Knowledge that Transforms

We study optimal monetary and fiscal policies in a New Keynesian model with heterogeneous agents, incomplete markets, and nominal rigidities. Our approach uses small‐noise expansions and Fréchet derivatives to approximate equilibria quickly and efficiently. Responses of optimal policies to aggregate shocks differ qualitatively from what they would be in a corresponding representative agent economy and are an order of magnitude larger. A motive to provide insurance that arises from heterogeneity and incomplete markets outweighs price stabilization motives.

This paper models the evolution of organizations that allow free entry and exit of members, such as cities and trade unions. In each period, current members choose a policy for the organization. Policy changes attract newcomers and drive away dissatisfied members, altering the set of future policymakers. The resulting feedback effects take the organization down a “slippery slope” that converges to a myopically stable policy, even if the agents are forward‐looking, but convergence becomes slower the more patient they are. The model yields a tractable characterization of the steady state and the transition dynamics. The analysis is also extended to situations in which the organization can exclude members, such as enfranchisement and immigration.

In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.

We consider estimation and inference on average treatment effects under unconfoundedness conditional on the realizations of the treatment variable and covariates. Given nonparametric smoothness and/or shape restrictions on the conditional mean of the outcome variable, we derive estimators and confidence intervals (CIs) that are optimal in finite samples when the regression errors are normal with known variance. In contrast to conventional CIs, our CIs use a larger critical value that explicitly takes into account the potential bias of the estimator. When the error distribution is unknown, feasible versions of our CIs are valid asymptotically, even when <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:msqrt> <a:mi>n</a:mi> </a:msqrt> </a:math>‐inference is not possible due to lack of overlap, or low smoothness of the conditional mean. We also derive the minimum smoothness conditions on the conditional mean that are necessary for <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:msqrt> <c:mi>n</c:mi> </c:msqrt> </c:math>‐inference. When the conditional mean is restricted to be Lipschitz with a large enough bound on the Lipschitz constant, the optimal estimator reduces to a matching estimator with the number of matches set to one. We illustrate our methods in an application to the National Supported Work Demonstration.

We study repeated independent Blackwell experiments; standard examples include drawing multiple samples from a population, or performing a measurement in different locations. In the baseline setting of a binary state of nature, we compare experiments in terms of their informativeness in large samples. Addressing a question due to Blackwell (1951), we show that generically an experiment is more informative than another in large samples if and only if it has higher Rényi divergences. We apply our analysis to the problem of measuring the degree of dissimilarity between distributions by means of divergences. A useful property of Rényi divergences is their additivity with respect to product distributions. Our characterization of Blackwell dominance in large samples implies that every additive divergence that satisfies the data processing inequality is an integral of Rényi divergences.

ABSTRACT We examine the extent to which deferred vesting of stock and option grants (deferred pay) helps firms retain executives. To the extent an executive forfeits all deferred pay if they leave the firm, deferred vesting will increase the cost (to the executive) of an early exit. The impact of deferred pay on executive retention, a key ingredient for firms to create shareholder value is hence an important empirical issue. Using pay duration proposed in Gopalan et al. (2014) as a measure of the extent of deferred equity, we find that CEOs and non‐CEO executives with longer pay duration are less likely to leave the firm voluntarily. The talent retention role of deferred pay is mitigated by performance‐vesting provisions and signing bonuses offered by industry peers. Moreover, we also find that voluntary turnover is less sensitive to pay duration for executives who are perceived to be more talented and have more firm‐specific skills. Overall, our study highlights a strong link between compensation design and turnover of top executives. It suggests that firms take into account the need for retaining managerial talent in designing executive compensation.

ABSTRACT Although internal control over financial reporting has gained increasing regulatory attention, its enforcement is far from perfect; thus, firm‐specific incentives to management become important to increase the quality of financial reports. We study how owners can motivate managers to invest in accounting quality even though it is costly to the managers. Using an agency model, we establish that a sufficiently conservative accounting system (which understates performance) is necessary to induce a manager to invest in accounting quality, and more conservatism increases this investment. The reason is that higher accounting quality mitigates the expected reduction of the manager's compensation from conservatively measured performance. Higher accounting quality makes the performance measure more precise, and the owner optimally lowers incentives, even though that entails some loss of productivity. In total, more conservatism increases both firm value and accounting quality. Our findings suggest that striving for neutral accounting can counteract incentives to improve accounting quality, and they provide support to using conservatism as a metric of financial reporting quality in empirical studies.

ABSTRACT We investigate the determinants and consequences of budget reallocations—that is, corrective changes to the budget made during the year. Using proprietary data from a large consumer goods manufacturer, we analyze the extent to which initial budgeting decisions drive reallocations. Examining this relationship is important because initial budget negotiations are often troubled by power struggles and politicking, which may give rise to the need for reallocations. We hypothesize that one important driver of reallocation decisions is the firm's aim to correct systematic deviations from the optimal initial budget that were driven by lobbying during the initial budgeting process. We find evidence that is consistent with this prediction. In a more exploratory analysis, we show that reallocations do not have the desired effects on market performance. In particular, budget cuts are negatively associated with a product's change in market share. More surprisingly, while budget increases do help product lines achieve their sales targets in the last quarter, they do not boost market share. Our results demonstrate that efficient investment planning is essential to achieve an improvement in market performance.

ABSTRACT Prior research finds that women receive lower salaries than men. Similarly, we show that female audit partners in Belgium receive significantly lower compensation than male partners. However, there are alternative explanations for the pay gap other than gender discrimination. For example, the gap in compensation could reflect that men are paid more because they have higher levels of productivity. We provide new predictions and tests of gender discrimination by comparing the fees generated by audit partners (a measure of partner productivity) and the types of clients assigned to partners. Consistent with our prediction of female partners having to meet higher performance thresholds than male partners, we show that female partners generate larger fee premiums, but they are less likely to be assigned to prestigious clients. To test whether these patterns are attributable to gender discrimination, we examine whether the results are stronger in male‐dominated offices, because this is where we would expect to find the most discrimination against women. We find the fee premiums generated by female partners are larger in male‐dominated offices, while the negative association between prestigious clients and female partners is stronger in male‐dominated offices. Collectively, our combined predictions and tests are consistent with female partners facing gender discrimination in audit offices that are dominated by male partners.

ABSTRACT The Clean Development Mechanism (CDM) is a flexible carbon market mechanism managed by the United Nations. The program grants tradable carbon emissions credits (Certified Emission Reductions) for carbon‐reducing projects in developing countries. A project can only be admitted to the program if it is not financially profitable, and thus would not take place without the emission credits granted through the CDM. In this paper, we examine how monitoring reduces incentives of companies to bias the reported expected financial viability of potential CDM projects to gain admission to the program. We find that reported rates of return, which are a key factor for admission to the program, tend to be downwardly biased and are negatively associated with the expected benefits stemming from forecasted greenhouse gas reductions. However, monitoring from various sources mitigates some of the distorted incentives and related reporting bias. Furthermore, the monitoring effect becomes much stronger after 2008, when the CDM Executive Board implemented a series of measures to strengthen the additionality testing that provides guidance for program applications.