Open Access

The levelling effect of product market competition on gender wage discrimination

IZA Journal of Labor Economics20143:19

https://doi.org/10.1186/s40172-014-0013-1

Received: 28 July 2014

Accepted: 23 October 2014

Published: 31 December 2014

Abstract

Using linked employer–employee panel data for West Germany that include direct information on the competition faced by plants, we investigate the effect of product market competition on the gender pay gap. Controlling for match fixed effects, we find that intensified competition significantly lowers the unexplained gap in plants with neither collective agreements nor a works council. Conversely, there is no effect in plants with these types of worker codetermination, which are unlikely to have enough discretion to adjust wages in the short run. We also document a larger competition effect in plants with few females in their workforces. Our findings are in line with Beckerian taste-based employer wage discrimination that is limited by competitive forces.

JEL codes: J16, J31, J71

Keywords

Gender pay gapDiscriminationProduct market competition

1 Introduction

Studies documenting a significant, persistent gender pay gap are legion, as are studies relating this gap to wage discrimination in the labour market1. The usual theoretical approach to wage discrimination originates in the pathbreaking work by Becker ([1971]). According to Becker, discrimination stems from personal prejudices among employers, co-workers, or consumers that constitute discriminatory preferences among these groups. As a case in point, discriminatory employers may suffer a disutility from employing women. To be compensated for the loss in utility following from employing women, these employers pay lower wages to women than to men, ceteris paribus. In the non-segregating equilibrium, all female workers receive a lower wage than men, no matter whether they work for an employer with or without discriminatory preferences.

Yet, as pointed out by Arrow ([1973]), in equilibrium, non-discriminatory employers employ more women at below-productivity wages than their discriminatory counterparts and therefore gain a competitive advantage over their competitors. Put differently, indulging discriminatory preferences comes at a cost in this framework. Indeed, empirical studies have documented that discriminatory employers make lower profits (Hellerstein et al.[2002]; Kawaguchi [2007]) and are more likely to exit the market (Weber and Zulehner [2014]). Employers actually seem to pay for discrimination.

For discrimination to prevail, discriminatory employers must possess some market power on their product markets, enabling them to indulge their costly preferences. Otherwise, discrimination would simply be competed away. And thus, the strength of product market competition should put limits on the viability of discrimination, with gender pay gaps being lower in more competitive environments. Following this line of reasoning, we investigate whether strong product market competition reduces gender wage discrimination in West Germany and whether this effect is more pronounced in the so-called codetermination-free sector, i.e., in plants with neither collective agreements nor a works council. In contrast with previous studies, we use linked employer–employee panel data that include a plant-level assessment of the strength of product market competition.

Almost exclusively, extant studies have used aggregate or indirect measures of competition such as the intensity of international trade (Black and Brainerd [2004]; Juhn et al.[2014]), the extent of market regulation (Black and Strahan [2001]), market structure (Winter-Ebmer [1995]; Heyman et al.[2013]), or combinations of these (Jirjahn and Stephan [2006]; Weichselbaumer and Winter-Ebmer [2007]; Zweimüller et al.[2008]; Heinze and Wolf [2010]). Such measures, however, suffer from at least two severe shortcomings: First, although these proxies may be informative on the average intensity of product market competition within a product market, they do not provide information on changes in competitive pressure at the level of the individual firm where decision-making takes place. Even worse, using these measures requires the researcher to implicitly define plants’ relevant product and geographical markets, which turns out to be far from trivial. Notably, the inevitable problems in doing so in a convincing and unambiguous way drove the German Monopolies Commission to cease reporting classic concentration measures, like the Herfindahl index or the concentration ratio, altogether (Monopolies Commission [2012], p. 12). Second, through the lens of the structure–conduct–performance paradigm, utilising these measures also claims that the market structure uniquely determines market participants’ behaviour, thereby abstracting from different ways of competition on different product markets that may add further ambiguity to the proxy at hand. As a case in point, Sutton ([1991]) shows that fierce competition on a market with substantial entry costs may lead to high concentration in an industry (the limiting case being two incumbents competing à la Bertrand). To sidestep these problems related to aggregate or indirect proxies of product market competition, we will use a direct plant-level measure of competitive pressure based on a self-assessment by plants’ managers. Therefore, we do not have to define plants’ relevant market ex ante, and basing our measure on the beliefs of plants’ managers about competition guarantees that it is relevant for managements’ decision-making.

To the best of our knowledge, Belfield and Heywood ([2006]) and Hirsch et al. ([2012]) are the only scholars in this strand of the literature to base their evidence on direct information on the competition faced by individual firms. While Hirsch et al. ([2012]) find a negative correlation between firms’ self-assessment on product market competition and the unexplained gender pay gap in West Germany, Belfield and Heywood ([2006]) document a negative link for the U.K. that is more pronounced in the non-unionised sector. Both studies, however, rely on cross-sectional data only – with Hirsch et al. ([2012]) utilising the 2008 cross section of the data set used in this study. They thus cannot rule out that differences in pay gaps, reflecting unobserved heterogeneity among firms facing different levels of product market competition and/or self-selection of workers into firms with different competitive pressure rather than a genuine competition effect.

Against this background, our study contributes to the literature in three ways: First, our panel data permit us to investigate the impact of plant-level product market competition on the unexplained gender pay gap, controlling for match fixed effects, i.e., time-invariant unobserved heterogeneity for every plant–worker pair. We thus base our investigation on direct information on the competition faced by plants, with identification relying on the differential impact of within-plant variation in competitive pressure on within-plant wage growth of men and women. As a consequence, estimated effects are neither contaminated by unobserved time-invariant plant heterogeneity nor by self-selection of workers.

Second, apart from identification, we add to the literature by investigating heterogeneities in the competition effect on gender wage discrimination by plants’ industrial relations regime. German industrial relations are characterised by a dual system of worker representation through (sectoral) trade unions and works councils that can be elected by the workforce in plants with at least five employees (for details, see Keller [2004] or Addison [2009]). As plants’ discretion to alter wages in response to changes in competitive pressure should be more limited if they are bound by a collective agreement or if they have a works council, we analyse whether the competition effect is stronger in the sector with none of these types of worker representation (which in Germany is usually referred to as the codetermination-free sector).

Third, we investigate whether the effect is larger in plants with a below-average share of females in their workforces. Since theory suggests that these plants have more pronounced discriminatory preferences, a stronger competition effect in these plants would further substantiate that fierce competition limits taste-based employer discrimination.

The remainder of this paper is organised as follows: Section 2 describes our data set and Section 3 our econometric approach. Section 4 presents and discusses our results, and Section 5 concludes.

2 Data

The data used in this paper consist of three waves of the cross-sectional model of the linked employer–employee data set of the Institute for Employment Research (LIAB) for the years 2008–2010 (for details, see Alda et al.[2005] and Heining et al. [2013]). The data set links the IAB Establishment Panel, a representative survey of German plants (not companies), to the administrative data on all those individuals who work for these plants and contribute to the social insurance system.

The administrative data are based on the notification procedure for the German health, pension, and unemployment insurances. This requires all employers to report the necessary information on their workers if covered by the system, where misreporting is legally prohibited. Thus, among others, civil servants, self-employed, and individuals in marginal employment are not included. All in all, about 80 per cent of all people employed in Germany are covered by the social security system. Inter alia, the data include information for every worker on the daily gross wage, age, education, sex, nationality, tenure, occupation, and industry at the 30th of June of each year.

The employer side of our data comes from the IAB Establishment Panel (for details, see Kölling [2000]), a stratified random sample of all plants that employ at least one worker covered by the social security system at the 30th June of each year. Starting in 1993, the IAB Establishment Panel has surveyed plants from all industries in West Germany. Response rates of units that have been interviewed repeatedly exceed 80 per cent. Questions deal, among other things, with the number of workers, the composition of the workforce, the plant’s commitment to collective agreements at the sector or plant level, the existence of a works council, the plant’s exporting activity, and its production technology.

From 2008 onwards, the data additionally include a plant-level self-assessment of product market competition on a four-point Likert scale that enables us to distinguish plants facing strong competition from other plants2. Using this information, we set up a panel data set for West Germany in the years 2008–2010 to investigate the effect of product market competition on the gender pay gap in profit-oriented plants3.

Whereas the wage information included in the LIAB is highly reliable, there are two shortcomings of the data crucial to our investigation: Firstly, the data set includes daily wages only and no detailed information on working hours. As a consequence, we have to exclude part-time workers. Secondly, wages are top-coded at the social security contribution ceiling, which affects 16.2 per cent of our male and 6.0 per cent of our female observations. To deal with this second issue, we apply the standard single imputation procedure proposed by Gartner ([2005]) and impute top-coded wages. In a first step, we estimate yearly Tobit models for each combination of sex and competition (e.g., females working for plants facing strong competition), where the regressand is the log daily gross wage and the regressors are those included in the subsequent analysis. In a second step, for every observation with a top-coded wage, a random value is drawn from a normal distribution left-truncated at the respective social security contribution ceiling, with the predicted log wage as mean and the standard deviation as estimated from the Tobit models4.

All in all, we end up with a sample of 1,239,911 observations of 627,076 male workers and 305,876 observations of 166,759 female workers employed by 6,114 plants. As can be seen from Table 1, 66 per cent of male and 61 per cent of female observations belong to plants facing strong product market competition. Furthermore, 6 per cent of male and 10 per cent of female observations are in the codetermination-free sector, i.e., work in plants that are neither bound by collective agreements nor have a works council. Turning to wages, women earn about 21 per cent lower wages than men on average. This number hardly changes when considering the subgroups of workers working for plants facing strong or weak competition, though workers’ wages are generally higher in plants with strong competitive pressure5. (For more descriptive statistics, see Table 2.)
Table 1

Wages by sex and product market competition

 

Women

Men

 

Mean

Std. dev.

Mean

Std. dev.

Full sample

    

Gross daily wages (in €)

104.36

45.90

132.41

50.47

Log gross daily wages

4.55

0.44

4.82

0.38

Strong product market competition (dummy)

0.61

0.49

0.66

0.47

Codetermination-free plant (dummy)

0.10

0.30

0.06

0.24

Observations

305,876

1,239,911

Workers

166,759

627,076

Plants

6,114

Plants facing weak product market competition

Gross daily wages (in €)

99.00

43.86

126.10

48.77

Log gross daily wages

4.50

0.44

4.77

0.38

Codetermination-free plant (dummy)

0.14

0.35

0.09

0.29

Observations

120,176

424,971

Plants facing strong product market competition

Gross daily wages (in €)

107.83

46.84

135.70

51.02

Log gross daily wages

4.59

0.44

4.84

0.37

Codetermination-free plant (dummy)

0.07

0.25

0.04

0.20

Observations

185,700

814,940

Notes: LIAB cross-sectional model, West Germany, 2008–2010. Wages are deflated by the consumer price index. Strong product market competition refers to the highest category on a four-point Likert scale of a plant-level self-assessment of competition.

Table 2

Descriptive statistics

 

Women

Men

 

Mean

Std. dev.

Mean

Std. dev.

Potential experience (years)

22.0

11.5

23.8

10.2

Tenure (years)

10.7

8.6

13.2

9.3

Tenure censored (dummy)

0.023

0.151

0.031

0.173

Apprenticeship, no Abitur (dummy)

0.600

0.490

0.674

0.469

No apprenticeship, Abitur (dummy)

0.021

0.142

0.010

0.102

Apprenticeship, Abitur (dummy)

0.100

0.300

0.044

0.206

Technical college degree (dummy)

0.040

0.195

0.070

0.255

University degree (dummy)

0.071

0.257

0.086

0.280

Non-German nationality (dummy)

0.069

0.254

0.074

0.261

Share of female workers

0.399

0.239

0.211

0.159

Share of lowly qualified workers

0.231

0.247

0.192

0.223

Works council (dummy)

0.813

0.390

0.885

0.319

Collective bargaining at firm level (dummy)

0.148

0.355

0.165

0.371

Collective bargaining at sector level (dummy)

0.645

0.478

0.704

0.457

Exporting activity (dummy)

0.607

0.488

0.767

0.423

New production technology (dummy)

0.837

0.370

0.840

0.366

Plant in rural area (dummy)

0.177

0.382

0.173

0.378

Plant size (number of workers)

5,420

11,967

8,610

15,100

Observations

305,876

1,239,911

Notes: LIAB cross-sectional model, West Germany, 2008–2010. Tenure is left-censored if the worker’s job with the plant started before 1975.

As the transition matrix in Table 3 makes clear, plants’ self-assessment on product market competition is extensively varied over time. During our period of observation, 1,715 transitions between strong and weak competition take place compared to 5,077 instances where competition stays constant. As a consequence, 23 per cent, or 171,369 out of 751,952 workers, are employed in plants with changing product market competition, thereby enabling us to rest identification on within-plant variation in competition. Notably, the number of transitions is not only reasonably large but is also roughly symmetrical with increases in competitive pressure being of similar magnitude as decreases. This renders it unlikely that plants with changing product market competition are a selective sample6.
Table 3

Transitions between strong and weak product market competition

Initial product market competition

Product market competition in the next period

 

Plants

Workers

 

Weak (t + 1)

Strong (t + 1)

Weak (t + 1)

Strong (t + 1)

Weak (t)

2,751

842

180,034

88,965

Strong (t)

873

2,326

82,404

400,549

Notes: LIAB cross-sectional model, West Germany, 2008–2010. Strong product market competition refers to the highest category on a four-point Likert scale of a plant-level self-assessment of competition.

3 Econometric Approach

To test the hypothesis that product market competition lowers the gender pay gap, we will run augmented Mincerian wage regressions. Our baseline specification is a fully interacted model
ln w i j t = π 1 f e m i + π 2 c o m p j t + π 3 c o m p j t × f e m i + x it ' α + β × f e m i + z j t ' γ + δ × f e m i + ε i j t ,
(1)

where ln w ijt is the log gross daily wage of worker i working for plant j in period t; fem i is a female dummy, and comp jt is a dummy indicating strong product market competition. comp jt  × fem i is the interaction of these two, and π1, π2, and π3 are the corresponding coefficients. Furthermore, x it denotes a vector of worker characteristics, z jt a vector of plant characteristics, ε ijt the idiosyncratic error component, and α , β , γ , and δ are the corresponding coefficient vectors. In using a fully interacted model, we allow each worker and plant characteristic to affect men’s and women’s wages differently and thus exploit a more flexible framework than the earlier contributions by Belfield and Heywood ([2006]) and Hirsch et al. ([2012]), which also utilise plant-level measures of product market competition.

Worker controls comprise potential experience and tenure (both linearly and squared), dummy variables for joining the plant before 1975 (i.e. censored job tenure), five levels of education, non-German nationality, year of observation, and three-digit occupation. Plant controls include log plant size and its square, the shares of female and low-skilled workers in the plant’s workforce, dummies for works council existence, a collective agreement at either firm level or sector level, exporting activity, new production technology, plant location in a rural area, and one-digit industry.

To ease interpretation, all regressors are centred around their sample averages, so that π1 can be interpreted as the average unexplained gender pay gap in the full sample. The coefficients of main interest are π2 and π3, where π2 gives the effect of strong product market competition on males’ wages, and π3 is the difference in the competition effect across the sexes. We expect strong competition to depress the overall rents accruing and thus workers’ wages in general. Moreover, strong competition should also confine employers’ ability to discriminate against women by sharing rents disproportionately with male workers (cf. Black and Strahan [2001]). Hence, we expect π2 to have a negative sign and π3 to have a positive sign, the latter indicating that strong competition reduces the gender pay gap by inducing a smaller adverse effect on females’ compared to males’ wages.

In this baseline specification, identification rests on both inter-plant and within-plant variation in competition. It is therefore unclear whether the results are driven by unobserved plant heterogeneity correlated with plants’ competitive pressure or by a genuine competition effect. To come closer to the true competition effect, we next add plant–sex fixed effects to the model, i.e., we control for permanent differences in plants’ sex-specific wage policies. As discussed in Section 2, this is viable in our data because we have a sufficient number of plants with varying product market competition over time. Our second specification thus is
ln w i j t = π 2 c o m p j t + π 3 c o m p j t × f e m i + x it ' β + γ × f e m i + z j t ' α + δ × f e m i + v j + ζ j × f e m i + ε i j t ,
(2)

where the plant fixed effect for men and women is v j and v j  + ζ j , respectively7. In this second specification, identification of π2 and π3 relies on within-plant variation in competition and the accompanying changes in male and female workers’ wages. Hence, π2 tells us how males’ wages and π3 how the unexplained gender pay gap responds to intensified competition.

That said, changes in workers’ wages due to varying competition may stem from two different sources: Either varying competition triggers wage changes for on-going jobs, i.e., for given worker–plant pairs, or wages change due to altering worker composition. And workers with different unobserved characteristics, like motivation, career outlook, or mobility, may self-select themselves into plants with different competitive pressure. As women and men have been found to differ considerably in both career aspirations and job mobility (e.g., Chevalier [2007] and Hirsch and Schnabel [2012]), self-selection of workers may contaminate within-plant comparisons of unexplained gender pay gaps. In order to address self-selection of workers, we next include match fixed effects to our model, i.e., we control for the permanent wage component of every worker–plant pair. Hence, our third specification is given by
ln w i j t = π 2 c o m p j t + π 3 c o m p j t × f e m i + x it ' β + γ × f e m i + z j t ' α + δ × f e m i + ϕ i j + ε i j t
(3)

with match fixed effect ϕ ij . In this final specification, identification of π2 and π3 rests solely on changes in workers’ within-plant wage growth, which is occurring simultaneously with within-plant variation in competition, i.e., wage changes within a given worker–plant pair. Estimated competition effects are thus free from biases stemming from unobserved heterogeneity in plants’ permanent sex-specific wage policies and self-selection of workers with different unobserved time-invariant characteristics. A fall in workers’ within-plant real wage growth may stem from various channels, such as decreased bonus payments, below-inflation nominal wage increases, or a reduction in the wage cushion between contract wages and effective wages.

We should make clear, though, that both specifications (2) and (3) just identify short-run effects of product market competition on workers’ wages, i.e., changes in wages occurring simultaneously with within-plant variation in competition. However, long-run effects, which we cannot identify given the short time horizon of our data, may be larger as it may take some time for employers to alter wages. Furthermore, plants’ discretion to adjust wages in the short run is likely to differ depending on their industrial relations regime. Arguably, plants bound by legally binding collective agreements may find it harder to cut wages in response to intensified competitive pressure. In a similar vein, works councils are likely to protect workers’ rents and lower plants’ discretion to reduce wages in such an event8. We thus expect to find marked competition effects only in plants belonging to the codetermination-free sector.

4 Results

Running the three wage regressions (1)–(3) on our full sample yields the results summarised in Table 4. In the OLS specification, we find a highly significant average unexplained gender pay gap of 14.6 log points, which is similar in magnitude to previous studies for West Germany, such as Gartner and Hinz ([2009]) and Hirsch ([2013]). As can be seen from the estimated coefficient of the competition dummy, male workers’ wages are 1.1 log points lower in plants facing strong product market competition, which is statistically insignificant at conventional levels. The interaction effect of the female and the competition dummy indicates that the competition effect is almost zero and statistically insignificant. Note that the estimated coefficients of other regressors (which we do not report in Table 4 for the sake of brevity) show no surprises and are in line with the previous literature.
Table 4

Wage regressions (full sample)

 

(1)

(2)

(3)

 

OLS

Plant–sex fixed effects

Match fixed effects

Strong product market competition

−0.0112

−0.0082**

−0.0094*

 

(0.0068)

(0.0041)

(0.0049)

Female × strong product market competition

0.0015

0.0019

0.0016

 

(0.0062)

(0.0033)

(0.0039)

Female

−0.1460***

  
 

(0.0032)

  

Observations

1,545,787

Notes: LIAB cross-sectional model, West Germany, 2008–2010. The regressand is the log gross daily wage. Standard errors in parentheses are clustered at the plant level. ***/**/* denotes statistical significance at the 1/5/10 per cent level. In Model 1, additional regressors are potential experience and tenure (linearly and squared), dummy variables for joining a plant before 1975, five levels of education, non-German nationality, the shares of female and low-skilled workers in the plant’s workforce, log plant size and its square, dummies for works council existence, a collective agreement at either firm level or sector level, exporting activity, new production technology, plant location in a rural area, one-digit industry, three-digit occupation, and year of observation. Model 2 additionally includes plant–sex fixed effects, whereas Model 3 includes match fixed effects. All regressors are centred around their sample averages and also interacted with the female dummy.

The picture hardly changes when resting identification on within-plant variation in competition in specifications (2) and (3). Intensified competition lowers male workers’ wages by 0.8 (0.9) log points in the specification with plant–sex (match) fixed effects, where the coefficient is statistically significant at the 5 (10) per cent level. Yet, the interaction effect of the female and the competition dummy is still very small and statistically insignificant throughout. So in the full sample, there is no indication of a differential short-run effect of intensified product market competition on female and male workers’ wages and thus no evidence of a (levelling) competition effect on gender wage discrimination.

However, as discussed in Section 3, short-run effects are expected to be only visible in plants that possess enough discretion to adjust wages in the short run. We therefore think that effects are more likely to be visible in plants not subject to worker codetermination. To check this, we split our sample in two subsamples: plants bound by a collective agreement and/or having a works council and codetermination-free plants. The results of running wage regressions on both subsamples are summarised in Table 5.
Table 5

Wage regressions by worker codetermination

 

(1)

(2)

(3)

 

OLS

Plant–sex fixed effects

Match fixed effects

Plants with some worker codetermination

   

Strong product market competition

−0.0092

−0.0073*

−0.0077

 

(0.0072)

(0.0043)

(0.0049)

Female × strong product market competition

−0.0002

0.0010

−0.0006

 

(0.0066)

(0.0034)

(0.0037)

Female

−0.1471***

  
 

(0.0035)

  

Observations

1,442,439

Codetermination-free plants

   

Strong product market competition

−0.0320***

−0.0152**

−0.0187**

 

(0.0111)

(0.0075)

(0.0087)

Female × strong product market competition

0.0209*

0.0116*

0.0176**

 

(0.0127)

(0.0067)

(0.0080)

Female

−0.2227***

  
 

(0.0250)

  

Observations

103,348

Notes: LIAB cross-sectional model, West Germany, 2008–2010. The regressand is the log gross daily wage. Standard errors in parentheses are clustered at the plant level. ***/**/* denotes statistical significance at the 1/5/10 per cent level. Regressors are those listed in Table 4.

In line with earlier contributions, like Jirjahn and Stephan ([2006]) or Heinze and Wolf ([2010]), the unexplained gender pay gap is lower if some sort of worker codetermination is present. In the OLS specification (1), the gap amounts to 22.3 log points in codetermination-free plants but just 14.7 log points in plants with worker codetermination. As expected, in codetermined plants workers’ wages do not respond much to intensified competition. In the specifications with plant–sex or match fixed effects, an increase in competition causes male workers’ wages to fall by 0.7–0.8 log points, where the effect is statistically insignificant in the specification including match fixed effects. Furthermore, as the interaction effect is almost zero, there is clearly no differential effect across the sexes.

Results are different for the codetermination-free sector in which workers’ wages are responding to intensified competition. This is in line with an earlier finding by Belfield and Heywood ([2006]) for the U.K. that the cross-sectional link between competition and the gap is driven by the non-unionised sector. Depending on specification, males’ wages decrease by 1.5–3.2 log points if competition gets fierce, where all effects are statistically significant at least at the 5 per cent level. As the interaction effect indicates that women’s wages are considerably less depressed than men’s, the unexplained gender pay gap is significantly reduced by 1.2–2.1 log points. In our preferred specification including match fixed effects and thus addressing unobserved plant heterogeneity and worker self-selection, intensified product market competition reduces males’ wages by 1.9 log points 9, while females’ wages are almost unaffected as the interaction effect also accounts to 1.8 log points10. This is in line with our expectation that intensified competitive pressure reduces the rents accruing and thus restricts employers’ ability to share rents disproportionately with male workers. Consequently, the unexplained gender pay gap falls by 1.8 log points if competition gets fierce. As the average gap in codetermination-free plants accounts to 22.3 log points, this means that it drops by about 8 per cent. Since this fall in the gap represents a short-run effect, and the long-run effect is arguably larger, we regard the competition effect on the gender pay gap as non-trivial from an economic point of view11.

To further scrutinise our results, we next split our sample by the share of females in plants’ workforces. In Becker’s ([1971]) model with taste-based employer discrimination, plants employing fewer women are not only more discriminatory employers but also less profitable. Therefore, we may expect the competition effect on the gender pay gap to be more pronounced in plants with a below-average share of female workers, which should be hit disproportionately by intensified product market competition.

To be precise, this notion is not literally true in Becker’s ([1971]) original model where the gender pay gap is the same for all women, no matter whether they work for discriminatory or non-discriminatory employers. However, this latter conclusion of the model hinges on the assumption that workers can instantaneously and costlessly switch employers. Once one builds in some forces restricting worker mobility, it does not hold anymore. For instance, Bowlus and Eckstein ([2002]) incorporate discriminatory employer preferences into Mortensen’s ([1990]) equilibrium search model with on-the-job search. They show that employers with more pronounced discriminatory preferences offer lower wages to women than employers with less pronounced tastes for discrimination and also end up with lower profits. Since intensified competition puts additional pressure on firm profitability, it restricts discriminatory employers’ ability to pay for discrimination, and it does more so for more discriminatory employers who already make lower profits than their competitors. Hence, we expect the unexplained gender pay gap to fall to a larger extent in plants with a lower share of females in their workforces12.

To check whether the effect of competition on the unexplained gender pay gap is more pronounced in plants employing fewer women, we run wage regressions separately for workers employed by plants with below-average and plants with above-average shares of female workers (in the respective two-digit industry)13. Table 6 presents the estimated competition effects from our preferred specification with match fixed effects.
Table 6

Wage regressions with match fixed effects by worker codetermination and the share of females in plants’ workforces

 

Effect of intensified product market competition on the unexplained gender pay gap

 

All plants

Plants with worker codetermination

Codetermination-free plants

All plants

0.0016

−0.0006

0.0176**

 

(0.0039)

(0.0037)

(0.0080)

Plants with below-average share of females

0.0045

0.0028

0.0196**

 

(0.0042)

(0.0041)

(0.0077)

Plants with above-average share of females

0.0008

0.0001

−0.0026

 

(0.0033)

(0.0036)

(0.0061)

Notes: LIAB cross-sectional model, West Germany, 2008–2010. The regressand is the log gross daily wage. The coefficient shown is the interaction effect of the competition and the female dummy. Standard errors in parentheses are clustered at the plant level. ***/**/* denotes statistical significance at the 1/5/10 per cent level. Regressors are those listed in Table 4.

When not distinguishing plants depending on worker codetermination, intensified competition lowers the gender pay gap by 0.5 log points in plants with a below-average female share, which is not statistically significant. This levelling effect of product market competition is somewhat smaller (0.3 log points) in plants with some worker codetermination and much larger (2.0 log points) and statistically significant at the 5 per cent level in codetermination-free plants. Remarkably, competition has no effect on the gender pay gap in plants with an above-average female share. Since, according to theory, plants with a below-average female share are more discriminatory employers, these findings are suggestive that intensified product market competition indeed reduces gender discrimination stemming from employer prejudices against women.

5 Conclusions

Using linked employer–employee panel data for West Germany for the years 2008–2010 that include a plant-level self-assessment on product market competition, we have investigated the effect of competition on the gender pay gap. In our preferred specification, we control for match fixed effects, thereby resting identification on the differential impact of within-plant variation in competitive pressure on within-plant wage growth of men and women. We thus address both unobserved time-invariant plant heterogeneity and worker self-selection into plants facing different levels of competition.

In line with Becker’s ([1971]) model of taste-based employer discrimination where competition limits employers’ ability to discriminate against women, we find that intensified competition significantly lowers the unexplained gender pay gap in plants without any worker codetermination by 1.8 percentage points. On the other hand, there is no levelling effect of competition in plants bound by collective agreements and/or that have a works council. There are three explanations for this latter finding that seem to be consistent with our evidence: First, institutions of codetermination may yield sluggish wage reactions, and longer-term estimates might show a response that mimics those without codetermination. Second, institutions of codetermination may resist any rent reduction (even if rents largely accrue to male workers). And third, institutions of codetermination may reduce the gender pay gap sufficiently on their own such that there is no added reduction associated with product market competition. While we do not have a strong preference for any of these explanations, we are also not aware of a convincing way how to discriminate among these in our data.

We also document that there is a more pronounced competition effect on the gender pay gap in plants with a below-average share of females in their workforces and that such an effect is generally absent in plants with an above-average female share. Since, according to theory, employers employing fewer women are more discriminatory, these findings are suggestive that competition indeed restricts gender wage discrimination, thus, reeling in employer prejudices against women.

Although we were able to improve on earlier contributions, particularly in terms of identification, the brief time horizon of our panel data only permited us to identify the short-run effect of product market competition on gender wage discrimination. While this short-run effect turned out to be non-trivial in magnitude, the long-run effect is likely to be larger, and we therefore regard our estimates as a lower bound of the competition effect. Investigating the long-run effect of product market competition on the gender pay gap seems to be a promising avenue for future research.

6 Endnotes

1Weichselbaumer and Winter-Ebmer ([2005]) provide a large meta-analysis of more than 260 international studies on the gender pay gap between the 1960s and the 1990s.

2In our sample, 4.3 per cent of plants report “no pressure from competition at all”, 11.1 per cent “minor pressure from competition”, 38.5 per cent “medium pressure from competition”, and 46.1 per cent “substantial pressure from competition”, where we take the latter category as an indicator of strong competition in the plant’s product market.

3Note that we do not find any impact of product market competition on both men’s and women’s wages when repeating our analysis for East Germany, which is in line with our earlier cross-sectional findings reported in Hirsch et al. ([2012]).

4As a check of robustness, we also redid our analysis excluding observations with top-coded wages from the sample, which did not change our insights.

5A multivariate analysis of the incidence of strong product market competition shows that plants facing strong competitive pressures are, ceteris paribus, larger, more often covered by collective agreements, and concentrated in (male-dominated) industries like manufacturing and construction. Hence, the descriptive evidence in Table 1 is likely to be driven by plant size, industry, and union effects.

6In addition to Table 3, Table 7 presents a detailed transition matrix for plants’ self-assessed pressure from product market competition across the whole four-point Likert scale. It shows that less than 1 per cent of plants change from the highest to the lowest category or vice versa, while 95 per cent of transitions are between neighbouring categories, thereby mitigating concerns that changes in plants’ self-assessed product market competition reflect inconsistent reporting. Moreover, excluding the few plants with extreme changes in product market competition in a check of robustness did not change our estimation results.
Table 7

Plant transitions between different levels of product market competition

Initial pressure from product market competition

Pressure from product market competition in the next period

 

No (t + 1)

Minor (t + 1)

Medium (t + 1)

Substantial (t + 1)

No ( t )

103

84

56

28

Minor ( t )

49

275

292

92

Medium ( t )

37

301

1,554

722

Substantial ( t )

37

94

742

2,326

Notes: LIAB cross-sectional model, West Germany, 2008–2010.

7Note that the coefficient of the female dummy π 1 is no longer identified when adding plant–sex or match fixed effects.

8Strictly speaking, German labour law precludes works councils from directly negotiating wages (which is the task assigned to trade unions). Yet, the extensive veto powers enjoyed by works councils in non-wage areas give them sufficient bargaining leverage to pressure management not to cut wages (cf. Addison et al.[2001]).

9Note that male workers’ wages drop somewhat less in the specification with plant–sex fixed effects, suggesting that specification (2) misses part of the competition effect due to workers self-selecting into plants where accruing rents are large.

10Testing the effect of competition on females’ wages, we also find that it is statistically insignificant in all three specifications (with p-values ranging from 0.38 to 0.78).

11We also experimented with different definitions of strong product market competition. In particular, we defined strong competition comprising the two highest categories (rather than the single highest category) of plants’ self-assessment of competition. Using this definition, 84.6 per cent of plants report strong product market competition, and only few changes between weak and strong product market competition take place (see Table 7). Redoing our analysis with this alternative indicator of strong product market competition, we find a similar sex difference in the impact of product market competition on workers’ wages in codetermined and codetermination-free plants using the OLS specification, though this interaction effect is generally statistically insignificant as estimates are considerably less precise. Furthermore, estimates of specifications with either plant–sex or match fixed effects turned out to be too noisy to draw any conclusions, which is not that surprising given the few changes between strong and weak competition in this alternative definition of strong product market competition.

12We are aware that a low share of females in plants’ workforce may not only reflect employers’ taste for discrimination but may also mirror that limited product market competition actually enables them to engage in costly discrimination against women.

13Note that we classify plants based on their time-average of the female share. Hence, we rule out that plants switch between groups, in particular due to changes in product market competition reflected in their workforce composition (see endnote 12).

Declarations

Acknowledgments

We would like to thank Christian Dustmann, Andrea Weber, Thomas Zwick, three anonymous referees, and the editor of this journal for very useful suggestions. We further appreciate the comments received at the 2014 annual meetings of the Ausschuss für Bevölkerungsökonomie im Verein für Socialpolitik, the European Society for Population Economics, and the European Association of Labour Economists as well as the 2014 BGPE workshop. Writing of this paper has been partially supported by a BGPE scholarship to Michael Oberfichtner.

Responsible editor: Klaus F. Zimmermann

Authors’ Affiliations

(1)
Chair of Labour and Regional Economics, University of Erlangen–Nuremberg
(2)
Chair of Labour and Regional Economics, University of Erlangen–Nuremberg
(3)
IZA

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