Analyzing the Influence of Macroeconomic Variables on Tourism Receipts in OIC Countries: A Quantile Analysis Approach Across Varying Income Levels

Analyzing the Influence of Macroeconomic Variables on Tourism Receipts in OIC Countries: A Quantile Analysis Approach Across Varying Income Levels

Saizal Pinjaman^1,2,*, Abdul Rahim Ridzuan^2,3, Suddin Lada², Shaidathul Jemin³

¹Centre for Economic Development and Policy, Universiti Malaysia Sabah, Malaysia

²Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah, Malaysia

³Universiti Teknologi Mara, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.9020180

Received: 31 January 2025; Accepted: 10 February 2025; Published: 10 March 2025

ABSTRACT

This study examines the heterogeneous effects of macroeconomic variables on tourism receipts across income tiers in Organization of Islamic Cooperation (OIC) countries, addressing a critical gap in tourism economics literature. Employing a panel quantile regression approach on data from 34 OIC countries (1995–2020), the research analyzes how exchange rates, income per capita, inflation, and trade openness differentially influence tourism receipts at lower, middle, and upper quantiles of tourism spending. The methodology leverages quantile regression for panel data (QRPD) to account for non-linear relationships and income-level heterogeneity, offering a nuanced alternative to traditional linear models. Key findings reveal significant income-tiered disparities. Exchange rate depreciation enhances tourism receipts in most contexts, except in upper-middle-income countries, where volatility signals instability. Income per capita exhibits positive yet inelastic effects in low- and lower-middle-income nations, but adverse impacts in high-income countries, suggesting market saturation. Inflation erodes receipts in low-income economies but stimulates demand in high-income OIC states, reflecting divergent tourist price sensitivities. Trade openness consistently suppresses tourism across quantiles, with high-income countries experiencing the steepest declines. These results challenge the universality of the Tourism-Led Growth Hypothesis, emphasizing context-specific dynamics. Policy recommendations advocate for income-tiered strategies: low-income countries should prioritize exchange rate stabilization, inflation control, and infrastructure investments via Islamic finance instruments, while high-income nations must pivot to premium niches like halal and cultural tourism. Regional cooperation through unified visa policies and OIC-wide halal certification standards is critical to harnessing collective tourism potential. Policymakers are urged to balance trade liberalization with targeted tourism-sector incentives to mitigate resource diversion. By aligning interventions with macroeconomic realities, OIC countries can transform tourism into an engine of equitable growth, advancing Sustainable Development Goals (SDGs) on economic diversification and inclusive employment.

INTRODUCTION

Tourism has emerged as a pivotal driver of economic growth, particularly in developing and emerging economies, where it serves as a vital source of foreign exchange earnings, employment generation, and economic diversification. For member countries of the Organization of Islamic Cooperation (OIC), the tourism sector holds immense potential, given their rich cultural heritage, historical landmarks, and natural attractions (Zamani-Farahani & Eid, 2016). However, despite these advantages, the tourism industry in OIC countries remains underdeveloped, with significant disparities in performance across nations. For instance, while countries like Turkey, Malaysia, and the United Arab Emirates have established themselves as global tourism hubs, others, particularly low-income OIC nations, struggle to attract international visitors and generate substantial tourism revenues (Mahmoudinia, 2024). This disparity highlights the uneven distribution of tourism benefits within the OIC bloc and underscores the need to examine the underlying factors influencing tourism receipts, particularly macroeconomic variables, which play a critical role in shaping tourism demand and expenditure patterns.

The OIC region, comprising 57 member states, is characterized by diverse economic structures, ranging from high-income oil-exporting nations to low-income agrarian economies (Nafar, 2019). This economic heterogeneity is reflected in the tourism sector, where income levels, infrastructure development, and policy frameworks vary significantly. For example, high-income OIC countries often benefit from advanced infrastructure, robust marketing strategies, and favorable exchange rates (Garini et al., 2025), which enhance their competitiveness in the global tourism market. In contrast, low-income OIC countries face challenges such as inadequate infrastructure, limited access to financing, and political instability, which hinder their ability to capitalize on tourism opportunities. These disparities are further exacerbated by external factors such as global economic fluctuations, geopolitical tensions, and health crises, which disproportionately affect tourism-dependent economies (Hamarneh, 2022).

Moreover, the tourism sector in OIC countries is uniquely positioned to leverage the growing global interest in cultural and religious tourism. The OIC region is home to some of the world’s most significant religious sites, including Mecca and Medina in Saudi Arabia, Al-Aqsa Mosque in Palestine, and the Blue Mosque in Turkey, which attract millions of pilgrims and tourists annually. Additionally, the concept of halal tourism, which caters to the needs of Muslim travelers, has gained traction in recent years, offering OIC countries a niche market to explore (Alrawadeh, 2024). Despite these opportunities, the full potential of cultural and religious tourism remains untapped in many OIC countries due to insufficient investment in infrastructure, marketing, and service quality. Furthermore, the lack of regional cooperation and integration in tourism development initiatives has limited the ability of OIC countries to collectively promote their tourism offerings on the global stage.

The relationship between macroeconomic factors and tourism receipts has been extensively studied in the context of developed and emerging economies (Rivera, 2017; Gopalan & Khalid, 2024). However, the unique economic, cultural, and geopolitical characteristics of OIC countries necessitate a tailored analysis. Existing literature highlights the influence of variables such as exchange rates, income levels, inflation, and trade openness on tourism demand. For instance, studies like Belloumi (2010) have demonstrated that exchange rates and income exhibit a positive relationship with tourism receipts, while inflation and trade openness present more complex, context-dependent effects (Soh et al., 2024). Despite these insights, there is a paucity of research examining how these relationships vary across different levels of tourism spending and income within OIC countries. This gap is particularly significant given the heterogeneous economic structures and development trajectories of OIC member states.

The primary objective of this research is to analyze the influence of macroeconomic variables on tourism receipts in OIC countries, employing a quantile regression approach to capture the heterogeneous effects across varying income levels. By doing so, this study aims to provide a nuanced understanding of how factors such as exchange rates, income, inflation, and trade openness differentially impact tourism receipts at lower, middle, and higher quantiles of tourism spending. This approach not only addresses the limitations of traditional linear models but also offers actionable insights for policymakers seeking to optimize tourism strategies tailored to specific economic contexts.

The relevance of this study is multifaceted. First, it contributes to the growing body of literature on tourism economics by extending the analysis to OIC countries, a region that has been relatively underexplored in this context. Second, the use of quantile regression represents a methodological advancement, enabling the identification of threshold effects and non-linear relationships that are often overlooked in conventional analyses. Third, the findings of this research have practical implications for OIC countries, providing evidence-based recommendations for enhancing tourism competitiveness and economic resilience. For instance, understanding the differential impact of macroeconomic variables can inform targeted interventions, such as currency stabilization measures for low-income countries or trade liberalization policies for high-income nations.

LITERATURE REVIEW

The relationship between macroeconomic variables and tourism receipts has been a focal point of academic research, driven by the tourism industry’s significant contribution to global economic growth and development. Theoretical frameworks such as the Tourism-Led Growth Hypothesis (TLGH) and the Economic Dependency Theory provide a foundation for understanding how macroeconomic factors influence tourism demand and receipts. The TLGH posits that tourism stimulates economic growth through foreign exchange earnings, job creation, and infrastructure development (Seetanah et al., 2022; Lin, 2024), while the Economic Dependency Theory emphasizes the sensitivity of tourism to external economic shocks, such as exchange rate fluctuations and income disparities (Monterrubio et al., 2018). These theories underscore the bidirectional relationship between tourism and macroeconomic variables, highlighting the need for empirical studies to explore these dynamics in diverse contexts.

Recent studies have extensively examined the impact of key macroeconomic variables—exchange rates, income per capita, consumer price index (CPI), and trade openness—on tourism receipts, employing advanced econometric techniques to capture nuanced relationships. Exchange rates, for instance, have been widely recognized as a critical determinant of tourism demand (Martins et al., 2017; Louail, 2020). A depreciation in the local currency often makes a destination more affordable for international tourists, thereby boosting tourism receipts. Martins et al. (2017) utilized panel data analysis to demonstrate that exchange rate volatility significantly influences global tourism demand, with stable or favorable exchange rates positively impacting tourism inflows. Similarly, Brida and Risso (2010) and Khan et al. (2020) highlighted the importance of relative prices, which are closely tied to exchange rates, in shaping tourism demand, particularly in developing economies.

Income per capita, both domestically and in source countries, is another crucial macroeconomic variable affecting tourism receipts (Culiuc, 2014; Khalid et al., 2020). Higher income levels generally correlate with increased disposable income, enabling greater spending on travel and tourism activities. Wijesekara et al. (2022) employed Granger causality and wavelet coherence analysis to reveal a bidirectional relationship between income levels and tourism demand across 105 countries, emphasizing the role of income in driving tourism growth. In the context of OIC countries, Soofi et al. (2018) found that income per capita exhibits a positive relationship with tourism receipts, although the strength of this relationship varies across countries with different economic structures.

The consumer price index (CPI), which measures inflation, also plays a significant role in shaping tourism demand. High inflation rates can erode purchasing power and increase the cost of travel, thereby deterring tourists. However, the relationship between CPI and tourism receipts is not always straightforward. For example, Pinjaman et al. (2023) found that while moderate inflation can signal economic growth and stimulate tourism demand, hyperinflation has a detrimental effect on tourism receipts, particularly in low-income OIC countries. This finding aligns with earlier studies by Lee and Chang (2008), who emphasized the importance of price stability in sustaining tourism growth, especially in developing economies.

Trade openness, which reflects a country’s integration into the global economy, has also been identified as a key determinant of tourism receipts (Shahbaz et al., 2017). Countries with higher levels of trade openness tend to attract more tourists due to improved infrastructure, enhanced connectivity, and greater exposure to international markets. Pinjaman et al. (2023) highlighted the positive impact of trade openness on tourism development, noting that it facilitates the flow of goods, services, and tourists across borders. However, the relationship between trade openness and tourism receipts can be complex, as increased trade openness may also expose countries to external economic shocks, which can negatively impact tourism demand (Mutoni, 2019).

Despite the wealth of research on the topic, significant gaps remain in the literature, particularly concerning the influence of macroeconomic variables on tourism receipts in OIC countries. While existing studies have explored the relationship between macroeconomic factors and tourism demand, few have employed a quantile analysis approach to capture the heterogeneous effects across varying income levels. This gap is particularly critical given the economic diversity within the OIC bloc, which includes high-income oil-exporting nations and low-income agrarian economies. Addressing these gaps would not only advance academic discourse but also provide actionable insights for policymakers seeking to enhance tourism competitiveness and economic resilience in the region.

METHODOLOGY

The current study utilizes the international tourism receipt per arrival in US dollars to represent the tourism demand while macroeconomic factors are represented by the exchange rate, income per capita, consumer price index, and trade openness. These macroeconomic factors are determined based on the findings of previous research.

Panel Quantile Regression Analysis

The analysis of panel quantile regression is employed to evaluate the correlation between tourism receipts and macroeconomic factors. Quantile regression, according to Koenker and Hallock (2001), entails analysing the quantiles of the dependent variable’s conditional distribution as functions of a number of variables. The goal of the quantile regression model is to estimate the entire range of conditional quantile functions, as opposed to the classical linear regression model, which estimates models for conditional mean functions (Buhai, 2004).

According to Wang et al. (2019), quantile regression analysis is appropriate when there is heterogeneity and the interaction between the variables varies across quantiles. It also permits varying elasticities of the factors along the independent variable’s distribution (Martinez-Zarzoso et al., 2017). This method helps researchers concentrate their attention on specific portions of a conditional distribution and produces convincing results, especially for misspecification errors brought on by non-normality and the presence of outliers (Koenker & Hallock, 2001; Lin et al., 2021).

The current paper specifically utilised the quantile regression for panel data, or QRPD, of Powell (2021), which allows non-additive fixed effects in the quantile functions and maintains the nonseparable disturbance term. Considering the uneven size of the tourism receipts of the OIC countries, this method offers the best approach.

The potential outcome framework of the model is described below:

\( Y_{it}^d = q(d_1, U_{it}^{(*d)}) \)

Where \( Y_{it} \) is the outcome and \( d \) is the random potential values of independent variables. Let \( U_{it}^{(*d)} \) be a function of some fixed and time-varying disturbance terms:
\( U_{it}^{(*d)} = f(\alpha_i, U_{it}) \) with \( U_{it}^{(*d)} \sim U(0, 1) \).
The quantile function of \( q(d, \tau) \) is strictly increasing in \( \tau \), a given quantile of the outcome distribution. To permit individual heterogeneity, the model assumes that:
\( P(Y_{it} \leq q(D_{it}, \tau) \mid Z_{it}) \) varies by individual and generally is not equal to \( \tau \). \( D_{it} \) is the general term for treatment variables and \( Z_{it} \) are the instruments.

The QRPD estimates the quantile treatment effects (QTE) which represent the causal effect of a change of the independent variable from \( d_1 \) to \( d_2 \) on \( Y_{it} \) at a particular quantile:
\( q(d_2, \tau) – q(d_1, \tau) \).

The structural quantile function of interest is:
\( S_Y (\tau \mid d) = q(d \mid \tau), \tau \in (0, 1) \)

The quantile function defines the quantile of the latent outcome variable:
\( Y_{it}^d = q(d_1, U^*) \)
for a fixed \( d \) and a randomly selected \( U^* \sim U(0, 1) \). Powell (2021) said QRPD produces consistent estimates for small \( T \).

The current study uses QRPD to investigate how the variables of interest interact at various income levels. Based on the World Bank classification, the OIC countries are then split into four income levels in the following model: low income, low-middle income, upper-middle income, and high-income countries. 34 OIC nations with complete data from 1995 to 2020 were selected for this study.

Pre- and post-estimations

Quantile regression minimises the weighted sum of absolute deviations, making it robust to outliers and non-normal distributions. According to Nouri (2022), this yields a more efficient estimate than conventional least squares when there are non-normal or asymmetric error term distributions. Furthermore, absolute estimation is preferred in dealing with multicollinearity, heteroskedastic variance, and model misspecification (Harter, 1975). This led to the relaxation of pre- and post-estimations for the quantile model.

Results analysis

Table 1a: Summary Statistics for Low Income Countries

  Table 1b: Summary Statistics for Low-Middle Income Countries

 Table 1c: Summary Statistics for Upper-Middle Income Countries

 Table 1d: Summary Statistics for High Income Countries

Table 1(a-d) presents summary statistics of the data transformed into natural logarithms, revealing small standard deviations for the variables across all income levels. Moreover, the medians of all variables closely resemble their respective arithmetic means, falling distinctly between the minimum and maximum values. This indicates minimal variability in the data, rendering the presence of outliers improbable.

Table 2: Panel quantile regression

Table 2 shows the results of the panel data quantile regression analysis, where the international tourism receipt per arrival in US dollars at different quantiles is treated as the dependent variable while the exchange rate, income per capita of the domestic market, inflation rate, and trade openness are considered the independent variables. The results align with the Tourism-Led Growth Hypothesis in lower-income contexts but challenge its universality, particularly in high-income OIC nations where macroeconomic stability and niche marketing outweigh traditional growth drivers. These findings underscore the need for income-tiered policies, as one-size-fits-all strategies fail to account for the unique economic structures and tourist preferences shaping OIC countries’ tourism trajectories.

Based on the analysis, it is evident that the exchange rate is highly related to tourism receipts across all percentiles, with the exception of the lower percentiles of the lower middle-income host countries. With the exception of the higher-middle-income host countries, where the impact is negative, it has been established that the exchange rate has a positive impact on tourism receipts. A 1% increase in the exchange rate, which indicates the depreciation of the domestic currency, will cause tourism receipts to rise by 0.5% to 10%, depending on the level of income of the host countries. In addition, the strength of the relationship is higher for hosts with high incomes, where the coefficients are higher and more significant. In light of this, it may be deduced that tourists are more sensitive to fluctuations in the exchange rate against the US dollar when they are staying with higher-income host countries, as opposed to those with lower incomes.

Income per capita for the host countries, on the other hand, is believed to be significant and positively related to tourism receipts across all points of receipts and for most of the OIC countries, as expected. This suggests that tourists are concerned about the level of income, which is commonly associated with the improvement in infrastructure and travel security, of the host countries. The small value of the coefficient postulates a significant yet inelastic relationship between the variables, where a 1 percent increase in the income per capita for the host countries will cause tourism receipts to increase by 0.09% to 0.39%. Furthermore, as seen by the higher level of coefficients, this is more obvious to the host countries that have lower incomes and lower middle status. It is worth mentioning that in high-income countries, income per capita is adversely impacting tourism receipts at the lower and upper ends of the percentiles.

The relationship between inflation and tourism receipts is negative, as demonstrated in low- and lower-middle-income countries. As the tourism receipt increases to higher points, the adverse impact of inflation is stronger, suggesting that higher-spending tourists in low- and low-middle-income countries are severely affected by the increase in price level. A 1 percent increase in inflation will cause tourism receipts to fall up to 1 percent at the 40th percentile for these countries. Meanwhile, inflation seems to have positively impacted the receipts for upper-middle and high-income host countries across all percentiles of tourism receipts, as well as the higher percentiles for the lower-income host countries. The positive impact is more visible for the high-income hosts, where an increase in inflation of 1 percent can lead to an increase in tourism receipts of up to 1.7 percent.

Trade openness is mostly negative in its relationship with tourism receipts for most of the tourism receipt percentiles and across all income levels of the host countries. Moreover, the strength of the relationship increases in the high-income host countries, with the change in tourism receipts falling up to 4 percent at the 60th percentile as trade openness increases by 1 percent. In the low-income host countries, trade openness is demonstrated to be insignificant in explaining the movement of tourism receipts at the higher points.

CONCLUSION AND RECOMMENDATIONS

This study’s quantile regression analysis reveals critical insights into the heterogeneous effects of macroeconomic variables on tourism receipts across income levels in OIC countries. Key findings underscore that exchange rate depreciation generally enhances tourism receipts, except in higher-middle-income countries, where currency stability may signal economic confidence. Income per capita exerts a positive, inelastic influence, particularly in low- and lower-middle-income countries, aligning with the Tourism-Led Growth Hypothesis, as argued by Seetanah et al. (2022) and Lin (2024), by emphasizing infrastructure and security improvements. However, in high-income countries, income per capita shows adverse effects at certain quantiles, potentially reflecting market saturation or shifting tourist expectations toward experiential over infrastructural quality. Inflation’s dual role—negative in low-income nations due to eroded purchasing power but positive in high-income contexts, possibly signaling economic vitality—mirrors earlier studies on price stability’s importance. Trade openness consistently reduces tourism receipts, likely due to resource diversion from tourism to trade sectors, a finding that challenges assumptions about globalization’s uniform benefits.

These results extend prior research by contextualizing macroeconomic dynamics within OIC’s economic diversity. The quantile approach validates non-linear relationships posited by the Economic Dependency Theory, particularly the asymmetric impacts of exchange rates and inflation. For instance, while Martins et al. (2017) emphasized exchange rate stability, this study nuances that high-income OIC countries may benefit less from depreciation due to premium market positioning. Similarly, the inflation dichotomy supports Pinjaman et al.’s (2023) contention that moderate inflation can stimulate demand in robust economies but harm fragile ones.

The findings of this study underscore the need for OIC countries to adopt income-tiered and context-specific strategies to maximize tourism receipts while addressing macroeconomic vulnerabilities. For low-income OIC countries, policymakers should prioritize exchange rate stabilization through prudent monetary policies to enhance destination affordability without inducing volatility, coupled with targeted inflation control measures such as fiscal discipline and subsidies for tourism-related goods and services. Concurrently, investments in infrastructure, safety, and service quality are critical to amplify the positive spillover effects of rising income per capita, which enhances tourist confidence. These nations could leverage international partnerships or Islamic financing mechanisms, such as sukuk bonds, to fund such initiatives. High-income OIC countries, in contrast, should shift focus from price competitiveness to value-driven tourism by capitalizing on niche markets like luxury travel, halal tourism, and cultural heritage. While inflation in these contexts may signal economic vitality, central banks must ensure it reflects quality upgrades in tourism services rather than macroeconomic instability.

Regional cooperation is pivotal for OIC-wide tourism growth. Member states should establish platforms to promote intra-regional religious and cultural tourism, harmonize visa policies, and launch joint marketing campaigns under a unified OIC tourism brand. A “Halal Tourism Corridor” initiative could standardize halal-certified services—from hospitality to logistics—across member states, enhancing their collective appeal to Muslim-majority markets. To counterbalance the adverse effects of trade openness on tourism, governments should incentivize tourism-linked investments—such as eco-resorts or heritage conservation projects—through tax breaks or public-private partnerships, ensuring trade liberalization does not divert resources from the tourism sector.

Finally, OIC countries must institutionalize data-driven monitoring systems to track the real-time impact of macroeconomic shifts on tourism. Income-level-specific tourism observatories, supported by the Islamic Development Bank or OIC-affiliated bodies, could provide actionable insights for adaptive policymaking. By tailoring interventions to economic contexts and fostering regional synergies, OIC nations can transform tourism into a catalyst for equitable growth, aligning with Sustainable Development Goals (SDGs) on decent work, industry innovation, and reduced inequalities.

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