Business regulation, labor force participation and employment in industrial countries

Journal of Economics and Business 61 (2009) 238–260

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Journal of Economics and Business

Business regulation, labor force participation and employment in industrial countries Horst Feldmann ∗ Department of Economics & International Development, University of Bath, Bath BA2 7AY, United Kingdom

a r t i c l e

i n f o

Article history: Received 27 April 2007 Received in revised form 7 April 2008 Accepted 30 June 2008 JEL classification: J21 J23 K23 L51 Keywords: Corruption Employment Labor force participation Regulation

a b s t r a c t Using data from 19 industrial countries for 5 years in the period 1990–2002, this paper analyzes to what extent anticompetitive business regulations, like price controls and administrative obstacles to start a new business, affect labor force participation and employment rates. According to the regression results, they appear to lower both. Corruption, which is one result of strict business regulation, is also found to lower labor force participation and employment rates. While most effects on the general population seem to be modest, the effects on the low-skilled are likely to be substantial. The results are robust to variations in specification. © 2008 Elsevier Inc. All rights reserved.

1. Introduction According to most theoretical studies, anticompetitive business regulations (e.g., entry restrictions, price controls) generally reduce equilibrium output and thus labor demand.1 They lead to fewer entries of new firms, lower competitive pressures and more inefficiencies. In addition, the lack of competition increases the price mark-up that firms are able to enforce. Insofar as employees are able to appropriate part of these rents via wage premia, firms will produce more capital intensive and less labor intensive

∗ Tel.: +44 1225 386853; fax: +44 1225 383423. E-mail address: [email protected]. 1 See, e.g., Hicks (1935), Nickell (1999), Fonseca, Lopez-Garcia, and Pissarides (2001), Blanchard and Giavazzi (2003), Pissarides (2003), Spector (2004). 0148-6195/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.jeconbus.2008.06.002

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than in a competitive situation.2 Strict business regulation may also induce firms to either relocate abroad or to relocate jobs abroad (Busse & Hefeker, 2007; Daude & Stein, 2007). All of these effects will cause employment to fall. In some sectors, such as public utilities, protection from domestic and foreign competitors can induce state-controlled firms to hoard labor. They can thus maintain employment at artificially high levels, at least for some time. However, the related productive inefficiencies are likely to spill over to the entire economy, reducing employment elsewhere. Moreover, the budgetary costs of subsidizing state-controlled enterprises on a large scale are likely to result in an increased tax burden, lowering the overall employment level (Boeri, Nicoletti, & Scarpetta, 2000). Furthermore, strict business regulations encourage bureaucratic extortion, i.e., bribing. As red tape and bureaucratic extortion increase the cost of starting a new business officially, they not only reduce official labor demand, they can also induce new firms to be started in the informal sector. As the cost of strict business regulations also makes official firms offer lower wages, they reduce official labor supply and tend to drive potential employees away into underground jobs, reducing official employment figures (Bouev, 2002). Most of the mechanisms that tend to reduce the employment rate are also likely to reduce the labor force participation rate. Specifically, low labor demand from and low wages offered by official firms as well as high taxes and high costs of starting a new business officially are likely to induce people to either not be economically active at all or to work in the shadow economy, rather than in the official economy. We thus hypothesize that tight business regulations lower not only the employment rate but also the labor force participation rate. Additionally, we hypothesize that low-skilled workers and possibly women are most severely affected when jobs are in short supply because of tight business regulations. The jobs of low-skilled workers can be relocated more easily to the shadow economy or to foreign countries than the jobs of high-skilled workers. Furthermore, it is relatively easy to substitute low-skilled workers by capital. In many countries, women often have lower levels of formal qualification and less vocational experience than men. Thus many of their jobs can also be substituted by capital or relocated to the shadow economy or to foreign countries relatively easily. In addition, many women take a career break to have children and later on try to get back into employment. Also, both low-skilled and female workers often do not have the personal connections necessary to get a job in protected industries. Furthermore, both often do not have the skills, financial resources and connections necessary to start their own business. For these reasons, anticompetitive business regulations are likely to have above-average effects on these groups. In sum, there are strong theoretical reasons suggesting that anticompetitive business regulations lower both the employment and the labor force participation rate. However, so far there has been a dearth of empirical studies on this topic. The studies that are available either analyze panels of industrial countries or individual industrial countries. Among the former, Nicoletti et al. (2001) find that anticompetitive product market regulations had substantial negative effects on employment rates in OECD countries. Similarly, according to Nicoletti and Scarpetta (2005), restrictive product market regulations have curbed employment rates in OECD countries where no product market reforms were implemented. Messina (2005) finds that, in OECD countries, heavier administrative burdens to business start-ups are associated with a lower share of employment in services. According to Griffith and Harrison (2004), higher mark-ups are associated with lower levels of employment in EU countries. Several studies have analyzed the effects of deregulation of various industries in the United States, which took place in the late 1970s and early 1980s (for a survey, see Peoples, 1998). These studies find that deregulation of the trucking and airline industries led to substantial employment gains; that employment held steady in telecommunications; and that it fell dramatically in railroads (possibly due to higher barriers to entry in this industry). According to Burda (2000), a non-negligible component of the recent Dutch employment miracle could be attributed to product market deregulation, in particular the liberalization of shop-closing laws effected in the mid-1990s. Finally, Bertrand and Kramarz (2002)

2

In addition to the literature cited in footnote 1, see Nickell, Vainiomaeki, and Wadhwani (1994) and Jean and Nicoletti (2004).

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find that, in France, zoning regulation introduced in the early 1970s to restrain the development of large retail stores slowed down employment growth. This paper innovates in four ways. First, it uses indicators that measure the strictness of business regulation in the entire economy.3 By contrast, previous studies have either focused on individual industries or have used indicators that cover only a subset of industries. For example, the widely used index of product market regulation constructed by the OECD covers seven energy and service industries only.4 Second, we make a first attempt at exploring the labor market effects of corruption, which, as mentioned above, is likely to be more widespread in an environment of strict business regulation. Third, whereas previous studies have focused on employment effects, we additionally analyze the effects on labor force participation. Fourth, this paper is the first to analyze the effects on female and low-skilled workers. The next section describes the data set and the econometric method used in this paper. Section 3 presents and discusses the regression results. Section 4 concludes. 2. Data and methodology To measure the strictness of business regulation, this paper uses the component ‘business regulations’ from the Economic Freedom of the World (EFW) index (Gwartney & Lawson, 2005).5 This component consists of five indicators: • • • • •

price controls, administrative conditions and new businesses, time with government bureaucracy, starting a new business, irregular payments.

These indicators are designed to identify the extent to which regulatory restraints and bureaucratic procedures limit competition and the operation of markets. The rating scale of the EFW index ranges from 0 to 10, with 0 representing the lowest and 10 the highest degree of economic freedom. In order to score high in the ‘business regulations’ portion of the index, countries must allow markets to determine prices and refrain from regulatory activities that retard entry into business and increase the cost of producing goods. They must also refrain from using their power to extract irregular payments. The following regressions measure the strictness of business regulation using the ratings for the component ‘business regulations’. The ratings for this component are calculated as the arithmetic means of the ratings for its five indicators. Some of the following regressions also use the ratings for the five indicators of this component. This allows us to analyze which aspects of business regulation have the strongest effects. As higher marks on the 0-to-10 scale indicate more flexible regulation, we label the variable based on the EFW component ‘business regulations’ ‘flexible business regulations’. The variables of the five indicators have been relabeled accordingly (e.g., ‘few price controls’ instead of ‘price controls’). For definitions, descriptive statistics and sources of all variables, see Table 1. The indicator ‘price controls’ measures the extent to which businesses are free to set their own prices. The more widespread the use of price controls, the lower the rating. In order to categorize countries with respect to this indicator, the authors of the EFW index use data from the Institute

3 In a companion paper, we use the same indicators to study the labor market effects of business regulation in a cross-section of 74 industrial, developing and transition countries (Feldmann, 2008). 4 Recently, the OECD has also constructed an economy-wide indicator of product market regulation. However, this indicator covers only two years, 1998 and 2003 (Conway, Janod, & Nicoletti, 2005). 5 The EFW index has been developed by a group of North American economists under the auspices of the Canadian Fraser Institute with the aid of a worldwide network of further economists and institutes. It measures the degree of economic freedom in five major areas: (1) size of government, (2) legal structure and security of property rights, (3) access to sound money, (4) freedom to trade internationally, and (5) regulation of credit, labor and business. Area (5) is divided into three components: (5A) credit market regulations, (5B) labor market regulations, and (5C) business regulations.

Table 1 List of variables. Mean

Labor force participation rate

Labor force (employed and unemployed) as a percentage of the population. Age group: 15–64 years. Harmonized series

72.65

Female labor force participation rate

Female labor force (employed and unemployed) as a percentage of the female population. Age group: 15–64 years. Harmonized series

Labor force participation rate among low-skilled workers

S.D.

Maximum

Source

5.85

58.10

84.00

ILO (2005)

64.28

8.79

41.90

81.90

ILO (2005)

Labor force (employed and unemployed) with less than upper secondary education as a percentage of the population with the same educational attainment. Age group: 25–64 years

64.16

6.45

53.19

86.40

OECD (1998, 2005a)

Employment rate

Employed aged 15–64 years as a percentage of the population in the same age bracket

68.07

7.18

48.28

83.12

OECD (2005a)

Female employment rate

Employed women aged 15–64 years as a percentage of the female population in the same age bracket

59.88

10.18

31.78

80.96

OECD (2005a)

Employment rate among low-skilled worker

Employed with less than upper secondary education as a percentage of the population with the same educational attainment. Age group: 25–64 years

58.64

7.02

46.30

77.70

OECD (1998, 2005a)

Flexible business regulations

Component of the Economic Freedom of the World (EFW) index, consisting of five indicators (see below). All indicators carry equal weights. Higher values on the 0-to-10 scale represent more flexible regulation

6.80

1.14

4.43

9.44

Gwartney and Lawson (2005)

Few price controls

Extent to which businesses are free to set their own prices. Higher values on the 0-to-10 scale represent less widespread use of price controls. Indicator of the EFW index

7.08

1.49

2.00

10.00

Gwartney and Lawson (2005)

Administrative procedures conducive to starting a new business

Indicator of the EFW index, based on results from the World Economic Forum’s annual Executive Opinion Surveys. The survey statement is “Administrative procedures are an important obstacle to starting a new business”. Higher values on the 0-to-10 scale indicate less burdensome procedures

4.75

2.10

1.83

8.20

Gwartney and Lawson (2005)

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Minimum

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Definition

242

Table 1 (Continued ) Definition

Mean

S.D.

Minimum

Maximum

Source

Indicator of the EFW index, based on results from the World Economic Forum’s annual Executive Opinion Surveys. The survey statement is “Senior management spends a substantial amount of time dealing with government bureaucracy”. Higher values on the 0-to-10 scale indicate a less time-consuming bureaucracy

7.02

1.14

3.50

9.35

Gwartney and Lawson (2005)

Generally easy to start a new business

Indicator of the EFW index, based on results from the World Economic Forum’s annual Executive Opinion Surveys. The survey statement is “Starting a new business is generally easy”. Higher values on the 0-to-10 scale indicate more ease of starting a new business

6.23

1.47

3.43

8.80

Gwartney and Lawson (2005)

Rare irregular payments

Indicator of the EFW index, based on results from the World Economic Forum’s annual Executive Opinion Surveys. The survey statement is “Irregular, additional payments connected with import and export permits, business licenses, exchange controls, tax assessments, police protection, or loan applications are very rare”. Higher values on the 0-to-10 scale indicate that irregular payments are more rare

8.20

1.25

2.91

9.99

Gwartney and Lawson (2005)

Trade union density

Percentage of employees in trade unions

36.63

21.73

9.60

87.50

OECD (2004a)

Wage bargaining at industry level

Industry level predominant in wage bargaining (dummy variable)

0.33

0.47

0.00

1.00

OECD (2004a)

Wage bargaining coordination

Degree of coordination in wage bargaining. The indicator ranges from 1 to 5, with higher values representing a higher degree of coordination

3.13

1.34

1.00

5.00

OECD (2004a)

Tax wedge

Income tax plus employee’s and employer’s social security contributions less cash benefits as a percentage of labor costs; one-earner family with two children; average production worker

28.44

8.84

13.50

44.90

OECD (2004b)

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Small amount of time dealing with government bureaucracy

Indicator for strictness of protection against individual dismissals and for strictness of regulation of temporary employment (fixed-term contracts, temporary work agency employment). The scale ranges from 0 (least restrictive) to 6 (most restrictive)

2.03

0.99

0.20

4.10

OECD (2004a)

Unemployment benefits replacement rate

Gross unemployment benefits as a percentage of previous gross wage earnings. Averages across two earnings levels, three family types, and three unemployment duration categories

32.37

13.02

2.58

68.50

OECD (2004c)

Active labor market policies

Expenditure on active labor market programs per unemployed person, divided by 1000

7.62

6.61

0.84

37.77

OECD (2004a)

Output gap

Deviations of actual GDP from potential GDP as a percentage of potential GDP

0.31

2.02

−8.18

4.44

Collective bargaining coverage

Percentage of salaried workers subject to union-negotiated terms and conditions of employment

67.61

25.95

14.92

97.50

OECD (2004a)

Minimum wage

Statutory minimum wage as a share of average wage

0.25

0.23

0.00

0.63

OECD (2004a)

GDP per capita

Gross domestic product per capita, converted to constant 2000 international dollars using purchasing power parity rates, divided by 1000

25.32

4.30

14.21

34.83

Product market regulation

Indicator of regulatory impediments to product market competition in the following seven non-manufacturing industries: gas, electricity, post, telecoms (mobile and fixed services), passenger air transport, railways (passenger and freight services) and road freight. The scale ranges from 0 (least restrictive) to 6 (most restrictive)

2.94

1.13

1.11

5.83

OECD (2003, 2005b)

World Bank (2005)

Conway et al. (2005)

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Employment protection legislation

243

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for Management Development, Price Waterhouse, the World Bank, the Economist Intelligence Unit and the U.S. State Department. Their ratings are based on objective data and on the assessments of experienced experts. The other four indicators are calculated using results from the World Economic Forum’s annual Executive Opinion Surveys. The respondents are a company’s CEO or another member of its senior management. In each country approximately 60–70 executives are interviewed. The industry structure of the companies questioned corresponds largely to the industry structure of the relevant economy (excluding the agricultural sector). Also, care is taken to question companies of various size categories and types (e.g., private and state-owned, domestically oriented and internationally active enterprises). The participants are asked to indicate on a numerical scale to which extent they agree or disagree with a specific statement. With regard to ‘administrative conditions and new businesses’, the statement says: “Administrative procedures are an important obstacle to starting a new business”. With regard to ‘time with government bureaucracy’, the statement says: “Senior management spends a substantial amount of time dealing with government bureaucracy”. With regard to ‘starting a new business’, the statement says: “Starting a new business is generally easy”. With regard to ‘irregular payments’, the statement says: “Irregular, additional payments connected with import and export permits, business licenses, exchange controls, tax assessments, police protection, or loan applications are very rare”. As with the first indicator, higher values on the 0-to-10 scale always represent less burdensome regulation. Not only is it advantageous that the selection of respondents in the Executive Opinion Surveys is largely representative. Additionally, the respondents have comprehensive knowledge of and practical experience with the business regulations of their countries. There is a potential drawback though: generally, each respondent could use his own yardstick when answering the questions. However, in the planning, implementation and analysis of the surveys, the World Economic Forum takes great care to ensure the use of a uniform yardstick. For example, the respondents are provided with a written explanation of the numerical scale. Also, the answers are examined for robustness and consistency using various methods. In one of these checks, half of the answers from each country are dropped at random. Up to now, the results remained stable in the process. Thus they have obviously not been distorted by individual peculiarities in responding (see, e.g., Cornelius & McArthur, 2002). To measure the effects on the labor market, we use overall labor force participation and employment rates as well as labor force participation and employment rates relating to female and low-skilled workers. This enables us to test all of our hypotheses, including the hypothesis that strict business regulations are likely to have above-average effects on these two groups. Our labor market performance data are exclusively based on labor force surveys. Most of them come from the OECD, which is the standard source for these data on its member countries. The data for the variables ‘labor force participation rate’ and ‘female labor force participation rate’ come from the ILO, which has recently developed a new harmonized series of labor force participation. This series reduces some of the previous limitations to comparability. Specifically, it accounts for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. We use the output gap to control for the state of the business cycle and year dummies to control for year-specific effects. Furthermore, we control for the impact of all major labor market institutions that have been considered in the recent literature. As numerous empirical studies have shown, certain labor market institutions appear to have a considerable impact on the performance of the labor market.6 We use data from the OECD, which has developed the best indicators of labor market institutions. Our baseline specifications control for the impact of the following institutions: trade union density, wage bargaining at industry level, wage bargaining coordination, tax burden on labor (‘tax wedge’), employment protection legislation, unemployment benefits replacement rate, active labor market policies. Additionally, in some of our robustness checks, we control for the impact of collective bargaining coverage and statutory minimum wages.

6 See, e.g., Scarpetta (1996), Elmeskov, Martin, and Scarpetta (1998), Feldmann (2003, 2005, in press), IMF (2003), Belot and van Ours (2004), and Nickell, Nunziata, and Ochel (2005).

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

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The estimation sample consists of 19 industrial countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States. Most of our regressions are based on data for the years 1995, 2000, 2001 and 2002. The OECD data on labor market institutions are unavailable for more recent years. Prior to 2000, the EFW index has been calculated for every fifth year, and annually since then. The component ‘business regulations’ was added to the index in 1995. As data for the indicator ‘price controls’ were also published for 1990, most regressions employing this variable are additionally based on data for this year. (The regressions to explain the labor force participation and employment rates among low-skilled workers are an exception as data on these rates are unavailable for 1990.) On the other hand, data for the indicator ‘administrative conditions and new businesses’ have only been published for years from 2000. The regressions employing this variable are thus based on data for the years 2000, 2001 and 2002. To relate business regulation to labor force participation and employment rates, we estimate the following model: Yit = ˛ + ˇXit + Lit + ıZit + t + εit , where Yit is a labor market performance variable of country i at year t, ˛ is a constant, Xit denotes a business regulation variable, Lit denotes a vector of labor market institutions variables, Zit is the output gap variable, t denotes year dummies and εit is the error term. We control for unobserved country effects by using the random effects, feasible generalized least squares (FGLS) procedure, specifically, the Swamy–Arora (1972) estimator that is cited most often in textbooks (e.g., Baltagi, 2001). Random effects estimates have the advantage of exploiting both the cross-country and the time-series variation included in the sample. By contrast, fixed effects estimates would be very imprecise because they only use the time-series variation within countries. The error term εit can be decomposed as εit = wi + uit , where wi denotes time-invariant country-specific characteristics and uit is the combined time-series and cross-section error term. The random effects estimation treats the country-specific effects (wi ) as random. However, it requires that they are uncorrelated with the explanatory variables included in the estimated equation. If this condition is violated, the random effects FGLS estimator yields inconsistent estimates. Therefore, a Hausman (1978) test for misspecification of the random effects model has been performed for each regression. In none of our baseline regressions and in only two of our robustness checks did we find any evidence for such a misspecification bias (Tables 3–10).7 Thus, in our case the random effects FGLS method is the appropriate choice. Finally, to correct for heteroskedasticity, we estimate robust t-statistics using the technique developed by White (1980). As indicated in the previous section, corruption is one result of strict business regulation. Thus we instrument for our ‘rare irregular payments’ variable, using ‘few price controls’, ‘small amount of time dealing with government bureaucracy’ and ‘generally easy to start a new business’ as instruments.8 Staiger and Stock (1997) use the rule of thumb that the first-stage F-statistic should be greater than 10, otherwise the instruments are weak. Our instruments pass the Staiger–Stock test in all but the regressions to explain the labor force participation and employment rates among low-skilled workers, in which the first-stage F-statistic, at 9.84, is only marginally below 10 (Tables 3–10). Thus our instruments are likely to be sufficiently strong. As the correlation matrix indicates, there is substantial correlation among most of our business regulation variables (Table 2). Therefore, we estimate specifications that include these measures one at a

7 As indicated in Tables 3–10, in some cases the variance matrix of the difference between the random and fixed effects estimates is not positive definite so that the test statistic cannot be computed. (This is probably due to the fact that data for some explanatory variables do not vary much through time.) Although we cannot rule out that misspecification bias may be a problem in these cases, there is no evidence for this. 8 From an economic point of view, ‘administrative procedures conducive to starting a new business’ would also be a suitable instrument for ‘rare irregular payments’. However, as data on the former variable are unavailable for the year 1995, we did not employ it as an instrument.

246

Table 2 Correlation matrix. Flexible business regulations

Administrative procedures conducive to starting a new business

Small amount of time dealing with government bureaucracy

Generally easy to start a new business

Rare irregular payments

Trade union density

Wage bargaining at industry level

Wage bargaining coordination

Tax wedge

Employment protection legislation

Unemployment benefits replacement rate

Active labor market policies

Output gap

Collective bargaining coverage

Minimum wage

GDP per capita

Product market regulation

1.00 −0.24 −0.08

1.00 −0.45

1.00

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Flexible business regulations Few price controls Administrative procedures conducive to starting a new business Small amount of time dealing with government bureaucracy Generally easy to start a new business Rare irregular payments Trade union density Wage bargaining at industry level Wage bargaining coordination Tax wedge Employment protection legislation Unemployment benefits replacement rate Active labor market policies Output gap Collective bargaining coverage Minimum wage GDP per capita Product market regulation

Few price controls

1.00 0.75 0.84

1.00 0.40

1.00

0.73

0.31

0.65

1.00

0.80

0.58

0.50

0.42

1.00

0.71

0.57

0.32

0.44

0.64

1.00

0.24 −0.15

0.07 −0.15

0.11 −0.02

0.10 −0.07

0.15 −0.25

0.33 −0.10

1.00 0.15

1.00

−0.26

−0.43

−0.01

−0.01

−0.37

−0.18

0.40

0.30

1.00

−0.15 −0.47

−0.10 −0.41

0.02 −0.12

−0.06 −0.24

−0.36 −0.68

−0.20 −0.50

0.44 0.08

0.49 0.39

0.43 0.57

1.00 0.60

1.00

−0.03

−0.10

−0.08

0.06

−0.18

0.21

0.27

0.19

0.43

0.31

0.34

1.00

0.01

−0.09

−0.08

0.06

−0.02

0.26

0.41

0.35

0.38

0.32

0.18

0.47

1.00

0.02 −0.21

−0.03 −0.21

0.44 −0.02

0.05 −0.19

0.04 −0.40

0.12 −0.08

−0.19 0.42

0.13 0.47

0.07 0.54

−0.02 0.69

0.10 0.65

0.11 0.51

0.20 0.33

1.00 0.07

1.00

−0.12 0.07 −0.27

−0.07 0.02 −0.36

−0.10 −0.00 0.05

−0.17 0.13 −0.07

−0.01 0.25 −0.56

−0.12 0.24 −0.57

−0.53 0.03 0.06

−0.11 −0.07 0.11

−0.49 0.08 0.34

−0.25 −0.11 0.37

−0.07 −0.45 0.62

−0.04 −0.02 0.14

−0.21 0.32 −0.01

0.01 0.05 0.05

−0.16 −0.19 0.38

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

247

Fig. 1. Business regulation and labor force participation. 19 industrial countries; averages over the years 1995, 2000, 2001 and 2002. X-axis: ratings for the component ‘business regulations’ of the Economic Freedom of the World (EFW) index; the index ranges from 0 to 10; higher marks on the ‘business regulations’ portion of the index represent more flexible, procompetitive regulation. Y-axis: labor force (employed and unemployed) as a percentage of the population; age group: 15–64 years.

time.9 Also note that one of our control variables, ‘employment protection legislation’, is fairly closely correlated with one of our variables of interest, ‘generally easy to start a new business’, and moderately correlated with another control variable, ‘tax wedge’. Controlling for the impact of employment protection legislation is indispensable when analyzing the determinants of labor market performance; thus we did not drop this variable from our baseline specifications. Instead, in order to check whether including this variable leads to biased estimates, we dropped it in some of our sensitivity checks. As it turned out, the estimates for our variables of interest were very similar (results not reported here). Multicollinearity is also a concern in some of our robustness checks that include additional variables or substitute one variable for another. Specifically, ‘collective bargaining coverage’ is highly correlated with two of our standard control variables, ‘tax wedge’ and ‘employment protection legislation’, while ‘product market regulation’ is moderately correlated with ‘employment protection legislation’ (Table 2). However, dropping these standard control variables one at a time hardly affected the coefficients on our variables of interest either (results not reported here). 3. Results Before we discuss the results from the multivariate regressions, let us briefly take a look at the bivariate associations between the ratings for the EFW component ‘business regulations’, on the one hand, and the labor force participation rate and the employment rate, on the other hand (Figs. 1 and 2). Both figures use country averages. Fig. 1 clearly indicates that countries with stricter (more flexible) business regulation have lower (higher) labor force participation rates. Fig. 2 indicates that stricter (more flexible) business regulation is generally associated with a lower (higher) level of employment. Of course, these bivariate associations do not prove that there is a causal impact of business regulation on labor force participation and employment. Therefore, the next step is to see whether there is a statistically significant effect once we control for the impact of labor market institutions and the other factors mentioned in the previous section.

9

The small sample size is an additional reason for including the business regulation variables separately, rather than jointly.

248

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Fig. 2. Business regulation and employment. 19 industrial countries; averages over the years 1995, 2000, 2001 and 2002. X-axis: ratings for the component ‘business regulations’ of the Economic Freedom of the World (EFW) index; the index ranges from 0 to 10; higher marks on the ‘business regulations’ portion of the index represent more flexible, procompetitive regulation. Y-axis: employed aged 15–64 years as a percentage of the population in the same age bracket.

Tables 3–8 present our main results. All estimates for the variable ‘flexible business regulations’ are statistically significant at the 1% level. With tighter business regulations, the labor force participation rate declines among the total working-age population as well as among female and low-skilled workers (Tables 3–5). Furthermore, tighter business regulations are associated with lower employment levels among the total working-age population as well as among women and the low-skilled (Tables 6–8). Our estimates suggest that liberalizing business regulation would have a noticeable pay-off in terms of higher labor force participation and employment, especially for low-skilled workers. For example, according to the EFW index, Italy had the strictest business regulations in our sample of 19 industrial countries. On average over the years 1995 and 2000–2002, its rating for the component ‘business regulations’ was 5.14. By contrast, Finland had the most flexible business regulations. Its score averaged 8.26. Finland also had substantially higher labor force participation and employment rates, both among the total working-age population and among each of the two demographic groups. According to our estimates, if business regulations in Italy had been as flexible as in Finland, the Italian labor force participation rate would have been 1.4 percentage points higher among the total working-age population, 1.5 percentage points higher among women and 4.2 percentage points higher among the low-skilled, ceteris paribus. Additionally, Italy’s employment rate would have been 3.1 percentage points higher among the total working-age population, 2.6 percentage points higher among women and 3.2 percentage points higher among low-skilled workers, ceteris paribus. Of course, these figures should be interpreted with some caution. However, they illustrate that anticompetitive business regulations are likely to involve non-negligible costs in terms of poorer labor market performance. The estimates for the five indicators individually measuring the impact of the various features of business regulation provide differentiated insights into the importance of such features to the labor market. The regression analysis produced the following results: • Price controls are likely to lower both labor force participation and employment rates among the total working-age population (Tables 3 and 6). Furthermore, they appear to lower labor force participation among women (Table 4). • More burdensome administrative procedures to start a new business are correlated with a lower employment rate among women (Table 7).

Table 3 Regressions to explain the labor force participation ratea . (1)

Number of observations R2 Standard error of regression F-Statistic Hausman testc

(3)

(4)

(5)

(6)b

0.45*** (4.44) 0.46*** (3.75) 0.11 (0.84) 0.19* (1.98) −0.37 (−1.40) 0.11*** (3.46) −2.67* (−1.68) −1.17*** (−3.64) −0.12*** (−5.28) −0.10 (−0.16) 0.03** (2.01) 0.14*** (4.57) 0.27*** (6.79) 76 0.61 0.84 8.34*** 15.53

0.08*** (8.70) 2.28* (1.78) −1.92*** (−3.15) −0.17*** (−4.61) −0.13 (−0.26) 0.03*** (2.75) 0.21*** (3.78) −0.04 (−0.41) 95 0.49 1.41 6.10*** n.a.d

0.10*** (60.72) −1.80 (−1.24) −0.75 (−1.57) −0.17*** (−3.45) −1.54 (−1.17) 0.07 (1.09)

0.11*** (3.32) −2.83* (−1.76) −1.34*** (−3.98) −0.09*** (−4.98) −0.20 (−0.39) 0.04* (1.74)

0.11*** (2.72) −3.02** (−2.31) −1.40*** (−2.95) −0.05 (−1.29) −0.62 (−0.81) 0.05** (2.53)

1.28* (1.70) 0.08*** (3.57) −3.33 (−1.57) −1.16*** (−6.65) −0.14** (−2.64) 1.40 (1.44) −0.02 (−0.34)

0.12*** (13.21) 0.39*** (4.30)

0.13*** (4.87) 0.28*** (6.40)

0.10*** (2.72) 0.29*** (5.87)

0.19*** (2.77) 0.40*** (4.65)

57 0.37 0.56 2.45** 6.28

76 0.62 0.80 8.54*** 9.89

76 0.64 0.76 9.35*** 11.39

76 0.47 0.97 8.87*** n.a.d

a Feasible generalized least squares estimates with country-specific random effects (Swamy–Arora method). The estimation sample consists of 19 industrial countries. Data are for the years 1995, 2000, 2001 and 2002, except for regression (2), which is based on data for the years 1990, 1995, 2000, 2001 and 2002, and regression (3), which is based on data for the years 2000, 2001 and 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain year dummies and a constant term. b Two-stage regression. ‘Rare irregular payments’ is instrumented with ‘few price controls’, ‘small amount of time dealing with government bureaucracy’ and ‘generally easy to start a new business’. The first-stage F-statistic is 11.88***. c 2 statistic. d The variance matrix of the difference between the random and fixed effects estimates is not positive definite so that the test statistic cannot be computed.

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Flexible business regulations Few price controls Administrative procedures conducive to starting a new business Small amount of time dealing with government bureaucracy Generally easy to start a new business Rare irregular payments Trade union density Wage bargaining at industry level Wage bargaining coordination Tax wedge Employment protection legislation Unemployment benefits replacement rate Active labor market policies Output gap

(2)

249

250

Table 4 Regressions to explain the female labor force participation ratea . (1)

Number of observations R2 Standard error of regression F-Statistic Hausman testc

(2)

(3)

(4)

(5)

(6)b

0.49*** (3.17) 0.43*** (2.64) 0.23 (1.56) 0.20 (1.63) −0.28 (−0.88) 0.19*** (6.28) −4.05* (−1.97) −2.14*** (−2.93) −0.12*** (−5.00) 0.39 (0.63) 0.06*** (2.78) 0.19*** (8.64) 0.34*** (6.17) 76 0.77 0.92 17.62*** 8.51

0.15*** (10.70) 3.84** (2.48) −3.37*** (−5.98) −0.19*** (−5.82) 0.31 (0.50) 0.07*** (5.63) 0.27*** (3.60) −0.04 (−0.29) 95 0.64 1.92 11.05*** n.a.d

0.19*** (71.43) −2.66** (−2.69) −1.66*** (−3.15) −0.14*** (−2.88) −2.07 (−1.32) 0.15* (1.83)

0.18*** (5.48) −4.23* (−1.85) −2.21** (−2.25) −0.09*** (−5.54) 0.27 (0.85) 0.07** (2.30)

0.17*** (4.89) −4.31* (−1.79) −2.17** (−2.42) −0.06 (−1.61) −0.18 (−0.27) 0.08*** (2.73)

1.39 (1.50) 0.14*** (6.59) −4.74* (−1.67) −2.04*** (−4.01) −0.16** (−2.49) 2.04* (1.73) 0.00 (0.05)

0.10*** (17.76) 0.37*** (2.81)

0.17*** (14.42) 0.35*** (6.22)

0.15*** (5.22) 0.35*** (6.20)

0.24*** (3.54) 0.48*** (4.49)

57 0.46 0.58 3.44*** 3.92

76 0.79 0.86 19.19*** 3.57

76 0.79 0.86 19.23*** 6.38

76 0.67 1.11 17.88*** n.a.d

a Feasible generalized least squares estimates with country-specific random effects (Swamy–Arora method). The estimation sample consists of 19 industrial countries. Data are for the years 1995, 2000, 2001 and 2002, except for regression (2), which is based on data for the years 1990, 1995, 2000, 2001 and 2002, and regression (3), which is based on data for the years 2000, 2001 and 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain year dummies and a constant term. b Two-stage regression. ‘Rare irregular payments’ is instrumented with ‘few price controls’, ‘small amount of time dealing with government bureaucracy’ and ‘generally easy to start a new business’. The first-stage F-statistic is 11.88***. c 2 statistic. d

The variance matrix of the difference between the random and fixed effects estimates is not positive definite so that the test statistic cannot be computed.

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Flexible business regulations Few price controls Administrative procedures conducive to starting a new business Small amount of time dealing with government bureaucracy Generally easy to start a new business Rare irregular payments Trade union density Wage bargaining at industry level Wage bargaining coordination Tax wedge Employment protection legislation Unemployment benefits replacement rate Active labor market policies Output gap

Table 5 Regressions to explain the labor force participation rate among low-skilled workersa . (1)

Number of observations R2 Standard error of regression F-Statistic Hausman testc

(3)

(4)

(5)

(6)b

1.36*** (4.21) 0.44 (0.98) 0.16 (1.01) 1.00** (2.10) −0.52 (−0.70) 0.10 (0.60) −4.72*** (−5.33) −1.24 (−0.64) −0.18*** (−4.65) 2.38*** (7.52) 0.14*** (4.38)

0.10 (0.60) −4.41*** (−5.36) −1.05 (−0.51) −0.17*** (−2.90) 1.27*** (5.12) 0.17*** (5.46)

0.04 (0.46) −3.25** (−2.02) 0.20 (0.36) −0.13** (−2.18) 0.56 (1.18) 0.10 (1.30)

0.12 (0.77) −4.95*** (−4.25) −1.66 (−0.85) −0.15*** (−3.89) 2.32*** (5.01) 0.13*** (4.82)

0.12 (0.64) −4.86*** (−3.13) −1.49 (−0.76) −0.10 (−0.95) 0.82 (0.98) 0.18*** (4.81)

3.14* (1.69) 0.03 (0.18) −6.52** (−2.50) −1.23 (−0.55) −0.08 (−1.13) 4.91*** (2.80) 0.02 (0.14)

0.23** (2.62) −0.11 (−1.10)

0.18 (1.65) −0.10 (−0.75)

−0.13 (−1.04) 0.18 (0.94)

0.23** (2.34) −0.10 (−1.28)

0.15** (2.20) −0.04 (−0.36)

0.28*** (3.10) −0.09 (−0.67)

75 0.34 1.96 2.70*** 12.22

75 0.31 2.04 2.34** 13.65

57 0.14 0.89 0.65 8.96

75 0.38 1.89 3.11*** 10.94

75 0.31 2.00 2.35** 10.81

75 0.14 2.22 2.62*** n.a.d

a Feasible generalized least squares estimates with country-specific random effects (Swamy–Arora method). The estimation sample consists of 19 industrial countries. Data are for the years 1995, 2000, 2001 and 2002, except for regression (3), which is based on data for the years 2000, 2001 and 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain year dummies and a constant term. b Two-stage regression. ‘Rare irregular payments’ is instrumented with ‘few price controls’, ‘small amount of time dealing with government bureaucracy’ and ‘generally easy to start a new business’. The first-stage F-statistic is 9.84***. c 2 statistic. d The variance matrix of the difference between the random and fixed effects estimates is not positive definite so that the test statistic cannot be computed.

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Flexible business regulations Few price controls Administrative procedures conducive to starting a new business Small amount of time dealing with government bureaucracy Generally easy to start a new business Rare irregular payments Trade union density Wage bargaining at industry level Wage bargaining coordination Tax wedge Employment protection legislation Unemployment benefits replacement rate Active labor market policies Output gap

(2)

251

252

Table 6 Regressions to explain the employment ratea . (1)

Number of observations R2 Standard error of regression F-Statistic Hausman testc

(2)

(3)

(4)

(5)

(6)b

0.98*** (3.53) 0.32** (2.07) 0.21 (1.48) 0.35** (2.65) −0.26 (−0.83) 0.12*** (5.72) −3.02*** (−4.62) −0.91 (−1.63) −0.28*** (−12.02) 0.67 (0.82) −0.03 (−1.49)

0.08*** (11.19) 1.64* (1.76) −1.20*** (−3.45) −0.31*** (−6.82) −0.36 (−0.63) −0.01 (−0.42)

0.10*** (8.60) −2.43 (−1.32) −0.91*** (−2.78) −0.21*** (−4.94) −1.18 (−0.91) 0.06 (0.89)

0.13*** (6.33) −3.14*** (−4.25) −1.11** (−2.12) −0.24*** (−23.30) 0.22 (0.28) −0.02 (−1.10)

0.34*** (8.99) 0.67*** (43.16)

0.37*** (6.72) 0.24 (1.42)

0.28*** (3.47) 0.63*** (6.20)

0.32*** (8.09) 0.68*** (16.04)

76 0.77 1.12 17.34*** 9.55

95 0.65 1.75 11.68*** n.a.d

57 0.45 0.60 3.35*** 8.18

76 0.76 1.15 16.26*** 8.97

0.12*** (3.56) −3.25** (−2.35) −1.12** (−2.15) −0.19*** (−6.46) −0.36 (−0.30) −0.00 (−0.23) 0.28*** (5.10) 0.68*** (20.75) 76 0.75 1.14 16.06*** 9.57

1.91*** (2.88) 0.08*** (3.97) −4.05*** (−10.67) −0.96 (−1.31) −0.29*** (−8.69) 2.64*** (2.88) −0.10** (−2.17) 0.39*** (5.05) 0.85*** (11.12) 76 0.73 1.20 17.50*** n.a.d

a Feasible generalized least squares estimates with country-specific random effects (Swamy–Arora method). The estimation sample consists of 19 industrial countries. Data are for the years 1995, 2000, 2001 and 2002, except for regression (2), which is based on data for the years 1990, 1995, 2000, 2001 and 2002, and regression (3), which is based on data for the years 2000, 2001 and 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain year dummies and a constant term. b Two-stage regression. ‘Rare irregular payments’ is instrumented with ‘few price controls’, ‘small amount of time dealing with government bureaucracy’ and ‘generally easy to start a new business’. The first-stage F-statistic is 11.88***. c 2 statistic. d

The variance matrix of the difference between the random and fixed effects estimates is not positive definite so that the test statistic cannot be computed.

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Flexible business regulations Few price controls Administrative procedures conducive to starting a new business Small amount of time dealing with government bureaucracy Generally easy to start a new business Rare irregular payments Trade union density Wage bargaining at industry level Wage bargaining coordination Tax wedge Employment protection legislation Unemployment benefits replacement rate Active labor market policies Output gap

Table 7 Regressions to explain the female employment ratea . (1)

Number of observations R2 Standard error of regression F-Statistic Hausman testc

(3)

(4)

(5)

(6)b

0.83*** (2.70) 0.28 (1.50) 0.33* (1.73) 0.25** (2.05) −0.18 (−0.46) 0.20*** (6.45) −4.74*** (−4.60) −2.00*** (−4.59) −0.22*** (−8.62) 0.57 (0.73) −0.01 (−0.40)

0.12*** (4.74) 4.18** (2.34) −2.45*** (−14.48) −0.28*** (−7.23) −0.59 (−0.69) 0.01 (0.40)

0.21*** (5.46) −3.68 (−1.07) −2.08*** (−15.96) −0.19*** (−7.51) −1.34 (−1.15) 0.12 (1.33)

0.20*** (6.69) −4.93*** (−3.50) −2.16*** (−3.33) −0.17*** (−8.43) 0.16 (0.27) 0.01 (0.28)

0.20*** (5.56) −4.97** (−2.25) −2.13*** (−3.74) −0.14*** (−5.53) −0.28 (−0.28) 0.02 (0.93)

1.52** (2.50) 0.15*** (5.37) −5.60*** (−16.57) −1.92*** (−2.90) −0.23*** (−6.84) 2.15** (2.35) −0.06 (−1.15)

0.40*** (13.20) 0.57*** (13.23)

0.47*** (5.88) 0.16 (0.78)

0.31*** (3.46) 0.58*** (3.39)

0.37*** (17.64) 0.58*** (11.78)

0.34*** (7.41) 0.58*** (14.27)

0.44*** (8.61) 0.73*** (11.21)

76 0.84 1.10 28.30*** 5.90

95 0.71 2.08 15.51*** n.a.d

57 0.53 0.66 4.67*** 4.41

76 0.84 1.09 28.24*** 3.34

76 0.84 1.10 27.73*** 4.72

76 0.83 1.15 28.38*** n.a.d

a Feasible generalized least squares estimates with country-specific random effects (Swamy–Arora method). The estimation sample consists of 19 industrial countries. Data are for the years 1995, 2000, 2001 and 2002, except for regression (2), which is based on data for the years 1990, 1995, 2000, 2001 and 2002, and regression (3), which is based on data for the years 2000, 2001 and 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain year dummies and a constant term. b Two-stage regression. ‘Rare irregular payments’ is instrumented with ‘few price controls’, ‘small amount of time dealing with government bureaucracy’ and ‘generally easy to start a new business’. The first-stage F-statistic is 11.88***. c 2 statistic. d The variance matrix of the difference between the random and fixed effects estimates is not positive definite so that the test statistic cannot be computed.

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Flexible business regulations Few price controls Administrative procedures conducive to starting a new business Small amount of time dealing with government bureaucracy Generally easy to start a new business Rare irregular payments Trade union density Wage bargaining at industry level Wage bargaining coordination Tax wedge Employment protection legislation Unemployment benefits replacement rate Active labor market policies Output gap

(2)

253

254

Table 8 Regressions to explain the employment rate among low-skilled workersa . (1)

Number of observations R2 Standard error of regression F-Statistic Hausman testc

(2)

(3)

(4)

(5)

(6)b

1.01*** (3.69) 0.23 (0.66) 0.17 (0.90) 0.80* (1.91) −0.93 (−1.34) 0.14 (0.84) −5.76*** (−3.31) −1.01 (−0.58) −0.25*** (−8.53) 2.65*** (3.72) 0.05 (1.57)

0.15 (0.81) −5.60*** (−6.20) −0.92 (−0.48) −0.23*** (−4.26) 1.80*** (2.82) 0.07** (2.26)

0.04 (0.60) −4.71*** (−9.93) 0.28 (1.17) −0.19*** (−3.35) 1.26 (1.30) 0.07 (0.78)

0.16 (1.00) −5.96*** (−3.01) −1.32 (−0.75) −0.23*** (−8.54) 2.66*** (6.13) 0.04 (0.89)

0.16 (0.87) −6.08*** (−4.46) −1.26 (−0.72) −0.15 (−1.42) 1.05 (0.79) 0.08** (2.38)

1.53 (1.14) 0.12 (0.58) −6.72** (−2.33) −1.10 (−0.55) −0.18*** (−4.94) 3.65** (2.38) 0.00 (0.06)

0.34*** (8.32) 0.56*** (10.57)

0.30*** (5.44) 0.58*** (7.81)

0.04 (0.69) 0.58** (2.26)

0.34*** (7.00) 0.55*** (19.97)

0.24*** (8.38) 0.59*** (6.81)

0.35*** (6.35) 0.58*** (15.71)

75 0.37 1.79 3.04*** 14.73

75 0.35 1.84 2.75*** 15.72

57 0.21 0.92 1.07 7.16

75 0.40 1.74 3.42*** 14.05

75 0.39 1.76 3.26*** 13.07

75 0.40 1.72 2.83*** n.a.d

a Feasible generalized least squares estimates with country-specific random effects (Swamy–Arora method). The estimation sample consists of 19 industrial countries. Data are for the years 1995, 2000, 2001 and 2002, except for regression (3), which is based on data for the years 2000, 2001 and 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain year dummies and a constant term. b Two-stage regression. ‘Rare irregular payments’ is instrumented with ‘few price controls’, ‘small amount of time dealing with government bureaucracy’ and ‘generally easy to start a new business’. The first-stage F-statistic is 9.84***. c 2 statistic. d

The variance matrix of the difference between the random and fixed effects estimates is not positive definite so that the test statistic cannot be computed.

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Flexible business regulations Few price controls Administrative procedures conducive to starting a new business Small amount of time dealing with government bureaucracy Generally easy to start a new business Rare irregular payments Trade union density Wage bargaining at industry level Wage bargaining coordination Tax wedge Employment protection legislation Unemployment benefits replacement rate Active labor market policies Output gap

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

255

• The more time senior management has to spend dealing with government bureaucracy, the lower labor force participation and employment rates, both among the total working-age population and among low-skilled workers (Tables 3, 5, 6 and 8). Furthermore, women’s employment rate is lower if dealing with government bureaucracy is time consuming for senior managers (Table 7). • Irregular payments to politicians and bureaucrats appear to lower both labor force participation and employment among the overall working-age population (Tables 3 and 6). They also appear to reduce low-skilled workers’ labor force participation and women’s employment rate (Tables 5 and 7). We also checked the robustness of our results. For brevity, we only report the respective regressions to explain the labor force participation and the employment rate (Tables 9 and 10). The results for our other four dependent variables are very similar to the estimates from the respective baseline regressions. Furthermore, we only report the regressions employing the variable ‘flexible business regulations’; the results for the individual indicators of business regulation also largely accord with the respective results from our baseline regressions. In our first two robustness checks we excluded outliers. Specifically, in our first check, we excluded Finland, the country with the highest ratings for the EFW component ‘business regulation’, while in our second check, we excluded the country with the lowest ratings, Italy. In our third robustness check, we substituted the variable ‘collective bargaining coverage’ for the variables ‘wage bargaining at industry level’ and ‘wage bargaining coordination’. We did not include the variable ‘collective bargaining coverage’ in our baseline specifications because, according to previous theoretical and empirical research, the extent of wage bargaining at industry level and of wage bargaining coordination constitute the most relevant features of national wage-setting systems (for surveys, see Flanagan, 1999, and Aidt & Tzannatos, 2002). In our fourth robustness check, we additionally controlled for the impact of statutory minimum wages. We did not include the respective variable in our baseline specifications as several industrial countries do not have a statutory minimum wage and as this variable thus is not normally used in cross-country labor market studies. In our fifth robustness check, we additionally controlled for the level of economic development using the variable ‘GDP per capita’. Although it seems likely that labor force participation and employment rates are systematically related to the level of economic development, we did not include the respective variable in our baseline specifications as this variable normally is not included in cross-country regressions that focus on industrial countries. As is evident from Tables 9 and 10, the coefficient on our variable of interest remained statistically highly significant in each of these robustness checks, and the size of the coefficient remained very similar. In our final robustness check, we substituted the OECD variable ‘product market regulation’ for the EFW variable ‘flexible business regulations’. As mentioned in Section 1, the OECD indicator only covers regulations in seven energy and service industries whereas the EFW indicator measures the strictness of business regulation in the entire economy. Interestingly, the OECD indicator is statistically insignificant in the regression to explain the labor force participation rate (Table 9). Thus, whereas the strictness of economy-wide business regulations is likely to adversely affect the labor force participation rate (Table 3), the regulation of the seven non-manufacturing industries covered by the OECD indicator does not have a noticeable impact. This seems plausible as it is more likely that economywide regulations have an impact on labor force participation than specific regulations covering only some industries. By contrast, the coefficient on ‘product market regulation’ is highly significant in the regression to explain the employment rate (Table 10), indicating that restrictive regulations of the seven energy and service industries have an adverse impact on the level of employment, similar to restrictive economy-wide business regulations. In sum, our regression results corroborate the theoretical hypotheses sketched in Section 1. They suggest that anticompetitive business regulations are likely to adversely affect labor market performance. According to our estimates, price controls, administrative obstacles to start a new business, time-consuming bureaucratic procedures and corruption appear to lower labor force participation and employment rates. The adverse labor market effects of strict business regulations are probably due to the relocation of jobs to foreign countries and the shadow economy, few entries of new firms, low competitive pressures, wage premia extracted by labor market insiders and high irregular payments extracted by politicians and bureaucrats.

256

Table 9 Robustness checks for regressions to explain the labor force participation ratea . Italy excluded

Collective bargaining coverage substituted for wage bargaining at industry level and wage bargaining coordination

Minimum wage added

GDP per capita added

Product market regulation substituted for flexible business regulations

(3)

(4)

(5)

(6)

0.60*** (7.15) 0.09*** (5.16) −2.17 (−1.40) −1.31*** (−5.33) −0.19*** (−6.84) 0.02 (0.02) 0.02 (1.13) 0.17*** (2.88) 0.24*** (5.39)

0.52*** (5.83) 0.10*** (3.53) −2.32 (−1.29) −1.33*** (−2.89) −0.12*** (−4.73) 0.11 (0.18) 0.05*** (3.78) 0.12*** (3.71) 0.19** (2.39)

0.12** (2.29) −2.99* (−1.75) −1.29*** (−3.19) −0.07*** (−3.12) −0.25 (−0.48) 0.05** (2.38) 0.11*** (2.75) 0.28*** (6.57)

(1)

(2)

Flexible business regulations Trade union density Wage bargaining at industry level Wage bargaining coordination Tax wedge Employment protection legislation Unemployment benefits replacement rate Active labor market policies Output gap Collective bargaining coverage Minimum wage GDP per capita Product market regulation

0.58*** (4.29) 0.11*** (4.82) −2.52 (−1.65) −0.97*** (−3.64) −0.16*** (−4.94) −0.13 (−0.18) 0.02 (0.98) 0.15*** (3.83) 0.32*** (4.59)

0.32*** (4.14) 0.09*** (3.07) −3.46** (−2.14) −0.59 (−1.36) −0.12*** (−3.87) −0.70** (−2.39) 0.07*** (2.78) 0.10*** (18.22) 0.30*** (4.48)

Number of observations R2 Standard error of regression F-Statistic Hausman testb

72 0.59 0.94 7.19*** 23.20**

0.45** (2.35) 0.13*** (24.29)

−0.10*** (−2.82) 0.18 (0.17) 0.05*** (2.78) 0.14*** (4.74) 0.30*** (2.99) −0.13*** (−3.76)

−4.07*** (−3.24) 0.39** (2.41) −0.36 (−1.19) 72 0.64 0.77 8.85*** 5.93

76 0.65 0.81 10.60*** 14.26

76 0.62 0.95 7.82*** 30.61***

76 0.64 0.81 8.63*** 12.73

76 0.62 0.78 8.66*** 7.34

a Feasible generalized least squares estimates with country-specific random effects (Swamy–Arora method). The estimation sample consists of 19 industrial countries, except for regressions (1) and (2), which are based on samples of 18 countries. Data are for the years 1995, 2000, 2001 and 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain year dummies and a constant term. b 2 statistic.

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Finland excluded

Table 10 Robustness checks for regressions to explain the employment ratea . Italy excluded

Collective bargaining coverage substituted for wage bargaining at industry level and wage bargaining coordination

Minimum wage added

GDP per capita added

Product market regulation substituted for flexible business regulations

(3)

(4)

(5)

(6)

1.03*** (4.40) 0.11*** (6.29) −2.71*** (−4.02) −0.99 (−1.42) −0.32*** (−13.90) 0.68 (0.76) −0.04** (−2.05) 0.36*** (6.60) 0.64*** (42.30)

1.08*** (3.78) 0.11*** (7.33) −2.47*** (−3.37) −1.23*** (−5.11) −0.27*** (−20.21) 1.08* (1.69) −0.00 (−0.11) 0.30*** (7.03) 0.54*** (6.27)

0.13*** (3.33) −3.51*** (−7.21) −0.83 (−1.24) −0.20*** (−9.49) 0.27 (0.33) −0.02 (−0.76) 0.25*** (4.28) 0.69*** (16.21)

(1)

(2)

Flexible business regulations Trade union density Wage bargaining at industry level Wage bargaining coordination Tax wedge Employment protection legislation Unemployment benefits replacement rate Active labor market policies Output gap Collective bargaining coverage Minimum wage GDP per capita Product market regulation

1.08*** (3.71) 0.14*** (5.70) −3.32*** (−3.19) −0.53 (−1.23) −0.32*** (−27.21) 0.62 (0.73) −0.06*** (−2.74) 0.34*** (6.39) 0.83*** (11.04)

0.84*** (3.11) 0.10*** (5.29) −3.89*** (−5.11) −0.32 (−0.67) −0.27*** (−14.63) 0.10 (0.21) −0.01 (−0.13) 0.31*** (21.89) 0.69*** (18.37)

Number of observations R2 Standard error of regression F-Statistic Hausman testb

72 0.75 1.19 14.64*** 12.63

0.97*** (3.22) 0.15*** (16.26)

−0.26*** (−19.20) 1.10 (1.00) −0.01 (−0.42) 0.35*** (8.80) 0.70*** (10.64) −0.14*** (−4.14)

−2.69** (−2.09) 0.66* (1.93) −1.17*** (−3.31) 72 0.78 1.10 17.09*** 5.05

76 0.78 1.09 21.13*** 7.15

76 0.76 1.20 15.15*** 14.75

76 0.80 1.05 19.13*** 6.13

76 0.76 1.12 16.93*** 9.30

H. Feldmann / Journal of Economics and Business 61 (2009) 238–260

Finland excluded

a Feasible generalized least squares estimates with country-specific random effects (Swamy–Arora method). The estimation sample consists of 19 industrial countries, except for regressions (1) and (2), which are based on samples of 18 countries. Data are for the years 1995, 2000, 2001 and 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain year dummies and a constant term. b

2 statistic.

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Our estimates corroborate our conjecture that anticompetitive business regulations are likely to have above-average effects on low-skilled workers (Section 1). As illustrated at the beginning of Section 3 using the variable ‘flexible business regulations’, the effects on the low-skilled seem to be exceptionally large in the area of labor force participation and slightly larger than average as far as employment rates are concerned. This demographic group appears to suffer more than the general population if senior management has to spend a substantial amount of time dealing with government bureaucracy and if corruption is widespread (Tables 5 and 8). By contrast, our hypothesis that stringent business regulations have above-average effects on women does not receive much support. While there is a slightly above-average effect on the female labor force participation rate, the effect on the female employment rate clearly is below average. This paper’s results are in line with previous empirical studies. Particularly, they are in line with their main result that anticompetitive business regulations have a negative impact on the employment rate. However, as noted in Section 1, this paper goes beyond previous empirical studies in several respects. Finally, let us briefly comment on our estimates for the control variables (Tables 3–10). By and large, they accord with those obtained in many earlier studies. For example, in line with most previous studies we find that an increase in the tax wedge is likely to reduce both labor force participation and employment rates (see, e.g., Daveri & Tabellini, 2000; Prescott, 2004) and that stringent employment protection legislation does not appear to affect aggregate labor force participation and employment rates (see, e.g., Heckman & Pagés, 2004; OECD, 2004a). In line with many previous studies, we also find that higher collective bargaining coverage and higher statutory minimum wages adversely affect the performance of the labor market (see, e.g., Aidt & Tzannatos, 2002; Neumark & Wascher, 2006). Furthermore, we find that wage bargaining at industry level appears to lower employment. This corroborates the Calmfors and Driffill (1988) hypothesis, according to which employment will be low if wages are negotiated at the industry level, and high if they are negotiated either at the firm or at the national level.10 We also find that higher expenditure on active labor market policies is associated with higher labor force participation and employment rates. This suggests that, in general, these policies appear to succeed in “activating” unemployed and economically inactive persons. However, extensive research suggests that the effectiveness of active labor market policies varies widely across different types of programs (e.g., Feldmann, 2002; OECD, 2005a). For example, public employment programs increase official employment figures but often fail to help the participants get permanent jobs in the open labor market. 4. Conclusion Our regression results indicate that anticompetitive business regulations adversely affect the performance of the labor market. Specifically, price controls, administrative obstacles to start a new business and time-consuming bureaucratic procedures appear to lower labor force participation and employment rates. Corruption, which is one result of strict business regulation, is also found to lower labor force participation and employment rates. While most effects on the general population appear to be modest, the effects on the low-skilled seem to be substantial. Our results are robust to variations in specification. Given these findings, countries with both strict business regulations and poor labor market performance should consider a liberalization of their business regulations as a means to raise labor force participation and employment rates. References Aidt, T., & Tzannatos, Z. (2002). Unions and collective bargaining: Economic effects in a global environment. Washington, DC: World Bank. Baltagi, B. H. (2001). Econometric analysis of panel data (2nd Ed.). Chichester: John Wiley & Sons.

10 The empirical evidence for the Calmfors and Driffill hypothesis from a wealth of other studies is mixed (see, e.g., Aidt and Tzannatos, 2002).

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