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A Practical Guide for the Computation of Domain-Level Estimates with the Socio-Economic Panel (and Other Household Surveys)

Author

Listed:
  • Natascha Hainbach
  • Christoph Halbmeier
  • Timo Schmid
  • Carsten Schröder

Abstract

There is a huge interest in deriving and comparing socio-economic indicators across societal groups and domains. The indicators are usually derived from population surveys like the German Socio-Economic Panel (SOEP) by direct estimation. Small sample sizes in the domains can limit the precision of these estimates. For example, while SOEP may be a suitable database for determining mean income in Germany, it is unclear whether this also applies to smaller domains (for example, women in Berlin). Here we show SOEP-based applications of Stata’s fayherriot package (Halbmeier et al., 2019). This package implements the Fay-Herriot model (Fay and Herriot, 1979), a small-area estimation technique designed to improve the precision of domain-level direct estimates using domain-level covariates from auxiliary datasets.

Suggested Citation

  • Natascha Hainbach & Christoph Halbmeier & Timo Schmid & Carsten Schröder, 2019. "A Practical Guide for the Computation of Domain-Level Estimates with the Socio-Economic Panel (and Other Household Surveys)," SOEPpapers on Multidisciplinary Panel Data Research 1055, DIW Berlin, The German Socio-Economic Panel (SOEP).
    • Handle: RePEc:diw:diwsop:diw_sp1055
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    References listed on IDEAS

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    1. Nikos Tzavidis & Li‐Chun Zhang & Angela Luna & Timo Schmid & Natalia Rojas‐Perilla, 2018. "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 927-979, October.
    2. Eibich, Peter & Ziebarth, Nicolas R., 2014. "Analyzing regional variation in health care utilization using (rich) household microdata," Health Policy, Elsevier, vol. 114(1), pages 41-53.
    3. Deckers Thomas & Falk Armin & Schildberg-Hörisch Hannah, 2016. "Nominal or Real? The Impact of Regional Price Levels on Satisfaction with Life," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 16(3), pages 1337-1358, September.
    4. Mario Piacentini, 2014. "Measuring Income Inequality and Poverty at the Regional Level in OECD Countries," OECD Statistics Working Papers 2014/3, OECD Publishing.
    5. Christoph Halbmeier & Ann-Kristin Kreutzmann & Timo Schmid & Carsten Schröder, 2019. "The fayherriot command for estimating small-area indicators," Stata Journal, StataCorp LP, vol. 19(3), pages 626-644, September.
    6. Mathias Bug & Martin Kroh & Kristina Meier, 2015. "Regional Crime Rates and Fear of Crime: WISIND Findings," DIW Economic Bulletin, DIW Berlin, German Institute for Economic Research, vol. 5(12), pages 167-176.
    7. Sugasawa, Shonosuke & Kubokawa, Tatsuya, 2015. "Parametric transformed Fay–Herriot model for small area estimation," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 295-311.
    8. Goebel Jan & Grabka Markus M. & Liebig Stefan & Kroh Martin & Richter David & Schröder Carsten & Schupp Jürgen, 2019. "The German Socio-Economic Panel (SOEP)," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(2), pages 345-360, April.
    9. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
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    Cited by:

    1. Schröder, Carsten & König, Johannes & Fedorets, Alexandra & Goebel, Jan & Grabka, Markus M. & Lüthen, Holger & Metzing, Maria & Schikora, Felicitas & Liebig, Stefan, 2020. "The economic research potentials of the German Socio-Economic Panel study," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 21(3), pages 335-371.

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    More about this item

    Keywords

    Disaggregated indicators; Small area estimation; Fay-Herriot model; Socio-Economic Panel;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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