This paper provides evidence on the difficulty of expanding access to credit through large institutions. We use detailed observational data and a large-scale countrywide experiment to examine a large bank's experience with a credit card that accounted for approximately 15% of all first-time formal sector borrowing in Mexico in 2010. Borrowers have limited credit histories and high exit-risk -- a third of all study cards are defaulted on or cancelled during the 26 month sample period. We use a large-scale randomized experiment on a representative
sample of the bank's marginal borrowers to test whether contract terms affect default. We find that large experimental changes in interest rates and minimum payments do little to mitigate default risk. We also use detailed data on purchases and payments to construct a measure of bank revenue per card and find it is generally low and difficult to predict (using machine learning methods), perhaps explaining the bank's eventual discontinuation of the product. Finally, we show that borrowers generating a favorable credit history are much more likely to switch banks providing suggestive evidence of a lending externality. Taken together these facts highlight the difficulty of increasing financial access using large formal sector financial organizations.
In the digital economy, user data is typically treated as capital created by corporations observing willing individuals. This neglects users' role in creating data, reducing incentives for users, distributing the gains from the data economy unequally and stoking fears of automation. Instead treating data (at least partially) as labor could help resolve these issues and restore a functioning market for user contributions, but may run against the near-term interests of dominant data monopsonists who have benefited from data being treated as "free". Countervailing power, in the form of competition, a data labor movement and/or thoughtful regulation could help restore balance.