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Women’s economic capacity and children’s human capital accumulation

Abstract

Programs that increase the economic capacity of poor women can have cascading effects on children’s participation in school and work that are theoretically undetermined. We present a simple model to describe the possible channels through which these programs may affect children’s activities. Based on a cluster-randomized trial, we examine how a program providing capital and training to women in poor rural communities in Nicaragua affected children. Children in beneficiary households are more likely to attend school 1 year after the end of the intervention. An increase in women’s influence on household decisions appears to contribute to the program’s beneficial effect on school attendance.

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Notes

  1. Duflo (2012) indicates that the effects of these policies are not necessarily uniformly positive for children. Investments in children’s health and nutrition, for instance, may come at the expense of investments in children’s education.

  2. See, for instance, Benhassine et al. (2015), Duflo (2003), and Edmonds (2006) for direct comparisons of the effects of providing cash grants to women versus men. See Baird et al. (2014), Fiszbein and Schady (2009), and Saavedra and Garcia (2012) for more general discussion of the effects of cash transfers on children’s education and de Hoop and Rosati (2014), Edmonds (2008), and Fiszbein and Schady (2009) for more general discussion of their effects on children’s work.

  3. Rangel (2006), for instance, shows that investment in children’s education increased when Brazilian women’s bargaining power improved as a result of extended alimony rights. Reggio (2011) argues that, in Mexico, female children’s participation in work is negatively associated with women’s bargaining power.

  4. Examples for multiple countries can be found in Banerjee et al. (2015), Cho and Honorati (2014), and McKenzie and Woodruff (2014).

  5. Theoretically, additional capital provided to the household might be a gross substitute for children’s time in the production function and hence reduce the marginal productivity of child work. However, given the kind of economic activities and technologies typically supported by productive interventions for the poor, this possibility appears unlikely.

  6. The present paper is an output of a United States Department of Labor funding initiative, which supported the collection and analysis of data on children’s productive activities as part of the experiment. The data collected for the project cover two domains of children’s outcomes: schooling and productive activities. Both of these domains are investigated in this paper. Data collection did not comprise other data for children’s individual level outcomes. While analysis of child time use variables was not pre-registered, measurement of the effects of the program on children’s schooling and work is a stand-alone output of this research project. Hatzimasoura et al. (2017) provide a more extensive discussion of the effect of the program on households and adult women’s outcomes, the primary output of the overall study.

  7. See, among others, Cho and Honorati (2014), McKenzie and Woodruff (2014), and Todd (2012).

  8. See for example Banerjee et al. (2015) for a multi-site study, Bandiera et al. (2013) for Bangladesh, Banerjee et al. (2011) for India, Blattman et al. (2014) for Uganda, or Macours et al. (2013) for Nicaragua.

  9. See for instance Karlan and Valdivia (2011) for Peru and de Mel et al. (2014) for Sri Lanka.

  10. Evidence on the effects of micro-credit programs, although conceptually somewhat different from the productive program we study, is more abundant. Various studies find increased work involvement among some specific groups of children (See Augsburg et al. (2012) for Bosnia and Herzegovina, Hazarika and Sarangi (2008) for Malawi, Islam and Choe (2011) for Bangladesh, and Nelson (2011) for Thailand). Yet, another study finds reductions in the probability that children are in work and not in school (Wydick 1999 for Guatemala).

  11. For simplicity of exposition, we do not consider that time can also be allocated to leisure. The implications of this assumption will be briefly discussed later.

  12. As it will become apparent, this assumption does not alter in any substantial way the results we are interested in.

  13. The figures in the remainder of this section, with the exception of those related to children’s economic activities, are drawn from the World Bank’s development indicator database: http://data.worldbank.org/country/nicaragua. After correcting for purchasing power parity, the GDP per capita translates to about US$3962.

  14. Latest figure is for 2007.

  15. Latest figure is for 2005.

  16. For more information on legislation, we refer to the website of the United States Department of Labor: http://www.dol.gov/ilab/reports/child-labour/nicaragua.htm.

  17. http://ucw-project.org/Pages/Tables.aspx?id=1602.

  18. For more information about FUMDEC, See http://fumdec.org.

  19. See Hatzimasoura, Premand, and Vakis (2017) for a more detailed description of the program as well as its overall impacts beyond education and child labor.

  20. The next section describes the beneficiary selection process.

  21. One additional component of the program, not yet implemented at the time of the follow-up survey, consisted of the creation of community banks. For this purpose, the program would provide training in management and organization to community leaders and initial technical support. It was envisioned that these banks would eventually become as a sustainable source of credit for the community.

  22. In addition, administrative costs for the pilot amounted to US$225 per beneficiary, for a ratio of administrative costs to total transfers of 37%.

  23. The 24 communities were selected on the basis of five criteria: (i) they had to be located in a rural area, (ii) they should not have benefitted from related interventions, (iii) they needed to contain a minimum of 20 households, (iv) they needed to be located in an area that was well-known to the local NGO, and (v) the local authorities had to agree with the (potential) implementation of the program in their community.

  24. In the context of the study, enrolment was closed after the initial enrolment period, and there was no new intake of beneficiaries until after the follow-up survey.

  25. Although we have information on individuals who moved into the households (and were not observed at baseline), we leave these individuals out of the analysis.

  26. We consider individuals to be attending school if the answer to the question “is … attending school this school year?” is “yes.”

  27. The non-agricultural activities on own account include household production, commerce, manufacturing, or services. Wage employment covers both agricultural wage work and non-agricultural wage jobs.

  28. We rely on two questions. The first is whether individuals worked in the week prior to the interview. The second, asked only to individuals who initially respond that they did not work in the week prior to the interview, is whether they participated in any of the following economic activities: (i) sale of goods, (ii) washing, ironing, or sewing for others, (iii) preparing and selling bread, tortillas, sweets, crafts, and other items, (iv) work as an apprentice, (v) agricultural work (cultivation or caring for livestock), (vi) tourism, (vii) fishery, or (viii) other economic activities (not further defined). We classify individuals as working if they answer “yes” either to the first or the second question.

  29. All figures reported in this subsection are for households and individuals that were observed also at follow-up.

  30. This is computed as the first principal component of 13 assets: radio recorder, kitchen, vehicle, refrigerator, fan, grinding machine, iron, TV, bicycle, eating utensils (plates, glasses, and cutlery), kitchen utensils, table, and chairs.

  31. Results not displayed.

  32. Administrative data also show that the application rate was similar in treatment and control communities.

  33. If a covariate is not reported for an individual or household, we code it with the value −1. We then include a dummy variable taking the value 1 for all individuals or households for whom the corresponding covariate is missing.

  34. We also examined potential spillover effects on non-applicant households. School participation of children in those households was not significantly affected. There may have been an increase in participation in work among children from non-applicant households. However, the estimated effect is only marginally significant and, given the absence of other spillover effects, we decided not to focus on this outcome in the present paper.

  35. With the exception of the difference between children living more and less than 1 km from school, none of the differences displayed in Table 4 (such as between the impact of the program on boys and girls or the impact of the program on older or younger children) is statistically significant (results not displayed in the table).

  36. We find similar results if we use education (ever attended primary school) of the household head and beneficiary, instead of literacy of the household head and beneficiary.

  37. We rely on a stata routine written by Judson Caskey to calculate bootstrapped significance levels: https://sites.google.com/site/judsoncaskey/data. Note that the bootstrap does not affect the point estimates and does not produce standard errors, but only P values.

  38. Hatzimasoura, Premand, and Vakis (2017) provide a broader discussion on the impacts of the program on intra-household decision-making and gender empowerment.

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Acknowledgements

The authors thank the anonymous referees of this journal for insightful suggestions for improvement of the paper. The authors are grateful to Chrysanthi Hatzimasoura for her contributions to data analysis and to the main study report. The authors would like to thank Verónica Aguilera, Soledad Cubas, Amer Hasan, Karen Macours, Marco Manacorda, Enoe Moncada, Ana María Muñoz Boudet, Amber Peterman, Teresa Suazo, and Egda Velez for contributions and advice at various points during the study design, its implementation, and analysis. The authors also thank participants in seminars at Bocconi University, UNICEF Office of Research—Innocenti, and Wageningen University. Finally, the authors would like to extend their gratitude to the team at Fundación Mujer y Desarrollo Económico Comunitario (FUMDEC) who implemented the intervention under the leadership of Rosa Adelina Barahona, Marlene Rodriguez, and Milton Castillo.

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Correspondence to Jacobus de Hoop.

Ethics declarations

This study was funded by the United States Department of Labor (grant number ILO-GAP-22509-11-75-K), the World Bank Gender-Action Plan (no grant number), and a BNPP trust fund (no grant number).

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Responsible editor: Junsen Zhang

This paper is based on a project initiated at the World Bank, and part of the research was carried out within UCW (Understanding Children’s Work), a joint ILO, World Bank, and UNICEF Programme. Funding was provided by the United States Department of Labor, the World Bank Gender Action Plan, and a BNPP trust fund. The views expressed here are those of the authors and should not be attributed to the ILO, the World Bank, UNICEF, or any of these agencies’ member countries. This document does not necessarily reflect the views or policies of the United States Department of Labor nor does mention of trade names, commercial products, or organizations imply endorsement by the United States Government. A more detailed presentation of the program as well as its overall impacts beyond child labor and education is provided in the main report of the study (See Hatzimasoura et al. 2017).

Appendix

Appendix

Table 8 Sample attrition
Table 9 Mean values of outcome variables in the control group at follow-up
Table 10 Robustness of program impact on children’s activities when estimated with the wild cluster bootstrap
Table 11 Robustness of measured program impact on adults
Table 12 Robustness of measured program impacts on household level outcomes

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de Hoop, J., Premand, P., Rosati, F. et al. Women’s economic capacity and children’s human capital accumulation. J Popul Econ 31, 453–481 (2018). https://doi.org/10.1007/s00148-017-0656-x

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Keywords

  • Women’s economic capacity
  • Female empowerment
  • Child labor
  • Human capital accumulation
  • Field experiment
  • Nicaragua

JEL classification

  • D13
  • H43
  • I25
  • J22
  • J24
  • O15
  • O22
  • Q12