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Workers who are worried about their personal finances may find it hard to focus at work. If so, reducing financial concerns could by itself increase productivity. We test this hypothesis in a sample of low-income Indian piece rate manufacturing workers. We stagger when wages are paid out: some workers are paid earlier and receive a cash infusion while others remain liquidity constrained. The cash infusion leads workers to reduce their financial concerns by immediately paying off debts and buying household essentials. Subsequently, they become more productive at work: their output increases by 7.1% (0.12 SDs), and they make fewer costly, unintentional mistakes. Workers with more cash-on-hand thus not only work faster but also more attentively, suggesting improved cognition. These effects are concentrated among more financially constrained workers. We argue that mechanisms such as gift exchange or nutrition cannot account for our results. Instead, our findings suggest that financial strain, at least partly through psychological channels, has the potential to reduce earnings exactly when money is most needed. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Algorithms (in some form) are already widely used in the criminal justice system. We draw lessons from this experience for what is to come for the rest of society as machine learning diffuses. We find economists and other social scientists have a key role to play in shaping the impact of algorithms, in part through improving the tools used to build them. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Many government services are provided at the state level such as unemployment insurance, Medicaid, and SNAP. Given the lack of competition, a natural worry is that customer support provided by states for these services is less than adequate. While there are many different measures of how a state can support beneficiaries, we focus on just one in this short and applied report: the ability to get a live representative on the phone to help with an application question. To do this, we take a “mystery shopping” approach and make 2,000 phone calls to state government offices. We find substantial heterogeneity in the availability of live phone representatives across states and types of service (UI, Medicaid, etc.). For example, live representatives in New Jersey and Georgia were reached less than 20% of the time while representatives in New Hampshire and Wisconsin were reached more than 80% of the time. We hope that this report provides a simple example for how academics, investigative reporters, and watch groups can help states be more accountable for their customer support systems. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

A debt trap occurs when someone takes on a high-interest rate loan and is barely able to pay back the interest, and thus perpetually finds themselves in debt (often by re-financing). Studying such practices is important for understanding financial decision-making of households in dire circumstances, and also for setting appropriate consumer protection policies. We conduct a simple experiment in three sites in which we paid off high-interest moneylender debt of individuals. Most borrowers returned to debt within six weeks. One to two years after intervention, treatment individuals were borrowing at the same rate as control households.

To evaluate how well economic models predict behavior it is important to have a measure of how well any theory could be expected to perform. We provide a measure of the amount of predictable variation in the data that a theory captures, which we call its "completeness." We evaluate the completeness of leading theories in three applications---assigning certainty equivalents to lotteries, initial play in games, and human generation of random sequences---and show that this approach reveals new insights. We also illustrate how and why our completeness measure varies with the experiments considered, for example with the choice of lotteries used to evaluate risk preferences, and explain how our completeness measure can help guide the development of new theories.

Existing learning models attribute failures to learn to a lack of data. We model a different barrier. Given the large number of dimensions one could focus on when using a technology, people may fail to learn because they failed to notice important features of the data they possess. We conduct a field experiment with seaweed farmers to test a model of “learning through noticing”. We find evidence of a failure to notice: On some dimensions, farmers do not even know the value of their own input. Interestingly, trials show that these dimensions are the ones that farmers fail to optimize. Furthermore, consistent with the model, we find that simply having access to the experimental data does not induce learning. Instead, farmers change behavior only when presented with summaries that highlight the overlooked dimensions. We also draw out the implications of learning through noticing for technology adoption, agricultural extension, and the meaning of human capital.

Firms sometimes know more about a consumer's expected usage than the consumer herself. We explore the consequences of this reversal in the information asymmetry. We analyze the consequences of making consumers more informed about themselves. While making consumers more informed decreases their expenditure conditional on a given set of prices, equilibrium prices may increase, offsetting the direct benefit of information. We discuss theoretical and practical issues surrounding so-called RECAP regulation that would require firms to provide each consumer with information about her own usage of the firm's product.

Do financial advisers undo or reinforce the behavioral biases and misconceptions of their clients? We use an audit methodology where trained auditors meet with financial advisers and present different types of portfolios. These portfolios reflect either biases that are in line with the financial interests of the advisers (e.g., returns-chasing portfolio) or run counter to their interests (e.g., a portfolio with company stock or very low-fee index funds). We document that advisers fail to de-bias their clients and often reinforce biases that are in their interests. Advisers encourage returns-chasing behavior and push for actively managed funds that have higher fees, even if the client starts with a well-diversified, low-fee portfolio.

Choosing a mortgage is one of the biggest financial decisions an American consumer will make. Yet it can be a complicated one, especially in today's environment where mortgages vary in dimensions and unique features. This complexity has raised regulatory issues. Should some features be regulated? Should product disclosure be regulated? And most basic of all, is there a rationale for regulation or will the market solve the problem? Current regulation of home mortgages is largely stuck in two competing models of regulation - disclosure and usury or product restrictions - neither of which take adequate account of behavioral psychology or market incentives. This paper seeks to use insights from both psychology and economics to provide a framework for understanding both these models as well as to suggest fundamentally new models. We understand outcomes as an equilibrium interaction between individuals with specific psychologies and firms that respond to those psychologies within specific markets. Regulation must then account for failures in this equilibrium.

Are minorities treated differently by the legal system? Systematic racial differences in case characteristics, many unobservable, make this a difficult question to answer directly. In this paper, we estimate whether judges differ from each other in how they sentence minorities, avoiding potential bias from unobservable case characteristics by exploiting the random assignment of cases to judges. We measure the between-judge variation in the difference in incarceration rates and sentence lengths between African-American and White defendants. We perform a Monte Carlo simulation in order to explicitly construct the appropriate counterfactual, where race does not influence judicial sentencing. In our data set, which includes felony cases from Cook County, Illinois, we find statistically significant between-judge variation in incarceration rates, although not in sentence lengths.

We present a model of coarse thinking, in which individuals group situations into categories, and transfer the informational content of a given message from situations in a category where it is useful to those where it is not. The model explains how uninformative messages can be persuasive, particularly in low involvement situations, and how objectively informative messages can be dropped by the persuader without the audience assuming the worst. The model sheds light on product branding, the structure of product attributes, and several puzzling aspects of mutual fund advertising.

We perform a field experiment to measure racial discrimination in the labor market. We respond with fictitious resumes to help-wanted ads in Boston and Chicago newspapers. To manipulate perception of race, each resume is randomly assigned either a very African American sounding name or a very White sounding name. The results show significant discrimination against African-American names: White names receive 50 percent more callbacks for interviews. We also find that race affects the benefits of a better resume. For White names, a higher quality resume elicits 30 percent more callbacks whereas for African Americans, it elicits a far smaller increase. Applicants living in better neighborhoods receive more callbacks but, interestingly, this effect does not differ by race. The amount of discrimination is uniform across occupations and industries. Federal contractors and employers who list Equal Opportunity Employer in their ad discriminate as much as other employers. We find little evidence that our results are driven by employers inferring something other than race, such as social class, from the names. These results suggest that racial discrimination is still a prominent feature of the labor market.

Most Difference-in-Difference (DD) papers rely on many years of data and focus on serially correlated outcomes. Yet almost all these papers ignore the bias in the estimated standard errors that serial correlation introduces. This is especially troubling because the independent variable of interest in DD estimation (e.g., the passage of law) is itself very serially correlated, which will exacerbate the bias in standard errors. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its "effect" as well as the standard error for this estimate. The standard errors are severely biased: with about 20 years of data, DD estimation finds an "effect" significant at the 5% level of up to 45% of the placebo laws. Two very simple techniques can solve this problem for large sample sizes. The first technique consists in collapsing the data and ignoring the time-series variation altogether; the second technique is to estimate standard errors while allowing for an arbitrary covariance structure between time periods. We also suggest a third technique, based on randomization inference testing methods, which works well irrespective of sample size. This technique uses the empirical distribution of estimated effects for placebo laws to form the test distribution.

In his famous article, "The Nature of the Firm," Ronald Coase (1937) raised two fundamental questions that have spawned a large body of research: Do firm boundaries affect the allocation of resources? And, what determines where firm boundaries are drawn? While the first of these questions has received some theoretical attention - notably Oliver Williamson (1975, 1985), Benjamin Klein, Robert Crawford, and Armen Alchian (1978) and Sanford Grossman and Oliver Hart, (1986) - it has largely been ignored empirically. Instead, the empirical work in this area, discussed in the other articles in this session, has addressed the second question by analyzing the determinants of vertical integration. Thus, while we know something about the forces that determine firm boundaries, we know relatively little about how these boundaries affect actual firm behavior. This is a major limitation in our understanding of the nature of the firm. To begin to assess how firm boundaries affect behavior, we analyze whether there are differences between integrated and non-integrated chemical manufacturers in their investments in production capacity. We focus on producers of vinyl chloride monomer (VCM), the sole use of which is in the production of the widely used waterproof plastic, polyvinyl chloride (PVC). VCM is a homogenous commodity and is traded in relatively liquid markets. Moreover, there is no obvious production link between VCM and PVC other than that one is an input into the other. For example, PVC is not a by-product of VCM production. Nevertheless, two thirds of VCM producers in our sample are integrated downstream into PVC. The existing literature would ask why we observe this degree of integration. We ask instead whether integrated and non-integrated VCM producers invest differently in production capacity.

How do memory limitations affect economic behavior? I develop a model of memory grounded in psychology and biology research to investigate this question. Using this model, I study the case where people apply Bayes rule to the history they recall as if it were the true history. The resulting beliefs exhibit over-reaction on average. They also exhibit under-reaction with the model providing enough structure to allow predictions about which effect dominates when. I then apply this general framework to an otherwise standard model of consumption. It predicts the broad structure of consumption predictability as well as differences in marginal propensity to consume across different income streams. Most important, because it ties the extent of bias to a measurable aspect of the stochastic process being forecasted, the model makes novel, testable empirical predictions.

Many surveys contain a wealth of subjective questions that are at first glance rather exciting. Examples include "How important is leisure time to you?" "How satisfied are you with yourself?"; or "How satisfied are you with your work?" Yet despite easy availability, this is one data source that economists rarely use. In fact, the unwillingness to rely on such questions marks an important divide between economists and other social scientists. This neglect does not come from disinterest. Most economists would probably agree that the variables these questions attempt to uncover are interesting and important. But they doubt whether these questions elicit meaningful answers. These doubts are, however, based on a priori skepticism rather than on evidence. This ignores a large body of experimental and empirical work that has investigated the meaningfulness of answers to these questions. Our primary objective in this paper is to summarize this literature for an audience of economists. Thereby turning a vague implicit distrust into an explicit position grounded in facts. Having summarized the findings, we integrate them into a measurement error framework so as to understand what they imply for empirical research relying on subjective data. Finally, in order to calibrate the extent of the measurement error problem, we perform some simple empirical work using specific subjective questions.

In many countries, controlling shareholders are accused of tunneling, transferring resources from companies where they have few cash flow rights to ones where they have more cash flow rights. Quantifying the extent of such tunneling, however, has proven difficult because of its illicit nature. This paper develops a general empirical technique for quantifying tunneling. We use the responses of different firms to performance shocks to map out the flow of resources within a group of firms and to quantify the extent to which the marginal dollar is tunneled. We apply our technique to data on Indian business groups. The results suggest a significant amount of tunneling between firms in these groups.

We empirically examine two competing views of CEO pay. In the contracting view, pay is used to solve an agency problem: the compensation committee optimally chooses pay contracts that give the CEO incentives to maximize shareholder wealth. In the skimming view, pay is the result of an agency problem: CEOs have managed to capture the pay process so that they set their own pay, constrained somewhat by the availability of cash or by a fear of drawing shareholders' attention. To distinguish these views, we first examine how CEO pay responds to luck, observable shocks to performance beyond the CEO's control. Using several measures of luck, such as changes in oil price for the oil industry, we find substantial pay for luck. Pay responds about as much to a "lucky" dollar as to a general dollar. Most importantly, we find that better governed firms pay their CEOs less for luck. Our second test examines how much CEOs are charged for the options they are granted. Since options never appear on balance sheets, they might offer an appealing way to skim. Here again we find a crucial role for governance: CEOs in better governed firms are charged more for the options they are given. These results suggest that both views of CEO pay matter. In poorly governed firms, the skimming view fits better (pay for luck and little charge for options) while in well governed firms, the contracting view fits better (filtering out of luck and charging for options).