Introduction

Gender inequality is present in many socioeconomic indicators around the world in both developed and developing countries. These gaps can be observed in education, earnings, occupation, access to productive inputs, political representation, or bargaining power inside the household, among others. ¹ See the World Development Report 2012 (World Bank, 2012) for a comprehensive review of these and other gender gaps discussed in the literature. One important aspect of gender inequality in the labor market that has been largely overlooked in the literature is the low representation of women in entrepreneurship. In this paper, we study this issue for Latin America and the Caribbean (LAC) using data from the World Bank Enterprise Survey[16] World Bank. (s. f.). Enterprise Surveys [Base de datos]. World Bank Group. Recuperado el 28 de julio de 2026, de https://www.enterprisesurveys.org
.² We acknowledge that there is substantial institutional and economic heterogeneity across Latin American and Caribbean countries. Differences in regulatory quality, access to finance, female labor force participation, and economic development may generate heterogeneous effects on entrepreneurial constraints and the associated efficiency losses. However, the availability of comparable data limits our ability to conduct a reliable country-level or subgroup analysis. Estimating the model requires a sufficiently large number of observations to identify the relevant parameters, which is not feasible for many countries individually. Therefore, a systematic analysis of regional heterogeneity is beyond the scope of this paper. Our objective is to provide an aggregate estimate for the region based on the best comparable data available, rather than to characterize cross-country differences. In that region, on average, female-run firms account for only about 25% of all firms, and the average firm managed by a woman is about 3 times smaller than that of a man.

We then use the general equilibrium model in Cuberes and Teignier (2022)[7] Cuberes, D., & Teignier, M. (2022). Size-dependent gender gaps in entrepreneurship: The case of Chile. Latin American Economic Review, 31, Article 5. https://doi.org/10.47963/laer.v31.55
to quantify the effects of gender gaps in entrepreneurship on aggregate productivity and income. In our theoretical framework, agents are endowed with a random entrepreneurship skill, on which they decide whether to become employers, self-employed, or workers. An employer in this model produces a homogeneous good using span-of-control technology that combines their entrepreneurial skills with capital and labor. We assume that men and women are identical in terms of their managerial skills- in the sense that both groups draw their managerial talent from identical distribution functions- but women are subject to exogenous constraints in their occupational choices, which results in a fraction of them being excluded from employership. This generates a loss in income per worker because less able managers run firms of sub-optimal size and productivity. An important question in determining the magnitude of this aggregate loss is which types of women are excluded from employership. To determine how entrepreneurial skills and selection into entrepreneurship are related, we examine our empirical results through the lens of our model. In our model, in the absence of gender frictions or when these frictions are independent of talent, both men and women manage firms of identical size. Since the data indicate that firms’ size is significantly smaller for women, this means, through the lens of our model, that the probability of being excluded from employership must increase with a woman’s talent. Our empirical analysis indicates that, in our set of countries, women tend to run smaller firms than men, which, in our framework, implies that more talented women are less likely to become employers.

We find large effects of the employer-gender gap on aggregate output and productivity. In the Latin America and Caribbean region (LAC), we estimate an 8.9% fall in aggregate output in the short run when we keep the stock of capital fixed, and 9.4% in the long run when this stock is also negatively affected. These losses are 1.3 times larger than those we would obtain using a model in which the exclusion probability is independent of talent. ³ See Cuberes and Teignier (2014) for a critical literature review of the two-directional link between gender inequality and economic growth. The intuition behind the output loss is as follows. When a woman with very good management skills cannot become an employer, a less skilled man takes her position and becomes the manager of a firm and, as a consequence, the average firm size and output per worker in the economy fall. On top of that, if more talented women face a higher probability of exclusion, the average talent of employers declines further, resulting in a larger loss.

To our knowledge, there are very few articles that quantify the macroeconomic effects of gender gaps in the labor market. The International Labor Organization provides estimates of the output costs associated with labor gender gaps but does not propose any specific theoretical model (International Labor Organization, 2014[11] International Labor Organization. (2014). Global employment trends 2014: Risk of a jobless recovery? International Labour Office. https://www.ilo.org/global/research/global-reports/global-employment-trends/2014/WCMS_233953/lang--en/index.htm
). One shortcoming of their exercise is that it does not shed light on the mechanisms by which gender gaps in the labor market may affect aggregate efficiency. Cavalcanti and Tavares (2016)[3] Cavalcanti, T., & Tavares, J. (2016). The output cost of gender discrimination: A model-based macroeconomic estimate. The Economic Journal, 126(590), 109-134. https://doi.org/10.1111/ecoj.11203
construct a growth model based on Galor and Weil (1996)[9] Galor, O., & Weil, D. N. (1996). The gender gap, fertility, and growth. The American Economic Review, 86(3), 374-387. https://doi.org/10.2307/2118202
that includes exogenous wage discrimination against women. Calibrating their model using U.S. data, they find very large effects associated with these wage gaps: a 50 percent increase in the gender wage gap in their model leads to a decrease in income per capita of a quarter of the original output. Their results also suggest that a large fraction of the actual difference in output per capita between the U.S. and other countries is indeed generated by the presence of gender inequality in wages. Hsieh et al. (2019)[10] Hsieh, C.-T., Hurst, E., Jones, C. I., & Klenow, P. J. (2019). The allocation of talent and U.S. economic growth. Econometrica, 87(5), 1439-1474. https://doi.org/10.3982/ECTA11429
use a Roy model to estimate the effect of changes in the occupational allocation of white women, black men, and black women between 1960 and 2008 on U.S. economic growth and find that the improved allocation of talent accounts for 17 to 20 percent of growth over this period. Finally, Cuberes and Teignier (2016)[6] Cuberes, D., & Teignier, M. (2016). Aggregate costs of gender gaps in the labor market: A quantitative estimate. Journal of Human Capital, 10(1), 1-32. https://doi.org/10.1086/684850
calculate the macroeconomic effects of gender inequality in the labor market using data from the International Labor Organization for a large sample of countries, including several countries from the LAC region, while Cuberes and Teignier (2022)[7] Cuberes, D., & Teignier, M. (2022). Size-dependent gender gaps in entrepreneurship: The case of Chile. Latin American Economic Review, 31, Article 5. https://doi.org/10.47963/laer.v31.55
analyze the case of Chile, and Cuberes and Teignier (2025)[8] Cuberes, D., & Teignier, M. (2025). Costos macroeconómicos de las desigualdades de género en un modelo con emprendimiento y producción doméstica: el caso de México. Ensayos. Revista de Economía, 44(1), 63-84. https://doi.org/10.29105/ensayos44.1-3
study the case of Mexico, taking into account the household sector production. An important difference is that, in this article, we use firm-level data for Latin America and the Caribbean, rather than labor aggregates, to conduct our analysis. Regarding the analysis of firm size by gender in Latin America, Arellano and Peralta (2015)[1] Arellano, P., & Peralta, S. (2015). Informe de resultados: Análisis de género en las empresas. Tercera Encuesta Longitudinal de Empresas. Ministerio de Economía, Fomento y Turismo, División de Política Comercial e Industrial. https://www.economia.gob.cl/wp-content/uploads/2015/05/Resultados-ELE-3.pdf
report significant differences in firm size and productivity in Chile. Still, they do not use any theoretical model in their analysis.

It is important to emphasize that our analysis constitutes a theoretical model based on a specific model and set of assumptions. Other factors, including credit constraints, sectoral segregation, differences in preferences, family responsibilities, business networks, and accumulated managerial experience, may also help explain the patterns observed in the data.

The rest of the paper is organized as follows. Section 1 presents the empirical facts, and Section 2 sketches the general equilibrium occupational choice model. The numerical results of the paper are presented and discussed in Section 3, while Section 4 contains the sensitivity analyses. Finally, conclusions.

1. Empirical facts

We begin the analysis by documenting existing differences in employership between men and women in the LAC region. First, using data for the latest available year for all countries in this region from the International Labor Organization (KILM, 8th edition)[15] Organización Internacional del Trabajo. (2013). Key indicators of the labour market (8.ª ed.). OIT. https://www.ilo.org/ilostat
on employers by gender, we find that women are clearly underrepresented ⁴ http://kilm.ilo.org/KILMnetBeta/default2.asp. among employers. As we can see in Table 1, the share of female employers in the working-age population is less than one-third of that of males.

We then compare the size distribution of firms with male and female top managers in LAC, using data from the World Bank Enterprise Survey[16] World Bank. (s. f.). Enterprise Surveys [Base de datos]. World Bank Group. Recuperado el 28 de julio de 2026, de https://www.enterprisesurveys.org
for 2010. ⁵ http://www.enterprisesurveys.org/ The World Bank Enterprise Survey[16] World Bank. (s. f.). Enterprise Surveys [Base de datos]. World Bank Group. Recuperado el 28 de julio de 2026, de https://www.enterprisesurveys.org
is a firm-level survey of a representative sample of an economy’s private sector conducted for a large set of countries.⁶ The Enterprise Survey is answered, in most cases, by business owners and top managers. Typically, 1200-1800 interviews are conducted in larger economies, 360 interviews are conducted in medium-sized economies and for smaller economies 150 interviews take place. The manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72. Formal (registered) companies with 5 or more employees are generally targeted for interview and microenterprises are not always included. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. In each country, businesses in the cities/regions of major economic activity are interviewed. The sampling methodology for Enterprise Surveys is stratified random sampling, that is, all population units are grouped within homogeneous groups and simple random samples are selected within each group. For Latin America and the Caribbean, it contains information on 13,855 firms, with sizes ranging from 5 to 21,955 employees, from 26 countries.⁷ The countries are Argentina (1054 firms), The Bahamas (150 firms), Barbados (150 firms), Bolivia (362 firms), Brazil (1802 firms), Chile (1033 firms), Colombia (942 firms), Costa Rica (538 firms), Dominican Republic (360 firms), Ecuador (360 firms), El Salvador (360 firms), Grenada (153 firms), Guatemala (590 firms), Guyana (165 firms), Honduras (360 firms), Jamaica (376 firms), Mexico (1480 firms), Nicaragua (336 firms), Panama (365 firms), Paraguay (361 firms), Peru (1000 firms), St. Kitts and Nevis (150 firms), St. Vincent and the Grenadines (154 firms), Trinidad and Tobago (370 firms), Uruguay (607 firms), and Venezuela (320 firms). The survey covers registered firms, excluding the informal sector and self-employment, where women are likely to be more heavily represented. We do not expect this limitation to significantly affect our estimates of the aggregate costs of gender gaps, as most productivity losses arise from medium-sized and large firms rather than from small firms or self-employment.

As we can see in Table 2, female-run firms -defined as firms where the top manager is a woman- are not only outnumbered by male-run firms, but they are also significantly smaller in terms of employees. The median size of firms with a female manager, for example, is 16 workers, while it is 30 workers for firms with a male manager. ⁸ We acknowledge that the number of employees is not a perfect measure of entrepreneurial capacity or firm performance, and that alternative indicators such as sales, value added, or total factor productivity could provide a more comprehensive assessment. However, the lack of homogeneous and comparable data across all countries in our sample substantially limits the set of measures available for our analysis.

Figure 1 shows graphically that the firm-size distribution of female-run firms is shifted to the left relative to the distribution of firms managed by men or, in other words, the distribution of female-run firms has a thicker density at low numbers of employees. ⁹ We plot the natural logarithm of the number of employees to help visualize the plots. Without taking logs, the large number of small firms in the data make it hard to appreciate the shape of the entire distribution.

2. Theoretical framework

3. Numerical results

4. Sensitivity analysis

In this section, we present our main results for other parameter values to assess the sensitivity of the results to different assumptions. The first row of Table 6 shows the results from the benchmark simulation, as in Table 4, while the remaining rows show the short- and long-run losses under different scenarios.

The first alternative scenario, presented in the second row, shows the loss in per-capita output due to the introduction of the employership gender gaps in a context where the self-employment gender gap is also present. To be precise, we infer from the data that about 16% of women barred from that occupation can become self-employed while the rest become workers. Taking this into account, we find that introducing the gender gaps in employers generates significantly larger losses, both in the short run and the long run. Intuitively, the output loss is now larger because there is a larger fall in the share of entrepreneurs and a larger fall in the equilibrium wage, and, consequently, in the average talent of entrepreneurs. Although self-employment is clearly central to LAC labor markets, we do not use this simulation as our baseline because we want to focus on gender gaps among employers, where we can compare the size of female- and male-run firms.

The third row displays the losses under the parameter values used in Cuberes and Teignier (2016)[6] Cuberes, D., & Teignier, M. (2016). Aggregate costs of gender gaps in the labor market: A quantitative estimate. Journal of Human Capital, 10(1), 1-32. https://doi.org/10.1086/684850
, namely $\left[\eta , \rho , \tau \right]=\left[\mathrm{0.79,} \mathrm{6.5,} 0.7\right],$ which were estimated using data for OECD countries. In this case, the model predicts significantly smaller losses (about one-third smaller) and a smaller difference relative to the $\gamma =0$ framework. The main difference with respect to the benchmark parametrization is a somewhat larger value for 𝜂, which reduces the contribution of managerial talent to firm productivity, and especially a larger value for $\rho$ , which increases the slope of the pdf and reduces the thickness of the talent distribution tail. As a result, the fall in the talent threshold due to the introduction of employership gaps is smaller, implying a lower loss of aggregate income.

The fourth row, on the other hand, shows that the income losses would also be somewhat smaller if the firm's profits (or compensation to managerial talent) were treated as labor income rather than capital income, i.e., if $\alpha \eta = 1/3 \text{and} \alpha = 0.41$ . The long-run results are unaffected because the capital stock adjusts to its steady-state value, making the value of 𝛼 irrelevant for determining the income fall. In the short run, the income fall is smaller because the importance of the fixed factor-namely, capital-is greater. Intuitively, in this case, there is a larger difference between the short- and long-run results because the long-run capital adjustment increases with $\alpha$ .

Finally, the last row shows the results under an alternative setup in which self-employment is never an optimal occupational choice but only a necessity for those unable to find a job as workers. In this setup, the loss in income would be somewhat lower than in the benchmark simulation, especially in the short run, although the ratio to the $\gamma =0$ case is the same. The explanation is that the parametrization of this alternative setup yields larger values of 𝜂 and 𝜌, which, as discussed above, imply smaller output losses. ¹⁴ In particular, under this alternative setup, matching the fraction of employers as well as the firm-size distribution implies a value of 𝜂=0.83 and 𝜌=7.24 .

Conclusions

In this paper, we document that in the Latin American and Caribbean (LAC) region, only about one-quarter of all firms are managed by women, and that female-managed firms are, on average, approximately three times smaller than those managed by men. We quantify the output losses associated with these gender disparities using an occupational choice model in which women face barriers to becoming firm managers, with the severity of these barriers increasing with managerial talent.

Our benchmark calibration indicates that these gender gaps generate aggregate output losses of approximately 8.9% in the short run and 9.4% in the long run. Misallocation is the root cause of the entire short-run loss, since entrepreneurial barriers exclude high-talent women from it, reducing average managerial talent and, consequently, aggregate productivity. The capital channel amplifies it by about half a percentage point in the long run.

We also show that these estimated losses are about 1.3 times larger than those obtained under an alternative specification in which the barriers to entrepreneurship are unrelated to managerial talent. Sensitivity analyses suggest that the short-run output losses range from 6.4% to 11.8%, while the corresponding long-run losses range from 8.1% to 12.5%.

Our framework attributes the observed differences in the size distributions of male- and female-managed firms to exogenous barriers that affect women's occupational choices, with these barriers assumed to be positively correlated with managerial talent. This assumption provides a tractable way of capturing the idea that more talented women are less likely to become entrepreneurs. Such barriers could reflect labor market discrimination, but they may also arise from other factors, including differences in preferences, such as a stronger preference for family time, or broader constraints affecting occupational choices. Admittedly, the welfare effects of closing gender gaps in entrepreneurship depend on the underlying causes of those gaps. Under a labor market discrimination mechanism, eliminating these disparities would likely increase both output and welfare. By contrast, if the gaps reflect a stronger preference for family among more talented women-driven, for example, by concerns about human capital transmission or by assortative matching in the marriage market-then requiring these women to operate larger firms could reduce welfare.

Our estimates should be interpreted as the outcome of the specific mechanism embodied in the model, rather than as definitive causal evidence regarding the origins of the observed differences in entrepreneurship between men and women. Our model is not intended to identify the underlying sources of these barriers. Determining the fundamental causes of the observed gender differences in entrepreneurial outcomes remains an important question for future research. Alternative explanations include gender differences in the accumulation of entrepreneurial human capital, educational choices, managerial experience, access to productive opportunities, or access to finance. Distinguishing among these competing mechanisms requires richer data than are currently available and lies beyond the scope of this paper.

We also leave for future research the examination of the evolution of the gender gaps documented in this article through a more recent period, as well as the cross-country heterogeneity within Latin American and Caribbean countries. Regarding the first point, we show in Cuberes et al. (2019)[4] Cuberes, D., Priyanka, S., & Teignier, M. (2019). The determinants of entrepreneurship gender gaps: A cross-country analysis. Review of Development Economics, 23(1), 72-101. https://doi.org/10.1111/rode.12564
that gender gaps in entrepreneurship participation do not exhibit a strong time trend. Still, we are not aware of any study on the evolution of gender gaps by firm size.

References

A APPENDIX: Model details

The economy we consider has a continuum of agents indexed by their entrepreneurial talent $x$ , drawn from a cumulative distribution $\Gamma$ that takes values between $B$ and $\infty$ . We assume the economy is closed and that it has a workforce of size $N$ and $K$ units of capital. Labor and capital are inelastically supplied in the market by consumers, in exchange for a wage rate $w$ and a capital rental rate $r,$ respectively. Firms then combine these inputs to produce a homogeneous good. Agents decide to become either firm workers, who earn the equilibrium wage rate $w$ -which we assume is independent of their entrepreneurial talent-or entrepreneurs, who earn the profits generated by the firm they manage. ¹⁵ In what follows we will refer to an entrepreneur as someone who works as either an employer or a self-employed. In the model, we also include a fourth category, namely the out-of-necessity entrepreneurs, who choose this occupation because they have no other occupational options but to run their own business. We denote by $1-\theta$ the fraction of both males and females that are out-of-necessity entrepreneurs.

An agent with entrepreneurial talent or productivity level $x$ who chooses to become an employer and hires $n\left(x\right)$ units of labor and $k\left(x\right)$ units of capital produces $y\left(x\right)$ units of output and earns profits $\pi \left(x\right)=y\left(x\right)-rk\left(x\right)-wn\left(x\right)$ , where the price of the homogeneous good is normalized to one. As in Lucas (1978)[12] Lucas, R. E., Jr. (1978). On the size distribution of business firms. The Bell Journal of Economics, 9(2), 508-523. https://doi.org/10.2307/3003596
and Buera and Shin (2011)[2] Buera, F. J., & Shin, Y. (2011). Self-insurance vs. self-financing: A welfare analysis of the persistence of shocks. Journal of Economic Theory, 146(3), 845-862. https://doi.org/10.1016/j.jet.2010.10.015
, the production function is given by

where $\alpha \in \left(\mathrm{0,1}\right)$ and $\eta \in \left(\mathrm{0,1}\right)$ . The parameter $\eta$ measures the span of control of entrepreneurs and, since it is less than 1, the entrepreneurial technology exhibits diminishing returns. On the other hand, an agent with talent $x$ who chooses to become self-employed uses the amount of capital $\tilde{k}\left(x\right)$ , produces $\tilde{y}\left(x\right)$ units of output, and earns profits $\tilde{\pi }\left(x\right)=\tilde{y}\left(x\right)-r\tilde{k}\left(x\right)$ . The technology they operate is

where $\tau$ is the self-employed productivity parameter; one interpretation of this parameter is that self-employed workers spend a fraction of their time on management tasks, implying that 𝜏 equals the fraction of time available for work raised to the power $\left(1-\alpha \right)\eta$ . As explained below, we estimate this parameter to match the average fraction of self-employed in the data.

A.1 Agents’ optimization

A.1.1 Employers

Employers choose the units of labor and capital to hire to maximize their current profits, $\pi$ . The optimal number of workers and capital stock, $n\left(x\right)$ and $k\left(x\right),$ respectively, depend positively on the productivity level $x$ , as equations (9) and (10) show:

A.1.2 Self-employed

When we solve for the problem of a self-employed agent with talent $x$ who wishes to maximize their profits, we find

A.1.3 Occupational choice

Figure (2) displays the shape of the profit functions of employers, ${\pi }_e\left(x\right)$ , and self-employed, ${\pi }_s\left(x\right)$ , as well as the wage earned by workers as a function of talent $x$ . The agents’ optimization determines the relevant talent cutoffs for the occupational choices. Here we present the equations that define these thresholds: the first one, $z_1$ , defines the earnings such that agents are indifferent between becoming workers or self-employed, and it is given by

If $x 〈 z\mathrm{1,} agents choose to become workers; if x 〉 z\mathrm{1,}$ they become self-employed or employers. The second cutoff, $z_2$ , determines the choice between being self-employed or an employer, and it is given by

so that if $x>z_2$ an agent wants to become an employer.

A.2 Competitive Equilibrium

We assume that women represent half of the population in the economy and that there is no unemployment. Moreover, any agent in the economy can potentially participate in the labor market, except for the restrictions on women described above. Under these assumptions, in equilibrium, the total demand of capital by employers and self-employed must be equal to the aggregate capital endowment (in per capita terms) $k$ :

The upper term is the demand for capital by men, and the two lower terms are the women’s demand for capital. The demand for capital by male-run firms has three components: the first represents the capital demand by employers, while the second and third represent the demand by the self-employed. i.e., those with the right ability to be self-employed, plus the capital required by those who become self-employed because they could not find jobs as workers. ¹⁶ As explained in Section 3, a fraction (1−𝜃) of both males and females with ability below 𝑧 1 become self-employed because they would like to be workers but are not allowed to do so and choose their second-best option. These out-of-necessity self-employed demand the optimal amount of capital given their talent or ability.

The demand for capital by female-run firms has four components. The first term represents the capital demand by female employers, i.e., those with sufficient ability to be employers and who are allowed to be so, while the second term represents the capital demand by women who can be self-employed and are allowed to work. The third term shows the capital demand by women who become self-employed because they are excluded from employership, and, finally, the last term shows the fraction of females who would like to be workers but, since they are “excluded” from this occupation, they choose to become out-of-necessity self-employed if they are not excluded from entrepreneurship.

Similarly, the labor market-clearing condition is given by

which shows that in equilibrium, aggregate labor demand equals aggregate labor supply. The first term is the labor demand by male employers, and the second corresponds to the labor demand by female employers, i.e., those women with sufficient ability to be employers who are allowed to choose their occupation freely. The labor supply shows the fraction of men and women who choose to become workers and are not forced to be self-employed out of necessity.

A competitive equilibrium in this economy is a pair of cutoff levels $\left(z_1,z_2\right)$ , a set of quantities $\left[n\left(x\right),k\left(x\right),\tilde{k}\left(x\right)\right],\forall x$ , and prices $\left(w,r\right)$ such that entrepreneurs choose the amount of capital and labor to maximize their profits, and labor and capital markets clear.

A.3 Details on size-distribution calculations

Given a level of talent $x$ , firms choose to have $n\left(x\right)={\left[x\eta \left(1-\alpha \right){\left(\frac{\alpha }{1-\alpha }\right)}^{\alpha \eta }\frac{w^{\alpha \eta -1}}{r^{\alpha \eta }}\right]}^{1/\left(1-\eta \right)}$ workers. We can invert this function to find

Substituting this expression into the cumulative density function 2 gives us.