THE IMPACT OF POLITICAL PROTESTS ON PLANNED … · tually scheduled. Thus, this transactional...

THE IMPACT OF POLITICAL PROTESTS ON PLANNED CONSUMER DEMAND FORTRAVEL: A MULTI-COUNTRY PANEL DATA REGRESSION ANALYSIS USING

TRANSACTION DATA FROM THE EXPEDIA GROUP

By

Amy Chen, Patricia Tang, James Zhao

Advised by Professor Aaron Yoon

Submitted in Partial Fulfillment of theRequirements for the Degree of

BACHELORS OF ARTS IN MATHEMATICAL METHODS IN THE SOCIAL SCIENCES

May 2020

ABSTRACT

This paper analyzes the impact of political protests in different regions on planned

future consumer demand for travel to those locations. In particular, we look at 8 political protests

in different geographic areas between 2017-2020. We use transaction panel data from The Expedia

Group to estimate planned consumer demand for future travel and Google Trends data to proxy for

the saliency and relevance of the protests in popular media. Finally, we regress the transaction data

on the Google Trends data, controlling for country-level fixed effects on transactions.

Even though existing literature has shown that political protests severely impact im-

mediate short-term travel demand to a country, we find that there is no evidence to suggest that

political protests significantly affects planned consumer demand for travel to a country, both as a

layover destination and as a final destination.

Overall, this paper highlights the short-term nature of the effect of political protests on

travel to affected areas. Though immediate travel demand to areas affected by political protests

decreases in the short run, our research found no evidence that travellers are changing their travel

patterns in the long run. This could be because people generally recognize that political protests

subside in a few months, and plan their future travel internalizing this assumption. This find-

ing contributes significantly to existing literature, showing the differences between long-term and

short-term effects of protests to travel to a country.

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ACKNOWLEDGEMENTS

We are immensely grateful to Drew Rubin and Tim Krick at Expedia for providing us

with an exceptional dataset, aiding us with helpful advice, and being our cheerleaders as we went

through the thesis-writing process from start to finish. Thank you for your time, energy, and efforts

– without your support, this paper and project would not have been possible.

We would also like to recognize the invaluable guidance of Professor Aaron Yoon.

From forming our topic to building out our model, Professor Yoon was there to provide insightful

recommendations and valuable insights – thank you for all the time you spent mentoring us, it was

invaluable to us! Additionally, we would like to thank Nicole Ozminowski for her advice on our

empirical analysis methodology as well as on the implications of our model.

We would also like to recognize the invaluable efforts of Professor Jeffrey Ely in facil-

itating a partnership between Expedia and MMSS. Without this partnership, we would not have

had access to Expedia’s resources. Special recognition goes to Nicole Schneider, who also helped

to facilitate this relationship. Lastly, we would like to thank our parents for providing us with the

resources necessary for our success.

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TABLE OF CONTENTS

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Chapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Chapter 2: Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.1 Algeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Egypt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3 Hong Kong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.4 Indonesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.5 Jordan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.6 Lebanon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.7 Tunisia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Chapter 3: Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Chapter 4: Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Chapter 5: Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Chapter 6: Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

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Chapter 7: Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Chapter 8: Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Appendix A: R Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Appendix B: Expedia Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Appendix C: Protest Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

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LIST OF TABLES

6.1 Number of Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

6.2 Total Destination Transactions, GBV . . . . . . . . . . . . . . . . . . . . . . . . . 25

6.3 Total Layover Transactions, GBV . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

6.4 Average Destination Transactions, GBV . . . . . . . . . . . . . . . . . . . . . . . 26

6.5 Average Layover Transactions, GBV . . . . . . . . . . . . . . . . . . . . . . . . . 26

6.6 Std. Dev for Destination Transactions, GBV . . . . . . . . . . . . . . . . . . . . . 26

6.7 Std. Dev for Layover Transactions, GBV . . . . . . . . . . . . . . . . . . . . . . . 27

6.8 Aggregate Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

6.9 Individual Country Layover Transactions . . . . . . . . . . . . . . . . . . . . . . . 29

6.10 Individual Country Destination Transactions . . . . . . . . . . . . . . . . . . . . . 30

C.1 Protest Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

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LIST OF FIGURES

4.1 Google Trends data for the search query ”hong kong protests” from 2017 to 2020 . 22

B.1 Expedia Data Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

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CHAPTER 1

INTRODUCTION

Large-scale political protests often have significant impacts on the countries and geo-

graphic areas that they occur within. Studies (Neumayer, 2004), (Sloboda, 2003), (Fielding and

Shortland, 2009) have shown that political protests often lead to adverse economic impacts, par-

ticularly in the tourism industry. Indeed, it is reasonable to believe that political protests in a

geographic area would lead to decreased international travel to these affected areas because of an

increased sense of fear or concern of travelling to these places.

This topic is salient in 2020 given the far-reaching effects of the 2019 Hong Kong

protests on travel to Hong Kong. Examining news coverage regarding the 2019 Hong Kong protests

show that flights to Hong Kong decreased significantly during the duration of the protests (Regan,

2019). This might be due to the well-reported effects of protests on the operations of airports and

airlines in Hong Kong during the protests. For example, protesters shut down the Hong Kong air-

port in August 2019 (Griffiths et al., 2019) and operations of Hong Kong’s flagship airline, Cathay

Pacific, were severely affected following the protests. This might contribute to the increased sense

of fear and logistical uncertainty which might lead to an immediate decrease in travel to a destina-

tion with political protests.

However, despite the short term negative effects of political protests on travel to Hong

Kong, documentation regarding the effects of the Hong Kong protests on long term consumer

demand and perception of travel to the region seems more ambiguous. For instance, New York-

based Travel + Leisure Magazine named Hong Kong as first on their list of 2019’s most-visited

international travel destinations even after the protests occurred (Fox, 2019), noting that travel to

Hong Kong was popular among consumers globally despite the 2019 protests and their effects.

Additionally, the effects of political protests on changes in demand of travel through the Hong

Kong airport as a transit hub for travel to connecting destinations are rarely examined in general.

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The fact that effects of the Hong Kong protests on long-term consumer demand and

perception for travel to Hong Kong as both a final destination and a layover point seem ambiguous

or unobserved brings up two key questions regarding consumer responses to political protests

affecting popular travel destinations. Firstly, will the existence of political protests in a travel

destination significantly affect long-term consumer demand for travel to that destination after the

protests are over? And, secondly, will the existence of these political protests significantly affect

long-term consumer demand for use of that destination as a transit hub to other final destinations?

While metrics for measuring consumer demand for travel in the short run might be

more accessible through public records such as air traffic data from airports of interest during the

time that political protests occur, metrics for measuring long-term demand for travel and consumer

perceptions for future travel are not as readily available. Notably, the entities most interested and

affected by consumer demand for travel in both the long run and the short run are firms that provide

travel services such as travel booking firms, which are often private entities that restrict data access.

Given this fact, more in-depth examinations of consumer demand for travel in both the long run

and the short run given political protests might be possible to those with access to data from these

private companies within the tourism industry that collect transactional data for destinations of

interest.

Thus, due to access to private sector data from The Expedia Group, an American online

travel booking company, we use transaction data from 2017 to 2020 to examine the impact of po-

litical protests on consumers’ planned future demand for travel to these destinations as both final

destinations and layover points in 8 different countries/regions. This transaction data is collected

for all flight bookings made through all platforms of The Expedia Group over a three year period

of time. Crucially, the dates during which these transactions are recorded correspond to when

consumers make a purchase for a future flight, and do not correspond to when the flights are ac-

tually scheduled. Thus, this transactional dataset from Expedia allows us to proxy for consumers’

planned future demand and thus long term demand for future travel at any given point in time.

Through this data, we use case studies of selected political protests from 2017 to early 2020 to

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examine long-term consumer demand and behavior as well as how perceived risk due to political

instability affects purchasing decisions for future travel dates with respect to travel.

In this paper, we draw distinctions between short-term consumer demand, planned fu-

ture demand, and consumer perception for future travel. We define short-term consumer demand

within our paper as consumers’ demand for travel in the current period; for example, short-term

consumer demand for travel might be measured through the number of individuals traveling in a

location during the current time period. We define planned future demand as consumers’ demand

for travel in the long-term or in future periods. In our paper, as we examine transactional data

of consumers seeking to purchase travel in future periods, we seek to measure planned future de-

mand. And, lastly, we define consumer perceptions for future travel as consumer perceptions on

the possibility of future travel plans. When consumers consider whether or not travel to a certain

destination in future periods is reasonable given uncertainty or risk in the current period, their

thoughts regarding travel in future periods would reveal consumer perceptions for future travel.

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CHAPTER 2

BACKGROUND

In our analysis, we examined the effects on travel of seven different political instability

events: the Algerian Hirak Movement of 2019 and 2020, the Egyptian protests of 2019, the Hong

Kong extradition protests of 2019 and 2020, the Indonesia protests of 2019, the Jordan protests of

2018, the Lebanon protest of 2019, and the Tunisia protests of 2018.

2.1 Algeria

The Algerian Hirak Movement of 2019 and 2020, Reuters News writes, was the biggest

political crisis in Algeria since the 1990s. Spanning a year or more, the protests began in February

2019 when it was clear to the Algerian public that incumbent president Abdelaziz Bouteflika,

then 81, would run again for a fifth term as president after 20 years in office. Protestors called

for Bouteflika and his inner circle to resign, claiming that Bouteflika was no longer fit to run

the country. They also argued for an overhaul of the Algerian political system in order to end

corruption and promote a more democratic society in Algeria.

As protests numbers grew in March, pressure on Bouteflika grew and, after failed at-

tempts to appease protestors, Bouteflika resigned from office on April 2, 2019. Despite this,

protests continued bi-weekly in Algiers and other Algerian cities. In May and June, authorities

began to detain key Bouteflika allies on corruption charges. Protests continued, intensifying after a

September announcement by interim president Abdelkader Bensalah regarding a December pres-

idential election date. Though months of mass demonstrations against the December presidential

election continued through December and protestors called for a boycott of the national election,

former Prime Minister Abdelmadjid Tebboune was elected president in December, winning 58%

of the vote.

Upon assuming office in January, Tebboune put a new government in place, gave a

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voice to protestors, and set up a commission to overhaul the Algerian constitution. However,

protests continued despite this until at least February of 2020.

2.2 Egypt

Demonstrations in Egypt began in September 2019 after Egyptian businessman and

actor Mohamed Ali accused current Egyptian President Abdel Fattah el-Sisi of corruption. Ali,

who worked as a military contractor for 15 years, accused el-Sisi of embezzling public funds and

called for his resignation. In a video, Ali rallied Egyptians to protest in the streets if el-Sisi did not

resign.

After el-Sisi did not resign, Egyptians marched in cities across Egypt, marking one of

the first public displays of dissent in the country in six years. These protests, spurred by both

Ali’s videos and public discontent regarding the socioeconomic ramifications of the Sisi regime’s

2016 policy decisions as part of a $12 billion loan package from the International Monetary Fund,

were met by Egyptian security forces, who attempted to rein in protestors with tear gas, rubber

bullets, and live bullets. The largest wave of arrests in Egypt since 2014 followed the protests, with

security forces arresting journalists, professors, and others for spreading fake news and joining

terrorist organizations. By October 2019, over 4000 arrests had been made.

Despite the September demonstrations calling for his resignation, el-Sisi remains in

office.

2.3 Hong Kong

On April 3, the Hong Kong government introduced legislation that would allow for

criminal suspects to be extradited to China. On June 9, demonstrations in Hong Kong began

against the bill, with critics of the bill claiming that the bill could be used to intimidate, silence,

and arrest political dissidents. On June 12, during another demonstration against the extradition

bill, Hong Kong police forces fired tear gas and rubber bullets into the protesting crowd, inciting a

skirmish that developed into the worst violence Hong Kong had seen in decades.

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Despite Hong Kong Chief Executive Carrie Lam’s June 15 announcements that she

would delay the bill indefinitely, millions of protestors gathered the next day to demand the com-

plete withdrawal of the bill. Protests continued through June and July and, in July, became more

violent when mobs of men wearing white-shirts attacked commuters in a subway station near

Mainland China. Demonstrations followed this, with police firing tear gas canisters into protesting

crowds.

In August, protests intensified: civil servants joined the protests, and protests became

much more violent. Police forces continued to use tear gas, rubber bullets, and bean bag rounds on

protestors, leading protestors to wear protective gear. On August 12, after violent skirmishes at a

train station the prior day that led to a protester being injured in her eye, protestors gathered at the

Hong Kong airport, leading to mass flight cancellations. Protests continued through August and

September, even after Lam announced the withdrawal of the extradition bill that began the protests.

Protests continued, with protesters demanding the withdrawal of the ”riot” description used about

the 12 June protests, amnesty for all arrested protesters, an independent inquiry into alleged police

brutality, and universal suffrage in elections.

Skirmishes in Hong Kong between protesters and police continued through the end of

2019, with police even trapping student anti-government protesters in the Hong Kong Polytechnic

University. Even after a major pro-democracy showing in 2019 local council elections in Novem-

ber, protestor demands were still unmet, and protests continued through early 2020.

2.4 Indonesia

Following legislation passed by Indonesia’s parliament in September 2019 that ap-

proved changes to a law governing Indonesia’s anti-corruption agency, the Corruption Eradica-

tion Commission. The Corruption Eradication Commission, or the KPK, had become one of In-

donesia’s most respected public agencies due to its work prosecuting hundreds of politicians and

officials since 2002 and alleviating Indonesia’s endemic problems with corruption. The Septem-

ber revisions to governing laws worked to establish an external board to oversee the KPK. Critics

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claimed that the legislation weakened the KPK’s ability to eliminate corruption in Indonesia and

reduced the independence of one of Indonesia’s most credible public institutions. Due to this and

other perceived infringements on Indonesian rights such as revisions of the Indonesian criminal

code that could limit free speech, student protestors took to the streets in major cities like Jakarta,

Bandung, Yogyakarta, and Malang a week after the legislation was passed.

Protests soon became violent after police fired tear gas and water cannons to disperse

thousands of rock-throwing students, sending hundreds of students and police to the hospital. On

September 25, students continued to riot, even throwing a Molotov cocktail toward police barri-

cades. Violence continued through the end of September.

2.5 Jordan

Protests in the Jordanian capital of Amman and in major cities across Jordan organ-

ised by an independent Jordanian group known as Hirak Shababi and 33 of Jordan’s professional

associations and civil society groups began in June of 2018 over price increases backed by the

International Monetary Fund and a new tax reform law. The proposed law aimed to increase the

percentage of Jordanians taxed from 4.5 percent to 10 percent, lower taxable income, reclassify tax

evasion as a felony, and ultimately increase government revenue in order to ameliorate Jordan’s fi-

nancial situation: at the time, Jordan had approximately $40 billion in debt, and had to pay annual

interest of about $1.5 billion. Critics of the price increases and tax noted that price increases and

tax rate increases were fairly common even prior to this new law, and claimed that Jordan’s de-

pendency on foreign aid bred rampant corruption and lack of accountability. Protestors demanded

that the prime minister of Jordan, Hani al-Mulki, step down due to his involvement in Jordanian

monetary policy. They also demanded that legislation regarding tax rate and price increases be

scrapped and that the Jordanian government become more accountable to the public.

Following protests, Mulki resigned from his position as prime minister, and Jordanian

King Abdullah asked former World Bank economist Omar al-Razzaz, Jordan’s education minister

at the time and a more “accepted” character from the perspective of the Jordanian people, to form

14

a new Jordanian government. Despite King Abdullah’s attempts to defuse public anger, protests

continued following Razzaz’s appointment, demanding the dissolution of Jordan’s parliament.

Following Razzaz’s commitment to withdrawing legislation regarding tax rate and price

increases, protests came to a halt on June 8, 2018.

2.6 Lebanon

On October 17, 2019, protests began after the government announced new taxes in a

variety of areas, including voice over internet protocol services (VoIP) such as WhatsApp. The

protest originated in Beirut but soon spread across the country, prompting hundreds of thousands

to join in. While the new regulation sparked the series of protests, Lebanese citizens were already

upset with the existent sectarian political system, lack of necessities such as water and electricity,

and poor economic conditions. Subsequent forest fires and the government’s failure to properly

handle them further fueled their anger. Protests quickly turned violent, with security forces using

tear gas and firing at protestors.

In response to the protests, the VoIP tax was dropped and Prime Minister Saad Hariri

resigned on October 29. The government also proposed several reforms a few days later, but the

demonstrators remained unsatisfied. Local businesses, banks, and schools closed as the protests

continued. A new prime minister Hassan Diab was nominated in December 2019 with support

from the protestors, but citizens were still unhappy with the cabinet member. Demonstrations

continued into 2020, briefly pausing during the coronavirus pandemic, but returning as the virus

outbreak slowed.

2.7 Tunisia

The Tunisian government introduced new laws to increase taxes on a wide variety of

goods and services starting from January 1, 2018. This caused large amounts of Tunisians to

protest across the country in over 20 towns and cities from January 2018 to February 2018 as they

were unhappy with the tax hikes that would have caused them to pay higher prices for everyday

15

goods such as gasoline, fruits and phone cards. The protestors included large numbers of students

and unemployed, who are particularly impacted by the tax hikes and rising unemployment in the

country.

In response, the Tunisian government made little efforts to engage with the protestors

regarding the issues that they were protesting about, but instead focused on maintaining order and

arresting protestors. The Tunisian government deployed thousands of police officers to locations

where protests were held, and more than 300 protesters were arrested. Eventually, the government

announced economic policy updates, such as increased government aid for needy families and

expanded healthcare coverage, that would benefit the poor and the jobless.

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CHAPTER 3

LITERATURE REVIEW

The majority of existing literature examining the connection between political instabil-

ity and travel to a country uses qualitative methods such as surveys and interviews to determine

how travel to a particular country is impacted by political instability events. This is especially the

case within earlier studies, which often focus on the impact of political instability on people’s per-

ceived willingness to travel rather than their actual travel patterns. For example, in the paper “The

impact of political instability on tourism: case of Thailand,” Hadyn Ingram, Saloomeh Tabari, and

Wanthanee Watthanakhomprathip used 100 questionnaires and four semi-structured interviews to

test and contrast the opinions of people who have and have not visited Thailand (Ingram et al.,

2013).

However, recent studies have incorporated more quantitative methods in understand-

ing the relationship between political instability and travel patterns. Quantitative, and especially

regression-based, analysis allows us to expand the scope of our analysis to examine the average

impact of independent variables on dependent variables across different regions and time.

A key paper in this field using a quantitative econometric approach is “The Impact of

Political Violence on Tourism” by Eric Neumayer. Neumayer employs a more quantitatively rigor-

ous approach in his paper by using two estimation techniques to test the impact of political violence

on tourism rates (Neumayer, 2004). In particular, Neumayer employs a fixed-effects panel estima-

tor with contemporaneous effects only and a dynamic generalized method of moments estimator in

his analysis. Neumayer’s approach is novel because it takes a far more quantitative approach to an-

swering his research question. As such, he was able to expand the scope of his analysis to a global

scale in order to understand the average effects of political violence on tourism (as compared to

previous studies which limited their research to individual countries and regions). Neumayer found

strong evidence that political violence is negatively correlated with tourist arrivals, and that the ef-

17

fects of decreased tourist arrivals even spills over to neighbouring regions. Neumayer also found

that autocratic regimes generally see a lower number of tourist arrivals as compared to democratic

regimes, even without the effect of political violence.

While Neumayer uses tourist arrivals as a proxy for short-term consumer demand for

travel, we found in our review of prior research that, notably, there was no consistent metric used

by researchers as a proxy for short-term consumer demand for travel. For example, Abraham

Pizam and Ginger Smith, in their paper “Tourism and Terrorism: A Quantitative Analysis of Major

Terrorist Acts and Their Impact on Tourism Destinations,” examined how terrorist events impacted

tourism by ranking, categorically, the magnitude of a terrorist event’s effect on tourism demand

(e.g. “No effect/Slight decline/Significant decline/Cessation”) as well as the length of effect in

months rather than quantitatively analyzing tourism rates (Pizam and Smith, 2000). This means

that, in our analysis, we are able to introduce new means by which we examine travel demand.

Importantly, our analysis differs from that of Neumayer’s in our measure of travel de-

mand, and builds upon his work as a result. In our analysis, we seek to track changes in the num-

ber of transactions over time in countries affected by political instability rather than the number

of tourist arrivals. This means that our model, in contrast to Neumayer’s, examines the long-term

effects of political instability events on travel and thus builds on existing literature by providing

novel insights. This is especially the case due to the fact that our dataset, generously provided to us

by the Expedia Group, is unique in its makeup. Our dataset, consisting of transactional data from

Expedia from 2017 to 2020 in key countries, captures the dates when transactions are made instead

of the dates of travel, meaning that we measure individuals’ decision-making and intent to fly on

a given date rather than their actual travel. Thus, this gives us a better ability to determine how

events of political instability affects people’s purchasing and travel decision-making processes.

Additionally, thanks to the Expedia Group, we have access to travel data beyond tourism data: in

our model, we use the Expedia Group’s transactional data, which includes transactions for both

tourism-related and non-tourism-related travel. As such, we are able to expand the scope of our

research and investigate the effect of political instability on travel in general.

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With regards to estimating the saliency of political instability events in tourist des-

tinations from the consumer’s perspective, researchers have adopted a wide variety of ways to

determine how significant a specific event is to a given consumer. For instance, in “Does tele-

vision terrify tourists? Effects of US television news on demand for tourism in Israel” by David

Fielding and Anja Shortland, Fielding and Shortland draw a distinction between the intensity of

conflict events in reality versus the intensity reported in U.S. television media. Notably, Fielding

and Shortland claim that, conditional on actual events, changes in reported conflict intensity could

influence tourists because alternative sources of information are costly (Fielding and Shortland,

2009). Through time series regression methods, Fielding and Shortland concluded that their find-

ings corroborated their hypothesized rational choice framework. This is relevant to our research

with regards to tracking the saliency of notable events: since reported intensity is shown to affect

tourism demand, we are thus able to use media reports to examine the saliency of events from the

consumer’s perspective.

Similarly, in “Destination Image as Quantified Media Messages: The Effect of News

on Tourism Demand”, Svetlana Stepchenkova and James Eales used, in their analysis, only the Big

Three U.K. newspapers (the Times, the Guardian, and the Independent) in their analysis, as they

claim that the Big Three act as representatives, or “spokespersons,” for all U.K. media coverage of

Russia (Stepchenkova and Eales, 2010). By this, Stepchenkova and Eales note that reports from the

Big Three newspapers on Russia would fully represent a U.K.-based consumer’s exposure to news

on the topic. This assumption significantly simplifies data collection for media-related sources,

and we aim to simplify our measure of the saliency of political instability events through similar

means.

In particular, we propose using Google Trends scores to measure saliency firstly be-

cause Google is a first-stop for consumers with regards to learning about topics of interest and

secondly because Google searches for these topics aggregate all news reports across news outlets.

There are precedents for this: earlier papers such as ”Public health insurance, labor supply, and

employment lock” by Craig Garthwaite, Tal Gross, and Matthew J. Notowidigdo have used Google

19

Trends data as a measure of topic saliency within their analyses. We believe that, because of this,

Google Trends data is representative of the overall intensity of news in the U.S. and can be used as

a measure for the saliency of political instability events from the perspective of the U.S. consumer

(Garthwaite et al., 2013).

With our means of measuring travel demand through the number of transactions for fu-

ture travel in a given period rather than through the actual number of travellers during that time, we

contribute to existing literature by providing an analysis of the long-term effects of political insta-

bility events on travel. Our analysis is able to determine both consumers’ planned future demand

for travel and consumer perceptions of future travel as defined earlier in this paper, allowing us to

examine consumer demand for travel in future periods rather than the present. Additionally, due to

our use of Google Trends data to indicate the saliency of given political instability events to U.S.

consumers, we are able to examine how reports of political instability in digital mediums affect

long-term consumer demand for travel. Thus, our research shows how the high volume of infor-

mation regarding topics of interest available to consumers in the digital age affects their planned

future demand for travel.

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CHAPTER 4

DATA SOURCES

Our primary dataset for estimating long-term consumer demand consists of transac-

tional data from Expedia. The transactional data contains booking information for flights between

the U.S. and the countries/regions examined from all of the Expedia Group’s platforms (including

but not limited to Expedia.com, Orbitz, and Travelocity), and includes both layover and destination

data. Specifically, our dataset reports the number of bookings from the U.S. to seven countries as

both end destinations and layover points. Our dataset also includes the gross booking value for

the same aforementioned set of transactions, which indicates the aggregate amount spent on flight

tickets per location. Our data is grouped at the weekly level, with the dates representing the date of

booking. This means that transactions in the current period actually denote travel in future periods,

as consumers usually plan for travel in advance and same-day booking of travel is often extremely

difficult to achieve. Due to limitations in Expedia’s data collection, our transactional data begins

in 2017 and ends in 2020. As a result, we limited the events we examined to those which began

after 2017.

We use Google Trends search term data as a proxy for the intensity of events. This

dataset includes weekly scores of search terms normalized across all searches on Google.com since

2004. Google Trends normalizes the data using total searches in a geography and time range, then

scales it to a value from 0 to 100. This allows for comparison between search terms across time

since the search terms are normalized by popularity. We used relevant search terms such as “Hong

Kong protests” to measure the intensity of the protests over time. The figure below represents

Google Trends data for the Hong Kong protests.

21

Figure 4.1: Google Trends data for the search query ”hong kong protests” from 2017 to 2020

This appears to be an accurate representation of the Hong Kong protests, which started in June

2019 and continued for the rest of the year and into 2020.

We looked at events related to political instability in Hong Kong (2019-2020), Indone-

sia (2019-2020), Tunisia, Jordan, Algeria (2019-2020), Egypt, and Lebanon. All these events

involved large political protests involving at least thousands of participants.

22

CHAPTER 5

METHODOLOGY

We propose two separate models with different specifications to estimate the effect of

political protests on consumers’ long term demand for travel. We run each regression on two

separate measures of our dependent variable, total flight transactions as a layover to a country and

total flight transactions as a final destination to a country, for a total of four regressions. In our first

model, we run a fixed effects regression on panel data for different countries across transactional

flight data (layover and final destination) at the weekly level. We also include time and country-

level fixed effects to control for both observable and unobservable differences that vary with time

and location, respectively. To obtain the overall effect of the protests on travel, we use the model,

yi,t = β0 + β1xi,t + γiAi + δmMm

Each country is represented by the subscript i and each week is represented by the sub-

script t. The variable yi,t represents the number of layover flights for each country i in week t. xi,t

represents the intensity of a political protest as measured by the normalized Google Trends score

for each search term corresponding to a protest. Ai represents country specific fixed effects, and

Mm represents time fixed effects per month m. Our coefficient of interest is β1, which represents

the effect of the intensity of the protest on the number of transactions.

We run this regression on both transactions involving the country i as a layover point,

and transactions with country i as the final destination to compare how the political protests af-

fect each type of transaction differently. Additionally, we correct the error structure by using

heteroskedasticity-robust standard errors and clustered standard errors by country because we are

utilizing panel data.

In our second regression, we observe each country individually using the model,

23

yt = β0 + β1xt + δmMm

We do this to compare β1 across all of the countries to determine if travel is affected differently for

each protest.

24

CHAPTER 6

RESULTS

Table 6.1: Number of Observations

Number of Weeks 164

Number of Countries 7

Table 6.2: Total Destination Transactions, GBV

Country Total Number of DestinationTransactions 2017-2019

Total Destination GBV 2017-2019

Algeria 7660 3564657.709Egypt 34342 30948247.77Hong Kong 94241 75899002.66Indonesia 87837 70122715.95Jordan 13833 13486708.12Lebanon 27347 22182701Tunisia 11517 6040445.772

Table 6.3: Total Layover Transactions, GBV

Country Total Number of LayoverTransactions, 2017-2019

Total Layover GBV, 2017-2019

Algeria 66252 32126023.8Egypt 399945 366339337.6Hong Kong 2091095 1971198653Indonesia 639137 483494001.7Jordan 170385 171987132.8Lebanon 331031 270897559.2Tunisia 119603 62876799.46

25

Table 6.4: Average Destination Transactions, GBV

Country Average Number of Destina-tion Transactions Per Week,2017-2019

Average Destination GBV,2017-2019

Algeria 46.70731707 21735.71774Egypt 209.402439 188708.8278Hong Kong 574.6402439 462798.7967Indonesia 535.5914634 427577.5363Jordan 84.34756098 82236.02513Lebanon 166.75 135260.3719Tunisia 70.22560976 36831.98641

Table 6.5: Average Layover Transactions, GBV

Country Average Number of LayoverTransactions, 2017-2019

Average Layover GBV, 2017-2019


Table 6.6: Std. Dev for Destination Transactions, GBV

Country Std. Dev, Destination Trans-actions 2017-2019

Std. Dev, Destination GBV2017-2019


26

Table 6.7: Std. Dev for Layover Transactions, GBV

Country Total Number of DestinationTransactions 2017-2019

Total Destination GBV 2017-2019

Algeria 7660 3564657.709Egypt 34342 30948247.77Hong Kong 94241 75899002.66Indonesia 87837 70122715.95Jordan 13833 13486708.12Lebanon 27347 22182701Tunisia 11517 6040445.772

Table 6.8 contains the regression output for the model with country fixed effects (1)

run on aggregate layover and destination transaction data. protest score represents the xi,t, or the

Google Trends score for the protest, from our models above. Neither of these coefficients are

significant at the 5% level, and they are only slightly negative, indicating that the intensity of a

protest does not appear to affect bookings for either layovers or destinations.

Table 6.9 displays the results of model (2) for flights to each country as a layover desti-

nation. Only Hong Kong has a significant coefficient. We can interpret these values as the number

of flights booked per week as the Google Trends score increases by one point. Generally dur-

ing periods of activity, the Google Trends score jumps by over 10 points. There is an average of

12,751 layovers per week for Hong Kong, so there is a decrease in transactions by about 3.6% if

the Google Trends score rises by 10 points. During the peak of the protest, the score rose by about

80 points, indicating that the decrease in layovers is notable.

Table 6.10 displays the results of model (2) for flights to each country as the final

destination. Similar to the results in Table 6.9, Hong Kong is the only country with a significant

effect. Despite the fact that the coefficients are lower in magnitude than for the layover data, these

results are more pragmatically significant. The average number of weekly flights to Hong Kong is

575. According to this model, if the Google Trends score increases by 10 points, then there would

be 52 less transactions to Hong Kong. This is 9.1% less flights. At the peak of the protest, the

Google Trends score reached 100 for both countries, but could theoretically be higher since the

27

score only goes up to 100. This is a significant decrease in flights.

Table 6.8: Aggregate Regressions

Layover Destination

(1) (2)

protest score −4.604 −0.491(4.215) (0.469)

as.factor(country)Egypt 2,042.911∗∗∗ 163.570∗∗∗

(7.505) (0.835)

as.factor(country)Hong Kong 12,324.870∗∗∗ 525.615∗∗∗

(19.893) (2.213)

as.factor(country)Indonesia 3,502.887∗∗∗ 489.917∗∗∗

(8.867) (0.986)

as.factor(country)Jordan 636.810∗∗∗ 37.838∗∗∗

(1.696) (0.189)

as.factor(country)Lebanon 1,580.255∗∗∗ 116.389∗∗∗

(31.357) (3.488)

as.factor(country)Tunisia 357.344∗∗∗ 26.935∗∗∗

(29.326) (3.262)

Constant 353.242∗∗∗ 49.385∗∗∗

(66.340) (6.583)

Time Fixed Effects Yes YesObservations 1,148 1,148R2 0.961 0.925Adjusted R2 0.960 0.924

Note: ∗p

Tabl

e6.

9:In

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ver

tran

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(4.4

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(7.8

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(5.8

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01

30

CHAPTER 7

DISCUSSION

The results of this paper suggest that, though existing literature has shown that political

protests impact immediate short term travel demand to a country, there is no evidence to suggest

that political protests significantly affect long-term consumer demand for travel to a country, both

as a layover destination and as a final destination. This can be seen from the results of our re-

gression, where we obtained small and statistically insignificant coefficients on the Google Trends

variable in both model specifications that we ran, both as a layover destination and as a final des-

tination. These coefficients suggest the variation in the number of searches about a protest in a

given country does not significantly affect the amount of flight tickets purchased for future travel

(since our independent variable captures consumer purchasing behaviour at a given date for future

travel).

Our results are significant in their implications regarding consumer optimism given

major political events. While we would expect “pessimistic” consumers to decrease future travel

due to protests in the current time period, as they would likely feel that things will not get better

after political instability events, our results indicate that political protest activity in the short-term

does not affect consumer demand in the long-term. This means that, according to our analysis,

consumers are likely optimistic about countries’ recovery with regards to safety and economic

vitality following a political instability event in a given area. Thus, though consumers seem to

decrease their demand for immediate travel to a destination during a political instability event

as shown by earlier studies, long-term consumer demand remains unaffected by these political

instability events as “optimistic” consumers expect these countries to bounce back.

This “optimism” could potentially be due to the short-term nature of political protests

and their resulting effects. Our results indicating that travellers did not drastically change their

long-term travel plans to affected areas evidence that consumers might generally recognize that

31

political protests usually subside within a few months. As such, consumers would plan their future

travel with this assumption in mind. This finding contributes significantly to existing literature in

its indication of the differences between long-term and short-term effects of protests on travel to a

country: from our findings, it seems that consumers are confident that political instability events

subside within a few months and confident that affected regions will rebound quickly after these

political instability events.

However, there are outliers to this general trend. This is particularly pronounced in the

case of Hong Kong, where political protests directly affected the regional economy, key regional

players in the travel and tourism industry, and the operations of important travel infrastructure

such as the Hong Kong International Airport (Harrison, 2019). To analyze the reason why Hong

Kong is an outlier, we collected categorical data regarding the attributes of the different events in

order to understand which aspects of a political protest most significantly affect consumer travel

consumption. More specifically, we collected information on the issue surrounding the political

protest (whether it was regarding change of power, economic issue, or human/citizen rights), level

of violence (dummy variable for whether event was violent), and total number of people involved

in the protests (categorical variable for thousands or millions), based on news reports. The number

of people who participated in the Hong Kong protests reached the millions in a small geographic

area (Kuo and Yu, 2019), as compared to the tens and hundreds of thousands of protestors in much

bigger countries for the other events. The scale and severity of the Hong Kong protests may have

increased the uncertainty of the situation in Hong Kong even in the long-term, and consumers may

not be able to confidently predict when the political protests would improve. This is likely why we

see a negative and statistically significant result for Hong Kong: long-term effects on Hong Kong’s

economy, travel industry, and travel infrastructure might be associated with our observed decrease

in long-term travel demand. As a result, consumers may decide to hold off their planned travel to

Hong Kong until there is more clarity on how the political protests in Hong Kong play out.

Overall, our findings indicate that political instability events usually have negligible

effects on long-term consumer demand for travel unless these political instability events disrupt

32

affected regions’ economy and infrastructure. These results are insightful for major players within

the travel industry in that they can indicate optimal business strategies and actions to take given

major political instability events in key destinations. In particular, for airlines, travel agencies and

other organizations within the travel industry, our findings could be useful in helping to plan for

future demand following major political instability events. Our results indicate that, while organi-

zations might want to plan for immediate decreases in consumer demand given political instability

events, they might not need to take significant actions to mitigate the potential negative effects of

a theoretical decrease in long-term consumer demand stemming from these political events unless

these political events have significant effects on affected regions’ economy and infrastructure.

Future studies could dive deeper into consumer perceptions of travel safety following

political instability events, something that we touched on briefly during this paper. While our anal-

ysis measures, quantitatively, the effects of political instability on planned future demand, more

qualitative or survey-based studies might be able to concretely determine consumer attitudes to-

ward travel in future periods given political instability events in key destinations in the current

period as well as key topics of interest for consumers with regards to long-term travel planning in

light of major political instability events. Additionally, future studies could extend our method-

ology to analyze other non-political instability events and their impacts on long-term consumer

demand.

33

CHAPTER 8

CONCLUSION

Through our analysis, we find that, in general, long-term consumer demand for travel

from the United States to foreign countries affected by in-country political instability is unaffected

by these political instability events. This has positive ramifications for players in the travel industry,

as, in most cases, travel industry firms can expect long-term consumer demand to remain stable

following political instability events in key destinations due to consumer “optimism” regarding the

future state of countries affected by political instability. However, outliers remain, and escalation of

political protests to key magnitudes may have significant effects on long-term consumer demand.

Limitations of our analysis such as the amount of countries examined and the timeframe

examined, however, should be ameliorated by future analyses. For instance, while we hoped to

examine variation in protest intensity within our empirical analysis, our limited sample size of

7 countries constrained our ability to include this in our analysis due to a lack of variation in our

data. Future analyses might consider expanding upon our scope to increase the amount of countries

examined, widening their possibilities with regards to what can be analyzed quantitatively.

Further research might also be able to both identify the distinction between political

instability events that do not affect long-term consumer demand and escalated political instability

events that affect long-term consumer demand, and might be able to conduct analyses regarding

consumer decision-making processes regarding travel to destinations affected by political instabil-

ity events at the individual level. Additionally, analyses may be conducted on major events outside

of the sociopolitical sphere such as natural disasters and key global culture events (e.g. the World

Cup, the Olympics).

34

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Appendices

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APPENDIX A

R CODE

l i b r a r y ( t i d y v e r s e )

l i b r a r y ( l m t e s t )

l i b r a r y (AER)

l i b r a r y ( s t a r g a z e r )

# read d a t a s e t s

f l i g h t data

’ l a y gbv ’ = ends wi th ( ’ Layover GBV’ ) ,

’ d e s t t r a n s ’ = ends wi th ( ’ D e s t i n a t i o n T r a n s a c t i o n s ’ ) ,

’ d e s t gbv ’ = ends wi th ( ’ D e s t i n a t i o n GBV’ )

) %>%

mu ta t e ( avg l a y c o s t = l a y gbv / l a y t r a n s ,

avg d e s t c o s t = d e s t gbv / d e s t t r a n s ) %>%

# l a b e l d e p e n d e n t v a r i a b l e as y

rename ( y = e x p e d i a c o l ) %>%

mu ta t e ( month = format ( date , ’%B ’ ) ,

y e a r = format ( date , ’%Y’ ) ,

c o u n t r y = s t r t o t i t l e ( c o u n t r y ) ) %>%

mu ta t e ( month = f a c t o r ( month , l e v e l s = ordered months ) )

}

# run i n d i v i d u a l r e g r e s s i o n

run r e g r e s s i o n % r o b u s t se ( ) )

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# d e s t i n a t i o n r e g r e s s i o n

c o u n t r i e s %>%

map ( f u n c t i o n ( x ) g e t data ( f l i g h t data , x , ’ d e s t t r a n s ’ ) %>%

run r e g r e s s i o n ( ) %>% r o b u s t se ( ) )

# ================== END INDIVIDUAL REGRESSIONS ==================

# ================== AGGREGATED REGRESSIONS ==================

# combined l a y o v e r da ta

l a y o v e r data %

c o e f t e s t ( vcovCL , c l u s t e r = ˜ c o u n t r y , t y p e = ’HC1 ’ )

# combined d e s t i n a t i o n da ta

d e s t i n a t i o n data %

c o e f t e s t ( vcovCL , c l u s t e r = ˜ c o u n t r y , t y p e = ’HC1 ’ )

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APPENDIX B

EXPEDIA DATA

Figure B.1: Expedia Data Format

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APPENDIX C

PROTEST CHARACTERISTICS

Table C.1: Protest Characteristics

Country Topic Violence Protest Numbers

Hong Kong Rights Yes MillionsIndonesia Rights Yes ThousandsAlgeria Change of power No Hundreds of thousandsEgypt Change of power Yes ThousandsLebanon Taxes / Economical Yes Tens of thousandsTunisia Taxes / Economical Yes ThousandsJordan Taxes / Economical No Thousands

46

Title PageTable of ContentsList of TablesList of FiguresIntroductionBackgroundAlgeriaEgyptHong KongIndonesiaJordanLebanonTunisia

Literature ReviewData SourcesMethodologyResultsDiscussionConclusionR CodeExpedia DataProtest Characteristics

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