방법 비교

	Watts Poverty Index	Foster-Greer-Thorbecke Index
분야	경제학	경제학
계열	Process / pipeline	Process / pipeline
기원 연도	1968	1984
창시자	Harold W. Watts (1968); axiomatized by Buhong Zheng (1993)	James Foster, Joel Greer & Erik Thorbecke
유형	Distribution-sensitive poverty measure	Parametric class of poverty measures
원전	Zheng, B. (1993). An axiomatic characterization of the Watts poverty index. Economics Letters, 42(4), 347–353.	Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3), 761–766.
별칭	Watts Index, Watts Poverty Measure, Log Shortfall Poverty Index	FGT Index, FGT Poverty Measures, P-alpha Poverty Index, Foster-Greer-Thorbecke Poverty Measure
관련	3	4
요약	The Watts index, proposed by Harold Watts in 1968, was the first poverty measure to be sensitive to the distribution of income among the poor, anticipating the axiomatic poverty-measurement literature by nearly a decade. It averages, over the whole population, the natural logarithm of the ratio of the poverty line to each poor person's income. Because the log gives ever-larger weight to incomes near zero, the Watts index satisfies the strong transfer principles that the headcount and the linear poverty gap fail, and Buhong Zheng's 1993 axiomatic characterization established it as the smallest distribution-sensitive measure satisfying a natural set of axioms.	The Foster-Greer-Thorbecke (FGT) index is a parametric class of poverty measures introduced by James Foster, Joel Greer, and Erik Thorbecke in 1984 that became the workhorse of applied poverty analysis. A single parameter alpha tunes how much weight the measure places on the depth and distribution of poverty: alpha = 0 gives the headcount ratio (the share of people below the poverty line), alpha = 1 gives the poverty gap (the average normalized shortfall), and alpha = 2 gives poverty severity (which weights larger shortfalls more heavily). Its defining virtue is additive decomposability — total poverty is the population-weighted sum of subgroup poverty — which makes it ideal for profiling poverty across regions, sectors, and demographic groups.