Introduction. Tourism produces a series of changes in both economic and social sectors and finally in environmental sectors (Soza, 2016) this is why tourism requires workers to constantly train themselves by promoting education and professional training for the effective development of these activities (Brida et al., 2013). On the other hand, tourism is an important generator of employment in many countries since it belongs to one of the largest industries in the world (Brida et al., 2008). In this way, tourism subsidizes a better distribution of income (Moral et al., 2016) since tourism promotes the economy through job creation (Gómez & Barrón, 2019).

In this way, tourism stimulates other economic activities (A. López & de Esteban, 2010) such as agriculture, manufacturing and commerce, generating greater production as well as national economic income (Brida et al., 2013) therefore, tourism creates direct and indirect employment opportunities (Casas et al., 2012), which contributes to reducing unemployment in the locality and in turn improving the quality of life of residents (Brida et al., 2013). 

Thus, tourism activity represents a critical point in the economy and is generally structured through commercial links (Salazar, 2006), since it is an activity that generates a margin of sustained growth and produces effects in different areas (Maldonado, 2006) such as financial, social and cultural areas (Jiménez, 2006).

Additionally, in the tourism sector we can determine four factors that intervene with each other for its development (Brazales et al., 2021), which are the tourist, the interactions with the local population, the structure of the tourism system and the sociocultural impacts (Moral et al., 2016) where these influence the planning and management of tourism development (Arnandis, 2018).

Therefore, tourism activity through tourism development seeks to minimize negative impacts and maximize positive ones (Tarlombani, 2005) through long-term benefits for local communities and the environment (Orgaz, 2013), by understanding and meeting the needs of the local population, it is possible to contribute to the development of sustainable tourism (Baldárrago, 2005).

But also, negative impacts can be generated in the tourism sector that arise from poor management (Condori & Flores, 2023; Violante, 2013) so that the excessive growth of tourism without adequate planning can overload the road infrastructure as well as health services and waste management (Bertram, 2002; Huízar et al., 2015; Salazar, 2006), generating problems in local communities (López, 2014), which is why sustainability is important to ensure tourism development (Condori & Flores, 2023; A. López & de Esteban, 2010; Ruiz et al., 2020; Violante, 2013).

Therefore, poorly managed tourism can cause environmental degradation (Amador, 2021; Ascanio, 1994; Gonzales, 2006; Picornell, 1993) which is why caring for the environment as well as social well-being helps to commit to the positive development of tourism and plan it in the future in a more accurate way (Amador, 2021; Carvajal, 2021; Ferreira, 2009) since the development of tourism calls for the implementation of sustainable management strategies that balance tourist exploitation while taking care of natural and cultural resources (Ruiz et al., 2020; Zhong et al., 2011).

In this sense, the objective of this article is to analyze the Impact of Tourism on Local Economic Sectors in Tungurahua Province, Ecuador. The hypothesis is: the tourism GVA is positively and significantly related to the GVA of other key economic sectors in the province of Tungurahua.

The province of Tungurahua, Ecuador, has demonstrated increasing economic importance (Banco Central del Ecuador, 2023; Ministerio de Turismo Ecuador, 2024), standing out for the influence of tourism on its local economy. Tourism activity in Tungurahua presents a significant opportunity to assess how tourism can relate to and contribute to the economic development of the primary, secondary, and tertiary sectors (Salazar, 2006).

Therefore, it is essential to examine the Gross Added Value (GVA) of tourism in these sectors and its consequent impact on the economy of Tungurahua. The tourism development model in Tungurahua must consider the diversification of the tourism offer and the integration of economic sectors. This includes not only the strengthening of the tertiary sector, but also the promotion of collaboration between the primary and secondary sectors (Banco Central del Ecuador, 2023).

The problem addressed in this analysis focuses on determining the series of challenges that the province of Tungurahua faces in maximizing tourism potential to contribute to the provincial economic dynamism, through the expansion of existing economic sectors. The main concern is to define the economic activities with the greatest influence on tourism GVA, which in turn impacts on the economic management of the province and its connection with tourism.

The hypothesis states that tourism Gross Added Value (GVA) has an impact on local economic sectors such as wholesale and retail trade; repair of motor vehicles and motorcycles, transportation and storage, and manufacturing of textile products and leather goods, in the province of Tungurahua, Ecuador (Gobierno Provincial de Tungurahua, 2023).

Hypothesis

H1: tourism GVA benefits wholesale and retail trade GVA; repair of motor vehicles and motorcycles.

H2: tourism GVA benefits transportation and storage GVA.

H3: tourism GVA benefits manufacturing of textile products such as clothing; manufacturing of leather and leather goods.

Economic Impacts of tourism. Various investigations have devoted much attention to empirical studies on the impacts of tourism on the economy because tourism drives economic development in a given time (Pizzolon et al., 2013) thus generating foreign currency, employment and infrastructure in the different localities that manage it (Brida et al., 2013).

The impacts of tourism play a crucial role in local economies (Gambarota & Lorda, 2017; Salazar, 2006) since it is intrinsically related to primary, secondary and tertiary economic activities (Atucha & Lacaze, 2018) especially activities related to trade (Mundial, 2021).

Thus, tourism generates foreign exchange income (Benavides, 2020; Ronquillo, 2015), lo which contributes to strengthening the local economy by stimulating other economic activities, such as agriculture, manufacturing and trade (Brazales, 2021; Brazales et al., 2024), generating a multiplier effect in the economy through the tourism sector (Benavides, 2005).

Thus, one way to examine the economic impact of tourism is through the balance of payments, gross domestic product and employment (Carvajal, 2021) in this way, we can highlight that through these factors, tourism generates long-term financial development (Apolo & Cantavella, 2002; Cardona, 2020; Dritsakis, 2004). Therefore, we can say that tourism companies are entities that form a microeconomy of the tourism sector and generate economic effects (Condori & Flores, 2023; Dichiara, s. f.; Méndez, 2020; Rubinfeld & Pindyck, 2013) such as improving the national balance of payments by generating foreign currency income (Brida et al., 2013; Calderón & Ruggeri, 2016; Knetsch & Var, 1976), so that tourism seen as an economic activity generates various impacts on society (Fontan, 2019; Hidalgo, 2018; Picornell, 1993; Salazar, 2006; R. Schneider, 2013) since this can be observed through the application of tourist services determined according to the region and the type of tourist destination (Condori & Flores, 2023).

On the other hand, through the input-output analysis it is possible to examine the economic effects of tourism (Bote, 1996; Brida et al., 2013; Riera et al., 2006; Tarancón, 2005) because the input-output analysis provides a holistic view of the economic impacts of tourism (Hernández, 2004) In this way, considering the indirect and multiplier effects, the input-output analysis comes to present some negative methodological limitations since an undue appropriation affects the precision of the results (Briassoulis, 1991).

In turn, the economic contribution is a direct effect of tourism on the economy of a destination and this is measured by the Tourism Satellite Account (CST) (Brida et al., 2008), la which records the expenses made by tourists in the destination (Zapata et al., 2017), such as accommodation, food, transportation and tourist activities (Hernández, 2004).

In this way, the tourism satellite account allows us to know its contribution to the economy in terms of employment, added value and income generation (Zapata et al., 2017) Therefore, the satellite account is a tool that allows us to analyze tourism (Padrón, 2020) having a more simplified understanding of the impact of the economy and the design of more effective public policies for balanced development in the sector (Varisco, 2005).

Likewise, the satellite account is executed as a tool that allows studying tourism in a more simplified way for the design of more effective public policies and thus better development in the area (Varisco, 2005) in this way tourism contributes to reducing the economic gap between developed and less developed areas (Mihalic, 2011). 

For which it is important to mention that the economic impacts of tourism influence the economy at a global level (Altimira & Muñoz, 2007; A. García & Lavalle, 2012) in turn the development of sustainable tourism strategies which benefit all the actors involved (Condori & Flores, 2023) where the providers of tourist services such as hotels, restaurants and tourist guides, are the key actors and these in turn belong to the value chain of tourism and its development (de Duque et al., 2012).

In this way, tourism is a key sector for the export of services and in turn becoming the generator of employment in many countries (Brida et al., 2008; Chérrez et al., 2021) in which the tourist environment influences because it is a complex system that includes various interconnected elements such as carrying capacity, quality assessment and measures (Zhong et al., 2011).

However, tourism in certain cases can concentrate wealth in certain sectors but at the same time generates inequalities in this way affecting traditional economic activities (Orgaz, 2015) On the other hand, its development has driven growth in the universal economy so that in 2018 it contributed to the world GDP in a way that is representative of world trade (Chérrez et al., 2021).

Thus, the relationship between economic activity and tourism is fundamental, especially for the economies of countries with emerging markets or in turn in development (Cervantez et al., 2023; Gambarota & Lorda, 2017; Salazar, 2006) in countries such as Ecuador, the canon of this sector in 2018 the GDP reached 5.51%; which caused 722,935 jobs where it contributes with 4.06% of the total net taxes of the local economy (Chérrez et al., 2021).

GVA of tourism. The main reason why the various regions of the world show a high interest in the development and growth of the tourism sector is the high economic growth that it can provide (Brida et al., 2013; Piñar et al., 2016) to such an extent that the contribution of the tourism sector to the global economy is estimated at approximately 10% of the world GDP (Papatheodorou, 1999; L. Schneider & Gonzalez, 2023; Severiche et al., 2017).

The VAB determined as the gross added value is the quantified measurement by which the facts and data of great economic relevance of a region are taken into account (García et al., 2022) which is responsible for measuring the additional benefit given to goods or services in the different stages of an economic area (Carvalho, 2003).

It is also important to mention that Gross Added Value is a parameter of macroeconomics and is of utmost importance because it helps measure economic performance in different industries, in this case the tourism sector, whether for its services offered or goods in a defined time and is used to evaluate the Domestic Product (Arriaga & González, 2019; Brito et al., 2019; Viveros et al., 2017; Zamora & Coello, 2015). 

Thus, through the GVA of a locality we can easily determine the GDP (Brito et al., 2019; Mondéjar & Vargas, 2008), likewise when we talk about the Gross Added Value of Tourism, which by its acronym is GVA of tourism, it is understood as an economic measure that indicates the role of tourism in the economy (Moral & Valverde, 2018; Zamora & Coello, 2015).

It is important to mention that in European countries, through the developed tourism sector in the hotel and restaurant area, the GVA has a significant impact and at the same time remains constant (Brida et al., 2008), so that the financial increase represents the development of the national impetus of the GDP (Samuelson & Nordhause, 2010).

Local Economic Sectors. The local economy together with tourism has as its primary objective the economic development of several countries (Carchipulla & Carchipulla, 2023). Thus, in local economies, tourism is a relevant sector that provides benefits to potential sites (Cedeño et al., 2016) as it is a globalizing phenomenon with significant economic impacts (Escobar et al., 2020).

Within the study of local economies we have in mind several theories such as regional development and location since they help us to have a competitive advantage within what classical economics proposes (Alarcon & Gonzalez, 2018), in turn it has been demonstrated how beneficial the relationship between development and culture is both for economic growth, territorial development and socio-cultural cohesion (Selva, 2016). 

The economic sector is based on a social science that aims to study the production, distribution and consumption of goods and services that aim to improve the quality of life of individuals (Carchipulla & Carchipulla, 2023). Depending on foreign investment contributes to the conditioning and scarcity of employment and business opportunities generated by tourism within the local population (Merino, 2021).

This is why alliances between local governments and emerging tourism entrepreneurs encourage the connection between various sectors and regions (Ramukumba et al., 2012), bearing in mind that the number of tourists together with the spending and added value of the tourism sector has a positive impact within local economies (Suhel & Bashir, 2018).

Considering that companies not only drive economic development by introducing new product and service segments to the market (Sharma & Kaur, 2022), they also generate and promote recreational events as these are increasingly considered as a means for local economic development and not just as entertainment activities (Hefner, 1990).

The relevance of this type of events is a beneficial investment for local municipalities, mainly when considering the contributions provided by non-governmental entities (Tohmo, 2005). To evaluate the total impact of these events on the economy, economic impact studies have been applied that are based on the multiplier concept(Hefner, 1990).

For this reason, it is important to understand the economic impact that tourism generates within the local economies of the region, since it seeks to generate a more detailed and precise vision of the economic situation in said area (Draghici et al., 2015). Thanks to this, if we take into consideration the role that tourism plays within the local sectors, we come to reconsider whether tourism should be introduced as an internal guide industry in local economies (Kim & Kim, 1998).

Materials and Methods. Quantitative study. Type of research

Correlational, longitudinal, using deductive and bibliographic logic as support for the quantitative, based on the analysis of secondary sources. Population and Sample Provincial gross added value. The sample is the provincial gross added value by industry from 2007 to 2020 of the province of Tungurahua from the Central Bank, in a convenience sampling since the province of Tungurahua presents high levels of tourist demand in relation to other provinces. Information gathering: Information collection: National accounts with provincial accounts, with provincial gross added value data by industry of the province of Tungurahua from 2007 to 2020 from the Central Bank (Banco Central del Ecuador, 2023). Validity: Validity: Since the national accounts are a public data source produced by the central bank (Banco Central del Ecuador, 2023), their validity and reliability are high and proven. Statistical Tool: First, a hypothesis verification was performed using the non-rotated indices of each variable. For this, the Pearson correlation coefficient was used, analyzing the significance of the results obtained (p<0.05). Secondly, a linear regression analysis was carried out between the indices corresponding to each variable. Data processing: Stata statistical tool.

Econometric model, linear regression:

                (1)

Where, from formula 1, β0 is the ordinate at the origin, β1 is the slope coefficient that indicates any change in the variables and ε is the error term.

 1. Econometric model 1: GVA of wholesale and retail trade; and repair of motor vehicles and motorcycles based on GVA accommodation and food services. From formula 1 we obtain econometric model 1:

2. Econometric model 2: GVA of transport and storage as a function of GVA accommodation and food services. Where formula 1 obtains econometric model 2:

3. Econometric model 3: GVA textile and clothing manufacturing from GVA accommodation and food services. Where formula 1 gives econometric model 3:

Results and Discussion.

Below is GVA data for the main economic activities of the province of Tungurahua (table 1).

Table 1

GVA of tourism, GVA of wholesale and retail trade; repair of motor vehicles and motorcycles; GVA of transportation and storage; GVA of manufacturing, thousands USD 2007 to 2020

Таблица 1

ВДС туризма, ВДС оптовой и розничной торговли; ремонт автотранспортных средств и мотоциклов, ВДС транспортировки и хранения, ВДС обрабатывающей промышленности тыс. долл. США 2007-2020 гг.

Note: Разработано в рамках (Banco Central del Ecuador, 2023).

Hypothesis testing

There is a significant and favourable association between the tourism GVA and the GVA of the most relevant economic activities in terms of their contribution to the provincial Gross Added Value. That is to say, it is expected that the increase in the tourism GVA will be related to a proportional increase in the GVA of said economic activities. From here, 3 hypotheses will be delineated from the analysis of secondary information sources with the three activities that contribute the most to the provincial GVA.

H1: The tourism GVA benefits the wholesale and retail trade GVA; motor vehicle and motorcycle repair.

Table 2

Correlation between GVA of wholesale and retail trade; repair of motor vehicles and motorcycles and GVA of tourism

Таблица 2

Соотношение ВДС оптовой и розничной торговли, ремонта автотранспортных средств и мотоциклов и ВДС туризма

Note: The Pearson correlation is 0.8162 between the GVA of wholesale and retail trade; repair of motor vehicles and motorcycles and the GVA of tourism. This positive value indicates a high positive correlation between the GVA of trade and the GVA of tourism. That is, as the GVA of tourism increases, the GVA of trade also tends to increase. This finding supports the hypothesis that tourism benefits trade in the province of Tungurahua. It is also statistically significant.

The results suggest that there is a strong and significant relationship between the growth of the tourism sector's GVA and the GVA of wholesale and retail trade in the province of Tungurahua. This finding supports the hypothesis that tourism development has a positive impact on trade in the province.

H2: The tourism GVA benefits the transport and storage GVA.

Table 3

Correlation of GVA of transport and storage and GVA of tourism

Таблица 3

Соотношение ВДС транспорта и хранения и ВДС туризма

Note: The coefficient of evaluation between transport and storage GVA and tourism GVA is 0.6881. This value suggests a moderate positive pressure. In practical terms, this means that when tourism GVA increases, transport and storage GVA also tends to increase. It is also statistically significant.

That is, the weighting analysis supports hypothesis H2, indicating that tourism GVA has a positive and significant impact on transport and warehousing GVA in Tungurahua Province, Ecuador. This finding is crucial for policymakers and stakeholders as it emphasizes the importance of promoting tourism as a strategy to strengthen the transport and warehousing sector in the region.

H3: Tourism GVA benefits the GVA of manufacturing textile products such as clothing; manufacturing of leather and leather goods.

Table 4

Correlation of GVA between the manufacture of textile products such as clothing;

the manufacture of leather and leather goods and GVA in tourism

Таблица 4

Соотношение ВДС между производством текстильных изделий,

таких как одежда, производством кожи и изделий из кожи и ВДС в туризме

Note: The coefficient of evaluation between GVA of textile products such as clothing, leather and leather goods manufacturing and GVA of tourism is 0.7370. This value indicates a strong positive activation, suggesting that an increase in GVA of tourism is strongly associated with an increase in GVA of textile and leather goods manufacturing. The correlation is statistically significant.

The assessment analysis supports hypothesis H3, suggesting that tourism GVA growth significantly benefits the GVA of textile and leather product manufacturing in Tungurahua Province. This finding is important for the development of economic strategies as it highlights the interconnection between tourism and the textile and leather industry, underlining the value of fostering tourism as a means to stimulate the manufacturing industry in the region.

Linear Regressions

Table 5

Linear regression 1 between wholesale and retail trade GVA from wholesale and retail trade; repair of motor vehicles and motorcycles as a function of tourism GVA

Таблица 5

Линейная регрессия 1 между ВДС оптовой и розничной торговли;

ремонт автомобилей и мотоциклов как функция ВДС туризма

Note: The model equation is GVA of wholesale and retail trade; repair of motor vehicles and motorcycles = 167652.6+1.942 × GVA of Tourism in Thousands of USD + ε.

F(1, 12) = 23.95, Prob > F = 0.0004: The F statistic indicates that the model as a whole is statistically significant. The associated probability (0.0004) is very low, suggesting that the model has good explanatory power.

The model presented the following parameters to validate the assumptions: Multicollinearity (VIF=1.00), Heteroscedasticity with a Breusch-Pagan P-value>.05, so the H0 of homoscedasticity is accepted, Normality in the errors with a P-value >.05 in the Jarque Bera test and independence in the errors with a correlation close to zero (=.0000) between the errors and the independent variable.

GVA of Tourism in Thousands of USD (Coef. = 1.942031, P > t = 0.000): This means that for every 1 unit increase in GVA of tourism (in thousands of USD), an average increase of 1.942 units is expected in GVA of wholesale and retail trade; motor vehicle and motorcycle repair. Since the p-value (0.000) is significantly less than 0.05, the coefficient is statistically significant, supporting the hypothesis that GVA of tourism has a positive impact on GVA of wholesale and retail trade; motor vehicle and motorcycle repair.

Constant = 167652.6, P > t = 0.001: The constant indicates the expected value of the GVA of wholesale and retail trade; repair of motor vehicles and motorcycles when the GVA of tourism is zero.

R-squared = 0.6662: The coefficient of determination R-squared is 0.6662, indicating that approximately 66.62% of the variability in the GVA of wholesale and retail trade; motor vehicle and motorcycle repair is explained by the variability in the GVA of tourism. This suggests that the model has a good level of fit.

Adj R-squared = 0.6383: The adjusted R-squared is 0.6383, slightly lower than the R-squared, which adjusts the R-squared to the number of predictors in the model.

Table 6

Linear regression 2 between GVA of transport and storage as a function of tourism GVA

Таблица 6

Линейная регрессия 2 между ВДС транспорта и хранения как функция ВДС туризма

Note: The model equation is GVA of transport and storage = 139553.5 + 1.05 × GVA of Tourism in Thousands of USD + ε.

F(1, 12) = 10.79, Prob > F = 0.0065: The F statistic is significant with an associated probability (0.0065) that is less than 0.05. This indicates that the model as a whole is significant and that tourism GVA is a valid predictor of transport and warehousing GVA.

The model presented the following parameters to validate the assumptions: Multicollinearity (VIF=1.00), Heteroscedasticity with a Breusch-Pagan P-value>.05, so the H0 of homoscedasticity is accepted, Normality in the errors with a P-value >.05 in the Jarque Bera test and independence in the errors with a correlation close to zero (=-.0000) between the errors and the independent variable.

R-squared = 0.4735: This value indicates that approximately 47.35% of the variability in transport and storage GVA can be explained by tourism GVA. Although this value suggests that there are other important factors affecting transport and storage, almost half of the variability is explained by tourism.

Table 7

Linear regression 3 between GVA of manufacturing of textile

products such as clothing; manufacturing of leather and leather goods as a function of tourism GVA

Table 7

Linear regression 3 between GVA of manufacturing of textile

 products such as clothing; manufacturing of leather and leather goods as a function of tourism GVA

Note: The model equation is GVA of manufacturing of textile products such as clothing; manufacturing of leather and leather goods = 91996.74 + 0.93 × GVA of Tourism in Thousands of USD + ε.

F(1, 12) = 14.27, Prob > F = 0.0026: The model is statistically significant as a whole, since the value of Prob > F (0.0026) is less than 0.05, indicating that the independent variable has a significant effect on the dependent variable.

The model presented the following parameters to validate the assumptions: Multicollinearity (VIF=1.00), Heteroscedasticity with a Breusch-Pagan P-value>.05, so the H0 of homoscedasticity is accepted, Normality in the errors with a P-value >.05 in the Jarque Bera test and independence in the errors with a correlation close to zero (=-.0000) between the errors and the independent variable.

R-squared = 0.5432: Approximately 54.32% of the variability in the GVA of textile manufacturing such as apparel; leather and leather goods manufacturing can be explained by the variability in the GVA of tourism. This suggests that tourism is an important factor, although there are other factors that also influence this sector.

Conclusions

The econometric analysis of the relationship between tourism Gross Domestic Product (GDP) and Gross Value Added (GVA) of various economic sectors in the province of Tungurahua reveals significant findings on the impact of tourism on the local economy. Through three linear regressions, it is examined how tourism GVA influences the GVA of the wholesale and retail trade, transportation and storage, and textile and leather manufacturing sectors.

Regression Results

Wholesale and Retail Trade:

Results: The model shows that an increase of 1,000 USD in tourism GVA is associated with an increase of approximately 1,942 USD in wholesale and retail trade GVA, with an R² of 66.62%.

Interpretation: This result indicates a positive and significant relationship, suggesting that tourism growth has a notable effect on local trade, which is vital to the economy of Tungurahua.

Transport and Storage:

Results: The regression indicates that for every 1,000 USD increase in tourism GVA, transport and storage GVA increases by 1,050 USD, with an R² of 47.35%.

Interpretation: Although the impact is smaller compared to the trade sector, it is still relevant. Approximately 47.35% of the variability in this sector is explained by tourism, suggesting that transport is a crucial enabler for tourism activity.

Textile and Leather Products Manufacturing:

Results: In this case, a 1,000 USD increase in tourism GVA is associated with a 935 USD increase in manufacturing GVA, with an R² of 54.32%.

Interpretation: Although the relationship is positive, it suggests that other factors also play an important role in the manufacturing sector. However, tourism remains a driver of growth in this industry.

The analysis reveals that tourism acts as a significant growth driver for several economic sectors in Tungurahua, especially in wholesale and retail trade. The positive and statistically significant relationship in all models suggests that strengthening the tourism sector could have multiplier effects on the local economy.

Although tourism is a key driver, it is crucial to recognize that other factors also influence the GVA of sectors such as transportation and manufacturing. This implies that economic development policies must consider a comprehensive approach, where tourism is encouraged, but other sectors are also strengthened to maximize the economic impact in the region.

In conclusion, the evidence presented underlines the importance of tourism as an essential component in Tungurahua's economic development strategy, and highlights the need for policies that promote the sustainability and growth of the tourism sector to benefit the entire local economy.

Conflicts of Interest: the authors have no conflict of interests to declare.

Информация о конфликте интересов: авторы не имеют конфликта интересов для декларации.