Economics of Digitization Spring 2020

Most existing digital identity solutions are either centralized (e.g., national identity cards) or individualistic (e.g., most "self-sovereign" identity proposals). Outside of digital life, however, identity is typically social (for instance, "individual" data such as birthdate is shared with parents) and intersectional (viz., different data and trust are shared with different others). Immorlica, Jackson, and Weyl formalize these ideas to provide a more robust and realistic framework for decentralized identity. They build upon the concepts web-of-trust and social collateral, from cryptography and economics, to provide a system of defining, verifying, and making use of identity through networks. The researchers exploit the redundancy created by intersectionality together with the fragmentation of identity suggested by self-sovereign schemes to minimize social collateral required for verification. They exploit the probabilistic structure of Bloom filters to provide uniqueness proofs to prevent Sybil attacks while conveying minimal compromising information to verifiers. The researchers discuss applications to "proof-of-personhood" blockchains and Radical Markets.

Increasingly, retailers have access to better pricing technology, especially in online markets. Through pricing algorithms, firms can automate their response to rivals' prices. What are the implications for price competition? Brown and MacKay develop a model in which firms choose algorithms, rather than prices. Even with simple (i.e., linear) algorithms, competitive equilibria can have higher prices than in the standard simultaneous Bertrand pricing game. Using hourly prices of over-the-counter drugs from five major online retailers, they document evidence that these retailers possess different pricing technologies. In addition, the researchers find pricing patterns consistent with competition in pricing algorithms. A simple calibration of the model suggests that pricing algorithms lead to meaningful increases in markups, especially for firms with superior pricing technology.

In a range of studies across platforms, online ratings have been shown to be characterized by distributions with disproportionately-heavy tails. Brandes, Godes, and Mayzlin focus on understanding the underlying process that yields such "j-shaped" or "extreme" distributions. They develop a simple analytical model to capture the most-common explanations: differences in utility or differences in base rates associated with posting extreme versus moderate reviews. They compare the predictions of these explanations with those of an alternative theory based on differential rates of attrition from the potential reviewer pool across people with moderate versus extreme experiences. The attrition rate, by assumption, is higher for moderate reviews. The three models yield starkly different predictions with respect to the impact on the relative prevalence of extreme versus moderate reviews of a review solicitation email: while existing theories predict a relative increase in extreme reviews, our attrition-based model predicts a decrease. The results from a large-scale field experiment with an online travel platform clearly support the predictions from the attrition-based explanation, but are inconsistent with those from the utility-based and base-rate explanations alone.

Claussen, Peukert, and Sen run a field experiment to quantify the economic returns to data and informational externalities associated with algorithmic recommendation in the context of online news. The results show that personalized recommendation can outperform human curation in terms of user engagement, though this crucially depends on the amount of personal data. Limited individual data or breaking news leads the editor to outperform the algorithm. Additional data helps algorithmic performance but diminishing economic returns set in rapidly. Investigating informational externalities highlights that personalized recommendation reduces consumption diversity. Moreover, users associated with lower levels of digital literacy and more extreme political views engage more with algorithmic recommendations.

Platform gig work such as rideshare driving involves workers supplying flexibility to the platform, for example, providing service when demand is high. It also can be attractive to workers who demand flexibility, for example, workers with irregular commitments in other jobs. Who benefits the most (and least) from flexible work arrangements? Workers who supply labor price elastically provide flexibilty to the platform and receive above the platform-average compensation. In contrast, workers with the most time-variation in their reservation wage are demanders of flexibility and benefit from the availability of flexible work options. Using an empirical Bayesian model, Chen, Chevalier, Rossi, and Currier estimate driver-by-driver both the level and time variation in the driver reservation wage. They characterize the demographics of Uber drivers and explore the characteristics of drivers who supply flexibility and the characteristics of drivers who would drop out if the arrangement were less flexible. The results run counter to several common intuitions about the costs and benefits of gig work.

Alyakoob and Rahman examine the potential economic spillover effects of a home sharing platform -- Airbnb -- on the growth of a complimentary local service -- restaurants. By circumventing traditional land-use regulation and providing access to underutilized inventory, Airbnb is attracting visitors of a city to vicinities that are not traditional tourist destinations. The novel nature of the home-sharing offering means that visitors are lodging in areas that are not accustomed to tourists and, as such, may not have the underlying infrastructure to fully benefit from their visits. Although visitors generally bring significant spending power, it is ambiguous whether or not the visitors use Airbnb primarily for lodging, thus, not contributing to the adjacent vicinity economy. To evaluate this, the researchers focus on the impact of Airbnb on the restaurant employment growth across vicinities in New York City (NYC). Specifically, they focus on areas in NYC that did not attract a significant tourist volume prior to the home-sharing service. The results indicate a salient and economically significant positive spillover effect on restaurant job growth in an average NYC locality. A 1% increase in the intensity of Airbnb activity (Airbnb reviews per household) leads to approximately 1.7% restaurant employment growth. The researchers also investigate the role of demographics and market concentration in driving the variation. Notably, restaurants in areas with a relatively high number of White residents disproportionately benefit from the economic spillover of Airbnb activity whereas the impact in their black counterparts is not statistically significant. The researchers validate the underlying mechanism behind the main result by evaluating the impact of Airbnb on Yelp visitor reviews -- areas with increasing Airbnb activity experience a surge in their share of NYC visitor reviews. This result is further validated by evaluating the impact of a unique Airbnb neighborhood level exogenous policy recently implemented in New Orleans.

Miric and Yin investigate the entrepreneurship gender gap in technology industries. While digitization has created vast economic opportunities in the technology sector, it has also lowered many barriers to entry, reducing traditional frictions to entrepreneurship and thus potentially increasing opportunities for female founders. Using individual career histories from more than 600 million LinkedIn profiles, the researchers study whether females exhibit a higher rate of founding in technology industries. They report three main results: 1) Females are only half as likely as males to found businesses in technology industries. 2) Although there are fewer females employed in tech industries, even when the researchers use the gender gap in labor force participation as a baseline, the gender gap in tech entrepreneurship relative to the share of females employed in tech is wider than in other industries. 3) The gender gap in tech entrepreneurship is largely driven by lower rates of entrepreneurship for females in lower positions in the organizational hierarchy, by contrast, females who reach the C-suite in technology sectors are actually 16% more likely to found firms than their female C-suite counterparts in non-tech industries. Together, these results paint a more nuanced picture of the gender gap and provide important facts to inform policies intended to ameliorate the gender gap in tech.