A report on
Effect of Crude Oil Prices on Growth of South East
Asian Economies
Submitted to
Prof Shyamal Roy
On
28th August 2007
In
Partial Fulfillment of the Requirements for the Course
Contemporary Concerns Study
By
Abhipsita Singh
Gautam Patil
0611213
0611230
Indian Institute of Management, Bangalore
TABLE OF CONTENTS
Sl.
Particulars
Pg No.
1
Introduction
3
2
Energy intensity as criterion
5
3
Methodologies used
8
4
Sectoral Breakdown of GDP
10
5
Causality tests
12
6
Inferences
14
7
Appendix
18
References
2
Introduction
The South East Asian economies are now classified as a part of the emerging markets and have been
registering impressive growth rates since 2000. Some of these economies are predominantly
manufacturing oriented and thus energy prices play a critical role in the sustenance of their growth
rates.
Growth of SE Asian Economies since 2000
12
yoy growth
10
8
Hong Kong
6
Malaysia
4
Philllipines
Singapore
2
Thailand
0
-2
2000
2001
2002
2003
2004
2005
2006
South Korea
-4
year
Figure 1: Growth trend of major economies in SE Asia
With Indonesia also turning into a net importer of oil in 2002, a common concern across these
economies is the resource dependence shown by them for the energy sources. Though the
consumption pattern varies across different countries, the economies considered for the study are net
oil importers. This may intuitively suggest that the growth in the region is susceptible to the changes
in the energy prices. This is the starting point of our analysis of the economies that we study.
Though the oil demand was sluggish until 2002 due to the Asian Economic crisis and the burst of the
IT bubble, it has been growing steadily since then. At these high consumption levels, these countries
have a much higher exposure to oil price volatility through factors like inflation and income transfer
to oil-producing countries. All these have an adverse impact on the real growth rate of these
economies.
3
Figure 2: Increase in Asian oil imports
The objective of this project is to determine if there is a link between growth and energy prices in
these economies. Because of the volatility in energy prices and the growth trend shown by the South
East Asian economies, the inferences given by the project will provide a broad framework to show
how the energy prices can play a role in making investment decisions in the economies that are
studied.
While there are many factors beyond supply-demand like geo-political situations and market
sentiment that influence oil prices; the focus of this project will mainly be on the quantifiable data
with due attention to the other factors. The scope of the project will be limited to identifying the
effect of various energy prices scenarios on the growth; however no attempt will be made at
predicting energy prices.
4
Criterion of Energy Intensity
The countries under study in this project are South Korea, Indonesia, Malaysia, Singapore and Hong
Kong. Many factors like availability of statistics on economy, quality of information, periodicity of
reporting of information, size of the economy, dominance in the region and sectoral diversity within
the economy have affected the choice of these countries. It can be noted that no single country has a
demand large enough to individually affect energy prices and thus is effectively a price taker. Also,
oil prices are taken as a proxy for energy prices for multiple reasons, namely:
a) Non availability of data on gas consumption and prices in some countries
b) Oil consumption in value terms is currently much higher than gas consumption
c) Other sources like coal, methane etc have been ignored for simplicity
d) Restriction on substitutability due to technological issues
As stated before there are other factors which contribute to oil price volatility as shown in the figure
below and no attempt is made to account for these factors.
Energy Intensity
Energy intensities are valuable indicators in describing the energy consumed in entire production
chains and provide insight into an economy's total energy use. Changes in energy consumption
reflect the combined effects of changes in energy intensities in various sectors and changes in the
volume and structure of demand. Energy needed per unit of production (referred to as energy
5
intensity or specific energy consumption) shows the sensitivity of an economy to changes in energy
prices. It is useful as criteria for comparison because it is not biased by the absolute value of the
GDP.
Figure3: Energy Intensity for East and South Asian nations
Note: Nepal and Sri Lanka measured on the secondary axis
Source: Data from EIA website www.eia.doe.gov/emeu/international/energyconsumption.html
The SE Asian region constitutes economies with distinctly different energy intensities.
The variation in energy intensity values can be due to several reasons:

Different levels of energy efficiency in the various economies: For instance, the high values of energy
intensities in Nepal, Pakistan with a modest growth rate compared to other economies in the region
may reflect inefficient use of energy (which in turn may be due to poor public transport or lack of
investment in newer technologies).

The varying nature of the different economies: While the economies dominated by the service industry
usually have low energy requirements, the ones which are manufacturing intensive would have
higher energy consumption. As a result, the energy intensity is likely to be much more in the case of
the latter.
In order to conduct the study, countries with energy intensity values in different ranges have been
selected (highlighted in Figure 4). However, all the four economies have comparable growth rates.
This will help in analyzing if there is a conclusively similar dependence of growth on energy prices
for countries with energy intensities in the same range. Also, the selected economies differ in their
composition and this will help determining if there are some factors other than energy consumption
per unit of GDP that can influence the dependence of GDP on energy prices.
6
Country
2003
2000
1990
Hong Kong
91.4
89
92.4
Bangladesh
97.9
98.1
102.1
Sri Lanka
120.8
115.1
136.8
Philippines
127.4
139
110.4
India
189.5
208.1
250.2
Thailand
199.1
193.3
176.3
Singapore
213.8
233.2
297.1
Viet Nam
227.3
236.7
303.2
China
231.3
243.1
504.5
Pakistan
236.1
240.5
257.6
Korea, Rep
238.2
251
220.7
Indonesia
239.3
233.6
241.5
Nepal
248.1
252.6
293.7
Malaysia
257.5
237.8
229.1
Figure 4: Ton Equivalent of Oil (ToE) per $ million of GDP
7
Methodologies Employed
Initially, a simple linear correlation measure between quarterly GDP and crude oil prices was taken
in order to get the expected negative relationship. This approach did not give the expected result
because:
1.
Since both nominal GDP and crude oil prices showed an increasing trend, the resulting
correlation measure would be negative.
2.
In some countries, as the retail fuel prices administered, the effect of crude oil prices is not
direct.
3.
Seasonality of data also posed a problem. For instance, South Korea’s utility consumption
peaks in December due to the extreme cold. An increase in crude oil price in this period
would only reduce the peak consumption but the consumption would nonetheless increase
over the previous quarter.
4.
Even a QoQ % change of GDP and crude oil prices linear correlation measure did not help
because the effect of price change was not direct (due to price administration, taxation,
refinement to other petroleum products) and linear.
To overcome these problems, two alternative approaches were considered.
1.
An increase in crude oil prices causes inflation (consumption does not reduce much due to
inelasticity of oil consumption to price in the short term) which is like an additional tax burden
on the consumer and it also reduces the real GDP growth. Thus, assuming a scenario of flat
crude oil prices, it is possible to negate this effect of inflation and account for the ‘lost’ GDP
growth. Due to problems related to data availability and many simplifying assumptions, this
approach was not taken up further.
2.
Since price administration distorted the effect of crude oil prices on the economy, using retail
fuel prices instead will result in a more ‘transparent’ relationship. Since retail fuel prices were
step-wise in nature, these could be interpolated and then used. The problem with this
approach however is that crude oil is further refined into different products like gasoline,
kerosene, naphtha, propane etc. Getting data for the required period and periodicity on the
consumption of these products for most of the countries was a problem.
Sectoral Breakdown of GDP
As the consolidated GDP data did not produce any conclusive results and lack of availability of data
was a problem, broad sectors of these economies were then studied. This approach was used because
8
the consumption of crude oil was distinctly different across each sector and in addition, the
contribution of a particular sector to the GDP varied in % terms across each country. Thus, the
impact of crude oil price changes would also be different on the growth of each of these countries.
The sectors were chosen so as to envelop all the activities which can contribute to a nation’s GDP.
100%
90%
Others
80%
Agriculture & Mining
70%
services
60%
real estate
50%
retail
40%
finance
30%
utilities
20%
transport
10%
manufacturing
0%
S. Korea
Indonesia
M alaysia
Hong Kong
Singapore
Figure5: The industry wise composition of the economies under consideration
Source: Bloomberg
As can be seen from Figure 5, the contribution of a sector to the GDP varies across the countries. The
intensity of crude oil consumption varies across each of these sectors. Manufacturing, transport and
utilities (water, electricity and gas) are traditionally seen as the sectors which are energy intensive
and thus may show a higher sensitivity to crude oil prices. On the other hand, sectors like services,
retail and finance may not be directly affected by changes in crude oil prices.
Crude oil prices can affect GDP growth via two ‘routes’, as listed below:
1.
An increase in prices results in reduced consumption and thus production of goods or
transportation may be directly hit. Thus, affecting GDP growth.
2.
Similarly, an increase in prices increases inflation. A sharp increase causes the inflation to
increase above acceptable levels which may induce central banks to use macroeconomic
policies like interest rate hikes which may reduce GDP growth rates. This is a more indirect
transmission mechanism and is not fully considered in this study.
Causality Tests
The sectoral GDP data of each country was obtained and a correlation measure was taken between
% QoQ changes of this data and crude oil prices. Some sectors did show markedly better
9
correlations while others showed very low correlation and did not yield any conclusive results. Since
correlation does not imply causality, it was decided to test the causality between the GDP of various
sectors and the crude oil prices using the Granger Causality Test.
Granger Causality is useful for determining if one time series can be used to forecast another
however, it places a prerequisite of stationarity (a stochastic process whose probability distribution
at a fixed time or position is same at all times and positions)
on the time series. The Unit Root Test (Dickey Fuller test, in particular, is used) is used to determine
the lag and difference for which a particular time series becomes stationary. The next step is to
perform a Cointegration test on pairs of these time series at the lag at which they become stationary
and by making certain assumptions about the nature of relationship (for instance, presence of linear
deterministic trend and intercept). The purpose of the Cointegration test is to check if a linear
combination of two or more non-stationary time series is possible and this is followed by the Granger
Causality test. The Granger Causality test determines is used on pairs of time series and checks the
presence and direction of causality between them.
The results from the causality tests between the sectoral GDP data and the crude oil prices are
provided in Appendix A for further perusal, however the important results are as tabulated below.
Sector
Manufacturing
Transport
Utilities
Construction
Services
Real Estate
Wholesale/Retail
Trade
Finance
Agriculture
Mining
Indonesia
X
X
N.A.
N.A.
X
N.A.
N.A.
X
X
N.A.
X
Malaysia
X
N.A.
N.A.
N.A.
X
N.A.
N.A.
N.A.
N.A.
X
√
[√ - Causality, X – No causality, N.A. – Not Applicable]
10
Singapore
√
N.A.
X
X
X
X
N.A.
N.A.
N.A.
N.A.
N.A.
South Korea
√
X
X
√
X
√
X
N.A.
X
N.A.
N.A.
Inferences
Hong Kong:
Outlook on growth of the economy and energy demand:

Service dominated economy: Almost 60% of the GDP is contributed by financial and other
services e.g. the traditional financial, logistics, property, tourism and producer services,
growth is supported by more knowledge-based and services industries such as fitness and
beauty, theme park, business consulting, and the environmental industry.
11

The absence of domestic energy sources has made Hong Kong, China a net importer for oil
(mainly from Singapore) and natural gas (from China).

By 2030, the share of GDP in the services sector is expected to reach more than 95 percent.

Final Energy Demand by various sectors : Outlook
Over the outlook period, final energy demand is projected to grow at 3.2 percent
per year, slower than the 5.3 percent annual growth rate in the past
two decades. In 2030, the transport sector will maintain the largest
share at 61 percent, followed by commercial (21 percent), residential (9
percent), and industry (9 percent).
Results derived from the causality tests:
Due to lack of availability of sectoral GDP on Hong Kong, only a qualitative analysis was
performed on the data. Hong Kong’s GDP has demonstrated steady growth since 2002 and has
been able to keep inflation at very low levels of about 1%. This can be attributed to low cost
imports from mainland China. The services industry in Hong Kong accounts for about 60% of
the GDP with the rest coming from transportation, wholesale & retail etc. Empirical evidence
suggests that the services industry is quite resilient to oil price changes in the short run (however,
an oil price shock like the one in the 1970’s may still have dire consequences). Transportation is
another major contributor since the port services at Hong Kong and its international airport
(with the largest passenger handling capacity in Asia) provide a hub for the region and also an
entrance in the Pearl Delta river region of mainland China. The GDP contribution resulting
from wholesale and retail will only be affected to the extent of inflation due to increase in price of
imports.
12
Figure 7: Final Energy Demand Projections in Hong Kong
From the figure above, it is clear that the transportation sector will become the major consumer
of energy while its contribution to GDP will still be lower than that of the services sector.
Consequences for investment decisions:
From the analysis above, it can be stated that the Hong Kong economy is very resilient to crude
oil price changes mainly due to the services industry. A cause of concern maybe the utilities and
the transport sector which will be huge consumers of energy in future. This dependence is now
being reduced by switching to nuclear and gas based electricity generation of electricity but the
transport sector still remains vulnerable.
Indonesia:
Outlook on growth of the economy and energy demand:

Indonesia is endowed with indigenous energy resources such as natural gas, coal and oil, and
is self sufficient in terms of energy supply except oil. Indonesia has been a net oil importer
since 2002.48 Oil production decreased from 70.6 Mtoe in 2000 to 54.6 Mtoe in 2004 due to
depleting reserves and lack of investment for exploration and development.

GDP is projected to grow at 4.6 percent per year, from US$790 billion in 2002 to US$2,795
billion in 2030. The growth in GDP will be largely attributed to the services sector and will
account for about 57 percent of the incremental GDP growth.
13

Indonesia’s urbanisation level is projected to increase from a low of 44 percent in 2002 to 68
percent in 2030. Over the outlook period, it is estimated that about 18 cities will have
population between 1 and 5 million. Growth of urban population will lead to higher demand
for oil in transport, and electricity in the residential and commercial sectors.

Final Energy Demand Outlook: Indonesia’s final energy demand is projected to grow at 2.9
percent per year, reaching 247 Mtoe in 2030, more than double that of
2002 at 112 Mtoe.
Results derived from the causality tests:
The causality tests indicate lack of any kind of causality between the crude oil prices and sectoral
GDP contributions. Following reasons can be attributed to this result:

Indonesia become a net energy importer only in late 2002, thus its economy was
insulated from world wide crude oil price fluctuations

Indonesia has provided high subsidies on the refined petroleum products and though
subsidies on gasoline and diesel were only recently abolished, rest of the subsidies still
continues. These subsidies distort the effect of energy prices on the economy by
influencing consumption and transferring the resulting burden to other sectors.
Effect on Investment:
This economy however enjoys the benefits of diversification, since no sector contributes to
more than 15% of the GDP and each of these sectors are quite different from the other, for
instance, agriculture, mining, trade, manufacturing, finance and services. The energy
consumption trend is skewed towards the private consumers currently but, as shown in the
figure below, the industrial consumption might outpace it in future. Thus, this economy
faces no immediate threat of an oil price increase to its growth prospects.
14
.
South Korea:
Outlook on growth of the economy and energy demand :

With very limited indigenous energy resources,Korea relies heavily on imports of oil, natural
gas and coal. Net imports have more than doubled from 72 Mtoe in 1990 to 190 Mtoe in
2005. In 2005, Korea was the world’s fourth-largest importer of oil and the second-largest
importer of both coal and liquefied natural gas (LNG).

Korea’s GDP is projected to grow at an annual rate of 3.6 percent over the outlook period –
a slower rate than the past two decades at 7.0 percent per year.

Final energy demand is projected to grow at 2.3 percent per year, much slower compared
with the annual growth in the previous two decades of 7.5 percent.
15

Korea’s primary energy consumption grew at an average annual rate of 3.2 percent between
2002 and 2005, much slower than that of the last decade at 6.9 percent per year. The sluggish
growth in energy consumption is due mainly to the economy’s rather slow GDP growth at
around 3.9 percent per year over the same period, dampened further by energy efficiency
improvements in the industry and transport sectors.

The shift from energy-intensive industry to non-energy intensive industry will result in the
lower projected growth for industrial energy demand. Over the past two decades, the value
added of energy intensive manufacturing sector has grown more than ten-fold, due to the
production capacity expansion in industries such as steel, cement, shipbuilding62 and
petrochemicals. By contrast, future growth of industrial value-added is expected to be led by
non energy- intensive industries such as IT, electronics, machinery and services sector.63
The impact of the changes in the economy’s industrial structure will result in significant
improvements in energy intensity, from 273 toe per US$ million in 2002 to 164 toe per US$
million in 2030.

Over the past two decades, income growth, improvements in living standards, expansion of
residential suburbs and development of vehicle manufacturing industries have all
contributed to a thirty-fold increase in the stock of vehicles, which have in turn resulted in a
ten-fold increase in gasoline and diesel consumption.65 Managing road transport
congestion66, and air pollution67 caused by passenger vehicles and freight trucks continues
to be a significant challenge for the economy.
Results derived from the causality tests:
16

The manufacturing sector of South Korea constitutes energy intensive sectors like the
cement and steel industry and thus displays heavy dependence on crude oil.

The construction sector of South Korea also displays a dependence on crude oil prices
mainly due to use of heavy fuel and other petroleum products in this industry.

The utilities sector is unaffected due to reliance on nuclear and gas based electricity
generation.
Effect on Investment:
Given the contribution of the manufacturing and the construction sector to GDP and its heavy
reliance on crude oil (97% of its requirements are imported), its economy is highly susceptible to
crude oil price changes. Thus, its GDP growth and growth prospects may suffer adversely.
Appendix
Indonesia
1)
Unit root test
Null Hypothesis: D(MANUFACTURING) has a unit root
17
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=7)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-4.726191
0.0008
Test critical values:
1% level
-3.689194
5% level
-2.971853
10% level
-2.625121
Null Hypothesis: D(MINING) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=7)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-4.068785
0.0040
Test critical values:
1% level
-3.689194
5% level
-2.971853
10% level
-2.625121
Null Hypothesis: D(AGRICULTURE) has a unit root
Exogenous: Constant
Lag Length: 6 (Automatic based on SIC, MAXLAG=7)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-3.169047
0.0359
Test critical values:
1% level
-3.769597
5% level
-3.004861
10% level
-2.642242
Null Hypothesis: D(SERVICES) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=7)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-4.122305
0.0035
Test critical values:
1% level
-3.689194
5% level
-2.971853
10% level
-2.625121
18
Null Hypothesis: D(TRADE) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=7)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-4.866217
0.0005
Test critical values:
1% level
-3.689194
5% level
-2.971853
10% level
-2.625121
Null Hypothesis: D(TRANSPORT) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=7)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-5.057363
0.0003
Test critical values:
1% level
-3.689194
5% level
-2.971853
10% level
-2.625121
Null Hypothesis: D(CONSTRUCTION) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=7)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-4.678305
0.0009
Test critical values:
1% level
-3.689194
5% level
-2.971853
10% level
-2.625121
2)
Pair wise co-integration test
Date: 08/28/07 Time: 03:07
Sample (adjusted): 4 30
Included observations: 27 after adjustments
Trend assumption: Linear deterministic trend
Series: AGRICULTURE CRUDE
Lags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
19
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None
0.122347
7.034413
15.49471
0.5736
At most 1
0.121931
3.510805
3.841466
0.0610
Trace test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
Mining
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.368494
17.63621
12.32090
0.0059
At most 1 *
0.156518
4.766063
4.129906
0.0345
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
Transport
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.387401
16.40579
12.32090
0.0098
At most 1
0.091423
2.684532
4.129906
0.1198
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
Manufacturing
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.316579
17.72792
15.49471
0.0227
At most 1 *
0.279977
8.211799
3.841466
0.0042
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
Series: SERVICES CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
0.05
Statistic
Critical Value
20
Prob.**
None *
0.342942
15.73600
12.32090
0.0129
At most 1
0.132393
3.976466
4.129906
0.0547
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
Series: TRADE CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.319442
12.70887
12.32090
0.0430
At most 1
0.066716
1.933274
4.129906
0.1936
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
3)
Granger’s causality test
CRUDE does not Granger Cause SERVICES
28
SERVICES does not Granger Cause CRUDE
TRANSPORT does not Granger Cause TRADE
28
TRADE does not Granger Cause TRANSPORT
CRUDE does not Granger Cause TRADE
28
TRADE does not Granger Cause CRUDE
CRUDE does not Granger Cause TRANSPORT
28
TRANSPORT does not Granger Cause CRUDE
CRUDE does not Granger Cause MINING
28
MINING does not Granger Cause CRUDE
CRUDE does not Granger Cause MANUFACTURING
28
MANUFACTURING does not Granger Cause CRUDE
CRUDE does not Granger Cause FINANCE
28
FINANCE does not Granger Cause CRUDE
Malaysia
21
1.34197
0.28104
0.79565
0.46332
0.11923
0.88815
0.23873
0.78956
0.67066
0.52109
0.77440
0.47263
0.46688
0.63277
1.40781
0.26499
0.22813
0.79780
9.32106
0.00108
1.03000
0.37290
0.19614
0.82326
0.55879
0.57947
0.57043
0.57308
1)
Unit root test of sectoral data
Null Hypothesis: D(AGRICULTURE) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic based on SIC, MAXLAG=10)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-7.174750
0.0000
Test critical values:
1% level
-3.552666
5% level
-2.914517
10% level
-2.595033
Null Hypothesis: D(MANUFACTURING) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=10)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-6.361328
0.0000
Test critical values:
1% level
-3.550396
5% level
-2.913549
10% level
-2.594521
Null Hypothesis: D(MINING) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=10)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-6.940643
0.0000
Test critical values:
1% level
-3.550396
5% level
-2.913549
10% level
-2.594521
Null Hypothesis: D(SERVICES) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=10)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-6.480503
0.0000
Test critical values:
1% level
-3.550396
5% level
-2.913549
22
10% level
2)
-2.594521
Pair wise co-integration test on sectoral data
Trend assumption: Linear deterministic trend
Series: AGRICULTURE CRUDE
Lags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.236232
21.32435
15.49471
0.0059
At most 1 *
0.105330
6.232824
3.841466
0.0125
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
Trend assumption: No deterministic trend
Series: MANUFACTURING CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.167446
15.37736
12.32090
0.0149
At most 1 *
0.082883
4.931668
4.129906
0.0313
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
Trend assumption: No deterministic trend
Series: MINING CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.237236
20.43028
12.32090
0.0018
At most 1 *
0.083891
4.994321
4.129906
0.0302
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
23
Trend assumption: No deterministic trend
Series: SERVICES CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.191425
14.97906
12.32090
0.0175
At most 1
0.049064
2.867561
4.129906
0.1069
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
3)
Pair wise Granger Causality test
Lags: 2
Null Hypothesis:
CRUDE does not Granger Cause AGRICULTURE
Obs
F-Statistic
Probability
57
0.73640
0.48376
0.62067
0.54152
3.40376
0.07043
0.92637
0.34002
0.40358
0.52788
1.45835
0.23236
0.15057
0.69949
1.08069
0.30309
AGRICULTURE does not Granger Cause CRUDE
CRUDE does not Granger Cause MINING
58
MINING does not Granger Cause CRUDE
CRUDE does not Granger Cause SERVICES
58
SERVICES does not Granger Cause CRUDE
CRUDE does not Granger Cause MANUFACTURING
58
MANUFACTURING does not Granger Cause CRUDE
Singapore
4)
Unit root test of sectoral data
Null Hypothesis: D(MANUFACTURING) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=10)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-8.375142
0.0000
Test critical values:
-3.574446
1% level
24
5% level
-2.923780
10% level
-2.599925
Null Hypothesis: D(UTILITIES) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=10)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-6.964056
0.0000
Test critical values:
1% level
-3.574446
5% level
-2.923780
10% level
-2.599925
Null Hypothesis: D(CONSTRUCTION,2) has a unit root
Exogenous: Constant
Lag Length: 2 (Automatic based on SIC, MAXLAG=10)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-12.13684
0.0000
Test critical values:
1% level
-3.584743
5% level
-2.928142
10% level
-2.602225
Null Hypothesis: D(SERVICES) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=10)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-5.984815
0.0000
Test critical values:
1% level
-3.574446
5% level
-2.923780
10% level
-2.599925
5)
Pair wise co-integration test on sectoral data
Date: 08/27/07 Time: 23:31
Sample (adjusted): 3 50
Included observations: 48 after adjustments
Trend assumption: No deterministic trend
25
Series: MANUFACTURING CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None
0.178500
12.20750
12.32090
0.0522
At most 1
0.056067
2.769594
4.129906
0.1136
Trace test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
Date: 08/27/07 Time: 23:37
Sample (adjusted): 3 50
Included observations: 48 after adjustments
Trend assumption: No deterministic trend
Series: CRUDE CONSTRUCTION
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.232708
13.18817
12.32090
0.0357
At most 1
0.009818
0.473569
4.129906
0.5546
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Date: 08/27/07 Time: 23:39
Sample (adjusted): 3 50
Included observations: 48 after adjustments
Trend assumption: No deterministic trend
Series: UTILITIES CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None
0.198420
12.18736
12.32090
0.0526
At most 1
0.032202
1.571142
4.129906
0.2465
26
Trace test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
Date: 08/27/07 Time: 23:39
Sample (adjusted): 3 50
Included observations: 48 after adjustments
Trend assumption: No deterministic trend
Series: SERVICES CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.250064
17.19974
12.32090
0.0071
At most 1
0.068129
3.386898
4.129906
0.0779
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
6)
Pair wise Granger Causality test
Pairwise Granger Causality Tests
Date: 08/27/07 Time: 23:42
Sample: 1 50
Lags: 2
Null Hypothesis:
Obs
F-Statistic
Probability
48
8.09506
0.00104
0.92001
0.40621
7.41912
0.00171
2.89227
0.06630
6.32121
0.00392
3.33917
0.04487
3.40879
0.04225
5.44058
0.00783
0.06162
0.94032
0.63214
0.53632
0.10740
0.89841
CONSTRUCTION does not Granger Cause
MANUFACTURING
MANUFACTURING does not Granger Cause CONSTRUCTION
UTILITIES does not Granger Cause MANUFACTURING
48
MANUFACTURING does not Granger Cause UTILITIES
SERVICES does not Granger Cause MANUFACTURING
48
MANUFACTURING does not Granger Cause SERVICES
CRUDE does not Granger Cause MANUFACTURING
48
MANUFACTURING does not Granger Cause CRUDE
UTILITIES does not Granger Cause CONSTRUCTION
48
CONSTRUCTION does not Granger Cause UTILITIES
SERVICES does not Granger Cause CONSTRUCTION
48
27
CONSTRUCTION does not Granger Cause SERVICES
CRUDE does not Granger Cause CONSTRUCTION
48
CONSTRUCTION does not Granger Cause CRUDE
SERVICES does not Granger Cause UTILITIES
48
UTILITIES does not Granger Cause SERVICES
CRUDE does not Granger Cause UTILITIES
48
UTILITIES does not Granger Cause CRUDE
CRUDE does not Granger Cause SERVICES
48
SERVICES does not Granger Cause CRUDE
7.18219
0.00204
1.85479
0.16879
0.94965
0.39484
3.44895
0.04081
2.04288
0.14205
0.10472
0.90080
0.64033
0.53207
0.97588
0.38505
2.92850
0.06422
South Korea
4)
Unit root test
Null Hypothesis: D(MANUFACTURING) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=8)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-10.66307
0.0000
Test critical values:
1% level
-3.661661
5% level
-2.960411
10% level
-2.619160
Null Hypothesis: D(UTILITIES) has a unit root
Exogenous: Constant
Lag Length: 2 (Automatic based on SIC, MAXLAG=8)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-4.550401
0.0011
Test critical values:
1% level
-3.679322
5% level
-2.967767
10% level
-2.622989
Null Hypothesis: D(CONSTRUCTION) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=8)
28
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-12.50798
0.0000
Test critical values:
1% level
-3.661661
5% level
-2.960411
10% level
-2.619160
Null Hypothesis: D(RETAIL) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=8)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-10.27515
0.0000
Test critical values:
1% level
-3.661661
5% level
-2.960411
10% level
-2.619160
Null Hypothesis: D(TRANSPORT) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=8)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-6.129146
0.0000
Test critical values:
1% level
-3.661661
5% level
-2.960411
10% level
-2.619160
Null Hypothesis: D(FINANCE) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=8)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-5.822840
0.0000
Test critical values:
1% level
-3.661661
5% level
-2.960411
10% level
-2.619160
Null Hypothesis: D(REAL_ESTATE) has a unit root
Exogenous: Constant
29
Lag Length: 0 (Automatic based on SIC, MAXLAG=8)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-9.399434
0.0000
Test critical values:
1% level
-3.661661
5% level
-2.960411
10% level
-2.619160
5)
Pair wise co-integration test
Trend assumption: No deterministic trend
Series: MANUFACTURING CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.314337
13.61583
12.32090
0.0302
At most 1
0.059978
1.917403
4.129906
0.1956
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
Trend assumption: No deterministic trend
Series: CONSTRUCTION CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.206677
12.85461
12.32090
0.0407
At most 1 *
0.167349
5.677359
4.129906
0.0204
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
Trend assumption: No deterministic trend
Series: FINANCE CRUDE
Lags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
30
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None
0.293300
11.86581
12.32090
0.0595
At most 1
0.047226
1.451341
4.129906
0.2675
Trace test indicates no cointegration at the 0.05 level
Trend assumption: No deterministic trend
Series: REAL_ESTATE CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.371048
16.08816
12.32090
0.0112
At most 1
0.053773
1.713448
4.129906
0.2239
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
Trend assumption: No deterministic trend
Series: RETAIL CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.330243
13.46185
12.32090
0.0321
At most 1
0.032862
1.035823
4.129906
0.3587
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
Trend assumption: No deterministic trend
Series: TRANSPORT CRUDE
Lags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.366224
14.83410
12.32090
0.0186
31
At most 1
0.037682
1.152313
4.129906
0.3298
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
Trend assumption: No deterministic trend
Series: UTILITIES CRUDE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.306489
12.62047
12.32090
0.0445
At most 1
0.040289
1.274815
4.129906
0.3023
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
6)
Granger’s causality test
CRUDE does not Granger Cause CONSTRUCTION
31
CONSTRUCTION does not Granger Cause CRUDE
CRUDE does not Granger Cause FINANCE
31
FINANCE does not Granger Cause CRUDE
CRUDE does not Granger Cause MANUFACTURING
31
MANUFACTURING does not Granger Cause CRUDE
CRUDE does not Granger Cause REAL_ESTATE
31
REAL_ESTATE does not Granger Cause CRUDE
CRUDE does not Granger Cause RETAIL
31
RETAIL does not Granger Cause CRUDE
CRUDE does not Granger Cause TRANSPORT
31
TRANSPORT does not Granger Cause CRUDE
CRUDE does not Granger Cause UTILITIES
31
32
3.10390
0.06182
1.99727
0.15599
1.52389
0.23669
1.87607
0.17335
3.85640
0.03414
4.27942
0.02474
4.33125
0.02379
3.55255
0.04325
0.31304
0.73394
6.24001
0.00612
1.52707
0.23602
0.48821
0.61924
0.10391
0.90168
UTILITIES does not Granger Cause CRUDE
0.68398
0.51346
References
1.APEC Energy Demand and Supply Outlook 2006, Asia Pacific Energy Research Centre, Institute
of Energy Economics, Japan
2.Energy Demands and Sustaining Growth in South East Asia 2006 , Pragya Jaswal and Mitali Das
Gupta , Institute of Development Studies
3. Developing Asia and the Pacific: Performance and prospects , Asia Development Outlook 2007
33
4. Bloomberg database
34
Criterion of Energy Intensity
The countries under study in this project are South Korea, Indonesia, Malaysia, Singapore and Hong
Kong. Many factors like availability of statistics on economy, quality of information, periodicity of
reporting of information, size of the economy, dominance in the region and sectoral diversity within
the economy have affected the choice of these countries. It can be noted that no single country has a
demand large enough to individually affect energy prices and thus is effectively a price taker. Also,
oil prices are taken as a proxy for energy prices for multiple reasons, namely:
a) Non availability of data on gas consumption and prices in some countries
b) Oil consumption in value terms is currently much higher than gas consumption
c) Other sources like coal, methane etc have been ignored for simplicity
d) Restriction on substitutability due to technological issues
As stated before there are other factors which contribute to oil price volatility as shown in the figure
below and no attempt is made to account for these factors.
Energy Intensity
35
Energy intensities are valuable indicators in describing the energy consumed in entire production
chains and provide insight into an economy's total energy use. Changes in energy consumption
reflect the combined effects of changes in energy intensities in various sectors and changes in the
volume and structure of demand. Energy needed per unit of production (referred to as energy
intensity or specific energy consumption) shows the sensitivity of an economy to changes in energy
prices. It is useful as criteria for comparison because it is not biased by the absolute value of the
GDP.
Figure3: Energy Intensity for East and South Asian nations
Note: Nepal and Sri Lanka measured on the secondary axis
Source: Data from EIA website www.eia.doe.gov/emeu/international/energyconsumption.html
The SE Asian region constitutes economies with distinctly different energy intensities.
The variation in energy intensity values can be due to several reasons:

Different levels of energy efficiency in the various economies: For instance, the high values of energy
intensities in Nepal, Pakistan with a modest growth rate compared to other economies in the region
may reflect inefficient use of energy (which in turn may be due to poor public transport or lack of
investment in newer technologies).

The varying nature of the different economies: While the economies dominated by the service industry
usually have low energy requirements, the ones which are manufacturing intensive would have
higher energy consumption. As a result, the energy intensity is likely to be much more in the case of
the latter.
In order to conduct the study, countries with energy intensity values in different ranges have been
selected (highlighted in Figure 4). However, all the four economies have comparable growth rates.
36
This will help in analyzing if there is a conclusively similar dependence of growth on energy prices
for countries with energy intensities in the same range. Also, the selected economies differ in their
composition and this will help determining if there are some factors other than energy consumption
per unit of GDP that can influence the dependence of GDP on energy prices.
Country
2003
2000
1990
Hong Kong
91.4
89
92.4
Bangladesh
97.9
98.1
102.1
Sri Lanka
120.8
115.1
136.8
Philippines
127.4
139
110.4
India
189.5
208.1
250.2
Thailand
199.1
193.3
176.3
Singapore
213.8
233.2
297.1
Viet Nam
227.3
236.7
303.2
China
231.3
243.1
504.5
Pakistan
236.1
240.5
257.6
Korea, Rep
238.2
251
220.7
Indonesia
239.3
233.6
241.5
Nepal
248.1
252.6
293.7
Malaysia
257.5
237.8
229.1
Figure 4: Ton Equivalent of Oil (ToE) per $ million of GDP
37
Causality Tests
The sectoral GDP data of each country was obtained and a correlation measure was taken between
% QoQ changes of this data and crude oil prices. Some sectors did show markedly better
correlations while others showed very low correlation and did not yield any conclusive results. Since
correlation does not imply causality, it was decided to test the causality between the GDP of various
sectors and the crude oil prices using the Granger Causality Test.
Granger Causality is useful for determining if one time series can be used to forecast another
however, it places a prerequisite of stationarity (a stochastic process whose probability distribution
at a fixed time or position is same at all times and positions)
on the time series. The Unit Root Test (Dickey Fuller test, in particular, is used) is used to determine
the lag and difference for which a particular time series becomes stationary. The next step is to
perform a Cointegration test on pairs of these time series at the lag at which they become stationary
and by making certain assumptions about the nature of relationship (for instance, presence of linear
deterministic trend and intercept). The purpose of the Cointegration test is to check if a linear
combination of two or more non-stationary time series is possible and this is followed by the Granger
Causality test. The Granger Causality test determines is used on pairs of time series and checks the
presence and direction of causality between them.
The results from the causality tests between the sectoral GDP data and the crude oil prices are
provided in Appendix A for further perusal, however the important results are as tabulated below.
Sector
Manufacturing
Transport
Utilities
Construction
Services
Real Estate
Wholesale/Retail
Trade
Finance
Agriculture
Mining
Indonesia
X
X
N.A.
N.A.
X
N.A.
N.A.
X
X
N.A.
X
Malaysia
X
N.A.
N.A.
N.A.
X
N.A.
N.A.
N.A.
N.A.
X
√
[√ - Causality, X – No causality, N.A. – Not Applicable]
Valuation of Nantucket Nectars
38
Singapore
√
N.A.
X
X
X
X
N.A.
N.A.
N.A.
N.A.
N.A.
South Korea
√
X
X
√
X
√
X
N.A.
X
N.A.
N.A.
The following methods were considered for valuation of Nantucket Nectars:

Sales multiple

EBITDA multiple

EBIT multiple

P/E multiple method

APV method
Sales multiple based valuations are typically used for internet firms or service industry
based firms which have very few tangible assets. In addition, a sales multiple based
valuation does not take into account the profitability of the business and could thus give
favorable valuations even for a loss making firm but with high revenues.
EBIT multiple should not be used because the value of this multiple is affected by
accounting practices which may vary considerably across firms. Thus, it doesn’t fully
reflect a given firm’s performance. The EBITDA multiple is a better option as it captures
operational efficiency more accurately. The thumb rule is that as the base of the multiple
goes lower it becomes more susceptible to manipulation and thus may give ‘spurious’
valuations.
A price earning multiple is used mostly to estimate the performance of companies whose
shares are traded in public and therefore reflect market expectation to a credible extent.
Nantucket Nectars being a privately held firm should not be valued using this method.
The multiples method has some drawbacks compared to APV method. Though the
multiples method is simple and much easier to implement than the APV method, it’s
useful only if the firm being valuated does not operate in a niche with characteristics
completely different from those of its industry. The drawback of the APV method
however is that it gives vastly different valuations for changes in the value of r and g
assumed (especially for new enterprises since terminal value forms a large proportion of
the valuation). In addition, the choice of the discount rates used for tax shields from
interest and operating losses carried forward can change valuations.
39
Based on the arguments presented above, the valuations of the firms using the APV
method and the EBITDA method have been presented in the annexure for further perusal.
Apart from the base case valuation as provided in the case, two more scenarios
corresponding to a 10% to 20% reduction in COGS (Cost of Goods Sold) have also been
considered1. This is to accommodate the cases in which NN founders benefit from the
favorable cost structure of the acquirer.
Recommendations
1
Harvard Business School Case Study, ‘Nantucket Nectars’, Valuation Analysis, Para 2, Pg. 10
40
The final recommendations to the founders of Nantucket Nectars (NN) on each of the
following issues:

Mode of exit: As can be seen from Table 1, the alternative of trade sale seems
most preferable in order to satisfy the stakeholders. The concerns of the
employees regarding further growth and culture fit can be alleviated if care is
taken to find an acquirer to fulfill these needs of the employees. Thus, trade sale
is the recommended mode of exit.

Acquirer: Among the potential bidders provided in the case2 the analysis as
presented in Table 2 presents Seagram and Ocean Spray as two strong contenders.
Ocean Spray additionally provides the advantage of culture fit thereby alleviating
the issue encountered above and it may also bid aggressively in order to prevent
NN from exploiting its loss of Pepsi distribution. Thus, Ocean Spray should be
considered as the potential acquirer.

Valuation: An EBITDA multiple based valuation is recommended and the
founders should expect the acquirers to demand a discount rate upwards of 18%
(to compensate them for the market risks associated with a relatively new firm
like NN). In addition, as NN will benefit from the favorable cost structure of
Ocean Spray an optimistic estimate of 10% reduction in COGS may be made.
Thus, final negotiation of valuations should be expected in the $70 million to $105
million bracket.

Structuring: As neither Michael Egan nor the founders have stated strong
preferences for any particular mode of trade sale, the structuring related issues can
be resolved only through negotiations.
Annexure
Exhibit 1 (in 000s)
Cash Flow Forecasts
2
Harvard Business School Case Study, ‘Nantucket Nectars’, Exhibit 9
41
Base Case
Total Revenues
EBITA
Income Taxes (benefit) on Unlevered Income
Unlevered Net Income (EBIAT)
Depreciation
Working Capital Requirements
Capex
Free Operating Cash Flow
Tax Rate
Tax loss Shield
EBIT
Interest
Taxable income
Loss available for carry forward
Loss set off
Tax
Tax Shield
Notes payable interest
Subordinated Debt
Interest Income
Notes payable shield
Subordinated Debt Shield
Interest Income Shield
Total Tax Shield
1995
1996
1997
1998
1999
2000
2001
2002
15335
-45
-18
-27
29493
722
286
436
50026
2025
802
1223
69717
4279
1695
2584
93700
6964
2758
4206
122981
10633
4211
6422
148499
14698
5820
8878
174635
19192
7600
11592
137
-185
-295
-370
247
-3232
-315
-2864
209
-1484
-350
-402
331
-1137
-488
1290
495
-1365
-656
2680
710
-2658
-861
3613
763
-2283
-1039
6319
947
-2401
-1222
8916
39.60%
39.60%
39.60%
39.60%
39.60%
39.60%
39.60%
39.60%
-45.00
139.00
-184.00
2252.00
0.00
0.00
722.00
301.00
421.00
2025.00
405.00
1620.00
4279.00
432.00
3847.00
6964.00
340.00
6624.00
10633.00
182.00
10451.00
14698.00
-74.00
14772.00
19192.00
-469.00
19661.00
-2436.00
421.00
166.72
-2015.00
1620.00
641.52
-395.00
395.00
156.42
3452.00
0.00
0.00
10451.00
0.00
0.00
14772.00
0.00
0.00
19661.00
0.00
0.00
337.00
102.00
34.00
323.00
204.00
95.00
323.00
204.00
187.00
323.00
204.00
345.00
323.00
204.00
601.00
323.00
204.00
996.00
1504.80
897.60
704.11
1698.29
Base Case:
Discount
rate
12%
14%
16%
18%
Terminal Growth rate
4%
6%
8%
$71,698
$92,778
$134,938
$54,177
$65,754
$85,048
$42,708
$49,783
$60,396
$34,680
$39,320
$45,815
Tax Shield
$1,698
$1,698
$1,698
$1,698
Tax Loss
$697
$683
$669
$656
4%
$74,094
$56,558
$45,076
$37,034
Total
6%
$95,174
$68,135
$52,151
$41,674
8%
$137,334
$87,430
$62,763
$48,169
4%
$170,465
$131,293
$105,531
$87,403
Total
6%
$217,060
$156,882
$121,170
$97,658
8%
$310,250
$199,531
$144,628
$112,016
10% COGS reduction:
Discount
rate
12%
14%
16%
18%
Terminal Growth rate
4%
6%
8%
$168,767
$215,362
$308,552
$129,594
$155,184
$197,832
$103,833
$119,471
$142,930
$85,705
$95,960
$110,318
Tax Shield
$1,698
$1,698
$1,698
$1,698
42
Tax Loss
$0
$0
$0
$0
20% COGS reduction:
Discount
rate
12%
14%
16%
18%
Terminal Growth rate
4%
6%
8%
$188,956
$240,858
$344,663
$145,280
$173,784
$221,290
$116,546
$133,966
$160,096
$96,317
$107,741
$123,733
Tax Shield
$1,698
$1,698
$1,698
$1,698
Tax Loss
$0
$0
$0
$0
4%
$190,654
$146,978
$118,244
$98,015
Total
6%
$242,556
$175,482
$135,664
$109,439
8%
$346,361
$222,989
$161,794
$125,432
Exhibit 2 (in 000,000s)
EBITDA
Multiples
$95
10.825
10% COGS
Reduction
$136
14%
$83
$124
$175
16%
$76
$114
$160
18%
$70
$105
$148
Disc Rate
12%
Base Case
20% COGS
Reduction
$191
Comparable: Includes the firms Celestial Seasonings, Ben & Jerry’s, Boston Beer and
Weider Nutrition (2001 figures for Nantucket Nectars were used)
43