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Preparation of Data from the New SOEP Consumption Module: Editing, Imputation, and Smoothing

Author Info

Listed author(s):
  • Jan Marcus
  • Rainer Siegers
  • Markus M. Grabka

Abstract

This documentation describes the data preparation of the new consumption module in the German Socio-Economic Panel Study (SOEP) and introduces the content and structure of the generated dataset "hconsum." In 2010, the SOEP for the first time included a detailed consumption module in the household questionnaire. This documentation discusses several methodological challenges of the new module and suggests possible remedies. The methodological challenges include inconsistencies between monthly and annual consumption information, missing values, and a high incidence of heaping.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.428029.de/diw_datadoc_2013-070.pdf
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Paper provided by DIW Berlin, German Institute for Economic Research in its series Data Documentation with number 70.

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Length: 38 p.
Date of creation: 2013
Handle: RePEc:diw:diwddc:dd70
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Keywords: SOEP; consumption; heaping; imputation; generalized beta of the second kind;

Find related papers by 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
  • D30 - Microeconomics - - Distribution - - - General
  • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

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  1. Singh, S K & Maddala, G S, 1976. "A Function for Size Distribution of Incomes," Econometrica, Econometric Society, vol. 44(5), pages 963-970, September.
  2. McDonald, James B, 1984. "Some Generalized Functions for the Size Distribution of Income," Econometrica, Econometric Society, vol. 52(3), pages 647-663, May.
  3. Royston, Patrick & White, Ian R., 2011. "Multiple Imputation by Chained Equations (MICE): Implementation in Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i04).
  4. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
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Cited by:

  1. Groß, Marcus & Rendtel, Ulrich, 2015. "Kernel density estimation for heaped data," Discussion Papers 2015/27, Free University Berlin, School of Business & Economics.
  2. Stefan Bach & Martin Beznoska & Viktor Steiner, 2016. "An Integrated Micro Data Base for Tax Analysis in Germany," Data Documentation 86, DIW Berlin, German Institute for Economic Research.
  3. Pestel, Nico & Sommer, Eric, 2015. "Shifting taxes from labor to consumption: More employment and more inequality," ZEW Discussion Papers 15-042, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  4. Heinz-Herbert Noll & Stefan Weick, 2015. "Consumption expenditures and subjective well-being: empirical evidence from Germany," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 62(2), pages 101-119, June.
  5. Loeffler, Max & Peichl, Andreas & Pestel, Nico & Siegloch, Sebastian & Sommer, Eric, 2014. "Documentation IZAΨMOD v3.0: The IZA Policy Simulation Model," IZA Discussion Papers 8553, Institute for the Study of Labor (IZA).
  6. Lamarche, Pierre, 2017. "Estimating consumption in the HFCS: Experimental results on the first wave of the HFCS," Statistics Paper Series 22, European Central Bank.

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