Multidimensional Poverty in Indonesia 1993-2007 (original) (raw)
Related papers
Multidimensional Approach to Poverty Measurement in Indonesia
2014
Poverty is multidimensional phenomenon. The poverty measurement that based on consumption level is insufficient in explaining the multiple deprivations faced by poor. Applying Alkire & Foster’s multidimensional methodology framework by utilizing the National Socio Economic Survey Indonesia data (2011), this study confirmed that the monetary measure of poverty should be complemented with multidimensional poverty measure to capture comprehensive picture of deprivation in Indonesia. Around 62.3% of populations that monetary poverty measurement declares them as non-poor are multidimensional poor. Using the logit and ordered logit model, this study also confirmed that a higher educational attainment of household head leads to a higher probability of being non-poor both in monetary and multidimensional poverty. The paper identifies that health is the major source of multidimensional poverty. Universal health insurance program is needed. Human investment is very important in efforts to red...
Multidimensional Poverty Index in Agricultural Households in Indonesia.pdf
POVERTY AND SOCIAL PROTECTION CONFERENCE , 2016
Poverty is one of the central issues in the development program of Indonesia. In 2014, more than 27 million people in Indonesia live in poverty and over than 60 per cent of them lived in rural areas which heavily rely on the agriculture sector as their livelihood (BPS-Statistics Indonesia, 2014). This fact confirms that poverty eradication in agriculture is the key factor in reducing poverty in Indonesia. Data and information about poverty are really needed in the fight against poverty. However, the formal poverty data that available counts only direct monetary income and neglects other qualitative dimensions of poverty like health and education. Therefore, researchers are interested in measuring the Multidimensional Poverty Index (MPI) in an agricultural household in Indonesia. This research used secondary data from the latest 2014 National Social Economic Survey of Indonesia (SUSENAS 2014). The total number of sample in this research was 285,400 household. The measurement of MPI was conducted exploratory by factor analysis. Data was entered and analyzed by using the Statistical Package for Social Science (SPSS) computer program. The analysis was performed on the 33 provinces in Indonesia. Based on the result of the analysis, we found that, in term of multidimensional poverty, poverty in Indonesia is also an agricultural phenomenon. Multidimensional Poverty Index (MPI) in an agricultural household in Indonesia was 0.30 and much higher than MPI in a non-agriculture sector that was only 0.11. More than 60 per cent of people in the agricultural household were considered as poor multidimensionality. The intensity of poverty in agriculture household was 0.50. It is also much higher than the intensity of poverty in non-agricultural sector and intensity of poverty in total that was 0.45 and 0.48, respectively. Therefore, we conclude that any effort to address poverty must consider the central place of agriculture in Indonesia. Besides, poverty reduction must not only focus on improving the income of farmers but also on boosting the capability (level of health and education) of poor people.
DETERMINANTS OF POVERTY LEVEL IN INDONESIA
Jurnal Ekonomi , 2022
This study observes that there is a slowdown in poverty reduction in Indonesia, so that in order to realize the achievement of no poverty in 2030 it is necessary to carry out further research related to the determinants of poverty in Indonesia. Researchers used panel data from 34 provinces in Indonesia for the 2015-2020 period. The model used is panel data regression using panel data regression analysis. The results of the study are that government spending directly has a significant effect on poverty and indirectly through unemployment has a significant effect but government spending does not have a significant effect on poverty through economic growth, investment has a significant effect on poverty but indirectly boththrough growth and unemployment have no significant effect.
Determinants of Poverty in Indonesian Provinces
Proceedings of the 3rd International Conference on Banking, Accounting, Management and Economics (ICOBAME 2020), 2020
Poverty is a global problem faced by a lot of countries, including Indonesia. In this research, the data used are 34 provinces in Indonesia from 2015-2018. The analysis technique used is data pooling that combines times series and cross section. The research result shows that the suitable model to analyze poverty is the fixed effect model. Variables that have a negative effect on poverty are Gross Regional Domestic Bruto per-capita, Sanitation, and net enrollment rate of senior high school, while Gini Ratio has a positive effect. Based on the analysis, there are 14 provinces that have higher poverty percentage than the average province poverty in Indonesia.
Social Indicators Research, 2022
Criticism on the use of the income/expenditure poverty line to estimate the number of the poor in Indonesia leads to questioning the use of the multidimensional poverty line (MPL) measurement. While current research on the defining variables, dimensions, and indicators to develop the MPL measurement in Indonesia was not based on direct views of the poor and the non-poor household heads, we complement this research gap by examining it based on direct views of the poor and the non-poor household heads. Methods used to collect the empirical data were conducted in four stages. The first stage was by organizing a Focus Group Discussion with twenty-five participants. The second stage was by conducting a pilot for the main survey on thirty poor and non-poor household heads. The third stage was by distributing the main survey questionnaire to 274 non-poor and 315 poor household head respondents in six representative locations in Indonesia. The fourth stage was by taking in-depth interviews with 8-12 key informants in each survey location. These data were further analysed by employing the qualitative technique. The results confirmed that the poor and the non-poor household head respondents, and the interviewees under the survey viewed the MPL measurement as a comprehensive and better poverty measurement. However, dimensions and indicators that were viewed to be important in developing the MPL measurement were mostly in the groups of three variables. These three variables were capability, empowerment, and opportunity. These three variables should be no hierarchy of importance in developing the MPL measurement as well as in formulating policy and programs to eradicate the incidence of poverty in Indonesia.
Determinant of Poverty in Indonesia
Economics Development Analysis Journal
This research aims at knowing the relationship between the labor force participation level, the average of expenditure per capita, the literacy level, and the capital investment and the poverty level in Indonesia at 2014. The secondary data in this research were obtained from the Central Bureau of Statistics (BPS) and the National Population and Family Planning Bureau (BKKBN) in 32 provinces in Indonesia (cross section). The method of research used is Ordinary Least Square Method with level of confidence of 95%. The research result shows that the average of expenditure per capita and the capital lending have a significant influence on the poverty level in 2014, while the labor force participation level and the literacy level have no significant influence on the poverty.
Gender Determinant on Multidimensional Poverty Index: Evidence from Indonesia
Jurnal Ilmu Sosial dan Ilmu Politik
Poverty measurement from a non-monetary aspect is needed as low-income individuals are not always multidimensionally poor, and vice versa. The focus should also be on the gender determinant potentially related to the inequality in wage, labour market, and the return of education, which can influence the household’s ability to achieve a higher standard of living and alleviate poverty. This paper discovers the contribution of gender determinants to multidimensional poverty conditions in Indonesia. This paper used logit estimation using National Socioeconomics Survey (Susenas) 2018. The data show that approximately 10% of the Indonesian population is considered vulnerably poor, and severely poor is 3%. The vulnerably and severely poor individuals are mostly measured from years of schooling, health insurance ownership, and assets ownership. Moreover, we find that variables of household size, dependency ratio, and household head age are the better explanators of poverty’s vulnerability. ...