how exchange rate affect tourism

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Moderation analysis of exchange rate, tourism and economic growth in Asia

Bosede Ngozi Adeleye

1 Dept of Accountancy, Finance and Economics, University of Lincoln, Lincoln, United Kingdom

2 Lincoln International Business School, University of Lincoln, Lincoln, United Kingdom

Jimoh Sina Ogede

3 Dept of Economics, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

Mustafa Raza Rabbani

4 Dept of Accounting and Finance, British University of Bahrain, Sar, Kingdom of Bahrain

Lukman Shehu Adam

5 Dept of Economics and Development Studies, Kwara State University, Malete, Nigeria

Maria Mazhar

6 School of Economics, Quaid-i-Azam University, Islamabad, Pakistan

Associated Data

All relevant data are within the paper and its Supporting Information files.

This study brings novelty to the tourism literature by re-examining the role of exchange rate in the tourism-growth nexus. It differs from previous tourism-led growth narrative to probe whether tourism exerts a positive effect on economic growth when the exchange rate is accounted for. Using a moderation modelling framework, instrumental variables general method of moments (IV-GMM) and quantile regression techniques in addition to real per capita GDP, tourism receipts and exchange rate, the study engages data on 44 Asian countries from 2010 to 2019. Results from the IV-GMM show that: (1) tourism exerts a positive effect on growth; (2) exchange rate depreciation hampers growth; (3) the interaction effect is positive but statistically not significant; and (4) results from EAP and SA samples are mixed. For the most part, constructive evidence from the quantile regression techniques reveals that the impact of tourism and exchange is significant at lower quantiles of 0.25 and 0.50 while the interaction effect is negative and statistically significant only for the SA sample. These are new contributions to the literature and policy recommendations are discussed.

1. Introduction

The tourism and hospitality industry has experienced development and expansion making it one of the biggest and fastest-growing sectors [ 1 ]. Many countries and destinations have grown in popularity, resulting in an increase in the number of visitors and tourism receipts. The tourism sector has the potentials to make significant contributions to economic growth and development through a variety of channels. It is a “currency earning sector” that permits the use of human and physical capital stock to drive innovation and development. Simultaneously, the tourism sector is either directly or indirectly related to other sectors like transportation, accommodation, or retailing through trickledown effect [ 2 ]. It also influences spending, and expands trade and global competitiveness [ 3 ]. International tourism, in particular, is a source of foreign exchange generation which improves the balance of payment position [ 4 ] and eases the acquirement of advance technologies and capital goods that can be used in other manufacturing processes [ 5 , 6 ]. Furthermore, it plays an important role in stimulating investments in new infrastructure and enhancing competition thereby creating jobs and improving overall living standard [ 2 ].

Similarly, the exchange rate influences economic growth. In this paper, an improvement/increase in the exchange rate indicates the appreciation of a domestic currency against a foreign currency. It is a significant indicator of economic progress as it essentially mirrors the competitiveness between a domestic economy and the rest of world. The exchange rate reflects a standard exchange among purchasers and merchants of foreign currency in the foreign exchange market of a particular country. Particularly, non-oil trades, oil exporters, international tourist expenditures, and foreign remittances all drive inflow of foreign currency. According to Rapetti et al. [ 7 ] the growth effect of exchange rate specifically the real exchange rate (RER) is both growth-amplifying and growth-dwindling. The exchange rate can significantly affect a country’s balance of payments position particularly if the country’s reliance on imported goods is high. In these circumstances, a more competitive RER would aid in relieving foreign exchange bottlenecks that would otherwise stymie the development process.

The connection between tourism and the exchange rate is not far-fetched. International tourism receipts are significant sources of foreign exchange earnings and highly linked to the exchange rate. Changes in exchange rates greatly affect tourism demand in a destination as changes in the exchange rate will have an impact on the currency value of the country of origin. Any adjustments in the exchange rate will prompt an appreciation or depreciation of the tourist’s currency, affecting transportation costs and the tourist’s decisions to visit the country. Thus, the exchange rate has an impact on the number of tourists’ visits as well as tourism receipts [ 8 ]. Less flexible exchange rates are supposed to advance global exchange and tourism by lessening vulnerability in worldwide transactions, wiping out exchange costs, and expanding market transparency. Furthermore, the exchanges rate mimics the relative price differential (as it affects global economic environment, purchasing power and overall wealth of tourists), which tourists have insufficient information about since they make travel arrangements in their own currency in advance before leaving their country. In this way, low-uncertainty exchange rate regimes could promote international tourism flows [ 9 ] that in turn speed up the development process through foreign direct investment and globalization [ 10 ].

Tourism as a commodity is very susceptible to exchange rate shocks which affects tourists’ inclination to visit a foreign country. We, therefore, hypothesize that changes in the exchange rate will influence the impact of tourism on economic growth. To the best of our knowledge, this is the first study to empirically test this hypothesis. That is, does the exchange rate tilt the tourism-growth dynamics? To probe the discourse, an unbalanced panel data on 44 Asian economies from 2010 to 2019 comprising tourism receipts, per capita GDP (proxy for economic growth), official exchange rate and a set of control variables is used. To ensure the robustness of the results, a blend of econometrics techniques is deployed. To control for possible endogeneity of the tourism variable, the instrumental variable technique nested within the generalised method of moments (IV-GMM) is used [ 11 – 13 ]. Lastly, the quantile estimator [ 14 – 16 ] is used in the event that the dependent variable has a non-normal distribution. This empirical approach makes the study novel and holistic in ensuring a critical examination of its core arguments. The rest of the paper is structured as follows: section 2 discusses the literature; section 3 outlines the data and empirical model; section 4 discusses the results, and section 5 concludes.

2. Literature review

Tourism activities are considered as one of the most important sources of economic growth and foreign exchange earnings around the globe [ 2 , 6 , 17 ]. The literature on tourism development and its impact on exchange rate and economic growth has increased exponentially in the last three decades [ 18 , 19 ]. The studies on tourism and growth nexus have proliferated mainly due to the fact that international tourism has grown over the years despite some ephemeral shocks [ 20 ]. The tourism growth literature mainly focuses on the causal relationship between tourism and economic growth [ 19 , 21 – 23 ] whereas, tourism and exchange rate literature focus mainly on exchange rate volatility and tourist flows [ 24 – 26 ]. We divide our literature review into two parts; the first part consists of available literature on tourism and economic growth whereas, the second part consists of tourism and exchange rate.

2.1 Tourism and economic growth

This section discusses the literature on tourism economics focusing on economic growth and tourism nexus. From a theoretical perspective, Lanza and Pigliaru [ 27 ] were among the first to document the tourism-growth nexus. They find that countries with high tourism sectors experienced high economic growth. They developed a Lucas type-two sector model where tourism is taken as one of the sectors which depends on the endowments of natural resources such that countries with abundant natural resources have high growth potential and achieve a faster rate of growth. Perles-Ribes et al. [ 28 ] studied the tourism and economic growth nexus using autoregressive distributed lag (ARDL) and Toda-Yamamoto model for the period 1957 to 2014 taking into consideration the economic crises. Their findings revealed a bi-directional relationship between economic growth and tourism development. There are many studies proposing the hypothesis that growth of tourism in the country is directly linked to economic prosperity [ 29 ]. The study reports that there is bidirectional causality between tourism and economic growth. Fuinhas et al. [ 22 ] report that in the long run, high frequency of tourist arrivals in the country leads to positive economic growth. In another study, Naseem [ 30 ] concludes that in the long run, tourism receipts, number of tourist arrivals, and total expenditure have a strong positive relationship with economic growth. The study empirically examined the data from Saudi Arabia and validated the popular hypothesis that tourism leads to economic growth in the country. Similar findings were obtained by [ 31 – 35 ], where they concluded that tourism has a positive impact on the economic growth of the country. The study by Sahni et al. [ 36 ] used a quantile regression approach and concluded that tourism growth has a more pronounced effect on economic growth below the threshold and above the threshold. The study further concluded that countries with lower economic growth have more benefits from tourism development. The study by Selvanathan et al. [ 37 ], applied ARDL, vector error correction model (VECM) and panel frameworks and concluded that in the long run tourism development positively contributes to growth. Tourism development is the significant predictor of the economic growth and financial development at frequency rather than the low frequency [ 38 ]. On the contrary, Croes et al. [ 39 ], revealed that tourism development has a very short term effect on economic development and a negative and indirect link to human development. Similar findings were obtained by Kyara et al. [ 23 ] where it was revealed that there is a unidirectional causality relationship between tourism development and economic growth.

2.2 Tourism and exchange rate

The effects of exchange rate on tourism development can differ across the country, territory and within the tourism jurisdiction [ 38 ]. The real and nominal appreciation of the currency leads to a negative impact on the tourism development in the country [ 40 ]. Exchange rate has asymmetric impact on tourism on tourism development in developing countries such as, India, Bangladesh, Pakistan and Nepal in the short run [ 41 ]. Boskurt et al. [ 42 ] applied dynamic common correlated effects (DCCE) approach in their study on demand and exchange rate shocks on tourism development and concluded that effects of the exchange rate shocks are temporary on the tourism development. To examine the response of tourism demand to exchange rate fluctuation in South Korea, Chi [ 43 ] used ARDL model and concluded that tourists are sensitive to the appreciation of the Korean Won, whereas they are insensitive to its depreciation. The findings of the study imply that foreign visitors in Korea are loss averse and with increase or decrease in the exchange rate volatility tend to affect the tourism demand in an asymmetric manner. Dogru et al. [ 44 ] used ARDL approach to examine the trade balance and exchange rate taking evidence from tourism development. The study concluded that depreciation and appreciation of the US Dollar affects the bilateral tourism with Canada, Mexico, and the United Kingdom (UK). The study further concluded that in the long-run the appreciation of the US dollar negatively affects the tourism trade balance with Canada and the UK while it does not affect the tourism development with Mexico in the long-run. A study by Belloumi [ 45 ], examined tourism receipts and exchange rate nexus in Tunisia and concluded that there is a cointegrating relationship between tourism and economic growth. An increase in foreign direct investment (FDI) and appreciation of the exchange rate contracts the tourism demand of the country while in the long-run the depreciation of domestic currency and decrease in FDI inflow results in more tourist inflow [ 41 ]. Similar findings were obtained by [ 46 ] and [ 47 ] where they revealed that reduction in FDI inflow and depreciation of foreign exchange rate results in positive tourism development.

2.3 Tourism, exchange rate and economic growth

There are few studies that investigated the nexus of exchange rate, tourism development and economic growth [ 23 , 48 , 49 ]. Primayesa et al. [ 50 ] probed the dynamic relationship among real exchange rate, economic growth and tourism development in Indonesia using variance decomposition and impulse response function approach. The study revealed that in explaining the tourism shock in Indonesia, the real exchange rate is less important than the economic growth. The study further concluded that the shock of economic growth and real exchange rate has a positive effect on tourism activity in the short- and long-term. Harvey et al. [ 25 ] applied bounds testing approach to cointegration and error-correction modelling to examine whether tourism development and exchange rate promote the economic growth in Brunei Darussalam, Indonesia, Malaysia, and the Philippines. The study revealed the Philippines is the only country that has the positive long-run and short-run impact from the tourism industry and exchange rate.

3. Data and methodology

This study uses data on nine variables sourced from World Development Indicators (WDI) for 44 countries located in East Asia and the Pacific (EAP) and South Asia (SA) from 2010 to 2019. Availability of sufficient data on the variables of interest–per capita GDP, tourism receipts, and official exchange rate—justify the inclusion of a country in the sample and to explore the heterogeneity of the sample countries, we disaggregate the full sample into EAP with 36 countries and SA having 8 countries. The countries are East Asia and the Pacific (36): American Samoa, Australia, Brunei Darussalam, Cambodia, China, Fiji, French Polynesia, Guam, Hong Kong SAR, China, Indonesia, Japan, Kiribati, Korea, Dem. People’s Rep., Korea, Rep., Lao PDR, Macao SAR, China, Malaysia, Marshall Islands, Micronesia, Fed. States, Mongolia, Myanmar, Nauru, New Caledonia, New Zealand, Northern Mariana Islands, Palau, Papua New Guinea, Philippines, Samoa, Singapore, Solomon Islands, Thailand, Timor-Leste, Tonga, Vanuatu, Vietnam. South Asia (8): Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka.

3.1 Dependent variable

Real GDP per capita is the proxy for economic growth. Studies on tourism-growth nexus have widely used it [ 51 – 53 ] likewise, those on exchange rate-growth relationship [ 54 , 55 ].

3.2 Main explanatory variables

From World Development Indicators, International tourism, receipts (% of total exports) is defined as: expenditures by inbound visitors including payments to foreign carriers for international transport. In other words, this composite variable captures the spendings of inbound tourists to Asia and the Pacific, among others. In line with the literature [ 56 – 60 ], tourism receipts which is the first main explanatory variable is proxied by tourism receipts in current US dollars. Existing literature have found a positive relationship between different dimensions of tourism and economic growth [ 61 – 65 ]. The second key explanatory variable is exchange rate [ 42 , 66 – 68 ]. The exchange rate captures the competitiveness of a country in the international market [ 69 – 73 ]. Lastly, to address the study questions, an interaction term of tourism receipts with exchange rate (TRPT*XR) is included to determine if exchange rate moderates the impact of tourism on growth.

3.3 Control variables

The set of control variables align with those used in growth models: mobile phone subscription [ 5 , 74 , 75 ], individuals using the Internet [ 76 , 77 ], labour force participation [ 78 ] (Niebel, 2018), foreign direct investment net inflows [ 79 ], domestic credit to the private sector [ 80 – 83 ] and services trade [ 84 , 85 ]. We expect positive coefficients in line with existing literature. Table 1 details the variables used.

Source: Authors’ Compilation from World Bank [ 86 ] World Development Indicators (WDI)

3.4 Empirical model

We specify two baseline linear models that expresses economic growth as a function of tourism receipts, exchange rate and a set of control variables which satisfies the first objective:

Where, ln PC it = natural logarithm of per capita GDP; ln TRPT it = natural logarithm of tourism receipts; XR it = official exchange rate; Z ′ it and K ′ it = vector of control variables in natural logarithms; α i , γ i = parameters to be estimated; φ t , δ t = year dummies (which controls for common shocks such as the global financial crises of 2007–2009), and u it , e it = general error term. To satisfy the second objective, we add an interaction term ( TRPT*XR ) to Eq [ 1 ] and the model becomes:

Where, R ′ it = vector of control variables in natural logarithms; η i = parameters to be estimated; ω t = year dummies (which controls for common shocks such as the global financial crises of 2007–2009), and v it = general error term. From Eq [ 3 ], η 3 provides two information. First, the sign of the coefficient indicates if exchange rate exerts a significant moderation effect on economic growth. That is, whether the interaction of both variables intensifies or hinders growth. Secondly, the magnitude of the coefficient may sustain or sway the impact of tourism on growth which is derived as:

3.5 Estimation techniques and strategy

Specifically, our econometric strategy consists of a three-step procedure. First, we examine linear impact of tourism on economic growth. Next, we estimate the linear effect of exchange rate on economic growth. Lastly, we perform the moderation analysis to show the interaction effect on economic growth. We engage these analyses using two techniques: the instrumental variables-two-step generalised method of moments (IV-GMM) techniques and the quantile estimator [ 14 – 16 ]. Specifically, the IV-GMM technique is used to correct for cross-sectional dependence, endogeneity, autocorrelation and heteroscedasticity in the data [ 11 , 87 ]. It uniquely deploys the ivreg2 routine in Stata version 16 developed by Baum, Schaffer, and Stillman [ 12 , 13 ]. The routine performs several variants of single-equation linear regression models including the generalized method of moments (GMM). Hence, the GMM variant which implements the two-step feasible GMM estimation (that is, gmm2s option) is adopted to ensure that our results are devoid of endogeneity, heteroscedasticity and autocorrelation [ 12 ]. On the other hand, the quantile regression is deployed to examine the potentially differential effects of tourism and exchange rate at different levels of growth. The quantile regression model is a defined solution to minimize the equation for the θ th regression quantile, 0< θ <1 and expressed thus:

Where, y t is the dependent variable and x t is a k x 1 vector of explanatory variables.

4. Results and discussions

4.1 summary statistics and correlation analysis.

The upper panel of Table 2 contains the correlation matrix’s results, illustrating the relationship between the regressors and the outcome variables. Our findings indicate a negative correlation between per capita GDP and official exchange rate, implying that rising income will decrease the exchange rates in Asia. Likewise, individuals use the internet and the official exchange rate. Trade in services is negatively associated with tourism receipts, official exchange rate, FDI, and MOB. These findings suggest that increasing individuals using the internet and trade in services will impact the official exchange rate, tourism receipts, FDI, and MOB.

*** p<0.01

** p<0.05

* p<0.1

ln = Natural logarithm; PC = per capita GDP; TRPT = tourism receipts; XR = Official exchange rate; DC = Domestic credit to the private sector; LAB = labour force participation rate; FDI = foreign direct investment; MOB = mobile phone subscriptions; NET = individuals using the Internet; TRS = trade in services; 9.08E+09 = 9,080,000,000.00

Source: Authors’ Computations

The lower panel of Table 2 indicates the summary statistics for the variables from 2010 to 2019. The average of per capita GDP, tourism receipts, official exchange rate, domestic credit to the private sector, labour force participation, foreign direct investment, mobile phone subscriptions, internet users, and trade in services are 12398.47, 9080000, 1295.02, 71.42, 69.06, 1540000, 92490468, 38.86, and 30.269, respectively, from the entire sample. At the same time, the standard deviation provides information on the deviation from sample averages.

4.2 IV-GMM results

Table 3 displays results for the instrumental variables-two-step generalised method of moments (IV-GMM). Across the Full, EAP, and SA samples, tourism receipts and exchange rate are instrumented with their first difference and level terms. Limiting to the variables of interest, the summary of the linear models from the full sample shows tourism receipts as a significant positive predictor of economic growth. The findings indicate that a percentage change leads to 0.88% rise in economic growth, on average, ceteris paribus . We argue that a well-structured tourist sector together with investments in modern infrastructure will boost growth supporting Tugcu [ 88 ], Alfaro [ 89 ], Calero and Turner [ 90 ], Cheng and Zhang [ 91 ], and Scarlett [ 92 ] all of which argue in favour of tourism-driven growth. The exchange rate shows a significant negative effect on growth. According to the findings, a percentage-point change in the exchange rate results in a 0.00005% drop in economic growth. The reason for this is not far-fetched. Exchange rate fluctuations influence potential travellers’ decisions to alter their destination or shorten their vacation resulting in revenue loss for economies. This may result in adjustments to visitors’ travel plans while in a particular nation [ 93 ]. These findings corroborate those of Lin, Liu, and Song [ 94 ], Meo et al. [ 95 ], Sharma and Pal [ 96 ], Chi [ 43 ], and Seraj and Coskuner [ 97 ]. For EAP countries, tourism increases economic growth by 0.62%, on average, ceteris paribus . On the other hand, the coefficient of the exchange rate is negative and significant at 1 per cent, which supports the argument of Vieira et al. [ 98 ] and Seraj and Coskuner [ 97 ]. These studies contend that local currency appreciation will decrease the spending power of international tourists with consequent decline on tourism demand and economic growth. In South Asia, the effect of tourism on growth is positive but statistically not significant but exchange rate significantly boosts growth by 0.007%, on average, ceteris paribus . This finding contradicts Seraj and Coskuner [ 97 ] and suggests that currency appreciation is growth-enhancing. For the moderation models, columns [ 3 , 6 , 9 ] reveal that the interaction effect is positive but statistically not different from zero for the full and EAP samples while it decreases growth in South Asia which contradicts Sharma, Vashishat, and Rishad [ 99 ]. In other words, the conditional effect of tourism on growth reduces when exchange rate appreciates in South Asia.

t -statistics in (); -5.40e-05 = 0.0000540; ln = Natural logarithm; PC = real per capita GDP; TRPT = tourism receipts; XR = Official exchange rate; DC = Domestic credit to the private sector; LAB = labour force participation rate; FDI = foreign direct investment; MOB = mobile phone subscriptions; NET = individuals using the Internet; TRS = trade in services.

On the reliability of the instruments used to validate the robustness of our estimations, we controlled for identification and exclusion restrictions which are indispensable for robust GMM estimations [ 12 , 13 ]. Having used the IV-GMM estimation in ivreg2 , the appropriate test of overidentifying restrictions and testing the validity of instruments used is the Hansen J statistic: the GMM criterion function. From the lower panel of Table 3 , the p -value of the Hansen-J statistic across the six models ranges between 0.085 and 0.3874 which is clearly above 0.05. Hence, it fails to reject the null hypothesis of instruments validity indicating that the instruments used are valid and robust to our analysis.

4.3 Quantile regression results

Table 4 presents the quantile regression results across the 25th, 50th, and 75th quantiles of economic growth. The topmost panel displays the full sample results where tourism significantly improves growth at the 25 th and 50 th quantiles by 0.23% and 0.12%, respectively. Noticeably, the positive effect of tourism receipts declines along the distribution. On the other hand, exchange rate appreciation shows a reducing effect on growth at the 25 th and 50 th quantiles by -0.000051% and -0.000059%, respectively. This reducing effect is larger at the 50 th quantile indicating that economic growth vulnerable to exchange rate fluctuations. Following our findings, we hypothesise that variations in the official exchange rate affects tourist purchasing decisions and economic growth in the long-run [ 100 ]. On the interaction effect, we find no significant impact on growth corroborating the results shown in Table 3 .

I-statistics in (); ln = Natural logarithm; PC = per capita GDP; TRPT = tourism receipts; XR = Official exchange rate; DC = Domestic credit to the private sector; LAB = labour force participation rate; FDI = foreign direct investment; MOB = mobile phone subscriptions; NET = individuals using the Internet; TRS = trade in services.

The results of East Asia and the Pacific displayed in the middle panel indicate that tourism significantly increases growth at the 25 th and 50 th quantiles by 0.44% and 0.31%, respectively. A reducing positive effect is observed similar to that of the full sample. Also, exchange rate appreciation shows a reducing effect on growth at the 25 th and 50 th quantiles by -0.000061% and -0.000067%, respectively. Similar to the full sample, this reducing effect is larger at the 50 th quantile and we find no significant interaction effect on growth. From the lowest panel, the results from South Asia indicate that tourism significantly increases growth at the 50 th and 7 th quantiles by 0.17% and 0.19%, respectively. An increasing positive effect is observed contrary to the full and EAP samples. Likewise, exchange rate appreciation increases economic growth across all the quantiles, though with a declining trend from 0.0087% to 0.0075%. Contrary to the full and EAP samples, a significant negative interaction effect is observed across the quantiles supporting the results shown in Table 3 .

5. Conclusion and policy recommendation

This current study highlights the role of exchange rate in influencing the effect of tourism on economic growth in Asia. To the best of our knowledge, this is the first study that critically evaluates the influence of exchange rate on the tourism-growth nexus. That is, it gauges the nonlinear effect of tourism on economic growth when the exchange rate is accounted for. This position differs from other tourism-growth studies [ 22 , 27 – 30 , 101 , 102 ] that investigated the direct and linear effect of tourism on economic growth but aligns with Adeleye et al. [ 103 ] who examined a similar nexus on Sri Lanka. For the most part, these studies affirm that tourism exerts a direct and positive effect on economic growth. However, we expand the frontiers of knowledge having recognized that the exchange rate is an important macroeconomic policy instruments for promoting sustainable economic growth and encouraging tourism flows as it serves as an essential factor influencing the decision of tourists regarding tourism destinations. To this end, this paper examines the moderating effect of exchange rate and tourism receipts on economic growth in Asia from 2010 to 2019. From the full sample, findings from IV-GMM and quantile regressions techniques revealed that tourism significantly boosts economic growth, and the exchange rate indicates a negative effect. Deductively, we conclude that tourism is growth-enhancing which supports the tourism-led growth conjecture and that exchange rate appreciation is also growth-reducing. On the interaction effect, though the coefficient is positive but statistically insignificant it suggests that currency appreciation may possess inherent potentials in sustaining the positive effect of tourism on economic growth. Results from the East Asia and the Pacific and South Asia are diverse.

Based on the findings, the following recommendations are made for the government and stakeholders in Asia: (1) Provide a sound and efficient financial system which does not only provide adequate funding for promoting the tourism sector but also ensure easy accessibility to aid foreign tourist’s transaction. (2) Initiate investment incentive policies for the tourism sector which will reduce the operating cost, investment outlay and provide security for the investment of tourist investors. (3) Initiate a well-managed exchange rate system that supports tourism flows and economic growth. For further studies and subject to data availability, the role of government regulation, real exchange rate and competitiveness in relation to the tourism-growth dynamics may be undertaken.

Supporting information

Funding statement.

The author(s) received no specific funding for this work.

Data Availability

• PLoS One. 2022; 17(12): e0279937.

Decision Letter 0

30 Aug 2022

PONE-D-22-18174Moderation analysis of exchange rate, tourism and economic growth in AsiaPLOS ONE

Dear Dr. Adeleye,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. 

In view of the referees’ feedback and my own reading of your paper, we believe your paper is some way from being publishable. In particular, there are serious doubts about the underlying hypotheses on which the research is based, as well as about the methodology used.

While we consider the issues identified to be major in nature, we are willing to offer you a chance to rework the paper if you feel able to address them fully and robustly. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Partly

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Reviewer #2: N/A

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Reviewer #3: No

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Reviewer #1: Idea of the paper and statistical parts have been performed appropriately and rigorously. And there is no problem. But some formal parts have mistakes and errors. Some of them:

1- Abstract part should be improved.

2- Too many references. Can be reduced according to the journal index (Scopus, Sci, ssci etc.)

3- No need to citations in Data and Methodology parts. They must be in Literature Review part.

4- In text there are citation errors. For example more than 3 authors use et al. Some parts it is true but some parts wrong.

5- Some citations are missed in references especially in page 12.

6- Use "literature review" instead of "Review of literature".

7- At conclusion part comparisons with previous studies can be made.

Reviewer #2: The paper attempts to examine “moderation analysis of exchange rate, tourism and economic growth in Asia”. After reviewing, I find that this paper is interesting. The paper is readable ragarding the case of economic growth in Asian in the background of exchange rate, tourism and their interactive association.

See the attachment

Reviewer #3: The paper under consideration looks at the impact of tourism on GDP growth and the interactions of the impact with exchange rate. In my opinion this paper has important shortcomings that will prevent it from being published in the current form. My suggestion is rejection. The issues that lead to my decision are as follows:

1. The paper largely ignores the growth regression literature and certainly aims to be a part of it.

2. The value added generated in the tourism sector is in fact part of the overall value added of the economy. This is largely correlated with the international tourism. What sense does it have to regress GDP on a component of it? We can find out quite precisely what is the EXACT contribution of tourism to GDP and GDP growth.

3. The models are estimated by GMM. However, what are the instruments? The paper does not seems to use any sort of Arellano-Bond, Arellano Bower System-GMM. So the description is vague. And in particular, the panel System-GMM methods are mainly used to solve the endogeneity caused by the lagged dependent variable and not the inherent endogeneity of the economic problem posed here. So this part clearly needs clarification and justification. It does not suffice to write that „results are devoid of endogeneity, heteroscedasticity and autocorrelation.”

4. The measurement of both TRPT and GDP in USD should be discussed, i.e., the volume of tourist services may be positively related to depreciating exchange rate but its value in USD may not.

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• Hospitality Industry

Exchange Rate trends, how do they impact hotel performance?

November 12, 2020 •

5 min reading

In this report we analyze how exchange rate fluctuations affect the hospitality industry. We consider the case of Switzerland, which is a small open economy located in the heart of the European monetary union.

Switzerland is a very special country because, thanks to its stability, it has always been considered a safe haven where international investors put their resources when there is economic turmoil and uncertainty in the rest of the world. The combination of these two features - a high degree of openness and a currency which has a tendency to appreciate - makes Switzerland a very interesting case study. Why? Because a strong currency reduces the ability of Switzerland to trade.

DOWNLOAD THE EXCHANGE RATE TRENDS REPORT NOW

In general, exchange rate fluctuations affect any generic sector exposed to trade with foreign countries, as follows:

In particular, Hospitality and Tourism industries fall into the category of export-oriented sectors because they export services. When we export goods, we physically move a good from the country where it has been produced to a foreign country where it is going to be consumed. In the case of services, instead, we export a service whenever the consumer (resident in a foreign country, the tourist) physically goes to the country of the producer, where he/she consumes the service. Consider for example a German resident going on holiday to the UK. Any night spent in a UK hotel is considered as an export from UK to Germany.

As explained in Table 1, a stronger Swiss franc not only reduces Germans’ incentives to go to Switzerland for their holidays, but also stimulates Swiss people to go to Germany for their vacation, given their relatively high purchasing power abroad.

Switzerland has historically been perceived as a “safe haven”. During turmoil, investors have a tendency to buy Swiss francs which produces the effect of strengthening the currency. In Figure , we show an index which represents the exchange rate between the Swiss franc and several other currencies. An increase in the index is an appreciation of the Swiss franc. As you can observe, we had strong appreciations during and after the 2008 Great Recession, as well as during the COVID-19, which was born as a health crisis, but soon turned into an economic crisis.

Figure 1: Nominal exchange rate between the Swiss franc and a set of other currencies

Figure 2: Exchange rate between CHF and Euro. How many CHF for 1 Euro.

If we focus more specifically on the exchange rate between the Swiss franc and euro and on the period between 2000 and 2018 (Figure 2), we can see that in 2000 the exchange rate between CHF and euro was 1.6 (1.6 CHF for 1 euro), while starting from the world financial crisis in 2008, we observe a progressive strengthening of the Swiss franc. During the crisis, Switzerland was perceived as a safe haven, which explains why investors started to strongly buy Swiss francs. Such a high demand increased the value of the Swiss franc, which almost reached parity with the euro (1 CHF = 1 euro) in 2011. This is why the Swiss National Bank (SNB) intervened in September 2011 introducing a limit to Swiss franc appreciations with respect to the euro. With this intervention, the SNB committed to acting on the forex market with the goal of preventing the exchange rate to go below 1.2 CHF for 1 euro. This intervention lasted until January 2015, when the SNB decided that it was time to let the exchange rate freely fluctuate. As you can see from the picture, that same day, the CHF strongly appreciated and reached parity with the euro (1 CHF for 1 euro).

Having observed the fluctuations in hotel demand and pricing, we study whether these fluctuations are associated to exchange rate movements.

• In order to do so, we classify hotels by geographic market , class (luxury, up upscale, upscale, up midscale, midscale and economy) and type of operation (independent, franchise and chain) and we analyze how the different categories of hotels respond to exchange rate appreciations.
• Additionally, we focus our attention on the intervention of the SNB and we study whether it affected the behaviors of hotels and clients between June 2011 and January 2015. Is it possible that pricing and consumption behaviors changed during the SNB intervention? Is it possible that agents (hotels and consumers) modified their behaviors knowing that the SNB was protecting them?

Hotel performance is surely related to local factors which go beyond hotel class and operation (business model). Northern areas of Switzerland seem to be more exposed to international competition and react more to exchange rate fluctuations. Southern and central areas are more touristic, but somehow seem more protected from international competition. One reason might be that their prices are relatively low with respect to the average Swiss hotel prices (central Switzerland) or another reason might be that their demand is quite rigid (Ticino for example has a relatively high average ADR and does not show any intention to reduce it because of exchange rate appreciations. Mountain regions have an average ADR but a relatively low occupancy).

Nevertheless, our results seem to suggest that chains and higher class hotels (luxury, upscale) have a better ability to insure themselves against exchange rate fluctuations. If necessary, independent hotels also limit their losses, but in a way that is different from chains. Independent hotels simply do not react to shocks at all, while chains are more prone to change prices in response to market forces.

Data suggest that over time the market is expanding in a stronger way in the regions that better react to exchange rate appreciations (Figure 3): Lake Geneva and Northern Switzerland. In fact, even if during the last decade hotels in this region had to face some negative shocks that implied some losses, we should always remember that, on average, their performance is well above the one of all the other parts of Switzerland.

Similarly, we observe in the last twenty years an important increase in luxury and upper scale hotels (Figure 4), and chains (Figure 5), which seems quite consistent with our results.

Figure 3: Evolution of hotels by region between 2000 and 2018

Figure 4: Evolution of hotels by class between 2000 and 2018

We also observe a larger increase in chains and franchises rather than in independent hotels, which still represent the vast majority of hotels in Switzerland.

Figure 5: Evolution of hotels by operation between 2000 and 2018

The present study was conducted before the COVID-19, using data between 2000 and 2018. Nevertheless, its main implications may apply also now. During the first months of this health crisis that soon turned into an economic crisis, the Swiss franc in fact showed a tendency to appreciate towards most of the currencies (Figure 1), replicating a situation similar to the one that we observed during the 2008 crisis. Future research will have to delve deeper into the analysis to understand whether the recent franc appreciations produced similar results on the hotel industry as the ones that we observed during and immediately after the Great Recession of 2008.

Associate Professor in Economics at EHL

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The consequences of exchange rate trends on international tourism demand: evidence from India

• Research Paper
• Published: 08 August 2019
• Volume 21 , pages 270–287, ( 2019 )

Cite this article

• Akhil Sharma 1 ,
• Tarun Vashishat 2 &
• Abdul Rishad   ORCID: orcid.org/0000-0003-1418-5619 1  

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Exchange rate is frequently considered as a key determinant in international tourism demand models. Tourism export is one of the major sources of India’s foreign exchange earnings. So understanding the dynamics of exchange rate and tourism is essential for planning and execution of tourism policies. This paper empirically investigates the extent to which exchange rate fluctuations affect India’s international tourism receipts. In order to achieve this goal, the paper employed quarterly data ranging from 2003Q1 to 2017Q4 within an Autoregressive Distributed Lag (ARDL) framework. Using Wald coefficients, the study found cointegration among the variables. It further discovered that variables are correcting the shock-induced disequilibrium at a high speed of 96%. Furthermore, the study established a significantly negative link between exchange rate and international tourism receipt. We also found that the overall impact of the exchange rate is time-invariant, i.e. having similar long-run and short-run impacts on international tourism demand, though the short-run magnitude is higher than the long-run one. The outcomes of this study help practitioners to frame suitable policies to manage their currency exposure. Based on findings, the study suggests better management of the exchange rate to protect the external competitiveness of rupee for attracting more foreign tourists. Moreover, development of innovative hedging instruments helps to reduce currency exposure of international tourists.

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Introduction

In the most recent decades, a remarkable growth has been witnessed by tourism across industrialised and emerging economies as one of the foremost drivers of socio-economic progress. The global tourism sector is now worth over US$ 1.6 trillion in terms of total export value, which is accounted for 10.4% to the global gross domestic product. Additionally, 7% of world exports are associated with tourism, which ranks third after chemicals and fuels (UNWTO Tourism Highlights  2018 ). Due to its growing importance, there is no surprise that multifarious studies have been conducted on different aspects of the tourism domain (De Vita 2014 ; Lee et al. 1996 ; Song and Li 2008 ; Prideaux 2005 ; De Vita and Kyaw 2013 ; Carey 1991 ; Garín-Muñoz 2006 ; Law et al. 2004 ; Lee 1996 ; Wang 2009 ; Chatziantoniou et al. 2013 ; Chao et al. 2013 ).

Broadly speaking, the variables in the international tourism studies are divided into qualitative and quantitative variables (Peng et al. 2012 ). Song and Turner ( 2006 ) pointed out that the latter is more prevalent in the empirical literature. Qualitative factors such as safety issues, cultural issues and socio-political instability play a significant role in influencing the tourist flow (Patsouratis et al. 2005 ). It is not easy to measure the impact of such qualitative factors, but their impact is clearly visible in the pattern of tourist flow. Apart from this, quantitative factors such as disposable income, transportation cost, cost of living and exchange rate fluctuations influence international tourism demand (Dwyer et al. 2002 ). Modelling these economic factors with suitable econometrics framework can help to explain the intensity of influence of these factors on tourist flow in an economy. Despite the above, other significant determinants which have been discussed in the literature include foreign direct investment flows (Abbott and De Vita 2011 ; Abbott et al. 2012 ), climate change (Lise and Tol 2002 ), destination promotion (Crouch et al. 1992 ; Law et al. 2004 ; Lee 1996 ), level of income distribution and inequality (Morley 1998 ), education level of tourist (Alegre and Pou 2004 ) and rate of unemployment (Cho 2001 ). However, earlier tourism demand studies focused on conceptualisation and identification of exogenous variables (Witt and Martin 1987 ; Martin and Witt 1988 ; Uysal and Crompton 1984 ), whereas recent studies are more inclined towards econometric modelling and forecasting techniques (Song and Li 2008 ; Shen et al. 2011 ; Dogru and Sirakaya-Turk 2016 ).

The growth of tourism in the last few decades has made a radical shift in the employment pattern and the sectoral contribution to the gross domestic product of Emerging Market Economies (EMEs) including India. With the adoption of a floating exchange rate regime to supplement new economic policy in 1991, India witnessed a remarkable growth in terms of foreign tourist earning. However, despite its potential as an exotic destination, India ranks at the 17th position globally in terms of international tourist arrivals with 27.3 US$ billion tourism export which accounts to 5.8% of the total export of India (UNWTO 2018 ; WTTC 2018 ). As tourism is the third highest contributor to India’s foreign exchange earnings (Kaur 2017 ), acceleration of tourism activities improves the non-debt portion of country’s reserve and helps in stabilising the exchange rate which further accelerates the trade and capital flow. Thereby, it is necessary to attract more foreign tourists towards India which in turn bring excess foreign currency that can help in reducing Balance of Payment (BoP) and boost the tourism export for the country.

In the last few years, exchange rate has emerged as one of the widely used indicators for measurement of the international tourism flows alongside other economic and social determinants (Patsouratis et al. 2005 ; Song and Li 2008 ; Zhang et al. 2009 ; Santana-Gallego et al. 2010 ; Kah and Lee 2013 ; De Vita 2014 ; Agiomirgianakis et al. 2015b ). The changes in exchange rate not only garner responses from the potential travellers, but also the absence of hedging market forces the business to be shifted from an economy with high exchange rate instability to an economy with low exchange rate instability having a more stable currency rate for managing economic exposure (Crouch 1994d ; Agiomirgianakis et al. 2015a ). Exchange rate fluctuation influences the potential travellers to change the destination or reduce the length of the holidays which results in revenue loss to the economies. This may cause changes in the travel itinerary of tourists while visiting a particular country (Webber 2001 ). Therefore, there is a need to monitor, track and predict these exchange rate fluctuations and formulate effective mechanisms (WTTC 2016 ). In the case of emerging Asian economies, the introduction of a more flexible exchange rate regime and devaluation of currency is attracting more foreign tourists (Chang and McAleer 2009 ). Such a radical shift in exchange rate regime brings countries to the list of international destinations. From the international trade point of view, countries keep their currency devalued for competitive advantages. But this competitive advantage on exchange rate elasticity in the case of tourist inflow depends on the risk-taking behaviour of the agents and the exchange rate of the destination country as well. For instance, the South East and South Asian currencies are relatively cheaper for tourists originating from Western countries. Therefore, small fluctuations in the exchange rate of these currencies may not be much influential in impacting the travel decision of the tourists.

For understanding such dynamic relationship between exchange rate and international tourist inflow, the previous studies have used the nominal exchange rate as a proxy for measuring potential risk in tourism demand model which may not be adequate as it neglects the relative price level at the destination. In order to rectify this issue, studies such as Kim and Lee ( 2017 ), Eilat and Einav ( 2004 ) and Edwards ( 1995 ) were given more emphasis on the relative price level in the destination countries. But including exchange rate and consumer price index simultaneously may cause multicollinearity in the model (Zhang et al. 2009 ).

Following the arguments of Chen ( 2008 ), this study fortifies that exchange rate is the key contributing factor of international tourism demand. By investigating in seclusioning the impact of exchange rate on global tourism inflow to India, the present study explains how this factor influences the international tourism demand. A unique combination of real effective exchange rate within an ARDL framework explicates elasticity of exchange rate in explaining the global tourism demand to ‘Incredible India’.

The next section of this paper provides an overview of the relevant empirical studies followed by the methodological aspects adopted in the study along with details of the data. There on, the outcomes of the empirical research are discussed. The penultimate portion concludes the research and lays down the policy recommendations.

Review of the related literature

The expansion of tourism as an industry has significantly contributed to the gross domestic product of nations and attracted more researchers and policymakers to investigate its various aspects. Both qualitative and quantitative factors were used as explanatory variables to estimate the tourism demand function in the second half of the twentieth century (Lim 1997 ). Later on, it was found that methodological and data-related constraints limited the scope of these studies (Narayan 2003 ). The development of econometric techniques and the availability of high-frequency data on different elements of tourism contributed to the development of an innovative methodological framework for the analysis to produce accurate and actionable results. Theoretical and empirical literature highlights four major determinants of cross-border tourist inflow in the recent scenario. It includes direct determinants such as the cost of transportation, exchange rate, the relative price level in origin and destination countries (Crouch 1994b ; Garin-Munoz and Amaral 2000 ; Li et al. 2005 ; Song and Li 2008 ). The previous researches have given more emphasis on the relative price level at the destination countries because tourism demand is highly price elastic (Crouch 1995 ; Patsouratis et al. 2005 ; Önder et al. 2009 ).

For computation of tourism price variable, the majority of studies have used Consumer Price Index (CPI), exchange rate and CPI-adjusted exchange rate as an alternative variable for the cost of destination (Li et al. 2005 ). There are contradictory arguments on the use of CPI as a proxy for relative price level in the host country. Earlier studies argue that CPI is a good proxy as it closely captures the price of travel and tourism (Martin and Witt 1987 ; Morley 1994 ; Uysal and Sherif El Roubi 1999 ; Culiuc 2014 ); however, certain issues were also identified in this approach. For instance, CPI represents the cost of a selected basket of goods and services consumed by an average household in a domestic country which is entirely different from the consumption pattern of the tourists (Chasapopoulos et al. 2014 ). To overcome this issue, studies such as Berkhout ( 2007 ) and Goral and Akgoz ( 2017 ) came up with a separate tourism price index for various countries after considering consumption patterns of tourists. However, the complexity and irregularity of its calculation limit the use of such indices in tourism models (Divisekera 2003 ; Rosselló et al. 2005 ). Using CPI with exchange rate may also cause multicollinearity in the model as the exchange rate indirectly absorbs the changes in CPI (Lim 1997 ; Zhang et al. 2009 ). Keeping this argument in mind, this study used the real effective exchange rate as an appropriate alternate for tourism price variable which replaces exchange rate as well as price level together.

The past studies have employed different variables for examining tourism demand. For instance, the amount of money spent by tourists, tourism receipts, imports and/or export of tourism are considered proxies for tourism demand in the monetary approach. In the non-monetary framework, number of tourist arrival/departure and number of nights/day spend, and the average length of stay per tourist in a particular destination is considered as a proxy for tourism demand (Crouch 1996 ; Lim 1997 ). However, obtaining reliable data on these variables is difficult. On the other hand, data on tourists arrival are easily accessible and more reliable, but their responses to the determinants are poor (Zhang et al. 2009 ). In order to avoid such issues, this study used the receipts from international tourism as a proxy for measuring the inbound tourism demand in India.

Moreover, including the gross domestic product of the host country in the model as a proxy for infrastructural development, the standard of living and economic condition helps to examine how these factors influence destination decisions of international tourists (Belloumi 2010 ). The connections between these variables are well explained by economic theories. This paper does not consider the income of origin countries as Patsouratis et al. ( 2005 ) argued that income of origin country could be ignored if the host countries’ currency is cheaper to the country of origin.

Gerakis ( 1965 ) made the first ever effort to measure the effects of exchange rate variations on the foreign tourist receipts while conducting a comparative study of Canada, Spain, France, Yugoslavia, Finland, Germany and Netherlands between 1954 and 1963. He found a remarkable increase in the tourist receipt of France, Spain and Yugoslavia and a modest increase in the case of Canada. But the currency revaluation of Finland, Germany and Netherlands negatively influenced the tourism receipts and significantly accelerated the receipt of main competitors. Similar findings were later reported by Gray ( 1966 ) and Chadeeand and Mieczkowski ( 1987 ) confirming that Canadian tourism imports were quite elastic to exchange rate. Subsequently, Artus ( 1970 ) assessed the effect of the revaluation of Deutsche Mark on the German receipts from foreign visitors, and it was found that the price sensitivity of the German receipts from foreign tourists was quite high. Lin and Sun ( 1983 ) in their research project on the tourism sector in Hong Kong found that international tourists’ flow was highly elastic to exchange rate. In addition, Garin-Munoz and Amaral ( 2000 ) studied the effects of exchange rates on demand for Spanish tourist services in the international markets. The resulting exchange rate elasticity of +0.50 showed that devaluation of the Peseta would boost the international tourist flows to Spain. Webber ( 2001 ) investigated the impacts of exchange rate volatility on Australia’s outbound leisure tourism demand with respect to nine countries in the long run. The results showed that exchange rate fluctuations were a significant determinant of tourism demand in the long run and tourists were likely to drop the plan of visiting a particular nation in 40% of the cases. Likewise, Dritsakis ( 2004 ) utilized the exchange rate as a factor to explain the long-run tourism demand for Greece by Germany and Great Britain. The analysis revealed that real exchange rate is inelastic for German tourists and elastic for Great Britain tourists. Patsouratis et al. ( 2005 ) established exchange rate as the predominant determinant of tourism demand of Greece with respect to alternate Mediterranean destinations such as Italy, Spain and Portugal offering the same product. The findings of Quadri and Zheng ( 2010 ) on Italian tourism demand showed that the exchange rate did not have any significance in 11 out of 19 country pairs. They further established that exchange rate volatility does not always affect international arrivals universally. Chang and McAleer ( 2009 ) examined daily and weekly tourist arrivals to Taiwan from different parts of the world including USA and Japan along with the world price, JPY/TWD and USD/TWD exchange rates, and their associated volatility. The analysis suggested that the exchange rate was having an expected negative impact, whereas volatility exerted either positive or negative effects on tourist arrivals to Taiwan. Yap ( 2012 ) studied the impact of the rising value of the Australian Dollar on its destination competitiveness as compared to the neighbouring countries. For nine countries of origin, the sensitivity to exchange rate volatility was examined, and it was inferred that sudden currency shocks would not have long-term implications for Australian tourism imports. More recently, researchers examined the relationship between tourist arrivals and exchange rate volatility for the UK and Sweden. The relationship turned out to be negative, indicating that the choice of travel destination is affected by exchange rate fluctuations (Agiomirgianakis et al. 2015a ).

Many academic studies in the past considered exchange rate as an independent variable as it is a theoretically strong proxy for relative price. Theoretical arguments justify its use for two reasons. Firstly, tourists are more aware of exchange rate than the prices of individual products and services in the host country (Artus 1970 ; Martin and Witt 1987 ; Crouch 1994a , b , c , e ; Webber 2001 ). Secondly, exchange rate fluctuation (especially the case of floating exchange regime) is directly linked to the cost of the trip and ultimately influences destination decisions (Lim 2006 ). Such a relationship is based on the idea that currency depreciation makes the destination cheap and increases the number of foreign tourists (Greenwood 2007 ). So, excluding the exchange rate from the international tourist demand model makes the model spurious. But the use of exchange rate exclusively as variables to measure the cost of living might be inaccurate. The benefit of a higher exchange rate scenario could be dampened by hyperinflation in the economy (Witt and Witt 1995 ).

All the above-mentioned studies highlight the fact that exchange rate variations influence the growth rate of international tourism across the globe. Understanding the impact of exchange rate fluctuation on tourist demand is highly useful for policymakers and academicians. There is a severe gap in research from developing economies, especially South Asian economies, which earn well from international tourism. There are a limited number of studies which have examined the tourism demand of India (Dhariwal 2005 ; Madhavan and Rastogi 2013 ). But these studies have not given proper attention to price level and exchange rate as key determinants of international tourist flow.

This study is an attempt to fill that gap and to contribute to an improved understanding of the relationship between exchange rate and international tourism demand. Using the ARDL model, the authors investigate how the exchange rate fluctuations have influenced international tourism receipt during the post-liberalisation period in India so as to facilitate relevant policy-making and academic pursuits.

Methodology and data

Theoretical framework.

International tourism can be considered as a form of international trade in services. However, specific models developed within the theoretical premises of international trade to understand tourism as a trade in services are absent. International tourism, as a trade, has unique characteristics. For instance, unlike many other trades, a customer in this case has to visit the exporting country to consume its products (Cheng et al. 2013 ). Moreover, its products are imperfectly substitutable due to the unique role played by multicultural and geographical factors. International trade theories postulate a negative relationship between price and demand of tourism products. Hence, this study adopted a theoretical model based on the assumptions of demand theory. As per the tenets of Neoclassical Consumer Demand Theory, international demand for tourism products depends on the relative price of all goods and services consumed by the tourists. An increase in the price of domestic goods and services reduces the demand for tourism export as it influences the purchasing power of money (as per the theory of Purchasing Power Parity).

According to theories of international trade, depreciation of domestic currency makes the products cheaper in international markets and increases trade volume (Kang and Dagli 2018 ). Considering international tourism as an export of services with specific features, it can be assumed that depreciation of currency increases tourist inflow. This phenomenon is reflected in the surge in tourism exports of emerging Asian and African economies in the recent past.

In this context, to understand exchange rate’s impact on international tourism demand, this study has based its theoretical approach as per the above discussion and by assimilating the insights of economic utility theory of tourism demand developed by Morley ( 1992 ). Further, it used the tourism demand model developed by Lim ( 1997 ) which described tourism demand as a function of domestic and foreign income and price. It can be described as:

where D T is the tourism demand, Y and Y * represent income of domestic and foreign countries. P and P * indicate the domestic and foreign prices. This assumes that tourists are price conscious, and variation of price significantly influences the demand. But including foreign and domestic price indices may bring a biased result as the products consumed by the foreign tourists are entirely different from the domestic consumption basket. Hence, the exchange rate is the most suitable variable for representing the relative price of the tourism products (Ghosh et al. 2003 ).

Though there are several indices which can be used for assessing the relative price of tourism products between economies, they do not reflect the comparative picture due to the heterogeneity of the price index baskets used for their estimation. In this situation, the exchange rate is the best proxy to compare the relative cost of tourism products between economies. Unlike the indices which are often vulnerable to biases from methodological frameworks used by the respective monetary authorities, the exchange rate is market-determined. This makes it a better proxy to accurately assess the relative price situation in case of tourism as an internationally traded product.

Variable construction and data

Several studies in the past have used numerous variables to understand the determinants of international tourism inflow. The current study utilised GDP and real effective exchange rate as the major explanatory variables as per the theoretical model of Lim ( 1997 ), and the empirical explanation by Quadri and Zheng ( 2010 ). Footnote 1

The proposed model has been trained focused on quarterly data stretching from the period of 2003Q1–2017Q4 for empirical analysis, and variables of the study are international tourism receipt measured as foreign exchange earnings from tourism sector in local currency (denoted by FEE), Footnote 2 Real Effective Exchange Rate (denoted by REER) Footnote 3 of India and Gross Domestic Product (denoted by GDP). Data prior to 2003Q1 are excluded in the study because of the non-availability of the tourism data. The quarterly series is formed by taking averages of monthly time-series data in case of REER and FEE. Scholars such as Balaguer and Cantavella-Jordá ( 2002 ), Oh ( 2005 ) and Gunduz and Hatemi-J ( 2005 ) suggested including the REER so as to deal with potential overlooked variable issues and to account for external competitiveness. In tourism business, seasonality is one of the unique phenomena, and the seasonal components appear in the majority of the tourism-related businesses (Soesilo and Mings 1987 ; Bonn et al. 1992 ; Butler 1998 ; Jang 2004 ). Therefore, to reveal the actual trends, we de-seasonalise the series with an X-13 Census method along with the GDP series as it is also affected by seasonal variation. Data are retrieved from Knoema, Reserve Bank of India (RBI), Ministry of Tourism (Tourism Statistics 2003–2017, India). Finally, to deal with the problem of normality and heteroscedasticity, the variables have been converted into natural logarithm form.

Econometric methodology

To estimate the factor determining the FEE, potentially two-factor ARDL equation is formulated. Keeping all the theoretical arguments in mind, we specify the implicit form of the proposed model as:

where lnFEE is the natural logarithm of foreign exchange earnings from the tourism sector, \(\beta_{1} \;{\text{and}}\; \beta_{2}\) are the coefficients of the natural logarithm of gross domestic product (lnGDP) and the natural logarithm of real effective exchange rate (lnREER), \(\beta_{3}\) is the coefficient of dummy variable for structural breaks, t is the time period and \(\mu_{t}\) is the white-noise error term.

Different from the traditional Ordinary Least Square (OLS) estimation, the study examines the impact of REER and GDP on FEE by applying robust ARDL, originally introduced by Pesaran ( 1997 ) and later extended by Pesaran and Shin ( 1999 ) and Pesaran et al. ( 2001 ). This advance methodology has several virtues over other techniques such as:

This test is based on the single ARDL equation, rather than on a VAR as in Johansen. Thus, it reduces the number of parameters to be estimated.

This test is comparably more efficient in small and finite sample sizes and potentially removes the problem of omission bias and autocorrelation (Pesaran and Shin 1999 ).

This test does not impose a restrictive assumption that the variables under study are to be integrated of the same order; it can be applied regardless of whether the regressors in the model are purely I (0), purely I (1) or fractionally integrated (Pesaran 1997 ).

ARDL representation does not call for symmetry of lag length; each length can have a different number of lag lengths.

This technique also provides the valid t -statistics and unbiased estimates of the long-run model even when some of the regressors are endogenous in the model (Harris and Sollis 2003 ; Jalil et al. 2010 ).

In order to implement the ARDL model, Eq. ( 1 ) is transformed into the Unconditional Error Correction Model (UECM) and that is indicated as follows.

The mathematical representation of the ARDL approach can be written as:

where \(a_{1}\) is the drift component, \(\rho\) is the maximum lag length of the dependent variable, whereas q and r are the maximum lag length of independent variables, ∆ denotes the first difference operator, the expression from a 2 to a 4 depicts the short-run dynamics, while on the right-hand side expression from \(\lambda_{1} \;{\text{to}}\;\lambda_{3}\) represents long-run dynamics, \(\lambda_{4}\) represents dummy variable for structural break and µ t is the Gaussian error term.

The ARDL bound testing procedure follows with the multiple steps and procedure. In the preliminary step, we examine the order of integration of the variable just to assure that none of the variables will be I (2). Ouattara ( 2004 ) explicitly mentioned that ARDL results would be spurious if it is generated by I (2) process or beyond. In the second step, Eq. ( 2 ) will be assessed with the OLS estimate, and joint F -statistics will be computed to test the existence of a long-run association among the variables. This joint F -statistics is actually a test of the null hypothesis (H 0 ) of non-existence of cointegration against the alternate hypothesis (H 1 ) of the existence of cointegration among the variables. Pesaran et al. ( 2001 ) demonstrated two critical bound values, i.e. lower bound value I (0) and upper bound value I (1). I (0) assume there is no cointegration among the variables, whereas I (1) assume there is cointegration among the variables. Henceforth, if the calculated F -statistics exceeds the I (1), we reject the null hypothesis(cointegration among variables), and if the F -statistics is less than I (0), we do not rule out the null hypothesis (no cointegration).

But the results are considered inconclusive if the F -statistics falls inside I (0) and I (1). After estimating the long-run association between the variables, the following equation has been formulated to estimate the short-run coefficients based on Error Correction Term (ECT):

\(\gamma\) denotes one lagged parameter of ECT that has to be negative and statistically significant. Further, it explains the speed at which dependent variable returns back to equilibrium due to the shocks in the independent variables. Footnote 4 Likewise, in Hendry ( 1995 ), the general-to-specific linear modelling approach is pursued. A wide variety of diagnostic tests such as autocorrelation, normality and heteroscedasticity test are performed to ensure the robustness of the model. Furthermore, Pesaran ( 1997 ) suggests conducting stability test and breakpoint test for the ARDL estimation. For this purpose, the cumulative sum (CUSUM) and the cumulative sum of squares (CUSUMQ) graphical plots suggested by Brown et al. ( 1975 ) were conducted.

Empirical results

Stationarity test.

To ensure that the variables are not integrated of order I (2) or higher, we first conducted two standard unit root tests, i.e. Augmented Dicky–Fuller (ADF) test and the Phillips Perron (PP) test (Dickey and Fuller 1981 ; Phillips and Perron 1988 ). For both ADF and PP, the null hypothesis for the presence of unit root was tested. For the ADF test, the Schwartz information criterion (SIC) with a maximum of 12 lags was used. Similarly, Bartlett kernel spectral estimation method and Newey–West Bandwidth were used for PP test. The empirical evidence of ADF and PP unit root tests is reported in Table  1 indicating non-rejection of the null hypothesis at levels; however, after transforming the data into first differencing, the variables become stationary.

Both of the above tests were biased towards non-rejection of the null hypothesis of unit root in the presence of structural breaks. To overcome this issue, we further conducted a breakpoint unit root test to identify unknown breaks in the series. Table  2 reports the results of the Breakpoint unit root test with unknown structural breaks. While considering structural breaks, the GDP series was found stationary at levels, and the rest were found stationary after first differencing. After incorporating all the breaks in the model, only 2006Q1 was found significant, and hence, we incorporate 2006Q1 as a dummy variable in the model.

Lag selection of ARDL

Selecting the optimum lag order is necessary to estimate the ARDL ( p , q , r ) model. We choose Akaike Information Criteria (AIC) based on the study of Liew ( 2004 ) as per which AIC performs better than other criterion in case of small sample size, as in the case of our study. Furthermore, Lütkepohl ( 2006 ) confirms that the AIC criterion is suitable for a comparatively small sample size. Figure  1 reports the top 20 models out of the 648 different ARDL models evaluated by Eviews. Out of the available combinations, we choose the ARDL (6,8,1) model with 6 lags of FEE (dependent variable), 8 and 1 lags of GDP and REER (independent variables).

Source : Author’s calculation using Eviews software, version 10

ARDL lag selection.

Diagnostic test results

The model passes all the assumptions as verified by the various diagnostic tests. The R-square is acceptable which explains 99% variation in FEE as explained by its regressors. The Durbin–Watson (DW) statistics is greater than 2, which indicates that the model is not spurious. Diagnostic tests such as for autocorrelation (Breusch–Godfrey Serial Correlation LM test), normality (Jarque–Bera test) and heteroskedasticity (Breusch–Pagan–Godfrey Heteroskedasticity test) were conducted for robustness of our results as shown in Table  3 . The result confirms that the model is not serially correlated, and there is no evidence of multicollinearity and heteroskedasticity as well.

ARDL bound test results

Since the model passes all assumptions of diagnostic tests, we then calculate the cointegration among the variables. To accomplish this task, we estimate Eq. ( 2 ) by OLS estimation procedure, and joint F -statistics are calculated with the null hypothesis of H 0 : \(\lambda_{1} = \lambda_{2} = \lambda_{3} = 0\) against the alternate hypothesis H A : \(\lambda_{1} \ne \lambda_{2} \ne \lambda_{3} \ne 0\) . As we have not coerced the intercept of the model and there is no linear trend included in the model, we opted Case III of Pesaran et al. ( 2001 ) for bound testing. As shown in Table  4 , the value of the F -statistics is 11.376, exceeding the upper bound value I (1) at 5% level of significance. Thus, the null hypothesis of the absence of cointegration is rejected, and therefore, we conclude that there is a long-run relationship between FEE, GDP and REER.

Long-run and short-run results

All the long-run variables are statistically significant and have expected sign as demonstrated in Table  5 . The estimated coefficient of the GDP has a positive sign which indicates that 10% increase in GDP will elevate FEE by 10.17%, which is similar to the findings of Belloumi ( 2010 ) and Agiomirgianakis et al. ( 2015b ), whereas the coefficient of REER entails that 10% increase in REER will reduce the share of FEE by 2.48% in the long run, which is supported by the results of Santana-Gallego et al. ( 2010 ) and Agiomirgianakis et al. ( 2014 ).

The coefficients of long-run estimation summarise that GDP and REER do have an effect on the FEE in the case of India. Further, we estimated the restricted error correction of ARDL model as shown in Table  6 .

Statistically significant (1% significance level) negative value of ECT t −1 shows that the model is getting adjusted to long-run equilibrium at a pace of 96% per period (one quarter). This signifies the arguments of Banerjee et al. ( 1998 ) that a highly significant ECT is further evidence of the presence of a long-run stable relationship.

Lag intervals of FEE and GDP have a positive and significant impact, whereas REER has a significant and negative impact on FEE in India. The current, first, third, sixth and seventh lagged values of the first-differenced GDP have a positive impact on FEE although the fourth and fifth lags of the first-differenced GDP negatively affect the FEE. However, the current-differenced value of REER significantly and negatively affects the FEE. Hence, it can be concluded that the overall impact of GDP and REER is time-invariant, i.e. having similar long-run and short-run impacts on FEE though the magnitude may vary.

Exchange rate fluctuation influences the potential travellers to change the destination or reduce the length of the holidays which results in revenue loss to the economies. This may cause changes in the travel itinerary of tourists while visiting a particular country (Webber 2001 ). Hence, the exchange rate emerges as the main factor which motivates potential travellers to visit a particular destination. Hence, the development of customised and innovative hedging instruments will help to reduce the currency exposure of international tourists. Policymakers should ensure the sustainable external competitiveness of rupee with a stringent control over the domestic price level.

Stability test

Further, to ensure the model stability and parameter constancy with the AIC-based ECT, we employed the CUSUM mean and CUSUMQ variance plot. A graphical demonstration of the CUSUM and CUSUMQ plots is provided in Figs.  2 and 3 . Both the plots show the absence of parameter instability as the graphs lie within the critical boundary lines at 5% level of significance.

The study aimed at investigating the impact of real effective exchange rate and gross domestic product of India on international tourism receipt using Pesaran et al. ( 2001 ) approach to cointegration. The ADF and PP unit root test suggest that all the variables were integrated of I (1). However, mix order of integration has been found when we applied the Breakpoint unit root test. The ARDL bounds test confirmed both short- and long-run cointegration between the international tourism receipts, the real effective exchange rate and gross domestic product of India. As required, the ECT coefficient is highly significant and negative. It shows that disequilibrium due to shock in the short-run period is adjusted back to long-run equilibrium at a speed of 96% for one period. Furthermore, by applying CUSUM and CUMUSQ tests to the model, it comes out to be stable.

These findings are considerably significant since the real effective exchange rate of India does affect the international tourism receipts. Our analysis reveals a favourable interaction between REER and international tourism receipt. More specifically, 10% depreciation in REER of India leads to 6.9% and 2.4% increase in international tourism receipt in the short run and long run, respectively. Therefore, international tourism receipts can be considered as a pivotal channel to accelerate economic growth. As a commodity, tourism is extremely vulnerable to exchange rate shocks that influence the willingness of tourists to visit a foreign country. Hence, the development of customised and innovative hedging instruments will help to reduce the currency exposure of international tourists. Policymakers should ensure the sustainable external competitiveness of rupee with stringent control over the domestic price level.

Due to its specific focus on price-related factors, the study has neglected non-price factors such as destination image, immigration procedures and visa processing which could also be potential determinants of international tourist in-flow in India. Future research could incorporate these variables in order to provide a more comprehensive outlook.

Inclusion of other determinants would bring potential interferences (Quadri and Zheng 2010 ). For instance, using exchange rate and price level as a determinant in the model can cause multicollinearity as the exchange rate indirectly absorbs the changes in the price level (Lim 1997 ; Zhang et al. 2009 ). Including real exchange rate in the model specifically explains the impact of exchange rate on tourism demand.

FEE is foreign exchange earnings from tourism sector. SOURCE: Market research and Statistics, Ministry of Tourism, Government of India.

REER is real effective exchange rate of India. The data are available in monthly time-series data which for analysis were converted into quarterly time-series data. SOURCE: Knoema.

Higher the magnitude of the error correction term (EC t ), better the speed of adjustment (Coakley et al. 2004 ).

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Sharma, A., Vashishat, T. & Rishad, A. The consequences of exchange rate trends on international tourism demand: evidence from India. J. Soc. Econ. Dev. 21 , 270–287 (2019). https://doi.org/10.1007/s40847-019-00080-2

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• Published: 11 January 2023

Is pass-through of the exchange rate to restaurant and hotel prices asymmetric in the US? Role of monetary policy uncertainty

• Uju Violet Alola 1 ,
• Ojonugwa Usman 2 &
• Andrew Adewale Alola   ORCID: orcid.org/0000-0001-5355-3707 3 , 4  

Financial Innovation volume  9 , Article number:  18 ( 2023 ) Cite this article

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This study examines the exchange rate pass-through to the United States (US) restaurant and hotel prices by incorporating the effect of monetary policy uncertainty over the period 2001:M12 to 2019:M01. Using the nonlinear autoregressive distributed lag (NARDL) model, empirical evidence indicates asymmetric  pass-through of exchange rate and monetary policy uncertainty. Moreover, a stronger pass-through effect is observed during depreciation and a negative shock in monetary policy uncertainty, corroborating asymmetric pass-through predictions. Our results further show that a positive shock in energy prices leads to an increase in restaurant and hotel prices. Furthermore, asymmetric causality indicates that a positive shock in the exchange rate causes a positive shock to restaurant and hotel prices. We found feedback causal effects between positive and negative shocks in monetary policy uncertainty and positive and negative shocks in the exchange rate. Additionally, we detected a one-way asymmetric causality, flowing from a positive (negative) shock to a positive (negative) shock in energy prices. Therefore, these findings provide insights for policymakers to achieve low and stable prices in the US restaurant and hotel industry through sound monetary policy formulations.

The drivers of restaurant and hotel business in tourism destinations are examined.

There is asymmetric pass-through of exchange rate and monetary policy uncertainty.

A stronger pass-through is observed during appreciation and a negative shock to monetary policy uncertainty.

There is asymmetric causality from positive shock in exchange rate to postive shock in restaurant and hotel prices.

Introduction

The tourism industry is increasingly boosting global economic expansion in a way that industry subsectors (e.g., air travel, medical tourism, restaurant, and hotel) are becoming critical components in many economies. Notably, restaurants and hotels are playing critical roles in the tourism industry’s advancement. Specifically, recent industry trends suggest that restaurants (e.g., coffee shops and fast food sectors) and hotels (for overnight accommodation, e.t.c) are continuously experiencing global growth in market and chain operations (International Labour Organization 2010 ). Hotels and restaurants may be experiencing continuous global growth owing to the industry’s highly competitive and segmented nature (Statista 2020a , b ). Hotels are categorized as either independent or unaffiliated, and restaurants (under the foodservice segment) are categorized as commercial or noncommercial (Statista 2020a , b ). In most advanced economies including the United States (US), restaurant and hotel industries are distinctly booming and competitive. Tourists’ purchase intentions significantly determined by destinations’ food being served in the restaurants, hotels’ hospitality and service quality, and other cultural representations (Lee and Choi 2020 ).

In the US restaurant and hotel industry, annual growth rate in 2023 is expected to be 2.8% higher than 2008 (Statista 2020b ). With over 1 million restaurant locations (including global brands and other retail restaurants) and about 15.6 million in jobs, the restaurant industry is expected to amass $899 billion (i.e., expected growth rate of 4% from 2019) in revenue in 2020 (National Restaurant Association 2020 ). The National Restaurant Association ( 2020 ) projects that 1.6 million more new restaurant jobs would be added by 2030, or 17.2 million jobs in total. Sales and jobs created in the restaurant industry would have increased from $590 billion and 12.2 million in 2010, respectively, to $1.2 trillion and 17.2 million in 2030, respectively. Moreover, while the US food service segment comprises more than 80% of the restaurants in the US, noncommercial categories account for about 20%. Moreover, hotel chains account for two-thirds of the restaurant and hotel market. Restaurants and hotels are significant not only to the US economy but also the global tourism industry.

Despite the restaurant and hotel industry’s strong performance, the industry remains vulnerable to various uncertainties, which inhibit the contributions of the industry to global tourism and economic expansion (Akadiri et al. 2019 , 2020 ; Othman et al. 2020 ; Alola et al. 2020 ).

In the US, the Federal Reserve executes five main functions to maintain economic stability: conducting monetary policy, promoting financial system stability, supervising and regulating financial institutions and activities, fostering payment and settlement system safety and efficiency, and promoting consumer protection and community development (Federal Reserve 2022 ). As exchange rates are key determinants of tourism demand, the tourism industry cannot work in isolation from the country’s monetary policy stance (See Usman et al. 2022 ). Hence, exchange rate and monetary policy uncertainty dynamics between the US and tourism destinations drive tourism development vis-à-vis the restaurant and hotel industry’s expansion.

Previous studies have examined the impact or nexus between exchange rate and tourism activities (Tang 2013 ; Alola et al. 2019 ; Usman et al. 2022 ). Tang ( 2013 ) found a short- and long-run Granger causality from exchange rates in real tourism receipts. Falk ( 2015 ) found that, especially during the winter season, Swiss overnight visitors in Western Austrian Ski resorts respond sensitively to exchange rate dynamics. Monetary policy dynamics have also consistently been linked to tourism activities (Chen 2010 ). Usman et al. ( 2021 ) examined the exchange rate pass-through to restaurant and hotel prices using a linear model that assumes that prices of restaurants and hotels react identically to positive and negative exchange rate fluctuations. Monetary policy uncertainty is increasing in the US, which may affect the prices of hospitality-related services. Moreover, positive exchange rate or monetary policy uncertainty shocks may behave differently from negative shocks of identical size.

In this study, we investigate whether an asymmetric pass-through of the exchange rate to restaurant and hotel prices exists while accounting for monetary policy uncertainty in the US. Hence, we incorporate the global price of energy (EPR) as a control variable in the hotel price model. Footnote 1 In 2021, US fast food chains emerged as the first among top eight performing industries locally, and twentieth among the total of twenty-five global performing restaurants (Brandirectory 2021 ). Moreover, Brandirectory ( 2021 ) noted that despite COVID-19 pandemic-related disruptions, major food chains exhibited remarkable adaptability, minimizing the pandemic-induced shocks and damage to the subsector. Hence, this study presents a significant extension of literature on tourism development for obvious reasons. First, considering that the US is a world-leading market for global fast food, restaurant, and hotel brands, this study specifically focuses on the restaurant and hotel industries and consists a considerably rare study on the macro-level of business and the economy. Second, this study employs a nonlinear autoregressive distributed lag (NARDL) modeling technique with a pass-through perspective that completely illustrates both the dimensions and directions of exchange rate shocks on restaurant and hotel service prices.

The succeeding sections are organized as follows. Section  2 presents a literature review. Section  3 describes the adopted dataset and empirical methods. Sections 4 and 5 discuss the results and conclusions of the study.

Literature review and research hypothesis development

Theoretical literature.

Exchange rate pass-through is theoretically embedded in purchasing power parity, derived from the law of one price. Purchasing power parity theory was first proposed in the sixteenth century at the University of Salamanca, while its modern version was popularized in 1916 by Swedish economist Gustav Cassel. This theory states that at equilibrium level, the market prices of tradable goods and services remain identical in different countries if goods and services prices are measured according to an identical unit of currency. Purchasing power parity theory is based on and follows perfect and existing competitive arbitrage activities, which compel exchange rates to adjust toward equilibrium, given no transport costs, tariffs, and imperfect competition. However, empirical studies have demonstrated that purchasing power parity or law of one price, either in its absolute or relative versions, does not hold owing to the stickiness of nominal prices resulting from weak competitive arbitrage activities (Balcilar et al. 2020 ).

By eliminating economic arbitrage activities, we have developed several theoretical models to address the difficulty and cost of achieving a unanimous agreement. Hence, researchers have emphasized a shift toward soft consensus models (see Kuo et al. 2014 , 2016 ; Zhang et al. 2019 ). By proposing soft consensus cost models for group decision-making based on loan consensus in online P2P lending, Zhang et al. ( 2019 ) demonstrate that P2P lending is beneficial to both borrowers and lenders by eliminating middlemen and their arbitrage activities, which reduces risks and maximizes returns. Moreover, Chao et al. ( 2021 ) apply a large-scale group decision-making model with cooperative behaviors and heterogeneous preferences in financial inclusion. Their experimental results indicate that by comparing a model’s performance with that of an existing model through a poverty reduction-targeted project in China, the efficacy of the proposed model can be validated owing to the difficulty in selecting beneficiaries in financial inclusion. This is because they lack not only credit history but a large number of participants, and participants have mixed views.

Empirical literature

Destination or border prices of commodities—especially tourism-related services—are competitively driven by both domestic and international factors (Dwyer et al. 2002 ). Campa and Goldberg ( 2005 ) and Usman et al. ( 2021 ) explored this observation further in the tourism industry by evaluating the existence of a pass-through effect of the exchange rate on tourism-related prices. In this section, we review existing studies with relevant hypotheses.

Exchange rates and restaurant-hotel prices

A study on the nexus of exchange rate and restaurant price by Fullerton et al. ( 2009 ) analyzed restaurant prices of eight international border businesses or franchises in El Paso, Texas, Ciudad, and Juarez. Employing seven and one US and Mexican multinational corporations or affiliate, respectively, Fullerton et al. ( 2009 ) surveyed the prices of 32 menu items, yielding a total of 132 for each pair of prices as the number of sampled observations. This study demonstrated that the price ratio of menu items in international restaurants in Ciudad, Juarez, El Paso, and Texas exhibit strong correlations with the peso/dollar exchange rate. Moreover, an exploratory analysis revealed a significant but very short half-life deviation for eight different products. Similarly, Tang ( 2015 ) employed a dataset of publicly traded restaurant firms over the period 1990–2012 in the US, which covered three business cycles. The study examines (i) the determinants of risk exposure and (ii) degree of risk exposure to commodity prices in the restaurant industry. While utilizing the modeling of the determinants of equity risk exposure via the discounted cash flow approach, 60-month rolling regression accounts for the risk exposure of the equity returns were estimated. Notably Tang ( 2015 ) showed that commodity price risk was confirmed in 35.39% of the sampled restaurant businesses. In these business, levels of equity risk exposure associated with periods of price booms and slumps were 1.148 and 1.031, respectively. However, more study findings revealed that while operating and financial leverages could minimize risk exposure, these could be ineffective tool during commodity price booms and slumps owing to asymmetric effects.

Moreover, Aalen et al. ( 2019 ), building on existing literature gaps, examined the extent to which exchange rate affects inbound hotel demand. Using Norway as a destination country, ten different source countries—Denmark, France, Germany, Italy, Japan, the Netherlands, Spain, Sweden, the United Kingdom, and the US—were examined over a 2007–2015 period. Using a panel of monthly hotel accommodations sold in the destination country to potential visitors from the aforementioned source countries, the study revealed that inbound hotel demand responded with an equal amount to the bed prices (i.e., a unitary elastic). Balcilar et al. ( 2020 ), using Nigerian time series data on quarterly frequency, found that exchange rate to prices pass-through is incomplete with evidence that the long-run pass-throughs are stronger than short-run pass-throughs. In a related US case, Usman et al. ( 2021 ) reported that an exchange rate appreciation affects restaurant and hotel prices but increased prices in energy and tourism development are responsible for restaurant and hotel price shocks in the US based on quarterly time series data over a 2001(Q4)–2017(Q4) period.

Thus, we present the following hypothesis:

Pass-through of the exchange rate to restaurant and hotel prices is asymmetric in the US.

Monetary policy and restaurant-hotel prices

Extant studies have revealed that monetary policy administered by apex banks exerts a varying degree of effects on all economic sectors, including hospitality-related sectors (Chen 2010 , 2012 ; Chen et al. 2010 ). However, most studies addressed the effect of monetary policy on the hospitality industry with a holistic approach (i.e., without considering the specificity of the restaurant and hotel prices). Chen ( 2010 ) and Chen et al. ( 2010 ) examined a shifting effect from the monetary policy of different economies. While Chen et al. ( 2010 ) examined the monetary effects associated with the stock performance in the airlines, hotels, restaurants, and tourism-related businesses, Chen ( 2010 ) outlined the same objective for the US. Chen ( 2010 ) classified changes in the discount or federal fund rates as either expansionary (for an expansive period) and contractionary (for a restrictive period) monetary policy tools respectively. While this study revealed that the monetary policy dimensions exert varying degrees of impact, the authors observed important changes due to the federal fund rates in the stock returns of the country’s restaurants with the discount rates causing any significant change in the hospitality stock prices. Chen et al. ( 2010 ) confirmed that discount rate a decrease (expansive monetary policy) significantly affected hotel and tourism stocks in Hong Kong.

Moreover, studies by Chen et al. ( 2012 ) and Fougère et al. ( 2010 ) have presented another dimension with determinants of restaurant and hospitality-related prices. Fougère et al. ( 2010 ) examined the observation in the Japanese hotel stock returns by exploring series of macroeconomic variables including percentage changes in money supply, unemployment, consumer price index (CPI), industrial production, oil price, total trade, and yen-dollar exchange rate alongside discount rate changes. Notably, the study outlined that changes in discount rate, unemployment rate, and oil prices can cause significant impact on national hotel stock returns, thus posing as determinants of the industry stock market. Similarly, while examining key determinant(s) of restaurant prices, Chen et al. ( 2012 ) estimated CPI from the individual price quotes and examined how minimum wages affect restaurant prices in France. Despite establishing a positive relationship between restaurant prices and minimum wages in the country, this study revealed that changes in minimum wage mostly pass through retail prices in not less than 1 year.

Pass-through of monetary policy to restaurant and hotel prices is asymmetric in the US

Munir and Iftikhar ( 2021 ), Irandoust ( 2019 ), and Ongan et al. ( 2018 ) have examined an empirical connection between exchange rates and tourism and recreation activities. For instance, Munir and Iftikhar ( 2021 ), while employing a hidden cointegration analysis within a likelihood-based panel framework for 10 European countries, examined the asymmetric effect of exchange rate on tourism demand. The investigation affirmed that tourism demand responds asymmetrically to the exchange rate fluctuations especially in the long run, further suggesting that depreciation and appreciation of exchange rates affect tourism demand in different dimensions.

Pass-through of energy prices to restaurant and hotel prices is asymmetric in the US.

Energy prices and restaurant-hotel prices

Studies on the effect of energy prices on tourism development have used oil prices as a proxy for energy prices (See Balcilar et al. 2022 ). Using the Bayesian vector autoregression with stochastic volatility, Clark and Terry ( 2010 ) showed that core inflation responded significantly to energy price shocks at the beginning of 1975 in the US. This response declined sharply and remained low. However, with effective monetary policy, responsiveness to energy inflation has decreased since 1985. Similarly, using the NARDL model, Lacheheb and Sirag ( 2019 ) examine the pass-through of oil price shocks to inflation in Algeria. Their empirical results suggested evidence of a nonlinear effect of oil price on inflation and further demonstrated that oil price does not have a significant relationship with inflation in Algeria. Moreover, using the NARDL model and an asymmetric causality test, Usman et al. ( 2020 ) detected an asymmetric pass-through of energy prices to US inflation. Moreover, they noted an asymmetric causal relationship flowing from positive and negative shock in energy prices to positive and negative shock in inflation. Sek ( 2022 ) assessed how oil price changes affect sectoral inflation in Malaysia. Results based on the Markov-Switching model suggest an asymmetric oil price effect on price inflation, industrial production, and producer price. The study showed that the effects of oil prices on industrial production and producer prices are quite stronger than that of other investigated indicators. Moreover, sectors linked to energy resources tend to experience a higher effect of oil prices on CPI, industrial production, and producer prices. By recognizing a significant amount of carbon emission from the transportation industry to the atmosphere, Kou et al. ( 2022 ) extend group decision-making and spherical fuzzy numbers to provide strategies to stimulate the effectiveness of solar energy investment projects. This novel methodology is based on hybrid decision-making, and the results suggested that dynamicity is the most critical TRIZ-based factor, and composite materials, with a weight of 0.255, have a critical impact. The study concluded solar panels have to be designed vertically to receive sunlight at different periods.

According to the previous studies, including the work of Usman et al. ( 2021 ) which is closer in perspective, no studies considered the role of monetary policy while examining the asymmetric role of the exchange rate in tourism-related aspects’ development. Particularly, Usman et al. ( 2021 ) failed to account for possible asymmetries in the exchange rate-tourism price nexus for the US.

Data source and methodology

Data and source.

In this study, we employed the logarithmic transformation of the US monetary policy uncertainty (MP), nominal effective exchange rate (NEER), restaurant and hotel prices (RHP) measured as the harmonized index of consumer prices for the US, index (2015 = 100), and the global price of energy index, (Index 2016 = 100) for the period 2001:M12 to 2019:M01. Notably, we retrieve NEER data from International Financial Statistics database of the IMF. We retrieved RHP and the global price of energy index from the Federal Reserve Economic Data of the Federal Reserve Bank of St. Louis, while MP is obtained from the Economic Policy Uncertainty Database.

Methodology

Price can react directly to exchange rate shocks, which is a central focus of the purchasing power parity (PPP) doctrine (Balcilar et al. 2021a , b; Usman 2020 ). In this study, we extend the original PPP equation, which shows the nexus between exchange rate and prices, by augmenting shocks to monetary policy uncertainty using the NARDL approach. Conversely, positive and negative partial sums of the explanatory variables are derived from the following decomposition:

Here, explanatory variables \(X_{j}^{ + }\) and \(X_{j}^{ - }\) in Eq. ( 1 ) represent the positive and negative exchange rate fluctuations (NEER), monetary policy uncertainty (MP), and global price of energy (EPR). Following Shin et al. ( 2014 ), we specify the NARDL model as follows:

where ln denotes the logarithmic transformation of the variables \(\gamma\) and represents the model intercept, while \(\beta\) and \(\varphi\) represent slopes of the long-run and short-run coefficients. Terms p and q denote orders of lags used for the estimation. Following the empirical studies of Delatte and Lopez-Villavicencio ( 2012 ), we include the global price of energy as a control variable to determine restaurant and hotel price changes in the US. Furthermore, \(\varepsilon_{t}\) is the error term, which follows a stochastic Gaussian process with zero-mean and variance \(\sigma^{2}\) , \(\varepsilon_{it} \sim iid\left( {0,{\upsigma }^{2} } \right)\) . Hence, procedures for the estimations are summarized as ( I ) testing the stationarity properties of the series for the avoidance of I(2) in the series, ( II ) testing the short-run asymmetry \((\varphi_{i}^{ + } = \varphi_{i}^{ - } )\) and long-run asymmetry \((\beta_{i}^{ + } = \beta_{i}^{ - } )\) by employing the standard Wald test, \(i = 1,2,3\) , Footnote 2 ( III ) testing the null hypothesis of no cointegration \(\beta_{i} = { }\beta_{i}^{ + } = \beta_{i}^{ - } = 0\) using F -statistic and t -statistic, and ( IV ) the long-run asymmetric coefficient is estimated as \(L\psi_{i}^{ + } - \frac{{\beta_{i}^{ + } }}{{\theta_{0} }}\) and \(L\psi_{i}^{ - } - \frac{{\beta_{i}^{ - } }}{{\theta_{0} }}\) , where \(L\psi_{i}^{ + }\) and \(L\psi_{i}^{ - }\) denote the positive and negative long-run coefficients, while the positive and negative short-run coefficients are represented by \(\varphi_{i}^{ + } {\text{and }} \varphi_{i}^{ - }\) , respectively.

Additionally, to examine the asymmetric causality between the variables, we perform an asymmetric causality test developed by Hatemi ( 2012 ). The asymmetric causality employed in this study considers the positive and negative shocks between two integrated variables. Specifically, the cumulative form in Eq.  1 is used to investigate the asymmetric causal relationship between the variables through a vector autoregressive model of order p , vector autoregression VAR ( p ) as suggested by Hatemi ( 2012 ).

Results and discussion

We first assessed the visual properties of the series employed. Essentially, we examined time plots of the series against the possibility of drift, seasonality, trend, and structural breaks. Figure  1 indicates that the log of the RHP slopes upward, which suggests that variables increased over the years covered. We characterized NEER and monetary policy uncertainty by fluctuations with no evidence of a particular trending pattern. Conversely, the log of energy price, although associated with structural breaks, trends upward after a global financial crisis. Breaks found in the series can be partly attributed to macroeconomic policy changes. Exchange rate and energy price graphs spikes in 2008 may be attributed to the global financial crisis that started in the US toward the end of 2007. This crisis disrupted the US dollar and consequently affected global energy prices. Moreover, crude oil prices fell considerably between 2014 and 2016, which subsequently decreased energy prices. Footnote 3 Furthermore, fluctuations in variables are more conspicuous in the case of monetary policy uncertainty for two main reasons: first, the variable is already an uncertainty variable; second, monetary policy rate is frequently adjusted to solve the country’s macroeconomic problems.

Time series plot of variables for the study

Table 1 depicts the descriptive statistics of the variables explored in this study. The average values of the variables in their natural logarithms are 4.468 for lnRHP, 4.684 for lnNEER, 4.1999 for lnMP, and 4.963 for energy price. Values of the standard deviation are less than 1 in all the variables, which suggests that the variables exhibit a low volatility level. The values of skewness of the variables are not far from zero in all variables. Hence, frequency distribution is considerably close to symmetry. lnRHP and lnEPR present a negative skewness, while lnNEER and lnMP present positive skewness. Furthermore, the kurtosis values for all variables indicate a flat-topped (platykurtic), and values for the Jarque–Bera statistics are high for all variables except lnMP. Hence, the null hypotheses of the normal distribution is rejected for all variables except lnMP. This implies that the distribution of the variables explored is not normal except for lnMP, which depicts a normal distribution.

Next, we test whether a nonlinear model is appropriate for this study. Hence, we conduct two different symmetric tests. The first test considers long- and short-run asymmetry differently using the standard Wald test. Panel A of Table 2 indicates that the results provide evidence that the null hypothesis of symmetric relationship is rejected in all cases, except the short run for lnMP. In the second test, we use the Broock, Scheinkman, and Dechert (BDS) linearity tests proposed by Brock et al. (1996). This test uses the residuals of dynamic interactions among the variables. Results in Table 2 , Panel B, demonstrate that the null hypothesis, wherein the residuals of the model are independently and identically distributed \(\left(i.i.d\right),\) is rejected. This implies that the relationship between the variables is characterized by nonlinearity. From these findings, we conclude that the dynamic relationship estimated in this study includes nonlinear characteristics. Therefore, nonlinear model can better produce robust findings for policy formulations.

Furthermore, we test for the unit root in the series explored by first applying the standard unit root tests via the augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and the Phillips-Perron (PP) tests. Table 3 indicates that both RHP and exchange rates are not stationary at levels except after their first differences. Monetary policy and energy prices remain stationary both at their levels and first differences. To circumvent the effect of structural breaks that may affect test outcomes, we apply a structural break unit root test from Lee and Strazicich ( 2013 ). Hence, results in Table 4 indicate that except for monetary policy uncertainty, which is stationary at levels, variables including RHP, exchange rate, and energy prices remain stationary after their first differences. This means that the series has a unit root with a break that cannot be held for these variables except monetary policy uncertainty, which is stationary at levels. These results imply that in this study, there is a mixed order of integration in the variables explored (i.e., I(0) and I(1)). This means that we can proceed with the estimation of our NARDL model.

Prior to model estimation, we conduct a series of diagnostic tests (Table 5 ). Estimated model residuals show that the Breusch-Godfrey Lagrange multiplier test for serial correlation, Breusch-Pagan-Godfrey conditional heteroskedasticity test, Ramsey regression equation specification error test (RESET) test, and Jarque–Bera normality test. As autocorrelation, heteroscedasticity, functional misspecification were not found, our results suggest that the NARDL model for this study is correctly specified. Moreover, the model residuals are normally distributed.

Table 6 presents the results of the long- and short-run asymmetric effects of exchange rate and monetary policy uncertainty on RHP. Before discussing the long-run and short-run coefficients, we present the results of the asymmetric cointegration tests, which are based on the bounds-testing approach. This test is a modified version of the F-statistic proposed by Pesaran et al. ( 2001 ) and the t-statistic by Banerjee et al. ( 1998 ). Test results indicate the values of the F-stat and t-stat (i.e., 6.4528 and − 5.7596) are greater than the critical values at a 1% significance level (Table 6 ). In the presence of structural breaks, cointegration exists between dependent and explanatory variables.

Notably, in the long run, a 1% positive shock to the exchange rate reduces RHP by 0.0868%. Conversely, a negative shock of the same magnitude increases RHP by 0.1697%. Both coefficients of positive and negative shocks in the exchange rate, in the long run, are statistically significant. Plausible economic reasons for these results are based on the fact that domestic currency appreciation inhibits exports and reduces imports price. Conversely, domestic currency depreciation stimulates exports and increases imports price. Hence, as domestic currency appreciates, prices of restaurants and hotels tend to decrease as imported commodities, including inbound tourism, become more expensive. Furthermore, a close examination of these results reveals that the pass-through of a depreciation of the exchange rate is stronger in magnitude than the pass-through of an appreciation of the exchange rate. Furthermore, in the short run, the pass-through of a 1% positive shock in the exchange rate to RHP is negative (i.e., − 0.0913%). Conversely, a 1% negative shock in the exchange rate increases RHP by 0.1334%. Pass-through is stronger in for depreciating exchange rates than in appreciating exchange rates. This finding is consistent with Usman ( 2020 ), who found evidence of a stronger pass-through effect for exchange rate depreciation. Moreover, our finding is consistent with Balcilar et al. ( 2020 ), who found evidence that exchange pass-through is larger in magnitude in the long run than the pass-through in the short run for Nigeria.

Our results indicate that a positive shock to monetary policy uncertainty causes RHP to fall significantly in the long run and short run. Conversely, a similar negative shock would cause RHP to increase with evidence of statistical significance only in the long run. Specifically, the magnitude of a 1% positive shock to monetary policy uncertainty significantly reduces RHP in the long run by roughly 0.0729%. Conversely, that of a 1% negative shock to monetary policy uncertainty significantly increases RHP by about 0.0807%. In the short run, our results indicate that positive shocks to monetary policy uncertainty would significantly result in a decline in RHP in the US by 0.0358%. However, a 1% negative shock in monetary policy uncertainty increases RHP significantly by 0.0392%. Our findings indicate that a high level of uncertainty reduces inbound tourism, which consequently results in a fall in RHP. However, when level of uncertainty shocks reduces, inbound tourism would increase. This increase may trigger restaurant and hotel owners to increase prices. This evidence is similar to Chen ( 2010 ), who found that an expansive monetary policy significantly impacted the hotel and tourism stocks in Hong Kong.

Furthermore, we incorporate energy prices as determinants of RHP. Our results show that in the long run (short run), a 1% positive shock in energy prices increases RHP by 0.0127% (0.0197%). Conversely, in both the long and short run, a 1% negative shock in energy prices is negative and insignificantly related to the prices of restaurant and hotel. This suggests that US energy prices are inflationary in the restaurant and hotel industry only in the long run. Hence, as energy prices increase, restaurants and hotel prices increase owing to the industry’s dependence on large amounts of energy in its operations. Moreover, the insignificant effect of a negative shock in energy prices suggests that RHP only respond to increases in energy prices. Furthermore, the coefficient of the error correction term (ECT) (− 0.1962) implies that RHP converge to the long-run equilibrium level by a 19.6% adjustment speed every month through positive and negative shocks in the exchange rate, monetary policy uncertainty, and energy prices.

To craft appropriate macroeconomic policies to sustain low and stable price levels in the US restaurant and hotel industry, we employ a nonlinear causality test proposed by Hatemi-J ( 2012 ). This test considers the asymmetric causal relation between two variables within the framework of Toda and Yamamoto’s (1995) causality. We use the Hatemi-J Criterion (HJC) for lag selection. Our results indicate that the null hypothesis of a positive shock in exchange rate not causing a positive shock in RHP is rejected at a 10% level of significance (Table 7 ). However, negative shocks in the exchange rate causing negative shock in RHP is unsupported, consistent with Aalen et al. ( 2019 ) who find equal responses of exchange rate to hotel prices in 10 countries. Our results do not detect any causality from lnRHP and exchange rate and vice versa.

Furthermore, our results find that a positive or negative shock in monetary policy uncertainty does not cause positive or negative RHP in a Granger sense. Similarly, a positive or negative shock in RHP does not cause a positive or negative shock in monetary policy uncertainty. While a positive energy price shock causes a positive restaurant and hotel price shock, there is no evidence to support that a negative shock in energy price Granger-causes a negative shock in RHP. Moreover, we could not detect any evidence to support that either positive or negative shocks in RHP cause energy price shocks.

Results of the asymmetric causality between monetary policy uncertainty and exchange rate present a feedback effect. The null hypothesis that a positive (negative) shock in monetary policy uncertainty not causing a positive (negative) exchange rate can be rejected at a 1% and 10% significance level. Similarly, the null hypothesis that a positive (negative) shock in exchange rate does not cause monetary policy uncertainty can also be rejected at a 10% significance level. These results imply that both monetary policy uncertainty and exchange rate shocks can be used to predict each other. Regarding asymmetric Granger causality between energy price and exchange rate, the null hypothesis that a positive (negative) shock in energy price does not cause exchange rate cannot be rejected. However, the null hypothesis that a positive (negative) shock in exchange rate not causing energy prices is rejected. Therefore, a one-way asymmetric causality moves from a positive shock in the exchange rate to that in energy price and from a negative shock in the exchange rate to that in energy price.

Given the discussion of the estimated results, it is evident that pass-through of exchange rate and monetary policy uncertainty to RHP is asymmetric, and the coefficient of both negative and positive shocks is inelastic and statistically significant. Hence, hypotheses (1) and (2) are supported by the empirical results of this study. Furthermore, for hypothesis (3), empirical results provide the support that the pass-through of energy price to restaurant and hotel price is asymmetric, but the coefficient of positive change in energy price is only statistically significant. This implies that the third hypothesis is not supported by empirical evidence.

Robustness check

To determine the robustness of our estimations, we capture the effect of structural breaks identified in the series via the NARDL modeling technique. Table 8 results suggest that in the presence of structural breaks, the Breusch-Godfrey LM test for serial correlation, Breusch-Pagan-Godfrey conditional heteroskedasticity test, Ramsey RESET test, and Jarque–Bera normality test provide the best model fit. Moreover, Table 9 indicates that all coefficients survive. Effects of structural breaks are not statistically significant in the model. Additionally, the ECT coefficient is − 0.2878, which suggests that RHP in the US converge to their long-run equilibrium level by a 28.8% adjustment speed every month. This is possible through positive and negative shocks in the exchange rate, monetary policy uncertainty, and energy prices.

Conclusion and policy implications

The US serves as a major host and world-leading market for global fast food, restaurant, and hotel and hotel brands. Moreover, the country has a resilient currency and effective monetary policy. However, with the recent incidences of global financial crises, the US economy has become unstable following an increasing level of monetary policy uncertainty. Considering that businesses and hospitality-related activities may be susceptible to financial distortions, this study extends the literature by examining not only the asymmetric effect of exchange rate and monetary policy uncertainty on RHP but also by identifying an asymmetric causality between these variables over the period 2001:M12 to 2019:M01. Empirical results from the NARDL provide evidence of asymmetry concerning the direction of exchange rate, monetary policy uncertainty, and energy price shocks. Furthermore, we found that a positive exchange rate shock (appreciation) causes RHP to fall, but a negative exchange rate shock (depreciation) of the same magnitude causes RHP to increase. Moreover, a positive shock in monetary policy uncertainty decreases the prices of restaurants and hotels, while a negative shock of identical size increases the prices of restaurants and hotels in the US. A close examination of the findings indicates that both negative exchange rate and negative monetary policy uncertainty shocks (depreciation) have stronger impact on RHP. Moreover, a positive energy price shock increases RHP, but a negative shock of the same magnitude has no significant impact on RHP both in the long and short run.

Furthermore, results of the asymmetric causality indicate that a positive shock in the exchange rate causes a positive shock to RHP. Positive and negative shocks in monetary policy uncertainty have predictive power for positive and negative shocks in the exchange rate and vice versa. This suggests asymmetric feedback effect between monetary policy uncertainty and exchange rate. Moreover, asymmetric causality is detected moving only from a positive (negative) shock in the exchange rate to a positive (negative) shock in energy price.

Therefore, these findings contain policy implications for stabilizing the US economy and achieving low and stable price levels. The findings provide insights for policymakers to attain price stability in the US hospitality-related industries. Particularly, our findings would provide insights to policymakers to help design appropriate monetary policies against domestic and global shocks. Recently, the US dollar and major exchange rates worldwide have experienced sharp responses to issues associated with fiscal policy arising from the political polarization on contentious issues of debt ceiling and other fiscal policy dichotomies.

Finally, our analysis contains some limitations. Our analysis excludes the COVID-19 pandemic period owing to data unavailability. Hence, we recommend that future studies conduct a similar investigation while extending the investigation period to accommodate the coronavirus pandemic period. Moreover, such studies can capture the effect of COVID-19 in the pass-through channels. Researchers can consider a panel study that includes tourist destinations that severely affected the COVID-19 pandemic (e.g., the US, Spain, Italy, Brazil, and others) in the future.

Availability of data and materials

Not applicable.

Change history

16 january 2023.

Country name in the second author's affiliation has been corrected to "Turkey"

Theoretically and empirically, energy prices are one of the major determinants of prices (see Delatte & Lopez-Villavicencio 2012 ).

Where subscript ‘ i ’ represents the variables we explored in this study, i  = 1, 2, 3.

Crude oil dominates the energy market. Whatever happens to oil prices affects all other energy commodities (See Balcilar et al. 2022 ).

Abbreviations

Augmented Dickey-Fuller

Autoregressive distributed lag

Broock, Scheinkman and Dechert

Coronavirus

Consumer price index

Error correction term

Price of energy

Economic policy uncertainty

Hatemi-J criterion

Independently and identically distributed

Kwiatkowski-Phillips-Schmidt-Shin

Logarithmic function

Large-scale group decision-making

Lee-Strazicich

• Monetary policy uncertainty

Non-linear autoregressive distributed lag

Nominal effective exchange rate

Phillips-Perron

Purchasing power parity

Regression equation specification error test

• Restaurant and hotel prices

United States

Vector Autoregression

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How Does The Exchange Rate Affect Tourism In The UK

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Introduction

Welcome to the United Kingdom, a country of rich history, stunning landscapes, and vibrant culture. As a popular tourist destination, the UK welcomes millions of visitors each year, contributing significantly to its economy. However, the travel industry is not immune to the fluctuations of the global economy, particularly in relation to exchange rates.

The exchange rate is the value of one currency in terms of another currency and plays a crucial role in shaping the tourism industry. It has the power to influence travel decisions, affect tourist spending patterns, and impact the overall competitiveness of a destination. Understanding the relationship between exchange rates and tourism demand is essential for both travelers and industry professionals.

In this article, we will explore the intricate relationship between exchange rates and tourism in the UK. We will delve into the economic factors that influence tourism, the significance of exchange rate fluctuations, and the effects they have on tourists’ behavior. Additionally, we will examine case studies that highlight the tangible impact of exchange rate changes on UK tourism and discuss strategies for mitigating the effects of exchange rate volatility.

Whether you are a curious traveler or a tourism industry enthusiast, this article will provide valuable insights into the fascinating world of exchange rates and their role in shaping tourism in the UK. So let’s embark on this journey together and explore the interplay between currency fluctuations and the travel industry.

Economic Factors Influencing Tourism in the UK

Before we dive into the specifics of exchange rates, it is important to understand the broader economic factors that influence tourism in the UK. The travel industry is highly sensitive to macroeconomic conditions, as travelers adjust their plans based on levels of disposable income, employment rates, and overall economic stability.

Disposable income plays a significant role in determining the extent to which individuals can afford to travel. During periods of economic prosperity, with rising incomes and low unemployment rates, people may have more discretionary funds available for vacations and travel experiences. Conversely, during economic downturns, when income levels stagnate or decline and unemployment rates rise, people tend to cut back on leisure travel and prioritize essential expenses.

Another important factor influencing tourism in the UK is the state of the global economy. The strength or weakness of major international economies can impact travel decisions and patterns. For example, if the economies of key source markets, such as the United States and European countries, are buoyant, it is likely to result in an increase in tourist arrivals to the UK. On the contrary, if these economies experience a downturn, it could lead to a decrease in visitor numbers.

Furthermore, geopolitical factors, such as wars, political instability, and natural disasters, can profoundly affect tourism. These events can lead to travel advisories, border restrictions, or a perception of unsafe travel, which can deter tourists from visiting the UK. Conversely, positive geopolitical developments and favorable international relations can boost tourism by fostering a sense of security and promoting the country as a desirable destination.

Government policies and regulations also impact tourism in the UK. Initiatives such as visa regulations, taxation policies, and marketing campaigns can shape travel trends. For instance, the implementation of visa relaxation programs or streamlined visa processes can attract more visitors by reducing barriers to entry. On the other hand, stricter visa requirements can create more hurdles for potential visitors.

Overall, a multitude of economic factors collectively influence tourism in the UK. From individual disposable incomes to global economic trends and government policies, these factors shape the demand for travel and play a crucial role in the fluctuation of tourist arrivals. Understanding these economic variables is fundamental in comprehending how exchange rates impact tourism in the UK, which we will explore in the subsequent sections.

The Exchange Rate and Its Importance

The exchange rate is the value at which one currency can be exchanged for another. It is a fundamental component of international trade and plays a crucial role in shaping the tourism industry. The exchange rate determines the cost of travel, influences tourist spending power, and affects the competitiveness of a destination in the global market.

For tourists, the exchange rate has a direct impact on the affordability of travel. When a traveler converts their home currency into the currency of their destination, they are subject to the prevailing exchange rate. A favorable exchange rate can make travel more affordable and appealing, as the amount of foreign currency they receive in exchange for their own currency is higher.

Moreover, the exchange rate influences the purchasing power of tourists. A stronger exchange rate enables tourists to buy more goods and services in the destination country, increasing their overall spending power. Conversely, a weaker exchange rate reduces purchasing power, potentially leading to a decrease in tourist spending and a shift towards more budget-conscious travel habits.

The exchange rate also affects the competitiveness of a destination within the global tourism market. A favorable exchange rate can make a country more attractive to international visitors, as it offers them better value for their money. This can lead to an increase in tourist arrivals and boost the tourism industry’s contribution to the national economy. On the other hand, an unfavorable exchange rate may make a destination less competitive, as it becomes relatively more expensive compared to other countries.

In addition to its impact on tourists, the exchange rate has implications for businesses operating in the tourism sector. Fluctuations in exchange rates can affect the costs and revenues of tourism enterprises. For example, a stronger domestic currency can increase the cost of imported goods and services, potentially impacting the profitability of tourism-related businesses that rely on such inputs. On the other hand, a weaker domestic currency can boost export-oriented tourism sectors by making their products or services more affordable in international markets.

It is worth noting that exchange rates are not fixed and can fluctuate due to various factors, including economic indicators, interest rates, inflation rates, and market speculation. These fluctuations can occur on a daily, weekly, or long-term basis, creating both challenges and opportunities for the tourism industry.

In the next section, we will examine the relationship between exchange rates and tourism demand to gain a deeper understanding of how changes in exchange rates can influence travel behavior and tourist preferences.

The Relationship between Exchange Rates and Tourism Demand

The exchange rate plays a pivotal role in shaping tourism demand, as it directly affects the cost of travel and the affordability of visiting a particular destination. Changes in exchange rates can have both positive and negative impacts on tourism demand, influencing the decision-making process of potential travelers.

When the exchange rate of a country’s currency strengthens, meaning it appreciates against other currencies, it generally leads to an increase in the number of international tourists. A stronger currency makes traveling to that country more affordable for foreigners, as they receive more local currency in exchange for their own currency. This can stimulate tourism demand, attract more visitors, and provide a boost to the local economy.

On the other hand, when the exchange rate of a country’s currency weakens, meaning it depreciates against other currencies, it can lead to a decrease in tourism demand. A weaker currency makes traveling to that country more expensive for foreigners, as they receive less local currency in exchange for their own currency. This can deter potential visitors, especially those on a tight budget, and result in a decline in tourist arrivals.

However, it is important to note that the relationship between exchange rates and tourism demand is not solely dependent on the direction of currency fluctuations. Other factors come into play, such as income levels, travel preferences, and the overall attractiveness of a destination.

For example, even if a country’s currency weakens, making it more expensive for foreign tourists, it may still experience an increase in tourism demand if other factors outweigh the impact of the exchange rate. These factors could include unique attractions, cultural experiences, safety, infrastructure, and promotional efforts. Conversely, even if a country’s currency strengthens, it may not see a substantial increase in tourism demand if other elements are not enticing enough for potential visitors.

It is also worth noting that the relationship between exchange rates and tourism demand is not always immediate or linear. Changes in travel patterns and tourist preferences may take time to adjust to fluctuations in exchange rates. Tour operators, travel agencies, and individuals may need time to assess and respond to changes in currency values. Moreover, long-term exchange rate trends and stability can also influence tourism demand, as travelers may feel more confident and secure when there is predictability in currency values.

Understanding the relationship between exchange rates and tourism demand is crucial for travel industry professionals and policymakers. It allows them to anticipate and adapt to changes in currency values, develop effective marketing strategies, and implement relevant policies to attract and retain tourists.

In the following section, we will explore the impact of exchange rate fluctuations on tourists’ behavior and spending habits to gain further insights into how changes in currency values can influence individual travel decisions.

Impact of Exchange Rate Fluctuations on Tourists’ Behavior

Exchange rate fluctuations have a significant influence on tourists’ behavior, shaping their travel decisions, spending habits, and even destination choices. These changes in currency values can create both challenges and opportunities for travelers, impacting various aspects of their travel experience.

One key impact of exchange rate fluctuations is on the affordability of travel. When a currency strengthens, making it more valuable compared to other currencies, travelers from that country will find it cheaper to visit foreign destinations. This can lead to an increase in outbound travel, as individuals take advantage of their stronger currency to explore new places or revisit favorite destinations. Conversely, when a currency weakens, it becomes more expensive for travelers from that country to go abroad, potentially leading to a decrease in outbound travel.

The changes in currency values also affect tourists’ spending power. A stronger currency means that travelers can enjoy more purchasing power in their destination country. They are likely to have more disposable income for accommodation, dining, shopping, and other activities. On the other hand, a weaker currency reduces purchasing power, forcing travelers to be more budget-conscious and potentially limit their spending. This can impact the overall tourism revenue generated by a destination and shape the type of experiences and accommodations that tourists seek.

Exchange rate fluctuations can also influence the destination choices of travelers. When a currency strengthens, it makes traveling to countries with weaker currencies more affordable and attractive. This can lead to an increase in visitors to these destinations, as they offer more value for money. Conversely, a weaker currency can deter tourists from visiting countries with stronger currencies, as they might perceive them as expensive or not offering good value. This can result in a shift in tourist flows and impact the market share of different destinations.

Moreover, exchange rate fluctuations can influence the timing and duration of trips. When a currency weakens, travelers may choose to extend their stay in a destination they have already arrived in to maximize the value of their money. On the other hand, a stronger currency might prompt tourists to reduce the duration of their stay or postpone their travel plans to a later time when the exchange rate is more favorable. These adjustments in travel behavior can have implications for businesses in the tourism industry, such as accommodations, restaurants, and attractions.

It is important to note that the impact of exchange rate fluctuations on tourists’ behavior is not uniform for all travelers. Factors such as individual budgets, travel preferences, and personal financial circumstances play a significant role in shaping how individuals respond to changes in currency values. Some travelers may be less sensitive to exchange rate fluctuations and prioritize other factors, such as safety, cultural experiences, or specific attractions, when making their travel decisions.

Understanding the impact of exchange rate fluctuations on tourists’ behavior is crucial for tourism businesses and destinations. By analyzing these effects, industry professionals can develop strategies to attract and retain visitors, tailor marketing efforts to specific target markets, and adjust pricing and offerings to align with travelers’ preferences and budgets.

In the next section, we will delve into case studies that examine the effects of exchange rate changes on UK tourism, providing real-life examples of how currency fluctuations can influence tourist behavior and industry dynamics.

Case Studies: Effects of Exchange Rate on UK Tourism

To understand the tangible impact of exchange rate changes on UK tourism, let’s explore some case studies that exemplify how currency fluctuations can influence tourist behavior and industry dynamics.

Case Study 1: The Impact of a Weaker Pound

In the aftermath of the Brexit referendum in 2016, the British pound experienced a significant depreciation against major currencies. This led to a boost in tourism to the UK, as international visitors found it more affordable to explore the country. The weakened pound made accommodations, dining, shopping, and attractions comparatively cheaper for tourists, attracting a surge in visitor numbers. As a result, the UK witnessed a notable increase in tourism revenue, benefiting various sectors of the economy.

Case Study 2: The Effect of a Stronger Pound

In contrast, when the British pound becomes stronger, it can impact the inflow of tourists to the UK. For example, during periods when the pound strengthened against the euro, UK destinations became relatively more expensive for European travelers. This led to a decline in tourist arrivals and affected the tourism industry’s contribution to the economy. To counteract this trend, tourism authorities and businesses often adjust their marketing strategies and pricing models to entice visitors and maintain competitiveness.

Case Study 3: The Influence of Exchange Rates on Travel Behavior

Exchange rate fluctuations not only affect the choice of destination but also impact travel behavior within the UK. When the pound weakens, domestic tourism may gain popularity as residents prefer to explore their own country rather than traveling abroad. This shift in behavior can benefit local destinations, attractions, and hospitality businesses, stimulating the domestic tourism market and boosting regional economies.

These case studies highlight the profound influence that exchange rate fluctuations can have on UK tourism. A weaker currency can attract more international visitors by offering better value for their money, while a stronger currency can make the UK relatively more expensive, potentially impacting tourism demand. Additionally, exchange rates can influence both outbound and domestic travel behavior, affecting the flow of tourists and revenue distribution within the tourism industry.

By examining these real-life examples, it becomes evident that monitoring and addressing exchange rate fluctuations is crucial for tourism stakeholders in the UK. Navigating the effects of currency volatility requires a thoughtful approach, including targeted marketing campaigns, pricing adjustments, and proactive industry collaboration to maintain the country’s competitiveness in the global tourism market.

In the next section, we will explore the broader implications of exchange rate fluctuations on the tourism industry in the UK and discuss strategies to mitigate the effects of currency volatility.

Implications of Exchange Rate on Tourism Industry in the UK

The exchange rate has significant implications for the tourism industry in the UK, affecting various stakeholders and shaping the overall competitiveness and profitability of the sector. Understanding these implications is crucial for industry professionals and policymakers to effectively navigate the challenges and opportunities associated with currency fluctuations.

1. Competitiveness:

The exchange rate directly impacts the competitiveness of the UK as a tourism destination. A favorable exchange rate can make the country more affordable and attractive to international visitors, leading to an increase in tourist arrivals and boosting the tourism industry’s contribution to the local economy. Conversely, an unfavorable exchange rate can make the country relatively more expensive, potentially deterring visitors and reducing market share. Maintaining a competitive exchange rate is vital in ensuring the sustainability and growth of the tourism industry in the UK.

2. Revenue Distribution:

Exchange rate fluctuations can lead to shifts in revenue distribution within the tourism industry. A weaker currency may result in an increase in domestic tourism as residents opt to explore their own country rather than travel abroad. This can benefit local destinations, attractions, and hospitality businesses. On the other hand, a stronger currency can impact the inflow of international tourists, affecting revenue generated by businesses reliant on international visitors. Finding the right balance and diversifying revenue sources can help mitigate the effects of exchange rate volatility.

3. International Collaboration and Partnerships:

Exchange rate fluctuations necessitate increased collaboration and partnerships within the tourism industry. Tourism businesses, destination management organizations, and government entities need to work together to address the challenges posed by currency volatility. This can include joint marketing efforts, pricing adjustments, and partnerships with commercial banks and foreign exchange providers to offer competitive exchange rates and minimize the impact on tourists’ spending power.

4. Investment and Business Confidence:

Exchange rate stability is crucial for attracting foreign investment in the tourism industry. A stable currency provides a predictable environment for businesses, reducing the risk associated with currency fluctuations. On the other hand, volatile exchange rates can deter investors and hinder the development of tourism infrastructure and services. By implementing policies that promote exchange rate stability and providing support to the tourism industry during periods of currency volatility, the UK can enhance business confidence and foster a favorable investment climate.

5. Industry Adaptation and Resilience:

Exchange rate fluctuations require the tourism industry to adapt and build resilience. Businesses must continually assess their pricing strategies, monitor currency trends, and develop contingency plans to navigate potential challenges. Diversifying target markets, focusing on niche segments, and offering unique experiences can reduce reliance on specific currencies and mitigate the impact of exchange rate fluctuations on business performance.

By recognizing these implications, the tourism industry in the UK can be better prepared to respond to exchange rate fluctuations and develop strategies to mitigate their effects. It requires close collaboration between businesses, industry associations, and government entities to ensure a sustainable and resilient tourism sector that can withstand currency volatility and thrive in an ever-changing global market.

In the next section, we will explore strategies for mitigating the effects of exchange rate volatility in the tourism industry and maintaining a competitive edge in the global market.

Strategies for Mitigating the Effects of Exchange Rate Volatility

Exchange rate volatility poses challenges to the tourism industry in the UK, but there are strategies that businesses and destinations can employ to mitigate its effects and maintain a competitive edge in the global market:

1. Diversify Target Markets:

Relying too heavily on a single market can leave tourism businesses vulnerable to fluctuations in that market’s currency. Diversifying target markets can help spread the risk and reduce the impact of exchange rate volatility. This involves identifying and investing in emerging markets, collaborating with travel agents and tour operators from different regions, and tailoring marketing efforts to attract a diverse range of international visitors.

2. Implement Dynamic Pricing:

Dynamic pricing allows tourism businesses to adjust prices in response to changes in exchange rates. By closely monitoring currency fluctuations, businesses can modify their pricing strategies to maintain competitiveness and attract customers. This may involve offering discounts during periods of currency appreciation or adjusting prices for different markets to maximize revenue and keep tourists engaged.

3. Offer Value-Added Experiences:

Providing unique and value-added experiences can help tourism businesses differentiate themselves and attract visitors, regardless of exchange rate fluctuations. Focusing on quality service, personalized experiences, and showcasing the unique aspects of the destination can create an emotional connection with travelers and make them more willing to spend, regardless of the exchange rate.

4. Collaborate with Financial Institutions:

Forming partnerships with commercial banks and foreign exchange providers can enable tourism businesses to provide competitive exchange rates to visitors. By offering convenient and favorable currency exchange services, businesses can enhance customers’ purchasing power and mitigate the impact of exchange rate fluctuations on their spending habits.

5. Invest in Technology and Innovation:

Utilizing advanced technology and innovative solutions can help tourism businesses streamline operations, improve efficiency, and manage costs effectively. Adopting digital payment platforms, implementing revenue management systems, and utilizing data analytics can assist in optimizing pricing strategies, forecasting demand, and identifying new market opportunities, making businesses more resilient to currency fluctuations.

6. Focus on Domestic Tourism:

During periods of currency volatility, emphasizing domestic tourism can help mitigate the effects of exchange rate fluctuations. Collaborating with local tourism boards and associations to highlight the unique attractions and experiences that can be enjoyed within the country can encourage residents to explore their own backyard, stimulating domestic tourism and reducing the reliance on international visitors.

7. Government Support and Policy Interventions:

Policies and initiatives from the government can play a crucial role in mitigating the effects of exchange rate volatility. Providing monetary incentives, tax breaks, and financial assistance to tourism businesses during periods of currency fluctuations can help cushion the impact on their operations. Additionally, collaborating with financial institutions to offer favorable loan terms and exchange rate hedging options can further support the tourism industry.

By implementing these strategies, businesses and destinations in the UK can minimize the effects of exchange rate volatility and maintain competitiveness in the global tourism market. A combination of proactive measures, innovative approaches, and supportive policies can build resilience and ensure long-term sustainability in the face of currency fluctuations.

In the final section, we will conclude our discussion on the impact of exchange rates on tourism in the UK and summarize the key insights obtained throughout this article.

Exchange rates play a significant role in shaping the tourism industry in the United Kingdom. The value of currencies impacts travel decisions, tourist spending patterns, destination competitiveness, and revenue distribution within the sector. Understanding the relationship between exchange rates and tourism demand is crucial for industry professionals and policymakers in navigating the challenges and opportunities presented by currency fluctuations.

Economic factors, geopolitical events, and government policies all contribute to fluctuations in exchange rates. These fluctuations can have both positive and negative effects on tourism demand, depending on the direction and magnitude of the currency movements. A stronger currency can attract more international visitors by making travel more affordable, while a weaker currency can impact tourism demand due to decreased purchasing power for foreign tourists.

Exchange rate fluctuations also influence tourists’ behavior, including their destination choices, spending habits, and travel duration. These changes in behavior can have implications for various tourism-related businesses and revenue distribution within the industry. Furthermore, exchange rate volatility requires businesses and destinations to adapt and develop strategies for mitigating its effects.

To mitigate the effects of exchange rate volatility, tourism businesses and destinations can diversify target markets, implement dynamic pricing strategies, offer unique experiences, collaborate with financial institutions, invest in technology and innovation, focus on domestic tourism, and seek government support and policy interventions.

By effectively addressing exchange rate fluctuations and capitalizing on the opportunities they present, the UK tourism industry can maintain competitiveness, attract visitors from diverse markets, and contribute to the country’s overall economic growth. Strategic planning, collaboration, and adaptation are essential for navigating the complexities of currency volatility and maximizing the benefits of a dynamic and ever-changing global tourism market.

In conclusion, exchange rates are not merely numbers on a screen; they are powerful variables that shape the tourism landscape. By understanding and responding to the implications of exchange rate fluctuations, the UK can harness the potential of its vibrant tourism industry and continue to enchant visitors from around the world.

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How Exchange Rates Affect Tourism and Businesses

Exchange rates may seem like a bit of an abstract concept, but they actually affect everything that we do, regardless of whether that includes going on a summer holiday or running an overseas business. How, you ask? Well, in this post, we’ll show you exactly how exchange rates affect tourism and businesses. But first, let’s have a look at why exchange rates move.

Why Exchange Rates Move

Exchange rates move as currencies are paired against each other. This means that as the value of one currency rises, the other’s value must fall.

The value of a currency is measured against a number of different factors . These include everything from employment rates to elections and even wars. The simplest of all economic announcements, including something on interest rates or farming data can cause the price of a currency to move up and down.

Also, it’s important to note that changes in the price of one currency (or even an asset) can have a huge knock-on impact. For example, if there’s an oil shortage in Saudi Arabia, then it can have effects on economies the world over. As well as lowering the value of middle eastern currencies, it also has an effect on currencies in the UK, US and beyond. That’s because, thanks to globalisation, these economies are dependent on each other. So, a fuel shortage in Iran will not only affect the price of oil, but the value of the pound, too.

So, exchange rates can move due to a number of different factors. But how does this affect you if you’re going on holiday or doing business deals? Let’s take a look.

The largest impact on tourism comes from foreign exchange rates when people are getting their holiday spending money.

When you go to a bureau de change, you may notice that the amount that you can buy on one day varies in comparison to another day. This change is only minimal, but it can make a big difference if you’re spending large amounts for your holiday spending money.

That’s why so many people shop around. The rate often varies hour to hour, and it certainly varies from location to location. However, on top of this, it’s also worth pointing out that although bureau de changes are convenient, they’re often not the best way of getting your money.

If you don’t want to be hit hard by unfavourable exchange rates and transaction fees, you’re better off looking online . This way, you’re less likely to pay extortionate fees for foreign exchange transactions.

Foreign exchange rates also have a large impact on businesses operating overseas. This is because, if you sign a contract to pay a certain amount of pounds for your goods regardless of what the host’s currency is worth, then you could end up paying way over or under the odds for your products.

Likewise, it is also worth noting that, if you agree to pay a fluctuating fee based on what each currency is worth at the time of purchase, then you may end up paying considerably more for one shipment than the last one.

As such, before you take your business global, you must consider the impacts of this. By paying different amounts for each pallet of goods, you may have to structure your finances differently. So you’ll have to plan for if the pound takes a nosedive, or even reconsider your pricing structure regularly .

To conclude, exchange rates affect everything that we do, regardless of whether it’s business or personal. So, before you travel or do a business deal, make sure you check the exchange rate carefully, you could save a fortune.

Zak Goldberg is a Law & Business Graduate from the University of Leeds who has chosen to follow his aspirations of becoming a full-time published writer, offering his expertise on all areas of law and finance.

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What causes exchange rates to change?

An exchange rate is how much of a given nation’s currency you can buy with a different nation’s currency. If you purchase foreign goods or travel abroad, you may need to convert your currency to another country’s money.

Exchange rates are a critical measure of a country’s financial health, and they constantly shift as the demand for a particular currency increases or decreases.

Many factors go into and can cause them to change. For instance, a currency’s value might go up or down due to international trading, policy decisions, investor expectations, the political climate, and the overall economic conditions of the home country.

9 common causes of exchange rate fluctuations

Pinpointing what causes exchange rates to change isn’t straightforward. Even the most accomplished economists sometimes struggle because of and the many interrelated factors involved.

There are two main —fixed and . In a fixed exchange rate system, a government or central money maintains a currency’s value, allowing little to no fluctuation. In contrast, floating exchange rates are based on current supply and demand forces within the foreign market.

Many things affect the supply and demand of a currency (and thus its value), including inflation, interest rates, stock market performance, and government debt.

Let’s dive into nine reasons why exchange rates change.

1. Inflation

Inflation occurs when the cost of goods and services increases, decreasing the purchasing power (and actual value) of a currency.

Typically, the perceived value of the money will decrease as well, deterring investors from buying it. As the currency loses its buying power and becomes less attractive in the foreign market, the exchange rate will likely drop in favor of stronger currencies.

2. Interest rates

Interest rates play a major role in a currency’s value and are an essential part of a country’s monetary policy. Governments often adjust interest rates to manage inflation and economic growth, which can push a nation’s exchange rate higher.

For example, a government will often raise interest rates in a high-inflation economy, discouraging borrowing and encouraging saving. Over time, prices for goods and services drop, enticing consumers to start buying again. This typically causes the currency to appreciate, resulting in a higher foreign exchange rate.

3. Recession

A country is in a recession when its gross domestic product (GDP), the total market value of all final goods and services produced within its borders, drops for two consecutive quarters. Often marked by high unemployment, a recession causes everyone to pinch pennies, including foreign investors.

When a nation’s economy is weak, its currency loses international appeal. As a result, the exchange rate will typically drop until the country’s financial situation improves.

4. Speculation

As investors try to earn a profit, their speculation on a currency’s value could cause the exchange rate to change.

Suppose investors believe a nation’s money is overvalued. They might sell their holdings to cash out before an anticipated dip, potentially driving down the currency’s value. On the other hand, if investors think a currency is undervalued, they may go on a buying spree that causes an artificial price hike.

5. Stock markets

The performance of a nation’s stock market is a significant indicator of its financial health and, thus, a potential cause of exchange rate fluctuations.

Stocks outperforming investor expectations is a sign of a strong economy. This makes a currency more appealing to foreign investors. Conversely, an underperforming stock market might drive foreign investors away from a currency.

6. Political instability

When a country’s economy is unstable, its money typically loses value on the international stage. Political instability often leads to the same result.

Political unrest and division create uncertainty, potentially discouraging foreign investors from investing in that country’s currency or businesses. Political instability can also drive up inflation, disrupt production and exports, and force governments to spend more. This combination can hurt a currency’s value.

 7. Current account deficits

A current account measures the money coming in and out from selling goods and services to other countries. The current account has a deficit if the nation imports more than it exports and borrows foreign currency to operate and grow.

While a current account deficit can benefit a country, it could eventually cause the nation’s money to lose value. Foreign investors may pull back if they don’t predict a high enough return on their investment, ultimately resulting in a lower exchange rate.

8. Terms of trade

Terms of trade (TOT) measures the ratio between a nation’s export and import prices. When export prices increase faster than import prices, the country’s revenue goes up, as does the demand for the nation’s currency. As more people want to buy the currency, the value increases.

When import prices increase faster than export prices, the opposite happens. The country’s revenue, currency demand, and exchange rate decrease.

9. Government debt

Governments sometimes take on debt to fund national improvements. However, too much debt might make a country’s currency less attractive to foreign investors.

Investors might speculate about the country’s ability to repay its debt, potentially leading to high inflation or a weaker currency. A poor credit rating can add to those concerns.

What are the causes of fluctuating exchange rates?

There are many causes of exchange rate fluctuations. Generally, exchange rates change when a country experiences economic or political shifts.

What are the factors affecting the exchange rate?

Factors that affect the exchange rate include but aren’t limited to economic standing, speculation, stock market performance, political stability, current account status, terms of trade, and government debt.

Is it better for the exchange rate to go up or down?

Generally, it’s better when the exchange rate for your nation’s currency goes up because it indicates a strong economy. However, another country’s currency losing value can be an opportunity to purchase an investment that may appreciate in the future.

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Costa Rica’s Tourism Sector Faces Job Losses Amidst Exchange Rate Crisis

The National Chamber of Tourism ( CANATUR ) disclosed that among its affiliated companies, 544 people were laid off during 2024. The companies indicated that they had no other option but to fire employees in the face of the negative effects of the exchange rate. If this steady devaluation of the U.S. dollar and the resulting appreciation of the local currency continue, the chamber expects another 918 layoffs in the coming weeks.

The private sector claims that the government maintains a destructive exchange rate policy, which has caused a lot of problems for companies in the tourism industry.

Shirley Calvo, executive director of CANATUR, said that since the pandemic, businesses are making an effort to survive. However, after the COVID-19 pandemic, right when the tourism industry was recovering, the colón began to appreciate. Employers must make the difficult decision as to whether or not the income is sufficient to maintain the payroll.

“Today we are here representing almost 1,500 families that are losing their jobs as a direct result of the exchange rate policy the BCCR is implementing in this country,” Calvo said at the press conference organized by the private sector.

In less than two years, the dollar went from being close to ¢700 to close to ¢500. Currently, the U.S. currency has the same price it had in 2014, that is, a decade ago.

The business community argues that these variations make it impossible for them to plan for the long term without incurring losses, make it difficult for them to invest, and jeopardize the stability of countless jobs.

It’s worth noting that the tourism sector is crucial to the Costa Rican economy, with thousands of families in rural areas, the most vulnerable, depending on it for their livelihoods.

Business owners reminded the authorities that there have been several occasions in which the Costa Rican productive sector has come together to warn about these layoffs as an effect of the decisions made by the government.

“The BCCR has sufficient legal and technical instruments to be able to restore balance to the exchange rate policy and sanity to the economic policy,” Calvo added.

While the private sector has already made several requests to the government to take actions, the Chaves administration has defended the current exchange rate and clarified it will not take further action to alter it.

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