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Beschreibung:
We show for Germany that labor productivity as reflected in wage is, ceteris paribus, higher for workers who previously acquired work experience in rather urban labor markets with a large local workforce than in rather rural labor markets which are small in terms of regional employment. Our empirical analysis provides new evidence on the magnitude of these dynamic agglomeration gains by estimating the elasticity of wages with regard to the (cumulated) size of the local labor markets in which workers acquired experience. It shows that this elasticity increases with the level of individual experience to more than 0.06 implying that today's wage of a worker with 20 years of experience or more would be about four to five percent higher if the worker would have gained all his or her experience in local labor markets double the size of the labor markets in which he or she actually was working in the past. These identified dynamic agglomeration gains are supposed to be related to learning externalities. The analysis uses information on individual employment biographies and regional employment from 1975 onwards. The wage information refers to more than 300,000 entry wages of new employment relationships in Germany in the period 2005 to 2011. The depreciation of human capital is taken into account and that high-skilled workers presumably are the ones other workers learn the most from. ; Mit dieser Studie zeigen wir für Deutschland, dass der heutige Arbeitslohn einer Person, ein Indikator für die individuelle Arbeitsproduktivität, unabhängig davon, wo eine Person heute tätig ist, ceteris paribus signifikant höher ist, wenn die Person in der Vergangenheit Arbeitserfahrung in großen, also eher städtischen statt in kleinen, eher ländlichen Arbeitsmärkten gesammelt hat, wobei wir die Arbeitsmarktgröße anhand der Beschäftigtenzahl messen. Die vorliegende Arbeit liefert neue Erkenntnisse über die Größe dieser dynamischen Agglomerationsvorteile, die mutmaßlich auf Lernexternalitäten in großen Arbeitsmärkten zurückzuführen sind. ...