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Air pollution and income inequality: a spatial econometric approach

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Abstract

This study re-examines the relationship between income inequality and emission in US counties, incorporating multiple measures for pollution and income inequality. The initial results show that a nonlinear relationship exists between emissions and income. Effects estimates from the spatial Durbin model show that inequality has no significant impact on SO2 emissions although a concave relationship exists between income, SO2, and CO emissions. Increased income inequality reduces CO, PM2.5, and NOx emissions for two of the three measures of inequality. The cubic term for income in the PM 2.5 specification reveals that pollution eventually increases as income rises. Our results indicate that income inequality reduces air pollution emissions for localized pollutants but has no impact of pollutants that are more geographically dispersed.

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Notes

  1. Readers interested in exploring attempts to provide a theoretical justification for the EKC should also see the summary in Pasten and Figueroa (2012). Examples of important papers include those of Lopez (1994); John and Pecchenino (1994); and Andreoni and Levinson (2001).

  2. The county-level aggregation of the data may mask relationships that exist at a more local level. A locality within the county may have high income inequality and low emissions, while an area may have low income inequality and low emissions. The aggregation of the data may show no relationship between emissions and income inequality, even though a significant relationship may exist at a smaller unit of analysis.

  3. The appendix contains images of the spatial distribution of Nitrogen Oxide, Sulfur dioxide and the income share of top 5% of wealth distribution. Thanks to Anthony Ponce for providing essential GIS maps.

  4. Other factors may influence spatial interaction, but we chose to use a contiguity matrix to capture spatial interdependence. The model is well specified eliminating the need for robustness checks using different spatial weight matrices. See Lesage and Kelley Pace (2014) for details.

  5. OLS HAC results are available from the authors upon request.

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Acknowledgements

The authors thank Anthony Ponce for providing essential GIS maps.

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  1. Department of Economics, California State University, Bakersfield, USA

    Nyakundi M. Michieka, John Deal & Kyle Lahman

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  1. Nyakundi M. Michieka
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Correspondence to Nyakundi M. Michieka.

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Appendix

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Table 3 Variables
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Table 4 Descriptive Statistics
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4 and Figs.

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Nitrogen oxide emissions (2011)

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Sulfur dioxide emissions (2011)

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Share of top 5% of wealth (2011)

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Michieka, N.M., Deal, J. & Lahman, K. Air pollution and income inequality: a spatial econometric approach. Ann Reg Sci 69, 1–31 (2022). https://doi.org/10.1007/s00168-021-01105-7

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