The Factors Affecting Poverty Rate in Central Java Province 2018-2020

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.


INTRODUCTION
Poverty is a global issue facing various countries worldwide. Thus, they pay attention to the issue (Todaro et al., 2000). According to (Arsyad, 1999), poverty can be broadly classified into two aspects, namely primary aspects and secondary aspects. The primary aspects of poor are assets, socio-political organizations, knowledge, and skills. Meanwhile, the secondary aspects of poor are social network, financial resources, and information. Poverty can also be interpreted as an absolute or relative condition in an area where people cannot meet their basic needs according to the prevailing values or norms (Nugroho et al., 2012). Based on data from the Statistics Indonesia (BPS), Indonesia's population in 2020 is 269.60 million people and ranks as the fourth most populous nation in the world.
Out of the 34 provinces in Indonesia, Central Java Province has 34.74 million people and is the third-most populous province in Indonesia (BPS; 2020). The large population in Central Java Province may undoubtedly cause social problems, such as poverty. The number of people living in poverty in Central Java Province in 2020 is 3.98 million people. Central Java Province has the second highest number of people living in poverty in Indonesia after East Java Province. Reducing the number of people living in poverty is one of the Central Java Provincial government programs. The government targeted to reduce the poverty rate in 2020 to 9.81 percent (Central Java Province Regional Poverty Reduction Coordination Team, 2020). Therefore, it is essential to conduct a study on poverty rate to determine factors affecting poverty rate in Central Java Province. The results of this study are essential for policymakers to identify factors that can reduce poverty rate in Central Java Province.
Several previous studies on poverty rate in Central Java Provinc includes Central Java Province poverty data modeling using fixed-effect spatial Durbin model (Alvitiani et al., 2019), spatial analysis of the effect of unemployment rate on poverty in Indonesia: a case study of Central Java Province (Rahmawati et al., 2015), MGWR modeling on poverty levels in Central Java Province (Darsyah et al., 2015), multidimensional poverty analysis in Central Java Province 2011-2013 (Indriani and Setiyono, 2018), analysis of the effect of per capita income, unemployment rate, HDI and population growth on poverty in Central Java in 2009-2013(Fadlillah et al., 2016 and analysis of factors affecting poverty: a case studies in Banjarnegara, Cilacap, Purbalingga, Kebumen, and Banyumas Regencies (Wijaya H et al., 2020).
Population is a fundamental problem because if the population grows rapidly and uncontrollably, it can hamper economic development goals, namely the welfare of the people and poverty reduction (Saputra W.A, 2011). Didu S and Fauzi F (2016) and Silastri N (2017) indicated that the population significantly affects poverty.
GRDP is the gross added value of all goods and services produced in the geographic boundaries of a country arising from various economic activities within a specific period (BPS, 2020). Total GRDP is an indicator to calculate economic growth. Economic growth provides an opportunity for a region to overcome various problems, including poverty. The poverty rate can be reduced if the GRDP increases in real terms. Susanty (2013) indicated that the GRDP negatively correlates with poverty.
The HDI provides an overview of the development of a decent life with three components, namely health, education, and purchasing power parity. Pratama (2014) stated that HDI has a negative effect on poverty. The previous study conducted by Wijaya H et al. (2020) also stated that GRDP and HDI negatively correlate with poverty rates.
Another factor causing the increase in poverty is disasters or disease outbreaks. The Covid-19 pandemic has begun to enter Indonesia since 2020. Due to the increasing number of positive cases with more evenly distributed areas throughout Indonesia, the President of the Republic of Indonesia has declared the Covid-19 pandemic as a non-natural national disaster. A study on the impact of the covid-19 pandemic on poverty in Indonesia has been carried out by Tarigan H et al. (2020). They stated that the Covid-19 pandemic had caused an increase in poverty rate in most provinces in Indonesia. This study used data from provincial level and from March 2020. The effect of the Covid-19 pandemic has not ended as of the end of 2020. Therefore, the effect of the Covid-19 pandemic in Central Java Province 2020 can be seen by looking at the conditions in the regencies/municipalities in 2018-2020.
The previous studies on poverty were limited until 2019. The study on poverty in Central Java Province in 2018-2020 has not been carried out. Thus, it was essential to conduct a study on poverty in Central Java Province in 2018-2020. This study aimed to analyze poverty in Central Java Province in 2018-2020 using a panel data regression approach. The variables used in this study are HDI, GRDP, and total population. This study also determined whether the coronavirus disease (Covid-19) pandemic in 2020 significantly affects poverty in Central Java Province.

METHODS
The scope of this study was all 35 regencies/municipalities in Central Java Province. It used the data series from 2018 to 2020. Hence, it used a total of 105-panel data as a combination of cross-section and time-series data. This study used the panel data regression method using secondary data from Statistics Indonesia (BPS). The dependent variable is poverty rate. Meanwhile, the independent variables are HDI, total population, GRDP, and the impact of the Covid-19 pandemic.
The following is the definition used in this study: (1) The poverty rate is the percentage of poor people whose income is below the poverty line in the regencies/municipalities in Central Java Province (in percentage units).
(2) HDI is an indicator in determining the quality of human development. Human development is a process of enlarging the choices of people. (3) Population is people who live in geographical areas in regencies/municipalities in Central Java Province for six months or more or those who live for less than six months but aim to settle down. The total population used in this study is the total population of the regency/municipality sourced from BPS (people). (4) GRDP is the total value of goods and services produced in an area at a specified time (in units of million Rupiah). In other words, GRDP is the amount of gross value added arising from all economic sectors in a region. Value added is the value-added from the combination of production factors and raw materials in the production process. Meanwhile, the calculation of added value is the value of production (output) minus intermediate costs. Gross value added includes the components of factor income (wages and salaries, interest, land rent, and profits), depreciation, and net indirect taxes. The GRDP (millions of Rupiah) was obtained by adding up the gross added value of each sector and the gross added value of all the sectors. (5) The impact of Covid-19 is the year or period experiencing the Covid-19 pandemic, in this case, 2020.
According to Widarjono (2009), panel data combines time-series data and cross-sectional data (individual). The formulation of the panel data model is as follows:

RESULTS AND DISCUSSION
Based on Table 1, the poverty rate in Central Java in 2019 is 10.8% or decreases by 0.52% compared to 2018. Meanwhile, in 2020, it is the opposite. The percentage increases by 0.61% compared to 2019. The increase in poverty rate will undoubtedly be a serious concern for the government, especially the Central Java Provincial Government because one of the government's programs is to reduce the poverty rate each year. The HDI and total population of Central Java in 2020 increase by 0.14 and 0.54% compared to 2019. Meanwhile, the GRDP in 2020 decreases by 2.65% compared to 2019. The panel data regression model can be estimated using three models, namely the CEM, FEM, and REM. Table 2 shows the results for the three models.

Source: Data Processing Results
The Chow test is used to choose the best estimation model between the CEM and FEM. The Chow test results in Table 3 show that p-value is 0.0000 or smaller than α = 0.05. It indicates that H0 is rejected, meaning that the best estimation model is FEM. The next step is to conduct the Hausman test to determine the best estimation model between the FEM and the REM. The result in Table 4 shows that p-value is 1.0000 or larger than α = 0.05. It indicates that H0 is accepted, meaning that the best estimation model is the REM. The process of selecting the best model is continued by conducting the Lagrange Multiplier test. This test aims to choose the best estimation model between the CEM and the REM. Table 5 shows the results of the test. In the Lagrange Multiplier test results, Prob. Breusch-Pagan for both columns has a value of 0.000, and it is smaller than α = 0.05. Therefore, it can be concluded that H0 is rejected, meaning that the best estimation model is the REM. From all the model selection tests, it is found that the REM is the best model. After obtaining the best estimation model, the classical assumptions test is conducted. The method developed by Jarque-Bera can formally detect the residual normality test. Figure 2 shows the result of the Jarque-Bera test. The probability value is 0.340731, greater than 0.05. It indicates that H0 is accepted, meaning that the residuals are normally distributed. Therefore, the assumption of normality is fulfilled. The multicollinearity can be detected by using the correlation value between independent variables. Table 5 indicates that none of the correlations between independent variables is larger than 0.8. Based on the results of the multicollinearity indicators above, the correlation between variables does not exceed +/-0.8 (Baltagi B.H, 2005). Therefore, there is no multicollinearity problem in the independent variables. The non-autocorrelation assumption is tested using the Durbin-Watson test.  Testing for the heteroscedasticity in panel regression can be carried out using the Breusch-Pagan-Godfrey test. Prob. Chi-Square(4) on Obs*R-squared is 0.0279, smaller than 0.05. Therefore, it can be concluded that Ha is accepted, meaning that there is heteroscedasticity in the residuals. To overcome the heteroscedasticity problem, the REM uses robust standard errors. Thus, this study can explain further the effect of each independent variable on the dependent one. Panel data regression analysis is used to determine the effect of HDI, total population, GRDP, and the impact of the Covid-19 pandemic on the poverty rate in 35 regencies/municipalities in Central Java. Table 8 shows the best estimation model of this study. The F-test shows a value of 0.000000. It indicates that HDI, total population, GRDP, and the impact of Covid-9 simultaneously have a significant effect on the poverty rate. Based on the equation model, the coefficient of determination (R 2 ) is 73.90%, meaning that the variables of HDI, total population, GRDP, and the impact of Covid-19 can explain the variability of the poverty rate in Central Java by 73.90%. In comparison, the remaining 26.1% can be explained by other variables that are not in the model.
From the partial or individual significance test results, the HDI, GRDP, and the impact of Covid19 have a probability value below 0.05. It indicates that the HDI, GRDP, and the impact of Covid-9 partially have significant effect on the poverty rate. Meanwhile, the probability of total population is 0.2627, greater than 0.05. It means that total population has no significant effect on the poverty rate.
The panel data regression equation shows that the HDI coefficient is -0.452933. It means that the HDI has a negative impact on poverty rate. Every one percent increase in HDI will cause a decrease in the poverty rate by -0.452933 percent with the assumption that the other variables are constant. This finding of this study is in line with studies conducted by Pratama (2014), Fadlillah et al. (2016), Yolanda (2017), and Wijaya H et al. (2020), stating that HDI has a negative and significant effect on the poverty rate Meanwhile, total population has a coefficient of 2.144701, meaning that every one percent increase in population will cause an increase in the poverty rate by 2.144701 percent with the assumption that other variables are constant. This finding is in line with studies conducted by Mustika (2011), Azizah et al. (2018, stating that total population has a positive impact on poverty rate. GRDP has a coefficient of -2.565675, meaning that every one percent increase in GRDP will cause a decrease in the poverty rate by 2.565675 percent with the assumption that other variables are constant. The finding of this study is in line with the findings of Mustika (2011), Wahyuningsih & Zamzami (2014), Silastri (2017), and Wijaya et al. (2020), stating that GRDP has a negative and significant effect on poverty rate. Meanwhile, the impact of the Covid-19 pandemic has a coefficient of 0.536672, meaning that the presence of Covid-19 pandemic causes an increase in the poverty rate by 0.536672 percent, assuming that other variables are constant.

CONCLUSION
Based on the results and discussion, this study comes to the following conclusions: (1) In 2020, the percentage of people living in poverty in Central Java increased by 0.61 percent compared to 2019.
(2) The Random Effect Model (REM) was the best model to analyze the factors affecting poverty rate in Central Java. (3) The variables of HDI, GRDP, and the impact of Covid-9 partially had a significant effect on the poverty rate. (4) Total population had no significant effect on the poverty rate. (5) Poverty is a multidimensional problem that requires extra efforts to eradicate. However, the results of this study highlighted the strong relationship between poverty rate and the Covid-19 pandemic, GRDP, as well as education, health, and poverty through HDI. (6) We recommended further study to have more independent variables or use other analytical methods to find out factors affecting the poverty rate in Central Java.