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Consequences of Parents’ Unemployment on Investments in Children’s Education in Brazil

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Abstract

This paper investigates whether parents’ entry into unemployment affects investment in children’s education through their decision to provide public or private education for their children. The empirical approach makes use of longitudinal data from the Continuous National Household Sample Survey (Pesquisa Nacional por Amostra de Domicílios Contínua–PNAD contínua) and propensity-score matching with difference-in-difference methods. According to estimates, parents’ unemployment reduces the probability of children’s enrollment in the private educational system, which usually has better quality but high costs, instead of in the public educational system, which is offered for free but typically has poor quality. Thus, evidence suggests that reductions in household income as a consequence of unemployment can have impacts on the quality of human capital accumulated by children.

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Notes

  1. Fernandes and Felicio (2005) find evidence that spouses increase their labor supply as a response to the household head’s unemployment in Brazil.

  2. According to the continuous PNAD, only 0.8% of the children aged 7–14 years were not enrolled in school in 2018. Among those in the age range analyzed by Duryea et al. (2007), 2.5% were out of school, most of them aged 15–16 years.

  3. Hanushek (2003) reports large differences in teaching quality lead to important differences in students’ performance.

  4. Menezes-Filho and Curi (2010), using data from the 2002/2003 Brazilian Consumer Expenditure Survey (Pesquisa de Orçamentos Familiares – POF), show that average education expenditures for the 5% richer families is 15 times higher than that of the 28% poorer families. Menezes-Filho and Curi (2010) include only families with positive expenditures on education in this calculation.

  5. As mentioned by Feldmann (2021), private schooling has increased in many developing countries motivated by better teaching and learning outcomes compared to public schools. The expansion of private schools, despite their charge fees, is an indication of the importance that parents attribute to their children’s education.

  6. The 2003 SAEB, the 2003 and 2005 Brazilian School Census, and the 2003 Census of Teaching Professionals (Censo dos Profissionais do Magistério).

  7. According to the 2017 Educational Census, the private system also has 18% of the students enrolled in primary school (INEP, 2018).

  8. Six-year-old children not enrolled in primary school are dropped to avoid transitions from pre-school. Children in secondary school are also excluded from the sample.

  9. Eighty-three percent of the children aged 6 to 13 in their first PNAD continua interview live in households where both the head and spouse are present. Estimates including households where spouse is not present (available upon request) are similar the ones reported in Sect. 6.

  10. This value is quite similar to the one that considers all children aged 6–13 years in the continuous PNAD.

  11. First time applicants need at least 12 months of employment in the last 18 months, second time applicants need at least 9 months of employment in the last 12 months. Third time applicants need six months of employment in the last job.

  12. Hoffmann (2009) shows that labor earnings represent three-quarters of the household income per capita in Brazil.

  13. Heckman et al. (1997) use a generalized version of the kernel matching implemented here, called local linear matching, which converges at a faster rate near boundary points and adjusts better to different data densities. Heckman et al. (1997) have a large share of their data at boundaries.

  14. Figure 4 in Appendix B shows the means of covariates before and after matching for treatment and control groups, where the former is defined according to column (1) of Table 5. In Tables 6 and 7, Z also includes characteristics of the spouse. Figure 4 shows the distribution of the predicted probability of the household head's entry into unemployment by children in control and treatment groups. Differences between those groups are accentuated. Figure 5 restricts the control group to nearest neighbors of children in the treatment group, and the distributions of the two groups are much more similar.

  15. Estimates (available upon request) usually do not indicate that the main conclusions depend on the timing of the unemployment spells. Shocks represented by unemployment only in interviews 2, 3, or 4 are nonsignificant, and although two periods of unemployment in interviews 3 and 4 seem to have a greater negative effect on the estimated probability of enrollment in a private school compared to a shock represented by unemployment only in interviews 2 and 3, the coefficients are significant at the 1% level in both cases. The results in Table 5 are not so different comparing households interviewed in the 2nd and 3rd quarters of the calendar year, in the middle of the school year in Brazil, with those interviewed in the 1st and 4th quarters. Also, estimates that drop children in rural areas, where private primary schools are much more scarce, show more pronounced impacts of unemployment than those reported in Table 5, although the main conclusions are similar.

  16. However, how well this method can deal with selection bias depends on the assumption that, conditional on variables in Z, non-treated outcomes are independent from the participation in treatment or control group, which is hard to be effectively verified (Blundell and Dias, 2008).

  17. Children in poorer households who attend a public school should be also affected by parents’ unemployment, throughout school evasion and worse educational attainment, for example.

  18. The analysis considers changes at one-year intervals. It is possible that longer periods of employment increase the probability of transition from public to private school.

  19. Nonetheless, Gruber (1997) shows that less than 20% of individuals who lose their jobs have savings of more than two months of income before job loss in the U.S. The capacity of workers’ savings to finance unemployment spells seems much more limited in Brazil. According to Moreira and Silveira (2019), even among families in the top household per capita income decile, savings are positive for only 15% of them.

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  1. Instituto de Pesquisa Econômica Aplicada, Av. Presidente Antonio Carlos, 51 (1409), Rio de Janeiro, RJ, 20020-010, Brazil

    Mauricio Reis

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Appendix

Appendix

See Figs 4 and 5, Tables 13, 14, 15, 16

Fig. 4
figure 4

Distribution of the propensity score of the household head's entry into unemployment by control and treatment groups

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Fig. 5
figure 5

Distribution of the propensity score of the household head's entry into unemployment by control (nearest neighbors) and treatment groups

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Table 13 The continuous PNAD rotating panel design
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Table 14 Covariates in the PSM (means in the tratment and control groups)
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Table 15 Unemployment and children's enrollment in private schools (Propensity score matching estimator)
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Table 16 Unemployment and children's enrollment in private schools (Double Robust estimator)
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Reis, M. Consequences of Parents’ Unemployment on Investments in Children’s Education in Brazil. Soc Indic Res 171, 373–404 (2024). https://doi.org/10.1007/s11205-023-03247-x

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