Working papers

This working paper looks at the impact of health on labor supply near retirement.

Divided by choice? Private providers, patient choice and hospital sorting in the English National Health service

Estimating the size and nature of responses to changes in income tax rates on top incomes in the UK: a panel analysis*

Updating and critiquing HMRC’s analysis of the UK’s 50% top marginal rate of tax

Frictions and taxpayer responses: evidence from bunching at personal tax thresholds

There is substantial evidence of a significant relationship between parents’ income and sons’ earnings in the UK, and that this relationship has strengthened over time. We extend this by exploring a broader measure of net family income as an outcome.

This paper provides a framework for identifying preferences in a large network where links are pairwise stable.

Testing for homogeneity in mixture models

Quantile regression 40 years on

Rebayes: an R package for empirical bayes mixture methods

Bayesian deconvolution: an R vinaigrette

This paper studies inference in randomized controlled trials with covariate-adaptive randomization when there are multiple treatments. More specifically, we study in this setting inference about the average effect of one or more treatments relative to other treatments or a control. As in Bugni et al. (2017), covariate-adaptive randomization refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve "balance" within each stratum. In contrast to Bugni et al. (2017), however, we allow for the proportion of units being assigned to each of the treatments to vary across strata.

Consideration set models relax the assumption that consumers are aware of all available options.

Multinomial choice models are fundamental for empirical modeling of economic choices among discrete alternatives. We analyze identifcation of binary and multinomial choice models when the choice utilities are nonseparable in observed attributes and multidimen- sional unobserved heterogeneity with cross-section and panel data.

We study social learning in a continuous action space experiment.

In nonlinear panel models with fixed effects and fixed-T, the incidental parameter problem poses identification difficulties for structural parameters and partial effects. Existing solutions are model-specific, likelihood-based, impose time homogeneity, or restrict the distribution of unobserved heterogeneity. We provide new identification results for the large class of Fixed Effects Linear Transformation (FELT) models with unknown, time-varying, weakly monotone transformation functions.

Robins et al. (2008, 2016b) applied the theory of higher order infuence functions (HOIFs) to derive an estimator of the mean of an outcome Y in a missing data model with Y missing at random conditional on a vector X of continuous covariates; their estimator, in contrast to previous estimators, is semiparametric efficient under minimal conditions. However the Robins et al. (2008, 2016b) estimator depends on a non-parametric estimate of the density of X. In this paper, we introduce a new HOIF estimator that has the same asymptotic properties as their estimator but does not require non-parametric estimation of a multivariate density, which is important because accurate estimation of a high dimensional density is not feasible at the moderate sample sizes often encountered in applications. We also show that our estimator can be generalized to the entire class of functionals considered by Robins et al. (2008) which include the average effect of a treatment on a response Y when a vector X suffices to control confounding and the expected conditional variance of a response Y given a vector X.

We examine the optimal auditing problem of a tax authority when taxpayers can choose both to evade and avoid. For a convex penalty function the incentive-compatibility constraints may bind for the richest taxpayer and at a positive level of both evasion and avoidance. The audit function is non-increasing in reported income, and is higher for progressive tax functions than for regressive tax functions. Higher marginal tax rates increase the incentives for non-compliance, overturning the well-known Yitzhaki paradox.

If the productive characteristics of workers and firms are truly multi-dimensional, what features of the data do we miss by modeling them as one-dimensional scalars? This is the question we ask in this paper.