First-Difference Estimates With Instrumental Variables

For my final results, I add two types of instrumental variable (IV) analysis to a FD model, where the relatedness of these instruments was explained in Table 2.2. Table 2.7 provides results using the continuous form of abundance-based instruments along with the physical output instruments. Recall that the instruments are defined as the total number of major and minor petroleum (oil and natural gas) fields, in the 1970’s, and the relative share of coal deposit areas to total district areas, in the 1980’s. For comparison purposes, I place the earlier OLS results side by side with IV-GMM results.

I start with whether the instruments satisfy the relevance and overidentification tests.

In general, the instruments are fairly strong, particularly for oil/gas and coal resource

56 To interpret dummy explanatory variables when the Y variable is in logs form, I follow the formula 100.[exp(𝛽̂ − 1] (see Wooldridge, 2016, p.212). _𝑖)

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dependence for models (3′)-(4′). As shown, the Kleibergen Paap F statistic ranges from 16.834 in model (1’), to 25.347 in model (3), greater than the recommended rule of thumb for instrument strength. Alternatively, as shown in Table 2.7, according to Cragg-Donald F statistic values, we see relevance increasing from models (1′) and (4′) to (2′) and (3′).

However, I emphasise Kleibergen F statistics that are robust in the presence of heteroskedasticity. Likewise, regarding overidentification, the Hansen J statistic fails to reject the null hypothesis of exogenous instruments in any models from (1′) to (4′), though with the lowest p-value in model (2’) of 0.1486. This implies that my instruments pass the necessary conditions of the two tests, which are consistent with validity.

With the performance of my combined instruments appearing fairly strong for all models, I next test whether the change in resource dependence, ∆𝑅𝐷_𝑖, is endogenous. In model (1’), the p value from a Hausman type endogeneity test cannot reject the null that mining dependence (model (1′)) is exogenous (p value 0.248). However, endogeneity tests reject exogeneity in IV-GMM models (2′) to (3′) at the 5% level, with p values of 0.015 and 0.018, respectively. In model (4′’) the p value is close to borderline, at just above the 10 per cent level (0.108). Therefore, with exogeneity rejected or borderline rejected for 3 of 4 models, I move next to describe second stage IV results.

Just as in the FD case without instruments, I find no evidence that higher non-renewable resource dependence creates an adverse effect on growth. As is clear from models (4’), (1’) and (2’), my resource dependence coefficients increase in their magnitudes with use of instruments, and are significant at the 1 per cent or 5 per cent levels. Under the IV-GMM estimator, a change in district government dependence on oil and gas revenues in model (3’) has the largest estimated coefficient. Here, an increase of a standard deviation in the change in oil and gas revenue dependence, on average, increases real income per capita by (0.091*1.765 = 0.1606) 16 per cent. This finding once again does not confirm Sachs and Warner’s negative findings in a within-country case. Instead, these results support the views of many earlier descriptive papers for Indonesia considering the effect of the oil boom of the 1970’s and 1980’s (see Gylfason, 2001; Rosser, 2007; Sovacool, 2010).

For its part an increase in coal revenue dependence continues to have no significant effect on long run growth if instruments are included, though the sign of the coefficient turns negative. The insignificant effect of coal may have been caused by the weak performance in individual instruments as shown by the first-stage regression results in both reduced form

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models. For example, as reported in Appendix 2.3, coal abundance and change in coal production have inconsistent signs in models (5) and (6), when they are respectively regressed on change in the share in coal revenue in total government budget or change in per capita income.

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Table 2.7. First difference model of effect of resource dependence on GRDP per capita (CONTINUOUS abundance levels plus change in production IV’s)

Dependent Variable: ∆GRDP per capita (in logs)

(1) (1’) (2) (𝟐’) (3) (𝟑’) (4) (𝟒’)

VARIABLES OLS IV-GMM OLS IV-GMM OLS IV-GMM OLS IV-GMM

∆Mining Dependence 0.738*** 1.356***

(0.190) (0.444)

∆Oilgas Revenue -0.160 1.765**

(0.473) (0.810)

∆Coal Revenue 0.469 -0.696

(0.522) (0.645)

∆Mining Revenue -0.076 1.164**

(0.389) (0.581) Earthquake -0.033** -0.031* -0.034*** -0.035*** -0.032*** -0.037*** -0.034*** -0.026**

(0.013) (0.018) (0.012) (0.013) (0.012) (0.012) (0.012) (0.011)

∆Labour force partic.rate 0.037 0.059 0.025 0.051 0.073 -0.045 0.020 0.215

(0.186) (0.189) (0.192) (0.234) (0.189) (0.201) (0.206) (0.179)

Ln GRDP per capita, 2005 -0.113*** -0.111*** -0.148*** -0.035 -0.150*** -0.119*** -0.141*** -0.097**

(0.032) (0.036) (0.037) (0.066) (0.034) (0.039) (0.029) (0.039)

Population, 2005 (in logs) 0.008 0.023 0.000 0.021 0.004 -0.006 0.000 0.032

(0.023) (0.020) (0.026) (0.026) (0.026) (0.025) (0.027) (0.021)

DURBAN 0.046 0.075* 0.040 0.012 0.047 0.018 0.036 0.048

(0.042) (0.042) (0.042) (0.059) (0.045) (0.047) (0.044) (0.045)

DJAVA 0.085* 0.114* 0.038 -0.048 0.036 0.029 0.034 -0.022

(0.048) (0.060) (0.047) (0.050) (0.044) (0.043) (0.045) (0.042)

Constant 0.730** 0.487* 0.983*** 0.351 0.938*** 0.973*** 0.961*** 0.397

(0.290) (0.282) (0.357) (0.323) (0.328) (0.320) (0.350) (0.256)

Cragg-Donald Wald F stat 8.900 29.017 111.683 27.509

Kleibergen-Paap Wald F 16.834 27.754 25.347 14.807

Hansen J statistic, P-value 0.223 0.355 0.635 0.149

Endogeneity test, P value 0.248 0.015 0.018 0.108

Observations 390 390 390 390 390 390 390 390

R-squared 0.164 0.103 0.082 -0.107 0.084 0.065 0.081 -0.001

Note:Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The reported negative R-squared in column (2’) and (4’) can be obtained when using IV estimation as the sum of squared (SSR) of residuals exceeds the total sum of squared (TSS) of dependent variable (see Wooldridge (2016), page 471; or https://www.stata.com/support/faqs/statistics/two-stage-least-squares/ for detailed discussion).

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Regarding the other control variables, effects are generally similar to the FD model without instruments. For example, the cumulative number of earthquakes over 10 years negatively affects the change in per capita income at district level in Indonesia in all four models of resource dependence. Initial GRDP per capita again has a strong negative association with district income per capita, indicating convergence as before.

Given no evidence of a resource curse with continous abundance level instruments (combined with change in physical production instruments), I next estimate the same models but now using binary abundance level measures, combined with change in physical production measures. As previously described, a binary variable for oil/gas abundance takes on a value of 1 if a district has a major oil field and 0 otherwise; that for coal abundance takes a value of 1 if the district has a proportion of 20 per cent or more with coal deposits, and 0 otherwise. Results are provided in Table 2.8. Kleibergen F statistics indicate that the binary abundance instruments generally are strong for models (4’), (2’) and (3’), with F values of 13.896, 30.976 and 27.580, respectively. More importantly, overidentification test p values are now everywhere far above rejection thresholds in all models. Thus the binary abundance instruments combined with physical production change instruments past tests consistent with validity, albeit still with some weakness in model (1’).

Moving to findings, Table 2.8 shows that results are similar when the binary abundance instruments are used in place of continous ones. The coefficients on resource dependence are positive, and significant for oil/gas and for oil/gas and coal combined. Taking oil and gas revenue dependence as an example in model (2’), a one standard deviation increase in the change of the share of oil and gas revenue over total government revenues is associated with an increase in long run per capita GRDP of about (0.091*1.359 = 0.1236) 12.36 per cent.

Once again, with binary instruments as without instruments, there is no significant association between rising coal revenue dependence and per capita income.

In the analagous endogeneity tests using binary abundance instruments, I again find that exogeneity cannot be rejected in model (1’), and can be rejected in model (3’). In contrast, evidence of endogeneity is now stronger in model (2’) rather than borderline (p value 0.063), and weaker in model (3’) (p value 0.167).

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Table 2.8. First difference model of effect of resource dependence on GRDP per capita (binary abundance levels plus change in production IV’s) Dependent Variable: ∆GRDP per capita in logs

(1) (𝟏’) (2) (𝟐’) (3) (𝟑’) (4) (𝟒’)

VARIABLES OLS IV-GMM OLS IV-GMM OLS IV-GMM OLS IV-GMM

∆Mining Dependence 0.738*** 1.143**

(0.190) (0.579)

∆Oilgas Revenue -0.160 1.359*

(0.473) (0.710)

∆Coal Revenue 0.469 -0.456

(0.522) (0.708)

∆Mining Revenue -0.076 1.059*

(0.389) (0.637)

Earthquake -0.033** -0.031* -0.034*** -0.034*** -0.032*** -0.035*** -0.034*** -0.027**

(0.013) (0.018) (0.012) (0.012) (0.012) (0.012) (0.012) (0.011)

∆Labour force partic.rate 0.037 0.135 0.025 0.068 0.073 -0.003 0.020 0.237

(0.186) (0.186) (0.192) (0.218) (0.189) (0.202) (0.206) (0.173)

Ln GRDP per capita, 2005 -0.113*** -0.108*** -0.148*** -0.056 -0.150*** -0.128*** -0.141*** -0.087**

(0.032) (0.035) (0.037) (0.056) (0.034) (0.038) (0.029) (0.039)

Population, 2005 (in logs) 0.008 0.029 0.000 0.020 0.004 0.001 0.000 0.031

(0.023) (0.020) (0.026) (0.025) (0.026) (0.026) (0.027) (0.021)

DURBAN 0.046 0.069 0.040 0.019 0.047 0.030 0.036 0.039

(0.042) (0.042) (0.042) (0.053) (0.045) (0.047) (0.044) (0.044)

DJAVA 0.085* 0.093 0.038 -0.032 0.036 0.025 0.034 -0.019

(0.048) (0.066) (0.047) (0.047) (0.044) (0.043) (0.045) (0.043)

Constant 0.730** 0.415 0.983*** 0.430 0.938*** 0.919*** 0.961*** 0.368

(0.290) (0.281) (0.357) (0.293) (0.328) (0.322) (0.350) (0.257)

Cragg-Donald Wald F stat 8.108 41.389 115.597 31.065

Kleibergen-Paap Wald F 7.759 30.976 27.580 13.896

Hansen J statistic, P-value 0.269 0.456 0.631 0.470

Endogeneity test, P value 0.697 0.018 0.167 0.063

Observations 390 390 390 390 390 390 390 390

R-squared 0.158 0.136 0.075 -0.036 0.077 0.072 0.074 0.012

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

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Regarding other control variables with binary abundance instruments, earthquake frequency still has negative and statistically significant effects, as I found in the previous results. The coefficient on initial district population level is not significant across models, suggesting no benefits of economies of scale on growth. The initial GRDP per capita in 2005 is statistically significant at the 1 per cent level across all specifications, again implying that convergence is occuring in real income levels between districts during the 2006-2015 period.

Interestingly, although the regression results have generally found that resource dependence is promoting income, the results in Appendix 2.2 which use initial resource dependence in 2006 have reported consistent negative signs and statistically significant at 1 per cent level according to IV-GMM specifications. For example, in column (2’), where the instruments’ strength and exogeneity have passed necessary requirements to be valid, a one standard deviation increase in the share of oil and gas revenue in total local government budget in 2006 lowers per capita income by (0.126*(-1.084)=) 0.136 or 13.6 per cent. These findings support resource curse argument as found in many early studies particularly those which relied on data at country level. Across time, however, concerns have raised due to the problem of unobserved time-invariant factors at national level which cannot be solved by putting resource dependence as a level variable in cross-section regressions as tried here.

While acknowledging these contrary findings, I therefore put more weight on my first difference specifications with instruments better approach has been used increasingly studies conducted at the local level, though to date mostly in developed country studies.