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AN EMPIRICAL INVESTIGATION BETWEEN FOREIGN DIRECT INVESTMENT (FDI) AND TOURISM IN BANGLADESH

Article History Received: 12 March 2020 Revised: 23 April 2020 Accepted: 25 May 2020 Published: 16 June 2020


INTRODUCTION
In the last few decades, Foreign Direct Investment (FDI) has boomed due to the globalisation and led to economic progress and transformation by employing foreign asset, technology and skills. FDI in the tourism sector generates employment, raises productivity, and transfers managerial skills and technology across the host countries (Selvanathan, Selvanathan, & Viswanathan, 2012;Tang, Selvanathan, & Selvanathan, 2007). Moreover, FDI is often considered as one of the better sources of financing for the tourism sector since the host country's access to funding is limited and is not sufficient to spend on the much-needed re-engineering of tourism tools in many cases (Rajapakse, 2016). Therefore, most governments in developing and emerging countries often place the highest priority on attracting FDI by experimenting with a variety of policies (Zhang, Chong, & Ap, 1999). growth, only a handful of literatures have focused on the relationship between FDI and tourism sector. Samimi, Sadeghi, and Sadeghi (2013) found that a causality between FDI and tourism using dynamic panel data analysis, where causality runs from FDI to tourism in the developing countries. Sanford and Dong (2000) found unidirectional causality running from tourism to FDI in the USA. On the other hand, Sadi and Henderson (2001) found a bidirectional relationship between FDI and tourism development in Vietnam. Several researchers also found evidence of the relationship between FDI and tourism in many developing and emerging countries like India, Turkey, and China (Selvanathan et al., 2012;Tang et al., 2007).  To our knowledge, no study has been conducted to examine the relationship between FDI and tourism sector in the context of Bangladesh. Therefore, this paper aims to examine the relationship between FDI and tourism in Bangladesh and analyse the policy framework required to attract FDI for tourism development in Bangladesh.
To check the stationary properties, we have employed the Augmented Dickey-Fuller (ADF), the Phillips-Perron (PP) and the Zivot-Andrews unit root tests with a structural break. Johansen cointegration test has been conducted to check the existents of meaningful long run association among the variables of interest. After the cointegration test, the Granger Causality test and the Vector Error Correction Model (VECM) have been run to observe the causality between the variables in the long run and short run, respectively. Dynamic Ordinary Least Square (DOLS) estimation and Autoregressive Distributed Lag (ARDL) are also used to estimate the long run coefficient. Finally, the Cumulative Sum (CUSUM) test has been incorporated for investigating the stability of the concerned variables. Our results are expected to provide the necessary platform for the policymakers of the government and other affiliated tourism stakeholders so that they can set a long-term tourism growth strategy and appropriate planning for development in the future.
The remaining paper is organised as follows: section 2 provides a review of the existing literature, section 3 discusses the methodologies used, section 4 discusses the results of the econometric analysis and finally, Section 5 provides the concluding remarks and policy implications of the results found.

LITERATURE REVIEW
The direction of causality between FDI and tourism is not cohesive as the result varies with different studies.
Empirical studies of FDI and tourism for the economy of Turkey had generated mixed results. Katircioglu (2011) studied tourism and FDI inflows growth by using bounds test with the data of Turkey from 1970 to 2005 which, showed that the two variables were in a level or relationship only when net FDI Inflows were the dependent variable in Autoregressive Distributed Lag Model (ARDL). However, Pham and Tran (2015) yielded different results. They examined the causal relationship between international tourist arrivals and FDI for the period of 1980-2012, which indicated a definite strong causality between the two variables and established tourist arrivals as a catalyst for foreign direct investment. The paper further highlighted that tourism in Turkey should be uplifted to enhance the economy's growth. Alam, Idris, Malik, and Gaadar (2016) conducted a study using data from the period 2000-2013 of the Kingdom of Saudi Arabia and applied time series cointegration method by using the variables: number of visitors and total receipt from the tourism sector and FDI. By using the Johansen Co-integration test, they showed that there was both a long run and short run positive relationship between FDI and tourist value. The Granger causality test showed bidirectional causality between tourism expenditure and FDI. Seetanah and Fauzel (2019) investigated the link between foreign direct investment (FDI) and tourism development for the case of the small island economy of Mauritius for the period 1980-2015. The research employed a dynamic time series econometrics framework, namely a vector error correction model (VECM), to account for a potential dynamic and endogenous relationship in the FDI-tourism nexus. They showed that FDI had a positive and significant effect, though relatively lower compared to the other classical factors of tourism development, in the long run. Interestingly, a bidirectional effect was observed in the long run, while an indirect link between FDI and tourism development via the economic growth channel was found. Tang et al. (2007) used the Granger causality test under a VAR framework using quarterly data from the time period of 1987 to 2001. They found a one-way causality from FDI to tourism in China. They also argued that more tourists would increase the demand for hotels, and consequently, the investment would expand. China played a leading role in absorbing FDI among emerging economies and developing countries. FDI could positively benefit large international hotel chains spread their brand and cater to the growing tourism demand. Craigwell and Moore (2007) applied panel causality tests to establish this relationship for selected Small Island Developing States (SIDS). The results indicated several causal relationships between the variables which were used.
The causality ran from FDI to tourism, validating that FDI indeed helped to reduce the financial gap in developing nations. Furthermore, the results suggested that SIDS would actively pursue foreign investment to generate funding which the country could not collect on its own. The lack of a bidirectional causal relationship between the two variables indicated that some SIDS might need to do a better job of marketing their tourism product to encourage more significant foreign investment.
On the other hand, Willem and Nair (2006) used panel regression analysis to establish whether Caribbean countries could increase the amount of FDI influx towards tourism using their trade negotiations. They found a positive association between FDI flows and the number of General Agreement on Trade in Services (GATS) commitment; however, they did not find a notable relationship between tourist arrival and FDI flows. The insignificant results could be liable for the small sample size of 9 countries and a short period of data collected. Samimi et al. (2013) documented the relationship between FDI and tourism development in a panel of selected 20 developing countries using a framework that confines both inter-country and inter-temporal variation for the period of 1995 to 2008. The results of panel cointegration tests proved that there was a cointegrated relationship between tourism-related FDI and tourism growth in the long run. Besides, there was a long term bilateral relation between business enterprise connected FDI and business enterprise development, whereas there was no short term relation between variables. Broadly, whenever the system suffered a shock, the variables made adjustments to recover long run equilibrium. Bank whereas the data for TR has been drawn from the different annual reports published by Bangladesh Bank 2, 3 .

Unit Root Test
To check the stationary properties of the variables, Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests have been conducted. However, one of the main limitations of these traditional unit root tests is that they cannot determine the time of structural breaks in time series data endogenously (Pahlavani, Valadkhani, and Worthington (2005). To overcome this problem, Zivot and Donald (1992) unit root test with structural break is used in this paper.

Cointegration Test
The Johansen procedure is applied, which is a unified framework for estimation and testing for co-integration relations in the context of the Vector Autoregressive (VAR) error correction model (Sjo, 2008). For this approach, one has to estimate an Unrestricted Vector of Autocorrelation of the form: (1) Where is the difference operator, is a vector of non-stationary variables (in levels) and is also 3.3. Causality Tests Engle and Granger (1987) and Granger (1988) proposed the Granger causality test which is used widely in empirical studies. In this paper, we can establish a long run relationship between non-stationary time series variables when the variables are cointegrated. The causality test between international Tourist Receipts (TR) and Foreign Direct Investment (FDI) will be conducted. We can determine whether the past value of TR augments the explanation of the present value of FDI given that by information in the past value of FDI itself and vice versa.
Therefore, the following two sets of equation will be estimated: (2) For all possible pairs of series in the group. The reported F-statistics are the Wald statistics for the joint hypothesis The VECM approach is considered in this paper because a possible limitation of the Granger causality test is that it takes into account causal relationships only in the long run but not for the short run while VECM provides information for both time frames. By doing so, the short run deviations of series from their long run equilibrium path are also captured by including an error correction term. Therefore, error correction models of cointegration under the bivariate system in this paper can be specified as follows: (4) Z t-1 is the error-correction term, which is the lagged residual series of the cointegrating vector. The errorcorrection term trials the deviations of the series from the long run equilibrium relation.

Dynamic OLS (DOLS)
The Dynamic OLS (DOLS) was introduced by Stock and Watson (1993) which is an updated version of the OLS approach where one can deal with smaller sample size and dynamic sources of biases. It is a robust single equation approach that corrects the regressor endogeneity by including lags and leads. DOLS can assess long run equilibrium where variables are integrated in the same or dissimilar order. Moreover, it has the same kind of optimality as Johansen distribution. This is one of the major benefits of this method (Amin & Khan, 2020). If TRt is (6)

Autoregressive Distributed Lag (ARDL)
Pesaran and Yongcheol (1999) introduced the Autoregressive Distributed Lag Model (ARDL) approach. This method has two important advantages. Firstly, it effectively corrects for possible endogeneity of explanatory variables. Secondly, the estimates exhibit desirable small sample properties (see also (Caporale & Pittis, 2004;Panopoulou & Pittis, 2004)). Another advantage of the ARDL test is that one can use variables which are I (0) or I (1) or the mixture of I (0) and I (1).
We will use long-term ARDL (p, q) as given below,

CUSUM Test
Usually, we use the Cumulative SUM (CUSUM) test to investigate any systematic changes or movements in which there are possibilities of change in values of the coefficient revealing structural instability. Existence of any unknown breakpoint results rejecting the specification throughout the period. Through hypothesis testing, we can find out whether the model parameters are stable or not by rejecting or accepting the null hypothesis (Farhani, 2012).
Under the null hypothesis, must be inside the corridor ( to ) We will reject the null hypothesis (i.e., the model is stable) if cuts the given range of corridors. Which actually means the variables are not stable enough.   Table 3. Therefore, it can be concluded that the variables have unit root in the presence of structural break, meaning the variables are not mean-reverting at their levels. Figure 2 and Figure 3 graphically illustrate the break points in our variables of interest.

Johansen Cointegration Test Results
The Johansen cointegration test that has been carried next in Table 4 to investigate whether the two series have at least one meaningful cointegrating relationship in the long run. Both the Trace test and the Maximum Eigen value tests have revealed the existence of one cointegrating relationship.

Causality Test Results
Results of the long run causal relationship between TR and FDI are summarised in Table 5. It can be seen that FDI causes TR in the long run. The finding suggests that FDI can stimulate the tourism sector in the long run. Tang et al. (2007) and Othman, Salleh, and Sarmidi (2012) also found similar significant causality running from FDI to Tourism. The tourism sector is combined with multi industries like service, transportation, accommodation, etc. The sector needs capital, developed infrastructures, technical skills, policy implementations etc. which are important for the long run sustainable development (Amin & Rahman, 2019). FDI inflow into this sector will eventually help to achieve these long run requirements for the development of this sector. The results of the VECM in Table 6 find a unidirectional causality running from FDI to TR in the short run. Previously, Selvanathan et al.
(2012) also found a similar result. One of the possible reasons for this is that in the short run, inflow of FDI can stimulate more tourism related services with existing resources through generating more jobs in the tourist regions.

Long run Coefficient Estimation
The DOLS and ARDL results are given in Table 7. Long run FDI coefficients are found to be 0.065 and 0.062 by DOLS and ARDL estimation, which are positive and significant as well. The results assert that a 1 million increase in FDI will increase the tourism receipts by 0.065 million USD in DOSL estimation and 0.062 million USD in ARDL estimation. It is worth mentioning that both estimation results show a similar pattern, which indicate the robustness of the estimated long run coefficient of FDI. However, the coefficient value is relatively small. It is because a larger portion of the FDI is directed to RMG, leather textile, and other manufacturing sectors of Bangladesh. In addition, tourism is a relatively new sector in Bangladesh and has not yet been able to give proper signals to the foreign investors. Therefore, the effect of FDI is not seen much in the tourism sector of Bangladesh. The Cumulative SUM (CUSUM) test was used to observe any possible change of coefficient value in any structural instability. Figure 4 reveals that Wm remains inside the 5 percent limit corridor. It means that parameters are stable.

CONCLUSION AND POLICY IMPLICATIONS
FDI plays a crucial role in many countries for facilitating the development of different sub-sectors and overall progress. FDI inflow into a country creates opportunities for employment, socioeconomic development, infrastructural development, and other aspects of an economy. Thus, FDI is given high priority for development by most governments in emerging countries (Khoshnevis Yazdi, Homa Salehi, & Soheilzad, 2017). On the other hand, tourism is considered as one of the important drivers for economic growth (Amin, Kabir, & Khan, 2019).
Consequently, FDI can play an important role in developing the tourism sector, which is comprise of multi-skill industries including technical skills, infrastructural capital, and supply chain system (Banerjee et al., 2015). The availability of financial assistance intensifies the development of the tourism sector, especially in the case of capitalintensive tourism projects, which require a huge setup cost.
Bangladesh needs export diversification to achieve sustainable development. Most of the developing and emerging countries like Bangladesh need capital, infrastructure, knowledge, access to global marketing and distribution chains for desired development progress (Stauvermann & Kumar, 2017). Since Bangladesh has huge potential in the tourism sector, it can be one of the prospective sectors for this country to accelerate economic growth and development. The government needs to promote the tourism sector considering its huge potential for the Bangladesh economy. To facilitate the tourism sector of a country, FDI among others is expected to play a significant role by providing the required financial assistance.
Although there are a few studies on the relationship between tourism and FDI from a range of perspectives, the direction of its causality remains an unsolved conundrum in Bangladesh. This paper aims to examine the relationship between FDI and tourism in Bangladesh to fill the gap in literature. respectively. CUSUM stability test shows that the parameters are stable given any unknown break as Wm remains inside the corridor.
As the result shows that FDI plays a positive role in expanding the tourism sector in Bangladesh, appropriate policies to explore tourism resources and plans to develop new tourist venues are important. The government should undertake the policies to give incentives for investing in the tourism infrastructures, and thus the potential of tourism will come under the spotlight. Policies for increasing both national and international tourism demands are highly recommended. It is because; increase in tourism demand can give signals to foreign investors to invest more in the tourism sector of Bangladesh. Regional and sectoral development is needed to create a tourism-friendly environment to attract international tourists. Underdeveloped regions such as hill tract areas of Bangladesh should be given priority because of natural scenic beauty. These areas can attract more tourists from the national and international arena. Tourist friendly services are needed, such as proper transportation facilities (Taxi or Car lift) after tourists arrive into the country. This is one of the important factors for Bangladesh. Besides, proper tourist guiding is needed to provide knowledge about tourism spots, culture, and culinary of Bangladesh.
There are other points worth mentioning about this paper. Firstly, we use a bivariate model to analyse the relationship between FDI and tourism. However, more explanatory variables such as exchange rate, GDP, political stability, and business environment can be added to the model to see more robustness of the findings. Secondly, the paper can be done in panel data, including other countries from the South Asian Region.
Funding: This study received no specific financial support. Competing Interests: The authors declare that they have no competing interests.