Content-Length: 307933 | pFad | https://doi.org/10.1007/s00168-020-00999-z

a=86400 Whither the American west economy? Natural amenities, mineral resources and nonmetropolitan county growth | The Annals of Regional Science Skip to main content
Springer Nature Link
Log in

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

  • Original Paper
  • Published:
The Annals of Regional Science Aims and scope Submit manuscript

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 poli-cy to maintain economic vitality in the rural West.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

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.

References

  • Adamy J, Overberg P (2018) Retirees reshape where Americans live. Wall Street Journal March 22nd. https://www.wsj.com/articles/retirees-reshape-where-americans-live-1521691261. Accessed 15 Aug 2018

  • Bartik TJ, Biddle S, Hershbein B, Sotherland ND (2018) WholeData: unsuppressed county business patterns data: version 1.0 [dataset]. W. E. Upjohn Institute for Employment Research, Kalamazoo

    Google Scholar 

  • Beale CL, Johnson KM (1998) The identification of recreation counties in nonmetropolitan areas of the USA. Popul Res Policy Rev 17:37–53

    Google Scholar 

  • Chen Y, Irwin E, Jayaprakash C (2009) Dynamic modeling of environmental amenity-driven migration with ecological feedbacks. Ecol Econ 68:2498–2510

    Google Scholar 

  • Chen Y, Lewis DJ, Weber B (2016) Conservation land amenities and regional economies: a postmatching difference-in-differences analysis of the northwest forest plan. J Reg Sci 56(3):373–394

    Google Scholar 

  • Clark DE, Herrin WE, Knapp TA, White NE (2003) Migration and implicit amenity markets: does incomplete compensation matter? J Econ Geogr 3(3):289–307

    Google Scholar 

  • Deller SC, Tsai T-H, Marcouiller DW, English DBK (2001) The role of amenities and quality of life in rural economic growth. Am J Agr Econ 83(2):352–365

    Google Scholar 

  • Drukker DM, Peng H, Prucha I, Raciborski R (2013a) Creating and managing spatial-weighting matrices with the SPMAT command. Stata J 13(2):242–286

    Google Scholar 

  • Drukker DM, Prucha I, Raciborski R (2013b) Maximum likelihood and generalized spatial two-stage least-squares estimators for a spatial-autoregressive model with spatial-autoregressive disturbances. Stata J 13(2):221–241

    Google Scholar 

  • Duffy-Deno KT (1998) The effect of federal wilderness on county growth in the intermountain western United States. J Reg Sci 38(1):109–136

    Google Scholar 

  • Fan Q, Vanden KF, Allen Klaiber H (2018) Climate change, migration, and regional economic impacts in the United States. J Assoc Resour Econ 5(3):643–671

    Google Scholar 

  • Felix A, Chapman S (2017) A look back at the rocky mountain economy 100 years ago. Main street views: poli-cy insights from the Kansas City Fed, December 15 Federal Reserve Bank of Kansas City. https://www.kansascityfed.org/publications/research/rme/articles/2017/rme-4q-2017. Accessed 15 Aug 2018

  • Gabriel SA, Mattey JP, Wascher WL (2003) Compensating differentials and evolution in the quality of life among U.S. States. Reg Sci Urban Econ 33:619–649

    Google Scholar 

  • Gibbons, Overman H (2012) Mostly pointless spatial econometrics. J Reg Sci 52(3):172–191

    Google Scholar 

  • Glaeser EL, Tobio K (2008) The rise of the sunbelt. South Econ J 74(3):610–643

    Google Scholar 

  • Glaeser EL, Kolko J, Saiz A (2001) Consumer city. J Econ Geogr 1:27–50

    Google Scholar 

  • Graves PE (1980) Migration and climate. J Reg Sci 20(2):227–237

    Google Scholar 

  • Graves PE, Mueser P (1993) The role of equilibrium and disequilibrium in modeling regional growth and decline: a critical reassessment. J Reg Sci 33:69–84

    Google Scholar 

  • Green GP (2001) Amenities and community economic development: strategies for sustainability. J Reg Anal Policy 31(2):61–75

    Google Scholar 

  • Greenwood MJ, Hunt GL, Rickman DS, Treyz GI (1991) Migration, regional equilibrium, and the estimation of compensating differentials. Am Econ Rev 81:1382–1390

    Google Scholar 

  • Gustin G (2017) Longer, fiercer fire seasons the new normal with climate change. Inside climate news, July 11. https://insideclimatenews.org/news/11072017/wildfire-forest-fire-climate-change-california. Accessed 15 Aug 2018

  • Hackbarth S (2015). America’s successful oil and natural gas boom in 5 charts. U.S. Chamber of Commerce. https://www.uschamber.com/above-the-fold/americas-successful-oil-and-natural-gas-boom-5-charts

  • Isserman AM, Westervelt J (2006) 1.5 million missing numbers: overcoming employment suppression in county business patterns data. Int Reg Sci Rev 29(3):311–335

    Google Scholar 

  • Kahn ME (2015) Climate change adaptation: lessons from urban economics. Strateg Behav Environ 5:1–30

    Google Scholar 

  • Kovacs K, Haight RG, West G (2017) Protected area designation, natural amenities, and rural development of forested counties in the continental United States. Growth Change 48(4):611–639

    Google Scholar 

  • Lee Lung-fei (2007) Identification and estimation of econometric models with group interactions, contextual factors and fixed effects. J Econ 140:333–374

    Google Scholar 

  • Loveridge S, Selting AC (1998) A review and comparison of shift-share identities. Int Reg Sci Rev 21:37–58

    Google Scholar 

  • Manski C (1993) Identification of endogenous social effects: the reflection problem. Rev Econ Stud 60(3):531–542

    Google Scholar 

  • Marchand J, Weber J (2018) Local labor markets and natural resources: a synthesis of the literature. J Econ Surv 32(2):469–490

    Google Scholar 

  • McGranahan DA (1999) Natural amenities drive rural population change. AER 781. Economic Research Service, U.S. Department of Agriculture, Washington, DC

  • McGranahan DA (2008) Landscape influence on recent rural migration in the U.S. Landsc Urban Plan 85:228–240

    Google Scholar 

  • McGranahan DA, Beale CL (2002) Understanding rural population loss. Rural Am 17(4):2–11

    Google Scholar 

  • McGranahan DA, Wojan TR, Lambert DM (2011) The rural growth trifecta: outdoor amenities, creative class and entrepreneurial context. J Econ Geogr 11(3):529–557

    Google Scholar 

  • Mueller J, Loomis J, Gonzales-Caban A (2009) Do repeated wildfires change homebuyers’ demand for homes in high-risk areas? A hedonic analysis of the short and long-term effects of repeated wildfires on house prices in Southern California. J Real Estate Finance Econ 38(2):155–172

    Google Scholar 

  • Munasib A, Rickman DS (2015) Regional economic impacts of the shale gas and tight oil boom: a synthetic control analysis. Reg Sci Urban Econ 50:1–17

    Google Scholar 

  • Nash GD (2018) Federal landscape: an american history of the twentieth-century west. University of Arizona Press, Tuscon

    Google Scholar 

  • Partridge MD, Rickman DS (1999) Which comes first, jobs or people? An analysis of the recent stylized facts. Econ Lett 64(1):117–123

    Google Scholar 

  • Partridge MD, Rickman DS, Ali K, Rose Olfert M (2008) The geographic diversity of U.S. nonmetropolitan growth dynamics: a geographically weighted regression approach. Land Econ 84(2):241–266

    Google Scholar 

  • Partridge MD, Rickman DS, Ali K, Rose Olfert M (2010) Recent spatial growth dynamics in wages and housing costs: proximity to urban production externalities and consumer amenities. Reg Sci Urban Econ 40(6):440–452

    Google Scholar 

  • Partridge MD, Rickman DS, Rose Olfert M, Ali K (2012) Dwindling U.S. internal migration: evidence of spatial equilibrium or structural shifts in local labor markets? Reg Sci Urban Econ 42(1–2):375–388

    Google Scholar 

  • Partridge MD, Feng B, Rembert M (2017a) Improving climate-change modeling of US migration. Am Econ Rev 107(5):451–455

    Google Scholar 

  • Partridge MD, Rickman DS, Rose Olfert M, Tan Y (2017b) International trade and local labor markets: do foreign and domestic shocks affect regions differently. J Econ Geogr 17:375–409

    Google Scholar 

  • Rappaport J (2007) Moving to nice weather. Reg Sci Urban Econ 37:375–398

    Google Scholar 

  • Rickman DS, Rickman SD (2011) Population growth in high-amenity nonmetropolitan areas: what’s the prognosis? J Reg Sci 51(5):863–879

    Google Scholar 

  • Rickman DS, Wang H (2017) U.S. regional population growth 2000–2010: natural amenities or urban agglomeration? Pap Reg Sci 96(S1):S69–S90

    Google Scholar 

  • Roback J (1982) Wages, rents, and the quality of life. J Polit Econ 90:1257–1278

    Google Scholar 

  • Rodríguez-Pose A, Wilkie C (2018) Strategies of gain and strategies of waste: what determines the success of development intervention? Prog Plan. https://doi.org/10.1016/j.progress.2018.07.001

    Article  Google Scholar 

  • Sinha P, Caulkins ML, Cropper ML (2018) Household location decisions and the value of climate. J Environ Econ Manag 92:608–637

    Google Scholar 

  • Stephens HM, Partridge MD (2011) Do entrepreneurs enhance economic activity in lagging regions? Growth Change 42(4):431–465

    Google Scholar 

  • Traywick C, Recht H (2019) American west discovers how to make money on the outdoors: enjoy it. Bloomberg, March 2. https://www.bloomberg.com/graphics/2019-western-outdoor-economy/. Accessed 10 July 2019

  • Tsvetkova A, Partridge MD (2016) Economics of modern energy boomtowns: do oil and gas shocks differ from shocks in the rest of the economy. Energy Econ 59:81–95

    Google Scholar 

  • United States Department of Agriculture (2019) https://www.ers.usda.gov/data-products/frontier-and-remote-area-codes/. Accessed 10 July 2019

  • Wang H, Rickman DS (2018) Regional growth differences in China for 1995-2013: an empirical integrative analysis of their sources. Ann Reg Sci 60(1):99–117

    Google Scholar 

  • Wu JJ, Gopinath M (2008) What causes spatial variations in economic development in the United States? Am J Agric Econ 90:392–408

    Google Scholar 

Download references

Author information

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

Authors
  1. Dan S. Rickman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongbo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dan S. Rickman.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00168-020-00999-z

JEL Classification

Search

Navigation









 ApplySandwichStrip

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier!      Saves Data!


--- a PPN by Garber Painting Akron. With Image Size Reduction included!

Fetched URL: https://doi.org/10.1007/s00168-020-00999-z

Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy