IDEAS home Printed from https://ideas.repec.org/a/iza/izawol/journl2017n352.html
   My bibliography  Save this article

The need for and use of panel data

Author

Listed:
  • Hans-Jürgen Andreß

    (University of Cologne, Germany)

Abstract

Stability and change are essential elements of social reality and economic progress. Cross-sectional surveys are a means of providing information on specific issues at a particular point in time, though without providing any information about the prevailing stability. Limited information on change can be obtained by retrospective questioning, but this is often impaired by “recall bias.” However, valid information on change is essential for assessing whether phenomena such as poverty are permanent or only temporary. Panel data analyses can address these problems as well as provide an essential tool for effective policy design.

Suggested Citation

  • Hans-Jürgen Andreß, 2017. "The need for and use of panel data," IZA World of Labor, Institute of Labor Economics (IZA), pages 352-352, April.
  • Handle: RePEc:iza:izawol:journl:2017:n:352
    as

    Download full text from publisher

    File URL: https://wol.iza.org/uploads/articles/352/pdfs/the-need-for-and-use-of-panel-data.pdf
    Download Restriction: no

    File URL: https://wol.iza.org/articles/the-need-for-and-use-of-panel-data
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edward P. Lazear, 2000. "Performance Pay and Productivity," American Economic Review, American Economic Association, vol. 90(5), pages 1346-1361, December.
    2. Marcel Das & Vera Toepoel & Arthur van Soest, 2011. "Nonparametric Tests of Panel Conditioning and Attrition Bias in Panel Surveys," Sociological Methods & Research, , vol. 40(1), pages 32-56, February.
    3. Card, David, 1999. "The causal effect of education on earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 30, pages 1801-1863, Elsevier.
    4. Joachim R. Frick & Jan Goebel & Edna Schechtman & Gert G. Wagner & Shlomo Yitzhaki, 2006. "Using Analysis of Gini (ANOGI) for Detecting Whether Two Subsamples Represent the Same Universe," Sociological Methods & Research, , vol. 34(4), pages 427-468, May.
    5. Johannes Van Der Zouwen & Theo Van Tilburg, 2001. "Reactivity in Panel Studies and its Consequences for Testing Causal Hypotheses," Sociological Methods & Research, , vol. 30(1), pages 35-56, August.
    6. Bartel, Ann P, 1995. "Training, Wage Growth, and Job Performance: Evidence from a Company Database," Journal of Labor Economics, University of Chicago Press, vol. 13(3), pages 401-425, July.
    7. O. Ashenfelter & D. Card (ed.), 1999. "Handbook of Labor Economics," Handbook of Labor Economics, Elsevier, edition 1, volume 3, number 3.
    Full references (including those not matched with items on IDEAS)

    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. Hans Dietrich & Harald Pfeifer & Felix Wenzelmann, 2016. "The more they spend, the more I earn? Firms' training investments and post-training wages of apprentices," Economics of Education Working Paper Series 0116, University of Zurich, Department of Business Administration (IBW).
    2. repec:dau:papers:123456789/4462 is not listed on IDEAS
    3. Christophe Muller & Christophe Nordman, 2004. "Which Human Capital Matters For Rich And Poor'S Wages: Evidence From Matched Worker-Firm Data From Tunisia," Working Papers. Serie AD 2004-28, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    4. Michael Waldman, 2012. "Theory and Evidence in Internal LaborMarkets [The Handbook of Organizational Economics]," Introductory Chapters,, Princeton University Press.
    5. Jozef Konings & Stijn Vanormelingen, 2015. "The Impact of Training on Productivity and Wages: Firm-Level Evidence," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 485-497, May.
    6. Domenico Depalo & Sabrina Di Addario, "undated". "Shedding Light on Inventors' Returns to Patents," Development Working Papers 375, Centro Studi Luca d'Agliano, University of Milano.
    7. Kristinn Hermannsson & Patrizio Lecca, 2016. "Human Capital in Economic Development: From Labour Productivity to Macroeconomic Impact," Economic Papers, The Economic Society of Australia, vol. 35(1), pages 24-36, March.
    8. María Arrazola & José de Hevia, 2003. "Evaluación económica de políticas educativas: Una ilustración con la Ley General de la Educación de 1970," Hacienda Pública Española / Review of Public Economics, IEF, vol. 164(1), pages 111-127, march.
    9. Kemptner, Daniel & Tolan, Songül, 2018. "The role of time preferences in educational decision making," Economics of Education Review, Elsevier, vol. 67(C), pages 25-39.
    10. Grossmann, Volker, 2008. "Risky human capital investment, income distribution, and macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 19-42, March.
    11. Campos, Nauro F. & Jolliffe, Dean, 2003. "After, before and during: returns to education in Hungary (1986-1998)," Economic Systems, Elsevier, vol. 27(4), pages 377-390, December.
    12. Emanuela di Gropello, 2006. "Meeting the Challenges of Secondary Education in Latin America and East Asia : Improving Efficiency and Resource Mobilization," World Bank Publications - Books, The World Bank Group, number 7173.
    13. Bas Jacobs, 2013. "Optimal redistributive tax and education policies in general equilibrium," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 20(2), pages 312-337, April.
    14. David Campbell, 2001. "Rates of Return to Schooling and the Quality of Education in England and Wales," Studies in Economics 0115, School of Economics, University of Kent.
    15. Huong Thu Le & Ha Trong Nguyen, 2018. "The evolution of the gender test score gap through seventh grade: new insights from Australia using unconditional quantile regression and decomposition," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 7(1), pages 1-42, December.
    16. Martin Schlotter & Guido Schwerdt & Ludger Woessmann, 2011. "Econometric methods for causal evaluation of education policies and practices: a non-technical guide," Education Economics, Taylor & Francis Journals, vol. 19(2), pages 109-137.
    17. Peydró, José-Luis & Jasova, Martina & Mendicino, Caterina & Panetti, Ettore & Supera, Dominik, 2021. "Monetary Policy, Labor Income Redistribution and the Credit Channel: Evidence from Matched Employer-Employee and Credit Registe," CEPR Discussion Papers 16549, C.E.P.R. Discussion Papers.
    18. Kai Barron & Luis F. Gamboa & Paul Rodríguez-Lesmes, 2019. "Behavioural Response to a Sudden Health Risk: Dengue and Educational Outcomes in Colombia," Journal of Development Studies, Taylor & Francis Journals, vol. 55(4), pages 620-644, April.
    19. María laura Alzúa & Guillermo Cruces & Carolina Lopez, 2016. "Long-Run Effects Of Youth Training Programs: Experimental Evidence From Argentina," Economic Inquiry, Western Economic Association International, vol. 54(4), pages 1839-1859, October.
    20. Kai Carstensen & Erich Gundlach & Susanne Hartmann, 2009. "The Augmented Solow Model with Mincerian Schooling and Externalities," German Economic Review, Verein für Socialpolitik, vol. 10(4), pages 448-463, November.
    21. Alan B. Krueger, 2002. "Inequality, Too Much of a Good Thing," Working Papers 845, Princeton University, Department of Economics, Industrial Relations Section..

    More about this item

    Keywords

    panel data; panel attrition; individual change; cohort analysis; omitted variable bias; selection;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J10 - Labor and Demographic Economics - - Demographic Economics - - - General

    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:iza:izawol:journl:2017:n:352. 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: Institute of Labor Economics (IZA) (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.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.