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DSTI/EAS/IND/SWP/AH(2001)3

TABLE OF CONTENTS

WORK WITH FIRM-LEVEL STATISTICS: SOME KEY APPLICATIONS..............................................3

1. Introduction...........................................................................................................................................32. Firm demographics: exit, entry and turnover of firms..........................................................................43. Entrepreneurship and the growth of firms.............................................................................................64. The size dimension: the role of SMEs in the economy.........................................................................7

4.1 The contribution of SMEs to overall performance......................................................................74.2 Work on high-growth firms.........................................................................................................9

5. Women entrepreneurship....................................................................................................................106. The dynamics of productivity growth.................................................................................................13

6.1 The contribution of firm-level dynamics to aggregate productivity growth..............................136.2 The role of upsizing and downsizing.........................................................................................16

7. Understanding the growth of firms.....................................................................................................177.1 Technology, innovation and the role of ICT..............................................................................177.2 Other factors...............................................................................................................................22

8. Wrapping up: the importance of firm-level statistics..........................................................................24

SELECTED STUDIES..................................................................................................................................26

General surveys of firm-level studies.........................................................................................................26Entry and exit, job flows............................................................................................................................26Entrepreneurship and SMEs.......................................................................................................................27Women entrepreneurship............................................................................................................................27Productivity................................................................................................................................................28Analytical studies.......................................................................................................................................29

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WORK WITH FIRM-LEVEL STATISTICS: SOME KEY APPLICATIONS

1. Introduction

Work with firm-level statistics covers many areas and summarising this work is no simple matter, although some surveys are available (OECD, 1998; Bartelsman and Doms, 2000; OECD, 2001a; Ahn, 2001). This paper provides a brief overview of the main areas of work, primarily covering those that will be discussed at the OECD workshop on firm-level statistics. No attempt is made to be exhaustive, however, and the survey primarily covers work that is based on work with official firm-level statistics in OECD member countries, as opposed to that based on sample surveys.1

The studies surveyed here rely on several firm-level sources, ranging from business register data to longitudinally linked results of production surveys. They all have in common, however, that they are based on information on the economic characteristics of individual firms or establishments. The survey distinguishes between six areas of work, that are discussed in turn:

Firm demographics: These studies focus on the processes of exit, entry, turnover and survival, i.e. the creation and destruction of firms. Such work is available from statistical offices in several OECD countries.

Entrepreneurship and the growth of firms: This work builds on the first area and looks in more detail at the growth and survival of firms, i.e. which types of firms grow, which entrepreneurs are most likely to survive and what the characteristics of growing and declining firms are. Some of this work also examines age and vintage effects.

The size dimension: A considerable amount of work focuses on this aspect, i.e. the role of small and medium-sized enterprises (SMEs), their growth over time, the role of high-technology SMEs and start-ups, and so on.

Women entrepreneurship: These studies focus on the gender dimension of entrepreneurship, i.e. the role of women entrepreneurs in overall entrepreneurial activity and the specific characteristics of women entrepreneurship.

The dynamics of productivity growth: This area of work focuses on the firm-level dimensions of productivity growth, notably the respective contributions of existing firms, exit and entry, and changes in market shares to overall productivity growth. Some work has also examined the link between productivity and employment growth at the firm level, i.e. the role of upsizing and downsizing firms.

Understanding the drivers of firm performance: This area of work examines the drivers of firm performance in more detail, i.e. the links between firm performance (e.g. productivity growth) and potential drivers of firm growth, such as technology use, human capital,

1 . This paper also does not cover work with firm-level data on labour turnover. Some OECD work using these data is available in OECD (1996; 1997).

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organisational change, innovation, exposure to foreign competition, etc. This is a very rich area of work, and the most diverse across countries, as it relies on the availability of firm-specific data on each potential driver of growth. The discussion in this section will only focus on the issues that are discussed during the workshop, notably the role of information technology and innovation in firm growth.

In practice, the distinction between these areas of work is difficult to make as they are closely interlinked. Work on SMEs, for example, may have an explicit focus on the dynamics of productivity growth within SMEs; or work on the drivers of firm performance may focus specifically on women-owned firms. The discussion in the different sections below therefore frequently makes cross-references to other sections, as these can be equally relevant.

2. Firm demographics: exit, entry and turnover of firms

Firm demographics, or entrepreneurial demography, is currently of great policy interest, as the creation of new businesses and the decline of unproductive firms are regarded key to the overall dynamism of OECD economies. Many statistical offices therefore provide official statistics on the exit, entry and turnover of firms. Several studies are also available at the international level, sometimes based on official statistics, in other cases based on more limited sample surveys. This work typically focuses on the following indicators (Ahn, 2001):2

The entry rate (or start-up rate), typically calculated as the number of entrants during a certain period, divided by the total number of firms in the sector. Occasionally, gross sales or employment are used as weights of the share of entrants. The gross sales measure is referred to as the entry penetration rate and the employment measure is referred to as employment-weighted entry rate. 3

The exit rate, typically calculated as the number of exiting firms during a certain period divided by the total number of firms in the sector. The analogous employment-weighted exit rate is calculated by dividing the employment of exiting firms by total (sectoral) employment.

The turnover rate is the sum of entry rate and exit rate in a given sector over a given period.

Cross-country comparisons of entry and exit are relatively rare, partly due to a range of measurement problems. The available studies show that a large number of firms enter and exit most markets every year (European Commission, 2000; OECD, 2001a; Bartelsman, et al, 2001). Must of the interest in international comparisons of exit and entry is linked to the assumption that countries that are more dynamic (i.e. experience better economic performance) should have higher rates of firm turnover. Cross-country studies of firm demographics provide evidence that there are indeed large differences in firm turnover, but do not always demonstrate that countries that perform better have the highest rate of firm turnover. The available studies also give quite different assessments of firm dynamics in OECD countries (Table 1). This may partly be due to differences in the timing of studies, but is also likely to result from methodological differences. The difference between the results by the European Commission's comparison, based on national estimates of firm turnover, and those of the recent OECD study, based on a more harmonised approach, is particularly marked.

2 . The issues paper for the workshop discusses the statistical problems in developing these indicators.3 . As the average size of entrants is much smaller than that of incumbents, entry penetration rates or

employment-weighted entry rates are usually much lower than entry rates.

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Alternative indicators of entrepreneurship and firm creation

Start-up activity, 19981

(1)

Firm turnover, 1998 (2)

Turnover in the business sector,

1989-94 (3)Australia 8.1 -- --Austria -- 13.0 --Belgium 1.4 17.0 --Canada 6.2 -- 21.7Denmark 3.0 -- 20.8Finland 1.9 19.1 29.2France 1.2 -- 20.2Germany 3.8 41.6 16.6Ireland 1.0 15.0 --Italy 3.4 3.5 16.5Japan 0.9 7.4 --Korea 5.3 -- --Luxembourg -- 17.5 --Netherlands -- 8.5 16.1Norway 5.5 -- --Portugal -- 13.9 21.5Spain 3.2 25.9 --Sweden 1.9 -- --United Kingdom 3.1 21.1 --United States 9.8 30.0 20.1

Col 1 - Col. 2 Col 2 - Col. 3 Col 1 - Col. 3Correlation coefficients: 0.49 0.04 -0.19

Note (1) Percentage of adults engaged in the process of creating a business in the past 12 months.Sources: Col. 1 from Reynolds et al. (2000); Col. 2 from European Commission (2000); Col. 3 from OECD (2001a ).

Some other findings of the work on enterprise demography are of interest. The recent OECD work with data covering the first part of the 1990s showed that firm turnover rates (entry plus exit rates) are around 20 per cent in the business sector of most countries. This implies that a fifth of firms are either recent entrants, or will close down within the year (OECD, 2001a; Bartelsman, et al. 2001). Turnover rates vary significantly across detailed industries in each OECD country, however, implying that differences in the industry composition influence international comparisons of average turnover. The OECD study covered 10 OECD countries, but similar studies exist for many other OECD countries (e.g. Australia, Belgium, Czech Republic, New Zealand, Poland, Spain, Sweden and Switzerland; see OECD, 2001b).

The OECD work also showed that the process of entry and exit of firms involves a proportionally low number of workers. In all but two countries (Finland and Denmark), less than 10 per cent of employment is involved in firm turnover, and in the United States, Germany and Canada, employment-based turnover rates are less than 5 per cent. The difference between firm turnover rates and employment-based turnover rates arises from the fact that entrants (and exiting firms) are generally smaller than incumbents (see discussion below). The OECD work on entry has confirmed three stylised facts of a well-known study by Geroski (1995), namely that:

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1. Entry is common. Large number of firms enter most markets every year, but entry rates are far higher than market penetration rates.

2. There is a large variation in entry across industries, but these do not persist for long.

3. Entry and exit rates are positively correlated, and net entry and penetration rates are only a small share of gross rates.

3. Entrepreneurship and the growth of firms

The entry of new firms and the exit of declining firms are only one part of the entrepreneurial process. It is also important to understand how firms grow, which firms succeed, and why they succeed. Research in this area is quite diverse. The focus in this section is on only aspect, namely the survival process and the growth of firms. Studies that deal more explicitly with the drivers of firm growth are discussed below, notably in section 7.

In his survey on entry, Geroski (1995) also offered a stylised fact about survival, namely that the survival rate of most entrants is low, and even successful entrants may take more than a decade to achieve a size comparable to the average incumbent. This finding is broadly confirmed by recent OECD work (OECD, 2001a). It found a high correlation between entry and exit across industries, which may be the result of new firms displacing old obsolete units, as well as high failure rates amongst newcomers in the first years of their life.

An examination of survival rates, i.e. the probability that new firms will live beyond a given age, shows that the survival probability for cohorts of firms that entered their respective market in the late 1980s declines steeply in the initial phases of their life. In fact, about 20 to 40 per cent of entering firms fail within the first two years. Conditional on overcoming the initial years, the prospect of firms improves in the subsequent period: firms that remain in the business after the first two years have a 60 to 70 per cent chance of surviving for five more years. Nevertheless, only about 30-50 per cent of total entering firms in a given year survive beyond the seventh year. A low survival rate is not necessarily a cause of concern. Entry by new firms can be seen as a process of experimentation and it is in the nature of this process that the failure rate will be high. This is particularly so if new entry leads incumbent firms to increase their efficiency and profitability.

Regression-based analyses of survival and growth of firms has considered various factors such as firm size, firm age, capital intensity, innovation, productivity, corporate governance structure, etc.4 Firm size and firm age are consistently important in explaining survival and growth of entrants. For firm size, smaller firms tend to have lower likelihood of survival but higher rates of post-entry growth. For firm age, older firms showed lower failure rates and lower growth rates in most regression analyses. In particular, survival analyses based on the hazard regressions suggest either negative duration dependence or a -shaped hazard function. Hence, small new firms have both a low probability of survival in the early stages, and a high probability of fast growth if they do survive.

These findings suggest that a heterogeneous group of entrants learn about their ability to survive and explore and adjust to the competitive environment. Each entrant starts business with different initial size reflecting differences in their own perceived ability and expectation. Those with inadequate competitiveness are forced to exit, while successful survivors grow and try to adjust themselves to the changing environment. The accumulation of experience and assets, in turn, strengthens survivors and lowers the likelihood of failure.

4 . This overview draws on Ahn (2001).

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Another important implication of this finding is that the technological environment and the degree of market competition influence firm dynamics. The product life cycle model also points out that the pattern of firm dynamics evolves along the product life cycle reflecting evolving stages in the market growth, scale economies, and the degree of competition. Major factors affecting firm dynamics include:

Innovative environment: Regression analyses has shown that entrants are exposed to higher risks of failure in industries where small firms tend to have the innovative advantage. This is consistent with the prediction of the product life cycle model. Industries at the early stage of the product life cycle tend to show more turbulent firm dynamics with higher turnover rates.

Economies of scale: In industries with large economies of scale, successful entrants would have to grow fast to reach the minimum efficient scale (MES). Regression analyses in several studies indeed report that an industry-wide measure of MES had positive correlation both with the probability of exit and with survivors’ growth.

Competitive environment: The observation that turnover rates are higher under more innovative environments seems consistent with more general findings that industries with higher entry rates also tend to have higher hazard rates. Firms in industries with higher capital intensity or higher innovative efforts (measured by R&D intensity, use of new technologies, etc.) do show higher failure rates on average, while an individual firm’s capital intensity or innovative efforts appeared to positively related with the firm’s survival or growth. It is also reported that hazard rates are lower in growing industries while macroeconomic downturns raise hazard rates.

4. The size dimension: the role of SMEs in the economy

Firm-level data also enable the construction of databases by size category and provide important insights in the role of SMEs in the economy. Only two strands of work are distinguished here:

Analysis of the contribution of SMEs to overall business performance, e.g. in the context of entry, productivity or employment growth.

Work on high-growth firms. Recent OECD work, for example, shows the importance of small firms to the process of creative destruction (Schreyer, 2000).

4.1 The contribution of SMEs to overall performance

Small establishments make an important contribution to overall employment and turnover, although there is considerable variation across countries (OECD, 2001c; Figure 2). In the United States, for example, more than 60 percent of all employment is in establishments with over 500 employees. In Japan and Italy, these establishments account for just over 20% of overall employment. The contribution of the smallest establishments (of less than 50 employees) to total employment also varies considerably; in Turkey and Italy, they account for 46 and 52 per cent of total employment, respectively. In the United States, they only account for 15 per cent.

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Distribution of employment in manufacturing, by size class

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

USA SWE FIN CZE BEL GBR AUT TUR KOR CHE JPN ITA NZL PRT

1-9 10-49 50-99 100-499 500 +

Source: OECD SME database, see OECD (2001c).

OECD surveys have also examined the contribution of SMEs to employment growth, based on work with longitudinal databases (Schreyer, 1996). This study showed that a number of common features emerge from the studies surveyed for the report:

First, both the rates of gross job creations and gross job losses were significantly higher among small firms than among large ones. This reflects the general volatility and dynamics of small firms. The greater turbulence among small firms is present in all studies although variations in the extent of turbulence exist across countries, sectors and over time.

Second, many studies find a clear negative relationship between net job creation rates and the size of establishments or firms. However, for certain countries it was found that the highest net job creation-employment ratios were among very small firms whereas small to medium-sized firms (i.e., the size class of 20-49 employees) did not perform significantly better than large firms.

Third, gross flows of employment creation and losses tend to be dissociated from net flows. In periods of overall strong employment losses (gains), there are still sizeable flows of gross job gains (losses).

Fourth, methodology matters, certainly for the magnitude of the relation between job creation and firm size and in several cases also for the direction and quality of the relation.

Recent OECD work has shown that small firms play an important role in the process of entry and exit (OECD, 2001a). Cross-country evidence suggests that new firms are only 20 to 50 per cent the average size of existing firms, and their relative size is less than a fifth of that of incumbents in the United States and Canada. The relatively small size of entrants in Canada and especially the United States reflects both the large size of incumbents (in the United States, twice that of most other countries) and the small average size of entrants compared to that in most other countries (in the United States, about three employees in the total economy and about six in manufacturing). In other words, entrant firms are further away from the average (or “optimal”) size in the United States than in most other countries for which data are available.

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There are a number of different possible explanations for this. First, the larger market of the United States may partly explain the larger average size of incumbents.5 Second, the wider gap between entry size and “optimal” size in the United States may reflect economic and institutional factors, e.g. the relatively low entry and exit costs may increase incentives to start up relatively small businesses.6

The likelihood of failure in the early years of activity is thus highly skewed towards small units, while surviving firms are not only larger but also tend to grow rapidly. Thus, the size of exiting firms is similar to the size of entering firms in most countries, and the average size of surviving firms increases rapidly to approach the average size of incumbents in the market in which they operate. The combined effect of exits being concentrated among the smallest members of a cohort and the growth of survivors makes the average size of the cohort almost double in the first seven years. Post-entry growth in average size is stronger in services than in manufacturing, given the smaller initial size and the higher failure of small businesses there. Moreover, both failure of small units and growth of survivors are stronger in the United States than in the other OECD countries, leading the average size of a given cohort to increase three-fold in the first three years. This could reflect the greater opportunities offered to small firms to enter the market in the United States, even though their failure rate is high. This greater experimentation of small firms in the US market may also contribute to explain the evidence discussed above of a lower than average productivity of US firms at entry.

The relationship between size and productivity growth has also been studied in a few studies on the link between employment and productivity growth, notably for the United States and the Netherlands (Bartelsman et al., 1995; Baily et al., 1996a). In both countries, SMEs were disproportionately represented among successful upsizers, i.e. firms combining productivity growth and employment growth. Surprising, however, was the disproportionate representation of the largest establishments (over 5 000 employees) among successful upsizers in the United States. In both countries, establishments with over 500 employees were strongly represented among successful downsizers, i.e. establishments combining positive productivity growth with negative employment growth. Among unsuccessful upsizers (establishments combining negative productivity growth and positive employment growth), small establishments were disproportionately represented in both countries. However, among unsuccessful downsizers (a combination of negative productivity and employment growth), small and medium-sized establishments (less than 250 employees) are somewhat under-represented and large establishments over-represented in the United States, whereas in the Netherlands small- and medium-sized establishments (below 500 employees) are over-represented and large establishments under-represented.

4.2 Work on high-growth firms

The work on high-growth firms suggests that a small group of firms is often responsible for a large share of new jobs created. The OECD work in this area is based on results from five OECD countries (Germany, Italy, Netherlands, Spain and Sweden) as well as from Quebec (Canada). Each of these studies used a firm-level data set to identify high-growth firms and their differentiating characteristics. Despite considerable differences in the underlying data and some of the methodology, a number of common findings emerge (Schreyer, 2000):

5 . Geographical considerations may also affect the average size of firms: firms with plants spreading into different US states are recorded as single units, while establishments belonging to the same firm but located in different EU states are recorded as separate units.

6 . As discussed in Nicoletti et al. (1999), regulations affecting the start up of firms are generally much less stringent in the United States than in most of Europe, with the notable exception of the United Kingdom.

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a.Small firms exhibit higher net job creation rates than large firms. At the same time, significant flows of gross job gains co-exist with large flows of gross job losses, especially among small firms. Both observations are in line with many earlier studies on the topic.

b.High-growth firms account for a disproportionately large part of gross jobs gained. High-growth firms are those firms that rank first according to a measure that combines relative (percentage) and absolute rates of employment expansion.

c.Among high-growth firms, job creation rates of small firms exceed those of large ones.

d.In absolute terms, larger firms are also significant job creators in the high-growth group. Specifically, they play a more important role as employment creators among high-growth firms than they do among growing firms. On the other hand, the rapid growth of large firms often reflects mergers and acquisitions rather than internal growth. This puts a question mark on the extent at which genuinely new jobs created by these units.

e.High-growth firms are found in all industries and in all regions of the countries examined. Fast-growing firms tend to be more concentrated in some sectors as opposed to growing firms but the concentration is not necessarily in the same industries.

f. High-growth firms are more R&D intensive than growing firms or than the average permanent firm.

g.Firms that are partly or wholly owned by others tend to be more than proportionally represented among the set of fast growers. More partial evidence shows also that fast-growing units are more often involved in alliances than the average firm.

h.Growing firms tend to be younger than firms on average. There is some evidence that job gains by new entrants match those by permanent firms.

These results fit well into the previous discussion on entrepreneurship. Entrepreneurship implies uncertainty and asymmetric distribution of information; it is thus an idiosyncratic, search-oriented process. Finding a), which reflects considerable heterogeneity among firms, is consistent with this view. It has been argued earlier that two aspects of entrepreneurship can usefully be distinguished: one that focuses on firm entry, start-ups, and exit in industries. The other aspect is innovation. Because it is difficult to establish a general link between innovation and firm size, high-growth firms were chosen on criteria that do not a priori favour a particular size class. The resulting set of high-growth firms comprises therefore both large and small firms, as pointed out in finding d). There is a clear positive link between the R&D efforts and the emergence of high-growth (finding f)). This lends additional support to the idea that a search process is observed and that it is reasonable to think of the set of high-growth firms as successful entrepreneurs. Finding h) on the significance of young age stands out, as it holds for samples of permanent firms with minimum size, thus excluding very small (and very young) firms as well as entrants. Hence, even among permanent firms of a certain maturity, younger firms tend to be relatively more successful in moving towards an expansion path. The age component becomes even more important when entrants are included in the analysis or when employment growth measures are restricted to internal growth, excluding mergers and acquisitions.

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5. Women entrepreneurship7

In recent years, there has been rapidly growing interest in the role of women in overall entrepreneurial activity. Efforts have been made in several OECD countries to gather information from firm-level data, to supplement other sources of data such as labour force surveys. The information that can be extracted from firm-level data is still quite limited in many OECD countries, however, as these statistics generally not capture gender differences in business ownership. Special sample surveys may provide more information, although their results can not always be generalised to the whole business population. In addition, differences in coverage and methodology make international comparisons of such work almost impossible.

The most comprehensive account of women-owned businesses is provided by economic census collections. A number of OECD countries, such as the United States, provide such statistics (Table 2). Such statistics have the advantage that they cover all business activity. However, they are typically less timely than sample surveys and are costly to produce. Many countries therefore (also) survey smaller sample of firms, primarily to collect information on the qualitative and quantitative characteristics of firms.

US Firms by Gender of Ownership, 1997

Firms (1000s)

Sales and receipts (million USD)

Firms (1000s)

Sales and receipts (million USD)

Employees (1000s)

Annual payroll (million USD)

All US firms 20,822 18,553,243 5,295 17,907,940 103,360 2,936,493Female-owned 5,417 818,669 847 717,764 7,076 149,116Equally male-/female-owned 3,641 943,881 1,029 828,390 8,285 160,989Male-owned 11,382 6,629,451 3,151 6,257,728 43,541 1,189,193Publicy held, foreign-owned and nonprofit 382 10,161,242 268 10,104,058 44,458 1,437,195

Source: US Census Bureau, "Women-Owned Businesses", 1997 Economic Census.

All firms Firms with paid employees

An example of such a survey is Statistics Sweden's collection of information on newly-started enterprises. The survey is based on a sample of firms drawn from the Swedish business register, which covers all firms. These statistics also cover the gender of new entrepreneurs (Table 3).

Newly-started enterprises in Sweden

MaleStart-up rate

per 1000 males1

FemaleStart-up rate

per 1000 females1

Mixed

1998 64 11.6 31 5.7 51999 64 12.3 31 5.8 5

Note: (1) Between 16 and 64 years.Source: Statistics Sweden, http://www.scb.se/sm/Nv12SM0001_tabeller.asp

[percentage distribution by gender of the entrepreneur]

Many countries currently undertake such sample surveys, sometimes specifically focused on women entrepreneurs. Two papers for this workshop provide more information on such surveys for Denmark (Boegh Nielsen, 2001) and France (Letowski, 2001), respectively. These available surveys point to a

7 . This section draws on OECD (2001d).

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number of common themes and characteristics8. They show that women entrepreneurs are a heterogeneous group, with wide-ranging skills, motivations and orientation. Nevertheless, certain characteristics can be traced across all groups. First, in terms of demographic characteristics, on average female entrepreneurs:9

Are in the age group of 35-44.

Are married and have children.

Have less formal or business related education or prior work experience than men. In industrialised countries, women entrepreneurs have relatively higher educational levels but still often lack prior entrepreneurial or management experience.

Start their business with economic motivations, such as generating extra income for the household, as well as with non-economic motives like being independent, and creative. Thus, there is comparatively a large “push group”, consisting of women who are more or less forced into setting up a business as an alternative to being unemployed and also a smaller but increasing “pull group” of women who are drawn to entrepreneurship by a wish to be independent, and use self-employment as a means to advance their specific skills.

Second, with respect to sector of activity and the type of their businesses, women entrepreneurs:

Are increasingly involved in non-traditional sectors, from transportation, communications, finance, insurance and real estate, even though women-owned businesses are still clustered in services and retail trade.

Select mostly sole proprietorship as the legal form of organisation for their businesses; only a small percentage of the businesses has the corporation as its legal form.

In general set up their businesses with little start-up capital; their businesses are thus young and small compared with the rest of the business population. On average, women-owned businesses also generate fewer revenues.

Third, most female entrepreneurs cite lack of capital and credit as their main start-up problem followed by lack of knowledge on how to effectively expand their business, i.e. financial and managerial know-how, in addition to constraints on access to networks and foreign markets (OECD, 2000). It should, however, be noted that the observed differences with respect to the size and revenue potential of women entrepreneurs may be due to the general business environment, sector of activity, or age of the business. It is not necessarily due to some inherent skill bias that might have held back women’s involvement in the economy.

Data on women entrepreneurs in SMEs are particularly limited due to two factors. First, business statistics have typically concentrated on larger firms, in particular those located in the manufacturing sector Second, the available data on SMEs, e.g. those derived from administrative sources, are not always designed to capture gender differences.

8 . This covers studies for Australia, Canada, Korea, Mexico, the United States (APEC Project, 1999); Brazil (SEBRAE, 2000); Denmark (Danish Agency for Trade and Industry, 1998); and Ireland (Network Ireland, 1998).

9 . The quantitative information in these surveys is not reported here, since the relative rankings of groups or cross-country comparisons using available information as a basis could be misleading. The individual surveys cover different time periods, different sample sizes, and are based on different sampling methods.

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6. The dynamics of productivity growth

6.1 The contribution of firm-level dynamics to aggregate productivity growth

Firm-level data also provide useful insights in productivity growth. Pioneering work by researchers such as Martin Baily, John Baldwin, Eric Bartelsman and John Haltiwanger has contributed to a large literature on the breakdown of productivity growth in its components. These components include the contribution of productivity growth within existing firms; increases in market shares of high-productivity firms; as well as the entry of new firms that displace less productive firms.10 Studies of this kind now exist for several OECD and non-OECD countries (see references). Recently, a first attempt has been made to make an international analysis of productivity growth using firm-level data (OECD, 2001a; Barnes, et al. 2001).11

Productivity growth within firms depends on changes in the efficiency and intensity with which inputs are used in production. Thus, this source of aggregate productivity growth is associated with the process of technological progress. Shifts in market shares between high and low productive units also affect aggregate productivity trends, as does the reallocation of resources across entering and exiting firms. The overall contribution of reallocation to productivity growth is generally identified with a competitive market process, although it may also reflect changes in demand conditions and, as argued above, may also be linked to technological progress. This simple taxonomy hides important interactions. The entry of highly productive firms in a given market may stimulate productivity-enhancing investment by incumbents trying to preserve their market shares. Moreover, firms experiencing higher than average productivity growth are likely to gain market shares if their improvement is the result of a successful upsizing, while they will lose market shares if their improvement was driven by a process of restructuring associated with downsizing.

Table 4 presents a decomposition of labour productivity growth rates in manufacturing into a within-firm component and the different components due to the reallocation of resources across firms. Such a decomposition will give different results depending on the time horizon considered (see below). In most countries for which data are available, labour productivity growth was largely accounted for by gains within individual firms. In the second half of the 1980s, the within component accounted for three-quarters or more of total productivity growth in all but one country (Italy), with a somewhat smaller, though still predominant, role in the first half of the 1990s. The impact on productivity via the reallocation of output across existing enterprises (the “between” effect) varies significantly across countries and over time, but is generally small and in a few instances even negative. The net contribution to overall labour productivity growth of the entry and exit of firms (net entry) is positive in most countries (with the exception of western Germany over the 1990s), accounting for between 10 per cent and 40 per cent of total productivity growth. Evidence also suggests that in most of the cases in which the net entry effect is positive and sizeable, exits made most of this contribution to overall productivity growth, i.e. exits involve low-productivity units.

10 . There are several possible approaches to this breakdown. These are not discussed here, but are covered in the issues paper for the workshop (DSTI/EAS/IND/SWP/AH(2001)1 and Baldwin and Gu (2001).

11 . This survey does not examine the various methods that can be used for the decomposition of productivity growth. See the issues paper and Baldwin and Gu (2001) for a discussion.

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Breakdown of labour productivity growth in manufacturing

PeriodAnnual average

growth rate

(per cent) Within Between Mix Entry Exit

Australia1 1994-95 - 1997-98

-3.0 93.3 -50.0 60.0 46.7 50.0

Canada 1973-79 2.2 42.0 32.7 -- 5.6 19.61979-88 1.4 83.0 -1.9 -- 3.3 15.51988-97 2.9 87.0 -5.2 -- 1.0 17.3

Finland 1985-90 5.4 72.5 7.0 -- 0.4 20.11989-94 4.6 68.4 16.1 -- -2.5 18.0

France 1985-90 2.0 84.7 1.9 -- -20.2 33.61987-92 0.0 85.0 11.0 -- -44.0 49.0

Germany2 1992-97 2.1 115.3 -12.1 -- -0.7 -2.6

Italy 1985-90 4.8 62.1 9.0 -- 10.7 18.31990-95 5.5 58.2 7.0 -- 15.7 19.1

Japan3 1987-94 9.2 73.9 27.0 -0.7 -- --

Korea4 1990-95 23.0 57.0 46.0 3.0 -- --1995-98 4.7 -2.0 65.0 38.0 -- --

Netherlands 1985-90 1.5 99.9 -8.1 -- 33.5 -25.21990-95 2.8 78.2 -10.8 -- 20.5 12.1

Portugal 1987-91 6.6 91.4 -9.7 -- -13.4 31.81990-95 6.8 62.6 -4.3 -- 5.3 36.4

United Kingdom 1985-90 1.6 98.3 -7.4 -- 13.7 -4.61990-95 1.7 59.9 3.1 -- 8.8 28.2

United States 1987-92 1.6 63.0 8.0 -- -24.0 53.01992-97 3.0 81.0 1.0 -- -13.0 31.0

Notes : (1) Decomposition based on approach by Foster, Haltiwanger and Kazan (1998). (2) Estimates refer to western Germany. (3) Excludes firms with less than 100 employees. (4) Breakdown based on approach by Baily, Hulten and Campbell (1992); estimates refer to multi-factor productivity growth.Sources : Australia from Bland and Will (2001) and Parham (2001); Canada from Baldwin and Gu (2001); Finland, France, Germany, Italy, Netherlands, Portugal, United Kingdom and United States from OECD (2001a ) and Barnes, et al. (2001); Japan from OECD (1998); Korea from Hahn (2000).

Breakdown (% contribution)

Percentage growth rate and contribution of each component to total productivity growth

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In countries where a sufficiently long time series is available, evidence suggests that year -to-year changes in the within-firm component are the main drivers of fluctuations in aggregate growth; the between and net entry components show only modest fluctuations. Moreover, in years of expansion (the second half of the 1980s in most countries), within-firm growth makes a stronger contribution to overall productivity growth, whilst in slowdowns (the early 1990s) the contribution from the exit of low-productive units increases in relative importance.12

The entry of new firms has variable effects on overall productivity growth: positive in Italy, the Netherlands and the United Kingdom; negative in France and the United States; and, on balance, small in Finland, western Germany and Portugal. The contribution of entry to productivity is, however, significantly influenced by the horizon over which productivity growth is measured: by construction, the contribution of entering firms is greater the longer the horizon considered.13 Moreover, if new entrants undergo a significant process of learning and selection, the time horizon is likely to affect the comparison between entering and other firms. For example, US studies focussing on long time horizons generally found a significantly higher contribution of entry to aggregate productivity growth than those using short time periods.14

Although the driving forces of aggregate labour productivity growth differ significantly across countries, a few common patterns can be identified. In particular, in the industries more closely related to information and communication technologies (ICT), the entry component makes a stronger contribution to labour productivity growth than on average,15 suggesting an important role for new (high-tech) firms in an area characterised by a strong wave of technological changes. The opposite seems to be the case in more mature industries, where a more significant contribution comes from either within-firm growth or the exit of obsolete firms.

The decomposition of labour productivity growth in service sectors gives far more varied results than that for manufacturing, perhaps because of the difficulties in properly measuring output in this area of the economy. But in two broad sectors, transport storage and communication and trade, the results are qualitatively in line with those for manufacturing (Barnes, et al. 2001). The within-firm component is generally larger than the component related to net-entry and reallocation across existing firms, although in the trade sector entering firms seem to have a lower than average productivity growth in general, driving down aggregate growth.

Decompositions of multifactor productivity (MFP) growth in the manufacturing sector for five countries suggest a somewhat different picture than that shown with respect to labour productivity (Barnes, et al. 2001). Thus, within-firm MFP growth provides a comparatively smaller contribution to overall MFP growth (although it still drives overall fluctuations), while the reallocation of resources across incumbents (i.e. the between effect) plays a somewhat stronger role. More important, a strong contribution to MFP

12 . The results are also broadly consistent with findings in Baily et al. (1992) and Haltiwanger (1997) for the decomposition of MFP growth in the US manufacturing sector: during a period of robust productivity growth (1982-87), the within-firm contribution is large and positive, while in a low growth period (1977-82) the contribution is negative.

13 . The share of activity (the weighting factor in the decomposition, see Box VII.1) of entrants in the end year increases with the horizon over which the end year are measured (see Foster et al., 1998).

14 . See Baily et al. (1996b) and Haltiwanger (1997).15 . The industry group is “electrical and optical equipment”. In the United States, most 3-4 digit industries

within this group had a positive contribution to productivity stemming from entry, contrary to the result for total manufacturing. In the other countries, there are cases where, within this group, the contribution from entry is very high, including the “office, accounting and computing machinery” industry in the United Kingdom and “precision instruments” in France, Italy and the Netherlands.

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growth generally comes from net entry. Indeed, the (limited) information available suggests that the entry of new high-productive firms has made a marked impact on aggregate trends in the more recent period. Combining the information on labour and MF productivity decompositions it could be tentatively hypothesised that in a number of European countries, incumbent firms were able to increase labour productivity mainly by substituting capital for labour (or by exiting the market altogether), but not necessarily by markedly improving overall efficiency in production processes.16 By contrast, new firms entered the market with the “appropriate” combination of factor inputs, and possibly new technologies, thus leading to faster growth of MFP.

6.2 The role of upsizing and downsizing

A few studies have also looked at the role of upsizing and downsizing in productivity growth. This method, attributed to Baily et al. (1996a), makes a distinction between four types (quadrants) of firms: those that increase employment and have positive productivity growth (successful upsizers); those that decrease employment and have positive productivity growth (successful downsizers); those that increase employment and have negative productivity growth (unsuccessful upsizers); and those that decrease employment and have negative employment growth (unsuccessful downsizers).

Upsizing and downsizing in productivity growthAnnual average growth rates, in per cent

United States 1977-871

United States 1987-921

Japan 1987-942

France, 1985-913

Netherlands 1980-914

All firms/establishments .. .. .. .. 3.0

Continuing firms 3.4 2.4 9.2 2.3 2.0 Of which: - Successful upsizers 2.2 1.7 9.1 1.2 1.2 - Successful downsizers 2.6 2.6 10.0 2.2 1.0 - Unsuccessful downsizers -0.6 -0.5 -4.7 -0.6 -0.1 - Unsuccessful upsizers -0.7 -1.3 -5.2 -0.5 -0.2

Effect of entry and exit .. .. .. .. 1.0

1. Based on establishment data.2. Based on firm data. Excludes firms with less than 100 employees.3. Based on firm data. Excludes firms with less than 20 employees.4. Based on firm data. Excludes firms with less than 10 employees.Source: United States (1977-87) from Baily et al ., 1996; Netherlands from Bartelsman et al. , 1995. Other estimates by OECD based on national data (see OECD, 1998).

This type of breakdown is only available for a few OECD countries (OECD, 1998; Table 5). Apart from a positive effect of net entry for the Netherlands, productivity growth is almost equally shared between successful upsizers and successful downsizers. This result is similar for the three countries. The first group, successful upsizers, added employment but increased productivity at the same time. This pattern may indicate increasing product demand, combined with increasing returns to scale, or technological innovation that allows the firm to lower the price of its output in the face of elastic product demand (Bartelsman, et al., 1995; Baily, et al., 1996a).

16 . This finding is consistent with aggregate data for a number of European countries (see Scarpetta et al., 2000). In particular, in many Continental European countries, high labour productivity growth in the 1990s was accompanied by significant falls in employment, especially in manufacturing, leading to low (as compared to the 1980s) GDP per capita growth rates. Moreover, the relatively high labour productivity growth was accompanied by significant falls in MFP growth with respect to the previous decade.

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The second group, successful downsizers, are representative of the view that productivity growth in manufacturing is associated with downsizing. The combination of rising productivity and falling employment may indicate technological innovations or efficiency improvements combined with falling or inelastic demand or, alternatively lower barriers to entry and lesser importance of economies of scale in expanding markets. The third and fourth group represent the less successful parts of the manufacturing sector. A combination of falling productivity and falling employment could indicate falling demand and increasing returns to scale, or falling demand and incomplete employment adjustment (Baily, et al., 1996a; Bartelsman, et al., 1995). The combination of falling productivity and increasing employment could suggest negative productivity and increasing demand, or rising demand and diminishing returns to scale.

The decomposition presented above can be taken further in a number of ways (Baily, et al., 1996a; Bartelsman, et al., 1995). For instance, it is possible to look at the size distribution of firms and study which size-classes contributed most to productivity and employment changes. The results of this type of breakdown were discussed above. In addition, sectoral patterns can be studied. These differ substantially between the two countries for which these data are available. For the United States, the following patterns can be observed. Among the successful upsizers, electronic equipment has a relatively high share and basic metals a very low share. Among the successful downsizers, petroleum refining and basic metals are strongly represented. The decline in the steel industry and the move towards minimills appears to be reflected in the data (Baily, et al., 1996a). For the Netherlands, a different pattern emerges.17 Here, the chemical industry is important among the successful upsizers, whereas the metal and electrotechnical industry are under-represented in this group. The latter two groups are over-represented in the category of successful downsizers, reflecting the decline of the steel industry in the Netherlands, and the considerable restructuring efforts by the electronics industry (which is dominated by Philips).

7. Understanding the growth of firms

Firm-level studies have also enabled the identification of some factors influencing productivity growth and the performance of firms. The decomposition analysis suggests that two types of processes are key. The first is productivity growth within firms. This may be due to technical change and the accumulation of human capital with the firm,18 but is also influenced by “softer” production factors, such as management, ownership and organisation. The second process is productivity growth among firms, due to changes in market shares and the entry and exit of firms. This is often linked to the role of competition and creative destruction. This section looks at some of the evidence on these factors, primarily focusing on the role of technological factors.

7.1 Technology, innovation and the role of ICT

Work with technology and innovation surveys

Empirical studies show a fairly close relation between investments in technology at the firm level and productivity performance. The relationship can still be shown at the sectoral level, though it is weaker, given the variation in firm behaviour. At the level of the economy as a whole, it is often difficult to establish a clear link between an indicator of technology effort and productivity growth. The difficulty in

17 . The breakdown for the Netherlands is less detailed than for the United States. Baily, et al. present some results for US 3-digit industries and indicate that there is considerable variation within the 2-digit categories.

18 . If the focus is on labour productivity, physical capital accumulation may also be an important factor driving productivity growth within firms.

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establishing this relationship is due to a number of factors. First, both innovative effort and productivity may be measured incorrectly. Second, there may be a lag between innovative effort and its translation into productivity gains. Third, it is difficult to disentangle the impact of technology from other factors that affect productivity. And finally, a large part of economy-wide productivity gains are due to the diffusion process (as shown above).

Micro-economic studies, based on firm-level longitudinal databases, are therefore of great help in linking technological change and technology use to productivity performance. Work with these databases has demonstrated the enormous heterogeneity in firm performance and enabled the identification of some factors influencing productivity growth. Two types of processes seem to be at work. One is productivity growth within firms, which may be due to technical change and the accumulation of human capital with the firm, but is also influenced by “softer” production factors, such as management, ownership and organisation. The other process is linked to productivity growth among firms. This is often linked to competition and creative destruction.

Work with longitudinal databases – in combination with technology surveys – offers fresh insights in the link between technology and productivity. The most extensive work on this issue has been done for the United States. Doms et al. (1995) constructed a database for the period 1987-91 for more than 6 000 manufacturing plants on the basis of the 1987 Census of Manufacturers (CM), the 1988 Survey of Manufacturing Technology (SMT), and the 1991 Standard Statistical Establishment List (SSEL). The 1988 SMT data distinguish 17 advanced (manufacturing or information) technologies used by a plant, whereas the CM and SSEL data provide information on size, age, productivity, capital use, and growth and failure variables. The authors found that increases in the capital intensity of the product mix and in the use of advanced manufacturing technologies are positively correlated with plant expansion and negatively with plant exit. A follow-up study (Doms et al. 1997) shows the interaction between technology, skills and wages. It finds that plants that use more sophisticated equipment employ more skilled workers and that workers that use more advanced capital goods receive higher wages. An inter-temporal analysis showed that the most technologically advanced plants paid higher wages prior to adopting new technologies and were more productive, both prior to and after the adoption of advanced technologies.

McGuckin et al. (1998) also examined the link between technology use and productivity, based on the US LRD database and the 1988 and 1993 Surveys of Manufacturing Technology. They found that firms that use advanced technologies exhibit higher productivity, even when controlling for factors such as size, age, capital intensity, labour force skills, industry and region. More productive plants used a wider range of advanced technologies and used them more intensively than other plants. Like Doms et al. (1997), they found that while the use of advanced technologies can help improve productivity, plants that perform well are more likely to use advanced technologies than plants that perform poorly. They also found that the process of technology adoption was not smooth and was characterised by substantial experimentation. In addition, the diffusion of particular technologies was very diverse.

Similar studies have been made for other countries. Studies for Canada (Baldwin and Diverty, 1995; Baldwin et al. 1995a) link panel data from the Census of Manufacturers to data from a technology survey. Baldwin et al. found that establishments using advanced technologies gain market share at the expense of non-users. Technology users also enjoy a significant labour productivity advantage over non-users, except for establishments that only use fabrication and assembly technologies. Relative labour productivity grew fastest in establishments using inspection and communications technologies and in those able to combine and integrate technologies across the different stages of the production process. Technology users were also able to offer higher wages than non-users. Baldwin and Diverty (1995) found that plant size and plant growth were closely related to the incidence and intensity of technology use, an indication that technology use is closely linked to the “success” of a plant.

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A study of the Netherlands (Bartelsman et al. 1996) found that adoption of advanced technology is associated with higher labour productivity, higher export intensity, and larger size. Firms that employed advanced technologies in 1992 had higher productivity and employment growth in the preceding period. For Canada, Baldwin et al. (1995b) found that use of advanced technology was associated with a higher level of skill requirements. In Canadian plants using advanced technologies, this often led to a higher incidence of training. They also found that firms adopting advanced technologies increased their expenditure on education and training. A follow-up study (Baldwin et al. 1997) found that plants using advanced technologies pay higher wages to reward the higher skills required to operate these technologies. Thus, most micro-level studies confirm the complementarity of technology and skills in improving productivity.

A study for Australia (Productivity Commission, 1999) explicitly associates some of the improved performance of Australia’s economy over the past years with innovation. Of particular importance are the introduction of new advanced technologies throughout the economy and a greater business involvement in innovation and R&D. The study shows that a growing number of Australian firms use advanced technologies, such as computer equipment and advanced manufacturing technologies. The same study also shows that business expenditure on R&D has increased considerably over the past years, and suggests that firms undertaking R&D have become markedly more innovative.

There are many other studies that have examined the role of technology with firm-level data. Most of these studies use smaller sets of firm data than the studies discussed above, however. The advantage of the longitudinal databases is that they cover virtually all firms in a sector, which enables an analytical link between the performance of individual firms and sectoral and/or aggregate economic performance. The firm-level evidence shows that technological change can bring significant productivity gains, but only when accompanied by organisational change, training, and upgrading of skills, i.e. when the new technologies are thoroughly “learned”. Firm-level evidence also shows that a firm’s integration in networks is an important factor for successful performance (OECD, 1999).

With the emergence of innovation surveys in several OECD countries, and notably in Europe, several studies have used these to examine the role of innovation in firm-level performance. Some of this work is currently underway and involves cross-country comparisons, being based on the Community Innovation Survey 2 (CIS-2). They show that innovation is widely distributed throughout the economy. Most firms, both in manufacturing and in services, innovate. Secondly, they demonstrate that expenditure on innovation goes considerably beyond expenditure on R&D. Thirdly, innovation surveys provide insights in a firm’s objectives to innovate. Increasing market share, improving service quality and expanding product or service range are the key objectives in both manufacturing and services. Other important goals are compliance with regulations and standards, and the reduction of material, energy and labour costs. Fourthly, innovation surveys offer insights in the key barriers to innovation, such as financial constraints, lack of skills, high risk or inappropriate regulatory frameworks. Fifthly, they permit a better understanding of the role of networks and external sources of knowledge, such as customers, equipment suppliers and universities. And finally, they are an important source of primary data for empirical analysis of innovation and economic performance.

Since innovation surveys are relatively recent and are still being improved, the empirical analysis of innovation surveys is still in its early stages. However, an analysis of the results of innovation surveys in Germany over the period from 1992 to 1997 suggests a clear link between innovation, firm survival and employment generation. A study for Belgium found a significant positive effect of the combination of product and process innovation on the growth of industrial firms (Federaal Planbureau, 1998). Micro data from innovation surveys will be a rich source of data for more detailed analysis of innovation patterns and their link to economic performance. It will be an important extension to the traditional micro-economic analysis that mainly relies on traditional data on firm performance.

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ICT and firm-level growth

The role of ICT in firm-level performance has received considerable attention in recent years. This work was initially primarily based on small sample surveys (e.g. the studies listed in Table 5), but has increasingly become part of the work by statistical offices. The studies typically find that the greatest benefits from ICT appear to be realised when ICT investment is combined with other organisational assets, such as new strategies, new business processes, new organisational structures and better worker skills. Because organisational change tends to be firm-specific, it is not surprising that these studies show on average a positive return to ICT investment, but with a huge variation across organisations (Brynjolfsson and Hitt, 1997).

The studies mentioned in Table 6 primarily look at the role of ICT in individual firm performance. However, the emergence of networks, such as the Internet, increasingly underscores the need to broaden the focus to a product’s entire value chain, particularly a firm’s interaction with suppliers and customers. Studies are now slowly emerging, using new data on ICT networks, that explore the role of such networks in firm performance (Atrostic and Nguyen, 2001).

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Selected firm-level studies on ICT and productivity

Study Sample Issue addressed Main findings

Lichtenberg (1995) US firms, 1988-91

Output contribution of capital and labour deployed in information systems

One information systems employee can be substituted for six non-information systems employees without affecting output

Hitt and Brynjolfsson

(1997); Brynjolfsson and

Hitt (1997)

More than 600 large US firms, 1987-94

The impact of the adoption of IT and organisational decentralisation on productivity

Firms that both adopt IT and organisational decentralisation are on average 5% more productive than those that adopt only one of these

Black and Lynch (1997 and 2000)

US firms, 1987-93, and 1993 and 1996

The impact of workplace practices, IT and human capital on productivity

The adoption of certain newer work practices, higher educational levels, and the use of computers by production workers have a positive impact on plant productivity

Brynjolfsson and Yang (1998)

Fortune 1000 US firms, 1987-94

The impact of IT and intangible assets on firm performance

The market value of USD 1 of IT capital is the same as that of USD 10 of capital stock. This may reflect the value of intangible investment associated with ICT

Brynjolfsson, Hitt and Yang (1998)

The impact of the adoption of IT and organisational decentralisation on productivity

The market value of USD 1 of IT capital is higher by USD 2-5 in decentralised firms

Bresnahan, Brynjolfsson and

Hitt (1999)

400 large firms, 1987-96

Complementarity between IT investment, human capital and decentralised organisational structure

IT combined with work practices such as higher skills, greater educational attainment, greater use of delegated decision making lead to a higher value of IT investment.

Source: OECD summary.

After the initial role of academic researchers, studies on ICT and firm-level performance have increasingly become part of the work by statistical offices. For example, two studies by Stolarick (1999a; 1999b) explore the link between IT spending and productivity performance in manufacturing for the United States. Stolarick (1999a) finds a positive relationship between IT spending and productivity, but a relationship that varies between industries. Industry mix is thus an important explanatory factor driving aggregate findings. Stolarick (1999b) finds that low productivity plants may sometimes spend more on IT than high productivity plants, in an effort to compensate for their poor productivity performance. The study suggest that management skill should therefore be taken into account as an additional factor when investigating the IT productivity paradox.

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For Canada, Baldwin and Sabourin (2001) find that a considerable amount of market share is transferred from declining firms to growing firms over a decade. At the same time, the growers increase their productivity relative to the decliners. Those technology users that were using communications technologies or that combined technologies from several different technology classes increased their relative productivity the most. In turn, gains in relative productivity were accompanied by gains in market share. Other factors that were associated with gains in market share were the presence of R&D facilities and other innovative activities.

For Italy, Milana and Zeli (2001) investigate how ICT affects production performance and technical efficiency. The study examines the correlation between ICT and technical efficiency by using cross-sectional regressions run on firm-level data within each industry. The main conclusion is that this correlation is not significantly rejected in the majority of the industrial sectors considered. In general, positive correlations are not rejected in all four groups of industries defined on the basis of R&D intensity of production. Paradoxically, technical efficiency does not seem to be affected by ICT in a significant share of high R&D intensity industries, where almost all firms operate at the highest relative level of efficiency and there are little margins for increases in ICT intensity of production.

For France, Crepon and Heckel (2000) use firm-level data to evaluate the contribution of ICT to productivity growth. They find that the effects of computer diffusion on growth are concentrated in a number of industries, and also that MFP growth in IT-producing industries contributes significantly to overall growth.

An increasing number of studies with firm-level data also focus on the impact of ICT in the services industry, as many services are intensive users of ICT. Broersma and McGuckin (1999), for example, use longitudinally linked data from the Annual Survey of Production Statistics to focus on productivity in wholesale and retail trade in the Netherlands. They find that computer investments have a positive impact on productivity and that the impact is greater in retail than in wholesale trade. The study also found flexibility of employment practices, particularly in retail trade, and found that these are related to computer use. Foster, Haltiwanger and Krizan (2001) and Jarmin, Klimek and Miranda (2001) focus on the trade sector in the US context.

7.2 Other factors

Human capital

A number of longitudinal studies also address the interaction between technology and human capital, and their joint impact on productivity performance (Bartelsman and Doms, 2000). Although few longitudinal databases include data on worker skills or occupations, some address human capital through wages, arguing that wages are positively correlated with worker skills. For the United States, Baily, Hulten and Campbell (1992) found a positive link between wages and productivity, although the causality is not clear. For France, the results are somewhat clearer, as the French data include details about worker characteristics. Entorf and Kramarz (forthcoming) found a strong complementarity between skills and technology and hence also between skills and productivity.

For Canada, Baldwin, Gray and Johnson (1995b) found that the use of advanced technology was associated with a higher level of skill requirements. In Canadian plants using advanced technologies, this often led to a higher incidence of training. The more advanced technologies a firm used, the more likely it was that the firm would engage in training. The study also found that firms that adopted advanced technologies increased their expenditure on education and training. A follow-up to this study (Baldwin, Gray and Johnson, 1997) found that plants using advanced technologies pay higher wages to reward the higher skills

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that are required to operate these technologies. The majority of these micro-level studies thus confirm the complementarity between technology and skills in improving productivity performance.

Management, ownership and organisation

Management and related factors are often difficult to capture in productivity analysis. However, LMDS provide some insights. For the United States, Baily, et al. (1992) found that plants that are part of a high-productivity firm will also have high productivity (where the plant in question is excluded from the firm’s productivity level). In their interpretation, multi-unit firms can improve performance across the whole range of plants, as they can easily transfer skills, technology, product design and production methods.

Lichtenberg and Siegel (1992a) focus on a sample of large establishments in the LRD and find that plants that undergo ownership changes tend to have below average productivity before they change owners, and that productivity in these plants grows slightly faster than the average once they have changed owners. A related study (Lichtenberg and Siegel, 1992b) found that ownership changes were often accompanied by a reduction in the share of employment at auxiliary offices.

Another study, by McGuckin and Nguyen (1995), focused on only one industry, food processing, but covers a more elaborate sample of establishments. It found that plants with above-average productivity are most likely to change owners. The acquiring firms also tend to have above-average productivity. Plants that changed owners generally experienced improved productivity performance following the change. In the interpretation of the authors, ownership changes appear associated with the purchase or integration of good properties into new firms. These results were confirmed by Baldwin (1995), in a study for the Canadian manufacturing sector.

A follow-up study (McGuckin and Nguyen, 1997) analyses ownership changes from the perspective of acquiring firms. It finds that acquisitions have a positive effect on acquiring firms’ productivity growth when single-unit firms are included in the analysis, but that there is no significant effect of ownership changes if multi-unit firms are the focus. In addition, acquired plants’ productivity growth is higher than that of non-acquired plants, for both single and multi-unit firms.19

The role of competition

The decomposition analysis with LMDS provides some important insights in the competitive process. As discussed above, it demonstrates the high degree of turnover in the manufacturing sector, the success of some firms, the failure of others, and the role of entry and exit. It also shows that intra-industry dynamics make an important contribution to productivity growth (Baldwin, 1995). New entrants are generally more productive than firms that close down, and firms that exit an industry tend to have below-average productivity performance. The effects of entry take time, however, as plants gain market share and become more productive as they mature. Furthermore, firms that gain market share provide an important contribution to overall productivity growth.

Firm-level data have also been used to explore influences of competitive environment (such as domestic competition and foreign trade) on firm-level productivity. For example, using UK data sources Nickell (1996) and Disney et al. (2000) experimented with several indicators of competition in productivity 19 . The study also draws an important methodological conclusion. It finds that the positive effect of

ownership changes can be obscured when the analysis includes large multi-unit firms. Composition effects associated with changes in the activities of such firms can introduce considerable measurement errors. Consequently, studies that are based on such firms -- as much of the empirical work on mergers is -- will be subject to considerable aggregation bias, possibly leading to erroneous conclusions.

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regressions and concluded that competition has positive effects on productivity. Nickell (1996) found that competition (measured by increased numbers of competitors or by lower levels of rents) was associated with higher productivity growth rates. Using a more recent and much larger data set of around 143 000 UK establishments, Disney et al. (2000) found that market competition significantly raised productivity levels as well as productivity growth rates.

Competition appears to be a major disciplining factor on firm performance, but not the only one. In a follow-up study by Nickell et al. (1997), the impact of competition on productivity turns out to be weakened when firms are under financial pressure or when they have a dominant external shareholder. This is interpreted as suggesting that the disciplining effect of competition in fact can be substituted by other pressures on firms. As circumstantial evidence of the influences of competition on firms’ productivity, Oulton (1998) points out that manufacturing sectors have significantly lower dispersion of productivity than the rest of the economy. A possible explanation is that manufacturing sectors are more exposed to international competition than service sectors.

Empirical studies using micro data generally show a positive association between exports and productivity.20 Using plant level data from the Longitudinal Research Database (LRD), Bernard and Jensen (1999) examine whether exporting has played any role in increasing productivity growth in US manufacturing. They find little evidence that exporting per se is associated with faster productivity growth rates at individual plants. The positive correlation between exporting and productivity levels appears to come from the fact that high productivity plants are more likely to enter foreign markets. While exporting does not appear to improve productivity growth rates at the plant level, it is strongly correlated with increases in plant size. Trade fosters the growth of high productivity plants, though not by increasing productivity growth at those plants. According to the results of a parallel study for Germany by Bernard and Wagner (1997), sunk costs for export entry appear to be higher in Germany than in the United States, but lower than in developing countries. It is also found that plant success (as measured by size and productivity) increases the likelihood of exporting.

8. Wrapping up: the importance of firm-level statistics

Firm-level data are the foundation for many types of aggregate statistics. But the short survey above has demonstrated also that firm-level data, whether derived directly from business registers, or from smaller sample surveys, are now a key ingredient for many types of analysis that rely on the individual experiences of firms. These applications range from the analysis of entrepreneurship and the role of SMEs, to productivity and the growth of firms. An important contribution of studies based on firm-level data is that they show the enormous heterogeneity of firms' performance. In addition, firm-level studies often provide analytical and policy-relevant insights that are impossible (or very difficult) to extract at higher levels of aggregation. For example, the role of exit and entry in productivity growth, or the role of organisational change in ICT-related productivity changes can not be observed with more aggregate data. Firm -level studies thus contribute to a better understanding of the drivers of economic performance and the interaction of different factors, and in this manner contribute to better policy making.20 . Micro level studies in the literature have focused exclusively on the link between export and

productivity, leaving the import part of the trade and productivity relationship unexplored. This is largely because micro data at the plant and firm level usually contain no information on imported inputs (Bernard and Jensen, 1999). Levinsohn (1993) is an exception which linked imports and productivity in an indirect way. Using the annual census data which cover all plants in the greater Istanbul area of Turkey from 1983 to 1986, he demonstrated that the imports-as-market-discipline hypothesis were supported by the data in the natural experiment of the broad and dramatic import liberalisation of 1984. In a similar indirect way, Bottasso and Sembenelli (2001) found a jump in productivity growth rates of Italian firms in industries where non-tariff barriers were perceived to be high, after the announcement of. the EU Single Market Program (SMP: a proposal of 282 specific measures to reduce non-tariff trade barriers in the EU).

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An important example is policies aimed at enhancing productivity. Productivity analysis based on micro-data has added considerably to the understanding of productivity growth and has demonstrated the enormous diversity of experiences in individual industries. In the United States, industry-specific factors explain less than 10 per cent of the cross-sectoral variation, a sign that firms react very differently to changing conditions and aggregate shocks. Aggregate trends, drawn from industry-level data, may thus fail to allow for a proper interpretation of behaviour. Analysis of microeconomic patterns may be needed to understand changes in macroeconomic patterns. This insight also affects the analysis of productivity. First, longitudinal analysis shows that competitive effects, such as entry and exit of firms and changes in market shares, make an important contribution to productivity growth. Technology-driven strategies to enhance productivity growth within firms may have to be embedded in a competitive framework, where a process of “creative destruction” enables entry and exit, growth of successful firms, and failure of unsuccessful ones. Policies that unduly restrict this process risk lowering productivity growth. Furthermore, the breakdown of productivity growth suggests positive effects from ownership changes and the growth of firms, suggesting that policies should not unduly restrict the expansion of firms. Longitudinal analysis also provides some fresh insight into the role of small- and medium-sized enterprises. It suggests that SMEs, where the process of creative destruction is greatest, are a dynamic component of the economy.

Work with microeconomic data also raises new issues. Principal among these is the diversity of firm behaviour. Analysis suggests that most productivity growth is the result of growth within firms. The use of advanced technologies and investment in skills are often associated with productivity growth within firms, but longitudinal studies also suggest that firms that adopt these technologies and invest in skills already perform better than the average firm. This suggests the need for a better understanding of why some firms do well and why others fail. "Soft" factors, such as management and organisation, may play an important role. For example, flexible firms appear to have a greater ability to adapt and to enhance the contribution of intangible assets, such as workers’ skills, to their performance.

While work with micro databases is expanding rapidly, much of this work still primarily covers the manufacturing sector. Although some databases now include parts of the services sector, less work has been done on these data, partly because of measurement problems. Further work on longitudinal data on services would be very important, however, as it would extend the analysis of microeconomic data to the largest part of the economy, thus improving the understanding of productivity growth at the macroeconomic level. As productivity growth in parts of the services sector has been more sluggish than in manufacturing, better understanding of the drivers of productivity in services would be very important. The recent focus of some firm-level studies on ICT in the services sector is therefore important.

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SELECTED STUDIES

General surveys of firm-level studies

AHN, S. (2001), "Firm Dynamics and Productivity Growth: A Review of Micro Evidence from OECD Countries", OECD Economics Department Working Paper, No. 297, Paris.

BARTELSMAN, E.J. and M.G. DOMS (2000), “Understanding Productivity: Lessons from Longitudinal Micro Datasets”, Journal of Economic Literature, Vol. 38, September.

McGUCKIN, R.H. (1995), “Establishment Microdata for Economic Research and Policy Analysis: Looking Beyond the Aggregates, Journal of Economics and Business Statistics, pp. 121-126.

OECD (1998), “The Dynamics of Industrial Performance: What Drives Productivity Growth”, OECD Science, Technology and Industry Outlook 1998, Chapter 4, Paris.

OECD (2001a), “Productivity and firm dynamics: evidence from microdata”, OECD Economic Outlook, No. 69, Paris.

OECD (2001b), "Firm-level data in OECD Member Countries: An Inventory of Existing Resources", DSTI/EAS/IND/SWP/AH(2001)2, Paris.

Entry and exit, job flows

BALDWIN, J.R., L. BIAN, R. DUPUY and G. GELLATLY (2000), Failure Rates for New Canadian Firms: New Perspectives on Entry and Exit, Statistics Canada, Ottawa.

BALDWIN, J.R., D. BECKSTEAD and A. GIRARD (2001), "The Importance of Entry to Canadian Manufacturing with an Appendix on Measurement Issues", Microeconomic Analysis Division, Statistics Canada, DSTI/EAS/IND/SWP/AH(2001)19.

BARTELSMAN, E., S. SCARPETTA and F. SCHIVARDI (2001), "Comparative Analysis of Firm Demographics and Survival: Micro-level Evidence for the OECD Countries", OECD Economics Department Working Paper, Paris, forthcoming.

CABALLERO, R.J., E.M.R.A ENGEL and J.C. HALTIWANGER (1997), “Aggregate Employment Dynamics: Building from Microeconomic Evidence”, American Economic Review, Vol. 87, March, pp. 115-137.

DAVIS, S.J., J.C. HALTIWANGER and S. SCHUH (1996), Job Creation and Destruction, MIT Press, Cambridge, MA.

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GEROSKI, P. (1995), “What do we know about entry?”, International Journal of Industrial Organization, Vol. 13, No. 4, pp. 421-440.

OECD (1996), “Employment Adjustment, Workers and Unemployment”, Employment Outlook, Chapter V, Paris.

OECD (1997), “Job Insecurity”, Employment Outlook, Chapter V, Paris.

Entrepreneurship and SMEs

EUROPEAN COMMISSION (2000), "Benchmarking Enterprise Policy", SEC(2000)1842, Commission Staff Working Document.

NICOLETTI, G., S. SCARPETTA and O. BOYLAUD (1999), “Summary indicators of product market regulation with an extension to employment protection legislation”, OECD Economics Department Working Paper No. 226, Paris.

OECD (2001c), "Progress Report on the statistical database on enterprises by size class: Available information and relevant indicators and graphs", DSTI/EAS/IND/SWP/AH(2001)8, Paris.

REYNOLDS, P.D., M. HAY, W.D. Bygrave, S.M. CAMP and E. AUTIO (2000), Global Entrepreneurship Monitor - 2000 Executive Report, Babson College/Kaufman Center/London Business School.

SCHREYER, P. (1996), "SMEs and Employment Creation: Overview of Selected Quantitative Studies in OECD Member Countries", STI Working Paper 1996/4, OECD, Paris.

SCHREYER, P. (2000), “High-growth firms and employment”, STI Working Paper 2000/3, OECD, Paris.

Women entrepreneurship

APEC (1999), Women entrepreneurs in SMEs in the APEC region, Policy level group on SMEs.

Bank of Montreal’s Institute for Small Business, Myth and realities: The economic power of women-led firms in Canada.

BOEGH NIELSEN, P. (2001), "Statistics on New Enterprises, the Entrepreneurs and the Survival of the Start-Ups", DSTI/EAS/IND/SWP/AH(2001)10.

Danish Agency for Trade and Industry (1998), More women needed among the entrepreneurs of the future.

LETOWSKI, A. (2001), "Les femmes chefs d’entreprise, créatrices d’entreprise en France : état des lieux, sources d’information et propositions pour accroître la connaissance et la comparabilité internationale ", DSTI/EAS/IND/SWP/AH(2001)12, Paris.

Network Ireland (1998), Businesswomen of Ireland: Concerns, plans and expectations.

OECD (2000), OECD Small and Medium Enterprise Outlook, Paris.

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OECD (2001d), "Issues Related to Statistics on Women Entrepreneurship", DSTI/EAS/IND/SWP/AH(2001)11, Paris.

Serviço Brasileiro de Apoio as Micro e Pequenas Empresas (SEBRAE) (2000), The SEBRAE poll 2000: The woman entrepreneur, Brazilian Support Service to Micro and SMEs.

UNITED STATES DEPARTMENT OF COMMERCE (2001), Women-Owned Businesses, 1997 Economic Census - Survey of Women-Owned Business Enterprises, Economics and Statistics Administration, U.S. Census Bureau, March.

Productivity

BAILY, M.N., E.J. BARTELSMAN and J. HALTIWANGER (1996a), “Downsizing and Productivity Growth: Myth or Reality”, Small Business Economics, Vol. 8, No. 4, August, pp. 259-278.

BAILY, M.N., E.J. BARTELSMAN and J. HALTIWANGER (1996b), “Labor Productivity: Structural Change and Cyclical Dynamics”, NBER Working Paper Series, No. 5503, NBER, Cambridge, MA.

BAILY, M.N., C. HULTEN and D. CAMPBELL (1992), “Productivity Dynamics in Manufacturing Plants”, Brookings Papers on Economic Activity: Microeconomics, pp. 187-267.

BALDWIN, J.R. (1996), “Productivity Growth, Plant Turnover and Restructuring in the Canadian Manufacturing Sector”, in: MAYES, D. (ed.) (1996), Sources of Productivity Growth, National Institute of Economic and Social Research, Cambridge University Press, Cambridge.

BALDWIN, J.R. and P.K. GORECKI (1991), “Entry, Exit, and Productivity Growth”, in: P.A. Geroski and J. Schwalbach (eds.), Entry and Market Contestability: An International Comparison, Blackwell, Oxford.

BALDWIN, J.R. and W.GU (2001), "Plant Turnover and Productivity Growth in Canadian Manufacturing", Micro Economic Analysis Division, Statistics Canada, June, DSTI/EAS/IND/SWP/AH(2001)20.

BARNES, M, J. HASKELL and M. MALIRANTA (2001), "The Sources of Productivity Growth: Micro-level Evidence for the OECD", DSTI/EAS/IND/SWP/AH(2001)14 and OECD Economics Department Working Paper, Paris, forthcoming.

BARTELSMAN, E.J. and P.J. DHRYMES (1992), “Productivity Dynamics: U.S. Manufacturing Plants, 1972-1986”, CES Discussion Paper, CES 92-1, US Bureau of the Census, Washington, DC.

BARTELSMAN, E.J., G. van LEEUWEN and H.R. NIEUWENHUIJSEN (1995), “Downsizing and Productivity Growth: Myth or Reality”, Netherlands Official Statistics, Autumn, pp. 23-28.

BLAND, S. and WILL, L. 2001, Resource Movements and Labour Productivity, and Australian Illustration: 1994-95 to 1997-98, Productivity Commission Staff Research Paper, AusInfo, Canberra.

FOSTER, L., J. HALTIWANGER and C.J. KRIZAN (1998), “Aggregate productivity growth: lessons from microeconomic evidence”, NBER Working Paper, No. 6803.

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GRILICHES, Z. and H. REGEV (1995), “Firm Productivity in Israeli Industry, 1979-1988”, Journal of Econometrics, Vol. 65, pp. 175-203.

HAHN, C.-H. (2000), “Entry, exit, and aggregate productivity growth: micro evidence on korean manufacturing”, OECD Economics Department Working Paper No. 272, OECD, Paris.

HALTIWANGER, J. 1997, "Measuring and Analysing Aggregate Fluctuations: The Importance of Building from Microeconomic Evidence", Federal Reserve Bank of St Louis, Economic Review, January/February.

MALIRANTA, M. (1997a), “The Determinants of Aggregate Productivity - The Evolution of Micro-Structures and Productivity within Plants in Finnish Manufacturing from 1975 to 1994”, ETLA Discussion Papers, No. 603, Helsinki.

PARHAM, D. (2001), "The role of exit and entry in Australian productivity growth", DSTI/EAS/IND/SWP/AH(2001)13, Paris.

SCARPETTA, S., A. BASSANINI, D. PILAT and P. SCHREYER (2000), “Economic growth in the OECD area: Recent trends at the aggregate and sectoral levels”, OECD Economics Department Working Paper No. 248, Paris.

WAGNER, J. (1997), “Productivity Decomposition Analysis based on Micro-Level Longitudinal Data from Manufacturing Firms in Lower Saxony, Germany, 1978-1994”, Lüneberg, mimeo.

Analytical studies

ATROSTIC, B.K. and S. NGUYEN (2001), "The Effect of Computer Networks on Manufacturing Productivity", Center for Economic Studies, forthcoming.

BALDWIN, J.R. (1995), The Dynamics of Industrial Competition: A North American Perspective, Cambridge University Press, New York.

BALDWIN, J.R., B. DIVERTY and D. SABOURIN (1995a), “Technology Use and Industrial Transformation: Empirical Perspective”, Working Paper No. 75, Micro-Economics Analysis Division, Statistics Canada, Ottawa.

BALDWIN, J.R. and B. DIVERTY (1995), “Advanced Technology Use in Canadian Manufacturing Establishments”, Working Paper No. 85, Micro-Economics Analysis Division, Statistics Canada, Ottawa.

BALDWIN, J.R., T. GRAY and J. JOHNSON (1995b), “Technology Use, Training and Plant-Specific Knowledge in Manufacturing Establishments”, Working Paper No. 86, Micro-Economics Analysis Division, Statistics Canada, Ottawa.

BALDWIN, J.R., T. GRAY and J. JOHNSON (1997), “Technology-Induced Wage Premia in Canadian Manufacturing Plants during the 1980s”, Working Paper No. 92, Micro-Economics Analysis Division, Statistics Canada, Ottawa.

BALDWIN, J.R. and D. SABOURIN (2001), "Impact of the Adoption of Advanced Information and Communication Technologies on Firm Performance in the Canadian Manufacturing Sector", DSTI/EAS/IND/SWP/AH(2001)15, Paris.

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BARTELSMAN, E.J., G. van LEEUWEN and H.R. NIEUWENHUIJSEN (1996), “Advanced Manufacturing Technology and Firm Performance in the Netherlands”, Netherlands Official Statistics, Vol. 11, Autumn, pp. 40-51.

BERNARD, A. and J.B JENSEN (1999), “Exceptional exporter performance: cause, effects or both”, Journal of International Economics, 47.

BERNARD, A. and J. WAGNER (1997), “Exports and success in German manufacturing”, Weltwirtschaftliches Archiv, Vol. 133, No. 1, pp. 134-57.

BLACK, S.E. and L.M. LYNCH (1997), “How to compete: the impact of workplace practices and information technology on productivity”, NBER Working Paper Series, No. 6120, August.

BLACK, S.E. and L.M. LYNCH (2000), “What’s driving the new economy: The benefits of workplace innovation”, NBER Working Paper Series, No. 7479, January.

BRESNAHAN, T.F., E. BRYNJOLFSSON, and L.M. HITT (1999), “Information Technology, Workplace Organization and the Demand for Skilled Labor: Firm-Level Evidence”, NBER Working Paper Series No. 7136, May.

BROERSMA, L. and R.H. McGUCKIN (1999), “The Impact of Computers on Productivity in the Trade Sector: Explorations with Dutch Microdata”, Research Memorandum GD-45, Groningen Growth and Development Centre, October.

BRYNJOLFSSON, E. and L. HITT (1997), “Computing Productivity: Are Computers Pulling Their Weight?”, mimeo MIT and Wharton, http://ccs.mit.edu/erik/cpg/.

BRYNJOLFSSON, E., L. HITT and S. YANG (1998), “Intangible Assets: How the Interaction of Information Systems and Organisational Structure Affects Stock Market Valuations”, forthcoming in the Proceedings of the International Conference on Information Systems, Helsinki, Finland.

BRYNJOLFSSON, E. and S. YANG (1998), “The Intangible Benefits and Costs of Computer Investments: Evidence from the Financial Markets, mimeo, May, http://ccs.mit.edu/erik/.

CREPON, B., E. DUGUET and J. MAIRESSE (1998), “Research, innovation, and productivity: an econometric analysis at the firm level”, NBER Working Paper, No. 6696, August.

CREPON, B. and T. HECKEL (2000), "Informatisation en France: une évaluation à partir de données individuelles", G2000/13, Document de Travail, INSEE.

CUNEO, P., J. MAIRESSE (1984), “Productivity and R&D at the Firm Level in French Manufacturing”, in R&D, Patents and Productivity, Griliches ed., University of Chicago Press.

DISNEY, R., J. HASKEL and Y. HEDEN (2000), “Restructuring and productivity growth in UK manufacturing”, CEPR Discussion paper series, No. 2463, May.

DOMS, M., T. DUNNE, and M.J. ROBERTS (1995), “The Role of Technology Use in the Survival and Growth of Manufacturing Plants”, International Journal of Industrial Organization 13, No. 4, December, pp. 523-542.

DOMS, M., T. DUNNE and K.R. TROSKE (1997), “Workers, Wages and Technology”, Quarterly Journal of Economics, Vol. 112, No. 1, pp. 253-290.

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http://ccs.mit.edu/erik/

http://ccs.mit.edu/erik/cpg/


DUNNE, T. (1994), “Patterns of Technology Usage in U.S. Manufacturing Plants”, Rand Journal of Economics, Vol. 25, No. 3, pp. 488-499.

DWYER, D. (1995b), “Technology Locks, Creative Destruction and Non-Convergence in Productivity Levels”, CES Discussion Papers, CES 95-6, US Bureau of the Census, Washington, DC.

ENTORF, H. and F. KRAMARZ (forthcoming), “New Technologies, Wages, and Worker Selection”, National Academy of Science Conference Volume.

FEDERAAL PLANBUREAU (1998), “De impact van innovatie op de groei van toegevoegde waarde en tewerkstelling” (The impact of innovation on growth of value added and employment), Working Paper 9-98, Brussels, December.

FOSTER, L., J. HALTIWANGER and C.M. KRIZAN (2001), "The Link between Aggregate and Micro Productivity Growth: Evidence from Retail Trade", paper presented at the 2001 CAED conference, Aarhus, October.

HITT, L. and E. BRYNJOLFSSON (1997), “Information Technology and Internal Firm Organisation: An Exploratory Analysis”, Journal of Management Information Systems, Vol. 14 (2).

HITT, L.M. and E. BRYNJOLFSSON (1998), “Beyond Computation: Information Technology, Organisational Transformation and Business Performance”, http://ccs.mit.edu/erik/, mimeo.

JARMIN, R., S. KLIMEK and J. MIRANDA (2001), Dynamics in the U.S. Retail Sector, 1975-1999, paper presented at the CAED 01 meeting, Aarhus, October.

JENSEN, J.B. and R.H. McGUCKIN (1997), “Firm Performance and Evolution: Empirical Regularities in the U.S. Microdata”, Industrial and Corporate Change, special issue, forthcoming.

KLOMP, L. and G. VAN LEEUWEN (2000), "The Importance of Innovation for Company Performance", Netherlands Official Statistics, pp. 26-35.

LICHTENBERG, F.R. and D. SIEGEL (1992a), “Productivity and Changes in Ownership of Manufacturing Plants”, in: F.R. Lichtenberg, Corporate Takeovers and Productivity, MIT Press, Cambridge.

LICHTENBERG, F.R. and D. SIEGEL (1992b), “Takeovers and Corporate Overhead”, in F.R. Lichtenberg, ed., Corporate Takeovers and Productivity, MIT Press, Cambridge, Massachusetts.

LICHTENBERG, F.R. (1995), “The Output Contributions of Computer Equipment and Personal: A Firm Level Analysis”, Economics of Innovation and New Technology, Vol. 3.

MAIRESSE, J. and B.B. HALL (1996), “Estimating the productivity of research and development: an exploration of GMM methods using data on French and United States manufacturing firms”, NBER Working Paper, No. 5501.

McGUCKIN, R.H. and S.V. NGUYEN (1995), “On Productivity and Plant Ownership Change: New Evidence from the LRD”, Rand Journal of Economics, Vol. 26, No. 2, pp. 257-276.

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http://ccs.mit.edu/erik/


McGUCKIN, R.H. and S.V. NGUYEN (1997), “Exploring the Role of Acquisition in the Performance of Firms: Is the “Firm” the Right Unit of Analysis?”, The Conference Board/U.S. Bureau of the Census, mimeo.

McGUCKIN, R.H., M. STRIETWEISER, and M. DOMS (1998), “The Effect of Technology Use on Productivity Growth”, Economics of Innovation and New Technology, Vol. 7, pp. 1-26.

MILANA, C. and A. ZELI (2001), "The Contribution of ICT to Production Efficiency in Italy: Firm-Level Evidence using DEA and Econometric Estimations", DSTI/EAS/IND/SWP/AH(2001).

NICKELL, S.J. (1996), “Competition and corporate performance”, Journal of Political Economy, Vol. 104, No. 4.

NICKELL, S., D. NICOLITSAS and N. DRYDEN (1997), “What makes firms perform well?”, European Economic Review, 41.

OECD (1999), Managing National Innovation Systems, Paris.

OULTON, N. (1998), “Competition and the dispersion of labour productivity amongst UK companies”, Oxford Economic Papers, No. 50, pp. 23-38.

PRODUCTIVITY COMMISSION (1999), Microeconomic Reform and Australian Productivity: Exploring the Links, Research Paper, AusInfo, Canberra.

STOLARICK, K.M. (1999a), "IT Spending and Firm Productivity: Additional Evidence from the Manufacturing Sector", CES 99-10, Center for Economic Studies, October.

STOLARICK, K.M. (1999b), "Are Some Firms Better at IT? Differing Relationships between Productivity and IT Spending", CES 99-13, Center for Economic Studies, October.

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