IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v525y2019icp761-770.html
   My bibliography  Save this article

Tampering with inflation data: A Benford law-based analysis of national statistics in Argentina

Author

Listed:
  • Miranda-Zanetti, Maximilano
  • Delbianco, Fernando
  • Tohmé, Fernando

Abstract

There is a widespread consensus that the national statistics on inflation were manipulated by the Argentinean government from 2006 to 2015. The best known tool to run a forensic analysis of this claim is to check for the validity of Benford’s law in the data series. We find that indeed, the inflation for that period fails to satisfy this statistical regularity. We further compare this behavior to that of Argentina’s inflation series for the same period but recorded independently of the government; to that of the national records of 1943–2006, as well as to historical series of other countries. We find again that Argentina in 2006–2015 is the only one in our sample that can be singled out as candidate for statistical manipulation.

Suggested Citation

  • Miranda-Zanetti, Maximilano & Delbianco, Fernando & Tohmé, Fernando, 2019. "Tampering with inflation data: A Benford law-based analysis of national statistics in Argentina," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 761-770.
    • Handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:761-770
    DOI: 10.1016/j.physa.2019.04.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711930411X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.04.042?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bernhard Rauch & Max Göttsche & Gernot Brähler & Stefan Engel, 2011. "Fact and Fiction in EU‐Governmental Economic Data," German Economic Review, Verein für Socialpolitik, vol. 12(3), pages 243-255, August.
    2. 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.
    3. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially Biased Statistics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(1 (Spring), pages 59-108.
    4. Carlos Dabús & Fernando Delbianco & Andrés Fioriti, 2016. "High Inflation, Price Stability and Hysteresis Effect: Evidence from Argentina," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 31(1), pages 59-73, April.
    5. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
    6. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially Biased Statistics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 52(1 (Spring), pages 59-108.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Manuel de Mier & Fernando Delbianco, 2023. "Cu\'anto es demasiada inflaci\'on? Una clasificaci\'on de reg\'imenes inflacionarios," Papers 2401.02428, arXiv.org.
    2. De Mier Manuel, 2023. "¿Cuánto es demasiada inflación? Una clasificación de regímenes inflacionarios," Asociación Argentina de Economía Política: Working Papers 4640, Asociación Argentina de Economía Política.
    3. Meller, Leandro & Larrosa, Juan M.C. & Delbianco, Fernando & Ramírez Muñoz de Toro, Gonzalo & Uriarte, Juan Ignacio, 2021. "Inflación semanal en galletitas: un enfoque de datos de panel. || Weekly Cookie Inflation: A Panel Data Approach," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 31(1), pages 417-440, June.
    4. Lee, Kang-Bok & Han, Sumin & Jeong, Yeasung, 2020. "COVID-19, flattening the curve, and Benford’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    5. Azevedo, Caio da Silva & Gonçalves, Rodrigo Franco & Gava, Vagner Luiz & Spinola, Mauro de Mesquita, 2021. "A Benford’s Law based methodology for fraud detection in social welfare programs: Bolsa Familia analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    6. Herteliu, Claudiu & Jianu, Ionel & Dragan, Irina Maria & Apostu, Simona & Luchian, Iuliana, 2021. "Testing Benford’s Laws (non)conformity within disclosed companies’ financial statements among hospitality industry in Romania," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van Bergeijk, P.A.G., 2017. "Measurement error of global production," ISS Working Papers - General Series 632, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
    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.
    3. 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.
    4. Das, Abhiman & Lahiri, Kajal & Zhao, Yongchen, 2019. "Inflation expectations in India: Learning from household tendency surveys," International Journal of Forecasting, Elsevier, vol. 35(3), pages 980-993.
    5. Peter A.G. van Bergeijk, 2017. "Making Data Measurement Errors Transparent: The Case of the IMF," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 18(3), pages 133-154, July.
    6. Pappalardo, Gioacchino & West, Grant Howard & Nayga, Rodolfo M. & Toscano, Sabrina & Pecorino, Biagio, 2022. "The effect of a UNESCO world heritage site designation on willingness to pay to preserve an agri-environmental good: The case of the dry stone walls in Mt. Etna," Land Use Policy, Elsevier, vol. 114(C).
    7. Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," NBER Working Papers 25200, National Bureau of Economic Research, Inc.
    8. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2017. "Inflation Expectations, Learning, and Supermarket Prices: Evidence from Survey Experiments," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(3), pages 1-35, July.
    9. Baqaee, David Rezza, 2020. "Asymmetric inflation expectations, downward rigidity of wages, and asymmetric business cycles," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 174-193.
    10. Andreas Fuster & Ricardo Perez-Truglia & Mirko Wiederholt & Basit Zafar, 2022. "Expectations with Endogenous Information Acquisition: An Experimental Investigation," The Review of Economics and Statistics, MIT Press, vol. 104(5), pages 1059-1078, December.
    11. Young Bin Ahn & Yoichi Tsuchiya, 2022. "Consumer’s perceived and expected inflation in Japan—irrationality or asymmetric loss?," Empirical Economics, Springer, vol. 63(3), pages 1247-1292, September.
    12. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    13. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    14. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2021. "Spurious relationships in high-dimensional systems with strong or mild persistence," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1480-1497.
    15. Christopher Dobronyi & Christian Gouri'eroux, 2020. "Consumer Theory with Non-Parametric Taste Uncertainty and Individual Heterogeneity," Papers 2010.13937, arXiv.org, revised Jan 2021.
    16. Natasa Omerzu & Iztok Kolar, 2019. "Do the Financial Statements of Listed Companies on the Ljubljana Stock Exchange Pass the Benford’s Law Test?," International Business Research, Canadian Center of Science and Education, vol. 12(1), pages 54-64, January.
    17. El Mouaaouy Florian & Riepe Jan, 2018. "Benford and the Internal Capital Market: A Useful Indicator of Managerial Engagement," German Economic Review, De Gruyter, vol. 19(3), pages 309-329, August.
    18. Rishab Guha & Serena Ng, 2019. "A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 403-436, National Bureau of Economic Research, Inc.
    19. Samà, Danilo, 2014. "Essays on economic analysis of competition law: theory and practice," MPRA Paper 103118, University Library of Munich, Germany.
    20. Clippe, Paulette & Ausloos, Marcel, 2012. "Benford’s law and Theil transform of financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6556-6567.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:761-770. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.