Do beliefs about inequality depend on distributive preferences? What is the joint role of preferences and beliefs about inequality for support for redistribution? We study these questions in a staggered experiment with a broadly representative sample of the Swiss population conducted in the context of a vote on a highly redistributive policy proposal. Our sample comprises a majority of inequality averse subjects, a sizeable group of altruistic subjects, and a minority of predominantly selfish subjects. Irrespective of preference types, individuals vastly overestimate the extent of income inequality. An information intervention successfully corrects these large misperceptions for all types, but essentially does not affect aggregate support for redistribution. These results hide, however, important heterogeneity because the effects of beliefs about inequality for demand for redistribution are preference-dependent: only affluent inequality averse individuals, but not the selfish and altruistic ones, significantly reduce their support for redistribution. These findings cast a new light on the seemingly puzzling result that, in the aggregate, large changes in beliefs about inequality often do not translate into changes in demand for redistribution.
We use the elements of a macroeconomic production function—physical capital, human capital, labor, and technology—together with standard growth models to frame the role of religion in economic growth. Unifying a growing literature, we argue that religion can enhance or impinge upon economic growth through all four elements because it shapes individual preferences, societal norms, and institutions. Religion affects physical capital accumulation by influencing thrift and financial development. It affects human capital through both religious and secular education. It affects population and labor by influencing work effort, fertility, and the demographic transition. And it affects total factor productivity by constraining or unleashing technological change and through rituals, legal institutions, political economy, and conflict. Synthesizing a disjoint literature in this way opens many interesting directions for future research.
We study the effect of interest rates on wealth inequality. While lower rates decrease the growth rate of rentiers, they also increase the growth rate of entrepreneurs by making it cheaper to raise capital. To understand which effect dominates, we derive a sufficient statistic for the effect of interest rates on the Pareto exponent of the wealth distribution: it depends on the lifetime equity and debt issuance rate of individuals in the right tail of the wealth distribution. We estimate this sufficient statistic using new data on the trajectory of top fortunes in the U.S. Overall, we find that the secular decline in interest rates (or more generally of required rates of returns) can account for about 40% of the rise in Pareto inequality; that is, the degree to which the super rich pulled ahead relative to the rich.
With a dependent variable in logs, the difference-in-differences (DD) term does not capture the outcome difference between treated and untreated groups over time. Rather it reflects an approximation of the proportional difference in growth rates across groups. As I show with both simulations and three published case studies, if the baseline outcome distributions are sufficiently different across groups, the DD parameter for a log-specification can be different in sign to that of a levels specification. I provide a condition, based on (i) the aggregate time effect, and (ii) the difference in relative baseline outcome means, for when the sign-switch will occur.
The study explores the effect of financial transparency on dividend smoothing behavior. We analytically show that dividend smoothing should increase when shareholders cannot observe cash flow realizations. Using exogenous variation in financial transparency created by the SOX, we find that less transparent firms as measured by higher dispersion of analyst forecasts and lower excess audit fees reduced dividend smoothing more after the legislative change, as compared to other dividend-paying firms in the sample. Our study complements recent studies considering the firm’s dividend smoothing as an alternative corporate governance mechanism.
Around 16% of Americans now work completely from home and 28.5% have hybrid work arrangements (Barrero, Blood, and Davis, 2023). At the same time, at least one study (Emanuel, Harrington, and Pallais, 2023) observes that workers particularly younger ones start to receive less feedback on their work when transferring to fully remote, suggesting negative effects on learning. This raises a question of how much the learning is affected for remote workers. A large proportion of workers working remotely and somewhat exogenous transition to remote arrangement due to COVID allows to measure returns to experience for those switching to work from home. It can be attempted with a large panel dataset of workers or even better with an employer-employee matched dataset.
Average-based updating is one of the most common and simplest approaches to model spread of information in social networks. In this approach, each agent forms their belief as the average of reported beliefs of its contacts in the previous period. Golub and Jackson (2012) show that if subjects form symmetric (undirected) connections then under some conditions the speed of convergence can be linked to a measure of network homophily. Additionally, knowing the probabilities of connections within each type and across types happens to be sufficient to accurately predict the speed of convergence. However, these results provide little guidance for more realistic cases in which the influence is asymmetric (John listens to Donald, but Donald doesn't listen to John). The question on the speed of convergence of beliefs with non-symmetric (undirected) connections still remains open. Theoretical derivations can potentially rely on the recent result by Chatterjee (2023) linking Markov chain convergence times to singular values of the chain's generator matrix. (inspired by Ben Golub).
The rational-choice framework for modeling matching markets has been tremendously useful in guiding the design of school-assignment systems. Despite this success, a large body of work documents deviations from the predictions of this framework that appear influenced by behavioral-economic phenomena. We review these findings and the body of behavioral theories that have been presented as possible explanations. Motivated by this literature, we lay out paths for behavioral economists to be directly useful to education market design.
Convincing research designs make empirical economics credible. To avoid confounding, quasi-experimental studies focus on specific sources of variation. This could lead to a reduction in statistical power. Yet, published estimates can overestimate true effects sizes when power is low. Using fake data simulations, we show that for all causal inference methods, there could be a trade-off between confounding and exaggerating true effect sizes due to a loss in power. We then discuss how reporting power calculations could help address this issue.
This paper proposes a Sieve Simulated Method of Moments (Sieve-SMM) estimator for the parameters and the distribution of the shocks in nonlinear dynamic models where the likelihood and the moments are not tractable. An important concern with SMM, which matches sample with simulated moments, is that a parametric distribution is required. However, economic quantities that depend on this distribution, such as welfare and asset-prices, can be sensitive to misspecification. The Sieve-SMM estimator addresses this issue by flexibly approximating the distribution of the shocks with a Gaussian and tails mixture sieve. The asymptotic framework provides consistency, rate of convergence and asymptotic normality results, extending existing results to a new framework with more general dynamics and latent variables. An application to asset pricing in a production economy shows a large decline in the estimates of relative risk aversion, highlighting the empirical relevance of misspecification bias.