Econometrica最新文献

Structural measures of higher order risk attitudes have well-developed foundations in Expected Utility Theory (EUT), but little is known about their empirical magnitudes. We introduce a novel experimental design and a companion econometric model that allows us to structurally estimate indices of risk aversion, prudence, and temperance under EUT without imposing restrictions on their interdependence. We find that indices of absolute risk aversion, prudence, and temperance exhibit distinct patterns of variation over income, and that predicted risk premia under EUT and Rank-Dependent Utility Theory gradually converge as the order of risk increases. These findings are obscured by regular parametric utility functions, which inherently bias results toward prudence and temperance when subjects are risk averse. The results remain robust in subsamples of moderate size, which suggests that our approach can be adopted in broader studies that link higher order risk attitudes to other domains of latent individual preferences and economic behavior.

We exploit the implementation of a rural pension policy in China to estimate the average rural-to-urban migration cost for workers affected by the policy and the average underlying sectoral productivity difference. Our estimates, based on a large panel data set, reveal significant migration costs and substantial sectoral productivity differences, with sorting playing a minor role in accounting for sectoral labor income gaps. We construct and structurally estimate a general equilibrium household model with endogenous labor supply and migration. The results of this model align with the reduced-form findings and illustrate how the rural pension policy influences migration, GDP, and welfare through improving within-household labor allocation. Counterfactual analyses based on the model show that the positive effects of the policy remain even if migration costs were significantly lower, and that scaling up the rural pension policy would lead to even larger improvements in labor allocation, GDP, and welfare.

Public disability insurance (DI) programs in many countries face growing fiscal pressures, prompting efforts to reduce spending. In this paper, we investigate the welfare effects of expanding the role of private insurance markets in the face of public DI cuts. We exploit a reform that abolished one part of German public DI and use unique data from a large insurer. We document modest crowding-out effects of the reform, such that private DI take-up remains incomplete. We find no adverse selection in the private DI market. Instead, private DI tends to attract individuals with high income, high education, and low disability risk. Using a revealed preference approach, we estimate individual insurance valuations. Our welfare analysis finds that partial DI provision via the voluntary private market can improve welfare. However, distributional concerns may justify a full public DI mandate.

We propose flexible Gaussian representations for conditional cumulative distribution functions and give a concave likelihood criterion for their estimation. Optimal representations satisfy the monotonicity property of conditional cumulative distribution functions, including in finite samples and under general misspecification. We use these representations to provide a unified framework for the flexible maximum likelihood estimation of conditional density, cumulative distribution, and quantile functions at parametric rate. Our formulation yields substantial simplifications and finite sample improvements over related methods. An empirical application to the gender wage gap in the United States illustrates our framework.

New technologies have recently led to a boom in real-time pricing. I study the most salient example, surge pricing in ride hailing. Using data from Uber, I develop an empirical model of spatial equilibrium to measure the welfare effects of surge pricing. The model is composed of demand, supply, and a matching technology. It allows for temporal and spatial heterogeneity as well as randomness in supply and demand. I find that, relative to a uniform pricing counterfactual in which Uber sets the overall price level, surge pricing increases total welfare by 2.15% of gross revenue. Welfare effects differ substantially across sides of the market: rider surplus increases by 3.57% of gross revenue, whereas driver surplus and the platform's current profits decrease by 0.98% and 0.50% of gross revenue, respectively. Riders at all income levels benefit. Among drivers, those who work long hours are hurt the most, especially women.

This paper investigates how children affect women in science, using biographies in the American Men of Science (MoS 1956), linked with publications. First, we show that mothers have a unique life cycle pattern of productivity: While other scientists peak in their mid-30s, mothers become less productive at that age and reach peak productivity in their early-40s. Next, we estimate event studies of marriage, comparing mothers and fathers with other married scientists. Event study estimates show that the productivity of mothers declines until children reach school age, while fathers experience no change. These differences have important implications for tenure and participation: Just 27% of mothers achieve tenure, compared with 48% of fathers and 46% of other women. When women carried the full burden of childcare, the time costs of raising the baby boom led to a great loss of female scientists.

Trade policy is often cast as a solution to the free-riding problem in international climate agreements. This paper examines the extent to which trade policy can deliver on this promise. We incorporate global supply chains of carbon and climate externalities into a multi-country, multi-industry general equilibrium trade model. By deriving theoretical formulas for optimal carbon and border taxes, we quantify the maximum efficacy of two trade policy solutions to the free-riding problem. Adding optimal carbon border taxes to existing tariffs proves largely ineffective, delivering only 3.4% of what could be achieved under globally optimal carbon pricing. In contrast, Nordhaus's (2015) climate club framework, in which border taxes are used as contingent penalties to deter free-riding, can achieve 33–68% of the globally optimal carbon reduction, depending on the initial coalition (EU, EU + US, or EU + US + China). In all cases, the climate club ensures universal compliance, thereby preserving free trade.

Econometric analysis typically focuses on the statistical properties of fixed estimators and ignores researcher choices. In this article, I instead approach the analysis of experimental data as a mechanism-design problem that acknowledges that researchers choose between estimators, sometimes based on the data and often according to their own preferences. Specifically, I focus on covariate adjustments, which can increase the precision of a treatment-effect estimate, but open the door to bias when researchers engage in specification searches. First, I establish that unbiasedness as a requirement on the estimation of the average treatment effect can align researchers' preferences with the minimization of the mean-squared error relative to the truth, and that fixing the bias can yield an optimal restriction in a minimax sense. Second, I provide a constructive characterization of treatment-effect estimators with fixed bias as sample-splitting procedures. Third, I discuss the implementation of second-best estimators that leave room for beneficial specification searches.

We thank Man Chon Iao—a Ph.D. student at NYU—for bringing to our attention that we had a mistake in our code that generated the results in the published version of our paper. In this erratum, we: (1) discuss the mistake, (2) highlight the changes we made to our code in response to the mistake, and (3) reproduce all the relevant tables and figures of the paper after correcting the mistake. In particular, Section 2 of this erratum discusses the mistake, Section 3 updates the paper's core tables and figures, and Section 4 updates all remaining motivating and robustness tables and figures. Any table or figure we did not reproduce means the table/figure was unchanged compared to the original.

In summary, the magnitudes of the reported estimates change, although the qualitative results remain.

At the heart of the empirical component of our paper is the creation of state level wage measures during the period surrounding the Great Recession. When we initially made our composition adjusted state level wage measures, we summed over the wages for those working in each of our detailed demographic groups within each state for each year using repeated cross-sectional data from the American Community Survey. We then divided the total wages paid in each state-demographic group-year cell by the total number of individuals within each state-demographic group-year cell. This step produced a measure of the average wage for each demographic group in each state in each year. We then aggregated the state level demographic groups in each year—holding the group weights fixed at some initial time period level—to make our measure of demographically adjusted state wages in each year. Our mistake stems from the fact that we should have divided by the total number of “working” individuals within each group instead of the total number of individuals (unconditional on work status) within each group.

The main empirical result in the paper is the estimation of a state level New Keynesian Wage Phillips Curve (Table V, Section 5). The main quantitative results are the implications for aggregate business cycles of incorporating regional data when estimating a DSGE model (Figures 4 and 5, Section 7). We update these results below.

Below, we present the updated results for Figure 1, Figures 3, 3, 4, 5, Appendix A5–A6, and Tables I, II, and IV, V, VI, VII, VIII of the main paper. All other tables and figures are unaffected by our changes.

Policymakers often test expensive new programs on relatively small samples. Formally incorporating informative Bayesian priors into impact evaluation offers the promise to learn more from these experiments. We evaluate a Colombian program for 200 firms which aimed to increase exporting. Priors were elicited from academics, policymakers, and firms. Contrary to these priors, frequentist estimation cannot reject null effects in 2019, and finds some negative impacts in 2020. For binary outcomes like whether firms export, frequentist estimates are relatively precise, and Bayesian posterior intervals update to overlap almost completely with standard confidence intervals. For outcomes like increasing export variety, where the priors align with the data, the value of these priors is seen in posterior intervals that are considerably narrower than the confidence intervals. Finally, for noisy outcomes like export value, posterior intervals show almost no updating from priors, highlighting how uninformative the data are about such outcomes. Future policy experiments could use these posteriors as priors in a Bayesian or empirical Bayesian analysis.