Measuring time inconsistency using financial transaction data

Improving financial conditions of individuals requires an understanding of the mechanisms through which bad financial decision-making leads to worse financial outcomes. From a theoretical point of view, a key candidate inducing mistakes in financial decision-making are so called present-biased preferences, which are one of the cornerstones of behavioral economics. According to theory, present-biased households should behave systematically different when it comes to consumption and saving decisions, as they should be more prone to spending too much and saving too little. In this policy letter we show how high frequency financial transaction data available in digitized form allows to precisely categorize individual financial-decision making to be present-biased or not. Using this categorization, we find that one out of five individuals in our sample exhibits present-bias and that this present-biased behavior is associated with a stronger use of overdrafts. As overdrafts represent a particularly expensive way of short-term borrowing, their systematic use can be interpreted as a measure of suboptimal financial-decision making. Overall, our results indicate that the combination of economic theory and Big Data is able to generate valuable insights with applications for policy makers and businesses alike.