Bounding program benefits when participation is misreported

Instrumental variables (IV) are commonly used to estimate treatment effects in case of noncompliance. However, program participation is often misreported in survey data and standard techniques are not sufficient to point identify and consistently estimate the effects of interest. In this paper, we show that the identifiable IV estimand that ignores treatment misclassification is a weighted average of local average treatment effects with weights that can also be negative. This is troublesome because it may fail to deliver a correct causal interpretation, and this is true even if all the weights are non-negative. Therefore, we provide three IV strategies to bound the program benefits when both noncompliance and misreporting are present. We demonstrate the gain of identification power achieved by leveraging multiple exogenous variations when discrete or multiple-discrete IVs are available. At last, we use our new Stata command, ivbounds, to study the benefits of participating in the 401(k) pension plan on savings

Measuring Women's Empowerment in Collective Households

Measuring women's empowerment within families is challenging. Social scientists often rely on close-ended survey questions on women's participation in household decisions, domestic abuse, and autonomy to measure women's power and agency. Recent advances in family economics have allowed researchers to identify and estimate structural measures of women's power and resource control based on the collective household model. We provide a brief overview of this literature. We then apply machine learning techniques to answer the following questions: How do such measures compare to women's responses to close-ended survey questions? Which survey questions are most predictive of model-based estimates of women's empowerment

How Cash Transfers Improve Child Development

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On Couples and Decisions

In Chapter 1, which is co-authored with Rossella Calvi and Arthur Lewbel, we show that a local average treatment effect (LATE) can sometimes be identified and consistently estimated when treatment is mismeasured, or when treatment is estimated using a possibly misspecified structural model. Our associated estimator, which we call Mismeasurement Robust LATE (MR-LATE), is based on differencing two different mismeasures of treatment. In our empirical application, treatment is a measure of empowerment: whether a wife has control of substantial household resources. Due to measurement difficulties and sharing of goods within a household, this treatment cannot be directly observed without error, and so must be estimated. Our outcomes are health indicators of family members. We first estimate a structural model to obtain the otherwise unobserved treatment indicator. Then, using changes in inheritance laws in India as an instrument, we apply our new MR-LATE estimator. We find that women's empowerment substantially decreases their probability of being anemic or underweight, and increases children's likelihood of receiving vaccinations. We find no evidence of negative effects on men's health. Then, using changes in inheritance laws in India as an instrument, we apply our new MR-LATE estimator. We find that women's empowerment substantially decreases their probability of being anemic or underweight, and increases children's likelihood of receiving vaccinations.In Chapter 2, which is co-authored with Alexander Wolf, we take the Dunbar et al (2013) (DLP) model and explore its strength and weaknesses at recovering information regarding household sharing of resources. DLP develop a collective model of the household that allows to identify resource shares, that is, how total household resources are divided up among household members. We show why, especially when the data exhibit relatively flat Engel curves, the model is weakly identified and induces high variability and an implausible pattern in least squares estimates. We propose an estimation strategy nested in their framework that greatly reduces this practical impediment to recovery of individual resource shares. To achieve this, we follow a shrinkage method that incorporates additional (or out-of-sample) information on singles and relies on mild assumptions on preferences. We show the practical usefulness of this strategy through a series of Monte Carlo simulations and by applying it to Mexican data. The results show that our approach is robust, gives a plausible picture of the household decision process, and is particularly beneficial for the practitioner who wishes to apply the DLP framework.Finally, in Chapter 3, which is co-authored with Bram De Rock and Tom Potoms, we exploit the experimental set-up of a conditional cash transfers (CCT) program in Mexico to estimate a collective model of the household and to investigate how parents allocate household resources. This is important to understand because the success of policies aimed at fighting poverty depends crucially on how parents respond to monetary incentives. If parents allocate resources inefficiently (or non-cooperatively), the resulting level of well-being is likely to fall behind the socially efficient optimum. This is undesirable given the prevalence of CCT programs over the last two decades which have occupied a large percentage of governments' annual anti-poverty budgets. Although there is evidence that they have been beneficial, their effectiveness may still be limited. Our aim is to tackle this research question by estimating a theoretically-consistent demand system and by applying at best a powerful test of household efficiency developed by Bourguignon et al (2009). Contrary to previous results, we show that households make efficient decisions only at the beginning of the program, but fail to cooperate later on. In order to rationalize these results, we propose a simple model of household behaviour where decision makers may change their preferences as a result of a treatment that gives information about the importance of a public good.Doctorat en Sciences économiques et de gestioninfo:eu-repo/semantics/nonPublishe

Control of Resources, Bargaining Power and the Demand of Food: Evidence from PROGRESA

I use a structural model of households to recover how much resources each individual controls in the context of the Mexican PROGRESA program. I find that the eligibility to receive the cash transfers induces a redistribution of resources from the father to both the mother and children, although the mother is the one benefiting the most. With these information I compute individual poverty rates and quantify to what extent the program reduces within-household inequality. I also combine these measures to construct a proxy for women’s bargaining power and, using causal identification techniques, I estimate its direct effects on household demand for food. Exploiting random assignment of the cash transfers as an instrumental variable for the treatment of interest, I show that mothers having majority control of household resources relative to fathers increase food consumption as a share of the household budget by 6.5-8.3 percent. I use these estimates to argue that, by knowing (i) The distribution of pre-program resources inside the household, and (ii) How much influence each decision maker can have on the desired policy outcome, a policymaker can improve the cost-effectiveness of a cash transfer program by targeting the cash to resource shares in addition to gender.info:eu-repo/semantics/publishe

Household Responses to cash Transfers

This paper estimates a collective model of the household and investigates how parents reach decisions to allocate household resources. Using data from PROGRESA, we test the restrictions of collective rationality on a large variety of specifications and show that, contrary to previous results, this modeling approach cannot rationalize the household decision process. We provide some evidence that the observed inefficiency is driven by the group receiving the cash transfers. These results are consistent with the idea that a possible indirect effect of CCT programs may be to enhance disagreements between the spouses which trigger an inefficient allocation of their resources.info:eu-repo/semantics/publishe

Overcoming Weak Identification in the Estimation of Household Resource Shares

Dunbar et al. (2013) develop a collective model of the household that allows to identify resource shares, that is, how total household resources are divided up among household members. We show why, especially when the data exhibit relatively flat Engel curves, the model is weakly identified and induces high variability and an implausible pattern in least squares estimates. We propose an estimation strategy nested in their framework that greatly reduces this practical impediment to recovery of individual resource shares. To achieve this, we follow an empirical Bayes method that incorporates additional (or out-of-sample) information on singles and relies on mild assumptions on preferences. We show the practical usefulness of this strategy through a series of Monte Carlo simulations and by applying it to Mexican data. The results show that our approach is robust, gives a plausible picture of the household decision process, and is particularly beneficial for the practitioner who wishes to apply the DLP framework. Our welfare analysis of the PROGRESA program in Mexico is the first to include separate poverty rates for men and women in a CCT program.info:eu-repo/semantics/publishe

Estimating household resource shares: A shrinkage approach

Collective models identifying resource shares are promising tools to analyze intra-household welfare and poverty. However, their empirical application has proven difficult in practice as authors contend with large standard errors and unstable estimates. This paper uses a prominent framework to show how a common feature of the structure of these models makes the task so difficult and proposes an empirical strategy to stabilize the estimates.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Time of Day and High-Stake Cognitive Assessments

A variety of external conditions may affect individual performance in high-stake cognitive assessments, with potentially lasting consequences on earnings and career. We provide the first causal evidence that the time of the day is an important condition affecting the performance at the moment of an evaluation. Exploiting a setting in which cognitive assessments are quasi-randomly assigned at a different time of day, we find that peak performance occurs in the early afternoon. The estimated time-of-day effects follow specific patterns consistent with the circadian rhythm, which suggests that biological factors are important determinants of performance even in economically meaningful settings

Mezzogiorno and Neue Bundesländer: What lessons can Germany learn from Italy?

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