RT Generic
T1 Estimating Engel curves: a new way to improve the SILC-HBS matching process using GLM methods.
A1 López Laborda, Julio
A1 Marín González, Carmen
A1 Onrubia Fernández, Jorge
AB 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. 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, to test the robustness of the proposed methodology, we extend the empirical analysis to the rest of the European Union countries, using the micro data from the surveys provided by Eurostat (EU-SILC, 2011; HBS, 2010).
PB Taylor & Francis
SN 0266-4763
YR 2020
FD 2020
LK https://hdl.handle.net/20.500.14352/130993
UL https://hdl.handle.net/20.500.14352/130993
LA eng
NO López-Laborda, J., Marín-González, C., & Onrubia-Fernández, J. (2021). Estimating Engel curves: a new way to improve the SILC-HBS matching process using GLM methods. Journal of Applied Statistics, 48(16), 3233–3250. https://doi.org/10.1080/02664763.2020.1796933
NO Ministerio de Economía (España)
DS Docta Complutense
RD 5 feb 2026