The proposal on adjusting innovation indicators for the occupational structure or rural economies comes from discussions with the OECD Expert Advisory Committee for Rural Innovation. During the sessions, several rural academics identified structural problems associated with how innovation is measured in rural areas and why the bias associated may not be territorially homogenous. To address this, work by Dotzel (2017[6]) and Wojan (2021[7]) proposes an occupation-driven approach for analysing regional invention. The authors argue that patenting rates should be computed on the subset of workers that might plausibly contribute to patenting. To do this, the authors regress the aggregate number of patents produced in the commuting zone during the period 2000-05 on the share of the workforce employed in a selection of detailed census occupations. The authors’ commuting zone-level regression includes controls on the patent stock, human capital share (working-age population with a bachelor’s degree or higher), population density, a natural amenity score and the wage-rental ratio. They apply the analysis to a core set of occupations (from the U.S. Department of Labor Employment and Training Administration O*NET database) defined by the National Science Foundation’s classification of science, engineering and technical (SET) occupations, along with an iterative random selection of other occupations that may have a strong association with patenting. Ten thousand regressions are estimated with 19 non-SET occupations randomly included in each estimation. The inventive subset inclusion criteria for the non-SET occupations are those occupations-associated coefficients that are positive and significant in at least 75% of their regressions in the metro or non-metro analysis and are characterised as inventive. Of the 300 non-SET occupations included in the analysis, 11 are identified as inventive, that is consistently associated with positive, significant coefficients.
Table A D.1 provides a list of occupations with a relatively high probability of patent. Furthermore, Figure A D.1. demonstrate the distribution of these occupations as a share of the total employed population (patent intensity) across the United States. As demonstrated in Figure 3.4, adjusting for these shares reduces disparities in patenting intensities between territories. In comparison, patent intensity over total employment, over just knowledge-intensive or high-tech sectors provided in Figure A D.2. Adjusting denominators in patenting intensity (TL2), descriptively points towards the same direction, but statistically does not provide as powerful an argument. This could be due to loss of precision in TL2 level aggregation and relevance of the occupations included.
