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Whither the American west economy? Natural amenities, mineral resources and nonmetropolitan county growth

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Abstract

Although the American West has long experienced strong economic growth, variation in natural amenities and mineral resources across the West has produced a diversity of economic outcomes and trends. In this paper, we assess whether there have been recent significant shifts in economic growth across the nonmetropolitan counties of the region. We find significant relative downward growth shifts in areas most abundant in natural amenities. Further analysis suggests the downward growth shifts in high-amenity counties resulted from the capitalization of the amenities into housing costs, not from diminished quality of life in the counties from growth or climate change. Both the shocks and multipliers associated with mineral resource extraction shifted across the periods. The uncertainty surrounding future climate change adjustments and volatility of mineral resource extraction suggests the need for place-based policy to maintain economic vitality in the rural West.

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Notes

  1. The eleven states are Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington and Wyoming.

  2. We use the 1990 definition of a metropolitan area, defined as consisting of an urban core of 50,000 or more people and contiguous counties where at least 15% of the workforce commuted across county lines.

  3. McGranahan et al. (2011) suggest that adding forest cover could improve the natural amenity scale. But because forest cover can change from growth, logging, and wildfires, and because there is a strong correlation between forest cover and the natural amenity scale, we do not attempt to incorporate forest cover into the natural amenity ranking and simply use the ERS scale in the regressions. Rickman and Rickman (2011) report that the counties with the highest natural amenity ranking also have the largest forest cover in the county; the mean forest coverage for amenity rank 7, 6, and 5 counties are, respectively, 61.5, 50.8, and 39.9%.

  4. https://www.ers.usda.gov/data-products/county-typology-codes/.

  5. https://www.economicmodeling.com/.

  6. Among the other results not shown in Table 4 for the 1990s, for employment growth no individual category of county along the rural–urban continuum is significant, and manufacturing dependence in 1989 was associated with statistically significant slower annualized compounded growth of about 1.2%. Population growth was slower in category 5 counties along the rural–urban continuum during the period and was unaffected by manufacturing dependence. Results for the other control variables are available by request from the authors.

  7. In results not shown, counties that were farm dependent in 1989 experienced statistically stronger employment growth during 2000-2010, while manufacturing dependent counties returned to zero differential employment growth post-2000 (relative to the effect captured in the industry mix term). There were no statistically significant employment shifts along the rural–urban continuum. A statistically significant downward population growth shift in rural–urban category 8 counties (adjacent to metropolitan areas but with population less than 2500) occurred during both 2000–2010 and 2010–2016 and the lower growth in category 5 counties during the 1990 s mostly was reversed.

  8. The sole exception is the statistically positive differential growth during the 1990s for counties with amenity rank 6.

  9. Not shown is the slower wage and salary growth in rural–urban continuum category 5 counties during the 1990s (at the 10% level), consistent with the slower population growth in the counties. Farm and manufacturing dependence was associated with significantly increased growth in nonfarm proprietor income during 2010–2016.

  10. We also considered whether wilderness designation during the 2000–2010 or 2010–2016 period affected estimated economic growth across the amenity spectrum. Designation as a wilderness area could adversely affect the area economy through reduced extractive activities but benefit the area economy through increased amenity attractiveness (Duffy-Deno 1998; Chen et al. 2016; Kovacs et al. 2017). The wilderness designation variables did not significantly shift growth in any of the six regressions based on Wald Chi-square tests.

  11. In another robustness test, we re-classified the counties into high-amenity and low-amenity counties; high-amenity counties were those with amenity ranking 5, 6 and 7. We also re-classified the counties as adjacent versus non-adjacent to metropolitan areas. We reran all six regressions by replacing the previous amenity and rural–urban continuum variables with these corresponding two-category variables. We also then added the interaction of high-amenity status counties with adjacency to metropolitan areas for each period. For each regression then three interaction variables were added, one for each period. Based on Wald Chi-square tests, the three interaction variables as a group were all highly insignificant in each regression, with p values ranging from 0.3 to 0.8, and none of the interaction variables were individually significant at or below the 0.05 level in any regression.

  12. The regressions producing the residuals include household characteristics for earnings and housing characteristics for housing costs. A value of 0.3 is used as the housing expenditure share in consumption to weight housing cost residuals (Rickman and Rickman 2011).

  13. The Cragg-Donald statistic equals 3.38 for all regressions.

  14. We first used the STATA command spmat (Drukker et al. 2013a) where we imposed a condition that assumed counties which are more than 200 miles apart would have zero effect on each other to create an inverse-distance row-normalized spatial-weighting matrix that can be used in the spatial error term of a cross-sectional model with spatial-autoregressive disturbances (SARAR model). We then used the STATA command spreg (Drukker et al. 2013b) to estimate the parameters by maximum likelihood (ML) for each of the models.

  15. Characteristics in the earnings regression include several age range shares, several industry employment shares, several occupation employment shares, educational attainment shares, ethnicity shares and the share of households with a disability. Characteristics in the housing cost equations include median number of total rooms, the median number of bedrooms, age shares, share with complete indoor plumbing, share with complete kitchen facilities.

  16. Rickman and Rickman (2011) used the definition of metropolitan areas based on the 2000 Census of Population, resulting in fewer nonmetropolitan counties.

  17. Included are changes in the shares of the houses with 1, 2, 3 and 4 bedrooms, the share of the houses with a kitchen, the share of the houses with indoor plumbing and the median number of rooms.

  18. The results are available from the authors on request.

  19. We further explored whether there were any other patterns across the natural amenity spectrum using the ACS 2012-2016 5-year estimates (not shown). The share of the population that had moved from another county during the last year, the share of the population that had moved from another state during the last year, the percent of houses that are owner occupied, and the percentage of population that was aged 65 and older, all did not statistically differ across the natural amenity spectrum. The share of the adult population with at least a bachelor’s degree statistically differed across the amenity spectrum; the further up a county was in the natural amenity spectrum the higher was its share of adult population with at least a bachelor’s degree. Although the amenity ranking variables are statistically significant as a group, the difference between any two successive amenity ranks is only statistically significant between amenity rank 5 and 6 counties for the bachelor’s degree population share.

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Authors and Affiliations

  1. Oklahoma State University, 256 Business College, Stillwater, OK, 74078, USA

    Dan S. Rickman

  2. Oklahoma State University, 272F Business College, Stillwater, OK, 74078, USA

    Hongbo Wang

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  1. Dan S. Rickman
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Correspondence to Dan S. Rickman.

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Rickman, D.S., Wang, H. Whither the American west economy? Natural amenities, mineral resources and nonmetropolitan county growth. Ann Reg Sci 65, 673–701 (2020). https://doi.org/10.1007/s00168-020-00999-z

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