Dutch Disease phenomenon and demand for international business travels: Panel ARDL/PMG estimation

Abstract

This article explores the long-run and short-run effect of natural resource rents on inbound and outbound business travels in resource-abundant economies. By applying panel ARDL/PMG models for 25 countries with annual data for 2005–2017, our results show that increases in dependency on natural resources lead to lower demand for inbound and outbound business travels in the long run. The short-run analyses indicate that while natural resource rents have a significant and positive impact on outbound business travels, they do not affect inbound business travels.

Keywords

business travel Dutch Disease natural resource rents panel ARDL/PMG

Introduction

The link between Dutch Disease phenomenon and leisure tourism has been analyzed in the literature in recent years (e.g. Dwyer et al., 2016; Forsyth et al., 2014; Pham et al., 2015). In this study, we extend this growing literature to the business tourism research. To the best of our knowledge, there is no study that explores the relationship between Dutch Disease phenomenon and inbound and outbound business travels in a set of resource-abundant economies over time. These economies often enjoy a temporary period of higher foreign exchange revenues that flow from their resource exports. These increased revenues result in real exchange rate appreciation and a fall in output and employment of the non-resource traded goods sector, often manufacturing (Esfahani et al., 2014).

We address this gap in the tourism literature by studying the impacts of rapid boosts to foreign income from the exporting of natural resources on demand for international business travels in the long run and short run. We expect that inbound and outbound business travels show positive responses to the natural resources boom in resource-abundant economies. The excess foreign incomes improve the spending capacity of residents of these countries. In the meantime, the appreciation of national currency leads to an exogenous rise in the price of domestically made products relative to the price of imports (Corden, 1984). The combination of these factors facilitates an environment prone to more imports, which most likely generates a higher volume of international business travels.

Contrary to this view, one may argue that Dutch Disease conditions, which result in a medium-term deindustrialization of the economy, would negatively impact the flows of international business travels. That is, the windfall of the resource boom attracts labor and capital to this thriving sector and crowds out other industries. Moreover, the appreciation of the domestic exchange rate erodes the price competitiveness of non-booming export industries and those industries that face strong import competition (Gregory and Sheehan, 2011). This may result in a significant reduction in exports of traditional and non-booming sectors and thus limit the overseas business travels that are required for visiting customers and suppliers or marketing and promotional activities. These contradicting views provide an interesting context to conduct an empirical analysis on the relation between Dutch Disease and inbound and outbound business travels across different timespans. This is because the macroeconomic variables react differently to exogenous shocks in the short and long run; therefore, it is reasonable to expect that the volume of international business travels also exhibits different reactions to Dutch Disease conditions across distinct time periods.

We apply the pooled mean group autoregressive distributed lag (ARDL/PMG) model of Pesaran et al. (1999), which estimates the long-run and short-run impact of variables. By using data from 25 resource-abundant economies over the period of 2005–2017, we find that a rise in natural resource rents lessens both inbound and outbound business travels in the long run. The short-run analyses suggest that a positive change in natural resource rents have a positive impact on outbound business tourism but have a statistically insignificant impact on inbound business tourism.

This study is not only motivated by the gap in existing literature but also by some notable observations on the growth of international business travels. For example, over the period of our study (2005–2017), global business departures rose from around 144 million trips in 2005 to approximately 250 million trips in 2017 (Euromonitor International, 2020). Unlike global average growth in business travels, most of the resource-based economies have experienced significant fluctuations in business arrivals and departures over the past decade (see Figures A.1 and A.2 in Online Appendix). These countries have also experienced a volatile movement in terms of natural resource rents (see Figure A3 in Online Appendix). This raises an empirical research question being how business travels respond to fluctuation in resource rents (in the long run and short run) in resource-based economies where most of the economic activities heavily rely on natural resource exports.

The remainder of this article is structured as follows. The second section reviews the literature on Dutch Disease and tourism-related variables. The third section describes the data and variables. The fourth section presents the empirical model and explains the panel ARDL/PMG estimator. The fifth section presents results of the long-run and short-run analyses. The sixth section concludes the article.

Literature review

Dutch Disease theory

The term, Dutch Disease, was introduced by economists in the late 1970s. Two of the most cited studies in this area are those of Corden and Neary (1982) and Corden (1984) that consolidated the literature and theorized Dutch Disease in academic terms. The theory explains the adverse impacts of natural resource discoveries and developments on the manufacturing sector and their long-term impacts on the economy. After discovery of large reserves of gas in the Netherlands in the 1960s, the Dutch economy experienced a natural gas export boom. As a result, three economic sectors emerged, including the booming sector, the lagging sector and the non-tradable sector (Corden, 1984; Corden and Neary, 1982).

The booming sector (e.g. natural gas and oil) requires additional labor and capital to meet the new demand. Thus, these scarce factors of production move from the lagging and non-tradable sectors to the booming sector. This is known as the resource movement effect (Corden and Neary, 1982; Rajan and Subramanian, 2011). In the short term, the skilled-labor wages increase which leads to the reduction of the profitability and competitiveness of non-booming sectors.

Additionally, higher incomes generated by the export of booming commodities enables residents to spend more on imported goods and services. This is known as the spending effect (Corden and Neary, 1982). The jump in export also contributes to the appreciation of domestic currency. The combination of these factors reduces the competitiveness of domestically produced products in local and foreign markets. Moreover, the crowding out impact of Dutch Disease on a non-booming sector delays the learning-by-doing experience that is required for manufacturing industry (Van Wijnbergen, 1984). These effects lead to a decline in traditional exports, further de-industrialization of the lagging sector, and an overall contraction of the economy in the long term (Forsyth et al., 2014; Inchausti-Sintes, 2015; Rajan and Subramanian, 2011).

Considerable attention has been given to the applications of Dutch Disease theory on resource-exporting economies, with high emphasis on the oil-exporting countries (Buiter and Purvis, 1980; Edwards and Aoki, 1983; Hasanov, 2013; Oomes and Kalcheva, 2007). However, this economic illness can be extended to any situation where a country faces a sudden inflow of foreign capital (Inchausti-Sintes, 2015). For instance, there is evidence that some developing economies react similarly to foreign financial aids, demonstrating signs of inflationary pressures on exchange rate and export reduction (Laplagne et al., 2001; Rajan and Subramanian, 2011; Usui, 1996; Van Wijnbergen, 1984; White, 1992).

Dutch Disease and tourism

A sudden tourism boom can have similar impacts as the discovery of natural resources on an economy (Capo et al., 2007; Holzner, 2011). Non-tradable goods are typically not exportable and can only be consumed within the borders of a country. However, according to Nowak and Sahli (2007), a tourism boom can convert a non-tradable sector to a booming sector and, therefore, expose it to both resource movement and spending effects. Majority of studies in this area concentrate on the influx of capital from inbound tourism. Tourism is a labor-intensive sector and relocates skilled workers from traditional sectors to the non-tradable sector. It also attracts capital resources from the lagging sector and increases the consumption of non-tradable goods in a country. Furthermore, higher demand for the local currency generated by tourists leads to the appreciation of the exchange rate. The combinations of these elements diminish the competitiveness of tradable goods, which may imperil the long-term economic growth of a country (Inchausti-Sintes, 2015). Chao et al. (2006) developed a dynamic framework to examine the impact of tourism on capital accumulation, sectoral output, and domestic welfare of an open economy. Authors showed that an expansion of tourism causes diversion of resources from the manufacturing sector to the non-tradable sector and results in capital decumulation.

There are other examples of Dutch Disease in tourism literature. Capo et al. (2007) investigated two different Spanish regions with tourism-oriented economies, the Balearics and the Canary Islands. Authors identified Dutch Disease symptoms and indicated concerns about the future of the economic developments of these regions. In the same vain, Inchausti-Sintes (2015) showed that although tourism has helped Spain’s economy to reduce the unemployment rate, the “lower capital intensity generated by tourism-led growth may jeopardize future productivity gains that would adversely affect Spain economic growth” (p. 186).

However, Santana-Gallego et al. (2011a) presented some contradictory evidence about the existence of a tourism-related Dutch Disease environment in Canary Island. They reported a long-term and positive relationship between tourism and international trade and showed that tourism and trade are increasing the size of the island’s economy.

The other country that has been considered as a case for Dutch Disease in tourism literature is Australia. In the 2000s, Australia experienced a mineral boom fueled by substantial demand in Asian countries, most notably China and India. However, not all industries benefited equally from this boom. The trade-based industries suffered from a strong Australian dollar and lost price competitiveness to foreign competitors (Dwyer et al., 2016). Tourism was one of the adversely affected sectors.

Dwyer et al. (2016) surveyed the CEOs of Australian tourism industry associations, who indicated concerns about the negative impacts of mining industry travelers on leisure tourism in regional Australia. The mining boom created a substantial demand for accommodation and air transport by fly-in fly-out workers, which pushed prices up. In this competition for resources, leisure travelers—to mining states and regional areas—were the disadvantaged groups. In addition, as mining activities usually occur in remote regions, which are unable to address the increased demand for skilled labor in the short term, the hospitality industry attracted skilled labors from non-mining states. In the meantime, mineral export led to the significant appreciation of the Australian dollar. As a result, the tourism industries of non-mining states faced higher costs and wage rates and lost their international competitiveness (Dwyer et al., 2016).

The other two studies that highlighted the existence of Dutch Disease in Australia’s tourism sector are those of Forsyth et al. (2014) and Pham et al. (2015). Both studies confirmed the role of the mining boom on the appreciation of domestic currency and its crowding out effect of labor and capital resources from non-mining to mining states. Forsyth et al. (2014) proposed four policy responses to mitigate the adverse impacts of Dutch Disease on the tourism industry. They include price changes through changes in tourism taxes, promoting inbound and domestic tourism, and improving tourism products. Pham et al. (2015), on the other hand, suggested that policy makers need to consider strategic investment in accommodation and aviation to support the tourism sector. They stated that while the higher exchange rate reduced the intentional inbound tourism, the real income generated by the booming sector might make domestic traveling more attractive.

As discussed above, the majority of these studies have looked at the consequences of Dutch Disease from the leisure tourism perspective. However, in this study, we focus on business inbound and outbound tourism, which has not been addressed by existing studies. We extend the literature on the link between Dutch Disease and tourism by analyzing the long-run and short-run impact of natural resource rents on demand for business travels in resource-based economies.

Data and variables

Sample

The sample includes 25 resource-abundant economies for the period of 2005–2017. The countries are selected based on the criteria of having around 20% of resource rents (% of GDP) on average for the period of 2005–2017. In addition, there is substantial evidence in the literature that these countries experienced the symptoms of Dutch Disease over the past two decades (see Table A.1 in the Online Appendix for list of academic papers that identified evidence of Dutch Disease in the sample countries). Our sample covers all those countries for which data on business arrivals and departures as well as resource rents are obtainable. The sample countries are Algeria, Angola, Azerbaijan, Brunei Darussalam, Burundi, Chad, Congo Dem. Rep., Congo Rep., Equatorial Guinea, Ethiopia, Gabon, Iran, Iraq, Kazakhstan, Kuwait, Liberia, Libya, Mauritania, Mongolia, Oman, Qatar, Saudi Arabia, United Arab Emirates, Uzbekistan, and Venezuela. Given that our sample includes countries from low-income (e.g. Ethiopia), lower-middle-income (e.g. Uzbekistan), upper-middle-income (e.g. Azerbaijan), and high-income countries (e.g. Kuwait), we have a significant variation in the variables across countries which provides an appropriate context for regression analyses. It is noteworthy that due to some missing values for the control variables, we use an unbalanced panel data for analyses.

Dependent variables

The dependent variables in this study are number of business arrivals (‘1000’ trips) divided by labor force population (INBOUND) and (2) number of business outbound (‘1000’ trips) divided by labor force population (OUTBOUND). Following Gholipour and Foroughi (2019b), we correct for country size by dividing number of inbound and outbound business trips by labor force population¹ (in million), so that measures from different countries could be compared. Data for these variables are collected from the Euromonitor International (2020). Table A.2 provides description, data sources, and descriptive statistics of variables. It is notable that the Euromonitor International’s inbound and outbound tourism data have been used by many researchers in recent years (e.g. Gholipour et al., 2014, 2016; Gholipour and Tajaddini, 2018; Saha and Yap, 2014). However, Euromonitor International’s tourism data have a limitation as it uses estimated values for the number of inbound and outbound tourists for some of our sample countries.

Main variable of interest

We use the World Bank’s total natural resources rents as a percent of gross domestic product (GDP) (RENT) as a measure to capture the size of economies’ reliance on natural resources. They are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. The average of RENT over the period 2005–2017 in our sample is around 30%, with the highest in Iraq (47.8%), Equatorial Guinea (46.6%), and Kuwait (45.7%). The World Bank defines each component of total natural resources rents as follows. Oil rents are the difference between the value of crude oil production at world prices and total costs of production. Natural gas rents are the difference between the value of natural gas production at world prices and total costs of production. Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production. Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Finally, forest rents are roundwood harvest times the product of average prices and a region-specific rental rate.

The reason that we use RENT measure in our study is that the data for other measures, such as exports of mineral products and fuel exports, are not completely available for many of our sample countries.

Control variables

In our estimations, we also control for GDP per capita based on purchasing power parity (PPP) as a proxy for income, trade (% of GDP) as a proxy for trade openness and foreign direct investment (FDI), net inflows (% of GDP) as measure for foreign investment, which are the key determinants of business travel flows at the macro-level (e.g. Gholipour and Foroughi, 2019b; Kulendran and Wilson, 2000; Kulendran and Witt, 2003; Tang et al., 2007; Tsui et al., 2018). Table A.2 explains the definition of all variables.

Methodology

We apply the ARDL/PMG² estimator of Pesaran et al. (1999). The model is suitable for our study since we are interested in understanding the long-run and short-run impacts of changes in natural resource rents on inbound and outbound international business travels over time and across countries. It should be noted that the ARDL/PMG models have been used by several researchers to understand the long-run and short-run relationship between tourism and economic variables (e.g. Gholipour et al., 2020; Narayan, 2004; Santana-Gallego et al., 2011b).

The ARDL/PMG model takes the cointegration form of the simple ARDL model and adapts it for a panel setting by allowing the intercepts, short-run coefficients, and cointegrating terms to differ across cross-sections (Pesaran et al., 1999). ARDLs are standard least squares regressions that include lags of both the dependent variable and explanatory variables as regressors (Greene, 2008). For selecting number of lags for dependent and independent variables, we rely on the model selection method of Akaike information criterion (AIC). It suggests that the preferred model is ARDL (1, 1, 1, 1, 1).

Model

Assume the long-run function

{TOUR}_{i t} = θ_{0 t} + θ_{1 t} {RENT}_{i t} + θ_{2 t} X_{i t} + μ_{i} + ε_{i t}

where the number of countries i = 1, 2,…, N; the number of periods t = 1, 2,…, T; TOUR _it represents the dependent variables (inbound and outbound business travels); RENT _it is total natural resources rents as a percent of GDP; and X_it represents the control variables. If the variables are I(1) and cointegrated, then the error term is I(0) for all i. The ARDL dynamic panel specification of equation (1) is

{TOUR}_{i t} = δ_{10 i} {RENT}_{i t} + δ_{11 i} {RENT}_{i, t - 1} + δ_{20 i} X_{i t} + δ_{21 i} X_{i, t - 1} + λ_{i} {TOUR}_{i, t - 1} + μ_{i} + ε_{i t}

The error correction reparameterization of equation (2) is

Δ {TOUR}_{i t} = Φ_{i} {(TOUR}_{i, t - 1} - θ_{0 i} - θ_{1 i} {RENT}_{i t} - θ_{2 i} X_{i t}) + δ_{11 i} Δ {RENT}_{i t} + δ_{21 i} Δ X_{i t} + µ_{i} + ε_{i t}

where Φ _i = − (1 − λ_i), θ_0i = μ_i/(1 − λ_i), θ_1i = (δ_10i + δ_11i)/(1 − λ_i), and θ_2i = (δ_20i + δ_21i)/(1 − λ_i).

The error-correction speed of adjustment parameter, Φ_i, and the long-run coefficients, θ_1i, are of primary interest. With the inclusion of θ_0i, a nonzero mean of the cointegrating relationship is allowed. It is expected that Φ_i to be negative if the variables exhibit a return to long-run equilibrium (Blackburne and Frank, 2007).

Results

Results of unit-root tests

We start with panel unit-root test to examine the stationarity of the data. Since our data sets are unbalanced panel, we perform the Im–Pesaran–Shin (IPS) unit-root test (developed by Im et al., 2003). Unlike other panel unit-root tests, the IPS test does not require balanced data sets. Moreover, the IPS test relaxes the assumption that all panels share a common autoregressive parameter. The null hypothesis of the test is that all panels contain a unit root. Table 1 presents the test statistics for the variables. The results indicate all variables are stationary in first difference.

Table 1.

Im–Pesaran–Shin unit-root test.

H_o: All panels contain unit rootsH_a: Some panels are stationary
	w-t-bar statistic
Variables	Level	1st difference
log(OUTBOUND)	−1.214	−2.850***
log(INBOUND)	0.784	−2.419***
RENT	−1.529*	−1.305*
TRADE	−3.117***	−5.231***
log(GDPC)	−0.885	−1.850**
FDI	−1.292*	−2.030***

Note: Panel means and time trend are included. Lag structure for ADF regressions = 1. FDI: foreign direct investment; ADF: augmented dickey-fuller.

***p < 0.01, **p < 0.05, *p < 0.10.

Estimation results

The long-run and short-run equations obtained from ARDL/PMG, when using OUTBOUND as a dependent variable, are presented in Table 2. The long-run estimation shows that RENT has a negative and significant effect on OUTBOUND with a coefficient of −0.377, which is significant at the 1% level (see long-run equation in Table 2). The short-run analyses are provided at the bottom section of Table 2. As can be seen, RENT has a positive and significant impact on OUTBOUND (p < 0.05) for our sample of 25 countries.

Table 2.

Results of ARDL/PMG: Business outbound and natural resource dependency.

Dependent variable: D(log OUTBOUND)
Dynamic regressors	Long-run equation
RENT	−0.377*** (0.042)
TRADE	0.125*** (0.022)
log(GDPC)	0.171*** (0.050)
FDI	0.010 (0.068)
	Short-run equation
COINTEQ	−0.392*** (0.083)
D(RENT)	0.157** (0.077)
D(TRADE)	0.140** (0.067)
D(log(GDPC))	0.109 (0.278)
D(FDI)	−0.058 (0.286)
Included observations	297

Note: Model selection metdod: AIC; selected model: ARDL (1, 1, 1, 1, 1). Fixed regressors: C; standard errors are presented in parentdeses. FDI: foreign direct investment; ARDL: autoregressive distributed lag; PMG: pooled mean group; AIC: Akaike information criterion.

***p < 0.01, **p < 0.05, *p < 0.1.

Table 3 reports the short-run coefficients of RENT on OUTBOUND for each sample country. We group countries based on the sign and significance of the coefficients. For almost 50% of the sample (12 of 25 countries), the coefficient of the short-run impact is positive and significant. Among these countries, the positive response of OUTBOUND to RENT is relatively stronger for Burundi, Ethiopia, and Iran. On the other hand, for 16% of sample countries, such as Angola and Kazakhstan, the coefficient is negative and significant. That is, when the share of natural resource rents in GDP increases the demands for international business travels decrease in these countries in the short run. Finally, the findings indicate that there is an insignificant short-run association between RENT and OUTBOUND for nine countries including Qatar, Saudi Arabia, and United Arab Emirates. The possible reason is because these countries have an established sovereign wealth fund (SWF) which acts as a shock absorber leading to a slower response of the economy to foreign income shocks.

Table 3.

Cross-section short-run coefficients: Business outbound and natural resource dependency.

Dependent variable: D(log OUTBOUND)
Coefficient of D(RENT)
Positive and significant impact
Azerbaijan	0.387*** (0.052)	Libya	0.166*** (0.025)
Burundi	0.914*** (0.148)	Mauritania	0.174*** (0.001)
Congo, Rep.	0.196** (0.042)	Mongolia	0.173*** (0.001)
Ethiopia	0.594*** (0.099)	Uzbekistan	0.200*** (0.030)
Gabon	0.174*** (0.014)	Iran	0.468** (0.138)
Iraq	0.201** (0.048)	Kuwait	0.193** (0.035)
Negative and significant impact
Angola	−0.079*** (0.010)	Chad	−0.141*** (0.043)
Equatorial Guinea	−0.120*** (0.012)	Kazakhstan	−0.814*** (0.131)
Insignificant impact
Algeria	0.059 (0.124)	Oman	−0.012 (0.024)
Brunei Darussalam	0.429 (0.423)	Qatar	0.307 (0.369)
Congo, Dem. Rep.	1.041 (0.625)	Saudi Arabia	−0.498 (0.354)
Liberia	0.214 (0.218)	United Arab Emirates	0.040 (0.048)
Venezuela	−0.087 (0.042)

Note: Standard errors are in parentheses.

***p < 0.01, **p < 0.05, *p < 0.1.

Table 4 presents the long-run and short-run impact of RENT on INBOUND. The long-run equation shows that a 1% increase in RENT leads to 0.83% decrease in INBOUND in our sample countries. We also find that a higher level of natural resource dependency does not influence INBOUND in the short run, as the coefficient of D(RENT) is statistically insignificant.³

Table 4.

Results of ARDL/PMG: Business arrivals and natural resource dependency.

Dependent variable: D(log INBOUND)
Dynamic regressors	Long-run equation
RENT	−0.838*** (0.125)
TRADE	0.417*** (0.080)
log(GDPC)	1.069*** (0.143)
FDI	−1.228*** (0.241)
	Short-run equation
COINTEQ	−0.311*** (0.083)
D(RENT)	0.053 (0.311)
D(TRADE)	−0.216 (0.192)
D(log(GDPC))	1.447* (0.808)
D(FDI)	−0.339 (0.814)
Included observations	297

***p < 0.01, **p < 0.05, *p < 0.1.

Although the coefficient of RENT is not significant in the INBOUND model for the whole sample in the short run, the higher levels of RENT lead to higher INBOUND in 8 of 25 countries such as Azerbaijan, Iran, and Uzbekistan (see Table 5). For more than 50% of the sample countries (13 of 25), the link between RENT and INBOUND is insignificant. Our short-run analyses also show that the demand for business travels to Algeria, Brunei Darussalam, Venezuela, and Equatorial Guinea decrease when reliance on natural resources increases in these economies (see Table 5).

Table 5.

Cross-section short-run coefficients: Business arrivals and natural resource dependency.

Dependent variable: D(log INBOUND)
Coefficient of D(RENT)
Positive and significant impact
Azerbaijan	1.179*** (0.062)	Libya	0.127* (0.045)
Gabon	0.090*** (0.013)	Saudi Arabia	1.737* (0.593)
Iran	0.934** (0.181)	United Arab Emirates	0.075*** (0.001)
Liberia	1.849** (0.385)	Uzbekistan	0.366*** (0.034)
Negative and significant impact
Algeria	−0.607** (0.125)	Venezuela	−0.374** (0.087)
Brunei Darussalam	−0.733* (0.297)	Equatorial Guinea	−0.165*** (0.002)
Insignificant impact
Angola	0.400 (0.422)	Ethiopia	−6.147 (4.150)
Burundi	2.662 (1.889)	Iraq	0.010 (0.046)
Chad	0.400 (0.457)	Kazakhstan	−1.468 (6.476)
Congo, Dem. Rep.	0.701 (5.013)	Kuwait	0.573 (0.685)
Congo, Rep.	0.609 (5.012)	Mauritania	0.005 (0.002)
Mongolia	0.019 (0.072)	Oman	−0.041 (0.085)
Qatar	−0.200 (0.093)

Note: Standard errors are in parentheses.

***p < 0.01, **p < 0.05, *p < 0.1.

In a nutshell, we can infer the following conclusions based on the findings. In the short run, business people of the resource-abundant countries increase their international travels. Countries that experience Dutch Disease conditions are exposed to a large windfall from the exploitation of their natural resources in the short run. This excess income increases the spending power of government and residents and thus surges demand for imported goods that the domestic industries (lagging sector) are not able to manufacture in the short run. In addition, the appreciation of the domestic exchange rate makes the goods that are made abroad more affordable for local consumers (Corden, 1984; Corden and Neary, 1982; Inchausti-Sintes, 2015). Accordingly, during such a period, business people of the resource-abundant countries seek to address the new demand by importing goods and services. Thus, it leads to more outbound international business travels.

On the other hand, our results suggest that there are no significant changes to the inbound business travels due to increases in resource rents. This can likely be justified by the fact that the appreciation of domestic currency reduces the price competitiveness of the non-booming sectors’ products. This means those products are less appealing to international consumers and foreign business people and firms. On the other hand, the booming sector may generate new interest among foreign professionals and investors and increase demand for inbound travels. These conflicting factors may justify the positive but nonsignificant coefficient of RENT in the short run in Table 4.

The implications of Dutch Disease for economies are different in the long run. Several studies have suggested that the growth of an economy hinders because of the Dutch Disease (Capo et al., 2007; Davis, 1995; Gylfason, 2001; Kremers, 1986). The notable boom of the resource-based sector comes at the expense of shrinking the manufacturing and agriculture sectors (Davis, 1995). The lower levels of labor training, which is usually required for the exploitation of natural resources, means that the economy faces a larger proportion of unskilled workers in the long run (Rajan and Subramanian, 2011). The manufacturing sector, which is subject to economies of scale or learning by doing, loses its capacity which is very costly to reverse (Esfahani et al., 2014).

Literature shows that the resource-rich countries not only often fail to benefit from a favorable but temporary capital inflow from their export earnings, but the crowding out impacts of Dutch Disease reduces capital investment, productive activities, and welfare in the long run (Bjørnland and Thorsrud, 2016; Boyce and Emery, 2011). Our findings suggest that as the production outputs of boom sector stabilizes, and exports of lagging sectors diminishes, the needs for inbound and outbound business travel decrease. Considering the adverse impacts of Dutch Disease on economic activities in the long run, there would be less demand for international business departures and arrivals related to meetings, conferences, exhibitions and promotional activities.

Robustness analysis

In our main analysis, we did not include the inflation and exchange rate. The reason is that the cost of business travel is usually paid by the employers of the business travelers (Kulendran and Wilson, 2000). As such, many studies argue that price level is not a relevant determinant of business travels (Chow and Tsui, 2019; Kulendran and Witt, 2003; Tsui et al., 2018; Turner and Witt, 2001). Nevertheless, it is still possible to argue that as destination continues to become more expensive, firms may begin to investigate alternative strategies to business travel such as relying on local agents or employing information and communication technology (Kulendran and Wilson, 2000).

As a robustness check, we re-estimate the link between RENT and INBOUND by including additional control variables, the World Bank’s official exchange rate and consumer price index (CPI) for 21 sample countries (excluding Iraq, Libya, Uzbekistan, and Venezuela). We remove these four countries due to a large number of missing values in the new control variables of these countries. Our results presented in Table 6 show that the coefficient of RENT is still negative and statistically significant at 1% after controlling for inflation and exchange rate. These results are consistent with our findings presented in Table 4.

Table 6.

Results of ARDL/PMG: Business arrivals and natural resource dependency excluding Iraq, Libya, Uzbekistan, and Venezuela.

Dependent Variable: D(log INBOUND)
Dynamic regressors	Long-run equation
RENT	−1.576*** (0.152)
TRADE	0.0297 (0.0490)
log(GDPC)	2.022*** (0.266)
EXCHANGE	−0.512** (0.219)
log(CPI)	−0.004 (0.078)
	Short-run equation
COINTEQ	−0.275** (0.138)
D(RENT)	0.087 (0.571)
D(TRADE)	0.103 (0.278)
D(log(GDPC))	0.393 (1.302)
D(EXCHANGE)	−2.788 (2.218)
D(CPI)	0.368 (1.092)
Included observations	251

Note: Model selection method: AIC; selected model: ARDL (1, 1, 1, 1, 1). Fixed regressors: C; standard errors are presented in parentheses. ARDL: autoregressive distributed lag; PMG: pooled mean group; AIC: Akaike information criterion.

***p < 0.01, **p < 0.05, *p < 0.1.

Conclusion

Using data from 25 resource-based economies from 2005 to 2017 and applying panel ARDL/PMG estimators, we find that higher levels of natural resource rents have a negative impact on inbound and outbound business travels in the long run. Our short-run estimations show that outbound business travels only respond to shocks in natural resource revenues. While several studies have investigated the effect of Dutch Disease on leisure tourism in high-income countries, no empirical research has examined the effect of Dutch Disease on business tourism in developing countries with an abundance of natural resources. Therefore, the results of this study provide new insights in this area of emerging literature.

Our findings offer an important implication for tourism and travel companies. These companies may promote more international business opportunities in countries that are experiencing resource boom in the short run. Tourism and travel companies may be able to capture a larger market share if they offer competitive business travel packages to business people in countries that are experiencing Dutch Disease. However, travel companies need to be aware that the period of high demand for international business travel in countries that are affected by Dutch Disease may not last very long.

In the long run, our results suggest negative effects of resource rents on business tourism. We introduced an additional mechanism on how reliance on natural resources can have unfavorable impacts on economic development via reduction in international business tourism. As shown by several studies, business tourism is an important contributor to the international trade and FDI (e.g. Gholipour and Foroughi, 2019a; Kulendran and Witt, 2003; Selvanathan et al., 2012). Therefore, reducing the number of international business travels can diminish the benefits of trade and FDI and consequently reduce the economic development of resource-based countries in the long run.

The results of this study should be considered in light of its constraints. Due to data limitations, we only evaluate the data of 25 countries and therefore generalizations of our findings should be made with caution. In terms of future research, it may be interesting to study the impact of natural resource exports on business travels for the booming sector and non-resource traded goods sector if information on business travel arrivals and departures for these two sectors become available for multiple countries over time. Future studies may also examine the link between various types of resource rents (e.g., coal, natural gas, forest and mineral) and business travels.

Supplemental material

Supplemental Material, sj-pdf-1-teu-10.1177_1354816620983131 - Dutch Disease phenomenon and demand for international business travels: Panel ARDL/PMG estimation

Supplemental Material, sj-pdf-1-teu-10.1177_1354816620983131 for Dutch Disease phenomenon and demand for international business travels: Panel ARDL/PMG estimation by Hassan F Gholipour, Reza Tajaddini and Usama Al-mulali in Tourism Economics

Footnotes

Declaration of conflicting interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Hassan F Gholipour

Usama Al-mulali

Supplemental material

Supplemental material for this article is available online.

Notes

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