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Can Understanding Spatial Equilibria Enhance Benefit Transfers for Environmental Policy Evaluation?

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Abstract

A conceptual model of consumer sorting in markets for housing, labor and health care is outlined and used to make three points about how benefit transfers are used for environmental policy evaluation. First, the standard approach to assessing benefits of air quality improvements by transferring the value of a statistical life from labor market studies embeds several untested (but testable) assumptions. Second, if the cost of an environmental policy exceeds its capitalized effect on housing prices, then the capitalization effect is an insufficient statistic for determining whether benefits exceed costs. Third, there are several ways in which equilibrium sorting models may be usefully extended to assess distributional welfare effects of environmental policies.

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Notes

  1. This yields a conservatively high $1.09 billion estimate for the annualized cost. Walker shows that the regulation’s effect on earning is indistinguishable from zero after 7 years.

  2. I refer to these studies as quasi-national because they use data from a large but incomplete portion of the United States, focusing on nonrandom subsets of the people who live in the areas they study. Specifically, Chay and Greenstone’s estimates are based on the median self-assessed value of owner occupied houses in approximately one third of US counties. Bayer, Keohane and Timmins focus on household heads under the age of 35 living in 242 metropolitan statistical areas.

  3. Specifically, I start with Chay and Greenstone’s (2005) household MWTP estimate of $243 per permanent microgram per cubic meter reduction of total suspended particulates (TSP) and Bayer’s et al. (2009) household estimate of $149 in annualized MWTP per microgram per cubic meter reduction in PM\(_{10}\). Then I convert TSP and PM\(_{10}\) to PM\(_{2.5}\) using a conversion factor of 1.82 to go from TSP to PM\(_{10 }\) as suggested by Bayer, Keohane and Timmins and a conversion factor of 0.55 to go from PM\(_{10}\) to PM\(_{2.5}\) based on my calculations from a population-weighted regression of PM\(_{2.5 }\)on PM\(_{10}\) in the year 2000. Since Chay and Greenstone measure MWTP for a permanent reduction in air pollution, I annualize their measure assuming a user cost of housing of 7.86% based on Blomquist et al. (1988). Finally, I assume 123 million households in 2020 based on multiplying the Census Bureau’s 1-year ACS estimate for 2015 (118 million) by their projection for U.S. population growth between 2015 and 2020 (4.1%). All dollar values are converted to year 2006 dollars using the GDP deflator.

  4. The capitalization based measure derived using Chay and Greenstone (2005) estimate is $79 billion compared to $66 billion in total costs defined by adding Walker’s (2013) estimate for labor market costs to EPA’s cost estimate.

  5. Sullivan (2017) makes a similar observation and suggests that at least part of the discrepancy is because the standard approaches that economists use to assign air pollution exposures to people at their residential locations introduce severe measurement error that attenuates hedonic estimates of their marginal willingness to pay for air quality.

  6. I abstract from other local public goods and non-environmental amenities that affect the quality of life in Roback (1982) style models to avoid extraneous notation and to focus attention on environmental policy. Other amenities could be added to without altering the main points of this paper.

  7. One might prefer to condition on a broader set of characteristics when defining baseline mortality risk such as gender, race and genetic markers. Here I condition on age alone for notational simplicity and to help relate the conceptual framework to VSL estimates from Hall and Jones (2007).

  8. For simplicity, I assume that these investments do not affect occupational or neighborhood mortality risk. However, it would be interesting to consider potential interactions. For example, people who take statins to address hypertension may face a lower risk of having an air-pollution induced heart attack.

  9. Hall and Jones (2007) give an example of how to estimate the age specific cost of reducing mortality risk.

  10. This composite numeraire good includes the physical characteristics of housing.

  11. If two locations provided identical bundles of amenities and mortality risk, then additional assumptions would be needed to rule out the possibility that they sell at different prices. For example, one could guarantee existence of a price function by adding the assumptions of free mobility and full information as in Bajari and Benkard (2005).

  12. While Eq. (5) describes an equilibrium relationship it is not a traditional “hedonic” price function in the sense that amenities and mortality risk are not direct attributes of the product being purchased.

  13. Specifically, I take the mortality distribution by age from Table 5-4 of EPA’s report.

  14. Additional assumptions are also required to interpret the change in housing prices as a measure of willingness to pay, as discussed in Sect. 5.

  15. This assumes weak complementarity holds in the sense that one must live near the improved rivers and lakes in order to derive utility from the quality improvement.

  16. Hence, capitalization is used to describe comparative statistics for the transition from an initial equilibrium to a new equilibrium. I follow this convention despite the fact that it has potential to create confusion. Banzhaf (2015) explains that this convention differs from prior literature that used “capitalization” to describe cross-sectional correlation between prices and amenities in a single equilibrium.

  17. Hedonic price functions may change shape over time due to changes in market primitives such as preferences, technology and institutions, macroeconomic shocks to wealth, or increased housing supply.

  18. Even if the quality change is small, concomitant changes in technology, preferences, and information may cause the shape of the price function to change. Indeed, such changes are likely to occur over the 10 to 30-year study periods that are common in empirical capitalization studies.

  19. Greenstone and Gallagher (2008) focus on median housing prices in neighborhoods around Superfund sites. Gamper-Rabindran and Timmins (2013) demonstrate that focusing on the median priced house may understate the capitalization effects of Superfund cleanups because those effects tend to be concentrated among houses at lower quantiles of the within-neighborhood distributions of housing prices.

  20. In some cases, the use of administrative data sets may also pose new challenges in terms of sample selection. For example, Walker’s (2013) use of LEHD administrative records to analyze the effects of air quality regulations on the labor market was necessarily limited to just four states and Ketcham et al. (2016) analysis of Medicare administrative records to assess the welfare effects of choice architecture polices proposed for health insurance markets necessarily excluded individuals who received low income subsidies because they faced a different choice structure that would have invalidated the research design.

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Authors and Affiliations

  1. Economics Department, Arizona State University, PO Box 879801, Tempe, AZ, 85287, USA

    Nicolai V. Kuminoff

  2. National Bureau of Economic Research, Cambridge, MA, USA

    Nicolai V. Kuminoff

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  1. Nicolai V. Kuminoff
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Correspondence to Nicolai V. Kuminoff.

Additional information

Guest Editor: V. Kerry Smith.

I am grateful to Jared Carbone and V. Kerry Smith for helpful comments and suggestions on this paper. I also benefitted from discussions with Kelly Bishop, Jonathan Ketcham, Alvin Murphy, Sophie Mathes, Nirman Saha and participants in the December 2016 EPA Workshop on “Benefit Transfer: Evaluating How Close is Close Enough”.

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Kuminoff, N.V. Can Understanding Spatial Equilibria Enhance Benefit Transfers for Environmental Policy Evaluation?. Environ Resource Econ 69, 591–608 (2018). https://doi.org/10.1007/s10640-017-0214-8

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