IERCU
Institute of Economic Research, Chuo University
50th Anniversary Special Issues
Discussion Paper No.212
Determination of Optimal Location in Fragmented Production System:
A comparison of the 80 Provinces in the Philippines.

Arianne Dela Rosa Dumayas

Graduate School of Economics, Chuo University
Toshiharu Ishikawa

Faculty of Economics, Chuo University

November 2013I

INSTITUTE OF ECONOMIC RESEARCH
Chuo University
Tokyo, Japan

Determination of Optimal Location in Fragmented Production System:
A comparison of the 80 Provinces in the Philippines.

Arianne Dela Rosa Dumayas
Graduate School of Economics, Chuo University

Toshiharu Ishikawa
Faculty of Economics, Chuo University

Abstract
Globalization has further intensified competition which compel the firms to lower their productions
cost by fragmenting the production processes and transferring them to appropriate locations.
Identification of optimal locations is very significant in this system of production. This paper
presents the steps undertaken in establishing prospective locations and analyzes the influence oftransfer price and corporate taxation system in the determination of locations. With the assumptionthat the Philippines is within the prospective area, the paper conducts a comparison of economic andsocial conditions among the 80 provinces in the Philippines. This study elucidates the two-steps indetermining prospective location: first, across countries, and second, among regions or provinces ofthe selected country. During the first-step, chaotic phenomenon has occurred which showed several
potential locations that can guarantee in achieving the target profits. On the second-step, based on
the comparison of the provinces, Metro Manila and the neighboring provinces have the highest
provincial dynamism scores which denote the favorability of both economic and social conditions
those provinces. On the other hand, provinces in Mindanao, particularly in the Autonomous Regionof Muslim Mindanao (ARMM) have relatively adverse economic and social conditions.
Keywords: Factory’s location, Chaotic phenomenon, Location targeted area, Regional development,
Spatial disparity

JEL: R.12. R.30, O.18, O.53.

1

1. Introduction
In order to complete expanding their businesses, firms lower their production costs by cutting the
production processes into different parts and assigning them to appropriate locations. Fragmentationof the production processes ranges from highly-complex process to simple production processes. In
the identification of the potential location of these production processes, the candidate location’s
presence of production factors and quality of living are taken into account. For example, simple
production processes are oftentimes dispersed to many developing countries where there areabundant supplies of cheap unskilled labor. This relocation of the factories entails a huge cost.
However, savings from the low labor costs offset this cost and allow profit-maximization. Therefore,fragmentation of production processes facilitates further expansion of the factories which havebecome widespread in the late 20th century.
Choosing a potential location is therefore an utmost important in this system of production. There
are some location factors to be considered when choosing potential locations in a large geographicalspace including several countries: characteristics of economic activity, corporate taxation system andsocial conditions in each country. The determination of the potential locations sites is first decided ata country-level and then at regional or provincial-level.
Selection of potential location at the country-level takes into account each country’s economic
and social conditions as well as the taxation system. Accordingly, firms will only considertransferring to potential sites with good economic and social conditions and relatively wide
production base1. Moreover, the country’s tax system should be considered because all profits madeby the factories are subject to taxes imposed by the country’s national or local government. On theother hand, the government also needs to understand the firm’s profit management to establish
appropriate taxes and transfer prices. Firms compare the different countries’ taxation system andestimate transfer price when choosing the potential location2.
Determination of the prospective locations at the specific region or provinces considers attributes
such as infrastructure, education, culture, housing, safety, and welfare. This is due to the fact thatthese factors play decisive roles in firms’ production, sales, and research and development.
Furthermore, the decision to reside in a particular place is influence by the natural environment,
educational institutions, culture, housing, safety and welfare support. Given the fact that the citizen’s
activities might have an impact on production efficiency as well as quality improvement, it isnecessary to evaluate the region’s economic and social features.
This paper has three main objectives. First, the analysis introduces the two-step decision processof the establishing prospective locations. Secondly, the analysis incorporates transfer price systemand corporation system in the analysis of prospective locations. Third, assuming that the Philippines
1
2

For more information about the potential location, see Ishikawa (2010).
This area is extensively studied by Hirshleifer (1956), Bond (1980) and Eden (1985).

2

is one of the prospective locations, this paper compares the economic and social conditions of 80
provinces.
The paper is organized as follows: Section 2 explains the framework for analyzing the location
decision of the firms and derivation of optimal transfer prices and prospective area. Section 3
analyzes the impact of country’s taxation system on the transfer price, location decision, andcompany’s profit. Section 4 evaluates the economic and social conditions of 80 provinces in thePhilippines. Section 5 concludes the paper.

2. Location decision-making and optimal transfer price
2.1 Derivation of the profit function of a firm
The following assumptions are placed and the framework for the analysis is formed in this section. A
transnational firm produces goods by using two production processes, the first process, and thesecond process. The factory 1 which is assigned to the first process manufactures intermediate goods,
m4, in home country. The intermediate goods produced at factory 1 are transported to the factory 2
by the transfer price mq4, which locates at a city in a foreign country. The factory 2 uses one unit of
the intermediate goods to produce one unit of the final goods. And the factory sells the finishedgoods by the price p to the market which is at the same city in the foreign country. The tax rates ofhome and foreign country are represented by t and t*, respectively.
The profits of the factory 1 is given by equation (1)Y1 = (1- t)[ mp4*mq – C(mq)－ F1](1)

where mq is quantity of the finished goods, C(mq) is the cost function and F1 is fixed costs. The costfunction C(mq ) of the factory 1 is derived on the basis of the following assumptions: The factory 1
uses two different kinds of materials m1, m2 to produce the final goods mq . And the factory uses
lubricating oil m3 to operate machines, The materials m1, m2 and oil m3 are produced at points M1,
M2 and M3 which are identified by coordinates（x1, y1）, (x2,y2), (x3,y3), respectively, on a large plain
space. These materials are transported to the factory 1 at point L indicated by (x, y). Freight rates ofthe materials m1, m2 are denoted by tm, and the rate of the oil m3 is given by te. Mill prices of theseare shown by p1, p2, and p3, and these prices are given. The intermediate goods are transported fromthe factory 1 to the factory 2 which locates at the market at point M4（x4,y4). The freight rate of theintermediate goods mq is tg. Figure 1 illustrates the geographical relationship between the factory 1,
the factory 2, the market and the points of the three materials.
The production function of the factory1 is represented by equation (2):mq= Am1αm2β(2)3where A, α and β are parameters and they are defined as A>0, 0<(α+β)<1.

Figure 1 Location figure of the factory 1y■ M4 (Market, Factory 2 )● L (x,y) ( Location of factory 1)

0

x

▲

●

M2 (m2)M1 (m1)◙ M3 (m3)

The distances between the material places, Mi (i=1, 2,3 ) and the factory1, L(x,y) are represented
by d1, d2 d3, respectively:d1＝((x – x1 )2 + (y + y1)2)0.5,
2

2 0.5

d2＝((x+x2 ) + (y +y2 ) ) ,
2

2 0.5

d3＝(x + (y + y3) ) .

(3a)
(3b)
(3c)

The distance between the factory1 and the factory 2 which locates at the finished market M4 is givenby d4:
d4＝(x2 + (y–y4)2)0.5.

(3d)

If the profits of the factory 1, Y1, is given by equation (4),Y1 =(1-t)[ mq((mp4- tg d4）- (p3+ te d3）)- (p1+tm d1) m1- (p2+tm d2 ) m２– F1]. (4)Making use of the law of equi-marginal productivity, that is, the ratio between the productivities4

of the two intermediate goods should be equal to the ratio between the delivered prices of them,quantities of these goods are derived as equations (5a) and (5b): (For simplicity, α and β are assumed
α=β=0.4):
m1 ＝A-1.25 mq1.25( (p2+tmd2) /( p1+tmd1))0.5,(5a)m2 ＝A-1.25 mq 1.25( (p1+tmd1) /( p2+tmd2))0.5 .

(5b)Since quantity of oil m3 is assumed to be a linear function of amount of the final goods, it is simply
given by (5c),
m3

＝ mq.

(5c)

From the above equations, the cost C(mq) of the factory 1 is obtained as equation (6),C(mq)=2A-1.25mq1.25 (ｐ1+ tm d1)0.5(p2+tm d2 ) 0.5 +x ((p3+ ted3)+ F1.

(6)

Thus, the profit function of the factory 1 is rewritten as equation (7):Y1=(1-t)[ mq ((mp4-tgd4)- (p3+ ted3) ) -2mq1.25 A-1.25(p1+tm d1)0.5(p2+tm d2 ) 0.5 – F1]. (7)The factory 1 determines the transfer price and its location in order to maximize the firm’s total
profits by using equation (7).
Then, let us derive the profits of the factory 2, Y2. The profits are obtained under the following
assumptions: The factory 2 uses one unite of intermediate goods to produce one final goods, thus,
the quantity of the final goods Q is equal to the intermediate goods mq, (that is, Q=mq). The market
price of the finished goods is shown by p which is determined by the market demand function given
by equation (9).
Y2 = (1- t*)[(p – mp4 )Q - C(Q)-F2](8)p=600 - Q

(9)where F2 is the fixed cost, and C(Q) is the costs of assembling the intermediate goods to the finishedgoods, C(Q) is given by equation (10).
C(Q) = 1.5Q(2+Q)2/B(10)
5

Since the factory 2 determines the quantity supplied at the market to maximize its profits, thequantity supplied to the market is derived by using equation (8), (9),and (10), it is shown by equation(11) where parameter B is assumed 200 for simplicity,
Q=0.22( - 206 + (582409 - 900mp4 )0.5 )(11)Since the supply quantity Q is a function of the transfer price mp4, the total profits of the firm can be
rewritten as a function of the transfer price as equation (12),Y= (1- t)[(0.22( -206 + (582409 - 900mp4)0.5))(mp4 - tgd4 ) - (p3+ ted3) ) –-2(0.22(- 206 + (582409-900mp4)0.5))1.25 A-1.25(p1+tm d1)0.5(p2+tm d2 ) 0.5 ―F1]＋(1-t*) [(600-(0.22(-206+(582409-900mp4)0.5))-mp4)(0.22(-206+(582409-900mp4)0.5)) - F2].

(12)(2) Derivation of the optimal transfer price and the factory’s location
The optimal transfer price mp4 and the location (X,Y) of the factory1 are derived by using equation(12). To derive the optimal values of the transfer price and the location of the factory 1, the Gradient
dynamics is used. The essence of this method is that first, an initial value set is given to xn, yn, andp4n in the following equations (13a,b, and c) as a temporal solution, and obtain the values of xn+1,
yn+1, and p4n+1 by calculations indicated by the three equations (13a,b,and c). And then, the same
calculation is iterated until a given tentative solution can be approximately judged as the solution. If
the values of (xn+1, yn+1, mp4n+1）in equations (13a,b, and c) become approximately the same as thoseof (xn, yn, mp4n), the values can be admitted as the solution.
ｘn+1= xn +j*∂Y /∂x,(13a)ｙn+1= yn +j*∂Y /∂y,(13b)

mp4n+1= mp4n +j*∂Y /∂mp4,(13c)

where j is the width of a step and n shows the number of the calculation. And ∂Y/∂x, ∂Y/∂y, and∂Y/∂mp4 are given by equations (14a, b, and c). In addition, In addition, the tax rates are assumed tobe the same in home and foreign country, t=t*=082. Other parameters’ value are assumed as follows:6(x1=3, y1=-0.5), （x2= -30.5, y2=-0.5）, （x3=0, y3= -1.5）, （x4=0, y4=1）, A=1, p1=0.25, p2=2,
p3=0.2, tm=0.11, te=0.01, tg=0.225,F1=5000, F2=2500、∂Y/∂x=0.18[-A-1.25Q1.25

tm [ {(p2+tm d2)0.5/(p1+tmd1 ) 0.5} (x – x1)/d1 ++{(ｐ1+tm d1)0.5/(p2+tmd2 ) 0.5 }(x + x2)/d2-tgx Q/d4++Q (- tg(x/ d4) – te(x/ d3))]=0(14a)∂Y/∂y=0.18[- A-1.25Q1.25 tm [ {(p2+tm d2)0.5/(p1+tmd1 ) 0.5} (y +y1)/d1 ++{(ｐ1+tm d1)0.5/(ｐ2+tmd2 ) 0.5 }(y +y2)/d2-tg(y-1)Q/d4++Q (-tg((y –y4)/d4)-te((y-y3)/ d3)) ]=0(14b)∂Y/∂mp4=0.18[(Q - 99mp4/(582409-900mp4)0.5 ) +(99mp4/(582409-900*mp4)0.5)tg(x/ d4) ++(99mp4/(582409-900mp4)0.5)te(x/d3)) ++2.5*0.5A-1.25(ｐ2+tmd2)0.5(p1+tmd1) 0.5Q)0.25 )) 0.18[-Q-99(600-Q))/(582409-900*mp4)0.5++(0.7425(2+Q)2)/(582409-900*mp4)0.5+(21.78Q)/ (582409-900*mp4)0.5 ++(0.3267(2+Q)*(-206+(582409-900*mp4)0.5)/(582409-900*mp4)0.5++99mp4/(582409-900mp4)0.5]]=0.
(14c)

The calculation result derived from the Gradient dynamics using equation (14a, b, and c) shows
the figure illustrated in Figure 2. Figure 2 indicates that the optimal transfer price is approximately
442, and although the optimal location of the factory 1 is not clearly specified, the best location iscan be found in the area where a chaotic phenomenon appears.
The graph shows that chaotic phenomenon appears along the area of M1, the source of raw
materials. It can be established that the optimal transfer price is at 442. However, it is difficult todetermine the optimal location of factory 1 which produces intermediate goods. Nevertheless, theoptimal location is proximate to the area of M1 or the source of raw materials. If the factory 1chooses to locate within that particular area, the profit will be almost at the same level. Total profits
will be Y=33083.
3Graph 2 shows that chaotic phenomenon occurred in the derivation of optimal location and price or the Cauchy
Convergence. This Cauchy Convergence needs further analysis but this will not be discussed in this study. However,
Puu (1998) explained that chaotic phenomenon appears when the derivation of optimal location does not include

7

Figure 2 Optimal transfer price and location expected area

y

mp4

x

3. Effects on factory’s production of corporate tax rate and agglomeration economy
3.1 Coexistence of the factories at a market place
It is assumed that when factory 1 and factory 2 locate at the market, agglomeration economies
generate. It is supposed in this subsection that the influence is represented in the reduction of costs inthe factories; the reduction of fixed cost (F1) in the assembly factory 1. Given this kind of
agglomeration economies, to what level this fixed cost can occur the co-existence of the two
factories at the market place. As shown in the Figure 1, when intermediate goods producing
factory 1 locates within (3, -0.5) are of the sources of raw materials m1, transfer price, level of price,production level will be the same. If the factory 1 locates to the market (0, 1), the total profits isshown in the right-most column of Table 1. The corporate tax rate is set at t=t*=0.82 for eachlocation points, and taking into account the influence of agglomeration economy, fixed cost (F1) will
be reduced from 5000 to 4936 and this can result to co-existence of intermediate goods producing
factory 1 and assembly factory 2 at the market location. On the other hand, in the case the fixed cost
reduction will not drop by 64, these two factories will have to be diffused to the two countries.4

4

price in the two-dimensional space.
For concise discussion about co-existence and dispersion of production processes, refer to Stigler (1956).

8

Table 1 Location of factories and changes in volume of production and profitt=t*=0.82t=t*=0.82

location point(3,-05)(0,1)

transfer price

442

442

product price

551

551

volume of production49

49

profit(Y)

3307

3296

factory(Y1)

2968

2957

factory(Y2)
Source: Authors’ calculations

339

339

3.2 Reduction in corporate tax rate and the changes in location and transfer priceSince factory 1 is located in a country with a lower corporate tax rate of 0.70, and factory 2 islocated abroad with tax are equivalent to t*=0.82. The transfer price for intermediate goods will drop
to 417. While, the prospective area will be within the about same area that was shown in Figure 2.
The firm's profit and production volume will be similar to the first column of Table 2.
If the factory 1 will be relocated abroad, what will happen to the co-existence of intermediate
goods factory and assembly factory 2?

Transfer price, product price, and production volume willremain the same as shown in the right-most column of Table 2. The optimal location of intermediate
goods factory is in a country where there is a source of raw materials M1 and where the corporate tax
rate is comparatively low as illustrated in Table 2. Thus, in this case, it is not reasonable to put theintermediate goods factory and assembly factory in the same location.

Table 2: Location of factories and the changes in volume of production and profit (different tax
rate)
t=0.70 t*=0.82

t=0.70, t*=0.82

location point(3,-05)(0,1)

transfer price

417

417

product price

545

545

volume of production

55

55

profit(Y)

5844

3723

factory(Y1)

5268

3147

factory(Y2)
Source: Authors’ calculations

576

576

9

It is however possible to locate these two factories within the market if the reduction of cost basedon agglomeration economy is big or when there is a support from the government. Assuming that
agglomeration-based fixed cost reduction F1 of the factory 1, if the firm's profit exceeds 5844, it ispossible to put the factory 1 in the same location. As reflected in the values in Table 2, even though
agglomeration-based fixed cost reduction drops to zero, the target profit of 5844 will not be achieved.
Therefore, given the two country's different tax rate, t=0.70, t*=0.82, the firm will not move theirintermediate goods factory 1 to the location of the market. Thus, in this case, if the firm would notreceive any support from the government, spatial dispersal of the firm’s factories will happen5.

3.3 Significance of a prospective area and new location factors
Chaotic phenomenon shown in Figure 2 can be interpreted as follow: If the firm decides the locationof the factory 1 and price of the intermediate goods in the range of the chaotic phenomenon, thefirm’s profits may not so decrease from the maximum level because the optimal solution iscontained in this sphere. It can be, therefore, considered that the range indicates a prospective area
for a possible factory's location. The firm can determine the search area in a large location space
into a small range for the factory’s location; they can reduce significantly the searching costs.
Chaotic phenomena may provide the firm with useful information, especially in the case firms do
not have adequate information about many regions in a large space.
It is also considered that even if the firm could identify the optimal site for the factory, it may notestablish a factory at that site by some reasons. For instance, the place has been occupied by another
firm, or land is too weak to build a factory. In this case, the firm has to search the second best sitesaround the optimal place. In this searching, chaotic phenomenon can be used for squeezing the
spatial range to be searched. The firm can easily find out the second best sites around the best point
in a relatively short period. Chaotic phenomena could be useful for alleviating the firms' location
problem.
The prospective area generates new location factors to firms: When a firm decides the factory’s
location in the prospective area, the firm becomes to consider the location issue in a broader
perspective. Besides profit level, the firm can incorporate many location factors such as education,
culture, housing, safety, and welfare in the area into its decision-making. Considering of thelocational effects of these new location factors, the firm decides the location of factory in theprospective area. That is, new location factors such as education, culture, housing, safety, andwelfare play important role in the determination of factory’s site in the prospective area.
5

On the other hand, if tax rates are t=0.82, t*=0.70, even without the benefits of agglomeration economies, theintermediate goods factory can co-locate with the market location. In such case, considering the fragmentation offactory 1 and 2, the amount of diseconomies of scale will be huge.

104. Comparison of the provinces in the Philippines
This section assumes that Philippines is within the prospective area and examines each provinces
economy and social condition. Based on the provinces characteristics, the firms can have an idea
where to possibly locate.

4.1 The Philippines
The Philippines is an archipelago located in the Southeast Asia. It is composed of 7, 107 islands
which totals to 300,000 square kilometers, the second biggest archipelagic country in the world. The
topography of these islands varies, from mountains, hills, and plains.
The Philippines is one of the fastest growing economies in the East Asia with 6-7% growth rate
in recent years. The services sector, fuelled by the recent growth in BPO businesses, contributes
more than half of the economic output. The manufacturing sector accounts for 30% of the national
output.
The Philippines is a republic with presidential form of government. The Philippines is divided
into
three main islands; Luzon, Visayas, Mindanao. The country is divided further into 17 regions, 80
provinces, 138 cities, 1,496 municipalities, and 42,025 barangays6. These local government units(provinces, cities, municipalities, and barangays) are responsible for the delivery of basic services
such as health, social welfare, natural resources and environment, agricultural extension, and public
works. Majority of the income of the local governments comes from central government in the formof internal revenue allotment (IRA) or the share from national tax collections.

4.2 Data
The primary sources of data in this study are the National Statistical Coordination Board (NSCB)
and National Statistical Office (NSO). The study used the latest data for each indicator. Reference
year, however, is not uniform, from 2000 to 2013.

Table 3 List of Indicators
Indicators
Demography
Land Area,
Population
Population density

6

Barangay, the smallest administrative unit, is derived from the Malay word, Balangay, whichmeans sailboat. Early inhabitants are said to have reached the islands through these sailboats.
11

Economy
Real per capita income
Wage Rate Non-Agriculture
Wage Rate Agriculture
Total number of banks
Total bank deposits (millions)
No of MSME
Employment in MSME
Poverty Incidence among Families (%)
Education
Mean Years of Schooling
Literacy rate (simple literacy)
Net Enrolment Ratio (NER) in Primary Education
Completion Rate in Primary Education
Net Enrolment Rate (NER) in Secondary Education
Completion Rate in Secondary Education
Number of Higher Education Institution(HEI)
Enrollment in HEI(SUC)
Graduates in HEI(SUC)
Security
Crime Incidence(Crime per Population)
Traffic Accident (Traffic Accident Per Population
Total Terrorists/Insurgency Incidences
Magnitude of Terror Attacks(No of Casualties)
Weather and Climate-related risks
Geophysical risk
Infrastructure
Paved Road Length KMs(Road Density)
Permanents Bridges Linear Meters(Bridge Density)
Total Number of Airports
Total Number of Ports
Energized Barangays (%)
Number of Special Economic Zones
HealthLife expectancy at birth (years)
Safe Water Supply per Population
Sanitary Toilet per Population
Total Health Works per Population
GovernanceGood Governance Index
State of Performance
Internal Revenue Allotment
Local Sources
4.3 Estimation of provincial dynamism scores
Provincial Dynamism Score is computed using various variables that falls into six categories:
12

economic condition, education, security, infrastructure, health, and governance. These variables are
standardized using equation 815),

S I vP ( X I v  AVEI v )
STIv

(15)

(I=a, b, e; V=1, 2, 3, 4…n; P=P1, P2, P3…P80)

Where X Iv is the value of the indicator of I of a province P; AVEIv is the mean value; and STIv
is the standard deviation of the indicator I of variable E.
Score of the indicator I was obtained using equation (16),In

S Iv  (1/ n)  S IvP(16)

I  vP

The provincial dynamism index was computed based on equation (17),e

PDp  (1/ 6) S Iv

(17)

I a

Principal Component Analysis (PCA) is then used to discern the similarities or difference in the setof variables and plot these variables into two-dimensional space. This representation could identify
characteristics of each province.

4.4 Provincial dynamism scores
High provincial dynamism index would mean vibrant local economy, good local governance,
presence of infrastructure, educated citizens, presence of health-support, and high-level of safety.
In the Figure 3, provinces with high provincial dynamism index are shaded in black color and thelow provincial dynamism index is shaded in grey or white.
The top provinces that have the highest computed provincial dynamism scores are the following:
Metro Manila7(3.040), Batanes(0.786), Cavite(0.539), Bulacan(0.420), Laguna(0.411), Rizal(0.330),
Batangas(0.306), Cebu(0.299), Ilocos Norte(0.292), and Palawan(0.283). Except for Batanes, Cebu,
Ilocos Norte, and Palawan, the rest of the provinces are located within regions that adjacent to Metro7

For comparison purpose, Metro Manila is considered as a province in this study.
13

Manila. On the lowest end of the ranking are the following provinces: Maguindanao(-1.200),
Sulu(-0.994), Basilan(-0.919), Lanao del Sur(-0.639), Tawi-tawi(-0.617), Masbate(-0.527),
Saranggani(-0.489), Ifugao(0.-429),Lanao del Norte(-0.418), and Zamboanga Sibugay(-0.413).
Except for Ifugao and Masbate, these provinces are located in Mindanao, primarily in ARMM.

Figure 3: Map of provincial dynamism scores

Source: Authors’ calculations

In terms of economic scores, except for Cebu, the provinces which rank the highest are locatedwithin the peripheries of Metro Manila: Laguna, Cavite, Rizal, Batangas, Bulacan, Pampanga,
Bataan and Quezon. In this aspect, neighborhood definitely seems to matter. In terms of education,
except for Pangasinan and Batanes, the provinces with the highest scores are again located near thecapital: Cavite, Rizal, Laguna, Bulacan, Batangas, Pampanga, and Bataan. Notice that in both
economic and education scores, no provinces from Mindanao have made it on the top. In terms of
security, the provinces that are considered to be the safest are the following: Apayao, Isabela, Ilocos
Norte, Negros Occidental, Palawan, Iloilo, Zamboanga del Norte, Batanes, and Tawi-Tawi. Metro
Manila is at the top of the rankings most of the time except for this component as it has the lowest
14

score in security. In terms of infrastructure, the provinces which have the highest score are thefollowing: Metro Manila, Leyte, Cebu, Palawan, Negros Occidental, Pangasinan, Batangas, Bohol,
and Occidental Mindoro. In terms of health, provinces with relatively good health support are thefollowing: Batanes, Palawan, Guimaras, Ilocos Norte, Aurora, Benguet, Abra, La Union, andSiquijor. In terms of governance score, the provinces which rank the highest are the following:
Metro Manila, Bulacan, Laguna, Rizal, Camiguin, Cavite, Compostela Valley, Benguet, Siquijor,
and Kalinga.

4.5 Results of principal component analysis (PCA)
For the sample of 80 provinces, the first principal component(PC) accounts for 54.91 percent of thetotal variance with Eigen value higher than 1, and the second principal component accounts for22.33 percent of the total variance, with Eigen value higher than 1. The first two principal
components explain 76.23 percent of the total variance. The first component puts higher weight oneconomic, education, governance, and infrastructure indicators. The second component emphasizes
security, and health indicators.
It is shown in Figure 4 that the higher the score in economic, education, governance, andinfrastructure, the higher the score of the first PC on the x-axis and the provinces which exhibitedthese features are found the right portion of the graph. Meanwhile, the higher the score in security
and health indicators, the higher the score of the second PC on the y-axis and provinces with suchcharacteristics are found on the upper portion of the graph.
Metro Manila is no longer shown by graph in Figure 4 because it is located on the far right but
lower portion of the graph. This implies that Metro Manila has a good economic situation, high levelof education, presence of infrastructure, and high-quality of governance but it is still found lacking
in terms of security and health. Batanes is also no longer shown because it is located in theupper-right portion of the graph. Batanes is the only provinces that have favorable economic
condition and relatively high-level of security. The provinces that have the highest computed
provincial dynamism scores are located in the upper right portion of graph. Most provinces tend tocluster near the center which means that these provinces have either relatively good economic
conditions but needs improvement in security and health.
This representation allows easy identification the characteristics of the provinces in thePhilippines. This is particularly useful for the firms who are planning to locate in the Philippines. For
instance, firms that conduct high-technology processes as well as research and development (R&D)
can select the provinces with high education-level. Or firms that handles assembly or logistics canchose to locate in the provinces with relatively well-built infrastructure. Furthermore, thisrepresentation can also be helpful to policymakers and local administrators who are aiming torevitalize local economy and improve social conditions.
15

Figure 4: Principal component analysis of the provinces in the Philippines
2.5
Palawan2

Abra
Ilocos Norte
Isabela1.5
IloiloQuirinoCagayanApayaoF2 (21.33 %)Cavite1Marinduque

0.5

Antique
Occidental Mindoro
Nueva Vizcaya
Guimaras
QuezonBohol

Capiz Camarines
Norte
Ilocos Sur
Sorsogon
Mt. Province Catanduanes Aurora
NegrosAlbay
Occidental
Camarines
SurEcija Tarlac
Nueva
Kalinga
Aklan
Oriental Mindoro

0
RomblonRizal

LagunaBenguetBataanSiquijor
ZambalesCebu Batangas

La Union

BiliranNegros Oriental-0.5

BulacanPampanga-1

Pangasinan

Ifugao

-1.5

-2
-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

F1 (54.91 %)Source: Authors’ calculations

5. Concluding Remarks
It is elucidated in this paper that the steps in establishing prospective location and effects to firm’s
location decision-making of transfer price system and corporate taxation system which are closely
related to the economic situation and economic policy of a country. There are two steps in thedetermination of prospective location: cross-country comparison and within country (regional orprovincial level). At the first step, countries’ economic and social conditions are evaluated.
Consequently, firms are likely to locate in countries with relatively good economic and social
conditions. As the profits made by firms are subject to taxation, the system of taxation is also
considered as one of the deciding factors, particularly as these production processes tend to disperse
across borders. At this first step of determination of optimal location, chaotic phenomenon occurred
which implied presence of several optimal locations. The analysis found out that as long as the firms
locate within the prospective area, firms can still achieve target profits. In the second step, regions or
16

provinces economic and social characteristics are analyzed. In this paper, it is assumed that thePhilippines are within the prospective area so comparison among provinces was performed. Using
various indicators that are available at the provincial-level, this study constructed a provincial
dynamism score of 80 provinces. The study has found out that among the ten provinces which havethe highest computed scores, there are four provinces which are not located near the primaryeconomic center of Metro Manila: Batanes, Cebu, Ilocos Norte, and Palawan. However, provinces in
Mindanao, particularly those within the ARMM are found to have the lowest provincial dynamism
scores.

6. References
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Bond, E.W (1980), “Optimal transfer pricing when tax rates differ.” Southern Economic
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Eden, L (1985). “The microeconomics of transfer pricing,” Lorraine Eden and Alan Rugman (eds.)
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Puu, T. (1998).”Gradient dynamics in weberian location theory,” Beckmann et.al, Knowledge andnetworks in a dynamic economy, Springer, 34, pp.569-589.
Stigler, G. (1956). “Industrial organization and economic progress” in The State of the Social
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