5. ANÁLISIS DE RESULTADOS
5.3. Escenario 3 – Carga cíclica
The discussion on the income disparity has emphasized the need for research in finding the growth determinant. The specific goal of this chapter is to investigate the determinants of regional growth of income per capita and specifically it uses the provincial panel data within Indonesia during 1983–2003.
In doing so, it brings up some issues that differentiate the application in the sub national to the cross country application. The concerns related to the application of the growth model among regions in one country studies are data availability, the impact of national policy and interaction among regions. While, the impact of national policy and data availability issue could be addressed by selection of variables and also year dummy, this chapter has not taken account of the impact of interrelationship among regions. This has to be dealt with by relaxing the independent identically distributed (iid) assumption of the observation in the estimation, since the growth of one region is dependent on another region.
This chapter utilises GMM dynamic panel estimation (Arellano and Bond 1991) and the reduced form of the Solow-Swan growth model to estimate a regional growth model. The technique is considered suitable for the Indonesian sub national case for two reasons. First, the estimation should be of less concern in coefficient heterogeneity than in cross country studies since the differences of characteristic of regions in a country is far less than among countries. As a result, the impact of growth determinant can be assumed to be similar. Second, for the Indonesian case, the income per capita data are not persistent, meaning income per capita has fluctuated considerably in regions, not only in the five yearly data but also in annual data. As a result, Arellano and Bond (1991) would not have some downward bias from the persistency of the instrument.
This chapter concludes that significant growth determinants are different for different income proxies. Nevertheless, the significant growth determinants of GDP non mining per capita are also significant for GDP per capita except for financial institutions. Meanwhile, the growth of household consumption per capita seems to have different determinants that are related to the spending behaviour of households rather than as a proxy of income.
The result of the growth determinants can be grouped to investment related and non investment related. The investment related determinant has shown these results. The overall investment (gross fixed capital formation) is estimated to have an
insignificant impact on the growth of all income proxies. The average year of schooling has a different impact on different proxies of income. It is weakly positively significant on GDP growth, insignificant on GDP non mining and surprisingly negative significant on household consumption. There are negative impacts on growth from local government spending on GDP per capita and GDP non mining per capita. The impact of transportation infrastructure in term of roads per capita is significantly positive on GDP per capita growth, and weakly significantly positive on household expenditure. Yet, it is surprising that the impact on non mining GDP per capita is insignificant.
In the non investment determinant, the ratio of trade to GDP, as a proxy of openness, is the only significant growth determinant of all income proxies. The result from institutional variable is interesting. It is positively significant for GDP per capita but not significant for GDP non mining and household consumption. On the other hand, financial institutions variable is only significant in determining GDP non mining growth. The common impact of the national macro economics condition on provincial growth can be seen from the year dummy variables. These year dummy variables are very significant for GDP and GDP non mining but not for household consumption.
There are three variables in this analysis that have a very close relationship since theoretically they all could be grouped into investment. In particular for the Indonesian case, the infrastructure used (roads) is maintained by public spending which cannot be separated from overall investment. This chapter has shown that controlling one of the variables, which means limiting the allocation of investment, would change the impact of this investment on growth.
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Appendix A. The result of Arrelano and Bover (1995) method
Table A1. Results for the Arrelano and Bover (1995) Dynamic Panel Estimation on Growth Regression
GDP per capita
GDP non mining per capita
Household consumption per capita
Coef. Std. Err. Coef.
Std. Err. Coef. Std. Err. ly L1. 0.905 *** 0.031 0.964 *** 0.027 0.962 *** 0.033 li 0.072 ** 0.032 0.036 0.027 0.034 0.029 lpopgr -0.032 * 0.017 -0.033 ** 0.013 -0.027 * 0.014 lysch -0.075 0.080 -0.056 0.066 -0.028 0.066 lgs -0.076 ** 0.030 -0.071 *** 0.024 -0.022 0.027 lrdpc 0.075 ** 0.038 0.043 0.031 0.016 0.033 ltrds 0.044 0.031 0.007 0.024 0.014 0.025 linst -0.014 0.015 -0.027 ** 0.012 0.002 0.013 lfs 0.028 0.023 0.019 0.017 -0.015 0.017 lagrs 0.000 0.021 0.010 0.016 -0.003 0.017 y93 0.070 ** 0.033 0.046 * 0.026 0.069 *** 0.027 y98 -0.047 0.039 -0.087 *** 0.032 0.074 ** 0.032 y03 -0.041 0.047 -0.094 ** 0.038 0.092 ** 0.040 _cons 0.779 *** 0.278 0.548 ** 0.230 0.199 0.237 Sargan test 0.124 0.408 0.142 Arellano- Bond 0.027 0.007 0 Arellano- Bond 0.071
Arellano and Bond (1991) GMM procedure would have some downward bias if the left-hand side variable is persistent and the time dimension is small relatively to number of individuals. The problem arises since the persistency in the data will make the lag value of this variable in level a bad instrument. In addition, with the small time dimension, there is not enough information available from the instrument.
Arellano and Bover (1995) offer a solution by adding the level equation back to the first difference equation but instrumented the level equation with the difference. This will add information to the estimator that is crucial in determining the equation, especially if there is some time-invariant information that could be added. Following this step the GMM panel estimator will use the following moment conditions
E [lnyit-s Δuit] = 0 for s≥10 (A1)
E [ln ZTit -s Δuit] = 0 for s≥10 ; (A2)
E [ln XTit -s Δuit] = 0 for s≥10 ; (A3)
E [(ηi + uit) ΔXTit -s] = 0 (A4)
E [(ηi + uit) ΔZTit -s] = 0 (A5)
There is a disadvantaged in using this model. As pointed out by Bond (2002), the model is unwilling to assume that the predetermined variables have no correlation with the individual effect but willing to assume that their first difference do not have any correlation. However, if these variables have such correlation in both level and first difference then the Arellano and Bover (1995) method could produce the similar bias as the random effect.
Table A2. Results for Random Effect Panel Estimation on Growth Regression
GDP per capita
GDP non mining per capita
Household consumption per capita Coef.
Std.
Err. Coef. Std. Err. Coef.
Std. Err. ly L1. 0.905 *** 0.034 0.960 *** 0.031 0.958 *** 0.038 li 0.072 ** 0.035 0.035 0.030 0.035 0.032 lpopgr -0.032 * 0.018 -0.033 ** 0.015 -0.026 * 0.015 lysch -0.075 0.086 -0.059 0.074 -0.037 0.075 lgs -0.076 ** 0.033 -0.073 *** 0.027 -0.023 0.030 lrdpc 0.075 * 0.041 0.046 0.034 0.021 0.036 ltrds 0.044 0.034 0.010 0.027 0.016 0.028 linst -0.014 0.016 -0.027 ** 0.014 0.003 0.015 lfs 0.028 0.024 0.020 0.018 -0.014 0.019 lagrs 0.000 0.023 0.009 0.018 -0.005 0.019 y93 0.070 ** 0.035 0.047 * 0.028 0.069 ** 0.029 y98 -0.047 0.042 -0.086 ** 0.035 0.075 ** 0.036 y03 -0.041 0.051 -0.093 ** 0.042 0.096 ** 0.044 _cons 0.779 *** 0.299 0.565 ** 0.255 0.237 0.265 R-sq: within 0.8363 0.8746 0.888 betwee 0.9957 0.9956 0.993 overal 0.9786 0.9789 0.9726
Appendix B. The result of PMGE (1995) method
The other issue is in regard to the time length of the estimation. Pesaran, Smith and Shin(1999) show that if the time length is relatively long compared to the number
of individuals or regions and there is an expectation of heterogeneity in the growth coefficient, the estimation using the Arellano and Bond (1991) dynamic panel data estimate would have a misleading result. To deal with this problem the pooled mean group estimator (PMGE) should be implemented. This methodology works similarly to panel error correction model (PECM) if it is assumed that there are uniform coefficients across regions in the short term difference equation.
Table B1. Results for the Pooled Mean Group Estimator (PMGE) on Growth Regression
D.ly Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Ln yit-50 -0.96012 *** 0.279137 -1.057 *** 0.288211 -1.28626 *** 0.239398 Ln sit-5 -0.01596 0.155663 0.159 0.137223 0.057248 0.116088 Ln (n)it-5 0.099408 0.066177 0.057 0.057652 0.039424 0.049897 Ln yschit-5 0.328597 0.334691 -0.124 0.277988 -0.59607 ** 0.245822 Ln gsit-5 -0.29065 ** 0.125436 -0.198 * 0.101257 -0.10954 0.081524 Ln rdpcit-5 0.172971 0.168663 0.077 0.130574 0.322675 ** 0.118627 Ln trdsit-5 0.191929 0.116571 0.169 * 0.087672 0.08631 0.083939 Ln instit-5 -1.19297 1.009722 0.345 0.788144 -1.20678 * 0.670629 Ln fsit-5 -0.0301 0.063697 0.089 0.053226 -0.07696 0.04929 Ln agrsit-5 -0.11641 0.191582 -0.027 0.147185 -0.15641 0.131688 DLn sit -0.0407 0.114264 0.032 0.092712 0.031604 0.083268 DLn (n)it 0.016627 0.032631 -0.005 0.026626 0.001755 0.024881 DLn yschit 0.128688 0.337016 0.111 0.280131 -0.37773 0.252928 DLn gsit -0.15573 * 0.077464 -0.201 *** 0.058673 -0.01859 0.050669 DLn rdpcit 0.159202 0.117151 0.152 0.101555 0.219841 ** 0.085208 DLn trdsit 0.131523 0.087309 0.129 0.075821 0.159696 ** 0.069156 DLn instit -2.14622 1.553997 0.17 1.184641 -1.81424 * 1.053258 DLn fsit -0.04324 0.070276 0.049 0.05719 -0.07756 0.053563 DLn agrsit -0.09712 0.182764 0.005 0.150776 -0.03012 0.138602 DLn yit-5 0.247452 0.236757 0.254 0.19898 0.170061 0.180839 y98 0.080011 0.10888 0.098 0.100461 0.281138 *** 0.074567 y03 0.164766 0.188794 0.237 0.168179 0.606325 *** 0.132922 _cons 4.800953 4.699649 -0.898 3.631006 7.008598 ** 3.040704 R-sq: within 0.8283 0.868 0.8239 betwee 0.177 0.007 0.0015 overal 0.1204 0.04 0.0007
Nevertheless, the method is more appropriate to be used with a lot of time observation so it is applied to the annual data instead of five yearly data. The result is shown in table B2.
Table B2. Results for the Pooled Mean Group Estimator (PMGE) on Annual Growth Regression
D.ly Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Ln yit-2 -0.163 *** 0.030 -0.209 *** 0.038 -0.271 *** 0.0466 Ln sit-1 -0.006 0.012 0.018 0.012 -0.002 0.0170 Ln (n)it-1 -0.004 0.006 -0.009 0.006 -0.015 * 0.0084 Ln yschit-1 0.098 *** 0.035 -0.006 0.034 -0.035 0.0483 Ln gsit-1 -0.017 0.011 -0.016 0.011 0.015 0.0152 Ln rdpcit-1 0.016 0.016 -0.007 0.016 -0.005 0.0227 Ln trdsit-1 0.034 *** 0.013 0.011 0.012 0.041 ** 0.0194 Ln fsit-1 -0.016 * 0.009 0.001 0.009 -0.021 * 0.0125 Ln agrsit-1 -0.044 ** 0.020 -0.013 0.019 -0.007 0.0261 DLn sit 0.146 *** 0.014 0.170 *** 0.014 0.128 *** 0.0211 DLn (n)it -0.010 * 0.005 -0.013 ** 0.005 -0.016 ** 0.0077 DLn yschit 0.072 0.050 -0.030 0.048 -0.047 0.0703 DLn gsit -0.024 ** 0.011 -0.024 ** 0.010 0.011 0.0152 DLn rdpcit 0.026 0.017 0.015 0.016 0.016 0.0232 DLn trdsit -0.012 0.017 -0.027 * 0.016 0.048 ** 0.0239 DLn fsit -0.038 ** 0.017 -0.020 0.017 0.009 0.0243 DLn agrsit -0.332 *** 0.048 -0.111 ** 0.046 -0.142 ** 0.0674 DLn yit-1 -0.241 *** 0.043 -0.252 *** 0.044 -0.187 *** 0.0522 y86 0.049 * 0.027 -0.012 0.029 -0.137 *** 0.0393 y87 0.046 * 0.025 -0.004 0.027 -0.133 *** 0.0367 y88 0.069 *** 0.024 0.018 0.025 -0.129 *** 0.0344 y89 0.072 *** 0.023 0.017 0.024 -0.128 *** 0.0330 y90 0.074 *** 0.022 0.031 0.022 -0.116 *** 0.0316 y91 0.092 *** 0.020 0.052 ** 0.020 -0.085 *** 0.0286 y92 0.095 *** 0.019 0.064 *** 0.019 -0.088 *** 0.0276 y93 0.090 *** 0.018 0.069 *** 0.018 -0.063 ** 0.0260 y94 0.089 *** 0.018 0.080 *** 0.017 -0.074 *** 0.0251 y95 0.108 *** 0.017 0.096 *** 0.016 -0.033 0.0231 y96 0.116 *** 0.017 0.111 *** 0.016 -0.004 0.0228 y97 0.103 *** 0.016 0.109 *** 0.016 -0.003 0.0218 y98 0.002 0.017 -0.010 0.017 -0.010 0.0229 y00 0.082 *** 0.017 0.090 *** 0.016 0.050 ** 0.0234 y01 0.082 *** 0.017 0.098 *** 0.017 0.024 0.0225 y02 0.080 *** 0.016 0.097 *** 0.015 0.037 * 0.0222 _cons -0.158 0.126 -0.035 0.122 0.071 0.1777 R-sq: within 0.6697 0.692 0.4165 overal 0.3617 0.234 0.0486