Poverty Line Research Articles

The Impact of Population and Unemployment Rates on Economic Growth and Poverty in East Kalimantan from 2011 to 2023

Economic growth serves as a fundamental gauge for assessing the progress of a region’s economic development within a specific time frame, primarily driven by production activities. In the context of Indonesia, the economic growth of East Kalimantan Province stands as a positive indicator. It ranks among the top 10 provinces in the country, boasting a growth rate of 3.18%. Nevertheless, this positive growth is juxtaposed with a concerning poverty rate of 6.31%, signifying areas within East Kalimantan Province that require focused attention. One of the prominent contributing factors to this high poverty rate in East Kalimantan Province is the combined influence of the total population and the open unemployment rate (TPT). This composition plays a pivotal role in the region’s poverty scenario. This narrative aims to examine the direct correlation between population size, unemployment rates, and economic growth in East Kalimantan Province. Additionally, it endeavors to analyze how population numbers and unemployment rates, either directly or indirectly, impact poverty levels through their influence on economic growth in the same province. The dataset utilized in this study comprises secondary time series data spanning from 2011 to 2022, sourced from the BPS of East Kalimantan Province. Employing the path analysis method through SPSS 25 analysis tools, this research concludes that population size and the unemployment rate exert a direct influence on economic growth in East Kalimantan Province. However, their direct effects on poverty levels, along with economic growth, do not show significant correlations. Interestingly, the indirect effect of population size and unemployment rates on poverty levels, mediated through economic growth, is discernible. This suggests that while these factors may not directly impact poverty rates, their influence is manifest when channeled through the conduit of economic growth. The practical implications and theoretical contributions of this study will be further developed in subsequent research endeavors. Keywords: population, unemployment rates, economics growth, poverty

TEACHERS’ SILENT SCREAM: QUIET QUITTING

The aim of the study is first to discover if quiet quitting act exists among teachers at schools, and then, if there are teachers who are a part of this movement, to disclose the indications of quiet quitting act and the reasons that push them to quiet quit. The study was designed in accordance with the qualitative phenomenology pattern and carried out with 8 teachers selected by the snowball sampling method, which is one of the purposive sampling methods. Semi-structured face-to-face interviews were conducted to collect data and the data was analyzed by content analysis technique. In the light of the analysis, “indications of quiet quitting among teachers” and “reasons for quiet quitting among teachers” themes emerged. The result of the study reveals that teachers are part of the quiet quitting act. Teachers’ ending their psychological contract, organizational commitment and belonging are internal indications, while holding back on duties that are outside the job description, not taking on new duties, and not taking responsibility for duties requiring them to spend overtime or outside working hours are external symptoms. As for the reasons, teachers quiet quit because the meaning they attach to their profession is deteriorated and their efforts are rendered worthless because of the financial challenges they experience. Moreover, due to the precarization they experience stemming from the paid teaching practice and the loss of prestige in the society, teachers quiet quit. Finally, the pandemic is among the reasons that lead teachers to quiet quit due to the loss of motivation it causes and the visibility of work-life imbalance. Accordingly, it is recommended to make the precarization process of the teaching profession visible, to abolish the practice of paid teaching, and to regulate teacher salaries below the poverty line.

PROVINCIAL SEGMENTATION IN INDONESIA: EXPLORING FACTORS INFLUENCING EDUCATION WITH SEM-PLS METHOD, INCORPORATING MODERATION EFFECTS AND FIMIX-PLS APPROACH

The significance of education as a developmental metric is underscored by its designation as the 4th goal in the SDGs, which emphasizes ensuring inclusive, equitable, and high-quality education while also expanding lifelong learning opportunities for all. This research relies on two primary sources: secondary data from publications by the Indonesian Central Statistics Agency (BPS RI) in 2023 and the BPS website. The educational variables examined in this study are believed to be influenced by latent variables, including school performance, infrastructure, and poverty levels. Employing the Finite Mixture Partial Least Squares (FIMIX-PLS) approach, the research identified 13 valid and reliable indicators of educational variables. It delineated three regional groups based on the lowest BIC and CAIC values. In this structural equation research, the moderation effect is seen in the significance of the indirect relationship, especially the influence of Regional Poverty on Education with School Outcomes as a moderating construct.

MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE

Poverty modeling is a crucial economic and social development issue in various regions, including in East Nusa Tenggara (NTT) Province. This research proposes using the Mixed Geographically Weighted Regression (MGWR) model with an adaptive Bisquare weighting function to analyze variables influencing poverty levels in NTT Province. The MGWR model is an extension of the Geographically Weighted Regression (GWR), which allows some variables in the model to have local effects while others have global effects. The adaptive weighting function in the MGWR model enhances the analysis by providing different weights at each location according to its local characteristics, thus making the results more accurate and representative for each area. The data includes economic, social, and infrastructure variables from 22 districts/cities in NTT Province for 2023. The MGWR model with an adaptive weighting function is applied to model the relationship between these variables and poverty levels. The analysis integrates statistical software to manage and analyze spatial data. The study findings show that the MGWR model with an adaptive weighting function offers better estimates than the global regression and GWR models. The results revealed the smallest AIC value for the MGWR model at 104.1888, compared to the global regression model at 140.1427 and the GWR model at 117.6174. This model successfully identifies significant local and global variables and shows variations in influence at different locations in NTT Province. These findings provide valuable insights for policymakers and practitioners in designing and implementing more effective poverty alleviation strategies tailored to local conditions in NTT Province.

Econometrics case study: Analysis of factors influencing the poverty rate in the Special Region of Yogyakarta Province

Background: This research is an econometrics case study aimed at addressing two research questions. Firstly, to provide an overview of the Special Region of Yogyakarta Province. Secondly, to identify the factors influencing the poverty rate in the province. The study utilizes the concepts of poverty, provincial minimum wage, Human Development Index (HDI), economic growth, and unemployment rate. Method: Methodologically, the province is chosen as the research location due to its poverty rate that requires attention and its distinctive social, economic, and geographical characteristics. The data used are secondary data, analyzed using multiple linear regression and normality tests. Findings: The findings of this study are as follows. Firstly, the Special Region of Yogyakarta Province is situated between 7°33' LS - 8°12' LS and 110°00' BT - 110°50' BT, with a land area of 3,185.8 km2. The province comprises 4 regencies and 1 municipality. Secondly, the factors influencing the poverty rate in the Special Region of Yogyakarta Province are the Human Development Index (HDI) and the unemployment rate. Conclusion: The conclusion is that the poverty rate in the Special Region of Yogyakarta Province is influenced by the Human Development Index (HDI) and the unemployment rate. The recommendations that can be given are to increase the HDI and reduce the unemployment rate through various government initiatives such as improving education, creating jobs, and encouraging economic activities. Novelty/Originality of this article: This study identifies factors that influence poverty levels using multiple linear regression. The results show that the Human Development Index (HDI) and poverty rates significantly influence poverty rates in this province.

Estimating the Impacts of Livelihood Support Interventions on Poverty Reduction in Rural Balochistan

The study evaluates the impacts of livelihood intervention programs implemented by the Balochistan Rural Support Program (BRSP) on poverty reduction in Balochistan. The estimation of livelihood impacts is gauged through several indicators, including the Income Generating Grant (IGG), Community Investment Fund (CIF), and Skill Development (TVET). To this end, data from 1500 households segregated into 950 IGG, 250 CIF, and 300 TVET are selected using a two-stage stratified sampling technique in 8 selected districts of Balochistan. This study mainly adopted mixed research approaches, including the descriptive analysis of the data to reflect the role of these interventions in poverty reduction in Balochistan. In addition, Poverty Score Cards (PSC) values have been used to determine the poverty levels. After implementing livelihood support intervention, it was obtained that Households (HHs) monthly income of IGG, CIF, and TVET beneficiaries increased by 17%, 20%, and 39%, respectively, on average. The meaningful tabulation of livelihood interventions positively impacts multifaceted aspects of rural HHs. The results also give evidence of employment generation and income-earning prospects in rural Balochistan. The results also show that gender equality and support for women's empowerment are positively changed in rural Balochistan. Therefore, the tabulated results of community and HHs’ specific funding programs and human capital formation via skills provisions to ultra-poor and poor rural people justify Balochistan Rural Development and Community Empowerment (BRACE) as a livelihood support initiative for poverty reduction in rural Balochistan. The government and non-government organizations (NGOs) may get insightful policy interventions from this study to bring effective socioeconomic development in rural Balochistan.

Variables Affecting Poverty Rate in East Java, Indonesia

Poverty is one of the fundamental problems faced by various countries, including Indonesia. The objective of this study is to identify the variables that have an effect on poverty rate in East Java Province. Applying panel data and regression analysis method, this study compares the three models – the common effect, fixed effect, and random effect. Using Chow Test and Hausman Test, this study found that the best model is Fixed Effect. The empirical outcomes show that education, unemployment, population, dependency ratio, and health insignificantly affect poverty. Variables of Gross Regional Domestic Product (GRDP) per capita, and per capita expenditure has a significant effect on poverty. The higher per capita expenditure tends to increase the poverty level. Then, the higher GRDP per capita tends to lower the poverty in East Java especially. Keywords: unemployment, per capita expenditure, dependency ratio, poverty

Analysis of economic growth and poverty levels in Indonesia based on digitalization and sharia banking financial inclusion

This research aims to analyze Indonesia's economic growth based on digitalization and Sharia banking financial inclusion. Using secondary data for the period 2014 to 2022 per quarter and the Seemingly Unrelated Regression (SUR) method, to find out which variables most influence economic growth and poverty levels in Indonesia. The results found that Sharia banking financial inclusion through the variable amount of financing, growth in funding to the private sector, and the value of sukuk ownership contributed to an increase in Indonesia's economic growth. Furthermore, both financial digitalization and financial inclusion in Sharia banking through the variables of several Sharia ATMs, total assets, amount of financing, and value of sukuk ownership can reduce poverty levels in Indonesia.

Aksi Sosial Mengajar Anak Taman Pendidikan Al-Qur’an Masjid Andalusia Islamic Center

Research problems, gap analysis, objectives, methodology, discussion, finding. Andalusia The Andalusia Islamic Center Mosque is a result of profound creativity to address the challenges faced by Indonesia and its people. These challenges include backwardness in education, high levels of poverty, and low morality observed in various layers of society, both in the government and private sectors. Thus in the empowerment program of the teaching action for the years 2021-2023, it became the focus of activities to address unresolved issues. The objectives of this teaching action include: 1) Applying knowledge and skills by developing teaching abilities, 2) Developing the character and potential of children to become a Quranic generation, 3) Motivating children to study outside of their busy school schedule, 4) Providing teaching and learning experiences by interacting with children, 5) Making a tangible contribution to the community and the campus in terms of children's education, thereby improving the quality of education.

The Youth Inner Mission and the Soli Deo Gloria

From the 19th century, as a parallel field of foreign missionary work, the Hungarian inner mission followed Western (English, German) models and established associations to revive the stagnating religious life and to help those less fortunate in a miserable situation. The urbanisation of the period affected greatly the families moving to the capital, where some of them had become disconnected from their church roots, and the lack of a social safety net led to a high level of poverty and the moral decay that accompanied it. In the first half of my study, I looked at the beginnings of the Hungarian inner mission and then I wrote about evangelising and educating associations for youth and children, such as the Protestant Orphans’ Association, the Sunday School Association and the Christian Youth Association, which was modelled on the YMCA. In the second part, I discussed the social and faith-based activities of the specifically Hungarian Reformed Soli Deo Gloria Student Movement. Keywords: inner mission, youth ministry, Reformed Church, SDG, communism

Flood risk assessment using machine learning, hydrodynamic modelling, and the analytic hierarchy process

ABSTRACT The objective of this study was to develop a theoretical framework based on machine learning, the hydrodynamic model, and the analytic hierarchy process (AHP) to assess the risk of flooding downstream of the Ba River in the Phu Yen. The framework was made up of three main factors: flood risk, flood exposure, and flood vulnerability. Hazard was calculated from flood depth, flood velocity, and flood susceptibility, of which depth and velocity were calculated using the hydrodynamic model, and flood susceptibility was built using machine learning, namely, support vector machines, decision trees, AdaBoost, and CatBoost. Flood exposure was constructed by combining population density, distance to the river, and land use/land cover. Flood vulnerability was constructed by combining poverty level and road density. The indices of each factor were integrated using the AHP. The results showed that the hydraulic model was successful in simulating flood events in 1993 and 2020, with Nash–Sutcliffe efficiency values of 0.95 and 0.79, respectively. All machine learning models performed well, with area under curve (AUC) values of more than 0.90; among them, AdaBoost was most accurate, with an AUC value of 0.99.

Cohort study on association between catastrophic costs and unfavorable tuberculosis treatment outcomes among TB-HIV and TB-diabetes comorbid patients in India

BackgroundIndia grapples with an alarming burden of tuberculosis (TB), reporting 2.6 million incident cases in 2023, necessitating intensified efforts toward TB elimination. The prevalence of catastrophic costs, defined as expenses exceeding 20% of annual household income, varies widely. Our objective was to determine the association between catastrophic costs from TB-HIV and TB-diabetes care and unfavorable TB treatment outcomes.MethodsWe conducted a cohort study in Bhavnagar, India, from July 2019 to January 2021, involving 234 TB-HIV and 304 TB-diabetes patients. Catastrophic costs were assessed using the World Health Organization’s tool. Unfavorable TB treatment outcomes included positive results from sputum smear, nucleic acid amplification, or culture tests at treatment completion, death during treatment, or treatment cessation for a month (for drug-sensitive TB) or two months (for drug-resistant TB). Firth regression was employed to address quasi-separation issues and identify predictors.ResultsAmong TB-HIV patients, 12% faced catastrophic costs, with 20% experiencing unfavorable TB outcomes. In this group, significant predictors included weight (OR: 0.93, 95% CI: 0.89–0.98), family type (OR: 2.5, 95% CI: 1.2–5.5), and initial hospitalization (OR: 2.6, 95% CI: 1.1–6.3). For TB-diabetes patients, 5% faced catastrophic costs, and 14% had unfavorable outcomes, with significant predictors being below the poverty line (BPL) (OR: 2.9, 95% CI: 1.5–5.9) and initial hospitalization (OR: 3.4, 95% CI: 1.1–11.1). Catastrophic cost incidence was higher in TB-HIV (12% vs. 4% in TB only) and TB-diabetes (5% vs. 4% in TB only) patients. However, catastrophic costs did not show a direct association with unfavorable outcomes in either group.ConclusionsOur study found no direct association between catastrophic costs and unfavorable TB outcomes among TB-HIV/TB-diabetes patients. Instead, factors such as weight, family type, BPL status, and initial hospitalization were significant predictors. These findings underscore the importance of socio-economic conditions and initial hospitalization, advocate for enhanced support mechanisms including nutritional and financial aid, especially for BPL families.

Prenatal Maternal Occupation and Child Epigenetic Age Acceleration in an Agricultural Region

Research on fetal epigenetic programming suggests that the intrauterine environment can have long-term effects on offspring disease susceptibility. To examine the association between prenatal maternal occupation and child epigenetic age acceleration (EAA) among a farmworker community. This cohort study included participants in the Center for the Health Assessment of Mothers and Children of Salinas, a prospective, Latino, prebirth cohort. Pregnant women were recruited from October 1, 1999, to October 1, 2000, from 6 community clinics in California's Salinas Valley agricultural region. Participants were 18 years or older, English or Spanish speaking, Medicaid eligible, and at 20 weeks' gestation or earlier at enrollment. Mother-child pairs who had blood DNA methylation measured at the ages of 7, 9, and 14 years were included. Data were analyzed from July 2021 to November 2023. Prenatal maternal occupation was ascertained through study interviews conducted during prenatal visits and shortly after delivery. Child EAA at 7, 9, and 14 years of age was estimated using DNA methylation-based epigenetic age biomarkers. Three EAA measures were calculated: the Horvath EAA, skin and blood EAA, and intrinsic EAA. Linear mixed-effects models were used to estimate longitudinal associations of prenatal maternal occupation and child EAA, adjusting for confounders and prenatal organophosphate pesticide exposure. Analyses included 290 mother-child pairs (mean [SD] maternal age at delivery, 26.5 [5.2] years; 152 [52.4%] female infants); 254 mothers (87.6%) were born in Mexico, 33 (11.4%) in the US, and 3 (1.0%) in other countries; and 179 families (61.7%) were below the federal poverty line during pregnancy. Mothers reported engaging in several types of work during pregnancy, including agricultural fieldwork (90 [31.0%]), other agricultural work (40 [13.8%]), nonagricultural work (53 [18.3%]), or no work (107 [36.9%]). Children whose mothers worked in agricultural fields during pregnancy had a mean of 0.66 (95% CI, 0.17-1.15) years of greater Horvath EAA, 0.62 (95% CI, 0.31-0.94) years of greater skin and blood EAA, and 0.45 (95% CI, 0.07-0.83) years of greater intrinsic EAA compared with children whose mothers did not work during pregnancy. In this cohort study, prenatal maternal agricultural fieldwork was associated with accelerated childhood epigenetic aging independent of organophosphate pesticide exposure. Future research on which factors related to agricultural fieldwork accelerate aging in the next generation can inform targeted prevention programs and policies that protect children's health.

Social Programs and Socioeconomic Variables: Their Impact on Peruvian Regional Poverty (2013–2022)

The aim of this research is to establish the extent to which social programs and socioeconomic variables have been influencing poverty in the 24 Peru regions (2013–2022). The study is quantitative, non-experimental, and correlational. We use secondary data obtained from official sources such as the National Institute of Statistics and Informatics, Ministry of Economy and Finance, as well as the Peruvian Institute of Economics. For estimations, we use the Generalized Method of Moments System and dynamic panel data. The results indicate that Juntos, Pensión 65, Qali Warma, and Trabaja Perú social programs, with p-values of 0.383, 0.715, 0.681, and 0.870, respectively, have not had favorable impacts on reducing poverty. On the contrary, negative coefficients for human capital and physical infrastructure mean that improving them will reduce poverty at the regional level. A year more in schooling for the population aged over 15 years reduces poverty between 1.7% and 1.2%. Increasing 10% of the proportion of national roads in paved condition reduces poverty levels between 1.9% and 2.4%.

Evolving trends in CT colonography: A 10-year analysis of use and associated factors

PurposeComputed tomographic colonography (CTC) is a non-invasive screening test for colorectal cancer (CRC) with high sensitivity and low risk of complications. We used a nationally representative sample of screening-eligible adults to examine trends in and factors associated with CTC use. MethodsWe examined CTC use among 58,058 adults in the National Health Interview Survey in 2010, 2015, 2018, 2019, and 2021. For each survey year, we estimated CTC use by sociodemographic and health factors. We used multivariable logistic regression to identify factors associated with CTC use. ResultsA total of 1.7 % adults reported receiving CTC across all survey years. CTC use was similar in 2010 (1.3 %), 2015 (0.8 %), 2018 (1.4 %), and 2019 (1.4 %) but increased in 2021 (3.5 %, p < 0.05). In multivariable analysis, survey year 2021 [vs. 2010, odds ratio (OR) 2.51, 95 % confidence interval (CI) 1.83–3.43], Hispanic (OR 1.73, 95 % CI 1.34–2.23), non-Hispanic Black (OR 2.07, 95 % CI 1.67–2.57), and household income <200 % federal poverty level (vs. >400 %, OR 1.25, 95 % CI 1.01–1.57) was associated with CTC use. Further, adults with a history of diabetes (OR 1.20, 95 % CI 1.01–1.45), chronic obstructive pulmonary disease (OR 1.58, 95 % CI 1.25–1.99), cancer (OR 1.29, 95 % CI 1.05–1.58), or past-year hospital admissions (OR 1.44, 95 % CI 1.18–1.78) were more likely to receive CTC. ConclusionCTC use remained low from 2010 to 2019 but increased in 2021. CTC use was more frequent among adults with chronic health conditions, minorities, and adults with lower income, and may help reduce disparities in CRC screening.

Correlations of Socioeconomic and Clinical Determinants with United States County-Level Stroke Prevalence.

Socioeconomic status (SES) is a multi-faceted theoretical construct associated with stroke risk and outcomes. Knowing which SES measures best correlate with population stroke metrics would improve its accounting in observational research and inform interventions. Using the Centers for Disease Control and Prevention's (CDC) Population Level Analysis and Community Estimates (PLACES) and other publicly available databases, we conducted an ecological study comparing correlations of different United States county-level SES, health care access and clinical risk factor measures with age-adjusted stroke prevalence. The prevalence of adults living below 150% of the federal poverty level most strongly correlated with stroke prevalence compared to other SES and non-SES measures (correlation coefficient = 0.908, R2 = 0.825; adjusted partial correlation coefficient: 0.589, R2 = 0.347). ANN NEUROL 2024.

Analysis of Financial Performance, Economic Growth and Level of Poverty in Regency/City in Maluku Province

Analysis of Financial Performance, Economic Growth and Level of Poverty in Regency/City in Maluku Province

The impact of widowhood on mental health: anxiety, depression, and stress among widowed women in Nepal.

In South Asia, particularly in regions with strong patriarchal norms, widowhood is stigmatized, compounding the negative impact of grief and partner loss. This study measured the prevalence of mental health symptoms among widows in Nepal and its relationship to demographic variables. This cross-sectional study surveyed 588 Nepalese widows from six districts in Nepal (mean age = 52.62, SD = 13.99) who had lost their spouses within the past two years. Participants completed the Anxiety, Depression and Stress Scale (ADSS). Analyses examined prevalence of anxiety, depression, and stress symptoms, using standard ADSS cut-points. Level of anxiety, depression, and stress symptoms measured by the ADSS in the sample were also compared with female psychiatric and nonpsychiatric normative ADSS data, and were compared with one available comparison sample (a sample of older Nepalese women). Measures of association between ADSS scores and demographic variables were computed. Results showed that a high percentage of the Nepalese widows reported moderate to severe symptoms of anxiety, depression, and stress. They also endorsed significantly higher levels of anxiety, depression, and stress symptoms relative to normative data and the comparison sample. Stress scores were significantly negatively correlated with age, Anxiety and Depression scores were associated with income under the poverty line, and Depression scores were associated with homemaker status. These findings confirm the high emotional distress among widowed women in Nepal, and establish the relationship between emotional distress and poverty, homemaker status, and age. These findings can inform public health efforts and mental health care providers regarding the mental health needs of widows in Nepal.

Spatial prediction of poverty in Gauteng province (South Africa) in‐between Censuses using land use datasets

AbstractTo realize the first sustainable development goal of ending “poverty in all its forms everywhere,” local governments in South Africa need to implement informed targeted policy interventions based on up‐to‐date data and sound analytics. Statistics South Africa (Stats SA) Censuses reveal the socioeconomic circumstances of people living in South Africa but are only conducted every 10 years. As a result, most analytical studies done in‐between Censuses rely on outdated socioeconomic data. This study demonstrates how poverty levels in one of the provinces of South Africa, Gauteng, can be predicted when up‐to‐date Census datasets are not available. The spatial lag model is used to explain the relationship between the South African Multidimensional Poverty Index (SAMPI) and statistically significant variables extracted from land use datasets (i.e., land areas classified as built‐up, informal, residential, township, and non‐urban), and to ultimately predict the levels of poverty. Out‐of‐sample predicted poverty levels obtained based on the spatial lag model correlate with the actual levels of poverty thereby reflecting known spatial patterns of the levels of poverty in Gauteng province.

Pengaruh Tingkat Kemiskinan dan Pertumbuhan Ekonomi terhadap Indeks Pembangunan Manusia Indonesia

The aim of this research is to explain the influence of poverty levels and economic growth on the human development index in Indonesia. This research is quantitative research using secondary data originating from the Central Statistics Agency (BPS) with panel data which is time series data for the 2019-2023 period and cross section data from 33 provinces in Indonesia. The data analysis method uses Descriptive Statistical Tests, uses the Classical Assumption Test, and also uses Hypothesis Testing. The conclusion from the results of this test is that the level of poverty and economic growth have an influence on the human development index.