Private Paternalism, the Commitment Puzzle, and Model-Free Equilibrium
David Laibson
Paternalism is a policy that advances an individual's interests by restricting his or her freedom. In a setting with present-biased agents, I characterize the scope of private paternalism—paternalism implemented by private institutions. Private paternalism arises from two channels: (i) agents who seek commitment because they hold sophisticated beliefs about their present bias, and (ii) agents (naive or sophisticated) who use model-free forecasts to choose organizations that have a history of generating high experienced utility flows for their members (O'Donoghue and Rabin 1999b). When naive consumers are common, private paternalism will be shrouded, explaining why commitment mechanisms are typically shrouded in the labor market (the commitment puzzle). Private paternalism has greater traction when production occurs in the formal sector instead of the informal (household) sector, where monitors are not always present, able, or willing to implement socially efficient forcing mechanisms.
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Algorithmic Fairness
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan and Ashesh Rambachan
Concerns that algorithms may discriminate against certain groups have led to numerous efforts to 'blind' the algorithm to race. We argue that this intuitive perspective is misleading and may do harm. Our primary result is exceedingly simple, yet often overlooked. A preference for fairness should not change the choice of estimator. Equity preferences can change how the estimated prediction function is used (e.g., different threshold for different groups) but the function itself should not change. We show in an empirical example for college admissions that the inclusion of variables such as race can increase both equity and efficiency.
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Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data
Susan Athey, David Blei, Robert Donnelly, Francisco Ruiz and Tobias Schmidt
We estimate a model of consumer choices over restaurants using data from several thousand anonymous mobile phone users. Restaurants have latent characteristics (whose distribution may depend on restaurant observables) that affect consumers' mean utility as well as willingness to travel to the restaurant, while each user has distinct preferences for these latent characteristics. We analyze how consumers reallocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants, comparing our predictions to actual outcomes. We also address counterfactual questions such as what type of restaurant would attract the most consumers in a given location.
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Estimating Economic Characteristics with Phone Data
Joshua E. Blumenstock
Historically, economists have relied heavily on survey-based data collection to measure social and economic well-being. Here, we investigate the extent to which the "digital footprints" of an individual can be used to infer his or her socioeconomic characteristics. Using two different datasets from Afghanistan and Rwanda, we show that phone data can be used to estimate the wealth of individuals in two very different economic environments. However, we find that such models are relatively brittle, and that a model trained in one country cannot be used to estimate characteristics in another. These results suggest several promising applications and directions for future work.
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