Measuring market discrimination is extremely difficult except in the increasingly rare case where physical output measures allow direct measurement of productivity. We illustrate this point with evidence on elections to offices of the American Economic Association. Using a new technique to infer...

With the American Time Use Survey of 2003 and 2004 we first examine whether additional market work has neutral impacts on the mix of non-market activities. The estimates indicate that fixed time costs of market work alter patterns of non-market activities, reducing leisure time and mostly...

We link information on the current earnings of college graduates from many cohorts to their high-school records, their detailed college records and their demographics to infer the impact of college major on earnings. We develop an estimator to handle the potential for non-response bias and...

This paper evaluates the tax reforms carried out in Sweden between 1980 and 1991. We use a recently developed nonparametric estimation technique to account for labor supply responses. We decompose the tax returns to study how the separate components influence hours of work, tax revenues...

We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our procedure is asymptotically equivalent to...

This paper considers estimation and testing of vector autoregression coefficients in panel data, and applies the techniques to analyze the dynamic properties of revenues, expenditures, and grants in a sample of United States municipalities. The model allows for nonstationary individual effects,...

From its inception, demand estimation has faced the problem of "many prices." This paper provides estimators of average demand and associated bounds on exact consumer surplus when there are many prices in cross-section or panel data. For cross-section data we provide a debiased machine learner...

This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions

This paper investigates identification and inference in a nonparametric structural model with instrumental variables and non-additive errors. We allow for non-additive errors because the unobserved heterogeneity in marginal returns that often motivates concerns about endogeneity of choices...