Table 2 Individual, societal, technological, and institutional factors that influence women’s paid work.

a. Factors related to individual characteristics

1. Nature. Individual skills and preferences are influenced by fixed biological predispositions such as physical, neurological, and reproductive processes (Welch 2000; Bertrand 2011; Jayachandran 2015)

Women may have a biological predisposition to assume a higher cost if children are not taken care of, and therefore may be more strongly motivated than men to take care of children. Childcare services have improved female labor participation in Argentina, Brazil, Colombia, and Ecuador (Attanasio and Vera-Hernandez 2004; Barros et al. 2011; Calderón 2012; Rosero and Oosterbeek 2011; Berlinski and Galiani 2007; Berlinski et al. 2011)

2. Nurture. Individual skills result from human capital investment such as education and job experience (Mincer and Polachek 1974)

Training for work in Peru, Chile, Argentina, Colombia, and Peru have led to increases in female employment (Valdivia 2015; Kaplan et al. 2015; Aedo and Nuñez 2004; Ñopo et al. 2007).

Courses for the unemployed in the region have been successful in increasing female labor participation (Gonzalez-Velosa et al. 2012; Urzúa and Puentes 2010)

3. Identity effects. Individuals’ preference to comply with their gender social ideals is determined by their identity (Akerlof and Kranton 2002)

The author could not identify rigorous studies on the effects of policies that aim to change individual depictions of social roles

b. Factors related to social preferences

4. Stereotypes or taste discrimination. Society pays a higher price when a good is made or a service is provided by a person of a specific sex (Becker 1971; Ellemers 2018)

There are no studies on the direct effects of taste discrimination on employment. However, Ganguli et al. (2010) analyzed marriage and skill patterns in the region and found that skilled women in Latin America are less likely to marry than unskilled women. They conclude that men assign a high value to having a stay-at-home wife, while women do not

c. Factors related to technology

5. Technology changes the relative productivity among gender groups (Hicks 1932; Albanesi and Olivetti 2015)

There is no evidence of the effect of technology on female employment. However, Cubas (2016) found that the price of household appliances and access to infrastructure explains cross-country female employment in Latin America

d. Factors related to institutions

6. Rights. Institutions protect labor or individual rights and provide social protection (Coase 1960; Blau and Kahn 1999)

The implementation of laws for firms to provide maternity leave and unemployment severance in Colombia has resulted in lower employment of female labor (Molinos 2012; Ramirez et al. 2015; Pagés and Piras 2010)

Laws that allow firms to hire part-time employees and that promote flexible work schedules in Argentina have promoted female employment (Bosch and Maloney 2010; Pagés and Piras 2010)

7. Statistical discrimination. Employers do not have complete information on job applicants. Employers extrapolate productivity based on the sex of the individual (Arrow 1973)

There are no studies on the effects of policies that aim to address specific types of discrimination. However, Arceo-Gomez and Campos-Vázquez (2014b) found that women have a higher probability of receiving a callback than men after submitting a résumé to apply for employment in Mexico. Married women were less likely to receive a callback than single women. Moreno et al. (2012) found limited evidence of gender discrimination by intermediary employment services in Peru