Deceptive Equivalence: Rethinking GDP per Capita PPP and Gini as Interchangeable Proxies for Inequality

The purpose of this study is to empirically evaluate the appropriateness and validity of two widely used economic indicators—GDP per capita at purchasing power parity (PPP) and the Gini Index—as interchangeable proxies in global inequality and deprivation research. These metrics, although prevalent in policy and academic discourse, are increasingly scrutinized for their potential to misrepresent ground-level realities of income distribution and absolute human deprivation. This paper explores whether these indicators reliably capture similar economic constructs or if their use in aggregation potentially distorts analytical outcomes. The research contributes to an underexplored yet critical gap in global development economics by empirically interrogating the assumption of proxy equivalence. This investigation is vital given the heavy reliance of global institutions and scholarly models on GDP and Gini metrics as interchangeable measures in inequality assessments and socio-economic policy formulations. In addressing this gap, the study provides essential insight into the methodological fidelity of economic proxies used to inform international development agendas. Using a cross-sectional dataset of 57 countries for the year 2019 extracted from the World Development Indicators (WDI), the study employs bi-directional univariate Ordinary Least Squares (OLS) regression analysis to test the empirical interdependence and substitutability of the two indicators. The results were statistically significant but paradoxical; both univariate regressions yielded a negative relationship at the 1% significance level, with an adjusted R² of only 0.288, thereby empirically refuting their presumed interchangeability. These findings strongly caution against the uncritical use of GDP per capita PPP and the Gini Index as synonymous proxies in inequality research. Policy recommendations include greater methodological rigor in proxy selection and the need for multidimensional composite indices that better capture nuanced socio-economic realities. Future studies should explore alternative metrics and consider qualitative assessments to complement quantitative models.