Knowledge that Transforms

Bitcoin prices are driven by upward as well as downward jumps and so the bitcoin implied volatility surface behaves differently from those of established options markets. We analyze tick-level Deribit option price data, demonstrating increasing support for the limits-to-arbitrage hypothesis. Hence market makers are managing order imbalance and inventory more effectively as Deribit bitcoin options trading volumes increases. On the demand side, volatility traders drive both at-the-money and out-of-the-money option prices, the latter also being driven by directional traders. Directional effects were most pronounced during the price bubble of 2021. Further refinements of our tests assess time-to-maturity and time-of-day effects.

I study regulation of noncompete employment contracts, assessing the trade‐off between restricting worker mobility and encouraging firm investment. I develop an on‐the‐job search model in which firms and workers sign dynamic wage contracts with noncompete clauses and firms invest in their workers' general human capital. Employers use noncompete clauses to enforce buyout payments when their workers depart, ultimately extracting rent from future employers. This rent extraction is socially excessive, and restrictions on these clauses can improve efficiency. The optimal regulation policy is characterized. In an application to the managerial labor market using a novel contract data set, I find the optimal policy to be quantitatively close to a ban.

Increasing mother’s labor supply during a child’s preschool years may reduce time investments, yielding a negative direct effect on midchildhood and teenage outcomes. But as mother’s work hours increase, income will rise. Can income compensate for the negative effect of hours? Our mediation analysis exploits exogenous variation in both mother’s hours and family income. Results suggest a negative, insignificant direct effect from increasing mother’s hours on child test scores. However, the positive mediating effect of income creates a positive total effect on test scores (26% of a standard deviation) for a 10-hour increase in mother’s weekly hours in preschool years.

This paper studies learning among coworkers when incentives change. We use a simple principal-agent model to show that when workers are not fully informed on the global shape of the production function, (1) their effort choice changes over time as information is disclosed and processed and (2) changing incentives can trigger this learning process. We test this prediction using personnel data from an egg production plant in Peru. Exploiting a sudden change in the contract parameters, we find that workers learn from each other over the shape of the production function. This adjustment process is costly for the firm.

We study how active labor market policies affect the exchange of information and support among job seekers. Leveraging a unique social network survey in Ethiopia, we find that a randomized job search assistance intervention reduces information sharing and support between treated job seekers and their active job search partners. Because of lower job search support, untreated individuals search less and, suggestively, have worse employment outcomes. These results are consistent with a model of networks where unemployed individuals form job search partnerships to exploit the complementarities of job search.

We propose a model of intergenerational transmission of education wherein children belong to either highly educated or low-educated families. Children choose the intensity of their social activities, while parents decide how much educational effort to exert. Using Add Health data, we find that, on average, children’s homophily acts as a complement to the educational effort of highly educated parents but as a substitute for the educational effort of low-educated parents. We also find that policies that subsidize kids’ socialization efforts can backfire for low-educated students because they tend to increase their interactions with other low-educated students.

We estimate the effect of an unexpected institutional financial aid (IFA) award on student outcomes using administrative data collected from nine universities, exploiting variation in IFA schedules within and across university-entry cohorts. Each £1,000 of IFA during the first year of college increases the chances of completing that year by 1.4 percentage points, improves test scores by 0.059 standard deviations, and increases the chances of graduating with a good degree by 3.4 percentage points. We find that high-ability and low-income students benefit the most and calculate outcome-maximizing and cost-minimizing IFA schedules for each university.