Poverty Data Research Articles

Forecasting the Poverty Rates using Holt’s Exponential Smoothing

As a developing country with many provinces, Indonesia has a poverty problem that needs to be overcome. This research aimed to predict the poverty level in the Special Region of Yogyakarta using poverty data provided by the Central Statistics Agency for the Special Region of Yogyakarta. The method used in this research was Holt exponential smoothing to predict poverty levels in Yogyakarta City and four districts (Sleman, Bantul, Kulon Progo, and Gunungkidul) in this province. Three performances were measured to evaluate forecast results: sum squared error, mean squared error, and root mean squared error. The research results showed that the best configuration for the cities of Yogyakarta and Bantul is , = 0.9, 0.4; Kulon Progo and Gunungkidul are , = 0.9, 0.9; and Sleman are , = 0.9, 0.6. The forecasting results for 2022 to 2024, using a 95% confidence interval, showed that the poverty rate will increase in every city and district in the Special Region of Yogyakarta.

Relation of poverty with treatment-seeking behavior and antibiotic misuse among UTI patients in Pakistan.

The current study aimed to assess the relation between multi-dimension poverty, treatment-seeking behavior, and antibiotic misuse among urinary tract infection (UTI) patients. A cross-sectional approach was utilized to recruit patients who had a history of UTI in the previous month from two provinces of Pakistan. The treatment-seeking behavior and antibiotic misuse data were collected on a self-developed questionnaire, whereas the poverty data were collected on a modified multi-dimension poverty index (MPI). Descriptive statistics were applied to summarize the data. The logistic regression analysis was carried out to assess the association of multi-dimension poverty with patient treatment-seeking behavior and antibiotic misuse. A total of 461 participants who had UTI symptoms in the previous month were recruited. Most of the participants in the severely deprived stage treated the UTI (p < 0.001); however, there was a high proportion of the participants who consulted with friends and family for UTI treatment (p < 0.001). The patients with deprivation status (deprived and severely deprived) were less associated with formal consultation. The poorer subgroups were less likely to practice antibiotic course completion. The current study highlighted that poverty plays an important role in antibiotic misuse. Poorer subgroups were associated with informal consultations and the incompletion of the antibiotic course. Further studies are needed to explore the potential role of poverty in treatment-seeking behavior and antibiotic misuse.

Poverty convergence clubs

AbstractGlobal eradication of extreme poverty requires absolute convergence of poverty rates worldwide towards zero. Empirical analysis of poverty data for 100 emerging and developing countries over four decades reveals that such a goal is likely to remain elusive. Rather than absolute convergence, we find club convergence: countries' long‐run poverty rates cluster into several distinct clubs, whose number depends on the specific poverty dimension considered. Only the lowest‐poverty club exhibits poverty rates approaching zero by the end of the sample. In contrast, the highest‐poverty club, which accounts for nearly half the world's poor, evokes a poverty trap: its average poverty barely budged over the entire period examined. Overall, income—its initial level and, especially, its growth rate—matters more than inequality for shaping countries' club membership, particularly for the highest‐poverty club. Nevertheless, inequality plays a substantive role for membership in the intermediate‐poverty clubs, which achieved the greatest poverty reduction.

Mapping Human Resources to Guide Ophthalmology Capacity-Building Projects in Honduras: Sub-national Analyses of Physician Distribution and Surgical Practices.

To map ophthalmologist locations and surgical practices as they vary sub-nationally within Honduras to maximize the impact of efforts to develop cataract surgical capacity. An anonymous survey was sent to all Honduran ophthalmologists with questions on surgical volume, department-level location, type of facility in which they work, surgical methods, and age. Surgical volume, population, and poverty data sourced through the Oxford Poverty Human Development Initiative were mapped at the department level, and cataract surgical rates (CSR; surgeries per million population per year) were calculated and mapped. Sixty-one of the 102 Honduran ophthalmologists contacted responded. Of those, 85% perform cataract surgery, and 49% work at least part time in a non-profit or governmental facility. Honduras has fewer surgical ophthalmologists per million than the global average, and though national CSR appears to be increasing, it varies significantly between departments. The correlation between CSR and poverty is complex, and outliers provide valuable insights. Mapping ophthalmological surgical practices as they relate to population and poverty at a sub-national level provides important insights into geographic trends in the need for and access to eye care. Such insights can be used to guide efficient and effective development of cataract surgical capacity.

Comparison of TOPSIS Method and AHP Method in Determining Poor Households in Koto Baru Simalanggang

Every year, some social assistance is distributed by the Ministry of Social Affairs to each village/Nagari aimed at low-income families. The problem of poverty data almost occurs in all village/Nagari governments. In Koto Baru Simalanggang, several issues arose, including social assistance being mistargeted, poverty assessment being very complex, no system helped in DTKS, and no empowerment/system was carried out in reducing social problems; thus, all were put into DTKS without being able to see the details. Thus far, for the examination of the data of the proposer of assistance, the Nagari government still uses manual methods; therefore, it takes a long time and is subjective to the settlement of low-income families. Posdaya Bundo Saiyo is assisting in Koto Baru Simalanggang for the poor beneficiaries. There needs to be a decision support system to determine people with low incomes in this Nagari; using the method to determine people with low incomes is more objective. The results of this study are in the form of a decision support system for the determination of people with low incomes, which is used as a reference for the Bundo Saiyo Posdaya to make recommendations for the determination of people with low incomes to the Mayor of Koto Baru Simalanggang using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP) methods. Based on the comparisons made in this research, the AHP and TOPSIS methods have the same ranking results

Modeling Poverty Data in Indonesia with Spatial Hierarchy Structure Using HLM, GWR, and HGWR Methods

Poverty causes the majority of the Indonesian population to face challenges in fulfilling basic needs such as clothing, food, and shelter. The factors that play a role in determining the poverty rate in Indonesia tend to vary in each province; this is due to the diverse conditions resulting from spatial heterogeneity. However, poverty in Indonesia is not only influenced by factors from various regions but also by the conditions of the districts/cities within them. Districts/cities within a province form a spatial hierarchy structure. Therefore, in this study, the Hierarchical Linear Model (HLM), Geographically Weighted Regression (GWR), and Hierarchical and Geographically Weighted Regression (HGWR) methods were applied to determine the best model among the three methods in analyzing the factors affecting the poverty rate in Indonesia with a spatial hierarchy structure. The results of the analysis show that the HGWR method is the best model compared to HLM and GWR, as evidenced by the higher R-squared value of 0.8004 compared to HLM and GWR. Based on the HGWR model, most of the local estimators for population dependency ratio (G1), adjusted per capita expenditure (G2), and economic growth rate (G3) showed significance in provinces located in eastern Indonesia. In addition, the fixed effects and random effects estimators, namely the percentage of households without access to electricity (X1), the ratio of per capita normative consumption to net product (X2), and the percentage of households without access to clean water (X3), also have a significant influence on the poverty rate in Indonesia.

The Role of the Social Welfare Service in Community Empowerment in NA IX-X District, North Labuhanbatu Regency

The policy of the Labura District Government plays a crucial role in overseeing and facilitating development in the region. The area can effectively and sustainably manage its development when complemented by a well-crafted RPJM (Regional Medium-Term Development Plan) and efficient organizational roles. The purpose of this study is to elaborate on the contribution of the Social Welfare Office to the empowerment of district communities. North Labuhanbatu Regency, NA IX-X. Obstacles to this program are usually found in remote locations, which may make it difficult for the Social Welfare Office to help those in need and strengthen communities in the NA IX-X District of North Labuhanbatu Regency. Qualitative data analysis methods such as literature review, interviews, and observations were used in this research approach. The study's findings suggest that, in line with community empowerment theory, they will help facilitate community participation to increase resource capacity. However, due to poor coordination, the role of the Social Welfare Office as a stakeholder in community empowerment for poverty alleviation, including planning, data collection, and assistance, is not optimal in fulfilling its obligations. Data collection of Family Hope Program recipients needs to be corrected on target in all local governments.

Small Area Poverty Estimation Under Heteroskedasticity

Abstract Multilevel models with nested errors are widely used in poverty estimation. An important application in this context is estimating the distribution of poverty as defined by the distribution of income within a set of domains that cover the population of interest. Since unit-level values of income are usually heteroskedastic, the standard homoskedasticity assumptions implicit in popular multilevel models may not be appropriate and can lead to bias, particularly when used to estimate domain-specific income distributions. This article addresses this problem when the income values in the population of interest can be characterized by a two-level mixed linear model with independent and identically distributed domain effects and with independent but not identically distributed individual effects. Estimation of poverty indicators that are functionals of domain-level income distributions is also addressed, and a nonparametric bootstrap procedure is used to estimate mean squared errors and confidence intervals. The proposed methodology is compared with the well-known World Bank poverty mapping methodology for this situation, using model-based simulation experiments as well as an empirical study based on Bangladesh poverty data.

Religion and Poverty Alleviation in South Sulawesi: Analysis at the Village Level

The influence of religion on poverty reduction has become an exciting study in various countries. Several studies have proven no relationship between religious observance and poverty alleviation. However, on the contrary, other studies have proven that religion affects poverty alleviation. In fact, religion should be able to help overcome poverty. Based on this phenomenon, this study will use raw data of Village Potential (Podes) data issued by BPS in 2018 for South Sulawesi Province, with around 3000 villages. The dependent variable of this study is the number of poor people in each village. Poverty data is taken from the number of poverty certificates issued by the village. This data explains the willingness of people to be considered poor, so that it can show voluntary poverty. Meanwhile, the independent variables include the number of religious places of worship, the number of educational institutions, the number of health institutions, the number of electricity customers, and the number of small-scale industries in the village. This study will use a simple regression method to find the relationship between the variables studied. Even though econometrically many variable relationships are not statistically significant which may be due to the completeness and abnormal distribution of the data, there are interesting variable relationships from this study. The relationship between religion and poverty is negative, indicating that religion can reduce poverty. Community-based health services (posyandu) can also reduce poverty. Markets that show economic aspects will also have an impact on reducing poverty. The same thing is technology proxied with electricity customers will also reduce poverty. Meanwhile, the geographic variable proxied by the distance between the village and the sub-district is not significant and the direction is not as expected.&#x0D; Keywords: religion, poverty, South Sulawesi

Construction of a geographically weighted nonparametric regression model fit test

One of the approach of Geographically Weighted Regression (GWR) models is the Geographically Weighted Nonparametric Regression (GWNR) has more parameters than the GWR model. Models with more parameters usually have better match values, which is an advantage, while models with fewer parameters have the advantage of being easier to use and interpret. However, a model with more parameters should be used if it is proven to be significantly superior. Therefore, the purpose of this study was to develop a hypothesis test of goodness of fit test for GWNR model. The goodness of fit test was performed for the real data. We found that the GWNR model was more suitable than the mixed nonparametric regression model. Some highlights of the proposed method are:•A new model for GWR to overcome the unknown regression function by using mixed estimator spline truncated and fourier series at nonparametric regression•Goodness of fit for GWNR to testing the model fit between the mixed nonparametric regression model and GWNR•Applied goodness of fit test to poverty data in Sulawesi Island and infant mortality in East Java

Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment

Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment

Productive Poor Perception Towards Easily Micro-Business Financing Based on Micro Waqf Banks in Indonesia

Indonesia still has the problem of poverty rates in the last five years, with the number of poverty data tending to decrease. The importance of ease of obtaining business financing is a great opportunity for the productive poor to improve welfare and get out of poverty. The research aims to determine the perception of micro waqf bank customers on the ease of obtaining micro-business financing, analyse the contribution to reducing poverty potential from the existence of micro waqf banks, and determine the performance of micro waqf banks in Banten province. The research used qualitative methods with a case study and descriptive approach analysis at a micro waqf bank in Banten province. The results showed that the agre or positive perception are ease of becoming a customer is 81%, the ease of the process of obtaining financing is 89.1%, the ease of guarantee is 97.9%, the ease of understanding the financing contract system is 77.8%, the ease of receiving financing is 95.1%, the ease of returning funding is 96.8% and the ease of participating in weekly halaqah activities is 98.9% and can reduce the potential for poverty by 0.49% in Banten Province. The research implications are that the easier perception it is for the productive poor to obtain micro-business financing, the faster it will reduce poverty and increase public trust in micro waqf banks in Indonesia. Further research needs to be strengthened to examine the relationship between aspects of convenience obtained by customers.

Policy framework for updating and utilizing poverty data using MULTIPOL method in Bekasi Regency, Indonesia

Poverty is a complex and multidimensional social problem faced by almost all countries including Indonesia. Poverty alleviation efforts require accurate and up-to-date data to ensure that social welfare programs can effectively run and target appropriate beneficiaries. Bekasi Regency, as one of the regions that faces significant challenges in managing poverty, needs the right strategy in updating and utilizing poverty data to support social welfare programs. This study analyzes a policy framework for updating and utilizing the poverty data. The data were collected from focus group discussions with experts and stakeholders who were competent in updating and utilizing the poverty data. MULTIPOL method was used to analyze the data. The results of the study show that the best strategy for updating and utilizing poverty data in the scenario is to integrate scenarios with priority policy with digital budgeting policy, as well as priority actions providing incentives. This study makes an important contribution to regional development planning, especially poverty alleviation strategies based on accurate and integrative data.

Analisis Penerapan Standar Masyarakat Miskin di BPS Kota Binjai

Poverty is a condition in which a person or region is unable to improve quality of life or standard of living. Binjai cities are mentioned in a good category in the presentation of poverty compared to other cities in northern Sumatra. This study aims to find out the application of the standards of the poor society in the Central Statistical Authority (BPS) of Binjai City, using qualitative descriptive methods. The research finds that the concept used is that poverty is seen as an inability on the economic side to meet the food base not food measured on the expenditure side, and the concept is confined only to indicators of the poverty line, the presentation of the poor, the depth index of poverty, the index of misery.And there are two types of poverty data: micropoverty data and macro-poverty data.

Geospatial Big Data Approaches to Estimate Granular Level Poverty Distribution in East Java, Indonesia using Machine Learning and Deep Learning Regressions

One of the economic development the focus of the Indonesian government's efforts is for reducing poverty. In Indonesia, collecting poverty data uses the conventional method, the name is National Socio-Economic Survey (SUSENAS) which takes a large cost, time, and effort. To overcome these limitations, there is a need for additional data to provide more detailed poverty data. Recent studies show that the use of geospatial big data could identify poverty at a granular level, with a lower cost and faster update because of their unique and unbiased capacity to identify physical and socioeconomic phenomena. The integrated multi-source satellite imagery data such as the normalized difference vegetation index (NDVI) for detecting rural areas based on vegetation, built-up index (BUI) for identifying urban areas through building distribution, normalized difference water index (NDWI) for land cover detection, day time land surface temperature (LST) for identifying urban regions based on surface temperature, and pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) to evaluate economic activities based on pollution. Additionally, point of interest (POI) density and minimum POI distance are used to measure area accessibility. Therefore, the contribution of this research is to implement the utilization of geospatial big data to estimate the numbers of poverties at a granular level to the 666 sub-districts in East Java Province using machine learning and deep learning regression models. The evaluation results to estimate sub-district level poverty shows that the best model development using Support Vector Regression (SVR) in machine learning was the best root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values of 0.365, 0.293, and 0.032 with R-squared of 0.59 and MLP in deep learning algorithm with 0.444, 0.345, and 0.039 values of RMSE, MAE, and MAPE with R2 0.52. In addition, the results of visual identification revealed that high estimates of lower poverty are typically found in urban areas with high accessibility, and these areas are not spatially deprived areas with limited accessibility.

Integrating Satellite Imageries and Multiple Geospatial Big Data for Granular Mapping of Spatial Distribution of Human Development Index in East Java, Indonesia

The availability of data on the Human Development Index (HDI) is crucial as a gauge of regional performance, particularly in terms of assessing the development of human resources. In Indonesia, the collecting of HDI data usesthe conventional method, such as undirect estimation, National Socio-Economic Survey (SUSENAS), The Ministry of Religion, or inventory of sectoral data that used the large cost, time, and effort. Additional data are required to provide more detailed poverty data at a lower cost and with more recent information to overcome these limitations. According to recent studies, the quality of life for measuring HDI can be identified down to the granular level using geospatial big data. Therefore, the contribution of this research is to implement the use of geospatial big data, such as integrated multi-source satellite imagery data and Point of Interest (POI). Besides that, this study develops the relative spatial human development index in 11 km x 11 km resolution for the granular mapping of the quality of life to measure the HDI in East Java, Indonesia. The kinds of weighted sum models used in this study such as equal weight (EWS), Pearson (PCCWS), Spearman (SCCWS) correlation-based weight, and Principal Component Analysis (PCA)-based weight (PCAWS). The best RSHDI PCCWS for representing the human development index in East Java in 2022, which was determined using a weight-sum model based on Pearson correlation, has a correlation coefficient of 0.7858 (p-value = 5.078 x 10-9) and is highly correlated with official HDI data. The use of this RSHDI as a predictor variable in the estimation of HDI data shows the ideal model had an RMSE of 3.098% and an R2 of up to 61.75% using RSHDI PCCWS. According to the findings of the descriptive analysis of this map, areas with low RSHDI scores typically in some regencies areas in Madura Island and the east area of East Java with geographically depressed, while areas with high RSHDI scores typically have dense populations and have better accessibility such as urban area in Surabaya and Kota Malang. As a result, the official human development index data can be supported by the RSHDI's ability to map spatially deprive areas

Optimizing Data Collection Strategies for Effective Poverty Alleviation: Insights from the Social Welfare System - Next Generation (SIKS-NG)

Malang Regency is the second-largest region in East Java with a diversity of geographical, social, and economic conditions. Data from the Malang Regency Social Service in 2018 shows that the number of impoverished families in Malang Regency reached 230,081 families. The highest number of impoverished families is found in the Dampit District, totaling 12,053 families, while the lowest is in the Kromengan District with 3,061 families. This indicates that the Dampit District has a significant number of impoverished families, surpassing other districts in Malang Regency. This makes the Dampit District a relevant location for research on poverty and its impacts. This research has dual objectives: first, to provide reliable, accurate, and valid information on existing poverty indicators, which will serve as a strong foundation for supporting poverty alleviation programs in Malang Regency. Second, this research aims to formulate strategies in the form of activity guidelines that will enhance the competence of employees in conducting poverty data collection, ultimately supporting poverty alleviation efforts. Data collection methods include interviews, documentation, observation, and Focus Group Discussions to obtain more in-depth information. In-depth interviews were conducted using purposive sampling techniques involving stakeholders related to poverty alleviation in the Dampit District Office, Malang Regency. The analysis results indicate that the government of the Dampit District and the villages in the area have not fully succeeded in implementing the Next Generation Social Welfare System (SIKS-NG) optimally. The poverty data collection has predominantly focused on collecting data on the poor only. Therefore, improvements in the approach and implementation of SIKS-NG are needed to enhance the effectiveness of poverty alleviation programs. The findings of this research highlight the importance of a participatory approach in collecting poverty data, involving village governments, utilizing information infrastructure like BDT, and the data consolidation process to ensure the effectiveness of poverty alleviation programs. Additionally, appropriate training for data collection teams is crucial in improving the accuracy of poverty data.

COMPARISON OF LOGISTIC MODEL TREE AND RANDOM FOREST ON CLASSIFICATION FOR POVERTY IN INDONESIA

Classification methods are commonly employed to ensure homogeneous data within each group, facilitating the prediction of specific categories. The most frequently used classification models are Logistic Model Tree (LMT) and Random Forest (RF). This study aims to assess the accuracy rate in predicting the poverty status of regencies or towns across Indonesia, utilizing eight independent variables. The entire dataset was obtained from the official Central Bureau of Statistics website. The study investigates the accuracy of various iterations and combinations of training data. The results indicate that RF outperforms LMT in terms of accuracy, achieving a 100% improvement in iterations k=10 and k=500 and a 75% improvement in iteration k=100. Consequently, the RF proves to be more effective than the LMT for analyzing Indonesian poverty data, especially when incorporating all eight independent variables.

Abstract B092: Area-level socioeconomic influences on rates of cervical and colon cancer screenings

Abstract Introduction and Study Purpose: Cancer screening is one of the most important preventive modifiers of cancer outcome. The general correlation between negative socioeconomic influences (SEIs) and poorer cancer screening rates is known. However, variations on SEI correlation with cancer screening need to be confirmed at a smaller local level since there could be significant local variations. CDC PLACES is a high resolution dataset that provides data on cancer screening rates at a census tract level with coverage across the US. It is made up of small area estimates constructed with demographic data in CDC BRFSS, poverty data from ACS, and other sources. With CDC PLACES and SEI data readily available on a census tract level, it is possible to calculate associations between cancer screening compliance and SEIs on a local level to better identify communities potentially benefiting from increased cancer screening. We hypothesize that cancer screening rates will be positively correlated with economic resources, education, insurance coverage, and decreased minority population. Methodology: We performed a retrospective cross-sectional study of cervical and colon cancer screening rates at a census tract level using data from CDC PLACES. This study included 378 of the 443 census tracts in Cuyahoga County, Ohio. 65 census tracts were excluded due to missing data. SEI census tract-level variables including race, gender, age, income, education, and health insurance coverage were obtained from ACS. Cancer screening rates were obtained from CDC PLACES. Linkage across datasource was conducted at the census tract level. Pearson correlation coefficients were calculated to examine associations between the selected SEI variables and cancer screening rates across all 378 census tracts. Results: Within the 378 census tracts examined, cervical and colon cancer screening rates in Cuyahoga County were correlated with a similar set of SEI variables. Positive correlations were observed between cervical and colon cancer screening rates with greater percent White population (r=0.35, p&amp;lt;0.001; r=0.36, p&amp;lt;0.001; respectively), formal education achievement (r=0.75, p&amp;lt;0.001; r=0.79, p&amp;lt;0.001; respectively), and private health insurance coverage (r=0.75, p&amp;lt;0.001; r=0.81, p&amp;lt;0.001; respectively). Conversely, cervical and colon cancer screening rates were negatively correlated with greater percent of Black population (r=-0.24, p&amp;lt;0.001; r=-0.27, p&amp;lt;0.001; respectively), lower English language fluency (r=-0.47, p&amp;lt;0.001; r=-0.42, p&amp;lt;0.001; respectively), higher unemployment (r=-0.47, p&amp;lt;0.001; r=-0.51, p&amp;lt;0.001; respectively), and publicly insured healthcare coverage (r=-0.65, p&amp;lt;0.001; r=-0.67, p&amp;lt;0.001; respectively). Conclusion: Small area correlations between cancer screening rates and SEIs has the potential to inform local policy and research that more accurately serves the health needs of smaller community regions. Citation Format: Zherui Xuan, Jennifer Cullen. Area-level socioeconomic influences on rates of cervical and colon cancer screenings [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B092.

Distribution Analysis and Ranking Analysis of Poverty Data From Three Data Sources in Bekasi Regency

In implementing evidence-based policies, it is necessary to support poverty data that is precise, accurate, and consistent. The goal is that various assistance, such as social assistance, PKH, groceries, pre-employment, and other subsidies, will be right on target for people experiencing poverty. The diversity of poverty data sources will have implications for the inaccuracy of targeting social assistance recipients. Until now, there are still three sources of poverty data, namely the Statistics Indonesia, the Ministry of Social Affairs (Kemensos) with Integrated Data on Social Welfare (DTKS), and TNP2K, which is now under the Coordinating Ministry for Human Development and Culture (Kemenko KMK) which also releases data targeting the Acceleration of Extreme Poverty Elimination (P3KE). The purpose of this study is to analyze the distribution of poverty data and rank analysis of poverty data from three data sources in the Bekasi Regency. The analysis method used ArcGIS 10.4 software and the administrative map shapefile for the Bekasi Regency area. In the ranking analysis of poverty data from three data sources in each sub-district in Bekasi Regency, the Preference Ranking Organization Methods For Enrichment Evaluation (PROMETHEE) method was used. The results of the analysis showed that the distribution of P3KE data is 1,317,098 people spread by decile-1 to decile-4 in all sub-districts of Bekasi Regency. Statistics Indonesia data is 229,367 people spread across each sub-district, and the number of DTKS in Bekasi Regency is 1,035,402 people, with the most distribution of data in Babelan District, namely 110,867 people out of 34,009. While the best ranking of the three sources of poverty data is Central Cikarang, Pebayuran District (0.7273), North Tambun (0.6364), Cibitung and Karang Bahagia (0.4242), North Cikarang (0.3939), South Tambun (0 .3636), Sukatani (0.2121), Serang Baru (0.1515), West Cikarang (0.1212), Tarumajaya (0.0909), East Cikarang (0.0606).