We design an experiment to study the implications of information networks for incentives to acquire costly information, market liquidity, investors' earnings, and asset price characteristics in a financial market. Social communication crowds out information production as a result of an agent's temptation to free ride on the signals purchased by her neighbors. Although information exchange among traders increases trading volume, improves liquidity, and enhances the ability of asset prices to reflect the available information in the market, it fails to improve price informativeness. Net earnings and social welfare are higher with information sharing due to reduced acquisition of costly signals.

We investigate whether business ties with portfolio firms influence mutual funds' proxy voting using a comprehensive data set spanning 2003 to 2011. In contrast to prior literature, we find that business ties significantly influence promanagement voting at the level of individual pairs of fund families and firms after controlling for Institutional Shareholder Services (ISS) recommendations and holdings. The association is significant only for shareholder‐sponsored proposals and stronger for those that pass or fail by relatively narrow margins. Our findings are consistent with a demand‐driven model of biased voting in which company managers use existing business ties with funds to influence how they vote.

Growing reliance on student loans and repayment difficulties have raised concerns of a student debt crisis in the United States, but little is known about the effects of student borrowing on human capital and long-run financial well-being. We use variation induced by recent expansions in federal loan limits combined with administrative data-sets to identify the effects of increased access to student loans on credit-constrained students’ educational attainment, earnings, debt, and loan repayment. Increased student loan availability raises student debt and improves degree completion, later-life earnings, and student loan repayment, while having no effect on homeownership or other types of debt.

Was the Depression forecastable? After the crash, how long should it have taken contempo rary forecasters to realize how severe the downturn was going to be? These questions are addressed by studying the predictions of the Harv ard Economic Service and Yale's Irving Fisher during 1929 and the ear ly 1930s. The data assembled by the Harvard and Yale forecasters, tog ether with modern historical data, are subjected to statistical analy sis to learn whether their verbal pronouncements were consistent with the data. Both the Harvard and Yale forecasters were systematically too optimistic. Yet, nothing in the data suggests that the optimism w as unwarranted.

We test Le Play's (1875) hypothesis that the French Revolution contributed to France's early fertility decline by imposing equal partition of inheritance among all children, including women. We combine new data on local inheritance rules before the Revolution and individual-level demographic data from historical sources and crowdsourced genealogies. Difference-in-differences and regression-discontinuity estimates show that the inheritance reforms enacted during the Revolution reduced completed fertility by 0.5 children. A key mechanism was the desire to avoid land fragmentation across generations. These reforms closed the fertility gap between regions with different historical inheritance rules and crucially contributed to France's demographic transition.

This paper endogenizes intervention in financial crises as the strategic negotiation between a regulator and creditors of distressed banks. Incentives for banks to contribute to a voluntary bail-in arise from their exposure to financial contagion. In equilibrium, a bail-in is possible only if the regulator’s threat to not bail out insolvent banks is credible. Contrary to models without intervention or with government bailouts only, sparse networks enhance welfare for two main reasons: they improve the credibility of the regulator’s no-bailout threat for large shocks, and they reduce free-riding incentives among bail-in contributors when the threat is credible.

Technology has facilitated new, nontraditional work arrangements, including the ride-sharing company Uber. Uber drivers provide rides anytime they choose. Using data on hourly earnings and driving, we document driver utilization of this real-time flexibility. We propose that the value of flexibility can be measured as deriving from time variation in the drivers' reservation wage. Measuring time variation in drivers' reservation wages allows us to estimate the surplus and labor supply implications of Uber relative to alternative, less-flexible work arrangements. Despite other drawbacks to the Uber arrangement, we estimate that Uber drivers earn more than twice the surplus they would in less-flexible arrangements.

In a recent paper, Acemoglu and Johnson (2007) argue that the large increases in population health witnessed in the 20th century may have lowered income levels. We argue that this result depends crucially on their assumption that initial health and income do not affect subsequent economic growth. Using their data we reject this assumption in favor of a model of conditional convergence, with income adjusting to its steady state over time. We show that, allowing for conditional convergence, exogenous improvements in health due to technical advances associated with the epidemiological transition appear to have increased income levels.