The economic crisis associated with the emergence of the novel corona virus is unlike standard recessions. Demand for workers in high contact and inflexible service occupations has declined, while parental supply of labor has been reduced by lack of access to reliable child care and in-person schooling options. This has led to a substantial and persistent drop in employment and labor force participation for women, who are typically less affected by recessions than men. We examine real time data on employment, unemployment, labor force participation and gross job flows to document the gendered impact of the pandemic. We also discuss the potential long-term implications of this crisis, including the role of automation in depressing the recovery of employment for the worst hit service occupations.
We fully solve an assignment problem with heterogeneous firms and multiple heterogeneous workers whose skills are imperfect substitutes, that is, when production is submodular.We show that sorting is neither positive nor negative and is characterized sufficiently by two regions.In the first region, mediocre firms sort with mediocre workers and coworkers such that output losses are equal across all these pairings (complete mixing).In the second region, high skill workers sort with a low skill coworker and a high productivity firm, while high productivity firms employ a low skill worker and a high skill coworker (pairwise countermonotonicity).The equilibrium assignment is also necessarily characterized by product countermonotonicity, meaning that sorting is negative for each dimension of heterogeneity with the product of heterogeneity in the other dimensions.The equilibrium assignment as well as wages and firm values are completely characterized in closed form.We illustrate our theory with an application to show that our model is consistent with the observed dispersion of earnings within and across U.S. firms.Our counterfactual analysis gives evidence that the change in the firm project distribution between 1981 and 2013 has a larger effect on the observed change in earnings dispersion than the change in the worker skill distribution.
Schools across the United States and the world have been closed in an effort to mitigate the spread of COVID-19. However, the effect of school closure on COVID-19 transmission remains unclear. We estimate the causal effect of changes in the number of weekly visits to schools on COVID-19 transmission using a triple difference approach. In particular, we measure the effect of changes in county-level visits to schools on changes in COVID-19 diagnoses for households with school-age children relative to changes in COVID-19 diagnoses for households without school-age children. We use a data set from the first 46 weeks of 2020 with 130 million household-week level observations that includes COVID-19 diagnoses merged to school visit tracking data from millions of mobile phones. We find that increases in county-level in-person visits to schools lead to an increase in COVID-19 diagnoses among households with children relative to households without school-age children. However, the effects are small in magnitude. A move from the 25th to the 75th percentile of county-level school visits translates to a 0.3 per 10,000 household increase in COVID-19 diagnoses. This change translates to a 3.2 percent relative increase. We find larger differences in low-income counties, in counties with higher COVID-19 prevalence, and at later stages of the COVID-19 pandemic.
Two central insights from the Schumpeterian approach to innovation and growth are that the pace of innovation is endogenously determined by the expectation of future profits and that growth is inherently a process of creative destruction. As international trade is a key determinant of firm profitability and survival, it is natural to expect it to play a key role in shaping both incentives to innovate and the rate of creative destruction. In this paper, we review the theoretical and empirical literature on trade and innovation. We highlight four key mechanisms through which international trade affects endogenous innovation and growth: (i) market size; (ii) competition; (iii) comparative advantage; (iv) knowledge spillovers. Each of these mechanisms offers a potential source of dynamic welfare gains in addition to the static welfare gains from trade from conventional trade theory. Recent research has suggested that these dynamic welfare gains from trade can be substantial relative to their static counterparts. Discriminating between alternative mechanisms for these dynamic welfare gains and strengthening the evidence on their quantitative magnitude remain exciting areas of ongoing research.
Early tests of cross-country convergence found evidence only for conditional convergence. In contrast, with more recent data, Kremer, Willis, and You (2021) find evidence that since the mid-1980s there has been a trend towards unconditional convergence culminating in absolute convergence since 2000. Additionally, they find suggestive evidence that one of the major drivers of this trend is an underlying convergence towards development-favored policies. We discuss the implications of this result through the lens of individual welfare and poverty, concluding that the news is not as welcome as it may seem for the world’s poor. We point out that absolute convergence has happened contemporaneously with rising within-country inequality, resulting in more of the world’s poor living in middle-income countries. Next, we argue that domestic redistribution is essential to spread the benefits from industrialization, since the labor share of manufacturing isn’t reaching the heights it did in industrialized countries. Finally, we argue that the democratic institutions that can facilitate this redistribution themselves face headwinds. Democratic backsliding, the Covid-19 pandemic, and a bleak climate outlook all present obstacles to transforming economic growth into economic justice for the poor.
Do antitrust laws influence corporate valuations? We evaluate the relationship between firm value and laws limiting firms from engaging in anticompetitive agreements, abusing dominant positions, and conducting M&As that restrict competition. Using firm-level data from 99 countries over the 1990-2010 period, we discover that valuations rise after countries strengthen competition laws. The effects are larger among firms with more severe pre-existing agency problems: firms in countries with weaker investor protection laws, with weaker firm-specific governance provisions, and with greater opacity. The results suggest that antitrust laws that intensify competition exert a positive influence on valuations by reducing agency problems.
We ask whether epidemic exposure leads to a shift in financial technology usage within and across countries and if so who participates in this shift. We exploit a datasetcombining Gallup World Polls and Global Findex surveys for some 250,000 individuals in 140 countries, merging them with information on the incidence of epidemics and local 3G internet infrastructure. Epidemic exposure is associated with an increase in remote-access (online/mobile) banking and substitution from bank branch-based to ATM-based activity. Using a machine-learning algorithm, we show that heterogeneity in this response centers on the age, income and employment of respondents. Young, high-income earners in full-time employment have the greatest propensity to shift to online/mobile transactions in response to epidemics. These effects are larger for individuals in subnational regions with better ex ante 3G signal coverage, highlighting the role of the digital divide in adaption to new technologies necessitated by adverse external shocks.
With the onset of the COVID-19 crisis in March 2020, small business lending through fintech lenders collapsed. We explore the reasons for the market shutdown using detailed data about loan applications, offers, and take-up from a major small business fintech credit platform. We document that while the number of loan applications increased sharply early in March 2020, the supply of credit collapsed as online lenders dropped from the platform and the likelihood of applicants receiving loan offers fell precipitously. Our analysis shows that the drying up of the loan supply is most consistent with fintech lenders becoming financially constrained and losing their ability to fund new loans.
This paper provides an introduction to buyouts and the academic literature about them. Buyouts are initiated by “buyout funds”, which are limited partnerships raised from mostly institutional investors. The funds earn returns for their investors by improving the operations of the firms they acquire and exiting them for a profit. Buyout funds have grown substantially and currently raise more than $400 billion annually in capital commitments. We first discuss the institutional environment that developed to foster such buyouts and to provide incentives for general partners and firm managers to earn returns for the fund’s investors. We then describe various strategies that funds use to increase the values of their portfolio companies. The paper provides up to date statistics on all aspects of the buyout industry. Finally, we present a summary of the academic literature on buyouts. This literature has paid particular attention to the extent to which buyouts earn risk-adjusted abnormal returns for their investors, as well as the sources of those returns.
The COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards1. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs2. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need3? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth4–6. Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our approach uses traditional survey-based measures of consumption and wealth to train machine learning algorithms that recognize patterns of poverty in non-traditional data; the trained algorithms are then used to prioritize aid to the poorest regions and mobile subscribers. We evaluate this approach by studying Novissi, Togo’s flagship emergency cash transfer program, which used these algorithms to determine eligibility for a rural assistance program that disbursed millions of dollars in COVID-19 relief aid. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo at the time, the machine learning approach reduces errors of exclusion by 4-21%. Relative to methods that require a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.
The increased popularity of college Grade Forgiveness policies, which allow students to retake classes and substitute the new grades for the previous grades in their GPA calculations, is controversial yet understudied. Our paper is the first to ask whether such policies benefit students and how. To answer these questions, we use student-level admissions and transcript data from a four-year public institution in the U.S. that underwent two major changes in its GPA policy. We find that Grade Forgiveness significantly incentivizes students, especially students with the strongest academic preparation, to take STEM courses and challenging courses and to enroll in more credits. The increased variations in within-term grades suggest that students may change their effort allocations between courses taken in the same semester and spend more effort on courses that promise a higher grade in return. We also find that repeaters whose first attempted grades are forgiven are more likely to persist in the failed subject and obtain better grades subsequently. Finally, we see an increase in graduation in STEM majors for students who were intensively exposed to this policy.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
We present the first estimates of long-run trends in intergenerational relative mobility for samples that are representative of the full U.S.-born population. Harmonizing all surveys that ask about father's occupation and own family income, we develop a mobility measure that allows for the inclusion of non-whites and women for the 1910s–1970s birth cohorts. We show a robust increase in mobility between the 1910s and 1940s cohorts, about half of which is driven by absolute convergence in racial income gaps. We also find that excluding Black Americans, particularly Black women, considerably overstates mobility throughout the 20th century.
The COVID-19 pandemic led to stark reductions in economic activity in India. We employ CMIE's Consumer Pyramids Household Survey to examine the timing, distribution, and mechanism of the impacts from this shock on income and consumption through December 2020. First, we estimate large and heterogeneous drops in income, with ambiguous effects on inequality. While incomes of salaried workers fell 35%; incomes of daily laborers fell 75%. At the same time, we observe that income fell more for individuals from households in the highest income quartile. Second, we document an increase in effort to buffer income shocks by switching occupations. We employ a Roy Model to estimate the gains from occupation churn and find, surprisingly, that reservation wages fell, implying that the risk of COVID did not reduce the value of employment. Third, we find that consumption fell less than income, suggesting households were able to smooth the idiosyncratic components of the COVID shock as well as they did before COVID. Finally, consumption of food and fuel fell less than consumption of durables such as clothing and appliances. Following Costa (2001) and Hamilton (2001), we estimate Engel curves and find that changes in consumption reflect large price shocks (rather than a retreat to subsistence) in sectors other than food and fuel/power. In the food sector, it appear that lockdown successfully distinguished essential and non-essential services, at least to the extent that it did not increase the relative price of food. There is some suggestive evidence that the price shocks outside the food sector were larger in places with greater COVID-19 cases, even during the lockdown.
Over ten million Native Americans live in the USA today, but their experiences are often obscured in empirical research. While the rise in despair, or chronic distress, among White Americans is much discussed, what is not discussed is what has happened for the first Americans. We demonstrate that levels of consistently poor mental health were higher among Native peoples than among White or Black Americans in every year between 1993 and 2020, and these levels have been rising. We find this pattern among those over the age of 30 but less so for the young. Chronic distress seems to be lowest among Native peoples living in the seven states with the largest proportion of Native Americans as a fraction of their population: Alaska, Arizona, Montana, New Mexico, North Dakota, Oklahoma and South Dakota. In our judgment, these facts are important and not widely known. This stands in stark contrast to the enormous scholarly and media interest in declining physiological well-being among White Americans.
This paper proposes a network model of the economy in which conglomerate firms transmit idiosyncratic shocks from one industry to another. The strength of inter-industry connections is determined by the conglomerate's share of total industry sales and by the industry's share of the conglomerate's total sales. The empirical results show that industry growth rates comove more strongly within industry pairs that are more closely connected in the conglomerate network. These results hold after controlling for industry-pair and year fixed effects, input-output connections, reverse causality, and in tests that exploit exogenous cross-sectional industry shocks from import tariff changes. Finally, our model also provides a new cross-industry extension for the widely-used Herfindahl index of concentration.
Digital platforms are not only match-making intermediaries but also establish internal rules that govern all users in their ecosystems. To better understand the governing role of platforms, we study two Airbnb pro-guest rules that pertain to guest and host cancellations, using data on Airbnb and VRBO listings in 10 US cities. We demonstrate that such pro-guest rules can drive demand and supply to and from the platform, as a function of the local platform competition between Airbnb and VRBO. Our results suggest that platform competition sometimes dampens a platform wide pro-guest rule and sometimes reinforces it, often with heterogeneous effects on different hosts. This implies that platform competition does not necessarily mitigate a platform's incentive to treat the two sides asymmetrically, and any public policy in platform competition must consider its implication on all sides.
Future electricity systems with tight constraints on carbon emissions will rely much more on wind and solar generation, with zero marginal cost, than today. We use capacity expansion modelling of Texas in 2050 to illustrate wholesale price distributions in future energy-only, carbon-constrained grids without price caps under a range of technology/system assumptions. Tightening carbon emissions constraints dramatically increases the frequency of very low prices. The frequency of high prices also increases, and all resources earn the bulk of their energy market revenues in relatively few hours. The presence of demand response, long-duration energy storage, dispatchable low-carbon generation, or a robust market for hydrogen for non-electricity use (and for energy storage) weakens but does not undo these results. Financial instruments to hedge price volatility will consequently be more costly and it is likely that we will need to redesign capacity remuneration mechanisms to provide adequate incentives for optimal investment in VRE generation and, particularly, storage. In order to encourage economy-wide electrification, the marginal retail price of electricity should be low whenever the wholesale price is low. With automated control of demand via demand response contracts, the risks of price volatility faced by retail customers can be mitigated without sacrificing efficiency. To encourage economy-wide electrification, the marginal retail price of electricity should be low when the wholesale spot price is low. We discuss ways of reducing consumers’ risk in this world while providing adequate investment incentives.
Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and predictable by forecast revisions.Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have precise knowledge of the data in question.We propose that these anomalies arise because professional forecasters don't know the model that generates the data.We show that Bayesian agents learning about hard-to-learn features of the data generating process (low frequency behavior) can generate all the prominent aggregate anomalies emphasized in the literature.We show this for two applications: professional forecasts of nominal interest rates for the sample period 1980-2019 and CBO forecasts of GDP growth for the sample period 1976-2019.Our learning model for interest rates also provides an explanation for deviations from the expectations hypothesis of the term structure that does not rely on time-variation in risk premia.
The transformation of the Child Tax Credit (CTC) into a more generous, inclusive monthly payment marks a historic (temporary) shift in U.S. treatment of low-income families.To investigate the initial impact of these payments, we apply a series of difference-in-difference estimates using Census Household Pulse Survey microdata collected from April 14 through August 16, 2021.Our findings offer three primary conclusions regarding the initial effects of the monthly CTC.First, payments strongly reduced food insufficiency: the initial payments led to a 7.5 percentage point (25 percent) decline in food insufficiency among low-income households with children.Second, the effects on food insufficiency are concentrated among families with 2019 pre-tax incomes below $35,000, and the CTC strongly reduces food insufficiency among low-income Black, Latino, and White families alike.Third, increasing the CTC coverage rate would be required in order for material hardship to be reduced further.Self-reports suggest the lowest-income households were less likely than higher-income families to receive the first CTC payments.As more children receive the benefit in future months, material hardship may decline further.Even with imperfect coverage, however, our findings suggest that the first CTC payments were largely effective at reducing food insufficiency among low-income families with children.
This paper documents several new facts about the relationship between discrimination and political exclusion and the motivation to fight in wartime. The Pearl Harbor attack triggered a sharp increase in volunteer enlistment rates of American men, the magnitude of the increase was smaller for Black men than for white men and the Black-white gap was larger in counties with higher levels of racial discrimination. Discrimination reduced the quantity and the quality of Black volunteers. The discouraging effects of discrimination were more pronounced in places that were geographically distant from Pearl Harbor and in states that had joined the Union relatively recently. For Japanese-American men, enlistment rates were higher where the Japanese-American community was not interred than where it was interred. These and other results provide empirical support for the theory that discrimination and political exclusion reduce support for the government when it is under threat.
