Why Do Some Countries Prosper More in Tourism than Others? Global Competitiveness of Tourism Development

Abstract

The purpose of this study is to investigate the competitiveness of tourism destinations, worldwide, utilizing a regional economic policy tool known as the shift-share analysis. The changes in tourist arrivals, tourism receipts, and per-tourist-dollars of each country are assessed and compared with the overall tourism development across 150 countries in the world using a 17-year period (2000-2017). The findings indicate that Japan, Thailand, and Turkey are the most competitive countries when tourist arrivals are specified, but Turkey drops to the least competitive country when tourism receipts are specified as the base indicator. Thailand and Japan rank among the most competitive destinations based on their tourism receipts. Yet further results show that Luxembourg is the most competitive country, demonstrating the largest growth in per-tourist-dollar. Overall, results imply that the competitiveness of the countries and regions vary based on the aggregated measure of tourism development, suggesting that destinations need to focus on growth mechanisms and capitalize on their tourism potential. Practical implications are discussed.

Keywords

tourism competitiveness tourist arrivals tourism receipts shift-share analysis

Introduction

The world’s second largest industry, tourism, has grown remarkably since the turn of the 21st century, accounting now for 10% of the world’s gross domestic product (GDP) with an estimated $1,340 billion in tourism receipts and approximately 13,260 billion international tourist arrivals, worldwide (United Nations World Tourism Organization [UNWTO], 2018). Destinations are striving to increase their competitiveness by attracting more and more tourists and by capturing larger shares of tourist dollars. Although countries often measure the growth of tourism through historical changes in tourism receipts and tourist arrivals, these indicators, alone, cannot account entirely for the changes related to tourism development. Measurement usually includes the tourism industry’s composition, strength, and performance relative to that of the regional and global economy. From a macroeconomic perspective, development in tourism can be attributed to overall global or regional economic trends, tourism trends in the world or the region, and the tourism industry’s competitiveness in the country under investigation (Dogru & Sirakaya-Turk, 2017; Perloff et al., 1960). However, the extent to which tourism development in a particular country is driven by these factors is largely not known.

The purpose of this study is, therefore, to investigate the worldwide competitiveness of tourism destinations utilizing a regional economic policy tool known as shift-share analysis (SSA). Since the early 1960s, the SSA has been applied in many fields, such as spatial economics (Curtis, 1972), political economics (Glickman & Glasmeier, 1989), geography (Plane, 1987), urban planning (Stilwell, 1969), and international trade (Cheptea et al., 2005). While a few studies have applied the shift-share technique to investigate the tourism industry’s economic competitiveness within a smaller economic region (Dogru & Sirakaya-Turk, 2017; Sirakaya et al., 2002), this powerful, yet simple, method has not been applied to assess tourism development and its competitiveness across worldwide destinations.

From an economic theoretical perspective, application of the shift-share technique in alternative contexts can demonstrate its efficacy and adaptability in analyzing the changes in tourism development in a particular country or region based on the global economic growth, global industry growth, and global competitive growth. Tourism development in a country can be attributed to the aggregate of three indicators, which we term as the global growth effect (GGE), the global industrial mix effect (GIME), and the global competitive share effect (GCSE).

GGE is a measure of tourism development in a country that would have occurred in the country if tourism had developed at the same rate as the world economy, which shows the extent to which tourism development in the country is attributable to the overall economic development in the world (Dogru & Sirakaya-Turk, 2017). GIME links the difference in the growth rate in tourism development between the country under investigation and the world with weaknesses and strengths of the tourism industry in the world considered. GIME shows the extent to which tourism development in the country is attributable to the overall tourism development in the world. GCSE measures the extent to which a country is more or less competitive in tourism development compared with other countries, securing a larger share of total tourist arrivals and/or tourism receipts than other countries and thus indicating a higher degree of competitiveness.

The shift-share technique is a useful method for researchers seeking to analyze the composition, performance, and strength of the tourism industry (Sirakaya et al., 1995). However, the SSA has not been extensively applied in the context of tourism. In this study, we utilized the SSA in the context of destination competitiveness. In so doing, this study attempts to contribute to tourism literature in the context of destination competitiveness, which has been a controversial subject matter in the extant literature because of the subjectivity involved in measuring the competitiveness of destinations. As a result, strategies can be developed for governments to efficiently allocate resources, reevaluate current policy, capitalize on existing tourism resources, and introduce new tourism activities. While the shift-share technique has been applied to measure the competitiveness of destinations within a specific region or industry in the context of employment, in this study, we investigate the competitiveness of countries on a global basis in the context of overall tourism development.

Literature Review

Tourist arrivals and spending from tourism provide economic benefits to destinations, promoting infrastructure development, and investment (Assaker et al., 2014; Fletcher & Archer, 1991). Many countries depend on tourism as their primary source of economic activity and plan for growth (Dogru & Bulut, 2018; Gooroochurn & Sugiyarto, 2005). The potential of economic growth from tourism has been noted in many recent studies (Armenski et al., 2018; Assaf & Josiassen, 2012; Tsai et al., 2009) and it is expected that tourism will continue to play a significant role in propelling increased growth and opportunities, particularly in many smaller countries (Croes, 2011). Thus, competitiveness among countries for market share is increasing. Understanding the determinants of destination competitiveness is therefore a top priority to researchers and important to policy makers. In fact, there has been a growing stream of academic research on destination performance and competitiveness for a decade now (Crouch, 2011; Gómez-Vega & Picazo-Tadeo, 2019).

Notably, Crouch and Ritchie (1994) developed the most comprehensive definition and framework for destination competitiveness based on Porter’s (1990) core diamond theory of competition and comparative advantages. Conceptually, key elements such as supporting resources, destination management policy, and planning and development determinants were included in the framework. Dwyer and Kim (2003) further determined how the supporting resources interact with tourism demand, thus influencing destination competitiveness. Subsequently, definitions have propagated as tourism has grown and more destinations appeared on the global market. Tourism researchers struggle with defining the concept of destination competitiveness, while most researchers focus on destination competitiveness through pricing factors—foreign exchange and economic welfare associated with tourism (Sequeira & Nunes, 2008)—others dwell on destination competitiveness as the quality of tourism experience, social, cultural, and environmental factors (Go & Govers, 2000; Hassan, 2000).

de Holan and Phillips (1997) recommended, specifically, Porter’s (1990) basis for destination competitiveness among countries, which postulates that success in international competition depends on the relative strength of the economy. As such, many econometric studies on tourism highlight the implicit market shares, spending in destinations, and employment generated to measure relative performance. In addition, the extant empirical studies include investigations on price competitiveness and comparisons of countries to analyze how price competitiveness affects regional economies and tourism (Croes, 2011; Dwyer et al., 2000; Dwyer et al., 2001; Giordano & Zollino, 2016).

Given the complexity of investigating the range of dimensions of destination competitiveness covered in the body of literature (for a detailed list of dimensions in the literature, see Peng et al., 2015), the current study investigates competitiveness from an economic perspective. In fact, a more specific notion of “competitiveness” exists in the economics literature and is “the capacity of businesses, industries, regions, nations or supranational associations exposed, and remaining exposed to, international competition to secure a relatively higher return on the factors of production and relatively high employment levels on a sustainable basis” (Turner, 2002, p. 17). Similar to the economic concept of competitiveness, “destination competitiveness” can focus on the economic impact of tourism. In this sense, tourism is an industry with the capacity to attract tourists and spending that sustains employment and develops resources for a destination while maintaining market positions in relation to tourism industries in competing destinations (Li et al., 2013). With regard to this definition, our study adopts economic indicators to measure relative destination competitiveness.

In acknowledgement of destination competitiveness comprising multidimensional demand indictors, we adopted a multifaceted approach to the global economic concepts thought to influence the competitiveness of countries included in our analysis. Appropriately, our approach is consistent with Ritchie and Crouch’s (2003) macroeconomic theoretical “onion skin taxonomy.” The onion skin taxonomy reflects the complexity and multidisciplinary of contextual industries affecting destinations. Studies of Ritchie and Crouch (2003) and Mazurek (2014) postulate that many external and internal factors shaping the opportunities in a destination build-up the body of what has been referred to as an “onion” from layers surrounding the destination. According to Ritchie and Crouch (2003), however, the economic inner layer of the onion may be the greatest determinant of competitiveness for particular destinations. Onionskin taxonomy applied to the present study’s destination competitiveness and tourism demand concept to analyze how a country’s tourism industry performs globally and regionally. Also, the process of developing models and knowledge of destination competitiveness is still open and not definite (Mazurek, 2014). The present study attempts to fill this gap by introducing a theoretical perspective on destination competitiveness in the context of tourism. The complexity of epistemologies on tourist arrivals in destinations, as highlighted in the literature by Getz (1986), include a “number of descriptive, explanatory, and predictive models which form the building blocks of theories and describe whole or subsystems.” Thus, destination competitiveness studies should seek broader pragmatic umbrellas to underlie their measurement. Our specific aim is to indicate the factors decisive in terms of a country’s competitiveness in tourism development both globally and regionally.

In addition, our study follows previous studies (particularly Dogru & Sirakaya-Turk, 2017, but see also Dogru & Sirakaya-Turk, 2016) to argue that the understanding of destination competitiveness requires the inclusion of a broader range of factors involved in growing the tourism industry. In other words, a tourist destination is competitive if its growth and development of resources are evidenced by increased tourism expenditures and tourist arrivals. Consistent with Sahli (2006) and Craigwell (2007), countries with more tourist arrivals and tourist receipts or with a higher market share in the world market relative to other countries are considered more competitive. Thus, our study addresses the lack of testing of this postulation and measurement of competitiveness across countries and provides an operationalization of measures of tourism development.

Moreover, by implementing the comparison on more than a single region, this study also includes a universal measure of competitiveness with respect to a significant proportion of the countries in the world. The question is whether the determinants of tourism development have the same relative impact on the competitiveness of each country. In the exploration of these issues, tourism development is restricted to shift-share determinants of GGE, the GIME, and the GCSE.

SSA, initially developed by Dunn (1960) to be employed in the areas of geography and regional economics, is effective at analyzing a region’s competitiveness in various sectors in comparison with a country’s overall economic progress (Dogru & Sirakaya-Turk, 2017; Sirakaya et al., 1995; Sirakaya et al., 2002). SSA delineates economic growth, measures the effects of policy, and predicts variational growth rates among different regions (i.e., cities, states, or countries; Andrikopoulos et al., 1990) and a region’s future growth (Sirakaya et al., 1995). Additionally, SSA is effective in measuring a country’s performance relative to its region across a given time frame (Chun-yun et al., 2008; Doeringer et al., 1987; Leigh & Blakely, 2013).

The traditional SSA measures an industry’s development in comparative terms. In other words, SSA is considered to be an accounting tool that provides comparative measures to policy makers regarding the relative industry development compared to the general economic development. In previous studies, employment statistics were utilized to gauge the economic growth of a country (Bendavid-Val, 1991). While a few studies have applied SSA and analyzed the tourism industry’s weaknesses and strengths of the tourism industry in the context of employment (see, e.g., Dogru & Sirakaya-Turk, 2017, Sirakaya et al., 1995; Sirakaya et al., 2002; Sobral et al., 2007; Toh et al., 2004; Yasin et al., 2005), and SSA with regression (see studies by Blien and Wolf [2002] and Kowalewski [2011]), the shift-share method has not been applied in the context of destination competitiveness.

Our study utilizes the SSA technique to examine the relative destination competitiveness of 150 countries. We utilized, specifically, a dynamic SSA which takes the year-over-year changes in tourism development as measured by tourist arrivals and tourism receipts into account, which is not the case in a static SSA (Andrikopoulos et al., 1990; Patterson, 1991). Although the shift-share regression technique provides statistical significance measures regarding the destination competitiveness of the countries, it requires extensive data, software packages, and statistical skills (Dogru & Sirakaya-Turk, 2017). Therefore, we utilize the dynamic SSA, which is an efficient tool and can be easily applied by policy makers.

Methodology

Sample and Data

Tourist arrivals, tourism receipts, and GDP data were obtained from the World Bank database, online. While the GDP data were available in constant terms (2010 US$), the tourism receipts data were in current form and hence adjusted for inflation using the consumer price index of respective countries (2010 = 100). The sample of this study consists of countries for which annual tourist arrivals, tourism receipts and GDP data were available on the World Bank database. Although the GDP data dates back to the 1960s on the World Bank database and the tourist arrivals and tourism receipts data were only available from the 1995s onward, one or more of these variables were not available for many countries prior to 2000. Therefore, our study period is delimited to the years between 2000 and 2017. The country observations with missing variables were removed from the analysis. Accordingly, the final sample—which is essentially the census of this study—consists of 2,700 countries—year observations with 150 countries. The list of countries included in the sample can be found in Tables 2, 3, and 4.

Table 1

Regional Shift-Share Analysis

Panel 1: Region Rankings Based on Tourist Arrivals (TA)
Regions	GGE	GIME	GCSE	Global Ranking (TA)
Southeast Asia	28,768,562	14,810,926	30,834,512	1
Northeast Asia	45,046,908	22,418,740	25,436,352	2
South Asia	5,284,947	3,002,494	11,494,359	3
Southern/Mediterranean Europe	79,745,633	37,712,937	7,153,429	4
South America	10,182,895	5,184,419	6,333,686	5
Central America	3,493,151	1,750,798	1,947,051	6
Sub-Saharan Africa	10,413,550	5,031,599	770,351	7
Middle East	10,339,110	4,826,865	555,525	8
North Africa	7,532,676	3,621,737	270,587	9
Oceania	4,513,583	2,131,693	(200,476)	10
Central/Eastern Europe	45,901,650	22,173,965	(1,305,415)	11
Northern Europe	26,559,611	12,493,801	(3,209,412)	12
Caribbean	8,722,024	4,066,709	(3,940,233)	13
North America	46,808,857	21,256,645	(22,541,502)	14
Western Europe	72,696,360	32,631,454	(53,598,814)	15
Panel 2: Region Rankings Based on Tourist Receipts (TR; in Thousands)
Regions	GGE	GIME	GCSE	Global Ranking (TR)
Southeast Asia	30,611,347	(4,230,245)	41,226,501	1
Northeast Asia	49,232,514	(4,683,525)	38,236,273	2
Oceania	15,937,159	(435,377)	14,311,804	3
South Asia	8,413,052	(294,943)	5,356,057	4
Central America	4,130,154	(330,611)	857,621	5
North Africa	4,656,555	(155,029)	(766)	6
Northern Europe	37,141,414	(2,029,182)	(436,701)	7
South America	9,722,562	(1,042,582)	(3,646,575)	8
Middle East	10,648,380	1,331,169	(5,430,136)	9
Western Europe	78,150,770	(2,984,139)	(5,580,306)	10
North America	92,333,167	(10,286,516)	(6,415,214)	11
Central/Eastern Europe	20,848,950	(1,005,828)	(7,043,501)	12
Sub-Saharan Africa	9,750,971	537,513	(8,865,335)	13
Caribbean	12,080,977	265,605	(21,547,643)	14
Southern/Mediterranean Europe	80,277,163	(3,425,289)	(41,022,077)	15
Panel 3: Region Rankings Based on Per-Tourist-Dollars (PTD)
Regions	GGE	GIME	GCSE	Global Ranking (PTD)
Oceania	1,670	(2,405)	1,654	1
Western Europe	512	(735)	350	2
Panel 3: Region Rankings Based on Per-Tourist-Dollars (PTD)
Regions	GGE	GIME	GCSE	Global Ranking (PTD)
North America	935	(1,348)	299	3
Northeast Asia	511	(745)	210	4
Northern Europe	666	(950)	111	5
North Africa	297	(407)	32	6
Southeast Asia	511	(721)	(41)	7
Central/Eastern Europe	221	(304)	(53)	8
Southern/Mediterranean Europe	490	(676)	(245)	9
Middle East	515	(632)	(338)	10
Sub-Saharan Africa	461	(602)	(366)	11
South America	468	(645)	(391)	12
Central America	586	(800)	(494)	13
South Asia	888	(1,087)	(918)	14
Caribbean	678	(870)	(935)	15

Note: GGE = global growth effect; GIME = global industrial mix effect; GCSE = global competitive share effect.

Table 2

Global Shift-Share Analysis: Country Rankings Based on Tourist Arrivals (TA)

Countries	GGE	GIME	GCSE	Global Ranking (TA)	RGE	RIME	RCSE	Regional Ranking (TA)
Japan	4,280,076	2,383,275	17,270,649	1	6,993,450	1,838,946	15,101,603	1
Thailand	8,114,416	4,370,213	13,528,371	2	14,838,720	6,039,468	5,134,812	1
Turkey	11,882,529	6,059,049	10,073,421	3	5,662,216	13,365,785	8,986,999	1
Belarus	979,770	836,473	9,183,958	4	683,271	281,805	10,035,124	1
India	2,881,186	1,666,414	8,346,399	5	6,506,764	3,735,253	2,651,983	1
Hong Kong	8,495,898	4,390,435	6,183,668	6	14,052,389	3,283,942	1,733,670	3
Portugal	3,496,406	1,747,155	4,589,439	7	2,02,452	3,793,769	4,036,779	2
Georgia	1,005,300	661,191	4,429,509	8	834,805	467,057	4,794,137	2
Macao (China)	5,071,378	2,579,683	4,406,939	9	8,404,797	1,900,953	1,752,250	2
Indonesia	3,203,954	1,600,602	4,171,445	10	5,881,694	2,394,078	700,229	5
Cambodia	1,113,305	658,126	3,364,569	11	2,047,892	846,314	2,241,793	2
Croatia	4,328,421	2,173,353	3,260,226	12	2,372,931	4,571,890	2,817,178	3
Kyrgyz Republic	829,345	532,621	3,147,034	13	649,290	346,220	3,513,491	3
Albania	833,708	528,301	2,963,991	14	443,144	1,122,244	2,760,612	4
Denmark	3,657,472	1,801,307	2,749,221	15	2,291,255	2,271,171	3,645,575	1
Chile	1,305,583	714,880	2,687,537	16	1,117,381	1,732,643	1,857,976	1
Kazakhstan	2,247,535	1,149,200	2,621,266	17	2,697,052	604,232	2,716,715	4
Greece	7,848,202	3,707,281	2,542,517	18	4,453,674	7,898,009	1,746,317	5
Colombia	794,963	439,616	2,321,421	19	718,006	1,048,195	1,789,799	2
Mexico	11,151,689	5,201,704	2,296,607	20	7,473,828	3,414,261	7,761,911	1
Bahrain	3,567,623	1,700,080	2,233,297	21	4,851,334	347,356	2,302,310	1
Philippines	1,581,937	837,717	2,209,346	22	2,905,519	1,252,089	471,392	6
Bulgaria	2,601,054	1,356,759	2,140,187	23	3,064,942	819,254	2,213,804	5
Myanmar	600,907	345,832	2,080,261	24	1,068,732	409,221	1,549,047	3
Lao People’s Democratic Republic	675,377	417,386	1,973,237	25	1,246,567	525,881	1,293,552	4
Singapore	4,049,626	2,004,448	1,786,925	26	7,438,200	3,000,263	(2,597,463)	7
Morocco	3,537,932	1,788,846	1,744,222	27	4,736,755	621,258	1,712,987	1
Malaysia	9,429,041	4,576,601	1,720,358	28	17,580,537	6,938,825	(8,793,362)	8
Korea Republic	4,092,899	2,213,332	1,707,769	29	6,684,696	1,701,365	(372,061)	5
Peru	996,254	549,838	1,685,908	30	846,986	1,425,548	959,466	3
Iceland	290,773	175,719	1,455,509	31	208,163	240,188	1,473,648	2
Iran	1,367,702	752,141	1,405,156	32	3,018,920	2,480,479	(1,974,399)	6
Slovenia	875,536	447,247	1,173,216	33	472,430	957,330	1,066,240	6
Cuba	1,164,274	594,637	1,094,089	34	826,854	410,234	1,615,912	1
Sri Lanka	395,735	241,083	1,079,581	35	867,215	651,377	197,807	2
Armenia	282,251	172,322	995,426	36	266,943	120,028	1,063,029	6
Paraguay	242,778	140,067	912,155	37	182,556	381,338	731,106	4
Oman	600,528	316,408	884,065	38	785,167	383,678	632,154	2
Romania	3,316,572	1,608,322	737,105	39	3,992,684	890,074	779,242	7
Czech Republic	3,139,250	1,529,327	718,423	40	3,875,560	786,534	724,906	8
Nicaragua	426,072	227,426	647,502	41	625,949	242,040	433,011	1
Uganda	357,738	207,700	643,561	42	552,423	(28,168)	684,745	1
Argentina	2,110,667	1,070,993	629,340	43	1,973,232	2,624,154	(786,387)	11
Germany	11,997,412	5,862,855	608,732	44	5,740,821	3,472,747	9,255,432	1
Panama	531,350	286,728	540,922	45	792,307	253,369	313,324	2
Costa Rica	896,167	458,634	517,199	46	1,325,892	523,093	23,015	3
Latvia	621,634	313,214	505,152	47	730,922	168,943	540,135	9
Sudan	174,907	104,015	496,078	48	221,764	29,039	524,197	2
Ethiopia	201,183	117,064	478,753	49	297,165	13,469	486,366	2
Lesotho	232,149	131,235	471,617	50	322,670	48,733	463,597	3
Estonia	1,054,334	546,924	423,742	51	1,218,383	364,318	442,299	10
Bolivia	289,712	154,918	370,370	52	260,345	387,004	167,651	5
Maldives	363,429	194,712	364,859	53	806,573	605,410	(488,983)	4
Nigeria	490,914	221,547	363,539	54	801,634	13,406	260,960	6
Guatemala	639,749	307,713	339,538	55	954,069	376,943	(44,011)	5
Algeria	821,230	427,625	336,145	56	1,097,631	130,224	357,145	3
Cabo Verde	146,517	85,199	321,285	57	223,907	(1,103)	330,196	4
Togo	79,050	49,137	307,814	58	114,698	4,267	317,035	5
Dominican	1,940,049	967,017	302,933	59	1,397,509	655,842	1,156,649	2
Tanzania	362,978	186,359	266,663	60	573,407	(10,000)	252,593	7
Ecuador	485,112	244,515	251,374	61	425,297	638,101	(82,398)	8
Macedonia	128,439	69,558	209,003	62	69,897	149,722	187,381	7
Bhutan	26,637	19,529	201,234	63	59,561	52,702	135,137	3
Lithuania	838,437	416,686	184,877	64	1,071,535	189,889	178,576	11
Uruguay	1,016,367	508,638	180,996	65	916,211	1,133,577	(343,789)	9
Lebanon	632,920	311,850	170,230	66	872,829	133,052	109,119	3
Malawi	290,162	148,895	169,942	67	470,621	(29,862)	168,241	8
Namibia	455,105	220,960	166,935	68	720,694	2,254	120,053	10
Burundi	70,578	35,762	163,660	69	115,657	(6,571)	160,914	9
Norway	2,011,989	976,979	159,032	70	1,333,267	1,401,270	413,463	3
Fiji	268,468	136,402	144,131	71	273,807	118,519	156,673	1
Haiti	126,157	63,886	136,958	72	80,093	68,556	178,351	4
Zambia	344,356	159,242	122,402	73	564,790	(26,285)	87,494	13
Malta	638,786	300,054	119,159	74	354,952	656,023	47,025	8
Congo Republic	54,595	33,966	98,440	75	73,361	14,084	99,556	11
Nepal	250,257	128,614	97,129	76	545,429	452,117	(521,545)	5
Suriname	79,413	44,554	97,032	77	71,647	109,969	39,383	6
Seychelles	83,006	41,561	95,433	78	130,065	1,806	88,129	12
Netherlands	5,342,816	2,482,831	95,353	79	2,578,617	1,343,522	3,998,861	2
Slovak Republic	703,687	325,069	80,244	80	906,625	128,324	74,051	12
Mauritius	413,853	198,868	73,280	81	671,144	(17,459)	32,314	16
Hungary	1,752,622	835,641	69,737	82	2,168,385	460,489	29,126	13
Mongolia	167,301	80,704	66,995	83	282,156	55,532	(22,688)	4
New Zealand	1,159,834	554,075	61,090	84	1,182,571	463,382	129,047	2
Niger	37,645	20,991	55,364	85	57,147	1,504	55,349	14
Belize	122,383	60,001	48,616	86	177,402	84,328	(30,730)	4
Benin	88,921	49,982	46,098	87	141,315	(1,024)	44,709	15
Papua New Guinea	53,138	28,440	39,423	88	54,366	31,970	34,664	3
Guyana	69,785	34,970	37,245	89	61,239	89,362	(8,601)	7
Mali	65,628	30,961	10,711	90	109,535	(7,836)	5,601	18
Solomon Islands	6,895	3,876	9,729	91	7,026	3,924	9,550	4
Ghana	325,353	163,561	9,086	92	523,951	(27,066)	1,115	19
Sierra Leone	19,646	10,933	8,621	93	32,183	(2,436)	9,453	17
Tonga	20,999	9,832	(3,332)	94	21,614	8,275	(2,388)	5
Marshall Islands	2,916	1,255	(3,371)	95	2,969	747	(2,916)	6
Vanuatu	37,696	18,035	(4,731)	96	38,662	16,175	(3,838)	7
El Salvador	524,753	242,164	(5,917)	97	783,496	309,229	(331,724)	6
Curacao	148,748	73,215	(13,964)	98	102,606	60,634	44,761	5
Samoa	52,551	24,365	(18,916)	99	54,052	18,969	(15,021)	8
Angola	147,820	83,845	(21,664)	100	223,774	(3,407)	(10,366)	20
Saint Kitts and Nevis	50,348	24,232	(25,580)	101	38,274	11,903	(1,177)	6
Jordan	1,543,867	745,621	(25,988)	102	2,166,666	511,703	(414,869)	4
Grenada	60,900	26,975	(48,875)	103	46,773	15,995	(23,768)	7
Dominica	36,458	16,115	(50,573)	104	27,952	8,042	(33,994)	10
St. Vincent and the Grenadines	37,993	16,313	(51,306)	105	29,617	6,679	(33,296)	9
Madagascar	100,573	49,628	(55,201)	106	177,383	(21,407)	(60,976)	21
New Caledonia	49,896	21,647	(60,543)	107	51,787	16,258	(57,045)	9
St. Lucia	143,482	65,963	(93,445)	108	108,787	36,945	(29,731)	8
Antigua and Barbuda	114,388	51,415	(125,803)	109	87,263	25,694	(72,957)	12
Honduras	352,677	168,132	(140,809)	110	520,356	222,528	(362,884)	7
Cayman Islands	143,579	62,012	(141,591)	111	108,242	38,269	(82,511)	13
British Virgin Islands	158,812	72,308	(168,119)	112	118,095	42,095	(97,190)	14
Jamaica	815,210	389,953	(175,163)	113	588,959	255,351	185,689	3
Australia	2,769,140	1,296,115	(181,255)	114	2,829,370	1,131,647	(77,017)	10
French Polynesia	92,050	37,651	(182,701)	115	96,602	22,107	(171,709)	11
Aruba	393,735	183,311	(227,546)	116	286,935	127,762	(65,196)	11
Bermuda	122,950	47,475	(232,425)	117	98,029	15,074	(175,103)	17
Barbados	258,976	114,954	(254,930)	118	198,474	52,882	(132,356)	15
U.S. Virgin Islands	275,959	123,161	(278,120)	119	209,715	63,843	(152,559)	16
Trinidad and Tobago	202,341	89,041	(295,382)	120	156,189	40,470	(200,659)	18
Sweden	2,399,243	1,127,492	(300,735)	121	1,624,394	1,730,058	(128,452)	4
Israel	1,016,310	488,751	(309,061)	122	499,609	1,157,258	(460,866)	9
Sint Maarten	216,974	97,915	(344,889)	123	165,618	49,351	(244,969)	19
Finland	1,114,837	512,257	(418,094)	124	745,175	776,865	(313,039)	5
Luxembourg	435,385	192,213	(433,598)	125	205,788	96,745	(108,533)	4
Kenya	614,375	295,396	(444,771)	126	1,025,721	(34,363)	(526,359)	22
Venezuela	303,617	134,980	(480,596)	127	285,775	296,261	(624,036)	10
Botswana	736,589	333,646	(600,234)	128	1,251,475	(154,766)	(626,709)	23
Cyprus	1,168,552	507,626	(710,178)	129	657,129	1,117,018	(808,147)	10
Zimbabwe	978,216	430,406	(952,622)	130	1,650,520	(92,373)	(1,102,147)	24
South Africa	3,716,602	1,724,757	(1,028,359)	131	6,196,430	(441,568)	(1,341,862)	25
Bahamas	705,293	296,775	(1,107,068)	132	552,956	122,928	(780,885)	20
Ireland	3,638,878	1,673,874	(1,620,752)	133	2,442,742	2,546,411	(1,297,153)	6
Puerto Rico	1,605,398	690,036	(1,839,434)	134	1,241,895	269,116	(1,055,011)	21
Tunisia	2,998,607	1,301,251	(2,305,858)	135	4,177,033	411,296	(2,594,329)	4
Brazil	2,488,646	1,146,450	(2,359,096)	136	2,334,888	2,641,283	(3,700,172)	12
Egypt	3,994,172	1,752,906	(2,706,078)	137	5,845,770	(176,055)	(2,628,715)	5
Belgium	3,385,361	1,492,622	(2,949,982)	138	1,585,551	774,473	(432,024)	5
Switzerland	3,794,926	1,655,602	(3,382,528)	139	1,786,565	904,131	(622,695)	6
Ukraine	7,786,155	3,689,819	(3,676,974)	140	9,692,704	1,365,528	(3,259,232)	14
Austria	10,353,405	4,836,662	(3,712,066)	141	4,886,865	2,703,999	3,887,137	3
Spain	26,981,703	12,477,177	(4,075,880)	142	14,968,867	26,671,853	(6,257,720)	11
Mainland China	22,939,356	10,771,311	(4,199,668)	143	38,013,817	9,689,957	(18,192,774)	6
The United Kingdom	13,446,418	6,226,174	(5,233,592)	144	8,928,814	9,304,227	(3,794,041)	7
Poland	7,064,305	3,035,629	(9,241,933)	145	9,421,055	1,362,932	(9,925,987)	15
Canada	8,499,258	3,463,008	(10,791,266)	146	5,677,173	1,294,724	(5,800,897)	3
Italy	20,547,041	9,207,385	(12,682,426)	147	11,104,179	20,089,618	(14,121,797)	12
Russia	11,679,400	5,164,769	(13,623,169)	148	14,589,046	2,551,369	(13,919,416)	16
The United States	27,157,909	12,591,934	(14,046,842)	149	18,131,024	9,532,990	(1,961,014)	2
France	37,387,054	16,108,669	(43,824,724)	150	17,485,129	8,164,048	(15,978,177)	7

Note: GGE = global growth effect; GIME = global industrial mix effect; GCSE = global competitive share effect; RGE = regional growth component; RIME = regional industrial mix component; RCSE = regional competitive share component.

Table 3

Global Shift-Share Analysis: Country Rankings Based on Tourism Receipts (TR; in Thousands)

Countries	GGE	GIME	GCSE	Global Ranking (TR)	RGE	RIME	RCSE	Regional Ranking (TR)
Thailand	11,605,759	(1,779,553)	33,183,857	1	21,406,260	4,498,687	17,105,116	1
Japan	7,068,993	(274,442)	22,959,570	2	11,475,267	(937,783)	19,216,638	1
Macao (China)	8,163,799	(1,528,999)	15,635,085	3	13,594,127	(2,625,355)	11,301,113	2
Hong Kong (China)	10,368,046	(1,652,901)	12,506,246	4	17,270,447	(1,823,616)	5,774,559	3
Australia	11,903,195	(407,422)	10,933,964	5	12,232,515	9,650,607	546,614	2
Singapore	5,058,032	(799,500)	7,105,029	6	9,438,524	2,403,595	(478,558)	5
Portugal	5,627,431	(542,786)	6,568,133	7	2,862,801	(414,748)	9,204,726	1
Ireland	3,738,627	(279,999)	5,645,643	8	2,422,816	803,506	5,877,949	1
India	5,621,405	(396,738)	5,596,195	9	12,680,529	(4,230,078)	2,370,412	1
Switzerland	7,325,473	(762,769)	4,130,159	10	3,390,379	2,422,078	4,880,406	1
Panama	1,214,235	(258,860)	3,878,964	11	1,797,714	(43,011)	3,079,636	1
New Zealand	3,054,996	(9,814)	3,568,353	12	3,115,172	2,929,801	568,563	1
Sweden	4,393,772	(104,050)	3,453,596	13	2,778,125	1,135,744	3,829,449	2
The United States	77,398,396	(9,935,778)	3,035,263	14	51,971,891	11,433,684	7,092,305	1
Croatia	3,872,194	17,055	2,688,605	15	1,902,366	(31,925)	4,707,413	3
Morocco	3,123,144	(142,909)	2,647,305	16	4,310,373	(1,860,699)	3,177,865	1
Germany	20,911,413	(872,327)	2,256,378	17	9,731,175	8,409,583	4,154,707	2
Malaysia	6,835,402	(713,153)	1,927,677	18	12,967,834	2,625,689	(7,543,598)	7
Cambodia	804,860	(85,913)	1,926,161	19	1,468,671	456,797	719,641	2
Georgia	398,608	(97,566)	1,896,549	20	370,094	(384,400)	2,211,897	1
Jordan	1,518,215	(109,713)	1,850,811	21	2,086,201	(1,854,917)	3,028,028	2
Sri Lanka	758,177	(54,755)	1,688,740	22	1,656,671	(264,286)	999,777	2
Peru	1,058,144	(149,677)	1,650,143	23	925,673	(392,919)	2,025,856	1
Oman	565,651	(68,989)	1,482,490	24	734,873	(337,956)	1,582,235	3
Philippines	1,931,658	(78,347)	1,425,112	25	3,548,052	758,434	(1,028,063)	6
Lebanon	3,179,051	1,012,741	1,406,921	26	4,806,858	(3,032,966)	3,824,822	1
Iceland	437,834	(33,287)	1,369,760	27	297,119	10,859	1,466,329	3
Bahrain	876,440	63,898	1,366,315	28	1,157,319	(378,381)	1,527,716	4
Slovak Republic	865,272	(107,343)	1,300,656	29	971,709	(774,961)	1,861,836	2
Maldives	638,853	(73,611)	1,100,657	30	1,459,105	(518,228)	725,021	3
Chile	1,080,868	(71,229)	1,063,052	31	1,028,435	(389,035)	1,433,291	3
Colombia	1,461,269	(192,950)	875,102	32	1,296,331	(751,440)	1,598,530	2
Romania	732,325	(148,417)	816,097	33	883,757	(618,237)	1,134,486	4
Albania	601,164	(77,384)	719,982	34	262,722	(105,307)	1,086,346	5
Finland	1,813,820	(112,074)	665,173	35	1,148,536	677,150	541,233	4
Armenia	235,170	(33,114)	635,762	36	243,338	(219,941)	814,422	5
Norway	2,352,290	(254,181)	512,282	37	1,526,564	660,189	423,638	5
Nigeria	202,173	(93,910)	487,794	38	385,610	(443,438)	653,885	1
Luxembourg	2,001,638	(47,038)	409,148	39	916,457	750,058	697,232	3
Latvia	344,554	(42,108)	382,956	40	389,219	(262,872)	559,056	7
Lithuania	513,051	3,872	314,036	41	642,091	(249,431)	438,299	9
Grenada	78,146	(22,425)	286,054	42	53,386	(92,059)	380,449	4
Estonia	660,631	(62,434)	270,096	43	822,927	(414,997)	460,363	8
Bolivia	206,223	9,829	256,819	44	190,015	(1,089)	283,944	4
Lao People’s Democratic Republic	147,375	(38,297)	250,095	45	272,024	42,725	44,424	3
Cabo Verde	137,803	(12,285)	221,162	46	231,674	(306,411)	421,419	3
Slovenia	1,085,287	(66,622)	202,871	47	523,149	(88,923)	787,310	6
St. Kitts and Nevis	67,701	(14,927)	201,827	48	48,038	(78,090)	284,652	6
Bulgaria	1,756,361	(51,658)	197,916	49	2,216,108	(1,007,144)	693,654	6
Kyrgyz Republic	114,803	(36,330)	197,338	50	118,209	(158,650)	316,251	11
Myanmar	274,116	(194,185)	195,322	51	476,163	(103,772)	(97,137)	4
Fiji	360,699	(10,292)	185,707	52	375,933	310,564	(150,384)	9
Antigua and Barbuda	192,382	(33,719)	170,748	53	141,651	(249,272)	437,032	3
Sudan	100,668	(2,782)	167,115	54	149,655	(110,613)	225,959	2
Belarus	292,015	(26,927)	160,818	55	332,342	(238,129)	331,693	10
Togo	47,207	(11,292)	149,963	56	67,370	(106,939)	225,446	7
Malta	529,520	(16,980)	142,581	57	285,991	(128)	369,258	8
Nicaragua	161,950	5,085	122,282	58	235,065	(47,383)	101,635	2
Macedonia	83,802	(16,980)	121,476	59	38,949	(17,310)	166,659	9
El Salvador	393,655	(7,255)	117,369	60	567,779	(123,603)	59,593	3
Czech Republic	3,099,566	(72,597)	91,489	61	3,825,698	(1,949,749)	1,242,510	3
Dominica	46,063	(10,900)	81,732	62	31,536	(51,069)	136,427	9
Vanuatu	84,697	(11,432)	69,925	63	86,262	56,542	385	6
Ecuador	386,254	(97,094)	61,810	64	318,291	(244,397)	277,076	5
Curacao	207,828	(37,706)	55,674	65	144,989	(217,317)	298,124	5
Moldova	100,134	(11,636)	54,466	66	121,010	(76,148)	98,102	12
Solomon Islands	15,821	(1,452)	39,094	67	16,490	9,480	27,492	3
Bhutan	23,066	(4,134)	34,940	68	52,127	(20,476)	22,221	4
St. Vincent and the Grenadines	59,611	(1,172)	33,752	69	44,871	(81,014)	128,334	10
Samoa	51,772	(1,242)	29,547	70	53,191	45,793	(18,907)	8
Mali	80,100	(1,232)	28,360	71	140,196	(139,342)	106,372	11
Niger	26,054	(5,395)	27,099	72	42,534	(53,342)	58,566	12
Paraguay	95,030	(7,865)	23,156	73	84,013	(54,407)	80,715	6
Tonga	11,778	(1,337)	14,936	74	11,822	10,046	3,508	5
Marshall Islands	2,691	461	11,557	75	2,555	4,614	7,539	4
Burundi	1,164	(6)	(2,907)	76	1,987	(1,194)	(2,541)	15
Congo Republic	21,407	2,582	(4,785)	77	34,514	(27,548)	12,239	14
Papua New Guinea	2,554	(323)	(4,817)	78	2,681	1,398	(6,665)	7
Uganda	275,668	(14,360)	(4,953)	79	450,027	(492,324)	298,653	5
Mongolia	131,531	47,424	(13,978)	80	214,102	117,827	(166,952)	4
Sierra Leone	21,784	6,961	(21,554)	81	35,855	(8,982)	(19,682)	16
Belize	122,174	1,107	(27,782)	82	181,558	(38,840)	(47,220)	4
Denmark	2,841,420	30,682	(30,709)	83	1,891,966	1,213,375	(263,948)	6
Lesotho	14,004	1,384	(34,851)	84	24,124	(15,876)	(27,712)	17
Tanzania	588,883	16,038	(36,894)	85	1,002,158	(902,150)	468,019	2
Benin	66,396	(5,594)	(47,948)	86	120,146	(128,306)	21,014	13
New Caledonia	111,000	(4,600)	(53,622)	87	114,736	108,099	(170,056)	10
Madagascar	208,541	(26,285)	(58,131)	88	348,012	(386,381)	162,495	9
Aruba	620,922	(42,542)	(59,261)	89	464,593	(821,801)	876,327	1
Guyana	31,610	(8,191)	(72,623)	90	27,643	(32,502)	(44,345)	7
Ethiopia	519,312	22,183	(82,109)	91	881,438	(840,532)	418,480	4
St. Lucia	194,215	(27,378)	(105,111)	92	153,368	(267,680)	176,037	8
Nepal	158,770	(19,720)	(115,811)	93	354,465	(87,111)	(244,115)	5
Malawi	30,336	9,968	(119,643)	94	54,065	(4,269)	(129,133)	21
Zambia	213,675	(4,013)	(129,966)	95	371,303	(452,720)	161,113	10
Zimbabwe	87,082	(4,083)	(134,924)	96	159,391	(179,906)	(31,411)	18
Algeria	135,269	(7,599)	(151,678)	97	193,879	(24,003)	(193,884)	3
Suriname	37,467	(7,201)	(152,130)	98	39,454	(16,257)	(145,061)	8
Seychelles	215,085	16,561	(234,340)	99	372,776	(291,189)	(84,282)	19
Namibia	230,679	36,435	(309,373)	100	399,080	(315,104)	(126,234)	20
Honduras	282,572	15,367	(321,554)	101	412,203	(103,947)	(331,871)	5
Haiti	149,558	(48,498)	(323,690)	102	108,846	(204,862)	(126,614)	17
Israel	2,204,790	(464,674)	(325,589)	103	1,105,743	(398,343)	707,127	7
Guatemala	558,711	(12,260)	(327,874)	104	817,918	(184,323)	(415,018)	6
Botswana	308,947	58,492	(383,734)	105	519,221	(270,016)	(265,500)	22
Kazakhstan	616,776	32,632	(394,179)	106	813,874	(258,330)	(300,316)	13
Mauritius	717,744	7,777	(412,712)	107	1,236,887	(1,091,113)	167,034	8
British Virgin Islands	215,461	7,661	(468,499)	108	176,565	(353,457)	(68,486)	15
French Polynesia	337,957	12,078	(482,839)	109	347,460	327,824	(808,088)	11
Cayman Islands	298,104	(2,317)	(559,439)	110	242,381	(468,460)	(37,573)	13
Trinidad and Tobago	343,144	18,851	(562,058)	111	276,510	(493,185)	16,612	12
Bermuda	227,989	17,457	(610,926)	112	189,210	(379,981)	(174,709)	18
Sint Maarten	357,303	(5,677)	(622,237)	113	280,478	(502,515)	(48,573)	14
Barbados	509,957	51,142	(793,979)	114	404,914	(828,682)	190,889	7
Uruguay	633,238	(141,656)	(868,830)	115	598,876	(566,914)	(409,210)	9
Cyprus	1,299,731	(16,856)	(884,484)	116	698,070	(8,141)	(291,537)	10
Bahamas	1,046,097	17,749	(911,813)	117	803,851	(1,490,807)	838,988	2
Angola	238,030	(139,067)	(952,861)	118	386,229	(581,086)	(659,040)	24
Venezuela	323,508	(35,406)	(1,024,850)	119	438,103	(235,899)	(938,952)	10
Greece	7,310,417	(490,275)	(1,025,543)	120	3,888,763	(805,637)	2,711,472	4
Kenya	688,564	70,491	(1,164,765)	121	1,205,926	(1,048,132)	(563,504)	23
Belgium	5,540,777	18,463	(1,501,075)	122	2,598,841	2,590,833	(1,131,509)	4
Korea Republic	6,318,321	(1,244,639)	(1,699,608)	123	10,455,927	347,145	(7,428,998)	5
Ghana	499,704	13,301	(1,807,746)	124	905,807	(672,878)	(1,527,670)	25
U.S. Virgin Islands	696,651	88,441	(1,867,204)	125	589,209	(1,160,238)	(511,084)	19
Netherlands	6,750,464	(313,927)	(1,879,366)	126	3,075,891	3,162,681	(1,681,400)	5
Hungary	2,973,318	(225,432)	(1,943,326)	127	3,851,893	(1,629,121)	(1,418,212)	14
Brazil	2,346,670	(132,675)	(1,995,461)	128	2,464,977	(734,045)	(1,512,399)	11
Cuba	1,243,057	61,316	(2,323,873)	129	1,012,476	(1,949,779)	(82,197)	16
Costa Rica	1,396,858	(73,795)	(2,583,785)	130	2,006,505	(820,472)	(2,446,755)	7
Austria	8,546,141	(77,875)	(2,591,423)	131	3,951,066	3,912,963	(1,987,187)	6
Tunisia	1,297,474	(1,738)	(2,680,659)	132	1,914,357	(72,189)	(3,227,092)	4
Iran	1,212,781	254,014	(2,948,665)	133	2,663,357	(271,910)	(3,873,316)	6
Ukraine	1,998,969	209,528	(2,974,669)	134	2,771,457	(1,021,449)	(2,516,181)	15
Puerto Rico	1,667,280	103,864	(2,993,805)	135	1,346,783	(2,654,482)	85,038	11
Argentina	2,062,281	(208,466)	(3,462,764)	136	2,163,442	(1,122,945)	(2,649,446)	12
South Africa	4,310,627	592,861	(3,835,517)	137	7,375,771	(6,569,773)	261,974	6
Mexico	6,766,644	(59,559)	(3,876,233)	138	4,418,729	669,833	(2,257,711)	2
Jamaica	1,333,071	130,663	(4,241,858)	139	1,157,068	(2,406,172)	(1,529,020)	21
Indonesia	3,954,146	(541,298)	(4,786,752)	140	7,319,859	28,063	(8,721,826)	8
Canada	8,168,128	(291,179)	(5,574,244)	141	5,391,833	1,745,466	(4,834,594)	3
Spain	25,944,554	(148,591)	(5,658,357)	142	13,472,903	(589,838)	7,254,542	2
Dominican Republic	2,526,436	15,725	(5,933,677)	143	2,103,150	(4,224,013)	(1,270,653)	20
France	27,074,865	(928,665)	(6,404,128)	144	12,387,550	12,286,770	(4,932,248)	7
Russia	6,147,399	(336,299)	(8,049,507)	145	7,971,876	(4,282,423)	(5,927,861)	16
Mainland China	17,181,823	(29,968)	(11,151,041)	146	28,642,434	6,054,741	(28,696,360)	6
Egypt	4,509,023	433,233	(11,536,673)	147	6,763,872	(3,395,488)	(9,962,801)	5
The United Kingdom	21,563,651	(1,276,272)	(12,052,445)	148	14,292,933	5,816,651	(11,874,650)	7
Italy	18,839,964	(89,090)	(13,342,962)	149	9,827,603	(583,091)	(3,836,601)	11
Turkey	12,878,309	(1,512,106)	(30,228,790)	150	6,782,608	(2,778,481)	(22,866,715)	12

Table 4

Global Shift-Share Analysis: Country Rankings Based on Per-Tourist-Dollar (PTD)

Countries	GGE	GIME	GCSE	Global Ranking (PTD)	RGE	RIME	RCSE	Regional Ranking (PTD)
Luxembourg	2,189	(3,197)	2,809	1	995	(735)	1,542	1
Marshall Islands	460	(684)	2,552	2	439	61	1,827	1
Lebanon	2,487	(2,702)	2,260	3	3,859	(6,997)	5,184	1
Grenada	583	(999)	2,223	4	406	(1,370)	2,770	1
St. Kitts and Nevis	624	(1,042)	2,063	5	454	(1,461)	2,651	2
Australia	2,037	(2,942)	2,057	6	2,115	(1,059)	95	5
Dominica	598	(1,031)	2,033	7	411	(1,364)	2,552	3
Panama	1,012	(1,515)	1,888	8	1,464	(2,348)	2,270	1
New Zealand	1,237	(1,778)	1,574	9	1,276	(541)	298	2
St. Vincent and the Grenadines	752	(1,150)	1,551	10	561	(1,771)	2,363	4
Antigua and Barbuda	795	(1,284)	1,543	11	595	(1,888)	2,346	5
Switzerland	906	(1,368)	1,281	12	418	(308)	709	2
Solomon Islands	971	(1,507)	1,240	13	1,059	(468)	114	4
Macao (China)	701	(1,130)	1,184	14	1,155	(1,371)	972	1
Vanuatu	1,029	(1,508)	1,051	15	1,054	(483)	1	6
Slovak Republic	583	(904)	944	16	675	(1,362)	1,311	1
Maldives	843	(1,190)	903	17	1,913	(3,296)	1,940	1
Ireland	484	(718)	874	18	313	(467)	794	1
Sweden	866	(1,258)	822	19	540	(814)	703	2
New Caledonia	1,060	(1,568)	796	20	1,091	(454)	(349)	9
French Polynesia	1,766	(2,481)	720	21	1,794	(729)	(1,061)	11
Finland	768	(1,121)	642	22	489	(657)	456	3
Jordan	454	(684)	560	23	610	(1,013)	732	2
Singapore	567	(855)	544	24	1,061	(1,281)	476	2
Thailand	662	(971)	538	25	1,229	(1,521)	521	1
Belgium	779	(1,100)	517	26	366	(156)	(14)	4
The Bahamas	708	(1,007)	508	27	536	(1,649)	1,323	6
The United States	1,355	(1,924)	487	28	910	(1,084)	92	2
Samoa	464	(674)	436	29	479	(206)	(46)	7
Oman	445	(675)	394	30	608	(1,017)	573	3
Tonga	258	(412)	376	31	261	(190)	151	3
Mali	586	(784)	356	32	1,015	(1,617)	760	1
Hong Kong	557	(814)	354	33	914	(931)	114	2
Japan	829	(1,143)	332	34	1,329	(1,314)	2	3
France	345	(501)	318	35	158	(78)	82	3
Canada	463	(687)	289	36	306	(339)	98	1
Togo	249	(391)	279	37	411	(809)	535	2
Morocco	425	(576)	250	38	613	(739)	225	1
Aruba	763	(1,030)	218	39	602	(1,814)	1,162	7
Portugal	785	(1,096)	193	40	384	(1,112)	609	1
Norway	552	(822)	189	41	363	(512)	68	4
Croatia	435	(600)	180	42	215	(595)	396	2
Lithuania	292	(420)	153	43	379	(586)	232	3
Germany	843	(1,156)	151	44	387	(103)	(444)	6
Bolivia	348	(449)	130	45	359	(708)	378	2
Macedonia	319	(447)	109	46	151	(461)	291	5
Cabo Verde	474	(624)	83	47	844	(1,398)	487	3
Malta	400	(543)	77	48	213	(542)	262	7
El Salvador	367	(492)	72	49	509	(961)	399	2
Bahrain	121	(163)	71	50	164	(274)	139	4
Brazil	453	(621)	65	51	504	(1,026)	419	1
Czech Republic	475	(657)	64	52	625	(987)	245	2
Latvia	255	(385)	59	53	326	(489)	93	4
Romania	106	(155)	56	54	142	(218)	83	5
Austria	398	(551)	54	55	183	(54)	(228)	5
Cyprus	534	(742)	50	56	279	(751)	314	4
Curacao	656	(945)	48	57	512	(1,494)	741	10
Nigeria	219	(386)	46	58	412	(803)	270	7
Spain	462	(649)	33	59	237	(661)	269	6
Malaysia	347	(481)	12	60	646	(807)	38	3
Moldova	7	(10)	11	61	8	(15)	15	6
Italy	442	(613)	11	62	234	(625)	230	8
Niger	347	(473)	6	63	615	(1,084)	348	5
St. Lucia	641	(974)	(12)	64	517	(1,559)	697	12
Zimbabwe	42	(65)	(18)	65	77	(149)	32	11
Fiji	648	(912)	(19)	66	682	(214)	(751)	10
The United Kingdom	761	(1,093)	(54)	67	512	(724)	(173)	5
Russia	255	(352)	(64)	68	349	(524)	13	7
Israel	1,075	(1,387)	(77)	69	601	(1,364)	373	3
Algeria	83	(115)	(86)	70	124	(129)	(113)	3
Georgia	262	(306)	(91)	71	394	(402)	(126)	8
Lesotho	37	(47)	(94)	72	70	(105)	(69)	13
Mainland China	355	(492)	(97)	73	580	(551)	(263)	4
Belize	482	(650)	(102)	74	712	(1,194)	211	3
Netherlands	611	(861)	(104)	75	270	(39)	(585)	7
Burundi	11	(17)	(105)	76	21	(30)	(101)	15
Greece	454	(636)	(113)	77	235	(651)	121	9
Botswana	205	(242)	(113)	78	342	(486)	(5)	12
Mongolia	407	(388)	(117)	79	630	(433)	(297)	5
Estonia	315	(427)	(126)	80	451	(540)	(149)	9
Madagascar	1,033	(1,353)	(130)	81	1,666	(2,540)	424	4
South Africa	561	(717)	(148)	82	954	(1,569)	311	6
Papua New Guinea	36	(48)	(156)	83	37	(4)	(202)	8
Ukraine	119	(152)	(159)	84	176	(208)	(159)	10
Mexico	296	(389)	(167)	85	193	(215)	(238)	3
Zambia	294	(405)	(170)	86	514	(1,003)	207	9
Sudan	383	(147)	(190)	87	658	(599)	(13)	2
Trinidad and Tobago	807	(1,082)	(213)	88	650	(1,924)	786	9
Nicaragua	201	(248)	(215)	89	284	(545)	0	4
Chile	422	(546)	(225)	90	453	(917)	115	4
Peru	512	(738)	(230)	91	525	(1,183)	203	3
Sint Maarten	787	(1,086)	(232)	92	625	(1,870)	715	11
Namibia	263	(312)	(246)	93	466	(692)	(70)	14
Bulgaria	334	(461)	(258)	94	470	(618)	(237)	11
Tunisia	207	(279)	(273)	95	310	(324)	(332)	4
Nepal	331	(440)	(295)	96	738	(1,107)	(34)	4
Puerto Rico	494	(647)	(308)	97	398	(1,194)	335	15
Kazakhstan	142	(191)	(345)	98	206	(262)	(339)	12
Malawi	69	(79)	(371)	99	127	(126)	(381)	18
Tanzania	831	(1,033)	(380)	100	1,470	(2,275)	223	8
Paraguay	211	(276)	(391)	101	221	(482)	(195)	7
Slovenia	608	(841)	(401)	102	305	(873)	(67)	10
Uruguay	295	(435)	(412)	103	305	(720)	(136)	5
Honduras	395	(517)	(423)	104	561	(1,042)	(65)	5
Barbados	940	(1,208)	(441)	105	750	(2,281)	822	8
Guatemala	435	(559)	(480)	106	612	(1,091)	(126)	6
Mauritius	856	(1,134)	(491)	107	1,503	(2,396)	124	10
Sierra Leone	559	(629)	(527)	108	928	(1,111)	(414)	19
Ecuador	385	(580)	(549)	109	363	(936)	(171)	6
Cambodia	402	(517)	(563)	110	724	(896)	(505)	4
Denmark	440	(523)	(564)	111	321	(323)	(644)	6
Benin	390	(498)	(570)	112	728	(1,056)	(350)	17
Hungary	834	(1,150)	(577)	113	1,150	(1,670)	(372)	13
Bermuda	872	(1,142)	(603)	114	702	(2,105)	530	14
Kyrgyz Republic	141	(157)	(607)	115	244	(207)	(660)	14
Kenya	549	(721)	(623)	116	968	(1,522)	(241)	16
Philippines	621	(790)	(623)	117	1,135	(1,418)	(509)	5
Congo Republic	380	(332)	(736)	118	660	(662)	(686)	20
Korea Republic	772	(1,099)	(737)	119	1,267	(1,112)	(1,219)	6
Cayman Islands	1,015	(1,279)	(756)	120	842	(2,461)	598	13
Sri Lanka	1,010	(1,260)	(777)	121	2,213	(3,286)	47	3
Guyana	232	(332)	(875)	122	220	(582)	(613)	8
British Virgin Islands	665	(853)	(930)	123	564	(1,671)	(11)	16
Armenia	440	(638)	(967)	124	631	(852)	(943)	15
Iceland	848	(1,054)	(1,008)	125	563	(632)	(1,144)	7
Lao People’s Democratic Republic	185	(230)	(1,012)	126	334	(525)	(866)	6
Argentina	491	(695)	(1,040)	127	531	(1,132)	(643)	9
Uganda	440	(546)	(1,085)	128	773	(1,157)	(806)	21
India	1,103	(1,385)	(1,096)	129	2,426	(4,014)	211	2
Albania	481	(634)	(1,157)	130	254	(671)	(894)	11
Cuba	553	(678)	(1,218)	131	480	(1,416)	(408)	18
Bhutan	726	(811)	(1,287)	132	1,603	(2,251)	(724)	5
Indonesia	637	(829)	(1,332)	133	1,171	(1,580)	(1,116)	7
Venezuela	568	(705)	(1,430)	134	759	(1,300)	(1,026)	10
Egypt	573	(673)	(1,462)	135	805	(1,114)	(1,252)	5
Iran	585	(556)	(1,759)	136	1,277	(1,580)	(1,429)	6
Dominican Republic	673	(821)	(1,963)	137	598	(1,781)	(928)	19
Virgin Islands	1,221	(1,469)	(2,021)	138	1,045	(3,074)	(240)	17
Seychelles	1,379	(1,620)	(2,078)	139	2,469	(3,598)	(1,190)	22
Suriname	283	(452)	(2,290)	140	287	(647)	(2,100)	12
Myanmar	254	(465)	(2,458)	141	448	(857)	(2,260)	8
Jamaica	856	(993)	(2,580)	142	784	(2,276)	(1,224)	20
Haiti	668	(920)	(2,606)	143	587	(1,712)	(1,733)	21
Colombia	1,049	(1,370)	(2,659)	144	1,017	(2,378)	(1,619)	11
Ethiopia	1,636	(1,905)	(2,664)	145	2,916	(3,763)	(2,087)	23
Costa Rica	843	(1,083)	(2,720)	146	1,162	(2,477)	(1,646)	7
Turkey	643	(863)	(3,163)	147	403	(948)	(2,838)	12
Ghana	887	(1,089)	(3,593)	148	1,623	(2,582)	(2,837)	24
Belarus	2,085	(2,222)	(4,852)	149	3,344	(3,892)	(4,441)	16
Angola	1,263	(2,568)	(20,405)	150	2,457	(5,058)	(19,109)	25

The Shift-Share Model

The SSA assesses tourism development via the changes in the tourism industry as measured by the following indicators: the GGE, the GIME, and the GCSE.

First, the GGE measures tourism development in a country that would have occurred in the country if tourism had developed equivalent to the development of the global economy (Tervo & Okko, 1983). The GGE shows the extent to which tourism development in the country is attributable to the overall economic development in the world. The country is considered to be at a disadvantage, in terms of tourism development, if the country grows as much as the global economy. The GGE is computed by multiplying the country’s base-year tourist arrivals and/or tourism receipts by the average economic growth in the world and then calculating the sum of the products (Sirakaya et al., 1995). The resulting amounts show the number of newly added tourist arrivals or the amount of newly generated tourism receipts that are attributed to trends in the global economy, alone.

Second, the GIME links the differential rate of growth in tourism development in the country under investigation to the weaknesses and strengths of the global tourism industry. Preferably, a large quantity of a country’s economy is expected to be concentrated in faster growing industries and also a smaller proportion of the economic development is preferred to be in the sectors of the economy with little or no growth. The difference in the global growth rate for the tourism industry and the average economic growth in the world is multiplied by the country’s tourist arrivals and/or tourism receipts to calculate the GIME. The GIME indicator shows the extent to which tourism development in the country is attributable to the overall tourism development in the world. The resulting figures illustrate the number of newly added tourist arrivals or the amount of newly generated tourism receipts that are attributable to the global tourism trends and nothing else.

Third, the GCSE measures the extent to which tourism development is driven by the country’s competitiveness in the tourism industry. That is, this measure indicates whether the country under examination (e.g., The United States) is relatively competitive in capturing a greater share of tourist arrivals and/or tourism receipts compared with the capture rate of other countries in the world. The GCSE is a production of the country’s tourist arrivals and/or tourism receipts and the difference in the growth rate of the tourism industry in both the country and the world. After completing this process, the outcomes are added to produce the differential-shift effect. A positive figure suggests that the country has a competitive advantage in terms of attracting more tourists and/or increasing tourism receipts. The GCSE is the vital factor in the SSA because it shows a country’s attractiveness to visitors and/or ability to increase tourism receipts. Hence, it is considered as an indicator of tourism development and a measure of a destination’s competitiveness.

The classical shift-share technique is presented in the following equation:

T_{i}^{t} - T_{i}^{t - 1} = Δ T_{i} = G G E + G I M E + G C S E

(1)

where i: the index referring to the countries in the global economy; ∆Ti: total change in tourist arrivals and/or tourism receipts in the i^th country; $T_{i}^{t}$ : tourist arrivals and/or tourism receipts in the i^th country at time t; GGE: global growth effect; GIME: global industrial growth effect; and GCSE: global competitive share effect.

The three accounting identities are calculated as follows:

G G E_{i} = T_{i} \times g_{W o r l d}^{G D P}

(2)

G I M E_{i} = T_{i} \times (g_{W o r l d}^{T o u r i s m} - g_{W o r l d}^{G D P})

(3)

G C S E_{i} = T_{i} \times (g_{i}^{T o u r i s m} - g_{W o r l d}^{T o u r i s m})

(4)

where the global growth component, GGE is given by the base period of tourism development (as measured by tourist arrivals, tourism receipts, and per-tourist-dollar) in the i^th country, $T_{i}$ times the overall economic growth in the world (as measured by GDP growth rate), $g_{W o r l d}^{G D P}$ ; the global industrial mix component, GIME given by base period tourism development in the i^th country, $T_{i}$ , times the tourism growth in the world, $g_{W o r l d}^{T o u r i s m}$ , less the overall economic growth in the world, $g_{W o r l d}^{G D P}$ ; and the global competitive share component, GCSE is given by the base period of tourism development in the i^th country, $T_{i}$ , times the growth in tourism in the i^th country, $g_{i}^{T o u r i s m}$ , less the tourism growth in the world, $g_{W o r l d}^{T o u r i s m}$ .

When the above-illustrated Equation (1) is employed to the data year-over-year, the model is named the dynamic SSA, which takes the yearly changes in tourist arrivals and/or tourism receipts into consideration and thus incorporates the year-over-year changes in tourism development. That is, the dynamic SSA is a more accurate comparison of the components of growth compared with static SSA. The dynamic SSA allows the identification of extraordinary years; for example, economic crisis, recession, or other regional and/or global economic transitions. The dynamic shift-share model is summarized in the following equation:

\sum_{k} (T_{i}^{t} - T_{i}^{t - 1}) = \sum_{k} Δ T_{i} = \sum_{k} G G E_{i k} + \sum_{k} G I M E_{i k} + \sum_{k} G C S E_{i k}

(5)

where k = 1, 2, . . . t is the sequence of adjacent years. The summation is for the periods 2000-2017 in this study. Accordingly, countries can be ranked based on the GCSE to identify their tourism development or tourism competitiveness as measured by tourist arrivals, tourism receipts, and per-tourist-dollar, which is calculated by dividing the tourism receipts by the tourist arrivals for each country.

We further decompose the changes in tourist arrivals, tourism receipts, and per-tourist-dollar in a particular country into alternative components to account for the potential differences in regional economic factors. Indeed, comparing the tourism and economic development of the countries in different regions might be less than perfect because of the economical differences across the regions. Instead of assessing the worldwide competitiveness of the countries, we assess their regional competitiveness to determine the extent to which tourism development in the country is attributable to the regional economic development, regional tourism development, and the extent to which tourism development is driven by the country’s competitiveness in the region. The UNWTO classifies 15 tourism regions in the world, mainly based on geographical structures. We conduct the regional analysis based on the 15 tourism regions defined by the UNWTO. In this context, the accounting identities for the regions are computed as follows:

R G E_{i} = T_{i} \times g_{j}^{G D P}

(6)

R I M E_{i} = T_{i} \times (g_{j}^{T o u r i s m} - g_{j}^{G D P})

(7)

R C S E_{i} = T_{i} \times (g_{i}^{T o u r i s m} - g_{j}^{T o u r i s m})

(8)

where the regional growth component, RGE is given by base period tourism development (as measured by tourist arrivals, tourism receipts, and per-tourist-dollar) in the i^th country, $T_{i}$ times the overall economic growth in the region j (as measured by GDP growth rate of the region), $g_{j}^{G D P}$ ; the regional industrial mix component, RIME given by base period tourism development in the i^th country, $T_{i}$ , times the tourism growth in the region, $g_{j}^{T o u r i s m}$ , less the overall economic growth in the region j, $g_{j}^{G D P}$ ; and the regional competitive share component, RCSE is given by base period tourism development in the i^th country, $T_{i}$ , times the growth in tourism in the i^th country, $g_{i}^{T o u r i s m}$ , less the tourism growth in the region j, $g_{j}^{T o u r i s m}$ . Accordingly, countries can be further ranked based on their tourism competitiveness as measured by RCSE within their respective regions using tourist arrivals, tourism receipts, and per-tourist-dollar.

Empirical Results

This section presents the findings from the SSA of world’s regions and countries. First, we investigate the development of the tourism industry across the world’s regions, which are classified by the UNWTO mainly based on their geographical locations. North Africa region includes Algeria, Morocco, Sudan, and Tunisia. Sub-Saharan Africa region includes Angola, Benin, Botswana, Burundi, Cabo Verde, Congo Republic, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritius, Namibia, Niger, Nigeria, Seychelles, and Sierra Leone. South Africa region includes Tanzania, Togo, Uganda, Zambia, and Zimbabwe, North America region includes Canada, Mexico, and the United States. Caribbean region includes Antigua and Barbuda, Aruba, the Bahamas, Barbados, Bermuda, British Virgin Islands, Cayman Islands, Cuba, Curacao Dominica, Dominican Republic, Grenada, Haiti, Jamaica, Puerto Rico, St. Lucia, St. Kitts and Nevis, Sint Maarten (Dutch part), St. Vincent and the Grenadines, Trinidad and Tobago, and U.S. Virgin Islands.

Central America includes Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama. South America region includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay, and Venezuela. North-East Asia region includes Mainland China, Hong Kong (China), Japan, Korea Republic, Macao (China), and Mongolia. Southeast Asia region includes Cambodia, Indonesia, Lao People’s Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, and Thailand. Oceania region includes Australia, Fiji, French Polynesia, Marshall Islands, New Caledonia, New Zealand, Papua New Guinea, Samoa, Solomon Islands, Tonga, and Vanuatu. South Asia region includes Bhutan, India, Iran, Maldives, Nepal, and Sri Lanka. Northern Europe region includes Denmark, Finland, Iceland, Ireland, Norway, Sweden, and the United Kingdom. Western Europe region includes Austria, Belgium, France, Germany, Luxembourg, Netherlands, and Switzerland. Central/Eastern Europe region includes Armenia, Belarus, Bulgaria, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Poland, Romania, Russian Federation, Slovak Republic, and Ukraine.

Southern/Mediterranean Europe region includes Albania, Croatia, Cyprus, Macedonia, Greece, Israel, Italy, Malta, Portugal, Slovenia, Spain, and Turkey. Middle East region includes Bahrain, Egypt, Jordan, Lebanon, and Oman. Table 1 presents these results.

Panel 1 of Table 1 presents the findings from the SSA of regions’ tourist arrivals. The GGE column shows the tourist arrivals growth in each region due to GGE. The resulting figures present the number of new tourist arrivals that are attributable to the global economic trends. The results show that the number of tourist arrivals has increased due to GGE in every region. That is, tourist arrivals in all of the world’s regions have experienced a gain due to positive developments in the global economy. The highest GGE is observed in Southern Europe and Mediterranean Europe along with the Western European region, while the lowest increase in tourist arrivals based on the GGE is seen in Central America.

The GIME column of Panel 1 of Table 1 shows the changes in tourist arrivals associated with the differential growth rate in tourism development (i.e., tourist arrivals) between the regions and the world. The increase in tourist arrivals across the regions due to the GIME overall tourism development in the world has led to increases in tourist arrivals in every region in the world. Tourist arrivals have increased substantially in Southern and Mediterranean Europe along with the Western Europe regions due to global tourism development, whereas the increase in tourist arrivals due to GIME was minimal in the Oceania and South Asia regions.

The GCSE column presents the strength of the competitiveness of the tourism industry in each region. The GCSE figures are the indicators of the competitive advantage (or disadvantage) of tourism industry in a region. The results show that Southeast Asia, Northeast Asia, South Asia Southern and Mediterranean Europe, South America, Central America, Sub-Saharan Africa, Middle East, and North Africa regions are more efficient at securing larger shares of tourist arrivals than Oceania, Central/Eastern Europe, Northern Europe, Caribbean, North America, and Western Europe regions. It is the GCSE component of the SSA that shows a region’s ability to attract more tourists and, hence, it is considered to be a measurement of the tourism competitiveness. We further ranked the regions based on their tourism competitiveness as measured by the GCSE component. Accordingly, the Southeast Asia region ranks first among tourism regions in the world as the most competitive, followed by the Northeast Asia and South Asia regions. Western Europe, however, appears to be the least competitive at increasing the tourist arrivals followed by the North America and Caribbean regions.

Although tourist arrival figures are a commonly used proxy to measure tourism development, these figures, alone, are not sufficient to capture the overall tourism development of the regions or countries. When the number of tourist arrivals increase, the amount of money tourists spend in the regions or countries may not increase in tandem with the number of tourist arrivals. Therefore, we further analyzed the tourism competitiveness of the regions via the shift-share method based on tourism receipts and per-tourist-dollar. Panels 2 and 3 of Table 1 illustrate these results. Accordingly, the results from Panel 2 of Table 1 show that tourism receipts have increased due to positive developments in the global economy across the regions. The highest growth in tourism receipts that is attributable to GGE is materialized in North America, whereas the increase in tourism receipts due to GGE was minimal in Central America. The results from the GIME in Panel 2 of Table 1 are different than those in Panel 1 of Table 1. The outcome shows that the overall tourism development in the world was not the primary driver of the tourism receipts in many of these regions. Tourism receipts have only increased in the Sub-Saharan Africa and Caribbean regions. These results suggest that only these two regions have a highly concentrated tourism industry within their economies, while other regions have a more diverse economy that is less concentrated in tourism. The GCSE column in Panel 2 of Table 1 shows a region’s ability to generate more revenues and, hence, it is considered to be a proxy for tourism competitiveness. The results show that the Southeast Asia, Northeast Asia, Oceania, South Asia, and Central America regions are more efficient at securing larger shares of tourism receipts than other regions and, hence, they have a competitive advantage. The ranking of tourism competitiveness appears to be different for many regions when compared with the rankings based on tourist arrivals and tourism receipts. While Southeast Asia and Northeast Asia are the most competitive regions based on both measures of tourism development, Western Europe was ranked the least competitive based on tourist arrivals but is the 10th most competitive regions, if tourism receipts are used as the measurement proxy.

In addition to the tourist arrivals and tourism receipts, which are the most commonly utilized proxies to assess tourism development, we propose an alternative measure of tourism development to analyze the tourism competitiveness of the regions and countries, which we label as per-tourist-dollar. This measure merely shows the amount of money a tourist spends in a given destination visited. Panel 3 of Table 1 presents these findings. Accordingly, per-tourist-dollar spending has increased in each region, albeit at varying degrees, due to developments in the global economy. However, the results from the GIME in Panel 3 of Table 1 shows that the overall tourism development in the world was not the primary driver of change in per-tourist-dollar. The regions’ capacities in generating more per tourist dollar is illustrated in column GCSE of Panel 3. According to the results, Oceania, Western Europe, North America, North-East Asia, Northern Europe, and North Africa are the most competitive tourism regions as these regions are more efficient at increasing per-tourist-dollar revenues compared with other regions. Increase in per tourist dollar in the regions with negative GCSE suggest that these regions do not possess a competitive advantage in generating higher per tourist dollar. The change in the GCSE in these regions are primarily driven by the overall growth in global economy. In fact, the negative GCSE combined with the negative GIME suggests that tourism has developed less than the overall global economy in these regions. In other words, tourism development in the regions with negative GIME and GCSE were not able to keep up with the overall global economic growth.

While measuring the tourism development of the regions can be insightful, there might be significant variations in destinations’ competitiveness among the countries within the regions. Therefore, a separate SSA based on individual countries’ tourist arrivals, tourism receipts, and per-tourist-dollar is necessary to identify the competitiveness of tourism in each country in the world. Also, comparing tourism industries and economic development of the countries in different regions might be less than perfect because of the economical differences across the regions. Therefore, further decomposition of the changes in tourist arrivals, tourism receipts and per-tourist-dollar in a particular country into alternative components are necessary to account for the potential differences in regional economic factors. Instead of assessing the worldwide competitiveness of the countries, assessing their regional competitiveness seems more plausible in order to determine the extent to which tourism growth in a country is attributable to regional economic development, regional tourism development, and the country’s competitiveness in the region. Table 2 presents the results from the SSA of countries based on tourist arrivals.

The GGE column shows the tourist arrivals growth in each country due to GGE. The resulting figures present the number of new tourist arrivals that are attributable to the global economic trends, alone. The results show that the number of tourist arrivals has increased due to GGE in every country. The results from the GIME column of Table 2 show that the overall tourism development has led to an increase in all countries’ tourist arrivals, suggesting that every country is focused on increasing tourist arrivals to boost their economies. The strength of the competitiveness of the tourism industry in each country is measured in CGSE column of Table 2. While 93 countries out of 150 countries examined in this study are efficient at securing larger shares of growth in tourist arrivals, the top five most competitive countries are Japan, Thailand, Turkey, Belarus, and India, respectively. The least competitive countries in capturing growth in tourist arrivals are Canada, Italy, Russia, the United States, and France. Although these findings may seem to be counterintuitive, these results suggest that these countries were not able to grow their tourist arrival numbers more than the overall global economic growth or global tourism development. That is, while tourist arrivals have increased, the growth in tourist arrivals is primarily driven by the global economic and tourism trends.

The regional rankings of these countries are also noteworthy. As can be seen in RCSE column of Table 2, the top five most competitive countries in the world are also the most competitive countries in their respective regions. Nevertheless, there is a greater variation in the regional competitiveness of the countries. For example, Morocco is the 27th most competitive country in capturing larger shares of growth in tourist arrivals in the world, but it is the most competitive in North Africa. Similarly, Germany is the 44th most completive country in the world, whereas it is the most competitive country in capturing larger shares of growth in tourist arrivals in Western Europe.

We also analyzed the competitiveness of the countries based on tourism receipts and per-tourist-dollar because changes in tourist arrivals may not necessarily reflect the changes in tourism revenues generated by the countries. Tables 3 and 4 present these findings.

The results presented in Table 3 show the country rankings based on tourism receipts, suggesting that tourist arrivals and tourism receipts yield different competitiveness figures for majority of the countries. While Thailand and Japan are still the most competitive countries in capturing larger shares of growth in tourism receipts, Turkey, Belarus, and India, which were among the top five most competitive destination based on tourist arrivals, have shifted down in ranking based on tourism receipts. Turkey appears to be the least competitive country, while India and Belarus are ranked as the 9th and 55th most competitive countries, respectively, in capturing the growth in tourism receipts.

A closer examination of the divergence between rankings based on tourist arrivals and tourism receipts suggests that tourists do not spend the same amount of money in each country. The primary reason of this divergence could be due to differences in purchasing power in each country, which can be caused by the exchange rate of the domestic currency against the U.S. dollar. To objectively rank the countries included in the sample of this study, we use the U.S. dollar as the currency, which is also the standard currency used by the World Bank, United Nations, and other major institutions. For example, US$1 was approximately equal to 1.20 Turkish Lira in 2007 and 2.91 Turkish Lira in 2016. Most recently, US$1 was approximately equal to 5.82 Turkish Lira. However, US$1 was approximately equal to 110 Japanese Yen in 2007 and 2016, whereas currently US$1 is approximately equal to 105 Japanese Yen. Considering the appreciation and depreciation of the domestic currency of these countries against the U.S. dollar, the amount of tourism receipts can vary greatly. While Turkey might be on the top in terms of the number of tourist arrivals, tourism spending in Turkey is much lower in comparison to tourism spending in Japan due to differences in cost of living and the value of local currency. Therefore, a country can be ranked on the top in terms of tourist arrivals, whereas it may not be very competitive in terms of tourism receipts.

The country rankings of tourism competitiveness further fluctuate when we utilized the per-tourist-dollar measure as a proxy for tourism development. The results in Table 4 shows that Luxembourg is the most competitive tourism destination in the world, followed by Marshall Islands, Lebanon, Grenada, and St. Kitts and Nevis. The least competitive countries appear to be Belarus, Ghana, Turkey, and Costa Rica and Angola. The results further show that 63 countries have a competitive advantage in generating higher per-tourist-dollar compared to that of 87 countries, which do not seem to possess a competitive advantage in capturing higher per-tourist-dollar.

Conclusion

SSA was essentially developed to analyze the reasons for differential economic growth in a region relative to its national counterparts (Tervo & Okko, 1983). This study is an extension of the widely accepted tool that has not previously been used to investigate tourism and destination competitiveness. We applied this effective accounting tool to analyze most countries around the world, where applicable, treating them as smaller regions and the world as their base counterpart.

In this study, we postulated that global competitiveness refers to the extent to which tourism development in a country is attributable to the economic development in the world, overall tourism development in the world, and geographical or industrial specialization (in tourism) of the countries or regions (the capacity of the country in tourism). Furthermore, regional competitiveness determined the extent to which tourism development in a country is attributable to the regional economic development, development in tourism in the region, and geographical or industrial specialization (in tourism) of the countries or regions (the capacity of the country in tourism). Accordingly, the countries and regions were ranked based on their tourism competitiveness as measured by GCSE using tourist arrivals, tourism receipts, and per-tourist-dollar. The competitiveness of the countries or the regions varied based on the proxy we utilized to measure the competitiveness.

Depending on the purpose of the policy makers and their agenda, their focus should be given to the appropriate measure. Higher per-tourist-dollars infers luxury tourist segments. The competitiveness in tourist arrivals suggests that the focus of the destinations is on the number of arrivals and the governments might be primarily focused on nonluxury travelers to reduce deficits in balance of payments, provide employment (it may even be seasonal), try to attract as many tourists as possible without improving current destination infrastructure, without capital investments through providing inexpensive vacation packages that are usually all-inclusive.

Competitiveness of tourism receipts suggest that these countries or regions are primarily focusing on attracting mass tourists but do not compromise their tourism by offering low-price tourism products. The mass tourism is happening in these countries but tourists still spend relatively larger sums of money per tourist thus causing countries generate higher revenues. Per-tourist-dollar competitiveness suggests that these countries primarily focused on luxury travelers and may be providing tourism products to the luxury tourism market. These countries or regions do not seem to focus on mass tourism but rather concentrate on specific and upper segment tourism markets. Less competitive countries or regions could closely examine the most competitive countries to learn from them and implement strategies to make their tourism sector more competitive depending on the policy makers’ or governments’ goals.

It is clear that focusing on tourist arrivals alone is not helpful. For example, Turkey is the 3rd most competitive country in attracting tourists but it does not seem to be effective on generating revenues from tourism with per capita foreign tourist spending of $589. Tourism receipts are relatively lower compared with tourist arrivals. How can countries like Turkey strategize and consider long-term planning in increasing their total tourism receipts in general and per-capita-tourism spending. It seems that while countries like Turkey seem to have short-term policies that are geared toward increasing overall visitor numbers without considering social, environmental and cultural costs of tourism, other countries seem to focus increasing revenues to decrease deficits in their balance of payments.

The political systems and the efforts made by the government and other stakeholders (e.g., marketing) might contribute to the development of the tourism industry. Some countries might depend more on tourism and hence might put more efforts into generating more tourism revenues and hence advance their tourism industry. In this study, we utilized the SSA as a tool to develop an objective measure of destination competitiveness. In this context, we used GDP, tourist arrivals, and tourism receipts of the countries, all of which are objective proxies. While some countries might put more emphasis on their tourism industries, the outcome is objectively measured and compared with tourist arrivals and tourism receipts. Our interest in ranking the countries according to their respective development of the tourism industry is primarily based on the outcome (i.e., tourist arrivals and tourism receipts). That is, we investigated the competitiveness of tourism destinations worldwide from a macroeconomic standpoint. Nevertheless, we also investigate the competitiveness of tourism destinations within their respective regions to account for possible geopolitical differences across the globe. However, the factors that lead to a greater tourism development or higher tourist arrivals/tourism receipts is beyond the scope of this study. Future studies should investigate the factors, such as political systems, resources devoted to tourism development, natural resources, marketing campaigns, and so on that contributes to tourism development to account for these differences in explaining why some countries are better ranked in generating higher tourist arrivals and/or tourism receipts.

Although the traditional SSA is an effective technique to compare an industry’s progress and growth relative to macroeconomic trends, the SSA may not answer the perplexing question of whether there are statistically significant changes in a particular industry’s development. The shift-share regression analysis, however, is an alternative approach that can void this gap in the traditional SSA technique (Andrikopoulos et al., 1990; Patterson, 1991). Future research is necessary to conduct shift-share regression analysis to provide policy makers a probabilistic analysis to measure the development of the tourism industry.

Furthermore, the SSA cannot answer the puzzling questions of the essential determinants of significant changes in the tourism industry from a statistical perspective. Therefore, proposing empirical techniques that can both statistically examine the changes in tourist arrivals, tourism receipts, and per-tourist-dollars and identify the underlying reasons of such changes persists as a challenge for future studies.

Also, future studies using a variety of shift-share methods can extend the analysis to other sectors of the economy. SSA is a simple accounting technique that has proved its efficacy by decomposing the growth in various sectors of the economy. Although the SSA technique has now matured, implementation of the same method using a variety of outcome proxies is still a fertile ground for research. Future studies should explore this venue by using other outcome variables to assess competitiveness within different subsectors of the tourism industry such as the ecotourism, specialty tourism activities, and so on. While the dynamic SSA is an efficient method that can be easily applied by policy makers (advanced statistical skills are not requisite), future studies may utilize alternative SSAs, such as shift-share regression technique, to compare their efficacy in measurement of destination competitiveness.

Footnotes

ORCID iD

Tarik Dogru

Tarik Dogru, PhD, CHE (corresponding author; e-mail: tdogru@dedman.fsu.edu), is an assistant professor at the Dedman School of Hospitality, Florida State University. Courtney Suess, PhD e-mail: csuess@tamu.edu), is an assistant professor at the Department of Recreation, Park and Tourism Sciences, Texas A&M University College Station. Ercan Sirakaya-Turk, PhD (e-mail: ercan@hrsm.sc.edu), is a professor at the College of Hospitality, Retail and Sport Management, University of South Carolina.

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