THE IMPACT OF COMPUTERS ON PRODUCTIVITY

DE ECONOMIST 151, NO. 1, 2003

THE IMPACT OF COMPUTERS ON PRODUCTIVITY IN THE TRADE SECTOR: EXPLORATIONS WITH DUTCH MICRODATA BY LOURENS BROERSMA,* ROBERT H. MCGUCKIN,** AND MARCEL P. TIMMER***

Summary

The impact of computers on productivity in the Dutch trade sector during the period 1988-1994 is examined. The analysis is based on a panel data set derived from the Production Survey of Statistics Netherlands, which includes data on output, employment, wages, and various types of investment. A new method is developed to estimate IT- and non-IT capital stocks for each firm based on investment flows and booked depreciation figures by firm. A Cobb-Douglas production function setting is used to study the effect of computer capital stock on productivity. We find that computers contributed positively to productivity, even when firm-specific effects such as labour quality are accounted for. In retail trade computers yielded returns above their relatively high rental price. For wholesaling, no evidence for excessive returns is found. The rates of return were not subject to a decline in the period studied, in contrast to findings for the US. This suggests that the Netherlands has been lagging in the application of IT compared to the US and that further productivity boosting effects can be expected. Key words: computer capital, productivity

1 INTRODUCTION

Strong growth of labour productivity in the US economy since the second half of the 1990s has been linked to information technology 共IT兲 both through increased * Corresponding author: Department of Economics and Department of Spatial Science, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands and The Conference Board, New York, phone: ⫹31 50 363 7053, fax: ⫹31 50 363 7337, e-mail: [email protected]. ** The Conference Board, New York. *** Department of Economics, University of Groningen. The authors would like to thank the participants of the CAED Conference in The Hague, August 19-20, 1999. In addition, we acknowledge the valuable comments of Bart van Ark of the University of Groningen and The Conference Board, Henry van der Wiel of the Netherlands Bureau of Economic Policy Analysis 共CPB兲 and Bert Balk of Statistics Netherlands. Last but not least, two anonymous referees are thanked for their comments that have improved the paper substantially. Financial support from the Netherlands Organisation for Scientific Research 共NWO兲 for Marcel Timmer is gratefully acknowledged. This research was carried out at the Center for Research of Economic Microdata 共CEREM兲 of Statistics Netherlands. The views expressed in this paper are those of the authors and do not necessarily reflect the policies of Statistics Netherlands. Statistics Netherlands ensures confidentiality of responses by requiring researchers to work on site at CEREM with output checked before leaving the premises. De Economist 151, 53–79, 2003. © 2003 Kluwer Academic Publishers. Printed in the Netherlands.

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L. BROERSMA, R.H. MCGUCKIN, AND M.P. TIMMER

use of IT capital goods and efficiency gains in the production of IT. 1 Moreover the strength of the evidence keeps growing and is now accepted by most researchers. For example, Oliner and Sichel 共2002兲, extending their earlier work 共Oliner and Sichel 共2000兲兲 to include data from 2001, find strong support for the importance of IT in the productivity revival even with the slowdown in the US economy. Their studies strongly complement other work in the US, including Jorgenson and Stiroh 共2000兲. There is more limited work below the aggregate levels but it suggests that industries such as trade and securities, which are both large and heavy IT users, are important contributors to the aggregate findings 共Stiroh 共2001兲兲. While most of these studies pertain to the US and so far little is known about the effects of IT investment in Europe 共van Ark 共2000兲兲. There are some recent aggregate growth accounting studies that have found a strong link between IT investment and productivity. See Schreyer 共2000兲 for a study on seven OECD countries, van der Wiel 共2001兲 for the Netherlands, and Roeger 共2001兲 in a study covering 16 European countries and the US. But these and other recent studies suggest that the pace of diffusion has been much slower in Europe than in the US and that this is likely an important reason for the expanding gap in productivity growth with the US since 1995 共McGuckin and van Ark 共2001兲 and van Ark, Inklaar, and McGuckin 共2002兲兲. This paper helps to fill the gap in European microdata studies by studying the impact of investment in computers on productivity growth in the Dutch retail and wholesale trade sectors. The trade sector is a particularly interesting object for study since it is not only one of the biggest sectors in the economy, accounting for over 20 percent of market sector GDP in the Netherlands 共van der Wiel, 2001兲, but also among the most important potential users of IT 共Pilat and Lee 共2001兲, Varian et al. 共2002兲兲. After telecommunications, it recorded the fastest labour productivity growth of all service industries, not only in the 1980s but also during the 1990s suggesting a possible strong link between IT investment and productivity growth 共van der Wiel 共2001兲兲. To measure the impact of IT investment on productivity growth a standard production function model is estimated with three inputs: hours worked, computer capital, and other, non-computer, capital. In addition an index of the firm’s wage bill is introduced to control for differences in labour quality 共see also van der Wiel 共1999兲兲. In contrast to previous studies, IT capital stocks are used as a measure of capital input. This provides a better measure of capital input than investment flow or counts of PCs. A major hurdle in using capital stock estimates in microlevel research is the lack of long investment series as normally only short panel data are available. To this end a new method for estimating capital stocks, 1 The terms ‘information technology’ 共IT兲 and ‘information and communication technology’ 共ICT兲 are often used interchangeably. In this paper we use the term IT, because focus is on the role of computer hardware.

IMPACT OF COMPUTERS ON PRODUCTIVITY

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the ‘booked depreciation method’, is proposed which allows derivation of long investment series on the basis of depreciation as reported by firms. Data are developed from a confidential longitudinally linked firm-level database derived from the annual census of Production Surveys 共PS兲 by Statistics Netherlands 共CBS兲 for the period 1988-1994. The principal aim of the PS is to provide data on the revenue and cost structure and employment of firms. The PS also provides information on investments in a number of capital assets, among which are direct investments in computers 共hardware兲. A balanced panel of 2,687 firms has been constructed covering the period 1988-1994. Although selection is an issue for a sample composed of successful surviving firms, this panel is fairly representative of trade firms with more than 20 employees, it also includes some smaller firms. The panel has a number of unique characteristics. Most importantly, it is derived from a single and official source. There is no need to link separate data sets on computers and production as all relevant information is provided in the PS. Moreover, there is no need to rely on the use of commercial surveys of computer usage by market research firms, such as International Data Corporation, Computer World or Informationweek magazines. These surveys, often used in other studies, rely on self-reporting and hence may suffer from sample selection bias. The period 1988-1994 has been chosen for a number of reasons. There were no definition changes during this period that caused breaks in series of variables drawn from the PS. The change in the Dutch industrial classification system in 1993 could easily be overcome. But from 1995 on a new questionnaire was used in the PS making comparisons with earlier years nearly impossible. Also, the 1988-1994 period is an interesting period to study as it pertains to the period where, even in the US, the question was if and how much impact IT was having on economic performance. Our principal finding is that computer capital has had a positive effect on productivity growth in the Dutch trade sector. In retailing the marginal productivity of computer capital compared to the marginal productivity of non-computer capital exceeded the ratio of marginal costs. Hence, Dutch retail trade has had excess returns to IT investments between 1988 and 1994. On the other hand, there is no evidence of excess returns in wholesaling, where rates of return to computers were significantly higher than to other capital, but not higher than could be expected on the basis of their higher rental prices. In the long run, under competition, returns and rental prices cannot diverge. However, in the short run just after the introduction of a radical new type of capital good, such as IT, uncertainty about its effects and opportunities may well drive a wedge between returns and rents. The existence of the wedge is the test we apply to see whether the adjustment, i.e. the diffusion of IT, is complete. The excessive returns to computer investment in retailing increased over the years 1988-1994, suggesting that the full effects of the use of IT still have to play out in this sector.

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It appears firms just started to provide the necessary organisational changes and restructuring of activities that appear to be important for reaping the full benefits of IT spending 共Brynjolffson and Hitt 共1998兲, Licht and Moch 共1999兲兲. Until recently, most firms moved along traditional lines and continued revamping traditional business functions with IT. This is in line with David 共1990兲 who argued that, like the case of electric power, it would take some time before new opportunities of a general-purpose technology are seized. As firms in this sector enhance their capabilities in the use of IT, competition will drive the returns to computer investment toward marginal costs in the long run. In the wholesale trade sector, where IT investment has a longer history than in retailing, returns to computer capital were constant during the period 1988-1994. These findings are in contrast with findings for the US suggesting that there computer productivity peaked at the end of the 1980s 共Lehr and Lichtenberg 共1999兲, Brynjolffson and Hitt 共1996兲兲. This implies that the Netherlands has been lagging in the application of IT compared to the US and that the productivity boosting effects of IT can still be expected to take place. This hypothesis is consistent with the surge in total factor productivity growth in the Dutch trade sector in the second half of the 1990s 共van der Wiel 共2001兲, Table 4C兲. The remainder of this paper is organised as follows. In section 2 an overview is given of the Dutch trade sector and the application of information technology in this sector. In section 3 we present our econometric production function model. Section 4 describes our data sources for output, labour input, and investment. In section 5 a new way to measure capital stocks on the basis of short investment series is proposed. A comparison of computer usage in various trade industries is given. Estimates of the model are given in section 6, using both panel and crosssection analysis. Firm-specific effects are accounted for which appear to upwardly bias returns to IT capital 共Brynjolffson and Hitt 共1995兲兲. Section 7 gives a summary and provides some concluding remarks. 2 THE DUTCH TRADE SECTOR

Trade firms show a large variety of activities, ranging from small, specialised bakery shops to large exporting firms. An important distinction is between retail and wholesale trade. Wholesale firms sell goods, which they do not manufacture themselves, to retailers and other large-scale buyers. Dutch wholesale firms are generally small with some 90% having less than 10 employees. Traditionally, the core business of wholesalers involves the storage and distribution of goods to firms that deal directly with the consumer. A significant fraction of Dutch wholesale firms are involved in international operations. Some 40% of all wholesale firms export their products. In 1995 about 32% of the wholesale revenue was obtained through sales abroad, primarily food and capital goods. On the other hand, about 35% of all wholesale purchases are received from abroad, mainly capital goods, non-food products and raw materials and intermediate goods. In


57

fact the share of wholesale trade in Dutch goods imports amounts to 40% 共de Jong, Muizer, and Van der Zwan 共1999兲兲. Recently there are indications of structural change in the industry with some firms specialising in physical storage and distribution of goods and others specialising in brokerage aspects of the business 共OECD 共2000兲兲. In this latter role a key aspect of the business is connecting retail buyers with producers and providing market information to both groups. Computers in wholesaling are mainly used for stock management, control, administration and communication with producers and consumers. They are also very important in ‘matching’ retailers with producers 共den Hertog et al. 共1997兲兲. In 1989 81% of the firms had automation expenses, defined as firms with computers valued at least at 500 Dutch guilders 共some 250 US $兲 or with automation personnel. This percentage had increased to 89% by 1999, well above the all industry average. Wholesale firms accounted for roughly 7.5% of all automation spending in 1999 in the Netherlands, which is about the same as their share in GDP for that year. 2 Retail firms sell goods, manufactured elsewhere, to households and private persons. The market structure of retailing firms has changed dramatically in recent years. There has been a shift from small-scale local operations to larger scale establishments and integrated retailing, facilitated through commercial co-operation, and franchising. 3 Despite this, the average Dutch retail firm is still very small with 95% of the firms having less than 10 employees. 4 Computers in retailing are used for inventory control and storage optimisation, pricing and promotion of the products 共scanning techniques兲 and administration 共den Hertog and Brouwer 共2000兲兲. A major new use in the period we study has been the introduction of electronic payment with debit bankcards in many retail stores, especially the larger ones. The number of bank-store connections grew from only 90 in 1986 to 40,000 by 1994 and 70,000 just one year later 共Vogelesang 共1996兲兲. Despite the huge increase in electronic payments in retailing, retail firms only account for 2.5% of total automation spending, while their share in total GDP is about 4% in 1999. Some 20% of the firms in retail had no automation expenses in 1999. 5 3 THE MODEL

To estimate the impact of computers on productivity, a production function framework is used with capital and labour as inputs. Since we want to investigate dif2 These data are drawn from Statline of Statistics Netherlands at http://www.cbs.nl. 3 Statistics Netherlands 共1999兲 reports that the number of private owners of retail firms has dropped dramatically in recent years. Nowadays one third of all retail firms is a subsidiary branch of a firm with five establishments or more or has some form of economic co-operation, like franchising. See also CBS, Press release, May 10, 1999. 4 Data from Statistics Netherlands. 5 Broersma and Brouwer 共2000兲 and den Hertog and Brouwer 共2000兲 do show that the use of advanced equipment like IT, which boosts the innovative process, is important in retail trade.

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ferences in marginal productivity of different capital goods, we focus on computer capital and non-computer capital separately. A simple Cobb-Douglas production function specification is used. 6 The flow of real production is ‘explained’ by the number of hours worked, real IT capital stock and real non-IT capital stock. We included the log of real wages per hour to control for the average skill level of the work force assuming wages to increase with labour quality. This takes account of firm differences in quality of labour, which is an important additional determinant of productivity. The model in logarithmic form is log Y i,t ⫽ ␮ i;0⫹␣ log L i,t⫹␤ 1 log K IT,i,t⫹␤ 2 log K non-IT,i,t⫹␥ log w i,t

共1兲

where index i refers to the individual firm and the index t refers to time. Y is real output, L is total hours worked, w is the real hourly wage rate, K IT is computer capital and K non-IT is non-computer capital. ␤ 1 is the output elasticity of IT capital and similarly for non-IT capital and labour. The ␮ 0 represents the intercept terms plus a large number of control dummies. Control dummies include nine size dummies to control for differences in productivity due to firm size and eight legal status dummies to control for differences in legal status of the firm. Six time dummies are added when pooled data is used. Finally, we included 227 industry dummies for the industry at a 5-digit level to which the firm belonged in order to control for the large variation within both retailing and wholesaling in terms of activity and prices. Various variants of this basic model are estimated to answer the following questions: 1. Is the marginal product of computers higher than for other capital? The estimated ␤’s from equation 共1兲 give the output elasticity of substitution of IT and non-IT capital. Multiplying this elasticity with the appropriate output-capital ratio gives an estimate of the marginal productivity of the capital good 共Lichtenberg 共1995兲兲. 2. Is there a difference in the productivity effects of computers in retail and wholesale trade? Previous studies have shown that the contribution of capital varies greatly across sectors. Retail and wholesale trade activities differ considerably and computers are put to quite different uses. Hence, differences in the effects of computer usage might be expected. The model is estimated separately for each sector. 3. Is there a trend in the marginal productivity of computers? The productivity enhancing effects of computers will change over time. Economic theory predicts that firms will increase investment in any input that achieves higher than normal returns, and that as investment increases, marginal productivity eventually falls to ‘normal’ levels as the more profitable applications of computers will first be uti6 Brynjolffson and Hitt 共1995兲 show that the parameter estimates are only little changed when use is made of a more flexible translog specification.


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lised. However, from a more dynamic perspective, one might expect an initial increase in productivity as firms start to learn about the new opportunities offered by IT, discover new applications and start to make necessary changes in organisational forms, etc. 共Brynjolffson and Hitt 共1998兲兲. If we are only in the early stages of an economic transformation as significant as the first and second industrial revolution 共David 共1990兲兲, a decline in marginal productivity in the period under study is less likely. To this end, the model is estimated on cross-section data to investigate time trends. 4. Do more productive firms have larger computer stocks? Previous studies have indicated that firms with relatively large computer stocks are also relatively productive for reasons other than having this large stock, for example due to better management skills 共Brynjolffson and Hitt 共1995兲兲. We partly take account of these differences by including a skill variable. However, to see whether other variables have been omitted, firm-specific effects models are estimated in order to derive the ‘pure’ contribution of IT spending. 4 DATA CONSTRUCTION

Balanced panel The data on output, employment, and investment are drawn from an important firm-level survey of Statistics Netherlands: the Production Surveys. The Production Surveys 共PS兲 is an annual survey that supports the official publications of Statistics Netherlands, like the National Accounts, for all industries. 7 The set of large firms - firms with 20 employees or more - is covered completely. Small firms - firms with less than 20 employees - are sampled for the PS and their results are weighted to give an adequate representation of the entire set of firms. We have used the confidential micro PS surveys for the Dutch retail and wholesale trade from 1988-1994. Due to a number of major changes introduced in 1995, the most important of which was a complete revision of the PS questionnaire to make the data consistent with the National Accounts and the Labour Accounts of Statistics Netherlands, it proved impossible to extend the period after 1994. 8

7 The questionnaire of the PS can be found in: CBS, ‘Detailhandel, samenvattend overzicht 1992’ 共in Dutch兲. 8 This revision was carried out to establish the so-called ESA guideline for National Account statistics across the EU. It had serious consequences for the reported numbers of part time and full time employees compared to the situation before the revision. Furthermore, employees that worked very few hours a week and were not insured under the Sickness Act were counted as employee of the firm from 1995 onwards. Before 1995 they were not counted as employee. In addition the reference date of the PS surveys moved from September 31 to December 31. All this caused a major break in the employment and output data of the PS. However, the effects of the industrial classification 共SBI兲 change in the Netherlands in 1993 was overcome relatively easy, because Statistics Netherlands organised the data in such a way that each individual firm could be reclassified on the new classification.

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To estimate the model a balanced panel of firms - firms that were observed continuously throughout the whole period - was constructed consisting of 2,687 firms in total trade, of which 1,705 in the wholesaling and 982 in retailing. It accounts for about 4% of the total number of firms in wholesale sector and 1% of the retail firms. As expected, the balanced panel accounted for a much greater proportion of economic activity than its count numbers would suggest because the sampling design concentrates on larger firms. In the period 1988-1994 this panel averaged 318 thousand workers, roughly one third of total trade employment according to official sources. Employment coverage was 27% for wholesale trade and 38% for retail trade. The number of firms in this study is much higher than in most other studies. The seminal study of Brynjolffson and Hitt 共1995兲 was based on a set of 367 large firms spread over all economic sectors. More recently Lehr and Lichtenberg 共1999兲 used a panel data set for 800 service firms in the US. Due to the balanced nature of the panel, only firms surviving over the period 1988-1994 are included. Hence one would expect output growth rates of the panel firms to be biased and generally higher than for all firms. This expectation is borne out by the figures for the retail sector where output and labour productivity growth of the firms included in the sample were slightly higher. However, for the wholesale firms the opposite was observed with marginally lower growth rates for output 共see Table 1兲. In terms of persons there is hardly a difference between employment growth in the balanced panel and the Labour Accounts, but in terms of the number of annual working hours, employment growth was actually lower in the balanced panel. This difference is explained by the fact that firms in the balanced panel are larger than in the population and large firms employ more part-time employees than small firms 共see also Table 2兲. On the basis of these tables one can conclude that the panel provides a good approximation of the total trade firm population. 9 The role of entry and exit of firms in the trade sector is not taken into account here. The main reason was that before 1993 no distinction could be made between a new firm start-up or a ‘new’ firm resulting from a merger, acquisition, management buy-out and so on, and between closure of existing firms and firms leaving the Register for similar reasons. 10

9 This is further corroborated by a more detailed analysis of the cost and revenue structures of both data sets 共see Broersma and McGuckin 共1999兲兲. 10 This is based on the General Firm Register 共‘Algemeen bedrijvenregister’兲 of Statistics Netherlands and the national Chambers of Commerce. Since 1993 Statistics Netherlands conducts a special survey among ‘new’ firms in the register and among firms that ‘left’ the register to assess the true entrants and exiters. From then on new start-ups and closure of firms can be identified.


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TABLE 1 – COMPARISON OF REAL OUTPUT AND LABOUR PRODUCTIVITY IN PUBLISHED PRODUCTION SURVEYS AND IN THE BALANCED PANEL, 1988-1994.

Production Surveys Average 1988-1994

Index 1994 共1988⫽100兲

Balanced panel Average 1988-1994

Index 1994 共1988⫽100兲

Real net revenue 共bn Dfl.兲 Wholesale trade Retail trade Total trade

313.1 100.4 398.3

120.7 107.5 109.8

98.8 37.3 135.7

124.7 113.9 120.4

Real gross profit 共bn Dfl.兲 Wholesale trade Retail trade Total trade

55.4 29.6 85.0

127.4 118.6 124.3

17.0 11.1 28.2

125.0 122.8 124.3

Real net revenue per worker 共1000 Dfl per worker兲 Wholesale trade 813.8 Retail trade 177.1 Total trade 418.7

107.4 92.3 95.6

939.0 175.0 426.1

108.4 99.6 105.1

Real gross profit per worker 共1000 Dfl per worker兲 Wholesale trade 143.9 Retail trade 52.2 Total trade 89.2

113.4 101.8 108.2

161.9 52.2 88.5

108.7 107.4 108.5

Output and labour input Output in the trade sector can be measured in various ways. The simplest measure is net revenue, which is defined as total sales revenue of delivering goods and services to third parties. 11 Net revenue is however not a proper measure of value added as it also includes the costs of purchasing the goods being resold. Therefore gross margins are often used. Gross profit margins can be directly drawn from the PS and are defined as net revenue minus purchasing costs. 12 This 11 Net of discounts, bonuses and so on. 12 Purchasing costs consist of the costs of goods sold and related freight, insurance, and taxes. Purchasing costs include import duties, costs of customs clearance, import levies, excise duties, costs of freight, costs of transport insurance, 共wage兲 costs of activities contracted out, depreciation of goods stock, and transit trade.

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is the output concept used in this study. 13 Several price indexes were used to deflate the revenue and cost variables. Our choices, however, were limited. The major change in industrial classification in 1993 forced us to work with price indexes at fairly aggregated levels. Second, detailed price indexes for the retail trade industry are only available from 1996 onwards. Since the retail industry is delivering to consumers, we used the overall CPI to deflate revenues in retail trade firms of the panel. In some cases, however, such as firms trading in food, clothes and shoes, it was possible to use a more detailed CPI. Firms in the wholesale industry deliver to other firms so the aggregate producer price index of domestic sales was used as deflator for wholesale trade. For the period 1988-1994 no adequate alternative price index was available. For labour input, hours worked is used rather than number of workers. This is important due to the high incidence of part-time workers, especially in the retail sector. However, the PS does not provide data on hours directly. The PS used the following classification: employees working less than 15 hours a week, between 15-30 hours, 30 hours or more, temporary employees and other labour 共owners, participating family members, managing directors, etc.兲. On the basis of this information we obtained an estimate of the total annual number of hours worked by firms in our sample. 14 Table 2 compares the employment measures of the balanced panel with those of official statistics of the Labour Accounts. The annual working hours per worker is a measure of employment that enables direct comparison between our panel and the Labour Accounts, which shows they match very well. Computer investments Section 3 described the variety of uses of computers in wholesale and retail trade. The amount of money invested in computers as share of total investment in gross fixed assets averaged 12.3% over the period. 15 The figure was much higher in wholesale, 15.7%, than in retail, 6.3%. These figures are based on using the same deflator for all investment goods. However, based on the US experience, the quality-adjusted fall in computer hardware prices was much greater than that for prices 13 A third output measure would be value added as used in the National Accounts 共NA兲. However, this is not directly reported in the PS and therefore not used in this study. Value added according to the NA is defined as the production 共basically gross margin兲 minus intermediate costs. These intermediate costs include all operating costs except payroll costs. 14 Employees in the class of less than 15 hours a week are assumed to work on average 8 hours a week, those in the 15-30 hours class to work 21 hours a week, and those with 30 hours or more to work 38 hours a week. Temporary employees were assigned 23 hours a week and ‘other labour’ was assumed to work 40 hours a week. Using a 40 hour working week as the official full time hours allowed us to get an estimate of the annual working hours. The distribution over these classes is derived from scattered evidence of Statistics Netherlands 共see ‘Sociaal-economische maandstatistiek’ 1997/11, Table 2.2.11兲. 15 In this case we can use the entire sample 1988-1995, because the break in 1995 共see earlier兲 does not affect the investment variables.


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TABLE 2 – COMPARISON OF EMPLOYMENT IN PUBLISHED LABOUR ACCOUNTS AND BALANCED PANEL, 1988-1994.

Labour Accounts Average 1988-1994 Employment 共1000 persons兲 Wholesale trade Retail trade Total trade

Index 1994 共1988⫽100兲

Balanced panel Average 1988-1994

Index 1994 共1988⫽100兲

384.5 567.6 952.1

112.4 116.5 114.9

105.1 213.1 318.3

115.0 114.3 114.6

Employment 共mln annual hours兲 Wholesale trade Retail trade Total trade

582.4 577.9 1160.3

116.0 115.7 115.8

163.9 220.2 384.2

112.7 111.4 111.9

Annual hours per worker Wholesale trade Retail trade Total trade

1515 1018 1219

1559 1033 1207

of other investment goods. The Netherlands Bureau of Economic Policy Analysis 共CPB兲 has developed a computer price index that indeed drops rapidly in the period under consideration and its trend is about the same as that of the hedonic price index used in the USA. If we apply this CPB computer deflator as a more representative measure of computer price movements and apply the overall investment price index from the National Accounts to all other investment goods, then the average share of investment moves to 21.4% for wholesale and 8.9% for retail. 16 Another way to look at the diffusion of computers is through movements in the percentage of firms with positive investments in computer hardware in a particular year. Table 3 shows that for total trade this went from about 42% to 58% over the 1988-1995 period. The increase was larger in retail trade - from 27% to 48% - than in wholesale trade, which grew from a base of 51% of the firms investing in computers to 65%. The proportion of firms that invested in computers was less for smaller firms than for larger ones, in part due to the relatively high costs involved in computers. However, Table 3 shows the falling computer price in a different way, as the number of small firms with positive computer 16

Computer price index available upon request from the authors.

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investments increased between 1988 and 1995. On average the number of small firms that invested in computers grew with 11% per year between 1988 and 1995. For the very large firms with more than 500 employees this was a mere 1.4% per year. Moreover, there is little growth in the share of firms investing in computers after 1991 among the larger firms. TABLE 3 – SHARE OF FIRMS WITH POSITIVE INVESTMENTS IN COMPUTERS BY SIZE AND INDUSTRY 共% OF ALL FIRMS兲.

Trade

by size 0-9

1988 1991 1995 Index in 1995 when 1988⫽100

by industry 10-99

100-499

ⱖ 500

wholesale

retail

42.2 50.3 58.4

19.8 24.8 40.6

43.6 50.3 60.1

49.5 63.5 62.1

51.7 58.2 57.1

51.0 56.8 64.6

26.9 36.4 47.5

137.3

202.3

136.4

127.3

106.7

126.2

173.7

5 THE ‘BOOKED DEPRECIATION METHOD’ FOR MEASURING CAPITAL STOCK

It is well known that for productivity analysis, the productive capital stock is the best measure of capital input 共OECD 共2001兲兲. However, due to a lack of data most studies use a proxy for productive capital stock. For example, Licht and Moch 共1999兲 use number of computers as a proxy of the computer capital stock. Book values of capital were used in Brynjolffson and Hitt 共1996兲 and Lichtenberg 共1995兲, while Lehr and Lichtenberg 共1999兲 used investment flows. Book values are imperfect measures of productive capital stocks as they are based on historic, rather than replacement cost and on accounting rules rather than economic depreciation. Investment flows are a noisy proxy as long as growth rates of investment are not constant, which is typically the case for computer investment. Productive capital stock can be derived using the Perpetual Inventory Method which essentially sums past investment flows, correcting for loss in productive capacity due to ageing. Assuming a geometric retirement pattern 共see e.g. Jorgenson and Stiroh 共2000兲兲, the capital stock is derived as K t ⫽ K t ⫺ 1 共1⫺␦兲 ⫹ I t

共2兲


65

with K t the capital stock at t, I t the real investment flow during year t, and ␦ the rate of economic depreciation. The main problem when using this approach, especially in microlevel studies, is the lack of long series on investment flows. Typically, data on investment is only available for a short period. Standard methods to circumvent this problem in macroanalysis, such as the Harberger method, cannot be used. In the Harberger method initial year capital stock is estimated by dividing investment in the initial year by the sum of the growth rate of investment and the depreciation rate. This method is based on a steady-state assumption: investment flows must be smooth and grow at a constant rate. This might not be a problematic assumption at the total economy or sectoral level, but it is at the firm level. Investment patterns at firmlevel are volatile, investment often coming in spikes. Here, we propose a new method to deal with this problem, using information on reported depreciation by firms. Most micro production surveys, which include investment variables, also include depreciation recorded in the firms’ books. This reported depreciation, which is determined by accounting rules rather than technical factors, contains information on past investment. This information can be retrieved when the accounting practice of firms is known using what we will call the ‘Booked Depreciation Method’. A standard accounting rule often used in practice is linear depreciation. This means that an investment made in year t is written off in equal parts during the lifetime of the asset. If the lifetime L of an asset is 15 year, each year one-fifteenth of the original investment value is recorded as depreciation. Hence booked depreciation in year t 共D t兲 is the summation of investment made in the period t⫺L to t, multiplied by 1/L. L

1

p⫽1

L

Dt ⫽ 兺

I t⫺p

共3兲

Using 共3兲 one can deduce that 1 1 D t ⫹ 1⫺D t ⫽ I t ⫹ 1 ⫺ I t⫺L . L L

共4兲

Rewriting I t⫺L ⫽ I t ⫹ 1 ⫹ L 共D t ⫹ 1⫺D t 兲.

共5兲

This equation shows past investment flows 共made before t兲 can be derived on the basis of investment and depreciation data at t and after.

66


The data set contains information on investment of nine asset types and total capital depreciation for the period 1988-1994. The ‘booked depreciation method’ has been used for the derivation of current price investment flows for total capital before 1988 共see the appendix for full details of this derivation兲. For computers, 17 separate depreciation figures are not available. Hence it was assumed that before our first observation 共1988兲 the capital stock of computer hardware was zero. This strong assumption can be defended by the fact that the surge of information technology and computers had not yet gained its momentum halfway the 1980s in more or less traditional trade industries. 18 Total investment was deflated with the price deflator for investment in fixed assets from the National Accounts. Investment in computers is deflated with the IT deflator of the CPB, discussed earlier, in order to take the rapid increase in computer quality into account. Using the long investment series in 共2兲, stocks are derived using a depreciation rate for non-computer capital of 0.067, based on an average life span of 15 years 共␦⫽1/L兲. For computer capital a lifetime of 5 years is used hence ␦⫽0.2. 19 Finally, the real stock of non-computer capital is simply derived by subtracting the computer stock from the total capital stock. Table 4 gives an overview of the share of computers in total real capital stock, and their growth rates, by trade industry. In 1995 the share of computer capital in wholesale trade was almost 7 %, which is almost three times the share in retailing 共2.5%兲. This corroborates the weight of computers, and in general of IT investments, in wholesaling, while indicating that the penetration of computers in retailing is lagging behind. However, retailing is catching up as shown by the higher growth rates of computer capital. Adequate comparison with IT stocks of other studies can only be made for the period 1991-1995. In our study we find an average annual growth rate for real capital stock of the trade sector of 5.5% and for computer capital of 13%. These growth rates are well in line with rates of van der Wiel 共1999兲, who reports 4% growth of capital in total trade for the same period. The Netherlands Bureau of Economic Policy Analysis, CPB, 共2000兲 reports 18% growth for IT capital in commercial services of which trade is a part. This higher figure than ours can be explained by the rapid growth of IT capital in business services, like computer service agencies, consultancy and technical engineering, which are part of commercial services, but not of the smaller subsector of trade.

17 Computers include all programmable information processing devices, including peripheral equipment. 18 The strongest growth in use of PC’s, especially those linked in a some network, started in The Netherlands only in the 1990’s, as can be concluded from Statistics Netherlands Automation Statistics. 19 These lifetimes come from Meinen et al. 共1998兲.


67

TABLE 4 – GROWTH RATES AND SHARE OF COMPUTERS IN TOTAL CAPITAL, 1988-1995

SBI

Industry

Real IT capital in real Index of real capital total capital growth in 1995 共1988⫽100兲 1988

1995

Total

IT

Whole sale trade of agricultural products and livestock food and stimulants other consumer products intermediate goods machines, equipment and accessories Other specialised wholesale trade

1.0

4.4

141.2

635.4

1.0 1.6 1.0 4.7

5.0 9.4 3.8 13.0

169.3 151.3 167.2 143.7

894.6 866.9 600.6 401.7

1.5

4.3

150.0

420.9

51

Wholesale trade

1.9

6.9

156.1

566.9

521 522

Non-specialised retail trade Specialised retail trade of food and stimulants Retail of pharmaceutical and medical products Other specialised retailing Retail of second hand goods and antiques Retail not in a shop 共street market, mail order兲

0.5 0.1

2.4 1.4

168.4 151.4

723.2 2757.6

0.4

2.4

173.6

1147.7

0.5 0.0

2.6 2.4

163.3 157.6

828.5 -

1.4

6.3

81.5

377.2

52

Retail trade

0.6

2.6

158.4

696.2

5

Total trade

1.3

4.9

157.2

595.5

512 513 514 515 516 517

523 524 525 526

There is substantial variability within detailed wholesale and retail industries. In 1995, 13 per cent of the capital stock of wholesaling of machines 共SBI 516兲 was computers, but computers only accounted for 3 per cent in wholesaling of intermediate goods. 20 The highest computer share in retailing can be found in ‘retailing which does not take place in shops’ 共SBI 526兲. In our data set this industry consists mainly of mail-order firms where automation is an important part of the production process. On the other hand, computer usage in specialised

20

The SBI number refers to the industrial classification system of Statistics Netherlands.

68


retailing of food is rather low, albeit growing. This industry consists mainly of relatively small, specialised shops like groceries, bakeries, etc. 6 EMPIRICAL RESULTS

The derived levels of computer and other capital stocks are used to estimate the returns to capital with the production function framework of equation 共1兲 and pooled firm observations for 1988-1994. Initially we focus on OLS estimates of the model. Specification and endogeneity issues are dealt with later in conjunction with difference estimates of the model. The estimated model includes fixed industry effects, represented by dummy variables for each 5-digit industry represented in our data set. 共Note this accounts for nearly 250 dummy variables with an average of a dozen firms per industry.兲 We take account of differences in the quality of labour by adding a firm-specific real wage variable. In addition, dummies for year, legal status of the firm and ten size dummies, used to minimise collinearity with labour and capital, are included in the statistical model. The first row of Table 5 shows estimates of the ‘baseline’ regression in which labour is not corrected for quality and the trade sector is taken as a whole. All inputs are highly significant and the coefficients on labour and capital input behave reasonably, although their sum 共0.90兲 is significantly less than one suggesting decreasing returns to scale. Clearly, the rate of return to IT investment is greater than zero. In the second row, the output concept is switched from gross profit to net revenue. Using net revenue, our less preferred output measure, actually leads to a higher estimated coefficient for IT capital. Many other studies on the impact of computers on productivity actually use sales as an output measures, so our results suggest that this can lead to overestimated effects of IT productivity in the trade sector, where the difference between net revenue and gross profit is quite substantial 共see also Table 1兲. In the third row, labour input is corrected for quality, which appears to be highly significant and substantially improves the fit of the model. More importantly, the coefficient on IT capital falls from 0.044 to 0.018 due to this correction. This corroborates that firm-specific levels of labour skills are an important determinant of productivity, suggesting that computers partly proxy for labour quality. This finding is in contrast with Lehr and Lichtenberg 共1999兲 who found no such effect. This is probably due to their sample of firms, which consists solely of large firms 共on average 6000 employees per firm兲 where the distribution of labour quality will be much narrower than in our sample of firms which includes both small and large firms 共see Figure 1兲. Rows 4 and 5 show the results for the wholesale and the retail sectors separately. For both sectors constant returns to scale are clearly rejected. The impact of skills is less in retail than in wholesale trade. Most importantly, the output elasticity of computer capital is almost the same and highly significant in each


69

TABLE 5 – REGRESSION RESULTS FOR FIXED INDUSTRY EFFECT MODEL OF OUTPUT, POOLED DATA 1988-1994

共1兲

Total trade

共2兲

Total trade

共3兲

Total trade

共4兲

Wholesale

共5兲

Retail

log L

log K non-IT

log K IT

0.631 共36.9兲 0.513 共32.9兲 0.865 共15.5兲 0.897 共8.8兲 0.797 共29.7兲

0.221 共36.8兲 0.255 共28.7兲 0.149 共29.8兲 0.143 共23.1兲 0.174 共27.7兲

0.044 共14.8兲 0.085 共19.3兲 0.018 共7.5兲 0.018 共5.5兲 0.017 共9.4兲

log w

0.807 共77.8兲 0.905 共18.3兲 0.548 共42.3兲

adj R 2

# obs

0.504

12236

0.557

12238

0.673

12204

0.551

8568

0.730

3635

Note: The variable w refers to the real hourly wages for each individual firm. The parameter values of the 251 control dummies are omitted for convenience. Dependent variable is real gross profit, except in 共2兲 where real net revenue is used. t-values are in parentheses

Figure 1 – Distribution of firms in balanced panel by size classes, averages 1988-1994

sector. This indicates that in both sectors there are positive returns to computer investment. The output elasticity of other capital is much higher than for computer capital. However, this does not mean that the marginal productivity of computer capital

70


is the same in both sectors, or that the marginal productivity of other capital is higher than of computer capital. The coefficients ␤ are estimates of the output elasticities of the two capital forms: ␤ IT⫽d log 共Y兲/d log 共K IT兲⫽共dY/d K IT兲共K IT/Y兲⫽MP IT 共K IT/Y兲 where MP IT is the marginal product of IT-capital and similarly for non-IT capital. Hence, marginal productivities are derived by multiplying ␤ by the average output-capital ratio of the sample. The resulting rates of return R 共defined as marginal productivity times 100兲 are given in Table 6. TABLE 6 – RATE OF RETURN TO CAPITAL, AVERAGE OVER 1988-1994 共%兲

Total trade Wholesale Retail

Average Y / K non-IT

Average Y / K IT

R共K non-IT兲

R共K IT兲

R共K IT兲/R共K non-IT兲

1.7 1.8 1.5

46.8 34.9 86.0

24.7 26.0 34.3

84.2 62.8 146.1

3.41 2.42 4.26

Table 6 shows that the rate of return to computer capital is twice as high in retail as in wholesale trade. This is primarily due to the smaller share of computer capital in retail than in wholesale trade since the output elasticities in retail trade are only 16% below those in wholesale trade. It also shows that the rates of return to computers are much higher than other capital, both in the trade sector, and in the two subsectors. In retailing the rate of return to computer capital is more than four times higher than for non-computer capital. As discussed in Lichtenberg 共1995兲 the excess returns to computers compared to other capital are not surprising since their rental prices are much higher. Computers have a much shorter lifespan and rapidly decreasing prices make capital losses a possibility. Hence, the cost of employing computer capital is much higher than for other capital. In the absence of taxation, the equilibrium rental price of an asset k can be written as 21 p kT ⫽ q kT⫺1 r T ⫹ ⭸q kT⫺1⫺关q kT⫺q kT⫺1兴

共6兲

with r a general rate of return to capital, ␦ the depreciation rate and q k the acquisition price of investment good k. This can be rewritten as p kT q kT⫺1

⫽ 共r T⫺␲ kT 兲 ⫹ 共1 ⫹ ␲ kT 兲 ⭸ k

共7兲

21 Even when such specific tax rates would have been available, inclusion of these tax rates in the calculation of rentals will influence rentals of IT and non-IT a like. It is however the difference between the two rentals, compared to their productivity differential which is of main interest in our paper, so leaving out taxes should have no harmful effect.


71

with ␲ kT ⫽ q kT⫺q kT⫺1 Ⲑ q kT⫺1, the rate of inflation in the price of investment goods. 22 To give an indication of the differences in rental rates of computer and other capital, the following values have been used. For r the nominal interest rate can be taken 共0.07兲. The decline in computer prices in the period 1988-1994 was about 7% annually, while prices of other capital increased by about 1%. Depreciation rates are taken as the inverse of the lifetime, being 5 years for computers and 15 years for other capital. Using these estimates the rental price ratio of computer and other capital is about 2.7. Together with rates of return of Table 6, this shows that in retailing there are substantial ‘excess’ returns to computer capital, while these cannot be found in wholesaling. This finding of excess returns to computer capital is consistent with most recent studies for the US. For example Lehr and Lichtenberg 共1999兲, using a sample of large firms for the total economy, found excess returns in both service and non-service sectors in the US for the period 1977-1993. The logical question to ask is whether the excess returns in retail have declined over time and whether wholesale returns always have been much lower than in retail. Our data set enables us to study the gross marginal product of computer capital over time. Because of its balanced nature and large number of observations, estimates can be made with considerable precision. Figure 2 shows the development of the marginal productivity of computer capital in the total trade sector 共a兲, wholesale trade 共b兲 and retail trade 共c兲 over the period 1989 to 1994. It shows that whereas in wholesale marginal productivity has fluctuated around 50% after a decline in 1990, the rate of return in retail shows an increasing trend with rates well above 100% in the 1990s. This contrasts findings for the US. Lehr and Lichtenberg 共1999兲 showed that computer productivity increased from 1977, reaching a peak in 1986-1987 and then began to decline. Brynjolffson and Hitt 共1996兲 find similar results for the US. These results suggest that the Netherlands has been lagging in the application of IT compared to the US and that the productivity-boosting effects are yet to be realised. Econometric issues There are many econometric issues of concern in estimating equation 共1兲. Of particular concern, there are reasons to expect upwardly biased capital coefficients from simple OLS estimation. This potential bias is due to an expected positive correlation between capital and the error term. Several factors could lead to such a positive correlation. For example, as argued in Brynjolffson and Hitt 共1995兲 and McGuckin et al. 共1998兲 firm specific effects not picked up in the various dummy variables included in equation 共1兲 are likely to be positively correlated with investment in computers. High returns to IT might be indicative of the benefits of computerisation, but may also simply be a marker for firms that are highly 22

See Jorgenson and Yun 共1991兲 for a derivation.

72


productive for other reasons. If IT investment is correlated with unmeasured productivity-enhancing characteristics, such as management skills, it will cause an omitted variables bias and simultaneity issues. Brynjolffson and Hitt’s results indicated that ‘firm effects’ accounted for as much as half of the productivity benefits imputed to IT in earlier studies, a finding corroborated by McGuckin et al. 共1998兲 and Lehr and Lichtenberg 共1999兲. In our study, we already corrected for an important firm-specific variable, namely the skill level of the labour force. Table 3 showed that skills were highly significant in explaining productivity differences between firms, and importantly, took away a large part of the productivity enhancing effects of computers. But this procedure does not ensure that we have purged the correlation from other omitted variables. To the extent that omitted variables are firm characteristics that do not change over the sample period, the bias can be mitigated by replacing the industry dummy variable with a dummy variable for each firm in the data set. However, given the large number of firms in our data set and the rather short sample period, this is not feasible. As an alternative, we followed Brynjolffson and Hitt 共1995兲 and used a ‘within transformation’ of equation 共1兲 that eliminates the firmspecific variable but leaves the other coefficients unchanged. Hence, we estimated: log Y i,t ⫺ log Y i ⫽ ␮ i,0 ⫹ ␣ 共log L i,t ⫺ log L i 兲 ⫹ ␤ 1 共log K IT,i,t ⫺ log K IT,i 兲 ⫹ ␤ 2 共log K non-IT,i,t ⫺ log K non-IT,i 兲 ⫹ ␥ 共log w i,t ⫺ log w i 兲

共8兲

where the barred variables refer to the average values for each firm i over the sample. This type of ‘difference’ procedure is similar to that suggested by Griliches and Mairesse 共1998兲 and employed in McGuckin et al. 共1998兲. 23 The results for the regressions with individual firm effects differenced out are given in Table 7. For total trade and for the retail subsector all coefficients in the regressions are still statistically significant at the 1% level or higher. However, for wholesale trade significance for the capital coefficients is now much lower compared to the fixed industry-effects model. The difference between the elasticities for computer and other capital is also much smaller. Importantly, for all sectors, the computer capital coefficients are still positive and significant at the 5% level. As found by 23 One might prefer to use some form of instrumental approach, but instruments of IT capital, or any of the explanatory variables for that matter, are difficult to find. Furthermore, given the relatively short time horizon of our panel, application of GMM estimators is no option either. One of the problems with this procedure is that by differencing the variables the noise to signal ratio increases as the effects of measurement error are magnified. This typically reduces estimated elasticities.


73

Figure 2 – Gross Marginal Product of Computer Capital Over Time

Brynjolffson and Hitt 共1995兲, the elasticity of computer capital in total trade output has dropped considerably and is more than halved from 0.018 to 0.007. Interestingly for retail the drop is much smaller from 0.017 to 0.012. These esti-

74

L. BROERSMA, R.H. MCGUCKIN, AND M.P. TIMMER TABLE 7 – REGRESSION RESULTS FOR FIRM EFFECTS MODEL OF OUTPUT, POOLED DATA 1988-1994

Total trade Total trade Wholesale trade Retail trade

log L

log K non-IT

log K IT

0.542 共54.9兲 0.781 共28.2兲 0.798 共19.7兲 0.745 共25.5兲

0.064 共6.5兲 0.023 共2.9兲 0.011 共1.1兲 0.063 共5.2兲

0.014 共5.1兲 0.007 共3.0兲 0.005 共1.8兲 0.012 共4.0兲

log w

0.623 共73.0兲 0.662 共60.2兲 0.519 共44.5兲

adj R 2

# obs

0.205

12236

0.449

12204

0.433

8558

0.537

3635

Note: t-values are in parentheses Dependent variable is real gross profit corrected for firm average over the sample

mates imply that for wholesale trade more than half and for retail about a third of the elasticity of computers is attributable to individual firm effects, while the remaining are attributable to the pure effect of computer spending. 7 SUMMARY AND DISCUSSION

This paper describes the results from the first large-scale micro level study on the impact of IT investment on productivity in the Netherlands. The study focused on the wholesale and retail trade sectors during the period 1988 to 1994. It was found that computers had a much higher rate of return than other capital in both sectors. However, only in retailing the returns to computers were higher than expected, given the higher rental prices of computers vis-à-vis other capital. The rates of return were not subject to a decline in the period studied. In retailing the rates showed an increasing trend, whereas in wholesale they were more or less stable. The increasing trend in returns in retailing may suggest that for using IT efficiently, strong complementary investments in shop concepts and other organisational changes are needed, and that these investments are increasingly made. In wholesaling on the other hand, IT can boost productivity without much additional investment effort. Combined with the fact that the share of computers in capital is still small in retailing, it indicates that in this sector opportunities for productive usage of computers were still high in the 1990s. These findings need to be qualified because of a number of potential measurement problems. We distinguished between computer capital and other capital, but neglected IT shares in other factors. In particular IT labour and IT services play an important role and constitute a sizeable part of IT budgets of firms 共Lichtenberg 共1995兲兲. This may lead to an overestimation of the marginal productivity of IT capital insofar that computer investment is correlated with expenditures on


75

non-capital IT. Also, other studies have shown that the strongest effects on output come specifically from the use of personal computers, and less so from other IT capital. As such, our focus on computers might bias the estimates in favour of IT capital investment 共Lehr and Lichtenberg 共1999兲兲. On the other hand, the effects of computers might be underestimated because of output mismeasurement. It is well known that many of the benefits of IT use, such as customer service and timeliness, other than cost saving, are not well captured in aggregate price deflators. This is particularly true for the trade sector. The use of a cross-sectional, within industry, design circumvents this problem partially. As long as the deflators are invariant across firms within industries, price mismeasurement does not affect the regression results 共Lichtenberg 共1995兲兲. However, insofar a greater use of IT of a firm would also lead to a higher quality of output, the marginal productivity of IT capital will be underestimated Despite these potential measurement problems, our findings of a positive productivity effect seem to be robust as a number of potential biases found in other studies have been accounted for. Use was made of only one data source 共the Production Survey of Statistics Netherlands兲, which ensures consistency between output and input measures, in particular computer and other capital. We used a balanced panel data set that is broadly representative of the trade sector, including smaller scale firms. The number of observations was much higher than in previous studies and concentrated on one sector, ensuring a high degree of homogeneity of the observations. Further, a new method was used to estimate capital stocks, which are a more accurate indicator of capital input than investment flows. Most importantly, firm-specific effects, such as labour skill and other 共nonmeasurable兲 effects were accounted for and appeared to be highly important. Our results confirm other studies, mainly pertaining to the US, that computers have had positive productivity effects. However, whereas in the US the rates of return seemed to decline by the end of the 1980s after a period of ‘excessive’ returns 共Brynjolffson and Hitt 共1996兲兲, in the Netherlands returns were still constant or even increasing at the beginning of the 1990s, at least in the retail trade sector. This suggests that the Netherlands has been lagging in the application of IT compared to the US and that the productivity-boosting effects may still take place. This hypothesis is supported by a recent growth accounting study for the Netherlands by van der Wiel 共2001兲, who found a surge in total factor productivity growth in trade in the second half of the 1990s. Further study on other sectors and use of longer time-series and more recent micro level data sets is needed to confirm this lagging-hypothesis. Linking production statistics with surveys of innovation behaviour, such as the Community Innovation Survey 共Klomp and van Leeuwen 共2001兲兲, appears to be an especially fruitful line of further research into the dynamic links between innovation, IT investment, and productivity. But, even if the slow diffusion is over, the question why IT diffusion was faster in the United States still remains. It is clear that IT equipment is sold in world-

76


wide markets and this means the technology is available to potential users about everywhere. In the end, business organisation and the opportunities to exploit technologies depend on the constraints and restrictions that firms face. For example in many European industries the opportunities to invest in IT are limited by regulations and structural impediments in both product and labour markets. Examples of product market restrictions include limits on shop opening hours, and transport regulations that make it difficult for manufacturers and wholesalers to supply customers frequently. Restrictive rules and procedures on working hours and employment protection limit flexibility in organising the workplace and hiring and firing of workers. Furthermore, barriers to entry and restrictions on the free flow of capital are still an issue in many countries. Examination of these factors is beyond the scope of this study. But as shown in McGuckin and van Ark 共2001兲 and van Ark, Inklaar, and McGuckin 共2002兲 such factors play a big role in European performance relative to the U.S.

APPENDIX

APPLICATION OF THE ‘BOOKED DEPRECIATION METHOD’

The specific application of the ‘depreciation method’ outlined in section 5 to the current case requires further assumptions as depreciation and investment data is only available for the period 1988-1994. Therefore we split the period 1973-1987 into two: 1973-78 and 1978-87 for reasons which will become apparent below. Let nK 88 be the nominal capital stock in 1988, then 87

nK 88 ⫽ 兺

共t⫺73 ⫹ 1兲

t⫽73

15

78

It ⫽ 兺

共t⫺73 ⫹ 1兲

t⫽73

15

87

It ⫹ 兺

共t⫺73 ⫹ 1兲

t⫽79

15

It .

共9兲

The first term on the right hand can be derived using 共5兲 I t ⫽ 15共D t⫹15 d ⫺ D t⫹16 兲 ⫹ I t⫹15 ,

t ⫽ 73 . . . 78

共10兲

The second term on the right hand side of 共7兲 is unknown. This can be approximated as follows 24: 87

兺

t⫽79

24

共t⫺73 ⫹ 1兲 15

It ⬇

11 15

87

兺

t ⫽ 79

It .

This approximation is exact when I t is constant over time.

共11兲


77

Because of 共3兲, the sum of depreciation over the period equals 94

1

t⫽88

15

兺 Dt ⫽

冉兺 93

92

88

87

t⫽78

t⫽74

t⫽73

冊

It ⫹ 兺 It ⫹ . . . ⫹ 兺 It ⫹ 兺 It ,

t⫽79

共12兲

which can be rewritten in the following three terms 94

7

t⫽88

15

兺 Dt ⫽

87

兺

t⫽79

93

共93 ⫹ 1 ⫺ t兲

t⫽88

15

93

共93 ⫹ 1 ⫺ t兲

t⫽88

7

It ⫹ 兺

78

共t ⫺ 73 ⫹ 1兲

t⫽73

15

78

共t ⫺ 73 ⫹ 1兲

t⫽73

7

It ⫹ 兺

It .

共13兲

It .

共14兲

Rewriting gives 87

93

15

兺I ⫽ 7 兺

t⫽79

t

t⫽88

Dt ⫺ 兺

It ⫺ 兺

The first and second term can be simply observed from the data and the third term can be derived from 共10兲 To get the capital stock in 1988 in constant 1990 prices, the investment flows in the period 1973-1978 are deflated by the appropriate aggregate investment price index from the National Accounts of Statistics Netherlands. For the second term in 共9兲 no annual investment flow is known and hence the deflator is approximated by the average index over the period 1979-1987. Hence the initial value of the real capital stock 共in 1990 prices兲 equals 78

K 88 ⫽ 兺

t⫽73

冉

t ⫺ 73 ⫹ 1 15

冊冉冊 .

P i,90 P i,t

It ⫹

11 15

87

兺I

t⫽73

冉冊 P i,90

t

P 79,87

共15兲

where P i,t is the investment price index for year t and P 79,87 is the average price index over 1979-1987.

REFERENCES Ark, Bart van 共2000兲, ‘Measuring Productivity in the ‘New Economy’: Towards a European Perspective,’ De Economist, 148, pp. 87-105 Ark, Bart van, Robert Inklaar, and Robert H. McGuckin 共2002兲, ‘Changing gear’ Productivity ICT and Service Industries: Europe and the United States,’ paper presented at the ZEW Conference ‘Economics of Information and Communication Technology,’ Mannheim, June 24-25, 2002. Broersma, Lourens and Erik Brouwer 共2000兲, ‘Innovation in Services: Explorations of Micro Data of Retail Trade and Technical Engineering in the Netherlands,’ paper in SIID-project on behalf of the Dutch Ministry of Economic Affairs, mimeographed, University of Groningen.

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