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Essays on Export SophisticationAbdulbasit AydinClemson University, [email protected]

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ESSAYS ON EXPORT SOPHISTICATION

A Dissertation Presented to

the Graduate School of Clemson University

In Partial Fulfillment of the Requirements for the Degree

Doctor of Philosophy Economics

by Abdulbasit Aydin December 2016

Accepted by: Dr. Scott L. Baier, Committee Chair

Dr. Michal M. Jerzmanowski Dr. Sergey Mityakov Dr. Robert F. Tamura

ii

Abstract

This dissertation consists of three chapters. These chapters use the export

sophistication index introduced by Hausmann, Hwang, & Rodrik, (2007) that the

sophistication of a country’s export bundle could have significant implications for

economic growth.

The first chapter investigates if countries that acquire the capability of

exporting more sophisticated products achieve higher growth rates. By examining

69, the results show that export sophistication is positively correlated with GDP

per worker. While export sophistication is highly correlated with income per

worker, some countries stand out as having relatively higher sophisticated scores

than predicted by their development levels. Those countries have succeeded in

transforming their exports from primary goods to more sophisticated commodities.

As a result, their export sophistication scores increased significantly over the years.

The objective of the second chapter is to use standard growth accounting

techniques and applies these to the export sophistication to analyze the factor content

of export and its sophistication level. I find that the exported products contain less

physical capital, human capital, and TFP than the countries’ average level of these

factors. Also, the results indicate that countries’ export sophistication growth has

important consequences for subsequent economic growth.

iii

The third chapter investigates the direction of causality between export growth

and total factor productivity as well as the causality between export growth and GDP

growth. The regression results show that TFP growth has significant impact in export

growth but export growth does not play an important role in TFP growth. The causality

is unidirectional, running from total factor productivity to exports.

Acknowledgments

First and foremost, I would like to offer a sincere gratitude and to thank the most

Merciful and All-Knowing God for providing me countless opportunities including this

success. Then, I would like to thank all the members of my dissertation committee for

their contributions to this project; particularly, my committee chair and advisor, Prof.

Scott L. Baier, for his invaluable help, editing, discussing, and mentoring. Without it,

completing this dissertation would have been undoubtedly much difficult.

Lastly, to my parents, M. Emin and Meryem, and my oldest brother,

Dr. Necati Aydin, and my wife for always are supporting me in my education.

iv

v

Table of Contents

Page

Title Page .......................................................................................................................... iAbstract ........................................................................................................................... iiAbstract .......................................................................................................................... iiiAcknowledgments ........................................................................................................ ivList of Tables ................................................................................................................. viiList of Figures .............................................................................................................. viii1 Export Sophistication Level and Economic Growth .................................................. 91.1 Introduction......................................................................................................................................................91.2 Export...............................................................................................................................................................111.3 ExportSophistication................................................................................................................................151.4 DataDescription..........................................................................................................................................171.5 MeasuringExportSophistication.........................................................................................................191.6 ProductSophisticationScores...............................................................................................................241.7 CategoricalSophisticationScores........................................................................................................261.8 CountrySophisticationScores...............................................................................................................261.9 RelationBetweenPerWorkerGDPandExportSophisticationLevel..................................281.10 Conclusion...................................................................................................................................................30References ........................................................................................................................ 312 Growth Accounting and Export Sophistication ..................................................... 332.1 Introduction.................................................................................................................................................332.1 Data...................................................................................................................................................................352.2 GrowthAccountingFramework...........................................................................................................362.3 GrowthinTotalFactorProductivity,PhysicalCapitalandHumanCapital.......................382.4 ExportSophisticationGrowth...............................................................................................................422.5 GrowthAccountingintermofExportSophistication.................................................................432.6 ExportSophisticationofPhysicalCapital,HumanCapital,andTFP.....................................462.7 CurrentSophisticationGrowthAgainsttheFutureEconomicGrowth...............................502.8 Conclusion......................................................................................................................................................52References ........................................................................................................................ 533 Export Growth and Total Factor Productivity ...................................................... 553.1 Introduction...................................................................................................................................................553.2 Data...................................................................................................................................................................573.3 TheEffectofTotalFactorProductivityonExport........................................................................583.4 TotalFactorProductivityGrowthEstimation................................................................................603.5 ModelSpecification....................................................................................................................................62

vi

Table of Contents (Continued)

3.6 Conclusion......................................................................................................................................................64APPENDICIES ............................................................................................................... 65A. CHAPTERONEAPPENDIX.........................................................................................................................66B. CHAPTERTWOAPPENDIX........................................................................................................................84References ........................................................................................................................ 96

vii

List of Tables

Table 1.1: Share of High and Medium-High Tech Products Exported by 3 Country Groups to the World Total Exported High and Medium-High Product.......................14

Table 1.2: RCA: Number of products with RCA>1 for Top 20 Sophisticated Countries in 2010 & GDP: Ranking for GDP Per Worker........................................................................20

Table 1.3: Most Sophisticated 20 Products in 2010......................................................................25

Table 1.4: Least Sophisticated 20 Products in 2010.....................................................................25

Table 1.5: Categorical Sophistication Scores..................................................................................26

Table 1.6: Countries Export Sophistication Ranking...................................................................27

Table 2.1:Production Factors Average Growth 1980-2010........................................................39

Table 3.1: Correlation Matrix of Selected Variables....................................................................59

Table 3.2: Export Growth Regression...............................................................................................63

Table 3.3: TFP Growth Regression....................................................................................................63

viii

List of Figures

Figure 1.1:Trade (% of GDP)...............................................................................................................11

Figure 1.2: Log-Income Per Worker (1970 versus 2010)...........................................................29

Figure 1.3: Sophistication Scores (1970 versus 2010).................................................................29

Figure 2.1:Relation Between TFP Growth and Physical Capital Growth.............................40

Figure 2.2: Relation Between TFP Growth and Human Capital Growth..............................41

Figure 2.3: Relationship Between Export Sophistication of Physical Capital Growth and Current Physical Capital Growth...............................................................................................47

Figure 2.4: Relationship Between Export Sophistication of Human Capital Growth and Current Human Capital Growth.................................................................................................48

Figure 2.5: Relationship Between Export Sophistication of TFP Growth and Current TFP Growth................................................................................................................................................50

Figure 3.1: Relation Between TFP Growth and Export Growth...............................................60

1

Chapter 1

1 Export Sophistication Level and Economic Growth

1.1 Introduction

One of the fundamental propositions of international trade theory is trade creates

opportunities for a country and its people to obtain a higher real income. Many

economists have conducted empirical studies to look at impacts of the international trade

on the growth of GDP.

Frankel and Romer (1999) analyze the impact of international trade on the

standards of living. They find that a one percent increase in the ratio of trade to GDP

increases income per capita by at least one and half percent. They relate the raise of

income to the increase in the accumulation of physical and human capital as well as

increasing productivity of output. Irwin and Terviö (2002) evaluated Frankel-Romer

(1999) finding to see if there are systematic differences between the OLS and

instrumental variables estimation of trade’s impact on income. They used a geography-

based instrument to control endogeneity of trade and find that countries that trade more

have higher incomes even after controlling for endogeneity.

2

In their analysis Dollar and Kraay (2004) look at the relation between trade and

growth for a group of developing countries in terms of increases in trade-to-GDP ratio

from 1970 to 1990. They labeled one group as “the globalizing group” and found that the

ratio of trade-to-GDP doubled in this group over the 20 years in response to their policies

to reduce tariff levels during the same period. They compared the “globalizing group”

with rest of the developing countries, which had reduction in trade openness in the same

period, and find that the later group’s growth rate declined on the average from 3.3% to

0.8% per year for 1970s and 1980s respectively. It has reached to 1.4% growth rate per

year during 1990s. On the other hand, the globalizing group’s growth rates continuously

increased from 2.9% to 3.5% and to 5.0% per year for 1970s, 1980s, and 1990s

respectively. They conclude that an increase in international trade volume has a strong

positive marginal impact on growth rate of GDP such that developing countries would be

able to catch up the rich countries by taking supportive actions to increase their

international trade.

Many countries that experienced rapid growth in per capita GDP after 1970s also

had a high share of trade in their GDP. For example, Hong Kong and South Korea had

more than doubled their share of trade in GDP from 1970 to 2010. Hong Kong’s share of

trade in GDP rose from 178.7% to 432.9% and South Korea’s rose from 35.4% to 95.7%.

In the same period, Japan, Singapore, and Thailand experienced more than 50 percent

increase in the share of trade in GDP for. Also growth rate of GDP per capita for those

countries have been steadily above of 4% on the average since 1970s.

3

Figure 1.1:Trade (% of GDP)

1.2 Export

Exports can be an important factor for the growth of an economy. Increasing export

helps to reduce the impact of external shocks on the domestic economy and to accelerate

integration of the country to the rest of the world economy. The growth of exports can

stimulate economic growth through technological spillovers, learning by doing, human

capital accumulation and other externalities. Romer (1990) suggests that as economies

become more open to international trade, the number of specialized inputs and growth

rates increase. Helpman and Krugman (1985) indicate, “Export expansion and openness

to foreign markets is viewed as a key determinant of economic growth because of the

positive externalities it provides. For example, firms in a thriving export sector can enjoy

the following benefits: efficient resource allocation, greater capacity utilization,

exploitation of economies of scale, and increased technological innovation stimulated by

0%50%100%150%200%250%300%350%400%450%500%

1970 1975 1980 1985 1990 1995 2000 2005 2010

Trade(%ofGDP)

HongKongSAR,China

Japan

Korea,Rep.

Thailand

Singapore

4

foreign market competition.''

Feder (1982) calculates that export expansion contributed more than 2.2% to the

growth of the semi-industrialized countries. He finds that the gains are substantially due

to beneficial externalities affecting the non-export sector. Marin (1992) finds that exports,

productivity, and the terms of trade move together in the long run. He claims that an

“outward looking regime” support productivity performance of both developing and

developed countries. Kravis (1970) points out that rises in export causes increase in

national income through both multiplier effects and induced investment that occurring

throughout the economy.

The trade increases total factor productivity and efficient allocation of resources for

less developed and developing countries by making more capital goods available. With

less (human and physical) capital, less developed countries will likely produce goods that

are “less sophisticated.” Accessing capital goods is an important factor for the production

of sophisticated goods. Developed countries produce most of capital goods and

sophisticated goods. Eaton and Kortum (2001) find eight countries produce almost 80

percent of capital goods in the world. For less developed countries, trade can provide

access to capital goods at lower cost than if provided at home. In the absence of trade,

those countries would produce relatively more capital goods at which they don’t have

comparative advantage. As result, they would have inefficient production and less

income. Mutreja, Ravikumar and Sposi (2014) find that in the absence of capital goods’

trade, poor countries would have 11 percent less income because of relying on domestic

5

production for capital goods.

Several Asian countries experienced high growth in per capita GDP between 1970s

and 1990s. These countries are mainly Thailand, Singapore, Korea, Japan, and Hong

Kong. Lee & Huang (2002) outline the reason of the rapid growth of Asian economies is

because of adopting “outwardly-oriented development strategy” which let the expansion

of their exports. Experiences of Asian economies could be viewed as examples of the

impact of the export into economic growth and development. The above-mentioned

countries exported a high share of its GDP while had exceptional economic growth from

1970s to 1990s. Most of them increased the share of exports of goods and services to

GDP by more than 50 percent. Thailand had 15% exports of goods and services as share

of GDP in 1970 and 71.3% in 2010. South Korea jumped from 12.9% to 49.4% and

Hong Kong from 93.2% to 219.4%.

The pattern of exports is shifting towards more technology-intensive goods. Exporting

technology-intensive products has significant contribution to the development of an

economy. Lall (2000) finds the share of exports for primary products are steadily

declining while the share of technology-intensive products are growing. I find similar

result with Lall (2000). I calculate the ratio of export for high-tech products to the export

of all products for 199 countries for the period 1970-2000. I find that the share of world

export of high-tech products increased from 39% in 1970 to 57% in 2000. That is, almost

50% increase in the world export of high-tech products in three decades.

6

Based on the World Bank’s income classification 1 for the world's economies, I

calculate the share of high and medium-high tech products exported by 3 country groups

to the world total exported high and medium-high products. As presented in Table 1,

high-income countries exported 92% of the world’s high-tech products in 1990 but only

75% in 2010. There is a significant increase in the export of high-tech products by upper-

middle class countries from 4% in 1990 to 22% in 2010. There is almost no change in the

lower and lower middle-income countries export pattern.

Table 1.1: Share of High and Medium-High Tech Products Exported by 3 Country Groups to the World Total Exported High and Medium-High Product

2010 2000 1990

High-income countries export for high and medium-high technology products/ The world total high and medium-high products export

0.75

0.81 0.92

Upper Middle-income countries export for high and medium-high technology products/ The world total high and medium-high products export

0.22 0.12 0.04

Lower and Lower Middle-income countries export for high and medium-high technology products/ The world total high and medium-high products export

0.03 0.07 0.04

1The World Bank income classifications by GNI per capita in 2013 are as follows: Low income: $1,035 or less Lower middle income: $1,036 to $4,085 Upper middle income: $4,086 to $12,615 High income: $12,616 or more

7

Why did a major shift take place from developed countries to developing

countries for the export of high-tech products in the last two decades? There might be

several explanatory factors for the shift. These factors are infrastructure, natural resource,

logistics, marketing, �and government policies such as trade restrictions and subsidies,

trading blocs and trade preferences. Another important factor changing the direction of

export is trade fragmentation. Trade fragmentation gives the opportunity to separate the

core technical characteristics and production processes of a product. Lall, Albaladejo and

Zhang (2004) find that about 45 percent of world electronics exports are made by

developing countries because electronics is highly fragmental. As result of trade

fragmentation developing countries have become some of the largest exporters of high-

tech products and more sophisticated products.

In this chapter, I analyze the export bundles of 69 countries by using sophistication

index created by Hausmann, Hwang and Rodrik (2007), which estimate the average

“income level of a country’s exports.” I calculate a measure to find a country level of

export sophistication and relate this to current and future growth performance. I analyze

if countries that acquire the capability of exporting more sophisticated products achieve

higher growth rates.

1.3 Export Sophistication

In order to analyze trade pattern and to apply and test theories of trade in the

literature, trade data are usually classified by various product characteristics. For

instance, products are classified by factor contents in the Heckscher-Ohlin theory.

8

Location theory of economic activity focuses on weight and transportability of goods.

Each way of classifications provides different approaches to analyze the trade pattern. I

use ‘product sophistication’ as a means of analyzing product characteristics. This method

of classifying is based on data for exports of each product and per worker level of income

of exporting countries. The product characteristics are derived from the characteristics of

the exporting countries. More sophisticated a product means higher the average income

of its exporters. When exporting a product, countries reveal their comparative advantage

and productivity through the characteristic of that product. For instance, when a product

exported by a high-income country, it shows that high wage producers compete in the

global market. Therefore, high income level countries expected to export more

sophisticated products which carriers high wage characteristics that include high

technology as an important input along with other factors. Thus, more sophisticated

products are in general expected to reflect high technology, high skills, and other

complex factors.

While technology is a major factor for product sophistication but it cannot be

separated from the other factors. Hausmann, Hwang and Rodrik (2007) argue that “what

a country exports matters” and show that their measure of economic sophistication is a

good predictor of future growth. They consider the impact of all products in terms of their

consequences for economic performance is not different. They classify the products

based on which countries mainly export and categorize as “rich-country products” if

exported by rich countries and “poor-country products” if exported mainly by poor

countries. They argue that countries that specialize in “rich-country products” most likely

9

grow faster than countries that specialize in other goods. They estimate that a 10 per cent

increase of the average sophistication cause growth by half a percentage. Specializing in

more sophisticated products will bring higher growth than specializing in others.

R. Anand, S. Mishra, and N. Spatafora (2012) indicate “Sophisticated sectors are

more likely to act as a catalyst for broad-based economic growth” and export

sophistication is a key component for the economic growth in the developing economies.

After controlling for financial development, human capital, and external liberalization,

they find output growth is associated with initial export sophistication.

Fortunato P and Razo C (2014) indicate “successful developing countries

progressively change their production structure, replacing low value added goods with

more sophisticated activities and a wider array of products.” They find that most

countries get stuck in the intermediate levels of export sophistication since they fail to

increase the sophistication of their production and export structures. P. Fortunato, C.

Razo and K. Vrolijk (2015) find “substantial differences in terms of average

sophistication, especially for less developed countries which would have found their

economies much closer to the sophistication frontier if they had been able to move to the

potential export basket.”

1.4 Data Description

I measure the products’ sophistication level, LNYPROD! k , for the period 1970-

2010 by using UN COMTRADE data that provides bilateral trade for 4 digits product

10

level for over 600 commodities. I construct the LNYPROD measure for a consistent

sample of 69 countries.

I use export data by industry and information on GDP per worker, capital per

worker, and human capital per worker. The UN COMTRADE database has detailed

cross-country information for the exported products by using a comparable standardized

classification across time. Using UN COMTRADE data have some limitations along with

its advantage. The data are not always complete. Some countries use different

classification systems and once the data are converted into the 4-digit SITC system,

information might get lost. The data do not include services but contain only goods.

There are two main advantages to classify the products on the basis of the sophistication

index created by Hausmann et al. (2007). First, they are defined at a highly disaggregated

level that allows analyzing in details. Second, it is based on outcomes not priori

assumptions such as agricultural products are less sophisticated than manufacturing

products.

I categorized 626 products according to Hatzichronoglou (1997)’s method. The

products are divided into 4 categories based on technological intensity: i) high-

technology, ii) medium-high-technology, iii) medium-low- technology and iv) low-

technology (see Annex). The products are classified by using the Standard International

Trade Classification, SITC Rev.1. The construction of classification of industries is based

on their technology intensities where direct and indirect R&D intensities were used as

criteria.

11

Low-technology product examples are mainly paper printing, textile and clothing,

beverages, tobacco, wood and furniture. Medium-low-technology products example

includes rubber and plastic, shipbuilding, non-ferrous metals, non-metallic mineral

products, fabricated metal products, petroleum refining and ferrous metals.

Medium-high and high technology products have advanced technologies with

high R&D and high levels of specialized technical skills. The product examples for

medium-high technology are scientific instruments, motor vehicles �, electrical machinery,

chemicals, other transport equipment, and non-electrical machinery. High technology

products example are aerospace�, computers, office machinery, electronics-

communications, and pharmaceuticals

1.5 Measuring Export Sophistication

We can not distinguish directly from the export sophistication measure of a

product or a country about its fundamental features such as technology embedded,

specialized skills required to produce it, and R&D investments. By the measure of export

sophistication, it is aimed to find out from the observed trade patterns, which products

require a high level of development in order to export.

The export sophistication measure assumes that each good 𝑘 that a country can

produce and export has an essential level of sophistication associated to it. I construct one

index for the products, 𝐿𝑁𝑌𝑃𝑅𝑂𝐷! 𝑘 , and another for sectors, 𝑙𝑛𝑦!. The indexes are

constructed from the weighted log-average of the income levels of good 𝑘 ’s exporters,

12

where the weights correspond a proportionate to revealed comparative advantage (RCA)

of each country i in good k. Using of RCA is to prevent the impact of country size on the

ranking of products.

I analyze top 20 sophisticated countries RCA progress for a period of ten years

from 1970 to 2010. I use Balassa’s (1965) measure of relative export performance to

construct the index of revealed comparative advantage (𝑅𝐶𝐴!"). The index is defined to

measure a country’s share of world export for a product divided by that product’s share

of total world exports. It is calculated as follows:

𝑅𝐶𝐴!" = (𝑋!"/𝑋!")/(𝑋!/𝑋!)

Where

𝑋!" = Country i’s export of good j

𝑋!" = World export of good j

𝑋! = Country i’s total export

𝑋! = World total export

The interpretation of RCA is quite simple. A country has a revealed comparative

advantage for a product if RCA for that product is greater than unity. Table 2 shows the

number of products with RCA>1 for the top 20 sophisticated countries in 2010.

Table 1.2: RCA: Number of products with RCA>1 for Top 20 Sophisticated Countries in 2010 & GDP: Ranking for GDP Per Worker

2010 2000 1990 1980 1970

RCA GDP RCA GDP RCA GDP RCA GDP RCA GDP

13

Ireland 78 14 79 7 138 23 154 32 139 37Switzerland 113 20 146 20 151 6 164 8 157 7Japan 134 17 124 18 125 8 149 30 170 29Korea,Rep. 94 13 117 26 127 37 129 74 84 69Hungary 147 60 143 55 3 58 3 67 1 73Singapore 71 4 77 10 105 27 103 37 103 49HongKong 112 3 138 6 137 13 122 34 96 58Germany 234 23 243 22 261 16 269 16 252 22Malta 28 29 30 30 55 35 63 52 65 104France 252 16 261 8 246 4 270 11 282 21Mexico 124 61 126 45 122 47 76 39 127 35Thailand 163 69 143 72 132 95 101 114 76 119UnitedStates 236 2 236 2 193 2 210 7 201 8Panama 105 70 73 67 70 74 47 44 20 62UnitedKing. 172 11 174 14 220 19 222 18 246 24Austria 207 12 192 9 191 11 213 22 200 23Malaysia 120 44 85 48 86 64 53 89 48 95Barbados 72 55 76 53 66 52 69 51 61 44Israel 83 9 89 16 100 7 107 28 89 18Italy 247 22 234 5 186 5 196 14 205 20

There is a significant decrease in the number of products with RCA>1 among

some of the developed countries. Ireland had 139 products with RCA>1 in 1970 but only

78 in 2010. Switzerland’s product numbers decreased from 157 to 113 from 1970 to

2010. United Kingdom, France and Germany are other developed countries that have

decline in the number of revealed comparative advantage products greater than one.

Malaysia, Thailand, South Korea, and Hungary increased their number of products with

RCA>1 since 1970. Hong Kong also increase its product numbers until 2000 but only

slight decrease from 2000 to 2010.

First, I define export sophistication of good 𝑘:

14

lnyprod! k = x!!(k)x!! k!

lny! !

x!! k : Country i’s export for product k, in p category, to the world

x!! k! : The world’s export for product k, in p category

𝑙𝑛𝑦!: The per worker level of income of country i, measured as the real log-GDP per

worker, in PPP. If mainly poor countries export a good, then it will have a lower

lnyprod! k , on the other hand, if mainly rich countries export it, then it will have a

higher lnyprod! k . The higher its LNYPROD!(k), log-average export value of a good,

the more sophisticated the product is. I also changed 𝑙𝑛𝑦! as capital per worker and

human capital per worker to see if they would make any different in the results.

Second, I define export sophistication for category p that accounts for four

categories mentioned above.

lny! = ( x!

!(k)!∈!

( x!! k )!∈!!

lny! !

After rearranging this can be written as

lny! = ( x!

!(k)!

( x!! k )!∈!!

x!! k!

x!! k!

lny! !∈!

Which is

lny! = ( x!

!(k)!

( x!! k )!∈!!

lnyprod! k !∈!

Or

15

lny! = !!(!)!!

lnyprod! k!∈! Which is the export

sophistication for categories where:

x!(k): Country j’s export in p category

x!: World’s export in p category

lnyprod! 𝑘 : Export sophistication of good k

lny!is the sophistication level for each category mainly; high-technology, medium-high-

technology, medium-low- technology and low-technology.

Finally, I define the export sophistication for each country:

lnesy! =x!!

x!lny!

!

Which is equal to

lnesy! =!!!

!!lny! + !!

!"

!!lny!" +!!

!"

!!lny!" +!!

!

!!lny!

x!!: Country i’s sum of exports to the world in p category

x!: Country i’s sum of exports to the world lny!:The export sophistication for categories Where p={L, ML, MH, H} LNESY! is sophistication level for country i’s export bundle which is the average

level of sophistication of its export basket. It is the log-weighted sum of the

sophistication levels for each exported good k, 𝐿𝑁YPROD! k , where the weights are the

shares of each good in the country’s total exports. LNESY! measures the degree of

specialization of a country in high sophisticated products. Since 𝐿𝑁YPROD! k is

16

calculated by using GDP per worker of the exporting country, rich countries naturally

will have a high LNESY! and poor countries will have low LNESY!. This is part of the

structure of sophistication in order to capture rich country goods and poor country goods.

We will see that there are variations in this relationship. Some of countries have higher

export sophistication than the others that have similar levels of GDP per worker.

1.6 Product Sophistication Scores

There is a general increase in sophistication scores of products over time. The

product sophistication ranking is not depending on how much advanced technology is

used, but in general, high-tech products score higher. When looking the sophistication

scores, I find that most of the high and medium high tech products have relatively high

scores and low-tech products have relatively low scores. Table 3 and 4 show the most

and the least sophisticated 20 products in 2010. The rest of products’ scores are presented

in the appendix. There is no high-tech product in the bottom 30 products sophistication

from 1970 to 2010 with the exception of two products in 1980. On the other hand, there

are low-tech products on the top 10 products sophistication for all periods. Some of these

low-tech products such as paper and lard have high sophistication score because either

their raw materials are located in rich countries or these countries are more efficient at

producing them. The reason some high-tech products have low sophistication scores

could be because of the fragmentation in the production process. Lall, Weiss and Zhang

(2006) point out that some high technology products that have low sophistication scores

could be because of production processes can be fragmented and therefore parts of the

production process located to lower wage countries. We see some low-tech products have

17

high sophistication scores. This could be because of natural resource, trade distortions,

logistical needs to be near main markets, or other things that poor countries can not

access.

Table 1.3: Most Sophisticated 20 Products in 2010

Rank SITC Code Product Category

1 2112 Calf skins and kip skins L 2 9510 Firearms of war & ammunition H 3 2219 Flour & meal of oil seeds,nuts,kernels, fat L 4 8960 Works of art,collectors pieces H 5 0913 Lard & other rendered piq & poultry fat L 6 2431 Railway sleepers ties L 7 2111 Bovine & equine hides excl. Calf & kip skins L 8 2511 Paper waste and old paper L 9 5152 Stable isotopes and their compounds MH 10 4221 Linseed oil L 11 2120 Fur skins,undressed L 12 5151 Radioactive chem.elements & isotopes/comp MH 13 7261 Electro medical apparatus H 14 6413 Kraft paper and kraft paperboard ML 15 5415 Hormones H 16 2512 Mechanical wood pulp L 17 6832 Nickel and nickel alloys, worked ML 18 6812 Platinum,unworked or partly worked ML 19 5153 Compounds & mixtures, uranium MH 20 0015 Horses,asses,mules and hinnies L

Table 1.4: Least Sophisticated 20 Products in 2010

Rank SITC Code Product Category

579 6521 Cotton fabrics, woven, grey, not mercerized ML 580 2713 Natural phosphates, whether or not ground L 581 4224 Palm kernel oil L

18

582 0611 Raw sugar,beet & cane L 583 4214 Groundnut /peanut/ oil L 584 6575 Carpets, carpeting and rugs, knotted ML 585 2634 Cotton,carded or combed L 586 2212 Copra, ex.flour and meal L 587 2654 Sisal and other fibres of the agave family L 588 6121 Machine leather belting & other articles ML 589 6986 Springs and leaves for srings/iron steel ML 590 4225 Castor oil L 591 6534 Jute fabrics, woven ML 592 4223 Coconut copra oil L 593 2640 Jute & waste L 594 2658 Vegetable textile fibres,nes and waste L 595 2655 Manila fibre and manila tow and waste L 596 0721 Cocoa beans,raw or roasted L 597 2213 Palm nuts & kernels L 598 2860 Ores & concentrates of uranium & thorium L

1.7 Categorical Sophistication Scores

I divided products into four categories according to their technology intensity.

Mainly these categories are high-technology, medium-high-technology, medium-low-

technology and low-technology. Sophistication score for each category shows increase

for each period. As presented in table 5, I found that the high-tech sector has the highest

scores and the low-tech sector has the lowest scores for all periods. Categorical scores

show that on average high tech products have higher sophistication scores.

Table 1.5: Categorical Sophistication Scores

Category 2010 2000 1990 1980 1970 L 10.44938 10.3967 10.23508 10.10961 9.88676 ML 10.41581 10.39208 10.25607 10.13232 10.03992 MH 10.62315 10.58125 10.43701 10.38418 10.15358 H 10.6306 10.57676 10.46977 10.37107 10.18609

19

1.8 Country Sophistication Scores

Export sophistications of countries are highly correlated with their income but

there are some interesting discrepancies in the scores. Some countries such as Hungary,

Hong Kong, Mexico, and Thailand have export sophistication levels that are much higher

than would be predicted based on their incomes. While these countries have less income

per capita than United States and United Kingdom, their sophistication levels are higher.

When looking the performance for countries’ export sophistication scores, some of the

countries showed significant improvements. Singapore ranked 26 in 1970 but jumped to

6th place top ranking in 2010. Hungary jumped 63 levels up from ranking 68 in 1970 to

ranking 5 in 2010. Malaysia, Malta, Mexico, and Thailand are other countries with high

improvements in their rankings. On the other hand, there is decline in the ranking of

some developed countries such as Italy, United Kingdom, and Austria. The increase in

the sophistication levels of these countries could be because of either increasing the

quality of existing products produced or adding more sophisticated products to their

manufacturing.

Table 1.6: Countries Export Sophistication Ranking

Country 2010 2000 1990 1980 1970 Ireland 1 3 9 14 33 Switzerland 2 6 2 2 1 Japan 3 2 1 1 3 Korea, Rep. 4 8 15 17 21 Hungary 5 11 65 69 68 Singapore 6 1 7 13 26 Hong Kong 7 16 16 10 12

20

Germany 8 10 3 3 2 Malta 9 5 4 22 23 France 10 13 11 7 8 Mexico 11 12 23 33 27 Thailand 12 19 24 43 51 United States 13 7 5 5 5 Panama 14 54 50 59 43 United Kingdom 15 14 6 6 4 Austria 16 20 12 11 9 Malaysia 17 9 19 31 36 Barbados 18 29 26 20 31 Israel 19 26 17 16 18 Italy 20 21 14 9 6

1.9 Relation Between Per Worker GDP and Export Sophistication Level

Figure 2 & 3 show log income per worker and sophistication level for countries in

1970 and 2010, respectively. It shows that there is a high variation in EXPY score among

countries. Some countries have succeeded in transforming their exports from primary

goods to more sophisticated commodities. As a result, their export sophistication scores

increased significantly over the years. For example, in 1970 Hungary and Ecuador had a

similar level of export sophistication, 9.88 (19,668) and 9.89 (19,752) respectively. By

the year 2010, Ecuador’s score reached 10.44 (34,265), while Hungary’s grew to 10.57

(39,093). Also, in 1970 Pakistan and South Korea had similar scores, 9.98 (21,748) and

9.99 (21,951) respectively. In 2010, South Korea increased its score to 10.57 (39,308)

while Pakistan reached 10.45 (34,790). Similarly, Colombia’s score increased from 9.91

(20,301) to 10.45 (34,585); whereas Thailand managed to raise its score from 9.91

(20,228) to 10.55 (38,223) during the same period of time.

21

Figure 1.2: Log-Income Per Worker (1970 versus 2010)

Figure 1.3: Sophistication Scores (1970 versus 2010)

Burkina Faso

Central African Republic

Benin

Jordan

Mali

Malta

Barbados

TogoMalawi

Paraguay

Uruguay

Fiji

Cyprus

Panama

Madagascar

Iceland

Senegal

Honduras

Nicaragua

Tunisia

Ecuador

El SalvadorBahrain

Cameroon

GuatemalaSri Lanka

Ghana

Morocco

Turkey

Greece

Thailand

Pakistan

ColombiaEgypt, Arab Rep.

IsraelKorea, Rep.

Portugal

Algeria

Ireland

PeruIndonesia

Philippines

Mexico

New Zealand

Chile

Singapore

Malaysia Argentina

India

Finland

Spain

Hong Kong, China

Brazil

AustriaNorway

Venezuela

DenmarkAustraliaSwitzerlandSwedenItaly CanadaFranceJapanUnited KingdomGermany

United States

Burkina Faso


Benin

Jordan

Mali

Malta

BarbadosHungary

TogoMalawi

Paraguay

Uruguay

Fiji

Cyprus

Panama

Madagascar

Iceland

Senegal

Honduras

Nicaragua

Tunisia

Ecuador

El SalvadorBahrain

Cameroon

GuatemalaSri Lanka

Ghana

Morocco

Turkey

Greece

Thailand

Pakistan


IsraelKorea, Rep.

Portugal

Algeria

Ireland

PeruIndonesia

Philippines

Mexico

New Zealand

Chile

Singapore

Malaysia Argentina

India

Finland

Spain

Hong Kong, China

Brazil

AustriaNorway

Venezuela


United States

Burkina Faso


Benin

Jordan

Mali

Malta

Barbados

TogoMalawi

Paraguay

Uruguay

Fiji

Cyprus

Panama

Madagascar

Iceland

Senegal

Honduras

Nicaragua

Tunisia

El SalvadorBahrain

Cameroon

GuatemalaSri Lanka

Ghana

Morocco

Turkey

Greece

Thailand

Pakistan


IsraelKorea, Rep.

Portugal

Algeria

Ireland

PeruIndonesia

Philippines

Mexico

New Zealand

Chile

Singapore

Malaysia Argentina

India

Finland

Spain

Hong Kong, China

Brazil

AustriaNorway

Venezuela


United States

Burkina Faso


Benin

Jordan

Mali

Malta

Barbados

TogoMalawi

Paraguay

Uruguay

Fiji

Cyprus

Panama

Madagascar

Iceland

Senegal

Honduras

Nicaragua

Tunisia

Ecuador

El SalvadorBahrain

Cameroon

GuatemalaSri Lanka

Ghana

Morocco

Turkey

Greece

Thailand

Pakistan


IsraelKorea, Rep.

Portugal

Algeria

Ireland

PeruIndonesia

Philippines

Mexico

New Zealand

Chile

Singapore

Malaysia Argentina

India

Finland

Spain

Hong Kong, China

Brazil

AustriaNorway

Venezuela


United States7

89

1011

ln20

10

7 8 9 10 11ln1970

IrelandSwitzerland

JapanKorea, Rep.Hungary

Singapore Hong Kong, China GermanyMalta FranceMexicoThailand United StatesPanama

United KingdomAustriaMalaysiaBarbados Israel ItalyCyprusPhilippines Spain SwedenJordanDenmarkMorocco Finland

Tunisia Portugal CanadaTurkeyIndiaArgentina

GreeceBrazilSenegalTogoMaliMadagascarHonduras El SalvadorGuatemalaNew ZealandEgypt, Arab Rep.Indonesia UruguayGhanaSri Lanka AustraliaFijiBeninMalawiNicaragua PakistanBurkina Faso Central African RepublicIceland BahrainPeru NorwayColombia ChileCameroonParaguayEcuador

VenezuelaAlgeria

10.4

10.4

510

.510

.55

10.6

Soph

istica

tion

Scor

es_2

010

9.9 9.95 10 10.05 10.1 10.15Sophistication Scores_1970

22

1.10 Conclusion

The findings show that there are increases in sophistication scores of both the

products and countries over time. In general, most of the high and medium high tech

products have relatively high scores and low-tech products have relatively low scores.

The results show that export sophistication is positively correlated with GDP per

worker. While export sophistication is highly correlated with income per worker, some

countries stand out as having relatively higher sophisticated scores than predicted by their

development levels. Those countries have succeeded in transforming their exports from

primary goods to more sophisticated commodities. As a result, their export sophistication

scores increased significantly over the years.

23

References B. Kravis., 1970. Trade as a Handmaiden of Growth, Economic Journal, pp.850-72. Dollar, D. & Kraay, A., 2004. Trade, Growth, and Poverty. The Economic Journal, 114(493), pp. F22–F49. Eaton, Jonathan and Samuel Kortum., 2001. Trade in Capital Goods. European Economic Review., 45:1195–1235. Feder G., 1982. On Exports and Economic Growth. Journal of Development Economics.,12; 59_73. Fortunato P and Razo C., 2014. Export Sophistication and the Middle Income Trap. In: Kozul-Wright R, Nu ̈bler I and Salazar J, eds. Transforming Economies: Making industrial policy work for growth, jobs and development. Geneva, ILO. Frankel, J. & Romer, D., 1999. Does trade cause growth? American Economic Review , 89 (3). 379–399. Hausmann, Ricardo, Jason Hwang and Dani Rodrik., 2007. What You Export Matters, Journal of Economic Growth, 12, 1-25. Helpman E, Krugman., 1985. P. Market Structure and Foreign Trade. Cambridge, MA: MIT Press. Irwin, D. & Terviö, M., 2002. Does trade raise income?: Evidence from the twentieth century. Journal of International Economics, 58(1), pp. 1–18. Lall, S., 2000. The technological structure and performance of developing country manufactured exports, 1995-1998, Oxford Development Studies, 337-369. Lall, S., Albaladejo, M. and Zhang, J., 2004. Mapping fragmentation: electronics and automobiles in East Asia and Latin America, Oxford Development Studies, 407- 432. Lee, C.H, and B.N., Huang., 2002. The relationship between Exports and Economic Growth in East Asian Countries: A Multivariate Threshold Autoregressive approach. Journal of Economic Development, Vol 27, No. 2, pp 45 – 68.

24

Marin D., 1992. Is the export-led growth hypothesis valid for industrialized countries? Review of Economics and Statistics. 74; 678_688. P. Fortunato, C. Razo and K. Vrolijk., 2015. Operationalizing The Product Space: A Road Map To Export Diversification. UNCTAD Discussion Papers. Piyusha,M., B. Ravikumar & M. J. Sposi., 2014. Working Papers, Federal Reserve Bank of St. Louis. R. Anand, S. Mishra, and N. Spatafora., 2012. Structural Transformation and the Sophistication of Production. IMF Working Paper. Romer P., 1990. Endogenous technological change. Journal of Politic Economics 98; 71_102. Weiss, J.; Zhang, J., 2006. The ‘sophistication’ of exports: A new trade measure. World Development, Vol. 34, No. 2, pp. 222–23

25

Chapter 2

2 Growth Accounting and Export Sophistication

2.1 Introduction

Why do some countries grow faster than other? Why do some countries stagnate?

When it comes to the question of how to accomplish economic growth and its main

driving force, identifying a set of factors that is empirically robust across time and space

remains elusive. Denison (1985), Young (1995), Collins and Bosworth (1996) argue that

technological advance and diffusion are the main drivers of economic growth, whereas

Abramovitz (1956), Solow (1957), and Kendrick (1961) attribute growth to physical and

human capital accumulation.

Among those that attribute physical and human capital accumulation as the main

driving force, Young (1995) argues that capital accumulation was the main determinant

of economic growth for the newly industrialized countries (NIC’s) 2 . Collins and

Bosworth (1996) examine the relationship between factor accumulation and productivity

growth in East Asian countries and found that growth is mainly driven by capital

accumulation. In earlier work, Jorgenson and Griliches (1967) find that if inputs and

output are accurately measured, the growth of input factors could explain most of the

output growth. Edward Denison (1985) finds that total output growth (not per capita) was 2Helookedatfortheperiodbetween1966-1990.

26

2.92% on average per year for the period 1973–1982 in the United States. Out of 2.92%

of this growth in GDP, the share of input factors explains two-third while labor growth

and capital growth account for 1.34% and 0.56% respectively.

On the other hand, some other studies find that total factor productivity is the

primary driver of economic growth. The earlier studies by Abramovitz (1956), Solow

(1957), and Kendrick (1961) indicate that a substantial percentage of growth in the

United States is associated with total factor productivity (TFP). Abramovitz estimates

that TFP growth accounts for 90% of output growth per person over 1878-1953

periods. Kendrick finds that three-fourths of the growth was associated with the increase

of productivity for 1889-1953 periods. Furthermore, Solow (1957) used the growth

accounting comprehensively to analyze total factor productivity where he identifies TFP

as technological progress. He used US data over 1909-1949 and showed that technical

change was Hicks neutral, and its growth rate was 1% for the first 20 years and 2% for

the rest. Solow defines technical change as a shift in the production function such as

economic shocks, human capital improvements, and other changes. He found that the

gross output per hour worked doubled in the United States from 1909 to 1949 with 88%

of it is due to the growth of technical change (TFP) and the remaining 12% is due to the

increase of physical capital.

Some recent studies also suggest similar findings of TFP. Baier, S., G. Dwyer,

and R. Tamura (2006) found that on average 14% of output growth is associated with

TFP growth for 145 countries. However, when they calculated TFP growth for country

groups, they found that TFP accounted for 34% of output growth per worker for the

27

Western countries including the United States; 26% for Southern Europe; and 26% for

newly industrialized countries (NICs). Yet, their estimate is quite lower than from early

estimates.

Recent research by Hausmann, Hwang, & Rodrik, (2007) showed that the

sophistication of a country’s export bundle could have significant implications for

economic growth. They constructed an index called “export sophistication” that is

intended to measure the productivity level associated with a country’s export basket.

They found that more sophisticated export bundles are positively correlated with the

growth. Presumably, this is because the productivity gains associated with producing a

set of sophisticated exportables are spread across the sectors. Therefore, they indicate, “It

is not how much you export, but what you export that matters.”

In this chapter, I use standard growth accounting techniques and apply these to the

export sophistication to analyze the factor content of export and its sophistication level.

First, I examine the relation between TFP, physical and human capitals. Second, I

construct an export sophistication measure to investigate the export sophistication of total

factor productivity, the export sophistication of human and physical capitals in economic

growth. Third, I discuss how export sophistication may lead to economic growth.

2.1 Data

The data set that I use in this article is from Baier, S., G. Dwyer, and R. Tamura

(2006)’s study. Penn World Table is used to construct the dataset. It includes 414

observations at 10-year intervals for 69 countries over 1960-2010 period. It contains per

28

worker values of output, physical capital and human capital for each country. Per worker

output and the stock of physical capital are presented in 1985 international dollars.

Human capital is presented by calculating the average level of education and experience

acquired by people employed.

2.2 Growth Accounting Framework

Different studies have analyzed how much of economic growth is accounted for

by the growth in physical capital, human capital, and total factor productivity. Solow

(1957) introduced the growth accounting methodology to measure the contribution of

different factors to economic growth. Since then, the growth accounting method is a

common method in economic growth literature to evaluate effects of determinants on

growth. Barro (1988) suggests that growth accounting exercises provide practical

information for both classical and endogenous growth approaches. Growth accounting

decomposes an economy’s total output growth rate into two parts: One part is the growth

that can be accounted for by the increases in the amount of physical and human capitals;

the other part is that the increase which cannot be explained by observable factors. While

physical and human capitals are two main factors for an economy’s output, they are not

sufficient to explain the output. The difference between growth rate of output and sum

of growth rates of inputs is attributed to technology, which is called the total factor

productivity (TFP).

29

In this section, I lay out the framework of growth accounting to analyze its

components, which are physical capital, human capital and TFP. I use an aggregate

production function for performing GDP growth accounting decomposition for a large

panel of countries for the period 1970-2010.

𝑌! = 𝐴!𝐾!!(𝐻!)!!! (1)

In the above equation, 𝑌! represents the gross output, 𝐴! is level of technology,

TFP, 𝐾! is physical capital and 𝐻! is human capital at 𝑡. If we take the natural logarithms

of the equation (1) for both sides, then, we get

ln ( 𝑌!) = Ln (𝐴!) + 𝑎ln ( 𝐾!) + (1− 𝑎) 𝑙𝑛 (𝐻!) (2)

If we take the difference from 𝑡 − 10 𝑖𝑛 the equation (2), we obtain equation (3),

which is average growth rate for 10 years period.

Ln (𝑌!) – Ln (𝑌!!!") = [( 𝑙𝑛 (𝐴!)- ln (𝐴!!!")] + 𝑎 [( 𝑙𝑛 (𝐾!)- ln (𝐾!!!")] + (1− 𝑎)[ ln (𝐻! - ln (𝐻!!!")] (3)

We can rewrite the equation (3) as below

𝑧 = 𝑦 − 𝑎𝑘 − 1− 𝑎 ℎ (4)

In the above equation, 𝑧 represent the growth rate of TFP, which is a residual,

computed from the other observable variables. Lowercase letter 𝑦 represent the growth

rate of output per worker, 𝑘 is the growth rate of physical capital, ℎ is growth rate of

human capital and input shares of physical capital and human capital are represented with

30

𝑎 and 1− 𝑎 , respectively. For the estimate in this article, I use capital share 𝑎 equal to

0.33 and human capital share to 0.67 for all years. These shares are in the range with

most of the previous studies. I use output per worker instead of output per capita. Baier,

S., G. Dwyer, and R. Tamura (2006) indicate “using output per worker instead of output

per person simplifies the empirical analysis with no obvious loss in the informativeness

of that analysis.” Equation (4) shows that the growth rate of output is equal to the sum of

growth rates of human capital, physical capital and TFP.

2.3 Growth in Total Factor Productivity, Physical Capital and Human Capital

As part of the growth accounting methodology in equation (4), I calculated the

contribution of each input factor and total factor productivity (TFP) to economic growth.

I have analyzed relationship between total factor productivity growth, physical and

human capital growth for 69 countries over 1970-2010 periods3. Table 1 summarizes the

results of growth accounting decomposition for these countries for ten-year average. It

demonstrates that physical capital has the highest average growth with 2.43% and human

capital growth with 0.99% while TFP growth is negative with -0.17%. The growth

accounting results illustrate that physical capital and human capital together account for

much of the growth in real per capita GDP. These are in line with the findings of Young

(1995), Collins and Bosworth (1996), Denison (1985), Baier, S., G. Dwyer, and R.

Tamura (2006) who attribute capital accumulation as the main determinant of economic

growth. As (Garrido, 2013) indicates, the growth accounting decomposition exercise does

3See Appendix 1 for growth accounting results in details

31

not offer any insights for understanding sources of growth in a country. However, it could

help to understand an initial picture about overall economic trends.

Table 2.1:Production Factors Average Growth 1980-2010

One would expect TFP growth and physical capital growth to be positively

correlated. Figure 1 depicts a scatter plot of TFP growth and physical capital growth and

it appears to be consistent with this view. Some countries such as Malta, Cyprus,

Mozambique, India, and Singapore are way above of the fitted values in different periods.

Malta’s outperforming TFP growth in 1980 was because of the outgrowth in the 1970s

that was driven by a strong rise in the capital stock. Cyprus’ 4% of TFP growth in the

1980s and 1990s could also be due to the acceleration of capital during those periods.

After the end of civil war in 1992, Mozambique experienced major physical capital

investments, which led significant TFP growth in the 2000s.

1970-1980

1980-1990

1990-2000

2000-2010 1980-2010

Physical Capital Growth 2.52 1.98 2.64 2.56 2.43

Human Capital Growth 0.96 1.15 0.98 0.85 0.99

Total Factor Productivity 0.32 -0.86 -0.03 -0.11 -0.17

32

Figure 2.1:Relation Between TFP Growth and Physical Capital Growth

Intuitively, one would also expect human capital growth to contribute TFP

growth positively. However, as depicted in Figure 2, I find there does not appear to

be a strong correlation between human capital growth and TFP growth. This is not

surprising because several other studies found similar results. Pritchett (2000) found

that the association of educational capital growth with conventional measures of TFP

was negative. Sacerdoti, Brunschwig, and Tang (1998), in a study carried out in

algeria

argentinaaustraliaaustria

bahrain

barbados

benin brazil

burkina faso

cameroon

canadacentral african republicchile

colombia

cyprus

denmark ecuador

egypt

elsalvadorfiji

finlandfrancegermany

ghana

greece

guatemala

honduras

hong kong

hungaryiceland

india

indonesia

ireland

israel

italyjapan

jordan

madagascar

malawimalaysia

mali

malta

mexico

morocco

mozambique

newzealand

nicaragua

norwaypakistan

panama

paraguay

peru

philippines

portugal

senegal

singaporespain

sri lanka

sweden switzerl

thailand

togo

turkeyuk

uruguay

usa

venezuela

-5-3

-11

35

79

Log_

TFP

Gro

wth

-5 -3 -1 1 3 5 7 9 11 13 15Log_Physical Capital Growth

tfp Fitted values

TFP Growth & Physical Capital Growth_1980

algeria

argentina

australiaaustria

bahrain

barbadosbenin

brazil

burkina faso

camerooncanadacentral african republicchile

colombia

cyprus

denmark

ecuador

egyptelsalvadorfiji


ghanagreece

guatemala

honduras

hong kong

hungaryiceland

india

indonesia

irelandisrael

italy

japan

jordanmadagascarmalawi

malaysiamali malta

mexico

moroccomozambique

newzealand

nicaragua

norway

pakistan

panama

paraguay

peru

philippines

portugal

senegalsingapore

spainsri lanka

swedenswitzerl

thailand

togo

tunisia

turkey

uk

uruguay

usa

venezuela-5-3

-11

35

79

Log_

TFP

Gro

wth


tfp Fitted values


algeria

argentinaaustraliaaustriabahrain

barbadosbenin

brazil

burkina faso

cameroon

canada

central african republic

chile

colombia

cyprus

denmark

ecuadoregypt

elsalvadorfijifinland

francegermanyghana

greece

guatemala

honduras

hong konghungaryicelandindia

indonesia

ireland

israel italyjapanjordanmadagascar

malawi malaysiamalimalta

mexico

moroccomozambique

newzealand

nicaragua

norway

pakistanpanama

paraguay

peruphilippinesportugal

senegal

singapore

spain

sri lankasweden

switzerl thailand

togo

tunisiaturkey

ukuruguayusa

venezuela

-5-3

-11

35

79

Log_

TFP

Gro

wth


tfp Fitted values


algeriaargentina

australiaaustria

bahrain

barbados

benin

brazilburkina faso

camerooncanada


chile

colombiacyprus

denmark

ecuadoregypt

elsalvadorfiji


ghana

greece

guatemala

honduras hong konghungary

iceland

india

indonesia

ireland

israel

italy

japanjordan

madagascarmalawi

malaysia

malimalta

mexicomorocco

mozambique

newzealandnicaraguanorwaypakistan

panamaparaguay

peruphilippines

portugalsenegal

singapore

spain

sri lanka

swedenswitzerl

thailand

togo

tunisiaturkeyuk

uruguay

usa

venezuela

-5-3

-11

35

79

Log_

TFP

Gro

wth


tfp Fitted values


33

Western Africa, investigated the effects of human capital on economic growth and

found that the human capital had little impact on economic growth due to the lack of

qualified individuals who were able to use advanced technology. They indicated that

for human capital growth to have a significant impact on TFP growth, structural and

institutional reforms needs to be accompanied that increase its social return,

promoting technology adoption and reducing opportunities for rent seeking. Overall,

the growth accounting results show that human capital growth has no much

contribution to TFP growth than would have been expected.

Figure 2.2: Relation Between TFP Growth and Human Capital Growth

algeria

argentinaaustralia austria

bahrain

barbados

benin brazil

burkina faso

cameroon


colombia

cyprus

denmark ecuador

egypt

elsalvadorfiji

finlandfrance germany

ghana

greece

guatemala

honduras

hong kong

hungaryiceland

india

indonesia

ireland

israel

italyjapan

jordan

madagascar

malawimalaysia

mali

malta

mexico

morocco

mozambique

newzealand

nicaragua

norwaypakistan

panama

paraguay

peru

philippines

portugal

senegal

singaporespainsri lanka

swedenswitzerl

thailand

togo

turkeyukuruguay

usa

venezuela

-6-4

-20

24

68

10Lo

g_TF

P G

row

th

0 1 2 3Log_Human Capital Growth

tfp Fitted values

TFP Growth & Human Capital Growth_1980

algeria

argentina

australiaaustria

bahrain

barbados benin

brazil

burkina faso

camerooncanadacentral african republicchilecolombia

cyprus

denmark

ecuador

egyptelsalvadorfijifinland

france germanyghana

greece

guatemala

honduras

hong kong

hungaryiceland

india

indonesia

irelandisrael

italy

japan

jordanmadagascar malawi

malaysiamali malta

mexico

moroccomozambique

newzealand

nicaragua

norway

pakistan

panama

paraguay

peru

philippines

portugal

senegalsingapore

spainsri lanka

swedenswitzerl

thailand

togo

tunisia

turkey

uk

uruguay

usa

venezuela

-6-4

-20

24

68

10Lo

g_TF

P G

row

th


tfp Fitted values


34

2.4 Export Sophistication Growth

Some early studies such as Emery (1967), Serven (1968), Kravis (1970),

Michaely (1977), Krueger (1978), Tyler (1981), and Lee & Cole (1994) argued that

export-oriented policies fueled economic growth. I explore this idea to see whether

current “Export Sophistication” implies higher rate of economic growth in the future.

Aghion and Howitt (1998), Hausman and Rodrik (2003), Stokey (1988) and Young

(1991) propose models that production structure is an important determinant of economic

growth. They argue that specializing in the sectors with comparative advantage is

important. Furthermore, specializing in more sophisticated products could create

additional gain. In this framework, “what a country exports matters”.

Hausmann, Hwang, and Rodrik (2007) developed a model, self-discovery, and

showed exporting more sophisticated products could pull an economy’s resources from

lower-productivity activities to the higher ones. The self-discovery model of economic

development is based on few successes such that few high-productivity activities could

lead as the lever for economic convergence. They found that countries with initially high

algeria

argentinaaustraliaaustria bahrain

barbadosbenin

brazil

burkina faso

cameroon

canada


chile

colombia

cyprus

denmark

ecuadoregypt

elsalvadorfijifinlandfrancegermany

ghanagreece

guatemala

honduras

hong konghungaryicelandindiaindonesia

ireland

israel italyjapanjordanmadagascar

malawi malaysiamalimalta

mexico

moroccomozambique

newzealand

nicaragua

norway

pakistanpanama

paraguay

peruphilippinesportugalsenegal

singapore

spain

sri lanka sweden

switzerl thailand

togo

tunisia turkey

ukuruguayusa

venezuela

-6-4

-20

24

68

10Lo

g_TF

P G

row

th


tfp Fitted values


algeriaargentina

australiaaustria

bahrain

barbados

benin

brazilburkina faso

camerooncanada


chile

colombiacyprus

denmark

ecuadoregypt

elsalvadorfiji


ghana

greece

guatemala

hondurashong konghungary

iceland

india

indonesia

ireland

israel

italy

japan jordan

madagascarmalawi

malaysia

malimalta

mexicomorocco

mozambique


panamaparaguay

peruphilippines

portugalsenegal

singapore

spain

sri lanka

swedenswitzerl

thailand

togo

tunisiaturkeyuk

uruguay

usavenezuela

-6-4

-20

24

68

10Lo

g_TF

P G

row

th


tfp Fitted values


35

levels of export sophistication subsequently experience higher growth in exports. Rodrik

(2006) also found a strong relationship between the initial level of a country’s export

sophistication and the subsequent rate of economic growth experienced by that country.

He estimated that a doubling of the productivity level of a country’s exports increases its

GDP per-capita growth around 6 percent.

2.5 Growth Accounting in term of Export Sophistication

To find the share of each factor in the export sophistication growth and whether

countries with exporting more sophisticated goods grow faster, I have examined the

export sophistication by using the growth accounting framework. First, I have redefined

the growth accounting, the equation (4), in terms of export sophistication. Then, to find

the export sophistication of physical capital and human capital, I modified Hausmann,

Hwan, and Rodrik (2007)’s export sophistication index. While Hausmann, Hwan, and

Rodrik (2007) only estimated the average income level of a country’s exports, I estimate

physical capital and human capital level of a country’s exports to investigate the content

of physical capital and human capital in a country’s export basket.

To calculate the export sophistication rate of both physical capital and human

capital, I used their formula by replacing income per capita with human capital per

worker and physical capital per worker for each country.

First, I define export sophistication of good k:

lnhprod! k = x!!(k)x!! k!

lnhc! !

36

x!! k : Country i’s export for product k, in p category, to the world

x!! k! : The world’s export for product k, in p category

𝑙𝑛ℎ𝑐!: The per worker level of human capital of country i, measured as the real log-

human capital per worker, in PPP (𝑙𝑛𝑝𝑐!: The per worker level of physical capital of

country i, measured as the real log-physical capital per worker, in PPP.)

Second, I define export sophistication for category p that accounts for four

categories; i) high-technology, ii) medium-high-technology, iii) medium-low- technology

and iv) low-technology.

lnh! = ( x!

!(k)!∈!

( x!! k )!∈!!

lnhc! !

After rearranging this can be written as

lnh! = ( x!

!(k)!

( x!! k )!∈!!

x!! k!

x!! k!

lnhc! !∈!

Which is

lnh! = ( x!

!(k)!

( x!! k )!∈!!

lnhprod! k !∈!

Or lnh! = !

!(!)!!

lnhprod! k!∈! Which is the export

sophistication for categories where:

x!(k): Country j’s export in p category

x!: World’s export in p category

37

lnhprod! 𝑘 : Export sophistication of human capital for good k

lnh!is the sophistication level for each category mainly; high-technology, medium-high-

technology, medium-low- technology and low-technology.

Finally, I define the export sophistication of human capital for each country:

lnesh! =x!!

x!lnh!

!

Which is equal to

lnesh! =!!!

!!lnh! + !!

!"

!!lnh!" +!!

!"

!!lnh!" +!!

!

!!lnh!

x!!: Country i’s sum of exports to the world in p category

x!: Country i’s sum of exports to the world lnh!:The export sophistication of human capital for categories Where p={L, ML, MH, H}) LNESH! is export sophistication of human capital for country i’s export bundle

which is the average level of sophistication of its export basket. It is the log-weighted

sum of the sophistication levels for each exported good k, 𝐿𝑁HPROD! k , where the

weights are the shares of each good in the country’s total exports.

I used the above process of export sophistication formula to find growth rate of

export sophistication of income per worker ( 𝑦𝑒𝑥𝑝 ) and growth rate of export

sophistication of physical capital per worker (𝑘𝑒𝑥𝑝). Then, to find export sophistication

of total factor productivity growth rate (TFPEXP), I modified the equations (2), (3), and

38

(4) in the above growth accounting framework by replacing income per-worker, human

capital and physical capital with their export sophistication scores.

𝑇𝐹𝑃𝐸𝑋𝑃 = 𝑦𝑒𝑥𝑝 − 𝑎𝑘𝑒𝑥𝑝 − 1− 𝑎 ℎ𝑒𝑥𝑝(5)

In the equation (5) 𝑇𝐹𝑃𝐸𝑋𝑃 is the export sophistication growth rate of TFP,

(𝑦𝑒𝑥𝑝) is the growth rate of export sophistication of output per worker, (𝑘𝑒𝑥𝑝) is the

growth rate of export sophistication of physical capital, (ℎ𝑒𝑥𝑝) is growth rate of export

sophistication of human capital and input shares of export sophistication of physical

capital and export sophistication of human capital are represented with 𝑎 and 1− 𝑎 ,

respectively.

2.6 Export Sophistication of Physical Capital, Human Capital, and TFP

I use the equation (5) to analyze the relationship between the growth of each

component of export sophistication, which are physical capital, human capital and TFP,

and current factors for each country. I also investigate if the growth rate of each factor in

a country’s exported products is higher than in its current factors. First, I calculate the

export sophistication growth in term of physical capital to find how much physical capital

is contained in the exported products comparing to the average current physical capital

level for each country. Figure 3 shows the relationship between growth of export

sophistication of physical capital and the current physical capital for all countries. I found

that there is a positive correlation for all four periods between 1980-2010. But, when

analyzing based on the development levels, I found different paths for developed and

developing countries. The developed countries have higher physical capital growth than

39

their export sophistication of physical capital growth, but the developing countries have

the reverse. That is, developing countries are exporting the products, which contain

higher physical capital than their average physical capital level. On the other hand, the

average level of export sophistication of physical capital growth is almost the same with

the physical capital growth in my sample countries.4 Almost half of the countries export

products that contain less physical capital than their average physical capital level.

Figure 2.3: Relationship Between Export Sophistication of Physical Capital Growth and Current Physical Capital Growth

4138 observations out of 265 have greater export sophistication of physical capital.

algeria


bahrain

barbadosbenin

brazil

burkina fasocamerooncanada

central african republicchile

colombia

cyprus

denmark

ecuador

egyptelsalvadorfiji

finlandfrance

germanyghana

greece

guatemala

honduras

hong konghungaryicelandindiaindonesia

irelandisrael

italy japanjordanmadagascar malawi

malaysiamali maltamexico

morocco newzealandnicaragua

norwaypakistan

panama

paraguayperu

philippinesportugal

senegalsingaporespainsri lanka

sweden switzerl

thailandtogo

tunisia

turkeyuk

uruguay

usa

venezuela

12

34

56

7Lo

g_Ex

p.So

phis

ticat

ion

of P

hysi

cal C

apita

l Gro

wth

-5 -3 -1 1 3 5 7 9Log_Physical Capital Growth

lnesyphysicalcapitalgrowth Fitted values

Physical Capital Growth & Exp.Sophistication of Physical Capital Growth_1980

algeriaargentinaaustraliaaustria

bahrainbarbadosbrazil

camerooncanada

chilecolombia cyprus

denmarkecuadoregyptelsalvadorfiji finland

francegermany greeceguatemala honduras hong konghungaryiceland india indonesia

irelandisrael

italy japan


malaysia

mali

maltamexico

morocco

newzealandnicaragua norway pakistanpanama paraguayperu

philippines

portugalsenegalsingaporesouth korea

spainsri lankasweden

switzerl

thailand

togotunisia

turkey

ukuruguay

usavenezuela

12

34

56

7Lo

g_Ex

p.So

phis

ticat

ion

of P

hysi

cal C

apita

l Gro

wth




algeria

argentinaaustralia

austria

bahrain

barbadosbrazilcamerooncanada chilecolombiacyprusdenmarkecuador

egyptelsalvadorfiji finlandfrance

germany

greece guatemalahondurashong kong

hungary

icelandindia

indonesiaireland

israel italyjapan

jordanmadagascar malawi malaysiamali

maltamexico

morocconewzealand

nicaragua

norway

pakistanpanamaparaguayperu

philippines

portugalsenegal singapore south koreaspain

sri lanka

swedenswitzerl

thailandtogotunisia

turkey

uk

uruguayusavenezuela

12

34

56

7Lo

g_Ex

p.So

phis

ticat

ion

of P

hysi

cal C

apita

l Gro

wth




algeriaargentina

australia

austriabahrain

barbadosbenin

brazilburkina fasocameroon canadacentral african republic chilecolombia

cyprus

denmarkecuadoregyptelsalvadorfiji

finlandfrancegermany ghanagreeceguatemala

honduras hong konghungaryiceland

indiaindonesia

irelandisraelitalyjapan jordanmadagascar malawi

malaysiamali

maltamexico

morocco


panama

paraguay peruphilippines

portugalsenegalsingapore

south koreaspain sri lankasweden

switzerl thailandtogo tunisiaturkeyuk uruguayusa

venezuela

12

34

56

7Lo

g_Ex

p.So

phis

ticat

ion

of P

hysi

cal C

apita

l Gro

wth




40

Second, I calculated the export sophistication growth in term of human capital to

find the level of human capital that contained in the exported products comparing to the

average current human capital level for each country. Figure 4 shows that the relationship

is mixed in different periods. While the correlation is relatively strong in the period of

1990 and 2010, it is negative in 1980 and relatively weak in 2000. Comparing the growth

rate of export sophistication of human capital to the current human capital, I found that

most of the countries have higher growth in the first rather than the latter5. These

countries have an average export sophistication of human capital growth of 1.51, which is

higher than the average human capital level 1.07. On average, countries contain more

human capital in their exported products than their average human capital levels.

Figure 2.4: Relationship Between Export Sophistication of Human Capital Growth and Current Human Capital Growth

5229 observations out of 311 have greater export sophistication of human capital.

algeria

argentinaaustralia

austria bahrain

barbadosbeninbrazil

burkina faso

camerooncanada


chile

colombia

cyprusdenmark

ecuadoregypt

elsalvadorfiji

finlandfrance germany

ghana

greece

guatemala

honduras

hong konghungary

icelandindia

indonesia

ireland

israelitaly

japanjordan

madagascarmalawi

malaysia

mali malta

mexico

morocconewzealandnicaragua

norway

pakistanpanama

paraguay

peru

philippines

portugalsenegal singapore

spainsri lanka

swedenswitzerl

thailand

togo

turkey

uk

uruguayusa

venezuela-10

12

34

5Lo

g_Ex

p.So

phis

ticat

ion

of H

uman

Cap

ital G

row

th


Log_Exp_Sophistication of Human Capital Growth Fitted values

Human Capital Growth & Exp.Sophistication of Human Capital Growth_1980

algeria

argentinaaustralia

austriabahrain

barbados brazilcameroon

canadachile

colombia

cyprusdenmark

ecuadoregypt

elsalvador fiji

finland

france germany

greece

guatemalahonduras

hong konghungary

icelandindia

indonesia

ireland

israel

italyjapanjordan

madagascar malawi

malaysia

mali

malta

mexico

morocco

newzealand nicaragua

norway

pakistan panama

paraguay

peru

philippines

portugalsenegal

singapore south korea spain

sri lankasweden

switzerl

thailandtogo

tunisia

turkey

uk

uruguayusa

venezuela

-10

12

34

5Lo

g_Ex

p.So

phis

ticat

ion

of H

uman

Cap

ital G

row

th




41

Finally, I calculated the export sophistication of TFP growth to find the content of

TFP in the exported goods comparing to current TFP level for each country. Figure 5

shows the scatter plot of TFP growth against export sophistication of TFP growth. I

found the correlation is negative for periods in 1980, 2000, and 2010 and weakly positive

in 1990. When decomposing the growth accounting in terms of export sophistication of

TFP, I found export sophistication of TFP growth is lower than the current TFP growth

for 170 out of 264 observations over the periods of 1980-2010. This means that the

exported products contain less TFP than the average level of TFP in these countries. This

result is surprising because when a country’s productivity level increases, its export

basket is expected to reflect the increases. But, the negative correlation here could be

because of the nature of TFP. In the literature, there are different views and explanation

about TFP and how it should be evaluated. Baier, S., G. Dwyer, and R. Tamura (2006)

indicate TFP growth might have measurement errors in output and physical and human

capital. As an example, they state that a useless road would count a part of physical

capital accumulation but may have no impact on GDP. They emphasize that there are

algeriaargentinaaustraliaaustriabahrain

barbados brazilcameroon

canadachilecolombiacyprus denmarkecuadoregypt

elsalvadorfiji


greeceguatemalahonduras

hong kong

hungary

iceland india

indonesiaireland

israel italyjapanjordan

madagascar malawi

malaysia

malimalta

mexico

morocconewzealand

nicaragua norwaypakistan

panamaparaguayperu

philippines

portugalsenegal

singaporesouth korea

spainsri lanka swedenswitzerl

thailand

togotunisia turkey

uk uruguayusavenezuela

-10

12

34

5Lo

g_Ex

p.So

phis

ticat

ion

of H

uman

Cap

ital G

row

th




algeriaargentinaaustraliaaustria

bahrain barbados benin

brazilburkina faso

cameroon

canada


chilecolombia

cyprus

denmarkecuadoregypt elsalvadorfiji


ghanagreece guatemala

honduras

hong konghungaryiceland

indiaindonesia

ireland

israelitaly

japan

jordanmadagascar malawi

malaysia

mali

maltamexico

morocco

mozambiquenewzealandnicaraguanorway

pakistan

panama

paraguayperu

philippines

portugalsenegal

singapore

south koreaspain

sri lanka

swedenswitzerlthailand

togo tunisiaturkey

uk

uruguay

usa

venezuela

-10

12

34

5Lo

g_Ex

p.So

phis

ticat

ion

of H

uman

Cap

ital G

row

th




42

different possible ways of explanations of changes in TFP. A change in TFP might not

only represent technological change. The change could arise from the economic regime,

property rights, managerial efficiency, etc.

Figure 2.5: Relationship Between Export Sophistication of TFP Growth and Current TFP Growth

2.7 Current Sophistication Growth Against the Future Economic Growth

Hausmann, Hwang, and Rodrik (2007) found that countries with initially high

levels of export sophistication subsequently experience higher growth in exports. Rodrik

algeria

argentinaaustraliaaustria bahrainbarbados beninbrazilburkina faso

camerooncanada

central african republicchilecolombia

cyprusdenmark

ecuador

egypt

elsalvadorfiji finlandfrancegermanyghana

greece

guatemalahonduras hong konghungaryicelandindia

indonesia

ireland

israel italyjapan

jordanmadagascar malawimalaysia

mali malta

mexico


norway

pakistan panamaparaguay

peru

philippinesportugalsenegal singapore

south korea spainsri lankaswedenswitzerl thailandtogo

turkeyuk

uruguay

usa

venezuela

-2.5

-2-1

.5-1

-.50

.51

1.5

2Ex

p.So

phis

ticat

ion

of T

FP G

row

th

-5 -3 -1 1 3 5 7 9TFP Growth

tfpsoph Fitted values

TFP Growth & Exp.Sophistication of TFP Growth_1980

algeria

argentinaaustralia

austriabahrain

barbadosbrazilcamerooncanada

chile

colombia

cyprusdenmark

ecuador egypt

elsalvadorfijifinland

francegermany

greece

guatemalahonduras hong konghungary

icelandindia

indonesia

ireland

israelitaly japan


malaysia

malimalta

mexico


norway

pakistanpanama

paraguay

peru

philippines

portugalsenegal

singapore south koreaspainsri lankasweden

switzerlthailand

togo

tunisia

turkeyuk

uruguay usa

venezuela

-2.5

-2-1

.5-1

-.50

.51

1.5

2Ex

p.So

phis

ticat

ion

of T

FP G

row

th

-5 -3 -1 1 3 5 7 9TFP Growth



algeria argentinaaustraliaaustriabahrainbarbadosbrazil

camerooncanadachilecolombia cyprusdenmarkecuador

egyptelsalvadorfiji

finlandfrancegermanygreeceguatemalahonduras

hong konghungaryicelandindia

indonesia irelandisraelitalyjapanjordan

madagascarmalawi

malaysia

malimalta

mexico

morocco newzealandnicaraguanorwaypakistanpanamaparaguayperu

philippinesportugalsenegal

singaporesouth koreaspain

sri lankaswedenswitzerlthailand

togotunisiaturkey uk

uruguayusa

venezuela

-2.5

-2-1

.5-1

-.50

.51

1.5

2Ex

p.So

phis

ticat

ion

of T

FP G

row

th

-5 -3 -1 1 3 5 7 9TFP Growth



algeriaargentinaaustraliaaustriabahrain barbadosbenin brazil

burkina fasocameroon

canada


chile colombiacyprusdenmarkecuadoregyptelsalvadorfijifinlandfrancegermany

ghanagreeceguatemala hondurashong konghungary

iceland indiaindonesiaireland

israelitalyjapan


malaysiamali

maltamexicomorocconewzealandnicaraguanorwaypakistanpanama

paraguayperu

philippinesportugalsenegal

singaporesouth koreaspain

sri lankaswedenswitzerl thailand

togo tunisiaturkeyuk

uruguayusa

venezuela

-2.5

-2-1

.5-1

-.50

.51

1.5

2Ex

p.So

phis

ticat

ion

of T

FP G

row

th

-5 -3 -1 1 3 5 7 9TFP Growth



43

(2006) also found strong relationship between the initial level of a country’s export

sophistication and the subsequent rate of economic growth experienced by that country.

In this section, I test the relation between current sophistication growth and subsequent

growth rate of GDP per worker, controlling for initial GDP per worker and initial human

capital per worker.

In order to investigate how export sophistication affects future growth I use the

following regression.

𝑌𝑔𝑟! = 𝑎 + 𝛽𝑙𝑛𝑦𝑒𝑥𝑝𝑔𝑟!! + 𝛿𝑙𝑛𝑦!! + 𝜃𝑙𝑛ℎ𝑐!! + 𝜖! (5)

where 𝑌𝑔𝑟!is GDP per worker growth rate at time t; 𝑙𝑛𝑦𝑒𝑥𝑝𝑔𝑟!! is the natural logarithm

of export sophistication growth at 𝑡! ; 𝑙𝑛𝑦!!𝑎𝑛𝑑 𝑙𝑛ℎ𝑐!! are the natural logarithm of initial

levels GDP per worker and human capital respectively. The estimates are reported in

Table 2. The results confirm previous findings that a country’s relative level of

sophistication has significant impact on the subsequent growth. The estimated coefficient

on 𝑙𝑛𝑦𝑒𝑥𝑝𝑔𝑟!! is positive and significant in 2000 and 2010 but negative and not

significant in 1990. Based on the results, holding other factors constant, a 1% increase in

export sophistication growth increases the average annual income per worker growth rate

over the following year by about 2.74% and 1.34% based on estimates year 2000 and

2010 respectively.

44

Table 2. Export Sophistication and Growth Regression ------------------------------------------------------------------------- 1990 2000 2010 ------------------------------------------------------------------------- lnyexpgr -1.169 2.734* 1.348* (0.66) (1.04) (0.66) lny -1.686** 0.276 0.258 (0.60) (0.43) (0.35) lnhc 5.537* 1.522 -0.705 (2.09) (1.52) (1.32) Cons 1.297** -7.224** -2.261 (4.09) (2.69) (2.56) ------------------------------------------------------------------------- R-sqr 0.149 0.214 0.065

2.8 Conclusion

In this chapter, I have examined the sophistication of a country’s export basket. I

have also examined the relationship between the initial level of a country’s export

sophistication and the subsequent rate of economic growth. The results show that the

exported products contain less physical capital, human capital, and TFP than the

countries’ average level of these factors. Also, the results indicate that countries’ export

sophistication growth has important consequences for subsequent economic growth.

Overall, the result shows that increasing sophistication of exports can contribute to

economic growth significantly.

45

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Barro, Robert., 1998. Notes on Growth Accounting. Harvard University. December, 1998.

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Kendrick, John W., 1961. Productivity Trends in the United States. Princeton University Press for the National Bureau of Economic Research, New York.

Kravis, I. B.,1970. Trade as a handmaiden of growth: Similarities between the nineteenth and twentieth centuries. Economic Journal, 80, 320 (December): 850-72.

Krueger, A. O.,1978. Foreign Trade Regimes and Economic Development: Liberalization

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Attempts and Consequences. Cambridge, MA: Ballinger.

Lee, F. Y. and Cole, W. E., 1994. Simultaneity in the study of exports and economic growth. International Economic Journal, 8, 1 (Summer): 33-41.

Michaely, M., 1977. Exports and growth: An empirical investigation. Journal of Development Economics, 4, 1 (March): 49-53

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47

Chapter 3

3 Export Growth and Total Factor Productivity

3.1 Introduction

A large number of studies have investigated the relationship between total factor

productivity (TFP) and export growth. Grossman & Helpman (1991), Edwards (1993),

Balassa, (1971), Kunst and Marin (1989), Bernard and Jensen (1999) and Bhagwati

(1988) suggest that the direction of causality between exports and productivity is mixed.

These studies mainly fall into three categories on the direction of causality between two.

First, export growth causes TFP growth. Second, TFP growth causes export growth.

Third, two-way causal relationship exists.

The first group of empirical studies includes Grossman & Helpman (1991),

Edwards (1993), Balassa, (1971), Weinhold and Rauch (1997). These studies find that

exports increase the productivity growth through learning by doing. Moreover, Edwards

(1993) explains that the productivity increase due to exports could lead spillover to the

non-export sector.

48

Another line of research finds that the causality goes from productivity growth to

export growth. A partial list of papers that support this view includes Kunst and Marin

(1989), Clerides et al. (1998), Bernard and Jensen (1999). Kunst and Marin (1989)

analyze the relationship between productivity and exports based on Austrian data using

time series analysis. They find no causal link from exports to productivity. However ,

they could not reject the causality from productivity to exports at conventional levels.

Bernard and Jensen (1999) find that exporters produce more than twice as much output

and they are more productive. They find that productivity growth leads the export growth

not the other way around. In addition, Clerides et al. (1998) find that only relatively

efficient firms export and exporting does not decrease unit production costs. Thus, they

argue that the causality is from productivity growth to exports.

The third group of studies related to this issue includes the paper by Bhagwati

(1988). He notes that export growth produces more income and more income facilitates

more export. As the result, there is two-way causality between two.

Some of the East Asian countries, Japan, South Korea, Hong Kong, Malaysia, and

Taiwan, experienced the highest rate of TFP growth in the world between 1960 and 1989.

How have these countries succeeded unprecedented TFP growth? Many studies have

looked at rapid economic growth of East Asian countries during the second half of the

last century. Most of them provided empirical evidence in support of export- driven

growth strategy; that is, exports had significant effects on productivity and economic

growth. In addition, the World Bank (1993) report supports that export and export-push

49

strategies had been a significant source of their productivity performance. The report

suggests that the relationship between exports and productivity growth might have arisen

from exports' role in helping economies to adopt international best-practice technologies.

On the other hand, in contrast to the export-led productivity, Liao and Liu (2009)

find that total factor productivity played significant role in the growth of exports in East

Asia. They found that causality is unidirectional, going from productivity to exports, for

China, Hong Kong, Indonesia, Malaysia and the Philippines. Rodrik (1992) similarly

argues that while increases in productivity have played significant role in the economic

growth of the developing countries but the trade liberalization overall did not have

positive impact on technological performance.

In this paper, I investigate the direction of causality between export growth and

total factor productivity as well as the causality between export growth and GDP growth.

I ask whether export growth increases productivity and output growth, or whether

productivity growth leads export growth. Most of the previous studies on this subject are

based on specific country analysis. To the best of my knowledge, this is the first study

that uses an extensive cross-country panel data to investigate the link between export

growth and productivity growth at the macro level.

3.2 Data

I use UN COMTRADE data for the export growth calculation. The data set that I

use to calculate TFP growths is from Baier, S., G. Dwyer, and R. Tamura (2006)’s study.

50

Penn World Table is used to construct the dataset. It includes 414 observations at 10-year

intervals for 69 countries over the period 1960-2010. It contains per worker values of

output, physical capital and human capital for each country. Per worker output and the

stock of physical capital are presented in 1985 international dollars. Human capital is

presented by calculating the average level of education and experience acquired by

people employed.

3.3 The Effect of Total Factor Productivity on Export

Kaldor (1967) argues that economic growth via increased productivity could act

as a stimulus to exports. The argument that productivity growth stimulates the export

growth implies that a country’s competitiveness in price and quality is enhanced as result

of increase in productivity. As result of TFP growth, the exporting country is more

competitive and has better chance of entering new markets or increasing the volume of

the sells to the importing countries. Therefore, the spillover from the productivity growth

could transform the nature of the basket of goods exported. Hausmann and Klinger

(2006) demonstrated that if a country’s existing exports have many nearby high-

technology goods, this increases the ability of its moving from the agriculture to the high-

technology manufacturing exports.

The productivity improvement could lead horizontal diversification as well as

vertical specialization. Furthermore, total factor productivity growth in the sectors

associated with revealed comparative advantage could create positive externalities and, in

return, that can lead more sophisticated products. This has important implications on

growth of export and economy. With higher productivity, a country can export more

51

sophisticated products and experience higher export growth. In addition, an export-

growth derived from sophisticated goods could be more self-sustaining against political

and social unrests in the exporting countries. Exporting high-technology products is one

of the manifestations of structural transformation for an economy by moving from

primary type of goods to more complex and high- technology products. Moreover,

manufacturing and exporting more sophisticated products creates a reliable and

sustainable economic infrastructure. After all, relatively productive firms are more likely

to export, because only the efficient firms could survive in very competitive foreign

markets and find it profitable to export. In other words, the causality may go from

productivity to exports.

The correlations between the selected variables are presented in Table 1. This

correlation table gives a preliminary idea of the relationship between the selected

variables. The total factor productivity growth is positively correlated with both export

growth and GDP per worker growth.

Table 3.1: Correlation Matrix of Selected Variables

TFP Growth GDP Per Worker Growth Export Growth

TFP Growth 1.0000

GDP Per Worker Growth 0.9225 1.0000

Export Growth 0.2802 0.3360 1.0000

Figure 1 depicts a scatter plot of TFP growth and export growth and it appears to

be consistent with the view of productivity growth stimulates the export growth.

52

Figure 3.1: Relation Between TFP Growth and Export Growth

3.4 Total Factor Productivity Growth Estimation

The conventional method for total factor productivity estimation is the Solow

residual method. It decomposes an economy’s total output growth rate into two parts:

One part is the growth that can be accounted for by the increases in the amount of

physical and human capitals; the other part is that the increase which cannot be explained

algeria


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expt_growth Fitted values

Relationship Between TFP Growth & Export Growth (1980)

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53

by observable factors. While physical and human capitals are two main factors for an

economy’s output, they are not sufficient to explain the output. The difference between

growth rate of output and sum of growth rates of inputs is attributed the total factor

productivity (TFP). However, in the literature, there are different views and explanation

about TFP and how it should be evaluated. Isaksson (2007) identified several

determinants associated with TFP growth, which are human capital (education and

health), infrastructure, openness, competition, financial development, geography and

capital intensity.

I use the Cobb-Douglas production function for calculation of TFP growth:

𝑌! = 𝐹(𝐾! , 𝐿!) = 𝐴!𝐾!!𝐿!! , where 0 < 𝛼 < 1 1

𝑌! is gross output, 𝐾 is the physical capital, 𝐿!is the human capital and 𝐴! represents

TFP. Then, taking natural logarithms and differentiating both sides of 1 with respect to

time t the growth rate of aggregate output can be expressed as

!Y /Y = !A / A+α( !K /K )+ (1−α)( !L / L) 2

For variable Z= 𝑌,𝐾, 𝐿 the term stands Z for the derivative of 𝑍 with respect to

time 𝑡 , and so Z/Z stands for the growth rate. The growth rates of physical capital and

labor are weighted by 𝑎 and 1− 𝑎 . These weights are corresponding to the

respective shares of rental payments for physical capital and human capital in total

income. The production function assumes of a constant returns to scale and neutral

technical change.

54

3.5 Model Specification

After calculating TFP growth, I regressed export growth on total factor

productivity growth and TFP growth on export growth. The link between TFP growth

and export growth are described in equation (3) and (4).

EXP! = δ+ αTPP!!! + lnTOTALEXP! + ϵ! 3

TFP! = µ+ βEXP!!! + ϵ! 4

TFP = Total Factor Productivity growth

EXP = Export growth

LnTOTALEXP = Log Total Export

ϵ = Error term

The estimates from equation 3 are reported in Table 2. The result suggests that a

country’s total factor productivity has significant impact on the subsequent export

growth. The coefficient of lag TFP growth is 0.8745. Based on the results, holding other

factors constant, a 1% increase in total factor productivity growth increases the average

export growth for the following year by about 0.87%. On the other hand, the regression

result in Table 3 suggests that there is no significant impact for a country’s export growth

on its total factor productivity growth.

55

Table 3.2: Export Growth Regression

Table 3.3: TFP Growth Regression

_cons 5.464637 3.024526 1.81 0.072 -.4915947 11.42087 lntotalexpt .2297747 .1862847 1.23 0.219 -.1370776 .5966271 L1. .8745929 .2107673 4.15 0.000 .4595265 1.289659 tfp expt_growth Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 12946.2141 257 50.3743739 Root MSE = 6.8752 Adj R-squared = 0.0617 Residual 12053.5252 255 47.2687263 R-squared = 0.0690 Model 892.688882 2 446.344441 Prob > F = 0.0001 F( 2, 255) = 9.44 Source SS df MS Number of obs = 258

_cons -.1690539 .1767279 -0.96 0.340 -.5172198 .1791119 L1. -.0150227 .0150068 -1.00 0.318 -.0445871 .0145418 expt_growth tfp Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 672.366911 237 2.83699119 Root MSE = 1.6843 Adj R-squared = 0.0000 Residual 669.523946 236 2.83696587 R-squared = 0.0042 Model 2.84296547 1 2.84296547 Prob > F = 0.3178 F( 1, 236) = 1.00 Source SS df MS Number of obs = 238

56

3.6 Conclusion

This paper investigates empirically the linkage between TFP growth and export growth.

My finding supports the line of research that argues the causality goes from productivity

growth to export growth. The regression results show that TFP growth has significant

impact in export growth but export growth does not play an important role in TFP

growth. The causality is unidirectional, running from total factor productivity to exports.

57

APPENDICIES

58

A. CHAPTER ONE APPENDIX

Table 1: Product Sophistication Scores in 2010 Ranking year productcode sector lnyprod_k(gdpperworker) lnyprodp

1 2010 2112 L 10.89933 10.449382 2010 9510 H 10.89046 10.63063 2010 2219 L 10.88499 10.449384 2010 8960 H 10.85337 10.63065 2010 913 L 10.83497 10.449386 2010 2431 L 10.83396 10.449387 2010 2111 L 10.83213 10.449388 2010 2511 L 10.82952 10.449389 2010 5152 MH 10.8281 10.6231510 2010 4221 L 10.82381 10.4493811 2010 2120 L 10.8005 10.4493812 2010 5151 MH 10.7994 10.6231513 2010 7261 H 10.79532 10.630614 2010 6413 ML 10.7901 10.4158115 2010 5415 H 10.78927 10.630616 2010 2512 L 10.78609 10.4493817 2010 6832 ML 10.78538 10.4158118 2010 6812 ML 10.78512 10.4158119 2010 5153 MH 10.78272 10.6231520 2010 15 L 10.78089 10.4493821 2010 6723 ML 10.77868 10.4158122 2010 6831 ML 10.77852 10.4158123 2010 6671 ML 10.77733 10.4158124 2010 8612 H 10.77674 10.630625 2010 8996 H 10.77633 10.630626 2010 6643 ML 10.77555 10.4158127 2010 2519 L 10.77366 10.4493828 2010 8613 H 10.77351 10.630629 2010 2733 L 10.77074 10.4493830 2010 6880 ML 10.76857 10.4158131 2010 8641 H 10.76827 10.630632 2010 6322 ML 10.76635 10.4158133 2010 6721 ML 10.76624 10.4158134 2010 8922 H 10.76619 10.630635 2010 7295 H 10.76502 10.6306

59

36 2010 8624 H 10.76381 10.630637 2010 2422 L 10.76183 10.4493838 2010 6894 ML 10.75979 10.4158139 2010 8623 L 10.75976 10.4493840 2010 5131 H 10.75918 10.630641 2010 811 L 10.75892 10.4493842 2010 5997 H 10.75434 10.630643 2010 6895 ML 10.7541 10.4158144 2010 4113 L 10.75399 10.4493845 2010 5416 H 10.75164 10.630646 2010 7198 H 10.75137 10.630647 2010 3325 ML 10.74936 10.4158148 2010 5713 H 10.74893 10.630649 2010 8942 H 10.74868 10.630650 2010 5128 H 10.74481 10.630651 2010 6642 ML 10.74441 10.4158152 2010 2215 L 10.74282 10.4493853 2010 7116 H 10.7413 10.630654 2010 7262 H 10.73998 10.630655 2010 2119 L 10.73995 10.4493856 2010 5134 H 10.73963 10.630657 2010 2820 L 10.73949 10.4493858 2010 410 L 10.73773 10.4493859 2010 7117 H 10.73602 10.630660 2010 4217 L 10.73405 10.4493861 2010 2836 L 10.73373 10.4493862 2010 452 L 10.73312 10.4493863 2010 6641 ML 10.73288 10.4158164 2010 5711 H 10.73277 10.630665 2010 8921 H 10.73134 10.630666 2010 5813 ML 10.7291 10.4158167 2010 6893 ML 10.72586 10.4158168 2010 5331 H 10.72376 10.630669 2010 7114 H 10.72239 10.630670 2010 8924 H 10.72185 10.630671 2010 7324 H 10.72185 10.630672 2010 8945 H 10.72139 10.630673 2010 8614 L 10.71982 10.4493874 2010 5512 MH 10.71798 10.62315

60

75 2010 5143 H 10.7177 10.630676 2010 7293 H 10.71744 10.630677 2010 2621 L 10.7159 10.4493878 2010 7353 H 10.71266 10.630679 2010 8619 H 10.71184 10.630680 2010 7142 H 10.7118 10.630681 2010 5123 H 10.71104 10.630682 2010 8912 H 10.71025 10.630683 2010 7195 H 10.70972 10.630684 2010 6538 ML 10.70969 10.4158185 2010 6574 ML 10.70738 10.4158186 2010 2850 L 10.70674 10.4493887 2010 7193 H 10.70594 10.630688 2010 8617 H 10.70573 10.630689 2010 1122 L 10.70443 10.4493890 2010 5126 H 10.70398 10.630691 2010 5999 H 10.70382 10.630692 2010 3217 ML 10.69935 10.4158193 2010 2116 L 10.69824 10.4493894 2010 7184 H 10.69798 10.630695 2010 7151 H 10.69599 10.630696 2010 121 L 10.69573 10.4493897 2010 5127 H 10.69547 10.630698 2010 6411 ML 10.69533 10.4158199 2010 5411 H 10.69533 10.6306

100 2010 116 L 10.69518 10.44938101 2010 8959 H 10.69466 10.6306102 2010 7181 H 10.69416 10.6306103 2010 2518 L 10.69286 10.44938104 2010 5149 H 10.69245 10.6306105 2010 2312 L 10.69202 10.44938106 2010 2432 L 10.69176 10.44938107 2010 5332 H 10.69065 10.6306108 2010 6952 ML 10.68752 10.41581109 2010 6742 ML 10.68697 10.41581110 2010 8642 H 10.68596 10.6306111 2010 6984 ML 10.68585 10.41581112 2010 7118 H 10.68524 10.6306113 2010 6635 ML 10.68441 10.41581

61

114 2010 430 L 10.68435 10.44938115 2010 6985 ML 10.68407 10.41581116 2010 8941 H 10.6836 10.6306117 2010 6559 ML 10.68356 10.41581118 2010 1124 L 10.68332 10.44938119 2010 8310 L 10.68281 10.44938120 2010 8615 L 10.68278 10.44938121 2010 7149 H 10.68256 10.6306122 2010 7113 H 10.6822 10.6306123 2010 7173 H 10.68178 10.6306124 2010 7122 H 10.68133 10.6306125 2010 2651 L 10.68121 10.44938126 2010 6130 ML 10.67988 10.41581127 2010 2611 L 10.67984 10.44938128 2010 6761 ML 10.67907 10.41581129 2010 7317 H 10.67856 10.6306130 2010 8919 H 10.67809 10.6306131 2010 619 L 10.6776 10.44938132 2010 6554 ML 10.67698 10.41581133 2010 8611 H 10.67658 10.6306134 2010 7185 H 10.67651 10.6306135 2010 7152 H 10.67497 10.6306136 2010 5419 H 10.67474 10.6306137 2010 2411 L 10.67438 10.44938138 2010 7196 H 10.67372 10.6306139 2010 5417 H 10.6698 10.6306140 2010 451 L 10.66966 10.44938141 2010 8972 H 10.66887 10.6306142 2010 6112 ML 10.6685 10.41581143 2010 7129 H 10.66774 10.6306144 2010 7197 H 10.6669 10.6306145 2010 8994 H 10.66566 10.6306146 2010 5333 H 10.66399 10.6306147 2010 7299 H 10.66305 10.6306148 2010 8995 H 10.66295 10.6306149 2010 5613 MH 10.66231 10.62315150 2010 421 L 10.66141 10.44938151 2010 8944 H 10.66043 10.6306152 2010 7199 H 10.65998 10.6306

62

153 2010 7312 H 10.65954 10.6306154 2010 6419 ML 10.65918 10.41581155 2010 2761 L 10.65808 10.44938156 2010 2741 L 10.65623 10.44938157 2010 7123 H 10.65464 10.6306158 2010 5712 H 10.65435 10.6306159 2010 7172 H 10.65357 10.6306160 2010 7333 H 10.65344 10.6306161 2010 6632 ML 10.65321 10.41581162 2010 6674 ML 10.65255 10.41581163 2010 819 L 10.65189 10.44938164 2010 8951 H 10.65031 10.6306165 2010 7171 H 10.65029 10.6306166 2010 2670 L 10.64923 10.44938167 2010 5543 MH 10.64916 10.62315168 2010 2840 L 10.64782 10.44938169 2010 2925 L 10.64687 10.44938170 2010 5812 MH 10.64664 10.62315171 2010 1121 L 10.64657 10.44938172 2010 6741 ML 10.64606 10.41581173 2010 7192 H 10.64524 10.6306174 2010 7121 H 10.64487 10.6306175 2010 2216 L 10.64321 10.44938176 2010 514 L 10.64319 10.44938177 2010 6942 ML 10.64308 10.41581178 2010 459 L 10.64224 10.44938179 2010 7341 H 10.64195 10.6306180 2010 2314 L 10.64152 10.44938181 2010 5811 MH 10.64052 10.62315182 2010 6532 ML 10.64029 10.41581183 2010 6762 ML 10.63999 10.41581184 2010 6983 ML 10.63877 10.41581185 2010 6636 ML 10.63613 10.41581186 2010 7125 H 10.63546 10.6306187 2010 7183 H 10.63465 10.6306188 2010 482 L 10.63459 10.44938189 2010 7313 H 10.63405 10.6306190 2010 5211 H 10.63392 10.6306191 2010 6743 ML 10.63348 10.41581

63

192 2010 2613 L 10.63312 10.44938193 2010 6649 ML 10.63228 10.41581194 2010 6658 ML 10.63158 10.41581195 2010 6634 ML 10.63153 10.41581196 2010 6648 ML 10.63113 10.41581197 2010 7222 H 10.631 10.6306198 2010 5124 H 10.63052 10.6306199 2010 129 L 10.63012 10.44938200 2010 2742 L 10.62944 10.44938201 2010 7291 H 10.62877 10.6306202 2010 8415 L 10.62751 10.44938203 2010 7349 H 10.62599 10.6306204 2010 8121 L 10.62517 10.44938205 2010 6412 ML 10.62414 10.41581206 2010 6785 ML 10.6239 10.41581207 2010 6422 ML 10.62349 10.41581208 2010 6537 ML 10.62348 10.41581209 2010 6551 ML 10.6233 10.41581210 2010 2623 L 10.62298 10.44938211 2010 5132 H 10.6227 10.6306212 2010 7112 H 10.62259 10.6306213 2010 8929 H 10.62209 10.6306214 2010 7316 H 10.62189 10.6306215 2010 2429 L 10.62167 10.44938216 2010 221 L 10.62113 10.44938217 2010 312 L 10.62088 10.44938218 2010 8923 H 10.62074 10.6306219 2010 488 L 10.62062 10.44938220 2010 2516 L 10.61929 10.44938221 2010 8124 L 10.61817 10.44938222 2010 8911 H 10.61692 10.6306223 2010 6631 ML 10.61633 10.41581224 2010 113 L 10.61617 10.44938225 2010 5530 MH 10.61526 10.62315226 2010 730 L 10.61501 10.44938227 2010 7334 H 10.61373 10.6306228 2010 4311 L 10.61266 10.44938229 2010 240 L 10.61205 10.44938230 2010 6782 ML 10.61108 10.41581

64

231 2010 7111 H 10.60965 10.6306232 2010 6639 ML 10.60865 10.41581233 2010 6533 ML 10.60812 10.41581234 2010 6747 ML 10.60743 10.41581235 2010 8420 L 10.6072 10.44938236 2010 230 L 10.60691 10.44938237 2010 7321 H 10.60287 10.6306238 2010 7221 H 10.6009 10.6306239 2010 2926 L 10.60038 10.44938240 2010 112 L 10.59596 10.44938241 2010 6512 ML 10.59551 10.41581242 2010 223 L 10.59472 10.44938243 2010 134 L 10.59451 10.44938244 2010 6727 ML 10.59376 10.41581245 2010 6852 ML 10.59338 10.41581246 2010 2734 L 10.59225 10.44938247 2010 7328 H 10.59159 10.6306248 2010 9310 L 10.59126 10.44938249 2010 6421 ML 10.59033 10.41581250 2010 5542 MH 10.59008 10.62315251 2010 7115 H 10.58999 10.6306252 2010 8930 H 10.58984 10.6306253 2010 7249 H 10.58928 10.6306254 2010 6989 ML 10.58802 10.41581255 2010 1110 L 10.58791 10.44938256 2010 6748 ML 10.58471 10.41581257 2010 7182 H 10.58462 10.6306258 2010 7232 H 10.58431 10.6306259 2010 6981 ML 10.58257 10.41581260 2010 13 L 10.58255 10.44938261 2010 5121 H 10.5825 10.6306262 2010 6623 ML 10.58207 10.41581263 2010 6811 ML 10.57965 10.41581264 2010 9410 L 10.57892 10.44938265 2010 6299 ML 10.57877 10.41581266 2010 6637 ML 10.576 10.41581267 2010 14 L 10.57501 10.44938268 2010 2622 L 10.57479 10.44938269 2010 6558 ML 10.57389 10.41581

65

270 2010 6941 ML 10.57261 10.41581271 2010 5141 H 10.57213 10.6306272 2010 7314 H 10.57172 10.6306273 2010 5995 H 10.57096 10.6306274 2010 7292 H 10.57083 10.6306275 2010 6933 ML 10.56917 10.41581276 2010 481 L 10.56752 10.44938277 2010 222 L 10.56678 10.44938278 2010 6735 ML 10.56656 10.41581279 2010 6294 ML 10.56641 10.41581280 2010 8630 H 10.56531 10.6306281 2010 5125 H 10.56424 10.6306282 2010 6518 ML 10.564 10.41581283 2010 6314 ML 10.56388 10.41581284 2010 6618 ML 10.56204 10.41581285 2010 6644 ML 10.56186 10.41581286 2010 6921 ML 10.56183 10.41581287 2010 990 L 10.56069 10.44938288 2010 7191 H 10.56005 10.6306289 2010 6842 ML 10.55988 10.41581290 2010 541 L 10.55664 10.44938291 2010 7294 H 10.55415 10.6306292 2010 6982 ML 10.55357 10.41581293 2010 2117 L 10.55319 10.44938294 2010 6841 ML 10.55257 10.41581295 2010 484 L 10.55231 10.44938296 2010 483 L 10.552 10.44938297 2010 6770 ML 10.55174 10.41581298 2010 6922 ML 10.54862 10.41581299 2010 6540 ML 10.54839 10.41581300 2010 6328 ML 10.54777 10.41581301 2010 6822 ML 10.54625 10.41581302 2010 6429 ML 10.54511 10.41581303 2010 7194 H 10.54416 10.6306304 2010 6793 ML 10.54284 10.41581305 2010 7322 H 10.54258 10.6306306 2010 2762 L 10.54149 10.44938307 2010 6647 ML 10.53968 10.41581308 2010 7329 H 10.53722 10.6306

66

309 2010 8123 L 10.53599 10.44938310 2010 6988 ML 10.53413 10.41581311 2010 6851 ML 10.53401 10.41581312 2010 4229 L 10.53323 10.44938313 2010 7358 H 10.53099 10.6306314 2010 2118 L 10.53093 10.44938315 2010 6911 ML 10.53021 10.41581316 2010 8616 H 10.53016 10.6306317 2010 7141 H 10.53002 10.6306318 2010 8993 H 10.52701 10.6306319 2010 6734 ML 10.52545 10.41581320 2010 6872 ML 10.5247 10.41581321 2010 311 L 10.52135 10.44938322 2010 3329 ML 10.52091 10.41581323 2010 6611 ML 10.5209 10.41581324 2010 6912 ML 10.51988 10.41581325 2010 6923 ML 10.51912 10.41581326 2010 8918 H 10.51842 10.6306327 2010 440 L 10.51836 10.44938328 2010 2711 L 10.51807 10.44938329 2010 6321 ML 10.51675 10.41581330 2010 546 L 10.51565 10.44938331 2010 5992 H 10.51346 10.6306332 2010 6732 ML 10.51042 10.41581333 2010 5122 H 10.51031 10.6306334 2010 8943 H 10.5094 10.6306335 2010 1222 L 10.50885 10.44938336 2010 6979 ML 10.5084 10.41581337 2010 6862 ML 10.50761 10.41581338 2010 7296 H 10.50441 10.6306339 2010 6562 ML 10.50369 10.41581340 2010 517 L 10.50307 10.44938341 2010 542 L 10.50126 10.44938342 2010 5819 ML 10.50053 10.41581343 2010 6731 ML 10.49968 10.41581344 2010 6311 ML 10.49921 10.41581345 2010 6960 ML 10.49918 10.41581346 2010 3510 ML 10.499 10.41581347 2010 8210 L 10.49775 10.44938

67

348 2010 8952 H 10.49767 10.6306349 2010 6971 ML 10.49512 10.41581350 2010 6536 ML 10.49083 10.41581351 2010 6646 ML 10.48956 10.41581352 2010 6515 ML 10.48904 10.41581353 2010 2218 L 10.48662 10.44938354 2010 7143 H 10.48479 10.6306355 2010 2625 L 10.48414 10.44938356 2010 8992 H 10.48309 10.6306357 2010 12 L 10.48304 10.44938358 2010 5135 H 10.48207 10.6306359 2010 2919 L 10.48037 10.44938360 2010 6577 ML 10.4796 10.41581361 2010 6555 ML 10.4774 10.41581362 2010 8914 H 10.47488 10.6306363 2010 6931 ML 10.47416 10.41581364 2010 5323 H 10.47038 10.6306365 2010 6416 ML 10.46662 10.41581366 2010 8999 H 10.46653 10.6306367 2010 6638 ML 10.46646 10.41581368 2010 5321 H 10.46571 10.6306369 2010 7331 H 10.46526 10.6306370 2010 3326 ML 10.4628 10.41581371 2010 118 L 10.4612 10.44938372 2010 6210 ML 10.45925 10.41581373 2010 7323 H 10.4591 10.6306374 2010 2769 L 10.45824 10.44938375 2010 6291 ML 10.45799 10.41581376 2010 7250 H 10.45613 10.6306377 2010 5142 H 10.45363 10.6306378 2010 5414 H 10.45336 10.6306379 2010 7325 H 10.44995 10.6306380 2010 6781 ML 10.44976 10.41581381 2010 6539 ML 10.4488 10.41581382 2010 6318 ML 10.44576 10.41581383 2010 6324 ML 10.44404 10.41581384 2010 4215 L 10.44367 10.44938385 2010 8618 H 10.44115 10.6306386 2010 2515 L 10.43652 10.44938

68

387 2010 6713 ML 10.43608 10.41581388 2010 6783 ML 10.43606 10.41581389 2010 11 L 10.43597 10.44938390 2010 5214 H 10.435 10.6306391 2010 5619 MH 10.4347 10.62315392 2010 6652 ML 10.43441 10.41581393 2010 6651 ML 10.43009 10.41581394 2010 2752 L 10.42986 10.44938395 2010 6664 ML 10.42717 10.41581396 2010 2663 L 10.42505 10.44938397 2010 6665 ML 10.42297 10.41581398 2010 6861 ML 10.42186 10.41581399 2010 6673 ML 10.41925 10.41581400 2010 6123 ML 10.41882 10.41581401 2010 6624 ML 10.41796 10.41581402 2010 4213 L 10.4127 10.44938403 2010 7327 H 10.4123 10.6306404 2010 551 L 10.41211 10.44938405 2010 6666 ML 10.41001 10.41581406 2010 6293 ML 10.40974 10.41581407 2010 8510 L 10.40909 10.44938408 2010 6122 ML 10.40867 10.41581409 2010 2517 L 10.40769 10.44938410 2010 555 L 10.40603 10.44938411 2010 5310 H 10.40538 10.6306412 2010 2632 L 10.39773 10.44938413 2010 6415 ML 10.39772 10.41581414 2010 7231 H 10.3964 10.6306415 2010 250 L 10.39515 10.44938416 2010 1123 L 10.39515 10.44938417 2010 2662 L 10.39473 10.44938418 2010 8412 L 10.39408 10.44938419 2010 533 L 10.39348 10.44938420 2010 5714 H 10.38961 10.6306421 2010 2837 L 10.38929 10.44938422 2010 6423 ML 10.38524 10.41581423 2010 7315 H 10.38254 10.6306424 2010 5996 H 10.38223 10.6306425 2010 6129 ML 10.38063 10.41581

69

426 2010 8971 H 10.3806 10.6306427 2010 5413 H 10.37851 10.6306428 2010 3214 ML 10.37623 10.41581429 2010 515 L 10.37547 10.44938430 2010 6114 ML 10.3724 10.41581431 2010 2664 L 10.37167 10.44938432 2010 914 L 10.36644 10.44938433 2010 2214 L 10.3661 10.44938434 2010 1223 L 10.36453 10.44938435 2010 6725 ML 10.36272 10.41581436 2010 2732 L 10.36197 10.44938437 2010 115 L 10.36055 10.44938438 2010 7359 H 10.3602 10.6306439 2010 6330 ML 10.35967 10.41581440 2010 6791 ML 10.35805 10.41581441 2010 554 L 10.35565 10.44938442 2010 2440 L 10.3545 10.44938443 2010 470 L 10.35264 10.44938444 2010 2423 L 10.35154 10.44938445 2010 6672 ML 10.35074 10.41581446 2010 2652 L 10.34782 10.44938447 2010 2911 L 10.34649 10.44938448 2010 5136 H 10.34607 10.6306449 2010 2763 L 10.34336 10.44938450 2010 6645 ML 10.34089 10.41581451 2010 6972 ML 10.33817 10.41581452 2010 8991 H 10.33764 10.6306453 2010 519 L 10.33612 10.44938454 2010 536 L 10.33412 10.44938455 2010 722 L 10.33105 10.44938456 2010 2712 L 10.32935 10.44938457 2010 4216 L 10.32921 10.44938458 2010 620 L 10.32781 10.44938459 2010 520 L 10.32712 10.44938460 2010 545 L 10.32646 10.44938461 2010 6531 ML 10.32558 10.41581462 2010 111 L 10.32411 10.44938463 2010 6511 ML 10.32305 10.41581464 2010 6119 ML 10.32286 10.41581

70

465 2010 6516 ML 10.3227 10.41581466 2010 2766 L 10.31581 10.44938467 2010 512 L 10.31568 10.44938468 2010 2627 L 10.31238 10.44938469 2010 7242 H 10.31167 10.6306470 2010 2924 L 10.30097 10.44938471 2010 3411 ML 10.29726 10.41581472 2010 6556 ML 10.29705 10.41581473 2010 511 L 10.29306 10.44938474 2010 6951 ML 10.29223 10.41581475 2010 6576 ML 10.28847 10.41581476 2010 460 L 10.2818 10.44938477 2010 5541 MH 10.27943 10.62315478 2010 539 L 10.27754 10.44938479 2010 6535 ML 10.27603 10.41581480 2010 6522 ML 10.2753 10.41581481 2010 114 L 10.27499 10.44938482 2010 2628 L 10.27215 10.44938483 2010 2629 L 10.2689 10.44938484 2010 2835 L 10.2668 10.44938485 2010 8411 L 10.26497 10.44938486 2010 4111 L 10.26165 10.44938487 2010 8122 L 10.25931 10.44938488 2010 138 L 10.25541 10.44938489 2010 8413 L 10.25427 10.44938490 2010 7351 H 10.25366 10.6306491 2010 1221 L 10.25275 10.44938492 2010 2922 L 10.25256 10.44938493 2010 8414 L 10.24171 10.44938494 2010 5511 MH 10.23778 10.62315495 2010 6714 ML 10.23426 10.41581496 2010 2839 L 10.23297 10.44938497 2010 5611 MH 10.23197 10.62315498 2010 2751 L 10.23177 10.44938499 2010 2626 L 10.23024 10.44938500 2010 3218 ML 10.22774 10.41581501 2010 7297 H 10.22694 10.6306502 2010 535 L 10.22687 10.44938503 2010 2813 L 10.2236 10.44938

71

504 2010 544 L 10.21841 10.44938505 2010 2765 L 10.21578 10.44938506 2010 2929 L 10.21325 10.44938507 2010 5133 H 10.20214 10.6306508 2010 616 L 10.20181 10.44938509 2010 713 L 10.1979 10.44938510 2010 812 L 10.196 10.44938511 2010 3310 ML 10.18981 10.41581512 2010 4212 L 10.17628 10.44938513 2010 6821 ML 10.17613 10.41581514 2010 4312 L 10.17345 10.44938515 2010 6327 ML 10.16885 10.41581516 2010 2631 L 10.16744 10.44938517 2010 2834 L 10.16489 10.44938518 2010 5129 H 10.16222 10.6306519 2010 723 L 10.16147 10.44938520 2010 2433 L 10.15508 10.44938521 2010 6566 ML 10.14386 10.41581522 2010 7241 H 10.1363 10.6306523 2010 6613 ML 10.13095 10.41581524 2010 6557 ML 10.12683 10.41581525 2010 2832 L 10.12497 10.44938526 2010 6514 ML 10.12358 10.41581527 2010 2764 L 10.10833 10.44938528 2010 6932 ML 10.10635 10.41581529 2010 532 L 10.09983 10.44938530 2010 2313 L 10.09682 10.44938531 2010 133 L 10.08708 10.44938532 2010 4313 L 10.07378 10.44938533 2010 6312 ML 10.06748 10.41581534 2010 313 L 10.05138 10.44938535 2010 6612 ML 10.04564 10.41581536 2010 814 L 10.04399 10.44938537 2010 6517 ML 10.04031 10.41581538 2010 8416 L 10.03607 10.44938539 2010 2612 L 10.02737 10.44938540 2010 813 L 10.02318 10.44938541 2010 320 L 10.02125 10.44938542 2010 2731 L 10.02074 10.44938

72

543 2010 2831 L 9.997386 10.44938544 2010 9610 L 9.990405 10.44938545 2010 5324 H 9.987166 10.6306546 2010 2927 L 9.985806 10.44938547 2010 6519 ML 9.972829 10.41581548 2010 2814 L 9.966008 10.44938549 2010 548 L 9.963091 10.44938550 2010 612 L 9.958277 10.44938551 2010 6715 ML 9.955535 10.41581552 2010 5612 MH 9.940853 10.62315553 2010 2633 L 9.932098 10.44938554 2010 3215 ML 9.894131 10.41581555 2010 6569 ML 9.888698 10.41581556 2010 4314 L 9.87947 10.44938557 2010 6513 ML 9.853362 10.41581558 2010 711 L 9.846224 10.44938559 2010 2923 L 9.84479 10.44938560 2010 6871 ML 9.83392 10.41581561 2010 6561 ML 9.818723 10.41581562 2010 742 L 9.786504 10.44938563 2010 1210 L 9.775996 10.44938564 2010 2211 L 9.763648 10.44938565 2010 2412 L 9.728057 10.44938566 2010 3412 ML 9.712757 10.41581567 2010 3216 ML 9.696671 10.41581568 2010 6578 ML 9.68116 10.41581569 2010 741 L 9.678336 10.44938570 2010 752 L 9.673696 10.44938571 2010 513 L 9.673099 10.44938572 2010 6712 ML 9.672759 10.41581573 2010 4222 L 9.663192 10.44938574 2010 751 L 9.648751 10.44938575 2010 422 L 9.6443 10.44938576 2010 615 L 9.629807 10.44938577 2010 2311 L 9.613351 10.44938578 2010 2833 L 9.595622 10.44938579 2010 6521 ML 9.583624 10.41581580 2010 2713 L 9.53549 10.44938581 2010 4224 L 9.52105 10.44938

73

582 2010 611 L 9.516431 10.44938583 2010 4214 L 9.510756 10.44938584 2010 6575 ML 9.483211 10.41581585 2010 2634 L 9.427506 10.44938586 2010 2212 L 9.426235 10.44938587 2010 2654 L 9.369188 10.44938588 2010 6121 ML 9.369188 10.41581589 2010 6986 ML 9.329352 10.41581590 2010 4225 L 9.269818 10.44938591 2010 6534 ML 9.259437 10.41581592 2010 4223 L 9.164895 10.44938593 2010 2640 L 9.160367 10.44938594 2010 2658 L 8.934501 10.44938595 2010 2655 L 8.934435 10.44938596 2010 721 L 8.925478 10.44938597 2010 2213 L 8.22379 10.44938598 2010 2860 L 7.876186 10.44938

Table 2: Ranking of Export Sophistication for 69 Countries in 2010 Reportername lnesy(gdpperworker)1 Ireland 10.594592 Switzerland 10.58663 Japan 10.580334 Korea,Rep. 10.579175 Hungary 10.573746 Singapore 10.568197 HongKong,China 10.567998 Germany 10.565629 Malta 10.56205

10 France 10.558811 Mexico 10.5576612 Thailand 10.551213 UnitedStates 10.5507714 Panama 10.5503115 UnitedKingdom 10.5448916 Austria 10.54435

74

17 Malaysia 10.5427418 Barbados 10.5407719 Israel 10.539220 Italy 10.5389321 Cyprus 10.5373222 Philippines 10.5369223 Spain 10.5365724 Sweden 10.5345525 Jordan 10.5342126 Denmark 10.5250127 Morocco 10.522928 Jamaica 10.5227429 Finland 10.5226330 Tunisia 10.5163331 Portugal 10.5115632 Canada 10.5086333 Turkey 10.5062334 India 10.4972335 Argentina 10.4939536 Greece 10.4882537 Brazil 10.4878238 Senegal 10.4848939 Togo 10.4844440 Mali 10.4801341 Madagascar 10.4794542 Honduras 10.4779743 ElSalvador 10.4773644 Guatemala 10.4758145 NewZealand 10.4721846 Egypt,ArabRep. 10.4717247 Indonesia 10.4707348 Uruguay 10.4691249 Ghana 10.4628850 SriLanka 10.462151 Australia 10.4611852 Fiji 10.4599753 Benin 10.4590954 Malawi 10.4588955 Nicaragua 10.45743

75

56 Pakistan 10.4570957 BurkinaFaso 10.45567

58CentralAfricanRepublic 10.45386

59 Iceland 10.4532160 Bahrain 10.4529961 Peru 10.4529662 Norway 10.4525663 Colombia 10.4512264 Chile 10.4481765 Cameroon 10.4438966 Paraguay 10.4435867 Ecuador 10.4418668 Venezuela 10.430469 Algeria 10.42433

76

B. CHAPTER TWO APPENDIX

Table2:SourceOfEconomicGrowth

PercentageRateOfChange Country Year GDPW HC PC TFP

Algeria 1970-0.4523563 1.09251 0.2779579 -1.276064

Algeria 1980 6.581154 1.82281 4.307938 3.938252

Algeria 1990 2.187605 1.980311 5.302029-0.8888729

Algeria 2000 -1.966276 1.687884-0.9256458 -2.791695

Algeria 2010 1.094818 1.191511 1.432905 -0.176353Argentina 1970 2.571726 0.8927774 3.230152 0.907615

Argentina 1980 2.031031 1.304399 3.716526-0.0693699

Argentina 1990 -3.58119 1.383399-0.6800652 -4.283646

Argentina 2000 2.539845 1.389452 0.4811478 1.450133Argentina 2010 1.164618 0.5208468 1.428413 0.3442743Australia 1970 3.273344 1.037719 3.230143 1.512125

Australia 1980 1.292419 0.9087324 2.350254-0.0920155

Australia 1990-0.0884056 0.884161 0.4837608

-0.8404346

Australia 2000 2.740924 1.234416 1.781209 1.326066

Australia 2010 1.383572 0.6911874 3.531246-0.2448347

Austria 1970 5.379868 1.667684 5.280743 2.519875Austria 1980 2.430619 1.493312 3.15911 0.3875937Austria 1990 1.953039 1.410313 2.610559 0.1466448Austria 2000 1.815901 1.277099 1.856031 0.3477544

Austria 2010 0.6194878 0.828253 2.128124-0.6377226

Bahrain 1970 2.868204 1.665934-0.7981491 2.015417

Bahrain 1980 2.908421 1.773019 -1.945438 2.362493Bahrain 1990 -1.295948 1.51951 4.393024 -3.763718Bahrain 2000 1.604958 1.76869 - 0.5143841

77

0.2862072Bahrain 2010 -2.896471 0.6126845 0.0992966 -3.339737Barbados 1970 5.363913 1.992782 5.173578 2.321468Barbados 1980 0.9932041 1.398947 3.342953 -1.047265

Barbados 1990-0.1056767 0.6765342 0.9135437 -0.860424

Barbados 2000 0.5150986 0.9815013 0.3088856-0.2444395

Barbados 2010 2.750111 1.272234 3.429003 0.7661432Benin 1970 1.007032 0.6114095 -2.851419 1.538356

Benin 1980 1.645465 1.091707-0.1419735 0.9608726

Benin 1990 1.218767 1.526034 2.520494-0.6354388

Benin 2000 0.8326435 0.8639002 3.903198 -1.034225Benin 2010 1.054802 2.047629 2.913818 -1.278669Brazil 1970 2.937717 1.062196 1.329956 1.78716Brazil 1980 4.226074 2.169296 6.184521 0.7317538Brazil 1990 -2.049656 1.627442 1.064701 -3.491394

Brazil 2000-0.3805065 1.360232 1.17424 -1.679361

Brazil 2010 1.58618 0.9509683 1.673012 0.3969373BurkinaFaso 1970 2.016683 0.1180851 -2.990203 2.924333

BurkinaFaso 1980 0.2457142 0.2564597 2.664442-0.8053797

BurkinaFaso 1990 0.8169031 0.5174661 0.6730032 0.2481098

BurkinaFaso 2000 3.129926 0.9407049 8.781643-0.3982885

BurkinaFaso 2010 0.4265594 1.275799 0.5671024-0.6153697

Cameroon 1970 2.133274 1.532257 -1.700249 1.667744Cameroon 1980 2.549529 1.605111 1.496239 0.9803458Cameroon 1990 1.456242 1.594284 4.341955 -1.044773Cameroon 2000 -1.670341 0.8501005 1.887989 -2.862945Cameroon 2010 0.7790852 0.5766904 1.030436 0.0526588Canada 1970 2.626934 0.6389356 2.536955 1.361652Canada 1980 0.3245354 1.211229 1.601452 -1.015467Canada 1990 0.4341316 1.512201 1.582508 -1.101271Canada 2000 2.405338 1.213129 1.623936 1.056643Canada 2010 0.4985523 0.4796743 2.908688 -

78

0.7826965Central AfricanRepublic 1970

-0.4934645 0.6958634

-0.1643276

-0.9054649

Central AfricanRepublic 1980

-0.9800148 1.303547 -1.182446 -1.463184

Central AfricanRepublic 1990

-0.6499147 1.176527 -2.004757 -0.776618

Central AfricanRepublic 2000 -1.434965 0.455513 1.391859 -2.199472Central AfricanRepublic 2010 1.13255 0.3351164

-0.1308346 0.9511974

Chile 1970 2.610025 1.122462 3.361864 0.7485604

Chile 1980 0.8369637 1.231167 1.739159-0.5618407

Chile 1990-0.8042622 1.148779 0.5694199 -1.761853

Chile 2000 4.21257 1.287459 4.759369 1.779381

Chile 2010 0.6713963 0.621109 3.555489-0.9180581

Colombia 1970 5.359575 1.568381 5.201329 2.592321Colombia 1980 -1.115731 2.286792 -1.35513 -2.200689Colombia 1990 -2.413235 1.158514 -1.011744 -2.855564

Colombia 2000 1.718922 1.320804 3.52747-0.3300818

Colombia 2010 1.68272 0.578444 1.912651 0.6639877Cyprus 1970 6.554794 2.102379 5.699606 3.26533Cyprus 1980 5.757246 1.19073 5.424347 3.169422Cyprus 1990 6.53367 0.2215409 6.965151 4.086738Cyprus 2000 5.332108 0.5100274 4.567404 3.483146Cyprus 2010 2.76885 0.6022799 3.673553 1.15305Denmark 1970 3.411255 1.006447 3.629322 1.539259Denmark 1980 0.2144814 1.242789 2.40036 -1.410306Denmark 1990 1.423416 1.426029 1.067572 0.1156778Denmark 2000 2.595663 1.03753 1.904202 1.272131Denmark 2010 0.4687023 0.8573461 3.404465 -1.229193Ecuador 1970 4.698173 0.930503 2.868772 3.128041Ecuador 1980 3.040218 2.856052 6.714201 -1.089023Ecuador 1990 -2.183447 1.946087 0.1114178 -3.524093Ecuador 2000 -1.225739 0.4381001 0.7535458 -1.767936Ecuador 2010 1.368418 0.5914724 2.780828 0.0544583

79

Egypt 1970 3.896656 1.390899 0.1147747 2.926878Egypt 1980 4.433575 1.357023 2.320137 2.758724Egypt 1990 0.3143883 1.678948 2.634811 -1.679994Egypt 2000 3.350573 2.382809 12.36449 -2.326191

Egypt 2010 1.089582 0.5033708 2.970772-0.2280312

Elsalvador 1970 2.097816 2.099558 1.444721 0.2143542

Elsalvador 1980-0.3505707 0.7958968 1.279513 -1.306061

Elsalvador 1990-0.7713223 0.4972148 2.398081 -1.895823

Elsalvador 2000 2.548494 0.854646 2.435818 1.172061

Elsalvador 2010-0.8161068 1.874306 1.784487 -2.660773

Fiji 1970 0.974617 0.7235181 0.2639294 0.4027632

Fiji 1980 0.1035309 0.973115 1.2185-0.9505612

Fiji 1990 0.3503513 0.9855998 0.9847164 -0.634957

Fiji 2000 1.643572 0.5624115-0.0561714 1.285293

Fiji 2010 -1.787672 0.6438601 1.804018 -2.814384Finland 1970 4.474087 1.975797 4.864941 1.544873Finland 1980 2.923155 1.491795 4.067478 0.5813846Finland 1990 1.540861 0.9822679 1.651478 0.3377538Finland 2000 2.020721 1.324552 1.957703 0.4872292Finland 2010 1.672373 0.4686642 3.535013 0.1918137France 1970 4.711008 1.148367 4.453135 2.472068France 1980 3.092249 1.595905 4.223403 0.6292697

France 1990 0.7966128 0.8511554 1.797477-0.3668287

France 2000 1.431656 1.250044 1.75813 0.0139436

France 2010 0.2413464 0.5451369 2.238922-0.8627396

Germany 1970 4.340225 1.386672 5.466504 1.607208Germany 1980 3.016148 1.84472 3.970184 0.4700249

Germany 1990 0.0016594 1.168674-0.9020615

-0.4836719

Germany 2000 1.578922 1.152884 2.57081-0.0418776

Germany 2010 0.3314209 0.7242918 2.213421 -

80

0.8842835Ghana 1970 0.6468678 1.182731 3.621845 -1.340771Ghana 1980 -1.944981 2.183595 -1.211929 -3.008053Ghana 1990 1.121836 1.826255 -0.906477 0.1973826Ghana 2000 1.597681 0.1020443 2.467003 0.7152003Ghana 2010 3.615551 0.8356619 3.71706 1.829028Greece 1970 8.046074 0.8727181 7.738504 4.907647Greece 1980 3.421148 1.227874 4.824977 1.00623Greece 1990 0.1829338 1.236398 1.185789 -1.036763

Greece 2000 0.7295513 1.021916 1.347609-0.3998434

Greece 2010 1.755648 1.12335 3.689604-0.2145658

Guatemala 1970 3.856182 0.9569724 2.49904 2.390327Guatemala 1980 6.340599 1.212222 4.625947 4.001848Guatemala 1990 -5.437517 1.008085 -2.574053 -5.263496Guatemala 2000 2.779531 1.545405 2.990503 0.7572436Guatemala 2010 -1.058044 1.579649 2.403555 -2.909582Honduras 1970 2.258873 0.9614536 2.999584 0.6248364Honduras 1980 2.378957 1.781482 1.336718 0.7442471Honduras 1990 -1.346703 1.773711 0.8196068 -2.80556Honduras 2000 -1.590948 0.8668911 2.585592 -3.02501Honduras 2010 1.779442 0.6956422 1.805906 0.7174128HongKong 1970 6.16169 1.776181 3.92005 3.678032HongKong 1980 4.956799 1.517165 4.645576 2.407258HongKong 1990 5.137253 1.143142 7.113113 2.024021HongKong 2000 2.745361 1.12835 4.913979 0.3677534HongKong 2010 2.424126 0.8304989 4.365692 0.4270134Hungary 1970 3.325701 0.8743 2.511387 1.911162Hungary 1980 2.461109 0.7968664 4.270325 0.5180013Hungary 1990 1.228266 0.600034 2.342987 0.0530575Hungary 2000 0.9395027 0.4708636 1.275778 0.2030174Hungary 2010 1.671276 0.7552731 3.17503 0.1174831Iceland 1970 2.50658 1.046606 3.360853 0.6962725Iceland 1980 3.14724 1.583149 2.851124 1.145659Iceland 1990 1.268826 0.5612147 2.364597 0.1124951Iceland 2000 0.4467201 0.5924273 0.0477314 0.0340424Iceland 2010 0.6728077 1.189777 3.017235 -1.12003India 1970 2.912846 1.195784 1.79533 1.519212

81

India 1980 -1.168569 1.192522 -1.49711 -1.473512India 1990 4.989171 1.249446 4.074354 2.807505India 2000 3.280497 1.450168 4.198275 0.9234537India 2010 5.95788 0.772959 6.064396 3.438747Indonesia 1970 1.481342 1.035344 0.5683708 0.6000991Indonesia 1980 3.156259 1.343869 5.589167 0.4114417

Indonesia 1990 2.413845 1.789405 5.515261-0.6050925

Indonesia 2000 2.249765 1.453515 4.592829-0.2397236

Indonesia 2010 2.727928 1.073189 1.827974 1.40566Ireland 1970 3.864737 0.5814397 2.519798 2.643639Ireland 1980 3.542476 0.7505695 3.637865 1.839099Ireland 1990 2.975473 1.175436 3.687572 0.9710321Ireland 2000 3.926325 0.6862175 0.5678844 3.279157Ireland 2010 0.1737404 0.1385736 4.48946 -1.400626Israel 1970 5.106623 0.2395326 5.865253 3.010603Israel 1980 0.4974246 0.7938221 3.716063 -1.260737Israel 1990 3.338766 0.7904446 3.638573 1.608439Israel 2000 0.8930779 0.7088864 1.094751 0.0568562

Israel 2010 1.272163 0.7776737 2.545147-0.0887769

Italy 1970 5.354967 1.041135 5.618505 2.8033Italy 1980 3.100671 1.623393 4.057746 0.6739415

Italy 1990 1.564856 1.240197 2.840843-0.2035542

Italy 2000 1.595345 1.261311 2.790604-0.1706327

Italy 2010-0.4125023 1.067744 2.853107 -2.069416

Japan 1970 8.11348 0.8672667 7.3421 5.109519Japan 1980 3.403397 1.087013 7.573938 0.1756988Japan 1990 3.575516 0.7197297 4.341345 1.660653Japan 2000 0.7840252 0.3504694 3.365002 -0.56124

Japan 2010 1.155748 0.4945195 3.160114-0.2184177

Jordan 1970 0.5635452 1.174147 0.2107334-0.2926753

Jordan 1980 2.971773-0.0514758 3.692255 1.787818

82

Jordan 1990-0.5036545 1.497405 3.067446 -2.519173

Jordan 2000 -0.101614 0.1520228 2.280893-0.9561639

Jordan 2010 2.173901 1.864463 4.083099-0.4227119

Madagascar 1970 1.032801 0.0349128-0.5962944 1.206187

Madagascar 1980 -1.259079 0.0842428-0.5036736 -1.149309

Madagascar 1990 -1.592364 0.1039279-0.2496433 -1.579613

Madagascar 2000 -1.47162 0.3424466 -1.211586 -1.301236Madagascar 2010 -1.135869 0.3758168 0.845356 -1.666634Malawi 1970 1.268525 0.1183277 0.6466293 0.9758578Malawi 1980 2.651658 0.17205 3.249302 1.464115

Malawi 1990-0.4120779 1.204258 0.1057053 -1.253814

Malawi 2000 1.295586 0.9821689 1.941261-0.0030833

Malawi 2010 1.442308 1.725016 3.065042-0.7249166

Malaysia 1970 3.109503 1.204411 3.290348 1.216733Malaysia 1980 4.389153 0.7567084 5.3057 2.131277Malaysia 1990 3.317575 1.165966 6.273432 0.4661452Malaysia 2000 3.035851 1.352679 5.376043 0.3554619Malaysia 2010 3.26417 0.8099353 3.058615 1.71217

Mali 1970 0.9072542 0.4157227-0.2289677 0.7042793

Mali 1980 2.260242 0.8461916 2.479248 0.8751418

Mali 1990 0.4442596 0.1088935 1.966553-0.2776615

Mali 2000 1.328859 0.3553402 1.036129 0.7488585

Mali 2010 1.915903 2.727828 0.2913475-0.0078864

Malta 1970 4.108944 1.084274 4.612532 1.860345Malta 1980 11.34568 1.425322 6.394167 8.280639

Malta 1990 3.168364 0.994848 8.1532-0.1887402

Malta 2000 3.579845 0.7402408 5.307322 1.332467

83

Malta 2010 1.814165 0.8063459 1.306009 0.8429303Mexico 1970 5.832424 1.142703 6.37949 2.961581

Mexico 1980 1.374722 1.645923 1.654844-0.2741449

Mexico 1990 -1.607561 1.547112 2.417116 -3.441774

Mexico 2000 1.026268 1.311922 1.737509-0.4260977

Mexico 2010-0.3313446

-0.2426195 2.025642

-0.8372514

Morocco 1970 2.048527 0.4926156 0.2124786 1.648357Morocco 1980 3.084547 1.217979 -3.231092 3.334761Morocco 1990 2.748632 2.10046 -1.19828 1.736756Morocco 2000 0.0004768 0.9190905 7.971296 -3.245842

Morocco 2010 3.205843 1.140313 7.962017-0.1856323

Mozambique 1970 2.723513 0.6882572 -4.119358 3.621769Mozambique 1980 -4.52425 0.426321 -5.43117 -3.017599Mozambique 1990 1.899462 1.954834 -1.265488 1.007334Mozambique 2000 1.194305 0.0228596 10.93879 -2.430812Mozambique 2010 4.772348 0.9247398 4.587383 2.638936Newzealand 1970 2.352228 1.175323 3.114214 0.537071

Newzealand 1980 0.1616192 0.7613194 1.95446-0.9934366

Newzealand 1990 -1.106615 0.6823266 0.6543541 -1.779711Newzealand 2000 2.251964 1.362254 0.2289348 1.263705

Newzealand 2010 0.9603786 0.9719932 1.495104-0.1842412

Nicaragua 1970 4.035044 1.834045 5.134869 1.111727

Nicaragua 1980 -3.977129 1.055825-0.2605226 -4.598559

Nicaragua 1990 -5.746393 2.070318 -3.12233 -6.103137

Nicaragua 2000 0.0963688 0.5042827 0.5737495-0.4308379

Nicaragua 2010 0.1588535 0.536406 1.167259 -0.585734Norway 1970 3.708296 1.095675 5.260735 1.238151

Norway 1980 1.246347 1.246425 1.773453-0.1739972

Norway 1990 2.120037 0.9581816 3.013945 0.4834535Norway 2000 2.537642 1.300024 1.616354 1.133229Norway 2010 1.071606 0.8250725 3.016491 -

84

0.4766346Pakistan 1970 3.963184 0.5118262 1.333601 3.180172

Pakistan 1980-0.0141886 0.9894596 -1.253234

-0.2635593

Pakistan 1990 5.835676 0.5843782 7.329063 3.025552Pakistan 2000 0.0270102 0.9088444 2.752389 -1.490204Pakistan 2010 1.048336 0.9180105 0.6391335 0.2223549Panama 1970 4.004889 1.190809 2.106848 2.511787Panama 1980 4.71858 1.642776 7.509699 1.139719Panama 1990 -4.413242 0.9864151 -2.226906 -4.339261

Panama 2000 0.6410599 0.711689 1.628942-0.3733226

Panama 2010 2.068377 0.5446136 1.462612 1.220824Paraguay 1970 2.686527 0.7064809 0.2226483 2.139711Paraguay 1980 5.187988 0.8898497 1.984096 3.937037Paraguay 1990 -1.591482 0.6395376 2.288809 -2.775279

Paraguay 2000-0.5708218 0.6059968 1.859064 -1.590331

Paraguay 2010 0.0374413 0.5596638 -1.465092 0.1459469Peru 1970 3.499444 1.262814 1.880662 2.03274

Peru 1980-0.2198458 1.756451 1.948237 -2.039586

Peru 1990 -5.533085 1.574466 0.2065659 -6.656144

Peru 2000 0.8652973 0.6832314-0.6749439 0.6302637

Peru 2010 2.808094 0.747025 1.730738 1.736444Philippines 1970 0.8920002 0.7568181 0.1141644 0.3472578Philippines 1980 1.764774 1.647553 1.938047 0.021358Philippines 1990 -1.086178 1.009862 2.145872 -2.470923

Philippines 2000 0.7110023 0.9223211 1.097565-0.2691493

Philippines 2010 1.825104 0.1802039 1.076221 1.349214Portugal 1970 6.718931 1.749781 4.483204 4.067121Portugal 1980 3.737555 1.749562 4.012623 1.241183Portugal 1990 1.124697 1.285034 4.129639 -1.099057Portugal 2000 1.929598 1.178504 3.14168 0.1032459Portugal 2010 0.2006531 1.190368 3.746119 -1.833113

Senegal 1970-0.4821968 0.3803617 -1.097631

-0.3748209

Senegal 1980 - 0.8767098 0.7693768 -1.107918

85

0.2666283Senegal 1990 1.57011 0.8315217 1.559525 0.4983472

Senegal 2000 0.3946495 0.907135 0.6838131-0.4387893

Senegal 2010 0.4517365 1.119217 2.859478 -1.241767Singapore 1970 5.793789 3.75949 4.493278 1.792149Singapore 1980 3.620148 1.494758 6.283236 0.5451922

Singapore 1990 2.206841 0.7521081 6.229258-0.3527266

Singapore 2000 5.530653 0.4262209 3.932037 3.947513Singapore 2010 2.050476 0.6746721 3.753595 0.3597593SouthKorea 1970 28.88491 22.17867 28.48783 4.624217SouthKorea 1980 -18.71844 -18.66786 -15.40027 -1.128885SouthKorea 1990 5.31498 1.401646 6.235809 2.31806SouthKorea 2000 5.188885 1.37027 7.922173 1.656487SouthKorea 2010 3.405285 1.241459 7.181768 0.203524Spain 1970 7.84936 1.256576 3.350439 5.901809Spain 1980 4.518805 1.821942 6.462793 1.165382Spain 1990 0.4273129 2.00568 2.263737 -1.663526

Spain 2000 1.491776 1.497525 2.56814-0.3590519

Spain 2010-0.3733826 0.689342 2.737665 -1.738671

SriLanka 1970 2.149272 0.3223017 1.71746 1.366568SriLanka 1980 3.074265 0.9805954 2.351418 1.641298

SriLanka 1990-0.4582024 0.8779144 0.0097275 -1.049615

SriLanka 2000 3.486495 0.5050209 5.240907 1.418632SriLanka 2010 3.93095 0.5515909 4.82379 1.969533Sweden 1970 4.047384 0.9974015 3.91818 2.086126Sweden 1980 0.2762032 1.411536 1.640205 -1.210794Sweden 1990 0.5713272 0.7901525 1.082582 -0.315327Sweden 2000 2.395792 0.8369541 2.179317 1.115858Sweden 2010 1.565456 0.8174884 1.750727 0.4399989Switzerl 1970 2.696915 0.4427671 4.380922 0.9545568

Switzerl 1980 1.009359 0.81815 3.710814-0.7633701

Switzerl 1990 0.166254 0.930506 0.8860683-0.7495875

Switzerl 2000 0.4331398 1.015588 0.8042717 -

86

0.5127138

Switzerl 2010 0.8973885 0.7921171 1.818104-0.2333043

Thailand 1970 5.307674 0.9499204 5.444355 2.87459Thailand 1980 3.574762 0.9086061 4.687929 1.418979Thailand 1990 5.005522 1.152694 5.488634 2.421968

Thailand 2000 3.477869 1.385401 7.917318-0.0630646

Thailand 2010 3.097754 0.7241023 3.46674 1.468581Togo 1970 4.612336 0.970217 0.5407524 3.783842Togo 1980 0.4891014 1.752751 5.259256 -2.420796Togo 1990 -2.112784 2.691462 0.00741 -3.918509Togo 2000 -3.108091 0.7504177 -2.021418 -2.943803

Togo 2010 -1.071897 0.6945729 -1.922693-0.9027721

Tunisia 1970 -9.347348 -7.107592 Tunisia 1980 14.22668 8.547722 Tunisia 1990 2.468734 0.6434176 2.872107 1.089849Tunisia 2000 2.372484 1.335379 6.172085 -0.559008Tunisia 2010 2.356882 0.9630489 4.152594 0.3412832Turkey 1970 4.118462 1.242181 3.506317 2.129116Turkey 1980 2.634563 2.025042 3.75823 0.037569Turkey 1990 4.958963 0.9859693 4.705858 2.74543

Turkey 2000 2.590599 1.755168 5.626221-0.4420165

Turkey 2010 1.499281 0.7598972 1.584444 0.4672834Uk 1970 2.20623 0.9436154 4.065933 0.2322498Uk 1980 2.953964 0.8766585 4.231718 0.9701359

Uk 1990 0.3674603 0.6148458 0.1763344-0.1026767

Uk 2000 2.719975 0.9312558 2.022285 1.42868

Uk 2010 1.037331 1.023331 2.263975-0.3954125

Uruguay 1970 0.3444672 1.260707 1.673698 -1.052527Uruguay 1980 2.670841 0.869782 1.35107 1.642234

Uruguay 1990 -1.731186 0.8260047-0.4129601 -2.148332

Uruguay 2000 1.997595 1.292188 1.124716 0.7606727Uruguay 2010 2.733917 0.7755625 4.069815 0.8712512Usa 1970 1.988916 0.5512011 2.023134 0.951977

87

Usa 1980 1.240597 0.6458592 1.932497 0.1701473Usa 1990 1.420164 0.6312478 1.88324 0.3757588Usa 2000 2.062855 0.4832351 2.559252 0.8945343

Usa 2010 0.9999561 0.5844259 3.686361-0.6081084

Venezuela 1970 2.824936 2.038889 1.12752 1.086799

Venezuela 1980-0.8608924 1.399598 3.317738 -2.893476

Venezuela 1990 -4.190454 1.168216 -1.295204 -4.545742Venezuela 2000 -1.676216 1.097854 -2.585382 -1.558602Venezuela 2010 1.417408 0.9566677 0.6253433 0.5700774

88

References Balassa, B., 1971. The Structure of Protection in Developing Countries, Baltimore: John Hopkins Press Bernard, A. B. and J. B. Jensen., 1999. Exceptional Exporter Performance: Cause, Bhagwati, J. Protectionism Cambridge, MA: MIT Press. Clerides, S., Lach, S. and J. Tybout.,1998. Is Learning by Exporting Important? Micro- Dynamic Evidence from Colombia, Mexico and Morocco. Quarterly Journal of Economics, 103, pp 903-947. Edwards, S., 1993. Openness, trade liberalization, and growth in developing countries. Journal of Economic Literature, 31, 1358–1393. Grossman, G.M. and Helpman, E., 1991. Innovation and Growth in the Global Economy. Cambridge: The MIT Press. Hansen Hausmann, R., B. Klinger 2006., Structural transformation and patterns of comparative advantage in the product space. Center for International Development Working Paper No. 128, Harvard University Isaksson, A., 2007., Determinants of Total Factor Productivity: A Literature Review, Research and Statistics Branch Staff Working Paper 02/2007, United Nations Industrial Development Organization. Kaldor, N., 1967. Strategic Factors in Economic Development, New York State School of Industrial and Labour Relations, Cornell University, Ithaca, NY. Kunst, R.M. and D. Marin., 1989. On Exports and Productivity: A Causal Analysis. The Review of Economics and Statistics, Notes, 699-703. Liao, H. and Liu, X., 2009. Export total factor productivity growth nexus in East Asian economies. Applied Economics 41 (13), 1663-1675. Weinhold, D. and J. E. Rauch.,1997. Openness, Specialisation and productivity Growth in Less Developing Countries. NBER Working Paper No. 6131. World Bank., 1993. The East Asian Miracle, Economic Growth and Public Policy-A World Bank Policy Research Report, Oxford University Press.

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