This study measures the significance of the mining industry on economic growth at the province level in Indonesia by using four different indicators, namely, share of employment, share of credit, share of domestic direct investment, and share of natural resources revenue sharing. These four mining indicators can be classified as the indicator of natural resource dependence instead of natural resource abundance (Mousavi & Clark, 2021). In their paper, Mousavi and Clark (2021) classified any measurement in the form of the percentage of the economy’s current flows (such as GDP) from renewable resource extraction as dependence, while the total non-renewable natural capital stock is classified as abundance. However, it is still true that Indonesia can be seen as a natural resource-rich country with a high potential to boost the economy through the primary sector (Hilmawan & Clark, 2019;
Komarulzaman & Alisjahbana, 2006).
When mining industry is represented as percentage of employment in mining, expanding mining industry is consistently correlated with a decrease in the province’s economic growth. This relationship holds even when changing the control variable or interacting employment with either school enrollment or immunization. Hence, increasing employment in the mining sector would slow down the economy because there might be a trade-off in reducing the share of employment in other sectors such as manufacture or agriculture in which these two sectors can give positive contributions to the economy. Based on the Indonesian Central Bureau Statistics (BPS), the biggest contribution to economic growth in 2018 came from the manufacturing sector at 0.91 percent followed by agriculture, forestry, and fishing at 0.49 percent. The mining sector, on the other hand, only contributed 0.17 percent to the country’s economic growth (BPS, 2019).
Furthermore, the education variable is found to be negatively correlated with growth. This result aligns with Gylfason (2001b; 2002), where the abundance of natural resources in a country can dis-incentivize investment in human capital, leading to lower economic growth. However, contrary to my expectation, the interaction between share of employment and education does not turn its negative impact on growth into positive. Some possible reasons are mining industry is not labor intensive as in manufacture; mining industry as a primary sector produces low
33 | P a g e value-added goods and provides low levels of job creation that do not require highly educated workers as in manufacture and services. Thus, highly skilled employees will go to different jobs. This argument is in line with the previous literatures that also found there is less demand for high skilled labor in primary productions compared to manufacture or services (Sachs & Warner, 1995; Gylfason 2001a).
The share of credit in mining sector contributes to boosting Indonesia’s economic growth. Although the impact turns into negative when the outliers of growth per capita are excluded, it suggests that the results depend on the mining-related factor outliers. One main hypothesis drawn from the positive correlations between credit and growth is that mining sector is relatively high capital-intensive industry that requires advanced technology and equipment especially for the non-surface and underground mining that are very expensive and mostly financed by the credit. Companies use the money they borrow from the national banks in Indonesia to cover their capital costs, such as purchasing technologies, machines and equipment to extract and process the commodities. For example, PT J Resources Nusantara (JRN), a leading gold mining company was reported to borrow credit of 231 million US dollars from Bank Negara Indonesia (BNI) in 2019 to buy capital and support its subsidiaries (Hutauruk, 2019). The capital, such as high technologies for production, will need high-skilled employees to operate it in return. This could explain why education in the share of credit becomes positively correlated with growth. The high-skilled employees are tasked to handle these high technologies which increase the productivity in mining sector and thus higher economic growth.
As for the last two mining indicators, the share of domestic direct investment and the share of revenue sharing are found not to significantly impact economic growth. Although an increase in domestic direct investment correlates with a decrease in growth per capita support findings of Gylfason and Zoega (2006), the results are consistently insignificant even when changing the specification. This might be because of the data period is too short to measure the significance of the share of domestic direct investment on growth per capita. Mining production consists of different steps, namely prospecting, exploration, exploitation, and processing/purification, meaning that the investment in mining factories today might not directly impact economic growth immediately. Since this mining indicator is
34 | P a g e measured as a share of domestic direct investment in mining sector to the total domestic direct investment in all sectors, it is unclear which production stage of the investment took place. If this investment is used for prospecting stage such as searching for economic minerals, the real impact on the economy might be limited.
As a consequence of this limitation, the share of domestic direct investment could not fully capture the province’s economic activity from mining sector.
Other mining indicator used in this paper is the share of revenue sharing from natural resources calculated by the central government based on the national revenue from mining sector located in the respective province. In other words, this revenue sharing depends on mining production and its price that affecting the central government revenue. For instance, even if coal-mining activity in East Kalimantan is high, the revenue from coal export could be low if the international coal price falls, and so the mining’s contribution to the national revenue and the revenue sharing in return. The volatility risk in the commodities’ price then indicates a possible flaw when using revenue sharing to measure the significance of mining industry on economic growth.
Overall, this paper confirms that the mining industry in Indonesia does matter for the province’s economic growth. In particular, the different ways of measuring the mining sector can generate different outcomes on the relation with Indonesia’s economic growth per capita. For researchers, this finding should be taken away: the choice of mining indicator measurement and its analysis on the relationship between the mining sector and economic growth should be carried out carefully. Since the credit in mining sector is correlated with an increase in economic growth, some policy implications can be drawn from this finding. Given the fact that abundant mining deposit in Indonesia is located not only on the surface but also underwater and underground, credit becomes an essential source of financing in mining productions. Indonesian policymakers could allocate more credit with lower interest for this sector and streamline access to credit so mining sector becomes more attractive for the mining companies and investors.
35 | P a g e
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39 | P a g e
Appendices
Appendix A1. Pairwise Correlations
Variables Growth GDP per
Capita
Initial GDP per Capita
Share of Employ–
ment
Share of Credit
Share of DDI
Share of Revenue Sharing
Mining Capacity
Enrollment Ratio at
Senior Level
Immunization Coverage
Villages with Road:
Asphalt
House–hold Access to Electricity
Share GDP to Mining Growth GDP
per Capita
1.000 Initial GDP
per Capita
–0.101** 1.000 Share of
Employment
–0.048 0.114*** 1.000 Share of
Credit
0.026 0.175*** 0.725*** 1.000
Share of DDI –0.086 0.025 0.088 –0.154 1.000
Share of Revenue Sharing
0.129 –0.241*** 0.126 0.102 –0.274 1.000
Mining Capacity
0.074 0.045 –0.053 0.078 0.071 0.162 1.000
Enrollment Ratio at Senior Level
–0.130*** 0.230*** –0.078* –0.088 –0.070 –0.047 –0.198*** 1.000
Immunization Coverage
0.141*** 0.208*** –0.003 0.009 0.135* –0.260*** 0.188*** 0.217*** 1.000
Villages with Road: Asphalt
–0.039 0.085 0.120* 0.211** –0.189 –0.001 0.215** 0.444*** 0.653*** 1.000
Household Access to Electricity
0.027 0.272*** 0.109** 0.135** –0.233*** –0.212** 0.247*** 0.671*** 0.552*** 0.659*** 1.000
Share GDP to Mining
–0.242*** 0.330*** 0.205*** 0.097* 0.246** 0.121 –0.088 –0.124** –0.266*** –0.380*** –0.250*** 1.000
*** p<0.01, ** p<0.05, * p<0.1
Source: Author’s calculations from BPS, INDO-DAPOER (World Bank), and NSWi
40 | P a g e Appendix A2. The Relationship between Domestic Direct Investment in Mining and Growth
(1) (2) (3) (4)
VARIABLES
Share of Domestic Direct Investment –0.00631 –0.00640 –0.00752 –0.00631
(0.00840) (0.0125) (0.00998) (0.0104)
Enrollment Ratio at Senior Level –0.168*** –0.144***
(0.0231) (0.0253)
Immunization Coverage 0.191*** 0.141*
(0.0649) (0.0745)
Household Access to Electricity –0.0857** 0.0207
(0.0426) (0.0318)
Initial GDP per Capita 0.0175 –0.0301 0.0185 –0.0118
(0.0212) (0.0365) (0.0321) (0.0263)
Constant 11.18*** –15.10** 9.813** –4.603
(1.486) (5.887) (4.126) (5.804)
Observations 173 148 173 148
Number of provinces 27 26 27 26
Province RE YES YES YES YES
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
Appendix A3. The Relationship between Revenue Sharing in Mining and Growth
(1) (2) (3) (4)
VARIABLES
Share of Revenue Sharing –0.00650 –0.00432 –0.0109 –0.0131
(0.00757) (0.00658) (0.0120) (0.0133)
Enrollment Ratio at Senior Level 0.0459 0.0391
(0.0573) (0.0845)
Immunization Coverage 0.0590 –0.0880
(0.0658) (0.113)
Household Access to Electricity 0.114* 0.116
(0.0679) (0.0733) Initial GDP per Capita –0.235*** –0.223*** –0.383*** –0.394***
(0.0552) (0.0442) (0.137) (0.150)
Constant 3.545 –0.0683 –2.805 3.594
(2.285) (5.924) (5.254) (6.250)
Observations 137 137 111 111
Number of provinces 29 29 29 29
Province RE YES YES YES YES
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
41 | P a g e Appendix A4. The Relationship between Employment in Mining and Growth
(With and Without Interactions, No Outliers)
VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9)
Share of Employment –0.0480 –0.0678*** –0.0212 –0.113*** –0.263*** –0.277*** –0.137** –0.116** –0.282***
(0.0380) (0.0248) (0.0264) (0.0310) (0.0852) (0.0779) (0.0571) (0.0475) (0.0797)
Enrollment Ratio at Senior –0.0680*** –0.130*** –0.0317 –0.0223 0.00430 –0.0238
Level (0.00928) (0.0191) (0.0226) (0.0251) (0.0255) (0.0261)
Employment*Enrollment –0.0243*** –0.0272*** –0.0283***
(0.00918) (0.00829) (0.00874)
Immunization Coverage 0.162*** 0.162*** 0.0578 0.00591 0.0241 0.0616
(0.0353) (0.0399) (0.0653) (0.0625) (0.0655) (0.0648)
Employment*Immunization –0.0144 –0.0332** 0.00478
(0.0139) (0.0156) (0.0183)
Household Access to –0.0207* 0.0346* –0.0309** –0.0294** –0.0311**
Electricity (0.0114) (0.0189) (0.0124) (0.0130) (0.0124)
Initial GDP per Capita –0.0206 –0.0778* –0.0445 –0.0422 –0.0380 –0.0234 –0.0513 –0.0397 –0.0232
(0.0404) (0.0410) (0.0461) (0.0289) (0.0294) (0.0270) (0.0374) (0.0350) (0.0271)
Constant 6.410*** –11.11*** 4.966*** –7.632** 2.957*** 5.916*** 3.547*** 6.061*** 5.922***
(0.469) (3.239) (0.995) (3.450) (0.275) (1.174) (0.310) (1.207) (1.178)
Observations 518 410 490 382 518 382 410 382 382
Number of provinces 31 31 31 31 31 31 31 31 31
Province RE YES YES YES YES YES YES YES YES YES
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
42 | P a g e Appendix A5. Log Transformation: The Relationship between Employment in Mining and Growth
(With and Without Interactions)
VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9)
Log Share of Employment 0.0435 –0.207 0.0957 –0.157 –0.159 –0.351* –0.309* –0.324* –0.357*
(0.184) (0.199) (0.215) (0.277) (0.175) (0.203) (0.178) (0.177) (0.192)
Enrollment Ratio at Senior –0.0515** –0.147***
Level (0.0222) (0.0266)
Log Enrollment Ratio at 0.817 –0.0801 0.207 –0.197
Senior Level (2.287) (1.338) (1.296) (1.334)
Log Employment* –0.701 –1.632** –1.723***
LogEnrollment (0.670) (0.747) (0.667)
Immunization Coverage 0.139*** 0.0734
(0.0391) (0.0644)
Log Immunization Coverage –0.370 5.176 –1.173 –0.371
(4.832) (5.980) (4.826) (5.091)
Log Employment* –2.800 –0.792 1.198
LogImmunization (3.030) (3.916) (3.929)
Household Access to –0.000330 0.0939** 0.0131 0.0143 0.0129
Electricity (0.0303) (0.0428) (0.0425) (0.0420) (0.0424)
Initial GDP per Capita –0.0502 –0.0732* –0.0827 –0.0774 –0.0734 –0.0749 –0.0646* –0.0797 –0.0748 (0.0547) (0.0412) (0.0664) (0.0481) (0.0526) (0.0554) (0.0384) (0.0572) (0.0565)
Constant 5.787*** –9.002** 3.433 –3.751 3.405*** 2.531 3.758*** 2.522 2.553
(1.095) (3.605) (2.607) (3.939) (0.381) (3.680) (0.303) (3.648) (3.665)
Observations 536 424 507 395 536 395 424 395 395
Number of provinces 31 31 31 31 31 31 31 31 31
Province RE YES YES YES YES YES YES YES YES YES
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
43 | P a g e Appendix A6: The Relationship between Credit in Mining and Growth
(No Outliers)
(1) (2) (3) (4)
VARIABLES
Share of Credit 0.0171 –0.0920*** 0.00774 –0.116***
(0.0713) (0.0165) (0.0716) (0.0156)
Enrollment Ratio at Senior Level 0.0440* –0.0667
(0.0234) (0.0431)
Immunization Coverage 0.110* 0.0620
(0.0625) (0.0653)
Household Access to Electricity 0.0394*** 0.0620***
(0.0118) (0.0237)
Initial GDP per Capita –0.0494 –0.0640 –0.0697 –0.0795
(0.0759) (0.0561) (0.0728) (0.0686)
Constant 1.511** –6.220 0.384 –3.724
(0.716) (5.780) (0.920) (5.920)
Observations 245 159 220 134
Number of provinces 30 29 30 29
Province RE YES YES YES YES
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
Appendix A7. Log Transformation: The Relationship between Credit in Mining and Growth
(1) (2) (3) (4)
VARIABLES
Log Share of Credit 0.0504 –0.108 0.590*** 0.548*
(0.312) (0.295) (0.211) (0.330)
Enrollment Ratio at Senior Level 0.0792 0.108
(0.0495) (0.100)
Immunization Coverage 0.132 –0.0490
(0.0812) (0.106)
Household Access to Electricity 0.0447 0.0979*
(0.0322) (0.0565)
Initial GDP per Capita –0.141* –0.0849 –0.180* –0.270*
(0.0782) (0.0648) (0.101) (0.151)
Constant 0.936 –8.317 1.645 –2.214
(2.605) (8.089) (2.507) (5.931)
Observations 256 165 230 139
Number of provinces 30 29 30 29
Province RE YES YES YES YES
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
44 | P a g e Appendix A8. The Relationship between Domestic Direct Investment in Mining and Growth
(No Outliers)
(1) (2) (3) (4)
VARIABLES
Share of Domestic Direct Investment –0.00630 –0.00658 –0.00743 –0.00723
(0.00902) (0.0140) (0.0106) (0.0124)
Enrollment Ratio at Senior Level –0.169*** –0.130***
(0.0218) (0.0249)
Immunization Coverage 0.205*** 0.178**
(0.0565) (0.0763)
Household Access to Electricity –0.0908** –0.00200
(0.0451) (0.0368)
Initial GDP per Capita 0.0125 –0.0381 0.0148 –0.0195
(0.0219) (0.0362) (0.0331) (0.0274)
Constant 11.38*** –16.16*** 10.45** –6.422
(1.420) (5.195) (4.367) (6.076)
Observations 171 146 171 146
Number of provinces 27 26 27 26
Province RE YES YES YES YES
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
Appendix A9. The Relationship between Revenue Sharing in Mining and Growth (No Outliers)
(1) (2) (3) (4)
VARIABLES
Share of Revenue Sharing –0.00421 –0.00325 –0.00607 –0.00617
(0.00716) (0.00674) (0.00921) (0.00812)
Enrollment Ratio at Senior Level 0.0140 –0.0465
(0.0436) (0.0587)
Immunization Coverage 0.0747 –0.0228
(0.0917) (0.115)
Household Access to Electricity 0.0194 0.0276
(0.0367) (0.0412) Initial GDP per Capita –0.204*** –0.205*** –0.249*** –0.239***
(0.0398) (0.0428) (0.0381) (0.0378)
Constant 4.551** –1.753 4.136 7.577
(1.885) (8.068) (3.532) (7.164)
Observations 132 132 107 107
Number of provinces 29 29 29 29
Province RE YES YES YES YES
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1