Strategy Seminar 2024-2025

I propose a model of a skill-replacing routine-biased technological change (SR-RBTC). In this model, technology substitutes the usage of skill in routine tasks in contrast to standard RBTC models, which assume technology replaces the workers themselves. The SR-RBTC model explains three key trends that are inconsistent with standard RBTC models: 1) why specifically middle wages declined even though workers in routine occupations are dispersed across the entire bottom half of the wage distribution, 2) why middle wages stopped declining while the technological change continued, and 3) why there is no substantial decline in the average wage of workers in routine occupations. I derive two new testable predictions from the model: a decrease in return to skill and a decrease in skill level in routine occupations. I use an interactive fixed-effects model to confirm both predictions. Since SR-RBTC violates the ignorability assumption required by standard decomposition methods, I introduce “skewness decomposition” to show that SR-RBTC is the main driver of bottom-half inequality trends.

The human mind has finite cognitive capacity, requiring cognitive functions to compete for shared neurobiological resources. Here, I introduce an extension of behavioral game theoretic approaches, decomposing strategic interactions into social and non-social arithmetic cognitive demands. Drawing on the economic concept of production functions, I develop a novel framework that models strategic sophistication as a product of participants' capabilities across these dimensions, processing time, and monetary incentives. In three studies, I demonstrate that social and arithmetic demands are contextual factors for sophistication with a lawful regularity, that subjects trade-off these capabilities as cognitive demands vary, and that sophistication emerges as a saturating function of processing time and monetary incentives. These findings provide a predictive new framework, offer mechanistic insights into reasoning in games and lay the foundation for a new detailed economic model of strategic choice.
 

We propose a novel interpretation of the AKM wage decomposition model, arguing that it reflects binding constraints on firms' ability to differentiate pay across worker skill levels. We develop a theoretical framework where firms face internal pay equity constraints, leading to a quantity-quality trade-off in hiring: firms that offer higher wages attract more skilled workers, but must forgo hiring less skilled workers due to profitability considerations. This mechanism generates workplace segregation and firm wage dispersion even among identical firms. Our model also shows how positive assortative matching between workers and firms can be reconciled with log additive wages. Using Israeli administrative data, we validate the model's predictions by documenting a hump-shaped relationship between firm size and pay, while skill share increases monotonically with pay. The model also provides new insights into several empirical regularities, including temporal changes in wage inequality, spatial variation in firm pay, constraints on regional wage differentiation within firms, and public-private sector wage differentials.

In many settings, individuals imitate their peers’ public decisions for one or both of two reasons: to adapt to a common fundamental state, and to conform to their peers’ preferences. In this model, the fundamental state and peers’ preferences are unknown, and the players learn these random variables by observing others’ decisions. With each additional decision, the public beliefs about these unknowns become more precise. This increased precision endogenously increases the desire to conform and can result in decisions that are uninformative about a player’s preferences or perceptions of the fundamental state. When this occurs, social learning about peers’ preferences and fundamentals ceases prematurely, resulting in inefficient decisions. In line with findings from social psychology, I show that interventions aimed at correcting misperceptions of peers’ preferences may lead to more efficient decision-making in settings where interventions aimed at correcting misperceptions of the fundamental state may have no effect.

This paper examines the role of compatibility across standalone technology products in anchoring consumers to brands. Through a novel experimental design, I identify the causal impact of compatibility, showing that participants’ willingness to pay for smartphones increases by 9% of their retail price when compatible with a laptop. I combine the experimental results with a smartphone demand model that incorporates compatibility with laptops to assess the welfare effects of (i) regulations mandating cross-brand compatibility (“open ecosystems”) and (ii) cross market mergers. I find that in 2018-2019, closed ecosystems benefit Samsung by locking non-Apple laptop owners into lower-quality Samsung smartphones, while the switch to open ecosystems boosts Apple’s smartphone market share. How ever, in 2020-2023, closed ecosystems benefit Apple, as Samsung’s top smartphones are superior, prompting Apple laptop owners to switch to Samsung smartphones in open ecosystems. In both periods, consumer surplus rises due to lower prices and greater product variety in open ecosystems. A counterfactual merger between Samsung and HP, Apple’s main smartphone and laptop competitors, respectively, results in lower smartphone market concentration but raises Samsung smartphone prices, disadvantaging consumers who value compatibility less.

People make correct inferences from observed events but make mistakes when reasoning about hypothetical events. I study the implications in competitive markets. I define Dynamic Cursed Expectations (DCE), where agents treat events occurring at different future periods as independent. I build on it to define Dynamic Cursed Expectations Equilibrium (DCEE) and prove its existence for a general financial economy. I apply DCEE to an asset pricing model and show that in DCEE agents underestimate the variance of risky assets, which under risk aversion leads to overvaluation relative to the Rational Expectations Equilibrium benchmark.
 

I apply the novel RD Aggregation method to assess the political impact of US trade unions. The method enables the aggregation of several discon tinuity events–close unionization elections–into a commuting zone level shock that measures the unions’ ”Luck” in each zone in each period. Using this methodology, I find that, on average, a newly unionized worker is worth 1.5 new votes for the Democratic party candidate in the following presidential elections and that unions shift local congressmen to the left. Further analysis suggests that the significant effects partly stem from increased campaign con tributions, strategic political resource allocation by unions in areas with new members, and direct impact on union members.

The adoption of Artificial Intelligence (AI) for hiring processes is often impeded by a scarcity of comprehensive employee data. We hypothesize that the inclusion of behavioral measures elicited from applicants can enhance the predictive accuracy of AI in hiring. We study this hypothesis in the context of microfinance loan officers. Our findings suggest that survey-based behavioral measures markedly improve the predictions of a random-forest algorithm trained to predict productivity within sample relative to demographic information alone. We then validate the algorithm’s robustness to the selectivity of the training sample and potential strategic responses by applicants by running two out-of-sample tests: one forecasting the future performance of novice employees, and another with a field experiment on hiring. Both tests corroborate the effectiveness of incorporating behavioral data to predict performance. At the same time, our field experiment comparing workers hired by the algorithm with those hired by human managers reveals that the algorithm hires better workers, though the treatment effects are marginally significant

This paper studies the prevalence and evolution of gender bias in the United States Patent and Trademark Office (USPTO) examination process and assesses the consequences of this bias on economic outcomes. Applying Natural Language Processing tools to patent applications submitted between 2001 and 2013, I estimate gender gaps conditional on the content of the patent application, comparing allowance probabilities between teams of inventors with different gender compositions but similar inventions. Despite a substantial raw gender gap in the probability of initial allowance, I document that there is no average difference in initial allowance rates between mixed-gender and all-male teams. This average masks important heterogeneity. Allowance rates for mixed-gender teams were significantly lower between 2001 and 2003, a gap that shrank to zero by 2005. Gender gaps also vary substantially across examiners, with bias against mixed-gender patents concentrated among senior examiners and bias in favor of women concentrated among young examiners. A mean zero gender gap with positive variance generates economic loss due to the misallocation of granting rights. Building on the methodology of Kogan et al. (2017), I estimate that these biases depressed the value of approved patents by $12.6 million per year.

We utilize the universe of customer bank accounts in Israel and a unique nationwide digital reform that substantially reduced the time and effort required for some customers to transfer their financial activity between banks. Employing a difference-in-differences methodology, we find that this reduction in switching costs led to a significant increase in customer mobility, with affected customers being 50% more likely to switch banks. Our findings indicate that this effect is persistent and cannot be explained by temporary increases in customer attention. These results provide valuable insights into switching barriers within the banking sector and suggest that digital transformation in the industry could profoundly influence customer behavior, with potential implications for bank competition, stability, and business models.