IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v169y2006i3p625-646.html
   My bibliography  Save this article

The effects of income imputation on microanalyses: evidence from the European Community Household Panel

Author

Listed:
  • Cheti Nicoletti
  • Franco Peracchi

Abstract

Summary. Social surveys are usually affected by item and unit non‐response. Since it is unlikely that a sample of respondents is a random sample, social scientists should take the missing data problem into account in their empirical analyses. Typically, survey methodologists try to simplify the work of data users by ‘completing’ the data, filling the missing variables through imputation. The aim of the paper is to give data users some guidelines on how to assess the effects of imputation on their microlevel analyses. We focus attention on the potential bias that is caused by imputation in the analysis of income variables, using the European Community Household Panel as an illustration.

Suggested Citation

  • Cheti Nicoletti & Franco Peracchi, 2006. "The effects of income imputation on microanalyses: evidence from the European Community Household Panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 625-646, July.
    • Handle: RePEc:bla:jorssa:v:169:y:2006:i:3:p:625-646
    DOI: 10.1111/j.1467-985X.2006.00421.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-985X.2006.00421.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-985X.2006.00421.x?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
    ---><---

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Lorenzo Cappellari & Stephen P. Jenkins, 2004. "Modelling low income transitions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 593-610.
    3. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Franco Peracchi, 2002. "The European Community Household Panel: A review," Empirical Economics, Springer, vol. 27(1), pages 63-90.
    6. Jeffrey M. Wooldridge, 1999. "Asymptotic Properties of Weighted M-Estimators for Variable Probability Samples," Econometrica, Econometric Society, vol. 67(6), pages 1385-1406, November.
    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. Hai Zhong, 2010. "The impact of missing data in the estimation of concentration index: a potential source of bias," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(3), pages 255-266, June.
    2. Andrew E. Clark, 2006. "A Note on Unhappiness and Unemployment Duration," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 52(4), pages 291-308.
    3. Frick, Joachim R. & Grabka, Markus M. & Groh-Samberg, Olaf, 2012. "Dealing With Incomplete Household Panel Data in Inequality Research," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 41(1), pages 89-123.
    4. Shunji Tada & Koyo Miyoshi, 2015. "Verifying household incomes in Japanese statistics," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 11(4), pages 531-546, September.
    5. Frick, Joachim R. & Grabka, Markus M., 2007. "Item Non-Response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective," IZA Discussion Papers 3043, Institute of Labor Economics (IZA).
    6. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2013. "Bayesian Model Averaging for Generalized Linear Models with Missing Covariates," EIEF Working Papers Series 1311, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.
    7. Nicoletti, Cheti & Peracchi, Franco & Foliano, Francesca, 2011. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 61-72.
    8. Fausta Ongaro & Stefano Mazzuco & Silvia Meggiolaro, 2009. "Economic Consequences of Union Dissolution in Italy: Findings from the European Community Household Panel," European Journal of Population, Springer;European Association for Population Studies, vol. 25(1), pages 45-65, February.
    9. Adel Bosch & Steven F. Koch, 2021. "Individual and Household Debt: Does Imputation Choice Matter?," Working Papers 202141, University of Pretoria, Department of Economics.
    10. Ronald Hagan & Andrew M. Jones & Nigel Rice, 2009. "Health and Retirement in Europe," IJERPH, MDPI, vol. 6(10), pages 1-20, October.

    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. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    2. Benzeval, Michaela & Davillas, Apostolos & M. Jones, Andrew, 2017. "The income-health gradient: evidence from self-reported health and biomarkers using longitudinal data on income," ISER Working Paper Series 2017-03, Institute for Social and Economic Research.
    3. Besstremyannaya, Galina, 2017. "Heterogeneous effect of the global financial crisis and the Great East Japan Earthquake on costs of Japanese banks," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 66-89.
    4. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    5. Peracchi, Franco, 2002. "On estimating conditional quantiles and distribution functions," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 433-447, February.
    6. Francesco Devicienti & Valentina Gualtieri & Mariacristina Rossi, 2014. "The Persistence Of Income Poverty And Lifestyle Deprivation: Evidence From Italy," Bulletin of Economic Research, Wiley Blackwell, vol. 66(3), pages 246-278, July.
    7. O.S. Mariev & N.B. Davidson & O.S. Emelianova, 2020. "The Impact of Urbanization on Carbon Dioxide Emissions in the Regions of Russia," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 19(3), pages 286-309.
    8. Chunbei Wang & Le Wang, 2011. "Language Skills and the Earnings Distribution Among Child Immigrants," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 50(2), pages 297-322, April.
    9. Strike Mbulawa & Francis Nathan Okurut & Mogale Ntsosa & Narain Sinha, 2020. "Dynamics of Corporate Dividend Policy under Hyperinflation and Dollarization: A Quantile Regression Approach," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 13(3), pages 70-82, December.
    10. Parente, Paulo M.D.C. & Smith, Richard J., 2011. "Gel Methods For Nonsmooth Moment Indicators," Econometric Theory, Cambridge University Press, vol. 27(1), pages 74-113, February.
    11. Duschl, Matthias & Schimke, Antje & Brenner, Thomas & Luxen, Dennis, 2011. "Firm growth and the spatial impact of geolocated external factors: Empirical evidence for German manufacturing firms," Working Paper Series in Economics 36, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    12. Agbeyegbe, Terence D., 2015. "An inverted U-shaped crude oil price return-implied volatility relationship," Review of Financial Economics, Elsevier, vol. 27(C), pages 28-45.
    13. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    14. G. Reza Arabsheibani & Francisco Galrao Carneiro & Andrew Henley, 2003. "Human capital and earnings inequality in Brazil, 1988-98 : quantile regression evidence," Policy Research Working Paper Series 3147, The World Bank.
    15. Duc Hong Vo & Thach Ngoc Pham, 2017. "Systematic Risk in Energy Businesses: Empirical Evidence for the ASEAN," International Journal of Economics and Financial Issues, Econjournals, vol. 7(1), pages 553-565.
    16. Makowsky, Michael, 2009. "Religious Extremism, Clubs, and Civil Liberties: A Model of Religious Populations," MPRA Paper 14358, University Library of Munich, Germany.
    17. Alex Coad & Rekha Rao, 2007. "The employment effects of innovation," Documents de travail du Centre d'Economie de la Sorbonne r07036, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    18. Coad, Alex & Segarra, Agustí & Teruel, Mercedes, 2016. "Innovation and firm growth: Does firm age play a role?," Research Policy, Elsevier, vol. 45(2), pages 387-400.
    19. Gustavsen, Geir Waehler, 2005. "Public Policies and the Demand for Carbonated Soft Drinks: A Censored Quantile Regression Approach," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24737, European Association of Agricultural Economists.
    20. Costa-Font, Joan & Fabbri, Daniele & Gil, Joan, 2009. "Decomposing body mass index gaps between Mediterranean countries: A counterfactual quantile regression analysis," Economics & Human Biology, Elsevier, vol. 7(3), pages 351-365, December.

    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:bla:jorssa:v:169:y:2006:i:3:p:625-646. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.