Suchergebnisse

ABSTRACTThe archetypal population‐relative measure of poverty is the Headcount Ratio, while the archetypal population‐absolute measure is the Aggregate Headcount. The overwhelming emphasis in the poverty measurement literature has been on population‐relative measures. The value‐basis for this preference has been seldom submitted to serious scrutiny, with the result that absolute and intermediate measures have generally been subjected to relative neglect. There would appear to be a strong case for the employment of indices which are intermediate between relative and absolute measures, and which avoid the extreme values underlying both. The present note, building on earlier work by scholars such as Arriaga, Krtscha, Del Rio, Alonso‐Villar, Mukherjee, Zheng and Zoli, aims to advance the cause of a workable and plausible population‐intermediate headcount index of poverty.

The methodology currently used to measure poverty in the European Union faces some important limitations. Capturing key aspects of poverty is done using a dashboard of indicators, which often tell conflicting stories. We propose a new income-based measure of poverty for Europe that captures in a consistent way in a single indicator the level of relative poverty, the intensity of poverty, poverty with a threshold anchored in time and a pan-European perspective on poverty. To do so, we work with a recently developed poverty index, the extended headcount ratio (EHC) and derive the relevant poverty lines to apply the index to poverty in Europe. We show empirically that our measure consistently captures the aspects typically monitored using a variety of indicators and yields rankings that seem more aligned with intuitions than those obtained by these individual indicators. According to our measure, Eastern Europe has a much higher level of poverty than Southern Europe, which, in turn, has a considerably higher level of poverty than North-Western Europe. In North-Western Europe, the evolution of our measure over time correlates most strongly with the at-risk-of-poverty rate, while in Southern and Eastern Europe, it correlates most strongly with at-risk-of-poverty with the threshold anchored in time.

AbstractThis article examines the effect of economic complexity on poverty in developing countries. It shows that greater economic complexity results in lower poverty headcount rates. This is particularly the case for countries that enjoy higher economic growth rates, lower levels of income inequality and lower degrees of economic growth volatility, including volatility due to smaller export demand and financial flows shocks. These findings have important policy implications for those developing countries that are exploring ways and means to recover from the current COVID‐19 pandemic crisis and to prepare for future crises.

Bhutan maintained solid growth and macroeconomic stability during 2016/17. The national poverty headcount rate fell from 12 percent in 2012 to 8 percent in 2017. However, the job challenges remained unaddressed. the youth unemployment rate increased from 10.7 percent in 2015 to 13.2 percent in 2016 indicating that high growth was not able to generate enough jobs for youth.

As awareness of the limitations of relying solely on income to measure poverty has become more widespread, attention has been increasingly focused on multi-dimensional approaches, to the point where the European Union (EU) has adopted a multidimensional poverty and social exclusion target for 2020. The rationale advanced is that the computation of a multidimensional poverty index is an effective way of communicating in a political environment, and a necessary tool in order to monitor 27 different national situations. In contrast with the rather ad hoc way in which the EU 2020 poverty target has been framed and rationalized, the adjusted head count ratio applied here has a number of desirable axiomatic properties. It constitutes a significant improvement on union and intersection approaches and allows for the decomposition of multidimensional poverty in terms of dimensions of deprivation and socio-economic attributes. Since understanding poverty as multidimensional does not necessarily require constructing a multidimensional poverty index, on the basis of our analysis we provide a more general consideration of the value of developing a multidimensional index of poverty for the EU.

Bangladesh has made remarkable progress in reduction of poverty headcounts, poverty gap, and squared poverty gap since 2000. While scores of studies rigorously looked into drivers of poverty reduction from the perspective of sources, both the cross country and single country literature on poverty convergence is scant. Even most of these convergence estimates are biased arising out of omitted variable due to ignorance of unobserved individual heterogeneity and endogeneity of at least a subset of regressors. This paper exploits a unique set of data to provide credible evidence of convergence in poverty across the districts of Bangladesh during 2000-2016 using the Arellano-Bond system dynamic panel estimator and panel generalized method of moments estimator. We find that poverty convergence is present during this period both through direct estimation and decomposition with relevant indirect estimates. Our results are robust to the alternative frequency of data (cross-section vis-à-vis panel) and the consequent estimation techniques, sources of data (direct estimates from the Household Income and Expenditure Surveys vis-à-vis small area estimates) and the alternative transformation of the dependent variables. Both growth-accounted poverty convergence effect and strong growth effect dominate the adverse effect of initial poverty on growth effectiveness to ensure strong overall poverty convergence found across the districts.

Following Amartya Sen's pioneering ideas on poverty and inequality measurement, the development economics literature proposes diverse classes of measures as well as poverty orderings. Yet in the Sustainable Development Goals (SDGs), the headcount ratio is the primary statistic for measuring monetary and multidimensional poverty. Rigorously analysing the trends of multidimensional poverty for India between 2005/6 and 2015/16, we illustrate how the headcount ratio is not able to observe certain centrally important requirements of the SDGs – such as whether anyone is being left behind, or how deprivations are interlinked. We propose using the adjusted headcount ratio or Multidimensional Poverty Index (MPI) as the primary poverty measure for policy assessment, supplemented by the headcount ratio, intensity, number of poor, and composition of poverty, to provide more accurate analyses. Exploiting cross-sectional data comprising of more than three million individuals and a panel of 29 states and several socio-economic subgroups, we show empirically how the reduction of multidimensional poverty by 271 million unfolded within a decade. In contrast to earlier periods in time, we find that the poorest of the poor saw the largest reductions in multidimensional poverty due to falling levels of intensity – a feature the headcount ratio alone cannot portray. Despite the importance of the MPI we recognise the inherent and enduring need to probe the headcount ratio and number of poor statistics. Hence we corroborate these stark findings with an assessment of the dominance of the distribution of attainment scores which establishes the relationship between MPI and H in both periods. To assess the robustness of the number of poor leaving poverty, 19 additional MPIs are constructed, each having different indicator definitions and combinations, and it is found that in all but one of these, more than 270 million people left poverty.