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Evaluating Data Fusion Methods to Improve Income Modelling

Author

Listed:
  • Jana Emmenegger
  • Ralf Münnich
  • Jannik Schaller

Abstract

Income is an important economic indicator to measure living standards and individual well-being. In Germany, there exist different data sources that yield ambiguous evidence when analysing the income distribution. The Tax Statistics (TS) – an income register recording the total population of more than 40 million taxpayers in Germany for the year 2014 − contains the most reliable income information covering the full income distribution. However, it offers only a limited range of socio-demographic variables essential for income analysis. We tackle this challenge by enriching the tax data with information on education and working time from the Microcensus. For that purpose, we ex- amine two types of data fusion methods that seem suited for the specific data fusion scenario of the Tax Statistics and the Microcensus: Missing-data methods on the one hand and performant prediction models on the other hand. We conduct a simulation study and provide an empirical application comparing the proposed data fusion methods, and our results indicate that Multinomial Regression and Random Forest are the most suitable methods for our data fusion scenario.

Suggested Citation

  • Jana Emmenegger & Ralf Münnich & Jannik Schaller, 2022. "Evaluating Data Fusion Methods to Improve Income Modelling," Research Papers in Economics 2022-03, University of Trier, Department of Economics.
    • Handle: RePEc:trr:wpaper:202203
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    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Rodgers, Willard L, 1984. "An Evaluation of Statistical Matching," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 91-102, January.
    3. Cowell, F.A., 2000. "Measurement of inequality," Handbook of Income Distribution, in: A.B. Atkinson & F. Bourguignon (ed.), Handbook of Income Distribution, edition 1, volume 1, chapter 2, pages 87-166, Elsevier.
    4. repec:hal:pseose:halshs-01157487 is not listed on IDEAS
    5. Charlotte Bartels & Maria Metzing, 2019. "An integrated approach for a top-corrected income distribution," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(2), pages 125-143, June.
    6. Michał Brzeziński & Michał Myck & Mateusz Najsztub, 2019. "Reevaluating distributional consequences of the transition to market economy in Poland: new results from combined household survey and tax return data," Working Papers 2019-18, Faculty of Economic Sciences, University of Warsaw.
    7. Thomas Blanchet & Juliette Fournier & Thomas Piketty, 2022. "Generalized Pareto Curves: Theory and Applications," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(1), pages 263-288, March.
    8. Anthony B Atkinson & François Bourguignon, 2014. "Handbook of Income Distribution," PSE-Ecole d'économie de Paris (Postprint) halshs-02923231, HAL.
    9. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    10. Stefan Bach & Giacomo Corneo & Viktor Steiner, 2009. "From Bottom To Top: The Entire Income Distribution In Germany, 1992–2003," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(2), pages 303-330, June.
    11. Thomas Piketty, 2015. "About Capital in the Twenty-First Century," American Economic Review, American Economic Association, vol. 105(5), pages 48-53, May.
    12. Ravallion, Martin & Chen, Shaohua, 1997. "What Can New Survey Data Tell Us about Recent Changes in Distribution and Poverty?," The World Bank Economic Review, World Bank, vol. 11(2), pages 357-382, May.
    13. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    14. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    15. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    16. Benjamin Okner, 1972. "Constructing a New Data Base from Existing Microdata Sets: The 1966 Merge File," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 325-362, National Bureau of Economic Research, Inc.
    17. Jacob Mincer, 1958. "Investment in Human Capital and Personal Income Distribution," Journal of Political Economy, University of Chicago Press, vol. 66(4), pages 281-281.
    18. Stefan Angel & Franziska Disslbacher & Stefan Humer & Matthias Schnetzer, 2019. "What did you really earn last year?: explaining measurement error in survey income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1411-1437, October.
    19. Jonathan Haughton & Shahidur R. Khandker, 2009. "Handbook on Poverty and Inequality," World Bank Publications - Books, The World Bank Group, number 11985.
    20. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
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