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Which Progress for Poverty Studies can we expect from new large Data Sources?

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  • Jürgen Friedrichs

Abstract

Large data sources would allow us to test the impact of neighborhood characteristics, such as poverty rates , on the attitudes and behavior ot residents. The article explores the feasibility of existing large date sets for such a purpose. Unfortunately, none of the three sets reviewed, the Microcensus, the ALLBUS and the SOUP, allows for such multi-level analyses, because data cannot be regionalized due to data protection or insufficient sample size. To overcome these problems in a limited sense, it is suggested to pursue a “puzzle strategy” to combine data from different existing data sets.

Suggested Citation

  • Jürgen Friedrichs, 2008. "Which Progress for Poverty Studies can we expect from new large Data Sources?," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 128(1), pages 65-73.
    • Handle: RePEc:aeq:aeqsjb:v128_y2008_i1_q1_p65-73
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    More about this item

    JEL classification:

    • 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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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