In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.

This article aims to identify the drivers of the life satisfaction and happiness according to basic demands explored in motivational theories and the inclusion of the need for association participation. Structured questionnaire was directly delivered to 454 poor families living in nine districts of Can Tho. The data was processed by using SmartPLS with partial least squares (PLS) technique. The study found that existence needs, growth needs and needs of association participation can mostly explain the model which remarkably contribute to the literature of life satisfaction and happiness. In addition, the needs for association participation and life satisfaction positively affect people’s life happiness. In order to improve the living standards of local people, the government should clearly understand what residents really target to in life and fundamental demands of survive and self-development should be carefully considered and supported by the local authority and private organizations.

Collecting poverty data through the National Socio-Economic Survey (SUSENAS) demands significant time, costs, and human resources. To enable more efficient policy-making, predicting the poverty rate before the release of Statistics Indonesia (BPS) data is essential. This research compares day and night satellite images to predict per capita expenditure in East Java, Indonesia, which has the highest number of poor people. The satellite images are processed using a transfer learning approach that employs a pretrained Convolutional Neural Network (CNN) model with VGG-16 architecture as a feature extractor. These extracted features are then used as independent variables to predict East Java's per capita expenditure using Support Vector Regression (SVR) with RBF and polynomial kernels. The findings indicate that night images are more reliable than day images, with the best model being a combination of transfer learning and the SVR polynomial kernel using night images. The prediction mapping aligns well with the unmodeled night image, demonstrating the effectiveness of this approach in predicting per capita expenditure.

This study aims to investigate the influence of green supply chain management practices on community empowerment and poverty reduction in the region of Indonesia. Primary data collection technique like questionnaire was adopted and a sample of 305 respondents was finally collected. For the better understanding, a structural model was developed for the green supply chain factors and community empowerment. For analyzing the data, descriptive findings, confirmatory factor analysis, structural equation modelling and finally regression analysis were applied. Through structural equation modelling, it is found that all three factors of green supply chain (eco design, environment friendly approaches, and green manufacturing and distribution) had positive and significant influence on community empowerment in Indonesia. In addition, findings under regression analysis show that for the poverty reduction factors green supply chain can also play significantly when integrated through all three factors. As per the implication, this research is a meaningful contribution in the field of green supply chain management practices, community development and poverty reduction.

The poverty becomes a serious problem because of the impact it causes. The factors that affect poverty are economic growth, low education, the limitation of natural resources, the limitation of employment opportunities, capital, and family burdens. All of these factors constitute a vicious circle in the problem of poverty. The problems studied are lag-1 investment, lag-2 investment, employment opportunities, and provincial minimum wages and their effects on the poverty rates in Riau Province, Indonesia. The fundamental problem faced by Riau Province today is the high level of poverty amidst government policies that have not met the expectations. The purpose of this study is to analyze government policies in order to reduce the poverty. The research method used was an explanatory study or hypothesis testing study that aims to explain and test hypotheses for the relationship among variables. The relationship described is a causal (cause-effect) relationship. The data were arranged in the form of time series during 1997-2018. The research model was formulated as a linear function based on the Nerlove's Partial Adjustment Model approach and was recursively analyzed using linear regression through the Ordinary Least Square (OLS) method. The findings of this research model are lag-1 investment, lag-2 investment, employment opportunities, and provincial minimum wages have a significant effect on the poverty rate in Riau Province.

The indicators of success in macro development can be measured from the economic growth, which is reflected in changes in the Gross Regional Domestic Product (GRDP). The factors which affect the economic growth are very complex. This study analyzes the economic growth and the factors that affect Sumatra island. The data used is panel data with descriptive analysis techniques and multiple linear regression. Based on the research results: Education, economic openness, road infrastructure, and investment have a positive and significant effect on economic growth and poverty and unemployment have a negative and significant effect on economic growth. Besides, the results of panel data explained that the openness of the economy to economic growth is very low, while the highest is education.

This paper examines how initial inequality and poverty rate are related to subsequent economic growth in the provincial level of Vietnam. The results show a robust negative relationship between initial poverty rate and subsequent economic growth. However, there is no link between initial inequality and subsequent economic growth. The results also show that lower inequality leads to lower poverty rate and poverty reduction could help to reduce inequality. Other determinants of inequality and poverty reduction include human capital, investment, gross domestic product (GDP) growth rate and trade openness. The main policy implication that emerges from this paper is that concentrating on poverty elimination will help us build a more equitable society without sacrificing economic growth.