Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Is Mining an Environmental Disamenity? Evidence from Resource Extraction Site Openings

Abstract

Extractive industries are often challenged by nearby communities due to their environmental and social impacts. If proximity to resource extraction sites represents a disamenity to households, the opening of new mines should lead to a decrease in housing prices. Using evidence from more than 6000 new extraction sites in Chile, this study addresses whether the heavy environmental and social impacts of digging activities outweigh their local economic benefits to the housing market in emerging economies. Findings from a spatial difference-in-difference nearest-neighbor matching estimator reveal that households near mining activity get compensated with lower rental prices, mostly in places with high perceptions of exposure to environmental pollution. Further analysis suggests that this compensation is lower among new residents of mining towns, which constitutes evidence of a taste-based sorting across space. Results in this study bring to light the need of incorporating welfare effects of potential social and environmental disruptions in future studies addressing the economic impact of new mining operations.

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

Fig. 1
Fig. 2

Notes

  1. 1.

    See more in Milu et al. (2002), Kitula (2006), Bebbington et al. (2008), and Kemp et al. (2010).

  2. 2.

    This is particularly true for countries with laxer environmental regulations. Broner et al. (2012) suggest that countries holding a comparative advantage in polluting industries might have laxer environmental regulations due their lobbying exerted to prevent the enactment of stringent standards. This is in line with the 2016 The Economist article titled “From Conflict to Co-operation”, on the mistrust existing among locals regarding the stringency of environmental impact assessments that are submitted by local mining projects. Source: From Conflict to Co-operation, The Economist, Online, accessed June 12, 2017.

  3. 3.

    See for instance Kohlhase (1991), Mendelsohn et al. (1992), Greenstone and Gallagher (2008), and Gamper-Rabindran and Timmins (2013) on proximity to hazardous waste sites or Superfund sites; Kiel and McClain (1995a, b) on waste incinerators; Currie et al. (2015) on industrial plants; Gamble and Downing (1982) on nuclear plants; and Davis (2011) on power plants.

  4. 4.

    This paper avoids a cross-sectional comparison of rental prices between cities with and without mining due to Chile’s historical mining tradition, which goes back to the 19th century. This situation challenges identification as it is unfeasible to control for city-level attributes and housing characteristics before the establishment of the first extraction sites. Instead, the opening of new deposits facilitates a clearer definition of the event study and the study period, which ultimately allows to proper control for potential differences across cities before these openings.

  5. 5.

    It is important to distinguish artisanal mining from conventional mining due to the potential small economic impacts of the former relative to large-scale operations, and their less-sustainable production and management practices that could impose higher net detrimental effects on households. See Sect. 4 for more details on the different impacts.

  6. 6.

    Chile is administratively divided into 346 communes- or cities- grouped into 15 regions. Comparing with the U.S. administrative division, cities are equivalent to counties, while regions are the equivalent to states.

  7. 7.

    I am very thankful to an anonymous referee for pointing this out.

  8. 8.

    For all the purposes, the study period covers years with low copper prices. Copper prices increased until 2011, declining afterward until their lowest drop in 2016. Source: Chilean Copper Commission, Online, accessed July 15, 2018. The effect of this price bust on rental prices is carefully taken into consideration in the empirical strategy.

  9. 9.

    In an ideal setting, effects of proximity to the disamenity would be measured by restricting the sample to houses that are located within a specific radius of distance. Unfortunately, the available data restrict this type of analysis. Findings in this study, however, bring to light the net impacts of cities’ concentration of extraction sites on the value of housing units for rent.

  10. 10.

    These results are in line with similar estimates on proximity to disamenities in other developing countries. For instance, Arimah (1996) estimates that households in Nigeria are willing to pay around 9% of their housing rent in order to live 1 km away from a major landfill boundary, while Deng et al. (2014) estimate a 12–14% increase in the price of properties within 5 km of two polluting power plants in China after the relocation of these facilities.

  11. 11.

    Evidence shows that subjective measures outperform objective indicators on environmental amenities (Berezansky et al. 2010; Chasco and Gallo 2013) by reducing the upward bias arising from a potential mismatch between these metrics (Michael et al. 2000). Another reason advocating for the use of subjective measures is the difficulty of interpreting objective measures of environmental quality, which prompts individuals to base their location decisions on their own insights of local pollution (Chasco and Le Gallo 2015).

  12. 12.

    New mining activity is expected to generate social impacts as well (see the previous section). For the sake of simplicity, however, this section only considers a change in local environmental quality.

  13. 13.

    For a more comprehensive review of hedonic price applications, see Mendelsohn and Olmstead (2009) and Freeman III et al. (2014).

  14. 14.

    An alternative approach to assessing a non-marginal change in a locational amenity is to estimate the bid functions as suggested by Rosen (1974). As shown later on the paper, however, a taste-based sorting prevents this estimation as preferences vary across individuals.

  15. 15.

    See “Organized Crime and Illegal Gold Mining in Latin America”, Global Americans, Online, accessed July 6, 2018.

  16. 16.

    An ownership distinction (i.e. state vs. private) is not feasible as this variable is omitted from the inventory. Notwithstanding, there are only two main state-owned mining companies in Chile: CODELCO and ENAMI. One of CODELCO’s division, “Ministro Hales”, started operations in 2013. Yet, this division is located in the city of Calama that belongs to the set of cities excluded from the analysis (see Fig. 3 in the Appendix), which implies that most of the openings studied here (if not all) correspond to private initiatives.

  17. 17.

    According to the National Service of Geology and Mining (SERNAGEOMIN), a small-scale mine has less than 80 hired workers; a medium-scale mine has between 80 and 400 workers; and a large-scale mine has more than 400 workers.

  18. 18.

    For a spatial location of the openings see Fig. 3 in Appendix.

  19. 19.

    At same time, this classification requires that none of these cities had experienced additional closures over the study period. In this way, the number of inactive or abandoned sites in the treatment cities (if any) is kept constant, and so their potential effect on rental prices is eliminated when taking the price difference over time.

  20. 20.

    Ideally, there would be a treatment group with cities experiencing mine closings as well. However, all the site closures identified in the data took place in cities that simultaneously host new openings, making it hard to isolate this event. Previous evidence on the closures of toxic plants, however, shows that housing prices remain unaffected after the shutdown of these facilities due to lasting visual effects or concerns on local contamination, which in turn implies that toxic facilities continue to negatively affect housing prices even after they cease operations (Currie et al. 2015), particularly if some of these sites are left abandoned (Newbold 2006).

  21. 21.

    The other 10,000 households are those homes located in cities that, due to the simultaneous opening and closing of mines, are dismissed from the analysis.

  22. 22.

    Identification of causal effects in a DID design comes from the counterfactual assumption that the trend in the rent prices for the control group is equivalent to the trend in the rent prices for the treatment group in absence of the treatment. Figure 6 (Appendix) fits this common trends assumption for the treatment groups, the control, and the placebo group, using the baseline sample. Pre-treatment periods correspond to 2009 and 2011. Unfortunately, missing data on pollution and other amenities at the city-level prevent the use of 2009 in the main analysis.

  23. 23.

    Figure 7 (Appendix) displays the common trends assumption for all the treatments and the placebo group using the matched sample. Relative to the trends in rental prices exhibited for the baseline sample (Fig. 6), the price gap between homes in treated and control cities shrinks once the comparability of the control group is improved. At the same time, there is a new price trend in cities with all-mine openings (panel a) and artisanal mine openings (panel c). Pre-treatment common trends on the matched sample suggest the existence of a negative treatment effect for all the three different treatments.

  24. 24.

    This is consistent with the house price index developed for Chile in Paredes and Aroca (2008). After matching houses across the entire country, they conclude that the region with the highest index is the region that has had the highest number of conventional mines over time.

  25. 25.

    The goal of this section is to study heterogeneous effects of mining on rental prices at different degrees of environmental pollution, regardless of the origin or source of this pollution.

  26. 26.

    Results for the other air pollutants are in Table 16 in the Appendix.

  27. 27.

    Strong heterogeneous effects are found for CO emissions in Table 16 as well. CO is one of the most common airborne pollutants from vehicle emissions. Large- and medium-scale operations in Chile are known for their high reliance on pickup trucks for their operations, as they facilitate the transportation of workers and equipment across different facilities (Paredes and Rivera 2017). Hence, it is not surprising that a high concentration of this type of mines could also boost CO emissions and other harmful pollutants from vehicle emissions. This could also explain the high magnitude of the estimates on \(\hbox {PM}_{2.5}\).

  28. 28.

    Limited households’ awareness regarding recreational water pollution, and the irrelevance of this characteristic when it comes to households’ location decisions (Boyle and Kiel 2001), might explain the disparity between subjective reports on water pollution and more objective indicators.

  29. 29.

    Households in 2015 CASEN report their 2010 city of residence, which defines people as either old residents (i.e. households living in their current city for at least five years), or new residents (i.e. households that during the last five years moved into their current city).

  30. 30.

    Another explanation is that newcomers, due to their recent relocation, might not be fully aware of the environmental and social changes affecting their neighborhoods. Even when this is a possibility, some of the disparities observed in Tables 5 and 6 between objective and subjective measures of environmental pollution reinforce the idea that some households may be indifferent to environmental degradation.

  31. 31.

    The fact that households can be split between old and new residents could indicate that estimations using only new residents would solve the issue of mobility costs common to hedonic applications as these new residents were recently part of an optimal decision process when moving. Inference regarding the equilibrium of these new residents, however, should also consider the hedonic wage function as these residents took their decision based not only on a rent-compensation but also on a wage-compensation (Roback 1982). Results in Table 7 constitute preliminary evidence that new residents may have weaker preferences for environmental quality as they receive lower rent-compensations when moving to less pleasant areas. Yet, more comprehensive conclusions should be derived after estimating a hedonic wage function, which is outside the scope of this paper.

  32. 32.

    As extraction sites are known to affect environmental quality and so to disrupt social activities, estimates in Table 8 constitute an overall measure of the local impact of mining that goes beyond air and water degradation.

  33. 33.

    Huang and Lanz (2018) derive a WTP of USD 40.50 for a unit reduction in \(\hbox {PM}_{{10}}\) in China. In 2018, China’s GDP per capita (PPP) was USD 18,210.1, while Chile’s was USD 25,283.9. Source: https://data.worldbank.org.

  34. 34.

    Particularly, these members are the heads of the Ministries of Environment, of Mining, of Economics, of Energy, of Health, and of Agriculture, with the minister of Environment as the chair of the entire committee.

  35. 35.

    Low levels of corruption in Chile minimize the possibility that bribery could be driving some of the results as well. Currently, Chile is second least corrupt country in Latin America, with a rank of 27/180 according to the Corruption Perception Index by Transparency International. Source: https://www.transparency.org/cpi2018.

  36. 36.

    Source: http://www.mining.com/chile-rejects-divisive-2-5-billion-dominga-copper-iron-project.

References

  1. Angrist JD, Pischke J-S (2008) Mostly harmless econometrics: an empiricist’s companion. Princeton University Press, Princeton

  2. Aragon FM, Rud JP (2013) Natural resources and local communities: evidence from a Peruvian gold mine. Am Econ J: Econ Policy 5(2):1–25

  3. Arias M, Atienza M, Cademartori J (2013) Large mining enterprises and regional development in Chile: between the enclave and cluster. J Econ Geogr 14(1):73–95

  4. Arimah BC (1996) Willingness to pay for improved environmental sanitation in a nigerian city. J Environ Manag 48(2):127–138

  5. Aroca P (2001) Impacts and development in local economies based on mining: the case of the chilean II region. Resour Policy 27(2):119–134

  6. Bebbington A, Bebbington DH, Bury J, Lingan J (2008) Mining and social movements: struggles over livelihood and rural territorial development in the Andes. World Dev 36(12):2888–2905

  7. Berezansky B, Portnov B, Barzilai B (2010) Objective versus perceived air pollution as a factor of housing pricing: a case study of the Greater Haifa Metropolitan Area. J Real Estate Lit 18(1):99–122

  8. Blundell R, Dias MC (2009) Alternative approaches to evaluation in empirical microeconomics. J Hum Resour 44(3):565–640

  9. Boslett A, Guilfoos T, Lang C (2016) Valuation of expectations: a hedonic study of shale gas development and new yorks moratorium. J Environ Econ Manag 77:14–30

  10. Boxall PC, Chan WH, McMillan ML (2005) The impact of oil and natural gas facilities on rural residential property values: a spatial hedonic analysis. Resour Energy Econ 27(3):248–269

  11. Boyle M, Kiel K (2001) A survey of house price hedonic studies of the impact of environmental externalities. J Real Estate Lit 9(2):117–144

  12. Broner F, Bustos P, Carvalho V (2012) Sources of Comparative Advantage in Polluting Industries. Technical report, National Bureau of Economic Research, Cambridge, MA, Cambridge, MA

  13. Bross I (1958) How to use ridit analysis. Biometrics 14(1):18–38

  14. Bulte EH, Damania R, Deacon RT (2005) Resource intensity, institutions, and development. World Dev 33(7):1029–1044

  15. Castro SH, Sánchez M (2003) Environmental viewpoint on small-scale copper, gold and silver mining in chile. J Clean Prod 11(2):207–213

  16. Chakroborty UK, Narayan B (2014) Socio-economic issues and dilemmas of mining induced displacement: a case of coal mining industry. J Econ Soc Dev 10(2)

  17. Chasco C, Gallo JL (2013) The impact of objective and subjective measures of air quality and noise on house prices: a multilevel approach for downtown Madrid. Econ Geogr 89(2):127–148

  18. Chasco C, Le Gallo J (2015) Heterogeneity in perceptions of noise and air pollution: a spatial quantile approach on the city of Madrid. Spatial Econ Anal 10(3):317–343

  19. Chay KY, Greenstone M (2005) Does air quality matter? Evidence from the housing market. J Polit Econ 113(2):376–424

  20. COCHILCO (2016) Propiedad Minera en Chile: Estado y Medidas de Perfeccionamiento. Retrieved fromhttps://www.cochilco.cl

  21. Consejo Minero (2017) Minería en Números. Retrieved fromhttps://consejominero.cl/apdm/mineria-en-chile/

  22. Currie J, Davis L, Greenstone M, Walker R (2015) Environmental health risks and housing values: evidence from 1600 toxic plant openings and closings. Am Econ Rev 105(2):678–709

  23. Davis LW (2011) The effect of power plants on local housing values and rents. Rev Econ Stat 93(4):1391–1402

  24. De Gregori I, Fuentes E, Rojas M, Pinochet H, Potin-Gautier M (2003) Monitoring of copper, arsenic and antimony levels in agricultural soils impacted and non-impacted by mining activities, from three regions in Chile. J Environ Monit 5(2):287–295

  25. Dehejia RH, Wahba S (2012) Causal effects in nonexperimental studies: reevaluating the evaluation of training programs. J Am Stat Assoc

  26. Delgado MS, Guilfoos T, Boslett A (2016) The cost of unconventional gas extraction: a hedonic analysis. Resour Energy Econ 46:1–22

  27. Deng G, Hernandez MA, Xu S (2014) When power plants leave town: environmental quality and the housing market in China. Available at SSRN 2502442

  28. Díaz Tobar JC (2015) Diseño de una estrategia de intervención basada en el desarrollo de capital humano para la minería artesanal

  29. Freeman AM (1979) The hedonic price approach to measuring demand for neighborhood characteristics. In: Segal D (ed) The economics of neighborhood, chap. 9, pp 191–217. Elsevier

  30. Freeman AM III, Herriges JA, Kling CL (2014) The measurement of environmental and resource values: theory and methods. Routledge, London

  31. Gamble HB, Downing RH (1982) Effects of nuclear power plants on residential property values. J Reg Sci 22(4):457–478

  32. Gamper-Rabindran S, Timmins C (2013) Does cleanup of hazardous waste sites raise housing values? Evidence of spatially localized benefits. J Environ Econ Manag 65(3):345–360

  33. Gopalakrishnan S, Klaiber HA (2013) Is the shale energy boom a bust for nearby residents? Evidence from housing values in pennsylvania. Am J Agric Econ 96(1):43–66

  34. Graham J, Irving J, Tang X, Sellers S, Crisp J, Horwitz D, Muehlenbachs L, Krupnick A, Carey D (2015) Increased traffic accident rates associated with shale gas drilling in pennsylvania. Accid Anal Prev 74:203–209

  35. Greenstone M, Gallagher J (2008) Does hazardous waste matter? Evidence from the housing market and the superfund program. Q J Econ 123(3):951–1003

  36. Greenstone M, Jack BK (2015) Envirodevonomics: a research agenda for an emerging field. J Econ Lit 53(1):5–42

  37. Gylfason T (2001) Nature, power and growth. Scott J Polit Econ 48(5):558–588

  38. Harrison D Jr, Rubinfeld DL (1978) Hedonic housing prices and the demand for clean air. J Environ Econ Manag 5(1):81–102

  39. Huang X, Lanz B (2018) The value of air quality in chinese cities: evidence from labor and property market outcomes. Environ Resour Econ 71(4):849–874

  40. Jacobsen GD (2019) Who wins in an energy boom? Evidence from wage rates and housing. Econ Inq 57(1):9–32

  41. James A, Smith B (2017) There will be blood: crime rates in shale-rich us counties. J Environ Econ Manag 84:125–152

  42. Kemp D, Bond CJ, Franks DM, Cote C (2010) Mining, water and human rights: making the connection. J Clean Prod 18(15):1553–1562

  43. Kiel KA, McClain KT (1995a) House prices during siting decision stages: the case of an incinerator from rumor through operation. J Environ Econ Manag 28(2):241–255

  44. Kiel KA, McClain KT (1995b) The effect of an incinerator siting on housing appreciation rates. J Urban Econ 37(3):311–323

  45. Kitula AGN (2006) The environmental and socio-economic impacts of mining on local livelihoods in Tanzania: a case study of Geita District. J Clean Prod 14(3–4):405–414

  46. Kohlhase JE (1991) The impact of toxic waste sites on housing values. J Urban Econ 30(1):1–26

  47. Kolstad I, Søreide T (2009) Corruption in natural resource management: implications for policy makers. Resour Policy 34(4):214–226

  48. Komarek T, Cseh A (2017) Fracking and public health: evidence from gonorrhea incidence in the marcellus shale region. J Public Health Policy 38(4):464–481

  49. Komarek TM (2018) Crime and natural resource booms: evidence from unconventional natural gas production. Ann Reg Sci 1–25

  50. Kuminoff NV, Parmeter CF, Pope JC (2010) Which hedonic models can we trust to recover the marginal willingness to pay for environmental amenities? J Environ Econ Manag 60(3):145–160

  51. Loayza N, Rigolini J (2016) The local impact of mining on poverty and inequality: evidence from the commodity boom in Peru. World Dev 84(C):219–234

  52. Mason CF, Muehlenbachs LA, Olmstead SM (2015) The economics of shale gas development. Annu Rev Resour Econ 7(1):269–289

  53. Mehlum H, Moene K, Torvik R (2006) Institutions and the resource curse. Econ J 116(508):1–20

  54. Mendelsohn R, Hellerstein D, Huguenin M, Unsworth R, Brazee R (1992) Measuring hazardous waste damages with panel models. J Environ Econ Manag 22(3):259–271

  55. Mendelsohn R, Olmstead S (2009) The economic valuation of environmental amenities and disamenities: methods and applications. Annu Rev Environ Resour 34:325–347

  56. Michael HJ, Boyle KJ, Bouchard R (2000) Does the measurement of environmental quality affect implicit prices estimated from hedonic models. Land Econ 76(2):283

  57. Milu V, Leroy J, Peiffert C (2002) Water contamination downstream from a copper mine in the Apuseni Mountains, Romania. Environ Geol 42(7):773–782

  58. Ministerio de Desarrollo Social (2009) Encuesta de Caracterización Socioeconómica Nacional CASEN. Retrieved from http://observatorio.ministeriodesarrollosocial.gob.cl/casen/basededatos_historico.php. Accessed 01 March 2016

  59. Ministerio de Desarrollo Social (2011) Encuesta de Caracterización Socioeconómica Nacional CASEN. Retrieved from http://observatorio.ministeriodesarrollosocial.gob.cl/casen/basededatos_historico.php. Accessed 01 March 2016

  60. Ministry of Environment (2011) Official environment status report. Retrieved from http://mma.gob.cl

  61. Monjezi M, Shahriar K, Dehghani H, Namin FS (2009) Environmental impact assessment of open-pit mining in iran. Environ Geol 58(1):205–216

  62. Muehlenbachs L, Spiller E, Timmins C (2013a) Shale gas development and the costs of groundwater contamination risk. SSRN Electron J

  63. Muehlenbachs L, Spiller E, Timmins C (2013b) Shale gas development and the costs of groundwater contamination risk. Resources for the Future Discussion Paper, (12-40)

  64. Muehlenbachs L, Spiller E, Timmins C (2015) The housing market impacts of shale gas development. Am Econ Rev 105(12):3633–59

  65. Muehlenbachs L, Staubli S, Chu Z (2017) The accident externality from trucking. Technical report, National Bureau of Economic Research

  66. Newbold J (2006) Chile’s environmental momentum: ISO 14001 and the large-scale mining industry-case studies from the state and private sector. J Clean Prod 14(3–4):248–261

  67. Palmquist RB (1992) Valuing localized externalities. J Urban Econ 31(1):59–68

  68. Papke LE, Wooldridge J (1996) Econometric methods for fractional response variables with an application to 401 (k) plan participation rates. J Appl Econom 11:619–632

  69. Paredes D, Aroca P (2008) Metodología para estimar un índice regional de costo de vivienda en chile. Cuadernos de Econ 45(131):129–143

  70. Paredes D, Rivera NM (2017) Mineral taxes and the local public goods provision in mining communities. Resour Policy 53:328–339

  71. Pegg S (2006) Mining and poverty reduction: transforming rhetoric into reality. J Clean Prod 14(3–4):376–387

  72. Petrova S, Marinova D (2013) Social impacts of mining: changes within the local social landscape. Rural Soc 22(2):153–165

  73. Ridker RG (1967) Economic costs of air pollution, studies in measurement. Frederick A. Praeger, Publishers, New York

  74. Ridker RG, Henning JA (1967) The determinants of residential property values with special reference to air pollution. Rev Econ Stat 49(2):246

  75. Rivera N, Aroca P (2014) Escalas de producción en economías mineras: El caso de chile en su dimensión regional. EURE (Santiago) 40(121):247–270

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

  77. Robinson JA, Torvik R, Verdier T (2006) Political foundations of the resource curse. J Dev Econ 79(2):447–468

  78. Rosen KT, Smith LB (1983) The price-adjustment process for rental housing and the natural vacancy rate. Am Econ Rev 73(4):779–786

  79. Rosen S (1974) Hedonic prices and implicit markets: product differentiation in pure competition. J Polit Econ 82(1):34–55

  80. Sánchez JM, Enríquez SM (1996) Impacto Ambiental de la Pequeña y Mediana Minería en Chile. Technical report, Universidad de Chile

  81. SERNAGEOMIN (2013) Concesiones Mineras de Exploracion y Explotacion Total Pais (%). http://www.sernageomin.cl/pdf/mineria/estadisticas/estadisticas%20de%20concesiones%20mineras/propiedad_minera_2_2013.pdf Accessed 17 July 2017

  82. SINIM (2017) Datos municipales. Retrieved from http://datos.sinim.gov.cl. Accessed 10 Aug 2017

  83. Transparency International (2018) Riesgos de corrupcin en concesiones mineras y otorgamiento de permisos ambientales: el caso de Chile. Retrieved from http://www.chiletransparente.cl/wp-content/files_mf/1517340532InformeMineriafull.compressed.pdf

  84. Urkidi L (2010) A glocal environmental movement against gold mining: Pascua–Lama in Chile. Ecol Econ 70(2):219–227

  85. U.S. Environmental Protection Agency (2013) America’s Children and the Environment. Retrieved from https://www.epa.gov/sites/production/files/2015-06/documents/ace3_2013.pdf

  86. USGS (2014) Minerals Yearbook—Chile. Retrieved from https://minerals.usgs.gov/minerals/pubs/country/2014/myb3-2014-ci.pdf

  87. Weber JG (2012) The effects of a natural gas boom on employment and income in Colorado, Texas, and Wyoming. Energy Econ 34(5):1580–1588

  88. Williams AM (2011) The impact of surface coal mining on residential property values: a hedonic price analysis

  89. World Bank (2018a) Chile overview. Retrieved from https://www.worldbank.org/en/country/chile/overview

  90. World Bank (2018b) Extractive industries overview. Retrieved from https://www.worldbank.org/en/topic/extractiveindustries/overview

Download references

Author information

Correspondence to Nathaly M. Rivera.

Additional information

Publisher's Note

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

Appendix

Appendix

Fig. 3
figure3

Resource extraction site openings between 2011–2016. Notes: The ores extracted are mostly copper, silver, gold, zinc, iodine, lithium, molybdenum, natural nitrates, and rhenium. Sites plotted with \(\hbox{ArcMap}\) 10.1 using information from SERNAGEOMIN, Online, accessed May 12, 2016. Red dots symbolize conventional site openings and blue dots are for Artisanal sites. (Color figure online)

Fig. 4
figure4

Example of treated, control, and Placebo cities, and treatments. Notes: Panel a depicts different scenarios that allow the classification of cities as either treated, control, or placebo cities, or cities to dismiss from the analysis. Type-A cities are cities that over time have a constant number of mining sites, and so they are part of a placebo group of cities. Type-B cities are cities that over time experienced the closure and the opening of different types of mines, and therefore, they are dismissed from the analysis. Type-C cities are cities that for each year show no records of mine sitings. These cities belong to the control group of cities. Finally, type-D cities are cities that experienced the siting of a new mine, and therefore, they are part of a treated group of cities. Panel b displays the treatments considered in the analysis. Type-A cities are cities that host the opening of an artisanal mine only. Type-B cities are cities that host the opening of a new conventional mine and a new artisanal mine, that is, “all-mine openings”. Type-C cities are control cities, while type-D cities are cities that host the opening of a conventional mine only

Table 10 Extraction sites over time
Fig. 5
figure5

Overview of treated, control, and placebo cities

Table 11 Descriptive statistics of main variables
Table 12 Ridits on air and water pollution perceptions

Results in Table 13 for air pollution indicate that \(\hbox{PM}_{2.5}\), CO, \(\hbox{NO}_{{X}}\), and \(\hbox{SO}_{{2}}\) emissions are all positively related with households’ perceptions of bad air quality. In terms of water pollution perceptions, results indicate a counterintuitive sign for TSS. Regarding stream flow and cities’ density, higher values for these variables are negatively related to water pollution perceptions. Higher stream flows nearby may be negatively related to water pollution perceptions because streams assimilative capacity (i.e. the stream capacity to reduce pollutant concentrations) increase with the streamflow magnitude. A higher population density, instead, might be negatively related to water pollution perceptions due to a lower variety of water reservoirs available for recreation in high dense areas, which might affect their perceptions on the contamination of these waterbodies (Table 14).

Table 13 Average marginal effects on pollution perceptions
Table 14 Descriptive statistics on cities’ average pollution perceptions

Two things from Fig. 6 are worth mentioning. Overall, the common pre-treatment trends assumption fits the data well. Pre-treatment rental prices of homes in treated cities show a trend that is parallel to the one for homes in control cities for all the three treatments (panels a, b, and c) and for the placebo group (panel d). In addition, rental prices are higher in cities that host artisanal openings, a feature that is especially noteworthy because, as Sect. 6 shows, these are the openings that are strongly correlated with reductions in rental prices. A plausible explanation for this trend is the reduced comparability among houses in treated and control cities as shown in panel A of Table 2.

Fig. 6
figure6

Overview of the common trends assumption. Baseline sample. Notes: Considering tenants with a single family nucleus. Pre-treatment periods are 2009, and 2011, with information retrieved from Ministerio de Desarrollo Social (2009, 2011)

Although pre-existing trends in rental prices are still common to homes in cities affected with the treatments and homes in the control group, the gap between these prices shrinks once units in the treatment groups are matched to comparable homes in the control group. Even more important is the change in these trends in cities affected with all-mine openings (panel a) and artisanal mine openings (panel c). A visual analysis of Fig. 7 suggest a negative treatment effect in all cities treated with mine openings of any kind (panels a, b, and c).

Fig. 7
figure7

Overview of the Common Trends Assumption. Matched Sample. Notes: Considering tenants with a single family nucleus. Pre-treatment periods are 2009, and 2011, with information retrieved from Ministerio de Desarrollo Social (2009, 2011)

Results in columns (1), (2) and (3) reveal that, on average, rental prices are 6–11% lower in cities that concentrate all-mine openings (panel A), around 19–25% lower in cities exposed to conventional openings (panel B), and around 11–12% lower in cities with artisanal mine openings (panel C). These results are statistically significant mostly for the richest specifications (column (3)). As expected, these impacts decrease in magnitude after the inclusion of the air and water pollution variables (indexes), particularly for all-mine openings and artisanal mine openings. On average, households are compensated with rental prices that are around 9–11% lower in cities with all-mine openings (panel A), around 18–26% lower in cities with conventional openings (panel B), and around 9–10% lower in cities with artisanal mine openings (panel C). All these estimates are statistically different from zero (Table 15)

Table 15 The impact of mine openings on rental prices: spatial DID

.

Table 16 The impact of mine openings by air pollution levels: CO and \(\hbox{NO}_X\)
Table 17 Robustness check: different matching procedures
Table 18 Robustness check: placebo group
Table 19 Robustness check: certificates of occupancy
Fig. 8
figure8

Overview of the common trends assumption on certificates of occupancy. Pre-treatment periods. Notes: Excluding two cities with abnormalities in their reports: Huasco (all-mine openings) and Vitacura (control). Source: SINIM (2017)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rivera, N.M. Is Mining an Environmental Disamenity? Evidence from Resource Extraction Site Openings. Environ Resource Econ 75, 485–528 (2020). https://doi.org/10.1007/s10640-019-00397-w

Download citation

Keywords

  • Extractive industries
  • Mining
  • Environmental valuation
  • Environmental disamenities
  • Hedonic models
  • Nearest-neighbor matching estimator

JEL Classification

  • Q53
  • Q51
  • Q32
  • Q34