ASSESSING THE CONTRIBUTION OF AIR ACCESSIBILITY
TO REGIONAL ECONOMIC GROWTH
IN THE PHILIPPINES
A Thesis
Presented to the
Faculty of the School of Economics
University of Asia and the Pacific
In Partial Fulfillment
of the Requirements for the Degree
Master of Science in Industrial Economics
By
Reynaldo B. Delos Santos Jr.
March 2015
© Reynaldo B. Delos Santos Jr. 2015. All Rights Reserved
ACKNOWLEDGMENTS
“Happiness is neither virtue nor pleasure nor this thing nor that
but simply growth. We are happy when we are growing.”
-- William Butler Yeats
This thesis is the culmination of months of doing research, writing drafts,
accepting rejections, changing directions, discovering errors, braving risks,
asking for help, waiting patiently, writing again, moving forward, learning so
much, and growing throughout the whole process. The path to finally finishing
this was surely a difficult one, but I wouldn’t have it any other way. My gratitude
extends to everyone who has accompanied me through the highs and lows of the
worthy struggle that is IEP, but some names deserve particular mention:
 SEC: To all the professors and staff in the office at ACB 6th, thank you
for letting me make SEC my second home. I’ve spent much of my 5th
year life in the cubicles and pantry of that office. Thank you Ms. Arlene,
Ms. Glenda, and Sir Perry for helping me in reserving stuff, contacting
people and getting forms. A huge thanks to Ate Rachel and Ate Tin for all
the advice and for always being there in times of need (and frustration).
 Ms. Chesca Villareal: For all the assistance you have extended in my
search for macroeconomic data, I am especially grateful.
 Ms. Dina Pilapil: Thank you for making time and sharing your thoughts
and expertise about logistics in the Philippines.
 NEDA, NSCB and CAAP: Data gathering was hard, but it would have
been worse if it wasn’t for all the helpful staff. Thank you very much!
 REID family: I never thought I’d find home in my internship, but there
you were. Special thanks to Ate V, Ms. Lynne, Ms. Beth and Ms.
Marianne for all your help, and to Kuya Third for guiding me during my
intern stint – I’ve learned so much from you. A huge thanks to Capt. Ben
and Ms. Twinkle for all of your guidance as I was writing my drafts. Your
work was one of my sources of inspiration in writing this thesis.
#ParaSaBayan!
 Business Economics Association: To the organization to which I put half
my heart (the other half I saved for myself) and has taught me the real
value of leadership and service, I am forever grateful. Thank you to all
the BEA officers who choose to go beyond what’s expected of them in
school and be excellent leaders. I think I’ve grown so much
professionally and personally because of BEA. I will definitely miss all
the brainstorming sessions, poster-making, and participating in the BEA
events. Thank you Franc for your dedication and great work, and Keren
for being with me in the Creatives Office for two terms (I never thought
we’d last that long! Haha).
 IEP Superseniors: Joe, Mon, Jose, Chela, Sarmie, Apple, Ivy, Althea,
Keren, Francis, Raf, Keng, Germs, Mar, Rige, Lyndon, Rose, Rap. I owe
much of the happiness I’ve experienced in the university to you guys. I
don’t know how I could’ve survived college without you. We’ve shared
almost everything there is to share: from our struggles, disappointments,
doubts, fears, to our strengths, chances, triumphs, hopes, and dreams, just
to name a few. Please know that you all have a place in my heart. Let’s
not let our different career paths cut our friendship. Now I will have to
endure the separation anxiety as graduation kicks in.
 Dr. Cid Terosa: Words are not enough to express my gratitude for
everything you’ve done for me. Thank you for honestly pointing out my
shortcomings, for always lifting my spirits whenever I’d feel discouraged
and lost with my thesis, for all the career and life lessons you’ve imparted
to me in the past two years. Thank you for seeing the better in me when
nobody else did, even myself. Your constant belief in me and in my
potential I used as motivation in pursuing the Masters. I wouldn’t have
achieved this much without your support.
 Alvin Religioso: Thank you for always cheering me on whenever I rant
about everything life throws at me, including thesis writing. You always
make me think that I can do all things. I truly admire your calmness and
grit. I shall see you real soon.
 My family: You guys are my source of strength. It may sound like it came
from a cheesy song but really, everything I do, I do it for you. Who I am
right now I owe to my parents who raised me well (or so I thought). This
is for you. As promised, I dedicate this space to Reisanne Delos Santos
for sponsoring the expenses for the bound copies of my thesis. ❤
 Myself: Congratulations, we’ve come this far! For not giving up, for
getting back when you get knocked down, thank you. We did our best to
fight procrastination, but let’s do better next time, ayt?
TABLE OF CONTENTS
Page
Acknowledgments
List of Tables
List of Figures
Executive Summary
iii
iii
iv
CHAPTER
I
II
III
IV
INTRODUCTION
A. Background of the Study
B. Statement of the Problem
C. Objectives of the Study
D. Significance of the Study
E. Scope and Limitations
F. Definition of Terms
1
6
6
7
7
9
REVIEW OF RELATED LITERATURE
A. The Regional and Sectoral Dimensions of Growth
B. Transport Infrastructure in the Philippines
C. The Economic Importance of Transportation
D. Accessibility and Location
E. Inter-regional Connectivity of the Philippine regions
F. The Air Accessibility-Economic Growth Nexus
G. Previous Work on Air Transport and Economic Growth
H. Theoretical and Empirical Issues
I. Synthesis of the Literature
11
15
19
22
24
36
45
55
60
THEORETICAL FRAMEWORK AND METHODOLOGY
A. Theoretical Framework
1. Endogenous Growth
2. Economic Geography
3. Location
B. Empirical Methodology
C. Data Sources
63
63
67
72
74
82
PRESENTATION, INTERPRETATION, AND ANALYSIS
OF RESULTS
A. Air Accessibility and Regional Growth
B. Impact on Core Regions
C. Impact on Peripheral Regions
D. Impact of Air Accessibility on Employment and
Productivity Per Sector
1. Agriculture, Hunting and Forestry
2. Fishing
3. Manufacturing
i
84
86
88
89
89
94
96
4. Wholesale and Retail Trade
5. Real Estate, Renting, and Business Activities
6. Financial Intermediation
V
SUMMARY, CONCLUSIONS, AND
RECOMMENDATIONS
A. Summary
B. Conclusions
C. Policy Recommendations
D. Recommendations for Further Research
99
102
104
107
109
110
113
APPENDIX
A.
B.
C.
D.
E.
F.
G.
H.
I.
J.
Domestic Commodity Flows via Air, 2013
Domestic Air Passenger Routes
2SLS Regression Results for All Regions
2SLS Regression Results for Core Regions
2SLS Regression Results for Peripheral Regions
OLS Results for Core Regions by Sector Productivity
OLS Results for Core Regions by Sector Employment
OLS Results for Peripheral Regions by Sector Productivity
OLS Results for Peripheral Regions by Sector Employment
Wholesale and Retail Trade Regional Share in National
Total Number of Establishments
K. Gross Value Added in Real Estate, Renting and Business
Activities by Region
BIBLIOGRAPHY
115
116
117
118
119
120
126
132
138
144
145
146
ii
LIST OF TABLES
Table
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Forces Affecting Geographical Concentration
Data Description
2SLS Regression Results across all Philippines regions
2SLS Regression Results for Philippine Core regions
2SLS Regression Results for Philippine Peripheral regions
OLS Regression Results for Agriculture, Hunting and
Forestry in Core regions
OLS Regression Results for Agriculture, Hunting and
Forestry in Peripheral regions
Food and live animals inter-island commodity flow pattern,
2009
OLS Regression Results for Fishing in Core regions
OLS Regression Results for Fishing in Peripheral regions
OLS Regression Results for Manufacturing in Core regions
OLS Regression Results for Manufacturing in Peripheral
Regions
OLS Regression Results for Wholesale and Retail Trade
in Core regions
OLS Regression Results for Wholesale and Retail Trade
in Peripheral Regions
OLS Regression Results for Real Estate, Renting and Business
Activities in Core regions
OLS Regression Results for Real Estate, Renting and Business
Activities in Peripheral regions
OLS Regression Results for Financial Intermediation in
Core regions
OLS Regression Results for Financial Intermediation in
Peripheral regions
67
83
85
87
89
90
90
91
95
95
98
98
99
100
103
103
105
105
LIST OF FIGURES
Figure
1
The Transportation-Regional Growth Relationship
iii
71
EXECUTIVE SUMMARY
The huge disparity in regional incomes and growth in the Philippines has
been a pressing concern for policymakers and economists for decades. As
prosperity seem to concentrate in the historical capital which has become a
technological and business hub, Metro Manila, the other regions have
experienced a slow and limited growth. One of the reasons commonly cited for
the stark inequality in regional economies is the insufficiency in the provision of
public infrastructure, an enabling factor to the productivity potential of the
regions’ human capital. One of the infrastructure crucial to the growth of any
economy is transport infrastructure. This thesis investigates on the impact of air
accessibility on the economic growth of the 17 administrative regions in the
Philippines by applying estimation methods to reveal where air transport
contributes to growth the most.
The regions in the country were classified into the prosperous core and
the laggard periphery, following Krugman’s economic geography framework.
Along with endowments and human capital, the spatial economic relationships of
the regions were tested of their significance in determining the growth of the
Philippine regions. Differences in the air accessibility – growth relationship were
measured for the core and periphery, as well as the differences in the impacts per
sector as measured by the employment and productivity of these sectors. Air
accessibility as represented by air traffic and air cargo volume were subject to
various models of panel regressions of two-stage least squares and ordinary least
iv
squares in order to quantitatively assess the contribution of air accessibility to
regional growth.
Growth in the peripheral regions exhibited fairly positive correlation with
increase in air accessibility. Growth in the core regions in the recent period
showed very low dependence to improvement in air accessibility, which may be
due to overcrowding effects. Economic sectors which are positively linked with
air accessibility are the Agriculture, Fishing, Manufacturing, and the Wholesale
and Retail Trade sectors. Sectors with significant positive correlation with air
accessibility were more commonly found in the peripheral regions. Unlike the
results of previous studies on air transport and economic growth which showed
positive correlation in the business and high-technology service sectors in
developed countries, results from this thesis imply the stronger link between air
accessibility and growth in goods-related sectors. The results from this thesis
support the characteristic of the peripheral regions in the Philippines as
producers and suppliers of various food and other traded goods and the core as
the biggest consumer market of the peripheral regions.
There is a possibility for the peripheral regions to catch up with the core
regions in terms of income, as long as government policies are geared towards
the integration of the periphery into the national economy in order to facilitate
the diffusion of investments and economic activities. All of these are highly
dependent on an efficient transport network, composed of land, water, and most
specially, air transport infrastructure that enable the movement of goods and
people across the archipelago.
v
CHAPTER I
INTRODUCTION
A. Background of the Study
Reducing regional inequality has long been the target of the Philippine
government. Disparities in income and level of employment across the
Philippine regions have not changed significantly over at least the past two
decades: Metro Manila and surrounding areas continued to have high socioeconomic indicators; while relatively poorer regions remained so. Per capita
gross regional domestic product (GRDP) of the richest region, NCR, is more
than twice the national average, and is more than tenfold that of the poorest
region, ARMM. 1 Moreover, only three regions have incomes greater than the
national average. One reason cited for the stark differences in regional growth is
the wide variation in these regions’ access to adequate infrastructure services
(Balisacan and Hill, 2007). The regions with the lowest GRDP are also those
suffering from the most severe basic infrastructure shortage. The country’s
richest regions in terms of contribution to GRDP, NCR and CALABARZON,
had above-average access to safe water, electricity, and roads, while the poorest
ones – Caraga, Cagayan Valley, and ARMM – had the lowest access to these
infrastructure.2
1
Based on 2013 NSCB Regional Accounts.
Balisacan and Hill (2007). The Dynamics of Regional Development: The Philippines in East Asia.
Cheltenham, UK: Edward Elgar.
2
1
Infrastructure and Integration
Infrastructure
is
essential
to
unifying
the
national
economy.
Transportation infrastructure, in particular, facilitate the integration of regional
economies separated by distance and time costs through its impacts on the
regions’ access characteristics – a factor crucial to improving regional
productivity in sectors highly dependent on efficient networks. Better transport
linkages enable investment and human capital to flow more freely across borders,
improving returns on investment. The composition of this infrastructure is also
important. However, an increasingly accurate characterization of the Philippine
situation is that efficient connections to the global economy occur alongside the
poorer provision of domestic networks, which result “in a series of
internationally oriented enclaves of economic activity weakly integrated to the
hinterland” (p.28, Balisacan and Hill, 2007).
The richer regions tend to provide better infrastructure, as these regions
possess more capacity to fund more and higher quality physical facilities. Using
road density, access to potable water, access to electricity, and telephone line
density data 3 it is observable that NCR scored the highest in the selected
infrastructure indicators except in access to potable water, which was topped by
Central Luzon with 95% of households having access to potable water in 2003.
The gap in ranking from the best to the second best provided infrastructure is
highest in road density – NCR ranks first with 5.72 km of road per square km of
land, followed by Ilocos with 0.54 km/km2. Discounting the leading region, the
rankings per infrastructure is more variable across the 16 remaining regions. The
3
Comparisons based on data from DPWH, PSA, and 2003 FIES
2
infrastructure record of the Philippines show obvious deficiencies which holds
back the process of efficient regional economic integration. The country’s poor
infrastructure performance is evident in the 2014 Global Competitiveness Report,
where the Philippines ranks 84th (from 89th in 2004) in transport infrastructure.
While small improvement was manifest, transport infrastructure still remains in a
dire state, with seaport facilities ranked 116th and airport facilities ranked 113th
(World Economic Forum, 2013).
Regional Air Transport
The characteristics of air transport which set it apart from land and
marine transport is that it can mobilize goods as well as people over longer
distances and at a shorter time period. Air accessibility, along with other modes
of accessibility, reduce logistics costs of firms, increasing their competitiveness
leading to the specialization and growth of regional economies. The country is
connected to the rest of the world mostly through its capital, Manila, with Cebu,
Davao and Clark also being internationally connected.
In the regional level, air accessibility is highest in NCR as it has the
biggest traffic and most widely-connected to other regions. NCR registers the
highest passenger enplanements share with 60% of all air passengers going
through Ninoy Aquino International Airport.4 Following the lead is the second
group composed of Central Visayas, Western Visayas, and Davao Region with
11%, 9%, and 6% air passenger share, respectively. Regions with the lowest
4
Author’s computations based on Civil Aviation Authority of the Philippines data, 2003 to 2013
average
3
share in air passengers are CAR, Calabarzon and Sockscsargen, having a
combined total share of 0.03%. Obtaining the air passenger share-to-GRDP share
ratio per region indicate that some of the relatively well-off regions have a low
air traffic. Calabarzon has an income share of 14.19% over 2003 to 2013 average,
ranking it second to NCR with 37.3% share in national income, but its average
air traffic share for the same period is less than zero. Ilocos and CAR share the
same characteristic. Nevertheless, richer regions such as NCR, Central Visayas
with 6.53% share in national GDP, Western Visayas with 5.15%, Davao with
4.16% and Northern Mindanao with 4% also rank high in air passenger shares.5
The contribution of air accessibility to regional growth can be dissected
into two: (1) facilitating movement of goods in air cargo, and (2) facilitating the
movement of persons in tradable services. This translates to air accessibility
having the most impact on the manufacturing and services sectors of the regional
economy, although most literature cite the services as the most significantly
affected by increased air accessibility. High technology and high value service
sectors such as the professional, finance, and information were found to benefit
from air services (LeFors, 2014; Ozcan, 2013; Breuckner, 2003; Alkaabi and
Debbage, 2007). The skilled labor market in the services sector utilize the
accessibility to markets provided by air infrastructure. Air service is one of the
critical factors in high-technology industry location decisions. Apart from
exporting services and goods, air accessibility also makes tourism possible.
Foreign tourist arrivals to the country have increased 119% from 2002 to 2012,
5
Author’s computations based on PSA Regional Accounts data, 2003 to 2013 average
4
and inbound tourist expenditure was pegged at Php 160 billion in 2012. 6 The
travel and tourism industry directly contributed Php 257 billion to the Philippine
economy, with spending of foreign visitors arriving by air amounting to Php 51
billion (Oxford Economics, 2009). With the skilled labor market and services
sector growing in the Philippines, increase in air accessibility of regions may
provide means for structural transformation of the economies to higherproductivity sectors and to specialization.
Economic activity is concentrated in regions which enjoy better linkages
with global markets due to their superior ports, airports, and other infrastructure.
These transportation infrastructure facilitates the accessibility of markets,
attracting business firms to places with sufficient human capital and resources,
boosting employment in the area. The archipelagic character of the country
further necessitates efficient maritime and air transport infrastructure for growth
and socioeconomic integration. According to Llanto, Basilio and Basilio (2005),
the integration of peripheral islands with urban economic nodes such as Metro
Manila, Cebu and Davao, as well as the diffusion of investments and economic
activities, fundamentally depends on an efficient transport network.
The
accessibility of Philippine regions are facilitated by road, maritime, and air
transport networks. Accessibility via air transport services, ‘air accessibility’
henceforth, demands special attention as its growth potential is the least explored
mode in studies describing transport-growth relationships in the Philippines. The
difference in the nature of air accessibility from land and water highlights the
6
Computed from the Department of Tourism tourist arrivals data
5
need for a separate study for the interaction between access facilitated by
transport and regional growth.
B. Statement of the Thesis Problem
In studying the role of air transport in economic growth, the researcher
asks: does the air accessibility of Philippine regions contribute to regional
economic growth? In line with this, the following questions need to be answered:
1) How does air accessibility affect regional growth?
2) Are there differences in the air accessibility-regional growth
relationship between economic core and peripheral regions?
3) How does air accessibility influence regional sectoral growth?
C. Objectives of the Thesis
The main objective of this study is to examine the relationship between air
transport services and regional growth in the Philippines. To achieve this, the
researcher shall do the following:
1) Estimate the correlation between air traffic growth and regional
growth in the average region.
2) Determine whether there are differences in the relationship across
regions.
3) Identify the sectors of the regional economy which are linked with
increased air accessibility of regions.
6
D. Significance of the Study
Studying the relationship between the provision of air transport services
through infrastructure and regional growth will help policymakers determine
investments for air transport infrastructure in the many regions characterized by
different endowments and different needs. Assessing the role of air transport to
regional economic development in the Philippines will aid the government in
crafting development plans to address regional inequality. A better understanding
of regional economies in the country through this study will be of use to local
government units as they implement national plans in targeting regional
economic issues. The academe will also benefit from the results of this study
through the insights gained from integrating the role of air transport in shaping
economic growth in the Philippines. This study shall enrich existing regional
economics as well as transport economics literature as it tackles a country where
air transportation provides a high potential for networking and connectivity
among individuals and firms across its thousands of islands.
E. Scope and Limitations of the Thesis
This study limits its coverage to assessing the contribution of air
accessibility to regional economic growth in the Philippines. Air accessibility is
treated in the aggregated sense of passenger enplanements. The division of the
country into regions is based on the latest government classification into 17
regions. Analysis is performed by classifying the Philippine regions arbitrarily
into core and periphery, consistent with Krugman’s theory. Classified as the core
regions are the following: NCR, CALABARZON, Central Luzon, Western
7
Visayas and Central Visayas. The rest of the country are classified as the
peripheral regions. The relationships between air accessibility of the regions and
the different sectors of the regional economy are measured solely by
employment and productivity of the sectors. This research has not focused on
employment and productivity in the air transportation sector itself. This thesis
investigates how the use of air transportation by other industries impacts the
productivity of the regional economy, not how the productivity of the aviation
sector impacts the economy. Also, the time period investigated is from 2009 to
2013 as determined by availability of data. The relationships are measured in the
context of geographical regional units in the country. Thus, results may be
different if another study shall be conducted on a provincial and/or urban level.
Due to data restrictions and unavailability, some regions may have insufficient
data in some of the variables used in the study, leading to an unbalanced number
of panel observations. All data were obtained from relevant government
authorities, thus accuracy of acquired data heavily depends on the gathering and
processing techniques of the respective authorities supplying the data.
8
F. Definition of Terms
 Air accessibility – is the degree by which a region can be reached by air
and is commonly measured by number of passengers enplaned. Dependent
on infrastructure present. Also used interchangeably with air connectivity.
 Accessibility – is defined as the measure of the capacity of a location to be
reached by, or to reach different locations.
 Air traffic – is the measure of the flow of individuals across regions via air
transport networks; synonymous with number of enplaned passengers.
 Air transport services – refer to the services provided by air transport
infrastructure (airports, runways, airplanes) that facilitate the flow of goods
and persons from one area to another.
 Core–refers to the economic centers in a country; characterized by high
income levels. Core regions refer to NCR, CALABARZON, Central Luzon,
Western Visayas and Central Visayas.
 Gross Regional Product (GRP) – is the output/income generated per region
 Logistics – refers to the movement and storage of goods, intermediate and
final, from the sourcing of inputs to the delivery of finished products.
 Market access – refers to the ability of transportation facilities and services
to provide households and businesses with access to opportunities they
desire.
 Periphery – refers to the poorer regions of a country which have exhibited
low growth rates. Peripheral regions refer to CAR, Ilocos Region, Cagayan,
9
MIMAROPA, Bicol Region, Eastern Visayas, Zamboanga, Northern
Mindanao, Davao, SOCCKSARGEN, Caraga and ARMM.
 Productivity – refers to the ratio of business output to production cost,
where the denominator is the total cost of all input factors.
 Regional growth – refers to the increase in wealth of a region as measured
by per capita GDP, employment and population.
 Transport infrastructure – is not reduced to mere components of the
‘aggregate’ neo-classical concept of physical capital but includes the
potential for networking and connectivity among individuals and firms.
10
CHAPTER II
REVIEW OF RELATED LITERATURE
This section presents a review of all relevant literature which has helped
the researcher formulate the framework to be used in the achievement of the
goals of this study. Earlier studies on the relationship between transportation and
growth as well as their various results are discussed. Then we focus on literature
tackling air transport infrastructure and regional growth.
A. The Regional and Sectoral Dimensions of Growth
Regional growth patterns across the 17 regions vary considerably. The
differences in human capital, natural resource endowments, and connectivity
characterize the variety in income growth. Metro Manila
generates
approximately a third of the Philippines’ GDP, and when combined with the rest
of Luzon, contributes more than half of the country’s GDP. The significant
differences in income and poverty levels across regions in the Philippines is
observable, with Metro Manila having a per capita income of Php37,855 in 2006
(in constant 1985 pesos). At the other extreme is ARMM with per capita income
of Php3,486 which is only one-fourth of the national average. Bicol, Eastern
Visayas and Caraga also belong to the poorest regions, with average per capita
GRDP levels of approximately one-half the national average. Based on 2000 to
2006 poverty headcount and income growth data, richer regions are found to be
less poor and exhibit faster income growth (World Bank, 2010).
11
The economic structure per region also show wide variety, with regions
having more than a third of their GRDP coming from agriculture: Ilocos,
Cagayan Valley, Western Mindanao, Central Mindanao, ARMM, and Caraga.
Similarly, these regions also have the lowest GRDP and GRDP growth rates.
Moreover, poverty is found to be more prevalent among agricultural households
than non-agricultural households.7 Poverty incidence was three times higher than
poverty incidence among non-agricultural households (World Bank, 2010).
Some regions are more heavily dependent on services, led by NCR having 62.9%
of its GRDP from the services sector, followed by Bicol (61.2%) and Central
Visayas (61.7%) in 2003. Regions exhibiting relatively high industry shares are
characterized by high levels of mining. Included in the list of heavily
industrialized regions in the Philippines are CAR and regions in Mindanao such
as Northern, Southern and Central Mindanao in 2003.
Identifying the determinants of regional growth in the Philippines has
long been part of the government’s efforts to drive up incomes in the poor
regions of the country. Pernia and Quising (2003) studied the contribution of
trade openness to regional economic growth in the Philippines by analyzing it
with other factors assumed to affect regional growth and poverty levels. Included
in the model explaining gross regional domestic product are local/domestic
factors such as public expenditures on economic development services and on
social services. Public expenditures on economic services refer to local
government unit (LGU) outlays on agriculture and natural resources, trade and
7
Agricultural households are defined as households with total income from agricultural activities
equal to or greater than income earned from non-agricultural activities.
12
investment, roads and other transport, power, water development and flood
control, among others. Social services expenditures are public spending on
education, health, and housing and community development. These LGU
expenditures were used as variables to illustrate the local conditions in the
regions. Pernia and Quising (2003) cites Solon, Fabella, and Capuno (2000) that
“Expenditure outlays directly imply the relative priority given to different types
of public services…local officials can readily influence the allocation of public
resources, more than they can local welfare. Educational attainment of the
population is also used to predict regional growth, and is proxied by the mean
schooling years of the household heads. This measure is said to reflect the ability
of the population to take advantage of the economic opportunities presented by
economic growth. Results of the study reveal that trade openness as well as
social expenditures of LGUs and human capital as measured by average
schooling years of household heads significantly influences GRDP per capita
growth.
Mapa, Balisacan and Briones (2006) investigate population as one of the
determinants of income growth of 74 provinces in the Philippines. They used
education, migration, neighborhood effects and infrastructure variables along
with the proportion of population of young dependents in models explaining
provincial income growth. The population, infrastructure, and migration
variables were found to be significant drivers of provincial growth. However,
education was not. The insignificant result is in contrast to the results from crosscountry regressions where education is a positive determinant of economic
13
growth. One reason cited for this is that the education variable in the model
failed to capture the level of human capital in the provinces. It is also interesting
to note that the neighborhood effect measure which was included in the models
represented spillover effects of neighboring provinces’ growth to a particular
province’s own growth.
The disparities in regional growth can be attributed to the economic
structures of the regions. Consistent with foreign studies, the agriculture sector is
the least productive sector. In the Philippines, it employs the majority of the poor
and has exhibited slow growth relative to other sectors. Agriculture’s overall
contribution to total GDP growth was only around 13 percent over 1997 to 2007
– same with the transport and communications sector, which accounted for a
much smaller share of GDP (World Bank, 2010).
The services sector grew the highest in terms of employment. Between
2001 and 2007, employment in the services sector grew by 17 percent, compared
to agriculture sector’s 9.4 percent and manufacturing’s 8.5 percent. The services
sector has overtaken the agriculture sector to account for the largest share in total
employment. In 2013, services accounted for 53 percent of total employment,
while manufacturing registered 8 percent and agriculture with 27 percent. The
services sector is the most skills-intensive, with 70 percent of its workforce
composed of skilled workers. Manufacturing and agriculture sectors have
employment skilled worker share of 60 percent and 26 percent, respectively.
In terms of growth in gross value added (GVA), the sectors that exhibited
the highest growth are finance, private services, mining and quarrying, and the
14
transport, communication and storage, with respective values of 80.5%, 66.4%,
58%, and 56.6% from 2002 to 2009.8
B. Transport Infrastructure in the Philippines
1. Land transport
Many studies investigating on the impact of land infrastructure to growth
in the Philippines focus on roads, specifically paved roads, as better roads lower
transactions costs of producers by making inputs more accessible and produce
more easily marketed. Manalili and Gonzales (2009) found that road
infrastructure as well as irrigation facilities contribute to farm profitability and
productivity in the Philippines. Farmers tend to purchase and use more nitrogen
fertilizer as production prices become lower due to decreased transport costs
from good roads. Olson (2008) found that improvements in road connectivity led
to changes in investment, production system, employment, transport service
supply and demand in a fishing community in the Philippines. Improved road
accessibility translated to a 35% decrease in fuel consumption, a 44% decline in
maintenance costs and a 40% reduction in travel time according to householdfirms in the fishing community. Before the road project in 1995, about half of the
medium-sized vessel owners delivered fish to Manila while the rest delivered to
Infanta, a small neighboring municipality. After the road project was completed,
almost all of those vessel owners delivered fish to Manila and a large
neighboring city. Lucena, A more recent study by Llanto (2012) looks into the
impacts of roads to rural output. He estimated that a percentage point increase in
8
Author’s computations from PIDS data, base year 1985
15
the length of paved roads as a ratio to total length of roads is correlated with
approximately Php 285,000 per agricultural worker increase in productivity.
Road infrastructure handles the great majority of passenger and freight
transporters, with about 90 percent of total passenger movement and about 50
percent of freight movement made possible by roads. The Philippine road
network is classified broadly into national and local roads stretching more than
200,000 kilometers. National roads account for 15 percent while provincial and
municipal roads account for around 24 percent of total road length. Road paving
extends about 4 percent annually on the average, and at this rate, the government
will take two decades before paving 80 percent of all Philippine roads
(Paderanga, 2007).More recent data show that the annual increases in paved road
length is consistent with the 4% average growth, with only the year 2010 as an
exception with 7.38% increase in paved road length.9
2. Maritime transport
While roads are factors affecting economic activities inside the islands,
maritime shipping is cited as the backbone of inter-island and international trade.
The Philippines has 414 operational ports all over the country connecting
production and consumption centers. The Philippine Ports Authority manages 19
ports of entry and 59 sub-ports of entry. Shipping connectivity is an important
determinant of trade costs, and is the major infrastructure by which the
Philippine islands are linked. In 2006, the predominant mode of transport was by
road carrying about 1.71 billion passengers (93.14%) and 25.9 million tons of
9
Department of Public Works and Highways
16
freight (58%), with maritime transport at 1.22% and 42% respectively
(UNESCAP, 2012).
The biggest bulk of domestic commodity trade in the Philippines is
transported via marine vessels. Products which dominate domestic trade via
water transport are food and live animals, inedible crude materials, mineral fuels
and related materials, and manufactured goods which are classified chiefly by
material. In 2013, more than 190,000 tons of food and live animals were shipped
throughout the archipelago, valuing more than six billion pesos in trade. The
value and quantity of trade by water as mode of transport is the largest among all
modes of transport across the regions. Metro Manila has the highest domestic
shipping of goods, with CALABARZON coming in at second. Domestic
maritime trade in the Western Visayas, Central Visayas and Northern Mindanao
regions have also shown high values. Cordillera, Ilocos, and Cagayan Valley
regions registered zero domestic trade via marine transport in 2012 and 2013.10
3. Air transport
Airports in the Philippines are classified into four categories according to
capacity and flights. The Civil Aviation Authority of the Philippines (CAAP)
lists 11 international, 14 principal class 1, 19 principal class 2, and 42
community airports, which gives a total f 86 airports in the country. International
airports are those with border control facility used for international flights. NAIA
handles the highest volume of passenger enplanements, followed by the MactanCebu international airport. Principal Class 1 airports are airports used for
domestic flights serving jet aircrafts such as B737 and A320 with passenger
10
Commodity Trade Flows, NSCB
17
capacities of more than 100. Among the airports falling under this classification
are the Tacloban, Nagaand Tagbilaran airports. Principal Class 2 airports are
those used for domestic flights serving propeller aircraft or jet aircrafts smaller
compared to Class 1. The capacity of Principal Class 1 airports range from more
than 19 to less than 100 passengers. Examples of these airports include Baguio,
Basco, Caticlan, Marinduque, and Siargao.
Following the industry deregulation of air transport, air traffic has
increased over the past years. The rise of low-cost carriers has also driven
competition in the market once dominated by the Philippine Airlines (PAL).
However, PAL is still the largest supplier of air services despite the recent
deregulation of the industry. PAL operates domestically in 1 of 16 regions and
is the sole carrier of the Philippine flag. The second leader in the airline industry
is Cebu Pacific, which offers low-cost travel. There are also some 10 small
players in the industry.
Air traffic, domestic and international flights combined, has shown
steady growth over the past ten years. The growth of air passenger volumes
from 2003 to 2013 have been positive. Air passenger volume has, on the
average, been increasing by 11% annually over the period.
A study of the economic benefits of air transport in the Philippines
(Oxford Economics, 2011) reveal that 0.4% of Philippine GDP is supported by
the aviation industry, with 0.3% of the workforce under the industry. Air
transport enables long-term economic growth through global connectivity. In
2010, there were 56 routes connecting major airports in the Philippines to urban
18
agglomerations around the world. On average there were four outbound flights
per day along these routes.11 The Philippines is connected to cities of more than
1 million inhabitants through 18 of these routes. Also, the cost of air transport
services in real terms has decreased by about one percent annually over the past
40 years, resulting to the rapid expansion in trade volumes in the same period. A
cross-country statistical analysis of connectivity and productivity by Oxford
Economic Forecasting in 2006 estimated that a 10% improvement in the
Philippines’ connectivity relative to GDP would result to a PHP 5.1 billion per
annum increase in long-run GDP.
The importance of the air transport network of the Philippines to the
international market has been emphasized in studies linking air accessibility to
growth; however, the interregional connections facilitated by air transport inside
the country has been of lesser focus to researchers.
C. The Economic Importance of Transportation
The importance of transportation has often been emphasized in studies
tackling economic growth and welfare of populations. Efficient transport
systems provide economic and social opportunities and benefits that spread
throughout the economy. On the other hand, “deficiencies in infrastructure
translate into poorly functioning domestic markets with little spatial and
temporal integration, low price transmission, and weak international
11
Route and frequency figures were obtained by Oxford Economics from airlines schedules
published by SRSAnalyzer. Urban agglomerations defined as contiguous built-up areas of at least
1 million population.
19
competitiveness” (Llanto, 2012). Rodrigue, Comtois and Slack (2007) classify
the economic impacts of transportation to be direct and indirect. Direct impacts
refer to accessibility change where transport enables larger markets and enables
time and cost savings. Indirect impacts are related to the economic multiplier
effect where the price of commodity drop and their variety increase due to firm
competition. Transportation systems can impact transport supply and demand at
both microeconomic (sector-wise) or macroeconomic (whole economy) levels.
Rodrigue, Comtois and Slack (2007) enumerates the economic benefits of
transportation into:

Direct transport supply: income from transport operations
(fares and salaries); access to wider distribution markets and
niches

Direct transport demand: improved accessibility; time and
cost savings; productivity gains; division of labor; access to a
wider range of suppliers and consumers

Indirect microeconomic: rent income; lower price of
commodities; higher supply of commodities

Indirect macroeconomic: formation of distribution networks;
attraction and accumulation of economic activities; increased
competitiveness; growth of consumption; fulfilling mobility
needs
Distinguishing the impacts on the macroeconomic and microeconomic
levels is necessary. At the macroeconomic level, transportation and the mobility
20
it provides are linked to a level of output, employment and income within an
economy. At the microeconomic level, or the importance of transportation for
specific sectors of the economy, transportation is linked to producer, consumer
and production costs. Thus, the importance of specific transport activities and
infrastructure can be assessed for each sector in the economy.
The positive link between the development of transport systems and
economic growth on the national scale is widely acknowledged; however, the
relationship between transportation and regional economic growth is difficult to
establish and has been debated for many years. This is due to the fact that
transport alone is a necessary but insufficient condition for growth. Moreover,
the lack of transport infrastructure is seen as a constraint to economic growth.
According to Rodrigue, Comtois and Slack (2007), the complexity of identifying
the relationship between transportation and economic growth is due to the
variety of both the timing of the development and the type of impact:

Timing of the development varies as the impacts of
transportation can either precede, occur during or take place
after economic development. Therefore, the lag, concomitant
and lead impacts make it difficult to separate the specific
contributions of transport to development. Each case study
appears to be specific to a set of timing circumstances that re
difficult to replicate elsewhere.

Types of impacts vary considerably. The spectrum of
impacts ranges from the positive through the permissive to the
21
negative. In some cases transportation impacts can promote, in
others they may hinder, economic development in a region. In
many cases, few, if any, direct linkages can be clearly
established.
D. Accessibility and Location
There is a need to distinguish between ‘access’ and ‘accessibility’: access
is not accessibility. To illustrate the point, consider the public highway system –
it can, in theory, be accessed by anyone. Truck delivery companies as well as an
individual on a car can access it. Therefore, wherever an individual is located,
access is the same provided that it is possible to enter or to exit. On the other
hand, accessibility is not uniform. Accessibility may vary according to where
one is in the transport system. Accessibility appears to be a relative concept. The
location which is positioned at the center in relation to the network is always the
more accessible. In the public highway example, the location most accessible
would be the one in the intersection of two highways. A less accessible location
is one with only a single road connected to it.
The location of economic activities is highly dependent on the different
levels of accessibility required by the various industries and businesses.
“Because of the level of accessibility they provide, transport infrastructures
influence the setting of economic activities (p.90, Rodrigue, Comtois and Slack,
2007).” It is important to note that not all economic activity is dependent on a
high level of accessibility. Each type of economic activity has its own set of
22
requirements for locating in a particular area or region. Nevertheless, some
general factors considered in location can be identifies by a broad classification
of the economic sectors:
Primary economic activities. Natural endowments and resources
determine the location of this type of economic activity. Examples of sectors
with primary economic activities are mining and agriculture. Mining is
constrained by the availability and extractability of mineral deposits, and
agriculture takes place where environmental factors such as soil fertility,
precipitation, and temperature determine yield. Primary activities are thus highly
dependent on transportation since they usually locate far from centers of demand.
The capacity to transport raw materials is crucial in developing primary activities.
Secondary economic activities. These have to consider many location
factors depending on the industrial sector, but commonly relate to labor (cost
and/or skill level), energy costs, capital, land, markets and/or proximity of
suppliers. Thus, location is an important factor in cost minimization. A wide
variety of industrial and manufacturing activities fall into this category.
Tertiary economic activities. The capacity to sell a product or service is
their most important location requirement, which necessitates market proximity
the most. Many of these activities are related to retail. Thus, their proximity to
consumers, as well as their level of income, is important and also determines
sales. They mainly focus on maximizing revenues, making location an important
revenue factor. The retail industry has significantly changed with the emergence
23
of large retail stores that maximize sales through economies of scale and local
accessibility.
Quarternary economic activities. These activities do not rely so much on
environmental endowments but are linekd to high-level services such as banking,
insurance, research development, and dominantly the high-technology sector.
The recent developments in telecommunications led to many of these activities
locating anywhere. However, there are still some strong locational requirements
for high technology economic activities which include proximity to highly
qualified and highly skilled labor pool (as well as cheap labor for supporting
services, a high quality of life, proximity to research centers and universities, and
access to efficient transportation and telecommunication facilities.
Accessibility influences location of economic activities by allowing more
customers to be served through an expanded market area, by increasing the
efficiency of distribution in terms of costs and/or time, or by enabling more
people to reach workplaces. The influence of transport systems on the
organization of economic activities can occur in either of the two: dispersion or
concentration.
E. Inter-regional Connectivity of the Philippine regions
The 17 regions of the country show differences when it comes to air
accessibility. This reflects the priorities of the government in the locations of
building infrastructure. The air accessibility of the regions shall be described
based on the movement of goods via the domestic commodity flow data
24
(Appendix A) and on the movement of people via the domestic air passenger
routes data from carrier Cebu Pacific (Appendix B).
National Capital Region
The country’s capital region does not only lead the country in terms of
income, but also in terms of air accessibility. Among all the regions, it is the one
most connected to the rest of the country. Commodities from NCR which are
transported via air reach all the regions in Visayas and Mindanao as well as in
the Ilocos region, MIMAROPA and Bicol Regions. It is important to note that
NCR trades the most with Central Visayas, Davao Region, and Western Visayas.
Available air passenger destinations from NAIA show that NCR has the most
access to the other regions in the country. Of the 16 other regions in the
Philippines, direct flights are served from Manila to 13 other regions. Regions in
which a person from NCR can go to another region through three or more
provinces in that region are MIMAROPA, Bicol Region, Western Visayas,
Central Visayas, and Zamboanga Peninsula.
Cordillera Administrative Region
The landlocked nature of the region makes it highly dependent on road
networks in transporting its predominantly-agricultural goods and people to
neighboring regions, namely, Ilocos and Cagayan Valley. Commodity flow data
and passenger routes data do not show flows of goods and people to and from the
region via air. It appears that the connectivity of CAR to the rest of the country’s
25
regions is only through land transport, where tourists go to Baguio by bus and
vegetable produce are brought to Manila and nearby urban areas by jeepney and
truck.
Ilocos Region
The Ilocos region is bound in the west by sea; however, commodity flow
data does not show figures traded from the region to the rest of the country
through both maritime and air transportation. However, commodity flows from
NCR to Ilocos exists. In 2013, approximately 5.5 million pesos worth of
commodities from NCR entered into Ilocos via air. Moreover, only NCR is the
region accessible to Ilocos via air.
Cagayan Valley
Cagayan Valley, like its neighbor region CAR, is more accessible
through land transport than through air. Available domestic flights from Cagayan
Valley only have Manila as a single destination. The region only registers
commodity flow via air with NCR as well, albeit with relatively small values. In
2013, Cagayan Valley transported through air about 177,000 pesos worth of
products to NCR, without any other region to trade with.
Central Luzon
Central Luzon is not very accessible via air, but it highly engages in
domestic maritime trade. Its biggest consumer market is NCR, with more than
26
half of all commodities from Central Luzon going to the region in 2013. Central
Luzon’s commodity flows heavily use maritime transport, which makes all the
16 regions in the country accessible to it in terms of trade. Other regions which
are primary destinations of goods from the region are CALABARZON, Central
Visayas, Davao Region, and Ilocos Region. In terms of air passenger transport,
Central Luzon is connected only to Central Visayas.
CALABARZON
Like its neighboring region Central Luzon, CALABARZON is not highly
accessible in terms of air transport. However, it also engages in maritime trade
with other regions in the country. The region’s connections to the rest of the
country in terms of domestic commodity flow by water are NCR, MIMAROPA,
Bicol Region, Western, Central, and Eastern Visayas, Zamboanga Peninsula,
Northern Mindanao and Caraga.
MIMAROPA
The region is completely separated from other regions by water, making
it impossible to reach its neighbors through land transport, initially.
MIMAROPA registered a high value of commodity flow via air to NCR, while
having minimal amounts of airborne commodity flows to other regions such as
Western Visayas, SOCCSKSARGEN, Central Visayas, and Northern Mindanao.
MIMAROPA has direct flights to NCR, Western Visayas and Central Visayas.
Providing an efficient transport network to interconnect the island provinces of
27
the region has remained a challenge. The road connecting the Calapan Port to
Roxas Port is part of the Strong Republic Nautical Highway which serves as the
primary link of MIMAROPA to mainland Luzon and the Visayas. The road
network project between San Jose in Occidental Mindoro and Bulalacao town in
Oriental Mindoro shall connect the province of Oriental Mindoro to the nautical
highway for better accessibility to the Visayas and CALABARZON regions.
Bicol Region
Bicol is connected to the other parts of the country through land, water,
and air transport. The primary destination of airborne goods trade from Bicol is
NCR, which received more than 19 million pesos worth of goods from Bicol in
2013. Other markets of air shipped commodities from the region are Western
Visayas and Central Visayas . To a smaller extent, the other regional markets of
commodities shipped via air are MIMAROPA, Davao Region, Northern
Mindanao and Zamboanga Peninsula. Domestic passenger flight destinations
from Bicol are NCR and Central Visayas. The region transport system heavily
relies on road networks and connecting its four mainland and two island
provinces to other regions remains an issue. The Bicol mainland is connected to
Metro Manila via the Maharlika Highway, the main trunkline road.
Western Visayas
Western Visayas is surrounded by water except in Negros Occidental
which is bounded on the southeast by Central Visayas. In terms of commodity
28
flows via air transport, Western Visayas is among the biggest suppliers of goods
to NCR, with a value of more than 100 million pesos going to NCR in 2013. It is
also the region with the most number of connections in airborne goods
destinations. Western Visayan products go to all the other regions in the
Philippines except for CAR, Central Luzon and CALABARZON. Region
destinations with more than a million pesos worth of commodity flows are Bicol
Region, Central Visayas, Eastern Visayas, Northern Mindanao, Davao Region
and SOCCSKSARGEN. Domestic passenger flight destinations from the region
include NCR, MIMAROPA, Central Visayas, Northern Mindanao, Davao
Region and SOCCSKSARGEN.
Central Visayas
The region has among the busiest seaports and airports in the country.
The Mactan Cebu International Airport serves as the airline hub of Cebu Pacific
and Philippine Airlines, with Kalibo also serving international flights. Central
Visayas is connected to Manila and most parts of the country through its airports.
Air passenger destinations from Central Visayas include Central Luzon,
MIMAROPA, Bicol Region, Eastern Visayas, Zamboanga Peninsula, Northern
Mindanao, Davao Region, SOCCSKSARGEN, and Caraga. Central Visayas is
most connected to Western Visayas, where passengers can from Central Visayas
arrive through Boracay, Kalibo or Iloilo airports. Domestic commodity flows
data show that the region engages in airborne trade the most with NCR. Other air
shipped commodity destinations are Western Visayas, Eastern Visayas, Davao
29
Region, Northern Mindanao, Zamboanga Peninsula, SOCCSKSARGEN, Bicol
Region, and to a small degree, Caraga and ARMM.
Eastern Visayas
Four out of the region’s 10 airports operate commercial flights. These
are: Tacloban, Ormoc, Calbayog and Catarman airport. Despite it being
separated to the other regions by sea, the inter-regional connectivity of Eastern
Visayas is very much limited, with air passenger flights only catering to NCR
and Central Visayas. Commodity flows via air are also limited to NCR being the
only destination of products from the region. However, commodity flows
through water transport are active. The most important destination of goods from
Central Visayas is Caraga, where 74 per cent of its goods went through water
transport in 2013. Other region destinations are Northern Mindanao, Central
Visayas, and Central Luzon.
Zamboanga Peninsula
Zamboanga’s commodity flow via air is highly concentrated in the NCR,
with approximately 113 million pesos of goods going to NCR in terms of value
in 2013. Zamboanga is also connected to the rest of the regions in airborne trade,
except for CAR, Central Luzon, and CALABARZON, where zero value of
airborne trade was recorded in 2013. Regions which received at least 100 million
pesos value of commodities from Zamboanga Peninsula include Central Visayas,
ARMM, Davao Region, Western Visayas, and MIMAROPA. Destination
30
regions for air passenger flights from the region are NCR, Central Visayas,
Davao Region, and ARMM.
Northern Mindanao
Northern Mindanao is accessible via air transport to NCR, Western
Visayas, Central Visayas and Davao Regions in terms of domestic flights. Goods
from the regions are able to reach NCR, Western Visayas, SOCCSKSARGEN
and Central Visayas via air. Zamboanga is connected to the Luzon mainland
through the Roll-on Roll-off (RORO) network, linking Zamboanga del Norte
through Negros, Panay and Mindoro to Batangas. This makes it possible for
waterborne trade to flow from Northern Mindanao to regions as far as
CALABARZON. By maritime transport trade links, the region is connected to
all the other regions in the country except for the landlocked CAR and Cagayan
Valley. The most important destinations of commodities from Northern
Mindanao in 2013 are Central Visayas, Western Visayas and CALABARZON.
A huge portion of waterborne trade also flows intra-regionally.
Davao Region
NCR is the biggest market of goods coming from Davao Region, with
approximately 22 million pesos worth of commodities reaching NCR via air in
2013. Other important destinations of goods shipped through air include Central
Visayas, Northern Mindanao, and Zamboanga Peninsula. For air passenger
flights, destination regions from Davao Region consist of NCR, Western Visayas,
31
Central Visayas, Zamboanga Peninsula, and Northern Mindanao. Davao is
considered as the gateway of the Mindanao mainland to Asia. As it is the link of
Mindanao to the export markets abroad, many of the products from neighboring
regions go through the Davao International Airport. Davao also transports
commodities via marine transport to SOCCSKSARGEN, Central Visayas, and
NCR.
SOCCSKSARGEN
Formerly known as Central Mindanao, SOCCSKSARGEN is composed
of South Cotabato, Cotabato, Sultan Kudarat, Sarangani and General Santos City.
Regional linkages in terms of domestic air passenger routes are Metro Manila,
Western Visayas and Central Visayas. Majority of goods from the region that are
transported through air go to Metro Manila, while small amounts in commodity
value reach the following regions: Western Visayas, Central Visayas,
Zamboanga Peninsula and Northern Mindanao. The most important destinations
of goods from the region in terms of water transport are NCR, Central Visayas,
and Zamboanga.
Caraga
Caraga is only accessible from two regions: NCR and Central Visayas for
air passengers. The top two destinations of commodities from the region are
NCR with 2.5 million pesos worth of trade via air, and MIMAROPA with
155,000 pesos in value. Caraga also transports goods through air to Visayas,
32
albeit at very small values. Inter-regional trade linkages via maritime transport is
stronger, as commodity from the region flows to almost all other regions in the
country, with the biggest markets being the Eastern Visayas and Central Visayas
regions.
Autonomous Region of Muslim Mindanao
ARMM is the only region in the Philippines which lack direct transport
connections to Metro Manila, even for air transport services. Air passengers can
only access the region through Zamboanga. Also, the region does not register
any value of commodity flows to all other regions in the country. ARMM
transports its goods via water, nevertheless. Commodity destinations are NCR,
Western Visayas, Central Visayas, and Zamboanga Peninsula.
Apart from PAL and Cebu Pacific, other carriers are emerging by
providing ‘niche’ routes – transversal lines not operated by the airline industry
leaders such as the Zamboanga-Davao route. From the sea and air transport
networks in the archipelago it can be observed that transverse service is very
poor, especially for east-west links perpendicular to the north-south axis. One
must go through Manila in order to reach Davao from Puerto Princesa. While
there exists good inter-island shipping links in the area, airlinks often involve a
transfer in the capital from which the networks are organized. It can be much
easier and faster to fly from Cebu to Seoul or Osaka than from Puerto Princesa to
Zamboanga. Due to the lack of island-connecting bridges except the San Juanico
33
Bridge, inter-island travel must be done either by plane or by boat. Connecting
the Philippine islands through the intergration of the three modes of transport:
road, water, and air remains a challenge.
The Strong Republic Nautical Highway System
That mobility problems across the Philippine regions dampens economic
growth is not unknown to the government. Significant efforts have been exerted
by the previous administration to connect regions separated by sea and long
distances. Former President Gloria Macapagal-Arroyo launched in 2003 the
Strong Republic Nautical Highway (SNRH) System, a collaboration among
shipping companies, motor carriers, trucking and intercity bus companies in an
attempt to improve the connections between roads and major transshipment hubs
through different islands, from Manila to Mindanao.
The Western Nautical Highway is the first SRNH project that was first
built in 2003, which encompasses a total of 703 kilometers and 137 nautical
miles of land and sea travel. Through its road transport and RORO scheme, it
linked together the following regions: NCR (Manila), Batangas City
(CALABARZON), Calapan (MIMAROPA), Roxas (MIMAROPA), Caticlan
(Western Visayas), Dumangas, Bacolod, Dumaguete (Central Visayas) and
Dapitan (Zamboanga Peninsula). After only five years, about 171 business
establishments have opened up in Roxas. Movement of cargo and people
dramatically increased. 12 Also, the new connections facilitated the southbound
12
Passenger and cargo traffic grew at rates of 438% and 677% respectively along the RoxasCaticlan RORO route for the period 2003 to 2006.
34
movement of passengers and freight. Southwestern Mindanao RORO
connections were also built, connecting ARMM to Zamboanga Peninsula.
The Eastern Nautical Highway, also known as the Pan Philippine
Highway or the Maharlika Highway, links Luzon, Visayas and Mindanao
through the integration of intermodal transport, covering a total distance of 2,500
kilometers in the network of roads, bridges and ferry services. The highway was
enhanced in 1997 when 600 kilometers of road from Batangas to Sorsogon was
built with the aid from Japanese government. The Eastern Nautical Highway
connects the via the provinces of Sorsogon, Samar, Leyte, Southern Leyte and
Surigao del Norte. The network extends from the northernmost terminal in Laoag
City and ends in Zamboanga City, Zamboanga del Sur. The Bicol Region, the
Eastern Visayas Region and the Northern Mindanao Region are linked by the
nautical highway.
In 2008, the last main trunk line of SRNH, the Central Nautical Highway,
was established to connect Sorsogon, Masbate, Cebu City, Bohol, Camiguin, and
Misamis Oriental. This nautical highway bridges Manila to Mindanao, as it is
possible to go to Sorsogon from Manila by land transport. The Central Nautical
Highway improved the connection of the following regions: Bicol Region,
Central Visayas, and Northern Mindanao. From Sorsogon, it is possible to reach
other parts of Mindanao through trucks and buses.
In just five years after the establishment of the first part of the nautical
highway system, benefits such as reduced transport costs, increased regional
trade, improved tourism and agricultural productivity, growth in investments and
35
the development of the countryside have already been observed (Basilio, 2008).
This intermodal system involved a huge effort to upgrade road infrastructure and
ports made possible by loans from the World Bank and Asian Development
Bank. The road transport-nautical highway connections reduced travel time by
10 hours and lowered costs of goods by 30 per cent and for passengers using
bus-ferry combination tickts by 40 per cent (Boquet, 2009). The SRNH network
was planned for expansion by the Arroyo government in order to improve not
only north-south links but also east-west links across the islands in the
Philippines.
F. The Air Accessibility-Economic Growth Nexus
Transportation infrastructure serves as an engine for regional growth
through its improvement of the region’s market access and connectivity, as all
economic activities are seen to be dependent on access to workers, input
materials, and customers. On the global scale, significant changes in business
operations and markets over the years has required faster long-distance transport
services. This has propelled the importance of air transport.
Air accessibility contributes to regional economic growth through five
channels: (1) trade in services, (2) trade in goods, (3) tourism, (4) business
investment and innovation, and (5) productivity. The connections created
between areas and markets through air transport services represent an important
infrastructure asset that generates benefits through enabling foreign direct
36
investment, business clusters, specialization, and other spillover effects on the
Philippine economy's productive capacity.
1. Trade in Services
Increase in air accessibility contributes to growth in the services sector by
facilitating trade in services across regions. The services sector benefits from
increased air accessibility due to its heavy dependence on efficient transport for
delivering quality service. Air transportation is the fastest way that trans-regional
transactions which require face-to-face contact can be executed. Highproductivity subsectors such as financial intermediation rely on efficient mobility
that can only be provided by air services. The relationship between the services
sector’s share of GDP and income per capita were investigated by Eichengreen
and Gupta (2009), and they find a positive correlation overall. This positive
relationship between the services sector share in the economy to income growth
can highlight the importance of air accessibility as one of the drivers of the
growth of the services sector.
It is important to note that trade in services (as well as in goods) may be
(1) intra-regional or (2) extra-regional. We are interested in extra-regional trade
in services, as this is directly affected by aviation being the fastest and most
reliable mode of long-distance transport. According to Jensen (2013), who takes
off from the General Agreement on Trade in Services of WTO, there are four
modes in "trade in services":
37
Mode 1: cross-border provision, for example when software is
produced in one country and shipped via the Internet to
another.
Mode 2: consumption abroad, for example when a vacationer
travels to a resort in another country and purchases hotel
accommodations, meals, and other services there.
Mode 3: commercial presence in a foreign country, for example
when a restaurant chain opens a branch outside its home
country.
Mode 4: temporary movement of natural persons across borders,
for example when a business consultant travels to visit a
foreign client.
Putting the definitions in the context of inter-regional trade, it can be said
that the financial sector may be benefiting from the improved accessibility of
Philippine regions by making it easier to open branches in places outside Manila
and Luzon. Also, though business trips do not directly contribute to GVA of the
services sector, the resulting business deals contracted through face-to-face
contact with clients help boost productivity in this sector. As with other services
industries, air accessibility makes it easier for managers and executives to
oversee far-flung operations, which infuses efficiency into those operations.
Apart from banking and finance, other sectors commonly mentioned in literature
on the air accessibility-employment growth correlation include other specialized
38
high-value, knowledge-intensive and globally-connected services such as legal,
IT, consultancy, business management, chemical, and the professional, scientific
and technical (PST) sector.
As literature on this subject study air accessibility-economic growth
relationships of other countries (US, UK, China), it is necessary to identify
which sectors are potentially traded by Philippine regions. By adopting a
geographic concentration approach by Jensen (2013), the researcher was able to
identify which sectors are traded by the regions,"tradable sectors", according to
location quotient in employment. Values far greater from 1 indicate
'exportability' of the sector's products, while values closer to zero indicate that
the region is more likely to import products of that particular sector. NCR
exhibited industrial concentration in services sectors such as information and
communication, finance and insurance activities, real estate activites,
professional, scientific, and technical activities, and administrative and support
service activities. CAR, Bicol, Davao, and Caraga have high location quotients
in mining and quarrying.
2. Trade in Goods
Air transport facilitate the movement of goods as well. Perishable,
compact, light and high unit value goods are transported via air freight. In a
global scale, air accounts for only 0.5% of global trade volume, but air freight
carries 34.6% of the total value of global trade (Oxford Economics, 2009).
Balisacan and Hill (2007) highlights the need of the country to immediately act
towards modernizing its air cargo services, as it can reduce transport costs which
39
will increase trade volumes in turn. A study by Leinbach and Bowen (2004)
reveals that air cargo is one of the most important infrastructure services for
manufacturers with internationalized production networks.
The supply
management strategies of private businesses require air cargo services that allow
electronics firms to respond effectively to global demand.
Transportation influence the trade in goods through logistics. Logistics is
a process of moving and handling goods and materials, from the beginning to the
end of the production, sale process and waste disposal, to satisfy customers and
add business competitiveness. Transport system is among the most important
activity in business logistics systems. At least one-third of the expenses of
logistics costs are spent on transportation. 13 Transportation plays a connective
role among the several steps that result in the conversion of resources into useful
goods for the consumer. The production, storage, transportation, wholesaling,
and retail sale, production/manufacturing plants, warehousing services, and
merchandising establishments are all about doing transportation. Air freight
logistics are needed by many industries and services to complete their supply
chain and functions. It makes possible delivery with speed, lower risk of damage,
security, flexibility, accessibility and good frequency for regular destinations, but
with the disadvantage of high delivery fees. Air freight logistics is only selected
13
Tseng, Y., Yue, W.L. and Taylor, M.A. (2005) The Role of Transportation in Logistics Chain.
Eastern Asia Society for Transportation Studies, Vol.5, pp.1657-1672.
www.siam.orgjournalsplagiary1657.pdf
40
as a mode “when the value per unit weight of shipments is relatively high and the
speed of delivery is an important factor”.14
There are different transport modes: via rail, road, ship, or air. Choosing
the appropriate mode requires the knowledge of costs associated with different
transport modes. Air freight might be more expensive than land transport but the
storage cost might be less. Thus in terms of total cost, air freight might be the
most reasonable transport mode for a particular transport purpose, for example,
transport of fresh seafood. In the Philippines, the mode of transportation which
dominates inter-regional trade is marine. However, based on value-to-volume
ratios, air transport carries goods which have higher unit value: goods
transported through water were worth Php13,025 per ton, while goods
transported via air were worth Php39,908 per ton.15In 2003, NCR had 52% of the
total national domestic trade transported through air, followed by Central
Visayas with 15%. Food and live animals comprise 55.8% of all air transported
goods by quantity and 38.8% by value. Other products which have high value
shares in all domestically transported goods by air are miscellaneous
manufactured articles (22.6%), and machinery and transport equipment (17%).
By 2013, the composition of goods transported via air changed. 16 Food and live
animal products’ share in quantity and value dropped to 32.6% and 23.54%
respectively. Chemical and related products occupy 20.63% in quantity and
15.58% in value of total air-transported goods, while miscellaneous
14
Reynolds-Feighan A.J. (2001) Air freight logistics, In A.M. Brewer, K.J. Button and D.A. Hensher
(eds.), Handbook of Logistics and Supply-Chain Management, Elsevier Science Ltd., UK,
431-439
15
Author’s computations based on PSA 2003 Commodity Trade Flow data.
16
Author’s computations based on PSA 2013 Commodity Trade Flow data
41
manufactured articles comprise 22.28% in quantity and 24.15% in value share.
Machinery and transport equipment share in value rose to 20%. The sectors
producing these products and its industry linkages are expected to respond to
increased air accessibility of Philippine regions more than the rest of the
economy.
3. Tourism
Improved air accessibility contributes to economic growth through
tourism. Tourists' demand for various goods and services further increase
employment and output in the tourism and travel sectors. Air is the most
important transport medium for tourism, as 98.2% of foreign visitors arrived to
the Philippines via air in 2009 (Oxford Economics).
4. Business Investment and Innovation
Improved air accessibility of the regions provides businesses greater
access to markets outside their locality. This encourages specialization in the
business processes of firms and innovation through competition among firms
which have access to similar markets. Improved accessibility through air can also
impact the economy by making foreign direct investments easier to do in the
country. The increased passenger traffic and trade that accompanies accessibility
of markets make an environment conducive for foreign firms to operate in. A
study by the IATA measures a 0.07% increase in Philippines GDP for every 10%
increase in air connectivity per Php billion of GDP of the Philippines.
42
5. Productivity
Productivity is commonly defined as a ratio of output to input.
Productivity can be raised by increasing output while maintaining the amount of
input, by producing the same output while minimizing input, or via changes to
both output and input such that the ratio of output to input increases. Aldstadt
and Weisbrod (2012) mention that improvement in the performance of
transportation facilities and services can enhance productivity of firms and
businesses in two ways. First, it can improve productivity by reducing time
and/or expense costs incurred during business operations. This consequently
grows productivity by lowering the value of the denominator of the ratio. Second,
it can enlarge market access or connectivity, which raises the numerator while
the denominator remains constant or grows proportionally less than the
numerator. The effects can occur as long as economies of scale and other
business operating efficiencies exist as consequences to access to bigger markets.
Krugman (1991) describes the relationship between market scale and economic
productivity through the occurrence of “agglomeration economies”. If imperfect
competition exists, regions naturally develop differentiated industry mixes,
resulting a disproportionately large concentration of some economic activities.
This “clustering” is typically enabled by access to larger markets, bringing
demand for greater product variety, thus enabling firms to achieve increasing
returns to scale. Increased market access makes it possible for firms to spread
fixed cost over a wider base to reduce unit cost, obtain cost and quality benefits
associated with greater ability to acquire specialized labor and materials, and
43
receive potential knowledge spillovers in the form of technology development
associated with clustering.
Stilwell and Hansman (2013) identify the mechanisms by which air
transportation potentially impacts productivity: (1) comparative advantage, (2)
economies of scale, (3) logistics improvements, (4) location choice, and (5) faceto-face communication. Air transportation allows for concentration of resources
in those regions where production is most efficient, which leads to increases in
productivity due to specialization. It also enables producers to target wider and
new markets, making it possible to lower input costs due to bulk purchases. Air
transportation also lowers logistics costs in cases where goods are of particularly
high value. High value goods usually incur significant holding costs, defined as
the value of the good multiplied by its depreciation over time, and it can be
cheaper overall to ship smaller quantities of such goods at frequent intervals by
air transportation. Air freight is more expensive than other modes, but the
savings from educing holding/storage costs can outweigh higher shipping costs
in some products. Face-to-face communication is observed to be important for
the services sector. Firms locate near good air service especially where there are
many destination reachable by non-stop flights. This reduces average travel time
and reduces the chances of delays incurred from stopovers.
44
G. Previous Work on Air Transport and Economic Growth
Much of the literature in assessing the role of infrastructure in regional
growth focused on land transport, with conclusions affirming location and
agglomeration theories. However, air transport deserves a rightful place in the
field, as it is distinct from land transport insofar as it is a more technologically
advanced transportation service. According to Debbage (1999), there are two
ways by which air transportation affects the regional economy. First, the
construction of an airport is a direct investment in the regional economy. An
airport sources most of its employment in the surrounding area. Moreover, the
multiplier effects of a large investment such as the airport may extend to sectors
such as wholesale goods and ground transportation. Second, air transportation
makes a region more accessible. This influences a region’s economic linkages
with other regions and alters regional competitiveness. Transportation
infrastructure – regional development relationship can be classified as nonspatial or spatial. In the non-spatial nature, infrastructure investment affects the
aggregate levels of economic activity in an economy. Spatial effects deal with
the role of infrastructure in differentiating performances between regions.
Numerous studies have focused on air transport in estimating the
relationship between infrastructure and regional growth, and empirical
methodologies always has to deal with the characteristic of “bi-directionality” or
“reverse causality” between air traffic as a proxy for air transport and gross
regional product and employment as measurements of regional growth.
Breuckner (2003) estimates the relationship between air service and growth of
45
US metropolitan areas by using instrumental variables to control for the
circularity of the relationship between air traffic and growth. Results show that a
10 percent increase in passenger enplanements in a metropolitan area increased
employment in service-related industries by approximately 1 percent. However,
air service growth has been found to have no significant correlation with growth
in manufacturing and other goods-related employment.
Alkaabi and Debbage (2007) focused on a particular sector of the
regional economy in analyzing the role of air transportation in shaping growth.
The links between the employment and number of establishments in the
professional, scientific, and technical (PST) sector and air passenger
enplanements were investigated. Significant relationships were found to exist
between the geography of air passenger demand and spatial distribution of PST
employment and establishments, with regressions giving correlation coefficients
of 0.90, 0.84, 0.68, and 0.39 for air passenger traffic and (i)professional,
scientific, and technical establishments; (ii) high-technology establishments;
(iii)professional, scientific, and technical employment; and (iv) high-technology
employment, respectively.
Goetz (2001) identified the relationship between air passenger flow
volume per capita and urban population and employment growth by accounting
for the “two-way road” causality: the effects of air passenger growth on urban
growth as well as the effects of subsequent urban growth to air traffic growth. A
distinguishing feature in Goetz (2001) is that it was able to analyze the time trend
in the causal relationships. Significant positive correlation was found betweenair
46
passengers per capita and both previous and subsequent metro area population
change for the 50 largest air passenger cities in each time period, from 1950 to
1987. However, the strength of the bidirectional relationship comparison
remained unclear. The positive link between air traffic and economic growth was
highest during the 1960s, and has become less important ever since.
While literature measuring air transport effects of economic growth via
bivariate regressions abound, a number have attempted to incorporate the
accessibility effects of air transport infrastructure in growth models explaining
regional growth. Blonigen and Cristea (2013) determined the role of air services
in regional development in the context of an exogenous deregulation shock in the
US airline industry. Air traffic is included in an endogenous model explaining
regional growth across metropolitan statistical areas (MSAs). For a given MSA,
a 50 percent increase in the growth rate of air passenger traffic leads on average
to a 0.8 percent increase in the annual population growth rate, while a 50 percent
increase in the air traffic growth rate for a given MSA leads to a 1.45 percent
increase in the annual income growth rate and to a 1.75 percent increase in the
annual employment growth rate. Blonigen and Cristea (2013) performed
robustness checks by investigating the employment effects further: which
industries do air services boost employment? Consistent with empirical literature,
service- and trade-related industries were found to be linked with air traffic
growth. Effects across communities, however, exhibited some differences.
Smaller communities located in close proximity of large hub cities appear to be
less sensitive to local changes in air services.
47
Ozcan (2013) uses panel data to measure the linkage between air
passenger traffic and local employment in Turkey. Employment was classified
into three: goods-related, services-related, and non-agricultural. Ozcan (2013)
used the two-stage least squares method (2SLS) with joint-use airport and
runway as instrument variables. Results show that a 10 percent increase in air
traffic per capita translates to 15,013 service jobs throughout the 13 provinces of
Turkey. Specifically, local employment is increased in the construction,
wholesale and retail trade, TCS, finance, insurance, real estate and business
services, aggregate service-related, aggregate non-agri industries, and in the
occupations clerical, commercial and sales, and service workers.
Green (2007) cites four strands of literature which touches on the topic of
air traffic and regional growth: public finance, development economics,
transportation and agglomeration economics, and airport literature. Green (2007)
builds the growth model by taking note of the following assumptions, ceteris
paribus:
(i) Areas with lower taxes perform better than areas with higher
taxes
(ii) Places with higher levels of government services perform
better
(iii)Places with industrial mixes focused in high employment
growth industries perform better (places concentrating in
business
services
have
performed
better
than
those
concentrating in manufacturing)
48
(iv) Areas with relatively high concentration of college graduates
perform better
Airport activity was used as one of the explanatory variables in a model
explaining growth, alongside human capital, climate, and industrial structure
variables. Green (2007) measures airport activity through four variables:
boardings per capita, originations per capita, cargo volume per capita, and hub
status. Boardings represent the impact of air service arising from business and
tourism, while cargo volume measures the impact of air service arising from
distribution or trade. It is interesting to note that only passenger boardings per
capita and passenger originations per capita were found to be powerful predictors
of population growth and employment growth. Also, the strong positive
relationship only applies in the nation’s largest metropolitan areas. This suggests
that, where airports are constrained by capacity, adding to capacity might have
significant economic growth impact.
Mukkala and Tervo (2012) applied a different approach in addressing the
bi-directionality of the relationship between air accessibility and regional growth.
By executing the Granger technique, the nature of the causality as well as the
differences in the causal relationship in core and peripheral regions were
analyzed. The method is lifted from Button et al. (1999) who conclude that air
traffic leads to growth. Mukkala and Tervo (2012) distinguishes between
prosperous economic centers and laggard regions as it follows Krugman’s New
Economic Geography framework, which is bent on answering the question of
whether reducing transportation costs between core and peripheral regions
49
allows the peripheral regions to capitalize on production cost advantages or
whether economies of scale predominate. While Krugman (1999) explains how
improving accessibility of peripheral regions can lead to further economic
decline, Mukkala and Tervo (2012) recognizes the benefits of improved
accessibility of peripheral regions through air transport infrastructure, “In
peripheral regions, the competitive and locational advantages may be strongly
influenced by airline networks because air traffic may weaken the negative
effects of long distances.” Regions which are more accessible make firms more
productive than firms in regions which are less accessible. Granger causality
tests reveal that regional growth-to-air traffic direction of causality is
homogenous across regions in Europe; however, air traffic-to-regional growth
direction of causality is more evident in peripheral regions than in core regions.
The spatial effects of air accessibility to growth were manifest in studies
citing differences in the relationship of air traffic growth and economic growth
among regions (Green, 2007; Mukkala and Tervo, 2012; Blonigen and Cristea,
2013). Smaller and peripheral regions with less economic activity are affected
differently from bigger economic centers. Crescenzi and Rodriguez-Pose (2008)
formally accounts for the spatial effects of air accessibility in their panel
regression model by including the spillover effects. The endogenous growth
models commonly used in studies assessing the impact of air traffic on regional
growth is extended by including the externalities, which further illuminates the
dynamics of regional growth. Crescenzi and Rodriguez-Pose (2008) argue that
capturing the shorter-run Keynesian effect of infrastructure, or the effect of
50
relocation of economic activities in response to changes in transport costs, is
insufficient in the analysis of the relationship between air accessibility and
growth. It is necessary to “provide a full appraisal of the impact of network
benefits arising when transport infrastructure allows for closer interactions with
economic agents from neighboring regions, thus increasing their interactions and
possibly spreading agglomeration benefits” (p.67). Analysis of the economic
impact of transport infrastructure as providers of connectivity is argued to be
always be placed in a spatial perspective by considering both the impact of
endogenous conditions as well as the conditions or neighboring regions. In the
full appraisal of the impact of air accessibility to regional growth in the EU, the
characteristics of surrounding regions are included to account for spillovers in
structural features/determinants of growth from these regions due to the
increased accessibility provided by air transport. The level of infrastructure has
been found to have a positive effect on regional growth, however, the change in
the level of infrastructure or growth in air accessibility does not. The capacity for
innovation as measured by R&D expenditure-to-GRP ratio performs a significant
role in stimulating growth. Innovative activities are found to have a much larger
effect on regional growth than increases in the air accessibility of regions.
Studies investigating on the contributions of air transportation on the
different economic sectors also abound. Foreign literature cite air accessibility as
one of the factors affecting the logistics and value-added of goods-related
industries. Services-related industries are also influenced by air accessibility by
establishing the coordination of stakeholders in business projects.
51
Larsen (2003) analyzed key performance indicators related to transport
activities, such as transport costs and transport time, in order to investigate how
infrastructure investments could add value to Norwegian fish production. Larsen
borrows from Porter (1985) in constructing the theoretical framework based on
the value chain.17 The value of a product bought by a consumer is the sum of the
values added by each industry where the product goes through to achieve its final
form. According to supply chain literature, the three key outcomes of success in
the supply chain is better, faster and cheaper. Adding value, or improving the
performance in the supply chain, can occur through any of the following: (1)
better quality/services, (2) lower transport costs, and (3) faster transport time.
Faster transport often leads to higher product prices, especially in fish markets.
Better service as flexibility with respect to delivery times and the possibility for
door-to-door transports, when transporting fresh fish, is necessary to deliver
products of high quality. Larsen (2003) concludes that the high time-sensitivity
for the Norwegian fresh products necessitate a more efficient transport system.
Value creation in the fishing sector depends highly on a faster and more efficient
transport technology.
An investigation on the transport costs of fresh flowers in Ecuador by
Vega (2008) uses a case study approach of Ecuador’s value chain and finds that
infrastructure and institutional constraints are observed to have a huge effect on
the efficiency of transport systems. Shortfalls in current airport operations such
as inadequate cargo facilities, runways incapable of accommodating bigger
17
Porter describes it as, “The value chain disaggregates a firm into its strategically relevant
activities in order to understand the behavior of costs and the existing and potential
sources of differentiation.”
52
aircrafts and high costs of air navigation services are among the constraints
found in the country. The US is the number one destination of Ecuador’s fresh
flower exports.18 Retailers are the final stop in the value chain of the flowers.
Retailers include florist shops, supermarket chains, roadside vendors, online
stores, etc. Supermarkets account for almost 40 percent of US flower sales. As
the value of fresh flowers highly depend on the time it takes to reach the
customer from the producer, the value chain structures are of interest. The
increase in air accessibility as manifested by the full deregulation of the aviation
sector as well as the declaration of open skies for all cargo is recommended for
South American countries, according to this study.
O’Connell, Mason and Hagen (2005) studied the expanding role of air
cargo services in exporting agricultural exports from California. Trade of
California’s agricultural produce via air may seem to be a small share of total
trade, but it has been growing and is seen to be the only effective means of
reaching markets overseas for high-value added crops such as cherries,
strawberries, asparagus and a range of fresh organically-raised produce.
Compared to other means of transport, air transport minimizes the risks
associated with improperly shipped edible products. O’Connell, Mason and
Hagen (2005) also finds that air is an overlooked mode of transport. However,
when measured by dollar value, California’s economy was found to be more
dependent on air cargo than on ships, trucks and trains. The reason is that more
of California’s merchandise exports are shipped by air than by either sea or land.
Waterborne shipments only usually account for approximately one-fifth of the
18
In 2006, 63% of flowers from Ecuador were exported to the US.
53
total value of California’s merchandise exports. It has also been noted that
approximately half of all air freight is carried on the bellies of passenger planes.
In the case of perishable food products, the share is even larger because
passenger flights to any particular overseas destination are more numerous and
frequent than all-cargo flight and are thus more attractive for shippers of timesensitive products.
Stilwell and Hansman (2013) finds that the government as well as the
services sector are the largest users of air transportation in the US. Their analysis
draws upon the Bureau of Labor Statistics labor productivity data where air
transport intermediate use, labor productivity, and indices of sector output have
been plotted. Moreover, correlations between air transportation use and labor
productivity was calculated, Results show that some positive correlations of
these two variables were observed especially for services-producing industries.
Goods-producing sectors of the economy, meanwhile, show negative
correlations. The Publishing industries (except Internet) sector reveals the
strongest positive correlation between air transportation use and labor
productivity, followed by Retail Trade, Food Services, and Accommodation
sectors. Industry sectors which reveals negative correlation with air transport use
include many goods-related sectors such as paper manufacturing, chemical
manufacturing, plastics manufacturing and wood products.
54
H. Theoretical and Empirical Issues
The development of infrastructure by the government originates from the
perceived potential benefits it provides as one of the assumed drivers of
economic growth. Lewis (1998) presents three ways by which infrastructure
influences the economy: (1) as an ‘unpaid factor of production’, directly
generating improvements in output, (2) as an ‘augmenting factor’, improving the
productivity of both capital and labor, and (3) as an incentive for the relocation
of economic activity. Aschauer (1989) attempted to explain the positive effect of
infrastructure endowment on the regional economy through labor costproductivity ratio effects. In Aschauer’s framework, differences in the levels of
national output can be explained by the relative differences in the stocks of
public infrastructure and private capital. Increasing the stock of public
infrastructure boosts capital productivity in the private sector while increasing
labor costs at a lower rate. This labor cost-productivity ratio serves as proxy to
the region’s competitiveness. In areas where productivity is greater than labor
costs, income and number of jobs will increase, stimulating inward migration
and capital inflows. Improving infrastructure endowment thus results to
productivity exceeding labor costs. Studies based on Aschauer’s framework
produced results estimating the return to public investment at values exceeding
100 percent (Holtz-Eakin 1993; Glomm and Ravi-Kumar 1994).Another
approach in explaining the role of infrastructure investment in better economic
performance is the classical location theory: locations with better accessibility
and lower transport costs have an advantage over other locations. Using duality
55
theory, Seitz and Licht (1995) proposed yet another perspective in identifying the
link between transport and regional growth: infrastructure investments improve
the competitiveness of regions by reducing production costs as well as transport
costs.
These results were met with a number of theoretical and empirical
criticisms. Vandhoudt et al (2000) has questioned the causality from public
investments to growth and found that growth stimulates public investment, not
the other way around as earlier concluded by Aschauer. Gramlich (1994) found
Aschauer-style analyses to contain significant measurement inconsistencies, and
also challenged the direction of causality of Aschauer’s regressions. Results from
analyses done at the micro-level by Button (1998) and Vandhoudt et al. (2000)
revealed that Aschauer’s rates of return to public investments are implausibly
high.
The direct relationship between accessibility and economic growth has
shown significant weakness when the simplified spatial model of the classical
location theory is replaced by a more realistic view of the territory and its
economic geography. Banerjee et al. (2012) finds that the accessibility of
counties in China to transportation networks does not affect income significantly.
The elasticity of per capita GDP with respect to distance from historical
transportation networks is approximately -0.07.Overall transport costs have also
become an increasingly smaller portion of total industrial cost (Vickerman et al.
1997), thereby decreasing the importance of transport costs as a source of
competitive advantage in regions. Furthermore, increases in accessibility through
56
infrastructure investments has exacerbated regional inequalities due to the
damaging effect to lagging regions of opening up to central regions. Increased
competition with firms in lagging regions pushes down the region’s economy
more.
Lakshamanan and Anderson (2007) observe that a huge number of
studies examining transport infrastructure productivity produce results where the
return to transport capital varies in a disturbing way. Productivity of
infrastructure across studies was found to differ sharply for the same country, for
countries at comparable stages of development and for countries at different
stages of development. Thus, Lakshamanan and Anderson ask: Are key
transport-economy linkages impossible to be modeled in the macroeconomic
level? One reason cited for the lack of consistency among empirical efforts in
estimating how transportation infrastructure contributes to economic growth and
productivity is related to how transport infrastructure is identified and measured.
Gramlich (1994) has highlighted the same point that the lack of an agreed
definition about the concept of infrastructure may have resulted to significant
measurement inconsistencies.
In this regard, Lakshamanan and Anderson (2007) argue that the
perspective to be taken in studying infrastructure should be according to one of
the following properties as mentioned in Johansson (2007):
(i) The capital value of infrastructure objects and the sum of such
values, where the capital values are included as production factors
57
in models that apply production, cost and profit functions to
determine the infrastructure impact on the economy.
(ii) Physical or tangible properties of infrastructure objects and of
infrastructure networks. Such measures include a specification of
capacity such as runway or road length and flow capacity and
transport costs.
(iii) Compound measures of physical and value properties of a network,
such as connectivity and accessibility of nodes to other nodes.
Accessibility measures provide a way to describe interaction
opportunities.
Johansson (2007) classifies attempts to model and estimate the role of
transport infrastructure into two. In the first approach, transport infrastructure is
measured by capital value, as one form of public capital. This relates to the
general question: Is public capital productive? Examples of studies along this
line of thought can be found in Aschauer (1989) where an aggregate productionfunction model of the US economy is formulated and in Aschauer (2000) with an
aggregate model for macro regions. The second approach is to represent
transport infrastructure in terms of its physical attributes. This approach is
commonly applied to regional cross-sectional or panel data from a set of regions.
In this perspective transport infrastructure may be represented by a variable such
a highway density, air trafficor degree of agglomeration. These two approaches
used in measuring the growth and productivity effects of infrastructure capital
are distinctly different. Infrastructure capital fails when applied to different
58
investments or different regions because of its one dimensionality in
measurement.
It can be said that aggregate models provide information that is relevant
in the macro perspective, by estimating effects which reflect consequences
attributed to an “average” infrastructure investment project. As with regard to
using GDP as dependent variable vs using sector-specific output values, the
meaningfulness of results has to rely on the law of large numbers.
Defining air transport infrastructure not as mere capital but as
accessibility measures facilitates the assessment of air transport and regional
growth linkages. Economic growth literature does not dwell much on the cost
production framework of air transport infrastructure, it focuses on the market
interactions between regions as facilitated by increased accessibility. The
economic activities which spring from better accessibility of regions further are
found to create impacts on population, employment, and income. However,
estimation results for the “average” region in a country differ in comparison to
appraisals of individual regions according to size, initial wealth, and air
passenger demand level. These observations call for a more thorough
investigation of the dynamics between air accessibility and regional growth, and
the interactions between economic factors within regions as well as across
regions shall be important in assessing the role of air accessibility in regional
growth.
59
I. Synthesis of the Literature
A significant number of studies on the impacts of air accessibility to
economic growth have been conducted; however, most, if not all, existing
literature focus on developed countries such as the US and in Europe. A study to
measure the air accessibility-economic growth relationship is yet to be done in a
developing country, much less an archipelagic country, such as the Philippines.
While there exists a study on the productivity impacts of air connectivity
involving the Philippines, the analysis is only on the international level. There is
a need to conduct analysis of air accessibility impacts in the sub-national level,
as differences in accessibility does not vary only across countries, but also
across regions within a country. Explaining the highly variable growth
trajectories of the regions in the Philippines has been of interest to academics
and policymakers alike, but as far as the researcher probed through related
literature, there have been no attempts to measure the regional growth effects of
air accessibility in the country. Regional and provincial growth differences in
the Philippines were modelled in regressions including education, health, trade,
and population, among others, as explanatory variables. However, there is a lack
in the literature of studies using air transport (either in terms of infrastructure or
air traffic) as a possible predictor of regional growth in the Philippines. In
literature that includes infrastructure impacts on regional growth, Philippine
studies focus on land transport such as road density and road accessibility.
Roads have been found to have significant positive effects on trade and
agricultural productivity in the country. However, the influence of road
60
networks to economic growth and productivity barely goes beyond the
intraregional level. Investigating the economic impact of transport accessibility
of a country at the interregional level has never been done in the Philippine
context.
Past studies have highlighted that transport, specifically in air, may have
positive macroeconomic and microeconomic effects, and that it affects growth
in the national, aggregate level of the economy. However, the economic growth
impacts of lower transport costs brought about by more efficient and connected
transport systems at the regional level have been less than conclusive. More
specifically, the capacity of air accessibility of regions to affect regional growth
in the Philippines remains a question. Related literature have used air traffic and
cargo volume data to measure air transport and population, employment, and
income variables to represent economic growth in regression models that
explain metropolitan and regional growth. It has been established that while
efficient transport infrastructure and networks contribute to growth, it is not
sufficient. Other factors are necessary to explain differences in growth across
regions.
From previous works it has been known that the air accessibility- growth
relationship is a self-reinforcing one. This led researchers to use econometric
methods such as the two-stage least squares in order to address this endogeneity
between the variables. Through instrumental variables, the effect of our variable
of interest air accessibility on regional growth can be measured while excluding
61
the effect of subsequent growth on air accessibility. Instrumental variables
commonly used in the literature are runway dimensions and population.
In terms of the effects of air accessibility on the different sectors of the
economy, researchers have observed positive significant impacts on the services
sector. More sector-specific studies focused on the PST, finance and business
sectors and also found positive results. In the Philippine setting, the sectors of
the economy linked with increased air accessibility remains unknown. Bulk of
related literature were from developed countries which are technologically
advanced and have more mature financial and business environments than
developing countries such as the Philippines. This gives room for uncertainties
regarding similarities in the linkages of air accessibility to the economic sectors
in the Philippines compared to that in the US, for example. The channels
through which air accessibility impacts regional growth may be different in
developing countries.
62
CHAPTER III
THEORETICAL FRAMEWORK AND EMPIRICAL METHODOLOGY
The theories explaining the dynamics between air accessibility and
regional growth are presented in this chapter. The methodology to quantitatively
measure the relationship between the two as well as the application of the
theories then follows.
A. Theoretical Framework
1. Endogenous Growth Theory
Endogenous growth theory suggest that economic growth is an outcome
generated primarily by internal characteristics of the economic system rather
than the result of the external forces to which it is exposed. It relies on
uncovering the public and private sector choices that cause the growth rate to
vary across countries. The theory implies the possibility of sustained differences
in the levels and growth rates of national income. Because of externalities,
mainly productivity gains obtained from specialized research-driven inputs,
diminishing return to human and physical capital do not occur and neither do
countries converge in income terms. Endogenous growth theory is more realistic
in its core assumptions than neo-classicism with imperfect competition,
increasing returns to scale and international interdependence all playing roles.
Endogenous growth theory explains output as the function of endogenous
factors and considers the positive externalities and spillover effects to the
economy. This study extends the model of Glaeser, Scheinkman and Shleifer
(1995) by including air services as a productivity shifter as applied by Blonigen
63
and Cristea (2013). Regions shall be treated as separate economies with free
flowing labor and capital. Thus, differences in regional growth cannot be
attributed to savings or exogenous labor endowments. In this respect, we can
assume that regions are only different in their level of productivity and quality of
life.
Let the total output of a region be given by:
𝑌𝑖𝑡 = 𝐴𝑖𝑡 𝑓(𝐿𝑖𝑡 )
(1)
where Ait denotes the level of productivity of region i at time t, Lit represents the
population of the region i at time t, and f(.) is a Cobb-Douglas production
function that is common across regions:
𝑓(𝐿𝑖𝑡 ) = 𝐿𝛼𝑖𝑡
(2)
Utility Uit is derived from the labor income earned by the individual Wit
and from the quality of life Vit. We assume that the two components are
multiplicative in the utility function:
𝑈𝑖𝑡 = 𝑊𝑖𝑡 𝑉𝑖𝑡
(3)
Labor income is given by:
𝑊𝑖𝑡 = 𝛼𝐴𝑖𝑡 𝐿𝛼−1
𝑖𝑡
(4)
Quality of life, Vit, refers to a wide collection of location-specific factors.
We assume that it has a negative relationship with the population size of the
region, because of its housing prices, traffic congestion, and crime effects. The
quality of life is also explained by factors exogenous to the production
64
technology such as the local amenities in the area. These factors are contained in
the vector Qit , which gives us:
𝑉𝑖𝑡 = 𝐿−𝛿
𝑖𝑡 𝑄𝑖𝑡
(5)
where δ > 0.
The free mobility of individuals makes utility the same across space at
any given point in time in equilibrium, which also implies the constancy of the
changes in utility over time across regions. Using equations (3) to (5) yields the
following:
𝑈𝑡+1
𝑊𝑖𝑡+1
𝑉𝑖𝑡+1
log (
) = log (
) + log (
)
𝑈𝑡
𝑊𝑖𝑡
𝑉𝑖𝑡
𝑈𝑡+1
log (
𝑈𝑡
𝐴𝑖𝑡+1
) = log (
𝐴𝑖𝑡
) + (𝛼 − 𝛿 − 1) log (
𝐿𝑖𝑡+1
𝐿𝑖𝑡
𝑄𝑖𝑡+1
) + log (
𝑄𝑖𝑡
)
(6)
The above equation must hold for each region. The left side of equation (6) is
identical across all regions. In order for this identity to apply for all i, population
growth in every region must adjust each period such that utility grows at a rate
that is common across all communities. Thus, from equation (6) we can express
the rate of population growth as:
𝐿𝑖𝑡+1
log (
𝐿𝑖𝑡
)=
1
1−𝛼+ 𝛿
𝐴𝑖𝑡+1
[log (
𝐴𝑖𝑡
) + log (
𝑄𝑖𝑡+1
𝑄𝑖𝑡
)] + 𝑘𝑡
(7)
Where kt is a constant.
Rewriting the labor income in equation (4) as an annual growth rate and
substituting for population growth using equation (7), we can derive the
following equation for the income growth at the regional level:
65
𝑊𝑖𝑡+1
log (
𝑊𝑖𝑡
1
)=
𝐴𝑖𝑡+1
[δ log (
1−𝛼+ 𝛿
𝐴𝑖𝑡
𝑄𝑖𝑡+1
) + (α − 1) log (
𝑄𝑖𝑡
)] + 𝛺𝑡
(8)
With Ωt ≡ (α - 1)kt as constant.
The effect of the provision of air transport services comes into the
equation when the determinants of population and income growth are identified
for equation (7) and (8). In order to achieve this, the stochastic process of
productivity and the exogenous factors defining the appealing characteristics of
an urban area should be specified. Initial or base year conditions are commonly
included as main determinants of subsequent growth in productivity and quality
of life, respectively. Because the primary goal of this paper is to measure the
links between air traffic and growth, we assume that:
𝐴𝑖𝑡+1
log (
𝐴𝑖𝑡
𝑄𝑖𝑡+1
log (
𝑄𝑖𝑡
𝐴𝐼𝑅𝑖𝑡+1
) = (𝑋𝑖𝑡 )′ ϓ1 + 𝛽1 𝑙𝑜𝑔 (
𝐴𝐼𝑅𝑖𝑡
𝐴𝐼𝑅𝑖𝑡+1
) = (𝑋𝑖𝑡 )′ ϓ2 + 𝛽2 𝑙𝑜𝑔 (
𝐴𝐼𝑅𝑖𝑡
) + 𝑣𝑖𝑡
) + 𝑣𝑖𝑡
(9)
(10)
Where Xit refers to the vector of characteristics for region i observed in the base
year and AIRit represents the volume of airline traffic in region i at time t. AIRit
serves as proxy for the local aviation network. Equation (9) and (10) are
substituted into (7) and (8) respectively, then structural coefficients are replaced
with their reduced forms and the log form of the main variables are relabeled
with corresponding lower case letters for notational simplicity, we get:
𝑙𝑖𝑡+1 − 𝑙𝑖𝑡 = 𝛽(𝑎𝑖𝑟𝑖𝑡−1 − 𝑎𝑖𝑟𝑖𝑡 ) + 𝑋′𝑖𝑡ϓ + Ԑ𝑖𝑡
(11)
𝑤𝑖𝑡+1 − 𝑤𝑖𝑡 = 𝛽̅ (𝑎𝑖𝑟𝑖𝑡−1 − 𝑎𝑖𝑟𝑖𝑡 ) + 𝑋′𝑖𝑡ϓ̅ + 𝜉𝑖𝑡
(12)
66
We are interested in the value of β. The effect of air traffic growth is
expected to be positive in a regression which explains the rate of growth of
population, per capita income, and employment respectively, across regions.
2. Economic Geography Theory
Economic activities tend to concentrate in certain areas, and many
industries tend to concentrate in a few areas. However, not all of a country’s
population live in one big region. Krugman answers this puzzle through the
economic geography theory. According to this theory there is a tug of war
between forces that tend to promote geographical concentration of economies
and those that tend to oppose it, namely, the centripetal and centrifugal forces. A
selection of some of these forces are presented in Table 1.
Table 1. Forces affecting geographical concentration
Centripetal forces
Market size effects (linkages)
Thick labor markets
Pure external economies
Centrifugal forces
Immobile factors
Land rents
Pure external diseconomies
The centripetal forces are the three classic Marshallian sources of external
economies. Market size effects are in terms of backward and forward linkages.
Sites with good access to large markets are preferred locations for the production
of goods subject to economies of scale (backward linkages), while a large local
market supports the local production of intermediate goods, which lowers costs
for downstream producers. The concentration of industries creates a thick labor
market, especially for specialized skills, leading to job matching. Also,
67
information and technological spillovers may occur with the local concentration
of economic activity.
The centrifugal forces limit the concentration of economic activity.
Immobile factors such as land and natural resources gravitate against the
concentration of production, both from the supply side (some production must go
to where the workers are) and from the demand side (dispersed factor create a
dispersed market, and some production will have an incentive to locate close to
the consumers). The concentration of economic activity increases demand for
local land, making land rents more expensive and thereby providing
disincentives for further concentration. Another effect which may prevent further
concentration is congestion, as many firms and establishments crowd in a single
area.
The role of efficient transport systems in economic growth can be best
observed through its impacts on firms – better transport services yield product
market and labor market effects. When areas are more accessible, the efficiency
of both the backward and forward linkages of firms and the access of the firm to
a labor supply improve. Firms can source their materials and deliver their
products at lower costs if transport infrastructure is more available. Employers
can select from a broader set of potential employees. These factors influence
where firms locate. More firms tend to locate in areas with better transport
infrastructure, leading to increases in output and employment.
Economic geography provides an economic explanation of the spatial
structure of the economy. The economic geography theory basically answers the
68
questions: ‘where do economic activities concentrate?’ and ‘why do some
economies grow faster than others? It incorporates the element of space in the
analysis of economic growth. Economic geography deals with the factors
influencing where economic activities are located. The role of transportation is
emphasized here due to its overarching influence over the location factors,
namely, (1) the site attributes, (2) the level of accessibility, and (3) the
socioeconomic environment.19
Location factors can be subdivided into three general functional
categories:
1. Site. Specific micro-geographical characteristics of the site, including the
availability of land, basic utilities, visibility (prestige), amenities (quality
of life) and the nature and level of access to local transportation (such as
proximity to a highway). These factors have an important effect on the
costs associated with a location.
2. Accessibility. Includes a number of opportunity factors related to a
location, mainly labor (wages, availability, level of qualification),
materials (mainly for raw materials dependent activities), energy, markets
(local, regional and global) and accessibility to suppliers and customers
(important for intermediate activities). These factors tend to have a
regional connotation.
3. Socioeconomic
environment.
Specific
macro-geographical
characteristics that tend to apply to political units (nation, region, and
19
From “Geography of Transport Systems” [book]
69
locality). They consider the availability of capital (investment, venture),
varied subsidies, regulations, taxation and technology.
Improvement in transport facilitates the spillover effects of benefits to the
whole economy from two sources: (1) positive externalities and (2) imperfect
competition. Externalities may be technological, meaning that accessibility
among regions results to direct efficiency benefits to firms in those connected
regions. An example is that freer flow of factors of production through transport
services helps spread technology and knowledge to firms, raising the efficiency
of production of these firms. Externalities may also be pecuniary, meaning that
improvement in accessibility among regions also leads to improvement in
competition in a less than competitive market. Transport improvements let firms
find it easier and profitable to sell in other regions, driving prices down in those
regions. When there are economies of scale in production, transport cost
reductions may reorganize production into larger units supplying wider markets
as firms take advantage of increasing returns to scale.
Linkage effects, which are mediated by transportation costs, are naturally
tied to distance; so is access to immobile factors. That transportation has a
positive effect to economic development is a widely accepted idea, however, it
does not tell the whole story. The positive relationship only refers to increasing
the ‘size of the cake,’ or to the economy as a whole. The regional distribution
impacts, or ‘how the cake is shared’ of improved transport infrastructure is of
higher interest.
70
Figure 1. The transportation-regional growth relationship
Transport Improvements
Product Market
Impacts
Labor Market
Impacts
Existing Firms
Expand
New Firms
Established
Regional Growth
Economic geography literature identifies regions as the core (center of
economic activity) or a periphery. The core is characterized by having a larger
market, while the periphery is characterized by cheaper production costs.
According to Krugman (1991), reduction in transport costs as caused by
improvement in infrastructure has two possible effects: (1) production
concentrates to where it is cheapest and firms transport goods to all markets from
there, or (2) production concentrates to where it can achieve economies of scale
i.e., where the larger market is located. In short, improvement in transport may
lead either to the further concentration of industries in the core, or to the
diversion of industries from the core to the cheaper periphery.
Answering the key question of whether reducing transport costs between
the core and the periphery allows the periphery to capitalize on its production
71
cost advantage, or economies of scale predominate can only be answered by
knowing the initial level of transport costs. The relationship between regional
inequality and transport costs follow an inverted U graph. At initially very high
transport costs, transport cost reduction increases regional inequality. Further
decreasing transport costs, however, eventually decreases regional inequality.
At the regional level, enhancing the accessibility among regions will not
necessarily result to the growth of all regions especially when there is an initial
disparity among them. Economic geography theory posits that effects of
increased transport, specifically air transport in this study, may have different
effects to the core and the peripheral regions in the country.
3. Location Theory
Location theory addresses the important questions of who produces what
goods or services in which locations, and why. The spatial characteristic of
regional growth as discussed by Krugman (1991) in the economic geography
theory traces its roots from the work of early location theorists, most notably
Johann-Heinrich von Thunen (1783-1850) who studied the optimal location of
cities and farms while balancing both land costs and transport costs. In von
Thunen’s model, concentric rings of agricultural activity develop around a city.
The production of perishable goods, or goods that need to reach the market in the
fastest way possible, and other activities such as ranching locate in outer rings. In
Walter Isard’s 1947 work entitled Location and Space Economy, Isard explained
the need to capture the essential impact of space on the location of economic
activities by offering three fundamental insights in establishing the general
72
theory of location. First, the general theory of location and space economy must
be more than the traditional general equilibrium analysis based on perfect
competition. Second, such a general theory should be identical with the general
theory of monopolistic competition; and third, the evolutionary approach should
be helpful to embody dynamic relations in the general theory.
The idea that transport cost influences organization of industries was
strongly supported by this theory, as shown in Isard’s correspondence with
Schumpeter20 in which Schumpeter defended the Hicksian analysis, stating that
“transport cost is implicitly contained in production cost, and thus Hicksian
analysis is sufficiently comprehensive.” Isard replied that production theory
cannot just treat some production costs explicitly and the other production costs
implicitly: “For a balanced treatment, the particular effects of transport and
spatial costs in separating producers from each other must be considered. They
are too vital to be sidestepped through implicit treatment, as Hicks and others
may be interpreted as having done.”
Feinberg (2007) lists the main assumptions of most economic location
theories:
(1) The production process for particular goods is uniform,
independent of locations. For example, corn production requires
certain quantities of a type of soil, farm machinery, fertilizer,
climate, and so on. Thus, some locations are more suitable for
growing corn than others. Factors are non-substitutable such
20
The correspondence can be found in the footnote in Isard’s Location and Space Economy,
page 26.
73
that superior farm machines cannot substitute for scarce land to
grow corn in the city.
(2) The demand for products and the supply or production are
separated. Therefore, transportation costs affect where goods
are produced.
(3) Factors of production such as land and most natural resources
are immobile, while some factors such as capital and labor can
be mobile.
Based on the above assumptions, the theorists can make predictions that
in order to minimize production and transportation costs, certain location will
specialize in the production of particular goods and services and “export” these
goods to other locations.
B. Empirical Methodology
1. Estimating air transport - regional growth correlation
Regional growth is a function of myriad of factors including factor
endowments, infrastructure, and governance, and many others. However, a
commonly significant variable predicting urban and regional growth has always
been the educational level of the population, which actualize the growth potential
of any infrastructure and policy targeted to growth. Education levels are usually
complemented with health indicators to capture the human capital of the area.
The efficient provision of government services is also crucial in utilizing this
human capital to generate output. Studies on the determinants of regional growth
74
in the Philippines, these aforementioned factors have shown significant power in
explaining regional differences in income levels and growth (Balisacan and
Briones, 2006). In considering these, the following model is formulated:
𝑖𝑛𝑐𝑜𝑚𝑒𝑟,𝑡 = 𝑐 + 𝑎𝑖𝑟𝑟,𝑡 + 𝑠𝑜𝑐𝑒𝑥𝑝𝑟,𝑡 + µ
Eq (1)
𝑎𝑖𝑟𝑟,𝑡 = 𝑐 + 𝑝𝑜𝑝𝑑𝑒𝑛𝑠𝑟,𝑡 + µ
where income = GRDP per capita
air = air accessibility (in passengers or cargo)
socexp = LGU expenditure on social development
in region r at time t.
The idea is hardly a new one that there is a circular, self-reinforcing
process in which the decision of individual producers to choose a location with
good access to markets and suppliers actually improves the market or supply
access of other producers in that location, thereby further driving up
productivity in incomes. Areas with high air accessibility are in general
observed to have higher output, while areas with more disposable income tend
to invest in air transport infrastructure more. Endogeneity thus exists. From a
mathematical standpoint, the difficulties that this endogeneity cause for
econometric analysis are that of omitted variables, and that of errors-invariables, or measurement error in the x variables. In each of these three cases,
OLS is not capable of delivering consistent parameter estimates. The general
solution to the problem of endogenous regressors is to use the instrumental
75
variable estimator. A popular form of this estimator that is usually employed in
the context of endogeneity is the two-stage least squares (TSLS). Endogeneity
of the air accessibility and the income variables in this study shall be addressed
by employing a two-stage least squares technique. Previous studies on air
accessibility use runway dimensions, airport plans, and population as predictors
of income and employment growth distinct from effects accruing from previous
income growth. Due to data limitations and the lack of reliability of runway
dimensions to explain air traffic n the Philippine setting, population density per
region is selected as the instrumental variable to be used in running the TSLS.
Earlier studies have shown that more populous areas tend to travel via air more
(Green, 2007; Breuckner, 2003).
Consistent with existing literature on air transport and economic growth,
air passenger volumes and air cargo data shall be used to represent air
accessibility of the Philippine regions. The proxy for human capital, the social
expenditure of LGUs, encompass the budget allocated by the local government
for the health and education projects and programs. These greatly affect the
quality of human capital in the regions, which in turn contributes to the
productivity of labor and the actualization of economic potential of regions with
particular physical endowments.
Fixed effects is employed in the models. The fixed effects model controls
for omitted variables that may differ between regions but are constant over time.
(This would be true if the F-test for all individual effects is significant, that is, pvalue < 0.05α.) It allows changes in the variables over time to estimate the
76
effects on regional productivity of the independent variables, the infrastructure
index and participation in education.
2. Differences in the relationship for core and periphery
In determining the differences, if there are, in the growth impacts of
increased air accessibility in the core and peripheral regions, the following
TSLS equation shall be employed for core and peripheral regions:
𝑖𝑛𝑐𝑜𝑚𝑒𝑟,𝑡 = 𝑐 + 𝑎𝑖𝑟𝑟,𝑡 + 𝑠𝑜𝑐𝑒𝑥𝑝𝑟,𝑡 + µ
Eq (1)
𝑎𝑖𝑟𝑟,𝑡 = 𝑐 + 𝑝𝑜𝑝𝑑𝑒𝑛𝑠𝑟,𝑡 + µ
where income = GRDP per capita
air = air accessibility (in passengers or cargo)
socexp = LGU expenditure on social development
in region r at time t.
The classification of the seventeen regions of the Philippines into core
and peripheral regions were done arbitrarily by the researcher with Krugman’s
definitions as the basis. Core regions exhibit relatively high incomes compared to
peripheral regions, and the economies of peripheral regions usually are
dominated by low-productivity sectors such as agriculture. The regions included
in the core are: National Capital Region, CALABARZON, Central Luzon,
Western Visayas, and Central Visayas. Due to the nature of air accessibility in
CALABARZON and Central Luzon and its close proximity to the region most
77
accessible via air, NCR, the three core regions in Luzon have been grouped into
Greater Luzon. This is to account for the fact that the people and goods flowing
through Metro Manila also pass through CALABRZON and Central Luzon to a
great extent. Therefore, the regions subject to econometric methods in this thesis
are as follows: Greater Luzon, Western Visayas, and Central Visayas. The
peripheral regions to be used in this study include Cordillera Administrative
Region, Ilocos, Cagayan Valley, MIMAROPA, Bicol, Eastern Visayas,
Zamboanga, Northern Mindanao, Davao, SOCCKSARGEN, Caraga, and
ARMM. This classification of regions applies to the first two problems which is
addressed by this thesis.
3. Impact of air accessibility per economic sector
The contribution of air accessibility to regional growth can be understood
in greater depths when the sectors of the regional economy are investigated.
Increase in air accessibility of regions can make the affected regions more
competitive for business attraction. Even where this business attraction does not
increase productivity within an industry, it can shift the mix of industrial activity
away from low-productivity sectors to high-productivity sectors. Also, there is
reason to believe that industry dynamics and productivity growth are two
outcomes of a single underlying process of economic transformation. In order to
capture the economic consequences of air accessibility changes per economic
sector, the relationship of economic activity to variation in worker skills and
accessibility shall be calculated via two equations:
78

Industry Employment Concentration. Equation 2 relates the air
accessibility variables as well as worker skill, a control variable, to
the concentration of employment in a single specific industry. This
measures industry concentration relative to other industries. Using a
panel perspective across all 17 Philippine regions, equation 2 reveals
how air accessibility affects industry concentration i in region r at
time t. Using regional population in the denominator also allows for
the effects of labor participation, household location, and land rent
change.
(𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑟,𝑡 ) = 𝑓(𝑊𝑜𝑟𝑘𝑒𝑟𝑆𝑘𝑖𝑙𝑙𝑟,𝑡 , 𝐴𝑖𝑟𝐴𝑐𝑐𝑒𝑠𝑠𝑟,𝑡 )
Eq (2)
where i = industry
r = region

Industry Labor Productivity. Equation 3 relates air accessibility and
worker skill to labor productivity for a single industry and reflects the
industry’s unique wage and skill mix.
𝑂𝑢𝑡𝑝𝑢𝑡 𝑖
𝑟,𝑡
(𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡
) = 𝑓(𝑊𝑜𝑟𝑘𝑒𝑟𝑆𝑘𝑖𝑙𝑙𝑟,𝑡 , 𝐴𝑖𝑟𝐴𝑐𝑐𝑒𝑠𝑠𝑟,𝑡 )
𝑟,𝑡
Eq (3)
where i = industry
r = region
Air accessibility is measured using passenger enplanements and air cargo
while worker skill level can be proxied by percent of workers with college
79
degree or the average years in school of household heads. Employment data by
industry is measured in thousands as well as population data given the
availability of data. Output is measured in million pesos and are rebased based
on 2006 regional CPI. Due to data limitations, regressions were run using
regional data from 2009 to 2013 only. Also, panel data are unbalanced due to
absence of data for some regions. All variables which are measured per capita
were computed by the researcher by dividing by the population per region. For
example, GRDP per capita is computed by the researcher by getting GRDP data
from the Regional Accounts and then dividing it by the population data. For the
worker skill variable, two separate measures were used: high school and college
education attainment of the labor force per region. Considering the differences in
the educational system of developed countries (to which much of the literature
on air accessibility has been written) and developing countries such as the
Philippines, the college worker skill variable is the most appropriate to use in
measuring the capacity of the workforce to take advantage of opportunities and
translate it to productivity growth.
Estimations are also done while accounting for the differences in the
nature of the regional economic sectors or industries. The Philippine Standard
Industrial Classification (PSIC) provides a list a sections in the economy
according to the kind of productive activities undertaken by these establishments.
The 2009 PSIC is divided into the following industries:

Agriculture, Hunting and Forestry

Fishing
80

Mining and Quarrying

Manufacturing

Electricity, Gas, and Water Supply

Construction

Wholesale and Retail Trade

Hotels and Restaurants

Transport, Storage and Communications

Financial Intermediation

Real Estate, Renting and Business Activities

Public Administration and Defense

Education

Health and Social work

Other Community, Social and Personal Service Activities

Private Households with Employed Persons
Due to the huge differences in the industrial composition of the regions
composing Greater Luzon, the researcher opt to split them into their original
administrative regional division. Thus, for this problem the core regions are as
follows: National Capital Region, CALABARZON, Central Luzon, Western
Visayas, and Central Visayas. Peripheral regions remain to be the following:
Cordillera Administrative Region, Ilocos, Cagayan Valley, MIMAROPA, Bicol,
Eastern Visayas, Zamboanga, Northern Mindanao, Davao, SOCCKSARGEN,
Caraga, and ARMM.
81
In order to solve all three problems, two models per estimation shall be
used: Model 1 which shall use air passenger volumes as proxy for air
accessibility, and Model 2 which shall use cargo in monetary value as proxy for
air accessibility.
The coefficients explaining the relationship of air accessibility and
income, as well as the relationship of air accessibility and employment and
productivity of the services sectors, are expected to be positive.
C. Data Sources
Air passenger data, with domestic and international passengers combined,
were obtained from the Civil Aviation Authority of the Philippines. GRDP and
population data were from the Philippine Institute of Development Studies
database. GRDP from 2003 to 2013 has been rebased using CPI base year 2006
to make values uniform. Employment data by industry classification were
obtained from the Philippine Statistics Authority and Bureau of Labor and
Employment Statistics of the Department of Labor and Employment. Human
capital indicators were obtained from the Family Income and Expenditure
Surveys which are conducted every three years. Local Government Unit
expenditure data by region were taken from the Department of Budget and
Management. Inter-regional commodity trade in terms of mode of transportation
were from the PSA commodity trade flows statistics. The summary of data
description is shown in Table 2 below.
82
Table 2. Data Description
indicates
proxy
source
Income
regional income
GRDP per capita
air
air accessibility
Air passenger volume per
capita
Air cargo in value
Regional Accounts of the
Philippines, PSA
CAAP, MIAA
socexp
quality of life
popdens
educ
population
density
worker skill
prod
productivity
Social development
expenditure per capita
Number of people per
square km of land area
Employees with high
school and college as
highest educational
attainment
Gross value
added/employment
Domestic Commodity Flows,
PSA
Statement of LGU income
and expenditure, BLGF
Regional Accounts of the
Philippines, PSA, DoH
Labor Force Survey
Regional Accounts of the
Philippines
83
CHAPTER IV
PRESENTATION, INTERPRETATION, AND ANALYSIS OF RESULTS
A. Air Accessibility and Regional Growth
Air accessibility, measured by passenger volume, is positively linked with
regional growth, represented by gross regional domestic product per capita, in
the regression of the Philippine regions spanning the period 2009 to 2013. The
human capital variable social expenditures of LGUs per capita also shows
significant positive correlation with regional growth, with a much higher impact
than air accessibility. A one percent increase in the social expenditures of LGUs
per capita on the average region translates to approximately 20 percent increase
in regional income per capita, while a one percent increase in air passengers per
capita on the average region raises GRDP per capita levels by 7.8 percent.
Consistent with literature investigating on the factors influencing regional
growth in the Philippines, social expenditure has a positive relationship with
regional incomes. Air accessibility as represented by air passenger volume
appears to be one of the factors influencing regional growth in the country, as
regression results in Table 3 show a positive, albeit smaller, value of the impact
of the change in air traffic on growth. Air accessibility contributes to regional
growth in the country by enabling the fast transport of people and goods over
long distances. Estimations using air cargo data as the air accessibility variable
84
failed to be significant in explaining income growth in the Philippine regions for
the period 2009 to 2013.21
Table 3. 2SLS Regression Results across all Philippine regions
Dependent Variable:
GRDP per capita
Model 1
Model 2
Coefficient Significance Coefficient Significance
CONSTANT
9.7021
***
9.1434
***
LOG(PASSPERCAP)
0.0780
***
--
--
--
--
-0.0003
not sig.
0.1960
***
0.2630
***
LOG(CARGO)
LOG(SOCEXPPERCAP)
At the level of the firm, it can be said that improvement in transport
accessibility has the effect of reducing the cost of assembling inputs at the
production site and delivering goods to customers, yielding direct efficiency
gains. The positive effect of air accessibility on regional incomes stems not only
from the cost savings of firms, but also from the value-added – both in the
provision of transportation services and in the production of goods and services
which use air freight services. In the macroeconomic perspective, increase in air
accessibility attracts and promotes economic activities in the regions. Time and
monetary costs in material assembly and acquiring raw materials become lower.
Also, the quality of transport service increases. While the economy as a whole
benefits from air accessibility improvements, the presence of differences
21
For estimations using air accessibility as a variable, the proxy air cargo failed to be statistically
significant. A possible explanation for this counterintuitive result is the inaccuracy and
inconsistency of cargo data compiled by the government. Due to this, results from all
subsequent estimations using air cargo data may be less reliable and less helpful in
explaining the link between air accessibility and regional growth. Nevertheless, the
author included it in an attempt to use both passenger and cargo as described in
Chapter 3.
85
between industries and regions may mean that particular regions may not be
benefitting from increases in accessibility of regions.
Air accessibility of the regions links together the factors of production in
a complex web of relationships between producers and consumers. This results
to a more efficient division of production through the exploitation of
geographical comparative advantages, as well as the means to develop
economies of scale and scope. Thus, the productivity of capital and labor is
enhanced with the efficiency of distribution. It can be said that the commodity
market and labor market impacts are felt leading to increases in income.
Increasing air accessibility throughout the Philippine regions boosts growth in
the average region and in the country as a whole.
B. Impact on core regions
While air accessibility is among the factors determining growth in the
average region, the process by which air accessibility influences regional growth
can be understood better when we consider it within the spatial context. The core
and peripheral regions respond differently to increases or improvement in
accessibility. According to the economic geography framework, increasing
accessibility between the core and the periphery may result to either the core
generating more income due to a wider customer market made possible by
transport, or the periphery becoming richer than before due to the spillover
effects from the more technologically-advanced core regions and the widening of
markets.
86
Regression results show that air accessibility is not a significant
determinant of growth in the core regions in the Philippines. Social expenditure
per capita, however, remains to be a strong indicator of per capita incomes across
the core regions in the country. This is due to the growth in core regions being
driven by other factors such as better intra-regional connectivity through roads
(Metro Manila has the highest paved road density of all regions), a more skilled
workforce, and more foreign investments, all of which may have paled air
accessibility in comparison to their effects to regional growth. Further increasing
air accessibility may not contribute, or even be detrimental, to the core regions’
growth due to overcrowding. NAIA, for example, is operating over capacity.
Centrifugal forces such as overcrowding in places that are most accessible
prevent further agglomeration and concentration of economic activity in the core
regions. Where significant diseconomies of agglomeration have arisen, further
improvements to already good transport networks are likely to be to the benefit
of peripheral regions.
Table 4. 2SLS Regression Results for Philippine Core regions
Dependent Variable:
GRDP per capita
Model 1
Model 2
Coefficient Significance Coefficient Significance
CONSTANT
9.0889
***
4.9373
not sig.
LOG(PASSPERCAP)
0.0895
not sig.
--
--
--
--
0.2142
not sig.
0.3311
***
0.5163
**
LOG(CARGO)
LOG(SOCEXPPERCAP)
87
C. Impact on peripheral regions
The impact of air accessibility on the peripheral regions is positive and
significant based on the regressions. Social expenditure per capita also show
positive correlation with income growth. This result highlights the importance of
air accessibility as a factor supporting the growth of peripheral regions
characterized by low incomes and slow income growth. Making peripheral
regions more accessible opens them to bigger consumer markets such as those in
the core regions and also makes consumers in the peripheral regions better off by
having more products and services coming in from outside the region.
Connecting peripheral to core regions as well as connecting peripheral to
other peripheral regions served as advantage to the economies of the peripheral
regions, as they were able to not only expand their consumer markets in distant
places, but also they were able to reach markets where their output has a higher
value than in its local market. Many of the peripheral regions in the Philippines
specialize on agricultural and mining production activities that are highly
dependent on existing endowments and natural resources not found elsewhere,
especially in highly urbanized regions such as the core regions. Air accessibility
makes it possible for firms and businesses in the peripheral regions to serve
customers in the core regions where their products and services are more highly
valued than in their own region, due to the relative scarcity of these goods in the
core regions.
88
Table 5. 2SLS Regression Results for Philippine peripheral regions
Dependent Variable:
GRDP per capita
Model 1
Model 2
Coefficient Significance Coefficient Significance
CONSTANT
9.9979
***
9.2633
***
LOG(PASSPERCAP)
0.0816
***
--
--
--
--
-0.0008
not sig.
0.1396
***
0.2303
***
LOG(CARGO)
LOG(SOCEXPPERCAP)
D. Impacts of air accessibility on employment and productivity per sector
The means through which air accessibility influences economic growth
can be identified through its linkages with the employment and productivity of
the sectors of the regional economy. Regression results show that the sectors
which are linked with increase in air accessibility in terms of either employment
or productivity impacts are: (1) Agriculture, Hunting and Forestry, (2) Fishing,
(3) Manufacturing, and (4) Wholesale and retail trade.22
1. Agriculture, Hunting and Forestry (AHF)
Regressions measuring the relationship between the Agriculture, Hunting
and Forestry sector and air accessibility of the region imply that there are
productivity benefits of increased air accessibility to the sector. When the
number of workers with high school as the highest educational attainment was
used as the worker skill variable, AHF sector employment decrease as air
accessibility increase, while productivity for all regions has a coefficient of 0.13
and for peripheral regions a coefficient of 0.14. When the number of workers
22
All subsequent tables of regression results use college as worker skill variable
89
who finished college education was used as the worker skill variable, only the
correlation between air accessibility and AHF productivity remained significant,
with a positive value. Based on the results it can be assumed that air accessibility
benefits the AHF sectors in the peripheral regions by improving the efficiency of
the distribution of agricultural produce, especially in high-value fruits and crops.
It is important to note that many regions in the periphery have agriculture as a
huge part of their economies. Air accessibility may be supporting the faster
delivery of perishable agricultural output such as fruit and other foodstuff.
Table 6. OLS Regression Results for Agriculture, Hunting and Forestry
in Core regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
yes
+
yes
LOG(PASSPERCAP)
-
no
+
no
LOG(CARGO)
LOG(WORKERSKILL)
+
no
-
no
EMP Sig? PROD
Sig?
+
no
+
yes
+
no
-
no
+
no
-
no
Table 7. OLS Regression Results for Agriculture, Hunting and Forestry
in Peripheral regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
yes
+
yes
LOG(PASSPERCAP)
-
no
+
yes
LOG(CARGO)
LOG(WORKERSKILL)
+
no
-
yes
EMP Sig? PROD
Sig?
+
yes
+
yes
+
no
-
no
+
no
-
yes
90
Table 8 presents the origins and destinations of agricultural goods and
live animals in the country. It can be observed that approximately half of the
produce of Luzon went to outside markets in Visayas and Mindanao. Visayas
consumed more than 75 percent of the food it produced. Mindanao is the biggest
domestic trader of food and live animals, with about 49 percent of its produce
going to Luzon and 44 percent being consumed in Visayas in 2009. Mindanao
produced 42 percent of the country’s food and live animals in 2009. With the
knowledge that much of the agricultural produce in a region is consumed in other
islands in the other major island groups, inter-regional flows of commodities is
supported by more reliable transport. Logistics costs, including transportation
costs, account for as much as one-third of the total cost of producing high-value
vegetables. 23 Inefficiency in the transport networks and port and shipping
services greatly affects the profitability of key players in the supply chain. The
perishable characteristic of agricultural goods demands a transport system that is
capable of fast delivery.
Table 8. Food and live animals inter-island commodity flow pattern, 2009
Origin
Luzon
Visayas
Mindanao
TOTAL
Luzon
7,104
835
7,493
15,431
Destination (million tons)
Visayas
Mindanao
5,198
2,217
4,741
699
6,806
1,069
16,745
3,985
Total
14,519
6,275
15,367
36,161
Source: PIDS
23
Llanto, G. 2012. obtained from Lantican, F.A. 2010. Supply chain analysis of high-value
vegetables in the Philippines. Philippine Institute of Development Studies
91
Although sea transport remains to be the dominant means of reaching the
domestic and foreign consumer markets, air transport usage has been growing
due to its more efficient logistics service. Shipping via maritime vessels are cited
to be prone to shipment delays, which jeopardize the quality (thus market value)
of the agricultural produce. 24 Nevertheless, mostly high-value agricultural
produce are shipped via air. Domestic markets for fresh fruits and vegetables
grown from northern Luzon and Mindanao are urban centers Metro Manila,
Cebu and Davao. The recent growth of supermarket chains and grocery store
chains has contributed to the increase in the demand for fresh produce in the
more urbanized regions. Agricultural products for export usually require air
freight to ensure quality and freshness, as preservation may decrease market
value in consumer export markets such as Japan and the US.
An industry under the agriculture sector which is heavily dependent on
air service in its production-distribution operations is horticulture, specifically
cut flowers. The most produced flower variety in the country, the
chrysanthemum, is primarily sourced from CAR25 and flown to Metro Manila
and Cebu for the flower shops and retail outlets. CAR is also the biggest
producer of roses, where approximately 60% of Philippine roses were traded in
2013. The largest importers of cut flowers from the Philippines are the
Netherlands, Japan and the UK. 26 Increasing the air accessibility of the other
24
A market chain analysis study for live reef fish trade cites delays as one of the factors shippers
and forwarders in Southeast Asia and the Pacific consider in exporting fish.
25
The Cordillera Administrative Region produced more than half of the national total volume of
chrysanthemums in 2013, according to BAS data.
26
The Netherlands, Japan, and the UK imported 2.3 million USD worth of flowers from the
Philippines during the period 2000 to 2007, with Netherlands having half the total.
92
regions to CAR will contribute to the expansion of its cut flower consumer
markets.
CAR is also a major regional producer of high-value vegetables such as
carrots, cauliflower, potato and cabbage. Apart from CAR, regions which will
benefit from the improvement of accessibility thru inter-modal transport
development consisting of road, water, and air transport are MIMAROPA and
regions in Mindanao. Production of high-value fruits 27 for inter-regional and
export trade are concentrated in the following regions: Davao producing 56% of
the country’s Cavendish bananas, of which half was exported, Northern
Mindanao producing more than half of the country’s pineapples and a quarter of
the country’s Cavendish bananas, Ilocos Region where a third of national mango
and 18% of national watermelon were sourced, MIMAROPA which produced
more than half of the country’s calamansi, and SOCCSKSARGEN with country
production shares of 33% in pineapples and 12% in Cavendish banana.
Opportunities for growth are seen in the agriculture sector of the peripheral
regions in the Philippines, as the logistics and supply chain structure in
transporting raw and processed products from the farmer down to the final
consumer is still far from efficient. Spoilage is a common problem across most
fruit and crop groups, due to the cheaper but slower delivery of maritime vessels.
Scheduling in sea cargo are frequently subject to delays, which is why traders
have resorted to preservation techniques in an attempt to maintain the quality of
their products when it lands on Manila. Another common problem is the lack of
27
High-value produce is defined as produce having a high profit/cost ratio, with corresponding
data provided by BAS in 2013.
93
farm-to-market roads. The airports which serve as gateways to the international
consumer market who are willing to pay a higher price to obtain fresh produce
from the Philippines are underdeveloped. Mindanao, for instance, holds high
prospects for agro-industrial development because of its climate suitable for
growing crops all year round. However, its potential for growth is hampered due
to the lack of international and domestic transport connections. Its widelydispersed islands and mountainous areas require airports and seaports to be
improved in order to strengthen its linkages to Luzon and Visayas. Davao,
General Santos and Zamboanga can be accessed through international airports,
but Davao handles more than 50% of passenger traffic in Mindanao.
Improvement of inter-modal transportation has been recommended by
government agencies such as NEDA as part of the road map for regional
development.
2. Fishing
Air accessibility shows positive coefficient (0.21) in the employment in
the fishing sector in the core regions and a negative coefficient (-0.27) for the
peripheral regions in regressions using high school worker skill variable. Using
college attainment in the work force as worker skill variable gives similar signs
for core (0.2) and peripheral (-0.22) regions. The positive relationship between
air accessibility and employment in the fishing sector in core regions can be
explained by the indirect effects of increased accessibility of regions to the
demand for products in the sector. Increases in the air accessibility of core
94
regions are linked to the increase in the demand for labor in the fishing sector
especially to production processes which may be located in the core regions. The
negative effect of air accessibility to fishing employment in the peripheral
regions may be attributed to the reorganization of the production process of
goods which sources inputs from the fishing sector. Firms dependent on marine
produce may have been growing in the core regions as goods can be delivered to
the core from the peripheral regions faster with the help of efficient transport
systems such as air. The value of most goods from the agricultural and fishing
sectors are highly dependent on perishability – the best way to add value to a fish
is to do nothing to it, except to get it to the consumer quickly. Fresh fish is worth
more than frozen, salted, or otherwise processed fish.
Table 9. OLS Regression Results for Fishing in Core regions
Model 1
Variable
EMP
Sig? PROD
Model 2
Sig?
CONSTANT
+
yes
-
no
LOG(PASSPERCAP)
+
yes
-
no
LOG(CARGO)
LOG(WORKERSKILL)
-
no
+
no
EMP Sig? PROD
Sig?
+
yes
-
yes
-
no
+
yes
-
no
+
yes
Table 10. OLS Regression Results for Fishing in Peripheral regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
-
no
+
yes
LOG(PASSPERCAP)
-
yes
-
no
LOG(CARGO)
LOG(WORKERSKILL)
+
yes
+
no
EMP Sig? PROD
Sig?
-
no
+
yes
+
yes
-
no
+
yes
+
no
95
Air accessibility from the marine producers in the peripheral regions to
the retailers and traders in the core regions facilitate the growth of the sector in
the core regions. Restaurants and other establishments selling raw and cooked
fish of higher value require the product, called live reef fish food, to be at its best
condition to ensure freshness and the satisfaction of customers. In terms of
exports, nearly all live reef fish food are delivered to Hong Kong by air. Air
transport is chosen by exporters as the means of delivering live fish food to the
market due to lower transport costs compared to sea transport.28 Air transport is
also preferred in domestic logistics of the product, because of higher reliability
of the service. Most of live reef fish food, such as wrasse, groupers, and trout, in
the Philippines are sourced from Palawan in MIMAROPA and Eastern Visayas
and are consumed in urban centers such as Metro Manila and export markets.
3. Manufacturing
Air accessibility is seen to have positive effects on the productivity of the
manufacturing sector especially in the peripheral regions. Regressions using high
school worker skill variable and air accessibility as independent variables
explaining sectoral employment and productivity reveal a coefficient of 0.27 on
the productivity of the manufacturing sector. Replacing the worker skill variable
with college educational attainment of workers give similar results. Increasing
the air accessibility of peripheral regions helps boost the manufacturing
industries in these regions due to improvements in both backward and forward
28
Philippine exports of live reef fish food to Hong Kong cost 3.7-4.7 USD/kg via air and 4.5-5
USD/kg via sea shipping, based on Sadovy et al. (2004)
96
linkages in the production and delivery of goods. Moreover, multinational
companies are more likely to open up shop in places which are more accessible
via air.
That air accessibility has a positive effect on the productivity of the
manufacturing sector through foreign direct investments is supported by a PIDS
study which show that productivity spillovers take place horizontally from
multinational corporations to domestic firms. 29 The reduction of travel time
through air transport has contributed to the increase in productivity of the sector,
as production networks become more efficient. Value of output per total
employment for manufacturing establishments in peripheral regions based on
recent figures are comparably high
30
: productivity in the Cordillera
Administrative Region was highest with 24 million pesos value of output per
total employment, followed by MIMAROPA with approximately 15.3 million
pesos, Eastern Visayas with about 14.9 million pesos, and Northern Mindanao
ranking fourth place at 6.4 million pesos. MIMAROPA and Northern Mindanao
appear to benefit from improvements in their air accessibility. CAR and Eastern
Visayas has the potential of further growing its manufacturing center by being
more domestically connected by air.Core regions NCR, CALABARZON,
Central Luzon, Western and Central Visayas have values ranging from three to
five million pesos in productivity of labor in the manufacturing sector. The
29
Aldaba and Aldaba. 2012. Do FDI inflows have positive spillover effects? The case of the
Philippine Manufacturing Industry. Philippine Institute of Development Studies
30
Based on the Indicators for Manufacturing Establishments with Total Employment of 20 and
Over by Region: Philippines 2012.
http://census.gov.ph/sites/default/files/attachments/itsd/specialrelease/Ratio%20Ta
ble_REG.pdf
97
ability to reach more markets through air accessibility of regions have
contributed to the rise in productivity in this sector.
Table 11. OLS Regression Results for Manufacturing in Core regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
yes
+
yes
LOG(PASSPERCAP)
+
no
+
no
LOG(CARGO)
LOG(WORKERSKILL)
+
no
-
no
EMP Sig? PROD
Sig?
+
no
+
yes
-
no
-
no
+
no
-
no
Table 12. OLS Regression Results for Manufacturing in Peripheral regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
yes
+
yes
LOG(PASSPERCAP)
+
no
+
yes
LOG(CARGO)
LOG(WORKERSKILL)
+
no
-
no
EMP Sig? PROD
Sig?
+
yes
+
yes
+
yes
-
yes
+
no
-
no
Manufacturing is an important element in the production value chain
because it transforms raw materials in order to add value to the intermediate or
final product to be sold by the wholesalers and retailers to consumers. As a
country abundant in natural resources, the Philippines produces many fresh fruits
and crops for domestic and international consumption. However, not all
agricultural produce go to consumers raw. A portion of the produce are
processed then sold as products different from its raw form. Multinational
corporations commonly partake in this process, as they are the biggest
manufacturers of foodstuffs grown in the country.
98
4. Wholesale and Retail Trade
Studies analyzing the impact of transport infrastructure find robust
evidence that wholesale and retail industries benefit the most from improved
market access and lower transportation costs. 31Since many goods do not pass
directly from the producer or factory to the consumer, transportation also plays a
key role in moving products from production through wholesale and retail trade
channels. Regression using high school worker skill and air accessibility as
explanatory variables to employment and productivity of the sector reveal that
the productivity in the wholesale and retail trade sector is positively affected by
increases in air accessibility. Using college worker skill and air accessibility in
the regression explaining employment and productivity in the sector show that
air accessibility only significantly impacts the peripheral regions’ employment
and productivity in the wholesale and retail trade sector. These results highlight
the importance of air transport to the trade of goods involving the peripheral
regions.
Table 13. OLS Regression Results for Wholesale and Retail Trade in
Core regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
yes
+
yes
LOG(PASSPERCAP)
+
no
+
no
LOG(CARGO)
LOG(WORKERSKILL)
+
yes
-
yes
EMP Sig? PROD
Sig?
+
yes
+
yes
+
no
-
yes
+
yes
-
yes
31
Michaels, 2008.
99
Table 14. OLS Regression Results for Wholesale and Retail Trade in
Peripheral regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
yes
+
yes
LOG(PASSPERCAP)
+
yes
+
yes
EMP Sig? PROD
+
yes
+
yes
-
no
-
yes
+
yes
-
yes
LOG(CARGO)
LOG(WORKERSKILL)
+
yes
-
yes
Sig?
According to the National Statistics Office (NSO), wholesale trade refers
to the resale of new and used goods to retailers, while retail trade refers to the
resale of new and used goods for personal and household consumption. In terms
of the type of product sold, the distribution sector can be broken down as
follows:
Wholesale Trade

Farm, forest and marine products

Processed food, beverages and tobacco products

Dry goods, textiles and wearing apparel

Construction materials and supplies

Office and household furniture, furnishings and appliance and
ware

Machinery equipment, including transport equipment

Mineral, metals and industrial chemicals

Petroleum and petroleum products

Wholesale trade not elsewhere classified (flowers, handicrafts)
100
Retail Trade

Books, office, school supplies

Food, beverage and tobacco

Dry goods, textiles and wearing apparel

Construction materials and supplies

Office and household furniture, furnishings and appliances

Transportation, machinery and equipment, accessories and
supplies

Medical supplies and equipment

Petroleum and other fuel products

Retail trade not elsewhere classified (toys, jewelry, beauty parlor
supplies)
Increasing the accessibility of the Philippine regions is seen to benefit the
peripheral regions as the Wholesale and Retail Trade sector bridge the
manufacturers and consumers. This sector of the regional economy is essentially
the final stage of the value chain: from the production point of the manufacturer
to the transaction point of the end-consumer. Agricultural producers are able to
reach more markets in the country with enhanced transport linkages. The
expansion of the Wholesale and Retail Trade sector in the peripheral regions can
be seen through the improvement in their relative shares in the total number of
sector establishments in the country (Appendix C). The National Capital Region
has exhibited a declining share in the total number of wholesale and retail trade
establishments, which was approximately 48% in 2006 and down to 30% in 2012.
101
NCR still has the largest share; however, the increases in the shares of the other
regions is proof that more and more establishments are setting up in places far
from Manila. Growth is observed to be dispersing to the peripheral regions, and
one factor that can be attributed to this is the increase in air accessibility of these
regions. Among the regions which posted improvements in regional shares over
the six-year period are: Ilocos Region, Cagayan Valley, Central Luzon,
CALABARZON, MIMAROPA, Bicol Region, Western Visayas, Central
Visayas, Eastern Visayas, Caraga and ARMM.
5. Real Estate, Renting and Business Activities
Previous studies on the economic impacts of air accessibility point to the
business services as one of the sectors positively linked with increased air traffic.
However, the relationship seem to lack strength when the econometric methods
are applied in the Philippine context. The regression results for the Real Estate,
Renting and Business Activities sector in the core regions show a positive
correlation between employment and passenger enplanements, but is
insignificant. The air accessibility variable becomes significant when the
regression model is applied to the peripheral regions; however, the model is
weak considering that the constant fails to be a significant variable in the model.
The productivity effects of air accessibility to the sector also fails to be
significant.
102
Table 15. OLS Regression Results for Real Estate, Renting and Business
Activities in Core regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
yes
+
yes
LOG(PASSPERCAP)
+
no
-
no
LOG(CARGO)
LOG(WORKERSKILL)
-
no
+
no
EMP Sig? PROD
Sig?
+
yes
+
yes
-
no
+
no
-
no
+
no
Table 16. OLS Regression Results for Real Estate, Renting and Business
Activities in Peripheral regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
no
+
yes
LOG(PASSPERCAP)
+
yes
+
no
LOG(CARGO)
LOG(WORKERSKILL)
+
no
-
no
EMP Sig? PROD
Sig?
+
no
+
yes
-
yes
-
no
+
no
+
yes
By looking into the gross value added of the sector by region, it can be
observed which regions will most likely benefit from the improvement of air
accessibility (Appendix K). For the period 2009 to 2013, the GVA of the sector
has increased by approximately 35%. However, a closer look shows that many
peripheral regions have GVA growth rates above the national average. Apart
from the core regions NCR, CALABARZON and Central Visayas, regions
which have exhibited above average growth in the Real Estate, Renting and
Business sector are the CAR, MIMAROPA, Northern Mindanao, and Davao
Region. Davao Region demands particular attention due to its remarkable growth
rate in this sector – in a span of only four years, GVA for the sector has grown in
103
the region by more than fifty percent. Davao’s strong growth is attributed to the
region being the country’s preferred destination for ICT products and services. In
2012, investments from private building construction in the region pegged at 14
billion pesos. Davao is fast becoming the convention, investment, and tourism
hub of Southern Philippines. The expansion of business process outsourcing and
ICT sector to knowledge process outsourcing such as health IT, engineering and
design also pose development opportunities in the region. A better accessibility
of the region through air will help contribute to increasing the importance of the
region as part of the business activity value chain in the global market.
6. Financial Intermediation
One of the services sectors commonly cited in studies investigating the
employment and productivity effects of air accessibility is the banking sector. In
this study of the contribution of air accessibility to regional economic growth in
the Philippines; however, air accessibility fails to be a significant determinant of
employment and productivity in the Financial Intermediation sector. For the core
regions, passenger volume per capita show a positive correlation with sector
employment and a negative correlation with productivity, but with both values
being statistically insignificant. In the peripheral regions, the air accessibility
variable coefficients are both positive in sector employment and productivity, but
still fall short in explanatory power. These results imply that while air
accessibility may form part of the industry linkages in the operations of the
Financial Intermediation sector, its share in the inputs or outputs of the sector
104
may be very minimal compared to other factors. Also, banking in the Philippines
is yet to grow and be highly dependent on air transportation.
Table 17. OLS Regression Results for Financial Intermediation in
Core regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
no
+
yes
LOG(PASSPERCAP)
+
no
-
no
LOG(CARGO)
LOG(WORKERSKILL)
+
no
-
no
EMP Sig? PROD
Sig?
+
no
+
yes
-
no
-
no
-
no
-
no
Table 18. OLS Regression Results for Financial Intermediation in
Peripheral regions
Model 1
Variable
EMP
Model 2
Sig? PROD
Sig?
CONSTANT
+
no
+
yes
LOG(PASSPERCAP)
+
no
+
no
LOG(CARGO)
LOG(WORKERSKILL)
-
no
-
no
EMP Sig? PROD
Sig?
+
yes
+
yes
-
no
-
no
-
no
-
no
Regression results show that across the sectors of the regional economy,
air accessibility significantly impacts goods-related sectors, more specifically, in
the peripheral regions. This implies the role of air accessibility of regions in
facilitating the trade of goods in the country. In the peripheral regions, air
accessibility is found to positively influence the following: the productivity of
the Agriculture, Hunting and Forestry sector, productivity of the Manufacturing
sector, and the employment and productivity of the Wholesale and Retail Trade
sector. Effects in the core regions are not much pronounced, which is due to the
105
fact that the core regions are the primary consumer markets of the peripheral
regions, and increased air accessibility enables more products from the periphery
to reach the core at a faster rate than using other modes of transportation.
Consequently, economic activity in the aforementioned goods-related sectors in
the periphery rises, as reflected by the need for more labor or more output in
these sectors.
106
CHAPTER V
SUMMARY, CONCLUSION AND RECOMMENDATIONS
A. Summary
The need for physical access to markets in order to spur growth in
regional economies is not a new concept. Regional growth is not only driven
from the inside (endowments, human capital) but also from connections to
economies outside (accessibility). However, the means through which
accessibility of regions could be achieved is not clear cut. The different modes of
transport, land, water and air, provide distinct advantages in linking places. Air
transportation has the advantage of facilitating the fast and reliable delivery of
goods and convenient travel of passengers over long distances. In an archipelagic
country such as the Philippines, the interaction among land, water, and air
transport in order to reach far places and markets is inevitable. This thesis is an
attempt to identify how air transportation connections across the Philippine
regions mostly separated by sea and long distances spur economic growth. This
thesis tries to assess the contribution of air accessibility to regional economic
growth in the Philippines through quantitative methods which involved the
human capital variable represented by social expenditures of LGUs per capita
and our variable of interest - air accessibility – represented by passenger volume
per capita, in an equation explaining the economic growth represented by income
per capita of regions over the period 2009 to 2013, consistent with the
endogenous growth and economic growth theories.
107
The two-stage least squares method was utilized to address the selfreinforcing relationship between air accessibility and regional growth, with
population density as the instrument variable. The regions were classified by the
researcher as the core or the periphery according to relative income. For this
study, the following regions were assigned as the core: NCR, CALABARZON,
Central Luzon, Western Visayas and Central Visayas. The theoretical framework
used in this thesis is grounded on the relationship between inter-regional
transport and economic activity: improvement in transport networks provide
access to larger markets and time and cost savings for businesses, leading to
economy-wide productivity changes and growth. However, the effects of
improving the accessibility may differ for the core and the peripheral regions.
Economic geography theory posits that either the core or the periphery will
benefit more from increasing access per region. The process by which air
accessibility impacts the regional economy is investigated further through the
employment and productivity effects of air passenger volumes to the different
sectors of the regional economy. This thesis focuses on the contribution of air
accessibility to regional growth through trade in goods, trade in services, and
employment and productivity effects of increased use of air transportation in the
regions and in the specific sectors.
108
B. Conclusions
Air accessibility is found to increase the incomes of the Philippine
regions overall. The human capital variable; however, exhibits a larger impact to
regional growth than air accessibility. Classifying the regions into the core and
periphery reveal that the growth impacts of air accessibility varies for the core
and the periphery, as the economic geography framework states. In the
Philippine case, the peripheral regions significantly benefit from increased air
accessibility. The explanatory power of air accessibility in determining growth in
the core regions has been proven to be weak.
Investigating further into the sectoral impacts of improved air
accessibility in the regions show that air accessibility contribute to the growth of
peripheral incomes through increasing the employment and/or productivity of the
following sectors: Agriculture, Hunting and Forestry, Manufacturing, and
Wholesale and Retail Trade sector. Air accessibility reveals a positive correlation
with employment in the Fishing sector in the core regions. Improvement in the
connections among the regions in the country will benefit the agriculture sectors
in Ilocos, Cordillera Administrative Region, MIMAROPA, and in the fruit
basket of the Philippines, Mindanao, regions Davao, Northern Mindanao and
SOCCSKSARGEN. NCR, being the biggest market of fresh fish and aquatic
produce, has achieved more access due to increase in transport from suppliers
such as Palawan and the Visayas regions. In terms of manufacturing, growth in
MIMAROPA and Northern Mindanao can be pushed further through better
linkages, of which air transport forms part. The promising manufacturing sector
109
output of CAR and Eastern Visayas can be actualized in the future by increasing
networks with other regions as part of the food production and distribution value
chain with the help of air accessibility of the regions. The Wholesale and Retail
Trade in Ilocos Region, Cagayan Valley, MIMAROPA, Bicol Region, Eastern
Visayas, Caraga and ARMM also appear to benefit from better transport linkages.
In areas where ships cannot reach destinations just in time, air accessibility
provides opportunities for flows of people and goods across these regions. The
rise in passenger enplanements in the MIMAROPA and Davao regions has made
these regions attractive for the Real Estate, Renting and Business Services sector,
most of which are oriented globally.
Contrary to results from existing literature in developed countries, air
accessibility is more significantly linked with goods-related sectors than with
services-related sectors. The results from this thesis support the characteristic of
the peripheral regions in the Philippines as producers and suppliers of various
food and other traded goods and the core as the biggest consumer market of the
peripheral regions.
C. Recommendations
1. Policy recommendations
The results from this thesis highlight that the government and other
policymakers involved in the construction and investment in transport
infrastructure should prioritize the improvement of air transport connections and
network of the peripheral regions. Creating more access to these regions may
110
pave the way for more economic activities in these regions and eventually lead to
growth in incomes. Results from this thesis imply that one of the strategies in
developing the agriculture, fishing, and other related industry sectors is by
supporting the fast transport of both intermediate and final goods through an
efficient air transport system. The development of domestic trade may be
stimulated by providing accessibility across all peripheral regions, and not just
by connecting Metro Manila to the rest of the country. This may be one of the
steps to achieve the goal of the national government of reducing regional
inequality in terms of incomes.
The efforts of the previous administration in establishing transport links
from Luzon all the way to Mindanao through the construction of the nautical
highways should be complemented by air transport networks across the
archipelago. The Strong Republic Nautical Highway System has strengthened
links in the north-south direction. There is still a need to enhance the east-west
connections of regions. Manila and Cebu ports and airports have been operating
beyond capacity due to the lack of direct routes or flights from other countries to
cities outside Manila and Cebu. Most of the Philippine regions appear to be
accessible only through Manila. Cebu Pacific, for example, does not offer Puerto
Princesa-Zamboanga City flights – passengers going this route often have to go
through Manila first before reaching their destination. Regions in the Luzon
mainland rely on Manila to get their people and goods to the Visayas and
Mindanao regions, when in fact there are existing airports in these areas.
Redirecting the flow of goods and people from Manila to other nodes in the
111
transport network will help ease the congestion in NCR. Enhancing the east-west
connections in the country by linking MIMAROPA to Eastern Visayas and
Zamboanga, or Northern Mindanao to Bicol Region would be a great addition to
the existing airline routes in the country. Increasing the air accessibility of the
regions such as Bicol, Eastern Visayas and Zamboanga to other regions in the
Visayas and Mindanao will help boost economic activity in these peripheral
regions. Bicol and Eastern Visayas are currently accessible only through Manila
and Cebu.
The emergence of low-cost airlines has made it possible for more people
and goods to flow across islands via air. Establishing routes between destinations
outside the Metro Manila and Central Luzon areas will help spur growth in
agriculture-dominated regions by expanding their markets even more.
Investment costs of creating new inter-regional connections may be high, but
proper collaboration with the private sector and organizations can make these
projects feasible, in the same way that the nautical highways were a big
government project that was successfully carried out years ago.
Air transport networks in the archipelago have to be improved, but it can
never be achieved without a good infrastructure. Capacity and frequency of
flights can only be attained if airports and airplanes are of optimum quality.
Local governments in the Bicol Region and MIMAROPA have been clamoring
for funds for the development of its airports to international status in order to
attract more people and trade.
112
The improvement of the air accessibility of the Philippine regions will
contribute to growth; however, the full economic potential of any region can
only be realized by integrating land, water, and air transport networks. The
SRNH is a laudable attempt in organizing road and RORO transport to connect
the islands, but the government should not stop there. Air transport should also
be integrated in the system of inter-modal transport that would boost passenger
and commodity flows not only inter-regionally but also globally. The Philippines
is increasingly becoming part of the global value chain, with multinational
corporations setting up in the country for production and distribution operations.
Improvements in the accessibility of the growing peripheral regions and the
commercial hub core regions through intermodal networks will certainly drive
economic activity and growth in the regions and in the country as a whole.
2. Recommendations for further research
The researcher recommends a more thorough investigation of the impacts
of air accessibility to economic growth through the use of a more extensive
database of the variables. A longer time frame to be used by future studies would
reveal the longer-term relationship between growth and air accessibility. Due to
data limitations, the researcher failed to include other transport variables that
may have significant effects to growth apart from air accessibility. As this thesis
did not dwell on the intermodal nature of transport in the country (land, water
and air), and focused instead on air accessibility, a valuable contribution to the
bank of knowledge in the Philippine transport and economic growth literature is
to assess the growth effects of an integrated transport system in the country.
113
Subsequent studies on the same topic may add value to current knowledge by
including road and marine transport data.
As this thesis focused on the macroeconomic growth impacts of air
accessibility, aggregated values per region were used in the analysis. Researchers
interested in measuring impacts by province or municipality may use the microapproach in the research. Data on prices and revenues on air fares and business
output can be beneficial in this microeconomic analysis. Identifying which cities
in the regions lead in regional growth and by exactly how much far flung towns
benefit from air services is another possible focus of future studies in the
Philippines. Also, further research on this topic may explore the cost-benefit
analysis of building the actual infrastructure such as airports and the perceived
economic returns of its consequent trade and mobility effects. The feasibility of
building and maintaining airports and airplanes is among one of the factors
considered by the government in passing projects that would improve
accessibility or poor regions.
114
115
Source: Regional Commodity Flow in the Philippines by Mode of Transport 2013, census.gov.ph
Domestic Commodity Flow via Air, 2013 (in thousand pesos)
APPENDIX A
116
accessed March 1, 2015
Source: Cebu Pacific Airlines Domestic Route Map, https://www.cebupacificair.com/Pages/route-map.aspx
Domestic Air Passenger Routes
APPENDIX B
APPENDIX C
2SLS Regression Results for All Regions
Dependent Variable: LOG(GRDPPERCAP)
Method: Panel Two-Stage Least Squares
Date: 04/04/15 Time: 10:10
Sample: 2009 2013
Periods included: 5
Cross-sections included: 14
Total panel (balanced) observations: 70
Instrument specification: LOG(PASSPERCAP) C LOG(POPDENS)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(SOCEXPPERCAP)
9.702096
0.078043
0.195996
0.277231
0.027765
0.042467
34.99648
2.810815
4.615247
0.0000
0.0069
0.0000
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
Instrument rank
0.992032
0.989819
0.049526
697.4609
0.000000
16
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
Second-Stage SSR
10.81543
0.490840
0.132451
1.982867
0.085364
Dependent Variable: LOG(GRDPPERCAP)
Method: Panel Two-Stage Least Squares
Date: 04/04/15 Time: 10:13
Sample: 2009 2013
Periods included: 5
Cross-sections included: 15
Total panel (balanced) observations: 75
Instrument specification: LOG(CARGO) C LOG(POPDENS)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(SOCEXPPERCAP)
9.143392
-0.000354
0.263036
0.359431
0.004513
0.054777
25.43849
-0.078370
4.801956
0.0000
0.9378
0.0000
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
Instrument rank
0.987105
0.983548
0.060824
692.0471
0.000000
17
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
Second-Stage SSR
10.81432
0.474204
0.214577
2.192020
0.086709
117
APPENDIX D
2SLS Regression Results for Core Regions
Dependent Variable: LOG(GRDPPERCAP)
Method: Panel Two-Stage Least Squares
Date: 04/04/15 Time: 10:49
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Instrument specification: LOG(PASSPERCAP) C LOG(POPDENS)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(SOCEXPPERCAP)
9.088971
0.089498
0.331100
0.484093
0.060710
0.075018
18.77528
1.474174
4.413598
0.0000
0.1712
0.0013
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
Instrument rank
0.994051
0.991672
0.046164
1469.390
0.000000
5
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
Second-Stage SSR
11.20937
0.505858
0.021312
1.634201
0.006085
Dependent Variable: LOG(GRDPPERCAP)
Method: Panel Two-Stage Least Squares
Date: 04/04/15 Time: 10:46
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Instrument specification: LOG(CARGO) C LOG(POPDENS)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(SOCEXPPERCAP)
4.937282
0.214153
0.516342
3.247413
0.162325
0.183849
1.520374
1.319285
2.808509
0.1594
0.2165
0.0185
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
Instrument rank
0.987711
0.982796
0.066351
1932.428
0.000000
5
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
Second-Stage SSR
11.20937
0.505858
0.044025
1.919091
0.004629
118
APPENDIX E
2SLS Regression Results for Peripheral Regions
Dependent Variable: LOG(GRDPPERCAP)
Method: Panel Two-Stage Least Squares
Date: 04/04/15 Time: 10:52
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Instrument specification: LOG(PASSPERCAP) C LOG(POPDENS)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(SOCEXPPERCAP)
9.997865
0.081605
0.139613
0.297989
0.027664
0.045094
33.55111
2.949881
3.096032
0.0000
0.0052
0.0035
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
Instrument rank
0.991776
0.989426
0.044424
496.7149
0.000000
13
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
Second-Stage SSR
10.70799
0.432018
0.082886
1.915525
0.070520
Dependent Variable: LOG(GRDPPERCAP)
Method: Panel Two-Stage Least Squares
Date: 04/04/15 Time: 10:54
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Instrument specification: LOG(CARGO) C LOG(POPDENS)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(SOCEXPPERCAP)
9.263341
-0.000797
0.230330
0.401558
0.004387
0.061895
23.06849
-0.181723
3.721313
0.0000
0.8566
0.0005
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
Instrument rank
0.984177
0.979705
0.059035
475.1576
0.000000
14
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
Second-Stage SSR
10.71556
0.414393
0.160314
2.165150
0.074891
119
APPENDIX F
OLS Regression Results for Core Regions by Sector Productivity
Dependent Variable: LOG(AHF)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:45
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
13.57820
0.102558
-0.298522
2.467119
0.097075
0.363275
5.503668
1.056486
-0.821752
0.0001
0.3086
0.4250
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.995288
0.993268
0.081734
0.093526
27.04966
492.8403
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.39666
0.996190
-1.909492
-1.561317
-1.833929
2.601746
Dependent Variable: LOG(AHF)
Method: Panel Least Squares
Date: 04/04/15 Time: 13:39
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
17.77680
-0.188289
-0.556006
3.614216
0.133593
0.440124
4.918578
-1.409419
-1.263295
0.0006
0.1890
0.2351
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.995896
0.994254
0.089912
0.080842
17.89077
606.6234
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.36084
1.186140
-1.718770
-1.482753
-1.721284
2.828162
120
Dependent Variable: LOG(FISH)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:45
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
-32.93704
-0.661519
6.317300
37.38468
1.470988
5.504764
-0.881030
-0.449711
1.147606
0.3932
0.6598
0.2704
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.420352
0.171932
1.238530
21.47538
-30.03275
1.692101
0.195456
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.52837
1.361048
3.526929
3.875103
3.602491
0.997394
Dependent Variable: LOG(FISH)
Method: Panel Least Squares
Date: 04/04/15 Time: 13:41
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
-133.4591
4.929296
11.16795
39.27629
1.451778
4.782900
-3.397955
3.395351
2.334974
0.0068
0.0068
0.0417
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.642673
0.499743
0.977088
9.547016
-17.89542
4.496399
0.024545
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.10056
1.381456
3.052722
3.288739
3.050208
1.927427
121
Dependent Variable: LOG(MANU)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:46
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
18.60460
0.089136
-0.858647
4.937452
0.194276
0.727022
3.768057
0.458814
-1.181047
0.0021
0.6534
0.2573
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.957822
0.939745
0.163574
0.374593
12.47990
52.98724
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
12.56174
0.666376
-0.521895
-0.173721
-0.446332
1.471522
Dependent Variable: LOG(MANU)
Method: Panel Least Squares
Date: 04/04/15 Time: 13:43
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
26.17683
-0.278484
-1.445238
6.156283
0.227556
0.749686
4.252052
-1.223805
-1.927791
0.0017
0.2491
0.0827
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.966086
0.952520
0.153152
0.234555
9.901800
71.21562
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
12.38405
0.702858
-0.653573
-0.417557
-0.656087
1.781516
122
Dependent Variable: LOG(TRADE)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:47
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
18.59095
0.110388
-0.983126
2.893175
0.113839
0.426010
6.425797
0.969685
-2.307753
0.0000
0.3487
0.0368
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.992617
0.989453
0.095849
0.128618
23.70429
313.7130
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.66267
0.933304
-1.590885
-1.242711
-1.515322
1.058246
Dependent Variable: LOG(TRADE)
Method: Panel Least Squares
Date: 04/04/15 Time: 13:44
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
25.09217
-0.284862
-1.341819
3.473731
0.128400
0.423016
7.223407
-2.218547
-3.172028
0.0000
0.0508
0.0100
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.994523
0.992333
0.086417
0.074679
18.48546
453.9925
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.92668
0.986921
-1.798061
-1.562045
-1.800576
0.912666
123
Dependent Variable: LOG(RERBA)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:47
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
12.39915
-0.109056
0.063648
2.216099
0.087198
0.326313
5.595036
-1.250677
0.195051
0.0001
0.2316
0.8482
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.962613
0.946591
0.073418
0.075463
29.30302
60.07760
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
12.96252
0.317682
-2.124097
-1.775923
-2.048534
2.136057
Dependent Variable: LOG(RERBA)
Method: Panel Least Squares
Date: 04/04/15 Time: 13:46
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
12.61406
0.006090
0.050175
3.304418
0.122142
0.402398
3.817331
0.049857
0.124691
0.0034
0.9612
0.9032
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.958838
0.942373
0.082205
0.067577
19.23499
58.23587
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
13.04398
0.342442
-1.897999
-1.661982
-1.900513
1.043998
124
Dependent Variable: LOG(FIN)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:47
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
17.77922
-0.032380
-0.597906
2.164233
0.085157
0.318676
8.215023
-0.380235
-1.876223
0.0000
0.7095
0.0816
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.983287
0.976124
0.071700
0.071972
29.80035
137.2753
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
13.67779
0.464016
-2.171462
-1.823288
-2.095899
1.884148
Dependent Variable: LOG(FIN)
Method: Panel Least Squares
Date: 04/04/15 Time: 13:47
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
18.19512
-0.126746
-0.383742
2.983341
0.110274
0.363299
6.098908
-1.149371
-1.056272
0.0001
0.2772
0.3157
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.983419
0.976786
0.074217
0.055082
20.76824
148.2734
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
13.81082
0.487119
-2.102432
-1.866416
-2.104946
2.069146
125
APPENDIX G
OLS Regression Results for Core Regions by Sector Employment
Dependent Variable: LOG(AHF)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:08
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
5.279877
-0.032689
0.059441
2.263281
0.089054
0.333260
2.332842
-0.367068
0.178362
0.0351
0.7191
0.8610
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.998761
0.998231
0.074981
0.078710
28.86061
1881.643
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
5.728084
1.782582
-2.081962
-1.733788
-2.006400
2.896874
Dependent Variable: LOG(AHF)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:29
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
2.562730
0.108996
0.192040
3.412025
0.126119
0.415502
0.751088
0.864225
0.462188
0.4699
0.4077
0.6538
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.998739
0.998235
0.084882
0.072050
18.75431
1980.740
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
5.379890
2.020548
-1.833908
-1.597891
-1.836422
2.913560
126
Dependent Variable: LOG(FISH)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:09
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
4.894979
0.200299
-0.062829
2.342036
0.092153
0.344857
2.090053
2.173555
-0.182187
0.0553
0.0474
0.8580
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.995211
0.993159
0.077590
0.084283
28.14230
484.9436
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.234460
0.938112
-2.013553
-1.665379
-1.937990
2.306329
Dependent Variable: LOG(FISH)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:33
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
7.217993
-0.126433
-0.181993
3.291085
0.121649
0.400774
2.193196
-1.039324
-0.454104
0.0530
0.3231
0.6595
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.996063
0.994488
0.081873
0.067032
19.29564
632.5251
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.231809
1.102818
-1.906085
-1.670069
-1.908599
2.112661
127
Dependent Variable: LOG(MANU)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:10
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
4.581161
0.025993
0.179873
1.529690
0.060189
0.225241
2.994830
0.431858
0.798579
0.0096
0.6724
0.4379
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.991523
0.987890
0.050678
0.035955
37.08737
272.9294
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
5.796715
0.460522
-2.865464
-2.517290
-2.789901
1.046497
Dependent Variable: LOG(MANU)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:34
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
4.061470
-0.008647
0.249617
2.245000
0.082982
0.273387
1.809118
-0.104202
0.913055
0.1005
0.9191
0.3827
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.990086
0.986120
0.055850
0.031192
25.03332
249.6659
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
5.667773
0.474055
-2.671109
-2.435092
-2.673623
1.135198
128
Dependent Variable: LOG(TRADE)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:11
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
2.809383
0.005282
0.546423
0.744345
0.029288
0.109602
3.774301
0.180343
4.985512
0.0021
0.8595
0.0002
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.995993
0.994276
0.024660
0.008513
52.21399
579.9888
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
6.585051
0.325934
-4.306094
-3.957920
-4.230531
1.809926
Dependent Variable: LOG(TRADE)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:35
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
2.080484
0.005996
0.631774
0.950064
0.035117
0.115695
2.189837
0.170736
5.460698
0.0534
0.8678
0.0003
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.997111
0.995956
0.023635
0.005586
37.93229
862.9895
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
6.527242
0.371666
-4.390973
-4.154956
-4.393487
1.525566
129
Dependent Variable: LOG(RERBA)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:12
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
7.674216
0.193689
-0.363912
3.963494
0.155953
0.583611
1.936225
1.241969
-0.623554
0.0733
0.2347
0.5429
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.979658
0.970940
0.131308
0.241385
17.09409
112.3725
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.937441
0.770272
-0.961342
-0.613167
-0.885779
1.630777
Dependent Variable: LOG(RERBA)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:36
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
11.39933
-0.272324
-0.394073
5.021136
0.185597
0.611453
2.270269
-1.467282
-0.644486
0.0465
0.1730
0.5338
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.986530
0.981142
0.124912
0.156031
12.95906
183.0937
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.953034
0.909605
-1.061208
-0.825191
-1.063722
1.380924
130
Dependent Variable: LOG(FIN)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:12
Sample: 2009 2013
Periods included: 5
Cross-sections included: 5
Total panel (unbalanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
3.474587
0.182220
0.081161
3.887559
0.152965
0.572429
0.893771
1.191253
0.141784
0.3866
0.2534
0.8893
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.969363
0.956234
0.128792
0.232224
17.50032
73.82842
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.830948
0.615629
-1.000031
-0.651856
-0.924468
0.930303
Dependent Variable: LOG(FIN)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:37
Sample: 2009 2013
Periods included: 5
Cross-sections included: 3
Total panel (balanced) observations: 15
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
9.950939
-0.250880
-0.394056
4.937191
0.182494
0.601230
2.015506
-1.374729
-0.655417
0.0715
0.1992
0.5270
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.979709
0.971593
0.122824
0.150857
13.21195
120.7092
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.797971
0.728737
-1.094927
-0.858910
-1.097441
0.685812
131
APPENDIX H
OLS Regression Results for Peripheral Regions by Sector Productivity
Dependent Variable: LOG(AHF)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:49
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
13.23801
0.133811
-0.396732
0.792387
0.031783
0.141205
16.70650
4.210145
-2.809618
0.0000
0.0001
0.0075
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.974149
0.966763
0.048058
0.097003
96.31797
131.8916
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
10.66053
0.263607
-3.029744
-2.555284
-2.846266
2.002263
Dependent Variable: LOG(AHF)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:42
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
13.42809
-0.006958
-0.464308
0.734974
0.004066
0.128481
18.27015
-1.711183
-3.613816
0.0000
0.0938
0.0007
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.965771
0.956097
0.054399
0.136126
97.51915
99.83655
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
10.67871
0.259625
-2.783972
-2.295291
-2.592822
1.467716
132
Dependent Variable: LOG(FISH)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:50
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
10.59806
-0.022788
0.164763
4.273851
0.171426
0.761609
2.479744
-0.132931
0.216336
0.0172
0.8949
0.8298
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.869523
0.832244
0.259209
2.821947
3.630802
23.32462
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.59707
0.632864
0.340698
0.815159
0.524176
1.310821
Dependent Variable: LOG(FISH)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:43
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
9.005839
-0.006980
0.469108
3.377638
0.018686
0.590447
2.666313
-0.373551
0.794496
0.0105
0.7105
0.4310
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.875560
0.840393
0.249996
2.874911
6.013370
24.89669
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.64289
0.625759
0.266221
0.754901
0.457371
1.306348
133
Dependent Variable: LOG(MANU)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:50
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
13.45967
0.333768
-0.155833
2.627709
0.105399
0.468263
5.122206
3.166713
-0.332789
0.0000
0.0029
0.7409
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.991119
0.988582
0.159370
1.066755
30.38296
390.6187
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.83471
1.491472
-0.632108
-0.157647
-0.448630
2.240633
Dependent Variable: LOG(MANU)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:45
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
16.43777
-0.030165
-0.729031
2.204071
0.012193
0.385295
7.457912
-2.473848
-1.892136
0.0000
0.0171
0.0648
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.990282
0.987535
0.163135
1.224192
31.62559
360.5715
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
11.92754
1.461194
-0.587520
-0.098839
-0.396370
1.496035
134
Dependent Variable: LOG(TRADE)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:51
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
15.63130
0.085482
-0.880075
0.975723
0.039137
0.173876
16.02023
2.184193
-5.061516
0.0000
0.0346
0.0000
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.997179
0.996372
0.059178
0.147083
84.87087
1237.011
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
10.37405
0.982541
-2.613486
-2.139026
-2.430008
1.319526
Dependent Variable: LOG(TRADE)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:46
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
15.64279
-0.009874
-0.888963
0.789980
0.004370
0.138097
19.80152
-2.259306
-6.437242
0.0000
0.0286
0.0000
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.997044
0.996208
0.058470
0.157264
93.18886
1193.446
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
10.41347
0.949568
-2.639629
-2.150948
-2.448479
1.431743
135
Dependent Variable: LOG(FIN)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:51
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
13.63020
0.114234
-0.015573
3.082352
0.123635
0.549281
4.422014
0.923963
-0.028351
0.0001
0.3608
0.9775
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.758407
0.689381
0.186944
1.467826
21.60604
10.98718
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
13.29164
0.335427
-0.312947
0.161514
-0.129469
1.299811
Dependent Variable: LOG(FIN)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:49
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
14.71909
-0.014261
-0.222250
2.418991
0.013382
0.422866
6.084808
-1.065650
-0.525581
0.0000
0.2921
0.6017
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.758394
0.690115
0.179042
1.474574
26.04295
11.10715
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
13.29323
0.321628
-0.401432
0.087249
-0.210282
1.156204
136
Dependent Variable: LOG(RERBA)
Method: Panel Least Squares
Date: 01/31/15 Time: 14:51
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
14.89731
0.039229
-0.297682
1.996417
0.080077
0.355766
7.462024
0.489888
-0.836737
0.0000
0.6268
0.4075
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.874120
0.838155
0.121083
0.615761
45.49468
24.30433
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
13.09659
0.300976
-1.181625
-0.707164
-0.998147
2.409719
Dependent Variable: LOG(RERBA)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:48
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
17.23626
0.012606
-0.743193
1.651012
0.009134
0.288615
10.43981
1.380113
-2.575037
0.0000
0.1742
0.0133
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.872579
0.836568
0.122200
0.686908
48.96065
24.23134
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
13.07400
0.302275
-1.165355
-0.676675
-0.974205
2.180695
137
APPENDIX I
OLS Regression Results for Peripheral Regions by Sector Employment
Dependent Variable: LOG(AHF)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:15
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
4.927823
-0.023910
0.242819
0.584270
0.023435
0.104118
8.434156
-1.020273
2.332155
0.0000
0.3134
0.0246
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.987029
0.983324
0.035436
0.052740
113.0757
266.3419
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
6.379048
0.274405
-3.639117
-3.164657
-3.455639
1.641863
Dependent Variable: LOG(AHF)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:52
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
5.526110
0.002924
0.146647
0.483521
0.002675
0.084525
11.42888
1.093091
1.734965
0.0000
0.2800
0.0894
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.986635
0.982858
0.035788
0.058916
122.6436
261.2141
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
6.401776
0.273339
-3.621452
-3.132771
-3.430302
1.528353
138
Dependent Variable: LOG(FISH)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:15
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
-1.730109
-0.220049
0.887150
2.285562
0.091675
0.407292
-0.756973
-2.400317
2.178167
0.4533
0.0209
0.0351
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.992492
0.990347
0.138619
0.807042
38.05548
462.6565
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.861035
1.410857
-0.911108
-0.436648
-0.727630
1.725949
Dependent Variable: LOG(FISH)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:54
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
-1.700182
0.020432
0.931155
1.900301
0.010513
0.332193
-0.894691
1.943539
2.803054
0.3756
0.0581
0.0074
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.991556
0.989170
0.140651
0.910003
40.52323
415.5106
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.878708
1.351515
-0.884108
-0.395427
-0.692958
1.802423
139
Dependent Variable: LOG(MANU)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:16
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
3.471211
0.068922
0.139098
1.325819
0.053179
0.236264
2.618164
1.296028
0.588739
0.0122
0.2020
0.5592
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.988956
0.985801
0.080411
0.271568
68.00749
313.4194
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.122568
0.674812
-2.000272
-1.525812
-1.816795
2.357942
Dependent Variable: LOG(MANU)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:56
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
3.885244
0.012773
0.023509
1.011398
0.005595
0.176803
3.841458
2.282789
0.132970
0.0004
0.0271
0.8948
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.989716
0.986810
0.074859
0.257776
78.36395
340.5358
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.149239
0.651800
-2.145465
-1.656785
-1.954315
1.871077
140
Dependent Variable: LOG(TRADE)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:16
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
2.281770
0.101556
0.586441
0.858776
0.034446
0.153036
2.657002
2.948279
3.832052
0.0111
0.0052
0.0004
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.990343
0.987584
0.052085
0.113938
91.89272
358.9394
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
5.439659
0.467436
-2.868826
-2.394365
-2.685348
1.340393
Dependent Variable: LOG(TRADE)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:58
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
3.623171
-0.003500
0.323340
0.787026
0.004354
0.137581
4.603625
-0.803923
2.350188
0.0000
0.4256
0.0231
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.986935
0.983243
0.058252
0.156090
93.41364
267.3012
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
5.453973
0.449998
-2.647121
-2.158441
-2.455971
1.141695
141
Dependent Variable: LOG(FIN)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:19
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
4.834212
0.096732
-0.428976
2.679618
0.107481
0.477513
1.804068
0.899992
-0.898353
0.0784
0.3733
0.3741
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.966560
0.957006
0.162519
1.109317
29.30706
101.1651
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
2.151719
0.783787
-0.592984
-0.118523
-0.409506
1.508812
Dependent Variable: LOG(FIN)
Method: Panel Least Squares
Date: 04/05/15 Time: 15:00
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
5.123991
-0.002937
-0.506027
2.135645
0.011815
0.373334
2.399272
-0.248606
-1.355429
0.0205
0.8048
0.1819
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.965727
0.956041
0.158070
1.149360
33.51786
99.70476
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
2.174243
0.753921
-0.650595
-0.161915
-0.459446
1.452344
142
Dependent Variable: LOG(RERBA)
Method: Panel Least Squares
Date: 01/31/15 Time: 13:20
Sample: 2009 2013
Periods included: 5
Cross-sections included: 11
Total panel (balanced) observations: 55
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(PASSPERCAP)
LOG(COLLEGE_EMP)
3.069286
0.236734
0.046548
2.471139
0.099119
0.440362
1.242053
2.388386
0.105704
0.2211
0.0215
0.9163
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.961332
0.950283
0.149874
0.943418
33.76180
87.01298
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
2.821825
0.672166
-0.754974
-0.280514
-0.571497
2.100359
Dependent Variable: LOG(RERBA)
Method: Panel Least Squares
Date: 04/05/15 Time: 14:59
Sample: 2009 2013
Periods included: 5
Cross-sections included: 12
Total panel (balanced) observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CARGO)
LOG(COLLEGE_EMP)
2.623380
-0.023069
0.078895
2.020183
0.011176
0.353150
1.298585
-2.064178
0.223404
0.2006
0.0447
0.8242
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.958618
0.946923
0.149524
1.028442
36.85269
81.96846
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
2.846906
0.649019
-0.761756
-0.273076
-0.570606
1.536632
143
APPENDIX J
Wholesale and Retail Trade, Regional Share
in National Total Number of Establishments
NCR
CAR
Ilocos Region
Cagayan Valley
Central Luzon
CALABARZON
MIMAROPA
Bicol Region
Western Visayas
Central Visayas
Eastern Visayas
Zamboanga Peninsula
Northern Mindanao
Davao Region
SOCCSKSARGEN
Caraga
ARMM
2001
48.31%
0.98%
2.66%
0.90%
5.83%
8.97%
0.75%
1.63%
4.35%
7.17%
1.78%
3.12%
4.29%
5.52%
2.70%
0.92%
0.13%
2006
48.05%
1.34%
2.89%
0.68%
7.01%
7.80%
0.51%
1.68%
5.64%
7.36%
1.61%
2.34%
4.25%
5.27%
2.27%
1.19%
0.11%
2012
30.31%
1.19%
4.51%
2.98%
9.28%
13.17%
2.17%
2.89%
5.77%
8.30%
2.80%
2.47%
4.07%
5.26%
2.93%
1.65%
0.23%
Source: Census of Philippine Business and Industry, 2001, 2006 and 2012
144
APPENDIX K
Gross Value Added in Real Estate, Renting and
Business Activities by Region
NCR
CAR
Ilocos Region
Cagayan Valley
Central Luzon
CALABARZON
MIMAROPA
Bicol Region
Western Visayas
Central Visayas
Eastern Visayas
Zamboanga Peninsula
Northern Mindanao
Davao Region
SOCCSKSARGEN
Caraga
ARMM
Philippines
2009
2013
288872356
390443752
8491675
12146685
12423928
15969804
6130901
7914728
37453723
49423320
73577435
103060813
5809528
8453926
11302995
14350272
18974945
20866064
34121183
47943021
8158232
7776340
6096561
8169458
9099977
12620017
13105909
20113208
7396717
9885604
3905912
5034029
2944335.828
3766121
547866311.6 737937161.4
growth
35.16%
43.04%
28.54%
29.10%
31.96%
40.07%
45.52%
26.96%
9.97%
40.51%
-4.68%
34.00%
38.68%
53.47%
33.65%
28.88%
27.91%
34.69%
Source: NSCB Regional Accounts of the Philippines.
Values are in ‘000 PHP at 2000 constant prices
145
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