Institutional quality,financial development and tourism market diversification in OECD and non-OECD countries: A panel quantile approach

Abstract

This study examines the determinants of tourism market diversification in 70 OECD and non-OECD countries, while considering the roles of institutional quality and financial development. A model of tourism diversification is developed using the novel method of moment quantile regression and estimated using data span of 24 years. The results show that improved institutional quality, financial development and an increase in average income of visitors lead to an increase in tourism market diversification in the full sample, OECD countries, and non-OECD countries. Increases in infrastructural facilities lead to lower tourism market diversification in the full sample and non-OECD countries, while generating more tourism market diversification in OECD countries. The results further indicate that appreciation of the local currency generates an increase in market diversification in non-OECD countries but a decrease in tourism market diversification in the OECD countries.

Keywords

tourism market diversification institutional quality financial development augmented tourism demand model panel quantile regression Z38 Z30 Z32 C23

Introduction

The diversification of tourism markets is a strategic imperative involving the attraction of visitors from diverse geographic locations and demographic segments (Yap et al., 2023). This approach serves as a vital strategy for mitigating reliance on specific tourist segments, thereby bolstering the overall resilience of the tourism sector. Despite the acknowledged importance of market diversification, a comprehensive exploration of its determinants and their specific impacts remain conspicuously absent in existing literature.

Research findings have demonstrated that diversifying tourism markets offers clear directives for managing risks in destination planning, diminishing reliance on a limited range of products and market segments (Sou and Siu, 2023). It has also been asserted that such diversification plays a crucial role in shaping rural tourism development, stimulating related sector activities, and fostering import substitution in tourism (Weidenfeld, 2018). The economic ramifications of diversifying tourism markets have been scrutinized to assess its potential for generating positive economic outcomes, potentially contributing to enhanced economic growth (Saboori et al., 2023; Solarin et al., 2023).

Moreover, attention has been drawn to the impact of market diversification on economic growth, underscoring the potential positive influence of diversification on economic growth (Gupta et al., 2023). Suggestions have been made that diversifying tourism markets can serve as a mitigating factor against adverse effects stemming from unfavorable social, economic, and political developments on tourism demand, providing innovative strategies for policymakers to consider (Solarin et al., 2023). Yap et al. (2023) delved into the capacity of tourism market diversification to offset the detrimental repercussions of a blockade on tourism. Their findings point out to the substantial role of diversification in fortifying the resilience of the tourism sector. This investigation holds relevance as it directly addresses the implications of tourism market diversification, aligning seamlessly with the primary focus of the current research.

Can and Gozgor (2018) revisited the intricate interplay between tourism and growth, offering valuable insights into the diversification of tourist arrivals. Sou and Siu (2023) delve into the impacts of diversification on tourism demand from a structural change perspective, shedding light on the potential impacts of diversification strategies in attracting visitors from diverse market origins. The researchers provide valuable information on the nuanced relationship between diversification efforts and the dynamics of tourism demand. While there is growing number of research on market diversification and its effect, its determinants have not received the same level of attention. Solarin et al. (2024) examine the factors influencing tourism market diversification in Australia and show that increases in visitors’ income and infrastructure facilities in Australia spurs tourism diversification, while increases in relative price decreases it.

This paper goes beyond Solarin et al. (2024) and investigates the intricacies of factors influencing tourism market diversification in OECD and non-OECD countries. It explores the roles of factors such as institutional quality, and financial development which are expected to influence market diversification. The quality of institutions can be an indication of the weak implementation of laws and violation of law and order, widespread violent activities, heightened corruption, communalism, political tensions, which all portend threat to the general development of a nation and particularly, the tourism industry. A country with deteriorating institutional quality is likely to attract decreasing number of tourists and tourists from a smaller number of home countries, thereby lower tourism market diversification.

Financial development and liberalisation on the other hand, can facilitate development of the tourism industry in several ways. Financial development is associated with increasing funding for firms including tourism firms (Naeem and Li, 2019), some of which are used to fund marketing activities aimed at attracting tourist arrivals from new markets. It may also fund new ventures such as infrastructure development, provision of services, hosting of events, development of new products and consolidation of existing one, all of which will make a destination more attractive and to a wider market. The effect of financial and institutional qualities on market diversification, however, remains an empirical question which this paper seeks to investigate.

Employing a panel quantile approach, the study unravels the nuanced relationships between the dependent variable, tourism market diversification and financial development and quality of institutions using other demand determinants including the average income of visitors, exchange rate, and infrastructural facilities as control variables. The use of panel quantile approach has the advantage of producing complete effects of the independent variables across the different distributions of tourism diversification. The data ranges from 1995 to 2018. This is a period of relative stability compared to 2019 and after where datasets are truncated and incomplete due to the uncertainty and stability resulting from Covid 19 travel restrictions.

The rationale for focusing specifically on OECD and non-OECD countries in this investigation is rooted in several key considerations. Firstly, these country groups represent distinct entities characterized by varying levels of economic development, financial openness, and market characteristics. This differentiation provides a unique opportunity for a comparative analysis of the determinants of tourism market diversification in economies with differing structural and institutional features (Kheng et al., 2023). Additionally, the substantial differences in financial openness between OECD and non-OECD countries (Kheng et al., 2023) further justify this selection, as these disparities can significantly influence the determinants of tourism market diversification.

The subsequent section is structured as follows; Section 2 investigates the connection between tourism market diversification and its independent factors. Section 3 provides the theoretical framework, while Section 4 contains tourism index derivation, model and data explanation, data description and source details, and econometric approach. Section 5 summarizes the study’s findings and Section 6 concludes with a summary of the key results and policy recommendations.

Literature review

Liutak et al. (2018) and Hor (2021) have highlighted the essential role of institutional quality in fostering diversity within the tourism market. Institutions, encompassing legal structures, governance frameworks, and regulatory mechanisms, form the foundation of a destination’s capacity to attract and host a varied spectrum of tourists. These institutions intricately shape the broader business landscape, influencing investment, entrepreneurial activities, and the development of numerous services and products relevant to tourism. The establishment of a robust institutional framework is crucial for attracting a diverse variety of tourists, as argued by Hor (2021). Destinations with strong institutions provide a secure and stable environment conducive to cultivating various tourism segments. Transparent and efficient legal systems, for instance, can stimulate investments across different sectors of the tourism industry, fostering a more varied and resilient market.

The link between institutional quality and the production of tourism products and services is studied in Kim et al. (2018). Destinations with effective institutions are better positioned to generate and oversee a range of tourism offerings. Effective governance serves as a catalyst for collaboration between the public and private sectors (Mushtaq et al., 2021), fostering the creation of infrastructural facilities and a broad array of attractions, accommodations, and activities catering to diverse market segments.

As discussed in Lyu et al. (2023), the ability to innovate of a destination is pivotal to its competitiveness and ultimate success. Innovative endeavors possess broader implications for destinations by augmenting their allure and capacity to adapt to dynamic market conditions. The diversification of the tourism market, achieved through innovative product offerings, can serve as a strategic approach for destinations to alleviate the repercussions of exchange rate fluctuations that exert an influence on their competitive standing. Through the introduction of novel and distinct tourism products, destinations can entice visitors from a broader spectrum of source markets, thereby diminishing their dependence on any single market.

The relationship between infrastructural facilities and tourism market diversification constitutes a central focus in contemporary scholarly investigations within the field of tourism. Numerous studies (e.g., Adeola and Evans, 2020) have underscored the pivotal role played by these facilities in shaping the overall tourism environment. The critical foundation for attracting and accommodating a diverse spectrum of tourists lies in the development of infrastructure, which includes essential components such as transportation, lodging, and communication networks (Nguyen, 2021). Notably, well-connected transportation systems have been linked to an expansion in the demographic spectrum of visitors, spanning from budget-conscious travelers to those seeking high-end travel experiences (Maleki et al., 2020). The provision of robust infrastructure significantly contributes to enhancing the attractiveness of a destination, thereby fostering diversification in the tourism market. Destinations endowed with diverse and well-maintained infrastructure are adept at accommodating the preferences and needs of distinct market segments, thereby broadening their allure to a more extensive spectrum of tourists (Nguyen, 2021; Salinas Fernández et al., 2020).

The diverse components of financial development, which include aspects such as banking infrastructure, credit accessibility, and investment mechanisms, are of paramount importance for the economic stability and advancement of a region. As a result, this significantly impacts the tourism sector by furnishing essential financial resources for infrastructure development, promotional initiatives, and the establishment of a varied range of tourism products and services (Panjaitan, 2022). Financial development actively contributes to tourism market diversification by facilitating the acquisition of funds for investment in various tourism-related ventures. Kim et al. (2018) posits that nations endowed with well-established financial frameworks can attract investments aimed at cultivating a spectrum of tourism offerings (Ehigiamusoe, 2021). Adequate financial support enables the establishment of new attractions, accommodation, and services catering to diverse market segments, thus broadening the range of experiences available to tourists.

Moreover, financial development significantly impacts the competitiveness of countries in the global tourism marketplace. Shahbaz et al. (2019) emphasizes that countries with robust financial systems are better equipped to implement effective marketing strategies, engage in promotional activities, and navigate crises. The financial resources available empower locales to enhance their market visibility and appeal to diverse tourist segments, fostering a diversified tourism market. Furthermore, the relationship between financial development and tourism market diversification extends to the support of small and medium-sized enterprises (SMEs) within the tourism sector (Ibrahim, 2021). Unhindered access to credit and financial services encourages entrepreneurship and the establishment of a diverse range of tourism-related businesses, thereby contributing to the comprehensive diversification of the tourism market (Dahles et al., 2020). The impact of financial development on tourism market diversification is also evident in its role in managing risks and uncertainties. Destinations with resilient financial systems are better prepared to handle economic fluctuations and external shocks, allowing for a more resilient and adaptable tourism market. This adaptability is crucial in attracting a variety of tourists, as locales can navigate challenges and continue to offer diverse experiences (Pyke et al., 2021).

The foregoing literature review indicates that there are several gaps in the existing empirical literature on tourism demand which this paper seeks to address as discussed in the theoretical framework section.

Theoretical framework

One of the gaps observed in the literature is that the role institutional quality in promoting tourism market diversification has not been investigated. Institutional quality signals the attributes of the set of institutions that govern laws, constitution, property rights, and traditions that are needed for the social relationship within a nation. Good institutional quality deters internal tension, rent seeking and free riding, is beneficial to economic development, promotes freedom and civil liberty indices, and fosters cooperation as well as trust (Balli et al., 2016). An increase in internal conflicts within a country is associated with decreases institutional quality and may reduce the number of tourism markets that account for significant number of tourists. This is because rising internal conflicts within a nation indicates that there is a reduced number of nations that the host country is better than in respect to institutional quality. Therefore, tourists from only a small number of nations will likely select such host nation for tourism activities (Lee and Chen, 2021). The foregoing analysis suggests that improved institutional quality should increase tourism market diversification.

The other gap which this paper addresses is the study of how financial development impacts on tourism market diversification. Financial develop is crucial for the modernisation of an economy. Financial development is also connected with a conducive atmosphere that fosters savings as well as investments while providing a high level of liquidity (Levine et al., 2016). The linkage between financial development and the tourism sector is based on numerous channels including the financial markets and institutions being able to facilitate lower cost transactions for tourists (Musakwa and Odhiambo 2022). By fostering investment, financial development is simultaneously enhancing sizeable investments in the tourism sector, therefore improving the destinations’ ability to host more visitors from new markets (Churchill et al., 2023). The widespread of cashless transactions, as enabled by financial development (Wright et al., 2017) at the destination has the ability of drawing visitors from new markets, thereby enhancing tourism market diversification. The omission of such variables in the tourism market diversification analysis might cause omitted variable bias.

Income, exchange rates, and infrastructural facilities are included as a control variables following the conventional tourism demand modelling process (Seetaram 2012). Income growth in the emerging markets boost demand for international travel which benefits destinations and may promote market diversification (Solarin et al., 2024). Therefore, a devaluation or depreciation of a country’s currency makes inbound international tourism less expensive, and attracting tourist flows from visitors from new markets that hitherto used to visit other destinations that are now more expensive (Seetaram, 2012). The development of infrastructural facilities embodies the features of the host countries, and they characterize the destination attractiveness. This is because the quantity and quality of facilities in a destination country indicates the level of preparedness of such destination to attract visitors for tourism purposes (Tsai et al., 2014).

The paper also looks into the effect of Infrastructural facilities development, which signifies the preparedness of the host country to receive both existing visitors and new ones is expected to influence both tourism arrivals and tourism market diversification. So far, the effect of this variable tourism market diversification has only been studied by Solarin et al. (2024) in Australia, a developed country. This paper seeks to provide a multi-country perspective on the subject and addressed it in the context less developed nations by including non-OECD countries in the sample. Tourism infrastructural developments may specifically aim at drawing tourists from new source countries to alleviate the problems of seasonality associated with the tourism industry (Sharpley, 2002) such as the expansion of airport and creation of new air routes and investing in tourism products that are culturally linked to tourists from new source markets (Lahura and Sabrera, 2023). Taking advantages of cultural proximities between destinations and markets can be an effective means of diversifying markets as demonstrated in Petit and Seetaram (2019), cultural proximity is an important determinant of tourism flow. Therefore, an expansion in such infrastructural facilities is anticipated to enhance diversification of the tourism markets.

Methodology

Derivation of the tourism index

The tourism diversification index initiated by Can and Gozgor (2018) has been used in this study. The index is originally based on Herfindahl-Hirshman export diversification index. The diversification index is estimated employing the following description:

T M C = \frac{\sum_{j = 1}^{n i} {(\frac{m_{i j}}{M_{i}})}^{2} - \frac{1}{k_{i}}}{1 - \frac{1}{k_{i}}} .

(1)

Where TMC is the tourism market diversification index, M is the frequency of total tourists into country i, m is the frequency of tourists originating from country j to country i, and k is the frequency of tourism markets in country i. The values that the index assumes ranges from 0 to 1. Values that are close to 1 suggests that the host country has a highly concentrated tourism market and tourists are from few countries. Values that are close to 0 indicates that the host country has a highly diversified tourism market and tourists are from several countries (Can and Gozgor, 2018).

Model and data

Based on the theoretical framework, the following model is used to analyze the determinants of tourism diversification:

{T M C}_{l t} = β_{0} + β_{1} {I N C}_{l t} + β_{2} E X C_{l t} {+ β_{3} {I F R}_{l t} + β_{4} I N S_{l t} + β_{5} {F I N}_{l t} + ϵ}_{l t}

(2)

In equation (2), l is the cross sectional dimension, t denotes time series dimension, INC denotes the average income of visitors, while EXC is the real value of the home country’s currency against the basket of trading partner’s currencies. The two independent variables are also recognized in the theory of consumer behaviour. In this regard, just like any normal good, the demand for tourism can be as a function of consumers’ income. An appreciation of the local currency indicates that the cost of travel or tourist price has increased, and this might inhibit tourism market diversification. IFR captures infrastructural facilities in the host countries, INS denotes institutional quality in the host countries and FIN is financial development in the host countries. These are pull factors that can serve as attractive factors to tourists to a particular destination. All the variables are in logarithmic form apart from institutional quality.

Data description and source

The tourism market diversification data are compiled from the BETA Akademi Social Science Research Lab for the period, 1995-2018. Institutional quality data is based on Polity IV figures, and it is derived from The Centre for Systemic Peace Database. The Polity data bears values from −10 (hereditary monarchy) to +10 (consolidated democracy). Hence higher values indicate better institutional quality. The financial development index data has been collected from the Financial Development Index Database of the International Monetary Fund. It is computed premised on the financial services accessibility, size of liquidity provided by financial system, and efficiency of services provided by the financial system. The average income of visitors is the net global real GDP per capita (constant, 2015 US$), after excluding the figures of each host country. The GDP per capita dataset has been collected from National Accounts - Analysis of Main Aggregates of the United Nations Statistics Division. The exchange rate data is the real effective exchange rate data involving 170 trading partners for each host country. The data is obtained from the Bruegel Datasets Section of the Bruegel AISBL Website. The number of mobile cellular subscriptions (per 100 people) is used as a proxy for infrastructural facilities following Asiedu and Lien (2011). Mobile cellular subscription rate is a good indicator for a country’s consumption capability and access to digital infrastructure (Kaya et al., 2025). Generally, access to mobile devices can promote better access to health and education services and improved efficiency in producing goods and services. Tourists can use mobile devices to gain access to tourism information, guide their journeys, and communicate with tourism authorities (Bala, 2024).

70 countries have been selected for this study because they are the only ones with consistent data for all the variables used in this paper for the 24-year span under study. Therefore, the total number of observations is 1680. The summary descriptive statistics of the variables and the countries under observation are itemised are reported in Table 1. The Kurtosis and Skewness tests indicate that the series are not normally distributed. Most of the variables are logged prior to the econometrics exercise. The tourism market diversification trend for all the clusters under review is presented in Figure 1. It shown that OECD countries have experienced a visible expansion in tourism market diversification.

Table 1.

Descriptive statistics and list of countries.

Variable	Mean	Median	Minimum	Maximum	Standard deviation	Kurtosis	Skewness
TMC	0.168	0.116	0.007	0.960	0.155	6.073	2.304
INS	5.526	8.000	−7.000	10.000	5.762	−0.184	−1.159
FIN	0.404	0.361	0.027	0.974	0.252	−1.017	0.442
INC	8817.881	8913.171	5350.573	12,683.026	1154.686	−0.843	0.021
EXC	102.269	100.000	32.983	234.929	20.016	5.804	1.357
IFR	67.320	72.489	0.003	202.043	50.794	−1.350	0.059
OECD countries				Non-OECD countries
Austria, Australia, Belgium, Chile, Canada, Costa Rica, France, Germany, Hungary, Ireland, Italy, Israel, Japan, Korea Republic, Luxembourg, Lithuania, Mexico, Netherlands, New Zealand, Norway, Portugal, Poland, Slovenia, Slovakia, Spain, Türkiye, United Kingdom, and United States				Algeria, Angola, Argentina, Belarus, Bolivia, Brazil, Bulgaria, Cambodia, China, Croatia, Cyprus, Ecuador, Egypt, El Salvador, Fiji, Georgia, Gambia, India, Jamaica, Jordan, Laos, Kuwait, Malawi, Madagascar, Mauritius, Myanmar, Morocco, Nicaragua, Peru, Paraguay, Philippines, Russia, Romania, Sri Lanka, Singapore, Tanzania, Tunisia, Thailand, Uruguay, Ukraine, Vietnam, and Zambia

Figure 1.

Tourism market diversification index.

Econometrics approach

The method of moment quantile regression (MMQREG) approach proposed by Machado and Santos Silva (2019) deals with the estimation of the quantiles of a regressand $Y (T M C)$ that has a distribution conditional on a set of regressors bundled in X $(I N S, F I N, I N C, E X C, I F R)$ can be specified as:

Y = α + X^{'} β + σ (δ + Z^{'} γ) U,

(3)

where

{(α, β^{'}, δ, γ^{'})}^{'} \in ¡^{2 (k + 1)}

are unknown parameters,

Z

is a set of known differentiable conversions of the regressors in X.

σ (\cdot)

is a known

C^{2}

function so that

\Pr {σ (δ + Z^{'} γ) > 0} = 1;

U is an unobserved random series that is not dependent on

X .

Empirical results

The empirical results with cross-sectional dependence tests in Table 2. The biased-corrected test of Baltagi et al. (2012) and the cross-dependence (CD) test of Pesaran (2021) are applied. The null hypothesis in each test is no cross-sectional dependence. The results of the full sample, which contains all the countries under observation, is reported first and it is shown that cross-sectional dependence is present. To gain a deeper knowledge of the determinants of tourism market diversification, two different clusters of countries including OECD countries and non- OECD countries are considered.¹

Table 2.

Cross sectional dependence test results.

Sample	Biased-corrected test	Pesaran CD test
Full sample	149.577^a (0.000)	3.396^a (0.001)
OECD income countries	83.536^a (0.000)	25.097^a (0.000)
Non-OECD income countries	83.712^a (0.000)	7.713^a (0.000)

^aImply significance at 1%. The parenthesis contains the probability values.

The results suggest that cross-sectional dependence is presented in the different clusters of countries. Fortunately, Machado and Santos Silva (2019)’ method does provide for the presence of cross-sectional dependence. Panel data estimations generate inaccurate outputs when the independence of the cross-sections is overlooked (Pesaran, 2015). The results of the MMQREG estimation technique of Machado and Santos Silva (2019) involving all the five explanatory variables considered in this paper are presented in Table 3.

Table 3.

MMQREG quantile results, full sample.

Independent variable	Location	Scale	Quantile
Independent variable	Location	Scale	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
INS	−0.005 (0.150)	0.003 (0.161)	−0.010^b (0.010)	−0.009^b (0.014)	−0.007^b (0.025)	−0.006^c (0.054)	−0.006 (0.107)	−0.005 (0.203)	−0.003 (0.433)	−0.002 (0.721)	0.001 (0.915)
FIN	−0.208^a (0.000)	−0.021 (0.307)	−0.174^a (0.000)	−0.186^a (0.000)	−0.193^a (0.000)	−0.200^a (0.000)	−0.205^a (0.000)	−0.211^a (0.000)	−0.219^a (0.000)	−0.229^a (0.000)	−0.245^a (0.000)
INC	−0.750^a (0.000)	0.480^a (0.000)	−1.511^a (0.000)	−1.250^a (0.000)	−1.080^a (0.000)	−0.936^a (0.000)	−0.814^a (0.000)	−0.687^a (0.001)	−0.483^b (0.041)	−0.258 (0.356)	0.107 (0.765)
EXC	−0.297^a (0.003)	0.015 (0.818)	−0.320^a (0.004)	−0.312^a (0.001)	−0.307^a (0.001)	−0.302^a (0.001)	−0.299^a (0.002)	−0.295^a (0.004)	−0.288^b (0.015)	−0.281^b (0.045)	−0.270 (0.134)
IFR	0.072^a (0.000)	−0.023^b (0.017)	0.109^a (0.000)	0.096^a (0.000)	0.088^a (0.000)	0.081^a (0.000)	0.075^a (0.000)	0.069^a (0.000)	0.059^a (0.001)	0.048^b (0.024)	0.030 (0.273)

^aImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^bImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^cImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

The results show that improved institutional quality and financial development generate an expansion in tourism market diversification in most of the quantiles. Besides, an appreciation of the local currency generates an expansion in tourism market diversification, across all the quantiles. Furthermore, increases in infrastructural facilities lead to tourism market concentration. The location parameters suggest that increases or improved institutional quality or financial development, or average income of visitors, or real exchange rate generate an increase in the average tourism market diversification in these countries. The location parameters further suggest that increases in infrastructural facilities increases tourism market concentration. The scale parameter suggests that increases in most of the variables lead to less dispersion in tourism market diversification series, although the impact of most of the series are insignificant.

The foregoing results are partly consistent with the findings of Solarin et al. (2024), which indicate that the increases in average income of visitors and infrastructural facilities promote tourism market diversification. The positive connection between institutional quality and tourism market diversification might be premised on improved higher institutional quality that several countries have experienced over the years. Higher institutional quality is associated with decreases in uncertainty in the tourism sector and many tourism markets have continuously faced less uncertainty over the years (Akbar and Tracogna, 2018). Uncertainty is likely to delay necessary decision making for effective tourism market diversification projects. Rising uncertainty-related costs facing destinations might affect the capacity of the destinations to effectively conduct tourism market diversification as the destinations pass the costs to consumers through higher prices. The positive connection between financial development and tourism market diversification can be ascribed to the financial sector growth that many nations have witnessed over the years. Financial development has facilitated different activities including enabling the capacity of tourism operators’ risky business endeavours (Shahbaz et al., 2019), such as tourism market diversification projects as such projects are frequently capital-intensive.

The findings hint that a rise in average income of visitors generates an increase in tourism market diversification, especially in countries with higher tourism market diversification.² The results further show appreciation of the local currency generates an expansion in tourism market diversification, across all the quantiles. The location parameters indicate that more average income of visitors and an appreciation of the local currency increase the average tourism market diversification series in these countries, although the impact of income of visitors is insignificant. The scale parameter indicates that an increase in average income of visitors or appreciation of the local currency generate less dispersion in tourism market diversification series, although the impact of currency appreciation is insignificant. The findings are very similar to the previous results as it is revealed that an increase in average income of visitors leads to an increase in tourism market diversification.

To check for robustness, two additional panel quantile techniques are applied. The first method is the robust panel quantile technique of Chamberlain (1994) and Koenker (2005), and the results are reported in Table 4. This method uses robust standard errors and addresses the possibility of heteroscedasticity. The second method is the fixed effect panel quantile method of Machado and Santos Silva (2019) and the results are reported in Table 5. This approach allows for the individual effects to influence the entire distribution of the dependent variable (Machado and Santos Silva, 2019). The rationale for conducting the robust analysis is to generate results from diverse approaches. There are few differences between the results produced by the two methods (as reported in Tables 4 and 5) and the outputs of the MMQREG analysis.

Table 4.

Robust quantile results, full sample.

Independent variable	Quantile
Independent variable	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
INS	−0.008^a (0.003)	−0.004 (0.298)	0.003 (0.406)	0.003 (0.365)	−0.005 (0.213)	−0.006 (0.223)	−0.004 (0.457)	−0.005 (0.427)	0.008 (0.220)
FIN	−0.118^a (0.000)	−0.123^b (0.015)	−0.244^a (0.000)	−0.282^a (0.000)	−0.281^a (0.000)	−0.328^a (0.000)	−0.323^a (0.000)	−0.271^a (0.000)	−0.120^b (0.026)
INC	−1.144^a (0.000)	−1.188^a (0.000)	−0.936^a (0.000)	−0.984^a (0.000)	−1.117^a (0.000)	−0.976^a (0.000)	−0.344 (0.206)	−0.062 (0.863)	0.995^b (0.016)
EXC	−0.410^a (0.000)	−0.338^a (0.008)	−0.060 (0.490)	0.007 (0.942)	0.029 (0.744)	−0.010 (0.927)	−0.100 (0.407)	−0.192 (0.297)	−0.246 (0.129)
IFR	0.097^a (0.000)	0.088^a (0.000)	0.059^a (0.001)	0.060^a (0.000)	0.078^a (0.000)	0.090^a (0.000)	0.077^a (0.001)	0.038 (0.196)	−0.022 (0.459)

^aimply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^bimply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^cimply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

Table 5.

Fixed-effect quantile results, full sample.

Independent variable	Quantile
Independent variable	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
INS	0.002 (0.856)	0.001 (0.892)	0.001 (0.930)	0.001 (0.998)	−0.001 (0.911)	−0.001 (0.835)	−0.002 (0.792)	−0.003 (0.774)	−0.004 (0.764)
FIN	0.109 (0.359)	0.080 (0.410)	0.060 (0.479)	0.035 (0.634)	0.007 (0.917)	−0.021 (0.784)	−0.051 (0.587)	−0.078 (0.492)	−0.110 (0.431)
INC	−1.118^a (0.000)	−1.026^a (0.000)	−0.961^a (0.000)	−0.881^a (0.000)	−0.791^a (0.000)	−0.698^a (0.000)	−0.602^a (0.000)	−0.515^a (0.002)	−0.413^b (0.043)
EXC	−0.183^c (0.085)	−0.237^a (0.007)	−0.275^a (0.000)	−0.321^a (0.000)	−0.374^a (0.000)	−0.427^a (0.000)	−0.483^a (0.000)	−0.533^a (0.000)	−0.593^a (0.000)
IFR	0.072^a (0.000)	0.067^a (0.000)	0.063^a (0.000)	0.059^a (0.000)	0.054^a (0.000)	0.048^a (0.000)	0.043^a (0.000)	0.038^a (0.007)	0.032^c (0.061)

^aImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^bImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^cImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

It is plausible that tourism diversification changes may affect income of visitors, infrastructural facilities, and institutional quality causing reverse causality in the model. In order to circumvent this eventuality, the instrumental variable approach of Kripfganz and Sarafidis (2021) is applied to re-estimate the model. The approach is based on a two-stage instrumental variable estimation process that use defactored covariates as instruments. The relationships in the series are estimated using a country-level and time-level fixed effects method. This is to alleviate the possibility of omitted variable bias. Lastly, the model is also estimated using a fixed effects method with clustering standard error at country level for robustness purpose. The results associated with the three methods are reported in Table 6 and it is shown that the coefficients of institutional quality, income of visitors, and exchange rate are mostly negative, while financial development and infrastructural facilities produce positive coefficients.

Table 6.

Additional variable estimation results.

Countries	Instrumental variable estimation results	Country-level and time-level fixed effects method	Fixed effects method with clustering standard error.
Independent variable
INS	−0.002^c (0.076)	−0.003 (0.449)	−0.001 (0.955)
FIN	0.141^a (0.000)	0.021 (0.647)	0.004 (0.969)
INC	−0.966^a (0.000)	1.593 (0.115)	−0.780^a (0.002)
EXC	−0.141^a (0.000)	−0.384^a (0.0000	−0.380^a (0.005)
IFR	0.236^a (0.000)	0.092^a (0.000)	0.053^b (0.012)
Frequency of instruments	55
Frequency of observations	1680	1680	1680
Frequency of factors in the first step	1
Frequency of factors in the second step	1
Hansen test	55.771 (0.267)

^aImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^bImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^cImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

The results of the MMQREG estimation technique for OECD countries are presented in Table 7. The results show that improved institutional quality, financial development and increases in infrastructural facilities generate an expansion in tourism market diversification in most of the quantiles in OECD countries. The positive connection between institutional quality and tourism market diversification in the developed countries can be ascribed to not only the high level of institutional quality but also the improvement in institutional quality in the OECD countries. According to The Centre for Systemic Peace Database, most of the OECD countries have institutional quality scores close to the perfect scores of 10. The database also suggests that institutional quality increased by more than 6% in the OECD between 1995 and 2018.

Table 7.

MMQREG quantile results, OECD countries.

Independent variable	Location	Scale	Quantile
Independent variable	Location	Scale	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
INS	−0.017 (0.462)	−0.007 (0.633)	−0.007 (0.799)	−0.010 (0.665)	−0.013 (0.577)	−0.014 (0.532)	−0.016 (0.476)	−0.019 (0.444)	−0.021 (0.431)	−0.024 (0.433)	−0.007 (0.633)
FIN	−0.060 (0.393)	0.083^c (0.054)	−0.186^b (0.023)	−0.144^b (0.046)	−0.116^c (0.090)	−0.099 (0.146)	−0.071 (0.310)	−0.041 (0.583)	−0.010 (0.902)	0.028 (0.770)	0.083^c (0.054)
INC	0.006 (0.985)	0.940^a (0.000)	−1.414^a (0.000)	−0.943^a (0.005)	−0.623^b (0.047)	−0.429 (0.171)	−0.110 (0.735)	0.229 (0.508)	0.577 (0.130)	1.003^b (0.021)	0.940^a (0.000)
EXC	−0.038 (0.865)	−0.628^a (0.004)	0.911^a (0.001)	0.596^b (0.010)	0.383^c (0.075)	0.253 (0.239)	0.039 (0.859)	−0.187 (0.430)	−0.419 (0.108)	−0.704^b (0.018)	−0.628^a (0.000)
IFR	−0.136^a (0.004)	−0.077^a (0.007)	−0.021 (0.706)	−0.059 (0.219)	−0.085^c (0.060)	−0.101^b (0.025)	−0.127^a (0.006)	−0.155^a (0.002)	−0.183^a (0.001)	−0.218^a (0.000)	−0.077^a (0.007)

^aImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^bImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^cImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

The increase in institutional quality may have been partly responsible for some of the improvements recorded in the tourism sector in the developed countries (Balli et al., 2016). Therefore, the improved institutional quality might be responsible for some of the successes of the tourism diversification strategy. The positive connection between financial development and tourism market diversification developed countries can be due to the financial sector improvement that OECD countries has witnessed the sample period. According to Financial Development Index Database, financial system in OECD countries improved by more than 35% during 1995–2018. This has facilitated the provision of loans, overdraft services, leasing and insurance facilities in addition to fixed asset financing arrangements to the tourism sector (Shahbaz et al., 2019). These financial facilities are needed in the tourism market diversification projects. The findings confirms that that an increase in average income of visitors leads to an increase in tourism market diversification in countries, especially countries with higher tourism market diversification.

The results of the MMQREG estimation technique for non-OECD countries are presented in Table 8. The findings revealed that an improved institutional quality and financial development generate an expansion in tourism market diversification in most of the quantiles.

Table 8.

MMQREG quantile results, non-OECD countries.

Independent variable	Location	Scale	Quantile
Independent variable	Location	Scale	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
INS	−0.010^a (0.008)	0.001 (0.803)	−0.011^b (0.018)	−0.011^a (0.007)	−0.011^a (0.005)	−0.010^a (0.005)	−0.010^a (0.007)	−0.010^b (0.010)	−0.010^b (0.025)	−0.010^c (0.056)	−0.009 (0.170)
FIN	−0.270^a (0.000)	−0.098^a (0.000)	−0.110^b (0.021)	−0.164^a (0.000)	−0.196^a (0.000)	−0.237^a (0.000)	−0.260^a (0.000)	−0.284^a (0.000)	−0.326^a (0.000)	−0.367^a (0.000)	−0.450^a (0.000)
INC	−0.438 (0.120)	0.346^c (0.055)	−1.001^a (0.004)	−0.812^a (0.006)	−0.698^b (0.012)	−0.553^b (0.043)	−0.473^c (0.088)	−0.388 (0.180)	−0.241 (0.457)	−0.097 (0.793)	0.196 (0.688)
EXC	−0.250^b (0.034)	0.331^a (0.000)	−0.789^a (0.002)	−0.608^a (0.000)	−0.499^a (0.002)	−0.360^a (0.002)	−0.283^b (0.014)	−0.202^c (0.096)	−0.062 (0.650)	0.076 (0.626)	0.357^c (0.084)
IFR	0.083^a (0.000)	−0.010 (0.320)	0.100^a (0.000)	0.094^a (0.000)	0.091^a (0.000)	0.086^a (0.000)	0.084^a (0.000)	0.081^a (0.000)	0.077^a (0.000)	0.072^a (0.001)	0.064^b (0.025)

^aImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^bImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

^cImply significance at 1%; 5% and 10% respectively. The parenthesis contains the probability values.

The positive connection between institutional quality and tourism market diversification in the developing countries can be ascribed to the improvement that non-OECD countries experienced during the sample period. According to The Centre for Systemic Peace Database, institutional quality increased by more than 8% in the developing countries between 1995 and 2018. Since the increase in institutional quality is partly responsible for tourism sector improvements in developing nations (Balli et al., 2016), institutional quality may also be responsible in the success of tourism diversification strategies in some of the developing countries. With very low institutional quality, several developing countries suffer from negative perception of the outlook for the country and therefore, require improvement in institutional quality to attract tourism from diverse markets.

The finding for non-OECD show that improvement in institutional quality and the development of the financial systems have positive effects market diversification. These two variables are highly significant in explaining the variation in the market diversification index of non-OECD countries across most quantiles. The effect development of financial systems is higher compared to other explanation variable but lower relative to OECD countries. Several developing countries have experienced improved financial sector, and new financial products and services have been introduced. According to Financial Development Index Database, financial system in non-OECD countries improved by more than 39% during 1995-2018. Some of the products and services have been beneficial to their tourism sectors. For instance, microfinance services deliver credit to micro- and small enterprises including tourism businesses, that commercial banks might considered too volatile to be granted loans. (Can and Gozgor, 2018). Surprisingly however, the results further show that improvement in infrastructural facilities will lead to tourism market concentration.

Conclusion and policy implications

The aim of this study is to examine the determinants of market diversification in both OECD and non-OECD. It uses data from 70 countries, expanding on 24 years. The paper focuses on the role of institution quality, financial development as determinants of market infrastructure. Infrastructure development, income of visitors, and exchange rates as control variables. The paper contributes to the literature by highlighting the importance of institutional quality and financial development on tourism market diversification. Both variables of interest are found to be significant on positive in explaining market diversification. One surprising additional contribution is that the finding that infrastructure development leads to market concentration in non-OECD countries. The findings for income and exchange rate confirm those from the literature see for example Solarin et al. (2024).

The theoretical implication of the findings is that the tourism demand framework is more informative when it is treated as a platform for explaining not only the volume of arrivals but also the composition of demand. Traditional demand models, following Morley (1998) and Seetaram (2012), are primarily concerned with tourist flows as a function of income, prices, exchange rates, and destination attributes. The present findings suggest that this approach remains necessary but incomplete when the outcome of interest is diversification. A destination may remain highly exposed to concentration risk even when tourist arrivals are strong. Explaining diversification, therefore, requires a broader framework that incorporates the institutional and financial conditions under which market broadening becomes feasible.

The practical implications of the findings are numerous. The findings reveal a strong positive relationship between institutional quality tourism market diversification in both OECD and non-OECD countries as expected. They suggest that governments should prioritise improving governance structures. This is relevant not only for tourism diversification but for the whole economy. High institutional quality implies that laws are consistently enforced, policies are predictable, and public institutions function efficiently and transparently. In the context of tourism, strong institutional quality reduces uncertainty and risk for both investors and visitors. It fosters a secure and trustworthy environment, encourages foreign investment in tourism infrastructure, and improves the overall visitor experience. As a result, countries with higher institutional quality are better positioned to attract tourists from a wider range of origin markets, thereby enhancing tourism market diversification.

Government policies which aim at reducing corruption, enhancing the quality of rules and regulations, strengthening the rule of law and reducing red tape, can increase tourism arrivals from a wider range of market and help reduce overreliance on a few markets only. The government and tourism authorities can promote more transparency and effectiveness in institution frameworks directly focussing on the tourism sector such as issuing of visas, data governance, promotion activities, enforcement of business regulations and destination safety would be an even more positive step in that direction.

Linked to the above factor is financial development which also support tourism market diversification. Improved efficiencies of financial intermediaries, access to credit, and stronger market and institutions will benefit the tourism in both OECD and non-OECD countries. It is recommended that government use the right mix of fiscal and monetary policies to strengthen their banking systems to expand access to small and medium tourism and tourism related enterprises will benefit the sector significantly. It can mean that more funds are available to develop tourism products and tourism marketing both of which will benefit the sector. It is also recommended that financial institutions and tourism businesses put in place measures to modernise payments system which is an aspect of financial development which can directly benefit market diversification. For example, as wider access to online payments, reducing and eliminating credit card fees from the destination on international transactions, accepting novel methods of payment such as Google Pay and Apple Pay will reduce the transaction costs for the individual tourist and make destinations more attractive to visitors from a wider range of markets including new ones.

Infrastructure improvements show contrasting effects between OECD and non-OECD countries. In the OECD countries, it is recommended that government at all levels and businesses continue to investments infrastructure to promote tourism market diversification. This will be enabled especially with improved local institutions which have been shown to promote investment especially from the private sector. In non-OECD countries, infrastructure development seems to instead lead to market concentration. It widely accepted in the tourism development literature that investment in infrastructure has nevertheless, a positive impact on the sector and on the economy of the destination as a whole. Non-OECD countries may need to complement infrastructure deployment with marketing campaigns targeting a wider range of tourism generating countries.

One limitation of this study is that uses access to mobile devices as a proxy for infrastructure. It does consider various types of infrastructure investment, even though each type may have distinct effects on tourism market diversification. It can be hypothesised that of investment in certain type of infrastructure will have a higher rate of return as opposed to other. For example, the expansion of airport infrastructure may be more beneficial for tourism market diversification than improvements in internet coverage at the destination. However, this study does not incorporate such granular distinctions due to lack of such data for the whole sample and therefore, cannot provide definitive conclusions on the relative effectiveness of specific types of infrastructure. Future research could address this gap.

Further studies can also look the effect of public as compared to private in investment in infrastructure. Higher level of public investment may entail crowding out in period of economic growth. Higher level of public investment can be funded by higher taxes or increased borrowing. Both will discourage spending from the private sector. Private investment on the other hand, will depend on the rate of return on investment which may not yield the most efficient allocation of resources, especially in the presence of externalities. It will be interesting to assess which has the higher positive impact on market diversification with the lowest associated costs to the economy. To develop more targeted and context-specific policy recommendations, future research should incorporate more detailed, disaggregated measures of infrastructure development.

Similarly, future studies can disaggregate institutional quality into components such as government effectiveness, regulatory quality, rule of law, control of corruption, and political stability to determine which dimensions matters most for tourism market diversification. The same is true for financial development, where future studies could separate access, depth, efficiency, and digital-finance components.

Footnotes

Acknowledgements

Taiwo Temitope Lasisi gratefully acknowledge the financial support of the Faculty of Informatics and Management of the University of Hradec Králové (FIM UHK) within the framework of the Project Excellence “Cognition and ICT in interpretation, anthropology and management of sustainable tourism”

ORCID iDs

Sakiru Adebola Solarin

Taiwo Temitope Lasisi

Neelu Seetaram

Funding

Declaration of conflicting interests

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

Notes

Author biographies

Sakiru Adebola Solarin read for his Ph.D. in Universiti Utara Malaysia. He is affiliated with Multimedia University Malaysia as a professor. Prior to joining the Multimedia University, he was an Associate Professor and Exam Officer at the School of Economics, University of Nottingham, Malaysia. He was the Chairperson of Centre for Globalisation and Sustainability Research (GCSR) for several years. He has written several journal papers on numerous areas of tourism economics and has participated in many national and international conferences.

Dr. Taiwo Temitope Lasisi is a postdoctoral researcher at the University of Hradec Králové, Czechia, where she contributes to projects on sustainable tourism and environmental management. Dr. Lasisi earned his Ph.D. in Tourism Management. Her scholarly work focuses on the intersections of tourism development, environmental quality, and economic growth, with particular attention to sustainable practices and destination competitiveness. Notably, she has served as a project member in a UNESCO-funded initiative, underscoring her engagement with international collaborations that link academic research to global sustainability goals.

Dr Neelu Seetaram is Professor of Tourism Economics at Leeds Beckett University, United Kingdom. She specialises in the study of the tourism and airline industries, with a keen interest in issues related to tourism demand modelling, the effect of taxation and immigration on tourism; and impact of the tourism industry on economies and poverty reduction. She regularly publishes her work in world leading academic journals and serves on the editorial boards of prestigious journals such as the Journal Travel Research and Tourism Economics.

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