Non-income Indicators Research Articles

Contextualizing Poverty along with the Sustainable Development Goals (SDG’s) 3, 6 and 9 as nonincome indicators in Ocampo, Camarines Sur Philippines: Evidences from CBMS 2019

Measuring poverty using non-income indicators enable countries to target and develop programs for poverty alleviation and understand its performance towards the sustainable development goals (SDG’s). This paper which seeks to evaluate the performance of the municipality of Ocampo in Camarines Sur, Philippines towards the SDG’s via the Community-based Management System (CBMS) would enable policymakers to target recipients of government programs and interventions in the municipality. This paper looked into the responsiveness of the municipality of Ocampo, Camarines Sur to the SDGs by identifying the household poverty index; poverty gap index and poverty severity index as well as estimating the probability of households in Ocampo, Camarines Sur to becoming poor using non-income indicators which are connected with the SDG’s. Employing the Foster-Greer-Thorbecke (FGT) poverty measures, and logistic regression, the researcher found that around 64.5% of the households in the municipality of Ocampo, Camarines Sur are poor. Likewise, majority of the poor households in the municipality don’t have access to clean and safe water, toilet facilities and internet and electric connectivity. However, despite them being poor, the residents of Ocampo visits medical facilities and received medical treatments if they are sick. The municipality does well in SDG 3, good health and well-being. To further strengthen the municipality’s responsiveness to the SDG’s, there is a need to strengthen barangay health units, develop local water sources and further access to internet connectivity.

Inequality and Welfare in Quality of Life Among OECD Countries: Non-parametric Treatment of Ordinal Data

The last few years have witnessed an increasing emphasis on going beyond GDP per capita when measuring a nation’s quality of life. Countries (e.g. UK, France, Canada, Germany, Italy, Japan, Korea, Spain) and international organizations (e.g. OECD) have been developing methods suitable for non-income indicators. However, this involves serious measurement challenges due to: (a) multidimensionality, and (b) ordinality (i.e. unlike income these indicators do not have a natural scale). This paper is the first summary of the methods developed in the last decade in the field of inequality and welfare measurement to address these challenges. Next, we utilize the presented methodology and provide evidence on the ranking of OECD countries in terms of welfare and inequality in education and happiness. We find that when dimensions are analysed separately, welfare dominance is frequent (42% of all comparisons in education and 31% in life satisfaction). The number drops to only 4.4% for bivariate dominance, which highlights the empirical relevance of multidimensional analysis. Greece, Portugal and Hungary feature the lowest joint welfare. Northern European countries are most often dominating and Southern European countries are most often dominated in both inequality and welfare analyses.

Multidimensional poverty in India: has the growth been pro-poor on multiple dimensions?

The present investigation is an attempt to synergize the uni-dimensional as well as multidimensional approaches to measure poverty as well as pro-poor growth. This analysis is based upon FGT indices for measuring unidimensional poverty, the Alkire and Foster (2008) methodology for multidimensional poverty and then pro-poor growth rates on non-income indicators have been computed by using Klasen (2008) approach which is based upon Ravillion and Chen (2003) index. It can be stated that both the uni-dimensional and multidimensional poverty in India had declined between 2004–05 and 2009–10. But, it had not been pro-poor across all the dimensions and for all social groups. It has been observed that the dimensions of education, expenditure and regular salary had not been pro-poor in most of the cases. Among the social groups, the SCs and the STs are the poorest categories and by household types, the labour households are the poorest one. These households suffer from the deprivations of multiple dimensions. It has been observed that the dimension of education and cooking fuel are the biggest contributors to overall poverty rate and the poorest suffer the most from these deprivations.

Human Poverty Measuring Relative Deprivation from Basic Achievements. A Comparative Study for 144 World Countries in the Time Span 1990-2010.

Referring to seminal works of Sen and many others (see e.g. Morris 1979; Sen 1985, 1992; Desai 1991; Ravallion 1993), we present an specific approach to poverty issues, treating low incomes as consequence – not a cause – of poverty. The main target of the work is twofold. Firstly we aim to develop a new complex measure of relative poverty to set the most recent worldwide poverty estimates. For this purpose we deploy methods based on fuzzy sets (e.g. see works of Zadeh 1965; Dubois & Prade 1980; Berenger & Verdier-Chouchane 2006), and we apply a wide array of non-income indicators (proxies) explaining level of socio-economic development. Following the logic of fuzzy sets theory, we explain poverty as deprivation from – for example – access to education or improved drinking water. Secondly, we run an analysis on disaggregated data, tracking for factors which contribute mostly to the level of poverty in different countries. Such approach let us to detect most backward areas of human development which shall be in the centre of interest of policy makers. All data applied in the estimates are derived from World Development Indicators Database 2012. The sample covers 144 world economies, and the set time framework is 1990-2010.

Growth Incidence Analysis for Non-Income Welfare Indicators: Evidence from Ghana and Uganda

Growth incidence analysis involves calculating the amount of growth in some welfare-indicator (e.g. income, life expectancy) at each quantile (e.g. quartile, decile,or percentile) of the distribution of that indicator. This information, and its graphical representation in the form of a growth incidence curve (GIC), gives a much fuller picture of changes in social welfare and poverty over any given period than can be provided by a single figure, such as the rate of per capita income growth, or the reduction in the poverty headcount. However, although it is now widely accepted that welfare and poverty are multi-dimensional, and cannot be reflected accurately by any one single indicator, the vast majority of GICs calculated to date have been based on growth rates in income (or expenditure).This paper begins filling this gap by providing estimates of GICs for non-income welfare indicators in Ghana and Uganda. The main practical difficulty is that most of such indicators found in standard household surveys are discrete, as opposed to continuous, variables – i.e. their values are limited to a fixed number of categories (e.g. good, poor), rather than a very large number (e.g. units of local currency). This limits the range of values the GIC can take; in the extreme, it limits it to one of only two values: growth or no growth. The best way to address this problem is by expanding and improving the coverage of non-income welfare indicators in standard household surveys. This will require an initial ‘bench-marking’ exercise to establish best-practice and standardise as much as possible across surveys in different countries. In the meantime, an alternative approach is to calculate so-called ‘conditional’ GICs, which involves calculating the growth in non-income indicators among different groups of the population ranked by income (or expenditure).From the household surveys for Ghana and Uganda in 1992 and 1999 it was possible to construct nine non-income welfare indicators, in the dimensions of education, health, household amenities (e.g. use of drinking water, sanitation and electricity), and household assets. The results show some significant and important differences across income and non-income welfare indicators, both in terms of aggregate trends and distributional patterns. In Ghana, despite relatively low rates of income growth at the lower end of the income distribution, there have been significant improvements and catching up in other non-income indicators, such as primary school enrolment, use of good drinking water, and the value of assets owned (in proportional if not always in absolute terms). In Uganda, despite reasonably high rates of income growth, particularly at the lower end of the income distribution, there has been a deterioration of other non-income indicators.Overall, the analysis cautions against over-reliance on purely income-based measures of poverty and welfare. It also suggests the need to consider ways in which potential trade-offs between income and non-income welfare indicators can be incorporated into policy analysis.

Measuring Chronic Non-Income Poverty

An increasing interest in poverty dynamics has lately also led to an extensive literature on the analysis of chronic poverty. Based on Amartya Sen’s groundbreaking work on capabilities and functionings static poverty measures have long used non-income indicators. In contrast, measures of poverty dynamics - including chronic poverty ‐ have in general conceptualised poverty only in an income dimension. Hence, this paper first critically discusses the conceptual and empirical potentials and limitations of analysing chronic poverty from a nonincome perspective. Second, it proposes methods to empirically measure chronic nonincome poverty, with an exploratory application to panel data from Vietnam from 1992 and 1997, which demonstrates that a range of useful insights can be generated from such an analysis. In particular, we find that the correlation between chronic income and non-income poverty is rather low which is mostly due to a low correlation between income and nonincome poverty in each period, while both move relatively closely over time. We also find a surprising amount of heterogeneity in static and dynamic non-income poverty within households.

Measuring Pro-Poor Growth in Non-Income Dimensions

Current concepts and measurement of pro-poor growth is entirely focused on the income dimension of well-being. This neglects non-income dimensions of poverty and the multidimensionality of poverty and wellbeing. In this paper we introduce the multidimensionality of poverty into the measurement of pro-poor growth measurement by applying the growth incidence curve to non-income indicators. We develop growth incidence curves and calculate the associated Ravallion-Chen Pro Poor Growth measures for a range of non-income indicators such as education, mortality, vaccinations, stunting, and a multidimensional wellbeing measure and are thereby able to study improvements in these dimensions of well-being at various points of the distribution of those indicators as well as at various points of the income distribution. This way we can determine whether improvements in non-income indicators were pro-poor in an absolute or relative sense. We illustrate this empirically for Bolivia between 1989 and 1998 and find that growth was relatively pro-poor in the non-income dimension; results for absolute improvements are less clear.

Economic Growth and Poverty Reduction: Measurement Issues using Income and Non-Income Indicators

Summary This paper first reviews the debate on the definition of pro-poor growth and argues that a relative definition has merits when defining the state of pro-poor growth, while an absolute definition is most suitable when analyzing the rate of pro-poor growth. The second contribution is to extend the pro-poor growth toolbox to non-income indicators. The analysis, applied to Bolivia, shows that this extension greatly improves our understanding of the distribution of change in non-income indicators, of the linkages between progress in income and non-income dimensions of poverty, and on the incidence of progress in non-income outcomes along the income distribution.

A composite index to explain variations in poverty, health, nutritional status and standard of living: Use of multivariate statistical methods

To calculate the Human Development Index (HDI) and Human Poverty Index (HPI) of Indian states; to trace the indicators useful for finding variations in poverty; and to develop a composite index that may explain variations in poverty, health, nutritional status and standard of living. Cross-sectional study. The HDI and HPI were calculated for different Indian states. A set of possible indicators varying between rich and poor states of India was identified with the use of discriminant function analysis. A composite index has been developed for measuring the standard of living of Indian states with the help of factor analysis. Demographic, socio-economic, health and dietary indicators play a major role in determining the real standard of living. Poverty, standard of living and human development depend on multiple factors. The existing indices, such as HDI and HPI, use income indicators to measure the standard of living, and do not take into account diet and nutritional status indicators. The proposed index was found to be more suitable for measuring the real standard of living and human development, as it is a comprehensive index of income and non-income indicators. Further validation may be carried out for different populations. Discriminant function analysis and factor analysis were used to assess health inequality and standard of living among Indian states. The proposed multi-dimensional index may provide a better picture of human development. Further work is of interest for other populations.

Economic Growth and Poverty Reduction: Measurement and Policy Issues

The aim of this Working Paper is to broaden the debate on “pro-poor growth”. An exclusive focus on the income dimension of poverty has neglected the non-income dimensions. After an examination of prominent views on the linkages between economic growth, inequality, and poverty reduction this paper discusses the proper definition and measurement of pro-poor growth. Bolivia serves as a case study to illustrate the usefulness of applying the analytical toolbox for pro-poor growth to non-income dimensions of poverty and offers some important new insights about differences in the evolution of both poverty dimensions. Growth in Bolivia has been more pro-poor in the non-income than in the income dimension. The analysis furthermore shows that extending use of the pro-poor growth toolbox to non-income dimensions of poverty greatly improves our understanding of the trends in non-income indicators. Such understanding is pivotal for a careful assessment of the linkages between income and nonincome poverty along the entire income distribution. It is equally important for poverty monitoring and for defining policy interventions. It also allows deeper analysis of the relative merits of economic growth, compared to direct intervention aimed at improving non-income dimensions of poverty. Ce document technique se propose d’elargir le debat sur « la croissance au benefice des pauvres ». Prendre exclusivement en compte le revenu comme dimension de la pauvrete neglige ses dimensions hors revenu. Apres une etude des theses en vue sur les liens entre croissance economique, inegalites et reduction de la pauvrete, l’article questionne la definition propre de « la croissance au benefice des pauvres » et la maniere de la mesurer. La Bolivie presente un cas d’ecole illustrant l’utilite d’elargir les outils d’analyse de cette croissance aux dimensions hors revenu de la pauvrete, et elle offre de precieux nouveaux reperes sur les differentes evolutions des deux spheres de la pauvrete. La croissance en Bolivie a davantage beneficie aux pauvres dans le registre hors revenu que dans le registre du revenu. L’analyse developpe qu’un recours aux outils d’une croissance benefique aux pauvres elargis aux dimensions hors revenu de la pauvrete ameliore sensiblement notre comprehension des tendances des indicateurs de la sphere hors revenu. Une telle comprehension est fondamentale pour une appreciation judicieuse des liens entre pauvrete en matiere de revenu et pauvrete hors revenu tout au long de la distribution du revenu global. C’est egalement important pour agir sur la pauvrete et pour definir des politiques d’intervention. Cette comprehension permet aussi d’approfondir en les comparant l’analyse des merites relatifs de la croissance economique et des interventions directes visant a ameliorer les parametres hors revenu de la pauvrete.