The Olympic impact on hosting candidate countries

In this paper, we analyze the impact of hosting the Summer Olympics on macroeconomic aggregates such as GDP, consumption, government consumption and investments per capita. The data is in panel structure and includes the period of ten years before and ten years after the event containing the Olympic Summer Games between 1960 and 1996. The sample countries comprise only candidates to host the games. This sampling strategy allows us to estimate the average treatment effect consistently, because it is assumed that these countries are comparable to each other, including those that ultimately hosted the games. The impact of hosting the Olympic games is measured by Fixed Effect and First Difference regressions. Moreover, we do a structural break test developed by Andrews (1993) to identify if hosting the Olympic Games creates anticipation effects for demand changes that stimulate current GDP, consumption, government consumption and investments. The results indicate a positive effect of the Summer Olympics in all variables of interest. However, the distribution in time and anticipation of these effects is unclear in the tests, changing significantly depending on the model and the significance level used.

Conditional treatment and its effect on recidivism

The objective of this paper is to evaluate the effect of the 1985 ”Employment Services for Ex-Offenders” (ESEO) program on recidivism. Initially, the sample has been split randomly in a control group and a treatment group. However, the actual treatment (mainly being job related counseling) only takes place conditional on finding a job, and not having been arrested, for those selected in the treatment group. We use a multiple proportional hazard model with unobserved heterogeneity for job seach and recidivism time which incorporates the conditional treatment effect. We find that the program helps to reduce criminal activity, contrary to the result of the previous analysis of this data set. This finding is important for crime prevention policy.

Lighting and violent crimes: evaluating the effect of an electrification policy in rural Brazil on violent crime reduction

This paper estimates the effect of lighting on violent crime reduction. We explore an electrification program (LUZ PARA TODOS or Light for All - LPT) adopted by the federal government to expand electrification to rural areas in all Brazilian municipalities in the 2000s as an exogenous source of variation in electrification expansion. Our instrumental variable results show a reduction in homicide rates (approximately five homicides per 100,000 inhabitants) on rural roads/urban streets when a municipality moved from no access to full coverage of electricity between 2000 and 2010. These findings are even more significant in the northern and northeastern regions of Brazil, where rates of electrification are lower than those of the rest of the country and, thus, where the program is concentrated. In the north (northeast), the number of violent deaths on the streets per 100,000 inhabitants decreased by 48.12 (13.43). This moved a municipality at the 99th percentile (75th) to the median (zero) of the crime distribution of municipalities. Finally, we do not find effects on violent deaths in households and at other locations. Because we use an IV strategy by exploring the LPT program eligibility criteria, we can interpret the results as the estimated impact of the program on those experiencing an increase in electricity coverage due to their program eligibility. Thus, the results represent local average treatment effects of lighting on homicides.

Bounds on functionals of the distribution treatment effects

Bounds on the distribution function of the sum of two random variables with known marginal distributions obtained by Makarov (1981) can be used to bound the cumulative distribution function (c.d.f.) of individual treatment effects. Identification of the distribution of individual treatment effects is important for policy purposes if we are interested in functionals of that distribution, such as the proportion of individuals who gain from the treatment and the expected gain from the treatment for these individuals. Makarov bounds on the c.d.f. of the individual treatment effect distribution are pointwise sharp, i.e. they cannot be improved in any single point of the distribution. We show that the Makarov bounds are not uniformly sharp. Specifically, we show that the Makarov bounds on the region that contains the c.d.f. of the treatment effect distribution in two (or more) points can be improved, and we derive the smallest set for the c.d.f. of the treatment effect distribution in two (or more) points. An implication is that the Makarov bounds on a functional of the c.d.f. of the individual treatment effect distribution are not best possible.

Effects on school enrollment and performance of a conditional transfers program in Mexico

We study the effects of a conditional transfers program on school enrollment and performance in Mexico. We provide a theoretical framework for analyzing the dynamic educational decision and process inc1uding the endogeneity and uncertainty of performance (passing grades) and the effect of a conditional cash transfer program for children enrolled at school. Careful identification of the program impact on this model is studied. This framework is used to study the Mexican social program Progresa in which a randomized experiment has been implemented and allows us to identify the effect of the conditional cash transfer program on enrollment and performance at school. Using the mIes of the conditional program, we can explain the different incentive effects provided. We also derive the formal identifying assumptions needed to provide consistent estimates of the average treatment effects on enrollment and performance at school. We estimate empirically these effects and find that Progresa had always a positive impact on school continuation whereas for performance it had a positive impact at primary school but a negative one at secondary school, a possible consequence of disincentives due to the program termination after the third year of secondary school.

Três ensaios sobre o mercado de trabalho no Brasil

Esta tese é composta por três artigos e uma nota, sendo um em cada capítulo. Todos os capítulos enquadram-se na área de Microeconomia Aplicada e Economia do Trabalho. O primeiro artigo estende o modelo tradicional de decomposição das flutuações na taxa de desemprego de Shimer (2012), separando o emprego formal do informal. Com essa modificação, os principais resultados da metodologia se alteram e conclui-se que os principais fatores para a queda do desemprego na última década foram (i) a queda na taxa de participação, principalmente pela menor entrada na força de trabalho; (ii) o aumento da formalização, atingido tanto pelo aumento da probabilidade de encontrar um trabalho formal quanto pela probabilidade de deixar a condição de empregado formal. O segundo capítulo apresenta estimativas para o retorno à educação no Brasil, utilizando uma nova metodologia que não necessita de variáveis de exclusão. A vantagem do método em relação a abordagens que utilizam variáveis instrumentais é a de permitir avaliar o retorno médio para todos os trabalhadores (e não somente os afetados pelos instrumentos) e em qualquer instante do tempo. Face aos resultados, concluímos as estimativas via MQO subestimam o retorno médio. Discute-se possíveis explicações para esse fenômeno. O terceiro artigo trata da terceirização da mão de obra no Brasil. Mais especificamente, mede-se o diferencial de salários entre os trabalhadores terceirizados e os contratados diretamente. Os resultados de uma comparação não condicional indicam que os terceirizados têm salário médio 17% menor no período 2007 a 2012. Porém, com estimativas que levam em conta o efeito fixo de cada trabalhador, esse diferencial cai para 3,0%. Além disso, o diferencial é bastante heterogêneo entre os tipos de serviços: aqueles que utilizam trabalhadores de baixa qualificação apresentam salário menores, enquanto nas ocupações de maior qualificação os terceirizados têm salários iguais ou maiores do que os diretamente contratados. Mais ainda, as evidencias apontam para a diminuição do diferencial ao longo do tempo no período analisado. Finalmente, a nota que encerra a tese documenta dois aspectos relevantes e pouco conhecidos da Pesquisa Mensal de Emprego do IBGE que podem levar a resultados imprecisos nas pesquisas que utilizam esse painel se não forem tratados adequadamente.

Revisiting the synthetic control estimator

The synthetic control (SC) method has been recently proposed as an alternative to estimate treatment effects in comparative case studies. The SC relies on the assumption that there is a weighted average of the control units that reconstruct the potential outcome of the treated unit in the absence of treatment. If these weights were known, then one could estimate the counterfactual for the treated unit using this weighted average. With these weights, the SC would provide an unbiased estimator for the treatment effect even if selection into treatment is correlated with the unobserved heterogeneity. In this paper, we revisit the SC method in a linear factor model where the SC weights are considered nuisance parameters that are estimated to construct the SC estimator. We show that, when the number of control units is fixed, the estimated SC weights will generally not converge to the weights that reconstruct the factor loadings of the treated unit, even when the number of pre-intervention periods goes to infinity. As a consequence, the SC estimator will be asymptotically biased if treatment assignment is correlated with the unobserved heterogeneity. The asymptotic bias only vanishes when the variance of the idiosyncratic error goes to zero. We suggest a slight modification in the SC method that guarantees that the SC estimator is asymptotically unbiased and has a lower asymptotic variance than the difference-in-differences (DID) estimator when the DID identification assumption is satisfied. If the DID assumption is not satisfied, then both estimators would be asymptotically biased, and it would not be possible to rank them in terms of their asymptotic bias.

Bounds on policy relevant parameters with discrete policy variation

When estimating policy parameters, also known as treatment effects, the assignment to treatment mechanism almost always causes endogeneity and thus bias many of these policy parameters estimates. Additionally, heterogeneity in program impacts is more likely to be the norm than the exception for most social programs. In situations where these issues are present, the Marginal Treatment Effect (MTE) parameter estimation makes use of an instrument to avoid assignment bias and simultaneously to account for heterogeneous effects throughout individuals. Although this parameter is point identified in the literature, the assumptions required for identification may be strong. Given that, we use weaker assumptions in order to partially identify the MTE, i.e. to stablish a methodology for MTE bounds estimation, implementing it computationally and showing results from Monte Carlo simulations. The partial identification we perfom requires the MTE to be a monotone function over the propensity score, which is a reasonable assumption on several economics' examples, and the simulation results shows it is possible to get informative even in restricted cases where point identification is lost. Additionally, in situations where estimated bounds are not informative and the traditional point identification is lost, we suggest a more generic method to point estimate MTE using the Moore-Penrose Pseudo-Invese Matrix, achieving better results than traditional methods.