Geographic Determinants of Hi-Tech Employment Growth in US Counties (original) (raw)
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Geography and high-tech employment growth in US counties
2012
This paper investigates the role of geography in high-tech employment growth across US counties. The geographic dimensions examined include industry cluster effects, urbanization effects, proximity to a research university, and proximity in the urban hierarchy. Growth is assessed for overall high-tech employment and for employment in various high-tech sub-sectors. Econometric analyses are conducted separately for samples of metropolitan and nonmetropolitan counties.
2006
The traditional view of cities as monocentric conglomerates of people clustered around an employment center, driving economic growth in cities that subsequently trickles down to the hinterland, is increasingly being challenged. In particular, the role of space, technological leadership, human capital, increasing returns to scale and industrial clustering as well as hierarchical organization principles have been emphasized in the more recent literature. This paper utilizes exploratory and spatial econometric data analysis techniques to investigate these issues for US counties using data from 1969 through 2003. Ultimately, contiguous and hierarchical organization and interaction patterns are captured using an endogenous growth model allowing for spatial effects, inspired by earlier work on human capital and technology gaps. We investigate a neoclassical growth model and compare it to a spatial version of an endogenous growth model allowing for "domestic" investment in human capital and catch-up to the technology leader, and find that human capital strongly contributes to growth in a neoclassical setting, but much less so in an endogenous setting. In the endogenous model the catch-up term dominates in comparison to "domestic" human capital effects.
The traditional view of cities as monocentric conglomerates of people clustered around an employment center, driving economic growth in cities that subsequently trickles down to the hinterland, is increasingly being challenged. In particular, the role of space, technological leadership, human capital, increasing returns to scale and industrial clustering as well as hierarchical organization principles have been emphasized in the more recent literature. This paper utilizes exploratory and spatial econometric data analysis techniques to investigate these issues for U.S. counties using data from 1969 through 2003. Ultimately, contiguous and hierarchical organization and interaction patterns are captured using an endogenous growth model allowing for spatial effects, inspired by earlier work on human capital and technology gaps. We investigate a neoclassical growth model and compare it to a spatial version of an endogenous growth model allowing for "domestic" investment in human capital and catch-up to the technology leader, and find that human capital strongly contributes to growth in a neoclassical setting, but much less so in an endogenous setting. In the endogenous model the catch-up term dominates in comparison to "domestic" human capital effects.
High tech manufacturing: Firm size, industry and population density
Small Business Economics, 1994
Location theorists have emphasized the importance of agglomeration economies in explaining the concentration of industrial activity. They have divided these economies into portions that relate to average industry size, firm size, and market size. This study examines these three factors, in the context of value created, and concludes that each is statistically different for high tech industries in comparison to non-high tech industries. This finding adds an important dimension to state industrial development strategies, particularly those focused on high tech.
The impact of growth and innovation clusters on unemployment in US metro regions
Regional Science Policy & Practice, 2017
Much has been written by various scholars and practitioners over the years about the benefits of industrial clustering. The benefits of such clustering or local agglomeration economies supposedly include greater regional exports, greater employment growth, greater payroll growth, and greater new business establishment creation, among other impacts. However, the work for this article has not uncovered much if any literature on how agglomeration affects United States regional unemployment rates. Additionally, most growing industrial and innovation clusters over the last two decades or so require highly educated and skilled workers. Since most of the unemployed at any given time tend to be less educated and less skilled than most workers on average, any local and state economic development policies that focus on clustering must be careful in targeting lower unemployment rates as a policy goal or outcome. From a theoretical point of view, if greater clustering leads to greater employment growth and employment levels then greater clustering in a US metro area should yield lower unemployment rates than those which have lesser degrees of industry clustering on average. On the other hand, greater clustering and greater industry concentration in either declining industries or in those industries not in decline but at the same time not immune to bad cyclical downturns could yield greater unemployment rates and levels in metro areas which have these types of industries. Therefore, before pursing any policies which promote greater industry clustering, it would be useful for policy-makers to know whether greater industry clustering or greater industry diversity makes a difference when it comes to unemployment rates for metro areas, which are usually defined as local job markets for most workers. This paper finds that, in general, greater clustering for 'growth and innovation' industries leads to lower unemployment rates in US metro areas, on average, after controlling for other factors which may impact local unemployment rates.
Beyond the Silicon Valley: University R&D and high-technology location
Journal of Urban Economics, 2006
In this paper, we examine high-technology location in US counties, focusing on the relationship with university research and development (R&D). The Dirichlet-Multinomial model (an extension of the conditional logit model that allows for overdispersion) is used to estimate the determinants of manufacturing establishment births in US counties. We test the hypothesis that university R&D generates spillovers captured locally as new high-technology establishments, after controlling for local costs, demand, agglomeration economies and other important location factors. Estimates show that R&D expenditures at universities exert a positive, statistically significant influence on the decision to locate plants in a county. The marginal impacts of increased R&D funding on county probabilities for new high-tech plant births, however, appear to be modest. Separate estimates indicate that the findings hold up across most individual high-tech industries. Our model also determines the distance from university R&D in which economic spillovers can be detected. Spillover effects may extend up to approximately 145 miles from universities.