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Economic development can be described as a process of system or non-social system in order to achieve a better condition. It is often related to public welfare as evidenced by the poverty phenomenon experienced by the population. The population growth in a country must be controlled in order to prevent a threat to the country’s welfare, since an increase in the population will lead to an increase in the need for fulfillment. This condition should be balanced with a proper income alignment. East Java Province has the second highest population in Indonesia. In fact, this condition has had an impact on the economic development process, particularly in East Java, where there has been an increase in the poverty rate. Over the past decade, the poverty rate in East Java has fluctuated. It showed an increase in a few years, namely 2015, 2020, and 2021, with an average increase of 0.88%. This study aims to identify the effects of several factors on the poverty rate in East Java between 2017 and 2022. Furthermore, this study used a quantitative method with a panel regression analysis. The independent variables included open unemployment rate, labor force participation rate, minimum wage, and education level. This study found that open unemployment rate and minimum wage affected poverty rate. In contrast, labor force participation rate and education level did not affect poverty rate.

Poverty is a challenge for all countries and its eradication is a common goal in the European Union. Our paper analyses the statistical and theoretical relationship between social inequalities and the at-risk-of-poverty rate. For a holistic understanding of the poverty phenomenon in the EU, other variables such as economic growth, social protection expenditure, and the old-age dependency ratio were also included in the analysis. The methodology is based on panel data models. A regression model was built with data for the period 2011-2022, for the 27 EU countries. The econometric model of the at-risk-of-poverty rate highlighted a series of factors, among which the Gini coefficient is one of the most influential. Other important factors were research and development expenditure, social protection expenditure, GDP per capita, the unemployment rate, and the old-age dependency ratio. The cluster analysis grouped the EU countries into three clusters. Understanding the phenomenon of poverty is a prerequisite for alleviating it, generating social solidarity, and rebuilding a fairer society. Our research contributes to the evaluation of the interaction between inequalities and poverty in Romania and the EU.

Poverty is the condition of having few material possession or little income. It can have various economic, social, and political causes and effects and has served as a major impediment to Indonesia's national development process. Using data on the rising poverty rate in West Java up to March 2021, we can use interpolation to construct a function that describes the pattern of the data. The function can then be used to predict poverty rate data beyond the scope of the already established data. There are several formulas of interpolation whose graphs pass through the interpolation points, such as Lagrange, Newton’s divided differences and cubic splines. We aim to compare Newton’s divided differences interpolation and a cubic spline in the context of predicting West Java poverty rates. The results obtained show that the interpolation using a cubic spline is superior to Newton’s interpolation polynomial because a cubic spline results in smaller errors. [ABSTRACT FROM AUTHOR]

This study examined the mastery of Technological Pedagogical Content Knowledge (TPACK) for Physical Education (PE) teachers candidates from low-income families. Candidate teachers from low-income families have less access to information technology, so this research examines the mastery level of TPACK among physical education teachers from low-income families. This research used a quantitative approach with a survey method. The survey instrument utilized a TPACK survey to assess the mastery of technology in the teaching and learning process. The participants involved in this study were 146 students who received the Kartu Indonesia Pintar (KIP) Scholarship (government scholarship) and came from low-income families. The results showed that the candidate teachers were included in a Good TPACK skills category. This result indicates that low income does not affect the ability to master TPACK skills. This study rejects the notion that low-income students have low mastery of technology.

This study seeks to examine the effect of local government expenditure on the improvement of provincial welfare in Indonesia. It is necessary to increase public capital such as basic infrastructure and public facilities, as well as to improve public services such as health, education, and social protection. Based on previous studies, research on government spending on poverty has a significant effect on poverty reduction, but some other studies are not the case, namely government spending has not been significant in reducing poverty and improving welfare. The data used in government expenditure is expenditure based on functions, particularly health, education, and social protection functions in the province, both district/city and provincial government expenditures obtained from the Directorate General of Financial Balance (DJPK), while the provincial poverty rate serves as a proxy for regional welfare. Over the period 2015-2021, the Fixed Effect model using the Generalized Least Square Estimation (GLSE) approach is used to estimate outcomes for 34 provinces in Indonesia. The results indicated that government expenditure on the health sector and education sector had a negative significant influence on reducing the poverty rate, whereas government expenditure on social protection did not. The control factors, such as economic growth, had a negative effect on poverty reduction in Indonesia, whereas the unemployment rate and informal labor in the agricultural sector had positive and significant effects. Government policies, especially government spending, have contributed well to reducing poverty, but the government should pay attention to the integration of spending programs with other programs.

Explicit poverty data are critical for policymaking and targeting humanitarian aid. Poverty rate is the most widely accepted definition of a poverty status. However, poverty rate data are commonly available at country-level. Here, we proposed an approach that estimates the poverty rate at different spatial scales with a consistent standard within a country. We first trained the model based on household survey data and publicly available remote sensing data to derive a wealth index map, and then we developed the relationship between the wealth index and the poverty rate at country-level. The relationship finally was applied to estimate the multi-spatial scale poverty rates in the study area. We made validation between the estimated and statistical province-level poverty rates using relative error and R-Square. A case study was carried out in Mozambique. The results showed that the proposed model has a good ability to estimate poverty rate with an overall accuracy of 85.21%, as well as an R-Square of 0.94. There was a huge gap within Mozambique, with the Northern Provinces holding high poverty rates and the Southern Provinces holding low poverty rates. The district-level poverty rate map might reflect the negative impact of climate disasters, as well as the positive influence of economic and trade exchange. Given that the data we use are publicly available, the proposed methodology can be applied to other countries to estimate poverty rates at various spatial scales.

This Study aims to analyze poverty rate, Gross Domestic Regional Product (GDRP), Minimum Wage on mean years of schooling in Gorontalo Province. The data was taken from Central Bureau of Statistics (BPS), which consists of six cities/districs from 2010-2019. The method that is used in this research is Panel Data Regression with the software used is Rstudio and Eviews. The results of this study can be concluded that GDRP and minimum wage affect the mean years of schooling in Gorontalo Province. Also, this study has limitations in terms of the amount of data in each time period which affect the dependence variable and there are also some data that cause failures in some assumptions. [ABSTRACT FROM AUTHOR]

Economic development can be described as a process of system or nonsocial system in order to achieve a better condition. It is often related to public welfare as evidenced by the poverty phenomenon experienced by the population. The population growth in a country must be controlled in order to prevent a threat to the country’s welfare, since an increase in the population will lead to an increase in the need for fulfillment. This condition should be balanced with a proper income alignment. East Java Province has the second highest population in Indonesia. In fact, this condition has had an impact on the economic development process, particularly in East Java, where there has been an increase in the poverty rate. Over the past decade, the poverty rate in East Java has fluctuated. It showed an increase in a few years, namely 2015, 2020, and 2021, with an average increase of 0.88%. This study aims to identify the effects of several factors on the poverty rate in East Java between 2017 and 2022. Furthermore, this study used a quantitative method with a panel regression analysis. The independent variables included open unemployment rate, labor force participation rate, minimum wage, and education level. This study found that open unemployment rate and minimum wage affected poverty rate. In contrast, labor force participation rate and education level did not affect poverty rate. [ABSTRACT FROM AUTHOR]

The methodology used to determine the at-risk-of-poverty rate commonly applied in the European context is often criticised for arbitrary steps in its construction. This study questions the first step – the equivalence scale applied to transform the disposable income of households of different sizes into comparable units. First, we hypothesise that economies of scale are lower in Central-Eastern European countries than in their Western counterparts. We assess the hypothesis using a simple descriptive analysis of the structure of household consumption expenditures based on Household Budget Survey data. Second, we demonstrate the sensitivity of the at-risk-of-poverty rate to an equivalence scale based on the Statistics on Income and Living Conditions data. We identify three different groups of countries according to the sensitivity of the income poverty rate to the relative adult and child household member weights assigned by the equivalence scale. The study contributes to the discussion on defining accurate, country-specific equivalence scales.

This paper elaborates some appropriate policies regarding regional poverty reduction in Central Java province. This research estimates a poverty model based on a set of panel data comprising 29 regencies and six cities from 2011 to 2016. A fixed-effect model presents that poverty rate has a negative association with regional economic growth, minimum wage level, number of unemployment, and the quality of human resources. The higher number of population significantly decreases poverty rate in each region. Also, this study indicates that there is more poverty rate in the eastern region than that in the west region. Moreover, the percentage of the poverty rate in regencies remains higher than the level in the cities. Overall, these results indicate that the local governments have successfully managed the poverty issues in among regencies and cities. This research finds that local governments are on the right way in their public policies in the development process. For more effective in poverty reduction, the local governments in the eastern region have to improve their human resources quality.

According to the definition of the National Institute of Statistics, the relative poverty rate represents the proportion of the poor (according to the relative estimation method) in the total population. It is considered poor people in households with an adult-equivalent income (including or exclusive of own resource consumption) lower than the poverty threshold. This indicator is currently determined for the 60% threshold of the median of adultequivalent income. The indicator is sometimes referred to as "the risk of poverty". To analyze the dynamics of the relative poverty rate for the period considered, we used the statistical indicators of the chronological series, respectively the absolute, relative and average indicators.

Spatial data is data that contains the influence of location with non-homogeneous variance at each location, or spatial heterogeneity. To address spatial heterogeneity, the Geographically Weighted Regression (GWR) model is used. However, in the GWR model, there is a phenomenon of multicollinearity, which is a strong relationship between independent variables that will reduce the accuracy of parameter estimation. To overcome multicollinearity in the GWR model, the Least Absolute Shrinkage and Selection Operator (LASSO) method is used. The LASSO method estimates the parameters of the GWR model by minimizing the sum of squared errors subject to a constraint function, which is solved using the Least Angle Regression (LARS) algorithm. This results in the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to address the problem of multicollinearity in spatial data. Based on the research results, the LASSO method can overcome multicollinearity by shrinking the coefficients of parameters that contribute less and have a strong correlation with other independent variables in the GWR model, resulting in 33 final models. One of the models is for Nias Regency, where the factors influencing the poverty rate are the open unemployment rate, life expectancy, average length of schooling, gross participation rate, and per capita income. In Nias Regency, the value of s is 0.288 with an R-squared value of 0.9403. In Nias Regency, 94.03% of the variation in the poverty rate is explained by the independent variables in the model, while the remaining 5.97% is attributed to external factors not covered by the model. Coefficient of the Human Development Index variable shrinks to exactly zero, indicating that it has no effect on the poverty rate in Nias Regency. [ABSTRACT FROM AUTHOR]

During the 2017-2019 period, Central Java performed well in poverty alleviation. It turns out that the reduction of poverty in Central Java has a major contribution in reducing the poverty rate nationally, Indonesia. This poverty reduction could occur due to an increase in the regional aggregate government expenditure budget in Central Java. This study aimed to determine the effect of regional spending by function on poverty levels in districts/cities in Central Java using panel data regression. The model chosen is the Fixed Effect Model with the SUR approach. The results show that regional spending on health, education, and social protection negatively affects poverty levels. On the other hand, spending in the economic sector has a significant but positive impact on the poverty level. Meanwhile, spending on infrastructure does not show a significant effect on reducing poverty in Central Java. The government needs to maintain financial management in the health, education, and social assistance functions, as well as carry out further studies related to budget allocations in the economic and infrastructure sectors. Keywords: poverty, poverty reduction, government spending, fixed effect model

This paper aims to provide an analysis of the determinants of poverty rate in ten member states of the European Union from the Central and Eastern Europe, and of the way how its influenced poverty in the 2009-2016 period. In order to estimate the impact, I used panel technique and Estimated Generalized Least Squares method with no effects, weighted by Period SUR option to provide more consistent estimators. To verify if the model is correctly specified and to confirm the maximum verisimilitude of the estimators, I tested the Gauss-Markov. The conclusion of the paper is that the unemployment rate, rural population, as well as the early leavers rate of people aged between 18-24, have a positive impact on the rate of people at risk of poverty rate after social transfers. On the other hand, the government spending on social protection has a negative impact on the used dependent variable.

The paper focuses on the poverty and social exclusion measurement. The aim of the paper is to analyse the factors influencing the differences in at-risk-of-poverty or social exclusion (AROPE) rates and their development across the Visegrad countries. As these factors, we consider the different structure of households by their social status (employed, unemployed, retired, other inactive) and the different highest attained level of education (primary, secondary, tertiary). We use data from the EU-SILC and decompose the AROPE rates as the price indices of unit value. We prove the significant impact of the structure of households by their social status in years 2005–2017 on the AROPE rate comparison for all Visegrad countries and the effect of educational composition on the AROPE rate development for all Visegrad countries except Hungary.

Poverty is currently a major problem that must be resolved in various regions in Indonesia, including areas in Java. Java Island is the island with the highest number of poor people compared to other islands in Indonesia. The aim of this study was to determine the determinants of poverty levels in Java and formulate policy recommendations that can be implied to overcome poverty. Research variables have used secondary data from six provinces in Java sourced from the Central Statistics Agency, namely poverty level data, Human Development Index (HDI), inflation rate data, open unemployment rate data, and Regional Minimum Wage data (UMR). The data was compiled into panel data and analyzed using OLS Model. The analysis showed that the determinants of poverty levels in Java were inflation rates, Human Development Index, Regional Minimum Wages, and open unemployment rates, so that all determinants need to be considered properly to formulate policy recommendations that able to overcome poverty in Java.

One of the Central Java Provincial Government programs is to reduce the number of people living in poverty. The Central Java Provincial Government target in 2020 was to lower poverty rate to below ten percent. Therefore, it is necessary to determine factors affecting the poverty rate in Central Java. This study aimed to analyze the effect of the Human Development Index, total population, Gross Regional Domestic Product (GDRP), and the impact of the Covid-19 pandemic on the poverty rate in regencies/municipalities in Central Java. The study on the impact of the Covid-19 pandemic on the poverty rate in Central Java Province has never been carried out. This study was different from the previous studies. This study used panel data regression by using 2018-2020 data from Statistics Indonesia (BPS). In this study, the result of the best model selection was the random effect model. The result of this study indicated that Human Development Index (HDI) had a negative and significant effect, total population had a positive and insignificant, GRDP had a negative and significant effect, and the impact of Covid-19 pandemic had a positive and significant effect on the poverty rate. Keywords: Impact of Covid-19, Human Development Index, Population, Poverty Rate, Gross Regional Domestic Product.