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Statistical Matching for Combining the European Survey on Income and Living Conditions and the Household Budget Surveys: An Evaluation of Energy Expenditures in Bulgaria

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  • Rude,Britta Laurin
  • Robayo,Monica

Abstract

Energy poverty has gained attention in the context of increasing energy prices and the recent energy crisis in Europe. However, measuring energy poverty and characterizing the energy poor are challenging, given that expenditure surveys (household budget surveys) often need more information to characterize the energy poor. Additionally, there is no consensus on how to measure and monitor energy poverty. It is also unknown how and why it differs from income poverty. While income poverty relies on a well-defined poverty line, energy poverty does not have a clearly defined energy poverty line that indicates the minimum energy necessary for satisfying basic needs. In addition, monetary poverty and other welfare measures are measured with income in EU countries using the European Survey of Income and Living Conditions. Therefore, it is not straightforward to characterize energy affordability among the monetary income poor or to estimate the overlap between official income poverty and energy poverty. This paper explores statistical matching as a potential strategy to overcome these data challenges in the context of Bulgaria. Via data fusion, a unique dataset is generated that contains information on energy spending shares, income-based indicators of poverty and inequality, and additional variables on households' living conditions and welfare. For this purpose, the paper first generates a harmonized dataset, which consists of the European Survey of Income and Living Conditions and household budget survey data. It then employs different imputation models and chooses the best-performing one to impute energy spending shares into data. Based on the resulting dataset, it overlays energy poverty with monetary poverty. The findings show that a large share of the energy poor is not income poor, calling for differentiated policy measures to tackle energy poverty. Importantly, these findings depend on the underlying definition of energy poverty. This paper contributes to a growing body of literature exploring the potential of statistical matching to improve the current data environment in the European Union.

Suggested Citation

  • Rude,Britta Laurin & Robayo,Monica, 2024. "Statistical Matching for Combining the European Survey on Income and Living Conditions and the Household Budget Surveys: An Evaluation of Energy Expenditures in Bulgaria," Policy Research Working Paper Series 10818, The World Bank.
  • Handle: RePEc:wbk:wbrwps:10818
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    References listed on IDEAS

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    1. Schaller, Jannik, 2021. "Datenfusion von EU-SILC und Household Budget Survey – ein Vergleich zweier Fusionsmethoden," WISTA – Wirtschaft und Statistik, Statistisches Bundesamt (Destatis), Wiesbaden, vol. 73(4), pages 76-86.
    2. 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.
    3. 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.
    4. 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.
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    6. Schenker, Nathaniel & Taylor, Jeremy M. G., 1996. "Partially parametric techniques for multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 22(4), pages 425-446, August.
    7. 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.
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