Poverty Data Research Articles

GEOGRAPHICALLY WEIGHTED REGRESSION WITH DIFFERENT KERNELS: APPLICATION TO MODEL POVERTY

Poverty is still a significant problem in Indonesian development. The poverty alleviation programs implemented have yet to pay attention to spatial aspects, so the policies are often not on target. This study aims to reveal the spatially varying relationships between the poverty level and its factors at the regional scale and compare three fixed kernels as a weighting matrix for GWR. The method used is geographically weighted regression (GWR) with poverty data for 2021. The study results show spatial autocorrelation and is grouped in 29 regencies/cities. The expenditure per Capita, life expectancy, percentage of houses and households with proper drinking water, open unemployment rate, labor force participation rate, and GDP at constant prices show different effects in each region. The results strengthen the argument that spatial aspects cannot be ignored in regional development, especially poverty alleviation. Therefore, area-based poverty alleviation can be used as a basis for determining/determining policies so that they can be more targeted.

Metode Penetapan Mustahiq Zakat Pada Mustahiq Center Baznas Kabupaten Wonosobo

This paper will analyze the method of determining mustahiq zakat through the mustahiq center at BAZNAS Wonosobo Regency. In this study the writer will use a qualitative approach with descriptive methods. The data source used is the method of determining mustahiq zakat through the mestahiq center used by the Wonosobo National Amil Zakat Agency (BAZNAS). The results of this study can be concluded that the National Amil Zakat Agency (BAZNAS) of Wonosobo Regency in determining mustahiq zakat by developing an integrated national Mustahiq database system (Mustahiq Data Center). This system is presented in an effort to sharpen the target of zakat recipients by utilizing existing poverty data in Wonosobo district.

북한의 지속가능발전목표 이행에 관한 연구

Purpose: Based on the compliance theory and the institutionalist approach, this article is intended to assess North Korea’s compliance levels of No Poverty goal under the Sustainable Development Goals (SDGs).
 Originality: Although SDGs stands for advanced global norms, its application constitutes significant challenges for the developing world. For the better achievement of SDG within the global community, this paper highlights country-specific limitations and opportunities for SDG compliance and implementation through the case of North Korea.
 Methodology: For the analysis, this study conducts content analysis on a range of documents and information released by the North Korean government, including the Voluntary National Review (2021) and the Multiple Indicator Cluster Survey (2018). In order to evaluate the direct and indirect availability of poverty data (including estimates), relevant academic works and reports are also reviewed.
 Result: To assess and explain North Korea’s weak compliance, this study does not stick to the issue of statistical capacity only. For incorporating the wider institutional contexts, the localization of the global discourse on poverty and the internal structure for SDG implementation are also highlighted. Despite the country’s willingness, due to the communist tradition as well as regime justification, we interpret that the country still shows strategic but roundabout behavior in adopting the global concept of poverty.
 Conclusion and Implication: This study concludes with recommendations that can help North Korea improve its compliance with the SDG.

Redesigning consolidated data for handling extreme poverty in rural areas based on SDGS DESA

Responses to extreme poverty include ensuring the authenticity of related data. Therefore, studies of data consolidation on handling extreme poverty in rural areas are important. The integration of poverty studies into social science and development fields is crucial for advancing knowledge in these disciplines. This study aims to describe data inequality and collection accuracy in Indonesia. It used a data consolidation approach based on SDGs Desa to explore the disparity in central and regional poverty data collection, which impacts the loss of access to basic rights. Furthermore, this study relied on survey data from 100 villages in 4 of the 5 regencies piloting the projects to tackle extreme poverty in East Java, Indonesia. The results showed that the accuracy of the data influenced the poor categorization, social assistance distribution, and the seriousness of the state in alleviating extreme poverty. Therefore, it becomes the basis for further study in unravelling the dynamics in the design of data collection in rural areas.

INFORMATION FOR THE IDENTIFICATION AND VERIFICATION OF PROSPECTIVE POVERTY SOCIAL ASSISTANCE RECIPIENTS

One important aspect of the poverty alleviation strategy is the availability of accurate poverty data, so that the government in providing social assistance (Bansos) can be right on target. The problem is that if there is an inaccuracy in the identification process when recording citizen data, it will affect the process of verifying and validating data on prospective Social Assistance recipients. Inaccuracies in prospective Social Assistance recipients can occur due to the process of collecting data on citizen identities and applying the wrong criteria for poverty requirements and the data is not up to date. The purpose of this research is to develop an information system to identify, verify, and validate data on prospective Social Assistance recipients. The identification stage for potential recipients of social assistance is carried out at the RT/RW and Kelurahan levels. The verification stage is carried out by fulfilling the requirements of the poverty criteria. Meanwhile, the validation stage is used to determine the ranking of potential social assistance recipients. The verification process is carried out at the Kelurahan and Kecamatan levels, while the validation process is at the Regency/City level. The waterfall method is used in the development of information system prototypes for prospective Social Assistance recipients with system analysis and design using use case, activity, and sequence diagrams. The prototype was tested with CRUD and trial & error, the result is a prototype system application that is able to collect citizen data by applying a priority scale for prospective recipients of social assistance based on a web by fulfilling the requirements of 14 poverty criteria according to the Central Statistics Agency (BPS).

Synthetic Minority Over-sampling Technique (SMOTE) for handling imbalanced data in poverty classification

Poverty data in official statistics data is important for development planning. The lower percentage of the poor recorded yearly indicates good development of a country. Moreover, there is always a problem when performing an inferential and classification analysis because of the imbalanced data, thereby leading to biases in the estimation results and prediction errors in the classification. One of the solutions to this problem is using Synthetic Minority Over-sampling Technique (SMOTE). Therefore, this study aims to evaluate the inference and classification quality using the binary logistic regression model without and with SMOTE. The data utilized was the poverty status of households in the rural and urban areas in East Java, Indonesia as contained in the 2019 National Socio-Economic Survey. Furthermore, the variables used are poverty status of the household, the age of the household head (HH), the ratio of household members who are employed, gender of the HH, number of household members, education level of HH, and occupation of the HH. It was concluded that the model with SMOTE approach was better at inference and classifying the results.

Keep out the cold: An analysis of potential gaps in fuel poverty policies in Ireland

Fuel poverty in Ireland is a significant issue faced by almost a quarter of the population as per government statistics. In this study, we analyse fuel poverty data in Ireland between 2008 and 2020 using the Irish subset of EU SILC data. We find that the increase in the proportion of households receiving Fuel Allowance and the decrease in the proportion of poor-quality dwellings have a significant positive impact on the decline in aggregate fuel poverty experienced. We also analyse a sample of low-income households that experience fuel poverty and compare groups that are targeted by official policies with others on key indicators. We find that the existing supports normally target lower-income households who live in energy-inefficient dwellings and are of retirement age. We also find that non-targeted larger urban families who live in rented or mortgaged dwellings experience significant fuel poverty due to difficulty paying bills. We identify some gaps in the current policies that target fuel poverty. We propose that fully funded energy efficiency retrofits and social transfers for vulnerable households to help with the cost of heating, which are policies already used in Ireland to target fuel poverty, should be expanded to effectively reach a cohort of the population that is facing challenges to keep warm.

Analysis of Factors Influencing the Percentage of Poverty in North Sumatra Using Robust Linear Regression

The percentage of poverty in North Sumatra Province in 2021 will reach 9.01%. The data used are poverty percentage data in North Sumatra for 2021, Gini ratio, Covid-19 pandemic, poverty depth index, education and unemployment rate are considered to contribute to the percentage of poverty in North Sumatra Province. The purpose of this research is to determine the variables that have a significant effect on the Percentage of Poverty in North Sumatra in 2021. The method used is Ordinal Least Square. The results of this study show that the Poverty Percentage model in North Sumatra in 2021 uses a robust M-estimation regression which is 4.432790; 0.07230 and 0.007035. Partially, the regression coefficient shows that the poverty depth index has a significant effect on the percentage of poverty with a probability of 9.087. Simultaneously, the Gini ratio, the Covid-19 pandemic, the poverty depth index, education and unemployment affect the percentage of poverty with a probability of 64,602.

Islamic Banks and Its Role in Reducing Poverty

This study aims to analyze the effect of financing disbursed by Islamic banks on reducing the poverty rate in Indonesia. This study used timeseries data tested using multiple linear regression analysis. Sharia bank financing data was obtained from Sharia Banking Statistics by the Financial Services Authority and poverty data in Indonesia obtained from BPS was used in this study. The results showed that murabahah has an effect on reducing the poverty rate, while the greater distribution of mudharabah financing has an impact on increasing poverty, and the existence of musyarakah financing has no effect on poverty in Indonesia at all.

Analisis Autokorelasi Spasial Global dan Lokal Pada Data Kemiskinan Provinsi Bali

Poverty is a crucial problem that often occurs in Indonesia, including in the province of Bali. The poverty rate rate in Bali has increased significantly over the last 3 years, so further analysis is needed so that this poverty rate can be reduced. The pattern of poverty between regions is different because it depends on the geographical and socio-cultural conditions in each area. The effect of location on poverty cases can be identified by global and local spatial autocorrelation methods. This study aimed to analyze the spatial autocorrelation of poverty data in Bali Province using the global autocorrelation test with the Moran’s and Geary’s C indices as well as the local spatial autocorrelation test with the Local Indicator of Spatial Association (LISA) and Getis-Ord G to obtain an overview of the spatial distribution of poverty data. Based on the global autocorrelation test, it is concluded that using Moran’s index there is a negative spatial autocorrelation in the 2020-2022 data for a=10%. Similar results were also obtained when using the Geary’s C test. In the local autocorrelation test using LISA, it was found that districts had negative spatial autocorrelation, namely in 2020 Buleleng Regency (high-low) and Klungkung (low-low), for 2021 there is Buleleng Regency (high-low) and Jembrana (low-low), while for 2022 only Buleleng Regency (high-low) has negative spatial autocorrelation. For local autocorrelation testing with the Getis-Ord G test, it was found that there were no districts/cities that showed spatial grouping or that there was no spatial autocorrelation locally.

Reducing energy poverty in small rural communities through in situ electricity generation

Energy is key in achieving sustainable societies. There have been great efforts towards improving energy access worldwide. Despite the advances in energy access, energy poverty remains a major problem in many parts of the world, particularly in rural communities. Modern energy, in particular electricity, can help rural communities develop through improving education and health. During the last two decades, there have been improvements on bioenergy technological innovations, e.g. electricity generation from bioenergy from residual biomass from several agricultural crops in biorefineries. Most research has focussed on large biorefineries, with limited research on small-scale gasifiers’ location and contribution to energy poverty. This paper is aimed at assessing technological options to generate electricity in situ from biomass to reduce energy poverty of rural communities. This is done using four analysis methods: (1) crops availability data; (2) poverty and marginalisation data; (3) electricity provision/distribution; and, (4) GIS/Geographical latitude and longitude to locate municipalities in Mexico. The results shows that the generating potential for electricity using residual biomass with gasifiers could improve the welfare of almost 10 million people communities using residual biomass from crops harvested in such communities. This research provides location solutions on the best places to locate small-scale biorefineries. The research also provides systemic analysis to reduce energy poverty through in situ electricity generation using cheap accessible small plant technologies and biomass as raw material, as well as their location. Generating electricity in a decentralised way through agricultural residual biomass can help lift rural communities from poverty and improve their well-being, and, thus, make societies more sustainable.

The Effect of Islamic Bank Financing on the Human Development Index: A Case Study of 15 Cities on Java Island

The Human Development Index (IPM) measures how people can access development results in obtaining income, health, and education. This study aimed to determine whether Islamic bank financing, poverty, and Gross Regional Domestic Product (GRDP) have a relationship and influence the Human Development Index in 15 cities in Java Island for 2015-2021. The quantitative approach uses secondary data from the Financial Services Authority (OJK) for Islamic bank financing data. In contrast, Poverty and GRDP data from the Central Bureau of Statistics (BPS). The data used in this study is annual data from 2015 to 2021. The estimation method used is panel data regression with the Random Effects model. The study results that the Islamic bank financing variable has a significant positive effect on HDI. Meanwhile, the Poverty and GRDP variables significantly and negatively affect the Human Development Index. Simultaneously, the variables of Islamic bank financing, poverty, and GRDP affect the Human Development Index.

The use of the consensual approach for the improvement of existing multidimensional poverty data in Latin America: an illustration based on data from the City of Buenos Aires

This article aims at contributing to the current literature on poverty data limitations and measurement by discussing the process for producing the first multidimensional poverty measure based on the consensual approach for the City of Buenos Aires. The results show a remarkable level of consensus about the necessities of life in the twenty-first century, underline the importance of generating more suitable indicators of deprivation and show that unmet basic needs-type variables are no longer adequate for measuring poverty in countries like Argentina. According to the valid and reliable poverty index, 20.3% of the city’s population live in households in multidimensionally poor households, this being the social dimension that shows the highest deprivation rate.

Relative deprivation and the differential effects of the COVID-19 pandemic on the poor in Mexico

One of the main challenges to studying the effects of the pandemic on poverty has been the lack of adequate data. Very few countries have short-term poverty data and most official surveys are rather rigid and were incapable of adding any special modules to jointly study COVID-19 and poverty. Furthermore, many poverty measures rely on indicators of unsatisfied basic needs (UBN) that, albeit useful to characterise exclusion from essential services, are not sensitive to short-term effects on the resources of the population. This study uses data from the Encovid-19 to jointly study poverty and some of the consequences of the pandemic on the Mexican population. This survey included a short module to measure poverty using the consensual deprivation approach. Once the poor were reliably identified in the survey, the study analysed differences in coping mechanisms, occupation, and perceived COVID-19 consequences between the poor and the not poor. From the methodological point of view, the study shows how the consensual approach has the advantage of producing valid and reliable poverty figures at a low cost. The findings clearly show that the poor were clearly most affected by the COVID-19 pandemic.

Penerapan Algoritma K-Means untuk Klasterisasi Penduduk Miskin pada Kota Pagar Alam

The purpose of this study was to obtain a poverty data cluster in Pagar Alam City. The data collection of beneficiaries of the Program Keluarga Harapan (PKH) is not correct, the provision of assistance only pays attention to the criteria for poverty in general, so there are still many poor people who feel more deserving of PKH assistance. To overcome the problem of PKH recipients, it is necessary to cluster the community into various levels, so that the government can know the level of poverty of the community and can provide PKH assistance appropriately. The methods used in this study are CRISP-DM and the K-Means clustering algorithm. The attributes used are Identity Number, Name, Family Family Card Number, Poverty Rate, Pregnant Women, Early Childhood, Elementary School, Junior High School, Senior High School, Elderly, and Family Hope Program Recipient Group. This clustering process produced three clusters, namely cluster_0 as many as 156 people, cluster_1 as many as 82 people, and cluster_2 as many as 233 people. Furthermore, it was developed into a system with the Rapid Application Development (RAD) system development method. Thus producing a K-Means algorithm system to classify the poor in Pagar Alam City. The system test method uses black box testing with the alpha method and obtained database test results with a value of 4, interfaces with a value of 4, functionality of 4.42, and algorithms with a value of 4. In the testing process with UAT, in the system aspect got 87% of users agreed, in the user aspect 86% agreed, and in the interaction aspect 87% of users agreed. So it can be concluded that this system is worth using.

Poverty reduction in Indonesia through utilization of social security fund application

The purpose of this study is to examine poverty reduction through the utilization of social security fund applications. The social security fund application is named SIKS-NG. This is an application that aims to collect data on social security funds. In distributing social security funds (bansos), it is necessary to have data on the recipient of the social security fund (bansos). The data must be accurate so that the social security fund (bansos) will be on target. This study uses a descriptive method with a qualitative approach. The study results show that SIKS-NG is a solution for integrated, effective, and efficient management of poverty data. It contributes to the reduction of poverty. This follows the Sustainable Development Goals (SDGs), ending poverty everywhere and in various forms. SIKS-NG implements appropriate social protection systems and measures for all levels of society in Indonesia.

Nexus between Public Debts, Poverty and Unemployment Rates in Nigeria: A Vector AutoRegression (VAR) Approach

Notwithstanding the upsurge in public debts, it is absurd and worrisome to note that socioeconomic indicators like poverty and unemployment have shown gloomy pictures in Nigeria. The absurd situation makes it unclear on the precise nexus between public debts and unemployment rate on the one hand and poverty rate on the other hand. Consequently, this paper analysed the nexus between public debts, poverty and unemployment in Nigeria. Secondary data of public debts (measured by internal and external debts), poverty and unemployment rates were obtained from the Central Bank of Nigeria statistical bulletin and National Bureau of Statistics during the period 1981-2021. Using an unrestricted vector auto-regression model, the study indicated that neither internal nor external debts had any significant impact on poverty; however, they influence the level of unemployment rates in Nigeria. Impliedly, most of the public debts incurred within the period of investigation were not growth-oriented and could partly be explained by the fact that most of the borrowings were mainly to finance trade deficits, which were mainly consumable goods. The paper recommended that the current debt-togross domestic product ratio of less than 20 percent should be sustained to ensure that debt remains within the internationally recommended threshold for developing economies like Nigeria. Also, future public borrowings should be targeted at specified productive sectors of the economy that would engender growth in the long-run in terms of job creation and poverty alleviation; this can be achieved via the procedure of tying every public borrowing to specific growth-driving project that are oriented towards job creation and alleviation of poverty

Reexaminando la “paradoja del desarrollo fronterizo” del norte de México: ¿mayores ingresos con rezagos en servicios públicos?

The objective of this paper is to verify if the urban population of the municipalities of the northern border of Mexico hashigher incomes than those of other municipalities in the country, while suffering worse living conditions; that is what someauthorshave called “border development paradox”. To verify this issue, proportions were compared based on multidimensional poverty data from Conevalfor the period2008-2020,and more municipalities and population were considered than in any previous research. The results show that urban residents of the northern border have higher incomes, but also better access to services and less social deprivation.These results—which contradictsome previous research,but confirm others—are maintained throughout the entireperiod analyzed and with different territorial groups. Therefore, it is concludedthat there is no evidence of said paradox.

Data Forecasting Model to Know the Social Impact of Poverty in the Era of Globalization in West Java Province, Indonesia

Globalization has a social impact in the form of poverty. Meanwhile, poverty data in West Java Province, Indonesia, will increase in 2021 by 999,960 people. In addition to education, a country's poverty level shows its citizens' welfare. Therefore the poverty level in that country must be considered. In the Sustainable Development Goals, poverty is the priority scale to be considered. Therefore, forecasting is quite crucial in planning to know in advance what will happen. ARIMA (Auto Regressive Integrative Moving Average) is a modeling approach that can calculate the probability of a future value between two specified limits. This study predicts the number of poor people in West Java Province, Indonesia, from 2022 to 2025. The data used are 15 years from 2007 to 2021 and are processed with the Eviews computer program to see patterns and results in the ARIMA model. The modeling stage starts from data stationarity testing, model identification, model estimation, and model verification to forecasting. Based on the results of this study, the prediction results of the number of poor people in 2022 are 3,618,866; in 2023, it will be 3,512,758; in 2024, there will be 3,406,651, and in 2025 it will be 3,300,543 people. This forecasting uses the ARIMA (Auto Regressive Integrative Moving Average) model (1, 2, 1) as the most accurate method with MAD (Mean Absolute Deviation) error parameters of 1,751,747, MSE (Mean Square Error) of 6,977,202,252. 995 and MAPE (Mean Absolute Percentage Error) of 8%.

On the Use of Logistic Regression Model and its Comparison with Log-binomial Regression Model in the Analysis of Poverty Data of Nepal

Background: Previous literatures have indicated that log-binomial regression model is an alternative for the logistic regression model for frequent occurrence of event of outcome. The comparison of the performance of these two models has been found with reference to clinical/epidemiological data. Nonetheless, the application of log-binomial model and its comparison with the logistic model for poverty data has not been described. 
 Objective: To compare logistic and log-binomial regression model in terms of variable selection, effect size, precision of effect size, goodness of fit, diagnostics, stability of the model, and the issue of failure convergence.
 Materials and Methods: Cross sectional data of 5988 households of Nepal Living Standard Survey 2010/11 has been used for the analysis. The performance of logistic and log-binomial model has been compared in terms of variable selection, effect size, and its precision for each covariate, goodness of fit using Hosmer - Lemeshow (H-L) test, diagnostics of the model, stability of the model using bootstrapping method, and the issue of failure convergence.
 Results: Logistic model overestimates the effect size, yields wider 95% confidence interval than that of log - binomial model for each covariate. The greater elevation in risk for covariates varies from 13% to 173%. Logistic model satisfies goodness of fit of the model (p = 0.534), diagnostics tests, and stability of the model. However, log-binomial model grossly violates the goodness of fit of the model (p = 0.0004) but satisfies the model diagnostics and stability criteria.
 Conclusion: Log-binomial model satisfies all criteria for model development and diagnostics except gross violation in goodness of fit of the model. However, logistic regression model satisfies all the criteria including goodness of fit of the model. On the basis of the entire comparison of model performance, logistic regression model is better fitted than the log-binomial model in fitting the poverty data set of Nepal.