We build a publicly available platform that tracks economic activity at a granular level in real time using anonymized data from private companies. We report daily statistics on consumer spending, business revenues, employment rates, and other key indicators disaggregated by county, industry, and income group. Using these data, we study the mechanisms through which COVID-19 affected the economy by analyzing heterogeneity in its impacts across geographic areas and income groups. We first show that high-income individuals reduced spending sharply in mid-March 2020, particularly in areas with high rates of COVID-19 infection and in sectors that require physical interaction. This reduction in spending greatly reduced the revenues of businesses that cater to high-income households in person, notably small businesses in affluent ZIP codes. These businesses laid off most of their low-income employees, leading to a surge in unemployment claims in affluent areas. Building on this diagnostic analysis, we use event study designs to estimate the causal effects of policies aimed at mitigating the adverse impacts of COVID. State-ordered reopenings of economies have little impact on local employment. Stimulus payments to low-income households increased consumer spending sharply, but had modest impacts on employment in the short run, perhaps because very little of the increased spending flowed to businesses most affected by the COVID-19 shock. Paycheck Protection Program loans have also had little impact on employment at small businesses. These results suggest that traditional macroeconomic tools - stimulating aggregate demand or providing liquidity to businesses - may have diminished capacity to restore employment when consumer spending is constrained by health concerns. During a pandemic, it may be more fruitful to mitigate economic hardship through social insurance. More broadly, this analysis illustrates how real-time economic tracking using private sector data can help rapidly identify the origins of economic crises and facilitate ongoing evaluation of policy impacts.

We build a publicly available database that tracks economic activity in the U.S. 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 impacts 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 lower in areas that were initially hard hit, indicating that the job losses due to the demand shock led to a persistent reduction in labor supply. Building on this diagnostic analysis, we evaluate the impacts 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.

This thesis consists of three chapters on the economics of health and social insurance. In the first chapter, I examine the distribution of income risk that adults face from severe illness and the social insurance provided by taxes and transfers using an event study research design with linked Canadian hospital and tax records. I find that adults with lower incomes face larger pre-tax earnings risk from hospitalization events, primarily due to extensive margin exits from employment. Canada’s tax and transfer system insures 44% of post-hospitalization income losses in the bottom income quintile and 12% of losses in the top income quintile. But less than two thirds of this insurance comes from replacing lost earnings with increased transfers. In the bottom income quintile, 30% of insurance is due to a stable stream of transfers; in the top income quintile, 30% of insurance is due to progressive taxation. Using a calibrated model, I find that the marginal value of additional insurance against hospitalization risk is approximately flat across the income distribution. In the second chapter, I show that employer-provided short-term disability insurance (STDI) increases long-term disability insurance (LTDI) take-up and imposes a negative fiscal externality on the government budget. Using variation in private STDI coverage caused by Canadian firms ending their plans, I find that private STDI raises two-year flows onto LTDI by 0.07 percentage points (33%). Extrapolating to Canada’s entire population, private STDI generated 18,300 LTDI recipients and CA$230 million dollars (5%) of public LTDI spending in 2015. In the third chapter, Raj Chetty, Sarah Abraham, Shelby Lin, Benjamin Scuderi, Nicholas Turner, Augustin Begeron, David Cutler and I examine the relationship between income and life expectancy in the United States from 2001 to 2014. Using 1.4 billion linked earnings and mortality records, we document the levels of life expectancy and changes in life expectancy over time by income group, at a national level and within local areas. We also examine the factors correlated with differences in life expectancy across local areas. JEL Classification: I38, H53, I14