New Indicators and Measurement Methods for Welfare in the Global Economy Era

This study discusses the inadequacy of GDP alone as a measure of welfare in the global economic age and examines alternative welfare indicators and measurement methods. This study, which discusses the human development index (HDI), the inequality adjusted human development index (I-HDI), the gender inequality index (GII), the multidimensional poverty index (MPI), the social progress index (SPI), the happy planet index (HPI), the better life index (BLI), the Legatum prosperity index(LPI), the human capital index (HCI), and the ecological footprint (EF) methods, shares the country rankings of these methods and reveals the differences in the results depending on the method. It also draws attention to the differences between the economic size and welfare level by sharing the rankings of the world's 10 largest economies in alternative methods. In addition, the study examines the obstacles to the inability to establish a complete, precise, and generally accepted method of measuring welfare.

Proposal of a Multidimensional Poverty Index for Spanish Municipalities

There are two objectives that impel this work: first, to propose a valid, reliable, and parsimonious poverty index, named Municipal Poverty Index-Urban Audit (MPI-UA), and second, to describe the evolution of the multidimensional poverty risk of the Spanish municipalities according to C. For the construction of the MPI-UA, the information provided by the URBAN AUDIT database on Spanish municipalities with a population in excess of 20,000 inhabitants has been used. The validated data have been analyzed with the PLS technique to identify the variables that allow the establishment of the MPI-UA, although previously certain variables have had to be transformed. This chapter has valuable implications insofar as there is no known multidimensional poverty index for Spanish municipalities. Moreover, it may be of probable use for academics. In the case of local managers, the index can be useful both for the knowledge of the situation and for the design of public policies to reduce poverty.

Multidimensional Relative Poverty of Farmers in Tibetan Areas – Based on a Data Analysis on Micro Surveys

In 2020, the issue of absolute poverty has been solved, and China is building a well-off society in an all-round way. The issue of relative poverty is an important content of poverty reduction. Based on a survey data from Danba County in October 2020, this paper uses the AF method to calculate the incidence of multidimensional poverty and the multidimensional poverty index. The results showed that 44.65% of the farmers have multidimensional deprivation of any three indicators of relative poverty, and 2.79% of the farmers have serious multidimensional deprivation; the incidence of one-dimensional poverty in terms of “educational level index of head of household,” “per capita non-transfer income of households in 2019,” and “per capita household income in 2019” is the highest; at the same time, the contribution rate of the three indicators to the multidimensional poverty index is also higher than other indicators. Therefore, several suggestions have been put forward to alleviate the multidimensional relative poverty in the region from the aspects of industry development and education.

Multidimensional Rural Poverty In Burji And Konso Area, Southern Ethiopia

Ethiopia is among the poorest countries in the world and the level of poverty is more challenging in rural areas compared to urban. Currently, there are great tendencies over the world to measure poverty using Alkire and Fosters' MPI approach among different approaches of poverty assessment. This study aimed at assessing multidimensional rural poverty status household's poverty in Burji and Konso area in Southern Ethiopia. To address this objective, 368 households were selected using simple random sampling techniques. The data were collected from both primary and secondary sources. Interview schedule, focused group discussion, key informant interview, and observation methods were implemented to collect primary data. Alkire & Foster Methodology with modified four dimensions and 14 indicators used to analyze multidimensional rural poverty. The study reveals as the highest three deprivations 97.8% of cooking fuel, 92.6% of the floor, and 76.1% of drinking water. The multidimensional Poverty Index (MPI) of the study area was 0.419 with 76.6% of incidence and 54.7% the intensity of multidimensional rural poverty. The highest (15%) contributor to MPI was deprivation in school attendants and the highest (34%) deprivation dimension was in living Standard out of four dimensions.

Migrants and non-migrants' welfare on cocoa farms in Ghana: Multidimensional poverty index approach

PurposeThe study assessed welfare of migrant and non-migrant labourers on cocoa farms in Ghana, using multidimensional poverty index (MPI) with four dimensions (education, health, dietary diversity, living standards) and 21 indicators. Specifically, we examined and compared non-monetary welfare of migrant and non-migrant labourers on cocoa farms in Ghana by adopting MPI approach. Also, we explored the factors affecting labourers' welfare.Design/methodology/approachA sample of 400 labourers was used. Qualitative and quantitative data were used. Quantile regression was used to investigate factors affecting labourers' deprivation in the different domains of non-monetary welfare.FindingsLabourers on cocoa farms are generally deprived in all the welfare indicators. Apart from having low education, labourers were underfed and lived under poor conditions. Though both migrants and non-migrants were multidimensionally poor, welfare of the later was higher than the former. Welfare of migrants and non-migrants on cocoa farms are influenced by similar factors: secondary occupation, income, credit accessibility, nature of contract and distance to social amenities.Research limitations/implicationsFor migrants, permanent status improves welfare. To improve labourers' welfare for enhanced productivity, cocoa farmers should provide permanent/long-term contracts for labourers and government should provide social amenities in cocoa-producing communities.Originality/valueMost previous welfare studies focused on farmers, with little attention paid to welfare of labourers on cocoa farms. We examined and compared the factors that affect migrant and non-migrant labourers' welfare on cocoa farms in Ghana. Moreover, we adopted the MPI (non-monetary) approach to assess labourers' welfare, instead of the expenditure and income approaches prevalent in literature.

Machine learning approach for multidimensional poverty estimation

In the social sciences, a theoretical analysis has predominated in its research. The scarcity of data and its difficulty in collecting and storing it, has been the main limitation for the social sciences to adopt quantitative approaches. However, the large amount of information generated in recent years, mainly through the use of the Internet, has allowed the social sciences to include more and more quantitative analysis. This study proposes the use of technologies such as Machine Learning (ML) are the answers to solving this data scarcity. The objective is to estimate the multidimensional poverty index at the personal level in a particular territory of Ecuador by using Machine Learning (ML) regression models based on a limited amount of data for training. Ten ML models are compared, such as linear, regularized, and assembled models and Random Forest performs outstandingly against the other models. An error of 7.5% was obtained in the cross-validation and 7.48% with the test data set. The estimates are compared with statistical approximations of the MPI in a geographical area and it is obtained that the average MPI estimated by the model compared to the average reported by the statistical studies differs by 1%.

Socioeconomic variation of multimorbidity in Colombian older adults

Multimorbidity (MM) prevalence among older adults is increasing worldwide. Variations regarding the socioeconomic characteristics of the individuals and their context have been described, mostly in high-income settings. However, further research is needed to understand the effect of the coexistence of infectious diseases along with socioeconomic factors regarding MM. This study aims to examine the variation of MM regarding infectious diseases mortality after adjusting for socioeconomic factors. A cross-sectional multilevel study with a nationally representative sample of 17,571 Colombian adults of 60 years of age or older was conducted. Individual socioeconomic, demographic, childhood and health related characteristics, as well as group level variables (multidimensional poverty index and infectious diseases mortality rate) were analyzed. A two-level stepwise structural equation model was used to simultaneously adjust for the individual and contextual effects. Multimorbidity prevalence was 62.3% (95% CI 61.7–62.9). In the multilevel adjusted models, age, female sex, having functional limitations, non-white ethnicity, high body mass index, higher income, physical inactivity and living in urban areas were associated with multimorbidity among the sample for this study. The median odds ratio for multidimensional poverty was 1.18 (1.16–1.19; p = 0.008) and for infectious diseases was 1.25 (1.22–1.28; p = 0.014). This paper demonstrates that MM varies regarding the mortality of infectious diseases and shows a strong association between MM and poverty in a low-middle income country. Differences in the factors involved in the etiology of multimorbidity are expected among wealthy and poor countries regarding availability and prioritization of health services.

Poverty in South Africa

The chapter examines poverty in the post-apartheid dispensation (in South Africa), taking into account the various studies that have been undertaken regarding poverty since 1994. Because the South African statistical agency—Statistics South Africa—has not collected poverty data since 2015, the chapter uses the National Income Dynamics Study (NIDS) dataset to estimate poverty for the 2008–17 period while analysing existing data and estimates prior to 2017. An attempt to cover the period after 2017 is done using the NIDS-Coronavirus Rapid Mobile Survey (NIDS-CRAM) which is the mobile survey that was undertaken using the NIDS respondents to gauge the impact of the coronavirus pandemic. The analysis focuses on income poverty although other measures such as the multidimensional poverty index are also estimated and analysed. Relative to expenditure, the income approach in measuring poverty provides descriptive information on household welfare and it is useful for policy analysis and programme evaluation as the literature explains. The chapter concludes that although it appears that income poverty has been declining, it remains very high and it is higher for women, for those living in rural areas, and for the African/black population group. In addition, the severity and intensity of poverty has not changed much since 1994.

Characterizing multidimensional poverty in Migori County, Kenya and its association with depression

Introduction Narrow, unidimensional measures of poverty often fail to measure true poverty and inadequately capture its drivers. Multidimensional indices of poverty more accurately capture the diversity of poverty. There is little research regarding the association between multidimensional poverty and depression. Methods A cross-sectional survey was administered in five sub-locations in Migori County, Kenya. A total of 4,765 heads of household were surveyed. Multidimensional poverty indices were used to determine the association of poverty with depression using the Patient Health Questionnaire (PHQ-8) depression screening tool. Results Across the geographic areas surveyed, the overall prevalence of household poverty (deprivation headcount) was 19.4%, ranging from a low of 13.6% in Central Kamagambo to a high of 24.6% in North Kamagambo. Overall multidimensional poverty index varied from 0.053 in Central Kamagambo to 0.098 in North Kamagambo. Of the 3,939 participants with depression data available, 481 (12.2%) met the criteria for depression based on a PHQ-8 depression score ≥10. Poverty showed a dose-response association with depression. Conclusions Multidimensional poverty indices can be used to accurately capture poverty in rural Kenya and to characterize differences in poverty across areas. There is a clear association between multidimensional poverty and depressive symptoms, including a dose effect with increasing poverty intensity. This supports the importance of multifaceted poverty policies and interventions to improve wellbeing and reduce depression.

Association of multidimensional poverty and tuberculosis in India

Background Reduction of multidimensional poverty and tuberculosis are priority development agenda worldwide. The SDGs aims to eradicate poverty in all forms (SDG 1.2) and to end tuberculosis (SDG 3.3.2) by 2030. While poverty is increasingly being measured across multiple domains, reduction of tuberculosis has been an integral part of public health programmes. Though literature suggests a higher prevalence of tuberculosis among the economically poor, no attempt has been made to understand the association between multidimensional poverty and tuberculosis in India. The objective of this paper is to examine the association of multidimensional poverty and tuberculosis in India. Methods The unit data from the National Family Health Survey-4, conducted in 2015–16 covering 628,900 households and 2,869,043 individuals across 36 states and union territories of India was used in the analysis. The survey collected information on the self-reported tuberculosis infection of each member of a sample household at the time of the survey. Multidimensional poverty was measured in the domains of education, health, and standard of living, with a set of 10 indicators. The prevalence of tuberculosis was estimated among the multidimensional poor and non-poor populations across the states of India. A binary logistic regression model was used to understand the association of tuberculosis and multidimensional poverty. Results Results suggest that about 29.3% population of India was multidimensional poor and that the multidimensional poverty index was 0.128. The prevalence of tuberculosis among the multidimensional poor was 480 (95% CI: 464–496) per 100,000 population compared to 250 (95% CI: 238–262) among the multidimensional non-poor. The prevalence of tuberculosis among the multidimensional poor was the highest in the state of Kerala (1590) and the lowest in the state of Himachal Pradesh (220). Our findings suggest a significantly higher prevalence of tuberculosis among the multidimensional poor compared to the multidimensional non-poor in most of the states in India. The odds of having tuberculosis among the multidimensional poor were 1.82 times higher (95% CI, 1.73–1.90) compared to the non-poor. Age, sex, smoking, crowded living conditions, caste, religion, and place of residence are significant socio-demographic risk factors of tuberculosis. Conclusion The prevalence of tuberculosis is significantly higher among the multidimensional poor compared to the multidimensional non-poor in India.