To design effective policy instruments that target the energy poor in Romania, it is crucial to understand who the energy poor are. However, these types of analyses are limited by the current data environment. While monetary energy poverty estimates rely on data from expenditure surveys, traditional welfare indicators and detailed information on access to social protection programs form part of the EU-SILC. Samples of both surveys differ
consequently, record linkage of both surveys is impossible. This paper propose an alternative solution to combine information from both surveys, namely statistical matching techniques. It applies several imputation models to impute information on energy spending shares from the HBS into the EU-SILC based on a set of matching variables, compare the performance of these models and apply the best-performing one. Based on the resulting matched dataset, the results show that nearly all the monetary poor are also energy poor, but that a significant additional share of the population in Romania is energy poor. Energy poverty rates are higher at the lower end of the welfare distribution. This result has significant welfare implications.