1

Oil Price Fluctuations and Macroeconomic Performances in Asian and Oceanic Economies

Youngho Chang

Division of EconomicsNanyang Technological University

30th USAEE/IAEE North American Conference9 – 12 October 2011

Capital Hilton, Washington, D.C.

2

Outline

• Introduction– Oil price fluctuations and the economy– Causality between oil prices and macroeconomic variables

• Objectives • Data• Test Results and Interpretations– Impulse response– Variance decomposition

• Conclusions

3

Oil Price Fluctuations and the Economy

• Macroeconomic implications of oil price shocks identified since the 1970s• Research largely indicated a negative relationship, with oil price increases

preceding almost all recessions in the United States after World War II– Hamilton (1983, 1996 and 2004)– Gisser and Goodwin (1986)– Burbridge and Harrison (1984)

• Since then, other country studies have been conducted that further support this stand;– New Zealand (Gounder and Barleet, 2007) – Greece (Papapetrou, 2001)

• However, a declining oil-price and macroeconomic relationship has also been found– Mork et al. (1989, 1994) – Abeysinghe (2001)

4

Summary of Literature Review Author Year Countries observed Macroeconomic Variables ConclusionsHamilton 1983, 1996, 2003 Significant negative relationship

Gisser and Goodwin 1986 Negative and relatively stable relationship

Burbridge and Harrison 1984 5 OECD countries SeveralSubstantial initial impact on macroeconomic indicators;

also a declining impact of oil price shocksJiménez-Rodríguez

and Sánchez2005 Several GDP Significant impact on macroeconomy

Gounder and Bartleet 2007 New Zealand GDP and inflation Direct relationship for GDP, indirect relationship with inflationPapapetrou 2001 Greece Significant negative casual relationship

Hooker 1996 USA Changing and unstable oil price-macroeconomic relationship Mork et al. 1990, 1994

Abeysinghe and Wilson 2000

Chang and Wong 2003 SingaporeGDP, inflation

and unemploymentMarginal impact on macroeconomic indicators

Blanchard and Gali 2007 Several GDP and inflation Changing impact of oil prices Barsky and Kilian 2001, 2004 Limited impact of oil price shocks

Kilian 2009 Little or no impact of oil price shocksFerderer 1996

Lardic and Mignon 2006 USA and EuropeCunado and Gracia 2005 6 Asian countries

Fuhrer 1995Hooker 2002

Barksy and Kilian 2004 Significant impact on inflationLeBlanc and Chinn 2004 Moderate impact on inflation

Lescaroux and Mignon 2008Chen 2009

Cunado and Gracia 2005 6 Asian countries Short run effect on inflationKumar 2005 India Significant impact on inflation

Loungani 1986 USABurbidge and Harrison 1984 5 OECD countries

Darby 1982 SeveralHamilton 1983

Gisser and Goodwin 1986Uri 1996

Dorgul 2010 TurkeyCarruth et al 1998 USA Lagged effect on unemployment

GDP

USA GDP

USA

Inflation

Unemployment

USA

Several

Several

USA

GDP

Several

Significant relationship

Long run relationship

Significant pass through effect to inflation

Declining impact on inflation

Declining oil price-macroeconomic relationship

Asymmetric relationship

5

Causality between Oil Prices and Macroeconomic Variables

• Early studies have found the inverse relationship with oil price and particularly GDP (Hamilton, 1983)

• Most studies indicated causality running from oil price to real GDP or economic growth, especially for oil-importing countries– Lescaroux and Mignon (2008); Du, He and Wei (2010); Cunado and

Gracia (2005)• However, there are also studies which show no causality between the two

– Bartleet and Gounder (2007); Li, Ran and Voon (2010)• General results of causality running from oil price to inflation has been

found – Lescaroux and Mignon (2008); Du, He and Wei (2010); Cunado and

Garcia (2005); Jalles (2009)• For unemployment, most countries indicated a causality running from oil

price to unemployment – (Lescaroux and Mignon, 2008)

6

Summary of Granger Causality Studies

Author Year Oil Price→GDP GDP→Oil Price GDP↔Oil Price No CausalityLescaroux and Mignon 2008 Saudi Arabia, UK, and to a less extent,

QatarDu, He and Wei 2010 China: Sample fom 2003-2008 shows causality

at 1% and 5% level of significance. Hanabusa 2009 Japan: In mean, with a 9-month lag, and in

variance with a lag of 1-month. In mean, with a 6-month lag. In variance,

with a lag of 1 month.Prasad, Narayan and

Narayan 2007 Fiji Islands: In the long run, at 5% significance

level.Jalles 2009 France

Li, Ran and Voon 2010 Hong KongBartleet and Gounder 2007 New Zealand for

Economic Growth

Author Year Oil Price→CPI CPI→Oil Price CPI↔Oil Price No CausalityLescaroux and Mignon 2008 Oil prices have a large influence on CPI for the

United Arab Emirates, United Kingdom, Mexico and Libya

Du, He and Wei 2010 China: Similar to the results for the causality in the GDP section, there are no significance in the

1995-2001 period sample but there are causality running from oil price to CPI at the 1% and 5%

level, but not the other way round.Cunado and Grancia 2005 Japan; Singapore; Thailand

Jalles 2009 France

Author Year Oil Price→Unemployment Unemployment→Oil Price Unemployment↔Oil Price

No CausalityLescaroux and Mignon 2008 Great influence of oil prices on the

unemployment rate in the United States, Luxembourg, France, Canada andVenezuela

Four oil importing countries (China, Greece, Spain and the United States)

Li, Ran and Voon 2010 Hong Kong

GDP

CPI

UNEMPLOYMENT

Note: → denotes the direction of Granger-Causality while ↔denotes bi-directional Granger-Causality

7

Objectives

• To explore the impact of oil price fluctuations on macroeconomic variables for economies in ASEAN, the Asia-Oceanic Region and South Asia

• To investigate the varied patterns of the impact by different categories of economies in the region– Oil-exporting economies– Small-open economies– Large countries with rapid economic growth

8

Vector Autoregression Model (VAR)

• Investigate the relationship between oil price and the macroeconomic variables – When they are not cointegrated

• Equation:

o y is an n-vector of endogenous variables o Bk is an (n × n) matrix of regression coefficients to be estimated. o The error term, ut, is assumed to be independent and identically

distributed with a zero mean and constant variance. o Selection of the appropriate lag length, p, is important. 4 is chosen

9

Data

• Variables – GDP, CPI and unemployment rate

• Scope – 17 countries (Asia-Pacific and ASEAN region)

• Sources– CEIC data manager – International Financial Statistics (IFS) CD-ROM 2010– Specific government sources and websites

• Type of Oil – Dubai crude “Arab Gulf Dubai” measured in FOB $US/BBL

10

Unit Root Tests for Stationarity

• Phillip-Perron (PP) unit root tests are conducted• Null hypothesis – Series are non-stationary– If the p-value is less that 0.1 (10% level of significance), the

null hypothesis of non-stationarity is rejected

11

Unit Root Tests for Stationarity: GDP

• GDP Time-Series– All series show non-stationarity except for the Philippines

and Vietnam

• Oil Price Series Corresponding to the GDP– All except for the Philippines

*Brunei and Vietnam were not be examined due to different orders of integration

12

Unit Root Tests for Stationarity: CPI

• CPI Time-Series – Australia, Brunei, China, Japan, the Philippines and South

Korea • Null hypothesis of non-stationarity is rejected• All other countries are non-stationary

• Oil Price Series Corresponding to the CPI– The Philippines: Null hypothesis for is rejected – Other countries: Rejected for the first differences– Australia, Brunei, China, Japan and South Korea

• Not studied due to different orders of integration

13

Unit Root Tests for Stationarity: Unemployment

• Unemployment Rate Series – More varied results due to smaller sample sizes– Brunei, Cambodia, Malaysia and Thailand

• Null hypothesis of non-stationarity is rejected – The remaining countries (except for China and Vietnam)

• Rejected for the first differences

• Oil Price Series Corresponding to the Unemployment Rate– Non-stationarity is rejected only for Indonesia– The remaining series except Cambodia

• Rejected for first differences– Analysis omitted for 7 countries due to different order of integration

14

Australia Brunei Cambodia China India Indonesia Japan Laos Malaysia

lnGDP I(1) I(2) I(1) I(1) I(1) I(1) I(1) I(1) I(1)

lnOil_GDP I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1)

lnCPI I(0) I(0) I(1) I(0) I(1) I(1) I(0) I(1) I(1)

lnOil_CPI I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1)

UN I(1) I(0) I(0) I(2) - I(1) I(1) - I(0)

lnOil_UN I(1) I(1) I(2) I(1) - I(0) I(1) - I(1)

MyanmarNew

Zealand Philippines SingaporeSouth Korea Taiwan Thailand Vietnam

lnGDP I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(0)

lnOil_GDP I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1)

lnCPI I(1) I(1) I(0) I(1) I(0) I(1) I(1) I(1)

lnOil_CPI I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1)

UN - I(1) I(1) I(1) I(1) I(1) I(0) I(2)

lnOil_UN - I(1) I(1) I(1) I(1) I(1) I(1) I(1)

33 (shaded) out of 49 pairs of variables proceeded with the cointegration test

15

Cointegration Test

• Engle-Granger cointegration test• The critical value calculated according to the equation is -1.61

by MacKinnon (2010)• If the absolute value of the statistic is greater than |-1.61|– Null hypothesis is rejected– Proceed with Vector Error Correction model (VECM)

• If the absolute value of the statistic is less than |-1.61|– No cointegration between the two variables – Variance auto-regression (VAR) model adopted

16

Cointegration Test

• GDP and Oil Price – Australia, India, Japan, South Korea and Thailand

• No cointegration

• CPI and Oil Price– India, Indonesia, Laos, Taiwan and Thailand

• No cointegration

– Malaysia, Myanmar, New Zealand, Singapore and Vietnam • Cointegration detected

• Unemployment Rate and Oil Price– Australia, Japan, New Zealand, the Philippines, Singapore,

South Korea and Taiwan • Cointegration

17

Cointegration Test

• Observations of cointegration– GDP and oil price series: 9 countries– CPI and oil price series: 6 countries– Unemployment rate and oil price series: 7

countries• Mainly in developed Asian countries • Australia, Japan, New Zealand, Singapore, South Korea

and Taiwan • Developing nations studied have no cointegrating

relationship

18

Australia Brunei Cambodia China India Indonesia Japan Laos Malaysia

lnGDP on lnOil_GDP -0.467643 - -4.342324 -2.433306 0.198985 -2.712333 -1.277553 -3.550048 -3.117576

lnCPI on lnOil_CPI - - -2.369161 - 0.333649 -0.179834 - -1.233173 -2.657299

UN on lnOil_UN -2.217284 - - - - - -2.160321 - -

Myanmar New Zealand Philippines Singapore South Korea Taiwan Thailand Vietnam

lnGDP on lnOil_GDP -3.381828 -2.701918 - -2.014756 -1.106708 -1.728377 -1.382402 -

lnCPI on lnOil_CPI -1.746361 -3.012051 - -1.819914 - -1.171726 0.103821 -1.626163

UN on lnOil_UN - -1.921504 -2.238887 -1.793251 -2.758843 -2.864638 - -

• Shaded boxes indicate cointegration• “-“ represents no cointegration between the variables (not integrated of the same order)

19

Vector Error Correction Model (VECM)

• For two cointegrated variables, the VECM describes the data-generating process

• The Error Correction Term (ECT) shows how fast the relationship between the two variables converges towards its long-run equilibrium

• Impulse-Response Analysis• Variance Decomposition

20

Impulse Response Functions

• Impact of a one standard deviation shock to the real oil price on three variables– GDP– CPI– Unemployment Rate

• Study of the 8-year impact• Depicted through graphical means

21

Impulse Response Analysis for GDP

Varied impact of an oil price shock on GDP• Singapore and Taiwan

– Small-open economies– Delayed negative impact– Consistent with findings of previous

studies

• Malaysia and Indonesia – Net oil exporters in the past– Long-run positive impact on GDP due

to critical nature of oil, short run inelastic demand

– Strong support from some studies

22

Impulse Response Analysis for GDP

Varied impact of an oil price shock on GDP• China

– Large economy with strong growth– Positive GDP impact from oil shock– Most energy needs met by coal, not oil– Robust economic growth in the past

despite increases in oil prices– Contradictory conclusions from some

studies

• New-Zealand– Another small open economy– Immediate negative impact followed by

positive trend– Positive and delayed effect from trading

partners, China and Australia

23

Impulse Response Analysis for CPI

• Malaysia, New Zealand, Singapore Vietnam– Instantaneous increase after oil price

shock– But the inflationary increase is small– Improved Central Bank credibility in

fighting inflation– Even smaller impact for oil exporting

nations

• Cambodia– Lagged inflationary impact– Transmission through trading

partners such as Vietnam and Thailand

24

Impulse Response Analysis for Unemployment• Australia, Japan, New Zealand, Singapore,

South Korea– Lagged positive impact of an oil price

shock on the unemployment rate– Increase in unemployment rate after

four or five years; support from past study

– However, scale of increase is nominal• Taiwan

– No lag; immediate uptick– Flexible labor market

• Australia– Delayed positive impact, but subsiding

effect on unemployment rate– Commodity-linked economy; benefits

from commodity price increase• Long-lasting impact on unemployment rate

(3 years)

25

Variance Decomposition

• Impact of real oil price fluctuations on the long-run volatility of three variables– GDP– CPI– Unemployment Rate

• Study of the 8-year impact• Depicted through graphical means

26

Variance Decomposition for GDP• Most economies including Cambodia, China, Indonesia,

Malaysia, Myanmar, Singapore – An oil price shock is a considerable source of GDP volatility– Impact not uniform over time

• Increasing impact for China and Indonesia • Decreasing impact for Cambodia and Singapore

27

Variance Decomposition for CPI and Unemployment

• Substantial source of disturbance to CPI volatility over all examined countries– Oil price shocks account for over 10%

of CPI variance with an increasing impact over time

– Limited studies for comparison– New Zealand and Singapore CPI

volatility through oil has been studied previously

• Little research has examined the importance of an oil price shock on unemployment rate– Varied results across economies– Substantial impact on New Zealand,

Philippines and Taiwan, but negligible for Australia, Japan and Singapore.

28

Granger-Causality

Cambodia Cambodia IndiaChina India Philippines

Indonesia IndonesiaLaos Laos

Malaysia MalaysiaMyanmar Myanmar

India ThailandThailand Vietnam

Australia New Zealand AustraliaNew Zealand Singapore Japan

Singapore Taiwan New ZealandTaiwan SingaporeJapan South Korea

South Korea Taiwan

UnemploymentRate

Emerging and Developing Economies

Advanced Economies

GDP CPI

In bold, Granger-Causality runs from oil price to the considered variable at 10% significance level.

29

Conclusions

• Countries are classified according to their macroeconomic characteristics to form three broad categories

1. Asian countries that export oil and are in a position to gain an advantage from a positive oil price shock

2. Small open economies for which trade plays a big role in their economic activity

3. Large, rapidly growing economies

30

Conclusions1. Asian Oil-exporting economies

– Includes Malaysia and Indonesia– Increase in the oil price causes their GDP to increase– Large percentage of the volatility in GDP is contributed by oil price variance– Signifies that oil price plays a substantial role in influencing their GDP

2. Small open economies– Includes Singapore, New Zealand and Taiwan– Negatively impacted by an oil price shock in the short-run but improves in

the long-run– Indirect positive effect through major trading partners causes resurgence in

their economic activity

3. Large and fast growing economies – Includes China and India– Negligible impact of an oil price shock on GDP– Small reliance on oil as a source of energy

31

Thank you for your attention!