Tourism and economic growth: a meta-analysis of panel data studies

1 TOURISM AND ECONOMIC GROWTH: A meta-analysis of panel data studies Mercedes Castro-Nuñoª, José A. Molina-Toucedo b and María del P. Pablo-Romero c a. Department of Economic Analysis and Political Economy, Avda. Ramon y Cajal, 1 University of Seville, E-41018 Seville, Spain Tel.: (+34) 954 55 44 77 Fax: (+34) 954 55 76 29 [email protected]s b. Department of Economic Analysis and Political Economy, Avda. Ramon y Cajal, 1 University of Seville, E-41018 Seville, Spain Tel.: (+34) 954 55 75 29 Fax: (+34) 954 55 76 29 [email protected] c. Department of Economic Analysis and Political Economy, Avda. Ramon y Cajal, 1 University of Seville, E-41018 Seville, Spain Tel.: (+34) 954 55 76 11 Fax: (+34) 954 55 76 29 mpablo[email protected] Abstract Although for decades it has been acknowledged that tourism likely contributes to economic growth, theoretical models that consider a causal relationship between both are a recent phenomenon. From a sample of 11 studies based on panel data techniques published through to 2011, and for a total of 87 heterogeneous estimations, a metaanalysis is performed by applying models for both fixed and random effects, with the main objective being to calculate a summary measure of the effects of tourism on economic growth. While the results obtained point to a positive elasticity between economic growth and tourism, the magnitude of the effect was found to vary according to the methodological procedure employed in the original studies for empirical estimations. KEYWORDS: Panel data, Tourism, Economic Growth, Meta-analysis, Elasticities. JEL Codes: C33, C83, L83, O40, O50. 2 1. INTRODUCTION Although for decades it has been recognized that tourism could contribute in some way to economic growth, theoretical models that consider a causal relationship between tourism and economic growth are a recent phenomenon (Kim et al., 2006). Lanza & Pigliaru (2000) were the first to investigate this relation from an empirical point of view, while Balaguer & Cantavella-Jorda (2002) were the first to analyse the tourism-led growth hypothesis (TLG) – i.e. the hypothesis according to which tourism generates economic growth – from an econometric perspective. From the first attempt, an increasing number of articles with the same objective – although for different countries, using different methodologies and obtaining different results –have been published. Most of the studies are based on time series and refer to a single economy. Among them, and without providing an exhaustive list, several warrant mention such as that of Dritsakis (2004), Durbarry (2004), Ongan & Demiroz (2005), Gunduz & Hatemi-J (2005), Oh (2005), Kim et al. (2006), Katircioglu (2007, 2009, 2010), Lee & Chien (2008), Brida & Risso (2009). Brida et al. (2010), Chen & Chiou-Wei (2009) Jin (2011), Lean & Tang (2010), and Arslanturk et al. (2011). Of these, most support the TLG hypothesis. Among these studies there is a group which uses panel data analysis to investigate the TLG hypothesis. Although the number of these studies is smaller than that for time-series studies, a larger number of countries are included. This permits an understanding to be gained of the relationships that occur across a group of countries (Lee & Chang, 2008) and an evaluation to be made of the broader or global impact of tourism (Sequeira & Nunes, 2008). Further to this, the relation between Gross Domestic Product (GDP) and tourism in these studies is usually not isolated because other variables that are essential for growth are also considered. 3 The aim of the present work is to verify whether tourism contributes to economic growth and to determine the magnitude of this contribution by calculating global measures based on published scientific evidence available through until 2011. To this end, meta-analysis techniques have been used, which allow the quantitative synthesis of numerous estimations obtained in previous studies, as well as to determine how certain methodological approaches influence values obtained in these estimates. We will only consider those studies based on panel data to corroborate the TLG hypothesis as they provide global estimations which, consistent with Lee & Chang (2008), refer to large samples of countries. Despite the extraordinary growth of the studies examining the relationship between tourism and economic growth using panel data, in such a short time, no quantitative systematic review that integrates all of the information has yet been made. The use of meta-analysis was introduced by Glass, (1976). In contrast to the traditional narrative review, the basic purpose of meta-analysis is to provide the same methodological rigor to a literature review that is required for experimental research (Rosenthal, 1995). In the case of economic growth and development studies, this technique has been used to integrate findings on the effects of fiscal policies (Nijkamp & Poot, 2004; Phillips & Goss, 1995), the influence of income inequality conditions or political structures (De Dominicis, Florax, & De Groot, 2008; Doucouliagos & Ulubasglu, 2008), the contribution of social capital to economic growth (Westlund & Adam, 2010) and population growth (Headey & Hodge, 2009), or the effectiveness of development aid (Doucouliagos & Paldan, 2008). In the field of tourism, general applications of this technique can be found in tourism research (Dann, Nash & Pearce, 1988), tourism forecasting (Calantone, Di Benedetto & Bojanic, 1987), and more specifically on tourist and economic impact studies (Wagner, 2002; Wagner & Wober, 2003). 4 Meta-analyses of particular importance in this field concern those performed on tourism income multipliers (Baaijens, Nijkamp & Van Montfort, 1998), regional tourism multipliers (Baaijens & Nijkamp, 2000), and tourism demand (Crouch, 1995; Lim, 1999). More recently, reports have been published concerning specific branches of tourism such as that by Carlsen & Boksberger (2011) on wine tourism, Weed (2009) on sports tourism and Sariisik, Turkay & Akova (2011) on yachting tourism. This work is divided into six sections which describe the meta-analysis approach taken with respect to tourism-economic growth. 2. METHODOLOGY. Following Glass et al. (1981) and Lipsey & Wilson (2001), the meta-analysis method consists of deducing a summary effect based on the combination of different estimations (effect sizes) from a selected sample of studies by means of different statistical techniques: the fixed-effects model (FEM) and the random-effects model (REM). Under a FEM (Borenstein et al. 2009) the selected studies are combined on the premise that there is no heterogeneity among them and the only determinants of the weight of each study in the meta-analysis would be its sample size and its own variance or within-study variance (inverse variance weighted method: Birge, 1932 and Cochran, 1937). Assuming a sample of "m" estimates or effect sizes, (i = 1, 2… m), representing a measure of an analyzed effect called T i , a summary effect   may be formulated as (Borenstein et al., 2009):   ∑  ∑ [1], where w i is the statistical weight of the i-th estimation:    1   [2] , and   the variance of the i-th estimation, so that: ∑   1 . It is possible that the variability among studies is higher than that expected by pure randomness, which would be detectable in the first instance by testing the 5 hypothesis of homogeneity. The most widely used test was originally developed by Cochran (1954); it calculates the parameter Q, according to:   ∑        [3]. Because of the low power of this test, highlighted by Takkouche et al. (1999) it is recommended that a subgroup analysis of studies or that additional procedures to quantify the possible heterogeneity, such as the parameter I 2 (Higgins et al., 2003), which indicates the proportion of the variation between studies (between-studies variance) in the total variation due to heterogeneity:       [4], where   (TauSquared) is the between-studies variance and   the within-study variance (due to randomness). If heterogeneity is detected, a REM should be appropriated (Borenstein et al., 2009), which considers that the estimated effects of the included studies are only a random sample of all those possible, and the true effect sizes for them would be distributed about a mean effect (with two possible sources of variation: that exist within the studies or random error and the variation between studies or true dispersion). Applying the variance weighted method, under the REM, expression [2] is transformed and we have, for each i-th estimation, adjusted weights (    according to [5]:         [5], where   (Tau-Squared) is the between-studies variance and w i the statistical weight for an i-th estimation under a FEM. With regards to the summary effect   (i.e. a mean effect obtained from "a distribution of effect sizes") we can calculate:   ∑  ∑  [6]. The possibility of obtaining a biased summary effect must be assessed, which is derived from the presence of publication biases as a result of the fact that many completed studies are not actually published because they do not achieve significant effects, because they are unfavorable or because they have negative outcomes (Sterne et al., 2000; Thornton & Lee, 2000). 6 Analytically, the publication biases can be detected by the statistical methods of Begg (Begg & Mazumba, 1994), and Egger (Egger et al., 1997), and graphically by the namely funnel plots diagrams. However, the limitations of these methods (Thorntoln & Lee, 2000; Macaskill et al., 2001), require application of Duval and Tweedie's Trim and Fill technique (Duval & Tweedie, 2000), which allows the number of missing studies to be determined and added to the analysis, following which the combined effect is recomputed. Finally, to assess the robustness or stability of the calculated summary effect, sensitivity analysis is performed. 3. PANEL DATA STUDIES AND ESTIMATIONS. Details of 13 studies published through to 2011 which use panel data to analyze the relationship between tourism and economic growth are given in Table 1. These studies were identified by literature search techniques using Scopus, ScienceDirect, Google Schoolar and the main journals in tourism research 1 , using terms as: tourism, economic growth, tourism led growth hypothesis and related terms. Papers from other studies identified were also used, which include not only articles in scientific journals listed in Journal Citation Reports (JCR) or other databases, but also working papers (Wpaper) published on the Internet that have reached a certain scientific recognition on account of their quality or number of citations. All the studies included in the analysis are shown in Table 1 with an identification code, and the number of estimations in each study. Each of these estimations differs depending on the estimation model, whether or not additional variables were used to explain economic growth, the type of variable used to measure 1 Ryan (2005) shows the ranking and rating of academics and journals in tourism research. 7 tourism, whether the sample was classified into subsamples, and the inclusion or not of instrumental variables or dummy variables for econometric estimation. Table 1: Panel data studies showing the relationship between tourism and economic growth. Author Year of study Code Classification of study Sample Analysed period No. of estimations EugenioMartin et al. 2004 Eug Wpaper Latin American countries 1985-1998 4 Sequeira & Campos 2005 Seca Wpaper 72 countries 1980-1999 6 Sequeira & Nunes 2008 Sequ JCR. Q3 Small, poor and normally developed countries 1980-2002 16 Fayissa et al. 2008 Fayi JCR. Q3 Sub-Saharan countries 1995-2004 4 Lee & Chang 2008 Lee JCR. Q1 OCDE, Asia, Latin American and subSaharan countries 1990-2002 20 CortésJiménez 2008 Cort JCR. Q3 Coastal regions of Italy and Spain 1990-2000 12 Proenca & Soukiazis 2008 Sou JCR. Q4 Portugal regions NUT II and NUT III 1993-2001 6 Fayissa et al. 2009 Fay Wpaper Latin American countries 1995-2004 4 Adamau & Clerides 2010 Adam open journal 162 countries 1980-2005 10 Narayan et al. 2010 Nara JCR. Q3 4 islands 1988-2004 2 Holzner 2011 Holz JCR. Q1 99 countries 1970-2007 4 Seetenah 2011 Seet JCR. Q1 Pacific Islands and developed countries 1995-2007 6 Dritsakis 2011 Drit JCR. Q3 Mediterranean countries 1980-2007 2 Source: Own elaboration. Two different empirical models are generally estimated: dynamic and nondynamic. The first is defined econometrically as follows [7], in general terms: itiititittit uXTyy ε λ β φ α + + + + + = −1 [7], where y is the logarithm of real per capita GDP, T is a measure of tourism development expressed in logarithmic terms, X 8 represents a vector of other explanatory variables, α is a period-specific intercept term to capture changes common to all countries, u is an unobserved country-specific and time-invariant effect, ε is the error term and the subscripts i and t represent country and time period, respectively. Non-dynamic models are specified similarly, but without the term 1−it y φ . They can be defined in general as follows in [8]: itiitittit uXTy ε λ β α + + + + = [8]. The parameter β, which reflects the estimated impact of tourism on the GDP, reaches a different interpretation: in non-dynamic models, it reflects the elasticity of productivity with respect to tourism (because the variables are expressed in natural logarithms); while in dynamic models; it reflects only part of the effect of tourism on productivity (which is produced in the same period). The effects of tourism expand in time, which is to say that tourism has an effect on productivity various periods thereafter depending on the value of φ in [7]. Irrespective of whether the models are dynamic or not, the studies also differ in terms of those that use additional variables such as education, physical capital, etc., to explain the growth of real per capita GDP, compared with those that relate only to the tourism growth variable (A or B respectively) . Other important differences can be summarized as follows: 1. Whether the temporal effect is included by virtue of the coefficient α in the estimation (time dummies used); 2. The proxy of tourism expansion, which is used to define T 2 (indicators of tourism arrivals vs. indicators of tourism receipts); 3. Those estimations that using instrumental variables in estimating the function or not; 4. Depending on the wide sample of countries or a specific set of countries that make up the panel data. 2 Soukiazis & Proenca (2008) use as a proxy for the tourism variable the accommodation capacity of the tourism sector. This proxy is not based on tourism arrivals and tourism receipts, and is therefore not classified in our meta-analysis following this criterion. 9 Given these differences, the meta-analysis considers different groups of similar estimations, as shown in Table 2. There are two sets of estimations (type 1 scenarios) – dynamic and non-dynamic – because as stated above, the estimated β coefficients are not directly comparable between the two model types. Within each type 1 scenario, 3 clusters can in turn be made: those that contemplate the whole sample for each scenario (overall), and estimations that include only type A or type B estimations (type 2 scenarios). Furthermore, within each type 2 scenario, 9 clusters can be formed: those that contemplate the whole sample of estimations for the scenario (overall) and type 3 scenarios. These combinations give rise to a total of 42 scenarios. 4. META-ANALYSIS RESULTS. Thirteen studies were identified in our search, but the estimations of Sequeira & Campos (2005) and Fayissa et al. (2009), were excluded from the meta-analysis because the data provided were insufficient. The study thus encompassed the empirical results from 11 previous studies (Table 1) that gave rise to a total of 87 estimations (the sample for our meta-analysis framework) reported in the form of elasticities , which express the impact of the tourism sector on economic growth. Table 2 summarizes the main results of the meta-analysis performed on that sample. 16 overall sample for dynamic and non-dynamic scenarios, as well to their respective A and B subgroups. It was found that the inclusion of temporary variables and the use of instrumental variables tended to decrease the random point estimations value in all of the type 2 scenarios, and that the random point estimations value obtained for these scenarios were higher when travel income was used to measure tourism than when the number of arrivals was used. Further to this, the random point estimation tended to be greater when the estimations that consider only large samples of countries (general countries) were used. In such cases, the estimated value is unbiased. If estimations refer only to specific countries, i.e. samples refer only to countries with a certain profile, the elasticity tends to be lower. However, it must be taken into account that groups of countries in this scenario were very diverse, ranging from Asian, Latin American, Mediterranean and sub-Saharan countries, to island groups, economically poor countries, small countries. This suggests that a more detailed study of elasticities is required based on the characteristics of the countries in those samples. 6. CONCLUSIONS Theoretical models that consider a causal relationship between economic growth and tourism are a more recent phenomenon. Since 2002, an increasing number of articles that have investigated this relation from an econometric perspective have been published. A considerable proportion of these studies are based on panel data to analyze effects of tourism on economic growth across a large number of countries. According to the meta-analysis presented in this paper, from 87 estimations obtained using panel data techniques we can conclude that tourism positively affects economic growth. However, 17 the duration of this positive effect depends on methodological features of estimations made in the original studies. Thus, the meta-analysis applied to estimations based on dynamic functions shows that elasticity (the productivity with respect to tourism) in the short term is small, yielding a random point re-adjusted estimation of 0.002. However, the initial effect is prolonged in time, so that in the long term the average value of the elasticity is raised to 0.179, for significant and stable estimations. The meta-analysis applied to estimations based on non-dynamic functions showed that the elasticities had an average value of 0.266 for the overall sample. The value of these elasticities is nonetheless affected by a range of features used in the estimations carried out. We found that as the model becomes more specific, the value of elasticity, irrespective of the case, tends to decrease. Thus, the inclusion of explanatory variables for economic growth, in addition to that of tourism, tends to reduce the value of the elasticity, especially with respect to non-dynamic models. Also, when temporary variables and instrumental variables are considered, the value of the elasticity tends to diminish. Furthermore, the variable used to measure tourism also affects the elasticity. If tourism is measured in terms of travel income, then elasticity tends to be higher than if the tourism is measured in terms of tourist arrivals. Finally, it should be noted that the average elasticity calculated in our metaanalysis was higher only when the studies included were based on a large sample of type-specific countries. 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Long-Term dynamic multiplier of tourism on economic growth CODE Model type β φ Dynamic multiplier (DM) Adam 1 A 0.002** (0.00056) -0.1** (0.0077) 0.02ª Adam 3 A 0.00018** (4.50E-05) -0.01** (0.0073) 0.018ª Adam 4 A 0.00012* (0.00006) -0.09** (0.017) 0.001ª Adam 6 A 0.0041** (0.00096) -0.101** (0.0076) 0.041ª Adam 7 A 0.0039** (0.0012) -0.101** (0.018) 0.038ª Adam 8 A 0.00048** (0.00012) -0.1** (0.0074) 0.004ª Holz 1 A 0.011** (2.00) 0.941*** (35.98) 0.186 Holz 2 A 0.018*** (2.84) 0.95*** (35.49) 0.360 Holz 3 A 0.008** (2.08) 0.97*** (52.93) 0.267 Cort 1 A 0.001** (n.a) 0.895*** (n.a) 0.010 Cort 2 A 0.006* (n.a) 0.884 *** (n.a) 0.052 Cort 3 A 0.001** (n.a) 0.919*** (n.a) 0.012 Cort 4 A 0.006*** (n.a) 0.907*** (n.a) 0.065 Cort 6 A -0.015 (n.a) 0.891*** (n.a) -0.138 Cort 8 A -0.017** (n.a) 0.942*** (n.a) -0.293 Cort 9 A 0.001* (n.a) 0.831*** (n.a) 0.006 Cort 10 A 0.007*** (n.a) 0.830*** (n.a) 0.041 Cort 11 A 0.001** (n.a) 0.869*** (n.a) 0.008 Cort 12 A 0.006*** (n.a) 0.857*** (n.a) 0.042 Eug 1 A 0.00036* (1.68) 0.777* (19.30) 0.007 Eug 2 A -0.0002* (2.54) 0.765* (12.64) -0.001 Eug 3 A 0.00063* (1.92) 0.738* (10.16) 0.002 Eug 4 A 0.00062* (2.63) 0.597* (4.14) 0.002 Fayi 3 A 0.0249*** (0.0081) 0.568*** (0.073) 0.058 Seet 1 A 0.12* (1,95) 0.24** (215) 0.158 Seet 2 A 0.06* (1.95) 0.23*** (2.52) 0.078 Seet 3 A 0.064* (1.96) 0.34*** (2.43) 0.097 Seet 4 A 0.14* (2.04) 0.17* (2.17) 0.169 Seet 5 A 0.033* (1.87) 0.25** (2.15) 0.044 Seet 6 A 0.08* (1.89) 0.37** (2.19) 0.127 Sequ 2 A 0.041** 0.927*** 0.562 25 (2.42) (17.47) Sequ 3 A 0.026* (1.92) 0.931*** (18.37) 0.379 Sequ 4 A 0.025* (1.85) 0.891*** (20.77) 0.229 Sequ 5 A 0.048** (3.77) 0.943*** (24.73) 0.842 Sequ 6 A 0.041** (2.69) 0.924*** (23.14) 0.539 Sequ 7 A 0.095*** (4.44) 0.87*** (7.96) 0.731 Average value of DM - - - 0.179 Note: Significance at ***1%, **5%, *10%, respectively. The estimated function is ttt tyy β θ + = ∆ −1 . So ttt tyy β θ + + = −1 )1( , and θ β =MD n.a. Not available. Source: Own elaboration.