Progressive taxes and firm births

Tax reform proposals in the spirit of the “flat tax” model typically aim to reduce three parameters: the average tax burden, the progressivity of the tax schedule, and the complexity of the tax code. We explore the implications of changes in these parameters for entrepreneurial activity, measured by counts of firm births. The Swiss fiscal system offers sufficient intra-national variation in tax codes to allow us to estimate such effects with considerable precision. We find that high average taxes and complicated tax codes depress firm birth rates, while tax progressivity per se promotes firm births. The latter result supports the existence of an insurance effect from progressive corporate income taxes for risk-averse entrepreneurs. However, implied elasticities with respect to the level and complexity of corporate taxes are an order of magnitude larger than elasticities with respect to the progressivity of tax schedules.     

A Probabilistic Clustering Model for Variables of Mixed Type

This paper develops a probabilistic clustering model for mixeddata. The model allows analysis of variables of mixed type: thevariables may be nominal, ordinal and/or quantitative. The modelcontains the well-known models of latent class analysis as submodels.As in latent class analysis, local independence of the variables isassumed. The parameters of the model are estimated by the EMalgorithm. Test statistics and goodness-of-fit measures are proposedfor model selection. Two artificial data sets show the usefulness ofthese tests. An empirical example completes the presentation.     

Revisiting the trans-situationality of values in Schwartz’s Portrait Values Questionnaire

Quality & Quantity - Schwartz in his famous theory of basic values follows Parsons and Rokeach in arguing that human values are trans-situational or context free. For any individual, the same...     

Goodness-of-fit measures for multiple correspondence analysis

This paper presents three results: (1) It is shown that the explained variance criterion is inappropriate for multiple correspondence analysis (MCA). (2) There are two strategies to interpret a MCA configuration: factor analytical interpretation and cluster analytical interpretation. Goodness-of-fit measures for both interpretations are constructed. (3) These measures give (a) a more adequate picture of the model fit and (b) allow to differentiate whether a factor analytical interpretation or a cluster analytical interpretation is more adequate.     

How to combat NEET? Evidence from Austria

The number young people not in education, employment or training (NEET) has been rising since the beginning of the economic and fi nancial crisis in 2008. This article contributes to the discussion about the reduction of the NEET rate by addressing the questions: Which structural factors can explain differences in the NEET rates among Austrian federal states? What causes an increased or reduced NEET risk at the individual level? What are the key factors for an exit from a NEET situation?