Do Foreign Direct Investment Inflow and Trade Openness Influence International Tourism Demand in Africa? A Study of the Post-Global Financial Crisis Era

Abstract

Following the paucity of evidence on the roles of foreign direct investment (FDI) inflow and trade openness in Africa’s tourism development, particularly in the post-Global Financial Crisis era, this study examined the roles of FDI inflow and trade openness as drivers of international tourism demand in Africa. We also investigated how infrastructural development, terrorism, climatic conditions, and institutional quality influence the FDI-tourism and trade openness-tourism relationships in Africa. The system GMM modeling framework and a panel of 42 African countries were used. We find that FDI inflow has not contributed significantly to the growth of tourism demand in Africa. However, infrastructure and climatic conditions on the continent have the potential to positively influence the FDI-tourism relationship, while terrorism hampers it. We also find that trade openness impacts positively on international tourism demand in Africa, but this impact is mainly insignificant. The study made some policy recommendations based on these findings.

Keywords

international tourism demand foreign direct investment trade openness global financial crisis system GMM Africa

Introduction

The role of the tourism sector in job creation, income generation, and stimulating overall growth and development has been acknowledged by some recent studies in the extant literature (Amin et al., 2020; Balsalobre-Lorente and Leitão, 2020; Roudi et al., 2019; Sokhanvar, 2019; Tu & Zhang, 2020). These studies particularly emphasized the fact that the tourism sector can turn the fortunes of countries and regions from one characterized by unemployment and poverty to one distinguished by stable and sustainable economic growth and development. In the view of Ehigiamusoe et al. (2023), tourism is considered a driver of economic growth since it boosts income, job opportunities, infrastructural development, international trade, foreign direct investment, capital flows, and technological diffusion. According to Signé (2018), the tourism sector provides 235 million jobs globally and also contributes 5% of global GDP and 30% of service exports. Studies like Ayoub and Alheety (2018) and Sharpley (2002) that have investigated the role of tourism as a driver of growth in the United Arab Emirates attest to this fact. Sharpley (2002) also demonstrated that the tourism sector can be harnessed as a means of achieving economic diversification. However, it has been observed that despite the high rates of unemployment, poverty, and overall underdevelopment in Africa, the region appears not to have explored its tourism potential in order to turn around its economic fortunes. Indeed, statistics from the World Bank’s World Development Indicators (WDI), as shown in Table 1, indicate that tourism arrivals in Sub-Saharan Africa have consistently remained low in the last decade relative to other regions of the world.

Table 1.

Regional Statistics on International Tourism Arrivals, 2010 to 2019.

Year	Number of international tourism arrivals				Tourism arrivals in percentage (%) of world international tourism arrivals
Year	EUU	LCN	SSF	World	EUU	LCN	SSF
2010	705,250,736	157,334,946	40,085,380	1,667,618,834	42	9	2
2011	739,701,833	155,232,840	40,255,802	1,700,643,829	43	9	2
2012	756,293,170	159,169,038	42,687,021	1,784,601,425	42	9	2
2013	780,872,157	162,604,212	44,195,479	1,852,336,696	42	9	2
2014	806,007,534	171,230,247	45,956,219	1,909,106,952	42	9	2
2015	832,208,137	182,041,193	48,394,851	1,966,289,744	42	9	2
2016	866,488,537	193,554,775	50,612,067	2,031,531,035	43	10	2
2017	906,805,888	202,719,238	52,268,614	2,135,215,495	42	9	2
2018	934,880,666	201,521,996	56,031,021	2,221,458,981	42	9	3
2019	966,435,421	201,852,567	55,251,254	2,280,131,088	42	9	2

Source. Authors, with data from World Bank’s World Development Indicators.

Note. EUU = European Union; LCN = Latin America & Caribbean; SSF = Sub-Saharan Africa.

The low level of tourism arrivals in Africa, as shown in Table 1, may be blamed on several factors. Some of these factors include: insecurity of lives and property, since tourists ordinarily would not go to unsafe places (Abrahamsen, 2013; Knight & Oriola, 2020); political conflicts and violence, which also make the region unsafe for tourists (Adefeso, 2018; Tosun et al., 2008); as well as corruption and poor governance, which generally render the tourist destinations on the continent unattractive relative to other parts of the world (Aldcroft, 2015; Hope, 2017). However, two important factors that could be relevant in explaining the dynamics of tourism arrivals in Africa are the level of foreign direct investment (FDI) inflow into the continent and the continent’s level of trade openness. Indeed, recent studies have shown that the level of FDI inflow is an important factor in the tourism development of countries and regions (Adeola et al., 2020; Al-Hallaq et al., 2020; Fauzel, 2021; Satrovic & Muslija, 2017). Unfortunately, records from the WDI, as shown in Table 2, indicate that Sub-Saharan Africa predominantly received the least FDI inflow in the last decade relative to other regions of the world. The poor FDI inflow into Africa may have somewhat accounted for the continent’s poor tourism development. This means that there is a need for empirical evidence on the FDI-tourism nexus in Africa. However, apart from Adeola et al. (2020), which did not control for terrorism, climatic conditions, infrastructural development, and several important institutional variables in their modeling framework, Osinubi et al. (2022), which were limited to the top 10 tourist destinations in Africa, Bezuidenhout and Grater (2016), which only examined the component of FDI in tourism, neglecting other components of FDI that may ultimately have a direct or indirect effect on tourism, and Huseynli (2022), which focused only on three selected African countries, no other recent study, to the best of our knowledge, has investigated this relationship for Africa as a whole. This means that a large scope still needs to be covered toward a more comprehensive understanding of this relationship in Africa. This study contributes to the literature in this regard.

Table 2.

Regional Statistics on Net FDI Inflows, 2010 to 2019.

Year	Net FDI inflows (BoP, Current US$)				Net FDI inflows in percentage of world net FDI inflows (%)
Year	EUU	LCN	SSF	World	EUU	LCN	SSF
2010	539,435,758,001.16	233,569,693,516.65	32,908,706,968.19	1,926,405,196,004.26	28	12	2
2011	877,970,479,520.76	285,584,798,425.22	42,038,716,324.16	2,366,284,993,861.19	37	12	2
2012	588,029,691,678.47	298,854,650,687.85	45,549,042,272.24	2,084,666,298,643.01	28	14	2
2013	609,534,393,592.38	385,184,087,955.15	40,691,175,231.21	2,210,905,102,245.70	28	17	2
2014	430,430,315,387.91	296,623,189,181.76	44,275,406,888.95	1,951,598,302,497.59	22	15	2
2015	772,807,165,358.88	284,291,003,636.56	44,341,897,381.70	2,680,118,822,829.96	29	11	2
2016	735,276,443,007.32	229,404,157,744.52	30,787,806,231.17	2,750,078,675,707.03	27	8	1
2017	546,910,235,188.94	221,640,856,518.24	27,581,381,827.88	2,213,224,049,932.92	25	10	1
2018	(67,219,122,748.30)	234,850,092,473.12	30,948,001,459.84	1,091,559,161,697.73	−6	22	3
2019	313,169,831,918.84	221,996,984,427.87	31,377,914,143.59	1,744,099,961,012.48	18	13	2

Source. Authors, with data from World Bank’s World Development Indicators.

Note. EUU = European Union; LCN = Latin America & Caribbean; SSF = Sub-Saharan Africa.

The second factor that could be important in understanding the dynamics of tourism development in Africa is the degree of trade openness. Some studies in the extant literature have emphasized the role of trade openness as a key determinant of international tourism demand (Ben-Jebli et al., 2019; Chaisumpunsakul & Pholphirul, 2018; Fry et al., 2010; Kulendran and Wilson, 2010; Leitão, 2010; Shan and Wilson, 2010; Turner and Witt, 2001; Santana-Gallego et al., 2011). However, in spite of the growing literature on the role of trade openness in tourism development across countries and regions, the relationship is yet to be empirically investigated for Africa as a whole. This leaves an important gap in the literature, which this study seeks to fill. Indeed, providing empirical evidence on the role of trade openness in tourism demand in Africa will enable policymakers on the continent to evolve policies, which will in turn enable the continent to harness its huge tourism potential and facilitate the attainment of the African Union’s Agenda 2063 as well as the accomplishment of the Sustainable Development Goals. Signé (2018) provides detailed documentation on Africa’s tourism potential, which clearly shows that the continent has significant tourism potential due to its rich cultural heritage and natural resources.

Consequently, the broad objective of this study is to investigate if FDI inflow and trade openness influence international tourism demand in Africa. Specifically, the study seeks to: (i) examine the impact of FDI inflow on international tourism demand in Africa; (ii) determine how infrastructural development, terrorism, as well as climatic and institutional variables influence the FDI-tourism relationship in Africa; (iii) ascertain the impact of trade openness on international tourism demand in Africa; and (iv) determine how infrastructural development, terrorism, as well as climatic and institutional variables influence the trade-tourism relationship in Africa. To achieve these objectives, the study adopted the dynamic system Generalized Method of Moments (GMM) and covered the period 2010 to 2020. The choice of the dynamic system GMM estimator is mainly because it has proven to be efficient in modeling short panels. However, we conducted the Bond (2002) test in order to ensure that our choice of the system GMM estimator rather than the difference GMM estimator is well informed. The chosen period lies within the post-Global Financial Crisis (GFC) era. Two main reasons informed the choice of this period. The first reason is that Africa has witnessed many events that have important implications for its tourism sector in recent years, but the existing literature has yet to take these events into consideration. Examples of such events include the recent upsurge in terrorism and xenophobic attacks on the continent, which escalated since 2016; the 2016 economic recession in Nigeria; the economic downturn in South Africa arising from weak manufacturing and trade; and the tensions created by the transition from one government to another on the continent since 2015, among others. Adeola et al. (2020) covered the period 1995 to 2014 and thus failed to account for these recent events.

The second reason why this study is focusing on the post-GFC era is because, apart from the ongoing COVID-19 pandemic, the GFC remains the worst economic crisis the global economy has witnessed in the last several decades, and knowledge gained in the period following the GFC would be useful in the post-Covid-19 era. This is because, just like the GFC, the ongoing COVID-19 pandemic has further exposed the vulnerabilities of African economies. Thus, the tourism sector in Africa can be exploited in the post-pandemic era not only to fight the challenges of widespread poverty and a lack of jobs on the continent but also to drive economic diversification and sustainable economic growth. To make this possible, this study provides empirical evidence on the roles of FDI and trade openness as drivers of international tourism demand in Africa during the post-GFC era.

The specific objectives of this study recognize the fact that economies around the world do not exist in a vacuum since institutional factors, the level of infrastructural development, the prevalence of terrorist activities, and climatic conditions can contribute considerably to the proper (or improper) functioning of every economy. In the case of institutional factors, it is presumed that any economy that is bedeviled by wide-spread corruption, lack of government effectiveness in implementing developmental programs, poor government regulations, willful disregard for the rule of law and civil liberties (including the rights to free expression, to organize or demonstrate, and to freedom of religion, education, travel, and other individual rights), and the absence of political rights (i.e., the freedom to participate in the political process) will find it difficult to function properly (Davidson et al., 2018; Ekeocha et al., 2023; Li et al., 2020; Ogbonna et al., 2021, 2022). Studies like Egbetunde and Akinlo (2015) have also argued that African countries cannot experience sustainable economic progress without high-quality institutions, while North (1990) explained that healthy and high-quality institutions have the capacity to create a conducive environment that can attract and retain investors’ confidence by ensuring that effective governance is entrenched and property rights are protected. Thus, this study examined how institutional variables are influencing FDI-tourism and trade-tourism relationships in Africa.

In addition to institutional factors, the extant literature has also shown that FDI inflow and trade openness are sensitive to the level of infrastructural development in an economy (Chakrabarti, 2003; Nchofoung & Asongu, 2022). Indeed, recent studies like Ekeocha et al. (2021) have emphasized the need for policymakers and governments in Africa to evolve and implement policies that will increase investments in basic infrastructure as a means of attracting investments, enhancing trade, and improving overall economic performance in the region on a sustainable basis. The economic expectation here is that improved infrastructure will not only enable trade and attract foreign direct investments to Africa but also enhance international tourism demand in the region since tourists will be able to enjoy better transport and communication facilities, among others. This presumes that infrastructural variables can exert some influence on FDI-tourism and trade-tourism relationships in Africa. Unfortunately, Bariu (2020) and Ekeocha et al. (2021) have also lamented that Africa’s share of developed infrastructures, particularly transportation and information and communication technology (ICT) infrastructures, has remained quite low compared to other regions of the world. Thus, it is important for policymakers in Africa to understand how the level of infrastructural development in the region is influencing FDI-tourism and trade-tourism relationships. This study provides empirical evidence to aid this understanding.

An emerging aspect of the literature has also shown that FDI inflow and trade openness are sensitive to terrorism and climatic conditions (Bandyopadhyay et al., 2018; Bezić et al., 2016; Khan et al., 2021; Kyriakopoulou et al., 2023; Martínez-Martínez et al., 2023; Nitsch & Rabaud, 2022). While terrorism can increase investment and trade costs because of stricter security measures, changes in climatic conditions resulting in extreme and sudden weather events can damage the transport and communication infrastructures that are required for trade, restrict the movement of people and goods, disrupt supply chains, and render the domestic economies unattractive for foreign direct investment. Besides, international tourism can also be disrupted in two ways: by terrorist activities and extreme climate and weather events. The first is through direct damage to tourism infrastructure, particularly transport and communication infrastructures, while the second is through changes in the tourist destinations that gradually render them unappealing over time. In spite of these presumed economic dynamics, the existing literature has yet to expose how terrorism and climatic conditions are influencing the FDI-tourism and trade openness-tourism relationships in Africa. This study also addressed this gap in the literature.

The remaining sections of the paper are organized as follows: An overview of the literature is presented in Section 2, while the data and the method used are presented in Section 3. The results are presented and discussed in Section 4, while Section 5 concludes the paper with some policy recommendations.

An Overview of the Literature

From a theoretical perspective, the eclectic theory explains that FDI is an integral part of tourism (Dunning, 2001, 2006a, 2006b). The theorist explains that an increase in tourism to a particular destination is borne out of the market-seeking goal of FDI, since tourism is a unique product that is produced and consumed on site and cannot be exactly replicated in the home country. Furthermore, FDI increases the quality of services, which in turn increases international tourism arrivals. Indeed, the eclectic theory is an economic and business method for analyzing the attractiveness of making a foreign direct investment. The theory follows the three-tier OLI framework of ownership (O), location (L), and internalization (I). First, ownership can be defined as the proprietorship of a unique and valuable resource that cannot easily be imitated or replicated, which creates a competitive advantage against potential foreign competitors. Second, the potential business host countries being considered for FDIs must present numerous competitive advantages, such as location. The location advantage focuses more on the geographic advantages of the host countries. An example of a geographic advantage can be access to the ocean for sea freight or other purposes that is not available to a landlocked country. Indeed, the moderator variables in this study, which are the levels of infrastructural development, terrorism, climatic, and institutional variables, can also offer locational advantages to a given host country, as emphasized by the eclectic theory.

Furthermore, in order for companies to choose which investment pathway or method is best suited for their needs, their management team must analyze the internalization advantage. They normally need to consider whether it would be more sensible to get the value chain activity performed locally with their own team or to outsource it to a foreign country. The advantages of outsourcing from different countries can include (but are not limited to) lower costs, better skills to perform value chain activities, and/or better knowledge of the local markets. In such a case, management can choose between two options on how to proceed. It can either outsource its production to an original equipment manufacturer or license its product design to an independent foreign company. However, it should keep control over its activities and engage in FDI by starting from scratch through a greenfield investment, entering into joint ventures with local partners, or purchasing existing local companies. Some studies lend support to the eclectic theory (Anastassopoulos et al., 2009; Dunning & Lundan, 2008; Endo, 2006; Selvanathan et al., 2012).

Other predominant theoretical perspectives in the literature include the tourism-led growth hypothesis, the conservation hypothesis, and the neutrality hypothesis. These theoretical aspects emphasize the role of tourism in the economic growth process. Some empirical studies lend support for the tourism-led growth hypothesis, which explains that tourism impacts significantly on growth (Osinubi & Osinubi, 2020; Ribeiro & Wang, 2020; Zuo & Huang, 2018). However, other studies lend support to the conservation hypothesis, which states that international tourism is being driven by economic growth (e.g., Beyene & Kotosz, 2020; Le & Van, 2020). Further still, studies like Nathaniel and Bekun (2021) and Salawu (2020) lend support to the neutrality hypothesis, which posits that tourism and economic growth do not impact significantly on each other.

Some studies in the literature have shown that the level of FDI inflow is relevant in explaining the degree of tourism development of countries and regions (Adeola et al., 2020; Fauzel, 2021; Al-Hallaq et al., 2020; Blamoh et al., 2020; Cannonier & Burke, 2019; Huseynli, 2022; Osinubi et al., 2022). However, while studies like Fauzel (2021) and Adeola et al. (2020) found a bidirectional causality relationship between FDI inflow and tourism demand, other studies like Al-Hallaq et al. (2020) and Cannonier and Burke (2019) found evidence in favor of unidirectional causality running from FDI inflow to tourism demand. Our review of the literature, however, shows that there is a growing but relatively small number of studies on the FDI-tourism relationship, especially in Africa. Unfortunately, out of the existing studies, only Adeola et al. (2020) focused on the whole of Africa. Our study differs from Adeola et al. (2020) in several ways. First, unlike Adeola et al. (2020), our study did not just control for some important variables; it also examined their moderating role in the FDI-tourism nexus. These variables include infrastructural development, terrorism, and climatic conditions. Indeed, the extant literature has shown that infrastructural development (J. Khadaroo & Seetanah, 2007; Mazrekaj, 2020; Mustafa, 2019; Ouariti & Jebrane, 2020; Seetanah et al., 2011), terrorism (Ahlfeldt et al., 2015; Drakos & Kutan, 2003; Feridun, 2011; Kuto & Groves, 2004), and climatic conditions (Agnew & Viner, 2001; Churchill et al., 2022; Hamilton & Tol, 2004; Mishev & Mochurova, 2008; Nyamwange, 2016) can influence tourism demand across regions. It is therefore essential to examine if they play any moderating role in the FDI-tourism relationship.

Second, our study accounted for the post-GFC period so that the knowledge gained can be used to shape policies for the post-Covid-19 pandemic era. Third, our study also accounted for other institutional variables like control of corruption, regulatory quality, government effectiveness, rule of law, and voice and accountability. This is unlike Adeola et al. (2020), who controlled for only political stability and the absence of violence. These gaps indicate that a large scope still needs to be covered for a more comprehensive understanding of the FDI-tourism nexus in Africa. This study contributes to this emerging literature by addressing the observed gaps.

The role of trade openness as an important driver of international tourism demand has also been examined by some studies in the extant literature (Chaisumpunsakul & Pholphirul, 2018; Fry et al., 2010; Kulendran & Wilson, 2010; Leitão, 2010; Santana-Gallego et al., 2011). Interestingly, some of these studies found significant evidence of bidirectional causality between trade openness and tourism demand (e.g., Fry et al., 2010; Santana-Gallego et al., 2011). In the work of Chaisumpunsakul and Pholphirul (2018), it was established that the degree of trade openness is positively correlated with international tourism demand in Thailand. According to their results, a percentage increase in trade share to GDP contributed about 0.046% of short-term foreign tourism demand and 0.807% of long-term tourism demand in Thailand. However, no study in the extant literature known to us has investigated the relationship between trade openness and tourism demand in Africa. This leaves an obvious gap in the literature, which our study addressed.

Data and Methodology

The Data

To examine the role of FDI inflow and trade openness in international tourism demand in Africa, data were drawn from 42 African countries (see Appendix 1) over the period 2010 to 2020. The scope, which is dictated by the availability of sufficient observations on the variables of interest, is needed to situate the study to the start date of 2010 in order to capture the post-global financial crisis era, which is also justifiable as most African countries show substantial losses in tourism and trade data in the pre-2010 years. A comprehensive description of the variables and the sources of data used is presented in Appendix 2.

The outcome variable in this study is international tourism. It is measured using international tourism receipts (t_receipt). The following studies have used international tourism arrivals as a proxy for tourism: Ben-Jebli and Hadhri (2018), Adeola and Evans (2020), Kumar and Kumar (2020), Tugcu (2014), Liu et al. (2021), among others. On the other hand, Anser et al. (2022), Sokhanvar (2019), Balsalobre-Lorente and Leitão (2020), and Eyuboglu and Eyuboglu (2020), among others, have proxied tourism with international tourism receipts. We believe that international tourism receipts are more appropriate since they measure the monetary value of tourism demand.

The main explanatory variables are foreign direct investment inflow (fdi) and trade openness (top). Other regressors included in the study are: infrastructure (infr), terrorism (gti), climatic conditions (temp), and institutional indicators (inst). These regressors were included in order to enhance the robustness of the estimates. They have also been observed to have direct impacts on tourism (Naudé & Saayman, 2005; Adeola et al., 2018; Seetanah et al., 2010).

The main explanatory variables (i.e., foreign direct investment and trade openness) were also interacted with infrastructure, terrorism, climatic conditions, and institutional indicators (such as control of corruption [cc], regulatory quality [rq], government effectiveness [ge], rule of law [rl], and voice and accountability [va]) in order to examine their moderating effects on the FDI-tourism and trade openness-tourism nexus in Africa. Thus, for FDI, the following interaction terms were included in the models: fdi*infr, fdi*gti, fdi*temp, and fdi*inst. Similarly, for trade openness, the following interaction terms were included in the models: trade*infr, trade*gti, trade*temp, and trade*inst. The institutional indicators (control of corruption, regulatory quality, government effectiveness, rule of law, voice and accountability) entered the model separately in order to avoid the problem of collinearity. The extant literature suggests that FDI (Bezić et al., 2016; Chakrabarti, 2003; Wang & Zhang, 2022) and trade openness (Appiah et al., 2022; Khan et al., 2021; Nchofoung & Asongu, 2022) are sensitive to these factors, thereby making it essential to examine how their interactions affect international tourism demand in Africa. It is important to note that the major limitation encountered in this study is a lack of adequate data. Some countries in Africa appear not to have available data on some of the variables of interest, and this limited our country coverage to 42 African countries.

Model Specification

Recall that the specific objectives of this study are to examine the impact of FDI and trade openness (and their interactions with infrastructure, terrorism, climatic conditions, and institutions) on international tourism demand in Africa. To achieve these objectives, the empirical approach of Adusei and Adeleye (2021), B. N. Adeleye et al. (2021), N. Adeleye and Eboagu (2019), and Ogbuabor et al. (2023) is adopted after slight modification by specifying tourism as a dynamic linear function of FDI, trade openness, and other explanatory variables:

l n Y_{i t} = α_{0} + δ l n Y_{i t - 1} + β I n X'_{i t} + φ I n Τ'_{i t} + γ I n Α'_{i t} + θ I n Ζ'_{i t} + μ_{i t}

(1)

where: Y_it = international tourism demand (measured by international tourism receipts); X′_it = (fdi, trade)′ is a 1 × 2 vector of the core explanatory variables, which are foreign direct investment and trade openness; T′_it (fdi*infra, fdi*gti, fdi*temp, fdi*inst)′ is a 1 × 4 vector of interaction variables obtained by interacting the infrastructure, terrorism, climatic condition, and institutional variables with FDI; A′_it = (trade*infra, trade*gti, trade*temp, trade*inst)′ is a 1 × 4 vector of interaction variables obtained by interacting the infrastructure, terrorism, climatic condition, and institutional variables with trade openness; Z′_it = (infra, gti, temp, inst)′ is a 1 × 4 vector of other exogenous variables included in the model. µ_it = λ_t + e_it; where λ_t is the country-specific effect and e_it is the error term; α, is the constant term; δ, β, φ, γ, and θ are the parameters to be estimated. Five institutional indicators (control of corruption, regulatory quality, government effectiveness, rule of law, and voice and accountability) will enter the models separately.

Estimation Technique

The econometric methodology employed for this study is based on the dynamic panel GMM estimation framework proposed by Arellano and Bond (1991) and later developed by Blundell and Bond (1998). This technique has been chosen based on its ability to address the problems of simultaneity bias and country-specific effects. For instance, Arellano and Bond (1991) suggested that to eliminate the country-specific effects and the simultaneity bias from equations such as model (1), such an equation can be transformed into a first-difference equation. However, it has recently been argued that this form of modeling could lead to wrong inferences, particularly when the explanatory variables are found to be persistent, which is mostly the case with institutional variables (Arellano & Bover, 1995). To address this problem, Blundell and Bond (1998) recommended a system GMM estimator as an alternative, which combines both the level and difference equations. Therefore, the lagged differences of the regressors are used as an added instrument for the level equation. They further stressed that this form of strategy reduces the bias and imprecision associated with the difference estimator. Thus, the system GMM estimator was chosen as the most preferred due to its consistency and lack of bias in the parameter estimates over the difference GMM estimators, pooled ordinary least squares (OLS) method, or fixed effect. This approach provides the most efficient estimate and handles the endogeneity problem better than the difference GMM or fixed effect models.

Although the system GMM estimator is divided into one-step and two-step systems, the two-step system is theoretically assumed to be the most efficient due to its optimal weighting matrices. However, Bowsher (2002) argued that using a system GMM estimator with a sample that has a small cross-section dimension could lead to biased estimated parameters and a weakened over-identification test. Windmeijer (2005) added that this could lead to biased standard errors. However, Roodman (2009) contended that too many instruments or instrument proliferation cause such problems. Therefore, he suggested an innovative solution that potentially decreases the dimensionality of the instrumental variable matrix. Thus, for a study with a dataset with a cross-sectional unit of N = 42 and T = 11, the moment conditions are limited to a maximum of two lags on the dependent variable. We, therefore, follow Roodman (2009) and reduce the dimensionality of the instrumental variable matrix. Since the regressors are possibly endogenous, they are consequently instrumented with two lags in the first-difference equation and one lag in the level equation.

More so, for the regressors to be used as valid instruments, two moment conditions must hold: E(X_it, T_it, A_it, Z_it, µ_it) = 0 and Cov(X_it, T_it, A_it, Z_it, λ_t) ≠ 0, that is, regressor exogeneity and panel level collinearity, respectively. In other words, the regressors must be independent of the error term e_it and of each other from country j to k, while at the same time, share some similarities between country j and k, provided j ≠ k. Therefore, this study employs the two-step system GMM estimator to estimate the roles of FDI and trade openness (and their interactions with infrastructure, terrorism, climatic conditions, and institutions) on international tourism demand in Africa. Most importantly, for the estimated results of this approach to be consistent, two basic specification tests must be considered. These are the Hansen and Singleton (1982) test of over-identifying restrictions and the second-order serial correlation test (AR2) in the disturbances (Arellano & Bond, 1991). We subjected all the models in this study to these tests.

Empirical Results

Summary Statistics and Correlation Analysis

The summary statistics and correlation matrix of the variables are presented in Tables 3 and 4, respectively. The data exhibited variations, as shown by the standard deviation. Furthermore, a cursory look at the correlations between the regressors indicates that there is no significant evidence of collinearity among the regressors, except for the institutional variables, which do not pose any challenge since they entered the models separately.

Table 3.

Summary Statistics.

Variable	Mean	Minimum	Maximum	Std. dev	Skewness	Kurtosis
t_receipts	19.17	11.51	23.38	2.06	−0.29	2.86
fdi	4.01	−11.20	39.83	5.89	3.36	17.6
top	71.70	0.78	347.99	45.47	3.32	18.4
infr	23.42	3.55	88.39	18.76	1.83	5.57
gti	2.63	0.00	9.31	2.60	0.50	1.92
temp	24.48	10.84	29.78	3.54	−1.09	4.73
cc	−0.61	−1.57	1.03	0.55	0.58	3.04
rq	−0.57	−1.67	1.13	0.50	0.72	4.05
ge	−0.67	−1.85	1.06	0.55	0.64	3.51
rl	−0.61	−1.82	0.97	0.55	0.30	3.17
va	−0.56	−1.85	0.94	0.65	0.28	2.19

Source. Authors.

Note. t_receipts = tourism receipts; fdi = foreign direct investment; top = trade openness; infr = infrastructural index; gti = global terrorism index; temp = temperature; cc = control of corruption; rq = regulatory quality; ge = government effectiveness; rl = rule of law; va = voice and accountability.

Table 4.

Correlation Matrix of the Variables.

	1	2	3	4	5	6	7	8	9	10	11
(1) t_receipt	1.00
(2) fdi	−0.14	1.00
(3) top	−0.12	0.25	1.00
(4) infr	0.70	−0.20	0.05	1.00
(5) gti	0.20	−0.03	−0.32	0.08	1.00
(6) temp	0.29	0.11	−0.08	−0.34	0.11	1.00
(7) cc	0.20	−0.10	0.19	0.44	−0.35	−0.40	1.00
(8) rq	0.29	−0.09	0.14	0.48	−0.35	−0.29	0.80	1.00
(9) ge	0.37	−0.11	0.09	0.62	−0.25	−0.38	0.85	0.88	1.00
(10) rl	0.30	−0.15	0.07	0.55	−0.36	−0.37	0.88	0.88	0.91	1.00
(11) va	0.14	−0.02	0.02	0.31	−0.33	−0.15	0.62	0.71	0.66	0.70	1.00

Source. Authors.

Model Estimation Results

Prior to estimation, this study checked for cross-sectional dependence among the variables using Pesaran’s (2004) cross-sectional dependence test. The test is applicable when N is greater than T, which is the case in the present study (i.e., 42 countries [N] > 11 years [T]). Appendix 3 reports the results of the tests for cross-sectional dependence. The results overwhelmingly revealed the predominance of cross-sectional independence in the panel for this study. Second, we also performed the Bond (2002) test in order to determine the suitable estimator between the difference and system GMM estimators. The results, which are presented in Appendix 4, overwhelmingly preferred the system GMM estimator. This estimator is generally considered superior to the difference GMM estimator because: it corrects for the upward bias in the difference GMM estimator; it uses the Windmeijer (2005) standard errors in a finite sample; and it assumes linearity of all moment conditions. In the next paragraphs, we discuss the results of the system GMM estimations on the impact of foreign direct investment and trade openness (and their interactions with infrastructural development, terrorism, climatic conditions, and institutional indicators) on international tourism demand in Africa.

Table 5 shows the results of the baseline model. Here, we included all the regressors in the model without the interaction terms, while the five institutional variables entered the model one at a time, giving a total of five panels. The results show that the lag of the dependent variable (i.e., initial tourism receipts) has a significant positive impact on current tourism receipts in all five panels. Our first core explanatory variable, foreign direct investment (FDI), shows a negative connection with tourism receipts across the panels, though its impact remained statistically insignificant throughout. This shows that FDI in Africa has not contributed to tourism demand on the continent. This finding is corroborated by the records from the WDI as shown in Table 2, which indicate that Sub-Saharan Africa predominantly received the least FDI inflow in the last decade when compared to other regions of the world. We therefore conclude that the poor FDI inflow into Africa may have somewhat accounted for the continent’s poor tourism development. The observed negative and insignificant impact of FDI on tourism demand in Africa may be due to acts of terrorism, banditry, kidnapping, and xenophobic attacks in some parts of the continent, as well as the Arab Spring crisis that affected many countries in the Middle East and North Africa. Also connected to this could be the global financial crises and the COVID-19 pandemic, which have ravaged most economies in the world. These recent events may have significantly reduced the level of FDI in Africa. This result is supported by the works of Clancy (1999) and Oppermann (1993). Besides, Brohman (1996) argued that although FDI helps boost tourism, the presence of multinationals in developing countries has the potential to exacerbate issues related to poverty and inequality that subsequently render a location less attractive as a tourist destination.

Table 5.

Baseline System GMM Regression Results (Response Variable: Lnt_receipt).

Regressors	(1)	(2)	(3)	(4)	(5)
l.Lnt_receipt	1.38***	1.72***	1.56***	1.42***	1.44***
fdi	−0.01	−0.01	−0.01	−0.01	−0.0004
top	0.01*	0.01*	0.01*	0.01	0.01
infr	−0.02**	−0.03*	−0.02**	−0.02**	−0.02**
gti	−0.02	−0.07	−0.05	−0.06	−0.05
temp	−0.004	0.01	−0.01	−0.002	0.002
cc	−0.32*
rq		−0.91
ge			−0.76*
rl				−0.63*
va					−0.35*
Groups	42	42	42	42	42
Instruments	13	13	13	12	10
AR(2)	0.19	0.21	0.19	0.20	0.20
Hansen	0.31	0.33	0.37	0.26	0.66
Observations	420	420	420	420	420

Source. Authors.

Note. All the regressors are included in the model, but the five institutional variables enter the model one at a time to give five panels. (***), (**), and (*) indicate significance at 1%, 5%, and 10%, respectively.

However, this result contradicts several other studies, like Adeola et al. (2018, 2020), Anastassopoulos et al. (2009), and Tang et al. (2007). More so, this result negates the eclectic theory. The theorist explained that an increase in tourism to a particular destination is borne out of the market-seeking goal of FDI, since tourism is a unique product that is produced and consumed on site and cannot be exactly replicated in the home country. Hence, the theory presumes that tourism demand in Africa is a unique product that cannot be easily replicated in other foreign locations, an indication that FDI will be a key contributor to tourism demand in Africa. Our finding is contrary to this theoretical postulation, and the implication is that Africa is yet to exploit the FDI channel toward harnessing its tourism potential. The theory holds that FDI increases the quality of services, which in turn increases international tourism arrivals. This, unfortunately, did not hold in the case of Africa. Our analysis focused on the post-global financial crisis era so as to disclose the nature of the underlying relationship in the aftermath of the crisis. The results indicate that, contrary to most existing studies, Africa has yet to maximize the gains of foreign direct investment in the post-crisis period to advance its tourism sector.

Trade openness, on the other hand, shows a positive relationship with tourism demand, though not strongly significant. This result is supported by the works of Chaisumpunsakul and Pholphirul (2018), Fry et al. (2010), and Ben-Jebli et al. (2019), all of which highlight the positive role trade openness plays in international tourism demand. Laying credence to this result is also the study of Khalid et al. (2022), whose findings underscore the importance of strong economic integration in fostering international tourism flows. We also find that infrastructure exerts a significant negative impact on tourism demand in Africa, with a negative coefficient in all five panels. The implication of this is that infrastructure in Africa is still at its lowest ebb, and as such, it has served as a huge impediment to the development of international tourism on the continent. This is a reflection of the weak infrastructural development that characterizes most countries in Africa (Ogbuabor et al., 2020).

Furthermore, based on the results in Table 5, terrorism indicates a negative connection with international tourism demand in Africa. In other words, terrorism in Africa is hampering international tourism demand on the continent. According to Boulal (2017), terrorism has continued to plague nations where tourism is a major source of income for their economies. Tourism benefits economies not just in terms of income but also in terms of the reduction in unemployment and the building of a diversified economy. An example is that of Kenya, Nigeria, Niger, and Chad, which have witnessed major terrorist attacks in recent times. Esmail (2016) also pointed out a decline in tourist activities in Egypt as a result of terrorism. The effect of temperature on international tourism remained insignificant throughout. While Ekeocha et al. (2021) reported that Africa has pleasant weather conditions to drive tourism-led growth, Sifolo and Henama (2017) explained that climate change remains a threat to the sustainability of the tourism sector globally and has negative impacts on the destination area, which must be managed to improve the standard of living and quality of life for the host community. However, our baseline results did not show any significant effect from the climatic variable (i.e., temperature). The institutional indicators showed negative effects on tourism demand in Africa. This is an indication that institutions in Africa are still weak and, as such, are unable to give the tourism industry the needed support to thrive. This is also reflected in the average negative values of the institutional indicators in the summary statistics in Table 3. Indeed, our results show that efforts toward improving the quality of institutions on the continent are important in stimulating the continent’s tourism sector.

In Table 6, we interacted the core regressors, foreign direct investment and trade openness with infrastructure. The results generally follow the same qualitative patterns as the baseline results in Table 5. However, unlike in the baseline model, the effect of FDI on tourism demand is now negative and statistically significant throughout. This further buttresses the fact that FDI in Africa has not contributed much to tourism demand on the continent. Interestingly, our results show that the interaction of FDI and infrastructure had a significant positive impact on all the panels. The implication of this result is that, even though the level of infrastructure in Africa is low, it nonetheless moderates the negative impact of FDI on international tourism demand on the continent. Studies like A. J. Khadaroo and Seetanah (2010), Kok and Acikgoz Ersoy (2009), Mlambo (2005), Bae (2008), and Rehman et al. (2011) have also highlighted the critical role of infrastructure in attracting foreign direct investment. Furthermore, our results indicate that the interaction between trade openness and infrastructure remained muted in all the panels. This shows that the weak infrastructural base of the continent is not significantly moderating the trade-tourism relationship. This is a reminder of the poor state of infrastructure in most African economies. The conclusion here is that while FDI impacts international tourism negatively, trade openness has a positive impact. However, while the interaction of FDI and infrastructure shows that infrastructure can moderate the adverse impact of FDI, the interaction of trade openness and infrastructure shows that the level of infrastructural development on the continent is not yet moderating the trade openness-tourism relationship. The effects of terrorism and the institutional indicators remained negative as before, while the effect of the climatic variable also remained muted.

Table 6.

System GMM Regression of the Moderating Effect of Infrastructure (Response Variable: Lnt_receipt).

Regressors	(1)	(2)	(3)	(4)	(5)
l.Lnt_receipt	1.38***	1.71***	1.55***	1.37***	1.37***
fdi	−0.04***	−0.05***	−0.04**	−0.04***	−0.04**
top	0.01*	0.01	0.01	0.01	0.01
infr	−0.02**	−0.04*	−0.03**	−0.02*	−0.02*
fdi*infr	0.003***	0.003**	0.003**	0.003***	0.003**
top*infr	−0.0001	−0.0001	−0.00003	−0.00002	−0.0001
gti	−0.02	−0.06	−0.04	−0.04	−0.02
temp	−0.01	−0.002	−0.02	−0.01	0.001
cc	−0.29*
rq		−0.87
ge			−0.74**
rl				−0.61*
va					−0.21
Groups	42	42	42	42	42
Instruments	16	15	15	15	15
AR(2)	0.20	0.21	0.20	0.21	0.20
Hansen	0.48	0.40	0.44	0.37	0.30
Observations	420	420	420	420	420

Source. Authors.

Note. Two interaction terms (fdi*infr and top*infr) were added to the baseline models in Table 5. Other notes in Table 5 apply.

Table 7 presents the results of the interaction between terrorism and our core explanatory variables, FDI and trade openness. Just as in our baseline results, the effect of FDI on international tourism demand remained statistically insignificant throughout. The coefficients of trade openness, infrastructure, terrorism, temperature, and the institutional indicators are in agreement with the results obtained in our baseline model. The interaction between FDI and terrorism is shown to have a significant negative impact on tourism demand in all the panels. This is a pointer to the enormous damage that terrorism has done to African economies. The prevalence of terrorism activities in most African countries has in no small measure affected the inflow of FDI into the continent, as most foreign investors will not be comfortable investing in a crisis-prone area, thereby hindering the development of the tourism sector on the continent. This finding is consistent with studies like Shahbaz et al. (2013) for Pakistan and Kinyanjui (2014) for Kenya, which indicate that terrorism reduces FDI significantly. However, the interaction between trade openness and terrorism gives a positive coefficient in all the panels. This is an indication that terrorism in Africa has not affected the trade-tourism relationship negatively. In other words, terrorism in Africa has not posed much challenge to the role of trade as a driver of international tourism demand on the continent. This finding may also be a reflection of the increased arms trading deals undertaken by many economies on the continent in response to the rising wave of terrorism in recent years. For instance, Nigeria, which is the largest and most populous country on the continent, has been procuring weapons and fighter jets from some advanced countries in recent years to enable it to combat terrorism.

Table 7.

System GMM Regression of the Moderating Effect of Terrorism (Response Variable: Lnt_receipt).

Regressors	(1)	(2)	(3)	(4)	(5)
l.Lnt_receipt	1.40***	1.06**	1.70***	1.36***	1.34***
fdi	0.01	0.01	0.01	0.01	0.01
top	0.04*	0.0004	0.01**	0.003	0.002
infr	−0.02**	−0.01	−0.03**	−0.02*	−0.02
gti	−0.11**	−0.04	−0.21**	−0.12*	−0.12*
fdi*gti	−0.01**	−0.01**	−0.01*	−0.01**	−0.01**
top*gti	0.002**	0.001	0.003*	0.002**	0.002**
temp	−0.01	−0.01	−0.02	−0.01	−0.004
cc	−0.36*
rq		−0.01
ge			−1.05**
rl				−0.55
va					−0.28
Groups	42	42	42	42	42
Instruments	14	16	14	14	14
AR(2)	0.19	0.20	0.21	0.19	0.19
Hansen	0.31	0.16	0.36	0.27	0.24
Observations	420	420	420	420	420

Source. Authors.

Note. Two interaction terms (fdi*gti and top*gti) were added to the baseline models in Table 5. Other notes in Table 5 apply.

In Table 8, we interacted the core explanatory variables (i.e., FDI and trade openness) with the climatic variable, which is temperature. This is to enable us to understand how the climatic variable is influencing the relationship between our core variables and tourism demand in Africa. The results generally follow the same patterns as our baseline estimates in Table 5. Interestingly, the interaction terms remained muted throughout, suggesting that the influence of temperature on the presumed relationships is not significant. However, the interaction of FDI and temperature showed positive coefficients in all the panels, suggesting that the climatic condition in Africa has the potential to enhance the FDI-tourism relationship on the continent. Furthermore, the interaction of trade openness and temperature showed a negative coefficient in all the panels, suggesting that changes in the climatic variable have the potential to deter the trade-tourism relationship in Africa. Abidoye and Odusola (2015) found that there exists a negative impact of climate change on economic growth, such that a 1°C increase in temperature reduces gross domestic product growth by 0.67 percentage points. In Table 9, we also reported the interaction of our core variables with the institutional indicators. The results in Table 9 are generally consistent with our baseline estimates in Table 5. In addition, the interactions of the institutional variables with FDI and trade openness did not yield any significant coefficients in all the panels. This is a reflection of the weak institutions on the continent, as shown by the negative average values of the institutional indicators in the summary statistics in Table 3.

Table 8.

System GMM Regression of the Moderating Effect of Temperature (Response Variable: Lnt_receipt).

Regressors	(1)	(2)	(3)	(4)	(5)
l.Lnt_receipt	1.44***	1.78***	1.61***	1.34***	1.32***
fdi	−0.10	−0.07	−0.05	−0.03	−0.03
top	0.02	0.04	0.02	0.02	0.02
infr	−0.02**	−0.03	−0.02**	−0.01*	−0.02**
gti	−0.03	−0.08	−0.05	−0.04	−0.03
temp	0.06	0.09	0.05	0.04	0.07
fdi*temp	0.003	0.003	0.002	0.001	0.001
top*temp	−0.001	−0.001	−0.001	−0.001	−0.001
cc	−0.41*
rq		−1.00
ge			0.83*
rl				−0.54
va					−0.27
Groups	42	42	42	42	42
Instruments	14	15	15	15	16
AR(2)	0.19	0.21	0.19	0.19	0.19
Hansen	0.32	0.29	0.38	0.23	0.36
Observations	420	420	420	420	420

Source. Authors.

Note. Two interaction terms (fdi*temp and top*temp) were added to the baseline models in Table 5. Other notes in Table 5 apply.

Table 9.

System GMM Regression of the Moderating Effect of Institutional Quality (Response Variable: Lnt_receipt).

Regressors	(1)	(2)	(3)	(4)	(5)
l.Lnt_receipt	1.42***	2.28***	1.52***	1.20***	1.30***
fdi	0.0004	−0.02	−0.003	0.01	−0.01
top	0.004	0.01	0.01	0.002	0.01
infr	−0.02**	−0.06*	−0.02*	−0.01*	−0.02**
gti	−0.03	−0.12	−0.07	−0.02	−0.02
temp	−0.01	−0.01	−0.01	−0.004	0.01
cc	−0.06
fdi*cc	0.01
top*cc	−0.004
rq		−1.18
fdi*rq		−0.01
top*rq		−0.01
ge			−1.03
fdi*ge			−0.001
top*ge			0.004
rl				−0.27
fdi*rl				0.02
top*rl				−0.002
va					−0.38
fdi*va					0.01
top*va					0.002
Groups	42	42	42	42	42
Instruments	15	11	12	17	16
AR(2)	0.19	0.28	0.20	0.20	0.19
Hansen	0.31	0.52	0.74	0.12	0.42
Observations	420	420	420	420	420

Source. Authors.

Note. The institutional variables are interacted with fdi and top. Other notes in Table 5 apply.

Overall, our results in Tables 5 to 9 indicate that terrorism and infrastructural development can be classified as pure moderator and quasi moderator variables, respectively, based on the typology of moderator variables advanced by Sharma et al. (1981). Following this typology, we performed sub-group analysis by splitting the sample into two sub-groups for the periods 2010 to 2015 (sub-group 1) and 2016 to 2020 (sub-group 2). The results of our sub-group analysis, as shown in Appendix 5, indicate that institutional quality and climatic condition belong to the class of moderator variables that Sharma et al. (1981) called homologizer variables, since the predictive validity coefficients varied remarkably over the sub-groups.

In Table 10, we allowed all the moderating variables to enter the model at the same time. This enabled us to provide an additional robustness check on the baseline estimates in Table 5. Interestingly, the results in Table 10 generally follow the same patterns as our baseline estimates in Table 5. All the models in this study were subjected to two important diagnostic tests. These are the Arellano-Bond second-order AR(2) test for serial correlation and the Hansen test for over-identifying restrictions. The p-values of the AR(2) tests for all the models indicate that the null hypothesis of no autocorrelation cannot be rejected at the 5% level of significance. In other words, the models do not suffer from the problem of autocorrelation. For the Hansen tests, the p-values are also found to be higher than 5% in all the models, indicating that the null hypothesis that the instruments are not correlated with the residuals cannot be rejected. Hence, the instruments used in the models are valid for the estimations. We, therefore, conclude that the estimated parameters are robust and adequate for policy purposes.

Table 10.

System GMM Regression of the Moderating Effect of Infrastructure, Terrorism, Temperature and Institutions (Response Variable: Lnt_receipt).

Regressors	(1)	(2)	(3)	(4)	(5)
l.Lnt_receipt	1.55***	1.84***	1.68***	1.40***	1.35***
fdi	−0.17**	−0.26*	−0.24**	−0.13	−0.10
top	0.02	0.03	0.02	0.01	0.02
infr	−0.03**	−0.04*	−0.02**	−0.02*	−0.02*
gti	−0.13**	−0.23*	−0.21**	−0.12*	−0.11
temp	0.03	0.02	−0.04	0.01	0.03
inst	−0.14	−1.28	−1.42**	−0.70	−0.30
fdi*infr	0.004***	0.004**	0.01***	0.004**	0.002**
fdi*gti	−0.01*	−0.01	−0.01**	−0.01	−0.01*
fdi*temp	0.003	0.01	0.01	0.002	0.003
fdi*inst	−0.05	−0.05	−0.06	−0.05	−0.02
top*infr	−0.0002	−0.0002	−0.0003*	−0.0001	−0.0001
top*gti	0.002**	0.003*	0.003**	0.002**	0.002**
top*temp	−0.001	−0.001	−0.002	−0.0003	−0.0004
top*inst	−0.003	0.01	0.01**	0.004	0.001
Groups	42	42	42	42	42
Instruments	20	18	20	20	20
AR(2)	0.20	0.23	0.21	0.22	0.19
Hansen	0.38	0.54	0.36	0.36	0.22
Observations	420	420	420	420	420

Source. Authors.

Note. All the regressors and the interaction terms are included in the model, but the five institutional variables enter the model one at a time to give five panels. Hence, inst is the institutional variable, so that in Panel 1, it denotes control of corruption (cc), in Panel 2, it denotes regulatory quality (rq), in Panel 3, it denotes government effectiveness (ge), in Panel 4, it denotes rule of law (rl), and in Panel 5, it denotes voice and accountability (va). As before, (***), (**), and (*) indicate significance at 1%, 5%, and 10%, respectively.

Concluding Remarks and Some Policy Recommendations

This study examined the roles of FDI inflow and trade openness as drivers of international tourism demand in Africa during the post-Global Financial Crisis era. We also investigated how infrastructural development, terrorism, climatic conditions, and institutional quality influence the FDI-tourism and trade openness-tourism relationship in Africa. The study used the system GMM modeling framework and a panel of 42 African countries during the post-Global Financial Crisis era. We summarize the findings as follows: First, we find that FDI inflow has not contributed significantly to the growth of tourism demand in Africa. This finding is contrary to the eclectic theory, which assumes that tourism demand in Africa is a unique product that cannot be easily replicated in other foreign locations, such that FDI will be a key contributor to tourism demand in Africa. The implication of this is that Africa is yet to exploit the FDI channel toward harnessing its tourism potential. However, infrastructural development and climatic conditions on the continent have the prospects of positively influencing the FDI-tourism relationship, while terrorism hampers this relationship. The role of institutional quality as a driver of the FDI-tourism relationship in Africa remained negligible throughout. Second, we find that trade openness promotes international tourism demand in Africa, even though its influence is not predominantly statistically significant. In addition, we find that the rising wave of terrorism on the continent is yet to adversely deter the trade openness-tourism relationship on the continent. However, the roles of infrastructural development and institutional quality in influencing the trade openness-tourism relationship remained negligible. Third, we find that in terms of their direct effects, infrastructural development, temperature, and institutional quality are not significantly promoting international tourism demand in Africa, while terrorism remains a deterring factor.

The foregoing findings have some policy implications in order to ensure that the tourism sector in Africa begins to benefit significantly from FDI inflow and trade. First, the present challenge of poor infrastructural development facing Africa should be addressed. This is necessary since the results showed that infrastructure has the potential to enhance the FDI-tourism relationship. For instance, while making efforts to enhance budgetary allocations to key infrastructural development projects, African countries can also explore increased infrastructural development funding from the African Finance Corporation and other development financing institutions, as well as private-public partnership infrastructural financing schemes. These will help in improving the power, transport, and information and communications technology infrastructures on the continent. Second, the adverse influence of terrorism on the FDI-tourism relationship suggests that there is a need for leaders and policymakers in Africa to form a common front in order to tackle the rising wave of terrorism on the continent. Indeed, instead of allowing individual African countries to wage the war on terrorism, a multinational task force can be established at the level of the African Union to address this cankerworm.

Third, since climatic conditions, measured by annual average temperature, have the potential to enhance the FDI-tourism relationship in Africa, we recommend that Africa leaders ensure that they adhere to global climate agreements in order to reduce greenhouse gas emissions and maintain a safe and clean atmosphere around the continent. Furthermore, following the finding that the role of institutional quality in enhancing the FDI-tourism and trade openness-tourism relationships in Africa remained predominantly muted, we recommend that policymakers and leaders in Africa focus on building high-quality institutions. Thus, high-quality institutions are needed to attract FDI inflows and enhance trade in Africa, and these, in turn, can then contribute toward improving the tourism sector on the continent.

Research Data

sj-xlsx-1-jtr-10.1177_00472875231202171 – Supplemental material for Do Foreign Direct Investment Inflow and Trade Openness Influence International Tourism Demand in Africa? A Study of the Post-Global Financial Crisis Era

Supplemental material, sj-xlsx-1-jtr-10.1177_00472875231202171 for Do Foreign Direct Investment Inflow and Trade Openness Influence International Tourism Demand in Africa? A Study of the Post-Global Financial Crisis Era by Jonathan E. Ogbuabor, Christian Agu and Ifeoma C. Mba in Journal of Travel Research

This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Footnotes

Appendices

Appendix 5.

Predictive Validity Coefficients for Sub-Group Analysis.

Model	Total sample	Sun-group 1	Sub-group 2
(1)	0.87 (420)	0.45 (210)	0.28 (168)
(2)	0.87 (420)	0.56 (210)	0.34 (168)
(3)	0.87 (420)	0.47 (210)	0.30 (168)
(4)	0.88 (420)	0.54 (210)	0.41 (168)
(5)	0.87 (420)	0.53 (210)	0.36 (168)

Source. Authors.

Note. Sample size is indicated in parentheses.

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 iD

Christian Agu

Data Availability Statement

The dataset used for this study has been provided by the authors and can be found in the Journal’s data repository.

Author Biographies

Jonathan E. Ogbuabor (PhD) is a Senior Lecturer in the Department of Economics, University of Nigeria, Nsukka, Nigeria. He is also an alumnus of the Department of Economics & Related Studies, University of York, United Kingdom. He currently teaches Econometrics and Research Methods to Masters and Doctoral students. He has published widely in journals indexed in the Social Science Citation Index compiled by Clarivate Analytics. His research papers cover macroeconomic connectedness and spillovers, economic asymmetries, and economic growth. His email address is jonathan.ogbuabor@unn.edu.ng

Christian Agu is a Lecturer and doctoral candidate in the Department of Economics, University of Nigeria, Nsukka, Nigeria. He currently teaches Microeconomics and Economic Statistics to undergraduate students. He has published in both national and international journals. His research interests include macroeconomic analysis, international economics, and development economics. He is the corresponding author for this article. His email address is chris.agu@unn.edu.ng

Ifeoma C. Mba (PhD) is a Senior Lecturer and current Head of the Department of Economics, University of Nigeria, Nsukka, Nigeria. She currently teaches Microeconomics and Public Sector Economics to Masters and Doctoral students. She has published widely in journals indexed in the Social Science Citation Index compiled by Clarivate Analytics. She has also presented papers in numerous international conferences and workshops. Her email address is ifeoma.mba@unn.edu.ng

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