We bring new evidence on the labor market effects of large minimum wage increases by using merged administrative datasets from Minnesota to examine the policy changes implemented by Minneapolis and Saint Paul. We begin by using synthetic difference-in-differences methods to estimate counterfactual outcomes at the zip code level from Minnesota and at the city level from the rest of the country. The minimum wage did not affect employment in most industries but exerted a negative impact on restaurants' employment, with an elasticity of −0.8. Next, using variation in exposure to the minimum wage across establishments and workers within the Twin Cities, we find employment effects that are half as large as those from the time series. The cross-sectional estimates difference out employment effects from the pandemic or civil unrest that could confound the time series comparisons, but they do not include potential effects of the minimum wage operating through equilibrium adjustments such as entry. We quantify a model of establishment dynamics to reconcile the different estimates and argue that they plausibly reflect lower and upper bounds of employment losses. We use the model to show that our estimates are consistent with an establishment elasticity of labor demand of −1 and illustrate how they can inform deeper parameters characterizing product and labor market competition, factor substitution, and establishment dynamics.

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Past research has provided evidence of clientelistic politics in delivery of program benefits by local governments (gram panchayats (GPs)), and manipulation of GP program budgets by legislators and elected officials at upper tiers in West Bengal, India.Using household panel survey data spanning 1998-2008, we examine the consequences of clientelism for distributive equity. We find that targeting of anti-poverty programs was progressive both within and across GPs, and is explained by greater 'vote responsiveness' of poor households to receipt of welfare benefits. Across-GP allocations were more progressive than a rule-based formula recommended by the 3rd State Finance Commission (SFC) based on GP demographic characteristics. Moreover, alternative formulae for across-GP budgets obtained by varying weights on GP characteristics used in the SFC formula would have improved pro-poor targeting only marginally. Hence, there is not much scope for improving pro-poor targeting of private benefits by transitioning to formula-based budgeting.

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