Welfare analysis of policy interventions is ubiquitous in economic research. It plays an important role in merger analysis and antitrust litigation, design of tax and subsidies, and informs the current debate on a universal basic income. This paper provides a survey of existing empirical methods, based on cross-sectional microdata, for calculating welfare effects and deadweight loss resulting from realized or hypothetical policy change. We briefly outline classical parametric methods that are computationally tractable, then discuss recent nonparametric approaches that avoid making statistical and functional-form restrictions on individual preferences. This makes the welfare estimates theoretically more credible, and clarifies exactly what welfare-relevant information is contained in demand distribution in various choice settings. However, these methods also demand greater in-sample variation in the data for practical implementation than classical parametric approaches. We then cover settings with externalities. The above results are theoretical, and take the demand function as known; therefore, we briefly discuss empirical problems around demand estimation. We conclude by suggesting areas for future research.

An important goal of empirical demand analysis is choice and welfare prediction on counterfactual budget sets arising from potential policy interventions. Such predictions are more credible when made without arbitrary functional‐form/distributional assumptions, and instead based solely on economic rationality, that is, that choice is consistent with utility maximization by a heterogeneous population. This paper investigates nonparametric economic rationality in the empirically important context of binary choice. We show that under general unobserved heterogeneity, economic rationality is equivalent to a pair of Slutsky‐like shape restrictions on choice‐probability functions. The forms of these restrictions differ from Slutsky inequalities for continuous goods. Unlike McFadden–Richter's stochastic revealed preference, our shape restrictions (a) are global, that is, their forms do not depend on which and how many budget sets are observed, (b) are closed form, hence easy to impose on parametric/semi/nonparametric models in practical applications, and (c) provide computationally simple, theory‐consistent bounds on demand and welfare predictions on counterfactual budge sets.

We consider empirical measurement of equivalent/compensating variation resulting from price-change of a discrete good using individual-level data, when there is unobserved heterogeneity in preferences.We show that for binary and unordered multinomial choice, the marginal distributions of EV/CV can be expressed as simple closed-form functionals of conditional choice-probabilities under essentially unrestricted preference-distributions.These results hold even when the distribution/dimension of unobserved heterogeneity are neither known nor identi-…ed and utilities are neither quasi-linear nor parametrically speci…ed.The welfare distributions take simple forms which are easy to compute in applications.In particular, average EV for a price-rise equals the change in average Marshallian consumer-surplus and is smaller than average CV for a normal good.These nonparametric point-identi…cation results fail for ordered choice if the unit-price is identical for all alternatives, thereby providing a connection to Hausman-Newey's (2014) partial identi…cation results for the limiting case of continuous choice.

How do elite universities balance diversity and academics during admissions? We develop a theory-based empirical framework to identify and quantify this potential trade-off, using postentry outcomes. We apply this to matched admission and exam-performance data from Cambridge. Comparing first- versus second-round admits from different demographic groups yields bounds on the trade-off magnitude, which (a) hold irrespective of whether we observe all applicant characteristics and (b) require no information on rejected applicants. We find robust evidence of trade-off between gender balance and future performance in math-intensive subjects but not for state/private school students or for gender in competitive nonmathematical disciplines.

Admission practices at high-profile universities are often criticized for undermining academic merit. Popular tests for detecting such biases suffer from omitted characteristic bias. We develop a bounds-based test to circumvent this problem. We assume that students who are better qualified on observableswould, on average, appear academically stronger to admission officers based on unobservables. This assumption reveals the sign of differences in admission standards across demographic groups that are robust to omitted characteristics. Applying our methods to admissions data from a British university, we find higher admission standards for men and slightly higher ones for private school applicants, despite equal admission success probability across gender and school background.

We introduce a notion of money-metric social welfare for discrete choice under unrestricted heterogeneity and income effects. It is the maximized indirect utility under normalization of the outside option. It also equals the amount of income necessary to achieve a given level of utility, while certain choices are prohibited. We show that the distribution of this quantity is nonparametrically identified as a closed-form functional of average structural demand for the outside option, making it useful for cost-benefit analysis and optimal targeting. An illustration with private tuition subsidies in India shows that the income path of usage-maximizing subsidies differs significantly from welfare-maximizing ones.

Many real-life settings of individual choice involve social interactions, causing targeted policies to have spillover effects. This article develops novel empirical tools for analysing demand and welfare effects of policy interventions in binary choice settings with social interactions. Examples include subsidies for health-product adoption and vouchers for attending a high-achieving school. We show that even with fully parametric specifications and unique equilibrium, choice data, that are sufficient for counterfactual demand prediction under interactions, are insufficient for welfare calculations. This is because distinct underlying mechanisms producing the same interaction coefficient can imply different welfare effects and deadweight-loss from a policy intervention. Standard index restrictions imply distribution-free bounds on welfare. We propose ways to identify and consistently estimate the structural parameters and welfare bounds allowing for unobserved group effects that are potentially correlated with observables and are possibly unbounded. We illustrate our results using experimental data on mosquito-net adoption in rural Kenya.