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

Alberto  Cavallo; Guillermo  Cruces; Ricardo  Perez-Truglia

Article | Brookings Papers on Economic Activity

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

by Alberto Cavallo, Guillermo Cruces and Ricardo Perez-Truglia

Abstract

When forming expectations, households may be influenced by perceived bias in the information they receive. 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. This period is interesting because attention was being given to inflation information and both official and unofficial statistics were available. Our evidence suggests that, rather than ignoring biased statistics or naively accepting them, households react in a sophisticated way, as predicted by a Bayesian learning model. 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 could also be 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 are not.

Keywords: inflation expectations; Bayesian Estimation; Inflation and Deflation; Information; Household; Behavior; Argentina;

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Cavallo, Alberto, Guillermo Cruces, and Ricardo Perez-Truglia. "Learning from Potentially Biased Statistics: Household Inflation Perceptions and Expectations in Argentina." Brookings Papers on Economic Activity (Spring 2016): 59–108.

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In early 2020, a coronavirus pandemic swept the globe, ushering in unprecedented disruptions to the global economy. Both supply and demand were decimated by lockdowns and border closings, as well as reduced spending by fearful consumers. In response, governments around the world made equally unprecedented policy moves, spending trillions of dollars on fiscal stimulus measures to bridge the gap during the self-imposed shutdown. As the pandemic progressed, governments had to weigh the costs between loss of life due to opening up their economies and the economic devastation caused by delaying a reopening.

The HBS COVID-19 Global Policy Tracker is an initiative by the Business, Government and the International Economy (BGIE) unit at Harvard Business School (HBS) to collect and standardize economic policies implemented as a response to the coronavirus pandemic around the world. We focus on fiscal policy, monetary policy, and lockdowns and quantify government responses in common variables, units, and currencies to facilitate cross-country comparisons. The database is updated in real time with the efforts of several dozens of students at HBS and other Harvard schools.

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

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