Reassessing the econometric measurement of inequality and poverty: toward a cost-of-living approach

Abstract

The econometric measurement of inequality and poverty in advanced capitalist economies has been preoccupied with aggregate measures of relative deprivation, namely, the Gini Index and a relative poverty rate, both of which are based on economic distances from the population median. Using the case of Hong Kong, this article demonstrates the limitations of relative measures: the Gini Index masks social mobility and the relative poverty line understates actual poverty. This article argues instead for a cost-of-living approach to measure poverty, where the poverty line is defined as the cost of essential goods and services. A cost-of-living approach produces a poverty line of HK$28,815 and attendant poverty rate of 44.47% in 2020, nearly double the poverty line of HK$13,450 and poverty rate of 23.6% according to the conventional relative measure of the poverty line set to 50% of median household income—capturing a shortfall of 551,400 poor households that have been overlooked by relative measures.

The heterogeneous relationship between public health expenditures and household medical expenditures: evidence from relative poverty households in China

“…Some researches have proposed that the coverage of social assistance can be used as a reference for determining the proportion of the relative poverty population ( 10 ). Similarly, Au ( 11 ) uses a cost-of-living approach to measure relative poverty, where the poverty line is defined as the cost of essential goods and services. Although similar measures are easy to apply, they cannot reflect the unique demand (or expenditure) dimension of relative poverty compared with absolute poverty, leading to a serious underestimation of the depth of poverty.…”

How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method

“…The application of modern econometrics and related measures (e.g., median household income, Gini coefficient and other measures of income inequality, and incidence of poverty) nested within geographical epidemiology and spatial statistics reveals promising features to motivate and advance timely public health policy interventions [ 7 ]. However, the rise of multi-modal approaches from different settings and theories underlying econometric metrics within the local environments of social mechanisms reveal variations in associations across different settings, sometimes challenging the development of clearly defined, real-life interpretations [ 6 , 8 ], a consequence that arises from the utilization of multi-sectoral secondary data sources [ 2 ].…”