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

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 is 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 (EU-SILC). 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, we generate a unique dataset 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, we first generate a harmonized dataset, which consists of EU-SILC and household budget survey data. We then employ different imputation models and choose the best-performing one to impute energy spending shares into EU-SILC data. Based on the resulting dataset, we overlay energy poverty with monetary poverty. Our 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.