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Conditional cash transfers and women’s reproductive choices

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Sonia Laszlo discusses the non-targeted effects of a large-scale national conditional cash transfer program—Peru’s Juntos—on the fertility and reproductive decisions of adult beneficiaries.

Image courtesy of interviewee. July 17, 2024

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This video is a presentation discussing the effects of conditional cash transfer programs (CCTs), specifically the Juntos program in Peru, on maternal fertility. It explores how these anti-poverty programs, which provide financial assistance to low-income households based on children's health and education criteria, may influence women's fertility choices.

Key points include:
1. **Objective of the Research**: The study aimed to understand the causal mechanisms through which conditional cash transfers might influence parental fertility, particularly since existing literature primarily focused on adolescent fertility without delving deeply into parental effects.

2. **Concerns about CCTs**: There is concern that linking cash transfers to children could incentivize higher fertility rates, potentially perpetuating cycles of poverty, especially in high-fertility contexts.

3. **Program Details**: Juntos is a national CCT program initiated in Peru in 2005, providing monthly transfers of around 100 sols (~$25) to poor households with pregnant women or children under 14. Beneficiaries must meet conditionalities related to education and health.

4. **Context and Evidence**: The study highlights the prevalence of excess fertility, where households have more children than desired, indicating unmet contraceptive needs. Previous studies suggest that CCTs can reduce adolescent fertility, but evidence on their effects on parents is limited and mixed.

5. **Methodology**: The researchers utilized demographic and health surveys to analyze fertility outcomes among women eligible for the program, contrasting them with similar women in non-beneficiary regions. They employed an event study design to understand the impact of program rollout over time.

6. **Findings**: The results showed a significant reduction in total fertility by approximately 0.14 fewer births per woman in treated districts, primarily from reduced unplanned pregnancies. The estimated national reduction in births since the program's inception is around 175,000, indicating a positive impact on maternal and child welfare.

7. **Mechanisms of Impact**: Investigating potential mechanisms, the study concluded that the reduced fertility was not due to changes in parental preferences for family size or increased decision-making power for women. Instead, the increased access to and use of reproductive health services was a significant factor.

8. **Policy Implications**: The findings suggest that anti-poverty programs like CCTs can lead to unanticipated benefits, such as improved fertility outcomes, and highlight the importance of considering long-term and intergenerational effects in program evaluations.

9. **Future Research Directions**: The study calls for deeper exploration into how CCTs empower women, including their roles in decision-making and access to reproductive health, particularly in contexts of high fertility.

The overall conclusion emphasizes that while traditional measures of empowerment may not capture the complete picture, there is a likely empowerment effect as evidenced by reduced excess fertility and improved access to contraceptive methods.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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We were interested in understanding

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how conditional cash transfers,

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which are an anti-poverty program in developing countries,

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can influence mother's fertility.

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Uh, what we're really talking about here are the sorts

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of programs that give money to poor households,

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conditional on both, children's health

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and education investments, on the part of the family.

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And so, why this question?

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Uh, well, we knew, when we started looking at this

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with my co-authors, Farhan, Maji,

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and that, conditional cash transfers have been linked

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to adolescent fertility.

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So this could be through increased school attendance,

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it could be through decreased household, poverty.

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But ultimately, there was not a lot

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of evidence on the impact

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that this had on parents fa fertility.

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And there's a concern that one sometimes hears, that,

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well, you know, if cash transfers are linked to children,

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then this could actually incentivize increased fertility.

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And if we're dealing with situations in which,

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we have high fertility rates to begin with, then,

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an anti-poverty program that increases,

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fertility could perpetuate the cycle of poverty.

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So we wanted to unpack this a little bit more

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and have a better understanding of the causal mechanisms,

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that conditional cash transfers could have

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on parents' fertility.

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Now, our prior, was that, well, you know,

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there might be some reasons to think

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that this could have a positive effect,

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but our prior was that, especially in, rural areas

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or in poor communities,

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where women may not be fully empowered, that

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by providing cash transfers, which are normally given

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to mothers, that this could lead to an empowerment effect,

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in which a mother could control her fertility

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and her shirt, her own fertility preferences.

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So with this in mind, we turned our attention

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to a program in Peru called Juntos.

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Uh, it's a national program

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of direct support to the poorest.

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Uh, it's a conditional cash transfer program

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that started in 2005, with a two,

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a typical two stage targeting.

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First stage is geographic,

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which targets district based on official poverty mapping.

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And then once a district has been identified, in

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that first stage of the targeting process,

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then households would be targeted within those districts.

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And these would be households, that are typically poor,

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where there are, either pregnant women

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or children under the year, under the age of 14.

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And so per juntos has these monthly transfers,

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about a hundred solis per month.

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So in today's terms, this is approximately equal

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to 25, 26 US dollars.

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Uh, and these are normally transfers

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that are given to mothers.

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Now that's the transfer, that's the cash part of it.

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What about the conditionality? Uh, the conditions part?

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Well, here, essentially, we're dealing with,

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both education conditions and health conditions.

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On the education side.

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Uh, the condition for, continued receipts

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of the transfer is

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that school-aged children should remain in school

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and should regularly attend school.

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And on the health conditionality, that, children,

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would be receiving regular health checks

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and, for pregnant mothers

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and infants, that there would be some,

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following up with the health clinics.

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And so why did we choose Peru for this particular study?

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We chose it, for a number of reasons.

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Um, first, there's a number of, features of,

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the Peruvian program, which is quite interesting,

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to be able to understand this question.

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On the one hand, we're dealing with a national program,

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although it's primarily rural, it does spread out to, a,

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a, a big part of the country.

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Secondly, the transfer is actually

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independent of the number of children.

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And so this is really important for us

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because we're interested in understanding the effects on

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fertility without necessarily incentivizing the

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number of children, right?

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So this rules out the program effect on the number

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of children, directly.

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Now, there are, a couple of other,

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interesting features of the Peruvian economy

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that make this a particularly interesting study,

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area to study.

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One is that, when we're dealing with fertility outcomes,

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in at least the data that we have,

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there is quite a bit of evidence of excess fertility.

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And so what does excess fertility mean?

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It means that households have more than their

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ideal, number of children.

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What is that? In terms of numbers?

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It's about 0.8 children per mother in our sample.

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And while this not, might not necessarily seem like a very

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large number, this is an average,

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and it does account for about 44% of women in our sample

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who claim or who report

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to have more than their ideal number of children.

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And so this points to two, potentially, important,

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reproductive health

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and overall, development concerns.

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One is this could mean

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that there is a significant unmet desire

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for, contraception.

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Um, and the second point is

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that e excess fertility is actually linked

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to potentially negative ALK effects on households economic

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wellbeing, and both maternal and child health.

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Overall. There's been some evidence

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that these conditional cash transfers,

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have had an impact on, adolescent fertility.

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In fact, there's a few very influential studies

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that have shown very clear, fertility effects.

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Um, and in, one in that I have in mind is a,

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randomized control experiment in Malawi by Baird, McIntosh

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and Osler that was published in 2011, where they were trying

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to understand the impact that the cash

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or the condition,

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of these cash transfer programs have on a number

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of education and, reproductive health outcomes of,

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adolescent girls.

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And they actually do document a significant decrease in,

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adolescent fertility.

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And this has also been found in a number of other contexts.

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Uh, more recently paper by Susan Parker.

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And, and, and her co-author have looked at, the impact

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of Mexico's Resa, cash transfer program

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and has found some similar, results as well.

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What is, however less clear is the effect on mother's

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fertility, right?

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So why do we think that this is different?

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Well, adolescent fertility, as I mentioned earlier, the fact

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that there is a conditionality on,

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on school attendance means that, well, you know, maybe the,

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fact that, adolescents are getting more educated,

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they're spending more time in school, that this is going

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to have a negative impact on fertility, whereas the channel

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of causality for moms, fertility is going

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to be very different here.

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And so we need to think a little bit about the theory,

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and unfortunately, the theory's a little ambiguous.

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On the one hand, we might think that, well,

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if people have more income, they may, desire

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to have larger families.

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Um, but, you know, it could also mean that, well,

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maybe they wanna spend more on existing children.

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So even in terms of thinking about the role

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that the increased cash have, it's less clear.

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And so this is why we were thinking

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that maybe this empowerment effect,

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of transferring the cash transfer to the mother

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may allow her to assert her own preferences towards lower

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fertility, and so reduce excess fertility.

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So, as, we do for any paper,

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we review the literature,

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and here the evidence was really mixed.

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Most studies that looked at fertility among, parents

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among the mother in particular, happened

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to look at this, as, one

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of many other different outcomes

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that are not necessarily focused on fertility.

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And so as a result, many of them don't necessarily try

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to get at possible mechanisms

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through which the cash transfer can lead

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to changes in fertility among, parents.

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And so the causal effects there are going

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to be difficult to identify.

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Um, and that's particularly true if we think

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that the cash transfers may be linked, somehow

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to fertility outcomes, like, for example,

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the number of children.

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So how do we contribute to this literature?

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Well, the features of the

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Peruvian program are really helpful.

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It is independent of the number of children.

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So that's already one issue.

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Uh, the other, thing that makes this, a particularly

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interesting place where we can make some contributions is

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the fact that, Juntos,

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the Peruvian program was rolled out over tenure program,

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which gives us some time and geo geographic variation.

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And both of these factors help us identify the causal

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effects of the cash transfer on fertility.

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So that's one,

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way in which we contribute to the literature.

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Another way in which we contribute

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to the literature is given the length of the program,

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which started in 2005, it allows us to,

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really look at the persistence of the cash transfer effects

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so that we can sort of have a better sense of, well, is the,

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the effect instantaneous?

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Does it persist? Does it happen a little bit later?

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And so we're able to speak to some

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of the dynamic effects of the program.

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And the third way in which we are able

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to make some contributions to this literature is

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by delving into the mechanisms.

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And here we really have three mechanisms in in mind.

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One is whether or not the cash transfer has had an impact on

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fertility intentions of parents.

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Uh, so do families prefer having larger families as a result

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of the cash transfer?

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The second mechanism is whether

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or not, the cash transfer somehow provides better access

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to reproductive health services for the families

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that would be beneficiaries of these programs.

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And the third mechanism is, whether

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or not, these cash transfers are empowering women.

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And so we can try to see whether

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or not intra household decision making is influenced

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with the cash transfer receipt.

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So we actually use two data sets, very importantly,

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to be able to, identify, who is potentially eligible

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for this, cash transfer program.

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Uh, we have administrative data on the rollout, which this,

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what this means is that this tells us,

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which districts in Peru, receive juntos

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and the year in which they receive juntos.

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And so this allows us to really pinpoint that time

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and geographic variation of the rollout.

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Um, and, what's also, important to note here in

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that administrative data, which is relevant

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to the way the program was rolled out, is

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that once a district, is eligible to receive,

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these cash transfers, then it remains in.

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So we don't necessarily have

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to worry about districts coming in and out of the program.

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So that's one, source of data that we have.

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The second source of data that we use is,

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PERS demographic and health surveys.

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Uh, in Spanish is called es.

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And,

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and this provides us individual level data on outcomes.

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So fertility, number of children contraceptive use

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and access fertility intentions,

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and as well empowerment outcomes.

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In addition to these outcome variables,

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there's a very large number of, individual

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and household control variables that allow us to control

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for socioeconomic status.

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But what's really unique about the Peruvian DHS survey,

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is that unlike other DHS surveys,

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which is normally every four years, starting in 2004,

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Peru started having an annual DHS.

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And so this allows us to have,

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a lot more repeated cross sections.

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And in fact, we're gonna utilize the cross sections

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from 2004.

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So year before, Juntos comes in

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to the first districts in Peru,

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and also the first year in which we're able

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to look at annual cross-sections.

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So from 2004 until 2017, the sample we use.

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Um, so we're gonna try to, reproduce a little bit,

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of a, of an experimental design here.

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So we're going to first restrict all women in the DHSF

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who fit the beneficiary eligibility criteria.

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So this means for mothers

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and pregnant women in rural districts, right?

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Uh, this allows us to construct plausible counterfactuals

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by comparing women who fit that eligibility criteria

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in districts that receive juntos to similar women

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in similar districts that either don't have juntos

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or don't have Juntos yet, right?

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So allowing us to make this sample restriction allows us

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to have this sort of treatment and counterfactual setup.

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Now, other restrictions that we're going to make here is

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that we're going to restrict this to women in a relationship

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who are not sterilized.

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Because ultimately, to be able to really understand

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what might be happening here, we need

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to get into the underlying mechanisms.

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And since our underlying mechanisms are about fertility

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intentions and inter household, intra household,

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decision making, then we do really need to make those two,

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two restrictions.

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Now, the identification strategy, how are we able to,

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look at this, from a, causal, mechanism?

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So, as, we recall, once a district is rule,

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ruled in, it stays, in, in the program.

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And, because we're able

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to observe fertility outcomes in the DHS, what we're going

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to be able to do is exploiting that time

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and geographic, variation.

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We're going to be able

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to conduct what's called an event study.

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And so what an event study does is it compares outcome

13:34.175 --> 13:37.515
of the program or outcomes of the program,

13:38.105 --> 13:40.035
once it's enrolled to outcomes

13:40.055 --> 13:42.355
before the program is enrolled, right?

13:42.415 --> 13:46.795
The annual DHS data allows us to test for preexisting trends

13:46.815 --> 13:49.435
and identify any persistent effects on

13:49.615 --> 13:51.715
of the cash transfer, right?

13:52.255 --> 13:55.035
And so our pretre tests, what are pre tests?

13:55.065 --> 13:57.435
Well, here, we just really wanna make sure that, well,

13:57.615 --> 14:00.195
you know, if we're seeing a reduction in fertility,

14:00.195 --> 14:02.755
we wanna make sure that it's not just, you know,

14:02.975 --> 14:05.035
for a decline, infertility

14:05.035 --> 14:07.315
that would've happened even in the absence of the program.

14:08.295 --> 14:11.835
And we conduct a number of preterm tests, pretre tests,

14:12.095 --> 14:14.515
that generally give us the confidence that the effects

14:14.515 --> 14:16.795
that we do measure can in fact be attributed

14:16.795 --> 14:18.875
to the Juntos program and not on those

14:18.945 --> 14:20.755
preexisting underlying trends.

14:21.255 --> 14:22.715
So our main results is

14:22.715 --> 14:24.595
that we do find strong negative effects

14:24.595 --> 14:26.835
of juntos on fertility outcomes from the

14:26.835 --> 14:27.915
number of children ever born.

14:28.855 --> 14:30.955
Uh, what does this mean in terms of numbers?

14:31.295 --> 14:34.195
Uh, women in treated districts have a four percentage,

14:34.195 --> 14:36.875
point reduction in total fertility, which translates

14:36.875 --> 14:39.715
to approximately 0.14 fewer births

14:39.715 --> 14:42.035
after the program is rolled into their district.

14:43.015 --> 14:45.715
Um, this may seem a little small,

14:46.095 --> 14:49.315
but it's, better if we sort of think about, how

14:49.315 --> 14:50.315
to contextualize that number.

14:50.535 --> 14:51.645
So the first thing I wanna mention,

14:51.715 --> 14:55.045
this is gonna come up when we talk about the,

14:55.225 --> 14:56.365
the mechanisms, is

14:56.365 --> 14:58.765
that this is a reduction in unplanned pregnancies.

14:58.835 --> 15:01.045
It's not like there's really much change in the

15:01.045 --> 15:02.085
number of planned pregnancies.

15:02.085 --> 15:03.885
It's really coming from unplanned pregnancies.

15:04.625 --> 15:06.005
The second thing is that

15:06.155 --> 15:09.485
what we are estimating here is women in treated districts,

15:09.645 --> 15:11.645
which means that we're not necessarily saying that women

15:11.705 --> 15:14.285
who receive the transfer we're saying that women,

15:14.475 --> 15:17.925
poor women, with, that are either pregnant

15:18.225 --> 15:20.925
or, have, children,

15:21.595 --> 15:23.125
that whole group, we see a reduction.

15:23.425 --> 15:26.965
So if we make assumptions about actual individual receipts

15:26.965 --> 15:30.525
of juntos, we estimate about 0.225 fewer

15:30.545 --> 15:31.925
births per per mother.

15:32.385 --> 15:34.325
So again, while this number might seem small,

15:34.505 --> 15:37.125
it actually does account for more than one quarter

15:37.425 --> 15:38.605
of excess fertility.

15:39.145 --> 15:41.965
And our back of the envelope calculations estimate a

15:42.165 --> 15:45.405
national reduction of about 175,000 births since the start

15:45.405 --> 15:47.165
of the program, right?

15:47.265 --> 15:49.485
So that already is very interesting.

15:49.535 --> 15:51.085
We're finding a reduction in fertility.

15:51.095 --> 15:53.925
We're finding a reduction in unplanned pregnancies,

15:54.065 --> 15:56.565
and we're finding a reduction in excess fertility.

15:56.945 --> 15:58.845
We believe that these are going

15:58.845 --> 16:00.485
to be important factors in terms

16:00.585 --> 16:02.205
of improving household welfare

16:02.385 --> 16:04.045
and also maternal and child health.

16:05.065 --> 16:08.765
Um, and we also find that these effects, per persist

16:08.765 --> 16:10.285
for persist for up to six years.

16:10.585 --> 16:12.085
So our event study allows us

16:12.085 --> 16:14.645
to really look at the dynamic effects of the program.

16:15.305 --> 16:18.925
Uh, and given the timeline in which, we're looking at,

16:19.105 --> 16:21.965
at these effects, we do see that these effects do persist

16:22.065 --> 16:24.565
for quite some time after the cash transfer is made

16:24.565 --> 16:25.645
available in the district.

16:27.445 --> 16:30.065
So I mentioned that one of the, contributions

16:30.065 --> 16:32.465
of our study is that we can delve into the mechanisms.

16:32.605 --> 16:34.585
And so, we do exactly that.

16:34.685 --> 16:37.785
So the first question that we may want to answer is whether

16:37.785 --> 16:39.505
or not that effect is happening

16:39.505 --> 16:42.945
through a change in parental preferences over family size.

16:43.485 --> 16:45.865
And according to our results, we're able

16:45.865 --> 16:47.265
to reject that hypothesis.

16:47.325 --> 16:51.905
It does not look, like, you know,

16:52.415 --> 16:54.105
it's really changing parental preferences.

16:55.365 --> 16:56.945
Uh, secondly,

16:57.055 --> 16:59.145
does the cash transfer empower women in

16:59.145 --> 17:00.305
household decision making?

17:01.285 --> 17:06.185
Um, so our typical, measures for, decision making.

17:06.485 --> 17:08.505
Um, so these are standard variables such as

17:08.505 --> 17:09.865
who decides about birth control

17:09.865 --> 17:12.385
or whether women conceal the use of birth control.

17:12.755 --> 17:15.065
We're not really finding anything there, so we fail

17:15.065 --> 17:17.625
to reject the null hypothesis in that context as well.

17:18.385 --> 17:21.565
So that leaves us a third hypothesis, which is whether

17:21.565 --> 17:24.325
or not the cash transfer gives women better access to.

17:24.665 --> 17:27.645
And, and also, you know, and,

17:27.645 --> 17:29.605
and use of reproductive health services.

17:30.345 --> 17:33.525
And our answer here, our results here are resoundingly, yes,

17:33.785 --> 17:35.765
we in fact find very strong effects.

17:36.105 --> 17:39.765
Uh, we find that there's an increased use of modern forms

17:39.765 --> 17:44.005
of contraception, decreased use of traditional forms of,

17:44.005 --> 17:47.445
contraception, which we know are, less effective,

17:47.985 --> 17:50.685
and also an increased propensity to use any form

17:50.685 --> 17:54.005
of birth control, whether it's traditional

17:54.025 --> 17:55.845
or conscious, or, or modern.

17:56.945 --> 17:58.765
And so this led us to sort

17:58.765 --> 18:00.845
of think about a little bit more what's happening there.

18:01.265 --> 18:04.405
Um, and so we looked at it a little bit more to try to say,

18:04.405 --> 18:07.565
okay, was it because they have better knowledge about,

18:07.735 --> 18:09.125
birth control or

18:09.155 --> 18:11.005
because it allows 'em to cover the cost

18:11.005 --> 18:12.125
of birth, birth control?

18:12.585 --> 18:15.325
But it turns out that, respondents in our, in our,

18:15.505 --> 18:18.325
in our data, don't seem to really point out

18:18.325 --> 18:21.845
that these are really big barriers to, adopting

18:22.025 --> 18:23.365
or using birth control.

18:24.425 --> 18:27.245
Uh, and in fact, we find that,

18:27.465 --> 18:30.765
the cash transfers associated with an increased probability

18:30.765 --> 18:33.565
that mothers discuss family planning at the health services.

18:33.905 --> 18:36.445
So, if you'll remember, one of the conditionalities is

18:36.475 --> 18:37.845
that mothers,

18:37.905 --> 18:40.685
and pregnant women would need to have regular health checks,

18:40.945 --> 18:42.725
and they would also have to,

18:43.145 --> 18:44.765
attend health checks with their children.

18:45.065 --> 18:48.805
And so this may give them access to, reproductive care,

18:49.145 --> 18:50.925
services and information.

18:51.745 --> 18:54.125
Um, so there are two policy implications here.

18:54.305 --> 18:56.645
The first is, that we highlight anti

18:56.795 --> 18:58.645
that anti-poverty policies can have an

18:58.645 --> 18:59.725
unanticipated effects.

19:00.265 --> 19:01.645
And in this case, we see

19:01.645 --> 19:05.485
that we have a welfare improving fertility outcomes

19:05.515 --> 19:08.285
because we do see reduction in excess fertility in

19:08.285 --> 19:09.405
unplanned pregnancies.

19:09.985 --> 19:12.245
And so the social returns of these sorts

19:12.245 --> 19:14.245
of programs may be larger than the

19:14.245 --> 19:16.165
policymaker, has in mind.

19:16.665 --> 19:18.005
The implication here is

19:18.005 --> 19:20.805
that program cost benefit analysis might want

19:20.805 --> 19:22.365
to take these into consideration.

19:22.705 --> 19:25.885
Of course, it is really difficult to predict ex ante,

19:26.235 --> 19:29.645
what those, what those unintended effects are,

19:29.745 --> 19:31.125
or unanticipated effects.

19:32.385 --> 19:35.805
The second policy implication is that our results point

19:35.805 --> 19:38.565
to long-term effects of social, protection policies.

19:38.945 --> 19:41.925
And this is particularly important,

19:41.925 --> 19:45.805
because they can have intergenerational implications,

19:46.445 --> 19:49.245
specifically given the relationship between lower fertility

19:49.305 --> 19:51.405
and poverty reduction over the long term.

19:51.605 --> 19:53.925
I really do think we should be pushing on the two angles

19:53.925 --> 19:55.645
that I highlighted in the policy implications.

19:56.385 --> 19:59.245
Uh, on the one hand, I think we do wanna think about the

19:59.245 --> 20:01.845
broader impacts on these non-con uncondition outcomes

20:02.105 --> 20:03.325
of cash transfers.

20:03.625 --> 20:07.445
Um, given now that we have seen, a lot

20:07.445 --> 20:09.445
of these cash transfer programs at the national,

20:09.465 --> 20:12.885
or more localized level, both in developing countries,

20:12.885 --> 20:14.725
but also since, COVID-19

20:14.745 --> 20:17.885
and developed countries, I think trying to understand

20:17.885 --> 20:22.725
that is, of, of, of very strong policy,

20:22.985 --> 20:24.685
and academic, importance.

20:25.385 --> 20:28.365
And the second, area coming out

20:28.365 --> 20:30.325
of those policy implications is

20:30.385 --> 20:32.725
to better understand the longer term effects.

20:33.025 --> 20:36.805
And, one area that I think is both difficult to answer,

20:37.225 --> 20:39.245
but I think is really worthwhile pushing is

20:39.245 --> 20:41.765
to understand the impacts that these programs have on family

20:41.865 --> 20:43.845
for formation over generations.

20:43.985 --> 20:46.125
And I think that, you know, now as we're seeing

20:46.685 --> 20:49.685
Juntos has been around for almost two decades,

20:49.965 --> 20:51.805
Progressa has been around for, for quite some time,

20:51.965 --> 20:54.285
I think now is a good time to really start looking at these

20:54.285 --> 20:56.605
longer term, generational outcomes.

20:57.625 --> 20:59.565
And I guess the last area, and,

20:59.565 --> 21:01.525
and maybe this is picking up one of the,

21:01.575 --> 21:06.005
surprising results, is, from our study is

21:06.035 --> 21:10.885
that we did not find, using our standard measures

21:10.885 --> 21:12.965
of empowerment strong results there,

21:13.665 --> 21:16.765
but we do see strong results in terms of reduced,

21:16.825 --> 21:18.365
excess fertility,

21:18.625 --> 21:20.685
and that this persists over longer term.

21:21.345 --> 21:24.085
And so, while the decision making outcomes didn't show

21:24.085 --> 21:27.725
significant access, we do think that these,

21:28.115 --> 21:30.965
results do point to some sort of empowerment effect,

21:31.065 --> 21:33.205
if nothing else, through access to birth control

21:33.305 --> 21:35.125
and that reduction in excess fertility.

21:35.865 --> 21:38.685
And so I think that's an area that needs to,

21:38.905 --> 21:40.685
to be unpacked a little bit more.

21:41.225 --> 21:43.205
Um, particularly given the focus

21:43.205 --> 21:47.005
that many development agencies have at empowering women,

21:47.105 --> 21:49.005
is an issue of needing better measures

21:49.185 --> 21:51.405
or is an issue of trying to find better ways

21:51.505 --> 21:53.605
to really understand what's happening

21:53.675 --> 21:54.845
with these underlying data.

"Exploring the Impact of Conditional Cash Transfers on Maternal Fertility: A Study of Peru's Juntos Program"

00:00:00 - We were interested in understanding how conditional cash transfers, which are an anti-poverty program in developing countries, can influence mother's fertility. what we're really talking about here are the sorts of programs that give money to poor households, conditional on both, children's health and education investments, on the part of the family. And so, why this question? well, we knew, when we started looking at this with my co-authors, Farhan, Maji, and that, conditional cash transfers have been linked to adolescent fertility. So this could be through increased school attendance,
00:00:43 - But ultimately, there was not a lot of evidence on the impact that this had on parents fa fertility. And there's a concern that one sometimes hears, that, well, you know, if cash transfers are linked to children, then this could actually incentivize increased fertility. And if we're dealing with situations in which, we have high fertility rates to begin with, then, an anti-poverty program that increases, fertility could perpetuate the cycle of poverty. So we wanted to unpack this a little bit more and have a better understanding of the causal mechanisms, that conditional cash transfers could have
00:01:23 - Now, our prior, was that, well, you know, there might be some reasons to think that this could have a positive effect, but our prior was that, especially in, rural areas or in poor communities, where women may not be fully empowered, that by providing cash transfers, which are normally given to mothers, that this could lead to an empowerment effect, in which a mother could control her fertility and her shirt, her own fertility preferences. So with this in mind, we turned our attention to a program in Peru called Juntos. it's a national program of direct support to the poorest. it's a conditional cash transfer program
00:02:05 - a typical two stage targeting. First stage is geographic, which targets district based on official poverty mapping. And then once a district has been identified, in that first stage of the targeting process, then households would be targeted within those districts. And these would be households, that are typically poor, where there are, either pregnant women or children under the year, under the age of 14. And so per juntos has these monthly transfers, about a hundred solis per month. So in today's terms, this is approximately equal to 25, 26 US dollars. and these are normally transfers
00:02:44 - Now that's the transfer, that's the cash part of it. What about the conditionality? the conditions part? Well, here, essentially, we're dealing with, both education conditions and health conditions. On the education side. the condition for, continued receipts of the transfer is that school-aged children should remain in school and should regularly attend school. And on the health conditionality, that, children, would be receiving regular health checks and, for pregnant mothers and infants, that there would be some, following up with the health clinics. And so why did we choose Peru for this particular study?
00:03:28 - first, there's a number of, features of, the Peruvian program, which is quite interesting, to be able to understand this question. On the one hand, we're dealing with a national program, although it's primarily rural, it does spread out to, a, a, a big part of the country. Secondly, the transfer is actually independent of the number of children. And so this is really important for us because we're interested in understanding the effects on fertility without necessarily incentivizing the number of children, right? So this rules out the program effect on the number of children, directly. Now, there are, a couple of other,

"Exploring the Impact of Conditional Cash Transfers on Excess Fertility and Reproductive Health Outcomes"

00:04:08 - that make this a particularly interesting study, area to study. One is that, when we're dealing with fertility outcomes, in at least the data that we have, there is quite a bit of evidence of excess fertility. And so what does excess fertility mean? It means that households have more than their ideal, number of children. What is that? In terms of numbers? It's about 0.8 children per mother in our sample. And while this not, might not necessarily seem like a very large number, this is an average, and it does account for about 44% of women in our sample who claim or who report to have more than their ideal number of children.
00:04:52 - reproductive health and overall, development concerns. One is this could mean that there is a significant unmet desire for, contraception. and the second point is that e excess fertility is actually linked to potentially negative ALK effects on households economic wellbeing, and both maternal and child health. Overall. There's been some evidence that these conditional cash transfers, have had an impact on, adolescent fertility. In fact, there's a few very influential studies that have shown very clear, fertility effects. and in, one in that I have in mind is a,
00:05:35 - and Osler that was published in 2011, where they were trying to understand the impact that the cash or the condition, of these cash transfer programs have on a number of education and, reproductive health outcomes of, adolescent girls. And they actually do document a significant decrease in, adolescent fertility. And this has also been found in a number of other contexts. more recently paper by Susan Parker. And, and, and her co-author have looked at, the impact of Mexico's Resa, cash transfer program and has found some similar, results as well.
00:06:15 - fertility, right? So why do we think that this is different? Well, adolescent fertility, as I mentioned earlier, the fact that there is a conditionality on, on school attendance means that, well, you know, maybe the, fact that, adolescents are getting more educated, they're spending more time in school, that this is going to have a negative impact on fertility, whereas the channel of causality for moms, fertility is going to be very different here. And so we need to think a little bit about the theory, and unfortunately, the theory's a little ambiguous. On the one hand, we might think that, well, if people have more income, they may, desire
00:06:53 - but, you know, it could also mean that, well, maybe they wanna spend more on existing children. So even in terms of thinking about the role that the increased cash have, it's less clear. And so this is why we were thinking that maybe this empowerment effect, of transferring the cash transfer to the mother may allow her to assert her own preferences towards lower fertility, and so reduce excess fertility. So, as, we do for any paper, we review the literature, and here the evidence was really mixed. Most studies that looked at fertility among, parents among the mother in particular, happened
00:07:36 - of many other different outcomes that are not necessarily focused on fertility. And so as a result, many of them don't necessarily try to get at possible mechanisms through which the cash transfer can lead to changes in fertility among, parents. And so the causal effects there are going to be difficult to identify. and that's particularly true if we think that the cash transfers may be linked, somehow to fertility outcomes, like, for example, the number of children. So how do we contribute to this literature? Well, the features of the Peruvian program are really helpful. It is independent of the number of children.

"Examining the Impact and Mechanisms of the Juntos Conditional Cash Transfer Program in Peru: A Longitudinal Analysis"

00:08:17 - the other, thing that makes this, a particularly interesting place where we can make some contributions is the fact that, Juntos, the Peruvian program was rolled out over tenure program, which gives us some time and geo geographic variation. And both of these factors help us identify the causal effects of the cash transfer on fertility. So that's one, way in which we contribute to the literature. Another way in which we contribute to the literature is given the length of the program, which started in 2005, it allows us to, really look at the persistence of the cash transfer effects so that we can sort of have a better sense of, well, is the,
00:08:58 - Does it persist? Does it happen a little bit later? And so we're able to speak to some of the dynamic effects of the program. And the third way in which we are able to make some contributions to this literature is by delving into the mechanisms. And here we really have three mechanisms in in mind. One is whether or not the cash transfer has had an impact on fertility intentions of parents. so do families prefer having larger families as a result of the cash transfer? The second mechanism is whether or not, the cash transfer somehow provides better access to reproductive health services for the families that would be beneficiaries of these programs. And the third mechanism is, whether
00:09:43 - And so we can try to see whether or not intra household decision making is influenced with the cash transfer receipt. So we actually use two data sets, very importantly, to be able to, identify, who is potentially eligible for this, cash transfer program. we have administrative data on the rollout, which this, what this means is that this tells us, which districts in Peru, receive juntos and the year in which they receive juntos. And so this allows us to really pinpoint that time and geographic variation of the rollout. and, what's also, important to note here in
00:10:25 - to the way the program was rolled out, is that once a district, is eligible to receive, these cash transfers, then it remains in. So we don't necessarily have to worry about districts coming in and out of the program. So that's one, source of data that we have. The second source of data that we use is, PERS demographic and health surveys. in Spanish is called es. And, and this provides us individual level data on outcomes. So fertility, number of children contraceptive use and access fertility intentions, and as well empowerment outcomes. In addition to these outcome variables,
00:11:08 - and household control variables that allow us to control for socioeconomic status. But what's really unique about the Peruvian DHS survey, is that unlike other DHS surveys, which is normally every four years, starting in 2004, Peru started having an annual DHS. And so this allows us to have, a lot more repeated cross sections. And in fact, we're gonna utilize the cross sections from 2004. So year before, Juntos comes in to the first districts in Peru, and also the first year in which we're able to look at annual cross-sections. So from 2004 until 2017, the sample we use.
00:11:53 - of a, of an experimental design here. So we're going to first restrict all women in the DHSF who fit the beneficiary eligibility criteria. So this means for mothers and pregnant women in rural districts, right? this allows us to construct plausible counterfactuals by comparing women who fit that eligibility criteria in districts that receive juntos to similar women in similar districts that either don't have juntos or don't have Juntos yet, right? So allowing us to make this sample restriction allows us to have this sort of treatment and counterfactual setup.

"Analyzing the Effects of Conditional Cash Transfers on Fertility Outcomes: Methodological Considerations and Initial Findings"

00:12:35 - that we're going to restrict this to women in a relationship who are not sterilized. Because ultimately, to be able to really understand what might be happening here, we need to get into the underlying mechanisms. And since our underlying mechanisms are about fertility intentions and inter household, intra household, decision making, then we do really need to make those two, two restrictions. Now, the identification strategy, how are we able to, look at this, from a, causal, mechanism? So, as, we recall, once a district is rule, ruled in, it stays, in, in the program.
00:13:18 - to observe fertility outcomes in the DHS, what we're going to be able to do is exploiting that time and geographic, variation. We're going to be able to conduct what's called an event study. And so what an event study does is it compares outcome of the program or outcomes of the program, once it's enrolled to outcomes before the program is enrolled, right? The annual DHS data allows us to test for preexisting trends and identify any persistent effects on of the cash transfer, right? And so our pretre tests, what are pre tests? Well, here, we just really wanna make sure that, well,
00:14:00 - we wanna make sure that it's not just, you know, for a decline, infertility that would've happened even in the absence of the program. And we conduct a number of preterm tests, pretre tests, that generally give us the confidence that the effects that we do measure can in fact be attributed to the Juntos program and not on those preexisting underlying trends. So our main results is that we do find strong negative effects of juntos on fertility outcomes from the number of children ever born. what does this mean in terms of numbers? women in treated districts have a four percentage, point reduction in total fertility, which translates to approximately 0.14 fewer births
00:14:43 - this may seem a little small, but it's, better if we sort of think about, how to contextualize that number. So the first thing I wanna mention, this is gonna come up when we talk about the, the mechanisms, is that this is a reduction in unplanned pregnancies. It's not like there's really much change in the number of planned pregnancies. It's really coming from unplanned pregnancies. The second thing is that what we are estimating here is women in treated districts, which means that we're not necessarily saying that women who receive the transfer we're saying that women, poor women, with, that are either pregnant or, have, children,
00:15:23 - So if we make assumptions about actual individual receipts of juntos, we estimate about 0.225 fewer births per per mother. So again, while this number might seem small, it actually does account for more than one quarter of excess fertility. And our back of the envelope calculations estimate a national reduction of about 175,000 births since the start of the program, right? So that already is very interesting. We're finding a reduction in fertility. We're finding a reduction in unplanned pregnancies, and we're finding a reduction in excess fertility. We believe that these are going to be important factors in terms of improving household welfare
00:16:05 - and we also find that these effects, per persist for persist for up to six years. So our event study allows us to really look at the dynamic effects of the program. and given the timeline in which, we're looking at, at these effects, we do see that these effects do persist for quite some time after the cash transfer is made available in the district. So I mentioned that one of the, contributions of our study is that we can delve into the mechanisms. And so, we do exactly that. So the first question that we may want to answer is whether or not that effect is happening through a change in parental preferences over family size.

"Assessing the Impact of Cash Transfers on Women's Empowerment and Reproductive Health Services"

00:16:45 - to reject that hypothesis. It does not look, like, you know, it's really changing parental preferences. secondly, does the cash transfer empower women in household decision making? so our typical, measures for, decision making. so these are standard variables such as who decides about birth control or whether women conceal the use of birth control. We're not really finding anything there, so we fail to reject the null hypothesis in that context as well. So that leaves us a third hypothesis, which is whether or not the cash transfer gives women better access to.
00:17:27 - and use of reproductive health services. And our answer here, our results here are resoundingly, yes, we in fact find very strong effects. we find that there's an increased use of modern forms of contraception, decreased use of traditional forms of, contraception, which we know are, less effective, and also an increased propensity to use any form of birth control, whether it's traditional or conscious, or, or modern. And so this led us to sort of think about a little bit more what's happening there. and so we looked at it a little bit more to try to say, okay, was it because they have better knowledge about,
00:18:09 - because it allows 'em to cover the cost of birth, birth control? But it turns out that, respondents in our, in our, in our data, don't seem to really point out that these are really big barriers to, adopting or using birth control. and in fact, we find that, the cash transfers associated with an increased probability that mothers discuss family planning at the health services. So, if you'll remember, one of the conditionalities is that mothers, and pregnant women would need to have regular health checks, and they would also have to, attend health checks with their children. And so this may give them access to, reproductive care,
00:18:51 - so there are two policy implications here. The first is, that we highlight anti that anti-poverty policies can have an unanticipated effects. And in this case, we see that we have a welfare improving fertility outcomes because we do see reduction in excess fertility in unplanned pregnancies. And so the social returns of these sorts of programs may be larger than the policymaker, has in mind. The implication here is that program cost benefit analysis might want to take these into consideration. Of course, it is really difficult to predict ex ante, what those, what those unintended effects are,
00:19:32 - The second policy implication is that our results point to long-term effects of social, protection policies. And this is particularly important, because they can have intergenerational implications, specifically given the relationship between lower fertility and poverty reduction over the long term. I really do think we should be pushing on the two angles that I highlighted in the policy implications. on the one hand, I think we do wanna think about the broader impacts on these non-con uncondition outcomes of cash transfers. given now that we have seen, a lot of these cash transfer programs at the national, or more localized level, both in developing countries,
00:20:14 - and developed countries, I think trying to understand that is, of, of, of very strong policy, and academic, importance. And the second, area coming out of those policy implications is to better understand the longer term effects. And, one area that I think is both difficult to answer, but I think is really worthwhile pushing is to understand the impacts that these programs have on family for formation over generations. And I think that, you know, now as we're seeing Juntos has been around for almost two decades, Progressa has been around for, for quite some time, I think now is a good time to really start looking at these

"Unpacking the Ambiguous Empowerment Effects of Conditional Cash Transfers on Fertility Outcomes"

00:20:57 - And I guess the last area, and, and maybe this is picking up one of the, surprising results, is, from our study is that we did not find, using our standard measures of empowerment strong results there, but we do see strong results in terms of reduced, excess fertility, and that this persists over longer term. And so, while the decision making outcomes didn't show significant access, we do think that these, results do point to some sort of empowerment effect, if nothing else, through access to birth control and that reduction in excess fertility.
00:21:53 - to be unpacked a little bit more. particularly given the focus that many development agencies have at empowering women, is an issue of needing better measures or is an issue of trying to find better ways to really understand what's happening with these underlying data.

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