In this paper, we study how individuals learn from potentially biased statistics using data from both a natural experiment and a survey experiment during a period (2007-15) when the government of Argentina was manipulating official inflation statistics.
To address these limitations in the observational data, we provide a simple model of Bayesian learners with potentially biased statistics and design a survey experiment to test its predictions.
More generally, the study of biased statistics goes back to the seminal contribution by Oskar Morgenstem (1963) on measurement, accuracy, and uncertainty in economics.
The most important prediction of this model is that a Bayesian learner is not expected to ignore biased statistics, but instead rationally adjust to the perceived bias.
To understand how households learn from potentially biased statistics, we utilize data from a natural experiment and a survey experiment based on the period when the Argentine government manipulated inflation statistics.