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.

Recent years have seen tremendous advances in the theory and application of sequential experiments. While these experiments are not always designed with hypothesis testing in mind, researchers may still be interested in performing tests after the experiment is completed. The purpose of this paper is to aid in the development of optimal tests for sequential experiments by analyzing their asymptotic properties. Our key finding is that the asymptotic power function of any test can be matched by a test in a limit experiment where a Gaussian process is observed for each treatment, and inference is made for the drifts of these processes. This result has important implications, including a powerful sufficiency result: any candidate test only needs to rely on a fixed set of statistics, regardless of the type of sequential experiment. These statistics are the number of times each treatment has been sampled by the end of the experiment, along with final value of the score (for parametric models) or efficient influence function (for non-parametric models) process for each treatment. We then characterize asymptotically optimal tests under various restrictions such as unbiasedness, α-spending constraints etc. Finally, we apply our our results to three key classes of sequential experiments: costly sampling, group sequential trials, and bandit experiments, and show how optimal inference can be conducted in these scenarios

Private signals model noisy information about an unknown state. Although these signals are called "private," they may still carry information about each other. Our paper introduces the concept of private private signals, which contain information about the state but not about other signals. To achieve privacy, signal quality may need to be sacrificed. We study the informativeness of private private signals and characterize those that are optimal in the sense that they cannot be made more informative without violating privacy. We discuss implications for privacy in recommendation systems, information design, causal inference, and mechanism design

We construct a dynamic model of child development where forward-looking parents and children jointly take actions to increase the child’s cognitive and noncognitive skills within a Markov perfect equilibrium framework. In addition to time and money investments in their child, parents also choose whether to use explicit incentives to increase the child’s self-investment, which may reduce the child’s future intrinsic motivation to invest by reducing the child’s discount factor. We use the estimated model parameters to show that the use of extrinsic motivation has large costs in terms of the child’s future incentives to invest in themselves.

We study the frictions in dealer-intermediation in residential real estate through the lens of "iBuyers," technology entrants, who purchase and sell residential real estate through online platforms. iBuyers supply liquidity to households by allowing them to avoid a lengthy sale process. They sell houses quickly and earn a 5% spread. Their prices are well explained by a simple hedonic model, consistent with their use of algorithmic pricing. iBuyers choose to intermediate in markets that are liquid, and in which automated valuation models have low pricing error. These facts suggest that iBuyers' speedy offers may come at the cost of information loss concerning house attributes that are difficult to capture in an algorithm, resulting in adverse selection. We calibrate a dynamic structural search model with adverse selection to understand and quantify the economic forces underlying the tradeoffs of dealer intermediation in this market.

National audience

The measurement of intelligence should identify and measure an individual’s subjective confidence that a response to a test question is correct. Existing measures do not do that, nor do they use extrinsic financial incentive for truthful responses. We rectify both issues and show that each matters for the measurement of intelligence, particularly for women. Our results on gender and confidence in the face of risk have wider applications in terms of the measurement of “competitiveness” and financial literacy. Contrary to received literature, women are more intelligent than men, compete when they should in risky settings, and are more literate.

An informed seller designs a dynamic mechanism to sell an experience good. The seller has partial information about the product match, which affects the buyer's private consumption experience. We characterize equilibrium mechanisms of this dynamic informed principal problem. The belief gap between the informed seller and the uninformed buyer, coupled with the buyer's learning, gives rise to mechanisms that provide the skeptical buyer with limited access to the product and an option to upgrade if the buyer is swayed by a good experience. Depending on the seller's screening technology, this takes the form of free/discounted trials or tiered pricing, which are prevalent in digital markets. In contrast to static environments, having consumer data can reduce sellers' revenue in equilibrium, as they fine-tune the dynamic design with their data forecasting the buyer's learning process.

This paper investigates the weekly evolution of skills as measured by unique data from a widely-emulated early childhood home-visiting program in rural China. The design of the study avoids input endogeneity issues and lack of comparable measures of skills that plague previous studies. Skills, nominally classified as the same, in fact, do not appear to share a common unit scale across levels. They are produced by skill-lifecycle-stage-specific learning processes. A novel dynamic stochastic skill production model for multiple skills is developed, aligning with empirical evidence. The model explains the "fadeout" of measures of learning through forgetting or depreciation of skills