Knowledge that Transforms

We state a sufficient condition under which choice data alone suffices to identify consumer preferences when choices are not fully informed. Suppose that: (i) the data generating process is a search model in which the attribute hidden to consumers is observed by the econometrician; (ii) if a consumer searches good j, she also searches goods which are better than j in terms of the non-hidden component of utility; and (iii) consumers choose the good that maximizes overall utility among searched goods. Canonical models will be biased: the value of the hidden attribute will be understated because consumers will be unresponsive to variation in the attribute for goods that they do not search. Under the conditions above and additional mild restrictions, an alternative method of recovering preferences using cross derivatives of choice probabilities succeeds regardless of the search protocol and is thus robust to whether consumers are informed. The approach nests several standard models, including full information. Our methods suggest natural tests for full information and can be used to forecast how consumers will respond to additional information. We verify in a lab experiment that our approach succeeds in recovering preferences when consumers engage in costly search.

This paper examines the impact of cultural diversity on innovation. Focusing on the United States from 1850 to 1940, we develop a novel surname-based measure of cultural diversity and combine this with patent data. Leveraging quasi-random variation in counties' surname compositions driven by historical immigration, we find that rising diversity increased both the quantity and quality of innovation within counties and for individual inventors. Examining mechanisms, we provide evidence suggesting that greater surname diversity accelerated innovation both by expanding the range of ideas, skills and perspectives available for recombination and by fostering the diverse social interactions that facilitate idea sharing.

Pay-as-bid (or discriminatory or multiple-price) auctions are used to sell homogenous goods such as treasury securities and commodities.We prove the uniqueness of their pure-strategy Bayesian Nash equilibrium and establish a tractable representation of equilibrium bids for symmetrically-informed bidders.Analyzing design, we show that supply transparency and full disclosure are revenue-maximizing in pay as bid, though not necessarily in uniform-price (or single-price) auctions, the main alternative auction format.Pay as bid raises weakly more revenue than uniform price and may lead to higher welfare.Our results provide an explanation for the revenue equivalence observed in empirical studies of treasury auctions.

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.

We present a dynamic quantitative trade and migration model that incorporates downward nominal wage rigidities and show how this framework can generate changes in unemployment and labor participation that match those uncovered by the empirical literature studying the China shock. We find that the China shock leads to average welfare increases in most US states, including many that experience unemployment during the transition. However, nominal rigidities reduce the overall US gains by around two-thirds. In addition, there are 18 states that experience welfare losses in the presence of downward nominal wage rigidity that would have experienced gains without it.

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.

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.

National audience

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.