You are not currently logged in.
Access your personal account or get JSTOR access through your library or other institution:
On Using Linear Regressions in Welfare Economics
Journal of Business & Economic Statistics
Vol. 14, No. 4 (Oct., 1996), pp. 478-486
Stable URL: http://www.jstor.org/stable/1392256
Page Count: 9
Preview not available
This article consists of two parts. The first part shows that the ordinary least squares regression coefficient is a weighted average of slopes between adjacent sample points. When applied to a linear regression, with income as the independent variable, the regression coefficient depends heavily on the slopes of high-income groups. The weight of the highest income decile may well exceed that of the other nine deciles. This may be undesirable, especially if the regression is used for welfare analysis, because the marginal propensities to consume attributed to the commodities are determined by the high-income groups. The second part of the article proposes alternative estimators, the extended Gini estimators, that enable investigators to control the weighting scheme and to incorporate their social views into the weighting scheme of the estimators.
Journal of Business & Economic Statistics © 1996 American Statistical Association