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Survey response and survey characteristics: Micro-level evidence from the ECHP

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  • Nicoletti, Cheti
  • Peracchi, Franco

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Abstract

This paper presents some micro-level evidence on the role of the socio-demographic characteristics of the population and the characteristics of the data collection process as predictors of survey response. Our evidence is based on the public use files of the European Community Household Panel (ECHP), a longitudinal household survey covering the countries of the European Union, whose attractive feature is the high level of comparability across countries and over time. We use individual-level information to predict response in the next wave given response in the current wave, focusing on how the probabilities of contact failure and refusal to cooperate vary with the socio-demographic composition of the national populations and the characteristics of the data collection process. We model the response process as the outcome of two sequential events; (i) the contact between the interviewer and an eligible interviewee, and (ii) the cooperation of the interviewee. Our model allows for dependence between the ease of contact and the propensity to cooperate, taking into account the censoring problem caused by the fact that we observe whether a person is a respondent only if she has been contacted.

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Bibliographic Info

Paper provided by University of Molise, Dept. EGSeI in its series Economics & Statistics Discussion Papers with number esdp04015.

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Length: 28 pages
Date of creation: 20 Jan 2004
Date of revision:
Handle: RePEc:mol:ecsdps:esdp04015

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Keywords: Panel data; survey response; bivariate probit model.;

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  1. Pamela Campanelli & Colm O'Muircheartaigh, 2002. "The Importance of Experimental Control in Testing the Impact of Interviewer Continuity on Panel Survey Nonresponse," Quality & Quantity: International Journal of Methodology, Springer, Springer, vol. 36(2), pages 129-144, May.
  2. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, Econometric Society, vol. 47(1), pages 153-61, January.
  3. Morimune, Kimio, 1979. "Comparisons of Normal and Logistic Models in the Bivariate Dichotomous Analysis," Econometrica, Econometric Society, Econometric Society, vol. 47(4), pages 957-75, July.
  4. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
  5. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0220, National Bureau of Economic Research, Inc.
  6. Franco Peracchi, 2002. "The European Community Household Panel: A review," Empirical Economics, Springer, Springer, vol. 27(1), pages 63-90.
  7. John M. Abowd & Bruno Crepon & Francis Kramarz, 1997. "Moment Estimation with Attrition," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0214, National Bureau of Economic Research, Inc.
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Cited by:
  1. Del Boca, Daniela & Sauer, Robert M., 2006. "Life Cycle Employment and Fertility Across Institutional Environments," IZA Discussion Papers 2285, Institute for the Study of Labor (IZA).
  2. Clark, Andrew E., 2006. "A Note on Unhappiness and Unemployment Duration," IZA Discussion Papers 2406, Institute for the Study of Labor (IZA).
  3. Nicole Watson & Roger Wilkins, 2012. "Experimental Change from Paper-Based Interviewing to Computer-Assisted Interviewing in the HILDA Survey," Melbourne Institute Working Paper Series, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne wp2012n06, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  4. Nicole Watson & Roger Wilkins, 2012. "The Impact of Computer-Assisted Interviewing on Interview Length," Melbourne Institute Working Paper Series, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne wp2012n10, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.

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