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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.

This paper argues that the link between poverty and drug-related crime might be spurious. We take an empirical approach to investigate the causality and plausibility of this link. Firstly, we regress crime against envisaged explanatory variables in order to estimate the contribution of poverty to crime. Secondly, data is analysed using an ARDL ECM. The quarterly sample data for our estimation is for the period 1995-2016. We found a strong association between crime and poverty both in the short and long run. We recommend the government should focus on none income linked factors in dealing with the scourge of drug-related crime. As demonstrated in this study, various drug-related crimes are driven by socio-economic factors.

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.

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.

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.

Nigeria has implemented structural adjustment policies for the past twenty years, with the intensity of reforms increasing each year. However, there is an ongoing debate as to whether strong adjustment is more effective than weak reforms in alleviating poverty in African economies. In this article, we use primary socio-economic data collected from two surveys, one conducted in 2001 and the other in 2007, of residents in the village of Umulwe in Southeastern Nigeria, to examine whether the intensity of structural adjustment policies has led to poverty reduction in the village. Specifically, we use changes in the poverty headcount index and the poverty gap index, as well as non-income measures to analyze the dynamics of poverty in the village. A cross-sectional OLS regression on the survey data does not indicate a direct link between structural adjustment and the poverty reduction observed in the village over time. By linking structural adjustment to changes in household behaviour, the article provides a micro-foundation for the analysis of the impact of structural adjustment on poverty.

The analysis and the comparison of poverty between regions and countries are important topics in social sciences, which have relevant demands of many national (Cáritas, Intermón Oxfam, Cruz Roja, etc) and international (UN, World Bank, OECD, Eurostat, IMF, etc) agencies and organizations. One of the most common poverty indicators in practice is the headcount index, which analyzes the proportion of individuals considered as poor in a population. In this paper, we first analyze the impact on the headcount index when different sampling designs are considered. Note that this study is based on real data sets taken from different countries of the European Union, and the empirical measures for comparisons are based on different Monte Carlo simulation studies. For instance, we observe that stratified sampling has the best performance in comparison to alternative sampling designs. Post-stratification performs similar to simple random sampling without replacement, and the use of auxiliary information provides similar results to ones derived from stratified sampling. Second, we also analyze the empirical performance of different variance estimators under the commented sampling designs. We conclude that they have a similar empirical performance, and they provide, in general, confidence intervals with desirable coverage rates.