Correcting the fixed-effect estimator for endogenous switching

In this paper, we propose a two-step estimator for panel data models in which a binary covariate is endogenous. In the first stage, a random-effects probit model is estimated, having the endogenous variable as the left-hand side variable. Correction terms are then constructed and included in the main regression.

Renda mínima no Brasil: avaliações de modelos implantados e dos modelos de James Meade

Esta dissertação reflete sobre diferentes programas de renda mínima existentes no Brasil - alguns já implementados e outros em fase de implantação - e sobre os modelos teóricos de James Meade. Inicialmente faz-se uma abordagem geral a respeito de cada um dos programas selecionados e, sobretudo, sobre como influenciam a oferta de mão-de-obra. Nessa abordagem, visualiza-se a existência de um grande mercado de trabalho informal no Brasil, o que leva à evidência da necessidade de criação de mecanismos de controle que possibilitem a implantação desses programas. Em seguida, são feitos levantamentos que permitem estimar os recursos financeiros necessários para cada uma das propostas de programas de renda mínima. Finalmente, são feitas considerações acerca das possibilidades de implantação de cada um dos programas discutidos e apresentam-se aqueles que são mais viáveis no Brasil de hoje.

Por que James Bond, o engenheiro, desistiu do Brasil

Após oito anos e meio, James Arthur Bond, de 48 anos, a contragosto, foi levado de volta às origens. A crise tirou-o do Rio de Janeiro, terra que escolheu para viver com a família, forçando-o a buscar estabilidade em solo britânico.

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.