IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01651144.html
   My bibliography  Save this paper

Étendue et conséquences des erreurs de mesure dans les données individuelles d'enquête : une évaluation à partir des données appariées des enquêtes emploi et revenus fiscaux

Author

Listed:
  • Cyrille Hagneré

    (THEMA - Théorie économique, modélisation et applications - UCP - Université de Cergy Pontoise - Université Paris-Seine - CNRS - Centre National de la Recherche Scientifique)

  • Arnaud Lefranc

    (THEMA - Théorie économique, modélisation et applications - UCP - Université de Cergy Pontoise - Université Paris-Seine - CNRS - Centre National de la Recherche Scientifique)

Abstract

Différents facteurs sont susceptibles d'introduire, un écart dans les données microéconomiques entre la vraie valeur des variables d'intérêt et les valeurs enregistrées dans les enquêtes : erreurs de déclaration (intentionnelles ou non), erreurs de saisie, erreurs de mémoire dans les données rétrospectives, ... Beaucoup d'études économétriques tendent encore à traiter ces erreurs de mesure comme un bruit négligeable ou sans conséquences pratiques. Pourtant, certains travaux récents ont révélé que la qualité des données utilisées et l'existence d'erreurs de mesure substantielles pouvaient avoir des conséquences critiques pour l'analyse économétrique. Les principaux enseignements théoriques en la matière sont, d'une part, que la présence d'erreurs de mesure conduit en général à biaiser les résultats d'estimations économétriques et, d'autre part, que le biais est d'autant important que le « bruit d'erreur de mesure » est grand, c'est-à-dire, en termes techniques, que la variance de l'erreur de mesure représente une part importante de la variance « vraie » de la variable considérée. La possibilité de tels biais plaide alors pour un examen empirique approfondi de l'étendue et des conséquences des erreurs de mesure dans les données recueillies dans les enquêtes individuelles. Plusieurs articles récents ont alors entrepris d'évaluer empiriquement l'ampleur des erreurs de mesure pour certaines enquêtes microéconomiques fréquemment utilisées, principalement des enquêtes nord-américaines. Les résultats obtenus sont évidemment difficilement généralisables : des différences, par exemple, dans la formulation du questionnaire ou dans les attitudes individuelles vis-à-vis des enquêtes statistiques sont en effet susceptibles d'engendrer des variations, d'une enquête ou d'un pays à l'autre, dans la qualité de l'information recueillie et dans la précision des déclarations. Toutefois, ces travaux mettent en évidence des effets substantiels des erreurs de mesure : par exemple, pour les déclarations de salaire, les biais possibles dans les estimations économétriques peuvent atteindre, voire dépasser 50%. En France, il n'existe pas d'études comparables. L'objet de cet article est de procéder à un tel examen à partir de l'enquête Emploi de l'INSEE, qui constitue l'une des principales sources de données individuelles pour l'étude du marché du travail français. 131 (*) OFCE, THEMA et IDEP

Suggested Citation

  • Cyrille Hagneré & Arnaud Lefranc, 2006. "Étendue et conséquences des erreurs de mesure dans les données individuelles d'enquête : une évaluation à partir des données appariées des enquêtes emploi et revenus fiscaux," Post-Print hal-01651144, HAL.
  • Handle: RePEc:hal:journl:hal-01651144
    Note: View the original document on HAL open archive server: https://hal.science/hal-01651144
    as

    Download full text from publisher

    File URL: https://hal.science/hal-01651144/document
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    2. Thierry Magnac & Michael Visser, 1999. "Transition Models With Measurement Errors," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 466-474, August.
    3. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    4. Mellow, Wesley & Sider, Hal, 1983. "Accuracy of Response in Labor Market Surveys: Evidence and Implications," Journal of Labor Economics, University of Chicago Press, vol. 1(4), pages 331-344, October.
    5. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-532, October.
    6. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
    7. Christine Lagarenne & Nadine Legendre, 2000. "Les travailleurs pauvres en France : facteurs individuels et familiaux," Économie et Statistique, Programme National Persée, vol. 335(1), pages 3-25.
    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. Arenas, Andreu & Malgouyres, Clément, 2018. "Countercyclical school attainment and intergenerational mobility," Labour Economics, Elsevier, vol. 53(C), pages 97-111.

    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. Jungmin Lee & Sokbae Lee, 2012. "Does it Matter WHO Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited," ILR Review, Cornell University, ILR School, vol. 65(1), pages 148-160, January.
    2. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    3. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    4. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    5. Lisa M. Dragoset & Gary S. Fields, 2006. "U.S. Earnings Mobility: Comparing Survey-Based and Administrative-Based Estimates," Working Papers 55, ECINEQ, Society for the Study of Economic Inequality.
    6. Paulus, Alari, 2015. "Tax evasion and measurement error: An econometric analysis of survey data linked with tax records," ISER Working Paper Series 2015-10, Institute for Social and Economic Research.
    7. Brownstone, David & Valletta, Robert G, 1996. "Modeling Earnings Measurement Error: A Multiple Imputation Approach," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 705-717, November.
    8. Akee, Randall K. Q., 2007. "Errors in Self-Reported Earnings: The Role of Previous Earnings Volatility," IZA Discussion Papers 3263, Institute of Labor Economics (IZA).
    9. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    10. Burt S. Barnow & David Greenberg, 2015. "Do Estimated Impacts on Earnings Depend on the Source of the Data Used to Measure Them? Evidence From Previous Social Experiments," Evaluation Review, , vol. 39(2), pages 179-228, April.
    11. Adam Bee & Joshua Mitchell & Nikolas Mittag & Jonathan Rothbaum & Carl Sanders & Lawrence Schmidt & Matthew Unrath, 2023. "National Experimental Wellbeing Statistics - Version 1," Working Papers 23-04, Center for Economic Studies, U.S. Census Bureau.
    12. Andrew S. Green, 2017. "Hours Off the Clock," Working Papers 17-44, Center for Economic Studies, U.S. Census Bureau.
    13. Randall Akee, Devesh Kapur, 2012. "Remittances and Rashomon- Working Paper 285," Working Papers 285, Center for Global Development.
    14. Lynn, Peter & Jäckle, Annette & Sala, Emanuela & P. Jenkins, Stephen, 2004. "Validation of survey data on income and employment: the ISMIE experience," ISER Working Paper Series 2004-14, Institute for Social and Economic Research.
    15. ChangHwan Kim & Christopher R. Tamborini, 2014. "Response Error in Earnings," Sociological Methods & Research, , vol. 43(1), pages 39-72, February.
    16. Bollinger, Christopher R. & Hirsch, Barry & Hokayem, Charles M. & Ziliak, James P., 2018. "Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch," IZA Discussion Papers 11710, Institute of Labor Economics (IZA).
    17. Javier Escobal & Sonia Laszlo, 2008. "Measurement Error in Access to Markets," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(2), pages 209-243, April.
    18. Mark C. Berger & Dan A. Black & Frank A. Scott, 1998. "How Well Do We Measure Employer‐Provided Health Insurance Coverage?," Contemporary Economic Policy, Western Economic Association International, vol. 16(3), pages 356-367, July.
    19. Li, Hao & Millimet, Daniel L. & Roychowdhury, Punarjit, 2019. "Measuring Economic Mobility in India Using Noisy Data: A Partial Identification Approach," IZA Discussion Papers 12505, Institute of Labor Economics (IZA).
    20. Akee, Randall, 2011. "Errors in self-reported earnings: The role of previous earnings volatility and individual characteristics," Journal of Development Economics, Elsevier, vol. 96(2), pages 409-421, November.

    More about this item

    Statistics

    Access and download statistics

    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:hal:journl:hal-01651144. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.