Improving Indonesia's Domestic Connectivity: An Inter ...service in Java and limited freight...

1

IMPROVING INDONESIA’S DOMESTIC CONNECTIVITY: AN INTER-REGIONAL CGE ANALYSIS

Mark Horridge, Arief A. Yusuf, Edimon Ginting, Priasto Aji

ADB PAPERS ON INDONESIA No. 17

August 2016

2

Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO)

© 2016 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 632 4444; Fax +63 2 636 2444 www.adb.org; openaccess.adb.org

Some rights reserved. Published in 2016.

The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent.

ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned.

By making any designation of or reference to a particular territory or geographic area, or by using the term “country” in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area.

Unless otherwise noted in this publication, “$” refers to US dollars.

This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) https://creativecommons.org/ licenses/by/3.0/igo/. By using the content of this publication, you agree to be bound by the terms of said license as well as the Terms of Use of the ADB Open Access Repository at openaccess.adb.org/termsofuse

This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributed to another source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material.

The ADB Papers on Indonesia is an ongoing, quick-disseminating, informal publication whose titles could subsequently be revised and developed into other forms of publication. Each paper is subject to formal revision and correction before they are finalized and considered published. The series is maintained by the ADB Indonesia Resident Mission based in Jakarta, Indonesia.

3

Improving Indonesia's Domestic Connectivity: An Inter-regional CGE Analysis

Mark Horridge1, Arief A. Yusuf2, Edimon Ginting3, Priasto Aji4

EXECUTIVE SUMMARY Indonesia's economic growth is slowed by poor infrastructure, especially transport infrastructure. Transport difficulties hamper all industries, by imposing extra costs on trade between regions, and on trade with other countries. Regional imbalances are exacerbated, since peripheral regions (which face higher transport costs) tend to be poorer. We present below some rough estimates, generated using a regional CGE model (IndoTERM), of the possible benefits to Indonesia of reducing transport costs. We simulate the effects of a 5% reduction in the costs of road and sea transport within and between the peripheral regions, or between any peripheral region and Java/Sumatera/BaliNT and also add a 1% reduction in iceberg trade cost. We find total benefits of around 5% of GDP. The great bulk of benefits arise in the core regions of Sumatera, Java and BaliNT -- simply because they account for so much of the Indonesian economy. However, we also notice that benefits accrued in the core spill over to peripheral regions. The combined transport improvement also reduces the country’s poverty incidence by 2.2% (4.7 million people). Interestingly, poverty reduction is higher in the eastern part of Indonesia as those provinces are suffering more from connectivity impediments. In addition, it is also due to a higher poverty base in those provinces. Keywords: Connectivity, transport infrastructure, logistics, CGE Model, IndoTERM

1 Monash University, Australia

2 Padjajaran University, Indonesia

3 Asian Development Bank

4 Asian Development Bank

4

I. INTRODUCTION

Although Indonesia’s economic growth edged up to 6.5% year-on-year in 2011 (the highest since the Asian crisis 1997–1998) and another year of 6%-plus growth in 2012, it is still significantly below the country’s growth potential. Deeper analysis of the sources of growth in the past decade highlights that growth has been mostly driven by the non-tradable sector, mainly services, while the performance of tradables, particularly manufacturing, has been lackluster (Ginting and Aji, 2012)5. Lackluster of the tradable sector is largely attributed to lack of infrastructure and high logistics costs. Logistics costs in Indonesia are estimated at about 14% of total production costs, compared with about 5% in, say, Japan. Indonesia’s overall ranking in the Global Competitiveness Index is at 50 out of 144 countries.6 However, the country only ranks 78 out of 144 on overall quality of infrastructure, far below its overall ranking, implying that infrastructure is a drag on the country’s competitiveness (Figure 1).

Investment is particularly needed in infrastructure, which would support growth of the more productive tradable sector, if the government is to achieve its 2014 growth target of 7%–8%. For 2014-2016, IMF estimates a baseline potential growth rate of 7.1%, but which would increase to 7.9% if infrastructure development and economic reforms are accelerated. The recent ADB Studies (ADB, 2010 and Hill et al, 2012) also identify inadequacies in infrastructure as one of the critical constraints to economic growth.

Figure 1. Global Competitiveness Index Ranking

2012-2013 (out of 142 countries)

Figure 2. Share of roads in bad or poor

condition

Source: World Economic Forum ( 2012) Source: The Public Expenditure Review of the Road Sector, the World Bank

The impact of lagging infrastructure appears in a number of forms. Deteriorating road systems in the provinces and districts increases domestic transport and logistics costs. Congested port and underdeveloped inter-island transport systems have led to expensive domestic shipping costs. Congested and underdeveloped international ports limit the efficient integration of Indonesia’s manufacturing sector not into international production networks.

5 Tradables refer to agriculture, mining and quarrying, and manufacturing. Non-tradables refer to utilities,

construction, services, including wholesale trade, transport, communication, financial and business services, and other activities. 6 World Economic Forum. 2011. Global Competitiveness Report, 2011–2012. Geneva.

2

32

46

78

98

2

25

38

50

65

Singapore Malaysia Thailand Indonesia Philippines

Infrastructure Overall 41%

24%

12%

District Provincial National

5

Linking rural areas with growth poles within islands is constrained by deterioration in road infrastructure and inadequate road infrastructure, and underdeveloped railway networks. Due to sustained low government spending in infrastructure spending, the quality of the existing road network has deteriorated significantly. The recent Public Expenditure Review of the Road Sector conducted by the World Bank finds that, respectively, 24% and 41% of provincial and district roads are in bad or poor condition (Figure 2). The performance is better at the national level where only 12% of the roads are in bad or poor condition. Inability to acquire land for public use has been one of the reasons for the delayed disbursement of government’s capital spending. It has also delayed the development of PPPs, particularly for toll roads. The private sector considers the land acquisition problem as a major source of uncertainties for project development, costs and implementation. The existing rail network mainly provided passenger service in Java and limited freight services in Sumatra.

As a large archipelagic country, reducing the cost of inter-island shipping will contribute significantly to economic growth and poverty reduction across islands of the country. At present, domestic shipping costs constitute a large portion of producers’ total costs, with these costs being passed on to consumers throughout the country. Reflecting poor logistics and inadequate transport infrastructure, about 70% of the rice price differences across provinces is explained by the degree of remoteness (World Bank, 2010). The high cost of shipping is due to a combination of inadequate hard and soft infrastructure in the ports. According to one estimate, the average turnaround time for domestic shipping at the 25 strategic ports is 2.7 days. With average sailing time being 1.5 days, ships spend about 75% of the total time between destinations at port.

The bulk of the country’s international trade is facilitated by five main ports. The largest is Tanjung Priok, the country’s main international gateway in Jakarta, currently serving approximately 70% of internationally traded goods. Tanjung Priok also serves 29% of container traffic between Java and other islands. While Tanjung Priok is Indonesia’s most efficient port, its level of productivity is much lower than that of major ports in the ASEAN region (Figure 3). Ship dwelling time in Tanjung Priok is 6.7 days in January 2012 compared with only 4.9 days in 2010. The cost of delivering one container from Cikarang (the nearest industrial zone) to Tanjung Priok with a distance of about 56 kilometers is $750, much higher than the cost ($450) from Pasir Gudang to Tanjung Pelepas with a similar distance in Malaysia. The 2012 Logistic Performance Index, which measured the efficiency of logistic services for international trade, confirms that Indonesia’s performance is much lower than the neighboring countries due to lagging infrastructure support (Figure 4).

Figure 3. Dwell time in selected countries Figure 4. Changes in Ranking of Infrastructure, in LPI 2010 and LPI 2012, selected countries

Source: Journal of the Indonesia Infrastructure Initiative

PRAKARSA, issue 10, April 2012 page 11

Source: World Bank, LPI 2012

6

5

4

4

3

3

2

1.1

0 2 4 6 8

Tanjung Priok

Thailand

Malaysia (Port Klang)

UK, Los Angeles (USA)

Australia, NZ

France

Hong Kong

Singapore

days

26

2 2 1 1 1 -6 -8 -14 -16

-20

-15

-10

-5

0

5

10

15

20

25

30

6

Figure 5. Access to shipping and per capita income gap index

Figure 6. Poverty incidence (% of population) in September 2012

Source: Ginting and Aji (2011) Source: BPS

The quality of access to shipping services partly explains the income gap within in the eastern part of Indonesia. The districts that have better access to the shipping services tend to have higher per capita income (Figure 5). As consequence, the progress in reducing poverty is mixed in the rural and in the eastern part of Indonesia (Figure 6). The national rural poverty rate of 15.6% is still much higher than the national urban poverty rate of 9.1%. Poverty rates in some provinces in eastern Indonesia are much higher than elsewhere in the country—for example, 25.3% in Maluku and Papua. Alleviating the country’s multidimensional poverty and income inequality will require not only accelerated economic growth but also a more inclusive growth process that provides rural areas and disadvantaged regions with greater economic opportunity. We present below some rough estimates, generated using an inter-regional CGE model, of the possible benefits to Indonesia of reducing transport costs. We do not investigate how the reductions might be achieved -- in practice this would require either costly investments, or, in some cases, managerial improvements that cost relatively little. Nor do we simulate the effects of repaying any debts that may be incurred to pay for the investments. We simulate only the benefits -- in effect performing just half of a full cost-benefit analysis. The next sections provide the model and data used in the analysis, followed by discussion of the results. Some concluding remarks are given in the final section.

II. THE MODEL AND DATA

The analysis is performed using a large inter-regional CGE model of Indonesia based on the Australian TERM model7. This framework has been applied to several countries before, including Indonesia, but the latest Indonesian version is the most ambitious to date, distinguishing all 30 provinces and up to 179 sectors. Most data comes from the BPS 2005 Indonesian national and inter-regional input-output tables, but several other sources have been used. The TERM framework is remarkable for its detailed treatment of inter-regional trade: for each commodity it models a full origin/destination trade matrix. And for each cell in that matrix, TERM

7 Information and references about TERM may be found at http://www.monash.edu.au/policy/term.htm

MTGH

ST

SBBURU

PM

KSHS

KATK HUMTBTER

MTHTIM

ASMMAP YW

HTGH AMBWARBN

TWNABJAYMER MAN

SARFAKKAISUP RA

SORTB MIM

-1000

-800

-600

-400

-200

0

200

0 50 100 150 200 250 300

Percapita Income gap index

Annual Frequency

8.69.9

8.7

11.8

4.2 5.6 6.1

14.712.9

15.1 16.6

8.2

14.4

31.7

11.7 11.7 11.3

14.7

6.5

11.4

24.1

National Sumatra Jawa Bali and Nusa

Tenggara

Kalimantan Sulawesi Maluku and

Papua

Urban Rural Urban and Rural

7

separately identifies both the basic (i.e. factory-gate) value of trade and the additional costs of transport (which may be further subdivided into road, rail, sea, etc.). It is possible then to say for example, that transport accounts for perhaps 5% of the cost of Javanese-produced clothing used in Bali, and to simulate the effects of reducing that cost (perhaps by improving road and sea links). For the simulations reported below, we aggregated the database sectors from 179 to 39 sectors, while preserving the full (30 province) regional detail. However, most regional results are reported, at a broader regional level: the 6 "corridor" regions identified for national planning purposes.

A. The Closure

The closure (or choice of exogenous variable) corresponds to a medium or long-run adjustment process:

Real wages adjust in each region to keep regional employment fixed.

Rates of return to capital are fixed

Nationally the ratio [Trade Balance/GDP] is held fixed.

The exchange rate is the fixed numeraire.

B. The Shocks

We simulate a portfolio of transport improvements, comprising the following components: 1. a generalized improvement in transport to or from peripheral regions 2. a generalized improvement in transport within non-peripheral regions 3. a reduction in the costs of foreign trade 4. a saving in logistic costs 5. a switching from Road to Rail

C. Poverty Impact Analysis

Change in poverty incidence is calculated as:

∆𝑃 = 𝑃(𝑧|𝜇′, 𝜎) − 𝑃(𝑧|𝜇, 𝜎), (1)

where 𝑦 is expenditure per capita, and 𝑧 is poverty line.

𝑃(𝑧|𝜇, 𝜎) = ∫1

√2𝜋𝜎𝑦𝑒

−1

2𝜎2(ln 𝑦−𝜇)2

𝑑𝑦𝑧

0 (2)

is the cumulative density function (c.d.f.) of expenditure per capita assumed to follow the lognormal distribution8 where its parameters 𝜇 and 𝜎 for each of the 30 provinces are estimated using Maximum Likelihood using SUSENAS data for the year 2005, the year representing the IndoTERM CGE model database. Whereas:

𝜇′ = ln ((1 +�̂�

100) 𝑒𝜇) (3)

8 Lognormal distribution is commonly used to proxy the distribution of income or expenditure (See for example

Bourguignon, 2003). Figure A1 at the appendix illustrates the goodness of fit of lognormal distribution of expenditure per capita of Indonesian households in year 2005 from SUSENAS data.

8

where �̂� is the percentage change in mean real consumption per capita taken from the results of the CGE model simulation. Poverty line (𝑧) for each province is calibrated using the inverse of

𝑃(𝑧|𝜇, 𝜎) to match the official provincial poverty incidences in 2005 published by Indonesian Statistics Office (BPS).

III. RESULTS

The impact of the shocks of the five components is described in the Table 1 below. As shown in Column 1 of the table, their total effect is to lift national GDP and other measures by around 5%. The remaining columns show how this total effect may be attributed to the 5 component groups of shocks. Components 2 and 1 are the largest.

Table 1: Percentage change national effects of transport improvements

% change Total effect Component

1 Component

2 Component

3 Component

4 Component

5

Real Household

Consumption 5.722 1.111 4.018 0.175 0.391 0.027

Real Investment

5.832 1.135 4.011 0.239 0.383 0.065

Exports to ROW

4.810 1.028 3.010 0.248 0.482 0.042

Real GDP 5.435 1.085 3.827 0.108 0.380 0.035

Real Wage level

4.917 0.939 3.483 0.161 0.308 0.026

Aggregate Capital Stock

6.126 1.181 4.258 0.218 0.415 0.054

CPI -0.252 -0.025 -0.328 0.143 -0.044 0.002

Source: model simulation, variable SomeMacro

It should be emphasized that the chosen magnitudes for all the transport cost savings shocks is quite speculative -- actual savings would have to be estimated in the context of specific improvement projects. Nevertheless, the simulations do indicate that quite small savings can yield substantial economic benefits.

A. Component 1: a generalized improvement in transport to or from peripheral regions

In a nation as diverse as Indonesia it is politically important that the benefits of economic growth are shared equitably between regions. Currently however economic activity is concentrated in the 3 regions Java/Sumatera/BaliNT. One reason is that expensive and inefficient transport to and from the other regions hinders their economic growth. In this component we simulate the effect of a general increase in transport efficiency for all trips that begin or end outside Java/Sumatera/BaliNT. That is, we simulate the effects of a 5% reduction in the costs of road and sea transport within and between the peripheral regions, or between any peripheral region and Java/Sumatera/BaliNT.

9

In addition we assume a 1% reduction in iceberg trade costs; e.g. we assume that where before only 90 in 100 tomatoes reached their destination unspoiled; now with better transport 90.9 tomatoes will arrive in good condition. This applies to all commodities.

Combined effects of the two groups of shocks are shown in the Table 2 below:

Table 2: Percentage change effects of better transport to or from peripheral regions

% change 1

Sumater 2

Java 3

Kalimantan 4

Sulawesi 5

BaliNT 6

PapuaMlku Total

Indonesia

Real Household

Consumption 0.348 0.479 4.697 3.692 1.007 4.366 1.117

Real Investment

0.208 0.423 4.938 3.798 0.938 3.046 1.140

Exports to ROW

-0.322 -0.509 5.048 5.848 -0.063 1.860 1.038

Real GDP 0.206 0.379 4.837 4.139 0.693 3.647 1.090

Real GDP (iceberg part)

0.162 0.309 4.409 3.772 0.563 3.047 0.958

Real Wage level

0.193 0.324 4.535 3.532 0.850 4.204 0.945


0.233 0.484 4.563 4.466 1.124 3.255 1.188

CPI 0.121 0.190 -0.835 -1.127 0.206 -1.179 -0.025


Effects are concentrated on the outer regions, although Java/Sumatera/BaliNT also benefit from easier trade with the rest. Above, an additional GDP row shows that most of the GDP change was due to the iceberg shock alone. Although the iceberg shock was small (1%) it applies to the whole value of each trade, while the larger 5% cut in transport costs applied to the transport part only.

B. Component 2: a generalized improvement in transport within Java/Sumatera/BaliNT

This component is analogous to the previous except that it affects transport costs only on trades within and between provinces in Java/Sumatera/BaliNT.

As with the previous component, affected regions enjoy GDP gains of over 4%, while the other regions get spill over gains of around 1% (Table 3). Because Java/Sumatera/BaliNT accounts for such a large share of national GDP, the national effect is also around 4%.

10

Table 3: Percentage change effects of better transport within Java/Sumatera/BaliNT

% change 1

Sumatera 2

Java 3

Kalimantan 4

Sulawesi 5

BaliNT 6

PapuaMlku Total

Indonesia

Real Household

Consumption 4.599 4.763 0.261 1.092 4.298 1.382 4.018

Real Investment

4.184 5.304 -0.220 0.426 3.722 0.557 4.011

Exports to ROW

4.840 4.597 -0.907 -1.666 0.389 -1.444 3.010

Real GDP 4.141 4.912 -0.073 0.335 3.673 0.305 3.827

Real Wage level

4.047 4.210 -0.279 0.549 3.747 0.838 3.483


4.451 5.574 -0.165 0.837 4.510 0.610 4.258

CPI -0.578 -0.443 0.358 0.516 -0.600 0.601 -0.328


C. Component 3: a reduction in the costs of foreign trade

In the GDP formula, GDP = C + I + G + X - M, exports X are valued FOB (exclusive of transport costs) while imports are valued CIF (including transport costs). When the costs of international trade are reduced, we expect in general that importing countries will pay less and exporters will receive more -- leading to a GDP increase. The actual benefit will be divided between exporters and importers according to the degree of competition in international trade.

According to the GTAP 7.1 database, transport costs add around 7% to the cost of Indonesia's exports and 5% to the cost of imports. Thus, if these costs could be reduced by 5%, for example by increasing the efficiency of ports, and if half the cost decrease was passed onto to Indonesia, we might expect that Indonesia would

enjoy increased export FOB prices of around 0.175% (=5% x 7% /2)

enjoy decreased import CIF prices of around 0.125% (=5% x 5% /2) These changes were applied as shocks, yielding the effects shown in the Table 4 below. The benefits are fairly well distributed between regions, although slightly larger in Java (which accounts for most of Indonesia's international trade).

11

Table 4: Percentage change effects of reduction in the costs of foreign trade

% change 1

Sumatera 2

Java 3

Kalimantan 4

Sulawesi 5

BaliNT 6

PapuaMlku Total

Indonesia

Real Household

Consumption 0.164 0.175 0.190 0.164 0.166 0.191 0.174

Real Investment

0.206 0.251 0.245 0.228 0.215 0.162 0.236

Exports to ROW

0.250 0.275 0.236 0.142 -0.034 -0.020 0.246

Real GDP 0.088 0.120 0.105 0.089 0.086 0.072 0.107

Real Wage level

0.151 0.162 0.176 0.150 0.152 0.177 0.160


0.182 0.235 0.198 0.216 0.208 0.145 0.215

CPI 0.143 0.143 0.147 0.142 0.143 0.141 0.143

Source: model simulation, variables SomeMacro

D. Component 4: a saving in logistic costs

A more efficient transport system allows for savings not only in direct transport costs, but also reduces logistic expenses (such as the costs of organizing and monitoring deliveries, and the costs of holding inventories). We model a reduction in this second, logistic, part of transport cost saving as a reduction in labor costs. In more detail, we simulate:

for each sector and region, a reduction in industry labor costs equal to 5% of the value of that sector's use of transport as a margin on inputs [delLOGISTIC1].

the benefit to final demanders is captured via a reduction in labor costs of the "Trade" sector, equal to 5% of the value of transport margins used on all final demands [delLOGISTIC2].

Some effects of these savings are shown in the Table 5 below:

12

Table 5: Percentage change effects of reduced Logistic costs

% change 1

Sumatera 2

Java 3

Kalimantan 4

Sulawesi 5

BaliNT 6

PapuaMlku Total

Indonesia

Real Household

Consumption 0.433 0.375 0.406 0.452 0.470 0.060 0.392

Real Investment

0.385 0.389 0.433 0.367 0.313 0.032 0.382

Exports to ROW

0.550 0.373 0.700 0.750 -0.059 0.146 0.483

Real GDP 0.370 0.356 0.498 0.411 0.363 0.447 0.379

Real Wage level

0.349 0.291 0.322 0.367 0.386 -0.022 0.308


0.382 0.413 0.506 0.431 0.410 0.281 0.415

CPI -0.065 0.008 -0.174 -0.128 -0.099 -0.412 -0.045


E. Component 5: a switching from Road to Rail

It is difficult to see how Indonesia's already-crowded road network can accommodate the increased traffic brought by future economic growth. Part of the remedy may be to encourage the use of rail transport instead, where appropriate. In this component we simulate the effect of a general movement from road to rail. One problem is that rail transport is little used, and unavailable in many places. Therefore, in some cases, rail capacity might have to grow by an implausibly large proportion to replace a significant fraction of road transport. The rule that we apply here is:

For each commodity, whether domestic and imported, and for each of the 30 possible origin and destination provinces, a portion of freight is shifted from road to rail. The amount of transport cost shifted is the lesser of: the existing use of rail transport, and 20% of the existing use of road transport.

In other words, road use decreases by at most 20%, and rail use increases by at most 100%. This means that in regions where there is currently no railway, the measure has no effect. Indeed national road use only declines by 1.1%, because of the above restrictions. We also assume that the movement from road to rail (where possible) is cost-reducing: each dollar not spent on road transport is replaced by only 80 cents worth of rail transport.

As with the other transport improvements modeled here, the switch to rail, and the cost savings, assumes that substantial investment has taken place to improve the rail network. We do not model here the demand effects of that investment, or simulate the effects of repaying any debts that may be incurred to pay for the investment.

13

Some effects of the road-to-rail switch are shown in the Table 6. As can be seen from the table, direct effects are mainly restricted to Java and Sumatra, which have railways (although the model says that some Bali trucking firms have lost through-Java business).

Table 6: Percentage change effects of Road-to-Rail switch

% change 1

Sumatera 2

Java 3

Kalimantan 4

Sulawesi 5

BaliNT 6

PapuaMlku Total

Indonesia

Real Household

Consumption 0.026 0.035 0.004 0.004 -0.011 0.021 0.027

Real Investment

0.072 0.091 0.001 -0.022 -0.080 0.011 0.064

Exports to ROW

0.038 0.062 0.014 0.024 0.006 -0.040 0.042

Real GDP 0.027 0.051 0.002 0.000 -0.005 0.008 0.035

Real Wage level

0.026 0.034 0.004 0.004 -0.012 0.020 0.026


0.044 0.076 0.004 0.001 -0.011 0.015 0.053

CPI 0.001 0.004 0.002 -0.001 -0.011 0.005 0.002

Rail sector output

33.512 29.876 - - - - 30.637

Road sector output

-0.857 -1.537 -0.051 -0.081 -0.249 -0.047 -1.127

Source: model simulation, variables SomeMacro, Zonextot, Natxtot

F. Detailed regional and sector results

Some detailed regional results are shown in the Table 7. The total effect is fairly uniform across regions, although individual shocks benefit particular regions.

Some detailed sector results are shown in the Table 8. In general the trade-exposed manufacturing sectors expand by 4-6%. The first 3, farm, industries expand only slightly, or even contract. The reason is that (a) they face inelastic local demand and (b) our imposed reduction in "iceberg" trade costs means that, for example, the farmer can grow 1% fewer tomatoes without reducing the amount received by the consumer.

The same argument explains the decline for the Government industry. Here again demand is fixed, and the "iceberg" cost reduction means that 1% less government output is needed. It may be more realistic to exclude this sector from the "iceberg" cost reduction.

14

Table 7: Percentage change effects on provincial GDPs

Total effect Component

1 Component

2 Component

3 Component

4 Component

5

NAD 4.597 0.068 4.048 0.092 0.364 0.025

SUMUT 5.624 0.259 4.812 0.099 0.426 0.028

SUMBAR 5.209 0.209 4.477 0.101 0.375 0.046

RIAU 4.362 0.170 3.737 0.079 0.356 0.022

JAMBI 4.333 0.265 3.666 0.076 0.304 0.021

SUMSEL 4.593 0.190 3.964 0.093 0.318 0.029

BABEL 5.512 0.722 4.317 0.094 0.337 0.042

BENGKULU 4.490 0.330 3.744 0.080 0.326 0.011

LAMPUNG 5.018 0.178 4.307 0.083 0.414 0.035

DKI 6.244 0.400 5.244 0.134 0.412 0.055

JABAR 6.018 0.547 4.915 0.133 0.367 0.057

BANTEN 6.305 0.466 5.283 0.130 0.364 0.061

JATENG 5.276 0.112 4.725 0.109 0.294 0.035

DIY 4.600 0.377 3.823 0.094 0.275 0.031

JATIM 5.392 0.252 4.665 0.100 0.328 0.048

KALBAR 5.240 4.546 0.178 0.091 0.420 0.005

KALTENG 6.472 5.716 0.028 0.107 0.616 0.004

KALSEL 5.996 5.350 -0.010 0.109 0.545 0.001

KALTIM 5.065 4.612 -0.148 0.107 0.492 0.002

SULUT 5.559 4.726 0.198 0.099 0.537 0.000

GORONTALO 4.510 3.576 0.514 0.080 0.345 -0.005

SULTENG 4.813 3.989 0.337 0.079 0.405 0.004

SULSEL 4.860 4.073 0.306 0.087 0.391 0.003

SULTRA 4.965 3.997 0.471 0.099 0.407 -0.009

BALI 5.433 0.752 4.169 0.105 0.405 0.002

NTB 4.219 0.511 3.359 0.076 0.292 -0.018

NTT 4.741 0.899 3.347 0.073 0.415 0.006

MALUKU 5.377 4.264 0.504 0.089 0.513 0.007

MALUT 5.591 4.447 0.445 0.089 0.601 0.008

PAPUA 4.315 3.525 0.280 0.069 0.433 0.008

Source: model simulation, variable xgdpexp

15

Table 8: Percentage change effects on sectoral outputs

1

Sumatera 2

Java 3

Kalimantan 4

Sulawesi 5

BaliNT 6

PapuaMlku Total

Indonesia

PADDY -0.085 -0.013 0.185 0.203 -0.051 -0.363 0.003

CROPS -0.283 -0.125 -0.469 -0.285 -0.462 -0.477 -0.208

ESTCR -0.459 -0.714 -0.793 -0.803 -0.944 -1.425 -0.619

ANIMA 2.437 2.232 2.396 2.319 2.254 2.563 2.305

FORES 1.773 1.622 1.688 2.094 1.783 2.030 1.770

FISHE 1.883 1.801 1.924 2.139 2.110 2.423 1.948

OILGS 0.705 0.428 0.791 0.916 0.774 0.948 0.711

OTMIN 0.981 0.429 0.939 1.084 1.014 1.304 0.955

OILRE 3.053 2.390 3.636 2.208 1.580 3.150 3.012

PALMO 1.237 0.480 1.514 2.586 2.089 3.673 1.227

FOSEA 1.028 0.310 0.816 1.704 1.028 3.463 0.935

FOODB 1.208 1.264 1.534 1.579 1.467 1.696 1.299

TEXTL 8.439 5.687 8.967 9.673 6.593 10.193 5.785

SHOES 4.378 1.885 5.654 5.790 3.279 8.055 1.907

WOODS 4.038 3.184 5.224 5.701 3.578 7.025 4.132

PULPP 3.987 2.813 6.319 3.895 3.048 6.677 3.420

RUBBR 4.892 3.066 5.126 4.748 3.632 6.814 3.987

CHEMI 4.342 3.358 6.848 5.221 3.383 7.196 3.758

CEMNT 3.305 3.005 2.820 4.616 4.891 2.672 3.472

BMETL 6.711 5.858 5.638 8.765 5.447 5.483 6.111

METAL 5.198 3.659 6.965 6.865 4.771 7.754 3.770

MACHI 6.614 4.358 8.456 8.398 5.942 8.666 4.526

VEHIC 6.837 5.061 7.227 8.220 8.542 9.764 5.095

OTIND 4.558 3.777 6.265 5.724 4.499 6.913 3.956

UTILI 2.991 3.098 2.832 2.522 2.657 2.529 3.014

CONST 3.775 5.043 4.234 3.303 3.489 2.489 4.472

TRADE 2.509 2.801 3.322 2.319 2.073 2.555 2.733

HOTEL 3.491 3.519 3.257 3.550 3.993 4.569 3.530

RailTrans 37.235 33.599 n/a n/a n/a n/a 34.359

RoadTrans -0.189 -0.394 0.997 0.645 0.556 0.509 -0.159

SeaTrans 0.441 0.709 1.684 0.506 -1.448 -1.539 0.730

RiverTrans 3.489 3.538 3.853 3.559 3.641 4.109 3.837

AirTrans 7.119 7.121 6.688 6.972 7.244 7.188 7.094

TransSvc 1.959 1.705 1.895 2.015 2.458 2.055 1.792

COMMU 2.981 2.830 3.138 3.058 3.020 3.013 2.884

FINAN 2.959 3.236 3.471 3.240 3.297 3.909 3.221

16

RealEstateDo 7.732 7.966 7.722 7.494 8.283 8.242 7.882

GGOVT -1.086 -1.021 -1.275 -0.976 -0.848 -0.831 -1.041

OTSER 2.548 2.691 2.512 1.980 2.553 2.892 2.594

Source: model simulation, variables zonextot, natxtot

G. The effects on poverty

Table 9 shows the impact of the 5 simulations, plus all 5 combines on the change in the rate of poverty incidence for each of the 30 provinces, 6 regions, and the whole Indonesia. Table 10 shows its equivalent impact on the change in number of poor populations. The change in the rate of poverty reduction is also illustrated in map of Indonesian province in Figure 1.

The combined transport improvement reduces Indonesian poverty incidence by 2.15% or equivalent to lifting 4.7 million people out of poverty. The biggest reduction in the rate of poverty incidence occurs in Eastern Indonesia (-3.08%) particularly Maluku Province (-3.59%). However, because Indonesian population is concentrated in Java, the reduction in the number of poor population is also concentrated in Java where 2.7 million people are lifted from poverty.

The reduction in the rate of poverty incidence depends on the size of the increase in the mean real consumption and its distribution within each province as reflected by the slope of the c.d.f. around the neighborhood of the poverty line (Figure A2). Provinces in eastern Indonesia experience the highest increase in mean consumption and this can explain why they also get the highest reduction in the poverty incidence. However, within eastern Indonesia, despite Maluku Utara having the highest mean consumption growth (6.65%), its reduction in poverty incidence (2.02%) is lower than Maluku (3.59%) and Papua Province (3.19%). Maluku Utara has lower initial poverty incidence, hence the slope of the c.d.f. around the neighborhood of poverty line is relatively lower.

Because economic activity is more concentrated in Java, Sumatera, and Bali Nusa Tenggara regions, the effect of the transport improvement in non-peripheral regions bring about higher reduction in national poverty incidence (1.58%) compared to similar improvement in peripheral regions. However, its poverty reduction spillover in eastern Indonesia is non-negligible. For example, poverty incidence in Maluku falls by almost 1%.

Figure 1. Reduction in poverty incidence due to overall transport efficiency (sim1, %)

PAPUA

KALTIM

KALBAR

RIAU

KALTENG

NAD

SUMSEL

JAMBI

JATIM

JABARJATENG

SUMUT

SULSEL

SULTENG

KALSEL

SUMBAR

NTT

NTB

SULTRA

LAMPUNG

MALUT

MALUKU

SULUT

BENGKULU

BABEL

BALI

BANTEN

DIY

GORONTALO

DKI

Legend

Poverty Change

0.57

0.58 - 1.20

1.21 - 1.45

1.46 - 1.70

1.71 - 1.91

1.92 - 2.10

2.11 - 2.54

2.55 - 2.78

2.79 - 3.31

3.32 - 3.59

®

17

Tabel 9: Simulated impact on in poverty Incidence (%)

Δ Real Cons

Pov Inc0

sim1 Periph Non

Periph Dp

ExpImp Logistic

Road2 Rail

Switch

Pop (000)

SUMATERA 5.57 17.21 -2.18 -0.15 -1.81 -0.07 -0.19 -0.01 46,030

NAD 5.27 28.69 -3.27 -0.14 -2.77 -0.11 -0.28 -0.02 4,032

SUMUT 5.68 14.68 -1.91 -0.13 -1.60 -0.06 -0.16 -0.01 12,451

SUMBAR 5.35 10.89 -1.45 -0.10 -1.21 -0.05 -0.13 -0.01 4,566

RIAU 5.68 12.51 -1.68 -0.10 -1.42 -0.05 -0.14 -0.01 5,854

JAMBI 5.34 11.88 -1.70 -0.15 -1.39 -0.05 -0.14 -0.01 2,636

SUMSEL 5.55 21.01 -2.64 -0.18 -2.24 -0.09 -0.18 -0.01 6,782

BABEL 6.37 9.74 -1.65 -0.23 -1.32 -0.05 -0.11 -0.01 1,043

BENGKULU 5.25 22.18 -2.53 -0.26 -2.01 -0.08 -0.23 -0.00 1,549

LAMPUNG 5.55 21.42 -2.64 -0.18 -2.15 -0.08 -0.27 -0.02 7,116

JAWA 5.83 16.06 -2.13 -0.17 -1.78 -0.07 -0.15 -0.01 128,471

DKI 5.65 3.61 -0.57 -0.05 -0.48 -0.02 -0.04 -0.00 8,860

JABAR 6.46 13.06 -2.10 -0.24 -1.70 -0.07 -0.14 -0.02 38,965

BANTEN 6.19 8.86 -1.29 -0.11 -1.08 -0.04 -0.08 -0.01 9,029

JATENG 5.18 20.49 -2.54 -0.14 -2.16 -0.09 -0.19 -0.01 31,978

DIY 4.81 18.95 -1.59 -0.20 -1.24 -0.06 -0.12 -0.00 3,344

JATIM 5.50 19.95 -2.46 -0.18 -2.06 -0.07 -0.18 -0.02 36,294

KALIMANTAN 5.52 10.92 -1.58 -1.29 -0.15 -0.05 -0.13 -0.00 12,098

KALBAR 5.71 14.24 -2.00 -1.53 -0.29 -0.06 -0.17 -0.00 4,052

KALTENG 5.47 10.73 -1.68 -1.36 -0.15 -0.06 -0.15 -0.00 1,915

KALSEL 5.73 7.23 -1.20 -1.00 -0.09 -0.04 -0.11 -0.00 3,282

KALTIM 5.41 10.57 -1.37 -1.23 -0.00 -0.05 -0.10 -0.00 2,849

SULAWESI 5.38 16.84 -2.03 -1.41 -0.44 -0.07 -0.18 -0.00 15,788

SULUT 5.27 9.34 -1.32 -0.99 -0.21 -0.05 -0.12 -0.00 2,129

GORONTALO 4.76 29.05 -2.65 -1.65 -0.77 -0.09 -0.20 -0.00 922

SULTENG 5.83 21.80 -2.78 -1.98 -0.54 -0.08 -0.27 -0.01 2,295

SULSEL 5.39 14.98 -1.86 -1.29 -0.40 -0.06 -0.17 -0.00 8,479

SULTRA 5.15 21.45 -2.37 -1.56 -0.62 -0.08 -0.19 -0.01 1,963

BALI NT 5.92 21.25 -2.63 -0.50 -1.91 -0.08 -0.23 -0.01 11,828

BALI 6.21 6.72 -1.15 -0.20 -0.87 -0.04 -0.10 -0.00 3,384

NTB 5.46 25.92 -3.13 -0.49 -2.42 -0.10 -0.22 -0.03 4,184

NTT 6.12 28.19 -3.31 -0.74 -2.24 -0.09 -0.33 -0.01 4,260

INDONESIA TIMUR 5.99 33.29 -3.08 -2.21 -0.74 -0.10 -0.09 -0.01 4,654

MALUKU 6.04 32.28 -3.59 -2.42 -0.92 -0.11 -0.23 -0.01 1,252

MALUT 6.65 13.23 -2.02 -1.41 -0.44 -0.06 -0.19 -0.01 884

PAPUA 5.94 40.83 -3.19 -2.38 -0.76 -0.11 -0.01 -0.01 2,518

INDONESIA 5.72 16.72 -2.15 -0.38 -1.58 -0.07 -0.16 -0.01 218,869

18

Tabel 10: Reduction in number of poor population (thousand of population)

Pov Head Count

sim1 Periph Non

Periph Dp

ExpImp Logistic

Road2 Rail

Switch Pop

SUMATERA 7,922 -1,002.5 -67.2 -834.8 -32.1 -85.2 -5.2 46,030

NAD 1,157 -131.9 -5.5 -111.8 -4.6 -11.4 -0.7 4,032

SUMUT 1,828 -237.6 -16.2 -198.6 -7.4 -19.9 -1.0 12,451

SUMBAR 497 -66.3 -4.5 -55.0 -2.3 -5.7 -0.5 4,566

RIAU 732 -98.2 -5.8 -83.2 -3.1 -8.0 -0.5 5,854

JAMBI 313 -44.7 -3.9 -36.6 -1.4 -3.8 -0.2 2,636

SUMSEL 1,425 -179.3 -11.9 -151.6 -5.9 -12.4 -0.9 6,782

BABEL 102 -17.2 -2.4 -13.8 -0.5 -1.1 -0.1 1,043

BENGKULU 344 -39.2 -4.0 -31.2 -1.2 -3.6 0.0 1,549

LAMPUNG 1,524 -187.9 -13.1 -152.9 -5.7 -19.2 -1.3 7,116

JAWA 20,635 -2,742.7 -222.1 -2,283.4 -88.5 -196.6 -18.6 128,471

DKI 320 -50.5 -4.2 -42.6 -1.9 -3.2 -0.2 8,860

JABAR 5,089 -819.0 -91.9 -664.3 -26.7 -56.5 -6.5 38,965

BANTEN 800 -116.2 -9.8 -97.6 -3.8 -7.5 -0.8 9,029

JATENG 6,552 -810.9 -43.5 -691.1 -27.2 -59.7 -4.3 31,978

DIY 634 -53.2 -6.7 -41.4 -1.9 -4.1 -0.1 3,344

JATIM 7,241 -893.0 -66.1 -746.4 -27.0 -65.6 -6.6 36,294

KALIMANTAN 1,321 -191.4 -156.2 -17.7 -6.6 -16.1 -0.3 12,098

KALBAR 577 -80.8 -62.1 -11.8 -2.4 -7.0 -0.2 4,052

KALTENG 205 -32.1 -26.1 -2.9 -1.2 -2.9 -0.1 1,915

KALSEL 237 -39.4 -32.8 -2.9 -1.5 -3.5 0.0 3,282

KALTIM 301 -39.0 -35.2 -0.1 -1.5 -2.8 0.0 2,849

SULAWESI 2,658 -320.6 -222.1 -69.8 -10.3 -28.7 -0.3 15,788

SULUT 199 -28.1 -21.1 -4.5 -1.1 -2.5 0.0 2,129

GORONTALO 268 -24.4 -15.2 -7.1 -0.8 -1.9 0.0 922

SULTENG 500 -63.8 -45.4 -12.4 -1.8 -6.2 -0.1 2,295

SULSEL 1,270 -157.7 -109.7 -33.7 -5.0 -14.4 -0.4 8,479

SULTRA 421 -46.5 -30.6 -12.1 -1.6 -3.8 -0.2 1,963

BALI NT 2,513 -311.0 -58.9 -226.0 -9.3 -27.0 -0.8 11,828

BALI 227 -38.9 -6.6 -29.4 -1.2 -3.4 0.0 3,384

NTB 1,085 -131.0 -20.5 -101.3 -4.3 -9.4 -1.3 4,184

NTT 1,201 -141.2 -31.7 -95.3 -3.8 -14.3 -0.5 4,260

INDONESIA TIMUR 1,549 -143.2 -102.6 -34.6 -4.6 -4.3 -0.5 4,654

MALUKU 404 -45.0 -30.3 -11.5 -1.4 -2.9 -0.1 1,252

MALUT 117 -17.8 -12.5 -3.9 -0.5 -1.7 -0.1 884

PAPUA 1,028 -80.4 -59.9 -19.1 -2.8 -0.3 -0.3 2,518

INDONESIA 36,598 -4,711.5 -829.1 -3,466.3 -151.5 -357.9 -25.7 218,869

19

IV. CONCLUSION

We have simulated the benefits of several ways of reducing Indonesian transport costs, and shown how the several ways affect different regions and sectors. We find total benefits of around 5% of GDP. A substantial part of this is due to our assumption of a 1% reduction in iceberg trade costs; meaning that in general all production becomes 1% more efficient than before. If applied to perishable goods only, that benefit would be much less.

From another point of view, the great bulk of benefits arise in the core regions of Sumatera, Java and BaliNT -- simply because they account for so much of the Indonesian economy. However, we also notice that benefits accrued in the core spill over to peripheral regions. Similarly, efforts to improve transport to or from peripheral regions have benefits too for the core region economies. This is fortunate, for the core regions would likely have bear most of the considerable costs of improving transport in the periphery.

The combined transport improvement also reduces the country’s poverty incidence9 by 2.2% (4.7 million people). Interestingly, poverty reduction is higher in the eastern part of Indonesia as those provinces are suffering more from connectivity impediments. In addition, it is also due to a higher poverty base in those provinces.

Thus, alleviating the country’s multidimensional poverty will require not only accelerated economic growth but also a more inclusive growth process that provides rural areas and disadvantaged regions with greater economic opportunity and access to social services. Improving and developing the country’s connectivity is key to reducing poverty incidence by (i) connecting rural areas with regional growth poles, thereby widening access to markets and services; (ii) connecting the poorer eastern parts of Indonesia with markets in western areas through more efficient interisland transport systems; and (iii) improving international connectivity to boost the competitiveness of the country’s productive sector. Each connectivity effort is expected to have a role in improving employment, reducing the size of the labor force employed in the informal sector, and thereby enhancing economic productivity and reducing overall poverty incidence.

9 Computed from the result of Indo-TERM using cumulative density function of change in expenditure per capita from

the simulation. Lognormal distribution is assumed as in Bourguignon (2003), ‘The growth elasticity of poverty reduction: explaining heterogeneity across countries and time periods’, in T. Eicher and S. Turnovsky (eds), ‘Growth and Inequality’, MIT Press.

20

V. ALTERNATE ASSUMPTION ABOUT ICEBERG COST REDUCTIONS

We noted above that the largest cost reductions come from Components 1 and 2, and within these from a 1% reduction in iceberg trade costs, applying to all commodities. An alternate set of simulations is generated by SIM2.CMF. The only difference for SIM2.CMF is within Components 1 and 2, where now there is 0.5% reduction in iceberg trade costs, applying only to non-services commodities.

This change is enough to roughly halve national GDP gains, as shown in the Table below.

Table 1A: Percentage change national GDP effects of transport improvements

% change Total effect Component

1 Component

2 Component

3 Component

4 Component

5

SIM1 Real GDP

5.435 1.085 3.827 0.108 0.380 0.035

SIM2 Real GDP

2.300 0.461 1.319 0.107 0.377 0.035

Source: model simulations, variable SomeMacro

Regional SIM2 results [for Components 1 and 2] are shown in Tables 2A and 3A below, which should be compared to Tables 2 and 3 above.

Table 2A: Percentage change effects of better transport to or from peripheral regions

% change 1

Sumatera 2

Java 3

Kalimantan 4

Sulawesi 5

BaliNT 6

PapuaMlku Total

Indonesia

Real Household

Consumption 0.175 0.247 1.909 1.514 0.488 2.207 0.505

Real Investment

0.040 0.166 1.614 0.934 0.340 1.199 0.372

Exports to ROW

-0.125 -0.242 2.366 2.647 0.006 0.717 0.484

Real GDP 0.106 0.199 1.879 1.510 0.342 1.597 0.461

Real Wage level

0.087 0.159 1.818 1.425 0.399 2.116 0.410


0.115 0.250 1.749 1.782 0.546 1.530 0.511

CPI 0.053 0.093 -0.094 -0.197 0.090 -0.240 0.043

Source: model simulation SIM2, variable SomeMacro

21

Table 3A: Percentage change effects of better transport within Java/Sumatera/BaliNT

% change 1

Sumatera 2

Java 3

Kalimantan 4

Sulawesi 5

BaliNT 6

PapuaMlku Total

Indonesia

Real Household

Consumption 1.698 1.720 -0.007 0.363 1.668 0.477 1.451

Real Investment

1.134 1.562 -0.276 0.023 1.069 0.137 1.128

Exports to ROW

2.113 1.805 -0.573 -0.887 0.260 -0.515 1.172

Real GDP 1.479 1.686 -0.094 0.094 1.287 0.090 1.319

Real Wage level

1.445 1.466 -0.257 0.112 1.415 0.225 1.205


1.594 1.934 -0.189 0.236 1.728 0.176 1.472

CPI 0.005 0.081 0.148 0.201 0.036 0.251 0.079

Source: model simulation SIM2, variable SomeMacro

22

APPENDIX 1

Cumulative Distribution Function of Expenditure Per Capita and Its Log Normal Estimate

23

APPENDIX 2

Calculation of Change in Poverty Incidence, by Provinces

(Pages 23-30)

NANGGROE ACEH DARUSSALAM

Poverty Incidence Ex-ante

28.69% SUMATERA UTARA

Poverty Incidence Ex-ante 14.68%

Poverty Incidence Ex-post

25.42% Poverty Incidence Ex-post 12.77%

Change 3.27% Change -1.91%

SUMATERA BARAT


10.89% RIAU Poverty Incidence Ex-ante 12.51%



Change -1.45% Change -1.68%

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

24

JAMBI Poverty Incidence Ex-

ante 11.88% SUMATE

RA SELATAN





BENGKULU


22.18% LAMPUNG





0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

25

BANGKA BELITUNG


9.74% DKI JAKARTA





JAWA BARAT


13.06% JAWA TENGAH





0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

26

DAERAH ISTIMEWA YOGYAKARTA


18.95% JAWA TIMUR





BANTEN Poverty Incidence Ex-

ante 8.86% BALI Poverty Incidence Ex-ante 6.72%




0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

27

NUSA TENGGARA BARAT


25.92% NUSA TENGGARA TIMUR





KALIMANTAN BARAT


14.24% KALIMANTAN TENGAH





0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

28

KALIMANTAN SELATAN


7.23% KALIMANTAN TIMUR





SULAWESI UTARA


SULAWESI TENGGARA


Poverty Incidence Ex-post 8.02%


Change -1.32

%

Change -2.78%

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

29

SULAWESI SELATAN


SULAWESI TENGGARA




Change -1.86%

Change -2.37%

GORONTALO


MALUKU Poverty Incidence Ex-ante 32.28%



Change -2.65%

Change -3.59%

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

30

MALUKU UTARA


PAPUA Poverty Incidence Ex-ante 40.83%



Change -2.02%

Change -3.19%

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 1.0

ex-ante

ex-post

povline

pov inc 0

pov inc 1

31

REFERENCES

ADB (2010), Indonesia: Critical Development Constraints.

Ginting and Aji (2012), Macroeconomic Management, in H. Hill, M. E. Khan, and J. Zhuong (eds). Diagnosing the Indonesian Economy: Toward Inclusive and Green Growth. Anthem Press, London; February 2012.

Ginting and Aji (2011), The Impact of Pioneer Shipping in Indonesia, unpublished research note.

Hill, Khan, and Zhuang eds. (2012), Diagnosing the Indonesian Economy: Toward Inclusive and Green Growth.

Bourguignon (2003), ‘The growth elasticity of poverty reduction: explaining heterogeneity across countries and time periods’, in T. Eicher and S. Turnovsky (eds), ‘Growth and Inequality’, MIT Press.

World Bank (2010), Logistics Performance Index 2010: Indonesia. Washington, DC.

World Bank (2012), Logistics Performance Index 2012: Indonesia. Washington, DC.

World Economic Forum (2012), The Global Competitiveness Report 2012-2013.

***

Improving Indonesia's Domestic Connectivity: An Inter ...service in Java and limited freight...

Documents

Transcript of Improving Indonesia's Domestic Connectivity: An Inter ...service in Java and limited freight...