Indonesia Growth Diagnostics - Bappenas

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


Ministry of National Development Planning/

National Development Planning Agency Directorate for Macroeconomic Planning and Statistical Analysis

ii | I n d o n e s i a G r o w t h D i a g n o s t i c s

Indonesia Growth Diagnostics


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

vi | I n d o n e s i a G r o w t h D i a g n o s t i c s


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. Mari Elka Pangestu, Ph.D. • Dr. Muhammad Chatib Basri, S.E., M.Ec. • William Wallace, Ph.D. • Prof. Geoffrey J.D. Hewings • Prof. Budy P. Resosudarmo, Ph.D. • Dr. Asep Suryahadi

• Prof. Arief Anshory Yusuf, Ph.D. • Faisal H. Basri, S.E., M.A. • Prof. Dr. Mohamad Ikhsan, S.E., M.A. • Anton Hermanto Gunawan, S.E., M.A., M.Phil. • Teguh Dartanto, S.E., M.Ec., Ph.D. • Haryo Aswicahyono, Ph.D. • Turro S. Wongkaren, Ph.D.

I n d o n e s i a G r o w t h D i a g n o s t i c s | iii

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

iv | I n d o n e s i a G r o w t h D i a g n o s t i c s


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

I n d o n e s i a G r o w t h D i a g n o s t i c s | v


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

ii | I n d o n e s i a G r o w t h D i a g n o s t i c s


I n d o n e s i a G r o w t h D i a g n o s t i c s | 1


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

2 | I n d o n e s i a G r o w t h D i a g n o s t i c s


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.



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.



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.



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.

Poor geography

Low human capital

Bad infrastructure

Micro risks: regulation,

institution i.e., legal system, corruption

Macro risks: financial, monetary,

fiscal instability

Information externalities:

“self-discovery”

Coordination externalities

Low domestic savings + bad international finance

Low competition

High risk High cost

Government failures

Market failures

Low appropriability

Low social returns

Bad local finance/ intermediation

Low return to economic activity High cost of finance

Problem: low levels of productive investment and

innovation

Figure 2. Diagnostic Tree



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.



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.



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.



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.



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.



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



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.



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.



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



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.



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.



References

Allen, E. R. (2016). Analysis of trends and challenges in the Indonesian labor market. Badan Perencanaan Pembangunan Nasional (Bappenas). (2017). Pertumbuhan Potensial Output Indonesia: Faktor

Penentu, Dampak Reformasi Struktural, dan Skenario ke Depan. Bappenas Study. Badan Perencanaan Pembangunan Nasional (Bappenas). (2018a). Paparan Visi Indonesia 2045. Unpublished. Badan Perencanaan Pembangunan Nasional (Bappenas). (2018b). Menutup Gap Infrastruktur Indonesia: Dampak

Pembangunan Infrastruktur terhadap Pertumbuhan Ekonomi dan Pemerataan di Indonesia. Kajian Bappenas. Calvo, S. (2006). Applying the growth diagnostics approach: the case of Bolivia. The World Bank, La Paz, Bolivia. Cirera, X., & Maloney, W. F. (2017). The innovation paradox: Developing-country capabilities and the unrealized

promise of technological catch-up. The World Bank. Enders, M. K. S. (2007). Egypt: Searching for Binding Constraintson Growth (No. 7-57). International Monetary Fund. Frasheri, E. et al. (2018). A Comparative View of Immigration Frameworks in Asia: Enhancing the Flow of Knowledge

through Migration. Center for International Development at Harvard University. Global Innovation Index. (2018). Energizing the World with Innovation. URL: https://www.globalinnovationindex.

org/gii-2017-report. Hausmann, R. (2008). In search of the chains that hold Brazil back. Hausmann, R. (2018). Sri Lanka Growth Diagnostic Executive Summary. Hausmann, R., & Klinger, B. (2008). Growth Diagnostic: Peru. Inter-American Development Bank. Hausmann, R., Espinoza, L., & Santos, M. (2016). Shifting gears: A growth Diagnostic of Panama. Hausmann, R., Klinger, B., & Wagner, R. (2008). Doing growth diagnostics in practice: a 'Mindbook' (No. 177). Center

for International Development at Harvard University. Hausmann, R., Rodrik, D. & Velasco, A. (2005). Growth Diagnostics. Ianchovichina, E., & Gooptu, S. (2007). Growth diagnostics for a resource-rich transition economy: the case of

Mongolia. The World Bank. IMF. (2014). Malaysia: Financial Sector Assessment Program. McKinsey Global Institute. (2013). Infrastructure Productivity: How to Save $1 Trillion a Year. Montenegro, C. E., & Patrinos, H. A. (2014). Comparable estimates of returns to schooling around the world. The World

Bank. Mundial, B. (2017). Doing Business 2018: Reforming to Create Jobs. The World Bank: http://www. doingbusiness.

org/~/media/WBG/DoingBusiness/Documents/Annual-Reports/English/DB2018-Full-Report. pdf. OECD. (2016). Skills Matter: Further Results from the Survey of Adult Skills. OECD Skills Studies. OECD Publishing. OECD. (2018). Economic Survey of Indonesia. The Organization for Economic Cooperation and Development:

http://www.oecd.org/economy/indonesia-economic-snapshot/. Oxford Economics, Global Infrastructure Hub. (2018). Global Infrastructure Outlook. Pritchett, L. (2016). The need for a pivot to learning: New data on adult skills from Indonesia. Samudra, R. R. (2018). Job Mismatch and Age-Earning Profile in Indonesia. Lembaga Demografi FEB UI. World Bank. (2015). Technical Note: Estimating Infrastructure Investment and Capital Stock in Indonesia. World Bank. (2018). World Development Report 2019: The Changing Nature of Work. World Economic Forum. (2018). The Global Competitiveness Report 2017-2018, Geneva: World Economic Forum.





Appendices

1. Figures on Growth Story Indicators

Figure 3. Indonesia Economic Growth

Sources: CEIC

Figure 4. GDP per Capita Trend

Sources: World Development Indicators

Figure 5. Indonesia Potential Growth

Sources: Bappenas (2018a)

Figure 6. Total Factor Productivity


0

2000

4000

6000

8000

10000

12000

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

GDP

per

Cap

ita (C

onst

ant 2

010

US$

) Indonesia China PhilippinesMalaysia Thailand Vietnam

0,3

0,4

0,5

0,6

0,7

1963 1969 1975 1981 1987 1993 1999 2005 2011 2017

TFP

leve

l at c

urre

nt P

PPs (

USA

=1)

Indonesia China MalaysiaPhilippines Thailand

-15

-10

-5

0

5

10

15

1968 1973 1978 1983 1988 1993 1998 2003 2008 2013 2018

Indo

nesi

a Ec

onom

ic G

row

th (%

YoY

)

Average 2000-2018

5.3%

Average 1980-1996

6.4%

Average 1968-1979

7.5%

commodity boom

manufacturing growth &

liberalization

low base growth oil

boom oil bust

Asia Financial Crisis

6,03

5,56

5,014,88

5,03 5,10 5,174,95 4,94 4,91 4,90 4,89 4,87

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Indo

nesi

a Po

tent

ial G

row

th (%

)

Bappenas projection of potential growth (“baseline" scenario)



Figure 7. Share of Manufacturing & GDP per Capita


Figure 8. High-Technology Exports


Figure 9. Accumulation of Fixed Capital Investment of Machinery and Equipment, 2007-2016

Sources: Indonesia – Prospera Infradashboard & McKinsey

Figure 10. Infrastructure Capital Stock

Sources: Indonesia – Prospera Infradashboard & McKinsey

Figure 11. FDI Net Inflows vs. GDP per Capita, 2017


Figure 12. Indonesia Incremental Capital-Output Ratio

Sources: CEIC

1991

19921993

19941995

1996

1997

1998

1999

2000

20012002

2003200420052006

20072008

2009

201020112012201320142015

20162017

1990

19911992199319941995

19961997

1998

19992000

200120022003

2004

20052006

2007

20082009201020112012201320142015

20162017

1990

19911992

1993199419951996

19971998

199920002001

2002

2003200420052006

20072008

2009

2010

2011

201220132014201520162017

20

23

26

29

32

8,3 8,8 9,3 9,8 10,3

Man

ufac

turin

g, v

alue

add

ed (%

of G

DP)

GDP per capita, PPP (constant 2011 international $), logIndonesia Malaysia Thailand

0

20

40

60

80

1990 1993 1996 1999 2002 2005 2008 2011 2014 2017

High

-Tec

hnol

ogy

Expo

rts

(% m

anuf

actu

red

prod

ucts

)

Indonesia China MalaysiaPhilippines Thailand Vietnam

20,1

57,9

49,1

64,4 62,9

40,9

Indonesia Malaysia Mexico Philippines Thailand South AfricaAccu

mul

atio

n of

Fix

ed C

apita

l Inv

estm

ent

of M

achi

nery

and

Equ

ipm

ent (

% o

f GDP

)

-

40

80

120

160

200

Indo

nesia

(201

7)

Braz

il

Uni

ted

King

dom

Cana

da

Indi

a

Uni

ted

Stat

es

Germ

any

Spai

n

Chin

a

Pola

nd

Italy

Sout

h Af

rica

Japa

n

Infr

astr

uctu

re C

apita

l Sto

ck (%

of G

DP)

Average excluding Japan (70%)

AFG

AGO

ALB

AREARGARM

ATG

AUSAUT

AZE

BDI

BEL

BENBFA

BGD

BGR

BHR

BHS

BIH BLRBLZBOL

BRA

BRB

BRN

BTN

BWACAF CAN

CHE

CHLCHNCIVCMRCOD

COG

COL

COM

CPVCRI CZE

DEU

DMADNK

DOM

DZAECU

EGY

ESP

ESTETH FINFJI

FRAGBR

GEO

GHAGIN

GMBGNBGNQ GRC

GRD

GTM

GUY

HKG

HNDHRVHTI

HUN

IDNIND

IRLIRN

IRQ

ISR

ITA

JAMJOR

JPNKAZ

KEN

KGZKIR

KNA

KORKWT

LAO

LBN

LBR LCA

LKALSO LTU

LUX

LVAMARMDA

MDG

MDV

MEX

MHL

MKDMLI

MLT

MMR

MNEMNG

MOZ

MRT

MUSMWI

MYSNAMNER

NGA

NIC

NLD

NORNPL NZL

OMN

PAK

PAN

PERPHL

PLW

PNG

POLPRT

PRYPSE QATROURUS

RWA

SAUSDNSEN

SGP

SLB

SLE

SLV

SRBSTP

SUR SVK

SVN

SWE

SWZ

SYC

TCDTGOTHATJK

TKM

TLSTON TTO

TUN TURTUV

TZAUGA UKR

URY

USAUZB

VCT

VNM

VUTWSM

YEMZAF

ZMB

ZWE

-10

0

10

20

30

40

6 8 10 12

FDI,

net i

nflo

ws (

% o

f GDP

)

GDP per capita, PPP (constant 2011 international $), log

0

2

4

6

8

10

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

2017

Incr

emen

tal C

apita

l-Out

put R

atio



2. Figures on Finance Indicators

Figure 13. Gross Domestic Savings vs. GDP per Capita, 2017

Sources: CEIC

Figure 14. FDI Net Inflows


Figure 15. Real Lending Rate vs. GDP per Capita, Average 2015-2017


Figure 16. Nominal Lending Rate, vs. GDP per Capita, Average 2015-2017


AFG

AGO

ALB

ARE

ARG

ARM

AUSAUT

AZE

BEL

BEN

BFA

BGD BGRBHS

BIH

BLR

BLZBOL

BRA

BRB

BRN

BTNBWA

CAF

CAN

CHE

CHL

CHN

CIVCMRCOD

COG

COL

COM

CPV CRI CYP

CZE

DEUDNK

DOM

DZA

ECU

EGY

ESPEST

ETH FINFRA

GAB

GBRGEOGHA

GIN

GNB

GNQ

GRC

GTM

GUY

HKG

HND

HRV

HTI

HUNIDNIND

IRL

IRN

IRQ

ISL

ITA

JAM

JOR

KAZ

KENKGZ

KOR

KWT

LAO

LBN

LKA

LTU

LUX

LVAMAR

MDG

MEXMKD

MLI

MLT

MMR

MNE

MNG

MOZ

MRT

MUSMWI

MYS

NAM

NER NGANIC

NLD NOR

NPL

OMN

PAK

PAN

PER

PHL

PLW

POL

PRT

PRY

PSE

QAT

ROU

RUS

RWA

SAU

SDN

SEN

SGP

SLESLV

SRB

SVKSVN SWE

SWZ

SYC

TCD

TGO

THA

TJK

TLS

TUN

TURTZA

UGAUKR

URYUZB

VCT

VNM

ZAF

ZWE

-20

0

20

40

60

80

6 8 10 12

Gro

ss d

omes

tic sa

ving

s (%

of G

DP)


-5

0

5

10

15

1990 1993 1996 1999 2002 2005 2008 2011 2014 2017Fore

ign

dire

ct in

vest

men

t, ne

t inf

low

s (%

of G

DP)

Indonesia China MalaysiaPhilippines Thailand Vietnam

AFG

AGO

ALB

ARG

ARM

ATG

AUS

AZE

BDIBENBFA BGD BGR

BHR

BHS

BIH BLR

BLZBOL

BRA

BRB

BRN

BTN

BWA CANCHE

CHLCHN

CIVCOD

COLCOM CPV CRI

CZE

DJI

DMA

DOM

DZA

EGYFJI

FSM

GEO

GMB

GNB

GRDGTMGUY

HKG

HND

HTIHUN

IDNIND

IRN

IRQ

ISLISRITA

JAMJOR

JPN

KEN

KGZ

KNA

KOR

KWT

LBN

LBR

LCALKALSO

MACMDA

MDV

MEX

MKDMLI

MMR

MNE

MNG

MOZ

MRT

MUS

MYSNAMNER

NGANIC

NZL

OMN

PAK PAN

PER

PHLPNG

PRY

PSE

QAT

ROU

RUS

RWA

SEN SGP

SLBSLE

SMRSRB

STP

SURSWZ

SYC

TGO THA

TJK

TLS

TON

TTOTZA

UGA

UKR

URY

USA

VCTVNM

VUT

WSMXKX

ZAFZMBZWE

-15

0

15

30

45

6 8 10 12Real

lend

ing

inte

rest

rate

(%),

aver

age

2015

-201

7

GDP per capita, PPP (constant 2011 international $), log, average 2015-2017

AFGAGO

ALB

ARG

ARM

ATG

AUS

AZEBDI

BENBFA

BGD

BGRBHRBHSBIH

BLR

BLZBOL BRB

BRN

BTN

BWA

CAN CHE

CHLCHN

CIV

COD

COL

COM CPV

CRI

CZE

DJI

DMA

DOM

DZA

EGY

FJI

FSM

GEO

GMB

GNB

GRD

GTMGUY

HKG

HND

HTI

HUN

IDNIND

IRN

IRQ

ISL

ISRITA

JAM

JOR

JPN

KEN

KGZ

KNA

KORKWT

LBN

LBR

LCALKA

LSO

MAC

MDA

MDV

MEXMKD

MLI

MMR

MNE

MNGMOZ

MRT

MUS

MYS

NAM

NER

NGA

NIC

NZLOMN

PAKPAN

PER

PHL

PNG

PRY

PSE

QATROU

RUS

RWA

SEN SGP

SLB

SLE

SMR

SRB

SSD

STP

SUR

SWZ

SYC

TGO THA

TJK

TLS

TON TTO

TZA

UGA

UKR

URY

USA

VCT

VEN

VNM

VUT

WSMXKX

ZAF

ZMB

ZWE

0

10

20

30

40

6 8 10 12

Nom

inal

ledi

ng in

tere

st ra

te (%

), av

erag

e 20

15-2

017

GDP per capita, PPP (constant 2011 international $), log, average 2015-2017



Figure 17. Real Lending Rate and Investment Rate


Figure 18. Biggest Obstacles in Doing Business in Indonesia

Sources: World Bank Enterprise Surveys

Figure 19. Indonesia Investment Composition

Sources: Prospera’s Calculation

Figure 20. Net Interest Margin

Sources: Prospera’s Calculation

Figure 21. Financial System Interlinkages, Indonesia

Sources: Bappenas and Prosperas’ calculation

Figure 22. Financial System Interlinkages, Malaysia

Sources: International Monetary Fund (2014)

2008

2009

2010

2011

2012

2013 2014

20152016

2017

-5

-1

3

7

11

27 30 33 36

Real

inte

rest

rate

(%)

Gross capital formation (% of GDP)0 10 20 30 40 50

Access to FinanceAccess to Land

Business PermitsCorruption

Business CourtsCrime and Security

Customs and Trade RegulationElectricity

Skilled LaborLabor Regulation

Political InstabilityInformal Practices

Tax AdministrationTax Rate

Transportation

%

2015

2009

-

5

10

15

20

25

30

35

2015

2016

2017

2018

p

2019

p

2020

p

2021

p

2022

p

2023

p

2024

p

Inve

stm

ent C

ompo

sitio

n (%

)

Banks

Capital Markets

Foreign

Financed not throughintermediation 0

1

2

3

4

5

6

2012 2013 2014 2015 2016 2017 2018

Net

Inte

rest

Mar

gin

(%)

Indonesia Philippines Egypt



3. Figures on Human Capital Indicators

Figure 23. Labour Force Distribution by Education, 2016

Sources: International Labour Organization

Figure 24. Labour Force with Tertiary Education


Figure 25. Agriculture Employment vs. GDP per Capita, 2017


Figure 26. Informal Employment vs. GDP per Capita, 2017


14

3

1

21

13

45

30

67

40

55

30

44

5

22

20

12

24

26

17

12

0% 20% 40% 60% 80% 100%

Indonesia

Malaysia

Philippines

Thailand

Vietnam

Less than Primary Primary Secondary Tertiary

8,1% 8,5% 9,5% 10,0% 10,4% 11,4% 12,3% 12,7%

5%

10%

15%

20%

25%

30%

35%

2010 2011 2012 2013 2014 2015 2016 2017

Labo

ur F

orce

–Te

rtia

ry E

duca

tion

Indonesia Malaysia PhilippinesThailand Vietnam

AFG

AGO

ALB

AREARG

ARM

AUSAUT

AZE

BDI

BEL

BEN

BFA

BGD

BGRBHR

BHS

BIH

BLRBLZ

BOL

BRA

BRBBRN

BTN

BWA

CAF

CAN CHECHL

CHN

CIV

CMR

COD

COG

COL

COM

CPV

CRI

CYPCZE DEUDNK

DOMDZA

ECUEGY

ESPEST

ETH

FIN

FJI

FRA

GAB

GBR

GEOGHA

GIN

GMB

GNB

GNQ

GTM

GUY

HKG

HND

HRV

HTI

HUN

IDN

IND

IRL

IRN

ISLISR

ITA

JAM

JOR JPN

KAZ

KEN

KGZ

KOR KWT

LAO

LBN

LBR

LBYLCA

LKA

LSOLTU

LUX

LVA

MARMDA

MDG

MDV

MEX

MLI

MLT

MMR

MNE

MNG

MOZMRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLD NOR

NPL

NZLOMN

PAK

PAN

PERPHL

PNG

PRT

PRY

PSE

QAT

ROU

RUS

RWA

SAU

SDNSEN

SGP

SLB

SLE

SLV

SSD

STP

SUR SVKSVNSWE

SWZ

TCD

TGOTHA

TJK

TKM

TLS

TON

TTO

TUN

TUR

TZAUGA

UKR

URY USA

UZB

VCTVEN

VNM

VUT

WSM

YEM

ZAF

ZMB

ZWE

-20

0

20

40

60

80

100

6 8 10 12

Agric

ultu

re E

mpl

oym

ent (

% o

f tot

al

empl

oym

ent)


ARM

BOL

CHL

CIV

COL

CRI

DOM

ECU

EGY

GTMHND

IDN

MDV

MNG

MUS

NAM

PAN

PER

PRY

PSE

SLV

SWZ

URY

VNM

ZAF

20

40

60

80

100

7,5 8,5 9,5 10,5

Info

rmal

Em

ploy

men

t (%

of t

otal

non

-ag

ricul

tura

l em

ploy

men

t)




Figure 27. Returns to Secondary Education

Sources: Montenegro and Patrinos (2014)

Figure 28. Unemployment Rate by Education


Figure 29. Returns to Tertiary Education

Sources: Montenegro and Patrinos (2014)

Figure 30. Skills of Working Age Population

Sources: Pritchett (2016) Note: Based on OECD Programme for International Assessment of Adult Competencies (PIAAC) 2016

Figure 31. Skills Mismatch in Indonesia

Sources: Samudra (2018) Note: Using Sakernas August round data

Figure 32. Net Wage Effects of being Skills Mismatch

Sources: Samudra (2018) Note: Using Sakernas August round data

3

8

13

18

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Retu

rns t

o ed

ucat

ion,

seco

ndar

y (%

)

Indonesia Thailand MalaysiaPhilippines India Pakistan

0

2

4

6

8

10

Indonesia(2017)

Malaysia(2016)

Philippines(2016)

Thailand(2016)

Vietnam(2017)

Une

mpl

oym

ent R

ate

(%)

Less than primary Primary Secondary Tertiary Total

8

13

18

23

28

33

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Retu

rns t

o ed

ucat

ion,

tert

iary

Indonesia Thailand MalaysiaPhilippines India Pakistan

169206

234 234264

292

Jakarta -Less than

Upper Secondary

Jakarta -Upper

Secondary

Jakarta -Tertiary

Denmark -Less than

Upper Secondary

Denmark -Upper

Secondary

Denmark -Tertiary

PIAA

C Li

tera

cy P

rofic

ienc

y

0 10 20 30 40 50

Underqualified

Well- Matched

Overqualified

Mismatch

Somewhat Match

Match

Vert

ical

Mis

mat

chHo

rizon

tal

Mis

mat

ch

Skills Mismatch (%)

2008 2015

-20-10

0102030

2008 2015 2008 2015 2008 2015

Base Controllingfor

Training

Interactingwith

Training

Net

Effe

cts o

n W

age

(%)

Vertical Mismatch

Underqualified Overqualified

2008 2015 2008 2015 2008 2015

Base Controllingfor Training

Interactingwith

Training

Horizontal Mismatch

Somewhat Match Mismatch



Figure 33. Mean Years of Schooling

Sources: Barro-Lee Dataset

Figure 34. Mean Years of Schooling vs. GDP per Capita, 2017

Sources: Barro-Lee Dataset & World Development Indicators

Figure 35. Gross Enrolment Ratio

Sources: UNDP (2018)

Figure 36. School Enrolment, Tertiary


Figure 37. Returns to Education vs. GDP per Capita, 2010

Sources: Montenegro and Patrinos (2014) & World Development Indicators

Figure 38. Returns to Education, Indonesia

Sources: Bappenas’ Calculation Note: Using Sakernas August round

3

5

7

9

11

1990 1995 2000 2005 2010 2015

Mea

n ye

ars o

f sch

oolin

g(P

opul

atio

n ag

ed 2

5 an

d ov

er)

Indonesia Thailand MalaysiaPhilippines Vietnam China

AFG

AGO

ALB

ARE

ARG

ARM

ATG

AUS

AUT

AZE

BDI

BEL

BEN

BFA

BGD

BGR

BHR

BHS

BIH

BLR

BLZ

BOL

BRA

BRB

BRN

BTN

BWA

CAF

CAN CHE

CHL

CHN

CIV

CMRCOD

COG

COL

COM

CPV

CRI

CYP

CZE

DEU

DMA

DNK

DZA

ECU

EGY

ESP

EST

ETH

FIN

FJI

FRA

FSMGAB

GBRGEO

GHA

GIN

GMBGNB

GNQ

GRC

GRD

GTM

GUY

HKG

HND

HRV

HTI

HUN

IDNIND

IRL

IRN

IRQ

ISL

ISR

ITAJAM

JOR

JPN

KAZ

KEN

KGZ

KIRKNA

KOR

KWT

LAO

LBN

LBR

LBY

LCA

LKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG MDV

MEX

MHL

MKD

MLI

MLT

MMR

MNE

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGANIC

NLDNOR

NPL

NZL

OMN

PAK

PAN

PERPHL

PLW

PNG

POL

PRT

PRY

PSE

QAT

ROU

RUS

RWA

SAU

SDN

SEN

SGP

SLB

SLE

SLV

SRB

SSD

STP

SUR

SVKSVN SWE

SWZ

SYC

TCD

TGO

THA

TJK

TKM

TLS

TONTTO

TUN

TUR

TZAUGA

UKR

URY

USA

UZB

VCT

VEN

VNM

VUT

WSM

YEM

ZAF

ZMB

ZWE

0

5

10

15

6 8 10 12

Mea

n ye

ars o

f sch

oolin

g(P

opul

atio

n ag

ed 2

5 an

d ov

er)


0

20

40

60

80

100

120

Indonesia(2017)

Malaysia(2017)

Philippines(2017)

Thailand(2016)

Vietnam(2016)

China(2017)

Gro

ss E

nrol

men

t Rat

io (%

)

Primary Secondary Tertiary0

10

20

30

40

50

60

1990 1993 1996 1999 2002 2005 2008 2011 2014 2017

Scho

ol e

nrol

lmen

t, te

rtia

ry (%

gro

ss)

Indonesia Thailand MalaysiaVietnam China Philippines

ARG

AUS

AUT

BEL

BGR

COL

CZE

DEU

DNK

DOM

ECUESP

ESTFIN

FRAGBR

GEOGRC

HND

HUN

IDN

ISLITA

KORLTU

LUX

LVAMDG

MEX MLTMNG

MUS

MYS

NLD

NORNPL

PAKPAN

PERPHLPNG

POLPRT

PRY

ROU

RWA

STP SVK

SVN

SWE

TUR

TUV

UGA

URY

USA

ZAF

ZMB

3

8

13

18

23

28

7 8 9 10 11 12

Min

ceria

n re

turn

s to

educ

atio

n


7,9

4

5

6

7

8

9

10

11

12

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

Retu

rns t

o Ed

ucat

ion

(%)



Figure 39. Trends in International Mathematics and Science Study (TIMSS), 2015

Sources: TIMSS, Martin et al (2015)

Figure 40. Programme for International Student Assessment (PISA), 2015

Sources: PISA, OECD (2016)

Figure 41. PISA Score Projection, Indonesia

Sources: World Development Report 2018, World Bank

Figure 42. TIMSS Score Projection, Indonesia

Sources: The SMERU Research Institute (2018)

Figure 43. Indicators Related to Quality of University

Global Innovation Index 2018

Tertiary inbound mobility

QS university ranking, average

score top 3 % Rank Index Rank

Singapore 19.2 5 70.2 13 Malaysia 9.3 21 49.3 25 Japan 3.4 58 80.4 8 South Korea 1.7 77 77.1 9 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 Philippines 0.1 104 24.4 48

Sources: Global Innovation Index (2018)

Figure 44. Indonesia University Ranking Classification

Sources: Ministry of Research, Technology & Higher Education (2018)

0 100 200 300 400 500 600 700

Singapore

Hong Kong

South Korea

Chinese Taipei

Japan

Average Score

Turkey

Chile

Indonesia

Mathematics Science0 100 200 300 400 500 600

Hong Kong

Singapore

Japan

South Korea

China

Vietnam

OECD Average

Thailand

Indonesia

Mathematics Reading Science

300

350

400

450

500

550

2000 2015 2030 2045 2060 2075 2090

PISA

Sco

re

Mathematics ReadingMathematics (Projection) Reading (Projection)Mathemtics (OECD Average) Reading (OECD Average)

300

350

400

450

500

1998 2000 2002 2004 2006 2008 2010 2012

TIM

SS S

core

Mathematics Score (Non-Islamic Schools)Mathematics ScoreInternational MeanLinear (Mathematics Score)

Cluster 11%

Cluster 23%

Cluster 315%

Cluster 473%

Cluster 58%



Figure 45. The Global Innovation Index 2018

Sources: Global Innovation Index (2018)

Figure 46. The Human Capital Index 2018

Sources: World Bank (2018)

Figure 47. Life Expectancy at Birth


Figure 48. Infant Mortality Rate


Figure 49. Maternal Mortality Ratio


Figure 50. Stunting Prevalence vs. GDP per Capita, 2016

Sources: UNICEF, WHO & World Development Indicators

INSTITUTIONS

HUMAN CAPITAL &RESEARCH

INFRASTRUCTURE

MARKETSOPHISTICATION

BUSINESSSOPHISTICATION

KNOWLEDGE &TECHNOLOGY OUTPUTS

CREATIVE OUTPUTS

INDONESIA MALAYSIA THAILAND

PHILIPPINES VIETNAM CHINA 0 0,2 0,4 0,6 0,8 1

Singapore

South Korea

Japan

China

Vietnam

Malaysia

Thailand

Philippines

Indonesia

India

Pakistan

12

346

4855

6584

8711

513

4

Coun

try

(Glo

bal R

ank

in 2

018)

59

64

69

74

79

1980 1985 1990 1995 2000 2005 2010 2015Life

exp

ecta

ncy

at b

irth,

tota

l (ye

ars)


0

20

40

60

80

1990 1993 1996 1999 2002 2005 2008 2011 2014 2017

Mor

talit

y ra

te, i

nfan

t (p

er 1

,000

live

birt

hs)


0

100

200

300

400

500

1990 1995 2000 2005 2010 2015

Mat

erna

l mor

talit

y ra

tio (m

odel

ed

estim

ate,

per

100

,000

live

birt

hs)


AFG

AGO

ALB

ARM

AUS

AZE

BDI

BEN

BFA

BGD

BIH

BLZBOL

BRABRB

BRN

BTNBWA

CAF

CHL

CHN

CIV

CMR

COD

COG

COL

COM

CRIDOM

DZA

ECUEGY

ETH

GAB

GEO

GHA

GIN

GMB

GNBGNQ

GTM

GUY

HNDHTI

IDNIND

IRQ

JAMJOR JPN

KAZ

KEN

KGZ

KOR

LAO

LBR

LCA

LKA

LSO

MAR

MDA

MDG

MDV

MEX

MKD

MLIMMR

MNEMNG

MOZ

MRT

MWI

MYSNAM

NERNGA

NIC

NPL

NRU

PAK

PAN

PER

PHL

PRYPSE

RWASDN

SEN

SLB

SLE

SLV

SRB

STP

SUR

SWZ

SYC

TCD

TGO

THA

TJK

TKM

TON

TTOTUN TURTUV

TZA

UGA

URY

USA

UZB

VNM

VUT

WSM

YEM

ZAF

ZMB

ZWE

0

20

40

60

6 8 10 12

Child

mal

nutr

ition

, stu

ntin

g (m

oder

ate

or se

vere

) (%

und

er a

ge 5

), 20

10-2

016




Figure 51. Immunization Rate, 2017


Figure 52. Cause of Death by Communicable Diseases and Maternal, Prenatal and Nutrition Conditions


Figure 53. Cause of Death by Non-Communicable Diseases


Figure 54. Mortality from CVD, Cancer, Diabetes or CRD


Figure 55. Health Facilities per 10,000 Population

Sources: World Health Organization & World Development Indicators Note: The latest data available in 2010-2017

Figure 56. Trend in Male Smoking Prevalence


0 20 40 60 80 100

Indonesia

Malaysia

Thailand

Vietnam

Philippines

China

DPT (% of children ages 12-23 months)HepB3 (% of one-year-old children)Measles (% of children ages 12-23 months)

0

10

20

30

40

2000 2002 2004 2006 2008 2010 2012 2014 2016

Caus

e of

dea

th, b

y co

mm

unic

able

di

seas

es &

mat

erna

l, pr

enat

al &

nu

triti

on c

ondi

tions

(% o

f tot

al)


55

60

65

70

75

80

85

90

2000 2002 2004 2006 2008 2010 2012 2014 2016

Caus

e of

dea

th, b

y no

n-co

mm

unic

able

dis

ease

s (%

of t

otal

)


AFG

AGO ALB AREARG

ARM ATG

AUS

AUT

AZEBDI

BEL

BEN

BFA BGD

BGR

BHS

BIH

BLR

BLZ

BOLBRABRB BRN

BTN

BWA

CAF

CANCHE

CHL

CHN

CIV

CMR

COD

COGCOL

COM

CPV

CRI CYP

CZE

DEUDNK

DOM

DZAECU

EGY

ESP

ESTETH

FIN

FJI

FRA

FSM

GAB

GBR

GEO

GHA

GIN

GMBGNB

GNQ

GRC

GRD

GTM

GUY

HND

HRV

HTI

HUN

IDN

IND

IRL

IRQ

ISLISRITA

JAM

JOR

JPN

KAZ

KEN

KGZ

KIR

KOR

LAO

LBNLBRLCA

LKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG

MDV

MEX

MKD

MLI

MLT

MMR

MNE

MNG

MOZ MRT

MUS

MWIMYS

NAMNER

NGA

NIC

NLD

NOR

NPL

NZL

PAK

PANPER

PHL

POL

PRT

PRY

QAT

ROU

RUS

RWA

SAU

SDN

SEN

SGP

SLB

SLE

SLV

SRBSTP

SUR

SVK

SVN

SWE

SWZ

SYC

TCDTGO

THA

TJK

TKM

TON

TTO

TUN TUR

TZA

UGA

UKR

URY

USA

UZBVCT

VNM

VUT

WSM

YEM

ZAF

ZMB

ZWE

5

10

15

20

25

30

35

6 8 10 12

Mor

talit

y fr

om C

VD, c

ance

r, di

abet

es, o

r CR

D be

twee

n ex

act a

ges 3

0 an

d 70

(%)


15,1

8,1

17,912,8

3,78,2

19 21

42

10 12

26

Malaysia Thailand China Philippines Indonesia Vietnam

Medical Doctors (per 10,000 people) Hospital Beds (per 10,000 people)35

45

55

65

75

85

2000 2004 2008 2012 2016

Smok

ing

prev

alen

ce, m

ales

(%

of a

dults

)




Figure 57. Male Smoking Prevalence vs. GDP per capita, 2016


Figure 58. Most Recent Survey of Youth Tobacco Use (Age 13-15)

Sources: World Health Organization

4. Figures on Infrastructure Indicators

Figure 59. Road Connectivity Index, 2017

Sources: WEF Global Competitiveness Index 2018 & World Development Indicators

Figure 60. Road Density, 2014

Sources: FAO Land Portal and World Development Indicators

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BEL

BEN

BFA

BGD BGR

BHS

BIH BLR

BRABRB

BRNBWA

CAN

CHE

CHL

CHNCOG

COL

COM

CPV CRI

CYP

CZE

DEU

DNKDOM

DZA

ECU

EGY

ESP

EST

ETH

FIN

FJI FRA

GBR

GEO

GHA

GMB

GRC

HRV

HTI

HUN

IDN

IND

IRL

ISL

ISR

ITAJAM

JPN

KAZ

KEN

KGZ

KIR

KOR

LAO

LBN

LBR

LKA

LSO

LTU

LUX

LVAMAR

MDA

MDV

MEXMLI

MLT

MMR

MNEMNG

MOZ

MUS

MWI

MYS

NAM

NER

NGA

NLD

NOR

NPL NRU

NZL

PAK

PAN

PHL

PLW

POLPRT

PRY

QAT

ROU

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRBSUR

SVK

SVN

SWESWZ

SYC

TGO

THA

TON

TUN

TUR

TZA

UGA

UKR

URY

USAUZB

VNM

VUTWSM

YEMZAF

ZMB

ZWE

0

20

40

60

80

6 7 8 9 10 11 12

Smok

ing

prev

alen

ce, m

ales

(%

of a

dults

)


0 5 10 15 20 25 30

Japan (2014)

Vietnam (2014)

Singapore (2012)

South Korea (2016)

China (2014)

Thailand (2015)

Indonesia (2015)

Malaysia (2016)

Prevalence (%) MALE FEMALE

AGO

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN

BFA

BGD

BGR

BIH

BOL

BRA

BRN

BWACAN

CHE

CHLCHN

CIV

CMRCOD COL

CPV

CRI

CYP

CZE

DEU

DNK

DOM

DZA

ECU

EGY

ESP

EST

ETH

FIN

FRA

GBR

GEO

GHA

GIN

GMB GRC

GTM

HND

HRV

HTI

HUN

IDN

IND

IRLIRN

ISL

ISRITA

JAM

JOR JPNKAZ

KEN

KGZ

KWT

LAO

LBNLBR

LKALSO

LTULVA

MAR

MDA

MEX

MKD

MLI

MNE

MNG

MOZ

MRT

MWI

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRT

PRY

QAT

ROU

RUS

RWA

SAU

SENSLE SLV

SRB SVK

SVN

SWE

SWZ

TCD

THA

TJKTTO

TUN

TUR

TZA

UGA

UKR URY

USA

VEN

VNM

YEM

ZAF

ZMB

ZWE

0

20

40

60

80

100

120

6 8 10 12

Road

conn

ectiv

ity in

dex,

0-10

0 (b

est)


ARG

ARM

AUT

AZE

BEL

BFA

BGD

BGR

BIHBLR

BRA CAN

CHE

CHLCHNCIVCMRCOD

CZE

DEU

DNK

DZAEGY

ESP

ESTFIN

FRA

GAB

GBR

GEOGRC

HRV

HUN

IDN

IND

IRL

IRNIRQ

ISRITA

JOR

JPN

KAZKGZ

KOR

LTU

LUX

LVA

MAR

MDA

MEX

MKD

MNGMOZMRT

MYS

NLD

NORPAK

PER

POL

PRT

ROU

R…SAUSDN

SRB

SVK

SVN

SWESWZ

THATJK TKM

TUN TUR

UKR

USA

UZBVNM

ZAF

0

3

6

9

12

6 8 10 12

Road

den

sity

(km

/100

km

2 )




Figure 61. Quality of Roads, 2017


Figure 62. Quality of Port Infrastructure, 2017


Figure 63. Airports per Million Square Kilometre, 2013

Sources: CIA World Factbook & World Development Indicators

Figure 64. Quality of Air Transport Infrastructure, 2017

Sources: CIA World Factbook & World Development Indicators

Figure 65. Railroad Density, 2017


Figure 66. Efficiency of Train Services, 2017


ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BENBGD

BGR

BHR

BIHBRA

BRN

BTN

BWA

CAN

CHE

CHL

CHN

CMR

COD

COL

CPV

CRI

CYP

CZE

DEUDNK

DOM

DZA

ECU

EGY

ESP

EST

ETH

FIN

FRA

GBR

GEOGHA

GIN

GMB

GRC

GTM

HKG

HND

HRV

HTI

HUNIDNIND

IRL

IRN

ISL

ISR

ITA

JAM

JOR

JPN

KAZ

KEN

KGZ

KOR

KWT

LAO

LBN

LBR

LKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG

MEX

MLI

MLT

MNE

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PERPHL

POL

PRT

PRY

QAT

ROURUS

RWASAU

SEN

SGP

SLE

SLV

SRB

SVK

SVN

SWE

SWZ

SYC

TCD

THATJK TTO

TUN

TUR

TZAUGA

UKR

URY

USA

VEN

VNM

YEM

ZAF

ZMB

ZWE

2

3

4

5

6

7

6 8 10 12

Qua

lity

of ro

ads,

1-7

(bes

t)


ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN

BGD

BGR

BHR

BIH

BRA

BRN

BTN

BWA

CAN

CHE

CHL

CHN

CMR

COD

COLCPV

CRI

CYP

CZE

DEUDNK

DOM

DZA

ECUEGY

ESPEST

ETH

FIN

FRA

GBR

GEO

GHAGIN

GMB GRC

GTM

HKG

HNDHRV

HTI

HUN

IDN

IND

IRL

IRN

ISL

ISR

ITA

JAM

JOR

JPN

KAZ

KEN

KGZ

KOR

KWT

LAO

LBN

LBR

LKA

LSO

LTULUX

LVAMAR

MDA

MDG

MEX

MLI

MLT

MNE

MNG

MOZ

MRT

MUS

MWI

MYSNAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRT

PRY

QAT

ROU

RUS

RWA

SAU

SEN

SGP

SLE SLV

SRB SVK

SVN

SWE

SWZ

SYC

TCD

THA

TJK

TTO

TUN

TUR

TZA

UGA

UKR

URY

USA

VEN

VNM

YEM

ZAF

ZMB

ZWE

1

3

5

7

6 8 10 12

Qua

lity

of p

ort i

nfra

stru

ctur

e,

1-7

(bes

t)


AFG AGO ALBAREARGARM

ATG

AUS

AUTAZEBDI

BEL

BENBFA BGD

BGR

BHR

BHS

BIH BLR

BLZ

BOLBRA

BRB

BRNBTN BWACAF CAN

CHE

CHL

CHNCIVCMR

COL

COMCPV

CRI

CYPCZE DEU

DJI

DMA

DNK

DOM

DZA

ECU

EGYESPEST

ETHFIN

FJI

FRA

FSM

GAB

GBR

GEOGHAGIN GMBGNB GNQ

GRC

GRD

GTM

GUY

HKG

HNDHRV

HTI HUNIDNIND

IRLIRNIRQ

ISL

ISR

ITA

JAM

JORJPN

KAZKEN

KGZ

KNA

KOR

KWTLAO

LBN

LBR

LCA

L…

LSO LTULUXLVA

MARMDAMDG

MEX

MKDMLI

MLT

MMRMNEMNGMOZ MRT

MUS

MWI MYSNAMNER NGA

NICNLD

NORNPL NZL OMNPAK

PAN

PER

PHL

PLW

PNG

POL

PRI

PRT

PRY

QATROU RUS

RWA SAUSDNSEN

SLB

SLE

SLV

SRB

STP

SURSVKSVN

SWESWZ

TCDTGO THATJK TKM

TON

TTO

TUN TURTZAUGA UKRURY

USA

UZBVEN

VNM

VUT

WSM

YEMZAFZWE

0

2000

4000

6000

8000

10000

6 8 10 12

Airp

orts

per

mill

ion

km2


ALB

ARE

ARG

ARM

AUSAUT

AZE

BDI

BEL

BEN BGD

BGR

BHR

BIH

BRA

BRN

BTN BWA

CANCHE

CHLCHN

CMRCOD

COLCPV

CRI

CYPCZE

DEUDNK

DOM

DZA

ECUEGY

ESP

EST

ETH

FIN

FRAGBR

GEO

GHA

GIN

GMBGRC

GTM

HKG

HNDHRV

HTI

HUN

IDNIND

IRL

IRN

ISLISR

ITA

JAMJOR

JPN

KAZ

KEN

KGZ

KOR

KWT

LAO LBN

LBR

LKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG

MEX

MLI

MLT

MNE

MNGMOZ

MRT

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRT

PRY

QAT

ROU

RUSRWASAU

SEN

SGP

SLE

SLVSRB

SVK

SVN

SWE

SWZ

SYC

TCD

THA

TJKTTO

TUN

TUR

TZAUGA

UKR

URY

USA

VEN

VNM

YEM

ZAF

ZMB

ZWE

0

2

4

6

8

6 8 10 12

Qua

lity

of a

ir tr

ansp

ort i

nfra

stru

ctur

e,

1-7

(bes

t)


ALB

ARG

ARM

AUS

AUT

AZE

BEL

BEN

BFA

BGD

BGR

BIH

BOLBRA

BWA

CAN

CHE

CHLCHN

CIVCMRCOD COL

CZE DEUDNK

DZA

EGY

ESP

EST

ETH

FIN

FRAGBR

GEO

GHA

GRC

HRVHUN

IDN

IND

IRL

IRN

ISRITA

JOR

JPN

KAZKEN

KGZ

KOR

LKA

LTU

LUX

LVA

MAR

MDA

MEX

MKD

MLI

MNE

MNG

MOZ

MRT

MWI MYS

NAMNGA

NLD

NOR

NZL

PAK

PANPERPHL

POL

PRT

ROU

RUS

SAU

SEN

SRB SVKSVN

SWE

SWZ

THA

TJK

TUNTUR

TZAUGA

UKR

URY

USA

VEN

VNM

ZAFZMB

ZWE

0

20

40

60

80

100

6 8 10 12

Railr

oad

dens

ity (k

m/1

000

km2 )


ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN

BGDBGR

BHR

BIH

BRA

BRN

BWA

CAN

CHE

CHL

CHN

CMR

CODCOL

CPV

CRI

CYP

CZE

DEU

DNK

DOM

DZA

ECU

EGY

ESP

EST

ETH

FIN

FRA

GBR

GEO

GHAGINGMB

GRC

GTM

HKG

HND

HRV

HUNIDNIND

IRL

IRN

ISLISRITA

JAM

JOR

JPN

KAZ

KEN

KGZ

KOR

KWT

LAO LBNLBR

LKA

LSO

LTU

LUX

LVA

MAR

MDA MEX

MLI

MLT

MNE

MNG

MOZMRT

MUS

MWI

MYS

NAM

NGANIC

NLD

NOR

NPL

NZLOMNPAK

PAN

PER

PHL

POL

PRT

PRY

QAT

ROU

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SVK

SVN

SWE

SWZ

TCD

THA

TJK TTO

TUN

TUR

TZA

UGA

UKR

URY

USA

VEN

VNM

YEM

ZAF

ZMB

ZWE

0

20

40

60

80

100

6 8 10 12

Effic

ienc

y of

trai

n se

rvic

es,

0-10

0 (b

est)




Figure 67. Problematic Factors for Doing Business in Indonesia

Sources: WEF Executive Opinion Survey 2017

Figure 68. Electrification Ratio, Indonesia

Sources: PLN Statistics

Figure 69. Electrification Ratio, Peer Countries


Figure 70. Electrification Ratio by Consumption Decile

Sources: Susenas & Podes, Prospera’s calculation

Figure 71. System Average Interruption Frequency Index (SAIFI)


Figure 72. System Average Interruption Duration Index (SAIDI)


0 2 4 6 8 10 12

Poor public healthInsufficient capacity to innovate

Foreign currency regulationsCrime and theft

Restrictive labor regulationsInadequately educated workforce

InflationTax regulation

Poor work ethic in national labor forceTax rates

Government instability/coupsPolicy instability

Inadequate supply of infrastructureAccess to financing

Inefficient government bureaucracyCorruption

% of firms

97,5

60

65

70

75

80

85

90

95

100

2010

2011

2012

2013

2014

2015

2016

2017

1H 2

018

Elec

trifi

catio

n ra

tio (%

hou

seho

lds)

60

70

80

90

100

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015Acce

ss to

ele

ctric

ity (%

of p

opul

atio

n)


88

90

92

94

96

98

100

1 2 3 4 5 6 7 8 9 10

Elec

trifi

catio

n ra

tio (%

hou

seho

lds)

Consumption decile

2012 2013 2017

02468

10121416

2010 2011 2012 2013 2014 2015 2016 2017

SAIF

I (tim

es/y

ear)

0

5

10

15

20

25

30

2010 2011 2012 2013 2014 2015 2016 2017

SAID

I (ho

urs)



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



Figure 76. Quality of Electricity Supply, 2017


Figure 77. Broadband Subscriptions, 2017


Figure 78. Broadband Speed, 2017

Sources: cable.co.uk

Figure 79. Access to Basic Water Services, 2015


Figure 80. Access to Basic Sanitation Services, 2015


Figure 81. Access to Water Supply by Quintile 2018, Urban

Sources: Susenas

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN

BGD

BGR

BHR

BIHBRA

BRN

BTN

BWA

CANCHE

CHL

CHN

CMRCOD

COL

CPV

CRICYP

CZEDEU

DNK

DOM

DZA

ECUEGY

ESP

EST

ETH

FINFRAGBR

GEO

GHA

GIN

GMB

GRCGTM

HKG

HND

HRV

HTI

HUN

IDNIND

IRL

IRN

ISLISR

ITA

JAM

JOR

JPN

KAZ

KEN

KGZ

KOR

KWT

LAO

LBN

LBR

LKA

LSO

LTU

LUX

LVAMAR

MDA

MDG

MEX

MLI

MLTMNE

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NGA

NIC

NLD NOR

NPL

NZLOMN

PAK

PANPER

PHL

POL

PRT

PRY

QAT

ROURUS

RWA

SAU

SEN

SGP

SLE

SLV SRB

SVK

SVN SWE

SWZ

SYC

TCD

THA

TJK

TTOTUN

TUR

TZAUGA

UKR

URYUSA

VEN

VNM

YEM

ZAF

ZMB

ZWE

0

2

4

6

8

6 8 10 12

Qua

lity

of e

lect

ricity

supp

ly, 1

-7 (b

est)


AGO

ALB

ARE

ARG

ARM

AUSAUT

AZE

BDI

BEL

BENBFA

BGD

BGR

BHR

BIH

BOL

BRA

BRN

BWA

CAN

CHE

CHL

CHN

CIVCMRCOD

COL

CPV

CRI

CYP

CZE

DEU

DNK

DOMDZAECU

EGY

ESPEST

ETH

FIN

FRA

GBR

GEO

GHAGINGMB

GRC

GTM

HKG

HND

HRV

HTI

HUN

IDNIND

IRL

IRN

ISL

ISRITA

JAM

JOR

JPN

KAZ

KEN

KGZ

KOR

KWTLAO

LBN

LBR

LKA

LSO

LTU

LUX

LVA

MAR

MDAMEX

MKD

MLI

MLT

MNE

MNG

MOZ MRT

MUS

MWI

MYS

NAM

NGA

NIC

NLDNOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRT

PRY

QAT

ROU

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SVK

SVN

SWE

SWZ

SYC

TCD

THA

TJK

TTO

TUN

TUR

TZA

UGA

UKR

URY

USA

VENVNM

YEM ZAFZMBZWE0

20

40

60

80

100

6 8 10 12

Fixe

d-br

oadb

and

inte

rnet

su

bscr

iptio

ns (p

er 1

00 p

opul

atio

n)


0 10 20 30 40 50 60 70 80

Singapore (2)

Malaysia (30)

Thailand (45)

Vietnam (89)

Indonesia (92)

Philippines (97)

China (152)

Mean download speed2017 2018 2019

AFG

AGO

ALB

AREARGARMATG

AUSAUT

AZE

BDI

BEL

BEN

BFA

BGDBGR BHR

BHSBIH BLRBLZ

BOL

BRABRB BRNBTN

BWA

CAF

CAN CHECHL

CHN

CIV

CMR

COD

COG

COMCPV

CRI CYPCZE DEU

DJI

DMADNK

DOMDZAECU

EGYESPEST

ETH

FIN

FJI

FRA

FSM GAB

GBR

GEO

GHA

GIN

GMB

GNB

GNQ

GRC

GTMGUY

HKG

HND

HRV

HTI

HUN

IDNIND

IRL

IRN

IRQ

ISLISRITA

JAM

JOR JPN

KAZ

KEN

KGZ

KIR

KOR KWT

LAO

LBN

LBR

LCA

LKA

LSO

LTULUXLVA MAC

MAR

MDA

MDG

MDV MEX

MHL

MLI

MLT

MMR

MNE

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLD NOR

NPL

NRU NZL

OMNPAK

PAN

PERPHL

PLW POLPRTPRY

PSE

QATROUR…

RWA

SAU

SDN

SEN

SGP

SLB

SLE

SLVSRB

SSD

STP

SUR

SVKSVN SWE

SWZ

TCD

TGO

THA

TJK

TKM

TLS

TONTTO

TUN

TURTUV

TZA

UGA

UKRURY USA

VCT

VNMVUT

WSM

YEM

ZAF

ZMB

ZWE

20

40

60

80

100

6 8 10 12

Acce

ss to

bas

ic w

ater

serv

ices

(%

of p

opul

atio

n)


AFG AGO

ALBARE

ARGARM

ATG

AUSAUT

AZE

BDI

BEL

BEN

BFA

BGD

BGR

BHR

BHSBIH BLR

BLZ

BOL

BRA

BRB BRN

BTNBWA

CAF

CAN CHECHL

CHN

CIV

CMR

CODCOG

COL

COM

CPV

CRICYPCZE DEU

DJI

DMA

DNK

DOMDZAECU

EGY

ESPEST

ETH

FINFJI

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GNB

GNQ

GRC

GRD

GTM

GUY

HKG

HND

HRV

HTI

HUN

IDN

IND

IRLIRN

IRQ

ISLISRITA

JAM

JORJPN

KAZ

KEN

KGZ

KIR

KOR KWT

LAO

LBN

LBR

LCALKA

LSO

LTULUX

LVA

MAR

MDA

MDG

MDV

MEXMHL

MKD

MLI

MLT

MMR

MNE

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLD NOR

NPL

NRU

NZLOMN

PAK

PANPERPHL

PLW POLPRT

PRYPSE

QAT

ROU

RUS

RWA

SAU

SDN

SEN

SGP

SLB

SLE

SLVSRB

SSD

STP

SUR

SVKSVN SWE

SWZ

TCDTGO

THATJK TKM

TLS

TON TTOTUNTUR

TUV

TZAUGA

UKR URYUSAUZB

VCT

VNM

VUT

WSM

YEM

ZAF

ZMB

ZWE

0

20

40

60

80

100

6 8 10 12

Acce

ss to

bas

ic sa

nita

tion

serv

ices

(%

of p

opul

atio

n)


0

25

50

75

100

1 2 3 4 5

% u

rban

hou

seho

lds

Consumption quintile

Piped on Premises

Other Improved

Other Unimproved

Surface Water



Figure 82. Access to Water Supply by Quintile 2018, Rural

Sources: Susenas

Figure 83. Firms Experiencing Water Insufficiencies

Sources: World Bank Enterprise Surveys & World Development Indicators

5. Figures on Market Failure Indicators

Figure 84. Indonesia’s Export by Type of Commodity

Sources: Prospera

0

25

50

75

100

1 2 3 4 5

% ru

ral h

ouse

hold

s

Quintile

Piped on Premises

Other Improved

Other Unimproved

Surface Water

AFG

AGO

ALB

ARG

ARM ATGAZE

BDI

BEN

BGDBGR

BHSBIH

BLR

BLZ

BOL

BRA

BRBBTN

BWA

CAF

CHLCHN(2012)

CMRCODCOL

CPV

CRI

CZE

DJI

DMA

DOMECU

EGY

ERI

EST

ETH

GEO

GHA

GIN

GRD

GTM

HND

HRV

HUNIDN(2015)

IND

IRQ

ISR

JAM

JOR

KAZ

KEN

KGZ

KHM

KNA

LAO

LBN

LCA

LKA

LSO

LTU

LVA

MAR

MDA

MDG

MEX

MKD

MLI

MMR MNE

MRT

MUS

MWI

MYS(2015)

NER

NGA

NIC

NPL

PAK

PANPER

PHL(2015)

POL

PRYPSE

ROU

RUSRWA

SDN

SEN

SLB SLV

SRB

SSD

SUR

SVKSVN

SWZ

TCD

TGO

THA(2016)

TJK

TLS

TTO

TUN

TUR

TZA

UGA UKR

URY

UZB

VCTVNM(2015)

XKX

YEM

ZMB

ZWE

0

20

40

60

6 7 8 9 10 11

Firm

s exp

erie

ncin

g w

ater

insu

ffici

enci

es (%

)


0

10

20

30

40

50

60

70

80

90

100

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

Indo

nesi

a's E

xpor

t by

Type

of

Com

mod

ity (U

SD B

illio

n)

Commodities

Resource-Based Manufactures

Low-Tech Manufactures

Medium-Tech Manufactures

High-Tech Manufactures

2000s Commodity Boom



Figure 85. Export Composition by Product, 1995 - 2017

Sources: CID Harvard Figure 86. Complexity Outlook Index & Economic Complexity Index, 2016

Sources: CID Harvard

Figure 87. Economic Complexity Index vs. GDP per Capita, 2016

Sources: CID Harvard & World Development Indicators

ABW

AFGAGO

ALBAND

AREARG

ARM ASM

ATG

AUS

AUT

AZE BDI

BEL

BEN

BFA

BGD

BGR

BHR

BHS

BIH BLR

BLZBMU

BOL

BRA

BRB

BRN BTNBWACAF

CAN

CHE

CHL

CHN

CIV CMRCODCOG

COL

COM

CPV

CRI

CUBCUW CYM

CYP

CZE

DEU

DJI

DMA

DNK

DOM

DZAECU

EGY

ERI

ESP

EST

ETH

FIN

FJI

FRA

FROFSM

GAB

GBR

GEO

GHA

GIN GMBGNBGNQ

GRC

GRD

GRL

GTMGUM

GUY

HKG

HND

HRV

HTI

HUN

IDN

IND

IRL

IRN

IRQ

ISL

ISR

ITA

JAM

JOR

JPN

KAZ

KEN

KGZ

KHM

KIR

KNA

KOR

KWTLAO

LBN

LBRLBYLCA

LKA

LSO

LTU

LUXLVA

MACMAR MDA

MDG

MDV

MEX

MHL

MKD

MLI

MLT

MMRMNE

MNG

MNP

MOZMRT

MUS

MWI

MYS

NAM

NCLNERNGA

NIC

NLD

NOR

NPL

NZL

OMN

PAKPAN

PER

PHL

PLW

PNG

POL

PRK

PRT

PRY

PSE

PYFQAT

ROM

RUS

RWASAU

SDN

SEN

SGP

SLB

SLE

SLV SMR

SOM

SRB

SSD

STP

SUR

SVKSVN SWE

SWZ SXM

SYC

SYR

TCA

TCD

TGO

THA

TJKTKM TLS

TONTTO

TUN

TUR

TUV

TZA

UGA

UKR

URY USA

UZBVCT

VEN

VNM

VUT

WSM

YEM

ZAF

ZMB

ZWE

-2,5

-1,5

-0,5

0,5

1,5

2,5

3,5

-2,5 -1,5 -0,5 0,5 1,5 2,5

Com

plex

ity O

utlo

ok In

dex

Economic Complexity Index(Controlling for GDP per Capita & Natural Resource Exports)

AFGAGO

ALB

AND

ARE

ARG

ARM

ASM

ATG

AUS

AUT

AZE

BDI

BEL

BEN

BFA

BGD

BGR

BHRBHS

BIHBLR

BLZ

BOL

BRA

BRB

BRN

BTN

BWACAF

CAN

CHE

CHL

CHN

CIV

CMRCOD

COG

COLCOM

CPVCRI

CYP

CZE

DEU

DMA

DNK

DOM

DZA

ECU

EGY

ESPEST

ETH

FIN

FJI

FRA

FSM

GAB

GBR

GEO

GHA

GIN

GMB

GNBGNQ

GRC

GRD

GRL

GTM

GUM

GUY

HKG

HND

HRV

HTI

HUN

IDNIND

IRL

IRN

IRQ

ISL

ISR

ITA

JAM

JOR

JPN

KAZKEN

KGZ

KHM KIR

KNA

KOR

KWT

LAO

LBN

LBRLBY

LCA

LKA

LSO

LTU

LUX

LVA MAC

MAR

MDA

MDG

MDV

MEX

MHL

MKD

MLI

MLT

MMR

MNE

MNG

MNP

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLD

NOR

NPLNZL

OMN

PAK

PAN

PER

PHL

PLW

PNG

POL

PRT

PRY

PSE

QAT

ROM

RUS

RWA

SAU

SDN

SEN

SGP

SLB

SLE

SLV

SMR

SRBSTP

SUR

SVK SVNSWE

SWZ SYC

TCD

TGO

THA

TJKTKM

TLS

TON

TTO

TUN TUR

TUV

TZA

UGA

UKRURY

USA

UZB

VCT

VNM

VUT

WSM

YEM

ZAF

ZMB

ZWE

-2,5

-1,5

-0,5

0,5

1,5

2,5

3,5

5 7 9 11

Econ

omic

Com

plex

ity In

dex




6. Figures on Macro Risk Indicators

Figure 88. External Debt & Reserve Adequacy

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 89. External Debt & Current Account Balance

Sources: International Monetary Fund

Figure 90. Central Government Debt


Figure 91. Tax Ratio vs. GDP per Capita, 2016


0

20

40

60

80

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

Cent

ral g

over

nmen

t deb

t, to

tal

(% o

f GDP

)

Indonesia Thailand Malaysia PhiippinesPhilippines

AGO

ALB

ARE

ARG

ARMAUS

AUT

BEL

BFA

BGD

BGR

BHS

BIH

BLRBRABTN

BWA

CAN

CHE

CHL

CHN

CIV

COLCRI

CYP

CZE

DEU

DNK

DOM ESP

ESTFIN

FRA

FSM

GBR

GEO

GRC

GTM

HUN

IDN

IRL

IRQ

ISL

ISRITA

JAM

JPNKAZ

KENKGZ

KIR

KORLAO LBN

LKA

LSO

LTU

LUXLVA

MAC

MDA

MEX

MHLMKD

MLI

MLT

MMR

MNG

MOZ

MUS

MWIMYS

NAM

NIC

NLD NOR

NPL

NZL

PERPHL

PLW

POL

PRT

PRY

PSE

ROU

RUS

RWASEN

SGP

SLB

SLV SVKSVN

SWE

SYC

TGO

THA

TUR

UGA

UKR

URY

USA

UZBVUT

WSM

ZAF

ZMB

0

10

20

30

40

6 7 8 9 10 11 12

Tax

Reve

nue

(% G

DP)




Figure 92. Government Expenditure on Education vs. GDP per Capita, 2015


Figure 93. Government Expenditure on Health


7. Figures on Regulation and Institution Indicators

Figure 94. The Missing Middle, 2013

Sources: Prospera

Figure 95. Regulatory Index, 2017

Sources: Worldwide Governance Indicators 2018

Figure 96. Legal System and Property Right Index, 2017


Figure 97. Rule of Law Index, 2017


AFGALB

ARG

ARM

AUSAUT

AZE

BDI BEL

BENBFA

BHR

BLR

BLZ

BRA

BTN

CHECHLCIV

CMR

COD

COG COLCOM

CPV

CRI

CYP

CZE

DEU

DMA

ECU

ESP

EST

ETH

FIN

FRAGBRGHA

GINGTM

GUY

HKG

HND

HTI

HUN

IDN IRL

IRN

ISL

ISR

ITA

JAM

KAZ

KEN

KGZ

KNA

KOR

LBRLCA

LKA

LTULUX

LVA

MACMDG

MDV

MEX

MLI

MLT

MNG

MUS

MWI

MYS

NER

NIC

NLD

NOR

NPL

NZL

PAK

PER

POLPRT

PSE

ROU

RUSRWA

SEN

SLV SRB

SSD

STP

SVKSVN

SWE

TGO TJK

TUN

TUR

UGA

UZB

VUTZAF

0

2

4

6

8

10

6 8 10 12

Gov

ernm

ent e

xpen

ditu

re o

n ed

ucat

ion,

tota

l (%

of G

DP)


0

1

2

3

4

1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Heal

th E

xpen

ditu

re, p

ublic

(% o

f GDP

)


-

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

0

20

40

60

80

100

120

Micro Small Medium Large

GDP Contribution (in Trillion IDR)

Empl

oym

ent (

Mill

ions

)

Employment Nominal GDP

4,18

6,37 6,55 6,40

8,72

7,136,65

Brazil China India Indonesia Malaysia Thailand Vietnam

Regu

lato

ry In

dex

4,45

5,635,10

4,52

5,76

4,77 5,02


Lega

l Sys

tem

& P

rope

rty

Righ

t Ind

ex

4,80 4,654,20 4,09

5,43

4,355,17


Rule

of L

aw In

dex



Figure 98. Cost of Redundancy Dismissal, 2018

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

Sources: World Bank Enterprise Surveys & World Development Indicators

Figure 100. FDI Regulatory Restrictiveness Index, 2018

Sources: OECD

Figure 101. Inward FDI Stock, 2018

Sources: Country Fact Sheets 2019 – UNCTAD

Figure 102. Time Required to Start a Business, 2019

Sources: Ease of Doing Business 2019 – World Bank

23,9

24,627,4

27,436

57,8

01020304050607080

New

Zea

land

Italy

Sing

apor

eU

nite

d St

ates

of…

Denm

ark

Japa

nBr

unei

Dar

ussa

lam

Uni

ted

King

dom

Cana

daSw

itzer

land

Finl

and

Peru

Fran

ceAu

stra

liaIc

elan

dSw

eden

Braz

ilIn

dia

Net

herla

nds

Gre

ece

Russ

ian

Fede

ratio

nSp

ain

Belg

ium

Uru

guay

Ger

man

yTu

nisia

Mex

ico

El S

alva

dor

Iran,

Isla

mic

Rep

ublic

…M

alay

siaVi

et N

amN

epal

Paki

stan

Chin

aKo

rea,

Rep

ublic

of

Phili

ppin

esPa

ragu

ayTu

rkey

Arge

ntin

aHo

ndur

asBa

ngla

desh

Ecua

dor

Thai

land

Egyp

tM

ozam

biqu

eG

hana

Zam

bia

Indo

nesia

Sri L

anka

Bahr

ain

Mau

ritiu

s

GNB

MOZ

ZAF

BFA

BRA

COG

CPV

ERI

FJI

GAB

MUS

TON

VUT

WSM

AGOATG

BHS

BLZ

BRB

BWA

CHLCRI

DMA

GRD

GUY

JAM

KNA

LCA

MEX

PAN

SUR

TTO

VCT

VEN

CAF

IRQ

LKA

RWA

CHN(2012)

RUS

ALB

ARM

AZEBGD

BGR

BIHBLR

COD

CZE

DJI

EST

GEO

GHA

HRV

HUNISR

JOR

KAZ

KEN

KGZ

LBN

LTU

LVAMAR

MDA

MDG

MKD

MNE

MNG

NPL PAKPOL

PSE

ROUSRB

SVKSVN

TJK

TUN TURTZA

UGA

UKR

UZB

XKX

YEM

ZMB

AFGBDI

IND

MRT

MWI

NAM

NGA

SDN

SENSSD

SWE

BTN

ETH

IDN(2015)

MYS(2015)

PHL(2015)

SLB

TLS

VNM(2015)BEN

CIVCMR

DOM

EGY

GIN

HND

LAO

LSO

MLI

MMR

NIC

SLV

TGO

THA(2016)

ZWE

ARG

BOL

COL

ECU

GTM

LBR

NER

PER

PRY

SLE

URY

0

20

40

60

80

6 7 8 9 10 11

Firm

s offe

ring

form

al tr

aini

ng (%

)


0,117 0,130

0,2090,251 0,252

0,313

0,374

Mya

nmar

Viet

nam

Indi

a

Chin

a

Mal

aysia

Indo

nesia

Phili

ppin

esFDI R

egul

ator

y Re

stric

tiven

ess I

ndex

(0

= o

pen,

1 =

clo

sed)

60,1

45,7 43,0

25,1 22,1

12,1

Vietnam Thailand Malaysia Philippines Indonesia China

Inw

ard

FDI S

tock

(% o

f GDP

)

1,5

4,5

5,5

8,6

13,5

17

19,6

31

Singapore

Thailand

Brunei Darussalam

China

Malaysia

Vietnam

Indonesia

Philippines

Time Required to Start a Business (Days)



Figure 103. Score on Trading across Borders, 2019


Figure 104. Rank in the Ease of Paying Taxes, 2019


Figure 105. Cost to Export and Import, 2019

Sources: Ease of Doing Business 2019 – World Bank Figure 106. Effective Rate of Protection, 2015

Sources: Mark (2015)

Figure 107. Most Problematic Factors for Doing Business in Indonesia

Sources: WEF Executive Opinion Survey 2017

93 90 88 87 85 83 77 72 71 70 69 67 61

Kore

a, R

ep.

Sing

apor

e

Mal

aysia

Japa

n

Thai

land

Chin

a

Indi

a

East

Asia

& P

acifi

c

Viet

nam

Phili

ppin

es

Indo

nesia

- Ja

kart

a

Indo

nesia

Indo

nesia

- Su

raba

ya

Scor

e on

Tra

ding

acr

oss B

orde

rs 131

112

94

7259

8

Vietnam Indonesia Phillipines Malaysia Thailand Singapore

Rank

in th

e Ea

se o

f Pay

ing

Taxe

s0

4080

120160200

Kore

a, R

ep.

Sing

apor

e

Phili

ppin

es

Mal

aysia

Japa

n

Thai

land

Chin

a

Indi

a

East

Asia

& P

acifi

c

Viet

nam

Indo

nesia

- Ja

kart

a

Indo

nesia

Indo

nesia

- Su

raba

ya

Cost

to E

xpor

t and

Impo

rt,

Docu

men

tary

Com

plia

nce

(USD

)

Cost to Import Cost to Export

-40 -20 0 20 40 60 80

Other MiningForestry

Oil & Gas ExtractionOil Refining & LNG

FisheriesWood ProductsPaper Products

Textile, Apparel & LeatherEstate and Other Crops

Other ManufacturingLivestock and Their Products

Machinery & Transport EquipmentNon-Metal Products

Food, Beverages & TobaccoChemical

Metals & Metal ProductsFood Crops

Effective Rate of Protection (%)0 2 4 6 8 10 12

Poor public healthInsufficient capacity to innovate

Foreign currency regulationsCrime and theft

Restrictive labor regulations

Inadequately educated workforceInflation

Tax regulationPoor work ethic in national labor…

Tax rates

Government instability/coupsPolicy instability

Inadequate supply of infrastructureAccess to financing

Inefficient government bureaucracy

Corruption

% of firms

