https://orcid.org/0000-0002-1539-8483
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Abstract
Microdata are required to evaluate the distributive impact of the taxation system as a whole (direct and indirect taxes) on individuals or households. However, in European Union countries this information is usually distributed into two separate surveys: the Household Budget Surveys (HBS), including total household expenditure and its composition, and EU Statistics on Income and Living Conditions (EU-SILC), including detailed information about households' income and direct (but not indirect) taxes paid. We present a parametric statistical matching procedure to merge both surveys. For the first stage of matching, we propose estimating total household expenditure in HBS (Engel curves) using a GLM estimator, instead of the traditionally used OLS method. It is a better alternative, insofar as it can deal with the heteroskedasticity problem of the OLS estimates, while making it unnecessary to retransform the regressors estimated in logarithms. To evaluate these advantages of the GLM estimator, we conducted a computational Monte Carlo simulation. In addition, when an error term is added to the deterministic imputation of expenditure in the EU-SILC, we propose replacing the usual Normal distribution of the error with a Chi-square type, which allows a better approximation to the original expenditures variance in the HBS. An empirical analysis is provided using Spanish surveys for years 2012–2016. In addition, we extend the empirical analysis to the rest of the European Union countries, using the surveys provided by Eurostat (EU-SILC, 2011; HBS, 2010).
Acknowledgments
The authors are very grateful to the referees and the Editor of the Journal for very useful suggestions that have helped them to improve the paper. Earlier versions of this paper were presented at workshop organised by the Joint Research Center, European Commission, Seville, Spain, on 12 February 2020; at the 24th Public Economics Meeting, Toledo, Spain, 26–27 January 2017; and at the 6th World Congress of the International Microsimulation Association, Torino, Italy, 21–23 June 2017. The authors gratefully acknowledge Daniel Santín (UCM) for his valuable suggestions in designing the Monte Carlo experiment, and the participants in the three conferences for their comments. All the remaining errors are entirely our responsibility.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 The EU-SILC project entered into force in 2004 and currently covers all EU countries, Iceland, Norway, and Switzerland. For more information, see https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions.
2 The extended study does not include all European Countries for several reasons. First, the HBS and EU-SILC surveys of Austria and Netherlands are not provided by Eurostat. Second, the Italian HBS does not include the variable disposable income. And third, the United Kingdom is not considered, as this country elaborates a survey with joint information of household income and expenditure called Living Cost and Food Survey.
3 Monetary expenditure does not include the rental imputed or expenditure from self-supply, self-consumption and wages in kind.
4 However, as warned by [Citation3] and [Citation15], there is a high probability that the EEA estimation algorithm will not converge.
5 A Chi-Squared distribution with 1 degree of freedom has a skewness of 2.82 and a kurtosis of 12. The HBS expenditure has a skewness of around 2 and a kurtosis of around 12.
6 On the microeconomic theoretical foundations of the Engel's curve, see [Citation11].