Does Tourism Growth Influence Economic Development?

Abstract

Having recognized the importance of tourism to economic growth, most international organizations have begun to argue that tourism growth can influence, as well, the economic and sociocultural development of society. However, recently, a new approach that criticizes the relationship between both dimensions has begun to be developed; suggesting that this is not an automatic relationship.

In this context, the aim of this study is to determine whether the economic growth experienced in some countries as a result of the expansion of the tourism activity over the last two decades influences an increase in the level of economic development. To that end, a sample of 144 countries has been used, which verifies that this relationship occurs, especially in more developed countries, which calls into question the conception of tourism as a driving force of economic development for the least developed countries, and even in developing countries.

Keywords

tourism economic growth economic development structural equation model

Introduction

In recent decades, most international organizations have argued that tourism can be conceived as a tool for economic development in many regions of the world. Moreover, many contributions from the economic literature recognize the potential of tourism in this sense.

However, a critical line of research regarding the relationship between these two dimensions has emerged in recent years, because of the fact that, for some countries, tourism growth not only has not implied an improvement of their socioeconomic conditions but has even contributed, as a consequence of the generation of significant costs, to a decrease in the level of welfare in their society.

There are, therefore, two opposing viewpoints regarding the conception of tourism as a tool for socioeconomic progress. Notwithstanding that, we should not radicalize in extreme positions. In other words, tourism is not the magic and automatic solution for all countries seeking to increase their levels of welfare, but it is not true either that tourism is unable to become a tool for progress. In fact, tourism has become an effective tool for progress in many areas that host a significant number of visitors, although this is certainly an economic activity that has found major limitations to developing this function in certain countries.

Thus, it is worth researching into the circumstances determining this relationship, the countries in which they are more easily found and the extent to which the availability, or deficit, of certain factors fosters or hinders this relationship.

Thus, the objective of this paper is to analyze, through an empirical study at a country level in which a sample of 144 countries has been used, whether the growth in the tourism activity that has occurred in these countries over the last two decades has helped to improve their level of economic development and verify, as well, whether this relationship is valid regardless of the initial socioeconomic status of each country.

Therefore, the hypothesis of this research study states that the economic growth that occurs as a result of an expansion of tourism activity contributes to an improvement of the economic development of a country.

Theoretical Framework

The expansion of the economic activity influences positively the economic growth of a country; however, the most important issue for the country in question is whether this economic growth is able to set in motion a more general process, the economic development of the population.

Tourism is considered as an economic activity with the potential to stimulate global economic growth because of its complementarity with other economic activities, its contribution to gross domestic product (GDP), job creation, and foreign exchange generation, etc. (Archer 1995; Durbarry 2002; Castro, Molina, and Pablo 2013; Fletcher and Archer 1991; Hall and Jenkins 2004; Sinclair 1998; Uysal and Gitelson 1994; West 1993, among others).

Nevertheless, the real importance of tourism lies not only in the fact that it contributes to the growth of the economy, in general, but also in the fact that this tourism growth can, given the right circumstances in its structural foundations, influence the economic and cultural progress of society, improving the welfare of the resident population (Ashley et al. 2007; Dwyer, Forsyth, and Spurr 2004; García 2005; Hernández and González 2013; Rosentraub and Joo 2009).

Even though the expansion of tourism is able to contribute to the economic prosperity of a country, the economic, social, and environmental benefits that it generates are not spontaneous. The different stakeholders involved in tourism need to manage it properly by the implementation of policies and actions that allow the channelling of tourism growth into the improvement of the socioeconomic conditions of the population. Furthermore, the relationship between the growth of tourism activity and economic development occasionally faces serious limitations; consequently, many countries committed to tourism as a tool for progress have realized that tourism has not become a key element that contributes to overcome their low levels of welfare (Sánchez-Rivero, Pulido-Fernández, and Cárdenas-García 2013).

Under this premise, the aim of the policies set by the public administration must be twofold: first, to base the economy on those activities that have the capacity to achieve real processes of economic growth; and second, using this economic growth as a basis, to improve the socioeconomic conditions in which people live.

Therefore, in principle, the economic growth resulting from the expansion of tourism acts as an essential tool for achieving a real economic development (Ashley et al. 2007; Balaguer and Cantavella-Jordá 2002; Cooper et al. 2008; Cortés and Artís 2005; Lickorish and Jenkins 1997; Sharpley and Telfer 2002; Tribe 2005, among others). This line of research has been advocated by most international organizations dealing with economic development (OECD 2010; UNCTAD 2011; UNWTO 2006; UNWTO 2012; WTTC 2010), which highlight that tourism has some characteristics that make it an activity to take into account when in the process of improving the socioeconomic conditions of a territory, so that their appropriate management can result in a series of positive effects in any territory.

However, a critical line of research has begun to develop that questions the role of tourism as a tool for economic development (Brohman 1996; Diagne 2004; Forsyth 1995; Kingsbury 2005; Kusluvan and Karamustafa 2001; Pérez 2001; Sahli 2007; UNDP 2011, among others), since tourism has implied for some countries or regions the loss of control over local resources, a limited pulling capacity in relation to other economic sectors, which results, in turn, in a significant leakage of the potential profits, vulnerability of tourism revenues, etc.

What seems clear is that tourism growth, with the ultimate goal of increasing the socioeconomic level, is a chronological process that requires a certain amount of time before the desired results begin to be observed (Figure 1).

Figure 1.

Transformation of tourism into economic development.

It seems obvious that the link between tourism growth and economic development must face greater limitations in countries with a previous lower level of economic development, as a result of the extreme poverty of their population and their weak economic, institutional, and human resources (often complicated by geographical difficulties). However, tourism has been considered hitherto as an effective tool for poverty alleviation, especially in the least developed countries (LDCs), to the point that tourism has become one of the main priorities in this group of countries (UNECA 2010).

Along these same lines, both the Canary Islands Declaration on Tourism in the Least Developed Countries in 2001, which emphasizes the importance of tourism as an alternative for promoting sustainable development and alleviating poverty in these countries (highlighting the significant comparative advantages of these countries in relation to the tourism industry; UNWTO 2003); and The ST-EP Program (Sustainable Tourism–Eliminating Poverty), which focuses on improving the UNWTO’s longstanding actions aimed at promoting sustainable tourism—from the social, economic, and ecological point of view, with activities specifically focused on poverty reduction that stimulate development and succeed in creating jobs for those living on less than one dollar a day (UNWTO 2006), have argued that tourism is an economic activity that contributes to improve the low levels of prosperity in less developed countries.

Likewise, the Fourth United Nations Conference on LDCs, held in May 2011 in Istanbul (Turkey), had among its objectives the adoption of new measures and strategies for the progress of LDCs in the next decade. In this regard, the UNCTAD has considered tourism as a driving force for development in the least developed countries (UNCTAD 2011):

Tourism is one of the most important economic areas in the least developed countries, as highlighted today in an international event on those States held in Istanbul, Turkey.

In an official statement, the UN Conference on Trade and Development maintained that tourism has a great potential to bring the population of those countries out of poverty and it is receiving the attention it deserves.

Research studies on the connection between tourism and economic growth were initiated by Ghali (1976), although it is from the papers published by Lanza and Pigliaru (2000) and Balaguer and Cantavella-Jordá (2002) that there is a growing body of literature on this topic, especially regarding the test of the so-called tourist-led growth (TLG) hypothesis. In a recent paper, Pablo-Romero and Molina (2013) conducted a thorough review of this literature, defending that, even today, this topic continues to generate great interest, as evidenced also by the research works published by Dritsakis (2012) or Surugiu and Surugiu (2013).

To a lesser extent, studies have been published that focus on the relationship between international tourism and trade (Khan, Toh, and Chua 2005; Kulendran and Wilson 2000; Lionetti and Gonzalez 2012, etc.) or between tourism spending and economic growth (Chou [2013] has done an interesting review of the literature on this topic).

Research on the relationship between the economic growth resulting from tourism activity and the economic development of the territories in which this activity takes place has not been so common. Lee and Chang (2008, p. 191), on the basis of their analysis of a significant number of countries, concluded that “unidirectional causality relationships exist from tourism growth to economic development in OECD countries, but bidirectional causality relationships are found between the two variables in non OECD countries.” Based on these results, the authors suggest that “all governments should commit to helping their tourism industry expand as much as possible, and at the same time, they should focus their attention on long-run policies. Aside from achieving regional effects, from the global standpoint, all countries can reap benefits from tourism development and economic growth.”

However, Sánchez-Rivero, Pulido-Fernández, and Cárdenas-García (2013), after analyzing 117 countries, have recently concluded that the tourism growth of a country does not automatically result in economic development, unless conditions are favorable for encouraging this process. And so, “not all types of interventions in the pursuit of tourism growth are equally effective in promoting a country’s economic development. Or, put another way, there are variables of tourism growth which are more strongly related to economic development than others, and therefore action should be directed primarily towards promoting these variables and not others” (Sánchez-Rivero, Pulido-Fernández, and Cárdenas-García 2013, p. 248). The authors argue that not identifying factors that truly favor this transformation of tourism growth into economic development is a very high opportunity cost for these territories and an unforgivable mistake for policymakers. These results support those obtained by Assaf and Josiassen (2012), who concluded that high-performance countries have well-established tourism industries.

This debate began in the 1990s. Hazari (1993) warned of the inflationary effects of tourism. Sinclair (1998) suggested that the development of tourism, in addition to requiring a large amount of physical capital, demands more skilled labor, so the destination country should increase investment in human capital in the tourism sector. Dunn and Dunn (2002) argue that another major problem affecting tourism implementation in a territory in some countries is crime and violence. Its solution (or at least control), through improvement of public safety, also generates costs for the destination. Other authors (Gursoy and Rutherford 2004; Jenner and Smith 1992 ) have referred to the impacts of tourism on the surrounding environment and the need to implement policies that ensure responsible tourism development. Dozens of authors who have studied the adverse effects of tourism that, in one way or another, determine its importance as a development tool could be cited.

Many of these findings are directly related to the advances that have occurred in recent years in the analysis of the competitiveness of tourism destinations. In fact, there is significant overlap between the factors affecting tourism growth transformation in economic development and the main aspects that explain tourism competitiveness. Thus, for example, Assaker et al. (2013) show that the infrastructure and the environment have a direct and positive impact on the generation and income from tourism activities. This justifies, according to these authors, that the countries that occupy the highest positions in the ranking of tourism competitiveness are industrialized countries with high organizational structures. Environmental resources are a key factor of competitiveness in tourism, as Mihalič (2013) recognized, but as Croes (2011) noted, “destinations cannot rely on their natural beauty alone.” This author proposes a competitiveness index of a destination more suitable, through the most important factors that affect the competitiveness of insular destinations. Interestingly, insularity is one of the factors determining the role of tourism as a development tool.

Meanwhile, Crouch (2011) identifies 10 factors of a total of 36 determinants of competitiveness as a destination that relate to the basic resources and the tourism attractions that make up the destination. According to Caber, Albayrak, and Matzler (2012), high-quality service and customer satisfaction are appropriate determinants of the competitiveness of a destination. Finally, Namhyun (2012) proposes a model tourist destination that is competitive in two groups of countries (high income and lower income) to recognize the relevant factors of this competitiveness. The results suggest that the determinants of destination competitiveness are the basic resources, destination management and globalization of the economy. However, the degree of impact of the factors is different between the two groups of countries analyzed; for the group of higher-income countries, the most important factor is the basic resources, whereas for lower-income countries, it is the globalization of its economy.

Consequently, both the scientific literature and most international organizations argue that tourism is able to become, in any territory, a tool for economic development, though it is noteworthy that without appropriate planning, tourism not only does not generate benefits but may contribute to environmental degradation, the displacement of local communities, and the creation of precarious employment conditions. Accordingly, strategies, policies, and regulations together with effective implementation mechanisms are necessary for tourism, not only to generate economic benefits but also to avoid its environmental and social impacts.

Methodological Approach

This research aims to find out whether a relationship exists between the growth of tourism in these countries and the increase in their level of economic development. In other words, it tries to determine whether tourism is, in fact, a strategic instrument that functions as an economic development tool. In order to meet this objective, the outline of this research, the research techniques used, and the sources of information consulted are stated below.

Previous Considerations

It seems reasonable that the design of the empirical study would have the objective of establishing whether tourism growth is transformed into economic progress using an analysis at the country level, discarding other, more partial, approaches that analyze the impact studies for a particular tourist resource or tourism area within the framework of cost–benefit analysis (Crompton, Lee, and Shuster 2001). Thus, a sample as large as possible was selected, composed of 144 countries, for all of which there are existing data for the time horizon analyzed and with regard to the selected variables.

In addition, an empirical analysis aimed at determining the link between tourism growth and economic development must consider the fact that these two dimensions are multidimensional. Thus, for their measurement, the use of multiple variables is required as the expansion of tourism activity contributes to economic growth through a broad set of factors and, in turn, these economic impacts affect various aspects within economic development.

Data Collection

The information used in this study to measure the growth of tourism has been obtained from the Tourism Impact Data & Forecast database. This database, prepared in accordance with the methodology of the Tourism Satellite Account by the World Travel & Tourism Council (WTTC) and its research partner, Oxford Economics, is used to measure the economic growth derived from tourism, quantifying the main contributions of tourism to economic growth since 1988, with the advantage that these variables allow comparison, in a homogeneous manner, across countries (WTTC 2012). Specifically, a total of six variables of tourism growth have been employed (Table 1).

Table 1.

Variables of Tourism Growth.

DCP, direct contribution to GDP	Gross domestic product generated by all sectors that are directly related to tourists, being equivalent to the total domestic expenditure on travel and tourism within a country minus the purchases made by these sectors (2011 U.S. dollars, in billion).
DCE, direct contribution to employment	Number of direct jobs generated within the travel and tourism industry (thousands).
ETI, international tourism exports	It includes the expenditure by international tourists in the country, both for business and leisure travel, including also the cost of transport (2011 U.S. dollars, in billion).
FBK, capital investment	This includes the expenditure on capital goods by all sectors directly involved in the travel and tourism industry. Thus, it is the capital investment of the industries in specific tourism resources, such as new accommodation facilities for visitors or the purchase of transport equipment (2011 U.S. dollars, in billion).
DTE, domestic tourism expenditure	Expenditure by residents during their tourism activities during the trips within their own country, so it does not include the expenditure by residents abroad (2011 U.S. dollars, in billion).
PSE, public sector expenditure	Public expenditure on individual services that are not market related, whose beneficiaries may be identified separately (2011 U.S. dollars, in billion).

Source: Authors’ own elaboration based on World Travel and Tourism Council (2012).

With regard to the quantification of the economic development, the information from the Human Development Report has been used. This report, updated periodically by the United Nations Development Programme (UNDP), is the result of the work carried out by a group of academics and development practitioners. It was first published in 1990 with the aim of placing the population in the center of the development process in terms of economic debates, formulation of policies, and promotion, and seeking to go beyond the issue of economic growth, which implies evaluating, in addition, the level of welfare in a given society (UNDP 2012). In this case, we used nine variables of economic development because although the main variable of the Human Development Report, HDI, was collected on an aggregate basis, one of the other individual variables, in turn, was of special interest in understanding the direct effect of tourism growth variables on different individual economic development variables, which are what make up the HDI (Table 2).

Table 2.

Variables of Economic Development.

HDI, human development index	Composite index that measures the achievements in three basic dimensions of human development: long and healthy life, access to knowledge, and a decent standard of living. The average of these three dimensions is defined on a scale of 0 to 1 (values 0–1).
LEB, life expectancy at birth	Number of years that a newborn will live, provided that the prevailing age-specific mortality patterns at the time of its birth remain the same throughout its life (years).
IMR, infant mortality rate	Infant mortality rate in children under 1 year of age. Probability of dying between birth and the exact age 1 year, expressed as 1,000 live births (per thousand births).
GPC, GDP per capita	Total production of final goods and services of a given economy, by both residents and nonresidents, divided by the population at midyear—converted into U.S. dollars according to the exchange rate in terms of purchasing power parity (U.S. dollars).
DOI, distribution of income	It measures the degree to which the distribution of income among individuals or households in a country deviates from a distribution in terms of perfect equality. The coefficient ranges from 0, which represents absolute equality, to 100, which represents perfect inequality (values 0–100).
PHY, physicians	Medical doctors and all professionals, graduates of any medical school, who are working in any field of expertise (per 1,000 inhabitants).
PEH, public expenditure on health	Current and capital expenditure from government budget (central and local), external borrowings, and grants and social health insurance funds (percentage of GDP).
ALR, adult literacy rate	The percentage of people aged 15 and older who can read and write, with understanding, a short and simple statement about their daily life (percentage of the population aged ≥15 years).
PEE, public expenditure on education	Public expenditure on public educational institutions plus subsidies to private educational institutions at primary, secondary, and tertiary levels, including expenses such as staff salaries and benefits, contracted services, books and teaching materials, furniture, equipment, repairs, and telecommunications (% of GDP).

Source: Authors’ own elaboration based on United Nations Development Programme (2012).

Despite the limitations that these two databases may have, the information obtained from both the Tourism Impact Data & Forecast and the Human Development Report is more consistent and reliable, in addition to the fact that these limitations are minor if compared to the information that could be obtained from other sources. Besides, by using another data source, the number of countries with available data would have been significantly lower.

Period Analyzed

An empirical work aimed at determining whether tourism growth influences the improvement of the socioeconomic conditions in which people live in a given country should be done within a time horizon long enough to measure whether the policies adopted by the various administrations and organizations, thanks to the existence of high rates of tourism growth for a certain period of time, have significantly influenced its economic development.

Thus, it does not seem reasonable to perform a short-term analysis of the relationship between these two dimensions; instead, it is considered much more logical to cover a broader time period. Thus, since the first data available in the databases used are for 1988 and 1990, respectively, the time horizon analyzed in this research ranges from 1991 to 2010, the last 20 years.

Methodology Applied

Structural equation modelling (SEM) (Blunch 2008; Iacobucci 2009; Kline 2011; Schumacker and Lomax 2004) makes it possible to measure the simultaneous relationships occurring between a set of independent variables and a set of dependent variables, allowing the identification of causal relationships between latent variables.

These models involve applying a multivariate statistical technique in order to test and estimate causal relationships based on statistical data and qualitative assumptions about causality. They combine factor analysis with linear regression to test the degree of adjustment of the observed data into a hypothetical model. They also provide the values belonging to each relationship and, more importantly, a statistic that expresses the degree to which the data fit the model proposed (Halpern and Pearl 2000).

SEM began to appear in tourism literature in the late 1990s. The first published paper presented SEM modeling as “a powerful tool enabling researchers to go beyond factor analysis into the arena of determining whether one set of unobserved constructs (dimensions) can cause (be seen to be likely to determine) another set of dimensions” (Reisinger and Turner 1999, p. 86).

Since then, SEM modeling has been used in a growing body of research on different aspects of tourism, as shown by the recent work of Nunkoo, Ramkissoon, and Gursoy (2013).

Moreover, Nunkoo and Ramkissoon (2012) describe the main advantages of using SEM with respect to other methodologies, especially regression analysis, and discuss the steps tourism researchers should follow when using this technique.

These techniques are based on analyses of covariance between the observed variables, which are used to measure the latent variables with a twofold aim: first, to understand the correlation structures among a set of observed variables, and second, to explain most of the variances of the related variables by an a priori model that incorporates latent variables.

The specification of the model is based on a graphical representation, in which, by the use of different signs, the hypothetical statistical relationships between the observed variables and the latent variables are formulated. This diagram of causal relationships between observed variables and latent variables can also be expressed through a system of simultaneous equations, or can summarize that system in a matrix expression. In this research study, we consider two latent variables in the model designed:

An endogenous variable: economic development (η₁).

A latent variable: tourism growth (ξ₁).

This structural equation model consists of two submodels:

A structural model, which shows the effect of one latent variable on the other latent variable of the model, formulated as follows:

η_{1} = γ_{11} ξ_{1} + ζ_{1}

A measurement model, which specifies the relationships existing between the latent variables and the observed ones, including, in turn, two sets of equations:

Endogenous latent variable (economic development)	Observed variable 1	$= λ_{11}^{Y} η_{1} + ε_{1}$
	Observed variable 2	$= λ_{21}^{Y} η_{1} + ε_{2}$
	Observed variable n	$= λ_{N 1}^{Y} η_{1} + ε_{N}$
Exogenous latent variable (tourism growth)	Observed variable 1	$= λ_{11}^{X} ξ_{1} + δ_{1}$
	Observed variable 2	$= λ_{21}^{X} ξ_{1} + δ_{2}$
	Observed variable n	$= λ_{N 1}^{X} ξ_{1} + δ_{N}$

Finally, the graphical representation of this structural equation model is shown in Figure 2.

Figure 2.

Structural equation model of the causal relationship between the latent factor “tourism growth” and the latent factor “economic development.

In the present study, as already mentioned, the growth of tourism as an explanatory variable of economic development has been considered; nevertheless, it is evident that economic development is, in turn, a determinant of tourist arrivals to a destination and, therefore, the growth of tourism in the country. However, the graph reflects the hypothesis stated at the beginning of this work.

The most important parameter of the structural equation model presented in Figure 2 is γ₁₁, since it quantifies the hypothetical relationship between tourism growth and economic development.

Results and Discussion

Previous Analysis

First, for the six variables of tourism growth (DCP, DCE, ETI, FBK, DTE, and PSE) and the nine variables of economic development (HDI, LEB, IMR, GPC, DOI, PHY, PEH, ALR, and PEE), their relative variation between 1991 and 2010, individually, was calculated as follows:

Relative variation (RV) = (\frac{V_{2010} - V_{1991}}{V_{1991}}) \times 100,

where V₁₉₉₁ and V₂₀₁₀ represent the value of the variable in question in 1991 and 2010, respectively.

After that, because of the heterogeneity that the measurement units of the variables of tourism growth and economic development used present (constant billions of dollars, thousands of employees, years of life, number of physicians, percentages, etc.), it is necessary to perform a previous process of homogenization of the information used by the normalization of the relative variation (RV) rates calculated. This normalization process is performed with the following relation:

Z_{R V} = \frac{R V - E (R V)}{sd (R V)},

where E(RV) is the mean or expected value of the relative variation rate and sd(RV) is the standard deviation thereof. In this way, not only is the normalization of the measurement units of the variables used achieved (since the normalized values are dimensionless, that is, are not expressed in any measurement unit) but also the comparability and use of variables of a very different nature is facilitated, as the normalized variables have an expected value of 0 and a variance of 1, thus avoiding the technical problems arising from differences in the variability of the variables analyzed (heterogeneity of variances).

This process of normalization has been performed with all the variables of tourism growth and economic development, except for two of them: infant mortality rate (IMR) and distribution of income (DOI). As it can be easily observed, unlike what happens with the rest, these two variables have an inverse relationship with the economic development of the countries, since their high value is symptomatic of a lower economic development and vice versa. Consequently, for these two variables, it has been necessary to carry out an inverse normalization by the following expression:

Z_{R V} = \frac{E (R V) - R V}{sd (R V)} = - \frac{R V - E (R V)}{sd (R V)} .

Thus, positive standardized values will be obtained in countries showing lower rates of infant mortality and a more equitable distribution of income (lower Gini index), while negative normalized values will be obtained in those countries registering higher rates of infant mortality and a less equitable distribution of income (higher Gini index).

After this previous preparation of the variables of tourism growth and economic development to determine whether a relationship exists between them, a structural equation model has been employed, in which an exogenous latent factor (tourism growth) and an endogenous latent factor (economic development) have been considered.

However, at the outset, we have analyzed the structure of correlations between variables of economic growth, once they have been standardized in order to detect possible problems of multicollinearity between these variables (Table 3), showing statistical correlations between 6 tourism-growth variables considered in this work. As you can see, there are no problems of perfect multicollinearity (correlations equal to 1) or approximate multicollinearity (correlations close to 1) between the variables that will be used to measure the latent exogenous variable of tourism growth.

Table 3.

Structure of Correlation between the Normalized Relative Rates of Tourism Growth.

	DCP	DCE	ETI	FBK	DTE	PSE
DCP	1	0.304* (0.000)	0.170* (0.041)	0.137 (0.101)	0.336* (0.000)	−0.119 (0.156)
DCE		1	0.066 (0.432)	0.045 (0.590)	0.205* (0.014)	−0.134 (0.110)
ETI			1	0.258* (0.002)	0.069 (0.413)	–0.175* (0.036)
FBK				1	0.187* 0.025	0.104 (0.214)
DTE					1	−0.110 (0.190)
PSE						1

Source: Authors’ own elaboration on the basis on the calculations made using SPSS 19.0.

Note: Significance (p values) associated with the correlation is denoted within parentheses.

Statistically significant correlation at 5%.

Finally, before moving on to the final model estimate structural equations, we used the technique of factor analysis to determine whether any of the 15 variables used in this work should be removed from the model. In the case of the tourism growth variables, it has been observed that, in general, the factor loadings between them and the extracted factor are high (lower factor loadings correspond to the variables FBK and PSE). Meanwhile, for the variables of economic development it has been shown that only five of the nine variables considered have high factor loadings with the first common factor, while the remaining four are virtually uncorrelated with this latent dimension. After extracting successive factors, it was found that the greatest correlation of the variable PEE occurs with the second factor, the highest correlation of the variable DOI is identified with the third factor, and the maximum correlation of the variables PHY and PEH with the extracted factors occurs with the fourth factor. Given these results, it was decided to remove these four variables from the structural equation model that was used to determine whether a relationship exists between the countries’ tourism growth and economic development.

Channelling Tourism Growth into Economic Development

The estimation of a structural equation model that relates tourism growth variables with the variables of economic development has been conducted with the EQS software that, together with LISREL and AMOS, is one of the main software used in the estimation of this type of models.

Having estimated the structural equation model of the causal relationship between tourism growth and economic development, and once those variables that were uncorrelated were removed, this study proceeds to the calculation of the nonstandard estimation of the parameters, their estimated standard deviations, the statistic t, the standardized estimates, the test of independence among the 11 observed variables—the six of tourism growth, and the five of economic development—and various measures of goodness of fit of the model¹ (Table 4). Although one software for estimation of SEM models provided TCD (total coefficient of determination) as the statistic of goodness of fit, EQS does not provide this statistic, despite which the goodness of fit of the model can be assessed by the various statistics presented in Table 4. Also, the estimates of the structural equation model that relate tourism growth to economic development (detailed in Table 4) are described in Figure 3.

Table 4.

Estimates of the Structural Equation Model (144 Countries).

Parameter	Nonstandard Estimate	Standard Deviation	Statistict	Standardized Estimate
$λ_{11}^{X}$	1.000 (R)			1.000
$λ_{21}^{X}$	0.921*	0.071	12.897	0.921*
$λ_{31}^{X}$	0.169*	0.083	2.048	0.170*
$λ_{41}^{X}$	0.136	0.082	1.644	0.137
$λ_{51}^{X}$	0.336*	0.082	4.090	0.336*
$λ_{61}^{X}$	−0.119	0.083	−1.424	−0.119
$λ_{11}^{X}$	1.000 (R)			0.994
$λ_{21}^{X}$	0.391*	0.102	3.814	0.380*
$λ_{31}^{X}$	−0.183*	0.090	−2.024	−0.178*
$λ_{41}^{X}$	−0.047	0.087	−0.537	−0.045
$λ_{51}^{Y}$	0.695*	0.131	5.316	0.683*
$γ_{11}$	0.250*	0.081	3.085	0.257*
χ² (ind.) = 614.729; 55 df; p value: 0.000Normed fit index = 0.683Nonnormed fit index = 0.653Comparative fit index = 0.729Incremental fit index = 0.734			MFI index = 0.590Goodness of fit index = 0.788Adjusted goodness of fit index = 0.675Root mean square residual statistic = 0.136Standardized root mean square residual statistic = 0.137

Source: Authors’ own elaboration based on the calculations done using EQS 6.1.

Note: (R) = parameter restricted to value 1 for model identification purposes.

Parameter statistically significant at 5%.

Figure 3.

Estimates of the proposed structural equation model (144 countries).

Regarding the goodness of fit statistics presented in Table 4, it is clear that values are at around 0.6, showing, from a strictly economic point of view, the existence of relationship between tourism growth and economic development. It is certainly true that it is recommended that all these goodness of fit indices are above 0.9, but in many cases, as in the present one, it is the data and the selected variables that determine the goodness of fit. On the other hand, any statistical model and the structural equation model are no exception; it will always be incomplete, since they can always leave out variables that help to improve the model. Thus, compared to other research in which raw statistical goodness of the model is prioritized, this research has prioritized the economic interpretation of the model beyond seeking artificially high goodness of fit values (if the estimates presented in Table 4 and Figure 3 are observed, it can be confirmed that it is impossible to eliminate some variables of the final model that, possibly, would improve the goodness of fit of the model, but that would lead, in essence, the same result from an economic point of view: the existence of a cause-effect relationship between tourism growth and economic development).

As it has been previously mentioned, the most interesting estimate, according to objectives pursued by the present work, corresponds to the parameter γ₁₁. Furthermore, it can be observed that this parameter, whose non-standardized maximum-likelihood estimate is 0.250 and its standard estimate is 0.257, is statistically significant at the 5 per cent level (value of the statistic t clearly above 2). In addition, its sign is positive, which confirms the working hypothesis that was taken as a starting point. As a result, the estimated structural equation model and particularly the estimate of the parameter γ₁₁ confirm that the economic development of the countries is conditioned by their level of tourism growth. In other terms, it is shown that tourism activity, and specifically the increase in the main variables that characterize it, contributes positively to improve the economic development of countries.

However, the above conclusion is quite general, as it has been achieved from a very large sample (144) of countries around the world, regardless of whether this causal relationship is verified for all countries, since, in some cases, it was considered that international tourism is an important source of income, while improving the conditions of development of the least developed countries (Chau and Turner 2009).

More Developed and Less Developed Countries

As highlighted in the review of the theoretical framework, both the scientific literature and international organizations consider that tourism is an effective tool for poverty alleviation in those countries with a lower level of economic development, although, in principle, it might seem that this economic activity finds greater limitations to become a tool for improvement in this kind of countries.

Therefore, the question posed in this research is, Does the previous level of economic development of a country determine the growth of its tourism activity that results in an improvement of its development?

To answer this question, we have classified the 144 countries of the total sample into two groups: one group, with the more economically developed countries in 1991 (the base year of the empirical analysis that has been performed); and a second group, with those countries with the lowest levels of economic development in 1991. (Although, in principle, the Human Development Report itself includes more categories to classify countries in their economic development function, in particular, four levels are established—very high, high, medium, and low—from the 2007 report, the first category refers to the developed countries and the last three refers to developing countries [UNDP 2007, pp. 157-58], so that has followed this same methodology to disaggregate between only two groups of countries.)

To carry out this grouping, and given the multivariate nature of economic development (in the present research, an approach to the measurement of economic development is performed through a total of nine variables), it was opted to use the methodology of Pulido-Fernández and Sánchez-Rivero (2009), based on the technique of principal component factor analysis (Pallant 2001; Tabachnick and Fidell 1996). Thus, once normalized, the nine standard variables of economic development considered in this work (bearing in mind the inverse normalization of the DOI and IMR variables), two common² factors have been extracted and rotated that explain 71.8% of the total variance of the observed variables. From these, and considering their correlations with the normalized variables of economic development, two intermediate indices have been built, whose mathematical expressions are as follows:

\begin{matrix} I_{D1} = 0.938 * HDI + 0.916 * LEB + 0.915 * IMR + 0.640 * GPC + 0.059 * DOI + \\ + 0.579 * PHY + 0.572 * PEH + 0.863 * ALR + 0.280 * PEE \end{matrix}

\begin{matrix} I_{D2} = 0.259 * H D I + 0.272 * LEB + 0.221 * IMR + 0.389 * GPC + 0.864 * DOI + \\ + 0.601 * PHY + 0.515 * PEH + 0.219 * ALR + 0.496 * PEE \end{matrix}

From these intermediate indices, and considering the value of the first two eigenvalues of the matrix X’ X (5.546 and 0.917, respectively), a final synthetic index has been obtained by weighting each previously calculated intermediate index on the basis of these eigenvalues. Therefore, the final expression of the synthetic index is as follows:

I_{DE} = 0.858 * I_{D1} + 0.142 * I_{D2} .

Finally, this synthetic index has been rescaled at a range from 0 to 100, using the so-called Calsamiglia’s transformation (1990), for a value $φ$ = 100:

S = f (I_{DE}) = {\begin{cases} 1 + \frac{φ - 1}{2} \exp (I_{DE}) si I_{DE} < 0 \\ φ - \frac{φ - 1}{2} \exp (- I_{DE}) si I_{DE} \geq 0 \end{cases}

The grouping sought in this study, which suggests the definition of two groups of countries of equal size, has considered the median of the above composite index (whose value is 57.47) as a cut-off value to classify the countries analyzed within a group or another, group A or group B. The use of the median as a cut-off value to define the two groups of countries responds, solely, to the intention to define the two groups of the same size, so that the possible comparisons between the estimates of structural equation models are possible. Furthermore, the identification of these groups has a purely exploratory purpose, and in no case confirmatory. It has ruled out the use of other classification techniques, such as discriminant analysis, which is eminently confirmatory of the a priori classification made and it can generate, based on Bayesian probabilities of belonging to each group, subsequent composition very unbalanced in terms of the number of countries comprising each group, with the consequent problems of robustness of the estimates in models with a small number of observations (breach of asymptotic theory) and potential comparisons between them.

The countries that make up Group A—higher value of the synthetic index of economic development in 1991—are shown in Table 5, and the countries of Group B—lower value of the synthetic index of economic development in 1991—are presented in Table 6.

Table 5.

Countries Included in Group A.

1. Norway (99.98)	19. Australia (99.82)	37. Latvia (98.79)	55. Trinidad and Tobago (91.73)
2. Sweden (99.97)	20. United Kingdom (99.78)	38. Belarus (98.72)	56. Saint Lucia (90.68)
3. France (99.96)	21. Spain (99.76)	39. Czech Republic (98.63)	57. Venezuela (90.41)
4. Iceland (99.96)	22. Seychelles (99.75)	40. Argentina (98.60)	58. Azerbaijan (90.40)
5. Germany (99.95)	23. Hungary (99.70)	41. Ukraine (98.23)	59. Mexico (88.03)
6. Switzerland (99.95)	24. Greece (99.67)	42. Slovakia (98.20)	60. Korea, Rep. (87.42)
7. Denmark (99.94)	25. Ireland (99.63)	43. Cyprus (98.14)	61. Romania (86.74)
8. Canada (99.94)	26. Uruguay (99.50)	44. Bahamas (98.05)	62. Arabia (85.72)
9. Netherlands (99.92)	27. Qatar (99.44)	45. Poland (97.89)	63. Jordan (83.91)
10. Austria (99.92)	28. Portugal (99.34)	46. Moldova, R. (97.87)	64. Jamaica (83.87)
11. Belgium (99.92)	29. Cuba (99.27)	47. Singapore (97.73)	65. Colombia (82.57)
12. Luxembourg (99.90)	30. Malta (99.19)	48. Costa Rica (97.66)	66. Malaysia (81.82)
13. Japan (99.90)	31. Estonia (99.16)	49. Panama (96.76)	67. Mongolia (78.66)
14. United States (99.89)	32. Lithuania (99.15)	50. Bahrain (94.42)	68. Suriname (74.77)
15. Finland (99.86)	33. Kuwait (99.08)	51. Kazakhstan (94.24)	69. Fiji (71.00)
16. Israel (99.84)	34. Russian, Fed. (99.02)	52. Armenia (93.64)	70. Ecuador (69.83)
17. Italy (99.83)	35. Bulgaria (98.87)	53. Albania (93.16)	71. Brazil (67.36)
18. New Zealand (99.82)	36. Hong Kong (98.81)	54. Chile (92.02)	72. Belize (59.09)

Source: Authors’ own elaboration.

Note: The value of the synthetic index of economic development in shown in parenthesis.

Table 6.

Countries Included in Group B.

1. Paraguay (55.85)	19. Maldives (16.77)	37. Ghana (2.21)	55. Uganda (1.08)
2. Thailand (55.06)	20. Vietnam (16.54)	38. India (1.88)	56. Benin (1.08)
3. Sri Lanka (52.32)	21. Vanuatu (15.18)	39. Yemen (1.80)	57. Sudan (1.08)
4. South Africa (50.13)	22. Iran, Rep. (13.63)	40. Comoros (1.74)	58. Burundi (1.07)
5. Oman (41.22)	23. Cape Verde (13.02)	41. Côte d’Ivoire (1.63)	59. Malawi (1.06)
6. Syria, Rep. (35.50)	24. Honduras (11.09)	42. Papua New Guinea (1.57)	60. Gambia (1.06)
7. China (34.23)	25. Lesotho (7.07)	43. Madagascar (1.45)	61. Rwanda (1.04)
8. Tunisia (33.04)	26. Swazi (7.05)	44. Zambia (1.44)	62. R. Central Africa (1.04)
9. Peru (31.41)	27. Gabon (7.00)	45. Pakistan (1.40)	63. Ethiopia (1.04)
10. Turkey (27.33)	28. Egypt (6.86)	46. Laos, Rep. (1.35)	64. Nigeria (1.04)
11. Guyana (24.57)	29. Indonesia (5.69)	47. Tanzania (1.27)	65. Chad (1.02)
12. Nicaragua (24.42)	30. Sao Tome (5.62)	48. Cameroon (1.26)	66. Mozambique (1.02)
13. Namibia (24.01)	31. Zimbabwe (5.31)	49. Cambodia (1.24)	67. Angola (1.02)
14. Philippines (22.41)	32. Bolivia (5.01)	50. Haiti (1.23)	68. Burkina Faso (1.01)
15. Botswana (21.53)	33. Kenya (4.29)	51. Togo (1.23)	69. Mali (1.01)
16. Algeria (20.18)	34. Congo (3.44)	52. Bangladesh (1.15)	70. Guinea (1.01)
17. R. Dominic. (18.76)	35. Morocco (3.25)	53. Nepal (1.13)	71. Niger (1.00)
18. El Salvador (17.26)	36. Guatemala (2.96)	54. Senegal (1.10)	72. Sierra Leone (1.00)

Note: The value of the synthetic index of economic development in shown in parenthesis.

Source: Authors’ own elaboration.

Once the two groups of countries are made up, the objective is to analyze again the causal relationships between tourism growth and economic development in both groups, using, again, a structural equation model in order to verify whether the previously obtained estimates remain the same in these two groups, as all the countries analyzed are considered together (results in Table 2), or whether, on the contrary, important changes occur regarding the estimated effects in both groups.

Table 7 and Figure 4 show the maximum-likelihood estimates of the structural equation model for Group A.

Table 7.

Estimates of the Structural Equation Model (Group A).

Parameter	Non-standard Estimate	Standard Deviation	Statistic t	Standardized Estimate
$λ_{11}^{X}$	1.000 (R)			0.923
$λ_{21}^{X}$	0.498	0.295	1.686	0.211
$λ_{31}^{X}$	1.727*	0.686	2.516	0.313*
$λ_{41}^{X}$	2.886*	0.511	5.653	0.685*
$λ_{51}^{X}$	2.438*	0.434	5.615	0.680*
$λ_{61}^{X}$	−0.047	0.815	−0.058	−0.007
$λ_{11}^{X}$	1.000 (R)			0.537
$λ_{21}^{X}$	1.376*	0.651	2.115	0.408*
$λ_{31}^{X}$	1.870	1.344	1.392	0.232
$λ_{41}^{X}$	3.119*	1.359	2.296	0.478
$λ_{51}^{Y}$	0.522	0.383	1.363	0.227
$γ_{11}$	0.351*	0.128	2.731	0.591*
χ² (ind.) : 116.043; 55 df; p value: 0.000Normed fit index: 0.481Nonnormed fit index: 0.446Comparative fit index: 0.567Incremental fit index: 0.596			Macdonald’s fit index: 0.602Goodness of fit index: 0.807Adjusted goodness of fit index: 0.705Root mean square residual statistic: 0.047Standardized root mean square residual statistic: 0.116

Note: (R) = parameter restricted to value 1 for model identification purposes.

Parameter statistically significant at 5%.

Source: Authors’ own elaboration based on the calculations done using EQS 6.1.

Figure 4.

Estimates of the proposed structural equation model (Group A).

In turn, the maximum-likelihood estimates of the structural equation model for Group B are shown in Table 8 and Figure 5.

Table 8.

Estimates of the Structural Equation Model (Group B).

Parameter	Nonstandard Estimate	Standard Deviation	Statistic t	Standardized Estimate
$λ_{11}^{X}$	1.000 (R)			1.000
$λ_{21}^{X}$	0.934*	0.109	8.559	0.949*
$λ_{31}^{X}$	0.128	0.098	1.300	0.154
$λ_{41}^{X}$	0.062	0.107	0.582	0.069
$λ_{51}^{X}$	0.214*	0.100	2.148	0.254*
$λ_{61}^{X}$	−0.080	0.088	−0.901	−0.107*
$λ_{11}^{X}$	1.000 (R)			0.340
$λ_{21}^{X}$	3.238*	1.640	1.974	1.000*
$λ_{31}^{X}$	−1.384*	0.536	−2.584	−0.502*
$λ_{41}^{X}$	0.349	0.381	0.915	0.113
$λ_{51}^{Y}$	0.730	0.404	1.807	0.257
$γ_{11}$	0.042	0.042	1.000	0.137
χ² (ind.) : 339.467; 55 df; p value: 0.000Normed fit index: 0.612Nonnormed fit index: 0.601Comparative fit index: 0.688Incremental fit index: 0.700			Macdonald’s fit index: 0.540Goodness-of-fit index: 0.727Adjusted goodness of fit index: 0.581Root mean square residual statistic: 0.217Standardized root mean square residual statistic: 0.151

Source: Authors’ own elaboration based on the calculations done using EQS 6.1.

Note: (R) = Parameter restricted to value 1 for model identification purposes.

Parameter statistically significant at 5%.

Figure 5.

Estimates of the proposed structural equation model (group B).

It is verified that in Group A the parameter γ₁₁ is statistically significant at a 5% level and with an even higher coefficient (0.250 for the whole set countries; 0.351 for Group A), which confirms the existence of a direct relationship between tourism growth and economic development within this first group of countries. However, in Group B the coefficient γ₁₁ is no longer statistically significant at a 5% level, which makes it possible to affirm that within this second group of countries, tourism growth does not turn into economic development, a circumstance that calls into question many of the statements made on this issue in recent years that consider tourism as the driving force for economic development in the least developed, or developing, countries.

Conclusions

Economic growth can be achieved through different economic activities, provided that its expansion is important enough to have a significant impact on the overall economic growth. Thus, it has been confirmed that tourism is a major activity, becoming in some countries an essential pillar of their economic activity.

Hence, tourism, like any economic activity that is able to influence the global economic growth of the economy, should allow that real economic development processes are achieved. However, it has been ascertained that the literature recognizes two opposing points of view regarding the relationship between these two dimensions.

In this duality, the first question that this research should answer is whether the relationship between the tourism growth of a country and the increase in its level of economic development exists. In this sense, the study shows that once the SEM for all countries included in the sample is estimated, tourism growth has an influence on the improvement of economic development.

Certainly, in general, for the sample as a whole, the hypothesis that the economic growth experienced by some countries due to the expansion of tourism activity influences the increase in the level of economic development that they have experienced has been demonstrated.

However, the sample analyzed includes a large number of countries with completely different socioeconomic structures: level of income per capita, infrastructure, training, or instability of the economic activity. Therefore, it has been examined whether the causal relationship between tourism growth and economic development is established in both developed and less developed countries. An additional analysis is then performed to verify whether the relationship between tourism growth and economic development occurs regardless of the level of development of the country, concluding that it is possible to distinguish two groups of countries with different behavior:

Group A: countries that showed a higher value of the synthetic index of economic development in 1991, where it has been demonstrated that tourism growth has led to an improvement of the economic development.

Group B: countries that had a lower value of the synthetic index of economic development in 1991 where tourism growth has not influenced the improvement of their economic development.

It is concluded, firstly, that the increase of tourist flows into a destination results in an expansion of tourism and, consequently, in a tourism growth that, in addition, contributes to improve socioeconomic conditions only in those countries with a higher level of development. This increased level of well-developed countries thanks to the growth of tourism is evident in all variables that were used in this study to measure the economic development of these countries (i.e., the Human Development Index, life expectancy at birth, infant mortality rate of under one year, GDP per capita, income distribution, medical, public spending on health, adult literacy rate, and public expenditure on education).

Likewise, secondly, there are countries (those forming Group B, which had lower rates of economic development in the year this study began, 1991) where tourism growth, in spite of having positively influenced the economic growth of the country, has not been become a tool with the ability to increase its level of prosperity.

Admittedly, the two groups of countries in which the sample has been divided for the second part of the analysis still contain a large heterogeneity and diversity of countries from the point of view of economic development, so a more selective grouping would yield more conclusive findings. It is a line of research that will be raised in the near future.

In any case, according to the results obtained, the approach of some organizations and international institutions (United Nations Conference on Trade and Development, United Nations Economic Commission for Africa or United Nations World Tourism Organization) that have considered tourism as an effective tool for poverty alleviation in the least developed countries must be questioned.

Despite the efforts of these organizations to determine that this is so (UNWTO 2010), this research shows that it is precisely in those countries where tourism growth has not led to an improvement of economic development. Therefore, we must be critical of such approaches, which accept the universal validity of tourism as a tool for development and poverty reduction, and lead to an enormous investment effort (not just of such organizations, but of the countries, driven by categorical statements that are not sufficiently contrasted; and of local investors themselves, who can squander their limited assets, relying on an activity that is presented as a manna that seems endless) in countries where the previous circumstances needed are not found and/or the key factors do not move forward at the right pace to achieve an appropriate environment that ensures that tourism growth ends up improving the quality of life of the population.

Consequently, given that in less developed countries there are no circumstances that ensure appropriate transformation of tourism growth into economic development, it might be argued that in these countries, tourism is less effective than other economic activities to improve the living conditions of the local population. Hence, a commitment to tourism without securing a real impact on the economic development of the country has a high opportunity cost for these countries. Another very different thing is foreign direct investment. Although the country’s tourism growth itself may be benefiting foreign investors (who recouped their investment through increased tourist flows, increased tourism expenditure, etc.), this does not necessarily involve a transfer of the benefit to the local population.

Finally, the existence of relationship between tourism growth and economic development, and the fact that this relationship is observed only within a certain group of countries, justifies the emergence, over the last years, of a series of works aimed at identifying, but still from a global and theoretical perspective, those factors that determine that tourism growth becomes a tool for economic development. The research study, whose results are presented in this article, is part of this new wave of research and provides enough data to hold the interest of this debate and draw attention to the need for further progress in the identification and analysis of these factors.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Pablo Juan Cárdenas-García is Lecturer in the Department of Economics at the University of Jaen, his main research interests focus on tourism growth and economic development, tourism impacts, competitiveness, and tax tourist.

Marcelino Sánchez-Rivero is Lecturer in the Department of Economics at the University of Extremadura, his research interests include statistical analysis of contingency tables, latent structure models, analysis of tourist behaviour, sustainability, and competitiveness.

Juan Ignacio Pulido-Fernández is Lecturer in the Department of Economics at the University of Jaen, his main research interests focus on destination management and economic development, tourism impacts, sustainability, and social network analysis and tourism.

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