Skip to main content

Advertisement

Log in

Cognitive and socioemotional skills and wages: the role of latent abilities on the gender wage gap in Peru

  • Published:
Review of Economics of the Household Aims and scope Submit manuscript

Abstract

The literature provides evidence on the positive connection between cognitive test scores and higher wages. Fewer and newer studies have explored the correlation between non-cognitive test scores and wages. However, these studies only focus on developed countries. The main objective of this study is to identify latent abilities and explore their role in the gender wage gap in a developing country: Peru. The main identification strategy relies on exploiting panel data information on test scores and arguing that time dependence across measures is due to latent abilities. We exploit two databases: the Young Lives Study and the Peruvian Skills and Labor Market Survey. The results show that when accounting for differences in actual latent abilities, socioemotional abilities account for important inter-gender differences in the endowment and returns of abilities. Moreover, inter-gender differences in latent abilities play an important role in not only wage profiles but in schooling, employment, and occupational decisions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. Information regarding socioemotional abilities was not collected during the first round for the older cohort.

  2. For self-esteem, the statements explored in the YL survey focused on positive and negative dimensions of pride and shame based on the Rosenberg Self-Esteem Scale that focused on dimensions of children’s living circumstances. For self-efficacy we focused on five items: “If we try hard we can improve my situation in life”; “Other people in my family make all the decisions about how we spend my time”; “I like to make plans for my future studies and work”; and “I (don’t) have choice about the work we do”. The degree of agreement was measured on a 4-point Likert scale that ranged from strong agreement to strong disagreement. We constructed two indices (one for each trait) as the average score of these items and used the standardized indices for our estimations.

  3. We should consider that the YL sample ignores children in the top 5% of the national income distribution.

  4. From now on, we will work with the composite measure of GRIT as the representative test for measuring socioemotional ability. We chose to work with GRIT as the literature on socioemotional abilities highlight its importance, and because Díaz et al. (2012), who also use the ENHAB, find that it plays an important role on wage equations. Nonetheless, every estimation has also been performed with the measures of the rest of personality traits and arrives at similar results.

  5. No measure of the caregiver’s cognitive ability was available in the dataset.

  6. We also analyzed the relation between latent abilities and wages using another measure of non-cognitive ability (self-efficacy) and obtained similar results.

  7. The sample size is higher than in the O-B section with measured abilities because we also consider those with no information on measured abilities. We proceed in this way in order to exploit the variability in the available data as much as possible.

References

  • Altonji, J. G., & Blank, R. (1999). Race and gender in the labor market. Handbook of Labor Economics, 3c, 3144–4259.

    Google Scholar 

  • Baccouche M. A., Arous I., Sellami H., & Elloumi A. (2014). Association between body mass index and cognitive performance in rugby players. International Journal of Scientific and Research Publications, 4(6).

  • Basatemur E., Gardiner J., Williams C., Melhuish E., Barnes J., & Sutcliffe A. (2013). Maternal prepregnancy bmi and child cognition: a longitudinal cohort study pediatrics. Pediatrics, 131(1), 56–63.

  • Behrman, J. R., Ross, D. R., & Sabot, R. H. (2008). Improving the quality versus increasing the quantit of schooling: evidence for rural pakistan. Journal of Development Economics, 85(1), 94.

    Article  Google Scholar 

  • Behrman J.R., Hoddinott J.F., Maluccio J.A., & Martorell R. (2011) Brains versus brawn: labor market returns to intellectual and physical human capital in a poor developing country. University of Pennsylvania

  • Bell, E. C., Willson, M. C., Wilman, A. H., Dave, S., & Silverstone, P. H. (2006). Males and females differ in brain activation during cognitive tasks. Neuroimage, 30(2), 529–538.

    Article  Google Scholar 

  • Bertrand, M. (2011). New perspectives on gender. In O. C. Ashenfelter, & D. Card (Eds.), Handbook of labor economics (Vol. 4B, pp. 1543–1590). Amsterdam: North-Holland. https://doi.org/10.1016/S0169-7218(11)02415-4

  • Blau, F. D., & Kahn, L. M. (2017). The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature, 55(3), 789–865.

    Article  Google Scholar 

  • Bowles, S., & Gintis, H. (1976). Schooling in capitalist America: educational reform and the contradictions of economic life. New York, NY: Basic Books.

    Google Scholar 

  • Cunha, F., & Heckman, J. (2007). The technology of skill formation. American Economic Review, 97(2), 31–47.

    Article  Google Scholar 

  • Cunha, F., & Heckman, J. (2008). Formulating, identifying and estimating the technology of cognitive and noncognitive skill formation. Journal of Human Resources, 43(4), 738–782.

    Article  Google Scholar 

  • Cunha F., Heckman J. J., Lochner L., & Masterov D. V. (2006). Interpreting the evidence on life cycle skill formation. In E. A. Hanushek, & F. Welch (Eds) Handbook of the economics of education. Edition 1, (Vol. 1, Chapter 12, pp. 697–812). Elsevier.

  • Cunha, F., Heckman, J. J., & Schennach, S. M. (2010). Estimating the technology of cognitive and noncognitive skill formation. Econometrica, 78(3), 883–931.

    Article  Google Scholar 

  • Deming, D. J. (2017). The growing importance of social skills in the labor market. Quarterly Journal of Economics, 132(4), 1593–1640. https://doi.org/10.1093/qje/qjx022.

    Article  Google Scholar 

  • Díaz J.J. Arias O., & Vera Tudela D. (2012). Does perseverance pay as much as being smart?: The returns to cognitive and non-cognitive skills in urban Peru, mimeo.

  • Duckworth, A., Peterson, C., Matthews, M., & Kelly, D. (2007). Grit: perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101.

    Article  Google Scholar 

  • Edwards, R. (1976). Individual traits and organizational incentives: what makes a “good” worker? Journal of Human Resources, 11(1), 51–68.

    Article  Google Scholar 

  • Fortin, N. M. (2008). The gender wage gap among young adults in the united states: the importance of money versus people. Journal of Human Resources, 43(4), 884–918.

    Article  Google Scholar 

  • Goldberg, L. R. (1990). An alternative “description of personality”: the big-five factor structure. Journal of Personality and Social Psychology, 59, 1216–1229.

    Article  Google Scholar 

  • Grogger, J., & Eide, E. (1995). Changes in college skills and the rise in the college wage premium. Journal of Human Resources, 30(2), 280–310.

    Article  Google Scholar 

  • Grove, W. A., Hussey, A., & Jetter, M. (2011). The gender pay gap beyond human capital: Heterogeneity in noncognitive skills and in labor market tastes. Journal of Human Resources, 46(4), 827–8.

    Article  Google Scholar 

  • Hansen, K. T., Heckman, J. J., & Mullen, K. J. (2004). The effect of schooling and ability on achievement test scores. Journal of Econometrics, 121(1-2), 39–98.

    Article  Google Scholar 

  • Hanushek, E. A., & Woessmann, L. (2008). The Role of Cognitive Skills in Economic Development. Journal of Economic Literature, 46(3), 607–668.

    Article  Google Scholar 

  • Heckman, J. J., Stixrud, J., & Urzúa, S. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics, 24(3), 411–482.

    Article  Google Scholar 

  • Hedges, L. V., & Nowell, A. (1995). Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science, 269(5220), 41–45.

    Article  Google Scholar 

  • Jones (1983). On decomposing the wage gap: a critical comment on Blinder’s method. The Journal of Human Resources, 18, 126–130.

    Article  Google Scholar 

  • Klein, R., Spady, R., & Weiss, A. (1991). Factors affecting the output and quit propensities of production workers. Review of Economic Studies, 58(2), 929–954.

    Article  Google Scholar 

  • Landes, L. (1977). Sex differences in wages and employment: a test of the specific capital hypothesis. Economic Inquiry, 15(4), 523–538. https://doi.org/10.1111/j.1465-7295.1977.tb01115.x. Retrieved from.

    Article  Google Scholar 

  • Manning, A., & Swaffield, J. (2008). The gender gap in early‐career wage growth. The Economic Journal, 118(530), 983–1024. https://doi.org/10.1111/j.1468-0297.2008.02158.x.

    Article  Google Scholar 

  • Mincer, J., & Ofek, H. (1982). Interrupted work careers: depreciation and restoration of human capital. Journal of Human Resources, 17(1), 3–24. https://doi.org/10.2307/145520.

    Article  Google Scholar 

  • Murnane, R. J., Willett, J. B., & Levy, F. (1995). The growing importance of cognitive skills in wage determination. The Review of Economics and Statistics, 77(2), 251–266.

    Article  Google Scholar 

  • Murnane, R. J., Willett, J. B., Duhaldeborde, Y., & Tyler, J. H. (2000). How important are the cognitive skills of teenagers in predicting subsequent earnings? Journal of Policy Analysis and Management, 19(4), 547–568.

    Article  Google Scholar 

  • Neal, D. A., & Johnson, W. R. (1996). The role of premarket factors in black-white wage differences. Journal of Political Economy, 104(5), 869–895.

    Article  Google Scholar 

  • Oaxaca, R. L., & Ransom, M. R. (1999). Identification in detailed wage decompositions. The Review of Economics and Statistics, 81, 154–157.

    Article  Google Scholar 

  • Paglin, M., & Rufolo, A. M. (1990). Heterogeneous human capital, occupational choice, and male-female earnings differences. Journal of Labor Economics, 8(1), 123–144.

    Article  Google Scholar 

  • Ragan, J. F., & Smith, S. P. (1981). The impact of differences in turnover rates on male/female pay differentials. Journal of Human Resources, 16(3), 343–365. https://doi.org/10.2307/145625.

    Article  Google Scholar 

  • Rosenberg, M., & Pearlin, L. I. (1978). Social class and self-esteem among children and adults. American Journal of Sociology, 84, 53–77.

    Article  Google Scholar 

  • Smith, E., Hay, P., Campbell, L., & Trollor, J. N. (2011). A review of the association between obesity and cognitive function across the lifespan: implications for novel approaches to prevention and treatment. Obesity Reviews, 12(9), 740–755.

    Article  Google Scholar 

  • Strand, S. (2003). Sex differences in cognitive abilities test scores: a national picture. Space, 98, 1–28.

    Google Scholar 

  • Twenge, J. M., & Campbell, W. K. (2002). Self-esteem and socioeconomic status: a meta-analytic review. Personality and Social Psychology Review, 6(1), 59–71.

    Article  Google Scholar 

  • Upadhayay, N., & Gurugain, S. (2014). Comparison of cognitive functions between male and female medical students: a pilot study. Journal of clinical and diagnostic research, 8(6), BC12.

    Google Scholar 

  • Urzúa S., Bravo D., & Sanhueza C. (2009). Ability, schooling choices and gender labor market discrimination: evidence for Chile. In: H. Ñopo, A. Chong, A. Moro (Eds.), Discrimination in Latin America: an economic perspective. Inter-American Development Bank.

  • Urzúa, S. (2008). Racial labor market gaps: the role of abilities and schooling choices. Journal of Human Resources, 43(4), 919–971. https://doi.org/10.3368/jhr.43.4.919.

    Article  Google Scholar 

  • Weinberger, C. J. (1999). Mathematical college majors and the gender gap in wages. Industrial Relations, 38, 407–413.

    Article  Google Scholar 

  • Weinberger, C. J. (2014). The increasing complementarity between cognitive and social skills. Review of Economics and Statistics, 96(5), 849–861. https://doi.org/10.1162/REST_a_00449.

    Article  Google Scholar 

Download references

Acknowledgements

We want to thank Sergio Urzúa, Omar Arias, Marcel Fafchamps, five anonymous referees, and seminar participants in the “Conference on Skills, Education and Labor Market Outcomes” at the University of Maryland, “Inequalities in Children’s Outcomes in Developing Countries Conference” at Oxford University, and the Lacea and Peruvian Economic Association for helpful comments and discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pablo Lavado.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lavado, P., Velarde, L. & Yamada, G. Cognitive and socioemotional skills and wages: the role of latent abilities on the gender wage gap in Peru. Rev Econ Household 20, 471–496 (2022). https://doi.org/10.1007/s11150-021-09556-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11150-021-09556-9

Keywords

JEL codes

Navigation