ESTIMATION OF DEMAND FOR TUNA IN THE PHILIPPINESUSING THE SOURCE –DIFFERENTIATED

ROTTERDAM MODEL

Aileen M. Galo1 , Tina Tan-Cruz2 and Edgardo Cruz3

Contact point: usep.saec@gmail.com

INTRODUCTION

Background of the Study

In 2008, the Philippines ranked 6th among the top fishing producing countries in the world with itstotal production of 4.97 million metric tons of fish, crustaceans, molluscs an aquatic plants(www.bfar.da.gov.ph). In 2000, it contributed a total of 387,680 metric tons and became 2nd in worldproduction of aquatic plants including seaweeds. The fishing industry contributed 3.9% to the country'sGross Domestic Product (GDP) in 2001. In the same year, its share to the country's Gross Value Added(GVA) in Agriculture, Fisheries and Forestry Group was 14.5% (P76.3 million, current prices) or 18.8%(P35.8 million, constant prices), respectively. The sector employs 10% of the active labor force inagriculture and 5% of total labor force (www.philexport.ph). Despite this formidable contribution of itsfishing industry, the Philippine’s position in the international tuna industry leaves much room forimprovement. The industry continues to be vulnerable to price fluctuations in the world market, stiffcompetition from existing and emerging tuna producing country, trade discrimination in some importingcountries, limited access to fishing grounds, increasing costs, and operational inefficiencies within theindustry. Of late, however, there has been a genuine recognition among the tuna industry players anddevelopment partners of the need to urgently address the obstacles to industry’s growth and the industryis now taking firm steps to do just that (www.gensantos.gov.ph).

Tuna are a group of salt water fish from the family Scombridae, particularly of the genusThunnus. They are fast swimmers, and some species are capable of speeds of 70 km/h (43 mph)(www.wikipedia.com). Tuna, prawns/ shrimps and seaweeds are high-value marine products in the worldmarket. Demand for these is growing both in absolute terms and in rates of consumption. For humanconsumption, they may be eaten fresh, frozen, canned or preserved or as food additives. They may alsobe processed in the form of fishmeal. Others are used as fertilizers for industrial purposes and as oilsource for medical and cosmetic industries (www.philexport.ph).

Demand for tuna in the world market falls under two main product categories: raw tuna in theform of sashimi and broiled tuna steaks, loins, jaws and other cuts preferred by the Japanese, andcanned tuna, preferred by North Americans and Europeans. Outside these markets, consumerpreferences are fairly evenly spread out between the two categories. The fresh and frozen formsaccount for the bulk of world tuna imports. World demand is growing, slowly but surely. From 1990 to1998, canned tuna production rose from 1.1 million tons to 1.4 million tons, while world importsincreased by an average of 8 percent per year in volume terms and 11 percent in value. The largestmarkets during this period were the European Union, which accounted for more than half of the totalimport value, and the United States (21 percent) (www.oneocean.org).

_______________________

1 BS Economics graduate, School of Applied Economics (SAEc), University of Southeastern Philippines(USeP), Davao City.

2 Adviser, SAEc, USeP, Davao City

3 Adviser, SAEc, USeP, Davao City

Figure 1 shows the Philippines total catches of tuna. The total tuna production from 1960 to 1977showed a steady increase from 20,585 metric tons to 118,149 metric tons. A fluctuating trend wasobserved since 1978 to 2001 and reached the highest catch in 2008 at around 360,000 metric tons.Unfortunately, annual catches of tuna fluctuated during 2009 to 2011 and this was a huge drop in tunaproduction.

Figure 1. Total catches of tuna in the Philippines, 1960-2011.Source: WCPFC Tuna Fishery Yearbook, 2011

Table 1 shows the major destination of top three tuna exports by kind, quantity and value. As of2010, tuna remained as the top export commodity with a collective volume of 106,449 MT forfresh/chilled/frozen, smoked/dried, and canned tuna products valued at US $337,719 million. Japan,USA and Thailand are the top tree importers of the fresh, chilled and frozen tunas of the country.

Table 1. Major destination of top three tuna exports by kind, quantity and value, 2010.

Commodity/Kind Quantity FOB Value

(MT) (‘000 $) (‘000 Pesos)

Tuna 106,449 337,719 15,116,320

Fresh/chilled/frozen 29,664 104,863 4,693,683

Germany 101 898 40,183

Hawaii 130 971 43,476Hongkong 93 396 17,741

Indonesia 116 189 8,458

Japan 3,112 24,423 1,093,168

Switzerland 190 1,470 65,779

Table 1 (continued)

Commodity/Kind Quantity FOB Value

(MT) (‘000 $) (‘000 Pesos)

Taiwan 3 16 722

Thailand 2,287 4,721 211,298

United Arab Emirates 44 209 9,360

USA 3,007 21,923 981,290

Others 20,561 49,647 2,222,208

Prepared/Preserved 76,801 232,835 10,421,692

Canada 3,483 11,202 501,406

Germany 14,973 44,048 1,971,607

Japan 404 1,684 75,356

Kuwait 1,482 5,228 234,022

Netherlands 2,387 6,741 301,708

Singapore 1,008 3,343 149,645

South Africa 207 651 29,140

Taiwan 408 1,134 50,753

United Kingdom 13,996 38,519 1,724,124

USA 20,378 63,467 2,840,800

Others 18,075 56,817 2,543,131

Dried/Smoked 4 21 945

Australia 1 4 159

New Zealand 2 15 683

Rep. of South Korea 1 2 102

Source: BFAR, 2010

Canned tuna constitutes the bulk of tuna products being exported. The major importers of thiscommodity include USA, UK and Germany. For dried/smoked tunas, Australia, New Zealand and Rep. ofSouth Korea are the regular importers from Philippines. In general, tuna export went up by 2% in termsof volume and 3% in terms of value in 2010. Among the major destination of Philippine fish and fisheryproduct exports (in terms of value) and their percentage share are USA, 25.1%; Japan, 10.8%;Germany, 7.7%; Hongkong 6.9%; UK, 5.7%; Spain, 4.9% ; France, 3.2%, Taiwan (ROC) 2.6%, Canada,2.4%, and Rep. of China, 2.3%; . Other countries have a cumulative share of 28% (BFAR, 2010).

Tuna fishing remains an important fishing sector of the country. It provides significant exportrevenues for the country and generates a great number of employments as well as source of nutrition fora large portion of the local community (Barut, 2002). Tuna RFMOs (Regional Fisheries ManagementOrganizations) have given little attention to economic criteria in determining management standards.The reluctance to do this is understandable given the diversity of economies and different economicobjectives of their members. Nevertheless, studies have demonstrated that modern fisheries are oftenextremely wasteful. The most obvious waste is the result of overexploitation of fisheries, which is thecase in some of the tuna fisheries. In addition, a fishery that is managed to produce the maximumsustainable yield can be wasteful for several reasons. Waste can occur as a result of management thatrestricts the use of available fishing capacity to achieve a target, for example, with the use of closedseasons because capacity is not fully utilized for other operational reasons or because, as is normallythe case, the economically optimal catch is less than the maximum sustained catch (Allen, 2010).

Tuna is a highly migratory species that can travel through thousands of miles of oceanthroughout its life and is fished in diverse regions around the globe. More than 70 countries worldwidefish tuna including major fishing nations such as the United States, Japan and Spain as well as states asdiverse as Ecuador, France, Ghana, the Philippines and the smallest island nation in the world, Nauru(www.healthytuna.com).

The 'true' tunas are those that belong to the genus Thunnus. Altogether, over fifty differentspecies, all belonging to the family Scombridae, are commonly referred to as tuna. Until recently, it wasthought that there are only seven species, and that Atlantic bluefin tuna and the Pacific bluefin tuna aresubspecies of the same species.

Table 2. List of true tuna species in the world.

Common Scientific Max. Length Common Max. Max. agename name length weight

Albacore tuna Thunnus alalunga 140 cm. 100 cm. 60 kg. 9 yrs.

Atlantic bluefin Thunnus thynnus 458 cm. 200 cm. 684 kg. 15 yrs.tuna

Bigeye tuna Thunnus obesus 250 cm. 180 cm. 210 kg. 11 yrs.

Blackfin tuna Thunnus atlanticus 108 cm. 72 cm. 20.6 kg. ___

Longtail tuna Thunnus tonggol 154 cm 70 cm. 35.9 kg. ___

Southern Thunnus maccoyii 245 cm. 160 cm. 260 kg. 20 yrs.

bluefin tuna

Pacific bluefin Thunnus orientalis 300 cm. 200 cm. 450 kg. 15 yrs.tuna

Yellowfin tuna Thunnus albacares 239 cm. 150 cm. 200 kg. 9 yrs.

Source: www.wikipedia.com

In addition, over forty related species with tuna-like characteristics, all in the family Scombridae,are also commonly referred to as tuna; genetically speaking, however, none are related to tuna of thegenus Thunnus. In most cases they are more closely related to the mackerel family.

Rationale of the Study

Tuna is one of the most important fishes in the oceans, from their role in maintaining the balanceof the ocean ecosystem to the millions of people that depend on them for protein. Unfortunately, theyare also in trouble—making the need for sustainable global fisheries clearer than ever before. The hugedemand for tuna—as a popular ingredient in sushi, as tuna steaks, and as mass produced, affordablecanned fish across much of Europe, Asia, and the United States—has resulted in overfishing andmismanagement of many tuna species.

In accordance with economic theory, the negative price elasticities of all the products mean that astheir prices increase the expenditure on them decreases. As they are inelastic, price increases wouldlead to proportionately slighter decreases in value demanded and, therefore, an increase in salesrevenue. Income increases, however, do not result in uniform changes in expenditure for all goods.Being a luxury good, the expenditure on tuna in oil increases by a proportionately larger amount thanany demand increases for tuna. The apparently complementary nature of tuna in brine, sauce, and oilsuggests that an increase in price of one would lead to a decrease in consumption of the other. Anincrease in the price of tuna in sauce, however, would cause an increase in the consumption of tuna inoil, being substitutes.

The truth is, many people either do not know about the tuna's status or do not believe it's as badas many scientists are saying. They are that endangered. Besides the species being highly endangeredand over-fished despite regulations, demand for the fish certainly isn't going away which means tuna willcontinue to be over-fished until the population collapses. Once it collapses there will have to be acomplete moratorium on tuna fishing to allow the population to come back, but even if given the chanceit will take decades due to the late sexual maturity of the fish, its particular needs and limited spawninggrounds (two thirds of which are heavily damaged (Mineo, 2012). If this will happen, demand for tuna willbe affected as well as the whole tuna industry, and this is the reason why demand for tuna should bestudied.

Despite being among the most important species in the world’s fisheries, little formal investigationis done and limited knowledge is available about the demand structure for tuna (Jaffry et al., (2008).Several other studies (e.i. Asche et al., (2005) and Reid et al., (2002) using aggregate grouping ofproducts (e.i. seafood products) were conducted.

Objectives of the Study

The general objective of the study is to estimate the demand for tuna in the Philippines using theRotterdam model. Specifically, the study attempts to:

1. present the trend of Philippine demand for tuna from 1991-2011; and2. estimate the demand and income elasticities for tuna.

Significance of the Study

Fisheries production in the Philippines has achieved strong growth during the past decade inspite of the impact of overfishing on fish stocks in a number of important fishing zones.

The result of the study divulges very important information about the demand for tuna in ourcountry. Information on the price elasticity of demand can be used by a business as part of a policy ofprice discrimination (also known as yield management). Knowing the elasticities is important becausethese will influence the behavior of the firm and it will affect the employment status of the industry. Firmscan use price elasticity of demand estimates to predict the effect of a change in price on the totalrevenue and expenditure on a product. Results will help provide information on transforming the globaltuna fisheries market and improving the way tuna fisheries are managed and governed. Depending onthe elasticity of a product, the firm can find an alternative marketing strategy that they can adopt toincrease revenue.

METHODOLOGY

Theoretical Framework

The law of demand states that consumers buy more of a good when its price is lower and lesswhen its price is higher (ceteris paribus). If price increases while income stays the same, demand willdecrease. It follows, then, that if there is an increase in income, demand tends to increase as well. Theratio of the percentage increase in demand to the percentage increase in income is referred to as theincome elasticity of demand. When the percentage increase in demand is equal to the percentageincrease in income, the income elasticity is unitary. Individual items of consumption that have elasticitiesgreater than unitary are said to have elastic demand, while those with elasticities below unitary arecharacterized as inelastic (Fuchs, 1965).

Estimation of demand functions consistent with economic theory has been a highly published areain the last forty years. The majority of the currently influential papers have appeared following theadoption of flexible functional forms, which rely heavily on duality theory. The Generalized Leontief, theTranslog, the Rotterdam, and the Almost Ideal Demand System or AIDS are examples of populardemand models. Although many functional forms are available, economic theory does not answer thequestion of which specification is the best to choose in estimating demand functions using a given dataset. This ambiguity is unavoidable, since the space of neoclassical functions cannot be spanned by anymodel having a finite number of parameters (Barnett and Seck, 2007).

Income - As people’s income rises demand for goods and services rise too. Goods which obeythis rule are called normal goods. However the exception to this is an inferior good. Demand for inferiorgoods will fall as income rises.

Consumer’s Taste and Preference - Customers’ tastes and preferences can changedepending on the season. As more customers want a product or service, the demand curve shifts to theright, and as they want it less, the demand curve shifts to the left.

Price of Related Goods - The price and availability of related or substitute goods also affectsthe demand curve. Substitutes - the higher the price of substitute goods, the higher the demand will befor this good. Complements - as the price of complements rises, demand for the complement falls andso too will demand for the good in question.

Future Expectations - If consumers expect the cost of a product or service to increase, theymay stock up on it now, to avoid paying the higher price later. This may temporarily shift the demandcurve to the right, but once prices increase, as expected, the demand curve shifts to the left.

Population - If there is a growing population more food is demanded. If the size of the marketdecreases, the demand curve shifts to the left, showing a higher price and fewer potential buyers. If thesize of the market increases, the demand curve shifts to the right, showing a lower price and a largernumber of potential buyers (Ebmer, 2012).

Conceptual Framework

Figure 2 exhibits that the major determinants of import demand are prices and real income. Asmentioned earlier, the law of demand states that as the price of Philippine tuna increases, the importdemand of China, Japan and USA from the Philippines decreases, ceteris paribus. Furthermore, theprices of other exporting countries influence the import demand for Philippine tuna. If the price of tunafrom the exporting countries (China, Japan and USA) increases, then the Philippine import woulddecrease from the said countries. In this case substitute exporting countries will now be considered. Onthe other hand, other countries are considered as complement if an increase in the price of one sourcewill lead to a decrease in the export demand from the other source of imported tuna.

The underlying ceteris paribus assumption implies that price elasticities describe how demandchanges in response to changes in prices with all factors held constant. Demand models provideinformation regarding the ways in which consumers will respond to changes in prices, income, and theirsocio-demographic circumstances (Tiffin, 2011).

Rotterdam Model

Figure 2. Factors affecting import demand for Philippine tuna.

The price of a product is one of the most basic factors affecting the demand for that product. Theamount of a certain product that a consumer buys depends on its price. An inverse (negative)

Import Price of Tuna

-USA

-Japan

-China

Real Income

(Proxied by totalexpenditure forimported tuna)

Import Demandfor Tuna

Elasticities

-price

-income

relationship exists between the price of the product and the quantity that consumers are willing topurchase. This means that as the price of a product goes up, people will buy less of that product. As theprice goes down, people will buy more (Sothern, 1999).

Another factor that affects the import demand for tuna is income. This effect will depend onwhether tuna is considered an inferior good or a normal good by the importing country. If the Philippineimport for tuna increases as income also increases, then tuna is considered to be a normal good.Otherwise, if the income of the Philippines increases but importation decreases, tuna is an inferior good(www.investopedia.com).

Data Source

The study mainly relies on secondary data coming from the Western and Central Pacific FisheriesCommission (WCPFC) Yearbook 2011. Annual data (1991-2011) involving import volumes and tunavalues will be taken from the UN Comtrade 2011. Prices were calculated as unit values (i.e. expendituredivided by quantities) from each exporting country as proxy for prices, while total expenditure was usedas proxy variable for income.

Statistical Model

In microeconomics, the AIDS and the Rotterdam models are frequently used, since each can beestimated in a linearized form with theoretical restrictions easily imposed and tested. Earlier, other lineardemand models possessed awkward connections with economic theory that tended to be difficult toimpose or test (Barnett and Seck, 2007). The functional forms of these models are locally flexible, in thesense that they do not put a priori restrictions on the possible elasticities at a point. These modelspossess enough parameters to approximate any elasticities at a given point. But locally flexiblefunctional forms often exhibit small regular regions consistent with microeconomic theory.

The Rotterdam model was the turning point in empirical demand analysis, offering many featuresnot available in modelling efforts that had been used up to that time, such as the double-log demandsystem and Working’s (1943) model. In particular, the Rotterdam model is entirely based on consumerdemand theory, has the ability to model the whole substitution matrix, has parameters that can easily berelated to underlying theoretical restrictions, and is linear in parameters and therefore easy toeconometrically estimate, and is econometrically regular (Barnett and Serletis, 2008).

Empirical studies of demand have found preference variables, along with prices and income, tobe important determinants of demand. Preferences have been conditioned on various demographicvariables, past consumption, advertising, and household composition variables.

In this study the Rotterdam model is given as:

nωi,t dlog qi,t = ai + Σ γij dlog pit + βi (DQ) + εi,t (1)

i=1

where:

ωi,t = the average weighted expenditure share of tuna from source i at time t.

dlog qit = first difference of the natural log of quantity demanded of tuna from source i at

time t.

DQ = Is the Divisia Volume index, expressed as:

DQ = Σi ωj, t-1 dlog qj,t (2)

pit = price of tuna from importing country i at time t

βi , ai, γij = are the parameters to be estimated

εi,t = normally distributed error term with zero mean and a constant variance.

Based on the consumer's budget constraint, the effects of preference variables, income, andprices obey adding-up restrictions. Theory indicates that the effects of prices further obey homogeneity,symmetry and negativity restrictions (Brown and Lee, 2002).

The general restrictions on demand are;

(a) Adding up

Σiβi = 1 (3)

where:βi = intercept coefficient in the ith share equation.

The adding-up condition restricts all budget shares ωi equal to 1, which requires the consumerto spend not more than its total budget. If the other two restrictions are fulfilled this condition will besatisfied.

(b) Homogeneity

Σiγij=0 (4)

where:γij = cross price coefficient associated with jth exporting country in the ith share equation.

The homogeneity restriction implies that a proportionate change in income and prices of allgoods will leave consumption of any one good unchanged (Seale et al., 2003).

(c) Symmetry

γij = γji (5)

where:

γij = cross price coefficient associated with jth exporting country in the ith shareequation.

Lastly, the symmetry restriction means the increase in the price of any good i will cause anincrease in the compensated quantity demanded of j equal to the increase in the compensated quantitydemanded of i caused by an increase in the price of j. Without this restriction, inconsistent choicesbetween products would be made and there would be no substitute or complement products (Seale etal., 2003).

Estimation

The relative and absolute price versions of the Rotterdam model can be estimated in a numberof ways. Applying the economic model discussed for Rotterdam, the following estimation models wereapplied for each importing country. The full specification of the import demand function for tuna inPhilippines each year is presented in double log form as follows:

ωC dlog qC = αC + γCC dlnPC + γCJ dlnPJ + γCUdlnPU+ γCROW dlnPROW + βC lnXC + εC (6)

ωJ, dlog qJ = αJ +γJC dlnPC +γJJ dlnPJ +γJU dlnPU +γJROW dlnPROW + βJ lnXJ +εJ (7)

ωU dlog qU = αU + γUC dlnPC + γUJ dlnPJ + γUU dlnPU + γUROW dlnPROW + βU lnXU + εU (8)

where:

ωi,t = market share of import of tuna from country i at time t.

dlog qit = first difference of the natural log of quantity demanded of source i at time t.

dlnP = first difference tuna from of the log of price of tuna

lnX = natural log form of real income of tuna

εit = error variance from source i at time t.

The three sources (China, Japan and USA) are represented by the subscripts C, J and Urespectively. The independent variables are prices of other countries and real income of the Philippines.

The dependent variable is the weighted expenditure share of the ith exporting country, i.e.,

ωi,t = (ωit + ωit-1) (9)2

where:ωi,t = is the share of ith source at time t.

The predictor variable for the Rotterdam demand system contains the product of the first differenceof the natural log of per capita consumption of tuna and the corresponding expenditure share of tuna fromthe ith exporting country or source. The first difference of the logarithm of the per capita consumption oftuna is obtained through:

dlogqit = logqi,t – logqi,t-1 (10)

where:

qi,t = quantity demanded of tuna from the ith source at time t.

qit-1 = quantity demanded of tuna from source i at year (t-1).

The nature of the data is time series which means that the observation on demand equationrepresents one point in time. This opens the possibility that contemporaneous correlation exists. For thesystem of equations (one system for each of the Rotterdam models), seemingly unrelated regression(SUR) estimation will be used when disturbances are contemporaneously correlated (Zellner, 1962).

The gain in efficiency from using the SUR estimator increases with the correlation betweenequation regressors. The reason for applying SUR lies in the fact that the common factors might existand influence all equations at the same time induce a correlation between the equation’s error terms.The Seemingly Unrelated Regression (SUR) of equations 6 to 8 can be expressed in matrix form as:

Yc = Xc βc + εc (11)

Yj = Xj βj + εj (12)

Yu = Xu βu + εu (13)

where Yc contains all time series observations of China’s share equation for Philippine imported tuna.The description for Yj and Yu follow for Japan and USA respectively. Similarly, Xc contains all time seriesobservations on the explanatory variables in the budget share equation of China. The description for Xjand Xu follow the above description accordingly.

The system in (11-13) could further be written compactly as the “super model”:

Y = X β + ε (14)

where:

Yc Xc 0 0 βc

Y = Yj X = 0 Xj 0 β = βj (15)

Yu 0 0 Xu βu

Elasticity Estimates

The demand elasticity is the percentage change in quantity demanded in response to a onepercent rise in market price. By convention, demand elasticities are often expressed as positivenumbers, even though the law of demand requires that quantity demanded falls as price increases (Reidet al., 2002).

Elasticity of demand is an important variation on the concept of demand. Demand can beclassified as elastic, inelastic or unitary (Hofstrand, 2007). Elasticity varies among products becausesome products may be more essential to the consumer. Products that are necessities are moreinsensitive to price changes because consumers would continue buying these products despite priceincreases. Conversely, a price increase of a good or service that is considered less of a necessity willdeter more consumers because the opportunity cost of buying the product will become too high.

Price elasticity of demand shows the responsiveness, or elasticity, of the quantity demanded ofa good or service to a change in its price. More precisely, it gives the percentage change in quantitydemanded in response to a one percent change in price, ceteris paribus. Income elasticity measures theresponsiveness between the change in quantity demanded for a particular good and a change in realincome. If income elasticity is positive, it indicates that the good is a normal good implying that asincome increases more goods are demanded at each price level. If income elasticity is negative, itindicates that the good is inferior which implies that an increase in income will result to a decrease inquantity demanded (Rodda, 2010).

Own-price elasticity measure the percentage change in the quantity demanded “caused” by apercentage change in price. Because the demand function is an inverse relationship between price andquantity the coefficient of price elasticity will always be negative.

(a) Own-price elasticity

eii = γii / ωi (16)

where:

eii = own-price elasticity associated with the ith exporting country.

γii = slope coefficient associated with the ith source

ωi = budget share of the ith source or exporting country

Since own-price elasticity is expected to be negative, it indicates that an increase in the price oftuna in the ith exporting country (USA, Japan or China), leads to a decrease of import demand of thePhilippines from that country.

The cross-price elasticity of demand measures the responsiveness of the quantity demanded ofone good when compared with a change in the price of another good. If two goods are substitutes, weshould expect to see consumers purchase more of one good when the price of its substitute increases.Similarly if the two goods are complements, we should see a price rise in one good cause the demandfor both goods to fall.

(b) Cross-price elasticity

eij = γij / ωi (17)

where:

eij = cross price elasticity (exporting country i vs. exporting country j)

γij= slope coefficient associated with the price of tuna in the exporting country i, andthe corresponding Philippine import demand from another exporting country j.

ωi = budget share of the ith exporting country.

Cross price elasticities can be negative, which imply that an increase in the price of tuna in oneexporting country i will lead to a decrease in the quantity demanded of another exporting country j (i.e.,the two exporting countries are complements). On the other hand, positive value indicate that if the priceof tuna from one exporting country i increases, then the Philippine import demand from anotherexporting country j rises (i.e., the exporting countries i and j are supplements).

Income elasticity of demand is an economic term that refers to the sensitivity of the quantitydemanded for a certain product in response to a change in consumer income.

(c) Income elasticity

βiei = 1+ ___ (18)

ωi

where:

ei = income elasticity with respect to exporting country i

ωi = budget share of the ith source, or ith exporting country

βi = slope coefficient of real total income (expenditure) with respect to exporting countryi

SHAZAM Version 9.0 was used for the computations of the parameters.

RESULTS AND DISCUSSION

Profile of tuna export prices

Figure 3 shows the movement of tuna export prices for USA, Japan and China from 1991-2011.The trend shows that USA exhibits the highest price from 2001 to 2010 with its highest point in 2003. Inthe US market, there was nearly a 9% decline in supplies during January-September 2009 compared in2008. This may be the reason for the constant decrease of Philippine’s total tuna import from USA. Also,seasonal tariffs on imported fishery products have been implemented. For each commodity, higher tariffsare imposed during the peak season. This is done in order to ensure a constant supply of fishery foodproducts, while protecting local producers from imported products flooding the market during the peakseason. The peak was reached on 2003 when it recorded a price of 7.17 US $ per kilogram. For themeantime, its price pattern is fluctuating every year from 1991 to 2001. Conversely, Japan exhibits asmooth and stable pattern over the years. This could be the reason why Philippines had the greatestvolume of import from this country (Figure 4). China shows an asymmetrical upward and downwardpattern throughout the year and had its lowest price in 2003 at 0.154 US $ per kilogram, which is alsorecorded as the lowest price among the three exporting countries (USA, Japan and China).

Figure 3. Export prices of tuna of the Philippines by source, 1991-2011.Source: UNComtrade, 2011

Figure 4 shows the volume of Philippine import of tuna from different sources. Export volume oftuna differs according to country of origin, similar to other traded commodities. Generally, Japan exhibitsthe highest total quantity/volume of imported tuna to the Philippines compared to other exportingcountries (USA and China). It shows a fluctuating trend from 1991 to 2011. Its highest point wasobserved in 1998 at 16,818,333 kilograms. Unfortunately, Japan’s importation of tuna showed a hugedrop from 1998 to 1999 at 2, 948, 073 kilograms and continued its fluctuating trend until 2011.

Profile of tuna imports

Figure 4. Volume of Philippine tuna imports by source, 1991-2011.Source: UNComtrade, 2011

China ranked second as the top exporter of tuna to the Philippines. It recorded its lowest volumeof importation from 2000 to 2008. Importation starts to increase from 2009 to 2011, and reached itshighest point in 2011 at 8, 244,652 kilograms of total tuna imported. Lastly, USA exhibits an irregulartrend from 1991 to 2000 and it continues to fluctuate until 2011. Its lowest point was observed in 2007 at8,342 kilograms only.

Estimation of import demand of tuna in the Philippines

The parameter estimates for the Rotterdam demand system are reported in Table 3.

Out of 12 parameters, 4 are found to be statistically significant at 10% level. The coefficients αiwhich reflects the budget share are all positive and significant while its values for the different exportingcountries satisfy the expected value between zero and one (0, 1). This requirement for the value of αi isimportant in satisfying the properties of an expenditure function where budget share of any exportingcountry should only be a portion of the total expenditure and the summation of all budget shares shouldadd up to one (adding up requirement).

Table 3. Estimates of parameters of the demand of Philippine tuna from top exporting countriesusing the Rotterdam model, 1991-2011.

USA Japan China

ai 0.228* 0.469* 0.303*(0.093) (0.125) (0.088)

βi -0.118ns -0.247ns -0.166ns

(0.141) (0.194) (0.136)

γuj 0.024ns -0.163* 0.139ns

(0.107) (0.096) (0.103)

γjj 0.040ns -0.179ns

(0.106) (0.146)

γcj 0.040ns

(0.106)

Values in parentheses are standard errors of estimates*-significant at 10% levelns- not significant

Results show that among the three top exporting countries, Japan capture the highest budgetshare while USA had the least. Interpretations of the signs and magnitude of the computed parametersare better understood and explained through the estimation of elasticities. The results of elasticities arefound in Table 4.

Table 4. Demand elasticities of Rotterdam model for top exporting countries, 1991-2011.

Price Elasticity Income

USA Japan China Elasticity

USA 0.105ns -0.348* 0.459ns -0.518ns

Japan 0.085ns -0.591ns -0.527ns

China 0.132ns -0.548ns

*-significant at 10% levelns- not significant

The price parameters for the Rotterdam model are reported in Table 4. All own-price parameterswere positive and not significant at 0.10 levels. USA, Japan and China’s demand for tuna is inelastic,indicating that demand for tuna is unresponsive to the increase in the price of tuna.

Cross price elasticities indicate that an increase in the price of tuna in one exportingcountry(USA) will lead to a decrease in the quantity demanded of another producer (Japan) The samesituation holds true for Japan and China. On the other hand, USA and China supplements each otheralthough it is not significant.

With regards to the income elasticity of demand of the different sources the model exhibits that,as the Philippines expands its expenditure on imported tuna by 1%, the market share of USA, Japan andChina will decrease more than proportionately 0.52%, 0.53% and 0.55% respectively, as shown in Table4. The price parameters for Rotterdam model are reported in Table 4. All own price elasticities do notconform to the Law of Demand which exhibits all positive relationship implying that tuna for USA, Japanand China is a Giffen good. Own price elasticities of the three countries show that demand is inelastic,indicating that demand for tuna in USA, Japan and China are not responsive to an increase in the priceof tuna.

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

Summary and Conclusions

The main objective of this study is to estimate the demand for tuna in the Philippines from thetop major exporting countries namely; USA, Japan and China from 1991 to 2011 using the Rotterdamdemand model through Seemingly Unrelated Regression estimation. It sought to present the trend ofPhilippine import of tuna within the period 1991 to 2011 and to estimate the parameter coefficients, own-price, cross price and income elasticities of demand using the Divisia quantity index.

Among the top three exporting countries, USA exhibits the highest price of tuna throughout theyear but has the lowest volume/quantity exported in 2007. Export pattern of Philippine tuna in the threemarkets is generally increasing. On a volume basis, the Philippine import demand is determined mainlyby Japan since 1991 at its highest peak in 1998 followed by China and USA respectively.

Based on the data on quantity and expenditure, the parameters and elasticities of the demandfor Philippine tuna were calculated using Rotterdam model. Results showed that tuna from all exportingcountries are considered inferior good which indicates that USA, Japan and China will not be affected asPhilippines increases their expenditure on tuna. The demands of tuna of the three exporting countriesare inelastic, indicating that the said countries are unresponsive to the changes in price of tuna. Also,import demand from these countries (USA, Japan and China) exhibits a positive relationship with exportprice, implying that tuna is a Giffen good.

Cross price elasticities showed that tuna coming from USA is substitute for tuna coming fromChina. Meanwhile, tuna coming from USA and Japan complement each other, implying that an increasein the price of USA will lead to a decrease in the quantity demanded of Japan. A similar result holds truefor Japan and China, which means that an increase in the price of tuna in Japan will lead to a decreasein the quantity demanded of tuna in China.

Recommendations

The study has a lot of rooms for improvement. Based on the results of the study, the followingrecommendations are suggested:

1. To make use of shorter intervals of time such as quarterly or monthly data as an alternative foryearly data to estimate a short term elasticities. This may further benefit decision makingprocess in the Philippine tuna industry.

2. For further study, consider additional explanatory variables which might influence the importdemand of tuna in the Philippines.

3. Include additional or other exporting countries.4. For the government to implement less strict policies regarding importation of tuna to encourage

more exporting countries to export tuna to our country.5. The National Tuna Management Plan provides management measures in a positive direction,

but a more dynamic and consultative plan and process must be developed in order to adapt to afast-changing and complex status of fishery resources.

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Appendix A. Quantity of imported tuna in the Philippines, 1991-2011.

Volume of Tuna Imported(kilograms per year)

Year USA Japan ChinaTotal

Imports

1991 2,477,625 1,905,382 724,000 5,107,0071992 2,310,687 1,089,312 360,000 3,759,9991993 37,429 3,054,875 342,460 3,434,7641994 430,710 47,074 200,000 677,7841995 353,359 1,704,375 850,000 2,907,7341996 863,503 3,771,937 490,314 5,125,7541997 261,151 13,043,515 496,198 13,800,8641998 729,625 16,818,333 2,864,000 20,411,9581999 1,191,689 2,948,073 3,230,625 7,370,3872000 50,724 2,728,051 24,475 2,803,2502001 29,501 1,785,473 181,681 1,996,6552002 97,829 6,213,580 194,797 6,506,2062003 74,344 2,777,403 12,690 2,864,4372004 75,300 841,779 26,000 943,0792005 66,333 5,885,638 474,467 6,426,4382006 52,782 2,473,148 280,589 2,806,5192007 8,342 6,904,917 490,038 7,403,2972008 211,424 3,931,244 52,000 4,194,6682009 36,244 9,826,400 1,979,775 11,842,4192010 24,603 8,037,978 3,083,662 11,146,2432011 16,093 6,152,435 8,244,652 14,413,180

Source: UNComtrade, 2011

Appendix B. Price of imported tuna in the Philippines, by source, 1991- 2011.

Price of Imported Tuna(Dollar per kilogram)

Year USA Japan China

1991 0.760636496 0.959939267 0.8981049721992 0.668997142 0.838356688 0.9399972221993 0.767907238 0.789522976 6.2092098351994 0.633704813 1.069380125 0.700631995 0.891863516 0.695209094 0.571996 0.883692355 0.73463104 1.8246674581997 1.039042546 0.725828429 1.8977646021998 0.602499914 0.625787942 0.652520951999 0.854206928 0.673858144 0.6452986652000 0.614383724 0.472930675 0.3226557712001 2.083251415 0.416087502 0.3055410312002 3.011315663 0.638083037 0.3139524742003 7.170329818 0.619170138 0.1540583142004 3.391142098 0.488497575 0.3112307692005 2.75954653 0.753369643 0.3248908782006 1.53298473 0.618605518 0.323833082007 6.394269959 0.937314091 0.282839292008 1.896866013 1.526272345 0.7213076922009 3.340359784 1.082574595 0.475601522010 1.448969638 1.002800953 0.4562079112011 0.570807183 1.220642396 0.455781396

Source: UNComtrade, 2011

Appendix C. Expenditure of imported tuna in the Philippines, by source, 1991-2011.

Trade Value(US Dollar)

Year USA Japan China

1991 1,884,572 1,829,051 650,2281992 1,545,843 913,232 338,3991993 28,742 2,411,894 2,126,4061994 272,943 50,340 140,1261995 315,148 1,184,897 484,5001996 763,071 2,770,982 894,6601997 271,347 9,467,354 941,6671998 439,599 10,524,710 1,868,8201999 1,017,949 1,986,583 2,084,7182000 31,164 1,290,179 7,8972001 61,458 742,913 55,5112002 294,594 3,964,780 61,1572003 533,071 1,719,685 1,9552004 255,353 411,207 8,0922005 183,049 4,434,061 154,1502006 80,914 1,529,903 90,8642007 53,341 6,472,076 138,6022008 401,043 6,000,149 37,5082009 121,068 10,637,811 941,5842010 35,649 8,060,492 1,406,7912011 9,186 7,509,923 3,757,759

Source: UNComtrade, 2011