government and industry performance: a comparative study

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Government and industry performance: acomparative studyKeith Hartley a , Richard White a & David Chaundy aa Centre for Defence Economics , University of York , York, YO1 5DDPublished online: 28 Jul 2006.

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Applied Economics, 1997, 29, 1227-1237

Government and industry performance: a comparative study

K E I T H HARTLEY, R I C H A R D W H I T E and DAVID C H A U N D Y

Centre for Defence Economics, University of York, York, Y O 1 5DD

A number of UK industries are heavily dependent on the government as a major purchaser. The Ministry of Defence and the National Health Service are essentially monopsonists for the industries supplying them. As a monopsonist, government can influence the size, structure, conduct and performance of the industries. This paper examines whether industries relying heavily on government purchases will differ in their structure, conduct and performance-characteristics and whether dependence on government can have favourable or adverse effects on industry performance. The hypothesis is tested by comparing the performance of industries dependent on government purchasing - defence, pharmaceuticals and medical equipment - with a control group of other non-dependent industries.

I . I N T R O D U C T I O N and performance-characteristics and that dependence on government can have favourable or adverse effects on in-

Public procurement is big business and a number of UK dustry performance. The hypothesis is tested by comparing industries are heavily dependent on the government as a group of industries dependent on government purchasing a major purchaser. The UK defence, pharmaceutical and (defence, pharmaceuticals and medical equipment) with medical equipment industries are examples of industries a control group of other non-dependent industries. where the government is a major buyer of the industry's output. For example, the Ministry of Defence is British Industry's largest single customer and is a major or monopsony buyer of the output of the UK aerospace, main battle 11. ANALYTICAL F R A M E W O R K

tank, nuclear-powered submarine, ordnance and warship building industries (Hartley and Hooper, 1995).

The Ministry of Defence (MOD) and the National Health Service (NHS) are essentially monopsonists for the industries supplying them. As a monopsonist, government can influence the size, structure, conduct and performance of the industries. The volume of government orders will affect the size of an industry; the government's willingness to 'open- up' public markets to competition will affect entry; support for national champions and bail-outs of bankrupt contractors can affect exits; controls can be imposed on the advertising and marketing behaviour of firms; and performance can be affected by the negotiation of prices, the regulation of profits and the control of exports (e.g. arms exports).

This paper examines whether MOD and NHS monopsony power has an impact on their supplying industries. It investigates the hypothesis that industries relying heavily on government purchases will differ in their structure, conduct

0003-6846 0 1997 Routledge

The hypotheses

Government purchasing can affect industry structure in a variety of ways. A government's demand for high technology products such as a new combat aircraft or its willingness to buy new pharmaceutical products and medical equipment can require large firms, either monopolies or oligopolies, to undertake research and development (R&D) at the scale needed to meet the government's requirements. Also, the production of high technology defence equipment is a decreasing cost activity reflecting economies of scale, learning and scope (e.g. aerospace). There have also been examples where governments have required firms to merge as the condition of receiving contracts (e.g. the UK aerospace industry 1958-60) or where government has permitted take-overs which have created a domestic monopoly (e.g. GEC acquisition of VSEL in 1995). Similarly, a government's demand for quality improvements in defence and health will be reflected in firm conduct leading to a focus on

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1228 K. Hartley et al.

R&D activities to achieve a competitive advantage. It is also possible that government dependency and the form of con- tracting (e.g. cost-based contracts) will affect wage levels, a firm's factor inputs and the factor mix.

The impact of government purchasing and dependency on industry performance can be beneficial or harmful. The hypothesis of beneficial impacts predicts that governments acting as a competitive buyer awarding fixed price contracts can create a 'culture of enterprise', shocking an industry into improved competitiveness reflected in improved export performance. Also, government as a major buyer can award contracts enabling firms to enter new markets (e.g. via research and development contracts) and to obtain scale economies from a large UK order.

The contrary hypothesis asserts that industries dependent on government will be inefficient and uncompetitive relying on preferential purchasing and cost-based contracts charac- terized by a 'cosy relationship' between the government spending Ministry and its major contractors. The result is likely to be a 'culture of dependency' rather than enterprise with adverse effects on industry performance. For example, previous studies of UK defence industries have found evidence of relatively low investment, poor labour productivity growth, a protected home market (buy British), a relatively poor export performance and the crowding-out of valuable civil investment (Levitt, 1985; Kaldor et al., 1986). However, during the 1980s, MOD introduced a competitive procurement policy with an emphasis on competition for contracts, the award of fixed price rather than cost-plus contracts, a greater willingness to buy from abroad, and the transfer of risks to contractors with the Ministry creating a more commercial relationship with its suppliers. The official view is that competition has resulted in significant cost savings

(possibly over £1 billion per annum, equivalent to some 10% of the equipment budget) and has been a major factor in improving the export competitiveness of UK defence industries (Hartley, 1991; Sandler and Hartley, 1995; HCP 390, 1994).

The impact of government purchasing and dependency on structure, conduct and performance is tested for the UK defence, pharmaceutical and medical equipment industries. These are government-dependent industries with some sim- ilarities. Government is a major buyer of their output; they are high technology industries; and they are subject to state regulation (via the Review Board for Non-Competitive Government Contracts in defence; the Pharmaceutical Profit Regulations Scheme ( PPRS) for the UK pharmaceutical industry and the 1968 Medicines Act which regulates the introduction of new medicinal products).

Industry dejinitions

Industries were selected from the manufacturing sector where the vast majority of government spending occurs. Target and control industries were identified according to the degree of sales dependency on a single branch of government. Dependency is either to MOD or to the NHS. Table 1 identifies the target and control industries used in the study. The table shows each industry's 1980 Standard In- dustrial Classification (SIC) and the relative size of each industry in terms of gross output. The target industries are considered to have a dependency on either MOD or NHS sales for a large portion of their market (i.e. aerospace, ordnance, shipbuilding, medical equipment and pharmaceuticals). They are referred to collectively as government dependent industries (GDIs). On the other hand, the control

Table 1. Target and control industries

Target Control

Government-dependent industries (GDI) Non-dependent industries (NDI)

SIC Industry name Size1 (f million) SIC Industry name Size1 (Emillion)

Defence-related industries

329 Ordnance 942 37 Instrument 3928 engineering

36 1 Shipbuilding 2368 32nes Mechanical 30 328 engineering nes2

364 Aerospace 12059 3 5 Motor vehicles 21 053

Medical-related industries

257 Pharmaceuticals 7518 25nes Chemical industry 26 035 nes2

372 Medical equipment 843 37nes Instrument 3085 engineering nes2

Notes: 'Size is denoted by 1991 gross output figures. 2 ' nes' = not elsewhere specified (e.g. 32nes = mechanical engineering except ordnance).

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Government and industry performance

group consists of industries considered not to have a dependency on any branch of government for their market (i.e. instrument and mechanical engineering, motor vehicles and chemicals). They are referred to collectively as non-dependent industries (NDIs). The industries are listed in the table as either defence-related or medical-related, but the analysis does not concentrate on the difference between dependence on the MOD versus dependence on the NHS.

Understandably, the results will depend on which industries are selected for each of the groups. This was resolved by selecting industries for the control group which had similar characteristics to industries in the target group barring their degrees of government dependency. The assumption is that any significant differences between the performance of the target and control groups is due at least partly to the effects of government monopsony power.

Methodology

A number of broad measures of structure, conduct and performance were considered (Chaundy, 1991). Concentra- tion ratios are used to measure industry structure. Effects on industry conduct are determined by examining R&D behaviour, capital expenditure patterns, wages and salary indices and capital and labour intensities. For industry performance, internal and external indicators are used. Internal indicators are represented by productivity and profitability while external indicators are measured by various international trade indices.

Where possible, data were collected and analysed for each year 1980 to 1991 inclusive. The choice of time period was influenced by two factors. First, the UK SICS are consistent throughout this period. The change in the industrial classification in 1980 and 1992 made it impossible to obtain a truly consistent time series for the variables used because some industry classifications were significantly affected by the re-classifications. Second, data on government expenditure before 1980 are generally not detailed by industry (CSO, 1979a).

The tables show arithmetic means of the yearly data for the indicator in each industry for the entire 1980-91 period, unless specifically noted otherwise. In all cases where industry figures are averaged together, the weighted average is reported. The weights are based on the size of the industry, so giving weight to each industry's relative prominence in the group.

Comparisons are based on the means of the indicators among the target group (GDIs) and control group (NDIs). The null hypothesis in each case is that the means are statistically the same. Initially, the standard t-test is used to

compare the difference between two sample means. Unfor- tunately, this requires two restrictive assumptions in order to be valid for the small sample (nl = 5, n2 = 5, in most cases) available in this analysis. These assumptions are that the performance indicators for all industries are normally distributed with the same variance. Alternatively, the Mann-Whitney test, which does not require the assumption of normality is also used in this analysis. To be reliable, however, it must be assumed that the variance in the indicators is the same for all industries. Hence, the alternative hypothesis in this test is that the two (unspecified) distribu- tions differ but only in regard to their means. As a result, the Mann-Whitney test is less powerful than the t-test.

The small sample size severely limits the opportunities for sophisticated statistical and econometric tecniques and correlation analysis is the only other statistical procedure used. Correlation ratios provide a measure of the correlation between the indicator under consideration and the percentage of MoD/NHS sales, denoted as the degree of dependency. The full, 12-year data set was used to calculate the correlation coefficients, not the means reported in the tables. As such, there may be times when the means figures presented in the tables appear to be uncorrelated, but in actuality there is some correlation among the entire 12-year data set. Finally, for each correlation the hypothesis was tested that there is no correlation in the data.'

111. D E P E N D E N C E O N SALES T O M O D A N D N H S

Table 2 shows the degree of industry dependency for sales to the MOD and NHS. The degree of dependency is calculated by dividing industry sales to the MOD or NHS by total industry sales. Dependence does not imply that these industries depend completely and permanently on the government for a market, but rather it shows the importance of a single branch of government as a major purchaser from these industries.

Although Table 2 shows sales to the MOD and NHS, it is possible that these industries also sell to other sectors of government. However, since the focus is on the monopsony effects of government on industry, it is important to examine those industries that depend heavily on one sector of government with one procurement policy. Table 2 shows that the degree of government dependency varied among the different GDIs with ordnance and medical equipment being especially dependent. Dependency among the GDIs as a group fell in the second half of the 198CL91 period from a weighted average of 41% to 34%, mainly due to the

'The correlation ratios are only a measure of linear correlation. A small absolute value could indicate no correlation or a non-linear correlation. The ratio is an absolute measure of correlation only and does not imply causation. They are also simultaneous correlations; but there might be leads and lags between dependence and industry performance. The correlation ratio is also sensitive to outliers.

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1230 K . Hartley et al.

Table 2. UK Defence, Pharmaceutical and Medical Equipment in- less than 6% in MOD sales and under 1% in NHS sales for d u s t r ~ sales to the MOD and the- NHS as a proportion of total the entire period. Given the clear difference between the industry sales GDIs and NDIs, the research question is whether is whether

(Degree of dependency: %) such differences are reflected in their structure, conduct and 1980-85' 1986-91' 1980-91' performance.

Ordnance 58.5 68.0 63.3 Shipbuilding Aerospace Instrum. Eng. 6.8 Mech. Eng. nes 1.8 Vehicles 1.8

Pharmaceutical 46.0 Chemical nes2 na Medical E q ~ i p . ~ 60.2 Instrum. Eng. nes2 na Overall weighted averages4 All GDIs 40.6' All NDIs 2.1'

3L.1 30.7 IV. S T R U C T U R A L I N D I C A T O R S

Notes: 'Figures are arithmetic averages for the time periods indicated. 'Based on data from the CSO Input-Output Tables for 1984 and 1990. 3Covers the years 1983 to 1991 only. 4Weights are based on volume of sales for each industry. ' (7 denotes averages are statistically different at the 5% level of significance. 6na = not available. It was not possible to calculate dependency for Chemicals and Instrument Engineering. 7GDIs = Government-dependent industries; NDIs = non-dependent industries.

Sources: Statement on the Defence Estimates, various years; Annual Bulletin for the Pharmaceutical Industry Annual Report, 1993; Busi- ness Monitor PA1002 (CSO, 1980-91c). Report on the Census of Production, Summary Volume, various years; Input-Output Tables, 1984 and 1990.

decline in dependency of the Aerospace industry. In fact, Ordnance and Shipbuilding became more dependent on MOD sales over the same period. The Aerospace industry saw the largest fall in government dependency from a high of42% in 1984 to half that in 1991. The Pharmaceutical and Medical Equipment industry peaked in dependency in 1983-84 and was followed by a subsequent decrease.

Despite the decreased reliance on government purchases, the importance of the MOD and NHS to these industries is clearly distinguished from the non-dependent group. For 1980-91, the NDIs on average relied on the government for some 2% of their sales. The most reliant industry in this group, Instrument Engineering, never exceeded 10% in sales to any single branch of government while averaging

Three measures of industry concentration based on gross output, total sales and employment were used to determine the possible effects of government dependency on industry structure. Only the gross output measures are shown in Table 3 because the results were virtually the same for all three indicators.

The most obvious difference in the degree of concentration was between the defence and control industries, with the exception of Motor Vehicles. In contrast, the medical and control industries showed a much smaller degree of difference in their concentration ratios. Interestingly though, Pharmaceuticals was less concentrated than its control group, Chemicals, even though its degree of dependency was higher. On the other hand, no consistent trend was evident between Medical Equipment and its control group, Instrument Engineering. From 1980-85, the Medical Equip- ment industry was slightly less concentrated than its control group, although, from 198691, it was more concentrated.

Table 3 shows that the Ordnance industry, which relied most heavily on government sales (63%), had the highest level of industrial concentration (83%). In contrast, the Motor Vehicle industry was the third most concentrated industry (71%), yet its reliance on government sales was one of the lowest (1.6%). While the defence related industries had a greater industrial concentration compared with their respective control groups, other factors may have been at work. For example, in some industries it is possible that the high cost of R&D plus opportunities for scale economies increased concentration irrespective of dependence upon government. Nonetheless, the correlation between government dependency and concentration levels among the GDIs and NDIs was significant at 0.287, suggesting that dependent industries are more concentrated than non-dependent industries.'

V. I N D U S T R Y C O N D U C T

This study used four indicators to assess the possible effects of government dependency on industry conduct, namely,

On an annual basis, concentration in Pharmaceuticals and Medical Equipment (both considered to be NHS dependent) increased slightly and steadily over the period, from 38% to 41% and 27% to 32%, respectively. At the same time, concentration levels dropped among Chemicals and Instrument Engineering (both considered not to rely on NHS sales), from 50% to 45% and 39% to 27%, respectively.

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Table 3. Concentration ratios

Gross Degree of output1 dependency (%I (%)

1980-91 198CL91

Ordnance Shipbuilding Aerospace Instrum. Eng. Mech. Eng. nes Vehicles

Pharmaceutical Chemical nes Medical Equip. Instrum. Eng. nes

Overall weighted averages

All GDIs All NDIs CorrelationZ

Notes: 'Concentration ratio is percentage of gross output accounted for by the five largest enterprises in each industry (based on number of employees). Figures for 2-digit SICs are weighted averages of the 3-digit SICs that comprise them. zCorrelations are between indicator denoted in the column head- ing and Degree of dependency. 3See also Table 2.

Sources: See Table 2.

R&D, capital investment, wages and salaries and capital and labour intensities.

Research and development

Table 4 shows various industry R&D indicators. In terms of proportions of employees and expenditure, Aerospace and Pharmaceuticals were the most R&D intensive industries. However, the UK government financed a significantly larger portion of R&D expenditure in the defence industries than it did for other industries.

As for wages and salaries, there was no significant difference in the amounts paid to R&D employees between the target and control groups as a whole. This is interesting in relation to claims that defence industries 'crowd-out' highly skilled labour and R&D funds from civil industries (Kaldor et al., 1986; Chaundy, 1991). Whilst R&D employees are paid more than other workers (see Tables 4 and 5), salaries for R&D employees in Aerospace (the largest UK defence industry) were amongst the lowest in the sample industries. This appears to refute the claim that defence industries crowd-out valuable R&D employees by paying higher salaries. But, it could be argued that civil industries had to offer higher salaries to attract labour from going to more 'excit-

ing and prestigious fields' in the defence industry (Kaldor et al., 1986, pp. 45-46).

For the period of 1981-89, on average, more than one in ten employees were devoted to R&D among the GDIs compared with about three in one hundred for the NDIs. Moreover, those industries that relied on government sales spent almost 24% of their gross value added on R&D over the 1980s compared with 7% for those not dependent on government sales. Similar results are obtained if R&D expenditure is expressed as a proportion of total sales. Overall, whilst the correlations between dependency and R&D employment and R&D expenditure are highly positive, the extremely small data set means that they are not statistically significant. As a result, it is difficult to draw any firm conclusions from the data except to note that sectors that sold a large share of their turnover to the government also devoted a large portion of their resources to R&D with the notable exception of Shipbuilding (where the high technology inputs required for modern warships are undertaken in the electronics industry). Possibly the form and extent of competition and an asymmetry of information between purchaser and supplier resulted in the GDIs being R&D- intensive. Alternatively, the nature of the products in Aero- space and Pharmaceuticals simply requires extensive R&D resources.

Investment, labour and capital

Table 5 summarizes the trends in investment, wages and salaries and capital and labour intensity indicators of industry conduct. It is difficult to make generalizations about capital expenditure as a proportion of value-added because the annual figures tended to vary so much for most industries. The scrapping of capital allowance in 1986 may also have distorted these figures. It does appear though, that capital expenditure in the GDIs was broadly in line with that of the NDIs, with less than two percentage points separating the means for both groups (with a non-significant correlation coefficient).

For the Aerospace industry, annual data showed an interesting trend. From 1980 to 1991, the capital expenditure rate within Aerospace became larger, while at the same time its dependency on MOD decreased. This could possibly reflect a move away from dependence on the MOD with firms in this industry diversifying into other areas precipita- ting increases in investment. Over the same period, both Ordnance and Shipbuilding showed large reductions in net capital expenditure, whilst their dependence on the MOD increased. The figures for the NHS-related industries did not show any discernible pattern of capital expenditure related to dependency over the same period. Similar results were obtained for net capital expenditure per employee.

Table 5 shows that over the period 1980-91, average wages and salaries per employee in GDIs were comparable to those in the NDIs: there was no statistically significant

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Table 4. R&D indicators of conduct

#. Hartley et al.

Percentage Wages and R&D Degree of R&D salaries per expenditure dependency employment R&D employee as a proportion (%) (%) (£ 1995 prices) of GVA (%)

1980-89' 1980-89' 1980-89' 1980-89'

Ordnance2 na na na 65.9 Shipbuilding 0.2 na 0.5 33.2 Aerospace 14.1 12 200 30.5 31.9 Instrum. Eng. 2.6 12 700 7.5 5.9 Mech. Eng. nes 1.5 10 600 2.5 2.0 Vehicles 4.0 13 600 7.3 1.6

Pharmaceutical 21.1 14 100 25.3 44.4 Chemical nes3 7.6 13 900 8.5 ( < 1.0) Medical Equip. na na na 58.2 Instrum. Eng. nes 3.3 12 700 8.6 ( < 1.0)

Overall weighted averages4

All GDIs 11.9 1 2 900 23.5 All NDIs 3.2 12 900 7.1 Correlation5 0.678 0.193 0.60 1

Notes: 'Figures are arithmetic averages for 1981, 1985 and 1989, as these were the only years for which data were available. Figures for Degree of dependency are averages for the same three years to be consistent in the comparisons. R&D includes funds from UK government, overseas and private industry. 'Source includes figures for Ordnance with Mechanical Engineering. However, this is not likely to have a significant impact as Ordnance accounts for less than 4% of total employment within Mechanical Engineering. 3Figures for Chemical nes includes Production of Man-made Fibres (SIC 26). 4Weights are based on total employment in each industry for columns 1 and 2, gross value added per industry for column 3 and sales per industry for column 4. 'All Manufacturing: 3.1%, £12400, 6.0%, and na for columns 1-4, respectively. 6See also Table 2.

Sources: M 0 1 4 (CSO 1980-91a) Industrial Research & Development Expenditure and Employment, various years; ABPI (1993); see also Table 2.

correlation between wages and government dependence. However, a more detailed analysis could be interpreted as offering some limited and tentative support for the crowding- out hypothesis. Wages were slightly higher in the GDIs, mainly due to the influence of Aerospace and Pharmaceut- icals; although Chemicals was the highest paid group in the sample. However, the lack of data on non-monetary benefits makes it difficult to assess crowding-out effects using money wage data (e.g. there might be greater non-monetary benefits in GDIs). Moreover, employees in defence industries consistently received marginally higher wages and salaries than those in similar but non-dependent industries.

Capital and labour intensity ratios are also shown in Table 5. Caution is needed, though, because data for capital and labour intensities were only available for 1985. There is no way of telling if these figures are outliers and do not reflect accurately the true levels of capital and labour intensities during the whole period.

The available data show that the amount of capital per unit of output used in the GDIs was some 14% lower than

in the NDIs. This does not support the idea that there was a capital bias in government dependent industries due to guaranteed rates of return on contracts. In fact the negative coefficient of correlation suggests otherwise; but this was not significant at the 5% level.

These results are mirrored in the capital-labour ratios. Almost all the GDIs had lower capital-labour ratios than their respective control industries, the exception being Ship- building. The average GDI ratio was below that of the NDIs by about 30%. This suggests that GDIs were more labour intensive than NDIs. Interestingly though, the fact that average wages in the GDIs were relatively higher than the NDls (albeit only slightly) makes the relative labour intensity in the GDIs rather surprising, which may indicate excess labour in the GDIs. However, Pharmaceuticals and Medical Equipment paid lower wages than their control industries which may partly explain their labour intensive production techniques. Finally, an industry by industry comparison showed almost no correlation between the level of labour intensity and the Degree of dependency.

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Table 5. Other indicators of conduct

Net capital expenditure as a Wages and salaries Capital-output Capital-labour Degree of proportion of GVA per employee ratio ratio dependency (%) (£ 1985 prices) (%) (8 (%)

1980-9 1 1980-91 1985' 1985' 1980-91

Ordnance 11.4 9400 0.86 25 300 63.3 Shipbuilding 9.5 9400 2.15 43 900 32.1 Aerospace 7.1 10300 1.03 29 300 30.7 Instrum. Eng. 8.4 8300 1.25 27 300 5.4 Mech. Eng. nes 7.2 9300 1.11 29 100 2.1 Vehicles 18.1 9700 1.44 53 100 1.6

Pharmaceutical Chemical nes2 Medical Equip. Instrum. Eng. nes


All GDIs All NDIs

Notes: 'Figures are based on estimates of the gross capital stock in 1985 at 1980 prices from source, but have been readjusted to reflect 1985 prices. The original data were based on 1968 SICS and have been reclassified according to 1980 SICS. A perfect correspondence could not be made, so the absolute values should be treated with caution. This is especially true of Shipbuilding, which includes the capital stock of the Marine Engineering industry. Figures for Chemical nes include Production of Man-made Fibres (SIC 26).

3Weights are based on net capital expenditure per industry for column 1, total wages and salaries per industry for column 2, and gross capital stock per industry for columns 3 and 4. 4All Manufacturing: 11.1 %, £8500, 1.36, £44 600, na for columns 1-4. 'See also Table 2.

Sources: O'Mahony and Oulton (1990) Industry-level estimates of the capital stock in UK manufacturing, 1948-1985. NIESR Discussion Paper No. 172; see also Table 2.

VI. I N D I C A T O R S O F I N D U S T R Y P E R F O R M A N C E

A variety of measures are used to assess industry performance embracing internal and external indicators. The internal indicators comprise labour productivity, profitability and technology. The external indicators are the UK trade balance, import penetration and export sales ratios. Throughout, the aim is to assess whether government dependence has a beneficial or harmful impact on industry performance.

Internal indicators of performance

Productivity. Labour productivity was measured using gross value added, sales and gross output per employee. The results for gross value added (GVA) per employee are reported in Table 6 along with two other measures of industry performance. The labour productivity measure shows that

overall the GDIs performed better than the NDIs for the 12-year period (about 10% better). This was confirmed by a detailed analysis of each target industry and its control on an annual basis. On this evidence, there is no reason to believe that government dependency has an adverse affect on productivity. If anything, there is a suggestion of a possibly favourable effect contrary to the pessimistic assumptions made in the literature. This is somewhat corrob- orated by the fact that the correlation between dependency and GVA per employee was significantly positive at the 5% level.

The story was different, however, when productivity was measured in terms of sales or gross output. In both these instances, the government dependent industries performed consistently worse than their control groups. The difference was most notable between the Pharmaceutical industry and the Chemical industry, with the Pharmaceutical industry productivity almost half that of the rest of the Chemical industry. However, the correlation ratios were very low, suggesting there is a much weaker relationship between

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Table 6. Productivity, projtability and technology

K . Hartley et al.

Productivity Profitability Technology

GVA per Price-cost UK share of USA Degree of employee margin1 patent applicationsZ dependency (£ 1995 prices) (%I (%) (%)



Overall weighted averages4 All GDIs All NDIs Correlation5

Notes: 'Price-cost margin calculated as net output minus expenditure on wages and salaries minus net capital expenditure all divided by gross output. 20riginal patent data was classified according to the US SICs. Data were reclassified to UK SICs as closely as possible using information contained in the correspondence table used by Soete (1981). A perfect correspondence is unlikely, so absolute values should be treated with some caution.

1989 data were not for the whole year. Weights are based on gross value added per industry for column 1, net output for column 2 and patents per industry for

column 3. All Manufacturing: £ 15 700, 20.1 %, na, na for columns 1-4.

6See also Table 2.

Sources: Data supplied by the Science Policy Research Unit, University of Sussex, by the Office of Information Systems, TAF Program, US Patent and Trademark Office; see also Table 2.

these measures of productivity and government dependency. These results probably reflect the difference in defini- tions between sales, gross output and gross value added.

Profitability. Profitability was measured using the p r i c ~ o s t margin, which is calculated as net output minus expenditure on materials, labour and capital, expressed as a proportion of gross output (Mayes et al., 1990). Results of the 198&91 industry averages are shown in Table 6. Overall the weighted averages show the GDIs with a higher rate of profitability than the NDIs; the correlation between dependency and profitability was weakly positive, but statistically insignificant. Nevertheless, more interesting insights emerge when the two groups are considered separately as defence and medical industries.

In defence, the MOD introduced a new competitive procurement policy in 1983. This involved a greater emphasis on competition and a move from cost-plus contracts to fixed price and incentive type of contracts. Profit rates in the

defence related industries fell over the next two years, but rose steadily after 1986. In fact, since 1988 profits among these industries consistently outpaced similar, but non-dependent industries with the gap widening every year through to 1991. Aerospace experienced the largest increase (20.0% to 25.5%) while Motor Vehicles suffered the largest drop (18.4% to 4.6%). Before 1988, the non-dependent defence related industries experienced slightly higher profit rates year by year.

These results do not suggest that the defence related industries were protected by lucrative contracts allowing them to achieve higher profits. However, it could be argued that potentially higher profits in the defence related industries were absorbed into higher wages and salaries and more labour intensive production techniques, as was noted earlier. This suggests that profits should be negatively corre- lated to the capital-output and capital-labour ratios, and there is some support for this possibility. The correlation coefficients between profitability and the two ratios were - 0.198 and - 0.326, respectively.

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The picture for medical related industries was very different. Pharmaceuticals had a consistently higher profit rate compared with Chemicals, almost double over the entire period. The profit rate in Pharmaceuticals increased slightly during the 1980s from an average of 35% for 1980-84 to 38% from 1985-89 and almost reaching 40% in the early 1990s. The relationship between Medical Equipment and its control group is much the same; however, the difference in margins was much less.

Technology. Expenditure and employment on R&D is a measure of technological inputs, with Aerospace and Pharmaceuticals as R&D-intensive sectors. But do these higher inputs yield greater technological outputs? In the absence of data on the number of commercially successful innovations, patent applications were used as an alternative. This assumes that industries which are technologically inno- vative seek to patent their discoveries.

Table 6 shows the number of UK patent applications granted in the US expressed as a proportion of the total number of applications granted there for each industry. The US is one of the largest patent markets, but in using this data it must be remembered that firms may only patent innovations in those countries to which they are major exporters: hence, variations in exports to the US market among the different industries may be a significant factor behind the diferent rates of US patent applications. Also, the fact that the US has a 'buy American' policy for defence equipment may influence negatively the number of UK patent applications for defence items. Moreover, the technological differences between industries may lead to different benefits from patenting innovations.

The evidence suggests that industries which rely on government for sales might have a technological advantage. In relative terms, the success rate of UK patent applications in the US for GDIs was just about 50% greater than for the NDIs. This can only be partly explained by government favouritism in allocating R&D funds. In Table 4 it was shown that Aerospace allocated a relatively large proportion of its resources to R&D; but more than half was funded by the government, whereas the figure was only about 5-10% for all the others, and less than 1% for Pharmaceut- icals. In fact, Pharmaceuticals, which funded its own R&D with virtually no direct reliance on government funding, performed best in terms of patent applications in the US when compared with all other industries in the sample. Unfortunately, data were not available for the Medical Equipment industry.

External indicators

Are industries which rely heavily on government sales competitive in the world market? Is there evidence that the government protects its supplying industries by providing advantages to exports and creating barriers to imports?

This section examines a number of external indicators to see if there is any evidence of such effects.

UK trade balance. Throughout the period the GDIs enjoy- ed a better balance of trade compared with their non- dependent counterparts. Trade balance is estimated by dividing exports minus imports by exports plus imports. The results are shown in Table 7. During the 12-year period, the GDIs had an average trade surplus of approximately 25'34, while the NDIs averaged 0% (neither a surplus nor a deficit). The correlation coefficient between trade balance and government dependency was positive and high at 0.653. This was the strongest correlation of all the indicators examined in the study. Whether or not this meant one industry was more competitive than another, or was protected more than the other cannot be determined from these data alone. The superior trade performance of the GDIs could reflect a bias in government procurement towards domestic industries and/or other measures which restrict imports yet promote exports. Thus, the difference in performance may well have been due to differing procurement and protectionist policies by government within each industry. A more detailed analysis of government subsidy, tariff and procurement policy is necessary to explain the trade surplus.

Some of the NDIs actually achieved large surpluses in the early 1980s. This was short lived as, by around 1982, all of the NDIs began a steady trend towards increasing trade deficits. The exception was Mechanical Engineering 'nes', which maintained a trade surplus, albeit a diminishing one. The same was true for the Chemical industry as a whole. Pharmaceuticals had a surplus from four to six times greater than the rest of the Chemical industry, but this too decreased towards the end of the period. The trade surplus in Medical Equipment followed the pattern of that in Pharma- ceuticals, failing from 29% in 1980 to 15% in 1991.

Import penetration and export sales ratios. Table 7 also provides information on import penetration and export sales ratios. The typical import penetration ratio shows imports as a proportion of home demand. It provides an indication of the degree to which imported products compete with domestically produced products. Similarly, the usual export sales ratio is defined as exports as a proportion of manufacturers' sales. This ratio provides an indication of how well domestically produced goods compete abroad. However, Wells and Imber (1977) contend that the absolute values of these figures are of limited use for making inter-industry comparisons because of measurement problems. They highlight the problem of double counting, point- ing out that often the ratios are inflated and may exceed 100% where re-exported and re-imported goods account for a large part of total trade. To account for these problems, the ratios in this study were adjusted. Imports are expressed as a proportion of home demand plus exports, and exports are expressed as a proportion of manufacturers' sales plus

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Table 7. External indicators of performance

K . Hartley et al.

UK trade Import penetration Export sales Degree of balance1** ratiols3 dependency (%) (Yo) (Yo)

1980-91 1980-91 1980-9 1 1980-91




All GDIs All NDIs Correlation6

Notes: 'Imports are valued c.i.f. and exports f.0.b. Commodities are classified according to the industry of which they are the principal product. Figures include imports for re-export and exports for re-import. Industries defined at source according to SITC Rev 2 which do not correspond exactly to UK SICS (1980). 'Trade balance is exports minus imports divided by exports plus imports. 31mport penetration ratio is imports as a proportion of home demand plus exports. Home demand is defined as - - manufacturers' sales plus imports minus exports. 4Export sales ratio is exports as a proportion of manufacturers' sales plus imports. 'Weights are based on sales per industry. 6All Manufacturing: 8.3%, 23.3%, 19.6%, na for columns 1 4 . 'See also Table 2.

Sources: Business Monitor MQlO (CSO 1980-91 b) Overseas Trade analysed in terms of Industries, various years; SITC (1975); see also Table 2.

imports. These two measures have a common denominator and allow for the influence of re-exports when calculating import penetration and the influence of re-imports when calculating export sales ratios (see Wells and Imber, 1977).

On average, over the 1980-91 period, the GDTs experienced lower import penetration rates and achieved higher export sales ratios. These results support the same conclusions drawn from the trade balance figures. It could either refute the idea that government dependent industries are not competitive abroad, or that they are protected against imports and subsidized to facilitate exports. The relatively high negative correlation coefficient for import penetration ( - 0.472) suggests that the greater the degree of dependency, the lower the import penetration. The coefficient for the export sales ratio was lower, but still statistically significant, suggesting that the export sales ratio increases with dependency.

A separate correlation was computed between the trade balance figures and the import penetration and the export sales ratios. The result was a correlation coefficient of

- 0.83 between the trade balance and import penetration. Between the trade balance and export sales ratio it was a relatively insignificant 0.062, all of which suggested that the trade surpluses were more a function of low import penetration as opposed to high export sales. This indicates that the favourable trade figures for the GDIs were probably more a function of preferential purchasing at home than of increased competitiveness abroad. In contrast, the NDIs do not receive preferential treatment and therefore are more subject to competition. Thus far, this is the strongest impact of the influence of government dependency on industry performance.

VII . C O N C L U S I O N S

It was predicted that industries dependent on government purchasing will differ in their structure, conduct and performance characteristics and that dependence on government can have favourable or harmful effects on industry

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performance. The evidence shows that, in terms of structure, government-dependent industries are more concentrated; but there was only limited evidence of any impact of government demand on industry conduct. There was, however, more convincing support for the hypothesis of government purchasing having a favourable impact on industry performance as reflected in productivity and external trade indicators.

The results are exploratory and suggestive. They are based on descriptive statistics a t the indusry level for the period 198&91. There remains considerable scope for fur- ther analytical and empirical work based on an extended time-series, more sophisticated estimating techniques, analysis a t the firm level and testing for the sensitivity of the results to different control groups. The government dependency variable needs to be disaggregated, separating purchasing from regulatory policy. Within procurement, it is likely that competitive purchasing has the favourable impact on industry performance. The evidence presented in this study provides some support for the hypothesis that government dependence can have a beneficial impact on industry performance.

A C K N O W L E D G E M E N T S

This paper is part of a research project funded by ESRC as part of its Contracts and Competition Programme (L114251031). Dr G de Fraja, D r S. Martin and A. Lozzi kindly offered comments: the usual disclaimers apply.

R E F E R E N C E S

ABPI (1993) Pharmaceutical Facts & Figures. Positive Print Group, Sevenoaks.

Chaundy, D. A. (1991) The international competitiveness of defence and pharmaceutical industries: a preliminary study. MSc, Dissertation, University of York, York, UK.

CSO (1980-91a) Business Monitor M014: Industrial Research & Development, Expenditure and Employment. HMSO, Lon- don.

CSO (1980-91b) Business Monitor MQIO: Overseas Trade Ana- lysed in Terms of Industries. HMSO, London.

CSO (198G91c) Business Monitor PA1002: Report on the Census of Production, Summary Volume. HMSO, London.

CSO (1979) Standard Industrial Classijcation: Revised 1980. HMSO, London.

Hartley, K. (1991) The Economics of Defence Policy. Brassey's ( U K ) , London.

Hartley, K. and Hooper, N. (1995) Study of the Value of the Defence Industry to the U K Economy: A Statistical Analysis, Centre for Defence Economics, York.

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Kaldor, M., Sharp, M. and Walker, W. (1986) Industrial competitiveness and Britain's defence. Lloyd's Bank Review, No. 162, October, pp. 31-49.

Levitt, M.S. (1985) The economics of defence spending. NIESR Discussion Paper No. 92. Published as: Managing the costs of defence, Public Finance Foundation, Discussion Paper, No. 10; PPF, London.

Mayes, D.G., Buxton, T. and Murfin, A. (1990) R&D innovation and trade performance. Ch. 7, pp. 107-30 in B. Dankbaar, J. Groenewegen, H. Schenk (Eds.) Perspectives in Industrial Or- qanisation: Kluwer Academic Publishers, Dordrecht.

o7~;hony , M. and Oulton, N. (1990) Industry-level estimates of the capital stock in UK manufacturing, 1948-85. NIESR Discussion Paper, No. 172.

Sandler, T. and Hartley, K. (1995) The Economics of Defense, Surveys of Economic Literature. Cambridge University Press, Cambridge.

SITC (1975) UN Statistical Papers, Series M , No. 34/Rev. 2. Soete, L.L.G. (1981) A general test of technological gap trade

theory. Weltwirtschaftliches Archiv, 117, 638-60. Wells, J.D. and Imber J.C. (1977) The home and export perfor-

mance of United Kingdom industries, Economic Trends, Au- gust, pp. 78-89.

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government and industry performance: a comparative study

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