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


  • Alberto Cavallo
  • Guillermo Cruces
  • Ricardo Perez-Truglia


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.

Suggested Citation

  • Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially-Biased Statistics: Household Inflation Perceptions and Expectations in Argentina," NBER Working Papers 22103, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:22103
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    References listed on IDEAS

    1. Paul E. Carrillo & M. Shahe Emran, 2012. "Public Information and Inflation Expectations: Microeconometric Evidence from a Natural Experiment," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 860-877, November.
    2. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2014. "Inflation Expectations, Learning and Supermarket Prices," NBER Working Papers 20576, National Bureau of Economic Research, Inc.
    3. Pérez Quirós, Gabriel & Pérez, Javier J. & Paredes, Joan, 2015. "Fiscal targets. A guide to forecasters?," Working Paper Series 1834, European Central Bank.
    4. Bruine de Bruin, Wändi & van der Klaauw, Wilbert & Topa, Giorgio, 2011. "Expectations of inflation: The biasing effect of thoughts about specific prices," Journal of Economic Psychology, Elsevier, vol. 32(5), pages 834-845.
    5. Andreas Fuster & David Laibson & Brock Mendel, 2010. "Natural Expectations and Macroeconomic Fluctuations," Journal of Economic Perspectives, American Economic Association, vol. 24(4), pages 67-84, Fall.
    6. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," Review of Economic Studies, Oxford University Press, vol. 76(3), pages 1071-1102.
    7. Tomasz Michalski & Gilles Stoltz, 2013. "Do Countries Falsify Economic Data Strategically? Some Evidence That They Might," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 591-616, May.
    8. Camacho, Maximo & Dal Bianco, Marcos & Martinez-Martin, Jaime, 2015. "Toward a more reliable picture of the economic activity: An application to Argentina," Economics Letters, Elsevier, vol. 132(C), pages 129-132.
    9. Sebastian J. Goerg & Johannes Kaiser, 2009. "Nonparametric testing of distributions—the Epps–Singleton two-sample test using the empirical characteristic function," Stata Journal, StataCorp LP, vol. 9(3), pages 454-465, September.
    10. Mary A. Burke & Michael Manz, 2011. "Economic literacy and inflation expectations: evidence from a laboratory experiment," Public Policy Discussion Paper 11-8, Federal Reserve Bank of Boston.
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    Cited by:

    1. Alberto Cavallo, 2017. "Are Online and Offline Prices Similar? Evidence from Large Multi-channel Retailers," American Economic Review, American Economic Association, vol. 107(1), pages 283-303, January.
    2. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.

    More about this item

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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