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Missed opportunities? Germany and the transatlantic labor-productivity gap, 1900-1940Veenstra, Joost

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Missed Opportunities?

Joost Veenstra

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Publisher: University of Groningen, Groningen, The Netherlands

Printer: Ipskamp Drukkers B.V.

ISBN: 978–90–367–6791–0 / 978–90–367–6790–3 (eBook)

c©2014 Joost Veenstra

All rights reserved. No part of this publication may be reproduced, stored in a retrieval

system of any nature, or transmitted in any form or by any means, electronic, mechan-

ical, now known or hereafter invented, including photocopying or recording, without

prior written permission of the publisher.

This document was prepared using LATEX.

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Missed Opportunities?Germany and the Transatlantic Labor-Productivity

Gap, 1900–1940

PhD thesis

to obtain the degree of PhD at theUniversity of Groningenon the authority of the

Rector Magnificus, Prof. E. Sterkenand in accordance with

the decision by the College of Deans.

This thesis will be defended in public on

Thursday 20 February 2014 at 11.00 hours

by

Joost Veenstra

born on 23 March 1984in Leiden

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Supervisors:Prof. H.J. de JongProf. M.P. Timmer

Assessment committee:Prof. S.N. BroadberryProf. J. StrebProf. J.L. van Zanden

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Contents

List of Tables v

List of Figures vii

Acknowledgement ix

1 Introduction 1

2 Catching-Up with the Global Labor-Productivity Leader? 15

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.4 The transatlantic labor-productivity gap . . . . . . . . . . . . . . . . . . 28

2.5 Labor-productivity growth in interwar Germany . . . . . . . . . . . . . 36

2.6 Drivers of growth and catch-up . . . . . . . . . . . . . . . . . . . . . . . 40

2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.A Representativeness of the industrial surveys . . . . . . . . . . . . . . . . 51

2.B Adjustment of the employment census . . . . . . . . . . . . . . . . . . . 54

3 The Yanks of Europe? 57

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

3.5 Technology in German manufacturing . . . . . . . . . . . . . . . . . . . 81

3.6 The long-term perspective . . . . . . . . . . . . . . . . . . . . . . . . . . 88

3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

3.A Distance function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

3.B Global best-practice frontiers . . . . . . . . . . . . . . . . . . . . . . . . 95

3.C Labor-productivity growth at the frontier . . . . . . . . . . . . . . . . . 98

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ii Missed Opportunities?

3.D Robustness check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

4 Industrial Output Growth in Pre-WW2 Germany 103

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

4.2 The time-series debate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

4.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

4.A German/UK comparative labor productivity in 1936 . . . . . . . . . . . 130

4.B Indices of German industrial output . . . . . . . . . . . . . . . . . . . . 132

5 Did a European Convergence Club Exist Before World War 1? 133

5.A Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

5.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

5.3 Purchasing power parities for pre-WW1 European countries . . . . . . . 141

5.4 Comparative productivity around 1910 . . . . . . . . . . . . . . . . . . . 145

5.5 Change of comparative labor productivity, 1870–1910 . . . . . . . . . . 150

5.6 Manufacturing and convergence at the country level . . . . . . . . . . . 154

5.7 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

5.A Sweden’s relative performance . . . . . . . . . . . . . . . . . . . . . . . . 163

5.B Value added estimates Germany . . . . . . . . . . . . . . . . . . . . . . 165

A Data Appendix 167

A.1 Pre-WW1 labor-productivity growth in German industries . . . . . . . . 167

A.2 Labor-productivity levels pre-WW1 Germany . . . . . . . . . . . . . . . 171

A.3 Labor-productivity levels interwar Germany . . . . . . . . . . . . . . . . 173

A.4 Labor-productivity levels pre-WW1 US . . . . . . . . . . . . . . . . . . 175

A.5 Labor-productivity levels interwar US . . . . . . . . . . . . . . . . . . . 177

A.6 Purchasing power parities (GER36/US35) . . . . . . . . . . . . . . . . . 178

A.7 Coverage and number of UVRs (GER36/US35) . . . . . . . . . . . . . . 179

A.8 Unit-value ratios GER09/US09 . . . . . . . . . . . . . . . . . . . . . . . 180

A.9 Unit-value ratios GER09/GER36 . . . . . . . . . . . . . . . . . . . . . . 185

A.10 Unit-value ratios GER36/US35 . . . . . . . . . . . . . . . . . . . . . . . 189

A.11 Value added (per employee) pre-WW1 Germany . . . . . . . . . . . . . 201

A.12 Value added (per employee) interwar Germany . . . . . . . . . . . . . . 203

A.13 Value added (per employee) pre-WW1 US . . . . . . . . . . . . . . . . . 205

A.14 Value added (per employee) pre-WW1 UK . . . . . . . . . . . . . . . . . 206

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Contents iii

References 207

Official publications 223

Nederlandse samenvatting 227

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List of Tables

2.1 Average number of employees per establishment . . . . . . . . . . . . . . 24

2.2 Purchasing power parities . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.3 Coverage and number of UVRs . . . . . . . . . . . . . . . . . . . . . . . 30

2.4 German/US comparative labor productivity in 1909 and 1936/35 . . . . 31

2.5 German/US comparative labor productivity: sample vs. full coverage . . 33

2.6 German average annual labor-productivity growth . . . . . . . . . . . . 37

2.7 Employment shares Germany and US . . . . . . . . . . . . . . . . . . . 39

2.8 Share of workers employed in large establishments . . . . . . . . . . . . 42

2.9 Distribution of employment over establishment-size classes . . . . . . . . 43

2.10 Representativeness of coverage statistical quarterlies . . . . . . . . . . . 53

2.11 Employment occupational census covered by the industrial surveys . . . 56

3.1 Electrification rates in German and US manufacturing . . . . . . . . . . 69

3.2 Horse power per 1,000 hours worked in manufacturing . . . . . . . . . . 75

3.3 Annual labor-productivity growth at the frontier . . . . . . . . . . . . . 77

3.4 Decomposition of the 1936/39 German/US labor-productivity gap . . . 80

3.5 Created labor-productivity potential at the frontier . . . . . . . . . . . . 99

3.6 Decomposition of the 1936/39 German/US labor-productivity gap . . . 100

4.1 Benchmark estimates of comparative labor productivity . . . . . . . . . 110

4.2 Unit-root test (augmented Dicky-Fuller) . . . . . . . . . . . . . . . . . . 117

4.3 Estimates of the state-space model . . . . . . . . . . . . . . . . . . . . . 120

4.4 Backward projections of comparative labor productivity . . . . . . . . . 124

5.1 Purchasing power parities . . . . . . . . . . . . . . . . . . . . . . . . . . 141

5.2 Idem, compared to other work . . . . . . . . . . . . . . . . . . . . . . . 142

5.3 Fisher purchasing power parities . . . . . . . . . . . . . . . . . . . . . . 144

5.4 Number of matched products . . . . . . . . . . . . . . . . . . . . . . . . 144

5.5 Comparative labor productivity . . . . . . . . . . . . . . . . . . . . . . . 146

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vi Missed Opportunities?

5.6 Laspeyres, Paasche and Fischer comparative labor productivity . . . . . 147

5.7 Employment share of manufacturing branches . . . . . . . . . . . . . . . 148

5.8 Comparative labor productivity in northwestern Europe . . . . . . . . . 149

5.9 Idem, compared to other studies . . . . . . . . . . . . . . . . . . . . . . 149

5.10 Comparative GDP per capita in northwestern Europe . . . . . . . . . . 155

5.11 Comparative labor productivity in sectors of the economy . . . . . . . . 157

5.12 Comparative labor productivity, alternative estimate . . . . . . . . . . . 163

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List of Figures

1.1 Comparative German/US labor productivity in manufacturing . . . . . 2

1.2 GDP per capita levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1 Peak and census years, 1900–1913 . . . . . . . . . . . . . . . . . . . . . 27

3.1 Estimating the frontier for industrial chemicals . . . . . . . . . . . . . . 63

3.2 Decomposition techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3.3 Change of the frontier in industrial chemicals . . . . . . . . . . . . . . . 72

3.4 Distribution of manufacturing employment over capital-labor ratios . . . 74

3.5 Labor-productivity vs. machine-intensity differences . . . . . . . . . . . 81

3.6 Labor-productivity catch-up in two sequential steps . . . . . . . . . . . 88

3.7 German catch-up in manufacturing after WW2 . . . . . . . . . . . . . . 90

3.8 Frontiers for the years 1909, 1919, 1929 and 1939 . . . . . . . . . . . . . 95

3.9 Decomposition of growth potential . . . . . . . . . . . . . . . . . . . . . 98

4.1 Time series of output in German industry . . . . . . . . . . . . . . . . . 108

4.2 Backward projection of labor productivity (LP) . . . . . . . . . . . . . . 109

4.3 Logarithms of output series with breaking trend . . . . . . . . . . . . . 118

4.4 The state series, observed series and observation disturbance . . . . . . 121

4.5 Reconciliation with 1907 German/UK benchmarks . . . . . . . . . . . . 125

4.6 Idem, alternative 1936/35 labor-productivity benchmark . . . . . . . . . 130

5.1 Comparative labor productivity before WW1 . . . . . . . . . . . . . . . 151

5.2 Initial performance and subsequent labor-productivity growth . . . . . . 152

5.3 Dispersion of comparative labor productivity before WW1 . . . . . . . . 153

5.4 Dispersion of comparative GDP per capita before WW1 . . . . . . . . . 156

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Acknowledgement

This dissertation is a product of my own labor as well as of those who have supported

me in the process of writing it. I would like to use this opportunity to thank several

people who in various ways have offered encouragement and assistance. Without their

help I could not have written this thesis.

First and foremost, I am indebted to my supervisors, Prof. Herman de Jong and

Prof. Marcel Timmer. They created a friendly environment that inspired me with the

confidence to explore new research avenues. Originally trained as a historian, at the

outset of the project I was not familiar with the research methods that I ultimately

applied in this study. Even though the prospect of learning to understand new tools of

analysis daunted from time to time, I have always felt able to face this challenge under

the guidance of my supervisors. My gratitude goes out to Dr. Jan Jacobs, too, for

encouraging me to use econometric analysis to study history and for patiently showing

me the way.

The time working on my dissertation has been made truly special by Jop Wolter,

with whom I shared the office. Over the years Jop has become one of my closest friends

and has proved instrumental in my development as a person and researcher. Because of

his willingness to listen to and answer my questions, I often think of him as my unofficial

third supervisor. My beautiful wife, Laurie Reijnders, deserves special mention. It would

not have been possible to bring this project to a successful end without her loving care

and kind understanding. Returning home to Laurie is a profound joy each day. Also, I

owe a large debt to my family for their unwavering belief in my ability to overcome the

difficulties I encountered in the process of writing this dissertation.

In addition, I would like to acknowledge the financial support from the Netherlands

Organisation for Scientific Research as well as the Faculty of Economics and Business

at the University of Groningen.∗ My appreciation goes out to the SOM Graduate

School and the N.W. Posthumus Institute for providing the administrative, scholarly

and scientific setting needed for my research to thrive. Furthermore, I would like to

* NWO Grant no. 360-53-102.

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x Missed Opportunities?

express my sincere gratitude to the assessment committee, Prof. Stephen Broadberry,

Prof. Jochen Streb and Prof. Jan Luiten van Zanden, for honoring me by reading and

commenting on my thesis.

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Chapter 1Introduction

This dissertation explores Germany’s inability to match American levels of manufac-

turing labor productivity in the pre-WW1 and pre-WW2 period. There is still much to

learn about labor-productivity growth and technological change during this period. In

the case of Germany, the need for more research is illustrated by an online discussion in

2007 among prominent scholars of German history on the question ‘Do we need a new

economic history of Germany?’.1 At the outset of the discussion the concern was raised

that in German historiography “economic processes are usually taken as a background

to social, political, demographic, and cultural transformations of greater immediate in-

terest to the profession”.2 In a contribution to this debate, Albrecht Ritschl noted in

similar vein that “in Germany, making the boring numbers speak is still a young and

less than well established tradition. In contrast to the US, German economic history

never experienced a Cliometric revolution”.3 With regard to the early half of the twen-

tieth century, a scarcity of reliable information discouraged quantitative research and

Ritschl argues that “the macroeconomic history of Imperial Germany has traditionally

been plagued by an abundance of low-quality data”.4

Despite the data difficulties, the growth trajectory of Germany is not a black box

entirely. For manufacturing, Stephen Broadberry shows that labor-productivity growth

between 1900–1980 comprised two phases.5 Figure 1.1, which plots Germany’s compar-

ative labor-productivity development in manufacturing vis-a-vis the US over the period

1900–1980, demonstrates a widening gap before WW2. This long period of divergence

1. W. Gray, “Forum: Do We Need a New Economic History of Germany?,” H-Net Online, June2007, www.h-net.msu.edu.

2. ibid.3. A. Ritschl, “Do We Need a New Economic History of Germany?,” H-Net Online, July 2007,

www.h-net.msu.edu.4. ibid.5. S.N. Broadberry, The Productivity Race: British Manufacturing in International Perspective,

1850–1990 (Cambridge: Cambridge University Press, 1997), 43–45.

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2 Missed Opportunities?

Figure 1.1: Comparative German/US labor productivity in manufacturing (US = 1.0)

0.0

0.2

0.4

0.6

0.8

1.0

1900 1910 1920 1930 1940 1950 1960 1970 1980

WW1 WW2

Divergence Convergence

Sources: Broadberry, The Productivity Race.

lasted up to the late 1940s. It was not before 1947 that the dynamics reversed and Ger-

many managed to close in on American levels of labor productivity. Other European

countries shared Germany’s relative backwardness before WW2 and across the Atlantic

a large labor-productivity gap persisted from the late nineteenth century up until the

post-WW2 period.

Figure 1.2 shows that the transatlantic labor-productivity gap manifested also at the

country level. As such, it is a main feature of economic development in the early twenti-

eth century. The persistence, widening even, of the gap is striking and points at the pres-

ence of systematic growth determinants that long-lastingly influenced economic devel-

opment. Of particular interest in this respect is the timing of the labor-productivity gap;

the emergence of the gap coincided with a period of rapid technological development,

a time also referred to as the second industrial revolution.6 If the “Great Inventions”

of the second industrial revolution determined the growth dynamics of the post-1870

period, differences between countries in the adoption of new technologies may explain

the pattern of diverging development.7 Manufacturing industries, employing about 30%

6. R. Lipsey, K. Carlaw, and C. Bekar, Economic Transformations: General Purpose Technologiesand Long-Term Economic Growth (Oxford University Press, 2005).

7. R. Gordon, “Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds,”

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Chapter 1. Introduction 3

of the labor force in developed countries during the first half of the twentieth century,

proved particularly receptive to technological change.8 Because many new technologies

were embodied in capital, manufacturing industries could reap the labor-productivity

benefits associated with innovation by adopting modern machinery.9

Figure 1.2: GDP per capita levels (1,000 $1990)

0

2

4

6

8

10

12

14

1870 1913 1929 1938 1950

Europe US

Sources: Bolt and van Zanden, “The First Update of the Maddison Project.” Europe is calculated on

the basis of British, French and German data.

The comparatively high pace of growth in America suggests that the US success-

fully caught the winds of change, while Europe spilled them. This notion of missed

opportunities implies a latent growth potential that Germany failed to fully explore.

In its 2007 Global Economic Prospects report, the World Bank hints at such a latent

and partially unused capacity for growth in Europe.10 The World Bank examined the

historical growth record of G-5 countries and on the basis of GDP data for the thirty

years running up to 1900 ‘predicted’ economic growth for the 50 following years. The

predicted rate of GDP growth turned out significantly higher than the G-5 countries

Centre for Economic Policy Research Policy Insight No. 63 (2012): 5.8. For employment shares, see B. Mitchell, International Historical Statistics. Europe 1850–2005.

Sixth Edition (London: Macmillan, 1951), 153–164 and M. O’Mahony, Britain’s Productivity Perfor-mance, 1950–1996; An International Perspective (National Institute of Economic / Social Research,1999), 12.

9. H. Jerome, Mechanization in Industry (National Bureau Economic Research, 1934); S. Schurr etal., Electricity in the American Economy. Agent of Technological Progress (Greenwood Press, 1990);W. Devine, “From Shafts to Wires: Historical Perspective on Electrification,” Journal of EconomicHistory Vol. 43, No. 2 (1983): 347–372.10. The World Bank, “World Bank Report: Challenge of Geopolitical Shifts for Long-Term Economic

Forecasts: Lessons of History,” Global Economic Perspectives. Managing the Next Wave of Globaliza-tion (2007): 55.

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actually realized during the first half of the twentieth century, a failure fully attributable

to a slowdown in Europe. As the prediction essentially projects the nature of annual

shocks between 1870–1899 on the years afterward, the discrepancy between forecasted

and realized growth implies that after 1900 Europe deviated from its long-run devel-

opment path. Because the forecasted growth trajectory captures the latent production

capacity, the deviation from it suggests that part of that potential remained unrealized.

Had Europe managed to continue after 1900 as before, the gap to the US would have

turned out considerably smaller in the early twentieth century.

For the case of Germany, the notion of missed opportunities does not correspond

well with the conventional outlook on historical development. The long phase of labor-

productivity divergence before WW2 defies the traditional, and largely qualitative, lit-

erature that attributed special features to the German growth experience. Adam Tooze

articulates this tension between the two strands of literature as follows:

“Was there anything peculiar about Germany’s experience of economic

growth? This seems to me to be a question that though obvious and once a

classic topic for student essays is in fact in need of reassessment. Certainly

in many accounts of Germany’s uneven modernization there was a strong

assumption that the modernity of its economy at least was not in ques-

tion. Indeed, in some interpretations of Europe’s economic development,

claims were made for a peculiar sophistication of the German economy. And

yet from a vantage point at the end of the twentieth century Germany’s

long-run economic trajectory surely looks less distinctive than previously

thought. During the era of steel, chemicals and heavy electrical engineering

German industrialism was no doubt surrounded by a formidable aura. And

it certainly was a considerable industrial competitor. However, even then

these dramatic elements of industrialism formed only a part of economic life

in Germany. And their status as defining elements of economic modernity

was not set to last.”11

The question, then, is how the paradoxical lack of fast labor-productivity growth

at a time of fast technological change ought to be perceived. Did the widening labor-

productivity gap result from a German failure to successfully ride the waves of techno-

logical change? With regard to German manufacturing in the early twentieth century,

this question is addressed in the present study.

11. A. Tooze, “Do We Need a New Economic History of Germany?,” H-Net Online, June 2007,www.h-net.msu.edu.

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Two issues receive particular attention. First, given the ‘plague of low-quality data’,

attention goes out to an accurate measurement of German labor-productivity levels

in manufacturing on the level of industries. This involves a critical review of both

the data and the measurement techniques traditionally used by researchers studying

historical labor-productivity patterns. Secondly, the contribution of technological

change to Germany’s labor-productivity performance is studied and its contribution to

the widening German/US labor-productivity gap quantified.

Chapter 2 sets the stage for this study by measuring German/US comparative

labor productivity in manufacturing industries for the years 1909 and 1936/35. As

the quality of the time series provided by the German Historical National Accounts

has been called into question, new estimates of labor-productivity growth in Ger-

man manufacturing are necessary.12 For this purpose state-of-the-art techniques are

employed to construct benchmarks of comparative labor productivity. To allow for

effects of composition and to capture inter-industry variation in performance, the

labor-productivity comparisons constructed in this study apply the industry-of-origin

approach, which breaks down the manufacturing sector in manufacturing industries.

Moreover, following the literature, German and US output values are converted to

a common currency using industry-specific purchasing power parities to enable an

international comparison.13 These measurement techniques have previously been

applied only to bilateral comparisons between the US/UK and Germany/UK for years

prior to WW1 and WW2, but never for a study of German/US labor-productivity

differences in periods before 1950.14

12. W.G. Hoffmann, Das Wachstum der Deutschen Wirtschaft Seit der Mitte des 19. Jahrhunderts(Berlin: Springer-Verlag, 1965); R. Fremdling, “German National Accounts for the 19th and Early 20thCentury: A Critical Assessment,” Vierteljahrschrift fur Sozial- und Wirtschaftsgeschichte Vol. 75, no. 3(1988): 339–357; A. Ritschl, “Spurious Growth in German Output Data, 1913–1938,” European Reviewof Economic History Vol. 8 (2004): 201–223.13. A. Maddison and B. van Ark, “Comparison of Real Output in Manufacturing,” Policy, Planning

and Research Working Papers Vol. 5 (1988): 1–33; B. van Ark, International Comparisons of Out-put and Productivity: Manufacturing Productivity Performance of Ten Countries from 1950 to 1990(Groningen: Groningen Growth / Development Centre, 1993), 1–233; B. van Ark and M.P. Timmer,“The ICOP Manufacturing Database: International Comparisons of Productivity Levels,” Interna-tional Productivity Monitor No. 3 (2001): 44–51; R. Inklaar and M. Timmer, “GGDC ProductivityLevel Database: International Comparisons of Output, Input and Productivity at the Industry Level.,”GGDC Research Memorandum No. 104 (2008): 1–81.14. Broadberry, The Productivity Race; S.N. Broadberry and D. Irwin, “Labor Productivity in the

United States and the United Kingdom During the Nineteenth Century,” Explorations in EconomicHistory Vol. 43 (2006): 257–279; S.N. Broadberry and C. Burhop, “Comparative Productivity in Britishand German Manufacturing Before World War II: Reconciling Direct Benchmark Estimates and TimeSeries Projections,” The Journal of Economic History Vol. 67 (2007): 315–349; R. Fremdling, H.J.de Jong, and M.P. Timmer, “British and German Manufacturing Productivity Compared: A NewBenchmark for 1935/36 Based on Double Deflated Value Added,” The Journal of Economic HistoryVol. 67, no. 2 (2007): 350–378; H.J. de Jong and P.J. Woltjer, “Depression Dynamics: a New Estimate

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The benchmarks uncover a large labor-productivity gap in both periods at the level

of total manufacturing, confirming earlier studies by, among others, Broadberry.15 The

variation of comparative performance on the industry level is substantial, however, and

shows that the diverging trend for total manufacturing described in figure 1.1 fails to

do justice to the dynamics in several underlying industries. This applies particularly to

German chemicals, textiles and primary metals, which displayed a labor-productivity

performance close to the level of their American counterpart. Not surprisingly, general

theories as regards the German-American productivity gap have difficulty accounting

for the cross-industry variation. There are nonetheless some patterns recognizable. For

one, production in the strong performing industries involves mainly goods used as in-

termediates in other industries. For instance, the pig iron obtained in primary metals is

further processed by fabricated-metals and transportation-equipment industries, while

the yarn and thread produced in spinning industries function as inputs for weaving in-

dustries. European markets have been associated with heterogeneous demand patterns,

which discouraged the adoption of standardized production processes, but industries

involved in the production of mainly basic goods may not have suffered from this.16

The comparatively strong performing German manufacturing industries share an-

other characteristic as well. There appears to be an overlap between German industries

with relatively high labor-productivity levels and those associated in the literature with

relatively large establishment size and a high degree of vertical integration. Both phe-

nomena are associated with economies of scale, which possibly endowed industries with

relatively high labor-productivity levels.17 Although in general the scale of production

and the degree of vertical integration was much smaller in Germany compared to the

US, the relatively strong-performing German industries lagged only little behind.18 Nev-

ertheless, these are necessary but not sufficient conditions for catch-up and conceivably

of the Anglo-American Manufacturing Productivity Gap in the Interwar Period,” Economic HistoryReview Vol. 64 (2011): 472–492.15. Broadberry, The Productivity Race.16. L. Rostas, “Industrial Production, Productivity and Distribution in Britain, Germany and the

United States,” The Economic Journal Vol. 53, no. 1 (1943): 39–54, 58-59; A. Chandler, Scale andScope: the dynamics of industrial capitalism (Harvard: Belknap Press of Harvard University Press,1990), 1–780, 47; D.S. Landes, The Unbound Prometheus: Technological Change and Industrial Devel-opment in Western Europe From 1750 to the Present (Cambridge University Press, 1969), 247; S.N.Broadberry, “Technological Leadership and Productivity Leadership in Manufacturing Since the Indus-trial Revolution: Implications for the Convergence Debate,” The Economic Journal Vol. 104 (1994):291–302, 291.17. L. Hannah, “The American Mircale, 1875–1950, and After: A View in the Europan Mirror,”

Business and Economic History Vol. 24, no. 2 (1995): 197–220; L. Hannah, “Logistics, Market Size,and Giant Plants in the Early Twentieth Century: A Global View,” Journal of Economic History Vol.68, no. 1 (2008): 46–78.18. J. Kinghorn and J. Nye, “The Scale of Production in Western Economic Development: A Com-

parison of Official Industry Statistics in the United States, Britain, France, and Germany, 1905-193,”Journal of Economic History Vol. 56, no. 1 (1996): 90–112.

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explain some, but certainly not all the observed variation in comparative labor produc-

tivity. For instance, the paper and cement industries, both of which can be argued to

produce standard goods, failed to match the high labor-productivity level vis-a-vis the

US displayed by textiles.

Other factors must have been at work also. Perhaps most importantly in this respect

are differences in the mix of factor inputs employed in production. A possible and

frequently-used explanation for observed labor-productivity differences between the US

and the UK in the nineteenth century has been put forward in the Rothbarth-Habakkuk

thesis, which emphasizes the importance of factor endowments for the capital-labor ratio

at which countries choose to operate.19 In the US a scarcity of skilled labor and an

abundance of natural resources provided an incentive to substitute machinery for labor.

This minimized costs and led to a capital-intensive production process. The supply

of factor inputs faced by European producers differed, which induced the adoption of

less capital-intensive technology. As some determinants of relative factor costs, such

as the availability of natural resources or the size and density of the population, are

exogenous to the production process, a country’s initial conditions influence the choice

of technology. In the extreme, if one assumes that these fixed initial conditions fully

determine the relative factor costs and the choice of factor-input mix, the existence of

different technological paths across the Atlantic was foreordained.20

If technological progress is directed toward the technology currently used by coun-

tries, differences in relative factor costs lead to technological lock-in.21 This begs the

question whether such path dependencies effectively blocked the traditional channels

of labor productivity catch-up described by standard neo-classical growth theories?

Provided that the necessary capabilities and resources are available (Gerschenkron’s

idea of ‘appropriate’ economic institutions and Abramovitz’ ‘social capabilities’)

countries distanced far away from the technological frontier can catch-up quickly by

importing or imitating technologies that are already in use in developed countries.22

19. E. Rothbarth, “Causes of the Superior Efficiency of U.S.A. Industry as Compared with BritishIndustry,” The Economic Journal Vol. 56, no. 223 (1946): 383–390; M Abramovitz, “Resource andOutput Trend in the United States Since 1870,” American Economic Review Vol. 63, No. 2 (1956):5–23; H.J. Habakkuk, American and British Technology in the Nineteenth Century. The Search forLabour-saving Inventions (Cambridge: Cambridge University Press, 1962), 1–222.20. N. Rosenberg, “Why in America?,” in Exploring the Black Box. Technology, Economics, and

History (Cambridge University Press, 1994), 109–120.21. P. David, Technical Choice, Innovation and Economic Growth. Essays on American and British

Experience in the Nineteenth Century (Cambridge: Cambridge University Press, 1975), 1–334. Theclassic reference for path dependency is P. David, “Clio and the Economics of QWERTY,” AmericanEconomic Review Vol. 75, no. 2 (1985): 332–327.22. A. Gerschenkron, Economic Backwardness in Historical Perspective; A Book of Essays (Cam-

bridge University Press, 1962); M. Abramovitz, “Catching-up, Forging Ahead and Falling Behind,”Journal of Economic History Vol. 46 (1986): 385–406.

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Also the recent economic-growth literature has often emphasized the importance of

investment-based strategies for follower countries.23 Yet if follower countries refrain

from adopting advanced technology because of different relative factor costs, Europe

may have been trapped on a labor-intensive and low-productive technological path.

For the case of Germany, recent studies have concluded otherwise, though. Re-

search on the machine-tool industry during the interwar years revealed a process of

technology adoption on the part of Germany and finds that at the outbreak of WW2

capital-intensity levels were as high as in the US.24 The tradition of copying and

adopting American machinery contests the notion of technological lock-in. But how

can large labor-productivity differences coexist with a diminishing machine-intensity

gap? Chapter 3 addresses this paradox by specifically accounting for the contribution

of machine-intensity differences to the German/US labor-productivity gap in 1936/39.

For this purpose I use data envelopment analysis techniques, which offer several

advantages over traditional Solow-based level accounting exercises. First, the analysis

allows for localized innovation, a main feature of technological progress ever since

the first industrial revolution.25 Second, the data envelopment analysis involves a

non-parametric approach and, as such, does not require information on capital and

labor prices to proxy the marginal factor returns.26 Third, the analysis uses horse

power per hour worked, which offers a more accurate indicator of machine intensity

than the total capital stock per employee statistics conventionally employed.27

The data envelopment analysis applied here estimates a global best-practice frontier

and the change thereof between 1899–1939. This best-practice frontier indicates for each

point in the range of operated capital-labor ratios the highest labor-productivity level

contemporaneously or previously attained. Positioning German and American manu-

facturing industries in relation to the global best-practice frontier permits a decompo-

sition of the labor-productivity gap in components of capital intensity and efficiency.

23. P. Aghion, “Higher Education and Innovation,” Perspektiven der Wirtschaftspolitik Vol. 9 (2008):28–45; Daron Acemoglu, “Directed Technical Change,” The Review of Economic Studies Vol. 68, no. 4(2002): 781–809; J. Vandenbussche, P. Aghion, and C. Meghir, “Growth, Distance to the Frontier andComposition of Human Capital,” Journal of Economic Growth Vol. 11 (2006): 97–127.24. C. Ristuccia and A. Tooze, “Machine Tool and Mass Production in the Armaments Boom: Ger-

many and the United States, 1929–44,” Economic History Review Vol. 66, no. 4 (2013): 953–974.25. R.C. Allen, “Technology and the Great Divergence: Global Economic Development Since 1820,”

Explorations in Economic History 49 (2012): 1–16.26. S. Kumar and R. Russell, “Technological Change, Technological Catch-up, and Capital Deepening:

Relative Contributions to Growth and Convergence,” The American Economic Review Vol. 92, no. 3(2002): 527–548; M.P. Timmer and B. Los, “Localized Innovation and Productivity Growth in Asia:An Intertemporal DEA Approach,” Journal of Productivity Analysis Vol. 23 (2005): 47–64.27. A.J. Field, “On the Unimportance of Machinery,” Explorations in Economic History Vol. 22

(1985): 378–401.

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The former element captures the difference between the labor-productivity level at

the frontier for German and American industries. As such, it measures the difference in

labor-productivity potential between machine-intensity levels operated in Germany and

the US. This difference in labor-productivity potential does not fully account for the

observed variation in labor-productivity levels for the reason that industries exploited

their labor-productivity potential only partly. The extent to which an industry manages

to exhaust its labor-productivity potential is expressed by the second component, i.e.

efficiency. The difference between the efficiency level in Germany and the US accounts

for the remainder of the labor-productivity gap that is left unexplained by variation

in the labor-productivity potential at the level of machine intensity explored in both

countries.

The labor-productivity gap decomposition shows that it was not a low machine-

intensity level that refrained German industries from matching the labor-productivity

performance of their American counterparts. Rather, a relatively low level of efficiency

accounts for more than two-thirds of the labor-productivity gap. The limited impor-

tance of machine-intensity differences can be attributed to a process of machine in-

tensification in Germany during the 1920s and 1930s.28 The rapid move toward high

capital-labor ratios in German manufacturing aligns well with theoretical models of

appropriate technology in which new production knowledge is appropriate only for one

capital-labor ratio; if innovation takes place exclusively at high capital-labor ratios, “fol-

lower countries” must adopt capital-labor ratios already explored by leader countries in

the past to prevent a further widening of the labor-productivity gap.29

However, as much of what one needs to know to employ new production knowledge

is implicit and not available from handbooks, it takes time to assimilate and operate

machinery at the level displayed by countries exploring that capital-labor ratio before,

an effect that possibly explains part of the initially low efficiency levels in German

manufacturing.30 Other factors came into play as well. The findings of chapter 2

hinted at the positive influence that economies of scale exerted on labor-productivity

levels. A relatively large establishment size, for instance, could have made possible

a labor-productivity performance that was otherwise unattainable at a particular

level of machine intensity. Especially interesting in this respect is the below average

28. R. Richter, “Technology and Knowledge Transfer in the Machine Tool Industry. The UnitedStates and Germany, 1870–1930,” Essays in Economic & Business History Vol. 26 (2008): 173–188;R. Richter and J. Streb, “Catching-Up and Falling Behind: Knowledge Spillover from American toGerman Machine Toolmakers,” Journal of Economic History Vol. 71, no. 4 (2011): 1006–1031.29. S. Basu and D. Weil, “Appropriate Technology and Growth,” The Quarterly Journal of Economics

Vol. 113 (1998): 1025–1054.30. B. Los and M. P. Timmer, “The ‘Appropriate Technology’ Explanation of Productivity Growth

Differentials: An Emperical Approach,” Journal of Development Economics Vol. 77 (2005): 517–531.

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scope for labor-productivity catch-up through enhanced efficiency levels in textiles and

primary metals, which were both identified in chapter 2 as relatively strong performing

German manufacturing industries. This suggests that the on average smaller scale of

production in the other German manufacturing industries restrained the full efficient

use of machinery, a notion also advanced by Cristiano Ristuccia and Adam Tooze.31

Chapter 4 moves on to critically assess the measurement techniques conven-

tionally employed in long-run economic history by addressing the debate on German

output growth over WW1. Studies on long-run economic growth are often plagued by

limited data availability, particularly for early periods. In the absence of, for instance,

production data, the unobserved output change can be proxied by the behavior of

correlates. However, the correlation between the proxy and target series is never

perfect, which introduces inaccuracy to the estimates and may spark off a debate

concerning the appropriateness of different proxies. This scenario unfolded in the debate

on industrial output growth in pre-WW2 Germany, leading to different time-series

estimates.32 The choice between output proxies carries important implications for the

assessment of Germany’s growth experience; when used to calculate labor productivity,

the different output estimates indicate a German performance prior to WW1, i.e. 1907,

either equal to or well above the British level. Because the exact fit between proxy

and target variables cannot be determined when the latter is unobservable, choosing

between proxies proceeds on the basis of circumstantial evidence. Given the historical

questions at stake, the debate would benefit from a less conjectural approach.

In chapter 4 I apply a new approach to this old debate. Instead of choosing between

the different estimates, I acknowledge that all series are based on correlates of output.

Consequently, the dynamic properties of each observed series must be captured by the

same component, i.e. output change, while the deviation between the series reflects the

different accuracy of the correlates in capturing the unobserved change in output. Us-

ing state space time series analysis, I filter this common component from the output

series.33 This way, I do not discard any data and thus make full and efficient use of all

information. In a second step, the filtered output series is combined with employment

data to derive an index of German labor-productivity change, which, expressed relative

31. Ristuccia and Tooze, “Machine Tool and Mass Production,” 9.32. R. Wagenfuhr, “Die Industriewirtschaft. Entwicklungstendenzen der deutschen und interna-

tionalen Industrieproduktion 1860 bis 1932,” in Vierteljahrsheftte zur Konjunkturforschung, vol. (Son-derheft) 31 (Berlin: Verlag von Reimar Hobbing, 1933); Hoffmann, Das Wachstum; Ritschl, “SpuriousGrowth in German Output Data.”33. J. Commandeur and S.J. Koopman, An Introduction to State Space Time Series Analysis (Ox-

ford University Press Inc., 2007); J. Durbin and S.J. Koopman, Time Series Analysis by State SpaceMethods, vol. 24, Oxford Statistical Science Series (Oxford University Press Inc., 2001).

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to its British counterpart, is extrapolated backward from a known German/UK com-

parative level of labor productivity in 1936/35.34 This exercise is repeated twice, using

the point and interval estimates of the filtered common component, respectively. The

former attributes a 15% lead to Germany in 1907, while the latter indicates a range of

about 11% around the point estimate that contains the estimated parameter with 95%

certainty.

This finding takes on significance for the reconciliation between time-series projec-

tions and benchmark estimates. Faced with the different time series of output presented

in the literature, scholars have previously employed 1907 labor-productivity benchmarks

to test the accuracy of the time series estimates.35 As with the latter, however, vari-

ous benchmark estimates are presented, which ascribe a lead to Germany of either 5%

or 25%. Previously, criteria for the fit between benchmark estimates and time series

projections were loosely defined and the procedure applied in this chapter moves the

debate forward by providing a statistical framework to quantify the margin for error.36

Although the benchmarks presented in the literature deviate markedly, they all fall

within the tails of the confidence interval around my time-series projections. All esti-

mates can therefore be reconciled. This suggests that when benchmarks are used as a

check upon time series, taking into account the measurement error leads to a different

assessment of the fit between both measures as compared to the exclusive use of point

estimates.

Of course, this raises the question if such a broad range of German labor-

productivity levels obtained by the methodology advanced in this chapter renders

impossible a concise assessment of Germany’s comparative performance? Paradoxically,

my answer to this question is that working with confidence intervals actually increases

the reliability of the conclusions regarding historical economic development. Any

conclusion drawn from the filtered time-series estimates are explicitly founded on

a solid statistical basis, which provides an increased certainty compared to studies

employing point estimates only. I can confidently infer that, first, Germany had

overtaken Britain in terms of labor productivity already before WW1, yet by a small

margin only. Second, over WW1 there was a statistically significant change in labor

productivity leadership with Germany dropping below the UK. And, third, given

Fremdling, de Jong and Timmer’s 1936/35 German/UK benchmark comparison,

34. Fremdling, de Jong and Timmer, 2007.35. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing”;

A. Ritschl, “The Anglo-German Industrial Productivity Puzzle, 1895-1935: A Restatement and APossible Resolution,” Journal of Economic History Vol. 68, no. 2 (2008): 535–565; S.N. Broadberryand C. Burhop, “Resolving the Anglo-German Industrial Productivity Puzzle, 1895–1935: A Responseto Professor Ritschl,” Journal of Economic History Vol. 68, Nr. 3 (2008): 930–934.36. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing,” 326.

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Britain’s lead evaporated again in the 1930s and both countries performed roughly on

par shortly before WW2.

In chapter 5 I explore the possibility of a European convergence club in manu-

facturing before WW1. Several of the potential constraints to productivity growth

in pre-WW1 Britain and Germany, e.g. small domestic markets and relative factor

costs less favorable to capital-intensive production in comparison to the US, are easily

extended to other European countries, too. This invites the question whether, or to

what extent, the condition of being ‘European’ determined the growth experience

of countries.37 The notion of convergence in manufacturing labor-productivity levels

is particularly relevant for the pre-WW1 era, as the period in between 1870–1913 is

characterized by openness to trade and globalization. The relative openness to trade

potentially promoted the convergence between European manufacturing industries

toward a common level of performance, as trade theory suggests that differences in

relative factor prices and thus in the mix of factor inputs used in production iron out

under conditions of free trade.38 Chapter 5 explores this possibility by constructing

benchmarks of comparative labor productivity for the US, UK, Germany, France, the

Netherlands and Sweden around the year 1910.

Despite the openness to trade, the benchmarks show that the level of labor produc-

tivity had not converged between European countries before WW1 and marked differ-

ences persisted, both for total manufacturing and manufacturing branches. Moreover,

backward extrapolation of comparative labor productivity to 1870 points out that the

dispersion of performance hovered around a constant level throughout the period and

showed no signs of convergence. These findings are in sharp contrast to total-economy

developments; GDP per capita levels converged steadily between 1870–1913.39 This

finding aligns well with Broadberry’s notion that convergence at the country level was

fueled mainly by changes in the structure of the economy rather than labor-productivity

developments in manufacturing.40 At the same time, it may also mean that the pre-

37. For conditional convergence, see R. Barro, “Economic Growth in a Cross Section of Countries,”The Quarterly Journal of Economics Vol. 106, No. 2 (1991): 407–443; R. Barro and X. Sala-I-Martin,“Convergence,” Journal of Political Economy Vol. 100 (1992): 223–258; J. Fagerberg, “Technologyand International Differences in Growth Rates,” Journal of Economic Literature Vol. 32, no. 3 (1994):1147–1175.38. E. Heckscher, “The Effect of Foreign Trade on the Distribution of Income,” Ekonomisk Tidskrift

(1919): 497–512; B. Ohlin, Interregional and International Trade (Cambridge, Mass.: Harvard Univer-sity Press, 1983); P. Samuelson, “International Trade and the Equalization of Factor Prices,” EconomicJournal (1948): 165–184; P. Samuelson, “International Factor-Price Equalization Once Again,” Eco-nomic Journal (1949): 181–197.39. J. Williamson, “Globalization, Convergence, and History,” Journal of Economic History Vol. 56,

no. 2 (1996): 277–306.40. S.N. Broadberry, “Manufacturing and the Convergence Hypothesis: What the Long-Run Data

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WW1 period did not witness convergence of capital-labor ratios in manufacturing. For

instance, Abramovitz argued that the social competence necessary to exploit the new

technology was limited before WW1.41 Indeed, within Europe the level of machine

intensity differed considerably.42

As chapter 3 revealed that Germany operated before WW1 at a relative machine-

intensity level vis-a-vis the US much lower than during the 1930s, I am inclined to

attach more importance to differences in capital-labor ratios in an explanation of the

transatlantic labor-productivity gap before WW1 than at the end of the interwar pe-

riod. This makes the first half of the twentieth century a period of transition in which

German manufacturing gradually moved toward capital-labor ratios that promised a

considerable scope for labor-productivity growth. The stationarity, even deterioration,

of Germany’s comparative labor-productivity performance between 1909–1936 relative

to the US did not reflect the lack of technological progress, but an incomplete adop-

tion of new technology hampered by learning effects. This transition phase is enclosed

on both ends by periods that arguably display very different dynamics. The relatively

low levels of machine intensity in pre-WW1 German manufacturing suggests that the

labor-productivity gap to the US in the period before 1900 was driven largely by the

use of different technology, while the post-WW2 era witnessed a rapid decrease of both

the labor-productivity and capital-intensity gap to the US.43 This process of capital-

intensity convergence, however, had already set in during the interwar years. While it

failed to bring German labor-productivity levels closer to the US in the short run, it

formed the necessary first step on the road to catch-up and may partly explain the

German growth miracle in the post-WW2 period.

Show,” Journal of Economic History Vol. 53, no. 4 (1993): 772–795.41. Abramovitz, “Catching-up,” 395.42. Hannah, “Logistics, Market Size, and Giant Plants,” 71.43. van Ark, International Comparisons of Output and Productivity.

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Chapter 2Catching-Up with the Global Labor-Productivity

Leader? German and US Industrial Labor Productivity

Compared Before and After WW1

2.1 Introduction

Germany’s rapid economic development from the late nineteenth century onwards has

traditionally been described as a typical example of catch-up growth.1 In particular the

rapid transformation of the new, science-based industries, such as engineering, chemical

production, and metal manufacturing during the second industrial revolution has re-

ceived much attention.2 To which degree these developments propelled Germany to the

vanguard of industrial development is still a topic of debate, as demonstrated by the

discussion recently held between Broadberry & Burhop and Ritschl, who fail to reach

consensus on the question whether or not Germany had surpassed Britain by the turn

of the twentieth century.3 It has not been attempted to compare Germany with the US,

which is a surprise given the latter’s well-established lead over Europe in manufactur-

ing; if German growth genuinely resulted from a catch-up process, i.e. the “benefits” of

lagging behind, than the universal productivity leader – and not Britain – seems the

appropriate point of reference.4

This chapter presents a German/US comparison of labor productivity in mining

and manufacturing for two benchmark years, i.e. 1909 and 1936/35. The results of

1. See for instance: Gerschenkron, Economic backwardness, 16; Landes, The Unbound Prometheus,236.

2. H.J. Braun, The German Economy in the Twentieth Century: the German Reich and the FederalRepublic (London: Routledge, 1990), 20.

3. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing”;Ritschl, “The Anglo-German Industrial Productivity Puzzle.”

4. America’s superiority in manufacturing is clearly demonstrated in Broadberry, The ProductivityRace.

15

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this exercise are confronted with two strands of literature, each highlighting a different

aspect of the German growth experience. First, the preconditions for growth may have

been less favorable in Germany than in the US. It has been argued that relative-factor

costs in Europe discouraged the substitution of machinery for skilled labor, which in

turn constrained the adoption of labor-productivity enhancing technology.5 Moreover,

the literature has suggested that the small scale of European production negatively

affected labor productivity. Large-scale production and standardization was only limited

applicable, because producers faced a small domestic market characterized by a demand

for customized goods.6 Although these conditions have been ascribed to Europe as a

whole, evidence in support of such theories are based mainly on the case of Britain. The

question, then, is whether the British constraints to labor-productivity growth applied

also to Germany?

Arthur Shadwell, a British contemporary, who traveled the UK, Germany and the

US shortly after the turn of the twentieth century in order to compare the qualities of

industrial life in these countries, took note of Germany’s remarkable success in the face

of circumstances potentially detrimental to development:

“Not a rich country, possessing no exceptional resources or facilities, no

extensive and convenient seaboard, with no tide of skilled immigrant labour

to make things easy, and with enemies in arms on both sides of her, she

has yet within the space of thirty years, and while bearing the burden of

an enormous system of military defense, built up from comparatively small

beginnings a great edifice of manufacturing industry which for variety and

quality of output can compete in any market with most of the finest products

of Great Britain. That is no exaggeration but a plain statement of facts, and

it can be said of no other country.”7

Having matched British performance, did Germany subsequently encounter the same

barriers for further growth that prevented the UK from catching-up to the US? This

was not necessarily the case, given that Chandler has likened several elements of the

German system of manufacturing to the US, rather than to the UK.8 Also, the unique

institutional setting in which German producers operated, in particular the cartel-tariff

5. Habakkuk, American and British Technology; David, Technical Choice, 66; P. Temin, “LabourScarcity in America,” Journal of Interdisciplinary History Vol. 1 (1971): 251–264, 162;Field, “On theUnimportance of Machinery,” 379.

6. Rostas, “Industrial Production, Productivity and Distribution,” 58-59; Chandler, Scale and Scope,47; Landes, The Unbound Prometheus, 247; Broadberry, “Technological Leadership,” 291.

7. A. Shadwell, Industrial Efficiency (Longmans, Green, / Co., 1906), 14-15.8. A. Chandler, “Organizational Capabilities and the Economic History of the Industrial Entreprise,”

Journal of Economic Perspectives Vol. 6, no. 3 (1992): 79–100.

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Chapter 2. Catching-Up with the Global Labor-Productivity Leader? 17

system, has been associated with labor-productivity benefits, which may have counter-

acted some of the ails that European countries suffered from.9

Second, in addition to questions of relative standing and catch-up, this chapter

addresses the issue of labor-productivity in Germany, too. The US is known for its

unprecedented growth spurt during the interwar period and by placing Germany’s labor-

productivity performance in relation to its American counterpart the growth record of

German industries between 1909 and 1936 may easily be underrated.10 In the presence

of rapid growth in the US, a stagnant level of comparative labor productivity reflects

fast growth in Germany, too. Conversely, a relative standing close to the US does not

by necessity imply a rapid development on the part of Germany.

A focus solely on German growth is called for also because doubt has been cast

upon the reliability of the output and employment indices of the German Historical

National Accounts (HNA) constructed during the 1960s under supervision of Walther

Hoffmann. The critique on Hoffmann’s series is directed toward his use of income data

to estimate output growth. Because of the increased bargaining power of labor unions

over WW1, the productivity to wage ratio changed and using the latter as a proxy for

output leads to spurious growth.11 As the quality of the time series has been called

into question, extrapolating backwards from known labor-productivity levels in the

interwar period could potentially lead to inaccurate estimates. New estimates of labor-

productivity growth in German manufacturing are therefore necessary.12

In response to the first issue, i.e. relative standing, this chapter presents a comparison

of labor-productivity levels between Germany and the US in 1909 and 1936/35. The

second question, i.e. growth in Germany, is subsequently addressed by an exclusively

German inter-temporal comparison of labor-productivity levels between 1909 and 1936.

In both cases the latest methodological developments for constructing productivity

comparisons are taken on board to allow for the most accurate analysis possible. This

involves the application of an industry-of-origin approach to the benchmark estimates,

which, among other things, entails a break down of manufacturing in industries to

provide the detail needed to map out an economy’s productivity profile, i.e. its strong

and weak elements. Having set out the methodology and data in sections 2.2 and 2.3,

the results, which are presented in sections 2.4 and 2.5, are finally positioned in the

literature in section 2.6.

9. Hannah, “The American Mircale,” 207–208; Kinghorn and Nye, “The Scale of Production,” 109;M.Levenstein and V. Suslow, “What Determines Cartel Success,” Journal of Economic Literature Vol.44, no. 1 (2006): 43–95, 85.10. A.J. Field, “The Most Technologically Progressive Decade of the Century,” The American Eco-

nomic Review Vol. 93, no. 4 (2003): 1399–1413.11. Ritschl, “Spurious Growth in German Output Data.”12. Hoffmann, Das Wachstum; Fremdling, “German National Accounts.”

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2.2 Methodology

Most research focusing on productivity comparisons is conducted on the total-economy

level, as, for instance, the well-known long-run series provided by Maddison.13 As a

result, Maddison’s time series outline major trends of economic development, but the

dynamics that play out within the economy go by unnoticed. As such, the drivers be-

hind the observed total-economy growth patterns remain hidden. For instance, studies

focusing on technological development conducted on the basis of total-economy data

may miss important historical developments, as effects of efficiency-increasing innova-

tions tend to be underestimated when their impact is measured by their contribution to

GDP. In fact, Paul David used this argument to criticize the conclusions of Robert Fo-

gel’s seminal work on the impact of railroads on American economic growth.14 Another

example that stresses the importance of research on the disaggregated level concerns

Broadberry’s analysis of Anglo-American GDP-per-capita difference, which he ascribes

to compositional effects. When employment shifts from low productivity to high produc-

tivity sectors, output per worker on the total-economy level increases and such a change

in employment structure was crucial for America’s growth spurt during the nineteenth

century.15

To allow for effects of composition and to capture inter-industry variance in per-

formance, the labor-productivity comparisons constructed in this study employ the

industry-of-origin approach, which dissects the manufacturing sector in its underlying

components, i.e. manufacturing industries. By accurately measuring the state of man-

ufacturing in a particular year, the industry-of-origin benchmark provides the starting

point for further research that aims to explain a country’s growth experience. In addi-

tion, a benchmark for the pre-WW1 period supplies a check upon time-series projections

extrapolated backward from more recent benchmark estimates. As it is difficult for back-

ward extrapolations to accurately allow for changes in the structure of an economy (or

manufacturing), especially when the projection covers periods characterized by turbu-

lence and rapid change, such as the World Wars or the Great Depression, problems

occur when time series are projected into the distant past.16 A large deviation between

13. A. Maddison, Phases of Capitalist Development (Oxford: Oxford University Press, 1982); A.Maddison, Dynamic Forces in Capitalist Development: A Long-Run Comparative View (Oxford: Ox-ford University Press, 1991), 1–333; A. Maddison, Monitoring the World Economy 1820–1992 (Paris:Organisation for Economic Cooperation / Development, 1995), 1–255.14. P. David, “Transport Innovation and Economic Growth: Professor Fogel On and Off the Rails,”

Economic History Review Vol. 22, no. 3 (1969): 506–525.15. Broadberry, The Productivity Race.16. A. Gerschenkron, “Soviet Heavy Industry: A Dollar Index of Soviet Machinery Output, 1927–28

to 1937,” The Review of Economics and Statistics Vol. 37, no. 2 (1955): 120–130.

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time-series projections and direct level estimates may indicate a degree of inaccuracy

on the part of the former.17 An additional advantage of the benchmark over time-series

projections is that because the level estimates of productivity produced for the former

refer to one year, data on output and employment can be obtained from a single primary

source, which guarantees a consistency between the output and input measures.18

The importance and appeals of the industry-of-origin approach were recognized by

the late 1940s. The first industry-of-origin benchmark was constructed by Rostas in

1948.19 In an attempt to assess the state of the British and American economies, he

broke down the manufacturing sector and pinpointed the comparative performance of

UK industries in relation to their US counterparts. Since then, the industry-of-origin

approach has been adopted by other scholars, most notably among which Stephen

Broadberry, to address the debate on (historic) patterns of convergence and divergence.

The research conducted here follows in this tradition and builds upon the work of these

early pioneers.

Although a productivity comparison on an industry level is a simple and straight-

forward concept, such a comparison can be made in a variety of ways. The methods

used here are refinements of the basic methodologies of comparison set out by Rostas,

Paige & Bombach and Broadberry. The first industry-of-origin benchmarks obtained

productivity figures by taking the ratio between output volume and employment on

the industry level. When output is expressed in volumes an international comparison is

straightforward, since the unit of measurement is the same for all countries, for instance

produced tons of coke per employee. However, as noted by Inklaar and Timmer, the

direct comparison of physical units of output for the measurement of productivity is

only possible for a specified product or a closely related group of products.20 Conse-

quently, this limits the ability to estimate productivity for industries producing a wide

array of heterogeneous goods, which is always the case when comparing productivity at

the industry or total-economy level. In view of these limitations, it is more practical to

compare output values, rather than output volumes.

Unfortunately, when the value approach is applied, the advantage of directly com-

paring productivity levels between countries is lost. While hectolitres and kilograms are

the same in the US and Germany, US$ and German Goldmark cannot be compared

directly. Therefore, a conversion factor is necessary to express the output value of dif-

ferent countries in a common currency. The exchange rate is not an optimal conversion

17. Chapter 4 provides a detailed discussion of this issue.18. van Ark and Timmer, “The ICOP Manufacturing Database.”19. L. Rostas, Comparative Productivity in British and American Industry (Cambridge: Cambridge

University Press, 1948), 1–263; Rostas, “Industrial Production, Productivity and Distribution.”20. Inklaar and Timmer, “GGDC Productivity Level Database,” 6-8.

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factor for this purpose, since it only signifies the price relation between internationally

traded goods. Moreover, the exchange rate is a particularly inconvenient instrument

for this research, as the countries under comparison operated under different monetary

regimes, at times. Both countries were on the gold standard at the start of the twen-

tieth century. Exchange rates were effectively fixed and domestic price movement was

determined by a country’s gold supply. However, by the late 1930s the US had abolished

payments stipulated in gold, while Germany still adhered to the gold standard. Finally,

the exchange rate is a total-economy measure of the price ratio that does not allow for

variation thereof between different sectors of the economy.

A more appropriate alternative to the exchange rate is an industry-specific conver-

sion rate based on producer prices. This technique has been set out by van Ark and is

referred to as the International Comparisons of Output and Productivity (henceforth,

ICOP) methodology.21 The building blocks of the conversion rates are formed by prod-

uct prices. As these prices are seldom available in the statistical records, they have to

be derived from data on the produced value and quantity of products. In a bilateral

country comparison, these product prices – referred to as unit values – are computed

for both countries as in equation (1).

pij =vijqij

(2.1)

Where pij is the unit value of product i in country j, vij the output value of that

product and qij the corresponding produced volume. Subsequently, identical products

are selected and matched between the two countries involved in the comparison. The

ratio between the unit value of the same commodity in both countries captures the

product-specific relative price expressed in terms of country n’s currency per unit of

the base country o’s currency, as in equation (2).

uvrio =pinpio

(2.2)

With uvrio as the unit value ratio (henceforth, UVR) of product i, which represents

the relative unit value in country n (pin) compared to the unit value in country o

(pio). In order to derive an industry-level conversion factor, a weighted average is taken

of the product-specific price ratios classified in the same industry group. The weights

allotted to the UVRs for the purpose of aggregation reflect the product’s share in

total industrial output (vi/∑

vi). The aggregated UVRs are traditionally referred to

as purchasing power parities (henceforth, PPPs). The process of aggregation proceeds

21. Maddison and van Ark, “Comparison of Real Output in Manufacturing”; van Ark, InternationalComparisons of Output and Productivity; van Ark and Timmer, “The ICOP Manufacturing Database.”

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in three sequential steps, as described by equations (3), (4) and (5). The UVRs are

aggregated, first, using base country o’s output weights and, second, using the weights

of the numerator country n to get a Laspeyres (Lgo) and Paasche (P go) gross output

PPP, respectively:

Lgo =

∑(vio · pin

pio

)∑

vio=

∑(vin · uvrio)∑

vio(2.3)

P go =

∑vin∑(

vin · pio

pin

) =

∑vin∑

(vin/uvrio)(2.4)

In a third step the geometric average of the Laspeyres and Paasche PPPs is taken (F go),

which is used throughout this chapter to convert industrial output:

F go =√Lgo · P go (2.5)

Equations (1)–(5) provide the tools needed to express German and US gross output

in a common currency. Labor productivity in country j (yj) is then expressed as in

equation (6), where goij denotes gross output of product i in country j and lij the

labor input employed in the production process.

yj =

∑goij∑lij

(2.6)

In the following analysis labor is defined initially as the number of employees involved

in production and subsequently as total annual hours worked. The adjustment for hours

worked takes on significance mainly for the interwar comparison, as the first half of the

twentieth century saw a rapidly decreasing length of the working week, especially in the

US.22 Combining equations (5) and (6), the level of labor productivity in country n as

compared to base country o is expressed as in equation (7) below:

LP =yn/F

go

yo(2.7)

2.3 Data

As mentioned, one of the advantages of comparing levels of labor productivity is the

possibility for each country to draw data on production and labor from the same pri-

mary source, ensuring consistency between the output and input measures. Generally,

I employ in this study the censuses of production published by the statistical offices of

22. Jong and Woltjer, “Depression Dynamics.”

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Germany and the US. The pre-WW1 analysis for the US is based on the Thirteenth Cen-

sus of the United States published by the US Bureau of Commerce.23 For 1935 US I rely

primarily on the Biennial Census of Manufactures 1935 and the Sixteenth Decennial

Census of the United States.24 The US censuses provide an extensive and consistent cov-

erage of the American manufacturing sector in both years. For interwar Germany I use

the comprehensive archival records of the German production census published in Die

deutsche Industrie: Gesamtergebnisse der amtlichen Produktionsstatistik (henceforth,

production census of 1936). This is the first official German census of manufactures

and is available in two forms; a published edition and the original archival records. The

former has been to set up to hide particular manufacturing activities that were related

to the war effort. The archival records contain considerably more detailed and accurate

information and is used in this study.25

Collecting data to calculate labor productivity for pre-WW1 Germany was less

straightforward. The statistical offices of the US and the UK published a census of

manufactures already before WW1. For manufacturing industries these censuses re-

port data on output, employment, installed capital, etc. and as such are ideally suited

for constructing benchmarks. Because the first German census of manufacturing was

not published until 1936, I have to rely on other sources for the prewar period. The

Kaiserlichen Statistischen Amte (henceforth, Imperial Statistical Office) monitored the

economy in a variety of ways from the turn of the twentieth century onwards. Using

a combination of official statistical publications the industry-level data needed for the

construction of benchmarks is obtained. Because it forms the weakest link in the chain

of benchmarks presented here, the computation of German labor-productivity levels

before WW1 requires further elaboration.

Labor productivity in pre-WW1 Germany

To calculate German labor-productivity levels for the prewar period, I mainly rely

on information obtained from the Vierteljahrshefte zur Statistik des deutschen Reichs

(henceforth, statistical quarterlies). In the statistical quarterlies of 1913 the results of

23. United States Department of Commerce: Bureau of the Census, Thirteenth Census of the UnitedStates Taken in the Year 1910, vol. VIII: Manufactures (Washington D.C.: United States GovernmentPrinting Office, 1913). For mining, United States Department of the Interior, United States GeologicalSurvey 1910.24. United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1935 ;

United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1940 (I);United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1940 (II).25. Reichsamt fur Wehrwirtschaftliche Planung, Die Deutsche Industrie 1936 ; for a detailed discus-

sion of this source see: R. Fremdling, H.J. de Jong, and M.P. Timmer, “Censuses Compared: A NewBenchmark for British and German Manufacturing 1935/1936,” Groningen Growth and DevelopmentCentre Memorandum no. 90 (2007): 1–36.

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industrial surveys for the years between 1907 and 1911 are published.26 The surveys

report output and employment data for a number of industries. For those industries

that are included, the surveys do not provide full coverage. Instead, the production of

a sample of firms is reported. Partly this is due to the fact that the surveys are only

sent to firms affiliated with the national health-insurance scheme for workers (Gewerbe-

Unfallversicherungsgesetze). The smallest workplaces are in effect not covered and the

scope of the surveys may be limited to the larger firms in German industries. This

could lead to compatibility problems when comparing Germany with the US. The US

census of manufactures provides almost full coverage as only household industries and

establishments with an annual output lower than $500 are excluded.27

If due to scale advantages labor productivity is higher in large establishments as

compared to small establishments, the benchmark results based on data obtained from

the statistical quarterlies could potentially overestimate the productivity performance

of German industries. Table 2.1 captures the employment coverage of the industrial

surveys. On the left side of the table the average number of employees working in estab-

lishments included in the industrial surveys is reported. On the right side, I included the

same statistic for comparable industries obtained from the Berufs- und Betriebszahlung

published in 1907 (henceforth, occupational census), which has full-employment cover-

age.28 The nomenclature does not match perfectly between both sources. Nevertheless,

the fit is close enough to be reasonably sure that the classification of the occupational

census refers to the same manufacturing activities. For all industries the comparison of

average establishment size shows that the statistical quarterlies report data on relatively

large establishments. This creates a potential bias in favor of Germany.

In order to quantify the potential bias, I need to know which establishment-size

classes are represented by the establishments included in the industrial surveys. If,

for instance, it turns out that the surveys exclude establishments with less than 10

employees, the part of employment covered by those establishments is not represented

by the surveys, which introduces an upward bias in my estimates. Note that I estimate

the representativeness of the surveys and not their coverage. I use the representativeness

of the surveys for two reasons. First, as for some industries the nomenclature between the

industrial surveys and the occupational census differs, a comparison between the number

26. Kaiserlichen Statistischen Amte, “Ergebnisse der deutschen Produktionserhebungen 1913”;Kaiserlichen Statistischen Amte, “Ergebnisse der deutschen Produktionserhebungen 1914.”27. United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1910

(VIII), 19. In the case of the British census of manufactures (1907) household industries, one-personestablishments, and establishments with less than 10 employees are excluded. As a consequence, about25% of employment is not covered. See: Board of Trade, UK Census of Production 1907, 8.28. Kaiserlichen Statistischen Amte, “Gewerbliche Betriebsstatistik,” Abteilung II, Heft 1, Tabelle 8,

1–27.

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Table 2.1: Average number of employees per establishment ( lini) in German

manufacturing industries, 1907/09

Statistical Quarterlies Employment Census

Description lini

Description lini

Kraftfahrzeug- und Hilfsindustrie 244 Fabrikation Kraftfahrzeugen 58

Eisenverarbeitungsindustrie 445 Grosseisen- und -Stahlindustrie 270

Eisen- und Stahlgiessereien 79 Eisengiesserie und -Emaillierung 79

Silber-/Blei-/Kupfer-/Zinkhutten 310 Silber-/Blei-/Kupfer-/Zink-/Zinnhutten 140

Schwefelsaure 69 Chemische Grossindustrie 36

Teer Destill. und Petroleum raff. 43 Kohlenteerschwelerei, Petroleumraff. 30

Kokereien 143 Verkokungsanstalten 131

Zementindustrie 166 Zement- und Trassfabrikation 81

Sources: Kaiserlichen Statistischen Amte, “Gewerbliche Betriebsstatistik,” in Berufs– undBetriebszahlung, Statistik des deutschen Reichs (Berlin, 1907); Kaiserlichen Statistischen Amte,“Erganzungsheft zu die Ergebnisse der deutschen Produktionserhebungen,” in Vierteljahrsheftezur Statistik des deutschen Reichs: Erganzungsheft, vol. Vol. 22, no. 3 (Berlin, 1913).

of employees reported by the surveys and the census, which covers total employment,

introduce a degree of inaccuracy. The compared statistics may not refer to exactly the

same unit of production. Second, even in cases where this is not a problem, the coverage

of the surveys does not provide information on the size of the establishments included

in the surveys. For instance, if 70% of an industry’s employment is covered by the

surveys, it is not clear whether the excluded 30% are employed in relatively small or

large establishments. Therefore, the sign of the bias associated with the surveys remains

unclear, too. Instead, the representativeness of the surveys, i.e. the establishment-size

classes represented by the surveyed establishments, does provide a tool to assess the

bias in the survey’s results.

Using a combination of the average firm size reported by the industrial surveys

and information obtained from the occupational census, I have estimated, first, which

establishment-size classes are represented by the surveys and, second, the share of total-

industry employment that is covered by these establishment-size classes.29 The results

indicate that in most industries the surveys represent all but the smallest establishment-

size classes. As, in general, between 95% and 100% of the manufacturing labor force is

employed in establishments-size classes represented in the surveys, there is no reason

to think that the surveys introduce a structural upward bias in the German labor-

productivity estimates.

29. See appendix 2.A for more detail and the results of this exercise.

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Additional sources for the pre-WW1 period

Another potential drawback of the statistical quarterlies is that several manufacturing

industries are not included in the surveys. Data on, for instance, the food industry,

electrical and mechanical engineering, or the instrument industry are not reported.

For some industries important activities are omitted as well. The chemical industry

is poorly represented by coal-tar distillations, potash and sulfuric acid: information

on inorganic chemicals is unavailable. Furthermore, the industrial surveys only report

output in the textile industry, but no employment, making it impossible to calculate

labor-productivity levels. Lastly, due to their incomplete coverage, the industrial surveys

do not provide a complete output structure of the manufacturing sector. Additional

sources are needed to provide the weights necessary to aggregate the UVRs and PPPs

for an analysis on the level of total manufacturing.

To fill the gaps in the data of the statistical quarterlies, three other publications of

the Imperial Statistical Office are used here. First, the Statistisches Jahrbuch fur das

Deutschen Reich (henceforth, statistical yearbook) provides annual data on a limited

number of industries (mostly the production of mines and blast furnaces).30 To a large

extent these industries are in more detail covered by the industrial surveys. However, the

statistical yearbook includes data on the production of taxable goods, such as sugar,

tobacco, and alcoholic beverages and thereby provides information on output in the

food & kindred industry, which remained outside the scope of the industrial surveys.

Unfortunately, the yearbook reports physical quantities only. Hence, labor-productivity

levels for sugar and tobacco are expressed in produced tons per employee.31 For alcoholic

beverages, output is measured in hectoliters. In contrast to output data, the number

of employees working in the food & kindred industry is not reported by the statistical

yearbook. As with the textile industry, for which only output is reported in the industrial

surveys, an additional source is needed to find employment data necessary to calculate

labor productivity. For this purpose the occupational census is used, both in the case of

textiles and food & kindred. The number of workers in the textile industry derived from

30. Kaiserlichen Statistischen Amte, Statistisches Jahrbuch fur das deutschen Reich (Berlin, 1909–1912), 52–133.31. The obtained comparative labor-productivity level is subsequently projected on the US nominal

level of labor productivity (in US$), to obtain the German level of labor productivity expressed in US$.Because the statistical quarterlies report information on output value and volume for some products,starch mainly, it was possible to construct a PPP for this industry, which is then used to convert laborproductivity from US$ to German Goldmark. Of course, the comparative level of German/US laborproductivity has not changed in any sense, but expressing the comparison in output value enables anaggregation scheme along the lines of equation (2.6), which would not have been possible when outputis expressed in volumes. For tobacco, the same procedure has been followed with the difference that noindustry-specific PPP could be obtained and I relied on the total-manufacturing PPP, instead.

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the occupation census is adjusted in line with the coverage of the industrial surveys.32

Secondly, the labor-productivity estimates for paper and glass production are based

on reports of trade unions, which were collected during the 1920s and used to recon-

struct developments on the industry level. These reports were subsequently published in

the Ausschuss zur Untersuchung der Erzeugungs- und Absatzbedingungen der deutschen

Wirtschaft.33 Finally, the estimates for tire production (rubber) are obtained from the

Industrielle Produktionsstatistik, a special edition of the Wirtschaft und Statsitik pub-

lished by the Statischen Reichsamt.34 Although these publications report predominantly

production figures for the period since 1925, the interwar data is sometimes comple-

mented with information on years before WW1 for purpose of comparison.

At this point, I am able to calculate labor-productivity levels for many manufac-

turing industries in pre-WW1 Germany. In most cases, the data employed to calculate

labor productivity does not cover an industry’s total output and employment. For the

purpose of aggregation, however, it is recommendable to allot total-industry weights

to the industry-level labor-productivity estimates. This way, the composition of manu-

facturing is properly taken into account. Because total-industry output is not reported

for Germany, but total employment is (by the occupational census), I have estimated

total output by multiplying the German nominal labor-productivity level by total em-

ployment. Essentially, the labor-productivity estimates are thus reweighted according

to industry-employment shares derived from the occupational census. Earlier research

already pointed out that this procedure underestimates the share of high-productivity

industries, but to a small extent only and is unlikely to affect the results substantially.35

This is confirmed by the 1936/35 German/US benchmark. Using the product of nominal

labor productivity and total-industry employment as a proxy for total-industry output

produces the same result as obtained by the use of actual total-industry output.

A potential problem is that most labor-productivity levels calculated on the basis of

the industrial surveys do not refer to the same year as the weighting scheme, i.e. 1907. In

fact, except for the textile and food & kindred industries, all productivity data refer to

either 1908, 1909 or 1910. If the results are to be interpreted as representative for 1907,

labor-productivity levels must have remained constant over this period, which seems

unlikely. In this study I pursue a less stringent approach by choosing the year for which

32. See appendix 2.B for more detail.33. Verhandlungen und Berichte des Unterausschusses fur allgemeine Wirtschaftsstruktur, “Die

deutsche Zellstof-, Holzschliff-, Papier- und Pappenindustries”; Verhandlungen und Berichte des Un-terausschusses fur allgemeine Wirtschaftsstruktur, “Die deutsche Glasindustrie.”34. Kaiserlichen Statistischen Amte, “Industrielle Produktionsstatistik”; Kaiserlichen Statistischen

Amte, “Industrielle Produktionsstatistik”; Kaiserlichen Statistischen Amte, “Industrielle Produktion-sstatistik.”35. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing,” 320.

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Figure 2.1: Peak and census years, 1900–1913

United States

0

2

4

6

8

10

12

14

16

realG

DP

(1913

=100)

1900 1902 1904 1906 1908 1910 1912 191450

60

70

80

90

100

110

0

2

4

6

8

10

12

unem

plo

ym

ent

rate

(%)

Germany

1900 1902 1904 1906 1908 1910 1912 191450

60

70

80

90

100

Real GDPa

Trend growth GDPc

Unemployment rateb

Census years

a Sources: Angus Maddison, “Historical Statistics of the World Economy: 1–2008 AD,”Groningen Growth and Development Centre, 2008, http://www.ggdc.net/maddison/, Table 2:GDP Levels, retrieved: 23 March 2011.

b Sources: [US] D.R. Weir, “A Century of U.S. Unemployment, 1890–1990,” Research inEconomic History Vol. 14 (1992): 301–346, 341–343; [GER] T. Pierenkemper, “The Standardof Living and Employment in Germany, 1850-1960: An Overview,” Journal of EuropeanEconomic History Vol. 16 (1987): 51–73, 58–59.

c The basic long-run trend growth is fitted as a least-squares polynomial of degree 2, for theperiod 1870–1913.

the most output data is available, i.e. 1908 or 1909, as the basis for the benchmark. This

setting assumes that the composition of the manufacturing labor force has remained

unaltered between 1907 and 1909. As the employment structure is much less volatile

than the movement of productivity levels, projecting the 1907 structure on either 1908

or 1909 does not give cause for concerns.36 As the prewar benchmark is used for a

comparison with America and the latter’s census of manufactures refers to 1909, I

designated 1909 as base for the German benchmark.

The choice of 1909 as the prewar benchmark-year was further strengthened by move-

ments of the business cycle. Whenever possible, I took care to avoid years which are

at a peak or in a through of the cycle. Figure 2.1 shows that the level of real GDP

at the selected census years for both countries was above the long-run trend, and that

36. On the basis of the industrial surveys I am able to calculate the annual change in labor productivitybetween 1907 and 1911 for several industries, see appendix A.1. In almost all of these industries laborproductivity increased (rapidly) over the years 1908–1911 (Δ LP). Assuming that labor productivitydid not change, even in this short period, is therefore problematic. Instead, the employment share ofthese industries changed little.

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the unemployment rate at that point in time was relatively low or stable. This is an

essential requirement for my analysis, as I strive to determine the level of potential pro-

ductivity differentials between the countries under comparison. I thus want to exclude

the effects of business cycles and capacity under-utilization as much as possible; which,

I am convinced, is the case for the selected benchmark year.37 Consequently, all German

labor-productivity estimates originally based on data from other years are adjusted to

a 1909-basis using Hoffmann’s industry-level time series of output and employment.

2.4 The transatlantic labor-productivity gap

The methodology and data described in the previous chapters enables me to compare

labor-productivity levels between German and US manufacturing industries. This is

necessary because the extent to which Germany lagged behind the global productivity

leader is not immediately evident from other studies. In the literature German/UK and

US/UK comparisons are presented for prewar and interwar years. Until now, direct Ger-

man/US comparisons were not available and the productivity gap between Germany

and the US could only be obtained indirectly using the German/UK and US/UK com-

parisons. The quality of an indirect German/US estimate depends on the consistency in

the applied methodology and the coverage of industries between the German/UK and

US/UK comparisons, which is never perfect.

Moreover, such a procedure is in particular problematic for the pre-WW1 period,

because the size of the gap between Germany and the UK before WW1 is not undis-

puted. Both Steven Broadberry & Carsten Burhop (henceforth, B&B) and Albrecht

Ritschl have presented German/UK benchmarks for 1907, reporting a productivity ra-

tio in manufacturing of 1.08 and 1.28, respectively.38 Contingent on the choice between

these benchmarks, Broadberry & Irwin’s estimate of a 2:1 American lead over Britain

in 1909/10 implies a German/US productivity ratio of either 0.54 (via B&B) or 0.63

(via Ritschl); a difference of about 15%, which is sizable for this type of research.39

As described in section 2.2, the industry-of-origin approach compares the gross out-

put by industries between countries using an industry-specific conversion factor or

PPP. The inter-industry variation illustrated by table 2.2 highlights the importance

of industry-specific conversion factors. The listed Laspeyres, Paasche, and Fischer gross

37. See Jong and Woltjer, “Depression Dynamics” for an elaborate discussion of the business cycleand capacity utilization effects and a sensitivity analysis for the interwar period.38. Ritschl, “The Anglo-German Industrial Productivity Puzzle,” 549; S.N. Broadberry, R. Fremdling,

and P. Solar, “European Industry, 1700–1870,” Jahrbuch fur Wirtschaftsgeschichte Vol. 2 (2008): 141–171, 93239. Broadberry and Irwin, “Labor Productivity in the United States and the United Kingdom,” 261.

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Table 2.2: Purchasing power parities

Industry PPP (GER/US)

1909 1936/35

Exchange rate 4.20 2.48

Lasp. Paas. Fisch. Lasp. Paas. Fisch.

Mining 7.18 6.19 6.66 5.36 5.44 5.40

Manufacturing 4.33 3.49 3.89 3.84 3.00 3.39

Food and kindred products 4.19 2.87 3.47 3.31 3.83 3.56

Textile mill products 2.85 2.85 2.85 3.29 3.29 3.29

Tobacco manufactures . . . . . . . . . 2.72 2.65 2.69

Paper and allied products 4.59 4.53 4.56 3.78 3.51 3.64

Chemical and allied products 3.64 2.83 3.21 3.09 2.63 2.85

Petroleum and coal products 6.63 6.05 6.33 3.96 2.04 2.84

Rubber products 7.71 7.71 7.71 4.99 3.53 4.20

Leather and leather products 4.86 5.42 5.13 4.22 4.25 4.23

Stone, clay, and glass products 4.13 4.12 4.12 3.14 2.78 2.95

Primary metal products 3.58 3.22 3.39 2.89 2.74 2.81

Transportation equipment 4.82 5.03 4.92 4.83 3.84 4.31

Sources: see text, section 2.3. For UVRs: see appendix A.8 and A.10.

output PPPs differ markedly between manufacturing industries and the exchange rate

functions poorly for the purpose of converting industrial output. On the aggregated

level, however, the 1909 Fischer PPP for industry closely resembles the official ex-

change rate, which signals that the latter reflects fairly accurately the average price

ratio between Germany and the US. This no longer holds for the interwar period, when

the official exchange rate overvalued the Reichsmark with considerable margin. In this

case, the use of the exchange rate to convert Reichsmark to US Dollar would introduce

a bias in the productivity comparison in favor of Germany.

In addition, in view of the high coverage of the UVRs, listed in table 2.3, the PPPs

constructed here provide a reliable as well as methodologically appropriate alternative

to the official exchange rate. On average, the output that is matched between pre-WW1

Germany and the US in the construction of UVRs covers, respectively, 61% and 50%

of the output that is compared in the labor-productivity benchmark. For the interwar

period the coverage of compared output is similar. In contrast, the number of product

matches is much larger for 1936/35 than it is for 1909. The fact that a substantially

larger number of matches does not lead to a correspondingly larger share of covered

output reflects the increased complexity and product diversity of manufacturing. The

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Table 2.3: Coverage and number of UVRs

Industry Share in compared output No.

1909 1936/35 1909 1935

GER US GER US

Mining 94 52 60 27 7 5

Manufacturing 61 50 74 48 74 125

Food and kindred products 4 8 70 47 6 9

Tobacco manufactures . . . . . . 57 118 1

Textile mill products 58 30 148 114 11 7

Paper and allied products 100 60 43 25 3 12

Chemical and allied products 143 38 94 21 15 33

Petroleum and coal products 86 86 59 9 8 3

Rubber products 64 98 69 73 1 3

Leather and leather products 79 120 133 251 5 7

Stone, clay, and glass products 43 42 70 57 2 7

Primary metal products 74 58 76 46 18 27

Transportation equipment 68 57 47 57 5 16

chemicals and primary-metals industries are a case in point. In both situations an

increased number of matches did not produce a higher coverage of output.

For some industries, the UVRs cover more than the output included in the labor-

productivity comparison. Products designated to a SIC category are sometimes classi-

fied in the ‘wrong’ industry group in the primary source. Although in such cases the

UVRs of these products are reclassified here in the ‘correct’ industry group, their out-

put is usually not included in the labor-productivity comparison. For instance, leather

gloves are classified as a product of the apparel industry in the historical statistics,

but, according to the SIC, belong to leather production. As such, the UVR of leather

gloves are included in this study in the PPP for the leather industry, but their output

cannot be included in the labor-productivity comparison for the leather industry when

the corresponding labor employed in the production of leather gloves is not separately

reported in the primary source.

Looking at the PPPs presented in table 2.2, for several industries the Laspeyres

and Paasche PPPs vary substantially, specifically in 1936/35. This is an indication of

structural differences between the countries under comparison. The deviation between

the Laspeyres and Paasche PPPs stems from the use of, respectively, base-country (US)

or non base-country (German) weights for the process of aggregation and variation

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between the two is evidence of dissimilar production structures. A clear example is the

case of the petroleum and coal industry in 1936/35; the early adoption of petroleum-

based production techniques in the US led to a large gap in output prices relative to

Germany. That is, US petroleum refining was more cost efficient and when assigned a

large share in industrial output the industry PPP (Laspeyres) takes on a high value

reflecting the relatively low production cost in America. Reversely, the specialized coal-

based chemical industry in Germany was still able to produce cost efficient against its

American counterpart. As in Germany coke production took on high importance relative

to petroleum refining – exactly the opposite of the American case – using German

output weights (Paasche PPP) produces a PPP very different from the conversion factor

obtained through application of US output shares.

Table 2.4: German/US comparative labor productivity (US = 100%),Single-deflated gross output per employee/hour

Description Per employee Per hour

1909 1936/35 1909 1936/35

Mining 40 29 44 26

Manufacturing 57 52 56 47

Food and kindred products 55 43 55 41

Tobacco manufactures 30 35 30 34

Textile mill products 90 111 86 103

Paper and allied products 53 52 53 46

Chemical and allied products 80 105 82 96

Petroleum and coal products 42 55 43 50

Rubber products 50 46 51 43

Leather and leather products 66 57 65 55

Stone, clay, and glass products 51 54 51 48

Primary metal products 67 103 64 88

Transportation equipment 30 24 30 21

Sources: see text, section 2.3. For output and employment data: seeappendices A.2, A.3, A.4 and A.5.

The conversion of industrial output with the PPPs reported in table 2.2 enables

a comparison of German and US labor-productivity levels in 1909 and 1936/35. The

results of these comparisons are listed in table 2.4. Because of the data constraints

discussed in section 2.3 above, comparative productivity for 12 pre-WW1 industries

could be calculated (11 manufacturing industries and mining). Although for 1936/35

it was possible to provide full-manufacturing coverage, for reasons of consistency and

comparability table 2.4 includes an estimate based on the same selection of industries as

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studied for the year 1909. The employment coverage of the prewar comparison amounts

to 34% for Germany and 47% for the US. In 1936/35 the coverage of the prewar industry-

sample was 32% and 33%, respectively.40

The lack of full-coverage data for the pre-WW1 period may introduce a bias in

the estimates. Indeed, on the aggregate level, a comparison between the sample and

full-coverage results for 1936/35, presented in table 2.5 below, shows that the former

overstate Germany’s performance by about 10%. The difference is accounted for by

two effects. First, the performance in some German industries is overestimated by the

sample data. For textiles, chemicals and, to a lesser extent, primary metals the total-

industry results show a much poorer performance on the part of Germany. This indicates

that the production activities covered by the 1909 sample displayed an a-typically high

performance level, a finding that helps explain the strong performance in some parts of

German manufacturing, an issue to which I later return. Second, several industries are

excluded by the sample data and these tended to perform relatively weak. Including

these industries thus drags down the overall level of German labor productivity. Then

again, even after downward adjusting the performance in some industries, the main

characteristics of German manufacturing remain unaltered, as do the conclusions drawn

on the basis of the comparison.

With regard to these conclusions, the top row of tables 2.4 and 2.5 reports the com-

parative performance on the aggregate level and shows that Germany tracked America

at considerable distance, both in 1909 and 1936/35. If I accept the idea that countries

lagging behind look to the universal productivity frontier for catch-up growth, as is

often suggested in the literature, Germany had yet a long way to go by 1909.41 De-

spite this large potential for catch-up growth, at the end of the interwar period German

and American levels of productivity had not converged. Instead, the US extended its

lead and the German/US productivity ratio dropped from 57% to 52%, which might

not come as surprise given the many calamities since 1914. Still, the distance to the

US before WW1 was larger for the UK than it was for Germany. Given Britain’s edge

over Continental Europe all through the nineteenth century, this change in European

productivity leadership signifies a success on the part of Germany in modernizing the

manufacturing sector since the second industrial revolution.

Up till this point I have looked at gross output per employee in Germany and the US.

40. For Germany 1909 95% of mining employment is covered. All employment is taken into accountfor US 1909, US 1935 and Germany 1936.41. The idea of catch-up is old and can be found in the works of, for instance, Gerschenkron, Economic

backwardness, 113, 116 and Abramovitz, “Catching-up,” 387. For more recent frontier analysis see,for example, Acemoglu, “Directed Technical Change,” 39 and Vandenbussche, Aghion, and Meghir,“Growth, Distance to the Frontier and Composition of Human Capital,” 98.

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Table 2.5: German/US comparative labor productivity(US = 100%), sample and full-coverage data

Description Per employee Per hour

Sample All Sample All

Mining 29 29 26 26


Food and kindred products 43 45 41 44

Tobacco manufactures 35 35 34 34

Textile mill products 111 74 103 69

Apparel products . . . 49 . . . 39

Lumber and wood products . . . 49 . . . 46

Paper and allied products 52 52 46 46

Chemical and allied products 105 72 96 66

Petroleum and coal products 55 56 50 51

Rubber products 46 41 43 39

Leather and leather products 57 50 55 48

Stone, clay, and glass products 54 48 50 43

Primary metal products 103 93 88 79

Fabricated metal products . . . 48 . . . 42

Machinery (excl. electrical) . . . 49 . . . 40

Electrical machinery . . . 49 . . . 43

Transportation equipment 24 25 23 22

Sources: see text, section 2.3. The coverage and number of the UVRs: see(for sample) table 2.3 and (for full coverage) appendix A.6 and A.7

There are, however, good reasons to adjust for working hours. Over the interwar period

both the US and European countries saw a rapid drop of hours worked per year. The

increased bargaining power of labor unions, but also the effects of the Great Depression

led to a shortening of the working week and an increasing number of holidays.42 Since

the change in hours worked was larger for the US, adjusting for hours will affect the

labor-productivity comparisons. For the US, total annual hours worked plummeted from

2,718 in 1909 to 1,817 in 1935; a drop of 33%. The corresponding figures for Germany

42. The correction for hours worked is based on data from M. Huberman, “Working Hours of theWorld Unite? New International Evidence of Worktime, 1870–1913,” Journal of Economic HistoryVol. 64, no. 4 (2004): 964–1000 and M. Huberman and C. Minns, “The Times They are not Changin’:Days and Hours of Work in Old and New Worlds, 1870-2000,” Explorations in Economic HistoryVol. 44, no. 4 (2007): 538–567. In addition, several primary sources have been used, i.e. KaiserlichenStatistischen Amte, Statistisches Jahrbuch fur das deutschen Reich, Hoffmann, Das Wachstum, UnitedStates Department of Commerce: Bureau of the Census, US Census of Manufactures 1910 (VIII),International Labour Office, Year Book of Labour Statistics 1939 (Geneva: International Labour Office,1939) and R. Matthews, C.H. Feinstein, and J.C. Odling-Smee, British Economic Growth, 1856–1973(Oxford: Clarendon Press, 1982), 1–712.

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are 2,723 hours in 1909 and 2,073 in 1936, which signifies a sharp decrease of hours

worked as well, but the reduction was not as pronounced as in the US. Table 2.4 reports

the comparative labor-productivity levels corrected for hours worked. Compared to the

output per employee results the correction for differences in working hours matters

hardly for the year 1909. In contrast, it makes a big difference for the interwar period

and places Germany even further at the back foot.

The benchmark results call for a moderate revision of German industry’s competi-

tiveness relative to the US. First, before WW1 German industry was somewhat stronger

than implicitly indicated by B&B, but certainly not as strong as suggested by Ritschl.

Second, the considerable drop in comparative performance over the interwar period

when labor input is measured by hours worked questions the stationary 2:1 ratio at-

tributed to the transatlantic productivity gap in Broadberry’s work, a conclusion also

drawn by de Jong & Woltjer for the case of the US and the UK.43 Nevertheless, the

findings on the aggregate level broadly align with the traditional view on the ‘produc-

tivity race’, in which the US enjoyed a commanding lead over Europe throughout the

first half of the twentieth century.44 So far as the contribution of the comparisons is

concerned, their value mainly derives from the new information provided on the disag-

gregated rather than the aggregated level. Productivity estimates on the level of total

manufacturing hide industry-specific dynamics and aggregate results do not always ef-

fectively capture the growth experience of underlying industries. Previous benchmark

studies have frequently found considerable inter-industry differences in comparative

labor-productivity levels. Such is also the case for the German/US comparison. Whereas

German manufacturing on average dropped far behind the US, comparative performance

in manufacturing industries ranged from very poor to impressively strong.

Variation in comparative performance between industries can be explained as an

economy’s productivity profile. Broadberry, for instance, has pointed at Britain’s char-

acteristic comparative advantage in light industries, where the productivity gap with

the US was smaller than on the level of total manufacturing.45 There is more than one

story to be told for German manufacturing industries, too. Classified according to their

distance to the frontier, German industries can be grouped in two categories. First,

many industries failed to keep-up with the US and performed at a level half that of

their American counterparts, or even less. At the low end of this group are tobacco

manufacturing and the transportation-equipment industry, while paper production and

leather performed somewhat better. A second group is formed by industries that man-

43. Jong and Woltjer, “Depression Dynamics.”44. Broadberry, The Productivity Race, 34.45. ibid., 26-27.

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aged to keep up with the US. This was a small group constituted by textiles, chemicals

and primary metals.

When we probe deeper and dissect the manufacturing sector on the SIC 3-digit

level, the pronounced variation between comparative levels of performance persists.46

For instance, a break down of primary metals in 1909 points out that the iron & steel

industries in Germany were not at all inferior to the US. A low efficiency in nonfer-

rous metals, however, depresses the productivity level for German primary metals as

a whole. Similarly, the large gap between Germany and the US in petroleum & coal

production was caused by a low level of productivity in German petroleum refining. In

coke production Germany was no less efficient than America. The comparative perfor-

mance of chemical industries in 1936/35 varied, too; while the German paint production

performed at a third of the US level, Germany enjoyed an advantage over America of

about 2:1 in the fertilizer industry. In short, the range of German industrial performance

relative to the American frontier was large.

In spite of the observed variation in comparative labor productivity between in-

dustries, the pattern of strong versus weak performers persisted over time. Industries

already performing distinctively weak or strong before 1909 did likewise in 1936/35. Es-

pecially industries at the lower end of the performance scale were predominantly station-

ary. Leather manufacturing, paper production, petroleum refining, and transportation-

equipment industries all persistently trailed the US at a large distance. At the opposite

end of the spectrum textiles, primary metals and chemicals, all of which already did

well in 1909, improved their comparative performance. Still, table 2.5 suggests that

the industries included in the sample performed a-typically strong compared to total-

industry comparative labor productivity. In particular, the spinning activities studied

in the sample does much better in relation to the US than the textile industry on the

whole. The same goes for chemicals and, to a lesser extent, primary metals. Even so,

between the sample- and full-coverage comparison the top-three German industries dis-

playing the strongest comparative performance is the same, only their relative levels

drop from parity to about two-thirds the level of the US. The recurrence in 1936/35 of

a productivity profile similar to the case of 1909 suggests that the level of comparative

performance was dictated by long-run growth determinants, rather than the turbulence

of the period.

With respect to this persistent productivity profile, an identifying trade mark for all

strong or weak performing German industries, such as the aforementioned distinction

between light versus heavy industries in the case of the UK, is not directly evident. Yet

46. Compare the data underlying the 2-digit labor-productivity levels in appendices A.2 and A.4. Forthe UVRs needed to express both output values in a common currency, see appendix A.8.

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there is, I think, a common denominator shared by all three of the industries that man-

aged to approach US levels of labor productivity: the well-performing industries produce

mainly basic, standardized goods. A large part of the primary-metals industry’s output

is processed further in the fabricated-metals, (electrical-) machinery or transportation-

equipment industries. The textile industries studied for 1909 concern spinning activities

producing yarn and thread, which is subsequently used in weaving or apparel industries.

The chemical products included in the prewar sample, in particular sulfuric acid and

potassium compounds, form the intermediate inputs needed for the production of fertil-

izers. Indeed, for textiles, chemicals as well as primary metals the 1936/35 comparison

shows that the prewar industry sample displays a level of labor productivity well above

the average for the industry as a whole (see table 2.5). Conversely, many of the indus-

tries facing a particular large gap to the US involved the production of predominantly

consumer goods, the food & drink and transportation equipment industries being prime

examples.

2.5 Labor-productivity growth in interwar Germany

In spite of the large productivity gaps in both 1909 and 1936/35, German industries

did not necessarily lack progress. Conditional on the rate of productivity growth in the

US, a German industry could rapidly increase productivity levels and still fail to catch-

up. Table 2.6 provides an overview of productivity growth in German manufacturing

industries between 1909 and 1936. As with the German/US comparisons, the results are

obtained through application of the ICOP methodology where industry-specific PPPs

are constructed on the basis of UVRs. Because it concerns a single-country intertemporal

comparison, the PPP is interpreted as a price index and used to convert nominal to real

output, i.e. 1909 Goldmark into 1936 Reichsmark. The labor-productivity difference

between 1909 and 1936 is expressed in average per annum growth to get a accurate

estimate of the pace of change.

The growth rates measured at the industry level can be contrasted with Hoffmann’s

time-series estimates constructed in the German HNA. As mentioned in the introduc-

tion, the output and employment indices presented in the HNA are contested and doubts

have been cast upon the reliability of these series. My intertemporal benchmark com-

parison does not suffer from the problems associated with the time series and, therefore,

offers a convenient alternative.47. Table 2.6 also reports the average annual growth rate

of output per employee in German industries between 1909 and 1936 calculated on

47. See chapter 4 for an elaborate discussion of the problems associated with Hoffmann’s time series

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Table 2.6: German average annual labor-productivity growth (%)

Description PPP Annual lab.-prod. growth

09/36 This study Hoffmann

Employees Hours Employees

Mining 0.94 2.2 2.6 2.2

Manufacturing 0.94 0.6 1.7

Food and kindred products 0.66 -1.2 -0.1 } -0.3Tobacco manufactures . . . 1.7 2.8

Textile mill products 0.76 -0.5 0.8 -0.5

Paper and allied products 1.11 1.5 2.4 1.9

Chemicals and allied products 1.16 1.7 2.8 } 2.1Petroleum and coal products 0.85 0.8 1.9

Rubber products 2.01 1.6 2.8

Leather and leather products 0.99 -0.5 0.6 -0.3

Stone, clay, and glass products 1.01 2.4 3.5 1.5

Primary metals products 0.91 0.7 1.7 0.6

Transportation equipment 2.07 5.7 6.8

Sources: see text, section 2.3. For UVRs: see appendix A.9. For output andemployment data: see appendix A.2 and A.3.

the basis of Hoffmann’s data. The intertemporal benchmark results correspond reason-

ably well with the Hoffmann estimates. The growth rates observed for mining, textiles,

leather and metal production differ little between the calculations of Hoffmann and my

own. Other industries deviate more, but the gap is nowhere large. An exception is the

case of Hoffmann’s building materials industry, which set out against the stone, clay,

and glass industry shows a relatively low rate of growth. This discrepancy is driven by

the composition of the industries, which differs between Hoffmann’s and my classifi-

cation. The growth of the stone, clay, and glass industry is driven mainly by cement

production, a process that underwent rapid change over the interwar years.

A second advantage of the intertemporal benchmark is that it provides more detail

as compared to Hoffmann’s estimates. For example, whereas in case of the latter the

food & kindred industry is combined with tobacco, the benchmark separates the two

and shows that the -0.3 average annual growth rate displayed by Hoffmann’s series is the

result of a decline in output per employee levels in food & kindred, which is in turn partly

offset by an increase in tobacco production. The same applies to chemical industries;

whereas the benchmark distinguishes between chemical and petroleum production, the

time series do not allow for such a break up. Moreover, transportation equipment and

rubber industries are not covered in the NHA, although they do include data on wood

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production and printing activities; industries for which the data required by the ICOP

methodology is not available.

Third and final, the intertemporal benchmark presented here corrects for differences

in working hours between the prewar and interwar period, which Hoffmann’s series do

not. As noted above, in Germany the total number of hours worked on an annual basis

decreased by 25%. Taking account of the reduction in labor input leads to an upward

adjustment of labor-productivity growth. As a result, the food, textile and leather in-

dustries no longer display a negative rate of labor-productivity growth, as implied by

Hoffmann. Rather, these industries stagnated or experienced little growth only. The

correction for hours increases the average annual growth rate in industries by about 1

percentage point across the board, with the exception of mining for which the adjust-

ment makes a small difference only. The reduction of hours worked was considerably

smaller in mining, mostly because the time spent below ground was already relatively

low in 1909.

Table 2.6 shows that on the aggregate level German industry realized a moderate

rate of labor-productivity growth, with mining doing better than manufacturing. On

the level of industries, the growth experience varied considerable. Some industries dis-

played rapid growth while others appeared to stagnate or even decline. With respect

to the former, the common denominator of fast-growing industries appears to have

been maturity. In particular ‘young’ industries developed rapidly, the transportation

equipment industry being a prime example. The latter’s fast-paced growth is reflected

by the price ratio between 1909 and 1935. The price level dropped sharply between

1909 and 1936, a characteristic feature of rapidly developing industries. Rubber, which

through the production of tires was closely related to the motor-vehicles industry, and

chemicals & allied belong to this category, too. Industries born (chemicals, motor ve-

hicles, tires, petroleum)48 or extensively modified (primary metals, tobacco)49 during

the late nineteenth century succeeded in raising productivity levels. In contrast, none

of the stagnated industries (e.g. food & kindred, textiles and leather) can be plausibly

typecast as young.50

The results presented in table 2.6 shed new light on the comparative German/US

productivity levels. Germany’s outstanding comparative performance in textiles over

the interwar period suddenly looks less impressive knowing that the relatively small

productivity gap resulted from a lack of any significant progress in both countries. To

48. Landes, The Unbound Prometheus, 234.49. ibid., 235.50. Although these industries modernized, too. In textiles, for instance, the ring spindle gradually

replaced the mule and the food industries witnessed the introduction of new techniques that conservedproducts for a longer time. Broadberry, Fremdling, and Solar, “European Industry,” 158, 161.

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all appearances, it involves a matured industry already well past its growth stage. On

the other hand, Germany’s success in keeping-up with the US in emerging industries,

e.g. chemicals & allied, and industries still in development, such as primary metals,

stands out more firmly. Furthermore, the large and persistent productivity gap in the

transportation-equipment industry was, perhaps, not the failure it at first seems to have

been; the persistent gap between Germany and the US might be understood best as a

success on the part of the latter rather than a failure of the former.

So despite an increasingly large gap to the US, Germany did not lack development.

This development is reflected by the fast labor-productivity growth experienced in sev-

eral industries in table 2.6, but can also be deduced from changes in the employment

structure. Table 2.7 reports the employment shares on the industry level in 1909 and

1936/35 for both Germany and the US. In Germany the combined share of typically

modern industries – i.e. (petro)chemicals & rubber and machinery & engineering – al-

most tripled from 9% in 1909 to 26% in 1936. In the US the same industries employed

12% of labor in 1909 and, like Germany, 26% in 1936. Moreover, in Germany the share

of food & kindred, a low-productive industry, rapidly declined, while several other ma-

tured industries, such as wood production, developed along similar lines. The combined

share of textiles and apparel remained stable over the years (from 24% in 1909 to 23%

in 1936), but it did likewise in the US (21% and 22%, respectively). Even though the

move of labor toward modern industries did not lead to catch-up growth, the German

manufacturing sector was restructuring between 1909–1936 in a fashion not dissimilar

to the US.

Table 2.7: Employment shares Germany and US (%)

Description GER US

1907 1936 1909 1935


Food & tobacco 16 8 12 12

Textiles & apparel, leather 31 24 26 26

Wood & furniture 9 5 14 7

Paper & printing 5 6 8 9

(Petro)chemicals & rubber 2 6 5 9

Metals 14 17 18 14

Machinery & engineering 7 20 7 17

Miscellaneous 17 12 10 6

May not sum to total due to rounding. Sources: see textsection 2.3.

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From the results presented in this section, I draw three conclusions. First, the Ger-

man manufacturing sector was characterized by high cross-industry variation in com-

parative performance, a feature not captured by total-industry estimates. Although on

average German manufacturing persistently lagged behind, several industries performed

on par with their US counterparts. There is no evidence of convergence, as most indus-

tries faced an increasingly large gap to the US. Secondly, despite a relative decline in

competitiveness, German industry did not stagnate. From the intertemporal productiv-

ity comparison it is clear that several industries realized fast growth. However, there is

no relation between the rate of growth over time and the distance toward the US frontier.

For example, labor productivity in the transportation-equipment industry increased at

an unprecedented rate, but proved not enough to close on its American counterpart. In

contrast, the textile industry failed to improve labor-productivity levels over time, yet

was able to deliver a strong comparative performance all through the period of study.

Thirdly, from this I infer that the drivers of growth and catch-up were not necessarily

the same. Related to this, I tentatively suggest that a strong performance as compared

to the US coincides with the production of basic, standardized goods, while fast growth

over the interwar period appears to depend on an industry’s maturity mainly.

2.6 Drivers of growth and catch-up

Given the political and social mayhem of the period, the increasing productivity gap

between Germany and the US on the aggregate level is, perhaps, not much of a surprise.

And, as Hannah puts it, “it seems obtuse to seek the reasons for these standings [in

comparative productivity levels] in the traditional subject matter of business history.

The laggards spent much of the first half of the twentieth century killing one another”.51

Yet the benchmark comparisons show a number of German industries that either man-

aged to close-in on the US or displayed fast growth over the interwar period. These

industries stand out sharply against the backdrop of comparative failure or stagnation

in German manufacturing and the stark contrast between successful development and

backwardness requires further examining. Moreover, the wide-spread range of compar-

ative industrial performance suggests that factors other than the period’s turbulence

in general were at play, a belief that is strengthened by the recurrence of a similar

productivity profile in 1909 and 1936/35. The latter’s persistence fuels the notion that

comparative performance was determined by factors present throughout the entire pe-

riod of study.

51. Hannah, “The American Mircale,” 209–210.

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Based on the literature I develop in this section an argument that helps explain

the benchmark results. Central to the line of argument pursued here is the idea that

large-scale production induces efficiency advantages.52 It has often been suggested that

the scale of production was relatively large in the US compared to Europe. As a result,

the former adopted standardization and high-throughput production technology, usu-

ally associated with high levels of capital intensity, more widely than the latter.53 It is

a popular belief that US producers were able to realize large-scale production because,

among other things, they enjoyed a large domestic market characterized by homogenous

demand. European markets, in contrast, were smaller and typically specialized in cus-

tomized production.54 From this line of reasoning it follows that in particular German

manufactures of consumer goods must have suffered from heterogeneous demand, not

producers of basic goods. Given that the strong performing German industries have been

associated with the production of basic goods, the relative scale of production between

Germany and the US may help understand the comparative productivity findings.

Establishment size

With the framework set out above in mind, the first question to answer is whether

the scale of production actually differed between the US and Germany. There are two

ways in which scale is related to production efficiency and, hence, measured. First, if

establishments (factories) are large, efficiency advantages can be obtained by standard-

izing production lines. Second, large firms that incorporate several (or all) stages of the

production chain, i.e. firms that are vertically integrated, may enjoy efficiency benefits

through a reduction of transaction costs, especially when markets function poorly.

Looking at the former measure first, the average establishment size in German man-

ufacturing was indeed smaller than in the US, as Kinghorn and Nye show for the

pre-WW1 period.55 Table 2.8 reports for several manufacturing industries the share of

industrial employment working in establishments with over 50 employees. The picture

is the same across the board; the employment share working in large-scale establish-

ments is in each and every case lower in Germany than it is in the US. Nevertheless, the

size of the gap varied between industries. The difference is almost nonexistent in iron

& steel and quite small for chemicals as well. In contrast, if the share of employment

working in large-scale establishments provides an indicator of industrial development,

52. Chandler, Scale and Scope, 23.53. Rostas, “Industrial Production, Productivity and Distribution,” 58-59; Chandler, Scale and Scope,

47; Landes, The Unbound Prometheus, 247.54. Broadberry, “Technological Leadership,” 291.55. Kinghorn and Nye, “The Scale of Production,” 99.

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the German textile, lumber, leather, and food industries lagged substantially behind; in

these industries the share of workers employed in establishments with over 50 workers

was 3, 4, or – in the case of the food industry – even 5 times smaller than in the US.

Table 2.8: Share of workers employed in establishmentsemploying >50 workers (%)

Description US 1909 Germany 1907

Textiles 93 38

Paper and printing 67 51

Lumber 81 22

Leather 90 25

Iron and steel 99 98

Food 67 13

Ceramics 85 55

Chemicals 85 70

Sources: J. Kinghorn and J. Nye, “The Scale of Production inWestern Economic Development: A Comparison of OfficialIndustry Statistics in the United States, Britain, France, andGermany, 1905-193,” Journal of Economic History Vol. 56, no.1 (1996): 90–112, 99.

For iron & steel and chemicals the small difference in average establishment size coin-

cides with a correspondingly small difference in labor-productivity levels. I am inclined

to relate Germany’s emphasis on large-scale production in these industries to Hannah’s

observations regarding giant plants (>1,000 workers) in Germany and the US. Hannah

states that giant production units were particularly representative for “modern” in-

dustries and in chemicals, shipbuilding, and electrical manufacturing Germany counted

more giant plants than the US.56 The opposite conclusion applies to tobacco and auto-

mobiles.57 With the exception of electrical engineering, the presence of giant plants or

the lack thereof corresponds well to the comparative productivity levels presented in this

research; chemicals performed on par with the US, while the transportation-equipment

industry and tobacco manufacturing trailed the American frontier at considerable dis-

tance. Moreover, following Kinghorn and Nye, Hannah underlines Germany’s overall

smaller average establishment size in manufacturing and suggests it might have been

the bulk of small workshops that drove Germany’s low overall labor productivity.58

So far as the data of Kinghorn and Nye go (table 2.8), the small share of employment

working in large-scale establishments reported for the German textile industry is difficult

56. Hannah, “Logistics, Market Size, and Giant Plants,” 68.57. ibid., 69.58. ibid., 72.

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Table 2.9: Distribution of employment over establishment-size classes (%) inGerman manufacturing industries, 1907

SIC Industry ≤ 50 51–1,000 ≥ 1, 001

Textiles Totala 28 72 0

Cotton spinning 31 69 0

Linen spinning 18 82 0

Jute spinning 7 93 0

Silk spinning 51 49 0

Chemicals General chemicals 29 58 13

Petroleum and coal Totala 21 79 0

Petroleum 66 34 0

Coke 18 82 0

Stone, clay and glass Cement 12 84 4

Primary metals Totala 15 59 27

Iron & steel 5 55 40

Cast iron 30 60 11

Nonferrous metals 9 82 9

Transportation equipment Motor vehicles 21 50 29a Industry totals are a weighted average calculated using employment weights. Foremployment data, see appendix A.2.May not sum to total due to rounding. Sources: Kaiserlichen Statistischen Amte,“Gewerbliche Betriebsstatistik,” in Berufs– und Betriebszahlung, Statistik des deutschenReichs (Berlin, 1907).

to reconcile with the strong labor-productivity performance it delivered in both 1909 and

1936/35. This puzzle can be explained by table 2.9, which reports for several industries

covered by the labor-productivity comparisons the distribution of employment over

establishment-size classes. For 1909 the textile industries included in the comparison

concern spinning activities and table 2.9 shows that in these industries the employment

share working in establishments with over 50 employees was much higher than the 38%

reported by Kinghorn and Nye for the whole of textiles. In cotton spinning, which was

the largest spinning industry in terms of employment, this share amounted to 69%. The

other textile industries, i.e. jute, linen and silk spinning, employed 93, 82 and 49% of

total labor in large-scale establishments, respectively. Clearly, the spinning industries

not only displayed above average labor-productivity levels, as the 1936/35 comparison

testifies, they were also characterized by relatively large establishments. For the other

two strong performers, i.e. iron & steel and chemicals, the employment share working

in large establishments differs not between the industry sample of the comparisons and

Kinghorn and Nye’s data. So each of the three German strong-performing industries

produced on a scale not much smaller than their American counterparts.

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Vertical integration: firms versus cartels

The establishment-size data provides some empirical support for the notion that in

textile spinning, iron & steel, and chemicals Germany faced little problems when it

comes to production scale. If these industries were indeed involved in the production of

predominantly basic goods and did not face a demand for customized consumer goods,

the comparable establishment size in both countries suggests that technical constraints

to standardized production should be no less in Germany than in the US. However,

the efficiency effect of establishment size may be offset by differences between German

and US firm size. Optimal firm size is partly determined by the relative costs of market

transactions and if markets function poorly these transaction costs can be lowered by

integrating several (or all) stages of the production chain within a single firm. As the

size of firms was typically larger in the US than in Europe, this potentially endowed

American producers with an advantage over their German competitors.59

But there is a problem with this argument. In the case of firm size, being larger is not

always better. More specifically, as optimal firm size is determined by transaction costs,

the smaller firm in Germany might simply reflect a well-integrated market that reduced

the incentive for firms to extent their control over more stages of the production chain.

Country-specific conditions conducive to low transaction costs can thus limit the size

of firms. Cartels – a much favored model of industrial organization in Germany – could

have provided such conditions and the smaller firm size in Germany need not have been

a sign of backwardness.60 Cartels offered an alternative way to attain a reduction of

transaction costs. Through the control exerted by the cartel over different stages of the

production chain, coordination problems could be addressed efficiently without having

to integrate these production stages in one firm. Related to this, the stability offered

by cartels potentially induced higher rates of investment, leading to capital deepening

and productivity growth.61

Although cartels are associated with a reduced intensity of competition, moving Ger-

many away from competitive capitalism, the literature on Germany is strikingly positive

about the effect of cartels on economic development.62 If German cartels tended toward

a monopoly control of the market, they could have closed the door on technological

development, yet Burhop and Lubbers conclude that in the case of German coal-mining

corporations productivity was not significantly affected by cartel membership.63 Over

59. Chandler, “Organizational Capabilities,” 83.60. Kinghorn and Nye, “The Scale of Production,” 109; Hannah, “The American Mircale,” 207–208.61. Levenstein and Suslow, “What Determines Cartel Success,” 85.62. J. Kocka, “Entrepreneurs and Managers in German Industrialization,” The Cambridge Economic

History of Europe Vol. 7 (1978): 492–589, 564.63. C. Burhop and L. Lubers, “Cartels, Magerial Incentives, and Productive Efficiency in German

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the period 1881–1913 there is no evidence that the Rheinisch-Westphalien Coal Syndi-

cate, one of the longest-lasting cartels, adversely influenced levels of technical efficiency.

In similar vein, Kinghorn argues that German coal and iron & steel cartels around

the turn of the century did not lead to true monopoly power, yet they did allow firm

members to use more efficient production technologies.64 Strikingly, the top three of

industries with the largest number of cartels included iron & steel, chemicals and tex-

tiles, i.e. precisely those industries that the benchmark comparisons showed to deliver

a strong performance relative to the US.65

Vertical integration: protectionist policy

Vertical integration was encouraged not only by the cartel system, but also by the pro-

tectionist policy that Germany maintained to restrict foreign competition. The case-

studies in the work of Webb are particularly useful in this respect. Being the leading

advocates of protective tariffs, the iron foundries, cotton-spinning mills, and large-scale

agriculturalists are centrally placed in Webb’s research. To interpret the impact of tariffs

on domestic production accurately, he measures the effective rate of protection, which

captures the tariff-instigated percentage increase in value added and thereby takes into

account the price change in both intermediate inputs and finished products.66 Webb

concludes that, together with cartels, protection encouraged vertical integration; as tar-

iffs raised domestic market prices above the world level, backward integration ensured

purchase of intermediate inputs against cost, rather than market prices.67 Moreover,

by stimulating vertical integration, the tariff system reinforced the stability of prices

already encouraged by cartels, which – as noted above – reduced the riskiness of invest-

ment in capital-intensive technologies.68 Smaller, non-vertically integrated firms faced

market prices above world level and, therefore, did not gain from protection. As a result,

trade tariffs favored the large-scale, more politically powerful enterprises.69

It is hard to say how the increased costs of intermediate inputs affected comparative

productivity between Germany and the US. As in the case of iron & steel and textiles –

Coal Mining, 1881–1913,” Journal of Economic History Vol. 69, no.2 (2009): 500–527, 502.64. J. Kinghorn, “Kartell or Cartel? Evidence from Turn of the Century German Coal, Iron and Steel

Industries,” Journal of Economic History Vol. 56, no. 2 (1996): 491–492, 492.65. Kocka, “Entrepreneurs and Managers,” 564.66. S. Webb, “Tariff Protection for the Iron Industry, Cotton Textiles, and Agriculture in Germany,

1879–1914,” Jahrbucher fur Nationalokonomie und Statistik Vol. 192 (1977): 336–357, 337.67. S. Webb, “Tariffs, Cartels, Technology, and Growth in the German Steel Industry,” Journal of

Economic History Vol. 40, no. 2 (1980): 309–330, 328.68. Additionally, for the pre-1950 period higher tariffs are associated with lower relative capital-

good prices and because the latter are negatively related to the rate of investment protection canstimulate growth through increased investment. W. Collins and J. Williamson, “Capital-Goods Pricesand Investment, 1870–1950,” Journal of Economic History Vol. 61, no. 1 (2001): 59–94, 80, 81.69. Webb, “Tariff Protection,” 353; Webb, “Tariffs, Cartels, Technology, and Growth,” 323.

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i.e. the industries studied by Webb – protectionist policy confers certain cost advantages

to vertically-integrated and large-scale firms. However, these advantages are relative

to other, small-scale firms in German industry. This goes to show that trade tariffs

hurt the competitiveness of small establishments in particular, while larger firms could

only hope to avoid the backlash of protectionism and stand their ground relative to

the US through vertical integration. It is not evident how protectionism improved the

comparative performance of German industries.

Then again, even though domestic firms faced prices above market level as a conse-

quence of tariff walls, protectionist policy may have secured the survival of developing

industries by shutting out foreign competition. Given that many of the German modern

industries were outperformed by their American counterparts, as the comparisons show,

such an infant-industry approach suited these industries well. Indeed, it has been sug-

gested in the literature that in contrast to the post-1950 period, in which – by and large

– growth benefited from free trade, trade tariffs around the turn of the century were pos-

itively correlated with growth.70 As all through the first half of the twentieth century

manufacturing products entering Germany were (increasingly) tariffed, protectionist

policy may explain the growth captured by the German intertemporal benchmark.71

It should be noted, however, that the existence of a ‘tariff-growth paradox’ has been

called into question. Many scholars have used regression analysis to test the hypoth-

esis that economic growth was a function of protection, but different specifications of

the model have led to results both confirming (O’Rourke, 2000; Jacks, 2006) and re-

futing (Capie, 1983; Schularik and Solomou, 2011) the tariff-growth paradox.72 In any

case, the cry for protectionism was fueled in Germany by notions much different than

those set out by the infant-industry argument. The textile industry is a case in point;

not only provided tariffs protection for the strong (instead of the weak), cotton spin-

ning can hardly be described as an emerging industry. To take another example, when

70. P. Bairoch, “Free Trade and European Economic Development in the 19th Century,” EuropeanEconomic Review Vol. 3, no. 2 (1972): 211–245, 242; J.A. Frankel and D. Romer, “Does trade causegrowth?,” American Economic Review Vol. 89, no. 3 (1999): 379–399, 394; K. O’Rourke, “Tariffs andGrowth in the Late 19th Century,” Economic J Vol. 110, no. 463 (2000): 456–483, 473; D. Jacks,“New Results on the Tariff-Growth Paradox,” European Review of Economic History Vol. 10 (2006):205–230, 221.71. V. Hentschel, “German Economic and Social Policy, 1815–1939,” in The Cambridge Economic

History of Europe, ed. P. Mathias and S. Pollard, vol. Vol. 8 (1989), 752–813, 786;C.P. Kindleberger,“Commercial Policy between the Wars,” in The Cambridge Economic History of Europe, ed. P. Math-ias and S. Pollard, vol. Vol. 8 (Cambridge University Press, 1989), 161–196, 180; Broadberry, TheProductivity Race, 141.72. F. Capie, Tariffs and Growth; Some Insights from the World Economy, 1850–1940 (Manchester

University Press, 1994), 42; M. Schularick and S. Solomou, “Tariffs and Economic Growth in the FirstEra of Globalization,” Journal of Economic Growth Vol. 16, no. 1 (2011): 33–70 49, 56. Schularick andSolomou claim that the real paradox is not that free trade was bad for growth, but that changes ininternational economic policies seems to have mattered little to countries’ growth trajectories.

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cheap foreign grain threatened domestic agricultural production, the politically power-

ful landowners successfully lobbied for tariffs.73 And to add insult to injury, because

tariffs on agricultural imports were on balance higher than for manufacturing goods,

the landowners effectively slowed down the rate of GDP per capita growth by delaying

the shift of employment toward high productive industries.74 Clearly, if protectionism

induces growth, it was an unintended byproduct of an otherwise strictly conservative

policy.

Relative factor costs

Apart from the differences in industrial organization between Germany and the US

described above, Europe’s inability to catch-up in general has been explained by the

Rothbarth-Habakkuk thesis. In Europe, factor and resource endowments as well as de-

mand patterns are said to have favored a labor-intensive way of production.75 Natural

resources were scarce and skilled labor was in ample supply, which provided an incentive

to economize on fixed capital in the form of machinery.76 In contrast, the US was well

endowed with natural resources, while skilled labor was relatively expensive. Machinery

was substituted for skilled labor, resulting in the use of capital-intensive production

techniques. This way, local circumstances determined the initial choice of technology.

Technological progress is subsequently directed toward the particular technological path

a country has chosen, leading to lock-in effects.77 As capital-intensive production tech-

niques are associated with higher labor-productivity levels, Europe could not catch-up

with the US.

A study of capital-intensity levels lies outside the scope of this paper. Chapter 3

returns to this issue and provides a thorough analysis of technological development in

both countries. Nevertheless, some remarks are in place here. The benchmark results

do not fit the Rothbarth-Habakkuk thesis exceptionally well. The German industries

that performed on par with their US counterparts challenge the deterministic nature

of the initial-conditions approach. Some scholars – most notably Rosenberg – have de-

scribed America’s lead as foreordained; US resource endowments acted as a benevolent

73. P. Bairoch, “European Trade Policy, 1815–1914,” in The Cambridge Economic History of Europe,ed. P. Mathias and S. Pollard, vol. Vol. 8 (Cambridge University Press, 1989), 1–160, 76; C.P. Kindle-berger, “The Rise of Free Trade in Western Europe, 1820–1875,” Journal of Economic History Vol.35, no. 1 (1975): 20–55, 46.74. S.N. Broadberry, “How Did the United States and Germany Overtake Britain? A Sectoral Analysis

of Comparative Productivity Levels, 1870-1990,” The Journal of Economic History Vol. 58 (1998): 375–407, 386; Hannah, “The American Mircale,” 201.75. Habakkuk, American and British Technology.76. Temin, “Labour Scarcity,” 162;Field, “On the Unimportance of Machinery,” 379.77. David, Technical Choice, 66

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Providence, inevitably setting the stage for America’s edge over Europe.78 However, the

strong German performance in textiles, primary-metals manufacturing, and chemicals

suggests that in these industries either similar production techniques (i.e. capital-to-

labor ratios) were employed by both countries or that higher levels of capital intensity do

not necessarily translate into higher labor-productivity levels. Either way, initial condi-

tions (whatever those were) did not prevent these particular industries from catching-up

and the Rothbarth-Habakkuk thesis – originally suggested as a possible explanation for

19th century Anglo-American, rather than 20th century German-American productivity

differences – sits uncomfortably with the case of Germany.

Some of the cross-industry differences in performance might be explained by the

variance in the degree to which industries rely on raw materials and capital-intensive

production techniques. However, the importance of natural resources in, for instance,

the iron & steel and the textile industries is undeniable, both in the form of raw materials

and as combustibles. A more likely explanation is that factor costs in the ‘successful’

German industries deviated only little from those in the US. In a case study on the pre-

WW1 iron & steel industry, Bob Allen accounted for price differences between German,

American, and British iron products by studying the costs of materials used and the

efficiency of production. Allen finds that in 1910 the price of used raw materials (ore

and scrap) was actually lower in Germany than in both the US and UK.79 Fuel (blast

furnace coke) was more expensive as compared to the US, but cheaper than in Britain.

In line with the benchmark results, Allen shows that productivity in Germany was

comparable to the US and higher than in the UK.80 Iron production in Germany was

characterized by low material costs and high efficiency levels.

Apparently, the costs of using capital in the primary-metals industry did not dif-

fer much between Germany and the US. Does this mean that both countries operated

similar production techniques? In the late nineteenth century, the Bessemer process for

the mass-production of steel from molten pig iron revolutionized the iron & steel indus-

try. Although the large-scale application of the Bessemer process was introduced first

in Britain, the technology was swiftly improved upon in the US so that by the 1880s

the coke-fueled blast furnaces developed in America formed the pinnacle of available

production techniques.81 Hyde shows that when American steel-producing technologies

78. Rosenberg, “Why in America?” 112.79. R.C. Allen, “International Competition in Iron and Steel, 1850–1913,” Journal of Economic His-

tory Vol. 39 (1979): 911–937 932. British iron ore mined in the East Midlands and Cleveland was atleast as cheap as the German ore from West-Phalia, but for some reason Britain mainly used the moreexpensive Spanish ore.80. ibid. 931.81. C. Hyde, “Iron and Steel Technologies Moving Between Europe and the United States, Before

1914,” in International Technology Transfer. Europe, Japan and the USA, 1700-1914, ed. D.J. Jeremy

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proved their superiority European manufacturers started to copy American designs.82

Adoption of American technology by German entrepreneurs is observed in other turn-of-

the-century industries, too. For instance, Richter and Streb present evidence of transat-

lantic technology transfer in the machine-tool industry, a tradition that continued well

into the twentieth century. They quote contemporary industry periodicals, which report

a good many cases where German manufacturers imported American machinery and

incorporated these technologies in their own production process without the slightest

adjustment.83 The implementation of American technology in German industries seems

difficult to reconcile with the idea of technological lock-in driven by local circumstances.

2.7 Conclusion

A more compelling advocate of research on the disaggregated level than the case of Ger-

many is hard to imagine. In the literature the German growth experience has been de-

scribed in different manners; traditionally, emphasis is placed on the success of German

industries during the second industrial revolution, but more recent research struggled to

find quantitative evidence of German catch-up growth. Contingent on the level of aggre-

gation, the German/US productivity comparisons presented here justify both stories.

There are no signs of convergence at the level of total manufacturing. Zooming in on the

performance of underlying industries, however, several clear-cut German successes are

observable, most notably in the production of chemicals, textiles, and primary metals.

The stark contrast between success and failure returns when manufacturing industries

are dissected even further and the focus shifts from the SIC 2-digit to the 3-digit level;

in primary metals, for instance, iron & steel industries performed comparatively strong,

while non-ferrous metal production failed to keep-up with the US.

Not surprisingly, general theories as regards to the German-American productivity

gap have a difficult time accounting for the cross-industry variation. There are nonethe-

less some recurrent patterns recognizable. First of all, while in general the scale of

production was smaller than in the US, there is a striking overlap between the German

industries listed in the literature as having relatively many large factories or even giant

plants, many cartels, and high tariffs and those that performed strong in comparison

to the US. According to the literature the cartel-tariff system had the potential of rais-

ing efficiency levels by encouraging large-scale production, lowering transaction costs

(Aldershot: Edward Elgar, 1991), 51–73 52.82. ibid. 68.83. R. Richter and J. Streb, “Catching-up and Falling Behind. Knowledge Spillover from American

to German Machine Tool Makers,” FZID Discussion Paper (2009): 1–24, 1-2.

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through vertical integration, and creating a low-risk environment for investment.

The notion that these institutions provided German industries with a competitive

edge certainly sits well with the benchmark results. As to why specifically these German

industries managed to upscale production, a possible explanation concerns the nature

of the manufactured products. It has been suggested that the smaller establishment

size in Germany resulted from a domestic demand for customized goods, which ham-

pered standardized production. However, textile spinning, iron & steel and chemical

industries produced predominantly basic, rather than consumer goods. Unconstrained

by heterogeneous demand patterns, large-scale production was attainable.

Looking at productivity growth in German industries between 1909 and 1936,

though, the maturity of industries takes on importance. To name an example, trans-

portation equipment, i.e. the fastest-growing German manufacturing industry, neither

had particularly many cartels nor comparatively large plants. However, ‘born’ around

the turn of the century, it was a typically modern industry. Emerging industries, such as

motor-vehicles and chemicals production, and industries extensively revised in the late

nineteenth century, for instance tobacco and primary-metals manufacturing, displayed

fast growth rates, too. But, as Landes notes, ‘there is a tendency to concentrate on

the most striking examples of German achievement’ and the overall success of German

manufacturing should not be exaggerated: even when German industries grew fast, it

was mostly too slow for catch-up with the US84.

Although the drivers of growth and catch-up seem different (an industry’s maturity

and its scale of production, respectively) they can be reconciled. As demonstrated by the

German industries that are associated with large establishments, the scale of production

serves to exhaust a technology’s potential. However, when technological development

in industries has come to a standstill, labor productivity can increase only through

more efficient exploitation of the technology in use. If scale is a factor in this and

the average establishment size was fairly large already, the scope for growth was little

indeed. Such was the case for textiles. In contrast, when industries experienced rapid

technological change, the level of labor productivity increased, even when the scale of

production was suboptimal. However, if for each new technology large-scale production

is associated with higher labor-productivity levels, industries that attained a sizable

scope of production benefited more from technological change. From this perspective,

the rapid productivity growth between 1909 and 1936 signifies fast technological change,

while the increasingly large gap to the US reflects the inability to benefit fully from new

developments.

84. Landes, The Unbound Prometheus, 317.

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2.A Representativeness of the industrial surveys

To calculate German labor-productivity levels for the prewar period, I mainly rely

on information obtained from the Vierteljahrshefte zur Statistik des deutschen Reichs

(henceforth, statistical quarterlies). In the statistical quarterlies of 1913 the results of

industrial surveys for the years between 1907 and 1911 are published. The surveys re-

port output and employment data for a number of industries. For those industries that

are included, the surveys do not provide full coverage. Instead, the production of a

sample of firms is reported. Partly this is due to the fact that the surveys are only

sent to firms affiliated with the national health-insurance scheme for workers (Gewerbe-

Unfallversicherungsgesetze). Small workplaces are in effect not covered and the scope

of the surveys is thereby limited to the larger firms in German industries. This could

lead to compatibility problems when comparing Germany with the US. The US census

of manufactures provides almost full coverage as only household industries and estab-

lishments with an annual output lower than $500 are excluded.

In order to quantify the potential bias, I need to know which establishment-size

classes are represented by the establishments included in the industrial surveys. If,

for instance, it turns out that the surveys exclude establishments with less than 10

employees, the part of employment covered by those establishments are not represented

by the surveys, which introduces an upward bias in my estimates. Table 2.10 shifts the

cut-off point in the occupational census upwards in a series of steps – i.e. it increases

the number of excluded establishment-size classes – and thereby raises the average

establishment size up till the point that it equals the average establishment size of the

industrial surveys. When that point is reached, I have an approximation of the survey’s

cut-off point and the establishment-size classes that are represented. The occupational

census reports the share of employment working in the represented establishment-size

classes, which helps to estimate the margin for bias. If the employment coverage of not-

represented establishment-size classes is low, the bias in my results is correspondingly

low as well, and vice versa.

Table 2.10 points out, for instance, that the establishments of the motor-vehicle

industry reported by the industrial surveys have on average 244 employees. According

to the occupational census, establishments in this industry have on average 58 employ-

ees. If establishments with less than 21 workers are omitted, that number rises to 198.

Raising the cut-off point even higher – covering all establishments with 51 employees

or more – the average establishment size in the motor-vehicle industry increases to 313.

Somewhere in between lies the average establishment size reported by the occupational

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census, i.e. 244. From this I conclude that the cut-off point of the statistical quarter-

lies for this particular industry is lower than establishment-size class ≥51, but higher

than establishment-size class ≥21. The occupational census reports that the share of

employment working in establishments with more than 51 workers amounts to 86%.

The coverage of establishments larger than 21 employees is 93%. The sample of estab-

lishments included in the industrial surveys is thus representative for at least 86% but

less than 93% of total employment in the motor-vehicle industry. Column l∗i reports the

arithmetic average of these lower and upper bound estimates.

Table 2.10 suggests that the cut-off point of the industrial surveys is high, especially

for the nonferrous-metals, secondary-metals, coke, and motor-vehicle industries. Estab-

lishments with less than 21 employees appear to not have been surveyed. In contrast,

the coverage of the cement, sulfuric-acid, and coal-tar distillation industries extents to

establishments from 6 employees and more. Yet even when the cut-off point is rather

high, the share of employment not covered by the relatively large establishments is very

small. In most industries about 90% of total employment works in establishments with

51 employees or more, as a consequence of which the surveys represent establishment-

size classes that cover almost all employment. Furthermore, because the employment

figures presented in the surveys of statistical quarterly are derived from insurance data

and report the total insured hours expressed in the number of full-time equivalent work-

ers, the actual number of employees in the surveyed industries was likely to be higher

and the average establishment size lower, which increases the establishment-size classes

included in the sample. Thus, although the industrial surveys cover only a small share

of total employment, the average establishment size suggests that the establishments in-

cluded in the industrial surveys cover a wide range of establishment-size classes. In turn,

the employment census points out that nearly all employees work in the establishment-

size classes covered by the industrial surveys, which leads me to conclude that there is

little evidence of a systematic bias toward large establishments in the latter.

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Tab

le2.10:Representativenessofcoverage(l

∗ i)statisticalquarterlies

StatisticalQuarterlies

EmploymentCen

sus

l i ni

l i nifordifferentcut-offpoints

Description

≥1≥6

≥11

≥21

≥51

≥101

l∗ i

Kraftfahrzeu

g-undHilfsindustrie

244

5898

143

198

313

428

89%

Eisenverarbeitungsindustrie

445

270

306

334

366

433

511

96%

Eisen

-undStahlgiessereien

79

79

9110

212

318

627

510

0%

Silber-/Blei-/K

upfer-/Z

inkhutten

310

140

158

175

215

270

324

94%

Schwefelsaure

6936

80

9913

320

730

699

%

TeerDestill.undPetroleumraff.

4330

36

45

6410

818

196

%

Kok

ereien

143

131

134

134

141

165

209

99%

Zem

entindustrie

166

81153

186

219

271

313

98%

Sources:Kaiserlich

enStatistisch

enAmte,“Gew

erblich

eBetrieb

sstatistik,”

inBerufs–und

Betriebszahlung,

Statistik

des

deu

tsch

enReich

s(B

erlin,1907);

Kaiserlich

enStatistisch

enAmte,

“Erganzu

ngsh

eftzu

die

Ergeb

nisse

der

deu

tsch

enProduktionserh

ebungen

,”in

Vierteljahrshefte

zur

Statistik

des

deu

tsch

enReich

s:Erganzu

ngsheft,

vol.Vol.22,no.3(B

erlin,1913).

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2.B Adjustment of the employment census

In contrast to output data, the number of employees working in the food & kindred

industry is not reported by the statistical yearbook. As with the textile industry, for

which only output is reported in the industrial surveys, an additional source is needed

to find employment data necessary to calculate labor productivity. For this purpose

the occupational census is used, both in the case of textiles and food & kindred. The

number of workers in the textile industry derived from the occupation census is adjusted

in line with the coverage of the industrial surveys. This is done according to the number

of establishments included in the surveys. As mentioned, the number of establishments

per establishment-size class are reported by the occupational census. Assuming that the

industrial surveys cover the largest establishments only, I have subtracted the number

of establishments in the largest establishment-size class of the occupational census from

the number of establishments reported by the surveys. In table 2.11 this process is

repeated for all subsequent establishment-size classes until all establishments reported

by the industrial surveys are accounted for. If the assumption that the industrial surveys

capture only the largest establishments in textile industries is violated, my computation

overestimates employment needed for the reported production and, thus, underestimates

labor productivity.

As the share of employment working in small establishments is usually not very

large, the adjustment of the employment number of the occupational census is lim-

ited, too, as can be seen in table 2.11 (in the column headedl∗iLi). Notable exceptions

are the silk and woolen (worsted) industries, where about 25% of total employment

works in establishments with less than 50 employees and which are not accounted for

by the industrial surveys.85 The reliability of these estimates depends on (a) the fit of

the nomenclature between both sources and (b) the assumption that the surveys only

capture the largest establishments being correct. For the linen industry (Flachspinnerei

und -zwirnerei) I can check the accurateness of the estimates, as for the year 1908 em-

ployment is also reported by the industrial surveys. The adjusted estimate, based on

the occupational census, overestimates actual employment by 5.2%, which is reassur-

ingly close. In fact, the difference dissolves entirely when I take into account that the

employment number reported by the industrial surveys refers to 1908 and not 1907,

which is the year for which I calculate labor productivity. Between 1907 and 1908 out-

put in the linen industry increased by 5.5%. If I assume that labor productivity did not

change between 1907 and 1908 (and 5.5% is subtracted from the employment number

85. See table 2.9 for the spread of employment over establishment-size classes in textile industries.

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of 1908 to offset the effects of output growth), the difference between employment as

reported by the surveys and as estimated by me on the basis of the occupational census

amounts to only 0.3%. For employment in the food & kindred industries I have also

used the occupational census. However, in this case no adjustments were needed as the

output data were obtained from the statistical yearbook and thus do not suffer from

the coverage problems associated with the industrial surveys.

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Tab

le2.11

:Employmentoccupational

censuscoveredbytheindustrial

surveys

EmploymentCensus(1907)

IndustrialSurveys(1907)

Description

l iDescription

l∗ il∗ i Li

Bau

mwollspinnerei

98,746

Bau

mwollspinnerei

und-zwirnerei

97,625

98.9%

Seiden

spinnerei

7,41

3Seiden

spinnerei

und-zwirnerei

5,71

277

.1%

Flachshechelei

und-Spinnerei

18,586

Flachsspinnerei

und-zwirnerei

16,439

88.5%

Jute-undZellstoffspinerei

12,868

Jutespinnerei

und-zwirnerei

12,866

100.0%

Wollspinnerei

58,498

Kam

mgarnspinnerei

und-zwirnerei

43,042

73.6%

EmploymentCensus(1907)

StatisticalYearbook

(1907)

Brauerei

111,779

Biergew

innung

111,779

100.0%

Ruben

zuckerfabrikation

37,380

Gew

innungvon

Roh

-und

37,380

100.0%

undZuckerraffinerie

Verbrauchszucker

Starkezuckerfabrikation

und

2,72

2Gew

innungvon

Starkezucker

2,722

100.0%

Melassenverarbeitung

Tab

akfabrikation

61,162

Roh

tabak

infabrikation

sreifem

61,162

100.0%

Zustan

de

Sources:Kaiserlich

enStatistisch

enAmte,“Gew

erblich

eBetrieb

sstatistik,”

inBerufs–undBetriebszahlung,

Statistik

des

deu

tsch

enReich

s(B

erlin,1907),

52–59;Kaiserlich

enStatistisch

enAmte,“Erganzu

ngsh

eftzu

die

Ergeb

nisse

der

deu

tsch

enProduktionserh

ebungen

,”in

Vierteljahrshefte

zurStatistik

des

deu

tsch

enReich

s:Erganzu

ngsheft,

vol.Vol.22,no.3(B

erlin,1913),

68–75;Kaiserlich

enStatistisch

enAmte,Statistisch

esJahrbuch

furdasdeu

tsch

enReich

(Berlin,1909–1912),

1909:‘V

erbrauch

rech

nungen

’–103,107,275.

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Chapter 3The Yanks of Europe? Labor Productivity and

Technology in German and US Manufacturing, 1899–1939

3.1 Introduction

The pattern of divergence between German and American manufacturing uncovered

in the previous chapter roughly aligns with the 2:1 ratio suggested by Broadberry for

the transatlantic labor-productivity gap in the early twentieth century.1 Traditionally,

Europe’s inability to catch-up has been partly ascribed to local circumstances, i.e. factor

and resource endowments as well as demand patterns, which favored labor-intensive

production.2 In Europe, natural resources were scarce, while skilled labor was in ample

supply, the combination of which provided an incentive to economize on fixed capital in

the form of machinery.3 In comparison, the US was well endowed with natural resources,

while skilled labor was relatively expensive. Therefore, machinery was substituted for

skilled labor, resulting in a capital-intensive production process.

Furthermore, as the American demand for goods was more homogenous and given

the size of the US domestic market, manufacturers could standardize production, imple-

ment high throughput systems, and thereby raise productivity levels.4 This advantage

was denied to European countries, which faced heterogeneous markets characterized by

a demand for customized goods. Thus, local circumstances determined the initial choice

of capital-labor ratios. If technological progress is directed towards the capital-labor ra-

tios currently in use, local circumstances can lead to technological lock-in.5 Assuming

that the increase of labor productivity achieved at high capital-intensity levels surpass

1. Broadberry, The Productivity Race, 3; Broadberry and Irwin, “Labor Productivity in the UnitedStates and the United Kingdom,” 265.

2. Habakkuk, American and British Technology.3. Temin, “Labour Scarcity,” 162; Field, “On the Unimportance of Machinery,” 379.4. Broadberry, “Technological Leadership,” 291.5. David, Technical Choice, 66.

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those realized at low capital-labor ratios, labor-productivity levels will differ across the

Atlantic.

However, three pillars of this explicative model have recently been called into ques-

tion. First, the notion of technological lock-in flies in the face of widespread transatlantic

technology transfer recorded during the early twentieth century. Contemporary industry

periodicals report a good many cases where German manufacturers imported Ameri-

can machinery and incorporated these in domestic production lines.6 Apart from a new

coat of paint, imported American machinery was often installed in its original form; ev-

idence that contradicts the development of dichotomous technological paths.7 Second,

case studies reveal a process of rapid capital deepening over the interwar period in the

German machine-tool industry.8 By the late 1930s the number of machines installed on

the factory floor available per worker was comparable between the US and Germany.

The potential external benefits may be substantial, as developments in the machine-tool

industry spill over to other manufacturing industries that extensively use machinery.

Third, the stereotypical US high-throughput model has been downplayed lately. Only

a minority of American industries actually employed thoroughgoing mass-production

systems; a much larger share of manufacturing focused on specialized, European-type

production processes.9 This begs the question whether the Germans ought to be seen

as ‘the Yankees of Europe’?10

In addition to these case-studies, quantitative research on the level of total manufac-

turing also failed to confirm the alleged importance of capital-intensity differences for the

labor-productivity gap. Decomposing the US/UK and German/UK labor-productivity

gap for years between 1870 and 1950 in effects of comparative total-factor productivity

and comparative capital intensity, Broadberry finds the latter component to explain lit-

tle of the observed labor-productivity differences.11 Using Broadberry’s data and decom-

position framework, only about 25% of the German/US labor-productivity gap in both

1909 and 1937 is explained by differences in capital intensity. Yet in spite of the modest

contribution assigned by the decomposition to the role of capital-intensity differences,

this finding has not led to a reinterpretation of the German/US labor-productivity gap.

Instead, the lack of strong empirical evidence in support of the supposed significance

6. Richter and Streb, “Catching-up and Falling Behind,” 1–2.7. Richter, “Technology and Knowledge Transfer.”8. C. Ristuccia and J. Tooze, “The Cutting Edge of Modernity: Machine Tools in the United States

and Germany 1930–1945,” Cambridge Working Papers in Economics No. 0342 (2003): 1–48.9. P. Scranton, Endless Novelty. Specialty Production and American Industrialisation 1865–1925

(Princeton University Press, 1997).10. Quote obtained from Kindleberger. See C. Kindleberger, Economic Response: Comparative Stud-

ies in Trade, Finance, and Growth (Cambridge, Mass.: Harvard University Press, 1978), 188.11. Broadberry, The Productivity Race, 105,106; Broadberry, “Manufacturing and the Convergence

Hypothesis,” 784.

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Chapter 3. The Yanks of Europe? 59

of variation in capital-labor ratios has been attributed to methodological weaknesses

of the level-accounting exercise based on the standard Solow model.12 Three problems

stand out in this respect.

A first deficit relates to the nature of technological change. In the conventional

Solow-based decomposition framework the effect of technological change is proportion-

ate at any level of capital intensity. New production knowledge increases the labor-

productivity potential by the same factor everywhere along the production possibility

frontier. Technological change was, however, not factor neutral, but localized and capital

biased; Allen shows that ever since the first industrial revolution innovation took place

at the highest capital-labor ratios in use, while low capital-intensive technology saw

little or no improvement.13 This has consequences for the impact of variation in capital

intensity on labor-productivity differences. If innovation was indeed localized, countries

operating at capital-labor ratios unaffected by technological change faced a widening

labor-productivity gap relative to countries that did enjoy the benefits of innovation.

A second inadequacy, related to the implementation of the level-accounting exercise,

concerns the use of total capital-stock data per worker as a measure for the capital

intensity of production. The size of the capital stock is determined largely by stocks

of buildings and inventories and the value of the machinery and equipment stock is

low in comparison.14 Because America’s alleged capital-intensity advantage pertains to

machinery, rather than to buildings, it is inappropriate to use total capital-stock data

to calculate capital intensity for the question addressed here, as I will show later on.

A measure of machine intensity is much better suited for the purpose and more apt to

capture the dynamics in German manufacturing as described above for the machine-tool

industry.

Thirdly, a Solow-based decomposition framework requires information on the shares

of capital and labor in output to proxy the marginal factor returns. Using a weighted

average of Germany, the UK and the US for 1975, Broadberry attributes a weight of 0.23

to capital.15 The capital-intensity gap between Germany and the US must be incredibly

large in this setting to make a substantial contribution to the labor-productivity gap.

Compared to 1975, wage levels were relatively low and capital costs relatively high in

the early twentieth century and, consequently, the effect of capital intensity may be

severely underestimated. Without additional factor price information these shares are

notoriously difficult to pin down.

12. Broadberry, The Productivity Race, 106–109.13. Allen, “Technology and the Great Divergence,” 6.14. Field, “On the Unimportance of Machinery.”15. Broadberry, The Productivity Race, 105.

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This chapter provides a decomposition of German-American labor-productivity dif-

ferences that does not suffer from these problems by employing a method that allows

for factor-biased technological change, focuses on the machine intensity of production

and does not rely on fixed and exogenously-given rates of marginal factor productivity.

Following Kumar and Russell, a global best-practice production frontier is constructed

through application of a data envelopment analysis.16 The data envelopment analysis

provides a non-parametric approach, which allows for localized innovation by letting

the empirical data dictate the shape of the frontier and, as a consequence, requires

minimal assumptions of functional form. This data-driven analysis is applied on the

disaggregated level and the effect of localized innovation on the shape of the frontier is

permitted to vary between manufacturing industries. Moreover, instead of using total

capital-stock data, I rely on applied horse-power statistics, a more direct measure of

machinery. Because this new approach allows for the localized nature of technological

change, requires minimal assumptions with regard to the functional form of the frontier

and employs an appropriate measure of capital intensity, it supplies a more sophisticated

instrument to analyze labor-productivity differences than conventional level-accounting

techniques.

The data envelopment analysis draws a global best-practice production frontier on

the basis of observed labor-productivity to capital-intensity ratios in manufacturing in-

dustries. The frontier indicates the maximum labor-productivity performance contem-

poraneously or previously attained over the full range of capital-labor ratios that are

currently in use or have been explored in the past. As a consequence, the frontier is based

only on best-practice observations, which assigns other observations a position below

the frontier. This enables me to calculate the difference between the labor-productivity

level actually realized by industries and the best-practice labor-productivity level indi-

cated by the frontier for the capital-labor ratio at which this industry operates. The

discrepancy between realized and potential labor-productivity levels can be interpreted

as the level of efficiency at which the machinery stock is operated and enables a richer

decomposition of the labor-productivity gap than possible with conventional level ac-

counting. It follows that differences in labor productivity are attributed to components

of, first, comparative machine intensity and, second, comparative efficiency.17 This way,

the part of the labor-productivity gap not accounted for by differences in capital-labor

ratios can be ascribed to a suboptimal utilization of the machinery stock, rather than

to different technological paths as in traditional decomposition techniques.

Although the production frontier analysis is purely data driven, requiring a mini-

16. Kumar and Russell, “Technological Change,” 530–531.17. ibid., 531,532.

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mum of assumptions, the decomposition framework itself is firmly grounded in theo-

retical growth models. Particularly relevant in this respect is Basu and Weil’s model

of appropriate technology in which new production knowledge is appropriate only for

a limited range of capital-labor ratios. This setting identifies two channels for labor-

productivity growth depending on a country’s initial position; through innovation for

“leader countries” and for “follower countries” by adopting capital-labor ratios already

explored by leader countries in the past.18 When innovation is confined to high capital-

labor ratios, as Allen demonstrates for my period of study, low-end countries must strive

for higher levels of capital intensity or face an ever increasing labor-productivity gap.19

Yet the process of rapid capital intensification can come at a cost, as empirical studies

point out.20 As much of what one needs to know to employ new production knowledge

is implicit and not available from handbooks, it takes time to assimilate and operate

machinery at the level displayed by countries exploring that capital-labor ratio before.21

According to Los and Timmer, these findings suggest a sequence in which developing

countries first create scope for labor-productivity growth by adopting high capital-labor

ratios and, subsequently, ‘learn’ to operate efficiently at that capital-intensity level, from

which point onwards the latent labor-productivity gains materialize.22

The two sources of labor-productivity growth set out by the model, i.e. capital inten-

sity and efficiency, provide a framework that possibly explains the paradoxical lack of

labor-productivity catch up at a time of capital-intensity convergence between German

and US manufacturing. If innovation was localized and took place at high capital-labor

ratios only, Germany – a follower country – faced a strong incentive to increase capital-

intensity levels, but the associated labor-productivity gains may not have materialized

in the short run as German industries struggled to learn how to operate efficiently at the

the new capital-labor ratios, a process that required time. The next section discusses

the analytical framework necessary to test whether such dynamics can be identified for

German manufacturing over the interwar period. Subsequently, section 3.3 describes the

data, while the results are presented in section 3.4. After positioning these results in the

more qualitative literature on German historical economic development in section 3.5,

the next section (3.6) adopts a long-term perspective and positions the findings of this

chapter in long-run developments. Finally, section 3.7 concludes.

18. Basu and Weil, “Appropriate Technology,” 1036.19. ibid., 1043–1045.20. Los and Timmer, “The ’Appropriate Technology’ Explanation,” 519.21. A. Atkinson and J. Stiglitz, “A New View of Technological Change,” The Economic Journal Vol.

79 (1969): 573–578; David, Technical Choice, 59–60.22. Los and Timmer, “The ’Appropriate Technology’ Explanation,” 529.

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3.2 Methodology

In order to see if the model set out above applies to German manufacturing in the early

twentieth century, I follow a two-stage research strategy. First, I look at the level of

capital intensity in German manufacturing in 1909 and 1936 and measure the rate of

labor-productivity growth if machinery was operated at full efficiency throughout the

entire period. In order to do so, I need to know what the maximum labor-productivity

potential for explored capital-labor ratios was. This requires a global best-practice labor-

productivity frontier that captures the highest attainable levels of labor productivity

over the full range of explored capital-labor ratios by any country in the world and

allows for localized innovation. Secondly, I decompose the labor-productivity gap in

manufacturing industries between Germany in 1936 and the US in 1939. This gap is

attributed to differences in both capital intensity and the efficiency at which machinery

is operated. Both components can be measured only by knowing what the full labor-

productivity potential is at the capital-labor ratios adopted by Germany and the US,

for which purpose the global best-practice frontier is used again.

The first part of the analysis quantifies the potential return to the adoption of high

capital-labor ratios and illustrates the (ex-post) incentive for capital-intensification in

German manufacturing. The second part measures how efficiently the machinery stock

was operated in German manufacturing by the late 1930s, following a period of capital

intensification, and indicates the degree to which the transatlantic labor-productivity

gap was sustained by differences in efficiency levels between Germany and the US.

Because both steps in the analysis require a global best-practice frontier, the estimation

of this frontier provides the basis for the analysis of labor-productivity differences.

Therefore, this section discusses the data envelopment analysis (henceforth, DEA) used

to construct the frontier first, before describing the decomposition of the German/US

labor-productivity gap in 1936/39.

The global best-practice frontier

Using DEA-techniques the global best-practice frontier is estimated in four sequential

steps. A first step involves collecting data on the level of capital intensity and labor

productivity for manufacturing industries. Subsequently, manufacturing industries that

produce similar products are sorted into groups. For this purpose the standard industrial

classification of 1945 (henceforth, SIC) is used.23 Thirdly, all industries classified in

23. For a detailed overview of the SIC, see United States Department of Commerce: Bureau of theCensus, Census of Manufactures 1947, vol. Industry Description (Washington: United States Govern-

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the same group are placed in 〈k, y〉 space, where k is capital intensity and y is labor

productivity. In a final step, the global best-practice frontier is drawn by enveloping

the data in the tightest-fitting convex cone using linear line segments.24 The upper

boundary of the envelop represents the global best-practice frontier.25 This exercise is

then repeated for several periods to also obtain the movement of the frontier over time.

Figure 3.1: Estimating the frontier for industrial chemicals, 1899–1939

0

1

2

3

4

5 10

y

k

(a) Observations up to and incl. 1909

0

1

2

3

4

5 10

y

k

(b) Observations up to and incl. 1939

0

1

2

3

4

5 10

F(’09)

F(’39)

y

k

(c) The frontiers in 1909 and 1939

0

2

4

5 10

F(’09)

F(’39)

(’09)

(’36)

y

k

ya

yb

(d) German chemical industry, 1909 and 1936

Figures 3.1a until 3.1c capture the procedure described above for the case of indus-

trial chemicals. The top-left pane draws the frontier for 1909 based on data from the

US, Germany and the UK. Although not truly global, the frontier contains the three

leading industrial nations, both in terms of size and labor-productivity levels, of the

ment Printing Office, 1949).24. Kumar and Russell, “Technological Change,” 530.25. For best-practice industries, see W. Salter, Productivity and Technical Change, second edition

(Cambridge University Press, 1966), 1–220.

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early twentieth century. The data on which the frontier for 1909 is based includes all

current and past observations. By including observations from earlier years, knowledge

previously generated is ‘remembered’ over time and remains accessible in the current

period.26 In practice, this means that the cloud of observations in figure 3.1a contains

data from 1899, 1905 and 1909 for the US, 1907 for the UK, and 1909 for Germany.

This information is derived from the published production censuses or other statistical

sources. Section 3.3 provides more detail on the dataset constructed for the DEA.

The top-right pane of the figure shows how the frontier changes over time, in this

case between 1909 and 1939. All observations already included in the frontier estimation

for 1909 are exported to figure 3.1b and show up as the gray dots. In addition, the

figure plots all available observations for the period afterward up to and including

1939, which, as before, are collected from the production censuses. This includes data

on 1936 for Germany, 1930 for the UK and all census years since 1909 for the US.

Subsequently, the frontier is drawn. Leaving out the observations that are not part of

the frontier, figure 3.1c clearly shows the upward shift of the frontier between 1909

and 1939. Throughout the rest of this chapter this outward movement of the frontier is

referred to as technological change. As the frontier captures the highest achieved levels

of labor productivity for contemporaneously or previously explored capital-labor ratios,

the vertical distance between an industry observation and the frontier determines the

efficiency level at which technology is operated. For instance, figure 3.1d positions the

German chemical industry relative to the frontier in 1909 and 1936. Clearly, the level of

efficiency declined with the increase of the distance to the frontier. Linear programming

techniques are used to accurately calculate an industry’s vertical distance to the best-

practice frontier.27

An appeal of the DEA approach is that the shape of the frontier can be revealed

without imposing a specific functional form on technology.28 The convexity of the en-

velop poses the only restriction on the functional form of the frontier. As the param-

eters of the production frontier are obtained from the data, rather than presupposed,

the DEA allows for any form of localized technical change.29 That is, an increase in

the labor-productivity potential at particular capital-intensity levels through innova-

26. The unlikeness of an ‘imploding frontier’ was already noted by Basu and Weil, “AppropriateTechnology,” 1031, 1036 and Kumar and Russell, “Technological Change,” 540, but the first to formallyexclude the possibility of technological degradation were Los and Timmer (Los and Timmer, “The’Appropriate Technology’ Explanation”).27. R. Fare, S. Grosskopf, and K. Lovell, Production Frontiers (Cambridge: Cambridge University

Press, 1994), 1–296; Kumar and Russell, “Technological Change,” 531. For the linear programmingtechniques, see appendix 3.A.28. Fare, Grosskopf, and Lovell, Production Frontiers, 12.29. Los and Timmer, “The ’Appropriate Technology’ Explanation,” 522.

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tion may shift the frontier upward only for a limited range of capital-labor ratios, while

leaving other parts of the frontier unchanged. In the example of industrial chemicals,

in spite of substantial technological change at high levels of capital intensity, the first

line segment of the frontier remains unaltered throughout the entire period. Because of

its non-parametric nature, the DEA is particularly suited for the issue at hand.

The global best-practice frontier and the change thereof over time permits the first

part of the analysis introduced above, i.e. measuring the rate of labor-productivity

growth at the frontier for German capital-intensity levels in 1909 and 1936. Using fig-

ure 3.1d as example again, I measure the labor-productivity level at the frontier for the

capital-labor ratios at which the German chemical industry operated, i.e. ya for 1909

and yb for 1936. Subsequently, the growth rate of labor productivity at the frontier

can be computed between 1909 and 1936. This is an annual growth rate of 3.4% in

this particular case, which can be interpreted as the created potential for growth as

a result of adopting higher capital-labor ratios between 1909 and 1936. Also evident

from figure 3.1d is Germany’s inability to exploit the full potential of the machinery

it operated in 1936, as testified by the vertical distance between the German chemical

industry and the frontier. The question remains to what degree this low efficiency level

contributed to the labor-productivity gap between Germany and the US in the late

1930s. A decomposition of the labor-productivity gap in components of relative capital

intensity and relative efficiency is necessary to answer that question.

The decomposition of labor-productivity gap

Having constructed the frontier, the labor-productivity gap between Germany and the

US can be decomposed into two elements. The stylized figure 3.2 illustrates these compo-

nents graphically. A German industry in 1936 and its American counterpart in 1939 are

positioned relative to the global best-practice frontier. The observed labor-productivity

gap between the two countries (y1/y0) is attributable, first, to the use of different

capital-labor ratios, because the labor-productivity potential (ya and yb) at the capital-

intensity levels of Germany and the US (ka and kb, respectively) differs. If both countries

operate at their respective capital-intensity levels with full efficiency, the US enjoys a

labor-productivity lead over Germany on account of the American capital-labor ratio

having a larger labor-productivity potential. Secondly, the labor-productivity gap is

determined by the efficiency level of Germany (y0/ya) relative to the US (y1/yb). If

Germany exploits relatively little of its labor-productivity potential compared to the

US, this contributes to the observed labor-productivity gap, too.

In equation 3.1 the combined effect of differences in capital intensity and efficiency

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Figure 3.2: Decomposition techniques

F(’39)

(GER’36)

(US’39)

y

0 k

y0

ya

y1

yb

ka

kb

explains the German/US labor-productivity gap. If the efficiency component takes on a

value of 1, the productivity gap surely results from the use of different capital-labor ra-

tios, a scenario much in line with the notion of technological lock-in mentioned above.

However, if the efficiency component takes on a value lower than 1, which indicates

that Germany exploits relatively little of the labor-productivity potential associated

with the capital-labor ratio at which it operates, the productivity gap cannot be under-

stood to reflect simply the use of different capital-labor ratios. If the first part of the

analysis indicates that Germany experienced a process of capital intensification over

the interwar period, the framework proposed by Los and Timmer suggests that the

labor-productivity gap decomposition may uncover low levels of efficiency in German

manufacturing in 1936; adoption of high capital-labor ratios creates growth potential,

which remains unexplored in the short run because it takes time to efficiently assimilate

production knowledge.

y0y1

=

(yayb

)︸︷︷︸

technology

·(y0/yay1/yb

)︸︷︷︸efficiency

(3.1)

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3.3 Data

As described in the previous section, for the DEA and the labor-productivity decom-

position industry-level data is required on labor input, capital input and output. The

complete data set used here entails approximately 105 separate industries, which are

classified in 28 SIC industry groups, and in total consists of nearly 1,200 observed

input-output combinations, including US, UK and German observations for years be-

tween 1899 and 1939. US data is available for 1899, 1905, 1909, 1914, 1919, 1929 and

1939, omitting only the census year 1935 on account of unreported capital data.30 For

the UK data is obtained for 1907 and 1930, while the data set includes German ob-

servations for 1909 and 1936.31 Thus, German manufacturing industries in 1936 must

be compared to their American counterparts in 1939 and the labor-productivity gap is

decomposed using the 1939 frontier.

Capital input

The level of technology is measured by capital intensity, but instead of using total

capital-stock estimates, as traditional level-accounting exercises have done for this pe-

riod, I rely on a measure of machine intensity.32 Much of the production knowledge

gained since the second industrial revolution was contained in tangible capital in the

form of machinery installed on the factory floor. So a measure of capital that captures

the stock of machinery is needed. Because total capital-stock data contains other invest-

ment components besides equipment, i.e. inventories and buildings, they do not accu-

rately capture the level of machine intensity; the share of machinery in the total capital

stock is typically small and conclusions regarding the process of machine intensification

drawn from total capital-stock data are bound to mislead.33 Indeed, it has been shown

for the period 1960–1985 that the correlation with GDP growth was much stronger for

changes in equipment than for any other component of investment.34 Although data on

30. United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1910(VIII); United States Department of Commerce: Bureau of the Census, US Census of Manufactures1914 ; United States Department of Commerce: Bureau of the Census, US Census of Manufactures 1920(VIII); United States Department of Commerce: Bureau of the Census, US Census of Manufactures1929 (II); United States Department of Commerce: Bureau of the Census, US Census of Manufactures1935 ; United States Department of Commerce: Bureau of the Census, US Census of Manufactures1940 (II).31. For the UK, Board of Trade, UK Census of Production 1907 ; Board of Trade, UK Census of

Production 1930. For Germany, see chapter 2.32. Broadberry, The Productivity Race, 105, 106.33. Field, “On the Unimportance of Machinery.”34. B. de Long and L. Summers, “Equipment Investment and Economic Growth,” The Quarterly

Journal of Economics Vol. 106, No. 2 (1991): 445–502; B. de Long and L. Summers, “EquipmentInvestment and Economic Growth: How Strong is the Nexus?,” Brookings Papers on Economic Activity

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investment in equipment is available from the historical national accounts of Germany

and the US, they do not provide the level of detail needed for an analysis on the industry

level. As data on the value of installed machinery is unobtainable, I rely on horsepower

statistics instead.35

Is applied horse power an accurate proxy for installed machinery? A possible concern

in this respect is the relation between the unobserved level of machine intensity and

the observed power generated by machinery per unit of labor. To produce the same

output, new machinery often applies less horsepower, i.e. machinery is increasingly

energy-efficient. This applies in particular to the introduction of electrical apparatus, the

application of which became widespread since the 1920s. Because electrical machinery

no longer relied on a single drive shaft, the efficiency of both machinery and factory-floor

design improved considerably.36 The reported horse power applied during production

thus tends to be high for countries employing predominantly steam engines relative to

users of mainly electric motors. Even though the former country applies more horse

power, it would be wrong to conclude that it operates at higher capital-labor ratios. If

the electrification rate in Germany lies well below the US, using horsepower as a proxy

for installed machinery might overestimate the machine-intensity level in Germany.

In practice, however, German and American manufacturing industries are charac-

terized by very similar electrification rates, as table 3.1 shows. Over WW1 the share

of electrical machinery in total applied horsepower increased rapidly in both countries.

Therefore, I feel comfortable using horse power per hour worked as a proxy for ma-

chine intensity. Moreover, as the US substituted electricity for steam power in a process

mirrored by Germany, table 3.1 does not furnish evidence suggesting the latter was

slow adopting new technology. Only in food, drink & tobacco and miscellaneous indus-

tries Germany proved unable to match American electrification rates. In contrast, the

difference between both countries was nonexistent in most modern industries, such as

chemicals & allied, petroleum & coke, (electrical) machinery and transportation equip-

ment.

A different source of potential worry when using horse-power statistics concerns the

danger of ‘double counting’. The horse power applied on the factory floor is supplied

by machinery running on either non-electric or electric power. In case of the latter, the

electricity needed to operate the machinery can be internally generated in the factory

(by electricity generators) or purchased from an electrical power network to which the

No. 2 (1992): 157–211.35. As reported by the Census of Manufactures for the US, the Census of Production for the UK,

and the Gewerbliche Betriebszahlung/Gewerbliche Betriebsstatistik for Germany.36. Schurr et al., Electricity in the American Economy, 32; Jerome, Mechanization in Industry, 250,

253.

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Table 3.1: Electrification rates in German and US manufacturing industries, ca. 1910and ca. 1930

Industry Share electric HP in total HP (%)

ca. 1910 ca. 1930

GER US GERUS GER US GER

US

Food, drink and tobacco 15.2 15.2 1.00 54.9 75.4 0.73

Textiles and apparel 11.1 20.1 0.55 72.3 77.4 0.94

Lumber and furniture 17.4 5.8 2.98 66.2 58.6 1.13

Paper and printing 22.3 21.4 1.04 80.3 74.9 1.07

Chemicals, petroleum, coke and rubber 25.1 29.4 0.85 76.0 78.3 0.97

Primary metals 21.4 27.9 0.77 80.1 87.7 0.91

Fabricated metal products 30.3 21.1 1.44 85.5 93.6 0.91

Machinery (incl. electric) 40.6 46.9 0.87 94.3 96.6 0.98


Miscellaneous 25.9 22.4 1.15 74.6 91.4 0.82

MANUFACTURING 20.3 22.2 0.92 75.9 81.9 0.93

Sources: see text.

factory is connected. When the electricity is internally generated, the horse power used

by electricity generators should be excluded in the analysis as it does not contribute

directly to the fabrication of goods. Only for pre-WW1 Germany the data does not

allow a correction for double counting.37 This is not a source of major concern, because

the share of electricity in total horse power was modest before the 1920s and the part of

electric power internally generated even smaller. If anything, the bias provides a lower

bound estimate of machine intensification between 1909 and 1936 and underestimates

the created potential for labor-productivity growth.

A final worry concerns the horse-power data for interwar Germany. The 1936

machine-intensity level is indicated by horse-power data obtained from the employment

census of 1933, a procedure that introduces a bias in the capital-intensity estimates.38

In 1933 unemployment in Germany stood at 36.2%, only slightly lower than the all-time

high level of 43.8% the year before.39 By 1936 the unemployment rate had decreased to

12.0% and the employed labor force in manufacturing was 38% larger than in 1933.40

As 1936 was the first year that saw employment levels equal to those of before the Great

37. Hoffmann, Das Wachstum, 263–264.38. Statistik des Deutschen Reichs, “Gewerbliche Betriebszahlung,” in Volks-, Berugs- und Be-

triebszahlung vom 1933 (Berlin: Verlag fur Sozialpolitik, Wirtschaft und Statistik, 1933).39. T. Pierenkemper, “The Standard of Living and Employment in Germany, 1850-1960: An

Overview,” Journal of European Economic History Vol. 16 (1987): 51–73, 59.40. ibid., 59; Hoffmann, Das Wachstum, 199.

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Depression, a part of the installed horse power reported for 1933 may have stood idle or

underutilized on the factory floor. Although the horse-power statstics only report the

frequently used horse power on the factory floor, capital-labor ratios may be spuriously

high.

Alternatively, horse-power data is available for 1938 as well, but these suffer from

three problems and cannot be used. First, the coverage of industries is low as compared

to the 1933 data. Second, by 1938 the German statistical publications hid production

activities related to the war effort. And, third, the build-up for war affected the employ-

ment structure of German manufacturing. Although these impairments have a limited

impact on total manufacturing, the distortions can be quite pronounced on the industry

level and very difficult to identify. Given the importance for this study of data on the

disaggregated level, I prefer the use of the 1933 horse-power statistics. Nevertheless, to

check the sensitivity of the analysis, the decomposition of the labor-productivity gap is

done using German machine-intensity data of both 1933 and 1938. The results do not

differ in any major way (see appendix 3.D) and the findings are robust to variation in

the German level of machine intensity.

Output and labor input

Output is measured by value added as reported by the statistical publications of the

US, the UK and Germany.41 This is necessary to avoid movements of the frontier that

are driven by changes in input prices, rather than improvements of the production

process. German and British output is expressed in US$ using industry-specific output

PPPs.42 Subsequently, nominal value added in US$ is converted to constant prices (with

a 1929 base) by applying price deflators at the industry level. Deflators are calculated

on the basis of Fabricant’s indices of physical output and nominal output series.43

After reclassification to fit the SIC, the modifications and extensions to the indices

of production proposed by Kendrick are incorporated, too.44 Labor input is expressed

in terms of hours worked. The necessity of the hours adjustment has been stressed

41. For the US the Census of Manufactures. For the UK the Census of Production (1907 and 1930)and for Germany the industrial surveys (1909, see chapter 2) and the first industrial census (1936, seechapter 2).42. For Germany/US the PPPs constructed in chapter 2 are used. For UK/US the PPPs are used

of de Jong and Woltjer (interwar period) as well as Veenstra and Woltjer (pre-WW1 period). SeeJong and Woltjer, “Depression Dynamics” and J. Veenstra and P.J. Woltjer, “The Yanks of Europe?Technological Change and Labor Productivity in German Manufacturing, 1909–1936,” XVIth WorldEconomic History Congress (2012).43. S. Fabricant, The Output of Manufacturing Industries, 1899–1937 (New York: National Bureau

Economic Analysis, 1940), 123–321; 605–639.44. J.W. Kendrick, Productivity Trends in the United States (Princeton N.J.: National Bureau Eco-

nomic Research, 1961), 416–421; 467–475.

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in chapter 2 and by de Jong & Woltjer in their study on US/UK labor-productivity

differences.45

3.4 Results

The main findings of this chapter can be summarized in four points. First, technological

change at the global production frontier over the period 1899–1939 was decidedly non-

neutral and biased toward capital. Second, in terms of machine-intensity levels Germany

gradually converged on the US over the interwar period. Third, due to the bias in

technological change, the process of adopting machine-intensive technology markedly

increased the scope for labor-productivity growth in German manufacturing. Fourth,

the German/US labor-productivity gap in the late 1930s was mainly due to a relatively

low level of efficiency in factor use, rather than different capital-labor ratios. The created

potential for growth was not realized before WW2 and Germany failed to efficiently

assimilate new production knowledge in the short run.

The global best-practice production frontier between 1899–1939

The movement of the global best-practice production frontier between 1899 and 1939,

as measured by the DEA, contains a bias toward machine-intensive technology. Tech-

nological change did not shift the frontier by the same proportional amount at all

capital-labor ratios. Instead, innovation was localized at the machine-intensive side of

the production frontier.

This finding aligns well with the DEA-literature discussed before. Using similar

techniques, Kumar and Russell concluded for the period 1965–1990 that technological

change has been decidedly nonneutral; outward movement of the frontier was localized

at predominantly high levels of capital intensity.46 Timmer and Los also uncover very

similar dynamics for a broad sample of OECD countries in the last quarter of the

twentieth century; innovation was highly localized and skewed toward the higher capital

intensities.47 Allen shows that the capital bias in technological change was not restricted

to the post-WW2 period. Since the first industrial revolution the global production

frontier shifted upward only at the highest capital-labor ratios in use, while low capital

45. The interwar period saw a substantial drop in the average hours of work for the interwar period. Asthe decrease in hours of work was more pronounced in the US relative to Europe, adjusting for hourswidens the labor-productivity gap between the US and Europe. See Jong and Woltjer, “DepressionDynamics,” 485–488.46. Kumar and Russell, “Technological Change,” 529, 538.47. Timmer and Los, “Localized Innovation,” 55.

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Figure 3.3: Change of the frontier in industrial chemicals

0

y

k

‘09

1

3

2 4

(a) 1909

0

y

‘29

‘09

1

3

k2 4

(b) 1909–1929

0

y ‘39

‘29

‘09

1

3

k2 4

(c) 1909–1939

intensities saw little or no improvement.48 For the first half of the twentieth century,

my findings confirm Allen’s conclusions. Under these conditions a country benefits from

technological change only if it adopts high capital-labor ratios.

The localized nature of and the capital bias in technological change are shown in

figure 3.3 for the case of industrial chemicals, an industry that was already introduced

earlier to exemplify the DEA-technique.49 The figure displays the global best-practice

frontier for three years, i.e. 1909, 1929 and 1939. In each year the labor-productivity

potential was largest for high capital-labor ratios. Industries moving along the produc-

tion frontier would thus increase labor-productivity levels, even if the frontier had not

changed as time progressed. However, the frontier did change and looking at the shifts of

the frontier from year to year, the largest gains in labor-productivity levels are observed

for high capital-labor ratios.

The dashed vertical lines capture the localized nature of change by indicating the

lowest capital-labor ratio at which the frontier shifted during a period of time. Between

1909–1929 the first line segment of the frontier remained unaffected by innovation at

other levels of capital intensity. The vertical line is placed further to the right for the

period 1929–1939, including the first two line segments, which implies that between the

period 1909–1929 and 1929–1939 innovation applied to increasingly machine-intensive

technology. In order to enjoy benefits from technological change, industries had to adopt

capital-labor ratios beyond the threshold indicated by the vertical lines. Industries that

48. Allen, “Technology and the Great Divergence,” 6.49. See figures 3.1a, 3.1b, 3.1c and 3.1d on page 63.

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failed to acquire a level of machine intensity in the range for which new production

knowledge was appropriate were inevitably left behind in the labor-productivity race.

Machine intensification in German manufacturing between 1909–1936

In view of the localized nature of and capital bias in technological change countries

faced a strong incentive to adopt high-end technology. The traditional notion of a static

technology gap between the US and Germany until the post-WW2 period, however,

suggests the prevalence of lock-in effects. This is certainly untrue in the narrowest

sense. Even if the relative machine-intensity level in German manufacturing lagged

equally far behind the US throughout the first half of the twentieth century, Germany

must have increased capital-labor ratios just as fast as the US did. Additionally, and

more importantly, a constant technology gap is observable only with total capital-stock

data. The horse-power definition of capital employed here uncovers different dynamics.

Although German manufacturing industries faced a very large machine-intensity gap

before WW1, the distance to the US narrowed over the interwar period. The increase

in machine-intensity levels in Germany proceeded at about 1.5 times the speed of the

US and in 1936/39 the machine-intensity gap was almost half its size in 1909. While

Germany still lagged well behind the US, the interwar period was a time of convergence

in capital-labor ratios, rather than technological lock-in.

The narrowing machine-intensity gap is captured by figure 3.4. Two Kernel-density

plots are drawn, each of which displays the distribution of manufacturing employment

over available capital-labor ratios in Germany and the US based on observations of

machine-intensity levels and employment shares at the industry level. Starting with

the plot for 1909, it can be seen that before WW1 German manufacturing produced

in a much less capital-intensive way than the US. However, the overlap of the US

and German distributions in the late 1930s demonstrates that machine-intensity levels

in Germany were increasingly similar to the US. The speed of convergence differed

between manufacturing industries, but the gap declined across the board. Looking at

table 3.2, the reduction of the machine-intensity gap in several industries stands out.

For instance, the German machinery industry more than halved the gap to the US.

Even more rapid was the machine-intensity convergence in electrical machinery. In 1936

German machine-intensity levels stood at about 90% the level of its direct American

counterpart. As will be discussed later, other industries displayed a remarkable catch-

up process, too. For instance, textiles, which closed the gap to an even greater extent

than the machinery branch, and chemicals. Other industries were less successful in this

respect. A noteworthy example is the slight increase of the gap in the primary-metals

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Figure 3.4: Distribution of manufacturing employment overcapital-labor ratios, US and Germany in 1909 and 1936/39

0.06 0.25 1.00 4.00 16.00

k

Kern

el

Densit

y

(a) 1909

0.06 0.25 1.00 4.00 16.00

k

Kern

el

Densit

y

(b) 1936/39

US GER Overlap

industry.

The improved fit of Germany’s machine-intensity profile with its American equiva-

lent in the 1930s suggests that Germany trailed the US by several years in an otherwise

very similar development. This leads me to conclude that while before WW1 both coun-

tries tracked different technological paths, such a distinction is no longer evident for the

interwar period. What is more, the comparatively high rate of machine intensification

in Germany implies that initial conditions did not stand in the way of machine-intensive

production. This could signify that the pattern of relative factor costs in German indus-

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Table 3.2: Horse power per 1,000 hours worked in manufacturing,Germany and the US in 1909 and 1936/39

Industry 1909 1936/39

GER US GERUS GER US GER

US

Food, drink and tobacco 0.48 1.15 41 1.81 2.63 69

Textiles and apparel 0.18 0.56 32 0.86 1.09 79

Lumber and furniture 0.30 1.23 25 1.51 3.01 50

Paper and printing 0.53 1.23 43 1.82 3.22 56

Chemicals, petroleum, coke and rubber 0.59 1.24 48 3.47 5.89 59

Primary metals 0.77 1.92 40 3.33 9.52 35

Fabricated metal products 0.24 0.69 34 1.05 1.89 56

Machinery 0.27 0.82 33 1.27 2.14 59

Electrical machinery 0.30 0.96 31 1.52 1.67 91

Transportation equipment 0.24 0.64 38 1.09 2.58 42

Miscellaneous 0.24 1.06 23 1.34 3.00 45

Total manufacturing 0.34 1.07 32 1.58 2.98 53

Sources: see text.

tries deviated only little from those in the US. Or if they did, it did not deter German

entrepreneurs from acquiring higher levels of machine intensity.

Nevertheless, in spite of the rapid increase in machine-intensity levels, German man-

ufacturing still lagged behind its American counterpart. With the exception of electrical

machinery, German industries failed to fully close the machine-intensity gap. The short

time between the hyperinflation and the Great Depression offered only so much room

for extensive revisions to the production process. When the depression hit Europe in

1929 Germany had enjoyed relative stability for less than a decade and many long-term

projects slowed down, stalled, or were canceled all together.50 With the exception of

the machine-tool industry, Germany never reached the level of mechanization displayed

by the forerunners of American industrial development, such as Ford.

It was an often entertained notion that due to labor unions’ increased bargaining

power after WW1 investment was constrained by rising wage costs.51 Although the

consensus view now is that labor costs failed to harm investment worse than it did before

1914, the “roaring twenties” in Germany are broadly agreed to have been confined to

50. M. Nolan, Visions of Modernity. American Business and the Modernization of Germany (OxfordUniversity Press, 1994), 132.51. K. Borchardt, “Zwangslagen und Handlungsspielrame in der großen Wirtschaft der fruhen

dreißiger Jahre,” Jahrbuch der Bayerischen Akademie der Wissenschaften (1979): 85–132; K. Bor-chardt, Perspectives on Modern German Economic History and Policy (1991); A. Ritschl, “Zu hoheLohne in der Weimarer Republik? Eine Auseinandersetzung mit Holtfrerichs Berechnungen zur Lohn-position der Arbeiterschaft 1925–1932,” Geschichte und Gesellschaft Vol. 16 (1990): 375–402.

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the brief period between hyperinflation and depression, i.e. between 1924–1928.52 As the

alleged investment boom associated with the armament race in the late 1930s effectively

crowded out investment in both the private and public sectors, all together, the window

of opportunity for capital deepening in interwar Germany was rather small, which helps

understand the lack of full catch-up in machine intensity.53

Creating potential for labor-productivity growth

The specific focus on capital in the form of machinery unveils a tradition of machine

intensification in German manufacturing. As the labor-productivity potential increases

with machine intensity this process created substantial scope for labor-productivity

growth in German manufacturing. Table 3.3 illustrates this potential for growth that

German industries realized as a result of increased capital intensity. The first column

lists the average annual labor-productivity growth at the frontier over the interwar

period at the capital-labor ratios operated by German industries in 1909. As such it

reports the increase in labor-productivity potential had German manufacturing failed

to increase machine-intensity levels after 1909 and provides the counter-factual scenario

of technological lock-in in the strictest sense. The second column captures the average

annual labor-productivity growth at the frontier as a result of the actual change in

machine-intensity levels in German manufacturing industries. The difference between

the columns can be interpreted as the created potential for labor-productivity growth

through machine intensification in Germany.

The first column clearly shows the potential danger of lock-in. For many industries

the frontier changed only little at the capital-labor ratios displayed in 1909, a conse-

quence of the localized and capital-biased nature of technological change. Innovation

and introduction of new technology on the frontier, in this case only by US industries,

took place chiefly at high machine-intensity levels. In the cases of, for instance, metals

and machinery there was practically no movement of the frontier at all at these low

capital-labor ratios. To increase the potential for labor-productivity growth, machine

intensification was a necessity in these industries. Even in textiles, in which technological

change did manifest at low capital-labor ratios, frontier movements were much greater

at higher levels of machine intensity. The second column lists the created potential for

labor-productivity growth at the frontier as a result of the actually realized increase in

machine-intensity levels between 1909 and 1936. The reported growth rates are much

52. H.J. Voth, “With a Bang, not a Whimper: Pricking Germany’s “Stock Market Bubble” in 1927and the Slide into Depression,” Journal of Economic History Vol. 63, no. 1 (2003): 66.53. J. Scherner, “‘Armament in Depth’ or ‘Armament in Breadt’? German Investment Pattern and

Rearmament during the Nazi Period,” Economic History Review Vol. 66, no. 2 (2013): 13.

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Table 3.3: Annual labor-productivity growth (ln %) at the frontierbetween 1909–1939 for German capital-labor ratios

Industry At machine intensity of

1909 1909–36

Food, drink and tobacco 0.5 4.7

Textiles and apparel 1.2 3.2

Paper and printing 0.8 3.4

Chemicals, petroleum, coke and rubber 0.6 4.3

Stone, clay and glass 0.7 3.2

Primary and fabricated metals 0.3 3.3

Machinery (incl. electric) 0.1 1.7

Transportation equipment 0.9 6.1

Miscellaneous 0.3 2.9

Total manufacturing 0.6 3.4

For more detail, see appendix 3.C. Sources: see text, section 2.3.

higher than those under the first column, indicating that the potential reward to capi-

tal deepening was very large. If German industries operated fully efficient, the increase

of machine intensity recorded in section 3.4 would have pushed up labor-productivity

levels in manufacturing at an average annual rate of 3.4% (ln), more than five times as

fast as in the counter-factual situation of stagnant machine-intensity levels. For several

industries, such as food, chemicals and transportation equipment, the potential gains

were even larger.

The growth rates in table 3.3 reflect the dynamics of the frontier at German levels

of machine intensity and, as such, capture the effects of innovation appropriate for

German industries. An improved understanding of the displayed patterns can therefore

be obtained by looking at the history of technological change over the interwar period.

Take for instance the transportation equipment industry. Table 3.3 reports almost no

change of the frontier at low levels of machine intensity, but rapid change at high levels

of machine intensity. This aligns with the literature, which has put emphasis on the key

position that the process of mechanization claimed in the development of this industry,

as illustrated by the introduction of Ford’s assembly line in the early 1920s.54 The

movement of the frontier corroborates the notion of rapid labor-productivity growth

induced by increasingly high levels of machine intensity.

The capital bias is less pronounced in the textile industry. Although adoption of

54. R.R. Nelson and G. Wright, “The Rise and Fall of American Technological Leadership: ThePostwar Era in Historical Perspective,” Journal of Economic Literature Vol. 30 (1992): 1931–1964,1944–45.

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high machine-intensity levels created substantial additional scope for labor-productivity

growth, the frontier moved outward for low-end technology, too. This development pat-

tern may be explained by the lack of major technological breakthroughs during the

interwar period. The latest technological revolution experienced in textiles dated from

the late nineteenth century with the introduction of the ring spindle, which replaced the

less productive self-acting spinning mule.55 Over the interwar period the ring spindle

was adopted widely and subsequent productivity gains derived from further improve-

ments of the spindle, such as an increase of the spindle’s rate of revolutions.56 This

suggests a tradition of learning-by-doing in textiles, as a result of which the labor-

productivity potential of the ring-spindle technology was exploited to an increasing

extent. Nevertheless, the first half of the twentieth century saw a reduction in the rate

of labor-productivity growth, which suggests that the gains derived from learning-by-

doing fell short of those obtained through the switch from the old self-acting mule to

the ring spindle at the end of the nineteenth century.57

As with the transportation equipment industry, in chemicals technological develop-

ment took place at predominantly high machine-intensity levels. It has been noted in

the literature that in chemicals the subsequent stages of production are closely linked,

resulting in a continuous production line that combines different steps of the produc-

tion chain.58 This promoted not only vertical integration and large-scale production, it

also encouraged mechanization and automation, which accounts for the capital bias in

chemicals. Moreover, the fast pace of change pertains to new technology introduced in

the early 1920s, such as the production of synthetic fuels, rubber, and artificial resins.59

Other industries experienced technological change as well. For example, in primary

metals the open-hearth furnace – a late-nineteenth century innovation – was widely

adopted, in food industries conservation methods revolutionized, and paper machines

were both widened to increase the surface of paper under process and equipped with

multiple engines to improve performance.60 Apart from these industry-specific techno-

logical changes, the whole of manufacturing enjoyed the benefits from electrification.

Although electricity was introduced already in the late nineteenth century, it was the

55. J. Radkau, Technik in Deutschland vom 18. Jahrhundert bis zur Gegenwart (1989), 185–86.56. G. Egbers, “Innovation, Know-How, Rationalization, and Investment in the German Textile In-

dustry During the Period 1871–1935,” Zeitschrift fur Unternehmensgeschichte Beiheft 22 (1982): 234–256, 243.57. ibid., 242 (diagram 3).58. R. Berthold, ed., Produktivkrafte in Deutschland, 1917/18 bis 1945 (Akademie-Verlag Berlin,

1988), 126.59. ibid., 125.60. U. Wengenroth, Enterprise and Technology. The German and British Steel Industries, 1865–

1895 (Cambridge University Press, 1994), 195; Berthold, Produktivkrafte in Deutschland, 141 andibid., 137–38.

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first half of the twentieth century that witnessed the widespread application of elec-

tricity in manufacturing industries.61 Among its many advantages, electricity allowed

for a more efficient (and flexible) lay out of factory-floor design as machinery no longer

relied on a single drive shaft for propulsion.62 Production processes characterized by

high machine-intensity by nature profited most from the productivity gains associated

with electricity.

Decomposition of the labor-productivity gap in 1936/39

It is clear that the reduction of the German/US machine-intensity gap over the

interwar period, although far from complete, created a large potential for labor-

productivity growth in German manufacturing. Moreover, this created potential for

labor-productivity growth was larger in Germany than in the US, a necessary condi-

tion for catch-up.63 But the increase of machine-intensity levels proved insufficient to

close the labor-productivity gap. This section presents the labor-productivity gap de-

composition for 1936/‘39, which shows that the potential for catch-up growth created

in German manufacturing was not fully realized, at least not in the short run. A large

German/US labor-productivity gap is still observed at the end of the 1930s, but the

bulk of the gap is ascribed to the inability of German industries to operate machinery

at American levels of efficiency and not to a lack of machine-intensive technology in

German manufacturing as suggested in the literature.64

Table 3.4 reports the results of the decomposition along the lines of equation (3.1)

on page 66. Germany’s labor-productivity gap to the US is decomposed in two elements,

i.e. machine-intensity differences and relative efficiency levels. The table demonstrates

that it was not the choice of capital-labor ratios that kept Germany from catching-up

with America. At the level of total manufacturing, Germany had to increase labor pro-

ductivity by 86% to match the performance of its American counterpart. If Germany

operated machinery at US levels of efficiency, labor productivity would have risen by

62% (which covers 72% of the labor-productivity gap). The complete closing of the

machine-intensity gap would augment German labor productivity by 24% only (which

covers the remaining 28% of the labor-productivity gap), a small increase only in com-

parison with the potential gains attainable through an improved efficiency level.

The relatively small effect of machine-intensity differences may surprise given that

Germany still employed capital-labor ratios about half the level in the US. This lim-

61. Nelson and Wright, “The Rise and Fall,” 1945.62. Schurr et al., Electricity in the American Economy, 32.63. For a comparison between the created growth potential in Germany and the US, see appendix 3.C.64. Broadberry, The Productivity Race, 108, 109.

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Table 3.4: Decomposition of the increase (in %) of Germanlabor-productivity levels needed to catch-up with the US, 1936/39

Industry Total Obtainable through:

Needed for Technical Machine

catch-up efficiency intensity

Food, drink and tobacco 99 74 25

Textiles, apparel and leather 55 19 36

Paper and printing 101 48 54

Chemicals, petroleum and rubber 74 58 21

Stone, clay, and glass products 97 68 29

Primary and fabricated metals 74 53 21

Machinery (including electrical) 97 94 3

Transportation equipment 101 75 26

Miscellaneous 120 96 24

Total manufacturing 86 62 24

Sources: see section 3.3.

ited impact on the labor-productivity gap can be explained by the convex shape of

the frontier. The returns to further increases in machine intensity diminish sharply at

the highest ranges of capital-labor ratios of the frontier. Industries that are positioned

beyond that point may enjoy a large machine-intensity lead, which does not necessarily

translate into a markedly different labor-productivity potential. This is still a rational

choice, however, if innovation takes place at high ranges of machine intensity. In fact,

this is exactly how Basu and Weil perceive the behavior of pioneer countries. In or-

der to grow, they acquire as yet unexplored capital-labor ratios and in time increase

the labor-productivity potential through a process of learning-by-doing.65 This is also

perfectly in line with Allen’s empirical frontier analysis on the total-economy level; pi-

oneer economies first invent technology that is more capital intensive and subsequently

improve labor productivity as the new technology is perfected.66

The example of industrial chemicals in figure 3.5 illustrates this mechanism and

shows that the labor-productivity gap between the US and German industry is dispro-

portionally small as compared to the machine-intensity gap. The American chemical

industry appears to have ‘overshot’ by acquiring a capital-labor ratio beyond k∗, but

may move the frontier outward in the next period.

So the machine-intensity contribution to the labor-productivity gap depends on the

65. Basu and Weil, “Appropriate Technology,” 1030.66. Allen, “Technology and the Great Divergence,” 11.

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Figure 3.5: Labor-productivity vs. machine-intensitydifferences in industrial chemicals

0

F(’39)

(GER ‘36)

ya

yb

(US ‘39)

ka

kb

k*

degree to which capital-labor ratios differ between both countries as well as the shape

of the frontier. For instance, the lack of catch-up potential by means of machine in-

tensification for machinery industries reflects a relatively small machine-intensity gap.

Then again, in textiles, apparel and leather, machine-intensity differences were rela-

tively small, too, but even so there was substantial scope for labor-productivity in-

crease through adopting higher capital-labor ratios. This is explained by the strong

capital bias of the frontier for apparel and leather, which attributes considerable labor-

productivity gains to an increase in machine intensity.67 This also applies to paper and

printing. Apart from these exceptions, however, in all other industries the bulk of the

labor-productivity gap is ascribed to low efficiency levels, rather than differences in

machine-intensity levels.

3.5 Technology in German manufacturing

The DEA and the subsequent decomposition of the labor-productivity gap demonstrate

that German industries acquired new growth possibilities through machine intensifica-

tion. The gained potential for labor-productivity growth provides in hindsight a jus-

tification for Germany’s rapid move toward machine-intensive production technology.

67. See appendix 3.B for the global best-practice frontiers.

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Yet these possibilities for labor-productivity growth remained largely unrealized. This

raises two questions. First, did German entrepreneurs purposefully create potential for

labor-productivity growth through capital deepening? An awareness of frontier devel-

opments is prerequisite to technological spillover and Germany could expect to create

additional labor-productivity potential only when new production knowledge was imme-

diately available to countries not on the frontier.68 Secondly, if German entrepreneurs

were aware of the potential gains associated with adopting high capital-labor ratios,

what obstructed the efficient use of new machinery stock? In this section I turn to the

literature for an understanding of these issues.

Frontier awareness

In the case of Germany, ‘frontier awareness’ translates to an understanding and ap-

preciation of American production technology among German industrialists. Such an

America-centered orientation is well documented in the literature on interwar Germany.

In a study on German modernization, Nolan notes that American influences on German

entrepreneurship were limited before the 1920s. From the 1890s onwards, the scientific

management of labor as proposed by Frederick Taylor gained a strong foothold in the

minds of American producers. Proponents of Taylorism traveled to Germany, too, but

found their message difficult to sell; partly because of working-class opposition for fear

of reform at the cost of the laborer and partly because Germany’s successful industrial

development before 1914 did not create a necessity for new concepts.69

In the 1920s the situation was different. WW1, the reparation payments demanded

at Versailles, and the hyperinflation of the early 1920s had left the German economy

weakened in general and technological backward in particular. Change was needed and

by that time an attractive alternative to Taylorism was offered by Ford’s achievements in

the Detroit motor-vehicle industry. Rather than improving performance by rationalizing

on the factor input labor only, Fordism stressed the importance of both labor and

technology in the production process.70 As a consequence, the Fordist approach to

production appealed strongly to German entrepreneurs and set the example for future

development in Germany:

“With the end of Germany’s acute postwar dependency and instability,

America came to be seen as an economic model. In the words of one ob-

68. N. Rosenberg, “Economic Development and the Transfer of Technology: Some Historical Perspec-tives,” in The Economics of Technical Change, ed. E. Mansfield and E. Mansfield (Aldershot: EdwardElgar Publishing Limited, 1993), 380, 397.69. Nolan, Visions of Modernity, 45.70. ibid., 48.

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server: ‘One seeks to learn from her, to study her organization, management,

and technology.”’71

The economic-growth literature has often emphasized the importance of investment-

based strategies for follower countries.72 Provided that the necessary capabilities and

resources are available (Gerschenkron’s idea of ‘appropriate’ economic institutions

and Abramovitz’ ‘social capabilities’) countries distanced far away from the frontier

can catch-up quickly by importing or imitating advanced technologies.73 Such an

investment-based catch-up strategy helps explain the strong German orientation on

America. Apart from the theoretical notions of modern economic-growth models, how-

ever, perhaps a more decisive incentive to follow the American example was provided

by the realities of the interwar period. In 1924, after the damage suffered by the econ-

omy during WW1 and the subsequent problems in the Weimar Republic had become

visible, industrialists and entrepreneurs sought ways to recover. At the time America

showed an unprecedented growth record.74 The growth experience of the US was not

just a theoretical possibility discussed in academic debate. More than anything, the

US demonstrated the feasibility of fast economic growth. What better way forward for

Germany than to follow the American example?

“For industrialists, (...) Fordist productivism offered a possible solution to

the economic problems of low productivity, inefficient technology, lack of

standardization, and the resulting high costs that plagued the economy as a

whole. (...) Rationalization, at least in the first instance, was defined by all

in technological and productivist terms. A shared perception of the prob-

lems of German production led to a shared belief that there was no better

place to start learning alternative production methods than from Ford, the

embodiment of American technological leadership, efficiency, and cost cut-

ting.”75

Many German entrepreneurs traveled to the US to study first hand the organization

of American manufacturing industries. Although the extensive application of machinery

and the high level of efficiency at which American manufacturing operated never failed

to impress the visitors, many Germans felt that the American example could not be

71. ibid., 23–24.72. Aghion, “Higher Education and Innovation,” 31; Acemoglu, “Directed Technical Change,” 39;

Vandenbussche, Aghion, and Meghir, “Growth, Distance to the Frontier and Composition of HumanCapital,” 98.73. Gerschenkron, Economic backwardness, 113, 116; Abramovitz, “Catching-up,” 387.74. Field, “The Most Technologically Progressive Decade.”75. Nolan, Visions of Modernity, 38.

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repeated in Germany. Market size, demand patterns, and wage structures differed just

too much between the US and Germany. Nevertheless, it was argued that the principles

of American production technology could be isolated and implemented in Germany as

well.76

Given the widespread enthusiasm about American production technology, it does

not surprise that in the 1920s German manufacturing industries deployed imitating

activities to catch-up with their American competitors.77 Well-known examples of both

imitating strategies and direct technology transfer concern the German machine-tool

industry. Richter and Streb, for instance, quote contemporary sources reporting that

American machine tools were copied by German engineers without any modification to

the original design:

“Information coming from Germany indicates that a number of American

machine-tools are (...) made without the slightest alteration.”78

But there were countless more examples of imitation by German machine-tool man-

ufactures. In the mid-1920s, the American trade commissioner listed over sixty US

machine-tool producers whose export suffered from German firms duplicating their

products.79 They suffered mainly because the changes implemented by the Germans on

American designs were negligible:

“But so far as the central idea and the means of carrying it our [were]

concerned, these tools [were] simply American out and out.”80

Richter concludes that not only thousands of American machine tools were in use in

Germany, but also the same amount or even more German copies of these tools.81 This

invites the question why Germany needed that many American machine tools if the

German production system was locked-in on a technological path essentially different

from the US, as traditionally has been uphold in the literature?82

In a recent paper, Ristuccia and Tooze quantify the prevalence of technology transfer

and imitating activities in the machine-tool industry. Although they base their analysis

on the number of purchased machines in Germany and the US, their conclusion aligns

76. Nolan, Visions of Modernity, 38.77. For a discussion on the channels of technology transfer between Germany and the US, see H.J.

Braun, “The National Association of German-American Technologists and Technology Transfer Be-tween Germany and the United States,” in History of Technology, ed. N. Smith (Mansell PublishingLimited, 1984), 15–35.78. Richter and Streb, “Catching-Up and Falling Behind,” 1007.79. Richter and Streb, “Catching-up and Falling Behind,” 17.80. Richter, “Technology and Knowledge Transfer,” 179.81. ibid., 180.82. ibid., 177.

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with my horse-power statistics; Germany converged on the US in terms of machine

intensity and by the 1930s both countries operated very similar production technology.83

Moreover, with respect to the nature of capital, they find no evidence to suggest that

Germany hoarded old machinery; additions to the machinery stock consisted of new

technologies not unlike those in America.

On the basis of this evidence, Ristuccia and Tooze reject the notion of dichotomous

technological paths across the Atlantic and note that “Germany by the late 1930s

showed all the signs of an economy tooling up for the mass production in internal

combustion engines on the lines pioneered by the US in the 1920s”.84 Furthermore, as

Scranton and Richter did before, they downplay the importance of mass production in

US manufacturing.85 Mass production was only one element in a much broader range

of technologies employed in America.86

As Fordist production methods cannot be taken as representative for the American

national technology system, so should the machine-tool industry not be interpreted as

archetypical to the whole of German manufacturing. My data show that only in the

electrical machinery industry Germany closed the machine-intensity gap with the US

(see table 3.2). Other industries still lagged behind and quite considerably in some cases.

Nevertheless, the broad pattern of capital deepening displayed most prominently by the

machine-tool industry are observed in a wide range of other manufacturing industries,

too, although to a lesser extent.

The adoption of American technology proceeded in two waves. As early as between

1870 and 1914 machinery was acquired in America, yet the practice of technology adop-

tion was limited to large and modern German establishments, mostly in metal produc-

ing and metal processing industries.87 In this first wave, the American technologies

were met with reservation concerning its applicability in Germany and implementation

was frequently deemed feasible only once machinery was modified to suit local condi-

tions.88 The second wave of Americanization took place in the 1920s and was much

more widespread.89 Small firms participated in imitating activities, too, as it was rec-

ognized that copying American production technology improved competitiveness and

reduced development costs.90 The tradition of technology transfer thus continued into

the interwar period, but on a much grander scale than before WW1.

83. Ristuccia and Tooze, “Machine Tool and Mass Production,” 13, 18.84. ibid., 11.85. Scranton, Endless Novelty, 341–43; Richter, “Technology and Knowledge Transfer,” 178.86. Ristuccia and Tooze, “Machine Tool and Mass Production,” 9.87. Radkau, Technik in Deutschland, 177.88. ibid., 179.89. ibid., 181, 269, 275.90. Richter, “Technology and Knowledge Transfer,” 181.

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In general, the process of Americanization was characterized by increased standard-

ization and mass production. In the primary metals industry, for instance, Germany

adopted the American method of positioning rolling mills in line to not have to move

metal in different directions or between different machines during the stages of produc-

tion.91 In iron production Germany took the Siemens-Martin process from America,

too. Although the latter was introduced as early as the 1870s, early designs were unable

to process pig iron with a high phosphor content, as in Germany.92 When this problem

was overcome, the open-heart furnace was readily adopted and spread over Germany

during the first half of the twentieth century.93

German constraints to high efficiency

Given the numerous examples of technology transfer it is difficult to maintain for the

early twentieth century the notion that Germany was stuck on a technology path very

different from the US. Then what prevented Germany from operating this new technol-

ogy at efficiency levels displayed by the US? Going back to table 3.4, which demonstrates

that low levels of efficiency formed the major impediment to labor-productivity catch-

up, the industry-level results provide some clues. Take for instance the transportation

equipment industry. Germany could have raised labor-productivity levels by 75% if it

improved its efficiency to the level displayed by its US counterpart, an increase three

times larger than the 26% obtainable through capital deepening. It has been suggested

that the industrial organization of the German motor-vehicle industry lay at the root

of the comparatively low level of labor productivity. Keck writes that before the turn of

the twentieth century German firms were among the early technical leaders, but failed

to turn that into a commercial lead, mainly due to the difficulties German automobile

producers encountered adopting mass-production technology.94

This reluctance of German producers to fully embrace the American system has

been ascribed to different causes – mostly associated with demand patterns, such as a

relatively small domestic market, a demand for heavy-built, custom-made and expensive

cars or a fluctuating demand for automobiles which encouraged flexible production –

and resulted in low productivity levels; over the year 1921 Daimler produced less cars

than Ford did in one day.95 Still, production was increasingly standardized in Germany,

91. Radkau, Technik in Deutschland, 122.92. Wengenroth, Enterprise and Technology, 195, 243.93. G. Milkereit, “Innovation, Know-How, Rationalization and Investments in the German Mining and

Metal-Producing Industries, Including the Iron and Steelmaking Industry, 1868/71–1930,” Zeitschriftfur Unternehmensgeschichte Beiheft 22 (1982): 159.94. O. Keck, “The National System for Technical Innovation in Germany,” in National Innovation

Systems. A Comparative Analysis, ed. R. Nelson (Oxford University Press, 1993), 129, 131.95. Radkau, Technik in Deutschland, 275, 278.

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but instead of using a single production line, as in America, German car assembly

remained divided into different production stages, each with its own assembly belt to

allow for flexible production.96 German industrial organization was aimed at minimizing

operating costs of machinery, rather than maximizing output with respect to labor. So

Germany failed to exploit the full potential of the technology in use and approach

American performance.

The notion that low levels of efficiency prevented convergence is illustrated most con-

vincingly by machine-producing industries. Table 3.4 reveals that the labor-productivity

gap in these industries was fully attributable to a lack of efficiency on the part of Ger-

many. Indeed, as shown in table 3.2 and more extensively described by Ristuccia and

Tooze, the level of capital intensity was similar between Germany and the US.97 So

differences in labor-productivity performance can only be ascribed to the level of effi-

ciency at which technology is operated. As with the transportation-equipment industry,

it has been noted that German machine-tool industries focused more on flexible produc-

tion than on high-throughput systems.98 The capital intensity did not differ between

both countries, but the composition of installed machinery did. High volume, autom-

atized machinery was underrepresented in Germany, which may explain the relatively

low levels of output per unit of labor input in this industry.99

In addition to these industry-specific factors that kept Germany from exploiting the

full potential of their technology stock, Ristuccia and Tooze suggest that the labor-

productivity gap stemmed from general differences between Germany and the US, such

as the latter’s cheap energy sources and larger scale of production.100 Also, by the

1930s it became evident that due to the emphasis on production and productivity, i.e.

supply-side factors, the productive capacity of industries had expanded much faster than

demand. In effect, many industries were overcapitalized and had excess capacity that

was left unused.101 As the new direction of technological development and industrial

organization was ill-matched to meet demand patterns, the success of the modernization

process was less than what was hoped for. Furthermore, autarkic policies related to the

build-up to WW2 may have acted as a barrier to efficiency, too. For instance, the food

industry saw major changes in the 1930s by government decree to suit the needs of

a country preparing for war.102 Together with protectionist policies that reduced the

incentive to improve efficient production, it may explain the low efficiency levels.

96. ibid., 278, 280.97. Ristuccia and Tooze, “Machine Tool and Mass Production.”98. ibid., 9; Radkau, Technik in Deutschland, 277.99. Ristuccia and Tooze, “Machine Tool and Mass Production,” 9.

100. Ristuccia and Tooze, “The Cutting Edge of Modernity,” 9.101. Nolan, Visions of Modernity, 132.102. Berthold, Produktivkrafte in Deutschland, 143–144.

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3.6 The long-term perspective

The labor-productivity gap decomposition points out that the gains of modernization

were limited, in the short run at least. I do not necessarily interpret the lack of catch-up

growth as a failure on the part of Germany. Previous applications of the DEA-approach

in the field of development economics led to findings not dissimilar to mine. That is,

fast capital deepening does not necessarily translate directly into correspondingly fast

labor-productivity growth. For a sample of countries in the period between 1975–1992,

Timmer and Los show that the created potential for labor-productivity growth due to

capital deepening was large, not unlike interwar Germany.103 Moreover, the country

that created the largest potential, i.e. Korea, experienced an increase of its distance to

the global best-practice frontier over time. Korea grew at just above halve the growth

potential it had created. Instead of interpreting the declining value for efficiency as

a failure, Timmer and Los conclude that these findings suggest a sequence in which

countries first create opportunities for growth by rapidly increasing capital intensities

and subsequently learn to operate the new technology at its full potential.104

Figure 3.6: Labor-productivity catch-up in two sequential steps

0

(pre-WW1)

(pre-WW2)

y

k

(post-WW2)

??

(2) ‘Learning’ and operating

technology efficiently

(1) Creating potential through

capital intensification

Timmer and Los’ interpretation of the Korean growth experience is a two-stepped

approach to catch-up. Follower countries (or industries) go through two sequential

phases of development in order to close the gap with the frontier, as depicted in fig-

ure 3.6. If the initial phase of catch-up – the adoption of high capital-labor ratios –

103. Timmer and Los, “Localized Innovation,” 58.104. ibid., 60.

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involves an extensive transformation of the production process, efficiency levels may

be low in the short run. Only after the economy has adjusted to the new situation

and has ‘learned’ to operate technology at its full potential, the labor-productivity gap

to the frontier narrows. The time lag between creating potential and moving toward

the frontier may therefore depend on the speed of capital deepening. For the case of

Germany, this implies that the implementation problems that German engineers and

industrialists encountered in the 1920s and 1930s were not necessarily signs of failed

industrialization. Instead, they were features of progress and inextricably linked to the

initial phase of catch-up growth.

By the 1930s Germany had created a large potential for labor-productivity growth

and, in theory, it should cash in this latent capacity in a later period, i.e. the 1940s, by

means of ‘learning’. It falls outside the scope of this chapter to extend the analysis to

include the post-WW2 period, mostly because the postwar data on capital is not di-

rectly compatible with the horse power measure employed here. Nevertheless, Germany

was not in the position to realize its growth potential during the period 1939–1945 for

obvious reasons. Equally well-documented is Germany’s change of fortunes after 1946,

from which year onwards it rapidly closed the productivity gap with America. In 1980

(West) German levels of labor productivity stood at about 80% of those in the US.105

Furthermore, the process of capital deepening picked up again after 1950 and contin-

ued until the early 1970s, when America had almost lost its lead over Germany.106

Figure 3.7 sets out German/US relative machine intensity against German/US rela-

tive labor productivity for both the pre-WW2 period, based on data from this study,

and the post-WW2 period, based on data from O’Mahony. Both series are not directly

compatible, as the capital data in this chapter refers to machinery, while O’Mahony

measures the total capital stock. Nevertheless, the figure suggests that the unexploited

potential for labor-productivity growth was gradually realized after 1950.

The unprecedented rate of labor-productivity growth in Germany during the early

postwar years has been explained partly by reconstruction dynamics; Vonyo demon-

strates that wartime destruction and dislocation left much of the capacity for growth

unrealized, a potential which was exploited during the late 1940s and early 1950s.107 In

similar vein, Wolf argues that Germany’s direct productive capacity was not severely

damaged during the war, as a result of which the postwar productive capability consid-

erable exceeded actual production.108 The ensuing rebound growth – or “soft growth”,

105. O’Mahony, Britain’s Productivity Performance, 16.106. ibid., 24, 25.107. Tamas Vonyo, “Postwar Reconstruction and the Golden Age of Economic Growth,” EconomicHistory Review Vol. 12, no. 2 (2008): 235, 239.108. H. Wolf, “Post-War Germany in the European Context: Domestic and External Determinants of

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Figure 3.7: German catch-up in manufacturing after WW2

0.20

0.40

0.60

0.80

1.00

0.40 0.60 0.80 1.00

19091936

1950

1973

1979

US = 1.00

US

= 1

.00

This study

O’M

ahony (1

999)

Com

para

tive l

abor

pro

ducti

vit

y

Comparative capital intensity

as Wolf calls it – explains most of Germany’s labor-productivity growth up till 1955,

while by 1960 this potential had been exhausted.109

To these explanations I add that rebound growth in postwar Germany was driven not

exclusively by the potential left unrealized as a result of wartime dislocations, but also

by the capacity for growth already build-up during the interwar period. Shortly after

1950 German industrial production had recovered up to the pre-WW2 level of 1938.

Therefore, according to Wolf, “hard growth” – i.e. expansion of the full-employment

production level – sets in from the 1950s onwards.110 The decomposition presented

here demonstrates that also at 1936 production levels the capacity for labor-productivity

growth had not yet been exhausted. While the postwar reconstruction dynamics were

limited mainly to the late 1940s, the realization of the growth potential left unused

before WW2 possibly provided some of the fuel for the growth spurt during the 1950s.

Indeed, Ristuccia and Tooze suggest that the postwar growth miracle was prepared by

the process of capital deepening in the machine-tool industry during the 1930s.111

It is not implied here that the high rate of capital deepening over the interwar

period provided the foundation for the German postwar growth miracle. Too much has

happened between the 1930s and 1950s to make such a connection. I do like to point out,

Growth,” in Europe’s Post-War Recovery, ed. B. Eichengreen (Cambridge, Mass.: Harvard UniversityPress, 1995), 326.109. Wolf, “Post-War Germany,” 328.110. ibid.111. Ristuccia and Tooze, “Machine Tool and Mass Production,” 20.

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however, that the emphasis in the reconstruction literature on rebound growth during

the late 1940s and early 1950s fits conceptually well within the framework proposed

here; with the only difference that the latent capacity for growth present after the war,

in my view, results from technological inefficiency incurred by both wartime dislocations

and rapid machine intensification in the interwar period.

3.7 Conclusion

This paper studied the growth experience of German manufacturing over the interwar

period. Qualitative evidence on technological change in Germany is difficult to align

with models of path-dependent technological progress used so far to explain the per-

sistent transatlantic labor-productivity gap between 1850 and 1950. Labor-abundant

and resource-scarce European countries were supposedly trapped on a labor-intensive

technological path that limited the scope for productivity growth. However, recent case

studies unveil a tradition of imitation and technology transfer in German manufac-

turing, particularly in the machine-tool industry. During the 1920s German industries

actively copied and duplicated American, capital-intensive, technology. This contradicts

the notion of a Germany-specific technological path.

This paper reassesses the productivity dynamics in German manufacturing and

adopted as point of departure Basu and Weil’s framework of appropriate technology

that predicts convergence in light of capital deepening. My findings show that, first,

over the interwar period Germany increased machine intensity at a rate higher than in

the US, as a result of which the machine-intensity gap between both countries narrowed.

This convergence does not align with models that imply stable capital-intensity levels or

gaps over the interwar period. Secondly, using DEA-techniques, I show that the change

of the global technology frontier was localized and biased toward capital. Consequently,

Germany’s process of capital deepening created a large potential for labor-productivity

growth. Third, and lastly, the decomposition of the labor-productivity gap between in-

terwar Germany and the US revealed that this potential for growth remained partly

unrealized, as Germany operated at low levels of efficiency. If efficiency levels equaled

those in the US, the labor-productivity gap would be a quarter of the size it actually

was.

These findings confirm the anecdotal evidence reported by case studies and reject

the notion of a dichotomous technological development across the Atlantic during the

first half of the twentieth century. The convergence in machine intensity occurred at a

time when German entrepreneurs and industrialists increasingly looked to America as

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the example for industrial development. Frontier awareness, i.e. knowledge of produc-

tion technology used at the frontier, was a prerequisite to technological spillover and

abundantly present among German industrialists in the late 1920s and early 1930s.

In spite of the infatuation with the US, reality could not match the pro-America

rhetoric employed by German engineers, industrialists, and entrepreneurs. Yet these

small gains in labor productivity were not a failure on the part of Germany. Following

Los and Timmer, the decrease of relative efficiency is understood as a feature of progress

inextricably linked to the first phase of catch-up growth, which is creating potential by

capital deepening. Only after an economy has adjusted to the new situation and exhausts

the full potential of the new technology, the labor-productivity gap to the frontier can be

narrowed. Indeed, a review of the literature shows that German entrepreneurs struggled

to fully embrace American industrial design, especially the adoption systems of mass

production.

In view of these findings, I see the interwar period as a time of transition in German

manufacturing. This transition phase is enclosed on both ends by periods that arguably

display very different dynamics. The relatively low levels of machine intensity in pre-

WW1 German manufacturing suggests that the labor-productivity gap to the US in the

period before 1900 was driven largely by the use of different technology, while the post-

WW2 era witnessed a rapid decrease of both the labor-productivity and capital-intensity

gap to the US. Yet the dynamics that propelled Germany to the frontier after the 1950s

should, perhaps, not necessarily be understood as a development strictly confined to

the postwar period, but as a process of technology catch-up that was already set in

motion in the 1920s and 1930s. Or, as Nelson and Wright note with regard to the

interwar period, the “global diffusion and adaption of American methods would surely

have continued, (...) either by imitation or by direct foreign investment, if it had not

been interrupted by World War II.”112

112. Nelson and Wright, “The Rise and Fall.”

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3.A Distance function

In this paper I emphasize the role of technological change as a driver behind the wave

of modernization that marked the interwar period and stress the importance of effi-

ciency behind the German productivity dynamics of the 1920s and 1930s, particularly

in relation to the US. By adopting a data envelopment analysis (DEA), which ap-

plies non-parametric linear programming techniques, I can decompose TFP into two

components: changes in technological efficiency and shifts in technology over time. In

addition, as the DEA does not require the imposition of a particular functional form

on the production frontier, it allows for any type of technological change, be it biased

or factor-neutral.113

In this appendix I will summarize the basic framework behind the DEA, based

primarily on the work of Fare, Grosskopf and Lovell.114 They illustrate that a distance

function can be used to determine the Farrell efficiency indices of a production set

for any number of inputs or outputs. On the basis of the efficiency scores, a (global)

production frontier can be constructed, which in turn allows me to determine the change

in technology over time.115 In this basic example I assume that all inputs and output

quantities are non-negative and that, for each time period t = 1, . . . , T , the production

technology St models the transformation of N inputs, xt ∈ RN+ , into M outputs, yt ∈

RM+ ,

St ={(xt, yt) : xt can produce yt

}(3.2)

The input distance function Dti(x

t, yt) at time t is defined as

Dti(x

t, yt) = min{θ : (θxt, yt) ∈ St

}(3.3)

For the constant returns to scale case and a technology set St, the input distance

113. The main advantage of the Data Envelopment Analysis technique is its flexibility and adaptability.A DEA allows for multiple inputs and outputs, does not require input- or output-prices and does notrequire behavioral assumptions such as cost minimization or profit maximization.114. Fare, Grosskopf, and Lovell, Production Frontiers.115. R. Fare et al., “Productivity Growth, Technical Progress, and Efficiency Change in IndustrializedCountries,” American Economic Review Vol. 84, no. 1 (1994): 68–69.

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function for production (xj,t, yj,t) can be specified as

min θ subject toθ,λ1,...,λJ

yj,t ≤∑k

λkyk,t (3.4)

θxj,t ≥∑k

λkxk,t

λk ≥ 0 ∀ k.

The solution to the linear program for the intensity vector λ∗ and efficiency index θ∗

can be interpreted as follows. There is a (hypothetical) composite producer formed as a

non-negative linear combination of all J observations using the components of λ∗. This

composite producer consumes no more than θ∗ times observation j’s inputs, while still

producing j’s output. The composite producer thus represents a fully efficient producer

who is located on the global production frontier at j’s output level, while θ∗ represents

the ratio between both the inputs of the composite producer and xtjrespectively. Note

that if (xt, yt) ∈ St, the Farrell efficiency index θ will take on a value between 0 and 1,

where a value of 1 implies full efficiency.

The observations for which the input distance function returns a θ equal to 1 together

determine the position and shape of the production frontier. The frontier is formed

by tightly enveloping the fully efficient observations, or ‘best practice’ activities, with

linear segments; as illustrated in figures 3.1a and 3.1b on page 63. The frontier is thus

a subset of all feasible techniques that attain the highest labor productivity for the

capital intensity levels they correspond to.116

116. Timmer and Los, “Localized Innovation,” 52.

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3.B Global best-practice frontiers

Figure 3.8: Frontiers for the years 1909, 1919, 1929 and 1939

0 2 4 6 8k

0

1

2

3

4

y

(a) Industry 20

0 0.2 0.4 0.6 0.8 1.0 1.2k

0

2

4

6

8

y

(b) Industry 21

0 0.5 1.0 1.5 2.0 2.5 3.0k

0

0.4

0.8

1.2

1.6

2.0

y

(c) Industry 22x

0 1 2 3 4k

0

1.5

3.0

4.5

y

(d) Industry 227

0 0.06 0.12 0.18k

0

0.5

1.0

1.5

2.0

2.5

3.0

y

(e) Industry 23

0 0.6 1.2 1.8k

0

0.4

0.8

1.2

1.6

y

(f) Industry 24t5

0 0.4 0.8 1.2 1.6 2.0 2.4k

0

0.4

0.8

1.2

1.6

2.0

2.4

y

(g) Industry 26x

0 4 8 12 16k

0

0.4

0.8

1.2

1.6

2.0

y

(h) Industry 261

0 0.3 0.6 0.9k

0

0.5

1.0

1.5

2.0

2.5

3.0

y

(i) Industry 27

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0 1 2 3 4k

0

1

2

3

4

y

(a) Industry 28x

0 1 2 3 4 5k

0

1

2

3

4

y

(b) Industry 281t2

0 0.2 0.4 0.6 0.8 1.0 1.2k

0

1.5

3.0

4.5

y

(c) Industry 283

0 0.5 1.0 1.5 2.0 2.5 3.0k

0

1

2

3

4

5

y

(d) Industry 284

0 3 6 9k

0

1

2

3

4

y

(e) Industry 2287t8

0 2 4 6 8 10k

0

1.5

3.0

4.5

y

(f) Industry 29

0 1 2 3 4 5k

0

0.5

1.0

1.5

2.0

2.5

3.0

y

(g) Industry 30

0 0.4 0.8 1.2 1.6k

0

1

2

3

4

y

(h) Industry 31

0 5 10 15 20 25 30k

0

0.5

1.0

1.5

2.0

2.5

3.0

y

(i) Industry 32

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0 1 2 3 4 5 6k

0

0.4

0.8

1.2

1.6

2.0

y

(a) Industry 33

0 0.6 1.2 1.8k

0

0.4

0.8

1.2

1.6

2.0

2.4

y

(b) Industry 34

0 0.4 0.8 1.2 1.6 2.0k

0

0.4

0.8

1.2

1.6

2.0

y

(c) Industry 35x

0 0.1 0.2 0.3 0.4 0.5k

0

0.6

1.2

1.8

y

(d) Industry 357t9

0 0.4 0.8 1.2 1.6 2.0k

0

0.4

0.8

1.2

1.6

2.0

y

(e) Industry 36

0 1 2 3 4k

0

0.4

0.8

1.2

1.6

y

(f) Industry 37x

0 0.4 0.8 1.2 1.6k

0

0.5

1.0

1.5

2.0

2.5

3.0

y

(g) Industry 371n25

0 0.6 1.2 1.8k

0

1

2

3

4

y

(h) Industry 38

0 0.2 0.4 0.6 0.8 1.0 1.2k

0

0.4

0.8

1.2

1.6

2.0

2.4

y

(i) Industry 39

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3.C Labor-productivity growth at the frontier

Section 3.4 described how the adoption of high capital-labor ratios in German manufac-

turing created a large potential for labor-productivity growth. The labor-productivity

growth at the frontier for the German change in capital-intensity levels between 1909

and 1936, presented in the second column of table 3.3, can be further decomposed in

two elements; pure technological change and capital intensification. Both effects are

captured by figure 3.9. The highest attainable level of labor productivity for German

capital-labor ratios in 1909 is ya and in 1936 yd. The growth at the frontier from ya to

yd can be ascribed to (1) a move along the frontier as a result of capital intensification

and (2) an upward shift of the frontier over time by means of technological change.

Figure 3.9: Decomposition of growth potential

F(’09)

F(’39)

y

0 k

ya

yb

yc

yd

kGER’09

kGER ‘36

The contribution of capital intensification is calculated as the geometric average of

the labor-productivity increase due to a move along the frontier in each period, i.e.

yb/ya for 1909 and yd/yc for 1936. The effect of technological change is then measured

as the geometric average of the change in labor-productivity at capital-intensity levels

of 1909 and 1936, i.e. yc/ya and yd/yb, respectively. Equation (3.5) explains the total

growth of labor productivity at the frontier over the period 1909–1936 by the combined

contribution of both elements.

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ybya

=

(ybya

· ydyc

)0.5

︸︷︷︸capital deepening

·(ycya

· ydyb

)0.5

︸︷︷︸technological change

(3.5)

Table 3.5 reports the results of this decomposition, for Germany as well as the US.

The upshot is clear; both Germany and the US created a large potential for labor-

productivity growth, but whereas the former achieved this mainly through capital in-

tensification, the latter did it by means of technological change. To be more precise, for

total manufacturing 59% of total labor-productivity growth at the frontier for German

capital-labor ratios is ascribed to capital intensification. In the US only 29% of the

total labor-productivity gains were realized through this channel. So America enjoyed

an increase in labor-productivity potential mainly as a result of technological change.

These results imply that innovation took place at capital-labor ratios displayed by the

US. Or, in other words, innovations was mainly US based during the interwar period.

Germany experienced a different path of development and created potential by adopting

capital-intensive technology already explored by forerunners.

Table 3.5: Created labor-productivity potential (ln %) in Germany and the USdecomposed in elements of technological change and capital intensity

Industry Created potential Contribution (%) of

Annual ln(%) Techn. change Capital int.

GER US GER US GER US

Food, etc. 4.67 3.25 29 53 71 47

Textiles, etc. 3.15 2.75 57 76 43 24

Paper, etc. 3.44 2.81 32 65 68 35

Chemicals, etc. 4.26 3.76 46 72 54 28

Stones, etc. 3.18 3.03 42 77 58 23

Metals, etc. 3.29 2.73 34 76 66 24

Machinery, etc. 1.66 1.22 34 88 66 12

Transportation equipment 6.12 5.21 48 73 52 27

Miscellaneous 2.90 2.18 29 77 71 23

Manufacturing 3.40 2.77 41 71 59 29

Sources: see text, section 2.3.

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3.D Robustness check

In the discussion of the data in section 3.3, it was suggested that the use of 1933 horse-

power statistics as a proxy for 1936 potentially overestimates machine-intensity levels

in German manufacturing. Because labor was laid off during the Great Depression,

the machine stock was underutilized and may have stood partly idle on the factory

floor in 1933. An alternative strategy is to take 1938 horse-power statistics for German

manufacturing. In 1938, however, factor utilization differed from 1936, too, although in

exactly the opposite way compared to 1933. Whereas the unemployment rate in 1936

was smaller than in 1933 by a factor three, it was larger than in 1938 by exactly the

same factor.117 I already justified my choice for the 1933 data on the grounds that the

1938 statistics suffer from a lower coverage and distortions due to the war effort that are

impossible to correct. Nevertheless, as a robustness check, it is possible to decompose the

labor-productivity gap using 1938 horse-power data for Germany, as done in table 3.6.

Table 3.6: Decomposition of the German labor-productivity increase(in %) needed to catch-up with the US, 1936/39

(German 1938 machine-intensity levels)

Industry Total Obtainable through:

Needed for Technical Machine

catch-up efficiency intensity

Food, drink and tobacco 99 55 45

Textiles, apparel and leather 55 18 37

Paper and printing 101 52 50

Chemicals, petroleum and rubber 74 57 17

Stone, clay, and glass products 97 51 45

Primary and fabricated metals 74 42 32

Machinery (including electrical) 97 86 11

Transportation equipment 101 64 37

Miscellaneous 120 86 34

Total manufacturing 86 55 31


The results in table 3.6 are obtained on the basis of a lower bound estimate of capital-

intensity levels in German manufacturing and, hence, an upper bound estimate of the

catch-up potential by means of capital intensification. At the same time, it increases

industries’ efficiency level as the frontier assigns a relatively small labor-productivity

117. Pierenkemper, “The Standard of Living and Employment,” 59.

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potential to relatively low capital-labor ratios. Consequently, the indicated potential

for labor-productivity catch-up by means of improving of technical efficiency presents

a lower bound estimate. It is argued in chapter 3 that standing in the way of German

labor-productivity catch-up was a relatively low level of technical efficiency, rather than

the use of different capital-labor ratios. The use of a lower bound estimate for machine

intensity in table 3.6 thus offers a check upon the strength of this argument. Looking

at the results, it is clear that the conclusions remain unaltered. Although, as expected,

the catch-up scope through machine intensification increases marginally, it is clear even

here that the bulk of the German/US labor-productivity gap is attributable to a low

technical efficiency, rather than a lack of sufficiently high capital-labor ratios.

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Chapter 4Industrial Output Growth in Pre-WW2 Germany.

A Reinterpretation of Time-Series Evidence∗

4.1 Introduction

The necessity of re-assessing the state of the German economy, as repeatedly stressed in

the previous chapters, stems for a large part from the difficulty encountered by previous

studies to construct a reliable time series of industrial output in pre-WW2 Germany.

Several series have been proposed, yet a final solution for the issue is not available. The

confusion surrounding the quality of these historical indices invites discussion concerning

the development of Germany’s performance around the turn of the twentieth century.

In particular Germany’s comparative performance in industry relative to the UK prior

to WW1, a time when the effects of modernization were not yet diluted by the economic

dislocations of the World Wars, has been the topic of debate.1

At stake in this debate is the labor-productivity leadership in Europe; if Germany

had already surpassed Britain – the labor-productivity leader of old – this would suggest

a failure on the latter’s part to benefit from the opportunities offered by the innovations

of the second industrial revolution as much as the former did. This line of reasoning

suggests that the barriers to growth frequently attributed to the UK, such as the relative

costs of factor inputs or market conditions discussed earlier, acted less as a constraint on

Germany’s development. The relative standing within Europe may carry implications,

too, for the growth process. If follower countries develop through catch-up mechanisms,

i.e. copying technology operated at the global productivity frontier, Germany could

no longer look to the UK for future growth. A further interest concerns the period

* This chapter is based on joint work with Jan P.A.M. Jacobs (University of Groningen).1. Ritschl, “Spurious Growth in German Output Data”; Broadberry and Burhop, “Comparative Pro-

ductivity in British and German Manufacturing”; Ritschl, “The Anglo-German Industrial ProductivityPuzzle”; Broadberry and Burhop, “Resolving.” Section 4.2 visits the debate in detail.

103

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after WW1. Prerequisite for understanding the impact of the war and the subsequent

upheaval in the Weimar Republic is an assessment of the state of the economy before

these shocks occurred.

The estimates so far presented in the literature differ to such a degree that two

stories can be told of Germany’s comparative performance before WW1 that are in fact

incompatible; Germany either performed on par with the UK or it outperformed the

UK by roughly 25%.2 With an eye to the historical questions touched upon above, the

ambiguity is unsatisfactory. This begs the question whether it is possible to confidently

draw conclusions regarding Germany’s historical growth record in the face of conflicting

data? I think it is and I arrive at that conclusion through application of a new approach

to this debate.

Much of the deviation between the various output series suggested in the literature

results from the use of different output proxies, which are used because data on value

added is unobtainable for the period before WW2. New releases of the German output

series are therefore valued at the accuracy of the proxies applied to estimate industrial

production. In the absence of value added data to evaluate the accuracy of the prox-

ies, the choice between alternative versions of the output index is not straightforward.

Nevertheless, once a revision is deemed more appropriate as a measure of value added

change, it has been custom to discard all other, older alternatives of the output index.

This chapter sets out to solve the time-series issue by casting the debate in a new

framework. Instead of choosing between different output proxies, I acknowledge that

all series estimate output change by studying variables that are assumed to correlate

strongly, but not perfectly with value added. It follows that the behavior of all series is

largely determined by the same underlying component, i.e. value added change, while

deviations in the observed series are contingent on the different correlation between

the employed output proxies and actual output growth. Using state space time series

analysis, I estimate value added change by filtering from all available data an unob-

served common component.3 This way, the analysis makes full and efficient use of all

information available, rather than choosing for one particular alternative only.

A second aim of the chapter is to shed light on the statistical error associated with the

estimation process. Due to incomplete information the estimates of output change are

essentially based on sample data, so the estimates are inaccurate to some extent. In the

debate on German output growth, however, indicators of statistical dispersion are not

2. Broadberry and Burhop, “Resolving,” 932; S.N. Broadberry and C. Burhop, “Resolving the Anglo-German Industrial Productivity Puzzle, 1895–1935: A Response to Professor Ritschl,” Warwick eco-nomic research papers Vol. 848 (2008): 16. See also table 4.1 on page 110.

3. Commandeur and Koopman, An Introduction; Durbin and Koopman, Time Series Analysis.

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Chapter 4. Industrial Output Growth in Pre-WW2 Germany 105

provided and point estimates are implicitly treated as true values. As such, important

properties of the data are omitted. By providing an indication of the statistical error

in my estimates, this chapter follows in the tradition of Charles Feinstein and Mark

Thomas, who argued that any new statistical series should be accompanied by a guide

to the associated margins of error.4

The statistical error is important for the reconciliation between time series and

benchmark estimates, i.e direct level estimates. If the output level is known for a par-

ticular year, the index of industrial production can be used to obtain output levels in

other years through extrapolation. The discrepancy between output levels obtained in-

directly through time series and directly by benchmarks is a frequently used measure of

the former’s accuracy. Because benchmark studies calculate output levels for particular

years, these snap shots of industrial performance are a popular measurement technique

in the debate on German output growth; new releases of the output index that do not

reconcile with benchmark estimates are ill-received. Yet conditional on the width of

the confidence interval, the lack of perfect reconciliation between the output index and

benchmark estimates does not necessarily disqualify the fit between both measures.

The innovative feature of my approach is the decomposition of the observed data in

an unobserved value added component and a noise factor resulting from the use of prox-

ies. As state space analysis is designed to uncover the dynamic evolution of time series

when these properties are not directly observable from the data, it is an appropriate tool

of analysis. In my case, the system of observed time series is modeled as a function of

an unobserved common process plus an irregular component containing index-specific

noise. By casting the debate in state space form, I offer a formal framework to sta-

tistically assess the similarity and dissimilarity between output series presented in the

literature. Moreover, because all information available is used, my analysis transcends

earlier contributions to the debate on German output growth by the application of an

integrated, rather than exclusive, approach.

4.2 The time-series debate

Hoffmann’s Historical National Accounts

In the early 1960s, a team of researchers under the auspices of Walther Hoffmann

constructed the German historical national accounts, as a part of which an output index

4. C.H. Feinstein and M. Thomas, “A Plea for Errors,” Historical Methods Vol. 35, no. 4 (2002):155; C.H. Feinstein and M. Thomas, “A Plea for Errors,” University of Oxford Discussion Papers inEconomic and Social History No. 41 (2001): 3.

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for German industry was produced.5 Hoffmann’s output index is a weighted average

of the estimated change in output in twelve manufacturing and utility industries for

the period between 1870 and 1938. As for the pre-WW1 period data on value added

change in German industries is not available, output proxies are used instead. Output

change in the majority of the industries is estimated using physical indicators, usually

manufactured tons of goods. In contrast, for metal processing – an industry class that

contains machine building, shipbuilding and electrical engineering – Hoffmann chose

to rely on labor-income data as a proxy of output change. The aggregate time series

for industry is subsequently constructed weighting the twelve industry indices by their

share of value added (the compound output index is plotted in figure 4.1a).

Hoffmann’s index of industrial production has received severe criticism and its reli-

ability has been called into question, in particular by Rainer Fremdling.6 The problems

associated with the Hoffmann series concern two issues. First, using the annual wage-

bill to proxy output change presumes a constant wage-productivity ratio. However,

Borchardt (1979) argued that after WW1 wages rose as a consequence of labor unions’

increased bargaining power, rather than raised labor-productivity levels.7 In light of

Borchardt’s thesis, the assumption of a constant wage-productivity ratio might not be

innocuous. More to the point, the dichotomous development between wages and la-

bor productivity leads to an upward bias in Hoffmann’s output estimates for metal

processing.

Second, value-added data for manufacturing industries is available for 1936 only

(based on the first German census of production). To construct value-added weights

Hoffmann multiplied the level of labor productivity in 1936 by employment in 1933

(derived from the employment census). Although the resulting value-added shares might

function as a weighting scheme for the interwar period, it cannot reasonably be imposed

on periods before WW1. Hoffmann ‘solved’ this problem by using proxy value-added

weights; that is, he multiplied the level of labor productivity in 1936 by employment

in 1882 and 1907 to obtain value-added shares for the years 1871-1895 and 1895-1913,

respectively. However, this assumes comparative levels of value added per employee

across German industries to have remained unchanged over the period 1870-1938, which

it did not.

5. Hoffmann, Das Wachstum.6. Fremdling, “German National Accounts”; R. Fremdling, “German Industrial Employment 1925,

1933, 1936 and 1939. A New Benchmark for 1936 and a Note on Hoffmann’s Tales,” Sonderdruck aus:Jahrbuch fur Wirtschaftsgeschichte Vol. 2 (2007): 171–195.

7. Ritschl, “Spurious Growth in German Output Data,” 202; Borchardt, “Zwangslagen und Hand-lungsspielrame.”

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Revisions of Hoffmann’s output series

As the problems associated with the weighting scheme are not easily solved in the

absence of value-added data, the wage-bill issue has been discussed most extensively in

the literature. The first to address Hoffmann’s output index for metal processing was

Albrecht Ritschl.8 In a comprehensive overview of already existing German time series

he wondered why Hoffmann chose to use the wage bill as a proxy for production in

metal processing while other, possibly less problematic, proxies are readily available for

the interwar period. In fact, production indices of various metal processing industries

were presented in 1933 by Wagenfuhr of the Institut fur Konjunkturforschung (IfK,

see figure 4.1a).9 When Hoffmann’s metal processing time series is compared with the

official production data provided by the IfK, deviations in output growth are manifest

mainly for the machine-building industry, which is part of metal processing. For this

reason Ritschl uses sales data of the Verband Deutscher Machinen- und Anlagenbau (the

German machinery producers’ association) to reassess output change in the machine-

building industry and he records an output growth of only half the magnitude suggested

by Hoffmann. Since metal processing has a weight of 17 percent in Hoffmann’s compound

index of industrial production, the revised data on machine building moderates German

output growth considerably. Ritschl’s revision reports a relatively low rate of growth

particularly over WW1, as can be seen in figure 4.1a.

The modified output index evoked a reaction from Stephen Broadberry and Carsten

Burhop for Ritschl’s proposed changes imply a revision of Germany’s performance rel-

ative to the UK that does not sit well with previous research.10 Combining Ritschl’s

output index with Hoffmann’s time series of employment to obtain the change in la-

bor productivity, the level of labor productivity in 1936 can be extrapolated backward,

as illustrated by figure 4.2. Adjusted to a manufacturing basis (i.e. excluding mining,

construction and utility industries), the extrapolated productivity levels suggest a com-

manding German lead over the UK in the pre-WW1 period, a performance on the

part of Germany much stronger than previously.11 A snap shot of comparative labor-

productivity levels for 1907 constructed by Broadberry & Burhop (table 4.1, first row)

points at an equality in performance between both countries rather than a distinct

German lead; a result seemingly at odds with the extrapolated labor-productivity lev-

els obtained using Ritschl’s output series. Hoffmann’s original index of output, on the

8. Ritschl, “Spurious Growth in German Output Data.”9. Wagenfuhr, “Die Industriewirtschaft.”

10. Broadberry and Burhop, “Comparative Productivity in British and German Manufacturing.”11. R. Fremdling, “Productivity Comparison Between Great Britain and Germany, 1855–1913,” Scan-

dinavian Economic History Review Vol. 1 (1991): 37. See also table 4.1.

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Figure 4.1: Time series of output in German industry, 1913 = 100

0

20

40

60

80

100

120

140

160

180

1880 1890 1900 1910 1920 1930

Ritschl Hoffmann Wagenfuhr

WW1

(a) Output levels in industry

-.4

-.2

.0

.2

.4

.6

1880 1890 1900 1910 1920 1930

Ritschl Hoffmann Wagenfuhr

WW1

(b) Annual growth rates of output

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other hand, does fit nicely with the benchmark results, which Broadberry & Burhop

interpret as proof of the superior quality of Hoffmann’s series.

Figure 4.2: Backward projection of labor productivity (LP)

Slow growth

Fast growth

LP

level

1907 1936 Time

High

Low

Known LP

level

Reconciliation between time series and benchmarks

Because Ritschl’s revised time-series estimate is rejected on the basis of the reconcili-

ation between time series and benchmarks, a sine qua non for Broadberry & Burhop,

the intrinsic quality of the modified index remains largely undiscussed in Broadberry &

Burhop. Consequently, Ritschl observes a trade off in Broadberry & Burhop between

the quality of the employed time series and the quality of its fit with the benchmark

estimates.12 After once more highlighting the potential bias in Hoffmann’s index of

industrial production, he concludes that any time-series projection of comparative pro-

ductivity has to deal with these problems. Nevertheless, Ritschl accepts Broadberry &

Burhop’s reconciliation principle – the notion that (point) estimates derived through

application of benchmark and time-series analysis must resemble – and, therefore, re-

works the 1907 benchmark, which leads to results in line with his modified time series

of industrial output (table 4.1, second row). In response, Broadberry & Burhop reject

Ritschl’s proposed changes to the benchmark and adhere to their own, slightly modified,

estimates. Furthermore, they resolve the reconciliation issue by combining a downward

adjusted version of Ritschl’s output index with a new employment series, proposed by

12. Ritschl, “The Anglo-German Industrial Productivity Puzzle.”

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Fremdling; this procedure conveniently leads to a German level of labor productivity

prior to WW1 in line with Broadberry & Burhop’s benchmark of comparative perfor-

mance (latest benchmark revisions; table 4.1, third row).13

Table 4.1: Benchmark estimates of comparative labor productivity

Source GER/UK labor productivity

Indus. Manuf.

1907

Broadberry & Burhop (2007) 1.02 1.05

Ritschl 1.25 1.28

Broadberry & Burhop (2008) 1.05 1.08

Fremdling 0.74 . . .

1935/36Broadberry . . . 1.02

Fremdling, de Jong, Timmer . . . 1.07

Sources: see text, section 4.2.

In short, the debate has been fueled to a large extent by the notion that point esti-

mates obtained by benchmarks and time-series analysis should reconcile. However, this

notion defies the literature that emphasized several causes for deviation between both

measures. This topic has been debated in relation with long-span time series projec-

tions and the problems associated with reconciling historical time series with benchmark

estimates are well documented.14 In general, deviations stem from methodological dif-

ferences between both measures. At the root of this inconsistency lies the difference

between weight structures employed in bilateral benchmarks and time series, a problem

solvable only by application of a single aggregation scheme for both spatial and tempo-

ral comparisons.15 As the structural composition of economies changes over time, the

13. Broadberry and Burhop, “Resolving”; Fremdling, “German Industrial Employment.”14. I. Kravis, A. Heston, and R. Summers, “World Product and Income: International Comparisons of

Real Gross Products,” World Bank Report (1982): 326; R. Summers and A. Heston, “The Penn WorldTable (Mark 5): An Expanded Set of International Comparisons, 1950–1988,” The Quarterly Journalof Economics Vol. 106 (1991): 327–368; A. Heston, R. Summers, and B. Aten, “Price Structure, theQuality Factor and Chaining,” 2001, http://www.oecd.org/std/prices-ppp/2425050.pdf and A.Deaton and A. Heston, “Understanding PPPs and PPP-based National Accounts,” American EconomicJournal: Macroeconomics Vol. 2 (2010): 1–35. In the field of economic history this issue has beenaddressed by M. Ward and J. Devereux, “Measuring British Decline: Direct Versus Long-Span IncomeMeasures,” The Journal of Economic History Vol. 63 (2003): 826–851; S.N. Broadberry, “Relative PerCapita Income Levels in the United Kingdom and the United States since 1870: Reconciling Time-SeriesProjections and Direct-Benchmark Estimates,” The Journal of Economic History Vol. 63 (2003): 852–863 and M. Ward and J. Devereux, “Relative U.K./U.S. Output Reconsidered: A Reply to ProfessorBroadberry,” The Journal of Economic History Vol. 64 (2004): 879–891.15. H.J. de Jong and P.J. Woltjer, “A Comparison of Real Output and Productivity for British and

American Manufacturing in 1935,” Groningen Growth and Development Centre Memorandum No. 108(2009): 16; G. Szilagyi, “Procedures for Updating the Results of International Comparisons,” Reviewof Income and Wealth Vol. 30 (1984): 156–157.

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weighting scheme of time series requires updating, which leads to inconsistency with

benchmark comparisons. While forcing a single weighting scheme on time series en-

sures consistency over time and across space, it renders the interpretation of the results

difficult and is therefore undesirable.16

So a perfect fit between time series and benchmarks cannot be expected nor de-

manded, and the time-series revision proposed by Ritschl does not necessarily provide

sufficient grounds to reject Broadberry & Burhop’s 1907 benchmark, or vice versa. In-

deed, the differences between Ritschl’s revised output index and Hoffmann’s original

are, in general, limited at the level of total manufacturing. Even though the data un-

derlying figure 4.1a displays a compound annual growth rate over the period between

1907–1936 of 1.20% for Ritschl’s index versus 1.93% for Hoffmann’s series, which is

substantial because small variations between annual growth rates can and indeed do

lead to large deviations in output levels in the long run, the difference between both

estimates can be almost fully ascribed to the period 1913–1925, while before and after

the annual growth rate hardly differs between the series. Figure 4.1b shows for both

series the compound annual growth rate over this period; whereas Hoffmann’s series

suggest a decade of (small) growth, Ritschl’s data indicate a continuous decline in out-

put. But over the other periods the annual growth rates are very similar. The question

is, then, if the inconsistency between benchmarks and time series can be accounted for

by index-number factors.

This effect can be quantified if the benchmarks and time series are constructed

exclusively on the basis of price and quantity data. This is impossible for pre-WW2

Germany, as the necessary data are not always obtainable; price information is often

unavailable and so are quantities for some industries, in which case proxies are used. The

nature of the data not only hinders a measure of the index-number induced deviation,

by itself it also presents a second source of inconsistency. The proxies employed in the

time series are associated with measurement errors, which introduce inconsistency with

the benchmarks.

Moreover, both the benchmarks and the time series suffer to a different degree from

a lack of representativeness in the data used, as the industry coverage varies between

both measures, an issue already briefly touched upon in chapter 2. The output se-

ries studied here apply to industry and include manufacturing, mining, construction

and some utility industries. Although the 1907 benchmarks include manufacturing and

16. E. Dalgaard and H. Sørensen, “Consistency Between PPP Benchmarks and National Price andVolume Indices,” in 27th General Conference of the International Association for Research in Incomeand Wealth (Stockholm: Sweden, 2002), 4; Jong and Woltjer, “A Comparison of Real Output andProductivity,” 16.

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mining, the other two sectors are not captured. This introduces a bias. Ideally, the

construction and utility industries are taken out of the time-series sample, but this pro-

cedure is rendered impossible by the lack of industry weights for Wagenfuhr’s series.17

Dalgaard and Sørenson note that the ensuing discrepancies cannot be accounted for by

index-number formulas and, therefore, pose genuine problems of consistency.18 It is the

inconsistency attributable to these genuine factors that arbitrates the quality of the fit

between benchmark estimates and time-series projections.

Using the state-space form I can assess this fit between both measures. A break down

of the inconsistency between benchmarks and time series in genuine and non-genuine

components is impossible here, but perhaps not necessary. As the model estimates a

common component containing the dynamic properties of the three observed time series,

the different benchmarks presented in the literature are confronted only with my filtered

time-series estimate. Given that all 1907 benchmarks use the same weighting scheme, i.e.

the employment structure obtained from the 1907 census, the deviation with the filtered

time series that is explained by index-number related factors is the same for each match

between benchmark and time series. It follows that variation in inconsistency between

my time series and the presented benchmarks traces back to genuine factors. Assuming,

first, that the estimated time series captures the change in output and, second, the 1936

benchmark from which the time series is extrapolated backward accurately measures

the level of output, the 1907 benchmark that shows the closest fit with the backward

projections suffers least from these genuine consistency problems.

This still leaves the question how much inconsistency one is willing to allow for and

not reject the fit between the filtered time series and the benchmarks? The uncertainty

associated with estimating the unobserved common component provides a yardstick of

measurement error in the time series (although not in the benchmarks). Using the vari-

ance of the model I construct a confidence interval to indicate a range around the point

estimates that contains the true value of the estimated parameter with high probability.

In case a benchmark estimate falls inside that range, the inconsistency can be explained

by measurement error in the time series and while both measures do not reconcile the

fit cannot be rejected. If a benchmark estimate falls outside the confidence interval,

the unexplained inconsistency is caused by additional genuine factors originating in the

benchmark, such as its measurement error or industry coverage, that introduce further

noise and thus impair the quality of the fit with the time-series projections.

17. In the absence of industry weights it is not feasible to aggregate the industry series to the levelof total manufacturing.18. Dalgaard and Sørensen, “Consistency Between PPP Benchmarks and National Price and Volume

Indices,” 9–10.

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4.3 Methodology

The purpose of state space time series analysis is to uncover the dynamic evolution

of observations measured over time when the dynamic properties cannot be directly

observed from the data.19 As I am interested in the unobserved change of industrial

output, which is assumed to determine the behavior of the observed time series, state

space modeling provides a tool of analysis particularly suited to my design. By using

the state-space form, I build upon a literature that has used such models before in the

field of economic history, in particular the research of Lee & Anderson, Crafts & Mills

and Pfister, Riedel & Uebele.20 All three study the interaction between economic and

demographic variables in early-modern times (the former for England and the latter

for Germany), using the state-space form to estimate the dynamics of, for instance,

technological change, the demand for labor or weather and disease prevalence, none of

which is observed.21

These analyses are all univariate, though, and the application of the state-space

form to filter a common state from multiple observed time series is new to the field of

economic history. Although not applied in the state-space form before, I am not the first

to estimate common components from different time series. When in the face of data

restrictions the dynamics of a particular variable can be extracted from the behavior

of other data that are assumed to relate with the variable of interest, such a procedure

provides a useful research strategy for periods characterized by poor data coverage.

This avenue has been explored by Sarfarez and Uebele, who ‘track down’ business-cycle

movements in Germany before WW1, a period that suffers from data scarcity, through

application of dynamic-factor analysis.22 Similarly, in a paper on market integration,

Uebele employs comparable techniques to estimate a common price change for regional,

national and international markets from multiple time series.23

In my case, using the state-space form to estimate a common component has several

advantages. As explained, by casting the time series of industrial output presented

in the literature in state-space form, I am able to estimate an unobserved dynamic

19. Commandeur and Koopman, An Introduction; Durbin and Koopman, Time Series Analysis.20. R. Lee and M. Anderson, “Malthus in State Space: Macro Economic-Demographic Relations in

English History,” Journal of Population Economics Vol. 15 (2002): 195–220; N. Crafts and T. Mills,“From Malthus to Solow: How did the Malthusian Economy Really Evolve?,” Journal of Macroeco-nomics Vol. 31 (2009): 68–93; U. Pfister, J. Riedel, and M. Uebele, “Real Wages and the Origins ofModern Economic Growth in Germany, 16th to 19th Centuries,” EHES Working Papers in EconomicHistory No. 17 (2012): 1–27.21. Crafts and Mills, “From Malthus to Solow,” 82; Pfister, Riedel, and Uebele, “Real Wages,” 13.22. S. Sarferaz and M. Uebele, “Tracking Down the Business Cycle: A Dynamic Factor Model for

Germany, 1820–1913,” Explorations in Economic History Vol. 46, no. 3 (2009): 368–387.23. M Uebele, “National and International Market Integration in the 19th Century: Evidence from

Comovement,” Explorations in Economic History Vol. 48, no. 2 (2011): 226–242.

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process. Moreover, in a multivariate setting information from multiple time series can

be used to improve the estimate of the target series, output in this case, by assuming

that the unobserved component is common to all observed series.24 A second appeal

of the state-space form is that stationarity of the time series is not required, because

it concerns a structural time series model in which the trend, seasonal and error terms

are explicitly modeled. This is an advantage, given that most real series in the field

of economics are non-stationary.25 Thirdly, the state-space framework can deal with

missing observations with relative ease; as the years covering WW1 are not accounted

for in the output indices, this is a benefit, too.26 In short, the state-space form provides

a flexible and easy to work with instrument to analyze the German output series.

Specification of the model

Using matrix notation, all multivariate state-space models can be written in the gen-

eral format of equations (4.1) and (4.2). The model contains two equations. First, the

observed series (yt) are modeled by the measurement (or observation) equation, which

defines the series by two components, i.e. the unobserved dynamic process called the

state (αt) and a disturbance term (εt). Second, the state equation models the unob-

served dynamic process as a function of its value in previous periods plus a disturbance

term (ηt). Both disturbance terms are normally and independently distributed (NID)

around a mean of zero with a variance of σε2 and ση2 , respectively.

yt = Ztαt + εt, εt ∼ NID(0, Ht) (4.1)

αt+1 = Ttαt +Rtηt, ηt ∼ NID(0, Qt) (4.2)

The specification used here is a local linear trend model, which is a special case of the

general state-space framework presented in the set of equations (4.1) and (4.2). Each

of the observed series is modeled as a function of a common state component and an

index-specific observation disturbance. In case of Wagenfuhr’s and Hoffmann’s series,

however, the common state is weighted by a coefficient a, because the literature has

credited Ritschl’s time-series revision with the highest reliability.27 My special case of

24. A. Harvey and C. Chung, “Estimating the Underlying Change in Unemployment in the UK,”Journal of the Royal Statistical Society Vol. 163, No. 3 (2000): 305, 314–315.25. Commandeur and Koopman, An Introduction, 134.26. ibid., 103.27. Broadberry & Burhop accepted Ritschl’s (adjusted) revisions to Hoffmann’s output series, but

combined it with a different employment series, i.e. Fremdling’s instead of Hoffmann’s, to reconcile thebackward extrapolation of labor productivity with their own 1907 German/UK benchmark estimate.See Broadberry and Burhop, “Resolving,” 933.

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the state-space form is then defined as:

yt =

⎛⎜⎜⎝

y(1)t

y(2)t

y(3)t

⎞⎟⎟⎠ , αt =

(μt

vt

), ηt =

(ξt

ζt

), Tt =

[1 1

1 0

]

Rt =

[0 0

0 1

], Zt =

⎡⎢⎢⎣

1 0

a1 0

a2 0

⎤⎥⎥⎦ , εt =

⎡⎢⎢⎣ε(1)t

ε(2)t

ε(3)t

⎤⎥⎥⎦ , Ht =

⎡⎢⎢⎣σ2ε(1)

0 0

0 σ2ε(2)

0

0 0 σ2ε(3)

⎤⎥⎥⎦ (4.3)

Qt =

[σ2ξ 0

0 σ2ζ

]

where y(1)t , y

(2)t and y

(3)t refer to Ritschl’s, Wagenfuhr’s and Hoffmann’s output series,

respectively. Writing out these components in scalar notation, this yields the following

measurement equations:

ln(Ritschl) = μt + ε(1)t

ln(Wagenfuhr) = a1μt + ε(2)t (4.4)

ln(Hoffmann) = a2μt + ε(3)t

For the state equation, I get:

μt+1 = μt + vt (4.5)

vt+1 = vt + ζt (4.6)

In this structural model the trend is captured by the common state component. The

dynamics of the state is determined by two trend components; a level μt and slope vt.

With respect to the former, the level component can be regarded as the equivalent of the

intercept in a classical regression model, with the difference that in state-space form the

intercept may be treated stochastically, in which case the level component is allowed to

change over time and its dynamics are contained by the level disturbance ξt.28 However,

from Rt it follows that I have modeled the level component deterministically by setting

the disturbance term at zero, as in a classical regression model, so the level disturbance

ξt does not return in equation (4.5), where the system of equations (4.3) is written out

in scalar notation. As with the level component, the slope vt can be conceived of as the

equivalent of a regression coefficient, but in contrast to the classical regression model

the value of the slope coefficient may differ from period to period. Therefore, the slope

28. Commandeur and Koopman, An Introduction, 9.

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is also referred to as the drift.29 The behavior of the slope is determined by the slope

disturbance ζt.

The values of the two hyperparameters, i.e. the measurement and state disturbances,

cannot be obtained analytically and the model is therefore estimated using maximum

likelihood based inference. The likelihood function associated with the model is ob-

tained through the application of an algorithm called the Kalman filter.30 In my case,

the estimated unobserved common component refers to the filtered state, which is the

estimate of the state vector based on all past observations and the current observation.

This means that the estimation process involves only a forward pass through the data.

Alternatively, I could have smoothed the state by also performing a backward pass and

thereby using all observations (i.e. past, current and future observations) to estimate

the state vector. As the name suggests, such a procedure effectively smooths the dy-

namics of the state. However, when ‘corners are cut’ the state series takes on a value

for the years before WW1 lower than of the observed series, because the output drop

over the war is already taken into account before it actually happened. With an eye to

the historical context to which the state vector refers, it does not make sense for shocks

to have backward effects and, therefore, I use the filtered state.

The unknown parameters are estimated using the log-likelihood function in Eviews,

which corresponds to the definitions of Durbin and Koopman.31 Estimation involves a

numerical search procedure that starts by choosing a set of starting values for the un-

known parameters and calculating the corresponding value of the log-likelihood func-

tion. Subsequently, the process is repeated, selecting different parameter values that

improve the log-likelihood function. These iterations are executed up to the point that

no further improvements are obtained and the log-likelihood function is optimized.

However, due to the multivariate nature of the model, the optimization process may

produce either a suboptimal or no solution for particular starting values. Following

Van den Bossche, I use a multiple random start procedure that runs the optimization

algorithm repeatedly, each time starting from a different set of initial values for the

unknown parameters.32 The whole estimation procedure is repeated 1,000 times and

the solution reported with the highest log-likelihood value is used henceforth.33

29. Commandeur and Koopman, An Introduction, 21.30. R.E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Journal of Basic

Engineering Vol. 82 (1960): 35–45.31. Quantitative Micro Software, Eviews 6 User’s Guide II (2007), 387; Durbin and Koopman, Time

Series Analysis, 138; A. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter(Cambridge University Press, 1989), 126; F van den Bossche, “Fitting State Space Models with Eviews,”Journal of Statistical Software Vol. 41, no. 8 (2011): 3.32. ibid., 10.33. Eviews provides different optimization procedures, i.e. Marquardt and Berndt-Hall-Hausman. I

used the former first derivative technique. For further specification of the program, see appendix.

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Data

It has been noted that before carrying out any estimation, it is important to deter-

mine the nature of the time series in hand.34 It is in particular crucial to examine the

properties of the observed series’ trend and establish whether it is deterministic (sta-

tionary) or stochastic (nonstationary). If the trend’s nature of one of the three series

studied here differs from the others, there is no common trend to estimate. Therefore,

before running the analysis, I test for stationarity using augmented Dicky Fuller (ADF)

tests. If the ADF shows that the series has a unit root, this points in the direction of a

non-stationary trend. Yet the results of a unit-root test do not provide definitive proof

of stationarity or the lack thereof. In the presence of structural breaks, unit root has

difficulty distinguishing stationarity from nonstationarity.35

Table 4.2: Unit-root test (augmented Dicky-Fuller)

Output series Adjusted sample τ -Statistic ρ

Hoffmann (1965) 1872–1938 -1.29 0.92

Ritschl (2004) 1872–1938 -1.10 0.93

Wagenfuhr (1933) 1872–1931 -2.59 0.84ρ Coefficient on the lagged dependent variable.* Significant at either the 0.10, 0.05 or 0.01 level.

A worry in this respect is the inclusion of WW1 in my period of study, as structural

breaks in the twentieth century often occurred at times of war.36 Figures 4.3a, 4.3b

and 4.3c show the logarithms of the observed series fitted with a linear breaking trend

function, where I allow both the intercept and the slope of the linear trend to change

after 1914. All series are clearly upward trending, hinting at the presence of unit roots.

Looking at the regression coefficients, in the case of Hoffmann’s and Ritschl’s series no

trend breaks are detected over WW1. Wagenfuhr’s series, on the other hand, displays

a significant decrease of the intercept at the 1% level and a significant increase of the

slope at the 5% level. This result is driven primarily by the coverage of the series; in

contrast to the other two series, the reconstruction phase after WW1 is included, while

the slump during 1930s is omitted. Nevertheless, as the slope of Wagenfuhr’s series

increases, there is no evidence of a break at which the generating process switched from

nonstationary to stationary. Indeed, table 4.2 suggests that a common trend can be

filtered from the three series.

34. J.P.A.M. Jacobs and J.P. Smits, “Historical Time Series Analysis: An Introduction and SomeApplications,” Jahrbuch fur Wirtschaftsgeschichte / Economic History Yearbook (2006): 5.35. ibid., 7.36. ibid., 10.

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Figure 4.3: Logarithms of output series with breaking trend

-.6

-.4

-.2

.0

.2

.4

3.0

3.5

4.0

4.5

5.0

5.5

1875 1890 1905 1920 1935

(a) Hoffmann

-.6

-.4

-.2

.0

.2

.4

3.2

3.6

4.0

4.4

4.8

5.2

1875 1890 1905 1920 1935

(b) Ritschl

-.4

-.2

.0

.2

.4

2.8

3.2

3.6

4.0

4.4

4.8

1875 1890 1905 1920

(c) Wagenfuhr

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4.4 Results

A common trend

The solution of the model obtained by Eviews is presented in table 4.3 below and fig-

ure 4.4a displays the filtered state with a 99% confidence interval. Some of the estimation

results reported in table 4.3 require further elaboration. First, although the coefficients

on each of the three observation disturbances are significantly different from zero at the

1% level, the variance of the error term associated with Wagenfuhr’s index is somewhat

larger than those of Ritschl and, in particular, Hoffmann. Figures 4.4b, 4.4c and 4.4d

provide an explanation. These graphs plot the observed series, the filtered state and

the difference between the two captured by the residuals. With respect to the latter,

the linear breaking trend functions in figures 4.3a, 4.3b and 4.3c already showed the

increased volatility of the output series after WW1, a feature which makes it difficult

for the filtered state to produce a tight fit with the series. Although all observed series

suffer from this, especially Wagenfuhr’s index shows a large variation in the disturbance

term between 1919–1925.

The state disturbance of the slope is significantly different from zero, too, which

together with the lagged value of the slope models the rate of change in the state series.

Moreover, the positive final value of the slope component signifies an increase of the

state series over the last period, as indeed can be seen in figure 4.4a. The steep angle

of the output estimate is explained by the build-up to WW2; German industry had

shifted gear and operated at full speed to meet the demands of the armaments race. All

output series presented in the literature agree in this respect, although they deviate for

earlier periods. Overall, the filtered state lies in between the observed output series and

over the period 1907–1936 displays a compound annual growth rate of 1.63%, which is

slower than the pace of growth set by Hoffmann’s series, but faster than in Ritschl’s

index. Over WW1 the state trails Wagenfuhr’s index, because the other series provide

no information on output change until 1925. At the time all three observed series pick

up, the state displays a level of output slightly above Ritschl, but well below Hoffmann.

The manner in which the filtered state, plotted in figure 4.4a, bridges the data gap of

WW1 provides a graphic account of how the estimation process functions; in the absence

of current-year information, the state is forecast on the basis of past observations only.

The forecast is driven purely by the change in the slope component, which takes on the

value of the previous period. Given that the pace of growth was high in the years prior

to WW1, the filtered state predicts a substantial increase of output levels over the war,

too. As can be seen in figure 4.4a, the growth of the state between 1913–1914 is linearly

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Table 4.3: Estimates of the state-space model

Parameter Description Estimate

Variance coefficients

ε(1) Obs. disturbance Ritschl 0.011*

ε(2) Obs. disturbance Wagenfuhr 0.013*

ε(3) Obs. disturbance Hoffmann 0.007*

ζ State (slope) disturbance 0.005*

Coefficients on common level

a1 Wagenfuhr 0.988*

a2 Hoffmann 0.997*

Final states

μ Level 5.183*

v Slope 0.106

Fit of the model

Log likelihood 76.999

Akaike information criterion -2.254* Significant at the 1% level.

extrapolated up till 1918, pushing the output estimate far above the level in 1914.

From 1919 onwards current-year data is available again. The estimation process

is updated and the estimated state vector instantly drops toward the output level at

which Wagenfuhr’s index picks up after the war. Nevertheless, while for years before

and after the war the filtered state estimate (in contrast to the smoothed state) does

not suffer from the data gap between 1914–1918, it should be born to mind that the

forecast for the missing observations does not contain interpretable information about

output change during the war. The economy was anything but in equilibrium over these

years and a forecast based on peace-time dynamics can impossibly account for war-time

shocks, as the exploding confidence limits attest.

Interval estimates and comparative labor productivity

In figures 4.4b, 4.4c and 4.4d vertical lines are drawn at two points in time, 1907 and

1936, to see how the observed series and the filtered state bridge the interwar period in

different ways. The level differences between the series are fairly limited for 1907 and

somewhat larger for 1936, with the state in between the series of Hoffmann and Ritschl.

It should be noted that the estimated latent output change depicted in figure 4.4a is

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Figure 4.4: The state series, observed series and observation disturbance

2.8

3.2

3.6

4.0

4.4

4.8

5.2

5.6

6.0

6.4

1875 1890 1905 1920 1935

(a) Filtered state and 99% confidence interval

-1

0

1

2

3

3.0

3.5

4.0

4.5

5.0

1875 1890 1905 1920 1935

(b) Hoffmann

-2

-1

0

1

2

3.0

3.5

4.0

4.5

5.0

1875 1890 1905 1920 1935

(c) Ritschl

-2

-1

0

1

2

3

3.0

3.5

4.0

4.5

5.0

1875 1890 1905 1920 1935

(d) Wagenfuhr

Observed series

Standardized residuals

Filtered state

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bound to be a weighted average of sorts of the three observed series and the fact that

the state lies in between the observations of Ritschl and Hoffmann should not come as a

surprise. Therefore, of prime interest is not the obtained dynamics of the state. Rather,

the measure of uncertainty associated with the state estimation contains the innovative

element of my research design.

As the state estimation error is a linear function of the initial state error and the

variance of both the measurement and state disturbances, it follows that the uncertainty

associated with the estimated state – and thus the confidence interval in figure 4.4a –

is determined partly by the deviations between the series of Wagenfuhr, Hoffmann and

Ritschl.37 Given that the latter are based on output proxies, the estimation uncertainty

reflects the variation in the accuracy with which the different proxies capture the latent

change of output. In practice, this means that the absence of output data in the available

historical sources introduces uncertainty to my estimates. Although this is an intuitive

notion, my approach has the benefit of quantifying the degree of uncertainty.

Traditionally, in the debate on German output growth, indicators of statistical dis-

persion are not provided and point estimates are implicitly treated as true values. Look-

ing at the upper and lower confidence limits, however, the margin for error can be quite

large: using a 99% confidence level, as in figure 4.4a, I find a range of over 10% around

my point estimate of output change. Actually, for most of the post-WW1 period all

observed series fall within the confidence bounds around the state. When measurement

error is allowed for the differences between the series of Hoffmann, Ritschl and Wa-

genfuhr with regard to the magnitude of output decline in German industry over WW1

– the issue which sparked off the debate in the first place, as explained in section 4.2 –

are not that dramatic.

The interval estimates are relevant in particular for the debate on labor productivity.

Ritschl’s output index was initially meant as a contribution to the discussion on German

historical growth developments, but the ensuing debate took shape mostly through

the (unintended) implications of the revision for comparative labor-productivity levels.

Combined with employment data and extrapolated backwards from a known level of

labor productivity in 1936, Ritschl’s revised output index translates into a level of labor

productivity in 1907 higher than in Britain; a result that calls into question the parity in

productivity performance between Germany and the UK (before WW1) pointed out by

previous research of Broadberry.38 To extrapolate backward German/UK comparative

labor productivity from 1936, I use equation (4.7).

37. Durbin and Koopman, Time Series Analysis, 15.38. See the previous section addressing the time-series discussion.

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ygetyukt

=

(yget /yge36yukt /yuk36

)· y

ge36

yuk36(4.7)

with yt as a country’s level of labor productivity in year t and yt/y36 the change of labor

productivity between year t and the base year 1936. As the level of labor productivity

is unobtainable, except for the benchmark year, the change of labor productivity yt/y36

is derived on the basis of the change in output and employment:

yty36

=ot/o36lt/l36

(4.8)

where ot/o36 captures the output change between year t and base year 1936, respectively,

and lt/l36 measures the change of employment over this period. By inserting different

values for ot/o36 as indicated by the output series of Hoffmann, Ritschl and my filtered

state, and keeping all other variables constant, it is possible to measure the implications

of the various indices for the German/UK labor-productivity gap in years before WW1.

Table 4.4 reports the results of this exercise. The data used for the UK comes from

Broadberry, while I obtain German employment from Fremdling’s latest estimates. Sub-

sequently, the change in labor productivity in the UK and Germany is calculated and in

case of the latter three versions are presented (Hoffmann, Ritschl and the filtered state).

In a next step, German/UK comparative labor productivity is computed in five vari-

ants. In addition to the estimates derived using the output series of Hoffmann, Ritschl

and the filtered state, I introduce the notion of measurement error, too, and include an

estimate of comparative labor productivity using the lower and upper confidence lim-

its of the filtered state. Lastly, as already mentioned, the relative series are projected

backward from a benchmark estimate in 1936.

On the basis of earlier research, I can choose between two benchmark estimates

of comparative German/UK labor productivity in 1935/36, i.e. between Broadberry &

Fremdling and Fremdling, de Jong and Timmer (see table 4.4. Since the former uses less

reliable data and less advanced methods, I have opted for the latter.39 Using this ratio of

105.4 in 1936, Germany outperformed the UK prior to WW1 by a margin of 15.5%. The

lower bound of the interval estimate reports a comparative labor-productivity level of

101.4 while the upper bound indicates a level of 131.5. I conclude from this that between

Germany and Britain the former attained the highest level of labor productivity in 1907,

although it is improbable that Germany’s lead extended to a margin over 125% or below

105% the level of the UK. Moreover, after WW1 the UK regained the upper hand again

39. See chapter 2 for more detail.

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and held on to that until at least the mid-1930s. Shortly before WW2 industry in

both countries performed approximately on par once more, although the width of the

interval estimate renders it impossible to say with certainty whether the real value of

comparative labor productivity favored Germany or the UK.

Table 4.4: Backward projections of comparative labor productivity

Variable Source 1907 1925 1933 1936

United Kingdom (1913=100%)

Employment } Broadberry (1997)93.0 93.4 89.4 101.1

Output 88.8 111.8 119.6 155.7

Germany (1913=100%)

Employment Fremdling (2007) 92.6 111.8 77.6 100.5

Output Hoffmann (1965) 78.7 103.4 83.1 137.1

Ritschl (2004) 80.7 90.6 71.5 114.2

Filtered state 79.4 93.1 78.5 132.2

Comparative labor productivity (UK=100%)∗

Labor productivity Hoffmann (1965) 105.9 91.9 95.2 105.4

Ritschl (2004) 130.4 96.7 98.4 105.4

Filtered state 115.5 87.2 89.1 105.4

Idem, upper bound 131.5 100.1 103.1 . . .

Idem, lower bound 101.4 76.1 76.9 . . .∗Extrapolated backward from a German/UK comparative level of 105.4, obtained fromFremdling, de Jong, and Timmer, “British and German Manufacturing ProductivityCompared,” 353.Sources: see text, section 4.2.

In a final step I take these estimates to the issue of reconciling time series and

benchmark estimates. As described in section 4.2, the different sides in the debate on

German/UK comparative labor productivity tried to ensure a close fit between their

time series projections and benchmark comparisons. However, the confidence interval

around the filtered state, and thus around the levels of comparative performance, already

showed that point estimates of time series estimates are associated with considerable

uncertainty. So I move away from the notion that benchmarks and time series estimates

need to align closely. Instead, I let the measurement error of my estimated value-added

change determine the deviation between both measures that I am willing to allow for.

The question is, then, which of the benchmarks presented in the literature (if any at all)

I am compelled to reject on the basis of the uncertainty associated with the estimation

process.

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Figure 4.5: Reconciliation with 1907 German/UK benchmarks ofcomparative labor productivity (UK=100%)

60

70

80

90

100

110

120

130

140

1907 1925 1933

________Ritschl (2008)

________B&B (2007)________B&B (2008)

________Fremdling (1991)

(a) Measurement error in time series(99% confidence level)

60

70

80

90

100

110

120

130

140

1907 1925 1933

________Ritschl (2008)

________B&B (2008)________B&B (2007)

________Fremdling (1991)

(b) Measurement error in time series(95% confidence level)

In answer to that question, I have combined the backward projections with direct

estimates of comparative labor productivity for 1907 in figures 4.5a and 4.5b (for the

levels indicated by the benchmarks included in the figures, see table 4.1 on page 110).

Under the assumption that the time-series estimate captures the true value of compar-

ative labor productivity, a 1907 German/UK benchmark estimate that falls outside the

confidence interval must represent something else instead. The message conveyed by ta-

ble 4.4 is simple; a deviation between both measures of a margin up till about 10% does

not imply a disqualification of the fit between both measures. Indeed, figure 4.5a shows

that all 1907 benchmark estimates can be reconciled with my time-series projections

when the state estimation error is taken into account.

I am willing to accept for 1907 a broad range of benchmark estimates, because the

uncertainty associated with the time-series’ estimation procedure is fairly large. Since

the confidence interval encapsulates all benchmark estimates, from my point of view, I

cannot exclude the possibility that the benchmark estimates are different draws from

the same probability distribution. Thus, they may well refer to the same parameter, even

though the estimates differ substantially. The message to take away from this is that

measurement error must be considered when benchmarks are used to check the accuracy

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of time series. In this case the estimation error of the time series is sizable partly because

output change is unobservable. While this may differ in other cases, my research suggests

that, in general, conclusions should not be based on differences between point estimates

only. In the particular case of the debate on German/UK comparative performance, the

confidence interval around the time series projection includes more than one benchmark

estimate.

Having said that, the benchmarks of Broadberry & Burhop (2007 vintage) lies almost

on top of the lower confidence limit. The probability of observing such a value is very low,

i.e. about 1%. From this, I derive that although in the extreme all benchmark estimates

can be reconciled with the time-series projection, the likelihood of such an event is

extremely small. Indeed, if I decrease the confidence level to 95%, as in figure 4.5b, this

first benchmark of Broadberry & Burhop falls well outside the confidence interval. Also,

I have difficulty accounting for Fremdling’s results. If my time series projection captures

the true value of comparative labor productivity, Fremdling’s benchmark must measure

something else. In contrast, at both 99% and 95% confidence levels neither Burhop &

Broadberry’s revised estimate (2008 vintage) nor Ritschl’s benchmark falls outside the

interval, which leads me to conclude that none of these estimates can be rejected on

the basis of the fit with the time-series projections.

In some respects, the finding that I cannot reject neither Broadberry & Burhop’s nor

Ritschl’s benchmark takes me back to where I started. That is, the uncertainty intro-

duced in my estimates by the absence of output data in the available historical sources

makes it impossible to choose between the 1907 German/UK benchmark estimates.

Then again, this is in itself an important conclusion, as it implies that the reconcili-

ation principle employed in the debate may not have been appropriate in the face of

the large uncertainty associated with the time series. Rather, broad margins should be

taken into account in the backward extrapolations. Of course, this begs the question

if such a broad range of German labor-productivity levels obtained by the method-

ology advanced in this chapter renders impossible a concise assessment of Germany’s

comparative performance?

Paradoxically, my answer to this question is that working with confidence intervals

around my point estimates actually increases the reliability of the conclusions drawn

with regard to historical economic development. Any conclusion drawn from the filtered

time-series estimates are explicitly founded on a solid statistical basis, which provides

an increased certainty compared to studies employing point estimates only. So looking

at figures 4.5a and 4.5b I can confidently infer that, first, Germany had overtaken

Britain in terms of labor productivity already before WW1, yet by a small margin only.

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Second, over WW1 there was a statistically significant change in labor productivity

leadership with Germany dropping below the UK. And, third, given Fremdling, de Jong

and Timmer’s 1936/35 German/UK benchmark comparison, Britain’s lead evaporated

again in the 1930s and both countries performed roughly on par shortly before WW2.

In view of earlier research, these findings confirm the trend over the last two decades

in the literature on German-Anglo productivity differences. Fremdling’s estimate of a

German/British comparative level of 74% seems very low now, but deviated much less

from other estimations presented in the literature in the 1980s and 1990s. For instance,

Bairoch (1973) placed Germany at 93% the level of Britain, Crafts (1983) suggested a

German performance of 87%, Dormois & Bardini (1994) found a comparative level of

82% and Burger (1994) indicated a level of 79%.40 Compared to these earlier works,

the findings of Broadberry & Burhop and Ritschl correspond much better with the

contemporary perspective on German-Anglo industrial relations. Arthur Shadwell, who

traveled the UK, Germany and the US shortly after the turn of the twentieth century in

order to compare the qualities of industrial life in the three leading industrial countries

of the time, wrote that:

“[Germany] built up (. . . ) a great edifice of manufacturing industry which

for variety and quality of output can compete in any market with most of

the finest products of Great Britain.”41

4.5 Conclusion

Several attempts have been made in the literature to quantify output and labor-

productivity growth in German industry for the period before 1950. Given that on

the basis of different time series of output two incompatible stories can be told of

Germany’s comparative performance before WW1, the uncertainty is uncomfortable.

This begs the question whether it is possible to confidently draw conclusions regarding

Germany’s historical growth record in the face of these conflicting data?

I contribute to this debate by casting the time-series discussion in a new framework.

All output series presented in the literature set out to measure the change in output,

but value-added data is not obtainable in which case proxies are used to estimate

output change. While output proxies are assumed to correlate strongly with value-

added change, they cannot do so perfectly. The underlying data used to construct the

40. J. Dormois, “The Impact of Late-Nineteenth Century Tariffs on the Productivity of EuropeanIndustries, 1870–1930,” in Classical Trade Protection, 1815–1914, ed. J. Dormois and P. Lains (London:Routledge, 2006), 179.41. Shadwell, Industrial Efficiency, 14-15.

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output indices differs little between the series, but for some industries different output

proxies are employed, which drives the deviation between the series.

Because in the absence of output data it is impossible to determine which of the

proxies captures output growth best, the choice between time series is arbitrary to some

extent. Therefore, I argue that it is inappropriate to choose between the series, discard-

ing information provided by the rejected series. As all series employ proxies that are

correlated with value-added change, the dynamic properties of the three observed series

must be contained by a common unobserved component. Using time-series analysis, in

a first step I filtered this common component from the series, which is then interpreted

as the actual change in value added.

In a second step I looked at the uncertainty associated with the process of estimating

the common component. This means that I look at point as well as interval estimates.

Although this seems obvious, it has been tradition in the literature addressed here to

exclude information on statistical error and implicitly treat the point estimates as the

‘true’ value of the parameter. Using an upper and lower confidence limit, I indicate

a range around the estimated common component which contains the true value of

value-added with 99% certainty.

In a third step the estimated change in output is combined with data on employ-

ment to get the change of German labor productivity, which I then compared with its

British counterpart. Extrapolated backward from a robust benchmark of comparative

labor productivity in 1935/36, the level of comparative labor productivity in 1907 is

obtained. This exercise is repeated thrice, replacing the filtered common component

with the upper and lower confidence bound, respectively. This way, I identify a range

of comparative labor productivity containing the true value of the estimated parameter

with a high probability.

With this approach I deviate from the traditional notion that benchmarks and time

series estimates need to align closely. Faced with the different time series of output pre-

sented in the literature, scholars have previously employed the 1907 labor-productivity

benchmarks to test the accuracy of the time series estimates. The idea is simple; if

the benchmark estimate does not provide a tight fit with the backward projections,

the latter must be flawed. Criteria for the fit between benchmark estimates and time

series projections are loosely defined and not supported by a theoretical justification

thereof.42

In this chapter, I move away from that notion. Instead, I let the measurement error

42. Broadberry suggests a range of 10% around the point estimates. See: Broadberry and Burhop,“Comparative Productivity in British and German Manufacturing,” 326 in which the authors refer toBroadberry, “Manufacturing and the Convergence Hypothesis.”

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of my estimated value-added change determine the deviation between both measures

that I am willing to allow for. The question is, then, which of the benchmarks presented

in the literature (if any at all) I am compelled to reject on the basis of the uncertainty

associated with the estimation process. Using a conservative 99% confidence level, all

benchmarks fall within the interval around my point estimate, while at the 95% level

Broadberry and Burhop’s 2007 benchmarks falls outside the confidence bounds. These

findings suggest a comparatively strong performance on the part of Germany.

The interval around the point estimates are fairly large and I am willing to accept a

broad range of German/UK comparative labor-productivity levels. The message to take

away from this is that measurement error must be considered when benchmarks are used

to check the accuracy of time series. Still, if I project the margin of error around the point

estimate (at the 95% confidence level) on the reliability scheme of Chapman, my series

falls into the B-category of “good estimates”.43 Moreover, the width of the intervals

dooes not prevent me from drawing conclusions regarding Germany’s comparative labor-

productivity development during the first half of the twentieth century. If anything, I

draw such conclusions with increased confidence. It is clear that Germany had a lead

over the UK before WW1 around the range of 10%–20%. The situation reversed over

WW1, when Germany fell behind. Although the German economy managed to catch-up

again by the late 1930s, it did not regain the advantage over the UK enjoyed before

WW1.

43. A. Chapman, Wages and Salaries in the United Kingdom, 1920–1938 (Cambridge: CambridgeUniversity Press, 1953), 231; Feinstein and Thomas, “A Plea for Errors,” 158; Feinstein and Thomas,“A Plea for Errors,” 16.

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4.A German/UK comparative labor productivity in

1936

In figures 4.5a and 4.5a comparative labor productivity is extrapolated backward from

a German/UK relative level of 105.4 in 1936/35. This level is directly obtained from

Fremdling, de Jong and Timmer (2007) and is based on a single-deflated value-added

measure of output. Alternatively, Fremdling, de Jong and Timmer also provide an esti-

mate based on output measured by double-deflated value added. This level of compar-

ative performance takes into account differences in the German/UK price relation be-

tween intermediate inputs and final outputs and raises Germany’s performance slightly

to a level 106.8% of Britain.

Figure 4.6: Reconciliation with 1907 German/UK benchmarks(UK = 100%)

60

70

80

90

100

110

120

130

140

1907 1925 1933

________B&B (2008)

________Ritschl (2008)

________B&B (2007)

________Fremdling (1991)

(a) Measurement error in time series(99% confidence level)

60

70

80

90

100

110

120

130

140

1907 1925 1933

________Ritschl (2004)

________B&B (2008)________B&B (2007)

________Fremdling (1991)

(b) Measurement error in time series(95% confidence level)

I have opted for the single-deflate output measure, as the 1907 German/UK bench-

marks are based on singel-deflated output, too. But if we extrapolate backward from a

1936 level of 106.8%, comparative productivity in 1907 increases from 115.5% to 117.0%,

as is depicted in figure 4.6. Looking at the interval estimate, the conclusions do not

change at the 99% confidence level. At the 95% confidence level, however, the interval

estimate envelopes only Ritschl’s 1907 benchmark, while both vintages of Broadberry

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& Burhop’s benchmark drop below the lower confidence limit.

Possibly more problematic is the fact that Fremdling, de Jong and Timmer’s esti-

mate measures comparative labor productivity in manufacturing. As the filtered state

estimated in this chapter captures output change in German industry, which includes

manufacturing, mining, construction and some utility industries (e.g. electricity pro-

duction), the state series ought to be tied to a level estimate for 1936 based on the same

selection of industries. Clearly, this is not the case, because a 1936 estimate for total

industry is not available. The 1907 German/UK benchmarks (see table 4.1) suggest

that German performance in industry is slightly worse than for manufacturing only. If

this applies to 1936, too, then the level of 105.4 should be adjusted downward.

By how much exactly, however, is impossible to say without re-estimating Fremdling,

de Jong and Timmer’s benchmark to include mining and construction. In all likeli-

hood, the adjustment will be small only. For instance, in the case of the 1936/35 labor-

productivity comparison between Germany and the US, presented in chapter 2, is 4 per-

centage points only. In this margin is projected on the 1936/35 estimate of Fremdling,

de Jong and Timmer, extrapolating the filtered state backward to 1907 leads to a com-

parative German/UK performance of about 111%. Correspondingly, the 99% upper and

lower confidence limits drop to 127% and 98%, respectively, a range that encompasses

all 1907 German/UK benchmark estimates. At the 95% level, then, Ritschl’s bench-

mark estimate of 124.5 falls just outside the upper confidence limit, while Broadberry

& Burhop’s 2007 estimate of 101.8 is very close to the lower confidence limit. This is

pure conjunction, however.

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4.B Indices of German industrial output

(1913 = 100)

Year State HM65 RL04 WF33 Year State HM65 RL04 WF33

1880 27.3 26.1 32.3 24.6 1910 85.5 85.5 87.8 88.6

1881 28.1 27.2 33.5 26.8 1911 91.5 90.7 93.8 96.0

1882 28.4 27.1 32.2 28.4 1912 97.1 97.2 98.3 98.9

1883 30.3 29.3 35.0 30.4 1913 100.0 100.0 100.0 100.0

1884 32.0 30.4 37.2 31.4 1914 104.0 . . . . . . . . .

1885 32.9 30.7 38.4 32.2 1915 108.1 . . . . . . . . .

1886 33.5 30.8 40.7 32.7 1916 112.4 . . . . . . . . .

1887 35.3 33.4 41.8 35.0 1917 116.9 . . . . . . . . .

1888 36.9 35.2 42.7 36.0 1918 121.5 . . . . . . . . .

1889 39.9 38.7 46.2 38.6 1919 39.6 . . . . . . 37.8

1890 41.5 39.9 46.2 40.3 1920 47.4 . . . . . . 55.1

1891 42.3 40.8 46.7 41.4 1921 60.3 . . . . . . 66.3

1892 42.8 41.7 48.3 40.0 1922 71.5 . . . . . . 71.4

1893 44.3 43.1 51.1 42.4 1923 57.1 . . . . . . 46.9

1894 47.0 45.4 55.0 44.9 1924 66.0 . . . . . . 70.4

1895 50.4 48.9 58.8 47.6 1925 90.9 103.4 90.6 82.7

1896 53.1 49.9 60.7 52.9 1926 90.1 93.7 81.9 79.6

1897 55.2 52.5 60.9 55.9 1927 106.6 118.8 105.6 100.0

1898 58.3 55.8 63.5 60.4 1928 112.8 119.1 107.5 102.0

1899 60.5 58.0 63.5 63.7 1929 113.7 121.4 106.5 103.1

1900 62.1 61.4 62.3 64.7 1930 100.3 106.1 88.7 90.8

1901 61.0 58.7 61.5 64.9 1931 80.1 85.1 70.4 73.5

1902 62.0 60.2 62.6 68.7 1932 67.1 72.8 61.6 . . .

1903 66.3 64.8 69.0 72.9 1933 72.0 83.1 71.5 . . .

1904 70.5 67.5 73.0 77.7 1934 89.3 103.1 88.0 . . .

1905 73.4 70.0 75.8 79.4 1935 109.7 121.2 102.9 . . .

1906 76.1 73.0 76.4 84.3 1936 127.0 137.1 114.2 . . .

1907 79.5 78.7 80.7 82.9 1937 142.0 152.9 127.0 . . .

1908 79.4 78.0 82.0 78.8 1938 156.0 168.1 140.4 . . .

1909 81.3 81.4 84.1 81.3

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Chapter 5Did a European Convergence Club Exist Before

World War 1? Comparative Labor Productivity in

Northwestern Europe, 1875–1913

5.A Introduction

Looking back on the previous chapters, much of the presented and discussed evidence

hints at the possibility of a common growth experience for European countries in the

period before WW1. First, chapter 2 showed that the US enjoyed a commanding labor-

productivity lead in manufacturing over Europe. This finding broadly aligns with other

manufacturing benchmarks presented in the literature to the degree that they demon-

strate an inability on the part of Europe to close in on America.1 Subsequently, in

chapter 3 it was noted that on the basis of capital-intensity data for the pre-WW1 pe-

riod the possibility could not be ruled out that Europe’s backwardness resulted from the

use of relatively labor-intensive technology, possibly induced by a skilled-labor abun-

dance. Furthermore, chapter 4 demonstrated that within Europe Germany and the UK

operated at roughly similar levels of labor productivity.

These findings suggest that while the preconditions for growth in the period run-

ning up to 1910 differed across the Atlantic, they may have been similar between Eu-

ropean countries. Indeed, according to Stephen Broadberry, the US’s substantial lead

over Europe in manufacturing labor productivity showed a great degree of stationarity

of comparative performance in manufacturing, which suggests the prevalence of dif-

ferent long-run growth paths across the Atlantic.2 This begs the question whether in

1. US/UK: Broadberry and Irwin, “Labor Productivity in the United States and the United King-dom”; Jong and Woltjer, “Depression Dynamics.” Germany/UK: Broadberry and Burhop, “Compar-ative Productivity in British and German Manufacturing”; Ritschl, “The Anglo-German IndustrialProductivity Puzzle”; Broadberry and Burhop, “Resolving.”

2. Broadberry, “Manufacturing and the Convergence Hypothesis,” 788.

133

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the decades leading up to WW1 European countries converged on a common level of

manufacturing performance?

Although the incapability of the UK and Germany to match US performance levels

has received most attention, several of the arguments presented in the literature aimed

at explaining the transatlantic labor-productivity gap are in principal easily extended

to include other countries as well. First, America’s advantage over Europe has been as-

sociated with its uniquely abundant supply of industrial mineral supplies and a scarcity

of skilled labor, the combination of which favored capital-intensive production.3 The

analysis in chapter 3 indeed uncovered a large capital-intensity gap in the pre-WW1

period in line with David’s and Broadberry’s view regarding differences in the choice of

technology. Given America’s unique supply of natural resources, all European countries

suffered from the same disadvantage as the UK and Germany did. The same holds for ar-

guments concerning market size and demand preferences; if these prevented Britain and

Germany from catching-up, they may well have constrained labor-productivity growth

in other European countries in a similar fashion.4

If European countries were indeed similarly affected by these local conditions in the

period before WW1, convergence with the US was unattainable for all. Instead, until

the catch-up mechanism described in chapter 3 kicked in after WW1, at least in the

case of Germany, countries may well have followed a European labor-productivity path,

characterized by low levels of performance as compared to the US. The notion of such

conditional convergence was introduced in the literature in response to the lack of em-

pirical support for unconditional convergence, as originally suggested by Solow.5 Since

the preconditions for unconditional convergence – i.e. countries are identical in levels of

technological knowledge, savings rates, population growth and depreciation rates – exist

in theory only, Solow’s model provides a bad fit with observed historical growth pat-

terns.6 However, controlling for differences across countries with respect to particular

parameters, such as the savings rate, human-capital formation or government consump-

tion, research revealed an inverse relation between initial per capita levels of income and

3. N. Crafts, “Forging Ahead and Falling Behind: The Rise and Relative Decline of the First Indus-trial Nation,” Journal of Economic Perspectives Vol. 12, No. 2 (1998): 202–203. See also chapter 3 fora more detailed discussion.

4. The lack of large-scale production has featured prominently, for instance, in explanations for theslow development in Dutch industry during a large part of the nineteenth century. See J.P. Smits,“The Determinants of Productivity Growth in Dutch Manufacturing, 1815–1913,” European Review ofEconomic History No. 2 (2000): 223–246.

5. R. Solow, “A Contribution to the Theory of Economic Growth,” Quarterly Journal of EconomicsVol. 70 (1956): 65–94.

6. L. Pritchett, “Divergence, Big Time,” Journal of Economic Perspectives Vol. 11 (1997): 1034–1052; W. Easterly and R. Levine, “It’s Not Factor Accumulation: Stylized Facts and Growth Models,”The World Bank Economic Review Vol. 15, no. 2 (2001): 177–219

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Chapter 5. Did a European Convergence Club Exist Before World War 1? 135

subsequent rates of growth predicted by Solow.7 If the conditions under which countries

develop are similar, which may be argued for turn-of-the-century Europe, convergence

is expected.

A factor that promoted similar conditions for growth in Europe at the start of

the twentieth century concerns the relative openness of European economies between

1870–1913.8 Trade theory (Heckscher-Ohlin-Samuelson) predicts that differences in rel-

ative factor prices and thus in the mix of factor inputs used in production disappear

over time under conditions of free trade.9 Openness to trade and perfect competition

induces a country to specialize in the commodities whose production requires the inten-

sive use of the country’s relatively abundant, and thus cheap, production factor. When

engaging in international trade, a labor-abundant country specializes in labor-intensive

production processes. Consequently, the demand for labor increases, wages rise and the

wage/interest ratio goes up, too, which in turn erodes the the country’s comparative ad-

vantage in the production of labor-intensive commodities. As capital-abundant countries

experience a change of the wage/interest ratio in the opposite direction, relative factor

costs equalize between countries. For these dynamics to occur, barriers to trade ought

to be minimal. Around 1900, Europe showed the potential for such a well-integrated

market.10

In fact, Williamson already documented strong patterns of convergence in Europe

in the period 1870–1913 on the total-economy level. Using Maddison’s GDP-per-capita

data as well as his own data on real wages, Williamson shows that in the pre-WW1

period present OECD countries converged at a steady pace, a pattern particularly dis-

tinct when the US and Canada are left out of the sample.11 Broadberry notes, however,

that in the case of Germany, Britain and the US convergence was stronger on the

total-economy level than for manufacturing only.12 This suggests that convergence was

fueled mainly by compositional effects (reallocation of labor from agriculture to either

industry or services) rather than driven by the use of increasingly similar production

techniques induced by relative factor-cost equalization between countries. This might

be because the equalization of relative factor costs was, perhaps, thwarted; although

European economies were relatively open, trade tariffs did persist throughout the pe-

7. Barro, “Economic Growth”; Barro and Sala-I-Martin, “Convergence”; Fagerberg, “Technology.”8. For globalization and catch-up, see Williamson, “Globalization,” 295. For globalization in general,

see K. O’Rourke and J.G. Williamson, Globalization and History: The Evolution of Nineteenth CenturyAtlantic Economy (Cambridge, 1999).

9. Heckscher, “The Effect of Foreign Trade”; Ohlin, Interregional and International Trade; Samuel-son, “International Trade”; Samuelson, “International Factor-Price Equalization.”10. Hannah, “Logistics, Market Size, and Giant Plants.”11. Williamson, “Globalization,” 284.12. Broadberry, “Manufacturing and the Convergence Hypothesis,” 780–781.

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riod 1870–1913, particularly in countries such as Germany and France.13. Alternatively,

even if relative factor costs differed little between countries, they may not have oper-

ated the same technology. Because the social competence necessary to exploit the most

advanced technology was still limited in the period before WW1, notes Abramovitz,

technology transfer left a weak mark on convergence.14 Therefore, the strong conver-

gence measured on the total-economy level before 1914 might not be visible when the

focus is on manufacturing only.

This chapter studies (sigma) convergence in manufacturing between five northwest-

ern European countries, i.e. the UK, Germany, France, the Netherlands and Sweden, in

the period leading up to WW1. First, I look at levels of manufacturing labor produc-

tivity in 1910 by constructing five bilateral industry-of-origin benchmarks. These are

needed because the time series of long-run productivity performance suffer from the

drawback that they do not adequately account for shifts in sectoral output and changes

in product prices, particularly when they are projected from a certain benchmark-year

into distant periods. In recent years, economic historians have stressed the need for new,

more detailed, comparisons of welfare and productivity for earlier periods, particularly

for the pre-WW1 era.15 As the previous chapters have emphasized, direct benchmark

comparisons between countries are a much wanted addition to the long-span projections.

Moreover, the best-known comparisons of long-run performance, i.e. those of Maddi-

son, are unsuited for the purpose of this chapter, as they capture developments at the

total-economy level only.16 Although I am not the first to measure comparative labor

productivity between pre-WW1 European countries, previous studies deviate from the

approach applied here in that they do not use the ICOP-technique to convert output

of different countries in a common currency.17

13. Dormois, “The Impact of Late-Nineteenth Century Tariffs,” 187.14. Abramovitz, “Catching-up,” 395. Williamson underlines this point, too, and ranks technology

transfer as a minor player in nineteenth-century convergence. See Williamson, “Globalization,” 299.15. E. Frankema, J.P. Smits, and P. Woltjer, “Comparing Productivity in the Netherlands, France,

UK and US, ca. 1910: A New PPP benchmark and its Implications for Changing,” Groningen Growthand Development Centre Memorandum no. 113 (2010): 1–34; L. Prados de la Escosura, “InternationalComparisons of Real Product, 1820–1990,” Explorations in Economic History Vol. 37 (2000): 1–41;K. Fukao, D. Ma, and T. Yuan, “Real GDP in Pre-War Asia: A 1934-36 Benchmark PurchasingPower Parity Comparison with the U.S.,” Review of Income and Wealth Vol. 53 (2007): 503–537; J.van Zanden, “Rich and Poor Before the Industrial Revolution. A Comparison Between Java and theNetherlands at the Beginning of the Nineteenth Century,” Explorations in Economic History Vol. 40(2003): 1–23.16. Maddison, Phases of Capitalist Development ; Maddison, Dynamic Forces in capitalist develop-

ment ; Maddison, Monitoring the World Economy.17. J. Dormois and C. Bardini, “Branch Comparisons of Manufacturing Labour Productivity for Eight

European Countries, Ca. 1910–1913,” Paper for N.W. Posthumus seminar on comparative historicalnational accounts for Europe in the 19th and 20th centuries (1994): 1–29; Dormois, “The Impactof Late-Nineteenth Century Tariffs”; J. Dormois, La Defense du Travail National? L’Incidence duProtectionnisme sur l’Industrie en Europe, 1870–1914 (Presses de l’Universite Paris-Sorbonne, 2009).

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In a second step, I study the growth of manufacturing labor productivity in these

northwestern countries over the period 1870–1913. The notion of convergence implies a

time dimension and it is necessary not only to look at comparative levels of performance

in 1910, but also at the increase or decrease of labor-productivity differences over time.

If the level comparison constructed in the first step demonstrates very similar levels

of performance between these countries, labor productivity in all probability converged

in the period running up to WW1. In case of widely different labor-productivity levels

in 1910, the time dimension may still provide evidence of a decreasing variation in

performance levels, but – in the light of Broadberry’s findings – it is not necessarily

anticipated. In addition, as in the case of America versus Europe, between European

countries the conditions for growth differed also. While the five countries studied here

may be the same in that they all faced less favorable conditions for labor-productivity

growth as compared to the US, although arguably to a different degree, dissimilarity

prevailed in many other respects.

5.2 Methodology

For the construction of benchmarks this chapter employs the approach set out in chap-

ter 2. The only differences are that, first, output is measured by value added (rather

than gross output) and, second, employment is not corrected for hours worked. With

respect to the latter, this choice is induced by the inability to find the necessary data

on hours worked on the industry level for all countries studied here. This is, however,

unlikely to introduce a bias in the results as chapter 2 already showed that such an

adjustment makes little difference for the pre-WW1 period. This means that relative

productivity at the industry level is estimated by the value of net output per employee

(in national currency), translated into a common currency with an industry-specific

PPP-adjusted price ratio based on factory-gate data.18

Previous work on economic performance in pre-WW1 European countries has been

conducted along different lines. The choice for a different strategy has been fueled by

the lack of data on the early twentieth century. In some cases, the limited availability of

data for the pre-WW1 period introduces difficulty implementing the analysis along the

lines of the ICOP approach. With the exception of the US and the UK, no other coun-

18. The benchmark method is formally defined in section 2.2 on page 20 of chapter 2. See also: D.Paige and G. Bombach, A Comparison of National Output and Productivity of the United Kingdomand the United States (Paris: Organisation for European Economic Co-operation, 1959), 1–245; vanArk and Timmer, “The ICOP Manufacturing Database”; Fremdling, de Jong, and Timmer, “Britishand German Manufacturing Productivity Compared”; Fremdling, de Jong, and Timmer, “CensusesCompared”; Jong and Woltjer, “Depression Dynamics”; Jong and Woltjer, “A Comparison of RealOutput and Productivity.”

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try published a census of manufactures and the quantity and quality of the data made

available through other sources, such as statistical yearbooks, lack full-manufacturing

coverage, as already demonstrated for Germany. Consequently, sometimes even the con-

struction of output conversion factors based on factory-gate prices can be difficult. In

such circumstances, one may proceed in different ways to convert output. First, as

is done here, PPPs can be constructed using the factory-gate prices that are available,

which possibly provide poor coverage of the output that is compared between countries.

Alternatively, price information can be obtained from other sources, for instance

from wholesale, retail or trade data. This approach perhaps increases coverage, but it

introduces an unknown bias in the PPPs, as these expenditure prices are determined

partly by factors outside the production process. Third and finally, when no price infor-

mation is available or the quality of the data is ill-regarded, the official exchange rate

presents a last resort. There are arguments against and in favor of each of these three

options and the choice of technique primarily depends on the type of question that is

confronted with the data. As the benchmarks presented here are valued for their break-

down of manufacturing in underlying industries, the exchange rate, which captures a

total-economy average relative price level, provides a poor instrument of analysis in this

case. As a result, either factory-gate or expenditure PPPs should be used.

Both measures of relative price do not necessarily return the same value and as

the PPPs form a main ingredient in the calculation of comparative labor productivity,

the choice between factory-gate or expenditure PPPs may affect our understanding of

historical development. This sensitivity of comparative labor productivity with regard to

relative price levels is clearly demonstrated by the debate between Broadberry and Ward

& Devereux in the Journal of Economic History. Ward & Devereux have constructed

expenditure PPPs – in line with the methods applied by scholars such as Gilbert, Kravis

and Maddison in the United Nations International Comparison Project – to obtain seven

benchmark estimates of US and UK income per capita and output per worker between

1872 and 1930.19 Their expenditure PPPs deviate markedly from conventional estimates

of US/UK relative price levels and imply a revision of America’s overtaking of Britain

in GDP-per-capita levels. While traditionally the view was held that the UK pertained

a lead up till 1900, the results obtained by Ward & Devereux suggest that the US had

overtaken Britain already before the 1870s.20 In this instance, the new PPPs change

our perception of the past.

19. Ward and Devereux, “Measuring British Decline”; Ward and Devereux, “Relative U.K./U.S. Out-put Reconsidered”; A. Maddison, The World Economy: a Millennial Perspective (Paris: Organisationfor Economic Cooperation / Development, 2001), 1–383.20. Broadberry, “Relative Per Capita Income Levels.”

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As the expenditure PPPs establish a direct link between comparative income levels

and consumption possibilities, those estimates are particularly suited for international

comparisons of income and living standards, as in the case of Ward & Devereux. How-

ever, for international comparisons of productivity and economic performance in gen-

eral, which is the purpose of this research, a direct comparison of output at an industry

level is preferable.21 Whereas expenditure PPPs take the impact on consumer prices

of imports, trade margins, transport costs and taxes into account, factory-gate PPPs

exclude such factors and thus produce a more refined comparison of labor productivity

levels. This is not to say that a factory-gate approach is a superior methodology, it is

suggested only that the choice for expenditure or factory-gate prices primarily depends

on one’s research objective: living standards as measured by real income or economic

performance as measured by real value added? Given that the focus in the current

chapter is on the latter, factory-gate prices are favored.

Previously, however, researchers studying comparative labor productivity in pre-

WW1 Europe have opted for one of the two alternative strategies for the purpose of

converting output values. Most relevant in this respect is the research conducted by the

French economic historian Jean-Pierre Dormois, who released several vintages of a pre-

WW1 European labor-productivity comparison, also on the disaggregated level. His first

attempt, together with Carlo Bardini, employed the price comparison approach using

PPP-adjusted price ratios to convert output, as advocated here. The PPPs, however,

are based on expenditure prices obtained from either wholesale, retail or export data

and calculated on the level of total manufacturing only.22 The problems with these

PPPs, duly acknowledged by the authors, not only concern the nature of expenditure

prices and the lack of detail on the industry level, but also the selection bias of the

commodities included in the basket of goods compared between countries; Dormois and

Bardini selected semi-finished products only, while finished goods are left out. They do

so because semi-finished products are of universal quality and thus easily comparable

between countries.23

In more recent years, Dormois introduced a new release of his European labor-

productivity comparison. Here, the construction of PPPs involves a two-step approach.

First, he takes the crude ‘real’ exchange rates published by the Economic and Financial

Department of the League of Nations in 1926, based on factory-gate prices, which in

a next step are extrapolated backward to 1910 using country-specific price indices.24

21. van Ark, International Comparisons of Output and Productivity; van Ark and Timmer, “TheICOP Manufacturing Database.”22. Dormois and Bardini, “Branch Comparisons,” 7.23. ibid., 8.24. Dormois, “The Impact of Late-Nineteenth Century Tariffs,” 177.

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Although this takes into account the difference between expenditure and factory-gate

prices, such a procedure suffers from two drawbacks. The use of price indices is haz-

ardous given the volatility of exchange rates in the post-WW1 period and, just as in his

previous work, the obtained conversion factor refers to the level of total manufacturing,

which prevents a further refinement of the analysis on the disaggregated level. In a third

and last comparison, Dormois simply uses an exchange-rate based conversion factor.25

Again, this procedure neither allows for inter-industry variation, nor does it take the

exchange rate’s bias into account.

Although Dormois is fully aware of the shortcomings associated with his approach,

it has certain advantages, also. Expenditure prices are relatively easy to come by, which

helps provide a broad data basis. Indeed, Dormois’ sample of countries is impressive,

truly representative of Europe. Hence, there is a tradeoff between the appropriateness

of the PPPs and the coverage of the data. Whereas Dormois placed more importance

on the latter, my concern goes out to the former. The data limitations mainly concern

the lack of manufacturing-wide censuses of production. In fact, only the US and the UK

published such a census. For several other countries, information on output volumes and

values is available, but these data usually have limited coverage. This inevitably limits

the scope of research, which is restricted to countries exclusively from northwestern

Europe.

Factory-gate prices in this study are based on the volumes and values of the items

reported in official statistical publications.26 These surveys contain detailed information

on produced items, average prices, gross output, intermediate input and employment,

enabling me to construct labor-productivity comparisons bottom-up. For the United

States the analysis is based on the Thirteenth Census of the United States taken in the

year 1910, published by the Bureau of the Census of the U.S. Department of Commerce.

For the United Kingdom I rely primarily on the First Census of Production of 1907

published under the census of production act of 1906. The data for the Netherlands

was taken from the Statistiek van de Voortbrenging en het Verbruik der Nederlandsche

Nijverheid in 1913 en 1916 published by the National Statistical Office (Centraal Bureau

voor de Statistiek). For France I employed on the Evaluation de la Production published

by the Chambers of Commerce (1910) and the Statistiques Administratives (1912). In

addition the Annuaire Statistique de la France for 1908 and the summary tables of

1966 are used, too. The Swedish data are obtained from Prado’s newly constructed

benchmarks, while for Germany I rely on the same sources as in chapter 2.

25. Dormois, La Defense du Travail National?, 187.26. The data are collected by me for Germany, Prado for Sweden and primary data For the UK,

France and the Netherlands comes from Frankema, Smits, and Woltjer, “Comparing Productivity.”

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5.3 Purchasing power parities for pre-WW1 Euro-

pean countries

This chapter’s main contribution to the literature is the application of the ICOP ap-

proach to construct industry-of-origin benchmarks for pre-WW1 European countries.

The PPPs constructed for the countries studied here are reported in table 5.1. The

table shows the official exchange rate and three PPP-variants; the Laspeyres, Paasche

and Fisher PPPs. Respectively, these refer to PPPs obtained by use of base-country

weights, non base-country weights and the geometric average thereof. Comparing the

Fisher PPP with the exchange rate, some deviation is observed, but in all cases to a

small extent only. For the UK and Germany, the official exchange rate slightly overes-

timates the domestic currency’s strength, while the reverse applies in all other cases.

The fact that the manufacturing PPPs resemble the exchange rate fairly closely signifies

that the former heavily influences the latter. Given that the exchange rate captures,

by and large, the relative price of traded goods, it suggests that trade consisted of

manufacturing products in the main, which – indeed – it did.27

Table 5.1: Purchasing power parities for total manufacturing, ca. 1910

UK GER FRA NL SWE

(£/$) (Mark/$) (Ffr/$) (Dfl/$) (Skr/$)

Exchange rate 0.21 4.20 5.18 2.49 3.73

PPP – Laspeyres 0.22 4.33 5.44 2.66 4.17

PPP – Paasche 0.18 3.49 5.60 1.99 3.98

PPP – Fisher 0.20 3.89 5.52 2.30 4.07


As compared to other pre-WW1 star comparisons presented in the literature, the

use of factory-gate prices in the construction of PPPs sets this study apart from earlier

work. So how do my results compare to the conversion factors used by others? Table 5.2

reports the PPPs used here and those introduced before in the research discussed in the

previous section. The first two rows of table 5.2 sets out my results against Dormois’

latest PPPs. As already explained, this last batch of PPPs by Dormois are obtained

by taking the relative factory-gate prices in the 1920s and then extrapolating these

27. By 1913, northwestern Europe was a net exporter of manufactured goods and a net importer ofprimary products, such as food and raw agricultural materials. Moreover, the bulk of the trade betweennorthwestern European countries studied here (imports plus exports) involved manufactured products,while primary goods were imported from other, less-developed parts of the world. See O’Rourke andWilliamson, Globalization and History, 412.

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backward to the pre-WW1 period using the price indices of the countries compared.

Of the several conversion factors proposed by Dormois, this latest batch comes closest

to mine with respect to the way they are constructed; both rely on factory-gate prices.

Indeed, the difference between both measures is very small on the total-manufacturing

level. For each country pair, the PPP reported by Dormois lies well within a 5% range

around the conversion factors constructed here.

The last three rows of table 5.2 report my PPPs and those of research conducted

during the early 1990s, which rely on expenditure prices. In contrast to the upper

two rows, the conversion factors in these studies are used to express foreign currency

into British pound, rather than US dollar. To compare these with the US-based PPPs

applied in this study, I calculated for each country the indirect UK-based PPP. To

obtain the latter, all US-based Fisher PPPs reported in table 5.1 are divided by the

price ratio of the UK relative to the US. For instance, dividing the German/US Fisher

PPP of 3.89 by the UK/US Fisher PPP of 0.20 leads to a German/UK relative price of

19.68. This procedure does not provide the most accurate estimate of a country’s price

level relative to the UK for two reasons. First, the weights to calculate Laspeyres and

Paasche PPPs differ between, for instance, a German/US and German/UK comparison,

an effect which is not taken into account in my short-cut procedure. Second, to stick

with the German example, the coverage of matched products is assumed to be identical

between the German/US and the UK/US PPPs, which is not the case in reality.

Table 5.2: Purchasing power parities of this study compared to other work

UK GER FRA NL SWE

(£/$) (Mark/$) (Ffr/$) (Dfl/$) (Skr/$)

Dormois (2006) 0.20 4.07 5.43 n.a. n.a.

This study 0.20 3.89 5.52 2.30 4.07

(£/£) (Mark/£) (Ffr/£) (Dfl/£) (Skr/£)

Dormois & Bardini (1994) 1.00 24.18 29.80 12.84 22.24

Burger (1994) 1.00 22.93 29.70 12.74 n.a.

This study 1.00 19.68 27.93 11.65 20.63

Sources: J. Dormois, “The Impact of Late-Nineteenth Century Tariffs on the Productivity ofEuropean Industries, 1870–1930,” in Classical Trade Protection, 1815–1914, ed. J. Dormoisand P. Lains (London: Routledge, 2006), 178, J. Dormois and C. Bardini, “BranchComparisons of Manufacturing Labour Productivity for Eight European Countries, Ca.1910–1913,” Paper for N.W. Posthumus seminar on comparative historical national accountsfor Europe in the 19th and 20th centuries (1994): 9 and A. Burger, “A Five CountryComparison of Industrial Labour Productivity, 1850–1990,” Paper for N.W. Posthumusseminar on comparative historical national accounts for Europe in the 19th and 20thcenturies (1994): 5.

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Nevertheless, the deviation between my indirect UK-based PPPs and those reported

in other literature is fairly small, Germany being the exception. Whereas for France,

the Netherlands and Sweden the difference between the PPPs of different studies never

exceeds the 10% margin, the German/UK conversion factor presented in this study

takes on a value well below previous estimates. As compared to my PPP, the use of

either Dormois & Bardini’s or Burger’s relative price level to convert output value in

a common currency will lead to a substantial underestimation of German comparative

labor-productivity performance. This implies that the labor-productivity comparisons

presented in the next section are expected to display a German performance that is

much stronger than reported in earlier research.

Overall, the cases of Germany, France, the Netherlands and Sweden appear to sug-

gest that expenditure PPPs overestimate the price level of manufacturing goods relative

to the UK. Indeed, in all cases the expenditure PPPs are higher than my factory-gate

PPPs and the difference between both measures directly translates to an underestima-

tion of output to the same degree. In the case of the Netherlands, for instance, the

expenditure PPPs of Dormois & Bardini and Burger are about 10% higher than my

factory-gate PPP, which means that the former underestimate the value of Dutch man-

ufacturing output expressed in units of British pound by about 10%, too. This affects

the comparative performance of the Netherlands correspondingly.

However, other than in the case of Germany these differences between the old ex-

penditure PPPs and my new factory-gate PPPs are quite limited. But then again, the

total-manufacturing results do not contain the innovative feature of this research. This

chapter’s contribution to the literature lies in its ability to breakdown manufacturing

into underlying branches by introducing branch-specific PPPs, which, to the best of

my knowledge, has never been done before in this way for the pre-WW1 period. These

branch-level PPPs allow for variation in relative price levels within manufacturing and

thereby provide a more refined analysis of comparative labor productivity. The results

are reported in table 5.3, which clearly shows the diversity between branch-level PPPs.

The degree of variation suggests that a uniform currency converter on the level of total

manufacturing will not generate accurate productivity comparisons at the branch level

as it rules out the possibility of inter-industry relative-price differences.

Looking at the PPPs in table 5.3, for each country branches can be identified that

enjoyed conditions favorable to participation on the international market and thus con-

ducive to specialization. In general, a PPP below average or below the formal exchange

rate indicates that products are produced at relatively low costs and therefore may be

internationally competitive. Such is the case, for instance, in Britain for the textiles,

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Table 5.3: Fisher purchasing power parities for manufacturing branches, ca. 1910

UK GER FRA NL SWE

Branch (£/$) (Mark/$) (Ffr/$) (Dfl/$) (Skr/$)

Food, drink & tobacco 0.19 3.47 6.18 1.92 4.15

Textiles, leather & clothing 0.16 3.68 3.81 2.07 4.20

Chemicals 0.21 5.28 7.68 3.39 3.10

Metals & machinery 0.22 3.46 5.83 3.86 n.a.

Miscellaneous 0.20 4.46 5.39 1.90 3.89

Exchange rate 0.21 4.20 5.18 2.49 3.73


leather & clothing branch and – to a lesser extent – for food, drink & tobacco as well.

In Germany, particularly chemicals is characterized by high price levels. This could

be misleading, though, because several industries – i.e. general chemicals, petroleum

& coke and rubber – are grouped in this branch. Chapter 2 already observed that in

particular the rubber and petroleum & coke industries displayed price levels well above

average, while the opposite applied to general chemicals. In France the textiles, leather

& clothing branch shows relatively low prices, while chemicals did not. In terms of rela-

tive price levels, Dutch manufacturing appears to be split in two. Relatively low prices

levels are found in food, drink & tobacco as well as in textiles, leather & clothing, while

heavy industries faced high production costs. The reverse applies to Sweden, where only

the chemical branch explicitly displays low relative prices.

Table 5.4: Number of matched products

Branch UK GER FRA NL SWE

Food, drink & tobacco 20 6 7 11 6

Textiles, leather & clothing 24 16 3 12 4

Chemicals 23 24 4 14 3

Metals & machinery 30 23 2 7 0

Miscellaneous 14 5 2 6 2

Total 111 74 18 50 15

Coefficient of variation 0.10 0.16 0.20 0.30 0.10


Although the chemical industry is covered for Sweden, no product matches could be

made for metals & machinery, which means that a branch-specific conversion factor is

unobtainable. This point is illustrated by table 5.4, which shows the number of product

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matches for each bilateral comparison. In general, a high number of product matches

indicates a broad coverage of production and ensures that the PPP is representative

for the branch it applies to. In contrast, when industry PPPs rely on a few product

matches only, the ensuing PPP may not reflect accurately the relative price level of a

branch, especially when the products included in a branch are highly divers, such as in

chemicals. With this in mind, table 5.4 invokes confidence in the British, German and

Dutch comparisons, as they provide a much higher coverage of products as compared

to, for instance, the earlier star comparisons of Dormois & Bardini and Burger.28 The

product coverage of the French and Swedish comparisons is similar to that in these

earlier studies.

Yet there are mitigating circumstances for the comparisons with few product

matches only. The coefficient of variation, which captures the spread between the branch

PPPs of a country, reported in the last row of table 5.4, is reassuringly low for France

and Sweden. Although in combination with a low number of matches, a variation of rel-

ative prices larger than for countries with better coverage may suggest that the branch

PPPs are based on an unrepresentative sample of products, the spread of the branch

PPPs in neither France nor Sweden points in that direction. Even if we adjust the co-

efficient of variation for all countries to exclude metals & machinery, as in the case of

Sweden, the country displaying the largest variation is the Netherlands.29 Given the

high number of product matches for the Dutch/US comparison, I am fairly confident

that this reflects actual differences in relative price levels between branches.

This belief is strengthened by the fact that manufacturing branches by 1910 were, in

terms of product variation, much less complex than in later periods and a large share of

total output was covered by fewer products. This means that a low number of matches

does not necessarily pose problems concerning the reliability of the comparison. Lastly,

both in the case of France and Sweden, the total-manufacturing PPP relate to the

expenditure PPPs presented before in the literature and the formal exchange rate in a

manner very similar to the countries with much higher coverage.

5.4 Comparative productivity around 1910

Using the PPPs introduced above to convert the labor-productivity data of the countries

studied here to a common currency, I obtain a measure of comparative performance

28. Dormois and Bardini, “Branch Comparisons”; A. Burger, “A Five Country Comparison of Indus-trial Labour Productivity, 1850–1990,” Paper for N.W. Posthumus seminar on comparative historicalnational accounts for Europe in the 19th and 20th centuries (1994): 1–27.29. Excluding metals & machinery the coefficient of variation for the UK, Germany, France and the

Netherlands is, respectively, 0.09, 0.16, 0.22 and 0.24.

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relative to the US. Table 5.5 reports the comparison of single deflated value added

per employee.30 Clearly, none of the European countries were able to catch-up with

America, a finding which does not come as a surprise. Germany approached American

productivity levels closest, but still faced a big gap. Moreover, the German/US gross

output per employee comparison presented in chapter 2 attributed a stronger relative

performance to Germany, i.e. a comparative level of 57%, which suggests that the share

of intermediate inputs in gross output was larger for Germany than for the US. The

country lagging behind furthest was the Netherlands, which attained a performance

of only a third the level realized across the Atlantic, while the other three countries,

i.e. France, the UK and Sweden, were evenly spaced in between these two European

extremes.31

Table 5.5: Comparative labor productivity (US = 100%), ca. 1910single deflated value added per employee

Branch UK GER FRA NL SWE



Chemicals 49 54 32 10 39



Manufacturing 41 50 38 32 36


The size of the US lead differed between manufacturing branches, with each Euro-

pean country having relatively strong and weak points. The most pronounced differ-

ences in comparative performance between manufacturing branches are observed for the

Netherlands. The Dutch economy displayed extremely low levels of labor productivity

in heavy industries, a finding which was already anticipated by the PPPs reported in

table 5.3. The high PPPs for these industries indicate that the Netherlands proved un-

able to produce at low costs, which, among other things, could result from low levels of

productive efficiency. The Dutch performance in more traditional and light industries

was much stronger. Germany showed a mixed experience, too, which has already been

pointed out in chapter 2. The UK, France and Sweden are characterized by less di-

verging levels of comparative performance, although even in these cases branches with

30. In case of single deflation, the purchasing power parities are based on final products only andnot corrected for possible deviations between German/US price relations of intermediate and finalproducts. See also section 2.2 in chapter 3.31. Sweden’s relative distance to the US has been calculated using a two-step procedure. See ap-

pendix 5.A for the details.

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relatively strong and weak performances are easily recognized.

Looking at the difference between the level of comparative labor productivity mea-

sured using the Laspeyres and Paasche PPP, it appears likely that the inter-industry

variation in performance relative to the US stimulated specialization in European coun-

tries. Table 5.6 reports these statistics and shows that for all countries except France

the use of the Paasche PPP leads to the highest estimates of comparative labor produc-

tivity. Given that the Laspeyres and Paasche PPPs are constructed using base-country

weights (always the US) and non base-country weights (European countries), respec-

tively, the gap between the two measures reflects deviations in the production structure

of the countries compared, which is known as Gerschenkron effects. The more favorable

outcome for European countries when the Paasche PPP is applied reveals an emphasis

on the strong-performing branches in the manufacturing composition of these countries.

Projecting the American structure of production on these European countries, as the

Laspeyres PPP does, leads to a decrease of comparative performance.

Table 5.6: Laspeyres, Paasche and Fischer comparativelabor productivity (US = 100%)

UK GER FRA NL SWE

Laspeyres 38 45 38 27 35

Paasche 45 56 37 37 37

Fisher 41 50 38 32 36


It follows that when a country is heavily specialized in the production of particular

goods, the industrial structure of manufacturing deviates markedly from countries with

a different specialization. However, the gap between the Laspeyres and Paasche indices

provides an imperfect measure of compositional differences, as they are calculated using

the matched value of output in the process of aggregation. Alternatively, one can reweigh

the branch-level PPPs with the share of total output of that branch in manufacturing.

This essentially assumes that the price ratios of the matched items are representative

for the whole branch. In view of the limited product coverage for some countries, I have

chosen not to do so. Table 5.7 offers a more accurate description of compositional differ-

ences by reporting the employment share of branches in total manufacturing.32 Much

of the manufacturing labor force in the Netherlands and, in particular, France was con-

centrated in textiles, leather & clothing, while heavy industries, such as chemicals and

metals, employed a relatively small part of the manufacturing labor force as compared

32. Sweden is not included due to a lack of full employment coverage by the data.

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to other countries. Furthermore, the emphasis on textiles in the UK is evident, while

the US and Germany had a broadly similar distribution of labor over manufacturing

branches that showed no pronounced specialization patterns.

Table 5.7: Employment share (%) of manufacturing branches, ca. 1910

US UK GER FRA NL



Chemicals 5 3 3 1 2




Ranked according to their distance to the US, the indirect comparative performance

between the European countries can be derived from table 5.5. Although in all bilateral

comparisons the US is set as base country, the reference country can be changed. For

instance, a relative productivity level of 50% for Germany/US and 41% for UK/US im-

plies a German/UK comparative performance of 122%. Setting the UK as the reference

country for the other comparisons, too, I obtain the relative levels reported in table 5.8.

Examining these figures with the possibility of a European convergence club in mind, it

cannot be concluded that the northwestern European countries studied here performed

at similar levels of labor productivity by 1910. All countries are contained in a range

of roughly 20% above and below UK levels of labor productivity, which means that

marked differences existed between the best and worst performers. With respect to the

latter, i.e. the Netherlands, it operated at a productivity level 64% of the former, i.e.

Germany. So the gap between Germany and the Netherlands was not much smaller than

between Germany and the US. In comparison, the deviation between the UK, France

and Sweden was relatively small.

Set out against previous star comparisons for Europe, as is done in table 5.9, my

results show a relatively strong performance of European countries across the board. As

expected, Germany does much better than indicated by Dormois. The previous section

already showed that the use of factory-gate PPPs increased Germany’s performance by

about 10% as compared to Dormois’ expenditure PPPs. The rest of the difference is

caused by the fact that Dormois relies on Hoffmann’s (adjusted) time series of output.

With an eye to the problems associated with Hoffmann’s series identified in chapter 4,

my comparison relies on level estimates directly obtained from contemporary statistical

sources. In a bilateral setting this point has already been stressed by Broadberry &

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Table 5.8: Comparative labor productivity in northwestern Europe(UK = 100%), ca. 1910

Branch GER FRA NL SWE CoV

Food, drink & tobacco 71 82 87 81 0.11

Textiles, leather & clothing 155 96 61 91 0.30

Chemicals 111 66 20 80 0.42

Metals & machinery 188 118 47 94 0.42

Miscellaneous 122 117 97 107 0.09

Manufacturing 122 91 77 88 0.16


Burhop and Ritschl, who both attribute Germany with a lead over Britain.

For other countries, the differences originate mainly in the use of new PPPs. For

France, for instance, the value-added data is derived from Dormois, which means that

the deviation between his and my estimates traces back to other origins. In this case,

the relatively weak performance of France in Dormois (2006) stems not only from the

different PPPs, but also his use of census-definition coverage for the UK. As the cut-off

point of the British census is very high, and a large, low-productive part of manufac-

turing remains unaccounted for, using census data increases British performance. The

comparisons presented here use full coverage, which help explain why France does better

relative to Britain as compared to previous work.

Table 5.9: Comparative labor productivity in northwesternEurope (UK = 100%) compared to other studies, ca.1910

GER FRA NL SWE

Dormois & Bardini (1994) 71 63 n.a. 77

Burger (1994) 86 79 n.a. n.a.

Dormois & Bardini (1995) 78 75 n.a. 67

Dormois (2004) 97 79 n.a. n.a.

Broadberry (1997) 116 71 n.a. 77

This study 122 91 77 88

A similar story applies to Sweden’s relative performance. Svante Prado estimated a

Swedish labor-productivity performance of 69% of the British level, which is substan-

tially lower than my indirect estimate of 88% presented here. His Swedish/US estimate

of 42%, on the other hand, aligns well with the figures presented above. Given that

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Prado’s figures imply a UK/US level of 61%, which seems unreasonably high set out

against the direct estimates composed by Broadberry & Irwin and Woltjer, he seems to

overstate the British performance prior to WW1. This results partly from Prado’s use

of census-definition UK data. My results are closer to Broadberry’s estimates, which

put Sweden and the UK on parity in 1913 and points at a Swedish performance of about

80% the level in Britain by 1909.

5.5 Change of comparative labor productivity, 1870–

1910

The notion of convergence implies a time element and a study of the levels of relative

performance in one year only cannot answer the question whether European countries

gravitated toward a common path typified by a performance about half the level of

the US. Even though the previous section recorded substantial differences between the

manufacturing performance of European countries, the spread of comparative labor-

productivity levels may still have been less by 1910 than in periods before. Having

established the relative levels of labor productivity for European countries around 1910,

these can be extrapolated backward using time series of output and employment for

each country. This is done by, first, calculating the change in labor productivity per

country and, second, multiplying the relative change of labor productivity between two

countries by the level of comparative labor productivity in a base year, which is the

benchmark year 1909 in this case, as described in equation (5.1):

yeurt

yust=

(yeurt /yeur09

yust /yus09

)· y

eur09

yus09(5.1)

with yt as a country’s level of labor productivity in period t and yt/y09 the change

of labor productivity between period t and base-year period 1909. As the level of la-

bor productivity is unobtainable, except for the benchmark year, the change of labor

productivity is derived from the change in output and employment:

yty09

=ot/o09lt/l09

(5.2)

where ot and o09 capture output in period t and base-year period 1909, respectively,

while lt and l09 refer to employment in these periods. The time-series data necessary

for this exercise is taken from the existing literature.33

33. UK and US: Broadberry, The Productivity Race – SWE: S Prado, Aspiring to a Higher Rank:Swedish Factor Prices and Productivity in International Perspective, 1860-1950 (University of Gothen-

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Figure 5.1: Comparative labor productivity, 1870–1909 (US=1.0)

.2

.3

.4

.5

.6

1870 1875 1880 1885 1890 1895 1900 1905

UK (Broadberry) Germany France

Netherlands Sweden


Figure 5.1 plots the projections. It does not reveal obvious signs of convergence. At

the start of the 1870s, the northwestern European countries studied here were divided

in two groups. The UK and France performed at slightly less than half the level of the

US, while the Netherlands and Sweden trailed further behind, operating at roughly a

third of American performance levels. For this period no information on Germany is

available. Shortly after 1880, when Germany does enter the sample, matters had started

to change in Europe. France’s comparative performance steadily declined on account of

a stagnant level of labor productivity at home. During this process France fell behind

of the UK and by 1890 joined ranks with the Netherlands and Sweden. Germany, which

in 1882 still trails behind the UK, appears to catch up with Britain around 1890 and,

particularly in the period 1900–1905, managed to move away, but by a small margin

only. During the same years, Sweden started to take over first the Netherlands and then

burg, 2008) – FRA: J.P. Dormois, “Tracking the Elusive French Productivity Lag in Industry, 1840–1973,” Hi-Stat Discussion Paper Series No. 152 (2006): 1–41 – NL: J.P. Smits, E. Horlings, and J.L.van Zanden, Dutch GNP and its Components, 1800–1913, GGDC Monograph Series 5 (Groningen:Groningen Growth / Development Centre, 2000), 1–246.

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France in a sudden surge of labor-productivity increase. As a result of Swedish catch-

up and the UK falling back on the level of Germany, a divide of countries in groups

of relatively strong and weak performers, as in the early 1870s, is no longer evident.

Instead, the performance of European countries was spread out at irregular intervals

between levels relative to the US of 51% and 32%.

Figure 5.2: Comparative labor-productivity in 1885 andsubsequent labor-productivity growth

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

30 32 34 36 38 40 42 44

Comparative productivity, 1885 (US=100%)

Co

effi

cien

t o

n l

inea

r tr

end

, 1

88

5-1

90

9

Sweden

Germany

Netherlands

France UK


There is little evidence of Solow-type convergence mechanisms in the comparative

growth dynamics captured by figure 5.1. The lack of a late-comer advantage is illustrated

in figure 5.2. For each European country the relation is plotted between the labor-

productivity gap to the US in 1885 and a measure of growth over the subsequent 25

years. The employed measure of labor-productivity growth over the period 1885–1909

reflects the slope coefficient on the fitted linear trend of labor-productivity growth.

Thus, the measure of growth is country specific and not expressed in relative terms to

the US. In case of catch-up growth, the countries displaying the largest distance to the

US in 1885 are expected to subsequently experience the highest growth rates. There

is no strong evidence in support of this notion. Although the regression line slopes

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downward, this negative relation between initial performance and subsequent growth is

driven by Sweden only. If Sweden is left out of the sample, the relation actually reverses.

In short, there is no clear pattern of catch-up growth in northwestern Europe between

1885–1909.

Figure 5.3: Dispersion of comparative labor productivity, 1875–1909(coefficient of variation)

.00

.05

.10

.15

.20

.25

1875 1880 1885 1890 1895 1900 1905

Average

Sources: see section 5.2.Countries: UK, Germany, France, the Netherlands and Sweden.

To press home the point, figure 5.3 plots the spread of comparative labor produc-

tivity in Europe over the period 1875–1909, as measured by the coefficient of variation.

Both at the start and the end of the period, the spread in performance is close to

the total-period average. In between, the coefficient of variation takes a dip first, then

steadily rises during the decade 1885–1895 before slowly sinking back again to the 1875

level at which it stabilizes after 1900. The convergence between 1875 and 1885 is driven

by a modest increase in Dutch comparative performance, a small drop in Britain’s posi-

tion relative to the US and France’s gradual decline. These co-occurring events brought

the performance of European countries closer together at first. However, because later

on the French plunge continued and the distance between the UK and the Netherlands

increased again, divergence set in. Over the entire period, these episodes of convergence

and divergence cancel out and the change of the coefficient of variation does not contain

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a trend.34 In contrast to total-economy developments, the first era of globalization saw

no convergence in manufacturing labor productivity between northwestern European

countries.

On the disaggregated level it was not possible to extrapolate the benchmark levels

backward. The data necessary for such an exercise are not available.35 Nevertheless,

looking at the dispersion of labor productivity at the industry level between European

countries reported in the last column of table 5.8, it seems unlikely that a break down of

the aggregate time series would show different results. Only in food, drink & tobacco the

dispersion is lower than on the total manufacturing level. Because the composition of

miscellaneous differs between the countries, the CoV thereof is difficult to interpret and

may not reflect the dispersion of performance between similar industries. That leaves

textiles, chemicals and metals, all of which show a high degree of variation. There is no

reason to expect industry-level patterns different from the lack of convergence observed

for total manufacturing. Rather, the disaggregated results clearly reject the notion of a

similar labor-productivity path across countries in northwestern Europe before WW1.

5.6 Manufacturing and convergence at the country

level

The time-series extrapolations demonstrate a stationarity of Europe’s comparative per-

formance relative to the US in the long run, a conclusion very much in line with Broad-

berry’s earlier work on the US, UK and Germany. Although the ranking of European

countries according to their comparative performance changed from time to time, e.g.

France’s relative decline between 1870–1890 and Sweden’s growth spurt after 1900, the

dispersion of performance in manufacturing remained unaltered on average. With re-

gard to a European convergence club, no evidence was found of a common long-run

equilibrium for northwestern European countries. The stationarity of European manu-

facturing performance applied to productivity differences both relative to the US and

between European countries.

At the same time convergence did take place on the total economy level. Table 5.10

reports for the five European countries included in this study the levels of GDP per

capita relative to the US and the dispersion of productivity across countries measured

34. The slope coefficient on the fitted linear trend is not statistically different from zero. Moreover,augmented Dicky Fuller tests do not suggest unit root, so the series appears stationary.35. For the US, the UK, Germany and the Netherlands time-series evidence is available at the level of

industries, for France and Sweden it is not. It may be hazardous to draw conclusions regarding Europe’sgrowth experience on an even further reduced sample of European countries (only 3 countries).

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Table 5.10: Comparative GDP per capita in northwestern Europe (US = 100%)

Country 1870 1880 1890 1900 1910

United Kingdom 131 109 118 110 93

Germany 75 63 72 73 67

France 77 67 70 70 60

Netherlands 113 92 94 81 76

Sweden 57 48 50 53 53

CoV 0.30 0.26 0.26 0.21 0.17

Sources: Bolt and van Zanden, “The First Update of the Maddison Project.”

by the coefficient of variation. The table clearly illustrates the relative decline of the

UK and the Netherlands, as well as France to a lesser extent. Germany and Sweden lost

less ground relative to the US, but still faced a large gap throughout the entire period.

Looking at the dispersion of GDP-per-capita levels, convergence is evident particularly

when the focus is on the club of European countries, as Williamson also noted for the

larger group of OECD countries.36 Much of the convergence took place since the 1890s,

during the last two decades of this era of globalization.

Comparing table 5.10 with the benchmark results presented in table 5.8, it is clear

that at first, around 1870, the spread of GDP-per-capita levels between northwestern

European countries was much larger than the spread of manufacturing labor produc-

tivity. By 1909 the dispersion of GDP per capita had declined, but it had not for

manufacturing labor productivity. As a result, the spread of performance between both

productivity measures turned out quite similar prior to WW1. Figure 5.4 captures this

pattern of convergence. The coefficient of variation of GDP per capita and manufactur-

ing labor productivity converged because the latter fluctuated around a constant level,

while the former decreased over the period 1875–1909. Whereas around 1880 the coef-

ficient of variation of GDP-per-capita levels more than doubled the dispersion of man-

ufacturing labor productivity, the gap had closed all but entirely by 1909. The lack of

convergence within manufacturing by implication means that the convergence observed

on the country level was a consequence of either compositional effects or a decreased

dispersion of productivity in services and agriculture between European countries.

With regard to developments outside manufacturing, table 5.11 lists the level of com-

parative labor productivity in agriculture, mining and services as reported by Frankema,

Smits and Woltjer.37 As compared to manufacturing performance, for all countries the

36. Williamson, “Globalization,” 284.37. Frankema, Smits, and Woltjer, “Comparing Productivity,” 13.

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Figure 5.4: Dispersion of comparative performance, 1875–1909(coefficient of variation)

.12

.16

.20

.24

.28

1875 1880 1885 1890 1895 1900 1905

Manuf. labor productivity GDP per capita

Average (GDPcap)

Average (manuf. lp)

Countries: UK, Germany, France, the Netherlands and Sweden.Sources GDP per capita: J. Bolt and J.L. van Zanden, “The First Update of the MaddisonProject; Re-Estimating Growth Before 1820,” Maddison Project Working Paper 4 (2013).Sources manufacturing labor productivity: this study.

gap to the US was much smaller in services. Moreover, particularly in the UK compara-

tive levels of labor productivity in agriculture were higher than in other sectors. Also in

the Netherlands agriculture had not fallen as far behind the US as manufacturing, even

though agriculture had experienced a relative decline in the decades running up to 1910.

Midway the nineteenth century the Dutch level of labor productivity in agriculture was

at 85% of the British level.38 And in services this figure was even as high as 92% (es-

pecially due to the strong performance of the Dutch trade sector, which had a level of

labor productivity which was 30% higher than in the UK). Both agriculture and services

witnessed a steady decline in comparative productivity rates vis-a-vis the United States

as well as the United Kingdom throughout the second half of the nineteenth century.39

In France, agriculture operated at low labor-productivity levels relative to other

38. Frankema, Smits, and Woltjer, “Comparing Productivity,” 21.39. ibid.

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Table 5.11: Comparative labor productivity in sectors of the economy(US = 100%), ca. 1910

UK FRA NL

Agriculture 56 37 47

Mining 38 39 10

Manufacturing 41 38 32

Services 84 68 85

Source: Frankema, Smits, and Woltjer, “Comparing Productivity.”

productive activities, including manufacturing. Surprisingly, during the phase of indus-

trialization after the 1850s France maintained a large labor force in agriculture. The

limited migration from rural to urban areas has been ascribed to a persistent cultural

belief in and adherence to small, traditional farming, which defied modernization and

suppressed agricultural labor productivity until well into the twentieth century.40 In

contrast, agriculture in the UK attained high levels of performance already early in the

nineteenth century. Whereas in France the move out of agriculture was delayed until

after the turn of the century, the share of agricultural employment was comparatively

small in Britain. These different dynamics help explain why the gap to the US in terms

of GDP-per-capita levels was much smaller than the manufacturing labor-productivity

gap for the UK and the Netherlands, but less so for France.

The comparative productivity levels obtained in this study also carry implications

for our understanding of the period after 1909 and may answer questions concerning

economic development in the interwar period. Van Ark’s data show that by 1950 the

UK, Germany, France and the Netherlands operated much closer together in terms

of manufacturing labor productivity than before WW1. Furthermore, over the period

1950–1989 the dispersion thereof did not reduce further.41 Given the lack of convergence

(or, for that matter, divergence) between 1875–1910, forces must have been active during

the period 1910–1950 that drove together levels of manufacturing performance between

European countries. An example of which is the Netherlands, which showed the lowest

levels of labor productivity before WW1, but closed the gap to Germany entirely over

the interwar years.42 In view of the findings in chapter 4, it seems likely that such

40. J.P. Dormois, The French Economy in the Twentieth Century (Cambridge: Cambridge UniversityPress, 2004), 102.41. Van Ark reports two series of comparative labor productivity (Sweden is not included), taking

first the US and then the UK as the base country. The CoV of the former increased between 1950–1989from 0.09 to 0.12, while in the latter’s case it remained constant at 0.10. See van Ark, InternationalComparisons of Output and Productivity, 290–291.42. H.J. de Jong, Catching Up Twice: the Nature of Dutch Industrial Growth During the Twentieth

Century in a Comparative Perspective (Berlin: Akademie Verlag, 2003), 66.

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convergence during the early twentieth century was fueled by a decrease of Germany’s

lead over the other countries, which resulted from setbacks encountered by the former,

rather than a catch-up process on the part of the latter.

Here again, the development in manufacturing differs from the patterns observed

on the total-economy level. The dispersion of GDP per capita levels in 1910 and 1950

were much the same for the sample of European countries studied here, while it de-

creased rapidly between 1950–1989.43 It is clear that the driving forces behind labor-

productivity development differed considerably between manufacturing and the total

economy. With respect to the former, the northwestern-European context in which the

UK, Germany, France, the Netherlands and Sweden operated did not provide a suf-

ficient condition for convergence in manufacturing labor productivity over the period

1875–1910.

5.7 Concluding remarks

The lack of labor-productivity convergence in manufacturing between 1875–1910 raises

questions regarding the role of technology in these developments. Chapter 3 argued

that the contribution of capital-intensity differences to the large German/US labor-

productivity gap in 1936/39 was comparatively small relative to the effect of tech-

nological efficiency. Does the same line of reasoning apply to the labor-productivity

differences observed for the period before WW1? For an answer to that question I rely

on the existing literature, because a study of capital intensity and its implications for

labor productivity lies outside the scope of this chapter. The remainder of this section

positions my labor-productivity findings in the literature and the results of the previous

chapters. Several elements of the analysis are based on conjecture and call for further

research.

Technology and labor productivity before WW1

With respect to the size of the distance European countries trailed behind the US,

it is not unlikely that differences in machine intensity played a more important role

before WW1 than at the end of the interwar period. For one, chapter 3 revealed a

German/US machine-intensity gap in 1909 much larger than in 1936/‘39. Before WW1

German manufacturing operated at a capital-labor ratio three times lower than the US.

Manufacturing industries in the UK and France faced a large machine-intensity gap with

the US, too, around 1910; Britain employed less than half as much horse power per unit

43. For 1910: Maddison, see table 5.10. For 1950 and 1989: Maddison.

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of labor as the US and France about a sixth only.44 These pronounced differences at the

start of the twentieth century between European countries and America suggest that

variation in capital-labor ratios contributed substantially to the US’s lead over Europe.

France is a case in point. According to Dormois, in the traditional consumer indus-

tries – which employed a large part of the French manufacturing labor force – it failed

to achieve a strong labor-productivity performance because of a limited adoption of

newly available technology, a feature common to much of French manufacturing.45 The

relatively weak Dutch performance during the late nineteenth century has also been

explained by the slow adoption of steam power.46 Traditional sources of energy, like

wind, water and peat prevailed. These technologies had remained unchanged from the

seventeenth century until about the 1850s.47 Levels of aggregate domestic demand were

so low that traditional types of production (i.e. based on the use of wind- and water

power) retained their cost advantage over the introduction of steam engines, a process

that induces high initial fixed costs.48

So among other factors, the Netherlands retained obsolete technology because it

was cost efficient, a point which has also been made repeatedly for nineteenth-century

Britain.49 Broadberry argues that for Britain, the increased competition from abroad

between 1870–1914, particularly from America and Germany, led to an efficiency in-

crease in flexible and labor-intensive production.50 It was a rational response to compe-

tition to cut-back on relatively expensive factor inputs in an attempt to minimize pro-

duction costs. In the face of small domestic markets, heterogeneous demand patterns,

scarcity of natural resources and abundance of skilled labor, this process of competition-

induced cost minimization discouraged the UK from acquiring machine-intensive tech-

nology and encouraged the further improvement of the technology already in use.

“Most industries were characterized by a high degree of competition, which

acted as a spur to efficiency, with existing rivals or new entrants ready to

44. Hannah, “Logistics, Market Size, and Giant Plants,” 71.45. Dormois, The French Economy, 14; Dormois, “Tracking the Elusive French Productivity Lag,” 8.46. Smits, “The Determinants of Productivity Growth in Dutch Manufacturing, 1815–1913,” 239–240.47. M. Jansen, De Industriele Ontwikkeling in Nederland (Amsterdam NEHA, 2000).48. Smits, “The Determinants of Productivity Growth in Dutch Manufacturing, 1815–1913,” 235–

238; Horlings and Smits point at the importance of demand constraints in the Dutch economy and itsimpact on the timing of modern economic growth, see: E. Horlings and J. Smits, “Private ConsumerExpenditure in the Netherlands, 1800–1913,” Economic and Social History in the Netherlands No. 7(1996): 15–40.49. See also: D. McCloskey, Economic Maturity and Entrepreneurial Decline: British Iron and Steel

(Cambridge, Mass.: Harvard University Press, 1973), C.K. Harley, “Skilled Labour and the Choiceof Technique in Edwardian Industry,” Explorations in Economic History Vol. 11 (1974): 391–414, L.Sandberg, Lancashire in Decline (Columbus, 1974).50. Broadberry, The Productivity Race, 158–159.

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take up opportunities neglected by incumbent producers.”51

Other than considerations of cost advantages, it has been suggested that countries

may refrain from adopting advanced technology due to a lack of necessary social ca-

pabilities.52 Yet before WW1 educational attainment differed little between countries

in the developed world, although some deviations were evident. Germany enjoyed a

lead over the US, UK and France in terms of average years of secondary schooling,

while the percentage of population gaining access to a college education was largest

in America.53 However, given the small share of science-based industries in manufac-

turing before WW1, the modest differences in educational attainment were of limited

consequence for convergence.54 What appears to have mattered more, at least on the

total-manufacturing level, are specialization patterns. An emphasis on mature industries

delays the wide-spread adoption of technologies introduced in modern industries. For

instance, in the industries in which the Dutch economy had strongly specialized, such

as food processing, the use of steam power proved difficult for technological reasons.55

The upshot of this literature is clear; instead of adopting high capital-labor ratios,

European countries improved their competitiveness by increasing the labor-productivity

performance of the technology already in use. It implies that variation in labor-

productivity levels within Europe stemmed from the degree to which countries success-

fully explored or even enhanced the potential of the machine-intensity level at which

they operated. This provides a useful perspective to address questions of relative stand-

ing that remained unanswered by a study of capital-intensity differences only. How could

Germany attain higher labor-productivity levels than the UK with a lower capital-labor

ratio? Why did France trail the UK at relatively close distance only, while it employed

half as much horse power per unit of labor?56 What prevented Sweden, which enjoyed a

machine-intensity level not dissimilar from the US, from outperforming all other Euro-

pean countries?57 If European countries indeed experienced labor-productivity growth

through a process of learning-by-doing, rather than by slavishly copying advanced tech-

51. Broadberry, The Productivity Race, 209.52. Abramovitz, “Catching-up,” 395.53. Nelson and Wright, “The Rise and Fall,” 1947–1948.54. ibid., 1942, 1949.55. W. Lintsen, Geschiedenis van de Techniek in Nederland. De Wording van een Moderne Samen-

leving, 1800–1890 (Zutphen: Walburg Pers, 1992), 269–271.56. Broadberry reports a French machine-intensity level of 77% of the UK. As, according to Broad-

berry, machine intensity in the UK stood at a level 47% of the US, France’s comparative machine-intensity relative to the US was 36%, i.e. similar to Germany’s level of machine intensity. Moreover,he reports a France/UK comparative labor-productivity level of 65%, which is much lower than myestimate of 91%. Broadberry, The Productivity Race, 109. Without going into detail about the differ-ences in the estimates, Broadberry’s figures imply a different question: why did France trail Germanyat considerable distance, while it employed just as much horse power per unit of labor?57. Hannah, “Logistics, Market Size, and Giant Plants,” 71.

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nology, the labor-productivity gaps between pre-WW1 European countries can, perhaps,

be understood best in terms of efficiency differences.

The above suggests that while machine-intensity differences take on importance for

the transatlantic labor-productivity gap before WW1 in particular, they fail to properly

explain variation in labor productivity within Europe. For the latter, it appears that

factors determining the labor-productivity performance of the technology in use play a

prominent role. Chapter 2 already discussed several potential efficiency augmenters, e.g.

a large establishment size or a high degree of vertical integration, and suggested that

the presence thereof in German industries coincided with a strong labor-productivity

performance. This is sill mostly conjecture and additional research is needed to shed

light on these issues. Especially because the efficiency component in labor-productivity

differences provides a measure of ignorance; a great many factors influence the labor-

productivity performance realized at particular machine-intensity levels and further

research is necessary to identify the main determinants thereof.

Technology and labor productivity over WW1

A next question concerns the changing dynamics over WW1. Tying the results of this

chapter to my previous findings, I am strengthened in the belief that the period 1900-

1940 was as a phase of transition in which European countries adopted increasingly high

levels of capital intensity, but operated machinery in conditions less favorable for labor-

productivity growth. Over WW2 there was a continuity in capital deepening, which after

the war coincided with a gradual improvement of technological efficiency. The dynamics

of the pre-1910 and post-1950 period are very dissimilar, while the period in between

witnessed a build-up of catch-up potential on account of European countries starting to

explore increasingly high capital-labor ratios. But the question remains why countries

like Germany changed their strategy for labor-productivity growth over WW1. Did the

barriers to technology adoption before WW1 disappear during the interwar years?

This question is not necessary limited to the German growth experience only. The

case of the Netherlands provides another example. In the nineteenth century, both a

delayed adoption of advanced technology and other factors, such as the lack of large-

scale production, constrained labor-productivity growth.58 At the end of the nineteenth

century the increase in trade lifted some of the barriers to large-scale production, but

even in the interwar period the average plant size was in many, if not most, industries

still smaller than in the UK and Germany.59 The machine-intensity level, on the other

58. Smits, “The Determinants of Productivity Growth in Dutch Manufacturing, 1815–1913,” 235.59. ibid., 240 and Jong, Catching Up Twice, 99.

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hand, was rapidly increasing and by 1930 relatively high in most Dutch industries as

compared to their British and German counterparts.60 Clearly, there was a continuity

over the interwar period for some constraints to labor-productivity growth, but machine

intensity does not appear to be one of them.

A possible explanation for the change over WW1 relates to the disintegration of mar-

kets. The decision to not adopt American production technology before WW1 seems

related to competition, which forced countries to produce cost efficiently, even though

innovation took place at predominantly high capital-labor ratios and an awareness of

these frontier movements provided an incentive for capital intensification. The outbreak

of WW1 ended the long period of openness and globalization between 1870–1914 and

countries resorted to protectionist policies after WW1. International markets disinte-

grated rapidly.61 As protectionist policies raise domestic prices above the world-market

level, they relieve the downward pressure on production costs. Firms are allowed to

produce with some degree of inefficiency without losing their domestic market share

to foreign competitors, which was not possible before WW1. Under these condition in-

dustries can adopt new technology, even when it is initially operated at low efficiency

levels.

This line of reasoning presents a variation on the infant-industry argument and

may shed new light on the differences in accumulation strategies over WW1.62 But

this perspective is at odds with the literature that associates the move away from

competition during the interwar years with a distortion to ‘adjustment mechanisms’,

which provided the possibility to retain old technology longer than would have been

feasible under conditions of competition.63 These adjustment mechanisms, however,

prevented capital intensification in the period before WW1 and, once blocked, may have

created the necessary opportunity for a move toward high machine-intensity levels. But

this assertion requires supporting empirical evidence, which calls for further research.

60. Jong, Catching Up Twice, 79.61. Broadberry, The Productivity Race, 210.62. H.J. Chang, Kicking Away the Ladder. Development Strategy in Historical Perspective (London:

Anthem Press, 2002).63. Broadberry, The Productivity Race, 291.

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5.A Sweden’s relative performance

Although Sweden’s comparative performance reported in table 5.8 (on page 149) is

based on a direct comparison with the US, the results have been adjusted. The third

column in table 5.12 presents the original numbers of the direct Swedish/US compari-

son. It shows that Sweden performed on 45% the level of America. If we compare this

figure with Britain’s distance to the US as measured by the value-added per employee

comparison in table 5.8, i.e. 41%, Sweden appears to have had a 10% lead over the UK

in 1909. This finding seems odd given that previous estimates have always pointed out

the reverse (see table 5.9). The data used here are obtained from Prado, who has con-

structed both a Swedish/US and a Swedish/UK comparison for 1909. My Swedish/US

estimate is close to Prado’s own figures (45% and 42%, respectively). In contrast, the im-

plicit Swedish/UK relative level presented in table 5.9 is not; Prado’s direct comparison

demonstrates a Swedish performance of 69% the level of Britain.

Table 5.12: Comparative labor productivity (US = 100%), ca. 1910single deflated gross output per employee

US=100% UK=100%

Branch UK GER SWE US GER SWE

Food, drink & tobacco 50 56 32 200 112 81

Textiles, leather & clothing 64 86 58 157 134 91

Chemicals 59 50 49 169 84 80

Metals & machinery 45 60 43 220 133 94

Miscellaneous 46 44 39 216 95 107

Manufacturing 51 57 45 198 113 88

The deviation between my indirect estimate of Swedish/UK comparative labor pro-

ductivity and the direct estimate of Prado stems from the fact that the Swedish data

refers only partially to value added. Most of the output value used in the comparisons

concerns gross output.64 As the Swedish/US comparison reflects to a large extent the

comparative level of gross output per employee, deriving an implicit Swedish/UK level

via a value-added based UK/US benchmark is a hazardous procedure. Given that much

of the underlying data of the Swedish/US comparison refers to gross output, it may be

more appropriate to use a gross-output UK/US comparison for the purpose of deriving

64. 59% of the covered output value for the Swedish/US comparison and only 25% for the UK/Swedishbenchmark. See Prado, 96.

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an implicit Swedish/UK level. Table 5.12 presents such a gross-output based compari-

son for the UK, Germany and Sweden. For France and the Netherlands no gross-output

data is available. Clearly, adjusting our output definition from value added to gross out-

put has a substantial effect on the comparative performance of the UK. The increased

British performance suggests that the share of intermediate inputs in gross output is

far lower in the US than in the UK.

If I now calculate for Sweden indirect levels of comparative productivity relative

to the UK (shown in the last three columns of table 5.12), Sweden’s performance is

adjusted downward from 110% to 88%.65 The Swedish performance is still substan-

tially higher than Prado’s estimates, but his figures imply a UK/US level of 61%, which

seems suspiciously high set out against the direct estimates composed by other re-

search.66 As with the case of France, the difference between Prado’s direct Swedish/UK

benchmark and the implicit relative level indicated here results mainly from Prado’s

use of census-definition UK data, which overestimates British performance. Also, the

PPPs constructed here favor Sweden. Sweden’s comparative performance reported in

table 5.8, is then obtained by projecting the Swedish/UK ratio presented above on the

UK/US relative levels of table 5.8.

65. For Germany I have included in table 5.12 an indirect productivity level relative to the UK usinggross-output definitions, too, which turns out slightly lower than in the 22% German advantage overBritain obtained using value added (see table 5.8).66. Broadberry and Irwin, “Labor Productivity in the United States and the United Kingdom”;

Frankema, Smits, and Woltjer, “Comparing Productivity”; Jong and Woltjer, “Depression Dynamics.”

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5.B Value added estimates Germany

Estimating value added in German manufacturing is less than straightforward. For

some industries the value of processed intermediate inputs is either not obtainable

from the sources or under-reported. In case of the latter, a value-added based labor-

productivity comparison presents a bias in Germany’s favor. The available data for

pre-WW1 Germany is listed in table A.11. Looking at the reported data, it is clear that

for tobacco, paper, rubber and stone, clay & glass VA/GO ratios need to be obtained

differently. In case of tobacco and rubber because no intermediate inputs can be derived

from the sources, while the reports for paper and stone, clay & glass clearly overestimate

the share of value added in gross output. For these industries the data from the German

census of 1936 are used, which are presented in table A.12.

Alternatively, I could have opted to use either US or UK VA/GO ratios as a substi-

tute (see tables A.13 and A.14. The advantage would be that for both countries data is

available for the same period, i.e. before WW1. Over the interwar VA/GO ratios may

very well have changed in Germany. On the other hand, using US VA/GO ratios means

that I do not correct for intermediate inputs at all, as the German/US comparison would

lead to exactly the same results as the gross-output estimate presented in chapter 2.

Moreover, given the emphasis both in the literature and in the findings of this chapter

on differences in factor costs between the countries studied here, it is unwise to force

Britain’s input-output structure on Germany. For these reasons I have chosen to use

German VA/GO ratios from the interwar period.

To see what impact the choice of different VA/GO ratios has on the aggregate

relative level of German/US labor productivity, for those industries for which I have

no (or no reliable) pre-WW1 German data on value added I have used in turn the

VA/GO ratio of Germany in 1936, the US in 1909 and the UK in 1907. Apart from

the four industries listed above (tobacco, paper, rubber and stone, clay & glass), I also

included the food industry in this exercise, because the VA/GO ratio of Germany in

1909 is based only on the starch industry. This may not be representative for the whole

of the food industry. Using for these industries the VA/GO ratio of Germany in 1936,

the US in 1909 and the UK in 1907 leads to a German/US comparative performance

of, respectively, 0.50, 0.61 and 0.57. Clearly, the data used in the chapter provides a

lower-bound estimate of Germany’s comparative labor-productivity performance.

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Appendix AData Appendix

A.1 Pre-WW1 labor-productivity growth in German

industries

Description Year LPa UVb Δ LPc Δ LPd

Manufacturing

Iron and steel 1908 11,854 88.70

1909 12,475 87.85 1.06 1.05

1910 13,241 87.32 1.07 1.06

1911 14,244 89.93 1.04 1.08

Zinc 1908 7,310 391.39

1909 8,540 420.22 1.09 1.17

1910 9,191 427.11 1.06 1.08

1911 10,614 453.21 1.09 1.15

Lead, silver, and copper 1908 24,358 554.41

1909 26,741 544.46 1.12 1.10

1910 27,869 541.78 1.05 1.03

1911 34,364 609.20 1.10 1.23

Sulfuric acid 1908 13,026 44.27

1909 15,948 42.98 1.26 1.22

1910 16,369 41.22 1.07 1.03

1911 17,878 43.74 1.03 1.09

Tin 1908 37,198 705.52

1909 45,151 1,128.55 0.76 1.21

1910 60,174 1,526.07 0.99 1.33

1911 77,399 1,495.63 1.31 1.29

Nickel, cobalt, bismuth, and ar-

senic

1908 19,026 2,280.86

Continued on next page. . .

167

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Table A.1 – Continued


1909 19,800 2,229.72 1.06 1.04

1910 21,725 2,260.75 1.08 1.10

1911 23,632 2,273.42 1.08 1.09

Coal-tar distillations 1908 11,954 44.45

1909 14,348 43.01 1.24 1.20

1910 14,446 41.41 1.05 1.01

1911 14,989 41.44 1.04 1.04

Lignite-tar distillations 1908 13,444 186.65

1909 12,604 166.68 1.05 0.94

1910 13,891 164.61 1.12 1.10

1911 13,501 158.42 1.01 0.97

Petroleum refining 1908 23,324 166.20

1909 23,424 147.41 1.13 1.00

1910 26,590 153.28 1.09 1.14

1911 28,278 154.94 1.05 1.06

Coke 1908 18,932 19.79

1909 18,621 18.33 1.06 0.98

1910 19,462 18.16 1.05 1.05

1911 20,253 n.a. 1.04

Motor vehicles 1907 4,537 5,542.41

1908 4,294 5,272.01 0.99 0.95

1909 4,179 5,203.07 0.99 0.97

1910 5,426 5,580.26 1.21 1.30

1911 5,681 6,293.17 0.93 1.05

Cement 1910 5,666 19.74

1911 6,357 20.05 1.10 1.12

Mining

Coal 1908 2,806 10.80

1909 2,608 10.41 0.96 0.93

1910 2,591 10.16 1.02 0.99

1911 2,622 9.93 1.04 1.01

Briquette 1908 24,558 14.22

1909 25,144 13.67 1.07 1.02

1910 25,587 13.21 1.05 1.02

1911 24,952 12.64 1.02 0.98

Lignite 1908 2,755 2.33

1909 2,623 2.33 0.95 0.95

1910 2,720 2.29 1.06 1.04

1911 2,782 2.24 1.05 1.02

Iron ore 1908 2,128 4.48

1909 2,132 4.01 1.12 1.00

1910 2,300 4.02 1.08 1.08


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Appendix A. Data Appendix 169



1911 2,421 4.06 1.04 1.05

Lead, silver, and zinc ore 1908 1,434 12.74

1909 1,676 14.74 1.01 1.17

1910 1,905 15.43 1.09 1.14

1911 2,044 16.00 1.03 1.07

Arsenic and copper ore 1908 1,322 29.59

1909 1,461 28.65 1.14 1.11

1910 1,680 27.81 1.18 1.15

1911 1,791 27.79 1.07 1.07

Pyrite 1908 2,162 7.21

1909 2,288 7.40 1.03 1.06

1910 2,154 7.41 0.94 0.94

1911 2,274 7.62 1.03 1.06

Uranium, tin, cobalt, nickel, bis-

muth, vitriol ore, and bauxite

1908 1,000 21.84

1909 1,048 23.86 0.96 1.05

1910 1,039 20.06 1.18 0.99

1911 1,251 19.98 1.21 1.20

Petroleum 1908 3,589 69.69

1909 4,896 67.67 1.40 1.36

1910 5,276 68.38 1.07 1.08

1911 5,022 68.97 0.94 0.95

Asphalt rock 1908 3,391 9.09

1909 3,242 8.34 1.04 0.96

1910 3,699 8.46 1.12 1.14

1911 3,497 7.56 1.06 1.04

Raw graphite 1908 1,087 45.57

1909 1,349 36.36 1.56 1.24

1910 1,293 33.18 1.05 0.96

1911 1,345 29.03 1.19 1.04

Salt (fine) 1908 4,818 30.05

1909 4,892 30.38 1.00 1.02

1910 5,046 30.51 1.03 1.03

1911 4,595 29.27 0.95 0.91

Salt (crude) 1909 8,023 16.66

1910 8,880 16.15 1.14 1.11

1911 9,315 16.40 1.03 1.05

Notes on next page. . .

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Notes to table A.1:a Output values are measured in Goldmarks.b Weighted average of the UVs of products produced in that industry.c Output is deflated using industry-specific PPPs; comparison based on real output values.d Output is not deflated; comparison based on nominal output values.


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A.2 Labor-productivity levels pre-WW1 Germany

Description Year Output Empl. LP

FOOD AND DRINKS – 157,556 –

Starch 1911 90,466,000 6,248 14,479

Glucose (starch) 1907 14,303,641 2,149 6,656

Brewing 1907 69,535,000a 111,779 622

Raw sugar 1907 2,138,731b 37,380 57

TOBACCO MANUFACTURES 97,390,000c 203,224 479d

Tobacco 1907 97,390,000c 203,224 479d

TEXTILES 1,134,115,084 175,216 6,473

Cotton spinning 1907 698,824,785 97,975 7,133

Silk spinning 1907 28,420,113 5,712 4,976

Woolen (worsted) 1907 263,273,841 43,042 6,117

Jute 1907 78,787,997 12,866 6,124

Linen 1907 64,808,348 15,622 4,149

PAPER 750,000,000 94,197 7,962

Paper, cardboard, and wood pulp 1913 750,000,000 94,197 7,962

CHEMICALS 233,542,686 21,560 10,832

Sulfuric acid 1909 92,481,257 5,799 15,948

Coal-tar distillations 1909 39,471,000 2,751 14,348

Potassium compounds 1909 85,591,429 12,058 7,430

PETROLEUM REFINING AND COKE 488,367,000 25,830 18,907

Petroleum refining 1909 36,073,000 1,540 23,424

Coke 1909 452,294,000 24,290 18,621

RUBBER 140,046,000 8,975 15,604

Tires 1912 140,046,000 8,975 15,604

LEATHER 656,507,000 42,750 15,357

Leather tanning and dressing 1910 656,507,000 42,750 15,357

STONE, CLAY, AND GLASS 402,846,000 114,228 3,527

Cement 1910 126,846,000 22,386 5,666

Glass 1910 276,000,000 91,842 3,005

PRIMARY METALS 4,176,161,000 325,661 12,824

Cast iron and steel 1909 467,564,000 125,057 3,739

Pig iron, wrought iron, etc. 1909 3,309,537,000 177,709 18,623

Zinc 1909 101,249,000 11,856 8,540

Lead, silver, and copper 1909 258,208,000 9,656 26,741

Tin 1909 21,763,000 482 45,151

TRANSPORTATION EQUIPMENT 80,325,000 19,211 4,181

Motor vehicles 1909 80,325,000 19,211 4,181

Notes on next page. . .

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Notes to table A.2:a Output is measured in hectoliter.b Output is measured in metric ton.c Output is measured in kilogram.d Output is the quantity of raw tobacco used in the production of tobacco.


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A.3 Labor-productivity levels interwar Germany



Starch 1936 109,604,000 5,828 18,806

Brewing 1936 33,856,000a 76,376 443

Sugar 1936 2,527,600b 60,197 42

TOBACCO MANUFACTURES 91,900c 154,255 596d

Tobacco products 1936 91,900c 154,255 596d

TEXTILES 1,651,310,000 217,540 7,591

Cotton spinning 1936 828,023,000 109,116 7,588

Artificial silk spinning 1936 275,492,000 36,955 7,455

Woolen (worsted) 1936 422,149,000 48,269 8,746

Jute 1936 66,741,000 10,927 6,108

Linen 1936 58,905,000 12,273 4,800

PAPER 819,204,000 76,291 10,738


CHEMICALS 471,382,000 32,450 14,526

Sulfuric acid 1936 67,914,000 4,990 16,610

Coal tar distillations 1936 215,567,000 9,703 22,217

Potash and potassium compounds 1936 187,901,000 17,757 10,582

PETROLEUM REFINING AND COKE 1,080,337,000 39,630 27,261


Coke 1936 709,696,000 23,541 30,147

RUBBER 195,922,000 14,878 13,169

Tires 1936 195,922,000 14,878 13,169

LEATHER 608,541,000 44,747 13,600

Leather tanning and dressing 1936 608,541,000 44,747 13,600


Cement 1936 267,552,000 20,030 13,358

Glass 1936 268,386,000 59,937 4,478

PRIMARY METALS 6,441,727,000 374,713 17,191

Cast iron and steel 1936 879,762,000 152,022 5,787

Pig iron (blast furnaces) 1936 847,973,000 27,495 30,841

Wrought iron 1936 2,393,000 226 10,588

Ingots 1936 1,533,646,000 46,253 33,158

Rolling works 1936 2,607,873,000 131,693 19,803

Zinc 1936 38,372,000 4,552 8,430

Copper refining 1936 183,232,000 1,825 100,401

Copper, lead, and silver 1936 184,738,000 8,197 22,537

Gold and silver refining 1936 129,005,000 701 184,030

Tin 1936 15,384,000 712 21,607


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Nickel, cobalt 1936 19,349,000 1,037 18,659

TRANSPORTATION EQUIPMENT 2,599,877,000 284,462 9,140

Motor vehicles 1936 1,445,742,000 111,261 12,994

Trailers 1936 266,254,000 37,991 7,008

Aircraft engines 1936 276,097,000 35,139 7,857

Aircraft 1936 611,784,000 100,071 6,113

Notes to table A.3:a Output is measured in hectoliter.b Output is measured in metric ton.c Output is measured in kilogram.d Output is the quantity of raw tobacco used in the production of tobacco.


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A.4 Labor-productivity levels pre-WW1 US



Starch 1909 15,868,393 1,925 8,243

Glucose (starch) 1909 32,930,918 2,848 11,563

Brewing 1909 66,176,940a 66,725 992

Sugar 1909 751,639b 12,536 60

TOBACCO MANUFACTURES 236,405,000c 197,637 1,196

Tobaccod 1909 236,405,000c 197,637 1,196

TEXTILES 841,021,276 318,061 2,644

Cotton: yarns and threads 1909 494,070,173 175,359e 2,817

Silk: throwing and winding mills 1909 17,145,992 17,646 972

Woolen: worsted goods 1909 312,624,663 114,422 2,732

Jute 1909 10,795,230 6,901 1,564

Linen 1909 6,385,218 3,733 1,710

PAPER 267,656,964 81,473 3,285


CHEMICALS 128,393,253 2,773 4,204

Sulfuric acid 1909 9,884,057 2,582 3,828

Coal-tar products 1905 820,309 170 4,825

Potash 1909 88,940 21 4,235

PETROLEUM REFINING AND COKE 332,694,281 47,866 6,951


Coke 1909 95,696,622 31,226 3,065

RUBBER 128,435,747 31,284 4,105

Tiresf 1909 128,435,747 31,284 4,105

LEATHER 327,874,187 67,100 4,886

Leather products 1909 327,874,187 67,100 4,886


Cement 1909 63,205,455 29,511 2,142

Glass 1909 92,095,203 72,573 1,269

PRIMARY METALS 2,113,637,262 362,351 5,833

Pig iron 1909 391,429,283 43,061 9,090

Steel works and rolling mills 1909 985,722,534 260,762 3,780

Wire 1909 84,486,518 19,945 4,236

Tin plate and terneplate 1909 47,969,645 5,846 8,206

Zinc: smelting and refining 1909 34,205,894 7,156 4,780

Lead: smelting and refining 1909 167,405,650 8,059 20,773

Copper: smelting and refining 1909 378,805,974 16,832 22,505

Gold and silver refining 1909 23,611,764 690 34,220

TRANSPORTATION EQUIPMENT 259,900,642 90,376 2,876


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Motor vehicles 1909 259,900,642 90,376 2,876

Notes to table A.4:a Output is measured in hectoliter.b Output is measured in metric ton.c Output is measured in kilogram.d Output data is derived from the ’Report of commissioner of internal revenue’. The numbers

of cigars, cigarettes and other tobacco products are converted to the input of raw tobacco.

This was feasible as the internal revenue reports the number of pounds of tobacco needed for

the production of 1,000 cigars or cigarettes or other products. Subsequently, the use of raw

tobacco for the production of cigars, cigarettes and other tobacco manufactures are summed.

Employment is obtained from the census of manufactures 1909 and represents the total number

of people working in the tobacco manufactures industry (so both workers and proprietors).e For cotton yarn and thread production, only the number of spinners are reported by the

census. Here, I added to the spinners an estimate of wage earners also working in this industry.f These data refer to ‘Rubber, not elsewhere classified’. Tire production is the main activity

of this industry. For 1909 I do not have information about the share of the value of tires in

total production. For 1914, tires formed 65% of the total output value of ‘Rubber, n.e.c.’.


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A.5 Labor-productivity levels interwar US


FOOD AND DRINKS 1,545,169,173 129,983 11,887

Starch 1935 103,631,751 8,616 12,028

Sugar 1935 498,654,860 29,775 16,747

Beverages 1935 942,883,102 91,592 10,294

TOBACCO MANUFACTURES 615,518,450 95,589 6,439

Tobacco 1935 615,518,450 95,589 6,439

TEXTILES 217,935,924 85,881 2,538

Yarn and thread mills 1935 66,243,746 33,938 1,952

Throwing and spinning together 1935 46,181,765 25,124 1,838

Worsted yarn 1935 83,608,832 19,846 4,213

Jute 1935 16,294,140 5,008 3,254

Linen 1935 5,607,441 1,965 2,854

PAPER 1,529,829,650 268,742 5,693

Paper 1935 1,529,829,650 268,742 5,693

CHEMICALS 944,823,119 154,157 6,129

Inorganic chemicals 1935 944,823,119 154,157 6,129

PETROLEUM REFINING AND COKE 2,423,292,920 137,898 17,573

Petroleum refining 1935 2,184,589,237 119,461 18,287

Coke 1935 238,703,683 18,437 12,947

RUBBER 446,091,602 65,715 6,788

Tires 1935 446,091,602 65,715 6,788

LEATHER 308,344,763 54,823 5,624

Tanning and finishing 1935 308,344,763 54,823 5,624


Cement 1935 120,417,129 23,311 5,166

Glass 1935 283,925,061 73,350 3,871

PRIMARY METALS 3,682,794,765 622,659 5,915

Blast furnaces and steel mills 1935 2,305,969,590 406,137 5,678

Iron and steel foundries 1935 288,330,311 112,087 2,572

Primary nonferrous metals 1935 533,867,769 79,168 6,743

Nonferrous metal rolling and drawing 1935 554,627,095 25,267 21,951

TRANSPORTATION EQUIPMENT 4,128,372,408 475,043 8,691

Motor vehicles and equipment 1935 3,942,014,123 425,045 9,274

Aircraft and parts 1935 45,347,030 14,931 3,037

Railroad equipment 1935 117,925,622 29,294 4,026

Motorcycles and bicycles 1935 23,085,633 5,773 3,999


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A.6 Purchasing power parities (GER36/US35)

Industry PPP (GER/US)

Sample All

Exchange rate 2.48 2.48

Lasp. Paas. Fisch. Lasp. Paas. Fisch.

Mining 5.36 5.44 5.40 5.36 5.44 5.40

Manufacturing 3.84 3.00 3.39 3.11 4.86 3.89

3.78 3.06 3.48

3.52 3.01 3.59 4.15 3.21 3.65

Food and kindred products 3.31 3.83 3.56 4.71 4.01 4.35

Tobacco 3.29 3.29 3.29 3.29 3.29 3.29

Textile mill products 2.72 2.65 2.69 4.05 2.97 3.47

Apparel and related 4.11 3.11 3.58

Lumber and wood 6.46 6.06 6.26

Paper and allied products 3.78 3.51 3.64 3.78 3.51 3.64

Chemical and allied products 3.09 2.63 2.85 3.57 3.21 3.39

Petroleum and coal products 3.96 2.04 2.84 3.96 2.04 2.84

Rubber products 4.99 3.53 4.20 4.99 3.53 4.20

Leather and leather products 4.22 4.25 4.23 4.22 4.25 4.23

Stone, clay, and glass products 3.14 2.78 2.95 3.06 2.64 2.84

Primary metal products 2.89 2.74 2.81 2.89 2.74 2.81

Fabricated metal products 4.29 2.96 3.56

Machinery 3.48 3.61 3.55

Electrical machinery 3.83 3.40 3.61


Instruments and related 5.36 5.41 5.39


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A.7 Coverage and number of UVRs (GER36/US35)

Industry Share in compared output No.

Sample All Sample All

GER US GER US

Mining 60 27 60 27 5 5

Manufacturing 74 48 43 38 125 202

Food and kindred products 70 47 67 45 9 26

Tobacco manufactures 57 118 57 118 1 1

Textile mill products 148 114 58 35 7 13

Apparel and related 55 59 10

Wood and lumber 71 60 2

Paper and allied products 43 25 43 25 12 12

Chemical and allied products 94 21 21 24 33 47

Petroleum and coal products 59 9 39 8 3 3

Rubber products 69 73 28 48 3 3

Leather and leather products 133 251 50 63 7 7

Stone, clay, and glass products 70 57 39 28 7 13

Primary metal products 76 46 58 43 27 27

Fabricated metal products 12 7 9

Machinery 3 2 3

Electrical machinery 9 16 5

Transportation equipment 47 57 37 56 16 19

Instruments and related 7 3 2


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A.8

Unit-valuera

tiosGER09/US09

Pro

duct

Unit

GER

1909

US

1909

UVR

GO

Vol.

GO

Vol.

Mining

2,017,384,000

654,109,829

Coalandlignite

Short

ton

1,685,365,000

235,505,173

405,486,777

379,744,257

6.70

Ironore

Longton

80,781,000

19,811,943

110,290,596

51,294,271

1.90

Pyrite

Longton

1,579,000

209,914

1,028,157

247,070

1.81

Uranium

ore,tinore,cobalt

ore,nickel

ore,bismuth

ore,

vitriolore

andbauxite

Longton

673,000

27,758

679,447

129,101

4.61

Petroleum

Barrel

9,297,000

987,919

128,248,783

182,134,274

13.36

Raw

graphite

Short

ton

224,000

6,790

32,238

5,096

5.21

Salt

Barrel

239,465,000

109,078,633

8,343,831

30,117,646

7.92

Manufacturing

6,765,599,461

2,824,473,286

Starch,potato

Lbs.

41,739,000

516,653,311

925,570

26,582,595

2.32

Starch,maize

Lbs.

5,176,000

52,866,850

15,962,916

638,825,366

3.92

Starch,wheat

Lbs.

11,775,000

63,765,623

626,337

12,127,686

3.58

Starch,glucose

sirups

Lbs.

13,044,000

124,333,661

17,922,514

769,660,210

4.51

Starch,dextrin

Lbs.

5,903,000

49,310,794

610,999

16,148,931

3.16

Starch,sugar

Lbs.

2,311,000

21,940,625

3,620,816

159,060,478

4.63

Total

79,948,000

39,669,152

Cotton,yarn

Lbs.

608,939,000

814,529,360

109,314,953

470,370,995

3.22

Cotton,spinningwaste

Lbs.

11,096,000

64,047,263

10,874,380

310,513,348

4.95

Cotton,thread:

Lbs.

36,698,000

26,053,185

20,516,269

23,706,957

1.63

Linen

,yarn

Lbs.

53,890,000

71,100,962

982,742

5,486,891

4.23

Continued

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Table

A.8

–Continued

Pro

duct

Unit

GER

1909

US

1909

UVR

GO

Vol.

GO

Vol.

Linen

,thread

Lbs.

4,589,000

3,622,960

3,407,008

6,530,503

2.43

Jute,yarn

Lbs.

62,769,000

294,848,170

4,361,550

62,512,247

3.05

Silk

Lbs.

20,371,235

3,611,584

2,104,066

779,462

2.09

Twistedraw

silk

Lbs.

11,633,000

1,016,117

6,341,719

1,088,780

1.97

Woolen,worstedthread

Lbs.

410,817,000

229,965,656

93,701,641

131,430,238

2.51

Twine

Lbs.

29,357,000

51,365,311

2,417,391

13,715,771

3.24

Ropes

andcables

Lbs.

11,359,000

27,602,232

1,164,526

7,603,907

2.69

Total

1,261,518,235

255,186,245

Pulp

Short

ton

230,000,000

1,668,238

30,177,366

910,846

4.16

Paper

Short

ton

460,000,000

1,775,824

101,654,400

1,870,459

4.77

Cardboard

Short

ton

60,000,000

407,855

29,497,735

883,088

4.40

Total

750,000,000

161,329,501

Copper

sulfate

Lbs.

1,958,000

11,677,886

1,569,200

37,357,501

3.99

Zincsulfate

Lbs.

303,000

11,951,259

472,302

25,054,213

1.34

Sulfuricacid

Short

ton

43,306,000

1,348,209

5,629,496

683,588

3.90

Pyrite

(iron(III)ox

ide)

Short

ton

5,779,257

590,079

6,807,265

1,217,401

1.75

Zincox

ide

Short

ton

42,775,000

418,720

953,467

12,360

1.32

Sulfurousacid(liquid)

Short

ton

484,000

6,398

476,135

31,349

4.98

Tar,

pitch

Short

ton

14,391,000

499,591

800,862

61,100

2.20

Coaltar,

distilled

Gallon

3,332,000

20,176,706

22,704

577,750

4.20

Coaltar,

creosote

Gallon

9,986,000

54,697,349

17,546

288,817

3.01

Ammonia,aqua

Lbs.

2,000

24,251

839,820

20,983,476

2.06

Ammonia,sulfuricacid

Lbs.

61,706,000

619,377,702

3,675,771

127,982,211

3.47

Potash

fertilizer

Short

ton

57,547,000

1,114,482

15,438,167

1,089,495

3.64

Potassium

chloride

Short

ton

68,097,000

545,936

6,497,364

177,372

3.41

Continued

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Table

A.8

–Continued

Pro

duct

Unit

GER

1909

US

1909

UVR

GO

Vol.

GO

Vol.

Potassium

sulfate

(over

42%

K2O,i.e.

potassium

oxide)

Short

ton

19,984,000

133,963

1,684,998

39,232

3.47

Kainite(patentkali)

Short

ton

5,083,000

69,385

3,939,263

448,885

8.35

Total

334,733,257

48,824,360

Coke

Short

ton

368,023,000

25,999,789

89,965,483

39,315,065

6.19

Tarandtarcompounds

Gallon

15,326,000

164,645,847

1,408,611

60,126,006

3.97

Ben

zine

Barrel

18,710,000

853,271

39,771,959

10,806,550

5.96

Kerosene

Barrel

3,797,000

173,848

94,547,010

33,495,798

7.74

Lube,

liquid

paraffin,

and

fuel

oils

Barrel

11,543,000

475,156

38,884,236

10,745,885

6.71

Alkaneoil,lignitetardistil-

lation

Barrel

3,989,000

281,209

9,473,975

3,239,230

4.85

Paraffin

Barrel

4,097,000

76,409

9,388,812

946,830

5.41

Ammonium

sulfate

(from

cokeov

ens)

Lbs.

420,000

4,027,846

3,227,316

123,111,197

3.98

Total

425,905,000

286,667,402

Tires

Number

78,076,000

1,283,000

125,780,035

15,928,722

7.71

Total

78,076,000

125,780,035

Leather,sole

Sides

194,683,000

8,618,321

306,476,720

35,610,504

2.62

Leather,upper

Sides

256,828,000

3,614,148

32,540,720

10,652,060

23.26

Leather,utensils

Sides

41,088,000

1,124,271

39,069,476

5,345,077

5.00

Leather,machinery

Sides

33,892,000

990,284

6,995,133

1,042,070

5.10

Leather,split

Sides

24,551,000

1,750,764

7,410,740

8,134,229

15.39

Total

551,042,000

392,492,789

Window

glass

50-footbox

es30,000,000

4,413,203

11,742,959

6,921,611

4.01

Continued

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�


Table

A.8

–Continued

Pro

duct

Unit

GER

1909

US

1909

UVR

GO

Vol.

GO

Vol.

Cem

ent

Barrel

125,857,000

37,546,400

52,797,973

65,399,889

4.15

Total

155,857,000

64,540,932

Pig

iron

Short

ton

633,541,000

12,540,434

391,429,283

25,651,798

3.31

Ingots

Short

ton

919,414,000

12,546,252

3,593,726

142,745

2.91

Steel

castings

Short

ton

41,334,000

147,784

38,862,448

504,856

3.63

Railway

-track

material

Short

ton

173,732,000

1,646,891

83,811,312

2,964,951

3.73

Beams

Short

ton

143,817,000

1,435,764

14,488,412

396,911

2.74

Ironbars

Short

ton

296,702,000

3,020,421

121,488,423

3,784,248

3.06

Hoops

Short

ton

39,922,000

336,802

10,429,681

341,043

3.88

Wirerods

Short

ton

101,148,000

954,239

61,947,958

2,295,279

3.93

Sheets

Short

ton

192,690,000

1,526,109

133,272,393

3,332,733

3.16

Tinplate

Lbs.

17,557,000

122,063,341

38,259,885

1,123,968,875

4.23

Tubes

Short

ton

108,572,000

434,975

75,109,011

1,386,605

4.61

Railway

rollingstock

Short

ton

52,733,000

263,880

3,831,344

102,348

5.34

Zinc

Short

ton

95,500,000

241,493

24,864,300

230,225

3.66

Lead

Short

ton

47,462,000

195,682

30,460,168

354,188

2.82

Silver

Troyounce

51,272,000

23,521,229

28,455,200

28,050,600

2.15

Gold

Troyounce

92,090,000

1,060,878

99,673,400

4,821,701

4.20

Copper

Lbs.

45,041,000

82,426,431

142,083,711

1,092,951,624

4.20

Tin

Short

ton

21,281,000

9,040

12,896

17

3.10

Total

3,073,808,000

1,302,073,551

Motorcycles

Number

2,246,797

3,703

2,985,866

18,496

3.76

Tricars

Number

2,050,586

936

30,122

132

9.60

Cars

Number

40,532,198

6,682

141,433,667

112,318

4.82

Trucks(deliverywagons)

Number

8,503,599

636

3,165,512

1,366

5.77

Continued

onnextpage...

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Table

A.8

–Continued

Pro

duct

Unit

GER

1909

US

1909

UVR

GO

Vol.

GO

Vol.

Engines

formotorb

oats

Number

1,378,789

1,739

294,152

2,796

7.54

Total

54,711,969

147,909,319

Sources:seesection2.3.

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A.9

Unit-valuera

tiosGER09/GER36

Pro

duct

Unit

GER

1909

GER

1936

UVR

GO

Vol.

GO

Vol.

Mining

2,029,694,000

2,683,195,000

Coal

Metricton

1,530,224,000

146,964,199

1,687,606,000

158,282,800

1.02

Lignite

Metricton

155,141,000

66,682,500

396,629,000

161,396,700

1.06

Briquette

Metricton

70,429,000

5,151,849

97,573,000

6,044,300

1.18

Lignitebriquettes

Metricton

132,289,000

14,601,690

367,596,000

36,074,500

1.12

Ironore

Metricton

80,781,000

20,129,863

23,581,000

3,542,500

1.66

Copper

ore

Metricton

23,098,000

797,408

11,827,000

1,149,700

0.36

Pyrite

Metricton

1,579,000

213,282

2,903,000

286,200

1.37

Petroleum

Metricton

9,297,000

137,382

46,491,000

443,000

1.55

Potash

rock

Metricton

6,941,000

1,391,738

23,594,000

2,378,400

1.99

Bituminousrock

Metricton

642,000

76,964

666,000

108,800

0.73

Refi

ned

rock

salt

Metricton

19,273,000

634,399

24,729,000

580,900

1.40

Manufacturing

7,281,327,205

9,176,445,813

Potato

starch

Dz

41,739,000

2,343,500

27,485,000

1,058,000

1.46

Maizestarch

Dz

5,176,000

239,800

23,491,000

680,000

1.60

Maizestarch,sirup

Dz

13,044,000

563,968

17,429,000

475,000

1.59

Total

59,959,000

68,405,000

Woolencarded

Kilogram

234,332,000

92,777,466

274,356,000

86,436,000

1.26

Cottonyarn

Kilogram

750,014,000

401,119,094

580,316,600

283,882,000

1.09

Flax

Kilogram

69,157,000

35,368,751

57,008,677

19,078,000

1.53

Jute

Kilogram

85,886,000

146,978,945

62,567,000

113,527,000

0.94

Woolweaving

Kilogram

774,509,000

113,355,019

820,908,213

86,756,000

1.38

Continued

onnextpage...

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“prnt˙thesis” — 2014/1/22 — 12:20 — page 186 — #200�

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Table

A.9

–Continued

Pro

duct

Unit

GER

1909

GER

1936

UVR

GO

Vol.

GO

Vol.

Worstedcombed

Kilogram

410,817,000

104,310,667

413,667,000

59,519,000

1.76

Total

2,324,715,000

2,208,823,490

Paper

Metricton

460,000,000

1,611,000

278,090,904

1,158,326

0.84

Cardboard

Metricton

60,000,000

370,000

22,231,468

85,071

1.61

Total

520,000,000

300,322,372

Sulfuricacid

Metricton

43,306,000

1,223,075

26,413,000

1,087,114

0.69

Sphalerite

Metricton

42,775,000

379,856

606,770

11,904

0.45

Ammonia

sulphate

Metricton

420,000

1,827

80,506,935

535,471

0.65

Ammonia

water

Metricton

2,000

11

43,013,525

176,464

1.34

Ben

zene

Metricton

2,097,000

19,122

90,841,000

449,064

1.84

Toluol

Metricton

585,000

2,791

12,588,704

24,189

2.48

Phen

ol

Metricton

1,505,000

2,211

5,755,880

6,853

1.23

Coaltar

Metricton

3,332,000

91,520

112,232,513

2,972,765

1.04

Tar(pitch

)Metricton

14,391,000

453,221

24,838,815

637,826

1.23

Total

108,413,000

396,797,142

Kerosene

Metricton

3,797,000

26,025

1,529,827

12,425

0.84

Bezine

Metricton

18,710,000

118,050

138,668,237

752,705

1.16

Lubricatingoils

Metricton

11,543,000

75,431

78,310,098

400,835

1.28

Total

34,050,000

218,508,162

Tires,motorveh

icles

Number

78,076,000

1,283,000

105,701,691

3,333,215

0.52

Tires,bicycles

Number

45,584,000

15,066,000

29,413,626

21,745,000

0.45

Total

123,660,000

135,115,317

Sole

leather

Kilogram

194,683,000

70,741,274

151,708,073

51,079,000

1.08

Upper

leather

Kilogram

256,828,000

29,665,808

125,868,281

15,572,000

0.93

Continued

onnextpage...

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“prnt˙thesis” — 2014/1/22 — 12:20 — page 187 — #201�

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Table

A.9

–Continued

Pro

duct

Unit

GER

1909

GER

1936

UVR

GO

Vol.

GO

Vol.

Leather

utensils

Kilogram

41,088,000

9,228,288

17,147,000

3,432,000

1.12

Total

492,599,000

294,723,354

Window

glass

Square

meter

30,000,000

20,500,000

28,453,701

24,770,453

0.78

Mirrorglass

Square

meter

22,000,000

1,700,000

19,645,020

1,986,097

0.76

Cem

ent(dry)

Metricton

121,917,000

5,867,088

256,220,000

11,612,582

1.06

Total

173,917,000

304,318,721

Pig

iron,foundry

Metricton

123,593,000

2,222,661

54,531,000

1,007,078

0.97

Pig

iron,Thomas

Metricton

367,685,000

6,985,507

541,885,000

10,363,123

0.99

Pig

iron,Martin

Metricton

83,350,000

1,202,215

150,364,000

2,650,799

0.82

Ingots:Thomas

Metricton

504,847,000

6,679,807

500,945,000

7,877,558

0.84

Ingots:Martin

(standard)

Metricton

356,039,000

4,313,673

803,779,000

10,395,653

0.94

Ironbars

Metricton

296,702,000

2,740,080

518,834,000

4,169,134

1.15

Hoops

Metricton

39,922,000

305,542

111,101,000

800,622

1.06

Wirerods

Metricton

101,148,000

865,671

142,932,000

1,175,248

1.04

Wheels

andaxes

Metricton

52,733,000

239,388

25,108,000

111,672

1.02

Forgings

Metricton

53,962,000

140,825

124,482,000

304,554

1.07

Scrap

Metricton

93,608,000

2,178,505

149,855,000

3,641,210

0.96

Cast

ironandsteel

Metricton

467,564,000

2,419,360

634,975,805

2,209,813

1.49

Railway

-track

materials

Metricton

173,732,000

1,494,034

75,975,558

224,572

2.91

Railstraps

Metricton

143,817,000

1,302,503

14,525,947

91,437

1.44

Sheets

(thick)

Metricton

94,899,000

768,789

45,179,885

311,752

1.17

Sheets

(thin)

Metricton

97,791,000

615,674

30,388,765

92,721

2.06

Matte

Metricton

639,000

1,266

26,866,000

82,752

0.64

Soft

lead

Metricton

42,289,000

161,985

27,475,000

120,253

0.88

Silver

Metricton

45,483,000

564

45,483,000

564

1.00

Continued

onnextpage...

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“prnt˙thesis” — 2014/1/22 — 12:20 — page 188 — #202�

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Table

A.9

–Continued

Pro

duct

Unit

GER

1909

GER

1936

UVR

GO

Vol.

GO

Vol.

Casted

copper

Metricton

20,039,000

16,152

101,230,000

75,510

1.08

Rolled

copper

Metricton

17,056,000

13,635

64,365,000

85,458

0.60

Gold

Metricton

92,090,000

33

61,989,000

22

1.01

Raw

zinc

Metricton

70,998,000

162,250

1,602,000

8,425

0.43

Tin

Metricton

21,281,000

8,201

6,278,000

2,372

1.02

Tin

ash

Metricton

21,000

55

4,139,000

14,589

0.74

Nickel

Metricton

11,297,000

3,779

13,308,000

5,427

0.82

Total

3,372,585,000

4,277,596,960

Aircraft

engines

Units

173,753

20

2,199,034

193

1.31

Motorcycleen

gines

Units

161,000

733

3,496,933

23,837

0.67

Caren

gines

Units

1,217,789

1,006

21,120,479

25,675

0.68

Total

1,552,543

26,816,446

Motorcycles

Units

2,246,797

3,703

82,123,390

140,844

0.96

Cars

Units

40,532,198

6,682

514,588,459

205,713

0.41

Goodsveh

icles

Units

8,503,599

636

166,087,000

30,739

0.40

Chasis

Units

18,594,069

2,126

182,220,000

30,810

0.68

Total

69,876,662

945,018,849


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“prnt˙thesis” — 2014/1/22 — 12:20 — page 189 — #203�

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A.10

Unit-valuera

tiosGER36/US35

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Mining

1,739,485,000

775,471,000

Coal

Short

ton

1,687,606,000

174,476,921

658,063,000

372,373,000

5.47

Ironore

Longton

23,581,000

3,486,552

83,035,000

33,426,000

2.72

Pyrite

Longton

2,903,000

281,680

1,583,000

514,192

3.35

Bituminousrock

Longton

666,000

107,082

10,952,000

3,042,000

1.73

Refi

ned

rock

salt:table

salt

Short

ton

24,729,000

640,333

21,838,000

7,927,000

14.02

Manufacturing

22,665,097,391

15,644,790,417

Flour:

rye

Metricton

380,910,000

1,625,600

5,914,056

134,168

5.32

Flour:

wheat

Metricton

851,830,000

2,773,200

664,567,583

9,097,311

4.20

Bread

Metricton

256,971,000

685,300

706,897,740

4,221,847

2.24

Biscu

its

Metricton

126,104,000

87,300

179,601,710

600,123

4.83

Cocao:pow

dered

Metricton

31,146,000

18,500

10,240,865

57,356

9.43

Chocolate:bars

andblocks

Metricton

157,313,000

73,800

43,937,504

123,467

5.99

Confectionery:ch

ocolate

Metricton

103,865,000

46,900

120,619,679

325,750

5.98

Confectionery:hard

candy,

caramel,etc.

Metricton

83,673,000

67,200

66,914,834

294,558

5.48

Curedfish:herring

Metricton

133,783,000

178,584

1,614,018

8,364

3.88

Curedmeat:bacon,sm

oked

-

andpickledpork

Metricton

107,341,000

64,071

393,507,389

892,718

3.80

Curedmeat:

cooked

hams

Metricton

93,567,000

40,509

35,581,320

49,132

3.19

Cans:

soup

Metricton

31,376,000

24,300

47,208,754

312,780

8.55

Sausages

andmeatpuddings

Metricton

126,301,000

45,217

233,077,321

613,847

7.36

Continued

onnextpage...

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“prnt˙thesis” — 2014/1/22 — 12:20 — page 190 — #204�

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Fresh

meat:

beefandveal

Metricton

113,479,000

56,440

655,532,197

2,453,131

7.52

Starch:potato

Metricton

27,485,000

105,800

883,315

15,321

4.51

Starch:corn

Metricton

23,491,000

68,000

26,637,504

343,043

4.45

Syrup:corn

Metricton

17,429,000

47,500

31,252,948

452,313

5.31

Anim

alfeed

s:poultry,cat-

tle,

andpigs

Metricton

148,974,000

691,100

222,698,070

5,644,475

5.46

Sugar:

unrefined

Metricton

350,647,000

999,900

16,297,431

243,996

5.25

Sugar:

refined

Metricton

673,696,000

1,527,700

95,916,815

1,150,737

5.29

Milk:

conden

sed,

evapo-

rated,andpow

dered

Metricton

87,298,000

92,311

152,656,076

1,253,180

7.76

Margarine

Metricton

320,650,000

437,079

47,256,720

176,399

2.74

Beer

Liters

873,305,000

3,008,500,000

336,490,207

3,815,434,600

3.29

Malt

Metricton

134,692,000

357,870

205,618,081

915,138

1.68

Brandy

Liters

62,491,000

21,836,600

6,299,741

21,227,336

9.64

Spirit,rectified

Liters

14,174,000

24,692,000

8,671,744

29,963,771

1.98

Total

5,331,991,000

4,315,893,622

Cigarettes

Thousands

655,083,000

38,470,701

723,249,455

139,903,223

3.29

Total

655,083,000

723,249,455

Yarn:cotton,single

Metricton

580,317,000

283,882

133,568,824

176,918

2.71

Yarn:cotton,mixed

Metricton

18,216,000

7,180

2,667,574

1,553

1.48

Woven

products:

cotton

Metricton

977,585,000

228,471

561,231,966

712,534

5.43

Yarn:wool,mohair,etc.

Metricton

1,508,931,000

232,711

101,595,298

44,428

2.84

Yarn:rayon

Metricton

222,251,000

51,537

3,198,407

1,452

1.96

Woven

products:

silk

Metricton

332,819,000

23,685

77,417,722

13,384

2.43

Yarn:flax

Metricton

51,856,000

16,576

778,756

706

2.83

Continued

onnextpage...

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Upholstery

filling:jute

and

flax

Metricton

5,152,000

2,502

22,143,047

97,133

9.03

Yarn:jute

Metricton

36,163,000

73,762

2,616,723

10,077

1.89

Yarn:jute

andflax,mixed

Metricton

26,404,000

39,765

3,692,084

13,294

2.39

Knittedfabric:

wool

Metricton

41,759,000

5,417

10,649,845

3,796

2.75

Hosiery:cotton,wool,

silk

andartificialsilk

Pairs

267,546,000

423,240,000

262,674,297

1,052,064,444

2.53

Gloves:

cotton,

wool,

silk

andartificialsilk

Pairs

47,266,000

60,000,000

6,470,974

13,933,728

1.70

Total

4,116,265,000

1,188,705,517

Suits

and

uniform

s:men

’s

andboy

s’

Number

161,297,000

5,291,251

330,087,375

23,576,405

2.18

Overcoats:men

’sandboy

s’Number

113,203,000

3,614,252

78,791,685

6,248,793

2.48

Dressinggow

nsandrobes

Number

24,175,000

1,780,489

21,478,983

6,457,057

4.08

Pants

and

trousers:men

’s

andboy

s’

Number

54,964,345

9,823,211

59,547,289

46,753,141

4.39

Workers

apparel,

aprons:

men

’sandboy

s’

Number

74,705,000

21,737,191

68,885,890

110,258,472

5.50

Coats:

women

’sand

chil-

drens’

Number

189,222,000

7,684,283

153,503,872

15,016,375

2.41

Suits

and

ensembles:

women

’sandch

ildren’s

Number

30,326,000

1,079,399

85,822,242

9,703,145

3.18

Dresses:women

’sand

chil-

dren’s

Number

118,811,000

8,096,232

517,467,190

209,438,322

5.94

Undergarm

ents:men

’sand

boy

s’

Number

91,984,000

25,358,419

147,567,857

225,434,508

5.54

Continued

onnextpage...

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“prnt˙thesis” — 2014/1/22 — 12:20 — page 192 — #206�

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Hats

andcaps

Number

63,034,000

26,087,008

175,087,243

233,255,796

3.22

Total

921,721,345

1,638,239,626

Lumber:softwoodandhard-

wood

Cubic

meter

547,776,017

10,825,260

399,182,036

46,106,271

5.84

Dressed

lumber

Cubic

meter

104,307,738

1,447,408

225,766,751

23,617,635

7.54

Total

652,083,755

624,948,787

Woodpulp:mechanical

Metricton

75,418,800

878,317

24,283,573

1,203,407

4.26

Woodpulp:fiber,bleached

Metricton

66,398,972

338,830

24,852,071

457,653

3.61

Wood

pulp:

fiber,

un-

bleached

Metricton

112,024,430

722,815

63,222,883

2,016,208

4.94

Paper:new

sprint

Metricton

79,424,525

473,041

33,353,967

859,754

4.33

Paper:

free

from

ground

wood

Metricton

92,210,992

216,794

86,829,954

868,704

4.26

Paper:

containing

ground

wood

Metricton

106,455,387

468,491

14,717,265

205,189

3.17

Paper:wrapping

Metricton

114,297,519

557,287

103,893,419

1,304,077

2.57

Paper:glassine

Metricton

40,000,295

94,321

8,011,249

33,880

1.79

Cardboard:raw

Metricton

22,231,468

85,071

8,768,545

93,488

2.79

Paper:ch

romo

Metricton

21,137,000

49,413

2,705,905

39,381

6.23

Paper:waxing

Metricton

13,165,000

18,959

5,217,626

41,856

5.57

Paper:parchment

Metricton

12,566,000

18,335

1,812,675

12,847

4.86

Total

755,330,388

377,669,132

Acid:sulphuric

Metricton

26,413,190

959,907

31,907,994

2,542,961

2.19

Sulphate:sodium

Metricton

3,849,000

122,528

4,262,546

251,021

1.85

Sodium

silicate,solid

Metricton

1,629,000

21,165

1,066,387

27,702

2.00

Continued

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Sodium

silicate,liquid

Metricton

6,556,000

62,697

6,607,204

142,579

2.26

Acid:boric

Metricton

2,526,000

5,348

1,245,874

13,035

4.94

Acid:tartaric

Metricton

4,871,000

4,161

1,609,027

3,124

2.27

Acid:citric

Metricton

1,291,000

980

2,768,377

4,760

2.26

Sodium

carb

onate

Metricton

56,171,000

706,105

31,357,132

1,733,697

4.40

Sodium

bicarb

onate

Metricton

2,163,000

25,453

3,658,321

123,882

2.88

Chloride:

ammonium

Metricton

7,622,000

46,354

1,583,613

15,814

1.64

Hydroxide:

sodium

Metricton

19,580,000

276,827

28,134,175

653,364

1.64

Hydroxide:

potassium

Metricton

12,139,000

61,343

1,260,031

8,635

1.36

Chlorine

Metricton

17,127,000

137,261

7,961,186

188,132

2.95

Hydrogen

gas

Metricton

7,775,000

2,817,000

1,556,658

1,494,959

2.65

Carb

onbisulphide

Metricton

6,939,000

40,038

3,384,851

53,414

2.73

Ammonia

Metricton

47,789,000

259,331

6,914,878

73,797

1.97

Acid:nitric

Metricton

10,797,000

64,782

2,142,817

22,226

1.73

Fertilizer:

nitrogen

ous

Metricton

110,804,000

1,140,718

93,091,644

3,811,610

3.98

Fertilizer:

superphosphate

Metricton

30,571,000

743,250

19,778,461

1,758,243

3.66

Acid:phosphoric

Metricton

14,226,000

36,416

1,333,702

10,293

3.02

Calcium

carbide

Metricton

73,070,000

412,750

6,234,380

133,440

3.79

Methanol

Metricton

16,437,000

50,384

5,075,683

56,817

3.65

Butanol

Metricton

6,146,000

6,478

2,601,983

16,274

5.93

Acetone

Metricton

4,517,000

5,603

2,642,149

31,262

9.54

Acetate:ethyl

Metricton

3,393,000

5,895

2,679,195

18,890

4.06

Ether

Metricton

4,543,000

4,664

1,305,459

3,590

2.68

Drugs:

ephed

rine

Metricton

1,040,000

5153,040

57.80

Sodium

bisulphite

Metricton

6,735,000

8,644

2,650,638

6,839

2.01

Acid:boric

Metricton

1,124,000

2,746

1,245,874

13,035

4.28

Continued

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Sodium

borate

(borax)

Metricton

3,212,000

17,182

3,693,129

96,280

4.87

Sulphate:aluminum

Metricton

7,868,000

92,785

8,007,782

320,434

3.39

Chloride:

aluminum

Metricton

2,439,000

2,893

397,295

2,122

4.50

Sulphate:copper

Metricton

3,706,000

13,986

2,002,099

24,838

3.29

Vitreousen

amels

Metricton

1,688,000

2,417

4,399,947

33,293

5.28

Cellulose

acetate

Metricton

20,267,000

6,978

7,986,489

4,715

1.71

Acid:acetic

Metricton

5,252,000

6,506

5,455,362

46,040

6.81

Explosive:

nitroglycerin

Metricton

24,335,000

8,752

818,748

1,126

3.83

Paint:

whitelead

Metricton

13,107,000

32,000

12,937,249

76,943

2.44

Paint:

zincox

ide

Metricton

1,920,000

3,485

143,819

566

2.17

Paint:

chem

icalcolors

Metricton

15,754,000

20,116

9,812,458

38,349

3.06

Varnish:non-cellulose

Metricton

80,794,000

88,040

50,025,338

215,428

3.95

Varnish:cellulose

Metricton

40,039,000

19,523

38,252,340

95,706

5.13

Linseed

oil

Metricton

24,854,000

67,596

43,271,858

219,097

1.86

Soap:toilet

Metricton

49,792,000

39,257

53,324,747

160,107

3.81

Soap:bar

Metricton

55,086,000

112,018

51,340,273

514,403

4.93

Soap:pow

der

Metricton

143,905,000

234,945

60,717,053

433,522

4.37

Soap:flakes

andliquid

Metricton

27,671,000

72,441

44,686,244

259,629

2.22

Total

1,029,532,190

673,485,509

Sulphate:ammonium

Metricton

4,313,000

94,060

2,073,258

87,298

1.93

Lubricatingoils

Metricton

78,310,098

400,835

186,533,605

4,046,171

4.24

Coke

Metricton

551,493,000

37,656,197

23,283,543

3,020,417

1.90

Total

634,116,098

211,890,406

Tires:passen

ger

car

Number

48,832,656

2,301,623

221,555,480

42,479,133

4.07

Tires:trucksandbus

Number

51,536,061

433,103

99,852,136

6,003,338

7.15

Continued

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Tires:cycleandmotor

Number

34,746,600

22,341,489

3,418,907

4,006,476

1.82

Total

135,115,317

324,826,523

Sole

leather:tanned

Metricton

184,629,073

56,312

89,678,756

156,956

5.74

Upper

leather:cattle

Metricton

18,426,000

4,537

51,928,960

66,816

5.23

Harness

and

saddlery

leather

Metricton

17,147,000

3,432

4,844,697

7,866

8.11

Gloves:leather,men

’sand

boy

s’

Pairs

12,054,126

3,056,112

14,576,068

21,935,256

5.94

Gloves:

leather,

women

’s

andch

ildren’s

Pairs

20,268,864

6,008,820

9,543,121

9,419,004

3.33

Belts:leather

Number

13,695,189

10,529,189

9,384,052

50,258,670

6.97

Boots

andshoes:leather

Pairs

543,218,000

79,574,000

593,391,703

330,714,911

3.80

Total

809,438,252

773,347,357

Asb

estos:

millboard

Metricton

2,726,000

6,036

398,934

3,846

4.35

Asb

estos:

yarn

Metricton

2,372,000

1,294

1,843,979

3,647

3.63

Asb

estos:

textiles

Metricton

703,000

398

2,700,488

5,902

3.86

Bricks:

common

Number

266,870,000

9,021,000,000

25,249,116

2,283,928,000

2.68

Tiles:floor

Metricton

25,875,000

180,292

1,083,771

21,411

2.84

Tiles:wall

Metricton

32,288,000

128,293

7,959,499

47,098

1.49

Cem

ent

Metricton

256,220,000

11,612,582

113,504,670

12,831,997

2.49

Glass:window

Square

meters

28,453,000

24,770,453

18,180,053

39,849,677

2.52

Glass:globes

Metricton

11,914,000

14,227

494,516

5,251

8.89

Bottles:

beer

Metricton

29,057,737

150,566

3,707,702

82,388

4.29

Bottles:

transparent

Metricton

8,159,200

30,912

29,355,858

509,436

4.58

Bottles:

med

icinaland

per-

fumery

Metricton

25,096,000

61,809

30,345,232

355,348

4.75

Continued

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Bottles:

bottlesandjars

Metricton

16,096,000

63,934

34,806,621

382,334

2.77

Total

705,829,937

269,630,439

Pig-iron

Metricton

788,898,000

14,779,136

314,164,787

20,887,926

3.55

Steel:ingots,blooms,billets,

andslabs

Metricton

1,304,724,000

18,273,211

143,505,275

4,339,025

2.16

Steel:

sheet

and

tin-plate

bars

Metricton

406,739,000

4,621,549

75,799,384

2,607,089

3.03

Steel:bars

Metricton

518,834,000

4,169,134

155,641,438

2,896,416

2.32

Wirerods

Metricton

142,932,000

1,175,248

35,980,306

834,644

2.82

Iron

and

steel:

flats,strips

andhoops

Metricton

267,763,654

1,318,708

86,657,854

1,598,814

3.75

Steel:platesandsheets

Metricton

189,058,000

1,545,000

288,156,378

5,096,508

2.16

Ironandsteel:scrap

Metricton

149,855,000

3,641,210

17,315,365

1,394,006

3.31

Ironandsteel:pipes

Metricton

66,246,798

445,335

25,185,189

556,765

3.29

Steel:castings

Metricton

56,092,156

119,299

67,441,571

367,389

2.56

Iron:castings

Metricton

63,359,784

105,500

50,540,532

393,712

4.68

Wireproducts,

ferrous:

ca-

bles,

ropeandstrands

Metricton

37,353,542

58,936

29,717,968

91,822

1.96

Wire

products,

ferrous:

drawnwire,

plain

Metricton

125,538,042

563,360

74,598,302

941,414

2.81

Ingots

andpigs:

lead

Metricton

27,475,000

120,253

11,428,581

117,491

2.35

Ingots

andpigs:

copper

Metricton

27,283,000

50,283

4,935,130

26,563

2.92

Ingots

andpigs:

zinc

Metricton

1,602,000

8,425

3,044,215

28,175

1.76

Ingots

andpigs:

tin

Metricton

6,278,000

2,372

2,756,568

2,532

2.43

Ingots

andpigs:

solder,soft

Metricton

3,253,000

2,750

18,729,891

39,583

2.50

Continued

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Plates,

sheets

and

tubes:

nickel

andnickel

alloy

s

Metricton

13,308,000

5,427

17,541,193

23,848

3.33

Ingots

and

pigs:

aluminum

andaluminum

alloy

s

Metricton

201,863,000

76,162

16,315,105

44,213

7.18

Plates,

sheets

and

tubes:

copper

Metricton

64,365,000

85,458

73,959,607

272,639

2.78

Plates,

sheets

and

tubes:

brass

andbronze

Metricton

210,088,000

244,931

144,332,364

453,110

2.69

Castings:

magnesium

alloy,

elektron

Metricton

20,643,000

3,706

1,748,977

1,242

3.95

Ingots

and

pigs:

white-base

alloy

s

Metricton

4,254,000

3,274

5,164,519

10,967

2.76

Platesandsheets:zinc

Metricton

23,677,000

73,861

8,130,743

45,297

1.79

Castings:

aluminum

and

aluminum

alloy

s

Metricton

88,449,000

26,743

32,428,531

42,175

4.30

Castings:

copper

Metricton

101,230,000

75,510

1,414,823

3,032

2.87

Total

4,911,161,976

1,706,634,596

Wire

products,

ferrous:

barb

edwire

Metricton

16,963,002

81,768

10,772,272

177,805

3.42

Wireproducts,

ferrous:

wire

netting

Metricton

39,627,465

94,395

42,498,252

372,648

3.68

Iron

and

steel:

machine

screws

Metricton

36,681,739

53,853

7,379,550

21,221

1.96

Wireproducts,

ferrous:

cut

andwirenails

Metricton

34,942,050

135,694

30,621,003

434,560

3.65

Tools:filesandrasps

Metricton

17,628,184

6,863

8,522,655

20,588

6.20

Continued

onnextpage...

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Razorblades

Number

8,441,995

101,982,755

17,733,193

1,241,383,291

5.79

Ironandsteel:woodscrews

Metricton

37,587,390

78,813

4,995,807

17,091

1.63

Wire

products,

ferrous:

other

chains

Metricton

11,378,699

28,586

4,926,322

23,698

1.91

Metalfoils:

other

thangold

foil

Metricton

40,458,000

10,194

14,216,043

28,848

8.05

Total

243,708,524

141,665,097

Business

machines:

type-

writers

Metricton

58,115,000

4,729

19,674,453

6,540

4.08

Pipe

and

fittings:

gas

and

water

Metricton

43,359,000

54,105

6,269,736

55,141

7.05

Electricalmach

ines:va

cuum

cleaners

Number

33,237,000

651,304

22,635,103

871,934

1.97

Total

134,711,000

48,579,292

Electricity

meters

Number

35,135,000

1,649,575

11,832,835

1,107,895

1.99

Radio

apparatus:

receiving

sets

Number

106,548,000

1,292,600

129,109,032

5,569,562

3.56

Incandescent

light

bulb:

large

Number

62,926,000

91,368,000

51,046,338

387,914,279

5.23

Incandescent

light

bulb:

small

Number

6,611,000

70,816,000

9,744,816

241,779,372

2.32

Incandescentlightbulb:car-

bon

Number

4,021,000

4,503,000

189,997

1,030,546

4.84

Total

215,241,000

201,923,018

Railway

wheels

andaxles

Metricton

25,108,000

111,672

10,951,005

113,878

2.34

Continued

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Motorveh

icle

parts:

veh

icle

chains

Metricton

20,543,284

17,989

7,258,405

30,039

4.73

Railway

material

Metricton

75,975,558

224,572

39,111,888

890,674

7.70

Vessels:steam

Gross

tonnage

65,312,088

130,120

37,610,218

168,727

2.25

Vessels:motor

Gross

tonnage

89,943,056

237,913

19,147,362

71,568

1.41

Vessels:lighters,scow

sand

barges

Gross

tonnage

4,787,154

39,088

7,714,131

131,146

2.08

Bicycles

Number

63,732,944

1,249,947

12,059,867

656,828

2.78

Motorscooters

Number

16,734,000

65,350

482,348

4,896

2.60

Priva

tecars

Number

514,588,459

205,713

1,752,794,114

3,212,835

4.59

Trucks:

capacity

exceed

ing

1.5

short

tons(30cw

ts.)

Number

166,087,000

30,739

43,808,889

34,814

4.29

Trailers

Number

65,238,066

22,059

15,918,524

22,951

4.26

Bodies:

cars

Number

84,702,000

81,687

351,682,014

2,083,205

6.14

Bodies:

busses

andtrucks

Number

42,789,000

28,150

39,479,304

200,863

7.73

Railway

equipmen

t:locomo-

tives

Metricton

69,551,000

53,366

20,245,112

25,425

1.64

Railway

equipmen

t:car-

riages

Number

37,237,000

691

4,318,363

142

1.77

Railway

equipmen

t:freight

cars

Number

23,706,000

4,132

20,990,050

8,805

2.41

Trams

Number

2,097,000

132

3,233,346

229

1.13

Engines:airplanes

Number

2,199,000

193

15,661,237

4,119

3.00

Motor-veh

icle

parts:

alu-

minum

and

aluminum

alloy

s

Metricton

7,724,000

2,202

13,400,209

18,890

4.94

Continued

onnextpage...

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Table

A.10–Continued

Pro

duct

Unit

GER

1936

US

1935

UVR

GO

Vol.

GO

Vol.

Total

1,378,054,609

2,415,866,386

Watches:pocket

Number

7,340,000

2,723,165

2,364,350

3,996,654

4.56

Watches:wrist

Number

28,374,000

3,923,095

5,871,305

4,615,406

5.69

Total

35,714,000

8,235,655


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A.11 Value added (per employee) pre-WW1 Ger-

many

Description VA VA/GO LP

FOOD AND DRINKS (184,139,574) 0.37 1,128

Brewing . . . . . . . . .

Raw sugar . . . . . . . . .

Glucose (starch) . . . . . . . . .

Starch 33,166,000 0.37 5,308

TOBACCO MANUFACTURES . . . . . . . . .

Tobacco . . . . . . . . .

TEXTILES (766,327,839) 0.36 (2,330)

Cotton spinning 225,433,762 0.32 2,301

Silk spinning . . . . . . . . .

Woolen (worsted) . . . . . . . . .

Jute 21,418,368 0.27 1,665

Linen 53,425,129 0.82 3,250

PAPER 556,000,000 0.74 5,903

Paper, cardboard, and wood pulp 556,000,000 0.74 5,903

CHEMICALS 108,830,924 0.42 5,048

Sulfuric acid 36,861,257 0.40 6,356

Coal-tar distillations 14,648,000 0.37 5,325

Lignite-tar distillations 8,150,000 0.68 1,127

Potash 49,171,667 0.55 1,286

PETROLEUM REF. & COKE (145,477,672) 0.29 (5,632)

Petroleum refining . . . . . . . . .

Coke 131,655,000 0.30 5,420

RUBBER . . . . . . . . .

Tires . . . . . . . . .

LEATHER 139,551,000 0.21 3,264

Leather tanning and dressing 139,551,000 0.21 3,264

STONE, CLAY, AND GLASS (365,391,450) 0.91 (3,199)

Cement 115,018,000 0.91 5,138

Glass . . . . . . . . .

PRIMARY METALS (1,259,417,314) 0.30 (3,867)

Cast iron and steel 303,941,000 0.65 2,430

Pig iron, wrought iron, ingots, and

rolling works

915,533,000 0.28 5,152

Zinc 37,010,000 0.37 3,122

Lead, silver, and copper 27,855,000 0.11 2,885

Tin 2,914,000 0.13 6,046

Nickel, cobalt, bismuth, and arsenic . . . . . . . . .


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TRANSPORTATION EQUIPMENT 40,590,000 0.51 2,112

Motor vehicles 40,590,000 0.51 2,112

Notes to table A.11:

Estimates of value added and value added per employee that are derived on the basis of an

industry’s average value-added/gross-output ratio are in parentheses. Only in case value added

for some of the underlying 3-digit industries is unobtainable.


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A.12 Value added (per employee) interwar Germany


FOOD AND DRINKS 1,404,606,000 0.49 8,197

Brewing 1,020,115,000 0.55 9,685

Sugar 330,696,000 0.29 5,494

Starch 53,795,000 0.49 9,230

TOBACCO MANUFACTURES 702,719,000 0.61 4,554

Tobacco 702,719,000 0.61 4,554

TEXTILES 696,231,000 0.42 3,200

Cotton spinning 319,188,000 0.39 2,925

Artificial silk spinning 166,405,000 0.60 4,503

Woolen (worsted) 156,513,000 0.37 3,243

Jute 29,387,000 0.44 2,689

Linen 24,738,000 0.42 2,016

PAPER 315,198,000 0.38 4,132


CHEMICALS 98,039,000 0.35 6,672

Sulfuric acid 39,220,000 0.58 7,860

Coal-tar distillations 58,819,000 0.27 6,062

Metal salts, chemicals 66,150,000 0.44 7,382

PETROLEUM REF. & COKE 265,697,000 0.25 6,704

Petroleum refining 111,512,000 0.30 6,931

Coke 154,185,000 0.22 6,550

RUBBER 98,617,000 0.50 6,628

Tires 98,617,000 0.50 6,628

LEATHER 270,437,000 0.44 6,044

Leather tanning and dressing 270,437,000 0.44 6,044

STONE, CLAY, AND GLASS 342,779,000 0.64 4,287

Cement 152,882,000 0.57 7,633

Glass 189,897,000 0.71 3,168

PRIMARY METALS 1,912,360,000 0.30 5,104

Cast iron and steel 592,893,000 0.67 3,900

Pig iron (blast furnaces) 212,657,000 0.25 7,734

Wrought iron 797,000 0.33 3,527

Ingots 304,582,000 0.20 6,585

Rolling works 722,006,000 0.28 5,482

Zinc 14,954,000 0.39 3,285

Copper refining 15,057,000 0.08 8,250

Copper, lead, and silver 28,765,000 0.16 3,509

Gold and silver refining 11,461,000 0.09 16,350

Tin 4,055,000 0.26 5,695


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Nickel, cobalt 5,133,000 0.27 4,950

TRANSPORTATION EQUIPMENT 1,302,448,000 0.50 4,579

Motor vehicles 635,272,000 0.44 5,710

Trailers 140,469,000 0.53 3,697

Aircraft engines 157,751,000 0.57 4,489

Aircraft 368,956,000 0.60 3,687

Sources: Reichsamt fur Wehrwirtschaftliche Planung, Die Deutsche Industrie 1936.

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A.13 Value added (per employee) pre-WW1 US


FOOD AND DRINKS 312,558,521 0.67 3,692

Brewing 278,134,000 0.74 4,168

Sugar 30,183,107 0.38 2,292

Starch 4,241,414 0.27 2,203

TOBACCO MANUFACTURES 239,509,000 0.57 1,212

Tobacco 239,509,000 0.57 1,212

TEXTILES 236,956,506 0.29 771

Cotton: yarns and threads 123,060,703 0.25 702

Silk: throwing and winding mills 9,058,076 0.53 513

Woolen: worsted goods 104,837,727 0.34 916

Jute . . . . . . . . .

Linen . . . . . . . . .

PAPER 102,214,623 0.38 1,255


CHEMICALS 4,779,921 0.45 1,737

Sulfuric acid 4,498,229 0.46 1,742

Coal-tar distillations 281,692 0.34 1,657

PETROLEUM REF. & COKE 69,396,352 0.21 1,450

Petroleum refining 37,724,257 0.16 2,267

Coke 31,672,095 0.33 1,014

RUBBER 46,243,926 0.36 1,478

Tires 46,243,926 0.36 1,478

LEATHER 79,595,254 0.24 1,186

Leather products 79,595,254 0.24 1,186

STONE, CLAY, AND GLASS 93,837,368 0.60 919

Cement 33,861,664 0.54 1,147

Glass 59,975,704 0.65 826

PRIMARY METALS 500,357,782 0.24 1,381

Pig iron 70,791,394 0.18 1,644

Steel works and rolling mills 328,221,678 0.33 1,259

Wire 23,943,587 0.28 1,200

Tin plate and terneplate 6,080,211 0.13 1,040

Zinc: smelting and refining 8,975,893 0.26 1,254

Lead: smelting and refining 15,442,628 0.09 1,916

Copper: smelting and refining 45,274,336 0.12 2,690

Gold and silver refining 1,628,055 0.07 2,360

TRANSPORTATION EQUIPMENT 123,172,337 0.47 1,363

Motor vehicles 123,172,337 0.47 1,363

Sources: United States Department of Commerce: Bureau of Foreign andDomestic Commerce, Statistical Abstract of the United States, 1913.

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A.14 Value added (per employee) pre-WW1 UK


FOOD AND DRINKS 44,512,000 0.61 487

Brewing 41,221,000 0.27 485

Sugar & molasses 3,291,000 0.56 506

TOBACCO MANUFACTURES 5,817,000 0.24 155

Tobacco 5,817,000 0.24 155

TEXTILES 56,221,000 0.27 74

Cotton 45,007,000 0.26 79

Silk 1,762,000 0.34 55

Jute, hemp, and linen 9,452,000 0.29 61

PAPER 4,542,000 0.33 111

Paper and allied 4,542,000 0.33 111

CHEMICALS 9,568,000 0.40 183

Chemicals, coal-tar products and

drugs

9,568,000 0.40 183

PETROLEUM REF. & COKE 4,037,000 0.29 254

Shale oil 777,000 0.33 229

Coke 2,993,000 0.30 273

Manufactured fuels 267,000 0.22 174

RUBBER 2,976,000 0.33 124

India rubber 2,976,000 0.33 124

LEATHER 3,385,000 0.19 117

Leather tanning and dressing 3,385,000 0.19 117

STONE, CLAY, AND GLASS 1,955,000 0.52 132

Cement 1,955,000 0.52 132

PRIMARY METALS 49,679,000 0.22 109

Iron and steel 30,048,000 0.29 115

Wrought iron and steel tubing 2,189,000 0.33 108

Wire 2,120,000 0.32 116

Tin plate 2,009,000 0.22 97

Lead, tin, and zinc 1,097,000 0.12 133

Copper and brass 2,930,000 0.17 137

Gold and silver refining 431,000 0.01 197

Anchor, chain, nail, bolts, etc. 2,314,000 0.41 83

Galvanized sheet, hardware, etc. 6,541,000 0.41 87

TRANSPORTATION EQUIPMENT 5,901,000 0.51 109

Motor vehicles 5,901,000 0.51 109

Sources: Board of Trade, UK Census of Production 1907.

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Nederlandse samenvatting

Na 1870 bracht een groeispurt van het welvaartsniveau, gemeten in bruto binnenlands

product per hoofd van de bevolking, de Verenigde Staten (VS) aan de vooravond van de

twintigste eeuw op gelijke hoogte met het Verenigd Koninkrijk (VK). Na de eeuwwisse-

ling zette de snelle Amerikaanse groei door en de VS namen het economisch leiderschap

over van het VK. Sindsdien hebben de VS die voorsprong niet meer afgestaan en het

transatlantisch productiviteitsgat vormt daardoor een kenmerkende eigenschap van eco-

nomische ontwikkeling in de moderne tijd. Deze divergentie werd voor het eerst zicht-

baar tijdens een periode van snelle technologische ontwikkeling, de tweede industriele

revolutie. Nieuwe technologieen creeerden ruimte voor arbeidsproductiviteitsgroei. De

relatief snelle Amerikaanse welvaartsgroei roept de vraag op of de VS de groeipotentie

van nieuwe technologie beter exploiteerden dan Europese landen?

Dit is de vraag die centraal staat in deze studie. In het bijzonder gaat de aandacht

uit naar de positie van de Duitse industrie in het transatlantisch productiviteitsver-

schil. Met name in de industrie, waarin tot halverwege de twintigste eeuw ongeveer

eenderde van de beroepsbevolking actief was, leidden de nieuwe productietechnologieen

tot grote verandering. Een studie naar de ontwikkeling van de industrie is bovenal

interessant voor Duitsland, dat gezien haar prominente positie binnen Europa onder-

vertegenwoordigd is in de kwantitatieve literatuur. Voor een deel volgt de onderbelichte

positie van Duitsland uit een gebrek aan consistente data voor de vroege twintigste

eeuw. De ontoereikendheid van de data compliceert de berekening van het Duitse in-

dustriele arbeidsproductiviteitsniveau en beperkt het zicht op de Duitse positie binnen

het transatlantisch divergentieproces.

Na een inleidend hoofdstuk zal in hoofdstuk 2 de prestatie van de Duitse indu-

strie ten opzichte van de VS worden geanalyseerd door middel van nieuwe niveauschat-

tingen van comparatieve arbeidsproductiviteit voor de steekjaren 1909 en 1936/35. Deze

analyse geeft in elk steekjaar een doorsnede van de industrie en brengt daarmee de

verschillen in comparatieve arbeidsproductiviteit tussen industriele bedrijfstakken in

kaart. Om de Duitse en Amerikaanse productiewaarden te vergelijken moeten ze in een

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gemeenschappelijke munteenheid worden uitgedrukt. De officiele wisselkoers, die een ge-

middelde prijsratio uitdrukt tussen Duitsland en Amerika, doet in dit geval geen recht

aan het gedetailleerde aggregatieniveau van deze studie. In plaats daarvan zijn aan de

hand van prijsinformatie industrie-specifieke conversiefactoren, koopkrachtpariteiten,

berekend.

De resultaten laten zien dat het arbeidsproductiviteitsverschil tussen de Duitse en

Amerikaanse industrie in 1909 weliswaar groot was, maar kleiner dan de literatuur aan-

geeft voor het verschil tussen het VK en de VS in dezelfde periode. De Duitse industrie

bereikte destijds een niveau van ongeveer 60% van de VS. Tijdens het interbellum ver-

slechterde Duitsland’s positie en de relatieve arbeidsproductiviteit daalde in 1936/35

tot een niveau onder de 50% van de VS. Echter, op het niveau van de afzonderlijke in-

dustriele bedrijfstakken blijken er grote verschillen te zijn. Lang niet alle bedrijfstakken

keken tegen een grote achterstand op. Bijvoorbeeld, de textiel, chemie en ijzer & staal

industrieen kwamen heel dicht bij het Amerikaanse arbeidsproductiviteitsniveau.

De geobserveerde verschillen in relatieve arbeidsproductiviteit staan op gespannen

voet met theorieen die de transatlantische verschillen verklaren aan de hand van al-

gemene land-gebonden kenmerken. Een prominent voorbeeld van het laatstgenoemde

is de Rothbarth-Habakkuk thesis. Deze stelling verklaart productiviteitsverschillen aan

de hand van verschillen in de inzet van productiefactoren. De schaarste aan geschoolde

arbeid en de relatieve overvloed aan natuurijke voorraden in de VS stimuleerden de

substitutie van kapitaal voor arbeid en leidden tot een kapitaalintensief productiepro-

ces gekenmerkt door een snellere arbeidproductiviteitsgroei dan in Europa. Maar de

resultaten van hoofdstuk 2 trekken deze stelling in twijfel, aangezien de nationale con-

text waarbinnen de Duitse industrie functioneerde leidde tot allesbehalve een uniform

niveau van relatieve arbeidsproductiviteit.

Het gegeven dat sommige Duitse industrieen het Amerikaans prestatieniveau dicht

benaderden suggereert dat verschillen in productietechnologie afwezig waren of geen

doorslaggevend effect hadden op de comparatieve arbeidsproductiviteit. Hoofdstuk 3

bestudeert dit vraagstuk. Hierin schat ik niveaus van kapitaalintensiteit in Duitsland

en de VS. Voor het steekjaar 1936 koppel ik de kapitaalintensiteit aan de niveaus van

arbeidsproductiviteit gemeten in hoofdstuk 2. Deze gegevens stellen mij in staat om

‘best-practice frontiers’ te schatten die voor elke bekende combinatie van de productie-

factoren kapitaal en arbeid het potentieel haalbare prestatieniveau aanduidt. Aan de

hand van deze best-practice frontiers ontleed ik arbeidsproductiviteitsverschillen tussen

Duitsland en de VS in twee componenten. Een eerste component wordt gedreven door

het ongelijke arbeidsproductiviteitspotentieel van de verschillende kapitaalintensiteit

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waarmee de landen produceren. Het resterende, tweede, component vormt een indicatie

van de technische efficientie waarmee de productiefactoren ingezet werden.

De decompositie verwerpt de Rothbarth-Habakkuk thesis als een mogelijke verkla-

ring voor het arbeidsproductiviteitsverschil tussen de Duitse en Amerikaans industrie

in de vroege twintigste eeuw. Verschillen in kapitaalintensiteit verklaren slechts een-

derde van het arbeidsproductiviteitsverschil, terwijl tweederde wordt gedreven door een

relatief laag efficientieniveau in Duitsland. In het licht van de snelle inhaalslag in kapi-

taalintensiteit die Duitsland maakte tussen 1909 en 1936 verbaast de beperkte bijdrage

van kapitaalintensiteitsverschillen aan de Duitse arbeidsproductiviteitsachterstand niet.

De best-practice frontiers laten zien dat het arbeidsproductiviteitspotentieel van kapi-

taalintensieve productietechnologieen het snelst toenam. Met andere woorden, Duitse

industrieen zagen zich gedwongen om net als de VS te investeren in kapitaalintensieve

technologieen om niet verder achterop te raken.

Dientengevolge droeg de ontwikkeling in Duitsland de belofte van substantiele ar-

beidsproductiviteitsgroei met zich mee, maar die belofte werd niet tijdens het inter-

bellum ingelost. De literatuur suggereert dat dit effect onlosmakelijk verbonden is met

snelle verandering. Een efficiente adoptie van moderne productietechnologie vergt tijd,

tijd om te leren en het productieproces aan de nieuwe situatie aan te passen. De relatief

lage efficientie in de Duitse industrie wijst niet op een gebrek aan ontwikkeling, maar

was een bijwerking van modernisering en een eerste, noodzakelijke, stap op weg naar

Amerikaanse niveaus van arbeidsproductiviteit in de periode na de Tweede Wereloorlog.

In hoofdstuk 4 verschuift de aandacht naar de eerder genoemde schaarste aan

Duitse data. Het gebrek aan informatie wreekt zich met name bij tijdreeksanalyse, aan-

gezien voor enkele Duitse industrieen data met jaarlijkse frequentie ontbreken. In deze

gevallen kan de toe- en afname van de productie alleen afgeleid worden uit andere, ge-

correleerde, proxyvariabelen. Inmiddels hebben onderzoekers voor dit doel verschillende

proxyvariabelen gebruikt en zijn er meerdere productiereeksen voorgesteld. Deze reeksen

laten over de periode 1914–1925 een andere groeidynamiek zien, wat leidt tot uiteenlo-

pende arbeidsproductiviteitsschattingen. Bijgevolg is het onduidelijk of Duitsland met

haar snelle industriele arbeidsproductiviteitsgroei in de late negentiende eeuw er wel of

niet in was geslaagd de traditionele Engelse hegemonie in Europa te doorbreken.

Hoofdstuk 4 gebruikt een nieuwe analysemethode om een antwoord op deze vraag te

geven. Alle in de literatuur voorgestelde reeksen zijn gecorreleerd met industriele pro-

ductie, maar meten de verandering daarvan met een afwijking die wordt veroorzaakt

door de imperfecte correlatie tussen de proxyvariabelen en productie. Met behulp van

tijdreeksanalyse schat ik de productieverandering door uit de beschikbare tijdreeksen

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een component te filteren dat het gemeenschappelijke dynamisch gedrag van alle reeksen

beschrijft. De geschatte productiegroei impliceert een niveau van Duitse arbeidsproduc-

tiviteit voor het jaar 1907 van ongeveer 15% boven het VK.

Hoewel Duitsland nog voor het uitbreken van de Eerste Wereldoorlog het VK had

gepasseerd, was de marge klein vergeleken met de grote achterstand ten opzichte van

de VS waartegen beiden landen aankeken. Dit roept de vraag op of de industrie in

Europese landen in de periode 1870–1914 convergeerde naar een gemeenschappelijk ar-

beidsproductiviteitsniveau? Convergentie tussen Europese landen werd in de periode

1870–1914 gestimuleerd door een mondiale globalisering en sterke integratie van Eu-

ropese markten. Klassieke handelstheorie voorspelt dat in deze situatie verschillen in

relatieve factorkosten, en dus de keuze van productietechnologie, verdwijnen. Daarnaast

leden Europese landen mogelijkerwijs onder vergelijkbare groeibarrieres, zoals een ge-

brek aan schaalvoordelen door een heterogene voorkeur voor producten.

Hoofdstuk 5 bestudeert de mogelijkheid van convergentie binnen Europa door het

industriele arbeidsproductiviteitsniveau te vergelijken tussen 6 geındustrialiseerde lan-

den, namelijk Amerika, Duitsland, Engeland, Franrijk, Nederland en Zweden, in het

steekjaar 1909. Daaruit blijkt dat alle Europese landen opereerden op een niveau ver

onder dat van Amerika, maar binnen Europa wees niets op een gemeenschappelijk ar-

beidsproductiviteitsniveau. Wanneer aan de hand van tijdreeksen de comparatieve in-

dustriele arbeidsproductiviteitv an de verschillende landen wordt teruggetrokken blijkt

dat de variatie in prestatieniveaus rond een constant niveau schommelde in de periode

1870–1909. In deze periode groeiden Europese welvaartsniveaus, gemeten in bruto bin-

nenlands product per hoofd van de bevolking, wel naar elkaar toe. Gezien het gebrek

aan convergentie in industriele arbeidsproductiviteit werd de afnemende variatie in het

welvaartsniveau tussen Europese landen klaarblijkelijk gedreven door andere factoren.

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