Household Poverty Status Research Articles

Evaluating the effects of digital finance on urban poverty

This study examines the impact of digital financial services development on urban poverty. Utilizing the vicious cycle of poverty theory, we established a multidimensional poverty measurement index to assess the poverty status of urban households and propose strategies for leveraging digital finance to address urban poverty. Through empirical research based on a comprehensive dataset from China covering the period from 2012 to 2020, we evaluated the moderating effect of digital financial growth on urban poverty. Our analysis, which categorized poor urban households into five groups—laid-off workers, unemployed individuals, migrants, the elderly, and disabled persons—revealed that only the unemployed and migrants experience significant benefits from digital financial development. Lastly, we explore the underlying mechanisms and emphasize the role of investment, financing, and entrepreneurship in utilizing digital finance for poverty alleviation.

Effect of Flood on Poverty Status: Evidence from Sugar cane Farmers in Kwara State and Osun State of Nigeria

In Nigeria, sugarcane is a prominent crop and the primary crop used to produce sugar. However, floods have been a major tragedy that has troubled Nigeria's small-scale farmers. The bulk of rural poor people are these small-scale farmers, and as poverty rates have been rising over time, corrective action is required. This study was therefore carried out to assess the poverty status of sugar cane farming households (affected by flood, and unaffected by flood) in Kwara/Osun State of Nigeria. The study employed two-stage sampling technique to select 120 sugarcane farming households in the study area. Descriptive statistics, the Flood Loss Estimation, Logit Regression and Foster Greer Thorbecke (FGT) are the empirical analytical tools employed to analyze the data collected for the study. The results of the poverty index analysis revealed that 46 percent of the respondents are relatively poor, the poverty depth was 0.16 which implies that 16 percent were below the poverty line and the poverty severity was 0.07 which implies that 7 percent of the respondents were severely poor. Also, it revealed that flood shocks, gender, household size, household income, access to credit and membership to social organization were significant determinants of poverty. The study then came to the conclusion that many coping mechanisms people employ are corrosive because they have detrimental long-term implications on the sustainability of household subsistence. For future risk assessments and flood mitigation, the study advises using a holistic strategy and adept models. The remedies outlined in this paper would help the government, private sector, and sugar cane farmers economically.

Multi-Dimensional Poverty in the Newly Merged Tribal Districts of Khyber Pakhtunkhwa: Identifying The Key Areas of Priority

Poverty measurement is a complex global challenge and a key target within the Sustainable Development Goals. Poverty is not a unidimensional issue tied solely to income or consumption but is instead a multidimensional phenomenon. This study investigates multidimensional poverty in the economically disadvantaged province of Khyber Pakhtunkhwa, Pakistan, with a specific focus on the newly merged tribal districts. Utilizing a nationally representative dataset, PSLM/HIES 2018-19, from the Pakistan Bureau of Statistics, the analysis is based on a sample size of 4464 households from Khyber Pakhtunkhwa. The study employs the Alkire-Foster methodology to measure multidimensional poverty, using dual cutoff points and constructing an index that incorporates nine dimensions: economic activity, living standards, environment, assets, education, health, food security, ICT access and women empowerment. The findings reveal a severe imbalance in poverty distribution across the province, with the seven recently merged tribal districts emerging as the poorest. Rural areas exhibit significantly higher multidimensional poverty compared to urban regions. Key covariates of poverty include remittances, the number of working household members, urban or rural location, and access to government canals for irrigation, all of which play a significant role in shaping household poverty status.

Contribution of agroforestry practices to income and poverty status of households in Northwestern Ethiopia

Agroforestry practices (AFPs) play a critical role in enhancing income and reducing poverty. This study assessed the effect of AFPs on income and poverty status of farmers in Lay Armachiho (LA), Bahir Dar Zuria (BDR), and Banja districts of Northwestern Ethiopia. 387 households, and 63 key informants were interviewed. Data were analyzed using descriptive statistics, FGT index, Gini coefficient, and multiple linear regression. About 49.35% of the respondents are categorized poor with a poverty gap of 18.93 and a poverty severity level of 9.7. Banja was the greatest with persons below poverty level (59.2%), followed by BDR (49.72%). Agroforestry practices contribute 28.43% to household income. Income from AFPs lowered the poverty ratio, poverty gap index, and poverty severity level of households by 13%, 9%, and 7%, respectively. Income from AFPs lowered the area between the line of equality and the Lorenz curve, as well as the Gini coefficient, by 7.97%. AFPs also lowered the income disparity of households in all districts. Age, AFPs land size, road accessibility, irrigation, AFPs experience, and AFPs types affect households AFPs income positively. Family size and membership to credit institutions had a negative effect. Thus, in order to lower poverty and raise household income, labor productivity, the credit service system, the road and irrigation infrastructure, and AFPs all need to be improved and scaled up.

Effect of Bio-fortified Vitamin A Cassava on Poverty Status of Farming Households in Nigeria, Evidence from Oyo State.

ABSTRACT Profitable farming is a prime target of all farmers regardless of the scale of production adopted and the veritable opportunity to attain the threshold of bumper harvest is not unconnected with the adoption of new technology. Bio-fortified vitamin A cassava remains the best option for farmers to experience improvement in cassava performance in both the quality and quantity terms and they are encouraged to adopt it in order to improve their households’ welfare. The survey was launched to investigate the effect of bio-fortified vitamin A cassava on the poverty status of the farming households in Nigeria using Oyo State as a case study. A Multistage sampling technique was used to sample a total of 186 cassava farmers comprising 92 adopters and 94 non-adopters of bio-fortified vitamin A cassava variety using copies of well-structured questionnaires. Parametric tools used were descriptive statistics and probit regression for socioeconomic characteristics and adoption status analysis. The socioeconomic results showed that both the adopters and non-adopters were in the productive ages of 42 years and 59 years respectively. The majorities of farmers in the two categories were married and had formal education. Years of experience possessed by them were 11 years and 18 years while both groups were members of associations. Poverty classification result showed 41.3% of adopters being poor while 83.0% of non-adopters were poor. Marital status (2.1271<p=0.05), education (1.1510; p<0.1), seasonal income (0.0757; p=0.05) and poverty status (I.6019; p<0.1) had influence on the farmers’ adoption status while lack of credit was voted highest among the factors militating against adoption of bio-fortified vitamin A cassava. It is therefore recommended that stakeholders in the crop enterprise should make frantic efforts in providing credit and other basic inputs that will enhance improved vitamin A adoption.

Multidimensional poverty among farming households in Katsina state, northwestern Nigeria

This study aimed to determine the multidimensional poverty status of farming households in Katsina state, northwestern Nigeria. A multi-stage sampling technique was used to elicit primary data from 480 farming households. Descriptive statistics and the multidimensional poverty assessment tool (MPAT-14) were used to analyze the data. The socio-demographic analysis showed that the mean age of respondents was 43 years while the average household size was 9 persons. The majority (86%) of respondents were male, and they were mostly married (85%) and educated (51.3%). The MPAT results showed that farmers in the study area experienced a high level of deprivation according to the majority of the fundamental human needs indicators, such as food and nutrition security (27.7%), domestic water supply (28.9%), health care (22.9), sanitation and hygiene (28.1%), housing, clothing, and energy (33.9%), and education (27.7%). The results indicate that the majority (60.6%) of the households are living in multidimensional poverty and that there is a higher level of multidimensional poverty among farming households. The government should upscale appropriate healthcare delivery systems to improve the healthcare services rendered.

Comparison of Chi-Square Automatic Interaction Detector (CHAID) and Random Forest Methods in the Classification of Household Poverty Status in Central Java

Central Java was in second position as the province with the highest number of poor people in Indonesia in March 2020. Poverty alleviation efforts have been carried out, but many are still not yet on target. The purpose of this study was to model the classification of household poverty status in Central Java using CHAID and random forest methods and compare the two methods. The data used in this study is data from the 2020 National Socioeconomic Survey (SUSENAS) conducted by the Central Bureau of Statistics (BPS) for Central Java. The number of poor households is much less than non-poor households. Therefore, Synthetic Minority Oversampling Technique (SMOTE) was performed to handle unbalanced data. The random forest method produced better classification performance than the CHAID method with accuracy, sensitivity, specificity, and AUC of 93,95%, 98,43%, 89,92%, and 0,9417, respectively. The important variables that build the random forest model are the floor area of the house, the age of the head of the household, cooking fuel, the place for the final disposal of feces, and ownership of the place to defecate.

Gender differences in the economic consequences of life‐long singlehood among older white U.S. adults

AbstractObjectiveDrawing on life course frameworks, this study examines how never married older adults differ from their married, cohabiting, divorced, and widowed peers with respect to three dimensions of late‐life economic security, and gender differences in these associations.BackgroundLifelong singlehood has become increasingly common over the past five decades, although little is known about the economic security of never married older adults relative to their currently and formerly married peers.MethodData are from the Wisconsin Longitudinal Study (WLS), which tracked Wisconsin high school graduates from ages 18 (1957) to 72 (2011). The 2011 analytic sample includes 5269 persons (2498 men and 2711 women). OLS and logistic regressions are used to predict total household income, wealth, and poverty status at age 72, adjusted for covariates.ResultsLifelong single men have higher poverty rates and lower income than men in all other marital categories, although divorced men evidence the lowest levels of wealth. Lifelong single women fare worse than married and cohabiting women but better than divorced women. Older men are more financially secure than women in every marital status category except lifelong singles.ConclusionBy centering the experiences of never married older adults, results reveal the economic precarity of lifelong single men and distinctions among subgroups of unmarried women. We document the persistence of gender inequality, where men consistently fare better than women across marital statuses.ImplicationsPublic policies should recognize growing heterogeneity in older adults' marital statuses and the implications thereof for their late‐life economic security.

The role of investment for poverty alleviation in Yogyakarta: Evidence from logit regression

This article discusses the Solow-Romet theory of economic growth, aiming to explain the relationship between household poverty in the Province of Daerah Istimewa Yogyakarta (DIY) through investments in education, quality of life improvement, and health. The study examines various influence variables, including Non-Formal Education (XNF), Educational Scholarships (XBS), Protein Consumption (PK), Disease Prevention Costs (XL), Calorie Consumption (IK)), Health Insurance Variables (XAS ), and Food Security (XT) in assessing the poverty status of households in the Special Region of Yogyakarta in 2021. The estimation model employs a logit mode approach, using data from Susenas (National Socioeconomic) data for the Special Province of Yogyakarta, with a sample size of 4044 households. The findings of this study indicate that investments in non-formal education, school fees, and educational scholarships do not significantly affect family poverty status. However, increased investment in quality of life (such as calorie consumption) and health (including disease prevention and health insurance spending) will affect the poverty status of households in the Special Province of Yogyakarta in 2021.

Socioeconomic Determinants of Poverty Reduction among Irrigating Farmers in Mberengwa District, Zimbabwe

Sustainable Development Goal 1 aims to end extreme poverty everywhere by the year 2030. Smallholder irrigation development is arguably a vital strategy to reduce rural poverty. The authors assessed the socioeconomic determinants of poverty reduction in Mberengwa district, Zimbabwe. Data were collected from 444 randomly selected households. Data were analyzed using SPSS version 27 and Microsoft Excel 2019 software packages. Chi-square tests, t-tests, and Foster–Greer–Thorbecke (FGT) poverty index and binary logistic regression model tests were performed. The chi-square test results show an association between access to irrigation and farmer’s level of education (p < 0.01). The t-test results show significant differences between irrigators and non-irrigators for household size (p < 0.01), household labor (p < 0.05), and rainfed plot size (p < 0.05). FGT indices show that the poverty incidence, depth, and severity were lesser for irrigators than non-irrigators. The binary logistic regression model results show that age, household size, access to irrigation and household income significantly influence household poverty status. In conclusion, access to irrigation reduces poverty in rural areas. However, access to irrigation is not a panacea for poverty reduction in rural areas. Smallholder irrigation development policies should consider socioeconomic determinants of poverty reduction to properly target and tailor interventions, and increase the relevance and effectiveness of poverty reduction efforts.

Determinants of Household Poverty: Identification Using Logistic Regression

The fight against poverty is considered one of the most critical tasks in achieving sustainable development goals. This study aims to identify the key determinants of household poverty in Algeria. The information base is data from the Sixth Cluster Survey (MICS 6) conducted by the National Statistics Office during 2018-2019 with a sample of 31,325 households in each province, which are divided into three groups (poorest, poor, non-poor) using a well-being index, which is based on the data about household assets and living conditions. Polynomial logistic regression was used to model the correlation between the poverty status of households and various demographic (household size, gender, age and education level of the head of the household), socio-economic (employment of the head of the household) and spatial (place of residence (urban/rural), region) characteristics. The results showed that a higher level of education of the head of the household significantly increased the probability of households moving from the “poorest” to the “poor” or “non-poor”. As the age of the household head increases, the likelihood that the household will be non-poor increases. The gender of the head of the household was not a statistically significant factor. The increase in the number of dependents as a factor of creating additional financial pressure and falling into poverty is essential only in a situation of a very drastic change in status – when moving from the status of “non-poor” to “the poorest”. In contrast, a slight change in well-being (a shift from “poor” to “poorest”) is not associated with an increase in the number of household members. Modeling showed that extreme poverty is more common in rural areas than in cities. Regional differences are also revealed, as the poverty level is higher in the southern regions than in the northern ones. The slight increase in household welfare (transition from the category of “poorest” to “poor”) in Algeria is not due to the economic activity of the head of the household. Still, it may be related to the nature of the analyzed data (only the fact of employment was considered without specifying the nature and type of activity). Instead, a significant change in welfare (moving from the category of “non-poor” to “poorest” households) directly depends on whether the head of the household is employed or unemployed. The results of the study are essential in the context of the development of a targeted policy to reduce the poverty risks of Algerian households: increasing the level of education and skills should be a priority, especially for young people and in disadvantaged regions, targeted programs for the development of rural areas and mechanisms for the fair distribution of public investments between provinces are needed to overcome regional disproportions.

Poverty and birth cohort effects of experiencing the 2007-2009 Great Recession during adolescence on major depressive episodes and mental health treatment of young adults in the United States.

Household economic adversity during adolescence is hypothesized to be a risk factor for poor mental health later in life. To test this hypothesis, we conducted a quasi-experimental analysis of an economic shock, the Great Recession of 2007-2009. We tested if going through adolescence during the Great Recession was associated with increased risk of major depressive episodes (MDE) and mental health treatment in young adulthood with potential moderation by household poverty to explore differences by economic adversity. We analyzed data on young adults age 18-29years from the 2005-2019 National Survey on Drug Use and Health (N = 145,394). We compared participants who were adolescents during the recession to those followed-up prior to the recession. Regression analysis tested effect modification by household poverty status. Adolescent exposure to the Great Recession was associated with higher likelihood of MDE during young adulthood (aOR = 1.30, 95% CI = 1.23, 1.37); there was no relationship with mental health treatment. Effects on MDE were stronger among those in households with higher incomes compared to those living in poverty. Findings support the hypothesis that exposure to the Great Recession during adolescence may have increased risk for MDE, but raise questions about whether the mechanism of this association is economic distress.

Does shifting from subsistence to commercial farming improve household nutrition and poverty? evidence from Malawi, Tanzania and Nigeria

The current study sought to assess the effect of smallholder crop commercialization on household nutrition security and poverty status. Recent government efforts have gone beyond investing in agricultural production, thereby establishing markets for smallholder farmers in order to commercialize the agricultural sector. As such, developing countries like Malawi, Tanzania, and Nigeria are slowly transitioning to a market economy in order to improve the livelihoods of their people. To that effect, the study used country-wide representative data from Malawi, Tanzania, and Nigeria collected under the World Bank Living Standards Measurement Surveys (LSMS) and employed an instrumented-censored (probit and tobit) model to solve for endogeneity bias. The results show that poverty and nutrition insecurity were higher among subsistence farmers, emphasizing the need for a shift towards commercialization of the country's agricultural sector. Furthermore, farmer social and institutional context significantly influenced market participation. The study hence recommends a tailor-made extension delivery system, cutting across gender divides and other social barriers among smallholder farm households, in order to improve crop production among subsistence farmers, ensure household food security, and increase income from the sale of surplus crop output.

The Influence of Social Capital and Farmers’ Specific Factors on the Poverty of Rural Farm Households in Akwa Ibom, State, Nigeria

This study analyzed the effect of social capital and other relevant factors on poverty of farm households and identified constraints militating against effective social capital formation among poor rural farm households belonging to associations in Akwa Ibom State, Nigeria. A multi-stage sampling technique was utilized to choose 180 rural farm households within the research location. The FGT methodology was used to generate indices of poverty. Descriptive statistics, Tobit model and factor analytical procedures were employed to analyze the objectives of the study. The estimated model showed high degree of fitness, thus justifying the estimation method used. The results revealed that, the following social capitals: farm size acquired from the association, income derived from membership of association, number of visits of extension agents, interest amount paid on loan acquired from association, fertilizer, seeds, pesticides and farm land acquired from the association have a significant negative relationship with the poverty of rural farm households in the study area. By implication, increase in the stock of these social capitals by poor farm households would significantly enhance the reduction of poverty depth among them. Moreover, other factors which affected farm household poverty status were sex of household heads, age, household size, years of formal education, farm income and ownership of assets. The results also found 8 categories of constraints militating against effective social capital formation among poor rural farm households. The constraints categories are: leadership, management style and low income; financial constraint and unconcerned attitude of members towards group activities; executive characters; ineffective coordination and unfair distribution of benefits to members; lack of seriousness of members; poverty; illiteracy and incompetence as well as defaults and partiality. From the result, it is concluded that social capital accumulations reduce the probability of being poor. Based on the high percentage of poverty prevalence in the State, it is concluded that poverty is endemic in the rural areas of Akwa Ibom State and specifically among farm households. This study lends support to recent emphasis on investing in social capital as a major means of poverty reduction in the rural areas.

The Impact of Rastra-BPNT Assistance on Household Food Expenditure Patterns and Poverty Status in Indonesia

This study uses Susenas March 2019 raw data in a cross-section of 34 provinces and 514 districts/cities in Indonesia with a coverage of 315,672 households, designed using a quasi-experiment on the treatment group and control group after program implementation. The treatment group was formed by beneficiary families while the control group was formed by non-beneficiary families. The sample of beneficiaries was 50,485 or 9,845,922 households if calculated with Susenas weighting (65.55% of the total 2019 realization of 15,020,323 national beneficiaries) and non-beneficiaries were 181,358 households. The data were analyzed using a probit regression model and impact estimation was conducted using the Propensity Score Matching (PSM) approach. The results suggest that education level is a bigger determinant for households to become Rastra-BPNT beneficiaries, especially for household heads who did not finish primary school and those who graduated from primary school. Rastra-BPNT food social assistance has a significant and positively correlated impact on beneficiary families with a pattern of food proportion to total expenditure > 50%. There was a shift in the food consumption budget share for Beneficiary Families in several dominant commodities such as grains; eggs and milk; processed food and beverages; and cigarettes, tobacco and betel. The distribution of Rastra-BPNT from November 2018 to February 2019 can reduce the gap between household groups, but there are still errors in the distribution of benefits.

DETERMINANTS OF POOR FARMING HOUSEHOLDS IN SOUTH KALIMANTAN PROVINCE IN 2023

South Kalimantan Province is one of the provinces in Indonesia that makes the agricultural sector a vital sector in its economy. According to BPS (2023), 48.86 percent of poor households in Indonesia have the main source of income in the agricultural sector. For this reason, it is necessary to measure poverty in agricultural households to provide input in overcoming poverty problems in the agricultural sector. By utilizing data from the March 2023 Socio-Economic Survey (Susenas) and two analysis methods, namely descriptive and binary logistic regression, this study aims to obtain an overview of the characteristics of agricultural households and identify what socio-demographic factors affect the poverty status of agricultural households. Based on the results of Susenas data processing in March 2023, 40.96 percent of agricultural households in South Kalimantan Province were obtained. The agricultural households are dominated by the plantation subsector which reaches 46.26%, followed by rice and palawija agriculture (32.50%), fisheries (10.59%), horticulture (4.94%), livestock (3.47%) and forestry and other agriculture (2.24%). The results of this study also show that agricultural households are dominated by those who live in rural areas, the head of the household is male, the average age of the head of the family is 45 years to 55 years, the last education below junior high school, the number of household members is 3 to 4 people, owns land and the head of the household has an agricultural business. Using the Poverty Line (GK) cut off point of each district/city, of the 3,400 agricultural households, 1.59% were poor, while 98.41% were not poor. The results of the binary logistic regression analysis show that the variables that have a significant effect on the poverty status of agricultural households in South Kalimantan Province in 2023 are the classification of regions, the gender of the head of the household, the age of the head of the household and the number of household members.

Children's Resilience to Not Working Before and During the Pandemic in Rural Indonesia

Children are an asset to a country and a resource that will further national and international development goals. Children who work will be threatened by their health, safety, education, and development. In the last 10 years, BPS has recorded the percentage of child labor fluctuating with the latest record in 2021 at 2.63 percent. The high percentage makes the phenomenon of child labor still a global concern to be addressed as contained in the Sustainable Development Goals. Therefore, the purpose of this study is to determine the determinants of children's resilience not to work in rural areas during the observation period before and during the COVID-19 pandemic. This study uses a three-level survival analysis method by utilizing Sakernas data for August 2019-2020 and data from the BPS website. The results of this study obtained that the percentage of children aged 5-17 years who worked increased during the pandemic by 5.62 percent to 18.88 percent in rural areas. The variables of child gender, child education, child status at individual level; household head gender, household head occupation sector, and household poverty status at household level; and percentage of poor population at districts level significantly affect the resilience of children aged 5-17 not to work in the period before and during the COVID-19 pandemic.

EFFECTS OF POST-HARVEST LOSSES ON POVERTY STATUS OF VEGETABLE FARMING HOUSEHOLDS IN KANO STATE, NIGERIA

The study focused on effects of post-harvest losses on poverty status of vegetable farmers in Kano State, Nigeria. Multi-stage sampling technique was used for sample selection and six (6) Local Government Areas (LGAs) were randomly selected from the list of the major vegetable producing LGAs in the State. Vegetable farmers that practice the production of both tomatoes and onions were identified and purposively selected and included in the study making a total of 192 respondents. The data collected were analyzed using frequency counts, percentages, mean, minimum, maximum, cross-tabulation, Post-harvest loss estimation (PHLE) model, Foster-Greer-Thorbecke (FGT) model, and T-test. The results of the socioeconomic characteristics analysis revealed that, 83.9%, 82.3%, and 96.9% of the vegetable farmers were males, married and were within an active working age (15-60yrs) respectively. The farmers indicated vegetable farming as their major source of income (81.2% of the respondents) and only 3.6% of the respondents had formal education up to the tertiary level. Among the two enterprises examined, the mean value of loss per hectare for Tomato was ₦120,905.2, and that of Onion was ₦36,860.16. For the two enterprises combined, the mean value of loss was ₦157,765.3 with a Post-harvest loss index (PHLI) value of 0.25. The study also revealed poverty incidence among vegetable farmers in the study area was 59.9% of the total respondents. From the households that were determined to be poor, 35.7% were only poor because of the post-harvest losses they incurred. It can be concluded based on the findings of this research that, post-harvest losses significantly affect the poverty status of the vegetable farming households in the study area. To reduce problems associated with post-harvest losses and its effects on poverty status, the study recommends improvement in the traditional harvesting and storage methods coupled with improved management practices, training farmers on integrated pest management as well as on post-harvest handling and storage of the crops they handle as this could go a long way to reduce poverty prevalence among the vegetable farmers.

The Energy Poverty Status of Off-Grid Rural Households: A Case of the Upper Blinkwater Community in the Eastern Cape Province, South Africa

This paper analyses the energy poverty status in off-grid rural households and its underlying socioeconomic factors. Employing the Foster–Greer–Thorbecke Technique and Probit regression on data from 53 households, the study uncovers a diverse array of energy sources in use, including firewood, paraffin, LPG, candles, and generators. Despite this energy source diversity, the poverty line threshold, as measured by the per capita energy expenditure line (92.40 ZAR) (1 US Dollar = ZAR 18.20), reveals the prevalence of energy poverty. Approximately 15% of respondents are experiencing severe energy poverty and 22% are facing moderate vulnerability to energy poverty, while over 50% are not energy poor. This indicates that, although they may lack access to electricity, their energy usage and expenditure in other forms might still be sufficient to meet their basic energy needs. This distinction highlights the importance of assessing energy poverty, extending beyond a simplistic assessment of absolute poverty but taking into account the dynamic nature of income levels. Gender, household size, formal education, and social grants emerge as key indicators shaping the energy landscape in the area. The results clearly indicate that male-headed households and larger households are less susceptible to energy poverty, while increasing formal education and social grants increases the risk of households being exposed to energy poverty. These findings suggest that the problem of energy poverty in the area is uniquely linked to social, economic, and cultural issues. Therefore, interventions targeted at addressing energy poverty problems must address the underlying social, economic, and cultural factors.

Classification of household poverty in West Java using the generalized mixed-effects trees model

Dealing with fixed effects and random effects can be accomplished by combining statistical modeling and machine learning techniques. This paper discusses the modeling of fixed effects and random effects using a statistical machine-learning approach. We used the generalized mixed-effects trees (GMET), a tree-based mixed-effect model for dealing with response variables that belong to the exponential family of distributions. In this study, both simulation and actual/empirical data utilized the GMET method to discover data conditions that were appropriate for employing this approach. The simulation data was generated using different response variable generations, as well as different values of the variance of random effect and fixed effect coefficients. The findings indicated that the GMET performs similarly for different response variable generation scenarios. However, it performed better when the fixed effect value and the variance of random effects were large. When applied to the empirical data, the GMET method describes fixed effects and random effects and classifies household poverty status quite well based on the area under curve (AUC) value. It has also revealed that important variables for poverty classification are the number of household members, owning land, the type of main fuel used for cooking, and the main source of water used for drinking. In order to address the socioeconomic disparity that leads to poverty, the government may become concerned about these factors. In addition to that information, the use of regional typology as a random effect in the model has also contributed to the variation of household poverty status. Based on research, the fixed effects in mixed models do not need to be linear and GMET may be employed in grouped data structures, giving the GMET technique the ability to compete with other approaches/methods.