Risk Sharing Across Countries: the Importance of Tourism Activity

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 well- known 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

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

1 School of Economics and Finance, Massey University, Palmerston North. New Zealand. E mail: [email protected]

2 School of Economics and Finance, Massey University, Palmerston North. New Zealand. E mail: [email protected]

3 Department of Economics and Finance, Vancouver Island University. E mail: [email protected]

1 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 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

2 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 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

3 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, 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)

4 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 on idiosyncratic output growth rates to estimate the magnitude of risk sharing empirically. In short, 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 ground-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 cross-sectional 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

5 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: (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: (4) where measures the co-movement of consumption and GDP growth rates. As approaches zero, there is perfect risk sharing. By contrast, equals 1 implies no risk sharing. Since measures co-movement, measures the extent of risk sharing. Accordingly, we estimate the following regression to quantify the extent of total risk sharing, : (5) where =1-. 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: (6)

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: (7) In contrast to Equation (4), both variables are expressed as a percentage change in GDP per capita prices minus their worldwide counterparts. For example, stands for the natural 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, 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 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: (8) where is the time-fixed effect. 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. is the ratio of international tourism receipts to nominal gross domestic product, which serves as proxy for

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. We used the Prais–Winsten estimation, since we have a smaller time series sample and cannot afford to lose a single observation.

7 the size of tourism receipts. is the share of international tourism receipts that originate from countries in the same continent as the recipient country. Similarly, is the share of international tourism receipts from high-income OECD economies. The coefficient represents the average risk sharing via tourism receipts. captures the trend changes in risk sharing. The estimates of - 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.

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,

6 See Appendix A for the construction of the variables and the data sources.

8 we use the Herfindahl–Hirschman Index to measure the concentration of tourist inflows based on their origin. Accordingly, the concentration ratio variable, (, is given by: , 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, () 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 (, ranging from 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 ( 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

9 tourism receipts, while 26 countries report a negative estimate, as we do not impose any restriction on the sign of the -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 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.

10 Table 3 presents our main findings based on the panel estimations of Equation (8). The coefficient 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 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

11 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 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

12 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 high- income countries do not necessarily enhance risk sharing for recipient countries.

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17 Appendix A: Data description and sources

Variables used to obtain the estimate of risk sharing via international tourism receipts (

International tourism In US$ from World Development Indicators (WDI). receipts Gross Domestic Source: IMF's International Financial Statistics (IFS). Product Consumer price index Source: World Development Indicators (WDI). Population Source: World Development Indicators (WDI). Exchange rate Units of local currency per US$ available from IFS.

Explanatory variables

Tourist Concentration The concentration index is created using the Herfindahl– index Hirschman Index. Accordingly, the concentration ratio variable is calculated as 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 19 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).

20 Table 1: Descriptive statistics for the main variables

Standar d Observation Mea Deviatio Maximu Minimu s n n m m Risk sharing via tourism receipts ( 87 0.07 0.11 0.44 –0.16 Tourist Concentration (CON) index 87 0.39 0.19 0.96 0.15 Tourism receipts to GDP ratio (INT) 87 0.06 0.05 0.20 0.03 OECD share (OECD) 87 0.64 0.30 0.97 0.03 Continent share (CONT) 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.

21 Table 2-a: The Extent of the Risk Sharing Distribution Across Country Groups

East Europe Middle Latin Sub- Asia & East and America & Saharan &Pacifi Central North Caribbean Africa c Asia Africa

Total count (TC) 14 37 14 7 15

Positive count (PC) 9 24 11 4 10

Positive and statistically 8 16 7 4 8 significant count (PSSC)

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

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.

22 Table 2: Samples and the estimates of risk sharing via international tourism receipts,

East Asia & Pacific Iceland –6 Haiti 4 Australia –8* Ireland –1 Honduras 5* Bhutan –16* Israel –21** Jamaica 16* Cambodia 15*** Italy 5 Nicaragua 5 Fiji 14*** Kyrgyz Republic 4 Panama –7* Indonesia 15** Latvia 0 Uruguay –6 Middle East & North Laos –5 Lithuania 16*** Africa Malaysia –8 Luxembourg –8* Bahrain 11** New Zealand 13** Malta 0 Egypt –4 Philippines 19** Moldova –16* Jordan 7** Samoa 20** Mongolia 3 Lebanon 21** Singapore –2 Nepal 15** Morocco –6 Solomon Islands 8* Netherlands 7 Syrian Arab Republic –11* Thailand 2 Poland 13** Tunisia 6* Tonga 9* Portugal 5 Sub-Saharan Africa Europe & Central Asia Slovenia 1 Benin 44*** Albania 1 Spain 14** Botswana 7 Armenia –3 Sri Lanka 22** Cape Verde –5 Austria 13** Sweden 16*** Ethiopia –15** Belgium 12*** Switzerland 14*** Ghana –6 Bosnia 11* Turkey 15** Kenya 38** Bulgaria 3 Ukraine 14** Lesotho 9 Latin America & Croatia –5 Caribbean Mali 14*** Cyprus –3 Bolivia 15* Mauritius 19*** Czech Republic 23** Costa Rica 11** Namibia 19** Denmark –10 Dominica 21* Senegal 21*** France 14* Dominican Republic 13** South Africa 16*** Estonia 0 El Salvador 4 Suriname –2 Georgia 17* Grenada 21** Swaziland 21*** Greece 11* Guatemala –3 Tanzania –6 Hungary –14* Guyana 2

Notes: quantifies the extent of risk sharing through tourism receipts by country in year, and is obtained from the following regression equation: (, where represents the idiosyncratic part of output calculated as the real GDP per capita growth rate of country i in period t minus the world’s real per capita GDP growth. Similarly, represents the international tourism receipts that country i obtained in year t. The estimated value of 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.

Table 3 Panel estimations: exploring the determinants of risk sharing via tourism receipts.

23 (1) (2) (3) (4) (5)

0.06 0.06 0.06 0.06 0.05 β0 (0.03)*** (0.04) (0.03)** (0.04) (0.03)

Trend 1.12 1.23 1.04 1.24 1.33 (1.34) (0.98) (1.24) (1.32) (1.32)

-22.31 -32.44 Concentration index (CON) (6.56)*** (-8.98)***

-45.16 -44.41 Continent share (CONT) (20.51)** (20.73)**

24.14 21.12 OECD share (OECD) (23.41) (30.35)

16.23 15.21 Tourism receipts to GDP ratio (INT) (8.33)** (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

(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.