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ABSTRACT
Most innovation-oriented studies use measures such as patent activity or research expenditures, likely ignoring the role of more home-grown upgrades or opportunity-recognizing activity common in businesses across the U.S. This study develops a broader ‘innovation index’ using a new survey of businesses that provides a wide lens for capturing innovative practices. The index is used in a series of regressions testing the relationship between innovation and both firm and regional-level economic outcomes. Results from the firm-level regressions show that the innovation index has a positive and significant relationship with wages paid to employees and product market growth. The regional analysis demonstrates that innovation is correlated with several regional economic variables, including median household income, and that spatial spillovers from innovation exist in some instances.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1. Tradable industries are defined as those in the mining (NAICS code 21), manufacturing (31–33), wholesale trade (42), transportation and warehousing (48), information (51), finance and insurance (52), professional/technical services (54), management of businesses (55), and arts/entertainment (71).
2. This attempt at measuring innovation with the REIS survey differs from the methodology used by Wojan and Parker (Citation2017), who differentiate between 3 categories of innovators (non/nominal/substantive) by using latent class analysis on 8 specific REIS questions.
3. A reviewer notes concern that some factors may be considered consequences of, rather than inputs to, innovation. However, the wording of each specific question generally connote the business’ approach to innovation. Further, we view the specific items noted in each question as potential inputs to the simple linear innovation model, where they are seen as components of basic research. As such, we believe that they are appropriate to use as variables in the factor analysis.
4. As noted in Kaiser (Citation1974), a KMO score in the 0.90’s is deemed ‘marvelous’, a score in the 0.80’s is ‘meritorious’, a score in the 0.70’s is ‘middling’, a score in the 0.60’s is ‘mediocre’, a score in the 0.50’s is ‘miserable’, and a score below 0.50 is ‘unacceptable.’
5. The ‘predict’ command in STATA performs this task. Although most of our variables are ordinal, we did not use a polychoric correlation matrix to smooth our variables. This is because the bias associated with using ordinal variables in PCA has been shown to be small with symmetric and unimodal data (Olsson Citation1979; Rigdon and Ferguson Citation1991). 12 of our 15 variables have skewness coefficients between −0.5 and 0.5. Our large sample size (10,000 observations) also reduces concerns about bias.
6. Wojan and Parker (Citation2017) also use commuting zones for their analysis.
7. 2011–2015 5-year American Community Survey (ACS) data was used to match the 2014 Business Innovation Survey.
8. The bivariate Moran’s I calculates the relationship between one variable and neighboring values for a different variable. It also ranges from −1 to 1, with a positive and significant relationship implying that high values of variable 1 are surrounded by high values for variable 2.
9. ERS defines these types of counties based on the industry’s percentage of total earnings or employment. The percentages vary by industry (ERS, 2017).
10. Low factor loadings indicate that the variable is not highly correlated with that particular factor. Note that all of the factor loadings in are > 0.3. The four questions listed in Appendix A that were excluded from the final factors were Q28 (ABANDONED), Q34 (EXTRACASH), Q38 (RECESSION) and Q39 (INCREASE13).
11. Moran’s I is a measure from −1 to 1 with 0 representing a purely random spatial distribution and 1 representing complete spatial autocorrelation (where a county’s value depends entirely on its neighbors).
12. USDA-ERS (2014) defines the creative class as those employed in ‘creative’ occupations, specifically occupations ‘developing, designing, or creating new applications, ideas, relationships, systems, or products, including artistic contributions.’
13. See Footnote 8 for a definition of the bivariate Moran’s I. The positive and significant values here suggest that regions with high (low) values of variable 1 are surrounded by high (low) values of variable 2.
