Risk Sharing Across Countries: The Importance of Tourism Activity
Faruk Balli1
Hatice Ozer Balli2
Rosmy Jean Louis3
Abstract
In this paper, we provide empirical evidence that international tourism receipts serve as an
important channel through which risks are shared among many countries beyond the wellknown channels found in the literature. Further investigation into the extent of risk sharing
across countries shows that the concentration of tourist flows for particular countries/regions
has a negative impact on the role of tourism receipts in providing insurance. However, the
share of tourist flows from off-continent countries has a positive impact on the extent of the
risk sharing via international tourist receipts. We also find that tourist flows originated form
separate continents are more likely increase the gains from risk sharing.
JEL classification: F24, F41
Keywords: diversification, international tourism demand, risk sharing, tourism receipts
1
School of Economics and Finance, Massey University, Palmerston North. New Zealand.
E mail: f.balli@massey.ac.nz
2
School of Economics and Finance, Massey University, Palmerston North. New Zealand.
E mail: h.ozer-balli@massey.ac.nz
3
Department of Economics and Finance, Vancouver Island University. E mail: Rosmy.JeanLouis@viu.ca
1
Introduction
In times of economic boom or depression, international tourism receipts represent a
reliable source of external financing for many developing and developed countries.
According to the World Tourism Barometer of the United Nations World Tourism
Organization (UNWTO), international tourism receipts have surpassed the 1 trillion USD
mark worldwide and have grown steadily in the last two decades, save, of course, for the
period covering the recent global financial crises. At the aggregate level, UNWTO estimates
that international passengers’ travel and transportation account for 30% of the world’s
exports of services and 6% of overall exports of goods and services, thereby making tourism
spending an important injection to domestic economies. This surge in international tourism
activity has proven beneficial all around the globe, particularly in those countries facing weak
domestic consumption as a result of fiscal austerity and monetary policy ineffectiveness.
As a key export and labour-intensive activity, international tourism serves as engine to
balance the current account and stimulate growth in the long run. With the globalization of
markets and advances in information technology, the internet has become a global space,
with popular social media sites such as YouTube and Facebook playing a key role in the
promotion of tourism destinations around the globe through informal sharing of pictures and
videos between friends and families, and formally through marketing and advertising.
Tourism has become the engine of growth for many regional economies and the most
important economic sector for many countries. As a result, policy makers and industry
stakeholders have allocated a sizable amount of domestic resources towards the creation,
promotion and enhancement of tourist destinations across countries.
Consequently, at the academic level, the literature has benefited greatly from various
contributions that focus on the economic impacts of tourism on domestic economies. These
encompass the works of Tosun (2001), Balaguer and Jorda (2002), Dritsakis (2004),
Durbarry (2004), Kim, Chen, and Cheng. (2006), Gunduz and Hatemi-J (2005), Proenca and
Soukiazis (2005), Lee and Chang (2008), Cuñado and Garcia (2006), Ongan and Demiroz
(2005), and He and Zheng (2011), among others. These authors have mostly studied the
impact of international tourism on economic growth by focusing on either long- or short-run
relationships, while applying different techniques and using different country samples, be
they emerging markets or OECD (the Organisation for Economic Co-operation and
Development).
The results have been mixed; some have found a positive relationship
2
between international tourism and economic growth, whereas others have found either a
negative or no relationship at all, depending on the time interval or the country sample used.
An earlier study by Chen and Devereux (1999) on the indirect effects of international tourism
suggests that tourism can, in fact, reduce welfare in countries with restrictive trade measures
(export taxes and import subsidies). It is worth noting that Song and Li (2008) have provided
the tourism literature with a comprehensive review on the diversity of research topics,
methodology, data, region and research themes used in tourism research for the post-2000
era, along with the diversity of findings. Reviews by Crouch (1994), Li, Song, and Witt
(2005), Lim (1997 a & b and 1999), and Witt and Witt (1995) that cover studies published
mostly during the period 1960-2000 are precursors to the review of Song and Li (2008).
However, despite the many contributions on the importance of tourism to economic growth,
the literature has remained, by and large, silent on the ability of tourism as a channel of risk
sharing across countries, i.e. the ability of tourism to act as insurance against economic
downturns. To that end, knowledge of the cyclical nature of tourism in relation to the
business cycle of the recipient country is summarily important. If international tourism
receipts are counter-cyclical (or at least less than 100% correlated) with the domestic output
shocks, countries or regions experimenting economic downturns may be likely to benefit
from tourist flows originating from countries less affected by global crises or with economic
abundance. In this vein, tourist expenditures possibly insulate the domestic economy by
smoothing income and consumption. Alternatively, little, no or negative risk sharing can
materialize if tourism revenues are pro-cyclical, given that tourism activity in domestic
countries are positively linked to economic well-being in foreign countries.
There are at least two compelling reasons for exploring the extent of risk sharing underlying
tourism flows across countries. As per the general risk sharing hypothesis documented in
Athanasoulis and van Wincoop (2000), and Pallage and Robe (2003), excessive consumption
fluctuations transmitted through output shocks— a feature of higher risk sharing—can have
adverse effects on the accumulation of human and physical capital. The welfare gains from
these risk sharings may exceed 100% of permanent consumption (Obstfeld 1994; van
Wincoop 1994). From another standpoint, in line with the theory of optimum currency areas
of Mundell (1961, 1973 a&b), if risk sharing emanating from tourism activity is indeed
effective in smoothing output shocks, just like any inflows to the economy, international
tourism receipts can be considered as a reliable channel to absorb the impact of asymmetric
3
shocks to domestic economies, thereby satisfying the requisites of higher economic
integration.
However, despite the growing importance of the risk sharing literature, the crucial aspect
of international tourism receipts as a shock absorber has not been formally investigated. The
few studies that come close to focusing on this issue empirically have only documented the
role of external inflows in promoting risk sharing. For example, Balli and Balli (2011) and
Balli, Basher, and Louis (2013) examine the contribution of remittance inflows; Balli,
Basher, and Balli (2013), the income inflows from international portfolio holdings; and
Sorensen and Yosha (1998), the impact of international transfers, exports and imports on risk
sharing, among other factors. Motivated by the limited research in the area and the
exceedingly important role international tourism receipts play in the overall macroeconomic
stabilization of developing economies, this paper makes a contribution to the existing
literature in filling this gap.
Using data for a sample of 87 countries over the period 1995–2010, we first measure the
extent of risk sharing via international tourism receipts for each country in our sample. Our
preliminary examination suggests that there is substantial cross-country variation in the
estimated degree of risk sharing via tourism receipts, ranging from 44% for Benin to –16%
for Moldova. In light of this notable gap, we further investigate the determinants of
international tourism receipts to uncover the source of this variation. We find that the
concentration of tourist inflows from limited number of countries is a leading explanation for
the extent of risk sharing via tourist receipts: the higher the diversification of the tourists from
different countries, the greater the amount of domestic output shocks buffered by the tourism
receipts. Another important finding is the impact of distance on risk sharing via tourism
receipts: the closer the country or region of origin of the tourists is to the tourist destinations
geographically, the lower the amount of risk shared via tourism receipts. As can be seen
easily, the further away that countries supplying tourists are from the tourist attraction
centres, the more likely it is that the two regions are subjected to asynchronous business
cycles, hence opening room for risk sharing to take place as financial resources flow to
smooth income in the less fortunate countries. In addition, we find evidence that international
tourism receipts originating from countries supplying tourists that are far away or in different
continents from countries that are tourist destinations produce more risk sharing than
countries that are close to each other or share the same continent. This is quite reassuring,
4
since business cycles are typically more synchronized among regional and neighbouring
economies, tourist inflows behave pro-cyclically with respect to domestic output, thus giving
rise to little or even dis-smoothing of output shocks. Last but not least, we investigate
whether the size of international tourism receipts as a ratio to gross domestic product (GDP)
facilitates more risk sharing. The results show that tourism receipts exert a positively strong
and statistically significant impact on risk sharing.
The rest of this paper is organized as follows: in Section 2, we present the underlying theory
of risk sharing that anchors the empirical model specification. Section 3 describes the
construction of the variables and the data sources, while Section 4 discusses the empirical
findings in detail. Finally, Section 5 concludes the paper.
2
The Empirical Model
Risk sharing indicates that economic agents or countries can share risk with each
other. In this section we briefly outline the basic ideas for endowment economies with one
homogeneous tradable good. For a fuller discussion interested readers are referred to Obstfeld
and Rogoff (1996).
Following the theories of the risk sharing, first Cochrane (1991) and Mace (1991) utilize
consumer-level data to investigate the degree of risk sharing between individual and
aggregate consumption. Subsequently, researchers have generally regressed idiosyncratic
(domestic minus world) consumption growth rates (∆𝑐𝑡𝑖 ) on idiosyncratic output growth rates
(∆𝑦𝑡𝑖 ) to estimate the magnitude of risk sharing empirically. In short, ∆𝑐𝑡𝑖 = 𝛼 + 𝑏Δ𝑦𝑡𝑖 + 𝜀𝑡
equation is used to test the risk sharing empirically. The slope coefficient, b, is equal to zero
if there is perfect risk sharing, implying that idiosyncratic consumption is uncorrelated with
idiosyncratic output. This equation used to test for full risk sharing at the country level, is
studied by Obstfeld (1994), Canova and Ravn (1996), and in the literature, most notably
Backus, Kehoe, and Kydland (1992), Baxter and Crucini (1995), and Stockman and Tesar
(1995) examined the prediction that the correlation of consumption across countries should
be equal to unity. From these studies, we have observed that the notion of perfect risk
sharing does not seem to be present in the data. A more realistic approach is to quantify the
extent of risk sharing between countries while identifying the channels through which risk is
shared and in what magnitude. This line of research was not possible until the ground5
breaking study of Asdrubali, Sorensen, and Yosha (1996) and Sørensen and Yosha (1998).
These researchers developed a simple accounting methodology to quantify the relative
contributions of various channels of risk sharing. Their method decomposes the crosssectional variance of GDP into various components to capture both market (capital and
credit) and non-market (fiscal) channels of risk sharing. Among various channels, Sørensen
and Yosha (1998) show that income risk sharing occurs primarily through cross-border
ownership of assets. The contribution of remittance inflows (Balli and Balli (2011) and Balli
et al. (2013)) and the income inflows from international portfolio holdings (Balli, Basher, and
Balli (2013)) on risk sharing has also been studied. However, the literature so far is silent on
quantifying the extent of risk sharing via trade or tourism channels.
2.1
Risk Sharing via Tourism Receipts
In order to quantify risk sharing via international tourism receipts, we follow the
methodology used by Sørensen and Yosha (1998) to uncover the role of international tourism
in absorbing output shocks. The starting point is the national accounts identity:
𝐺𝐷𝑃 = 𝐶 + 𝐼 + 𝐺 + 𝑋 − 𝑀.
(1)
Since, at the aggregate level, total output is equal to total income, it follows that output
(GDP) equals savings (S) plus consumption and taxes (T), where consumption includes both
private (C) and public (G) expenditure on goods and services. Algebraically:
GDP=C+S+T.
(2)
Under the assumption that T=G for a balanced budget, by setting Equation (1) equal to
Equation (2), we obtain
S= I + X – M,
(3)
where I stands for gross public and private investment, and X and M are exports and imports
of goods and services, respectively. Equation (3) can now be used to perform risk sharing
analysis by decomposing savings to bring to light the contribution of tourism receipts
incorporated in exports, which is our focal point. The basic consumption risk sharing
regression equation estimated by Sorensen and Yosha (1998) and Obstfeld (1996) can be
written as follows:
∆(𝐶 + 𝐺)𝑡 = 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛽 ∗ ∆GDPt ,
(4)
6
where 𝛽 ∗ measures the co-movement of consumption and GDP growth rates. As β
approaches zero, there is perfect risk sharing. By contrast, 𝛽 ∗ equals 1 implies no risk
sharing. Since 𝛽 ∗ measures co-movement, 1 − 𝛽 ∗ measures the extent of risk sharing.
Accordingly, we estimate the following regression to quantify the extent of total risk sharing,
β:
∆GDPt − ∆(𝐶 + 𝐺)𝑡 = 𝑎 + β ∆GDPt.
(5)
where β =1-𝛽 ∗ . Setting T = G in Equation (2) and solving for (C + G) to substitute in
Equation (5), we obtain an expression that can be used to decompose the total risk sharing
into channels:
∆GDPt − ∆(𝐺𝐷𝑃 − 𝑆)𝑡 = 𝑎 + β ∆GDPt .
(6)
As per Equation (3), S = I + X – M, we are able to measure risk sharing via different
channels. Since the variable X contains international tourism receipts, further decomposition
followed by substitution leads to the regression equation used to estimate the extent of the
risk sharing via tourism receipts:
̃t − ∆(GDP − Tourism
̃ receipts) = 𝛼𝑖 + 𝛽𝑖 ∗ ∆ GDP
̃t .
∆ 𝐺𝐷𝑃
t
(7)
In contrast to Equation (4), both variables are expressed as a percentage change in GDP per
̃t stands for the natural
capita prices minus their worldwide counterparts. For example, ∆ GDP
logarithm of annual real GDP per capita growth rate for country i minus the world aggregate
GDP per capita growth rate.4 The slope coefficient, βi , measures the extent of differential
output shocks buffered by international tourism receipts after discounting aggregate shocks
on tourism receipts. Each time series regression is estimated via the feasible generalized least
squares (FGLS) method to adjust for the serial correlation and heteroskedasticity among the
error terms.5
Upon quantifying the amount of risk insured via international tourism receipts, we turn
attention to its possible underlying determinants. We postulate that risk sharing via
4
Sørensen and Yosha (1998) estimate their risk sharing equations using cross-sectional estimation techniques
and obtain the idiosyncratic component (i.e. the deviation of a country’s growth rate from the aggregate growth
rate) by removing the time fixed effect. Since we estimate the risk sharing on a country-by-country basis, we
drop the aggregates from each variable to obtain the idiosyncratic components.
5
The FGLS method is asymptotically more efficient than the OLS when the autoregressive order 1 exists. The
FGLS estimation of the autoregressive order 1 model has two different names, originating from different
methods of estimating 𝜌. We used the Prais–Winsten estimation, since we have a smaller time series sample and
cannot afford to lose a single observation.
7
international tourism may be linked to tourism concentration, the size of tourism receipts,
continent of origin, and the importance of the OECD as a high-income bloc and a major
supplier of tourists. The justification for using these variables, along with their associated
details, is discussed below.
In order to take advantage of both the time series and cross-sectional dimensions of the data,
we follow Mélitz and Zumer (1999) and Sørensen, Wu, Yosha, and Zhu (2007) to estimate
the panel equation:
∆𝐺𝐷𝑃it − ∆(𝐺𝐷𝑃 − 𝑇𝑜𝑢𝑟𝑖𝑠𝑚 𝑟𝑒𝑐𝑒𝑖𝑝𝑡𝑠)it = 𝑣𝑖𝑡 + 𝛽0 ∆𝐺𝐷𝑃it + 𝛽1 ∆𝐺𝐷𝑃it ∗ (𝑡 − 𝑡̅) +
̅̅̅̅̅̅̅𝑡 ) + 𝛽3 ∆𝐺𝐷𝑃it ∗ (𝐼𝑁𝑇𝑖𝑡 − ̅̅̅̅̅̅
𝛽2 ∆𝐺𝐷𝑃it ∗ (𝐶𝑂𝑁𝑖𝑡 − 𝐶𝑂𝑁
𝐼𝑁𝑇𝑡 ) + 𝛽4 ∆𝐺𝐷𝑃it ∗ (𝐶𝑂𝑁𝑇𝑖𝑡 −
̅̅̅̅̅̅̅̅̅
𝐶𝑂𝑁𝑇𝑡 ) + 𝛽5 ∆𝐺𝐷𝑃it ∗ (𝑂𝐸𝐶𝐷𝑖𝑡 − ̅̅̅̅̅̅̅̅̅
𝑂𝐸𝐶𝐷𝑡 )+𝜀𝑖𝑡 ,
(8)
where 𝑣𝑖𝑡 is the time-fixed effect. 𝐶𝑂𝑁i is an index that captures the degree of
concentration of tourist inflows in each country. This variable informs us which country or
countries are relatively more important in supplying domestic economies with tourists. 𝐼𝑁𝑇𝑖
is the ratio of international tourism receipts to nominal gross domestic product, which serves
as proxy for the size of tourism receipts. 𝐶𝑂𝑁𝑇𝑖 is the share of international tourism receipts
that originate from countries in the same continent as the recipient country. Similarly, 𝑂𝐸𝐶𝐷𝑖
is the share of international tourism receipts from high-income OECD economies. The
coefficient 𝛽0 represents the average risk sharing via tourism receipts. 𝛽1 captures the trend
changes in risk sharing. The estimates of 𝛽2 - 𝛽5 measure the impact of the concentration
ratio, the size of international tourist receipts, continent share and OECD shares, respectively.
The explanatory variables are all demeaned in order to clear the cross-sectional effect.
Following Sørensen and Yosha (1998), we estimate Equation 8 by using a two-step
Generalized Least Squares (GLS) procedure. To take into account autocorrelation in the
residuals, we assume that the error terms in each equation/country follow an AR (1) process.
We restrict the autocorrelation parameter to be identical across countries and equations due to
the short sample period. Additionally we allow for country-specific variances of the error
terms. The GLS regression involves the following steps: first, the entire panel is estimated
using ordinary least squares (which is equivalent to a seemingly unrelated regression type
equation, since the model contains identical regressors); second, residuals from the first step
are used to estimate variance for each country and corrected for heteroskedasticity.
8
3
Data and Descriptive statistics
The data for this research were taken from various sources.6 The international tourism
receipts data7 came from the World Development Indicators database. This dataset is
available in US dollars annually for the period 1995–2010. Our sample consists of 87
developing and developed countries, and their average international tourist to GDP ratio
hovers around 3%. GDP (nominal) , the consumer price index and population data for each
country are from the International Monetary Fund’s International Financial Statistics
database. All variables were converted into US dollars using annual exchange rate provided
by the International Financial Statistics database.
Data for the decomposition of the tourism inflows by nationalities for each sample country
were extracted from World Tourism Organization (2012) and the Compendium of Tourism
Statistics database, United Nations World Tourism Organization (UNWTO).
To determine
whether such decomposition matters for risk sharing, we created a number of variables. First,
we use the Herfindahl–Hirschman Index to measure the concentration of tourist inflows
based on their origin. Accordingly, the concentration ratio variable, (𝐶𝑂𝑁𝑖 ), is given by:
2
𝐶𝑂𝑁𝑖 = ∑𝑚
𝑗=1 𝑠𝑗 ,
where sj is the share of tourists from country j visiting country i. This index takes values
between 0 and 1. A value of 1 indicates that tourist diversification based on country of origin
is limited to or dominated by only a single nationality or region, whereas a value of zero
indicates wide diversification.
Second, using the same database, we construct the variable, continent share, (CONTi ) as the
share of tourists coming from countries in the same continent as country i. This variable
measures the diversification of tourist flows based on nationality. Third, we specify OECD
share variable using data from UNWTO database as the share of tourists coming from the
OECD countries out of the total tourist inflows to country j.
The descriptive statistics for the variables are presented in Table 1. There is considerable
variation in the estimates of risk sharing via international tourism receipts (𝛽̂𝑖̇ ), ranging from
6
See Appendix A for the construction of the variables and the data sources.
9
a maximum of 44% for Benin to a minimum of –16% for Moldova and Bhutan. The standard
deviation is around 11%. The sample countries have an average tourism receipts to GDP ratio
of 6%. For countries such as Fiji, Samoa and Grenada, this ratio is close to 20%.
On average, 64% of tourism receipts in host countries originate from the OECD group.
Eastern European countries (with Poland at the top) receive the bulk of tourist inflows from
developed economies, whereas South African countries (Mauritius, Lesotho and Botswana)
receive the lowest number of tourists from the OECD region. 68% of tourists flow from
countries that belong to the same continent as the recipient country. Eastern European
countries (e.g. Latvia, Estonia and Croatia) attract the highest share of tourists originating
from the same continent, while Cape Verde, Egypt and Nepal experienced a negligible share
of tourism receipts in this regard.
4
Empirical Results
4.1
Individual countries’ estimates of risk sharing via tourism receipts
Table 2 reports the individual country regression estimation results for risk sharing via
tourism receipts (𝛽̂𝑖̇ ) based on Equation (7). Each time series regression is estimated by the
FGLS (Prais–Winsten estimation method) to adjust for serial correlation of the error terms.
Out of the sample countries, 57 countries exhibit a positive degree of risk sharing through
tourism receipts, while 26 countries report a negative estimate, as we do not impose any
restriction on the sign of the 𝛽-coeffecients.
At first glance, Table 2 displays mixed patterns of the risk sharing estimates across countries
for individual regions. Nonetheless, a closer examination of the results reveals some common
trends that warrant judicious discussion. We observe that 64% of the countries from the East
Asia and Pacific region benefit from positive and statistically significant risk sharing via
tourism receipts, whereas for Europe and Asia, this figure is 43%. This gap may be explained
by the relatively larger sample we obtain from the database for this bloc (37 countries in
total). For Sub-Saharan Africa, we found mostly significant impacts: about 80% of the
countries had positive risk sharing gains. The magnitudes are also relatively higher, perhaps
due to the larger tourism receipts to total GDP ratio. Arguably, we note that 79% countries
from Latin America and the Caribbean enjoy a positive contribution from tourism, though
63% of these gains are statistically significant. Results for the Middle Eastern and North
10
African countries show that four of the seven countries are able to smooth domestic
consumption via tourism.
Percentage-wise, a closer look at Table 2-a (extracted from Table 2) shows that the share of
countries experiencing positive risk sharing are quite close across groups: East Asia and
Pacific (64%), Europe and Central Asia (65%), Sub-Saharan Africa (67%), Latin America
and the Caribbean (79%), and Middle East and North Africa (57% ). Ratios or percentages
usually leave a number of stories untold. It is in this spirit that, in the next section, we look
into the underlying determinants of risk sharing via tourism receipts in search of an
explanation of the negative and differing positive values found.
4.2
Determinants of risk sharing via tourism receipts
Despite our contribution in measuring countries’ risk sharing via economic tourism
activity possibly being a first in the tourism literature—perhaps to the overall risk-sharing
literature—what we have done thus far is primarily based on findings of other studies that, at
best, may partially explain the cross-country patterns of income smoothing. There is therefore
a need to systematically investigate the factors underlying the differences observed in the
estimated degree of risk sharing via tourism receipts. Hence, in this section, we endeavour to
do just that. Since our study is at the border of international tourism receipts and risk sharing
research literature, we survey both these strands and shortlist some important indicators that
may possibly explain the magnitude of smoothing via tourism receipts.
Table 3 presents our main findings based on the panel estimations of Equation (8). The
coefficient 𝛽0 reflects the average risk sharing via tourism receipts, which is somewhat
comparable to the average of the estimated extent of risk sharing obtained by individual
countries as reported in Table 2. In average, the extent of the risk sharing via tourism receipts
is around 6% for the corresponding sample. The trend variable (TREND) does not have any
significant coefficients in any of the 5 columns of the Table 3. This result indicates that there
is no clear direction on the extent of the risk-sharing via tourism receipts.
Regarding the variables we created in the data section; first, we consider our variable of
interest to be the measure, capturing the extent of the concentration of tourist flows (based on
nationality, CONi) for each country. From a risk sharing perspective, it is expected that
11
tourists from a more diverse range of countries inflowing to a specific country may ensure
that inbound tourist numbers to that country are relatively more stable and less affected by
world shocks. Accordingly, tourism receipts are expected to generate some room for
buffering the domestic output shocks. In the first column of Table 3, we test whether the
CONi variable is significant. We find a negative and significant coefficient (–22.31 with a
standard deviation (STD) of 6.56). A similar result holds when all variables are tested jointly
in the last column. This is an intuitive finding about the effect of the composition of tourists
on the extent of risk sharing. It indicates that a higher concentration of the inbound tourist
from a particular nationality reduces the extent of risk sharing via tourism receipts. The basic
idea here is that diversification of inbound tourism (i.e. having tourists visiting from a wider
range of countries) is beneficial for domestic risk sharing in the destination countries when
not all countries supplying tourists are subject to the same business cycles. This gives rise to
various sources of tourism receipts to draw on and thereby increases income smoothing. In
other words, as the concentration ratio increases to approximately 1 in the extreme case,
tourist inflows, along with tourism receipts, become extremely volatile, depending on the
source countries’ economic conditions. In this case, stable tourism receipts are incapable of
smoothing domestic output shocks.
In column 2, we present the results pertaining to testing whether the continent share variable
(CONT) that captures the effect of tourist inflows originating from neighbouring countries
has any impact on the extent of risk sharing. If countries share the same regions (higher
continent share), they are most likely to experience similar output shocks, and tourism
receipts between these countries are more affected by those shocks than if the source
countries were in a different continent. Even when we considered the recent global shocks,
not all countries were affected to the same magnitude. Put differently, a more diversified
cross-continent inflow of tourists should be able to generate more gains from risk sharing via
tourism receipts due to the likely asynchronous cyclical behaviour of domestic and foreign
output. As anticipated, the coefficient estimate for the continent share variable, is negative
and statistically significant, (-45.16 with a STD 20.51 and in the last column -44.41 with a
STD 20.73) irrespective of whether other variables are incorporated in the model estimated
(Column 6). At this stage; there is overwhelming evidence that countries that are far apart
from each other are better insurers against domestic output shocks than those that are closer
together. We test a similar variable OECD, captures the shares of the tourist inbounds from
OECD countries. However in any of the specifications, we have not observed a significant
12
effect of the OECD share. We aimed to test whether tourism flows from rich countries matter
for risk sharing. Economically speaking, it is a truism that high-income countries supply
richer tourists who are perhaps more “generous” in spending due to their lavish lifestyle. It is
to be expected that their expenditures are to be less influenced by aggregate shocks, since
these expenditures are, by and large, financed by wealth accumulated over the years.
However, we could not find any significant evidence to support our claim.
Lastly, we test whether a larger tourism receipts to GDP ratio gives rise to more risk sharing.
As per columns 4 and 5, we find the coefficient estimates to be statistically significant (16.23
and 15.21, in columns 4 and 5, respectively) at 5% levels, thereby confirming that size of
tourism receipts as a share of GDP matters when it comes to smoothing domestic income and
hence consumption in periods of economic downturns.
This finding is consistent with
Sorensen et al. (2007) where they found that risk sharing via financial asset receipts increases
with the amount of the financial assets held abroad.
Conclusion
The literature on risk sharing postulates that countries that are subjected to asynchronous
business cycles can insure themselves against adverse domestic economic shocks by
purchasing financial assets from each other to smooth income and consumption. A number
of studies have made contributions to that effect by looking at the importance of money
markets, credit markets, stock markets, fiscal federalism, foreign aid and other forms of
international transfers, among others. Despite the importance of tourism for domestic
economies (developed or developing), there has been no previous research on the linkage
between international tourism receipts and risk sharing. To the best of our knowledge, this
paper is the first in the literature. Not only do we quantify the extent of risk sharing for each
country in our sample, we also provide irrefutable empirical evidence on its underlying
determinants. We find: (a) tourist flows originated form separate continents are more likely
increase the gains from risk sharing—distance matters; (b) countries with tourist inflows from
a wide range of countries tend to benefit more than those with inflows from the same or
fewer countries; (c) countries with a larger tourism receipts to GDP ratio absorb more gains
for risk sharing—size matters; and (d) despite their accumulated wealth, tourists from highincome countries do not necessarily enhance risk sharing for recipient countries.
13
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17
Appendix A: Data description and sources
Variables used to obtain the estimate of risk sharing via international tourism receipts (𝛽̂𝑖̇ )
International tourism
receipts
Gross Domestic Product
Consumer price index
Population
Exchange rate
In US$ from World Development Indicators (WDI).
Source: IMF's International Financial Statistics (IFS).
Source: World Development Indicators (WDI).
Source: World Development Indicators (WDI).
Units of local currency per US$ available from IFS.
Explanatory variables
Tourist Concentration
index
The concentration index is created using the Herfindahl–Hirschman
Index. Accordingly, the concentration ratio variable is calculated as
2
𝐶𝑂𝑁𝑖 = ∑𝑚
𝑗=1 𝑠𝑗 , where Sj is the share of international tourists from
country j visiting country i. The index takes values between 0 and 1.
Values close to 1 mean that tourist diversification in terms of the
number of countries is limited, whereas as the variable approaches
zero, this means that there is a wide diversification in terms of the
nationalities of incoming tourists. Source: World Tourism
Organization (2012) and the Compendium of Tourism Statistics
database, United Nations World Tourism Organization (UNWTO).
OECD share
This measures the share of total tourist numbers originating from
OECD countries out of the total number of tourists. The bilateral
tourist flows data have been obtained from World Tourism
Organization (2012), and the Compendium of Tourism Statistics
database, United Nations World Tourism Organization (UNWTO).
Continent share
This measures the share of tourist inflows coming from countries
belonging to the same continent as the recipient country.
18
List of countries
Sample countries
Albania (ALB), Armenia (ARM), Australia (AUS), Austria (AUT),
Bahrain (BHR), Belgium (BEL), Benin (BEN), Bhutan (BHT), Bolivia
(BOL), Bosnia and Herzegovina (BIH), Botswana (BWA), Bulgaria
(BUL), Cambodia (KHM), Cape Verde (CPV), Costa Rica (CRI), Croatia
(HRV), Cyprus (CYP), Czech Republic (CZE), Denmark (DNK),
Dominica (DMA), Dominican Republic (DOM), Egypt (EGY), El
Salvador (SLV), Estonia (EST), Ethiopia (ETH), Fiji (FJI), France
(FRA), Georgia (GEO), Ghana (GHA), Greece (GRC), Grenada (GRD),
Guatemala (GTM), Guyana (GUY), Haiti (HTI), Honduras (HND),
Hungary (HUN), Iceland (ISL), Indonesia (IDN), Ireland (IRL), Israel
(ISR), Italy (ITA), Jamaica (JAM), Jordan (JOR), Kenya (KEN), Kyrgyz
Republic (KGZ), Laos (LAO), Latvia (LVA), Lebanon (LBN), Lesotho
(LSO), Lithuania (LTU), Luxembourg (LUX), Malaysia (MYS), Mali
(MLI), Malta (MLT), Mauritius (MUS), Mongolia (MON), Morocco
(MAR), Namibia (NAM), Nepal (NPL), the Netherlands (NLD), New
Zealand (NZL), Nicaragua (NIC), Panama (PAN), the Philippines (PHL),
Poland (POL), Portugal (PRT), Samoa (WSM), Senegal (SEN),
Singapore (SGP), Slovenia (SVN), Solomon Islands (SOL), South Africa
(ZAF), Spain (ESP), Sri Lanka (LKA), Suriname (SUR), Swaziland
(SWZ), Sweden (SWE), Switzerland (CHA), Syrian Arab Republic
(SYR), Tanzania (TAN), Thailand (THA), Tonga (TON), Tunisia (TUN),
Turkey (TUR), Ukraine (UKR), Uruguay (URY).
19
Table 1: Descriptive statistics for the main variables
Risk sharing via tourism receipts (𝛽̂𝑖̇ )
Tourist Concentration (CON) index
Tourism receipts to GDP ratio (INT)
OECD share (OECD)
Continent share (CONT)
Standard
Observations Mean Deviation Maximum Minimum
87
0.07
0.11
0.44
–0.16
87
0.39
0.19
0.96
0.15
87
0.06
0.05
0.20
0.03
87
0.64
0.30
0.97
0.03
87
0.68
0.26
0.99
0.03
Notes: For a detailed description of the variables, see Appendix A. All variables are averaged across
time for each country.
20
Table 2-a: The Extent of the Risk Sharing Distribution Across Country Groups
East
Europe
Asia
&
&Pacifi
Central
c
Asia
Total count (TC)
14
37
14
7
15
Positive count (PC)
9
24
11
4
10
8
16
7
4
8
PSSC share of PC
0.88
0.67
0.63
1.00
0.80
PSSC share of TC
0.64
0.43
0.50
0.57
0.53
PC share of TC
0.64
0.65
0.79
0.57
0.67
Latin
America &
Caribbean
Middle
East and
North
Africa
SubSaharan
Africa
Positive and
statistically
significant count
(PSSC)
Notes: This table is extracted from Table 2 and indicates the distribution of the extent of the risk sharing via tourism
receipts for different regions.
21
Table 2: Samples and the estimates of risk sharing via international tourism receipts, 𝛽̂𝑖̇ (%)
East Asia & Pacific
Australia
Bhutan
Cambodia
Fiji
Indonesia
–8
–16*
15***
14***
15**
Iceland
Ireland
Israel
Italy
Kyrgyz Republic
Latvia
–6
–1
–21**
5
4
0
–5
–8
13**
19**
20**
–2
8*
2
9*
Lithuania
Luxembourg
Malta
Moldova
Mongolia
Nepal
Netherlands
Poland
Portugal
16***
–8*
0
–16*
3
15**
7
13**
5
Haiti
Honduras
Jamaica
Nicaragua
Panama
Uruguay
Middle East & North
Africa
Bahrain
Egypt
Jordan
Lebanon
Morocco
Syrian Arab Republic
Tunisia
Sub-Saharan Africa
Slovenia
Spain
Sri Lanka
Sweden
Switzerland
Turkey
Ukraine
Latin America &
Caribbean
Bolivia
Costa Rica
Dominica
Dominican Republic
El Salvador
Grenada
Guatemala
Guyana
1
14**
22**
16***
14***
15**
14**
Benin
Botswana
Cape Verde
Ethiopia
Ghana
Kenya
Lesotho
44***
7
–5
–15**
–6
38**
9
Mali
Mauritius
Namibia
Senegal
South Africa
Suriname
Swaziland
Tanzania
14***
19***
19**
21***
16***
–2
21***
–6
*
Laos
Malaysia
New Zealand
Philippines
Samoa
Singapore
Solomon Islands
Thailand
Tonga
Europe & Central
Asia
Albania
Armenia
Austria
Belgium
Bosnia
Bulgaria
1
–3
13**
12***
11*
3
Croatia
Cyprus
Czech Republic
Denmark
France
Estonia
Georgia
Greece
Hungary
–5
–3
23**
–10
14*
0
17*
11*
–14*
15*
11**
21*
13**
4
21**
–3
2
4
5*
16*
5
–7*
–6
11**
–4
7**
21**
–6
–11*
6*
Notes: 𝛽𝑖 quantifies the extent of risk sharing through tourism receipts by country 𝑖 in year 𝑡, and is obtained from the following regression
̃t − ∆(GDP − Tourism
̃ receipts) = α + β ∗ ∆ GDP
̃t, where ∆ 𝐺𝐷𝑃
̃t represents the idiosyncratic part of output calculated as
equation: (∆ 𝐺𝐷𝑃
t
the real GDP per capita growth rate of country i in period t minus the world’s real per capita GDP growth. Similarly, 𝐼𝑛𝑡. 𝑟𝑒𝑐𝑒𝑖𝑝𝑡𝑠 represents
the international tourism receipts that country i obtained in year t. The estimated value of 𝛽𝑖 is reported in percentage terms in this table. The
time series estimations are conducted for 87 developing and developed countries for the period 1995–2010. ***, ** and * denote statistical
significance at the 1%, 5% and 10% level, respectively.
22
Table 3 Panel estimations: exploring the determinants of risk sharing via tourism receipts.
β0
Trend
Concentration index (CON)
(1)
(2)
(3)
(4)
(5)
0.06
(0.03)***
0.06
(0.04)
0.06
(0.03)**
0.06
(0.04)
0.05
(0.03)
1.12
(1.34)
1.23
(0.98)
1.04
(1.24)
1.24
(1.32)
1.33
(1.32)
-22.31
(6.56)***
-32.44
(-8.98)***
-45.16
(20.51)**
Continent share (CONT)
-44.41
(20.73)**
24.14
(23.41)
OECD share (OECD)
Tourism receipts to GDP ratio (INT)
21.12
(30.35)
16.23
(8.33)**
15.21
(8.97)**
R2
0.24
0.15
0.12
0.16
0.33
Observations
1305
1305
1305
1305
1305
Notes: The sample period is 1995 to 2010. The estimated equation is
∆𝐺𝐷𝑃it − ∆(𝐺𝐷𝑃 − 𝑇𝑜𝑢𝑟𝑖𝑠𝑚 𝑟𝑒𝑐𝑒𝑖𝑝𝑡𝑠)it = 𝑣𝑖𝑡 + 𝛽0 ∆𝐺𝐷𝑃it + 𝛽1 ∆𝐺𝐷𝑃it ∗ (𝑡 − 𝑡̅) + 𝛽2 ∆𝐺𝐷𝑃it ∗ (𝐶𝑂𝑁𝑖𝑡 − ̅̅̅̅̅̅̅
𝐶𝑂𝑁𝑡 ) +
̅̅̅̅̅̅̅̅̅𝑡 ) + 𝛽5 ∆𝐺𝐷𝑃it ∗ (𝑂𝐸𝐶𝐷𝑖𝑡 − ̅̅̅̅̅̅̅̅̅
𝛽3 ∆𝐺𝐷𝑃it ∗ (𝐼𝑁𝑇𝑖𝑡 − ̅̅̅̅̅̅
𝐼𝑁𝑇𝑡 ) + 𝛽4 ∆𝐺𝐷𝑃it ∗ (𝐶𝑂𝑁𝑇𝑖𝑡 − 𝐶𝑂𝑁𝑇
𝑂𝐸𝐶𝐷𝑡 )+𝜀𝑖𝑡 ,
(8)
Feasible GLS estimation is employed to remedy heteroskedasticity and autocorrelation problems. Standard errors are given
in parentheses. ***, ** and * denote statistical significance at the 1%, 5% and 10% level, respectively. For a detailed
description of the explanatory variables, see Appendix A.
23