Working Papers
Janeway Institute Working Paper (2022)
I examine inequalities arising from biases brought about by the incentives and externalities present in data markets, where a data collector ultimately provides an end-service which is beneficial. Agents receive different prices for their data, which is valued according to the extent that it is representative of the data of non-participating agents. The service provider estimates the characteristics of high-cost and minority groups with less accuracy, leading to these groups receiving lower quality services on average, and lower utility in equilibrium. Data privacy policies tend to reduce such inequalities but at the cost of consumer surplus, while a subsidy strategy targeted at increasing the utility of those disadvantaged by data markets increases consumer surplus but may also widen inequality.
Janeway Institute Working Paper (2022)
We examine the effect of social trust on a network in which agents communicate with each other and information sources, changing their opinion with some probability. Agents whose peers are more likely to spread misinformation are consequently less trusting than agents whose neighbours are more informed, and therefore change their views with less probability. When echo chambers are strong, weakening them results in there being more interaction between high and low social trust agents, increasing the spread of misinformation. When echo chambers are weak, however, weakening them further reduces the differences in social trust, decreasing the asymmetries in communication and hence the probability agents are misinformed. As a result of the non-linear relationship between the strength of echo chambers and the spread of misinformation, optimal interventions in network structure depend on why agents form links in the first place.
With Ruslan Momot and Marat Salikhov, 2022
Janeway Institute Working Paper (2021)
Online platforms shape the pattern of observation between buyers and sellers. We model buyer-seller interactions as a series of bipartite graphs, which are each realised with a probability chosen by the platform owner. To maximise profit, the platform owner ensures that the size of the neighbourhood of each buyer is consistent and randomises observation across every seller. When products are vertically differentiated, the platform owner faces a trade-off between biasing observation towards high-quality products and increasing competition. The extent to which platforms highlight high-quality products depends on the characteristics of the market(s) in which they operate.
A recent survey showed that 33% of businesses grant their employees access to all company data, with at least another 35% granting accesses to more data than is needed. Such overly permissive data access strategy allows the firms to run more efficiently, but at the same time, such strategies present growing cybersecurity risks. With work-from-home becoming more popular, remote employees are being increasingly exploited by the malicious adversaries to gain access to their organizations' data. To address this issue, we investigate the optimal design of data access architectures - who should have access to what data. Our economic model captures a firm managing a set of employees and a set of datasets. For each employee the firm chooses which datasets this employee should have access to. An employee may be attacked by a potentially sophisticated adversary whose goal is to steal all their data. The firm trades off the efficiency benefit of the more permissive data access architecture with the adversarial risk it incurs. We characterize the firm's optimal data access architecture and investigate how it depends both on the adversarial environment
Janeway Institute Working Paper (2021)
A platform holds information on the characteristics of its users and wants to maximise the joint profits of two firms, one popular and one niche. It does so by revealing some of these characteristics to the firms, generating a segmentation of the data which is partially informative of the preferences of the buyers. The platform prefers to reveal no information than disclose the consumer's type for certain, but they improve profits by showing both firms a segmentation where the niche firm is relatively popular, but still less popular than the other firm. The platform can potentially improve its profits further by revealing information asymmetrically. The platform has an incentive to provide more granular data in markets in which the niche firm is particularly unpopular or in which broad demographic categories are not particularly revelatory of type; the benefit of holding consumer data differs depending on market characteristics.
Janeway Institute Working Paper (2021)
Product ratings are commonplace on large online platforms, like Airbnb and Amazon Marketplace. One use for these ratings is to order search results. Platform owners are able to choose the extent to which ratings can be used to determine the probability a given seller is observed by a sets of buyers. Since demand is higher for high quality products, there is an incentive to increase the probability that highly-rated sellers are observed by biasing search results towards them. However, biasing search results in this way results in competition being more concentrated, reducing prices. The extent to which it is profitable to use ratings as a means of ordering search results depends on the properties of the market(s) the platform operates in.
Work-in-progress
Big data, competition and strategic location
With Ozan Candogan (Chicago Booth)
Spatial analytics is an increasingly popular and powerful tool for firms. We analyse the effect this data has on competition in a setting in which firms choose both their location in a city and prices. A known proportion of agents commute into the centre of a city, which is relatively dense compared with its suburbs. Data is informative of where agents work and live, and they can shop in either location. Without data, firms both always choose to locate in the centre if there is a sufficient probability that a single competitor in the centre attract enough commuters from their opponent’s suburb. With data, firms can distinguish states of the world in which such behaviour is profitable, leading to a reduction in competition and a decrease in consumer welfare. We characterise the topography of cities in which the effect of data on competition is greatest.