A Living Cost Index for SMSAs

Cebula, Richard (1985): A Living Cost Index for SMSAs. Published in: Social Science Quarterly , Vol. 67, No. 4 (14 December 1986): pp. 887-891.

Abstract

A number of recent studies have been concerned with the determinants of either housing cost differentials or overall living cost differentials among SMSAs. Given the obvious significance of such differentials, the purpose of this research note is to develop a geographically comparable cost-of-living index for the 100 largest (as of 1980) SMSAs. From 1966 until 1979 the U.S. Bureau of Labor Statistics compiled comparable cost-of-living data for some 39 metropolitan areas. In more recent years (starting with 1979) the data have been compiled for a maximum of only 24 metropolitan areas. By generating a geo­graphically comparable living cost index for the 100 largest SMSAs, it is hoped that a data base useful to studies of wage differentials, geographic mobility, and other areas of concern and interest can be developed. The index is based on estimation of a reduced-form equation involving population size and density, per capita income, and the presence of right-to-work legislation.

• Item Type: MPRA Paper
• Language: English
• Keywords: living cost index; population size; population density; per capita income
• Subjects: J - Labor and Demographic Economics > J1 - Demographic Economics > J10 - General; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R2 - Household Analysis > R21 - Housing Demand; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R2 - Household Analysis > R23 - Regional Migration ; Regional Labor Markets ; Population ; Neighborhood Characteristics

References

Cebula, Richard J. 1983. Geographic Living-Cost Differentials (Lexington, Mass.: Lexington Books).

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Cobas , Jose A. 1978. "A Method to Estimate the Bureau of Labor Statistics Family Budgets for All Standard Metropolitan Statistical Areas," Social Science Quarterly, 59 (December): 546-52.

Hogan, Timothy D. 1983. "Estimating the Urban Family Budget with a Pooled Cross-Section Time­ Series Model," Social Science Quarterly, 64 (June):412-6.