Automatic Stabilisers and Economic Crisis : Us Vs Europe

This paper analyzes the effectiveness of the tax and transfer systems in the European Union and the US to act as an automatic stabilizer in the current economic crisis. We find that automatic stabilizers absorb 38 per cent of a proportional income shock in the EU, compared to 32 per cent in the US. In the case of an unemployment shock 47 per cent of the shock are absorbed in the EU, compared to 34 per cent in the US. This cushioning of disposable income leads to a demand stabilization of up to 31 per cent in the EU and up to 28 per cent in the US. There is large heterogeneity within the EU. Automatic stabilizers in Eastern and Southern Europe are much lower than in Central and Northern European countries. We also investigate whether countries with weak automatic stabilizers have enacted larger fiscal stimulus programs. We find no evidence supporting this view. 1 This paper uses EUROMOD version D21 and TAXSIM v9. EUROMOD and TAXIM are continually being improved and updated and the results presented here represent the best available at the time of writing. Our version of TAXSIM is based on the Survey of Consumer Finances (SCF) by the Federal Reserve Board. This paper uses data from the European Community Household Panel (ECHP) User Data Base made available by Eurostat; the Austrian version of the ECHP made available by Statistik Austria; the Panel Survey on Belgian Households (PSBH) made available by the University of Liège and the University of Antwerp; the Estonian Household Budget Survey (HBS) made available by Statistics Estonia; the Income Distribution Survey made available by Statistics Finland; the Enquête sur les Budgets Familiaux (EBF) made available by INSEE; the public use version of the German Socio Economic Panel Study (GSOEP) made available by the German Institute for Economic Research (DIW), Berlin; the Greek Household Budget Survey (HBS) made available by the National Statistical Service of Greece; the EU Statistics in Incomes and Living Conditions (SILC) made available by Eurostat; the Living in Ireland Survey made available by the Economic and Social Research Institute; the Survey of Household Income and Wealth (SHIW95) made available by the Bank of Italy; the SocioEconomic Panel for Luxembourg (PSELL-2) made available by CEPS/INSTEAD; the Socio-Economic Panel Survey (SEP) made available by Statistics Netherlands through the mediation of the Netherlands Organisation for Scientific Research Scientific Statistical Agency; the Polish Household Budget Survey (HBS) made available by the Polish Central Statistical Office and prepared by the Economics Department of Warsaw University; the Slovenian Household Budget Survey (HBS) and Personal Income Tax database made available by the Statistical Office of Slovenia; the Income Distribution Survey made available by Statistics Sweden; and the Family Expenditure Survey (FES), made available by the UK Office for National Statistics (ONS) through the Data Archive. Material from the FES is Crown Copyright and is used by permission. Neither the ONS nor the Data Archive bears any responsibility for the analysis or interpretation of the data reported here. An equivalent disclaimer applies for all other data sources and their respective providers. 2 This paper is partly based on work carried out during Andreas Peichl’s visit to the European Centre for Analysis in the Social Sciences (ECASS) at the Institute for Social and Economic Research (ISER), University of Essex, supported by the Access to Research Infrastructures action under the EU Improving Human Potential Programme. Andreas Peichl is grateful for financial support by Deutsche Forschungsgemeinschaft DFG (PE1675). Clemens Fuest acknowledges financial support from the ESRC (Grant No RES-060-25-0033). We would like to thank Danny Blanchflower, Dean Baker, Horacio Levy, Torfinn Harding, participants of the 2009 IZA/CEPR ESSLE and IZA Prize conferences and the 6th German-Norwegian Seminar on Public Economics (CESifo) as well as seminar participants in Bonn, Cologne, Nuremberg, Siegen and at the Worldbank for helpful comments and suggestions. We are grateful to Daniel Feenberg for granting us access to TAXSIM and helping us with our simulations. We are indebted to all past and current members of the EUROMOD consortium for the construction and development of EUROMOD. The usual disclaimer applies. JEL Classification: E32, E63, H2, H31