Knowledge that Transforms

We survey a large, representative sample of retail investors in China to elicit their memories of stock market investments and their return expectations. We merge these survey data with administrative transaction data to test a model in which investors selectively recall past experiences to form their beliefs. Our analysis uncovers new facts about investor memory and highlights similarity-based recall as a key mechanism of belief formation in financial markets. A rising market prompts investors to recall their past experiences more positively, leading to more optimistic forecasts of future returns. Recalled experiences can explain cross-investor variation in return expectations and, in our setting, dominate actual experiences in their explanatory power. In the transaction data, we confirm that recalled experiences are reflected in investors’ trading decisions through a belief channel.

We estimate a matching model of teachers and elementary schools with rich data on teachers' applications and principals' ratings from a large, urban district in North Carolina. Both teachers’ and principals’ preferences deviate from those that would maximize the achievement of economically disadvantaged students: teachers prefer schools with fewer disadvantaged students, and principals' ratings are weakly related to teacher effectiveness. In equilibrium, these two deviations combine to produce a surprisingly equitable current allocation, where teacher quality is balanced across advantaged and disadvantaged students. To close achievement gaps, policies that address deviations on one side alone are ineffective or harmful, while policies that address both could substantially increase the achievement of disadvantaged students.

Economists often measure discrimination as disparities arising from the direct effects of group identity. We develop new tools to model and measure systemic discrimination, capturing how discrimination in other decisions indirectly contributes to disparities. A novel experimental design, the iterated audit, identifies systemic discrimination. We illustrate these new tools in two field experiments. The first experiment shows how racial discrimination can accumulate across multiple rounds of hiring through the interaction of two forces: greater discrimination against inexperienced workers, which affects the opportunity to obtain experience, and high subsequent returns to experience. The second experiment shows how gender-based differences in the language of recommendation letters can translate into systemic gender discrimination in STEM hiring. We discuss how our findings qualify previous results on direct discrimination and how our tools can be used to target policy interventions.

We estimate the returns to IRS audits of taxpayers across the income distribution. We find an additional $1 spent auditing taxpayers above the 90th income percentile yields more than $12 in revenue, while audits of below-median income taxpayers yield $5. We construct our estimates by drawing from comprehensive internal accounting information and audit-level enforcement logs. We begin by estimating the average initial return to all audits of U.S. taxpayers filing in tax years 2010–2014. On average, $1 in audit spending initially raises $2.17 in revenue. Audits of high-income taxpayers are more costly, but the additional revenue raised more than offsets the costs. Audits of the 99–99.9th percentile have a 3.2:1 initial return; audits of the top 0.1% return 6.3:1. We then exploit the 40% audit reduction between tax years 2010 and 2014 to examine the returns to marginal audits. We find they exceed the returns to average audits. Revenues remain relatively unchanged, but marginal costs fall below average costs due to economies of scale. Next, we use randomly selected audits to examine the effect of an initial audit on future revenue. This individual deterrence effect produces at least three times more revenue than the initial audit. Deterrence effects are relatively consistent across the income distribution. This results in the 12:1 return above the 90th percentile. We conclude by estimating the welfare consequences of audits using the MVPF framework and comparing audits to other revenue-raising policies. We find that audits raise revenue at lower welfare cost.

We develop a framework to measure the welfare impact of macroeconomic shocks throughout the distribution. The first-order impact of a shock is summarized by the induced movements in agents’ feasible sets: their budget constraint and borrowing constraints. We combine estimated impulse response functions with micro-data on household consumption bundles, asset holdings, and labor income for different U.S. households. We find that inflationary oil shocks are regressive, but monetary expansions are progressive, and there is substantial heterogeneity throughout the life cycle. In all cases, the dominant channel is the effect of the shock on the cost of accumulating assets, not movements in goods prices or labor income.

Observed international investment positions and cross-country heterogeneity in rates of return to capital are hard to reconcile with frictionless capital markets. This article develops a theory of international capital allocation: a multi-country dynamic spatial general equilibrium model in which the entire network of cross-border investment is endogenously determined. Our model features cross-country heterogeneity in fundamental risk, a demand system for international assets, and frictions that cause segmentation in international capital markets. We measure frictions affecting international investment and apply our model to data from nearly 100 countries, using a new dataset of international capital taxes and cultural, linguistic, and geographic distances between countries (geopoliticaldistance.org). Our model performs well in reproducing the composition of international portfolios, the cross section of home bias and rates of return to capital, and other key features of international capital markets. Finally, we carry out counterfactual exercises: we show that barriers to international investment reduce world output by 7% and raise the cross-country dispersion of capital per employee, contributing in a meaningful way to global inequality.

A researcher can use a tightly parameterized structural model to obtain internally consistent estimates of a wide range of economically interesting targets. We ask how reliable these estimates are when the researcher’s model may be misspecified. We focus on the case of multivariate, potentially nonlinear models where the causal variable of interest is endogenous. Reliable estimates require that the researcher’s model is flexible enough to describe the effects of the endogenous variable approximately correctly. Reliable estimates do not require that the researcher has correctly specified the role of the exogenous controls in the model. However, if the role of the controls is misspecified, reliable estimates require a property we call strong exclusion. Strong exclusion depends on having sufficiently many instruments that are unrelated to the controls. We discuss how practitioners can achieve strong exclusion, and illustrate our findings with an application to a differentiated goods model of demand for beer.

This article investigates how competition to publish first and thereby establish priority affects the quality of scientific research. We begin by developing a model where scientists decide whether and how long to work on a given project. When deciding how long they should let their projects mature, scientists trade off the marginal benefit of higher-quality research against the marginal risk of being preempted. Projects with the highest scientific potential are the most competitive because they induce the most entry. Therefore, the model predicts these projects are also the most rushed and lowest quality. We test the predictions of this model in the field of structural biology using data from the Protein Data Bank (PDB), a repository for structures of large macromolecules. An important feature of the PDB is that it assigns objective measures of scientific quality to each structure. As suggested by the model, we find that structures with higher ex ante potential generate more competition, are completed faster, and are lower quality. Consistent with the model, and with a causal interpretation of our empirical results, these relationships are mitigated when we focus on structures deposited by scientists who—by nature of their employment position—are less focused on publication and priority. We estimate that the costs associated with improving these low-quality structures are between $1.5 and $8.8 billion since the PDB’s founding in 1971.

Market-based environmental regulations are seldom used in low-income countries, where pollution is highest but state capacity is often low. We collaborated with the Gujarat Pollution Control Board (GPCB) to design and experimentally evaluate the world’s first particulate-matter emissions market, which covered industrial plants in a large Indian city. There are three main findings. First, the market functioned well. Treatment plants, randomly assigned to the emissions market, traded permits to become significant net sellers or buyers. After trading, treatment plants held enough permits to cover their emissions 99% of the time, compared with just 66% compliance with standards under the command-and-control status quo. Second, treatment plants reduced pollution emissions, relative to control plants, by 20%–30%. Third, the market reduced abatement costs by an estimated 11%, holding constant emissions. This cost-savings estimate is based on plant-specific marginal cost curves that we estimate from the universe of bids to buy and sell permits in the market. The combination of pollution reductions and low costs imply that the emissions market has mortality benefits that exceed its costs by at least 25 times.

Satoshi Nakamoto (2008) invented a new kind of economic system that does not need the support of government or rule of law. Trust and security instead arise from a combination of cryptography and economic incentives, all in a completely anonymous and decentralized system. This article shows that Nakamoto’s novel form of trust, while undeniably ingenious, is deeply economically limited. The core argument is three equations. A zero-profit condition on the quantity of honest blockchain “trust support” (work, stake, etc.) and an incentive-compatibility condition on the system’s security against majority attack (the Achilles heel of all forms of permissionless consensus) together imply an equilibrium constraint, which says that the “flow” cost of blockchain trust has to be large at all times relative to the benefits of attacking the system. This is extremely expensive relative to traditional forms of trust and scales linearly with the value of attack. In scenarios that represent Nakamoto trust becoming a more significant part of the global financial system, the cost of trust would exceed global GDP. Nakamoto trust would become more attractive if an attacker lost the stock value of their capital in addition to paying the flow cost of attack, but this requires either collapse of the system (hardly reassuring) or external support from rule of law. The key difference between Nakamoto trust and traditional trust grounded in rule of law and complementary sources, such as reputations, relationships, and collateral, is economies of scale: society or a firm pays a fixed cost to enjoy trust over a large quantity of economic activity at low or zero marginal cost.