The Long and Short-term Determinants of Inflation in Indonesia’s Regions
Donni Fajar Anugrah1

Abstract
Although inflation rate was down from 6.96% in 2010 to 3.79% in 2011, theaverage inflation in Indonesia has been relatively high since 1998. The high and persistent
inflation recently becomes important issues for both central bank and Indonesia
government.

Therefore, the controlling inflation is a key policy concern in Indonesia.

However, this is a difficult task due to differences across regions. Since each region has
specific characteristics, determinants of inflation may be different in each region.
This research aims to analyze the factors of inflation in the long-term and shortterm. The result will be important to understand the differences between long-term andshort-term factors of inflation among regions. This study develops the long-term and shortterm models of inflation by applying panel co-integration techniques (Smith and Pesaran,
1995; Im, Pesaran and Shin, 1997; Breitung 2001; Breitung and Pesaran 2005). It also
applies an extended Engle Granger method of error correction mechanism (ECM). This
study extends previous research approaches (Leheyda, 2005) which applied Johansen’s
procedure and based on purchasing power parity theory. The model will be classified infive regions: Sumatra, Java, Kalimantan, Sulawesi and Papua. Cross section data are
Indonesia’s provinces in each region and time series is over the period 1989-2010. Data
are provided by Statistic Bureau of Indonesia and Bank Indonesia.
The empirical results report that inflation in each region has been determined bysimilar factors which are foreign price and exchange rate in the long term. These findings
confirm previous studies found that both foreign price and exchange rate have long-term
effects on domestic prices (e.g. Listiani, 2006; Endri, 2008; Kandil and Morsy, 2009; Khanand Gill, 2010). The effects of both factors are different in each region which the highest
effect of foreign price to inflation is in Sumatra and the highest effect of exchange rate toinflation is in Sulawesi. In the short term, the factors of inflation are different in eachregion. They are wage, total factor productivity (TFP), money supply, interest rate and theprice of kerosene.

Keywords : inflation rate, regional economics, panel co-integration
JEL: E31, R11, C33
1

Donni Fajar Anugrah is currently a PhD student in Economics at The University of Western Australia. His
study has been sponsored by Bank Indonesia as his employer. He has been working as an economist at Bank
Indonesia since 1999.

The Long and Short-term Determinants of Inflation in Indonesia’s Regions

1. Introduction

Since the Act No.23 about Bank Indonesia independence was issued in 1999 andrenewed in 2003 by the Act No.4, the main goal of Bank Indonesia as central bank ofIndonesia is to achieve a low and stable inflation. Bank Indonesia has applied Inflation
Targeting Framework (ITF) policy to achieve their goal. The high and persistent inflation
recently becomes important issues for both central bank and Indonesia government.
Controlling inflation is a key policy concern in Indonesia. However, this is a difficult task
due to differences across regions. Since each region has specific characteristics, thedeterminants of inflation may be different in each region.
There are limited studies that examined the determinants of regional inflation inIndonesia (Wimanda 2006; Anugrah, Dewati & Chawwa 2007). Wimanda (2006) foundthat the main factors of inflation in all provinces in Indonesia are expected inflation andexchange rate. Similarly, another study (Anugrah, Dewati and Chawwa, 2007) found thatfactors of inflation in East Java, Central Java, Yogyakarta and Bali are expected inflation,exchange rate and output gap. However, their studies fail to address the issue of differential
factors contributing for inflation in province in the long and short-term. Moreover, none ofthese studies analyzed regional differences across Indonesia applying panel data cointegration techniques.
These issues raise an important and related question which will be addressed in thischapter: What are the long and short-term determinants of inflation in Indonesia’s regions?
The main focus of this thesis is to analyze the determinants of inflation in the long andshort-term. The result will be important to understand the differences long and short-term
factor of inflation among regions. Therefore, policy makers can issue their policies byconsidering those factors in each region.
This study will use different methods, to address the different objectives andspecific topics. Developing long and short-term models of inflation by applying panel cointegration techniques (Smith & Pesaran 1995; Im, Pesaran & Shin 1997; Breitung 2001;
Breitung & Pesaran 2005) using an extended Engle Granger method of error correction
mechanism (ECM). This study will build on previous research approaches (Leheyda, 2005)
Page 1

which applied Johansen’s procedure with VAR and based on purchasing power parity
theory. This chapter will focus on Indonesia’s regions which have not been studied yet.
This chapter is structured as follows. Section 2 presents the study literature relatedwith determinants of inflation. Section 3 presents data, methodology and econometricmodels. Section 4 reports and discusses empirical results. The last part presents our main
conclusion and policy recommendation.

2. Literature Review

Inflation which generally means an increase in price can be determined by the longand short term factors. In the long run, price can be influenced by changes in aggregate
demand and/or aggregate supply. Romer (2001) mentioned that technology shock and labor
supply can be as sources of inflation in aggregate supply side. Meanwhile money stock andmoney demand can influence aggregate demand. He also stated that money supply growth
can be the main factor of inflation in the long term. The formula of demand for real money
is below:M L(i, Y )P
Noted: M is money stock, P is price level, i is nominal interest rate, and Y is real
income.
Since nominal interest rate can influence price level and policy rate directly
influence interest rate, policy rate can be the factor of inflation rate. When policy rate goesup or monetary policy tightening, domestic prices can be going down. An increase in policy
rate is followed by reducing consumption or shifting aggregate demand to the left.
Therefore, inflation goes down as a response of monetary tightening.
The monetary explanation for inflation has dominated the literature since the late1970s. This theory suggests that money growth determines inflation, and that there is astrong link between money growth and inflation (Friedman & Schwartz 1982). A numberof studies have confirmed Friedman’s analysis to argue that money growth is positively
linked to inflation in some countries, see for example studies from Australia (Fahrer &Myatt 1991), Nigeria (Moser 1995), Albania (Domac & Elbrit 1998), Indonesia (Listiani2006), Paraguay (Monfort & Pena 2008) and Tunisia (Boujelbene & Boujelbene 2010).

Page 2

Another important factor of inflation is exchange rate which can influence prices inthe short and long term. Some studies (e.g. Nicolae, 2002; Listiani, 2006; Endri, 2008;
Gachet, Maldonado and Perez, 2008; Kandil and Morsy, 2009; Khan and Gill, 2010) foundthat depreciation of exchange rate can increase inflation. Indeed, Indonesian study (Siregar& Rajaguru 2002) found that the main factors of inflation in Indonesia are base money andexchange rate during the post Asian financial crisis period (1997). The role of exchange
rate can be explained by the theory of purchasing power parity. This theory suggests thatdomestic prices are influenced by both foreign prices and the exchange rate (Hallwood &MacDonald 2000). The formula of purchasing power parity is below:

P  SP*
Noted: P is the domestic price, S is exchange rate, and P* is foreign price.
As foreign prices are positively correlated with domestic prices, an increase inforeign prices can lead to an increase in domestic prices. Leheyda’s (2005) study from
Ukraine confirms this since the foreign prices were found to have long-term effects on
domestic prices. Meanwhile, depreciation of exchange rate can increase domestic prices.
The foreign exchange rate is regarded as a particularly important factor influencing
inflation in the long and short-term. Recent evidence suggests that exchange rate
depreciation is found to generate inflation, due to an increase in imported product prices(e.g. Domac and Elbirt, 1998; Dlamini, Dlamini and Nxumado, 2001; Nicolae, 2002;
Listiani, 2006; Endri, 2008; Gachet, Maldonado and Perez, 2008; Kandil and Morsy, 2009;
Khan and Gill, 2010).
In the short term, the sources of inflation can be more factors than in the long term.
The popular theory of the long term factors of inflation is Philips Curve theory. This theory
argued that the main factors influencing inflation are expected inflation, output gap andsupply shock. The assumption that individual’s expectations of future inflation are based onrecent inflation is known as adaptive expectations. Monfort and Pena (2008) showed that
inflation expectation was an important factor explaining the inflation trends in Paraguay.
An increase in output gap is positively related to inflation across European countries(Boschi & Girardi 2007; Andersson, Masuch & Schiffbauer 2009). Other explanations
include supply shocks due to exogenous events, such as an increase in oil price (Mankiw,2007). Another important factor of inflation is wage. An increase in wage can raise
domestic prices, because aggregate demand shifts to the right. On contrary, a decrease in
Page 3

wage can decrease domestic prices. Inflation study in Swaziland (Dlamini, Dlamini &Nxumado 2001) found that wage is source of inflation in the long and short term.

3. Data and Methodology
3.1 Data

The data which are used by this study are from Statistic Bureau of Indonesia, Bank
Indonesia, and CEIC data. The data cover the 26 provinces of total 33 provinces inIndonesia. The other seven provinces which have limited data are Bangka Belitung, Riau
Islands, Banten, Gorontalo, West Sulawesi, North Maluku and West Papua. Provinces inIndonesia can be classified by five regions based on location. They are Sumatra, Java,
Kalimantan, Sulawesi and Papua. The data frequencies are annual data over the 1989-2010
period and monthly data over the 1993-2010 period.
Those data are consumer price index (CPI), exchange rate (Indonesia Rupiah agains
US dollar), the consumer price index (CPI) of US, money supply (broad money), wage,
TFP (Total Factor Productivity), oil price (kerosene price), expected inflation and policy
rate (Bank Indonesia rate). Data of money supply in each province are calculated byproportion of province’s GDP to national GDP. This method is used by previous research(Laksono 2005). Meanwhile, for expected inflation variable, we use the lag of inflation as
adaptive inflation (Mankiw 2007). Author obtains data of TFP by developing Solow
growth model and calculating TFP by growth accounting method (Tjahjono & Anugrah
2006).
Table 1. The List of Data
No

Data CodeDefinition

1

CPI

Consumer Price Index*

2

CPI US

US Consumer Price Index***

3

ER

Exchange Rate**

4

M2

Money Supply (broad money)

5

W

Wage*

6

TFP

Total Factor Productivity

7

Krs

Price of Oil (Kerosene)***8

CPI (lag)

Expected Inflation

9

IP

Policy Rate (Bank Indonesia rate)**Sources: *Statistic Bureau of Indonesia, **Bank Indonesia and ***CEICPage 4

3.2 Methodology

This paper applied econometric approach which mainly applies error correction
mechanism (ECM). In the region cases, we applied co-integration techniques using panel
data (Breitung and Pesaran, 2005) to find the long-term factors of inflation. We develop 5
panel data sets with provinces in each region as cross-section data and over the period 1989
to 2010 as time series. Meanwhile, in the province cases, this study applied co-integration
techniques using time series (Granger & Engle 1987). We also develop 26 models of eachprovince in the long and short term with monthly data from 1993 to 2010.
The long run models must be tested whether they have co-integration in the longterm or not. If they pass the stationary test, it means the long term relationship exists.
Therefore, the residual of the long run model can be used as ECM. The function of ECM
on the short-term model is to keep the short run model in the equilibrium of the long run
model (Granger & Engle 1987; Katarina & Johansen 1994).
The economic models of inflation are classified by panel data for regional models
and time series data for provincial models. The regional models are in the long and shortterm. The long-term equation based on purchasing power parity theory (equation 1) andmodified by adding money supply (equation 3).

log( P)ij  1   2 log(er )ij  3 log( P*)ij  eij(1)

where j is time index, Pij is the domestic price in province i , er is the exchange rate inprovince i and P * is the foreign price in province i . Furthermore, we find ECM variable
by reformulating equation 1 to be equation 2:

ecmij  log( P)ij  1   2 log(er )ij  3 log( P*)ij(2)

The alternative model of the long-term equation is put money supply as long andshort-term factor based on quantity theory of money:log( P)ij  1   2 log(er )ij  3 log( P*)ij   4 log(M *)ij  eij(3)Page 5

where j is time index, Pij is the domestic price in province i , er is the exchange rate inprovince i , P * is the foreign price in province i and M is the broad money in province

i . Furthermore, we find ECM variable by reformulating equation 3 to be equation 4:
ecmij  log( P)ij  1   2 log(er )ij  3 log( P*)ij   4 log(M )ij(4)

For long-term model procedure, we do panel unit root test for ECM variable. If it
has no unit root, we put it in the short-term equation. The short-term equation:

k

k

k

t 0

t 0

t 0

 log( P)ij     ecmi , j 1    t  log( P*)i , j t   t  log(er )i , j t    t  log(TFP)i , j t 

k

k

t 0

t 0

...   t  log( Krs)i , j t  t  log(W )i , j t   ijt  0,1, 2....k(5)

where j is time index, t is time lag,  log( P)i , j is price changes (inflation) in province i , log(er )i , j is exchange rate changes in province i ,  log( P*)i , j is foreign price changes inprovince i ,  log(TFP)i , j is Total Factor Productivity in province i ,  log(W )i , j is wagechanges in province i ,  log( Krs)i , j is kerosene price changes in province i
Similarly, inflation models for provincial monthly data are in the long and shortterm. The long-term equation based on purchasing power parity theory (equation 6).
log( P)t  1   2 log(er )t  3 log( P*)t  et(6)

where t is time index, Pt is the domestic price, er is the exchange rate and P * is theforeign price. Furthermore, we find ECM variable by reformulating equation 6 to beequation 7:
ecmt  log( P)t  1   2 log(er )t  3 log( P*)t

(7)

Page 6

Furthermore, we do unit root test for ECM variable. If it has no unit root, it meansthat the long-term model exists. Therefore, we put ECM variable in the short-term
equation. The formula of the short-term equation:
k

k

k

i 1

i 0

i 0

 log( P)t     ecmt 1   t  log( P)t i   t  log(er )t i    t  log( P*)t i 

k

k

k

k

i 0

i 0

i 0

i 0

...   t  log( M )t i  t  log(W )t i    t (iP )t i    t  log( Krs)t i   ti  0,1, 2....k(8)

where t is time index, i is time lag,  log( P)t is price changes (inflation),  log( P)t i is
expected inflation changes,  log(er )t is exchange rate changes,  log( P*)t is foreign pricechanges,  log(M )t is money supply changes,  log(W )t is wage changes, (iP )t is policy
rate (Bank Indonesia rate) changes and  log( Krs)t is kerosene price changes.

4. Empirical Results

In this part, we begin with the unit root test of the data in both regions andprovinces to find the order of integration in each data. We applied panel unit root test in theregional cases and time series unit root test in the provincial cases. Furthermore, wedevelop the long term model based on the variables which have the similar order ofintegration. We get the ECM from the long term model and applied it in the short term
model.

4.1 The Order of Integration

Empirical results of panel unit root test indicate that CPI province in all regions isunit root at level (Table 2). Different result is found by Breitung’s method that CPI
province is stationary at level in Kalimantan and Papua. However, other methods foundthat the data are unit root at level in both regions. Table 2 also reports that CPI province is
not unit root at first difference in all regions. This results mean that order of integration of
CPI province in all provinces is one due to the data is stationary at first difference.
Page 7

Table 2. Panel Unit Root Test of CPI Province
MethodBreitung

SumatraJava

Kalimantan

Sulawesi

Papua

Level

1st DiffLevel

1st DiffLevel

1st DiffLevel

1st DiffLevel

1st Diff

-1.55

-8.59

-0.78

-6.49

-1.80

-5.66

-0.54

-4.24

-1.97

-5.89

***

**

***

***

**

***

-7.32

8.05

-5.63

-5.75

9.47

-5.62

t-stat***Im, Pesaran
and Shin Wstat

10.85

ADF - Fisher
Chi-square

0.01

-8.38

10.85

***

***

89.94

0.01

62.61

***

Observation

***
0.002

***

176

7.36

42.62

***
0.01

43.58

***

110

***
0.004

47.52

***

88

***

88

110

Notes: Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *The data of CPI US as representative of foreign price are not stationary at level by
three methods which are Im, Pesaran and Shin, ADF and PP methods. Meanwhile, othertwo methods found different results (Table 3). When we put the data at first difference, we
found that all methods found the similar results (Table 3). Foreign price (CPI US) is
stationary at first difference in all regions. It means that order of integration of CPI US isone.
Table 3. Panel Unit Root Test of CPI US
MethodSumatraJava

Kalimantan

Sulawesi

Papua

Level

1st DiffLevel

1st DiffLevel

1st DiffLevel

1st DiffLevel

1st Diff

Breitung-2.31-15.91-12.58-1.64-11.25-1.83-12.58

**

***

***

*

***

1.64*

-11.25t-stat1.83**

***

**

***

Im, Pesaran
andShin
W-stat1.42-18.95

1.12-14.981.00-13.40

1.00-13.401.12-14.98ADF
Fisher Chisquare

5.27

Observation***

211.95
***

176

***

3.30

132.47
***

110

***

2.64

105.97

***

2.64

***

88

105.97
***

88

***

3.30

132.47***110

Notes: Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *Page 8

Similarly, the results of panel unit root test of exchange rate at level indicate thatexchange rate is unit root in all regions. Levin, Lin and Chu’s method found exchange ratein Sumatra is significant at 10%. It means that exchange rate in Sumatra is not unit root atlevel. However, by other methods we found that all regions are not significant meaningdata are not stationary at level (Table 4). At first difference, the data of exchange rate arestationary in all regions. Table 4 reports that all regions are significant at 1%. Therefore,
the order of integration of exchange rate is one in all regions.
Table 4. Panel Unit Root Test of Exchange Rate
MethodBreitung

SumatraJava

Kalimantan

Sulawesi

Papua

Level

1st DiffLevel

1st DiffLevel

1st DiffLevel

1st DiffLevel

1st Diff

0.91-13.81

0.72-10.92

0.64

-9.77

0.64

-9.77

0.72

-10.92***t-statIm, Pesaran
andShin
W-stat0.83

ADF
Fisher Chisquare

7.36-13.12

***
0.65

***

147.37

0.58

-9.28

***

4.60

***

92.10

***
0.58

3.68

73.68

110

***

-9.28

***

***

176

Observation-10.37***0.65-10.37

***

3.68

***

73.68

***

***

88

88

4.60

92.10
***

110

Notes: Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *Table 5 reports that variables of money supply at level in all regions are notsignificant. These results mean that money supply is unit root at level. Therefore, we
continue to test the variable of money supply at first difference. At first difference, the dataof money supply are stationary in all regions with Breitung t-stat method (Table 5). It
means that the order of integration of money supply is one in all regions.

Table 5. Panel Unit Root Test of Money Supply
MethodBreitung
t-statSumatraJava

Kalimantan

Sulawesi

Papua

Level

1st DiffLevel

1st DiffLevel

1st DiffLevel

1st DiffLevel

1st Diff

14.61

-5.29

14.43

-4.11

11.11

-3.64

14.02

-3.54

9.49

-4.10

***

***

***

***

***

Page 9Im, Pesaran
andShin
W-stat10.01

ADF
Fisher Chisquare

2.51

Observation

2.37

11.07

3.92

8.08

2.04

11.28

4.39

5.88

-1.66
**

15.31

0.0002

0.64

0.01

2.58

0.0001

0.23

1.91

35.99
***

176

110

88

88

110

Notes: Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *Similarly, we do unit root test for monthly data which is from 1993 to 2010 for 26
provinces. The data are domestic price (CPI), foreign price (CPI US) and exchange rate.
The results report that those variables are not stationary at the level. At the first difference,
they significantly reject unit root. It means the data are stationary. Therefore, we canconclude that the data have order of integration at one.
4.2 Long-term Determinant of Inflation

Since the empirical results found that domestic price, foreign price, exchange rate
and money supply are stationary at the first difference. It means the data have the similarlevel of order of integration which is one. Therefore, we can use the data in the long run
model. The results of panel OLS for domestic price (CPI province) can be seen on Table 6.
In the long run, the foreign price which is represented by CPI US has positively affected
domestic price which is represented by CPI province. The effect of CPI US in all regions isdifferent. The highest effect of foreign price is in Sumatra and the lowest effect of that is in
Java. The elasticity of foreign price in Sumatra and Java are 4.28 and 1.81 respectively. Itmeans an increase price in foreign price by 1% can increase domestic price by 4.28% in
Sumatra and by 1.81% in Java.
Meanwhile, exchange rate represented by US Dollar against Indonesia Rupiah ispositively associated with domestic price. Empirical results found that exchange rate issignificant and positive with elasticity by 0.49 in Sulawesi and 0.25 in Kalimantan. Itmeans depreciation of exchange rate by 1% can increase domestic price by 0.49% in
Sulawesi and by 0.25% in Kalimantan. This finding was supported by the fact that demandof import in Indonesia is high. The high value of import explains that foreign price andexchange rate are the main factor of domestic inflation. The domestic price can be
Page 10determined by foreign price because the import value is high. Therefore, the domestic priceis sensitive to the foreign price. Meanwhile, depreciating exchange rate will be followed byincreasing domestic price.
Table 6. The Long-term Model (Panel Fixed Effect-Period)
Variable

SumatraJava

Kalimantan

Sulawesi

Papua

CPI US

4.28

1.81

2.06

4.08

3.96

(0.16)***(0.54)***(0.56)***(0.23)***(0.19)***

0.45

0.27

0.25

0.49

0.43

(0.02)***(0.05)***(0.06)***(0.03)***(0.03)***

ER

0.32

0.29

(0.08)***(0.08)***-20.92-10.47-10.76-20.30-19.15(0.64)***(2.17)***(2.48)***(0.90)***(0.64)***

R2

0.98

0.99

0.99

0.98

0.98

Observation

176

110

88

88

110

Provinces

8

5

4

4

5

M2
C

Notes: standard errors are shown below the parameter estimates
Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *Money supply represented by broad money (M2) is positively associated withdomestic price. Empirical results found that money supply is only significant and positivein Java and Kalimantan. The elasticity of money supply in Java and Kalimantan is 0.32 and0.29 respectively. It means an increase of money supply by 1% can increase domestic priceby 0.32% in Java and by 0.29% in Kalimantan. The role of money supply in both regions isimportant which is comparing to other inflation factors.
To prove that the long run model is robust, we do panel unit root test for the
residual of the long run model (Table 7). The empirical results found that the residual in all
regions are significant at 1%. Therefore, we can conclude that the long run model is valid.
Furthermore, we can use the error terms of those long-term models as error correction
mechanism (ECM) in the short-term models.
Table 7. Panel Unit Root Test of Residual at Level
MethodSumatraJava

Kalimantan

Sulawesi

Papua

Levin, Lin & Chu t-5.85***-5.48***-5.28***-4.98***-6.73***

Breitung t-stat-3.12***-4.22***-4.46***-2.93***-5.12***

Page 11

ADF - Fisher Chi-square

56.37***

42.85***

37.41***

39.25***

60.89***

Notes: Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *In the provincial case we found that the order of integration of domestic price,foreign price and exchange rate is one. Therefore, we those variables can be used in thelong run regression. The empirical results of OLS for domestic price in each province canbe seen in appendix. The results shows that the foreign price has positively affected
domestic price in the long term. However, the impact of CPI US on domestic price in eachprovince is various. The highest effect of foreign price is in Banda Aceh (6.06).
Meanwhile, the lowest effect of that is in Gorontalo (3.34). It means an increase price inforeign price by 1% can increase domestic price by 6.06% in Banda Aceh and by 3.34% in
Gorontalo.
Similarly, the exchange rate is significantly affecting domestic price. The effect ispositive meaning that depreciation of Indonesian Rupiah against US Dollar can increase thedomestic price. The highest effect is in North Maluku with elasticity by 0.50 and the lowest
is in Papua with elasticity by 0.29. This finding confirms that exchange rate is theimportant factor of inflation (Kandil & Morsy 2009; Khan & Gill 2010). The test results forthe error of the long term model in each province has significantly rejected that it has unit
root. Therefore, the long term model exists in all provinces. Thus, the error of those longterm models can be applied as ECM in the short term models.

4.3 Short-term Determinant of Inflation
In the short-term inflation model, we put ECM variable that we got from the longterm inflation model. The ECM variable in each equation is expected to be negative, since
the role of ECM is to restore the equilibrium (Gujarati 2003). The empirical results showthat ECM variable is negative as expected in all equations (Table 9). The coefficient of
ECM in Sumatra, Java, Kalimantan, Sulawesi and Papua is -0.10, -0.11, -0.15, -0.13 and 0.10 respectively.
In Sumatra, CPI US as representative of the inflation in US has positively affected
inflation in Sumatra and Kalimantan with one lag. The elasticity of US inflation is 0.36 in
Sumatra and 0.22 in Kalimantan. Those mean that an increase in US inflation one year ago
by 1% can increase inflation in Sumatra and Kalimantan now by 0.36% and by 0.22%
Page 12

respectively. However, the effect of US inflation is significant in Sumatra, but it is not
significant in Kalimantan. From 2000 to 2010, the value of import in Sumatra is higher
than that of in Kalimantan (Figure 1).

Figure 1. Import in Sumatra and Kalimantan
2000
1800
Sumatera

1600

Kalimantan

million USD

1400
1200

1000
800
600

400
200
0
2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Source: Bank Indonesia

Meanwhile, the changes of exchange rate can positively affect inflation in all
regions. The effect of exchange rate changes is highly significant at 1%. The elasticity of
exchange rate changes in Sumatra, Java, Kalimantan, Sulawesi and Papua is 0.40, 0.34,
0.32, 0.37 and 0.34 respectively. An increase the changes of exchange rate depreciation by
1% can increase inflation by 0.40% (Sumatra), by 0.34% (Java), by 0.32% (Kalimantan),by 0.37% (Sulawesi) and by 0.34% (Papua). These findings support previous studies foundthat exchange rate significantly affects inflation (Endri, 2008; Gachet, Maldonado andPerez, 2008; Kandil and Morsy, 2009; Khan and Gill, 2010).

Table 8. The Short-term Model (Panel Fixed Effect-Period)
Variable

SumatraJava

ECM (-1)-0.13(0.04)***DCPI US (-1)
DER
DW

Kalimantan

Sulawesi

Papua

-0.13
(0.06)**-0.14(0.06)**-0.16(0.05)***-0.21(0.05)**

0.42(0.18)**

0.28(0.18)

0.30(0.18)

0.21(0.23)

0.19(0.24)

0.36(0.02)***

0.34(0.03)***

0.31(0.02)***

0.37(0.03)***

0.29(0.02)***

0.05(0.04)
Page 13

DW (-1)

0.09(0.04)**

DTFP

-0.34
(0.15)**

DKrs

0.08
(0.02)***

DKrs (-1)-0.31(0.16)*

0.03(0.04)

0.06(0.05)

0.10(0.05)**-0.46(0.15)***-0.37(0.19)*-0.52(0.17)***

0.05(0.02)**

0.09(0.03)***

0.06(0.02)**

0.04(0.02)*

C

0.06(0.01)***

0.05(0.01)***

0.06(0.01)***

0.05(0.01)***

0.05(0.01)***

R2

0.88

0.89

0.92

0.91

0.88

Observation

160

100

80

80

100

Provinces

8

5

4

4

5

Notes: standard errors are shown below the parameter estimates
Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *Wage changes positively affected inflation in all regions with different lag. In Javaand Papua, the wage changes affected inflation without lag and insignificant. Meanwhile, it
significantly affected inflation in Sumatra and Kalimantan with one lag. The elasticity ofwage changes in both regions is 0.08 and 0.25 respectively. It means than an increase ofwage changes in previous year by 1% can increase inflation in current year by 0.08% in
Sumatra and by 0.25% in Kalimantan. These findings confirm previous research (Leheyda
2005) found that wages has significantly affected inflation.
Money supply growth positively affects inflation in Papua with one lag, but it is not
significant. Theoretically, money growth is positively associated with inflation (Monfort &Pena 2008; Boujelbene & Boujelbene 2010). Increasing money growth can cause toimprove aggregate demand which positively affects inflation. The elasticity of money
growth is 0.01 meaning that an increase money growth by 1% in previous year can increaseinflation by 0.01% in Papua in current year.
Meanwhile, interest rate represented by time deposit rate and policy rate represented
by Bank Indonesia rate are positively related with inflation in Java, Kalimantan and
Sulawesi in the short-term. However, these relationships are not significant. Theoretically,
interest rate and policy rate have to negatively affect inflation. An increase policy rate canincrease time deposit rate and lending rate. Thus, increasing both rates can reduce
consumption and investment. Finally, aggregate demand will reduce in which reduces priceindex. Therefore, both interest rate and policy rate are negatively associated with inflation.
Page 14

Furthermore, we will discuss the short-term model of inflation in each province ineach region separately. Table 9 reports that ECM in each province in Sumatra has anegative coefficient as expected. The elasticity of ECM is relatively similar in eachprovince in Sumatra with the range between -0.03 and -0.05. They are significant at 1% in
all provinces in Sumatra. Meanwhile, expected inflation denoted by inflation lag 1 monthhas positive effect on inflation in each province. The lowest impact is in Jambi (0.24%) andthe largest impact is in Riau and South Sumatra (0.38).
The changes of exchange rate can positively affect inflation in all provinces. They
are significant at 1% with the elasticity of exchange rate changes between 0.04 (North
Sumatra) and 0.10 (Bengkulu). An increase the changes of exchange rate depreciation by
1% can increase inflation by 0.10% in Bengkulu. These findings confirms previous studies
found that exchange rate are sources of inflation (e.g. Kandil and Morsy, 2009; Khan and
Gill, 2010). Another short-term factor, money supply changes is significant only in Bandar
Lampung with lag a month. As expected, kerosene price changes are positive andsignificant (1%) in all provinces. The lowest impact is in Jambi that its elasticity is 0.06.
Meanwhile, the others are around 0.10.
Table 9. The Short-term Model (Sumatra)
Variable

Aceh

North
SumatraWest
Sumatra

Riau

Jambi

South
SumatraBengkulu

BandarLampung

ECM
(-1)

-0.03
(0.01)***-0.04(0.01)***-0.04(0.01)***-0.03(0.01)***-0.04(0.01)***-0.03(0.01)***-0.04(0.01)***-0.05(0.01)***

DCPI
(-1)

0.28
(0.06)***

0.33(0.05)***

0.27(0.05)***

0.38(0.05)***

0.24(0.06)***

0.38(0.05)***

0.32(0.05)***

0.34(0.05)***

DER
(-1)

0.06
(0.02)***

0.04(0.01)***

0.09(0.01)***

0.08(0.01)***

0.07(0.01)***

0.08(0.01)***

0.10(0.01)***

0.05(0.01)***

DM2

0.01
(0.01)

0.01(0.01)

0.004(0.01)

0.004(0.01)

0.002(0.01)

0.003(0.01)

0.01(0.01)

DM2
(-1)

0.01
(0.01)**

DKrs

0.10
(0.02)***

0.10(0.01)***

0.09(0.01)***

0.06(0.01)***

0.06(0.01)***

0.08(0.01)***

0.10(0.01)***

0.09(0.01)***

C

0.01(0.001)***

0.004(0.001)***0.01(0.001)***

0.004(0.001)***0.01(0.001)***

0.004(0.001)***

0.004(0.001)***

0.004(0.001)***

R2
Obs

0.36
214

0.51
214

0.51
214

0.53
214

0.41
214

0.57
214

0.55
214

0.51
214

Notes: Standard errors are shown below the parameter estimates
Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *Page 15

Meanwhile, table 10 shows that ECM with one month lag in each province in Java
has a negative coefficient between -0.03 and -0.04. The expected inflation variable is alsosignificant at 1% in all provinces. The lowest impact of inflation expectation is in Central
Java (0.34) and the highest impact is in West Java (0.49). That means an increase in
expected inflation by 1% can increase inflation by 0.34% and 0.49% in Central Java andWest java respectively. The US inflation significantly affects domestic inflation in
Yogyakarta only. Its elasticity is 0.21 meaning an increase in US inflation by 1% can raise
inflation in Yogyakarta by 0.21%.
Similar with Sumatra, the changes of exchange rate with one month lag canpositively affect inflation in all provinces in Java. They are significant at 1%. The highest
elasticity of exchange rate changes is in East Java (0.10) and the lowest of that is in WestJava (0.04). An increase the changes of exchange rate depreciation by 1% can increaseinflation by 0.10% and 0.04% in East Java and West Java respectively.
Table 10. The Short-term Model (Java)
Variable

JakartaWest Java

Central
Java

ECM
(-1)

-0.04
(0.01)***-0.04(0.01)***-0.03(0.01)***-0.03(0.01)***-0.04(0.01)***

DCPI
(-1)

0.47
(0.05)***

0.49(0.05)***

0.34(0.05)***

0.46(0.05)***

0.41(0.05)***DCPIUSDER(-1)

East Java

0.21(0.09)**
0.05(0.01)***

0.04(0.01)***

0.09(0.01)***

DM2
DM2
(-1)

Yogyakarta

0.09(0.01)***

0.10(0.01)***

0.001(0.004)0.01(0.004)*0.01(0.004)*0.01(0.004)*DIP-0.001(0.0004)*

DKrs

0.04
(0.01)***

0.06(0.01)***

0.08(0.01)***

0.02(0.01)***

0.05(0.01)***

C

0.003(0.001)***

0.003(0.001)***

0.003(0.001)***

0.003(0.001)***

0.003(0.001)***

R2
Obs

0.58
214

0.56
214

0.57
214

0.65
214

0.54
215

Notes: standard errors are shown below the parameter estimates
Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *Page 16

Money supply changes with a month lag is significant in Jakarta, West Java andCentral Java. They have similar elasticity (0.01). Policy rate in this model is significant
only in East Java with low elasticity (-0.001). This situation can be explained that policy
rate affects inflation indirectly and the lag range 3-6 months. The last factor in this model iskerosene price changes. It is positive and significant (1%) in all provinces with the lowest
effect in Yogyakarta (0.02). It means an increase in kerosene price changes by 1% canincrease inflation by 0.02%.
Table 11. The Short-term Model (Kalimantan)
VariableWest
Kalimantan

ECM
(-1)

-0.04
(0.01)***

DCPI
(-1)

0.30
(0.05)***

DCPI
(-3)

CentralKalimantan-0.75(0.07)***South
Kalimantan

East Kalimantan-0.03(0.01)***-0.03(0.01)***

0.30(0.05)***

0.29(0.05)***

0.003(0.05)DCPIUS

0.19(0.11)*

0.18(0.11)*

0.18(0.09)*

DER
(-1)

0.09
(0.01)***

0.12(0.01)***

0.09(0.01)***

DM2

0.003
(0.01)

0.001(0.01)

DM2
(-1)

0.01
(0.004)*

DKrs

0.05
(0.01)***

0.004(0.16)

0.06(0.01)***

0.06(0.01)***

C

0.01(0.001)***0.01(0.01)

0.004(0.001)***

0.004(0.001)***

R2
Obs

0.54
215

0.39
212

0.58
215

0.59
214

Notes: Standard errors are shown below the parameter estimates
Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *The short-term inflation model of provinces in Kalimantan can be seen on table 11.
The ECM with lag by 1 month in each province is significant at 1%. The elasticity of ECM
in each province is relatively similar around -0.03, except in Central Kalimantan (-0.75).
Meanwhile, expected inflation has positive effect on inflation in all provinces. They are
significant at 1%, except in Central Kalimantan. The elasticity of inflation expectation is0.30 (West Kalimantan and South Kalimantan) and 0.29 (East Kalimantan). Foreign
Page 17

inflation significantly affects domestic inflation in West Kalimantan (0.19), South
Kalimantan (0.18), and East Kalimantan (0.18). Therefore, an increase in US inflation by
1% can raise inflation by 0.18% in South Kalimantan and East Kalimantan.
Meanwhile, the changes of exchange rate with one month lag have positive impact
on inflation in three provinces in Kalimantan, such as West Kalimantan, South Kalimantan,
and East Kalimantan. The highest elasticity of exchange rate changes is in South
Kalimantan (0.12) and followed by West Kalimantan and East Kalimantan (0.09).
Meanwhile, money supply changes with a month lag is significant only in East Kalimantan.
Its elasticity is 0.01 which means an increase in money supply changes by 1% can increaseinflation by 0.01% in East Kalimantan. Kerosene price changes have positive impact on
inflation in all provinces in Kalimantan and significant at 1%, except in Central Kalimantan
which is insignificant. The elasticity of kerosene price changes is 0.05 in West Kalimantan
and 0.06 in South Kalimantan and East Kalimantan.
Table 12. The Short-term Model (Sulawesi)
Variable

North Sulawesi

Central Sulawesi

South Sulawesi

Southeast Sulawesi

ECM
(-1)

-0.07
(0.01)***-0.05(0.01)***-0.05(0.01)***-0.05(0.01)***

0.21(0.06)***

0.25(0.06)***

0.24(0.06)***

DCPI
(-1)
DCPI
(-3)

0.12
(0.06)***DCPIUS
DER
(-1)

0.31
(0.14)**
0.03(0.01)*

DM2

0.06(0.02)***

0.07(0.01)***

0.07(0.02)***

0.004(0.01)

0.001(0.01)

0.01(0.01)

DM2
(-3)

0.01
(0.01)**

DKrs

0.05
(0.01)***

0.03(0.02)**

0.03(0.01)**

0.08(0.02)***

C

0.01(0.001)***0.01(0.001)***0.01(0.001)***0.01(0.001)***

R2
Obs

0.41
212

0.36
214

0.43
214

0.47
214

Notes: standard errors are shown below the parameter estimates
Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *Page 18

In Table 12, there are four short-run inflation province models. Similar withprevious regions, ECM with one month lag in each province in Sulawesi is significant at
1%. The coefficient of ECM is -0.05 in all provinces, except in North Sulawesi (-0.07). The
inflation expectation in all provinces is significant at 1% with positive coefficient. North
Sulawesi has the lowest coefficient which is 0.12 meaning an increase in expected inflation
by 1% can lead inflation by 0.12%. Meanwhile, the other province’s coefficients are 0.21
(Central Sulawesi), 0.24 (Southeast Sulawesi) and 0.25 (South Sulawesi). Foreign inflationinfluences inflation in Southeast Sulawesi with elasticity 0.31.
A month lag of exchange rate changes positively affects inflation in all provinces in
Sulawesi. The lowest elasticity of exchange rate changes is in North Sulawesi (0.03).
Meanwhile, the other three provinces (Central Sulawesi, South Sulawesi, and Southeast
Sulaesi) have an elasticity range 0.6-0.7. Money supply changes have no significant
affecting inflation in all provinces in Sulawesi, except North Sulawesi. An increase in threemonth lag of money supply changes by 1% can lead inflation in North Sulawesi by 0.01%.
Another short-term factor, kerosene price changes, has positive effect on inflation in allprovinces in Sulawesi. The highest effect of that is in Southeast Sulawesi (0.08).
Table 13 reports the short-term inflation factor in all provinces in Papua. The ECM
with one month lag in each province is significant with coefficient between -0.03 and -0.07.
Meanwhile, the expected inflation positively influences inflation in Maluku (0.12), East
Nusa Tenggara (0.27), Bali (0.35), and West Nusa Tenggara (0.40), and. An increase ininflation expectation by 1% can raise inflation by 0.12%, 0.27%, 0.35% and 0.40% in
Maluku, East Nusa Tenggara, Bali, and West Nusa Tenggara respectively.
Table 13. The Short-term Model Papua
VariableBali

West Nusa
Tenggara

East Nusa
Tenggara

Maluku

Papua

ECM
(-1)

-0.03
(0.01)***-0.05(0.01)***-0.03(0.01)***-0.07(0.01)***-0.05(0.01)***

DCPI
(-1)

0.35
(0.05)***

0.40(0.05)***

0.27(0.06)***

0.12(0.06)*

DER
DER
(-1)
DER
(-2)

0.03
(0.01)**
0.06(0.01)***

0.08(0.02)***

0.03(0.02)

0.05(0.01)***
Page 19

DM2

0.002
(0.01)

0.001(0.01)

0.003(0.01)

0.01(0.01)

DM2
(-1)

0.01
(0.01)

DKrs

0.05(0.01)***

0.07(0.01)***

0.07(0.01)***

0.04(0.02)**

0.03(0.02)*

C

0.004(0.001)***

0.004(0.001)***0.01(0.001)***0.01(0.001)***0.01(0.001)***

R2

0.50

0.49

0.34

0.31

0.13

Obs
214
214
215
215
Notes: standard errors are shown below the parameter estimates
Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *215

The exchange rate changes significantly influences inflation in all provinces in
Papua region, except in the Province of Papua. The highest elasticity of exchange ratechanges is in Maluku (0.08) and followed by Bali (0.06%), East Nusa Tenggara (0.05), andWest Nusa Tenggara (0.03). On the other hand, money supply changes are insignificant inall provinces in Papua, even their impacts are positive to inflation. Meanwhile, kerosene
price changes have significantly affected on inflation in all provinces. The lowest effect isin the Province of Papua (0.03). It means an increase in kerosene price changes by 1% canincrease inflation by 0.03% in this province.

5. Conclusion and Policy Recommendation

In the long run, inflation in each region and province has been determined bysimilar factors which are foreign price and exchange rate. The foreign price represented by
CPI of United States positively affects domestic price. It means an increase in foreign pricecan raise domestic price. The fact that the value of import in Indonesia has shown apositive trend, therefore the domestic price is sensitive to foreign price. This condition
verifies the finding that foreign price positively associated with domestic price. In the
short-term, foreign price also affects domestic price in Sumatra and Kalimantan.
Meanwhile, exchange rate represented by US Dollar against Indonesia Rupiah also
positively affects domestic price in the long and short-term. Depreciating Indonesia Rupiah
against US will be followed by increasing domestic price. Since many products inIndonesia are from foreign countries, the domestic price is more sensitive with exchange
rate. Empirical findings also prove that exchange rate is significantly affecting inflation inPage 20

the short-term in all regions. This finding strengthens previous studies (Siregar & Rajaguru
2002; Wimanda 2006) found that main determinant of inflation in Indonesia is exchange
rate.
Money supply represented by broad money has also positively affected domestic
price in the long-term in Java and Kalimantan. This finding confirms previous study thatfound a strong link between money growth and inflation (Listiani 2006; Monfort & Pena
2008; Boujelbene & Boujelbene 2010). Money supply also affects inflation in the shortterm in Papua, but it is not significant. Other short-term factors of inflation are wage, time
deposit rate and policy rate. Wage is positively affecting on inflation in all regions as
expected. However, these effects are only significant in Sumatra and Kalimantan with one
lag. On the other hand, time deposit rate and policy rate are positively related with
inflation, but they are not significant.
According to the empirical findings, Bank Indonesia as central bank of Indonesia
must maintain the stability of exchange rate since the role of exchange rate is really
important to influence inflation. Moreover, Bank Indonesia should concern with theirpolicy instrument, since Bank Indonesia rate as the policy rate is not significantly affecting
inflation. However, another monetary instrument, base money is still effective to control
inflation. The finding that money supply is significantly affecting inflation should not be
neglected. Therefore, Bank Indonesia could not rely on their policy rate only, but they canalso use their other policy instrument, such as base money.
The effort of Bank Indonesia to achieve a low and stable inflation is needed the
support of government. Based on the conclusion, the government can support to reduce theimpact of foreign price by issuing import policy. The policy can limit a number of good
imports and prevent domestic price from any various foreign inflation. Moreover, thegovernment can also release the export policy which supports local entrepreneurs toincrease the value of export. This policy can help Bank Indonesia to strengthen Indonesia
rupiah against foreign currencies.

Page 21References

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Page 23

Appendix

The Long-term Model of CPI Province
No
1

2

3

4

5

6

7

8

9

10

11

12

13

14

Province
Banda Aceh

North
SumatraWest Sumatra

Riau

Jambi

South
SumatraBengkulu

Lampung

JakartaWest Java

Central Java

Yogyakarta

East Java

Bali

C

CPIUS

ER

-29.106.15

0.31(0.61)***(0.15)***(0.02)***-23.084.75

0.42(0.54)***(0.13)***(0.02)***-22.804.70

0.42(0.53)***(0.13)***(0.02)***-23.254.84

0.38(0.56)***(0.13)***(0.02)***-21.764.62

0.34(0.49)***(0.12)***(0.02)***-24.225.02

0.40(0.56)***(0.13)***(0.02)***-21.694.55

0.38(0.50)***(0.12)***(0.02)***-22.744.71

0.40(0.53)***(0.13)***(0.02)***-20.314.23

0.40(0.46)***(0.11)***(0.02)***-20.774.39

0.36(0.49)***(0.12)***(0.02)***-20.094.32

0.32(0.48)***(0.11)***(0.02)***-21.904.57

0.39(0.50)***(0.12)***(0.02)***-20.674.32

0.39(0.54)***(0.13)***(0.02)***-19.544.09

0.39(0.54)***(0.13)***(0.02)***

R2

Obs

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.96

216

Page 24

15

16

17

18

19

20

21

22

23

24

25

26

West
Kalimantan-21.194.35

0.43(0.48)***(0.11)***(0.02)***

CentralKalimantan-20.304.22

0.40(0.89)***(0.21)***(0.03)***South
Kalimantan-20.554.35

0.36(0.46)***(0.11)***(0.02)***

East
Kalimantan-21.884.60

0.37(0.47)***(0.11)***(0.02)***

North
Sulawesi-21.494.44

0.41(0.52)***(0.12)***(0.02)***

CentralSulawesi-25.555.18

0.45(0.63)***(0.15)***(0.02)***

South
Sulawesi-19.964.20

0.38(0.51)***(0.12)***(0.02)***Southeast
Sulawesi-24.985.15

0.41(0.58)***(0.14)***(0.02)***

West Nusa
Tenggara-20.404.26

0.39(0.53)***(0.13)***(0.02)***

East Nusa
Tenggara-23.735.06

0.32(0.57)***(0.14)***(0.02)***-19.524.07

0.40(0.52)***(0.12)***(0.02)***-25.055.35

0.30(0.55)***(0.13)***(0.02)***

Maluku

Papua

0.98

216

0.91

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.97

216

0.96

216

0.97

216

0.97

216

Notes: standard errors are shown below the parameter estimates
Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *Page 25