Distribution Pattern of Capital across Provinces in Indonesia
Mitsuhiko Kataoka
Department of Economics, Chiba Keizai University, Chiba, Japan
I. Background and Objectives
On account of Indonesia’s size, insular geography, large population (fourth largest in the world),
and extraordinary economic and social diversity, the nation is beset by an uneven distribution of
resources and unequal economic growth among regions. This is indicated by the oft-cited fact that theJava-Bali region constitutes less than 10% of Indonesia’s total land area but accounts for almost twothirds of its economic activity (Hill, 2000). To address this issue, the Indonesian government has
implemented various policies; however, the target outcomes have not yet been achieved. Despite
significant policy issues, the spatial distribution of resources has not been studied because of the data
constraints on regional capital.
The objective of this study is to examine the distribution pattern of (public) capital acrossprovinces, comparing actual investment share (θit) and equivalent distribution share θ it* , using
provincial capital estimates. This study employ the conventional Solow growth accounting approachand Kataoka’s (2010) panel data set of the 26 contiguous Indonesian provinces for 1986–2007.
II. Method

To compute an equivalent distribution share θ it*,we use following two empirical results.
1. Estimation of the regional production function with panel data analysis.
*
2. Computation of equivalent distribution share θ itin liner simultaneous equations.

II.1 Estimation of Provincial Production Function
The main scope of this study is to analyse the spatial distribution pattern of factor inputs, andtherefore, the individual provincial effects of production factors can be taken into account.
Type of production function: Cobb-Douglass production function for each province i.
Assumptions
The behaviors differ by production factors.
a. Labor (L) and Private Capital (KP) follow profit-maximizing behavior of firms.
→It relocates to regions offering higher returns.
b. Public capital (KG) follows the government’s resource distribution policies.
→It relocates to regions offering either higher or lower returns.
Formula Specification
a. Elasticity values of L and KP are the same across provinces.
b. Elasticity values KG differ by provinces.

Yit = α it + β1 Lit + β 2 K Pit + β 3i K Git + ε it(1)

Y : GDP L : Labor
K P : Private Capital K G : Public Capital()α : constant β k : elasticity ∑k β k = 1

i: province t: year k: prodcution factor
II.2 Computation of equivalent distribution share in liner simultaneous equations
Derived from equaiton (1), equation (2) can be defined as Solow’s growth accounting equation ineach province i.
K&
K&
(2)
Y&it
L&
= α& it + β1 it + β 2 P it
+ β3i G itYitLit
K P itK G it
The first term on the right-hand side of equation (2) represents technical change, while the threelatter terms are the contributions of input factors to output growth in province i in year t.
Next, we calculate an interprovincial equivalent distribution share of public capital growth(hereafter referred to as equivalent distribution share) in province i in year t, denoted as θ it* . The-1-equivalent distribution share is a provincial share of public capital growth in the nation, where thecorresponding province’s public capital growth contribution to its overall output growth,

β 3i

K& G it, in equation (2), is the same across provinces in year t. This relationship can be

K G itdescribed as
β 3i

K& G itK G it= β3 jK& G jt
K G jt(∀ i ≠ j ) ,(3)

where j represents province j, which differs from province i. The retirement values of public capital inprovince i (j) are assumed to be zero, and therefore, the periodic change in public capital in provincei(j) in year t, denoted as K& G it , is equal to the corresponding annual public investment I G it . Equation(3) can be rewritten asI θ*
β3i G t it

K G itI θ*= β3 j G t jt(∀ i ≠ j, 0 ≤ θ ( ) ≤ 1) ,*i j t

K G jt(4)

where IGt is national public investment in year t. Since we use the n province’s data set in year t, we
solve the n linear simultaneous equations with n unknown variables as follows:I θ*
β3i G t it

K G itI θ*− β3 j G t jt

∑θ = 1 .

K G jt(∀ i ≠ j, i( j ) = 1L n ) ,

=0

(5)

n

*
it

(6)

i =1

Note that there are (n − 1) equations in the form of equation (5).
III. Data
III. 1 Output and Input Variables• GDP (Constant Price 2000, 1986-2007)– Gross Regional Domestic Product of Provinces in Indonesia (BPS)• Labor (1986-2007)– Employment from Labor Force Situation in Indonesia (BPS)• Capital (Estimated, Constant Price 2000 , 1983-2007 )– Data on capital stock have not been officially published in Indonesia. As substitutes,
estimates are employed from Kataoka (2010), based on perpetual inventory method (PIM).
– In PIM, capital is calculated by the past summation of gross fixed capital formation (GFCF)
from provincial expenditure statistics.
In addition, GFCF values in Indonesia’s provincial/national expenditure statistics are shown
through the aggregated values of private and public capital. This study is constrained to use theaggregated capital data, assuming the coefficients of capital differ across provinces in this study. The
equations (2) and (5) are thus rewritten as

Y&it

β 2i

Yit

= α& it + β1

I tθ it*

K it− β2 jL&it

Lit+ β 2iI tθ *jt

K jt

K& it

=0

K it

,

(7)

(∀ i ≠ j, i ( j ) = 1L n )(8)where K , I , and β 2 i are the aggregate figures of capital and investment and the coefficient of
capital, respectively. All values of GDP, capital, and investment are deflated into the constant 2000
price.
III.2 Number of Provinces: Aggregated 33 provinces into 26 Provinces– From 1998, 7 new provinces were split from the existing ones and East Timor became
independence. (27 provinces →33 provinces)

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–
–

BPS published 33 provincial GDP variables only from 2002 and employment variables from2006, respectively.
No retroactive adjustment of those data series has been published so far.

Thus, we use data pertaining to 26 provinces and aggregate the data of the new and existing
provinces, and define the sum of the capital stock values for the 26 provinces as the national capitalstock value for each year.
IV. Empirical Results and Major Findings
IV.1 Estimation of Provincial Production Function– 572 Panel Observations: 26 provinces and 22 years (1986–2007)– Crisis dummy takes a value of 1 in the case that GDP values in a province for 1998–2003 wereless than the corresponding values in 1997, and takes a value of 0 in other cases. We assume
that the crisis affected economies until the year 2003, as the post-crisis national GDP was lower
than the values for 1997 until 2003.
– A Levin–Lin–Chu test rejects the null hypothesis of a common unit root in log-transformed
GDP variables.
– Regression results with periodic fixed effect specifications are omitted in Table 1 due to theirstatistically insignificant and economically meaningless coefficient values.
Table 1: Estimates of Provincial Production Function

Model

1: Pooled Model
2: Fixed Effects (FE)Model
3:Capital Dummy Model
4:FE and Capital Dummy
Model

Dummy onEconomic
CrisisConstant

Ln L

Ln K

(α+α1i)β1

β2 (β2i)

0.009
(0.1)
a

0.096
(4.6)**
0.297

0.876
(57.2)**
0.722-0.143(-3.8)**-0.112

0.424(5.5)**
0.216(34.1)
c(-7.8)**-0.109(1.9)*b(4.5)**
0.816

d(-7.5)**-0.100**F-Statistic(Null Hypothesis)(-8.8)**(12.6)

Adj-R2

0.943
0.992

146.1
(H0:α1=α1i, β2=β2i)
16.1(H0:α1i=α1i, β2=β2i)

0.992
0.995

i: t-ratios are shown in parentheses.
ii: **and * denotes significance at the 0.01 and 0.05 level, respectively.
iii: The notations a–d differ by prefecture, ranging as follows.
a ranges between -0.375 and 0.920; b ranges between -4.185 and 13.882; c ranges between 0.702 and0.825; d ranges between -0.758 and 0.854. For more details in the notation b, see Table 2.

Estimation Results by Models
1. Model 1 (Pooled Model) and Model 2 (Provincial FE Model) shows economically meaningful
coefficient values with statistical significance, and a sufficiently high coefficient of determinant.
However, F-statistics strongly rejected the both null hypotheses, confirming that the presences ofprovince-specific individual effects on the parameters of production factors (capital and labour)
should be taken into account.
2. Model 3 (Provincial Capital Dummy w/o FE Model) shows economically meaningful parametervalues with statistical significance and a sufficiently high coefficient of determinant. The
coefficients of capital dummy all take on positive values, ranging from 0.702 to 0.825, as isshown in Table 2.
a. Provinces specialising in capital-intensive mining, such as Aech (0.825), East Kalimantan(0.813), Riau (0772), and North Sumatra (0.769) take on relatively higher parametervalues for capital.
b. Provinces specialising in labour-intensive agriculture and tourism-oriented service, suchas West Nusa Tenggara (0.702), East Nusa Tenggara (0.712), and Yogyakarta (0.720).
c. The parameter ratio of capital to employment ranged between 3:1 and 4:1, and income
shares of capital ranged between 0.764 and 0.792, which are higher than the-3-corresponding national figures found in previous studies (Table 3). These figures seemplausible, considering the labour surplus in Indonesia.
3. Model 4 (Provincial Capital Dummy w FE Model) involves a sufficiently high coefficient ofdeterminant, but several coefficients of capital dummy have negative or not statistically significant
values. The coefficient of labour shows much higher values (0.816) for developing countries.
Therefore, we consider that the estimation result of Model 3 is reliable among all four regressions.
Table 2: Coefficients for Capital in Model 3β2iProvince
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

0.825
0.769
0.772
0.757
0.732
0.738
0.748
0.749
0.758
0.751
0.742
0.720
0.751
0.748

Province
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 Indonesia
23. West Nusa Tenggara
24. East Nusa Tenggara
25. Maluku
26. Papua
Mean

β2i
0.724
0.723
0.813
0.784
0.759
0.737
0.755
0.744
0.702
0.712
0.722
0.768
0.750

i: All coefficient values are statistically significant at the 0.01 level.
Table 3: Income Shares of Capital in Indonesia Found in Previous Studies
Income Share
Study
Periodof Capital
Ikemoto (1986)
1970–1980
0.405
Sigit (2004)
1980–2000
0.643
Sutanto (2004)
1992–2002
0.625
van der Eng (2009)
1971–2007
0.528
This study
1986–2007
0.764-0.792
IV.2 Equivalent distribution shares and distribution pattern of capital across provinces
The differences in actual provincial (regional) share, denoted as θ it , from the equivalent

distribution share θ it* , are shown for 1987 – 2007 in Figure 1 – 4; positive values indicate more
provincial capital distribution than computed distribution figures, where provinces’ capital growth
contributions to corresponding output growth are the same across province (region). The opposite istrue for negative values. In essence, positive values mean capital concentration in a province (region),while negative values mean dispersion.

()

Findings from Figure 1-4: Difference between actual investment share and equivalent*
distribution share θ it − θ it in provinces (region)
Findings from Figure 1• Dividing the nation into the Java-Bali region and the rest of the nation (the non-Java-Bali region),
Figure 1 shows differences in the Java-Bali region. It presents how the difference between actual
share and equivalent distribution share in the Java-Bali region changed from having a positive
value to a negative one in the year of 1998. This indicates a structural change in the investment
distribution pattern in the region before and after the economic crisis. Investment concentration in

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•

the Java-Bali region for 1987–1997 turned into dispersion in 1998 and after, with the opposite
being true in the non-Java-Bali region.
In 1999, Indonesia began a process of rapid government decentralisation, moving away from its
formerly strong centralised government structure. Under the decentralisation process, thegovernment reduced investment activities in the capital-intensive Java-Bali region, whichconstitutes nearly two-thirds of the economic activity in the nation.

Findings from Figure 2•

Jakarta, where over one-fourth of the national investment for the observation period was made,obtained more investment distribution than its equivalent distribution share until the year of 2000,
and less following that year.

•

Two other major provinces in the Java-Bali region show different dispositions. East Java, which
has the third-largest investment distribution share, followed the same trend as Jakarta, while West
Java, which has the second largest distribution share, obtained more than its equivalent
distribution share of investment in the decentralisation era.

Figure 1: The Java-Bali, 1987–2007

Figure 2: 3 major provinces in Java island

•
•
•
•

Findings from Figure 3••

Greater investment distributions were observed in most years in the resource-rich provinces of
Riau, North Sumatra, East Kalimantan, and Papua.
However, in the province of Aceh, which has a long history of resistance to any form of external
authority, investment distribution was stagnant until the 2004 tsunami disaster, becoming morevibrant after the subsequent peace agreement with the central government.

Findings from Figure 4••

Decreased investment distributions were seen in several sparsely populated provinces.
The provinces of Jambi, West Nusa Tenggara, and East Nusa Tenggara, which are far from thenational centre and specialise in labour-intensive agriculture, have had consistently less than theequivalent share of actual capital distribution for many years.

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Figure 4: Remote off-Java provinces
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•
•
•
•
•
•
•
•

•
V. Future Improvements and Extensions
1. Data manipulation for crisis dummy variables could be improved. Assuming that the economiccrisis affected several provinces during 1998–2003, we introduced a dummy variable for thepresence of effects of the economic crisis on provincial economies, and set a value of 1 for cases
in which GDP values in a province were less than the corresponding values in 1997. Considering
province-specific business cycles, these effects must still be technically confirmed.
2. The historical events affecting interregional capital distribution could still be examined in greater
depth. Province-specific development projects associated with regional development plans could
contribute to further discussions on understanding policy implications in provincial economies.
3. With the parameters and variables employed in this study, the estimations of total factor
productivity (TFP) and marginal productivity of production factors at the provincial level could
be extended in numerous ways.
4. The factor input variable of human capital could be taken into account to estimate provincial
production function. As for proxy variables of human capital, the data on working-age population(labour force) with territory education from Labor Force Situation in Indonesia (BPS, various
years) can be employed.
REFERENCES
Baltagi, B. H. (2005) Econometric Analysis of Panel Data, Wiley, West Sussex.
Hill, H. (2000) The Indonesian Economy, Cambridge University Press, Cambridge, MA.
Ikemoto, Y. (1986) ‘Technical progress and the level of technology in Asian countries’, The
Developing Economies, 34 (4): 368–90.
Kataoka, M. (2010) Economic growth and interprovincial resource allocation in Indonesia, Paper
presented at the 12th International Conference of the East Asian Economic Association, Seoul
King, R. G. and Levine, R. (1994) ‘Capital fundamentalism, economic development, and economic
growth’, Carnegie-Rochester Conference Series on Public Policy, 40: 259–92.
Sigit, H. (2004) ‘Indonesia’, in Total Factor Productivity Growth: Survey Report, ed. Noriyoshi
Oguchi, Asian Productivity Organization, Tokyo: 98–133.
Sutanto, A. B. (2004) ‘Konsep dan aplikasi total faktor produktivitas (TFP)’, Majalah Nakertrans, 24(3), pp.10–11.
Van der Eng, P. (2009) ‘Total factor productivity and economic growth in Indonesia’, Working Papers
in Trade and Development 2009(01).
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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