Learning from Potentially-Biased Statistics: Household Inflation Perceptions and Expectations in Argentina

Alberto Cavallo, Guillermo Cruces and Ricardo Perez-Truglia

No 22103, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: When forming expectations, households may be influenced by the possibility that the information they receive is biased. In this paper, we study how individuals learn from potentially-biased statistics using data from both a natural and a survey-based experiment obtained during a period of government manipulation of inflation statistics in Argentina (2006-2015). This period is interesting because of the attention to inflation information and the availability of both official and unofficial statistics. Our evidence suggests that rather than ignoring biased statistics or navively taking them at face value, households react in a sophisticated way, as predicted by a Bayesian learning model, effectively de-biasing the official data to extract all its useful content. We also find evidence of an asymmetric reaction to inflation signals, with expectations changing more when the inflation rate rises than when it falls. These results are useful for understanding the formation of inflation expectations in less extreme contexts than Argentina, such as the United States and Europe, where experts may agree that statistics are unbiased but households do not.

JEL-codes: C83 C93 E31 E58 (search for similar items in EconPapers)
Date: 2016-03
New Economics Papers: this item is included in nep-mac and nep-mon
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Citations: View citations in EconPapers (8)

Published as Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially Biased Statistics," Brookings Papers on Economic Activity, vol 2016(1), pages 59-108.

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