On Using Linear Regressions in Welfare Economics
指出普通最小二乘回归系数受高收入群体影响过大，在福利分析中可能不合适，并提出扩展基尼估计量，让研究者能控制权重并融入社会价值判断。
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