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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

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  • 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
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    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.
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    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.
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    Cited by:

    1. Arenas, Andreu & Malgouyres, Clément, 2018. "Countercyclical school attainment and intergenerational mobility," Labour Economics, Elsevier, vol. 53(C), pages 97-111.

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