Poverty Data Research Articles

Do surprisingly low child maltreatment rates in rural southern counties reflect lower rates of substantiation?

This study aims to help explain surprisingly low official child maltreatment rates in rural southern U.S. counties with many child maltreatment risk factors. It investigates whether comparatively lower child maltreatment rates in rural, majority African-American counties are due to lower rates of substantiation. County-level data from the National Child Abuse and Neglect Data System (NCANDS) were linked with demographic and poverty data from the U.S. Census. Fractional response probit regression models assessed whether substantiation rates were associated with county racial composition, rurality, and poverty rate. Substantiation rates in rural, majority African-American counties were similar to those of other counties in the region, even when accounting for the county poverty rate. The findings clarify that lower maltreatment rates in rural, majority African-American counties in the South are a consequence of lower maltreatment report rates, and not also a consequence of substantiation decisions. The discussion addresses some implications of low levels of formal child welfare intervention in high risk, low resource counties with histories of marginalization.

Analisa Clustering K-Means Pada Data Informasi Kemiskinan Di Jawa Barat Tahun 2018

Abstract— Poverty is a condition of life that is understaffed by a person or household so that it is unable to meet the minimum or proper needs for his or her life. The poverty Data in each region will differ. It is influenced by many of its supporting indicators. By determining and measuring the indicators of poverty, it will facilitate and recognize the poverty level of the region. Grouping characteristics of a region based on poverty indicators, so that the government can precisely and quickly take policies to mitigate poverty in a region. The method used in this study uses the K-Means Clustering method. The Clustering method is selected because this method has the ability to classify large amounts of data with faster process times efficiently. The object in this study used data published by the BPS (Badan Pusat Statistik) on poverty Data and information in the Regency/city in 2018. Based on the results of this study, the results of the characteristic mapping of each group formed based on the highest and lowest value of poverty indicator of West Java province year 2018. With the characteristics found in each region, it will certainly be a solid foundation for government organizers to provide the right and quick policy/approach to overcome the poverty that is found in the region.

ANALISIS FAKTOR-FAKTOR KEMISKINAN KABUPATEN DI SUMATERA

Poverty is one Fundamental issues serious concern which become from the government. An important aspect of supporting poverty tackling strategies is the provision of accurate and targeted poverty data. BPS presents data set and poverty information (data mining) and indicators related to poverty issues in level regency/city and province. The island of Sumatra is the region with the densest population after Java with the distribution of poverty of 21.6%. The purpose of this research was to analyze the poverty factors of regencies/cities in Sumatera. The research consist of 6.468 cell data covering 154 regencies/cities in Sumatera and 42 poverty indicators which are processed with factor analysis. The research results showed that the factors of poverty of the Regency and City in Sumatera are: 1) economic factors, employment and housing facilities, 2) under-five health factors, 3) factors of labor and clean water facilities, 4) social factors and assistance of raskin, 5 ) Maternal health factors, education and government facilities.There fore, the government shoul dpayattentito the ways of poverty alleviation by looking at various factors that exist in each region.

THE EFFECT OF DISTRIBUTION FUNDS OF ZAKAT INFAQ SHADAQAH, SOCIAL ASSISTANCE EXPENDITURE, AND SUBSIDY EXPENDITURE ON POVERTY IN INDONESIA

This study aims to analyze the effect of the distribution funds of Zakat Infaq Shadaqah (ZIS), social assistance expenditure and subsidy expenditure on poverty in Indonesia 2013-2017 period. The sample from this study consisted of ZIS data obtained at the National Alms Agency (known as BAZNAS). Then social assistance expenditure and subsidy expenditure and the latest poverty data obtained at the Central Statistics Agency (BPS). In this research, the method used multiple linear regression analysis by testing classical assumptions. The results of this study are according to the results of the F test that the three independent variables (ZIS, social assistance expenditure and subsidy expenditure) have a simultaneous effect on the dependent variable (poverty) by having a significance value of 0,000, which means less than 0.05.Furthermore, it is supported by the results of the T-test that the three independent variables partially have a significant effect on the dependent variable, with a significance value in the ZIS variable was 0,000. Then, social assistance expenditure was 0.025. The last, subsidy expenditure was 0,000. It means lower than 0.05

THE EFFECT OF DISTRIBUTION FUNDS OF ZAKAT INFAQ SHADAQAH, SOCIAL ASSISTANCE EXPENDITURE, AND SUBSIDY EXPENDITURE ON POVERTY IN INDONESIA

This study aims to analyze the effect of the distribution funds of Zakat Infaq Shadaqah (ZIS), social assistance expenditure and subsidy expenditure on poverty in Indonesia 2013-2017 period. The sample from this study consisted of ZIS data obtained at the National Alms Agency (known as BAZNAS). Then social assistance expenditure and subsidy expenditure and the latest poverty data obtained at the Central Statistics Agency (BPS). In this research, the method used multiple linear regression analysis by testing classical assumptions. The results of this study are according to the results of the F test that the three independent variables (ZIS, social assistance expenditure and subsidy expenditure) have a simultaneous effect on the dependent variable (poverty) by having a significance value of 0,000, which means less than 0.05.Furthermore, it is supported by the results of the T-test that the three independent variables partially have a significant effect on the dependent variable, with a significance value in the ZIS variable was 0,000. Then, social assistance expenditure was 0.025. The last, subsidy expenditure was 0,000. It means lower than 0.05

Discriminant AnalysisFor Cluster Validation In A Case Study of District Grouping In Jember Regency Based On Poverty

Cluster validation is a procedure to evaluate the results of cluster analysis quantitively and objectively on a data. The validation process is very important to get the results of a good and appropriate grouping. In the validation process, the author uses internal validation, stability, and discriminant analysis test. This study aims to obtain validation results from the hierarchy and kmeans method. This data grouping uses “iris” simulation data, which results from the grouping method used can be applied to the original data to see which vaidation method is used for all data and produce an optimal grouping. The result of the study show that in the “iris” data, a single linkage link is an appropriate grouping method because the result of the grouping are optimal for all validations and classification of group members whose groups are significant. In District poverty data in Jember Regency with a single linkage link optimal grouping was obtained and complete linkage links were also used as a method that resulted in optimal groupig for all validation. Cluster validation discriminant analysis test is appropriate for various types of data in general annd shows that single linkage methods are better than other methods for grouping and validation methods for “iris” data and District data in Jember Regency based on variabels of poverty status. Keywords: Cluster Analysis, Diskriminant Analysis, Multivariate Analysis, Validation Cluster.

Observations of Participation in Development Initiatives on Enterprise Income and Asset in Peninsular Malaysia

This study examined the impact of access to working capital and microenterprise development training programmes on microenterprise income and assets among participants from various development initiatives in the eKasih (National Poverty Data Bank) in Peninsular Malaysia. Adopting a cross-sectional design, we collected data from randomly selected 300 micro-entrepreneurs from the list of development organizations available in the eKasih (National Poverty Data Bank), located in four states in Peninsular Malaysia. Quantitative data were collected through structured interviews from October to November 2017. Findings revealed that the ‘length of participation in development programs’ increased microenterprise income and assets among low-income participants in Peninsular Malaysia. The total amount of economic loan received was also found to increase microenterprise assets. However, the effect on microenterprise income was not clear. In addition, development training programmes were found to have no substantial effect on both microenterprise income and assets. This article ascertained a mixed result of participation in development programmes on microenterprise income and assets among low-income households in Peninsular Malaysia. Development policymakers and organizations should review the effectiveness of the programmes and redesign their products and services to achieve their objectives.

Comparison of hierarchical clustering methods (case study: data on poverty influence in North Sulawesi)

Grouping of Large data has been carried out in various fields. One method for grouping is cluster analysis where this method consists of hierarchy and non-hierarchy method. The aim of this study was to compare the use of cluster analysis on aspects of the causes of poverty data. The method used is agglomerative hierarchical clustering, that is, the average linkage, centroid methods and ward methods. The results obtained are compared with the RMSSTD value and the smallest value is the ward method with a value of 2.0937. So the ward method is good for this case.

The nonparametric regression model using Fourier series approximation and penalized least squares (PLS) (case on data proverty in East Java)

Poverty is a problem all countries in the world, particularly developing countries, should encounter. The poverty level of a country can be a benchmark to see its social and economic conditions. Java is an island with the highest poor population density in Indonesia with the level of population density of 14.83 million people. East Java province had percentage of poverty of 11.85% in 2016 and it was included in hard core poverty (above 10%), indicating that East Java was on the highest level of poverty. Therefore, regression model is required to find out factors significantly influencing poverty. The regression model used is nonparametric regression model since the scatter plot does not form certain plot and even tend to have repeated patterns of data on certain time interval. For that reason, Fourier series approximation is needed in nonparametric regression. On such approximation, the model parameter can be estimated based on penalized least squares (PLS) and minimum generalized cross validation (GCV). The study investigated and applied the regression model on data of poverty in East Java. The study resulted in parameter estimation of 18 parameters and values of λ of nonparametric regression of Fourier series ranging from 0.05 to 0.1 on M=1 and GCV value of 5.83028 with coefficient of determination (R2) of 83.28.

Poverty Mapping of the Coastal Areas Using Spatial Empirical Best Linear Unbiased Prediction Method

This research aims to map out the poverty data in the Coastal Areas of Mukomuko District. The estimation method used is Spatial Empirical Best Linear Unbiased Prediction (EBLUP). So, the poverty estimator in the coastal area of Mukomuko District is obtained. The evaluation of parameter estimator is calculated by the value of MSE (Mean Square Error) using Bootstrap resampling method. Based on the result of the study is seen that the MSE value of Spatial EBLUP estimators is smaller than the MSE value of the direct estimator (Y) in each village. Finally, the estimation is presented as poverty map. The estimation of spatial correlation is positive and strong (0.52).

Income, educational attainment, and green infrastructure in Houston, Texas, USA

Previous studies suggest that when the residents of an area have lower educational attainment or income levels, residents are less likely to be exposed to the concepts of alternative stormwater management such as green infrastructures or have financial resources to install them. Therefore, fewer green infrastructures will be located in areas with higher poverty levels and lower educational attainment levels. However, previous studies failed to address that some socioeconomically disadvantaged areas are highly prone to hydrological disasters, such as hurricanes and flooding. Geographical specificity may illustrate very different research results than what scholars have previously studied. This study fills the gap in the literature by focusing on areas that desperately need these structures because of its low elevation and adjacent location to hurricane prone coastal areas by investigating educational attainment, poverty, and ethnicity data taken from the United States Census Bureau and combining it with Google Earth Street View data in order to identify green infrastructures. Within Houston, Texas the socioeconomically disadvantaged have more green infrastructures than those of higher educational and income levels do. We argue that the pattern of green infrastructure development in Houston is not so much due to the residents’ interest and concern for the environment, but it is more likely the result of past flood events. Results from this research indicate that ethnicity may correlate more closely to the location of green infrastructures within the Houston area than previously indicated. We will discuss our study results with the historical perspective as well as the federal and local government’s effort and potential policy implications.

PENGARUH TINGKAT PENGANGGURAN, INVESTASI, PENDIDIKAN TERHADAP KEMISKINAN DI KABUPATEN/KOTA PROVINSI BANTEN

Poverty is one of the fundamental problems that is the center of attention of governments in any country not least in developing countries, namely Indonesia. One important aspect in supporting poverty reduction strategies is the availability of accurate and targeted poverty data. Poverty is caused by various factors, namely slow economic growth, investment levels that are still below standard, low levels of education and high levels of unemployment. This study aims to determine the effect of unemployment, investment and education on poverty in Banten Province. The method used is panel data regression analysis. The results show that unemployment has a positive and significant effect with a coefficient value of 0.173297 and a probability value of 0.0005, investment has a positive and insignificant effect with a coefficient of 0.005261 and a probability value of 0.4127, while education has a negative and significant effect with a coefficient of -0.290174 with a probability value of 0.0147 on poverty in Regency / City of Banten Province. And simultaneously unemployment, investment and education variables affect poverty in the Regency / City of Banten Province.

Higher pricing of fresh produce is more likely in SNAP-Ed eligible neighborhoods when adjacent non-program eligible neighborhoods are mixed income.

This analysis describes the socioeconomic attributes of neighborhoods adjacent to low-income neighborhoods with ≥50% of households that are Supplemental Nutrition Assistance Program Education (SNAP-Ed) eligible. It compares the pricing, availability, and quality of fresh produce between these neighborhoods in Los Angeles County. The Los Angeles County Department of Public Health utilized 2013–2014 community-level data from the Communities of Excellence in Nutrition, Physical Activity and Obesity Prevention (CX3) Project to examine the geographic patterns of fresh produce purchases and accessibility in SNAP-Ed eligible census tracts. Community indicators collected by CX3 included information on pricing, availability, and quality of fruits and vegetables from grocery stores (n = 108) in these eligible neighborhoods (n = 21). Correlation statistics were generated to explore the effects of adjacent neighborhoods' socioeconomic status on fruit and vegetable pricing, availability, and quality in the selected neighborhoods (“CX3 neighborhoods”). Poverty data were obtained from the United States Census' American Community Survey. Residents of CX3 neighborhoods that were surrounded by mixed income neighborhoods paid 43% more for fresh produce than CX3 neighborhoods surrounded by other similarly low-income neighborhoods (median produce price, $1.50 versus $1.05). Study results suggest that while quality of produce remains an issue, it is the higher pricing of fresh produce in CX3 neighborhoods – i.e., in the presence of other surrounding mixed income neighborhoods (those with relatively higher income) – that appeared to potentiate food access barriers. Future SNAP-Ed efforts should take this pricing pattern under consideration when designing, planning, and/or implementing nutrition-related programs in these neighborhoods.

Cáncer y la pobreza están fuertemente correlacionados en el Perú

Esta es una investigación ecológica retrospectiva de múltiples grupos, cuyo objetivo es encontrar la correlación entre la vulnerabilidad al cáncer y la pobreza en el Perú durante el 2013. En la metodología, se utilizó el muestreo de conveniencia no aleatoria, incluidos todos los departamentos del Perú. Las principales fuentes utilizadas para el análisis fueron: la situación del Cáncer en Perú, 2013, del Ministerio de Salud, y los datos de pobreza del país, del Instituto Nacional de Estadística e Informática para el mismo año (2013); ya que estas son las últimas en publicaciones de datos nacionales sobre el cáncer y la situación de pobreza en Perú, respectivamente. Para la validación de los rangos para prueba de hipótesis estadística se utilizó la correlación de Spearman. Los resultados muestran una fuerte correlación estadística de 0,761 directa y significativa entre la vulnerabilidad al cáncer y la pobreza en Perú, durante 2013. Asimismo, la tasa y el alcance de una mayor vulnerabilidad al cáncer en las regiones del Perú, y jerárquica identificada como Análisis del estado del cáncer en Perú - 2013 , por Ministerio de Salud es: Huánuco, Ayacucho, Huancavelica, Cajamarca, Amazonas, Loreto y Pasco, todos muy vulnerables. Se concluye indicando que existe una alta correlación entre la vulnerabilidad al cáncer y la pobreza en Perú en 2013.

A New Dataset on Horizontal Structural Ethnic Inequalities in Thailand in Order to Address Sustainable Development Goal 10

Thailand is the world’s fourth most unequal country by wealth, but past research on this phenomenon has mainly been along individual or geographic lines as the census is insufficiently detailed to provide ethnic-based measures. This article introduces an innovative methodological solution to measure horizontal inequality in countries where the census excludes ethnicity. We investigate the roots of a structural ‘ethnic penalty’ by contextualising in some detail an ethnic gap in Thai public policies concerning poverty and inequality. Then, using data from the United Nations Development Programme’s ‘Human Achievement Index’ for Thailand, the Thai Office of the National Culture Commission’s Ethnolinguistic Maps of Thailand report, and Thai National Statistical Office population and poverty data, we compile the country’s first dataset on horizontal (between-group) inequality by ethnicity. Employing this novel approach, we then examine the eight sectors in the Human Achievement Index: health, education, employment, income, housing and living environment, transport and communication, family and community life, and participation. Describing how Thailand’s major ethnic groups fare in each sector and comparing inequality amongst sectors, this ethnolinguistics-based approach provides evidence of a significant ‘ethnic penalty’ in Thailand and makes initial recommendations for addressing the issue.

Poverty Data Retrieval Services Blockchain based on local governments

Poverty Data Retrieval Services Blockchain based on local governments

Multilevel Regression for Small-Area Estimation of Mammography Use in the United States, 2014.

The U.S. Preventive Services Task Force recommends biennial screening mammography for average-risk women aged 50-74 years. County-level information on population measures of mammography use can inform targeted intervention to reduce geographic disparities in mammography use. County-level estimates for mammography use nationwide are rarely presented. We used data from the 2014 Behavioral Risk Factor Surveillance System (BRFSS; n = 130,289 women), linked it to the American Community Survey poverty data, and fitted multilevel logistic regression models with two outcomes: mammography within the past 2 years (up-to-date), and most recent mammography 5 or more years ago or never (rarely/never). We poststratified the data with U.S. Census population counts to run Monte Carlo simulations. We generated county-level estimates nationally and by urban-rural county classifications. County-level prevalence estimates were aggregated into state and national estimates. We validated internal consistency between our model-based state-specific estimates and urban-rural estimates with BRFSS direct estimates using Spearman correlation coefficients and mean absolute differences. Correlation coefficients were 0.94 or larger. Mean absolute differences for the two outcomes ranged from 0.79 to 1.03. Although 78.45% (95% confidence interval, 77.95%-78.92%) of women nationally were up-to-date with mammography, more than half of the states had counties with >15% of women rarely/never using a mammogram, many in rural areas. We provided estimates for all U.S. counties and identified marked variations in mammography use. Many states and counties were far from the 2020 target (81.1%). Our results suggest a need for planning and resource allocation on a local level to increase mammography uptake.

Analyzing Spatial Distribution of Poverty Incidence in Northern Region of Peninsular Malaysia

Poverty has been a major problem around the world for many years. Therefore eradicating poverty has become the first agenda in the United Nations Sustainable Development Goals (SDGs) as its achievable target. In Malaysia, poverty rate is relatively low, decreasing from 49.3% in 1970 to 15% in 1990, then, to merely 0.6% in 2014. Although poverty rate is very small, it is reported at a state level which is to general to visualize its actual distribution. Furthermore, it fails to capture geographic variation within the state. This study aimed to analyze the spatial distribution of poverty incidence in the northern region of Peninsular Malaysia at a sub-district level using Geographic Information System (GIS). GIS was used to map poverty rate, demographic burden and poverty hotspot in the study areas. The poverty data were obtained from e-Kasih database. Furthermore, the accessibility of each sub-district to major urban centers, higher education institutions, and health facilities were also measured. Results indicated that poverty rate was highly correlated with regional differentiation, where location played a significant role in identifying areas with a high number of poor populations. Sub-districts with high poverty rate were less accessible to major urban centers, higher education institutions and health facilities. The findings also indicated that access to opportunities and facilities remained the major concerns in solving the poverty issues in Malaysia. It is timely, therefore, a spatial dimensional approach used to complement the existing poverty eradication strategies.

Entrepreneurship in a Non-Profit Healthcare Organization

Columbus regional health (CRH), a not-for profit organization located in Columbus, Indiana, evolved from a traditional local hospital to an award-winning regional community health-care provider by embracing the concept of innovation and its systematic implementation. The “A” case includes a detailed account of the innovation journey undertaken by CRH and the impact of the devastating flood in 2008. On the other hand, this second (“B”) case deals with WellConnect, an innovative health-care venture that CRH considered for investment in 2013. This new health-care concept was aimed at providing differentiated health-care services in downtown Columbus, away from CRH's main satellite campus. Students, thus, have the opportunity to step into CRH's President and CEO, Jim Bickel's shoes to decide the feasibility of investing $1.5 million in WellConnect based on a thorough assessment and synthesis of multiyear data provided in the case, including community demographic, income and poverty data, competitor information, and CRH's select financial statistics. This case tested in several undergraduate and graduate courses was received well. A unique aspect of this case is that it can be used in online as well as traditional face-to-face classes.

Logit-Normal Spatial Model for Small Area Estimation: Case Study of Poverty in Bengkulu

The main objective of this research is to develop a Logit-Normal model with spatial effects. Spatial effects are characterized by a spatial weighted matrix. The weighted matrix used is the distance of the amount of auxiliary variables of area. It was used to estimate the proportion/parameters using Hierarchical Bayesian method. The design of the research is a case study. Data used in the case study is poverty data in Bengkulu Province. The results show that the spatial effect can improve the value of the Root Mean Square Error.