The Effect of Cash Transfers on Fertility: Evidence from Argentina

Abstract

In 2009 Argentina introduced a large poverty-alleviation program (AUH) that provides monthly cash transfers per child to households without workers in the formal sector. In this paper we study the potential unintended effect of this program on fertility. We apply a difference-in-difference strategy comparing the probability of having a new child among eligible and ineligible mothers both before and after the program inception. The intention to treat estimations suggest a significant positive impact on fertility in households with at least one child (around 2 percentage points), but no significant effect on childless households. Given the short time window since the implementation of the AUH, we are unable to identify whether this positive effect reflects changes in the timing of births or in the equilibrium number of children.

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Fig. 1

Source: Author’s estimations based on official information from the AUH program and CPIs from Estudio GB

Fig. 2

Source: Author’s estimations based on EPH microdata (2004–2012).

Notes

  1. 1.

    For a more extensive review of this empirical literature in the United States see Blau and Robbins (1989), Fairlie and London (1997), Grogger et al. (2002), Dyer and Fairlee (2003), Rosenzweig (1999), Joyce et al. (2003) and Kearny (2004).

  2. 2.

    Some countries have introduced programs to explicitly affect fertility rates. In some developed countries the literature of financial incentives on fertility decisions has focused mainly on the evaluation of policies specifically designed to counteract the fall in birthrates (see Milligan 2005; Cohen et al. 2013). Also, some developing countries have used financial incentives to intentionally discourage fertility. See for instance Oster (2005) and Ebenstein (2010) for a detailed explanation of China's policy in this regard.

  3. 3.

    This objective places our study within a more extensive literature of CCTs that also analyzes the effects on other variables associated with long-run poverty. For instance, on the impact of CCT programs on labor market outcomes see Alzúa et al. (2013), Gonzalez-Rozada and Pinto (2011) and Azuara and Marinescu (2013).

  4. 4.

    However, the main goal of this study is to evaluate the impact of the PANES program on birth outcomes, particularly on birthweight.

  5. 5.

    Todd et al. (2012) find that the Red de Protección Social in Nicaragua generates an increase in birth spacing.

  6. 6.

    Registered workers already had a similar benefit. A public system of child allowances for formal workers was first introduced in Argentina in 1957, partly as the result of advocacy by the Catholic Church and labor unions. The current system was implemented by law 24.714 in 1996. See Groisman et al. (2014), Moreno (2006), and Rofman and Olivieri (2011).

  7. 7.

    The program also provides transfers to households with disabled children with no age restrictions, and to pregnant women.

  8. 8.

    Given the discrepancies regarding the accuracy of official CPI statistics, we use private CPI estimations from Estudio GB for the Greater Buenos Aires area.

  9. 9.

    Unfortunately, we cannot explore whether the impact started with the announcement of the program or with the effective delivery of the cash transfers, because the program was announced and implemented in just two months. The program was announced on October 29th, 2009 and the decree that created the new program (1602/09) was signed by the president that day. The first transfers were made in November and by December the program had reached 3,408,222 beneficiaries, almost its equilibrium size. For instance, one year later, in December 2010, the number of beneficiaries was 3,513,064 (ANSES 2012). In addition, the announcement of a large conditional cash transfer program in October 2009 was hardly expected, since just four months before, in June 2009, the government had announced that social policy was going to be based on workfare programs aimed at specific geographic areas and at-risk groups, and not on broad-based cash transfer programs (Groisman et al. 2014; Rofman and Olivieri 2011; Garganta and Gasparini 2015).

  10. 10.

    20% of the monthly benefit can only be made effective if the following requirements are met: vaccination and health checks for children under 4 years old and attendance at a public school for those between 5 and 18 years old. In addition, the transfer is contingent on the beneficiary not receiving any allowances from existing contributory and tax-relief programs.

  11. 11.

    The ENGHo is the only source of information in Argentina that enables one to capture both eligible households and effective beneficiaries of the AUH. The ENGHo only provides information for the year 2012, and hence it is not useful for the purpose of our study.

  12. 12.

    Cigno and Pinal (2004) study fertility decisions in a poor province in Argentina in the framework of the endogenous survival probability hypothesis, although for a period prior to the implementation of the AUH.

  13. 13.

    See for instance the model in Stecklov et al. (2007) based on Becker (1960), Beker and Lewis (1973) and Rosenzweig and Schultz (1985).

  14. 14.

    The conditions of the AUH may be more relevant only for the last years of high school, when drop-outs increase. However, it is unlikely that fertility decisions now are highly affected by the potential cost of sending a youngster to (a free of charge) high school in about 16 years.

  15. 15.

    Of course, there are cases where fertility is not a decision (e.g. impossibility to get pregnant, or forced pregnancy), and the role of economic incentives are null.

  16. 16.

    Our data comes from the national household survey (EPH) and not from an evaluation survey of the program. It is thus very unlikely that people misreport any variable in our data source in order to affect the probability of becoming eligible to the AUH program. Although there are concerns that self-reported income in the survey is under-reported, the ranking in the distribution is probably not affected very much by this phenomenon (Deaton 2003; Gasparini et al. 2013), as a result, households in the first four deciles of the self-reported income distribution are likely those in the bottom deciles of the actual income distribution.

  17. 17.

    We also carry out a separate analysis for these two groups.

  18. 18.

    Our newborn variable allows for some waiting time for pregnancy. Since the EPH does not provide information on age in months for children under 1 year old, even if we assume that the fertility decision is taken in time t, we cannot know the exact duration of the delay. Also, even for a given delay there can be some inclusion error, i.e. children conceived before time t who were born in t + 2 or t + 3, when we do not observe the household. Unfortunately, the EPH has no information on pregnancy either. If this data existed, it would be possible to extend the window and also to more accurately distinguish whether births in early 2010 correspond to fertility decisions that were taken after or before the AUH inception.

  19. 19.

    We also include region and time fixed effects in the estimations.

  20. 20.

    These variables are never statistically significant to explain the probability of being a beneficiary of the AUH using alternative specifications of a linear probability model.

  21. 21.

    We carried out several robustness checks by changing the definition of the treatment group (in particular changing the definition of “poor” to more or less than four deciles). All the results hold. Results are available upon request.

  22. 22.

    See Badaracco et al. (2016) for a recent assessment of the direct impact of the changing patterns of fertility in Latin America on income poverty and inequality.

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Correspondence to Santiago Garganta.

Additional information

This research project was carried out in part while Mariana Marchionni and Leonardo Gasparini were visiting scholars at the University of British Columbia. The paper includes material from Santiago Garganta’s Ph.D. dissertation at Universidad Nacional de La Plata. We are grateful to seminar participants at the Annual Conference of the Canadian Economic Association (Vancouver), Asociación Argentina de Economía Política (Posadas), Network of Inequality and Poverty (Buenos Aires), Universidad de San Andrés and Universidad Nacional de La Plata, and to Kevin Milligan, Alessandro Cigno, Ana Navarro, Jorge Paz, Roxana Maurizio, María Laura Alzúa, Alberto Porto, and Walter Cont, for their valuable comments and suggestions. We are also grateful to three anonymous referees for their insightful suggestions that contributed to improve a previous version of the paper. All errors and omissions are our sole responsibility.

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Garganta, S., Gasparini, L., Marchionni, M. et al. The Effect of Cash Transfers on Fertility: Evidence from Argentina. Popul Res Policy Rev 36, 1–24 (2017). https://doi.org/10.1007/s11113-016-9417-x

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Keywords

  • Fertility
  • Cash transfers
  • Social protection
  • AUH
  • Argentina

JEL Classification

  • H55
  • I38
  • J13