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.
To cite this article: Keith Hartley , Richard White & David Chaundy (1997) Government and industry performance: acomparative study, Applied Economics, 29:9, 1227-1237, DOI: 10.1080/00036849700000013
To link to this article: http://dx.doi.org/10.1080/00036849700000013
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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 mono- psony 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 indus- tries 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 con- tractors can affect exits; controls can be imposed on the advertising and marketing behaviour of firms; and perfor- mance 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 mono- psony 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
Government purchasing can affect industry structure in a variety of ways. A government's demand for high techno- logy products such as a new combat aircraft or its willing- ness 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 aero- space 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 govern- ment's demand for quality improvements in defence and health will be reflected in firm conduct leading to a focus on
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 per- formance. 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 evid- ence 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 procure- ment 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 pharmaceut- ical industry and the 1968 Medicines Act which regulates the introduction of new medicinal products).
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 govern- ment. 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 pharma- ceuticals). They are referred to collectively as government dependent industries (GDIs). On the other hand, the control
Table 1. Target and control industries
Government-dependent industries (GDI) Non-dependent industries (NDI)
SIC Industry name Size1 (f million) SIC Industry name Size1 (Emillion)
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
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).
Government and industry performance
group consists of industries considered not to have a de- pendency 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-depen- dent 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 indus- tries 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.
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 be- haviour, capital expenditure patterns, wages and salary in- dices and capital and labour intensities. For industry perfor- mance, internal and external indicators are used. Internal indicators are represented by productivity and profitability while external indicators are measured by various interna- tional 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 classi- fication 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 expendi- ture 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 indus- try 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 indi- cators 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 cor- relation analysis is the only other statistical procedure used. Correlation ratios provide a measure of the correlation between the indicator under consideration and the percent- age of MoD/NHS sales, denoted as the degree of depend- ency. 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 present- ed 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 da...