FORECASTING FUTURE CONSUMPTION OF CONIFEROUS WOOD IN INDIA:
A QUANTITATIVE APPROACH

by
Krzysztof Sas- Zmudzinski
MScF, Lakehead University, 1998

PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF BUSINESS ADMINISTRATION

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
April2012

© Kris Zmudzinski, 2012

llNIVERS11Y of NORTHERN
BRITISH COLUMBIA
p
LIBRARY
rlnc. George, a.c.

Abstract
Over the last few years, Canada has been very successful in increasing its trade in wood
products with China. India however, still remains an elusive market. There is a large amount of
peer reviewed literature on the specifics of the Indian wood market, and the potential for trade in
softwood products. Whereas the majority of studies describe in great detail the opportunities and
constraints in dealing with India, very little quantitative information is available about the trends
and patterns that determine the Indian wood market. This study uncovered and described one
such trend by identifying the relationships between the level of imports of softwood products and
such factors as India's Gross Domestic Product (GOP), domestic production, the price of lumber
on international markets, tariffs, and the price of Teak logs as a substitute for softwood products.
This study analyzed 13 years of quarterly data using the ordinary least square regression
technique. Diagnostics were conducted using Akaike and Schwartz criterions, the DurbinWatson test, and the Breusch-Pagan-Godfrey test for heteroscedasticity. Results suggest that the
indicated variables collectively explain 74% of variability in import levels. Two variables in
particular, real GDP and the price of Teak have a significant, positive impact on the level of
imports of softwood products with 0.45 and 0.49 as respective elasticities. Continuing growth of
India's GDP will ensure an ever increasing demand for imported wood products in the years to
come. To maximize this oppot1unity, North American exporters should not compete with New
Zealand's low quality pine, but should instead focus on competing with dark coloured tropical
hardwoods that are becoming prohibitively expensive as world wide supplies of Teak and other
tropical hardwoods continue to diminish.

----~--

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-~------

Table of Contents
ABSTRACT ......................... .. ........................................... ............... ... .... i
TABLE OF CONTENTS ............... ...... .. . ... ................................................... ii
LIST OF TABLES ...... ......... .................. ................... ........ .. . ........... . ......... iii
LIST OF FIGURES .. ................. ... .... ............. ................ ...... ..... .......... ... .. ... iv
Chapter l ............ ......... ... ............ ... ..... .. ... .......... . ... ...... ..................... ...... 1
INTRODUCTION ............ .. ... . .. . .. . ........... . .......... .. ... .................................. .1
1.1 Study Objectives .. . .. ................ . .................................................... 6
Chapter 2 ..... . ... ... ... ... .... .... . .. . .............. . .. .. .... . .. . .. . .. . .. .. ... ........................... 7
REVIEW OF LITERATURE .. ... . .. . ... ............ .......... ... ... .......... . .. . ....... ........... .7
Chapter 3 ....... ....... . ......... ........................................................................ 16
DATA BASE AND METHODOLOGY ........... ... .... .... .. ................... ..... ............ 16
3.1 Domestic Production and Imports .. . .... ....... .. .......... . .. . .................. ...... 17
3.2 Price of Softwood Lumber ........... ... . ........... ..................................... 18
3.3 Price of Teak .. . .. ................. ... ... .. ...... .. .... ......... ... .. . ... ... ... ... .. . ....... 18
3.4 Tariffs .... ........ ... ... ....... ... ...... ..................... .. ................. . ... ....... . 19
3.5 Real GDP ........ ...... .. ....................... ... ... ................... ............ .. .. .. 19
3.6 Population .. . .................................. ..... .... ... .... .. .. .... .. ... ... ....... .... . 19
3.7 Economic Model ................................... .... .. . ..... .. .. .. . .................... 20
3.8 Hypothesis ......... .. ................ ... ...... ... ... .. ......... .. .. .. . .. . .................. 22
Chapter 4 ................ .. .. . .... .......... . .... .. .. .. ...... ... ... ....................... .... .. .... .. ... 23
EMPIRICAL RESULTS .................. .... ... ... .. ................................................ 23
Chapter 5 ............... .... ................. ............................... ... ........ ... .. . ... .... ... .. 32
DISCUSSION AND CONCLUSIONS .................... ......................................... 32
5.1 Discussion ...... ..... ... . .............. .... .............................. ... ...... ......... 32
5.2 Conclusions ...... .. . ... ............... ......... ... ..... ................................... 34
Bibliography ................................................... ............................. . .. . .. . ..... 36
APPENDIX .... ..... ........ . ... ............................ ... ... ... ...... ......... .... . .. . ............ 39

11

List of Tables

TABLE 1: LIST OF INDEPENDENT VARIABLES ................. . ........................... 16
TABLE 2: DESCRIPTIVE STATISTICS .. .. ... ............. .... .. . .. . .. ..... .... ......... ........ .. 26
TABLE 3: FINAL REGRESSION RESULTS ........ . .................... . .................... .... 31

lll

List of Figures

FIGURE 1: EXPORT OF CANADIAN LUMBER TO US IN 2011 BY PROVINCE ...... I
FIGURE 2: EXPORT OF CANADIAN LUMBER TO US BY YEAR ........................ 3
FIGURE 3: EXPORT OF CANADIAN LUMBER IN 2011 BYDESTINATION .... ... .. 4
FIGURE 4: TIME PLOTS OF VARIABLES ........... . .. . .... .... ...... . .. . .. . .. . .. . .. . .... ... .. 23
FIGURE 5: SCATTERGRAMS OF INDEPENDENT VARIABLES .. . ... .. .. ..... ..... .... . 28
FIGURE 6: ACTUAL, FITTED, RESIDUAL GRAPH .... .. .. ... .... ... .. . ... ..... . .... .. ...... 30

lV

Chapter 1
Introduction
British Columbia (BC) is endowed with a rich forest resource. Forests cover about two-thirds
(60 million hectares) of the province' s total land mass. Major species oftrees harvested in the
province are coniferous, and include Western Red Cedar, Hemlock, Douglas-fir, White
Spruce, Ponderosa Pine, and Lodgepole Pine. Due to the fact that 95% ofBC ' s land base is
publicly owned, the management of forest resources rests largely with the provincial
government, which allocates the right to log crown land through the sale of stumpage fees
and the regulation of annual allowable cut. Almost all of the wood produced in BC is
softwood ie. produced from coniferous species, and is used to make lumber (sawn wood),
plywood, shakes, shingles, newsprint, and pulp and paper products. In this paper, I define
coniferous or softwood products as unprocessed coniferous logs and coniferous sawn wood
or lumber. Over half of the lumber produced in Canada comes from BC, as measured by
quantities exported (Figure I) .
Figure 1. Export of Canadian Lumber to the US in 2011 by Province

Export of Canadian Lumber to US in 2011 ( m3)

,-.··-·········-··-···---····-······---··

o Newfoundland & Labrador

11 Prince Edward Is.

o Nova Scotia
o New Brunswick
11 Quebec

oOntario
11 Manitoba

o Saskatchewan
II Alberta
tl British Columbia

Source: COFI, 20 II
1

Forest products are the province's most important export commodity, historically accounting for
more than halfofthe total value ofBC's international goods exports. This has changed in recent
years, as the value of energy and industrial goods sold to other countries has been rising, while
forest product exports have fallen .
The forest sector has faced many challenges in recent years. The downturn in the United States
(US) housing market is one reason for the current difficulties in the province 's forest sector.
Other challenges include lower prices for forest products, a long dispute over softwood lumber
exports to the US (which began in the 1980s and continued until 2006, when the Canadian and
US governments signed a framework agreement to end the dispute) and a mountain pine beetle
epidemic which devastated forests in the Province ' s interior.
The housing situation in the US is of particular importance to the well being of the forest
industry in BC. Housing starts in the US have prolonged and significant effect on Canadian
lumber production, exports, and prices (Jennings et al., 1991). The collapse of the housing
market occurred in 2008 and, to this day, it has not recovered .
As the construction of new housing units fell from the historic level of 1.5mln to 0.55 min in
2009 (US Census Bureau, 2012), the export of Canadian lumber to the US decreased
dramatically (Figure 2).

2

Figure 2. Export of Canadian Lumber to the US by Year

Export of Canadian Lumber to US
35,000,000
30,000,000
25 ,000,000 20,000,000 I""'
15,000 ,000
10,000,000
5,000,000
0

v

co
co

Ol

..Ol
Ol

...,.
Ol
Ol

.......
Ol
Ol

/\

\

/

.......--......

0
0
0

N

''

(")
0
0

N

<D
0
0

N

\

\.....,_

J- Quantity (m3) J

Ol
0
0

N

Source: COFI, 2011

This situation illustrates the larger phenomenon ofthe decoupling of Canadian natural
resources exports from the US economy.
Substantial reduction in the demand from the US, has forced the Canadian forest industry to
look elsewhere for export oppOitunities.
Over the last several years the demand from traditional overseas markets such as Taiwan,
South Korea, Japan and the EU has remained flat or has grown only slightly. However, new
markets have began to grow in importance, particularly China, and to a lesser extent, India.

Between 2006 and 2010, the export of logs from Canada to China increased 13-fold, and the
exp01t of lumber increased 11-fold. Trade figures show the value of softwood lumber
exported to China surpassed the U.S . for the first time in May of2010, underscoring the
importance of the Asia-Pacific markets for B.C.'s economy . According to Pat Bell, the
former BC Forests Minister and the current Minister of Jobs, Tourism and Innovation, China

3

has become the most important market for British Columbia's lumber. At the same time, the
export of logs to India has also increased, but has still remained marginal.
Figure 3 illustrates the size of the major export markets for Canadian coniferous products.
India remains insignificant in this comparison.

Figure 3. Export of Canadian Lumber in 2011 by destination

Export of Canadian lumber in 2011 ('000 $)

~---- ---- ----- ---- -----

1
China l..,..-----,----'1
Ll. .

I

1

~----- -

Japan

•

&'-- - ~~~~~~~
/

lndia _l!t

0

500000

-~~ -1000000

1500000

2000000

/

2500000

/'

3000000

Source: COFI, 2011

In spite of the minor role India plays as a market for Canadian coniferous products, several
factors point to future growth of this market, perhaps in the magnitude of what has happened
in China over the last four to six years. Factors such as the need for housing, the growing
wealth oflndia, the emergence of the middle class, and the shortage ofraw material for the
wood products industry, are creating real opportunities for countries such as Canada that
export wood products. Canada is presently very successful in exporting some forest products

4

to India. In particular, Canada is a leading provider to India of chemical wood pulp
dissolving grades (73mln USD in 2009-2010), and newsprint (50 min USD in 2009-2010).
This trade is estimated to reach 272 mln USD by 2015 . In contrast, as mentioned above,
sawn wood (lumber) exports are negligible, and amounted to little more than 2 min USD in
2009-2010. This represents only 2% ofindia' s total imports of sawn wood (Ace Global ,
2011). Canada's export ofunprocessed logs is also negligible.
The question then is this : why can the successful market penetration achieved by pulp and
paper products not be repeated with solid wood products? Why does India remain an "elusive
market" for BC's coniferous products?
Several studies of this issue have concluded that, to improve its trading opportunities, Canada
must focus on the following areas:
Finding specific opportunities for attractive positioning against tropical hardwoods;
Raising consumer/industry awareness;
Building partnerships with wood product manufacturers to promote Canadian wood,
and
Maintaining final price competitiveness vis a vis hardwoods
Before embarking on such a costly and time consuming undettaking, it is crucial to have a
good understanding of the trends in the future consumption of coniferous product and of
factors that influence this consumption.

5

1.1 Study Objectives

The purpose of this study is to predict future consumption of coniferous products in India.
The particular objectives are as follows :
To identify the most important factors or independent variables that determine
consumption of coniferous products and find out in what way they influence
consumption
To construct a statistically valid econometric model that would best describe the
relationships between identified independent variables and the consumption of
coniferous products.

6

Chapter 2
Review of Literature

In 1991, India initiated sweeping reforms ofthe economy. The new policies included
opening for international trade and investment, deregulation, initiation of privatization, and
tax reforms. The Government's initiative to abandon socialism and to allow for a free market
economy resulted over time, in high economic growth, reduction of poverty, and creation of
a 300 mln large middle class. The Gross Domestic Product (GDP) reached 9. 7% in 2008 and
is currently at 7.8% . India became one of the fastest growing major economy in the world,
second only to China.

The rapid economic growth of India in the past two decades has set the stage for fundamental
change among the country ' s consumers. It resulted in creating a massive middle class
centered in the cities. A study by the McKinsey Global Institute (Ablett, et al., 2007)
suggests that if India continues its recent growth, average household incomes will triple over
the next two decades. India will become the world ' s 5th-largest consumer economy by 2025,
and the country ' s middle class will grow from about 5 percent of the population to more than
40 percent. Along the way, spending patterns will shift significantly as discretionary
purchases capture a majority of consumer spending. India' s potential should make it a high
priority for most consumer goods businesses, but to succeed in this complex market they
must overcome major challenges.

7

The current population oflndia is 1.2 bln people and is growing by 1.4% annually adding 15 min
new residents per year. This fact, coupled with rising incomes, created a strong demand for
housing and as a result, the construction sector grew by 6.5% in 2010 (ITT0,2010). The World
Bank estimates that India' s housing shortage is between 20 and 70 min residential units (The
Economic Times, 201 0). Recently, the Government oflndia announced the start of a low-cost
housing scheme which will benefit 32 min slum dwellers in 250 cities (India Business Insight,
2011).
All these factors suggest that the size of the Indian market for wood products can be substantial,
and some early estimates put it at as high as l 50 min people (Cintrafor, 2007).

Until 1982 India was a net exporter of wood products. However, the dramatic reduction of the
forest cover from 23% to 8% prompted the government to undeiiake several forest conservation
measures. These included a drastic ban on fell ing in Indian forests, allowing imports of wood
and wood products, and a ban on exports of round as well as sawn timber. Conservation policies
still continue, and today India is a net impOiier of timber and panel products. In consequence, the
forest cover has increased to 24%.
The majority of domestic logs are produced from forest plantations of fast growing species.
These logs support the local plywood and fibre board industry. Approx.imately 22% of logs are
produced from natural forests . Since the harvest of natural forests has been very much restricted,
India is looking for replacement by using many imported species. Among the most accepted
substitutes has been New Zealand Radiata Pine which is a substitute for Himalayan conifers.
Tropical hardwoods imported from Malaysia, Indonesia, and West Africa supplement and

8

replace India's domestic hardwood supply. Log imports are supporting 20% ofthe demand
for logs (GAIN Report, 2008).

In 2010, India imported just below 600,000 m3 of softwood logs and lumber. India' s
approach to economic growth differs from China ' s in two ways. First, the Indian government
is less interventionist, does not control economic activity and does not intervene in capital
markets. Secondly, foreign direct investment is not embraced to the same degree as in China.
(Khanna, 2004). As a result, manufacturing industries that rely on infrastructure, and the
infusion of foreign capital do not fare well in India for the time being. Forest products
manufacturing is very fragmented, and very labour intensive. In spite ofthese constraints,
several wood product exporting countries have paid attention to this emerging market.
Australia , New Zealand, the US, and Canada have investigated trade opportunities, and
literature on this subject is abundant. Recent Australian study points out that India has a
culture which appreciates the use of wood, the country will not be able to increase domestic
wood production, so imports of wood will continue to grow (Salwood, 2007).
The New Zealand Trade and Enterprise Commission (NZTEC, 2008) states that the market
demand for that country's timber is rapidly increasing , and the long term demand for Radiate
Pine is excellent. The US Department of Agriculture in its GAINS Reports (USDA, 2008;
USDA, 2009) while recognizing opportunities, also pointed to potential challenges such as
fragmented marketing chain, small scale manufacturing units, and a general lack of
awareness of high quality of softwood species.

9

The biggest opportunities exist in exporting unprocessed logs or round wood. This category
includes raw logs and roughly squared logs with some bark still left on them.
The imp011 oflogs is preferred by India because a large number of sawmills and inexpensive
labour can process these logs into lumber at a low cost. Log imports are favored by low import
tariffs of 5%.
India imported 5mln m3 of logs in 2010 (ITTO, 2010). The majority of imports (4.5mln m3) are
of tropical hardwood species like Teak, Red Meranti, and Kapur from Malaysia, Myanmar, and
Ghana. New Zealand, Australia and the US also participate by exporting primarily softwood logs
ofDouglas-fir, Hemlock, Cedar, and Pine with New Zealand fulfilling 7.5% of total log imports
to India. While Australia' s participation in log imports increased recently by 230%, Canada's
share fell by 38% and represents only 0.07% of the total oflndia's imports. (Government of
India, 2011 ).

Australia and New Zealand provide the Indian market with Radiata Pine logs. These logs are
used primarily in construction for scaffolds and concrete forms ie. low end uses.(NZTEC, 2008)

The US and Canada have the opportunity to tap into the higher end market with the softwood
species of Douglas-fir, Western Red Cedar and Hemlock, which are all attractive because of their
versatile nature and wide range of applications including woodwork/joinery, interior paneling,
flooring and specialty products. The value of their imports from the US has increased five-fold to
2.7 mln USD from 2005 to 2009 (GAINS Report, 2009) indicating great future potential of this
presently small market.

10

In the case of Canada, and particularly BC, the trade in logs is severely restricted. Provincial
legislation states that, unless exempted, timber harvested must be used or manufactured in
BC (Forest Act, 1996).

Softwood species must compete with Teak and other "dark" tropical hardwoods for
acceptance by the Indian consumer (Ace Global, 2011).
Whereas round wood comprised of84% of all wood products imports to India, sawn wood
contributed only 3% to the total in 2010 (Ace Global, 2011).
Major exporters of sawn wood to India, besides West African, and South East Asian
countries, are New Zealand, the EU, and the US. Canada's share ofthe market was 0.7% in
2009 (Government ofindia, 2011), and in 2010, Canada exported to India lumber worth 3.2
mln CAD, consisting mostly of softwood species such as Douglas-fir, Larch, Hemlock and
SPF (Spruce, Pine, Fir). To put these numbers in perspective, in the same year Canada
exported lumber worth 651 mln to Japan, 722 min to China, and 3 bln to the US (COFI,
2011).

Canadian lumber exports can be roughly divided into two categories:
The first is lumber for high-end uses like doors, windows, architectural timbers, and other
interior decorative applications. This category includes tree species such as Douglas-fir,
Western Red Cedar, and Hemlock from BC's Coast. These softwoods can replace or
complement the use of Teak and other high priced, and difficult to obtain hardwoods like
Meranti, Merbau, and Kapur.

11

The second category is what is known as SPF, a mixture of Lodgepole Pine, White Spruce and
Balsam fl.r originating in BC's Interior. The best grades of these species can be used for higher
end purposes as appearance grades. To successfully penetrate the Indian market, these species
should be priced only slightly higher ( 10-15%) than Radiata Pine - a major competing species
from New Zealand (Ace Global, 2011).
At the beginning of2011, India conducted a pest-risk assessment of Canadian Spruce, Pine and
Fir sawn wood and determined it was suitable for entry into India. Higher grade lumber can now
be exported to India, and it is estimated that this market can dramatically increase within four to
five years (B. Zak, personal communication, May 29, 2011).

Canada's closest competitor, the US exported lumber worth of3.8 min USD in 2010. This export
of softwood lumber grew by 740% between 2010 and 2011 (USDA, 2011).

The consumption of imported softwood products is influenced by many factors. Some ofthem
are tariffs, local preferences, competition, price, and no awareness of Canadian products (Luna,
2006).
Tariffs: Since the early 1990's, India has moved gradually away fiom a strategy of
industrialization to a more open, market-oriented trade and investment regime. Nevertheless,
policies continue to protect some domestic producers from foreign competition (Muthoo, 2004).
India recognizes the imporiance of log imports to supply its sawmills, therefore the total import
duties for logs and squared logs (cants) is 5%. Import duties for sawn wood are much higher at
14, 7%, and for value added products such as plywood and wood-based panels are the highest at
24.4% (GAIN Report, 2009). Over the last 20 years tariffs were lowered several times with the

12

last adjustments being made in 2005 (ITTO, 2005). Presently, Canada and India are negotiating a
free trade agreement. One of the objectives of this agreement is to "contribute to trade and
investment facilitation through minimizing tariffs and non-tariff barriers, reducing any
administrative costs" (Canada -India Joint Study Group Report, 2010).

Preference for Teak: Indian customers show a strong preference for dark-colored wood like
Teak (Rattan, 1999). All other species are compared to Teak for appearance, strength and
resistance to insects. Teak however, is loosing its pre-eminent position because of declining
quality and escalating price. Supplies of highest quality Teak imported from Myanmar,
Malaysia, and Burma are dwindling. The price of Teak logs on international markets have
more than doubled in the last decade as indicated by the ITTO by-weekly Market
Information Reports for this period. Traditional sources of log imports located in ASEAN
countries might over time become unreliable due to heightened controls over illegal logging,
and increased competition for logs from China and Europe. In addition, the highest quality
logs are purchased by Japan, leaving India with logs of lower quality (Cintrafor, 2007).

Domestic production: As reported by the ITTO in their annual reports, and other publications
(Muthoo, 2004), domestic production of coniferous wood products since 1990 continues to
decline with production ofindustial roundwood declining by 0.7% and the production of
coniferous sawn wood declining by 8.7% over the last decade. This situation indicates
inadequate domestic supplies and restrictions on forest logging.

13

Real GDP: Economists use the gravity model of world trade (Krugman et al. , 2012) to explain
the relationship between gross domestic product and the level of trade between countries. The
value of trade between any two countries is proportional to the product of the two countries'
GDP, and diminishes as the distance between the two countries increases. Essentially, large
economies tend to spend large amounts on imports because they have large incomes. India's
economy is rapidly expanding, and has grown in the last decade by an average of7% annually.
An economic forecast prepared by Goldman Sachs (Wilson eta/., 2003) predicts that India has
the potential to attain the fastest growth among the BRIC countries over the next 30 years.
Growth is expected to be higher than 5% annually if development proceeds successfully.

A few publications provide quantitative analysis of the factors influencing the trade in forest
products between countries. A study of export demand for Ghana' s timber products (Nanang,

2009) found that the trade was influenced mostly by the income of impOiiing countries,
exchange rates, and world prices for timber products. A study done in India (Malik, et al., 2003)
predicts that the combined effect of economic growth and increasing population size on demand
for forest products will be significant in the future.

A study of Canadian lumber imports into the US revealed that US domestic producer prices,
and the level of housing starts in the US had significant impact on imp011 prices. Among trade
barriers examined, a softwood quota raised the import prices, but the high tariffs lowered it
(Pandit, 2009) . Another study (Sarker, 1996) concluded that lumber price, disposable income,
housing starts, construction wage rate, and exchange rate significantly impacted the level of

14

imp01ts of Canadian lumber to the US, and that these factors are more powerful determinants of
the lumber trade than the alleged stumpage subsidy.

15

Chapter 3
Data Base and Methodology

In this paper, I attempted to predict the future consumption of coniferous products in India. The
historic consumption data in India is incomplete, unreliable, and for some periods of time,
completely unavailable. Furthermore, the data does not separate the use of wood for fuel from
industrial use. For these reasons I used the import of coniferous products to India as a proxy for
consumption. This variable (IMP) became the dependent variable.
Six independent variables are listed in Table 1:
Table 1: List oflndependent Variables
Variable

Description

Source

PRO

Domestic Production in '000m 3

ITTO

POP

India's Population

Gol

PRL

Price of Lumber (random length) in USD per
m3

NAHB

PRT

Price of Myanmar Teak in USD per m3

ITTO

RealGDP

Real Gross Domestic Product

Gol

TAR

Tariffs imposed by Indian government on
imports

Go I

Go I - Govemment of India
ITTO - Intemational Tropical Timber Organization
NAHB - National Association of Home Builders

16

Data was collected from a variety of sources. Below is a more detailed account of data
ongm:

3.1 Domestic Production (PRO), and Imports (IMP)

Data for these variables was collected from materials published by the International Tropical
Timber Organization. The ITTO is an intergovernmental organization, promoting the
conservation and sustainable management, use, and trade of tropical forest resources. Its 59
members represent about 80% ofthe world' s tropical forests and 90% ofthe global tropical
timber trade. Every year the ITTO publishes its Annual Review. This review contains several
tables presenting information on trade, production, imports, exports, and the prices of major
forest products. Table 1-2 in the Annual Review contains data for logs (separately
coniferous and non-coniferous), and for sawn wood (separately coniferous and nonconiferous) (ITTO, 201 0).
The ITTO relies on information provided by member states. They admit, that many of the
statistics received trom members contained significant and obvious errors in one or more
data categories. India is listed as one of the countries that reported at least some categories of
data. To make data complete, the ITTO uses supplementary sources of information such as
the UN COMTRADE, and FAOSTAT.
Both domestic production and imports figures are expressed in thousands of cubic metres.

17

3.2 The Price of Softwood .Lumber (PRL)

Softwood lumber, or coniferous sawn wood for the purpose of this paper, is defined as sawn
wood in the size of2x4, random length, produced rrom Spruce, Pine or Balsam, kiln dried. This
commodity is traded on the Chicago Mercantile Exchange under ticker LB, and price is
expressed in USD per 1000 board feet. Data was obtained rrom the National Association of
Home Builders (NAHB, 2012). Prices were converted to USD per m3 .

3.3 The Price of Teak

Teak is a hardwood species highly valued in India. Teak is considered to be a substitute for
imported coniferous products. Limitations on domestic logging of Teak resulted in increased
imports from countries like Myanmar, Malaysia, Gabon, and Tanzania. Data from India is
incomplete to allow for capture of changes in price of Teak over the study period. For this
reason, as a proxy, Myanmar Teak log prices of sawlog quality grade 2 were used. The original
numbers were collected from the bi-weekly Market lnfonnation Service publication. The ITTO
Tropical Timber Market (TTM) Report, an output of the ITTO Market Information Service
(MJS), is published in English every two weeks with the aim of improving transparency in the
international tropical timber market. The TTM provides market trends and trade news from
around the world, as well as indicative prices for over 400 tropical timber and added-value
products. Original prices expressed in Euro per Hoppus ton and were converted to USD per m3 .

18

3.4 Tariffs (TAR)

Tariff figures for the period under study were obtained from the Government ofindia,
Central Board of Excise and Customs (Government oflndia, 2012).

3.5 Real GDP (ReaiGDP)

Figures were obtained from the Reserve Bank of India and its publication: The Handbook of
Statistics on Indian Economy : Quarterly Estimates of Gross Domestic Product at Factor Cost.
Numbers are expressed in rupee crore.

3.6 Population (POP)

Population figures were obtained from the Government oflndia' s Office of the Registrar
General & Census Commissioner. Numbers are based on census results conducted in 1991 ,
2001 , and 201 1.

In order to meet the objective of this study, the relationship between the lMP variable and a
set of independent variables was examined using statistical methods. In the first step, an
economic model was formulated .

19

3. 7 Economic Model

An economic model is a theoretical construct that explains economic processes by a set of
variables and quantitative relationships between them. The economic model is a framework
designed to illustrate complex processes, using statistical techniques.

The economic model in this study was formulated as follows :
The consumption of coniferous products in India as expressed by the level of import of these
products is a function of price of coniferous products imported, import tariffs, real GDP of
India, price of substitute (Teak), level of domestic production of coniferous products, and India' s
population.

IMP= f(PRO, POP, PRL, PRT, ReaiGDP, TAR)

Next, descriptive statistics were derived and analyzed.
Descriptive statistics provide summaries about the data sample and the measures. Together with
simple graphics analysis, they form the basis of quantitative analysis of data. Descriptive
Statistics are used to present quantitative descriptions in a manageable form . Univariate analysis
involves the examination across cases, one variable at a time. There are three major
characteristics of a single variable that were looked at:
•

Distribution

•

Central tendency

20

•

Dispersion

Univariate analysis was complemented by creation of scattergrams.
A scattergram can suggest various kinds of correlations between variables with a certain
confidence interval. One of the most powerful aspects of a scattergram, is its ability to show
the nonlinear relationships between variables. Furthermore, if the data is represented by a
mixture model of simple relationships, these relationships will be visually evident as
superimposed patterns.
After determining the type of relationship between variables, the economic model was
formulated in a testable form for acceptance or rejection .
A mathematical equation was written that describes the relationship between import and
independent variables, and a random error (e) to reflect simplification and imprecision of the
theoretical model.

Regression analysis was used to estimate the unknown parameters Po, P1 , P2, p3, ~

Ps,PGin

the relationship, using available data. The model was then tested for statistical significance as
to whether a change in independent variables is associated with a change in the quantity of
imports.
In the classic Keynesian consumption model, consumption function describes the
relationship between consumption and income. Economic theory suggests a negative
correlation between the demand for a commodity and its price. Therefore we can expect that

21

the level of import will be atTected by the price of lumber, and the level of tariffs. The more
expensive the softwood lumber is, and the higher the import tariffs are, the less products will
be imported . The domestic market will try to switch to substitutes like Teak, or will limit the
demand . The price of the substitute will be positively correlated with the import of softwood
products.
Higher income, as expressed in real GOP, and population growth imply greater demand for
forest products, therefore coefficients on the GDP variable, and population variable are expected
to be positive.
Finally, a decrease in domestic production (supply) at a time of increased demand will positively
favour imports, therefore the domestic production coefficient is expected to be negative.
The variables chosen for inclusion in the model can have a positive, negative or neutral effect on
the level of imports.

3.8 Hypothesis
I expect that all independent variables are significantly related to the level of import, therefore
my null hypothesis is:

the alternative hypothesis is

22

Chapter 4
Empirical Results
Quarterly data (Appendix) collected from various sources was analyzed using ordinary least
squares (OLS) regression technique.
Each variable was plotted against time to observe its time dependent behaviour (Figure 4).

Figure 4. Time Plots of Variables

----

DomestiC Production

Imports

j

1200

4100
4000
3900
3800
3700
3800
3500
3400
3300
3200
3100

I

1000

1- Domestic Production I

~ -~- ~~

600

400

I

I

200

N

N

N

N

0

§ 8 ~ 0

8l 8
00 8
15
l'3
0
0
0
0

~ ~

0

N

N

N

N

I

.,

8l "'
~

.,

~ ~ ~ "'~ 8 8"' "'~ ~ ~ 8""' "0
N

0

N

··-------·------·----·--·-·-··-····-····.

-------

Lumber Ffice x Tariffs

Price ofTeak
4500

2500

4000
3500

2000

3000
2500

Price x

2000

1000

1500

500
0

.,

"'~ "'"'~

0
0
0
N

0

~

N
0
0
N

., m
"' "' ...

0 .

#' ,# # #' ~ #' #' #' #' ~ -5''0

8"'
8N 8N 8N ~ ~ ~ ~
N
0

Real GOP

1,400,000,000

2000000
1800000 .
1600000
1400000
1200000 .
1000000
800000 .
600000
400000
200000

o.

.,

PoptJation

1,200,000,000
1,000,000,000

~

800,000,000

- ~

600,000,000

~~ ~

400,000,000

0>

"'~ "'~

0
0

0

N

0

0
N

N
0

0

N

"'
0

0

N

. "' "' ... "'
0
0
N

0
0
N

0

0

N

0

0
N

0

0

N

200,000,000
m
0
0
N

0

0

N

.,

.,

ffi § §" 8 8"' 8 8"' 8 ~ ~ 0
~

N

N

N

01

N

0

N

23

Domestic Production of coniferous wood products fluctuated significantly until 2004 when it
reached its peak. After 2004 it gradually decreased, and by 2006 it reached a steady level of
approximately 3.5mln m3 .
The price of Teak logs almost doubled over the study period, perhaps reflecting a dwindling
domestic supply, international bans on illegal logging, and increased competition from other
developing markets such as China.
India ' s GDP remained flat until 2005 and then substantially increased in the last five years of the
study. As the domestic production decreased, and the price of the substitute increased, the level
of imports of coniferous wood products increased for every year within the study period, and
currently provides 26% of demand. North American softwood lumber prices t1uctuate cyclically,
and do not show a long term pattern of systematic change. On the other hand, import tariffs are
gradually decreasing in India. While tariffs for logs have decreased more dramatically to very
low levels (5%), tarifis for sawn wood have remained much higher in order to protect the
domestic sawmill industry. However, these tariffs also show a tendency to become lower over
time. For the purpose of this study, I combined the tariffs for logs and for sawn wood into one
variable by calculating an average of both figures for each time period. In order to better capture
the effect of diminishing tariffs on the price of softwood lumber, I created an interactive term
which is the cross product of price oflumber and tariffs. As noted by Krugman et al. (2012), a
tariti can be viewed as a cost of transportation. Exporters will be unwilling to move the product
unless the price difference between the importing and exporting markets is at least equal to the
tariff The effect of the tariff in India is that it raises the prices of the imported goods in the
country imposing the burden by the full amount of the tariff. Production of the imported good

24

rises, while consumption of the good falls . As a result of the tariti, imports fall in the country
imposing the tariff India's import market is too small to affect the change of foreign export
prices. On the other hand, domestic production cannot compensate for lower imports simply
because there are not enough logs available. This situation results in the systematic lowering of
tariffs.

The population grew in India at an average rate of 1. 7% per year during the study period
(Government oflndia, 2011). Population growth together with increasing GDP indicates that
the size of the consumer market increased. Indeed, it is reported (Cintrafor, 2007) that this
market is currently150mln consumers strong.

Among the variables listed, the price ofNorth American softwood lumber is not a demand
shifter. These prices will not shift demand, but instead will cause movement along the
demand curve.
Variables that are demand shifters are : real GDP, the price of substitutes (Teak), and the
number of buyers on the market (population that is increasing, getting richer, and is not
ageing).
Table 2 shows basic statistics for independent variables.

25

Table 2. Descriptive Statistics
Variables
IMP
POP
PRL
PRO
PRT
REALGDP
TAR

Mean

Median

532.32
1,084,223 ,619
117.12
3,677.56
1,274 .63
830,364.38
16.28

485
I ,083,398,553
121
3,600 .5
1,138 .5
637,091.07
19.6

Maximum
Minimum
970
188
1,202,541 ,306 966,483,581
168
58
3,970
3,425
1,997
830
1,716,765
568,339.38
25.5
8.75

Std. Dev. ewness
230 .03
0.38
69,047,728
0.01
24.48
-0.15
164.64
0.51
361.43
0.70
317,265 .2
1.19
7.02
0.00

In the second step of analysis, I created scattergrams of the dependent variable IMP and each
independent variable separately to see what kind of relationship exists in the pair. Possible
outcome included linear relationship, non linear relationship or no relationship at all. Figure 5
shows the results.

The scattergrams indicate that the dependent variable (IMP) has a positive linear relationship
with POP and PRT. It seems logical that the more consumers enter the market, and the more
expensive a substitute product is, the more demand there will be for imported coniferous wood
products.
IMP has a positive logarithmic relationship with REALGPD, and a negative logarithmic
relationships with PRL *TAR, and PRO . As the cost of bringing the forest products to India
declines, and as domestic production diminishes, this creates positive conditions for increases in
imports.
From these scattergrams, I concluded that the coefficients for variables POP, PRT, and
REALGDP will be positive, and for variables PRL *TAR and PRO will be negative. I proposed
four models that would express the relationships between the dependent variable and the
independent variables, and analyzed them for best fit.
26

Obs.
52
52
52
52
52
52
52

Model 1- Double Log:
lnlMP = ~ + ~

+~

+~

~

+c.

Model2- Linear:
IMP = ~ + ~~ REALGDP + ~

+~

~

*T AR)+c.

Model 3- Semi log:
IMP = ~ + ~

+~

~

+~

lnlMP = ~ + ~ REALGDP + ~

~

+~

Model 4- Semi log:

27

Fibrure 5. Scattergrams of independent variables.

1,000

1,0CXl

90C

"

80C
70C

IMP

60C
50C
40C
30C
20C

~

,.
.._t

t

~

.

9CXl

am

#

7CXl

IMP

•

6CXl
5CXl

...

•o:-

4CXl

•

"

3CXl

I

2CXl

10C
40QOOO

.:-

---~--~--- --~--~--~
3,400 3,50( 3.6CXl 3,700 3,800 3,9CXl 4.000

1.6CXl.OOC

1,2CXl,OCXl

800,000

. ' .,

itA LGoP

..
...

1,0CXl
900

1,000

··.• .

700

.. ....
.• ..•• •

:00
4JO
300

....

a

.,.._._ *

800

e::xJ

700

IMP

600

..

500

J'•

.p.V.-4

./•'

400
300
200

::00
1CXl

."

900

I

8Xl

IMP

PRO

100+------r-----,------r-----4

0

1,CXlO

2,0CXl

3,0CXl

900,000,000

4,000

1,000
90(

..

80(

IMP

60(
50(
40(
30(
20(

1,300,000,000

POP

PRL 'TAR

70(

1,100,000,000

.

·.,·.-;,AlA
.

..

:

..

..

a

"'• ...

10( 4----r---r---r---r---r---r---4

800 1,CXlO 1,20C 1,400 1,6CXl 1,80C 2,CXlO 2,2CXl

PRT

28

Final regression results are presented in Table 3 with four ditTerent model specifications to
check the sensitivity of the estimates. This sensitivity was tested by the inclusion and
exclusion of certain variables. As discussed earlier, the population variable (POP) had a very
minimal effect on IMP, and therefore was removed from all models.
Model 1 was chosen as the one that best explained the economic theory in terms of the size
of variables, and their signs. This model has good explanatory power with R 2=0.74.
Furthermore, the joint test of all independent variables, the F-test is high and equals 37. It is
statistically valid at 1%. Other diagnostics like the Akaike, and Schwartz criterions indicate
that the double log model is preferable. The model has some evidence of minimal serial
correlation as indicated by the Durbin-Watson stat and the BG test. This is quite a normal
occurrence in the demand function. There is no heteroskedasticity in model 1 as indicated by
the BPG test.

Figure 6 illustrates the distribution of residuals. The residuals are plotted against the left
vertical axis, and both actual and fitted series are plotted against the vertical axis on the right.
The actual and titted values follow each other quite closely indicating good fit of the model.
The graph illustrates also that model I fits better in the latter part of the sample than in the
earlier years since the residuals become smaller in absolute value.

29

Figure 6. Actual, Fitted, Residual Graph.
,------------------------------------------------------------r7 .2
6 .8
6.4
6 .0
.4

5 .6

.2
~~--~------- ~----~~----------~----~~~--~~~~~~

5 .2

-.2
-.4
- .6
-

..~~..~--~..~~..~--~..~~..~--~..~~..~- ~~

~

98

99

00

01

02

03

04

05

06

07

08

09

10

I - - Residual - - Actual - - Fitted I

30

1.80*

-0.94

0.47
-0.83
2.14**

-1. 52

-0.19

3.83

5.67

***, * *, and * refer to 1%, 5%,10% level of significance

18.88 ***

27.25 ***

Serial Correlation BG Test (Chi 2)
Heteroskedasticity BPG Test
(Chi 2 )

0.86
81.90** *
11.81
12.00
0.85
0.16

-----

-0.05

-----

0.20

-----

-0.13

-----

Coefficient
587.16
0.00

-1.56***

3.21***

-1.47

3.85***

1. 75*

t-Stat

Model 2- Linear

0.74
37.72***
0.02
0.21
0.58
0.04

-----

0.49

-----

-----

-----

0.55

t-Stat

Coefficient
4.62

Model 1-Double Log

------··- ------

Adj R
F-statistic
Akaike info criterion
Schwarz criterion
Durbin-Watson stat
S.E.E.

2

Intercept
REALGDP
LOG(REALGDP)
PRO
LOG( PRO)
PRT
LOG(PRT)
PRLxTAR
LOG(PRLxT AR)

lndep.Variables

Dependent Variable- IMP

Table 3. Final Regression Results

5.08

19.05***

0.87
85.67 ***
11.77
11.96
0.84
0.16

-----

-0.03

-----

-423 .02
0.18

-----

351.58

-----

Coefficient
-932.58

-1.51

2.88***

-1.35

4.13***

-0.31

t-Stat

Model3 Semi-log

4.81

26.04***

0.74
37.70***
0.02
0.21
0.62
0.04

-----

0.00

-----

0.00

-----

0.00

-----

Coefficient
6.58
0.00

31

-2.91 ***

2.21 ••

-J.f)2

1.69*

7.13***

t-Stat

Model 4 Semi log

Chapter 5
Discussion and Conclusions
5.1 Discussion

Model 1 indicates, that f1uctuations in domestic production, price ofTeak, price of lumber,
tariffs, and real GDP ofindia explain 74% of the import demand for coniferous logs and sawn
wood. The remaining 26% of variability in trade are influenced by factors not identified in this
study.

None of coefficients oftested independent variables are equal to zero, therefore I reject the null
hypothesis. Fm1hermore, as expected in the economic model, coefficients for PRO and
PRL *TAR have negative signs indicating that the lower the domestic production of coniferous
wood products is, and the lower international prices for softwood lumber paired with lower
tariffs are, the higher the import level of coniferous wood products is.

Model I indicates that statistically significant variables which impact the level of imports are:
real GDP, and the price of the substitute (Teak). A 1% increase in the real GDP increases the
imports of coniferous wood products by 0.47%. This finding is consistent with the gravity
model of trade proposed by Tinbergen in 1962 (Nello, 2009), and the findings of Sarker (1996),
and Nanang (20 l 0). As the economy oflndia grows, its size slowly approaches the size of its
softwood producing trading partners, and this causes the increase in the volume of trade.

32

On the basis of the gravity model one can assume that countries located closer to India such
as Australia and New Zealand will enjoy higher trade levels than countries located farther
away such as the US and Canada.

The price of Teak on international markets, which more than doubled over the last decade,
affects the amount ofTeak logs and sawn wood consumed in India. As the price increases,
less Teak is consumed, and the demand for imported coniferous logs and sawn wood
increases. My statistical analysis shows that a 1% increase in Teak prices increases level of
imports by 0.49%.

Other variables, although not statistically significant, also have some impact on imports.
The price of North American softwood lumber fluctuates cyclically and imports to India are
poorly correlated with this variable. However, when this variable is coupled with Indian
import tariffs, the model indicates that together they have some influence in that as they
decrease, the level of imports increases. This suggests price sensiti vity of the market, and a
beneficial effect of lower tariffs.

Domestic production of coniferous wood products has been slowly decreasing over the last
decade, primarily as a result of government imposed restrictions on logging of natural forests
in the Himalayan subalpine zone. My statistical analysis indicates that the continuous decline
in domestic production helps to increase the level of imports.

33

5.2 Conclusions

The objective of this study was achieved. The statistical model constructed on the basis of 13
years of data, provides a business intelligence tool for predicting future demand as expressed by
impot1s for coniferous wood products. The constructed model indicates that the change in two
variables, real GDP, and the price of Teak has a direct and positive impact on the level of
imports. While these two variables were statistically valid shifters of the demand curve, others
like domestic production, tariffs and the price of lumber were less significant, and negative.

Over the next decades, as the economy oflndia grows, the demand for forest products will be
increasingly met through impot1s. This trend creates very good trade opportunities for Canada.
India, however is not a second China. It is located father away from Canada, the wood
processing and construction industries are highly fragmented, and consumers have different
preferences.

Trade statistics quoted earlier in this paper support the assumption that countries located closer to
India will enjoy higher trade levels than countries located farther away. This indicates that
Canada and the US are in competitive disadvantage as compared to New Zealand and Australia.
Whereas the competitive advantage of Canada and the US is not in proximity to the Indian
market, the key to successful trade might lay elsewhere.

34

Extensive literature on the topic, as well as the results of this paper, indicate that Canada and
the US should concentrate on trade in high quality softwood species such as Douglas-fir,
Cedar and Hemlock. This strategy will position them to successfully compete with tropical
hardwoods, particularly Teak, for use in joinery, furniture, and decorative applications.
Severe restrictions on domestic harvest of Teak and other tropical hardwoods, as well as
diminishing availability of high quality imported logs for the Indian market have resulted so
far and will continue to result in ever higher prices. This paper found that higher prices of
Teak result in higher levels of coniferous wood imports. The Indian consumer will look for
viable alternatives. Provided that they are aware of the advantages of North American
softwood products, these high quality softwoods are a natural choice.

To secure future success in the Indian wood products market, Canada should consider
allowing a larger amount of logs to be exported as the current ban on expo tis of unprocessed
wood "denies Canada a major opportunity in India's current wood imports
scenario"(AceGlobal, 2011 ). Exports of softwood lumber can be greatly increased if Canada
obtains preferential tariffs, and this topic should be on the agenda of current Free Trade
negotiations.

Finally, government agencies and industry groups in Canada should sta11 developing
patinerships with wood products manufacturers to promote Canadian wood in high potential
applications.

35

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38