A Bayesian Approach to Determine the Impact of Institutions on the Unemployment Rate

Large differences in the unemployment rates of industrialized countries and the underlying causes of unemployment have been subject of recurring discussion for a long time. Since the early 90's, labor and product market institutions like employment protection legislation, the unemployment benefit system or the labor tax system moved towards the center of attention. However, while theoretical papers provide clear predictions about the impact institutional factors should have on the labor market, empirical contributions disagree as to which factors are of empirical relevance. One major problem is that institutional concepts used in theoretical work like the bargaining power of workers are unobservable in reality. Although a large number of indicators is available, the low number of observations prevents the inclusion of all of them. Empiricists have therefore to decide which indicators to use in order to capture the effect of a specific institutional concept. However, this pre-selection could give rise to model mis-specification and biased results. This paper offers a solution to this problem by using a bayesian model averaging approach. The major advantage of this method is that a large set of institutional indicators can be tested for significance without running into a degrees-of-freedom problem and without requiring to specify one particular model. Rather, information of a large number of models and, particularly, model uncertainty can be taken into account. The results show that eight institutional indicators are significant. Each equation claiming to explain unemployment in industrialized countries should include these indicators as explanatory variables. More specifically, the payroll and the consumption tax, the first year and the fourth/fifth year benefits, the barriers to entry and the public ownership, the bargaining coordination, and the employment protection legislation are of importance. I check the robustness and reliability of the results by considering heteroskedasticity and endogeneity. Furthermore, the inclusion of additional control variables and the reduction of the sample by excluding countries or periods do not change the results substantially.

Sachs, Andreas (2010), A Bayesian Approach to Determine the Impact of Institutions on the Unemployment Rate, ZEW Discussion Paper No. 10-058, Mannheim.