Welfare Gains of the Poor: An Endogenous Bayesian Approach with Spatial Random Effects

We introduce a Bayesian instrumental variable procedure with spatial random effects that handles endogeneity, and spatial dependence with unobserved heterogeneity. We find through a limited Monte Carlo experiment that our proposal works well in terms of point estimates and prediction. Then, we apply our method to analyze the welfare effects on the poorest households generated by a process of electricity tariff unification. In particular, we deduce an Equivalent Variation measure where there is a budget constraint for a two-tiered pricing scheme and find that 10% of the poorest municipalities attained welfare gains above 2% of their initial income.

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