Industrial resilience, regional diversification and related variety during times of crisis in the US urban–rural context

ABSTRACT

This paper evaluates the role of related variety in the industrial resilience of US counties against the 2008 economic shock. We use employment data on six-digit industries and measure industrial resilience by the extent to which a county maintained or improved entry rates of new industrial specializations in the post-crisis period of 2009–14 as compared with 2002–07. We find that metropolitan counties are more resilient than other types of areas. Related variety exhibits a strong positive effect on industrial resilience. This effect appears to be driven by intermediate and rural counties, which particularly benefit from related variety.

KEYWORDS:

• regional resilience
• diversification
• related variety
• urban–rural divide

JEL:

• O14
• R11
• R58

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. See https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions/.

2. Source: FRED, https://fred.stlouisfed.org/series/A191RP1Q027SBEA/.

3. See https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx/.

4. We excluded 2008 because it is the only full year within the recession period announced by the NBER (December 2007–June 2009). Since the onset and end of the crisis is not clearly defined in all counties, we consider a more generous timeframe in the robustness checks.

5. Localization economies refer to the economies of agglomeration associated with specialization and are measured by means of the Los index (Los, 2000), which takes into consideration the technological proximity of industries. We use Hidalgo et al.’s (2007) co-occurrence analysis to infer proximity from the probability of co-specializations of two industries in the same county. By doing so, we obtain a 675 × 675 proximity matrix φ. Equation (8) shows the mathematical notation of the Los index, where i and j refer to a pair of industries in a county c; φ is based on the minimum of the pairwise conditional probability that a county has a specialization of one industry (x_i) given its co-specialization of another (x_j) (Hidalgo et al., 2007):

$Lo{s}_c={\displaystyle \frac{{\sum }_{i=1}^I{\sum }_{j=1}^J{E}_{ci}{E}_{cj}{\varphi }_{ij}}{{\sum }_{i=1}^I{\sum }_{j=1}^J{E}_{ci}{E}_{cj}}}$; ${\varphi }_i=min\left\{p\right(x_i\left|x_j\right),p\left(x_j\right|x_i\left)\right\}$

6. Since we are applying a multinomial logit estimation strategy, we tested if the independence from irrelevant alternatives (IIA) assumption holds for our regressions. A Hausman test confirms that the odds are not affected by omitting one outcome category in the regression, which suggests that the IIA holds. The results are available from the authors upon request.

7. For the full regression table, see Table A3 in the supplemental data online.

8. Nonetheless, we are aware that a more formal method should be applied. A Bartik strategy might solve any further doubts.