Allocation of Human Capital across Regions and Economic Growth in Indonesia
Mitsuhiko KATAOKA
Department of Economics, Chiba Keizai University, Chiba, Japan
I. Background and Objectives
There has been a growing consensus that human capital is a major factor in economic growth
process in the modern neoclassical economic literatures since the 1950s (Mincer 1958, Schultz 1961,
Becker 1964). In the competitive market economy, mobility of skilled labor to a subnational region
offering higher returns results in increased income in that region and causes its economy to become
skilled-labor intensive. In contrast, a subnational region offering the lower return becomes a lessskilled-labor intensive economy because of the presence of regional comparative advantage.
However, the existing empirical literatures in the cross-country (-region) study have notreached common results. The difference is generated by the diversity in institutions, difference inlabor markets and education quality across economies, presence of direct and indirect role of humancapital on the economies, the measurement error of human capital, and construction of human capitalproxies (Van Leeuwen and Folvali 2008, Son 2009, Yueh 2013).
Given an Indonesia’s horizontally long insular geography, large population, and extraordinary
natural and social diversity, its subnational regions show various stages of economic developmentfrom a traditional labor-intensive agricultural economy to a knowledge-intensive business service
economy. The nation is beset by an uneven distribution of resources among regions. Besides, this isindicated by the oft-cited fact that the Java-Bali region constitutes less than 10% of Indonesia’s total
land area but accounts for almost two-thirds of its economic activity (Hill, 2000). There are several
studies to examine the role of human capital on the economies in Indonesia (Table 1). The existing
literatures vary by application of human capital proxy variables and don’t focus on human capital
distribution across provinces.
Table 1 Existing Literatures
Studies
Van Leeuwen andFolvali (2008)
Garcia andSoelistianingsih(1998)
Balisacan et al.
(2003)

Vidyattama (2010)

Proxy
Average years of education
Province’s working-age population
share with secondary education
and # of the student per teacher
Adult literacy, years ofschooling, or distance to thesecondary school.
Years of schooling.
Average year of schooling in theworking-age population

Findings: Effects of HC
Positive effects on the LR national
economic growth for 1890–2000
Positive effects on the provincial
income growth and on reduction inregional income inequality for 1975–
1993
Positive effects on the regional
economic growth, not on the poverty
reduction.

Positive effects on the provincial per
capita GDP for 1985-2005, but weakstatistical significance.
There are two main objectives in this study: 1) to explore the factors contributing to inequality
in per capita human capital across subnational regions, employing the Theil population-weighted
decomposition index and 2) to estimate the regional production functions to examine the effects ofhuman capital on the economies.
II. Method
II.1 Inequality decomposition of per capita employment with tertiary education: Theil L index
Let N it , Lit , Eit , LHit and E Hit represent population, labor force, employment, labor force andemployment with tertiary education attainment, in a region i and year t, respectively. We disaggregate
labor force and employment into two-education group: those with and without the tertiary education.
The variable of labor (employment) with the tertiary education is defined as human capital variable. A
nation consists of n regions.

-1-

The per capita employment with tertiary education in region i, xhit = E Hit N it , can then be
multiplicatively expressed asxhit = lit ⋅ hlit ⋅ ehit (i = 1L n ) ,(1)

where lit = Lit N it is the per capita labor force in region i,hlit = LHit Lit is labor force with tertiary education per labor force in region i, andehit = EHit LHit is the employment rate with tertiary education in region i; this relation in anation can be expressed as
xht = lt ⋅ hlt ⋅ eht .
(2)
The aforementioned variables without the subscript i represent the corresponding national values.
Interregional inequality in per capita employment with tertiary education—as measured by the
Theil population-weighted method, so-called Theil L index—is given by

T ( xhit ) = ∑ pit log( xht xhit )
n

(3)

i =1

where pi = N i N stands for the share of region i in the national population. Using the relations
shown in equations (1) and (2) and the properties of natural logarithms, equation (3) can be additively
decomposed as follows:

T (xhit ) = ∑ pit log(lt lit ) + ∑ pit log(hlt hlit ) + ∑ pit log(eht ehit )n= T (l ) + T (hl ) + T (eh )
i =1

n

n

i =1

i =1

(4)

Equation (4) shows the additive decomposition of the Theil L index of per capita employment withtertiary education into the sum of three factors, T (l ), T (hl ), and T (eh ) , referring to the approach ofDuro and Esteban (1998). The first two terms are regarded as interregional inequalities in endowment
while the last term is regarded as interregional inequality in efficiency. Note that in equation (4), each
additive decomposition factor, with the exception of T (l ) , is not a strict Theil L index, which isdefined as the multiplicative probability represented by its population share and its occurrence.
Similarly, Equation (1) can be rewritten as
xhit = lit ⋅ eit ⋅ heit
(5)
where eit = Eit Lit is employment rate in region i, andheit = EHit Eit is employment with tertiary education per employment in region i. Similarly,
T ( xhit ) = T (l ) + T (e ) + T (he )Equation (4) can be represented as(6)II.1 Regional Production Function
Type of production function: Cobb-Douglass production function for each province i.
Assumptions
The behaviors differ by production factors.
a. Labor (L), human capital (H) and private capital (KP) follow profit-maximizing behaviors.
→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, H and KP are the same across provinces.
b. Elasticity values KG differ by provinces.

Yit = α it + β1 Lit + β 2 H it + β 3 K P it + β 4i K G it + ε it ,()

Y : GDP L : Labor H : Human Capital K P : Private Capital K G : Public Capitalα : constant β k : elasticity ∑k β k = 1 i: province t: year k: prodcution factor

-2-

(7)

III. Data
III. 1 Output and Input Variables• GDP (Constant Price 2000, 1986-2010)– Gross Regional Domestic Product of Provinces in Indonesia (BPS)• Labor and employment by educational attainment (1986-2010)– Labor Force Situation in Indonesia (BPS)• Capital (Estimated, Constant Price 2000 , 1983-2010 )– Data on capital stock have not been officially published in Indonesia. As substitutes, theestimates are employed from Kataoka (2013), 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.
– GFCF values in Indonesia’s provincial/national expenditure statistics are shown through theaggregated values of private and public capital. This study is constrained to use theaggregated capital data, assuming the coefficients of capital differ across provinces in thisstudy. The equations (7) are thus rewritten as

Yit = α it + β1 Lit + β 2 H it + β 3i K it (= K P it + K G it ) + ε it ,(8)

where K is the aggregate figures of private and public capital. In this estimation, we define the labor
force with and without tertiary education as variables of human capital and labor, respectively.
III.2 Number of Provinces: Aggregated 33 provinces into 26 Provinces• From the year of 1998, 7 new provinces were split from the existing ones and East Timor province
became independence. (27 provinces →33 provinces)• BPS published 33 provincial GDP variables only from 2002 and employment variables from 2006,
respectively. However, 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 each variable value for the 26 provinces as the national value for eachyear.
IV. Empirical Results
IV.1 Inequality factor decomposition
Figure 1 shows the inequality decomposition in per capita employment with tertiary education
across provinces and its determinant factors, using Equation (4). Comparing the inequality in labormarket efficiency by education attainment, the inequality in overall employment rate T(e) is also
shown. Several interesting observations are summarized as follows.
Figure 1: Population weighted Theil decomposition analysis
0.14
T(xh)

T(l)

T(eh)

T(e)

T(hl)

0.12
0.10
0.08
0.06
0.04
0.02
0.00
1988

1992

1996

•

2000

2004

2008

year

-0.02

The population weighted Theil index of per capita employment with tertiary education T(xh)decreased over the years with several fluctuations. It infers the employment opportunity with thehigher education attainment has been spreading across provinces.

-3-

•
•
•

In the factor decomposition analysis, the interprovincial inequality in labor force with tertiary
education per labor force T(hl) was a predominant factor in T(xh). It accounts for more than 85
percent in overall inequality throughout years, with an exception in 2008.
Other two factors play minor roles in most years. In other words, endowment in labor force withhigher education varies across provinces, but neither endowment in labor force nor efficiency in
its labor market does much.
The inequality in employment rate with the tertiary education T(eh) plays a major role in 2008, theyear of the global recession. On the other hand, the inequality in overall employment rate T(e)does not. It infers that the impact from the 2008 recession was more serious in the employment
with the higher education and varied by provinces. The resource rich provinces, such as Aceh,
North Sumatra, and South Kalimantan, which have a strong linkage with global demands in themining goods, increased more unemployment with higher educational attainment than otherprovinces.

IV.2 Provincial Production Function– 650 Panel Observations: 26 provinces and 25 years (1986–2010)– Crisis dummy takes a value of 1 in the case that GDP values in a province after 1998 were lessthan the corresponding values in 1997, and takes a value of 0 in other cases.
– 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 2 due to theirstatistically insignificant and economically meaningless coefficient values.
Table 2: Estimates of Provincial Production Function
Model
1: Pooled Model
2: Fixed-Effects(FE) Model
3: Capital
Dummy Model

Constant

L

H

K

Adj-R
sq.

F-Statistic

0.939

Fc = 147.2

0.991

Fd =16.1

c

β1

β2

Β3(3i)

CrisisDummy-0.059(-0.4)
a0.157***(6.8)
0.158***-0.037*(-1.8)
0.116***

0.850***(48.9)
0.517***-0.074**(-2.1)-0.141***

(2.7)

(6.6)

(21.3)(-9.3)

b-0.138**

2.968

***

0.090

*

0.111

***

0.990

(8.2)
(1.7)
(6.3)
(-8.8)
Notes: t-ratios are shown in parentheses.
aDiffers by prefecture, ranging between 1.879 and 3.264.
bDiffers by prefecture, ranging between 0.503 and 0.625. See Table 4 for more details.
cShows the null hypothesis H0: A = Ai, B = Bi.
dShows the null hypothesis H0:Ai = Ai, B = Bi.
*, **, and *** denotes significance at the 0.10, 0.05, and 0.01 level, respectively.

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 both null hypotheses of the different constant term (A) andparameters (B), confirming that the presences of province-specific individual effects on theparameters of production factors 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 0.503 to 0.625, as isshown in Table 3.
• The human capital has positive effects on the regional economies, but shows one fifth ofcoefficient values of capital. This indicates that one percent increase in human capital contribute

-4-

•

the increase in an economy in a region by 0.1%, which is about one-fifth of the contribution incapital increase.
Table 3 shows that the coefficients of the capital dummy are positive by provinces. The
resource rich provinces, such as Aceh (0.622), East Kalimantan (0.625), and Riau (0.599) takeon the higher coefficient values for capital. The less-developed provinces, such as West Nusa
Tenggara (0.504), East Nusa Tenggara (0.503), and Maluku (0.507), take on the lower
coefficient values. The provinces of Yogyakarta and Bali, which have higher per capita laborwith tertiary education attainment, show the relatively lower coefficient values of capital.
Human capitals in those provinces take relatively more significant roles to contribute to theeconomy.
Province

β3i

Aceh
0.622
N. Sumatra 0.590
Riau
0.599
W. Sumatra 0.556
Jambi
0.522
Bengkulu
0.507
S. Sumatra 0.570

Table 3: Coefficients for Capital in Model 3
Provinceβ3i

Lampung
0.557
W. Java
0.599
Jakarta
0.588
C. Java
0.579
Yogyakarta 0.513
E. Java
0.595
Bali
0.543

Provinceβ3i

W. Kalimantan 0.536
C. Kalimantan 0.520
E. Kalimantan 0.625
S. Kalimantan 0.575
N. Sulawesi
0.541
C. Sulawesi
0.520
S. Sulawesi
0.558

Province

β3i

SE. Sulawesi
0.515
W. Nusa Tenggara 0.504
E. Nusa Tenggara 0.503
Maluku
0.507
Papua
0.563
Mean
0.554

i: All coefficient values are statistically significant at the 0.01 level.
IV.3 Effects of human capital on TFP
It is broadly believed that human capital facilitates development and adoption of new technology.
We examine effects of human capital on technological progress, referring to the approach of Benhabib
et al. (1994). Using total factor productivity (TFP) growth as its proxy and taking into account on thehuman capital spillover of the technology across region, our specification can be shown as•

TFP it max Y jt − Yit j (i, j = 1,L n, i ≠ j )= c + g (H it ) + m(H it )
TFPit
Yit





(9)

where a dot is indicative of a periodic change. Equation (9) presents that the TFP growth depends on
the three factors: the exogenous technological growth, the technological growth associated with theability of a region to innovate domestically (endogenous growth), and the technological spillover
effects from the leading region. The second term indicates that the human capital independently
enhances technological progress and the third term infers catch-up effects that the region with thelower initial output experiences faster productivity growth. The notation of g and m are parametervalues. The human capital and output variables are taken in natural logarithm value.

Table 4 Effects of human Capital on TFP growth (n=26)
Aggregate
Per capita
C-2.963 **0.749(-3.949)(0.257)
g
0.409
*
0.649
(2.363)(1.144)m
2.454**-0.039 **(5.097)(-2.433)
Adj-R
0.490
0.149
Notes: t-ratios are shown in parentheses.
* and ** denotes significance at the 0.05 and 0.01 level, respectively.
Table 4 shows the empirical results, which consist of two valuations of the dependent andindependent variables expressed by the aggregate and per capita values, using Equation (9).

-5-Considering the effects of business cycle, we use the periodical change in TFP for 1987–2010 to
examine the presence of the long-run effects of human capital on TFP.
The estimation results in the aggregate values model shows economically meaningful parameterswith the statistically significant values and the fit is sufficient with adjusted R 2 . Human capital has
exogenous, endogenous growth effects and spillover effects on technological progress in thecorresponding provinces. Regarding spillover effects, technology adoption from other provinces ismore effective for provinces at the smaller economies. On the other hand, per capita value model show
most of parameters are statically insignificant in terms of effects of human capital.

V. Future Improvements and Extensions
Our empirical results are sensitive with respect to the human capital proxy and the specificationof regional production functions. Therefore, there are several potential extensions for this study.
1. Data employment for human capital variables could be considered. In the existing literature, some
use the average years of schooling and employment/labor with the secondary education
attainment as a proxy. Besides, Indonesia’s Ministry of National Education publishes the numberof the university, student, and lecturer by provinces.
2. This study employs the simple Cobb-Douglass province-specific production functions with
provincial capital dummy. The specification form varies by assumptions. The alternative
approach could be applied.
REFERENCES
Balisacan, A. M., Pernia, E. M. and Asra, A., 2003, Revisiting growth and poverty reduction inIndonesia: what do subnational data show?, Bulletin of Indonesian Economic Studies 39: 329–51.
Becker, G.S., 1964, Human Capital: A Theoretical and Empirical Analysis. NewYork: Columbia
University Press for the National Bureau of Economic Analysis.
Benhabib, J. and Spiegel, M. M., 1994, The role of human capital in economic development evidence
from aggregate cross-country data, Journal of Monetary Economics 34: 143–73.
Duro, J. A. and Esteban, J., 1998, Factor Decomposition of Cross-Country Income Inequality, 19601990, Economics Letters, 60: 269–75.
Garcia, J. G. and Soelistianingsih, L., 1998, Why do differences in provincial incomes persist inIndonesia?, Bulletin of Indonesian Economic Studies, 34, pp. 95.
Folloni, G. and Vittadini, G., Human capital measurement: a survey, Journal of Economic Surveys 24:
248–79.
Hill, H., 2000, The Indonesian Economy, Cambridge University Press, Cambridge, MA.
Kataoka, M., 2013, Economic Growth and Interregional Resource Allocation in Indonesia’, Studies inRegional Science (forthcoming).
Le, Trinh, John Gibson, and Les Oxley, 2003, Cost and Income-Based Measures of Human Capital,
Journal of Economic Surveys 17(3): 271–307.
Mincer, J. ,1958, Investment in human capital and personal income distribution. Journal of Political
Economy 66(4): 281–302.
Rowley, C. and Redding, G., 2012, Building human and social capital in Pacific Asia, Asia Pacific
Business Review: 295–301.
Schultz, T.W., 1961, Investment in human capital. American Economic Review 51(1): 1–17.
Son Hyun H. , 2010, Human Capital Development, ADB Economics Working Paper Series, No. 225
Sundrum, R. M., 1986, Indonesia’s rapid economic growth: 1968–81, Bulletin of Indonesian
Economic Studies 22: 40–69.
Van Leeuwen, B. and Foldvari, P., 2008, Human Capital and Economic Growth in Asia 1890–2000: A
Time-series Analysis, Asian Economic Journal 22: 225–40.
Vidyattama, Y., A Search for Indonesia's Regional Growth Determinants, ASEAN Economic Bulletin
27: 281–94.
Yueh Y.L., 2013, China's growth : the making of an economic superpower, Oxford University Press
Acknowledgment
This study is supported by Grant-in-Aid for Scientific Research C (23530285) from the Japan
Society of the Promotion of Science, and Visiting Research Fellowship from the Southeast Asia
Program, Cornell University.

-6-