Essays on Regulatory Design

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This dissertation consists of three essays on the design of regulatory systems intended to inform market participants about product quality. The central theme is how asymmetric information problems influence the incentives of customers, regulated firms, and certifiers, and the implications these distortions have for welfare and market design. The first chapter, Regulation by Information Provision, studies quality provision in New York City's elevator maintenance market. In this market, service providers maintain machines and are inspected periodically by city inspectors. I find evidence that monitoring frictions create moral hazard for service providers. In the absence of perfect monitoring, buildings rely on signals generated by the regulator to hold service providers accountable, cancelling contracts when bad news arrives and preserving them when good news arrives. Regulatory instruments, such as inspection frequency and fine levels, can therefore influence provider effort in two ways: (i) by directly changing the cost of effort (e.g. fines for poor peformance); (ii) by changing expected future revenue (through building cancellation decisions). Using a structural search model of the industry, I find that the second channel is the dominant one. In particular, I note that strengthening the information channel has two equilibrium effects: first, it increases provider effort; and second, it shifts share towards higher-quality matches since buildings can more quickly sever unproductive relationships. These findings have important policy implications, as they suggest that efficient information provision --- for example, targeting inspections to newly-formed relationships --- is a promising avenues for welfare improvement. The second chapter, Quality Disclosure Design, studies a similar regulatory scheme, but emphasizes the incentives of the certifier. In particular, I argue that restaurant inspectors in New York City are locally averse to giving restaurants poor grades: restaurants whose inspections are on the border of an A versus a B grade are disproportionately given an A. The impact of this bias is twofold: first, it degrades the quality of the information provided to the market, as there is substantial heterogeneity in food-poisoning risk even within A restaurants. Second, by making it easier to achieve passing grades, inspector bias reduces incentives for restaurants to invest in their health practices. After developing a model of the inspector-restaurant interaction, counterfactual work suggests that stricter grading along the A-B boundary could generate substantial improvements in food-poisoning rates. The policy implications of these findings depends on the source of inspector bias. I find some evidence that bias is bureaucratic in nature: when inspectors have inspection decisions overturned in an administrative trial, they are more likely to score leniently along the A-B boundary in their other inspections. However, it's not clear whether this behavior stems from administrative burden (a desire to avoid more trials) or a desire to avoid looking incompetent. Pilot programs that reduce the administrative burden of giving B grades are a promising avenue for future research. The last chapter, Real-Time Inference, also studies the incentives of certifiers, namely MLB umpires charged with classifying pitches as balls or strikes. Unlike in \textit{Quality Disclosure Design}, I find that umpire ball/strike decisions are remarkably bias-free. Previous literature on this topic has noted a tendency for umpires to --- for a fixed pitch location --- call more strikes in hitter's counts and more balls in pitcher's counts. I propose a simple rational explanation for this behavior: umpires are Bayesian. In hitter's counts, such as 3-0, pitchers tend to throw pitches right down the middle of the plate, whereas in pitcher's counts, they throw pitches outside the strike zone. For a borderline pitch, the umpire's prior will push it towards the strike zone in a 3-0 count and away from the strike-zone in an 0-2 count, producing the exact divergence in ball/strike calls noted in previous work. While implications for broader policy are not immediately obvious, I note several features of the environment that are conducive to umpires effectively approximating optimal inference, particularly the frequent, data-driven feedback that umpires receive on their performance.