Does gender of firm ownership matter? Female entrepreneurs and the gender pay gap

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Does gender of firm ownership matter? Female entrepreneurs and the gender pay gap © 2024 the Authors Published version Kritikos, Alexander S.; Maliranta, Mika; Nippala, Veera; Nurmi, Satu Kritikos, A. S., Maliranta, M., Nippala, V., & Nurmi, S. (2024). Does gender of firm ownership matter? Female entrepreneurs and the gender pay gap. Journal of Population Economics, 37, Article 52. https://doi.org/10.1007/s00148-024-01030-x 2024 Vol.:(0123456789) Journal of Population Economics (2024) 37:52 https://doi.org/10.1007/s00148-024-01030-x 1 3 ORIGINAL PAPER Does gender offirm ownership matter? Female entrepreneurs andthegender pay gap AlexanderS.Kritikos1,2,3,4 · MikaMaliranta5,6 · VeeraNippala5 · SatuNurmi7 Received: 22 March 2023 / Accepted: 2 May 2024 © The Author(s) 2024 Abstract We examine how the gender of business owners is related to the wages paid to female relative to male employees working in their firms. Using Finnish register data and employing firm fixed effects, we find that the gender pay gap is—starting from a gender pay gap of 11 to 12%—two to three percentage points lower for hourly wages in female-owned firms than in male-owned firms. Results are robust to how the wage is measured, as well as to various further robustness checks. More importantly, we find substantial differences between industries. While, for instance, in the manufacturing sector, the gender of the owner plays no role in the gender pay gap, in several service sector industries, like ICT or business services, no or a negligible gender pay gap can be found, but only when firms are led by female business owners. Businesses with male ownership maintain a gender pay gap of around 10% also in the latter industries. With increasing firm size, the influence of the gender of the owner, however, fades. In large firms, it seems that others—firm managers—determine wages and no differences in the pay gap are observed between maleand female-owned firms. Responsible editor: Klaus F. Zimmermann * Alexander S. Kritikos [email protected] Mika Maliranta [email protected] Veera Nippala v[email protected] Satu Nurmi [email protected] 1 German Institute forEconomic Research, DIW Berlin, Berlin, Germany 2 University ofPotsdam, Potsdam, Germany 3 GLO, Essen, Germany 4 IAB, Nuremberg, Germany 5 Labour Institute forEconomic Research, Helsinki, Finland 6 University ofJyväskylä, Jyväskylä, Finland 7 Statistics Finland, Helsinki, Finland A.S.Kritikos et al. 1 3 52 Page 2 of 31 Keywords Entrepreneurship· Gender pay gap· Discrimination· Linked employeremployee data JEL classification J16· J24· J31· J71· L26· M13 1 Motivation Faced with a still significant gender pay gap between female and male employees (Blau and Kahn 2017), one strand of literature argues that the gender composition of firm management may matter for the size of the “unexplained” part of the gap. To the extent that this part of the pay gap is rooted in discriminatory practices against female workers and to the extent that female superiors are more motivated than male superiors to reduce this kind of discrimination or are concerned about gender pay equality, female superiors may be able to reduce the gender pay gap.1 Most (but not all) research in this field points to small, but significant, effects when comparing firms where male managers dominate with firms where female managers dominate (see, e.g., Abendroth etal. 2017; Theodoropoulos etal. 2022), or when the effect of a change from male to female manager is analyzed for the employees reporting to that specific manager (see, e.g., Cardoso and Winter-Ebmer 2010; Hensvik 2014). However, as female managers are usually neither major shareholders nor owners of these firms, they are not necessarily able to freely determine how wages are set. Their bounded involvement in these firms and their constrained ability to determine wages might limit their moderating influence on the gender pay gap. Previous research focuses on management gender but does not investigate how the gender of firm ownership influences the gender pay gap. Female entrepreneurs and firm owners have different access to organizational power through their capital investment and profit-sharing than female managers, but they also have more skin in the game. As owners, they may decide about the wages of their salaried employees in a different way than managers—in small firms, for instance, there might be no interference from other managers or executives. Moreover, women who become managers in established firms may get involved in the wage-setting process where a gender pay gap already exists. By contrast, female entrepreneurs may have the ability to implement equal pay irrespective of gender from day 1. In that sense, the wage structure deserves separate attention with regard to the gender pay gap among firms run by female versus male entrepreneurs. Therefore, in this paper, focusing on entrepreneurs and firm owners, we investigate what role their gender plays in the wages paid to women relative to the wages paid to men in their respective firms. The gender of the entrepreneur (as much as of the manager) may matter for the gender pay gap when there exists some kind of pay discrimination against female in comparison to male employees. Following the distinctions made in psychological 1 The data set used in this study contains only a binary indicator of gender. Therefore, in this study, we use both terms—women and female—when we refer to individuals labelled as women in the data. We are, however, aware that individual’s gender identity can deviate from their assigned sex at birth. 1 3 Does gender offirm ownership matter? Female entrepreneurs… Page 3 of 31 52 research when investigating gender pay gaps with regard to discrimination, women employees may either suffer from explicit discrimination (which is forbidden in most European countries), when male superiors explicitly favor male over female employees, or from implicit discrimination. Implicit discrimination occurs when women confront unconscious or unintentional forms of bias (Ellemers 2018), where male managers, who still often comprise the majority in management, might—for instance because of their homophilous preferences—be inclined to support or promote male employees more strongly than female employees (Ertug etal. 2022). Parallel research in economics also discusses discrimination, with literature basically distinguishing between two models. There is “taste-based” discrimination (Becker 1971; Charles and Guryan 2008), where managers may experience disutility if they employ workers of the opposite gender. To compensate for such disutility, employees with a different gender than their manager must agree to relatively lower wages if they want to be employed. A second approach in economics argues that gender pay gaps are the consequence of statistical discrimination. Tracing back to the model of Phelps (1972), it is claimed that employers have incomplete information in the sense that the expected future productivity of women is less predictable than men’s expected future productivity. This is because women are more likely to quit their jobs for a variety of reasons (including, for instance, motherhood or moving to another job because their partner moves; see, e.g., Cooke etal. 2009). Female individuals, when they are not able to signal their individual future productivity value, must accept—as a consequence of their group membership—lower wages, are denied access to jobs that involve further investments like firm-specific training, or may face search frictions (Sulis 2012). Recent reviews claim that the psychological approach of classifying discrimination into implicit and explicit aspects also captures these two main economic models of taste-based and statistical discrimination (Bertrand and Duflo 2017). The aim of this paper is to investigate whether female entrepreneurs and firm owners are willing and able to reduce the part of the observable gender pay gap that is related to any of the described discriminatory practices. Having the ability is a necessary, but not sufficient, condition to reduce this part of the pay gap. Female entrepreneurs (as much as female managers) also need to have the willingness to do so. Theoretical research is divided on whether women superiors have the willingness to rectify the gender wage gap to the extent that it is rooted in discrimination. On the one hand, research argues in favor of the “same gender approach,” according to which female superiors—here female entrepreneurs—have homophilous preferences that lead to common interests between them and female workers within a firm (McPherson, Smith-Lovin, and Cook 2001; Rudman and Goodwin 2004). This may unfold effects in various ways. Female entrepreneurs may support, help, or mentor female employees, for instance by promoting them more often or by paying them higher wages (Baron and Pfeffer 1994; Matsa and Miller 2011). Female entrepreneurs may also serve as role models (Ely 1994), creating positive spillover effects on female employees (Zimmermann 2022). An alternative explanation for homophilous bonds is that managers are better able, due to differences in communication styles, to assess the skills of their employees if they are of the same gender (Flabbi etal. 2019; Theodoropoulos etal. 2022). Thus, applying the same gender approach A.S.Kritikos et al. 1 3 52 Page 4 of 31 to the present analysis means that female entrepreneurs and firm owners will act as “agents of change” (Cohen and Huffman 2007), seeking to nullify that part of the gender pay gap that is owed to discriminatory practices in the workplace, perhaps even creating different discriminatory practices against male employees. On the other hand, the opposing argument suggests that the principal-agent relationship between female entrepreneurs and female employees may counteract homophilous preferences, resulting in a gender pay gap that remains the same, if it is not increasing. Women may share men’s taste for discrimination with respect to women in lower positions (Deaux 1985). Female superiors who work in male-dominated industries need to become “one of the team.” Therefore, female superiors may feel pressure to maintain the status quo and to not ease the discrimination against female workers in such firms. This may also hold for female entrepreneurs, according to which they have to behave like “cogs in the machine” to receive acceptance from their male workers when they operate in a male-dominated industry (Kanter 1977). Ridgeway (1997) argues that there might exist a culturally driven persistent gender status belief according to which both female and male superiors implicitly expect superiority and greater competence of men. In a similar direction, according to the “queen bee syndrome” (Bednar and Gicheva 2014), individually successful women (in particular, in male-dominated environments) may feel competitive threats from other women or may hold negative stereotypical views about other women’s career commitment (Derks etal. 2011). Thus, applying the “cogs in the machine” approach or the “queen bee syndrome” to the present analysis means that female entrepreneurs will not seek to reduce the part of the gender pay gap that is owed to discriminatory practices in the workplace; they may even work to increase discriminatory practices against female employees. Overall, it is not fully clear which of the two effects will dominate, thus what kind of wages female entrepreneurs and firm owners pay to their female relative to their male employees in comparison to male entrepreneurs and firm owners. Yet, it becomes obvious that approaches—like the “cogs in the machine” approach—arguing in the direction that female workers will face the same pay gap irrespective of the gender owner are mostly explained by settings where female entrepreneurs act in a male-driven environment. In such an environment, like in the manufacturing sector, there is not only a strong gender gap in entrepreneurship (Caliendo et al. 2015) but also the working population is mostly male. Accordingly, the wage-setting process for female entrepreneurs might be different in environments where there is more gender balance or a majority of female workers. Hence, it is interesting to conduct a heterogeneity analysis that takes these kinds of differentiations into account. Based on these theoretical considerations, we empirically investigate in what way the gender of entrepreneurs and firm owners matters for the gender pay gap. More specifically, using Finnish register data, we analyze—for the first time, to the best of our knowledge—the size of the gender pay gap in firms started and owned solely or predominantly by female entrepreneurs in comparison to firms started and owned solely or predominantly by male entrepreneurs. We hypothesize, first, that the gender of entrepreneurs and firm owners matters in the sense that the gender pay gap will be smaller in firms that are started and owned solely or predominantly by female entrepreneurs. Secondly, given the contrasting expectations from earlier research 1 3 Does gender offirm ownership matter? Female entrepreneurs… Page 5 of 31 52 about the influence of female managers (not owners), which may be rooted in the gender structure of industries, we explicitly take the industry and the related gender structure of employees into account. By investigating gender pay gaps for firms operating in different industries at the one-digit level, we aim to find out whether the observed gender structure of the industry matters for how female entrepreneurs and business owners pay their female employees relative to their male employees. Third, as we also have information on firm size, we are able to analyze what kind of wages are set in small versus large firms run by entrepreneurs and how the gender pay gap changes as firms become larger, controlling for the gender of the entrepreneurs. By doing so, we reveal to what extent the potential influence of the gender of the owner on gender pay gaps depends on firm size. To investigate our research questions, we rely on unique Finnish register data provided by Statistics Finland for 2006–2015. The data links various sources delivering information on firms in the private business sector of Finland as well as wages paid in these firms (for details on data sources, see Kankaanranta and Melakari 2021). Using these data for our empirical setting, we analyze differences in person-level hourly or monthly wages between different firm owner groups: female-owned, maleowned, mixed or balanced ownership, and unknown or more dispersed ownership. We study the pay gap and differences in the pay gap, i.e., the difference in the estimates between different owner groups, controlling for various background factors. We further divide firms into subgroups by their industry, firm size, and relative productivity to analyze the differing pay gaps between these subgroups. Starting from an hourly wage gap of about 11 to 12% for the hourly wages, we observe that the gender pay gap is—depending on whether we include firm fixed effects in our estimations—two to three percentage points lower for hourly wages in female-owned firms than in male-owned firms. Moreover, we find in several service sector industries no or a negligible gender pay gap, but only when firms are led by female business owners. In male-owned businesses, pay gaps are still at around 10%, also in these industries. Finally, in large firms, the influence of the gender of the owner on this pay gap disappears. 2 Previous empirical research Previous research in this area concentrates on the question of whether the gender of managers, and other dependently employed supervisors, influences the gender wage gap.2 These studies can be divided according to their use of different identification strategies. Some focus on the impact of female manager on the gender pay gap by 2 There is, of course, a huge number of studies on the gender pay gap from a more general point of view, which we do not discuss here (for an overview, see Blau and Kahn 2017). However, from this research, it is important to note that the largest part of the difference between the unexplained and explained gap is described by industry and occupational differences as well as by work experience (Blau and Kahn 2017), while it is also shown in various studies that, for most industrialized economies, the gap increases in the upper percentiles of the wage distribution; this includes Finland (Christofides etal. 2013). Another study investigates for the first time firm size effects on the gender pay gap (Jones and Kaya 2023). A.S.Kritikos et al. 1 3 52 Page 6 of 31 analyzing how the switch from male to female managers influences the pay gap of their direct female and male subordinates. Others use a comparison by examining differences of gender wage gaps in firms where the share of female managers is high in comparison to firms with no or a low share of female managers, with earlier studies being restricted to cross-sectional analysis and few more recent studies exploiting the panel structure of the data at hand. The analysis of Portuguese firms between 1987 and 2000 by Cardoso and WinterEbmer (2010) is one of the first type: they investigate to what extent the wage differentials are reduced if the management in a firm is changed from male to female. They pool all kinds of female managed firms, but restrict the analysis to firms with more than 10 employees. Their main finding is that, under such a management change, the monthly wage differential is reduced by 1.5 percentage points.3 Hensvik (2014) finds similar results—a narrowing of the gender pay gap by 1.2 percentage points—for Swedish data when the gender of the manager changes from male to female in a firm. In her further analysis, she reveals that the gap reducing effect mostly owes to worker sorting behavior. Thus, instead of actively reducing the wage gap among the existing staff, female managers hire more highly skilled women who then receive higher wages. In contrast, Srivastava and Sherman (2015), who also analyze the influence of female managers on the wage levels of their employees after a change from male to female manager, but who restrict their investigation to one single firm in the information services (a male-dominated industry), find no support for a reduction of the gender wage gap. They even observe, in a subsample of highperforming supervisors and low-performing employees, that low performing women who switched from a male to a female supervisor had a lower salary in the following year than the same type of men who made the same switch, thus increasing the gender pay gap in this specific group. Flabbi etal. (2019), who restrict their analysis to Italian manufacturing firms, another male-dominated industry, also do not find that female managers reduce the gender pay gap and rather observe (like Srivastava and Sherman 2015) increases in the wage gap for less productive female workers. Cohen and Hofman (2007) and Hirsch (2013) report observations within the second domain of comparisons. Based on cross-sectional analyses of US census data from 2000 and German data from 2008, both papers examine whether the gender wage gap is lower if the share of female management is high. Both observe that a high share of female managers is associated with a slightly lower pay gap, but they report a diverging effect in one respect: while Hirsch finds for Germany a smaller wage gap if the number of female managers is high in the second level-management, Cohen and Hofman (2007) reveal the exact opposite for the US—the gap is lower if the share of females at the top management level is high. Further research by Lucifora and Vigani (2022) of 30 European countries, for the 1995–2010 period, finds 3 Their analysis remains silent with respect to the question of how large the size of the pay gap is in female-led firms. Moreover, one must emphasize that the segregation effect in Portugal of the 1990s was still huge, leading to substantially lower wages paid to women in female-led firms than women in maleled firms. 1 3 Does gender offirm ownership matter? Female entrepreneurs… Page 7 of 31 52 an overall gender earnings gap of 20%, with the pay gap being 5 percentage points lower if female employees work in firms with female owners. More recent analysis exploits the panel structure of these data and includes—in contrast to the previous approaches—among others firm fixed effects. Abendroth etal. (2017) use similar German data as Hirsch (2013), but from 2012 and 2013 and they concentrate on large firms, where they investigate the within-firm variance of wages. In this subset of firms, they find a gender earnings gap of 12% for monthly incomes, which was 2.4 percentage points lower if the firm had female managers. Zimmermann (2022) and Sondergeld and Wrohlich (2023) observe similar results for different time periods between 2004 and 2018. Starting with an overall gender pay gap of about 15%, reductions in the gender pay gap range from 1.2 percentage points for the first level female management and a stronger effect for the second level female management. This confirms the earlier suggestive observations of Hirsch (2013) that the female management at the second level has a higher influence on the reduction of gender pay gap in Germany. Hence, the reviewed empirical research on the influence of female managers on the gender pay gap shows that although firms with a larger share of female managers tend to pay wages that are associated with a lower gender wage gap, a substantial gap remains. Analyzing the influence of a change in the supervisor’s gender finds very small positive effects, no effect at all, or even negative effects for specific subgroups. It should be noted, however, that the latter two observations (of no or negative influences of female managers on the gender pay gap) are found in maledominated industries, the manufacturing sector (Flabbi etal. 2019) in information services (Srivastava and Sherman 2015). Yet, the access of female managers to organizational power is limited. Therefore, our approach takes a new direction. We examine how the gender of the entrepreneur or of the firm owner is related to the gender wage gap of those who are working for these entrepreneurs. There are several reasons for analyzing the wage-setting of female entrepreneurs and business owners separately from female managers. Female entrepreneurs and business owners are, through their ownership, involved in the capital investment and profit-sharing of their own firms in a different way than female managers. Therefore, as a principal, they have a different relationship with their employees than do managers and, as such, may have greater autonomy. Typically, entrepreneurs and business owners directly decide on wages, in particular when firms are small and there is no further management in between them and their employees.4 By contrast, female managers usually enter existing firms and are confronted with existing wage inequalities that they need to correct, if they aim to address the problem. At the same time, another important difference between entrepreneurs and managers is that female (as much as male) entrepreneurs will be aware that any wage changes will affect their own earnings. 4 Maliranta and Nurmi (2019) examine the (initial) characteristics of the entrepreneurs (gender, education, previous experience, the productivity performance of the firm where they previously have worked as an employee, etc.) starting their new business and, in particular, how these characteristics are related to the performance of their firm in terms of productivity, survival, and growth. A.S.Kritikos et al. 1 3 52 Page 8 of 31 We explore in the subsequent empirical analysis the gender pay gap in firms run by female entrepreneurs, comparing it with the pay gap of firms that are run by male entrepreneurs. Thus, we aim to identify what kind of gender pay gap we observe in firms that are owned solely or predominantly by female entrepreneurs. With a comprehensive analysis of the structures of gender pay gaps with the linked owneremployer-employee data along several dimensions, we test the research questions that can be derived from the economic and psychological literatures, as presented in the introduction. 3 Data andsummary statistics 3.1 Data description For our analysis, we construct a unique data set based on Finnish register data comprising limited liability firms in the private business sector and their employees by linking various data sources maintained for research purposes by Statistics Finland, among them the Structure of Earnings Statistics (SES), the Finnish Longitudinal Employer-Employee Data (FLEED), and the Finnish Longitudinal Owner-Employer-Employee (FLOWN), as well as the Finnish Patent and Registration Office data. The data is comprehensive and rich in its content, allowing versatile wage analysis by employee gender and firm owner gender over the 2006–2015 period, as FLEED was discontinued in 2016. The data is repeated cross-section data, meaning that firms can disappear from and enter the sample, as they enter and exit the market. Similarly, employees can exit and enter the job market or change firms. The repeated nature of the data allows us to follow employees and firms over time and allows the use of firm fixed effects. Wages are observed at the end of the year, so an employee’s employment status at the end of the year defines if they contribute to the sample that year and who their employer is. One advantage of our data is that it is a matched owner-employer-employee data set, providing us with information on employment structure within firms, i.e., the gender of all employees and their wages, which can be linked to the gender of the firm owner(s) and other firm features. Our target population is limited liability companies in the Finnish Business Register data with the size of at least one person. We include private non-agricultural business sector firms but exclude specific sectors, namely mining and quarrying; coke and refined petroleum products; electricity, gas and steam, and air conditioning supply; and water supply, sewerage, and waste management and remediation activities; as well as financial and insurance activities (according to NACE Rev. 2 the included sectors are 10-18, 20-33, 41-43, 45-47, 49-53, 55-56, 58-63, 68-82). Exclusion of these sectors is rationalized by the challenges in the measurement of productivity, which is one dimension of our analysis. Firm age is defined according to its oldest establishment in a given year to mitigate the effects of organizational changes, like mergers and acquisitions, in the firm codes. In comparisons between firm groups, we use information on productivity, dividing firms into productivity quartiles according to employment-weighted labor productivity within each 2-digit industry. 1 3 Does gender offirm ownership matter? Female entrepreneurs… Page 15 of 31 52 where wages are measured in natural logs and Female is an indicator variable that is 1 if the employee is female and 0 if the employee is male. The included Control variables are described above. This model is estimated separately for each firm-owner group and further for the sub-groups mentioned before. We extend this model by including firm fixed effects: where 𝛼j represents firm fixed effects that control for unobserved firm qualities that may influence the wage-setting and selection of employees to firms. Since the model is estimated separately for femaleand male-owned firms, the estimations have different samples and seemingly unrelated estimation methods are used to build a variance-covariance matrix that allows us to test for the statistical significance of the differences in the pay gap. Last, but not least, we make quantile regressions that are performed at the 10th, 25th, 50th, 75th, and 90th percentiles of the income distribution. Quantile regressions are useful for determining how the distribution of the wage gap is skewed, thus where the wage gaps are largest—at the bottom or the top incomes. 4.2 General estimation results Table5 provides the main results of our estimation of the gender pay gap for the full sample. In Finland, the hourly adjusted pay gap, during the observation period of 2006 to 2015, was 12.4%, when controlling for human capital, industry, and occupation beyond the basic variables, and 11.6% when also including firm fixedeffects. This gap is significantly lower in firms run by female entrepreneurs, by 3.3 percentage points in specification (1), and by 2.3 percentage points in specification (3) ln(Wageij)=𝛽 1 +𝛽 2 Femalei+𝛼j+Controls +𝜇ij Table 5 Wage regression, all firms The models include as controls age, number of children <7, education (2-digit), occupation (3-digit), firm size, firm age, industry (2-digit), and year dummies. The model with firm fixed effects additionally includes firm level fixed effects. The models are estimated over 2006–2015. Robust standard errors with clustering by firm. *p < 0.05; **p < 0.01; ***p < 0.001 Source: Authors’ calculations based on the linked research data (1) (2) Female −0.124*** (0.004) −0.116*** (0.003) Female owner −0.031*** (0.008) −0.014* (0.005) Female x female owner 0.033** (0.011) 0.023** (0.007) Obs. 5,024,475 5,024,475 R2 0.650 0.500 Firm fixed effects No Yes A.S.Kritikos et al. 1 3 52 Page 16 of 31 (2) when we include firm fixed effects. Moreover, we observe a negative relationship between hourly wages and female owners that is offset for female employees through the positive interaction effect. This means that the reduction in the gender pay gap in female-owned firms is mostly realized through lower wage payments to male employees, while female employees realize similar wage payments in maleand female-owned firms. In Table6, we present estimations separated by the gender of firm owners to further understand the heterogeneous effects between the genders—and confirm these observations for both specifications (i.e., without and with fixed effects). In firms with mixed gender ownership, pay gaps are similar to female-owned firms; in firms with unknown ownership, the pay gap is of the same size as in male-owned firms. The result of a 3 percentage points lower gender pay gap among female entrepreneurs and firm owners (when not including firm fixed effects) is generally confirmed for all kinds of variations of control variables (see panels A–D in Appendix TableA1). We find the same difference in the gap for additional payments (bonuses) or when occupational variables are not controlled for. In the latter case, gender pay gaps are at higher levels, while more controls lower the overall gender pay gaps (Meara et al. 2020), but differences between male and female business owners remain similar. This also holds for firms where the CEO and firm owner are the same person (see panel E in Appendix TableA1): again, the gender wage gap is 3 percentage points lower (as among female owners when compared to male owners). For monthly wages, the gender pay gap in female-owned firms is about 4 to 5 percentage points lower than in male-owned firms. When comparing the pay gaps for firms run by male and female CEOs (groups defined by CEOs instead of the owners of the firms, see panels F–G in Appendix TableA1), we observe that, in firms run by a male CEO, the wage gap is, at 12.2%, very similar to male owners; under female CEOs, wage differences are only 1.6 percentage points lower when compared to male CEOs. Hence, the difference in hourly wages (i.e., the difference in the gap) between male and female employees is (in estimations without firm fixed effects) about 3 percentage points in female-owned firms when compared to male-owned firms, thus larger than the difference under female CEOs in comparison to male CEOs. We also estimate quantile regressions to reveal the pay gaps over the wage distribution. Figure2 presents exemplary results for 2 years, 2011 and 2015. In both years, the same patterns are observed. The gender pay gap is getting larger in both maleand female-owned firms, the higher we move up the distribution ladder. At the same time, the increase in the gap between male and female workers is getting larger at a lower rate in female-owned firms. This means that gender pay gaps are highest in the upper percentiles of the wage distribution, at 12 percentage points in femaleowned and 16 in male-owned firms.6 Thus, the difference in the gap between male and female workers is also largest, at 4 percentage points, in the upper percentiles when comparing femalewith male-owned businesses, while the difference in the 6 Albrecht etal. (2003) and Arulampalam etal. (2007) show that gender pay gaps also increase in other countries in the upper percentiles. 1 3 Does gender offirm ownership matter? Female entrepreneurs… Page 17 of 31 52 Table 6 Gender pay gap by firm-owner groups The coefficient of “female” in Mincer type wage equation (wage gap) in firms with different owner structures. The models include as controls sex, age, number of children <7, education (2-digit), occupation (3-digit), firm size, firm age, industry (2-digit), and year dummies. The model with firm fixed effects additionally includes firm level fixed effects. The models are estimated over 2006–2015. Robust standard errors with clustering by firm. +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001. Source: Authors’ calculations based on the linked research data Male-owned firms Female-owned firms Mixed firms Unknown owner firms Panel A: Pooled OLS Coefficient of “female” −0.126*** (0.003) −0.097*** (0.008) −0.103*** (0.005) −0.123*** (0.004) Difference relative to male-owned firms – 2.9% 2.3% 0.3% Significance of difference – *** *** n.s. Obs. 599,291 114,970 116,071 4,194,143  R2 0.551 0.610 0.646 0.660 Panel B: With firm fixed effects Coefficient of “female” −0.113*** (0.003) −0.090*** (0.008) −0.092*** (0.005) −0.116*** (0.004) Difference relative to male-owned firms – 2.3% 2.1% −0.3% Significance of difference – – – – Obs. 599,291 114,970 116,071 4,194,143  R2 0.429 0.450 0.439 0.512 A.S.Kritikos et al. 1 3 52 Page 18 of 31 Fig. 2 Quantile regression for gender pay gaps in 2011 and 2015. Note: The figure shows how the gender pay gap develops over the wage distribution in femaleversus male-owned firms. The figure graphs the coefficients of indicator “female” in a quantile regression with hourly (SES) wage as dependent variable and as controls “female,” age, no of children <7, education (2-digit), occupation (3-digit), firm size, firm age, and industry (2-digit). Source: Authors’ calculations based on the linked research data 1 3 Does gender offirm ownership matter? Female entrepreneurs… Page 19 of 31 52 gap is around 1 percentage point in the lower tails, with differences being significant only in the upper five deciles of the distribution. 4.3 Further analyzing thegender wage gap We continue by examining firms in various industries, with various firm sizes, and various productivity levels. Industries It is important to differentiate the analysis of wage gaps according to industries as there are industries with more male than female employees and vice versa. Earlier research points to potential differences: in male-dominated industries, female entrepreneurs (like female managers) may be inclined to turn against female employees (see Flabbi etal. 2019) to signal to male employees that they, the female entrepreneurs, are “one of the boys” (see Srivastava and Sherman 2015, p. 1783). Our differentiation according to industries at the 1-digit level delivers several insights (Table7). Gender pay gaps in the secondary-sector industries (manufacturing and construction) and the more traditional third-sector industries (wholesale and retail) are generally larger than those in the other service-sector industries. Looking more deeply into differences, in the first three industries (with 1-digit codes C, F, and G), we observe gender pay gaps between 13.5 and 15% in male-owned businesses in the estimations without fixed effects and between 12 and 13% in the estimations with fixed effects. There are hardly lower pay gaps in female-owned business—only in construction the pay gap is significantly lower, by around 3 percentage points in female-owned firms. Much in contrast to this, in female-owned firms of service sector industries like information and communication (J), real estate (L), and administrative and support services (N), we observe no pay gaps at all, and in transportation and storage (H), and professional, scientific, and technical services (M) only a small gap of less than 5%. In these industries, gender pay gaps in male-owned businesses are still around 10%; thus, gender pay gaps are significantly lower in female-owned businesses with huge differences in (H) and, interesting enough, in information and communication (J). From the descriptive statistics, we know that, in these sectors, the share of women in the businesses greatly differ between maleand female-owned businesses. For instance, in information and communication (J), there are between 50 and 60% female employees in female-owned businesses, while in male-owned businesses, the share of male employees is above 80%. Last, but not least, the industry accommodation and food service (I) is an exception to these two “rules,” in the sense that the gender pay gap is relatively low with around 4% in the fixed effects estimation and does not differ across owner gender. Firm size From earlier research, we know that the size of the firm may influence the size of the gender pay gap (Jones and Kaya 2023). Table8 reports results for three different firm sizes (small, medium, and large firms). Two patterns can be observed. On the one hand, gender pay gaps grow larger with increasing firm size. On the other hand, the influence of female owners on reducing the gender pay gap becomes A.S.Kritikos et al. 1 3 52 Page 20 of 31 Table 7 Gender pay gap by firm owner and industry Male-owned firms Female-owned firms Significance of the difference Panel A: Pooled OLS Manufacturing −0.151*** (0.006) −0.135*** (0.010) n.s. Obs. 171,010 23,247 Construction −0.135*** (0.009) −0.102*** (0.010) * Obs. 97,305 20,174 Wholesale and retail −0.145*** (0.011) −0.133*** (0.016) n.s. Obs. 134,422 23,054 Transportation and storage −0.120*** (0.013) −0.049*** (0.011) *** Obs. 38,760 19,661 Accommodation and food service −0.057*** (0.005) −0.054*** (0.009) n.s. Obs. 37,968 8725 Information and communication −0.103*** (0.008) 0.010 (0.060) * Obs. 29,404 248 Real estate −0.127*** (0.026) 0.082 (0.135) + Obs. 2754 242 Professional, scientific, and technical activities −0.107*** (0.006) −0.039* (0.017) *** Obs. 58,518 5920 Administrative and support services −0.099*** (0.008) −0.039+ (0.023) * Obs. 29,150 13,699 Panel B: With firm fixed effects Manufacturing −0.128*** (0.005) −0.127*** (0.008) – Obs. 171,010 23,247 Construction −0.127*** (0.008) −0.097*** (0.006) – Obs. 97,305 20,174 Wholesale and retail −0.119*** (0.008) −0.114*** (0.008) – Obs. 134,422 23,054 Transportation and storage −0.103*** (0.013) −0.047*** (0.010) – Obs. 38,760 19,661 Accommodation and food service −0.039*** (0.004) −0.038*** (0.008) – Obs. 37,968 8725 1 3 Does gender offirm ownership matter? Female entrepreneurs… Page 21 of 31 52 smaller the larger the firms are. While in small firms the gender pay gap in femaleowned businesses is at 4% (for the estimation with fixed effects), half of the gender pay gap in male-owned businesses, in large firms the pay gaps are more or less identical at around 12%. Thus, in large firms, the influence of female owners on the gender pay gap seems to fade and it might be more in the hands of managers to decide about what kind of wages are paid in large firms. We also examine the impact of firm size by investigating the development of gender pay gaps when firm size increases. Table9 reports the results for male-owned and female-owned firms separately. The negative coefficient of the interaction term in both maleand female-owned firms with firm fixed effects confirms the finding of the pay gap growing larger with increasing firm size. In the model with firm fixed effects, this firm size penalty for women is larger in female-owned firms than maleowned firms, which provides additional evidence of female firm owners having less influence on the gender pay gap in larger firms. Productivity levels With respect to the productivity levels, we sort firms into four groups, low, medium-low, medium-high, and high productivity firms. We define productivity of the firm as value added by hour worked (Table10). The highest differences in gender pay gaps can be found by comparing hourly with monthly wages. The analysis of hourly wages shows that the generally observed pay gaps of 11 and 12% (with and without fixed effects) as well as the 2 and 3 percentage point difference between femaleand male-owned businesses are confirmed for all but the low productivity firms. At the latter group, pay gaps and differences of pay gaps between maleand female-owned firms are slightly lower. Different to Table 7 (continued) Male-owned firms Female-owned firms Significance of the difference Information and communication −0.101*** (0.006) −0.008 (0.054) – Obs. 29,404 248 Real estate −0.114*** (0.026) 0.081 (0.129) – Obs. 2,754 242 Professional, scientific, and technical activities −0.102*** (0.005) −0.038* (0.018) – Obs. 58,518 5920 Administrative and support services −0.081*** (0.007) −0.034 (0.022) – Obs. 29,150 13,699 The coefficient of “female” in Mincer type wage equation (wage gap) in firms with different owner structures and industries. The model includes as controls sex, age, number of children <7, education (2-digit), occupation (3-digit), firm size, firm age, and year dummies. Robust standard errors with clustering by firm. +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001 Source: Authors’ calculations based on the linked research data A.S.Kritikos et al. 1 3 52 Page 22 of 31 this, the monthly gender pay gap is getting larger between maleand female-owned businesses, the higher the productivity levels are. The difference between maleand female-owned businesses in the gender pay gap peaks at 8 percentage points for high-productivity firms. Thus, differences in the pay gaps are much stronger for monthly than for hourly wages, as productivity levels get higher. Hence, for monthly wages, gender wage gaps are sensitive to firm productivity. 4.4 Robustness check In this section, we assess the robustness of our results with respect to several specifications. First, we note that the main results are robust to different definitions of wages (see panels H–L in Appendix TableA1). With different wage measures that use SES data, the difference in the gender pay gap between femaleand male-owned firms ranges from around 2 percentage points to 4 percentage Table 8 Gender pay gap by firm owner and firm size groups The coefficient of “female” in Mincer type wage equation (wage gap) in firms with different owner structures and different sizes. The model includes as controls age, number of children <7, education (2-digit), occupation (3-digit), firm size, firm age, industry (2-digit), and year dummies. Robust standard errors with clustering by firm. +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001 Source: Authors’ calculations based on the linked research data Male-owned firms Female-owned firms Significance of the difference Panel A: Pooled OLS Small firms (<10 employees) −0.092*** (0.006) −0.066*** (0.011) * Obs. 44,285 10,260 Medium-sized firms (10–50 employees) −0.120*** (0.003) −0.079*** (0.008) *** Obs. 236,057 26,153 Large firms (>50 employees) −0.130*** (0.005) −0.101*** (0.011) * Obs. 317,220 78,420 Panel B: With firm fixed effects Small firms (<10 employees) −0.086*** (0.005) −0.040*** (0.012) – Obs. 42,880 9991 Medium-sized firms (10–50 employees) −0.106*** (0.003) −0.067*** (0.007) – Obs. 234,878 26,289 Large firms (>50 employees) −0.122*** (0.004) −0.114*** (0.010) – Obs. 233,335 31,700 1 3 Does gender offirm ownership matter? Female entrepreneurs… Page 23 of 31 52 points. With FLEED data, the difference is slightly larger, around five percentage points. The second robustness check concerns the financial crisis of 2008 and 2009. According to Calligaris etal. (2023), the Finnish economy suffered a long period of stagnation between 2008 and 2016, which falls in our sample period. They summarize that the financial crisis only had a limited role. Instead, the mostly exogenous downfall of mobile phone manufacturer Nokia around year 2010 had a more important role. Additionally, the financial crisis did lead to reduced global demand, which hit another important Finnish industry at the time, the forest industry. To examine the impact of these crisis industries, we perform a robustness check by excluding the electronics and forestry industries from our sample. Eliminating these industries from the sample (see panel B in Appendix TableA2) results in virtually the same wage gaps in maleand female-owned firms as in the whole sample, and the difference in the pay gap between maleand female-owned firms remains at around 3 percentage points. We continue our robustness check with an analysis where we exclude firms with unknown owners from our estimation procedure (see panel C in Appendix TableA2). Estimation results in the sense of observed gender pay gaps and differences between maleand female-owned firms are again fully confirmed. Table 9 Wage regression, separately for male-owned and female-owned firms The model includes as controls age, number of children <7, education (2-digit), occupation (3-digit), firm age, industry (2-digit), and year dummies. Robust standard errors with clustering by firm. +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001. Source: Authors’ calculations based on the linked research data. Male-owned firms Female-owned firms Significance of the difference Panel A: Pooled OLS Female −0.088*** (0.011) −0.086*** (0.016) n.s. Log firm size 0.024*** (0.003) 0.025*** (0.004) n.s. Female × log firm size −0.009*** (0.003) −0.003 (0.004) n.s. Obs. 599,291 114,970  R2 0.551 0.610 Panel B: With firm fixed effects Female −0.081*** (0.010) −0.040** (0.015) – Log firm size 0.009* (0.004) −0.003 (0.007) – Female × log firm size −0.008** (0.003) −0.011* (0.004) – Obs. 599,291 114,970  R2 0.429 0.450 A.S.Kritikos et al. 1 3 52 Page 24 of 31 Table 10 Gender pay gap by firm owner for various productivity levels The coefficient of “female” in Mincer type wage equation (wage gap) in firms with different owner structures and different productivity levels. The model includes as controls age, number of children <7, education (2-digit), occupation (3-digit), firm size, firm age, industry (2-digit), and year dummies. Firms were split into quartiles according to their productivity levels. Robust standard errors with clustering by firm. +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001 Source: Authors’ calculations based on the linked research data Male-owned firms Female-owned firms Significance of the difference Panel A: SES hourly wage Low productivity firms −0.110*** (0.004) −0.085*** (0.016) n.s. Obs. 127,181 30,227 Medium-low productivity firms −0.125*** (0.005) −0.0919*** (0.010) ** Obs. 175,364 35,697 Medium-high productivity firms −0.125*** (0.004) −0.089*** (0.009) *** Obs. 166,379 30,415 High productivity firms −0.122*** (0.007) −0.099*** (0.009) * Obs. 128,638 18,494 Panel B: SES hourly wage, with firm fixed effects Low productivity firms −0.097*** (0.004) −0.081*** (0.017) – Obs. 127,181 30,227 Medium-low productivity firms −0.119*** (0.005) −0.091*** (0.010) – Obs. 175,364 35,697 Medium-high productivity firms −0.118*** (0.004) −0.084*** (0.011) – Obs. 166,379 30,415 High productivity firms −0.112*** (0.007) −0.099*** (0.008) – Obs. 128,638 18,494 Panel C: FLEED monthly wage Low productivity firms −0.134*** (0.002) −0.110*** (0.006) *** Obs. 779,588 125,241 Medium-low productivity firms −0.165*** (0.004) −0.094*** (0.007) *** Obs. 749,243 103,301 Medium-high productivity firms −0.169*** (0.003) −0.102*** (0.007) *** Obs. 698,319 85,343 High productivity firms −0.180*** (0.004) −0.102*** (0.008) *** Obs. 592,789 53,920 1 3 Does gender offirm ownership matter? 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