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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
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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
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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
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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-
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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
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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
Algeria 403.9756098 195890.389Egypt 2438.689024 2233776.449Hong Kong 12750.57927 12019503.98Indonesia 3897.176829 2948134.156Jordan 1038.932927 1048702.029Lebanon 2018.481707 1651814.385Tunisia 729.2865854 383395.1186
Table 6.6: Std. Dev for Destination Transactions, GBV
Country Std. Dev, Destination Trans-actions 2017-2019
Std. Dev, Destination GBV2017-2019
Algeria 16.84901396 8564.548329Egypt 32.42503757 37840.03752Hong Kong 122.2683304 112722.3963Indonesia 88.08186474 82368.24597Jordan 18.98646418 26968.15923Lebanon 44.38418098 45970.18729Tunisia 19.62136253 12641.56118
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
divi
dual
Cou
ntry
Lay
over
Tran
sact
ions
layo
ver
tran
sal
geri
aeg
ypt
hong
kong
indo
nesi
ajo
rdan
leba
non
tuni
sia
(1)
(2)
(3)
(4)
(5)
(6)
(7)
prot
est
scor
e0.
219
2.94
9∗−
45.4
54∗∗
∗−
2.00
00.
441
−2.
358
0.03
4(0
.409
)(1
.572
)(8
.103
)(1
.734
)(0
.871
)(3
.198
)(0
.636
)
Con
stan
t38
2.24
2∗∗∗
2,39
2.40
6∗∗∗
12,1
82.9
70∗∗
∗4,
117.
704∗
∗∗93
5.00
7∗∗∗
1,94
5.19
0∗∗∗
713.
274∗
∗∗
(33.
860)
(64.
525)
(560
.698
)(1
41.6
67)
(30.
465)
(87.
685)
(51.
254)
Tim
eFi
xed
Eff
ects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obs
erva
tions
164
164
164
164
164
164
164
R2
0.20
30.
270
0.28
50.
348
0.30
60.
232
0.28
9A
djus
ted
R2
0.13
90.
212
0.22
80.
297
0.25
10.
171
0.23
3R
esid
ualS
td.E
rror
(df=
151)
100.
202
247.
209
1,84
4.87
445
1.88
213
6.86
235
6.72
012
6.57
3
Not
e:∗ p<
0.1;
∗∗p<
0.05
;∗∗∗
p<0.
01
29
-
Tabl
e6.
10:I
ndiv
idua
lCou
ntry
Des
tinat
ion
Tran
sact
ions
dest
inat
ion
tran
sal
geri
aeg
ypt
hong
kong
indo
nesi
ajo
rdan
leba
non
tuni
sia
(1)
(2)
(3)
(4)
(5)
(6)
(7)
prot
est
scor
e−
0.07
50.
107
−5.
240∗
∗∗0.
128
0.01
1−
0.14
4−
0.03
5(0
.072
)(0
.168
)(1
.355
)(0
.299
)(0
.161
)(0
.407
)(0
.091
)
Con
stan
t46
.323
∗∗∗
211.
032∗
∗∗57
6.15
8∗∗∗
562.
215∗
∗∗73
.423
∗∗∗
142.
135∗
∗∗68
.771
∗∗∗
(4.9
94)
(7.2
82)
(36.
127)
(23.
088)
(4.4
95)
(7.8
85)
(5.8
10)
Tim
eFi
xed
Eff
ects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obs
erva
tions
164
164
164
164
164
164
164
R2
0.15
10.
210
0.33
90.
342
0.28
80.
236
0.23
3A
djus
ted
R2
0.08
40.
147
0.28
70.
289
0.23
20.
176
0.17
3R
esid
ualS
td.E
rror
(df=
151)
16.1
2629
.947
103.
245
74.2
5716
.641
40.3
0017
.849
Not
e:∗ p<
0.1;
∗∗p<
0.05
;∗∗∗
p<0.
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
41
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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