Brazil's Bolsa Familia and young adult workers: A parallel RDD approach to large datasets

Dourado, Aloisio; Carvalho, Rommel N.; Erven, Gustavo Cordeiro Galvão Van

doi:10.1109/ijcnn.2017.7965830

Cited by 3 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We adopt the RD design framework by employing the eligibility rule based on per capita household income, used by the Brazilian government to select eligible families and authorize cash transfers. Regression discontinuity analyses have already been used to evaluate the BF program on a number of outcomes, e.g., fertility, schooling, labor choices (Superti 2020;Nilsson and Sjöberg 2013;Dourado et al 2017; Barbosa and Corseuil 2014).…”

Section: Introductionmentioning

confidence: 99%

Selecting Subpopulations for Causal Inference in Regression Discontinuity Designs

Forastiere¹,

Mealli²,

Pescarini³

et al. 2022

Preprint

View full text Add to dashboard Cite

The Brazil Bolsa Família program is a conditional cash transfer program aimed to reduce short-term poverty by direct cash transfers and to fight long-term poverty by increasing human capital among poor Brazilian people. Eligibility for Bolsa Família benefits depends on a cutoff rule, which classifies the Bolsa Família study as a regression discontinuity (RD) design.Extracting causal information from RD studies is challenging. Following Li et al. (2015) and Branson and Mealli (2019), we formally describe the Bolsa Família RD design as a local randomized experiment within the potential outcome approach. Under this framework, causal effects can be identified and estimated on a subpopulation where a local overlap assumption, a local SUTVA and a local ignorability assumption hold. We first discuss the potential advantages of this framework, in settings where assumptions are judged plausible, over local regression methods based on continuity assumptions, which concern both the definition of the causal estimands, as well as the design and the analysis of the study, and the interpretation and generalizability of the results. A critical issue of this local randomization approach is how to choose subpopulations for which we can draw valid causal inference. We propose to use a Bayesian model-based finite mixture approach to clustering to classify observations into subpopulations where the RD assumptions hold and do not hold on the basis of the observed data. This approach has important advantages: a) it allows to account for the uncertainty in the subpopulation membership, which is typically neglected; b) it does not impose any constraint on the shape of the subpopulation; c) it is scalable to high-dimensional settings; e) it allows to target alternative causal estimands than average effects; and f) it is robust to a certain degree of manipulation/selection of the running variable. We apply our proposed approach to assess causal effects of the Borsa Família program on leprosy incidence in 2009, for Brazilian households who registered in the Brazilian National Registry for Social Programs in 2007-2008 for the first time.

show abstract

Section: Introductionmentioning

confidence: 99%