Licensed Unlicensed Requires Authentication Published online by De Gruyter February 15, 2022

Aggregate Costs of a Gender Gap in the Access to Business Resources

Javier Gonzalez and Francisco Parro
From the journal The B.E. Journal of Macroeconomics
https://doi.org/10.1515/bejm-2021-0125

Abstract

We quantify the aggregate costs of a discriminatory restriction against women in the access to business resources. To do so, we develop a general equilibrium model with an endogenous size distribution of production units, which are run by either female or male entrepreneurs. In this setting, we introduce a distortion that limits the amount of capital that women can use to run their businesses. We calibrate the model to match data from benchmark economies that exhibit relatively egalitarian labor market results between women and men, except in entrepreneurship. Our counterfactual analyses show that a gender-specific capital constraint causes an output loss between 14% and 28% and a fall in aggregate productivity between 12% and 20%. Furthermore, we show that most of the output loss is accounted for by a fall in total factor productivity. Lastly, we show that the aggregate cost of the distortion is mainly triggered by preventing the most skilled women from running bigger businesses, and not the exit of women from entrepreneurship.

Keywords: gender gap; entrepreneurship; aggregate productivity
JEL Classification: E2; J21; J24; O40
Corresponding author: Francisco Parro, School of Business, Universidad Adolfo Ibáñez, Santiago, Chile, E-mail:

Appendix A: Inputs Demands

In this appendix, we derive the demand level for each of the inputs of the production technology of our model economy. We first derive the demands from unconstrained agents, and then, the demands from constrained agents.

We use first Eqs. (3), (4), and (6) to get:

(A1) k ( z ) = ζ α R 1 1 − ζ α A 1 1 − ζ α z 1 − ζ 1 − ζ α n ( z ) ζ ( 1 − α ) 1 − ζ α .

Then, we plug (A1) in (3) and use (4) to get:

(A2) y ( z ) n ( z ) = ζ α R ζ α 1 − ζ α A 1 1 − ζ α z n ( z ) 1 − ζ 1 − ζ α .

Let Ω 0 = ζ α R ζ α 1 − ζ α A 1 1 − ζ α . Then, we can express Eq. (A2) as:

(A3) y ( z ) n ( z ) = Ω 0 z n ( z ) 1 − ζ 1 − ζ α .

Next we use Eqs. (7) and (A3) to get:

(A4) n ( z ) = Ω 1 1 − ζ α 1 − ζ n ( z ) n f ( z ) 1 − ζ α ( 1 − ζ ) σ z ,

where Ω 1 = ζ ( 1 − α ) Ω 0 w f .

We can substitute back (A4) in (A3) to get:

(A5) y ( z ) n ( z ) = Ω 0 Ω 1 n f ( z ) n ( z ) 1 σ .

We derive now expressions for nf(z)/n(z) and nf(z)/nm(z). From (2) we get:

(A6) n ( z ) n f ( z ) = n m ( z ) n f ( z ) σ − 1 σ + 1 σ σ − 1 .

From Eqs. (7) and (8) we get:

(A7) n f ( z ) n m ( z ) = w m w f σ .

From Eq. (8) we compute:

(A8) n m ( z ) = ζ ( 1 − α ) ( y ( z ) / n ( z ) ) w m σ n ( z ) .

We directly express:

(A9) n f ( z ) = n f ( z ) n m ( z ) n m ( z ) .

From Eq. (6) we can get:

(A10) k ( z ) = ζ α R y ( z ) n ( z ) n ( z ) .

Let M = {wf, wm, R}. Substituting back the expressions derived for y/n, n(z), n(z)/nf(z), and (A7) into Eqs. (A8)–(A10) we can express the inputs demand system as:

(A11) k ( z ; M ) = Φ 0 ( M ) z ,
(A12) n f ( z ; M ) = Φ 1 ( M ) z ,
(A13) n m ( z ; M ) = Φ 2 ( M ) z ,

We now consider the case in which k = k ̄ . Then, we plug this condition in (3) and use (4) to get:

(A14) y = A z 1 − ζ k ̄ ζ α n ( 1 − α ) ζ ,

Next we use Eqs. (7) and (A14) to get:

(A15) n ( z ) = Ω 0 ̄ 1 ω 0 z 1 − ζ ω 0 k ̄ α ζ ω 0 ( n f ( z ) ) − 1 σ ω 0

where Ω ̄ 0 = ζ ( 1 − α ) A w f and ω 0 = 1 − 1 σ − ( 1 − α ) ζ .

From Eqs. (7) and (8) we get:

(A16) n m ( z ) n f ( z ) = w f w m σ .

From (2) we get:

(A17) n ( z ) = n m ( z ) n f ( z ) σ − 1 σ + 1 σ σ − 1 n f ( z ) .

Let Ω ̄ 1 = n m ( z ) n f ( z ) σ − 1 σ + 1 σ σ − 1 . Then, using (A15) and (A16) in (A17), we get:

(A18) n ̄ f ( z ) = Ω ̄ 0 σ 1 + σ ω 0 Ω ̄ 1 − σ ω 0 1 + σ ω 0 k ̄ α ζ σ 1 + σ ω 0 z ( 1 − ζ ) σ 1 + σ ω 0

Then, substituting back (A18) in (A16), we can get n ̄ m ( z ) . Then, the demand of constrained entrepreneurs is given by the following system:

(A19) k ( z ; M ) = k ̄ ,
(A20) n f ( z ; M ) = n ̄ f ( z ; M ) ,
(A21) n m ( z ; M ) = n ̄ m ( z ; M ) ,

Appendix B: Solving the Benchmark Model

We first discretize the ability space so it contains Z n evenly spaced points between z l and z h . Then, we use a lognormal density distribution to compute the probability vector for each of the points in the space Z n . Denote by p the latter vector. Once we have built the ability space and the discrete density function for them, we solve the household problem for fixed wages and rental price of capital. In Appendix A we showed that, for unconstrained entrepreneurs:

(B1) k ( z ; M ) = Φ 0 ( M ) z ,
(B2) n f ( z ; M ) = Φ 2 ( M ) z ,
(B3) n m ( z ; M ) = Φ 1 ( M ) z .

and for constrained entrepreneurs:

(B4) k ( z ; M ) = k ̄ ,
(B5) n f ( z ; M ) = n ̄ f ( z ; M ) ,
(B6) n m ( z ; M ) = n ̄ m ( z ; M ) ,

Then, we use the inputs demands to compute the profits, which is the value of being a manager: π i (z; M), for i ∈ {f, m}. The value of being a production worker is simply w i . Then, considering the value of being a manager and the value of being a production worker, we build the indicator function that identifies the members of the household who become a manager:

(B7) I i ( z ; M ) = 1   if π i ( z ; M ) > w i   0   if π i ( z ; M ) ≤ w i .

Notice that the vector I i ( z ; M ) also identifies the skill size of the marginal manager:

(B8) z i * ( M ) = min { z ∈ Z : I i ( z ; M ) = 1 } .

Next, we build the aggregate demands and supply of labor and capital services. Denote by N i j the aggregate demand for labor service type j by group i. Analogously, denote by K i the aggregate demand for capital services by group i. The aggregate demand for labor services type j of agent type i is:

(B9) N i j ( M ) = θ i ∑ z = z l z h n i j ( z ; M ) I i ( z ; M ) p ( z ) ,

The aggregate demand for capital services by agent type i is:

(B10) K i ( M ) = θ i ∑ z = z l z h k i ( z ; M ) I i ( z ; M ) p ( z ) .

Next, we compute the aggregate demand for each of the four inputs:

(B11) N f ( M ) = ∑ i ∈ { f , m } N i f ( M ) ,
(B12) N m ( M ) = ∑ i ∈ { f , m } N i m ( M ) ,
(B13) K ( M ) = ∑ i ∈ { f , m } K i ( M ) ,

Then, we compute the supply of labor inputs:

(B14) S i ( M ) = θ i ∑ z = z l z h ( 1 − I i ( z ; M ) ) p ( z )

For the supply of capital we initially set K = K ̂ . Then, given the guess for the capital stock, we compute equilibrium prices. We iterate on prices until all markets clear and find equilibrium prices for a given stock of capital. Then, we evaluate if the resulting rental rate for capital services differs from ( 1 + x c ) / β + δ − 1 and we iterate on capital until the R is equal to ( 1 + x c ) / β + δ − 1 , using an bijective algorithm.

Appendix C: Additional Results

This appendix presents further analysis considering an economy in which entrepreneurial ability and labor income are correlated. This alternative model captures the idea that entrepreneurs with higher ability also have better outside options and are generally more skilled, which would enable them to earn higher wages.[24]

In general, labor income can be modeled as wz γ with γ ≥ 0. In our baseline model, we follow Guner, Ventura, and Xu (2008), Buera and Shin (2013), Buera, Moll, and Shin (2015), among others, and set γ = 0. The additional analysis presented in this appendix considers γ = 1 − ζ; that is, we set the same degree of concavity for the production function and the expression for labor income, as in Cagetti and De Nardi (2006). Then, the market clearing condition for production labor was modified accordingly to match the supply of labor in efficiency units with the demand, the model was recalibrated, and the capital thresholds that define the counterfactual scenarios were modified to make them consistent with those of the main analysis. Tables C1–C4 show the results. We observe that the magnitude of the effects is relatively similar to that of the original model, the effect being somewhat lower for some outcomes and higher for other outcomes.

Table C1:

Aggregate output losses in benchmark relative to counterfactuals.

S1 S2 S3 S4 S5
Aggregate output, Y (%) 13.7 17.1 21.0 23.7 34.1
Ratio output per female manager, r f (level) 2.3 2.8 3.4 4.0 7.0

Table C2:

Aggregate output per worker losses in benchmark relative to counterfactuals.

(%) S1 S2 S3 S4 S5
Output per efficiency unit of labor, q1 16.1 19.8 24.1 27.0 37.8
Output per production worker, q2 9.9 11.9 14.0 15.6 21.8
Output per manager, q3 24.7 30.9 37.9 42.5 58.6

Table C3:

TFP losses in benchmark relative to counterfactuals.

(%) S1 S2 S3 S4 S5
TFP1 7.1 8.3 9.8 11.1 12.1
TFP2 7.1 8.3 9.8 11.1 12.1

Table C4:

Contribution to output per worker losses.

(%) S1 S2 S3 S4 S5
Output Y 1 / L 1
Capital intensity 29.0 31.8 31.7 30.8 47.5
TFP 71.0 68.2 68.3 69.2 52.5
Output Y 2 / L 2
Capital intensity 29.0 31.8 31.7 30.8 47.5
TFP 71.0 68.2 68.3 69.2 52.5

References

Agier, I., and A. Szafarz. 2013. “Microfinance and Gender: Is There a Glass Ceiling on Loan Size?” World Development 42: 165–81. https://doi.org/10.1016/j.worlddev.2012.06.016.Search in Google Scholar

Aidis, R., F. Welter, D. Smallbon, and N. Isakova. 2007. “Female Entrepreneurship in Transition Economies: The Case of Lithuania and Ukraine.” Feminist Economics 13 (2): 157–83. https://doi.org/10.1080/13545700601184831.Search in Google Scholar

Aristei, D., and M. Gallo. 2016. “Does Gender Matter for Firms’ Access to Credit? Evidence from International Data.” Finance Research Letters 18: 6–75. https://doi.org/10.1016/j.frl.2016.04.002.Search in Google Scholar

Bardasi, E., S. Sabarwal, and K. Terrell. 2011. “How Do Female Entrepreneurs Perform? Evidence from Three Developing Regions.” Small Business Economics 37 (4): 417–41. https://doi.org/10.1007/s11187-011-9374-z.Search in Google Scholar

Bellucci, A., A. Borisov, and A. Zazzaro. 2010. “Does Gender Matter in Bank-Firm Relationships? Evidence from Small Business Lending.” Journal of Banking & Finance 34 (12): 2968–84. https://doi.org/10.1016/j.jbankfin.2010.07.008.Search in Google Scholar

Blau, F., and L. Kahn. 2017. “The Gender Wage Gap: Extent, Trends, and Explanations.” Journal of Economic Literature 55 (3): 789–865. https://doi.org/10.1257/jel.20160995.Search in Google Scholar

Bruhn, M. 2009. “Female-Owned Firms in Latin America: Characteristics, Performance, and Obstacles to Growth.” In Policy Research Working Paper 5122. World Bank.10.1596/1813-9450-5122Search in Google Scholar

Buera, F., J. Kaboski, and Y. Shin. 2011. “Finance and Development: A Tale of Two Sectors.” The American Economic Review 101 (5): 1964–2002. https://doi.org/10.1257/aer.101.5.1964.Search in Google Scholar

Buera, F., and Y. Shin. 2013. “Financial Frictions and the Persistence of History: A Quantitative Exploration.” Journal of Political Economy 121 (2): 409–36. https://doi.org/10.1086/670271.Search in Google Scholar

Buera, F., B. Moll, and Y. Shin. 2015. “Entrepreneurship and Financial Frictions: A Macro-Development Perspective.” Annual Review of Economics 7 (1): 409–36.10.3386/w21107Search in Google Scholar

Cagetti, M., and M. De Nardi. 2006. “Entrepreneurship, Frictions, and Wealth.” Journal of Political Economy 114 (5): 835–70. https://doi.org/10.1086/508032.Search in Google Scholar

Carranza, E., C. Dhakal, and I. Love. 2018. “Female Entrepeneurs: How and Why Are They Different?” In Jobs Working Paper Issue No. 20. World Bank.10.1596/31004Search in Google Scholar

Cavalcanti, T., and J. Tavares. 2016. “The Output Cost of Gender Discrimination: A Model-Based Macroeconomic Estimate.” Economic Journal 126 (590): 109–34. https://doi.org/10.1111/ecoj.12303.Search in Google Scholar

Cavalluzzo, K. S., L. C. Cavalluzzo, and J. D. Wolken. 2002. “Competition, Small Business Financing, and Discrimination: Evidence from a New Survey.” Journal of Business 75 (4): 641–769. https://doi.org/10.1086/341638.Search in Google Scholar

Cuberes, D., and M. Teignier. 2014. “Gender Inequality and Economic Growth: A Critical Review.” Journal of International Development 26 (2): 260–76. https://doi.org/10.1002/jid.2983.Search in Google Scholar

Cuberes, D., and M. Teignier. 2016. “Aggregate Effects of Gender Gaps in the Labor Market: A Quantitative Estimate.” Journal of Human Capital 10 (1): 1–32. https://doi.org/10.1086/683847.Search in Google Scholar

Cuberes, D., and M. Teignier. 2018. “Macroeconomic Costs of Gender Gaps in a Model with Entrepreneurship and Household Production.” The B.E. Journal of Macroeconomics 18 (1): 1–15. https://doi.org/10.1515/bejm-2017-0031.Search in Google Scholar

Doepke, M., and M. Tertilt. 2009. “Women’s Liberation: What’s in it for Men?” Quarterly Journal of Economics 124 (4): 1541–91.10.3386/w13919Search in Google Scholar

Esteve-Volart, B. 2009. “Gender Discrimination and Growth: Theory and Evidence from India.” Manuscript.Search in Google Scholar

Erosa, A. 2001. “Financial Intermediation and Occupational Choice in Development.” Review of Economic Dynamics 4 (2): 303–34. https://doi.org/10.1006/redy.2000.0117.Search in Google Scholar

Fernandez, R. 2009. “Women’s Rights and Development.” In NBER Working Paper No 15355.10.3386/w15355Search in Google Scholar

Galor, O., and D. Weil. 1996. “The Gender Gap, Fertility, and Growth.” The American Economic Review 85 (3): 374–87.10.3386/w4550Search in Google Scholar

GEM. 2021. Global Entrepreneurship Monitor. Also available at https://www.gemconsortium.org/report/gem-20202021-global-report.Search in Google Scholar

Greenwood, J., A. Seshadri, and M. Yorukoglu. 2005. “Engines of Liberation.” The Review of Economic Studies 72 (1): 109–33.10.1111/0034-6527.00326Search in Google Scholar

Greenwood, J., J. Sanchez, and C. Wang. 2013. “Quantifying the Impact of Financial Development on Economic Development.” Review of Economic Dynamics 16 (1): 194–215.10.3386/w15893Search in Google Scholar

Guner, N., G. Ventura, and Y. Xu. 2008. “Macroeconomic Implications of Size-Dependent Policies.” Review of Economic Dynamics 11 (4): 721–44. https://doi.org/10.1016/j.red.2008.01.005.Search in Google Scholar

Hsieh, C., E. Hurst, C. Jones, and P. Klenow. 2019. “The Allocation of Talent and U.S. Economic Growth.” Econometrica 87 (5): 1439–74. https://doi.org/10.3982/ECTA11427.Search in Google Scholar

Lagerlof, N. 2003. “Gender Equality and Long Run Growth.” Journal of Economic Growth 8 (4): 403–26.10.1023/A:1026256917489Search in Google Scholar

Meunier, F., Y. Krylova, and R. Ramalho. 2017. “Women’s Entrepreneurship: How to Measure the Gap between New Female and Male Entrepreneurs?” In Policy Research Working Paper 8242. World Bank.10.1596/1813-9450-8242Search in Google Scholar

Midrigan, V., and D. Yi Xu. 2014. “Finance and Misallocation: Evidence from Plant-Level Data.” The American Economic Review 104 (2): 422–58. https://doi.org/10.1257/aer.104.2.422.Search in Google Scholar

Morazzoni, M., and A. Sy. 2021. “Female Entrepreneurship and Financial Frictions.” Manuscript.10.1016/j.jmoneco.2022.03.007Search in Google Scholar

Moro, A., T. Wisniewski, and G. Mantovani. 2017. “Does a Manager’s Gender Matter when Accessing Credit? Evidence from European Data.” Journal of Banking & Finance 80: 119–34. https://doi.org/10.1016/j.jbankfin.2017.04.009.Search in Google Scholar

Muravyev, A., D. Schaefer, and O. Talavera. 2009. “‘Entrepreneurs’ Gender and Financial Constraints: Evidence from International Data.” Journal of Comparative Economics 37 (2): 270–86. https://doi.org/10.1016/j.jce.2008.12.001.Search in Google Scholar

Ngai, L. R., and B. Petrongolo. 2017. “Gender Gaps and the Rise of the Service Economy.” American Economic Journal: Macroeconomics 9 (4): 1–44. https://doi.org/10.1257/mac.20150253.Search in Google Scholar

OECD. 2012. Closing the Gender Gap: Act Now. Also available at https://www.oecd-ilibrary.org/social-issues-migration-health/close-the-gender-gap-now_9789264179370-en.Search in Google Scholar

OECD. 2016. Entrepreneurship at a Glance. Also available at https://www.oecd.org/gender/data/do-women-have-equal-access-to-finance-for-their-business.htm.Search in Google Scholar

Parro, F. 2012. “International Evidence on the Gender Gap in Education over the Past Six Decades: A Puzzle and an Answer to it.” Journal of Human Capital 6 (2): 150–85. https://doi.org/10.1086/666849.Search in Google Scholar

Piacentini, M. 2013. “Women Entrepreneurs in the OECD: Key Evidence and Policy Challenges Key Evidence and Policy Challenges.” In OECD Social, Employment and Migration Working Papers, No. 147. Paris: OECD Publishing.Search in Google Scholar

Sabarwal, S., and K. Terrell. 2009. Access to Credit and Performance of Female Entrepreneurs in Latin America. Washington, DC: Mimeo, World Bank.Search in Google Scholar

UN. 2016. “Leave No One behind: Taking Action for Transformational Change on Women’s Economic Empowerment.” In UN Secretary-General’s High-Level Panel on Women’s Economic Empowerment.Search in Google Scholar
Received: 2021-06-10
Revised: 2022-01-25
Accepted: 2022-01-26
Published Online: 2022-02-15

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