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Natural amenities and skill sorting in rural communities: a case study of land conservation policy

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Abstract

Recent research finds evidence that improvements in rural natural amenities can increase median household income in communities close to the amenities. In this paper, a simple theoretical model is sketched out as an explanation of a hypothesized underlying mechanism: improvement in natural amenities can induce skill sorting across rural communities. Using community-level data in the state of Oregon before and after a large federal land conservation program was implemented, we test this hypothesis along with several competing mechanisms. Our empirical results support the hypothesized mechanism of amenity-induced skill sorting. Other mechanisms like the out-migration of the low-income population and the in-migration of the elderly population are not supported. The findings are important for policy discussions about land conservation, development and their distributional impacts.

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Data availability

Data are available upon request from the authors.

Code availability

Stata code used in data processing and estimation is available upon request from the authors.

Notes

  1. The implementation of the NWFP entails a large reduction in extractive management on government-owned forestland. This land preservation program can improve the natural amenities on those protected forest lands by restricting extractive uses and leaving more standing trees. Compared to places with unprotected natural amenities and continuing urbanization pressure, the implementation of NWFP creates a relative amenity improvement.

  2. The results in this paper are robust to the choice of radius. Different specifications result in similar estimates. However, the significance on high-skilled occupation ratio disappears if the radius is set too low (<=3 miles) or too high (>=8 miles) because around two thirds of the baseline observations are dropped from the matching process.

  3. The direct employment impacts associated with mill closure and the Northwest Economic Adjustment Initiative are region wide, so is the impact of the North American Free Trade Agreement (Christensen et al. 1999; Chen et al. 2016). This difference in the spatial extent between amenity-driven migration and these direct employment impacts provides the identification of the amenity effect of the NWFP on community skill composition.

References

  • Adamson DW, Clark DE, Partridge MD (2004) Do urban agglomeration effects and household amenities have a skill bias? J Reg Sci 44(2):201–223

    Article  Google Scholar 

  • Anderson K, Weinhold D (2008) Valuing future development rights: the costs of conservation easements. Ecol Econ 68(1–2):437–446. https://doi.org/10.1016/j.ecolecon.2008.04.015

    Article  Google Scholar 

  • Arntz M (2010) What attracts human capital? Understanding the skill composition of interregional job matches in Germany. Reg Stud 44(4):423–441

    Article  Google Scholar 

  • Autor D (2010) The polarization of job opportunities in the U.S. labor market: implications for employment and earnings. Center for American Progress and the Hamilton Project, Washington, DC

  • Banzhaf HS, Walsh RP (2008) AssociationDo people vote with their feet? An empirical test of Tiebout’s mechanism. Am Econ Rev 98(3):843–863

    Article  Google Scholar 

  • Brasington D, Hite D (2005) Demand for environmental quality: a spatial hedonic analysis. Reg Sci Urban Econ 35(1):57–82

    Article  Google Scholar 

  • Brown WM, Scott DM (2012) Human capital location choice: accounting for amenities and thick labor markets. J Reg Sci 52(5):787–808

    Article  Google Scholar 

  • Brueckner JK, Rosenthal SS (2008) Gentrification and neighborhood housing cycles: will America’s future downtowns be rich? Rev Econ Stat 91(4):725–743

    Article  Google Scholar 

  • Bureau of Labor Statistics (2018) Labor force statistics from the current population survey. Retrieved from https://www.bls.gov/cps/cpsoccind.htm

  • Buch T, Hamann S, Niebuhr A, Rossen A (2017) How to woo the smart ones? Evaluating the determinants that particularly attract highly qualified people to cities. J Urban Aff 39(6):764–782. https://doi.org/10.1080/07352166.2017.1282765

    Article  Google Scholar 

  • Carlino GA, Saiz A (2019) Beautiful city: leisure amenities and urban growth. J Reg Sci 59(3):369–408

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chen Y, Weber B (2012) Federal policy, rural community growth, and wealth creation: the impact of the federal forest policy and rural development spending in the pacific northwest. Am J Agric Econ 94(2):542–548

    Article  Google Scholar 

  • Christensen HH, Raettig TL, Sommers P, tech. eds. (1999) Northwest forest plan: outcomes and lessons learned from the Northwest economic adjustment initiative: proceedings of a forum. Portland, OR. Gen. Tech. Rep. PNW-GTR-484. Portland, OR, U.S

  • Cullen JB, Levitt SD (1999) Crime, urban flight, and the consequences for cities. Rev Econ Stat 81:159–169

    Article  Google Scholar 

  • Dalmazzo A, de Blasio G (2011) Amenities and skill-biased agglomeration effects: some results on Italian cities. Pap Reg Sci 90(3):503–527

    Article  Google Scholar 

  • Diamond R (2016) The determinants and welfare implications of US workers’ diverging location choices by skill: 1980–2000. Am Econ Rev 106(3):479–524

    Article  Google Scholar 

  • Economic Research Service (ERS), United States Department of Agriculture (2017), Rural Education at a Glance. Economic Information Bulletin 171. https://www.ers.usda.gov/webdocs/publications/83078/eib171_forprinting.pdf?v=0#:~:text=Between%202000%20and%202015%2C%20the,11%20to%2014%20percentage%20points. Accessed Dec. 12 2020

  • Eichman H, Hunt GL, Kerkvliet J, Plantinga AJ (2010) Local employment growth, migration, and public land policy: evidence from the northwest forest plan. J Agric Resour Econ 35(2):316–333

    Google Scholar 

  • Fallah B, Partridge MD, Rickman DS (2014) Geography and high-tech employment growth in US Counties‡. J Econ Geogr 14(4):683–720

    Article  Google Scholar 

  • Ferraro PJ, Pattanayak SK (2006) Money for nothing? A call for empirical evaluation of biodiversity conservation investments. PLoS Biol 4(4):0482–0488

    Article  Google Scholar 

  • Ferraro PJ, Miranda JJ (2017) Panel data designs and estimators as substitutes for randomized controlled trials in the evaluation of social programs. J Assoc Environ Resour Econ 4(1):281–317

    Google Scholar 

  • Garber-Yonts B, Kerkvliet J, Johnson R (2004) Public values for biodiversity conservation policies in the Oregon Coast Range. For Sci 50(5):589–602

    Google Scholar 

  • Glaeser E, Gottlieb JD (2009) The wealth of cities: agglomeration economies and spatial equilibrium in the United States. J Econ Lit 47(4):983–1028

    Article  Google Scholar 

  • Glaeser E, Saiz A (2004) The rise of the skilled city [with comments]. Brook-Wharton Papers Urban Aff 2004:47–105. https://doi.org/10.1353/urb.2004.0005

    Article  Google Scholar 

  • Guerrieri V, Hartley D, Hurst E (2013) Endogenous gentrification and hosing price dynamics. J Public Econ 100:45–50

    Article  Google Scholar 

  • Imbens GW, Wooldridge JM (2009) Recent developments in the econometrics of program evaluation. J Econ Lit 47(1):5–86

    Article  Google Scholar 

  • Jakus PM, Akhundjanov SB (2018) Neither boon nor bane: the economic effects of a landscape-scale national monument. Land Econ 94(3):323–339

    Article  Google Scholar 

  • Jakus PM, Akhundjanov SB (2019) The Antiquities Act, national monuments, and the regional economy. J Environ Econ Manag 95:102–117

    Article  Google Scholar 

  • Kahn ME, Vaughn R, Zasloff J (2010) The housing market effects of discrete land use regulations: evidence from the California coastal boundary zone. J Hous Econ 19(4):269–279

    Article  Google Scholar 

  • Klaiber HA, Phaneuf DJ (2010) Valuing open space in a residential sorting model of the Twin Cities. J Environ Econ Manag 60(2):57–77

    Article  Google Scholar 

  • Lee S (2010) Ability sorting and consumer city. J Urban Econ 68:20–33

    Article  Google Scholar 

  • Lee H-K, Kim H-B (2019) Regional preferences for the living environment and mobility of researchers and general workers: the case of Korea. Ann Reg Sci 62(1):169–186

    Article  Google Scholar 

  • Lewis DJ, Hunt GL, Plantinga AJ (2003) Does public lands policy affect local wage growth? Growth Change 34(1):64–86

    Article  Google Scholar 

  • Liu Y, Shen J, Xu W, Wang G (2017) From school to university to work: migration of highly educated youths in China. Ann Reg Sci 59(3):651–676

    Article  Google Scholar 

  • Lorah P, Southwick R (2003) Environmental protection, population change, and economic development in the rural western United States. Popul Environ 24(3):255–272

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Meltzer R, Ghorbani P (2017) Does gentrification increase employment opportunities in low-income neighborhoods? Reg Sci Urban Econ 66:52–73

    Article  Google Scholar 

  • Partridge MD, Rickman DS, Olfert MR, 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

    Article  Google Scholar 

  • Rasker R (2006) An exploration into the economic impact of industrial development versus conservation on western public lands. Soc Nat Res Int J 19(3):191–207

    Article  Google Scholar 

  • Roback J (1982) Wages, rents, and the quality of life. J Polit Econ 90(6):1257–1278

    Article  Google Scholar 

  • Rosenbaum PR, Rubin DB (1985) Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat 39(1):33–38

    Google Scholar 

  • Rossi-Hansberg E, Starte PD, Owens R (2010) Housing externalities. J Polit Econ 118(3):485–535

    Article  Google Scholar 

  • Sieg H, Smith VK, Banzhaf HS, Walsh R (2004) Estimating the general equilibrium benefits of large changes in spatially delineated public goods*. Int Econ Rev 45(4):1047–1077

    Article  Google Scholar 

  • Walls M, Lee P, Ashenfarb M (2020) National monuments and economic growth in the American West. Sci Adv 6(12):8523. https://doi.org/10.1126/sciadv.aay8523

    Article  Google Scholar 

  • Zheng SQ, Kahn ME (2013) Does government investment in local public goods spur gentrification? Evid Beijing Real Estate Econ 41(1):1–28

    Article  Google Scholar 

  • Zipp KY, Lewis DJ, Provencher B (2017) Does the conservation of land reduce development? An econometric-based landscape simulation with land market feedbacks. J Environ Econ Manag 81:19–37

    Article  Google Scholar 

Download references

Acknowledgements

The data were collected with funding from the Economic Research Service, U.S. Department of Agriculture, under cooperative agreement 58-6000-0-0053.

Funding

The data were collected with funding from the Economic Research Service under cooperative agreement 58–6000-0–0053 and with support from the Oregon Agricultural Experiment Station, Multistate project NE-1749.

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Correspondence to Yong Chen.

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Supplementary information

Appendices

Appendix 1: Proofs of propositions

Proposition 1

An improvement in \(G_{2}\) pushes up housing price \(p_{2}\): that is \({\text{d}}p_{2} /{\text{d}}G_{2} > 0\).

Proof

This can be proved by contradiction. If \(dp_{2} /dG_{2} \le 0\), together with the improvement in amenity \(G_{2}\), community 2 becomes more attractive to all residents at the new equilibrium, including those at the boundary \(\overline{\alpha }_{1}\) and \(\overline{\alpha }_{2}\). Now workers at these two boundaries strictly prefer community 2. So the housing demand in community 2 increases and the housing market clearing condition (Eq. 7) implies the housing supply has to increase and so does housing price. This contradicts the assumption \({\text{d}}p_{2} /{\text{d}}G_{2} \le 0\). Therefore, \({\text{d}}p_{2} /{\text{d}}G_{2} > 0\). QED.

Proposition 2

An improvement in \(G_{2}\) attracts workers with higher skill levels into the community: that is, \({\text{d}}\overline{\alpha }_{2} /{\text{d}}G_{2} > 0\) .

Proof

\({\text{d}}\overline{\alpha }_{2} /{\text{d}}G_{2} \le 0\) implies that workers with skill level \(\overline{\alpha }_{2}\), the upper boundary level of community 2, either strictly prefer community 3 or remain indifferent between communities 2 and 3. That is,

$$ {\Delta }V\left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{2} } \right)} \right) \approx V_{G} \left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{2} } \right)} \right) + V_{p} \left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{2} } \right)} \right) {\text{d}}p_{2} /{\text{d}}G_{2} \le 0. $$

Similarly, the change in the indirect utility of workers with skill level \(\overline{\alpha }_{1}\) can be approximated by:

$$ {\Delta }V\left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{1} } \right)} \right) \approx V_{G} \left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{1} } \right)} \right) + V_{p} \left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{1} } \right)} \right) {\text{d}}p_{2} /{\text{d}}G_{2} . $$

Given that \(V\left( {G_{k} ,{ }p_{k} , w\left( \alpha \right)} \right) = U\left( {1,{\text{w}}\left( \alpha \right) - p_{k} ,{ }G_{k} } \right)\), an increase in housing price implies a decrease in the numeraire consumption by the amount equal to \(p_{2}^{^{\prime}} - p_{2}\). \(U_{xx} < 0\) implies that this decrease in consumption hurts workers with lower skills more than those with higher skills, that is, \(V_{p} \left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{1} } \right)} \right) < V_{p} \left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{2} } \right)} \right) < 0\). In addition, \(V_{{{\text{Gw}}}} > 0\) implies \(V_{G} \left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{1} } \right)} \right) \le V_{G} \left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{2} } \right)} \right)\). Therefore, \({\Delta }V\left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{1} } \right)} \right) < {\Delta }V\left( {G_{2} ,{ }p_{2} ,w\left( {\overline{\alpha }_{2} } \right)} \right) \le 0\). The workers with skill level \(\overline{\alpha }_{1}\) strictly prefer community 1. As a result, the housing demand in community 2 decreases, its equilibrium housing price has to decrease. This contradicts Proposition 1. Therefore, we must have \({\text{d}}\overline{\alpha }_{2} /{\text{d}}G_{2} > 0\). QED.

Corollary

An improvement in \(G_{2}\) leads to a price decrease in community 3, that is, \({\text{d}}p_{3} /{\text{d}}G_{2} < 0\) .

As workers migrate out of community 3, the housing demand in community 3 decreases. Consequently, the housing price \(p_{3}\) will decrease.

Proposition 3

The impact on the low-skilled workers ( \({\text{d}}\overline{\alpha }_{1} /{\text{d}}G_{2}\) ) is indeterminate.

Proof

For workers with skill level \(\overline{\alpha }_{1}\) that choose to stay in community 2, because \(V_{G} > 0\) and \(V_{p} < 0\), the sign of the change in their indirect utility is generally indeterminate. This implies boundary skill level \(\overline{\alpha }_{1}\) may move up or down depending on the relative strength of the amenity effect (\(V_{G}\)) or the price effect (\(V_{p}\)). QED.

Appendix 2: Propensity score matching

Because conservation siting could be correlated with local economic conditions, we apply propensity score matching to create balanced samples across the treatment and control groups. Table 3 reports the covariance imbalance check for both the outcome variables and the explanatory variables. Following Imbens and Wooldridge (2009, p. 24), for each covariate, we calculate the difference in the mean from the treated group and the mean from the control group, normalized by the square root of the sum of the variances across both the control and treatment groups. The rule of thumb is that normalized differences in means that are greater than 0.25 indicate that linear regression methods will be sensitive to the linear specification. Matching significantly reduces the difference in normalized means across treated and control communities, with important reduction arising from a better balance associated with the mill closure variable, percentage of high-skilled, low-skilled workers and poverty—now treatment and control communities have a similar number of closed timber mills and population under poverty.

Identification of the treatment effects depends on controlling for potential confounding factors that change over time, like NWFP-induced regional employment shocks. The implementation of the NWFP is expected to reduce timber harvest and affect timber related employment in addition to local amenities. In the model description, we argue that the employment effect is regional in that it affects both the communities nearby and further away from the protected NWFP lands. The assumption is critical in the difference-in-differences analyses: control communities (those far from the protected NWFP lands) were subject to the same underlying trends as the treatment communities (those close to the protected NWFP lands). The validity of this assumption is confirmed in Chen et al. (2016) using a binary Probit model of the annual mill closure probability as a function of distance from the mill to the nearest protected NWFP land. The implementation of the economic assistance program under the NWFP combined with the imposition of NAFTA affected both the treatment and control communities in the region, and their impacts are therefore controlled by the difference-in-differences method.

Identification of treatment effects with observational data requires overlap in the propensity score of the treatment (Imbens and Wooldridge 2009). Overlap implies that for all possible values of the covariates, there are both treated and control units. In practice, overlap is assessed by plotting histograms of the propensity score of treatment for both treatment and control units and visually inspecting whether the two histograms overlap.

Fig. 1
figure 1

Overlap in the estimated propensity score

Figure 1 presents histograms of the propensity score and shows substantial overlap for our sample of communities for the matched sample when the propensity score is estimated with pre-treatment levels.

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Chen, Y., Lewis, D.J. & Weber, B. Natural amenities and skill sorting in rural communities: a case study of land conservation policy. Ann Reg Sci 67, 649–669 (2021). https://doi.org/10.1007/s00168-021-01060-3

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