Indonesia Growth Diagnostics: Strategic Priority to Boost Economic Growth
Ministry of National Development Planning/ National Development Planning Agency Directorate for Macroeconomic Planning and Statistical Analysis
Indonesia Growth Diagnostics: Strategic Priority to Boost Economic Growth
Ministry of National Development Planning/ National Development Planning Agency Directorate for Macroeconomic Planning and Statistical Analysis Indonesia Growth Diagnostics
Indonesia Growth Diagnostics: Strategic Priority to Boost Economic Growth
Supervisor Eka Chandra Buana, S.E., M.A.
Authors Mochammad Firman Hidayat, S.E., M.A. Adhi Nugroho Saputro, M.Sc. Bertha Fania Maula, S.E.
Cover Design Hamdan Hasan, S.Kom.
Data Visualization Bertha Fania Maula, S.E. Sekar Sanding Kinanthi, S.E.
Ministry of National Development Planning/ National Development Planning Agency Directorate for Macroeconomic Planning and Statistical Analysis
Jalan Taman Suropati Nomor 2 Jakarta 10310 Tel. (021) 3193 6207 Fax. (021) 3145 374 www.bappenas.go.id
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Indonesia Growth Diagnostics
Acknowledgments
The Growth Diagnostics study, to identify the most binding constraint of Indonesia’s economic growth, started in early 2018. Previously in December 2017, we invited Prof. Ricardo Hausmann from Harvard University to give a public lecture and workshop regarding Growth Diagnostics in Jakarta with funding from the Australian Government through the Department of Foreign Affairs and Trade (DFAT). We would like to thank DFAT for making it possible for us to learn about the tools and mechanics of Growth Diagnostics directly from Prof. Ricardo Hausmann.
The whole research activities and writing process of this report is a joint work between Directorate for Macroeconomic Planning and Statistical Analysis Bappenas and PROSPERA. Therefore, we would like to thank PROSPERA for doing this collaborative research from the beginning until the report is published. We received valuable support from PROSPERA for the analytical process of the study, the preparation of final outputs, and the facilitation of holding a discussion and in-depth interviews with related stakeholders.
We are indebted by many stakeholders that gave us inputs for preparing the study through a series of Focus Group Discussion (FGD) held in 2018.
The cross-directorate discussion in internal Bappenas helped us mapping the initial findings of the study and thus we are thankful for their inputs and supports.
We also want to thank private sector representatives who actively participated in our FGDs sharing their perspective on factors that hindering business activities in Indonesia. We benefited from discussion with Deloitte Indonesia, PWC (Pricewaterhouse Coopers), Bukalapak, Tokopedia, PT GE Operations Indonesia, PT Tira Austenite Tbk, HighScope Indonesia, PT Pacto Ltd, Maersk Line, PT Naku Freight Indonesia, and PT Mayora Indah Tbk. Moreover, we also gained perspectives from the smaller scale business through the SME representatives under the supervision of UKM Center FEB UI, IncuBie IPB, PEAC Bromo, and PT Permodalan Nasional Madani (PNM).
Besides, we also received valuable inputs on several discussion topics from public and research institutions that contributed in our FGDs, namely Ministry of Trade, Ministry of Industry, Otoritas Jasa Keuangan (OJK), the SMERU Research Institute, Lembaga Demografi FEB UI, and Centre for Strategic and International Studies (CSIS).
Most importantly, we would like to thank experts in economics and related field that we approached for in-depth interviews, invited in FGDs, and asked for the feedback to improve our study:
• Prof. Dorodjatun Kuntjoro-Jakti • Prof. Arief Anshory Yusuf, Ph.D. • Prof. Mari Elka Pangestu, Ph.D. • Faisal H. Basri, S.E., M.A. • Dr. Muhammad Chatib Basri, S.E., M.Ec. • Prof. Dr. Mohamad Ikhsan, S.E., M.A. • William Wallace, Ph.D. • Anton Hermanto Gunawan, S.E., M.A., M.Phil. • Prof. Geoffrey J.D. Hewings • Teguh Dartanto, S.E., M.Ec., Ph.D. • Prof. Budy P. Resosudarmo, Ph.D. • Haryo Aswicahyono, Ph.D. • Dr. Asep Suryahadi • Turro S. Wongkaren, Ph.D.
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Ministry of National Development Planning/National Develoment Planning Agency
Table of Contents
FOREWORD ...... III FOREWORD ...... IV FOREWORD ...... V ACKNOWLEDGMENTS ...... VI TABLE OF CONTENTS ...... III LIST OF FIGURES ...... IV EXECUTIVE SUMMARY ...... 1 1. INDONESIA GROWTH STORY ...... 3
1.1. DECLINING TREND GROWTH ...... 3 1.2. PRODUCTIVITY PROBLEM ...... 3 1.3. GROWTH QUESTION ...... 4 2. GROWTH DIAGNOSTICS ...... 5 3. REGULATIONS AND INSTITUTIONS AS THE MOST BINDING CONSTRAINT ...... 6
3.1. INVESTMENT FINANCING: ISSUE WITH INTERMEDIATION ...... 6 3.2. GEOGRAPHY: UNDERLINING THE NEED FOR INFRASTRUCTURE ...... 7 3.3. HUMAN CAPITAL (FUTURE BINDING CONSTRAINT): SKILLS, BASIC EDUCATION, AND HEALTH IMPROVEMENT IS CRITICAL ...... 7 Skills ...... 7 Education ...... 8 Health ...... 9 3.4. INFRASTRUCTURE: LACKING PARTICULARLY FOR CONNECTIVITY ...... 10 Connectivity ...... 10 Energy ...... 11 Digital ...... 12 Water and Sanitation ...... 12 3.5. MARKET FAILURE: UNREALIZED POTENTIAL ...... 12 3.6. MACRO RISK: LOW TAX RECEIPT LIMITS PUBLIC GOODS DELIVERY ...... 13 3.7. REGULATIONS AND INSTITUTIONS (THE MOST BINDING CONSTRAINT): BETTER COORDINATED POLICIES TO BOOST GROWTH ...... 13 CONCLUSION ...... 16 REFERENCES ...... 17 APPENDICES ...... 19
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List of Figures
Figure 1. Binding Constraint Illustration ...... 1 Figure 2. Diagnostic Tree ...... 5 Figure 3. Indonesia Economic Growth ...... 19 Figure 4. GDP per Capita Trend ...... 19 Figure 5. Indonesia Potential Growth ...... 19 Figure 6. Total Factor Productivity ...... 19 Figure 7. Share of Manufacturing & GDP per Capita ...... 20 Figure 8. High-Technology Exports ...... 20 Figure 9. Accumulation of Fixed Capital Investment of Machinery and Equipment, 2007-2016 ...... 20 Figure 10. Infrastructure Capital Stock ...... 20 Figure 11. FDI Net Inflows vs. GDP per Capita, 2017 ...... 20 Figure 12. Indonesia Incremental Capital-Output Ratio ...... 20 Figure 13. Gross Domestic Savings vs. GDP per Capita, 2017 ...... 21 Figure 14. FDI Net Inflows ...... 21 Figure 15. Real Lending Rate vs. GDP per Capita, Average 2015-2017...... 21 Figure 16. Nominal Lending Rate, vs. GDP per Capita, Average 2015-2017 ...... 21 Figure 17. Real Lending Rate and Investment Rate ...... 22 Figure 18. Biggest Obstacles in Doing Business in Indonesia ...... 22 Figure 19. Indonesia Investment Composition ...... 22 Figure 20. Net Interest Margin ...... 22 Figure 21. Financial System Interlinkages, Malaysia ...... 22 Figure 22. Financial System Interlinkages, Indonesia ...... 22 Figure 23. Labour Force Distribution by Education, 2016 ...... 23 Figure 24. Labour Force with Tertiary Education...... 23 Figure 25. Agriculture Employment vs. GDP per Capita, 2017 ...... 23 Figure 26. Informal Employment vs. GDP per Capita, 2017 ...... 23 Figure 27. Returns to Secondary Education ...... 24 Figure 28. Unemployment Rate by Education ...... 24 Figure 29. Returns to Tertiary Education ...... 24 Figure 30. Skills of Working Age Population ...... 24 Figure 31. Skills Mismatch in Indonesia ...... 24 Figure 32. Net Wage Effects of being Skills Mismatch ...... 24 Figure 33. Mean Years of Schooling ...... 25 Figure 34. Mean Years of Schooling vs. GDP per Capita, 2017 ...... 25 Figure 35. Gross Enrolment Ratio ...... 25 Figure 36. School Enrolment, Tertiary ...... 25 Figure 37. Returns to Education vs. GDP per Capita, 2010 ...... 25 Figure 38. Returns to Education, Indonesia ...... 25 Figure 39. Trends in International Mathematics and Science Study (TIMSS), 2015 ...... 26 Figure 40. Programme for International Student Assessment (PISA), 2015 ...... 26 Figure 41. PISA Score Projection, Indonesia ...... 26 Figure 42. TIMSS Score Projection, Indonesia ...... 26 Figure 43. Indicators Related to Quality of University ...... 26 Figure 44. Indonesia University Ranking Classification ...... 26 Figure 45. The Global Innovation Index 2018 ...... 27 Figure 46. The Human Capital Index 2018 ...... 27 Figure 47. Life Expectancy at Birth ...... 27 Figure 48. Infant Mortality Rate ...... 27 Figure 49. Maternal Mortality Ratio ...... 27 Figure 50. Stunting Prevalence vs. GDP per Capita, 2016 ...... 27 Figure 51. Immunization Rate, 2017 ...... 28 Figure 52. Cause of Death by Communicable Diseases and Maternal, Prenatal and Nutrition Conditions ...... 28 iv | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
Figure 53. Cause of Death by Non-Communicable Diseases ...... 28 Figure 54. Mortality from CVD, Cancer, Diabetes or CRD ...... 28 Figure 55. Health Facilities per 10,000 Population ...... 28 Figure 56. Trend in Male Smoking Prevalence ...... 28 Figure 57. Male Smoking Prevalence vs. GDP per capita, 2016 ...... 29 Figure 58. Most Recent Survey of Youth Tobacco Use (Age 13-15) ...... 29 Figure 59. Road Connectivity Index, 2017 ...... 29 Figure 60. Road Density, 2014 ...... 29 Figure 61. Quality of Roads, 2017 ...... 30 Figure 62. Quality of Port Infrastructure, 2017 ...... 30 Figure 63. Airports per Million Square Kilometre, 2013 ...... 30 Figure 64. Quality of Air Transport Infrastructure, 2017 ...... 30 Figure 65. Railroad Density, 2017 ...... 30 Figure 66. Efficiency of Train Services, 2017 ...... 30 Figure 67. Problematic Factors for Doing Business in Indonesia ...... 31 Figure 68. Electrification Ratio, Indonesia ...... 31 Figure 69. Electrification Ratio, Peer Countries ...... 31 Figure 70. Electrification Ratio by Consumption Decile ...... 31 Figure 71. System Average Interruption Frequency Index (SAIFI) ...... 31 Figure 72. System Average Interruption Duration Index (SAIDI) ...... 31 Figure 73. Electrification Ratio (% of Households) by Region, 2017 ...... 32 Figure 74. System Average Interruption Frequency Index (SAIFI) by Region, 2017 ...... 32 Figure 75. System Average Interruption Duration Index (SAIDI) by Region, 2017 ...... 32 Figure 76. Quality of Electricity Supply, 2017 ...... 33 Figure 77. Broadband Subscriptions, 2017 ...... 33 Figure 78. Broadband Speed, 2017 ...... 33 Figure 79. Access to Basic Water Services, 2015 ...... 33 Figure 80. Access to Basic Sanitation Services, 2015 ...... 33 Figure 81. Access to Water Supply by Quintile 2018, Urban ...... 33 Figure 82. Access to Water Supply by Quintile 2018, Rural ...... 34 Figure 83. Firms Experiencing Water Insufficiencies ...... 34 Figure 84. Indonesia’s Export by Type of Commodity...... 34 Figure 85. Export Composition by Product, 1995 - 2017 ...... 35 Figure 86. Complexity Outlook Index & Economic Complexity Index, 2016 ...... 35 Figure 87. Economic Complexity Index vs. GDP per Capita, 2016 ...... 35 Figure 88. External Debt & Reserve Adequacy ...... 36 Figure 89. External Debt & Current Account Balance ...... 36 Figure 90. Central Government Debt ...... 36 Figure 91. Tax Ratio vs. GDP per Capita, 2016 ...... 36 Figure 92. Government Expenditure on Education vs. GDP per Capita, 2015 ...... 37 Figure 93. Government Expenditure on Health ...... 37 Figure 94. The Missing Middle, 2013 ...... 37 Figure 95. Regulatory Index, 2017 ...... 37 Figure 96. Legal System and Property Right Index, 2017 ...... 37 Figure 97. Rule of Law Index, 2017 ...... 37 Figure 98. Cost of Redundancy Dismissal, 2018 ...... 38 Figure 99. Percent of Firms Offering Formal Training ...... 38 Figure 100. FDI Regulatory Restrictiveness Index, 2018 ...... 38 Figure 101. Inward FDI Stock, 2018 ...... 38 Figure 102. Time Required to Start a Business, 2019 ...... 38 Figure 103. Score on Trading across Borders, 2019 ...... 39 Figure 104. Rank in the Ease of Paying Taxes, 2019...... 39 Figure 105. Cost to Export and Import, 2019 ...... 39 Figure 106. Effective Rate of Protection, 2015 ...... 39 Figure 107. Most Problematic Factors for Doing Business in Indonesia ...... 39
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Ministry of National Development Planning/National Develoment Planning Agency
Executive Summary
Countries need many things to grow, but they are highly complementary, and resources are limited. Hence, the development plan needs to prioritize and resolve the most binding constraint to get the maximum return. The “most binding constraint” is the one constraint that will prevent the economy from growing faster even if other reform needs are addressed. The growth diagnostic is a framework for identifying the binding constraints to growth. It is an iterative process that starts with identifying the growth question and follows a diagnostic decision tree.
This study is produced as a background study for the 2020-2024 National Development Plan. The study itself adopts the original diagnostic tree and seeks to answer a growth question: what the most binding constraint to the low level of innovation and productive investment i.e., how to get investment that delivers higher productivity. This is on the background that Indonesia’s growth has been declining in the past decades. At the same time, Indonesia’s investment to income ratio is one of the highest in the world suggesting that investment effectiveness is low.
The study found that regulations and institutions are the most binding constraint to growth. Existing regulations do not support business creation and development and tend to be restrictive. Institutions here refer to the setting which produces those regulations, in particular: lack of strategic alignment, weak supervision, and overlapping institutional responsibilities. It also points to corruption and bureaucratic inefficiency. This conclusion was a common theme not only with private business but also in social sectors like health and education.
Inefficient regulations create high fixed costs. Therefore, it generates a missing middle phenomenon in Indonesia: large companies can bear high fixed costs, medium companies cannot compete, and small companies choose to be outside of the regulation – causing a large informal sector. Compared to other countries, existing regulations tend to be protectionist and the cost related to labour and taxation is very high. Widespread middlemen practice also indicate regulations and institutions as the most binding constraint as economic agents attempt to bypass it.
Figure 1. Binding Constraint Illustration
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There are three main areas identified as the regulatory constraints: labour, trade, and investment.
First, in labour regulation, the cost of firing workers in Indonesia is high. This means firms employ staff on temporary contracts and do not develop their professional skills through training. Indonesian firms also struggle to navigate costly and complex regulations to hire skilled foreign workers. This places Indonesian firms at a disadvantage.
Second, exporters and importers face high administrative costs owing to excess licenses and regulations. At the same time, “non-tariff barriers” to trade such as licenses and quotas increase the cost of living in Indonesia by 8%.
Third is investment policy, Indonesia is among the most restrictive countries in the world for foreign direct investment. The negative list discourages foreign firms from setting up businesses in Indonesia that could attract technology, create jobs and boost exports. This issue is not only for the manufacturing sector but even worse for the services sector. There are also skewed competition treatment, for instance, tax exemptions towards small businesses. This discourages Indonesian firms from expanding and becoming more productive through economies of scale. Even more, there are sectors closed for the domestic private competition such as seaports industry which is dominated by less efficient state-owned firms.
The evidence is also apparent across sectors. In the education sector, foreign nationals could not obtain academic tenure in Indonesian universities preventing know-how transfer. In the health sector, there is a lengthy process to obtain BPOM license making some drugs unavailable in Indonesia. Some evidence also points to the weak institutional setting. For instance, in 2018 there were up to 30 ready-to-operate ports with no road access. This is due to weak coordination between central government who built the ports and local government who has the authority to build the roads. Within the central government itself, there is a conflicting role on the budget process between three agencies: Bappenas and Fiscal Policy Agency, MoF and Treasury, MoF resulting in a budget that doesn’t reflect development priority.
The study also identifies human capital as the future binding constraint, particularly given the development of the digital economy and the aspiration to adopt industry 4.0 – the development of high technology manufacturing sector. Skills and education will become the next binding constraint in the rapid advancement of technology particularly as the quality of education in Indonesia are worrying.
Overall the study suggests that priority should be given to improvements in regulations particularly the ones that hamper business development and productivity growth. And, to the institutional improvement, mainly on the clarity of roles and authority, including the role as the policy conductor and development regulator. In addressing the future constraint on human capital, policy actions need to focus on reforming basic education and teaching, opening investment in tertiary education, incentivizing diaspora engagement and focus on children’s nutrition.
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Ministry of National Development Planning/National Develoment Planning Agency
1. Indonesia Growth Story
Indonesia’s growth story is one of the most impressive in the world. Since the early 1960s Indonesian GDP per capita has grown more than six-fold. Only a handful of other countries have matched this achievement, namely China, India, and South Korea. Indonesia’s poverty rate fell to single digits for the first time in 2018. This is significant because almost half of the population was poor after the Asian financial crisis in late 1990s.
1.1. Declining Trend Growth The pace of Indonesian economic growth today is slower that it was before Asian financial crisis (Figure 3). During 2000-2018 Indonesia’s economy expanded by 5.3 percent a year on average compared with 7.0 percent during 1980- 1997.
Slower growth means that Indonesia has failed to match the economic advances of its peers in East Asia. During the past decade, Indonesia’s progress in closing the gap in GDP per capita with Malaysia and Thailand has stalled (Figure 4). During the same period, the Philippines and Vietnam managed to narrow the gap in GDP per capita with Indonesia. China’s GDP per capita doubled during this period.
Indonesia’s slower and stagnant growth stems from structural rather than cyclical issues. The declining growth rate is a result of declining production capacity – potential output. Bappenas (2017) shows that current potential GDP growth is 5.1-5.3 percent and this figure continues to decline (Figure 5).
At the same time, Indonesia aspires to become a high-income country within the next two decades. Bappenas (2018a) shows that Indonesia need to grow at least by six percent each year in order to avoid a middle-income trap. Sustainable and targeted structural reform is necessary to obtain higher growth and achieve this goal.
1.2. Productivity Problem Slower productivity growth is the main reason for the declining growth of potential output. Before the Asian crisis, Indonesia’s productivity was one of the highest in the region—above Malaysia, Thailand, Vietnam, the Philippines, and China1 (Figure 6). But Bappenas (2017) estimates show a decline in Indonesia’s total factor productivity growth during the past 15 years. Today Indonesia’s productivity is among the lowest in the region.
Productivity in agriculture and services are especially low. This indicates that the structural transformation of the economy has not gone smoothly. Ideally an economy would advance from being built around agriculture and natural resources to higher value-added activities such as manufacturing then services. However, this does not mean that one sector is more important than the others: the transition from resources to high-value manufacturing would, for example, also require supporting services such as logistics, insurance, and accounting.
In Indonesia, more than 30 percent of the labour force works in the agricultural sector where productivity is low. Meanwhile, manufacturing’s share of economy activity is declining (although it is still high compared with other countries). Nonetheless, the declining trend is worrying because it is happening at an earlier stage of the economy’s development compared with other countries such as Malaysia and Thailand (Figure 7). In those two countries, the decline in manufacturing’s share of the economy began after peaking at around 30 percent of GDP and at a higher level of per capita GDP.
Indonesia’s manufacturing sector is more productive than other parts of the economy, but it is still not productive enough to compete globally. Indonesia’s manufacturing exports are low compared with its peers. Its exports are dominated by commodities and simple manufacturing products, mainly garments. Indonesia has been left behind when it comes to making more complex products that require more advanced technology (Figure 8). There has been little export diversification in recent decades. In 1970 Indonesia’s exports were dominated by commodities, mainly rubber and oil. The composition is not much different today, with exports still dominated by commodities (palm oil and coal). Fifty years ago, Thailand and Malaysia also exported mostly commodities but since then they successfully diversified. Today their exports are dominated by manufactured products, mainly electronics.
1 In this study, when referring to peer countries, it refers to Malaysia, Thailand, Vietnam, the Philippines, and China.
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1.3. Growth Question Indonesia successfully transformed its economy during the 1980s when it built up the manufacturing sector. However, after the Asian crisis structural transformation has stalled, as evidenced by the failure to develop exports of more advanced manufactured goods.
Lack of innovation is a major obstacle to export diversification. Indonesia ranks 85th out of 126 countries in the Global Innovation Index (2018). Compared with peer countries, Indonesia is wanting in both product and process innovation. Indonesia introduced only four new export products during 2000-2015, much lower than Vietnam and the Philippines, which created 51 and 27 new export products, respectively. It is widely known that innovation could drive productivity and in turn contribute to higher economic growth.
In Indonesia, the share of investment to GDP is one of the highest in the world. It means that investment has not been directed toward activities that could support higher productivity growth. This is shown by four main observations. First, the accumulation of machinery and equipment in Indonesia during the past decade has been significantly lower than in peer countries (Figure 9). Without this type of investment, the manufacturing industry cannot grow optimally owing to the depreciation of machinery and equipment as well as outdated technology. Second, Indonesia’s infrastructure capital stock as a percent of GDP is low compared with peer countries. Massive infrastructure investment in recent years has managed to stop the decline but more is required to return the capital stock to the level seen before the Asian crisis (Figure 10). Third, foreign direct investment (FDI) is low in Indonesia (Figure 11). FDI is important because it enables the transfer of “know-how”. Fourth, Indonesia’s overall investment effectiveness is declining and low compared with its peers (Figure 12). This points to a declining return on investment.
This study seeks to answer a growth question: what is the most binding constraint to the low level of innovation and productive investment, i.e. investment with high productivity return. This study follows the growth diagnostic method introduced by economists Ricardo Haussmann, Dani Rodrik and Andres Velasco.
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2. Growth Diagnostics
Countries need many things to grow, but they are highly complementary, and resources are limited. Hence, the development plan needs to prioritize and resolve the “most binding constraint” to get the maximum return. The most binding constraint is the one that will prevent the economy from growing faster even if other problems are addressed.
The growth diagnostic is a framework for identifying the binding constraints to growth. It is an iterative process that starts with identifying the growth question and then follows a diagnostic decision tree: posit a hypothesis that can account for the symptoms and search for further testable implications of the hypotheses and repeat these steps until they converge and the most binding constraint is identified.
The method was developed by Ricardo Hausmann, Dani Rodrik and Andres Velasco in 2015 and has been adopted by over 20 countries. If a constraint is binding, then: (i) the (shadow) price of the constraint should be high; (ii) movements in the constraint should produce significant movements in the objective function (e.g. GDP); (iii) agents in the economy should be attempting to overcome or bypass the constraint; and (iv) “camels and hippos”: agents less intensive in that constraint should be more likely to survive and thrive, and vice versa.
The study itself adopts the original diagnostic tree and seeks to answer a growth question: what is the most binding constraint to raising the low level of innovation and productive investment, i.e. how to get investment that delivers higher productivity.
Figure 2. Diagnostic Tree
Problem: low levels of productive investment and innovation
Low return to High cost of finance economic activity
Low social Low Low domestic savings + Bad local finance/ returns appropriability bad international finance intermediation
Government Market failures failures
Poor Low Bad Micro risks: Macro risks: Information Coordination Low High risk High cost geography human infrastructure regulation, financial, externalities: externalities competition capital institution i.e., monetary, “self- legal system, fiscal discovery” corruption instability
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3. Regulations and Institutions as the Most Binding Constraint
The diagnostic tree is the guiding framework for this section. First, we look at the right side of the tree on the cost of finance. Once we conclude that the cost of finance is not the main issue, we then move to the left side of the tree, on the return to economic activity. We look at social issues first, starting with geography followed by human capital and then infrastructure. We then move to the appropriability issue, both for the market and the government. The latter encompass both macro and micro risks.2
3.1. Investment Financing: Issue with Intermediation In this section, we look at the issue of financing investment. First, we look at the supply of financing both from foreign and domestic sources and then examine in more detail domestic financing where government has more power to intervene. We found that investment financing is not a binding constraint for investment. Shopping malls, for instance, are considered to offer higher returns over more productive investments such as factories. This suggests that the issue is more on the appropriability or the social return. Low foreign financing (FDI) is also supportive of this argument because foreign financing is independent of issues surrounding the domestic financial system.
Nevertheless, there is an important caveat here. The size of the domestic financial system in Indonesia is small and it is dominated by banks. More importantly, intermediation is low—thus driving inefficient investment. This is because investors have to compete for intermediated finance and the competition ensures finance is allocated to investments with the highest returns. Without the competition made possible by intermediation, finance flows to less productive investments.
At 31.6 percent of GDP in 2018, domestic savings in Indonesia are higher than most peer countries at similar stages of development (Figure 13). At the same time, non-resident investors’ trust and appetite for Indonesian assets is strong. Indonesian government paper is considered investment grade by all major credit rating agencies. Nevertheless, foreign direct investment in Indonesia is low compared with other countries (Figure 14)—even with assured access to foreign finance. Therefore, the issue is not the supply of financing but must be something else. Next, we look at access to finance.
Only half of adults in Indonesia have a bank account. This figure is low compared with Malaysia and Thailand where at least 80 percent of people have access to bank. It is a similar story for business where only six out of ten Indonesian companies have a bank account. The cost of finance is also not an issue given that lending rates are low and comparable to other countries in real and nominal term, respectively (Figure 15 and Figure 16). Changes to the lending rate do not have a meaningful correlation with the investment level. In fact, Figure 17 shows the contrary: investment tends to pick up when inflation-adjusted lending rates increase.
Indonesia also has high investment figures despite financing issues. The latest World Bank Enterprise Survey shows that financing is not considered as the biggest obstacle to doing business (Figure 18).
Nonetheless, most investment is not directed into areas with the highest returns. Figure 19 suggests that the issue is the lack of financial intermediation, as only 15 percent of investment is delivered through intermediation. This lack of intermediation means that the competition that would allocate investment to areas with the highest returns is limited.
The lack of intermediation also came up during our discussion with financial institutions. Indonesia’s domestic savings is high compared with its peers. Yet only a small fraction goes to the formal financial system. Hence deposit-taking institutions (banks) are competing for limited funds by offering high deposit rates. Among others, these three issues were identified as the root causes: low financial literacy, lack of trust in the financial system, and low financial inclusion.
The issue surrounding financial intermediation is exacerbated by inefficiency among banks themselves. Banks are highly segmented, limiting competition. This causes high operational costs and in turn drives the high net interest margin (Figure 20). Risk is another important driver of high net interest margin. Long-term investors such as those involved in insurance and pensions are scarce in Indonesia. This causes a mismatch in maturities where deposits are
2 The flow in this report has been adjusted to tell the story in a more coherent way. While it may not reflect the actual process which has many iterations, the findings are consistent.
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Ministry of National Development Planning/National Develoment Planning Agency
mostly short term but high-return investments usually require long-term financing. As shown in Figure 21, Indonesia’s financial intermediation is heavily concentrated in banks only. The size is also small and less diversified compared with Malaysia (Figure 22).
Although lack of financial intermediation is a significant shortcoming that needs to be addressed, the financial system overall is not currently a binding constraint. Investment with lower economic returns, such as shopping malls over factories, can thrive in Indonesia. This suggests that the issue is more about appropriability or social return. Low foreign financing (FDI) is also supportive of this argument because foreign financing has nothing to do with the issues surrounding the domestic financial system.
3.2. Geography: Underlining the Need for Infrastructure Since the cost of finance is not a binding constraint, we look at the left side of the diagnostic tree, starting with geography. On the one hand, Indonesia is a vast archipelagic country with more than 70 percent sea area. This poses huge challenges for transport and communication between locations in Indonesia. Large numbers of remote and isolated areas separated by sea, dense forests, or mountainous terrain contributes to high logistics cost in Indonesia. Geography could also explain the evolution of economic development that differs between regions. On the other hand, Indonesia’s geography could also be an asset. Indonesia’s has an economically strategic location, situated on major international maritime trade routes.
Further research is needed here. However, we argue that improving infrastructure, especially connectivity, is key for Indonesia to overcome its geographical challenges and reap the rewards of its strategic location.
3.3. Human Capital (Future Binding Constraint): Skills, Basic Education, and Health Improvement is Critical This section looks at human capital issues, covering skills, education and health. Human capital determines the labour quality which in turn affects productivity. Labour productivity improvement has significant potential to boost Indonesia’s growth given almost three quarter of its population is of working age.
Although access to education has been improved significantly in Indonesia, the quality of education has been slower to improve. The slow pace of improvement in PISA Assessments, for instance, suggests a wider gap in the quality of education with peer countries. Meanwhile, despite significant improvement, health indicators and facilities are still left behind compared with peer countries. Furthermore, there is a higher risk coming from poor health conditions indicated by high prevalence of stunting, rising non-communicable diseases, and high smoking prevalence among teenagers. With the rise of disruptive technologies and global competition, we conclude that education and health may constrain economic growth in the future.
We found that skills, in particular the lack of high-skilled labour, to be one of the binding constraints to growth. Employment is still dominated by those with primary education or less. The proportion of high-skilled workers across industries is also below that of peer countries. Indonesia’s low skills reflect the high rate of agricultural and informal employment. Skills mismatches are also a concern. More than half of Indonesian workers do not have the skills wanted by employers. Although training can significantly increase wages of those who learn the right skills, this itself points to shortages of skills in the labour market.
Skills Indonesia’s workforce is dominated by those with primary education or less (Figure 23). Compared with its peers, the proportion of labour with secondary education background is relatively high while those with tertiary education is low despite increasing in recent years (Figure 24).
High agriculture and informal employment in Indonesia suggesting that low skilled labour dominate the workforce. Approximately 30.2 percent of workers are farmers as of 2018 (Figure 25), higher than the average of peer countries. Moreover, the rate of informality in agriculture is more than 90 percent in Indonesia, based on OECD estimates (2018). Meanwhile, informal employment in non-agricultural sectors is above 70 percent. The number is significantly higher than other countries (Figure 26). Women, young people, and less educated people are more likely to work in the informal sector (OECD, 2018). The same study suggests that informality exists across regions in Indonesia, typically contributing to lower productivity, lower wages, less training, and poorer working conditions.
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High informality often exists when workers do not possess skills for jobs in the higher-paying formal sector (OECD, 2018). However, strict labour regulation in Indonesia that make it costly to fire workers also discourages employers from hiring low-skilled labour (Allen, 2016) and thus contributes to informality. As the problem of high costs discourages employers from adding formal or permanent workers, this is also associated with less training (Figure 99). More on this will be explained in the regulations and institutions section.
Mid-skill, that is labour with secondary education background, comes second in Indonesian workforce demography. The returns to secondary education are higher compared with peer countries (Figure 27). This indicates a high demand for labour with this level of education. However, unemployment among secondary-educated workers is also higher than other countries and other levels of schooling (Figure 28). This points to the uneven quality of secondary education as well as premise that the high return to the secondary education may be rooted from the minimum wage regulation in Indonesia.
The returns to tertiary education in Indonesia are low compared with its peers (Figure 29), indicating that the economy may not be looking for high-skilled labour. The fastest-growing economic sectors in Indonesia are also those that make intensive use of low-to-medium skills such as agriculture, trade and low-end manufacturing. In other words, despite limited supply, there is also a low demand for high-skilled labour.
It is important to note that the actual skills of labour may not be fully reflected by education level. Pritchett (2016) found that the skills of workers with tertiary education in Indonesia is similar to those of workers with less-than-upper- secondary education in Denmark (Figure 30). Hence the issue could be worse than what the figures suggested as they do not capture the quality of education.
Training is one of the solutions to address mismatches between people’s skills and the skills required for jobs in Indonesia. Horizontal and vertical skills mismatches3 4 existed for more than half of total workers in Indonesia with no significant improvement from 2008 to 2015 (Figure 31). Moreover, a study by the OECD (2016) found that Indonesia has the largest prevalence of mismatch by field of study, with one in two workers is doing jobs unrelated to their studies.
The skills mismatch keeps wages low (Samudra, 2018) (Figure 32). However, training could compensate for this negative wage effect. Samudra (2018) argues that training acts as a “cushion” for the wage penalty of being horizontally and vertically mismatched. There is a significant increase in wages from training, indicating a high price of skills owing to skills shortages in labour market. Furthermore, it is crucial that Indonesia’s labour force is able to adapt by improving its skills in an age of automation and other technological disruption.
Education Indonesia has recorded a significant improvement in educational quantity over the past two decades. The average number of years an Indonesian aged 25 or older has spent in school doubled from four years in 1990 to eight years in 2017 (Figure 33). Although this improvement is impressive, the average years of schooling in Indonesia is still less than peer countries with similar levels of per capita income (Figure 34).
Indonesia’s gross participation rate for primary and secondary education is on a par with peer countries (Figure 35). Meanwhile, the gross participation rate for tertiary education has improved significantly since 1990, although it is still less than peer countries (Figure 36).
The return on every year of additional education is similar with peer countries (Montenegro and Patrinos, 2014) (Figure 37). Nonetheless, the return has been declining since the mid-1990s (Figure 38). This suggests that education is not the binding constraint to growth.
Even so, the quality of education is still wanting compared with peer countries. Indonesia’s scores in both the OECD’s Programme for International Student Assessment and the IEA’s Trends in International Mathematics and Science Study are evidence education quality is lagging (Figure 39 and Figure 40). Indonesia is in the bottom third of countries, with lowest PISA score in 2015, far behind the OECD average. The World Bank (2018) shows that the improvement in Indonesian education quality is very slow. At the current pace, Indonesia will not match the average PISA score of
3 Horizontal Mismatch: the type/field of education or skills is inappropriate for the job. 4 Vertical Mismatch: the qualification/education level is lower (underqualified) or higher (overqualified) than the requirement.
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OECD countries until 2065—and this assumes there is no improvement in those countries (Figure 41). Indonesia’s TIMSS score points to declining abilities in mathematics (Figure 42).
The Global Innovation Index (2018) shows that tertiary education in Indonesia is shut off from the rest of the world as reflected by the low mobility of home students (Figure 43). Malaysia’s tertiary education is much more open. Three of its universities are ranked among the world’s 25 best institutions. Indonesia’s best university come 37th in the world rankings. That is better than Thailand, the Philippines and Vietnam. Yet there is huge variance in the quality of Indonesian universities. Based on Ministry of Higher Education’s rankings for 2018, only 14 universities across Indonesia (0.7 percent) belong to the first cluster (Figure 44). The rest are not nearly so good.
Although we do not believe that education is a binding constraint at this stage, there is a high probability of it constraining economic growth in the future. The quantity and quality of education in Indonesia is not sufficiently high to prepare the workforce for increased global competition and technological disruption. This is particularly concerning given that education is the main factor to produce skilled labour and innovation that will ensure high productivity in the long term.
The existing skills mismatch may suggest that the education system is poor at teaching the skills needed for work. Moreover, the World Economic Forum (2016) predicts that science, technology, engineering, and mathematics (STEM), as well as soft skills, will be the skills most sought after by employers in the future (by 2020). Indonesia comes near the bottom of PISA and TIMSS assessments in these areas. But the OECD (2018) estimates that ensuring today’s students are equipped with basic skills by raising the PISA score to Thailand’s current level would increase Indonesia’s average GDP growth by 0.6 percentage points a year from 2020 to 2060. Unfortunately, soft skills are not yet an important part of education in Indonesia. This contrasts with advanced countries such as Singapore and South Korea which put them at the core of their national curriculums.
The Global Innovation Index and Human Capital Index reveal separate concerns about Indonesia’s human capital, including education. Indonesia’s Global Innovation Index score for human capital and research has declined steadily since 2013. The country also scored lower than peer countries in 2018 (Figure 45). This indicates that human capital and research is insufficient to support innovation in Indonesia. Indonesia also ranks below neighbouring countries such as Singapore, Vietnam, Malaysia, Thailand and the Philippines in the Human Capital Index5 (World Bank, 2018) (Figure 46). This could mean that the next generation of Indonesian workers will be less productive than those in other countries.
Considering all the evidence, we categorise education as a future binding constraint to Indonesia’s economic growth.
Health Indonesia’s health outcomes have improved significantly. Life expectancy at birth has risen sharply although it is still below peer countries (Figure 47). Infant and maternal mortality has also been reduced significantly in the past two decades to rates nearly similar with peer countries (Figure 48 and Figure 49).
However, the quality of children’s health and nutrition is relatively low compared with peer countries. This is reflected in high prevalence of stunting (Figure 50) although in recent years the figure has dropped. Immunisation rates for children below the age of 5, including those for measles and hepatitis B, are still below those of peer countries (Figure 51).
Deaths from communicable diseases, deaths during pregnancy and childbirth, and deaths from poor nutrition are still relatively high in Indonesia (Figure 52). There has been improved in the recent years, however, suggesting that health intervention is working. Nevertheless, the government budget for improving health infrastructure and health workers is lacking compared with peer countries (Figure 93).
Meanwhile, deaths from non-communicable disease have increased, as expected as there are growing middle class (Figure 53). In particular, the prevalence of cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases in Indonesia is higher than peer countries (Figure 54). A higher rate of non-communicable disease will increase demand
5 The index is measured in terms of the productivity of the next generation of workers relative to the benchmark of complete education and full health.
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Indonesia Growth Diagnostics
for health facilities. Yet health facilities such as number of doctors and hospital beds per 10,000 people is still far below peer countries (Figure 55).
An important consideration is the high prevalence of smoking in Indonesia. Among adult men, smoking rates continue to increase, in contrast with other countries (Figure 56). In fact, Indonesia is one of the countries with highest rates of male smokers in the world (Figure 57). Recent surveys also suggest that the smoking among teenagers is also widespread in Indonesia (Figure 58).
Although several indicators show improvements to health, there are still many areas of concern that could impact labour productivity negatively over the long term. Moreover, the Human Capital Index released by the World Bank (2018) based on the current state of health, along with education, shows that Indonesia is being left behind by neighbouring countries. Taking into account the potential impact of current health and education, Indonesia’s long- term labour productivity is predicted to be lower than that in Singapore, Vietnam, Malaysia, Thailand and the Philippines.
Stunting, in particular, could mean that children’s brains fail to develop to full cognitive potential (World Bank, 2018). This would hinder children’s abilities to perform well at school and in turn make them less productive when they enter the labour market. Meanwhile, the high prevalence of smoking among teenagers heightens the risk of premature deaths from non-communicable diseases. This could lower lifetime earnings.
Improvements to health are essential for a more productive labour force going forward. Human capital itself is categorised as a nation’s productive wealth. Poor health would retard growth in human capital as it affects lifetime earnings (World Bank, 2018). As Indonesia is approaching its lowest-ever dependency ratio, with more than half the population in their productive years, poor health will give penalty to reap the optimal growth.
Looking at current condition of human capital and its possible condition in the future, we categorise health, along with education, as future binding constraints to economic growth.
3.4. Infrastructure: Lacking Particularly for Connectivity In this section, we look at infrastructure as part of our examination into whether social return is the most binding constraint for generating investment with higher returns. We found that infrastructure, in particular connectivity, is still a major constraint for Indonesia. Spanning almost 2 million square km and spread across 17,000 islands, Indonesia needs a vast amount of infrastructure. However, a lack of investment in the past has led to a decline in the infrastructure capital stock. Recent government’s effort to boost infrastructure have halted the decline but not enough to return the capital stock to previous levels or to put it on a par with peer countries.
Poor logistics limit opportunities for economic diversification and contribute to price disparities. For example, regions with limited access to markets owing to poor freight logistics (such as Papua) have less diversified economies than well-connected regions (such as West Java). This is because higher value-added goods need to meet tight delivery schedules cheaply, reliably and predictably.
We identify water and sanitation as potential problems in the future. The supply of surface water is projected to decline mainly owing to an increase in economic activity that is not environmentally friendly. The natural carrying capacity, i.e. the ability of natural ecosystem to support continued growth within the limit of abundance of resource and within the tolerance of environmental degradation, is also in declining and so making economic growth sustainable must be factored in the development agenda.
Connectivity High logistics costs are a major drag on the economy. In 2016 logistics costs were equivalent to 27 percent of Indonesian GDP.6 High logistics cost drag down firms’ profitability. The World Bank (2015) shows that total logistics costs incurred by Indonesian manufacturers represent 25 percent of sales, higher than both Thailand (15 percent) and Malaysia (13 percent). The World Bank also suggests that poor infrastructure contributes to high logistics costs by generating congestion, inefficiency, and unreliability. It also shows that poor logistics limit opportunities for economic diversification and contribute to price disparities.
6 INDii calculation.
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The cost of congestion is most relevant for roads. Underinvestment has adversely affected the capacity as well as the quality of the nation’s road network and driven up logistics costs. Travel speeds are relatively low (approximately 40 km/hr) due to a high volume-to-capacity ratio. Only 18 percent of vehicles travel without experiencing jams. To travel 100 km, it takes between 2.5 and 4 hours, which is much longer than in neighbouring countries. Approximately 40 percent of national roads in Java and Bali are congested. Nearly 60 percent of roads are less than seven metres wide.
Indonesia’s road transport infrastructure lags other countries’ in term of connectivity, density, and quality (Figure 59, Figure 60, and Figure 61). Demand for road transport rose by 7 percent a year between 2013 and 2016. This outstrips the supply of new roads, which grew by about 5 percent a year between 2009 and 2016 to 87,800 lane-km. Extensive congestion is prevalent in main areas. This trend is expected to continue as vehicle-ownership increases. Current levels of vehicle ownership in Indonesia are still relatively low, with 87 motor vehicles (excluding motorcycles) per 1,000 people.
The road transport network is struggling to cope with this exponential growth, mainly because of delays building new roads, persistent and substantial underinvestment, and weak planning capability at all levels of government for network expansion. This, in turn, has led to imbalanced growth of the network and uneven access in different regions of the country (especially in rural areas). The sector also faces other major challenges, such as road safety, congestion, and pollution in urban areas. Congestion is concentrated on major roads in arterial corridors—reflecting a lack of high- capacity expressways and dual-carriageways.
Inefficient and unreliable sea transport also increase costs for firms. Indonesia’s ports perform poorly relative to global benchmarks (Figure 62). Average productivity of ports under Pelindo 3 and 4 is approximately 22 containers per hour. Some ports are much more productive than others, with the worst ports managing to process only 13 containers per hour, and the best dedicated container terminals processing to 29 boxes per hour. The average vessel turnaround time is about 2.1 days. This is much slower than the global average of 1.4 days and is comparable to the slowest tier of ports in South Asia (which do not face much competition). Vessels also spend only around half (54 percent) of their berth time effectively (loading and unloading). World Bank study also found that sea freight costs are driven by the high value of time required for transportation (due to congestion and inefficiency), not the direct tariff or price paid for the transportation service.
Indonesia’s air transport infrastructure—as measured by airports per million square km, and air transport quality (Figure 63 and Figure 64)—is in line with that of other countries at a similar stage of development. The Government aims to develop tourism. Airport investment could support this ambition.
Indonesia’s rail transport infrastructure is slightly below that of other countries at a similar stage of development but similar to other ASEAN countries, measured by railway density and efficiency of train service (Figure 65 and Figure 66). As an archipelago, railway won’t be freights. Nonetheless, rail development should be the big and fast-growing cities.
Infrastructure is still one of the top constraints for doing business in Indonesia, according to the latest WEF survey (Figure 67), although it is not considered to be as much as a problem as it was in the past. our discussions with large manufacturing companies produced interesting findings. For large players, it is cheaper to build multiple factories to make the same products in different parts of Indonesia than it is to pay the high costs of moving goods around the archipelago. This means the producers do not achieve economies of scale, costing productivity.
Energy The electrification ratio (the proportion of households with some form of electricity) increased rapidly over 2010-2018 (Figure 68 and Figure 69. Electrification Ratio, Peer CountriesFigure 69) to 97.5 percent. At the same time, access to electricity improved for lower-income households so that the electrification rate is now broadly similar across all income groups (Figure 70). As such, improving electrification is no longer a pressing priority, at least at the national level.
Demand for electricity grew more slowly than expected in recent years, meaning that investment plans exceeded requirements. The largest demand for electricity came from Java where the manufacturing sector is thriving. As a result, PLN was able to maintain electricity reserves close to, or in excess of, its targets in most years.
However, the national electrification ratio masks substantial regional disparities between western and eastern Indonesia. Almost all districts in western Indonesia have electrification rates of more than 80 percent, while there are
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10 districts in Papua where less than 20 percent of households have electricity (Figure 73). Connecting these remaining districts will be challenging and expensive because most of them are in remote areas.
There is also a need to improve the quality and reliability of electricity, as demonstrated by the average duration of power cuts and the number of power cuts per year. Both indicators have worsened in recent years (Figure 71 and Figure 72) and are higher in eastern Indonesia (Figure 74 and Figure 75). Many industries run their own back-up electricity generators, especially outside Java.
Improving access to electricity for remote areas is important. However, Indonesia’s national electrification rate is higher than those of other countries at a similar level of development (Figure 69). However, the quality of electricity supply is still below the average (Figure 76). Given that most electricity demand has been met, electricity is not a pressing constraint for Indonesia, although intervention is still needed to improve reliability and extend coverage to all households.
Digital ICT infrastructure, as indicated by the number of broadband subscriptions and connection speeds, is below the average of countries at the same level of development (Figure 77 and Figure 78). However, there is a megaproject in place to significantly improve ICT infrastructure: the “Palapa Ring” broadband project. Improved ICT infrastructure is the backbone of the digital economy. At the same time, soft infrastructure, such as technology regulation and skills development, is needed. Even so, digital infrastructure is not currently a binding constraint, given the rapid development of Indonesian digital start-ups. Unequal access to technology is still the main challenge. At present the digital economy can only grow rapidly in Java and Bali where digital infrastructure is more developed than in other regions.
Water and Sanitation Access to water supply and sanitation (WSS) services has improved over the past decade. Indeed, Indonesia reached its Millennium Development Goal (MDG) for water supply, with 89 percent of its citizens benefiting from access to improved water supply in 2016 (Figure 79). The MDG for sanitation was missed by a narrow margin (Figure 80). The Government is now targeting universal access to water supply and sanitation services, in line with the new Sustainable Development Goals (SDGs).
However, access to WSS services is unequal between the rich and poor, between rural and urban households (Figure 81 and Figure 82), and between the different regions of Indonesia. For example, in rural areas, 57 percent of the poorest quintile have access to water services compared to 93 percent in the highest quintile. In sanitation, only 66 percent of the poorest quintile have access to improved sanitation compared to 89 percent in the highest quintile. Improving access to WSS services is particularly important because of the recognized link between poor sanitation, water-borne diseases, malnutrition and stunting (chronic malnutrition).
While improving access to clean water and sanitation to provide basic service for the low-income population is important, access to clean water seems to not be a constraint for firms (Figure 83). Nonetheless, Bappenas study (2018b) shows that water scarcity will become a challenge in Indonesia going forward. Unsustainable business practice has worsened the environment condition. This requires immediate actions to stop the deterioration of water quantity.
3.5. Market Failure: Unrealized Potential This section looks at market failure—the inability of the market to generate new economic activity. As discussed previously, Indonesian exports have not developed or diversified in recent decades. Also, Indonesia lags behind in term of innovation. Nonetheless, this study sees slow self-discovery as a consequence, not cause, of the growth constraint.
Since the 1980s Indonesian exports have been dominated by resource-based manufacturing products. This peaked during the commodity boom (Figure 84). Low-value industries such as garments and footwear have been established but these have not developed into higher value-added manufacturing exports. As a result, exports remain dominated by resource-based manufacturing (Figure 85) and are heavily dependent on the cycle in commodity prices. Since the end of the commodity boom, export performance has been declining.
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The complexity outlook index, an index that shows how easy it is for a country to develop a new product, suggests that Indonesia is actually in a good position. Indonesia has higher potential compared with other countries. However, this potential is yet to be realized, as shown by a low score in the economic complexity index (Figure 86 and Figure 87).
Not only does Indonesia lack high-value-added manufacturing, the services sector is also dominated by the low-end activities such as retails (micro and small kiosk) and tourism (particularly car rental). The modern financial services, business services and logistics services that could support manufacturing are lacking. Limited data on the services sectors means that it is not possible in this study to provide a similar level of analysis for services as for other sectors.
3.6. Macro Risk: Low Tax Receipt Limits Public Goods Delivery This section examines whether the macroeconomic and fiscal positions are the binding constraint to growth. We found that the macroeconomic condition is relatively stable and supportive of business, despite some risks related to the external balance, i.e. financing the current-account deficit. Macroeconomic stability is also reflected in more manageable inflation, lower exchange-rate volatility, adequate official reserves, and prudent fiscal management.
However, we note that fiscal revenue is low owing to low tax receipts. Compared with peer countries, Indonesia has one of the lowest tax-to-GDP ratios. This translates into low fiscal spending. For instance, even though Indonesia spends at least 20 percent of its annual budget on education, the amount it actually spends is less than peer countries.
Indonesia’s macroeconomic management is good. On the external side, reserves have been built up and are at a safe level (Figure 88). The current account is still manageable although more could be done to attract more sustainable financing, FDI. External debt is also relatively low compared with the peer countries (Figure 89). On the domestic side, inflation is low and manageable, particularly in recent years.
Fiscal sustainability has also improved. Governments have kept to the commitment to keep the fiscal deficit under 3 percent of GDP. Government debt as share of GDP has declined and is low compared with other countries (Figure 90). The fiscal rule also states that the government must keep public debt under 60 percent of GDP.
Nonetheless, low revenue combined with the commitment to prudent fiscal management have limited the government’s ability to provide necessary public goods. More than 80 percent of revenue comes from taxation but receipts have followed a declining trend. Indonesia collects the equivalent of less than 11 percent of GDP in taxes (Figure 91) compared with 15 percent in peer countries such as Thailand and Malaysia. This represents a significant decline compared with the years before the Asian financial crisis when Indonesia collected as much as 16 percent of GDP in taxes.
Tax collection is largely determined by the performance of commodity-related sectors. In the past five years, Indonesia’s declining tax receipts were driven by a fall in income tax from oil and gas. from 0.9-1 percent of GDP to 0.3- 0.4 percent of GDP. The tax base is also small owing to low compliance. In 2018, the number of registered taxpayers was 39.2 million. Of that figure, only 18 million earned taxable income, of which only 60 percent filed a tax report. Nonetheless, the main issue is tax policy. VAT receipt is low compared with other countries, mainly because of multiple exemptions.
Low tax receipts translate into to low public expenditure. This limits the delivery of necessary public goods, such as on education and health (Figure 92 and Figure 93). Indonesia’s capital spending is low compared with other countries. When the government removed fuel subsidy in late 2014, it created space for more capital spending. This enabled the delivery of many long-awaited infrastructure projects.
3.7. Regulations and Institutions (the Most Binding Constraint): Better Coordinated Policies to Boost Growth This section examines regulations and institutions. It concludes that they are the most binding constraint to growth. Existing regulations do not support business creation and development and they tend to be restrictive. Institutions here refer to the setting which produces those regulations, in particular: lack of strategic alignment, weak supervision, and overlapping institutional responsibilities. It also points to corruption and bureaucratic inefficiency. Weak regulations and institutions were a common complaint not only among private business but also among social sectors such as health and education.
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Indonesia Growth Diagnostics
Inefficient regulations create high fixed costs. Therefore, it generates a “missing middle” phenomenon in Indonesia (Figure 94): large companies can bear high fixed costs, medium companies cannot compete, and small companies choose to be outside of regulation – causing a large informal sector. Compared with other countries, existing regulations tend to be protectionist, and costs related to labour and taxation is very high. Widespread middlemen also indicate regulations and institutions to be the most binding constraint as economic agents attempt to bypass them.
Compared with peer countries, Indonesia has a low regulatory index score (Figure 95). The index reflects perceptions of the government’s ability to make and implement regulations/policies that support business development. In other business-related regulations, such as property rights, legal system, and rule of law, Indonesia also ranks low compared with other countries (Figure 96 and Figure 97).
Data from Global Trade Alert suggest that the number of new regulations issued by Indonesia is greater compared with other countries. While that figure could mean active participation of government in the international trade and investment, the type of intervention suggests that these interventions are mostly protective. They do not support international trade and investment.
Regulatory constraints cover three main areas: labour, investment, and trade.
First, labour regulation. The cost of firing workers in Indonesia is high. This means firms employ staff on temporary contracts and do not develop their professional skills through training (Figure 99). Figure 98 shows that in order to fire Indonesian workers with 1, 5, and 10 years tenure, it costs 57.8 weeks of salary on average. This is two times more than in Turkey, four times more than in Brazil, and six times more than in South Africa.
Indonesian firms also struggle to navigate costly and complex regulations to hire skilled foreign workers. This places Indonesian firms at a disadvantage. As an illustration, for a firm to be able to hire foreign skills, it needs to acquire RPTKA, IMA, VITAS, KITAS, MERP, and a residence permit. The annual cost to get IMTA is USD 1,200, or 35 percent of income per capita in Indonesia. Foreign skills are also limited into few sectors only with services sector being the most restrictive. With these limitations, only about 74,000 permits were granted in 2016. This is equivalent to just 0.03 percent of the population.
Second is investment policy. Indonesia is among the most restrictive countries in the world for foreign direct investment (Figure 100). Restrictions on FDI through the negative list discourage foreign firms, especially export- oriented firms, from setting up business in Indonesia. The service sector is one example where developed is hindered by highly restrictive regulation. As a result, Indonesia has low foreign direct investment (Figure 101).
Despite recent improvement in investment climate, as reflected in improvements in the ease of doing business rank, Indonesia still ranks poorly in few important areas such as starting a business, trading across border, and ease of paying taxes (Figure 102, Figure 103, and Figure 104). For instance, it takes 19.6 days to start a business in Indonesia compared with only 4.5 and 1.5 days in Thailand and Singapore, respectively. Moreover, Indonesia ranks 112th in the ease of paying taxes whereas Singapore and Thailand rank 3rd and 36th, respectively. It should be remembered that Indonesia is not alone in seeking to improve the ease of doing business. Indonesia is competing with other countries that are undertaking similar reforms.
Regulations do not create right incentives for businesses to grow. For instance, tax exemptions for small businesses discourage Indonesian firms from expanding and becoming more productive through economies of scale.
Third, Indonesian exporters and importers face high administrative costs owing to excess licenses and regulations. It takes longer and is more expensive to export from Indonesia compared with neighbouring countries such Malaysia, Thailand and Singapore (Figure 105). At the same time, “non-tariff barriers” to trade such as licenses and quotas increase the cost of living in Indonesia by 8 percent (Figure 106).
The evidence of regulatory constraints is apparent across sectors. In education, foreign nationals are not permitted to obtain academic tenure in Indonesian universities, preventing the transfer of valuable “know-how”. In health, there is a lengthy process to obtain a BPOM license, making some drugs unavailable in Indonesia.
There are examples of success when Indonesia has pursued more open investment policies. Indonesia recently has recorded a success story in opening its investment policy. The domestic film has grown rapidly since rules on foreign ownership were relaxed in 2016, with 600 new cinema screens opening during the past three years. The number of cinema-goers also grew, from 16 million in 2015 to 43 million in 2017. The larger market has created opportunities that
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have allowed domestic film-makes to match foreign competitors. A locally produced film, “Dilan 1991”, beat the Hollywood blockbuster “Avengers: Infinity War” in Indonesia. Another example of a successful domestically produced film is “The Night Come for Us”, which became the most-watched action film on Netflix.
Corruption and Inefficient bureaucracy are the first and second as the most problematic factors for doing business in the latest Executive Opinion Survey by the World Economic Forum (Figure 107). Policy instability is also considered one of the most problematic factors for doing business. This is consistent with our own focus group discussions and interviews with the private sector. Businesses complained about conflicting regulations between various government ministries and agencies. On top of that, regulations tend to be short-lived. Within the government itself, officials recognise weak coordination across agencies and high “sectoral ego” as the underlying reason for weak coordination.
Weak coordination is evident between the various layers of public administration. At the central level, planning, budgeting, implementation, and monitoring and evaluation are done by different agencies that do not communicate well with each other. As a result, what is implemented often differs from what was planned. For instance, dams are built but this is not followed by irrigation systems, or ports are built but there is no road access. Weak monitoring and evaluation, along with a lack of enforcement mechanisms, mean that these are recurring issues.
A lack of alignment between central and local government adds further complexity. The policy direction set by the central government is often not followed by local authorities. This is evident in the latest central government effort to open up to investment by simplifying the business licensing process. It was not matched by a similar spirit by most local authorities.
There is also an issue of overlapping institutional responsibility. This is especially apparent in the case of government intervention for small and micro enterprises. Small and micro enterprises themselves complain that they receive the same government training or other support over and over again. Data collection is another area of overlap. Each government agency collects its own data and does not share its findings, resulting in different interpretations, and sometimes leading to conflicting government intervention.
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Conclusion
The study found that regulations and institutions are the most binding constraint to economic growth in Indonesia. Existing regulations tend to be restrictive and they do not support business creation and development. Institutions here refer to the setting which produces those regulations, in particular: a lack of strategic alignment, weak supervision, and overlapping institutional responsibilities. Corruption and bureaucratic inefficiency are also problems. These complaints were a common theme among private businesses and also among social sectors such as health and education.
Inefficient regulations create high fixed costs. This generates a “missing middle” phenomenon in Indonesia: large companies can bear high fixed costs, medium companies cannot compete, and small companies choose to be outside of the regulation – causing a large informal sector. Compared with other countries, existing regulations tend to be protectionist and the costs related to labour and taxation are very high. Widespread middlemen also indicate that regulations and institutions are the most binding constraint as economic agents attempt to bypass them.
The study also identifies human capital as the future binding constraint, particularly given the development of the digital economy and the aspiration to develop Industry 4.0–high-tech manufacturing. Skills and education will become the next binding constraint as technology advances rapidly. The low quality of education and health in Indonesia is worrying.
Overall the study suggests that priority should be given to improvements in regulations, particularly ones that hamper the growth of business and productivity. Institutional improvements should focus on the clarity of roles and authority, including the role of policy conductor and development regulator. To address the future constraint of human capital, policy need to focus on reforming basic education and teaching, opening investment in tertiary education, incentivizing diaspora engagement, and focus on children’s nutrition.
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References
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Appendices
1. Figures on Growth Story Indicators
Figure 3. Indonesia Economic Growth Figure 4. GDP per Capita Trend 12000
low base 10000 growth oil oil manufacturing boom growth & 15 bust commodity 8000 liberalization boom 10 6000
5 4000 Average Average 0 Average 1968-1979 1980-1996 2000-2018 2000 -5 7.5% 6.4% 5.3%
GDP per Capita (Constant 2010 US$) 2010 (Constant Capita per GDP 0 -10 Asia Financial 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Crisis -15 Indonesia China Philippines Indonesia Economic Growth (% YoY) (% Growth Economic Indonesia 1968 1973 1978 1983 1988 1993 1998 2003 2008 2013 2018 Malaysia Thailand Vietnam
Sources: CEIC Sources: World Development Indicators
Figure 5. Indonesia Potential Growth Figure 6. Total Factor Productivity
0,7
6,03 0,6 Bappenas projection of potential 5,56 growth (“baseline" scenario) 0,5 5,17 5,03 5,10 5,01 4,95 4,88 4,94 4,91 4,90 4,89 4,87 0,4 Indonesia Potential Growth (%) Growth Potential Indonesia
FP level at current PPPs (USA=1)PPPs current at level FP 0,3 T 1963 1969 1975 1981 1987 1993 1999 2005 2011 2017 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Indonesia China Malaysia Philippines Thailand
Sources: Bappenas (2018a) Sources: World Development Indicators
Indonesia Growth Diagnostics | 19
Indonesia Growth Diagnostics
Figure 7. Share of Manufacturing & GDP per Capita Figure 8. High-Technology Exports
32 80 19992000 2004 2010 2003 20072008 2001 29 2001 2006 2002 60 2002 20032005 1998 2003 20042009 1997 2004 2008 2011 2000 2006 2002 1996 2005 19992000 20052006 1991 20072001 2012 1997 201320141994 199219982009 201520161995 40 26 19991990 199219932017 2007 1996 19951997 1991 1998 19931994 1996 2008 1995 1990 Technology Exports Technology
2009 - 1994 201020112012 20 23 201320142015 1993 2017 2010 2016 High 1992 2011 2012 products) manufactured (% 1991 201320142015 2016 20 2017 0 8,3 8,8 9,3 9,8 10,3 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017
Manufacturing, value added (% of GDP)of (% added value Manufacturing, GDP per capita, PPP (constant 2011 international $), log Indonesia China Malaysia Indonesia Malaysia Thailand Philippines Thailand Vietnam
Sources: World Development Indicators Sources: World Development Indicators
Figure 9. Accumulation of Fixed Capital Investment of Figure 10. Infrastructure Capital Stock Machinery and Equipment, 2007-2016 200
64,4 62,9 57,9 160
49,1 120 Average excluding Japan (70%) 40,9 80
40 20,1
- Italy India Spain Brazil China Japan Poland Infrastructure Capital Stock (% of GDP)of (% Stock Capital Infrastructure Canada Germany of Machinery and Equipment (% of GDP)of (% Equipment and Machinery of
Accumulation of Fixed Capital Investment Investment Capital Fixed of Accumulation Indonesia Malaysia Mexico Philippines Thailand South Africa South Africa South United States United United Kingdom United (2017) Indonesia Sources: Indonesia – Prospera Infradashboard & McKinsey Sources: Indonesia – Prospera Infradashboard & McKinsey
Figure 11. FDI Net Inflows vs. GDP per Capita, 2017 Figure 12. Indonesia Incremental Capital-Output Ratio 40 NLD 10 HKG
30 MLT 8 Output Ratio Ratio Output -
20 SGP MOZ 6
SLE COG VCTMNG MRT GEO PLW MNE MDV LUX 10 LAO GRD STP 4 ALB PANSYC LBR MMR LCASRBAZE ATG NIC VNMCPV GIN GHAHND GUYJAMFJI TKMBRB ESTSVK FINSWECHE ETH JOR LBNSURDOMCRI KNABHSISR NERMWI MDGHTIBFA NAM COL HRVLVA CZEOMN BRN TGO TCDRWA ZMB PHL EGYPERMKDBRA BGR PRT AUSAUT COD UGA SLBSENTZAVUTCMR MARUKR BIHTUNBWABLRMEXGNQ MYSLTU GBR ARE MLIBEN LSO CIV MDAINDBOL ARM THAARGMUSCHLROUTURGRCRUSKAZPOLSVN FRACANDEUUSA CAF COMGNBZWE TJKKENBGD PSESDNPAKNGAWSMSLVGTMBLZPRY CHN IRN KORNZL BHR GMBAFGKIR NPL TUVMHL UZBTLS IDNECULKAZAFDZA ESPITAJPN DNKSAU NOR QAT
KWT Capital Incremental FDI, net inflows (% of GDP)of (% inflows net FDI, BDI 2 0 YEM PNG BTNDMA IRL KGZ TON SWZ URY TTO IRQ
AGO BEL -10 HUN 0 6 8 10 12 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 GDP per capita, PPP (constant 2011 international $), log
Sources: World Development Indicators Sources: CEIC
20 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
2. Figures on Finance Indicators
Figure 13. Gross Domestic Savings vs. GDP per Capita, 2017 Figure 14. FDI Net Inflows
80 15
60 QAT IRL 10 BRNSGPLUX COG ARE CHN GAB
40 MNGDZA IRNPAN MLT KAZ KOROMN THA SAUCHE 5 TLS BWA MYSCZE BTNIDN AZEBLR NLD NORKWT AGOIND LKA GNQRUSHUNSVN SWE MMR EST DEUAUTDNK BGD VNM ECU TURSVK BELISL ETH TZA PRY BGRCHLPOL ESP AUS MRT LAOMAR PER MEX HRVROU FRAFIN HKG GHASDN UZB GEO LVASYC ITA CAN 20 CMRCIV ZAFMKDDOMCRI URY BHSLTU COD NERUGABFA CPVGUY COLBRAIRQ ARG PRTCYP GBR GDP)of (% TCD SEN NICNGA PHLBLZ GIN UKR SRB MDG BENNPL BOL GRC 0 RWA MUS AFG HND JAMARMTUNALB MWIMOZTGO MLI PAK NAM MNE GNB KEN GTM PLW COM KGZ JOR EGYBIHLBN BRB 0 CAF ZWE TJK SLV VCT SLE SWZ HTI Gross domestic savings (% of GDP)(% of savings domestic Gross -5 PSE
Foreign direct investment, net inflows net inflows investment, direct Foreign 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 -20 6 8 10 12 Indonesia China Malaysia GDP per capita, PPP (constant 2011 international $), log Philippines Thailand Vietnam
Sources: CEIC Sources: World Development Indicators
Figure 15. Real Lending Rate vs. GDP per Capita, Average Figure 16. Nominal Lending Rate, vs. GDP per Capita, Average 2015-2017 2015-2017
45 40 IRQ BRA TLS
GMB ARG 30 30 TJK
GMB 2017 KGZ TJK - UGA MOZ STP VEN COD HND UKR MNG UGA FSMKGZ SLE PRY MRT PRY BHR KWT QAT 15 STP HND ARM MNG IRN BRN 20 RWA MRT NGA AZE PERDOM OMN TZA AGO JAMARM PER IRN 2017 RWA TTO KENFSM URY - AFG DOM MOZ AFG DJI SYC BDI BTN EGY LBR TZA GUYJAMBTN CRIAZE LBR ZMBTLS SUR BLR COM NGACPVGTM COLDZABRB HTI MDAMMR GUYGTM COL RUS SLB PSE MMRWSMBOLBLZ IDNALBVCT URYATGKNA GEOIDN CRIIRQ SYC COD SLE PNG PAK IND XKXJORGEODMA MDVGRD MUSRUS SSD LSODJI NIC BFAZWEBEN LSOKENCIV MDAVNMNICTON LKALCALBN PAN COM BGD MDV NER SWZ ZAF AUSISL SLB CPV SWZBLZ ZAF 2015 BDI TGO SEN BGDZMB AGOPHL BIHMKDSRBMNEBGRROU NZL CHE SGP IND NAMLKA ATG MLI NAM SURBLR MYS CZEISRITA HKG MAC PAKWSM VCTGRD KNATTO HTI EGYCHNTHABWA CANUSASMR PNG TON BOL JORDMALCAALBLBNDZABRBMUS FJI KOR 2015 average ISL VUT MEX CHLBHSHUN JPN 10 ZWE VNM XKX MKDBWAMNE PAN 0 ARG PSE BGRROU GNB PHL FJI NZL AUS BRNSGPMAC NER TGOGNBBFA MLIBENSEN CIV BIH SRB MEX CHLBHSMYS OMNBHRHKG CHNTHA CZE KWT QAT UKR VUT ISRKORITA USA HUN CAN SMRCHE JPN Nominal leding interest rate (%), rate interest leding Nominal -15 0
Real lending interest rate (%), average (%), rate interest lending Real 6 8 10 12 6 8 10 12 GDP per capita, PPP (constant 2011 international $), log, GDP per capita, PPP (constant 2011 international $), log, average 2015-2017 average 2015-2017
Sources: World Development Indicators Sources: World Development Indicators
Indonesia Growth Diagnostics | 21
Indonesia Growth Diagnostics
Figure 17. Real Lending Rate and Investment Rate Figure 18. Biggest Obstacles in Doing Business in Indonesia
11 Transportation Tax Rate 2016 2015 Tax Administration 2012 Informal Practices 7 2014 20172013 Political Instability 2009 2011 Labor Regulation Skilled Labor 3 Electricity Customs and Trade Regulation Crime and Security Real interest rate (%) interest Real -1 2015 2010 Business Courts Corruption 2009 2008 Business Permits -5 Access to Land 27 30 33 36 Access to Finance Gross capital formation (% of GDP) % 0 10 20 30 40 50 Sources: World Development Indicators Sources: World Bank Enterprise Surveys
Figure 19. Indonesia Investment Composition Figure 20. Net Interest Margin 35 6 30 5 25 Banks 4 20 Capital Markets 3 15 Foreign 2 10 1 Financed not through 5 intermediation (%) Margin InterestNet 0
Investment Composition (%) Composition Investment - 2012 2013 2014 2015 2016 2017 2018 Indonesia Philippines Egypt 2015 2016 2017 2018p 2019p 2020p 2021p 2022p 2023p 2024p Sources: Prospera’s Calculation Sources: Prospera’s Calculation
Figure 21. Financial System Interlinkages, Indonesia Figure 22. Financial System Interlinkages, Malaysia
Sources: Bappenas and Prosperas’ calculation Sources: International Monetary Fund (2014)
22 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
3. Figures on Human Capital Indicators
Figure 23. Labour Force Distribution by Education, 2016 Figure 24. Labour Force with Tertiary Education
35% Vietnam 13 55 20 12 30%
Thailand 21 40 22 17 25%
20%
Philippines 1 67 5 26 Education Tertiary – 15% 12,7% 11,4% 12,3% 10,4% Malaysia 3 30 44 24 9,5% 10,0% 10% 8,1% 8,5%
Indonesia 14 45 30 12 Force Labour 5% 2010 2011 2012 2013 2014 2015 2016 2017 0% 20% 40% 60% 80% 100% Indonesia Malaysia Philippines Less than Primary Primary Secondary Tertiary Thailand Vietnam
Sources: International Labour Organization Sources: International Labour Organization
Figure 25. Agriculture Employment vs. GDP per Capita, 2017 Figure 26. Informal Employment vs. GDP per Capita, 2017
100 100 - BDI CAF TCD MWI GNB 80 COD CIV NER MDG MRT MOZ NPL SLB 80 UGAETHZWEGIN CPV SWZ BOL SSDRWA TZAVUT AFG CMR LAO HND 60 SLE GNQ GTM MLI BTN COM SEN ZMBSDN IDN TJK AGO CIV MMR PRY SLV ECU LBR YEM BEN PAK IND HTI GEO GAB NAM 40 BGDGHA VNM FJI ALB 60 TGO KEN COGNGA MAR AZE PER ARM VNM COL MDATON THA HNDNIC BOLGTM IDNMNG SWZ DOM MUS GMBBFA KGZ ECULKAPER PSE TLSPHL EGY BWA EGY UZB PRY ROU PNG SLV BIHNAM TUR MDV employment) 20 JAM CHN KAZ STP BLZ LCA IRNMEX GUYUKR TUN COLDZA PAN DOMCRILBY PAN LSO PSE BRABLR MYS TKMMNE LTU 40 CHL WSM VCT MDVBGRMUSHRVRUSLVAPRT OMNSAU ZAF VEN CHLHUNBHSESTSVNESPITAKORNZLFINAUT IRL JOR LBNSURBRB TTOSVKCYPCZEFRAJPNCANAUSISLDNKNLDCHENORKWT employment) agricultural CRI 0 ARGURY ISRMLTGBRBELBHRDEUSWEUSAHKGAREBRNSGPLUX QAT ZAF MNG
ARM
Agriculture Employment (% of total total of (% Employment Agriculture URY Informal Employment (% of total non total of (% Employment Informal -20 20 6 8 10 12 7,5 8,5 9,5 10,5 GDP per capita, PPP (constant 2011 international $), log GDP per capita, PPP (constant 2011 international $), log
Sources: World Development Indicators Sources: World Development Indicators
Indonesia Growth Diagnostics | 23
Indonesia Growth Diagnostics
Figure 27. Returns to Secondary Education Figure 28. Unemployment Rate by Education
18 10
8
13 6
4
8 2 Unemployment Rate (%) Unemployment
0
Returns to education, secondary (%) secondary education, to Returns 3 Indonesia Malaysia Philippines Thailand Vietnam 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 (2017) (2016) (2016) (2016) (2017) Indonesia Thailand Malaysia Less than primary Primary Secondary Tertiary Total Philippines India Pakistan
Sources: Montenegro and Patrinos (2014) Sources: International Labour Organization
Figure 29. Returns to Tertiary Education Figure 30. Skills of Working Age Population
33 292 264 234 234 206 28 169
23
18 Jakarta - Jakarta - Jakarta - Denmark - Denmark - Denmark - Less than Upper Tertiary Less than Upper Tertiary
13 Proficiency Literacy PIAAC Upper Secondary Upper Secondary Secondary Secondary
Returns to education, tertiary education, to Returns 8 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Sources: Pritchett (2016) Indonesia Thailand Malaysia Note: Based on OECD Programme for International Assessment of Philippines India Pakistan Adult Competencies (PIAAC) 2016
Sources: Montenegro and Patrinos (2014)
Figure 31. Skills Mismatch in Indonesia Figure 32. Net Wage Effects of being Skills Mismatch
Underqualified Vertical Mismatch Horizontal Mismatch
Well- Matched 30
Vertical 20 Mismatch Overqualified 10 Mismatch 0 -10 Somewhat Match -20 2008 2015 2008 2015 2008 2015 2008 2015 2008 2015 2008 2015 Mismatch Horizontal Match Base Controlling Interacting Base Controlling Interacting
0 10 20 30 40 50 (%) Wage on Effects Net for with for Training with Skills Mismatch (%) Training Training Training Underqualified Overqualified Somewhat Match Mismatch 2008 2015
Sources: Samudra (2018) Sources: Samudra (2018) Note: Using Sakernas August round data Note: Using Sakernas August round data
24 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
Figure 33. Mean Years of Schooling Figure 34. Mean Years of Schooling vs. GDP per Capita, 2017
15 DEU 11 CAN USACHE LTUISR GBRAUS GEO LVA ESTCZE JPN SVK NZL DNK NORIRL PLW BLR POL FIN ISLSWE SVNCYPKOR AUTNLD LUX RUSHUN HKG ARM BGR KAZ BEL MDAUZB FRA SGP UKR MNE HRV MLT TON SRB BHS KGZMHL LKA ROU TTO FJI AZE GRC ARE BLZ BRB 9 TJK WSM JOR VEN CHL MNGZAF PAN MYS ITA ALB ARG 10 JAM BIH TKMIRN ESP QAT MKD SYC OMN SAU PHL BWA MUS BHR PSE PER ATG PRT BRN BOL LCA ECU LBNGRDCRI URY VCT SUR MEX GUY PRY COL GAB KNA ZWE VNM DZA KIR FSM CHN TUR DMAIDNBRATHA 7 KWT GHA EGYTUN ZMB SLV LBY COD VUT NIC NAM IRQ KEN HND GTMSWZ LSOSTPCMR COG IND MDV MDGUGA NGACPV TZA BGD SLB MAR GNQ HTI CIV PAKAGOLAO NPL MMR 5 LBR COMTGOSSD MWI MRTPNG TLS 5 CAF RWA AFG SDN MOZ SLEGMB BEN BDI YEMGNB SEN BTN ETH GIN TCDMLI Mean years of schooling of yearsMean NER 3 BFA Mean years of schooling of yearsMean 1990 1995 2000 2005 2010 2015 over) 25 and aged (Population (Population aged 25 and over) 25 and aged (Population 0 Indonesia Thailand Malaysia 6 8 10 12 Philippines Vietnam China GDP per capita, PPP (constant 2011 international $), log
Sources: Barro-Lee Dataset Sources: Barro-Lee Dataset & World Development Indicators
Figure 35. Gross Enrolment Ratio Figure 36. School Enrolment, Tertiary 60 120
100 50
80 40 60 30 40 20 20 Gross Enrolment Ratio (%) Ratio Enrolment Gross
0 10 Indonesia Malaysia Philippines Thailand Vietnam China School enrollment, tertiary (% gross) tertiary enrollment, School (2017) (2017) (2017) (2016) (2016) (2017) 0 Primary Secondary Tertiary 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 Indonesia Thailand Malaysia Vietnam China Philippines Sources: UNDP (2018) Sources: World Development Indicators
Figure 37. Returns to Education vs. GDP per Capita, 2010 Figure 38. Returns to Education, Indonesia
28 12
11 23 RWA 10
ZAF 9 18
UGA 8 7,9 MUS DEU AUS HUN 13 TUV KOR USA 7 ZMB HND LTU LVAMYS MDG COL ROU PAK URY POL LUX 6 IDN PANMEX PRTSVN DOM CZEMLT NLD (%) Education to Returns NPL MNG TURARG FRA AUT PRY SVK GBR STP PHL PER BGR ESP NOR 8 PNG GEO ECU FIN Mincerian returns to education to returns Mincerian 5 ISL DNK EST GRC ITA BEL 4 SWE 3
7 8 9 10 11 12 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 GDP per capita, PPP (constant 2011 international $), log Sources: Bappenas’ Calculation Note: Using Sakernas August round Sources: Montenegro and Patrinos (2014) & World Development Indicators
Indonesia Growth Diagnostics | 25
Indonesia Growth Diagnostics
Figure 39. Trends in International Mathematics and Science Figure 40. Programme for International Student Assessment Study (TIMSS), 2015 (PISA), 2015
Singapore Hong Kong Hong Kong Singapore South Korea Japan Chinese Taipei South Korea Japan China Average Score Vietnam Turkey OECD Average Chile Thailand Indonesia Indonesia
0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 Mathematics Science Mathematics Reading Science
Sources: TIMSS, Martin et al (2015) Sources: PISA, OECD (2016)
Figure 41. PISA Score Projection, Indonesia Figure 42. TIMSS Score Projection, Indonesia
550 500
500 450
450 400 TIMSS Score TIMSS
PISA Score PISA 400 350
350 300 1998 2000 2002 2004 2006 2008 2010 2012
300 Mathematics Score (Non-Islamic Schools) 2000 2015 2030 2045 2060 2075 2090 Mathematics Score International Mean Mathematics Reading Linear (Mathematics Score) Mathematics (Projection) Reading (Projection)
Mathemtics (OECD Average) Reading (OECD Average) Sources: The SMERU Research Institute (2018) Sources: World Development Report 2018, World Bank
Figure 43. Indicators Related to Quality of University Figure 44. Indonesia University Ranking Classification
QS university Cluster 1 Global Tertiary inbound ranking, average Cluster 5 1% Innovation mobility score top 3 8% Index 2018 % Rank Index Rank Cluster 2 3% Singapore 19.2 5 70.2 13 Malaysia 9.3 21 49.3 25 Cluster 3 Japan 3.4 58 80.4 8 Cluster 4 15% South Korea 1.7 77 77.1 9 73% Thailand 0.5 88 32.9 38 China 0.3 97 82.3 5 Vietnam 0.2 99 0 78
Indonesia 0.1 103 34.9 37 Sources: Ministry of Research, Technology & Higher Education (2018) Philippines 0.1 104 24.4 48
Sources: Global Innovation Index (2018)
26 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
Figure 45. The Global Innovation Index 2018 Figure 46. The Human Capital Index 2018
1 Singapore INSTITUTIONS 2 South Korea
HUMAN CAPITAL & 3 Japan CREATIVE OUTPUTS RESEARCH
46 China
48 Vietnam KNOWLEDGE &
INFRASTRUCTURE 55 Malaysia TECHNOLOGY OUTPUTS Country
65 Thailand (Global Rank in 2018) in Rank (Global
BUSINESS MARKET 84 Philippines SOPHISTICATION SOPHISTICATION
87 Indonesia India 115 INDONESIA MALAYSIA THAILAND Pakistan 134 PHILIPPINES VIETNAM CHINA 0 0,2 0,4 0,6 0,8 1
Sources: Global Innovation Index (2018) Sources: World Bank (2018)
Figure 47. Life Expectancy at Birth Figure 48. Infant Mortality Rate
79 80
74 60
69 40
20 Mortality rate, infant infant rate, Mortality 64 births) live 1,000 (per
59 0 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 Life expectancy at birth, total (years) total birth, at expectancy Life 1980 1985 1990 1995 2000 2005 2010 2015 Indonesia Thailand Malaysia Indonesia Thailand Malaysia Philippines Vietnam China Philippines Vietnam China
Sources: World Development Indicators Sources: World Development Indicators
Figure 49. Maternal Mortality Ratio Figure 50. Stunting Prevalence vs. GDP per Capita, 2016
500 60
BDI
MDG 400 2016 - YEM GTM PAK NGALAO MOZ COD NER CAF AFG 40 TCD ZMB ETH SDN 300 MWI SLE AGOIND RWA NPL BGD IDN TZA BEN LSO PHLBTN LBR GIN COM SLB CMR BWA MLI UGA MMR VUT MRT 200 TGOGNBBFA ZWE ZAF TJKKEN GNQ SWZ GMB VNM ECU NRU ALB HND NAMEGY HTI CIV IRQ COG MYS UZB BRN 20 GHA PAN MDV AZE SEN STP NIC LKA GAB 100 BOL MARBLZ SLV PER KGZ COL TKMMEX DZA GUY GEO TTO MNG THAMNE URY TUV TUN TUR ARM BIHSUR TON JOR CHN KAZSYC PSE BRABRB JPN MDA JAM estimate, per 100,000 live births) live 100,000 per estimate, 0 PRY SRBDOMCRI
Maternal mortality ratio (modeled ratio mortality Maternal WSM MKD LCA KOR 1990 1995 2000 2005 2010 2015 CHL AUS USA 0 or severe) (% under age 5), 2010 5), age under (% or severe) Indonesia Thailand Malaysia (moderatestunting malnutrition, Child 6 8 10 12 Philippines Vietnam China GDP per capita, PPP (constant 2011 international $), log
Sources: World Development Indicators Sources: UNICEF, WHO & World Development Indicators
Indonesia Growth Diagnostics | 27
Indonesia Growth Diagnostics
Figure 51. Immunization Rate, 2017 Figure 52. Cause of Death by Communicable Diseases and Maternal, Prenatal and Nutrition Conditions
Indonesia 40 Malaysia
Thailand 30
Vietnam 20 Philippines
China 10 0 20 40 60 80 100 nutrition conditions (% of total) (% of conditions nutrition diseases & maternal, prenatal & & prenatal maternal, & diseases DPT (% of children ages 12-23 months) communicable by death, of Cause 0 HepB3 (% of one-year-old children) 2000 2002 2004 2006 2008 2010 2012 2014 2016 Measles (% of children ages 12-23 months) Indonesia Thailand Malaysia
Philippines Vietnam China Sources: World Development Indicators Sources: World Development Indicators
Figure 53. Cause of Death by Non-Communicable Diseases Figure 54. Mortality from CVD, Cancer, Diabetes or CRD
35 90 SLE YEM GUYFJI 30 AFG MNG CIV TKM 85 KIR EGY - LAO HTI LSO PHLSWZ KAZ FSM SDN IDNZAF 80 TJK RUS 25 KGZ PAKMDA UKRGEO MLI MMRUZB TGO TCDSLB BLRBGR CAFBDI MDGCOM VUT TONIND BTN VCT HUN GIN NGA ARM AZE MUSATG 75 BFAUGA NPL BLZ SUR LVAGNQ CMRBGD NAM GRD IRQ ROUSYCTTO GHA WSM MNE LTU 20 NER GNBGMB MKDBWA COD ZWEBEN JOR 70 LCA SRBDOM POL MOZ ETHRWA SEN STPMRT LBR TZA ZMB BIHLBN VNMCPVBOL PRY LKA MYSSVK COG ALB BRACHN URYHRV EST AREBRN MWI AGO TUN BRB TUR SAU COL MEXARGBHS 65 CZE QAT 15 GTMJAM THA USA HNDNIC SLV DZAGAB KEN MDV ECUPER PAN SVN 60 MAR CHLGRC DEU CRI PRTCYP BELAUTDNKNLD MLTFRAGBR Cause of death, by non by death, of Cause NZLFIN IRL 10 ISRESP CAN LUX ITA AUSISLSWE NOR SGP 55 JPN CHE KOR 2000 2002 2004 2006 2008 2010 2012 2014 2016 communicable diseases (% of total) of (% diseases communicable CRD between exact ages 30 and 70 (%) 30 and ages exact between CRD
Mortality from CVD, cancer, diabetes, or diabetes, cancer, CVD, from Mortality 5 Indonesia Thailand Malaysia 6 8 10 12 Philippines Vietnam China GDP per capita, PPP (constant 2011 international $), log Sources: World Development Indicators Sources: World Development Indicators
Figure 55. Health Facilities per 10,000 Population Figure 56. Trend in Male Smoking Prevalence
42 85
75 26 21 19 17,9 65 15,1 12,8 12 10 8,1 8,2 55
3,7 adults) of (%
45
Malaysia Thailand China Philippines Indonesia Vietnam males prevalence, Smoking 35 Medical Doctors (per 10,000 people) Hospital Beds (per 10,000 people) 2000 2004 2008 2012 2016
Sources: World Health Organization & World Development Indicators Indonesia Thailand Malaysia Philippines Vietnam China Note: The latest data available in 2010-2017 Sources: World Development Indicators
28 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
Figure 57. Male Smoking Prevalence vs. GDP per capita, 2016 Figure 58. Most Recent Survey of Youth Tobacco Use (Age 13-15) 0 5 10 15 20 25 30
IDN 80 Malaysia (2016) TUN Indonesia (2015) KIR RUS GEO LSO MDV 60 COG ARM GRC CYP KGZ LAO EGYALB LVA Thailand (2015) UKR BIH CHNMNE VNMMAR MNG BLR BGD MDATON BGR SUR KAZ SLE AZE CHLMYS PHL LBNSRB MUSHRVTUR KOR WSM THA ESTCZE NPL PAK NRU ROU LTUSVK ARE China (2014) (% of adults) of (% VUT MMR FJI HUNSYC ISRFRA 40 NAM ZAF BWA POL JPNDEU GMBZWE ESP BELAUT BRN MOZ YEM DZA PRT MLT JAM ARG ITA CHE SGP TZA LKA NLD LUXQAT MWI ZMB UZB SVN GBR SAUUSAIRL South Korea (2016) COMBFAHTIMLI PRY PLWMEX FIN RWA KEN IND URYBHS NOR Smoking prevalence, males prevalence, Smoking LBR SLV DOMBRA DNKSWE UGA SEN CPV SWZ CRI NZL CANAUS NER TGO BRB ISL 20 BEN ECU COL Singapore (2012) NGA PAN ETH GHA Vietnam (2014) 0 Japan (2014) 6 7 8 9 10 11 12 GDP per capita, PPP (constant 2011 international $), log Prevalence (%) MALE FEMALE
Sources: World Development Indicators Sources: World Health Organization
4. Figures on Infrastructure Indicators
Figure 59. Road Connectivity Index, 2017 Figure 60. Road Density, 2014
120 ) 2 12 CZE BEL ESP SAUUSA 100 SWE NAMZAF MEXARG FRACANDEU BWA CHL OMN LUX CHN PRT AUS CZE FINBEL LTUITA ISR NLD CHE ZWE DZA LVATURHUN GBRAUT DEU 80 MAR RUSPOLEST DNK AREBRN SRBVENIRN SVK CHEIRL QAT 9 BEN CIV URY KWT UKRJOR EGY HRV CYP HUN SWZ THA KAZ SVNNZLJPN ZMB PAKNGA GRC MWI GMBBFA GEOTUN BGR ROU BRADOM SVK UGA MDAIND NLD 60 GHA SLV GBR MOZSLE MLI KEN AGO ISL 100 (best) 100 SENTZA PAN - POL MRT AZE NOR 6 SVN 0 NIC ECUBIHPERMKDCOLLBN AUT COD TCD PRY ISRITAFRA GIN LSO HND VNM LKAMNG LBR CMR ARM CRI DNK 40 BDI BOL ALB HRV YEM KGZ JAM SRB ROU JPN NPLTJK BGD IDN ETH KOR Road connectivity index, connectivity Road CPV TTO MDA UKR BGR MNE km (km/100 density Road RWA MYS ESP PHL 3 MKD LVAPRTLTU IRL LAO BLR 20 ARM AZE EST SWEUSA GTM IND GEOBIH BGD SWZ ZAF GRC FIN TUN MEX TUR HTI PAKUZBVNM THA ARG NOR JOR CHNTKM CHLMYS MOZ TJK MAR EGY IRQPERBRAIRN RKAZ… CAN 0 0 COD BFA CMRCIVKGZMRTSDN IDN DZAMNGGAB SAU 6 8 10 12 6 8 10 12 GDP per capita, PPP (constant 2011 international $), log GDP per capita, PPP (constant 2011 international $), log
Sources: WEF Global Competitiveness Index 2018 & World Sources: FAO Land Portal and World Development Indicators Development Indicators
Indonesia Growth Diagnostics | 29
Indonesia Growth Diagnostics
Figure 61. Quality of Roads, 2017 Figure 62. Quality of Port Infrastructure, 2017
7 7 NLD SGP HKG CHEARE SGP PAN FIN ARE HKG BEL JPN NLD ISL PRT FRA USA 6 AUT DNK EST QAT ESPNZLGBRDEUSWE NOR USA MYS CAN MLTJPN ESPKOROMNDEUDNK NAM PRT KOR 7 (best) 7 HRV FINSWE LUX QAT LVA FRABHR IRL - CAN MAR SVN MYS 5 JAM URYCHL AUS CHL ZAF DOM LTU CYPISR GBRBHR EGY AZE ISR SAU NAMECU TUR IND ECU CHN HRV CYPOMN LUX 5 RWA KEN JOR LKA GRCTURSYC CHE SAU BRN GMB SEN HND ITA AZE AUS THAMEX SWZ LTUEST NZL MUSRUSPOL CHN IRL GEOALB MNEBGR MAR MUSGRC ITA BELISL PAK IRN ZAF PAN SVN BEN IDN AUT BRN NIC IND ALB DOMMEX SYC (best) 7 COL TTO KWT KEN THA NOR - VNM PER ARG LKA HUN MOZMDG BGDGHA CPVGTMSWZ JORBTN POLTTO KWT 1 UKR LBN ROU CZE GMB TJK SLV IDN IRN GIN TZA SLV DZACRI 4 PAK CPV SVKCZE SLE PRY TUN GHA JAMEGY LBR KAZHUN HND GEO BWA ZWE CMR NIC BRABWA SEN ARMTUN LSO SRB SVK MLI ZMB 3 RWA TZA DZAMNE BDI NGA PHL UGA VNM BGR COD ETH VEN ETH LAO ARGURY YEMHTIUGA MRT SRB MLT ARM SLE PHL MNGBRA MDA Quality of roads, 1 roads, of Quality BGD GTM PER LVA ZMB LAO BDILBR BIHCOL 3 BEN KAZRUS MWI BIH MWI ZWE NPL VEN infrastructure, port of Quality TCD TJK BTN ROU MLI TCD KGZ LBNCRI LSOCMR NGA MOZ MDA UKR NPL PRY KGZ MNG YEM GIN MDG 2 COD HTI MRT 1 6 8 10 12 6 8 10 12 GDP per capita, PPP (constant 2011 international $), log GDP per capita, PPP (constant 2011 international $), log
Sources: WEF Global Competitiveness Index 2018 & World Sources: World Development Indicators Development Indicators
Figure 63. Airports per Million Square Kilometre, 2013 Figure 64. Quality of Air Transport Infrastructure, 2017 10000 8
GRD FSM TON SGP NLDHKGARE
2 8000 KNA FIN CHE QAT DNK 6 PAN CANUSANOR ESPKOR DEUISLSWE MYS NZLMLTFRABEL ATG ZAF AZE CYPJPNGBR LUX PLW JOR TURPRTISR IRL URYLVA CZE AUSAUT BHS JAM EGYECU THA EST 6000 KEN CHN SAU MARARM MUSGRC BHR RWA NAMIDNDOM RUSSYC ITAOMN IND CRI CHL BRN GMB MEX POL BHR TJK GEO MNEBGRHRV LTUSVN ETH SEN MDA SWZBTN LKAALBPERCOLSRB ARG HUN PAK BWA ROUKAZTTO 4 MLI GHAHND CPVSLVUKR TUN BRA ZWE NICVNMLAO LBNDZA IRN 7 (best) 7 MDG SVK 4000 - MOZ BENGIN TZA BGD GTM 1 ZMB MNG KWT BDI UGATCD KGZ COD NGA PHL SLV LCA PRI LBR SLE CMR CRI MLT HTI PRY BIH VEN MWI NPL MRT GTM DMA VUT JAM MUS YEM BRB ISR 2
Airports per million km million per Airports CPV STP BLZPRY 2000 COM GBRDNKHKG FJI ECU PAN CZECYP DEU CHE WSM BEL USA SLB PNG NIC HRV KOR LSO HND MEX LTU ISL LSO BOLPHL SWZ DOMCOLLBN URY SVKSVNTTOFRANLD LUX DJI GUY BGR LVACHLGRCPRT SWEAUTIRL HTIZWE SRB BIH BRAZAFAZEVENARGHUNESTPOLNZLITAJPNFINOMN ARE QAT MWI NPL KEN UKRARMGEO MKDMNESURBLR MYS KWT infrastructure, transport air of Quality BDILBR GNBRWAUGA TZATJK PAKMDALAO JORL… IDN THAIRQTUNIRNGABROU ESPGNQ NOR BRN 0 CAF NERMOZGINTGOETHMDGBFAGMBSLEMLIAFGBENTCDSEN CMRBGDCIVKGZMRTGHAYEMSDNMMRUZBINDVNMNGAAGOMARBTN EGYALBCHNNAMPERTKMDZAMNGBWA TURKAZRUS CANAUSSAU 0 6 8 10 12 6 8 10 12 GDP per capita, PPP (constant 2011 international $), log GDP per capita, PPP (constant 2011 international $), log
Sources: CIA World Factbook & World Development Indicators Sources: CIA World Factbook & World Development Indicators
Figure 65. Railroad Density, 2017 Figure 66. Efficiency of Train Services, 2017
100 SRB HRVROUHUNPOLSVKSVNCZEISRITAKORFRAJPNGBRBELDEUAUTDNKNLDCHE LUX 100 )
2 JPN CHE UKR BGR HKG MDA 80 80 SGP ESP KOR IRL FIN NLD LVALTU ESP AUT FRADEUUSA PRTEST MYS MKD AZE LUX RUSPRTCZE 60 AZE PAN EST NOR USA 60 IND CHN HUNLTU MLTGBRCANDNKSWE ARE ARM LKA LVA ISL SWE IDN MUSKAZ AUS SVKISR BEL IRL IND GEOBIH UKRGEO POL ITA BGD MNE TJK IRN FIN MAR TTO BRN SWZ URYGRC NZLOMNSAU
ZAF (best) 100 PAK EGY 40 ZMB - VNM MNGDZABWA SVN NZL 40 ZAF BGR TUR ALB 0 RWA SWZ LKA QAT TUN MDA ARM GRC MEX KENBGD DOMMNEMEX TUR TUN CHL PAK ETH KGZ HRV NOR CMR NAM ROU BHR KWT THAARG SENTZA PER THA CPV SRBBRA ARG MOZSLE MLI JOR TCD MRT ECU Railroad density (km/1000 km (km/1000 density Railroad BIH 20 CHL services, train of Efficiency 20 BDI ZMB CRI MWI ZWEBEN KEN VNM CHN MYS GMB LAOPHLJAM LBRCOD GIN GHA LBN JOR KAZ CAN MWI SLV COL UGA SENTZA MAR EGY IRN RUS ZWE NIC VEN MOZ TJK GHA NGA NAM UGA BRA YEM BEN NGA CYP BFA CMRKGZ BOL IDN PRY URY COD CIV PHL PERCOLDZABWA NPL ALB MNG PAN AUS HND 0 ETHMLI MRT VEN SAU 0 LSO GTM 6 8 10 12 6 8 10 12 GDP per capita, PPP (constant 2011 international $), log GDP per capita, PPP (constant 2011 international $), log
Sources: World Development Indicators Sources: World Development Indicators
30 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
Figure 67. Problematic Factors for Doing Business in Figure 68. Electrification Ratio, Indonesia Indonesia 100 97,5 Corruption 95 Inefficient government bureaucracy Access to financing 90 Inadequate supply of infrastructure 85 Policy instability 80 Government instability/coups Tax rates 75 Poor work ethic in national labor force 70 Tax regulation Inflation 65 Inadequately educated workforce households) (% ratio Electrification 60 Restrictive labor regulations
Crime and theft 2010 2011 2012 2013 2014 2015 2016 2017 Foreign currency regulations 1H2018 Insufficient capacity to innovate % of firms Sources: PLN Statistics Poor public health
0 2 4 6 8 10 12 Sources: WEF Executive Opinion Survey 2017
Figure 69. Electrification Ratio, Peer Countries Figure 70. Electrification Ratio by Consumption Decile
100 100
98 90 96
80 94
92 70 90
60
Electrification ratio (% households) (% ratio Electrification 88
Access to electricity (% of population) of (% electricity to Access 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 1 2 3 4 5 6 7 8 9 10 Indonesia Thailand Malaysia Consumption decile Philippines Vietnam China 2012 2013 2017 Sources: World Development Indicators Sources: Susenas & Podes, Prospera’s calculation
Figure 71. System Average Interruption Frequency Index Figure 72. System Average Interruption Duration Index (SAIFI) (SAIDI)
16 30 14 25 12 20 10 8 15 SAIDI (hours) SAIDI 6 10 SAIFI (times/year) SAIFI 4 5 2 0 0 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017
Sources: PLN Statistics Sources: PLN Statistics
Indonesia Growth Diagnostics | 31
Indonesia Growth Diagnostics
Figure 73. Electrification Ratio (% of Households) by Region, 2017
Sources: PLN Statistics Notes: Yellow indicates better electrification ratio
Figure 74. System Average Interruption Frequency Index (SAIFI) by Region, 2017
Sources: PLN Statistics Notes: Yellow indicates better SAIFI
Figure 75. System Average Interruption Duration Index (SAIDI) by Region, 2017
Sources: PLN Statistics Notes: Yellow indicates better SAIDI
32 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
Figure 76. Quality of Electricity Supply, 2017 Figure 77. Broadband Subscriptions, 2017
8 100
CHE DNKHKGCHENOR SGP FRADNK FRAJPNGBRFINISLNLD LUX NLD CZENZLBELCANAUTSWE ARE 7 (best) 7 QAT KOR SVNISRKOR IRL MLT NOR - ESP USA 80 CHL PRT OMNBHRDEUSAU GBRDEUISL URY SVK BELSWE 6 BTN CRI HRVMYS ITA CAN GTMJOR EST AUS HKG MAR LTUCYP LUX NAM AZE MUSGRCPOL KWTBRN NZL USA TUN THA PANROURUSLVAHUN GRCPRTCYP GEOEGYPER CHN IRN SYCTTO JPNFIN SLV ECUBIH MEX MLT ESP AUS LAOIND JAMARM SRBCOL BGR 60 ESTLTU ALB MNE KAZ HUNSVNCZE AUT IRL NIC UKR BRA TUR ISR RWA MDAVNM IDN URY KEN PHL DZA LVA ITA SGP 4 LKAMNGZAF HRV SVK SWZ BWA BGR SENTJKKGZBGD CHN ROU GMBUGA HND CPV LBN SRB ETH TZA GHA MOZ ZWE ARG 40 BIH RUSPOL MLI NPLLSO PAK MKDAZEMNE TTO broadband internet internet broadband GEO MUS SLE ZMB PRY - ARGCHL BHR BDILBR GIN DOM SYC TCD CMR MDA ARE COD BEN MRT VEN BRACRI KAZTUR 2 MWI MDG UKR COL MEX LBN THA PAN VNM JAMARM SAU QAT HTI NGA Fixed 20 ALB IRN ECU VEN MYS BRN YEM MNG PERDZADOM OMN SLV TUN KGZBGD JOR EGYLKA subscriptions (per 100 population) (per subscriptions PRY TZA NICCPVGTMMARPHLNAM HND BOL ZAFBWA KWT YEM ZWE IND IDN LBR GMBUGAETHRWABENNPLSENKENCMRMRTCIVZMBGHAPAKAGOLAOSWZ Quality of electricity supply, 1 supply, electricity of Quality 0 0 BDICOD MWIMOZSLEHTIBFATCDMLIGIN LSOTJK NGA 6 8 10 12 6 8 10 12 GDP per capita, PPP (constant 2011 international $), log GDP per capita, PPP (constant 2011 international $), log
Sources: WEF Global Competitiveness Index 2018 & World Sources: World Development Indicators Development Indicators
Figure 78. Broadband Speed, 2017 Figure 79. Access to Basic Water Services, 2015
TUV TON NRUPLWCRIBGRMUSARGURYROUHRVCHLGRCHUNPOLPRTESTSVNCZECYPISRESPKORITAMLTNZLFRAGBRFINBELISLDEUAUSAUTBHRDNKSWENLDSAUUSAHKGCHENORAREKWTBRNSGPLUXMACQAT 100 ARMJORPRYEGYLCA THABRBMEXBLR TURLVAR… SVK JPNCAN IRL BGD UKRBTNBLZ MDVBIH BRAMNE ATGBHS LTUTTO Singapore (2) WSM DMA CHN MYS GUY VCTTUN SURTKMIRN PAN BOLGTMSLVJAMFJIGEOECU DOMDZA HND ALBLKALBNSRB KAZ VUT VNM PHL PER OMN PAK IDN NPLPSE KGZFSM IND GAB Malaysia (30) MDACPV IRQ COM ZAF AZE NIC MAR MNG GMB STP LAO 80 MHL NAM BWA DJI GHA Thailand (45) MLISEN TJK CIV LSO LBR TLS YEM MRT GNB COG MWIGIN ZWEBEN MMRNGA SWZ CMR HTIKIRSLB Vietnam (89) TGO AFG ZMB 60 SLE KEN SDN BDI RWA Indonesia (92) CAF BFA MDG SSD TZA GNQ MOZ NER Philippines (97) population) of (% TCD COD AGO 40 ETHUGA
China (152) services water basic to Access
0 10 20 30 40 50 60 70 80 20 6 8 10 12 2017 2018 2019 Mean download speed GDP per capita, PPP (constant 2011 international $), log Sources: cable.co.uk Sources: World Development Indicators
Figure 80. Access to Basic Sanitation Services, 2015 Figure 81. Access to Water Supply by Quintile 2018, Urban
UZB PLW CHLMYSESTISRESPKORMLTNZLJPNAUSAUTBHRDNKSAUUSACHEAREKWTSGP QAT 100 GRCHUNPOLPRTSVKSVNCZECYPITAFRAGBRFINOMNBELISLCANDEUSWE NOR KGZ WSM JOR ALB CRITKMBRB HRVTURKAZ NLDHKG BRN LUX 100 TJKPSE UKRFJI BIHMDVLBNSRBTHAMNEARGURY TON EGYTUNLKA BLRMUSLVALTU TUV SLVARMPRY LCAMKD BHS TTO IRL MEXAZE RUS BLZ VCT DZAIRN ATG MHL GUYJAM ECU BRAIRQBGR MARGEO COL DOM ROU HND 80 MDAVNM DMAGRDSUR NIC PER PAN 75 PHL CHN GNQ LAO ZAF Piped on Premises GTM IDN MMRCPV NRU RWA BTN 60 YEM MNG BWA PAK SWZ 50 Other Improved VUT BOL BDI DJI SEN NPL BGD MWI TLS LSO MRT IND GMB GAB 40 AFGKIRZWE STPCMR AGO Other Unimproved COM SDN NGA NAM 25 % urban households % urban (% of population) of (% HTIMLISLB KENCIVZMB CAF MOZ TZA GINGNBBFA Surface Water 20 COD UGA LBR SLE COG NER TGO BEN GHA SSD 0 Access to basic sanitation services sanitation basic to Access MDG TCD ETH 1 2 3 4 5 0 Consumption quintile 6 8 10 12
GDP per capita, PPP (constant 2011 international $), log
Sources: World Development Indicators Sources: Susenas
Indonesia Growth Diagnostics | 33
Indonesia Growth Diagnostics
Figure 82. Access to Water Supply by Quintile 2018, Rural Figure 83. Firms Experiencing Water Insufficiencies
100 60
MWI YEM
SWZ 75 AGO DJI CAF Piped on Premises LSO 40 ETH Other Improved 50 KEN GHA CPV Other Unimproved TZA MDG ZMB ERI BEN NIC SUR MLISLB SLV LBN HND % rural households % rural MRT BTN 25 Surface Water NERBDI BRB PSE PRYBLZ 20 JOR COL TLS GTM IRQ RUS COD SEN MUS MYS(2015) CMR NGALAO XKX RWA ZWE GRD LKA DOM TGO NPL CRISRB UGA TJK KGZ SDN UKRECU ARG BHS TTO PAK BOL KNATUR 0 TCDSSD BIH KAZ AFG VNMMMR(2015) BWAMNEMEX LVA PHL(2015)VCT KHM ALB PAN EST CZE 1 2 3 4 5 PER BRA BGR GEO URYBLR BGD MDAIND JAM EGY ROU CHN(2012)AZE HRV MKDTHA(2016)ATG POL ARM CHL Quintile MAR TUN LTU 0 GIN UZB DMALCA HUNSVKSVNISR Firms experiencing water insufficiencies (%) insufficiencies water experiencing Firms IDN(2015) 6 7 8 9 10 11 Sources: Susenas GDP per capita, PPP (constant 2011 international $), log
Sources: World Bank Enterprise Surveys & World Development Indicators
5. Figures on Market Failure Indicators
Figure 84. Indonesia’s Export by Type of Commodity
100 2000s Commodity 90 Boom 80 Commodities 70 60 Resource-Based Manufactures 50 Low-Tech Manufactures 40 Medium-Tech Manufactures 30 High-Tech Manufactures
Commodity (USD Billion) (USD Commodity 20 Indonesia's Export by Type of of by Type Export Indonesia's 10 0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
Sources: Prospera
34 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
Figure 85. Export Composition by Product, 1995 - 2017
Sources: CID Harvard
Figure 86. Complexity Outlook Index & Economic Complexity Figure 87. Economic Complexity Index vs. GDP per Capita, Index, 2016 2016
3,5 3,5 IND
TUR JPN 2,5 2,5 CHE ESP KOR DEU PRT SGP AUT IDN BGR CZE SWE EGY HUNMNP GBRFINUSA LTU NLD SVK SVN IRL TUV ITAFRA SRBCANHRV THADNKBEL LUX 1,5 GRC LVA ROMEST LUX 1,5 CHN MEX POL ANDBELSMR BRA LBN GBRFIN ROMMYS KNAEST NLDDNK TUN BLR POLCHN CHE THA MLT HKG UKR MYS FRASVKHUN KOR PHL WSM GUMISR ZAF BIH ISR MEX SVN SGP BLR HRV ESP VNM CYP HKG SWE STP PAN LTU PRT CAN UGA BIHSRB BRB NOR PAN BGRGRDASM LVA CYP MAC RUS GTM PHL SMR CPVTONTUN DMALBN TUR PAK LKA SLVPSE NORGUM IRL UKR CRIRUSSYC BHR ISL KGZNZL CZE IND PSE MHLSLVSWZ PLWBRA SAU ARE 0,5 ARG JORCRI ITA 0,5 KGZ VNM FSMJOR MNELCA ATG NZL AND IDN COL URY GRCBHS TZA KEN MDA NPL BTN ARM SYRMARALB MUS NPLURYARE MAC WSMMLT USA JPN SLE MDA EGY FJIBLZMKD ZAF CHL PER AUS DOMFJI COL BRB COM GEOJAM DOM TTOOMN HND GEOMKDCHL DMA UGA GTM VCT MUS KWT SEN MNESWZBHR ABW SXM MNP RWAMLI LKA NAM QAT KAZ PRK SEN HND MARGUYALB ARGKAZ MMR ARM ISL ASM STP KNA AUT KEN PRY IRNSURMDV AUS IRN OMNJAMBLZ DJI SAUTCATONCPVGRD KHMPAKLSO KIRUZB TLS MDG RWANAMMLI SLE BDI LBR TGO HTIBEN MMR NIC PERBWA GRL -0,5 UZBKHM ATGPYFMHLSYC -0,5 CAF YEM BGD DZA ECUETHGHALAOZWE TGONICBENPRY QATKWTTTOCUW BHSFSMLCAPLWCYM BMU TUV NER ZWE LAO VUT TKM MNG DZALBR GUY VCTCOMBTN MDGETHGMB TZATJK SLBGHA BOL LBY GNQIRQGAB BRNAGOMOZCODAFGCMRBOLSLBTJKLBYNERTKMBGDCAFYEMBWAGRLLSOKIRTLSCUBSURMDVNCL COD AFG CMR MNG ECU TCD SSD CIVCOG VEN GMBERIVUTSOMBDIHTIFRO MOZ SDN Complexity Outlook Index Outlook Complexity PNGGINGNB AZEMWIZMBSDN ZMB AGO NGAMRT MWI AZE BRN BFA Index Complexity Economic BFA MRT GNB CIV COG GIN PNGNGA GAB -1,5 -1,5 IRQ GNQ DEU TCD
-2,5 -2,5 -2,5 -1,5 -0,5 0,5 1,5 2,5 5 7 9 11 Economic Complexity Index GDP per capita, PPP (constant 2011 international $), log (Controlling for GDP per Capita & Natural Resource Exports)
Sources: CID Harvard Sources: CID Harvard & World Development Indicators
Indonesia Growth Diagnostics | 35
Indonesia Growth Diagnostics
6. Figures on Macro Risk Indicators
Figure 88. External Debt & Reserve Adequacy Figure 89. External Debt & Current Account Balance
Sources: International Monetary Fund Sources: International Monetary Fund Note: Assessment of Reserve Adequacy (ARA) metric measures a country's potential FX liquidity needs in adverse circumstances against which reserves could be held as a precautionary buffer (IMF, 2019).
Figure 90. Central Government Debt Figure 91. Tax Ratio vs. GDP per Capita, 2016
40 ISL 80 LSO DNK SYC 30 60 NAM ZAF NZL SWE MAC SLB GRC MLT JAM GBRAUT LUX KIR WSM LVA CYP GEO URY HUN ISRITAFRABEL MOZ PRTEST AUSNLD NOR ARM BWA FIN 40 20 UKR BIH PLW BGR TGO MDA IRL NPL MUSTUR SVN MHL SLV ALB CHL SVK BFA VUTKGZ UZB MKD ROU LTU SEN KENCIV NIC POL (% of GDP)of (% MWI MLI THA RWA ZMB KOR BHS CZE UGA PHL PERLBNDOMCRIBLRMEX MYS ESP SGP LAO BTN COLBRA 20 LKA ARG CAN Tax Revenue (% GDP) (% RevenueTax MNG JPNDEUUSA GTM 10 AGO PRY IDN KAZ CHE BGD CHN RUS
PSE FSM MMR Central government debt, total total debt, government Central 0 IRQ ARE
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 0 6 7 8 9 10 11 12 Indonesia Thailand Malaysia PhilippinesPhiippines GDP per capita, PPP (constant 2011 international $), log
Sources: World Development Indicators Sources: World Development Indicators
36 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
Figure 92. Government Expenditure on Education vs. GDP per Figure 93. Government Expenditure on Health Capita, 2015
10 4
8 ISL SWE NOR 3 BTN SEN UZB CRI FIN BLZ TUN BDI HND BEL BRA CYPNZL NER KGZGHA ZAF 6 ARG CZEISR MWI VUT GBR JAM ESTMLTFRAAUTNLD KEN CPV LVA KOR AUS 2 TGO TJKPSE MEX CHE ECU MUS MYS SVN ETH CIV BLR CHLPOL DEU COG MDVCOL HUNPRTSVK COM BEN LCA TUR ESP LBR BFA NIC MNG LTU ITA 4 STP SLV PERSRB LUX RWAMLINPL RUS IRL IDNALB DMA HKG HTIAFG ROU 1 GTM AZE MAC UGA CMR ARM IRN KAZKNA BHR GINMDG PAK education, total (% of GDP)of (% total education, Government expenditure on expenditure Government 2 COD LKA SSD 0 Health Expenditure, public (% of GDP)of (% public Expenditure, Health 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 0 GUY 6 8 10 12 Indonesia Thailand Malaysia GDP per capita, PPP (constant 2011 international $), log Philippines Vietnam China
Sources: World Development Indicators Sources: World Development Indicators
7. Figures on Regulation and Institution Indicators
Figure 94. The Missing Middle, 2013 Figure 95. Regulatory Index, 2017
120 4.000 GDP Contribution (in TrillionIDR) 8,72 3.500 100 7,13 6,37 6,55 6,40 6,65 3.000 80 2.500 4,18 60 2.000 1.500 40 1.000 Index Regulatory
Employment (Millions) (Millions) Employment 20 500
0 - Brazil China India Indonesia Malaysia Thailand Vietnam Micro Small Medium Large
Employment Nominal GDP Sources: Worldwide Governance Indicators 2018
Sources: Prospera
Figure 96. Legal System and Property Right Index, 2017 Figure 97. Rule of Law Index, 2017
5,63 5,76 5,43 5,10 5,17 4,77 5,02 4,45 4,52 4,80 4,65 4,20 4,09 4,35 Right Index Right Rule of Law Index Law of Rule Legal System & Property& SystemLegal
Brazil China India Indonesia Malaysia Thailand Vietnam Brazil China India Indonesia Malaysia Thailand Vietnam
Sources: Worldwide Governance Indicators 2018 Sources: Worldwide Governance Indicators 2018
Indonesia Growth Diagnostics | 37
Indonesia Growth Diagnostics
Figure 98. Cost of Redundancy Dismissal, 2018
80 70 57,8 60 50 40 36 27,4 27,4 24,6 30 23,9 20 10 0 Italy Peru India Spain Brazil Egypt China Japan Nepal Ghana France Turkey Tunisia Greece Iceland Mexico Finland Zambia Canada Bahrain Sweden Belgium Ecuador Pakistan Uruguay Thailand Malaysia Australia Sri Lanka Denmark Germany Paraguay Viet Nam Mauritius Honduras Indonesia Argentina Singapore Philippines El Salvador Bangladesh Switzerland Netherlands New Zealand Mozambique United States of… United Kingdom Korea, Republic of Brunei Darussalam Russian Federation Iran, Islamic Republic… Sources: Global Innovation Index 2018 Note: Sum of notice period and severance pay for redundancy dismissal (in salary weeks, averages for workers with 1, 5, and 10 years of tenure, with a minimum threshold of 8 weeks)
Figure 99. Percent of Firms Offering Formal Training Figure 100. FDI Regulatory Restrictiveness Index, 2018
80 WSM CHN(2012) 0,374
ECU SWE 0,313
PER KGZ GUY COL 0,251 0,252 FJI MNG 60 PHL(2015) NIC CHL 0,209 XKX VEN RWA GTM CRI CZE SLV URY MRT BIH BWA MEXBLR CAF BOL HRVKNA VUT HND MKD 0,130 PRYVCTGRD RUS 0,117 BGR SVK SLB BRA LTUSVN 40 KEN GHA ARGROU CMR COG ZAFSRB BHS CIV IND BRB EST TGOUGA POL TJK closed) 1 = open, (0 = BDI MWI NPL PAKMDA AFG TZA LSO NGA GAB TUN NER ZMB TURKAZ TTO ERI ZWE MARBTNJAM LBN BFA NAM MUS ATGLVA AGO ALB DOMMNE LBRMOZ BGD VNM(2015)UKR IRQ SLEETH DJI
BEN AZE Index Restrictiveness Regulatory FDI 20 DMA MYS(2015)ISR MLISEN LKA THA(2016)
SSD India COD GIN CPV ARM HUN China YEM BLZ
Firms offering formal training (%) training formal offering Firms LCA GNBMDG
PSE TONUZB PAN Vietnam SDN GEO EGY Malaysia Myanmar Indonesia
MMRLAO IDN(2015) Philippines JOR TLS SUR 0 6 7 8 9 10 11 Sources: OECD GDP per capita, PPP (constant 2011 international $), log
Sources: World Bank Enterprise Surveys & World Development Indicators
Figure 101. Inward FDI Stock, 2018 Figure 102. Time Required to Start a Business, 2019
60,1 Philippines 31
45,7 Indonesia 19,6 43,0 Vietnam 17
25,1 Malaysia 13,5 22,1 China 8,6 12,1 Brunei Darussalam 5,5 Thailand 4,5 Inward FDI Stock (% of GDP)of (% Stock FDI Inward Vietnam Thailand Malaysia Philippines Indonesia China Singapore 1,5
Time Required to Start a Business (Days) Sources: Country Fact Sheets 2019 – UNCTAD Sources: Ease of Doing Business 2019 – World Bank
38 | Indonesia Growth Diagnostics
Ministry of National Development Planning/National Develoment Planning Agency
Figure 103. Score on Trading across Borders, 2019 Figure 104. Rank in the Ease of Paying Taxes, 2019 93 90 88 87 85 83 77 131 72 71 70 69 67 61 112
94
72 59 India China Japan Vietnam Thailand Malaysia Indonesia Singapore Philippines Korea, Rep. Korea, Score on Trading across Borders across Trading on Score
Rank in the Ease of Paying Taxes Paying of Ease in the Rank 8 East Asia & Pacific & Asia East Indonesia - Jakarta - Indonesia
Indonesia - Surabaya - Indonesia Vietnam Indonesia Phillipines Malaysia Thailand Singapore
Sources: Ease of Doing Business 2019 – World Bank Sources: Ease of Doing Business 2019 – World Bank
Figure 105. Cost to Export and Import, 2019
200 160 120 80 40 0 India China Japan Vietnam Thailand Malaysia Indonesia Singapore Philippines Korea, Rep. Korea, Cost to Export and Import, and Export to Cost Documentary Compliance (USD) Compliance Documentary East Asia & Pacific & Asia East Indonesia - Jakarta - Indonesia
Cost to Import Cost to Export Surabaya - Indonesia
Sources: Ease of Doing Business 2019 – World Bank
Figure 106. Effective Rate of Protection, 2015 Figure 107. Most Problematic Factors for Doing Business in Indonesia Food Crops Metals & Metal Products Corruption Chemical Inefficient government bureaucracy Food, Beverages & Tobacco Access to financing Non-Metal Products Inadequate supply of infrastructure Machinery & Transport Equipment Policy instability Livestock and Their Products Government instability/coups Other Manufacturing Tax rates Estate and Other Crops Poor work ethic in national labor… Textile, Apparel & Leather Paper Products Tax regulation Wood Products Inflation Fisheries Inadequately educated workforce Oil Refining & LNG Restrictive labor regulations Oil & Gas Extraction Crime and theft Forestry Foreign currency regulations Other Mining Insufficient capacity to innovate % of firms -40 -20 0 20 40 60 80 Poor public health Effective Rate of Protection (%) 0 2 4 6 8 10 12
Sources: Mark (2015) Sources: WEF Executive Opinion Survey 2017
Indonesia Growth Diagnostics | 39
Ministry of National Development Planning/National Develoment Planning Agency
Indonesia Growth Diagnostics | 1