Poverty Estimates Research Articles

Accounting for survey design in Bayesian disaggregation of survey-based areal estimates of proportions: An application to the American Community Survey

Understanding the effects of social determinants of health on health outcomes requires data on characteristics of the neighborhoods in which subjects live. However, estimates of these characteristics are often aggregated over space and time in a fashion that diminishes their utility. Take, for example, estimates from the American Community Survey (ACS), a multiyear nationwide survey administered by the U.S. Census Bureau: estimates for small municipal areas are aggregated over 5-year periods, whereas 1-year estimates are only available for municipal areas with populations >65,000. Researchers may wish to use ACS estimates in studies of population health to characterize neighborhood-level exposures. However, 5-year estimates may not properly characterize temporal changes or align temporally with other data in the study, while the coarse spatial resolution of the 1-year estimates diminishes their utility in characterizing neighborhood exposure. To circumvent this issue, in this paper we propose a modeling framework to disaggregate estimates of proportions derived from sampling surveys, which explicitly accounts for the survey design effect. We illustrate the utility of our model by applying it to the ACS data, generating estimates of poverty for the state of Michigan at fine spatiotemporal resolution.

Multi-source satellite imagery and point of interest data for poverty mapping in East Java, Indonesia: Machine learning and deep learning approaches

This study proposes a novel approach to provide a more granular poverty map in terms of coverage (up to a grid level with the spatial resolution of 1.5 km) with less cost and time to update to support better poverty monitoring. Two poverty estimation model development scenarios using machine learning and deep learning were evaluated in this study. First, the model is constructed using multisource satellite imagery and geospatial point of interest location of economic infrastructures using zonal statistics feature extraction, which is the proposed approach in this study. The specific multisource satellite imagery to be used as poverty indicators are night-time light intensity (NTL) as an approach to economic activity, normalized difference vegetation index (NDVI) as an approach to detect rural areas based on vegetation, built-up index (BUI) as an approach to detect urban areas based on building distribution, normalized difference water index (NDWI) to detect land cover, land surface temperature (LST) as an approach to detecting urban areas based on surface temperature, carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) as an approach to detecting economic activities based on pollution, as well as point of interest (POI) density, and POI distance which show the accessibility of an area. Second, the model is constructed from daytime multiband and nighttime light intensity satellite imagery using the deep learning architecture of Resnet-34 transfer learning feature extraction. In each scenario, we compared the performance of different machine learning (support vector regression/SVR, decision tree regression/DTR, and random forest regression/RFR) algorithms along with deep learning (multiple layer perceptron/MLP and one dimensional-convolutional neural network/CNN-1D) algorithms. Based on the results of the model development evaluation, the CNN-1D model was chosen as the best model in the first scenario, and the Resnet-34 + MLP model was chosen as the best model in the second scenario. The two best models from the first and second scenarios are then used to predict poverty at the grid level with a spatial resolution of 1.5 km. Based on official poverty data, the estimated poverty mapping built using the CNN-1D model in the first scenario was chosen as the best map with the value of root mean squared error (RMSE) 1.95 and adjusted R2 0.84 at the district level. The results of visual identification revealed that high estimates of poverty are typically found in sparsely inhabited areas surrounded by unoccupied land, which is usually an agricultural area. Otherwise, low poverty estimates, on the other hand, are more likely to be found in highly populated regions with convenient access. Our finding is aligned with the official report of Statistics Indonesia, which claims that poverty in rural regions is higher than in urban areas.

Lower Inequality and Lower Development: A Contradiction Sensitivity Analysis of Jammu and Kashmir

Abstract The standard method of poverty estimation uses unadjusted per capita income or expenditure to calculate population below the poverty line. However, recent empirical advancements have validated this method to be essentially flawed in nature. It does not take into consideration nor allows for household composition and economies of scale. Empirical investigations have confirmed the facts that the measures of poverty and inequality are sensitive to various choices of equivalence scales. Therefore, standard measures provide mostly overestimated poverty and inequality estimates. Further, poverty measurement across groups or overtime is sensitive to different poverty lines and measures. Any alteration in these can reverse the ranking. The current research attempts to test adult equivalence and scale economies in Jammu and Kashmir region to validate whether poverty estimates are sensitive to these scales or not. It also employs stochastic dominance technique to check whether poverty reduction is robust through time over a wide range of poverty lines and measures. The paper does so by employing three waves of monthly consumption expenditure rounds conducted by National Sample Survey Organization. For sensitivity analysis, the paper estimates FGT, Gini and Atkinson indices. Despite being industrially backward and politically fragile, J&K has shown better economic indicators than most other Indian states. The findings of the current study validate lower poverty in the region and at the same time discover a growth in inequality over time. While a mixed result is derived for adult equivalence, the economies of scale highlight the fact that standard measures are overstated and welfare rank reversal ensues when household size and gender of household is tested for. Further, stochastic dominance results show that poverty reduction is only robust during 61st and 66th round, and not during 66th and 68th rounds.

Pemodelan Jumlah Penduduk Miskin di Jawa Timur dengan Generalized Linier Model

Poverty as a development issue is an important thing to pay attention. Therefore, it is necessary to analyze to prevent an increase in the number of poor people in an area, one of which is by considering the things that affect poverty estimates and knowing the model of the number of poor people that can later be used as predictions. East Java is the region in Indonesia with the highest population in Indonesia in 2019. One way to find out the influencing factors and at the same time to make a model of the number of poor people in East Java is by modeling with the Generalized Linear Model (GLM) method. General Linear Model (GLM). GLMs aim to determine the causal relationship and the influence of the independent variable on the dependent variable. The GLMs model in this study uses the Gaussian family because the research response variable (Y) is an integer. The results show that of the seven estimated models, only one model that meets the significance is the model with the dependent variable population density (X3). So it can be concluded that between Area (X1), Open Unemployment Rate (X2), and Population Density (X3), population density has a significant effect on the number of poor people with a 95% confidence level.

Small area estimation of non-monetary poverty with geospatial data

This paper evaluates the benefits of combining household surveys with satellite and other geospatial data to generate small area estimates of non-monetary poverty. Using data from Tanzania and Sri Lanka and applying a household-level empirical best (EB) predictor mixed model, we find that combining survey data with geospatial data significantly improves both the precision and accuracy of our non-monetary poverty estimates. While the EB predictor model moderately underestimates standard errors of those point estimates, coverage rates are similar to standard survey-based standard errors that assume independent outcomes across clusters.

Poverty mapping in Latin America: ECLAC experiences on small area estimation

Poverty mapping is a valuable tool for governments and international organizations to identify the geographical areas and population groups that are most deprived or vulnerable. This approach can lead to designing and monitoring development policies more effectively. This article presents the recent experience of the Statistics Division of the UN Economic Commission for Latin America and the Caribbean (ECLAC) in using Small Area Estimation (SAE) methods to combine information from household surveys, censuses and satellite imagery to deliver poverty estimates at the provincial and municipal levels, which could not be attained using the household surveys alone.

Connecting Water Access with Multidimensional Poverty: The Case of Tupiza River Basin in Bolivia

In developing countries, where economic expansion depends on extractive activities such as agriculture and mining, water quantity and quality considerations need to be examined in tandem with GDP growth and poverty reduction efforts. Poorest households in the Tupiza watershed in Bolivia are located in rural areas where water access for irrigation and safe drinking water is becoming increasingly scarce. Small-scale unregulated mining offers an alternative for revenue making in rural households, although wastewater from industry threatens water quality and new technologies to reduce water pollution are not implemented in this region yet. This study analyses water access and poverty linkages using the Multidimensional Poverty Analysis (MDPA) framework from the Swedish International Development Cooperation Agency (Sida) to guide a survey and to develop indicators using the Shapley-Owen decomposition method and multivariate regressions. A set of household-level policies were included to predict the influence of these policies on poverty reduction estimates. Results have shown that remote communities in rural areas have the lowest value of multidimensional poverty and for some of these communities mining activities represent an alternative that could be considered if their water access conditions worsen over time. While mining can bring better monetary benefits, it can cause the degradation of ecological flows from the produced wastewater. Under the current technologies and processes, it can pose negative impacts on water quality and threatens the public health of these communities.

Improved maize adoption and impacts on farm household welfare: Evidence from rural Ethiopia

AbstractThe use of improved crop varieties can increase agricultural productivity and enhance the welfare of farmers. This study examined whether the adoption of improved maize varieties (IMV) is associated with the increased welfare of farmers in rural Ethiopia. A panel data set with 1886 observations collected in three waves from 2009/10 to 2014/15 were used for the analysis. The adoption decision was modelled using a double‐hurdle model, and the welfare effect of IMV adoption was estimated using a fixed‐effects instrumental variable approach. Our findings reveal that IMV affects the welfare of farmers. Specifically, we found that IMV adoption increases households' income, asset ownership and maize consumption while also reducing income poverty. The poverty estimates indicate that a 10% increase in the area allocated to IMV was associated with a 4.79% reduction in the probability of being below the $1.90 poverty line. However, the poverty‐reducing effect of IMV adoption was heterogeneous across households, with the most pronounced effect experienced by households with extensive landholdings. Our findings suggest that facilitating access to IMV and land under cultivation can effectively improve farmers' welfare and reduce poverty in rural Ethiopia.

Health and Poverty Linkages for Population just above the Poverty Line: A Study done in Slums of Jaipur, India

Introduction: Underdeveloped and developing countries are unable to provide essential healthcare to all of their inhabitants, and those who remain uninsured are at a huge risk of financial hardship. It’s never easy to divide limited resources. Method: This study attempts to examine that, If urban poors, a vulnerable section of the society is protected against catastrophic health expenditure. Is there any deepening of poverty among urban poor of the city of Jaipur due to catastrophic health expenditure? 426 households in Jaipur’s urban slums were surveyed. Results: The incidences of Catastrophic Health Expenditure were 8.1% among urban slum households. The mean positive overshoot was 33%. Poverty increased by 1% at National Poverty Line and by 2.6% at International Poverty Line estimates. The increase in the normalised mean positive poverty gap from 29.8% to 45.3% suggests that the existing poor are becoming more impoverished. There was a significant association between increasing health spending and household cuts in food and apparel spending, at p = 0.0001 and p = 0.05, respectively. Conclusion: The results show a huge disparity between poverty estimates based on national poverty standards (2.8%) and international poverty standards (37.1%) indicating the necessity for developing sensitive poverty criteria. It is also vital to make an evidence-based decision on whether to employ assurance, insurance, or a combination of the two healthcare delivery systems. The assurance approach might expand accessibility while also lowering healthcare expenditures for the entire community. Rather than creating two distinct insurance or assurance systems, the government should pool its resources and efforts into one.

Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation

The increasing growth of forced displacement worldwide has brought more attention to measuring poverty among refugee populations. However, refugee data remain scarce, particularly regarding income or consumption. We offer a first attempt to measure poverty among refugees using cross-survey imputation and administrative and survey data collected by the United Nations High Commissioner for Refugees (UNHCR). Employing a small number of predictors currently available in the UNHCR registration system, the proposed methodology offers out-of-sample predicted poverty rates that are not statistically different from actual poverty rates. These estimates are robust to different poverty lines, perform well according to targeting indicators, and are more accurate than those based on asset indexes or proxy means tests. They can also be obtained with relatively small samples. We additionally show that it is feasible to provide poverty estimates for one geographical region based on existing data from another similar region.

Estimation of poverty and inequality in small areas: review and discussion

Estimation of poverty and inequality in small areas: review and discussion

Estimating Monthly Poverty Rates in the United States

AbstractOfficial poverty estimates for the United States are presented annually, based on a family unit's annual resources, and reported with a considerable lag. This study introduces a framework to produce monthly estimates of the Supplemental Poverty Measure and official poverty measure, based on a family unit's monthly income, and with a two‐week lag. We argue that a shorter accounting period and more timely estimates of poverty better account for intra‐year income volatility and better inform the public of current economic conditions. Our framework uses two versions of the Current Population Survey to estimate monthly poverty while accounting for changes in policy, demographic composition, and labor market characteristics. Validation tests demonstrate that our monthly poverty estimates closely align with observed trends in the Survey of Income & Program Participation from 2004 to 2016 and trends in hardship during the COVID‐19 pandemic. We apply the framework to measure trends in monthly poverty from January 1994 through September 2021. Monthly poverty rates generally declined in the 1990s, increased throughout the 2000s, and declined after the Great Recession through the onset of the COVID‐19 pandemic. Within‐year variation in monthly poverty rates, however, has generally increased. Among families with children, within‐year variation in monthly poverty rates is comparable to between‐year variation, largely due to the average family with children receiving 37 percent of its annual income transfers in a single month through one‐time tax credit payments. Moving forward, researchers can apply our framework to produce monthly poverty rates whenever more timely estimates are desired.

Iterative Kernel Density Estimation Applied to Grouped Data: Estimating Poverty and Inequality Indicators from the German Microcensus

Abstract The estimation of poverty and inequality indicators based on survey data is trivial as long as the variable of interest (e.g., income or consumption) is measured on a metric scale. However, estimation is not directly possible, using standard formulas, when the income variable is grouped due to confidentiality constraints or in order to decrease item nonresponse. We propose an iterative kernel density algorithm that generates metric pseudo samples from the grouped variable for the estimation of indicators. The corresponding standard errors are estimated by a non-parametric bootstrap that accounts for the additional uncertainty due to the grouping. The algorithm enables the use of survey weights and household equivalence scales. The proposed method is applied to the German Microcensus for estimating the regional distribution of poverty and inequality in Germany.

Bayesian models for spatial count data with informative finite populations with application to the American community survey

The American Community Survey (ACS) is an ongoing program conducted by the US Census Bureau that publishes estimates of important demographic statistics over pre-specified administrative areas. ACS provides spatially referenced count-valued outcomes that are paired with finite populations. For example, the number of people below the poverty line and the total population for each county are estimated by ACS. One common assumption is that the spatially referenced count-valued outcome given the finite population is binomial distributed. This conditionally specified (CS) model does not define the joint relationship between the count-valued outcome and the finite population. Thus, we consider a joint model for the count-valued outcome and the finite population. When cross-dependence in our joint model can be leveraged to ‘improve spatial prediction’ we say that the finite population is ‘informative.’ We model the count given the finite population as binomial and the finite population as negative binomial and use multivariate logit-beta prior distributions. This leads to closed-form expressions of the full-conditional distributions for an efficient Gibbs sampler. We illustrate our model through simulations and our motivating application of ACS poverty estimates. These empirical analyses show the benefits of using our proposed model over the more traditional CS binomial model.

Disaggregation of poverty indicators by small area methods for assessing the targeting of the “Reddito di Cittadinanza” national policy in Italy

In Italy, a crucial anti-poverty policy “Reddito di Cittadinanza” (RdC), a measure of guaranteed minimum income, was introduced in April 2019. We aim to evaluate the targeting of the RdC policy at the local level, as aggregated analyses could mask important misalignments between the share of beneficiaries of the RdC and the share of poor households. To measure the poverty share in the local areas of interest, two main indicators to capture and monitor poverty are used in Europe: the At-Risk-of-Poverty Rate based on the EU Statistics on Income and Living Conditions survey and the Absolute Poverty Index based on consumption data collected through the Household and Budget Survey. To obtain reliable estimates of these indicators at the local level, it is necessary to introduce small area estimation models that allow the use of data from different sources. We apply a bivariate Fay and Herriot model to provide reliable estimates of absolute and relative poverty for the assessment of RdC policy targeting in the 59 areas represented by the region by degree of urbanisation level in Italy. The degree of urbanisation is indeed a key geographical variable in the study of the poverty phenomenon. Our results suggest that the RdC policy implemented at the national level shows heterogeneous targeting performance at the local level, excluding large shares of poor households from the program. These findings yield a set of policy implications for improving the targeting of the measure.

A Correlation of Socio-economic Determinants and Food Security Status in Pakistan

This study gives details of the interrelationship among important socioeconomic variables and food security status in Pakistan. The major socio-economic explanatory variables; education, livestock, poverty, receipts of foreign remittances, and female house-head status were analyzed against food security. The data was derived from Pakistan Social and Living Standard Measurement survey dataset 2019-20. A binary logistic model has been applied for the estimation of poverty and food security indices. The study results showed that education, livestock, foreign remittances, and female house-head have a positive significant impact on food security while poverty has a negative and significant impact on food security. The study recommendations are that government must focus to increase agriculture growth, increased dependence on livestock, foreign remittances, and education.

The level of pension provision of the population is a guarantee of social security of the person

The level of pension provision of the population is a guarantee of social security of the person

Spatial and Temporal Trends in Multidimensional Poverty in the United States over the Last Decade.

This paper undertakes a comprehensive analysis of multidimensional poverty in the United States over the last decade. It provides estimates of multidimensional poverty over more than a decade, from 2008 to 2019, which covers the Great Recession and the recovery following the recession when major policy changes such as the Affordable Care Act were implemented. For the first time, spatial trends in estimates of multidimensional poverty are also provided. We measure annual poverty levels in 4 regions, 50 states and examine the relation between multidimensional poverty and neighborhood characteristics. We find that on average, 13 percent of the United States population was multidimensional poor. Poverty rates were high in the South and the West and among young adults, immigrants and Hispanics. Alternative indices of multidimensional poverty show consistent trends; multidimensional poverty in the United States rose between 2008 and 2010 and then gradually declined. However, more than a quarter of individuals with incomes above the poverty threshold remained multidimensional poor. This underscores the fact that income does not always capture deprivation experienced by individuals. Policies geared towards affordable housing, health insurance and higher education will help reduce multidimensional poverty in the United States.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11205-022-02902-z.

Air pollution is linked to higher cancer rates among black or impoverished communities in Louisiana

Despite longstanding concerns about environmental injustice in Louisiana’s industrialized communities, including the area known as Cancer Alley, there is a lack of environmental health research in this state. This research gap has direct consequences for residents of industrialized neighborhoods because state regulators have cited a lack of evidence for adverse health outcomes when making industrial permitting decisions. We investigated how cancer incidence relates to cancer risk from toxic air pollution, race, poverty, and occupation across Louisiana census tracts, while controlling for parish-level smoking and obesity rates, using linear regression and Akaike information criterion model selection. We used the most recent cancer data from the Louisiana Tumor Registry (2008–2017), estimates of race, poverty, and occupation from the US Census Bureau’s American Community Survey (2011–2015), and estimated cancer risk due to point sources from the US Environmental Protection Agency’s 2005 National Air Toxics Assessment (accounting for cancer latency). Because race and poverty were strongly correlated (r = 0.69, P < 0.0001), we included them in separate, analogous models. Results indicated that higher estimated cancer risk from air toxics was associated with higher cancer incidence through an interaction with poverty or race. Further analysis revealed that the tracts with the highest (i.e. top quartile) proportions of impoverished residents (or Black residents) were driving the association between toxic air pollution and cancer incidence. These findings may be explained by well-established disparities that result in greater exposure/susceptibility to air toxics in Black or impoverished neighborhoods. Regardless, our analysis provides evidence of a statewide link between cancer rates and carcinogenic air pollution in marginalized communities and suggests that toxic air pollution is a contributing factor to Louisiana’s cancer burden. These findings are consistent with the firsthand knowledge of Louisiana residents from predominantly Black, impoverished, and industrialized neighborhoods who have long maintained that their communities are overburdened with cancer.

Out-of-Pocket Health Care Expenditure and Poverty Impact in a Fragile Indian State of Assam

Out-of-Pocket (OOP) expenditure on health care is one of the debilitating factors in pushing households into poverty. Households, especially the lower income groups and which do not have security measures like medical insurance, are the worst sufferers when faced with health shocks. Conventional methods of poverty estimation do not take into account health care consumption expenditure, which might understate the poverty headcount. In this study, poverty headcount and impoverishing effects of OOP health care expenditure have been analysed. The data have been collected from Chirang district of Indian state of Assam. The state is a fragile state due to ethnic violence, militancy and natural calamities like flood that the region has been experiencing for decades. The recall period for the inpatient care is 365 days, and six months for outpatient care. The poverty headcount is the difference between post Hp and pre Hp. The pre-payment (i.e. pre-OOP) ‘poverty headcount’ is calculated by comparing household’s consumption expenditure gross of payments for health care with the poverty line defined by the planning commission of India (2001). The post-OOP payment ‘poverty headcount’ is computed by netting out health care payments from a household’s consumption expenditure and then comparing with the poverty line. The intensity of poverty has been estimated with the help of the methods introduced by Wagstaff and Doorslaer (2003). The higher income households make higher OOP expenditure than the lower income households. The burden of OOP is higher on the lower income households because the proportion of OOP expenditure to total income of households is higher amongst the poor households compared to higher income households. OOP expenditure pushed households into poverty. This is more serious with the inpatient care compared to the OOP expenditure on outpatient care. The poverty gap or the intensity of poverty of the overall OOP expenditure is Rs. 279.28, which varies between outpatient and inpatient OOP expenditure.