Hostname: page-component-848d4c4894-pjpqr Total loading time: 0 Render date: 2024-06-28T19:37:59.343Z Has data issue: false hasContentIssue false
  • English
  • Français

Using DEA and VEA to Evaluate Quality of Life in the Mid-Atlantic States

Published online by Cambridge University Press:  15 September 2016

Elizabeth Marshall  and
James Shortle
Elizabeth Marshall
Affiliation:
World Resources Institute in Washington, D.C.
James Shortle
Affiliation:
Department of Agricultural Economics and Rural Sociology at Pennsylvania State University in University Park, Pennsylvania
Get access

Abstract

In this study we use data envelopment analysis (DEA) and an extension of DEA called value efficiency analysis (VEA) to explore the “production” of quality of life within counties in the mid-Atlantic region and the extent to which production frontiers and efficiency differ between rural and urban counties. These methods allow us to identify counties that are inefficient in their quality of life production, and to rank (using DEA) those counties according to their distance from a performance standard established by other observed counties, or (using VEA) by a single unit designated as “most preferred.”

Keywords

data envelopment analysisvalue efficiency analysisquality of life
Type
Contributed Papers
Information
Agricultural and Resource Economics Review , Volume 34 , Issue 2 , October 2005 , pp. 185 - 203
Copyright
Copyright © 2005 Northeastern Agricultural and Resource Economics Association 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, R., Athanassopoulos, A., Dyson, R.G., and Thanassoulis, E. 1997. “Weights Restrictions and Value Judgements in Data Envelopment Analysis: Evolution, Development and Future Directions.” Annals of Operations Research 73(January): 13–34.Google Scholar
Banker, R.D., Charnes, A., and Cooper, W.W. 1984. “Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis.” Management Science 30(9): 1078–1092.Google Scholar
Banker, R.D., and Morey, R.C. 1986. “Efficiency Analysis for Exogenously Fixed Inputs and Outputs.” Operations Research 34(4): 513–521.CrossRefGoogle Scholar
Blomquist, G.C., Berger, M.C., and Hoehn, J.P. 1988. “New Estimates of Quality of Life in Urban Areas.” American Economic Review 78(1): 89–107.Google Scholar
Bowlin, W.F. 1998. “Measuring Performance: An Introduction to Data Envelopment Analysis (DEA).” Journal of Cost Analysis (Fall): 3–27.Google Scholar
Charnes, A., Cooper, W., Lewin, A.Y., and Seiford, L.M. 1994. Data Envelopment Analysis: Theory, Methodology and Applications. Boston: Kluwer Academic Press.CrossRefGoogle Scholar
Charnes, A., Cooper, W.W., and Rhodes, E. 1978. “Measuring Efficiency of Decision Making Units.” European Journal of Operations Research 2(6): 429–444.CrossRefGoogle Scholar
Cinca, C.S., Molinero, C.M., and Garcia, F.C. 2002. “On Model Selection in Data Envelopment Analysis: A Multivariate Statistical Approach.” Discussion Paper No. M02-7, Department of Management, University of Southampton, United Kingdom.Google Scholar
Cooper, W.W., Seiford, L.M., and Tone, K. 2000. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References, and DEA-Solver Software. Boston: Kluwer Academic Press.Google Scholar
Deller, S.C., Tsai, S.T., Marcouiller, D., and English, D.B.K. 2001. “The Role of Amenities and Quality of Life in Rural Economic Growth.” American Journal of Agricultural Economics 83(2): 352–365.Google Scholar
Douglas, S., and Wall, H.J. 1993. “‘Voting With Your Feet’ and the Quality of Life Index: A Simple Non-Parametric Approach Applied to Canada.” Economics Letters 42(2/3): 229–236.Google Scholar
Economic Research Service, U.S. Department of Agriculture. 2004a. “Measuring Rurality: 2004 County Typology Codes—Methods, Data Sources, and Documentation.” Available online at http://www.ers.usda.gov/Briefing/Rurality/Typology/Methods/ (accessed July 19, 2005).Google Scholar
Economic Research Service, U.S. Department of Agriculture. 2004b. “Measuring Rurality: Rural-Urban Continuum Codes.” Available online at http://www.ers.usda.gov/Brief[-]ing/Rurality/RuralurbCon/ (accessed July 19, 2005).Google Scholar
ERS [see Economic Research Service, U.S. Department of Agriculture].Google Scholar
Gabriel, S.A., Mattey, J.P., and Wascher, W.L. 2003. “Compensating Differentials and Evolution in the Quality-of-Life Among U.S. States.” Regional Science and Urban Economics 33(5): 619–649.Google Scholar
Golany, B., and Thore, S. 1997. “Restricted Best Practice Selection in DEA: An Overview with a Case Study Evaluating the Socio-Economic Performance of Nations.” Annals of Operations Research 73(1): 117–140.Google Scholar
Halme, M., Joro, T., Korhonen, P., Salo, S., and Wallenius, J. 1999. “A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis.” Management Science 45(1): 103–115.CrossRefGoogle Scholar
Korhonen, P., Siljamake, A., and Soismaa, M. 1998. “Practical Aspects of Value Efficiency Analysis.” IIASA Interim Report No. IR-98-042, International Institute for Applied Systems Analysis, Laxenburg, Austria.Google Scholar
Korhonen, P., Tainio, R., and Wallenius, J. 2001. “Value Efficiency Analysis of Academic Research.” European Journal of Operational Research 130(1): 121–132.Google Scholar
McGranahan, D. 1999. “Natural Amenities Drive Rural Population Change.” Agricultural Economic Report No. 781. Economics Research Service, U.S. Department of Agriculture, Washington, D.C.Google Scholar
Roback, J. 1982. “Wages, Rents, and the Quality of Life.” Journal of Political Economy 90(6): 1257–1278.Google Scholar
Rosen, S. 1979. “Wage-Based Indexes of Urban Quality of Life.” In Mieszkowski, P. and Straszheim, M., eds., Current Issues in Urban Economics. Baltimore: Johns Hopkins University Press.Google Scholar
Tiebout, C. 1956. “A Pure Theory of Local Expenditures.” Journal of Political Economy 64(5): 416–424.Google Scholar
Walden, J.B., and Kirkley, J.E. 2000. “Measuring Technical Efficiency and Capacity in Fisheries by Data Envelopment Analysis Using the General Algebraic Modeling System (GAMS): A Workbook.” NOAA Technical Memorandum No. NMFS-NE-160, National Oceanic & Atmospheric Administration, Washington, D.C. Google Scholar