AN ECONOMIC ANALYSIS OF THE THAILAND TUNA FISH … Kuldilok_s_Thesis.pdf · AN ECONOMIC ANALYSIS OF...
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vi AN ECONOMIC ANALYSIS OF THE THAILAND TUNA FISH INDUSTRY BY KULAPA SUPONGPAN KULDILOK A thesis submitted to the Newcastle University for the degree of DOCTOR OF PHILOSOPHY School of Agriculture, Food and Rural Development Faculty of Science, Agriculture and Engineering Newcastle University
Transcript of AN ECONOMIC ANALYSIS OF THE THAILAND TUNA FISH … Kuldilok_s_Thesis.pdf · AN ECONOMIC ANALYSIS OF...
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AN ECONOMIC ANALYSIS OF
THE THAILAND TUNA FISH INDUSTRY
BY
KULAPA SUPONGPAN KULDILOK
A thesis submitted to the Newcastle University
for the degree of
DOCTOR OF PHILOSOPHY
School of Agriculture, Food and Rural Development
Faculty of Science, Agriculture and Engineering
Newcastle University
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ABSTRACT
AN ECONOMIC ANALYSIS OF THE THAILAND TUNA FISH INDUSTRY
Although Thailand is currently the leading tuna fish exporter in the world, this thesis asks
whether the Thai tuna industry really sustainable. Almost all the raw tuna is imported prior to
processing for re-export, and tuna stocks are known to be over-fished. This thesis examines
the economic, environmental, and social sustainability aspects of the Thai tuna industry. The
thesis has three major parts - forecasting future tuna demand, internal and international
competitiveness analysis, and sustainable livelihoods of processing workers analysis.
First, tuna demand forecasts were estimated by a simple ARIMA model between 2007 and
2011. The results are interpreted in the light of factors involved in tuna demand: population;
income; tuna price. The simple projection of the past history of Thai exports indicates that
there are two sensible forecast trends (medium and low levels), as informed by consideration
of the major drivers of world demand. The low forecast level is considered more realistic
given the over-fishing of global tuna stocks. Hence, the Thai industry faces a likely future of
declining exports, implying a declining Thai processing sector.
Second, the potential of Thai tuna processors depends on key internal and external
relationships. For internal relationships, the tuna processing and fishing sectors have been
investigated here. The Structure Conduct Performance (SCP) paradigm has been used to
identify internal relationships in the tuna processing sector. The Thai processing structure is
oligopolistic. The firms’ conduct indicates that tuna processing operates through price
leadership by a dominant firm. Branding strategy is only used for the canned product. Vertical
and horizontal integration have been adopted by a few larger firms to explicit economies of
scale and scope and reduce transaction costs. According to a price-cost-margin analysis, two
canning processors are performing poorly, although no fresh and freezing firms are (yet) in
this high risk category. One effective fishing sector strategy would be to replace tuna imports
with an increased potential for negotiation for rules of origin requirements. However, there is
very limited potential for investing in Thai tuna vessels because both purse seine and long-
line vessels are experiencing losses.
Revealed comparative advantage analysis shows that Thailand has had a comparative
advantage and has constantly maintained the comparative advantage in the world and with
respect to two main importers, the US and Canada, but its comparative advantage has not
been sustained in Australia, the EU, the Middle East, and Japan. It is also clear that this
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advantage depends critically on low labour costs in Thailand, which is not consistent with
continued economic growth in Thailand. Trading tariffs, especially in the EU, and rules of
origin are contributing to a decline in competitiveness. Porter’s double diamond model
identifies that a low labour wage rate country has been a strong source of competitiveness
until now but this will decline as wages improve with economic growth and competition in
the labour market increase. International demand seems likely to continue to grow in the face
of limited supplies, leading to increasing prices for tuna, but the costs (especially fuel and
labour) of supply are also likely to rise in the future. Related industries are adequate for tuna
processing, but most have alternative activities which could become more profitable and
sustainable than tuna trade in the medium term. The Thai industry may be sufficiently strong
to cope with these changing circumstances, but it is likely to become more concentrated and
not grow in either absolute or relative importance as in the past. The greatest opportunity for
the processing sector would seem to be the development of tuna aquaculture in Thailand,
which has the necessary marine resources, though this development will need to avoid
environmental damage, and also to avoid simply shifting the over-fishing problem upstream
to fish feed stocks.
Third, sustainability of the Thai tuna industry also involves the livelihoods of workers. We
found that larger firms can support better welfare, income, environment, and convenient
facilities, though they currently employ relatively few workers. In worker living areas,
workers were vulnerable to economic crisis, seasonality of tuna catches, natural disasters, and
the insecurity of a personal living place. In the longer term, economic growth within Thailand
will generate competitive earning opportunities for many of the present labour force, while
the processing sector, if it is to survive, will need to match these earning opportunities and
working conditions. If it cannot, it can be expected to decline as labour finds better things to
do, as happened to the tuna processing industry in the US.
The findings of this thesis are rather pessimistic. The Thai tuna industry will not probably be
environmentally, economically, and socially sustainable without substantial adjustment. The
industry faces many severe problems in the near future as reflected in lower demand
forecasts, lack of raw material, unprofitable fishing operations, emerging shortages of
motivated, well-paid, skilled labour, and binding rules of origin and tariff restrictions. As this
analysis clearly demonstrates, maintaining both tuna fishing and the processing industry in
Thailand will be difficult. Nevertheless, there are opportunities as well as threats, and with
innovative and sound management there is still a future for the industry, albeit not with the
growth rates which have characterised its past.
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ACKNOWLEDGEMENTS
It is a pleasure to thank many people who made this thesis possible. It is difficult to
overstate my gratitude to my Ph.D. supervisors, Dr. John Lingard and Dr. Philip
Dawson. Throughout my thesis-writing period, they provided encouragement, sound
advice, good teaching, good company, and lots of good ideas. I would have been lost
without them. I would like to express my gratitude to Professor David Harvey and Dr.
Noel Russel for their time examining this thesis and their constructive comments.
I am indebted to interviewees who were managers and workers from tuna companies
and fishermen who sacrificed time for providing long answers. I am especially
grateful to Ms Praulai Nootmorn, Director of Andaman Sea Fisheries Research and
Development Center, and Miss Phairaoh Kanoklukana from Songkla, Director of
Marine Fisheries Research and Development for Lower Gulf of Thailand who offer
all facilities in Phuket and Songkla during my data collection time. My thanks are
extended to other officers in the Department of Fisheries and the Southeast Asian
Fisheries Development Center in Thailand who gave many suggestions and
information.
I am also grateful to my husband for his love and support and his accompany during
these four years in Newcastle. Next, and most importantly, I wish to thank my
parents. They raised me, supported me, taught me, and loved me. To them I dedicate
this thesis.
I would like to express my gratitude to everybody else who extended the hand of
backing and supporting me throughout my PhD study. Forgive me for not mentioning
you by name.
Last but not the least, I am grateful to the Royal Thai Government who sponsors my
four year study in the UK.
Newcastle, October 2009.
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I declare that this thesis for the degree of
Doctor of Philosophy at Newcastle University
has not been submitted by me for a degree at
any other university
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This thesis is respectfully dedicated to my beloved parents
Table of Contents
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Chapter 1 ....................................................................................................................... 1
Introduction ................................................................................................................... 1 1.1 The Sustainability of the Tuna industry ............................................................... 1 1.2 The World Tuna Market ....................................................................................... 2
1.2.1 World Tuna Consumption ................................................................................................ 2 1.2.2 World Processed Tuna Market ......................................................................................... 5 1.2.3 World Tuna Fisheries ....................................................................................................... 9 1.2.4 Tuna Stock Situation ...................................................................................................... 12
1.3 The Thai Tuna Industry History ......................................................................... 15 1.4 The Role of the Tuna Industry in the Thai Economy ........................................ 21 1.5 The Problem Statement ....................................................................................... 31 1.6 Objectives of the Study ........................................................................................ 33 1.7 Format of the Thesis ............................................................................................. 33
Chapter 2 ..................................................................................................................... 35
Forecasting Exports of Tuna from Thailand ............................................................ 35 2.1 Introduction .......................................................................................................... 35 2.2 Description of Data ............................................................................................... 38 2.3 Selecting the Best Forecasting Model and Forecasting ..................................... 40
2.3.1 Results of Forecasting using Exponential Smoothing Methods ..................................... 41 2.3.2 Result of Forecasting using ARIMA Models ................................................................. 48
2.4 Factors Influencing the Export Demand for Tuna ............................................ 60 2.4.1 Population, Income and Tuna Consumption ................................................................... 60 2.4.2 Tuna Product Price ......................................................................................................... 62 2.4.3 Trend for Tuna Catches .................................................................................................. 63
2.5 Conclusions ........................................................................................................... 65 Chapter 3 ..................................................................................................................... 67
The Competitiveness of the Thai Processing and Fishing Sectors ........................... 67 3.1 Introduction .......................................................................................................... 67
3.2 Literature Review ..................................................................................................... 74
3.3 A Structure, Conduct and Performance Analysis ............................................. 76 3.3.1 Data Sources ................................................................................................................... 76 3.3.2 The Structure of the Thai Tuna Industry ........................................................................ 77 3.3.3 The Relationship between Structure and Conduct .......................................................... 84 3.3.4 Performance Measurement ............................................................................................. 90
3.4 Analyses of Costs and Returns of Tuna Fishing Vessels and Break-Even ...... 96 3.4.1 Data sources ................................................................................................................... 96 3.4.2 Costs and Returns of Purse Seiners ................................................................................ 97 3.4.3 Costs and Returns of Long-Liners ................................................................................ 100 3.4.4 Break-Even and Sensitivity Analyses of Purse Seiners ................................................ 103 3.4.5 Break-Even and Sensitivity Analyses of Long-liners ................................................... 106
3.5 The Analysis of Market Share and the RCA Indices ...................................... 110 3.5.1 Data Sources ................................................................................................................. 110 3.5.2 An Analysis of World Exports ..................................................................................... 110
Table of Contents
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3.5.3 The Analysis of Main Importers ................................................................................... 113 3.6 Extending Porter’s Diamond Model and Multinational Activities through internationalization for the Thai Tuna Industry .......................................................... 123
3.6.1 Factor conditions .......................................................................................................... 123 3.6.2 Expansion Demand ....................................................................................................... 128 3.6.3 Firm Strategy, Structure and Rivalry ............................................................................ 130 3.6.4 Related and Supporting Industries ................................................................................ 130 3.6.5 The Role of Government .............................................................................................. 132 3.6.6 External factors ............................................................................................................. 132
3.7 Conclusions and Discussions ............................................................................. 141 Chapter 4 ................................................................................................................... 145
Livelihoods of Workers in the Thai Tuna Industry ................................................. 145 4.1 Introduction ........................................................................................................ 145 4.2 Methodology and Research Design ................................................................... 147
4.2.1 Area Selection .............................................................................................................. 147 4.2.2 The Sustainable Livelihoods Framework ..................................................................... 150 4.2.3 Statistical Analysis ....................................................................................................... 151
4.3 Background of the Selected Thailand Areas .................................................... 152 4.4 Livelihoods Analysis in the Living Place .......................................................... 157
4.4.1 General Province Characteristics .................................................................................. 157 4.4.2 The Vulnerability Context ............................................................................................ 162 4.4.3 Livelihoods Descriptions .............................................................................................. 168
4.5 Livelihood Conditions in Factories ................................................................... 183 4.5.1 Ambient Conditions ...................................................................................................... 183 4.5.2 Opinion of Workers in Tuna Factories ......................................................................... 184 4.5.3 Income Measurement ................................................................................................... 185
4.6 Livelihood Strategies and Outcomes ................................................................. 186 4.7 Conclusions ......................................................................................................... 188
Chapter 5 ................................................................................................................... 190
Conclusions ............................................................................................................... 190 5.1 Introduction ........................................................................................................ 190 5.2 Main Conclusions and Factors Relating to the Thai Tuna Industry ............. 190
5.2.1 Tuna Processing and Fishing Sectors ........................................................................... 191 5.2.2 Livelihoods of Workers ................................................................................................ 194 5.2.3 Tuna Supply ................................................................................................................. 195 5.2.4 Demand Forecasting ..................................................................................................... 196
5.3 Necessary Conditions for Improved Sustainability of the Thai Tuna Industry ....... 198 5.3.1 Tuna Demand ............................................................................................................... 198 5.3.2 Tuna Supply ................................................................................................................. 199 5.3.3 Tuna Processing and Fishing Sectors ........................................................................... 200 5.3.4 Unskilled Labour .......................................................................................................... 204
5.4 Contributions ...................................................................................................... 205 5.5 Limitations of the Study ..................................................................................... 206
5.5.1 Validation of Financial Statement and Tuna Prices ...................................................... 206 5.5.2 Estimation of Tuna Fishing Operations ........................................................................ 206
5.6 Scope for Further Study .................................................................................... 207
Table of Contents
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5.7 Overall Conclusions ............................................................................................ 209 References ................................................................................................................. 212
APPENDICES .......................................................................................................... 221
List of Tables
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Table 1.1 Summary of the Tuna Fisheries and Market Species ............................................................. 12
Table 1.2 The Levels of Exploitation of Tuna Stocks ............................................................................ 15
Table 1.3. Number and Percentage of Employed Persons (1,000 persons) by Industry (2007) ............. 26
Table 1.4 Average Salary of New Employees from Private Employment, 2006 ................................... 27
Table 2.1 The Value of Total Tuna Exports (million baht at current price) compared with GDP and
Seafood Exports in Thailand, 1999-2006. ...................................................................................... 36
Table 2.2 Estimates of the Exponential Smoothing Methods ................................................................. 41
Table 2.3 Initial Smoothing State for the Linear Trend Model with Multiplicative Seasonality ........... 46
Table 2.4 Estimates of the Linear Trend Model with Multiplicative Seasonality ................................. 46
Table 2.5 Parameter Estimates for ARIMA Model ................................................................................ 51
Table 2.6 Estimates of the Autocorrelation Function and Box-Ljung Q*-Statistics for the ARIMA
(0,1,1)(0,1,1)12 Model ..................................................................................................................... 52
Table 2.7 Comparing Forecasts from Preferred Models ........................................................................ 56
Table 2.8 Comparison of Within-sample Forecasts Performance Measures .......................................... 58
Table 2.9 Tuna Exports Forecasts from the ARIMA model (tones), 2007-2011 ................................... 59
Table 2.10 Population and Population Growth, 1985-2006 ................................................................... 61
Table 2.11 GDP per capita and GDP growth rate, 1985-2006 ............................................................... 62
Table 2.12 Tuna Product Import and Tuna Product Growth Rate, 1985-2006 ....................................... 62
Table 3.1. The Number of Firms in the Canned Tuna Sector, 1975-2005 ............................................. 68
Table 3.2. Number of Firms in the Chilled and Frozen Tuna Sector, 1986-2004 .................................. 68
Table 3.3 Number of Foreign Tuna Vessels landing in Thailand, 1996-2006 ........................................ 69
List of Tables
x
Table 3.4 Thai Purse Seiners Recorded in IOTC, 2005-2007 ............................................................... 70
Table 3.5 Thai Purse Seiners Recorded in IOTC, 2007-2008 ................................................................ 71
Table 3.6 Thai Long-liners Recorded in IOTC, 2004-2008 ................................................................... 71
Table 3.7. Market Shares of the Tuna Cannery Sector, 2005 ................................................................. 78
Table 3.8 Indices of Concentration for the Canned Tuna Sector, 2005 .................................................. 80
Table 3.9. Market Share of Fresh and Freezing Sector, 2005 ................................................................ 80
Table 3.10 Indices of Concentration for the Fresh and Frozen Tuna Sector, 2005 ................................ 81
Table 3.11. EU Tariff Quota (tonnes) ................................................................................................... 84
Table 3.12 Market Shares of Dominant Firms in Canning and Fresh and Freezing Sectors, 2002-2006
........................................................................................................................................................ 85
Table 3.13. Price-Cost Margin and Accounting Profit Ratios for the Canned Tuna Sector, 2005 ......... 91
Table 3.14 Performance Ranking for the Canned Tuna Sector, 2005 .................................................... 94
Table 3.15. Price-Cost Margin (PCM) and Accounting Profit Ratios of the Fresh and Frozen Sector,
2005 ................................................................................................................................................ 95
Table 3.16 Performance Ranking of the Fresh and Freezing Sector ...................................................... 96
Table 3.17 Catch Income of Tuna Purse Seine Vessels in 2006 ............................................................ 98
Table 3.18 Costs and Returns of Operating a Purse Seine for Two Rates of Interest (ARI) for Capital
in 2006 ............................................................................................................................................ 99
Table 3.19 Catch Income of Tuna Long-line Vessels in 2006 ............................................................. 101
Table 3.20 Costs and Returns of Operating Long-line Vessels for Two Rates of Interest (ARI) for
Capital in 2006 ............................................................................................................................. 102
Table 3.21 Tuna Catches and Revenues Needed to Reach Break-Even: a Purse Seiner, 2006 ............ 104
List of Tables
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Table 3.22 Break-Even Revenues and Catches Sensitivity around Changes in Tuna Price and AVC at
ARI 10%, a Purse Seiner, 2006 .................................................................................................... 104
Table 3.23 Break-Even Revenues and Catches Sensitivity around Changes in Tuna Price and AVC at
ARI 15%, a Purse Seiner, 2006 .................................................................................................... 105
Table 3.24 Tuna Catches and Revenues Needed to Reach Break-Even: a Long-liner, 2006 ............... 106
Table 3.25 Break-Even Revenues and Catches Sensitivity around Changes in Tuna Price and AVC at
ARI 10%: a Long-liner, 2006 ....................................................................................................... 107
Table 3.26 Break-Even Revenues and Catches Sensitivity around Changes in Tuna Price and AVC at
ARI 15%: a Long-liner, 2006 ....................................................................................................... 107
Table 3.27. Market Share of Canned Tuna from Global Exporters, 1996-2006 ................................... 111
Table 3.28 Abbreviations for Countries ............................................................................................... 111
Table 3.29 Market Shares of Importers from Thailand, 1996-2005 ..................................................... 114
Table 3.30. Market Shares of Exporters to the US, 1996-2005 ............................................................ 114
Table 3.31. Market Shares of Exporters to the EU, 1996-2005 ............................................................ 115
Table 3.32. Market Shares of Exporters to the Middle East, 1996-2005 .............................................. 118
Table 3.33. Market Shares of Exporters to Japan, 1996-2005 .............................................................. 119
Table 3.34. Market Shares of Exporters to Australia, 1996-2005 ........................................................ 121
Table 3.35. Market Shares of Exporters to Canada, 1996-2005 ........................................................... 122
Table 3.36. Minimum Wages in Tuna Canneries ................................................................................. 124
Table 3.37. Tuna Catches of the Six Main Canned Tuna Exporters, 1996-2006 ................................. 127
Table 3.38 The Double Diamond Model of the Thai Tuna Industry .................................................... 139
Table 4.1 Company Lists and the Abbreviations for the Companies ................................................... 147
List of Tables
xii
Table 4.2 Background Data of Three Provinces ................................................................................... 157
Table 4.3 General Characteristics and Province Resources in Three Provinces .................................. 160
Table 4.4 Vulnerability Context in Three Provinces ............................................................................ 166
Table 4.5 Status, Age, and Sex of Workers ......................................................................................... 169
Table 4.6 Household Characteristics of Workers by Provinces ........................................................... 170
Table 4.7 Household Access to Capital by Province ............................................................................ 176
Table 4.8 Workers in Tuna Factories in the Summary Pentagons ........................................................ 182
Table 4.9 The Security of Labour in the Tuna Factories ...................................................................... 184
Table 4.10 Income per month comparing with GPP, GRP, and GDP in 2006 ..................................... 186
Table 5.1 Summary of Import Tariff from Main Tuna Importers ........................................................ 203
List of Figures
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Figure 1.1 The Global Food Consumption and Population Growth Rates ............................................... 3
Figure 1.2 The World Fish Consumption, 1979-2003 .............................................................................. 4
Figure 1.3. Preserved Tuna Production Shares by Main Countries, 1980-2006 ....................................... 6
Figure 1.4 Preserved Tuna Exporters, 1980-2006 .................................................................................... 7
Figure 1.5 Preserved Tuna Importors, 1980-2006 .................................................................................... 9
Figure 1.6 World Tuna Catches, 1980-2006 .......................................................................................... 10
Figure 1.7 Tuna Catch by Species, 1980–2006 ...................................................................................... 11
Figure 1.8 Main Processed Tuna Producers 1979–2006 ........................................................................ 16
Figure 1.9 Tuna Demand Forecasts, Market Share, and Thai Tuna Catch, 1970-2011 .......................... 20
Figure 1.10 Fresh and Frozen Tuna Imports 1980-2006 ........................................................................ 21
Figure 1.11. Real GDP Growth Rate 1980-2006 .................................................................................... 22
Figure 1.12 GDP at Current Price including Agriculture, Non Agriculture, and Manufacturing 1980-
2006 ................................................................................................................................................ 23
Figure 1.13. Total Export Values,1996-2006 ......................................................................................... 24
Figure 1.14. Total Seafood Export Values and Tuna Export Values 1996-2006 ................................... 25
Figure 1.15 Inflation Rate in Thailand between 1979 -2006. ................................................................. 29
Figure 1.16 Exchange Rate of Thai Currency and the US Dollar, 1979-2006 ....................................... 30
Figure 1.17 Average Minimum Wage Rate in Thailand, 1979-2006 ..................................................... 31
Figure 2.1 Total Monthly Tuna Exports (tonnes), 1996-2006 ................................................................ 39
Figure 2.2 The ACF and PACF the Linear Trend Model with Addictive Seasonality ........................... 42
Figure 2.3 The ACF and PACF for the Exponential Trend Model with Additive Seasonality .............. 43
List of Figures
xiv
Figure 2.4 The ACF and PACF for the Linear Trend Model with Multiplicative Seasonality .............. 44
Figure 2.5 The ACF and PACF for the Exponential Trend Model with Multiplicative Seasonality ...... 45
Figure 2.6 Tuna Forecast using the Linear Trend Model with Multiplicative Seasonality ................... 48
Figure 2.7 Estimates of the ACF and PACF .......................................................................................... 49
Figure 2.8 Tuna Exports after First-differencing and Seasonal First-differencing ................................. 50
Figure 2.9 ACF and PACF for the ARIMA (0,1,1)(0,1,1)12 Model ..................................................... 53
Figure 2.10 Actual Tuna Exports (1996 –2006) and Forecasts (2007-2011) by ARIMA model ........... 54
Figure 2.11 Forecasts from Preferred Models (2007-2011) ................................................................... 55
Figure 2.12 Within-sample Forecasts and Actual exports from Preferred Models (2003-2006) ............ 57
Figure 2.13 Canned Tuna Price compared to Canned Tuna Import, 1989-2006 .................................... 63
Figure 2.14 World Tuna Captures and Growth Rate, 1976-2006 ........................................................... 64
Figure 3.1 Concentration Curve of Canning Sector, 2005 ..................................................................... 79
Figure 3.2 Concentration Curve of Fresh and Freezing Sector, 2005 .................................................... 81
Figure 3.3 Concentration Curves of Canning and Fresh and Freezing Sectors, 2005 ............................ 82
Figure 3.4. The Strategies of Thai Union Group .................................................................................... 87
Figure 3.5 The Strategies of Sea Value Group ....................................................................................... 89
Figure 3.9. RCA Indices for Exporters, 1996-2006 .............................................................................. 112
Figure 3.10. RCA Indices of Exporters to the US, 1996-2005 ............................................................. 115
Figure 3.11. RCA Indices of Exporters to the EU, 1996-2005 ............................................................. 116
Figure 3.12. RCA Indices of Exporters to the Middle East, 1996 - 2005 ............................................. 119
Figure 3.13. RCA Indices of Exporters to the Japan, 1996 - 2005 ....................................................... 120
List of Figures
xv
Figure 3.14. RCA Indices of Exporters to Australia, 1996 - 2005 ....................................................... 121
Figure 3.15. RCA Indices of Exporters to Canada, 1996-2005 ............................................................ 123
Figure 3.16. Skipjack and Yellowfin Prices, Thailand, 1996-2006 ...................................................... 128
Figure 3.17 World Demand, 1989-2006 ............................................................................................... 129
Figure 3.18 Effect of Tuna Prices between Fishing and Processing Sectors ........................................ 136
Figure 3.19 Oil Price and Raw Tuna Price, 1997-2009 ........................................................................ 136
Figure 3.20 Relationship among Exchange Rate, Tuna Price, and Thai Tuna Export 1989-2006 ....... 138
Figure 4.1 Samut Sakhon, Songkhla, and Phuket Provinces in Thailand ............................................. 148
Figure 4.2 Distribution of Samples in Nine Thai Tuna Firms Surveyed in 2006 ................................. 149
Figure 4.3 Sustainable Livelihood Frameworks ................................................................................... 151
Figure 4.4 Map of Samut Sakhon Province .......................................................................................... 153
Figure 4.5 Map of Songkhla Province .................................................................................................. 155
Figure 4.6 Map of Phuket Province ...................................................................................................... 156
Figure 4.7 Asset Capital Indicators ...................................................................................................... 179
Figure 4.8 Asset Pentagons by Province (weight data). ....................................................................... 183
Figure 4.9 Improvement in Livelihoods for Workers ........................................................................... 187
Figure 5.1 Main Factors in the Thai Tuna Industry .............................................................................. 191
Figure 5.2 Relationships of Tuna Demand Forecasts, Market Share, Thai Tuna Catch, and Thai Tuna
Export, 1970-2011 ........................................................................................................................ 198
Introduction
CHAPTER1
1
Chapter 1
Introduction
This chapter provides a background to the sustainability of the tuna industry, the
world tuna market, the Thai tuna industry and the role of the tuna industry in Thai
economy. Against this background, the central research question is defined in the
problem statement and objectives of the study, and the format of the thesis is shown at
the end of the chapter.
1.1 The Sustainability of the Tuna industry
The question of “ how is the tuna industry sustainable?” is the central issue in this
research. The general answer requires consideration of the three central dimensions of
sustainability: environmental; economic; social. In terms of environmental
sustainability, sustainable harvesting of the world’s tuna stocks is critical.
Stakeholders (tuna fishermen, processors, consumer, government, and other fishery
management organizations) have responsibility for sustainability. For instance, the
sustainable harvesting requires effective management to avoid over-fishing and stock
collapses, such as controlling catches, allocating fleet capacity limits (Joseph, 2003b)
and avoiding illegal, unrecorded and unregulated fishing. Edeka, the largest
supermarket chain in the German market, has already announced that it will only
stock fish from sustainable sources by 2011 (Infofish, 2009). The World Wide Fund,
which has also been campaigning for a substantial cut in catch, is now seeking an
international trading ban on bluefin tuna (Economist, 2008).
Introduction
CHAPTER1
2
To be economically sustainable, the tuna industry has to be able to match available
supplies with demand, and to do so efficiently and effectively while providing
competitive incomes and returns to those earning their livings from industry. In
addition, good management and market leadership are required for success. Success
also depends on how effective companies are in taking ideas, especially from their
own staff, and turning them into improved practices, products and services. The social
sustainability of the tuna industry has also been called into question recently,
especially in the West. For example, workers have been shown living and working in
dreadful conditions, with no prospects for improvement, on British television (BBC
Three, 2009). Not only are these workers poorly paid, with insecure of jobs, and low
welfare but also they do not have the potential for long-term maintenance of
wellbeing. To sum up, the tuna industry needs three main elements in terms of
ecology, commercial and social aspects to be sustainable.
1.2 The World Tuna Market
The world tuna market history shows how world tuna consumption, processed tuna
production, tuna fisheries and tuna stocks have changed dramatically in the last 30
years.
1.2.1 World Tuna Consumption
Figure 1.1 shows that global total food consumption has been growing at a rate of
2.2 % per year since 1980, at a faster rate than the global population, at 1.5% per year
(WHO, 2009).
Introduction
CHAPTER1
3
Figure 1.1 The Global Food Consumption and Population Growth Rates
0
1
2
3
4
5
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
%
Food consumption Population
Source: WHO (2009).
Protein is needed by human body at between 10-15% of dietary intake, for growth and
repair. Animal and fish proteins are different. Although protein from animal sources
contains the full range of essential amino acids needed in an adult’s diet, red meats
have high levels of saturated fat, which may raise blood levels of ‘unhealthy’ LDL
cholesterol. Moreover, a high consumption of saturated fat can give an increased risk
of cardiovascular disease and other related disorders. On the other hand, oil-rich fish
such as salmon, mackerel, herring, tuna, trout and sardines help to reduce the risk of
developing cardiovascular disease (The MRC Human Nutrition Research, 2001). The
protein from fish accounts for between 13.8% and 16.5% of the animal protein intake
of the human population (WHO, 2009) and, as can been seen from Figure 1.2 fish
consumption has been increasing, especially, in the Eastern world, and to a lesser
Introduction
CHAPTER1
4
extent in the West, while African consumption has, so far, not increased very much.
Figure 1.2 The World Fish Consumption, 1979-2003
-
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Thounsand Tonnes
World Eastern part
Western part Africa part
Source : FAO(2009a).
Fish consumption has been growing fast thanks to increases in income, population,
and urbanisation (Delgado et al., 2003). Tuna is popular fish in this rapidly increasing
demand because it has been relatively cheap and easily processed. Tuna is classified
into two types: the tropical tunas like bigeye, skipjack, and yellowfin; and the
temperate tunas - albacore and bluefin. Albacore, small yellowfin and skipjack are
preserved in cans and pouchs. Bigeye, bluefin and big yellowfin are used for the
sashimi (fresh) market.
Introduction
CHAPTER1
5
In 2005, 82% of world tuna supply was consumed canned product, 18% fresh. Japan
is the largest market for fresh tuna, consuming 78% of the world fresh supply. In
2004, canned tuna consumption was highest in the European Union (734,444 tonnes)
followed by the U.S. (445,847 tonnes), together combined accounting for 83% of the
total global consumption of canned tuna (Gilman and Lundin, 2008).
1.2.2 World Processed Tuna Market
World processed tuna production has experienced sustained growth from 1980-2006,
from 0.63-1.68 million tonnes (Figure 1.3), though from the peak of 1.68 million
tonnes in 2004 there is a slight decline to 1.67 million tonnes in 2006. The US was a
main producer but its capacity has sharply declined. Thailand is now the main
producer: in 1981, production was 4,700 tonnes rising to 400,000 tonnes in 2006.
Introduction
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6
Figure 1.3. Preserved Tuna Production Shares by Main Countries, 1980-2006
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
1,000 Tonnes
Thailand Spain USAEcuador Iran Others
Source FAO, 2008.
Thai exports are by far the most substantial since 1986 with 142,000 tonnes until 2006
with 501,000 tonnes or about 37 % of total world exports (8% from Ecuador, 5%
from Spain, and 4 % from Mauritius and Indonesia) in 2006 (Josupeit, 2008) thanks to
the shift of processing from the US to the Far East, reflecting, especially, rising labour
costs in the US. Figure 1.4 show that the growth rate of the Thai tuna export had been
constantly fluctuating at an average of 8% whereas those of Ecuador Mauritius and
Spain have been dramatically fluctuating about 20% from 1988-2006, particular for
the growth rate from Spain reached at peak over 140% since 1996-1997. Only the
growth rate in Mauritius has been increasing during 2004-2006.
Introduction
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7
Figure 1.4 Growth Rate of Preserved Tuna Exporters (tonnes), 1988-2006
Thailand
-40%
-20%
0%
20%
40%
60%
80%
100%
120%
140%19
88
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Growth rate (%)
Ecuador
-40%
-20%
0%
20%
40%
60%
80%
100%
120%
140%
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Growth rate (% )
Introduction
CHAPTER1
8
Mauritius
-40%
-20%
0%
20%
40%
60%
80%
100%
120%
140%
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Growth rate (%)
Spain
-40%
-20%
0%
20%
40%
60%
80%
100%
120%
140%
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Growth rate (% )
Source:FAO, 2008.
The US was the largest producer in the World, but now the US is the largest preserved
tuna importer (17% of total tuna import-Figure 1.5) in the World. The closure of
many tuna canneries in the US has been associated with the rapid increase in
Introduction
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9
processing (and export) in the Far East, especially Thailand. The UK accounts for
11% of total tuna imports followed by France, Germany and Italy.
Figure 1.5 Preserved Tuna Importers, 1980-2006
0
200
400
600
800
1,000
1,200
1,400
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
1,000 Tonnes
USA UK France Germany
Italy Japan Netherlands Others
Source:FAO, 2008.
1.2.3 World Tuna Fisheries
There are four major tuna fishing areas in the world: the Pacific islands, the eastern
Pacific, West Africa, and the western Indian Ocean (FAO, 2003). Total tuna catches
between 1980 and 2006 expanded from 1.8 to 4.4 million tonnes although the annual
growth rate is declining at an average of 3%. As shown in Figure 1.6, the main tuna
catching countries are Japan, Taiwan, Indonesia, Spain, the Philippines and Korea.
Japan was the world leader with 39% of total global capture in 1985, before it
Introduction
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10
decreased to about 12% in 2006. From 1980-2006, Taiwan had on average 9% of the
total catch, Spain caught 8%, Indonesia had 7%, Korea caught 6%, while Philippines
had the smallest catch amongst the major fisheries, of 5%.
Figure 1.6 World Tuna Catches, 1980-2006
-
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
1,000 Tonnes
Others
Korea
Philppines
Spain
Indonesia
Taiwan
Japan
Source: FAO, 2008
Figure 1.7 shows tuna catches by species from 1980 to 2006. Skipjack is by far the
major species caught and the catches increased threefold from 1980 to 2006. In 2006,
Skipjack catches reached a maximum of two million tonnes. The second main species
is yellowfin where production also grew: in 1980, catches were about 0.5 million
tonnes and grew almost threefold by 2006. Albacore, bluefin1 and bigeye catches are
much smaller and now more stable because of concerns about overfishing
(FAO/GLOBEFISH, 2006).
1 Blue fin tuna includes Northern blue fin and Southern blue fin
Introduction
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11
Figure 1.7 Tuna Catch by Species, 1980–2006
Skipjack
Yellowfin
Bigeye
Albacore
Bluefin-
500
1,000
1,500
2,000
2,50019
80
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
1,000 Tonnes
Source:FAO (2008).
Skipjack and yellowfin make up the largest proportion of catches. There are three
major types of fishing gear used in tuna commercial fisheries: purse seine; longline;
pole-and-line. The purse seines may be very large and operated by one or two boats,
but the most usual case is a purse seine operated by a single boat, with or without an
auxiliary skiff2 (Ne'de'lec and J.Prado, 1990), targeting species skipjack and
yellowfin. The fish caught tend to be smaller than those caught by long line. Most
catches from purse seines are processed for canning. Long line fishing is, as implied
by the name, carried out with long main line about 250 to 800 m., with short lines
carrying hooks attached at regular intervals. These vessels mainly catch large bigeye,
albacore and yellowfin in tropical waters as well as Northern bluefin and Southern
bluefin, swordfish and marlin in temperate waters. A pole and line is comprised of a 2 a small light boat for rowing or sailing, usually used by only one person
Introduction
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12
hooked line attached to a pole. The main tuna species caught by pole-and-line vessels
are skipjack, small yellowfin, albacore and bluefin tuna. Most catches are canned.
Currently, tuna fishing is dominated by the large purse seine vessels and their number
is increasing. Compared with other types of fishing vessel or fishing method, the
number of purse seine vessels is highest in the Pacific Ocean (70%), the Indian Ocean
(45%), and the Atlantic Ocean (55%) (Hinton, 2006). A summary of the tuna
fisheries, market species and prices are shown in Table 1.1.
Table 1.1 Summary of the Tuna Fisheries and Market Species
Tuna market species Fishery operation Tuna product
Prices for
sashimi Prices for canning
Albacore tuna Longline (mostly) Fresh and frozen tuna (sashimi)
High
Pole-and-line Canned tuna/ loins/chunks
Highest
Bigeye tuna Longline (mostly) for large fish
Fresh whole fish and fillets (sashimi)
High
Blue fin tuna Pole-and-line and long-line
Fresh (sashimi) Highest
Skipjack tuna Purse seine Canned tuna (mostly) Lowest Frozen loins/
fillets/chunks
Yellow fin tuna Purse seine (small fish) Canned tuna (mostly) Higher than
Frozen loins/ fillets/chunks
Skipjack prices
Long-line (large fish) Fresh tuna (sashimi) High Source:Applied from Josupeit (2006).
1.2.4 Tuna Stock Situation
Tuna stocks around the world, especially of the five main commercially harvested
species–skipjack, bigeye, yellowfin, bluefin and albacore–are suffering from
increasing fishing pressure, because of the high value of the catch. Fishery biologists
Introduction
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13
estimate fish stocks from the concept of the maximum sustainable yield3 to identify
the best utilization of resources. Hinton (2006) and Moreno and Majkowski (2003)
summarise the levels of exploitation of tuna resources and the maximum sustainable
yield as shown in Table 1.2.
Hinton (2006) defines the levels of exploitation in five categories:
- Not fully exploited where the biomass of the stock has not been decreased to
levels under MSY. There is a potential to increase sustainable fishing;
- Nearly fully exploited where the biomass of the stock is very close to MSY
though the biomass of spawning individuals is above that necessary to
maintain MSY;
- Fully exploited where further fishing efforts would not result in sustained
increases in catch and are likely to reduce the biomass of spawning individuals
below MSY. It is necessary to maintain the stock biomass above MSY;
- Overexploited where the biomass of stock is below the level corresponding to
the MSY, or the spawning biomass is below that required to provide for
sufficient reproduction of the stock to levels that will support fishing at levels
that will produce MSY harvests. Here, fishing efforts should be reduced to
allow the biomass to increase to levels to support MSY;
- Unknown where assessments have not determined the level of exploitation
with respect to a management objective established for the stock, or the
information on the exploitation of the stock is not up-to-date or incomplete,
that the assessment is no longer useful.
3 The maximum sustainable yield is defined as: the highest theoretical equilibrium yield that can be continuously taken (on average) from a stock under existing (average) environmental conditions without affecting significantly the reproduction process. Also referred to sometimes as Potential yield (FAO, 2007).
Introduction
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14
The levels of exploitation of tuna resources and maximum sustainable yields are
summarised in Table 1.2. There are five market tuna species within the Atlantic,
Indian, and Pacific Ocean. Twenty-three tuna stocks have been identified for the
purposes of management and conservation. Only the skipjack tuna catch level is not
currently sustainable (catches within the MSY level), and even for this species, the
Eastern Atlantic catch is unsustainable. Yellowfin is everywhere fully-exploited and
close to being unsustainable. Bigeye fishing is unsustainable in all but the Atlantic,
which is presently at maximum estimated capacity. Albacore is over-fished and nearly
full-exploited in the Pacific while the rest of albacore tuna is fully-exploited.
Restrictions to prevent over-fishing are required for all stocks except albacore in the
South Pacific and four skipjack stocks (excluding the Eastern Atlantic). It is clear that
present levels of production in the world are unsustainable. The historic growth rates
in production (and consumption) are now evidently history, and the future will see,
one way or another, a stabilization, and possibly a decline in world production and
consumption.
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15
Table 1.2 The Levels of Exploitation of Tuna Stocks
Species Ocean Sub-Area Exploitation Status MSY Catch (tonnes) (tonnes)
2003 2004 2005 2006 Skipjack Atlantic Eastern Fully unknown 113,610 119,046 91,756 Western Not Fully unknown 3,620 1,971 2,194 Indian Not Fully unknown 455,897 527,283 595,635 Pacific Eastern Not Fully unknown 219,117 285,607 322,434 Western Not Fully 1,600,000 1,378,062 1,496,813 1,503,386 Yellowfin Atlantic Fully unknown 117,543 106,295 105,909 Indian Over 280,000 - 250,000 497,914 472,302 401,374 Pacific Eastern Fully 250,000 (MSY) 296,321 289,863 181,939 Western Fully 381,000 - 554,000 307,892 378,626 344,922 Bigeye Atlantic Fully 79,000 - 105,000 86,537 72,737 64,516 Indian Over 102,000 129,579 114,409 106,035 Pacific Eastern Over 77,000 112,489 114,151 103,322 Western Over 40,000 - 80,000 150,968 134,899 112,013 Albacore Atlantic North Fully 32,600 24,653 34,649 35,520 South Fully 30,915 22,525 18,840 24,459 Med. Sea unknown unknown 2,308 1,181 5,947 Indian unknown unknown 22,341 20,557 23,567 Pacific North Over unknown 88,955 61,515 65,198 South Nearly Fully unknown 65,356 61,131 102,377 Bluefin Atlantic Eastern Over unknown na na na Western Over 3,500 - 7,200 na na na Pacific Fully unknown na na na Southern Over unknown 12,371 13,589 11,492 Source:Hinton (2006) and Moreno and Majkowski (2003).
1.3 The Thai Tuna Industry History
The tuna canning industry originated in Japan where the production of canned tuna
first occurred on an experimental basis in 1906. The second producer was the US
where tuna canning began in 1909 following depletion of sardines and the search for a
substitute (Wage and Hour Division (WHD), 2009; Corey, 1993). The Japanese
canned-tuna industry had been oriented to the export market. Japan had a rapid
expansion of tuna export to the US during 1931-1934 but a sharp drop in shipments in
1934 was caused by significant import barriers. Japan re-entered the US market
during 1948-52 and Japan was the largest exporter to the US exploiting the low-cost
Introduction
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16
Japanese advantage. However, in the 1960s, the relatively rapid rise in Japanese
incomes had contributed to higher production costs in tuna manufacture and
increasing competition from lower-cost foreign exporters which forced Japan out of
its long-lived position of market dominance (Corey, 1993). By then the US had
become the largest producer. Figure 1.8 shows that the US was the largest tuna
producer followed by Japan between 1979-1985. It also illustrates the remarkable
growth in the Thai industry since 1979, albeit in three apparently distinct periods.
Figure 1.8 Main Processed Tuna Producers 1979–2006
0
50
100
150
200
250
300
350
400
450
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Thousand tonnes
Thailand Spain USA Ecuador Japan
Source: Calculated from Josupeit (2008)
The Thai tuna industry started in the 1970s. In 1972, the cooperation of an Australian,
Thai, and Hong Kong partnership took a leap of faith to build the first Thai tuna
cannery under the brand SAFCOL, namely SAFCOL (THAILAND), LTD., (now
Introduction
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17
Kingfisher Holdings, Ltd (Kingfisher Holding Ltd., 2006)) fro the export market. Thai
Union Manufacturing then began a small tuna cannery operating in 1977 to produce
canned tuna by ordering from the house brands from the US. But it was not until 1983
that Thailand began to become significant in the world market. The rapid expansion
between 1983 and 1991 reflected both the rapid increase in world demand and
consumption, and the demise of the US canning industry, which became
uncompetitive because of rising labour costs (and the decline in local fish stocks).
Thus, there has been a steadily increasing share of the market gained by imports from
low-wage Asian countries in Southeast Asia - Thailand, Philippines, and Indonesia
(Wage and Hour Division (WHD), 2009).
The acquisition of two valuable US brand names (Chicken of the Sea and Bumble Bee
acquired by Unicorn, Thaialnd, in 1989) gave Thailand a significant advantage over
other foreign exports, especially in the important US market. Even though there are
no significant tuna resources within Thailand’s waters, there are many reasons why
Thailand became a “tuna superpower” (Corey, 1993) apart from the low wage costs
and high quality production (building on modern technology, foreign direct
investment, and marketing expertise (especially branding).
First, Thailand already had many tuna canneries that were converted from shellfish
and fruit/vegetable canneries, whose owners for many years had conducted business
with the US firms (importers and distributors), and had the necessary trading contracts
and networks. This is an advantage over other countries that did not have history of
doing business in the US (Corey, 1993). Second, Thailand uses English as the most
Introduction
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18
common second language and the political and economic environments are highly
compatible with western interests – the major markets. The Thai currency (baht) is
kept within a tight band around the US dollar, reducing the risk of loss from currency
fluctuations. Finally, Thailand has an ideal geographic location between two
important fishing grounds (the Western pacific and Indian Oceans), as well as good
marine transport connections to the major markets. By mid 1980s Thailand became
the second canned tuna producer and the largest exporter Figure 1.8. Production in the
Philippines, which used to account for 70% of the total U.S. import, stagnated
because of the limited supply of tuna, while the raw material import was also
prohibited until 1986 (Yamashita, 2000). Moreover, the number of tuna canneries in
Philippines has been reduced due to declining tuna catches, stiff competition with
other processed tuna exporting countries (particularly Thailand) and difficulty in
accessing new markets (Vera and Hipolito, 2006). Third, it was a result from the US
Federal restrictions on catching dolphin and tuna together, reflecting the public
concern over the killing of dolphins. Consequently, fishermen shifted harvesting from
Eastern to Western Pacific Ocean, where tuna do not run with dolphin. These US
restrictions also contributed to the reduction of the industry located on the mainland
US (Maryland and Astoria), as well as processors in Hawaii and Puerto Rico. Many
US tuna canneries closed between 1977-2001. In 2001 the last large full scale cannery
of the ‘chicken of the sea’ on the US mainland closed, providing the opportunity for
Thailand to become the largest tuna producer.
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19
Thailand is now the world’s largest producer and exporter of canned tuna4 (Josupeit,
2008). Over 80 countries import tuna products from Thailand with the biggest market
for canned tuna being the US (27%) followed by the European Union (15%), the
Middle East (14%), Japan (9%), Australia (8%) and Canada (7%). In addition,
Thailand is also an important exporter of fresh and frozen tuna product5. Fresh and
frozen tuna exports dramatically increased from 1,123 million baht in 2005 to 1,753
million baht in 2007 (Thai Frozen Foods Association, 2008). Thailand’s tuna
processing generates about 50,375 million baht6 and added value earnings of 19,470
million baht/year7 from export trade (Thailand Customs Department, 2006).
However, this rapid expansion of the Thai industry came to a halt in the early 1990s,
(Figure 1.9), partly because intense tariff and non tariff barriers by the EU were
associated with a drop in Thai export value (Kijboonchoo and Kalayanakupt, 2003).
Since 1997 they have been increasing again because of the weakness of the Thai baht
exchange rate and quota imports from the EU between 2003-2007, and also because
of the rapid increase in world demand, especially from Asia.. However, export
slightly drops again in 2008 as a result of 24% tariff from the EU and rules of origin
requirement from Japan. On the other hand, fresh and frozen tuna product for sashimi
market is also the important tuna product. Although the quantity of export is much
less than that of preserved tuna product, there are many longline vessels landing in
Thai ports and there has been an increase in exports of around three times from 4,903
to 22,230 during the last decade (FAO, 2009a).
4 HS Classification Code 160414 5 HS Classification Code 0302 and 0303 6 The exchange rate was average 70 baht:£ in 2006 7 The added value earning is calculated by total tuna export value minus total tuna import value.
Introduction
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20
Nevertheless, Thailand is heavily dependent on imports of raw fish, since she has
relatively few tuna fishing vessels. As can be seen in Figure 1.9 Thailand established
a fishing sector since 1976. Although it has been stable since 1985, raw fish from the
fishing sector has been still not sufficient for the tuna preserved sector.
Figure 1.9 Thai Tuna Exports (t), Market Share, and Thai Tuna Catch (t), 1970-
2006
0
100,000
200,000
300,000
400,000
500,000
600,000
1970
1975
1980
1985
1990
1995
2000
2005
Tonnes
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Thai world tuna market share (%)
Total Thai tuna exports Total tuna catches Thai World Market Share
Source:FAO (2008) and Calculated from Josupeit (2008).
Figure 1.10 shows that Thailand is the largest raw tuna importer followed by Japan.
The main sources of the tuna in Thailand are the Indian and Western Pacific Oceans
because these Oceans are close to Thailand.
Introduction
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21
Figure 1.10 Fresh and Frozen Tuna Imports 1980-2006
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,00019
80
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
1,000 Tonnes
Others
Philppines
Côte d'Ivoire
Seychelles
Mauritius
Spain
Japan
Thailand
Source:FAO (2008).
1.4 The Role of the Tuna Industry in the Thai Economy
Thailand is a developing country economy. As shown in Figure 1.11, real GDP
growth between the years 1980-1985 was approximately 5%. It increased to 13% in
1986/87 and declined thereafter to -10% in 1998 following the national financial crisis
of 1997: Since then, it has recovered to about 5%. The Thai economy has been
affected by external factors such as the terrorist attacks in 2001 on 9/11 in the United
States, the severe acute respiratory syndrome (SARS) outbreak and the Tsunami
disaster, both in 2004.
Introduction
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22
Figure 1.11. Real GDP Growth Rate 1980-2006
-15%
-10%
-5%
0%
5%
10%
15%19
80
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Percent
Source: National Economics and Social Development Board (2007).
GDP at the current prices rose slightly between 1980-1987 (Figure 1.12).
Subsequently, it increased rapidly until July 1997 when the financial crisis of a failure
of monetary policy resulted in GDP falling. After 1998, it resumed its former trend.
Non-agriculture output was three times higher than that from agriculture in 1980 and
this increased to eight times in 2006. Manufacturing output was about 22% of GDP in
1980 and 35% in 2006, as is typical in rapidly growing economies.
Introduction
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23
Figure 1.12 GDP at Current Price including Agriculture, Non Agriculture, and
Manufacturing 1980-2006
Agriculture
Non-Agriculture
Manufacturing
GDP
-
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
9,000,00019
80
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Million baht
Source: NESDB (2007).
Exports are important potential sources of economic growth. High and increasing
exports by encouraging specialization according to comparative advantage improve
static and dynamic efficiency and promote economic growth (Gylfason, 1997). The
National Economics and Social Development Plan during 1977-2006 has
concentrated on the development of exports since the Thai economy depends on
exports of goods and services as a significant source of income. Figure 1.13 presents
total export values, 1996-2006, showing an increase of total exports of 14% over this
period. Total export value increased slightly to an average of 2,300,000 million baht
between 1996-2002 and then rose rapidly after 2003, to 4,937,372 million baht in
2006.
Introduction
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24
Thailand is an emerging economy which depends on export trade for over 70% of
Gross Domestic Product (GDP) (National Economics and Social Development Board,
2007).
Figure 1.13. Total Export Values,1996-2006
Total Exports
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Million baht
Figure 1.14 shows seafood export values and tuna export values from 1996-2006.
Seafood exports are the important export sector. Although it earns only 6% of total
exports, the Thai economy received income from the seafood export values
approximately 105,000 million baht in 1996 and it increased to twice this amount in
2006. Tuna export contribution to revenue rose from 14,000 million baht in 1996 to
50,400 million baht in 2006.
Introduction
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25
Figure 1.14. Total Seafood Export Values and Tuna Export Values 1996-2006
Total seafood exports
Total tuna exports
0
50,000
100,000
150,000
200,000
250,00019
96
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Million baht
Employment is also important to Thai economy. If labour resources can be used in the
most economically efficient way, it is one of the key drivers for economic growth of
the national economy. It also directly affects people. If people are unemployed, it
means they lost their income and a reduced standard of living. Unemployed workers
represent a wasted production capability. It also means that there is less money being
spent by consumers, which has the potential to lead to more unemployment,
beginning a cycle. Based on 2007 labour statistics, Thailand’s total labour force is
36,942 thousand people with males (20,073 thousand) edging out females (16,869
thousand) with 508,000 unemployed (an unemployment rate of less than 1.5%). 42%
of Thai labour force is in agriculture, while the rest are employed in non-agriculture
(Table 1.3). Manufacturing, including tuna processing, employs 14.7% of the labour
force (National Statistical Office, 2008).
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26
Table 1.3. Number and Percentage of Employed Persons (1,000 persons) by
Industry (2007)
Industry Number Percentage
Agricultural 15,354.8 41.6 1. Agriculture, hunting and forestry 14,889.5 40.4 2. Fishing 465.3 1.3 Non-Agricultural 21,517.9 58.4 1. Mining and quarrying 61.1 0.2 2. Manufacturing 5,417.5 14.7 3. Electricity, gas and water supply 93.2 0.3 4. Construction 1,868.7 5.1 5. Wholesale and retail trade, repair of 5,485.4 14.9 motor vehicles motorcycles and personal and household goods 6. Hotel and restaurants 2,358.1 6.4 7. Transport, storage and communication 1,082.6 2.9 8. Financial intermediation 368.4 1.0 9. Real estate, renting and business activities 722.0 2.0 10. Public administration and defence, 1,255.0 3.4 compulsory social security 11. Education 1,063.6 2.9 12. Health and social work 669.9 1.8 13. Other community, social and 765.6 2.1 personal service activity 14. Private households with employed persons 245.6 0.7 15. Extra-territorial organizations and bodies 2.3 0.0 16. Unknown 59.0 0.2 Total 36,872.7 100 Source: National Statistical Office (2008).
Earnings depend on education. Basically, employees will be hired in the higher salary
if they graduate higher level. Table 1.4 presents the difference of average salary in
each education level. Employees who graduate from elementary/lower secondary
education will only be employed as unskilled labours as minimum wage levels of
about 3,700 baht/month whereas people who graduate with Masters degrees will have
Introduction
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27
the highest salaries. People with limited education normally work in the
manufacturing sector. Although working in factories is hard work, people who are
poor, landless, without much formal schooling, without other potential sources of
livelihood, and without social safety nets (Delgado et al., 2003) do not have much
choice.
Table 1.4 Average Salary of New Employees from Private Employment, 2006
Education Income (baht)
Elementary-lower secondary 3,768
Vocational 6,240
Higher 7,077
Bachelor 10,893
Master 18,944 Source: National Statistical Office (2008).
Tuna processing is a labour intensive process with an unskilled labour force as a key
factor and processors cannot produce efficiently without a reliable labour force. About
40,000 people work in this sector, which accounts for approximately 2,090 million
baht8 in terms of employment income (an average income per person employed of
5,225 baht). Most tuna production jobs require little formal education or training as
with other food production jobs. The average employees in tuna manufacturing work
five-six days per week and start working at 7 am-5 pm (BBC Three, 2009). Many
tuna production jobs in tuna factories involve repetitive, physically demanding work.
Working conditions depend on tuna stocks and customer orders. Sometimes 8 Employment income is estimated from the averarage minimum wage rate*working days (6 days/week excluding 15 public holidays*40,000 people
Introduction
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28
employees work at night or an weekends and spend much of their shift in the high
temperature conditions (BBC Three, 2009). In a case of high customer orders, tuna
factories need more workers and it is often that local people are not available.
Consequently, unskilled labour migrants from rural areas in other provinces in urban
area and migrants from neighbouring countries such as Myanmar are recruited to
support the industry. However, since foreigners need to be legally registered with the
government, there are limits on hiring foreign migrants.
Apart from Thai economy and employment, other effects such as inflation, exchange
rates and minimum wage rate are relevant to the tuna industry. Inflation affects
pricing by increasing tuna production costs which must be passed along to middlemen
and subsequently consumers. High inflation has tended to be associated with low
exports (Gylfason, 1997) and hence also a decrease in tuna export. Figure 1.15 shows
changes in the rate of inflation in Thailand from 1979-2006, which is average 5
percent. It has been fluctuating and uncertain.
Introduction
CHAPTER1
29
Figure 1.15 Inflation Rate in Thailand between 1979 -2006.
0
5
10
15
20
2519
79
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Inflation (%)
Source: Bank of Thailand (2007b).
The exchange rate has a significant impact in tuna exports. When Thai currency is
strong or revalued, Thailand loses competitive advantage on the international market.
Conversely, the weakness of Thai baht is a positive effect on the tuna exports. Figure
1.16 shows that the Thai currency had been stable from 1979-1996 but was very weak
during 1997-1999 because of 1997 Asian financial crisis. It has strengthened again
between 2001-2006.
Introduction
CHAPTER1
30
Figure 1.16 Exchange Rate of Thai Currency and the US Dollar, 1979-2006
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.0019
79
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Baht:1 USD
Source: Bank of Thailand (2007a)
Since the industry uses unskilled labour force, the minimum wage is also relevant to
the sector. Although Thai minimum wage is very low compared to that of other
produced tuna producing countries, there has been an increasing trend from 1979 to
2006. Increasing labour costs threaten the competitiveness of Thai exports in the
future-as they have in the past for the US, for instance.
Introduction
CHAPTER1
31
Figure 1.17 Average Minimum Wage Rate in Thailand, 1979-2006
0
20
40
60
80
100
120
140
160
18019
79
1981
1983
1986
1988
1991
1993
1995
1997
1999
2001
2003
2005
Baht/Day
Wage (baht:day)
Source:Ministry of Labour (2008).
1.5 The Problem Statement
Thailand has successfully expanded a major exporting industry as a result of being
geographically well placed to land, process and export tuna, and exploiting the low
wages of a developing country, as well as being well integrated with the major export
markets in the west, as a consequence of language and business connections.
Associated with a rapid increase in world demand for tuna (rising incomes and
demand, especially for animal proteins, and, especially, urban populations, divorced
from traditional land based food production), the success of the Thai industry in the
past has been spectacular.
Introduction
CHAPTER1
32
However, there are very strong reasons to suppose that this success cannot be
sustained. First, world tuna stocks are almost universally over-fished, or close to their
physical limits. World demand cannot continue to grow at past rates, and is very
likely to be restrained by rising prices of raw materials (Delgado et al., 2003). Thus,
the tuna sector is not environmentally sustainable without increasing fish prices, and
consequent curtailment of demand. Second, the economic competitiveness of the Thai
industry critically depends on low wages. Low wages cannot be sustained in a country
with rapidly rising living standards. The experience of the US is likely to be repeated
in Thailand, where the processing sector is likely to move to low wage economies
elsewhere, as the current labour force and its successors find better ways of earning
improved better wages and incomes. Third, and building on the second point, neither
the present labour force, nor the world’s consumers are likely to continue to tolerate
poor and oppressive working conditions in the factories. As they do so, Thailand’s
historic advantage of low wages will disappear.
This study explores the longer term future of the Thai tuna industry. The questions
that the study seeks to answer include: How sustainable is world tuna demand
growth?, Can the competitive strength of tuna processors be sustained?, Are tuna
processors or workers effective in their work and lives?, Is tuna supply balanced to
meet tuna demand?
Introduction
CHAPTER1
33
1.6 Objectives of the Study
The primary objectives of the present study are as follows:
1. To examine the development of the world tuna market and Thailand’s place in the
market. More specifically, to predict tuna exports and then apply the forecast results
to the constituent parts of the Thai tuna industry. This result can indicate forecast
impacts on the tuna industry.
2. To examine the structure-conduct-performance of the Thai tuna industry and to
investigate the international competitiveness between Thailand and other foreign
countries.
3. To study the socio-economic aspects of labour in the Thai tuna industry both in the
working places and living places and to assess the likely impact of developments in
the tuna sector on the labour force.
1.7 Format of the Thesis
This Chapter has introduced the study by highlighting the context of the Thai tuna
processing industry. In Chapter 2, empirical measures of export demand forecasts are
made using the exponential smoothing and autoregressive integrated moving average
methods (commonly used to forecast future patterns from data histories). The export
demand forecast is one indicator which might be used by the industry in planning and
developing its future. Chapter three presents a structure-conduct-performance (SCP)
of domestic tuna firms, a cost and return analysis for the fishing sector, and an
analysis of international competitiveness using revealed comparative advantage and
Introduction
CHAPTER1
34
the diamond model. It shows the market structure of the tuna industry using the
concentration measurements. The conduct aspect deals with how the tuna firms set
prices using the oligopoly theory, while the performance measure assess which tuna
companies are good and poor performers. The results can inform the strategies of tuna
processors and the profitability of tuna processing operations. Moreover, it also
demonstrates the comparative and competitive advantages between Thailand and its
main foreign competitors. The sustainable livelihoods of unskilled labourers who
work in tuna factories are investigated in Chapter four. Finally, suggestions are made
about the future of the Thai tuna industry in Chapter five.
Forecasting Exports of Tuna from Thailand
CHAPTER2
35
Chapter 2
Forecasting Exports of Tuna from Thailand
2.1 Introduction
Thailand is the largest tuna exporters and the tuna industry is an important industry in
Thailand. Table 2.1 shows the value of total tuna exports (million baht) compared with
gross domestic product (GDP) and seafood exports in Thailand for 1999-2006. In 2006,
the value of tuna exports was 24% of the seafood product export earnings (Department
of Fisheries; the National Economic and Social Development Board, 2007 and Thai
Customs Department, 2006). The largest quantity of tuna exports is canned tuna, which
comprise 47% of total canned tuna exports of world trade in 2006 (FAO/GLOBEFISH,
2006). The income from the tuna industry in 1996 was 14,373 million baht and this
increased continuously until 2006 to about 50,375 million baht.
Accurate forecasting and planning of production is necessary for businesses and
forecasting is important in a wide range of planning or decision-making situations. A
company’s goal is normally profit maximization and decisions about investment
depend on expected profit. It may thus be necessary to make accurate and reliable
demand forecasts (Pearce, 1971, pp.13-19). Forecasts are needed in finance,
marketing, personnel and production areas as well as by government (Hanke and
G.Reitsch, 1940, pp.2-3).
Forecasting Exports of Tuna from Thailand
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36
Table 2.1 The Value of Total Tuna Exports (million baht at current price)
compared with GDP and Seafood Exports in Thailand, 1999-2006.
Year GDP Seafood Exports Tuna Exports
Million baht Million baht Million baht % of GDP % of Seafood exports
1999 4,637,079 165,718 24,776 0.5% 15.0%
2000 4,922,731 185,750 20,887 0.4% 11.2%
2001 5,133,502 190,901 29,689 0.6% 15.6%
2002 5,450,643 169,186 29,946 0.5% 17.7%
2003 5,917,368 175,102 34,897 0.6% 19.9%
2004 6,489,847 176,522 37,089 0.6% 21.0%
2005 7,087,660 194,087 46,308 0.7% 23.9%
2006 7,816,474 213,986 50,375 0.6% 23.5% Source: Department of Fisheries, The Office of the National Economic and Social
Development Board, and Thai Custom Department, Thailand (2007).
In marketing, forecasts can be used to plan advertising and to direct sales and other
promotional efforts. In addition, forecasts aid decision making on market size and
market characteristics (Makridakis and Wheelwright, 1989, p.19). Production,
inventory and purchasing units need forecasts in the area of product demand. These
departments can then plan production schedules and inventory control of raw
materials to meet market requirements. The accuracy of prediction can lead to the
right decision. If product demand can be predicted, then manufacturers ensure that
there will be sufficient raw materials to meet that demand (Pearce, 1971, pp.13-19).
Forecasts are also beneficial for material requirements, labour scheduling, equipment
purchases, maintenance requirements, and plant capacity planning. These are all
pertinent to the Thai tuna industry.
Forecasting Exports of Tuna from Thailand
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37
In finance and accounting, demand forecasts can be used to forecast cash flows and
the rates at which expenses and revenues need to take place if they are to maintain
company liquidity and operating efficiency. Demand forecasts can also be used to
predict the supply level of skilled labourers or specialists (Pearce, 1971, p.13-19) in
the earning and frozen fish factories. Forecasts relate to all functional areas in an
organization. Moreover, a number of forecasts can be used across functions in
coordinating and integrating decision-making. For example, demand forecasts can be
useful for the R&D department and top management.
In this chapter, we forecast monthly Thai tuna exports for the five year period 2007-
2011 using data for 1996-2006. We use univariate time series methods because of
data limitations relating to, amongst other variables in terms of monthly data, tuna
prices, consumer index, and income. Monthly tuna export forecasts are more useful
than annual ones for business planning since tuna capture is seasonality. Our five year
forecasts relate to the Tenth National Economic and Social Development Plan (2007-
2011). The two methods used are exponential smoothing and autoregressive
integrated moving average (ARIMA) methods and they are appropriate for short and
medium forecasting. We also informally examine other influences on demand, such as
population growth, income growth, and tuna consumption from secondary data and
previous studies.
The rest of the chapter is organized as follows. In Section 2, we present the data for
forecasting, Section 3 describes the best forecasting model and presents the
forecastings, Section 4 discusses the results and considers other determinants of
Forecasting Exports of Tuna from Thailand
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38
demand, and the last section concludes.
2.2 Description of Data
This research uses monthly data for the physical quantities of tuna exports (source:
Information and Communication Technology Bureau, Thai Customs Department in
Thailand). Continuous monthly data are available between January 1996 and
December 2006 (132 observations).9 Total tuna exports are for three types of tuna:
fresh tuna, frozen tuna and canned tuna. Figure 3.2 shows the total quantities of
monthly tuna exports, 1996-2006.10
9 Data were collected between 1981 and 2006. Unfortunately, between 1992 and 1995, the data were annual and between 1981 and 1990 they were reported incompletely. Some data are missing for all 12 months in some years and there are no data for 1990. 10 Canned tuna exports decreased from 21,140 tonnes into 15,180 tonnes in May 2000 because of the falling prices in the US and the higher raw material skipjack price. In early 2006, canned tuna prices in Europe were the highest in the last five years because of higher raw materials like higher fuel prices, canning material costs and transportation costs. Accordingly, canned tuna exports declined dramatically from 42,963 tonnes into 34,707 tonnes in April 2006.
Forecasting Exports of Tuna from Thailand
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39
Figure 2.1 Total Monthly Tuna Exports (tonnes), 1996-2006
-
10,000
20,000
30,000
40,000
50,000
60,00019
96
1996
1998
1999
2000
2001
2002
2003
2004
2005
2006
Tonnes
Source: Thai Custom Department (2006).
In 2006, canned tuna exports made up 97% of total exports, with the remainder being
fresh and frozen tuna. For the main species (yellowfin and skipjack) for 1997-2005
(Table A 2.2 in Appendix 2), most landing are in November while the least are in
April. In 1996, total tuna exports averaged 17,880 tonnes and this reached an average
of 43,530 tonnes in 2006. The growth rate in total tuna exports was positive in all
years except for 2000 and 2004. The highest growth rate was 24% in 2003 while the
lowest of 5% was in 1997. In 2006, the total tuna export growth rate was 12%; the
average highest seasonal tuna export was 32,802 tonnes in November and the lowest
was 24,865 tonnes in April (Table A2.3 in Appendix 2).
Forecasting Exports of Tuna from Thailand
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40
2.3 Selecting the Best Forecasting Model and Forecasting
The forecasting frameworks that we use are exponential smoothing and ARIMA
methods. They are detailed in A2.1 Appendix 2. Both embody a number of alternative
models. A key issue therefore is one of selecting between the best exponential
smoothing model and the best ARIMA model and we need to compare the accuracy
of both forecasts. Our basis for validating forecast methods is to consider a
comparison of the forecast data (Ft) and the observations (Yt) in the validation period,
i.e. within the sample period.
To assess the forecast accuracy, we estimate each model from the whole sample of
1996-2006, and then forecast within the sample over 2002-2006. These forecasts (Ft)
are then compared with the actual observations (Yt) and the predictive accuracy is
measured by the root mean square error (RMSE), the mean square error (MSE), the
mean absolute error (MAE), and Theil's U-statistic11 (Pindyck and Rubinfeld, 1991,
p.340). We compare these measures between the preferred exponential smoothing
model and the preferred ARIMA models to determine our preferred forecasting model
overall.
11 RMSE = ∑ −n
ttt )FY(
n1
and Theil's U =
∑∑
∑
==
=
+
−
n
1t
2t
n
1t
2t
n
1t
2tt
)Y(n1)F(
n1
)YF(n1
where 1U0 ≤≤ .
If U=0, the forecast is a perfect fit; and if U=1, the forecast has the least accuracy.
Forecasting Exports of Tuna from Thailand
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41
2.3.1 Results of Forecasting using Exponential Smoothing Methods
Using exponential smoothing methods, we examine the three basic models of no
trend, a linear trend, and an exponential trend, and admit the possibility of no
seasonality, additive seasonality or multiplicative seasonality (see Table A2.1 in
Appendix Table 2). Thus, we estimate nine models and the results are shown in Table
2.2. We choose between these models on the basis of the minimum sum of square
errors. We consider the three basic models of no trend, a linear trend, and an
exponential trend, and admit the possibility of no seasonality, additive seasonality or
multiplicative seasonality. The values of the sum of squared errors for the linear trend
and exponential trend models with both types of seasonality are lowest and similar,
and we examine their autocorrelations. Although the linear trend and additive
seasonal method reveal the minimum sum of squared errors, the auto correlation
function (ACF) and the partial autocorrelation function (PACF) in Figure 2.2 show
significant peaks at lag five and the forecast error is not white noise. Thus we do not
choose the linear trend model with additive seasonality.
Table 2.2 Estimates of the Exponential Smoothing Methods
Trend Seasonality α (Level)
β (Trend)
γ (Seasonality)
Sum of Squared Errors
None None 0.402 - - 1,600,129,563None Additive 0.600 0.000 1,110,457,824None Multiplicative 0.600 - 0.000 1,118,606,679Linear None 0.301 0.000 - 1,395,309,133Linear Additive 0.501 0.000 0.000 960,683,786Linear Multiplicative 0.412 0.001 0.278 992,172,166Exponential None 0.400 0.000 - 1,503,392,141Exponential Additive 0.500 0.000 0.000 965,970,677Exponential Multiplicative 0.500 0.000 0.000 996,449,846
Forecasting Exports of Tuna from Thailand
CHAPTER2
42
Figure 2.2 The ACF and PACF the Linear Trend Model with Addictive
Seasonality
16151413121110987654321
AC
F
1.0
0.5
0.0
-0.5
-1.0
16151413121110987654321
Par
tial
AC
F
1.0
0.5
0.0
-0.5
-1.0
Similarly, we do not select the exponential trend model with addictive seasonality,
where the ACF and PACF are shown in Figure 2.3, for the same reason.
Forecasting Exports of Tuna from Thailand
CHAPTER2
43
Figure 2.3 The ACF and PACF for the Exponential Trend Model with Additive
Seasonality
16151413121110987654321
AC
F
1.0
0.5
0.0
-0.5
-1.0
16151413121110987654321
Part
ial A
CF
1.0
0.5
0.0
-0.5
-1.0
Figure 2.4 and Figure 2.5 show the ACFs and PACFs for both the linear trend and
exponential trend models both with multiplicative seasonality and neither are
statistically significant. Accordingly, we choose that with the lower minimum sum of
squared errors, i.e. the linear trend model with multiplicative seasonality (or the Holt-
Winters’ multiplicative exponential smoothing model).
Forecasting Exports of Tuna from Thailand
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44
Figure 2.4 The ACF and PACF for the Linear Trend Model with Multiplicative
Seasonality
16151413121110987654321
AC
F
1.0
0.5
0.0
-0.5
-1.0
16151413121110987654321
Part
ial A
CF
1.0
0.5
0.0
-0.5
-1.0
Forecasting Exports of Tuna from Thailand
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45
Figure 2.5 The ACF and PACF for the Exponential Trend Model with
Multiplicative Seasonality
16151413121110987654321
AC
F
1.0
0.5
0.0
-0.5
-1.0
16151413121110987654321
Part
ial A
CF
1.0
0.5
0.0
-0.5
-1.0
The initial smoothing state is presented in Table 2.3. The initial of level (Lt) for the
estimated period of 1996-2006 starts at 16,602 tonnes and the trend (bt) is 214.
Seasonal indices are shown for each of the 12 months in percentage terms. The initial
values of the multiplicative seasonal indices (Ss) are estimated as: 94.76S1 = ,
99.54S2 = , …, S12 = 100.32.
Forecasting Exports of Tuna from Thailand
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46
Table 2.3 Initial Smoothing State for the Linear Trend Model with
Multiplicative Seasonality
Month 1 94.76 Month 2 99.54 Month 3 106.01 Month 4 90.29 Month 5 97.86 Month 6 100.57 Month 7 98.67 Month 8 95.57 Month 9 95.47 Month 10 103.37 Month 11 117.52 Month 12 100.32 Level 16,602.05 Trend 213.67
The parameter estimates from the linear trend model with multiplicative seasonality
(the Holt-Winters’ multiplicative model) are shown in Table 2.4. The β-parameter is
not significant but this is the best fitting exponential smoothing model.
Table 2.4 Estimates of the Linear Trend Model with Multiplicative Seasonality
Estimate p-value Alpha α (Level) 0.412 0.000 Beta β (Trend) 0.001 0.964 Gamma γ (Season) 0.278 0.004
Table 2.4 presents the results of the initial values for the level, trend, and seasonal
indices. The Holt-Winters’ multiplicative model can be written as:
Forecasting Exports of Tuna from Thailand
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47
Eq.2.1 )bL)(412.01()SY
(412.0L 1t1tst
tt −−
−
+−+=
Eq. 2.2 1t1ttt b)001.01()LL(001.0b −− −+−=
Eq. 2.3 12tt
tt S)278.01()
LY
(278.0S −−+=
Eq. 2.4 12mtttmt S)mbL(F −++ +=
Monthly forecasts of aggregate tuna exports for 2007-2011 using the linear trend
model with multiplicative seasonality are shown in Figure 2.6. Also shown are the
95% confidence intervals. The forecast trend continuously increases. Monthly
seasonal forecast patterns show that the highest forecast is in November of each year,
the second highest is in March, and April is the lowest level in each year.
Forecasting Exports of Tuna from Thailand
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48
Figure 2.6 Tuna Forecast using the Linear Trend Model with Multiplicative
Seasonality
100,000
80,000
60,000
40,000
20,000
0
2011
2010
2009
2008
200
7
2006
2005
2004
200
3
2002
2001
200
0
1999
1998
1997
1996
ForecastLCLUCLFitObserved
Tonnes
2.3.2 Result of Forecasting using ARIMA Models
ARIMA methods involve three main steps: identification, estimation; and diagnosis
checking and validation. Consider identification. The time series in Figure 3.2 shows
that tuna exports are non-stationary with both a trend and seasonality and we use
seasonal ARIMA methods. Figure 2.7 shows the ACF and PACF with the 5%
significance level and again the series is non-stationary because the ACF does not fall
to zero. The PACF shows a large spike close to unity at lag 1.
Forecasting Exports of Tuna from Thailand
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49
Figure 2.7 Estimates of the ACF and PACF
AC
F
1.0
0.5
0.0
-0.5
-1.0
16151413121110987654321
Part
ial A
CF
1.0
0.5
0.0
-0.5
-1.0
16151413121110987654321
Accordingly, we take both first-differences and seasonal difference of the series and
the result is shown in Figure 2.8. This transformed series appears stationary.
Forecasting Exports of Tuna from Thailand
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50
Figure 2.8 Tuna Exports after First-differencing and Seasonal First-differencing
L N b16151413121110987654321
ACF
1.0
0.5
0.0
-0.5
-1.0
Lag Number16151413121110987654321
Part
ial A
CF
1.0
0.5
0.0
-0.5
-1.0
200620052004200320022001200019991998 1997
15,000
10,000
5000
0
-5,000
-10,000
-15000
Forecasting Exports of Tuna from Thailand
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51
The ACF shows significant values at lags 1 and 12 with a positive θ1 and the non-
seasonal MA and seasonal MA are of order one. The PACF also demonstrates spikes
at lags 1 and 12. Consequently, an ARIMA (0,1,1)(0,1,1)12 model seems appropriate.
Following Melard (1984), we estimate the parameters of this model using the exact
maximum likelihood method and the results are given in Table 2.5. The p-values
show that the non-seasonal MA(1) and seasonal MA(1) are significant.
Table 2.5 Parameter Estimates for ARIMA Model
Estimates p-value Non-Seasonal Lags MA(1) (θ1) 0.525 0.000 Seasonal Lags Seasonal MA(1)( 1Θ ) 0.921 0.000 Constant 20.940 0.602
We now perform diagnostic checking to assess model adequacy. The Ljung-Box Q*-
statistics are shown in Table 2.6 and they are not significant at lags 1-16. However,
the ACF and PACF plots of residuals in Figure 2.9 show that autocorrelations and
partial autocorrelations at lag 5 are significant, but only just. On balance, this model is
considered to be a good forecasting model.
Forecasting Exports of Tuna from Thailand
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52
Table 2.6 Estimates of the Autocorrelation Function and Box-Ljung Q*-
Statistics for the ARIMA (0,1,1)(0,1,1)12 Model
Lag Autocorrelation Standard Error(a) Q*-Statistic Value p-Value(b)
1 0.033 0.091 0.717 2 -0.010 0.090 0.931 3 0.061 0.090 0.897 4 -0.029 0.089 0.951 5 -0.196 0.089 0.354 6 -0.087 0.089 0.369 7 -0.173 0.088 0.169 8 0.049 0.088 0.221 9 0.010 0.087 0.298 10 -0.019 0.087 0.379 11 0.046 0.087 0.442 12 -0.022 0.086 0.522 13 0.009 0.086 0.603 14 -0.030 0.085 0.669 15 -0.041 0.085 0.720 16 -0.113 0.085 0.654
a The underlying process assumed is independence (white noise). b Based on the asymptotic chi-square approximation.
Table 2.5 presents the parameter estimates for the ARIMA (0,1,1)(0,1,1)12 and the
forecast model can be written in full as:
Eq. 2.5 ts
tt )B()B(cZw εΘθ+=
Eq. 2.6 t12
t112
1 )B()B(cY εΘθ+=ΔΔ
Eq. 2.7 t12
11t12 )B1)(B1(cY)B1)(B1( εΘ−θ−+=−−
Eq. 2.8 13t1112t11t1t13t12t1tt yyyCY −−−−−− εΘθ+εΘ−εθ−ε+−++=
Eq. 2.9 13t12t1tt13t12t1tt 49.092.053.0yyy94.20Y −−−−−− ε+ε−ε−ε+−++=
Forecasting Exports of Tuna from Thailand
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53
Figure 2.9 ACF and PACF for the ARIMA (0,1,1)(0,1,1)12 Model
16151413121110987654321
ACF
1.0
0.5
0.0
-0.5
-1.0
16151413121110987654321
Part
ial A
CF
1.0
0.5
0.0
-0.5
-1.0
We use the ARIMA (0,1,1)(0,1,1)12 model to forecast tuna exports for 2007-2011 and
Figure 2.10 illustrates this with 95% confidence intervals. The forecasts show a
continuously increasing trend and seasonality. As in our preferred exponential
smoothing model, the highest forecast tuna exports are in November of each year, the
second highest are in March and the lowest in April.
Forecasting Exports of Tuna from Thailand
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54
Figure 2.10 Actual Tuna Exports (1996 –2006) and Forecasts (2007-2011) by
ARIMA model
2011
2010
2009
200
8
2007
200
6
200
5
2004
2003
200
2
2001
200
0
199
9
199
8
1997
199
6100,000
80,000
60,000
40,000
20,000
0
ForecastLCLUCLFitObservedTonnes
We now compare the forecasts of our preferred exponential smoothing model with
those from our preferred ARIMA model and these are illustrated in Figure 2.11.
Forecasts from the ARIMA model are a little higher than those from the exponential
smoothing model.
Forecasting Exports of Tuna from Thailand
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55
Figure 2.11 Forecasts from Preferred Models (2007-2011)
2011
2010
2009
2008
2007
2006
2005
2004
2020
2002
2001
2000
1999
1998
1997
1996
70,000
60,000
50,000
40,000
30,000
20,000
10,000
Forcast_ARIMA(0,1,1)(0,1,1)Forecast_Holt_Winter_MultiTotal_tuna
Tonnes
Table 2.6 shows the MAPE, MAE, and normalized BIC statistics to compare the
models. Exponential smoothing gives slightly better forecasts than the ARIMA model
at around 8.03% in MAPE. With MAE, the exponential model is smaller than the
ARIMA model (6.2%). BIC for the exponential smoothing model (16.14) is less than
that of the ARIMA model (16.22). The exponential smoothing model fits the data
better than the ARIMA model.
Forecasting Exports of Tuna from Thailand
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56
Table 2.7 Comparing Forecasts from Preferred Models
Statistic Exponential Smoothing Model ARIMA Model
MAPE 8.579 9.268 MAE 2,319.772 2,463.366 Normalized BIC 16.146 16.225
We now compare the accuracy of within-sample forecasts before selecting between
the two models. We estimate forecasts for 2003-2006 by using the data set for 1996-
2002. Figure 2.12 illustrates both forecasts together with the actual observations
(2003-2006). The forecasts from the ARIMA model appear to be nearer to the actual
observations than those from the exponential smoothing (Holt-Winters multiplicative)
model.
Forecasting Exports of Tuna from Thailand
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Figure 2.12 Within-sample Forecasts and Actual exports from Preferred Models
(2003-2006)
N 2
006
N 2
005
N 2
004
N 2
003
N 2
002
N 2
001
N 2
000
N 1
999
N 1
998
N 1
997
N 1
996
60,000
50,000
40,000
30,000
20,000
10,000
Forecast_ARIMAForecast_HoltWinterTuna_export
We also compare each set of forecasts with actual observations using the RMSE, the
MSE, the MAE, and Theil's U-statistic and the results are shown in Table 2.8. From
all statistics, the ARIMA model is the more accurate forecasting model and the
ARIMA(0,1,1)(0,1,1)12 model is preferred.
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Table 2.8 Comparison of Within-sample Forecasts Performance Measures
Exponential Smoothing Model ARIMA Model RMSE 7,195 5,057 MSE 51,769,444 20,570,296 MAE 6,097 4,139 U 0.14 0.09
The forecasts from our preferred ARIMA model for 2007-2011 are shown in Table
2.9. The maximum forecast of total tuna exports in 2007 is in November at 49,154
tonnes and the confidence limits are 60,004 and 38,304 tonnes; the lowest forecast in
2007 is 40,977 tonnes in April with confidence limits of 48,735 and 33,219 tonnes.
The forecasts trend upwards and in November 2011 are highest at 58,843 tonnes with
confidence limits of 86,063 and 31,622 tonnes. On the other hand, the lowest forecast
values are 50,665 tonnes with the upper confidence interval 75,769 tonnes and lower
25,561 tonnes. Thus the demand for tuna exports from foreign customers is increasing
over time. Table 2.9 represents the average annual growth rate for 2007-2011
including a pessimistic (lower confidence level-LCL) and an optimistic (upper
confidence level - UCL) average annual growth rate. Forecasts of total tuna exports
averaged 44,179 tonnes in 2007 and reaches 53,868 in 2011. The actual growth rate is
5.5% in 2008 and then it slightly decreases during 2009 (5.2%) to 2011 (4.7%). The
more pessimistic average annual growth rate for 2007–2011 is -7.4% in 2008 and the
more optimistic average annual growth rate for 2007–2011 is 14.1% in 2008.
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Table 2.9 Tuna Exports Forecasts from the ARIMA model (tones), 2007-2011
Month/Year 2007 2008 2009 2010 2011
Forecast LCL UCL Forecast LCL UCL Forecast LCL UCL Forecast LCL UCL Forecast LCL UCL
Jan 41,789 35,828 47,749 44,211 32,433 55,989 46,633 30,367 62,898 49,055 28,734 69,376 51,477 27,310 75,644
Feb 42,986 36,372 49,600 45,408 33,208 57,609 47,830 31,153 64,507 50,253 29,517 70,988 52,675 28,088 77,261
Mar 44,813 37,604 52,022 47,235 34,632 59,838 49,657 32,665 66,650 52,080 31,078 73,082 54,502 29,679 79,324
Apr 40,977 33,219 48,735 43,399 30,405 56,393 45,821 28,497 63,145 48,243 26,941 69,546 50,665 25,561 75,769
May 43,961 35,691 52,232 46,384 33,008 59,759 48,806 31,146 66,465 51,228 29,614 72,842 53,650 28,248 79,053
Jun 44,502 35,748 53,255 46,924 33,176 60,671 49,346 31,353 67,339 51,768 29,839 73,697 54,190 28,483 79,898
Jul 44,161 34,949 53,372 46,583 32,473 60,693 49,005 30,683 67,327 51,427 29,184 73,670 53,849 27,836 79,863
Aug 43,795 34,148 53,442 46,217 31,753 60,681 48,639 29,992 67,287 51,062 28,507 73,616 53,484 27,165 79,803
Sep 43,661 33,597 53,725 46,084 31,274 60,893 48,506 29,538 67,473 50,928 28,065 73,791 53,350 26,728 79,972
Oct 45,647 35,183 56,112 48,069 32,922 63,217 50,492 31,209 69,774 52,914 29,746 76,082 55,336 28,413 82,258
Nov 49,154 38,304 60,004 51,576 36,098 67,054 53,998 34,405 73,591 56,420 32,951 79,890 58,843 31,622 86,063
Dec 44,707 33,485 55,929 47,129 31,327 62,931 49,551 29,653 69,450 51,973 28,206 75,741 54,396 26,881 81,910
Mean 44,179 35,344 53,015 46,602 32,726 60,477 49,024 30,888 67,159 51,446 29,365 73,527 53,868 28,001 79,735
Growth rate 5.5% -7.4% 14.1% 5.2% -5.6% 11.0% 4.9% -4.9% 9.5% 4.7% -4.6% 8.4% UCL and LCL denote the upper and lower 95% confidence intervals.
Forecasting Exports of Tuna from Thailand
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2.4 Factors Influencing the Export Demand for Tuna
Export forecasts are a useful information for the Thai tuna industry for planning sales,
inventory control etc. However, these forecasts may not adequate for planning
because other factors like the population growth rate, income and tuna consumption,
tuna price, and tuna capture are likely to have an effect on the quantity of fish
demanded. We now consider these factors.
2.4.1 Population, Income and Tuna Consumption
Population growth has been the main factor behind an increasing demand for food.
Table 2.10 shows population growth in the major tuna importing countries. World
population increased from 4.8 billion in 1985 to 6.6 billion in 2006 while the average
growth rate declined to 1.29% in 2006. The population growth rate declined will
likely decline further over the next ten years (Delgado et al., 2003). The populations
in Asia (a half of world population), Africa, EU and the US will mean that, despite
comparatively low population growth rates, thus these countries will account for a
large share of the growth of food demand.
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Table 2.10 Population and Population Growth, 1985-2006
Counties/Areas Population (1,000 person) Population growth rate (%) 1985 1995 2006 1976-85 1986-95 1996-2006
United States of America 243,063 270,245 302,841 1 1.07 1.04 European Union 439,295 478,453 491,954 0.32 0.93 0.26 Middle East 158,737 208,091 259,174 3.41 2.68 2.00 Japan 120,837 125,472 127,953 0.77 0.36 0.17 Australia 15,669 18,072 20,530 1.41 1.43 1.16 Canada 25,843 29,302 32,577 1.10 1.26 0.97 Africa 554,296 726,330 943,300 2.91 2.72 2.39 Asia 2,835,132 3,451,675 3,983,882 1.91 2.00 1.29 World 4,845,419 5,719,040 6,592,899 1.76 1.66 1.29
Source: FAO (2009a)
Income changes are involved tuna demand. Asche and Bjørndal (1999) noted that
income elasticity of demand for fisheries products is generally high, often over unity.
Table 2.11 presents per capita GDP for the major tuna importing countries for 1985-
2006. GDP has increased in the world but the average GDP growth rate declined from
5.58% to 3.64%. Tuna consumption will possibly grow to mirror increases in GDP.
Table 2.12 presents tuna imports for the main importers. Tuna imports have been
growing from 1985–2006 but average tuna product import growth has been generally
declining outside the US which has the higher growth rate at 4% during 1986-95 and
at 9% during 1996-2006. Moreover , Delgado et al. (2003) stated that per capita food
fish consumption will grow throughout the developing world, while developed-
country consumption will remain virtually constant in 2020. As we have known
consumption of tuna has been limited by the relative difficulty of the already high
levels of exploitation in capture fisheries.
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Table 2.11 GDP per capita and GDP growth rate, 1985-2006
Counties/Areas GDP per capita (US$) GDP per capita growth rate 1985 1995 2006 1976-85 1986-95 1996-2006
United States of America 17,228 27,169 43,366 8.72 4.67 4.32 European Union 5,548 13,893 22,569 4.57 7.69 5.12 Middle East 6,618 6,290 16,210 2.14 2.09 9.59 Japan 11,146 41,823 34,200 10.08 11.16 -0.39 Australia 11,376 21,253 38,379 3.98 7.19 5.70 Canada 13,764 20,152 39,138 5.36 4.28 6.77 Africa 694 720 1,198 4.12 1.12 5.34 Asia 980 2,565 3,181 7.37 8.95 2.89 World 2,670 5,202 7,401 5.58 5.98 3.64
Source: United Nations Statistics Division (2009)
Table 2.12 Tuna Product Import and Tuna Product Growth Rate, 1985-2006
Counties/Areas Tuna import (tonnes) Tuna import growth rate (%) 1985 1995 2006 1976-85 1986-95 1996-2006
United States of America 74,299 97,637 192,436 15 4 9 European Union 102,604 340,333 742,642 8 12 7 Middle East 1,633 20,250 96,145 58 37 16 Japan 4,826 46,352 82,497 14 35 7 Australia 2,648 10,648 34,434 52 20 15 Canada 11,019 27,336 35,943 5 8 4 Africa 4,667 18,618 37,816 154 53 11 Asia 15,647 72,375 176,207 35 26 9 World 218,112 609,811 1,351,482 10 11 8
Source: FAO (2009a).
2.4.2 Tuna Product Price
Increasing tuna price affects demand of that product. Wessells and Wilen (1993a;
1993b) and Johnson et al. (1998) indicate that the retail demand elasticity for tuna in
Japan is close to –1, but slightly inelastic. Wallström and Wessells (1995) indicate
that the demand for canned tuna in the US is highly inelastic and changes in price do
not have a large effect on tuna demand. Figure 2.13 shows that world tuna product
imports have been growing although the average tuna import price had been stable
Forecasting Exports of Tuna from Thailand
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from 1989-1998. However, canned tuna price had been decreasing during 1998-2001
as a consequence of the weakness of Thai baht in the 1997-1998 Asian financial crisis
therefore total imports have been continuously increasing.
Figure 2.13 Canned Tuna Price compared to Canned Tuna Import, 1989-2006
0
200
400
600
800
1,000
1,200
1,400
1989
1991
1993
1995
1997
1999
2001
2003
2005
Tonnes
-
5.00
10.00
15.00
20.00
25.00
US$/carton
World import Tuna price into US
Source: Josupeit (2008) and FAO (2009b).
Note: Canned tuna price into the US (the highest market share imports) originated from Thailand (the highest market share export).
2.4.3 Trend for Tuna Catches
Tuna capture has been steadily rising in the past three decades from 1.7 million tonnes
to 4.1 million tonnes with an oscillating growth rate (Figure 2.14). Nonetheless, the
sustainability of tuna capture is not secure since it is a mirror of worldwide fish
captures. Delgado et al. (2003) conclude that most wild fisheries are near maximum
sustainable exploitation levels and capture fisheries production will most likely grow
Forecasting Exports of Tuna from Thailand
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64
slowly to 2020. However, prediction in long-term trends for fish stocks is extremely
difficult, and forecasting for the world as a whole is an extraordinarily uncertain
exercise at best. As known, all most tuna species have been fully exploited with much
over-fishing. An exception is skipjack which is still not fully exploited. Therefore,
tuna capture is unlikely to increase substantially to balance tuna demand in the future.
Figure 2.14 World Tuna Captures and Growth Rate, 1976-2006
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
4,500,000
5,000,000
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Tonnes
-10%
-5%
0%
5%
10%
15%
20%
Growth rate (%)
Source: FAO (2009b).
To sum up, according to Figure 2.10, there are three possible forecast results: a high
forecast level (an optimistic level), a medium forecast level, and a low forecast level
(a pessimistic level). The medium forecast is possible as a result of increasing in
population, income, tuna consumption. However, with lower population growth,
income growth, and tuna import growth and with unsustainable tuna stocks, the low
Forecasting Exports of Tuna from Thailand
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demand forecast is more probable given unsustainable future tuna capture.
2.5 Conclusions
This chapter presents forecasts of tuna exports in Thailand. The export demand for
tuna is important with regard to future planning, job security and sustainable
livelihood for the labour force. Our forecasts are based on monthly data between
1996-2006 and we use exponential smoothing methods and ARIMA methods. These
methods are applied to forecast the aggregate tuna exports for 2007-2011.
Our results show that the best fitting exponential smoothing model is the linear trend
and multiplicative seasonal method (or Holt-Winters’ multiplicative exponential
smoothing model) and the best fitting ARIMA model is ARIMA(0,1,1)(0,1,1)12. We
compare the Holt-Winters’ multiplicative exponential method and
ARIMA(0,1,1)(0,1,1)12 and the former significantly fits the data better. However, we
also compare the accuracy of forecasts between the two models within the actual data
period for 2002-2006 and the ARIMA model is the better fitting model. On balance,
the ARIMA model is preferred.
The forecasts of total tuna exports from the ARIMA model have an upward trend. The
highest annual growth rate is 5.5% in 2008, which decreases slightly during 2009 to
5.2% and to 4.7% by 2011. Thus, export forecasts are growing but at a falling rate.
Estimation of confidence intervals for these forecasts shows that the most pessimistic
average annual growth rate for 2007-2011 is negative at -7.4% and the most
optimistic is 14.1%. The plausible demand forecasts are the medium and low forecasts
Forecasting Exports of Tuna from Thailand
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with the latter more probable considered by population, income, tuna consumption
and tuna stocks.
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
67
Chapter 3
The Competitiveness of the Thai Processing and Fishing Sectors
3.1 Introduction
The Thai tuna industry is divided into canning, and fresh and freezing sectors. The
canning sector began operating about 35 years ago. From one tuna cannery operating
in 1972, the number grew to 11 by 1985, to 20 by 1996, and to 31 by 2005 when
output reached 450,000 tonnes (Department of Business Development, 2008). There
has been enormous growth since 1995 because of the formal establishment of the
WTO in 1995 and the introduction of the two GATT 1994 rules, namely the
commitment to reduce fish tariffs and the attempt to subject health-justified
restrictions on trade. This increase has also coincided with the reduction of operations
in other countries. Table 3.1 illustrates growth in the Thai industry, 1975-2005. Most
companies mainly produce canned tuna but some also produce other seafood. The
fresh and freezing sector has operated since 1986 and the number of firms rose to five
by 1996 and to 10 by 2004 (Table 3.2).
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
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Table 3.1. The Number of Firms in the Canned Tuna Sector, 1975-2005
Year Number of firms Industry output (tonnes) Output (tonnes) per firm12 1975 2 na na 1980 7 na na 1985 11 na na 1990 16 na na 1996 20 210,297 10,515 1998 23 249,128 10,832 2000 24 265,727 11,072 2002 24 320,241 13,343 2004 27 377,518 13,982 2005 31 453,517 14,630
Source: DBD(2008).
Table 3.2. Number of Firms in the Chilled and Frozen Tuna Sector, 1986-2004
Year Number of firms Industry output (tonnes) Output (tonnes) per firm 1986 1 na na 1994 2 na na 1996 5 4,312 862 2000 6 6,411 1,068 2004 10 11,919 1,191
Source: DBD(2008).
With more opportunities from foreign supports such as the US and the lowest cost
tuna production, Thailand has become the largest tuna exporter. However, since 1996,
exports have been increasing but with a decreasing growth rate. Thailand has a
comparative advantage but its average revealed comparative advantage (RCA) indices
showed a decreasing trend during 1982-1998 (Kijboonchoo and Kalayanakupt, 2003).
Putthipokin (2001) reports Thailand’s RCA indices for canned tuna exports for five
major importers, the US, EU, Japan, Canada, and Australia: those for exports to the
EU, Australia, and Japan showed a decreasing trend between 1994-1999 and
12 Output per cannery is calculated from export volumes divided by the number of firms each year.
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
69
Putthipokin cites insufficient domestic tuna, lack of labour and higher wage costs,
international competition, and tariff and non-tariff barriers in international trade as
well as other trade agreements as the principal causes. The competitiveness of the
Thai tuna industry may not be strong in future.
Event though Thailand is the largest tuna exporter, it loses money from raw tuna
imports about 33,000 million baht in 2006 (Josupeit, 2008). Raw fish stocks are from
foreign catches from Japan, Taiwan, China, and Indonesia. Table 3.3 shows the
average number of foreign and Thai vessels landing in Thailand, 1997-2006. Purse
seine fleets have an increasing trend almost doubling the number of vessels,
particularly in the Western Pacific Ocean while the number of long-line vessels
doubled from 1997-2006, mainly in the Indian Ocean.
Table 3.3 Number of Foreign Tuna Vessels landing in Thailand, 1996-2006
Average Number of Vessels Year Purse-seine Purse-seine Total Long-line Long-line Total Indian Ocean Pacific Ocean Purse-seine Indian Ocean Pacific Ocean Long-line 1997 54 137 191 191 0 191 1998 28 161 189 163 5 168 1999 56 139 195 273 0 273 2000 35 166 201 284 4 288 2001 22 179 201 325 8 333 2002 48 173 221 420 1 421 2003 56 199 255 254 7 261 2004 45 233 278 300 25 325 2005 114 234 348 295 41 336 2006 67 268 335 357 53 410
Source: Calculated from Department of Fisheries (2006).
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
70
Not only Thailand has spent money on tuna imports but have tuna exports faced a
serious obstacle, namely rules of origin. Thailand has put more efforts to establish
own tuna fleets which aim to increase tuna supply, decrease the loss from imports,
and satisfy rules of origin but it has not been too successful. Now only a few Thai
tuna vessels from private companies operate because heavy capital investment in
fishing vessels and the associated risks often discourage entry into the distant water
fishing business. Table 3.4 and Table 3.5 show the number of Thai purse seiners
recorded in IOTC from 2005-2008. There were six Thai purse seiners operating in the
Indian Ocean during 2005-2007. Four vessels were bought by another owner, the
largest canning company-Thai Union Group-and are still operating in 2008. Two
fishing vessels, “Crystal Crown” and “Glorious Harmony” are no longer operating.
Table 3.4 Thai Purse Seiners Recorded in IOTC, 2005-2007
Vessel name Company Tonnage
Gross tonnage) Length (Metre)
Crystal Crown THAI TUNA FISHING CO., LTD. 2,660 na Eternity INTERNATIONAL FISHING CORPORATION
PUBLIC CO., LTD. 2027 79 Glorious Harmony THAI TUNA FISHING CO., LTD. 2,660 na Golden Success SIAM DEEP SEA FISHING CO., LTD. 1,413 72.5
Longevity INTERNATIONAL FISHING CORPORATION PUBLIC CO., LTD. 2,027 79
Prosperous SIAM DEEP SEA FISHING CO., LTD. 2,027 79
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
71
Table 3.5 Thai Purse Seiners Recorded in IOTC, 2007-2008
Vessel name Company Tonnage
(Gross tonnage) Length (Metre) Thai Union 1 PHANG-NGA ISHING CO., LTD 1,948 79 Thai Union 2 SONGKLA FISHING CO., LTD. 1,948 79 Thai Union 3 SAMUI FISHING CO., LTD. 1,948 79 Thai Union Dream 13 SAMUI FISHING CO., LTD. 470 47.69 Thai Union Star PHUKET FISHING CO., LTD. 1,413 72.5
Long-line vessels, with lower capital investment, can be operated for longer and are
more likely to increase in numbers. During 2004-2008, six long-liners under the same
owners were licensed of IOTC. Table 3.6 shows the number of Thai long-line vessels
from 2004-2008. There are six long-liners established by three companies.
Table 3.6 Thai Long-liners Recorded in IOTC, 2004-2008
Vessel Name Company Tonnage
(Gross tonnage) Length (Metre) Mook Andaman 018 Operating 2004-2008 SIAM TUNA INDUSTRY CO., LTD. 434 53.57 Mook Andaman 028 Operating 2003-2008 SIAM TUNA INDUSTRY CO., LTD. 372 52.1 Prantalay 1 Operating 2005-2008 P.T. INTERFISHERY CO., LTD. 758.5 56 Prantalay 2 Operating 2005-2008 P.T. INTERFISHERY CO., LTD. 758.5 56 Tuna Hunter 1 Operating 2005-2008 FIVE STAR TUNA LINE CO., LTD. 151 28 Tuna Hunter 2 Operating 2006-2008 FIVE STAR TUNA LINE CO., LTD. 175 30.5
The aim of this chapter is to examine the sustainable competitiveness of the Thai tuna
processing and fishing sectors in the long term. We consider external relationships
and internal capabilities, that is its own distinctive capabilities which are derived from
a firm’s relationship with its suppliers, and customers and which is identifies and
applied to relevant markets (Kay, 1993). The Thai tuna industry can be sustainable if
13 It is a searching supply vessel
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
72
it has strong internal competition and is competitive internationally.
For the internal capabilities, two sectors are examined. First, the domestic competition
of the Thai tuna industry is measured. Competition is defined as conditions for market
competition such as freedom of entry and exit, whether firms are price takers or price
setters, information availability, and the existence of differentiated products. The
study of domestic competition determines the potential of the market which it
measures internally by the strength of domestic competition. A commonly-used
approach for examining domestic competition is the structure-conduct-performance
(SCP) paradigm of Mason (1939) and Bain (1956; 1951) which postulates that key
market attributes affect the conduct of the firm, which in turn affects profitability (see
Appendix 3). It is used to analyse competitive conditions in industries by examining
how the structure of the industry is related to the behaviour and performance of firms.
Resende (2007) notes that the SCP paradigm has an enduring empirical tradition in
industrial economics and has the advantage of clarifying the basic building blocks of
competitive mechanisms. The research undertaken here provides evidence that firms
can alter market structure, and implement competitive strategies to increase
performance. Second, we consider input in the industry. The tuna fishing investment
is important for the industry and international trading since it decreases imports and is
a solution for rules of origin. The potential of Thai tuna fishing sector is assessed by
considering costs and returns and using break-even analysis.
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
73
In the case of external relationships, we examine comparative advantage.
Comparative advantage is a key determinant of international production that affects
resource allocation, trade patterns, and trade volumes. The concept of revealed
comparative advantage (RCA) pertains to the relative trade performances of
individual countries in particular commodities (Balassa, 1965). Balassa (1977) claims
that comparative advantage is revealed by observed trade patterns, such as high
market shares in export markets. Although there are some weaknesses in these
indexes (see Appendix 3), they are commonly used in comparative advantages’
analyses since there are no techniques for directly measuring a country’s comparative
advantage as it requires knowledge of pre-trade relative prices that are not observed.
However, Putthipokin (2001) argues that the RCA method is inadequate for
competitive analysis since the calculation of the index only uses import or export data.
Gupta (2009) argues that models of comparative advantage used with models of
competitive advantage have the potential to offer a much richer analysis of
international trade/business that is normally not available with either alone. Porter
(1990, p.72) argues that the RCA index does not link production and other relevant
factors, such as trade barriers, the role of government, demand conditions, the related
industries, input factors, and the structure and strategy of firms. Although Porter’s
model suggests some important determinants of a nation’s global competitiveness,
Moon et al. (1998) argue that Porter’s original diamond model is incomplete because
it does not incorporate multinational activities. A new approach, the generalized
double diamond model (Moon et al., 1995) is an extension14.
14 The SCP framework, cost and return estimation and break-even analysis methods, the revealed comparative advantage method, Porter’s diamond and double diamond models and a general theoretical literature review are detailed in Appendix 3
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
74
This remainder of this chapter is divided into six sections. Section 2 presents a
literature review of previous Thai tuna industry studies. Section 3 analyses domestic
competition using the SCP framework. Section 4 estimates costs and revenues and
performs a break-even analysis of tuna fishing. Section 5 examines comparative
advantage for Thailand’s main export competitors for 1996-2006 and for the main
importers of the US, EU, the Middle East, Japan, Australia, and Canada from 1996-
2005. Section 6 examines Porter’s Diamond model and multinational activities of the
Thai processing and fishing sectors. Section 7 provides a discussion and concludes.
3.2 Literature Review
Competition with the Thai tuna industry has been recently investigated. Putthipokin
(2001) investigated concentration of Thai tuna firms using the Harfindahl-Hirschman
(HH) index and the results showed that Thai canneries were not concentrated and
operated in monopolistic competition. There are now 31 tuna canneries in Thailand
and more than a half of the total market share is dominated by two firms. This suggest
that the market may be oligopolistic and the conclusions of Putthipokin (2001) need
to be updated. Moreover, Putthipokin did not distinguish between canning, and fresh
and freezing sectors.
The comparative advantage of the Thai tuna industry has been examined in recent
studies. Putthipokin (2001) used the RCA index to analyse the canned tuna industry in
Thailand, the Philippines and Indonesia (which were Thailand's main competitors) for
five main importers, the US, the EU, Canada, Australia, and Japan, for 1994-1999.
Results show that most Thai RCA indices of exports were much greater than the two
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
75
other countries with four main importers, except for the EU, and Thai exporters had
the largest comparative advantage. Kijboonchoo and Kalayanakupt (2003) analysed
comparative advantage and competitive strength of Thai canned tuna exports in the
world market. They compare RCA indices with the main exporters for four periods
1982-1986, 1987-1991, 1992-1995, and 1996-1998. Results show that Thailand’s
comparative advantage for canned tuna exports had been declining steadily;
Philippines’ and Indonesia’s comparative advantage were lower; and Côte d'Ivoire,
Mauritius, Ghana, Seychelles and other ACP countries had become Thailand’s
competitors; and most competitors had better tuna resources and an efficient high-sea
fleet for catching tuna at lower cost. However, the main exporters have subsequently
changed: for example, Côte d’Ivoire was the third largest canned tuna export until
2003 but exports have since declined substantially because of political instability
(Ababouch and Catarci, 2008); and the market share of the Philippines has changed
from second largest exporter to sixth.
Competitiveness of the Thai tuna industry has also been examined. Putthipokin (2001)
investigated the Thai tuna industry using Porter’s diamond model for the competitive
advantage of the tuna canneries of Thailand, Philippines, and Indonesia in 1999 with
the main importing countries: the US, the EU, Japan, Australia, and Canada. Results
show that Thailand had a greater competitive advantage in firm strategy, structure,
and rivalry than both competitors. Also, Thailand had a competitive advantage in
related and supporting industries of can packaging and sea transport. However,
Thailand had a competitive disadvantage with factor conditions of raw tuna materials
and the supporting fishery industry. Kohpaiboon (2006) notes that Thai tuna
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
76
processing gains competitive advantage of tuna export expansion from multinational
enterprises (MNEs). MNEs can involve themselves in host countries, through foreign
direct investment (FDI) and non-FDI channels. FDI is the outcome of a firm’s
decision to diversify all or some operational activities across countries. FDI has the
potential to generate impact on host countries’ economies such as injecting additional
funds, influencing the performance of locally-owned firms, creating upstream and
downstream linkages, bringing in superior technology, etc. MNE involvement in FDI
channels in the canned tuna industry introduces new opportunities to local
entrepreneurs, such as superior technologies, marketing and managerial practices.
Through non-FDI, MNE buyers play a significant role in assisting local firms to gain
a foothold in foreign market by using well-established brands, providing advice on the
food safety regulation, and organising factory visits from buyers.
3.3 A Structure, Conduct and Performance Analysis
3.3.1 Data Sources
Data on the Thai tuna factories were collected from two sources. Primary data were
collected by interviewing nine factory managers between September and December
2006. Simple random sampling was employed to collect data. The factories sampled
were in Samutsakhon, Phuket and Songkhla provinces. Secondary data were collected
from government organisations including the Ministry of Commerce, the Department
of Fisheries, and other organisations. The financial statement of each firm was
collected from the Ministry of Commerce (Department of Business Development,
2008).
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
77
3.3.2 The Structure of the Thai Tuna Industry
3.3.2.1 Concentration Measurement
We examine the structure of both the canning, and fresh and frozen tuna sectors in
2005 using five concentration measures: the concentration ratio (CR), the Gini
coefficient (G), the Herfindahl-Hirschman (HH) Index, the Hannah and Kay (HK)
Index, and the entropy (E) measure. Table 3.7 shows the market share in the canning
sector. The Thai Union Group had a market share of 37% in 2005 while Sea Value
had a market share of 15 %. Other companies have less than 7 % each. The
concentration curve in Figure 3.1 lies significantly above the diagonal straight line,
which is the line where all firms are the same size, and the canning sector is highly
concentrated.
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Table 3.7. Market Shares of the Tuna Cannery Sector, 2005
Company Sales 2005 Million Baht15 Market Share (%)
1 THAI UNION GROUP CO., LTD. (a+b+c) 30,026 37.4 a. THAI UNION FROZEN PRODUCTS PUBLIC CO., LTD. b. THAI UNION MANUFACTURING CO., LTD. c. S.C.C FROZEN SEAFOOD CO., LTD.
2 SEAVALUE CO., LTD. (d+e) 11,973 14.9 d. I.S.A. VALUE CO., LTD. e. UNICORD CO., LTD.
3 CHOTIWAT MANUFACTURING CO., LTD. 5,192 6.5 4 SOUTHEAST ASEAN PACKAGING AND CANNING CO., LTD. 4,439 5.5 5 PATTAYA FOOD CO., LTD. 4,248 5.3 6 KINGFISHER HOLDINGS LIMITED CO., LTD. 3,886 4.8 7 TROPICAL CANNING (THAILAND) PUBLIC CO., LTD. 3,044 3.8 8 GOLDEN PRIZE CANNING CO., LTD. 2,868 3.6 9 R.S. CANNERY CO., LTD. 2,495 3.1 10 ASIAN SEAFOODS COLDSTORAGE (SURATTHANI) CO., LTD. 1,882 2.3 11 M.M.P. INTERNATIONAL CO., LTD. 1,717 2.1 12 HI-Q FOOD PRODUCT CO., LTD. 1,652 2.1 13 SIAM TIN FOOD PRODUCTS CO., LTD. 1,364 1.7 14 PATTANI FOOD INDUSTRIES CO., LTD. 1,233 1.5 15 SEA HORSE PUBLIC CO., LTD. 895 1.1 16 PREMIER CANNING INDUSTRY CO., LTD. 818 1.0 17 AURORA POUCH PRODUCTS INDUSTRY CO., LTD. 568 0.7 18 PAN ASIA (1981) CO., LTD. 559 0.7 19 SAMUI CO., LTD. 534 0.7 20 P.B. FISHERY PRODUCT CO., LTD. 433 0.5 21 MAHACHAI MARINE PRODUCTS CO., LTD. 178 0.2 22 KIAT CHAROEN FOOD CO., LTD. 124 0.2 23 S.P.A. INTERNATIONAL FOOD GROUP CO., LTD. 99 0.1 24 S.V. FOOD CO., LTD. 95 0.1 25 SIRINAN FOOD CO., LTD. 47 0.1
15 70 baht = £1.
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Figure 3.1 Concentration Curve of Canning Sector, 2005
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 15 20 25
Cumulated processing firms
Cumulated market share (sales)
In Table 3.8, CR4 (64%) is above 40% and the sector is oligopolistic, that is
moderately concentrated. A Gini coefficient of 0.63 indicates that firms are of unequal
size. The HH-index is 0.18 and this sector is moderately concentrated, and there are
only five equal-sized companies. For the HK-index, α=2.4 because there is a
dominant firm, and the HK-index indicates again that there are five equal firms. An E-
coefficient of 0.99 indicates that the sector is concentrated.
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Table 3.8 Indices of Concentration for the Canned Tuna Sector, 2005
Index CR4 64% Gini coefficient 0.63 HH-index 0.18 The number of equivalent firms (HH) 5.55 HK-index 0.21 The number of equivalent firms (HK) 4.84 The entropy coefficient 0.99
Table 3.9 shows market shares in the fresh and frozen sector which is composed of
eight firms. The market is dominated by the largest firm, the Siam Chai International
Food company, which control 67% of the market while the Thai Ocean Venture
company has 14 %. The remaining 18% is in the hands of six other firms.
Table 3.9. Market Share of Fresh and Freezing Sector, 2005
Company lists Sales 2005 Million Baht Market share (%) 1 SIAM CHAI INTERNATIONAL FOOD CO., LTD. 1,498 66.7 2 THAI OCEAN VENTURE CO., LTD. 305 13.6 3 PHUKET DONGHER TRADING CO., LTD. 117 5.2 4 SIMIRAN CO., LTD. 90 4.0 5 SIAM TUNA SUPPLY CO., LTD. 80 3.6 6 TUNA PARADISE CO., LTD. 76 3.4 7 GGC. TWN. CO., LTD. 68 3.0 8 SIAM TUNA FISHERY CO., LTD. 13 0.6
The concentration curve for the fresh and freezing sector is shown in Figure 3.2. It
indicates inequality because it is dominated by the largest firm, the Siam Chai
International Food company. Table 3.9 shows that CR4 (89%) is above 40%, and this
sector is very concentrated and extremely oligopolistic. This inequality is highlighted
by the Gini coefficient of 0.65, and the HH-index of 0.47 indicate two equal-sized
firms as does the HK-index. The E-efficient of 0.52 shows that this sector is more
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concentrated than the canning sector.
Figure 3.2 Concentration Curve of Fresh and Freezing Sector, 2005
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8
Cumulated processing firms
Cumualted market share (sales)
Table 3.10 Indices of Concentration for the Fresh and Frozen Tuna Sector, 2005
Index CR4 89% Gini coefficient 0.65 HH-index 0.47 The number of equivalent firms (HH) 2.12 HK-index 0.52 The number of equivalent firms (HK) 1.90 The entropy coefficient 0.52
In conclusion, the concentration curves of both sectors in Figure 3.3 illustrate that the
fresh and freezing sector is more concentrated than the canning sector and other
concentration indices of both sectors show that the canning, and the fresh and freezing
sectors are oligopolistic.
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Figure 3.3 Concentration Curves of Canning and Fresh and Freezing Sectors,
2005
CanningFresh and Freezing
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Cumulated Number of firms
Cumulated market share (sales)
3.3.2.2 Barriers to Entry
There are three main barriers to entry: legal barriers, Bain barriers and geographical
barriers. The barriers restricting exports consisted of four conditions. First, no canned
tuna firms are allowed to export without being members of Thai Food Processors’
Association. Second, canned tuna products need a health certificate from the
Department of Fisheries and a Non-GMO certificate. Third, canned tuna products
have to pass a third-party investigation by Intertek Testing Services (ITS). Fourth,
government policy barriers exist in the form of import and export tariffs. In Thailand,
canned tuna and fresh and frozen tuna products are exempt from tariffs if they are
exported by ship, but if exported by air, they have a 5% tariff for fresh and frozen
tuna and 20% for canned tuna. Conversely, there are two categories of import tariffs:
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
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firms have to pay 5% for domestic consumption but they are exempted if they import
raw tuna material for export.
Bain barriers refer to economies of scale and absolute cost advantages. The largest
canned tuna firm, including its two subsidiary firms, has an annual production
capacity of 280,000 tonnes for canned tuna and 34,000 tonnes for tuna loin per year; it
has production cost advantages attributed to economies of scale (TRIS, 2008;
Bangkok post, 2007). The second largest firm, including its three subsidiary firms, has
a production capacity of 250,000 tonnes/year. The remaining canneries produce about
270,000 tonnes/year. With a high production capacity, existing firms can access
cheaper sources, particularly raw tuna imports. By contrast, new entrants may
experience higher input costs.
Geographical barriers refer to the restriction faced by Thai tuna companies attempting
to enter foreign domestic markets. The restrictions of tariffs and quotas that affect
Thai processors in 2005 were the EU tariff duty and bilateral trade agreements with
other countries. The EU single duty in Table 3.11 shows the tariff quota from 2003-
2006. Thailand received a quota of about 13,000 tonnes at 0%; exports of canned tuna
above this attract a tariff of 24%. This agreement ended in 2007 and the EU now
imposes a 24% tariff on all canned tuna products. This barrier affects existing firms
and new entrants as the EU is the second largest canned tuna importer.
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Table 3.11. EU Tariff Quota (tonnes)
Year Tariff quota Thailand Philippines Indonesia Others 100% 52% 36% 11% 1% Jul 2003 - June 2004 25,000 13,000 9,000 2,750 250 Jul 2004 - June 2005 25,750 13,390 9,270 2,832 258 Jul 2005 - June 2006 25,750 13,390 9,270 2,832 258 Source: Chalisarapong (2006).
The Thai tuna industry is also affected by Free Trade Agreements (FTAs). The Thai
government has opened negotiations with all major countries for duty-free access.
These agreements provide Thai exports with a competitive advantage in terms of
reduced trade tariffs. However, this is a controversial issue because tuna exports are
constrained by strict rules of origin. However as the next section indicates, Thai tuna
fishing vessels are largely unprofitable and are operating well below break-even catch
levels.
3.3.3 The Relationship between Structure and Conduct
Conduct refers to the competitive behaviour of firms. The aim of conduct studies is to
understand how firms attract customers and how they react to competitive action. This
section identifies a price leadership theory, product and market strategies, and vertical
and horizontal integration strategies.
3.3.3.1 Price Leadership Analysis
An oligopoly is a market where there are only a few producers or there is a high
concentration and profits can be made in both the short and long run. New firms can
enter with difficultly because of entry barriers. This analysis is based on the
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concentration measurement and other relevant evidence. Concentration measures
show that both the canning and the fresh and freezing sectors are oligopolistic. The
most appropriate economic model here is one of price leadership model where the
dominant firm has a considerable market share and is a price leader; and smaller
firms, each of them having a small market share, follow Koutsoyiannis (1980, pp.
244-248) and Shaffer (1985). Pananond (2004) also noted that Thai Union Group is a
dominant firm and a large family business group. Table 3.12 shows that the canned
tuna leader is the Thai Union Group with a market share average of 40%, and the
fresh and frozen tuna leader is Siam Chai International Food with a market share
67%.
Table 3.12 Market Shares of Dominant Firms in Canning and Fresh and
Freezing Sectors, 2002-2006
Year Market Share (%)
THAI UNION GROUP (Canning sector)
SIAM CHAI INTERNATIONAL FOOD(Fresh and Freezing sector)
2002 40 75 2003 43 66 2004 41 75 2005 37 67 2006 40 n.a.
In the long run, the number of small firms in the market will decline and the share of
the competitive fringe will decrease. Small firms typically merge or are acquired by
larger firms to increase market power, and to give each other access to their respective
know-how, capital system, image and reputation (Fisher, 2009). An acquisition took
place in the Thai tuna industry when Unicord and I.S.A. Value were acquired by Sea
Value.
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3.3.3.2 Advertising Strategies Analysis
A variety of product lines and product brand names are used in canning. Product lines
vary depending on customers: local demand requires particular value-added products
such as tuna in different flavours (sauces, spread, and curry) in cans and pouches,
while foreign customers demand basic tuna (in brine and oil) in cans and pouches, and
frozen tuna for canning. There are two strategies used for product brand names. Local
brand names are used for the domestic market and include SEALECT, HI-Q, TCB,
SEA HORSE and NAUTILUS. Other canned tuna sold in the domestic market use
foreign brand names, such as AYAM, KINGFISH, HEINZ, JOHNWEST, and
SAFCOL. For exports, processors typically use foreign brand names, but local brand
names are also used, such as NAUTILUS. The Chicken of the Sea, which is the brand
name of the third largest company in the US, is labelled by Thai Union Group while
Sea Value uses the Bumble Bee brand name for the second largest US tuna company.
The remaining tuna products are produced to order and labelled with the customer’s
own brand name. The fresh and freezing sector does not use a brand name strategy.
3.3.3.3 Vertical and Horizontal Integration Strategies Analysis
The two largest firms, the Thai Union Group and Sea Value, are emerging and
establishing themselves in other sectors or extending their product ranges or markets.
Figure 3.4 shows the strategies of the largest canned tuna firm, the Thai Union Group.
There are three products in the two subsidiaries: tuna product (A), pet food (B) and
seafood (C). With the vertical integration, there are 24 subsidiary companies. Number
3 is the production stage. Stages 1 and 2 are upstream, backward vertical integration
and stages 4 and 5 are downstream, forward vertical integration. For production stage
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3, there are three tuna cannery companies, Thai Union Frozen Products, Thai Union
Manufacturing, and S.C.C. Frozen Seafood. The Thai Union Frozen Products mainly
produce seafood products (C), such as frozen shrimp and tuna products (A) while
Thai Union Manufacturing produces only canned tuna products (A) and pet foods (B)
(by-product from canned tuna), and S.C.C. Frozen Seafood produces canned tuna (A)
and canned seafood products (C).
Figure 3.4. The Strategies of Thai Union Group
Raw materials (Import and tuna fleets)
Components (can, package, graphic)
Processors (Local factories and foreign factories)
Market service
(R&D and Quality management)
Distribution activities
(Importer and distributor)
A A B B C C
1
2
3
4
5
Backward integration
Forward integration
A B C
A A B B C C
C
A B C
Solid lines indicate domestic transfers
Dashed lines show imports
A = tuna product
B = pet food
C = seafood product
Raw materials (Import and tuna fleets)
Components (can, package, graphic)
Processors (Local factories and foreign factories)
Market service
(R&D and Quality management)
Distribution activities
(Importer and distributor)
A A B B C C
1
2
3
4
5
Backward integration
Forward integration
A B C
A A B B C C
C
A B C
Raw materials (Import and tuna fleets)
Components (can, package, graphic)
Processors (Local factories and foreign factories)
Market service
(R&D and Quality management)
Distribution activities
(Importer and distributor)
A A B B C C
1
2
3
4
5
Backward integration
Forward integration
A B C
A A B B C C
C
A B C
Solid lines indicate domestic transfers
Dashed lines show imports
A = tuna product
B = pet food
C = seafood product
Solid lines indicate domestic transfers
Dashed lines show imports
A = tuna product
B = pet food
C = seafood product Source: Thai Union Group (2008) and adapted from Harrigan (1985).
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At the production stage, the firms also established foreign plants in Indonesia (A and
B) and share investment in the US under the brand name of the Chicken of the Sea, in
Papua New Guinea, and in Vietnam (A and B). Backward vertical integration (stages
1 and 2) is a process of establishing subsidiaries for increasing control of supply. It
serves to streamline the organization to provide better cost controls and eliminate
middleman.
In stage 1, the Thai Union Group established fishing companies and component
companies. Due to the high tuna import price, this company invested heavily in five
tuna fishing companies to reduce tuna imports and solve the rules of origin. This
investment provides tuna supplies of about 8% of the total of tuna for canning (The
Thai Union Group, 2007). The fishing companies support canned tuna product and pet
foods (A and B). In stage 2, the company established two companies to support the
main plant. These are packaging product and printing companies to support all three
products. The forward vertical integration in stage 4 and 5 is where the company sets
up subsidiaries to distribute products. There are farming and breeding companies for
marine products as well as for the quality development to support product C.
Figure 3.5 presents the strategies of the second largest canned tuna firm, Sea Value.
This firm also uses vertical and horizontal integration. Horizontal integration is a
process of merging or taking over other firms operating with similar products. Sea
Value took over three large tuna factories from two tuna companies (Unicord and
I.S.A. Value) in 2004 and the Bumble Bee Food Company merged with its 10% of
market shares. There are three main products: tuna products, pet food and sardine
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products. The Sea Value has tried to establish the Siam International Fishery and a
joint venture with Indonesia to increase tuna catches for canning but it has not been
successful yet.
Figure 3.5 The Strategies of Sea Value Group
Raw materials (Imports, tuna
fleets)
Processors (Local factories)
A A B B
1
2
Backward vertical
integration
A A B B
A B
Solid lines indicate domestic transfersDashed lines show imports
Horizontal integration
A = tuna productB = pet foodC = Sardine product
Distribution activities
(Importer and distributor)
3
C C
C C
C
Raw materials (Imports, tuna
fleets)
Processors (Local factories)
A A B B
1
2
Backward vertical
integration
A A B B
A B
Solid lines indicate domestic transfersDashed lines show imports
Solid lines indicate domestic transfersDashed lines show imports
Horizontal integration
A = tuna productB = pet foodC = Sardine product
Distribution activities
(Importer and distributor)
3
C C
C C
C
Source: SEA Value Group (2008) and adapted from Harrigan (1985).
Because of economies of scale, these firms are able to use vertical and/or horizontal
integration as competitive strategies. Transaction costs are consequently reduced since
it is cheaper for these firms to perform the role of supplier and distributors than to
spend time and money interacting with other suppliers and distributors. Backward and
forward integrations are also a means of increasing a company’s value-added margins
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for a specific chain of processing from ultra-raw materials to ultimate consumers. By
contrast, new entrants face difficulties in operating at more than one level of
production. In addition, the largest firm is vertically integrated as a consequence of
capital availability and is able to use it own facilities from subsidiary firms to provide
extra profit (Piñeros and Lewis, 2005).
3.3.4 Performance Measurement
Performance is measured by profitability in financial statements and is available as
secondary data. In 2005, financial statements were available for 25 firms from the
canning sector and for eight (of 10) firms in the fresh and freezing sector. The most
common measures of profit are price-cost margin (PCM), the accounting rate of profit
on asset (ROA), the accounting rate of profit on equity (ROE), and the accounting rate
of profit on sales (ROS). In relation to the SCP paradigm, a larger market share leads
to greater power in terms of the capability to increase prices and thus increase
performance (Jedlicka and Jumah, 2006).
Table 3.13 shows performance measures in the canning sector. The profit margin is a
measurement of potential profitability per amount of sales revenues. The average
PCM ratio is 11% for the top four firms while that for smaller firms is 9%. Although
accounting rates of profit have many problems (Lipczynski and Wilson, 2001), their
measures are frequently used in competition studies because data are readily available
from annual reports. The return on assets can also a sure-fire way to gauge the asset
intensity of a business. The return to investment as a mechanism for generating profit
is shown by the ROA and ROE.
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Table 3.13. Price-Cost Margin and Accounting Profit Ratios for the Canned
Tuna Sector, 2005
Company Market share (%) PCM (%) ROA ROE ROS 1 THAI UNION GROUP 37.4 15 0.16 0.21 0.12 2 SEA VALUE CO., LTD. 14.9 6 0.98 4.23 0.48 3 CHOTIWAT MANUFACTURING CO., LTD. 6.5 12 0.05 0.09 0.02 4 SOUTHEAST ASEAN PACKAGING AND CANNING CO., LTD. 5.5 10 0.10 0.18 0.04 5 PATTAYA FOOD CO., LTD. 5.3 9 0.01 -0.01 0.00 6 KINGFISHER HOLDINGS LIMITED CO., LTD. 4.8 12 0.09 0.12 0.04 7 TROPICAL CANNING (THAILAND) PUBLIC CO., LTD. 3.8 5 0.02 0.02 0.01 8 GOLDEN PRIZE CANNING CO., LTD 3.6 6 0.04 0.33 0.01 9 R.S. CANNERY CO., LTD. 3.1 16 0.40 0.39 0.13 10 ASIAN SEAFOODS COLDSTORAGE (SURATTHANI) CO., LTD. 2.3 14 0.05 0.41 0.08 11 M.M.P. INTERNATIONAL CO., LTD. 2.1 3 0.01 0.02 0.00 12 HI-Q FOOD PRODUCT CO., LTD. 2.1 13 0.00 -0.01 0.00 13 SIAM TIN FOOD PRODUCTS CO., LTD. 1.7 15 0.23 0.23 0.10 14 PATTANI FOOD INDUSTRIES CO., LTD. 1.5 4 0.01 0.03 0.01 15 SEA HORSE PUBLIC CO., LTD. 1.1 17 0.05 0.05 0.01 16 PREMIER CANNING INDUSTRY CO., LTD. 1.0 7 0.13 1.04 0.04 17 AURORA POUCH PRODUCTS INDUSTRY CO., LTD. 0.7 9 0.06 0.07 0.01 18 PAN ASIA (1981) CO., LTD. 0.7 20 0.05 0.01 0.01 19 SAMUI CO., LTD. 0.7 10 -0.02 -1.08 -0.02 20 P.B. FISHERY PRODUCT CO., LTD. 0.5 12 0.09 0.24 0.04 21 MAHACHAI MARINE PRODUCTS CO., LTD. 0.2 -12 n.a. n.a. n.a. 22 KIAT CHAROEN FOOD CO., LTD. 0.2 -2 -0.01 0.03 -0.02 23 S.P.A. INTERNATIONAL FOOD GROUP CO., LTD. 0.1 14 0.06 0.02 0.00 24 S.V. FOOD CO., LTD. 0.1 9 0.00 -0.25 -0.02 25 SIRINAN FOOD CO., LTD. 0.1 4 -0.06 5.68 -0.08
Sea Value had the highest ROA (98%) but this ratio is abnormally high because this
firm was taken over in 2004 (Prachachat Turakit, 2006) and two companies were sold
with all their assets. The ROA for the RS Cannery, which is a small firm shared only
3% of total sales, is 40% because the balance sheet and the total assets show a
dramatic decline between 2003 (-49%) and 2005 (-19%). The ROA of Siam Tin Food
Products is 23% because its profit increased fourfold. For the remaining companies,
ROAs varied widely: Thai Union Group’s ROA is 16%, followed by Premiere
canning industry (13%), Southeast Asian packing and canning (10%), and P.B fishery
product (9%) and these companies show effective managerial use of assets. ROAs for
seven other enterprises are moderately high (4%-6%) and such enterprises show
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medium efficiency of asset management. The performance of the other nine firms is
poor and there is inefficient use of assets.
In general, a company’s success is measured by its return on equity (ROE). The
normal benchmark for ROE figure is over 20%. Companies that could generate ROE
of 20% or more are considered as a very good investment. Unusually, the ROE for
Sirinun Food has the highest value (568%), this company may have lost profit over
time because earnings turned negative on the income statement and shareholders'
equity is negative. The ROE of Sea Value is high (423%) because of merger. Premier
canning industry has the highest ROE (104%). The following common companies are
Asian Seafoods Coldstorage (Suratthani) (41%), R.S Cannery (39%), and Golden
Prize Canning (33%). The managements of these companies are more efficient and
the owners’ investment would be satisfied with the performance. Thai Union Group’s
ROE is only 21%. The lowest of ROE for S.V. Food is -25%.
The final ratio, ROS, shows how efficiently management uses the sales, thus
reflecting its ability to manage costs and overhead and operate efficiently. It also
indicates a company's ability to withstand adverse conditions such as falling prices,
rising costs, or declining sales. The higher the figure, the better a company is able to
endure price wars and falling prices. Results show that Sea Value has the highest ROS
(48%) with unstable sales in 2004-2005. The second rank is R.S. Cannery with ROS
of 13% followed by Thai Union Group (12%). The remaining companies have low
performance with ROSs between 2% and -8%.
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The profit margin ratio is used to compare the profitability of different companies
since it shows deeper insight into management efficiency. When a company has a
high margin, it usually means that it has one or more advantages over its competition.
Companies with high profit margins have a bigger cushion to protect themselves
during hard time while those with low profit margins leave the industry in a downturn
(McClure, 2004). Comparing the performance of firms using price-cost-margin
(PCM), we divide into normal, high survival, low survival, and high risk stages. The
medium survival state is defined as the mean of each ratio16. A high survival stage is
indicated as a ratio greater that an average ratio; a low survival state is referred being
positive but less that the average ratio; and a high risk stage is defined as a negative
ratio. These criterions are used in Table 3.14 and Table 3.16. Table 3.14 shows the
range of profitability measurement for the canned tuna sector. The average PCM is
9%. Thirteen companies have higher profit margins, three have a medium profit
margin, and seven have a lower profit margin. Only two firms suffer a high risk, and
of these only Kait Charoen Food is still operating17 (Thai Food Processors'
Association, 2009; Department of Business Development, 2008).
16 The mean of the ratio calculated in each ratio does not include abnormal company values or negative values. 17 Mahachai Marine Product is not in the lists of processing companies in 2009.
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Table 3.14 Performance Ranking for the Canned Tuna Sector, 2005
State PCM High THAI UNION GROUP CHOTIWAT MANUFACTURING SOUTHEAST ASEAN PACKAGING AND CANNING KINGFISHER HOLDINGS LIMITED R.S. CANNERY ASIAN SEAFOODS COLDSTORAGE (SURATTHANI) HI-Q FOOD PRODUCT SIAM TIN FOOD PRODUCTS SEA HORSE PUBLIC PAN ASIA (1981) SAMUI
P.B. FISHERY PRODUCT S.P.A. INTERNATIONAL FOOD GROUP
Normal S.V. FOOD PATTAYA FOOD AURORA POUCH PRODUCTS INDUSTRY Low SEA VALUE TROPICAL CANNING (THAILAND) GOLDEN PRIZE CANNING M.M.P. INTERNATIONAL PATTANI FOOD INDUSTRIES PREMIER CANNING INDUSTRY SIRINAN FOOD High risk MAHACHAI MARINE PRODUCTS KIAT CHAROEN FOOD
Table 3.15 shows the profitability in the fresh and freezing sector. The average PCM
ratio is 7 % for the largest four firms while the average is 13% for the remaining
firms. Siam Tuna Fishery has the highest average margin (21%) followed by GGC
TWN (17%) and Thai Ocean Venture (14%). This sector has scope to maintain
margins and to extend into foreign markets with higher demand. Siam Chai
International Food, Thai Ocean Venture, GGC TWN, and Siam Tuna Fishery have
positive ROAs and they can manage assets efficiently while the remaining firms have
negative ROA and managed assets inefficiently. Using the ROE, Tuna Paradise
(261%), Thai Ocean Venture (156%), and Simiran (105%) show good performance;
Siam Tuna Fishery, Siam Tuna Supply, and GGC TWN have a lower ROEs between
12%-22%; showing less efficient generating of shareholder’s earnings. Siam Chai
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International Food and Phuket Dongher Trading with ROEs of -1% and 5% are the
worst performers. Using the ROS, Siam Chai International Food, Thai Ocean
Venture, GGC TWN, and Siam Tuna Fishery have the largest ROSs and an average of
4% show good performance; Phuket Dongher Trading, Simiran, Siam Tuna Supply,
and Tuna Paradise have ROSs of -11% on average and they performed poorly.
Table 3.15. Price-Cost Margin (PCM) and Accounting Profit Ratios of the Fresh
and Frozen Sector, 2005
Company lists 2005 Market share (%) PCM (%) ROA ROE ROS 1 SIAM CHAI INTERNATIONAL FOOD CO., LTD. 66.7 8 0.05 0.05 0.02 2 THAI OCEAN VENTURE CO., LTD. 13.6 14 0.08 1.56 0.03 3 PHUKET DONGHER TRADING CO., LTD. 5.2 4 -0.09 -0.10 -0.05 4 SIMIRAN CO., LTD. 4.0 1 -0.18 1.05 -0.11 5 SIAM TUNA SUPPLY CO., LTD. 3.6 3 -0.35 0.19 -0.16 6 TUNA PARADISE CO., LTD. 3.4 11 -0.35 2.61 -0.13 7 GGC. TWN. CO., LTD. 3.0 17 0.10 0.22 0.04 8 SIAM TUNA FISHERY CO., LTD. 0.6 21 0.04 0.12 0.08
The range of profitability measurements of the fresh and freezing sector is shown in
Table 3.16. Using PCM, all firms are not high risk. Three firms at high level and one
firm at a medium level can gain a good operating profit (TR-TVC). Four firms need to
improve either an increase in total revenue or a decrease in total variable costs.
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Table 3.16 Performance Ranking of the Fresh and Freezing Sector
State PCM High THAI OCEAN VENTURE GGC. TWN. SIAM TUNA FISHERY Normal TUNA PARADISE Low SIAM CHAI INTERNATIONAL FOOD PHUKET DONGHER TRADING SIMIRAN SIAM TUNA SUPPLY
3.4 Analyses of Costs and Returns of Tuna Fishing Vessels and Break-Even
3.4.1 Data sources
Primary data were collected by interviewing vessel owners from Phuket, Samut
Sakhon and Bangkok provinces during the period September to December 2006 and
the data were used to calculate average costs of tuna vessels, both purse seine and
long-line. Since there was a time constraint, different languages were spoken and
difficulties in communication and access to landing sites was restricted, the data were
collected from only six owners of twelve vessels18. Foreign purse seine vessels do not
land at Thai fishing ports: their production is transferred to Thai landing ports via
carrier vessels. These problems and it was not possible to interview more purse seine
boat owners. For long-line vessels, there was good cooperation with owners in
allowing interviews. Secondary data were also collected from the databases of IOTC
(2006), DOF (2006), and FAO (Josupeit, 2008) and were used to calculate the
revenues of tuna fisheries. Data on catches and prices of tuna between 1996-2006
were employed to estimate average revenues.
18 Thai owners of six Thai purse seine vessels from Siam Deep Sea, Thai Tuna Fishing, and Thai Deep Sea Fishing and from four foreign owners who are import companies and an owner from two Thai long -line vessels of Five Star Tuna Line.
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3.4.2 Costs and Returns of Purse Seiners
Table 3.17 presents an estimate of catch income for purse seine vessels in 2006. This
income was calculated from all catches and value of tuna delivered in Thailand. The
catch composition consists of skipjack (78%), yellowfin (21%), bigeye and albacore
(less than 1%). Total average income was 46 million baht/boat. Table 3.18 shows the
costs of purse seine vessels at accounting rates of interest (ARI) of 10% and 15 %.
Total cost is dependent on the number of day trips and fuel use. The number of day
trips is approximately 20 days/month and the fishing period is about nine months/year
(180 days/year). The major cost is fuel at 42-45% of total cost. Total average cost was
about 61 and 66 million baht/vessel/year at ARIs of 10% and 15%. Each owner had
approximately 3 million baht/vessel/year for operating profits but owners had net
losses of 15 and 20 million baht/vessel/year at ARIs of 10% and 15%.
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Table 3.17 Catch Income of Tuna Purse Seine Vessels in 2006
Month
Yellowfin Skipjack Bigeye Albacore Total Income Tonnes Million baht Tonnes Million baht Tonnes Million baht Tonnes Million baht Million baht
Jan 13,443 617 33,373 1,041 306 10 205 22 1,690 Feb 5,229 254 14,380 509 - - - - 763 Mar 7,736 403 33,145 1,295 259 11 50 5 1,714 Apr 6,272 318 40,508 1,424 212 8 - - 1,749 May 5,371 269 36,295 1,275 781 1 - - 1,545 Jun 6,137 320 33,763 1,269 22 1 - - 1,590 Jul 4,307 221 16,166 628 151 6 17 1 856 Aug 3,948 212 27,135 973 - - - - 1,185 Sep 5,093 280 21,497 811 140 5 - - 1,096 Oct 6,607 387 24,673 914 105 4 - - 1,305 Nov 10,454 630 3,470 128 178 7 - - 765 Dec 7,228 420 19,047 673 - - - - 1,093 Total income 15,351 Average income/vessel (334 vessels) 46 Note: All purse seine vessels landing in Thailand include foreign and Thai vessels
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Table 3.18 Costs and Returns of Operating a Purse Seine for Two Rates of Interest (ARI) for Capital in 2006
Average Costs Life span Annual sum to be provided for replacement at Fishing expenses (Baht) (Years) 10 % ARI 15 % ARI
% of total cost Million Baht % of total cost Million Baht 1. Capital costs Hull 70,000,000 25 13 8 16 11 Engine 30,000,000 10 8 5 9 6 Equipment 20,000,000 5 9 5 9 6 Total capital costs 29 18 35 23 2. Variable costs - Maintenance cost 2 2 2 2 Crew cost 14 9 13 9 Skipper 720,000 - Engineer 648,000 - Crews 40 persons 7,200,000 - Fuel 45 28 42 28 Food 3 2 3 2 Communication costs 5 3 5 3 Harbour costs 0.2 0 0.2 0 Total variable costs 43 43 3. Total costs 100 61 100 66 4. Income 46 46 5. Profit/(Loss) of Operating 3 3 6. Net Profit/(Loss) (15) (20)
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3.4.3 Costs and Returns of Long-Liners
Costs and returns of operating long line fishing vessels are shown in Table 3.19 and
Table 3.20. Income for long-line is mainly from yellowfin (71%), albacore (15%) and
bigeye (9%) which command higher prices when compared to purse seiners. Total
revenue in 2006 was 915 million baht and total revenue for a vessel was estimated at
approximately 2.3 million baht/year. The number of day trips is less than that of purse
seiner an average of 14 days/month and the fishing period is about nine months/year
(126 days/year). 30% of total cost is fuel cost. Total cost was 12.2 and 12.6 million
baht/vessel/year at ARIs of 10% and 15%. Unlike purse seine vessels, long-line
operating could not make either operating profits or net profits. Owners had a net loss
of 9.97 and 10.33 million baht/vessel/year at ARIs of 10% and 15%.
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Table 3.19 Catch Income of Tuna Long-line Vessels in 2006
Month Yellowfin Skipjack Bigeye Albacore Other fish Total income Tonnes Million baht Tonnes Million baht Tonnes Million baht Tonnes Million baht Tonnes Million baht Million baht
Jan 1,198 74 301 9 101 6 237 24 4 0.2 112.9 Feb 971 62 2 0 199 12 304 32 4 0.2 105.7 Mar 957 66 111 7 11 1 1 0.1 73.5 Apr 431 32 0 0 45 3 18 2 - - 36.5 May 574 41 30 3 346 21 234 26 0 0.0 91.2 Jun 682 48 1 0 58 4 420 48 16 0.9 101.3 Jul 480 35 20 1 55 3 180 21 7 0.4 59.9 Aug 415 29 1 0 42 2 222 25 5 0.3 57.2 Sep 346 27 - - 77 5 - - 13 0.8 32.4 Oct 660 58 224 8 49 3 295 29 2 0.1 98.0 Nov 750 51 - - - - - - - - 51.2 Dec 1,460 95 - - - - - - - - 95.5 Total income 915.2 Average income/vessel (405 vessels) 2.3 Note: Longline vessels include foreign and Thai vessels landing in Thailand
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Table 3.20 Costs and Returns of Operating Long-line Vessels for Two Rates of Interest (ARI) for Capital in 2006
Fishing expenses Average Costs (Baht) Life span (Years)Annual sum to be provided for replacement at
10 % ARI 15 % ARI % of total cost Million baht % of total cost Million baht
1. Capital costs Hull 19,485,056 25 18 2.15 24 3.01 Engine 2,009,387 10 2 0.26 2 0.31 Gear Equipment 2,907,040 5 6 0.77 4 0.45 IT Equipment 2,166,400 5 0.57 3 0.34 Total capital costs 31 3.75 33 4.11 2. Variable costs Maintenance cost 1,042,387 9 1.04 8 1.04 Crew cost 22 2.67 21 2.67 Number of people Skipper 1,335,000 Engineer 198,000 Crews 1,135,200 Fuel costs 30 3.67 29 3.67 Food 5 0.58 5 0.58 Bait 2 0.22 2 0.22 Communication costs 3 0.32 3 0.32 Total variable costs 8.48 8.48 3. Total costs 100 12.23 100 12.59 4. Income 2.26 2.26 5. Profit/(Loss) for operating (6.22) (6.22) 6. Net Profit/ (Loss) (9.97) (10.33)
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3.4.4 Break-Even and Sensitivity Analyses of Purse Seiners
Catching is unprofitable but the break-even point can show how many catches are
required to reach the point at which costs and revenues are equal. Table 3.21 presents
the tuna catches needed to break-even for a purse seiner. To break-even, a purse
seiner must to catch 4.6 times and 5.8 times its actual catch (1,161 tonnes) and reach
fishing revenues of 216 and 274 million baht/year at ARIs of 10% and 15%.
Table 3.22 summarise break-even sensitivity calculations for different tuna prices and
average variable cost (AVC) for a purse seiner at ARI 10%. A combination choice,
which is the most suitable, is a 20% increase in the price and a 20% fall in AVC
requiring for a catch of 944 tonnes. The break-even sensitivity calculation for changes
in the price and AVC for a purse seiner at ARI of 15% are shown in Table 3.23.
Break-even suitably occurs for a combination of an increase of a 25% price and a
25% cost decrease where the catch is 998 tonnes.
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Table 3.21 Tuna Catches and Revenues Needed to Reach Break-Even: a Purse Seiner, 2006
Description Unit ARI 10% ARI 15% Fixed cost (FC) baht 17,870,077 22,772,831 Average variable cost (AVC)1 baht 37,231 37,231 Average tuna price/tonne2 baht 40,593 40,593 Break-even revenues baht 215,738,265 274,927,254 Break-even point tonnes 5,315 6,773 Variances with actual landings times 4.6 5.8 Note: 1. AVC = total variable costs / catches (tonnes) = 43,224,800 baht/1,161 tonnes 2. Average tuna price is calculated from average yellowfin price and average skipjack price caught by purse seiners in 2006 (40 baht: $US)
Table 3.22 Break-Even Revenues and Catches Sensitivity around Changes in Tuna Price and AVC at ARI 10%, a Purse Seiner, 2006
Description Unit ARI 10% Increases in tuna price and fall in AVC P = +5% P = 10% P = +15% P = +20% P = +25% Break-even AVC= -5% AVC= -10% AVC= -15% AVC= -20% AVC= -25%
Fixed cost (FC) baht 17,870,077 17,870,077 17,870,077 17,870,077 17,870,077 17,870,077 Average variable cost (AVC) baht 37,231 35,369 33,508 31,646 29,785 27,923 Average tuna price: tonne baht 40,593 42,623 44,652 46,682 48,712 50,741 Break-even revenues baht 215,738,265 105,005,975 71,597,730 55,481,044 45,991,135 39,737,833 Break-even point tonnes 5,315 2,464 1,603 1,188 944 783 Variances with actual landings times 4.6 2.1 1.4 1.0 0.8 0.7
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Table 3.23 Break-Even Revenues and Catches Sensitivity around Changes in Tuna Price and AVC at ARI 15%, a Purse Seiner, 2006
Unit ARI 15% Increase in tuna price and fall in AVC Description Break-even P = +5% P = 10% P = +15% P = +20% P = +25%
AVC= -5% AVC= -10% AVC= -15% AVC= -20% AVC= -25% Fixed cost (FC) baht 22,772,831 22,772,831 22,772,831 22,772,831 22,772,831 22,772,831 Average variable cost (AVC) baht 37,231 35,369 33,508 31,646 29,785 27,923 Average tuna price: tonne baht 40,593 42,623 44,652 46,682 48,712 50,741 Break-even revenues baht 274,927,254 133,814,947 91,240,964 70,702,576 58,609,058 50,640,127 Break-even point tonnes 6,773 3,140 2,043 1,515 1,203 998 Variances with actual landings times 5.8 2.7 1.8 1.3 1.036 0.9
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3.4.5 Break-Even and Sensitivity Analyses of Long-liners
Long-line fishery operating is also unprofitable. The break-even point is estimated in
Table 3.24. The fishermen need to increase fishing by over 1.8 and 2 times of the
actual catch (31 tonnes) and total revenue must attain 19.6 and 21.5 million baht at
ARIs of 10% and 15%. The summary of the break-even revenue and catch sensitivity
analyses for a long-liner is shown in Table 3.25 and Table 3.26. At an ARI of 10%, a
reduction in average cost of 10% and an increase in price of 10% yield a break-even
catch is 30 tonnes (Table 3.25). For an ARI of 15%, a rise in the price and a fall in
costs of 15% each yield a break-even catch of 26 tonnes (Table 3.26).
Table 3.24 Tuna Catches and Revenues Needed to Reach Break-Even: a Long-
liner, 2006
Description Unit ARI 10% ARI 15% Fixed cost (FC) baht 3,749,173 4,110,036 Average variable cost (AVC)1 baht 273,632 273,632 Average tuna price: tonne2 baht 338,320 338,320 Break-even revenues baht 19,608,373 21,495,701 Break-even point tonnes 58 64 Variances with actual landings times 1.9 2.0 Notes: 1. Average variable costs = total variable cost / catches (tonnes) = 8,482,602 baht/31 tonnes
(catches: vessel) 2. Average tuna price = average tuna price caught by long-liners in 2006 (40 baht: $US)
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Table 3.25 Break-Even Revenues and Catches Sensitivity around Changes in Tuna Price and AVC at ARI 10%: a Long-liner, 2006
Description Unit ARI 10% Increase in tuna price and fall in AVC P = +5% P = 10% P = +15% P = +20% P = +25% Break-even AVC= -5% AVC= -10% AVC= -15% AVC= -20% AVC= -25%
Fixed cost (FC) baht 3,749,173 3,749,173 3,749,173 3,749,173 3,749,173 3,749,173 Average variable cost (AVC) baht 273,632 259,951 246,269 232,587 218,906 205,224 Average tuna price: tonne baht 338,320 355,236 372,152 389,068 405,984 422,900 Break-even revenues baht 19,608,373 13,977,406 11,083,810 9,321,820 8,136,196 7,283,886 Break-even point tonnes 58 39 30 24 20 17 Variances with actual landings times 1.9 1.3 1.0 0.8 0.6 0.6
Table 3.26 Break-Even Revenues and Catches Sensitivity around Changes in Tuna Price and AVC at ARI 15%: a Long-liner, 2006
Unit ARI 15% Increase in tuna price and fall in AVC Description Break-even P = +5% P = 10% P = +15% P = +20% P = +25%
AVC= -5% AVC= -10% AVC= -15% AVC= -20% AVC= -25% Fixed cost (FC) baht 4,110,036 4,110,036 4,110,036 4,110,036 4,110,036 4,110,036 Average variable cost (AVC) baht 273,632 259,951 246,269 232,587 218,906 205,224 Average tuna price: tonne baht 338,320 355,236 372,152 389,068 405,984 422,900 Break-even revenues baht 21,495,701 15,322,747 12,150,639 10,219,056 8,919,314 7,984,968 Break-even point tonnes 64 43 33 26 22 19 Variances with actual landings times 2.0 1.4 1.1 0.8 0.7 0.6
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These changes in the tuna price and AVC for vessels to break-even might be possible.
First, an increase of tuna price to increase revenue depends on factors such as, type of
gear and fish characteristics, including species, fat content, type of handling, and fish
size and quality. To increase price, it should have a reduction in the number of vessels
and catch limit. Decreasing tuna catch will increase tuna price for higher income. For
the long-liners, harvesters need to be concerned about fish form and whole fish
provide highest value as well as a reduction of fishing. Oil and labour costs, which are
the main variable costs, need to be reduced. The oil price depends on the world oil
price but the harvests can use carrier vessels to reduce the quantity of fuel used. They
should stay longer on the high sea or reduce vessels size. Reducing labour cost is also
important. Thai fishermen could also improve expertise to reduce variable costs.
Nonetheless, there are factors to discourage the potential of the Thai tuna fishing
sector. First, tuna stock is limited and is conserved. The general policy objectives of
most fisheries may be divided into the biological sustainability of fish stocks and the
maximum economic returns from fisheries (Petersen, 2006). First, it is concerned with
the maximum sustainable yield (MSY). According to tuna capacity, the current
situation in which most of the stocks of tuna are fully exploited while in some regions
skipjack tuna is capable of sustained increases in yield, is an example of the
complicating factors in trying to set optimal limits on fleet capacity. If capacity
limitations are set on the basis of skipjack productivity, there might be over-
exploitation of yellowfin and bigeye. Unless a means of harvesting skipjack without
capturing yellowfin and or bigeye is developed, a difficult decision as to whether to
forego increased production of skipjack to protect the other species will have to be
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
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made (Joseph, 2003a). Another alternative target for fisheries management is
maximum economic yield (MEY) 19 which is the yield or effort that results in the
highest net economic returns. The WCPO (Greenpeace International, 2007) found that
the maximum economic yield for bigeye and yellowfin tuna occurred at a stock level
around 40-50% and 15-30% higher respectively that at which the maximum
sustainable yield was obtained. This policy may increase revenues for the fishing
sector and conserve tuna stocks but it may reduce fishing capacity and the number of
vessels affecting fish supplies for the processing sector.
Second, there is a fishing labour force with inexpert Thai captains and a shortage of
suitable crews. Foreign fishermen have expertise in tuna fishing on the high seas
while Thai fishing crews have less skill due to their lack of relevant experience,
particularly in fishing technology. The basic education of the fishermen does not
make it easy for them to access new fishing technology, and their lack of language
skills also deprives them of communicating with knowledgeable foreigners.
Moreover, there is no motivation to persuade unskilled labour to work as crews
because Thai labourers can easily work in other jobs and receive a similar income.
Furthermore, the fact that fishing hands work in less secure conditions, far away from
home with higher risks and comparatively less pay, have turned most Thai workers
away from the fishing sector. Nowadays, commercial fishing vessels are principally
operated by foreign crews (Department of Fisheries, 2008). Entrepreneurs need to hire
skippers with high salaries and foreign crews; both increase fishing costs.
19 In economics, maximum economic yield or MEY is the level of effort that maximizes the difference between total revenue and total cost or, where marginal revenue equals marginal cost. This level of effort maximizes the economic profit, or rent, of the resource being utilized.
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3.5 The Analysis of Market Share and the RCA Indices
3.5.1 Data Sources
RCA indices of canned tuna20 exporters for 1996–2006 were estimated from data on
world exports by value (sources: Josupeit, 2008; United Nations Statistics Division,
1996-2006). RCA indices for Thai exports are compared with the main canned tuna
exporters, mainly, Ecuador, Spain, the Seychelles, Mauritius, Indonesia, and the
Philippines. Next, the RCA indices of the main exporters to the largest importers from
Thailand - US, the EU,21 the Middle East,22 Japan, Australia, and Canada - are
calculated from export and import data for 1996-2005 (source: International Trade
Centre, 2008) for the six digit level of HS classification. The evaluation of
competitive advantage is also made by applying Porter’s diamond model with
multinational activities.
3.5.2 An Analysis of World Exports
3.5.2.1 Market Shares of World Exports
The basic measure of international competitiveness is the world export shares which
are defined as a country’s exports divided by total world exports. Table 3.27 shows
the market share of tuna exports. The main exporter is Thailand with over a third of
all canned tuna exports; no other country has a market share of more than 10 %.
20 Canned tuna export includes all tuna products from HS 160414 code 21 The EU is composed of 15 countries: France, United Kingdom, Italy, Germany, Spain Netherlands, Luxembourg, Austria, Greece, Denmark, Belgium, Portugal, Sweden, Finland, and Ireland. 22 The Middle East consists of Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, United Arab Emirates, and Yemen.
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Table 3.27. Market Share of Canned Tuna from Global Exporters, 1996-2006
Country Market share (%)
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Average Thailand 37 39 35 37 33 38 35 35 37 40 41 37 Spain 4 7 9 8 9 11 10 10 10 10 10 9 Ecuador 5 5 5 6 6 8 10 9 8 9 9 7 Seychelles 2 3 4 6 7 7 8 8 7 6 6 6 Mauritius 3 2 2 2 2 4 3 3 3 4 5 3 Indonesia 5 4 5 5 6 5 4 4 5 5 4 5 Philippines 9 8 7 5 4 4 5 5 5 2 3 5 Others 35 32 33 32 32 23 26 26 25 25 23 28
3.5.2.2 RCA Indices for the World Market
The abbreviations in Table 3.28 are used in graphs.
Table 3.28 Abbreviations for Countries
Abbreviations Descriptions
AUS Australia CAN Canada CIV Côte d'Ivoire ECU Ecuador ESP Spain EU the European Union FIJ Fiji IDN Indonesia ITA Italy JPN Japan KOR Korea ME the Middle East MUS Mauritius PHL the Philippines SLB Solomon Island SYC Seychelles THA Thailand TWN Taiwan USA the United States
VNM Vietnam
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Figure 3.6 shows RCA indices for the main exporters. The Seychelles, Mauritius,
Ecuador, and Thailand have very high RCA indices. The Seychelles had comparative
advantage, and RCA indices grew from 824 in 1996 to a high of 2,423 in 2004 and
then declined to 1,730 in 2006. Mauritius had RCA index which trended upwards
from 137 in 1996 to 254 in 2006. An average RCA of Ecuador was 85. Thailand
maintained its comparative advantage; its RCA index was relatively stable over the
sample period, ranging around 30 from 2000 to 2003 and growing a peak at 40 in
2005, and then declining to 34 in 2006. Spain’s RCA index rose between 1996-2006.
The biggest change was in the Philippines which performed worst and its RCA index
fell from 21 to 7. Indonesia could maintain RCA indices between 1996–2004 but by
2006, had fallen.
Figure 3.6. RCA Indices for Exporters, 1996-2006
SYC
MUS
ECU-
400
800
1,200
1,600
2,000
2,400
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
RCA indices
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THA
ESP
PHLIDN
-
5
10
15
20
25
30
35
40
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
RCA indices
3.5.3 The Analysis of Main Importers
The second RCA index (Eq. A 3.1) is calculated for Thai importers and then
compared with the main exporting competitors in Table 3.29. The US was the largest
importer at 27% of total exports, the EU and the Middle East each share imports of
15%, and Japan, Australia, and Canada share about 8%. The US primarily imports
canned tuna from Thailand, Ecuador, the Philippines, and Indonesia. The EU imports
from Spain, Seychelles, Ecuador, Côte d'Ivoire, Thailand, and the Philippines. The
Middle East imports mainly from Thailand, Indonesia, Italy, and the Philippines.
Imports to Japan are from Thailand, Indonesia, the Philippines, and Solomon Island
but Solomon Island export values are not available. Australia imports from Thailand,
the Philippines and Fiji. Canada imports from Thailand, the Philippines, Fiji, and
Italy.
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Table 3.29 Market Shares of Importers from Thailand, 1996-2005
Country Market share (%) 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average
USA 27 28 32 34 28 30 28 30 30 27 29 EU 20 17 16 14 12 13 14 13 11 15 14 Middle East 14 13 19 14 17 19 16 14 14 14 15 Japan 10 10 7 8 11 9 10 9 11 9 9 Australia 6 5 6 7 7 7 8 8 8 8 7 Canada 9 10 8 10 10 9 9 7 8 7 9 Others 14 16 13 14 15 13 15 19 19 20 16
3.5.3.1 Market Shares and RCA Indices for the US Market
Table 3.30 shows the market shares of the main exporters-Thailand, Ecuador, the
Philippines and Indonesia-to the US. Thailand is the largest supplier and, on average,
its markets share is 43%.
Table 3.30. Market Shares of Exporters to the US, 1996-2005
Country Market Share (%) 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average
Thailand 44 48 48 43 39 44 38 41 41 44 43 Ecuador 19 14 12 18 23 18 28 25 19 17 19 Philippines 11 11 8 3 5 12 11 12 13 12 10 Indonesia 11 5 7 6 9 9 7 8 8 8 8
Figure 3.7 shows the RCA indices of the main countries exporting to the US which
were all greater than one. Ecuador is a relatively new exporter and it had a
comparative advantage of over 80 in 1996; this increased to 160 in 2002 but had since
fallen to 58 in 2005.. Thailand is ranked second: its RCA index was stable, showing a
slight upward trend from 36 to 44 between 1996-2005. The Philippines and Indonesia
are similar indices and they are stable over the period.
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Figure 3.7. RCA Indices of Exporters to the US, 1996-2005
THA
ECU
PHL
IDN
-
20
40
60
80
100
120
140
160
18019
96
1997
1998
1999
2000
2001
2002
2003
2004
2005
RCA indices
3.5.3.2 Market Shares and RCA Indices for the EU Market
Table 3.31 shows the market share of the main exporters for the EU. Spain was the
largest exporter with 14% in 2005; the Seychelles’s share was 10% and Thailand had
a 9% share.
Table 3.31. Market Shares of Exporters to the EU, 1996-2005
Country Market Share (%) 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average
Spain 10 12 11 12 14 18 13 13 13 14 13 Seychelles 3 5 5 9 13 0 0 12 10 10 7 Thailand 10 9 8 7 6 8 7 7 6 9 8 Ecuador 1 3 2 3 3 4 4 4 5 8 4 Côte d'Ivoire 22 16 16 11 11 8 10 8 9 6 12 Philippines 3 4 3 2 1 2 2 2 0 1 2
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Figure 3.8 presents RCA indices of countries that exported canned tuna to the EU.
The Seychelles, Ecuador, Côte d'Ivoire, Thailand, Spain, and the Philippines had very
high RCA indices. The Seychelles had the highest index which increased from 1,410
in 1996 to 1,885 in 2000. These RCA indices are very high because over 80% of total
exports are canned tuna products. Ecuador’s RCA indices showed an increasing trend
between 1996-2001 but suddenly rose during 2003-2005. Côte d'Ivoire’s RCA index
was 201 in 1996 but in 2005, this had fallen to 80. Thailand maintained its RCA of 22
in 1996 but it declined between 1997-2000 before increasing to 23 again in 2005. The
Philippines experienced a declining RCA index during 1996-2000 before recovering
in 2006. RCA index in Spain was stable below 5 all the period.
Figure 3.8. RCA Indices of Exporters to the EU, 1996-2005
SYC
-
400
800
1,200
1,600
2,000
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
RCA indices
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CIV
ECU
-
50
100
150
200
250
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
RCA indices
ESP
THA
PHL
-
5
10
15
20
25
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
RCA indices
3.5.3.3 Market Shares and RCA Indices for the Middle East Market
There are four large exporting countries to the Middle East: Thailand, Indonesia,
Italy, and the Philippines (Table 3.32). Thailand has by far the largest market share of
82% in 2005 followed by Indonesia with 9%, Italy 4% and the Philippines 2%.
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Table 3.32. Market Shares of Exporters to the Middle East, 1996-2005
Country Market share (%) 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average
Thailand 72 74 78 75 75 75 76 78 78 82 76 Indonesia 7 8 8 9 13 11 13 12 13 9 10 Italy 3 3 2 3 4 4 4 4 4 4 3 Philippines 1 2 3 3 2 3 1 3 2 2
Figure 3.9 shows the RCA indices for the four main exporting countries to the Middle
East. RCA>1 for Thailand, Indonesia, and the Philippines, but not for Italy. Thailand
maintained the comparative advantage but within fluctuate RCA indices during the
sample period. Its RCA index was 46 in 1996 and this increased to 62 in 2005 but it
declined to below 50 during 1998-2000. The Philippines’ RCA index generally
trended oscillate over the period. However, its comparative advantage was relatively
weak in 2000-2002. Its maximum RCA index was 33 in 2003 but this had fallen to 22
in 2005. Indonesia held a comparative advantage of only 6 in 1996; this had increased
slightly to 10 in 2005. Italy had disadvantage comparative during 1996-2005.
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Figure 3.9. RCA Indices of Exporters to the Middle East, 1996 - 2005
THA
IDN
ITA
PHL
-
10
20
30
40
50
60
70
8019
96
1997
1998
1999
2000
2001
2002
2003
2004
2005
RCA indices
3.5.3.4 Market Shares and RCA Indices for the Japanese Market
Japan is the fourth largest importer of canned tuna products from Thailand. Table 3.33
shows that the market share of Thailand was 55% in 2005. Indonesia and the
Philippines have market shares of 22% and 1% respectively.
Table 3.33. Market Shares of Exporters to Japan, 1996-2005
Country Market Share (%)1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average
Thailand 47 45 39 44 54 53 54 54 58 55 50 Indonesia 16 20 28 25 25 31 24 25 26 22 24 Philippines 8 7 4 4 4 4 6 6 0 1 4
The RCA indices of these three countries are shown in Figure 3.10. Thailand had
comparative advantage at around 17. Indonesia increased its RCA indices from 1996-
1998: its RCA gently trended upwards from 4 in 1999 to 12 in 2004 but this fell
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
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subsequently to 6 in 2005. The Philippines faced a weakness of comparative
advantage over the sample period particularly in 2005.
Figure 3.10. RCA Indices of Exporters to the Japan, 1996 - 2005
THA
PHL
IDN
-
5
10
15
20
25
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
RCA indices
3.5.3.5 Market Shares and RCA Indices for the Australian Market
The market share of canned tuna exporters to Australia is shown in Figure 3.12.
Thailand dominates with a market share of 97%. Vietnam and Italy only had average
market shares of 1.2% and 0.5%.
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Table 3.34. Market Shares of Exporters to Australia, 1996-2005
Country Market Share (%)1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average
Thailand 96.8 97.8 95.0 96.0 95.5 98.1 95.1 94.5 96.0 97 96.2 Vietnam n.a. n.a. n.a. n.a. n.a. 0.1 1.8 2.6 0.5 n.a. 1.2 Italy 0.4 0.3 0.2 0.6 0.7 0.5 0.5 0.6 0.9 0.5 0.5
Table 3.11 shows that Thailand had the strongest comparative advantage and was the
only exporter where RCA>1. Nonetheless, it lost comparative advantage and its RCA
declined continuously over the sample period. The RCA index for Vietnam was 3 in
2003. Italy has never had a comparative advantage in the Australian market.
Figure 3.11. RCA Indices of Exporters to Australia, 1996 - 2005
THA
VNMITA
-
10
20
30
40
50
60
70
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
RCA indices
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3.5.3.6 Market Shares and RCA Indices for the Canadian Market
Canada is the fifth largest importer of canned tuna from Thailand. In Table 3.35, the
market shares of Thailand’s exports to Canada averaged 77% during 1996-2005. The
next ranked exporters were the Philippines at 13% and Fiji at 3%. RCA>1 for all three
countries. In Figure 3.12, Fiji had comparative advantage: its RCA indices generally
trended upwards and was 1,963 in 1996 and was highest at 2,256 in 2004. Thailand
had a stable RCA index which was lowest at 191 in 1996 and highest at 260 in 2005.
The Philippines had an average RCA index of over 150, but it lost some comparative
advantage at the end of the period.
Table 3.35. Market Shares of Exporters to Canada, 1996-2005
Country Market Share (%)1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average
Thailand 67 78 65 75 80 87 77 78 76 85 77 Philippines 23 16 15 16 10 10 16 14 - 7 13 Fiji 8 1 2 2 1 1 3 3 3 3 3
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Figure 3.12. RCA Indices of Exporters to Canada, 1996-2005
THA
PHL
FIJ
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2,200
2,40019
96
1997
1998
1999
2000
2001
2002
2003
2004
2005
RCA indices
3.6 Extending Porter’s Diamond Model and Multinational Activities through internationalization for the Thai Tuna Industry
This section applies Porter’s diamond model (Porter, 1990) and double diamond
model (Moon et al., 1998; Moon et al., 1995) to the Thai canned tuna industry where
the determinants of competitive advantage. The industry in each cluster is classified
into four conditions and two exogenous factors. We further analysis the Thai tuna
industry combining the diamond model with multinational activities to drive a double
diamond model.
3.6.1 Factor conditions
3.6.1.1 Global Sourcing in Low Labour Cost
Global sourcing has been available in Thailand. Countries such as Japan the US,
which were large tuna producers, shut down their factories and then imported tuna
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
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product from countries, particular Thailand, with lower labour costs. Moreover,
Thailand has an advantage from learning how to do business in a potential market,
tapping into skills or resources unavailable domestically, developing alternative
supplier/vendor sources to stimulate competition, and increasing total supply capacity.
Thailand has strong competitiveness with its basic factor of lower labour costs
(Konuntakiet, 1991). Most workers are unskilled. Comparisons with the main canned
tuna processors - US in California and Puerto Rico, US in America Samoa, the
Seychelles, Mauritius Ecuador, and the Philippines - are shown in Table 3.36. Labour
costs in Thailand are the lowest, and Thailand has a competitive advantage in labour.
However, this competiveness will disappear in future because labour costs have
increased with higher standard living. Thailand has recently experienced a lack of
unskilled labour and firms sometimes have to employ foreign labour from
neighbouring countries, such as Myanmar, Laos, and Cambodia.
Table 3.36. Minimum Wages in Tuna Canneries
Country Wage (US$/hour) US -California and Puerto Rico 5.15 US -American Samoa 3.26 Seychelles 1.90 Mauritius 0.90 Ecuador 0.77 Philippines 0.67 Thailand 0.66
Source: Ababouch and Catarci (2008) and Campling and Doherty (2007).
The tuna industry also needs advanced factor, which is skilled labour, such as
scientists for controlling the quality and safety of food, engineers for controlling the
machines, accountants for financial planning and controlling the budget, and
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marketers. The number of unemployed graduates in Thailand is 33,649 in 2008
(National Statistical Office, 2008) and this skilled labour capacity could easily support
the tuna industry.
3.6.1.2 High Production and Processing Technologies and Quality Controls
The role of multinational enterprises (MNEs) significantly influences the Thai tuna
industry especially for food safety controls and superior technologies. Kohpaiboon
(2006) states that multinational enterprise buyers (non-Foreign Direct Investment
(FDI)) play a significant role in supporting local firms to understand the complicated
food safety regulations of importing countries (Kohpaiboon, 2006). Foreign experts
from originated tuna processing companies can give a competitive advantage. For
instance, most Thai tuna companies, such as Thai Union Group, M.M.P., and Sea
Value, have hired high-profile staff from originated countries such as the US for
training and providing advice. MNE buyers mainly emphasize sanitary concerns in
the production process. Consequently, the quality of the raw tuna material in
Thailand, which is measured by freshness, is higher than for other competitors,
particularly the Philippines and Indonesia because of better cold storage facilities
(Putthipokin, 2001, p.106). The Thai processors have also hired high-profile staff to
train, set-up procedures, and establish Q.C. programs to enable their factories to be
GMP, and HACCP.
3.6.1.3 Infrastructure Connecting to International Trading
The infrastructure in Thailand is adequate with an extensive air transport network that
encompasses 28 commercial airports, the most extensive road transportation network
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of more than 250,000 kilometres, more than 40% of which are international standard
highways that provide links to every province. 122 ports, wharves, and jetties are able
to accommodate sea-going vessels engaging in international trade, including eight
international deep sea ports and fixed line telephones and mobile phones are readily
available. Access to the internet is available though ADSL, satellite modems dial-up
and broadband connections (The Board of Investment of Thailand, 2008).
3.6.1.4 Raw Tuna Material from Imports
Raw tuna material imports and prices are uncertain for processors. In the case of raw
tuna material, Thailand has disadvantage competitiveness with main competitors and
uncertain raw tuna material. First, Thai processors import 90% of tuna for canning,
with only 10% coming from local supplies. In comparison, Thailand’s main
competitors - Ecuador, Spain, Indonesia, Philippines, and the Seychelles - have their
own tuna fishing fleets.23 Table 3.37 presents the tuna catches of the six main
competitors. The highest catch from Indonesia averages 377 tonnes/ year, followed by
Spain and the Philippines. Those countries have their own raw tuna material except
Indonesia and the Philippines where they have the experience to catch tuna but they
still import raw materials from other countries because of their lack of investment in
cold storage (Putthipokin, 2001). Second, tuna catches from the Indian and the
Western and Central Pacific Oceans, mainly yellowfin and skipjack, have been
limited. Tuna stocks in Chapter 1, we noted that tuna stocks show that yellowfin is
being fully exploited and this is an obstacle for tuna processors.
23 Others competitors, like Mauritius, also need to import tuna for canning.
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Table 3.37. Tuna Catches of the Six Main Canned Tuna Exporters, 1996-2006
Country Tonnes 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Average
Indonesia 341 341 399 432 413 387 372 339 369 382 372 377 Spain 263 258 234 307 290 255 278 308 275 287 314 279 Philippines 171 177 200 203 206 191 212 270 278 261 313 226 Ecuador 75 101 129 205 171 144 135 194 160 211 203 157 Seychelles 0 9 20 29 26 44 55 80 94 100 86 49
Source: Adapted from Josupeit (2008).
Uncertainty of import prices of raw materials also affects canned tuna processing. The
price trends of tuna for canning are inferred from skipjack and yellowfin prices.
Thailand is the largest importer of frozen tuna and the price of skipjack is determined
in the Bangkok market (Ababouch and Catarci, 2008), while the yellowfin price is
determined in Italy. Figure 3.13 shows the skipjack and yellowfin monthly price from
1996-2006. Both yellowfin and skipjack prices are unstable over period of time and
they lead to higher production costs.
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Figure 3.13. Skipjack and Yellowfin Prices, Thailand, 1996-2006
Skipjack
Yellowfin
-
500
1,000
1,500
2,000
2,500Ja
n-96
Jan-
97
Jan-
98
Jan-
99
Jan-
00
Jan-
01
Jan-
02
Jan-
03
Jan-
04
Jan-
05
Jan-
06
$/Tonnes
Source: Josupeit (2008).
3.6.2 Expansion Demand
The growing global demand has led to competitive advantage in certain industries
where there are economies of scale, and firms are encouraged to invest in large-scale
facilities and technology development. Strong international demand helps Thai
companies gain global market leadership. World tuna market share measures the
competitiveness. Thailand has had the strongest market share since 1984. Even
though, market share growth rate had been declined during 1990-1998, as shown in
Chapter 1, it has been slightly increasing again since 1998.
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There may also be a disadvantage from global demand. Figure 3.14 presents world
canned tuna export demand in terms of volume. Thai export demand is increasing and
it follows the growth rate of world export demand. However, its growth rate is less
than world growth rate. Consequently, Thailand’s exports may decline in future.
Figure 3.14 World Demand, 1989-2006
THA
Total
0
200
400
600
800
1,000
1,200
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
1,000 Tonnes
Thailand has a competitive disadvantage in the domestic market because consumers
have many choices of fish products. Domestic demand reduces the risk associated
with international trade. Processors need to increase domestic customers by using a
strategy based on local promotion, such as advertising, and R&D to improve the
health and variety of products. Several canned tuna producers have currently turned to
focus more on the domestic market after confronting problems overseas (The
Industrial Finance Corporation of Thailand, 2000).
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3.6.3 Firm Strategy, Structure and Rivalry
The structure of the tuna industry is oligopolistic market. The dominant firms are
price leaders while other smaller companies are price-takers and follow the leaders’
price. The strengths of competitiveness are low entry barriers and the extension of
related and supporting industries. The tuna industry needs new entrants to increase
production to satisfy increasing demand from both domestic and foreign customers.
Vertical and horizontal strategies increase production capacity and reduce transaction
cost. Only the large companies have the potential to invest in foreign countries
because of their expertise. If smaller firms can use these strategies, the industry may
have a stronger competitive advantage in terms of reducing transportation costs,
improving supply chain coordination, providing more opportunities to differentiate by
means of increased control over inputs, increasing economies of scales, increasing
Thai market power in the world market, and reducing in the cost of international trade
by operating factories in foreign markets.
3.6.4 Related and Supporting Industries
Related and supporting industries are those firms that coordinate or share activities in
the value chain or those that involve complementary products. The main related
industries are cold storage, shipping, ports, packaging, logistics, and the fishing
sector. Competitive advantages are derived from strong supporting industries and
good infrastructure. First, some large processors have efficient cold storage to keep
frozen tuna in good condition before processing. Second, canning factories are mainly
situated near ports for efficient transshipments. Third, there are about 20, highly
competitive packaging companies producing tuna cans (Putthipokin, 2001). Fourth,
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logistics and shipping line companies are important for exporting: shippers number
around 40 and there are many good ports.
Thai tuna processors face fall in raw tuna fish import values and they now are
concerned about rules of origin. Preserved tuna heavily depend on imported inputs
and this will have a high impact if rules of origin are not in line with production
processes because of low local content. Although preserved tuna products are
classified as substantial transformation, Julintron and Chalatarawat (2007) note that
the origin of fish or fishery products is also determined by specific features of
fisheries requirement (see Appendix 3) from foreign partners. Investment of Thai tuna
fishing is important. Nonetheless, there are problems for fishing sectors. First, the cost
of investing in fishing vessels is very high. Only two large processors have invested in
fishing vessels. The Thai Union Group invested in five fishing vessels - about 1,400
million baht - these vessels can supply 8-10% of the total tuna raw material for their
firms in 2007 (The Thai Union Group, 2007); and Sea Value invested about 1,000
million baht in 2007 (Prachachat Turakit, 2007). In addition, Thai private companies
invested in six long-line vessels for supplying fresh and frozen tuna sector. Other
processors have made limited investment because their firms are medium or small and
they lack funds. Secondly, our break-even analysis finds that fishermen have been
facing losses from both purse seine and long line as a result of limited tuna stocks,
inexpert Thai captains, and shortage of crews.
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3.6.5 The Role of Government
The government has an important role in influencing competitiveness. It encourages
firms to raise their aspirations and move to greater levels of competitive advantage.
Nevertheless, the role of government is not always proactive and efficient especially
in negotiating trading barriers and Thailand faces trade barriers from countries. For
example, the Thai-US Free Trade Area (FTA) Agreement was suspended after the
military coup in 2006 and the ASEAN-EU Free Trade Agreement has been delayed
because of insecurity caused by the Thai political situation. In addition, the
development of deep sea fisheries and fishery on the high seas is a principal function
of Department of Fisheries (DOF). The objectives are to encourage tuna fleet
establishment, to support funds, to survey fishing grounds in high seas, to enhance
and develop standard deep sea ports and other facilities, and to control and regulate
Thai fisheries (Thummachua, 2005) but, these objectives have not yet been successful
because of Thai political insecurity (Thummachua, 2005).
3.6.6 External factors
External (uncontrollable) factors are trade agreements, consumer protection
requirements, environment protections, and tuna prices with changes in fuel costs
exchange rates. First, free trade agreements can benefit the Thai tuna industry if rules
of origin do not affect tuna exports to other countries. Presently, Thailand does not
have any problem with the Middle East market because the tariff is quite low at 0-5%
(Chalisarapong, 2006). In addition, Thailand has a benefit in the Thailand-Australia
Free Trade Agreement (TAFTA) started on 1 January 2005. Australia only considers
rules of origin from substantial transformation in change of chapter 2 digit level
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criteria and there is no special feature of fisheries. Consequently, the agreement of
TAFTA has been decreasing tariff. Thailand faced a 5% tariff on canned tuna
products in 2005, but it fell to 2.5% by 2006, and there has been no tariffs since 2007
(Department of Trade Negotiations, 2008). Thailand has disadvantages from imports
in the form of tariff and quotas. The main foreign markets, the US, EU and Canada,
still use high tariffs and quotas for imports. Most Thai canned tuna products in brine
are imported by the US and they are subjected to a 6 % tariff for a quota equivalent to
4.8% of US consumption, while beyond this volume, tariffs are 12.5%. In the EU
market, the import tariff quotas for canned tuna ended in 2007, and Thailand now
faces a tariff of 24%. Compared to the Africa, Caribbean and the Pacific Group (ACP)
of countries, Thailand is disadvantaged compared with the Seychelles and Mauritius
which can export canned tuna to the EU market without tariffs. Canada is also a
canned tuna importing country and a tariff of 4.5% is imposed on Thai imports
(Canada Border Services Agency, 2008).
In the case of Japan and Thailand Economic Partnership Agreement (JTEPA), there is
a specific feature of fishery requirement (see Appendix 3) that demands that all non-
originating tuna raw materials must be caught in Association of Southeast Asian
Nations (ASEAN) country territories or taken by vessels of Indian Ocean Tuna
Commission (IOTC) member countries. Thailand’s tuna imports mostly come from
Vanuatu, Japan, Taiwan, South Korea, Maldives, South Africa and the Philippines.
All these countries are registered with IOTC, except for Taiwan and Maldives. Hence,
if producers need a preferential tariff rate from the JTEPA agreement, they must avoid
using tuna fish from Taiwan and Maldives (Julintron and Chalatarawat, 2007). Even
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
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though Thailand imports over 780,000 tonnes tuna in 2006 (Josupeit, 2008) from
worldwide countries and almost half of total tuna imports come from IOTC members,
rules of origin are still biased against using tuna from some other countries,
particularly from the Western Pacific Ocean under the Secretariat of the Pacific
Community (SPC) which supplies over 50% of total tuna imports for Thailand
(Julintron and Chalatarawat, 2007), and it is currently the most important tuna-fishing
region in the world and is 50% of the global tuna catch (Secretariat of the Pacific
Community, 2004). It will be possible that other FTA partners will have specific
feature fishery conditions in their agreements and Thailand must confront this issue.
The second is consumer protection requirements. The basic requirements of importers
are Codex Codes of Practice and canned tuna standards such as Good Manufacturing
Practice (GMP), a Hazard Analysis and Critical Control Points (HACCP)-based
safety system, and quality assurance programme. Although all Thai tuna canneries
have the potential to acquire this certification to guarantee their products, they have
also to maintain it to retain customers’ confidence. Third, the canned tuna product is
controlled by the environmental conditions during its production, processing, and
distribution, and many countries are concerned about these issues. For example, tuna
fisheries are the first to deal with eco-labelling which are certifications given to products
that have a lower negative impact on the environment than other similar products.
Fourth, preserved tuna price change is caused by raw tuna price, fuel prices and the
exchange rate. Raw tuna price is the main cost of processing and in 1 kg of canned
tuna, raw tuna accounts for about 60% of total cost. Figure 3.15 shows the
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relationship between raw tuna and canned tuna prices. It can be seen that when raw
tuna prices rise, canned tuna prices also increase. Fuel constitutes the main cost of
tuna fishing and the fuel price is one of the main determining factors for raw tuna
price and subsequently the preserved tuna price. Figure 3.16 shows that the oil price
was constant during the period of 1989-1998 and steadily rose from 1999 until 2008.
Increasing oil price had an impact on increasing raw tuna fish price from 1989-1990
and from 2000-2006. In addition, FAO (2008) noted that tuna markets also were
rather unstable owing to large fluctuations in catch levels, and they declined in 2007
as a result of the increased fuel price, which made long fishing trips uneconomical for
the world tuna fleet. Prices increased in all main markets, and canned tuna prices
escalated for the first time in 20 years. However, fuel price has been dramatically
decreasing in 2009.
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Figure 3.15 Effect of Tuna Prices between Fishing and Processing Sectors
-
200
400
600
800
1,000
1,200
1,400
1,60019
89
1991
1993
1995
1997
1999
2001
2003
2005
US$/tonnes
0
5
10
15
20
25
30
US$/carton
Raw tuna price Canned tuna price
Source: Calculated by the data from Josupeit (2008)
Figure 3.16 Oil Price and Raw Tuna Price, 1997-2009
-
200
400
600
800
1,000
1,200
1,400
1,600
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
US$/tonnes
0
10
20
30
40
50
60
70
80
90
100
US$/Barrel
Raw tuna price Average oil price/year Source: Energy Information Administration (2009).
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
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The exchange rate impacts on tuna prices. For exchange rate management, the Bank
of Thailand maintains the principle, under the managed float exchange rate system, of
letting the exchange rate reflect the underlying fundamentals of the economy. The
Thai exchange rate is however volatile and the tuna industry will be consequently
affected, and by other foreign currency rates, especially the US dollar. A weaker baht
has positively impacted on the tuna processing sectors and sale revenues may increase
in future. Figure 3.17 shows that tuna price is inversely related to the exchange rate
(baht/US$). The weakness of baht24 effects increase in tuna exports due to declining
in tuna prices during 1989-2006. All determinants above are concluded in the double
diamond model of the Thai tuna industry in Table 3.38.
24 The exchange rate has been fluctuating since 1997 because the Baht was fixed to the US$ at an exchange rate of 20 baht/1 US$ between World War II and 1980. It proceeded to slowly decrease in value, and was again pegged to an exchange rate of 25 baht/US$ from 1985 until July 2, 1997 when the Asian financial crisis took its toll on Thailand. After that it has been placed on a floating exchange rate (Bank of Thailand, 2007)
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
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Figure 3.17 Relationship among Exchange Rate, Tuna Price, and Thai Tuna
Export 1989-2006
0
5
10
15
20
25
30
1989
1991
1993
1995
1997
1999
2001
2003
2005
US$/carton
-
5
10
15
20
25
30
35
40
45
50
Baht/1US$
Canned tuna price Exchange rate
0
100
200
300
400
500
600
1989
1991
1993
1995
1997
1999
2001
2003
2005
1,000 Tonnes
-
5
10
15
20
25
30
35
40
45
50Baht/US$
Thai export Exchange rate
Source: Bank of Thailand (2007a) and Josupeit (2008)
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139
Table 3.38 The Double Diamond Model of the Thai Tuna Industry
Double diamond model Current status Solutions for competitiveness 1. Factor condition 1.1 Global sourcing in low labour cost growing Finding other countries with lower-labour costs to establish factories 1.2 Advanced skilled human resources adequate 1.2 High production and process technologies and quality controls 1.3 Infrastructure connecting to international trading 1.4 Raw tuna material import Finding certain tuna supplies but also see the related industry for fishing sector 2. Demand condition 2.1 Global demand increasing but lower growth rate 2.2 Low domestic demand 3. Firm strategy structure and rivalry 3.1. Oligopolistic market 3.2 Vertical and horizontal integration from the dominant firms 3.3 Low barrier to entry 3.4 Foreign global brandname
4.Related industry 4.1 Strong support industries (cold storage, fishing port facilities, packaging)
4.2 Fishing sector Rule of origin forcing Foreign investment with export fishing countries Raw tuna material requirement Tuna farming Loss in fishing operation Hiring foreign expert captains Decreasing tuna stocks and conservation Shortage of fishermen
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
140
Double diamond model Current status Solutions for competitiveness Uncontrollable factors 5.Governmant roles 5.1 Negotiation for trade agreement 5.2 Support funding 5.3 Quality controls 5.4 Fisheries conservation 6. External factors 6.1 Trade agreement (FTA, bilateral and multilateral agreement)
6.2 Consumer protection requirement 6.3 Tuna price with fuel price and exchange rate 6.4 Exchange rate
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3.7 Conclusions and Discussions
This chapter examines the sustainable competitiveness of Thai tuna industry both the
processing and fishing sectors. To sustain its competitiveness, the tuna industry is
required the strengthen its internal and external relationships. The Thai tuna industry
is not sustainable yet with obstacles in both sectors. From internal relationships
identified in a SCP framework, the Thai processing structure is characterized by a
high market share of production being in the hands of a few firms. Both the canning
and fresh and freezing sectors are highly concentrated and are oligopolistic. This
contrasts with Putthipokin (2001) who concluded from HH-index measures that the
canned tuna sector was characterised by monopolistic competition.
Tuna exporters face three main entry barriers. First, legal barriers include government
policy, membership of the Thai Food Processors Association, regional organization,
registration system for vessels and crew, health certification from the Department of
Fisheries and Non-GMO certification, and third-party quality control investigation.
Second, Bain barriers affect new entrants seeking to gain access. Economies of scale
with a high production capacity and absolute cost advantage, with the low production
costs of the largest cannery and fresh and freezing company, affect new entrants.
Third, geographical barriers affect existing firms and new entrants.
The relationship between structure and conduct shows how firms react to competition.
First, economic analysis indicates that each sector is characterised as price leadership
by a dominant firm. Thai Union Group in the canning sector and Siam Chai
International Food in the fresh and freezing sector are dominant firms with large
The Competitiveness of the Thai Processing and Fishing Sectors CHAPTER 3
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market shares. Second, brand strategy is used in the canning sector. The domestic
market mainly uses local brands while the export market uses well-established brands
in the global market. The foreign market for the fresh and freezing sector does not
have a brand name strategy. Multinational enterprises impact on industry performance
and competitiveness. Vertical and horizontal integrations have been adopted by a few
larger canning firms to increase economies of scale and reduce transaction costs. Firm
performance is measured by profitability. In the canning sector, 19 firms have a good
performance while two firms perform poorly. In the fresh and frozen sector, four
firms have a good performance but four others have low performance.
For the fishing sector, the results show that purse seine vessels have an operating
profit but net losses. In contrast, a long-line vessel faces losses. However, these
results should be treated with caution since the primary data obtained from the fishing
vessels were from a small sample caused by inaccessible foreign vessel owners.
Break-even analysis can address how the owners of a single tuna vessel might
survive. One way to reach break-even output is for tuna prices to increase and
simultaneously AVC fall. For the purse seiner, the increase in the tuna price and the
decrease in AVC are both 20% for an ARI 10%, and are both 25% for an ARI of 15%.
In the case of the long-liner, the owners have to increase by the tuna price and reduce
AVC both by 10% for ARI 10%; corresponding figures for an ARI of 15% are 15%
each.
The international competitiveness of the Thai canned tuna industry, which has the
largest market share in the world, has strong competitiveness. The market share of
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Thailand in all main importers is also highest except in the EU. The revealed
comparative advantage in the world is found that Thailand maintained its comparative
advantage and is ranked fourth; its RCA index was relatively stable over the sample
period, ranging around 30 from 2000 to 2003 and growing a peak at 40 in 2005, and
then decline to 34 in 2006. We have updated Kijboonchoo and Kalayanakupts’(2003)
study which found that Thailand’s RCA indices were on the decreasing trend from 60
to 25 and its market shares both in terms of export volume and export value also
gradually declined from 50% to 30% during 1987-1998. We found that Thailand’s
RCA indices have not been declining but increasing during our study period except
for 2006 although they have not reached the peak of 70 as it happened in 1982 and the
Thai market share has been slightly increasing at around 37-41% from 1999-2006.
For the main importers of Thai tuna processors, in the US market, Ecuador had the
strongest comparative advantage and Thailand was ranked second with a constant
trend. In the EU market, Thailand faces trade restrictions whereas the Seychelles,
which is an ACP country, receives import duty exemption. RCA indices show that the
Seychelles had the strongest comparative advantage for canned tuna exported to the
EU25 whereas Thailand was ranked fourth and it had a fluctuating trend. For imports
to the Middle East and Japan, Thailand had the strongest comparative advantage and
it was fluctuating. In Australia, Thailand was the strongest comparative country but
with a declining trend. In Canada, Thailand’s import share was at a maximum in
2005; Fiji had the strongest comparative advantage although it did not have the largest
market share.
25 Canned tuna is the main exporting product from the Seychelles.
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The double diamond model is designed to suggest how an industry can achieve
competitive advantage in the global market. It indicates competitive advantage and
disadvantage in government roles, and external factors including multinational
activities. Since the Thai tuna industry mainly is dominated by international demand,
there are four features of international activities. For factor conditions, Thailand gains
from production capacity and processing technologies, and from infrastructure links
to international customers. Thailand has until now been a low labour wage rate
country but this will change because the minimum wage is increasing. We therefore
agree with Kijboonchoo and Kalayanakupt (2003) that Thailand might not gain
comparative advantage from low labour costs. The reduction of raw tuna imports is
less possible since Thailand has failed to operate tuna harvesting. In the case of an
increasing demand, the competitiveness is less strong as Thai’s tuna exports have
been increasing with lower growth rate. Related industry supports the sustainability of
tuna processing. Cold storages, shipping, ports, packaging, and logistics are adequate
for international demand. However, the Thai fishing sector has problems with factors
such as losses in operation, high investment, unskilled fishermen and limited tuna
stock and conservation.
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Chapter 4
Livelihoods of Workers in the Thai Tuna Industry
4.1 Introduction
The tuna trade significantly contributes to the Gross Domestic Product (GDP) of
Thailand and provides much needed employment for local workers. The industry
needs many people to produce tuna products because almost all production processes,
such as receiving raw materials, grading, butchering, skinning, cleaning, and filling,
are labour-intensive (Suwanrangsi et al., 1995). Even though this labour force works
hard they are often paid the minimum wage rate. Their factory income is the main
element to support their family livelihoods. The purpose of this chapter is to
investigate living and working conditions for workers which are required for the
social sustainability of the tuna industry.
In order to examine livelihoods of workers, we use the sustainable livelihoods
framework. The sustainable livelihoods framework (SLF) is one of a number of recent
approaches to sustainable development and is genuinely transdisciplinary as it is
produced, disseminated and applied in the borderland between research, policy, and
practice (Knutsson, 2006). The SLF, which is increasingly important in the
development, is used to investigate how sustainable livelihoods are accomplished
through access to a range of livelihood resources or capitals (natural, physical, financial,
human, and social) which are combined in the pursuit of different livelihood strategies.
Central to the framework is analysis of the range of formal and informal organisational
and institutional factors that influence sustainable livelihood outcomes (Scoones, 1998).
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Although this framework is often used in rural area, it has rarely been applied to urban
areas. Rigg (1998) states that “the diversification of the household economy and the
interpenetration of rural and urban have created multiple hybridities where individuals
and households shift between agricultural and industrial pursuits and cross between
rural and urban areas”. Knutsson (2006) noted that the SLF can be used to solve
problems in rural or urban areas. Consequently, both areas cannot be separated in this
study because labourers often work in an urban area and in a rural area.
To survey working condition, livelihoods in the tuna factories are examined by
indicating ambient conditions, security of labour and income measurement.
Environment in factories is very important for workers. For example, temperature in
the plant should make workers comfortable during the long working hours. A stable
labour force will promote skills, welfare and social harmony. Income payment should
ideally be equal to the average income of people in that country. A place to eat also
affects the work environment. Since a balanced diet plays an important role in
improving employees’ health, and boosting productivity, maintaining a good canteen
can contribute to a productive work environment.
This chapter is divided into three sections. The second section describes the
methodology of the sustainable livelihood framework and research design. Section 3
shows the background of the selected Thailand Areas. Section 4 details the result of
livelihood analysis in the living place. Section 5 provides livelihood conditions in
factories. Section 6 identifies livelihood strategies and outcomes and Section 7 is a
conclusion.
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4.2 Methodology and Research Design
4.2.1 Area Selection
To complete this study, the data collection was categorised into two sources. Firstly,
primary data were collected by interviewing workers from nine factories during the
period September to December 2006. Data for the analysis were gathered from the
Thai tuna factories. The abbreviations for companies are shown in Table 4.1. Workers
were interviewed with open-ended questionnaires (Appendix 1B).
Table 4.1 Company Lists and the Abbreviations for the Companies
Abbreviations The company lists TUM THAI UNION MANUFACTURING CO.,LTD. TUF THAI UNION FROZEN PRODUCTS PUBLIC CO., LTD. SCC S.C.C FROZEN SEAFOOD CO.,LTD. PFI PATTAYA FOOD INDUSTRY CO., LTD. UFP THE UNION FROZEN PRODUCTS CO., LTD. HYC HATYAI CANNING CO., LTD. TOV THAI OCEAN VENTURE CO., LTD. DC PHUKET DONGHER TRADING CO., LTD. STS SIAM TUNA FISHERY CO., LTD.
We selected three areas; one located in the central part and two located in the southern
part of Thailand, shown in Figure 4.1. Simple random sampling was employed from
the chosen firms that have good collaboration. The distribution sampling method is
presented in Figure 4.2. The number of samples was 331 people in nine factories.
Total females were 278 people (84 %), in contrast to total males which were 58
people (16 %). There were six canned tuna firms and three fresh and frozen tuna firms
in the three provinces (Samut Sakhon (SS), Phuket (PK), and Samut Sakhon (SK)).
The samples were 137 workers from the four canned tuna firms in Samut Sakhon
(TUM, PFI, TUF, and UFP), 93 workers of the two canned tuna firms in SK (SCC
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and HYC), and 101 workers from the three fresh and frozen tuna product firms (TOV,
DC, and STS) in Phuket.
Figure 4.1 Samut Sakhon, Songkhla, and Phuket Provinces in Thailand
Phuket
Samut Sakhon
SongkhlaPhuket
Samut Sakhon
Songkhla
Source: Phukhao advertising (2008)
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Figure 4.2 Distribution of Samples in Nine Thai Tuna Firms Surveyed in 2006
The Thai Tuna Industry
31 Caned tuna firms10 Fresh and
Frozen tuna firms
13 FirmsSamut Sakhon
5 FirmsSongkhla
13 FirmsOthers
5 FirmsPhuket
4 FirmsOthers
Samples
TUM5,000
PFI2,000
TUF3,000
tuna workers
UFP120 tunaworkers
49 M=12FM=37
34M=3
FM=31
29M=2
FM=27
25M=9
FM=16
SCC4,000
HYC150
59M=4
FM=55
34M=1
FM=33
TOV300
DC80
STS80
49 M=18FM=31
27M= 7
FM=20
25M=2
FM=23
The Thai Tuna Industry
31 Caned tuna firms10 Fresh and
Frozen tuna firms
13 FirmsSamut Sakhon
5 FirmsSongkhla
13 FirmsOthers
5 FirmsPhuket
4 FirmsOthers
Samples
TUM5,000
PFI2,000
TUF3,000
tuna workers
UFP120 tunaworkers
49 M=12FM=37
34M=3
FM=31
29M=2
FM=27
25M=9
FM=16
SCC4,000
HYC150
59M=4
FM=55
34M=1
FM=33
TOV300
DC80
STS80
49 M=18FM=31
27M= 7
FM=20
25M=2
FM=23
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Secondly, the secondary data were collected from Thai Government organisations
including the National Statistical Office, Ministry of Agriculture and Cooperative, and
other relevant organisations. These two sources of data were used to analyse the
livelihoods of workers.
4.2.2 The Sustainable Livelihoods Framework
The SLF26 is used for analysis of livelihoods in this chapter. It is a tool to promote
understanding of livelihoods and this framework is used to enhance livelihood
development. The sustainable livelihoods framework was originated by Robert
Champers (1983). It is mainly used to investigate peoples’ livelihoods in developing
countries and less-developed countries. Chambers and Convey (1991) noted that a
livelihood is sustainable when it can manage and recover from vulnerability, also
maintain or develop its capabilities and assets, and provide the opportunities for
sustainable livelihoods for the next generation.
DFID (1999) developed the sustainable livelihoods framework in order to improve
development activity. This framework facilitates analysis of the relationships between
poverty and environment by highlighting aspects relevant to decisions about
livelihood strategies. Ellis (2000, p.30) states that a framework for livelihoods
analysis can be utilised for thinking through diversified rural livelihoods and is
concerned with poverty reduction, sustainability, and livelihoods strategies. Carney
(1998, p.6) points out that the expected outcome of this framework will provide a
definition of the scope of, and provide the analytical basis for, livelihood analysis,
26 More details for the sustainable livelihood framework is included in Appendix 4
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helping to understand and manage complicated rural livelihoods. Moreover, the
outcome of this framework provides a point of reference for all concerned with
supporting livelihoods and the basis for further development. The sustainable
livelihood framework is shown in Figure 4.3.
Figure 4.3 Sustainable Livelihood Frameworks
KEYH = Human Capital S = Social CapitalN = Natural Capital P = Physical CapitalF = Financial Capital
LIVELIHOOD ASSETS
POLICIES,INSTITUTIONS
PROCESSESLIVELIHOODSTRATEGIES
H
N
FP
S
VULNERABILITYCONTEXT
ShocksTrendsSeasonality
Influence & Access
LIVELIHOODOUTCOMES
More income
Increased well-being
Reduced vulnerability
Improved food security
More sustainable use of natural resource base
Levels of governmentPrivate Sector
Laws CulturePoliciesIntuitions
SUSTAINABLE LIVELIHOODSFRAMEWORK
Source: Carney (1998, p.5)
4.2.3 Statistical Analysis
Data management and analysis were performed using SPSS version 15. The main
statistical methods used throughout the analysis were cross-tabulations for categorical
data. Most collected data are nominal therefore we use Cross-tabulation for statistic
tests. Cross-tabulation is one of the most useful and popular tools in social science
research and is also called joint contingency analysis. It uses a statistic known as Chi-
Square. The Chi-Square test of independence is a test of significance that is used for
discrete data in the form of frequencies, percentages, or proportions. Chi-square is one
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of a number of tests of significance and measures of association known as non-
parametric statistics (Walsh, 1990). Cross-tabulations, with two sets of categorical
variables, were constructed to investigate the existence and strength of an association
between these with Pearson’s Chi-square, Likelihood’s Chi-square, and Cramer’s V
parameter test. Morgan et al. (2004) suggested that Chi-square ( 2χ ) or phi/ Cramer’s
V are good choices for statistics when analysing two nominal variables. We use the
Pearson Chi-square to interpret the results of the test because of the variation in size
of the contingency tables (phi coefficient is usually used in 2x2 tables, while
Pearson’s Chi-square is used in 5x5 or larger tables). Significance was sought at the
5% level (p-values ≤ 0.05) to reject H0 (groups are independent). Critical
assumptions for using these statistics are: random sample data, sample size larger than
20 observations, cell frequencies larger than 5, and independent and categorical
observations.
4.3 Background of the Selected Thailand Areas
The three Thailand tuna areas were selected: Samut Sakhon, Songkhla, and Phuket
(Figure 4.1). These were selected because they have the main fish landing ports of
tuna catches, including a large number of workers. Samut Sakhon in Figure 4.4 is
located in the lower area of the central part of Thailand which is next to Bangkok and
covers an area of 872 km2. The province is a major fishing centre providing fresh fish
catches for nearby Bangkok. Table 4.2 shows that the province had a population of
462,510 people in 2006 corresponding to some 136,205 households. The literacy rate
in 2000 was 93.3%. A large number of people 59% worked in the manufacturing
sector. The next two popular workplaces were agriculture and the wholesale and retail
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trade sectors, accounting for approximately 10%. Gross provincial product per capita
was 553 thousand baht in 2006. The minimum wage per hour was 191 baht. The
average monthly income of people was 19 thousand baht.
Figure 4.4 Map of Samut Sakhon Province
Source: Tourism Authority of Thailand (2007)
Songkhla shown in Figure 4.5 is situated in the southern part of Thailand on the Gulf
of Thailand. The area of this province is 7,393 km2 and it is also the third biggest
province in the south (The Ministry of Interior, 2008). Its population in 2006 was
around 1.3 million people within 315 thousand households (Table 4.2). 90% of people
can read and write in the Thai language. The main occupation was in the agriculture
sector (36%) and around 20% of people worked in the wholesale and retail trade
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sector. There were only 12% of people working in the manufacturing sector. People
had 117 thousand baht of GPP per capita. The minimum wage rate per hour was 152
baht. People had an average monthly income of about 22 thousand baht.
Phuket (Figure 4.6) is a small island lying in the Andaman Sea in the south of
Thailand with 543 km2 of total area. The province had a population of around 300
thousand people corresponding to 70 thousand households. The literacy rate was
higher at about 94%. There were 30 percent of people working in hotels and
restaurants because Phuket is a famous tourist destination. A number of people, about
19% worked in the wholesale and retail trade sector. Only a small number of people
worked in the manufacturing sector. GPP per capita was 190 thousand baht in 2006.
People had an average monthly income of about 25 thousand baht. The minimum rate
wage was 186 baht.
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Figure 4.5 Map of Songkhla Province
Source: Tourism Authority of Thailand (2007)
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Figure 4.6 Map of Phuket Province
Source: Tourism Authority of Thailand (2007)
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Table 4.2 Background Data of Three Provinces
SS SK PK
Number of households (2000) 136,205 315,732 70,483 Total population (people) (2006) 462,510 1,317,501 300,737 Literacy rate % (2000) 93.3 90.5 93.8 Main labourers (2006) % of agriculture labourers 10 36 5 % of manufacture labourers 59 12 7 % of hotels and restaurants 5 9 30 % of wholesale and retail trade, repair of motor vehicles, 11 20 19 motorcycles and personal and household goods GPP per capita (2006) 533,159 baht 117,861 baht 190,421baht Minimum wage rate per hour (2006) 191 baht 152 baht 186 baht Total monthly income (2006) 19,555 baht 22,093 baht 25,630 baht
Source: National Statistical Office of Thailand (2007) and National Economics and Social Development Board (2007)
4.4 Livelihoods Analysis in the Living Place
The primary data and secondary data as analytical tools are to be embedded in the
content analysis of the SLF. The concept of assets and capital is key for the
explanation of livelihoods of workers in the Thai tuna industry. This section is
composed of the sustainable livelihood framework analysis of the three areas. It
focuses on changes and trends in the five main areas of livelihood which generally
affect the Thai tuna industrial workers: general province characteristics; province
resources; vulnerability context. Then access to assets of workers are analysed by an
asset pentagon model.
4.4.1 General Province Characteristics
In the case of Samut Sakhon province (National Statistical Office, 2002) in Table 4.3,
the general characteristic record shows that the annual growth rate of population
increased 1.3%. In Songkhla, The population increased averaged 1.1%. For Phuket,
the growth rate of the population increased by 4% per year. Land tenure in Thailand is
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classified into two types: private land and public land. The distribution of land tenure
is complicated in history. Private land is granted to claimants who have cleared
occupied and utilized land during the first phase of land history when people were
encouraged to bring land under cultivation. Public land refers to all remaining land
not claimed by private ownership. Public land now is distributed to people for
agriculture (Srisawalak, 2006). Efficiency of land distribution policy is required to
allocate land to poor people for cultivation and better living. Thailand has faced the
problem of land distribution for a long time. Many poor people are landless. Some
people had their own land but were taken advantage of by land speculators. In Samut
Sakhon, the total cultivated area, including rearing livestock and aquaculture in fresh
water accounted for 18,618 hectares. The main crops cultivated in Samut Sakhon
were limes, young coconuts, and mangoes. People in Songkhla generally work in
agriculture and fisheries. Farmland is itself divided between two main purposes:
intensive rubber, rice farming and livestock. Fishery is also a main occupation since
Songkhla is near the Gulf of Thailand and Songkhla Lake. Phuket’s households
mainly worked in agriculture, fishery, and tourism including hotels and restaurants.
The main crops are intensively-produced rubber, coconut, and stink bean (Parkia
speciosa).
For facilitates, although there are many water resources and water supplies, Samut
Sakhon is still in a water shortage situation. Fortunately, they can utilise electricity in
every household. Moreover, there are plenty of telephone lines to the buildings
(houses and offices). Transportation is also available by rail, road, and boat. The
infrastructure in Songkhla includes water resources, electricity, telephone lines, and
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transportation. Major water resources are canals and ditches, ponds, and concrete
weirs. These resources are used for cultivation. Electricity is available in every
household. Telephone lines are adequate for local people, both house telephone lines
and public telephone lines. Transportation includes 11 public highways, 22 train
stations, 3 river ports, and 2 airports. Phuket infrastructure includes water resources,
electricity, telephone lines, and transportation. Major water resources are canal and
ditch, pond, and concrete weir. Electricity is available in 99% of total households
since 2005. There are 58,568 telephone lines for local people. Transportation includes
one public highway, one large fishing port and 14 small fishing ports, and an
international airport.
Apart from its infrastructure, fishing is a popular job because Samut Sakhon is near
the sea and fishermen can catch fish throughout the Gulf of Thailand. There are also
some fresh water and brackish water rivers for fishing. 90% of people worked in
manufacturing factories (food processing, plastic, and mining) (The Ministry of
Interior, 2002). The main industries in Songkhla are agro-industry, food processing
industry and the rubber industry. The popular industries in Phuket are the mineral
industry, transportation, and the food processing industry. Samut Sakhon provides
sources of loans from cooperatives and banks. For welfare, there are support centres
(306 places) and health care services, such as hospitals, clinics, and health centres.
The data show that welfare and health services of both government agencies and
private centres provide sufficiently for local people in Songkhla and Phuket.
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Table 4.3 General Characteristics and Province Resources in Three Provinces
Samut Sakhon Songkhla Phuket 1. General province characteristics Population Annual growth rate 1.3% Annual growth rate 1.1% Annual growth rate 4% Area Total areas 872 km2 Total areas = 7,393 km2 Total areas 570 km2 Total cultivated areas 18,619 hectares Total cultivated areas 245,946 hectares Total cultivated areas 10,101 hectares Crops 14,375.68 hectares Crops 146,174 hectares Crops 8,079 hectares Livestock 27.68 hectares Livestock 1,044 hectares Livestock 80 hectares Aquaculture 4,216.32 hectares Aquaculture 118 hectares Aquaculture 4 hectares Crops and livestock 89,110 hectares Crops and Livestock 1,751 hectares Crops and aquaculture 3,166 hectares Crops and aquaculture 60 hectares Livestock and aquaculture 56 hectares Livestock and aquaculture 3 hectares Crops, livestock and aquaculture 6,278 hectares Crops, livestock and aquaculture 122 hectares Main cultivated crops Lime 2,959.2 hectares Rubber 167,347 hectares Rubber 15,860 hectares Young Coconut 1,886.24 hectares Rice 46,668 hectares Coconut 1,885 hectares Mango 1,882.88 hectares Stink Bean (parkin) 418 hectares
2. Province resources Water resources Concrete wire 5 places Reservoirs 21places Reservoirs 6 places Dam 5 places Concrete wire 77 places Concrete wire 20 places Pond 1 place Pond 91 place Pond 61 place Canal, ditch 8 places Canal, ditch 125 places Canal, ditch 16 places Water supply Water capacity 231,203,500 Cubic metre Water capacity 50,191,437 Cubic metre/year Water capacity 22,784,320 Cubic metre/year Water production 62,956,610 Cubic metre Water production 35,450,260 Cubic metre Water production 21,436,624 Cubic metre Electricity Available all villages Available all villages Available 26,805 households not available 180 villages Telephone line Telephone lines support on 21,358 numbers Telephone lines support on 85,730 numbers Telephone lines support on 58,568 lines
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Samut Sakhon Songkhla Phuket Transportation four main roads to Bangkok (capital city) 11 public highways 1 public highways trains 22 train stations 1 large fishing port and 14 small fishing ports rivers 3 river ports 1 international airport 2 airports Fisheries The Gulf of Thailand The Gulf of Thailand Andaman sea Songkhla lake Fresh water Brackish water Cooperative Agriculture (8 places), Fishery (1 place), Agriculture and Fishery (118 place), 106 places Manufacturing factories Manufacture (1 place), others (23places) Others (52 places) Mining industry 91 factories Food processing factories 357 factories Food processing industry 184 factories Transportation industry 85 companies Plastic factories 427 factories Agriculture industry 540 factories Food processing industry 39 factories Mining factories 632 factories Rubber industry 181 factories Wood industry 36 factories Mining industry 91 factories Transportation industry 106 companies Welfare Support centres 306 places Support centres 9 places Support centres 5 places for elderly people, children, disable people, for elderly people, children, disable people, for elderly people, children, disable people, low incomes, victims from disaster low incomes, victims from disaster low incomes, victims from disaster Health care Doctor 1:2,999 people Doctor 1:2,136 people Doctor 1:1,022 people Dentist 1:19,492 people Dentist 1:14,715 people Dentist 1:5,127 people Pharmacist 1:11,285 people Pharmacist 1:6,682 people Pharmacist 1:4,639 people Nurse 1:1,736 people Nurse 1:4,351 people Hospitals 6 places Hospitals 8 places Hospitals 23 places Health centre 24 places Health centre 56 places Health centre 175 places Clinics 90 places Clinics 348 places Women's health centres 47 places Government health service 29 places Pharmacy Drug stores 159 places Pharmacy Drug stores 428 places Health service centre 77 places
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4.4.2 The Vulnerability Context
The vulnerability context including shocks, trend, and seasonality affecting people’s
livelihoods are shown Table 4.4 as follows:
(i) Shocks
In the case of Samut Sakhon, people have confronted many shocks. For example, in the
case of the Asian financial crisis in 1997, GPP was down by 14% in 1998 and 19% in
1999 comparing with the GPP in 1997 (National Economics and Social Development
Board, 1999). Next is the relationship to people’s health. Severe Acute Respiratory
Syndrome (SARS) and Avian Influenza (Bird flu) during 2003 to 2004 touched the lives
of people in many ways affecting their health, employment, lifestyle, and self-assurance.
Thirdly, there is a migrant worker impact. Local workers lose their opportunities for
being hired because employers preferred hiring migrant workers, from Myanmar,
Cambodia, and Laos who can be more patient and industrious and are paid lower wages.
People had faced environmental pollution, such as waste pollution, coastal pollution, air
pollution, and water pollution, caused by local industries that are the major emitters of
such pollution.
Songkhla has experienced many shocks, such as Songkhla Lake pollution and the
southern Thailand insurgency. Songkhla Lake pollution affects the fishery and
aquaculture because now this lake faces water pollution, decreasing aquatic animals, and
lower water levels due to sedimentation and land infilling. Songkhla is one of four
provinces affected by the violence in the south of Thailand. As a result, local people try to
move to other places and change jobs. The resulting emigration problem is a cause of the
labour shortage, therefore migrant labour from neighbouring countries is increasing.
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Other problems will occur in the future, such as higher crime rates, lower quality of life,
and sexual harassment. Moreover, Thai tourists and foreign tourists are declining because
they are less confident about safety. Business investment stops growing.
Phuket experienced shocks, namely the tsunami disaster and water shortage. The tsunami
disaster was the worst shock occurring in Phuket in 2004. This shock has affected people
since 2004. For example, stakeholders in the tuna industry affected by the tsunami were
fisherfolks, employers and employees of small-scale business groups. Furthermore, the
infrastructure, such as harbours, bridges, roads, and electricity, was badly damaged.
Moreover, this shock destroyed livestock, crops, houses, and schools. Water shortage is
one problem in Phuket because water demand from consumers is very high, caused by
increasing economic growth.
The three provinces face drug problems and HIV/AIDS. Drugs may be consumed by
workers in manufacturing industries and by fishermen. The impact of drug addiction is on
the living conditions of people. Moreover, drug use is one of the main modes of
HIV/AIDS transmission by injected drug use. An impact on drugs and AIDS infection is
health problems in Songkhla as similar as in Samut Sakhon. For Phuket, the drugs
problem and AIDS infection problem have more effects. The number of addicts in 2002
was 1,579 people and decreased to 1,228 people in 2007 or by 22% (The Ministry of
Interior, 2006). However, there are still a lot of addicts. The large number of HIV infected
people has been increasing from 123 infected people in 2001 to 128 infected people/100
thousand people in 2006 (Epidemiological Information Section, 2008).
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(ii) Trends
Samut Sakhon has a trend of increasing investment in industries which will support
higher employment. However, environmental pollution is growing because of the
concentration of industries. Secondly, price trends are increasing in several ways. The
cost of living, the price of consumer goods and inflation will affect the living conditions
of people if wages do not increase at the same time as price trends are increasing. The
third is population and the quality of life. The enhancement of the foundation for a better
life will be higher in the long term, such as increases in numbers of health care centres,
public activities, religious activities for children, and career training courses. In the case
of Songkhla, the market system covers a large area and its population is very high,
therefore enhancement of the market system will extend in the short term and the medium
term, such as increasing the number of traders both wholesalers and retailers. This
province needs rising efficiency of people in the labour market. This is a career
opportunity for workers to have more than one permanent job. For Phuket, the cost of
living, consumer price, and inflation have been increasing because of the economic
growth. Phuket’s population is increasing because the number of tourists and foreign
investors is increasing. Building investment in real estate, hotels, and restaurants has been
increasing as well. This provides a career opportunity for workers to have more choices
for other jobs. Labour and activities will be changed in three provinces. Increases in the
minimum wages in manufacturing industries will provide additional income for workers.
This supports a better life for people.
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(iii) Seasonality
Seasonality is a factor that directly affects workers in the tuna industry with regard to tuna
catches, and an indirect impact of seasonality is the cultivation of crops. Tuna capture for
Samut Sakhon and Songkla in the high season is in April and November and the low
season of tuna capture is May. The low season causes declining employment in tuna
factories. However, workers can take the opportunity to have temporary jobs during the
low season to increase income by cultivating plants on their land because there is rainfall
all year round this province. On the other hand, the high season for the tuna capture in
Phuket is between October and January whereas the low season of tuna capture is in
September, and declining employment in tuna factories often happens in the low season.
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Table 4.4 Vulnerability Context in Three Provinces
Samut Sakhon Songkhla Phuket Vulnerability context
Shocks Asian Financial Crisis (1997) impact on people Songkhla lake's pollution TSUNAMI (2004)
Severe Acute Respiratory Syndrome (SARS) (water pollution, decreasing aquatic animal, lower land) Water storage for consumption and agriculture
impact on health (April 2003) The South Thailand Insurgency (2002-2008) Drug problem Avian Influenza (2004) impacts on health Drug problem AIDS Migrant workers impact on local workers AIDS Flooding (the monsoon, the higher sea level, and low land) Waste pollution Coastal pollution Drug problem Water shortage AIDS Trends Labour/Activities Labour/Activities Labour/Activities Rise in the minimum wages paid Rise in the minimum wages Rise in the minimum wages paid (191 baht/day) (152 baht/day) (186 baht/day)
The main income remains manufacture labour Population and quality of life Population and quality of life
Population and quality of life Increase infrastructure Increase population Increase health centre Increase the efficiency of market system Increase the number of tourists Increase temporary migration to Rising building investment industrial centres for work purposes in hotel and restaurant industry Increase participating in public activities Increase migrant labour Increase religious activities for children
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Samut Sakhon Songkhla Phuket Seasonality Tuna resources Tuna resources Tuna resources High season in April and November High season in April and November High season in October - January Low season in May Low season in May Low season in September The employment may decline The employment may decline The employment may decline Second jobs, such as planting or livestock Second jobs, such as planting Second jobs in tourism industry Main crops , livestock, and fishery People can cultivate plants all the time.
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4.4.3 Livelihoods Descriptions
A key challenging task in this section is the analysis of livelihoods and assessment of
its components. The SLF shows that the main point of the framework is the five
capitals. Brugere (2002) importantly noted that assets and access to capital are
different. Assets refer to things that belong to household members or people, such as
financial, natural or physical capital, while access identifies the capability of
household members to actually access the capital; for example, people can access a
school. Results are difficult to present clearly as there may well be an overlap in the
results. Therefore, we divided them into household characteristics, access, and assets.
4.4.3.1 Household Characteristics
Household characteristics of workers are presented according to categorical data and
provinces. Table 4.5 shows that most workers in the three provinces are female and
married. Average age of male workers about 27-28 years old is younger than female
workers about 32-35 years old. Results from statistical tests are presented after the
related descriptive tables. Table 4.6 shows household characteristics in the three
provinces. In general people are paid in the lowest payment (under 6,000 baht) based
on minimum wage rate of this province The number of landowners in Songkhla is the
highest and they therefore can gain additional household income from cultivation.
Female workers with the basis of education at a primary – high school level are
largely responsible to be the head of the household in terms of income contribution,
particular in Songkhla. Furthermore, a house with piped water can be a measure of the
prosperity of a community and the infrastructure which can access local areas. The
available water in the three provinces is sufficient in all accommodations. Although
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houses with piped water of employees in Songkhla are the lowest number they have
other water supplies, such as artesian and well water. The higher number of houses is
in Songkhla and Phuket while workers in Samut Sakhon mainly live in rented flats.
Table 4.5 Status, Age, and Sex of Workers
Samutsakhon Phuket Songkhla Count Mean Count Mean Count MeanSex Male 26 27 5 Female 111 74 88 Total 137 101 93 Status Single Male 8 14 1 Female 46 20 15 Total 54 34 16 Married Male 17 13 4 Female 62 48 70 Total 79 61 74 Divorce Male 1 0 0 Female 3 6 3 Total 4 6 3 Average age Male 28 27 28 Female 32 30 35
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Table 4.6 Household Characteristics of Workers by Provinces
Province SS % of total SK % of total PK % of total Total Significance? (1) (1)/137 (2) (2)/93 (3) (3)/101 n=137 n=93 n=101 N=331 Household size (no.) 2.55 3.31 2.81 Land status a Landless 136 99 54 58 100 99 290 Landowner 1 1 36 39 1 1 38 Tenant farmer 0 0 3 3 0 0 3 Wage per month b under 6,000 baht 30 22 76 82 28 28 134 6,001-8,000 baht 70 51 8 9 31 31 109 8,001-10,000 baht 17 12 8 9 29 29 54 over 10,000 baht 20 15 1 1 13 13 34 Female-headed in households 48 35 59 63 38 38 145 c Family in households Living with father 26 19 29 31 26 26 81 no Living with mother 32 23 35 38 33 33 100 no Have children 33 24 61 66 36 36 130 d Living with spouse 74 54 64 69 55 54 193 no Education level of workers e - Illiterate 0 0.0 0 0.0 0 0.0 0 - Primary school 6 education years 66 48.2 54 53.5 43 42.6 163 - Secondary school 9 education years 28 20.4 8 7.9 33 32.7 69 - High school 12 education years 30 21.9 20 19.8 9 8.9 59 - Diploma 14 education years 5 3.6 7 6.9 3 3.0 15 - Bachelor degree 16 education years 8 5.8 4 4.0 13 12.9 25
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Province SS % of total SK % of total PK % of total Total Significance? (1) (1)/137 (2) (2)/93 (3) (3)/101 n=137 n=93 n=101 N=331 House with water f - buy water 14 10.2 2 2.0 6 5.9 22 - piped water 100 73.0 25 24.8 76 75.2 201 - artesian water/well water 23 16.8 66 65.3 19 18.8 108 House status g - House owners 46 33.6 84 83.2 40 39.6 170 - Rent 91 66.4 9 8.9 61 60.4 161 House construction type h - Single 39 28.5 86 85.1 50 49.5 175 - Townhouse/Commercial 12 8.8 1 1.0 13 12.9 26 - Flat 84 61.3 6 5.9 37 36.6 127
Significant (two-tailed) tests (Pearson’s Chi-Square, χ2 and Cramer’s V statistic)
a) Cramer’s V= 0.561; p = 0.000 b) Cramer’s V= 0.408; p=0.000
c) Cramer’s V= 0.256; p=0.000 d) Cramer's V= 0.351, p = 0.000
e) Cramer’s V= 0.215; p=0.000 f) Cramer’s V= 0.365; p=0.000
g) Cramer’s V= 0.490; p=0.000 h) Cremer’s V = 0.381; p=0.000
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4.4.3.2 Household Access
According to access to the various capitals, the results revealed that workers in the
tuna industry had the capability to access resources and some had ownership. The
possibility of access to capital is described as follows:
Access to natural capital
• Number of water resources available to workers
• Conditions of use of each water source (common property)
• Number of workers collecting vegetables
• Number of fisheries available for workers
• Land for cultivation
• Number of activities in the community (religious activities, Thai traditional
activities, charity activities)
Access to financial capital
• Number of people saving and loans from government organisations, private
organisation, and informal structures (private money lenders and relatives).
Borrowing can also be a sign of weak or strong social ties of workers.
• Ownership and the current value of liquid assets such as gold, jewellery,
livestock and vehicles for occupation.
Access to human capital
• Household size
• Educational level of workers
• Number of people contributing to the household income
• Opportunity of jobs
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Access to physical capital
• House ownership
• Availability of electricity, telephone and piped water
• Type of house (single house, town house, flat, commercial building)
• Infrastructure (roads, train, ships, and public highway)
• Vehicles
Access to capital is shown in Table 4.7. Access to all capital is significant and
relevant to workers. The most relevant access is house ownership in physical capital
and the second important access is to natural capital and the opportunity of a second
job is the third. For access to natural capital, a larger number of Songkhla’s workers
were able to access water resources, such as artesian water and well water followed
by Samut Sakhon and Phuket. Phuket had the highest number of workers who gathers
wild vegetables for consumption whereas only 12% of worker in Samut Sakhon
accesses in wild vegetables and there was no access in Songkhla. Only a small
number of workers in the three areas were participated in fisheries.
Referring to access to social capital, the survey revealed that the largest number of
people, with 68% in Songkhla participated in customary Thai activities (culture,
religious, and Thai wedding ceremony) followed by Samut Sakhon (59%) and Phuket
(38%). However, other contributions such as cooperative activities: representative
savings groups, rice banks, and village cooperative shops and other voluntary
activities do not generally take place with these workers since they have little time to
contribute.
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Next, financial assets determine how households of workers access capital. There
were a similar number of significant associations for savings and credit. For access to
saving money, 77% of total workers in Samut Sakhon had a savings account, gold,
and jewels for saving but there were 23% of households with no savings. Workers in
Phuket and Songkhla had the same proportion of saving money methods about 30%
of total workers and people with no saving their money were also about 30 %. In the
case of access to credits, more than a half of the employees in the three provinces
never borrowed money. Access to credit facilities in commercial banks and
government agent banks are very difficult as a result of the inability of people to
provide a form of collateral as security for the advancement of loans. Most people
prefer to borrow from private lenders despite the high interest rate because these
lenders more readily accept borrowing.
Access to physical capital shows ownership of vehicles and houses. A considerably
larger percentage of Phuket and Songkhla workers (79% and 70%) with access to
physical capital used motor bikes as their major transportation because their
workplaces are far from their accommodation and public transport is not convenient
therefore they need motor bikes but, in Samut Sakhon, there was the lowest number of
workers using motor bikes because that area is near the capital city (Bangkok). They
can also use public transportation, such as local buses, city buses, or coaches for 24
hours. About 90% of Songkhla’s workers access ownership of a house. Most of
Songkhla’s employees were local people since their houses are inherited by their
family. Conversely, the majority of workers in Phuket and Samut Sakhon are migrant
people from other provinces therefore they do not have their own houses near the
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workplace and they usually live in rented accommodation which is near workplace.
Some workers move to live near the workplace and never go back to their home town
but others send monetary remittances back to their rural areas for family use.
Access to human capital is another crucial factor that affects the development of
workers. However, we found that the majority of workers in the three areas had only
attended basic education (primary school). This confirms that working in tuna
manufacturing is regarded as a preserve of the less fortunate in education. Only a
small number of total workers in Samut Sakhon and Phuket have the opportunity to
work in a second job. Only in the Songkhla area, about 47% of Songkhla’s workers
had a second job because workers who have their own land can grow rubber, fruit,
and rice in its seasonality and they also do fisheries and aquaculture. Other workers
may have a small business such as convenient stores and bakery shops. In addition,
some workers are salesmen for insurance and traditional herb medicine companies.
Diversified income sources are the norm rather than the exception for many tuna
factory worker household.
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Table 4.7 Household Access to Capital by Province
Province SS % of total PK % of total SK % of total Total Significance (1) (1)/137 (3) (3)/101 (2) (2)/93 n=137 n=101 n=93 N=331 Access to natural capital a - Water resources 21 15 12 12 64 69 97 - Gathering wild vegetables 17 12 21 21 0 0 38 - Fisheries 3 2 0 0 1 1 4 - Gathering and water resource 3 2 6 6 4 4 13 - Fisheries, gathering and water/Fisheries and water 4 3 5 5 2 2 11 Access to social capital b - No participation 8 6 20 20 7 8 35 - Culture 10 7 7 7 2 2 19 - Religious 3 2 3 3 5 5 11 - Thai wedding 2 1 10 10 1 1 13 - Culture and Religious 4 3 13 13 4 4 21 - Culture and Thai wedding 9 7 4 4 1 1 14 - Religious and Thai wedding 20 15 6 6 10 11 36 - Culture religious and Thai wedding 81 59 38 38 63 68 182 Access to financial capital Means of saving c - do not save 31 23 32 32 33 35 96 - bank account 17 12 14 14 14 15 45 - gold/jewels 19 14 24 24 19 20 62 - both bank account and gold/jewels 70 51 31 31 27 29 128
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Province SS % of total PK % of total SK % of total Total Significance (1) (1)/137 (3) (3)/101 (2) (2)/93 n=137 n=101 n=93 N=331 Access to credit d - no loans 94 69 56 55 62 67 212 - commercial bank loans 2 1 0 0 1 1 3 - government bank loans 11 8 8 8 17 18 36 - private loans 30 22 37 37 12 13 79 Access to physical capital Ownership of vehicle e - no 58 42 9 9 15 16 82 - car/van 5 4 0 0 1 1 6 - motor cycle 65 47 80 79 65 70 210 - bicycle 0 0 0 0 1 1 1 - car and motor cycle 8 6 11 11 10 11 29 - car and bicycle 0 0 0 0 0 0 0 - motor cycle and bicycle 1 1 1 1 0 0 2 - motor cycle , car and bicycle 0 0 0 0 1 1 1 House ownership f - House 46 34 40 40 84 90 170 - Rent 91 66 61 60 9 10 161
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Province SS % of total PK % of total SK % of total Total Significance (1) (1)/137 (3) (3)/101 (2) (2)/93 n=137 n=101 n=93 N=331 Access to human capital Educational level of workers i - Primary school 66 48 43 43 54 58 163 - Secondary school 28 20 33 33 8 9 69 - High School 30 22 9 9 20 22 59 - Diploma 5 4 3 3 7 8 15 - Bachelor degree 8 6 13 13 4 4 25 Opportunity job (second job) 23 17 15 15 44 47 82 l Significant (two-tailed) tests (Pearson’s Chi-Square, Likelihood χ2 and Cramer’s V statistic)
a) Cramer's V=0.434 ; p=0.000 f) Cramer's V=0.490 ; p=0.000
b) Cramer's V=0.292 ; p=0.000 i) Cramer's V=0.215 ; p=0.000
c) Cramer's V=0.157 ; p=0.012 j) Cramer's V=0.168 ; p=0.005
d) Cramer's V=0.180 ; p=0.002 k) Cramer's V=0.230 ; p=0.000
e) Cramer's V=0.283 ; p=0.000 l) Cramer's V=0.327 ; p=0.000
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4.4.3.3 Asset Pentagons
Plotting assets used in a pentagon highlights the relative wealth in each category of
assets. Household assets are illustrated by a pentagon in the SLF. The construction of
asset pentagons is carried out to explain our findings for workers. An attempt is made
to show all 17 indicators on a single polygon. It is decided to average standardised
values to obtain a single figure for each capital asset. The problem lies in the arbitrary
choice of indicators to represent each capital (Figure 4.7).
Figure 4.7 Asset Capital Indicators
Social capitalCulture, religion, wedding
activities
Human capital 1Educational levelMedical careWelfareSecond job
Physical capitalHouse ownershipPiped WaterVehicle ownership
Natural capitalWater resourceGathering wild vegetableFisheries
Financial capitalSaving
LoanAnimalPrecious thing
Social capitalCulture, religion, wedding
activities
Human capital 1Educational levelMedical careWelfareSecond job
Physical capitalHouse ownershipPiped WaterVehicle ownership
Natural capitalWater resourceGathering wild vegetableFisheries
Financial capitalSaving
LoanAnimalPrecious thing
As the number of samples in each province is not equal, the number of Phuket
workers (101 people) and Songkhla workers (93 people) was adjusted to be 137
people by using the standard of the maximum samples in Samut Sakhon’s workers
(137 people). Initial attempts used many indicators per capital. The value of each
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indicator was standardised on a 0 to 1 scale in Table 4.8. Social capital indicates what
activities workers participate in their neighbourhood. High participation refers to high
social capital. Participation in voluntary, club, and associations are activities to
support the development of the community, such as voluntary work for building a
school, women’s clubs for career support, association for cooperatives etc. Culture,
religion, and Thai wedding are Thai traditions for some vacations and people will help
to prepare for ceremony together. Physical capital refers to what workers have as their
houses. Three physical capitals are house ownership, private piped water and vehicle
ownership. High physical capital measures the wealth of workers and lower physical
capital means poorer households. Natural capital refers to land ownership, water
resource, vegetation collection, and fisheries. It measures how workers can use natural
assets and resources for their livelihood. Financial capital provides the potential of
workers to manage their budget. Wealthier workers have more money savings,
livestock and precious things. More loans indicate a deficit budget in households or
investment for building house and a small business. Human capital is classified as
education level and opportunity for other careers.
Table 4.8 and Figure 4.8 are the livelihood asset analysis and the summary of
livelihood assets pentagons for each province. The livelihood asset pentagon is
divided into five main axes: social capital, physical capital, natural capital, financial
capital, and human capital. A typical case is Samut Sakhon, with strong financial
capital due to saving money and gold collection. They are strong in social capital
because of participation in Thai culture, religious ceremony, and Thai wedding
ceremony. Thai culture and religious ceremony are individual activities so whoever
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wants to join they can join in the temples. Thai wedding ceremony is generally
extended to their friends in the tuna factories where they can participate easily. They
are strong on human capital due to the compulsory education level whist they are
weak on natural capital and medium physical capital. On Phuket, financial capital is
the main constraint for workers’ households. Natural and social capitals are in the
medium level and workers here are strong in human and social assets. In Samut
Sakhon, workers are strong in all capitals although they are not the strongest in social,
physical, and financial capitals. Workers in Samut Sakhon are stronger in financial
and social assets than Phuket and Songkhla whereas workers in Songkhla have more
advantage in natural and human assets than Phuket and Samut Sakhon. Physical asset
is the most sufficient in Phuket.
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Table 4.8 Workers in Tuna Factories in the Summary Pentagons
Province Samutsakhon (std n=137) Phuket (std n=137) Songkhla (std n=137) Count/Mean Std value Count/Mean Std value Count/Mean Std value Social capital Voluntary (#yes) 11 1.000 4 0.370 10 0.937 Club(#yes) 4 1.000 1 0.307 4 1.000 Association(#yes) 2 0.491 4 1.000 1 0.362 Culture(#yes) 104 1.000 84 0.809 103 0.992 Religion(#yes) 109 0.902 81 0.674 121 1.000 Wedding(#yes) 113 1.000 79 0.696 110 0.978
average 0.899 0.643 0.878 std dev 0.203 0.263 0.254
Physical capital House ownership (#yes) 46 0.372 54 0.438 124 1.000 Pipe water (#yes) 100 0.970 103 1.000 37 0.357 Vehicle ownership(#yes) 79 0.688 125 1.000 115 0.921
average 0.676 0.813 0.759 std dev 0.299 0.324 0.350
Natural capital
Land owner (#yes) 1 0.019 1.36 0.026 53 1.000 Water sources available Underground water (#yes) 25 0.298 9 0.113 84 1.000 Well water (#yes) 0 - 19 0.921 21 1.000 Vegetable collection (# yes) 23 0.565 41 1.000 6 0.145 Fisheries (#yes) 8 1.000 0 0.625 4 0.552
average 0.376 0.537 0.739 std dev 0.418 0.450 0.385
Financial capital With savings (#yes) 89 1.000 75 0.618 68 0.517 With bank loan (#yes) 42 1.000 11 0.258 27 0.631 Pigs/Cows/Chicken ownership (#yes) 13 0.250 8 0.115 77 1.000 Precious things (#yes) 87 1.000 62 0.717 59 0.677
average 0.813 0.427 0.706 std dev 0.433 0.259 0.252
Human capital Education level (years) 9 1.000 9 1.000 9 1.000 Medical care 136 1.000 134 0.987 133 0.975 Welfare 135 0.985 137 1.000 122 0.892 Second jobs 23 0.355 20 0.314 65 1.000
average 0.835 0.825 0.967 std dev 0.320 0.341 0.051
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Figure 4.8 Asset Pentagons by Province (weight data).
0.84
0.81
0.38
0.68
0.90
0.83
0.43
0.54
0.81
0.64
0.74
0.88
0.71
0.76
0.97
Social capital
Physical capital
Natural capitalFinancial capital
Human capital
Samut Sakhon Phuket Songkhla
4.5 Livelihood Conditions in Factories
4.5.1 Ambient Conditions
Environmental conditions in tuna processing plants affect with workers. There are
many processes in tuna production with different conditions: preserving in storage,
thawing, butchering and pro-cooking. The storage rooms have to be preserved at a
temperature of – 18 ºC. The temperature of the thaw water might be increased until it
reaches approximately -7 ºC. The frozen tuna after thawing should have a maximum
temperature of 5 ºC at the butchering table. The suitable temperature for tuna during
the cooking process must be brought up to approximately 60-66 ºC (Suwanrangsi et
al., 1995). It is clear that the ambient temperature, associated high and low
temperatures and high humidity in tuna process rooms affects worker emotions and
health. They work in physically stressful condition for over 8 hours per day.
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4.5.2 Opinion of Workers in Tuna Factories
It has been revealed that the tuna industry is participated in mainly by female
labourers. More security of labour can be measured by work years, off duty time,
working late, and lay-offs. The area with the longest number of work years (8 years)
of employees occurred in Samut Sakhon and workers took the least number of
holidays compared with Songkla and Phuket. Moreover, the lay-off policy of
companies was the lowest. This is because of the size of the companies there and their
financial status. Even though labour welfare, such as the national social insurance and
the worker’s compensation fund, is limited and covers only a minor proportion, most
employees (over 80%) are satisfied to be working in the tuna factories. Companies
supported employees in a good relationship and a friendly family-like atmosphere.
Some companies such as TUF, TUM, SCC, PFI, and TOV subsidise the price of
meals, cafeterias, bus services, cooperate for loans, accommodation, and travel. It
seems that larger firms actually provide better conditions than the smaller companies.
Table 4.9 The Security of Labour in the Tuna Factories
SS SK PK Mean Count % Mean Count % Mean Count %
Years of work 8.56 5.84 2.53 Off duty 2.54 3.40 8.92 Work late Never 121 89 79 85 62 63 Yes 15 11 14 15 37 37 Total 136 93 99 Layoff Never 94 69 78 88 62 63 Rarely 42 31 10 11 35 35 Normally 1 1 1 1 1 1 Often 0 0 0 0 1 1 Total 137 89 99 Welfare Yes 135 99 83 91 101 100 No 1 1 8 9 0 0 Total 136 91 101 Work satisfaction Satisfied 127 93 80 86 84 84 Indifferent 9 7 5 5 13 13 Unsatisfied 1 1 8 9 3 3 Total 137 93 100
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4.5.3 Income Measurement
To compare income of workers, it can be measured by comparing with Gross
Provincial Product (GPP) which is an average income per person of a province, Gross
Regional Product (GRP) which is an average income per person in a region, such as
North, South, Central, and East of Thailand, and Gross Domestic Product (GDP).
Workers’ incomes compared to GPP, GRP, and GDP in Table 4.10 were calculated
from household income data in the three areas. Employees in Songkhla earned the
lowest income because the minimum wage rate is the lowest compared with the two
other provinces while workers in Samut Sakhon and Phuket have the higher
proportion for income. The salary of Samut Sakhon’s employees is very low when
compared to GPP per capita in Samut Sakhon and GRP per capita in Central part.
They have less than a sixth of GPP and a third of GRP. While Songkhla’s workers
receive the lowest salary, their income is less than two-thirds of GPP per capita in
Songkhla. Phuket’s workers obtain a salary less than half of GPP per capita in Phuket.
Although workers’ salary is higher than the poverty line 5,307 baht (NESDB, 2006),
they are very close to the subsistence level of existence. Average earning of tuna
factory in the three areas are lower than GDP per capita per month.
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Table 4.10 Income per month comparing with GPP, GRP, and GDP in 2006
Income/ month/person (baht)
Average income of workers in Samut Sakhon 7,467 GPP per capita per month in Samut Sakhon 44,430 GRP per capita in the central part per month 24,143 Average income of workers in Songkhla 6,387 GPP per capita per month per month in Songkhla 9,822 Average income of workers in Phuket 7,683 GPP per capita per month in Phuket 15,868 GRP per capita in the southern part per month 7,558 Thailand's GDP per capita per month 10,003 Source: Survey (2006) and NSO and NESDB (2008; 2007).
4.6 Livelihood Strategies and Outcomes
Livelihood strategies react to changing pressures and opportunities and point out the
method of household survival. Analysis of livelihood strategies showed that three
major strategies occur in workers’ household to improve their livelihoods: migration,
job diversification, and agricultural intensification. People in Samut Sakhon and
Phuket moving from rural area are likely to migrate to unskilled low-paying jobs, and
earn more income from remittances. People work in factories because the salary from
factories is more secure than income from farming. Workers in three areas tend to
improve their livelihood security by occupational diversification toward more non
farm work such as direct sales and self-business. Fewer workers are still cultivators in
rural areas with their own land (Songkhla). Crop production with local plants is
largely from rubber, rice, coconut, stink bean, and lemon.
The outcome of livelihood strategies points to changes in livelihoods. For workplace
in tuna factories, people have to work hard for many hours together and to be willing
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to obey orders. Most people have moved from their regions which are farming homes
in order to obtain a better life and more income. They work as many hours as possible
but they are still paid low payment. Normally the permit of working is 48 hours per
week but it is possible to work overtime organised on a voluntary basis. They
generally have fewer than 10 days paid holidays and medical care and welfare are
basic. However, workers are largely satisfied in their job, welfare, and medical care
and there is a good job security with a low proportion of lay off. The question is how
sustainable are workers sustainable in their live existence in the factories?
Figure 4.9 Improvement in Livelihoods for Workers
Low income Basic welfare
Basic medical careSix day working
Help or HarmBalance of assets
Financial asset
Physical asset
Human assetSocial asset
Low income Basic welfare
Basic medical careSix day working
Help or HarmBalance of assets
Financial asset
Physical asset
Human assetSocial asset
Workers in the tuna processing factories remain poor, vulnerable and unskilled. They
face a difficult future. Most of the respondents have invested in their children’s
education and some of them are investing in themselves, especially by youths in
higher education, trainings, and skills. Workers can enhance their working skills but
they don’t improve their education because of long working hours. Social capital is a
mutual relationship within, and among households and communities. This relationship
is based on trust and reciprocity. More precisely, social capital pays more attention to
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family networks, kinship, and close friends that the household will depend on in time
of crisis (DFID, 1999). Workers also have some human activities that are offered by
companies such as holiday trips and New Year party. Nevertheless, social activities
such as cooperation in voluntary, club, and association are declining due to time limit.
4.7 Conclusions
This chapter investigates the sustainable livelihood of labourers, who work in tuna
factories. The sustainable livelihood of workers including their living and working
conditions supports the competitiveness and social sustainability of the Thai tuna
industry. The sustainability of the living place requires the potential for long-term
maintenance of wellbeing. Additionally, in workplaces, socially responsible
enterprises would support workers in their places. Income, welfare, medical care,
atmosphere for working and motivation of working are main factors in their
workplaces. People should have a stable job, suitable payment at least equal to GDP
per capita, adequate holidays, and suitable welfare. For living analysis, the results
show that vulnerability context has an impact on workers in their livelihoods. The
vulnerability context in each of the three provinces is quite different. In Samut
Sakhon, people panicked because of various shocks, such as the impact of the Asian
Crisis in 1997, environmental pollution, flooding, migrant workers, and diseases. In
Songkhla, there is a different type of shock: the South Thailand Insurgency. Because
of this, people feel insecure and may move to other areas. The trend and seasonality
are practically the same as that of Samut Sakhon. Lastly, Phuket’s people have been
seriously shocked by the TSUNAMI disaster. They need TSUNAMI preparedness in
the future. Trends and seasonality of the three provinces are virtually similar. The
Livelihoods of Workers in the Thai Tuna Industry CHAPTER 4
189
trend on the good side is an increase of the minimum wage every year but it depends
on the rise of the cost of living affecting the workers. The seasonality of tuna
resources often caused overtime working by employees.
For working condition, workers spend a long time in an awful environment: very high
and low temperatures, basic welfare, few holidays, and their incomes are pretty low
based on national income per capita. The poor environment, low salary, hard work,
poor health, and insufficient welfare have affected worker fulfillment. It is quite hard
for households to be sustainable in their livelihoods if people only work in the
factories. The solution for workers requires higher payment and better welfare but this
means the tuna production cost will increase as well thereby threatening international
competitiveness. In the short term worker households will need to maintain job
diversification strategies and agricultural intensification if they are to survive future
shocks, declining fish catches, seasonal low-capture rates and market uncertainties.
As with many other asset-poor, ill-educated, people world wide, tuna factory workers
in Thailand face a harsh, difficult and uncertain future.
Conclusions CHAPTER 5
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Chapter 5
Conclusions
5.1 Introduction
The Thai tuna industry is central within the Thai economy both in terms of export
earnings and in generating employment. The threats to the long term sustainability of
the Thai tuna industry are thus very important for the Thai economy. Three key
dimensions of the sustainability are an assured supply of fish stocks and catches, the
maintenance of international competitiveness and the sustainable livelihoods of
workers who provide the low-cost labour which underlies and underpins Thailand’s
competitiveness. The purpose of this final chapter is to discuss the relationships
between these aspects and make linkages between them. The chapter is divided into
seven sections. Section 2 discusses the major findings of the study and the
relationships between the above issues. Section 3 discusses the improvements needed
for the future sustainability of the industry. Sections 4, 5 and 6 indicate contributions,
limitations and areas for further study and Section 7 presents an overall brief
conclusion.
5.2 Main Conclusions and Factors Relating to the Thai Tuna Industry
Figure 5.1 shows relationships between the main factors in the supply chain of the
Thai tuna industry.
Conclusions CHAPTER 5
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Figure 5.1 Main Factors in the Thai Tuna Industry
Tuna Fisheries • Tuna Stock
ConservationOverexploitation
Thai Tuna Fisheries
Potential of Tuna Processors- Internal Relationship- External Relationship
Tuna Supply-Catches-Prices
Demand (Canning and Fresh and Frozen tuna) Export and Domestic
The Sustainable Livelihoods of Unskilled Labour
-Working place- Living place
Exogenous Factors
-Currency exchange rate- Labour shortage- Oil price- Tuna price
Tuna Fisheries • Tuna Stock
ConservationOverexploitation
Thai Tuna Fisheries
Potential of Tuna Processors- Internal Relationship- External Relationship
Tuna Supply-Catches-Prices
Demand (Canning and Fresh and Frozen tuna) Export and Domestic
The Sustainable Livelihoods of Unskilled Labour
-Working place- Living place
Exogenous Factors
-Currency exchange rate- Labour shortage- Oil price- Tuna price
5.2.1 Tuna Processing and Fishing Sectors
The potential of Thai tuna processors depends on key internal and external
relationships. For internal relationships, the tuna processing and fishing sectors have
been investigated here. In the tuna processing sector, the Structure Conduct
Performance (SCP) paradigm has been used to identify internal relationships. The
Thai processing structure in both the canning and fresh and frozen markets is highly
concentrated and oligopolistic. Tuna processors must meet legal barriers to entry in
the form of government policy controls which are:- (i) the need to be a member of the
Thai Food Processors Association, (ii) the registration system for vessels and crew
health certification from the Department of Fisheries and (iii) Third-party quality
control investigation.
Conclusions CHAPTER 5
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There barriers, coupled with economies of scale where high production capacity
generates absolute cost advantage, affect potential new entrants. Externally,
geographical barriers (import tariffs and other requirements from importers) affect
both existing firms and new entrants. The structure is reflected in the economic
analysis of processing firms’ conduct, which indicates that tuna processing in
Thailand operates through price leadership by a dominant firm. Branding strategy is
used for the canned product but not for fresh and frozen products. Local brands are
labelled for the domestic market while well-established brands are used for the export
market, again supporting both the concentration and conduct of the industry. In the
canning companies, vertical and horizontal integration has been adopted by a few
larger canning firms to increase economies of scale and reduce transaction costs. Only
the largest canning firm use a backward integration into fishing to solve rules of
origin problems while the second largest firm has not been able to establish fishing
companies and smaller firms lack sufficient funds. In the fresh and freezing
companies, vertical integration is typically used for processing, fishing, and
distributing. Using price-cost-margin analysis to examine the performance of firms,
we found that two canning processors are performing poorly, although no fresh and
freezing firms are (yet) in this high risk category.
It might be thought that one effective fishing operating sector strategy would be to
replace tuna imports with an increased potential for negotiation for foreign trade
agreement and rules of origin requirements. However, there is very limited potential
for investing in Thai tuna vessels. The results of this study show that both purse seine
and long-line vessels are experiencing losses. Break-even analyses indicate that both
Conclusions CHAPTER 5
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increased tuna prices and falling average variable cost of boat operation are required.
Although world tuna prices might be expected to rise, as tuna supplies tighten against
rising demand (following increased incomes), the costs of fishing are also likely to
rise as both fuel and (for Thailand) labour costs rise With unprofitable tuna fishing,
reducing raw tuna imports is less possible because the Thai fishing sector experiences
continuing losses in operation, due to high investment costs, unskilled fishermen, tuna
stock limitation, and conservation regimes.
The Thai canned tuna industry currently exhibits international competitiveness with
the largest market share in the world and in all main importers except in the EU.
Revealed comparative advantage analysis shows that Thailand has had a comparative
advantage and has constantly maintained the comparative advantage in the world and
two main importers, the US and Canada, over the last ten years but its comparative
advantage is declining in Australia and fluctuating in the EU, the Middle East, and
Japan. However, it is also clear that this advantage depends critically on low labour
costs in Thailand, which is not consistent with continued economic growth.
Peter’s double diamond model identifies how an industry can achieve competitive
advantage in the global market. Four features of international activities have been
revealed. Factor conditions show that Thailand gains from production capacity and
processing technologies, and from infrastructure links to international customers. A
low labour wage rate country has been a strong source of competitiveness until now
but this will decline as higher labour wage and labour shortages occur. Demand
conditions rely upon the international demand and the competitiveness of other
Conclusions CHAPTER 5
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exporting countries. Related industries, such as cold storages, shipping, ports,
packaging, and logistics are all adequate for tuna processing, but most have
alternative activities which could become more profitable and sustainable than tuna
trade in the medium term. However, the tuna industry relies on international demand,
which seems likely to continue to grow in the face of limited supplies, while the costs
(especially fuel and labour) of supply are also likely to rise in the future. It may be
that the Thai industry is sufficiently strong to cope with these changing circumstances
to remain a strong processor and exporter, albeit not growing in either absolute or
relative importance as in the past.
5.2.2 Livelihoods of Workers
Sustainability of the Thai tuna industry also involves the livelihoods of workers. We
found that employment in tuna production is as high as 40,000 workers and most are
unskilled and mainly females. Although the Thai labour force, especially women, can
accept working in factories because there are not many jobs in agricultural sector and
they are landless and lack funds, the payment for working in the factories is low,
conditions are poor with uncomfortable temperature and poor worker-welfare with
long working hours. Although larger firms can support better welfare, income,
environment, and convenient facilities, they currently employ relatively few workers.
In worker living areas, findings showed that workers were vulnerable to economic
crisis, seasonality of tuna catches, natural disasters, and the insecurity of a personal
living place. In the short term maintaining workers’ job diversification strategies
including agricultural intensification where are possible will ensure family survival
for the vulnerability. In the longer term, economic growth within Thailand will
Conclusions CHAPTER 5
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generate competitive earning opportunities for many of the present labour force, while
the processing sector, if it is to survive, will need to match these earning opportunities
and working conditions. If it cannot, it can be expected to decline as labour finds
better things to do.
5.2.3 Tuna Supply
Tuna capture is currently seriously threatened by limited supply, and the global tuna
market continues to experience a shortage of fish stocks. Limited tuna supply impacts
on fishers and fishery policy affects tuna harvesting. For biological sustainability of
fish stocks at the maximum sustainable yield level, it was indicated that yellowfin and
bigeye are fully exploited in the Indian Ocean, while yellowfin is fully exploited and
bigeye and albacore are overfished in the Pacific Ocean. Only skipjack is not fully
exploited and skipjack stock may still be sufficient for the tuna industry in the Indian
and the Western and Central Pacific Oceans. To date, fishery management has
focused on biological research. Now it is important to pay more attention to economic
considerations. Common policy goals for the fishery include long-term biological
sustainability (the maximum sustainable yield, MSY) and maximization of sustainable
economic returns (the maximum economic yield, MEY) where the tuna harvest is less
than the MSY level. The MEY level aims to identify the level of fishing activity
which yields both maximum economic rent (Kula, 1992) and maintains a larger stock
of tuna. Both objectives imply that access to the future tuna stock will be more
restricted.
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While the trend of tuna demand has been growing and supply catches increasing, the
problem of limited natural resource is one that cannot be avoided. Current tuna supply
needs to be conserved for sustainability while the tuna industry increasingly needs
raw tuna for processing. Companies are experiencing increasingly difficult access to
raw fish supplies. A shortage of raw tuna increases raw tuna prices. In addition,
producers may need to reduce their productive capacity or have at least a seasonal
closing-down of factories, and their income and profit will therefore decline. Negative
effects may occur in the small and medium enterprises with poor levels of
performance, whereas the dominant firms, such as the Thai Union Group and Sea
Value, could survive with their vertical and horizontal integration strategies, implying
increasing concentration (and associated specialization) in the industry.
5.2.4 Demand Forecasting
Here, demand “forecasts” were estimated by a simple ARIMA model, although the
main factors involved in tuna demand; population; income; tuna price; have also have
been discussed. The simple projection of the past history of Thai exports indicates that
there are two sensible forecast trends27 levels that use for the Thai tuna industry. At
the actual (or medium) forecast level, annual growth rate is 5.5% in 2008, which
decreases slightly during 2009 to 5.2% and to 4.7% by 2011 thus exports are growing
but at a falling rate. At the low forecast level, its growth rate is a decrease of -7.4% in
2007 with smaller declines of about -5.6% in 2009, -4.9% in 2010, and -4.6% in
2011.
27 The ARIMA ‘High’ forecast has been ignored as being inconsistent with known trends in fish stocks.
Conclusions CHAPTER 5
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Figure 5.2 shows the relationships of two forecasts for the next five years with the
trend of the Thai world tuna market share, Thai tuna export, and Thai tuna catch. An
increasing export demand at the medium level might be possible if there is a plenty of
tuna supply, increasing population and income, hence in tuna consumption, less
international competitions, lower import tariffs and less stringent rules of origin
criteria and improved conditions for fisheries. These are less likely and the low
forecast level is considered more realistic because global tuna stock policy has been
concerned with over exploitation leading to over fishing. There are decreases in
population growth, income growth, and tuna consumption growth. It is also possible
that import tariffs will be uncertain, rules of origin criteria become binding and, more
importantly, the fishing sector is largely unprofitable as a consequence of decreasing
tuna stock. The tuna industry survival will be slightly declining. Hence, the Thai
industry faces a likely future of declining exports, implying a declining thai
processing sector
Conclusions CHAPTER 5
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Figure 5.2 Relationships of Tuna Demand Forecasts, Market Share, Thai Tuna
Catch, and Thai Tuna Export, 1970-2011
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
1970
1975
1980
1985
1990
1995
2000
2005
2010
Tonnes
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Thai world tuna market share (%)
Total Thai tuna exports Total tuna catches Export forecast (M)
Export forecast (L) Thai World Market Share
Source:FAO and Calculated from Josupeit (2008).
5.3 Necessary Conditions for Improved Sustainability of the Thai Tuna Industry
5.3.1 Tuna Demand
International demand and domestic demand are very important to the Thai tuna
industry. Stronger international demand helps Thai tuna companies expand their share
leadership of the global market and higher export demand provides a competitive
opportunity to share in the expansion. However, tuna demand is not likely to grow as
strongly in the future as it has in the immediate past, as population growth rates slow,
and as real prices increase with increasingly tight supplies. The quantities of exports
will almost certainly not grow much in the future, since they will be constrained by
Conclusions CHAPTER 5
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fish stocks and catches, either as fisheries around the world take steps to conserve
existing stocks, or (failing such conservation) the stocks themselves fall, and catching
effort necessarily has to increase and become more costly and less effective.
However, given continued income growth, underlying demand is likely to continue to
increase, which will lead to rising real prices of tuna. It is not clear whether this will
lead to declining or increasing real value of tuna exports. In any event, in this
scenario, tuna processors will need to have strategies to increase other supplies, such
as tuna from aquaculture, substitute seafood products, and new non-tuna products..
Even in the optimistic case of sufficient tuna supply, to increase international demand,
processors may extend tuna exports into countries which have a higher growth rate of
income such as the Middle East, Canada, and Africa. Tuna producers may also give
attention to their domestic markets by improving local promotion such as advertising
and R&D to improve the health and variety of products.
5.3.2 Tuna Supply
With globally unbalanced tuna supply, it is difficult to increase tuna supply from the
high seas. Tuna farming is an opportunity to substitute for the world’s natural tuna
supplies. There are currently more than 30 bluefin tuna farms in the Mediterranean
Sea region (Turkey, Italy and Croatia) and bluefin farming is also carried out in
Northern Mexico (Aqua Fauna Consulting, 2008). South Bluefin Tuna farming in
Australia has developed since 1991 (Hidaka and Torii, 2005). In addition, yellowfin
farming is carried out in Mexico and there are developments and trial operations of
yellowfin and bigeye tuna farming and fishing operations in South East Asia (Aqua
Fauna Consulting, 2008; Hidaka and Torii, 2005). Hidaka and Torii (2005) noted that
Conclusions CHAPTER 5
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the advantages of tuna farming in Australia included freshness, flesh quality, low
farming costs, and low transportation costs to buyers. Given Thailand’s marine
resources, there would seem to be good opportunities to develop tuna farming in Thai
waters, and this is an opportunity for the Thai processing sector to secure a better
future than is otherwise likely. Although the target for tuna farming is now only for
the Japanese sashimi market, which is not for canning (Catarci, 2001), there is a
possibility of tuna farming development for the tuna industry in future. Thailand is
located in the Gulf of Thailand linking to the Pacific Ocean and Andaman Sea linking
to Indian Oceans. There are marine scientists who are successful with aquacultures
such as shrimp, shell, pelagic fish etc. Nevertheless, as with salmon farming, there are
difficulties to be overcome – especially the environmental effects of large scale
commercial farming and the upstream effects of the increased demand for fish feed,
itself a significant threat at present to declining stocks of other fish for farmed fish
feed. The development of non-fossil fuel dependent substitute feeds, ideally from re-
cycled waste products, would offer a major competitive advantage to the developers
and users in the future.
5.3.3 Tuna Processing and Fishing Sectors
5.3.3.1 Merging Small and Medium Enterprises Processing and Fishing Sectors
Merging is a solution for increasing the strength of competitiveness for smaller
enterprises. The efficiency–related reasons why mergers might occur are that mergers
involve economies of scale, attempts to create market power, take advantage of
opportunities for diversification by exploiting internal capital markets (Andrade et al.,
2001). . The Thai tuna industry has the potential for merger. Few larger companies
Conclusions CHAPTER 5
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have more advantages in the industry with successful forward and backward vertical
integration strategies and have been successful from merger activity. For instance, the
Thai Union Group resulted from multinational companies with the acquisition of US
company, and has many other subsidiary companies. Sea Value acquired two
companies which were not successful in their performance and shared with a US
company and it has now become the second largest tuna company in Thailand.
Consequently, smaller processing and fishing companies are likely to be amalgamated
with stronger companies to reduce fixed costs and risks in fishing, and possibly to
increase profit margins and market sales.
5.3.3.2 Effective Negotiations in Bilateral, Regional and Multilateral Trade Agreements
The main foreign importers of tuna are the US, the EU, the Middle East, Japan,
Canada, and Australia. Thailand has faced import tariffs and some restricting criteria
from Free Trade Agreement as shown in Table 5.1. Thailand does not have problems
with low tariffs in the Middle East and Canada and has a zero tariff with the Thailand
Australia Free Trade Agreement (TAFTA). However, Thailand has confronted high
tariffs from EU (24%) and the US (12.5%). In Japan, Thailand has become more
restricted by rules of origin, because of its present need to import raw fish (a treat
which tuna farming could overcome). Effective negotiation is required as follows.
The first goal expected is the positive impact of the EU preferential trading
arrangement which affect revenues, investment, and opportunities such as a decrease
in import tariffs and an extension of import quotas. The second expectation is
successful trade relations with FTA partners in the tuna industry. The effective
agreements relating to tuna products are the Japan-Thailand Economic Partnership
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Agreement (JTEPA) and the Thailand-Australia Free Trade Agreement (TAFTA).
JTEPA requires the additional condition that tuna needs to be caught by a vessel of a
country registered with the IOTC or by Thai tuna vessels. Again tuna farming offers
an opportunity here. For TAFTA, there has been no tariff since 2007 (Department of
Trade Negotiations, 2008). Apart from the two main agreements, there are other
foreign negotiation positions in process. For example, Thailand and the EU under
ASEAN-EU FTA are at an early stage of the framework; talks between Thailand and
the US are currently suspended due to political problems. The expectations of
resolving the rule of origin agreement are that raw tuna fish can be fished by Thai flag
vessels, and vessels of a country registered with the IOTC and the WCPC which
organise tuna capacity in the Indian and the Western Pacific Oceans where are main
raw material into Thailand or where is no specific feature requirement from FTA
partners.
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Table 5.1 Summary of Import Tariff from Main Tuna Importers
Importer Import tariff for preserved tuna Free Trade Agreement (FTA) Status FTA Criteria
The United States in oil = 35% Negotiating not in oil in airtight container = 12.5% not in airtight container = 1.1 cent/kg European countries 24% Negotiating The Middle East 5% na Australia TAFTA Require change in chapter two-digit level in Harmonised System Classification (see Appendix 3) Japan JTEPA 1. Require change in chapter two-digit level in Harmonised System Classification 2. Require fishery features Each of originating materials is taken by the authorized fishing vessels on the IOTC record Canada 7% na
Source: Canada Border Services Agency (2008), Department of Trade Negotiations (Department of Trade Negotiations, 2008), Government of Dubai, Royal Oman Police (2008), United States International Trade Commission (2008)
5.3.3.3 Tuna Conservation from the Industry
Tuna management and conservation, in the long run, is one of factors necessary to
support the sustainability of the tuna industry. The decline of tuna stocks will make it
more difficult for the industry to maintain profit levels, survival, and competitiveness.
Thus, all involved in the tuna industry should take more action to save and conserve
tuna stocks on which the industry. Again, tuna farming offers an opportunity.
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5.3.4 Unskilled Labour
Unskilled labourers in the Thai tuna factories are never paid higher than the minimum
wage because of restrictions on their capability such as education, social class and
prosperity. In the workplace, workers spend around 72 hours/week which is less the
maximum hours (84 hours/week) determined by the Thai Labour Protection Act.
However, most multi-national clients adhere to international labour standards which
limit working hours to 60 hours/week, due to concerns about workers’ health and
safety conditions as well as the quality of workers’ personal lives (Thai Labour
Development Network, 2006). A higher minimum wage, welfare, and security of
work are required for unskilled labour to have better family lives. With low
performance especially in small companies, an increasing minimum wage rate, less
strong competitiveness, and unsustainable tuna supply, some companies inevitably
may shut down in future. Thus workers might become redundant. Workers may find
other unskilled job and move back to work in farming.
However, so long as the Thai economy continues to grow, the present labour force in
the industry can expect to have greater opportunities to better their lot by getting
employment elsewhere, while their children can expect to better educated and trained
than their parents. In response, to secure their labour force, the tuna processors will
have to improve their wage levels and working conditions to compete effectively in a
continually changing Thai labour market. If they cannot, they can expect to lose
business to other less well developed countries.
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5.4 Contributions
This thesis contributes to the empirical research into the Thai tuna industry in a
number of ways. First, it updates the results on Thai tuna market structure.
Putthipokin (2001) concluded that the Thai tuna cannery industry was a monopolistic
market, but the Thai tuna fresh and freezing sector was not included in Putthipokin’s
study. Here, we conclude that the canning and fresh and freezing sectors are
oligopolies with a dominant price-leading firm. Second, this thesis improves the
results of the RCA in the tuna industry in the world. Putthipokin (2001) examined the
RCA of Thailand, the Philippines and Indonesia with the five main importers, the US,
the EU, Japan, Australia and Canada. Apimukvorasakul (2002) investigated the RCA
index of Thailand, the Philippines and Spain with regard to the import markets of the
US, the EU and Japan while Kijboonchoo and Kalayanakupt (2003) examined the
RCA for the world market. This thesis updates the RCA of Thailand and other major
exporters for the world market and the six major importers, the US, the EU, the
Middle East, Japan, Australia and Canada.
Third, the analysis of the potential of Thai long-line and purse seine vessel investment is
extended. Boonchuwong (2003) concluded that there was feasibility for investment in
long-line vessels in Thailand. The result shows that the benefit ratio was 1.12, net
present value was 3.87 million baht, the interest rate of return was 14.67%, and the
payback period was nine years. However, the depreciation costs for fixed cost were
calculated from the straight-line depreciation method not from the annuity method which
is often used to calculate vessel depreciation costs. It also did not capture the feasibility
of the purse seine vessels that mainly obtain tuna supplies for the cannery sector.
Conclusions CHAPTER 5
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Fourth, this thesis supports the sustainable livelihoods analysis for some Thai people.
The findings concerning the livelihoods of workers in the Thai tuna industry will
provide information for improving the situation for unskilled labour in other
industries and for unemployed workers.
5.5 Limitations of the Study
5.5.1 Validation of Financial Statement and Tuna Prices
Updated, accurate and valid financial statements from tuna processing companies are
insufficient to use the structure-conduct-performance framework. The available data
are for 2005, and we can not examine the development of companies over time. It is
necessary for government agencies to monitor and update their data. In addition, the
price leadership analysis of tuna companies needs historical tuna product price data
from companies to examine how the dominant firm sets the price and how small firms
follow and react. There were limitations in collecting these data because of the short
time scale, insufficient funds, and lack of cooperation from processors.
5.5.2 Estimation of Tuna Fishing Operations
There are weaknesses in the estimation of tuna fishing operations. Actual costs and
revenues of tuna vessel samples are restricted by languages, times and landing. Raw
tuna prices and tuna product prices are averages from the landing port organisations.
Landing port organisations cannot collect raw tuna prices from foreign vessel owners
due to confidential business reasons. One element missing from this thesis is the
optimum tuna fishery in terms of bio-economics due to data limitations.
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5.6 Scope for Further Study
There are several ways to extend this thesis. The first is to study the possibility of
merging small and medium tuna firms to examine if this will be beneficial in making
them more sustainable businesses: it also increases the power of price-setting and
diversification. Second, the possibility of tuna aquaculture in Thailand is a new
opportunity. Tuna aquaculture offers a major opportunity of increased and more
reliable input supply for tuna processors in the future. Third is the opportunity to
cooperate with tuna processing and fishing companies in foreign countries to resolve
rules of origin criteria. For example, Philippines, Japan, Indonesia, Vietnam, China,
and countries from Africa, the Caribbean and Pacific Group (Solomon Islands,
Vanuatu, Papua New Guinea, Cote d'Ivoire, Fiji, Mauritius and the Maldives) where
they have their own tuna fishing fleets and have expert tuna harvesters.
The fourth extension is for a more intensive study of sustainable tuna fisheries in the
Indian and the West and Central Pacific Oceans, which has not been possible here.
There is excess vessel capacity of tuna fisheries in both oceans, corresponding to
over-fishing. The optimum size of the tuna fisheries could be estimated using a bio-
economic model relating to the maximum economic yield level that will maximize
rents and continue the reduction of excess fishing capacity, thereby ensuring
sustainable tuna resources. Economists have more concern to define sustainability of
tuna harvesting in another way. They identify the maximum economic yield (MEY)
as the harvesting level that has the maximum resource rent (the total social benefit
obtained from the resource (consumers’ surplus, producers’ surplus and resource rent).
There are three things to note about MEY. MEY will be the equilibrium stock of fish
Conclusions CHAPTER 5
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which is larger than that associated with MSY for most realistic discount rates and
costs. In the economic objective, MEY is more conservationist than MSY and should
in principle help protect the fishery. If price of fish increases it pays to exploit the
fishery more intensively, although at yields still less than MSY. If the cost of fishing
rises, it is preferable to have larger stocks of fish and thus less effort and catch. Profits
may be low when the price of fish is low and the cost of fishing is high, but it will still
be in the maximised (Kompas et al., 2009).
Few studies focus on tuna fishery management in the Western and Central Pacific
Ocean. Bertignac et al. (2001) examined the maximization of tuna resource rent from
the Western and Central Ocean. Several models, including a population dynamics
model, species and fleets specific model, spawning recruitment, and movement
models, were used in their study. Hannesson and Kennedy (2007) investigated rent-
maximization versus competition in the Western and Central Pacific tuna fishery
using an age-structured steady-state bio-economic model. Some studies have partially
addressed the issue of optimal fleet composition in the Indian Ocean. Mohamed
(2007) investigated the optimum number of skipjack fisheries in the Maldives using
the surplus production models of Schaefer (1954) and Fox (1970).
However, there is no study which examines the backward-bending supply curve
model which is appropriate for fisheries in the long-term. Copes (1970) stated that this
model was related in nature to a long-run supply curve. At the overfishing level,
fisheries’ demand levels have pushed operations to a point on the backward slope of
the supply curve, where increased effort is accompanied by lower output and a higher
Conclusions CHAPTER 5
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price (Copes, 1970). This model seems very relevant to the current world wild fish
industry.
5.7 Overall Conclusions
This thesis seeks to answer the question “Is the Thai tuna industry sustainable in the
medium to long term?” The answer is that it depends critically on how efficiently the
major stakeholders (tuna processors, government agencies, tuna fishers, other private
organisations) deal with difficult and changing situations. The objectives of this
thesis, were to predict the tuna exports and then apply the forecast results to the
constituent parts of the Thai tuna industry, to examine structure-conduct-performance
in the domestic market, to estimate the costs and returns of tuna fishing vessels as
well as to investigate the international competitiveness between Thailand and other
foreign countries, and to study socio-economic aspects of unskilled workers in plants.
These objectives have now been achieved but the results are worrying for the
industry’s sustained future.
The results show that Thailand still has a comparative advantage with major foreign
customers. Thailand has also competitive advantages of sufficient production
capacity, low labour costs, high quality of the product, high process technology, good
production facilities, new and large cold-storages, and good infrastructures. However,
the Thai tuna industry has not been sustainable in three key dimensions. The first is
that the industry is unsustainable, facing overfishing of world tuna supply to balance
an increasing tuna demand. Exhaustion of natural tuna resources resulting in insecure
tuna supplies is the most important problem in obtaining enough raw materials for
Conclusions CHAPTER 5
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processors. Insecure tuna supplies, which are caused by a decrease in tuna stock from
overfishing or excess fishing capacity, seriously affect the certainty of production.
Moreover, fishery conservation and management in fishery policies, such as gear
restrictions, closed season, catch quota, licensing acts, and the number of vessel
controls have more restrictions for tuna harvesters and thereby increasing the problem
of limited tuna supply.
Second, the Thai tuna industry is not economically sustainable with internal and
international competitiveness. The insecurity of tuna processors performances
influences the strength of competiveness. Some producers have been facing
unprofitable market conditions and are finding difficult to survive. In addition, the
comparative advantage based on low wages cannot be sustained due to rising
minimum wages and increasing competition in the Thai labour market as the economy
continues to grow. The Thai industry faces a serious threat of decline for the same
reasons that the US industry declined (though, note, without any serous long term
consequences for the US economy, or even, after adjustment, to the local tuna
processing areas within the US). Rules of origin become a serious problem for
increasing competition in the world market. One solution is the development of
domestic fleets but Thailand lacks potential in tuna fisheries with unprofitable fishing
operation as a consequence of a lack of funding, a lack of skilled skippers, a decrease
in tuna stock, and conservation and fisheries management controls. The development
of a Thai based tuna aquaculture would offset this threat considerably, if it can be
managed sustainably.
Conclusions CHAPTER 5
211
The findings of this thesis are, regrettably for a Thai citizen, rather pessimistic. The
Thai tuna industry will not probably be environmentally, economically, and socially
sustainable and the industry faces many severe problems in the near future as
reflected in lower demand forecasts, lack of raw material, unprofitable fishing
operations, emerging shortages of motivated, well-paid, skilled labour, and binding
rules of origin and tariff restrictions. As this analysis clearly demonstrates,
maintaining both tuna fishing and the processing industry in Thailand will not be
easy. The Thai tuna industry faces many major problems and is heading for difficult
times. Nevertheless, there are opportunities as well as threats, and with innovative and
sound management there is still a future for the industry, albeit not with the growth
rates which have characterised its past.
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APPENDICES
221
APPENDICES
APPENDIX 1
Questionnaire forms for interviewing managers, workers, and fishermen
APPENDIX 2
Forecasting exports of tuna from Thailand
APPENDIX 3
The competitiveness of the Thai processing and fishing sectors
APPENDIX 4
Livelihoods of workers in the Thai tuna industry
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