We examine the association between capital inflows and industry growth in a sample of 22 emerging market economies from 1998 to 2010. We expect more external-finance-dependent industries, in countries that host more capital inflows, to grow disproportionately faster. This is indeed the case in the pre-crisis period of 1998–2007. In a panel regression analysis using annual changes, this relationship is driven by debt, rather than equity inflows. But when we consider the long-run effects using panel cointegration tests, equity inflows are those that contribute to growth. Further, we observe a reduction in output volatility, and this association is more pronounced for equity, rather than debt inflows. These relationships, however, break down during the crisis, indicating the importance of an undisrupted global financial system for emerging markets, if they are to harness the growth benefits of capital inflows. In line with this observation, we also document that the inflows-growth nexus is stronger in countries with well-functioning banks and better institutional quality.

In Das et al. (2010), an agent divides his or her wealth among mental accounts that have different goals and optimal portfolios. While the moments of the distribution of asset returns are exogenous in their normative model, they are endogenous in our corresponding positive model. We obtain the following results. First, there are multiple equilibria that we parameterize by the implied risk aversion coefficient of the agent’s aggregate portfolio. Second, equilibrium asset prices and the composition of optimal portfolios within accounts depend on this coefficient. Third, altering the goal of any given account affects the composition of each portfolio.

We investigate the determinants of analysts' target price implied returns and the implication of our findings for investment decision-making. We identify four broad sets of factors that help explain the cross-sectional variation in target price implied returns: future realized stock returns, errors in forecasting fundamentals, errors in forecasting the expected return to risk, and biases relating to analysts' incentives. Our results suggest that all four sets help explain target price implied returns, with errors in forecasting the expected return to empirical risk proxies having the greatest impact. Collectively, these variables explain nearly a quarter of the cross-sectional variation in target price implied returns. We use our model to predict the optimistic bias in target price implied returns and evaluate whether investors correctly ignore the predictable bias. The results suggest that investors make similar valuation errors to analysts and/or do not perfectly back out the predicted bias in target prices.

Growing evidence suggests that women are more likely to repay collateral-free microloans than men. However, we know little about what explains such gender differences. We hypothesize that better repayment performance of women microcredit borrowers can largely be explained by gender differences in innate trustworthiness. We conduct a trust game and a microloan repayment game in rural Bangladesh. We find that women are more trustworthy than men and that they are more likely to repay their loans irrespective of any control mechanisms, such as joint liability or dynamic repayment incentives. The results of a mediation test suggest that the gender effect on loan repayment is significantly mediated by differences in innate trustworthiness. We conduct a sensitivity test to check the extent to which unobserved confounders might have influenced the mediation effect, and find no evidence of significant omitted variables bias.

Consider a researcher estimating the parameters of a regression function based on data for all 50 states in the United States or on data for all visits to a website. What is the interpretation of the estimated parameters and the standard errors? In practice, researchers typically assume that the sample is randomly drawn from a large population of interest and report standard errors that are designed to capture sampling variation. This is common even in applications where it is difficult to articulate what that population of interest is, and how it differs from the sample. In this article, we explore an alternative approach to inference, which is partly design‐based. In a design‐based setting, the values of some of the regressors can be manipulated, perhaps through a policy intervention. Design‐based uncertainty emanates from lack of knowledge about the values that the regression outcome would have taken under alternative interventions. We derive standard errors that account for design‐based uncertainty instead of, or in addition to, sampling‐based uncertainty. We show that our standard errors in general are smaller than the usual infinite‐population sampling‐based standard errors and provide conditions under which they coincide.

On September 20, 2012, the rewards-based crowdfunding platform Kickstarter.com added a “risks and challenges” section to all project pages. While the section header became a mandatory part of the platform, discussion of risks within that section is voluntary and unverified, making this setting particularly useful for identifying the effects of disclosure on both crowdfunders and entrepreneurs. Consistent with increased salience of risks, we find that backer support for high-risk projects decreases after the introduction of this section, but that lengthier risk disclosures mitigate this decrease in backer support. Further analysis reveals that creators who provide lengthier risk disclosures also increase other non-risk disclosures, and that these non-risk disclosures are primarily responsible for the increased backer support. Collectively, we provide evidence that the introduction of a voluntary and unverified risk disclosure reduced information asymmetry within this unregulated market.

We study the stock market reactions to the Tax Cuts and Jobs Act (TCJA), the most significant structural U.S. tax reform in over 30 years. In line with the stated intent of TCJA proponents, we find that the Act benefited highly taxed firms. However, the Act hindered firms with international operations as well as firms with high interest expense and tax losses. Counter to claims that the TCJA would quickly spur economic growth, we find that financially constrained and high growth opportunity firms did not benefit. Rather, market participants anticipate that most of the TCJA's benefits will be passed on to shareholders via higher corporate payouts. We confirm these market expectations by documenting that firms did increase payouts via repurchases after the TCJA, but did not increase their corporate investments.

We provide evidence about allocations of cash flow freed up by not paying taxes (“tax-related cash”). Uncertainty about future repayments suggests firms may use tax-related cash more cautiously than other cash flow. We utilize a flow-of-funds model from finance to quantify the relative amounts of tax-related cash associated with various potential uses of operating cash flow. We find firms allocate tax-related cash differently than other after-tax cash flow. Prior studies find tax avoiders hold more cash, and our results suggest this is because firms invest less (and save more) tax-related cash. We also find that the allocation of tax-related cash varies with relative financial constraints, economic uncertainty, and firms' multinational status in ways consistent with prior findings. For example, firms facing relatively higher levels of financial constraints invest a lower (higher) percentage of tax-related cash in capital expenditures (marketable securities and R&D), possibly to preserve funds for future investment opportunities.

Our new model of consumption-based habit formation preferences generates loglinear, homoskedastic macroeconomic dynamics and time-varying risk premia on bonds and stocks. Consumers' first-order condition for the real risk-free interest rate takes the form of an exactly loglinear consumption Euler equation, commonly assumed in New Keynesian models. Estimating the model separately for 1979-2001 and 2001-2011 explains why the exposure of US Treasury bonds to the stock market changed from positive to negative. A change in the comovement between inflation and the output gap explains changing bond risks, but only when risk premia change endogenously as predicted by the model.

This paper investigates whether Tether, a digital currency pegged to the U.S. dollar, influenced Bitcoin and other cryptocurrency prices during the 2017 boom. Using algorithms to analyze blockchain data, we find that purchases with Tether are timed following market downturns and result in sizable increases in Bitcoin prices. The flow is attributable to one entity, clusters below round prices, induces asymmetric autocorrelations in Bitcoin, and suggests insufficient Tether reserves before month‐ends. Rather than demand from cash investors, these patterns are most consistent with the supply‐based hypothesis of unbacked digital money inflating cryptocurrency prices.