THE ECONOMIC IMPACTS OF COVID-19: EVIDENCE FROM A NEW PUBLIC DATABASE BUILT USING PRIVATE SECTOR DATA.

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.

Social capital I: measurement and associations with economic mobility.

Social capital-the strength of an individual's social network and community-has been identified as a potential determinant of outcomes ranging from education to health1-8. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers9, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES-which we term economic connectedness-is among the strongest predictors of upward income mobility identified to date10,11. Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality12-14. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org .

Social capital II: determinants of economic connectedness.

Low levels of social interaction across class lines have generated widespread concern1-4 and are associated with worse outcomes, such as lower rates of upward income mobility4-7. Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper7. We show that about half of the social disconnection across socioeconomic lines-measured as the difference in the share of high-socioeconomic status (SES) friends between people with low and high SES-is explained by differences in exposure to people with high SES in groups such as schools and religious organizations. The other half is explained by friending bias-the tendency for people with low SES to befriend people with high SES at lower rates even conditional on exposure. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness). Using fluctuations in the share of students with high SES across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Thus, socioeconomic integration can increase economic connectedness in communities in which friending bias is low. By contrast, when friending bias is high, increasing cross-SES interactions among existing members may be necessary to increase economic connectedness. To support such efforts, we release privacy-protected statistics on economic connectedness, exposure and friending bias for each ZIP (postal) code, high school and college in the United States at https://www.socialcapital.org .

The fading American dream: Trends in absolute income mobility since 1940.

We estimated rates of "absolute income mobility"-the fraction of children who earn more than their parents-by combining data from U.S. Census and Current Population Survey cross sections with panel data from de-identified tax records. We found that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Increasing Gross Domestic Product (GDP) growth rates alone cannot restore absolute mobility to the rates experienced by children born in the 1940s. However, distributing current GDP growth more equally across income groups as in the 1940 birth cohort would reverse more than 70% of the decline in mobility. These results imply that reviving the "American dream" of high rates of absolute mobility would require economic growth that is shared more broadly across the income distribution.

The Association Between Income and Life Expectancy in the United States, 2001-2014.

IMPORTANCE: The relationship between income and life expectancy is well established but remains poorly understood. OBJECTIVES: To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation. DESIGN AND SETTING: Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy. EXPOSURE: Pretax household earnings as a measure of income. MAIN OUTCOMES AND MEASURES: Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas. RESULTS: The sample consisted of 1,408,287,218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61,175 per year). There were 4,114,380 deaths among men (mortality rate, 596.3 per 100,000) and 2,694,808 deaths among women (mortality rate, 375.1 per 100,000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% (P < .001 for the differences for both sexes). Third, life expectancy for low-income individuals varied substantially across local areas. In the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking (r = -0.69, P < .001), but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low-income individuals was positively correlated with the local area fraction of immigrants (r = 0.72, P < .001), fraction of college graduates (r = 0.42, P < .001), and government expenditures (r = 0.57, P < .001). CONCLUSIONS AND RELEVANCE: In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.

The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment.

The Moving to Opportunity (MTO) experiment offered randomly selected families housing vouchers to move from high-poverty housing projects to lower-poverty neighborhoods. We analyze MTO's impacts on children's long-term outcomes using tax data. We find that moving to a lower-poverty neighborhood when young (before age 13) increases college attendance and earnings and reduces single parenthood rates. Moving as an adolescent has slightly negative impacts, perhaps because of disruption effects. The decline in the gains from moving with the age when children move suggests that the duration of exposure to better environments during childhood is an important determinant of children's long-term outcomes.

Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: Evidence from Danish Tax Records.

We show that the effects of taxes on labor supply are shaped by interactions between adjustment costs for workers and hours constraints set by firms. We develop a model in which firms post job offers characterized by an hours requirement and workers pay search costs to find jobs. We present evidence supporting three predictions of this model by analyzing bunching at kinks using Danish tax records. First, larger kinks generate larger taxable income elasticities. Second, kinks that apply to a larger group of workers generate larger elasticities. Third, the distribution of job offers is tailored to match workers' aggregate tax preferences in equilibrium. Our results suggest that macro elasticities may be substantially larger than the estimates obtained using standard microeconometric methods.

How does your kindergarten classroom affect your earnings? Evidence from Project Star.

In Project STAR, 11,571 students in Tennessee and their teachers were randomly assigned to classrooms within their schools from kindergarten to third grade. This article evaluates the long-term impacts of STAR by linking the experimental data to administrative records. We first demonstrate that kindergarten test scores are highly correlated with outcomes such as earnings at age 27, college attendance, home ownership, and retirement savings. We then document four sets of experimental impacts. First, students in small classes are significantly more likely to attend college and exhibit improvements on other outcomes. Class size does not have a significant effect on earnings at age 27, but this effect is imprecisely estimated. Second, students who had a more experienced teacher in kindergarten have higher earnings. Third, an analysis of variance reveals significant classroom effects on earnings. Students who were randomly assigned to higher quality classrooms in grades K­3­as measured by classmates' end-of-class test scores­have higher earnings, college attendance rates, and other outcomes. Finally, the effects of class quality fade out on test scores in later grades, but gains in noncognitive measures persist.

Effects of the Src inhibitor saracatinib (AZD0530) on renal function in healthy subjects.

BACKGROUND: Saracatinib (AZD0530), a potent Src inhibitor, is a subject of current evaluation as an anticancer therapy. Increased plasma creatinine levels have previously been observed after saracatinib administration in healthy subjects and this study was undertaken to characterize the underlying mechanism of this increase. SUBJECTS AND METHODS: 56 healthy male subjects were assigned to either single- (n=28; randomised to placebo or saracatinib 500 mg) or multiple-dose oral treatment (n=28; randomised to placebo or saracatinib 125 mg for 14 days). Renal function variables assessed included inulin clearance and tubular secretion of creatinine. RESULTS: Saracatinib led to a reduction in mean creatinine fractional excretion ratio, which was due to a reduction in tubular secretion of creatinine. Increased plasma creatinine was not associated with decreased glomerular filtration rate or increased creatinine production. CONCLUSION: The observed increase in plasma creatinine after saracatinib administration was due to reduced tubular secretion of creatinine, but was not considered to be clinically relevant in the context of this study.

Recommendations to qualify biomarker candidates of drug-induced liver injury.

Certain compounds that induce liver injury clinically are not readily identified from earlier preclinical studies. Novel biomarkers are being sought to be applied across the pharmaceutical pipeline to fill this knowledge gap and to add increased specificity for detecting drug-induced liver injury in combination with aminotransferases (alanine and aspartate aminotransferase)--the current reference-standard biomarkers used in the clinic. The gaps in the qualification process for novel biomarkers of regulatory decision-making are assessed and compared with aminotransferase activities to guide the determination of safe compound margins for drug delivery to humans where monitoring for potential liver injury is a cause for concern. Histopathologic observations from preclinical studies are considered the principal reference standard to benchmark and assess subtle aminotransferase elevations. This approach correlates quite well for many developmental compounds, yet cases of discordance create dilemmas regarding which standard(s) indicates true injury. Concordance amongst a broader set of biomarker injury signals in a qualification paradigm will increase confidence, leading to accepted and integrated translational biomarker signals during safety assessment processes across the pharmaceutical industry, with academia, in government and in contractor laboratories.

Enhancing the utility of alanine aminotransferase as a reference standard biomarker for drug-induced liver injury.

Drug-induced liver injury (DILI) is the most frequent cause of discontinuation of new chemical entities during development. DILI can either be intrinsic/predictable or an idiosyncratic type. These two forms of DILI are contrasted in their manifestation and diagnosis. Even with regulatory guidance (FDA, 2009), there is still a gap in our ability to identify predictable DILI, both specifically and sensitively. Alanine aminotransferase (ALT) is the principal reference standard biomarker to diagnose DILI, yet its current application in preclinical to clinical translation for decision-making purposes has imperfections: (1) analytical ALT assay uniformity across industry would be aided by common analytical processes; (2) assessment of ALT toxicological performance in a large preclinical analysis would help to establish a true threshold of elevation for predictable DILI and improve translational use across various stages of pharmaceutical development and finally, (3) clinical evaluation of ALT elevations prospectively and retrospectively is recommended to define and manage variations in clinical study subjects including rising body mass index (BMI) range and ALT upper limit of normal (ULN) in the broader population over time. The emergence of new hepatotoxicity biomarkers necessitates a parallel and equivalent assessment to the aminotransferases in a regulatory qualification model.