This research provides a status-based explanation for the high rates of female labor force non-participation (FLFNP) and the sustained increase in these rates over time that have been documented in many developing economies. This explanation is based on the idea that households or ethnic groups can signal their wealth, and thereby increase their social status, by withdrawing women from the labor force. If the value of social status or the willingness to bear the signaling cost is increasing with economic development, then this would explain the persistent increase in FLFNP. To provide empirical support for this argument, we utilize two independent sources of exogenous variation – across Indian districts in the cross-section and within districts over time – to establish that status considerations determine rural FLFNP. Our status-based model, which is used to derive the preceding tests, is able to match the high levels and the increase in rural Indian FLFNP that motivate our analysis. Counterfactual simulations of the estimated model indicate that conventional development policies, such as a reduction in the cost of female education, could raise FLFNP by increasing potential household incomes and, hence, the willingness to compete for social status. The steep increase in female education in recent decades could paradoxically have increased FLFNP in India even further.
We study agents who are more likely to remember some experiences than others but update beliefs as if the experiences they remember are the only ones that occurred. To understand the long-run effects of selective memory, we propose selective-memory equilibrium. We show that if the agent’s behavior converges, their limit strategy is a selective-memory equilibrium, and we provide a sufficient condition for behavior to converge. We use this equilibrium concept to explore the consequences of several well-documented biases. We also show that there is a close connection between selective-memory equilibria and the outcomes of misspecified learning.
In this paper, we explore a scenario where a sender provides an information policy and a receiver, upon observing a realization of this policy, decides whether to take a particular action, such as making a purchase. The sender’s objective is to maximize her utility derived from the receiver’s action, and she achieves this by careful selection of the information policy. Building on the work of Kleiner et al., our focus lies specifically on information policies that are associated with power diagram partitions of the underlying domain. To address this problem, we employ entropy-regularized optimal transport, which enables us to develop an efficient algorithm for finding the optimal solution. We present experimental numerical results that highlight the qualitative properties of the optimal configurations, providing valuable insights into their structure. Furthermore, we extend our numerical investigation to derive optimal information policies for monopolists dealing with multiple products, where the sender discloses information about product qualities.
It has become common practice for researchers to use AI-powered information retrieval algorithms or other machine learning methods to estimate variables of economic interest, then use these estimates as covariates in a regression model. We show both theoretically and empirically that naively treating AI- and ML-generated variables as “data” leads to biased estimates and invalid inference. We propose two methods to correct bias and perform valid inference: (i) an explicit bias correction with bias-corrected confidence intervals, and (ii) joint maximum likelihood estimation of the regression model and the variables of interest. Through several applications, we demonstrate that the common approach generates substantial bias, while both corrections perform well.
We develop a state-space model with a transition equation that takes the form of a functional vector autoregression (VAR) and stacks macroeconomic aggregates and a cross-sectional density. The measurement equation captures the error in estimating log densities from repeated cross-sectional samples. The log densities and their transition kernels are approximated by sieves, which leads to a finite-dimensional VAR for macroeconomic aggregates and sieve coefficients. With this model, we study the dynamics of technology shocks, GDP (gross domestic product), employment, and the earnings distribution. We find that spillovers between aggregate and distributional dynamics are generally small, that a positive technology shock tends to decrease inequality, and that a shock that raises earnings inequality leads to a small and insignificant GDP response.
A common tactic to estimate willingness-to-travel exploits variation in the relative proximity of consumers to supplier locations. The validity of these estimates relies on the exogeneity of that consumer-supplier distance. We argue that distance to suppliers is endogenous because suppliers strategically choose locations to target consumers; we introduce a novel instrument to address this form of endogeneity. Using geolocation data from millions of smartphones, we estimate consumer preferences for specific retail chains across income groups and regions. We show that accounting for distance endogeneity significantly alters willingness-to-travel measures. Contrary to the prevailing “retail apocalypse” narrative, we find that consumer surplus per trip to general merchandise stores did not significantly decline from 2010 to 2019. For the lowest-income consumers, the expansion of national chains, particularly dollar stores, nearly compensates for the closure of traditional department stores and regional chains. Notably, failing to account for distance endogeneity leads to the erroneous conclusion that lower-income households experienced statistically significant consumer surplus declines.
Climate policy by a coalition of countries can shift activities—extraction, production, and consumption—to regions outside the coalition. We build a stylized general-equilibrium model of trade and carbon externalities to derive a coalition’s optimal Pareto-improving policy in such an environment. It can be implemented through: (i) a tax on fossil-fuel extraction at a rate equal to the global marginal harm from carbon emissions, (ii) a tax on imports of energy and goods, and a rebate of the tax on exports of energy but not goods, all at a lower rate per unit of carbon than the extraction tax rate, and (iii) a goods-specific export subsidy. This combination of taxes and subsidies exploits international trade to expand the policy’s reach. It promotes energy efficient production and eliminates leakage by taxing the carbon content of goods imports and by encouraging goods exports. It controls the energy price in the non-taxing region by balancing supply-side and demand-side taxes. We use a quantitative version of the model to illustrate the gains achieved by the optimal policy and simpler variants of it. Combining supply-side and demand-side taxes generates first-order welfare improvements over current and proposed climate policies.
We consider a broad class of spatial models where there are many types of interactions across a large number of locations. We provide a new theorem that offers an iterative algorithm for calculating an equilibrium and sufficient and "globally necessary" conditions under which the equilibrium is unique. We show how this theorem enables the characterization of equilibrium properties for one important spatial system: an urban model with spillovers across a large number of different types of agents. An online appendix provides 12 additional examples of both spatial and nonspatial economic frameworks for which our theorem provides new equilibrium characterizations.
In the introduction to Activity Analysis of Production and Allocation (Cowles Monograph No. 13), Tjalling C. Koopmans recalled that he developed the model of his “Optimal Utilization of the Transportation System” (in the proceedings of 1947 International Statistical Congress, which were reissued as an Econometrica supplement, 1949) “under the stimulation of statistical work for the Combined Shipping Adjustment Board, the British-American board dealing with merchant shipping problems during the second world war.” Similarly, the contributions of George B. Dantzig and Marshall K. Wood to Cowles Monograph No. 13 (two revised journal articles and five new chapters) emerged from wartime work for the US Army Air Force and postwar work for the Department of the Air Force. This article examines the context and consequences of the wartime roots of these foundational contributions to activity analysis and linear programming, with particular attention to Koopmans's 1942 memorandum for the Combined Shipping Adjustment Board titled “Exchange Ratios between Cargoes on Various Routes” (first published in his Scientific Papers, 1970).
We study a dynamic contribution game where investors seek private benefits offered in exchange for contributions, and a single, publicly minded donor values project success. We show that donor contributions serve as costly signals that encourage socially productive contributions by investors who face a coordination problem. Investors and the donor prefer different equilibria, but all benefit in expectation from the donor's ability to dynamically signal his valuation. We explore various contexts in which our model can be applied and delve empirically into the case of Kickstarter. We calibrate our model and quantify the coordination benefits of dynamic signaling in counterfactuals.