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UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 1
Preprint, to cite:
Park, S. Y., Kim, J. Y., & Pan, B. (in press). The impact of Uber introduction on the tourism
industry in Sub-Saharan African countries. To be published in Journal of Travel Research.
The impact of Uber introduction on the tourism industry in sub-Saharan Africa
Abstract
Unreliable and inefficient public transportation systems can deter tourism destination
competitiveness. Uber, a convenient ride-hailing platform, can complement
underdeveloped public transport and play a significant role in cultivating a destination’s
tourism industry by increasing tourists’ mobility and accessibility to attractions and
service facilities. Using the data of 48 sub-Saharan African countries and difference-in-
differences (DID) method, this study empirically validates the impact of the Uber
service availability on a country’s tourism industry. Our analyses show that since 2013,
the introduction of Uber contributed to 88 million USD in total tourist spending—99
USD per tourist spending—to a country’s economy. However, Uber did not have a
significant impact on the number of international arrivals. The results confirm that the
Uber service can generate a country’s tourism receipts by providing a reliable and
efficient means of travel.
Keywords: Uber; sharing economy; tourist expenditure; tourist spending; sub-Saharan
Africa; difference-in-differences; propensity score matching; economic impact
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 2
Transportation plays a crucial role in the tourism industry and is an indispensable
element in destination development (Prideaux, 2000). An efficient and convenient
transportation system in a destination improves tourists’ satisfaction and increases the
number of attractions visited, leading to better destination image and competitiveness
(Gutierrez & Miravet, 2016; Le-Klaehn & Hall, 2015; Prideaux, 2000; Xiao, Jia, &
Jiang, 2012). It also allows effective management of visitors and reduces traffic
congestion and crowding (Gutierrez & Miravet, 2016). Hence, a well-developed
transportation system does not only increase tourist activity within the destination but
can also encourage tourists to form a positive image of a destination, leading to
increased tourist volume and expenditures (Albalate & Bel, 2010; Israeli & Mansfeld,
2003). By the same logic, an unreliable and unfriendly transportation system in a
destination can work as a barrier to tourist mobility and accessibility (Israeli &
Mansfeld, 2003; Lew & McKercher, 2006; Prideaux, 2000), which is the case for many
countries in sub-Saharan Africa (SSA) (Economic Commission for Africa, 2009; Trans-
Africa Consortium, 2008).
Though the specific situation differs from country to country, transportation systems
are considered to be poorly organized across the SSA region, mainly due to the lack of
transportation infrastructure and poor maintenance of the existing infrastructure (Trans-
Africa Consortium, 2008). Intra-city transportation is generally considered unstable and
inactive, which increases waiting time and decreases passenger mobility (Trans-Africa
Consortium, 2008). Transport safety and security are also issues recognized by the United
Nations (Economic Commission for Africa, 2009). Such an insecure and unorganized
public transportation system can deter tourism industry development as it hinders tourists’
accessibility to attractions and service facilities and limits their scope of movement.
The best-case scenario would be to build a quality public transportation system.
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 3
However, infrastructure development requires a considerable amount of resources and
time. Thanks to information technology, we now have alternative transit that utilizes
already existing vehicles: Uber. Uber is a convenient ride-hailing mobile platform which
helps people travel to a location by connecting them to private drivers (Tham, 2016a). An
individual can register oneself as a driver after being screened for one’s driving record and
criminal history. Passengers can request a drive from a specific location to another using
an app on their mobile devices (Uber, 2018). It is considered a representative form of the
sharing economy, which, according to Botsman (2013), is an economy built on distributed
networks of connected individuals and communities instead of centralized institutions,
transforming how we can produce, consume, finance, and learn.
Since first introduced to SSA in 2013, Uber has experienced continued growth
(Dahir, 2017) and is believed to provide a safe, transparent, and efficient transportation
mode for international tourists visiting the area (Henama & Sifolo, 2017; Wyk, 2016). For
the countries in SSA where the public transportation system is relatively under-developed,
an alternative intra-destination transportation system such as Uber can play a significant
role in increasing tourist mobility and vitalizing the tourism industry. However, research
on the economic importance of intra-destination transportation, especially nonpublic
services like Uber, is scarce (Albalate & Bel, 2010; Van Truong & Shimizu, 2017).
In this paper, we examine the role of Uber in the tourism industry for destinations
with underdeveloped public transportation system using data of SSA countries. We expect
to find that Uber positively influence the tourism industry as its availability increases
tourist accessibility to attractions and service facilities, which can lead to increased
expenditures as well as their satisfaction with the destination, resulting in positive
destination image and repeated visits (Chi & Qu, 2008; Virkar & Mallya, 2018).
Moreover, allowing Uber, we argue, represents a country’s openness and hospitality
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 4
toward foreign tourists, which can indirectly manifest a positive impact on tourists’
perceptions regarding the country, again building a positive image in addition to its
destination competitiveness.
The choice of SSA countries as a study area is based on two reasons. First, to
investigate the impact of Uber’s introduction on a country’s tourism economy, it is
essential to identify an area where we can conduct a quasi-natural experiment; the service
should have entered some countries while not the others so that a comparison can be made
between the two groups. SSA provides an ideal setting for such a quasi-natural
experiment: as of 2018, only six out of 48 countries in SSA have Uber operating in them.
Therefore, we can compare the growth of the tourism industry in the six countries to that
of the rest to investigate the impact of Uber’s introduction.
Second, it is easier to detect the impact of Uber on countries in SSA compared to
highly developed countries where public transportation systems are already well-
developed and marginal utility increase from Uber may be minimal. As SSA overall has
limited public transit, the advantage of Uber should be more prominent. Hence, SSA
provides an ideal condition to observe the influence of Uber overtime as Uber
compliments the underdeveloped public transport system and as we can compare the
countries that have had Uber against those that have not.
To examine the impact of Uber on the tourism industry, the current study compares
the trend of international tourist expenditures and arrivals in the countries that adopted
Uber against that of the countries that did not. For the tourist expenditures, we examine
both the total and per capita expenditure by international tourists obtained from the United
Nations World Tourism Organization (UNWTO). Including the per capita spending allows
us to distinguish the increase in total tourism expenditures from the increased tourist
volume. We also investigate the number of international tourist arrivals along with
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 5
expenditure. For both expenditures and arrivals, data from 1995 to 2015 are used to
examine the trends before and after the introduction of Uber in 2013.
Since the introduction of Uber is an intervention under a natural setting, this paper
employs the difference-in-differences (DID) method to analyze the causal effect. The DID
method is often used to estimate the effect of a specific policy intervention, often called
treatment, by comparing the changes in outcomes between a treatment group (a population
that experienced an intervention) and a control group (a population that did not) before
and after the intervention (Bertrand, Duflo, & Mullainathan, 2004).
Besides, we employ the propensity score matching (PSM), which matches countries
with similar characteristics and removes outliers to minimize the differences in outcomes
due to factors other than treatment. As for the matching characteristics, we include
Internet usage, urbanization, political stability, price competitiveness, ground and port
infrastructure, and tourism service infrastructure.
The current study contributes to the existing literature by empirically investigating
the role of Uber and quantify its impact on the tourism industry, especially its influence on
the tourist expenditures and the number of visits, for the first time. Understanding the
influence of Uber on the tourism economy is crucial for the countries with limited public
transport as it can ease the discomfort of the tourists with existing infrastructure. The
following section explores the previous literature on the relationship between
transportation and international tourist arrivals and expenditures.
Literature Review
The development of a public transport network between and within tourist
destinations is an essential factor in the creation and development of new destinations and
the growth of existing ones, as well as contributing to the economy (Hall, 1999; Kaul,
1985; Marlina & Natalia, 2017; Prideaux, 1996, 2000). Transportation infrastructure
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 6
affects tourist movement at various levels, from the highest level of country-to-country to
the lowest level of within a tourist attraction (Hall, 1999; Prideaux, 2000). Country-level
transportation connects tourist-generating countries to tourism destination countries
(Prideaux, 2000); within-tourist attraction transport includes shuttle services at airports
and amusement parks. The current study focuses on the within-destination transport that
provides accessibility inside a destination.
Though scholars have extensively researched the importance of transportation
systems that links countries and wider regions (e.g., Khadaroo & Seetanah, 2008;
Lundgren, 1982; Rey, Myro, & Galera, 2011), few have focused on the systems inside a
destination (Albalate & Bel, 2010). Hall (1999) considers within-destination mobility and
accessibility as one of the four roles of tourist transportation and researchers have found
that intra-destination transport affects satisfaction and attractiveness of destinations (e.g.,
Avgoustis & Achanca, 2002; Echtner, 1991; Echtner & Ritchie, 1991; Sarma, 2003;
Thompson & Schofield, 2007; Virkar & Mallya, 2018).
In their review of destination image, Echtner and Ritichie (1991) identify local
infrastructure/transportation as one of the top features that researchers used to measure
destination image. More recent studies have also considered local transportation as an
attribute to measure destination image and satisfaction (Avgoustis & Achanca, 2002;
Joppe, Martin, & Waalen, 2001; Sarma, 2003; Thompson & Schofield, 2007; Virkar &
Mallya, 2018). Thomson and Schofield (2007) and Virkar and Mallya (2018) provide an
extensive review of the role of tourism transport in tourist satisfaction and destination
image.
Positive destination image then has positive impact on the tourist satisfaction and
loyalty (Chon, 1990; Chi & Qu, 2008; Hernández-Lobato, Solis-Radilla, Moliner-Tena, &
Sánchez-Garcí, 2006; Zhang, Fu, Cai, & Lu, 2014), which in turn can lead to repeat visits
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 7
(Campo-Martínez, Garau-Vadell, & Martínez-Ruiz, 2010; Zhang et al., 2014) as well as
creation of new visits via word-of-mouth (Garín-Mun, 2006; Song, Wong, & Chon, 2003).
Increased number of visitors can potentially increase the total tourism receipt, assuming
the spending pattern persists. Hence, quality local transportation can result in increased
number of tourists and tourism spending by building positive destination image.
However, as Thomson and Schofield (2007) and Virkar and Mallya (2018) point
out, the research on the impact of local transportation on tourism is still limited. We were
not able to locate studies that explored intra-destination transport infrastructure besides
public transportation nor those that empirically linked transportation systems to tourism
receipts to examine economic impact, even though well-developed transportation
infrastructure can lead to growth in a country’s tourism industry (Kaul, 1985; Page, 2005).
Another path through which efficient local transportation can increase tourist
arrivals and receipts is by improving tourist mobility and accessibility. Better mobility and
accessibility can increase tourist expenditures as tourists can reach more attractions and
facilities; the number of attractions visited is positively correlated with expenditures
(Kaul, 1985; Leones, Colby, & Crandall, 1998; Marlina & Natalia, 2017; Spotts &
Mahoney, 1991). Mode of travel has often been correlated with tourist expenditures in
previous studies (e.g., Downward & Lumsdon, 2004; Lee, Var, & Blaine, 1996;
Marcussen, 2011; Mok & Iverson, 2000; Wang, Rompf, Severt, & Peerapatdit, 2006).
However, most of them examine the expenditure differences between modes of travel to a
destination, such as air and road (e.g., Lee et al., 1996; Marcussen, 2011; Wang et al.,
2006). One exception is Downward and Lumsdon (2004), who compare the spending of
tourists who drive cars with those who use public transportation and find the former spend
more than the latter. However, we were unable to locate any research that examines the
expenditures of tourists who utilize ride-sharing services such as Uber.
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 8
Uber is a unique transportation system that is non-public and affects the movement
within a destination. It enables tourists to reach attractions and facilities that are
inaccessible or cumbersome to access with public transportation, and to travel faster, safer
and more accessible compared to public transit (Henama & Sifolo, 2017; Wyk, 2016).
Uber can compensate for a weak transportation system by providing safe, transparent, and
efficient transportation (Henama & Sifolo, 2017; Wyk, 2016).
Wan, Mohamad, Shahib, Azmi, and Kamal (2016) describe four benefits of using
Uber—safety, price, convenience, and accessibility—that are some of the critical factors
tourists take into consideration when choosing a transportation mode (Westlake &
Robbins, 2005). Previous studies have confirmed that passengers prefer Uber to traditional
taxi service and public transit due to reduced time and cost, along with greater
convenience, accessibility, and flexibility (Ngo, 2015; Rayle, Dai, Chan, Cervero, &
Shaheen, 2016; Tham, 2016b; Wan et al., 2016). Rayle et al. (2016) surveyed residents in
San Francisco and identified ease of payment, short wait time, time to reach the
destination, ease of calling a ride, reliability, comfort/safety, and cost as some of the
reasons they utilize ride-shares such as Uber. Ngo (2015) found that Uber improved
overall transportation service and increased travel options; the number of taxi complaints
in Chicago and New York decreased due to increased service quality as a response to
competition from Uber.
Thus, we can expect tourists to benefit from Uber’ convenience, safety, efficiency,
and transparency when traveling to locations with limited public transportation.
Convenience and efficiency due to Uber can lead to both improved mobility/accessibility
and better destination image. The former can facilitate tourists’ movement within a
destination, helping them reach more attractions and service facilities. The latter can result
in greater number of tourists with repeated visits and new visits via word-of-mouth. Either
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 9
way can contribute to the growth in total tourism expenditure
[Figure 1 about here]
The current study contributes to the literature on the role of intra-destination
transportation in destination development by examining the capacity of Uber to increase
tourist expenditures and arrivals. Figure 1 graphically represents the conceptual
framework employed in this study. We empirically examine the relationship between Uber
and the tangible outcomes of the tourism industry using data from countries in SSA and a
quasi-experimental design. The following section elaborates on the data and methods
employed to investigate the relationship between the introduction of Uber and tourist
expenditures. Spell out H1 and H2 if you want to keep two hypotheses.
Methods
Data and Variables
The dependent variables that represent the tourism economy of the current study are
tourist arrival and expenditure (Table 1). First, the tourist arrival is measured via an annual
number of international inbound tourist arrivals obtained from the World Bank (World
Bank, 2018a). The World Bank defines international inbound tourists as those "who travel
to a country other than that in which they have their usual residence, but outside their
usual environment, for a period not exceeding 12 months and whose main purpose in
visiting is other than an activity remunerated from within the country visited" (2018a,
World Bank, para.1). The number of arrivals is sourced from the World Tourism
Organization’s Yearbook of Tourism Statistics, Compendium of Tourism Statistics and
data files (World Bank, 2018a). The investigation of the relationship between the number
of arrivals and Uber availability examines our hypothesis of Uber leading to positive
destination image building and increased loyalty.
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 10
Second, the tourist expenditure is examined to investigate the role of Uber in
increasing the spending of tourists via increased mobility and more attractions visited. The
expenditures data is a collection of records from the World Tourism Organization (WTO),
International Money Fund (IMF) and World Bank imports estimates (World Bank,
2018b). In addition to the goods and services expenditures made by inbound tourists, the
data also includes payments to national carriers for international transport and receipts
from same-day visitors as well as passenger services performed within an economy by
foreign carriers (World Bank, 2018b). We also use expenditure per tourist as an additional
dependent variable, by dividing the total expenditures by the number of inbound tourist
arrivals. Both tourist arrivals and expenditure data are collected for the years 1995 to
2016.
The treatment variable is represented in a binary format that shows whether Uber is
available or not—1 if Uber is available and 0 if not. From Uber’s official website
(https://www.uber.com/cities), we found data on the countries and the dates of Uber’s
introduction in the SSA region. Of 48 countries in the SSA, six have Uber operating in
them since 2013 (with the cities with Uber operation listed in brackets): Ghana (Accra,
Kumasi), Kenya (Nairobi, Mombasa), Nigeria (Abuja, Lagos), South Africa (Cape Town,
Durban, Johannesburg, Pretoria, Port Elizabeth), Tanzania (Dar Es Salaam), and Uganda
(Kampala). Figure 2 graphically presents the locations in a map.
[Figure 2 about here]
The intervening variables that can influence the dependent variables—tourist
arrivals and expenditures—are added to the analyses to account for any differences due to
country-specific characteristics other than the existence of Uber. First, we control for
global economic and societal development variables that are critical determinants of
tourism industry such as Internet usage, urbanization rate, and political stability and
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 11
absence of violence and terrorism (Naudé & Saayman, 2005; Ivanov, Gavrilina, Webster
& Ralko, 2017). Internet usage is measured as the percentage of the population who are
Internet users and is sourced from the International Telecommunication Union’s World
Telecommunication/ICT Indicators Database. The urbanization rate is the percentage of
the total population living in urban areas, which represents the level of industrialization
and societal development (World Bank, 2018c). The source of data is the United Countries
Population Division’s 2018 revision of World Urbanization Prospects. The political
stability and absence of violence and terrorism is one of the Worldwide Governance
Indicators from World Bank and assesses the "perceptions of the likelihood that the
government will be destabilized or overthrown by unconstitutional or violent means
(World Bank, 2018d, para. 1)." The three datasets are collected for the years from 1995 to
2016.
[Table 1 about here]
Second, we take tourism industry competitiveness into account since the increase in
tourist volume and expenditure can depend on how developed that country’s tourism
industry is. As a proxy for tourism competitiveness, we use the World Economic Forum
(WEF)’s Travel & Tourism Competitiveness Index (TTCI). The TTCI provides 14
variables that evaluate competitiveness of a country’s tourism industry, from its business
environment to natural resources. Of the 14, we use three variables we consider most
relevant to Uber: the ground and port infrastructure index, the tourism service
infrastructure index, and the price competitiveness index.
First, the ground and port infrastructure index measures the quality of the
transportation infrastructure, such as paved road density and quality of roads. Second, the
tourism service infrastructure index includes quality of tourism infrastructure, the number
of hotel rooms, presence of major car rental companies and automated teller machines
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 12
(ATM) per adult. Third, the price competitiveness index, which measures how costly it is
to travel or invest in a country. The price competitiveness index incorporates ticket taxes
and airport charges, a hotel price index, purchasing power parity, and fuel price levels.
The three variables are available every two years from 2007 and 2015 and their scale
ranges from one to seven. Due to the significant presence of missing data, we calculate the
average indexes for the years 2007, 2009 and 2011 to create a tourism competitiveness
index before Uber and the average indexes for the years 2013 and 2015 to generate an
index after Uber. Table 1 provides descriptive statistics of all the variables.
Analysis method
Difference-in-differences estimation. An experimental design is the most ideal for
evaluating the introduction of a new system or policy, which can be considered as a
treatment. The effectiveness of a treatment can be determined by comparing the group that
received the treatment (treatment group) and that did not (control group) in an identically
controlled environment. However, it is nearly impossible to experiment with a policy in
real-world settings due to ethical and resource-related issues (Asgari & Baptista Nunes,
2011).
The difference-in-differences method (DID) is a quasi-experimental method which
utilizes repeated cross-section and time-series to examine the impact of an intervention such as
policy implementation in natural settings (Bryman, 2016). DID is one of the most widely used
methods for estimating the effectiveness of policies since it complements the shortcomings of
both the cross-section and time-series analyses. The cross-section analysis measures policy
effects by comparing the treatment and control groups in the same period. Thus, the difference
caused by an unobserved time differences can be controlled. However, a cross-section analysis
does not capture the differences that occur from the different characteristics between the
treatment and control groups. On the other hand, a time-series method estimates a policy’s
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 13
effects by comparing the before and after characteristics of a treatment group. It is
advantageous for controlling the unobserved characteristics of a treatment group. However,
the estimation results of time series analysis cannot separate the unobserved impact from the
lag of time from policy effectiveness as there is no control group for comparison.
The DID controls for unobserved time differences and group characteristics
differences, as well as observed or complementary information (Angrist & Pischke, 2008).
The method includes a time-invariant assumption, i.e., the unobserved differences between
treatment and control groups are the same over time in the absence of treatment. With this
assumption, DID estimates the causal effect of specific intervention by comparing the
changes in outcomes over time between the treatment group and the control group
(Lechner et al., 2011).
Figure 3 graphically describes how DID works. The line T1–T2 represents the
outcome of the treatment group whereas the line C1–C2 represents the outcome of the
control group. The outcomes of both groups are measured before either group has received
the treatment, which is represented by the points T1 and C1. Their difference is marked as
D1. The treatment group then receives the treatment, marked as 'Uber' in Figure 3; and the
outcomes of both groups are measured again, presented as T2 and C2.
The DID then calculates the counterfactual difference in the outcome variable
between the two groups—the difference that would still exist if neither group experienced
the treatment. The counterfactual difference is represented by D2, the difference between
points C2 and Q. Q is the outcome of the treatment group reflecting the change of the
control group with the assumption of no Uber. The DID effect is the impact of Uber after
eliminating the difference from time and country characteristics. It is calculated as the
difference between the observed outcome and the counterfactual outcome, T2–Q (3).
Table 2 provides more detailed explanation.
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 14
The DID approach allows us to measure the difference in outcomes between the
treatment and control groups that comes solely from the intervention. It removes biases in
the post-intervention comparisons between the two groups that could arise from the
permanent differences between the two groups because it takes into account the
counterfactual outcome. Moreover, it removes biases from comparison over time in the
treatment groups that could be the result of the passing of time.
[Figure 3 about here]
Since Uber was introduced to SSA in 2013, the paper uses data from 1995 to 2012
as the before treatment and data from 2013 to 2015 as the after treatment. The treatment
group includes six countries: Ghana, Kenya, Nigeria, South Africa, Tanzania, and Uganda.
We set tourist arrivals and expenditures in the treatment group after Uber as T2.
Therefore, (T2 − T1) represents the change of tourists in the treatment group before and
after Uber’s introduction. The control group includes 42 countries, and their tourist
expenditures before and after 2013 are represented as C1 and C2, respectively. D1 and D2
account for the differences between the tourist expenditures of the treatment and control
groups pre- and post-Uber introduction, and D2 − D1 represents the treatment effect
(Table 2).
[Table 2 about here]
Linear Estimation of DID. The equation below is the linear model used for the DID
analysis in the study:
tour
it
= β
0
+ β
1
T
t
+ β
2
D
i
+ β
3
(T
t
∗
D
i
) + X
i
γ + e
i
(1).
The dependent variable tourit is the total or per capita expenditure by tourist(s) or
tourist arrivals for country i at year t. We define the treatment as the introduction of Uber
to the country and assume that there exist two periods, before treatment, Tt = 0 (t < 2013),
and after treatment, Tt = 1 (t ≥ 2013). For the qualification variable (Di), we assign the
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 15
value 1 if a country belongs to the treatment group (Di = 1) and 0 if not (Di = 0).
According to time and group variables, we identify the conditional value of equation (1) as
the following:
1) E11 = E(tour|D = 1, T = 1) = β0 + β1 + β2 + β3 + γ
2) E01 = E(tour|D = 0, T = 1) = β0 + β1 + γ (2)
3) E10 = E(tour|D = 1, T = 0) = β0 + β2 + γ
4) E00 = E(tour|D = 0, T = 0) = β0 + γ
Table 2 describes equation (2) by each condition. According to the DID method,
the pure effect of the treatment is defined as β3 since (E11 − E10) − (E01 − E00) = (β2 + β3)
− β2 = β3. The coefficient of the interaction term between the time variable and the
qualification variable (Ti
∗
Di) can be interpreted as a pure effect of treatment.
However, when we only conduct a simple DID estimation, a selection bias can
occur. For example, it is easy to assume that Uber is prone to select more developed
markets with a strong demand for their service because it recognizes the markets with
strong tourism growth potential. Considering this bias, it is crucial that the treatment and
control groups have similar characteristics and differ only as far as Uber availability. One
of the advantages of DID is that it can be combined with other procedures, such as the
Propensity Score Matching (PSM) method to ensure the validity of the comparison
(Heckman, Ichimura, & Todd, 1997, 1998).
[Figure 4 about here]
Propensity Score Matching. PSM reduces the heterogeneity between the treatment
and the control group and creates a situation similar to the random assignment of an
experimental design. Figure 4 explains how PSM works generally. The size of a circle
represents a country’s Propensity Score (PS) based on its characteristics, such as political
stability, urbanization, and tourism service infrastructure. The white circles are countries in
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 16
the treatment group and the black circles are countries in the control group. A subset of the
control group is matched to that of the treatment group with similar characteristics for
comparison. Since it is difficult to find observations that exactly match, the kernel matching
method estimates PS through a logistic model of covariates between treatment and control
groups and matches the sample of observations included in the treatment and control groups
with the nearest PS (Smith & Todd, 2005). If there is no close match, the observation is
excluded.
In our study, we matched the treatment and control groups using variables
influencing a country’s tourism industry: Internet usage, political stability, urbanization,
price competitiveness, ground and port infrastructure, and tourist service infrastructure.
Initially, we match Internet distribution, political stability, and urban populations of SSA
countries from 1995 to 2016. Then, we also match the ground and port infrastructure
index, tourist service infrastructure index, and price competitiveness index for the years
2007 to 2016. As a result, the number of control groups decreased from 42 to 20 that had
similar characteristics to those in the treatment group; the final analysis data included 20
controls and six treatments.
[Table 3 about here]
Table 3 compares the characteristics between the control and treatment groups
before and after the PSM. We can observe that the characteristics influencing tourism
industries are more similar between the control and treatment groups after the matching
process. For instance, the individual Internet use rate difference between the treatment
and control groups is 2.5 percentage points before the matching whereas the difference is
0.64 after. Moreover, the difference in tourist service infrastructure scores between the
two groups decreased from 0.2 to 0.09 after the matching.
Results
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 17
Table 5 presents the analysis results. The Travel & Tourism Competitiveness
Index (TTCI) data are only provided for limited years compared to Internet usage,
political stability and urbanization. Hence, when we control for TTCI variables, many
observations are lost. To mitigate the loss of observations, we first estimate by only
including political stability, Internet usage, and urbanization. We can observe a
significantly positive impact of Uber’s introduction represented as ‘Treatment*time’ for
both total and per capita expenditure.
To start with, we examine the results for the tourism receipts in columns (0)
through (4). The first column (0) shows the simple DID estimation result without
controlling any covariates. We observe a significant difference between the control and
treatment groups pre- and post-Uber service availability, which encouraged further
investigation. From the columns (1) and (3), we can observe that the impact of Uber on
total and per capita tourist expenditure is approximately 140 mil USD and 129 USD
respectively since 2013. On yearly average, the countries that had Uber operating gained
47 mil USD in total tourist expenditure and 43 USD in per capita tourist expenditure.
That is, each year, the tourists who visited the countries that had Uber operating spent 43
USD more on average, which contributed to 47 mil USD gain for tourism industry as a
whole.
[Table 5 about here]
The estimation result is lower but consistent, when we include the TTCI variables.
Column (2) and (4) are the estimation result when we control all explanatory variables.
The DID effect is positive and significant. For the countries with Uber, the total and per
capita tourist expenditure is greater than those without Uber by 88 mil USD and 99 USD
respectively. That is, since 2013, tourists to the countries with Uber spent 33 USD more
on yearly average than those to the countries without Uber, resulting in 29 mil USD
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 18
average tourism receipt annually. The findings affirm our hypothesis that the existence of
Uber promotes tourist spending by increasing their mobility and accessibility.
The establishment of Uber has a positive impact on tourist expenditures because
first, Uber helps tourist overcome insecure public transportations system in SSA
countries. Uber can provide a reliable and efficient alternative means to travel intra-
destination as many countries in SSA have inefficient and inadequate quality public
transportation (Economic Commission for Africa, 2009; Trans-Africa Consortium, 2008).
At the same time, Uber provides a familiar, accessible and transparent mode of travel for
international tourists (Ngo, 2015; Rayle et al., 2016; Tham, 2016b; Wan et al., 2016).
Tourists can request a ride directly from their location and inquire about pricing, even if
they are unable to speak the native language. They can also pay for the service using
Uber’s mobile app, which increases convenience. When tourists are more comfortable
with a transportation system, they are more likely to travel to more places where they are
likely to spend money.
We now examine the impact of Uber on tourist volume to investigate our second
hypothesis of Uber benefiting destination image. The analysis of Uber’s effect on the
number of tourist arrivals tells a different story. Columns (5) and (6) of Table 5 shows the
DID estimation results. The results indicate that the coefficient between treatment and
time is insignificant despite the positive result. The significant results for the treatment
dummy are probably due to the inherent differences in the countries which have Uber
versus those which do not, e.g., the size of the tourism economy. This indicates that Uber
did not significantly contribute to attracting more tourists to those countries. Though
previous literature has found transportation system as a critical factor in building positive
destination image (Gutierrez & Miravet, 2016; Le-Klaehn & Hall, 2015; Prideaux, 2000;
Xiao et al., 2012), we were not able to find the connection between Uber, destination
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 19
image, and increased arrivals.
Conclusion and Discussion
Using a difference-in-differences (DID) method, this research empirically validated
the impact of the introduction of Uber service on a country’s tourism economy. Since 2013,
Uber service brought in 88 million USD tourist spending to sub-Saharan African countries,
which is 29 mil USD annually. The extra income is due to an increase of 99 USD per capita
spending from each tourist and not from an increased number of tourist arrivals. The results
confirmed our hypothesis that Uber service could improve a country’s tourism economy by
providing cheaper and more convenient services and breaking down transportation barriers.
However, contrary to our initial thoughts, Uber did not help attract more international
tourists through an improved destination image. This might be due to the short time, two
years, since Uber’s introduction. Increased destination image may need more time to
propagate through customer word-of-mouth; thus, the impact has not materialized yet. It will
be interesting to conduct similar research a few years into the future to validate this
assumption.
The results call for sub-Saharan African countries to be open to the introduction of
new technologies. The researchers are not aware of the criteria for Uber’s decision to enter a
particular country. However, the substantial growth in tourist spending makes it worthwhile
to invest in those conditions which makes its introduction viable. Even though Uber is facing
several lawsuits (Marcano, 2018) in South Africa and around the world due to its disruption
to the traditional transportation industry, the results of the current study confirm its value to a
country’s tourism economy, at least in developing countries where transportation
infrastructure is usually inferior. Uber should be considered a partner of a country’s tourism
and hospitality industry, not a foe. The conclusions are also valuable to the company Uber for
justifying its value not only by fulfilling local transportation needs but by aiding the tourism
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 20
and hospitality industry in bringing in more dollars from international visitors and thus
creating more jobs and boosting a country’s economy.
The limitation of the study lies in its limited scope in sub-Saharan African countries.
Would Uber have the same effect on tourist spending in other developing or developed
countries with similar or relatively superior transportation infrastructure? Measuring the
number of jobs created due to Uber’s introduction while considering the number of jobs lost
in the traditional transportation industry could also be an exciting and meaningful future
research direction. Quantifying the impact of other sectors of the sharing economy, such as
Airbnb or bike sharing services, on the tourism industry could also be fruitful. Another
critical research would be to confirm the process through which Uber impacts the spending of
the tourists and the destination image.
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Figures
Figure 1. Conceptual framework for the role of Uber in tourism industry. Dashed line
represents a feedback loop. H1 and H2 respectively represents hypothesis 1 and 2.
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 28
Figure 2. Uber operating countries in sub-Saharan Africa. The circles represent the cities in
which Uber operates as of December 2018.
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 29
Figure 3. Difference-in-Difference (DID) method. T1 and C1 respectively represents the
initial value of tourism receipt/number of international tourist arrivals of the country that has
Uber operating (treatment group) and that does not (control group) prior to the introduction of
Uber. T2 and C2 respectively represents the tourism receipt/number of international tourist
arrivals of the treatment and the control group after the introduction of Uber. Q is the
counterfactual outcome of the treatment group reflecting the change of the control group as
well as its trend. (say, Uber Introduction in the figure above)
UBER INTRODUCTION AND TOURISM INDUSTRY IN SSA 30
Figure 4. Propensity Score Matching (PSM) method. White circles represent countries that
have Uber operating (treatment group) and black circles those that do not have Uber
operating (control group). The size of a circle indicates the Propensity Score calculated based
on its political stability, Internet usage, urbanization rate, ground and port infrastructure,
tourist service infrastructure, and price competitiveness.
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