Interprovincial differences in the endowment and utilization in labor force by educational
attainment in Indonesia’s post-crisis economy
Mitsuhiko KATAOKA
Department of Economics, Chiba Keizai University, Chiba, Japan
ABSTRACT
We comprehensively explore the factors contributing to interregional inequalities in the laborendowment and utilization by educational attainment in the Indonesia’s post-crisis economy,
introducing a variable of the employment with j educational attainment per capita (where j consists offive different levels of educational attainment) and employing Cheng and Li’s (2006) additive
inequality decomposition method. Further, we employ Shorrocks’ (1980) one-stage Theildecomposition method to measure the factors contributing to overall inequality in employment rate,
which is the divergence between the employment rate in a nation and employment rates with j
education attainment in province i.
We find that the industrial structures and business functions seem to vary more widely fromprovince to province than the other characteristics, such as demographic structures and labor market
efficiency, do. The interprovincial differences in labor market efficiencies tend to be greater for thehighly educated groups than the less educated. This is affected by several factors: no universal social
security system, province-specific compositions of formal/informal sectors, and different minimumwage provisions across provinces. Moreover, the interprovincial inequities in employment rates withthe junior secondary education attainment have the most significant influences among all educationgroups. The recent increase in the corresponding labor force share could make this a crucial issue forthe country. Consequently, policies for improving the efficiency in the corresponding labor market
must be implemented.
Keywords: Education, interprovincial allocation, Indonesia, inequality decomposition
JEL classification code: R11, R12, R58
I.

Background and Objectives

Education develops skills and knowledge of labor force. It promotes economic growth in a subnational region and its development stage determines the education level. Educational endowment in labor
force and its utilization differ by sub-national region. This subject is one of major growing publicconcerns, especially, in the developing nations which faces an interregional resource imbalance and has
experienced a rapid educational expansion.
Given its insular geography and the world's fourth largest population, its extraordinary economic,
demographic, and cultural diversity, and richly endowed with natural resources, Indonesia faces unevenresource distribution across subnational regions as Java Island, which constitutes 6% of the national landarea, has almost 60% of economic activities. Over the past two decades, Indonesia has experienced amassive increase and significant changes in its labor force from 77.4 million in 1990 to 116.5 million in2010 and the labor force annually grew by 2.1%, which is significantly faster than the annual population
growth rate (1.5%). The annual growth rates of the different education groups of labor force varied widely:No primary, -1.5%; Primary, 1.1%; Junior secondary, 5.6%; Senior secondary, 6.2%; and Tertiary, 9.8%
(BPS various years b; BPS various years d).
Generally, the highly educated enjoys greater employment stability; however, in Indonesia, thehighly educated are less employed. This is most likely because the absence of a universal social security
system made it so that the less educated could not afford to remain unemployed.
Observing the existing literature, few studies thoroughly focused on the endowment and utilization inlabor force across Indonesia’s sub-national regions by educational attainment. We comprehensively
examine this subject in the Indonesia’s post-crisis economy, employing Cheng and Li’s (2006) additive
inequality decomposition method. Further, we employ Shorrocks’ (1980) one-stage Theil decomposition
method to measure the factors contributing to overall inequality in employment rate, which is the-1-divergence between the employment rate in a nation and employment rates with j education attainment inprovince i.

II. Method
II.1 Cheng and Li’s (2006) inequality decomposition method
Cheng and Li (2006) method shows the additive inequality decomposition analysis by using causal
factors when the decomposition variable is expressed with multiplicative components. Their technique,1
which improved upon that of Duro and Esteban (1998), presents the additive interpretive inequality
decomposition in per capita income, consisting of Theil second measures in productivity and labor
participation rates and their interaction terms. We use the variable of employment with j educational
attainment per capita, which is obtained by three multiplicative components (shown by Equation (1)), we
can apply Cheng and Li’s (2006) method to our study.
Now, let Pi , Li , and Ei represent the population, labor force, and employment in province i,respectively. Furthermore, let the nation in question have a total of m provinces. We divide the labor force
and employment variables into n groups based on educational attainment; as such, Lij and Eij represent
labor and employment with j educational attainment in province i. When the subscript i is omitted, theaforementioned variables represent the corresponding national values.
We introduce a variable of the employment with j educational attainment per capita in province i,denoted as xij = Eij Pi . This can be multiplicatively expressed as

xij = li ⋅ lij ⋅ eij (i = 1L m, j = 1L n, xij > 0 for all i and j.) ,(1)

where li = Li Pi is the labor force participation rate (LFPR) in province i,lij = Lij Li is the share of labor force with j educational attainment in province i, andeij = Eij Lij is the employment rate among those with j educational attainment in province i.
The first two multiplicative terms at the right hand side of Equation (1) is regarded as endowments
for the entire labor force and the labor force with j educational attainment, while the third term is regarded
as labor utilization/market efficiency with j educational attainment in a province. The provincial mean ofthe each variable in Equation (1) is expressed as µ x j , µl , µl j , and µ e j , that is, µ e j = (1 m )∑i =1 eij .
m()
x ) (T (µ , x ) ≥ 0, for all j )The interprovincial inequality in employment with j educational attainment per capita, as measured

by the Theil second measures, T j µ x j , x j , are given by()

Tj µxj , x j = 1

m

m∑
i =1

(

ln µ x jij(µ ⋅ µ

j

xj

(2)

j

)

Now, substituting Equation (1) into the right hand side of Equation (2) and multiplying quotient
inside the natural logarithm by()

T j µ x j , xij = 1

µle

le j

µl ⋅ µle yields
j

µx




ln ⋅
⋅
m∑  l l ⋅e µ ⋅µ 
i =1=1
where µ le j = 1 m

µ

m

l

l

 i

ij

j

ij

j

l

le j

(3)



m

 µl 

m

 µle j 

 µxj

i =1

 i 

i =1

 ij






 + lnln  + 1 ∑ lnm∑  l m l ⋅e  µ ⋅µ 
ij



l

le j



∑ (l ⋅ e ) . Similarly, multiplying the quotient inside the natural logarithm in the
m

i =1

ij

ij

second equity of the right hand side of Equation (3) by(µ ⋅ µ µ ⋅ µ ), we obtain

1

lj

ej

lj

ej

The additive inequality decomposition terms in Duro and Esteban (1998) can take positive or negativevalues, although a strict Theil index maintains a non-negative value for its property. It is difficult tointerpret the role of the negative values, which indicates that the inequality of the corresponding factor
negatively affects the inequality (Gisbert 2001; Cheng and Li 2008).

-2-

(

)

T j µ x j , xij = 1=1m

 µl 

m

 i 

i =1

 µl j µ e j

µle

 ij

lj



 µx j

 + lnln  + 1 ∑ ln⋅⋅m∑  l m l µ ⋅µ e µ ⋅µ 
i =1

ij

j

ej

l

le j

m

 µl 

m

 µl j m µe j  µle j

i =1

 i 

i =1

 ij 

i =1







(4)



 µxj







ij

lj

ej

Now, we may express the covariance of l and (l ⋅ e ) (denoted as σ ((σ (l , l ⋅e ) = (1 m )∑i =1 (li − µl ) lij ⋅ eij − µle
m

i

ij

ij

(

j

)

ij

i

li , l ij ⋅eijij

= (1 m )∑i =1 li ⋅ lij ⋅ eij − li ⋅ µle j − µl ⋅ lij ⋅ eij + µl ⋅ µle jm= µ x j − µl ⋅ µle j
If we divide all of the terms in Equation (5) by

µx

=

j

µl ⋅ µlej + ln + 1 ∑ ln
 + lnln  + 1 ∑ lnm∑  l 
m
m
 l 
e 
 µ ⋅µ  µ ⋅µ σ (l , l ⋅e )
i

ij

j

µl ⋅ µ le

l

le j



) ) as follows:)(5)(µ ⋅ µ ) , we get
l

le j

+1

(6)

j

Similarly, the covariance of lij and eij (denoted as σ (lij , eij ) ) can be expressed as

(

)(

)

σ (l , e ) = (1 m )∑i =1 lij − µl eij − µ e = µle − µl ⋅ µ e
m

ij

ij

j

j

j

(

j

(7)

)

j

Then, by dividing all of the terms in Equation (7) by µ l j ⋅ µ e j , we obtainµ leµl ⋅ µej=jj

σ (l , e )ijijµl ⋅ µ e
j

+1

(8)

j

Then, we may substitute Equations (6) and (8) into Equation (4) in order to finally obtain()

T j µ x j , xij = 1mmm
 σ (l , l ⋅e )  σ (l , e ) µl 
 µe 
µ ln l  + 1 ∑ ln j  + 1 ∑ ln j  + ln i ij ij + 1 + ln ij ij + 1
m∑
m
m








i =1
i =1
i =1
 li 
 lij  eij  µ l ⋅ µ le j µl j ⋅ µ e j


(

) (

)

= T j (µ l , li ) + T j µ l j , lij + T j µ e j , eij + Cov j (li , lij ⋅ eij ) + Cov j (lij , eij )(9)Equation (9) shows that the interprovincial inequality in employment with j educational attainment
per capita is the sum of three inequality terms and two interactions terms. The first three terms are strict
2
Theil second measures and take on non-negative values. Each inequality is governed by different forces:
The first term depends on regional demographic patterns, the level of economic development, and theexistence of unemployment benefits. The second depends on regional industrial structures, business
functions, and education systems. The third depends on regional shocks and labor market efficiency.
The last two interaction terms take on positive (negative) values when the component variables are
positively (negatively) correlated. They are equal to zero when the component variables are totally
uncorrelated. It should be noted that the last two terms never take on undefined values: as all of the meanvariables on the left-hand side of Equations (6) and (8) are positive, so too are the terms within the natural
logarithms.
Especially, the last interaction term indicates an interesting implication as it shows the correlation
between the relative size of labor market and its employability (market efficiency) in a province. The labormarket efficiency in a province is negatively associated with wage inflexibility, which is determined byseveral factors such as minimum wage provisions, union activity, and proportion of large firms(Armstrong and Taylor 2000). The implementation of a minimum wage will reduce wage flexibility since
wages will be prevented from falling below the legal minimum even in the face of high unemployment.
Strong unions can prevent wage cuts during the recessions, thereby reducing wage flexibility. Large firms

2

The equation forms of the Theil first and second measures are the divergence between the shares of two
variables, weighted by the numerator of variables inside natural logarithm (Gisbert 2001). The quotient
inside the natural logarithm of first three terms in the right hand-side of Equation (9) are expressed as(µl li ) = ((1 m) (li ∑m li )) , (µl lij ) = ((1 m) (lij ∑m lij )) , and (µe eij ) = ((1 m) (eij ∑m eij )) . Those are satisfied with the
i =1

j

i =1

j

property of Theil second measure.

-3-

i =1

are unlikely to be on the edge of competitiveness and do not therefore monitor their wage costs very
closely.
The dual economy, which separately coexists the formal and informal sectors, normally exists in thedeveloping economies. The aforementioned determinates factors do not apply in the informal sector(Comola and de Mello 2009). Like other developing nations, most less-educated work in the informal
sector in Indonesia (ADB 2010). In general, (less) developed provinces are richly endowed with highly(less) educated and with large (small) firms. Conclusively, the highly educated abundant provinces, which
are more likely (unlikely) to lose wage flexibility, show lower employability. Then, it can be assumed thatthe last interaction term σ (l j , e j ) takes the negative (positive) values for the higher (less) educated labor.

II. 2 Shorrocks’ (1980) one-stage Theil decomposition method
The second method was derived by Shorrocks (1980), which can decompose the overall inequalities
in employment rate into those between education subgroups and those between provinces within eachsubgroup based on the Theil second measure. As stated above, labor and employment are divided into neducation groups, which are classified into m mutually exclusive and collectively exhaustive provinces inaccordance with working location. The relationships can be expressed as L =
n

n

m

j =1

i =1

∑ ∑ L andijm

E = ∑ j =1 ∑i =1 Eij . Based on the aforementioned structure, overall inequality in employment rates can bemeasured by the following Theil second measure: (Anand 1983; Fields 2001)

T (e, eij ) = ∑ j =1 ∑i =1 (Lij L )ln (e eij )
n

m

(10)

where e = (L E ) . Equation (10) can be additively decomposed into between-group inequality and withingroup inequality as follows (Shorrocks 1980):

T (e, eij ) = ∑ j =1 (L j L )ln (e e j ) + ∑ j =1 (L j L )⋅ Tw jnn

= TB + TW

where TW j =(11)∑ (L L )ln(e e ). This term is the Theil second measure index for the within-group
m

i =1

ij

j

j

ij

inequality, which is a weighted average of the between-province inequalities in employment rates for eacheducation group.

III. Data
The data used in this study consists of annual observations of 30 contiguous Indonesian provinces’
populations, labor forces, and employment figures from 2002 to 2010. The population data are from the
Population Census (BPS various years b) and the Intercensal Population Survey (BPS various years c).
The data on the provincial labor forces and employment are from Labour Force Situation in Indonesia(BPS various years d).
The present study aggregates labor and employment statistics into five groups in order to conduct a
decomposition analysis: (1) no primary (no schooling or incomplete primary education); (2) primary; (3)junior secondary; (4) senior secondary and (5) tertiary education.
BPS redefined labor force and employment status twice for the last two decades. Currently, the labor
force is defined as persons aged 15 and above, while before 1994, it was those aged 10 years and above.
This change affected all of the provincial labor force statistics recoded from 1998 onward. In 2001,
unemployment status was redefined to include those who were not working and had given up actively
searching for a job, whereas previously, it had only included those who were seeking employment.
However, no retroactive adjustment of past relevant data (by province and educational attainment) has
been officially made thus far. Consequently, we use the data on those variables covering the years 2002 –2010 in conducting our analysis.
It should also be noted that after the economic crisis of 1998, political reforms led to the creation ofeight new provinces and that the province of East Timor gained independence. Consequently, the numberof provinces changed from 27 to 34. However, only four provinces established prior to the year of 2002
has released data in 2002 and after. As of yet, no effort has been made to adjust the historical data in orderaccount for these changes. As such, we study only 30 provinces and aggregate the data from the new andexisting provinces for each year.

-4-IV. Empirical results
IV.1 Cheng and Li’s (2006) inequality decomposition method
Figures 1 through 5 present the inequality decompositions of the education groups’ employment by
3
a number of factors (which were calculated using Equation (9)).
First, T j (µl , lij ) , which is mainly determined by province-specific industrial structures, business
functions, and education systems, appears to be a significant factor in determining the overall inequalities
T j µ x j , x j . The observations are fairly uniform across the education groups. These findings show that theindustrial structures and business functions seem to vary more widely from province to province than theother characteristics, such as demographic structures and labor market efficiency, do. Those values for no
primary education group (ranging between 0.060 and 0.141) and tertiary education group (ranging between
0.038 and 0.092) are more uneven than those with other education group are. This may be because lesseducated workers are more often employed in the labor intensive agriculture sector, which has a greater
presence in less developed provinces, while highly educated workers are more attracted to the valueadding manufacturing and service sectors, which are more common in developed provinces.
Second, interprovincial inequalities in labor market efficiency by education group T j (µ ej , eij ), whichtakes on small values, has very little influence on the overall inequality for each education group. However,the cross-group comparisons shown at Figure 6 provide interesting observations. The interprovincial
differences in labor market efficiencies for the highly educated groups tend to be greater and more
fluctuate than the less educated. This is simply because there is no universal social security system, so
there is no province in which the less educated can afford to remain unemployed. Additionally, minimumwage provisions do not apply in the informal sector, which more consist of less educated. The reverse isalso true for the higher educated. In 2001, the nation decentralized the minimum wage provisions toprovinces and districts and the minimum wages rapidly increased by the mid-2000s and those increases
varied across provinces (Islam and Chowdhury 2010). Besides, the province-specific compositions offormal/informal sectors also affect interprovincial differences in labor market efficiencies. The more
fluctuation values for the period for the higher educated group infers that the regional shocks affects thecorresponding group’s provincial labor market more greatly than less-educated counterparts.
Finally, the interaction terms Cov j (lij , eij ) take on positive (negative) values for lower (higher)
education groups; though, these values are small and fluctuate cyclically. For instance, the annual
arithmetic mean values for Groups 1 through 5 are 0.0029, 0.0007, -0.0003, -0.0030, and -0.0013,
respectively. The values of Cov j (lij , eij ), which is not standardized, can range from zero to positive infinity.
Thus, we employ a scaled version of covariance, correlation coefficient, which is takes on a value between
1 and – 1. The annual arithmetic mean values for Groups 1 through 5 are 0.4103, 0.1425, -0.0041, -0.1847,
and -0.0548 respectively (Table 1). This observation does not support our hypothesis that the highly
educated abundant provinces, which are likely to have the lower wage flexibility, show lower
employability.

()

IV.2 One-stage Theil decomposition of the employment rate by educational attainment
Table 2 shows the results of the one-stage Theil decomposition analysis of the inequality inemployment rates for three selected years. The overall inequality increased from 0.0020 in 2002 andpeaked to 0.0028 in 2005, and then decreased to 0.0012 in 2010. Decomposition analysis reveals that theinequalities between the education groups’ employment rates (TB) played a crucial role in determining theoverall inequality. The between-group inequality (TB) increased from 0.0013 in 2002 to 0.0019 in 2005
and then decreased 0.0008 in 2010 and the corresponding contribution share increased from 67.4% in 2002
to 68.8% in 2005 and then decreased 64.8% in 2010.
Decomposition of the within-group inequality (TW) also showed the divergence and convergenceprocess and the inequality values increased from 0.0007 in 2002 to 0.0009 in 2005 and then decreased to
0.0004 in 2010. In the contribution shares of each group’s (TWj), those in the junior secondary groupshow the most significant values among the education group, 12.1% in 2002, 12.1% in 2005, and 11.5% in
The interaction term cov(li , lij ⋅ eij ) are excluded from Figure 1 to 5 as those do not provide significant
economic interpretations.
3-5-

2010. Generally, higher interprovincial variations in the employment opportunities could lead to increased
interprovincial migration. Then, if provinces with greater employment opportunities were to restrict labor
immigration, interprovincial tensions would rise precipitously. Since the ongoing increase in the labor
force share of junior secondary education group could make this a crucial issue for the country.

Major references
Asian Development Bank (2011) The informal sector and informal employment in Indonesia, Manila,
Philippines
BPS (Badan Pusat Statistik, Central Bureau of Statistics) (various years a) ‘Statistical Yearbook ofIndonesia”, Jakarta.
BPS (various years b) ‘Population Census’, Jakarta.
BPS (various years c) ‘Intercensal Population Survey’, Jakarta.
BPS (various years d) ‘Labour Force Situation in Indonesia’, Jakarta.
Cheng, Y.-S. and S.-K. Li. (2006) ‘Income inequality and efficiency: A decomposition approach andapplications to China’, Economics Letters 91 (1): 8–14.
Chun, C. and Khor, N. (2010) ‘Minimum wages and changing wage inequality in Indonesia’, ADB
Economics Working Paper Series No. 196, Asian Development Bank, Manila.
Duro, J.A. and Esteban, J. (1998) ‘Factor decomposition of cross-country income inequality, 1960–1990’,
Economics Letters 60 (3): 269–75.
Gisbert G. F. J., (2001), ‘On factor decomposition of cross-country income inequality: Some extensions
and qualifications’, Economics Letters 70 (3): 303-309.
Islam, I. and Nazara, S. (2000) ‘Minimum wage and the welfare of Indonesian workers’, International
Labour Organization Occasional Discussion Paper Series No. 3, Jakarta.
Shorrocks, A.F. (1980) ‘The class of additively decomposable inequality measures’, Econometrica, 48 (3):613–25.

Figures 1–5 Cheng and Li’s (2006) inequality decomposition method by education attainment
Figure 1: No primary

Figure 2: Primary

Figure 3: Junior secondary

Figure 4: Senior Secondary-6-

Figure 5: Tertiary

Figure 6 Interregional inequalities employment rate with j education attainment
T(μe1, ei1)
T(μe4, ei4)

Theil

0.0035T(μe2, ei2)T(μe5, ei5)T(μe3, ei3)0.00300.00250.00200.00150.00100.00050.0000
02

04

06

08

10

(Year)

Table 1 Correlation coefficient between labor force share and employment ratewith j education attainmentCor(li1, ei1)Cor(li2, ei2)Cor(li3, ei3)Cor(li4, ei4)Cor(li5, ei5)

Minimum
ValueYear
0.28882004-0.00092007-0.16902008-0.30922007-0.2355
2008

Maximum
ValueYear
0.5312
2007
0.29142010
0.28382005-0.03252003
0.1700
2003

Mean
Value
0.41030.1425-0.0041-0.1847-0.0548

Table 2 One-stage Theil decomposition analysis of the inequality in employment rates

T(e, eij)
TB
TW
TW1
TW2
TW3
TW4
TW5

2002
0.00200.00130.00070.00010.00020.00020.00020.0000

Theil Value
2005
2010
0.00280.00120.00190.00080.00090.00040.00010.00010.00020.00010.00030.00010.00020.00010.00000.0000-7-% of Contribution
2002
2005
2010
100.0
100.0
100.0
67.4
68.8
64.8
32.6
31.2
35.2
2.8
2.5
4.8
8.8
7.4
8.4
12.1
12.1
11.5
8.2
8.1
7.7
0.7
1.1
2.7