Reichtum in Niedersachsen und anderen Bundesländern - Ergebnisse aus der Einkommensteuer-Geschäftsstatistik 2003 für Selbständige (Freie Berufe und Unternehmer) und abhängige Beschäftigte
This contribution analyzes top incomes in the German Federal States for 2003 with focus on Lower-Saxony. Based on the microdata of the German Wage and Income Tax Current Statistics (Einkommensteuer-Geschaftsstatistik) for the first time richness rates and intensities of richness are examined for the self-employed as professions and entrepreneurs as well as for employees. We found large differences between the German Federal States and occupational groups. Even if the share of rich persons in the group of self-employed are substantially higher than in the group of employees the situation of the self-employed varies much between the German Federal States.
The Distribution of Income of Self-Employed, Entrepreneurs and Professions as Revealed from Micro Income Tax Statistics in Germany
As simple as it is, results describing the world are heavily dependent on the quality of the underlying data. One of the very crucial variables in microanalytical analyses of well-being and human resources is income. The more, when the situation of the self-employed is regarded. This paper focus on the distribution of income based on very sound data: the German Income Tax Statistic (Einkommensteuerstatistik) 1992. New is the actual possibility to use for the first time such a sound microdatabase to analyze the self-employed in particular: a 100.000 microdata sample of the population wide German Income Tax Statistic. New is the comparison between income from dependent and self-employed work with emphasis on the entrepreneurs and professions, and new is the indepth decomposition inequality analysis of the aggregated groups and of the single professions based on an inequality generalized entropy decomposition approach. One overall striking result is: the occupational status as an employee, entrepreneur or as a profession with its connected low between inequality share is by far not the overall driving factor to ‘explain’ the overall income distribution and inequality picture of the re-unified Germany; it is the within group inequality which counts in particular.
Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.
Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) introduces other new Complexity-based models/theories of Structural Change in industries; iii) reviews the relationships among Operations Research, Complexity and Structural Change; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of Structural Change.