In this paper, we examine the effect of IMF (imposed) programs on countries income inequality for the period 1963-2015. To deal with selection bias, we use a potential outcomes framework, which does not rely on the selection of matching variables and has the further advantage of uncovering the...

This paper proposes a methodology to incorporate bivariate models in numerical computations of counterfactual distributions. The proposal is to extend the works of Machado and Mata (2005) and Melly (2005) using the grid method to generate pairs of random variables. This contribution allows...

We consider semiparametric asymmetric kernel density estimators when the unknown density has support on [0, ∞). We provide a unifying framework which contains asymmetric kernel versions of several semiparametric density estimators considered previously in the literature. This framework allows...

We examine the (potentially nonlinear) relationship between inequality and growth using a method which does not require an a priori assumption on the underlying functional form. This approach reveals a plateau completely missed by commonly used (nonlinear) parametric approaches - the economy...

I examine how changes in the receipt of social transfers benefits associated to program reforms have affected the Canadian income distribution over the 1996-2006 period. Using the Survey of Labour and Income Dynamics, I apply nonparametric decomposition methods to construct density...

Poverty and inequality are often estimated from grouped data as complete household surveys are neither always available to researchers nor easy to analyze. In this study we assess the performance of functional forms proposed by Kakwani (1980a) and Villasenor and Arnold (1989) to estimate the...

When Barret and Donald (2003) in Econometrica proposed a consistent test of stochastic dominance, they were silent about the asymptotic unbiasedness of their tests against (square root) n-converging Pitman local alternatives. This paper shows that when we focus on first-order stochastic...