Global Hub Economic Impact Study...Overview of key assumptions and selected values 29 Table 8....

Global Hub Economic Impact Study

A REPORT PREPARED FOR THE GREATER TORONTO AIRPORTS

AUTHORITY

February 2014

February 2014 | Frontier Economics i

Contents

Global Hub Economic Impact Study

(GHEIS)

Executive Summary 1

1 Introduction 6

1.1 Background to the study ............................................................ 6

1.2 What is the study’s objective? .................................................... 6

1.3 How does this study differ from most others? ............................ 7

1.4 How is the report structured? ..................................................... 9

2 How does air connectivity facilitate economic value? 10

2.1 What do we mean by economic value? ................................... 10

2.2 What about causality? .............................................................. 12

2.3 What is our approach? ............................................................. 13

2.4 What is the significance of connecting passengers? ................ 18

2.5 How do we estimate economic value in the future? ................. 19

2.6 Why are our results additional to the DII approach? ................ 20

3 How do we quantify Toronto Pearson’s contribution to

economic value? 21

3.1 “What-if” scenario ..................................................................... 21

3.2 Values for key assumptions ..................................................... 22

4 What are our results? 31

4.1 Economic value facilitated by Toronto Pearson today ............. 31

4.2 Combined results today ........................................................... 33

4.3 Economic value facilitated by Toronto Pearson in 2030 .......... 33

5 Conclusion 39

Appendix 1: Methodology – Economic value today 41

Appendix 2: Methodology – Economic value in 2030 61

Appendix 3: Methodology – Competitive scenarios in 2030 67

Appendix 4: Sensitivity tests 80

Appendix 5: References 82

ii Frontier Economics | February 2014

Contents

Figure 1. Key relationships .................................................................. 2

Figure 2. Overview of project scope .................................................... 7

Figure 3. Summary overview of our approach ..................................... 9

Figure 4. Drivers of economic value considered in analysis .............. 10

Figure 5. The virtuous circle between connectivity and economic value

.................................................................................................... 13

Figure 6. Key relationships ................................................................ 14

Figure 7. How hub status enables more direct connections .............. 19

Figure 8. Passenger volumes to different continents in 2011 ............ 34

Figure 9. Passenger volumes to different continents in 2030 ............ 34

Figure 10. Overview of steps to calculate economic value facilitated

today ........................................................................................... 42

Figure 11. Illustration of differences in trade barriers ......................... 46

Figure 12. Return on investment ........................................................ 48

Figure 13. Outbound flights versus exports ....................................... 50

Figure 14. Trade and business travel by country ............................... 51

Figure 15. Inbound flights versus inward FDI..................................... 52

Figure 16. FDI differential between connected and unconnected

countries ...................................................................................... 52

Figure 17. Evidence on relationship between face-to-face meetings

and trade ..................................................................................... 53

Figure 18. Evidence on relationship between face-to-face meetings

and FDI........................................................................................ 54

Figure 19. Impact of FDI on productivity ............................................ 59

Figure 20. Overview of methodology for future economic value ........ 61

Figure 21. World Bank Oil Price Forecast .......................................... 64

Figure 22. Share of total passenger volumes at Toronto Pearson and

11 hubs in the US ........................................................................ 66

Figure 23. Hub airports considered in our 'competitive scenarios' ..... 68

Figure 24. Growth in passenger volumes 2011-2030 ........................ 69

Figure 25. The distribution of 'contestable' passengers ..................... 74

Figure 26. Network effect on a given route ........................................ 75

February 2014 | Frontier Economics iii

Contents

Figure 27. An example of capturing contestable connecting

passengers .................................................................................. 76

Figure 28. Comparison of passenger volumes at Toronto Pearson ... 79

Table 1. Total value facilitated by Toronto Pearson 5

Table 2. Economic value facilitated by trade, FDI and tourism spending

11

Table 3. Assumptions on passenger types 23

Table 4. Assumptions on Key Relationship 2 24



Table 7. Overview of key assumptions and selected values 29

Table 8. Economic value facilitated by Toronto Pearson today 32

Table 9. Economic value facilitated by Toronto Pearson in 2030 36

Table 10. Overview of economic input data - Trade and FDI links

between Ontario and each geography 43

Table 11. Tourism spending per passenger-visit 56

Table 12. Summary of GDP growth assumptions 63

Table 13. 2030 Passenger volumes at Toronto Pearson and 11

competitor hubs 64

Table 14. Connecting passengers at rival hubs in the US (m) - 2030 72

Table 15. Frequency elasticities of demand (FEDs) 77

Table 16. Worked example of frequency elasticity of demand – Peru

2030 78

February 2014 | Frontier Economics 1

Executive Summary

Executive Summary

Background and project objective

Toronto Pearson currently serves 35 million passengers per year who fly to and

from more than 180 destinations worldwide. 25 per cent of travel facilitated by

Toronto Pearson is outside North America. As a significant and growing hub

airport in North America, Toronto Pearson serves connecting as well as local

passengers, thereby increasing the number of routes it is able to offer. The

airport is closely linked to the local economy and contributes to economic value

in several ways.

It is common for studies on the economic impact of airports to focus on the role

of airports as an employer, and to quantify the direct, indirect and induced

benefits to the economy. This traditional approach to quantifying the economic

value of an airport has been completed by HRD/HLB Decision Economics for

Toronto Pearson and has revealed that in 2012 the airport generates the

following within Ontario:

124,000 direct, indirect and induced jobs;

$12.7 billion of Ontario’s GDP;

Total employment income of $6.3 billion; and

$2.8 billion taxes paid to governments.

However, in only focusing on the airport as an employer – like any other

business – this traditional approach does not capture the unique activities that an

airport facilitates, therefore, it only tells part of the economic value story. The

challenge of this study is to find a way to quantify the airport’s impact on the

economy, focusing on the economic activities facilitated by the connectivity the

airport provides. Our results, relating to the wider economy, can then be added

to the traditional approach of quantifying the direct, indirect and induced impacts

of the airport itself.

The objective of this study is to quantify the economic benefits from air travel

facilitated by Toronto Pearson today and in the future to the economy of

Ontario. Our methodology estimates the contribution of Toronto Pearson to

the economy based on business relationships and leisure spending facilitated by

air connectivity. Our results demonstrate the economic value facilitated today

(based on 2012 data) and in 2030.

How does Toronto Pearson facilitate economic value?

When we refer to ‘economic value’ we mean the impact of the airport on

Ontario’s GDP and employment, as these two metrics are most closely related to

2 Frontier Economics | February 2014

Executive Summary

living standards. We acknowledge that there is a two-way relationship between

air connectivity and economic growth, and that there are many other factors that

influence both connectivity and economic value. As such, we interpret our results

as the economic value facilitated by the airport rather than the economic value

generated by the airport.

The best way of thinking about this is a virtuous circle that links connectivity and

economic growth. Toronto Pearson contributes to economic growth, but is not

the only factor. At the same time, economic growth can create more demand for

connectivity. There is extensive evidence to show that causation works both

ways. Connectivity plays a key part in helping a well-functioning and open

economy to achieve its full potential.

Viewed in this way, it is clear that Toronto Pearson makes a vital contribution to

the Ontario economy. Although connectivity does not itself create economic

activity directly, economic activity would suffer if the connectivity provided by

Toronto Pearson were removed or inhibited in some way.

The way in which air travel relates to GDP and employment – through trade,

foreign direct investment and tourism – is indirect. We estimate the link between

connectivity and economic value by breaking the relationship down into a

number of steps, as illustrated in Figure 1.

Figure 1. Key relationships


Executive Summary

To develop a quantitative value for each of the key relationships, we undertook

an extensive literature review. We considered academic papers, institutional

reports, and industry expertise. Based on this evidence, we selected conservative

assumptions to underpin our results. Section 3.2 and the Appendices provide a

detailed justification of our assumptions.

We estimated the value of Toronto Pearson as a hub airport by comparing the

airport against a “what-if” scenario. Our “what-if” scenario assumes that

Toronto Pearson does not provide direct flights, so all passengers have to take

indirect flights via another hub airport to get to their final destinations. This

“what-if” scenario measures the economic value of being directly connected to

destinations. We concluded that this provides a realistic approach to valuing the

Toronto Pearson’s connectivity as a hub airport.

This study just focuses on the economic value facilitated by passengers travelling

through Toronto Pearson. As such, the economic value facilitated by air freight

that passes through Toronto Pearson is beyond the scope of this study.

Therefore the results can be considered conservative in this respect.

What is the economic value facilitated by Toronto Pearson today?

Based on our approach, we estimated that the economic value to Ontario

facilitated by direct connections from Toronto Pearson today is equal to 3.6 per

cent of Ontario’s GDP, equivalent to $22.7 billion. Based on this estimate,

Toronto Pearson currently facilitates 153,000 jobs. This is comparable to the

total employment in the retail or service sectors in the City of Toronto as

indicated by the 2012 employment survey (City of Toronto, 2012).

Approximately 55 per cent of the results can be attributed to connections within

Canada and to the US, and 45 per cent to connections with other countries. It is

beyond the scope of this report to comment on the type of jobs and sectors

facilitated by connectivity through Toronto Pearson. It is likely the jobs will be

from trade and FDI intensive industries and will also depend largely on

developments in the Ontario economy.

We can combine our results with those from the direct, indirect and induced

analysis because the latter effects relate to the impact of the airport itself and the

spending by people employed in airport-related activities. Our estimates are

additional as they consider the benefits facilitated to the wider economy by the

use of the airport by passengers.

Combining the results from the direct, indirect and induced analysis with our

results, the total economic value facilitated by Toronto Pearson today is:

277,000 jobs or 4.2 per cent of total Ontario employment; and

$35.4 billion or 5.6 per cent of Ontario GDP.


Executive Summary

What is the economic value facilitated by Toronto Pearson tomorrow?

Our baseline projections of travel demand at Toronto Pearson (based on income

growth only) suggest that the airport will handle 60 million passengers in 2030.

This is equivalent to average annual growth of 3 per cent.

Our results suggest that Toronto Pearson will facilitate economic value to

Ontario equal to 4.1 per cent of Ontario’s GDP in 2030, equivalent to $37

billion. The result is slightly greater than for 2012, as demand for travel is partly

based on GDP growth in high-growth economies, which are growing faster than

Ontario GDP.

We estimate that by 2030, Toronto Pearson will facilitate 247,000 jobs in

Ontario. This is equivalent to the combined employment of the manufacturing

and retail sectors in the City of Toronto’s in 2012 (City of Toronto, 2012).

Approximately 49 per cent of this result can be attributed to international

connections (excluding the US), which is a slight increase from the results for

today of around 45 per cent. This is because GDP growth in Canada and the US

is expected to be relatively low compared to emerging markets, such as Brazil,

India and China.

Toronto Pearson has estimated that the direct, indirect and induced economic

value facilitated by the airport in 2030 is 210,000 jobs and $21.6 billion of

Ontario GDP. Combining these results with our results, we estimate that the

total economic value facilitated by Toronto Pearson in 2030 is:

457,000 jobs; and

$58.6 billion or 6.6 per cent of total Ontario GDP.

In addition to the baseline results for 2030, we estimate the additional economic

value that could be facilitated by Toronto Pearson if it increased its market share

in the North American connecting passenger market. We estimate that there will

be 131 million passengers connecting via one of the 11 North American hubs

we examine that would consider Toronto Pearson as a substitute. The

connecting passengers through a hub airport make a significant contribution to

the number of direct connections and the frequency of flights that are viable.

Therefore, the greater number of passenger’s connecting via Toronto Pearson,

the greater the economic value it will facilitate. As an illustrative example, we

estimate the additional economic value if Toronto Pearson attracted 10 per cent

of passengers that start or end their journey in North America, using a US hub

(equivalent to 7.9 million passengers). In this case our approach suggests that

Toronto Pearson would facilitate an additional 0.4 percentage points of Ontario’s

GDP alongside 17,000 jobs. Adding the results from the direct, indirect and

induced analysis undertaken by Toronto Pearson for this scenario, the total

economic value facilitated by Toronto Pearson under this scenario increases by


Executive Summary

19,000 jobs. This scenario illustrates that the success of Toronto Pearson as a

hub airport has direct implications for Ontario’s economy.

Table 1 below summarises the total value facilitated by Toronto Pearson today

and in 2030. The results combine the estimates of the HDR/HLB Direct,

Indirect and Induced study with the results of the Global Hub Economic Impact

Study.

Table 1. Total value facilitated by Toronto Pearson

2012 2030 2030 with an additional

10% of passengers

connecting that start or

end their journey in

North America

GDP facilitated $35.4bn $58.6bn $ 62.1bn

As % of Ontario

GDP

5.6% 6.6% 7.0%

Jobs 277,000 457,000 478,000

Note: This table combined the results of HDR/HLB Direct, Indirect and Induced study with GHEIS study

conducted by Frontier Economics.


Introduction

1 Introduction

1.1 Background to the study

Toronto Pearson currently connects 35 million passengers per year to more than

180 destinations worldwide. 25 per cent of travel facilitated by Toronto Pearson

is outside North America. As a hub airport, Toronto Pearson serves connecting

as well as local passengers, thereby increasing the number of routes it is able to

offer.

The airport is closely linked to the local economy to which it contributes in a

number of ways. This ranges from concrete contributions, such as the number

of employees at the airport, to less tangible impacts, such as the contribution of

Toronto Pearson to the attractiveness of southern Ontario. It is common for

airports to quantify their direct, indirect and induced economic impacts. This

traditional approach to quantifying the economic value of an airport has been

completed by HRD/HLB Decision Economics for Toronto Pearson and has

revealed that in 2012 the airport generates the following within Ontario:





However, in only focusing on the airport as an employer – like any other

business – this traditional approach does not capture the unique activities that an

airport facilitates, therefore, it only tells part of the economic value story. The

challenge of this study was to find a way to quantify the impact of airport on the

economy that focuses on the travel activities facilitated by the airport. Our

results can then be added to the traditional approach of quantifying direct,

indirect and induced impacts.

Toronto Pearson already estimates direct, indirect and induced effect of the

airport. This study completes the picture by considering the economic value

facilitated by the air connectivity itself in addition to the employment generated

by Toronto Pearson.

1.2 What is the study’s objective?

The objective of this study is to quantify the GDP and employment facilitated by

air travel for business purposes and tourism spending. Our methodology

estimates the contribution of Toronto Pearson to the economy from the business

relationships and tourism spending facilitated by air connectivity. Our results


Introduction

demonstrate the economic value to Ontario facilitated today (based on 2012 data)

and in 2030. For 2030, we also illustrate the opportunities for increasing

economic value if Toronto Pearson can attract an increasing share of connecting

passengers from other North American hubs. Figure 2 illustrates that our study

is aimed at providing results for Toronto Pearson today and in the future.

Our results estimate Toronto Pearson’s contribution to Ontario’s economy as the

airport facilitates most of the economic value in the surrounding region. There

are additional benefits from Toronto’s connectivity to other provinces in Canada,

but these cannot be quantified with the same level of certainty. Our results can

therefore be interpreted as conservative estimates.

Figure 2. Overview of project scope

1.3 How does this study differ from most others?

Studies on the direct, indirect and induced impact of airports focus on the role of

airports as an employer. Toronto Pearson has estimated that 124,000 jobs are

generated by the airport. In contrast, this study focuses on the contribution of air

travel to the economy by considering air travel as an input to business activities.

We chose not to use an econometric model to estimate the relationship between

flights and economic growth, as we acknowledge that the airport is not the only

factor that contributes to economic growth, and it would be erroneous to suggest

that the airport is the sole cause. Even though it is possible to account for this

using a sophisticated econometric model, the available data is unlikely to provide

robust results.


Introduction

The best way of thinking about this is that connectivity represents an element in

a virtuous circle of economic activity and growth. Air connectivity through

Toronto Pearson contributes to economic growth, but is not the only factor. At

the same time, economic growth creates more demand for connectivity. There is

extensive evidence to show that the causation works both ways. Connectivity

plays a key part in helping a well-functioning and open economy to achieve its

full potential.

We estimate the link between connectivity and economic value by breaking the

relationship down into a number of steps illustrated in Figure 3. Our

methodology is based on the following premises:

a) Improved connectivity reduces travel times and hence travel costs, which

has a positive impact on the number of business travellers connecting

with Ontario.

b) An increase in the number of business travellers to and from Toronto

implies that they engage in a greater number of face-to-face meetings.

This increases the likelihood of creating or maintaining a successful

business relationship.

c) More successful business relationships have a positive impact on closing

deals, which increases trade and/or foreign direct investment (FDI).

Both trade and FDI have a positive long-term impact on productivity and

therefore GDP and employment.

In addition to the impact of connectivity on economic value via trade and FDI,

we also estimate the contribution to tourism spending. Tourism spending

includes expenditures such as accommodation, food and beverages,

entertainment and land transport.


Introduction

Figure 3. Summary overview of our approach

1.4 How is the report structured?

This report is structured as follows:

Section 2 provides an overview of the key concepts that underpin the

methodology;

Section 3 provides our detailed assumptions for quantifying economic

value;

Section 4 provides our results;

Section 5 provides our conclusions.

We have included more detail on our methodology in the Appendices.

Appendix 1 provides an overview of our key assumptions and data sources

for the estimation of economic value today.

Appendix 2 details assumptions and data sources for our analysis in the year

2030.

Appendix 3 provides more detail on how we estimated the potential market

size for North American connecting passengers;

Appendix 4 provides the results of our sensitivity tests; and

Appendix 5 provides a list of references.

Trade

FDI

Tourism

spending

CONNECTIVITY

Number of

routes and

frequency of

flights

Ease of

accessing

more

destinations

(valued by

monetising the

value of time)

Number of

people

travelling which

has an impact

on the

likelihood of

face-to-face

meeting

ECONOMIC

VALUE

Output

Productivity

Employment

Likelihood of

creating and/or

maintaining

successful

business

relationships


How does air connectivity facilitate economic value?

2 How does air connectivity facilitate

economic value?

This section provides an overview of the key elements of our approach. We first

clarify how we define economic value in the context of connectivity and discuss

the issue of causality. We also provide a detailed description of the key

relationships that underpin our analysis. Lastly, we discuss the role of connecting

passengers and our approach to estimating economic value in the future.

2.1 What do we mean by economic value?

Our analysis is aimed at estimating the economic value facilitated by Toronto

Pearson Airport. It is therefore useful to clarify what we mean by economic

value. Ultimately we are interested in Toronto Pearson’s contribution to

Ontario’s GDP and employment. GDP is generally defined as the sum of all

goods and services produced in the economy, and is therefore closely related to

living standards. Similarly, employment is one of the key factors that determine

economic well-being.

The way in which air travel relates to GDP and employment – through trade,

foreign direct investment and tourism – is indirect.

Figure 4. Drivers of economic value considered in analysis

Table 2 provides an overview of the types of trade, FDI and tourism spending

we include and the types of passengers to which they relate.



Table 2. Economic value facilitated by trade, FDI and tourism spending

Trade FDI Tourism

What is included?

All traded goods regardless of whether they are transported by air, sea or road

All FDI, for example, acquiring production facilities abroad or establishing a subsidiary abroad

All types of visitor spending including accommodation, food and beverages, entertainment, land transport, etc.

Type of passenger

Passengers that travel for business purposes (regardless of their class of travel)

All types of passengers

Inward travel (originating outside of Ontario)

Imports of goods and services facilitated by business travel, including international and interprovincial trade

Investment by non-Canadians in Ontario (interprovincial investment is not included)

Spending by visitors in Ontario

Outward travel (originating in Ontario)

Export of goods and services facilitated by business travel including international and interprovincial trade

Investment by Ontarians in other countries (interprovincial investment is not included)

Spending by Ontario travellers abroad

Note: For more detail on the relevance on interprovincial investment, see Appendix 4

We have reviewed a number of Canadian government policy documents that

advocate that trade and FDI are important drivers of future economic prosperity.

Canada’s State of Trade (2012), a report states that to further position

Canada for long-term prosperity, the government aims to create new and

deeper trade and economic relationships, particularly with large, dynamic

and fast-growing economies.

Advantage Ontario (2012), a report by Ontario’s Jobs and Prosperity

Council highlights key areas that should be targeted to maintain prosperity in

the global economy. These include ‘going global’, driving productivity

growth and encouraging innovation. The report recommends a shift in

export activity to strategically target emerging economies and meet rising

demand.

Collaborating for Competitiveness (2013), a report by the City of Toronto

discusses the importance of attracting companies and investment to Toronto



to encourage business investment and formation. It also emphasizes the

importance of establishing cross-border business partnerships and

developing new markets and trade alliances.

The City of Mississauga’s International Marketing Strategy (2011) outlines

its three key objective areas to increase its competitiveness on the global

stage. This includes creating more global connections and marketing

Mississauga’s advantages to a broad global audience.

All of the policy documents emphasize the importance of trade and investment,

and imply that future economic growth depends partly on the ability to engage

with high-growth potential countries such as Brazil, India and China. In all cases,

the policies support diversifying Canada’s trade links which are currently

dominated by trans-border traffic. This is considered an important factor for

future economic growth and resilience.

Tourism also is considered an important sector, as evidenced by a range of

policies such as Ontario’s Tourism Investment Strategy and Implementation Plan

(2011). Further, the City of Toronto’s report on the Premier-Ranked Tourist

Destination Project (2007) outlines the importance of tourism to the Toronto

economy. It suggests tourism is a key export industry for Toronto that plays an

important role in the growth of the economy by generating employment, foreign

exchange earnings, investment and regional development.

2.2 What about causality?

Studies on the relationship between connectivity and economic value are often

criticized as there are a range of other factors that influence economic value.

This implies that connectivity should be viewed as one of the factors contributing

to economic value.

While connectivity is one important factor that enables international business

relationships to develop, alone connectivity is not a sufficient condition for

economic growth. Clearly, other factors influence both connectivity and

economic value. The best way to describe this relationship is a virtuous circle

(shown in Figure 2 below). The relationship goes both ways: economic growth

creates demand for connectivity, but connectivity enables growth. Both

connectivity and economic value are also influenced by a range of other factors.

In addition, studies on connectivity and economic value often do not take into

account the issue of reverse causality. We acknowledge that there is a two-way

relationship between connectivity and economic value. As such, we interpret our

results as the economic value facilitated by the airport rather than the economic

value generated by the airport.



We are aware of the two-way relationship between connectivity and economic

value and have chosen not to use an econometric approach as this is unlikely to

provide robust results. Instead we identify a number of key steps that link

connectivity to economic value.

But the fact that causation works both ways does not devalue the vital and

unique contribution that an airport like Toronto Pearson makes to its local

economy. The best way of thinking about this is that connectivity represents an

element in a virtuous circle of economic activity and growth. The connectivity

enabled by Toronto Pearson is not a sufficient condition on its own for creating

economic activity, but the role the Airport plays in the economy is a necessary

condition in helping a well-functioning and open economy to achieve its full

potential.

Figure 5. The virtuous circle between connectivity and economic value

2.3 What is our approach?

In order to quantify Toronto Pearson’s contribution to economic value, we

divided the relationship between connectivity and economic value into a number

of steps. Detail on each of the steps is in Appendix 1 and 2. Figure 6

summarizes the four key relationships we have identified. Each of the

relationships is explained in detail below.



Figure 6. Key relationships

Key relationship 1 – Macroeconomic trends and hub competition

determine connectivity at Toronto Pearson

We define connectivity at Toronto Pearson as the number of direct connections

and the frequency of flights. The level of connectivity at Toronto Pearson is

largely determined by two factors:

a) Economic growth in Ontario and in the countries around the world that

can be reached via Toronto Pearson. There is established literature on

the income elasticity of air travel which shows how air travel demand

increases with income. This includes, for example, the International Air

Transport Association (IATA) (2007) and UK Department for Transport

(2013).

b) Different scenarios of competition with other North American hub

airports have an impact on the number of passengers connecting through

Toronto Pearson. Should Toronto Pearson capture a greater market

share of the North American connecting passenger market the number of

destinations served and the frequency of flights at Toronto Pearson

would increase.

Both of these factors influence the level of connectivity.



Key relationship 2 – A change in connectivity has an impact on

demand

A change in connectivity has an impact on the travel times for local passengers.

For example, an indirect flight may take an additional 2-3 hours in travel time

when compared to a direct flight. A passenger makes a decision to travel based,

in part, on the travel time. As a direct connection is always faster than flying

indirect via another airport, some passengers will choose not to travel if there is

no direct connection available or may travel less frequently. However,

connectivity therefore has an impact on demand. More detail on this relationship

is provided in Appendix 1.

Key relationship 3 – A change in the number of local passengers

impacts tourism spending and international business deals

A change in the number of local passengers has two impacts. The first impact is

a direct effect on tourism spending: if fewer people visit Ontario (regardless of

their travel purpose), tourism spending decreases. Tourism spending includes

accommodation, food and beverages, entertainment and land transport.

The second impact applies to business passengers only. A drop in the number of

business passengers indicates a decrease the number of face-to-face meetings.

While some face-to-face meetings can be substituted by using video or

teleconferencing, face-to-face meetings still play an important role in facilitating

business deals. They are a key mechanism for building trust, which is important

for establishing new business relationships, and even more important when

conducting business across different cultures. In situations where business

partners do not share a common language or culture and where business

regulations vary significantly, it is important to get to know the other parties to

ensure a successful relationship.

We reviewed a large body of literature from a range of sources that provides

evidence on the importance of face-to-face meetings for business. Here are

some examples.

The World Travel and Tourism Council (WTTC) (2011) conducted a survey

of business travellers and asked about the importance of personal contact

which revealed that:

28 per cent of existing business could be lost without face-to-face

meetings; and

Sales conversion rates are estimated to be 20-25 per cent higher with

face-to-face meetings.



Frankel (1997) illustrates the importance of face-to-face meetings as follows:

Consider a kind of export important to the United States: high-tech capital goods. To

begin sales in a foreign country may involve many trips by engineers, marketing people,

higher ranking executives to clinch a deal, and technical support staff to help install the

equipment or to service it when it malfunctions.

A survey by the UK Institute of Directors (2008) asked about the impact on

businesses if the amount of business travel by air was significantly curtailed.

30 per cent of respondents said that there would be significant adverse

effect, while 44 per cent indicated small adverse effects.

Poole (2010) finds that business travel to the United States by non-residents,

non-citizens has a positive impact on the extensive export margin.

Connectivity is also one of the factors that influence decisions on where to

locate business headquarters. For example, Strauss-Kahn and Vives (2005)

find that:

Headquarters relocate to metropolitan areas with good airport facilities, low corporate

taxes, low average wages, high levels of business services, and agglomeration of

headquarters in the same sector of activity. The effects are quantitatively significant

(airport facilities in particular).

Overall this literature review suggests that a drop in the number of business

passengers is likely to have an adverse effect on trade and FDI, as face-to-face

meetings are an important factor in establishing and consolidating business

relationships. More detail on this relationship is provided in Appendix 1.

Key relationship 4 – A change in trade, FDI and tourism spending has

an impact on GDP and employment

Changes in trade, foreign direct investment and tourism spending affect GDP

and employment. Tourism spending directly impacts on GDP. Spending by

Ontarians abroad has a negative impact on GDP, given that the economic

benefits accrue outside of Ontario. Spending by visitors in Ontario positively

impacts on GDP, given it involves an inward flow of economic value.

With respect to trade and FDI, we have distinguished the short-term static

impact on GDP and the long-term dynamic impact. The short-term view of

trade is that exports have a positive impact on GDP and imports have a negative

impact – this is based on a country’s trade balance in an accounting context. The

same holds for inward and outward investment. An equal increase in exports

and imports would therefore have no impact on GDP, as the positive impact of

exports would cancel out the negative impact of imports.



However, this short-term view does not take account of the long-term dynamic

effects of having an open economy. An open economy that trades with the rest

of the world – both importing and exporting – is likely to be more productive in

the long term. Productivity is one of the key drivers of GDP growth as it

describes the efficiency of production. For example, if the same output can be

produced with fewer inputs, productivity increases. We reviewed a large body of

academic research that investigates the positive impact of imports and exports as

well as inward and outward investment on long-term productivity. Most of the

literature is focused on examining the impact of trade and FDI on productivity at

the firm level. The literature suggests that not only do exports and inward

investment have a positive impact on productivity growth but imports and

outward investment also contribute to the level of “openness” of the economy,

which has a positive impact on productivity.

There are three main channels by which imports, exports, inward and outward

investment can increase long-term productivity.

a) Innovation – Trade is one of the key “transmitters” of innovation as it

exposes companies to a wider range of products and processes in other

countries. FDI can provide access to new technologies and cheaper

inputs, which has a positive impact on productivity. This is particularly

true for imports and outward investment.

b) Competition puts pressure on companies to be more efficient.

Exporting companies are faced with more competition as they compete

in a larger market. Imports also put more pressure on domestic firms as

they compete with a greater number of competitors.

c) Economies of scale – Larger market sizes imply that production

processes can benefit from economies of scale. Both trade and FDI can

provide access to markets outside Ontario so that firms can reduce costs

by realizing economies of scale. This is particularly true for exporting

firms who can access foreign markets and therefore increase their size.

For example, the OECD, (2012) finds that:

A main channel through which trade increases income is productivity growth.

Importing creates competition that forces domestic firms to become more efficient and

provides access to inputs of international calibre; exporting creates incentives for firms

to invest in the most modern technologies, scales of production and worker training.

The combined effect is to spawn a process of continual resource reallocation, shifting

capital and labour into activities with higher productivity.

This illustrates the combined effect of exports and imports. Similarly, the

Canadian federal government (2012) states that the US-Canada free trade

agreement has increased productivity in Canadian manufacturing by 13.8 per

cent, which is considered a remarkable trade-related achievement. This



achievement is based on both exports and imports. More detail on this

relationship is provided in Appendix 1.

Instead of focusing on the short-term impact of trade and FDI on GDP our

methodology emphasises the long-term benefit that trade and FDI generate by

increasing “openness” of the economy. Therefore, our conclusion is that both

exports, imports alongside inward and outward investment, have positive long-

term effects on an economy.

2.4 What is the significance of connecting

passengers?

Connecting passengers often only spend a few hours at the airport while they

wait for their connecting flight. It is therefore a common misperception that

connecting passengers contribute little to economic value compared to local

passengers. However, the traffic of connecting passengers through a hub airport

makes a significant contribution to the number of direct connections and the

frequency of flights. In order for a direct route to become viable, a minimum

level of demand is required. If this level of demand cannot be met by local

passengers, connecting passengers can play an important role as they can take the

level of demand above the threshold needed for a direct flight. Connecting

passengers can therefore contribute to route profitability. Local passengers then

benefit from the availability of additional direct services that might not be viable

without connecting passengers.

Figure 7 illustrates how connecting passengers can facilitate economic value. An

increase in connecting passengers may be the result of a new or expanded route

or may be the result of an increase in demand for connecting via Toronto

Pearson. A higher number of connecting passengers can result in a further

increase in destinations served and in flight frequencies. In addition, airlines may

also operate larger aircraft. As a result, travel time is reduced for local passengers

which can have a positive impact on demand. As some of these are business

passengers, they contribute towards FDI and trade, which have a positive impact

on GDP.



Figure 7. How hub status enables more direct connections

2.5 How do we estimate economic value in the

future?

For our analysis of the future, we assumed that demand for travel to and from

Toronto Pearson grows in proportion with economic growth. We used GDP

projections and income elasticities of demand for air travel as an input and then

apply the same approach for the year 2030.

Any projection of the future is inevitably uncertain. Our approach was a

“business as usual” one in which traffic at Toronto Pearson grows at the

expected rate, given economic growth, but not taking into account any changes

in market share between hub airports that might come about as a result of the

competitive interaction in the market over the next twenty years. This results in

projected average annual growth of 3% which is in line with GTAA’s experience

over the past 20 years.

To examine the potential impact of these competitive interactions, we also

considered the opportunities for Toronto Pearson to grow over and above this

business as usual scenario by attracting connecting passengers from other North

American hubs. We estimated the potential size of the market for these

connecting passengers by considering 10 other North American hubs: Atlanta,

Charlotte, Denver, Dallas, Detroit, Newark, Houston, New York, Los Angeles,

Miami and Chicago. We estimated the number of connecting passengers that

could consider Toronto Pearson to be a substitute hub based on the differences

in travel time (see Appendix 2 for more detail).

We applied the conditions above to estimate the total size of the connecting

passenger market for which Toronto Pearson competes. We then ran different

types of scenarios of hub competition. Linking these scenarios to our economic

Increase

number of

connecting

passengers

Increased number of

direct connections (that

were previously

indirect)

Reduced journey time

for local travellers

Increased frequency on

routes (e.g. twice a

week to daily)

Reduced journey costs,

improved access for

local travellers

Increase in

number of

local OD

passengers

These additional local OD passengers

create additional economic value (FDI,

trade and tourism spending)



value calculation enabled us to estimate the size of the opportunity for further

growth.

2.6 Why are our results additional to the DII

approach?

The analysis of direct, indirect and induced benefits has been undertaken by

HRD/HLB Decision Economics for Toronto Pearson and has revealed that in

2012 the airport generates the following within Ontario:





In 2030, Toronto Pearson has estimated that the airport generates:


$21.6 billion of Ontario’s GDP.

These measures record the contribution that Toronto Pearson Airport makes to

the local economy as a major employer. Not only are there many people directly

employed by the airport, but these individuals then spend a significant

proportion of their incomes on Ontario and Canadian goods and services, which

further contributes to local GDP and employment.

However, these direct, indirect and induced effects can be computed for any

employer in the region and are only one aspect of the contribution that the

airport makes. The airport also plays a significant role as a facilitator of economic

growth across the local economy. The airport helps to support growing output

and employment in many sectors because businesses use the airport’s services to

develop trade and investment links with other businesses around the world. In

addition the airport facilitates tourism, which results in visitor spending in the

local economy.

Without the connectivity provided by Toronto Pearson, Ontario would be less

accessible to the global economy and, as a consequence, Ontario could miss out

on significant opportunities for growth.

This report quantifies the dynamic impact that Toronto Pearson has on Ontario’s

GDP and employment in addition to the direct, indirect and induced effects

quantified by HRD/HLB Decision Economics. It does so by examining the links

between connectivity, trade, investment and productivity growth. These impacts

can then be added to the direct, indirect and induced impacts.


How do we quantify Toronto Pearson’s contribution to economic value?

3 How do we quantify Toronto Pearson’s

contribution to economic value?

This section describes our detailed approach to quantifying the economic value

facilitated by Toronto Pearson. We first discuss how we developed the “what-if”

scenario and then describe the values and justifications of our key assumptions.

More detail on our assumptions is provided in Appendix 1.

3.1 “What-if” scenario

To quantify Toronto Pearson’s’ contribution to economic value today, we

consider the economic value that would be lost if Toronto Pearson did not

provide the current level of connectivity. The size of the loss can then be

interpreted as the value facilitated by the current level of connectivity. There are a

number of options for defining the “what-if” or counterfactual scenario.

First, we considered a “what-if” scenario in which Toronto Pearson does not

exist. In this scenario air connectivity to and from Toronto would be severely

decreased and travel times would increase substantially. However, we do no not

think this is a credible approach as it would lead to an unrealistically large

estimate of Toronto Pearson’s value.

Instead, we took a more conservative approach. Our “what-if” scenario assumes

that Toronto Pearson does not provide any direct flights, so all passengers have

to take indirect flights via another hub airport to get to their final destinations. As

such, our “what-if” scenario measures the economic value of being directly

connected to destinations. We concluded that this provides a realistic approach

to valuing Toronto Pearson’s connectivity as a hub airport.

To develop a realistic view of the alternative travel times of indirect connections,

we selected four North American hub airports for indirect international

connections from Toronto. These were: Chicago, Atlanta, New York and Los

Angeles. For indirect connections in Canada we added 2.5 hours of travel time

to reflect the availability of a range of airports that could be used for connections.

In addition, we also considered road and rail alternatives to destinations within

800 kilometres of Toronto to capture the possibility that some passengers would

use these modes of transport as an alternative to flying.

We can illustrate the “what-if” scenario with the following example: passengers

travelling on a direct flight from Toronto to London, UK take about 7 hours. In

the “what-if” scenario the travel time increases by just less than three hours, as

passengers would have to fly via New York. As a result, a small proportion of

passengers would choose not to take the trip as the increase in travel time implies



that the trip is not worthwhile. It is the impact of this reduction in passengers

that measures the economic value of a direct connection to London, UK

provided by Toronto Pearson.

In addition to the reduction in passengers in the what-if scenario, there may also

be loss of productivity for the remaining passengers who must spend more time

on essential business travel. However, we do not attempt to measure this effect

as it requires a number of assumptions on the effect of increased travel time on

economic output. This can be considered a conservative assumption.

3.2 Values for key assumptions

3.2.1 General assumptions

To apply the key relationships outlined in section 2.3, we have quantified these

relationships. We have undertaken an extensive literature review, including

academic papers, industry research and Canadian as well as other government

reports. This section provides an overview of the key values we have chosen for

different assumptions. For more detail on the literature we have reviewed, see

Appendix 1. All of the assumptions we made are based on the best available

evidence and we chose conservative values in all cases. We acknowledge that the

evidence base for some of the assumptions is still evolving, and therefore

changes in the assumptions may be required in the future. We have attempted to

show all of our assumptions in an open and transparent way.

Business passengers

To estimate the economic value facilitated by Toronto Pearson we need to

distinguish different passenger types. Table 3 shows that we have assumed 40

per cent business passengers, based on survey data provided by Toronto Pearson.

As detailed information on the split of Canadian and foreign passengers on each

route is not available, we have used an aggregate figure from Statistics Canada

that is applied to all routes.



Table 3. Assumptions on passenger types

Parameter Assumed value Rationale / Source

Business passengers 40 per cent Based on Toronto Pearson

survey data

Proportion of Canadian/

non-Canadians on each

route

70 per cent Canadian /

30 per cent non-

Canadian

Based on Statistics Canada

this is an aggregate figure for

all routes.

Impact of connectivity on demand

Table 4 provides the assumptions we made to quantify Key Relationship 2. If

only indirect flights are available, passenger demand would drop as a response to

the increase in travel time. First, we calculated the increase in travel time based

on the additional distance travelled and added two hours of layover time at the

connecting airport. Second, we monetized the additional travel time by applying a

“value of time” to the additional journey time. This approach is commonly used

in land transport evaluation. For business travellers, we assumed a value of time

of $75 per hour and for leisure travellers we assumed a value of $22.50 per hour.

These are based on average wage rates as shown in Table 4. We further assumed

that there would be no change in ticket prices between direct and indirect routes.

This assumption was informed by an analysis of price data from Sabre that shows

no difference in average ticket prices for indirect and direct flights on the same

route. Finally, we used price elasticities of demand to estimate the change in

demand as a result of the price increasing due to an increase in travel time. We

distinguish different price elasticities for different countries, based on a study by

IATA (2007).



Table 4. Assumptions on Key Relationship 2


Flight speed 500mph during flight,

250mph for take-

off/landing

Based on industry standards.

Average airport

connecting time

2 hours Based on a conservative estimate of

the minimum connection time.

Travel Time Value

Business

Travellers

$75 per hour Double the average wage rate of

management occupations (Statistics

Canada Table 282-0070 Labour force

survey estimates (LFS), wages of

employees by type of work)

Travel Time Value

Leisure and VFR

(visiting friends

and relatives)

$22.50 per hour Based on average wage (Statistics

Canada Table 282-0070 Labour force

survey estimates (LFS), wages of

employees by type of work)

Price Increase for

direct v. Indirect

Routing

Zero Based on data from Sabre on fares

which revealed that there is no price

difference between direct and indirect

flights on the same route from/to

Toronto Pearson

Price elasticities Transatlantic: -0.72

Transpacific: -0.36

Intra America

(including of North and

South America): -0.60

Based on IATA (2007)

Relationship between connectivity and trade, FDI and tourism spending

Table 5 presents the assumptions for Key Relationship 3. A change in the

number of business passengers leads to a change in trade and FDI. We

acknowledge that these two assumptions are the most difficult to evidence, as the

literature focuses on qualitative evidence rather than quantitative data. We

considered a range of sources and also analysed flights and trade and FDI in

Ontario. We selected 0.3 as the elasticity for both trade and FDI, as we consider

this to be at the conservative end of the scale.

We have distinguished these elasticities for air travel between different countries,

because the relationship between face-to-face meetings and trade and FDI is

unlikely to be the same between Ontario and the US and other Canadian



provinces as it is with the rest of the world. Face-to-face meetings are likely to

play a bigger role in overcoming trade barriers between economies that are more

dissimilar. The most common trade barriers include:

Product market regulation – A range of different types of regulation

(for example, product standards, safety regulation, etc.) can inhibit trade

and FDI across borders.

Tariffs and quotas, local content requirements – Formal trade

barriers such as tariffs also reduce the likelihood of trade.

Exchange rate – The risk of changes in the exchange rate can pose a

significant barrier to trade and FDI as exchange rate volatility can

increase the spread of potential returns.

Cultural differences – Language differences and different business

cultures can impede business relationships across cultures as it is more

difficult to build trust.

Business travel is one way to reduce or overcome some of these barriers, as face-

to-face meetings enable a better understanding of local product market regulation

and formal trade barriers. Face-to-face meetings are also one of the key ways to

build trust across cultures. Trade barriers between Ontario and the US are almost

certainly lower than the trade barriers between Ontario and the rest of the world.

This is because cultural differences are much smaller (for example, common

language), formal trade barriers have been removed by NAFTA, and product

marker regulations are more likely to be aligned. As a result, we think that face-

to-face meetings facilitated by air travel in the US have a smaller impact on trade

and FDI. Trade barriers between Ontario and other provinces in Canada are

likely to be even lower, as there is no exchange rate risk and product market

regulation is even more likely to be harmonized. We believe that similar

reasoning applies to the relationship between air travel and FDI.

Tourism spending per person is based on Statistics Canada and the Ontario

Ministry of Tourism, Culture and Sport, and is distinguished by country.




Parameter Assumed

value

Rationale / Source

Business travel elasticity of

trade - change in trade as a

result of a 1 per cent drop in

business travel

0.3 For travel between Ontario and

international countries except the US.

Based on literature review, see

Appendix 1 for more detail

0.2 For travel between Ontario and the

US.

0.1 For travel between Ontario and other

provinces in Canada.

Business travel elasticity of

FDI - Change in FDI as a result

of a 1 per cent drop in

business travel.

0.3 For travel between Ontario and

international countries except the US.

Based on literature review, see

Appendix 1 for more detail

0.2 For travel between Ontario and the

US.

0.1 For travel between Ontario and other


Tourism spending $460 -

$1,280

Average outward tourist spend per

visit by Ontarians, depending on

country visited

$440 -

$1,990

Average inward tourist spend in

Ontario per visit depending on

country of origin

Relationship between trade & FDI and long-run GDP

Table 6 provides the assumptions underpinning Key Relationship 4. Trade and

FDI have a positive impact on GDP. The relationship between trade and GDP

is a well-established research topic and the value we use to relate openness to

GDP is based on OECD research quoted by the Government of Canada. We

have used a lower value for interprovincial trade as the impact on productivity is

likely to be lower. For example, Therrien and Hanel (2012) provide evidence

supporting the idea that the productivity gains from trade are stronger with trade

to foreign markets compared to the domestic market. They find that Canadian

firms who export to foreign markets have higher labour productivity.



It is more difficult to obtain evidence on the relationship between outbound and

inbound FDI and GDP so our assumptions are based on the best available

evidence. This includes research by the German Institute for Economics

Research on outward FDI and economic growth and the Korea Institute for

International Economic Policy on the impact of inward FDI in Ireland.

Our assumption on Ontario’s GDP per job is based on the same ratio that is

used by the federal government in Free Trade Agreement impact assessments.


Parameter Assumed

value

Rationale / Source

Openness

elasticity of GDP

(Openness is

defined as

trade/GDP)

0.44 For international trade, based on OECD

study quoted by Canada’s State of Trade

and Investment Update (2012) by Foreign

Affairs and Trade International

0.2 For interprovincial trade, see Appendix 1 for

more detail

Outbound FDI

elasticity of GDP

0.19 Based on literature review, see Appendix 1

for more detail

Inbound FDI

elasticity of GDP

0.24 Based on literature review, see Appendix 1

for more detail

GDP per job $150,000 Based on Canadian government figures for

free trade agreement impact assessments.

3.2.2 Assumptions specific to the 2030 analysis

There are a number of assumptions that are specific to the analysis of projected

future economic value. Table 7 summarizes the values and sources we have used

for the analysis in 2030. First, we have used GDP projections by HSBC Bank for

each country. We have used the HSBC source as it provides projections for a

large number of countries up until 2030. There are few alternative sources that

provide projections for so many counties over such a long time period. To

ensure the robustness of the HSBC projections we have cross-checked them

against projections by international institutions such as the International

Monetary Fund. Appendix 4 provides a sensitivity test on the assumption about

US and Ontario growth. Second, we assume that ticket prices do not increase in

real terms. Our research indicates that oil prices are expected to fall over the

medium-term (as a result of the ongoing global recession), so we have used zero

real price increases as a conservative assumption. This assumes that nominal



prices will increase in line with inflation, on average. Third, we assume that

aircraft size is expected to increase on average at 1 per cent per year. Industry

projections around this figure vary and we consider 1 per cent to be a

conservative assumption.

As passenger demand increases in the future, the route network can change in

two ways: new direct connections become viable and the frequency of

connections increases. If the demand on an indirect route from Toronto

Pearson grows sufficiently to justify a new direct connection, we assumed that

this direct connection would be provided by 2030. We then estimated an

increase in passengers due to the reduced travel time. If demand increases on an

existing direct route, frequency of flights can also increase. We used frequency

elasticities to estimate additional demand generated by more frequent

connections. We used two different frequency elasticities depending on the

initial frequency, as there are likely to be diminishing returns to frequency. For

example, increasing frequency from three times a week to daily is likely to have a

larger impact than increasing frequency from once to twice daily.

Our assumptions on income elasticities are based on IATA (2007). We

distinguish different income elasticities for countries with different levels of

income. Countries with high levels of income are likely to have lower income

elasticities than countries with lower levels of income.



Table 7. Overview of key assumptions and selected values


Annual GDP

forecast by

country 2012-

2030

0.4 per cent - 7.9 per

cent depending on

country

HSBC (2012) growth forecasts, cross-

checked against IMF forecasts

Annual real

ticket price

change

Zero change The key input is oil prices (accounts for

34 per cent of total airline costs

according to IATA), oil price forecast to

decrease so we used zero as a

conservative assumption. This is in line

with Airbus’ assumption (Airbus, 2012).

This assumes nominal prices will

increase in line with inflation.

Annual

technology

growth in

aircraft size

1 per cent We expect aircraft size to grow and have

used 1 per cent as a conservative

assumption.

Frequency

elasticity

For low-frequency

countries: 0.8

For high-frequency

countries: 0.6

The frequency elasticities are based on

a literature review. Frequency cut-off

(flights per day based on 2011 data): 0.5

Income

elasticities

Various between

1.22 and 2.03

Based on IATA (2007)

To estimate the size of the North American connecting passenger market that

could use Toronto Pearson airport as a substitute, we applied the following

conditions:

a) Connecting passengers who start and end their journey in the US who

cannot connect via Toronto Pearson;

b) To be a potential Toronto Pearson connecting passenger, the travel

distance via Toronto Pearson must be less than the travel distance of any

rival US hub airport that is active in the market; or

c) If the travel distance via Toronto Pearson is longer than all the rival hubs

that are active in the market, then the travel distance via Toronto Pearson

must be within 10 per cent of the shortest travel distance.



d) For routes that only have connections via one hub airport, the condition

is that travel time is less than 10 per cent longer than the existing

connection.

We consider these assumptions to be reasonable, as Toronto Pearson is clearly

not a substitute for all connecting passengers. For example, it would not be

reasonable to assume that journeys from the western US to Asia could connect

via Toronto Pearson.


What are our results?

4 What are our results?

4.1 Economic value facilitated by Toronto Pearson

today

Based on the approach described in section 2 and the assumptions described in

section 3, the economic value to Ontario facilitated by Toronto Pearson today

equates to 3.6 per cent of Ontario’s GDP, equivalent to $22.3 billion. This is

the value of having direct as opposed to indirect air connections from Toronto

Pearson.

Based on this estimate, Toronto Pearson currently facilitates 153,000 jobs within

Ontario. If Toronto Pearson only provided indirect connections instead of direct

connections, 153,000 jobs would be lost. This is comparable to the total

employment in the retail or service services sectors in the City of Toronto as

indicated by the 2012 employment survey (City of Toronto, 2012).

Approximately 55 per cent of the results can be attributed to connections within

Canada and to the US, and 45 per cent can be attributed to connections with

other countries.

GDP and jobs are driven by trade and FDI that is facilitated by connectivity to

and from Toronto Pearson, as our results show:

Exports: $9.7 billion exports, which is equivalent to 5.2 per cent of

Ontario’s total exports. Approximately, 65 per cent of those exports are to

the US, 20 per cent to other international countries and 15 per cent to other


Imports: $12.6 billion imports, which represents 4.8 per cent of Ontario’s

total imports. Approximately, 54 per cent of those exports are to the US, 33

per cent to other international countries and 13 per cent to other provinces

in Canada.

FDI: $40.3 billion of the total inward and outward FDI stock, which is close

to 5 per cent of the Ontario’s total FDI stock.

GDP and jobs are also influenced by tourism spending. Tourism spending

facilitated by Toronto Pearson has a net negative impact on GDP, as spending by

Ontarians abroad is greater than spending by visitors in Ontario. The tourism

spending facilitated by Toronto Pearson can be summarized as follows:

$310 million of tourism spending by visitors in Ontario, which is

equivalent to 4.3 per cent of total tourism spending in Ontario; and



$807 million of tourism spending by Ontarians abroad, which is

equivalent to 4.3 per cent of total tourism spending by Ontarians abroad.

This is because Ontario has a negative tourism spending balance. In 2010, visitor

spending in Ontario totalled $7.3 billion whereas spending by Ontarians abroad

totalled $17.9 billion (Ontario Ministry of Tourism, Culture and Sport, 2010).

Table 8 below shows a breakdown of our results for today.

Table 8. Economic value facilitated by Toronto Pearson today

$ million

Exports 9,686

Imports 12,623

Total trade 22,300

GDP facilitated by trade 9,253

Outward FDI 20,672

Inward FDI 19,632

Total FDI 40,300

GDP facilitated by FDI 13,960

Tourism exports 310

Tourism imports 807

Net tourism - 497

GDP facilitated by tourism - 497

Total GDP facilitated 22,700

% of Ontario GDP 3.6%

Jobs 153,000

Source: Frontier analysis, numbers may not add up due to rounding

We have cross-checked our results against two econometric studies.

The International Air Transport Association (IATA) (2007) estimates that a

10 per cent rise in connectivity relative to a country’s GDP will increase



labour productivity levels by 0.07 per cent. Applying the IATA study results

to our “what-if” scenario, IATA would suggest a much bigger impact of air

travel on GDP than we have estimated.

The World Travel and Tourism Council (WTTC) (2011) estimates that for

each 1 per cent drop in business travel, GDP decreases by 0.12 per cent.

Our results are in the same ballpark, as a 1 per cent drop in business travel

would lead to a 0.128 per cent decrease in GDP based on our results.

Our cross-check implies that our results are conservative and reasonable.

Recall, the above results are a quantification of the economic value facilitated by

the airport as a result of air travel. The economic value generated by the airport

as a result of employment is quantified by earlier, traditional economic modelling

conducted by HRD/HLB Decision Economics on behalf of the Airport.

4.2 Combined results today

Combining our approach with the results of the direct, indirect and induced

analysis, the total value facilitated by Toronto Pearson today is:

277,000 jobs or 4.2 per cent of total Ontario employment; and

$35.4 billion or 5.6 per cent of Ontario GDP.

4.3 Economic value facilitated by Toronto Pearson in

2030

4.3.1 Passenger volumes in 2030

Our baseline projections of travel demand at Toronto Pearson (based on income

growth only) suggest that the airport will handle 60 million passengers in 2030.

This is equivalent to average growth of 3 per cent per year. Figure 8 and Figure

9 illustrate that growth is not evenly distributed across the world. Travel to and

from high growth countries such as Brazil, India and China will increase faster

than travel to and from North America and Europe. The baseline scenario only

takes into account income growth, and it is assumed that Toronto Pearson’s

market share of the North American connecting passenger market remains

unchanged.



Figure 8. Passenger volumes to different continents in 2011

Figure 9. Passenger volumes to different continents in 2030

4.3.2 Results in 2030

Our results suggest that Toronto Pearson will facilitate economic value to

Ontario equal to 4.2 per cent of Ontario’s GDP in 2030, equivalent to $37.0

billion. The result is slightly bigger than for 2012, as demand for travel grows

faster than Ontario’s GDP growth as it is partly based on GDP growth in high

growth economies.

4.2m pax1.7m pax

2.5m pax

0.5m pax

25m pax

7m pax4m pax

7m pax

1m pax

41m pax



We estimate that by 2030 that Toronto Pearson will facilitate 247,000 jobs in

Ontario. This is equivalent to the combined employment of the manufacturing

and retail sectors in the City of Toronto’s in 2012 (City of Toronto, 2012). It is

estimated that 49 per cent of this result can be attributed to international

connections (excluding the US), which is a slight increase from 45 per cent in

2012. This is because GDP growth in Canada and the US is expected to be

relatively low compared to some of the emerging markets, such as Brazil, India

and China.

Our results for the trade and FDI figures, that underpin the economic value

results for 2030, are:

Exports: $15.5 billion of exports;

Imports: $21.8 billion of imports; and

FDI: $67.5 billion of FDI stock.

In 2030 the impact of tourism spending is still negative with the amount

facilitated by Toronto Pearson estimated to be:

$431 million of spending by visitors in Ontario; and

$1,103 million of spending by Ontarians abroad.

Table 9 below shows a breakdown of our results for 2030.



Table 9. Economic value facilitated by Toronto Pearson in 2030

$ million

Exports 15,501

Imports 21,846

Total trade 37,347

GDP facilitated by trade 15,428

Outward FDI 38,696

Inward FDI 28,837

Total FDI 37,347

GDP facilitated by FDI 23,302

Tourism exports 431

Tourism imports 1,103

Net tourism -671

GDP facilitated by tourism -671

Total GDP facilitated 37,058

% of Ontario GDP 4.12%

Jobs 247,056

Source: Frontier analysis, numbers may not add up due to rounding

Combined results in 2030

Combining our approach with the results of the direct, indirect and induced

analysis, the total value facilitated by Toronto Pearson today is:

457,000 jobs; and

$58.6 billion or 6.6 per cent of total Ontario GDP.



4.3.3 Size of the opportunity

Our baseline results for 2030 represent a “business as usual” projection of traffic

at Toronto Pearson, assuming that it maintains its market share of connecting

passengers relative to other North American hub airports. Clearly in a dynamic

world this cannot simply be relied upon to happen. There are both threats to this

position if other hub airports seek to win market share from Toronto Pearson,

and opportunities if Toronto Pearson itself is successful in increasing its share.

To illustrate the economic value associated with these competitive uncertainties

we created an additional scenario in which we estimated the additional economic

value that could be facilitated by Toronto Pearson if it increased its market share

in the North American connecting passenger market by 2030.

We estimate that by 2030 there will be 131 million passengers connecting via

one of the ten North American hubs that could consider Toronto Pearson as a

substitute. This market estimation is based on the two factors described in

section 2.5. The results are based purely on the routing of passengers. They

therefore include a number of Star Alliance hubs as the structure of the airline

market may be changed in 2030. The market size of 131 million can be broken

down as follows:

82 million connecting passengers are flying on routes that Toronto Pearson

is already connected to; and

49 million connecting passengers are flying on routes that Toronto Pearson

is not connected to.

This implies that there are considerable growth opportunities for Toronto

Pearson as a hub airport.

As an illustrative example, we estimated the additional economic value if Toronto

Pearson attracted an additional 10 per cent connecting passengers that use a rival

hub in the US and originate in North America (equivalent to 7.9 million

passengers). In this case Toronto Pearson would facilitate an additional 0.4

percentage points of Ontario’s GDP and an additional 17,000 jobs. Adding the

results from the direct, indirect and induced analysis undertaken by Toronto

Pearson for this scenario, the total economic value facilitated by Toronto

Pearson under this scenario increases by 21,000 jobs. This scenario illustrates that

the success of Toronto Pearson as a hub airport has direct implications for

Ontario’s economy, and visa-versa.



4.3.4 Factors that influence success

This study does not seek to determine which competitive outcome is most likely.

Rather, we have reviewed key lessons from three larger hub airports that are

located in similar sized economies, and identified the following key success

factors:

a) Deregulated domestic and liberalized international air markets are key

factors and provide the best opportunities for significant growth of a hub

airport.

b) Reliance on single dominant hub air carrier is good for the hub airport

provided the commercial interests of the air carrier and the hub airport

aligned, but risky if those interests change.

c) The ability to diversify across carriers in liberalized markets is an

important mechanism for sustainable hub airport growth.

d) In a competitive hub market cost and quality competitiveness are

essential.

All of these factors should be considered to ensure that Toronto Pearson

competes effectively in the market for connecting passengers.


Conclusion

5 Conclusion

Our approach

The objective of this study was to quantify the economic benefits to Ontario

from air travel and air connectivity facilitated by Toronto Pearson today and in

the future. We developed a methodology that allowed us to estimate the

economic value facilitated by air travel. This approach is different from the most

common studies of economic value, which tend to focus on the airport as an

employer and determine the level of direct, indirect and induced employment. As

noted, this traditional type of economic analysis has been completed by Toronto

Pearson, and when considered together this the results of this study, paint a more

complete picture of the total economic value of Toronto Pearson.

In this analysis, our approach examines the value, in GDP and employment

terms, facilitated by the activity of air connectivity. Our results can therefore be

added to those from the direct, indirect and induced analysis.

We acknowledge the two-way relationship between economic value and

connectivity. We think that the relationship is best characterized by a virtuous

circle. We acknowledge that there are a range of other factors that influence both

connectivity and economic value. Our approach is based on breaking the

relationship between connectivity and economic down into a number of steps.

For each of the key relationships in our approach, we undertook an extensive

literature review to develop conservative assumptions. We appreciate that the

most appropriate assumptions for this analysis may change. Assumptions can be

updated but we think that we have developed a sound framework to estimate the

contribution of Toronto Pearson to the economy.

Our results

Overall, this study demonstrates that Toronto Pearson makes an important

contribution to Ontario’s economy as it facilitates a substantial number of jobs

beyond direct and indirect employment. Our results show that Toronto Pearson

facilitates economic value equivalent to:

3.6 per cent of GDP or 153,000 jobs in 2012; and

4.1 per cent of GDP or 247,000 jobs in the baseline scenario for 2030.

We consider our results to be conservative, as they are similar or lower than

those implied by previous studies by IATA and the WTTC.

There is also a substantial opportunity to facilitate additional economic value in

the future if Toronto Pearson can attract additional connecting passengers from


Conclusion

other North American hub airports. By 2030, 131 million passengers that

connect via other North American hubs could potentially to consider Toronto

Pearson as a substitute airport.

Combined results – summary

Combining our results with those of the direct, indirect and induced analysis

undertaken by Toronto Pearson, the total economic value facilitated by Toronto

Pearson is estimated to be:

Today: 277,000 jobs or 5.6 per cent of Ontario GDP

In the year 2030: 457,000 jobs or 6.6 per cent of Ontario GDP

Policy implications

This study demonstrates the important role that Toronto Pearson plays in

facilitating strong business relationships with countries around the world. Many

of the policies we reviewed imply that Canada’s future economic prosperity partly

depends on its ability to diversify trade links. Stronger trade and FDI

relationships with fast-growing economies such as Brazil, India and China require

face-to-face meetings between businesses from both sides. Toronto Pearson can

play an important role in facilitating these relationships.


Appendix 1: Methodology – Economic value today

Appendix 1: Methodology – Economic value

today

Section 2 provides an overview of our approach and section 3 provides the key

assumptions that we had to make. This Appendix provides more detail on our

methodology and the literature we reviewed to inform our assumptions. It is

structured as follows:

Overview of key steps in the methodology;

Key relationship 2 – detailed approach and evidence to underpin

assumptions;

Key relationship 3 – number of passengers and trade/FDI – detailed

approach and evidence to underpin assumptions; and

Key relationship 3 – number of passengers and tourism spending –

detailed approach and evidence to underpin assumptions;

Key relationship 4 – trade and productivity: detailed approach and

evidence to underpin assumptions; and

Key relationship 4 – FDI and productivity: detailed approach and

evidence to underpin assumptions.

The focus of this Appendix is on the method for calculating the economic value

facilitated by Toronto Pearson today. Details of our method for determining the

future value are in Appendix 2.

Overview of methodology

Our methodology follows the steps illustrated in Figure 10. Our starting point is

the number of travellers on direct connections from Toronto Pearson to each

country. The analysis is undertaken on a country rather than a city level as trade

and FDI data is only provided at the country level. We determine the additional

travel time for the indirect connection by considering the additional distance

flown and connecting time at the airport. Distance is determined using a great

circle route mapping tool. Switching from a direct to an indirect flight leads to a

greater percentage increase in travel time for destinations that are closer to

Toronto. For example, adding 3 hours of travel time to a 5 hour journey

represents a bigger percentage increase than adding 3 hours of travel time to a 12

hour journey. As a result, the impact of an indirect flight is greater for

destinations that are closer.



We convert the additional travel time into a monetary value by applying the value

of time derived from hourly wage rates. The change in the price is then related

to the price of the original ticket to determine the percentage change in the ticket

price. Using a price elasticity of demand, we can determine the change in total

demand for travel to each country. We relate the percentage drop in passengers

to a change in trade, FDI and tourism spending by using the elasticities discussed

below. Changes in trade, FDI and tourism spending can then be related to the

impact on GDP and employment.

Figure 10. Overview of steps to calculate economic value facilitated today

Economic input data

Table 10 provides an overview of our input data. Ontario trade data by country

is available from the Conference Board of Canada. FDI by country is only

available for Canada, so we have used 60 per cent of the FDI stock for Canada as

an estimate for Ontario FDI data. This is based on Liang Liang (2008). There is

no data on interprovincial investment available. Appendix 4 provides a

sensitivity test that considers the impact on the results if an estimate for

interprovincial investment in included. The table shows the input data for those

countries with direct connections. This covers approximately 84 per cent and 74



per cent of total trade and FDI stock respectively. All our input data is currently

based on 2012.

Table 10. Overview of economic input data - Trade and FDI links between Ontario

and each geography

Connected countries/State/Provinces only CAN$m (2012)

Geography Exports per

year

Imports per

year

Outward FDI

stock

Inward FDI

stock

International

(excl. US) 31,788 97,653 173,903 156,109

US 124,318 105,995 126,746 142,994

Canada (excl.

Ontario) 45,795 50,331 * *

Total

Connected 201,901 253,978 300,649 299,103

* Data for inter-provincial FDI is not available

Key relationship 2: Travel time and passenger

numbers

A change in travel time impacts on the demand for travel as some passengers will

choose not to travel. The relationship can be seen in the following formula:

((Additional travel time * Value of time)/ Ticket price) * Price elasticity of demand =

Change in number of passengers

The change in travel time is calculated on the basis of additional travel distance

multiplied with average speed. We distinguish speed for take-off and landing

from the speed during the flight and use the following assumptions:

a) average speed during flight: 500 mph; and

b) average speed for take-off and landing: 250mph.

Distance is calculated on the basis of great circle routes. We add additional

connecting time at the airport. Our results are based on an assumption of 2

hours of connecting time. This implies that passengers would need 2 hours

between landing and take-off for their connecting flights. We consider this

assumption to be conservative, as this is likely to be close to the minimum rather



than the average connecting time. The total additional connecting time is

therefore equal to the additional flight time plus the connecting time. Our results

show that the additional travel time varies from 2.4 hours to 3.5 hours.

We monetize the value of time by using wage rates from Statistics Canada. There

are a number of ways to put a value on travel time including stated preference1

and revealed preference surveys. We have chosen to use wage rates as this is the

most common approach for valuing “in work” time (see for example, UK

Department for Transport, 2012). We use a similar approach for non-work time

to ensure consistency. For business travellers our value of time is $75 which is

informed by the wage rate of management occupations (Statistics Canada table

282-0070). We double the average wage rate as it is likely that international

business travel is undertaken by more senior management. An hourly wage rate

of $75 is equivalent to an annual pre-tax salary of $135,000. This is equivalent to

the average income of the top 20 per cent (Human Resources and Skills

Development Canada, 2013). We adjust wage rates for other countries using

Purchasing Power Parity. For non-business passengers, we use $23 as the value

of time which is equal to the average wage rate (Statistics Canada table 282-0070).

Ticket prices are based on Sabre data. We reviewed a number of studies on the

price elasticity of demand. The most disaggregated values are available from

IATA (2007). Separate elasticities are provided for transatlantic, transpacific and

intra-North America and values range from -0.36 to -0.72.

Key relationship 3: Face-to-face meetings and

trade and FDI

Our analysis of the value of Toronto Pearson’s connectivity requires us to make

an assumption on the relationship between face-to-face meetings, trade and FDI.

Face-to-face meetings increase the likelihood of closing business deals which has

a positive impact on trade and FDI. Face-to-face meetings are also important to

manage increasingly globalized supply chains. This relationship is supported by

qualitative literature, but it is difficult to quantify the relationship.

Concept

Despite the rise of technologies such as videoconferencing, face-to-face meetings

still play an important role in developing and maintaining successful business

1 Stated preference is based on what consumer’s say their preferences are, whereas revealed

preference measures consumers’ preferences based on their actual purchasing behaviour. For

example, a passenger may say they would pay $100 to reduce their travel time by an hour (their

stated preference), however, when buying a ticket they reveal they would only be willing to pay $70

for a flight with a travel time of one hour less (their revealed preference).



relationships. Most relationships are built on trust between business partners and

face-to-face meetings are still the most effective way to build and establish trust.

In addition, in-person meetings can be used to inspect production sites and meet

larger teams which cannot be done through videoconferencing.

The relationship between face-to-face meetings and trade and FDI is unlikely to

be the same for all of Ontario’s business relationships. We think that the

relationship is likely to differ for transactions between Ontario and other

provinces, the US and other international countries. This is because face-to-face

meetings are likely to play a bigger role in overcoming trade and FDI barriers

between economies that are more dissimilar. The most common barriers include:

a) Product market regulation – a range of different types of regulation

(product standards, safety regulation, etc.) can inhibit trade and FDI

across borders;

b) Tariffs and quotas, local content requirements – formal trade barriers

such as tariffs also reduce the likelihood of trade;

c) Exchange rate – the risk of changes in the exchange rate can pose a

significant barrier to trade and FDI, as exchange rate volatility can

increase the spread of potential returns; and

d) Cultural differences – language differences and different business

cultures can impede business relationships across cultures as it is more

difficult to build trust.

Business travel can reduce or overcome some of these barriers, as face-to-face

meetings enable a better understanding of local product market regulation and

formal trade barriers. Face-to-face meetings are also one of the key ways to build

trust across cultures.

These barriers are much lower when considering trade and FDI between Ontario

and the US compared to international transactions. This is because cultural

differences are much smaller (for example, common language), formal trade

barriers have been removed by NAFTA and product marker regulations are

more likely to be aligned. Trade barriers between Ontario and other provinces in

Canada are likely to be even lower as there is no exchange rate risk and product

market regulation is even more likely to be harmonized. Figure 11 illustrates this

concept.



Figure 11. Illustration of differences in trade barriers

Review of evidence

There is a range of qualitative, survey-based evidence that suggests face-to-face

meetings play an important role in business relationships. We discuss these

below. The importance of in-person meetings for trade facilitation is also

supported by the existence of trade missions. For example, the Canada Trade

Commissioner Service organizes a number of trade missions to different

countries each year. These trade missions provide access to foreign markets,

including networking opportunities, first-hand experiences and opportunities to

initiate business relationships (Government of Canada, 2012).

The World Travel and Tourism Council (2012) finds that sales conversion rates

with an in-person meeting are 50 per cent, compared to conversion rates of 31

per cent without an in-person meeting. The results are based on surveys in

Brazil, China, Germany, the UK and the USA and are consistent across these

countries. In 2011, the WTTC conducted another survey on the importance of

business travel and found that 28 per cent of existing business could be lost

without face-to-face meetings and sales conversion rates are estimated to be 20-

25 per cent higher with face-to-face meetings. This is further supported by a

range of qualitative studies.

Frankel (1997) illustrates the importance of face-to-face meetings as follows:

Consider a kind of export important to the United States: high-tech capital goods. To

begin sales in a foreign country may involve many trips by engineers, marketing people,



higher ranking executives to clinch a deal, and technical support staff to help install the

equipment or to service it when it malfunctions.

A survey by the UK Institute of Directors (2008) asked about the impact on

businesses if the amount of business travel by air was significantly curtailed.

30 per cent of respondents said that there would be significant adverse

effects while 44 per cent indicated small adverse effects.

Poole (2010) finds that business travel to the United States by non-resident,

non-citizens has a positive impact on export margins.

Aradhyula & Tronstad (2003) find that their results support the hypothesis

that both formal business exploration and casual exposure to cross-border

business opportunities have a positive impact on trade.

Strauss-Kahn & Vives (2005) find that headquarters relocate to metropolitan

areas with good airport facilities, low corporate taxes, low average wages,

high levels of business services, and an agglomeration of headquarters in the

same sector of activity. The effects are quantitatively significant (for airport

facilities in particular).

The City of London (2008) surveyed finance and insurance companies on

the importance of air travel. They found that 69 per cent of firms consider

air travel to be critical for business travel by their staff, with only 2 per cent

viewing it as not important.

Boeh & Beamish (2012) demonstrate that travel time between different

locations has a significant predictive power in firm governance and location

decisions, as travel time could otherwise be employed for productive

purposes.

Napier University (2004) finds that “[…] air transport per se is not a necessary

condition, but what is important are: the extent to which that area is plugged directly into

other major international hubs - availability and efficiency of routes (direct, hubbed); costs

and the level of competition in global transport market, and; perceived and actual

interchange efficiencies. This is a key consideration in the level of foreign investment into an

area and is most important for firms with international trading or contacts such as, high-

tech firms, financial services and pharmaceutical firms”.

Survey-based evidence also suggests that the importance of face-to-face meetings

depends on differences between business partners. Evidence from the World

Travel and Tourism Council (WTTC) and the Harvard Business Review indicates



that international business travel plays a more improtant role in generating and

sustaining business than domestic travel. The WTTC (2012) found that:

One extra dollar invested in international business travel would generate

on average US$17 in trade; and

One extra dollar invested in domestic US business travel by companies

results in an increase in revenue of US$9.50.

This implies that the return on investment for international travel is roughly half

of domestic travel. Figure 12 illustrates the difference in the return on

investment.

Figure 12. Return on investment

Source: World Travel and Tourism Council, 2011

Similarly the Harvard Business Review (2009) confirms the role of face-to-face

meetings in facilitating and sustaining business deals and also provides some

evidence for the specific role of business travel to overcome barriers to trade

across different cultures. For example, it found that:

a) 93 per cent of survey respondents agreed that in-person meetings are

helpful in negotiating with people from different language and cultural

backgrounds;

b) One survey respondent said that “Communicating with our Chinese partners is

enough of a challenge without face-to-face, because it is very difficult to explain a

difference in perspective without body language”; and

c) A number of respondents described the need to work with clients in their

own environment to get a full picture of the challenges and opportunities

they face.

There is a small amount of literature that supports this view.



Cristea (2011) found robust evidence that the demand for business-class air

travel is directly related to volume and composition of exports in

differentiated products. The paper finds that trade in R&D intensive

manufactures and goods facing contractual frictions is most dependent on

face-to-face meetings. Contractual frictions are more likely to occur with

higher trade barriers so this would support a lower elasticity for trade

between Ontario and the US/Canada compared to the rest of the world.

Poole (2010) finds that business travel for the purpose of communication

acts as an input to international trade. The effect is stronger for

differentiated products and for higher-skilled travellers, reflecting the

information intensive nature of differentiated products. The effect is driven

by travel from non-English speaking countries, for which communication

with the U.S. by other means may be less effective. The findings therefore

also confirm our view that business travel plays a bigger role when

connecting firms from different cultural backgrounds.

Selection of assumption values

Quantitative evidence on the relationship between face-to-face meetings and

trade/FDI is difficult to obtain. This is because it is difficult to pick out the

impact of face-to-face meetings from the other factors that influence trade and

FDI. Even though we know that a simple regression between flights and

trade/FDI will not provide sufficient evidence for the quantitative relationship,

we have performed this analysis for Toronto Pearson to establish an upper

bound. The regression coefficient will be overstated as the regression omits

other explanatory variables that influence trade and FDI. However, we can

interpret the coefficient as the upper value elasticity, as introducing other

variables would always reduce the coefficient. Figure 13 provides the results for

outbound flights from Toronto Pearson and Ontario exports. The coefficient is

1.153, which implies that a 1 per cent increase in flights leads to a 1.15 per cent

increase in exports. We consider this to be the upper bound of the relationship

and acknowledge the issues of omitted variable bias and endogeneity. As a result,

this analysis is only intended to provide additional evidence.



Figure 13. Outbound flights versus exports

Note: Outbound flights are estimated based on the number of outbound passengers from

Toronto Pearson airport and an assumption about aircraft size for long haul and short haul

flights.

The World Travel and Tourism Council (WTTC) performed a similar analysis for

a range of countries as shown in Figure 14. The figure shows the correlation

coefficient as well as the results of the Granger test for causality. The figure

shows that the correlations vary between 0.17 for outbound business travel from

Italy to 0.98 for outbound business travel from Brazil.



Figure 14. Trade and business travel by country

Source: WTTC, 2012

Figure 15 provides the same analysis for inbound flights and inward FDI. The

coefficient suggests that a 1 per cent increase in flights leads to a 0.6 per cent

increase in FDI. Again, we acknowledge issues of omitted variable bias and

endogeneity and consider this analysis to provide an upper bound only.



Figure 15. Inbound flights versus inward FDI

We also consider the FDI differential between connected and unconnected

countries as shown in Figure 16. The figure shows that FDI with connected

countries is approximately double the FDI with unconnected countries. We

acknowledge that the causality between FDI and connections goes both ways.

Figure 16. FDI differential between connected and unconnected countries

Given the lack of robust quantitative evidence on this relationship, we first

consider the elasticity for transactions between Ontario and international

countries (excluding the US). Figure 17 and Figure 18 show that we considered

a range of values. We conclude that an assumption of 0.3 is reasonable as this



value is at the lower end of the spectrum. So we assume that a 1 per cent

increase in face-to-face meetings increases trade and FDI by 0.3 per cent.

Our evidence discussed above suggests that the elasticities should be lower for

trade/FDI between Ontario and the US and even lower for trade/FDI between

Ontario and other provinces as compared to the rest of the world. As there is

little evidence on the magnitude of the difference, we consider the following

assumptions to be conservative estimates:

a) Ontario and rest of the world: 1 per cent increase in face-to-face

meetings increases trade and FDI by 0.3 per cent;

b) Ontario and US: 1 per cent increase in face-to-face meetings increases

trade and FDI by 0.2 per cent; and

c) Ontario and other Canadian Provinces: 1 per cent increase in face-to-face

meetings increases trade and FDI by 0.1 per cent.

These assumptions are broadly consistent with the WTTC findings.

Figure 17. Evidence on relationship between face-to-face meetings and trade



Figure 18. Evidence on relationship between face-to-face meetings and FDI

Key relationship 3: Number of passengers and

tourism spending

Concept

Passengers who travel to and from Ontario will inevitably generate tourism

spending regardless of their trip purpose. This suggests that a decrease in the

number of passengers travelling to Ontario results in a decrease in inbound total

tourism spending (or tourism exports). Likewise, a decrease in the number of

outbound passengers from Ontario results in a decrease in outbound tourism

spending (or tourism imports).

In order to estimate the impact of connectivity on tourism spending we have

obtained data on tourism spending per passenger-visit. We then multiply these

values by our passenger reduction in the “what-if” scenario. This provides an

estimate of the value of tourism spending facilitated by Toronto Pearson.

Review of evidence

Evidence on tourism spending on a country by country basis is limited. In

general, most evidence is based on tourism surveys. We have reviewed the

following sources:



a) Statistics Canada, International Travel 2010

b) Ministry of Tourism, Culture and Sport, The Economic Impact of

Tourism in Ontario and its Regions 2010

c) Canadian Tourism Commission, Tourism Snapshot: 2012 Year-in-review

d) Ontario Ministry of Tourism, Culture and Sport, Outbound Visits and

Spending statistics

Spending by non-Canadians in Ontario

Our assumptions on tourism spending per passenger-visit by country are based

on the Statistics Canada International Travel 2010 survey. It provides the most

comprehensive country-level data. It provides data on a person’s average

spending per trip for 14 countries across four continents, as well as data by

continent and region. We cross-checked our assumptions with the other sources

to ensure they were consistent.

Given that data on tourism spending by non-Canadians in Ontario was not

available for every country, we used either the respective continent and regional

values or a geographically similar country where there was missing data. For

example, for Taiwan we used China’s average spending per person-trip and an

‘Other European’ average for Albania.

Spending by Ontarians abroad

Tourism spending by Ontarians travelling to the rest of the world (excluding the

US) is based on the Ministry of Tourism, Culture and Sport: The Economic

Impact of Tourism in Ontario and its Regions 2010. This provides a figure of

CAN$1,279 in 2011. Due to data limitations we have applied this uniformly to all

outbound countries.

For the US, we again used data from Statistics Canada International Travel 2010

survey which provides a figure of CAN$555 in 2011.

Tourism spending in other provinces

We use the Ontario Ministry of Tourism, Culture and Sport’s visits and spending

statistics for our assumption on tourism spending between different provinces.

This provides an average tourism spend per visit for Ontarians to other

provinces of CAN$461 and by Canadians from other provinces in Ontario of

CAN$366, in 2011.

Table 11 below summaries our assumptions on tourism spending per passenger-

visit.



Table 11. Tourism spending per passenger-visit

Direction Location Average tourism

spending per passenger

visit

Outbound (tourism

imports)

Ontario to Rest of World $1,280

Ontario to the US $550

Ontario to Canadian

provinces

$460

Inbound (tourism

exports)

Rest of world (including

US) to Ontario

$440 - $1,990

Canadian provinces to

Ontario

$360

Note: These figures are rounded and are in Canadian dollars

Key relationship 4: Trade and productivity

Concept

A large body of academic research investigates the positive impact of trade on

productivity at the firm level. At the economy-wide level, there are also some

studies which suggest additional trade leads to higher productivity. The key

mechanisms by which trade influences productivity can be characterized in three

ways:

a) Innovation – trade is one of the key “transmitters” of innovation as

it exposes companies to a wider range of products and processes in

other countries. This applies regardless of whether the partner

country is a developed or developing economy.

b) Competition – as trade increases the market size companies that

export or import are faced with more intense competition.

Competition puts pressure on companies to be more efficient. This

applies to trade with any partner country.

c) Economies of scale – larger market sizes imply that production

processes can benefit from economies of scale. This also applies to

trade any partner country.

For example, the OECD, (2012) found that: “A main channel through which trade

increases income is productivity growth. Importing creates competition that forces domestic firms



to become more efficient and provides access to inputs of international calibre; exporting creates

incentives for firms to invest in the most modern technologies, scales of production and worker

training. The combined effect is to spawn a process of continual resource reallocation, shifting

capital and labour into activities with higher productivity”.

Importantly, the impact of trade on productivity holds for both exports and

imports. This is because we are considering the long-term impact on trade on

productivity instead of the short-term. In the short-term import substitution can

lead to structural changes in the economy that require some adjustments.

However, once resources are allocated to more productive uses, imports have a

long-term positive impact on productivity. The study that underpins our main

assumption uses a measure of “real openness” which is the sum of exports and

imports over GDP.

Review of evidence

The OECD has undertaken a study with data from 21 high-income countries

over nearly 30 years controlling for other factors: every 10-percentage point

increase in trade exposure (as measured by trade share of GDP) contributes a 4-

percent increase in GDP per capita. This study is quoted by the Canadian

government in “The State of Trade 2012” and provides the main evidence source

for our assumption.

We have also reviewed evidence to suggest that the impact of trade on

productivity may be lower when comparing domestic trade to international trade:

a) Therrien and Hanel (2012) provide evidence supporting the idea that the

productivity gains from trade are stronger with trade to foreign markets

compared to the domestic market: they find that Canadian firms who export

to foreign markets have higher labour productivity. Their results are based

on the following steps.

They find that Canadian firms who export to non-US markets and US

markets are more likely to innovate than firms who do not.

Canadian firms who innovate more have higher innovation-related sales.

Finally, firms that have higher innovation-related sales also have higher

labour productivity.

b) Ito (2011) examines whether first-time Japanese exporters achieve

productivity improvements through learning-by-exporting effects. The results

suggest that exporting to North America or Europe has a strong positive

effect on sales and employment growth, R&D activity, and productivity

growth. On the other hand, exporting to Asia does not have any strong

productivity enhancing effects. This would suggest that exporting to



countries that are more similar (or geographically close) has a lower impact

on productivity.

However, on the other hand we also found a range of papers that do not identify

a difference. For example, Wagner (2012) undertakes a literature review of the

impact of trade on productivity and finds that exporters are more productive

than non-exporters but finds no difference to where you export.

On the specific question of the impact of Canadian internal trade on

productivity, we have found that:

Agnosteva and Anderson (2013) estimate the existence and impact of intra-

provincial trade barriers. They find that there is substantial intra-provincial

‘home bias’. Home bias is the tendency to trade much more within a region

than to another region, and is often a sign of the presence of formal or

informal trade barriers. This suggests that the Canadian provinces and

territories are not fully integrated yet and there is significant scope for

internal trade policy intervention.

This would suggest that inter-provincial trade still has some impact on

productivity.


We have relied on the findings by the OECD (reported by the Canadian

government) to assume that a 1 per cent increase in real openness increases GDP

by 0.4 per cent. We apply this assumption to international trade. The evidence

suggests that the impact of interprovincial trade is likely to be lower. We

therefore assume that a 1 per cent increase in interprovincial trade increases

GDP by 0.2 per cent.

To convert the contribution of GDP into employment, we have used the same

conversion rate as Foreign Affairs and International Trade in their analyses of

free trade agreements: for every $150,000 of GDP, one full-time job is created.

Key relationship 4: FDI and productivity

Concept

Both inward and outward FDI have a positive impact on productivity and

competitiveness. Our research suggests that access to new markets, cheaper

inputs and new technology or know-how boosts the scale and efficiency of

domestic production. The underlying theory is similar to that applied to free

trade agreements. Figure 19 summarizes how FDI can impact on productivity.



Figure 19. Impact of FDI on productivity

Review of evidence

Evidence on the specific impact of FDI on productivity is limited. We have

found the following studies:

a) DIW (2009) studies the relationship between outward FDI and economic

growth. They find that FDI enables firms to enter new markets, import

intermediate goods from foreign affiliates at lower costs and access foreign

technology. As a result the domestic economy benefits from outward FDI

due to increased competitiveness of the investing companies and associated

productivity spill-over to local firms. The analysis shows that for every 1 per

cent increase in outward FDI stock, local GDP increases by 0.19 per cent.

b) Korea Institute for International Economic Policy (2008) studies the

relationship of inward FDI and productivity using Ireland as a case study.

They find that FDI advances new foreign technology or import of new

intermediary goods and enhances growth by accumulation of human capital

by means of labour training or absorption of technology and new

management techniques. Their analysis shows that for a 1 per cent increase

in inward FDI stock, local GDP increases by 0.24 per cent.

We have investigated the potential to use a different elasticity for the US. For

example, Borensztein, Gregorio and Lee (1998) analyse FDI flows from

industrial to developing countries. They find that FDI contributes to economic

growth only if a minimum level of human capital is met in the receiving country.



This is likely to hold for most connected countries. Similarly, Alfaro, Chanda,

Kalemli-Ozcan and Sayek (2006) find that holding FDI constant, financially well-

developed economies experience higher growth rates. They identify human

capital as one of the key factors that influences this effect. However, none of the

literature that we reviewed indicated that the FDI between the US and Canada

would be expected to have a different impact on Ontario than FDI with other

countries.


Based on the quantitative analysis we reviewed, we make the following

assumptions:

a 1 per cent increase in inward FDI increases productivity by 0.24 per

cent; and

a 1 per cent increase in outward FDI increases productivity by 0.19 per

cent.


Appendix 2: Methodology – Economic value in 2030

Appendix 2: Methodology – Economic value

in 2030

This appendix provides the detailed methodology for estimating economic value

in 2030. The methodology is largely based on the assumptions described in

Appendix 1 but some additional assumptions are required to project the results

to 2030. More detail on how the competitive scenarios are developed is provided

in Appendix 3.

Overview of methodology

Figure 20 provides an overview of the methodology for the 2030 results. The

key step for the baseline scenario is to project passenger numbers for 2030 for

Toronto and the 11 competitor airports.

Figure 20. Overview of methodology for future economic value



Key relationship 1: Macroeconomic trends impact

on passenger numbers

Passenger numbers in the 2030 baseline scenario are based on the following

assumptions:

a) GDP growth

b) Income elasticities

c) Ticket price growth

d) Price elasticities

We have obtained projections of GDP growth from HSBC Bank (2012). We

have used the HSBC source as it provides projections for a large number of

countries up until 2030. There are few alternative sources that provide

projections for so many counties over such a long time period. To ensure the

robustness of the HSBC projections we have cross-checked them against a range

of international sources including the IMF. Appendix 4 provides a sensitivity test

on the assumption about US and Ontario growth. Table 12 provides a summary

of the GDP growth assumptions for a selection of countries. Table 12The table

shows that growth in the BRIC economies (Brazil, Russia, India and China) is

expected to be substantially higher than growth in the developed economies such

as Germany and the UK.



Table 12. Summary of GDP growth assumptions

Country Average annual growth to 2030

Canada and Ontario 2.2 per cent

US 1.3 per cent

Brazil 3.1 per cent

China 6.0 per cent

Russian Federation 4.1 per cent

India 5.6 per cent

Australia 2.3 per cent

Germany 1.4 per cent

Japan 0.7 per cent

United Kingdom 1.7 per cent

The income elasticity describes the increase in demand for travel for every 1 per

cent increase in GDP. We have reviewed a number of sources (such as IATA

(2007) and UK Department for Transport (2013)) that suggest that the income

elasticity is likely to be between 1 and 2. We also found evidence to suggest that

the income elasticity is higher in countries with a lower GDP per capita. As a

result, we have differentiated income elasticities for countries with different levels

of GDP per capita and have used a range between 1.3 for developed countries

(including Canada) and 2.2 for developing countries.

We have researched likely movements in the ticket price based on changes in cost

inputs. IATA (2012) suggests that the oil price is one of the main drivers of

changes in ticket prices as it accounts for as much as 34 per cent of total input

costs. Oil price projections by the World Bank (shown in Figure 21) show a

slight decline in the oil price. This would suggest a potential reduction in ticket

prices.



Figure 21. World Bank Oil Price Forecast

Source: World Bank, (2013), Commodity Price forecast

We have assumed no change in ticket prices as the oil price decline may be offset

by increases in other input costs.

The assumptions on GDP growth, income elasticity and ticket price growth

result in demand projections shown in Table 13. Toronto Pearson has a slightly

higher average annual growth rate in passengers than the competitor airports.

This is because Toronto Pearson has a higher proportion of international

passengers. As GDP growth is expected to be relatively low in the US and

Ontario compared to the BRIC countries, international passenger demand is

expected to grow quicker than local demand. Figure 22 shows each hub airport’s

share of combined passenger volumes for Toronto Pearson and the 11 hub

airports in 2011 and 2030. Toronto Pearson’s share increases from 5.7% in 2011

to 6.6% in 2030.

Table 13. 2030 Passenger volumes at Toronto Pearson and 11 competitor hubs

Airport

2011 Passengers

(million)

2030 Passengers

(million) Growth

Compound Average Annual Growth

Toronto 33.4 59.6 78 per cent 3.1 per cent

Atlanta 92.4 136.7 48 per cent 2.1 per cent



Chicago 66.6 97.3 46 per cent 2.0 per cent

Los Angeles 61.8 92.3 49 per cent 2.1 per cent

Dallas 57.8 84.4 46 per cent 2.0 per cent

Denver 52.7 74.3 41 per cent 1.8 per cent

JFK 47.7 72.9 53 per cent 2.3 per cent

Houston 40.2 62.7 56 per cent 2.4 per cent

Charlotte 39.0 57.4 47 per cent 2.0 per cent

Miami 38.3 68.3 78 per cent 3.1 per cent

Newark 33.7 50.2 49 per cent 2.1 per cent

Detroit 32.4 46.6 44 per cent 1.9 per cent


Figure 22. Share of total passenger volumes at Toronto Pearson and 11 hubs in the

US

Source: Frontier analysis

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%2011 2030


Appendix 3: Methodology – Competitive scenarios in 2030

Appendix 3: Methodology – Competitive

scenarios in 2030

Our analysis considers the value facilitated by Toronto Pearson today and in

2030. In the 2030 scenario, we assume a business as usual scenario in which

Toronto Pearson grows in line with economic growth and standard income

elasticities. This implies that Toronto Pearson neither gains nor loses share in the

market for connecting passengers.

To illustrate the opportunity for Toronto Pearson’s growth, we have estimated

the market size of connecting passengers. We can then link different scenarios

for hub competition to the economic value calculations. This allows us to

determine the additional economic value facilitated by Toronto Pearson if

Toronto Pearson were to attract a greater share of connecting passengers from

other North American hubs.

The economics of hub networks are such that connecting passengers are crucial

for a hub to operate successfully. This is because in the absence of connecting

passengers, an airport is solely reliant on OD demand – and this is limited to the

local catchment. By pooling connecting passengers from all over the world, a hub

airport can increase its demand for particular flights and therefore fly more

regularly. In fact, without connecting passengers some routes might not be viable

at all. Therefore by adding extra connecting passengers, Toronto Pearson is able

to:

a) make new direct connections; and/or

b) increase the frequency of flights on existing routes.

Local business passengers get a direct benefit if increased numbers of connecting

passengers allow Toronto Pearson to support direct connections to new

destinations. With lower numbers of connecting passengers some destinations

could not be served meaning that local business passengers would have to fly

indirect to reach those destinations – and because this is inconvenient and time-

consuming they might respond by flying less frequently or not flying at all.

If having more connecting passengers Toronto Pearson is able to increase the

frequency of flights on existing routes, then this also benefits business

passengers. This is because Toronto Pearson can offer more flights per week or

per day. This might create flights that are more convenient to some business

passengers who might not have flown otherwise.



Airports considered

In our competitive scenarios, we consider passenger volumes at Toronto Pearson

and 11 hubs in the US.2 We have used these hubs because, according to Airports

Council International, they all handled either the same number of passengers as

Toronto Pearson in 2011 or more. We therefore consider them to represent the

largest opportunities to Toronto Pearson with respect to competition for

connecting passengers. Figure 23 presents an illustration of the airports

considered in the analysis.

Figure 23. Hub airports considered in our 'competitive scenarios'


Expected passenger volumes in 2030

As discussed in section 2.5 we have forecast passenger volumes for Toronto

Pearson in 2030. In order to consider the competitive scenarios, we have also

forecast passenger volumes at the 11 US hubs, by following the same approach.

Figure 24 summarizes the expected growth in passenger volumes.

2 These hubs are Atlanta, Chicago, Los Angeles, Dallas, Denver, JFK, Houston, Charlotte, Miami,

Newark and Detroit

Airport

Total

Passengers

(million)

Atlanta 92.4

Chicago 66.6

Los Angeles 61.8

Dallas 57.8

Denver 52.7

JFK 47.9

Houston 40.2

Charlotte 39.0

Miami 38.3

Newark 33.6

Toronto 33.4

Detroit 32.4

Toronto

Atlanta

Chicago

Los Angeles

Dallas

Denver JFK and Newark

Houston

Charlotte

Miami

Detroit

ACI 2011



Figure 24. Growth in passenger volumes 2011-2030


Our analysis suggests that Toronto Pearson has the potential to experience the

highest level of growth out of all the hubs in our analysis. We expect Toronto

Pearson to grow to 60 million passengers by 2030 which represents a 78 per cent

increase in size over the period 2011-2030. This implies an average annual

growth rate of 3.1 per cent. Meanwhile, the US hubs are expected to grow by

about 50 per cent on average over the same period. This implies a lower average

annual growth rate of around 2.2 per cent. The main driver behind this result is

that, according to HSBC (2012) growth forecasts:

a) Canadian GDP is set to grow by 2.2 per cent a year on average between

2011-2030; while

b) US GDP is expected to increase by only 1.3 per cent a year on average

over the same period.

Our analysis suggests that there will be around 840 million passengers in total at

the US hubs in 2030. And around 370 million of them, or about 44 per cent, will

be connecting passengers.

The first important consideration is that of these 370 million connecting

passengers, 220 million - or 60 per cent - are actually domestic connections –

making a journey which can be described as a ‘US to US, via the US’ journey.

This means that they start their journey in the US, connect via one of the US

0

20

40

60

80

100

120

140

160

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2011 2030



hubs, and fly on their final destination also in the US. However, Toronto Pearson

is not able to compete for these passengers as this type of traffic cannot be

served from Canada under existing air service agreements. This rules out a large

number of potential passengers for Toronto Pearson.

But the remaining connecting passengers are not all likely to consider Toronto

Pearson a viable hub because:

a) For some routes Toronto might be poorly placed geographically. This

might mean that connecting via Toronto instead of a US hub adds more

travel time, and is therefore unattractive to connecting passengers; and

b) For some connecting passengers, Toronto Pearson might not offer both

legs of the journey that are required in order to make the connection.

To arrive at an appropriate market size we have considered both these factors in

some detail.

Travel distance and market definition

The relevance of travel distance for the market definition is best explained by

using an example.

Our baseline results suggest that in 2030, around 15,000 passengers will travel

from the United Kingdom to Los Angeles via JFK in New York and 22,000

passengers will make the same journey but connect via Toronto Pearson instead.

In terms of travel distance, there is little difference between the two hub airports,

in fact the journey via Toronto is about 3 per cent shorter than the journey via

New York. For a passenger, JFK and Toronto Pearson are likely to be

substitutable as the travel time is likely to be similar.

It is much harder to attract connecting passengers if the use of Toronto Pearson

increases their travel times substantially. For example, Toronto Pearson is

connected to Seattle and China. Therefore, in principle, Toronto could connect

passengers wishing to travel between the two destinations. But in 2011 no

passengers travelled from Seattle to China via Toronto even though around 4,000

passengers chose to travel between Seattle and China via LAX in Los Angeles.

This can partly be attributed to the difference in travel time. The distance via

Toronto is around 8,600 miles compared to only 7,200 miles via Los Angeles, so

the connection at Toronto adds an extra 20 per cent of travel distance.

The two examples suggest that travel distance is an important factor in

determining whether hubs actually compete with each other for connecting

passengers. However, we also need to recognize that travel distance also depends

on the particular route. This means that two hubs might compete with each other

in one market, but not in another.



Staying with the example of passengers originating in Seattle – while Toronto

may not be able to compete with LAX for passengers wishing to fly to China, it

is likely to be able to attract passengers wishing to fly to Europe.

In 2011, Toronto Pearson and LAX both acted as a hub for passengers wishing

to travel between Seattle and the UK, France, Germany and Italy. Depending on

the precise European destination, routing via Toronto reduces journey distance

by 3 per cent to 4 per cent compared to LAX.

To account for differences in travel distance we introduced a distance test into

our market definition.

First, we calculated the total travel distance of connecting every destination in the

world to every other destination in the world via Toronto. And we calculated the

same figures for the other 11 hubs in our analysis.

Taking each possible connection in turn, we estimated whether Toronto Pearson

is ‘in the market’ by considering whether it can pass our distance test. This is

described in the box below.

Distance test

Toronto Pearson is ‘in the market’ on a particular route if it can satisfy one of the

following tests:

1. The travel distance via Toronto Pearson is less than the travel distance of

any rival hub that is active in the market; or

2. If the travel distance via Toronto Pearson is longer than all the rival hubs

that are active in the market, then it must be within 10 per cent of the

shortest travel distance.

If Toronto Pearson does not satisfy either of these tests, then it is not ‘in the

market’

By applying a market definition as described above, we identified a total pool of

131m ‘contestable’ connecting passengers for which Toronto Pearson could

compete for in 2030.

By capturing connecting passengers from rival hubs Toronto Pearson

might be able to create new connections

In 2011, 50,000 passengers flew from destinations all over the world to Bolivia

via one of the hubs in our analysis. However, none of them flew via Toronto

because there is no direct connection to Bolivia. But of these 50,000 passengers,

around 93 per cent of them started their journey in a destination where Toronto

Pearson does have a direct connection. Therefore, in practice Toronto would

have been able to offer half the journey but not complete the full connection.



In our analysis we also make the distinction between pairs of destinations that

Toronto can currently connect, and those that it cannot. With this in mind, our

analysis recognizes that Toronto Pearson competes for connecting passengers in

one of two markets:

1. On connecting routes where Toronto can offer both legs of the journey; and

2. On connecting routes where Toronto cannot offer both legs of the journey.

This is an important distinction because it allows us to consider whether Toronto

Pearson can make a viable new connection by supplementing the underlying OD

demand with connecting passengers from rival hubs. Table 14Table shows that

by 2030 the majority of ‘contestable’ connecting passengers travel on routes

where Toronto Pearson could offer both legs of the connection.

Table 14. Connecting passengers at rival hubs in the US (m) - 2030

Connecting passengers per cent of total

Market 1 82 63 per cent

Market 2 49 37 per cent

Total (1+2) 131 100 per cent


This implies that by 2030, there will be more contestable connecting passengers

moving through the rival hubs in the US than all the passengers at Toronto

Pearson itself. So, capturing even a fraction of these connecting passengers could

result in a large increase in passenger volumes at Toronto Pearson.

Linking competitive scenarios to economic value

Our analysis allows us to consider the 131 million contestable connecting

passengers in more detail. We can split the figure by region and consider the total

figure on a region-by-region basis. Figure 25 reports how the 82 million

passengers in Market 1 are split between the different geographic regions.

We see that of the 82 million contestable passengers in Market 1, 24 million of

them – or around 30 per cent - both start and finish their journey in North

America.3

3 This figure controls for the fact that Toronto Pearson cannot connect US-to-US domestic transfers.

Therefore this figure is made up of passengers either starting their journey in Canada and flying to

the US, or vice versa – starting their journey in the US and flying to Canada.



The second most popular region-to-region route is North America to Asia. In

2011, 13 million passengers flew between North America and Asia via one of the

rival North America hubs. And again it is important to note that these are

passengers whose origin and destination are both served by Toronto Pearson,

and that Toronto passes the test for market definition.



Figure 25. The distribution of 'contestable' passengers


Interpretation: The row heading represents the direction of travel. The column heading represents the origin. For example, 55,192 passengers flew from Central America & Caribbean to Asia via one

of the North American hubs, and 52,346 passengers flew from Asia to Central America & Caribbean via one of the North American hubs.

Note: The entries in this matrix are not symmetric. For example while 52,346 passengers fly to Central America & Caribbean from Asia via one of the North American hubs, 55,192 passengers fly in

the opposite direction. This is because in reality, while inbound and outbound passengers are roughly equal on average there is some variation – as observed in the 2011 data.



Figure 26 illustrates the size of the total connecting markets. Our analysis allows

for the consideration of any fraction or combination of these passengers.

Adding connecting passengers is likely to have a positive impact on the number

of Origin-Destination4 (or O/D) passengers, as connecting passengers add to the

network effect at the airport. Having more connecting passengers on a given

route increases the total number of passengers flying on that route, which means

that airlines can offer greater frequencies. As a result, the increased frequency of

flights means that there is greater flexibility in flying on that particular route –

this is because there are now more flights per day or per week. Some potential

O/D passengers might have been unwilling to fly before because the flights

available at the time were not at convenient times of the day or days of the week.

But the greater availability means that some O/D passengers are now willing to

fly. And this results in an increase in O/D demand. This effect is shown in

Figure 26 below.

Figure 26. Network effect on a given route

Source: Frontier illustration

4 Origin-Destination (O/D) passengers are all passengers that are not connecting via Toronto

Pearson, i.e. passengers for whom Toronto Pearson is either their origin or final destination.

Increase in

connecting

passengers

Increase in

total

passengers

Increase in

flights

Greater

flexibility in

flying

More

attractive to

marginal OD

passengers

Increase in

OD

passengers



Figure 27. An example of capturing contestable connecting passengers


Note: This is a symmetric matrix. This means that the input that appears in the cell for from Asia to North America will also appear in the cell for from North America to Asia. The underlying assumption

is that every connecting passenger that Toronto Pearson captures from a rival hub will also make the same return journey in the opposite direction and therefore needs to be counted twice. In the

model, this means that the model user can input a figure in the green cells and the same figure will also appear in the opposite / return cell.

In this particular example, we have selected 5 per cent of all connecting passengers travelling to and from North America to every other region in the world.



It is challenging to estimate network effects. In our analysis we have relied on

frequency elasticities of demand (FED). In our analysis, we apply two separate

FEDs. This is because we believe that it is necessary to distinguish between

routes that already have a high frequency of flights, and those that have a low

frequency of flights. For example, a route which is already served by a high

frequency of flights is probably less likely to see a big change in O/D demand if

there is a further increase in flights than a route which is poorly connected.

Table 15 lists the assumptions that appear in our model.

Table 15. Frequency elasticities of demand (FEDs)

Model assumptions

Definition of high frequency flights Greater than 0.5 flights per day

FED – high frequency flights 0.6 per cent

FED – low frequency flights 0.8 per cent


Note: The 0.8 per cent is an estimate of FED based on our research of Castelli, Pesenti, Ukovich (2003),

Jorge-Calderon (1997) and Prof. Dan Graham.

Worked example

As shown in Table 16, if by 2030 Toronto Pearson were to capture 5 per cent of

the connecting passengers travelling to and from North America via one of the

US hubs on routes where Toronto Pearson can offer both connections (Market

1), then this would add an extra 3.9 million connecting passengers at the airport.

For example, our analysis suggests that by 2030, over 850,000 passengers will

travel from North America to Peru, connecting via one of the North American

hubs. We estimate that 660,000 of these passengers are contestable for Toronto

Pearson on routes already offered from that airport. If Toronto were to capture 5

per cent of these passengers, then this would result in an extra 33,000 connecting

passengers.

In our baseline projection by 2030 75,000 passengers will fly from Toronto

Pearson to Peru. And 20,000 of these will be O/D passengers flying direct.

Therefore the extra 33,000 passengers would represent a 44 per cent increase in

total passengers flying on the leg. We consider this to be a proxy for flight

frequency, so the extra connecting passengers would result in 44 per cent more

flights to Peru from Toronto.



Table 16. Worked example of frequency elasticity of demand – Peru 2030

Passengers

Base case

Total passengers - Toronto to Peru 75,000

- of which direct O/D 20,000

Scenario

Extra connecting passengers 33,000

New total 108,000

per cent increase 44 per cent

Network effect

Frequency elasticity of demand 0.8

Extra O/D passengers ( per cent) 35 per cent

Extra O/D passengers 7,000

Final result

Total passengers – Toronto to Peru 115,000

- of which direct O/D 27,000

Overall increase in total passengers 40,000


Table 16 above shows the underlying calculations behind the Peru example. By

attracting an extra 33,000 connecting passengers to the airport, Toronto Pearson

can increase flight frequency by 44 per cent. Given that flight frequency from

Toronto to Peru is less than 0.5 flights per day, this is an example of a low-

frequency flight, and therefore we apply the higher frequency elasticity of

demand of 0.8.

As a result, this implies that there will be an extra 7,000 O/D passengers

attracted to the connection. And therefore the final result is that total passengers



on the leg could increase from 75,000 in our base case to 115,000 under our

competitive scenario.

Putting it all together

By considering not just the Peru example, but all the routes affected by the extra

connecting passengers, we see that if Toronto Pearson were to capture 5 per cent

of the connecting passengers travelling to and from North America via one of

the US hubs on routes where Toronto Pearson can offer both connections

(Market 1), passenger volumes in 2030 could increase from 59.6 million in our

base case to 64.9 million. 3.9 million of the increase is made up of the traffic won

from other hubs while 1.4m is additional O/D traffic generated by new

destinations and increased flight frequencies. This is shown in Figure 28.

Figure 28. Comparison of passenger volumes at Toronto Pearson


Having then established the passenger volumes at Toronto Pearson under the

competitive scenario in 2030, we then conduct the same “what-if” analysis in

order to estimate economic value.

33.4

59.6 59.6

3.9

1.4

0

10

20

30

40

50

60

70

2011 2030 - base case 2030 - scenario

Passengers

(m

illio

ns)

Extra connecting passengers Extra OD passengers


Appendix 4: Sensitivity tests


Interprovincial investment

As data was not available on interprovincial investment, we did not include it in

our main results. Our results are therefore a conservative estimate. This

sensitivity attempts to include some measure of interprovincial investment.

We assume that 50 per cent of Ontario's investment comes from all the other

provinces together. The share from each province is based on the share of

Canadian GDP it accounts for. For simplicity, we assume investment from

Ontario to each province is equal to the investment it receives from each

province.

Including interprovincial investment would increase the results to 157,000 jobs

facilitated by Toronto Pearson today from 153,000, an increase of 4,000 jobs.

The results for 2030 would increase by 5,000 jobs to 252,000.

These increases are not substantial so we consider our results to be slightly

conservative and, given the absence of good quality data, more robust.

GDP forecasts

As explained above, we have used the HSBC source as it provides projections for

a large number of countries up until 2030. There are few alternative sources that

provide projections for so many counties over such a long time period. To

ensure the robustness of the HSBC projections we have cross-checked them

against projections by international institutions such as the International

Monetary Fund. In addition to this cross-checking we have conducted a

sensitivity test for the growth forecasts for the US and Ontario as they are two

key drivers of the results.

According to HSBC (2012) growth forecasts:

a) Canadian GDP is set to grow by 2.2 per cent a year on average between

2011-2030; while

b) US GDP is expected to increase by only 1.3 per cent a year on average

over the same period.

We have tested a lower Ontario growth scenario of 2.0 per cent and a higher US

growth scenario of 2.5 per cent.

The lower Ontario growth scenario would decrease the results by 6,000 jobs to

241,000 in 2030. The higher US growth scenario would increase the result by

24,000 jobs to 271,000.



The changes from the lower Ontario growth scenario are not substantial. The

changes from the higher US growth scenario are more substantial; however it is

likely to represent an upper bound for US growth. Therefore, we prefer the

central scenario as it is more conservative and also ensures we use a consistent

source for growth rates across all countries.


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