How networks matter in economic development: Interdependence among industries, occupations and labor force skills: Measuring the dark matter & energy driving regional economic performance, part 2.

• Author: Slaper, Timothy F.

In an earlier IBR piece, we presented recent research on how to measure the invisible forces, only indirectly observable, that drive innovation. (1) In this article, we explore another dimension of economic performance that only recently has come to be recognized: the invisible interdependencies among industries, occupations and labor force skills. In short, the presence of (invisible) linkages among people and entities that inhabit a region.

In this article, we compress cutting-edge research conducted by a research professor at the School of Complex Adaptive Systems at Arizona State University and a newly minted Ph.D. from the Schar School of Policy and Government at George Mason University, with whom the IBRC had the pleasure to work on a project designed to explore unconventional ways to understand and measure local, regional and state economic development. In other words, the research presented below is not my own but that of project research partners Shutters and Waters and the "we" that follows refers to the three of us. (2)

There are so many key takeaways, we list them as an executive summary and expand upon them in the body of the article:

* Tightness is the big idea. It is a measure of invisible interdependencies within the regional economic landscape.

* A tightness score (T-score) is the metric for the interdependencies of regional industries, occupations and labor force skills.

* Tightness is derived from a type of network analysis and complex adaptive systems approach to understanding the nodes and linkages (or edges in the parlance of networks) between the nodes.

* The nodes are comprised of either industries, occupations, or the skills and occupational characteristics of the labor force.

* The concentration, density or regional specializations of the linkages--the T-scores--provide insights into regional economies.

* The T-scores explain a non-trivial portion of the economic performance differences among regions.

* The T-scores are also shown to relate to several regional characteristics, such as the presence and concentration of certain types of industry clusters--like business services and finance--as well as the dependency upon external sources of income and regional educational attainment.

* Interpreting the tightness of regions has its challenges. This leads to asking questions that start with "why." The data and the questions will enlighten policymakers and economic development practitioners as to the latent structure of a region's economy.

* Generating and raising questions about what drives economic performance is the motivation to conduct the research about tightness.

* The by-products of the tightness analysis can help inform policy directions about transitioning workers from one type of job (that may be shrinking in economic importance) to another type of occupation that better aligns with workers' skills and is in greater demand now and in the future.

* Indiana has several counties that rank well in terms of T-scores among the broader Great Lakes states.

* There is a positive statistical association between T-scores and performance measures, such as job growth and productivity (measured by GDP per worker) growth.

* With tightness comes efficiency and productivity, but also fragility and lower economic resilience.

* Increasing tightness has been shown to be statistically associated with higher economic growth but negatively associated with resilience to economic shocks, such as the Great Recession.

* Tightness may also help to explain the labor market effects of COVID-19 given that it helps identify the structural elements of labor market and income fragility.

This article is based on five research papers--a couple published and a couple not yet--that are cited and linked in the references. This research summary is structured as follows. First, we explain the motivation for presenting this material. Second, we describe the interdependence concept--tightness--its strengths and weaknesses and highlight the methods used to describe and measure the interconnectedness between each analytical category type--industry, occupation and skills. Third, tightness is placed in a network analysis framework. Following that, we present maps of Indiana and the Great Lakes region showing tightness scores by county. Fifth, we present the empirical results and statistical associations between tightness and measures of economic performance, especially for Indiana and the Great Lakes region. We conclude with several questions, a couple of answers, and several additional suggestions for applying the concept of tightness and conducting additional research.

Motivation

A perennial question in regional economic development is: What drives economic performance (which can be measured by job creation or productivity or rising income and living standards)? A more recent salient question arose from the ashes of the Great Recession and, more recently, due to the COVID-19 pandemic and natural disasters: What is resilience and how do we build it? (For purposes of this article, we define resilience as a regional economy's capacity to withstand and/or recover from an economic shock.)

Answers to these questions have been largely about regional characteristics for which there are readily available data from official government statistical agencies like the U.S. Bureau of Labor Statistics, the Bureau of Economic Analysis, the Department of Education and the Census Bureau, among others. These data are relatively easily conceptualized, measured and accessed.

As with latent innovation or economic "dark matter" discussed in our earlier article, there are invisible relationships that can be teased out of the official data. In the case of latent innovation, one can tease out the degree of regional complexity and specialization associated with production. In the case of tightness--which we introduce here as an aggregate measure of interdependences--one can tease out relationships that exist between pairs of, say, industries or occupations. These relationships are based on two dimensions: 1) the similarity (or dissimilarity) of the entities like industries or occupations and 2) the geographic concentration or density of a pair of the entities that co-locate within a region. The operating assumption is that when a concentrated pair of industries inhabit or are co-located in a region (in our case, county), that implies that those two industries are somehow related.

Similarity and co-location tells us much about the economic structure and the performance and resilience of a region.

What is tightness?

Regional economies are complex adaptive systems. The analytical framework we take is one from ecology: If two species inhabit the same landscape, they are somehow related. If the two species rise and fall in tandem--if the demise of one species is correlated with the decline in the health or presence of another--that is a dead ringer for species relatedness, even if neither one is on the other's typical dinner menu.

So the tightness concept takes its form by employing ecological techniques of co-occurrence analysis to infer interactions. In our case, those interactions are between industries, skills and occupations. We then create an aggregate measure of economic tightness, capturing the degree of integration or interconnectedness among a region's industries/skills/occupations. Spoiler alert: Industry tightness is positively correlated with a region's economic productivity but negatively correlated with one definition of resilience--bounce-back or a region's recovery to pre-shock employment and productivity following a shock. This is why the concept and measure are important.

A pair of two industries may be related because they are both links in a supply chain, for example, from foundries to forging to auto parts to auto assembly. The industries may be related because they are both needed in combination with each other for a specific production activity, for example, the robotics hardware and the systems integration that create a seamless assembly line. This type of relatedness is captured in the production-based NAICS classification system. But relatedness (or interdependence) isn't just about common classification schemes. Industries, outside of production input structures, may be related because the workers or clients of a regional anchor industry--for example, a university or hospital--want specific goods and services. So bicycle shops, electronic scooters and bars provide the services that college students spend their money on. The bicycle shops and bars share a customer base anchored by a university. In a similar...