Tourism: Economic growth, employment and Dutch Disease

Annals of Tourism Research 54 (2015) 172–189

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Tourism: Economic growth, employment and Dutch Disease Federico Inchausti-Sintes ⇑ Universidad de Las Palmas de Gran Canaria, Spain

a r t i c l e

i n f o

Article history: Received 12 May 2014 Revised 24 July 2015 Accepted 29 July 2015 Coordinating Editor: Larry Dwyer Keywords: Economic crisis Unemployment Economic growth Dutch Disease Dynamic CGE model

a b s t r a c t Since 2008, Spain has sustained a significant economic recession. Tourism is seen as a possible substitute for weakened domestic demand and, thus, an opportunity for to economic revitalisation the economy. Nevertheless, tourism also has profound consequences on the economy at the microeconomic level regarding resource allocation, exchange rate appreciation and demand for non-tradable goods, which can trigger the so-called Dutch Disease. A recursive-dynamic CGE model is developed to assess this possibility. Tourism implies a boost in the economy, although the Dutch Disease acts at the sectoral level causing a shift in resources towards non-tradable sectors which may jeopardize productivity gains, generate a persistent appreciation of the real exchange rate and thus affect the economic growth in the long term. Ó 2015 Elsevier Ltd. All rights reserved.

Introduction Since 2008, Spain has sustained a significant economic recession (a 0.92% reduction in real GDP from 2008 to 2012 and an unemployment rate of 24.3% in 2012). The main causes and effects of the actual downturn situation are the following: high levels of private debt (driven by years of low interest rates, which also spurred a real estate bubble), a high unemployment rate, lower wages, low levels of private consumption, credit shrinkage (banking crisis) and higher interest rates for public bond emissions. ⇑ Tel.: +34 928458212; fax: +34 928458183. E-mail address: fi[email protected] http://dx.doi.org/10.1016/j.annals.2015.07.007 0160-7383/Ó 2015 Elsevier Ltd. All rights reserved.

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Beyond this brief diagnosis, the Spanish crisis has two characteristic factors: Spain’s membership in the European currency union (euro) and, as a consequence, a public control deficit to fulfil the EU deficit commitment. The first factor implies that Spain acts as if it had a fixed exchange rate, forcing internal devaluation through lower salaries to earn external competitiveness. The second factor does not permit Spain to fall into persistent public budget deficits and reduces the possibility of carrying out demand policies to foster economic growth. Internal devaluation is already reducing the country’s deficit and salaries, but it is also causing domestic demand to falter. Spain, as with most of developed economies, relies strongly on the domestic demand to boost the economy. Paralleling the economic situation described above, tourism in Spain has been increasing since 2010 as a consequence of the Arab Spring that began on December of 2010 in Tunisia and rapidly spread to other Arab countries in the region. The United Nations World Tourism Organisation (United Nations World Tourism Organisation, 2013) does not share this point of view and asserts that the rise in tourism in Spain beyond the regional average (i.e., the Mediterranean area) is due to internal improvements, such as the modernisation of supply, human resource training, quality improvements and improvements in marketing and promotion. This new situation, together with more optimistic economic forecasts (International Monetary Fund, 2013), has fed the idea that tourism could substitute for weakened domestic demand and revitalise the economy. The success of some Asian countries in the eighties in promoting economic growth through export-oriented industries (World Bank, 1993) and the export orientation of tourism has guided studies about tourism and economic growth around the export-led hypothesis (Balassa, 1978). Authors such as Dritsakis (2004) and Sequeira and Maças (2008) and agree that tourism, a non-technology-intensive sector, can promote economic growth and enhance capital accumulation. These conclusions contradict Solow (1956) and some other authors’ findings, such as those of Aghion and Howitt (1998) and Grossman and Helpman (1991), about the relation between high-technology sectors and long-term growth. Lanza, Temple, and Urga (2003) affirm that the lower growth in productivity in tourism-based economy could be overcome by a progressive specialisation on tourism that could improve the terms of trade and compensate the lost in productivity. Moreover, these authors also highlight the importance of the high price elasticity and income elasticity of demand for tourism that may compensate the loss in productivity in the long term. Additionally, there are more profound consequences to the relationship between tourism and the economy beyond enhanced growth or the productivity gains that should not be neglected. Differences in the intensive use of capital and labour also have important implications at the sectorial level. Copeland (1991) and Chao, Hazari, Laffargue, Sgro, and Yu (2006) underscore the importance of non-tradable goods in tourism-based economies. According to these authors, tourism enhances the consumption of non-tradable goods and improves the terms of trade, although it could produce capital decumulation from the manufacturing sector (capital intensive) to the non-tradable sectors (labour intensives). Moreover, the appreciation of the real exchange rate because of tourism can also undermine the external competitiveness of traditional exports. Both the displacement of capital and labour endowment from traditional sectors to the non-tradable sectors and the appreciation of the real exchange rate can generate an economic ‘‘illness’’ known as Dutch Disease, by which the positive effect of tourism on the economy in the short term causes the economy to shrink in the long term (Corden & Neary, 1982). The Dutch Disease has been traditionally associated with oil-exporting countries such as Saudi Arabia, Qatar, Venezuela or Norway. Many of these countries avoid changing most of the revenues obtained from their oil exports into local currency to prevent the Dutch Disease. However, this economic illness can be generalised to any situation in which a country begins to receive an important amount of foreign money that triggers the consequences explained above. For instance, Laplagne, Treadgold, and Baldry (2001), Usui (1996), VanWijnbergen (1986) and White (1992) show how developing countries can suffer from this illness due to the receipt of external aid. VanWijnbergen (1986) argues that the negative connotation of the term Dutch Disease should not hide an important trade theory behind it by which an economy tends to produce those goods that require the intensive use of the most abundant factor of production in the country (Heckscher-Ohlin trade theory). Tourism-led countries are especially sensitive to the Dutch Disease due to the entrance of foreign money. So far, the literature regarding tourism and Dutch Disease has followed a theoretical/descriptive perspective.

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The use of recursive-dynamic CGE models permits us to work at these two levels. On the one hand, these models allow the impact of tourism on GDP and unemployment to be quantified. On the other hand, they also permit one to analyse the effect of such a shock on resource reallocation (capital and labour) among sectors and lost competitiveness and, consequently, to test the existence of the Dutch Disease in the economy at the micro-level over time. Towards this end, a recursive-dynamic model with five periods is developed. The scenario is based on the projections of the International Monetary Fund (International Monetary Fund, 2013) and the Economic Bulletin of the Bank of Spain (Bank of Spain, 2013), i.e., post-crisis scenario. Additionally, the model is based on three datasets: the Input-Output Table (IOT), the Tourism Satellite Account (TSA) and the National Account for Spain. According to Blake, Durbarry, Sinclair, and Sugiyarto (2001), the Input-Output framework overestimates the total effect of GDP and underestimates the total effect on the tourism sector. The TSA is the dataset that is able to compensate for the lack of tourism information in the IOT. Thus, the IOT and the TSA are combined to obtain a deeper representation of the tourism sector that the IOT is not able to provide. To conclude, this manuscript is the first to show, empirically, the economic process by which tourism trigger the Dutch Disease; as well as the consequences of this economic ‘‘illness’’ in term of sectoral production, employment, capital accumulation, external competitiveness, productivity or the kind economic growth that can be achieved in a tourism country in the long term. Literature review Economic growth theories Theories and studies about the sources of economic growth are not new. Smith (1776), Marx (1867) and Keynes (1936) are well-known examples of famous first attempts. Moreover, economic depletion contexts such as the Great Depression or the post-Second World War period were also a source of inspiration for many other economic theories concerning the possible causes of economic growth. The most remarkable theories are those of Fisher (1933), Domar (1946), Lewis (1954) or Rostow (1960). All of these theories turn on assuming the importance of capital accumulation, the presence of an unlimited supply of labour and the existence of industries, such as manufacturing, with high demand for low-qualification workers. Solow (1956) rejects the importance of capital accumulation and focuses on technological change in the long term to explain a steady economic expansion. The Great Depression also propitiated the advent of two economic policies broadly applied in developing countries: import substitution and export-oriented policies (Krugman & Obstfeld, 2006). The success of some Asian countries in the eighties in promoting economic growth through export-oriented industries (World Bank, 1993) and the export orientation of tourism (tourism-led) have guided studies on tourism and economic growth around the export-led hypothesis (Balassa, 1978). Tourism and economic growth The export-led growth hypothesis must be refashioned to explain growth generated by tourism, i.e., the tourism-led growth hypothesis. Thus, the theory should be responsive to the following four ideas:    

The strength of tourism, a non-technology-intensive sector, to promote long-term growth. The capacity of tourism to generate enough capital flows to ensure capital accumulation. Efficiency improvement with increasing international competition in the tourism enterprises. The promotion of increasing returns to scale (scale economies or economies of density) (Sinclair & Stabler, 1997). This paper is focused on the first two aspects.

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Tourism and economic growth by countries The first question tries to answer Solow’s conclusion (Solow, 1956) and some other findings, such as those of Aghion and Howitt (1998) and Grossman and Helpman (1991), who explore the relationship between the high-technology sector and long-term growth. Ghali (1976), Dritsakis (2004), Durbarry (2004), Gunduz and Hatemi-J (2005), Kim, Chen, and Jang (2006), Sequeira and Maças (2008), and Tang and Abosedra (2012) apply their respective studies in specific countries. All of these authors assert that tourism generates economic growth in the long term, despite the low technological intensity of the sector. However, not all studies find a positive causal relationship between tourism and economic growth. For instance, Oh (2005) concludes that the tourism-led hypothesis is not supported in the case of South Korea. Tourism has played a key role in the Spanish economy, Balaguer and Cantavella-Jorda (2002) conclude, finding that tourism has fostered the economic growth of the last three decades and has brought about positive effects on income and external competiveness. Capó, Riera, and Rosselló (2007b) also assert the importance of tourism on the Spanish economy, but these authors are more skeptical about the productivity gains and economic growth in the long term. Tourism and economic growth by regions Other authors such as Brau, Lanza, and Pigliaru (2007), Eugenio-Martín, Martín, and Scarpa (2004), Lanza et al. (2003), Lee and Chang (2008) and Di Liberto (2013) also analyse the impact of tourism on the economic growth, but they base their studies on wider datasets. Brau et al. (2007) examine small tourism countries and conclude that tourism specialisation generates economic growth and that these countries grow faster than other, larger countries. This conclusion is in accordance with a more general conclusion set out by Easterly and Kraay (2000). These authors conclude that, on average, small countries have higher GPD per capita, although they are more vulnerable to international trade shocks. Lanza et al. (2003) analyse the OECD countries. According to these authors, the lower growth in productivity in tourism-based economies could be overcome by a progressive specialisation on tourism that would improve the terms of trade and compensate for the productivity lost other sectors. Moreover, these authors also highlight the importance of the high price elasticity and income elasticity of demand for tourism that may in the long term compensate for the productivity loss. Lee and Chang (2008) expand their analysis to include both OECD and non-OECD countries. They find a long-run relationship between the tourism and real GDP per capita. Eugenio-Martín et al. (2004) conclude that countries with lower and medium incomes in Latin America benefit from tourism and the corresponding economic growth. Tourism, capital accumulation and Dutch Disease The literature has mainly followed a theoretical perspective regarding capital accumulation and, especially, Dutch Disease. The motivation for this perspective has been based on the distinction between tradable and non-tradable goods. Tradable goods are more capital intensive and can be exported. Non-tradable goods, however, are more labour intensive and can be consumed only within the producing country. The existence of tourism in the country increases the consumption of non-traded goods. Thus, tourism produces a reallocation of resources from tradable to non-tradable sectors (capital decumulation). At the same time, appreciation in the real exchange rate because of tourism erodes the external competitiveness of tradable goods. This is the theoretical reasoning followed by Copeland (1991) and Chao et al. (2006), although for Copeland (1991) de-industrialisation is not necessarily harmful, unless external economies are important to the industry. Hazari and Sgro (1995) and Albadalejo and Martínez-García (2013) develop a dynamic model to explain the capacity of tourism to enhance economic growth and capital accumulation. The latter group of authors affirm that tourism provides for inflows of foreign capital. Moreover, the model can endogenously increase tourism attractions in reaction to tourism demand. Thus, tourism enhances economic growth and capital accumulation. Poirier (1995) and Nowak, Sahli, and Cortes-Jimenez (2007) carry out an empirical analysis of the capital accumulation process promoted by tourism in Spain and Tunisia, respectively. Poirier (1995) follows a descriptive analysis. That study notes the positive effect of

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tourism on capital accumulation, deficit reduction and the balance of trade. Finally, Nowak et al. (2007) combine both theoretical and empirical results. These authors develop a theoretical proof of the so-called EKIG hypothesis (exports-capital imports-growth) and confirm, with econometrics tools, its positive and significant impact in Spain due to the capital inflows. Nonetheless, there is another profound consequence to the relationship between tourism and the economy; in which the de-industrialisation highlighted by Copeland (1991) and Chao et al. (2006) is one of the symptoms, the so-called Dutch Disease. Corden and Neary (1982) and Corden (1984) were the first to model Dutch Disease in academic terms. These papers differentiate among three sectors: the booming sector, the lagging sector and the non-tradable sector. The booming sector begins to produce and export strongly. Such a sector demands workers and capital from other sectors (lagging sectors and non-tradable sectors) to keep producing (resource effect). At the same time, the foreign income generated by the booming sector increases the real exchange rate, which erodes the external competitiveness of traditional exports and, together with the increase in the demand for non-tradable goods produced by the income generated in the booming sector, causes the expenditure effect. This rise in non-tradable goods also increases the demand for workers and capital in such sectors to the disfavour of the lagging sector (another resource effect). As a result, the rise produces a de-industrialisation (lagging sector), a strong increase in domestic prices and the real exchange rate that, eventually, reduces competitiveness and shrinks the economy. In the case of tourism countries, Nowak and Sahli (2007) remark that the effect of a tourism boom is twofold because the booming and the non-tradable sector are the same. Thus, the non-tradable sector is affected by both the resource effect and the expenditure effect. These authors also remark the importance of urban unemployment, labour migration or the use of land so as to quantify the net effect on welfare (positive or negative) in small tourism island. According to Capó, Riera, and Rosselló (2007a), there are evidences of Dutch Disease in the Balearic and the Canary Islands. These authors follow a descriptive analysis and highlight the consequences in the long term of the specialization on tourism in term of productivity and innovation. Assuming the existence of Dutch Disease, the following question is how to prevent from it. Many of the oil producing countries avoid changing most of the revenues obtained from their oil exports into local currency. In tourism countries, Sheng (2011) dissuades from taxing tourism as well as subsidizing non-tourism sector to prevent the Dutch Disease because of the negative effect on local welfare. In contrast, Chang, Lu and Hu (2011) argue that taxing tourism can prevent the ‘‘illness’’ if such tax revenues are publicly reoriented to the manufacturing sector. Finally, Dutch Disease can work in reverse, with tourism being adversely affected by an export boom (mining sector), as Forsyth, Dwyer, and Spurr (2014) and Pham, Jago, Spurr, and Marshall (2015) detect in the case of Australia. Tourism and CGE models Copeland (1991) develops a theoretical CGE framework to analyse the effect of tourism on the economy. Many of his conclusions have been tested by other researchers such as Adams and Parmenter (1995) and Narayan (2004), among others. Authors such as Adams and Parmenter (1995), Zhou, Yanagida, Chakravorty, and Leung (1997) and Narayan (2004) have quantified the impact of increasing tourism demand on the economy. A general conclusion regarding the impact of a rise in tourism (new arrivals or increased spending) on the economy is that a positive tourism shock produces an appreciation of the exchange rate that erodes traditional exports and increases imports. Nonetheless, the tourism shock overcomes the decline in traditional exports and the rise in imports. Thus, it improves the terms of trade. The impact of taxes on tourism has also been a recurrent topic. Gooroochurn and Sinclair (2005) focus their studies on the impact of taxes on tourism and the economy. According to these authors, tourism taxes can bring welfare gains because international tourists bear most of the taxes. Meng, Siriwardana, and Pham (2013) combine both the increase in total tourism demand and the change in taxes in their study of Singapore. CGE modelling can also be combined with other methodologies to provide a more precise insight into the shock considered. In this sense, Blake et al. (2006) combine econometric estimations based on tourism indicators with a CGE model. In a first step, a structural equations model is used to forecast

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tourism spending. In the second step, a CGE model is carried out to quantify the shock predicted in the first step. In their study of Scotland, Blake et al. (2006) calculate that a 10% increase in tourism spending will increase GDP by between 25.3 million and 42.6 million pounds in the short run and the long run, respectively. This boost in GDP will also generate 3326–4455 more jobs. Gago, Labandeira, Picos, and Rodríguez (2009) and Blake (2000) are some of the few authors to include the tourism sector in their CGE models of Spain. Previous CGE studies of Spain, such those of Polo and Sancho (1993) and Kehoe, Polo, and Sancho (1995) did not include tourism. Gago et al. (2009) and Blake (2000) evaluate the impact of indirect taxes on tourism and the economy. Moreover, Gago et al. (2009) also study the impact of such shocks on the employment rate. The negative effect of the increasing VAT rate on employment varies from 0.88% to 3.36%, depending on the shock considered. The shock assumed in any CGE model has deeper effects on the economy beyond GDP or the unemployment rate. It also produces changes in the income distribution within households which, depending on one’s goal, should not be neglected. The effect of tourism on the economy has also been extended to poverty relief analysis, and, under these circumstances, the inclusion of different households is vital. Blake, Arbache, Sinclair, and Teles (2008) conduct an analysis of the consequences of tourism on poverty relief in Brazil. Their main conclusion is that, although tourism benefits all households, it benefits low-income households the most. On this same topic, Wattanakuljarus and Coxhead (2008) assess the impact of tourism on poverty in Thailand. According to these authors, and assuming full employment, tourism is not a pro-poor activity because low-income households work in agriculture and other tradable sectors not especially related to tourism activities. In any case, the boost in tourism positively affects all types of households, but high-income households benefit the most. According to Pratt (2014), tourism is an attractive source of economic growth in lesser-developed provinces in China. These provinces can benefit from both tourism sectors and sectors with links to these sectors. Some authors have also followed a wider perspective regarding the impact of tourism on the economy. For instance, Blake and Sinclair (2003) quantify the impact of the terrorist attack of September 11th in USA. These authors highlight the importance of the US government intervention to relieve the negative impact of the 9/11 crisis on tourism, albeit their decisions lacked of suitable cost-effective analysis. On the other hand, Dwyer, Forsyth, Spurr, and VanHo (2006) assess the impact of the Iraq War and SARS on tourism and the economy in Australia. Their main finding is that both inbound and outbound tourism falls due to the crisis. As a result, savings, domestic tourism and other non-tourism consumption increase, although the net effect on GDP is negative. Finally, Blake, Sinclair, and Sugiyarto (2003) study the economic effect of the accession of Malta and Cyprus to the EU, both rely strongly on tourism to grow. Malta and Cyprus will increase their GDP by approximately 4% and 3.5% in the long term, respectively, acceding to the EU. In terms of employment, Malta will be able to generate 3,559 additional full-time jobs, and Cyprus will be able to generate 8,543 additional full-time jobs. Regarding the use of dynamic CGE models and tourism, Dwyer, Forsyth, Spurr, and Hoque (2010) use a dynamic CGE model to study the impact of reductions in greenhouse gases on tourism in Australia. Other authors, such as Blake (2009) and Pratt, Blake, and Swann (2013), have opted for including unanticipated shocks, which imply the inclusion of uncertainty in the model. These models imply an important change regarding traditional dynamic CGE models (perfect foresight expectation). Blake (2009) examines the effects of anticipated and unanticipated tourism in a dynamic framework. Pratt et al. (2013) develop a dynamic CGE model incorporating uncertainty. These authors assume different unanticipated shocks in tourism demand with uncertainty regarding the future path of the economy. One conclusion is that the simple expectation of the shock affects the behaviour of agents and, thus, the economy. Finally, Pratt (2013) provides a comprehensive historical review of CGE models and tourism. Case study This study is based on three datasets: the Input-Output table (IOT), the Tourism Satellite account (TSA) and the National Accounts for Spain in 2006. The first two datasets are combined to construct a

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new IOT. The combination of both datasets is a standard practice, as highlighted by Chen and Yang (2010). This combination has been focused mainly on the improvement of the tourism demand vector. However, in this paper, the aim pursued is to disentangle all goods and services of the IOT (supply table and use table) into tourism and non-tourism categories. Finally, the new IOT is combined with data from the national accounts to construct the Spanish Social Accounting matrix (SAM), following the criteria of Fernández and Manrique de Lara (2006). The SAM is composed of seventeen commodities and activities. Methodology Forward-looking versus recursive dynamic models CGE models can be classified as static (one single period) or dynamic (more than one period). Dynamic models can also be divided into forward-looking models (Classical Ramsey model (Ramsey, 1928)), also known as fully inter-temporal optimization problem, and recursive-dynamic models. The main difference between these two dynamic models comes from the different representation of the expectation of the economic agents. Agents in forward-looking models have perfect expectations, while agents in recursive-dynamic models form their expectations in the period of the decision. Thus, these agents do not consider future expectations in their current decision period. These differences in behaviour imply differences in the savings-investment decision. Forward-looking model can also be modified to include uncertainty in the expectations (Pratt et al., 2013). According to Babiker, Gurgel, Paltsev, and Reilly (2009) it is not clear which models are preferred. The authors agree that the assumption of agents’ expectations in forward-looking models is a desirable representation of agents’ decisions, but these models imply simplifications of the economic structure such that the necessary computations are feasible, whereas the recursive-dynamic model maintains the same structural detail. Recursive-dynamic CGE model (BLOVIFIS model) The BLOVIFIS model is written as a mixed complementarity problem (Rutherford, 1999). The model is programmed in MPSGE language (Rutherford, 1994) using Generalised Algebraic Modelling System (GAMS) software (Brooke, Kendrick, & Meeraus, 1988). The general structure of the model is the following one. In order to produce outputs, each industry uses factors (labour and capital) and intermediate goods. These outputs are disentangled into exports and domestic goods. Domestic and import goods are combined to produce final demand goods (consumption and investment) and intermediate demand goods (intermediate inputs). Final demand goods are purchased by the institutions to maximise their utility. Towards this end, institutions (households, firms and government) are endowed with their respective incomes. Households receive net factor incomes (labour and capital) and incomes from the rest of the world (ROW). Firms receive net factor income (capital) and net investments from ROW. The Government receives incomes from tax payments and incomes from ROW. This structure represents the circular flow of incomes. In mathematical term the aforementioned structure is as follows. A CGE models written as a mixed complementary problem is based on three conditions: the zero profit condition, the market clearance condition and the balance income condition. The equations shown below are in static form for the sake of clarity. Sub-index a represents activities, Sub-index c represents goods and services and sub-index t represents tourism and non-tourism categories. All elasticities have been taken from Hertel (1998).



1r 1

Ac;t ¼ fc;t ðac;t Mc;t

dm





1r 1 dm

þ ð1  ac;t ÞDc;t



rdm rdm 1

Þ

ð1Þ

Import goods (Mc,t) and domestic goods (Dc,t) are both used to produce the Armington goods (Eq. (1)) (CES production function). This composite good attempts to reflect the imperfect substitution between domestic and import goods (Armington, 1969). rdm is the domestics-import elasticity, ac,t is a distribution parameter and fc,t is a scale parameter. The Armington goods (Ac,t) are used to satisfy

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final consumption (non-resident households (tourists), resident households and government), investment (gross fixed capital formation and change of inventories) and intermediate demand (inputs) that is demanded by the activities to produce their outputs.

    iia;c;t v aa actv a ¼ min min ; aa;c;t ba

v aa ¼ ca



ð2Þ

1 rv a  1 ba Lqa þ ð1  ba ÞK qa q ; being q ¼

rv a

ð2:1Þ

According to Eq. (2), in the first nest, labour (L) and capital (K) are combined through a CES production function to produce the composite goods (vaa), rva being the elasticity of substitution between labour and capital (Eq. (2.1)). In the second nest, intermediate inputs (iia,c,t, level of Act used in sector a) are combined according to a Leontief production function. In the top nest (Leontief production function), both intermediate inputs and composite goods are combined to produce the industry output (actva). aa,c,t ba and ba are distribution parameters, and ca is a scale parameter. Each industry divides its output amongst products and services according to Eq. (3).

Y c;t ¼

17 X

ua;c;t actv a

ð3Þ

a¼1

Finally, products and services from Eq. (3) are disentangled into domestic (Dc;t ) and export goods/services (Ec;t ) (Eq. (4)). ua;c;t and dc;t are distribution parameters, vc;t is a scale parameter and T is the elasticity of transformation.

 1=ð1þTÞ ð1þTÞ Y c;t ¼ vc;t dc;t Dc;t þ ð1  dc;t ÞEð1þTÞ c;t

ð4Þ

Finally, the capital accumulation process is as shown in Eq. (5):

Caph;time ¼ ð1  dÞCaph;time1 þ gosh;time¼1 þ inv time1 inv h ðr þ dÞ

ð5Þ

In this case, the time sub-index is included to appreciate the way that capital accumulation works. The variable Caph;time represents the capital accumulation by institutions (h) (households, government and enterprises) and year (time). The capital accumulated at any time is formed by the capital of the previous year, less the depreciation of capital ½ðCaph;time1 ) (1  d], plus the gross operating surplus (gosh;time¼1 ) in the first year, plus the investment generated in the economic process (inv time1 inv h ðr þ dÞ). The investment in the previous year is multiplied by the investment endowment by institutions (inv h ) and the sum of the economic growth rate (r) plus the depreciation of capital (d)). According to Roca (1998), diagnosis of the Dutch Disease can be assessed following four hypotheses: appreciation of the real exchange rate, decline in the exports of the lagging sector, decline in the outputs of the lagging sector and a likely increase in the outputs of the non-tradable sector. The first two hypotheses capture the expenditure effect, and the last two capture the resource effect. In this regards, a CGE model captures the change in prices and allows to assess the change in the real exchange rate. On the other hand, Eq. (4) allows to analyse the change in both traditional exports and domestic production in tradable and non-tradable sectors. Finally, a fifth hypothesis should be added in regard to the resource effect: the likely increase in employment in the booming and non-tradable sectors, and the respective likely declines in the lagging sectors. Eq. (2) allow to quantify these changes in resources allocation (labour and capital) among sectors. Model closure The equations shown above are a brief summary of the main equations of the BLOVIFIS model. Other equations related to market clearance conditions and income balance conditions have been omitted along with those related to the behaviour of households (household´s income level acts as numeraire in the model), the government, enterprises and tourists. However, there are other equations

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whose assumptions have a deep impact on the results, and they have to be highlighted. These equations compose the model closure and are related to government balance, investment-savings, unemployment and balance of payment.  Government balance: income equals expenditure (zero deficit).  Investment-savings: total savings (domestic and abroad) equals investment. Additionally, any change in total savings in financed through capital flows (changes in capital account).  Unemployment: A wage curve is chosen to model unemployment.  Balance of payment: Current account equals capital account. Changes in the current account deficit are allowed. Spain is considered a small, open economy. Thus, world prices are fixed exogenously. Shock, scenario and calibration path The shocks simulated are:  a 2% increase in tourism  a 10% increase in tourism The 2% increase in tourism comes from the projections of the United Nations World Tourism Organisation (United Nations World Tourism Organisation, 2013). The second shock aims at testing the ability of tourism to provide economic growth and to reduce the unemployment rate, assuming an unlikely 10% increase in tourism. A dynamic model is based on future projections of economic growth, the interest rate and capital depreciation. These projections are used to capture the stationary state of the economy over time. The scenario projected is: 5% interest rate, 0.7% GDP and 5% capital depreciation (post-crisis scenario). This scenario forms the business as usual situation (BAU). The projections are based on International Monetary Fund projections (International Monetary Fund, 2013) and those of the Economic Bulletin of the Bank of Spain (Bank of Spain, 2013). The scenario projected does not match with the datasets. Hence, an ‘‘estibration’’ is applied to calibrate the dataset according to the scenario projected (Balistreri & Hillberry, 2003). Additionally, another economic scenario was also considered (coined as pre-crisis scenario). This scenario was based on the performance of the Spanish economy in the five years prior to the economic crisis (buoyant situation). The two different scenarios projected attempt to provide a more reliable view of the impact of tourism on the economy under different economic situation. The pre-crisis scenario simply reinforced the results and conclusions shown below regarding the post-crisis scenario. Results Gross real added value (GRAV), foreign account deficit and capital accumulation Tourism affects the GRAV through three mutually related processes: exports, consumption and investment. The first effect is an increase in exports, which produces an appreciation of the real exchange rate. This appreciation makes traditional exports more expensive, while imports become cheaper. The net effect (exports minus imports) on the GRAV is positive, and the foreign account deficit is reduced. Furthermore, tourists have a high propensity to consume non-tradable goods such as accommodation and catering services. The higher demand for non-tradable goods increases both the demand for workers and the revenues in the non-tradable sectors (resource movement effect), which increases both household consumption and investment. At the same time, the investment fosters the capital accumulation of the economy. According to Fig. 1, the increase of 2% in the tourist flows boosts the GRAV from 0.05% in the first year to 0.44% in the fifth year. The unlikely 10% increase means a strong bump in the economy from 0.25% in the first year to 2.58% in the fifth year. During the five years, the accumulative growth of the GRAV is 1.21% and 6.81% for the 2% and 10% cases, respectively. On the other hand, the improvement

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Gross real added value

Current account (deficit)

Capital accumulation

Fig. 1. Change in gross real added value, the current account deficit and capital accumulation with respect to the BAU situation (%).

in the current account deficit is similar to the change in GRAV. In the case of the 2% increase, the deficit is reduced by up to 0.06% in the first year and by up to 0.45% in the last year. In the 10% case, the reduction in the deficit increases from 0.28% to 2.75% in the last year. At the end of the fifth year, the deficit is reduced by approximately 1.27% and 7.35% for the 2% and 10% shocks, respectively. The change in GRAV because of the increase in tourism is significant but modest for the 2% case. Nonetheless, the unlikely 10% increase produces a strong effect on the economy. The increase in

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tourism boosts the real exchange rate, which decreases traditional exports and raises imports. The overall effect in the terms of trade (deficit reduction) is positive because of tourism. This result is in accordance with Copeland (1991), Adams and Parmenter (1995) and Narayan (2004). This general improvement in the terms of trade is especially useful in the current economic situation in Spain, as the country has to repay the money borrowed during the economic boom. To summarize, the increase in GRAV reinforces the first hypothesis highlighted in the state of the art by which tourism is able to promote economic growth, despite of the lower technological use. On the other hand, tourism also enhances capital accumulation (Fig. 1), but only in modest rates, which reinforces the conclusion of Capó et al. (2007b) about the low productivity gains generated by tourism in the long term. Unemployment The wage curve is the functional form chosen to model unemployment. This curve requires elasticity of employment as an input. Blanchflower and Oswald (1995) estimate elasticity of employment, but their estimate is formed with respect to a normal economic context, which is far from the current situation. Thus, two elasticities of employment are assumed to provide a better insight into the change in the unemployment rate depending on the elasticity considered: the elasticity obtained from Blanchflower and Oswald (1995) (0.1%), and a much more elastic one of 0.001% (elast), which may be more in accordance with the current unemployment situation in Spain. As can be seen in Fig. 2, elast allows for a greater reduction in unemployment, as was expected. With the 2% shock in tourism, both elasticities have a similar impact on the unemployment rate. The impact on unemployment is very small. In the last year, the unemployment rate decreases by 1.30% (25.74%) and 2.03% (25.47%) for Blanchflower´s elasticity and elast, respectively. These results imply approximately 76,651 and 119,948 new jobs, respectively. With the 10% shock in tourism, the differences in unemployment are higher with the elasticities. In the last year, the unemployment rate decreases by 7.53% (24.04%) and 12.03% (22.87%) for Blanchflower’s elasticity and elast, respectively. These results imply approximately 443,991 and 709,324 new jobs, respectively. Thus, neither shock implies a strong reduction in the unemployment rate, though both provide relief. The rest of the results of the paper are based on the elast elasticity. Domestic demand The disentangling of the tourism and non-tourism categories allows for a more accurate insight into the effect of tourism on the economy as depicted on Fig. 3. As a consequence of the reduction in the unemployment rate, the non-tourism side (nt) of the domestic demand (resident

Fig. 2. Unemployment rate with change in employment elasticity’s respect to the BAU situation (%).

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Fig. 3. Changes in domestic demand with respect to the BAU situation (%).

consumption, government consumption and investment) increases. On the contrary, the tourism side (t) of domestic demand declines due to the rise in domestic prices caused by the tourism. More precisely, in the 2% case, domestic tourism demand falls by 0.48% in the first year and by 1.79% in the last year. On the contrary, domestic non-tourism demand increases by 0.44% in the first year and by 2.64% in the last year. These results begin to show the negative side of tourism, which will be explained in greater detail in the next section. Up until now, the 10% shock has been used as a hypothetical upper-bound scenario to provide a better insight into the strength of tourism to generate economic growth. Hereafter, the remainder of the paper is focused on the most likely 2% shock. Winners and losers: Dutch Disease From a macroeconomic perspective, the positive effect of tourism on the economy has already been highlighted. However, at the sectoral level, the tourism shock has diverse effects that depend on the type of goods produced or the services provided, which, in the last term, produces a reallocation of resources among the economic sectors. Such changes can trigger the Dutch Disease. As highlighted in the methodology, the Dutch Disease can be assessed following four hypotheses (Roca, 1998): Expenditure effect Appreciation of the real exchange rate It should be noted that the exchange rate does not account for the nominal, but rather the real, exchange rate. The exchange rate in a CGE model adjusts to changes in domestic and world prices (Burfisher & E., 2011). Thus, the exchange rate ensures that the same amount of goods and services can be bought both domestically and abroad. As depicted on Fig. 4, a 2% increase in the tourist flows appreciates the real exchange rate by nearly 0.4% in the fifth year. Decline in the exports of the lagging sectors The lagging sectors are associated with Agriculture, energy and mining, and industry. As shown in Table 1, the exports in the lagging sectors decline in spite of the high unemployment rate. Two driving forces explain this decline. On the one hand, the appreciation of the real exchange rate erodes the external competitiveness of the lagging sector. Additionally, the less labour-intensive structure of these activities reduces the positive effect of the lower salaries due to the high unemployment rate.

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Fig. 4. Change in the real exchange rate (%).

Table 1 Change in exports in the lagging sectors (%). Goodsntime Agriculture Energy and mining Industry

t nt t nt t nt

1

2

3

4

5

1.68 0.50 – 0.45 0.65 0.24

3.08 0.80 – 0.70 1.11 0.31

4.58 1.19 – 1.04 1.65 0.44

6.06 1.58 – 1.37 2.19 0.57

7.52 1.95 – 1.70 2.71 0.70

On the other hand, tourists have a higher propensity to consume non-tradable goods, which causes a shift in demand towards this type of goods. The decline in traditional exports is especially considerable in tourism and, more precisely, in the agriculture commodities, which decrease by 7.52% in the last year. The exports of the lagging sectors account for approximately 70% of total exports in Spain. Thus, any negative impact on these exports will severely affect the current account. Resource effect Decline in the output of the lagging sectors The output of the lagging/traditional sector (agriculture, energy and mining and industry) falls slightly during the five years, as shown in Table 2. The greatest decline occurs in ‘‘Energy and mining’’. This decline is explained by two main forces. First, the higher demand for non-tradable goods promotes a reallocation of resources from lagging sectors to non-tradable and booming sectors. Second, imports become more profitable as the real exchange rate appreciates, which reduces the domestic production of these sectors. An increase in the outputs of the non-tradable sectors As can be appreciated from Table 3, output by activities increases in the booming (B) and non-tradable sectors (N). The accommodation sector, air transport sector and the travel agencies sector are benefit the most from tourism, showing increases of 5.29%, 3.01% and 13.65% for the fifth year, respectively. This result is one of the positive consequences of the decline in output from the lagging sectors previously noted. Nevertheless, in the long term, as soon as the economy grows and the unemployment rate reduces, the differences in demand for tradable and non-tradable goods may imply a persistent lack of external competitiveness. The so-called Balassa-Samuelson effect (Balassa, 1964;

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F. Inchausti-Sintes / Annals of Tourism Research 54 (2015) 172–189 Table 2 Change in production in the lagging sectors (%). Sectorsntime

1

2

3

4

5

Agriculture Energy and mining Industry

0.24 0.31 0.16

0.35 0.48 0.20

0.53 0.72 0.29

0.69 0.95 0.38

0.85 1.18 0.46

Table 3 Change in production in the booming and non-tradable sectors (%). Sectorsntime

1

2

3

4

5

Construction (N) Trade (B) Accommodation (B&N) Catering services (B&N) Railways transport (B) Road transport (B) Maritime transport (B) Air transport (B) Other transport services (B) Travel agencies (B&N) Real estate (B&N) Rent a car (B&N) Entertainment (B&N)

0.07 0.01 0.99 0.22 0.49 0.04 0.07 0.54 0.01 2.67 0.03 0.13 0.08

0.19 0.13 2.06 0.56 1.08 0.19 0.27 1.18 0.09 5.35 0.17 0.36 0.25

0.30 0.20 3.12 0.86 1.63 0.29 0.41 1.78 0.14 8.08 0.26 0.55 0.38

0.42 0.28 4.20 1.17 2.20 0.41 0.56 2.39 0.20 10.85 0.36 0.75 0.52

0.55 0.36 5.29 1.49 2.78 0.53 0.72 3.01 0.27 13.65 0.47 0.96 0.67

and Samuelson, 1964). More precisely, Spain acts as if it had a fixed nominal exchange rate. So, the real exchange rate rely more strongly on the rate between tradable and non-tradable goods. As tourism is intensive in non-tradable goods, it may imply a persistent increase in the real exchange rate. A likely increase/decrease in the employment in the booming-non-tradable sectors/lagging sectors This fifth hypothesis have been added in regard to the resource effect because of the Dutch Disease and its impact on employment. The lagging sectors (L) reduce their hiring of workers in spite of the high unemployment rate and the increase in tourism (Table 4), with the exception of the industries that increased employment from the second to the fifth year. On the other hand, the booming and non-tradable sectors increase their demand for labour. The sectors that benefited the most from the

Table 4 Change in employment by sectors (%). Sectorsntime

1

2

3

4

5

Agriculture (L) Energy and mining (L) Industry (L) Construction (N) Trade (B) Accommodation (B&N) Catering services (B&N) Railways transport (B) Road transport (B) Maritime transport (B) Air transport (B) Other transport services (B) Travel agencies (B&N) Real estate (B&N) Rent a car (B&N) Entertainment (B&N)

0.20 0.26 0.11 0.11 0.05 1.04 0.27 0.54 0.08 0.12 0.58 0.03 2.72 0.07 0.18 0.12

0.17 0.31 0.05 0.49 0.44 2.36 0.87 1.32 0.53 0.65 1.47 0.39 5.70 0.56 0.69 0.39

0.24 0.45 0.09 0.76 0.69 3.58 1.34 2.01 0.82 1.00 2.23 0.60 8.64 0.87 1.05 0.61

0.30 0.58 0.14 1.05 0.94 4.83 1.82 2.72 1.13 1.36 3.01 0.83 11.63 1.19 1.44 0.84

0.35 0.70 0.21 1.36 1.22 6.12 2.33 3.45 1.45 1.75 3.80 1.08 14.67 1.52 1.84 1.08

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high unemployment rate are those of accommodation, catering services, railway transport, air transport and travel agencies, which increased their demand for workers by 6.12%, 2.33%, 3.45%, 3.8% and 14.67%, respectively. This resources allocation is related to the increase in demand for non-tradable goods raised by the tourism. This allocation of resources implies a shift toward labour-intensive production (non-tradable goods), which could have important consequences regarding the lower productivity gains associated with non-tradable sectors. To conclude this section, tourism unleashes the Dutch Disease, although its degree varies depending on the effect considered. On the one hand, the expenditure effect is significantly reduced. Traditional exports decline because of the appreciation of the real exchange rate, but, with the exception of the tourism side of agriculture commodities, the decline is not remarkable. On the other hand, the resource effect has important and positive consequences for production and employment in the non-tradable sectors. The most tourism-oriented sectors, such as accommodation, air transport and travel agencies, benefited the most from tourism. By contrast, traditional sectors saw their production levels decline.

Conclusions Both the disentangling of the IOT into tourism and non-tourism categories and the recursive-dynamic CGE model provide a detailed insight into the effect of tourism on the Spanish economic. In addition, the recursive-dynamic CGE model also allows one to analyse the linkages between sectors and their effects on economic growth over time. In this ever-changing world, the relationship between tourism and economic growth is so opaque and complex that any conclusion in this regard should be taken with caution. For instance, the advent of the internet and new technology has converted some non-tradable goods into tradable goods, such as accounting services. Countries like India are already taking advantage of this new situation. These constant and unanticipated changes will force us to reconsider many theories and conclusions previously settled in the field. Keeping this in mind, and based on the assumptions and projections established here, this paper affirms that tourism provides economic growth, reduces the unemployment rate, improves the terms of trade and boosts the domestic demand in the medium term in Spain. The effect on these macroeconomic variables varies in intensity. For highly indebted countries like Spain, improvement in the current account deficit is very beneficial in the short and medium term. The effect on real gross value added is also positive and remarkable. Although the effect on unemployment is not a solution, it is a relief. Tourism has a double effect on domestic demand. On the one hand, domestic demand grows on its non-tourism side due to the increase in employment demand. On the other hand, the tourism side of domestic demand declines due to the rise in domestic prices produced by tourism. Nonetheless, at microeconomic level, the positive effects of the tourism sector are not as clear and positive as they are at macroeconomic level. Tourism fosters the appreciation of the real exchange rate which undermines traditional sectors such as agriculture, energy and mining and industry—a clear consequence of the Dutch Disease. In the medium term, its effects vary and are not especially harmful. Its negative consequences are overcome by the positive consequences. Additionally, the empirical results support the claim that a rise in tourism demand alleviates a decline in non-tradable goods. This last result is especially interesting in the context of economic crisis or lower growth. Thus, after the conclusions previously drawn, the question is not whether tourism generates economic growth, but what type of economic growth is desirable in the long term. From a policymaker´s perspective, the aforementioned conclusions provide important issues that deserve to be addressed. On the one hand, tourism-led growth regions base their success on the production of non-tradable sectors. Such sectors are less capital intensive than other tradable ones. In a context of economic depletion, in the short term, the economy can take advantage of this tourism to boost the economy and alleviate the unemployment rate. However, in the long term, the lower capital intensity generated by tourism-led growth may jeopardize future productivity gains that would adversely affect economic growth. On the other hand, as soon the economic grows and the unemployment rate reduces, the gap between the demand for tradable and non-tradable goods could also

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generate additional pressure on the real exchange rate that, in turn, could lead to a persistent lack of external competitiveness. It should be remembered that Spain acts as if it had a fixed nominal exchange rate and thus, the rate between tradable and non-tradable goods is key to ensure a competitive real exchange rate. In this sense, policymakers should focus on the improvement of the economic endowments (capital and labour) and the productivity in order to absorb the increase in tourism demand and avoid the increase in domestic prices in the long term. Further research may be oriented towards improving the expectation in dynamic CGE models—for instance, the combination of both forward-looking and backward-looking behaviour in the same framework. Additionally, the forward-looking behaviour should base future decisions on suitable economic indicators such as the inflation index. On the other hand, there is a potential cause of economic depletion that should be taken into account in future research. The environment acts as a pull factor, and its sustainability in the long term is vital to the economic growth in tourism-led countries. The lack of suitable management in this regard could be as harmful as the Dutch Disease. Thus, both economic revenue and environmental sustainability are necessary for steady and balanced economic growth. Finally, new technologies are playing a key role in both economy and society. More skilled workers are required to cope with them in all sectors. These changes could also affect the kind of workers currently hired by tourism firms and, perhaps, higher productivity gains may be affordable in the sector in the near future.

References Adams, P. D., & Parmenter, B. R. (1995). An applied general equilibrium analysis of the economic effects of tourism in a quite small, quite open economy. Applied Economics, 27(10), 985–994. Aghion, P., & Howitt, P. (1998). Market structure and the growth process. Review of Economic Dynamics, 1(1), 276–305. Albadalejo, I. P., & Martínez-García, M. P. (2013). An endogenous growth model of international tourism. Tourism Economics, 19(3), 509–529. Armington, P. S. (1969). A theory of demand for products distinguished by place of production. IMF Staff Papers, 16(1), 170–201. Babiker, M., Gurgel, A., Paltsev, S., & Reilly, J. (2009). Forward-looking versus recursive-dynamic modeling in climate policy analysis: A comparison. Economic Modelling, 26(6), 1341–1354. Balaguer, J., & Cantavella-Jorda, M. (2002). Tourism as a long-run economic growth factor: The Spanish case. Applied Economics, 34(7), 877–884. Balassa, B. (1964). The purchasing power parity doctrine: A reappraisal. Journal of Political Economy, 72, 584–596. Balassa, B. (1978). Exports and economic growth: Further evidence. Journal of Development Economics, 5(2), 181–189. Balistreri, E. J., & Hillberry, R. H. (2003). Estibration: An illustration of structural estimation as calibration. Mimeograph. Golden, CO: Colorado School of Mines. Bank of Spain (2013). Economic bulletin: Spanish economic projections report. Madrid, Spain: Bank of Spain. Blake, A. (2009). The dynamics of the economic impact of tourism. Tourism Economics, 15(3), 615–628. Blake, A., Arbache, J. S., Sinclair, M. T., & Teles, V. (2008). Tourism and poverty relief. Annals of Tourism Research, 35(1), 107–126. Blake, A., Durbarry, R., Eugenio-Martín, J. L., Gooroochurn, N., Hay, B., Lennon, J., Sinclair, M. T., & Yeoman, I. (2006). Integrating forecasting and CGE models: The case of tourism in Scotland. Tourism Management, 27(2), 292–305. Blake, A., Durbarry, R., Sinclair, M. T., & Sugiyarto, G. (2001). Modelling tourism and travel using tourism satellite accounts and tourism policy and forecasting models. Christel DeHaan Tourism and Travel Research Institute. Blake, A., & Sinclair, M. T. (2003). Tourism crisis management: US response to September 11. Annals of Tourism Research, 30(4), 813–832. Blake, A., Sinclair, M. T., & Sugiyarto, G. (2003). Tourism and EU accession in Malta and Cyprus. Christel DeHaan Tourism and Travel Research Institute, Nottingham University Business School. Blake, A. (2000). The economic effects of tourism in Spain. Christel DeHaan Tourism and Travel Research Institute. Blanchflower, D., & Oswald, A. J. (1995). An introduction to the wage curve. Journal of Economic Perspectives, 9(3), 153–167. Brau, R., Lanza, A., & Pigliaru, F. (2007). How fast are small tourism countries growing? Evidence from the data for 1980–2003. Tourism Economics, 13(4), 603–614. Brooke, A., Kendrick, D., & Meeraus, A. (1988). GAMS: a user’s guide. San Francisco: The Scientific Press. Burfisher, M. E. (2011). Introduction to computable general equilibrium models. Cambridge: Cambridge University Press. Capó, J., Riera, A. ., & Rosselló, J. (2007a). Dutch Disease in tourism economies: Evidence from the Balearics and the Canary Islands. Journal of sustainable Tourism, 15(6), 615–627. Capó, J., Riera, A., & Rosselló, J. (2007b). Tourism and long-term growth. A Spanish perspective. Annals of Tourism Research, 34(3), 709–726. Chao, C. C., Hazari, B. R., Laffargue, J. P., Sgro, P. M., & Yu, E. S. (2006). Tourism, Dutch Disease and welfare in an open dynamic economy. Japanese Economic Review, 57(4), 501–515. Chen, K.-H., & Yang, H.-Y. (2010). Appraising the economic impact of the ‘‘Opening up to Mainland Chinese Tourist Arrivals. Policy on Taiwan with a Tourism-CGE model. Asia Pacific Journal of Tourism Research, 15(2), 155–175. Copeland, B. R. (1991). Tourism, welfare and de-industrialization in a small open economy. Economica, 58(232), 515–529. Corden, W. M. (1984). Booming sector and Dutch disease economics: Survey and consolidation. Oxford Economic Papers, 359–380.

188

F. Inchausti-Sintes / Annals of Tourism Research 54 (2015) 172–189

Corden, W. M., & Neary, J. P. (1982). Booming sector and de-industrialization in a small open economy. The Economic Journal, 825–848. Di Liberto, A. (2013). High skills, high growth: Is tourism an exception? The Journal of International Trade and Economic Development, 22(5), 749–785. Domar, E. D. (1946). Capital expansion, rate of growth, and employment. Econometrica, 137–147. Dritsakis, N. (2004). Tourism as a long-run economic growth factor: An empirical investigation for Greece using causality analysis. Tourism Economics, 10(3), 305–316. Durbarry, R. (2004). Tourism and economic growth: The case of Mauritius. Tourism Economics, 10(4), 389–401. Dwyer, L., Forsyth, P., Spurr, R., & Hoque, S. (2010). Estimating the carbon footprint of Australian tourism. Journal of Sustainable Tourism, 18(3), 355–376. Dwyer, L., Forsyth, P., Spurr, R., & VanHo, T. (2006). Economic effects of the world tourism crisis on Australia. Tourism Economics, 12(2), 171–186. Easterly, W., & Kraay, A. (2000). Small states, small problems? Income, growth, and volatility in small states. World Development, 28(11), 2013–2027. Eugenio-Martín, J. L., Martín, M. N., & Scarpa, R. (2004). Tourism and economic growth in Latin American countries: a panel data approach. Fondazione Eni Enrico Mattei Working Papers, February 26, 2004. Fernández, F. M., & Manrique de Lara, C. (2006). La matriz de contabilidad nacional. Un método alternativo de presentación de las cuentas nacionales. Revista Asturiana de Economía, 37, 85–106. Fisher, I. (1933). The debt-deflation theory of great depressions. Econometrica, 337–357. Forsyth, P., Dwyer, L., & Spurr, R. (2014). Is Australian tourism suffering Dutch Disease? Annals of Tourism Research, 46, 1–15. Gago, A., Labandeira, X., Picos, F., & Rodríguez, M. (2009). Specific and general taxation of tourism activities. Evidence from Spain. Tourism Management, 30(3), 381–392. Ghali, M. A. (1976). Tourism and economic growth: An empirical study. Economic Development and Cultural Change, 527–538. Gooroochurn, N., & Sinclair, M. T. (2005). Economics of tourism taxation: Evidence from Mauritius. Annals of Tourism Research, 32(2), 478–498. Grossman, G. M., & Helpman, E. (1991). Trade, knowledge spillovers, and growth. European Economic Review, 35(2), 517–526. Gunduz, L., & Hatemi-J, A. (2005). Is the tourism-led growth hypothesis valid for Turkey? Applied Economics Letters, 12(8), 499–504. Hazari, B. R., & Sgro, P. M. (1995). Tourism and growth in a dynamic model of trade. Journal of International Trade and Economic Development, 4(2), 143–252. Hertel, T. W. (1998). In T. W. Hertel (Ed.), Global trade analysis: modeling and applications. Cambridge: Cambridge University Press. International Monetary Fund (2013).World economic outlook, April 2013. Washington, US: International Monetary Fund. Kehoe, T. J., Polo, C., & Sancho, F. (1995). An evaluation of the performance of an applied general equilibrium model of the Spanish economy. Economic Theory, 6, 115–141. Keynes, J. M. (1973). 1936. The general theory of employment, interest and money. London: Macmillan and Co. Kim, H. J., Chen, M. H., & Jang, S. (2006). Tourism expansion and economic development: The case of Taiwan. Tourism Management, 27(5), 925–933. Krugman, P. R., & Obstfeld, M. (2006). Economía Internacional. Pearson: Addison Wesley. Lanza, A., Temple, P., & Urga, G. (2003). The implications of tourism specialisation in the long run: An econometric analysis for 13 OECD economies. Tourism Management, 24(3), 315–321. Laplagne, P., Treadgold, M., & Baldry, J. (2001). A model of aid impact in some South Pacific Microstates. World Development, 29, 365–383. Lee, C. C., & Chang, C. P. (2008). Tourism development and economic growth: A closer look at panels. Tourism Management, 29(1), 180–192. Lewis, W. A. (1954). Economic development with unlimited supplies of labour. The Manchester School, 22(2), 139–191. Marx, K. (1976). 1867. Capital, Volume I. Trans. Ben Fowkes. New York and London: Penguin Books. Meng, X., Siriwardana, M., & Pham, T. (2013). A CGE assessment of Singapore’s tourism policies. Tourism Management, 34, 25–36. Narayan, P. K. (2004). Economic impact of tourism on Fiji’s economy: Empirical evidence from the computable general equilibrium model. Tourism Economics, 10(4), 419–433. Nowak, J. J., & Sahli, M. (2007). Coastal tourism and ‘Dutch disease’ in a small island economy. Tourism Economics, 13(1), 49–65. Nowak, J. J., Sahli, M., & Cortes-Jimenez, I. (2007). Tourism, capital good imports and economic growth: Theory and evidence for Spain. Tourism Economics, 13(4), 515–536. Oh, C. O. (2005). The contribution of tourism development to economic growth in the Korean economy. Tourism Management, 26(1), 39–44. Pham, T., Jago, L., Spurr, R., & Marshall, J. (2015). The Dutch Disease effects on tourism–The case of Australia. Tourism Management, 46, 610–622. Poirier, R. A. (1995). Tourism and development in Tunisia. Annals of Tourism Research, 22(1), 157–171. Polo, C., & Sancho, F. (1993). Insights or forecasts? An evaluation of a computable general equilibrium model of Spain. Journal of Forecasting, 12(5), 437–448. Pratt, S., Blake, A., & Swann, P. (2013). Dynamic general equilibrium model with uncertainty: Uncertainty regarding the future path of the economy. Economic Modelling, 32, 429–439. Pratt, S. (2013). Estimating Tourism Impacts using CGE Models: A historical review and future developments. In C. Tisdell (Ed.), Handbook on tourism economics: Analysis, new applications and case studies (pp. 467–488). Singapore: World Scientific Publishing. Pratt, S. (2014). Potential economic contribution of regional tourism development in China: A comparative analysis. International Journal of Tourism Research. Ramsey, F. P. (1928). A mathematical theory of saving. Economic Journal, 38(152), 543–559. Roca, A. M. (1998). Dutch Disease and banana exports in the Colombian Caribbean, 1910–1950. Latin American Studies Association. Rostow, W. W. (1960). The process of economic growth: A non-communist manifesto. Cambridge University Press.

F. Inchausti-Sintes / Annals of Tourism Research 54 (2015) 172–189

189

Rutherford, T. F. (1994). The GAMS/MPSGE and GAMS/MILES user notes. Washington: GAMS Development Corporation. Rutherford, T. F. (1999). Applied general equilibrium modeling with MPSGE as a GAMS subsystem: An overview of the modeling framework and syntax. Computational Economics, 14(1–2), 1–46. Samuelson, P. (1964). Theoretical notes on trade problems. Review of Economics and Statistics, 23, 1–60. Sequeira, T. N., & Maças, N. P. (2008). Does tourism influence economic growth? A dynamic panel data approach. Applied Economics, 40(18), 2431–2441. Sinclair, M. T., & Stabler, M. (1997). The economics of tourism. London: Routledge. Smith, A. (2005). 1776. La Riqueza de las naciones. Madrid: Alianza Editorial. Solow, R. M. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65–94. Tang, C. F., & Abosedra, S. (2012). Small sample evidence on the tourism-led growth hypothesis in Lebanon. Current Issues in Tourism, (ahead-of-print), 1–13. United Nations World Tourism Organisation. (2013). Tourism 2020 vision, volume 4, Europe. Madrid, Spain: United Nations World Tourism Organization. 12 October. Usui, N. (1996). Aid induced structural change in developing countries: An extension of the two-gap model. Singapore Economic Review, 41, 53–66. VanWijnbergen, S. (1986). Exchange rate management and stabilization policies in developing countries. Journal of Development Economics, 23, 227–247. Wattanakuljarus, A., & Coxhead, I. (2008). Is tourism-based development good for the poor? A general equilibrium analysis for Thailand. Journal of Policy Modeling, 30(6), 929–955. White, H. (1992). The macroeconomic impact of development aid: A critical survey. Journal of Development Studies, 28, 163–240. World Bank (1993). The east Asian miracle. Economic growth and public policy. New York: Oxford University Press. Zhou, D., Yanagida, J. F., Chakravorty, U., & Leung, P. (1997). Estimating economic impacts from tourism. Annals of Tourism Research, 24(1), 76.