THE ROLE OF INTEREST RATES AND PROVINCIAL

MONETARY AGGREGATE IN MAINTAINING

INFLATION IN INDONESIA

Chandra Utama, Miryam B.L. Wijaya, and Charvin Lim1

Abstract

Monetary policy may employ interest rate or money supply to derive the assigned national inflation target. In this manner, most studies investigate monetary policy effectiveness using national data. However, based on the idea that inflation is a regional phenomenon, the application of provincial data is more appropriate in explaining the relationship between monetary instrument and inflation. This study elaborates the impact of changes in provincial money supply, BI Rate (interest rates of central bank), and PUAB (money market interest rates) to regional inflation in Hybrid New Keynesian Phillips Curve (HNKPC) framework. This study employs Generalized Method of Moments (GMM) techniques on panel data of 32 provinces from 2005-III to 2013-III. The data is classified into 4 groups, which are Java-Bali (W1), Sumatera (W2), Kalimantan-Sulawesi (W3), and Papua-Maluku-Nusa Tenggara (W4). The estimation result shows that each monetary instrument has diverging effectiveness in different regions. Provincial monetary aggregate is only effective in Sumatera, while BI Rate can manage inflation in Sumatera and Kalimantan-Sulawesi. PUAB, on the other hand, is significantly affecting inflation in almost all Indonesian regions, except Kalimantan- Sulawesi. We conclude that interest rate (BI rate and PUAB) is a more appropriate instrument, compared to provincial monetary aggregate, to control provincial inflation in Indonesia.

Keywords: Monetary policy, regional inflation, hybrid NKPC

JEL Classification: E31, E52, R19

1Authors are researcher on Center for Economic Studies (CES) Parahyangan Catholic University. Chandra Utama (chandradst@unpar. ac.id); Miryam B. L. Wijaya (mlwijaya@unpar.ac.id); Charvin Lim (charvin.kusuma@unpar.ac.id).

Acknowledgement: The authors are grateful to participants of CES discussion sessions, the 2015 IRSA International Seminar in Denpasar, and the 2015 BEMP International Conference in Jakarta. They also especially thank BEMP anonymous referees for their comments and suggestions which improved this paper. All remaining errors are ours. Financial support from Parahyangan Catholic University, grant # III/LPPM/2015-02/47-P is acknowledged.

The Role of Interest Rates and Provincial Monetary Aggregate in Maintaining Inflation in Indonesia 271

2.2.Regional Inflation Variation and The Impact Of Monetary Policy on Regional Inflation

Monetary policy is designed structurally and purposely for an ultimate national goal – price stability. Yet, the impact of monetary policy may differ among regions in a country. Differences may occur because of varying regional conditions, such as industrial competitiveness, financial structure, trading activities, and institutional environment. However, studies regarding the effectiveness of monetary policy are still commonly performed using national-level data. In this section, we review studies which exploits regional aspects in assessing monetary policy effectiveness.

Studies regarding the effectiveness of ITF in provincial level had been undertaken in China. Mehrotra et al. (2007) found that in 1978-2004 there is a variation of inflation among China provinces. They also found that there are 22 out of 29 provinces where forward looking inflation component is statistically significant in determining the actual inflation. In ITF, the significance of forward looking inflation component will increase the effectiveness of monetary policy.

Similar to China, Indonesia is a country with broad geographical territory and diverse social and economic condition. Ridhwan et al. (2011) shows that, while tight monetary policy might be conducive for the economy of Java, it may have a destructive effect on the economy of non- Java regions. This result indicates that one monetary conduct might bring about constructive effect on a region and a neutral or, even, destructive effect on the other.

Another research by Chaban and Voss (2012) found that there is inflation variation in 10 provinces in Canada. All of the provinces, aside from Alberta, indicated the existence of anchored inflation expectation which supported the effectiveness of ITF. They assert that the success measure of ITF can be exhibited by its capability to certainly determine the expected inflation to be strictly around the inflation target and that the deviation cannot be predicted. In provincial-level, we can question the deviation of provincial inflation to its national target. If there is a considerable gap between provincial and national deviation, the nationally constructed inflation target is not equally successful in each region.

2.3. Hybrid NKPC model

In this research, we estimate inflation using a theoretical framework developed by Gali and Gertler (1999) so called Hybrid NKPC model. According to Gali and Gertler, every firm adjust

their prices in every periods with the fixed probability of . There are two types of firm, are firms with forward-looking behavior as Calvo (1983) stated in his study while the rest, , are firms with backward-looking behavior. Based on this condition, aggregate price

can be constructed as equation (1). If the price set by forward looking firm is and backward looking firm is , hence the new price is:

272Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 3, Januari 2017

(1)

Forward looking firms behave as Calvo’s (1983) assertion, therefore can be derived as

(2)

While , equal to the average of adjusted price in the last period. The price can be denoted as

(3)

Hence, the specification form of hybrid NKPC is

(4)

In empirical testing, equation (4) is estimated using non-linier instrumental variable (GMM) estimator. In their study, Gali and Gertler (1999) provided several strong findings regarding inflation behavior. First, the real marginal cost is statistically significant and is an important determinant of inflation (in this study we used output gap). Second, the behavior of forward looking is crucial because most of the firms have this kind of behavior. T; they found that 60-80% of the firms are forward looking behavior. Third, the behavior of backward looking is statistically significant. Therefore, even though forward looking behavior is plausible, pure forward looking model cannot be accepted. Last but not the least, it takes time for prices to change (sticky price).

III. METHODOLOGY

In this research, we use quarterly panel data of 32 provinces in Indonesia covering the period of 2005-III to 2013-III. Most of the data are obtained in quarterly report of Perkembangan Perekonomian Daerah (Pekda) and Kajian Ekonomi Regional (KER) which are published by Kantor Bank Indonesia (K.BI) in provinces. Table 1 explains the definition of variable which are observed in this study.

The Role of Interest Rates and Provincial Monetary Aggregate in Maintaining Inflation in Indonesia 273

Table 1. Proxy and Measurement of Variables

Variable	Sign	Measure				Explanation
Inflation	πi,t	Percentage	Year-on-year inflation
Actual output	yita	Trillion rupiah	Real regional output (base: year 2000)
Potential output	yit*	Trillion rupiah	Estimated using Hodric-Prescott Filter
Output gap	yit	Percentage	yit =	( yita − yit*	)	× 100%
				y *
				it
Changes in real	mit	Trillion rupiah	mit is the changes in real currency money ( ∆Kit ) added by real demand
money supply			depositn ( ∆Git ). ∆Kit is the net flow of currency in regional office central
			bank. ∆Git is the changes in demand deposit in each province’s banking.
BI Rate	r1t	Percentage	Central bank interest rate; national data
PUAB	r2t	Percentage	Money market interest rate; national data

We use year-on-year inflation to compare the development of inflation among provinces based on inflation in the previous year. Inflation is calculated using provincial consumer price index (CPI). Even though the original data of CPI is the general price in city, Pekda and KER have provided provincial CPI data. For Papua and Papua Barat, including Banten and Jakarta, the CPI data or inflation is compounded in a particular proportion based on Pekda and KER.

For economic output indicator, we use real RGDP (Regional Gross Domestic Product) with year 2000 as its base year. Hodrick-Prescott Filter (HP Filter) is applied in order to obtain potential output, , based on the real PDRB data. The output gap, ,is the reduction of actual output to potential output, divided by potential output, multiplied by 100%.

Because of the unavailability of provincial money supply data, we use the net flow of currency in provincial central bank branch offices (Kantor Bank Indonesia) as proxy of currency changes. If the outflow is greater than inflow (net outflow), there is an increase of currency supply in the region, vice versa. We also use demand deposit changes in commercial bank (conventional and Islamic banking) as the proxy of provincial demand deposit changes. The sum of currency changes and demand deposit changes is utilized as the changes of money supply in each province.

For interest rate instruments, we use BI Rate, r1t, and PUAB, r2t. These variables are commonly used as the national monetary policy references. In contrast to mit, which is a provincial level data, r1t and r2t is a national-level data therefore the value for these variables are identical for each period in every provinces.6

6Depending on the covariance structure of the equation block representing the panel data, using SUR is plausible when the set of exogenous variables are uniform across equations. However, in this paper we simply use one equation while the possible correlation across provinces may be adjusted during the estimation (i.e. white cross section). We thank to the anonymous reviewer of this journal for a good discussion and suggestion on this issue.

The Role of Interest Rates and Provincial Monetary Aggregate in Maintaining Inflation in Indonesia 275

Table 2.

Classification of Regions and Its Provinces8

		Provinces
Region 1 (W1)	Banten and Jakarta	Jawa Tengah	Bali
	Jawa Barat	Yogyakarta	JawaTimur
Region 2 (W2)	Aceh	Sumatera Barat	Sumatera Selatan
	Sumatera Utara	Riau	Lampung
	Kepulauan Bangka Belitung	Jambi
	Kepulauan Riau	Bengkulu
Region 3 (W3)	Kalimantan Selatan	Kalimantan Tengah	Sulawesi Tenggara
	Kalimantan Timur	Sulawesi Utara	Sulawesi Tengah
	Kalimantan Barat	Sulawesi Selatan	Gorontalo
Region 4(W4)	Nusa Tenggara Barat	Maluku
	Nusa Tenggara Timur	Maluku Utara
	Papua and Papua Barat

Based on equation (7), the impact of monetary instruments on inflation in Region 1, 2, 3, and 4 are consecutively , ( + ), ( + ), and ( + ). In order to identify the significance of each policy impact coefficient, we run the Wald test. Using equation (7) we can identify the coefficient differences between Jawa-Bali region (W1) and the other regions by learning coefficient , , and . However, we cannot identify the coefficient differences among regions except of using W1 as the comparator. For that purpose, we use Wald test in comparing the coefficient difference of each regions.

IV. RESULT AND ANALYSIS 4.1. Provincial Inflation Model

Table 3 displays the estimation result of three equations using Hybrid NKPC framework. Each equation has different policy variable, which are money supply, policy rate (BI Rate), and interbank money rate (PUAB). The results indicate the existence of backward looking and forward looking behavior in the determination of Indonesian inflation. Forward looking tends to be more dominant, suggested by its significance and coefficient level. In concordance with the theoretical prediction, output gap has also a significant role in the construction of inflation.

8Jakarta and Banten is reported in the same account, and so, in this research, they are combined for all period. So do Papua and Papua Barat. Moreover, although Indonesia has 34 provinces in 2013, Kalimantan Utara and Sulawesi Barat are not included as observations because the data for those provinces are not available in all observation period (Sulawesi Barat was just established in 5th October 2004, while Kalimantan Utara was established in 25th October 2012). Table 2 shows the provinces included as observations in this research.

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Table 3.

Estimation Result of Provincial Inflation Model

	π it				π it		π it
	Coef.		Prob.	Coef.	Prob.	Coef.	Prob.
πi,t−1	0.4429		0.0000	0.0887	0.0000	0.3252	0.0000
πi,t+1	0.6330		0.0000	0.2992	0.0000	0.6579	0.0000
yit	0.2020		0.0000	0.0364	0.0011	0.1063	0.0000
Sit = mit	-1.4E-07		0.0001
Sit = r1it				1.5515	0.0000
Sit = r2it						0.4736	0.0000
N			900		900		900
Instrument rank			30		30		30.0000
J-stat.			29.96381		29.85828		29.6851
prob.			0.269113		0.273524		0.2809

Note: We never use the money supply, policy rate, and the interbank money rate simultaneously.

Within this limitation, we compare the effect of these three variables on inflation.

On the role of money supply, BI Rate, and PUAB in determining the level of inflation, Table 3 shows significant contradictive directions. The result suggests that an increase in money supply would lower inflation, while an increase in interest rate raises inflation. We suspect this peculiar result is in accordance with Friedman’s proposition on short-run monetary ineffectiveness. According to Friedman, monetary instruments need time in order to effectively affect the objective variable. Friedman argued that the long period of lag required by the economy to respond to monetary policy produce peculiar short-term relationship between monetary instrument and inflation. Batini and Nelson (2001) further confirm Friedman’s proposition of monetary policy ineffectiveness in the short-run using data from 1953 to 2001 in the United States and United Kingdom.

Since the application of policy variables without time lag yield a conflictive result and that monetary policy need time to affect the economy, we try to re-estimate the model by adding time lag for the policy variables until we found each policy variable to have significant and corresponding effect to the policy objective. The lag would then demonstrate the nearest time in which monetary policy would produce the intended effect on inflation. The result is estimated and presented in Table 4.

In concordance with the earlier result, the estimations show that backward looking and forward looking behavior have positive significant effects on inflation, with forward looking having greater influence. In accordance with the theory, output gap is also found to have a positive effect on inflation. By adding time lag worth one quarter, the estimation results of

The Role of Interest Rates and Provincial Monetary Aggregate in Maintaining Inflation in Indonesia 277

monetary policies influence have changed. Money supply is shown to have insignificant effect on inflation, while BI Rate a contradictive effect. However, the estimation result on PUAB has shown the expected result. PUAB has a negative significant effect on the future inflation, with a time lag of one quarter. It suggests that, different with the other two policies, PUAB will become an effective policy in three months after its implementation. The value of J-statistic probability professes that there is no over-identification in the model.

Table 4.

Estimation Result of Provincial Inflation Model with1 Period Time Lag

	π it				π it		π it
	Coef.		Prob.	Coef.	Prob.	Coef.	Prob.
πi,t−1	0.444342		0.0000	0.202718	0.0000	0.468151	0.0000
πi,t+1	0.636456		0.0000	0.615281	0.0000	0.618738	0.0000
yit	0.158972		0.0000	0.165596	0.0000	0.155991	0.0000
Sit = mi,t-1	-4.9E-09		0.7151
Sit = r1i,t-1				0.683177	0.0000
Sit = r2i,t-1						-0.07558	0.0000
N			900		900		900
Instrument rank			30		30		30
J-stat.			29.8360		29.7515		29.8454
prob.			0.2745		0.2780		0.2741

Table 5.

Estimation Result of Provincial Inflation using 2 and 3 Periods of Time Lag

	πit			πit			πit				πit
	Coef.		Prob.	Coef.		Prob.	Coef.		Prob.	Coef.	Prob.
πi,t−1	0.5094		0.0000	0.5392		0.0000	0.4903		0.0000	0.5669	0.0000
πi,t+1	0.4993		0.0000	0.5385		0.0000	0.5017		0.0000	0.5163	0.0000
yit	-0.0582		0.0000	-0.0308		0.0369	-0.0036		0.6988	-0.0322	0.0063
Sit = mi,t-2	-2E-07		0.0000
Sit = mi,t-3				2.1E-7		0.0000
Sit = r1i,t-2							0.0616		0.0000
Sit = r1i,t-3										0.12075	0.0000
N			870			840			870		840
Instrument rank			30			30			30
J-stat.			29.5054			29.6032			29.9087		29.4934
prob.			0.2886			0.2844			0.2714		0.2891

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We applied two quarter and three quarter periods of time lag in order to find the best time lag for BI Rate and money supply. All of the estimation results indicate that inflation level in Indonesia is significantly affected by backward looking and forward looking behavior. In contrast with the earlier results, these estimation suggest that backward looking behavior has a greater impact on inflation, meaning that the economy take into account past inflation more considerably than expected future inflation in constructing their expected inflation. In Table 5, we show that money supply and BI Rate need 3 quarters to effectively influence the inflation.

4.2. Inflation Model with Regional Consideration

In order to identify which monetary policy is better in maintaining regional inflation, we estimate each monetary policy’s impact on each region. We started by assessing monetary aggregate, followed by BI Rate and PUAB sequentially. In order to identify differences in each region’s response, we use dummy variable on monetary policy. Wald test is executed in order to evaluate the significance of monetary policy in each region and to diagnose if the policy effect is different.

Table 6 shows the estimation result of Hybrid NKPC model with regional consideration and monetary aggregate as the policy variable. W1 is used as the base comparator in this equation. The estimation result suggest that forward looking and backward looking behavior have significant roles in determining future inflation, with forward looking being a slightly greater determinant. Output gap is found to have no significant effect in determining inflation.

Table 6.

Estimation Result with Monetary Aggregate and Regional Response

		πit
	Coef.	Prob.
πi,t-1	0.5200	0.0000
πi,t+1	0.5515	0.0000
yit	0.0142	0.5505
mi,t-3	9.02E-08	0.1444
mi,t-3d2i,t	1.43E-06	0.0000
mi,t-3d3i,t	-3.8E-07	0.1934
mi,t-3d4i,t	-1.1E-06	0.0029
N	840
Instrument rank	30
J-statistic	29.32421
Prob(J-statistic)	0.1698

The Role of Interest Rates and Provincial Monetary Aggregate in Maintaining Inflation in Indonesia 279

Table 7 displays the effect of monetary aggregate on each region’s inflation. The Wald Test result shows that money supply is only significantly affecting the inflation of W2 (Sumatera) and W4 (Papua-Maluku-Nusa Tenggara) regions. While it has a positive effect on W2’s inflation, it turns out to have negative effect on W4’s. It suggests that employing monetary aggregate as the monetary instrument will only be effective for managing inflation in W2 region.

Table 7.

Wald Test: Effect of Monetary Aggregate on Each Region's Inflation

	Jawa-Bali	Sumatera	Kalimantan -	Papua - Maluku -
			Sulawesi	Nusa - Tenggara
Coef.	9.02E-08	1.52E-06	-2.87E-07	-1.00E-06
F - statistic	2.1345	76.9455	1.0900	8.4129
Prob.	0.1444	0.0000	0.2968	0.0038

Table 8.

Wald Test: Monetary Aggregate’s Impact Differences Between Regions

		Jawa-Bali	Sumatera	Kalimantan -	Papua - Maluku -
				Sulawesi	Nusa Tenggara
Jawa-Bali	F-Stat		75.3142	1.6946	8.9352
	(Prob.)		(0.0000)	(0.1934)	(0.0029)
Sumatra		75.3142		30.1851	40.8928
		(0.0000)		(0.0000)	(0.0000)
Kal-Sul		1.6946	30.1851		4.3247
		(0.1934)	(0.0000)		(0.0379)
Pap-Mal_Nusa		8.9352	40.8928	4.3247
		(0.0029)	(0.0000)	(0.0379)

Displayed by the estimation result in table 8, we found that money supply has no different impact on inflation in W1 and W3. The policy impact is especially unique in W2 and W4, where it is shown that there is no indifferent effect on other regions.

The Role of Interest Rates and Provincial Monetary Aggregate in Maintaining Inflation in Indonesia 281

Table 11.

Wald Test: Diagnosis of BI Rate’s Impact Differences between Regions

		Jawa-Bali	Sumatera	Kalimantan-	Papua-Maluku-
				Sulawesi	Nusa Tenggara
		(W1)	(W2)	(W3)	(W4)
Jawa-Bali	F Stat.		4.2467	3.2403	1.8336
	Prob.		0.0396	0.0722	0.1761
Sumatra		4.2467		1.1297	0.0741
		0.0396		0.2882	0.7855
Kal-Sul		3.2403	1.1297		2.0217
		0.0722	0.2882		0.1554
Pap-Mal_Nusa		1.8336	0.0741	2.0217
		0.1761	0.7855	0.1554

The other alternative for monetary policy operational target is PUAB. Table 12 shows the estimation result of Hybrid NKPC model with PUAB as the shock variable. In this estimation result, we found that backward looking and forward looking behavior are significant in determining the level of inflation. We found forward looking behavior to be a greater determinant of inflation. It indicates that the expected future inflation has a bigger role in determining inflation than the inflation track records. In this equation result, we also found that the output gap impact on inflation is in line with theory.

Table 12.

Inflation Model with PUAB and Regional Response

		πit
	Coef.	Prob.
πi,t-1	0.4697	0.0000
πi,t+1	0.6145	0.0000
yit	0.1463	0.0000
r2i,t-1	-0.8723	0.0110
r2i,t-1d2i,t	0.6892	0.0555
r2i,t-1d3i,t	1.6945	0.0025
r2i,t-1d4i,t	0.2270	0.3969
N	900
Instrument rank	30
J-statistic	29.3568
Prob(J-statistic)	0.1688

282Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 3, Januari 2017

Table 13 presents the impact of PUAB on each observed region’s inflation. We found that PUAB stands as the best monetary instruments compared to the two others in this study. PUAB has significant and in-line-with-objective effect on W1 (Jawa-Bali), W2 (Kalimantan-Sulawesi) and W4 (Papua-Maluku-Nusa Tenggara) regions. Still to be put in our concern, PUAB has a positive effect on W3 region (Kalimantan-Sulawesi) which contradict the policy objective.

Table 13.

Wald Test: Effect of PUAB on Each Region's Inflation

	Jawa-Bali	Sumatera	Kalimantan-	Papua-Maluku-
			Sulawesi	Nusa Tenggara
	(W1)	(W2)	(W3)	(W4)
Coef.	-0.8723	-0.1830	0.8222	-0.6453
F-statistic	6.4851	11.4296	8.3421	4.7564
Prob.	0.0110	0.0008	0.0040	0.0295

Diagnosing the impact differences of PUAB between each region, we found that W1, W2, and W3 are having indifferent response toward the changes of PUAB (see table 14). On the other hand, we found that the impact of PUAB on inflation in W4 is only indifferent with its impact on W3. Considering the result, we assert that PUAB is a fair monetary instrument in controlling inflation level across regions in Indonesia.

Table 14.

Wald Test: Diagnosis of PUAB’S Impact Differences between Regions

		Jawa Bali	Sumatra	Kal-Sul	Pap-Mal-Nusa
Jawa-Bali	F-Stat		3.6769	9.2163	0.7184
	Prob.		0.0555	0.0025	0.3969
Sumatra		3.6769		12.8614	1.9798
		0.0555		0.0004	0.1598
Kal-Sul		9.2163	12.8614		7.0423
		0.0025	0.0004		0.0081
Pap-Mal_Nusa		0.7184	1.9798	7.0423
		0.3969	0.1598	0.0081

The Role of Interest Rates and Provincial Monetary Aggregate in Maintaining Inflation in Indonesia 283

V. CONCLUSION

Designing an effective and reliable monetary policy is necessary to ensure national price stability. To achieve the desired level of inflation, monetary authority can employ quantity-based approach, which make use of monetary aggregate as its instrument, or price-based approach, which exploit interest rate. The utilization of interest rate as the monetary operational target may have its own advantages in directing national inflation, but then again interest rate is a one-for-all ‘nationally-designed’ instrument. Different from interest rate, money supply can be regulated in regional level, accommodating each region’s economic needs. Since inflation is a regional phenomenon, a detailed assessment of each region’s idiosyncrasy is a necessity. If authorities are concerned about the inflationary gap among regions, they need policy instrument(s) which is or are capable of managing regional inflation evenly. Through this study, we found that each region in Indonesia have different responses on monetary policy instruments.

Using Hybrid NKPC model, we found that backward looking and forward looking behavior have significant roles in determining inflation in Indonesia. Further, our finding supports Gali and Gertler (1999) in asserting forward looking behavior to have greater influence than backward looking. It suggests the importance and effectiveness of inflation targeting framework in directing societies’ expectation regarding future inflation. Conforming macroeconomics theory, output gap is also found to have a positive impact on inflation.

Through regional analysis, we found that both monetary instruments (money supply and interest rate) are incapable of influencing inflation in the short run. Monetary policy needs time to effectively influence regional inflation. Our analysis suggests that PUAB instrument needs the least time lag to yield desirable influence on inflation. PUAB needs 1 quarter to effectively influence inflation while money supply and BI Rate needs 3 quarters. Furthermore, the estimation of PUAB alone delivers consistent backward and forward looking and output gap influence to regional inflation.

Taking into account regional aspect in the model, we found that money supply has a significant impact on inflation in Sumatera (W2) and Papua-Maluku-Nusa Tenggara (W4). This instrument, however, is only effective in Sumatera region, since the estimation result suggests that it would have conflicting effect in Papua-Maluku-Nusa Tenggara.

Moving to the second instrument, we found that BI Rate is an effective instrument in controlling the inflation in Sumatera (W2) and Kalimantan-Sulawesi (W3). In addition, through Wald test we found indifferent impact of BI Rate to inflation in these regions.

Lastly, we found that PUAB is the most favourable instrument in controlling inflation in Indonesia. Based on the estimation result, we found that PUAB is potent to manage inflation in most of the observed regions, including Java-Bali (W1), Sumatera (W2), and Papua-Maluku- Nusa Tenggara (W4). In those regions, the influence of PUAB to inflation is in concordance with the policy objective. Figure 1 shows the favorable instrument(s) in controlling regional inflation in Indonesia.

284Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 3, Januari 2017

W2

W3

W4

Rp

W1

Rp Provincial Money Supply

BI Rate

PUAB

Figure 1

Most Favorable Instrument in Controlling Regional Inflation in Indonesia

Each region has their own favorable monetary instrument in controlling inflation. However, in Java-Bali (W1) and Papua-Maluku-Nusa Tenggara (W4), inflation can only be managed using PUAB, while Kalimantan-Sulawesi’s (W3) inflation can only be controlled using BI Rate. Combining BI Rate and PUAB, monetary authority can effectively control inflation in every region in Indonesia. Hence, we conclude that the implementation of interest rate as the monetary instrument in Indonesia is more appropriate than money supply.

The Role of Interest Rates and Provincial Monetary Aggregate in Maintaining Inflation in Indonesia 285

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