Exploring Innovation Gaps in the American Space Economy

Mulligan, Gordon F.; Reid, Neil; Carruthers, John I.; Lehnert, Matthew

doi:10.1007/978-3-319-50547-3_2

Cited by 6 publications

(4 citation statements)

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“…A loading indicates the degree of correlation of a given variable with a particular factor, while a score represents the performance of a given metropolitan economy on a particular factor. The factor loadings have been discussed in great detail elsewhere [6] but, for purposes of clarification, they are shown again in the Table A1. Each of these factors can be interpreted by the loadings and these interpretations are given in Table 1.…”

Section: Initial Resultsmentioning

confidence: 99%

“…Some successful cities are narrowly productive in one or more high-valued activities, others are very entrepreneurial and exhibit an abundance of business startups, and even others are more inclined towards the formation of new ideas and the production of patents. Each city can be ranked on each dimension according to its factor score and a listing of the top-15 or top-20 cities on each of those 10 dimensions is very informative [6]. The important thing to note here is that some cities appear much more frequently than others near the top of each of these lists.…”

Section: Initial Resultsmentioning

confidence: 99%

“…Once the primary dimension of general innovation has been identified, the other less important dimensions are examined to demonstrate how the cities variously combine their knowledgeable and educated workforces in order to produce patents, engage in entrepreneurship, and create high value-added outputs. Therefore, as a first step, the overall innovative index of each metropolitan economy has been estimated by generating its score on each of the latent dimensions and then summing up those performance scores across all of the dimensions [6].…”

Section: Introductionmentioning

confidence: 99%

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Metropolitan Innovation in the New Economy

2017

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This paper analyzes the high-tech economies of 350-plus metropolitan areas across the U.S. during 2010. Attention is given to 20 different production attributes-including the age and education of the workforce, patent production, business startups, per capita productivity of the workers, and the like. Multivariate analysis is used to reduce these 20 attributes down to 10 orthogonal dimensions; then the scores on these dimensions are used to identify eight different innovation and entrepreneurial clubs. Basically the exercise deconstructs the metropolitan economies into various parts so that each economy is assigned a signature score on each of the independent factors. High-tech places, which are especially active in both patents and startups, are shown to be more heterogeneous than low-tech places. Moreover, the recent growth and change seen in many metropolitan areas appears to be associated with the incidence of very different factors: population growth has been driven by forces that are different from those that have induced either employment change or productivity growth in those metropolitan areas.

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Section: Initial Resultsmentioning

confidence: 99%

Section: Initial Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Metropolitan Innovation in the New Economy

2017

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“…Therefore, it is not surprising to see an entire part in this edited volume focused on technology, innovation, and entrepreneurship. In this part, the paper by Mulligan et al (2017) presents a nice empirical analysis on the innovation capacity and its local determinants in major US cities (20 variables are used to measure each city's innovation ecosystem), which clearly shows that place, particularly the uniqueness of place, matters for innovation and entrepreneurship. The great amount of heterogeneity in those cities' innovation indices also means that the prospects for future metropolitan growth are highly polarized where there will be winners and losers.…”

Section: Kevin Willardsenmentioning

confidence: 99%