the determinants of inflation: an asean...

A Contemporary Business Journal

ISSN: 2232-0172 Vol 6, August 2016

pp. 49-72

49Taylor’s Business Review, Vol 6, August 2016

Correspondence: Malarvilly Ramayah, Taylor’s Business School, Taylor’s University, Malaysia. Email :[email protected]

The Determinants of Inflation: An ASEAN Perspective

Teoh EdwardMalarvilly Ramayah*

Taylor’s University, Malaysia

© The Author(s) 2016. This article is published with open access by Taylor’s Press.

Abstract: Countries around the world are progressively adopting inflation-targeting policies in achieving their primary objective of maintaining price stability. This paper is primarily interested in the determinants of inflation and looks at a few selected Southeast Asian economies, namely Singapore, Malaysia and Indonesia to study this phenomenon. The ASEAN economies share similar financial and sectorial landscapes and are affected by common regional shocks. The independent variables that were chosen include money supply (M2), oil prices and nominal exchange rate while the selected dependent variable was inflation. This paper utilised the Ordinary Least Squares (OLS) regression method in determining the significance of the independent variables in causing inflation. The serial correlation test, heteroscedasticity test and unit root test were also employed in order to discover the properties of data collected from The World Bank. Overall, the results have found that money supply (M2) is a significant predictor for inflation across all three studied countries, in agreement with Milton Friedman’s proposition that “Inflation is always and everywhere a monetary phenomenon”. Oil prices have been found to be a significant predictor of inflation only in Singapore and Indonesia, most probably due to their net oil importer status. Nominal exchange rate has also been found to yield disappointing results.

Key words: Inflation, money supply, oil price, exchange rate

JEL Classification: E30, E31, E51, F31

1. INTRODUCTION

Countries around the world are progressively adopting inflation-targeting policies in achieving the primary objective of maintaining price stability (Gathogo & Sohn, 2015). The popularity of the strategy in large part denotes the grave impact that inflation poses to any economic system. The economies of Venezuela and Ukraine

Teoh Edward & Malarvilly Ramayah*

Taylor’s Business Review, Vol 6, August 201650

are currently being chastised for their unusually high inflation rates, while Japan, Greece and Spain face deflationary pressures (Duggan, 2015). Undisciplined inflation will lead to a state of hyperinflation, while an over-restriction of inflation, whether carelessly or mistakenly, will lead to deflation – a double-edged sword.

Generally, a low-inflation environment is believed to be a quintessential condition for growth in macroeconomics (Munir, Mansur & Furuoka, 2009). However, to what extent does this conventional wisdom hold true? Inflation, as a topic, has been one of the most rigorously debated areas, both in macroeconomic planning and academia. The full extent of the ramifications and consequences of inflation has yet to be fully understood, especially when the economy consists of intertwined processes no different than that of cogs and parts in a machine.

The Philips Curve, bedrock of macroeconomic planning in the United States during the 1960s, has been profoundly shaken by high inflation and unemployment episodes in the ensuing decades (Hornstein, 2008). Efficacy of policies to ensure sound monetary system and low inflation has yet again been challenged by the 2008 Financial Crisis, as millions were left jobless and economies across the globe slumped into recessions (Ivashina & Scharfstein, 2010). The rules and norm were twisted and bent with the Quantitative Easing programmes that the Federal Reserve undertook to prevent a complete meltdown in the financial markets, which forever altered the course of entire economies (Irwin, 2013).

At the time of this writing, America was flushed with liquidity in all sectors, with companies’ cash holdings reaching historical highs and commercial loans being lent out at a faster rate than ever due to cheap interest rates and strong market confidence (Forbes, 2015; CNN Money, 2015). However, the velocity of money being circulated in the economy experienced a slowdown. The active transfer of wealth mechanism is being exploited by inflated property and real estate prices, which pushes home-ownership to an all-time-low. Graduates who bore excessive tertiary education debts find themselves in a difficult position looking for jobs, let alone purchasing a home (Nasiripour, 2015).

This paper is primarily interested in the determinants of inflation as a whole in a few selected Southeast Asian economies. The study on the determinants of inflation could aid policymakers in question by wielding the double-edged sword in a more masterly manner. Therefore, understanding that these factors can lead to inflation is important and demands great attention.

Thus far, economists have been unanimous in their views of the exposure of ASEAN countries to inflation. It would be interesting to validate the relevance of the variables of previous studies through an up-to-date econometric analysis. Does the expansion of money supply lead to inflation in the Southeast Asian countries? Do world average oil prices affect inflation in this region? Does the depreciation of a local currency lead to domestic inflation in Southeast Asia?

The research objectives of this study are to identify the impact of rising inflation rate, as a result of expansion in money supply, change in average oil prices and

The Determinants of Inflation: An ASEAN PerspectiveTeoh Edward & Malarvilly Ramayah*

Taylor’s Business Review, Vol 6, August 2016 51

depreciation of domestic currency, on Southeast Asian countries.This study contrasts the pervasiveness of the factors of inflation in Southeast

Asia, in particular Singapore, Malaysia and Indonesia - economies with different economic statuses. Singapore is a high-income nation status; Malaysia is labelled as an upper-middle-income economy; Indonesia is considered a lower-middle-income economy. A time-series econometric analysis will be employed using data from 1980 to 2014.

The findings of this study are of paramount importance to aid in identifying and providing clear analysis of inflationary factors in different regions, which could then be utilised for sound economic planning. It is up to the authorities to decide whether to allow or curb the underlying workings of the inflation-causing factors, stemming from inflationary or deflationary policies. Hopefully, this relatively well-rounded approach in undertaking this study would satisfy monetarists, structuralists and other theorists or proponents alike.

This paper will begin with a review of current literature on inflation and its determinants. This will contribute to the choosing of dependent and independent variables. Next, methodology and a set of findings based on the econometric analysis will be used to evaluate the viability of each determinant, either to qualify or disqualify the determinants that were hypothesised. This paper then pursues further the topic by specifying the determinants of inflation and also the difference in the aforementioned countries through literature review.

2. ASEAN: THE FOCAL POINT

Various postulations regarding the sources from which inflation is derived include the quantity theory of money, demand-pull inflation, cost-push inflation, monetary theory of inflation, structural theory of inflation and a kaleidoscopic of other modern inflation theories. However, the decomposition of inflation into these components requires a more complex treatment, with analysis showing a “sophisticated dynamic interaction” between the aforementioned theories (Lim & Sek, 2015: Totonchi, 2011). Cost-push inflation or supply-side inflation may be caused by a continual rise in costs of production, which shifts the aggregate supply curve to the left while holding aggregate demand constant. As a result of the increase in costs, firms cut back on quantity produced and pass the costs on to consumers (Sloman, Wride & Garratt, 2012).

On the other side of the coin, demand-pull inflation may cause a rise in the prices of goods and services due to the shifting of the aggregate demand curve to the right, holding aggregate supply constant. The contributing factor to a rising aggregate demand is derived from households and businesses that made the decision to spend at a larger real output at different price levels (Jackson, McIver & Bajada, 2007). Expansion and contraction of money supply as a determinant of inflation is one of the few antiquated surviving economic propositions, stemming from the



classical monetary analysis of the 19th Century (Totonchi, 2011). According to the quantity theory of money, the expansion of monetary supply is always one of the leading causes of inflation in the long run (Templeman, 2010). Furthermore, this situation is exacerbated by the appearance of an inflation bias in monetary policy and a deficit bias for fiscal policy, with the tendency for policy makers making less than socially optimal decisions (Romer, 2012).

Several researchers have chosen certain variables to explain increase in inflation by taking into account various economic theories in different geographical landscapes. In a study of the Iranian economy, GDP, import prices and money supply have been used as independent variables (Armesh, Salarzehi, Mohammad Yaghoobi & Heydari, 2010). In a study of the Saudi Arabian economy, non-oil GDP, money supply, interest rate, foreign inflation and real exchange rate were used as independent variables (Altowaijri, 2011). Gross domestic product and money supply were used as independent variables by Adunega, Bello and Ejumedia (2012) in studying determinants of inflation in Nigeria.

Osorio and Unsal (2013) argued that 60% of the fluctuations in inflation levels in the Asian region stem from domestic factors, especially for larger and more advanced countries such as Indonesia, Japan and China. However, inflation fluctuations in the ASEAN economies are relatively more dependent on external factors and inflation is a result of their openness in trade. It has also been found that the primary causes for Asia’s inflation are supply shock and monetary shock, with demand-pull inflation playing a relatively small role.

However, this paper will not attempt to examine all the available theories and variables but will only take into account, determinants of inflation that are ASEAN-centred. Therefore, with sufficient empirical backing, the theoretical framework of this paper would be based on money supply, oil prices and nominal exchange rate, all of which are positively correlated to inflation.

Figure 1. Theoretical framework

Money supply, oil prices, nominal exchange rate are all positively correlated to inflation in the ASEAN countries being examined.



2.1 Money Supply and Inflation

Inflation is defined as the “rising general level of prices” (Jackson et al., 2007). There is a wide range of variation in definitions, with endless debates on semantics. Even so, modern economists have accepted the general theme of definition for inflation as “a ‘persistent’ and ‘appreciable’ increase in the general level of prices.” It is indeed interesting to note that not every price rise should be arbitrarily accepted as inflation. Among the phenomena that do not amount to inflation include qualitative improvement of goods and services due to technological changes, a substitution of low-price products of an agricultural nature with high-price industrial products and price indexing (Dwivedi, 2011).

As a result of high-inflation episodes in the 1970s, monetary authorities have ardently pursued policies to achieve low inflation levels by restricting expected inflation without foregoing output or causing high unemployment levels. Fixed exchange rate regimes in the emerging economies have opted for inflation-targeting mechanism instead of adopting the floating exchange rate system (Taguchi & Kato, 2011).

Historically, Asian economies have experienced a relatively steady and low inflation policy (Gerlach, Giovannini, Tille & Vinals, 2009). Findings from 12 Asia-Pacific countries have shown that there are minor differences in inflation between countries that formally apply inflation targeting strategy and those that do not. It has been found that all three countries in question - Singapore, Malaysia and Indonesia - practice “price stability” as a target in their various monetary policy objectives. This may partly be due to the common regional shocks that the neighbouring countries experience in terms of commodity price fluctuations. This also postulates that inflation can be tamed regardless of the type of policies that are being pursued (Filardo & Gernberg, 2010).

Milton Friedman’s work (1970) left an indelible mark on modern economics with the famous saying, “Inflation is always and everywhere a monetary phenomenon”. Despite the continuous dispute on many aspects of inflation, economists have arrived at a consensus that inflation is caused by the growth rate of money supply surpassing the growth rate of output in a given economy (Ball, 1993).

The Austrian Economists’ approach of excess money supply and inflation portrays a far greater causal relationship than standard macroeconomic models suggest, and to a certain extent, of greater accuracy in explaining the 2008 financial crisis (Templeman, 2010). The recent crisis involving the housing bubble has left millions of low-income homeowners perplexed by the vanishing of their perceived wealth. Critics have questioned and blamed the Federal Reserve for an expansive monetary system by keeping interest rates low “beyond prudent bounds” in funding the bubble, while others have excluded the Federal Reserve to be a cause altogether (McDonald & Stokes, 2013; Schwartz, 2009).



2.2 Oil Price and Inflation

In fact, history suggests that turbulence in price levels in America has always been co-occurring with the comparable change in money supply (Templeman, 2010). This may arise from the error of Federal Reserve’s monetary policies or from due compliance to the federal government’s fiscal requirements and budget deficit financing. For instance, the American Dollar broad money, M4 quantity, has increased by 165% in a 10-year period, from 2005 to 2014, while the Australian Dollar has increased in quantity by 239%, Japanese Yen by 117%, Singapore Dollar by 233% (The World Bank Group, 2015). This implies that the expansion of currency supply is a worldwide phenomenon. Several authors have validated the role that monetary policies play in causing inflation (e.g. Bernanke & Mishkin, 1997).

The debauchery of currency from the gold standard (presumably the root of expansion of monetary supply at will by monetary systems) is truly the surest way to cripple capitalism (Horwitz, 2002). An economic expansion is only credible and sustainable through investments from savings, and unsustainable when unrestrained credit creation exceeds the equilibrium level of supply and demand through lower-than-natural interest rates (Tempelman, 2010). It is of no surprise that Rogoff (2003) names inflation as the “cruellest and most regressive of all taxes” due to the poor’s inability to invest in inflation-proof assets.

Oil is considered to be an indispensable source of energy, playing a significant role as both fuel for transport and also as raw material used in manufacturing processes (Chang & Wong, 2003). A hike in real oil prices, up to the tune of 3 times the price, primarily caused the 1973 to 1974 inflation and recession of the United States and the rest of the world. Oil is believed to be a component of the aggregate production function, wherein a relative rise in oil price will lead to a fall in aggregate supply and higher prices (Darby, 1982; Cheng & Tan, 2002). Leblanc and Chinn (2004) reiterated the importance of monetary policies, taking into account oil prices, as empirical evidence has shown that oil prices are correlated to inflation. However, in recent years, Rogoff (2003) has noted that compared with the 1970s period, the impact of fluctuations in oil prices on global economic growth has been reduced.

International Monetary Fund (2015) has pointed out that the dwindling oil and other commodity prices, coupled with weakening demand from several countries such as Japan and the Eurozone, had contributed to the fall in headline inflation of advanced economies. Developing economies, on the other hand, also experience a fall in inflation due to the declining oil price, even to the point of deflation in certain Eurozone countries, except for Russia whose Rouble devaluation offsets the downward pressure in inflation. However, it is expected that the reduction of oil prices will not be passed on completely to consumer products.

The price levels of goods and services are doubtlessly susceptible to and dependent on oil prices. Higher oil price will theoretically lead to higher cost in factors of production. It has also been found that oil price shock do indeed Granger



cause inflation. The CPI captures the inflation in the energy component, with up to 60% of changes in the CPI being accounted for by oil prices alone (Shaari, Hussain & Abdullah, 2012).

ASEAN economies in general, except Indonesia, are found to be more heavily dependent on oil and food imports leading to commodities prices being the major contributor of inflation (Osorio & Unsal, 2013). Given that most of the Asian countries are net oil importers, stable and low oil prices play a significant role in ensuring cheap raw materials in industries and low cost of electricity and transportation. As a net oil importer, Singapore maybe severely exposed to the vagaries of the international oil market (Chang & Wong, 2003).

2.3 Exchange Rate and Inflation

The absence of government intervention in controlling exchange rates can lead to the depreciation of the local currency. As the appreciation of foreign currencies causes a price hike in imports, countries may experience “imported” inflation (Sloman et al., 2012). Bleany and Fielding (2002) in their findings, demonstrated that inflation in a country which adopts a floating exchange consistently outpaces that of a country which has a pegged exchange rate regime. Thus, developing nations with a floating exchange rate regime are poised to pay an exorbitant price for having inflation rates above 10% per year.

An interesting theory that encompasses the theory on exchange rate and inflation is the exchange rate pass-through theory. This theory signifies the sensitivity of locally-priced import goods in response to changes in exchange rates. The importance of this measure is unprecedented as inflation and output levels are dependent on the degree to which exchange rates affect domestic prices through imports (Sheets et al., 2005). A low exchange rate pass-through indicates that a 1 unit appreciation or depreciation of the nominal exchange rate leads to a lesser than 1 unit of change in import prices. Obstfeld (2002) concurred with other authors who suggest a sound monetary mechanism should have inflation-targeting policies to ensure prices of domestic goods do not fluctuate too much , while allowing nominal and real exchange rate to float and move accordingly. It has also been found that inflation-targeting policies have reduced the effect of exchange rate pass-through in ASEAN countries (Prasertnukul, Kim & Kakinaka, 2010).

However, having expressed the relationship between exchange rate and inflation through the pass-through theory, empirical evidence shows that exchange rate fluctuations and inflation are not related unitarily. Developed economies do generally produce low exchange rate pass-through results, given the erroneous stereotype that developing economies always produce a relatively higher exchange rate pass-through. It has also been found that import prices are more sensitive towards exchange rate changes compared to consumer prices (Ca’Zorzi, Hahn & Sánchez, 2007). Given the findings, we should expect that the proportion of import goods in the CPI, which



is being used as the dependent variable, should reflect the proportion to which exchange rates affect prices of imported goods.

As pointed out by Osorio and Unsal (2013), ASEAN economies are vulnerable towards external factors as a result of their openness in trade. However, recent literature has shown that changes in exchange rates have only resulted in disappointing effects on domestic inflation in small open economies (Takhtamanova, 2010). It has been found that the exchange rate pass-through of countries in Asia, specifically Singapore, has exhibited weak findings (Ca’Zorzi et al., 2007). Perhaps the lower exchange rate pass-through is contributed by monetary policies such as those that target inflation to curb a nation’s exposure to exchange rate risks.

3. METHODOLOGY

The World Bank group (2015) classifies countries by evaluating the gross national income per capita (GNI per capita) using the World Bank Atlas method. This sets Singapore as a “High-income” economy with a GNI per capita of US$12,736 or more; Malaysia as an “Upper-middle income” economy with a GNI per capita between US$4,126 and US$12,735; Indonesia as a “Lower-middle income” economy with a GNI per capita between US$1,046 and US$4,125. The only “lower-income” economy in ASEAN, Cambodia, is omitted from the test as its data is insufficient and incomplete, rendering it unfit for this econometric analysis.

Data sets on Consumer Price Index (CPI), money supply (M2), average spot price of oil (OILAVG) and nominal exchange rate per USD (EXRATE) have been extracted from The World Bank group; M2 was converted to its natural logarithm form, LM2. The databases that contain this relevant information are from the Global Economic Monitor (GEM) Commodities and World Development Indicators. The time period is from 1980 to 2014, featuring a 35-year period of annual data. The World Bank group’s data were chosen due to their credibility and accuracy.

Having evaluated and considered various literature, the proposed model explains inflation in the ASEAN countries by using the formulae below:

CPI = f(LM2, OILAVG, EXRATE) (1)

Functional form of the regression equation is as follows:

LnCPI = 0 + 1LnM2 + 2LnOILAVG + 3LnEXRATE + (2)

In light of validating the determinants as proposed in the theoretical framework and model specification, an econometric analysis was conducted to detect the degree of effectiveness of each of the factors in different countries using E_Views 7.0. The Ordinary Least Squares (OLS) method was the baseline specification of the ongoing analysis. In addition, Breusch-Godfrey test was employed to check for the presence of serial correlation and Breusch-Pagan-Godfrey test for heteroscedasticity. In order



to check for the presence of unit root, spuriousness and stationarity in the data set, Augmented Dicky-Fuller (ADF) test was performed.

4. FINDINGS

4.1 Findings for Singapore

Table 1. Ordinary Least Squares (OLS) - Singapore Findings

Dependent Variable: CPISample: 1980-2014Included observations: 35

Variable Coefficient Std. Error t-Statistic Prob.

CLNM2OILAVGEXRATE

-138.56779.0717690.117431

-8.437724

31.952961.0680340.0215983.478925

-4.3366168.4938975.437175

-2.425383

0.00010.00000.00000.0213

R-squaredAdjusted R-squaredS.E. of regressionSum squared residLog likelihoodF-statisticProb (F-statistic)

0.9673550.9641952.782791240.0617

-83.35994306.20000.000000

Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan-Quinn criterion.Durbin-Watson stat

82.4638714.706574.9919965.1697505.0533570.366757

Estimated regression equation:

LnCPI = -138.5677 + 9.071769LnM2 + 0.117431LnOILAVG – 8.437724LnEXRATE (1.068034) (0.021598) (3.478925)

At the 95% confidence interval level, t-statistic of the independent variables that exceed the critical value are deemed as significant explanatory variables for the dependent variable. As such, all three variables were found to statistically significant as the t-statistic value exceeded the critical value. Money supply, average spot price of oil and nominal exchange rate are significant independent variables in predicting for inflation.

With a beta coefficient of 9.071769 for the Natural Logarithm form of money supply (M2), this means that for every 1-percentage point increase in money supply (M2), holding other variables constant, the Consumer Price Index (CPI) will rise by 9.071769 points. This finding is consistent with empirical findings from various journal articles, and harmonises with the quantity theory of money. The increase in money supply (M2) in Singapore does indeed lead to inflation.



Analysing the beta coefficient of 0.117431 for the average spot price of oil, this signifies that for every USD per barrel increase in the average world price of oil, holding other variables constant, the CPI will rise by 0.117431 points. Once again, this finding coalesces with the empirical findings of previous authors who identified that Singapore’s inflation has a great degree of exposure to the world oil price fluctuations due to its net oil importer status.

In terms of nominal exchange rate, for every Singapore Dollar (SGD) increase against the US Dollar (USD) signifying the depreciation of the Singapore Dollar, holding other variables constant, the Consumer Price Index will fall by 8.437724 points. This finding is not consistent with the theoretical propositions and empirical findings. It may be due to a suspected spuriousness in the variable.

An analysis of the summary statistics reveals that the R-squared, with a value of 0.967355, implies that 96.74% of the changes in inflation in Singapore can be explained by the changes in the money supply, oil prices and nominal exchange rate. However, the results may not be accurate, as it will increase as the number of independent variables that are added rises. As such, we should consider the adjusted R-squared, that adjusts for the extra number of variables, which produces the value of 0.964195. The variation after adjusting for the number of variables is negligible. This data set is a good fit given such a high R-squared value and only the remaining 3.6% of the changes in inflation are not explained by the changes in the independent variables.

Table 2. Breusch-Godfrey Serial Correlation LM Tests - Singapore Findings (1)

F-statisticObs*R-squared

30.7222623.77765

Prob. F(2,29)Prob. Chi-Square(2)

0.00000.0000

Dependent Variable: RESIDSample: 1980-2014Included observations: 35Variable Coefficient Std. Error t-Statistic Prob.CLNM2OILAVGEXRATERESID(-1)RESID(-2)

16.01153-0.5177920.012593

-1.8888321.133056

-0.301125

19.148900.6381190.0132332.1126830.1743280.207465

0.836159-0.8114340.951620

-0.8940446.499567

-1.451452

0.40990.42370.34920.37870.00000.1574

R-squaredAdjusted R-squaredS.E. of regressionSum squared residLog likelihoodF-statisticProb(F-statistic)

0.6793610.6240791.62918576.97303

-63.4547212.288900.000002


-3.64E-142.6571863.9688414.2354724.0608821.987600



The Breusch-Godfrey Serial Correlation LM test measures whether the data set is serially correlated. The result in Table 2 indicates that the probability Chi-square of the Observed R-squared has a value of 0.0000. This implies that the alternative hypothesis is accepted and null hypothesis can be rejected. As the alternative hypothesis suggests, the data has a serial correlation problem. The residual of one period is correlated to lag values of itself.

Table 3. Breusch-Godfrey Serial Correlation LM Tests - Singapore Findings (2)


7.82077111.10351


0.00190.0039

Dependent Variable: RESIDSample: 1981-2014Included observations: 34Variable Coefficient Std. Error t-Statistic Prob. D(OILAVG)D(LNM2)D(EXRATE)RESID(-1)RESID(-2)

0.022700-1.181520-3.0661130.742655

-0.250186

0.0235501.9802493.6689970.1911560.183964

0.963915-0.596652-0.8356813.885068

-1.359973

0.34310.55540.41020.00050.1843

R-squaredAdjusted R-squaredS.E. of regressionSum squared residLog likelihoodDurbin-Watson stat

0.3265740.2336871.25703545.82396

-53.317501.900807

Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan-Quinn criter.

0.2708231.4359663.4304413.6549063.506990

Even after adjusting for first-differences, the data set for Singapore’s variables still possess a high degree of serial correlation problem. Table 3 shows the probability Chi-square of the Observed R-squared as 0.0039.

Table 4. Heteroscedasticity Test - Singapore Findings

F-statisticObs*R-squaredScaled explained SS

7.01714914.1552415.98568

Prob. F(3,31)Prob. Chi-Square(3)Prob. Chi-Square(3)

0.00100.00270.0011

Dependent Variable: RESID^2Sample: 1980-2014Included observations: 35Variable Coefficient Std. Error t-Statistic Prob. CLNM2OILAVGEXRATE

-28.762250.6376820.2642535.090477

109.58003.6627330.07406811.93068

-0.2624770.1741003.5677040.426671

0.79470.86290.00120.6726



Table 4 (con’t)R-squaredAdjusted R-squaredS.E. of regressionSum squared residLog likelihoodF-statisticProb (F-statistic)

0.4044350.3468009.5433492823.341

-126.49367.0171490.000979

Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan-Quinn criter.Durbin-Watson stat

6.85890511.808047.4567777.6345317.5181370.818671

Based on Table 4, the data set is found to be heteroscedastic, as the F-test has a p-value of 0.0010. This means that the null hypothesis can be rejected, and the alternative hypothesis accepted. The data set’s variance of residuals is not constant.

In Singapore’s data set, the test has revealed that all variables are not significant in level. Due to the high p-value, which is >0.05, the null hypothesis cannot be rejected. This suggests that there is a unit root for each of the variables for Singapore. The data is spurious and is not stationary.

TABLE 5. Augmented Dicky-Fuller (ADF) Unit Root Test for Singapore

Variables Level 1st differenceCPI 0.1957 0.0088*LM2 0.7917 0.0125*

OILAVG 0.6679 0.0000*EXRATE 0.0598 0.1013

* Indicates that data is significant in level.** Indicates that data is significant in the first order at 99% Confidence Interval.

However, by taking the 1st difference data into account, CPI, LM2, OILAVG were found to be significant in the first order. Only EXRATE was found to contain unit root at the 1st difference, suggesting that it is spurious and non-stationary.

4.2 Findings for Malaysia

Table 6. Ordinary Least Squares (OLS) - Malaysia Findings


Variable Coefficient Std. Error t-Statistic Prob. CLNM2OILAVGEXRATE

-336.204515.105640.1224952.212621

18.618350.8362400.0209871.179026

-18.0576918.063765.8366161.876651

0.00000.00000.00000.0700



Table 6 (con’t)


0.9892090.9881652.206174150.8834

-75.23313947.26470.000000

Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan-Quinn criter.Durbin-Watson stat

73.9565420.279284.5276084.7053624.5889681.077342

Estimated regression equation:

LnCPI = -336.2045 + 15.1056LnM2 + 0.122495LnOILAVG + 2.212621LnEXRATE (0.836240) (0.020987) (1.179026)

At the 95% confidence interval level, only 2 of the 3 variables were statistically significant. Money supply and average spot price of oil are significant independent variables in predicting for inflation. The nominal exchange rate is insignificant in predicting for inflation.

With a beta coefficient of 15.10564 for the Natural Logarithm form of money supply (M2), this means that for every 1-percentage point increase in the M2, holding other variables constant, the CPI shall rise by 15.10564 points. As can be found in the Singapore data, this finding holds true and obeys the quantity theory of money. The increase in money supply in Malaysia will raise the inflation levels captured in the CPI.

For Malaysia, the beta coefficient is 0.122495 for the average spot price of oil. This means that for every USD per barrel increase in the average spot price of oil, holding other variables constant, the Consumer Price Index will rise by 0.122495 points. Empirically, this finding holds true as the increase in international oil prices causes the CPI to increase through imported inflation.

The nominal exchange rate variable has a beta coefficient of 2.212621. In terms of nominal exchange rate, for every MYR increase against the USD, signifying the depreciation of the Malaysian Ringgit, holding other variables constant, the Consumer Price Index will rise by 2.212621 points. As expected, the imported goods that are denominated in the local currency unit, MYR, will be more expensive when the nominal exchange rate depreciates against the US Dollar.

The summary statistics shows the value of R-squared at 0.989209, implying that 98.92% of the changes in inflation in Malaysia can be explained by changes in the money supply, oil prices and nominal exchange rate. After adjusting for error in adding extra number of variables, the adjusted R-squared was 0.988165. The variation after adjusting for the number of variables is negligible. This data set is a good fit given such a high R-squared value and only the remaining 1.18% of the changes in inflation not explained by changes in the independent variables.



Table 7. Breusch-Godfrey Serial Correlation LM Tests - Malaysia Findings


4.8241018.737459


0.01550.0127

Dependent Variable: RESIDSample: 1980-2014Included observations: 35


CLNM2OILAVGEXRATERESID(-1)RESID(-2)

3.044969-0.1473470.0043180.2294560.582620

-0.290575

18.149100.8194810.0205411.1407770.1892270.200841

0.167775-0.1798060.2102340.2011403.078944

-1.446789

0.86790.85860.83500.84200.00450.1587

R-squaredAdjusted R-squaredS.E. of regressionSum squared residLog likelihoodF-statisticProb (F-statistic)

0.2496420.1202701.975859113.2166

-70.207051.9296400.119843


-1.10E-132.1065964.3546894.6213204.4467301.815851

Based on above output, the probability Chi-square of the observed R-squared has a value of 0.0127. At the 99% confidence interval level, the null hypothesis can be accepted and alternative hypothesis is rejected. As the null hypothesis suggests, the data does not have a serial correlation problem.

Table 8. Heteroscedasticity Test - Malaysia Findings


0.9043722.8166803.998119


0.45020.42080.2617

Dependent Variable: RESID^2Sample: 1980-2014Included observations: 35


CLNM2OILAVGEXRATE

116.9946-4.9607980.0812024.935183

70.516353.1672300.0794894.465520

1.659113-1.5662891.0215541.105175

0.10720.12740.31490.2776



Table 8 (con’t)


0.080477-0.0085108.3558062164.405

-121.84250.9043720.450175

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criterion. Durbin-Watson stat

4.3109538.3204807.1910017.3687557.2523620.769343

Based on Table 8, the data set for Malaysia is found to be homoscedastic, as the F-test has a p-value of 0.4502. This means that the null hypothesis cannot be rejected. The data set’s variance of residuals is constant.

In Malaysia’s data set, the test revealed that all variables are not significant in level. Due to the high p-value, which is >0.05, the null hypothesis cannot be rejected. This suggests that there is unit root for each of the variables for Singapore. The data is spurious and is not stationary.

Table 9. Augmented Dicky-Fuller (ADF) Unit Root Test for Malaysia

Variables Level 1st differenceCPI 0.9954 0.0006**

LNM2 0.1750 0.0004**OILAVG 0.6679 0.0000**EXRATE 0.8181 0.0019**

* Indicates that data is significant in level.** Indicates that data is significant in the first order at 99% Confidence Interval Level.

However, by taking the 1st difference data into account, CPI, LM2, OILAVG and EXRATE were found to be significant in the first order. This suggests that the variables are not spurious but are stationary.

4.3 Findings for Indonesia

Table 10. Ordinary Least Squares (OLS)-Indonesia Findings



CLNM2OILAVGEXRATE

-203.30046.2319300.6474400.002612

35.961911.1699550.0385340.000556

-5.6532145.32663916.801924.694764

0.00000.00000.00000.0001



Table 10 (con’t)


0.9838900.9823315.108159808.8920

-104.6184631.08410.000000


44.0460738.428726.2067666.3845206.2681271.676461

Estimated regression equation for Indonesia:

LnCPI = -203.3004 + 6.231930LnM2 + 0.647440LnOILAVG + 0.002612LnEXRATE (1.169955) (0.038534) (0.000556)

At the 95% confidence interval level, all three variables were found to be statistically significant. Money supply, world average spot price of oil and nominal exchange rate are significant independent variables in predicting for inflation.

With a beta coefficient of 6.231930 for the Natural Logarithm form of money supply (M2), this means that for every 1-percentage point increase in the M2, holding other variables constant, the Consumer Price Index (CPI)) shall rise by 6.231930 points. This finding is consistent with the findings from Singapore and Malaysia. The increase in money supply in Indonesia will raise inflation levels.

The beta coefficient for the average spot price of oil is 0.647440. This means that for every USD per barrel increase in the average spot price of oil, holding other variables constant, the Consumer Price Index will rise by 0.647440 points. This finding is held true across Singapore and Malaysia.

The nominal exchange rate variable has a beta coefficient of 0.002612. In terms of nominal exchange rate, for every IDR increase against the USD, signifying the depreciation of the Indonesian Rupiah, holding other variables constant, the Consumer Price Index will rise by 0.002612 points.

The summary statistics revealed a value of 0.983890 for R-squared, implying that 98.39% of the changes in inflation in Indonesia can be explained by changes in the money supply, oil prices and nominal exchange rate. After adjusting for error by adding extra variables, the adjusted R-squared revealed a value of 0.982331. The variation after adjusting for the number of variables is negligible. This data set is a good fit given such a high R-squared value and only the remaining 1.77% of the changes in inflation is not explained by changes in the independent variables.



Table 11. Breusch-Godfrey Serial Correlation LM Tests - Indonesia Findings


0.9173142.082462


0.41090.3530

Dependent Variable: RESIDSample: 1980-2014Included observations: 35


CLNM2OILAVGEXRATERESID(-1)RESID(-2)

-0.6034740.0193370.006165

-6.28E-050.117187

-0.247599

36.317881.1815740.0405030.0005840.2024870.203460

-0.0166160.0163660.152217

-0.1076580.578737

-1.216946

0.98690.98710.88010.91500.56720.2334


0.059499-0.1026565.121839760.7638

-103.54490.3669250.866996


-7.71E-154.8775966.2597096.5263406.3517501.874717

The Breusch-Godfrey Serial Correlation LM test measures whether the data set is serially correlated. As can be seen from table above, the probability Chi-square of the Observed R-squared has a value of 0.3530. This implies that the null hypothesis can be accepted and alternative hypothesis is rejected. As the null hypothesis suggests, the data does not have a serial correlation problem.

In the case of Indonesia, based on Table 12, the data set is found to be heteroscedastic, as the F-test has a p-value of 0.0239. This means that the null hypothesis can be rejected, and the alternative hypothesis is accepted. The data set’s variance of residuals is not constant.

Table 12. Heteroscedasticity Test - Indonesia Findings


3.6183729.07724310.33586


0.02390.02830.0159

Dependent Variable: RESID^2Sample: 1980-2014Included observations: 35



Table 12 (con’t)


CLNM2OILAVGEXRATE

173.2306-5.8288690.2574360.006084

253.50018.2417660.2716290.003921

0.683355-0.7067720.9477501.551457

0.49950.48500.35060.1309


0.2593500.18767436.0080740194.02

-172.97003.6183720.023854


23.1112039.9516510.1125710.2903310.173931.401785

Table 13. Augmented Dicky-Fuller (ADF) Unit Root Test for Indonesia

Variables Level 1st difference

CPI 0.9520 0.0022**

LNM2 0.9871 0.0030**

OILAVG 0. 6679 0.0000**

EXRATE 0.2412 0.0000**

* Indicates that data is significant in level.** Indicates that data is significant in the first order at 99% Confidence Interval Level.

However, by taking the 1st difference data into account, CPI, LM2, OILAVG and EXRATE were found to be significant in the first order. This suggests that the variables are not spurious but are stationary.

4.4 Comparison of Findings

The pervasiveness of the LM2 variable between the three countries can be identified. Singapore’s LM2 variable has a beta coefficient of 9.071769, Malaysia’s LM2 variable has a beta coefficient of 15.10564 and Indonesia’s LM2 variable has a beta coefficient of 6.231930. Among the three countries, Malaysia’s beta coefficient is the highest. This suggests that a percentage increase in the money supply (M2) in Malaysia, holding other variables constant, causes an increase in CPI compared with Singapore and Indonesia.

Of the four countries that were investigated, Malaysia’s money supply variable is the most prominent factor in causing inflation compared with Singapore, Thailand



and Philippines. Structural change played a relatively insignificant role for Malaysia compared with Singapore. This finding is consistent with the higher beta coefficient in Malaysia for the money supply variable.

According to Japan Research Institute Limited (1998), the co-integration test for the Malaysian data implies that there is a strong and stable relationship between money supply and inflation rates. An effective solution to the sound management of money supply will be through open market operations, which is not feasible due to the weak bond market development in the region. As suspected, in tandem with the quantity theory of money, unnecessary money supply expansion does indeed lead to higher levels of inflation. The quantity theorist proposes that when money supply growth rate exceeds real output growth rate, inflation will result. Therefore, it is believed that the rate of expansion of money supply (M2) in Malaysia exceeds the growth rate in real output more than in Singapore and Indonesia.

On the other hand, Singapore’s OILAVG variable has a beta coefficient of 0.117431, Malaysia’s OILAVG variable has a beta coefficient of 0.122495 and Indonesia’s OILAVG variable has a beta coefficient of 0.647440. This suggests that for every dollar per barrel increase in the average spot price of oil, holding other variables constant, has a higher impact on the Indonesian Consumer Price Index.

As governments play a huge role in controlling fuel prices, the coefficients that are displayed may be distorted in accordance to the government’s discretion on subsidy policies. Since Indonesia and Singapore are both net oil importers, the significance of the OPEC oil shock would, theoretically, affect their inflation rates. As for Malaysia, it had only recently switched its role from a net oil exporter into a net oil importer in 2014 (Kok, 2015).

As such, Indonesia’s exposure to volatility in the international oil prices should be higher than that in Malaysia and Singapore. As pointed out in the literature review, Indonesia faced an inflation rate of about 10-12% per year during the 1960s until the 1990s, with up to 35% and 20% inflation rates during the OPEC oil shocks in 1973 and 1979-1980, respectively (Hossain, 2005). Indonesia has consistently experienced higher inflation rates than Malaysia and Singapore, where the 2014 CPI level based on 2010’s 124.38 exceeded that of the latter (The World Bank group, 2015).

A probable reason as to why oil price fluctuations affect Indonesia’s inflation rate the most is due to its political instability. During the Asian Financial Crisis in 1998, with an inflation rate of up to 60%, a precondition for the US$43 billion bailout programme by the International Monetary Fund for Indonesia was the hiking of fuel prices by the then president, Suharto. This led to a major upheaval by the people which brought about his resignation. During that period, tens of millions of Indonesians were living below the poverty line on less than a dollar a day, where a price hike in oil will inevitably and negatively affect their standard of living (“Fuel in Indonesia”, 2014).

Indonesia and Malaysia have kept their price of fuel artificially low through gigantic subsidies, about 40% cheaper than neighbouring emerging markets such



as Cambodia and Laos. In 2013, Malaysia’s fuel subsidies reached a staggering 2.4% of GDP, while 20% of the government budget in Indonesia accounted for fuel subsidies (Shankleman, 2015). The previous fuel price appreciations in 2005, 2008 and 2013 has led to a rise in food, housing, transportation and clothing prices in Indonesia. Therefore, with active efforts in reducing subsidies to balance the budget, for 2015, Indonesia removed all fuel subsidies in tandem with the global fuel price slump except for a small subsidy for diesel (Asmoro, 2015).

Singapore’s nominal exchange rate may not be significant in explaining inflation due to its data’s spuriousness. Indonesia’s nominal exchange rate was a significant predictor of inflation, but its effect is so small that it should be deemed irrelevant. Even though the coefficient sign is consistent with past empirical findings, nominal exchange rate as a variable in Malaysia has exhibited a statistically insignificant result. The t-test did not exceed the critical value and therefore, the null hypothesis of no relationship is accepted.

Generally, it can be concluded that nominal exchange rate, that is, the amount of local unit currency needed to exchange for a unit of USD, is found to be irrelevant in predicting for inflation, having taken into account the spurious data for Singapore, the variable’s statistical insignificance in Malaysia and Indonesia’s disappointing findings.

5. SUMMARY AND RECOMMENDATIONS

Overall, despite the imperfection in the data sets in satisfying the classical assumptions for the Ordinary Least Squares (OLS) test, the findings were found to be very promising. The money supply and average spot price of oil displayed significance in predicting for inflation as captured in the Consumer Price Index for Singapore, Malaysia and Indonesia.

Money supply is the most prominent factor in explaining inflation in Malaysia. This is consistent with findings from past studies; it is empirically evident that money supply plays the biggest role in Malaysia compared with other ASEAN countries. This may be attributed to the country’s over-expansion of money supply that is faster than real output growth rate. As such, the Malaysian government is advised to utilise a more stringent monetary policy in managing money supply in the country to control inflation rates. The government has done this by switching from manipulating money supply into manipulating interest rates to control money supply.

Average spot price of oil, on the other hand, has the most prominent effect on the Indonesian inflation rate. It has been found that Indonesia had always exhibited an inflation-prone characteristic from previous data, with higher than usual inflation rates compared with Malaysia and Singapore. This may be due to political instability in the 1998 era during the Asian Financial Crisis as oil prices went up, resulting in tens of millions living below poverty line suffering the worst impact. Therefore,



the Indonesian government’s action of subsidy removal may, for this short period, reduce government spending, but in the long run, when global oil prices recover to 2013 highs, inflation may creep in and destroy the purchasing power of the people. The Indonesian government is advised to not only consider the short-run benefits due to market conditions, but also set in place, policies to hedge against a potential oil price hike in the future to protect its people.

The nominal exchange rate, on the other hand, did not produce the expected results based on past studies. The Singapore data set is found to be spurious, Malaysian data is statistically insignificant in explaining inflation and Indonesian data produced a weak correlation in explaining inflation.

The first recommendation for further research would be to swap nominal exchange rate with real exchange rate. By utilising the real exchange rate, instead of looking at nominal figures in the exchange rate, the actual fluctuations in the local currency units needed to purchase a basket of goods and services in the counterpart country can be obtained. This may lead to a more accurate finding.

This paper did not evaluate the impact of individual independent variables that lead to inflation in the respective countries, but rather evaluated the impact of the variables across countries. This calls for a deeper evaluation into the individual pervasiveness of the factors for the respective countries to assist policy makers make more informed decisions to ensure stability in the monetary system and inflation.

Open Access: This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0) which permits any use, distribution and reproduction in any medium, provided the original author(s) and the source are credited.

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