ISLAND, INDONESIA
1. Results of the Panel Data Regression Analyses with The Pooled OLS, Fixed Effects, and Random Effects Models
In the panel data regression model, the dependent and independent variables are all converted to the natural logarithm; thus, the coefficients of the independent variables are % change in the dependent variable due to 1 % change in the independent variables (i.e., elasticity). According to the Hausman test, the Chi-squared statistic is 10.71 and the probability is 0.4678; thus, we cannot reject the null hypothesis that the preferred model
is the random effects model. Therefore, we choose the result of the random effects model as the appropriate model. Furthermore, according to the Breusch-Pagan Lagrange multiplier (LM) test, the Chi-squared statistic is 554.7 and the probability is 0.0000; thus, we can reject the null hypothesis that there is no significant difference across districts (i.e., no panel effect). Therefore, the random effects model is appropriate. We will thus discuss the result of the panel data regression analysis with the random effects model.
According to the result of the random effects model (appropriate model), the coefficient of per capita GDP (pc_GDP) is significant at the 1% level and has an expected sign (i.e., negative). In other words, after controlling for expenditure inequality, mean years of education and district-specific effects, districts with larger per capita GDP tend to have smaller poverty head count ratios. The estimated coefficient is - 0.241. This means that if per capita GDP is increased by 1%, then poverty head count ratio will decrease by 0.241%. Therefore, economic growth is conducive to the reduction of poverty, ceteris paribus.
The coefficient of the Gini coefficient (gini) (one year lag) is also significant and has expected sign (positive). In other words, after controlling for per capita GDP, mean years of education and district-specific effects, districts with smaller expenditure inequality tend to have smaller poverty head count ratios. The estimated coefficient is 0.218. This means that if the Gini coefficent is decreased by 1%, then poverty head count ratio will decrease by 0.218%. Therefore, lowering expenditure inequality is conducive to the reduction of poverty, ceteris paribus.
The coefficient of mean years of education (my_edu) is also significant and has expected sign (negative). In other words, after controlling for per capita GDP, expenditure inequality and district-specific effects, districts with larger mean years of education tend to have smaller poverty head count ratios1. The estimated coefficient is – 0.848. This means that if mean years of education is increased by 1%, then poverty head count ratio will decrease by 0.848%. Therefore, raising the average level of education is conducive to the reduction of poverty, ceteris paribus.
In sum, the results of the panel data regression analyses suggest that in order to effectively reduce poverty, district governments should promote economic growth while decreasing inequality among households. At the same time, district governments should raise the average level of education to reduce poverty.
Since the incidence of poverty is related, to some extent, to poverty rates in the previous years, we conducts dynamic panel data regression analysis with the model given by equation (4) using the difference GMM estimator (the Arellano Bond estimator).
The result is presented in Table 5.2. In the dynamic panel data regression model, all the dependent and independent variables are also converted to the natural logarithm; thus, the coefficients of the independent variables are % change in the dependent variable due to 1 % change in the independent variables (i.e., elasticity). According to the Arellano- Bond test for zero autocorrelation in first-differenced errors, we can reject the null hypothesis at order 1, but not at order 2. This indicates that the error term is not serially correlated; thus, the estimated coefficients are consistent. On the other hand, according
to the Sargan test, we cannot reject the null hypothesis that overidentifying restrictions are valid. This implies that the population moment conditions are correct.
The Wald test shows that the dynamic panel data regression model presented in equation (4) is valid. The coefficient of per capita GDP (pc_gdp) is significant at the 1%
level and has an expected sign (i.e., negative). In other words, after controlling for poverty rates in the previous two years, expenditure inequality, mean years of education and district-specific effects, districts with larger per capita GDP tend to have smaller poverty head count ratios. The estimated coefficient is -0.179. This means that if per capita GDP is increased by 1%, then poverty head count ratio will decrease by 0.179%. Therefore, economic growth is conducive to the reduction of poverty, ceteris paribus.
The coefficient of the Gini coefficient (gini) is also significant and has expected sign (positive). In other words, after controlling for poverty rates in the previous two years, per capita GDP, mean years of education and district-specific effects, districts with smaller expenditure inequality tend to have smaller poverty head count ratios. The estimated coefficient is 0.165. This means that if the Gini coefficent is decreased by 1%, then poverty head count ratio will decrease by 0.165%. Therefore, lowering expenditure inequality is conducive to the reduction of poverty, ceteris paribus.
The coefficient of mean years of education (my_edu) is also significant and has expected sign (negative). In other words, after controlling for poverty rates in the two previous years, per capita GDP, expenditure inequality and district-specific effects, districts with larger mean years of education tend to have smaller poverty head count ratios. The estimated coefficient is –1.523. This means that if mean years of education is increased by 1%, then poverty head count ratio will decrease by 1.523%. Therefore, raising the average level of education is conducive to the reduction of poverty, ceteris paribus.
These results are qualitatively similar to the results obtained by the random effects model discussed earlier (in Table 5.1). Therefore, the results are robust. That is, in order to effectively reduce poverty, district governments should promote economic growth while decreasing inequality among households. At the same time, district governments should raise the a verage level of education to reduce poverty. Since mean years of education has a larger elasticity of poverty (-0.848 in the case of the random effects model and -1.523 in the case of the dynamic panel data regression model) than per capita GDP and the Gin i coefficient, the district government should expand education to reduce poverty, and this would also raise per capita GDP and in turn reduce poverty.
D. Conclusion
This study analyzed the factors of poverty by conducting panel data regression analyses with poverty head count ratio as the dependent variable. The following variables are considered as possible factors of poverty: per capita GDP, expenditure inequality among households (as measured by the Gini coefficient) and mean years of education. With the panel data set of 52 district for 10 years from 2006 to 2015 in the Sulawesi Island, this
study found that per capita GDP, expenditure inequality and mean years of education are all important factors of poverty in the Sula wesi Island.
The empirical results are summarized as follows. First, according to the Hausman test and the Breusch-Pagan Lagrange multiplier (LM) test for the panel data regression analyses with the pooled OLS, fixed effects and random effects models, we found that the random effects model is the most appropriate model for the panel data set consisting of 52 Sulawesi districts for 10 years from 2006 to 2015. Second, based on the result of the random effects model, the coefficients of per capita GDP, the Gini coefficient and mean years of education are all statistically significant and have expected sign. The estimated coefficient of per capita GDP is -0.241. This implies that poverty head count ratio will be reduced by 0.241% if per capita GDP is increased by 1%. Next, the estimated coefficient of the Gini coefficient is 0.218. This means that poverty head count ratio will be reduced by 0.218% if the Gini coefficient is decreased by 1%. Finally, the coefficient of mean years of education is -0.848. This means that poverty head count ratio will be reduced by 0.848%
if mean years of education is increased by 1%.
Fourth, to investigate the robustness of the result of the random effect model, a dynamic panel data regression analysis is also conducted. This is because poverty rate is related, to some extent, to poverty rates in the previous years. That is, districts with high (low) poverty head count ratio in the previous year tend to have high (low) poverty head count ratio this year. The result of the dynamic panel data regression analysis is qualitatively very similar to the result of the random effects model discussed above.
This shows that the result of the random effects model is robust, though the estimated coefficients are different from the ones by the random effects model.
E. Recommendation
Some policy implications can be drawn from the results of the panel data regression analyses presented above. Since poverty head count ratio is negatively associated with per capita GDP and positively associated with the Gini coefficient, district governments should promote economic growth while decreasing inequality among households to reduce poverty. Since poverty head count ratio is negatively associated with mean years of education and has a larger elasticity of poverty reduction than the other two independent variables (i.e., per capita GDP and the Gini coefficient), district governments should increase the level of education. This would enhance the productive capacity of human resources and would thus promote economic growth on the one hand and narrow inequality among households, thereby reducing poverty.
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► Nama : Rizki Akbar Maulana
► Unit Organisasi : Sekretariat Badan Penelitian dan
Pengembangan Kementerian Pekerjaan Umum dan Perumahan Rakyat
► Program Studi : Magister Perencanaan Ekonomi dan Kebijakan Pembangunan
► Negara Studi : Indonesia-Jepang
► Universitas : Universitas Indonesia