Result 1: The Effects of Resource Dependence on Development Indicators

This section considers the effects of resource dependence in Indonesian districts on development indicators as described in Section 4.3.3.1. I begin without spatial effects. Table 4.3 presents resource effects on the first outcome: local GDP (GRDP) per capita. Again I present first difference models to remove district fixed effects and then IV-GMM to account for potential endogeneity of my measure of resource dependence. With a greater emphasis on spatial factors in this chapter, I now also control for three absolute geographic factors as controls.

For efficient exposition, I move first to the instrument validity and endogeneity tests provided in IV-GMM results. Using the instruments listed in Appendix 4.2. Table A2, I find as in Chapter 1 that in all specifications (5) – (8), the instruments are individually significant in first stage regressions, and that the majority of first-stage F statistics are at or well above 10, suggesting that the instruments are sufficiently strong. Overidentification tests based on Hansen J also fail to reject the null, meaning that my instruments for each resource dependence measure pass necessary conditions for validity. Using these instruments, p-values in endogeneity tests shown show that three out of four measures of resource dependence are endogenous, leaving the preferred specifications to be (1), (6), (7) and (8). Here, again as I found in Chapter 1, an increase in budget dependence on resources significantly raises GRDP per capita for three of four resource dependence measures, showing a blessing effect on growth. For example, from (1), an increase in mining’s share of district GRDP significantly increases GRDP per capita. In particular, a standard deviation increase in a change in mining’s share is associated with a 10.5 (=0.141*0.745 = 0.105) percent increase in income per capita. Only rising coal revenue dependence is not associated with a rise in income.

Next, Table 4.4 reports the OLS estimates of resource dependence on change in the poverty rate, change in educational attainment and change in life expectancy. Corresponding IV-GMM results are presented in Table 4.5. I begin again by examining the validity of my instruments, and results of tests for endogeneity. As seen from the IV-GMM estimates in Table 4.5, the first-stage F statistic for each resource dependence measure for all outcomes generally exceeds 10, with the exception of columns (10)-(11) for life expectancy.

Particularly for column (11) (life expectancy) this may raise the issue of weak identification, where my instruments are only weakly correlated with GRDP resource dependence.

However, the p-value of jointly excluding the instruments for either of these two

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specifications shows them to be jointly significant, which indicates that the instruments are at least significantly correlated with the specified resource dependence measure. The p-values of Hansen J-statistics indicate that the instruments used have passed the over-identification test for all resource dependence measures where they can be run, with values ranging from 0.188 to 0.810. Note that for specifications in columns (7) and (11), the p-values cannot be calculated as the specifications become just-identified. Tests for endogeneity shows that, except for columns (1), (7), (10) and (11), the p-values can not reject the null hypothesis that resource dependence measures are exogenous for these broader development outcomes, suggesting that the OLS estimator is preferable. The preferred specifications are thus columns (2), (3), (4), (5), (6), (8), (9) and (12) from Table 4.4 and columns (1), (7), (10) and (11) from Table 4.5.

Moving to the results, neither in OLS nor in IV-GMM do I find that the poverty rate (columns (1) – (4)) is significantly influenced by variations in resource dependence. The insignificant effects imply that the blessing effects of resource reliance on local GRDP per capita have not been transmitted to improving living standards for people with the lowest incomes. Even more striking than resource dependence’s lack of effect on poverty, all measures are negatively associated with the share of the local population at least completing high school, though generally only significant at the 10% level in columns (5) – (8) of the relevant table.

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Table 4.3. OLS and IV-GMM, Dep Var: ∆GRDP per capita (log)

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES mindep oilgasrev coalrev minrev mindep oilgasrev coalrev minrev

∆Mindep 0.745*** 1.359*** Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

In column (7) of Table 4.5, for example, I find a standard deviation increase in a change in share of coal revenues in government budget is associated with a 1.4 (=0.046*(-0.324) = 0.014) percentage point drop in educational attainment. The magnitude of the effect is smaller for other resource dependence measures broader than coal, which may suggest that it is coal dependence in particular that is most strong negatively associated with high school completion, just as it is not positively associated with growth in per capita income. Taken together, these findings suggest that a higher dependence on resource extraction may have benefitted growth in per capita income within Indonesia in recent years, but has had no beneficial effect in reducing poverty, and may even have lowered high school completion.

Results regarding life expectancy are less consistent than those for poverty, but again with some evidence of a negative effect. In particular, while the point estimates of all four relevant resource dependence measures are negative, only one, mining dependence in GRDP

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(without instruments), is close to being significant, at the 10% level, as shown in column (9).

For example, a standard deviation increase in a change in mining’s share reduces life expectancy by (0.141*(-0.019) = 0.0019) 0.19 percent. I thus find weak evidence of a curse effect of resource dependence on life expectancy.

4.4.2 Results 2: The Effect of Resource Dependence on Development