For Official Use
DSTI/EAS/IND/SWP/AH(2001)3
Organisation de Coopération et de Développement Economiques
Organisation for Economic Co-operation and Development
19-Nov-2001
___________________________________________________________________________________________
_____________
English text only
DIRECTORATE FOR SCIENCE, TECHNOLOGY AND INDUSTRY
DSTI/EAS/IND/SWP/AH(2001)3
For Official Use
COMMITTEE ON INDUSTRY AND BUSINESS ENVIRONMENT
Working Party on Statistics
WORK WITH FIRM-LEVEL STATISTICS: SOME KEY APPLICATIONS
WORKSHOP ON FIRM-LEVEL STATISTICS, 26-27 NOVEMBER 2001
This paper was prepared by the Secretariat as a background paper for the workshop on firm-level statistics. The
paper provides a brief overview of the main areas of analytical work with firm-level data. It mainly covers areas
that will be discussed at the OECD workshop on firm-level statistics. No attempt is made to be exhaustive, and
the survey primarily covers work that is based on work with official firm-level statistics in OECD member
countries.
Contact: Dirk PILAT: Tel: 33 1 45 24 87 49; Fax: 33 1 44 30 62 58; E-mail: [email protected]
English text only
JT00116883
Document complet disponible sur OLIS dans son format d'origine
Complete document available on OLIS in its original format
DSTI/EAS/IND/SWP/AH(2001)3
TABLE OF CONTENTS
WORK WITH FIRM-LEVEL STATISTICS: SOME KEY APPLICATIONS ..............................................3
1.
2.
3.
4.
Introduction ..........................................................................................................................................3
Firm demographics: exit, entry and turnover of firms ..........................................................................4
Entrepreneurship and the growth of firms ............................................................................................6
The size dimension: the role of SMEs in the economy ........................................................................7
4.1
The contribution of SMEs to overall performance ......................................................................7
4.2
Work on high-growth firms .........................................................................................................9
5. Women entrepreneurship ....................................................................................................................11
6. The dynamics of productivity growth .................................................................................................13
6.1
The contribution of firm-level dynamics to aggregate productivity growth .............................13
6.2
The role of upsizing and downsizing .........................................................................................16
7. Understanding the growth of firms .....................................................................................................17
7.1
Technology, innovation and the role of ICT .............................................................................18
7.2
Other factors ..............................................................................................................................22
8. Wrapping up: the importance of firm-level statistics .........................................................................24
SELECTED STUDIES ..................................................................................................................................26
General surveys of firm-level studies ........................................................................................................26
Entry and exit, job flows ............................................................................................................................26
Entrepreneurship and SMEs ......................................................................................................................27
Women entrepreneurship ...........................................................................................................................27
Productivity ................................................................................................................................................28
Analytical studies .......................................................................................................................................29
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WORK WITH FIRM-LEVEL STATISTICS: SOME KEY APPLICATIONS
1.
Introduction
1.
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
2.
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 hightechnology 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.
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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DSTI/EAS/IND/SWP/AH(2001)3
 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,
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 firmspecific 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.
3.
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
4.
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 employmentweighted 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.
5.
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
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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DSTI/EAS/IND/SWP/AH(2001)3
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.
Table 1. Alternative indicators of entrepreneurship and firm creation
Australia
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Ireland
Italy
Japan
Korea
Luxembourg
Netherlands
Norway
Portugal
Spain
Sweden
United Kingdom
United States
Start-up
Firm turnover,
activity, 19981
1998 (2)
(1)
-8.1
-13.0
17.0
1.4
-6.2
-3.0
19.1
1.9
-1.2
41.6
3.8
15.0
1.0
3.5
3.4
7.4
0.9
-5.3
-17.5
-8.5
-5.5
-13.9
25.9
3.2
-1.9
21.1
3.1
30.0
9.8
Correlation coefficients:
Col 1 - Col. 2
0.49
Col 2 - Col. 3
0.04
Turnover in the
business sector,
1989-94 (3)
---21.7
20.8
29.2
20.2
16.6
-16.5
---16.1
-21.5
---20.1
Col 1 - Col. 3
-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 ).
6.
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).
7.
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
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DSTI/EAS/IND/SWP/AH(2001)3
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:
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
8.
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.
9.
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.
10.
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.
11.
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.
12.
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
4.
This overview draws on Ahn (2001).
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DSTI/EAS/IND/SWP/AH(2001)3
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.
13.
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
14.
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
15.
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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DSTI/EAS/IND/SWP/AH(2001)3
Figure 1. Distribution of employment in manufacturing, by size class
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
USA SWE
FIN
CZE
BEL
GBR
1-9
10-49
AUT
TUR KOR CHE
50-99
100-499
JPN
ITA
NZL
PRT
500 +
Source: OECD SME database, see OECD (2001c).
16.
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 mediumsized 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.
17.
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
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DSTI/EAS/IND/SWP/AH(2001)3
are available. 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
18.
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.
19.
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
20.
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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DSTI/EAS/IND/SWP/AH(2001)3
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. Fastgrowing 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.
21.
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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DSTI/EAS/IND/SWP/AH(2001)3
5.
Women entrepreneurship7
22.
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.
23.
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.
Table 2. US Firms by Gender of Ownership, 1997
All US firms
Female-owned
Equally male-/female-owned
Male-owned
Publicy held, foreign-owned
and nonprofit
All firms
Sales and
Firms
receipts
(1000s)
(million
USD)
20,822
18,553,243
5,417
818,669
3,641
943,881
11,382
6,629,451
382
Firms
(1000s)
5,295
847
1,029
3,151
10,161,242
268
Firms with paid employees
Sales and
Annual
receipts
Employees
payroll
(million
(1000s)
(million
USD)
USD)
17,907,940
103,360
2,936,493
717,764
7,076
149,116
828,390
8,285
160,989
6,257,728
43,541
1,189,193
10,104,058
44,458
1,437,195
Source: US Census Bureau, "Women-Owned Businesses", 1997 Economic Census.
24.
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).
Table 3. Newly-started enterprises in Sweden
[percentage distribution by gender of the entrepreneur]
1998
1999
Male
Start-up rate
per 1000
males1
64
64
11.6
12.3
Female
Start-up rate
per 1000
females1
Mixed
31
31
5.7
5.8
5
5
Note: (1) Between 16 and 64 years.
Source: Statistics Sweden, http://www.scb.se/sm/Nv12SM0001_tabeller.asp
7.
This section draws on OECD (2001d).
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DSTI/EAS/IND/SWP/AH(2001)3
25.
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 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.
26.
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.
27.
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.
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.
12
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28.
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.
6.
The dynamics of productivity growth
6.1
The contribution of firm-level dynamics to aggregate productivity growth
29.
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
30.
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.
31.
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.
13
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Table 4. Breakdown of labour productivity growth in manufacturing
Percentage growth rate and contribution of each component to total productivity growth
Period
Annual
average
growth rate
Breakdown (% contribution)
(per cent)
Within
Between
Mix
Entry
Exit
1994-95 1997-98
-3.0
93.3
-50.0
60.0
46.7
50.0
Canada
1973-79
1979-88
1988-97
2.2
1.4
2.9
42.0
83.0
87.0
32.7
-1.9
-5.2
----
5.6
3.3
1.0
19.6
15.5
17.3
Finland
1985-90
1989-94
5.4
4.6
72.5
68.4
7.0
16.1
---
0.4
-2.5
20.1
18.0
France
1985-90
1987-92
2.0
0.0
84.7
85.0
1.9
11.0
---
-20.2
-44.0
33.6
49.0
Germany2
1992-97
2.1
115.3
-12.1
--
-0.7
-2.6
Italy
1985-90
1990-95
4.8
5.5
62.1
58.2
9.0
7.0
---
10.7
15.7
18.3
19.1
Japan3
1987-94
9.2
73.9
27.0
-0.7
--
--
Korea4
1990-95
1995-98
23.0
4.7
57.0
-2.0
46.0
65.0
3.0
38.0
---
---
Netherlands
1985-90
1990-95
1.5
2.8
99.9
78.2
-8.1
-10.8
---
33.5
20.5
-25.2
12.1
Portugal
1987-91
1990-95
6.6
6.8
91.4
62.6
-9.7
-4.3
---
-13.4
5.3
31.8
36.4
United Kingdom
1985-90
1990-95
1.6
1.7
98.3
59.9
-7.4
3.1
---
13.7
8.8
-4.6
28.2
United States
1987-92
1992-97
1.6
3.0
63.0
81.0
8.0
1.0
---
-24.0
-13.0
53.0
31.0
Australia1
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).
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32.
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
33.
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
34.
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.
35.
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.
36.
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
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.
15
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(i.e. the between effect) plays a somewhat stronger role. More important, a strong contribution to MFP
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
37.
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).
Table 5. Upsizing and downsizing in productivity growth
Annual average growth rates, in per cent
United States United States
Japan
All firms/establishments
Continuing firms
Of which:
- Successful upsizers
- Successful downsizers
- Unsuccessful downsizers
- Unsuccessful upsizers
Effect of entry and exit
France,
Netherlands
1977-871
..
1987-921
..
1987-942
..
1985-913
..
1980-914
3.0
3.4
2.4
9.2
2.3
2.0
2.2
2.6
-0.6
-0.7
1.7
2.6
-0.5
-1.3
9.1
10.0
-4.7
-5.2
1.2
2.2
-0.6
-0.5
1.2
1.0
-0.1
-0.2
..
..
..
..
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).
38.
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
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.
16
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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).
39.
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.
40.
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
41.
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.
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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7.1
Technology, innovation and the role of ICT
Work with technology and innovation surveys
42.
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
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).
43.
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.
44.
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.
45.
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.
46.
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
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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.
47.
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.
48.
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.
49.
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).
50.
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.
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51.
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.
ICT and firm-level growth
52.
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).
53.
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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Table 6. Selected firm-level studies on ICT and productivity
Study
Lichtenberg (1995)
Sample
Issue addressed
Main findings
US firms, 198891
Output contribution of capital
and labour deployed in
information systems
One information systems employee
can be substituted for six noninformation systems employees
without affecting output
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, 198793, 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
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
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.
Hitt and
Brynjolfsson
(1997);
Brynjolfsson and
Hitt (1997)
Brynjolfsson, Hitt
and Yang (1998)
Bresnahan,
Brynjolfsson and
Hitt (1999)
400 large firms,
1987-96
Source: OECD summary.
54.
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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55.
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.
56.
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.
57.
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.
58.
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
59.
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.
60.
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
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higher skills 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
61.
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.
62.
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.
63.
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.
64.
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
65.
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 belowaverage 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.
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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DSTI/EAS/IND/SWP/AH(2001)3
66.
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 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.
67.
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.
68.
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
69.
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
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).
24
DSTI/EAS/IND/SWP/AH(2001)3
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.
70.
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.
71.
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.
72.
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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DSTI/EAS/IND/SWP/AH(2001)3
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.
26
DSTI/EAS/IND/SWP/AH(2001)3
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.
27
DSTI/EAS/IND/SWP/AH(2001)3
OECD
(2001d),
"Issues
Related
to
DSTI/EAS/IND/SWP/AH(2001)11, Paris.
Statistics
on
Women
Entrepreneurship",
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: Microlevel 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.
28
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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 MicroStructures 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.
30
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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.
31
DSTI/EAS/IND/SWP/AH(2001)3
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.
32