Poverty Disparities Research Articles

Impacts of Productive Safety Net Program on Households ‘Calorie Intake per Adult Equivalent, Expenditure and Livestock Asset Holding; Empirical evidence from Eastern Ethiopia

Chronic food insecurity is one of the main problems, which affected millions of Ethiopians for centuries. To solve the food insecurity problem and move away from the previous system of annual emergency appeals, the Ethiopian government in collaboration with development partners launched social protection program called productive safety net program. This study evaluated the impacts of productive safety net program on household poverty alleviation, by measuring the wellbeing of rural households using outcome indicators of the program i.e. calorie intake, consumption expenditure and livestock asset using cross-sectional survey data collected from 200 households in ECC-SDCOH facilitated kebeles of productive safety net program in eastern Ethiopia. Multistage stage sampling technique was used to randomly select representative household heads. Both primary and secondary data were analyzed using descriptive and econometric statistics. Applying a propensity score matching technique, the study found that the program has significantly increased participating households’ calorie intake by 30% (i.e., 856 calories), the consumption expenditure by 2.84% and livestock asset by 40% compared to that of non-participating households. The results of the logit model also indicated that program participation is significantly influenced by family size, education, marital status, dependency ratio, size of land holding and credit use. Therefore, to reduce poverty level of the country, due attention has to be given by the program to reducing gender disparity in poverty and sustained effort is needed to government-administered productive safety net program districts to accelerate the rate of poverty reduction. And the program should consider roles of significant variables in the selection of participant households for the desired impact under related locations.

Do socioeconomic inequalities increase the spread of COVID-19 in Turkey?

ABSTRACT This paper clarifies the medical and socio-economic factors affecting the prevalence of COVID-19 by using clinical and survey data in a binary probit model. Socio-economic factors are associated with risk of infection and can increase exposure to and mortality from COVID-19. Inequalities in socio-economic variables affect the prevalence to different degrees. Disparities in education and poverty are more important than being employed or being a smoker for the spread of COVID-19, we find evidence that confirms the hypothesis.

Reducing poverty strategy through educational participation, clean water, and sanitation in Indonesia

Addressing the poverty gap is the key to improving economic welfare and improving the structure of purchasing power in society. This paper wants to analyze how increased education participation, access to clean water, and sanitation water reduce poverty disparities. The data analyzed in this study covered 501 districts and cities throughout Indonesia in 2018. The methodology used was an econometric approach with multiple regression equation models with robustness provisions. The calculation results prove that the model has been robust and all variables selected have a significant effect on the degree of freedom of 5% for all districts and cities in Indonesia. This study suggests that education policy priorities, the availability of clean water and adequate distribution, and sanitation facilities are needed to reduce the poverty gap.

Socioeconomic Inequity in the Screening and Treatment of Hypertension in Kenya: Evidence From a National Survey.

Non-communicable diseases (NCDs) account for 50% of hospitalisations and 55% of inpatient deaths in Kenya. Hypertension is one of the major NCDs in Kenya. Equitable access and utilisation of screening and treatment interventions are critical for reducing the burden of hypertension. This study assessed horizontal equity (equal treatment for equal need) in the screening and treatment for hypertension. It also decomposed socioeconomic inequalities in care use in Kenya. Cross-sectional data from the 2015 NCDs risk factors STEPwise survey, covering 4,500 adults aged 18-69 years were analysed. Socioeconomic inequality was assessed using concentration curves and concentration indices (CI), and inequity by the horizontal inequity (HI) index. A positive (negative) CI or HI value suggests a pro-rich (pro-poor) inequality or inequity. Socioeconomic inequality in screening and treatment for hypertension was decomposed into contributions of need [age, sex, and body mass index (BMI)] and non-need (wealth status, education, exposure to media, employment, and area of residence) factors using a standard decomposition method. The need for hypertension screening was higher among poorer than wealthier socioeconomic groups (CI = -0.077; p < 0.05). However, wealthier groups needed hypertension treatment more than poorer groups (CI = 0.293; p <0.001). Inequity in the use of hypertension screening (HI = 0.185; p < 0.001) and treatment (HI = 0.095; p < 0.001) were significantly pro-rich. Need factors such as sex and BMI were the largest contributors to inequalities in the use of screening services. By contrast, non-need factors like the area of residence, wealth, and employment status mainly contributed to inequalities in the utilisation of treatment services. Among other things, the use of hypertension screening and treatment services in Kenya should be according to need to realise the Sustainable Development Goals for NCDs. Specifically, efforts to attain equity in healthcare use for hypertension services should be multi-sectoral and focused on crucial inequity drivers such as regional disparities in care use, poverty and educational attainment. Also, concerted awareness campaigns are needed to increase the uptake of screening services for hypertension.

Urban–rural disparity of child poverty in China: Spatio-temporal changes and influencing factors

Eradicating child poverty and shrinking urban–rural disparity are the common goals of humanity. In contemporary China, significant differences exist in child poverty between urban and rural regions, despite the remarkable achievements that have been made in child poverty reduction. Using the national datasets of China Family Panel Studies, this study explored the spatio–temporal changes and influencing factors of urban–rural disparity in child poverty (URDCP) in China between 2010 and 2018 based on the Alkire–Foster approach, spatial autocorrelation analysis and Geodetector method. Results indicated that the URDCP has been shrinking since 2010 and that the urban–rural gap in child poverty increased from east to west due to the combined influence of urban–rural differences and regional poverty disparity. The urban–rural differences in child poverty were mainly reflected in the dimensions of nutrition and living standards. Generally, family conditions, mobility and medical resources were the main contributors of spatial variations in URDCP. The findings may provide useful insights into child poverty alleviation in developing countries.

Caring of the Fringe? Mediterranean Desertification between Peri-Urban Ecology and Socioeconomics

This commentary debates on the role of multiple socioeconomic drivers of fringe land degradation (including, but not limited to, population and social dynamics, economic polarization, and developmental policies), as a novel contribution to the desertification assessment in Southern European metropolitan regions, a recognized hotspot of desertification at the global scale. Expanding rapidly all over the world, metropolitan regions are a geographical space where land degradation drivers and processes assume typical relationships that require further research supporting dedicated policy strategies. To assure a better comprehension of the environmental-economic nexus at the base of land degradation in peri-urban areas, we provided a classification of relevant socioeconomic and territorial dimensions in both macro-scale and micro-scale degradation processes. We also identified the related (contextual) factors that determine an increased risk of desertification in metropolitan regions. Micro-scale factors, such as agricultural prices and off-farm employment, reflect some potential causes of fringe land degradation, with a mostly local and on-site role. Technological change, agricultural prices, and household income influence land vulnerability, but their impact on fringe land degradation was less investigated and supposed to be quite moderate in most cases. Macro-scale factors such as population density, rural poverty, and environmental policies—being extensively studied on a qualitative base—were taken as important drivers of fringe land degradation, although their impact still remains undefined. Regional disparities in land resource distribution, rural poverty, and unsustainable management of environmental resources like soil and water were indirect consequences of land degradation in peri-urban districts. Based on a comparative review of theoretical and empirical findings, strategies mitigating degradation of fringe land and reducing desertification risk in potentially affected metropolitan regions were finally discussed for the Northern Mediterranean basin and generalized to other socioeconomic contexts.

A Multidimensional Approach to the Gender Gap in Poverty: An Application for Turkey

Conventional poverty measures have long been criticized as a poor gauge of quality of life. Household-level income or expenditure data used in these measures are silent on intrahousehold inequalities and capture means to an end rather than outcomes and opportunities individuals face. This article constructs a multidimensional poverty index (MPI) to address these problems. It calculates multidimensional poverty for Turkey in four equally weighted dimensions: education, health, employment, and household living conditions. The study introduces employment as a distinct dimension of well-being, which is especially pertinent for the gender gap in poverty in the Turkish context. It finds a significant (30–34 percent) gender poverty gap, which is gradually narrowing over time. However, there is very little convergence between regions. Finally, results show households with multidimensionally poor women and non-poor men as the most common sub-group and an increase in the share of households with no poor members. HIGHLIGHTS The study evaluates the multidimensional poverty of individuals to analyze gender gaps. Employment proxies for ignored functionings like self-respect and social inclusion. Gender poverty gap was between 30 and 34 percent during 2006–15. Gender poverty gap is only closing for the youngest cohorts. Within-household poverty disparities are high and stable during 2006–15.

Gender and ethnic disparities in energy poverty: The case of South Africa

The study ascertained the differentiated energy poverty of female-headed households based on their race/ethnicity in South Africa. The study made use of the 2016 General Household Survey (GHS). A sample of 7322 male-headed households and 6170 female-headed households was utilised. An Energy Vulnerability Index (EVI), Independent t-test and Propensity Score Matching (PSM) were used to analyse the data. The results show that male-headed households had more exposure, adaptive capacity and sensitivity to energy poverty compared to female-headed households. Overall, female-headed households were more vulnerable to energy poverty. Compared to White, Indian/Asian and Coloured female-headed households, Black/African female-headed households exhibited more vulnerability to energy poverty. The study concludes that gender of the household head and race/ethnicity of the female-headed households had impact on the energy poverty in South Africa. The study recommends the need to identify relevant gender and race/ethnic issues concerning energy. Furthermore, there is need to counter gendered-ethnic disparities in renewable energy programmes for poorer households, to avail alternative energy sources. Energy policy such as the Free Basic Electricity, besides being pro-poor, should also consider gender and the ethnic divide. Economic opportunities for low income female-headed households should also be promoted to reduce vulnerability to energy poverty.

In-work poverty and regional disparities. An analysis of the relationship between work intensity and the probability of being and feeling poor across Italian territories

Work intensity is considered one relevant factor in shaping the risk of experiencing in-work poverty, i.e., being a worker with a household income below the relative poverty line. However, little attention has been paid to how work intensity is associated with workers' subjective poverty (i.e., their feeling of being in-work poor) and to how this association varies across geographical areas. Therefore, the present work intends to fill this gap and investigate the relationship between work-intensity and the risk of experiencing in-work poverty, in both objective and subjective terms as well as differences among local contexts, i.e., regions and degrees of urbanisation. The analysis is based on 2018 cross-sectional data from the Italian module of the Eu-Silc survey. Empirical results show that work intensity is negatively associated with both objective and subjective in-work poverty, but the relation is stronger with the former. Furthermore, densely, intermediate and thinly populated areas show similar trends, whereas there is a persistent gap between, on the one hand, the North-Center of Italy and, on the other, the South, which has the highest risk of objective and subjective in-work poverty. However, the latter is also the area where the association between work intensity and in-work poverty is stronger.

Ending Child Poverty by Investing in Children and Families.

Ending Child Poverty by Investing in Children and Families.

Unequal From the Start? Poverty Across Immigrant Generations of Hispanic Children.

Recent cohorts of U.S. children increasingly consist of immigrants or the immediate descendants of immigrants, a demographic shift that has been implicated in high rates of child poverty. Analyzing data from the 2014-2018 Current Population Survey and using the U.S. Census Bureau's Supplemental Poverty Measure, we describe differences in child poverty rates across immigrant generations and assess how these disparities are rooted in generational differences in the prevalence and impact of key poverty risk factors. Our estimates show that poverty rates among Hispanic children are very high, particularly among first-generation children and second-generation children with two foreign-born parents. Low family employment is the most significant risk factor for poverty, but the prevalence of this risk varies little across immigrant generations. Differences in parental education account for the greatest share of observed intergenerational disparities in child poverty. Supplemental comparisons with third+-generation non-Hispanic White children underscore the disadvantages faced by all Hispanic children, highlighting the continued salience of race and ethnicity within the U.S. stratification system. Understanding the role of immigrant generation vis-à-vis other dimensions of inequality has significant policy implications given that America's population continues to grow more diverse along multiple social axes.

Why poverty is higher in Spain: a comparison with the Czech Republic

ABSTRACT This paper estimates the poverty level in Spain and the Czech Republic during 2013–2016 and analyzes the reason why poverty in Spain is higher. The contribution of this paper lies in decomposing horizontal poverty disparities between countries with similar development level. We find that in Spain inequality is higher, and the poor (especially the ultrapoor) are poorer than those in the Czech Republic, which offsets the poverty reduction effect of Spain’s higher mean income and renders its poverty level higher.

Illuminating and mitigating the evolving impacts of COVID-19 on ethnocultural communities: a participatory action mixed-methods study.

BACKGROUND:The COVID-19 pandemic has exacerbated disparities in poverty and illness for people in vulnerable circumstances in ethnocultural communities. We sought to understand the evolving impacts of COVID-19 on ethnocultural communities to inform intersectoral advocacy and community action.METHODS:The Illuminate Project used participatory action research, with cultural health brokers as peer researchers, from Sept. 21 to Dec. 31, 2020, in Edmonton, Alberta. Twenty-one peer researchers collected narratives from members of ethnocultural communities and self-interpreted them as they entered the narratives into the SenseMaker platform, a mixed-method data collection tool. The entire research team analyzed real-time, aggregate, quantitative and qualitative data to identify emerging thematic domains, then visualized these domains with social network analysis.RESULTS:Brokers serving diverse communities collected 773 narratives. Identified domains illuminate the evolving and entangled impacts of COVID-19 including the following: COVID-19 prevention and management; care of acute, chronic and serious illnesses other than COVID-19; maternal care; mental health and triggers of past trauma; financial insecurity; impact on children and youth and seniors; and legal concerns. We identified that community social capital and cultural brokering are key assets that facilitate access to formal health and social system supports.INTERPRETATION:The Illuminate Project has illustrated the entangled, systemic issues that result in poor health among vulnerable members of ethnocultural communities, and the exacerbating effects of COVID-19, which also increased barriers to mitigation. Cultural brokering and community social capital are key supports for people during the COVID-19 pandemic. These findings can inform policy to reduce harm and support community resiliency.

Effects of medicaid expansion on poverty disparities in health insurance coverage

BackgroundMore than 30 states have either expanded Medicaid or are actively considering expansion. The coverage gains from this policy are well documented, however, the impacts of its increasing coverage on poverty disparity are unclear at the national level.MethodAmerican Community Survey (2012–2018) was used to examine the effects of Medicaid expansion on poverty disparity in insurance coverage for nonelderly adults in the United States. Differences-in-differences-in-differences design was used to analyze trends in uninsured rates by poverty levels: (1) < 138 %, (2) 138–400 % and (3) > 400 % federal poverty level (FPL).ResultsCompared with uninsured rates in 2012, uninsured rates in 2018 decreased by 10.75 %, 6.42 %, and 1.11 % for < 138 %, 138–400 %, and > 400 % FPL, respectively. From 2012 to 2018, > 400 % FPL group continuously had the lowest uninsured rate and < 138 % FPL group had the highest uninsured rate. Compared with ≥ 138 % FPL groups, there was a 2.54 % reduction in uninsured risk after Medicaid expansion among < 138 % FPL group in Medicaid expansion states versus control states. After eliminating the impact of the ACA market exchange premium subsidy, 3.18 % decrease was estimated.ConclusionPoverty disparity in uninsured rates improved with Medicaid expansion. However, < 138 % FPL population are still at a higher risk for being uninsured.

Improving estimates of Medicaid's effect on poverty: Measures and counterfactuals.

To re-evaluate the effect of Medicaid on poverty using a poverty measure that accounts for health insurance needs and benefits and an evaluation approach that reflects disparities in access to alternative coverage. The Current Population Survey (CPS) for calendar year 2015. We estimate the effect of losing Medicaid on poverty, combining two previous approaches: (1) A propensity impact, which simulates a no-Medicaid counterfactual incorporating changes to health insurance and medical out-of-pocket spending, using the Supplemental Poverty Measure (SPM). This measure does not reflect a need for health care access nor how health benefits meet that need. (2) An accounting impact, which assumes that those losing Medicaid remain uninsured and does not incorporate any behavioral changes, using the health-inclusive poverty measure (HIPM). This measure includes a need for health insurance in the threshold and health insurance benefits in resources. Not applicable. Using the propensity-matched approach, we attributed a 2.5 percentage point reduction in health-inclusive poverty among those younger than age 65 to the Medicaid program, between the 1.0-point SPM propensity-match impact and the 3.9-point HIPM accounting impact. Medicaid's antipoverty impact and HIPM-SPM differences are greater among those who would become uninsured. HIPM propensity-matched estimates reveal much larger impacts of Medicaid on poverty disparities linked to race/ethnicity and single parenthood than SPM-based propensity estimates. Both the poverty measure and the method used to estimate the counterfactual make substantial, policy-relevant differences to estimates of Medicaid's impact on poverty. A poverty measure that fails to incorporate health insurance needs and benefits substantially underestimates Medicaid's effect. Failing to consider adjustments in insurance coverage and out-of-pocket spending substantially overestimates Medicaid's effect and underestimates its reduction of disparities.

Inter‐County Migration and the Spatial Concentration of Poverty: Comparing Metro and Nonmetro Patterns*

AbstractWe use panel data from the Panel Study of Income Dynamics (PSID) to document the changing volume and rate of intercounty migration among the poor. We evaluate whether the exchange of migrants between metro and nonmetro counties has exacerbated spatial disparities in poverty and whether some nonmetro counties have become “collecting grounds” for America's poor. The PSID highlights exceptionally high rates of intercounty migration among the nonmetro population, but especially among the poor. The results show that rural poor migrants circulate among nonmetro counties, often from one poor county to an even poorer one. This circulation of poor rural migrants has exacerbated the concentration of poverty in the most economically disadvantaged nonmetro counties. Concentrated poverty also is exacerbated by the migration of the “best and brightest” from nonmetro‐to‐metro counties, which also is associated with upward residential attainment (to counties with lower poverty rates). Significantly, metro‐to‐nonmetro migration is both selective of the poor and strongly associated with downward residential mobility to poorer nonmetro counties. Our findings are consistent with the hypothesis that poor nonmetro counties have become collecting grounds for America's poor.

Clinical characteristics and risk factors for death among hospitalised children and adolescents with COVID-19 in Brazil: an analysis of a nationwide database

BackgroundCOVID-19 is usually less severe and has lower case fatality in children than in adults. We aimed to characterise the clinical features of children and adolescents hospitalised with laboratory-confirmed SARS-CoV-2 infection and to evaluate the risk factors for COVID-19-related death in this population.MethodsWe did an analysis of all patients younger than 20 years who had quantitative RT-PCR-confirmed COVID-19 and were registered in the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe, a nationwide surveillance database of patients admitted to hospital with severe acute respiratory disease in Brazil), between Feb 16, 2020, and Jan 9, 2021. The primary outcome was time to recovery (discharge) or in-hospital death, evaluated by competing risks analysis using the cumulative incidence function.FindingsOf the 82 055 patients younger than 20 years reported to SIVEP-Gripe during the study period, 11 613 (14·2%) had available data showing laboratory-confirmed SARS-CoV-2 infection and were included in the sample. Among these patients, 886 (7·6%) died in hospital (at a median 6 days [IQR 3–15] after hospital admission), 10 041 (86·5%) patients were discharged from the hospital, 369 (3·2%) were in hospital at the time of analysis, and 317 (2·7%) were missing information on outcome. The estimated probability of death was 4·8% during the first 10 days after hospital admission, 6·7% during the first 20 days, and 8·1% at the end of follow-up. Probability of discharge was 54·1% during the first 10 days, 78·4% during the first 20 days, and 92·0% at the end of follow-up. Our competing risks multivariate survival analysis showed that risk of death was increased in infants younger than 2 years (hazard ratio 2·36 [95% CI 1·94–2·88]) or adolescents aged 12–19 years (2·23 [1·84–2·71]) relative to children aged 2–11 years; those of Indigenous ethnicity (3·36 [2·15–5·24]) relative to those of White ethnicity; those living in the Northeast region (2·06 [1·68–2·52]) or North region (1·55 [1·22–1·98]) relative to those in the Southeast region; and those with one (2·96 [2·52–3·47]), two (4·96 [3·80–6·48]), or three or more (7·28 [4·56–11·6]) pre-existing medical conditions relative to those with none.InterpretationDeath from COVID-19 was associated with age, Indigenous ethnicity, poor geopolitical region, and pre-existing medical conditions. Disparities in health care, poverty, and comorbidities can contribute to magnifying the burden of COVID-19 in more vulnerable and socioeconomically disadvantaged children and adolescents in Brazil.FundingNational Council for Scientific and Technological Development, Research Support Foundation of Minas Gerais.

Family and household sources of poverty for Black, Hispanic, and White newborns

AbstractObjectiveTo understand the importance of family and household structure, including cohabiting partners and nonkin adults, in explaining Black‐White versus Hispanic‐White newborn poverty disparities in the United States.BackgroundThe official poverty measure (OPM) has typically been used in scholarly studies of newborn and childhood poverty, but this measure excludes cohabitors' and nonkin household members' presence and income. Both are likely to have poverty‐preventing roles when a birth is nonmarital.MethodA household poverty measure inclusive of all household members is compared to the OPM, using data on households of White, Black, and Hispanic newborns from the 2005–2017 American Community Survey (N = 342,048). Regression decompositions are used to investigate the roles of mother's education and partner and other coresident adult presence and resources.ResultsLower maternal educational attainment explains most of the Hispanic‐White poverty disparity at birth. The prevalence and poverty‐alleviating effectiveness of cohabitation and living with other adults, however, is greater for Hispanic than for Black unmarried mothers, and is greater for foreign‐born than for US‐born Hispanics.ConclusionBlack mothers' lesser access to the resources of a partner or other adults in the year of the newborn's arrival is critical in producing a remarkably high Black‐White newborn poverty disparity. Official poverty obscures the greater benefits of cohabitation and living with other adults for newborns of Hispanic mothers overall, and of foreign‐born Hispanic mothers in particular.

Across the Rural-Urban Universe: Two Continuous Indices of Urbanization for U.S. Census Microdata.

Microdata from U.S. decennial censuses and the American Community Survey are a key resource for social science and policy analysis, enabling researchers to investigate relationships among all reported characteristics for individual respondents and their households. To protect privacy, the Census Bureau restricts the detail of geographic information in public use microdata, and this complicates how researchers can investigate and account for variations across levels of urbanization when analyzing microdata. One option is to focus on metropolitan status, which can be determined exactly for most microdata records and approximated for others, but a binary metro/nonmetro classification is still coarse and limited on its own, emphasizing one aspect of rural-urban variation and discounting others. To address these issues, we compute two continuous indices for public use microdata-average tract density and average metro/micro-area population-using population-weighted geometric means. We show how these indices correspond to two key dimensions of urbanization-concentration and size-and we demonstrate their utility through an examination of disparities in poverty throughout the rural-urban universe. Poverty rates vary across settlement types in nonlinear ways: rates are lowest in moderately dense parts of major metro areas, and rates are higher in both low- and high-density areas, as well as in smaller commuting systems. Using the two indices also reveals that correlations between poverty and demographic characteristics vary considerably across settlement types. Both indices are now available for recent census microdata via IPUMS USA (https://usa.ipums.org).

Racial disparities in energy poverty in the United States

This study uses data on residential energy consumption from large nation-wide surveys in 1990, 1997, 2005, and 2015 to assess the racial inequality in residential energy consumption. The results indicate that the overall inequality in energy consumption/cost varies little over the past 25 years while showing widespread energy poverty in the U.S. In terms of racial differences, African-American households are more vulnerable than white and Asian households, and about one-third of them lived in energy poverty by 2015. However, the share of households in energy poverty witnessed the greatest growth among white households during 1990–2015. In addition, the energy burdens of different racial groups vary greatly with respect to energy types, end-use demands, and regions, given the differences in climate and households’ socio-economic characteristics. The study suggests that besides low-income African-American households, urgent attention should be given to the increasing number of white households in energy poverty.