Revisiting the Boston data set - Changing the units of observation affects estimated willingness to pay for clean air
DOI:
https://doi.org/10.18335/region.v4i1.107Keywords:
Spatial Data Analysis, Spatial Econometrics, System Articulation, EntitationAbstract
Harrison, Rubinfeld (1978c) used a hedonic model to find out how house values were affected by air pollution in Boston, when other variables were taken into consideration. Their primary interest was in estimating willingness to pay for cleaner air. They chose to use 506 census tracts as units of observation because median house values for these units of aggregation were published in the 1970 census tabulations. Air pollution values from the model output, represented by nitrogen oxides (NOX), were available for 122 model output zones, of which only 96 fell within the study area defined by the chosen census tracts. These NOX values were then assigned proportionally to all census tracts falling within each model output zone. By re-aggregating the house value data to the 96 air pollution model output zones and re-fitting the regression model, the total impact of air pollution on house values, and thus the estimated willingness to pay, increases markedly. By extending the analysis to include spatially lagged independent variables, the total impact of air pollution on median house values, and consequently on the willingness to pay analysis, increases by over three times. Use of weighting to adjust the units of observation for the relative numbers of housing units behind each median house value further buttresses this conclusion. It is shown conclusively that the choice of observational units matters crucially for the estimation of economic parameters of interest in this data set.References
Belsley, D. A., Kuh, E., and Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons, New York.
Bivand, R. S. (2008). Implementing representations of space in economic geography. Journal of Regional Science, 48:1–27.
Dray, S., Couteron, P., Fortin, M.-J., Legendre, P., Peres-Neto, P. R., Bellier, E., Bivand, R., Blanchet, F. G., de C ́aceres, M., Dufour, A.-B., Heegaard, E., Jombart, T., Munoz, F., Oksanen, J., P ́elissier, R., Thioulouse, J., and Wagner, H. H. (2012). Community ecology in the age of multivariate multiscale spatial analysis. Ecological Monographs, 82:257–275. pp. 19.
Egger, P. H. and Lassmann, A. (2015). The causal impact of common native language on international trade: Evidence from a spatial regression discontinuity design. The Economic Journal, 125(584):699–745.
Gelfand, A. E. (2010). Misaligned spatial data: The change of support problem. In Gelfand, A. E., Diggle, P., Guttorp, P., and Fuentes, M., editors, Handbook of Spatial Statistics, pages 517–539. Chapman & Hall/CRC, Boca Raton. pp. 23.
Gilley, O. W. and Pace, R. K. (1996). On the Harrison and Rubinfeld data. Journal of Environmental Economics and Management, 31(3):403–405.
Gotway, C. A. and Young, L. J. (2002). Combining incompatible spatial data. Journal of the American Statistical Association, 97:632–648. pp. 17.
Haggett, P., Cliff, A. D., and Frey, A. (1977). Locational Analysis in Human Geography, second edition. Edward Arnold, London.
Haining, R. P. (2010). The nature of georeferenced data. In Fischer, M. and Getis, A., editors, Handbook of Applied Spatial Analysis, pages 197–217. Springer, Heidelberg. pp. 21.
Harrison, D. and Rubinfeld, D. L. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management, 5:81–102.
Hidano, N., Hoshino, T., and Sugiura, A. (2015). The effect of seismic hazard risk information on property prices: Evidence from a spatial regression discontinuity design. Regional Science and Urban Economics, 53:113 – 122.
Keele, L. J. and Titiunik, R. (2015). Geographic boundaries as regression discontinuities. Political Analysis, 23(1):127–155.
LeSage, J. P. (2014). What regional scientists need to know about spatial econometrics. Review of Regional Studies, 44:13–32.
Ord, J. (1975). Estimation methods for models of spatial interaction. Journal of the American Statistical Association, 70(349):120–126.
Pace, R. K. and Gilley, O. (1997). Using the spatial configuration of the data to improve estimation. Journal of the Real Estate Finance and Economics, 14:333–340.
Paelinck, J. H. P. and Nijkamp, P. (1975). Operational theory and method in regional economics. Saxon House, Farnborough.
Wakefield, J. C. and Lyons, H. (2010). Spatial aggregation and the ecological fallacy. In Gelfand, A. E., Diggle, P., Guttorp, P., and Fuentes, M., editors, Handbook of Spatial Statistics, pages 541–558. Chapman & Hall/CRC, Boca Raton. pp. 18.
Waller, L. A. and Gotway, C. A. (2004). Applied Spatial Statistics for Public Health Data. John Wiley & Sons, Hoboken, NJ.
Wilson, A. G. (2000). Complex spatial systems: The Modelling Foundations of Urban and Regional Analysis. Prentice Hall, Harlow.
Wilson, A. G. (2002). Complex spatial systems: Challenges for modellers. Mathematical and Computer Modelling, 36:379–387.
Wilson, A. G. (2012). The science of cities and regions: lectures on mathematical model design. Springer, Dordrecht.
Published
How to Cite
Issue
Section
License
REGION is an open journal, and uses the standard Creative Commons license: Copyright We want authors to retain the maximum control over their work consistent with the first goal. For this reason, authors who publish in REGION will release their articles under the Creative Commons Attribution license. This license allows anyone to copy and distribute the article provided that appropriate attribution is given to REGION and the authors. For details of the rights authors grant users of their work, see the "human-readable summary" of the license, with a link to the full license. (Note that "you" refers to a user, not an author, in the summary.) Upon submission, the authors agree that the following three items are true: 1) The manuscript named above: a) represents valid work and neither it nor any other that I have written with substantially similar content has been published before in any form except as a preprint, b) is not concurrently submitted to another publication, and c) does not infringe anyone’s copyright. The Author(s) holds ERSA, WU, REGION, and the Editors of REGION harmless against all copyright claims. d) I have, or a coauthor has, had sufficient access to the data to verify the manuscript’s scientific integrity. 2) If asked, I will provide or fully cooperate in providing the data on which the manuscript is based so the editors or their assignees can examine it (where possible) 3) For papers with more than one author, I as the submitter have the permission of the coauthors to submit this work, and all authors agree that the corresponding author will be the main correspondent with the editorial office, and review the edited manuscript and proof. If there is only one author, I will be the corresponding author and agree to handle these responsibilities.
