Knowledge that Transforms

A new form of secured lending using “digital collateral” has recently emerged, most prominently in low- and middle-income countries. Digital collateral relies on lockout technology, which allows the lender to temporarily disable the flow value of the collateral to the borrower without physically repossessing it. We explore this new form of credit in a model and a field experiment using school-fee loans digitally secured with a solar home system. Securing a loan with digital collateral drastically reduced default rates (by 19 percentage points) and increased the lender’s rate of return (by 49 percentage points). Using a variant of the Karlan and Zinman (2009) methodology, we decompose the total effect on repayment and find that roughly two-thirds is attributable to moral hazard, and one-third to adverse selection. In addition, access to digitally secured school-fee loans significantly increased school enrollment and school-related expenditures without detrimental effects on households’ balance sheets.

We build a publicly available database that tracks economic activity in the United States at a granular level in real time using anonymized data from private companies. We report weekly statistics on consumer spending, business revenues, job postings, and employment rates disaggregated by county, sector, and income group. Using the publicly available data, we show how the COVID-19 pandemic affected the economy by analyzing heterogeneity in its effects across subgroups. High-income individuals reduced spending sharply in March 2020, particularly in sectors that require in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in affluent, dense areas. Those businesses laid off many of their employees, leading to widespread job losses, especially among low-wage workers in such areas. High-wage workers experienced a V-shaped recession that lasted a few weeks, whereas low-wage workers experienced much larger, more persistent job losses. Even though consumer spending and job postings had recovered fully by December 2021, employment rates in low-wage jobs remained depressed in areas that were initially hard hit, indicating that the temporary fall in labor demand led to a persistent reduction in labor supply. Building on this diagnostic analysis, we evaluate the effects of fiscal stimulus policies designed to stem the downward spiral in economic activity. Cash stimulus payments led to sharp increases in spending early in the pandemic, but much smaller responses later in the pandemic, especially for high-income households. Real-time estimates of marginal propensities to consume provided better forecasts of the impacts of subsequent rounds of stimulus payments than historical estimates. Overall, our findings suggest that fiscal policies can stem secondary declines in consumer spending and job losses, but cannot restore full employment when the initial shock to consumer spending arises from health concerns. More broadly, our analysis demonstrates how public statistics constructed from private sector data can support many research and real-time policy analyses, providing a new tool for empirical macroeconomics.

Studies of human memory indicate that features of an event evoke memories of prior associated contextual states, which in turn become associated with the current event’s features. This retrieved-context mechanism allows the remote past to influence the present, even as agents gradually update their beliefs about their environment. We apply a version of retrieved-context theory, drawn from the literature on human memory, to explain three types of evidence in the financial economics literature: the role of early life experience in shaping investment choices, occurrence of financial crises, and the effect of fear on asset allocation. These applications suggest a recasting of neoclassical rational expectations in terms of beliefs as governed by principles of human memory.

We propose and implement a new approach that allows us to estimate income-specific changes in household welfare in contexts where well-measured prices are not available for important subsets of consumption. Using rich but widely available expenditure survey microdata, we show that we can recover income-specific equivalent and compensating variations from horizontal shifts in what we call “relative Engel curves”—as long as preferences fall within the broad quasi-separable class (Gorman 1970, 1976). Our approach is flexible enough to allow for nonparametric estimation at each point of the income distribution. We apply the methodology to estimate inflation and welfare changes in rural India between 1987 and 2000. Our estimates reveal that lower rates of inflation for the rich erased the real income convergence found in the existing literature that uses the subset of consumption with well-measured prices to calculate inflation.

In the 1960s, two landmark statutes-the Equal Pay and Civil Rights Acts-targeted the long-standing practice of employment discrimination against U.S. women. For the next 15 years, the gender gap in median earnings among full-time, full-year workers changed little, leading many scholars to conclude that the legislation was ineffectual. This article revisits this conclusion using two research designs, which leverage (i) cross-state variation in preexisting state equal pay laws and (ii) variation in the 1960 gender gap across occupation-industry-state-group cells to capture differences in the legislation's incidence. Both designs suggest that federal antidiscrimination legislation led to striking gains in women's relative wages, which were concentrated among below-median wage earners. These wage gains offset preexisting labor market forces, which worked to depress women's relative pay growth, resulting in the apparent stability of the gender gap at the median and mean in the 1960s and 1970s. The data show little evidence of short-term changes in women's employment but suggest that firms reduced their hiring and promotion of women in the medium to long term. The historical record points to the key role of the Equal Pay Act in driving these changes.

The money metric utility function is an essential tool for calculating welfare-relevant growth and inflation. We show how to recover it from repeated cross-sectional data without making parametric assumptions about preferences. We do this by solving the following recursive problem. Given compensated demand, we construct money metric utility by integration. Given money metric utility, we construct compensated demand by matching households over time whose money metric utility value is the same. We illustrate our method using household consumption survey data from the United Kingdom from 1974 to 2017 and find that real consumption calculated using official aggregate inflation statistics overstates money metric utility in 1974 pounds for the poorest households by around 0.5% a year and understates it by around a third of a percentage point per year for the richest households. We extend our method to allow for missing or mismeasured prices, assuming preferences are separable between goods with well-measured prices and the rest. We discuss how our results change if the prices of some service sectors are mismeasured.

This article examines the consequences and causes of low enrollment of Black patients in clinical trials. We develop a simple model of similarity-based extrapolation that predicts that evidence is more relevant for decision-making by physicians and patients when it is more representative of the group being treated. This generates the key result that the perceived benefit of a medicine for a group depends not only on the average benefit from a trial but also on the share of patients from that group who were enrolled in the trial. In survey experiments, we find that physicians who care for Black patients are more willing to prescribe drugs tested in representative samples, an effect substantial enough to close observed gaps in the prescribing rates of new medicines. Black patients update more on drug efficacy when the sample that the drug is tested on is more representative, reducing Black-white patient gaps in beliefs about whether the drug will work as described. Despite these benefits of representative data, our framework and evidence suggest that those who have benefited more from past medical breakthroughs are less costly to enroll in the present, leading to persistence in who is represented in the evidence base.

Policy makers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner’s curse, since we are more likely to select a given option precisely when we overestimate its effectiveness. This winner’s curse biases our estimates for selected options upward and can invalidate conventional confidence intervals. This article develops estimators and confidence intervals that eliminate this winner’s curse. We illustrate our results by studying selection of job-training programs based on estimated earnings effects and selection of neighborhoods based on estimated economic opportunity. We find that our winner’s curse corrections can make an economically significant difference to conclusions but still allow informative inference.

Frictions that impede the mobility of workers across occupations and space are a prominent feature of developing countries. We disentangle the role of insecure property rights from other labor-mobility frictions for the reallocation of labor from agriculture to nonagriculture and from rural to urban areas. We combine rich household and individual-level panel data from China and an equilibrium quantitative framework featuring sorting of workers across locations and occupations. We explicitly model the farming household and the endogenous decisions of who operates the family farm and who potentially migrates, capturing an additional channel of selection in the household. We find that land insecurity has substantial negative effects on agricultural productivity and structural change, raising the share of rural households operating farms by over 40 percentage points and depressing agricultural productivity by more than 20%. Comparatively, these quantitative effects are as large as those from all residual labor-mobility frictions. We measure a sharp reduction in overall labor-mobility barriers over 2004–2018 in the Chinese economy, all accounted for by improved land security, consistent with reforms covering rural land in China during the period. JEL Codes: O11, O14, O4, E02, Q1.

Domestic abuse encompasses a range of damaging behaviors beyond physical violence, including economic and emotional abuse. We analyze the impact of cohabiting with an abusive partner on victims’ economic outcomes. In so doing, we highlight the systematic role of economic suppression in such relationships. Using Finnish administrative data and a matched-control event-study design, along with a within-individual comparison of outcomes across relationships, we document three new facts. First, women who begin relationships with (eventually) physically abusive men suffer large and significant earnings and employment falls immediately upon cohabiting with the abusive partner. Second, the decline in economic outcomes is non-monotonic in women’s pre-cohabitation outside options. Third, men who are violent against women in any capacity impose economic costs on all their female partners, even those who do not report physical violence. To rationalize these findings, we develop a new dynamic model of abusive relationships where women do not perfectly observe their partner’s type, and abusive men have an incentive to use economic suppression to sabotage women’s outside options and their ability to later exit the relationship.