determinants of regional disparities in indonesia

Working Paper in Economics and Development Studies

Department of EconomicsPadjadjaran University

Center for Economics and Development Studies,Department of Economics, Padjadjaran UniversityJalan Cimandiri no. 6, Bandung, Indonesia. Phone/Fax: +62-22-4204510http://www.ceds.fe.unpad.ac.id

For more titles on this series, visit:http://econpapers.repec.org/paper/unpwpaper/

Determinants of Regional Disparities in Indonesia : Lessons from Provincial Level

Muhammad RefqiAchmad Kemal Hidayat

Master of Applied Economics, Universitas Padjadjaran

December, 2019

No. 201906

1

Determinants of Regional Disparities in Indonesia : Lessons from Provincial

Level

By MUHAMMAD REFQI, ACHMAD KEMAL HIDAYAT *

The phenomenon of regional income inequality, which is experienced by both

developed and developing countries, might also occur at the district level. This study measured the conditions of difference at both the national and

provincial level. Measured using the Theil Inequality Index, income inequality

was narrowed on the national scale and varied at the provincial scale. Furthermore, panel data regression was used to find the explanatory factors

of regional income inequality using data from 2010 to 2017. According to the

result, general allocation funds, road and education might be significant factors in decreasing income inequality, while GDP per capita, DBH

(Revenue Sharing Funds), and spatial planning policies might produce the opposite impact. There is considerable influence of natural resources on

inequality, and fiscal transfers were not able to quickly overcome these

conditions. The government was advised to be more considerate about the importance of underdeveloped areas through fiscal transfer reformulation,

potential economic maximization, and equitable development with proper spatial planning to promote income convergence and equalize welfare.

* Master of Applied Economics, Universitas Padjadjaran. West Java ([email protected])

1. Introduction

One of the most discussed economic issues in the past few decades has been regional

inequality within a country. The theory of Kuznets (1955) found that income distribution

tended to be widened in the early phase of economic growth and shrank in the future. This

theory has been clarified by Wang and Fan (2004) through their studies on regional disparity

in China. They found that after being reformed and opening up its economy, China’s inter-

provincial disparities tended to enlarge at the beginning and then narrowed since 2004. On the

other hand, this theory has been tested many times and has not been proven in various

countries (Gallup, 2012). Charoenphandhu and Yukio (2012) discovered that income

inequality in Thailand did not indicate any decreasing trends despite high economic growth as

predicted by Kuznets. Inequality conditions might also undergo fluctuations trend as

experienced by Indonesia from 1984 to 2008 (Kharisma & Saleh, 2013). They detected

factors that might have been affecting disparities in Indonesia, such as economic crisis, crime,

and fiscal transfer. These previous studies suggest that inequalities do not automatically

decrease even in a growing economy.

Many countries including Indonesia have attempted to overcome regional disparity issues.

The first constitution (1945) states in both its opening and its body the importance of

manifesting welfare among societies within the country with equity as one of its main

concerns. One of the solutions proposed by the government of Indonesia was the

implementation of regional autonomy through Law No. 32 (2004), Law No. 33 (2004b), and

2

Law No. 23 (2014). These regulations widened the authority of local governments in political,

fiscal, and administrative matters. In fiscal aspects, local governments received budgetary

transfers from the central government to carry out development and reduce the imbalance in

financial capacity between regions more flexibly. In the long run, local governments were

expected to perform better and began to show balanced inter-regional development and create

prosperity for the entire society.

However, even though decentralization has been found to have a negative impact on

regional inequalities (Irawan, 2015; Katamso & Junadi, 2018), the level of inequality in

Indonesia remains high. Lessman (2012) noted that regional inequalities in Indonesia were

quite severe when compared to other countries in East Asia and the Pacific regions. Currently,

based on data from the Indonesia Statistical Bureau, Daerah Khusus Ibukota (DKI) Jakarta

citizens’ per capita income is 14 times higher than people in Nusa Tenggara Timur. This

might be because of the failure to determine the right system of decentralization. This issue

raises the question of whether decentralization suggests a detrimental effect on the regional

disparity of Indonesia or vice versa. Some of the results of previous studies (Liu, Martinez-

vazquez, & Wu, 2014; M. A. B. Siddique, Wibowo & Wu, 2008; Rodriguez & Ezcurra,

2009). stating that fiscal decentralization has a positive impact on inequality, have caused this

issue to be investigated further.

Additionally, researchers have conducted many studies to discover the appropriate

determinants of regional growth using different variables and methodologies. Examining

factors that promote economic growth are indicated as an invaluable input to detect variables

that affect the condition of inequality within a region. Resosudarmo and Vidyattama (2006)

defined physical investment, trade openness, and the role of oil and gas as significant

variables for regional growth. Vidyattama (2010) in his next study on this topic added other

factors such as human capital, local government spending, and roads as factors affecting the

economic growth at the regional level. Other determinants emerged from the research

conducted by Supartoyo, Tatuh, & Sendouw (2013). They revealed that labor force and net

export might impact economic growth at the regional level. Until recently, studies that analyze which regional growth determinants might affect the

movement of inequality index were limited. According to Theil Entropy Index, economic

growth and population are the two variables used in the measurement of inequality. This

indicates that all variables affecting regional growth might also potentially influence the

inequality index. However, studies addressing the significant determinants of regional

disparity in Indonesia are quite limited, especially at the provincial level considering

inequalities at the district level within the province. This issue becomes an urgent matter since

the inequality issue might affect the unity of a country (Williamson, 1965) and the societal

welfare is a primary goal of a nation. The impact of the Spatial Planning Law (2007) is also

less observed in this issue. The creation of provincial and district spatial planning policies is

mandated by law and part of decentralized authorities, which has economic development as

one of its purposes. Therefore, while highlighting the issues mentioned above, this paper will

focus on understanding the inequality conditions and the causes of regional disparities, and

then develop alternative policies and inputs for the decentralization system to lessen the

inequality level more effectively.

2. Literature Review

This section will explain some theories related to regional income inequality. Some

findings from previous studies will also be presented to provide an overview of and essential

information regarding the inequality issue, to uncover possible research gaps that will be used

in this study.

3

2.1 Regional Income Inequality

Indonesia is one of the countries that experienced high regional income inequality (Akita &

Kawamura, 2002). To measure the regional disparities, researchers often used a weighted

coefficient of variation (Williamson, 1965), the Theil Entropy Index (Theil, 1967), or the

variance of log-income. According to Theil, if the regional income and population change are

proportionate, there will be no change in the inequality level. This means that a shift in both

variables might impact the disparity of a country or region. The Theil Inequality

Decomposition Method is related to Theil Inequality Indices (T and L). These are additively

decomposable and satisfy several desirable properties as a measure of regional income

inequality, such as mean independence, population-size independence, and the Pigou-Dalton

Principle of Transfers (Bourguignon, 1979; Shorrocks, 1980).

A Two-stage Nested Inequality Decomposition Analysis is conducted to explore the factors

determining regional income inequality. This method, developed by Akita (2003), is

correspondent to a two-stage nested formula from the Analysis of Variance (ANOVA). It

decomposes overall regional inequality as measured by a Theil index based on district-level

GDP and population data into three components: between-region, between-province, and

within-province inequality. Within-province describes the inequality between districts, and

between-province means the inequality conditions between provinces within a region. This

model is appropriate to be applied in examining the issue of inequality in Indonesia which has

the same hierarchy pattern as depicted inError! Reference source not found..

Figure 1. Hierarchy of Government authority

However, until recently, studies which measure the inequality condition in Indonesia at the

provincial level and simultaneously observe what factors that might influence the inequality

condition were quite rare.

2.2 Fiscal Transfer and Regional Disparity Nexus

The link between fiscal transfers from the government, better known as fiscal

decentralization, has become an important issue that continues to be examined from time to

time around the world. Fiscal decentralization and regional inequality are categorized as

closely related issues in terms of the problem-solution nexus among them. At the national

scale, Lesmann (2012) identified that the impact of fiscal decentralization on regional

inequality varies around countries based on their economic development level. He assumed

that adequate institutions and high distributional capacities, which are usually depicted in

developed countries, benefit fiscal decentralization to address inequality. On the other hand,

developing countries might experience the opposite results due to their negative redistribution

capacities.

Country

Province

Province

Region 1

Province

Province

Region 2

Disctrict/Municipality Disctrict/Municipality Disctrict/Municipality




4

The results of several studies on the impact of decentralized fiscal conditions on inequality

are still varied from one researcher to another. Some researchers revealed that fiscal

decentralization might increase the inequality level of a country or region. Dyah (2012) found

that fiscal decentralization has a positive and significant relationship with income inequality

in Indonesia. She determined that fiscal decentralization has distributive consequences which

lead to a higher degree of income inequality. She also assumed that local governments might

put less emphasis on the impact of income redistribution. Siddique, Wibowo, and Wu (2008)

found similar results on this issue. They stated that fiscal decentralization increased

expenditure inequality among people in Indonesia. They explained that this causal

relationship came from the implementation of the hold-harmless clause, which only benefited

most of the fertile regions. Thus, the positive impact of implementing fiscal decentralization

was not proportionally distributed among districts.

On the other hand, Suwanan and Sulistiani (2009) illustrated in their study that degree

differences among regions in decentralization have an essential role in income redistribution.

They determined that fiscal decentralization can lower regional disparities. This negative

association was also established by Sacchi & Salotti (2011a) and Irawan (2014). They

concluded that the geographical gap might be decreased through financial assistance from the

central government. An additional revenue given to the local government might generate

regional growth and narrow down the disparities between regions. These varied results

indicate the need for further research, especially in Indonesia, which has already been

implementing fiscal decentralization for about 20 years.

2.3 Factors Affecting Inequality

Until recently, studies that examine inequality in Indonesia and the influencing factors have

been conducted by many researchers but have been under debate. The debatable issues

particularly concern factors that might trigger economic growth in the regions. Bahmani,

Hegerty, and Wilmeth (2008), in their studies on regional inequality determinants in 16

countries, determined economic growth as the factor that might affect the inequality in two

directions, positive and negative. Using Gini coefficient as inequality index for the dependent

variable, they found that in the short-run economic growth increases the inequality level of

India, Iran, and Kenya as a proof that inequality tends to be worse in the early stages of the

economy as explained on the Inverted-U phenomenon from Kuznets (1955). On the other

hand, an opposite impact was observed by Chile, Indonesia, Malaysia, Panama, Philippine,

and Zimbabwe. Subsequently, the results were insignificant for the rest of the countries they

observed.

Many studies were also conducted to find variables that correlated to inequality. Estimated

at the provincial scale, Wang and Fan (2004) found that the level of infrastructure,

urbanization, and education might promote economic growth and increase regional disparities

in China. In Indonesia, schools were also determined as a significant factor that might

encourage the economic growth of provinces (Kharisma & Saleh, 2013; Kuncoro &

Murbarani, 2016). Another variable that could create regional growth was infrastructure

investment. Fan, Kanbur, and Zhang (2011) suggested that investment in infrastructure is

essential to overcome inequality in China. The opportunity to link coastal and central regions

through investment might significantly lessen the gap among areas. However, they mentioned

that the observing type of infrastructure might promote growth on a more substantial scale.

Researches on inequality issues also contributed by some researchers in developed

countries to compare and enrich studies about this issue. Obradović, Lojanica, and Janković

(2016) discovered in their research on The Organization of Economic Co-operation and

Development (OECD) countries that economic growth caused an increase in regional

inequality. Their results contradict Kuznets’ theory, which states that if a country has become

a developed country and reached the turning point of its economy, the inequality level tends

to slowly narrow down. This finding shows no significant change in the country based on

5

previous research (Atkinson, 2003). Atkinson estimated an increase in regional inequality in

some OECD countries. Based on his research, Norway, Finland, and Italy experienced high

inequality growth; US, Canada, and West Germany rose moderately; the UK and Netherlands

did not undergo any changes.

Moreover, the Foreign Direct Investment (FDI) also played a significant role in promoting

growth to address inequality between interior and coastal areas. Zheng and Chen (2007)

mentioned in their studies that the shifting development and foreign funding from the seaside

to the interior has successfully reduced the regional development gap. The FDI might have

given a comparative advantage to interior regions for improving their development.

The influence of labor force participation rate was also detected as a factor of economic

growth in several studies. An increase in the labor force participation rate could promote

economic growth in Bangladesh, Pakistan, India, and Sri Lanka (Rahman, 2014). In line with

this finding, Shahid (2011) stated that there was a significant positive relationship between

economic growth and the labor force participation rate. However, he also implied the

importance of regional convergence as an essential part of maintaining positive growth.

According to the existing literature on this topic, there are several research gaps which need

further observation. The use of Theil index as the dependent variable in econometric

regression, the various impacts of fiscal transfers on inequality, and the most influential

factors of creating provincial growth convergence in Indonesia are several issues that might

be answered by this study.

3. Data and Methodology

This section will provide information about the type and amount of data used in this study

and their sources. Subsequently, the method used for measuring inequality and finding its

determinants will also be explained in detail in this section.

3.1 Data

Statistical data used in this study are secondary data taken from the World Bank Database,

Central Bureau of Statistics Indonesia (BPS), and several related ministries and bureaus from

2010 to 2017. Furthermore, Table 1 will explain it in detail.

Variable Sources

GDRP province and districts World Bank synchronized with BPS

Population World Bank synchronized with BPS

Fiscal Transfers Directorate General of Financial Balance,

Ministry of Finance

Education BPS

Labor BPS

Investment Capital Investment Coordinating Board

Spatial Plan Minstry of Agrarian and Spatial Affairs

Road Ministry of Public Works and Housing

Table 1 Dependent and Independent variables data sources

The data used are divided into provincial and district levels from provinces in Indonesia.

Each will be used in a different calculation method and presented in a balanced panel. The

provincial inequality level will be calculated for 32 provinces, and the region's inequality is

based on the division of island groups: Sumatera, Jawa, Kalimantan, Sulawesi, Bali-Nusa

Tenggara, and Maluku-Papua.

The OLS estimation will be measured using 29 provinces, as described in Table 2, based on

several reasons. DKI Jakarta is independent from fiscal transfers and omitted from the

calculation. Kalimantan Utara is also excluded because it existed in 2013 and the data

6

availability is quite problematic. Some provinces are removed from regression since they are

outliers in terms of their inequality level.

Inequality Index (districts level) OLS Estimation (provincial level)

DKI Jakarta No districts

specified data

DKI Jakarta No Fiscal

Transfer

Kalimantan Utara New Province

Incomplete Data

Kalimantan Utara New Province

Incomplete

Data

Papua Outlier

Papua Barat Outlier

Nusa Tenggara Barat Outlier

Table 2 Omitted provinces and the reasons

To indicate the amount of inequality between regions in Indonesia, following the

decomposed Theil formula from Akita (2003) , the data used are GDP per capita and

population. For income inequality within provincial levels of Indonesia, fiscal

decentralization was used (General, Specific, and Revenue Sharing Allocation Funds) as the

primary variable.

According to Law No. 33 (2004b), general allocation funds (DAU) sources are from

national revenue, transferred to construct horizontal fiscal balance and fulfill the region's

necessities in implementing decentralization law. The government estimates the basic needs

of the area added with the fiscal gap, which is derived from fiscal needs minus the budgetary

capacity of each region to determine the allocation. The necessary allocation is used for local

official salary payment, and fiscal needs are the regional budgetary requirement to finance the

construction of public services. Fiscal budgetary capacity is independent financing

capabilities by the region. DAU are unconditional grants, which means they can be used

based on subnational priorities without any intervention.

Specific allocation funds (DAK) are conditional grants from the central government to

finance particular needs. The projects funded by DAK are usually related to the national

priorities program. The fundamental decisions in determining DAK recipients are based on

general, specific, and technical criteria, such as linkages with national priorities, condition of

facilities and infrastructure, and the absorption of the previous year's budget.

Revenue sharing funds (DBH) have two subjects: taxes and natural resources. According to

the law, DBH is an unconditional grant from national revenue that is allocated to regions

based on a percentage-basis calculation. The proportion used differs between taxes and

natural resources. DBH natural resources from general mining are shared between the central,

provincial, and local governments at the percentage of 20%, 16%, and 64%, respectively. The

principle used is by origin, means those who will receive a more significant portion will be

districts or cities where the resources originated.

Other variables used in this study are variables that are assumed to be the control variables

for regional income. Net Enrollment Ratio (NER) is released annually by BPS to describe the

human capital development in each region. The labor force variable will be presented through

a participation ratio measured from the total labor force divided by the total population. The

influence of infrastructure will be illustrated through the entire length of roads in the province

divided by the area size. The investment variable will be depicted by the foreign direct

investment per capita ratio and the spatial planning effect will be derived in dummy variables

about provincial spatial planning law enactment. For more detailed information, all the

variables used are described in Table 3.

7

DEPENDENT VARIABLE

Inequality • Theil Entropy

Index

2010 – 2017 Theil Measured using

Stata Application

INDEPENDENT VARIABLE (PROVINCIAL LEVEL)

Fiscal

Decentralization Special Allocation Funds 2010 – 2017

Standarized using

Population

Presented in Log Form

General Allocation Funds 2010 – 2017

Standarized using

Population


Revenue Sharing Funds 2010 – 2017

Standarized using

Population


Education Net Enrollment Ratio of

junior high school 2010 – 2017 -

Labor Labor Force Participation

Rate 2010 – 2017 -

Development GDP Per capita 2010 – 2017 Presented in Log Form

Spatial Plan Enactment Years of

Spatial Law Varies

Presented in Dummy

Variable

Infrastructure Length of Road 2010 - 2017

Standarized using Area

Size (Road per per Km2),

in log form

Investment FDI inflow to Provinces 2010-2017

Divided by Annual

Average of Currency and


Table 3 List of variables used and its form

3.2 Methodology

As explained in the literature review, this study applies the Theil index (1967), which is

well-known as the theory for measuring inequality, used by many studies (Lesmann, 2012).

The Theil index was used by many scholars to depict the inequality level of a region

(Charoenphandhu & Yukio, 2012; Rodriguez & Ezcurra, 2009). The Theil index used in this

study is the Two-Stage Nested Theil Decomposition Method handed down by Akita (2003)

followed by Wang and Fan (2004). Using the district as the underlying regional unit, overall

regional income inequality might be calculated by the following methodology, which is well-

known as Theil index T. The formula is explained as follows:

(1) d ∑ ∑ ∑ ( ijk

)k ji (

ijk ⁄

ijk ⁄)

where Td = Theil Entropy Index

yijk = Total income in district k in province j in region i

Y = Total income of all districts

nijk = Total population in district k in province j in region i

N = Total population of all districts

8

If Tdi is the equation used to measure between-district income inequality for region i as

follows,

(2) ∑ j ∑ k ( i⁄

i⁄)

then Td in Eq. (1) can be decomposed into

(3) d ∑ (

)

i ∑ (

)

i ( ⁄

⁄) ∑ (

)

i

Where Yi = Total income of region i

Ni = Total population of region i

TBR = between region inequality ; ∑ (

)

i ( ⁄

⁄)

Thus, it can be seen that Td is the summary of the within-region and between-region

components. The following phase is a decomposing equation for measuring within-province

inequality. If Tij is defined as within-province income, inequality measures for province j in

region i is as follows:

ij ∑ (

) ( ij⁄

⁄ ij

)

then Tdi in equation (2) can be further decomposed into:

(4) ∑ (

i)j ∑ (

i)j (

i⁄

i⁄)

∑ ( ij

i)j ij Tpi

where Yij = the total income of province j in region i

Nij = the total population of province j in region i

Tpi = income inequality between provinces in region i or,

∑ (

i)

j ( i⁄

i⁄)

By subtituting Tdi in Eq. (4), the following equation is obtained

(5) ∑ (

)

i *∑ (

) ij pi+

∑ ∑ (

i)

i ij ∑ ( i

) pi

wp pi

Equation (5) is a Two-stage Theil Inequality Decomposition Equation which means it

consists of a Theil within-province component, between-province component, and between

region component. The within-province inequalities (Tij ) will be further used in this study as

a dependent variable to check which factors influence it. The Theil index has been used in

many studies to describe conditions of inequality. Mahardiki and Santoso (2013) used the

9

Theil index to depict the situation of Indonesia from 2006 to 2011. Similarly, Akita and

Kawamura (2002) conducted a comparative analysis of Indonesia and China using the Theil

index.

The model used to find the relationship between fiscal transfers and the movement of

inequality indexes at the province level is a modified OLS version from Siddique et al.

(2008), Suwanan and Sulistiani (2009), and (Dougherty, 2012) and Dyah (2012) . The

difference in this study is the use of the Theil index as the dependent variable.

Ineq = β0 + β1 F scal + β2 Income + β3 Control + β4 D_Spatial + e

where,

Ineq : Inequality Index (Theil T) of 29 Provinces

β0 : Beta Coefficient

Fiscal : Fiscal transfer to local government

L_DAU (Log of General Allocation Funds/Population)

L_DAK (Log of Specific Allocation Funds/ Population)

L_DBH (Log of Revenue Sharing Funds/Population)

Income : L_GDPPC (Log GDP per capita)

Control : Variables assumed that influence economic growth

NER (Net Enrollment Ratio of junior high – school)

Labor Force Participation Rate

Infrastructure (Total Road Length/Total Area)

L_FDI (Log of Foreign Direct Investment in yearly avg currencies)

D_Spatial : Dummy of Spatial Planning Law Implementation

e : error term

Several classical assumption tests are conducted in this study to produce useful findings

and secure problems such as multicollinearity, and heteroscedasticity, which might appear in

the panel data regression.

4. Result and Discussion

This section will explain inequality measurement from 29 provinces in Indonesia and things

that have been captured based on observations during this period. Moreover, the results of the

econometrics calculation are also illustrated here to find out more clearly which factors

influence inequality at the provincial level.

4.1 Condition of Inequality

Using the Two-Stage Nested Theil Decomposition Method, the condition of inequality in

Indonesia is measured at both provincial and national level. As depicted in Figure 2, on the

island of Sumatera a trend of decreasing inequality was experienced by most provinces except

Sumatera Utara, Sumatera Barat, and Bangka Belitung. The most likely factors that cause

variations to increase in North Sumatera were fluctuations in economic growth that differed

between municipal districts, as well as an increase in mining activities in the region of

Tapanuli Selatan which sharply increased their economic growth by as much as 16.5% in

2013. In the case of Sumatera Barat, the cause was a fluctuating district economic growth,

which increased inequality on a miniscule scale of 0.0009. Lastly, the increase of inequality in

the Bangka Belitung was caused by economic growth, which slowed in a few districts. The

highest decline was experienced by Riau provinces as much as 0.071%, followed by Jambi

10

0

0.1

0.2

0.3

0.4

JAWA BARAT JAWATENGAH

DIYOGYAKARTA

JAWA TIMUR BANTEN

2010

2017

00.020.040.060.08

0.10.120.140.160.18

0.2

2010

2017

with 0.029 in this period of observation. Overall, there is a sign of regional convergence

within Sumatera’s provinces.

Figure 2 Inequality Index of Sumatera Island in 2010 and 2017

Within Jawa island, Yogyakarta and Jawa Timur were the two provinces with increased

inequality in the observation period, as explained in Figure 3Error! Reference source not

found.. The most influential factor that might explain this trend in Jawa Timur is the

production activity in the oil and mining sector in Bojonegoro and Sumenep districts. The

increasing sector share caused a significant growth of regional income in both regions and

widened the income gap among districts by as much as 0.012 at the end of the observation

period. In Yogyakarta, economic growth tended to be stable at around 3-4%. There was no

significant increase or decrease. However, the gap was slightly wider at 0.0024. Jawa Tengah

experienced the highest inequality decrease with 0.022, followed by Jawa Barat and Banten

with 0.004 and 0.007 respectively. The majority of provinces in Jawa island experienced

regional convergence, where the dawdling districts start catching up with the other areas.

Figure 3 Inequality Index of Jawa Island in 2010 and 2017

In Kalimantan island, Kalimantan Barat was the only province which experienced a slight

increase in inequality, as much as 0.0021, as represented in Figure 4. This presumably

originates from fairly steep fluctuations in several districts within. A significant decline was

experienced by Kalimantan Timur, which successfully reduced their districts’ income gap by

0.0773. As one of the provinces rich in natural wealth including mines and oil, the

government's prohibition on raw material exports has resulted in an economic slowdown in

several districts which affected the gap between regions. This trend was found in Kalimantan

11

0

0.05

0.1

0.15

0.2

0.25

KALIMANTANBARAT

KALIMANTANTENGAH

KALIMANTANSELATAN

KALIMANTANTIMUR

2010

2017

00.020.040.060.08

0.10.120.140.16

2010

2017

Tengah and Kalimantan Selatan, which successfully decreased their inequality level by 0.007

and 0.018 respectively, as a sign of regional income convergence occurring among districts.

Figure 4. Inequality Index of Kalimantan Island in 2010 and 2017

According to Figure 5, the inequality level in Sulawesi island was also exciting to observe.

The Theil calculation shows a very high increase experienced by Sulawesi Tengah. This

province has inequality level of as much as 0.026 in 2010 measured by the Theil index which

increases significantly to 0.10 in 2017. The 0.08 increase was mostly caused by significantly

high economic growth in Banggai and Morowali districts. Banggai started to grow its

economy using natural resources such as gas. The growth was noted to be as much as 30% in

2015 and 36% in 2016. Morowali’s economy benefited significantly from the production of

nickel. Its economic growth was very rapid compared to other regions and reached its peak in

2015 with 36%. As a result, the income inequality gap widened. Inequality in Sulawesi Utara

and Sulawesi Barat increased slightly with the same magnitude of 0.01.

In Sulawesi Utara, there is no specific reason for the increasing trend except for the

difference of economic growth among districts with a size that is not too flashy. For Sulawesi

Barat, the income gap is widened which might be caused by higher productivity in the

agricultural sector, especially in Mamuju Utara district. The increased investment in crude

palm oil and higher productivity of cocoa and rice are the influential factors behind it. In the

other three provinces, Sulawesi Selatan, Sulawesi Tenggara, and Gorontalo, the inequality

decreased by 0.005, 0.001, and 0.004, respectively. This indicates the growth of the economy

in the underdeveloped regions, causing a decrease in income inequality between regions in

these three provinces.

Figure 5. Inequality Index of Sulawesi Island in 2010 and 2017

Among the rest of the provinces, Nusa Tenggara Timur is the only province where the

increase of inequality occurred. Sumba Barat Daya, Manggarai Timur, Manggarai Barat are

three districts lagging behind. These districts need to catch up with other regions since they

12

0

0.2

0.4

0.6

0.8

1

1.2

BALI NTB NTT MALUKU MALUKUUTARA

PAPUA PAPUABARAT

2010

2017

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

2010 2011 2012 2013 2014 2015 2016 2017

Theil Index of Indonesia

(between provinces and region)

Theil Index

still have lower growth compared to the others. The slow growth deepens and enlarges the

gap as much as 0.019 at the end of the observation period. Nusa Tenggara Barat, Papua, and

Papua Barat are districts which successfully diminished their inequality gap to an insignificant

number in these eight years. Papua Barat has decreased its inequality index as much as 0.259,

followed by Nusa Tenggara Barat and Papua with 0.234 and 0.128 respectively. This trend

was caused by the decrease in mining and oil shares on regional GDP due to a government

ban on the export of raw materials and the obligation to make smelters at each site. As a

result, the difference per capita income between municipal/districts in these provinces with

abundant natural resources narrows down, decreasing their inequality index as depicted in

Figure 6. However, their inequality is still considered high compared to other provinces in

Indonesia.

Figure 6. Inequality Index of Nusa Tenggara, Maluku and Papua Island in 2010 and 2017

However, based on the calculation of the Theil index value depicted in Figure 7, the

disparity in Indonesia declined from 2010 to 2017 as much as 0.07. This indicates that at the

national level, Indonesia is able to overcome the inequality issue based on the narrowing

income inequality between provinces. However, while looking at the conditions of inequality

that occur at the provincial level, there are variations in trends over this period.

Figure 7. Theil Index of Indonesia from 2010 to 2017

13

In short, regional inequality between districts and cities in Indonesia at the provincial level

varies greatly, but the majority of provinces in this study have experienced a declining trend.

On the other hand, nationally Indonesia was able to manage the income disparity between

provinces by showing a decreasing trends.

4.2 Inequality Determinants

Pooled OLS, fixed effect, and random effect were performed to define the determinant

factors of inequality within a province. These approaches are used to clarify the consistency

of econometric calculation and to produce relevant results.

Firstly, the correlation test was conducted to check if there were any multicollinearity

among regressors in the model. According to Wooldridge (2012), multicollinearity might be

found using the correlation test on all independent variables. If the correlation occurred with a

value of more than 0.75, there would be multicollinearity among those variables and might be

a problem to the Best Linear Unbiased Estimator (BLUE) assumption. Table A in the

appendix section will clearly explain the correlation value among variables in this study. It

was found that there is high correlation among DAU and DAK in the model. Hence, DAK is

omitted from the regression.

Secondly, the regression was performed using the pooled-least square, fixed effect, and

random effect methods. To define which was the most econometrically appropriate method,

following Wooldridge (2012), chow test, likelihood ratio, and Hausman test were used. As a

result, OLS with the fixed effect was the most appropriate method to identify the determinants

of inequality in this study. Table B in the appendix section will show the result of each test

mentioned above. The significance of fixed-effect estimation fulfilled econometric rules with

less than 5% α. Another classical assumption test used in this model is the heteroscedasticity

test. In the heteroscedasticity test, the probability of all variables is above 5%, which means

there is no heteroscedasticity problem in the model. Autocorrelation is to be found in this

model, considering that the decentralized fiscal scale is influenced by the ability to absorb the

budget of the previous year.

As defined in Table 4, there are four significant variables at 1% α, one significant variable

at 5% α, and one at 10% α. The intercept is found to be negative with a value of -1.137. The

existence of a negative intercept value is quite confusing; however, in some studies, it is a

possible occurrence and not a severe problem. Gujarati (2009) and Wooldridge (2012), the

two famous authors on basic econometrics, stated in their books the possibilities of having

negative intercept (β0) in a model, and that the intercept does not always have to be

interpreted because sometimes it could be inconsequential. If there is no zero value used in

the data set, according to the realistic condition, a negative intercept might occur.

Additionally, Dougherty (2012) supports the same argument about this issue in his book. He

explains that a literal interpretation of an intercept or a constant might lead to a nonsensical

conclusion.

Variable Coefficient SE

C -1.13785 0.147293

L_DAU -0.07415*** 0.009549

L_DBH 0.010047** 0.004966

L_FDIPOP -0.00086 0.001629

LOG(RAT_ROAD) -0.04827*** 0.008585

L_GDPPC 0.218058*** 0.023442

NER -0.041* 0.021467

DPR 0.004911*** 0.001724

LFPR 0.007627 0.034142

14

R2 = 0.90

Prob = 0.0000

Table 4 Regression Result

4.2.1 The Regional Economy and Inequality

According to the fixed effect estimation, the impact of GDP per capita is positive and

significant. Any 1% increase of GDP per capita will enlarge the inequality as much as 0.0021.

It means that economic growth is one of the factors negatively affecting the disparity within a

province in Indonesia. If it is associated with the Kuznets (1955) theory, this indicates that not

all provinces experience an inverted-u trend. However, on the national level, the income

inequality between provinces is narrowed down. This finding strengthens the study by

Kuncoro and Murbani (2016), which revealed that the inverted-u phenomenon tends to exist

at the national level in Indonesia, and the decreasing trend or regional disparity at the national

level existed in Indonesia during 1995 to 2015.

Since GDP per capita is the average of income from all districts within a province divided

by population, it can be assumed that economic development is not evenly distributed among

areas. The magnitude of economic assertion is unequal among districts leaving the rich area

getting richer and the poor area still lagged behind. The regions producing natural resources

such as oil, mineral, and gas played a major role in deepening regional income gaps in the

economy of Indonesia.

The emergence of new mines or large investments in natural resources cause greater

difference in the per capita income between districtss and cities. For instance, Sulawesi

Tengah has accelerated its within-province inequality significantly due to the mining sector

increase in Banggai and Morowali districts. On the contrary, government intervention to

restrict mining activities and export of raw materials, through Law No. 4 (2009), has

successfully lessened the regional disparity in Nusa Tenggara Barat, Papua, and Papua Barat.

Accordingly, we can assume that the natural resources sector plays an important role in

increasing regional income and also has a strong influence in decreasing inequality between

regions.

4.2.2 Fiscal Decentralization and Regional Inequality

Even though DAU is mostly used for officers’ salaries, it is a statistically significant

variable for lessening regional disparities even if only on a small scale. A 1 % increase in

DAU would lead to a decline in inequality as much as 0.0007. DAU might promote economic

growth by sustaining the number of skilled government officers to make effective policies.

With good policy implementation, monitoring and evaluation, especially in underdeveloped

districts, opportunities for the economy might widen. Interestingly, Suwanan and Sulistiani

(2009) found the same results in their study. They stated that the impact of local government

authority in decision making and local autonomy over revenue sources could narrow

inequality between regions.

On the contrary, DBH has the opposite impact on regional inequality. This form of the

block grant is given proportionally based on which natural resources and tax producing

regions significantly gain a bigger portion compared to non-producing regions. This might

slightly contradict the efforts made by the government to balance financial capabilities

between regions. As noted in the estimation result, any 1% increase in DBH might increase

disparity by as much as 0.0001. This finding confirmed the research conducted by M.

Siddique et al (2008) which declared that the benefit of fiscal transfer was not proportionally

assorted among regions and might have increased expenditure inequality.

15

4.2.3 Human Capital and Regional Inequality

The issue of differences in human capital between regions is one of the important factors

which needs to be addressed. In this model, the Net Enrollment Ratio (NER) is chosen to be a

variable determining the impact of human capital on regional disparities. The estimation

shows that NER has a negative and significant impact on regional disparities. This indicates

that inequalities between regions might be decreased by improving the quality of human

capital, especially in the region lagging behind.

Any 1% increase of NER might decrease the regional disparity by as much as 0.041. This

finding might be important in the future because the quality of human development and

education determines the quality of the workforce and improves entrepreneurship skills which

might reduce migration between regions in the long run. Vidyattama (2010) also stated in his

study the importance of human capital in accelerating the economic growth of a region.

Hence, there is a strong correlation between human capital and efforts to improve the

economy of a region that has low per capita income, which will also be able to narrow the

income gap between regions in the future.

4.2.4 Labor Force and Regional Inequality

The participation of the labor force describes the availability of jobs in the regions. A

higher participation rate might be assumed as a lower unemployment rate. In this model, the

labor force participation rate is found to have a positive but insignificant impact on regional

disparity. However, it is important to create inclusive job opportunities among regions. If jobs

are still concentrated in certain areas it might create labor migrations between regions and the

origin areas might experience higher unemployment rate in the future.

4.2.5 Infrastructures and Regional Inequality

In this model, variables of infrastructures are represented by the total length of roads

divided by the area size. Any 1% increase in Road Ratio might decrease disparity by as much

as 0.004. The availability of infrastructure is undoubtedly very important to create economic

growth in the regions by increasing its accessibility and openness to become more involved

and attractive. With adequate infrastructure support the economy of a region will improve in

the future owing to the ease of logistics and transportation. Kharisma and Saleh (2013) and

Vidyattama (2010) also shared the same results about the role of infrastructure on the

economy. They revealed that infrastructure might increase regional income growth in

Indonesia.

4.2.6 Investment and Regional Inequality

To check the impact of investment on inequality, the variable used is the FDI represented in

log form divided by the average of currency within a year. From the estimation, FDI has a

negative but insignificant impact on regional inequality. Ledyaeva and Linden (2006) shared

the same result in their research about FDI in Russian regions. They found that FDI is an

insignificant factor to explain the growth in the regional economy.

However, funds might be invested in a certain location which is rich in natural resources or

has adequate infrastructure support. These requirements usually cause economic activities to

become increasingly agglomerated in an urban area or near the resource. This was confirmed

by Figini and Görg (2006) who stated in their study that an FDI inward investment could

hamper regional inequality but potentially decrease it in the future.

In Indonesia, most of the regions that produce natural resources are still exporting raw

materials to Jawa because of the economic and industry agglomeration. There are still a few

large-scale foreign investments placed in areas outside Jawa, especially in eastern Indonesia.

This might have happened because the multiplier effects of economic agglomeration

experienced by Jawa are greater than those compared to other regions. In the long run, this

might hamper the process of economic equalization between regions.

16

5. Conclusion and Policy Implication

This paper has examined the condition of inequality in Indonesia on both the national and

provincial levels. Furthermore, factors that influence inequality index within provinces are

also econometrically calculated in this study.

This study found that on a smaller scale, income inequality within province in several

regions has increased. Based on this study, the role of natural resources, especially mining

activities, is assumed to be a potential factor that affects the increase of income inequality

within the province. Sulawesi Tengah, Nusa Tenggara Barat, Papua, and Papua Barat are four

provinces which function as evidence for these findings. The role of fiscal transfers from the

central government is allegedly not optimal to overcome the disparities between districts that

have occurred. DAU might decrease the income gap, while DBH offers the opposite effect.

Human capital and infrastructure support are expected to reduce the inequality gap even if

they do so on a small scale, while spatial planning law is suspected to have an opposite

impact. Economic growth itself has the potential to increase inequality among the regions.

Therefore, the role of other factors that contribute negatively to the inequality index is

significant. Subsequently, labor force participation rate and foreign direct investment (FDI)

are econometrically unable to be explanatory factors. However, Indonesia's level of income

inequality was nationally decreased, indicating the occurrence of regional income

convergence among provinces.

Lastly , the data used in this study is an 8-year panel data on the provincial scale. The

multicollinearity problem which occurred among DAU and DAK might also be resolved

using a provincial data set from a more extended period. With a longer data set, the initial

conditions of inequality between different provinces that potentially disrupt the estimation can

also be overcome. Hence, a broader period of observation is highly recommended for future

studies and might produce better and more comprehensive findings.

Policy implications proposed based on this study are:

(i) The government is suggested to observe the economic potential in the areas

lagging behind and develop them to stimulate economic growth.

(ii) Reformulation of fiscal transfers might be required to create a more

powerful impact on decreasing income inequality.

(iii) The government might need to increase infrastructure and educational

budget in dawdling areas to support their economic activities, promote

economic growth, and lessen the income gap.

(iv) Tightening supervision on local spatial planning regulations from the

central government might be required to maintain its quality and its

relevance to national policy.

17

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i

APPENDIX

A. Inequality Index of Indonesia from 2010 to 2017

(measured using Two-Stage Nested Theil Decomposition Method)

National Level (Among Provinces and Regions)

2010 2011 2012 2013 2014 2015 2016 2017

INDONESIA 0.334 0.322 0.313 0.302 0.288 0.281 0.271 0.264

Provincial Level (Within Provinces)

PROVINCE 2010 2011 2012 2013 2014 2015 2016 2017

ACEH 0.088 0.084 0.082 0.077 0.069 0.061 0.048 0.061

SUMATERA UTARA 0.079 0.082 0.084 0.082 0.083 0.083 0.085 0.086

SUMATERA BARAT 0.034 0.034 0.033 0.033 0.033 0.034 0.034 0.035

RIAU 0.135 0.137 0.120 0.102 0.086 0.080 0.071 0.063

JAMBI 0.129 0.128 0.120 0.116 0.113 0.108 0.102 0.100

SUMATERA SELATAN 0.144 0.143 0.145 0.144 0.141 0.140 0.138 0.140

BENGKULU 0.062 0.062 0.062 0.062 0.061 0.062 0.062 0.062

LAMPUNG 0.028 0.028 0.027 0.027 0.027 0.027 0.028 0.028

BANGKA BELITUNG 0.017 0.016 0.017 0.017 0.017 0.018 0.019 0.019

KEPULAUAN RIAU 0.152 0.141 0.137 0.130 0.126 0.121 0.116 0.111

JAWA BARAT 0.188 0.188 0.188 0.189 0.188 0.186 0.184 0.183

JAWA TENGAH 0.196 0.194 0.190 0.186 0.185 0.183 0.178 0.174

DI YOGYAKARTA 0.104 0.106 0.107 0.107 0.108 0.108 0.109 0.109

JAWA TIMUR 0.299 0.299 0.301 0.302 0.305 0.308 0.310 0.312

ii

BANTEN 0.196 0.195 0.197 0.198 0.194 0.191 0.189 0.188

BALI 0.035 0.034 0.034 0.033 0.032 0.031 0.030 0.029

NTB 0.520 0.326 0.199 0.192 0.174 0.399 0.401 0.285

NTT 0.122 0.127 0.130 0.134 0.136 0.138 0.139 0.141

KALIMANTAN BARAT 0.027 0.028 0.029 0.030 0.030 0.029 0.030 0.030

KALIMANTAN TENGAH 0.027 0.027 0.026 0.025 0.023 0.022 0.020 0.019

KALIMANTAN SELATAN 0.123 0.124 0.123 0.122 0.114 0.113 0.108 0.104

KALIMANTAN TIMUR 0.211 0.183 0.176 0.164 0.153 0.143 0.139 0.137

SULAWESI UTARA 0.066 0.070 0.071 0.073 0.074 0.074 0.076 0.076

SULAWESI TENGAH 0.026 0.034 0.044 0.057 0.050 0.084 0.097 0.106

SULAWESI SELATAN 0.141 0.139 0.140 0.140 0.138 0.137 0.136 0.135

SULAWESI TENGGARA 0.056 0.059 0.063 0.061 0.043 0.042 0.041 0.043

GORONTALO 0.015 0.014 0.013 0.013 0.012 0.011 0.011 0.011

SULAWESI BARAT 0.040 0.044 0.047 0.047 0.056 0.057 0.052 0.051

MALUKU 0.048 0.045 0.044 0.042 0.038 0.036 0.034 0.031

MALUKU UTARA 0.036 0.037 0.037 0.037 0.033 0.034 0.036 0.034

PAPUA 1.013 0.848 0.766 0.772 0.732 0.729 0.753 0.754

PAPUA BARAT 0.652 0.661 0.634 0.620 0.589 0.569 0.550 0.523

iii

B. OLS Regression

*** = Significant at 10%; ** = Significant at 5%; * = Significant at 1%

Fixed effect was chosen as the appropriate model after conducting both Chow and Hausman tests.

Variable Fixed Effect Common Effect Random Effect

Coefficient SE Coefficient SE Coefficient SE

C -1.137846 0.147293

1.390484 0.216645

-0.866208 0.135392

(0.000)*** (0.000)*** 0.000

L_GDPPC 0.218058 0.023442

-0.05281 0.030028

0.188 0.020924

(0.000)*** (0.08)* (0.000)***

L_DAU -0.074152 0.009549

-0.19381 0.015852

-0.07934 0.008782

(0.000)*** (0.000)*** (0.000)***

L_DBH 0.010047 0.004966

0.024244 0.013661

0.008 0.004913

(0.044)** (0.077)* (0.099)*

L_FDIPOP -0.000857 0.001629

0.008745 0.005928

-0.000672 0.001618

0.600 0.142 0.678

LOG (RAT_ROAD) -0.048272 0.008585

0.006392 0.007236

-0.026 0.007316

(0.000)*** 0.378 (0.000)***

NER -0.040996 0.021467

0.159071 0.063253

-0.027511 0.021224

(0.058)* (0.0126)** 0.196

LFPR 0.007627 0.034142

0.004997 0.013078

0.015908 0.025212

0.824 0.7027 0.529

DPR 0.004911 0.001724

0.015231 0.008516

0.004648 0.001714

(0.044)** (0.0751)* (0.007)***

R-square 0.99 0.52 0.42

Probability 0.000 0.000 0.000

iv

Chow Test

H0 = Common Effect is Appropriate H0 is Rejected

Hausman Test

H0 = Random Effect is Appropriate H0 is Rejected

C. Classical Assumption Test

Multicollinearity

No correlation value between two variables above 0.75. Thus, no multicollinearity problem detected among variables.

Cross-section F 327.095761 (28,195) Prob. 0.0000

Cross-section Chi-square 897.961965 28 Prob. 0.0000

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 43.265886 8 0.0000

L_DAU L_DBH L_GDPPC LOG (RAT_ROAD) L_FDIPOP NER LFPR DPR

L_DAU 1.000000 -0.046456 -0.345950 -0.315379 -0.020198 -0.115926 0.078740 0.150915

L_DBH -0.046456 1.000000 0.691637 -0.611538 0.381544 0.084946 -0.135212 -0.484229

L_GDPPC -0.345950 0.691637 1.000000 -0.193546 0.553024 0.443258 -0.121304 -0.234371

LOG(RAT_ROAD) -0.315379 -0.611538 -0.193546 1.000000 -0.275276 0.316190 0.045196 0.379109

L_FDIPOP -0.020198 0.381544 0.553024 -0.275276 1.000000 0.308328 -0.251676 0.001259

NER -0.115926 0.084946 0.443258 0.316190 0.308328 1.000000 -0.189252 0.349229

LFPR 0.078740 -0.135212 -0.121304 0.045196 -0.251676 -0.189252 1.000000 -0.031685

DPR 0.150915 -0.484229 -0.234371 0.379109 0.001259 0.349229 -0.031685 1.000000

v

Heteroscedasticity Test (Glejser test)

Regressing all independent variables using resabs (abs – residual) as the dependent variable. Probability value of all variables are > 5% which means

there was no heteroscedasticity in this study.

C 0.2572

L_GDPPC 0.3136

L_DAU 0.6491

L_DBH 0.3374

L_FDIPOP 0.4431

LOG(RAT_ROAD) 0.6743

NER 0.7034

LFPR 0.8809

DPR 0.9946

determinants of regional disparities in indonesia

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