Poverty Criteria Research Articles

A Study on the Measurement of Relative Poverty in Developing Countries with Large Populations

Under the goal of global poverty eradication, it has become a forward-looking research aim to establish relative poverty criteria and identify people experiencing relative poverty in countries with different demographic characteristics. This paper introduces a new method to measure the relative poverty standard, which is to use the fuzzy decision tree algorithm to objectively estimate the relative poverty standard. The advantage of this algorithm lies in (1) it not only maintaining the regression idea of measuring absolute poverty, but also emphasizing the nonlinearity when the demand is increasing, which can reflect the change in human needs. (2) It overcomes the division of the traditional method which clearly distinguishes between those experiencing poverty and those who are not by means of a subjective threshold, and it also avoids the subjectivity of the selection of multidimensional indicators. (3) It overcomes the problems of data skewness and extreme value issues that traditional methods have, and can exhibit multi-dimensional characteristics. (4) Most importantly, this method can overcome the gap problem caused by the complex population structure in developing countries with huge populations, and is more adaptable under big data conditions than traditional methods. Taking China as an example, using data from the China Household Finance Survey for validation, the validation results show that the relative poverty standard in China in 2019 can be approximately delineated as 5288.5 RMB; this result is higher than the absolute poverty standard line delineated in China in that year, lower than the relative poverty standard line measured using the proportion method, and it can satisfy the average per capita food, tobacco, and alcohol consumption expenditure of Chinese residents in that year. Thus, compared with other methods, the fuzzy decision tree algorithm can better match the identification of relative poverty in developing countries with large populations.

Unravelling the influence and mechanism of agricultural inputs on rural poverty vulnerability: Evidence from China

Agricultural inputs have a significant catabatic effect on rural poverty at present. However, few studies have assessed whether agricultural inputs inhibit the intending probability of being vulnerable to rural poverty, referred to as poverty vulnerability. Poverty vulnerability studies, which focus on the likeli-hood of poverty among households in the future, have forward-looking and prognostic features in poverty research. Such research provides essential tools for describing mechanisms that can eradicate poverty. This study investigates the impact of agricultural inputs on rural poverty vulnerability and the mechanisms of action, using data from the 2015 China Household Finance Survey (CHFS). The empirical results show that agricultural inputs significantly suppress the probability of rural poverty vulnerability. The results hold after replacing the poverty criterion, poverty vulnerability criterion, and core explanatory variables. To overcome the endogeneity problem created by possible reverse cau-sality and omitted variables in the baseline model, this study uses the instrumental variables method to test endogeneity. The study also applies a treatment effects model and propensity score matching method to estimate and address any endogeneity problems due to explanatory variables. The findings also remain robust and significant after addressing the endogeneity problem. Further analyses indicate that the inhibitive effect of agricultural inputs on rural poverty vulnerability is mainly achieved through increased agricultural yields, improved physical health, and increased opportunities for entre-preneurial behavior. A heterogeneity analysis finds that the inhibitive effect of agricultural inputs on poverty vulnerability is more pronounced in Midwest China, in households with physically healthy household heads, and in families whose leaders have risk preferences. Our findings provide empirical evidence that can help inform forward-looking and prognostic policies for developing countries when formulating anti-poverty strategies.

Decision System for Beneficiaries of the Family Hope Program Using the Rank Order Centroid Method and Weighted Product Method

Poor refers to a state of having little or insufficient material possessions or resources relative to others in a particular society or community. Many factors become the criteria of poverty. These factors identified and measured using the Rank Order Centroid (ROC) method and Weighted Product (WP) method to decide on prospective beneficiaries’ social assistance of Program Keluarga Harapan (PKH) or the Family Hope Program. There are 8 criteria used in this research; income of the head of the family, number of dependents, occupation, home ownership, vehicle ownership, condition of the house using Geographical Information system or Global Positioning System (GPS), and having received assistance. The purpose of this study is to facilitate the Samarinda City Social Service in providing appropriate assessments and decisions. The results indicate that the ROC and WP method was successfully applied in decision support systems for the best alternatives with a preference value of 0.000991677.

Intimate Partner Violence among women living in families with children under the poverty line and its association with common mental disorders during COVID-19 pandemics in Ceará, Brazil

BackgroundIntimate partner violence (IPV) is a pervasive public health issue that affects millions of women worldwide. Women living below the poverty line experience higher rates of violence and fewer resources to escape or cope with the abuse, and the COVID-19 pandemic has significantly impacted women’s economic well-being worldwide. We conducted a cross-sectional study in Ceará, Brazil, on women in families with children living below the poverty line at the peak of the second wave of COVID to assess the prevalence of IPV and its association with common mental disorders(CMD).MethodsThe study population comprised families with children up to six years of age who participated in the cash transfer program “Mais Infância”. The families selected to participate in this program must meet a poverty criterion: families must live in rural areas, in addition to a monthly per capita income of less than US$16.50 per month. We applied specific instruments to evaluate IPV and CMD. To access IPV, we used the Partner Violence Screen (PVS). The Self-Reporting Questionnaire (SRQ-20) was used to assess CMD. To verify the association between IPV and the other evaluated factors with CMD, simple and hierarchical multiple logistic models were used.ResultsOf the 479 participant women, 22% were positively screened for IPV (95% CI 18.2–26.2). After multivariate adjustment, the chances of CMD are 2.32 higher in women exposed to IPV than in those not exposed to IPV ((95%CI 1.30–4.13), p value = 0.004). CMD was also associated with job loss during the COVID-19 pandemic (ORa 2.13 (95% CI 1.09–4.35), p-value 0.029). In addition to these, separate or single marital status, as well as non-presence of the father at home and food insecurity were associated with CMD.ConclusionWe conclude that the prevalence of intimate partner violence in families with children up to six years of age living below the poverty line in Ceará is high and is associated with greater chances of common mental disorders in mothers. Also, job loss and reduced access to food caused by the Covid 19 pandemic exacerbated both phenomena, constituting a double burden generator factor on mothers.

Evaluation of Poor Standards Implementation in Sorong City

The purpose of this study is to determine the results of the assessment of poor standards by the Central Statistics Agency (BPS) of Sorong City. This research uses a descriptive method with a qualitative approach. The population in this study consisted of BPS Sorong City staff and Sorong City residents who came from slums. The sampling technique uses purposive sampling with the criteria of BPS Sorong City employees who are qualified to collect information on poor people and community leaders who have information (data). Data collection techniques use observation, interviews, literature review, and documentation with data collection, data reduction, and data presentation. Data analysis techniques use source triangulation. The results show that BPS's poverty criteria standard is considered less effective as a guideline for determining poor people. Because they use the same criteria from year to year and do not pay attention for a long time, moreover the criteria also apply to all of Indonesia, with BPS ignoring the different characteristics of poverty in each region in Indonesia.

An Evaluation of the Effectiveness of Vietnam's Vocational Training Project for Rural Laborers

This study focused on evaluating the project on vocational training for rural laborers in Vietnam with respect to its effectiveness in employment creation and poverty reduction among rural laborers. The investigation took the form of a case study; official policy statistics, a survey of 300 project participants and interviews with eight vocational teachers were collected to triangulate the data. Our findings found that while the project met its initial objectives of increasing the trained laborer rate, creating jobs, and reducing poverty, Vietnam's traditional culture in addition to a lack of funding and vocational training poses potential obstacles to further increases in rural laborers’ income and industrial transformation. In contrast to previous work, this study incorporated multidimensional poverty criteria with three sources of data from different stakeholders’ perspectives to provide a more comprehensive and realistic picture of the long-term poverty reduction effect of the vocational training policy. Further implication for policy practice and policy evaluation method were discussed.

Analisis Daya Dukung Lahan Pertanian di Desa Penyangga Kawasan Ekonomi Khusus Mandalika - Kabupaten Lombok Tengah

The Mandalika Special Economic Zone in Central Lombok Regency plays a role in accelerating the distribution of national economic development in the region and strengthening the economy of the surrounding village communities. This study aims to analyze the carrying capacity of food crop farming land to farmer households in the buffer village of the Mandalika Special Economic Zone. The research method is descriptive and data collection using survey techniques. The research location is in the food crop center supporting villages (Sengkol, Rembitan and Sukadana villages) and the respondents are 45 farmers. (criteria for poverty in Sajogyo and the World Bank) and technical support using the Carrying Capacity Ratio formula. The results showed that the economic carrying capacity equivalent to rice was 312.52 kg/capita/year or US$ 0.55/capita/day and the welfare of farmers was classified as poor. The contribution of economic carrying capacity to the ability to fulfill staple food of farmer households is 65.11%. The carrying capacity of the land is technically high (CCR=1.99) so that the land is still able to: support the life of the population and receive additional population, as well as the development of food crop agriculture which is expansive and explorative in nature. Each farming household can have a planting area that is 1.2 ha larger than the average land ownership. Farming households can own a planting area of ​​1.2 ha larger than their average land ownership. In order for the carrying capacity of food crop farming land to prosper farmers, it is necessary to develop expansive and exploratory land, especially in the dry season and limit the conversion of agricultural land to food crops due to population growth in the buffer villages of KEK Mandalika - NTB.

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).

PERBANDINGAN SMOTE DAN ADASYN PADA DATA IMBALANCE UNTUK KLASIFIKASI RUMAH TANGGA MISKIN DI KABUPATEN TEMANGGUNG DENGAN ALGORITMA K-NEAREST NEIGHBOR

Poverty is a global problem that has occurred in various countries with various impacts. Poverty conditions are characterized by the inability of a person or household to meet the basic needs of life. Socio-economic problems, such as poverty, can be handled using machine learning, one of which is classification. The classification of households based on poverty criteria is expected to assist the government in preparing programs that are right on target. K-Nearest Neighbor is one of the easy-to-use classification algorithms. this classification is based on the closest neighborliness. The problem that can be experienced when classifying is if the data used is imbalanced. The data imbalance will causing the classification process to focus more on the majority class. SMOTE and ADASYN are used to solve the problem of imbalanced data. This study resulted in the addition of SMOTE and ADASYN to imbalanced data can improve classification performance, especially on the G-mean value. G-mean is a performance measure that is widely used in the case of imbalanced data. The result of this study is that SMOTE can increase the G-mean value to 58.5%, while ADASYN is 57.3%. Therefore, it can be concluded that SMOTE-KNN is the best classification model for household poverty classification.

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.

IMPLEMENTATION OF THE AHP-TOPSIS METHOD IN DECISION MAKING OF SOCIAL ASSISTANCE RECIPIENTS IN KARANGANYAR GUNUNG SEMARANG

The government's program of Social Assistance (Bansos) aims to improve the welfare of poverty in order to increase economic value, targeting that the recipients of the social assistance are the poor. There are still some obstacles in receiving social assistance, which are considered to be uneven, duplicate data and mistargeted. There are various models of determining poverty, one of which is based on calculations from the Central Statistics Agency (BPS). These provisions are used by the government in providing social assistance to be right on target. The determination of social assistance recipients according to BPS has 14 poverty criteria, namely: eating as much as a day, medical expenses, sources of income, sources of lighting, cooking fuel, toilet facilities, consumption of types of food, floor area, types of walls, sources of drinking water, savings , type of floor, buying clothes, and education of the head of the household. Using the Analytical Hierarchy Process (AHP) and the Technique Order Preference by Similarity to Ideal Solution (TOPSIS) which are used to rank Social Assistance recipients. The AHP method is used to weight the poverty criteria, while the TOPSIS method is used to rank alternative social assistance recipients, so as to produce recommendations for social assistance recipients. The test was carried out using 45 data from prospective social assistance recipients, the result was that the eligible recipients were in first place A4 with a value of -11.09028, in second place A5 with a preference value of -9.40301, third place A2 with a value of -7.30247 , fourth in A1 with a preference value of -4.33412, and fifth in A3 with a preference value of 9.35849.

The contribution of the carrying capacity of dry land agriculture to the socio-economic level of farming communities in west Lombok Regency

Sekotong District is the area with the largest dry land in West Lombok Regency with a low level of agricultural productivity, but the agricultural sector is still the main livelihood for farmers. The purpose of the study was to determine the carrying capacity of dry land agriculture and its contribution to per capita income as well as to the socio-economic level of farming families in Sekotong District - West Lombok Regency. The method used in this research is a descriptive research and data collection with survey techniques. The results showed that the carrying capacity of dry land agriculture was very low at 0.284 (less than 1.00) and the contribution of the carrying capacity to the income per capita of farmers was Rp.784.454.30/capita/year, and this indicates that the socioeconomic level of farmers are at a poor level. This per capita income, both according to the poverty criteria of Sajogjo, the Central Statistics Agency and also according to the poverty criteria of the World Bank, is still classified as very poor. This condition shows that the contribution of the carrying capacity of agricultural land in Sekotong District to the level of farmers’ welfare is very low. In order to increase the carrying capacity of dry land agriculture which can improve the welfare of farmers, it is necessary to increase production through the intensification of agriculture on the land and suppression of population growth.

Poverty during Maintenance and Relapse Risk in Children with Acute Lymphoblastic Leukemia (ALL): A Report from Children's Oncology Group Study (COG-AALL03N1)

Introduction: There is conflicting evidence regarding the association between poverty and survival in children with cancer. Most studies have relied on surrogate indicators of poverty, such as zip code-based median income for area of residence or on insurance status (Bona, PBC 2016; Kehm, Cancer 2019; Bona, JNCI 2021). We hypothesized that household poverty (measured at the individual level, using self-reported annual household income) during maintenance would be associated with a greater hazard of relapse among children with ALL. Methods: In this secondary analysis of COG-AALL03N1 (primary aim: adherence to oral mercaptopurine during maintenance), we examined the association between household poverty during maintenance and relapse risk in children with ALL. Eligibility for enrollment on AALL03N1 included (1) age <21y at ALL diagnosis; (2) receiving maintenance therapy in first remission. The current analysis was restricted to patients living in US who provided information on annual household income and the number of household members. We used national yearly median poverty thresholds provided by CDC; these take into account the annual household income and the size of family unit (i.e., number of adults and children <18y). For example, the annual income of a three-person household (two parents and one child [patient]) would need to be >$14,480 to be above the poverty threshold in the year 2002 (or >$21,831 in the year 2021). Among those below poverty threshold, we classified families with annual income under 120% federal threshold (median percent below poverty threshold for this cohort) as living in extreme poverty. Hazard of relapse from study enrollment during ALL maintenance therapy was estimated using proportional subdistribution hazard regression models, treating extreme poverty as the primary exposure and adjusting for age at study, sex, NCI risk group, blast cytogenetics, average 6MP and methotrexate dose intensity and time from start of maintenance to study entry. Results: We summarize sociodemographic and disease characteristics of 592 eligible patients in the Table. Median household income of the cohort was $35,000/year (range $10,000->$150,000). Overall, 73 (12.3%) patients met criteria for extreme poverty. Families living in extreme poverty were more likely to self-report their race/ethnicity as Hispanic or African-American and report parental education as high school (HS) or lower. Of note, clinical factors did not differ by poverty level. In a logistic regression, non-white race/ethnicity (odds ratio [OR]=7.9, 95%CI=2.4-26.1, P=0.007; reference: non-Hispanic white) and low parental education (≤HS: OR=4.2, 95%CI=2.4-7.4, P=<0.001; reference: college degree or higher) were associated with greater odds of living in extreme poverty. Cumulative incidence of relapse at 3y (Figure) from study entry among patients living in extreme poverty was higher compared to those without extreme poverty (14.3% [95%CI, 7-24] vs 7.6% [95%CI, 5-10], P=0.04). Patients living in extreme poverty had a 1.97-fold higher hazard of relapse compared to those not living in extreme poverty after adjusting for above mentioned covariates (95%CI=1.03-3.76, P=0.04). The reasons for the association between individual level poverty and relapse risk are likely multifactorial and possibly include poor adherence to oral chemotherapy. We found that patients living in extreme poverty had lower adherence to 6MP compared to those not living in extreme poverty (85.2±19.6% vs 90.6±14.8%, P=0.03). Conclusions: Extreme household poverty is associated with increased hazard of relapse in children receiving maintenance therapy for ALL compared to those living without extreme household poverty. Mechanisms investigating this association need to be explored in future studies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

DECISION SUPPORT SYSTEM FOR SOCIAL ASSISTANCE’S BENEFICIARIES USING AHP – PROMETHEE METHOD IN KELURAHAN KARANGANYAR GUNUNG

Indonesia as a developing country has problems related to poverty. The government tries to overcome the problem of poverty from year to year by launching various aid programs, one of which is the Family Hope Program (PKH). The distribution of PKH social assistance (bansos) can actually help millions of beneficiaries every year, but there are also obstacles related to the lack of precise targeting of beneficiaries, which often occurs because there is no definite standard in the hierarchy of criteria for beneficiaries. Using the Decision Making System (SPK) with the Analytical Hierarchy Process (AHP) - Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method can provide an alternative to prioritizing aid recipients to assist decisions made by officers and related office holders. The AHP method is used to weight the poverty criteria, while the PROMETHEE method is used to rank the priority of social assistance recipients. Using 45 data on prospective PKH social assistance recipients in Karanganyar Gunung Village who were selected based on 14 poverty criteria, the Central Statistics Agency (BPS) resulted in a priority ranking of social assistance recipients. The ranking is based on the netflow value, the higher the value, the higher the priority level. The ranking using this method gives results in the form of residents with the first priority of receiving social assistance with a net flow value of 0.342 owned by Sumirah followed by Meliyani Kusrini with a net flow value of 0.325.

Heterogeneity Perspective on the Dynamic Identification of Low-Income Groups and Quantitative Decomposition of Income Increase: Evidence from China

Common prosperity is an essential requirement of socialism. To achieve common prosperity, unbalanced urban–rural development, insufficient regional development, and progressively increasing income disparity between groups must be solved. The measure of raising the low and expanding the middle is receiving more and more attention in gradually achieving common prosperity. In order to formulate long-term income increase strategies for low-income groups with regional characteristics, this article, based on income group data, uses the weak relative poverty criterion to dynamically identify low-income groups and decomposes the changes in the income increase in low-income groups from urban–rural, regional and provincial perspectives to explore the causes and the extent of their influence on the income increase in low-income groups. This paper concludes that the size of low-income groups is generally on a declining trend, while the size of urban low-income groups has increased. Inequality in income distribution reduces the income increase in low-income groups; individual heterogeneity is a key factor that promotes the income increase in low-income groups. It is more difficult to increase the income of low-income groups in central and western rural areas than in eastern rural areas. In the future, on the basis of classifying the paths of income increase for low-income groups in provincial districts and implementing the corresponding income increase policies by combining the characteristics of different categories, it helps to formulate a long-term income increase mechanism for low-income groups that combines the characteristics of regional development and meets the actual regional development. It will promote the long-term income increase in low-income groups and gradually realize common prosperity.

Making Ends Meet: Employment, Cash Transfers, and Poverty in Post–9/11 Era Wounded Military Veterans

Over 36% of post–9/11 era veterans have a disability, but little research has examined wounded veterans’ finances. We investigated main and interaction effects of income sources and demographic, military, and mental health characteristics on household poverty and deep poverty to better understand wounded veterans’ financial outcomes. Data were drawn from the 2018 Wounded Warrior Survey ( n = 33,067). Two logistic regression models investigated direct associations between independent variables and poverty outcomes; predictive margins and average marginal effects were calculated for employment and cash transfers on outcomes. 56% of respondents reported full- or part-time employment, and 17% met criteria for household poverty. Of those, 75% met criteria for deep poverty. Cash transfers were negatively associated with poverty and deep poverty, and part-time employment was positively associated with poverty compared with not working. Employment was insufficient protection against poverty in this sample of wounded veterans—transfers were a critical bulwark.

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.

Penerapan Metode SAW (Simple Aditive Weight) Dan AHP (Analitical Hirarchi Process) Dalam Menentukan Kategori Penduduk Miskin

Residents are all people who are domiciled in the territory of the Unitary State of the Republic of Indonesia for 1 year or more or those who are domiciled for less than 1 year (2020 Population Census concept). Based on the mandate of the 1945 Constitution, especially articles 33 and 34, the state is responsible for protecting the entire Indonesian nation and promoting general welfare in the context of realizing social justice for all Indonesian people. To facilitate interpretation, changes in welfare levels were assessed according to ten (10) criteria for poverty, with the variables set by the Social Service and the Central Statistics Agency.Poverty is a condition of limited ability to meet the needs of a decent life such as limitations in income, skills, health conditions, mastery of economic assets, or access to information. Measurement with a monetary approach can be done using expenditure data as an approach to household income. Then this expenditure data is compared with a limit of the rupiah exchange rate needed to meet the minimum living needs. This limit is often referred to as the poverty line. There are various indicators in determining the poor population, namely based on the status of the house, occupation, number of dependents, fuel for cooking, water sources, and electricity. Determining the poor has problems because they have to sort the data values of each data sheet in the form of paper,The decision support system using the Simple Additive Weighting (SAW) and Analytical Heirarchy Process (AHP) method takes a case study in Sembawa sub-district. The decision support system can assist decision makers in Sembawa District in determining the poor, and can be used as a basis for providing assistance. Keywords : DSS, SAW, AHP, Poor, Criteria, weight.

Subjective satisfaction and objective electricity poverty reduction in Vietnam, 2008–2018

PurposeThe authors estimate the reduction of electricity poverty in Vietnam. The essential argument is that human development is about subjective feeling as much as technology and income.Design/methodology/approachThe authors use a self-reported satisfaction indicator as complementary to objective indicators based on national household surveys from 2008 to 2018.FindingsIn 2010, the fraction of households with access to electricity was over 96%. However, over 24% declared their electricity use did not meet their needs. Since 2014, the satisfaction rate is around 97%, even if 25% of the households used less than 50 kWh/month. Today there is electricity for all in Vietnam, but electricity bills weigh more and more in the budget of households.Practical implicationsThe subjective energy poverty measure allows better international statistics: unlike poverty or needs-based criteria, self-assessed satisfaction of needs compares across income levels and climates.Social implicationsInequalities in electricity use among Vietnamese households decreased during the 2008–2018 period, but are not greater than inequalities in income, contrary to the findings of Son and Yoon (2020).Originality/valueEngineering and econometric objectivist approaches dominate the literature on sustainability monitoring. Out of 232 sustainable development goal (SDG) indicators, only two are subjective. Yet the findings show that subjective indicators tell a different part of the story. Access is not grid building, but the meaningful provision of electricity to satisfy the needs.

Impacts of Payment for Forest Environmental Services in Cat Tien National Park

This paper assesses the impacts of Payment for Forest Environmental Services (PFES) in Cat Tien National Park, Vietnam. We analyze the impacts and additionality of PFES on local livelihoods by comparing the socio-economic situations in four pairs of villages before and after its implementation, and between places where PFES is and is not applied. In total, 149 people participated in focus group discussions, while 244 households (123 in areas with PFES and 121 in areas without) took part in household surveys. Our research shows that 92% of the people interviewed are from ethnic minorities participating and benefiting directly from PFES. In villages with PFES, the numbers of participating households ranged from 45% to 88% of all poor households in those villages. Of the poor households participating in PFES in the studied villages, 22% have no source of cash income other than their forest protection contracts, while 81.4% have escaped poverty, based on self-defined poverty criteria, through additional income from forest protection. Since the implementation of PFES, the area of forests allocated for community and household management is estimated to be three to 3.64 times higher than it had been previously. Although the number of communities under PFES contracts has not changed, the number of households participating in forest protection contracts is now much lower than before PFES started. On average, PFES contributes 16% to 74% of total household income in villages with PFES. Incomes in places with PFES are significantly higher than in places without. Although our research demonstrates immediate positive socio-economic impacts on livelihoods, it also highlights weaknesses in the current monitoring and evaluation system and a lack of reliable data for measuring PFES impacts in Cat Tien National Park.