Using a new instrumental variable strategy, we examine whether bilateral development aid increases military expenditure in recipient countries. The instrument is the interaction of donor government fractionalization and the probability of receiving aid. The dataset includes new data on military...

We consider the identification of empirical models of supply and demand. As is well known, a supply-side instrument can resolve price endogeneity in demand estimation. We show that, under common assumptions, two other approaches also yield consistent estimates of the joint model: (i) a...

It is customary to assume that an indicator of a latent variable is driven by the latent variable and some random noise. In contrast, a background indicator is also systematically influenced by variables outside the structural model of interest. Background indicators deserve attention because in...

We consider the problem of regressions with selectively observed covariates in a nonparametric framework. Our approach relies on instrumental variables that explain variation in the latent covariates but have no direct e ffect on selection. The regression function of interest is shown to be a...

This paper shows how in Medicare Part D insurers' gaming of the subsidy paid to low-income enrollees distorts premiums and raises the program cost. Using plan-level data from the first five years of the program, I find multiple instances of pricing strategy distortions for the largest insurers....

This paper considers identification and inference about the sign of the average effect of a binary endogenous regressor (or treatment) on a binary outcome of interest when a binary instrument is available. In this setting, the average effect of the endogenous regressor on the outcome is...

This paper introduces a new identification‐ and singularity‐robust conditional quasi‐likelihood ratio (SR‐CQLR) test and a new identification‐ and singularity‐robust Anderson and Rubin (1949) (SR‐AR) test for linear and nonlinear moment condition models. Both tests are very fast to...