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This article investigates the strategies of a data broker when selling information to one or to two competing firms that can price discriminate consumers. The data broker can strategically choose any segment of the consumer demand (the information structure) to sell to firms that implement third-degree price discrimination. We show that data broker's equilibrium profits are maximized when (1) information identifies consumers with the highest willingness to pay; (2) consumers with a low willingness to pay remain unidentified; and (3) the data broker sells two symmetrical information structures. The data broker therefore strategically sells partial information on consumers to soften competition between firms.Extending the baseline model, we prove that these results hold under first-degree price discrimination. * We would like to thank

This paper investigates the strategies of a data broker in selling information to one or to two competing firms that can price-discriminate consumers. The data broker can strategically choose any segment of the consumer demand (information structure) to sell to firms that implement third-degree price-discrimination. We show that the equilibrium profits of the data broker are maximized when (1) information identifies the consumers with the highest willingness to pay;(2) consumers with a low willingness to pay remain unidentified; (3) the data broker sells two symmetrical information structures. The data broker therefore strategically sells partial information on consumers in order to soften competition between firms. Extending the baseline model, we prove that these results hold under first-degree price-discrimination.

Mobile financial services such as M-PESA in Kenya are said to promote inclusion. Yet only 7.6 per cent of the Kenyans in the 2013 Financial Inclusion Insights dataset have ever used an M-PESA account to save for a future purchase. This paper uses a novel, three-step probit analysis to identify the socio-demographic characteristics of, successively, respondents who do not have access to a SIM card, have access to a SIM but do not have an M-PESA account, and, finally, have an account but do not save on it. We find that those who are excluded in the early stages are predominantly poor, non-educated, and female. For the final stage, we find that those who are in a position to save on their phone—the phone owners, the better educated—are less likely to do so. These results go against the traditional optimistic discourse on mobile savings as a prime path to financial inclusion. As such, our findings corroborate qualitative research that indicates that Kenyans have other needs, and want their money to circulate and ‘work’.