The Economic Benefits and Costs of Entrepreneurship (Foundations and Trends in Entrepreneurship)

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C. Mirjam van Praag and Peter H. Versloot The Economic Benefits and Costs of Entrepreneurship examines to what extent recent empirical evidence can collectively and systematically substantiate the claim of the economic benefits of entrepreneurship. The authors review the empirical literature and provide an answer to the question “what is the contribution of entrepreneurs to the economy in comparison to their counterparts?” Using four measures, the authors quantify the economic benefits of entrepreneurs along the following parameters — employment generation and dynamics, innovation, productivity and growth, and the role of entrepreneurship in increasing individuals’ utility levels. The Economic Benefits and Costs of Entrepreneurship is the first review of the primary empirical literature in this area. More precisely, it is the first review of high quality economics and management studies, focusing on various types of contributions that entrepreneurs can make to the economy in terms of quantifiable measures and evaluating the entrepreneurs’ performance in these areas relative to their counterparts.

FnT ENT 4:2 The Economic Benefits and Costs of Entrepreneurship

The Economic Benefits and Costs of Entrepreneurship: A Review of the Research

Foundations and Trends® in Entrepreneurship 4:2 (2008)

The Economic Benefits and Costs of Entrepreneurship: A Review of the Research C. Mirjam van Praag and Peter H. Versloot

C. Mirjam van Praag and Peter H. Versloot

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This book is originally published as Foundations and Trends® in Entrepreneurship, Volume 4 Issue 2 (2008), ISSN: 1551-3114.

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The Economic Benefits and Costs of Entrepreneurship: A Review of the Research

The Economic Benefits and Costs of Entrepreneurship: A Review of the Research C. Mirjam van Praag Amsterdam Center for Entrepreneurship University of Amsterdam Roetersstraat 11, 1018WB Amsterdam The Netherlands [email protected]

Peter H. Versloot Tinbergen Institute University of Amsterdam The Netherlands

Boston – Delft

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R Foundations and Trends in Entrepreneurship Vol. 4, No. 2 (2008) 65–154 c 2008 C. M. van Praag and P. H. Versloot

DOI: 10.1561/0300000012

The Economic Benefits and Costs of Entrepreneurship: A Review of the Research* C. Mirjam van Praag1,2,3,4 and Peter H. Versloot2,5 1

2 3 4 5

Amsterdam Center for Entrepreneurship, University of Amsterdam, Roetersstraat 11, 1018WB, Amsterdam, The Netherlands, [email protected] Tinbergen Institute, University of Amsterdam, The Netherlands Max Planck Institute of Economics, Germany IZA Institute for the Study of Labour, Germany University of Amsterdam, The Netherlands

Abstract Many studies in the entrepreneurship literature are motivated by the statement that entrepreneurship has important economic value, for instance, in terms of productivity and growth, employment generation or, innovation. This claim is often substantiated by a reference to (at most) one or two studies finding supporting evidence. However, whether the cited reference was one of the few out of many studies that “happened” to find supportive evidence is not yet clear. This paper examines to what extent recent empirical evidence can collectively and system*A

shorter version of this article is being published as What is the Value of Entrepreneurship? A Review of Recent Research by C.M. van Praag and P.H. Versloot in Small Business Economics, volume 29, number 4, pp 351–383.

atically substantiate the claim that entrepreneurs cause important economic benefits. Hence, a systematic review is provided that answers the question: What is the contribution of entrepreneurs to the economy in comparison to non-entrepreneurs? We study the relative contribution of entrepreneurs to the economy based on four measures that have most widely been studied empirically. Hence, we answer the particular question: What is the contribution of entrepreneurs to (i) employment generation and dynamics, (ii) innovation, and (iii) productivity and growth, relative to the contributions of the entrepreneurs’ counterparts, i.e., the “control group?” A fourth type of contribution studied is the role of entrepreneurship in increasing individuals’ utility levels. Based on 57 recent studies of high quality that contain 87 relevant separate analyses, we conclude that entrepreneurs have a very important — but specific — function in the economy. They engender relatively much employment creation, productivity growth and produce and commercialize high quality innovations. They are more satisfied than employees. More importantly, recent studies show that entrepreneurial firms produce important spillovers that affect regional employment growth rates of all companies in the region in the long run. However, the counterparts cannot be missed as they account for a relatively high value of productivity and growth, a less volatile and more secure labor market, higher paid jobs and a greater number of innovations and they have a more active role in the adoption of innovations. Keywords: Entrepreneur; entrepreneurship; self-employment; productivity; economic development; growth; employment; innovation; patents; R&D; utility; remuneration; income. JEL codes: D24, D31, E23, E24, J21, J28, J31, L26, M13

Contents

1 Introduction

1

2 Data: Sample Selection and Definitions

5

2.1 2.2 2.3 2.4

Sample Selection Procedure Definitions of the Entrepreneur and the Counterpart Indicators of Contributions to Economic Outcomes Final Search and Sample Statistics

5 7 9 11

3 Contributions to Employment

15

3.1 3.2 3.3

15 22 30

Generation of Employment Remuneration and Satisfaction of Employees Summary of the Contributions to Employment

4 Contributions to Innovation

31

4.1 4.2 4.3 4.4

31 35 36 37

The Quantity and Quality of Innovations The Commercialization of Innovations The Adoption of Innovations Summary of the Contributions to Innovation

5 Contributions to Productivity and Growth

41

5.1 5.2

41 50

Levels of Value Added and Productivity Growth of Value Added and Productivity ix

5.3

Summary of the Contributions to Productivity and Growth

59

6 Contributions to Utility

61

6.1 6.2 6.3 6.4

61 64 66 69

Remuneration Levels Remuneration Inequality and Volatility Job Satisfaction Summary of the Contributions to Utility

7 Conclusion

71

7.1 7.2 7.3 7.4

72 74 75 75

Employment Innovation Productivity and Growth Utility

Appendix

79

Acknowledgments

83

References

85

1 Introduction

Almost without exception, academic studies on entrepreneurship are motivated by the economic benefits of entrepreneurship. Most studies refer to one or two academic studies showing that entrepreneurship indeed leads to substantial benefits in terms of, for instance, employment generation or innovations. However, whether the motivation is based on a very carefully selected study that “happens to find” positive benefits, has, so far, remained unclear. This paper examines to what extent recent empirical evidence can collectively and systematically substantiate this claim. Entrepreneurs and their counterparts are defined and compared in terms of their contribution to the creation of economic value. Hence, the aim is to review recent empirical literature that provides an (statistically supported) answer to the following question: What is the contribution of entrepreneurs to the economy in comparison to their counterparts, i.e. non-entrepreneurs? Given the existing base of empirical studies into this subject, we arrive at four measures to quantify the economic benefits of entrepreneurs. Hence, we answer the following particular questions: What is the contribution of entrepreneurs to (i) employment generation and dynamics, (ii) innovation, and (iii) productivity and growth, relative to the contributions 1

2

Introduction

of the entrepreneurs’ counterparts, i.e., the “control group?” A fourth type of contribution that we study is the role of entrepreneurship in increasing individuals’ utility levels. Besides emphasizing what our study might contribute, it is also worthwhile to acknowledge what it does not contribute. Economic or management theories about why and how entrepreneurs would contribute more or less to specific aspects of economic value creation, such as employment or innovation, are not included. They are beyond the scope of our study and provided elsewhere, as for instance in Parker (2004) and in most of the studies reviewed. We only provide an (rather thorough) overview of empirical studies such that we can evaluate the extent of contributions to economic value creation of entrepreneurs in practice.1 Surprisingly, given the relevance of showing the relationship between entrepreneurship and economic outcomes, this paper is the first review of the (primary) empirical literature in this area. More precisely, it is the first review of high quality economics and management studies, focusing on various types of contributions that entrepreneurs can make to the economy in terms of quantifiable measures and evaluating the entrepreneurs’ performance in these areas relative to their counterparts, i.e., larger, older or incumbent firms. In these senses, our study is unique. Moreover, previous reviews were (obviously) based on older empirical studies. To our knowledge, five previous and recent studies are somewhat related. The review by Acs and Audretsch (2005) studies the relationship between entrepreneurship and innovation based on an older database and a somewhat different strand of — both theoretical and empirical — literature. Therefore, their sample of studies is mostly non-overlapping with ours. Hence, Acs and Audretsch (2005) and the current paper can be considered complements. As a matter of fact, the results of both reviews are remarkably similar. The same holds for Carree and Thurik 1 Moreover,

we do not relate the behavior of entrepreneurial firms to economic outcomes, as is done in, for instance Wynarczyk and Watson (2005), Maes et al. (2005), or Norton and Moore (2006). Nor do we assess how institutional factors affect the growth and productivity of entrepreneurial and other firms differently, i.e., Hartarska and Gonzalez-Vega (2006).

3 (2003) who discuss empirical macroeconomic studies on the relationship between entrepreneurship and economic growth, Biggs (2002), who discusses the importance of small and medium-sized firms (SMEs) for employment generation and innovation, Caves (1998), who focuses on firm entry, exit and turnover, as well as firm growth and production efficiency, and Sutton (1997), who reviews studies on employment generation (Gibrat’s Law specifically) published prior to 1995.2 The remainder of the paper is structured as follows. Chapter 2 elaborates on the definitions of the key variables studied in this review, i.e., entrepreneurs, the entrepreneurs’ counterparts, employment generation and dynamics, innovation, productivity and growth, and the indicators used in the literature of utility derived from entrepreneurship. Moreover, we discuss the details of the sample selection procedure and some statistics of the resulting sample. In Chapter 3, the entrepreneur’s contribution to employment is evaluated by measuring the levels of and growth in employment they realize relative to the “control group.” Moreover, employee remuneration levels are compared as an indicator of employment quality. Chapter 4 discusses entrepreneurs’ relative contributions to innovation in terms of the production, commercialization, and adoption of innovations. In Chapter 5, the contribution of entrepreneurs to productivity and growth is assessed in terms of value added, labor productivity, and total factor productivity. Chapter 6 focuses on utility levels derived from entrepreneurship as compared to wage employment in terms of expected income levels, income volatility, and job satisfaction levels. Chapter 7 concludes.

2 Literature

reviews prior to 1990 on the contributions to employment, in terms of employment generation and employee remuneration can be found in Brown et al. (1990) and Storey and Johnson (1987).

2 Data: Sample Selection and Definitions

This chapter discusses how the literature reviewed is selected, based on the definitions of the entrepreneur used and requirements imposed on each individual study. Details on the search procedure are shown and the indicators for the contributions of entrepreneurs to the economy are discussed in detail. The section closes with some statistics of the resulting sample of studies.

2.1 2.1.1

Sample Selection Procedure Initial Search

An initial — exploratory — search of studies analyzing the “benefits of entrepreneurship” showed that the foremost benefits analyzed in the literature pertain to employment, innovation, productivity and growth, and individuals’ utility levels. The second stage of search therefore focused on the (published and more recent) literature examining the relationship between these economic outcomes and entrepreneurship. Using keywords and relevant JEL-codes, a search through Google Scholar and databases of economic studies such as EconLit, Ebsco host, and the Social Science Research Network (SSRN) produced 5

6

Data: Sample Selection and Definitions

an unmanageable number of results.1 Additional requirements were imposed. 2.1.2

Requirements of Studies to be Included in the Review

The available empirical literature has been categorized and selected according to systematic rules. These rules should result in a database with sufficient coverage (i.e., representative of the population of published and unpublished studies) and precision (i.e., provides high quality information on the issue). Therefore, in narrowing the literature eligible for this review, we focused on the most recent studies published in journals with the highest impact (and probably quality). That is, only primary English language studies from the AA or A ranked economic journals, as defined by the widely acknowledged Dutch Tinbergen Institute Research School, were considered (see Appendix A, Table A.1).2 In addition, and as shown in Table A.1, two very influential small business and entrepreneurship field journals, i.e., The Small Business Economics Journal (the leading entrepreneurship journal in the field of economics) and The Journal of Business Venturing (the leading entrepreneurship journal in the field of management), and three top management journals (Strategic Management Journal, the Academy of Management Journal, and Administrative Science Quarterly)3 were considered in this review. Books, or book chapters, are not reviewed and only referred to for relevant background. To describe results that apply to the current economic environment, and that are based on state-of-the-art research methods, only literature published in the period 1995 to March 20074 — the date of complet1 Examples

are the following, used as single search terms and in combinations: entrepreneurship, economic development, economic growth, productivity, firm growth, employment (generation), job creation, utility, income, remuneration, innovation, patents, R&D, job duration. 2 See www.tinbergen.nl for the ranking system for economic journals as constructed by the Tinbergen Institute. 3 Management Science is included in the Tinbergen list. 4 There is one exception: the articles soon forthcoming in one Small Business Economics special issue which is very relevant.

2.2 Definitions of the Entrepreneur and the Counterpart

7

ing the sample — pertaining to industrialized countries is reviewed.5 Recent discussion papers — 2002 to March 2007 — were considered an additional source of literature, providing the most recent results.6 The before-final requirement prescribes that studies employ a quantitative measure of the outcome variables defined, i.e. employment, innovation, productivity and growth, and individuals’ utility levels. The final requirement imposed is that the study contains an explicit empirical test of whether the quantifiable contribution of the entrepreneur is significantly different from the contribution of the control group, i.e., the counterparts. Thus, eligible studies include observations on (the rate of) firms/individuals that can be considered entrepreneurial as well as (the rate of) firms/individuals that can be considered counterparts, based on the definitions given below.

2.2

Definitions of the Entrepreneur and the Counterpart

Some common empirical definitions of “the entrepreneur” or “entrepreneurial firm” are employed in this study. Entrepreneurial firms are defined as firms that satisfy one of the following conditions: (i) They employ fewer than 100 employees; (ii) They are younger than 7 years old; (iii) They are new entrants into the market. Hence, the “control” group — or counterpart — to which the contribution of entrepreneurial firms is compared consists of firms that (i) employ more than 100 employees; (ii) are older than 7 years; (iii) are incumbent firms. The terms “entrepreneur” and “entrepreneurial firm” are used interchangeably. The section on utility examines individuals and thus requires other definitions. Individuals are considered entrepreneurs if they are selfemployed or the owner–manager of an incorporated business, where the size or age of her firm is not a deciding factor. The control group 5 Considerable

attention has been paid to the benefits of entrepreneurship in countries in development or transition. We focus on industrialized countries. Given the structural differences between industrialized and less-developed countries the contribution of entrepreneurship is likely to differ (Van Stel et al. (2005), Sternberg and Wennekers (2005), Wennekers et al. (2005)). 6 In order to prevent double counting, working papers have been omitted that appeared more recently as publications (sometimes with a different title or authorship). The primary (virtual) search engines for working papers are the SSRN, and working papers series of well-known research institutes such as NBER, CEPR, and IZA.

8

Data: Sample Selection and Definitions

is formed by those individuals that are employees, i.e., individuals employed by a firm in which they have no (major) ownership share. Entrepreneurship is studied in the relevant literature in terms of these definitions both at the micro level, i.e., at the level of the individual firm or entrepreneur, and at the macro or meso-level. In the latter case, the rate of (i) small firms, (ii) young firms, (iii) new firms or (iv) entrepreneurs is measured at the regional or national level. But how did we arrive at these definitions? In line with the Schumpeterian entrepreneur, the definition of the entrepreneur as being a market entrant (or a young firm that has recently entered the market) is straightforward and these definitions — entrants or young firms — are often employed in entrepreneurship research. Though most entrepreneurial firms are small,7 small firms are not always entrepreneurial and identifying small firms as entrepreneurs is therefore less straightforward, though a common practice among entrepreneurship policymakers and academics to which we comply. Moreover, following the majority of empirical studies of entrepreneurs, we view individuals who have started up a business or who own a business, i.e., who are self-employed or the owner–manager of an incorporated business, as entrepreneurs too. This may be inappropriate as self-employment is often not associated with the creation of firms, whereas entrepreneurship is. Nevertheless, without an accepted superior empirical definition, we chose not to deviate from what seems to be conventional.8 Moreover, with respect to the boundaries (between young and old or small and large firms) there is no theoretical basis to feed our choices. Instead, again, we followed apparent conventions. With respect to size, several measures (in terms of personnel or sales) are used. For the most often used size measure, i.e., personnel, the most common cutoff point is 100 employees. However, various size classes are often observed and analyzed separately, e.g., 10–20, 20–50, 50–100, 100+ employees. 7 Nurmi

(2006) analyzes the determinants of the start-up sizes of plants. and Koo (2005) acknowledge the problem of the ad hoc nature of the definitions and measurement of the entrepreneur(ial firm) used in the literature. They arrive at a “superior empirical definition,” which is however difficult to employ in existing databases.

8 Luger

2.3 Indicators of Contributions to Economic Outcomes

9

In such cases, again, 100 employees is used as the boundary between small and large. A significant proportion of studies relates our measures of an economic contribution to continuous firm size measures. In that case, the actual boundary between entrepreneurs and the control group is less relevant and conclusions result about the relationship between a measure of economic benefits and firm size (entered linearly or otherwise). The same holds for the measure “firm age,” and if boundaries are chosen they are often set at 5 or 7 years. Please note that the various definitions are often, though implicitly, combined, i.e., entrants are young by definition (defining age as the time since start-up, not since entering the market) and rarely start out with more than 100 employees. Finally, we note that the resulting sample size does not allow for a distinction between the various definitions of entrepreneur(ial firm)s; for example, we do not pursue analyses of the extent to which young firms are innovative as compared to new market entrants or small firms. This is a limitation of our study.

2.3

Indicators of Contributions to Economic Outcomes

At the initial search phase, the specific indicators of employment, innovation, productivity and growth, and individual utility that are analyzed were administered. During the search through the content of the journals the following indicators appeared to be most commonly employed.9 2.3.1

Employment

Firms may contribute to the amount of employment generated, i.e., quantity, or to the quality of employment. Firm growth, measured by the number of jobs created (relative to the size of the firm), is often used as an indicator of the quantity of employment generated. The quality of employment is measured in terms of the remuneration offered to employees. The primary indicators used are wage levels, benefits (e.g., health insurance), and the use of productivity-related-pay (PRP). 9 The

selection of studies is not limited to those studies examining the variables mentioned in the main text. Studies using a different indicator for the contribution of the entrepreneur that fulfilled every requirement were also selected.

10

Data: Sample Selection and Definitions

Job satisfaction levels of the employees in entrepreneurial firms relative to employees in counterpart firms came up as a final indicator of employment quality.10 2.3.2

Innovation

Innovation is a broad concept for which a multitude of indicators is employed. Regarding a firm’s innovative output, i.e., the production of innovations, both measures of its quantity and quality are used. For quantity, commonly used empirical measures include research and development expenditures — although it measures input rather than output — patents, and the introduction of new products or technologies. The quality of those innovations is indicated by patent citations and the importance of the innovations, however measured. Moreover, the commercialization of innovations as well as the adoption of innovations are used as measures of contributions to economic value through innovation. 2.3.3

Productivity and Growth

Productivity and growth are measured by (a firm’s or region’s contribution to) a country’s gross domestic product (GDP) or a combination of variables used to calculate GDP or GDP growth. Therefore, studies are included in our review if they measure a firm’s (or region’s) value added, labor productivity — i.e., a firm’s (or region’s) contribution to the GDP per worker — or total factor productivity (TFP), i.e., output per unit of capital and labor input combined. Studies measuring the value and/or growth of any of these indicators of productivity and growth are considered relevant and are discussed. 2.3.4

Utility

Indicators for an entrepreneur’s individual utility relative to employees relate to specific sources of utility. The first source is any form of remu10 The

job satisfaction levels of employees in entrepreneurial firms are compared to employees in counterpart firms. In the studies related to utility (see Section 2.3.4), the relevant comparison is the level of job satisfaction of entrepreneurs themselves relative to employees.

2.4 Final Search and Sample Statistics

11

neration, i.e., expected incomes. Risk is another element affecting the utility of risk-averse individuals (negatively). Finally, job satisfaction levels are used as an indicator of utility.

2.4 2.4.1

Final Search and Sample Statistics Final Search

Having formulated the definitions and literature requirements, all issues of each selected journal and working paper series within the relevant publication period were studied. If an article’s title suggested relevance, its abstract was analyzed to determine inclusion into the review. Finally, the article’s content was studied to check whether the study actually fulfils all requirements defined before. This method of gathering literature is a thorough attempt to create an exhaustive sample of relevant studies — given the requirements — but is sensitive to errors. For example, we may miss a study if the title is formulated too generally. The potential for this error was reduced by checking each study whose title only vaguely hinted at the topic of interest. Another way to check whether the sample was complete was to browse the references of the studies selected. If one such reference seemed relevant the entire study was checked. Hence, if not exhaustive, our sample may be representative of the relevant literature. 2.4.2

Sample Statistics

The sample consists of 57 unique studies that measure the contribution of entrepreneurs relative to their counterparts to one or more of the indicators defined. Table 2.1 shows the number of studies per publication category (economics AA journals, economics A journals, small business journals, management journals, and working papers) and year. Two thirds of the studies in the sample have been published in Small Business and Entrepreneurship journals, the remainder elsewhere, i.e., mostly in economics journals, or not yet. One-third of the studies has been published in 2004 or later, indicating an increased interest in the

12

Data: Sample Selection and Definitions

Table 2.1 Studies per publication category and publication year. Publication year/Publication category 1995–1997 1998–2000 2001–2003 2004–2007 Total

EcAA 0 2 0 0 2

EcA 4 3 3 1 11

SB 8 7 6 13 34

M 0 1 0 1 2

WP X X 4 4 8

Total 12 13 13 19 57

Notes: ∗EcAA and EcA — Economic journals with this (Tinbergen Institute) rank, SB — Small Business and Entrepreneurship journals, M — Management journals, and WP — Working Papers.

topic. However, this increase is only evident in the field journals and not in the more general economics or management journals. Anyway, more than half of the studies on which we base our conclusions are published after 2002. This holds for all categories of studies. The periods observed by the various studies in the sample are obviously less recent. Most analyses pertain to the nineties and eighties of the previous century. The 57 studies include in total 87 observations, i.e., several studies analyze various relevant relationships. Table 2.2 shows the distribution of the 87 observations in the sample over the (sub-)categories of indicators of the contribution to the economy studied. The number of studies in the first three main outcome categories, i.e., employment, innovation, and productivity and growth is similar. Table 2.2 Studies per outcome indicator (sub-)category. Main category of outcome indicator Employment

Observations 27

Innovation

21

Productivity and growth

25

Utility

14

Total

87

Sub-category of outcome indicator Employment generation Employment dynamics Employee remuneration Production Commercialization Adoption Value added Labor productivity Total factor productivity Income levels Volatility Satisfaction

Observations 15 2 10 10 6 5 7 11 7 8 3 3 87

2.4 Final Search and Sample Statistics

13

Table 2.3 Frequency of using the various definitions of the entrepreneur. Definition of entrepreneur(ial firm) Smalla Youngb Newc Self-empl.d Total Employment 18 1 6 2 27 Innovation 14 3 4 0 21 Productivity and growth 15 4 4 2 25 Utility 0 0 0 14 14 Total 47 8 14 18 87 (a), (b), (c), and (d) refer to the definitions for the entrepreneur introduced in Section 2.2.

The category “utility” is smallest with 14 studies. More than half of the studies on employment belong to the sub-category of employment creation, whereas the vast majority of the remainder of studies in this category study the extent to which entrepreneurs contribute to the quality of employment. Moreover, half of the innovation studies within our sample study the relative contribution of entrepreneurs to the production of innovations, whereas a quarter of the studies measure the contribution of entrepreneurs to the commercialization and adoption of innovations, respectively. Productivity and growth are most often quantified in terms of labor productivity, whereas the relative utility levels of entrepreneurs are indicated by incomes in most studies (8 out of 14), and less frequently by the measure of income volatility (3 studies) or satisfaction (3 studies). Table 2.3 shows the definitions of the entrepreneur used in the various categories of studies. Studies on employment are mostly based on a distinction between small(er) versus large(r) firms (and new versus incumbent firms, but to a lesser extent). This implies that our conclusions with respect to the contribution of entrepreneurs in terms of employment pertain for two-thirds to small firms. The definition of entrepreneurship that is used in our study of the relative contribution of entrepreneurs to innovation is more scattered. The same holds for studies on productivity and growth. The category of studies on utility uses “self-employment” or business ownership as the dominant definition of the entrepreneur. All in all, our definition of an entrepreneurial firm refers to small firms in most cases. Most of our observations result from analyses at the individual firm (or entrepreneur) level. However, the “employment” and “productivity and growth” categories also include studies based on observations

14

Data: Sample Selection and Definitions

at more aggregate levels (regions, industries, or even countries). The respective numbers are 11 (of 27) and 8 (of 25). We conclude from the description of the sample that the results and conclusions described in what follows are mainly based on very recent articles in high quality economics and small business and entrepreneurship journals and working papers. The results pertain in most cases to small firms in the 1980s and 1990s.

3 Contributions to Employment

This chapter reviews the recent evidence of the role of entrepreneurial firms in generating employment, both in terms of quantity and quality, where the latter refers to aspects of employees’ remuneration.

3.1

Generation of Employment

The number of jobs created by entrepreneurs vis-`a-vis their counterparts is discussed first. Second, some of the dynamics underlying the job creation process are addressed. 3.1.1

Do Entrepreneurs Create More Jobs?

The empirical literature on the generation of employment can be categorized based on whether aggregated (macro or meso) or disaggregated (micro) data are analyzed. Based on all these studies, the conclusion is that entrepreneurial firms have a disproportionately high contribution to the creation of jobs. Based on aggregated data, Fritsch (1997) shows a positive correlation between start-up rates and employment growth in all German industries based on regional data in the period 1986–1989, 15

16

Contributions to Employment

i.e., a region characterized by a higher start-up rate is associated with a higher employment increase.1 F¨olster (2000) demonstrates such a positive effect for Sweden: “an increased share of self-employment by one percent of the population increases total employment by about 1.3 percent of the population” (p. 143). Baldwin (1998) examines the changes in employment shares of certain size classes of Canadian manufacturing plants. He concludes that the smallest size class, i.e., plants employing fewer than 100 employees, has increased its employment share by 0.57 percent annually over the period 1973–1992, whereas the larger size classes experienced decreasing employment shares. Moreover, Johansson (2005) — studying active Swedish IT firms in the period 1994–1998 — establishes a U-shaped relationship between an industry’s average firm size and its employment growth, with a minimum value for employment growth at an average firm size of around 240 employees. Shaffer (2006) provides additional evidence for this relationship, also based on aggregated data, namely at the county/sector level. Shaffer’s study makes an interesting first attempt to including the measurement of externalities in terms of job creation: “Most previous studies of employment patterns have focused on job changes within individual firms, overlooking potentially important aggregate effects that may reflect externalities and which are essential to an assessment of overall welfare. This paper represents a step toward filling that gap by presenting what appears to be the first exploration of an empirical association between average establishment size and subsequent growth rates of employment by sector at the county level. The cross-sectional sample comprises four sectors — manufacturing, retail, wholesale, and services — across more than 2000 U.S. counties. In most cases, smaller establishments are found to be associated with faster subsequent growth rates of employment, both within and across sectors” (in the period 1982–1987), (p. 439). Hence, entrepreneurs contribute strongly to employment growth. A recent stream of research, actually initiated by Michael Fritsch (2008), studies aggregated data, mostly at the regional level to analyze 1 However

after a few years, the effect turns out to be negative. Future research as discussed below supports this as being a “mid term” effect.

3.1 Generation of Employment

17

both direct and indirect effects of entrepreneurial activity on employment growth (and other outcomes). Start-ups, or market entries lead to new business development, whereas incumbent firms might be forced to dissolve by the increased competition of the new firms. More indirectly, the new businesses and the removal of older, perhaps less efficient businesses, might lead to improved competitiveness and economic growth. In particular, as a consequence of taking into account these indirect effects, another relevant question arises: How many years after the inception of new firms do these effects of business turnover, improved efficiency and economic growth arise? What are the short term and what are the long term effects? These questions are answered in a handful of recent studies. Mueller et al. (2008) relate start-up rates, or the number of new firms per 1000 employees created annually in British regions to regional employment changes (over two years). Higher start-up rates are associated with higher immediate levels of employment (in these new firms themselves). However, after some years the relationship with employment growth is negative due to the competitive pressure that leads (inefficient) incumbents to shed labor or exit the market. In the longer run though, the net effect is positive due to increased competitiveness. Acs and Mueller (2008) find a remarkable similar pattern for the United States. The higher the regional start-up rate, the higher is the regional employment growth rate. The positive effect is strongest in the first year and reduces to zero after some years and increases thereafter.2 Fritsch and Mueller (2008) corroborate this result based on German data, Baptista et al. (2008) on Portuguese data, Van Stel and Suddle (2008), who distinguish between manufacturing and service industries, on Dutch data.3 2 By

looking at the impact of firm and regional heterogeneity on employment effects they find that only start-ups with more than 20 and fewer than 500 employees have persistent employment effects over time and only in large diversified metropolitan regions. Therefore, both the type of entry (Gazelles) and the characteristics of the region are important for employment growth. 3 They show that the effects are bigger in the manufacturing industry than in the service industry. Fritsch and Weyh (2006) though show that the contribution that the German start-up cohorts of the 1984–2002 period made to total employment in 2002 is nearly three times as high in services as in manufacturing. Their study is not included in our analyses due to the lack of a control group.

18

Contributions to Employment

Based on country level data, Carree and Thurik (2008) find evidence for the same pattern: Increased business ownership rates are shown to go together with an instantaneous small effect on employment generation, a mid term negative effect and a long term positive effect. Studies using disaggregated data to examine the relationship between firm size or age and the proportional number of jobs a firm has created follow a framework derived from (actually one of the assumptions underlying) Gibrat’s “Law of Proportionate Effect” (Gibrat, 1931). The derivation of Gibrat’s Law is based on the assumption that mean growth rates are the same for all firm sizes. A very popular generalization of Gibrat’s framework, allowing for heterogeneous growth rates, is the following (see Equation 9.5, p. 214 in Parker, 2004): ln qit+1 = αi + β ln qit + uit+1 .

(3.1)

Estimating the coefficient of firm size (q) shows whether large (β > 1) or small (β < 1) firms have grown faster and is relevant as long as size is measured in terms of the number of employees. Studies in our sample that follow this methodology are Calvo (2006), Hart and Oulton (1996), Konings (1995), and Oliveira and Fortunato (2006). All four lead to the conclusion that smaller (surviving) firms have the highest percentage-rate growth. Thus, proportional to their size, small firms created more jobs than did large firms. That is, Calvo (2006) and Hart and Oulton (1996) estimate Equation (3.1) and find a beta that is significantly lower than 1. Calvo’s (2006) result is based on a sample of nearly thousand Spanish firms in the period 1990–2000. Hart and Oulton (1996) use a large sample of more than 50,000 U.K. firms in the period 1989–1993. Konings (1995) and Oliveira and Fortunato (2006) estimate an equation slightly different than (3.1) where a beta significantly smaller than 0 indicates that smaller firms grow faster. Konings’ (1995) significant results pertain to cross-sections of between 1674 and 1884 British firms in 1980, 1984, and 1990, respectively. The study by Oliveira and Fortunato (2006) renders marginal support for a faster growth rate among small firms based on a sample of 7653 firms in Portugal in the years 1990–2001. The negative relationship between firm growth and size is consistent with numerous earlier empirical studies. In fact, as Parker puts it

3.1 Generation of Employment

19

more broadly (2004, p. 215) “While many disparate results have been published, one of the most important and widely verified is the following: Firm growth rates are decreasing in firm size among firms of the same age; and are decreasing in firm age among firms of the same size.” In addition, Hart and Oulton’s large British panel dataset of firms reveals a nonlinearity in the basic loglinear regression, implying “that the growth process of the very smallest firms (especially those below eight employees) does differ from that of larger firms. Small firms were growing more quickly than large firms over the period 1989–1993 and hence were generating proportionately more jobs” (p. 1251).4 Another method for examining job creation (and employment dynamics, see the next subsection) by small versus large firms is a method most often ascribed to Davis and Haltiwanger (1992). This method does not examine whether any statistically significant relationship exists between firm size and growth but relies on descriptive analysis. Therefore, studies using this type of analysis do not belong to this review. However, due to the impact of this kind of studies and their widespread use to analyze employment (dynamics), we provide a short (non-exhaustive) overview of the method employed and the results obtained in this category of studies (which is not included in the tables in the main text). The results on employment generation from this category of studies have an advantage over the studies using the framework based on Gibrat’s Law that they share with studies based on aggregated data like regions or industries: They include the effects of firm entry and exit. Simply put, this method first sorts firms by whether they have created or destroyed jobs, i.e., grown or shrunk, and by size class. Whether a firm has created (destroyed) jobs depends on whether the firm has a larger (smaller) size (in employees) at time t + 1, than at time t. Thus, firms that created employment are firms that have grown or entered the market, while the firms that destroyed employment are firms that have shrunk or exited the market. Employment generated by a given size class is the sum of the jobs created by the growing (or entering) firms 4 Some

authors have questioned the results from studies using this framework altogether because of apparent regression to the mean (e.g., Davis et al. (1996), Konings (1995), p. 216, and Leonard (1987)).

20

Contributions to Employment

within that size class. Employment destroyed is analogously defined. These numbers are converted into job creation and destruction rates by dividing them by the average size of the firms within the size class. Whether an entire size class (i.e., all growing and shrinking firms within a size class) has created jobs depends on the “net employment growth rate” which is given by subtracting the job destruction rate from the job creation rate. Using this method, one is able to infer whether small or large firms have created and destroyed more jobs proportional to their size. Davis and Haltiwanger (1992) find that in United States manufacturing (1972–1986) the size class with between 1 and 99 employees has higher job creation and job destruction rates than larger firms. The effects offset and ultimately result in rather similar net employment growth rates across size classes (see p. 841). Davis et al. (1996), studying the United States manufacturing sector in 1972–1988, also find similar net employment growth rates for various size classes. Younger firms have higher net employment growth rates, see Davis and Haltiwanger (1992). Baldwin and Picot (1995) show that smaller firms have higher net employment growth, based on a dataset of the Canadian manufacturing (1970–1990). This result is supported by Broersma and Gautier (1997) for the Dutch manufacturing sector in 1978–1991, and by Picot and Dupuy (1998) for the Canadian economy in general in the period 1978–1992. Thus, although the studies may find different rates, smaller and younger firms tend to have higher net employment growth rates. Therefore, the net contribution to employment generation will be higher for entrepreneurs, relative to their own size.5 In sum, small and young firms, and thus entrepreneurs, have generated the largest number of jobs proportionate to their size. This result is based on three categories of studies, each having specific drawbacks (and advantages). Studies using aggregated data may miss important determinants of the employment generation process, studies using the framework based on Gibrat’s Law may utilize flawed empirical designs and 5 Davis

et al. (1996) note that large firms actually create a larger absolute number of jobs. “The reason is that large employers account for the bulk of the manufacturing jobs base. Over the 1972–1988 period as a whole, firms with at least 500 employees accounted for 65 percent of manufacturing employment” (p. 308).

3.1 Generation of Employment

21

neglect the effects of firm entry and exit, and studies using the framework attributed to Davis and Haltiwanger make no statistical comparisons.6 The upper panel of Table 3.1 shows the unambiguous results. 3.1.2

Employment Dynamics

The studies above measure the inter-temporal change in a firm’s employment level and derive the extent of employment generation. However, a single job may be filled by more than one individual in a specific period of time (in which changes in firm sizes are measured). Burgess et al. (2000) control for this possibility of worker reallocation across jobs. The difference between labor reallocation and job reallocation is indicated by the magnitude of the churning flows. Based on a large panel of both manufacturing and non-manufacturing firms located in the state of Maryland, U.S.A., they find quarterly churning rates of 19 percent in non-manufacturing firms, and 11 percent in manufacturing. While the rate declines with size and age of the employer, it remains around 10 percent in the oldest and biggest employers. Since churning flows dominate job reallocation as the source of worker reallocation we can conclude, based on this, that worker movements are larger in entrepreneurial firms. Moreover, Burgess et al. (2000) observe an inter-temporal pattern of job creation and job reallocation that differs across firm sizes and is supported by Lever (1996) who uses observations from the Netherlands (1974–1986). If both a small and large firm create one job, the small firm is less likely to create an additional job in the following period. This suggests that small firms may have rather volatile growth rates over time, while large firms grow at a more steady rate. The “Davis and Haltiwanger” method generates a measure of the employment dynamics of a size class, which is given by the sum of the employment creation and destruction rates, i.e., the “job reallocation rate.” Davis and Haltiwanger (1992) show that the smallest size class of firms (1–99 employees) in the United States manufacturing (1972– 1986) sector has higher job creation and job destruction rates, and 6 The

Davis and Haltiwanger type of studies are not included in the tables that describe our sample. We use seven studies of this type.

22

Contributions to Employment

thereby higher job reallocation rates, than larger firms. The same holds for younger firms. Hence, both smaller and younger firms contribute relatively much (little) to employment dynamics (security). Davis et al. (1996), Baldwin and Picot (1995), Broersma and Gautier (1997), and Picot and Dupuy (1998) all support this result with respect to firm size for various countries, sectors, and time periods. The (relative) effect of entry and exit of firms on employment dynamics has also been studied using the framework ascribed to Davis and Haltiwanger (see also Anyadike-Danes et al. (2005)). According to Neumark et al. (2005), the entry of new firms has a three times stronger effect on job creation than expansion of incumbent firms in California in the period 1992–2002. Moreover, this effect of entry is dominated by the entry of entirely new firms instead of the opening of “branches” of existing firms. Furthermore, the exit of firms accounts for close to four times as much of job destruction than shrinking incumbents. Spletzer (2000) also finds a substantial effect of entry and exit on employment dynamics in the state of West-Virginia. Firm exit is most likely for smaller and younger firms (see Calvo (2006), Parker (2004), and Heshmati (2001)). 3.1.3

Summary of the Generation of Employment and Employment Dynamics

The studies on the generation of employment and employment dynamics generally show that entrepreneurial firms grow, proportionately, faster than other firms. Moreover, in the long run, entrepreneurial firms create positive externalities leading to more employment, also in other, i.e., older, larger, and incumbent firms. However, these non-entrepreneurial firms grow at a more steady rate over time and have lower rates of job reallocation and higher survival probabilities. Table 3.1 summarizes the results. The conclusion is that small firms, and thus entrepreneurs, have a higher, but more volatile contribution to the generation of employment.

3.2

Remuneration and Satisfaction of Employees

This chapter compares wages, other forms of remuneration, and job satisfaction levels of employees employed by entrepreneurs and coun-

SB

SB

SB

SB

SB

Baldwin (1998)

Johansson (2005)

Shaffer (2006)

Mueller et al. (2008)

Acs and Mueller (2008) Fritsch and Mueller (2008) SB

SB

F¨ olster (2000)

320 U.S. regions (’90–’03) 74 German regions (’83–’02)

59 U.K. regional firm start-up rates (’81–’03)

26 Swedish IT industries (’94–’98) 2038 U.S. regions (’82–’87)

24 Swedish regions (’76–’95) Canadian mnf plants (’73–’92)

Study Journal Sample Employment Generation (3.1.1) Fritsch (1997) SB 75 Western German regions (’86–’89)

Regional empl. growth in 3 years Regional empl. growth in 2 years

Regional empl. growth in 2 years

Regional empl. growth

Industry empl. growth

Empl. shares

Regional empl. rates

Regional empl. growth in 1 year

Aspect of Employment

New firm start-ups per 1000 empl. New firm start-ups per 1000 empl.

New firm start-ups per 1000 empl.

Regional av. firm size (employees)

New firm start-ups per (i) 1000 empl.; (ii) stock of firms Regional self-empl. rate Empl. share of size classes (employees) Industry av. firm size (employees)

Entrepreneur definition Main finding

...

Higher start-up rates lead to direct empl. creation. Mid term effect negative Self-empl. leads to higher empl. rates Growth of empl. share larger for small size class Greater av. firm size reduces ind. empl. growth Greater av. firm size reduces regional empl. growth Higher start-up rates lead to empl. creation, directly and indirectly, in the long run ...

Table 3.1 Evidence of the relative contribution of entrepreneurs to the quantity of employment.

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Firm entry/ exit

+

+

+

+

+

+

+

+/−

Evidencea

3.2 Remuneration and Satisfaction of Employees

23

SB SB SB SB

Carree and Thurik (2008) Calvo (2006)

Konings (1995)

Oliveira and Fortunato (2006) Hart and Oulton (1996)

21 OECD countries (’72–’02) 967 Spanish mnf firms (’90–’00) 1800 U.K. plants (’80,’84 & ’90) 7653 Portuguese mnf firms (’90–2001) 50,441 U.K. firms (’89–’93)

40 Dutch regions (’88–’02)

Firm growth (Gibrat’s Law)

National empl. growth Firm growth (Gibrat’s Law) Firm growth (Gibrat’s Law) Firm growth (Gibrat’s Law)

Regional empl. growth in 3 years

Aspect of Employment Regional empl. growth in 2 years

Firm size (employees)

Entrepreneur definition New firm start-ups per (i) 1000 empl.; (ii) stock of firms New firm start-ups per 1000 labor years Changes in ownership rates Firm size (employees) Firm size (employees) Firm size (employees) Smaller firms grow faster

Smaller firms grow faster Smaller plants grow faster Smaller firms grow faster

...

...

Main finding ...

No

No

No

No

Yes

Yes

Firm entry/ exit Yes

+

+

+

+

+

+

Evidencea +

Burgess et al. (2000)

EcA

26,835 U.S. mnf Worker reallocation Firm size Reallocation higher in No − and non-mnf (employees) small firms firms (’85–’94) Lever (1996) SB Dutch mnf firms Speed of empl. Firm size Smaller firm empl. Yes − (’74–’86) adjustment (employees) gen. more volatile Overall Entrepreneurs have a higher, but more volatile, contribution to employment generation + aEvidence is positive (+) if findings indicate that entrepreneurial firms’ contributions are relatively large. It is negative (−) if the opposite is found and indeterminate (0) if the study does not show significant differences between the contribution of entrepreneurs and their counterparts.

Employment Dynamics (3.1.2)

EcA

SB

Journal Sample SB 30 Portuguese regions (’82–’02)

Van Stel and Suddle (2008)

Study Baptista et al. (2008)

Table 3.1 (Continued).

24 Contributions to Employment

3.2 Remuneration and Satisfaction of Employees

25

terparts. In this manner, it is possible to draw conclusions on the quality of the employment generated by entrepreneurs relative to their counterparts. Higher quality employment is (assumed to be) associated with higher levels of remuneration and higher levels of job satisfaction. 3.2.1

Wages

The standard to investigating “firm size wage differentials” is relating an individual’s wage to the size of the firm at which the individual is employed. All studies reach a similar conclusion: Smaller and younger firms pay their employees lower wages. For example, Wunnava and Ewing (2000) find that in 1989, U.S. firms with between 101 and 499 employees paid male employees 18 percent more than otherwise identical employees of firms with fewer than 100 employees (p. 56). The average wage differential between firms employing more than 500 individuals and entrepreneurial firms amounts to 27 percent for male employees.7 However, what are the determinants of the marked difference in the wage levels paid by large and small firms? Several explanatory factors play a role within a regression framework. Firm characteristics (like industry) appear to play a small role relative to the characteristics of the work forces employed by firms in different size (or age) classes. Regressions at the employee level (instead of the firm level) show that larger firms pay their employees higher wages due to higher skill levels, education and/or experience. Controlling for this worker heterogeneity substantially decreases the firm size wage differential initially found by Troske (1999) and Winter-Ebmer and Zweimuller (1999).8 Nevertheless, there remains an unexplained large firm wage premium. Brown and Medoff (2003), who study firm age wage differentials show that the positive correlation between firm age and employee wages even turns into a negative relationship when controlling for worker heterogeneity. “The higher wages paid by established firms are completely explained by the observable characteristics of their workers. Indeed, 7 Smaller

firm size wage differentials are found for female employees. and Troske (1999) use the same database as Troske (1999), but analyze other industries besides manufacturing. Because the results are rather similar, the results of Bayard and Troske (1999) are not included in our database.

8 Bayard

26

Contributions to Employment

after controlling for these characteristics, the relationship between firm age and wages is negative, at least over much of the firm-age distribution. Moreover, it is not just experience and tenure but also education, occupation, and other demographic characteristics associated with higher wages that are positively related to the age of firm” (p. 693). Table 3.2 shows an overview of the studies in our sample on firm size (age) wage differentials with(out) controls for worker heterogeneity: The firm size wage differential does not disappear, but becomes smaller when controlling for worker heterogeneity, whereas the firm age wage differential even turns negative (for the largest part of the age distribution). However, the latter result is based on one observation only. Troske, who examines seven theory-based explanations for the employer size wage premium by means of a large U.S. manufacturing establishment-employee matched dataset (such that both employer and employee characteristics can potentially explain the puzzle), finds evidence of a second explanatory factor (besides worker heterogeneity) for the wage premium of workers in larger firms: The presence of a complementarity between capital and skill. The complementarity implies that workers working in more capital intensive firms are paid higher wages. If larger firms are more capital intensive than smaller firms, this could be a (partial) solution to the puzzle. Troske (1999) finds empirical support for the explanatory power of this firm characteristic by controlling

Table 3.2 Regression results with and without controlling for worker heterogeneity (WH). w/o WH w/WH Additional details Study Regressor β β Country Period Brown and Medoff (2003), p. 684. Dependent variable: Ln(wage/hour) Age of business/10 0.022∗∗∗∗ −0.001 U.S.A. 1992 Ln(age of business) 0.042∗∗ −0.035∗∗ Winter-Ebmer and Zweimuller (1999), p. 90. Dependent Variable: Ln(wage) Size class 0–4 Ref. Ref. Switzerland 1991–1996 Size class 5–9 0.046∗∗∗∗ −0.010 Size class 11–99 0.095∗∗∗∗ 0.025∗∗ Size class 100+ 0.129∗∗∗∗ 0.030∗∗∗ Troske (1999), p. 19. Dependent variable: Ln(wage) Log firm size 0.033∗∗∗∗ 0.026∗∗∗∗ U.S.A. 1989 Log plant size 0.064∗∗∗∗ 0.047∗∗∗∗ ****, ***, and ** denote significance levels of 0.1%, 1%, and 5%, respectively.

N 1067

7453

129,901

3.2 Remuneration and Satisfaction of Employees

27

for the (log of the) capital-labor ratio. However, a significant premium remains and the puzzle has not been solved entirely.9 A third cause for the differential may simply be differences in wage policies followed by large and small firms. Oosterbeek and Van Praag (1995) find that large Dutch firms (> 500 employees) pay 9.4 percent higher wages than small firms (< 25 employees). The difference between small and medium-sized firms is 6.7 percent (p. 177). They find that the difference between small and medium-sized firms is due to mediumsized firms offering employees higher returns to their personal characteristics. The difference between small and large firms is 25 percent due to large firms employing individuals with higher levels of education and 75 percent due to large firms offering higher returns (pp. 179–180). They cannot explain the latter phenomenon. Thus, the wage premium earned by employees in larger firms has three observed causes: First, entrepreneurs employ individuals with lower levels of human capital. Second, entrepreneurs offer lower returns to those personal characteristics. Finally, entrepreneurs run firms in which the capital-skill complementarity is lower. On top of the differences in wages between smaller and larger firms that can be explained by these factors, an unexplained difference in wages remains. As Troske (1999) summarizes: “However, none of the explanations can fully account for the employer size-wage premium. In the end there remains a large, significant, and unexplained premium paid to workers of large employers” (p. 15). 3.2.2

Other Forms of Remuneration

Besides finding that large firms pay their workers higher wages, Wunnava and Ewing (2000) also derive from their cross-sectional database of 3625 U.S. individuals that the probability a given individual will receive medical insurance, life insurance, maternity leave and retirement benefits increases with firm size (see pp. 52–55 of their study). Our sample does not contain more studies analyzing differences across firm sizes in terms of benefits. 9 The

other five potential explanatory factors of the premium are not supported empirically in Troske’s study.

28

Contributions to Employment

There is one study analyzing the occurrence of productivity-relatedpay (PRP) schemes. Cowling (2001) examines the payment schemes offered to 15,800 individuals of the EU15 countries and finds that entrepreneurs use PRP less frequently than the counterparts in 1996 (pp. 200–201). In sum, it seems that entrepreneurs are less likely to offer employees other forms of remuneration. 3.2.3

Job Satisfaction Levels

Winter-Ebmer and Zweimuller (1999) infer job satisfaction levels from actions taken by employees in Switzerland: on-the-job-search (for alternative employment) and actual job changes. Both activities are undertaken less frequently by employees of larger firms (p. 92) and we can infer that employees of smaller firms must be less satisfied with their job. In contrast, Frey and Benz (2003), who examine actual scores on a job satisfaction questionnaire, find that employees of smaller German, British, and Swiss firms have higher average job satisfaction scores than employees of larger firms in the same set of countries (pp. 22–23). This is consistent with the findings by Clark and Oswald (1996) for U.K. employees. While studying the influence of, among other factors, education on job satisfaction scores, they control for firm size classes. The mean scores for small (< 25 employees), medium (25–199), and large (> 199) firms are significantly different and show that employees of the smallest firms are more satisfied. Furthermore, the percentage of workers reporting that they are very satisfied is highest among the smallest firms. Thus, these three studies show ambiguous results. Based on two direct measures, we conclude that employees in entrepreneurial firms are more satisfied. However, this is in conflict with the result of one indirect measurement of job satisfaction. All studies pertain to Europe. 3.2.4

Summary of Employee Remuneration and Satisfaction

Table 3.3 summarizes the results pertaining to employee remuneration and satisfaction. It shows that entrepreneurs pay their workers lower

3625 U.S. (’89) 15,800 across EU15 (’96)

SB SB

A

WP

7453 Swiss (’91–’96) 28,392 in Switzerland, U.K. and Western Germany (’84–’00)b 5195 U.K. (’91)

569 Dutch (’83)

SB

AA

1067 U.S. (’92)

7453 Swiss (’91–’96) 129,901 U.S. (’89)

A

A

AA

Sample Journal (individuals) SB 3625 U.S. (’89)

Job satisfaction

Productivityrelated-pay (PRP) On the job search and turnover Job satisfaction

Benefits

Wages

Wages

Wages

Wages

Aspect of Employment Wages

Size classes (employees)

Size classes (employees) Size classes (employees)

Size classes (employees) Size classes (employees) Size classes (employees)

Entrepreneur definition Size classes (employees) Size classes (employees) Firm and establishment size (employees) Firm age

Small firm employees more satisfied

Small firm employees less satisfied Small firm employees more satisfied

Main finding Small firms pay lower wages Small firms pay lower wages Small firms and establishments pay lower wages Younger firms pay lower wages Small firms pay lower wages Small firms offer less benefits Small firms offer less PRP

+

+

−

−

−

−

−

−

−

Evidencea −

Overall

Entrepreneurs pay lower wages, but, nevertheless, their employees appear to be more − satisfied aEvidence is positive (+) if findings indicate that entrepreneurial firms’ contributions are relatively large. It is negative (−) if the opposite is found and indeterminate (0) if the study does not show significant differences between the contribution of entrepreneurs and their counterparts. bPrecise numbers of individuals and years observed differ per country for Frey and Benz (2003).

Clark and Oswald (1996)

Winter-Ebmer and Zweimuller (1999) Frey and Benz (2003)

Brown and Medoff (2003) Oosterbeek and Van Praag (1995) Wunnava and Ewing (2000) Cowling (2001)

Study Wunnava and Ewing (2000) Winter-Ebmer and Zweimuller (1999) Troske (1999)

Table 3.3 Evidence of the relative contribution of entrepreneurs to the quality of employment.

3.2 Remuneration and Satisfaction of Employees

29

30

Contributions to Employment

base wages and offer fewer benefits and less productivity-related-pay than the counterparts. As such, we should conclude that entrepreneurs have a lower contribution to the quality of facilitated employment than the counterparts. However, this lower contribution is partly justifiable by recognizing that entrepreneurs employ individuals with lower levels of skill, education, and/or experience and run firms with lower levels of capital-skill complementaries. Nevertheless, job satisfaction levels of employees in entrepreneurial firms tend to be higher. Apparently, more research is required to explain why employees are more satisfied with less pay in entrepreneurial firms.

3.3

Summary of the Contributions to Employment

More entrepreneurship does not only lead to more employment in the short term, but also in the long term, due to an indirect positive effect of increased competition. Although entrepreneurs create more jobs, the jobs they create are less secure due to higher volatility and higher probabilities of firm dissolution. Furthermore, entrepreneurs offer their employees lower remuneration levels than these individuals would earn if they were employed by large firms. This result could be considered a stylized fact because of the unambiguous results across studies, even though different countries, periods, and sample sizes are examined. Moreover, employees in non-entrepreneurial firms obtain more benefits and are more frequently remunerated on a performance related basis. Nevertheless, employees in entrepreneurial firms — although they earn less and face higher risks of losing their job — are more satisfied with their jobs than employees in the control group of firms. Future research might explain some of the remaining puzzles. All in all, entrepreneurs have a higher contribution to the generation of employment, but a lower contribution to the quality of that employment.

4 Contributions to Innovation

This chapter examines the relative contribution of entrepreneurs to innovation. Recent studies have measured the contribution of entrepreneurs to innovation in terms of quantity and quality (Section 4.1); to value creation through the commercialization of innovations (Section 4.2), and to the stimulation of a society’s level of innovativeness, i.e., the adoption of third party innovations (Section 4.3).

4.1

The Quantity and Quality of Innovations

To quantify a firm’s innovativeness, researchers have focused on three measures. The first is the firm’s Research and Development (R&D) expenditures (4.1.1). Second, the number of patents it produces (4.1.2), and third, the number of new products or technologies introduced (4.1.3). The measurement of quality is related to patent citation rates, and the (subjectively) assessed importance of new products/technologies (4.1.4). 4.1.1

Research and Development

R&D expenditures are considered an input for innovations. And since “It is said that industrial R&D, particularly, basic research, tends to 31

32

Contributions to Innovation

be less developed than the socially optimal level” (Koga, 2005, p. 53), higher levels of R&D expenditure can be considered valuable. Castany et al. (2005) compare the mean R&D expenditure per employee of large and small Spanish firms (cut-off point at 200 employees) and find that large firms have allocated around 2.5 times more resources to R&D than small firms (in 1990 and 1994). In contrast, Arvanitis (1997) finds identical levels of R&D expenditure per employee for the largest part of the Swiss firm size distribution, i.e., for all firms with fewer than 300 employees (80 percent of the firms in the sample). The proportional R&D expenditure per employee is decreasing in firm size for firms with more than 300 employees (see Arvanitis (1997), p. 482). Based on these two studies, we can only conclude that entrepreneurs devote no more resources per employee to R&D than the control group. However, Yang and Huang (2005) find evidence that R&D expenditures induces higher growth rates, especially for small firms (in the Taiwan electronics sector). This would imply that each dollar spent on R&D in a small firm is more valuable than a dollar spent in a large firm. 4.1.2

Patents

Patents are considered a proxy for a firm’s level of innovations. There is conclusive evidence that entrepreneurs produce fewer patents than their counterparts. Almeida and Kogut (1997) and Sørensen and Stuart (2000) find such evidence for the U.S. semiconductor industry. The first study shows that start-up firms receive fewer patents on average than “other” firms in 1990, whereas Sørensen and Stuart provide evidence that the time between patent applications decreases with firm age and size in that sector. They find the same result for biotechnology firms. 4.1.3

New Products and Technologies

The measure of innovation that is related to new products and technologies is most often quantified based on subjective answers from firm-managers as to whether they have introduced a new product or technology. So far, studies have examined firms from the manufacturing sector only. Love and Ashcroft (1999) find that the number of innovations increases with plant size in a sample of more than 300 Scottish

4.1 The Quantity and Quality of Innovations

33

plants. Huergo and Jaumandreu (2004) examine the probability that a Spanish firm introduces a product or process innovation. Large firms (more than 500 workers) turn out to have a significantly higher probability of introducing innovations than small firms (20 or fewer workers). The difference is 37 percentage points for process innovations and 27 percentage points for product innovations. “In both cases [the] probability increases monotonically with size” (Huergo and Jaumandreu (2004), p. 202). They find the same sort of relationship between the probability of innovating and firm age (see Figure 3, p. 201). The finding that larger firms (are more likely to) introduce more innovations is not striking: Larger firms may simply have more product lines to improve upon. Love and Ashcroft (1999) use a second measure of innovativeness, i.e., innovations per employee, and find that this measure actually decreases with firm size.1 Hence “smaller plants are indeed more “innovation intensive” than their larger counterparts” (Love and Ashcroft (1999), p. 107).2 One study distinguishes between mere product improvements and radically new products, i.e., Acs and Gifford (1996). Based on records of firm innovations by the U.S. Small Business Administration, they find that larger firms introduce more radically new products, as a fraction of total product innovations (see p. 215). In sum, small and young firms produce fewer new products and/or technologies, which are also less radical. However, entrepreneurs produce more innovations per individual employed, which suggests that entrepreneurs produce innovations more efficiently. 4.1.4

Quality

Arvanitis (1997) uses firm-managers’ subjective assessments of the importance of their firm’s innovative behavior to gauge quality. Smaller 1 In

fact, they find a U-shaped relationship but the minimum is located at plants with 1000 employees and only 0.5 percent of the manufacturing plants in Scotland employ more than 1000 workers. 2 Love and Ashcroft observe individual plants, not firms. However, they control for whether the plant is part of a multi-plant firm (variable MULTI, see p. 102). This variable is insignificantly related to the number of innovations and the number of innovations per employee.

34

Contributions to Innovation

firms turn out to assess their own innovative behavior as a less important contributor to economic value creation.3 A more objective measure of quality is patent citations. If a patent is cited more often, it is reasonable to assume that the underlying product has given rise to more patents and innovations. However, the number of citations should be corrected for self-citations. “By excluding all patents that make one or more self-citations, we should largely eliminate marginal patents filed for the purpose of filling in small holes in an intellectual property estate.” (Sørensen and Stuart (2000), p. 106). Sørensen and Stuart (2000) find that in the semiconductor industry the time between patent citations made by other firms than the patent holder increases with firm age. However, they do not find evidence of this (or any other) relationship between firm size and citations in the biotech industry. Hence we conclude that — based on rather weak evidence for citation rates — entrepreneurs produce innovations that are of the same or even higher quality than the innovations of other firms. 4.1.5

Summary of the Quantity and Quality of Innovations

Based on somewhat ambiguous results we conclude cautiously as follows.4 Entrepreneurs invest no more in innovation than their 3 Arvanitis

(1997) finds an inverted U relationship between assessed (technological and commercial) importance and firm size, where the maximum of the curve lies beyond the largest firms in the sample. 4 The ambiguity of the results has several causes. Various definitions and indicators of “innovativeness” are used. Most of the indicators are — more or less — distorted, i.e., they do not capture all relevant aspects of innovation simultaneously. For example, Audretsch and Vivarelli (1996) report that large firms rely more on in-house R&D, while small firms rely more on external sources of knowledge, e.g., university research. Thus, firms’ R&D expenditures do not fully reflect their innovativeness. Their study thus provides a rationale for the finding that small firms show lower levels of R&D expenditure than large firms. Moreover, Roper (1999) mentions that R&D spending may be underreported by small firms, another (partial) explanation why R&D expenditure of entrepreneurs is found to be not higher than for their counterparts. Other potential flaws in the research discussed here are that patents may not represent economically viable products and may be reflective of a firm’s fear of expropriation (Kortum and Lerner, 2000); new products may simply be product improvements that represent limited value creation. One final issue weakening the conclusion is that a limited number of industries and countries is examined. That is, the U.S. semiconductor and biotechnology is studied most often. The service industries are examined less often (not at all by the reviewed studies), whereas these industries are growing rapidly.

4.2 The Commercialization of Innovations

35

counterparts and they produce fewer innovations (in terms of patents and new technologies/products). However, the quality of their innovations may be higher and these innovations seem to be produced more efficiently. If anything, this section shows a major shortcoming in analyzing innovativeness: benchmarking the number of innovations against the size of the firm is not common. The result of larger firms producing more innovations is not striking, and the lack of studies on the number of innovations produced per employee prevents us from drawing any hard conclusions on contributions of the entrepreneur relative to the control group. Future research is needed to provide hard evidence.

4.2

The Commercialization of Innovations

Two measures of commercialization are used: first, (the probability of) sales from innovations in general, and second, (the probability of) generating sales given some specific innovation. Using the first measure, Brouwer and Kleinknecht (1996) conclude that larger firms are more likely to have sales from innovative products, i.e., those new to the firm itself, during 1990–1992 in the Netherlands. With a more stringent definition of innovative products, i.e., those new to the sector, the positive effect of firm size becomes smaller. However, the analysis by Brouwer and Kleinknecht (1996) of the share of total sales realized with innovative products, “given that a firm has some sales of innovative products” (p. 196), shows that smaller firms in the service sector outperform larger firms, whereas they find no significant relationship with firm size in the manufacturing sector. Thus, entrepreneurs in the service sector are less likely to have sales from innovative products, but if they do have such sales, they will derive a higher fraction of their total sales from those innovative products. Hence, this is weak evidence in favor of entrepreneurs who are relatively good at commercializing their innovations. Czarnitzki and Kraft (2004) corroborate this result based on a dataset of firms in seven European countries that includes firms with and without any sales from innovative products: “the share of sales resulting from significantly improved or new products developed during [the years 1997–1999]” (p. 327) decreases considerably with the

36

Contributions to Innovation

firm’s number of employees. Thus, on average, the value created with innovations proportionate to sales is higher for smaller firms. The second measure of commercialization, i.e., the generation of sales with a given innovation, is analyzed by Lowe and Ziedonis (2006). They analyze innovations by the University of California exclusively licensed to a firm from 1981 to 1999. They find that “Start-ups and established firms are equally likely to commercialize inventions generated by the same university department,” i.e., have some sales from inventions with a similar type of technology (Lowe and Ziedonis (2006), p. 180). Dechenaux et al. (2003) conclude from a similar analysis that start-ups realize a first sale quicker than incumbent firms from commercializing an MIT university invention. Hence, if anything, the likelihood of realizing sales from a university invention is higher for entrepreneurs than for their counterparts. Moreover, the royalty revenues received by the university from startups are higher than royalties received from established firms, suggesting “start-ups outperform established firms” (Lowe and Ziedonis (2006), p. 182). On the other hand, start-ups continue to pursue unsuccessful commercializations longer than established firms, suggesting start-ups destroy more value (p. 181). In sum, we have the following observations: The likelihood of turning innovations into sales is lower for entrepreneurs, whereas their share of sales from innovations in general is higher than for other firms. Entrepreneurs are also more likely to generate sales and higher levels of royalties from a given (university) invention. However, entrepreneurs were found to destroy more value through prolonging unsuccessful commercialization strategies. Hence, the level of commercialization of entrepreneurs can be concluded to be relatively high. Nevertheless, the economic benefit of commercialization by entrepreneurs vis-`a-vis their counterparts depends on the trade-off between resources wasted and value created by entrepreneurs over and above that wasted and created by other firms. This trade-off has not yet been examined.

4.3

The Adoption of Innovations

The type of innovations adopted by firms having been in the spotlight recently is ICT-related technologies. Chandrashekaran and Sinha

4.4 Summary of the Contributions to Innovation

37

(1995) examine the volume and timing of “adopting” personal computers (PCs) by 3236 U.S. firms in 1978–1984. They find that first purchases are made earlier by smaller firms, whereas larger firms buy, unsurprisingly, larger volumes. BarNir et al. (2003) survey 150 U.S. magazine publishing firms in 2001 and find that older firms use the Internet more frequently for specific business purposes, e.g., communication with customers (see p. 802). However, the difference between firms of different ages, though significant, is small. Lucchetti and Sterlacchini (2004) do not find a difference across firm sizes in the use of Internet and e-mail by nonproduction workers in Italy, both for general applications and as a marketing tool. However, larger firms use more complicated ICT, e.g., Intranet or data-servers, more frequently than small firms (in the year 2000). In sum, smaller firms were found to adopt ICT-products earlier than large firms, but its volume and use may be independent of firm size. Small firms are less inclined to adopting high-cost innovations, such as data-servers. Thus, entrepreneurs and counterparts are equally likely to adopt low cost innovations, whereas the counterparts are more likely to adopt higher cost innovations.

4.4

Summary of the Contributions to Innovation

Table 4.1 shows the rather complex results pertaining to the contribution of entrepreneurs in terms of innovation. Entrepreneurs invest no more in innovation than their counterparts and they produce fewer innovations (in terms of patents and new technologies/products). However, the quality of their innovations may be higher and these innovations seem to be produced more efficiently, i.e., entrepreneurs produce more innovations per employee and they are cited more often. Concerning the commercialization of innovations, the levels are relatively high for entrepreneurs (in terms of the share in sales). Nevertheless, the relative benefit of commercialization by entrepreneurs vis-`a-vis their counterparts is not clear yet: Entrepreneurs create more value by the commercialization of innovations, but they destroy more value too by pursuing commercializing innovations for too long in case of failure.

Plant size (employees) Firm size (employees) Plant size (employees)

New prod./techn.

New prod./techn. per employee % radical innovations

Acs and Gifford (1996)

Sørensen and Stuart (2000)

MJ

Quality of Innovations (4.1.4) Arvanitis (1997) SB 564 Swiss mnf firms (’93) 387 U.S. semicond and bio firms (’86–’92)

Importance of innovations Patent citations

Firm size (employees) Firm size (empl.) & age

Firm size (employees)

Firm size (empl) and age

Patenting frequency

632 U.S. firms (’82)

Firm size (employees) Entrant

R&D expense/ employee Patents

SB

Firm size (employees)

R&D expense/ employee

New prod./techn.

Entrepreneur definition

Measure of Innovation

Journal Study status Sample Quantity of Innovations (4.1.1–4.1.3) Castany et al. WP Spanish mnf (2005) firms’, 523 in ’90; 668 in 94 Arvanitis (1997) SB 564 Swiss mnf firms (’93) Almeida and SB 40 U.S. semicond. Kogut (1997) firms (’90) Sørensen and M 387 U.S. Stuart (2000) semicond/ biotech firms, 86–92 Love and Ashcroft SB 304 Scottish mnf (1999) plants (’92) Huergo and SB 2356 Spanish mnf Jaumandreu firms (’91–’98) (2004) Love and Ashcroft SB 304 Scottish mnf (1999) plants (’92)

Table 4.1 Evidence of the relative contribution of entrepreneurs to innovation.

Measure decreases with firm size Time between patent citations increases with size & age, in semicond ind.

Increases with plant size P(introduction) higher for larger and older firms # per employee decreases with plant size Measure increases with firm size

Decreases with firm size Entrants produce fewer patents Time between patents decreases with size and age

Small firms devote less to R&D

Main finding

+

−

−

+

−

−

−

−

+

−

Evidencea

38 Contributions to Innovation

M

M

Lowe and Ziedonis (2006)

Lowe and Ziedonis (2006) Dechenaux et al. (2003)

150 U.S. publishing firms (’01) 168 Italian mnf firms (’00) 168 Italian mnf firms (’00)

3236 U.S. firms (’78–’84)

734 university inventions (81–99) 805 university inventions (80–96)

474 firms (97–99, 7 EU countries, 5 ind) 734 university inventions (81–99)

Use of internet/ e-mail Use of high-cost ICT

Use of internet

Time/volume PC-adoption

Time until first sale

Firm size (employees) Firm size (employees)

Age

Firm size (employees)

Entrant

Generated royalties Entrant

Entrant

Firm size (employees) Firm size (employees) Firm size (employees)

P(sales with innovations) Share of sales from inn. % sales from innovations P(sales given innovation)

Entrepreneur definition

Measure of Innovation

Smaller firm adopts quicker but lower volume Older firms use Internet slightly more Measure not related to firm size Use of high-cost ICT increases with size

Measure equal for entrants and incumbents Entrants generate more royalties Entrants make first sale with invention faster

−

0

0

+/−

+

+

0

+

+

Decreases with firm size Measure decreases with firm size

−

Evidencea

Increases with firm size

Main finding

Entrepreneurs contribute equally importantly to innovation but through 0 different aspects aEvidence is positive (+) if findings indicate that entrepreneurial firms’ contributions are relatively large. It is negative (−) if the opposite is found and indeterminate (0) if the study does not show significant differences between the contribution of entrepreneurs and their counterparts.

Overall

SB

Lucchetti and Sterlacchini (2004) Lucchetti and Sterlacchini (2004) SB

SB

BarNir et al. (2003)

Adoption of Innovations (4.3) Chandrashekaran and A Sinha (1995)

SB

SB

Czarnitzki and Kraft (2004)

Journal Study status Sample Commercialization of Innovations (4.2) Brouwer and SB 3784 Dutch (’92) Kleinknecht (1996) mnf/service firms

Table 4.1 (Continued).

4.4 Summary of the Contributions to Innovation

39

40

Contributions to Innovation

Furthermore, entrepreneurs and counterparts are equally likely to adopt low cost innovations, whereas the counterparts are more likely to adopt higher cost innovations. To conclude, entrepreneurs and their counterparts contribute equally importantly to the innovativeness of societies. However, they serve different goals in terms of quality, quantity and efficiency, as well as in terms of producing (and adopting) more radical (and higher cost) innovations. It might be interesting to note that our results are not in contradiction to results obtained at the country level. Based on a panel of 36 countries, Wennekers et al. (2005) show that the correlation between the extent of entrepreneurial activity in a country and a country’s innovative capacity (“a country’s potential to produce a stream of commercially relevant innovations”, p. 297) is positive for more developed countries such as the United States and Europe. Likewise, Acs and Varga (2005) find a positive relationship between entrepreneurial activity and technological change in the European Union.

5 Contributions to Productivity and Growth

The contributions of entrepreneurs to productivity and growth are measured by their relative contribution to components of GDP, i.e., all indicators reviewed are related to the calculation of GDP. They are total value added, as well as labor and factor productivity. A distinction is made between contributions to the level of GDP (Section 5.1) and the growth of GDP (Section 5.2).

5.1

Levels of Value Added and Productivity

A direct measure of contributions to a country’s GDP is a firm’s value added, since GDP is the sum of the amount of value added per firm, summated over all firms operating in that particular country. The second main indicator is related to the efficiency of production or the contribution to GDP per worker, i.e., labor productivity. Total factor productivity (TFP) is used as the final indicator. It is often referred to as the “residual” or the indicator of “technical progress” and is defined as output per unit of capital and labor combined (Nadiri, 1970).

41

42

Contributions to Productivity and Growth

5.1.1

Value Added

The relationship between entrepreneurship and levels of value added (unlike growth of value added) has been little studied and is not very insightful since value added is a type of size measure. Thus, the contribution of an entrepreneurial firm (often small) to value added will be lower by definition than for a firm in the control group. At the macro level, analyses of the small size class share in the total value added would be conceivable, but not informative about contributions to economic value creation, unlike changes in size class shares (see Section 5.2.1). 5.1.2

Labor Productivity

Brouwer et al. (2005) relate the firm’s value added and the firm’s gross output to the cost of labor based on a cross-section of 4566 Dutch manufacturing firms in 1999.1 They find that both value added and gross output per euro of labor increase with firm size (see p. 29). Thus, entrepreneurs appear to have a lower level of labor productivity than their counterparts in the Dutch manufacturing industry. Jensen et al. (2001) acknowledge various difficulties of comparing productivity levels across plants of different ages (and age is positively correlated with size) — besides the need of controlling for trends, cohort and age effects. They distinguish three different effects on productivity as plants grow older. The first is the positive age effect, i.e., older plants are more productive due to the management accumulating experience, gains from learning by doing, or the achievement of economies of scale. Second, older plants are more productive due to survival: Samples of young plants include potential successful as well as potential failing plants, whereas samples of older plants are self-selected based on performance. Hence, the selection effect based on survival biases the results from a comparison of the productivity of younger and older 1A

firm’s gross output is seen as another indicator, even though this may result in the inclusion of the value of intermediate goods. This is justified by Baldwin (1998), who shows that the relative contribution per firm size class to (the growth of) economic value is not affected by whether shipment, production or added value is examined, i.e., “the results [. . . ] are essentially the same” (p. 361).

5.1 Levels of Value Added and Productivity

43

plants in favor of older plants. Third, there is a possibly offsetting negative “vintage” effect: The best-practice technologies are embodied in new capital, i.e., start-up plants. Hence, younger plants in a given year embody more productive technologies. They distinguish the three effects empirically and find that all three are sizeable. First, age has a positive effect on productivity. Surviving plants improve their relative standing in the productivity distribution as they age. Second, selection matters. “Recent entrants show productivity levels below industry averages, but this is largely due to a large number of small, low-productivity plants that subsequently fail. Rapid failure of these plants leaves behind larger, high-productivity survivors” (p. 332). Third, vintage matters: “New plants embody better production technology and, even after controlling for labor quality and capital intensity, show higher productivity than do earlier cohorts of entrants” (p. 332). Taken together, the effects entail a relatively low contribution of younger firms to labor productivity: Productivity increases significantly with plant age. However, once the quality of labor (using the cohort of entrants’ average wages per hour worked as a proxy) and capital intensity are controlled for, productivity differentials with respect to age become insignificant (p. 331). Especially labor quality has a substantially significant effect. This implies that the differentials between older and younger plants may be due to older plants employing higher quality labor or having higher capital intensity. The conclusion might even be generalized to explaining the results by Brouwer et al. (2005) that show that larger (instead of older) firms are more productive than smaller (instead of younger) firms. Jensen et al. (2001) confirm the virtual irrelevance of whether plant age or plant size is studied: “If average size (mean value added) is included in the regressions reported in Table 3, the age coefficients decline and are no longer statistically significant. Although the causality is difficult to interpret — are more productive plants growing or does size increase productivity? — this suggests size is closely linked with survival and productivity gains” (p. 331). However, Disney et al. (2003) provide evidence in contrast to the above. Establishments younger than 1 year, i.e., entrants, have an average annual labor productivity (output per person hour) that is 2.4 per-

44

Contributions to Productivity and Growth

cent higher than for incumbent establishments, and 5 percent higher than for exiting establishments (see p. 674). These results hold for the U.K. manufacturing sector during 1980–1992, on average. Foster et al. (2006) also compare labor productivity levels of entrants and incumbents, while moreover distinguishing exiting firms as a separate category. Their sample consists of establishments of U.S. firms in the retail trade sector in the 1990s. An entering (exiting) establishment is defined as an establishment entering (exiting) the U.S. retail industry in any of the years 1987–1997. Incumbents are those establishments existing prior to 1987 and surviving beyond 1997. Their results show that exiting establishments are far less productive than entering establishments, and entering and incumbent establishments have similar productivity levels (p. 753). However, due to a major restructuring trend in the sector and period studied “Among entering establishments, the establishments associated with a national chain have a very large productivity advantage relative to single unit incumbents [and single unit entrants]. Entering establishments associated with increasingly larger scope firms, have increasing productivity advantages” (p. 754). Since national chains are more productive entrants than single-unit entrants, the first are likely to drive the average productivity of entrants up to a point where this group’s productivity is insignificantly different from incumbent firms. Thus, although Foster et al. do not examine this, truly entrepreneurial entrants may be less productive than the other firms.2 The results described, pertaining mostly to manufacturing firms in various countries and time periods, are rather mixed, but mostly not in support of relatively high levels of entrepreneurs’ labor productivity. The results by Brouwer et al. (2005) and Jensen et al. (2001) show that entrepreneurs lag behind in terms of labor productivity in the manufacturing sector. Jensen et al., employing the most extensive research design, find that the negative results are completely explained by the fact that entrepreneurs employ lower quality labor or have lower capital 2 It

could though be the case that some chains franchise individual establishments, whereas others do not. Franchisees operate on their own account and risk and could therefore be considered entrepreneurs as opposed to employed managers of chain subsidiaries. However, neither Foster et al. (2006), nor Disney et al. (2003) make such a distinction.

5.1 Levels of Value Added and Productivity

45

intensity than the control group. On the contrary, the results by Disney et al. (2003) indicate that the labor productivity of entrepreneurs in the manufacturing sector in the United Kingdom in the period 1980–1992 was relatively high. We infer from Foster et al., who study the retail trade sector that entrepreneurs probably contribute relatively less than their counterparts in terms of labor productivity in the retail trade sector. All in all, we conclude that entrepreneurs tend to have lower, at maximum similar, levels of labor productivity than others. 5.1.3

Total Factor Productivity

Total Factor Productivity (TFP) has been considered an important ingredient of a firm’s or nation’s production function ever since Solow (1957) introduced the concept as an indicator of the effect of technical change on productivity and a driver of economic growth. It is the multiplier A in the production function, here shown in Cobb–Douglas form with two inputs, i.e., capital input (K) and labor input (L): Y = A × K α × L1−α .

(5.1)

The level of A is a measure of the efficiency of the use of production factors, whereas the change in A over time measures efficiency changes. However, Solow’s interpretation only holds empirically if firms whose TFPs are compared use identical, or at least similar, production factor inputs. This reduces the applicability of TFP as an empirical indicator of contributions to economic development. Nevertheless, it is a much studied driver of economic growth. Disney et al. (2003) find that entrants have higher average TFP levels than incumbents and exiting establishments, i.e., 3.9 percent and around 9.4 percent, respectively (p. 674). Castany et al. (2005) calculate the mean TFP levels of small and large Spanish firms where the boundary lies at 200 employees. They find that large firms had marginally significantly higher mean levels of TFP in both 1994 and 1998 (see their Table 1, p. 27). Moreover, TFP turns out to be an increasing function of firm age.3 Hence, older 3 More

in particular, Castany et al. (2005) show that the positive relationship between age and TFP only holds in the sub-sample of small firms.

46

Contributions to Productivity and Growth

firms have, in general, higher levels of TFP than younger firms. The differences between the results of Disney et al. in favor of young firms and these of Castany et al. (2005) in favor of larger and older firms can possibly be traced back to various differences in the samples, i.e., different countries and time periods studied. Perhaps more importantly, Castany et al. (2005) exclude firms with fewer than 10 employees and since entrants start out small, Castany et al. could have excluded the firms Disney et al. (2003) found to be most productive. Brouwer et al. (2005) examine the ratio of value added and the cost of factor inputs and relate this productivity measure to the firm’s size, measured by the wage bill. Based on a cross-section of 4566 Dutch manufacturing firms in 1999, they find that as firm size increases, value added divided by the cost of labor and capital inputs increases (see p. 29). Thus, larger firms create more value, proportionate to the costs of factor inputs. Nguyen and Lee (2002) obtain estimates of the returns-to-scale with respect to multiple factors for various size classes of U.S. manufacturing plants. “A production unit is “efficient” when it operates on a constant long-run average cost curve (i.e., constant returns-to-scale)” (p. 43). The smallest size class they distinguish is a plant size of between 20 and 50 employees, whereas the largest contains plants with more than 1000 employees. Each of the six size classes they distinguish exhibits constant returns-to-scale according to their estimates (based on separate production functions per size class). Hence, their work supports “the proposition that [in 1999] small establishments are as efficient as large establishments in producing goods” (p. 48) and is in line with previous work pertaining to the manufacturing sector in the United States. We conclude that TFP levels of entrepreneurs are not different from or lower than those of their counterparts.

5.1.4

Summary of the Contributions to the Levels of Value Added and Productivity

The contribution of entrepreneurs to the level of productivity relative to their counterparts is indicated by labor and total factor productivity. The mixed results tend to indicate that entrepreneurs have no higher,

5.1 Levels of Value Added and Productivity

47

and probably lower, levels of productivity than their counterparts. Differences between entrepreneurs and their counterparts are insignificant (or attributable to specific factors) in many cases. Table 5.1 shows an overview of the results. One important point remains to be discussed: The studies reviewed in this section use two distinct observation-levels, i.e., individual firms and individual plants/establishments. This may lead to problems in interpreting and reconciling the results. That is, our observations are obtained from six unique studies of which four are based on samples of individual plants and two of firms. When observing plants, it is not clear whether the plant is operated by an entrepreneur, i.e., a single owner–manager, or is a subsidiary of a larger, non-entrepreneurial firm. Thus, studies observing plants produce results that may not pertain to entrepreneurs. The two studies at the firm level, that therefore apply to our definition of the entrepreneur, find results that are not in favor of a relatively large contribution of entrepreneurs to economic value. One of the plant-observing studies, i.e., Foster et al. (2006), allows for a distinction between single unit plants/establishments, and plants/establishments belonging to a larger group, where the former obviously corresponds to our notion of entrepreneurs. They find that the higher levels of labor productivity associated with entrants is mainly caused by the group of entrants that belong to a chain in the retail trade sector studied. Hence, based on this result we could ultimately degrade all results pertaining to analyses at the establishment level. However, the study by Foster et al. (2006) pertains to the retail trade sector, whereas the most commonly studied sector in this area is the manufacturing sector where chains are not as dominant in general and not among entrants in particular.4 Therefore, so far, we conclude based on all studies summarized in Table 5.1 that entrepreneurs are less productive than their counterparts. 4 Dunne

et al. (1988) report that in the U.S. manufacturing sector (1963–1982), “on average, single-plant firms account for 93.4 percent of the total number of firms in each year” (p. 500) and the remainder being multi-plant firms. Furthermore, 55.45 percent of the entrants are single-unit firms, with the remainder being multi-plant firms (see p. 504). In contrast, out of all establishments observed by Foster et al., 64 percent are single-unit establishments, and the remainder multi-unit plants (footnote 13, p. 753). Foster et al. (2006) do not show such statistics about entrants.

A

A

A

Disney et al. (2003)

Jensen et al. (2001)

Foster et al. (2006)

±1, 5 m U.S. retail establishments (1987–1997)

142,722 U.K. mnf establishm (1980–1992) ±200, 000 U.S. mnf plants (1963– 1992)

Study Journal Sample Value Added (5.1.1) Labor Productivity (5.1.2) Brouwer et al. (2005) WP 4566 Dutch mnf firms (1999)

Output/hours worked

Value added/hours worked

(Value added)/ (wage bill) AND (Gross output) /(wage bill) Output/person hour

Measure of value

Entrant: plants

Plant age

Entrants: firms < 1 year

Firm size (wage bill)

Entrepreneur definition

Table 5.1 Evidence of the relative contribution of entrepreneurs to levels of productivity.

Age relates negatively to labor productivity Age relates positively to labor productivity. (unless controlling for labor quality and capital intensity Productivity similar for incumbents and entrants (chains)

Size relates positively to labor productivity

Main finding

−/0

−/0

+

−

Evidencea

48 Contributions to Productivity and Growth

Entrants: establishments < 1 year Firm size (small is 10-200 empl) and Age Firm size (wage bill)

Firm TFP level

Entrepreneur definition

Firm TFP level

Measure of value

Small and young firms have lower TFP levels Size relates positively to value

Age relates negatively to TFP

Main finding

−

+

Evidencea

(Value added)/ − (cost of factor inputs) Nguyen and Lee SB 10,318 U.S. mnf Elasticity of Plant size No relation with size 0 (2002) plants (1991) output to all (employees) factor inputs Overall (5.1.4) Entrepreneurs do not have higher productivity levels than their counterparts −/0 aEvidence is positive (+) if findings indicate that entrepreneurial firms’ contributions are relatively large. It is negative (−) if the opposite is found and indeterminate (0) if the study does not show significant differences between the contribution of entrepreneurs and their counterparts.

Study Journal Sample Total Factor Productivity (5.1.3) Disney et al. (2003) A 142,722 U.K. mnf establishm (1980–1992) Castany et al. (2005) WP Spanish mnf firms (523 in 90 and 668 in 94) Brouwer et al. (2005) WP 4566 Dutch mnf firms (1999)

Table 5.1 (Continued).

5.1 Levels of Value Added and Productivity

49

50

Contributions to Productivity and Growth

5.2

Growth of Value Added and Productivity

In addition to examining the level of the indicators reviewed above, most of the studies examine the growth of that indicator too. In general, researchers have shown more interest in the analysis of the growth of value added and productivity than in the analysis of their levels. 5.2.1

Value Added Growth

Baldwin (1998) examines the changes in shipment shares of Canadian plant size classes in manufacturing in the 1980s. The smallest size class, i.e., plants with fewer than 100 employees, has increased its shipment share by 0.18 percent on average per annum, while the shares of larger size classes have decreased or remained the same during 1973–1992 (p. 362). Actually, Baldwin shows that economic activity has been shifted towards small firms (possibly without any actual growth of total shipment value, i.e., GDP). Whether the effect of such a shift is positive in terms of economic value added, depends on the relative performance of small versus large firms and the performance improvements of large firms due to the improved competitiveness as a consequence of more small firms. Audretsch et al. (2002) have studied the relationship between size class shares and economic growth and indeed find a positive effect of a larger small size class. Robbins et al. (2000) provide direct support of the relatively large contribution of entrepreneurial firms to value added growth, also based on aggregated data and accounting for possible spillovers between large and small firms. They find that labor productivity growth is significantly associated with the growth rate of a state’s Gross State Product.5 Thus, by affecting productivity growth positively, the smallest businesses provide a relatively large indirect contribution to the growth of a state’s value added. A difference between the studies by Baldwin (1998) and Robbins et al. (2000) is the level at which the performance measure has been defined. Baldwin analyzes the outcome variable “shipment share by size 5 Moreover,

they show that the employment share of the smallest businesses has a positive association with labor productivity growth at the state level in the United States (see Section 5.2.2).

5.2 Growth of Value Added and Productivity

51

class,” whereas Robbins et al. analyze labor productivity growth at the state level. Thus, Robbins et al., unlike Baldwin, include the possible effects of externalities between small and large firms. Carree (2002) examines links between changes in the employment share of large firms (> 500) over the period 1977–1990 to subsequent changes in a value added index in industry I (1990–1994) in country J. An increase in large firm employment shares leads to lower value added index changes. Thus, “on average, a shift towards small units has led to increased growth” (p. 248). Brouwer et al. (2005) provide additional support. They use firmlevel data and find that the percentage growth rates in productivity, in terms of output as well as value added relative to the costs of the factors of production, decrease with firm size (p. 31), i.e., smaller firms have higher productivity growth rates. Rodr´ıguez et al. (2003) use the framework of Gibrat’s Law and corroborate Brouwer et al.’s result. Based on a sample of 1092 firms located on the Spanish Canary Islands, they conclude that smaller firms experienced higher value added growth than larger firms during 1990–1996 (see pp. 299–300). Carree and Thurik (2008), also discussed in Section 3.1 in relationship to employment growth, relate the growth of the number of business owners as a percentage of the labor force to (national) GDP growth. They establish that the initial effect on GDP growth of a higher business ownership rate is positive and there is no significant evidence of business ownership having an indirect effect later on. Thus, entrepreneurs’ production value grows relatively fast in comparison to the control group according to all six studies. These unambiguous results have been found while using a definition of the entrepreneur based on firm size or new business formation and based on micro as well as macro data, where the latter incorporate spillover effects of entrepreneurial firms on their counterparts.6

6 Carree

et al. (2002) actually show that any deviation from the “optimal” level of business ownership rates given a country’s level of GDP per capita affects economic growth. Both having too little and too much entrepreneurial activity has a negative impact on economic growth. The deviation from the “equilibrium” level is what determines economic growth.

52

Contributions to Productivity and Growth

5.2.2

Labor Productivity Growth

Baldwin (1998) connects his finding from the distributions of shipment shares and employment shares over size classes to enable conclusions about labor productivity growth. He finds that “the smaller size class starts at 84 percent of the average productivity [level] but declines to 68 percent by the end of the period [1973–1992]. The larger plants with more than 500 employees start at 116 percent of the average and increase to 138 percent of the average” (p. 361). The entrepreneurs’ relative labor productivity shrunk, implying that the growth in productivity was smaller than that of the control group. As is shown below, this negative finding by Baldwin (1998) is an exceptional result in this category of studies. Baldwin does not include possible spillover effects of small firms on large firms in his results. Robbins et al. (2000) examine the relationship between the employment share of small businesses and a measure of labor productivity growth — the annual percentage change in the ratio of Gross State Product to employment — both defined at the (U.S.) state level (and including possible spillover effects). They use panel data for 48 states over the years 1986–1995. Their result is opposite to Baldwin’s, possibly due to large spillover effects. Defining small businesses as firms employing fewer than 20 employees, the employment share of this category has a significantly positive relation with productivity growth (p. 297). However, when defining the small business sector as firms employing fewer than 500 employees, the relationship turns out insignificant. Brouwer et al. (2005) measure the relationship between firm size and the firm’s growth of labor productivity based on micro data. The relationship they find is negative, implying that growth decreases with firm size (p. 31), i.e., the productivity of small firms grows faster than of large firms. Disney et al. (2003), who had established significant differences between productivity levels of entrants, incumbents and exiting firms in favor of entrants (see Section 5.1.2), also analyze differences in labor productivity growth. More precisely, they decompose industry-

5.2 Growth of Value Added and Productivity

53

wide labor productivity growth –based on individual establishment data — into (1) growth due to incumbent establishments increasing their labor productivity, so called “internal restructuring,” and (2) growth due to the entry and exit of establishments, i.e., the sum of the loss of labor productivity due to establishments exiting and the gain in labor productivity due to entrants, the so called “external restructuring.” Disney et al. find that effects (1) and (2) are each responsible for around 50 percent of industry-wide productivity growth. Given that entrants are a small fraction of all establishments investigated, we infer that entrants have a relatively high contribution to labor productivity growth.7 However, in their study of labor productivity growth, Disney et al. (2003) make the same distinction between single-unit entrants and entrants belonging to chains as Foster et al. (2006) do in their studies of both the level and the growth of labor productivity. They then find that the effect of net entry, i.e., effect (2), is dominated by the latter type of entrants in the manufacturing sector, too. That is, “net entry by singles raised productivity growth, accounting for about 16 percent of overall [labor] productivity growth. Interestingly however, the net entry effect of establishment groups accounts for about double this amount” (Disney et al. (2003), p. 681). Hence, although single-unit entrants contribute to labor productivity growth in the manufacturing sector, the majority of the net entry effect is caused by “non-entrepreneurial” entrants. Foster et al. (2006) use a decomposition method similar to Disney et al. (2003). They find that “net entry accounts for virtually all of the labor productivity growth in retail trade” (p. 757). However, besides showing that establishments belonging to large chains have the highest productivity levels (see Section 5.1.2), Foster et al. show that “Much of the contribution of net entry to overall productivity growth is associated with the displacement of single-unit establishments by the entry of highly productive establishments from national chains”

7 We

have to nuance this result somewhat. Balk and Hoogenboom-Spijker (2003) apply several decomposition methods to a sample of Dutch manufacturing firms in the period 1996–2001 and find that the relative contributions of effect (1) or (2) depend on the design of the decomposition method.

54

Contributions to Productivity and Growth

(p. 757). Hence, their evidence might not relate to our notion of an entrepreneur. Carree and Thurik (2008) study to what extent and when, i.e., short versus long term, direct and indirect effects of new business creation are translated into increased labor demand (Section 3.1), GDP (Section 5.2.1) and labor productivity growth. For labor productivity growth they only find evidence of a direct immediate (marginally significantly) positive effect. To conclude, the evidence suggests that labor productivity growth is higher in entrepreneurial firms than in other firms.8 Both studies based on micro and macro data show that the effect of increased entrepreneurial activity engenders labor productivity growth. As in the previous section, a remark is in order. Whereas one of the three micro studies using firm-specific data distinguishes entrepreneurs from others based on firm size, two of the three studies distinguish entrants from incumbents/exits. Hence, in these two studies our definition of the entrepreneur is the entrant. However, since these two studies are based on analyses of establishments rather than firms, the entrants possibly belong to incumbent (and large scale) chains. Both of the studies, i.e., Foster et al. (2006) and Disney et al. (2003), acknowledge that the contribution in productivity growth of entrants is mainly due to entering establishments of larger chains. This does not correspond to our notion of the entrepreneur. 5.2.3

Total Factor Productivity Growth

The growth in TFP represents growth in production due to a more efficient use of production factors. Three studies have measured the relative contribution of entrepreneurs to TFP growth, two of these for Spain. Callejon and Segarra (1999) show, based on a study of 13 manufacturing industries in 17 Spanish regions in the period 1980– 1992, that both entry and exit rates contribute positively to the growth of TFP in industries and regions. This leads thus to the 8 Wong

et al. (2005) show that only specific types of entrepreneurs engender growth.

5.2 Growth of Value Added and Productivity

55

conclusion that entrepreneurial activity is related positively to TFP growth. Castany et al. (2005) show that the growth rates of TFP levels in Spanish manufacturing firms appear rather similar for small and large firms. The evidence is (only) based on descriptive statistics: The reported mean TFP levels for small (10–200 employees) and large firms have increased between 1994 and 1998 by a similar magnitude.9 Using more advanced statistical methods, Disney et al. find that establishment entry (net of establishment exits) is responsible for 80 percent to 90 percent of industry-wide TFP growth (p. 680). Thus, entrepreneurs would have very high contributions to TFP growth. However, as was the case with labor productivity, the effect of net entry is dominated by establishment groups, contributing three times more to TFP growth than single-unit establishments (Disney et al. (2003), Table 8, p. 682). We conclude that, if anything, entrepreneurs contribute to TFP growth proportionally.

5.2.4

Summary of the Contributions to Growth of Value Added and Productivity

The majority of the studies into the contributions to growth in terms of value added and productivity show that entrepreneurs experienced higher growth in production value and labor productivity, see Table 5.2. The evidence for growth in TFP levels is meager. All in all, entrepreneurs have had a greater contribution to economic growth. The results pertaining to studies where the definition of the entrepreneur is a new entering plant or establishment should be interpreted with great caution since entrants can belong to existing large chains and this group of entrants turns out to experience relatively high growth, but is not necessarily entrepreneurial. 9 Changes

are based on Castany et al.’s (2005) Table 1 p. 27. Exponentiation (to e) of the lnTFP levels results in absolute TFP levels. Relating the change over 1994–1998 to the 1994 values gives the percentage-rate change. Because the data are not balanced, the changes do not pertain to changes in specific firms’ TFP levels, but only to the groups’ averages.

WP

SB

Brouwer et al. (2005)

Rodr´ıguez et al. (2003) Carree and Thurik (2008) SB

SB

Carree (2002)

1092 Spanish firms (1990–1996) 21 OECD countries (1972–2002)

4566 Dutch mnf firms (1999)

26 mnf industries in 5 countries

National GDP growth

(i) Value added; (ii) (Value added)/(cost of factor inputs) Value added

Change in value added index 1977–1990

Gross state product growth

SB

Robbins et al. (2000)

48 U.S. states (1986–1995)

Shipment shares

Measure of value

Study Journal Sample Growth of Value Added (5.2.1) Baldwin (1998) SB Canadian mnf plants (1973– 1992)

Firm size (employees) Changes in business ownership rates

(inverse) change in large firm (+500) share of employment Based on size (wage bill)

Employment share in state of (i) firms < 20 empl. and (ii) firms < 500 empl.

Firm size classes (employees)

Entrepreneur definition

Table 5.2 Evidence of the relative contribution of entrepreneurs to growth of economic value.

Size relates negatively to value growth Higher start-up rates lead to direct GDP growth, not indirectly in the long run

Size relates negatively to value growth

Entrepreneurial size class’ relative shipment share increased GSP growth is indirectly related positively to the share of small firms in a state only if small is defined as < 20 empl A shift towards small firms leads to increased growth

Main finding

+

+

+

+

+

+/0

Evidencea

56 Contributions to Productivity and Growth

SB

WP

A

A

Robbins et al. (2000)

Brouwer et al. (2005)

Disney et al. (2003)

Foster et al. (2006)

±1, 5 m U.S. retail establishments (1987–1997)

142,722 U.K. mnf establishm (80–92)

4566 Dutch mnf firms (1999)

48 U.S. states (1986–1995)

Study Journal Sample Growth of Labor Productivity (5.2.2) Baldwin (1998) SB Canadian mnf plants (1973– 1992)

Table 5.2 (Continued).

Output/hour worked

(Value added)/ (wage bill) AND (Gross output) /(wage bill) Output/person hour

Gross state product/ employment

Shipment share/ employment share

Measure of value

Entrants

Entrants: plants < 1 year

Firm size (wage bill)

Employment share in state of (i) firms < 20 empl. and (ii) firms < 500 empl.

Firm size classes (employees)

Entrepreneur definition

Entrants have a large effect on industry-wide labor productivity growth Entrants (together with exiting establishments) have a large effect on labor productivity growth

Relative labor productivity has decreased for small plants Labor productivity is related positively to the share of small firms in a state only if small is defined as < 20 empl Size relates negatively to productivity growth

Main finding

−/0

+

+

+

−/0

Evidencea

5.2 Growth of Value Added and Productivity

57

Journal Sample SB 21 OECD countries (1972–2002)

Firm TFP related to industry wide TFP growth

Measure of value National GDP growth/labor

Entrants: firms < 1 year

Entrepreneur definition Changes in business ownership rates Main finding Higher start-up rates lead to direct labor prod. growth, not indirectly in the long run

Evidencea +

Entrants have a large + effect on industry-wide TFP growth Castany et al. WP Spanish mnf firms, Firm TFP level Firm size (small is Small and large firms 0 (2005) 523 in 90 and 10-200 emp) show similar levels 668 in 94 and Age of TFP growth Callejon and SB 13 Spanish mnf Industry/region/year Firm entry and Firm entry and exit + Segarra (1999) industries in 17 TFP level exit rates are related regions (1980– positively with 1992) TFP growth Overall (5.2.4) Entrepreneurs contribute more than their counterparts to growth of value added and + productivity aEvidence is positive (+) if findings indicate that entrepreneurial firms’ contributions are relatively large. It is negative (−) if the opposite is found and indeterminate (0) if the study does not show significant differences between the contribution of entrepreneurs and their counterparts.

Growth of Total Factor Productivity (5.2.3) Disney et al. A 142,722 U.K. mnf (2003) establishm (1980–1992)

Study Carree and Thurik (2008)

Table 5.2 (Continued).

58 Contributions to Productivity and Growth

5.3 Summary of the Contributions to Productivityand Growth

5.3

59

Summary of the Contributions to Productivity and Growth

We used several indicators to measure productivity and growth, assuming that the indicators are complementary to each other. Given that the studies observe different periods, sample sizes, and countries, while using various methodologies, the similarity of the findings is striking. Entrepreneurs may lag behind in the levels of productivity, but they are catching up to the production efficiency of the control group due to a higher growth rate.

6 Contributions to Utility

This chapter will address whether individuals, given their personal characteristics, are better off being self-employed (i.e., entrepreneurs) than being wage-workers. “Better off” is understood as having a higher utility level, and the indicators used are remuneration levels (Section 6.1), remuneration inequality and volatility (6.2) as well as job satisfaction (6.3).

6.1

Remuneration Levels

In what follows, we discuss the differences between the levels of “incomes” for entrepreneurs vis-`a-vis employees. Various measurement issues are to be dealt with to enable an insightful comparison between entrepreneurs’ and employees’ incomes (see Parker (2004), pp. 14–16). In particular, three different measures of entrepreneurs’ income can be seen as comparable to employees’ incomes (Hamilton, 2000): (i) net profit; (ii) a periodic wealth transfer from the firm to the entrepreneur, much like a regular wage, labeled “draw,” and (iii) draw plus changes in the firm’s equity value. Another measurement issue is that the distribution of entrepreneurs’ incomes is very different from the distri61

62

Contributions to Utility

bution of employees’ incomes. Both the variance and skewness appear to be much larger, see below. Due to the presence of some “superstar”1 entrepreneurs, “mean earnings may not characterize the selfemployment returns of the majority of business owners” (Hamilton (2000), p. 605). Therefore, comparisons based on averages are likely to produce different results from those based on medians or other quantiles of the income distribution. Another issue, which has not been addressed much, but has been widely recognized, is that entrepreneurs’ incomes relative to those of employees may be under-estimated due to underreporting (Feldman and Slemrod (2007), Parker (2004)). Over-estimates may be due to omitting negative incomes from empirical studies (Van der Sluis and Van Praag, 2007). Hamilton (2000) is, in fact, the only study in our sample that analyzes the income differentials between entrepreneurs and wage employees very thoroughly (for the three different measures of entrepreneurial income, as well as for various quantiles of their distributions in 1984) for a broad sample of the U.S. male population. His results show that entrepreneurs have lower median incomes than employees, i.e., that entrepreneurs “have both lower initial earnings and lower earnings growth than in paid employment, implying a median earnings differential of 35 percent for individuals in business for 10 years” (p. 604). The differences are smaller (or even of the opposite sign, dependent on the definition of entrepreneurial income) when average income levels are compared. The negative relative income for entrepreneurs is supported by the more recent findings of Kawaguchi (2002). Hamilton shows convincingly that the differential cannot be explained by the selection of low-ability employees into self-employment and is similar for three alternative measures of self-employment earnings and across industries. On average, entrepreneurs would benefit from higher incomes and higher growth rates of their incomes had they switched to employment. The upper quartile of the entrepreneurs’ income distribution forms the exception. “Overall, it appears that many workers are willing to enter and remain in self-employment despite receiving returns substantially below their alternative paid employment wage” 1 See

Rosen (1981).

6.1 Remuneration Levels

63

(p. 606). Hamilton concludes that “The non-pecuniary benefits of selfemployment are substantial: Most entrepreneurs enter and persist in business despite the fact that they have both lower initial earnings and lower earnings growth than in paid employment” (p. 606). Fairlie (2005) shows that, for youth from disadvantaged families in the United States, the picture is somewhat different for males and comparable to the picture drawn by Hamilton (2000) for females. His numbers are based on averages (and the profit definition of entrepreneurial incomes). Female entrepreneurs tend to earn — insignificantly — lower levels of income than female wage-workers, while male entrepreneurs apparently had higher incomes than otherwise identical wage-workers.2 Fairlie controls for unobserved heterogeneity in individual characteristics by estimating a (individual) fixed effects model. Rosen and Willen (2002), in turn, find that entrepreneurs — given their educational level — and controlling for personal characteristics including gender, have higher mean and median income levels than wage-workers. Holtz-Eakin et al. (2000) analyze the mobility of individuals in the income distribution. They attempt to predict the change in the individual’s percentile position, conditional upon being self-employed or a wage-worker. Among the low-earning individuals, the self-employed experience higher income growth than wage-workers, keeping characteristics constant. In contrast, among the top-earning individuals, the self-employed experience smaller income growth than wage-workers. This suggests that the individual’s benefit from being self-employed depends on her initial income. This result is in line with the combination of Hamilton’s and Fairlie’s findings. However, the study’s basic model may produce a “regression-to-the-mean” effect (as noted by Holtz-Eakin et al. (2000) and Pannenberg and Wagner (2001)). 2 The

results are sensitive to changes in the definition of the dependent variable. Male entrepreneurs earn significantly more than male wage workers with absolute incomes as the dependent variable, whereas the difference is insignificant when the logarithm of incomes is used. The estimates of the income differential between female entrepreneurs and wage workers also depends on the definition of the dependent variable, but in a different way: Female entrepreneurs earn lower incomes than their employed counterparts when the logarithm of earnings is used, see p. 231. In the “no-log specification,” the income differential between female entrepreneurs and employees is insignificant.

64

Contributions to Utility

Van der Sluis et al. (2006) — while aiming at comparing the returns to education for entrepreneurs versus employees — estimate income equations for a combined panel sample of entrepreneurs and employees from the U.S. population (NLSY). By including interactions of one’s occupational status, i.e., entrepreneur or employee, with all the usual control variables in the (log hourly) income equation, they allow the returns to various characteristics to be different for entrepreneurs and employees. The remaining unexplained differential in average incomes between entrepreneurs and employees turns out insignificantly different from zero. Moreover, when acknowledging the endogeneity of the choice for entrepreneurship in such an income equation, the average earnings differential between entrepreneurs and employees remains insignificant. Based on the same dataset, Hartog et al. (2007) estimate income equations for entrepreneurs and employees in order to quantify the returns to (various kinds of) intelligence and ability for entrepreneurs vis-`a-vis employees. Before allowing the returns to the various kinds of intelligence, ability and education to differ between entrepreneurs and employees (again by including interaction terms), they find that entrepreneurs earn approximately nine percent lower incomes than employees, on average. However, as soon as they allow the returns to these measures of human capital to differ between the groups, the unexplained difference between entrepreneurs’ and employees’ income turns out insignificant. In short, entrepreneurs in the United States seem to earn lower median incomes than wage employees. However, for the upper and lower parts of the income distribution, the differences can be positive. Average incomes seem to be of similar levels for entrepreneurs and employees in regression frameworks that allow the returns to broad sets of indicators of human capital to differ across entrepreneurs and employees. Entrepreneurship might be good for social mobility and for becoming a “super income earner.”

6.2

Remuneration Inequality and Volatility

One of the stylized facts in the economics of entrepreneurship is that the distribution of entrepreneurs’ incomes is much less equal, i.e., has a higher variance, than the income distribution of wage-employees.

6.2 Remuneration Inequality and Volatility

65

Descriptive statistics of the income distributions of entrepreneurs and employees (mostly in terms of their averages and variances) in numerous studies have supported this claim (see Parker (2004) and all studies mentioned in the previous section). In most studies, negative incomes are equated to zero (Van der Sluis and Van Praag (2007), Parker (2004)). Since entrepreneurs’ incomes can be negative, whereas this is impossible for wage-workers, this would only add to the difference in variance already observed. Hence, income inequality and uncertainty is higher for entrepreneurs than for employees. However, it should be noted that this observation is based on an unconditional comparison of cross-sectional variances. To assess income uncertainty for individual labor market participants, insight should be obtained in the variance of income over time for a given individual, i.e., income volatility. Carrington et al. (1996) investigate how entrepreneurs’ and wage-workers’ hourly incomes are affected by changes in the unemployment rate and GNP, i.e., events related to systematic risk. Based on a large sample of individuals in the United States observed from 1967 to 1992, the authors conclude that the incomes of entrepreneurs are significantly more responsive to both decreases and increases in the GNP and the unemployment rate, ceteris paribus. This is consistent with relatively risky entrepreneurial incomes. We know only two studies that assess whether entrepreneurial incomes are more risky for a given individual in terms of variances in incomes over time conditional on a broad set of individual characteristics, i.e., Van der Sluis et al. (2006) and Rosen and Willen (2002). The evidence is supportive of riskier incomes for entrepreneurs than for employees. That is, Rosen and Willen (2002), using the Panel Study of Income Dynamics (1968–1993), show that “the income stream associated with self-employment is more variable than that associated with wage-earning, both in absolute terms and relative to income.” (pp. 5–6).3 Thus, entrepreneurial incomes are riskier and more volatile than the incomes of employees, for otherwise identical individuals. 3 The

relative measure can be interpreted as the risk per unit of income, i.e., standard deviation divided by the mean income.

66

6.3

Contributions to Utility

Job Satisfaction

Job satisfaction scores are important indicators of utility levels. Blanchflower and Oswald (1998) show in their seminal article “What makes an entrepreneur?” that (i) entrepreneurs are significantly more satisfied with their work than wage-workers on average; (ii) Entrepreneurs are significantly more satisfied with their work, controlling for various individual and work-related characteristics; (iii) The same holds for “life satisfaction.” Their satisfaction data and findings pertain to the United States. Benz and Frey (2003) execute a similar study pertaining to various countries and periods of time. Using the same type of analysis as Blanchflower and Oswald (1998), Benz and Frey reach the same conclusion: Entrepreneurs are more satisfied with their job than (identical) wage-workers (given income and working hours). They study the causes of job satisfaction in more detail than Blanchflower and Oswald by incorporating many job characteristics into the regressions. The difference in satisfaction levels between entrepreneurs and employees decreases, or even becomes insignificant, upon including controls for the individuals’ evaluation of job content and autonomy. We can infer that entrepreneurs are more satisfied, mainly due to them having more interesting jobs and/or more autonomy. Hence, these results collectively provide some evidence that entrepreneurs get higher utility than employees. But as Blanchflower and Oswald state “One caveat should be borne in mind when interpreting this study’s findings. The use of satisfaction and happiness data to proxy utility levels is unconventional in economics research. It may be that reported satisfaction levels are subject to important biases. For example, self-employed people may be intrinsically more optimistic and cheerful than others, or may feel psychologically compelled, because their business is in their own hands, to answer in the way they do” (p. 49). Frey and Benz (2003) performed a study that addresses this critique, by studying changes in satisfaction levels for individuals who change employment status, i.e., from entrepreneur to employee and vice versa or from job to job in wage employment for the United Kingdom and Western Germany. Individuals flowing into self-employment are

SB

WP

SB

Fairlie (2005)

Rosen and Willen (2002)

Holtz-Eakin et al. (2000) Van der Sluis et al. (2006)

Hartog et al. (2007)

WP

Kawaguchi (2002)

WP

WP

AA

Hamilton (2000)

Study Journal Remuneration Levels (6.1) Hamilton (2000) AA

U.S. 3,000 individuals (1979–2001)

8771 U.S. male individuals (1984) 8771 U.S. male individuals (1984) 2661 U.S. male individuals (1985–1998) 12,686 U.S. individuals (1979–1998) 10,533 U.S. individuals (1968– 1993) ±5, 000 U.S. families (1969–1990) U.S. 3000 individuals (1979–2001)

Sample

Average income

Mobility in income distribution Average income

Average income

Difference is dependent on initial income level Controlling for unobserved heterogeneity, no difference Controlling for unobserved heterogeneity, no difference

Entrepreneurial income is higher

Male (disadvantaged) entrepreneurs earn more

Average income

Median income growth Median income

Entrepreneurs have lower income levels Entrepreneurs have lower income growth Entrepreneurs have lower incomes

Main finding

Median incomes

Aspect of utility

Table 6.1 Evidence of the relative contribution of entrepreneurship to utility.

0/−

0/−

0

+

+

−

−

−

Evidencea

6.3 Job Satisfaction

67

Income volatility

Sensitivity of income to economic indicators Income volatility

±29, 000 U.S. individuals (1967–1992) U.S. 3000 individuals (1979–2001) 10,533 U.S. individuals (1968–1993)

Aspect of utility

Sample Entrepreneurial income more responsive to changes in indicators Entrepreneurial income has higher variance over time Entrepreneurial income has higher variance

Main finding

−

−

−

Evidencea

7874 U.K. individuals Job satisfaction scores Entrepreneurs are more + (1981) satisfied WP 9332 individuals in Job satisfaction scores Entrepreneurs are more + W-Europe and satisfied N-America (1997)b Frey and Benz WP 28,392 individuals in Job satisfaction scores Becoming self-employed + (2003) Switzerland, U.K. has a stronger positive and Western effect than becoming Germany a wage-worker (1984–2000)b Overall Despite having lower and riskier incomes, entrepreneurs are more satisfied 0 aEvidence is positive (+) if findings indicate that entrepreneurial firms’ contributions are relatively large. It is negative (−) if the opposite is found and indeterminate (0) if the study does not show significant differences between the contribution of entrepreneurs and their counterparts. bPrecise number of individuals and years observed differs per country/region.

A

WP

Rosen and Willen (2002)

Job Satisfaction (6.3) Blanchflower and Oswald (1998) Benz and Frey (2003)

WP

Van der Sluis et al. (2006)

Study Journal Remuneration Volatility (6.2) Carrington et al. A (1996)

Table 6.1 (Continued).

68 Contributions to Utility

6.4 Summary of the Contributions to Utility

69

more satisfied than those flowing out of self-employment (see p. 14). Furthermore, those becoming entrepreneurs are also more satisfied than wage-workers that change their job (but remain wage-workers). Hence, these results, that are not affected by unobserved individual differences, such as the extent of cheerfulness or optimism, are also supportive of higher satisfaction levels for entrepreneurs than for employees.

6.4

Summary of the Contributions to Utility

The main question posed was: Is an individual with a given set of characteristics better off being an entrepreneur? The answer is interesting. Although entrepreneurs have lower incomes, that are more volatile and less secure, entrepreneurs are more satisfied with both their jobs and their lives. Table 6.1 provides an overview. What could explain this result? Do entrepreneurs severely underreport their incomes (Feldman and Slemrod (2007), Parker (2004))? Do entrepreneurs not mind that their incomes are more volatile because they are less risk averse?4 This cannot be the entire explanation since switchers into entrepreneurship gain more satisfaction than switchers in the opposite direction (Frey and Benz, 2003). Does entrepreneurship require start-up capital that many people are not able to acquire (e.g., Astebro and Bernhardt (2005))? Does entrepreneurship bring so much non-pecuniary benefits?5 These questions require more research.

4 Van

Praag and Cramer (2001), Cramer et al. (2002), and Ekelund et al. (2005) are three studies providing evidence that individuals with higher risk aversion are less likely to become entrepreneurs. 5 Some related evidence, but from a very different angle, pointing at intangible benefits of entrepreneurship is provided by Moskowitz and Vissing-Jørgensen (2002). They find that households’ investments in private companies are concentrated in a single, risky, privately held firm in which the household has an active management interest. Thus, these households have a preference for investing in private equity over investment in publicly traded funds although their private equity investments are associated with higher levels of risk and lower returns. Possible non-pecuniary explanations for this seemingly irrational behavior are higher risk tolerance of entrepreneurs, cognitive problems (e.g., over-optimism and inadequacy of risk assessment), and benefits as “being your own boss” and flexibility and autonomy of self-employment (see pp. 771–773).

7 Conclusion

We have reviewed the fruits from 12 years of high quality empirical research into the economic benefits and costs of entrepreneurship. The research reviewed was selected based on specific rules such that statistical measurement of the relative benefits to the creation of economic value by entrepreneurs is enabled. Entrepreneurs or entrepreneurial firms are defined as small firms, young firms, entrants or self-employed. Their counterparts are defined as bigger firms, older firms, incumbent firms or wage employees, respectively. At a more aggregated level, these definitions of entrepreneurship translate into the share of small or young firms, the number of entering firms as compared to the number of employees or incumbent firms in a region or country, and the rate of self-employment. Economic benefits are defined in terms of employment generation and dynamics, innovation, productivity and growth and the creation of utility. The picture that emerges, both about the state of research and the results, is scattered. The resulting sample of studies consists of 57 studies that analyze 87 relationships between entrepreneurship and economic outcomes in total. This is a surprisingly low number given the great variety of indicators of economic outcomes, countries, time periods and industries that have been studied, while using various definitions of the entrepreneur. 71

72

Conclusion

Therefore, the number of studies that is really comparable in a quantitative fashion is low for each and every indicator, such that a genuine meta-analysis of the studies is not yet possible. The small number of studies might be due to our strict requirements in terms of (journal) quality and the required explicit comparison between entrepreneurs and some control group. Given the economic importance of the question, more high quality studies would be valuable. Some of the ambiguities of our conclusions provided below are due to the different definitions for the entrepreneur we have perforce used. Of course, the studies reviewed do not explicitly examine entrepreneurship but rather use a variable which allows for a distinction between entrepreneurs and counterparts. If many studies use the exactly same definition of entrepreneurship, this would allow for a better reconciliation of the literature, with fewer ambiguities. Nonetheless, our study has resulted in answers to the question: “What are the economic benefits and costs of entrepreneurs?” Table 7.1 serves as a guideline to the answer, which is complex due to the issues mentioned, i.e., many different indicators of economic value creation, definitions of the entrepreneur and time periods and industries studied.

7.1

Employment

Entrepreneurs create more employment than their counterparts, relative to their size. This result is unambiguous.1 Small and young firms are required to boost employment generation. This remains true when one accounts for the higher firm dissolution rate among young and small firms, which destroys jobs. Indeed, the net contribution of entrepreneurs to employment creation relative to their counterparts is positive. However, the greater job destruction, in turn, leads to less job security and a more volatile process of employment creation. Hence, entrepreneurs do create more jobs, but they do so in a rather dynamic way, which is disadvantageous for the stability of the labor market. Another important

1 The

results from studies following the Davis and Haltiwanger-methodology, which are not reported here, only add to the credibility of this result.

Utility Total

Productivity and growth Productivity and growth Productivity and growth Productivity and growth Productivity and growth Utility Utility

Innovation

Category Employment Employment Employment Employment Employment Innovation Innovation Innovation Innovation Innovation Innovation Innovation Innovation

7 7 3 8 3

Growth of value added Growth of labor productivity Growth of total factor productivity Remuneration levels Remuneration volatility

Growth of value added and productivity Growth of value added and productivity Growth of value added and productivity Remuneration levels Remuneration volatility Satisfaction Satisfaction

3 87

4

4

5

Total factor productivity

Adoption of innovations

Value of productivity

Studies 15 2 5 2 3 2 2 2 1 1 1 1 6

Labor productivity

Sub-category Employment generation Employment dynamics Wage levels Benefits Job satisfaction R&D expenses per employee Number/frequency of patents New products and technologies New products and technologies/employee Percentage of radical innovations Self-assessed importance of innovations Patent citations Commercialization of innovations

Specification of Category Employment quantity Employment quantity Employment quality Employment quality Employment quality Innovations quantity Innovations quantity Innovations quantity Innovations quantity Innovations quantity Innovations quality Innovations quality Commercialization of innovations Adoption of innovations Value of productivity

Table 7.1 Overview of the results.

3 44

2 0

2

5

6

1

1

1

Positive 14 0 0 0 2 1 0 0 1 0 0 1 4

0 7

1 0

1

0

1

1

0

2

Zero 0 0 0 0 0 0 0 0 0 0 0 0 1

0 36

5 3

0

2

0

2

3

2

Negative 1 2 5 2 1 1 2 2 0 1 1 0 1

7.1 Employment

73

74

Conclusion

aspect of entrepreneurial activity is the effect new firm creation has on incumbents. The evidence suggests rather convincingly that there is a positive long term effect of more entrepreneurial activity on labor demand, also by non-entrepreneurial firms. The quality of the jobs created by entrepreneurs is lower than for the counterparts. This is, among others, due to the fact that entrepreneurs hire employees with lower levels of human capital than other firms. However, even if one accounts as much as possible for all kinds of differences between entrepreneurial and other firms, such as the complementarity of capital and skills, and the differences in returns to skills that are paid to employees, an unexplained wage premium for employees in counterpart firms remains. Entrepreneurs pay not only lower wages, but also fewer benefits and offer performance-dependent pay systems less frequently. However, apparently, entrepreneurs offer other intangible benefits to their employees because their employees are more satisfied with their (lower paid and less secure) jobs than the employees of their counterparts. Entrepreneurs generate relatively much employment, relative to their size. Moreover, since new firms have an effect on the market by increasing competition, increased entrepreneurial activity has a long term positive effect on total labor demand. Entrepreneurs add relatively much volatility to the labor market. The jobs they create are less secure and paid worse. Nevertheless, employees in entrepreneurial firms are more satisfied than other employees

7.2

Innovation

Entrepreneurs do not spend more on R&D than their counterparts. They produce fewer patents and produce fewer new products and technologies. Moreover, the percentage of radical innovations is lower among entrepreneurial firms. Nevertheless, the efficiency with which innovations are produced is higher and so is the quality of innovations as measured by the number of patent citations. Entrepreneurs commer-

7.3 Productivity and Growth

75

cialize innovations to a larger extent, but score lower on the adoption of innovations than their counterparts. Entrepreneurs produce relatively few innovations. However, they produce innovations more efficiently and they are of higher quality. Entrepreneurs also lag behind in the adoption of innovations. Entrepreneurs have a relatively high contribution to the commercialization of innovations.

7.3

Productivity and Growth

The relative contribution of entrepreneurs to the value of productivity levels is low. This holds basically for all indicators considered, i.e. labor and total factor productivity. However, entrepreneurs contribute relatively much to the growth of value added and productivity. Hence, entrepreneurs are relatively important for productivity growth. Entrepreneurs have relatively low levels of productivity. However, they have relatively high growth rates of value added, as well as labor and total factor productivity.

7.4

Utility

The majority of the entrepreneurs would earn higher incomes as wage employees. The mean incomes of entrepreneurs can reach quite high levels due to some “superstar” entrepreneurs. Accordingly, the upper quartile of the income distribution of entrepreneurs is positioned at a higher income level than for employees. Nevertheless, the mean incomes of entrepreneurs appear to be lower or similar — but not higher — than the mean incomes of employees (conditional on various individual characteristics). This would lead to lower levels of utility. Entrepreneurs’ incomes are also more variable over time than employee incomes, which reduces the utility of risk averse individuals, too. However, there

76

Conclusion

must be various less tangible benefits to entrepreneurship like greater autonomy, or else, entrepreneurs are very irrational, optimistic, or risk seeking (or underreport their incomes): Entrepreneurs have higher levels of job satisfaction than employees. Entrepreneurs have higher levels of job satisfaction than employees (the counterparts), although they earn lower and more variable incomes. All in all, we conclude that entrepreneurs have a very important — but specific — function in the economy. They engender relatively high levels of employment creation, productivity growth and produce and commercialize high quality innovations. They are more satisfied than employees. However, the counterparts cannot be missed as they account for scale in terms of labor demand and GDP, a less volatile and more secure labor market, higher paid jobs and a greater number of innovations and the adoption of innovations. It is clear that we have refrained from discussing the implications these findings have for policymakers. While most of the studies reviewed in the paper give certain proposals based on their respective findings, we acknowledge the limitations of our analysis. The manner in which we reviewed the studies allows conclusions about the relative contribution of entrepreneurs to the various economic areas, but we have not investigated the causes. This warrants an entire study in itself. Without clear knowledge on the causes of higher percentage-rate employment growth, for example, we cannot provide tailored policy proposals. For example, entrepreneurs are found to have a relatively high contribution to employment growth and productivity growth and perhaps these findings are both reflective of some underlying variable. There are multiple possible causes for ours findings, both at a micro-economic level and the macro-economic level. Moreover, interrelationships may exist between the types of contributions we have considered and spillover effects to non-entrepreneurial firms, especially at the regional level (Scott, 2006). Some research into these interrelationships has been initiated recently and discussed here. It is clear from this handful of studies that indirect spillover effects in

7.4 Utility

77

all areas cannot be ignored and that they should be measured much more extensively. For example, it may well be that a more profitable entrepreneurial firm is better (or less) able at facilitating employment and producing innovations, whereas the innovativeness of entrepreneurs may be the result of non-entrepreneurial firms in the same area and/or sector that produce innovations. Such interrelationships and spillover effects should be measured and taken into account when designing policy.2

2 Hewitt-Dundas

(2006), for instance, shows that the factors constraining firms ability to innovate are very different for small firms than for large firms. Thus, policy to stimulate innovations would therefore be different when targeted to large firms than to small firms.

Appendix

Table A.1 Sources of literature, journals, publishers, latest issues included, and working paper outlets. Journal Economic Journals ranked AA American Economic Review Econometrica Journal of Political Economy Quarterly Journal of Economics Review of Economic Studies

Economic Journals ranked A Accounting Review Econometric Theory Economic Journal European Economic Review Games and Economic Behavior

Online publishers

Latest issue

IngentaConnect, EBSCO, JSTOR Blackwell Publishing, JSTOR University of Chicago Press, EBSCO, JSTOR MIT Press Journals, EBSCO, JSTOR Blackwell Publishing, EBSCO, JSTOR

March 2007 March 2007 February 2007

EBSCO, JSTOR Cambridge University Press Blackwell Publishing, EBSCO, JSTOR Elsevier Science Direct Elsevier Science Direct

March 2007 April 2007 February 2007

79

February 2007 April 2007

April 2007 February 2007 (Continued)

80

Appendix

Table A.1 (Continued). Journal International Economic Review Journal of Accounting and Economics Journal of Business and Economic Statistics Journal of Econometrics Journal of Economic Literature Journal of Economic Perspectives Journal of Economic Theory Journal of Environmental Economics and Management Journal of Finance Journal of Financial Economics Journal of Health Economics Journal of Human Resources Journal of International Economics Journal of Labor Economics Journal of Marketing Research Journal of Monetary Economics Journal of Public Economics Management Science Mathematics of Operations Research Operations Research RAND Journal of Economics Review of Economics and Statistics Review of Financial Studies World Bank Economic Review

Online publishers Blackwell Publishing, EBSCO, JSTOR Elsevier Science Direct

March 2007

American Statistical Association

April 2007

Elsevier Science Direct IngentaConnect, EBSCO, JSTOR IngentaConnect, EBSCO, JSTOR

May 2007 March 2007 January 2007

Elsevier Science Direct Elsevier Science Direct

March 2007 March 2007

Blackwell Publishing, EBSCO, JSTOR Elsevier Science Direct Elsevier Science Direct IngentaConnect, EBSCO, JSTOR Elsevier Science Direct

April 2007

University of Chicago Press, EBSCO, JSTOR EBSCO Elsevier Science Direct Elsevier Science Direct Extenza, EBSCO, JSTOR Extenza, EBSCO

Latest issue February 2007

April 2007 March 2007 Spring 2007 April 2007 April 2007 February 2007 March 2007 June 2007 March 2007 February 2007

Extenza, EBSCO, JSTOR EBSCO, JSTOR MIT Press Journals, EBSCO, JSTOR Oxford University Press, JSTOR Oxford University Press

February 2007 Summer 2006 February 2007 March 2007 No. 1 2007

EBSCO, JSTOR

February 2007

Management Journals Academy of Management Journal Administrative Science Quarterly Strategic Management Journal

EBSCO, JSTOR

September 2006

Wiley InterScience, JSTOR

May 2007

Small Business Field Journals Journal of Business Venturing Small Business Economics

Elsevier Science Direct SpringerLink

July 2007 April 2007 (Continued)

Appendix

81

Table A.1 (Continued). Journal Working Paper Outlets Centre for Economic Policy Research (CEPR, U.K.) Institute for the Study of Labor (IZA, Germany) Max Planck Gesellschaft (Germany) EIM on SMEs and Entrepreneurship (Netherlands) National Bureau of Economic Research (NBER, U.S.A.) Social Science Research Network (SSRN, U.S.A.) Tinbergen Institute (TI, Netherlands)

Online publishers www.cepr.org www.iza.org www.mpg.de www.eim.net

www.nber.org www.ssrn.com www.tinbergen.nl

Latest issue

Acknowledgments

The authors are grateful to Andre van Stel, David Audretsch, Zoltan Acs, Thomas Hellman, Randolph Sloof, and Niels Bosma for their helpful comments. The usual disclaimer applies.

83

References

Acs, Z. J. and D. B. Audretsch (2005), ‘Entrepreneurship, innovation and technological change’. Foundations and Trends in Entrepreneurship 1(4), 1–65. Acs, Z. J. and S. Gifford (1996), ‘Innovation of entrepreneurial firms’. Small Business Economics 8(3), 203–218. Acs, Z. J. and P. Mueller (2008), ‘Employment effects of business dynamics: Mice, gazelles and elephants’. Small Business Economics, forthcoming. Acs, Z. J. and A. Varga (2005), ‘Entrepreneurship, agglomeration and technological change’. Small Business Economics 24(3), 323–334. Almeida, P. and B. Kogut (1997), ‘The exploration of technological diversity and the geographic localization of innovation’. Small Business Economics 9(1), 21–31. Anyadike-Danes, M., M. Hart, and M. O’Reilly (2005), ‘Watch that space! the county hierarchy in firm births and deaths in the UK, 1980–1999’. Small Business Economics 25(3), 273–292. Arvanitis, S. (1997), ‘The impact of firm size on innovative activity — An empirical analysis based on Swiss firm data’. Small Business Economics 9(6), 473–490.

85

86

References

Astebro, T. and I. Bernhardt (2005), ‘The winner’s curse of human capital’. Small Business Economics 24(1), 63–78. Audretsch, D. B., M. A. Carree, A. J. Van Stel, and A. R. Thurik (2002), ‘Impeded industrial restructuring: The growth penalty’. Kyklos 55, 81–98. Audretsch, D. B. and M. Vivarelli (1996), ‘Firm size and R&D spillovers: Evidence from Italy’. Small Business Economics 8(3), 249–258. Baldwin, J. and G. Picot (1995), ‘Employment generation by small producers in the Canadian manufacturing sector’. Small Business Economics 7(4), 317–331. Baldwin, J. R. (1998), ‘Were small producers the engines of growth in the Canadian manufacturing sector in the 1980s?’. Small Business Economics 10(4), 349–364. Balk, B. M. and E. Hoogenboom-Spijker (2003), ‘The measurement and decomposition of productivity change: Exercises on the Netherlands’ manufacturing industry’. Statistics Netherlands Discussion Paper 03001, Voorburg/Heerlen, Netherlands. Baptista, R., V. Escaria, and P. Madruga (2008), ‘Entrepreneurship, regional development and job creation: The case of Portugal’. Small Business Economics, forthcoming. BarNir, A., J. M. Gallaugher, and P. Auger (2003), ‘Business process digitization, strategy, and the impact of firm age and size: The case of the magazine publishing industry’. Journal of Business Venturing 18(6), 789–814. Bayard, K. and K. R. Troske (1999), ‘Examining the employer-size wage premium in the manufacturing, retail trade, and service industries using employer-employee matched data’. The American Economic Review 89(2), 99–103. Benz, M. and B. S. Frey (2003), ‘The value of autonomy: Evidence from the self-employed in 23 countries’. University of Zurich Working Paper #173, Zurich, Switzerland. Biggs, T. (2002), ‘Is Small Beautiful and Worthy of Subsidy?’. Washington: International Finance Corporation, U.S.A. Link: rru.worldbank.org/PapersLinks/Open.aspx?id=2482.

References

87

Blanchflower, D. G. and A. J. Oswald (1998), ‘What makes an entrepreneur?’. Journal of Labor Economics 16(1), 26–60. Broersma, L. and P. Gautier (1997), ‘Job creation and job destruction by small firms: An empirical investigation for the dutch manufacturing sector’. Small Business Economics 9(3), 211–224. Brouwer, E. and A. Kleinknecht (1996), ‘Firm size, small business presence and sales of innovative products: A micro-econometric analysis’. Small Business Economics 8(3), 189–201. Brouwer, P., J. De Kok, and P. Fris (2005), ‘Can firm age account for productivity differences?’. EIM SCALES-paper N200421, Zoetermeer, Netherlands. Brown, C., J. Hamilton, and J. Medoff (1990), Employers Large and Small. Cambridge, MA: Harvard University Press. Brown, C. and J. L. Medoff (2003), ‘Firm age and wages’. Journal of Labor Economics 21(3), 677–697. Burgess, S., J. Lane, and D. Stevens (2000), ‘Job flows, worker flows, and churning’. Journal of Labor Economics 18(3), 473–502. Callejon, M. and A. Segarra (1999), ‘Business dynamics and efficiency in industries and regions: The case of Spain’. Small Business Economics 13(4), 253–271. Calvo, J. L. (2006), ‘Testing Gibrat’s law for small, young, and innovating firms’. Small Business Economics 26(2), 117–123. Carree, M. A. (2002), ‘Industrial restructuring and economic growth’. Small Business Economics 18, 243–255. Carree, M. A. and A. R. Thurik (2003), ‘The impact of entrepreneurship on economic growth’. In: Z. J. Acs and D. B. Audretsch (eds.): Handbook of Entrepreneurship Research. Boston, MA: Kluwer Academic Publishers, pp. 437–471. Carree, M. A. and A. R. Thurik (2008), ‘The lag structure of the impact of business ownership on economic performance in OECD countries’. Small Business Economics, forthcoming. Carree, M. A., A. J. Van Stel, A. R. Thurik, and A. Wennekers (2002), ‘Economic development and business ownership: An analysis using data of 23 OECD countries in the period 1976–1996’. Small Business Economics 19, 271–290.

88

References

Carrington, W. J., K. McCue, and B. Pierce (1996), ‘The role of employer/employee interactions in labor market cycles: Evidence from the self-employed’. Journal of Labor Economics 14(4), 571–602. Castany, L., E. L´opez-Bazo, and R. Moreno (2005), ‘Differences in total factor productivity across firm size. A distributional analysis’. University of Barcelona Working Paper. Caves, R. E. (1998), ‘Industrial organization and new findings on the turnover and mobility of firms’. Journal of Economic Literature 36(4), 1947–1982. Chandrashekaran, M. and R. K. Sinha (1995), ‘Isolating the determinants of innovativeness: A split-population tobit (SPOT) duration model of timing and volume of first and repeat purchase’. Journal of Marketing Research 32, 444–456. Clark, A. E. and A. J. Oswald (1996), ‘Satisfaction and comparison income’. Journal of Public Economics 61(3), 359–381. Cowling, M. (2001), ‘Fixed wages or productivity pay: Evidence from 15 EU countries’. Small Business Economics 16(3), 191–204. Cramer, J. S., J. Hartog, N. Jonker, and C. M. Van Praag (2002), ‘Low risk aversion encourages the choice for entrepreneurship: An empirical test of a truism’. Journal of Economic Behaviour and Organization 48(1), 29–36. Czarnitzki, D. and K. Kraft (2004), ‘Firm leadership and innovative performance: Evidence from seven EU countries’. Small Business Economics 22(5), 325–332. Davis, S. J. and J. Haltiwanger (1992), ‘Gross job creation, gross job destruction, and employment reallocation’. The Quarterly Journal of Economics 107(3), 819–863. Davis, S. J., J. Haltiwanger, and S. Schuh (1996), ‘Small business and job creation: Dissecting the myth and reassessing the facts’. Small Business Economics 8(4), 297–315. Dechenaux, E., B. Goldfarb, S. A. Shane, and M. C. Thursby (2003), ‘Appropriability and the timing of innovation: Evidence from MIT inventions’. NBER Working Paper 9735, Cambridge, MA, U.S.A. Disney, R., J. Haskal, and Y. Heden (2003), ‘Restructuring and productivity growth in UK manufacturing’. The Economic Journal 113, 666–694.

References

89

Dunne, T., M. J. Roberts, and L. Samuelson (1988), ‘Patterns of firm entry and exit in U.S. manufacturing industries’. The RAND Journal of Economics 19(4), 495–515. Ekelund, J., E. Johansson, M. R. J¨arvelin, and D. Lichtermann (2005), ‘Self-employment and risk aversion — Evidence from psychological test data’. Labour Economics 12(5), 649–659. Fairlie, R. W. (2005), ‘Entrepreneurship and earnings among young adults from disadvantaged families’. Small Business Economics 25(3), 223–236. Feldman, N. E. and J. Slemrod (2007), ‘Estimating tax non-compliance with evidence from unaudited tax returns’. Economic Journal 117(518), 327–352. F¨olster, S. (2000), ‘Do entrepreneurs create jobs?’. Small Business Economics 14(2), 137–148. Foster, L., J. Haltiwanger, and C. J. Krizan (2006), ‘Market selection, reallocation, and restructuring in the U.S. retail trade sector in the 1990s’. The Review of Economics and Statistics 88(4), 748–758. Frey, B. S. and M. Benz (2003), ‘Being independent is a great thing: Subjective evaluations of self-employment and hierarchy’. CESifo Working Paper #959, Munich, Germany. Fritsch, M. (1997), ‘New firms and regional employment change’. Small Business Economics 9, 437–448. Fritsch, M. (2008), ‘How does new business formation affect regional development?’. Small Business Economics, forthcoming. Fritsch, M. and P. Mueller (2008), ‘The effect of new business formation on regional development over time. The case of germany’. Small Business Economics, forthcoming. Fritsch, M. and A. Weyh (2006), ‘How large are the direct employment effects of new businesses? An empirical investigation for West Germany’. Small Business Economics 27(2), 245–260. Gibrat, R. (1931), Les In´egalit´es Economiques. Paris, France: Librairie du Recueil Sirey. Hamilton, B. H. (2000), ‘Does entrepreneurship pay? An empirical analysis of the returns to self-employment’. The Journal of Political Economy 108(3), 604–631.

90

References

Hart, P. E. and N. Oulton (1996), ‘Growth and size of firms’. The Economic Journal 106(438), 1242–1252. Hartarska, V. and C. Gonzalez-Vega (2006), ‘What affects new and established firms’ expansion? Evidence from small firms in Russia’. Small Business Economics 27(2), 195–206. Hartog, J., J. Van der Sluis, and M. Van Praag (2007), ‘Returns to intelligence: Entrepreneurs versus employees’. University of Amsterdam Working Paper, Amsterdam, Netherlands. Heshmati, A. (2001), ‘On the growth of micro and small firms: Evidence from sweden’. Small Business Economics 17(3), 213–228. Hewitt-Dundas, N. (2006), ‘Resource and capability constraints to innovation in small and large plants’. Small Business Economics 26(3), 257–277. Holtz-Eakin, D., H. S. Rosen, and R. Weathers (2000), ‘Horatio alger meets the mobility tables’. Small Business Economics 14(4), 243–274. Huergo, E. and J. Jaumandreu (2004), ‘How does probability of innovation change with firm age?’. Small Business Economics 22(3–4), 193–207. Jensen, J. B., R. H. McGuckin, and K. J. Stiroh (2001), ‘The impact of vintage and survival on productivity: Evidence from cohorts of U.S. manufacturing plants’. The Review of Economics and Statistics 83(2), 323–332. Johansson, D. (2005), ‘The turnover of firms and industry growth’. Small Business Economics 24(5), 487–495. Kawaguchi, D. (2002), ‘Compensating wage differentials among selfemployed workers: Evidence from job satisfaction scores’. Osaka University Working Paper, Osaka, Japan. Koga, T. (2005), ‘R&D subsidy and self-financed R&D: The case of japanese high-technology start-ups’. Small Business Economics 24(1), 53–62. Konings, J. (1995), ‘Gross job flows and the evolution of size in U.K. establishments’. Small Business Economics 7(3), 213–220. Kortum, S. and J. Lerner (2000), ‘Assessing the contribution of venture capital to innovation’. The RAND Journal of Economics 31(4), 674–692.

References

91

Leonard, J. S. (1987), ‘On the size distribution of employment and establishments’. Institute of Industrial Relations Working Paper iirwps’005’87, University of California, Berkeley, U.S.A. Lever, M. H. C. (1996), ‘Firm size and employment determination in Dutch manufacturing industries’. Small Business Economics 8(5), 389–396. Love, J. H. and B. Ashcroft (1999), ‘Market versus corporate structure in plant-level innovation performance’. Small Business Economics 13(2), 97–109. Lowe, R. A. and A. A. Ziedonis (2006), ‘Overoptimism and the performance of entrepreneurial firms’. Management Science 52(2), 173–186. Lucchetti, R. and A. Sterlacchini (2004), ‘The adoption of ICT among SMEs: Evidence from an Italian survey’. Small Business Economics 23(2), 151–168. Luger, M. I. and J. Koo (2005), ‘Defining and tracking business startups’. Small Business Economics 24(1), 17–28. Maes, J., L. Sels, and F. Roodhooft (2005), ‘Modelling the link between management practices and financial performance’. Small Business Economics 25(1), 17–34. Moskowitz, T. J. and A. Vissing-Jørgensen (2002), ‘The returns to entrepreneurial investment: A private equity premium puzzle?’. The American Economic Review 92(4), 745–778. Mueller, P., A. Van Stel, and D. Storey (2008), ‘The effects of new firm formation on regional development over time: The case of Great Britain’. Small Business Economics, forthcoming. Nadiri, M. I. (1970), ‘Some approaches to the theory and measurement of total factor productivity: A survey’. Journal of Economic Literature 8(4), 1137–1177. Neumark, D., J. Zhang, and B. Wall (2005), Business Establishment Dynamics and Employment Growth. Available at SSRN: http://ssrn.com/abstract=909249. Nguyen, S. V. and S. H. Lee (2002), ‘Returns to scale in small and large U.S. manufacturing establishments: Further evidence’. Small Business Economics 19(1), 41–50.

92

References

Norton, W. I. and W. T. Moore (2006), ‘The influence of entrepreneurial risk assessment on venture launch or growth decisions’. Small Business Economics 26(3), 215–226. Nurmi, S. (2006), ‘Sectoral differences in plant start-up size in the finnish economy’. Small Business Economics 26(1), 39–59. Oliveira, B. and A. Fortunato (2006), ‘Firm growth and liquidity constraints: A dynamic analysis’. Small Business Economics 27(2–3), 139–156. Oosterbeek, H. and M. Van Praag (1995), ‘Firm-size wage differentials in the Netherlands’. Small Business Economics 7(3), 173–182. Pannenberg, M. and G. G. Wagner (2001), ‘Overtime work, overtime compensation and the distribution of economic well-being. Evidence for the West Germany and Great Britain’. IZA Discussion Paper #318, Bonn, Germany. Parker, S. C. (2004), The Economics of Self-Employment and Entrepreneurship. Cambridge, UK: Cambridge University Press. Picot, G. and R. Dupuy (1998), ‘Job creation by company size class: The magnitude, concentration and persistence of job gains and losses in Canada’. Small Business Economics 10(2), 117–139. Robbins, D. K., L. J. Pantuosco, D. F. Parker, and B. K. Fuller (2000), ‘An empirical assessment of the contribution of small business employment to U.S. state economic performance’. Small Business Economics 15(4), 293–302. Rodr´ıguez, A. C., M. A. Molina, A. L. G. P´erez, and U. M. Hern´andez (2003), ‘Size, age and activity sector on the growth of the small and medium firm size’. Small Business Economics 21(3), 289–307. Roper, S. (1999), ‘Under-reporting of R&D in small firms: The impact on international R&D comparisons’. Small Business Economics 12(2), 131–135. Rosen, H. and P. Willen (2002), ‘Risk, return and self-employment’. University of Chicago/Princeton University Discussion Paper, U.S.A. Rosen, S. (1981), ‘The economics of superstars’. The American Economic Review 71(5), 845–858. Scott, A. J. (2006), ‘Entrepreneurship, innovation and industrial development: Geography and the creative field revisited’. Small Business Economics 26(1), 1–24.

References

93

Shaffer, S. (2006), ‘Establishment size and local employment growth’. Small Business Economics 26(5), 439–454. Solow, R. M. (1957), ‘Technical change and the aggregate production function’. The Review of Economics and Statistics 39(3), 312– 320. Sørensen, J. B. and T. E. Stuart (2000), ‘Aging, obsolescence, and organizational innovation’. Administrative Science Quarterly 45(1), 81–112. Spletzer, J. R. (2000), ‘The contribution of establishment births and deaths to employment growth’. Journal of Business and Economic Statistics 18(1), 113–126. Sternberg, R. and S. Wennekers (2005), ‘Determinants and effects of new business creation using global entrepreneurship monitor data’. Small Business Economics 24(3), 193–203. Storey, D. J. and S. Johnson (1987), Job Generation and Labour Market Change. London: Macmillan. Sutton, J. (1997), ‘Gibrat’s legacy’. Journal of Economic Literature 35(1), 40–59. Troske, K. R. (1999), ‘Evidence on the employer size-wage premium from worker-establishment matched data’. The Review of Economics and Statistics 81(1), 15–26. Van der Sluis, J. and C. M. Van Praag (2007), ‘Returns to education for entrepreneurs and employees: Identification by means of changes in compulsory schooling laws’. University of Amsterdam Working Paper, Amsterdam, Netherlands. Van der Sluis, J., M. Van Praag, and A. Van Witteloostuijn (2006), ‘Why are the returns to education higher for entrepreneurs than for employees?’. University of Amsterdam Working Paper, Amsterdam, Netherlands. Van Praag, C. M. and J. S. Cramer (2001), ‘The roots of entrepreneurship and labor demand: Individual ability and low risk aversion’. Economica 68(269), 45–62. Van Stel, A., M. Carree, and R. Thurik (2005), ‘The effect of entrepreneurial activity on national economic growth’. Small Business Economics 24(3), 311–321.

94

References

Van Stel, A. and K. Suddle (2008), ‘The impact of new firm formation on regional development in the Netherlands’. Small Business Economics, forthcoming. Wennekers, S., A. Van Stel, R. Thrurik, and P. Reynolds (2005), ‘Nascent entrepreneurship and the level of economic development’. Small Business Economics 24(3), 293–309. Winter-Ebmer, R. and J. Zweimuller (1999), ‘Firm-size wage differentials in Switzerland: Evidence from job-changers’. The American Economic Review 89(2), 89–93. Wong, P. K., Y. P. Ho, and E. Autio (2005), ‘Entrepreneurship, innovation, and economic growth: Evidence from GEM data’. Small Business Economics 24(3), 335–350. Wunnava, P. V. and B. T. Ewing (2000), ‘Union-nonunion gender wage and benefit differentials across establishment sizes’. Small Business Economics 15(1), 47–57. Wynarczyk, P. and R. Watson (2005), ‘Firm growth and supply chain partnerships: An empirical analysis of U.K. SME subcontractors’. Small Business Economics 24(1), 39–51. Yang, C.-H. and C.-H. Huang (2005), ‘R&D, size and firm growth in Taiwan’s electronics industry’. Business Economics 25(5), 477–487.