A DYNAMIC PANEL DATA ANALYSIS
D. Conclusion and Recommendations
In this study, we have tried to analyze the effect of infrastructure on regional economy using a panel data set of 33 provinces in Indonesia for the period from 2006-2016.
Specifically, by conducting panel data regression analyses, we have examined the effects of road, electricity, and water infrastructures on provincial economy. In the dynamic
panel data regression analysis, we have also examined the conditional - convergence among provinces, that is, investigated whether poorer provinces tend to grow faster than richer provinces.
According the coefficient of variation (CV), the Gini coefficient and the Theil index, interprovincial inequality in per capita GDP has been declining over the period from 2006 to 2016. That is, there was a ß-convergence among 33 provinces in this period. However, the KBI (western Indonesia including the Sumatera and Java islands) still dominates Indonesia with the Java Island as the center of the economy by accounting for 61.9% of total GDP in 2016. There is also a large disparity in per capita GDP between the richest province (DKI Jakarta) and the poorest province (Maluku), and the ratio in per capita GDP between these provinces is very high at 15.6.
To reduce interregional inequality in economic welfare, Indonesian government has been developing infrastructures. However interprovincial inequality in per capita amount of electricity was still very high at 0.369 by the Gini coefficient in 2016, despite its declining trend. Meanwhile, interprovincial inequality in per capita road length has been relatively stable at around 0.30-0.32 by the Gini coefficient and interprovincial inequality in the proportion of households who can access to safe water has been increasing gradually.
According to the result of the panel data regression analysis base on the fixed effects model, per capita amount of electricity and the proportion of households who can access to safe water are found to have significant and positive effects on per capita GDP. However, per capita road length is not significant determinant of per capita GDP.
On the other hand, mean years of education in the previous year has a significant and positive effect on per capita GDP, while unemployment rate in the previous year has a significant but negative effect on per capita GDP. Poverty rate in the previous year is also a significant determinant of per capita GDP, but its coefficient does not have an expected sign (negative).
To further analyze the effects of infrastructures on per capita GDP, we also conducted a dynamic panel data regression analysis by including the one-year lag of per capita GDP as an independent variable, since the current per capita GDP is, to some extent, related to per capita GDP in the previous year. The result of the Arellano-Bond estimator shows that road and electricity infrastructures have significant and positive effects on per capita GDP with the elasticities of 0.134 and 0.120, respectively. Unlike the fixed effects model, however, there is no significant effect of other independent variables on per capita GDP, including the proportion of households with access to safe water. Meanwhile, we found an evidence of conditional ß-convergence across provinces as the coefficient of the one- year lag of per capita GDP is positive and smaller than 1. That is, provinces with smaller per capita GDP tend to grow faster than those with larger per capita GDP over the study period from 2006-2016.
These observations suggest that infrastructures such as road, electricity and water infrastructures appear to have been playing important roles in the development of provincial economy. According to the result of the dynamic panel data model (Arellano- Bond estimator), road and electricity infrastructures, as proxied by per capita road length
and per capita amount of electricity distributed, have the elasticities of 0.134 and 0.120, respectively. This means that if these infrastructures are increased by 1%, then per capita GDP will increase by 0.134% and 0.120%.
Regarding road infrastructure, Banten and West Java in the Java Island and North Sumatera, Riau Islands, South Sumatera and Lampung in the Sumatera Island have a relatively low per capita road length; therefore, the government should further develop and strengthen road infrastructure in these provinces. The government should also develop electricity infrastructure in the eastern region (including Kalimantan, Sulawesi, Maluku and Papua provinces) since provinces in this region have smaller per capita amount of electricity distributed than those in the western region. Though the dynamic panel data model does not show a significant and positive effect of water accessibility on regional economy, water accessibility seems to have played an important role in the development of regional economy as indicated by the fixed effects model. Therefore, the government should develop water infrastructure in provinces with relatively low water accessibility, such as Riau Islands, Bantenand DKI Jakarta.
There are several limitations and extensions for the future research. First, due to the lack of data, this study did not include some other important infrastructure variables in the panel data regression models, such as infrastructures for irrigation, telecommunication, seaports and airports. Second, the coefficient of per capita road length possibly takes both the positive and negative values. Intuitively, the relatively higher income provinces are densely populated and intensively accumulated in road length. Accordingly, the value per capita road length possibly shows either greater or smaller values in the higher income provinces than the lower income provinces. Therefore, road length per km2 might be used in a future study. Lastly, this study used provincial data. But, it is better to use district-level data to analyze the effects of infrastructures on regional economy since fiscal and administrative decentralization which was implemented in 2001 is introduced to strengthen the capacity of district governments rather than provincial governments.
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► Nama : Agreta Indah Gusumawati
► Unit Organisasi : Bappeda Pemerintah Kota Medan
► Program Studi : Magister Perencanaan Ekonomi dan Kebijakan Pembangunan
► Negara Studi : Indonesia-Jepang
► Universitas : Universitas Indonesia