Essays on the effects of human capital, innovation and technology on economic performance
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This thesis is on human capital (HC) and innovation in Germany and comprises of three essays. The first essay provides a comparative analysis on the economic performance impacts of creative class and HC at regional level with the purpose of testing whether the contemporary occupation-based creative class or the conventional education-based HC can be used as a better driver of regional economy. In doing so, I disaggregate creative class into creative core (scientific experts), creative professionals (associate scientists), and art experts while HC is categorized into primary and secondary school graduates with and without vocational training, and university graduates. Further, I proxy the outcome variable regional economic performance by inflation adjusted economic growth, employment growth, and wage growth. All economic performance, HC, and creative class data are drawn from the Sample of Integrated Labor Market Biographies (SIAB 1975–2008) and from the Federal Statistical Office of Germany which cover the years 1998–2008 inclusive for 394 administrative regions (Nomenclature of Territorial Units for Statistics – NUTS3). Estimations, using system generalized method of moments (SGMM), reveal that human capital (share of university graduates) is superior to creative class (share of scientific and associate scientists) in generating economic growth, employment growth is better predicted by creative class, and that creative class and HC appear to have equivalent influence on wage growth. The estimation further indicates that art experts have a deterring effect on wage, employment, as well as on economic growth, hence, rejecting Florida’s (2002) thesis. The second essay analyzes the impacts of innovation input and innovation output on the performance of 3124 manufacturing and service firms based on rich longitudinal data that cover the years 2003–2010 inclusive. The data—which are drawn from Mannheim Innovation Panel (MIP)—share many of the characteristics of the Community Innovation Survey (CIP) data but also have at least two unique features. First, unlike CIS, MIP is an annual panel survey which provides more opportunity to analyze persistence of innovation activities and causal effects between innovation input and innovation output, and between innovation and firm performance. Second, and more importantly, MIP survey goes beyond the standard CIS questionnaires and includes data on firm profitability, firm’s market competition, innovation input, and innovation output. Third, annual survey data further minimizes the risk of data jumps or gaps over years, therefore, yield more chance to use a dynamic panel estimator that can effectively trace innovation persistence. Such unique features of the survey panel data— in conjunction with the method developed by Oslo manual of innovation—allow to measure innovation input by R&D intensity, investment innovation intensity, and total innovation intensity; proxy innovation output through product innovation to firm, product innovation to market, and process innovation; and explain firm performance by employment, sales and labor productivity. The analysis uncovers that innovation input and innovation output do affect growth of employment, sales, and labor productivity differently within and between manufacturing and service firms. More importantly, innovation input compared to innovation output appears to better explain innovation and, in turn, drive firm performance. The third essay, an extension of the second essay, analyzes the influences of technological innovation (measured by R&D intensity, patents, and share of researchers) on economic performance [growth of per capita income (PCI), employment, and wage] in 96 planning regions on the basis of five years (every two years) panel data over the years 2001-2009. The five year average cross-section and panel data estimations show that most of the employed innovation indicators have not only a statistically robust but also an economically strong impact on the economic performance of the planning regions. More specifically, the panel estimation provides information that the lagged dependent variables of investment in R&D, patent claim, and the share of high-tech and knowledge intensive employees impact PCI, employment, and wage growth immensely. However, the elasticity of the effects differ. Non high-tech and knowledge intensive services employees—who are identified in the dataset as non-researchers—have also positive and substantial impact on regional economy. This may suggest that non-researchers can innovate and, in turn, play a crucial role in harnessing regional economy. 
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