A Methods on tracing income distribution from group data - Yarcia, Damaris
Abstract (Inequality is on the rise and tool are constantly being developed to measure income distribution despite data constraints. Access to unit-level income data is one major limitation even with wider support for statistical agencies to conduct household surveys on income and expenditure. Current tools often used to estimate inequality from grouped data, which are typically the only readily available data on household income, are based on fitting parametric Lorenz Curves. While such tools can accurately predict inequality of the entire population, they generally fail to capture the structure of the tail-ends of the distribution. Conditions for validity of parametric Lorenz Curves also constrain estimation. This study propose a method to mitigate this constraint by the generating random variable ordinates based on summary statistics. The proposed method produces more accurate estimates of inequality at decile level. Level for mode-sensitive indicators like the Gini coefficient, the best-fitting distribution function of each decile may hold across several survey rounds).