IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v138y2018i2d10.1007_s11205-017-1672-7.html
   My bibliography  Save this article

Differences Between Household Income from Surveys and Registers and How These Affect the Poverty Headcount: Evidence from the Austrian SILC

Author

Listed:
  • Stefan Angel

    (WU Vienna University of Economics and Business)

  • Richard Heuberger

    (Statistics Austria)

  • Nadja Lamei

    (Statistics Austria)

Abstract

We take advantage of the fact that for the Austrian SILC 2008–2011, two data sources are available in parallel for the same households: register-based and survey-based income data. Thus, we aim to explain which households tend to under- or over-report their household income by estimating multinomial logit and OLS models with covariates referring to the interview situation, employment status and socio-demographic household characteristics. Furthermore, we analyze source-specific differences in the distribution of household income and how these differences affect aggregate poverty indicators based on household income. The analysis reveals an increase in the cross-sectional poverty rates for 2008–2011 and the longitudinal poverty rate if register data rather than survey data are used. These changes in the poverty rate are mainly driven by differences in employment income rather than sampling weights and other income components. Regression results show a pattern of mean-reverting errors when comparing household income between the two data sources. Furthermore, differences between data sources for both under-reporting and over-reporting slightly decrease with the number of panel waves in which a household participated. Among the other variables analyzed that are related to the interview situation (mode, proxy, interview month), only the number of proxy interviews was (weakly) positively correlated with the difference between data sources, although this outcome was not robust over different model specifications.

Suggested Citation

  • Stefan Angel & Richard Heuberger & Nadja Lamei, 2018. "Differences Between Household Income from Surveys and Registers and How These Affect the Poverty Headcount: Evidence from the Austrian SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 575-603, July.
  • Handle: RePEc:spr:soinre:v:138:y:2018:i:2:d:10.1007_s11205-017-1672-7
    DOI: 10.1007/s11205-017-1672-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-017-1672-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11205-017-1672-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Claus Thustrup Kreiner & David Dreyer Lassen & Søren Leth-Petersen, 2014. "Measuring the Accuracy of Survey Responses Using Administrative Register Data: Evidence from Denmark," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 289-307, National Bureau of Economic Research, Inc.
    2. Inmaculada Martínez-Zarzoso, 2013. "The log of gravity revisited," Applied Economics, Taylor & Francis Journals, vol. 45(3), pages 311-327, January.
    3. Ulrich Rendtel & Rolf Langeheine & Roland Berntsen, 1998. "The Estimation Of Poverty Dynamics Using Different Measurements Of Household Income," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 81-98, March.
    4. Diana Worts & Amanda Sacker & Peggy McDonough, 2010. "Re-Assessing Poverty Dynamics and State Protections in Britain and the US: The Role of Measurement Error," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 97(3), pages 419-438, July.
    5. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    6. Francesco Figari & Maria Iacovou & Alexandra Skew & Holly Sutherland, 2012. "Approximations to the Truth: Comparing Survey and Microsimulation Approaches to Measuring Income for Social Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 105(3), pages 387-407, February.
    7. Santos Silva, J.M.C. & Tenreyro, Silvana, 2011. "Further simulation evidence on the performance of the Poisson pseudo-maximum likelihood estimator," Economics Letters, Elsevier, vol. 112(2), pages 220-222, August.
    8. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    9. Kathleen McGarry, 1995. "Measurement Error and Poverty Rates of Widows," Journal of Human Resources, University of Wisconsin Press, vol. 30(1), pages 113-134.
    10. 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.
    11. 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.
    12. Chadi, Adrian, 2013. "The role of interviewer encounters in panel responses on life satisfaction," Economics Letters, Elsevier, vol. 121(3), pages 550-554.
    13. Detlef Landua, 1992. "An attempt to classify satisfaction changes: Methodological and content aspects of a longitudinal problem," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 26(3), pages 221-241, May.
    14. 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.
    15. Richard Breen & Pasi Moisio, 2004. "Poverty dynamics corrected for measurement error," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 2(3), pages 171-191, July.
    16. Mark Wooden & Ning Li, 2014. "Panel Conditioning and Subjective Well-being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 117(1), pages 235-255, May.
    17. Kirstine Hansen & Dylan Kneale, 2013. "Does How You Measure Income Make a Difference to Measuring Poverty? Evidence from the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(3), pages 1119-1140, February.
    18. Pirmin Fessler & Kasy, Maximilian & Peter Lindner, 2012. "Survey mode effects on income inequality measurement," Working Paper 48766, Harvard University OpenScholar.
    19. Frauke Kreuter & Richard Valliant, 2007. "A survey on survey statistics: What is done and can be done in Stata," Stata Journal, StataCorp LP, vol. 7(1), pages 1-21, February.
    20. Frick, Joachim R. & Goebel, Jan & Schechtman, Edna & Wagner, Gert G. & Yitzhaki, Shlomo, 2006. "Using Analysis of Gini (ANOGI) for Detecting Whether Two Subsamples Represent the Same Universe: The German Socio-Economic Panel Study (SOEP) Experience," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 34(4), pages 427-468.
    21. 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.
    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. García-Suaza, A & Lobo, J & Montoya, S & Ordóñez, J & Oviedo, J. D, 2022. "Impact of the collection mode on labor income data. A study in the times of COVID19," Documentos de Trabajo 20396, Universidad del Rosario.
    2. S.T, Pavan Kumar & Lahiri, Biswajit, 2023. "Conditional selection of multifactor evidence for the levels of anaemia among women of reproductive age group," Evaluation and Program Planning, Elsevier, vol. 100(C).
    3. Alessio Terzi, 2021. "Economic Policy-Making Beyond GDP An Introduction," European Economy - Discussion Papers 142, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    4. Sabatini, Serena & Martyr, Anthony & Gamble, Laura D. & Jones, Ian R. & Collins, Rachel & Matthews, Fiona E. & Knapp, Martin & Thom, Jeanette M. & Henderson, Catherine & Victor, Christina & Pentecost,, 2023. "Are profiles of social, cultural, and economic capital related to living well with dementia? Longitudinal findings from the IDEAL programme," Social Science & Medicine, Elsevier, vol. 317(C).
    5. Sabatini, Serena & Martyr, Anthony & Gamble, Laura D. & Jones, Ian R. & Collins, Rachel & Matthews, Fiona E. & Knapp, Martin & Thom, Jeanette M. & Henderson, Catherine & Victor, Christina & Pentecost,, 2023. "Are profiles of social, cultural, and economic capital related to living well with dementia? Longitudinal findings from the IDEAL programme," LSE Research Online Documents on Economics 117728, London School of Economics and Political Science, LSE Library.
    6. Ahnert, Henning & Kavonius, Ilja Kristian & Honkkila, Juha & Sola, Pierre, 2020. "Understanding household wealth: linking macro and micro data to produce distributional financial accounts," Statistics Paper Series 37, European Central Bank.
    7. R. Bollinger, Christopher & Valentinova Tasseva, Iva, 2022. "Income source confusion using the SILC," ISER Working Paper Series 2022-04, Institute for Social and Economic Research.
    8. Andrea Cutillo & Michele Raitano & Isabella Siciliani, 2022. "Income-Based and Consumption-Based Measurement of Absolute Poverty: Insights from Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 689-710, June.

    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. Dean R. Hyslop & Wilbur Townsend, 2020. "Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 457-469, April.
    2. ChangHwan Kim & Christopher R. Tamborini, 2014. "Response Error in Earnings," Sociological Methods & Research, , vol. 43(1), pages 39-72, February.
    3. Quinn Moore & Irma Perez-Johnson & Robert Santillano, 2018. "Decomposing Differences in Impacts on Survey- and Administrative-Measured Earnings From a Job Training Voucher Experiment," Evaluation Review, , vol. 42(5-6), pages 515-549, October.
    4. Hyslop, Dean R. & Townsend, Wilbur, 2017. "Employment misclassification in survey and administrative reports," Economics Letters, Elsevier, vol. 155(C), pages 19-23.
    5. 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.
    6. Crossley, Thomas F. & Fisher, Paul & Hussein, Omar, 2023. "Assessing data from summary questions about earnings and income," Labour Economics, Elsevier, vol. 81(C).
    7. Christian Imboden & John Voorheis & Caroline Weber, 2023. "Self-Employment Income Reporting on Surveys," Working Papers 23-19, Center for Economic Studies, U.S. Census Bureau.
    8. 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.
    9. O'Neill, Donal & Sweetman, Olive, 2012. "The Consequences of Measurement Error when Estimating the Impact of BMI on Labour Market Outcomes," IZA Discussion Papers 7008, Institute of Labor Economics (IZA).
    10. Michele Lalla & Patrizio Frederic & Daniela Mantovani, 2022. "The inextricable association of measurement errors and tax evasion as examined through a microanalysis of survey data matched with fiscal data: a case study," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1375-1401, December.
    11. 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.
    12. Michele Lalla & Maddalena Cavicchioli, 2020. "Nonresponse and measurement errors in income: matching individual survey data with administrative tax data," Department of Economics 0170, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    13. Donal O'Neill, 2015. "Correcting for Self-Reporting Bias in BMI: A Multiple Imputation Approach," Economics Department Working Paper Series n263-15.pdf, Department of Economics, National University of Ireland - Maynooth.
    14. 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).
    15. Markus Jäntti & Stephen P. Jenkins, 2013. "Income Mobility," SOEPpapers on Multidisciplinary Panel Data Research 607, DIW Berlin, The German Socio-Economic Panel (SOEP).
    16. Bruce D. Meyer & Nikolas Mittag, 2015. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," NBER Working Papers 21676, National Bureau of Economic Research, Inc.
    17. Whalley, Alexander, 2011. "Education and labor market risk: Understanding the role of data cleaning," Economics of Education Review, Elsevier, vol. 30(3), pages 528-545, June.
    18. Peter Valet & Jule Adriaans & Stefan Liebig, 2019. "Comparing survey data and administrative records on gross earnings: nonreporting, misreporting, interviewer presence and earnings inequality," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 471-491, January.
    19. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," IZA Discussion Papers 10943, Institute of Labor Economics (IZA).
    20. 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).

    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:spr:soinre:v:138:y:2018:i:2:d:10.1007_s11205-017-1672-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.