conduct welfare analysis

Standard difference-in-differences (DID) methods struggle to identify counterfactual distributions under regulatory policies—such as minimum wage laws—when confronted with mass points, distributional discontinuities, nonstationarity, or unobserved selection bias. This paper proposes a unified partial identification framework grounded in a copula stability assumption, applicable to discrete, continuous, and mixed outcome variables. Under continuity and monotonicity, the framework collapses to the point-identification result of Athey & Imbens (2006), and it is transformation-invariant. Integrating DID, copula modeling, and partial identification theory, the approach yields sharp bounds on the counterfactual distribution. Empirically, it precisely quantifies the causal impact of minimum wage increases on the joint distribution of employment and earnings. The resulting bounds are highly informative, substantially extending both the applicability and robustness of policy evaluation methods in settings where conventional DID assumptions fail.

This paper addresses the challenge of welfare analysis for dynamic models in high-dimensional state spaces. Methodologically, it proposes an estimable and inferential welfare metric framework grounded in doubly robust estimation and dynamic dual representation, enabling unbiased inference on average welfare and its marginal effects without explicit value function estimation. The approach accommodates arbitrary value function estimators—including Lasso and deep neural networks—and automatically corrects their estimation bias without imposing restrictive assumptions on bias structure. Theoretically, it establishes consistent estimation and asymptotically valid inference procedures for average welfare, average marginal welfare effects, and decomposition into direct and indirect effects under high-dimensional dynamic environments. Empirically, the method is applied to a dynamic model of teacher absenteeism, successfully estimating average teacher welfare and demonstrating strong performance, validity, and robustness in a real-world high-dimensional dynamic setting.

This paper addresses the challenge of identifying welfare effects of price changes under consumer heterogeneity. We propose a robust local approximation to Hicksian compensated demand based on uncompensated demand moments, which avoids strong parametric assumptions about preference heterogeneity and yields a preference-structure-robust approximation to compensated demand—enabling both average welfare and welfare distribution analysis. Theoretically, we are the first to systematically employ uncompensated demand moments for Hicksian welfare approximation and prove that a simple nonparametric representative-agent model can outperform complex heterogeneous-parameter models in welfare estimation. Empirically, using UK Household Budget Survey data, our method delivers robust estimates of tax elasticities, price indices, and general-equilibrium welfare, substantially mitigating identification bias induced by preference heterogeneity.

This paper studies the multidimensional Bayesian utility maximization problem under unit-demand settings where buyers’ valuations for items are independent and identically distributed (i.i.d.). It designs prior-independent mechanisms to approximate the social welfare benchmark. Methodologically, it extends the Hartline–Roughgarden single-dimensional analysis framework to the multidimensional setting, establishing a general information-theoretic reduction from multidimensional unit-demand environments to homogeneous-item settings. Theoretically, it proves tight approximation guarantees: a $(1-1/e)$-approximation when the number of items $m$ is at least the number of buyers $n$, and a tight $Theta(log(n/m))$-approximation when $n > m$. These results uncover counterintuitive structural complexity in multidimensional utility maximization and establish fundamental limits on the approximability of social welfare—revealing both its intrinsic difficulty and optimality as a benchmark.

This paper examines the welfare implications of monopolistic price discrimination enabled by consumer data. We develop a theoretical model featuring endogenous market segmentation under residual uncertainty and adopt a weighted total surplus—i.e., a convex combination of consumer and producer surplus—as the welfare metric. We introduce the novel concept of “surplus monotonicity” to characterize the monotonic effect of information refinement on welfare, and derive a general necessary and sufficient condition that reduces welfare evaluation across multiple demand curves to a closed-form test involving only two basis demand curves. Rigorously delineating the boundaries under which information is universally beneficial or harmful, we establish that data collection enhances social welfare if and only if demand satisfies three conditions: domain overlap, two-basis separability, and hyperbolic monotonicity. Our results provide an operationally tractable theoretical benchmark for data regulation.

This study addresses the critical challenge of reliably estimating sharp lower bounds for the standard errors of moment condition estimators when cross-sample correlation information is either absent or only partially available. By leveraging geometric inequalities, the authors derive explicit and tight lower bounds on standard errors and show that the general problem can be reformulated as a semidefinite programming (SDP) problem amenable to efficient computation. This approach yields the first sharp error bounds in settings with no knowledge of cross-sample correlations. Integrating insights from moment condition estimation and statistical inference theory, the method demonstrates both validity and practical utility across several applications, including menu cost models, heterogeneous-agent New Keynesian frameworks, and two-sample instrumental variable settings.

This study addresses revenue-maximizing mechanism design in a single-item monopoly setting where buyers possess both private valuations and private budgets. Leveraging tools from mechanism design theory, probabilistic distribution analysis, and menu complexity measures—alongside metrics such as the Gap from Optimal Revenue (GFOR), Maximal Value Ratio (MVR), and the revenue non-monotonicity gap—the work systematically evaluates the robustness of approximately optimal mechanisms under budget constraints. The main contributions show that, under bounded-support value distributions, simple mechanisms with polylogarithmic menu size can arbitrarily approximate the optimal revenue. However, for unbounded or unit-square concentrated distributions, no finite or sublinear-menu mechanism can guarantee a constant fraction of the optimal revenue, revealing a fundamental limitation of simple mechanisms in achieving robust approximation guarantees.

This work addresses the long-standing limitation in bilateral trade mechanisms, where fixed-price rules are known to achieve at most a 0.7381 approximation of optimal social welfare. Challenging this theoretical barrier, the paper introduces a novel buyer-offer mechanism with a seller-specified reserve price: the buyer submits a single offer no lower than the reserve, and the seller, following a dominant strategy, decides whether to accept it. By integrating mechanism design theory with probabilistic analysis, the authors optimize the choice of the reserve price and construct—for the first time—a non-fixed-price mechanism that guarantees at least 0.746 times the optimal social welfare. This result refutes the presumed optimality of fixed-price mechanisms and establishes a new benchmark for incentive-compatible welfare guarantees in bilateral trade.

This study addresses a critical flaw in the existing Beta Lorenz curve, whose parameter space fails to satisfy the theoretical constraints inherent to Lorenz curves, leading to systematic bias in estimating poverty and inequality from grouped income data. The authors explicitly identify this deficiency for the first time and propose a novel four-parameter family of Lorenz curves that rigorously adheres to all formal properties of genuine Lorenz curves while retaining practical usability. Through parametric modeling, derivation of necessary constraints, and extensive empirical validation across more than 2,000 datasets, the new model demonstrates superior performance in estimating poverty and inequality metrics. Specifically, it significantly reduces the systematic underestimation of poverty levels observed in over 80% of cases compared to the widely used General Quadratic (GQ) Lorenz curve.