Female workers earn $0.89 for each male-worker dollar even in a unionized workplace where tasks, wages, and promotion schedules are identical for men and women by design. We use administrative time card data on bus and train operators to show that the earnings gap can be explained by female operators taking, on average, 1.5 fewer hours of overtime and 1.3 more hours of unpaid time-off per week than male operators. Female operators, especially those who have dependents, pursue schedule conventionality, predictability, and controllability more than male operators. Analyzing two policy changes, we demonstrate that while reducing schedule controllability can reduce the earnings gap, it can also make workers—particularly female workers—worse off.
We study the mental health of graduate students at Economics PhD programs in the U.S. Using clinically validated surveys, we find that 18% of graduate students experience moderate or severe symptoms of depression and anxiety — more than three times the population average — and 11% report suicidal ideation in a two-week period. The average PhD student reports greater feelings of loneliness than does the average retired American. Only 26% of Economics students report feeling that their work is useful always or most of the time, compared with 70% of Economics faculty and 63% of the working age population. Depression and symptoms of anxiety increase with time in the program: 25% of students in years 5+ of their programs experience moderate or severe symptoms of depression or anxiety compared with 14.5% of first-year students. Many students with significant symptoms of mental distress are not in treatment. We provide recommendations for students and faculty on ways to improve student work conditions, productivity, and mental health.
We use administrative Swedish data to show that, conditional on parent income, immigrant children have similar incomes and higher educational attainment in adulthood than native-born Swedes. This result, however, masks the fact that immigrant children born into poor families are more likely than similar natives to both reach the top of the income distribution and to stay at the bottom. Immigrant children from high-income families are also more likely than natives to regress to the economic bottom. Notably, however, children from predominantly-refugee sending countries like Bosnia, Syria, and Iran have higher intergenerational mobility than the average immigrant child in Sweden.
The U.S. government spends about 1% of GDP (165B USD) each year on highway and bridge investment, often employing scaling auctions to procure construction work from private firms. Bidders in a scaling auction submit unit price bids for each piece of material required to complete a project. The winner is determined by the lowest total cost given government estimates of the amount of each material needed, but paid based on the amount used. This creates incentives to skew bids (placing high unit bids on items bidders expect to exceed the government's quantity expectations and low bids on others), and raises concerns of rent-extraction among policymakers. If bidders are risk averse, however, scaling auctions provide a distinctive way to generate surplus: they enable bidders to limit their risk exposure by placing low unit bids on items with greater uncertainty. To assess this effect empirically, we develop a structural model of scaling auctions with risk averse bidders. Using data on bridge maintenance projects undertaken by the Massachusetts Department of Transportation (MassDOT), we demonstrate reduced form evidence that bidding behavior is consistent with optimal skewing under risk aversion. We then estimate bidders' risk aversion, the risk in each auction, and the distribution of bidders’ private costs. Finally, we simulate equilibrium item-level bids under counterfactual settings to estimate the fraction of MassDOT spending that is due to risk and evaluate alternative mechanisms under consideration by MassDOT. We find that scaling auctions provide substantial savings to MassDOT relative to lump sum auctions and suggest several policies that might improve on the status quo.
Using a network approach to characterize the evolution of the federal funds market during the Great Recession and financial crisis of 2007-2008, we document that many small federal funds lenders began reducing their lending to larger institutions in the core of the network starting in mid-2007. But an abrupt change occurred in the fall of 2008, when small lenders left the federal funds market en masse and those that remained lent smaller amounts, less frequently. We then test whether changes in lending patterns within key components of the network were associated with increases in counterparty and liquidity risk of banks that make up the core of the network. Using both aggregate and bank-level network metrics, we find that increases in counterparty and liquidity risk are associated with reduced lending activity within the network. We also contribute some new ways of visualizing financial networks.
I use regression discontinuity analysis to measure the effect of one of the Affordable Housing Goals, the Underserved Areas Goal (UAG), on the number of whole single-family mortgages purchased by Fannie Mae and Freddie Mac (GSEs) in undeserved census tracts for 1996–2002. Focusing additionally on tracts that became UAG-eligible in 2005–2006, I measure the effect of the UAG during peak years for the subprime market. The results suggest a small UAG effect and challenge the view that the goals caused the GSEs to supply substantially more credit to high-risk borrowers than they otherwise would have supplied during the subprime boom.