The Individual Poverty Incidence of Growth

Abstract

The canonical approach to analyse the poverty impact of growth is based on the comparison of poverty before and after growth. Measurement tools endorsing this approach fail to capture the different experiences of poverty dynamic in the population: there can be groups of the population made poorer or non-poor made poor by growth. We propose an approach that allows measuring this individual poverty incidence of growth and show how it is related with existing models. We apply our framework to evaluate the poverty impact of growth in

What’s behind pro-poor growth? The role of shocks and measurement error

“…The year 2000 marked the transition from the autocratic rule of Suharto, the recovery from the Asian financial crisis, the beginning of a process of decentralization, and, subsequently, the commodity boom-four different economic, political, and social events that arguably had an impact on people's lives and so on their income trajectories. Several studies (Bresson et al 2017;Grimm 2007;Lo Bue and Palmisano 2020), including the present one, have shown that there has been growth in this period, and that the incidence of growth has been larger among the initially poor. But why do the poor exhibit higher growth rates than those individuals initially belonging to richer percentiles?…”

“…The observed negative slope of our IGICs and CIGICs might, however, simply result from 'convergence' of incomes or 'regression towards the mean'. This is indeed a leitmotif of the na-GICs that have been estimated so far in the literature for different countries, including Indonesia (Grimm 2007;Lo Bue and Palmisano 2020). In order to understand to what extent the observed pattern is a pure Galtonian process, we need to first test if the actual expenditure dynamics simply result from non-classical measurement error, generating a spurious relation between the base-year reported income and the associated income change.…”

“…Thus, the na-GIC plots the income growth rate of all individuals as a function of their quantile in the initial distribution. A growing strand of recent literature adopts this non-anonymous approach to evaluating pro-poor growth (see among others Jenkins and Van Kerm 2016;Lo Bue and Palmisano 2020;Palmisano 2018;Palmisano and Peragine 2015). 2 The individual income growth rate is also used by another relevant strand of literature aimed at measuring income mobility.…”

Dynastic measures of inter-generational mobility with empirical evidence from Indonesia

“…This literature, focusing again mainly on intra-generational mobility interpretations and applications, assumes that individual-level mobilities are represented by concepts of 'distance' between first-and second-period incomes (Fields et al 2002;Jenkins and Van Kerm 2016;Van Kerm 2009). It has been extensively used to characterize where income growth has benefited specific segments of the population, notably the poor, using non-anonymous growth incidence curves (Berman and Bourguignon, 2021;Bourguignon 2011;Grimm 2007;Jenkins and Van Kerm 2016;Lo Bue and Palmisano 2020;Palmisano 2018;Van Kerm 2009). Our approach is closely related to this literature but adapted to the inter-generational perspective.…”

“…For these reasons, the IFLS has been applied to many development studies, including those focusing on intra-generational mobility (see Grimm 2007;Lo Bue and Palmisano 2020). It has also been used to investigate the heterogeneous effect of the INPRES school construction programme on girls' educational achievements between ethnic groups practising bride price and the others (Ashraf et al 2020) or the spillover effects of the programme on the second generation's human capital outcomes (Akresh et al 2018;Mazummder et al 2019).…”