Economic Growth and Structural Changes in Indonesia
Mitsuhiko Kataoka
Department of Economics, Chiba Keizai University, Chiba, JapanI.

Background and Objectives Introduction
Under market-oriented factor mobility, factor inputs (capital and labor) relocate to regions offering

higher returns. If producers move to the most profitable locations for plants and workers move to provinceswith the highest wages, the more (less) the factor inputs in subnational regions with higher productivities,
the greater the increase (decrease) in national output.
The standard neoclassical economics literature that analyzes national growth considers two sources:
increase in factor inputs and productivity growth. However, the extent to which interregional factor
mobility has contributed to a national economy has been infrequently examined because of regional data
constraints. This research examines efficiency growth in interregional resource allocation, which isattributed to interregional allocation of factor inputs from low-productivity to high-productivity regions.

II. Methodology and Data
II.1 Methodology: Efficiency Index and Growth
The efficiency growth in interregional resource allocation, measured by growth in efficiency index, iscomputed by the following two empirical results: (i) estimation results in the province-level regional

production functions and (ii) Pareto-optimal interregional allocation of the factor inputs.
The efficiency index is calculated by comparing actual national output values with theefficiently allocated national value at which social welfare is maximized. The index comprises
efficiencies in resource utilization and resource allocation in the national economy, employing twotypes of outputs: efficiently utilized GDP (hereafter referred to as “potential GDP”) and efficiently
allocated GDP (hereafter referred to as “optimal GDP”). The potential GDP is the value of GDP
when resources are fully employed without reallocating resources across provinces, while the optimal
GDP is the value of GDP when total social welfare is maximized by reallocating resources acrossprovinces.
*Let Yt , Yt , and Yt**

be actual GDP, potential GDP, and optimal GDP in a national economy inyear t, respectively. The efficiency index, Et , represents the interrelationships among the three GDP
types, and can be expressed as the actual-to-optimal GDP ratio of a nation in year t,Et = Yt Yt** = Yt Yt * ⋅ Yi * Yt ** = EtU ⋅ EtAU(1)A

where Et and Et refer to efficiency in utilization (hereafter referred to as the “utilization efficiencyindex”) and efficiency in allocation (hereafter referred to as the “allocation efficiency index”),respectively. Similarly, taking the first derivative of the right-hand side of Equation (1) with respect
to time t, the efficiency index can be expressed as∂Et ∂t = ∂EtU ∂t + ∂EtA ∂t(2)-1-Equation (2) presents the additive decomposition of the growth in efficiency index into the sum ofgrowth in utilization efficiency and allocation efficiency indexes.
II.1.1 Provincial Production Function
A simple Cobb-Douglass aggregate production function for each province i is employed and it isassumed coefficients for variables may change over timeln (Yit ) = α 0 + α1i + α 2t + ∑ j β1 j ⋅ ln (X ijt ) + ∑ j β 2 j ⋅ ln (X ijt )⋅ t + ε it ,(3)where Y = GDP, α = coefficient indicating total factor productivity (TFP), and X = input factors;

β represents elasticity values of the corresponding input factor j. The input factor j refers to both
capital and labor variables in this study. For each provincial function, a constant return of scale withineach time period t is assumed:∑ B = 1.
jj

II.1.2 Welfare Maximizing Interregional Resource Allocation
In the first fundamental theorem of welfare economics, a perfect competition across provinces
leads to a Pareto-optimal interprovincial resource allocation. For any given year, efficient allocationof resources can be determined by a computational problem in resource allocation, which isequivalent to solving a small computable general-equilibrium model with the objective function of
maximizing social welfare, subject to technology and resources constraints. The technology constraint
refers to the estimation results of the aggregate provincial production function in Equation (3). The
result is a set representing efficient resource allocation, defined as optimal resource allocation of****

inputs, X ijt , and optimal GDP, Yit .
Then, we can solve a central planner’s problem to obtain the optimal input allocations:max W = ∏it YitSi ,(4)(Yit , X it )subject to( )production technologies: Yit = F X ijt
Endowments:(5)∑ X = X ( X ≥ 0 for all i )iittit(6)where W is a social welfare function of the Cobb–Douglas form with the actual provincial output
shares S i used as weights. In endowments, input variables without the subscript i indicate thecorresponding national values as well.
II.2 Data
This study employs data on 26 provincial GDP and factor inputs for the period 1986–2007. As aresult of political reforms in Indonesia after the 1998 economic crisis, 7 new provinces were carved out
from the existing ones and the province of East Timor was granted independence. Thus, the number ofprovinces increased from 27 to 33; however, no retroactive adjustment of relevant data has been carried-2-

out so far. In order to avoid inconsistency, we use the data for 26 provinces and aggregate the statistics forthe new and existing provinces. The sources are summarized as follows.

•GDP: The data are sourced from Gross Regional Domestic Product of Provinces in Indonesia byExpenditure (BPS, various years a) and all figures are converted into the 2000 constant price.

•Employment: The data are sourced from Labor Force Situation in Indonesia (BPS, various yearsb).

•Capital: Data on capital stock have not been officially published in Indonesia; therefore, theprovincial capital data are employed from Kataoka (2010), which estimated on the basis of theperpetual inventory method. The data are constructed by accumulating each year’s investment,
obtained from the Gross Regional Domestic Product of Provinces in Indonesia by Expenditure,and subtracting the retirement values of the past investment.

III. Empirical Results
III. 1 Province-level Regional Production Function
In order to avoid a heteroskedasticity problem due to large regional differences, either regionaldummy variables in the resource-rich provinces or regional fixed effects were employed. The fiveresource-rich provinces are Aceh, North Sumatra, Riau, East Kalimantan, and Papua. Additionally,
considering the impact of the financial crisis on the provincial economies in Indonesia, a dummy
variable that takes a value of 1 in the post-crisis period and 0 at other times is also employed.
Table 1 Provincial Production Function Estimates
R1
R2
R3
R4-1.416-1.561-1.679-0.106(-10.29) (-20.08)** (-21.09)** (-0.29)
0.842
0.833
0.807
0.687
Cpital(47.05)** (50.19)** (74.5)** (22.62)**
Labor0.161
0.184
0.213
0.056
(6.79)** (12.1)** (15.37)** (-1.08)
Time Trend-0.0140.011
0.022(-1.28)(4.86)** (9.82)**
Capital*t-0.003-0.003**(-2.49)(-2.14)*
Labor*t0.005
0.003(2.75)** (4.11)**
Regional Dummy
0.246
0.246
0.244
(10.97)** (10.96)** (10.83)**
Post-Crisis Dummy-0.229-0.236-0.232-0.230********(-7.87)(-8.23)(-7.95)(-14.36)
Provincial Fixed Effect
yes
0.979
0.980
0.979
0.994
R2
Constant

Note 1: Figures in parenthesis are t-values.
Note 2: * and ** indicate that estimates are at the 5% and 1 % significance levels, respectively.
Note 3: Fixed-effect coefficients are not reported.

Shown at Table 1, R3 is employed as production technologies of maximizing problem because
all coefficients are statistically significant and their signs are economically meaningful. Capital played
an important role in Indonesia’s provincial production with elasticities 0.807 and that of labor input isfar smaller than that of capital at 0.213. Strong and positive coefficients for the time-trend variables-3-implies that technical change played a vital role in promoting Indonesia’s provincial production
during the study period. The coefficient of the regional dummy indicates a positive and strong value,
implying that resource-rich provinces have much greater output than other provinces. The coefficientof the post-crisis dummy shows a negative and strong value, implying that production values
decreased significantly in the year 1998 and after.
III. 2 Pareto-optimal Interregional Allocation of Factor Inputs
We calculate the Pareto-optimal interregional factor inputs in year t. Tables 2 and 3 present thebiggest and the smallest gap between the national share of actual and optimal values of input
allocations for each province, with the year. The positive (negative) value indicates more (less)allocations than the optimal. Several interesting findings are summarized as follows.
•Table 2 shows that, apparently, there exists a large surplus of capital in the province of Jakarta.
The actual provincial capital shares for Jakarta ranged from 25.5% in 1986 to 27.3% in 1997.
This indicates that Jakarta, which, as the capital city, has of a number of capital-intensive
industries, faces an overconcentration of economic activity.

•The resource-rich off-Java provinces show different dispositions. Riau province, the booming
special economic region, shows a higher-than-optimal rate of capital formation, whereas twoprovinces, Aceh and North Sumatra indicate lower-than-optimal rates.
Table 2 Gap between actual and optimal allocation by province – CapitalMaxMin
MeanGap (%) Year Gap (%) Year

Sumatra01. Aceh
02. North Sumatra
03. Riau
04. West Sumatra
05. Jambi
06. Bengkulu
07. South Sumatra
08. Lampung
Java-Bali
09. West Java
10. Jakarta
11. Central Java
12. Yogyakarta
13. East Java
14. Bali

•-0.4
-0.2
1.8
-0.3
0.1
0.1
0.7
0.0

2007
1992
1993
1986
1997
1996
1997
2005

-2.3
-0.9
0.3
-0.6
-0.2
-0.1
0.1
-0.5

1991
2007
1987
1993
2007
2007
1986
1991

-1.4
-0.5
1.2
-0.5
0.0
0.0
0.5
-0.2

-1.910.2
-1.1
0.2
-1.0
0.01998
1999
1994
1986
2000
1987

-4.9
7.8
-1.7
0.0
-1.9
-0.3

2002
1986
2005
1999
1994
2007

-3.6
9.0
-1.3
0.1
-1.4
-0.1

Max
MinMeanGap (%) Year Gap (%) Year
Kalimantan
15. West Kalimantan
16. Central Kalimantan
17. East Kalimantan
18. South Kalimantan
Sulawesi
19. North Sulawesi
20. Central Sulawesi
21. South Sulawesi
22. Southeast Sulawesi
Eastern Indoneisa
23. West Nusa Tenggara
24. East Nusa Tenggara
25. Maluku
26. Papua

0.3 1986
0.4 2007
0.2 1996-0.3 2002-0.2 1998
0.0 1998-1.3 1986-0.6 2006

0.00.1-0.5-0.5-0.1 1986
0.0 1996-0.1 1987
0.0 2002-0.3 1998-0.2 1994-0.6 1998-0.1 1986

-0.2
-0.1
-0.4
-0.1

0.1 1999
0.0 1997
0.1 2000
0.6 2007

0.0 2000-0.1 1998-0.2 1991-0.5 1998

0.1
-0.1
-0.1
0.0

Table 3 shows that the actual labor allocation in Jakarta is far less than what it should be. The
reverse is true of West Java, the adjacent province of Jakarta. Considering that some parts ofWest Java is within commuting distance of Jakarta, the province has a sizable residential
population working in Jakarta.

•In Ache and East Kalimantan, the actual labor allocation is far less than the optimal levels
because these provincial economies specialize in non-labor-intensive mining sectors. On thecontrary, the three large Java provinces, West, Central, and East Java, show far more actual-4-

allocation of labor input than they should as these economies specialize in the labor-intensive
manufacturing sector.
Table 3 Gap between actual and optimal allocation by province – LaborMaxMinMeanGap (%) Year Gap (%) Year
Sumatra01. Aceh
02. North Sumatra
03. Riau
04. West Sumatra
05. Jambi
06. Bengkulu
07. South Sumatra
08. Lampung
Java-Bali
09. West Java
10. Jakarta
11. Central Java
12. Yogyakarta
13. East Java
14. Bali

-0.3
1.2
-4.0
0.5
0.6
0.5
0.5
2.5

2003
1987
2007
1994
1996
1996
2002
1989

-3.3
-0.2
-6.7
0.1
0.3
0.3
-1.5
1.5

1987
2007
1987
2007
2007
1988
1986
2006

-1.8
0.4
-5.1
0.3
0.5
0.4
-0.1
1.8

2.6
-12.4
9.6
1.1
7.0
1.0

1998
1999
1986
1989
1986
1988

0.4
-14.6
7.7
0.7
3.2
0.6

1999 1.1
1997 -13.8
1997 8.3
1998 0.9
2006 4.4
1998 0.8

MaxMinMeanGap (%) Year Gap (%) Year
Kalimantan
15. West Kalimantan
16. Central Kalimantan
17. East Kalimantan
18. South Kalimantan
Sulawesi
19. North Sulawesi
20. Central Sulawesi
21. South Sulawesi
22. Southeast Sulawesi
Eastern Indoneisa
23. West Nusa Tenggara
24. East Nusa Tenggara
25. Maluku
26. Papua

0.7 1995
0.2 2006-4.1 2007
0.6 1994

0.4 1998-0.1 1986-5.6 1986
0.2 2006

0.6
0.1
-4.6
0.4

0.7 1987
0.5 1996
1.9 1987
0.5 2002

0.3 2002
0.3 20030.9 2003
0.2 1987

0.5
0.4
1.3
0.4

1.4 1994
1.9 1987
0.6 2000
0.1 1987

1.0 2005
1.3 1999
0.1 1988-0.7 1998

1.2
1.5
0.4
-0.3

III. 3 Efficiency Growth
Given the previous estimation and calculation results, the three efficiency indices as well as
their growth rates are computed and shown at Table 4. The major interesting observations aresummarized as follows.
Table 4 Efficiency Indexes and Growth Rates
Efficiency
Utilization
Allocation
Growt
Growt
Growt
Value
Value
Value
h (%)
h (%)
h (%)
1986 0.896
0.9920.903
1987 0.894 -0.24 0.989 -0.34 0.904 0.10
1988 0.905 1.25 1.001 1.25 0.904 0.00
1989 0.920 1.68 1.017 1.56 0.905 0.12
1990 0.935 1.54 1.030 1.29 0.908 0.25
1991 0.943 0.94 1.038 0.79 0.909 0.14
1992 0.954 1.14 1.047 0.83 0.912 0.30
1993 0.945 -1.00 1.034 -1.23 0.914 0.24
1994 0.941 -0.42 1.027 -0.68 0.916 0.27
1995 0.932 -0.87 1.014 -1.24 0.920 0.38
1996 0.911 -2.27 0.990 -2.32 0.920 0.05
1997 0.881 -3.30 0.956 -3.44 0.922 0.15
1998 0.935 6.07 1.016 6.28 0.920 -0.20
1999 0.930 -0.49 1.010 -0.57 0.920 0.09
2000 0.924 -0.64 1.004 -0.64 0.920 -0.01
2001 0.915 -1.01 0.994 -1.00 0.920 -0.01
2002 0.914 -0.06 0.995 0.06 0.919 -0.12
2003 0.927 1.34 1.004 0.95 0.923 0.39
2004 0.930 0.39 1.008 0.43 0.922 -0.04
2005 0.942 1.29 1.019 1.03 0.925 0.26
2006 0.953 1.18 1.027 0.80 0.928 0.37
2007 0.959 0.61 1.034 0.66 0.928 -0.05
0.330.20
0.13
Mean 0.927
1.011
0.917
(100)
(60.8)(39.1)•The efficiency index ranges from 0.881 to 0.959 over the observation period. The reciprocal of
the efficiency index indicates national welfare growth prospects; the prospects of welfare growth
are relatively greater at a potential growth rate of 4.2% to 13.4%.

•

The efficiency index has grown at an arithmetic average rate of 0.33%, annually. The growth
rates in the utilization efficiency and the allocation efficiency indices were 0.20% and 0.13%,-5-respectively, and their contribution to efficiency growth accounted for 60.8% and 39.1%,respectively. These results imply that better resource utilization contributed more to national
economic growth in Indonesia than did resource allocation.
•With regard to annual variations, the allocation efficiency index constantly rose from 1986 to1997, but the growth slowed down afterwards. This fact implies a structural change ininterregional allocation of factor inputs between the pre- and post-crisis periods. In the pre-crisis
period, labor and capital moved to more profitable provinces, which implies a pro-efficiency
interprovincial allocation. The post-crisis period was not characterized by pro-equity
interprovincial allocation.

IV. Conclusions
Given the estimation results of province-level production functions and computations of optimal
resource allocation, this study examined the efficiency in interprovincial resource allocation. A
structural shift can be found from a pro-efficiency allocation before the economic crisis to a nonefficiency allocation after the crisis. The overconcentration of economic activity in Jakarta did not
improve the efficiency in the interregional resource allocation.
There are several the scope for improvements in this study. First, the panel-data estimation
techniques for the province-level production functions can be improved. In order to examine thestructural change during the economic crisis, this study employed post-crisis dummy variables.
Several other econometric techniques such as CUSUM test can be an alternative application. In thismodel, the post-crisis dummy variables were the same across provinces. However, Akita andAlisjahbana (2002) found that the impact of the economic crisis differed across regions and that theeffects were more severe in the Java provinces. Their empirical findings should be analyzed by othereconometric techniques in the future.
Second, the welfare-maximizing resource allocation model can be another improvement. The
model entails the application of the Cobb–Douglass production function and weighs its output by
actual output values; however, there are no constraints in terms of interprovincial mobility of factor
inputs. If relocation costs are assumed in the analysis, for plant and equipment and in regard to thesocial ties in the residential community, the results would be more relevant.
References
Akita, T., and A. Alisjahbana, 2002, Regional Income Inequality in Indonesia and the Initial Impact of theEconomic Crisis, Bulletin of Indonesian Economic Studies, 38(2), pp.201–222.
Badan Pusat Statistik (BPS, Central Bureau of Statistics), various years a, Gross Regional Domestic
Product of Provinces in Indonesia by Expenditure, Jakarta.
̶̶̶, various years b, Labor Force Situation in Indonesia, Jakarta.
Kataoka, M., 2010, Provincial capital stock in Indonesia, 1983-2007, The 10th International Conference ofIndonesian Regional Science Association, Surabaya, July 29, 2010.

Acknowledgment
This study is supported by Grant-in-Aid for Scientific Research C (20530211) from Japan
Society of the Promotion of Science.

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