Demand, volatility and post-war tourism in Sri Lanka


Research Paper (postgraduate), 2016
25 Pages

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Table of Content

1. Introduction

2. Risks of Political Violence and Tourism Industry in Sri Lanka

3. Empirical Model for the Volatility of Tourism Demand in Sri Lanka
3.1 Methodology
3.2 Model Specification
3.3 Results

4. Post-war Tourism Demand and Forecasting in Sri Lanka
4.1 Empirical Model for Tourism Demand
4.2 Evaluation of the Targets

5. Issues in tourism Sector in Sri Lanka

6. Conclusion

7. References

Abstract

Historically Sri Lanka has always been a tourist destination for centuries because of its strategic location and uniqueness. Although Sri Lankan tourism sector has been growing since 1967, International tourist arrivals to Sri Lanka have experienced notable fluctuations during the nearly three decades of civil war, particularly between 1983 and 2009. Sri Lankan tourism has managed to recover quickly since the end of war in 2009. After recognising the role of post-war tourism, the Sri Lankan government has launched the Tourism Development Strategies for the period 2011-2016. Firstly, The paper carried out systematic statistical analysis for Sri Lankan tourism focussing in particular on the civil war and associated political violence. Secondly, the paper empirically explored whether the targets set in the TDS by the Sri Lankan government are achievable or realistic by using a simple econometric model. Furthermore, post- war tourism development and the tourism boom are being evaluated within the context of the current political and economic situation. The empirical results of the first study demonstrating that the Sri Lankan tourism industry is very sensitive to political violence, exchange rate changes, and seasonal variations. The analysis suggests that significant increases in political violence lowered tourist arrivals in Sri Lanka and created a substantial amount of volatility in tourism demand. The results of the second study indicate that the Sri Lankan tourism industry was in a favorable position to achieve the targets until 2014 but missing the targets in 2015 and it is a challenge to achieve of 2.5 million tourism arrivals by 2016. A number of policy inferences can be drawn from this study. Given the limited resources available to the Sri Lankan government and the competing claims on these resources, development of strategies for and active promotion of public-private partnerships aimed at creating new tourism related infrastructure (hotel resorts, cruise line facilities, road transport upgrades, etc.) are recommended. There is should be a consideration for promoting the country as a price competitive and safe tourism destination. Finally, the country has to maintain political stability and work towards reconciliation process with improved governance and maintaining rules of law in order for the development strategy to be fully realized.

1. Introduction

Historically Sri Lanka has always been a tourist destination for centuries because of its strategic location and uniqueness. Although there have been ups and down in the number of tourist arrivals to Sri Lanka during the post-independence period, Sri Lanka satisfies the criteria required to be considered as one of the ‘tourism countries’ (TC) (Brau, Di Liberto, & Pigliaru, 2011). Although the Sri Lankan tourism sector has been growing since 1967, International tourist arrivals to Sri Lanka have experienced notable fluctuations during the nearly three decades of civil war, particularly between 1983 and 2009 (Fernando, 2017). Following the end of nearly three decades separatist civil war in Sri Lanka in May 2009, Sri Lanka has witnessed an unprecedented post-war tourism boom beyond its expectation (Smith, Bandara, Liyanaarachchi, & Fernando, 2014). The number of international tourist arrivals to Sri Lanka has sharply increased breaking all previous historical annual and monthly tourist arrivals records. The post-war peace time has given Sri Lankans and the Sri Lankan tourism sector new hopes and optimism (Fernando, 2016).

This study carried out systematic statistical analysis for Sri Lankan tourism focussing in particular on the civil war and associated political violence which significantly affected the Sri Lankan tourism industry for the three decades prior to 2009. Furthermore, post-war tourism development and the tourism boom are being evaluated within the context of the current political and economic situation. For this purpose, two econometric models are developed in order to evaluate the sensitivity of the Sri Lankan tourism to political violence and its potential growth during the post-war period. Furthermore, using the results of the tourism demand model the post war tourism targets are evaluated. The rest of the paper is organised as follows. Section two discusses risk in the tourism industry in the case of political violence and civil war. Section three develops an empirical model for investigating volatility of tourism demand in Sri Lanka. Section four identifies empirically trends and fluctuations in Sri Lankan tourism demand in between the various episodes of war and peace using monthly tourism data for last four decades. Further, it investigates post-war tourism targets and feasibilities in achieving those targets in Section five. Section six concludes the paper.

2. Risks of Political Violence and Tourism Industry in Sri Lanka

Given the main objective of relaxing and enjoying an uninterrupted holiday in the visited country of choice, it is a well-recognized fact that international tourists are very sensitive to war, political violence and criminal activities. This relationship between tourism and war, terrorism and political stability is well explored within literature (see Pizam & Mansfeld, 1996; Sevil, 1998 ). Particularly amongst tourism analysts, there is a widespread view that international visitors are very concerned about their personal safety and thus international tourism can only thrive under peaceful conditions (Hichcock, King, & Parnwell, 1993). As Neumayer points out (2004, pp. 259-260) “tourists are only willing to travel to foreign places in mass numbers if their journey and their stay are safe and shielded from events that threaten a joyous holiday experience”. Continuous political violence, prolonged wars and suicide bombings in popular tourist centres can subsequently lead international tourists to choose alternate destinations (Bhanugopan, 2001). Furthermore, Western governments tend to issue travel warnings to protect their citizens when political violence and criminal activities are taking place in popular tourist destinations. It is well documented that wars and political violence have detrimental effects on economic growth, particularly in the tourism sector (see Murdoch & Sandler, 2002; Neumayer, 2004). According to this body of literature, creating a peaceful environment and political stability in a country like Sri Lanka is an important pre-condition to accelerate economic growth and inclusive economic development led by tourism during the post-war period.

As a result of risk in tourism at some destinations, there is another branch of tourism literature which focuses on volatility in international tourist arrivals. This relatively new body of research emphasizes the importance of examining the volatility in tourist arrivals with regard to policy and decision making in both the public and private sectors and future tourism planning (see Chan, Lim, & McAleer, 2005; Shareef & McAleer, 2005). To explore this association, Sri Lanka provides an opportunity for an excellent case study (Fernando, Bandara, & Smith, 2012). As is well-known, Sri Lanka has experienced political violence since the late 1970s. Including war, and terrorist attacks, this political violence, was particularly strong between 1983.and the end of war in May, 2009. As a result of this end of war, Sri Lanka is currently experiencing a tourism boom and revenue from this boom has become a main source of foreign exchange earnings which help to cushion the current balance of payment crisis in Sri Lanka. The Sri Lankan government therefore believes that international tourism can play a crucial role in the post war development of Sri Lanka (Ministry of Economic Development, 2011). Understanding volatility in tourism demand is vital in terms of planning and making strategic decisions by both the public and private sectors for future development in the tourism sector.

Although the Sri Lankan tourism sector has been growing since 1967, it has experienced significant fluctuations due to political violence (Fernando, Bandara, & Smith, 2013). Despite its importance in the Sri Lankan economy in terms of contribution to GDP, employment generation and foreign exchange earnings, there has not been much attention on the tourism sector with the exception of a small number of studies (Bandara, 1997; Buultjens, Ratnayake, & Gnanapala, 2015; Fernando et al., 2013; Fernando, Bandara, & Smith, 2016; Gamage, 1978; Gamage, Shaw, & Ihalanayake, 1997; O'Hare & Barrett, 1994; Selvanathan, 2006; Tisdell & Bandara, 2005; Wickremasinghe & Ihalanayake, 2006). These studies have been mainly in qualitative in nature, with the exception of Selvanathan (2006) and Wickramasinghe and Ihalanayake (2006). While Selvanathan (2006) analysed factors that determine international tourist arrivals to Sri Lanka, Wickramasinghe and Ihalanayake (2006) attempted to examine the causal relationship between tourism and economic growth in Sri Lanka.

Although international tourist arrivals to Sri Lanka have experienced dramatic fluctuations during the separatist war and a number of peace episodes during the period 1983-2009 (see more details Fernando et al., 2013), no systematic study has been carried out to examine the volatility that affects decision making and future planning of tourism. Given this the purpose of this chapter is to analyse the conditional variance (or volatility) in international tourist arrivals to Sri Lanka, focusing particularly on the period of nearly three decades of separatist war that included political violence and terrorist attacks. Such an analysis is useful for designing development and management strategies for the tourism sector which is expected to play a significant role in post war development in Sri Lanka.

3. Empirical Model for the Volatility of Tourism Demand in Sri Lanka

Following the above brief historical overview on risk of tourism of political violence in Sri Lanka, this section examines the volatility in international tourist arrivals to Sri Lanka precipitated by political violence and the separatist war. This will be achieved using a data set that comprises monthly international tourist arrivals from January 1967 to July 2012. For the purpose of this study the data set was assembled using various publications of the Sri Lanka Tourist Board and the Central Bank of Sri Lanka. This represents the most comprehensive monthly tourist arrivals data set created for Sri Lankan tourism to date. The data set comprises 547 observations.

Figure 1 plots the first difference of the logarithm of monthly tourist arrivals to Sri Lanka. At the beginning of the monitored period, an increase in total number of tourist arrivals is recorded, with three noticeable deeper drops observed, in the month of April 1971, August 1983 and August 2005. Furthermore, as seen in Figure 1, the numbers of tourist arrivals between 1983 and 2012 have proven to be highly volatile.

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Figure 1: First Difference of Monthly Arrivals in Sri Lanka January 1967 - July 2012 Source: Based on Sri Lanka Tourist Board, various annual reports

This finding is consistent with the general view in the tourism literature. As Neumayer (2004, p.261) points out, “events of political violence are likely to affect tourism both contemporaneously and with lagged effects”. He further points out that “because tourists are sensitive to the negative image of tourist destination, events of violence can affect a tourist destination, and events of violence can affect long after the event has passed and stability has, in effect, been restored”(Neumayer, 2004, p.262).

The time series also indicates a considerably pronounced seasonality with the focus of intensity of total arrivals in some months, especially in July and December, because of Sri Lanka weather condition and festival season. This is seen by a simple visual inspection of the natural logarithm of the total arrivals in Sri Lanka as shown in Figure 2.

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Figure 2: Monthly Seasonal Indices for Sri Lankan Tourism Demand Source: Based on Sri Lanka Tourist Board, various annual reports

3.1 Methodology

In analysing demand for tourism, a number of statistical and econometric models have been used in the literature. Song and Li (2008) have reviewed a large number of published studies on tourism demand modelling and forecasting since 2000. These models mainly include structural equation and time series techniques (Sinclair & Stabler, 1997; Song & Witt, 1999), VAR models and the cointegration technique (Sergo, Tomcic & Poropat, 2005) and AIDS models (De Mello, Pack, & Sinclair, 2002; Syriopoulos & Sinclair, 1993; White, 1985).

Tourism demand can be measured by either using the number of inbound tourist arrivals or using foreign exchange receipts from international tourism (Neumayer, 2004). Many studies have used the number of tourist arrivals to measure tourism demand. These studies have mainly forecasted the changes in the number of tourists over time and usually consider a random term which incorporates all the unknown effects on tourism demand over time. Until recently, the variability in the random component of tourism demand had not been of major concern to tourism researchers (as often is the case also in financial or another macroeconomics research). That is, apart from the standard approaches of testing for heteroscedasticity and/or serial correlation (Sergo, Poropat & Grzinic,2010). Heteroscedastic and/or serially correlated errors can lead to imprecise estimates of tourism demand, thereby reducing the forecasting performance of the models (Hoti, Leon, & McAleer, 2005). Several studies have found that tourism demand data exhibit volatility (Shareef & McAleer, 2005).

Although the Sri Lankan separatist war was well recognized as one of Asia’s longest running wars and the negative impact of the war on the Sri Lankan tourism sector is easy to observe, there has not been any serious attempt to quantify the impact of this war on tourism (Fernando et al., 2012). This is with the exception of Selvanathan (2006). Using an econometric model, Selvanathan (2006) found that there was a significant decline in tourist arrivals due to war and that the Sri Lankan tourism is sensitive to the exchange rate. In this study, we attempt to investigate the link between war, peace and tourism and other relevant variables further by looking at volatility.

3.2 Model Specification

As pointed out in McAleer and Divino (2008) and Chang, et al. (2009), the Generalized Autorregressive Conditional Heteroskedasticity (GARCH) model (proposed by Engle, 1982) can be used to explain time-varying conditional variances in financial and tourism data series. The generalized GARCH(p,q) model proposed by Bollerslev (1986) can be used “when the time-varying conditional variance has both autoregressive and moving average components” (Chang, et al., 2009). This technique has widely been used to estimate conditional volatility using monthly international tourism arrival data in recent years (see for example Chan, Lim, & McAleer, 2005; Hoti, Leon, & McAleer, 2005; Shareef & McAleer, 2005, 2007 & 2008; Divino & McAleer, 2008). Following this literature we use the GARCH model introduced by Bollerslev (1986) to analyse Sri Lankan monthly international tourism arrival data and to capture the volatility characteristics inherent within it.

In order to estimate the impact of some important variables on the number of tourist arrivals, the following model is used as the mean equation:

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where

ln y denotes the natural logarithm of tourist arrivals to Sri Lanka,

ln EXR and ln TPI are the natural logarithm of exchange rate and tourism price index respectively, and

WAR is a dummy variable equal to 1 if the country was in the peak of the civil war and 0 otherwise. After considering war related incidents, 1967-1983, 1992-1997, 2000-2001 and 2003-2007 periods were considered as peaceful time periods for which the dummy variable takes the value 0, with the value 1. assigned to other years. In addition, dummy variables were included to capture the effect of seasonal variation on tourism demand. There are eleven dummy variables included ranging from ܦଶ to ܦଵଶ where ܦଶ ൌ ͳ if the month is February, 0 otherwise. Likewise, the other seasonal dummy variables represent in turn each month except for January which is considered as the base category. Inclusion of these seasonal dummy variables within the model acts to remove seasonal fluctuations in the data. This enables us to be able to more easily isolate the impact of other events on tourist arrivals.

The GARCH (p,q) is used as the model for conditional variance of equation based on equation 1 above.

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The conditional variance is a function of four terms, namely [illustration not visible in this excerpt] and WAR. Among the two terms and [illustration not visible in this excerpt] , the first refers to the ARCH term and the second term captures the previous period forecast variance (hence the conditional variance). The variable WAR, as defined above, captures the impact of civil war on tourism volatility. If WAR =1 means that tourism sector is facing a negative impact and 0 means no such impact from the civil war. According to the GARCH model, [illustration not visible in this excerpt] are the necessary conditions to generate a positive number for the conditional variance. The sum of coefficients [illustration not visible in this excerpt] of the GARCH model should be less than 1 to guarantee that the conditional variance,[illustration not visible in this excerpt] is stationary.

3.3 Results

The maximum likelihood procedure is used to estimate the parameters of the above model using a data set collected from different sources in Sri Lanka covering the sample of monthly data from 1967 to 2012. Total number of tourist arrivals was taken from the Sri Lanka Tourism Development Authority (SLTDA) and annual reports of the Central Bank of Sri Lanka were used to obtain data on the exchange rate and the tourism price index. Table 1 shows the estimated parameters of the conditional mean equations and conditional variance equations according to the GARCH(1,1) volatility model.

The error term [illustration not visible in this excerpt] obtained from OLS estimation of the mean equation (1) and the estimated t volatility,[illustration not visible in this excerpt], are shown in Figures 3 and 4. The error term of the regression does not satisfy the condition of independent and identically distributed random variables, with a mean of 0, a variance of 1, and independent of y, k 1 for all t. In other words, the error term in the mean equation does not have the property of homoscedasticity in variance which shows autocorrelation in the error variance. This indicates that the error term has ARCH effects.

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Figure 3: Time Series Plot of OLS error term ࢿ࢚

Source: Based on Sri Lanka Tourist Board, various annual reports

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Figure 4: Volatility of Tourism Demand for Sri Lanka.

Source: Based on Sri Lanka Tourist Board, various annual reports

Changes in the variance of shocks to tourism demand over time are often called conditional or stochastic volatility. As a result of many factors that can affect the tourism market, it is clear that shocks to demand may not have the same variability over time. In the case of tourism, volatility may be present due to various unexpected factors which can affect tourist decisions, such as the severity of war, changes in disposable income, advertising campaigns, oil price shock, wealth effects, and other random events.

Figure 4 shows a volatility clustering phenomenon in which other large aftershocks continuously follow a larger shock. In addition, a large variation in t was noted during the period after 1978 to the present period. This reflects the existence of a large volatility in monthly tourist arrivals into Sri Lanka resulting from negative publicity in the print and electronic media and travel warnings issued by Western countries relating to war and peace in the country. These periods were clearly identified when the war was in its peak as well as when major attacks were carried out by the LTTE on Sri Lanka’s important economic and political targets in Colombo (the main economic and political centre of the country).

All the variables used in the estimates are tested for unit roots using the Augmented Dickey Fuller test. These unit root tests imply that all the variables integrated in order one. The seasonal unit root test is performed at different seasonal frequencies using the extended (Hylleberg, at el.,1990 ) [henceforth, HEGY (1990 )] procedure for the case of monthly data used in this study. The HEGY test is conducted by estimating the following regression,

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Where [illustration not visible in this excerpt] are auxiliary variables obtained by appropriately filtering the variable polynomial is a remainder with roots outside the unit circle which allowsכunder study. The [illustration not visible in this excerpt] the augmentation necessary to whiten the errors in the estimation of the above equation.

After estimating the above regression using the OLS, tests are conducted for [illustration not visible in this excerpt] in each frequency and joint test hypothesis to determine the presence of unit roots and seasonal unit roots (refer Table 1). In this case, the data do not reject the presence of unit roots at seasonal frequencies of 2,3,8,9, and 12 including the existence of a unit root at the zero frequency. This means that tourist arrival is non-stationary both at seasonal and non-seasonal frequencies. These results further imply that there is no deterministic cycle at these frequencies but there is a stochastic cycle.

Table 1: Regression Results to Test the Seasonal Unit Root Using HEGY Procedure

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Source: Authors’ calculations.

Notes: *** and ** are significant at the 0.01 and 0.05 level, respectively

We begin our discussion with the results obtained in the mean equation regression model. As shown in Table 2, lnEXR and WAR are significant at the 1% significance level. This positive relationship between lnEXR and tourist arrivals indicates that Sri Lanka will be more attractive for incoming tourists when the Sri Lankan rupee depreciates against US dollar. The variable that was created to capture the effects of war on tourist arrival indicates a negative coefficient - which is consistent with expectations, in particular the fact that tourist arrivals will on average be 5.2 per cent lower when the war is in the peak. What can thus be seen is that a major war related incident is strong enough to reduce the tourist arrival by 5.2 per cent per month compared to a period when peace is restored in the country. Although the tourism price index was not statistically significant, it shows a negative impact in explaining the tourist arrivals in Sri Lanka.

Table 2: The Estimated Results for Volatility model

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Source: Authors’ calculations.

Notes: *** and ** are significant at the 0.01 and 0.05 level, respectively

A significant seasonal effect is evident in data of tourist arrivals to Sri Lanka. Significant seasonal dummy variables with negative coefficients for April, May and September showed that there was a decline tourist arrivals in these months compared to months without seasonal variation. Peak levels of tourist arrivals are observed in November to January. An arguable reason for this result is the seasonal variations in those countries which generate the majority of Sri Lankan tourism, mainly European countries in the Northern Hemisphere. Their winter weather begins in November and ends in March, the same times in which Asian countries attract more tourists from Northern Hemisphere countries. This will apply to Sri Lanka too and, therefore, months from November to March can be considered as peak months with positive impacts in terms of tourist arrivals to Sri Lanka. The respective dummy variables in these months are significant at the 5% level and they do, as expected, have a positive sign. Apart from a mini-peak in tourist arrivals in July and August due to the cultural pageant in the Hill Capital of Sri Lanka, Kandy, months from April to September can be considered as a season with low tourist arrivals.

The GARCH (1, 1) parameters in the conditional variance model are stable in both high and low seasons because their sum is less than one and because the volatility shows a dampening pattern.

The coefficients on the lagged squared residuals , [illustration not visible in this excerpt] and WAR are significant at the 1% level

in both cases. The ARCH effect which is captured by the coefficient explains short term persistence of shocks on tourist arrivals while the GARCH captures long run persistence. The ultimate impact on the prevalence of volatility in the long run depends on the magnitude of The coefficient equals 0.5731 while [illustration not visible in this excerpt] is 0.1076 and the sum of all three [illustration not visible in this excerpt] is 0.7935. A

comparison of the two coefficients indicates that the ARCH effect is stronger than the GARCH effect implying that events which shock the tourism demand have very strong short run effect. Further, the

sum of coefficients, are less than one, which implies that a particular shock on tourism

demand has only temporary effects on the growth rate of tourism demand. In other words, the impact of deceasing tourism demand upon exhausting of a negative shock will only be quite short, and if nothing happened thereafter, it would restore the previous velocity. Since there was a sequence of events that innovated shocks including war, tourism demand remained highly volatile until 2009. The negative impact of these recurrent incidents of war, have had the effect of bringing the tourist industry to a critically low performance level compared to the other countries in the region. Given this, the policy implications of empirical results of the GARCH estimate highlight the importance of uncertainty on the regularity of monthly tourist arrivals. The impact of the war related incidents that have taken place in Sri Lanka were strong enough to create considerable uncertainty which has been captured by the volatility of the model.

4. Post-war Tourism Demand and Forecasting in Sri Lanka

Following the end of nearly three decades of brutal separatist war between the separatist rubbles and government security forces in Sri Lanka in May 2009, Sri Lanka has witnessed an unprecedented post-war tourism boom beyond its expectation. The number of international tourist arrivals to Sri Lanka has sharply increased breaking all previous historical annual and monthly tourist arrivals records. It can clearly be identified from Figure 5 that tourist arrivals to Sri Lanka fluctuated after 1983 during the different war and peace episodes.

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Figure 5 Tourist Arrivals to Sri Lanka and year on year growth from 1966 to 2015

Source: Based on Sri Lanka Tourist Board Annual Reports, various issues

After recognising the key role that the tourism industry can play in post-war development the Sri Lankan government launched a Tourism Development Strategy (TDS) with a five-year master plan for 2011-2016, setting a number of important targets centred on attracting a large number of international tourists. This demonstrates that the Sri Lankan government is very keen to accelerate economic development through tourism. It is also important for Sri Lanka to implement marketing and management strategies to rebuild its image as an attractive and safe tourist destination after decades of negative international publicity highlighting the ongoing political violence, the war and persistent acts of terrorism prior to 2009 as well as concerns about alleged human rights abuses in the final stages of the war. In addition to the TDS, Sri Lanka has launched a massive marketing campaign under the tourism branding slogan of “Sri Lanka - the wonder of Asia”. This strategy is important for Sri Lanka considering its effort to recreate its image and the competition it faces from other destinations in terms of attracting international tourists. As results of these influences, Sri Lanka Tourism has surged to a new high record of 1,798,380 arrivals in 2015, transcending all time high hits in the history. As a result, the experience of the short history of the post-war period shows that the tourism sector has now become a main driver of the Sri Lankan economy in terms of foreign exchange earnings, employment generation and attracting foreign direct investment (FDI). In 2015, tourism generated 319,436 both direct and indirect employment opportunities and Rs. 405,492 million (US$ 2,980.6 million) foreign exchange earnings in the Sri Lankan economy (SLTDA, 2015).

Against the above background, it is important to address some important issues related to the post- war tourism development, marketing and management strategies of Sri Lanka. Firstly, it is important to consider developing tourism demand model in Sri Lanka in order to recognise the TDS’s targets is meaningful. There has not been any explicit systematic quantitative studies, with rare exceptions such as Selvanathan (2006), exists in the Sri Lankan tourism literature aimed at setting targets based on empirical evidence. Secondly, it is important to identify the necessary development that need to be managed to accommodate a large influx of international tourists. Therefore, this part of the paper intends to make a number of contributions in terms of tourism strategy and management in Sri Lanka. The main contribution is to evaluate the projections of tourist arrivals stipulated by the Sri Lankan government by using a simple econometric model and further discuss the constrains that need to be fulfil to accommodate TDS targets.

4.1 Empirical Model for Tourism Demand

In this study attempt to extend the Selvanathan study to accommodate the post-war scenario and evaluate the TDS’s targets. According to Sevanathan’s (2006) results, while inbound tourist arrivals were badly affected by the separatist war, open economic policies introduced in 1977 have created a positive impact on tourism. Other variables such as the exchange rate, the consumer price index (CPI) of Sri Lanka and per capita world income for the period 1972 - 2002 were included in her model. Although the model developed in this paper is somewhat similar to Selvanathan (2006), it differs in a number of ways. Firstly, this model focusses on the role of tourism in post-war development in Sri Lanka. Secondly, this data series is different from Selvanthan (2006) and this study uses a more comprehensive data set which includes coverage across more than four decades. In particular, this study uses a data series for the period between 1966 and 2015, in contrast to the data series covered for a shorter period between 1972 and 2002 in Selvanathan (2006). Thirdly, this study uses directly a tourism price index rather than using a consumer price index as a proxy for changes in the cost of tourism. Finally, this study uses a dummy variable for the periods which showed upward trends or positive periodical growth in tourist arrivals (Table 1) as a result of the absence of the war instead of effect of war. This is the significant different with previous study as this study is going to predict the tourism arrivals in absence of the war.

Recent econometric studies of tourism demand have used similar variables found in the standard economic theory of demand. Therefore, the demand for tourism has been modelled as a function of tourists’ income, tourism prices in a destination relative to those in the origin country, tourism prices in the competing destinations, and exchange rates. In modelling the tourism sector, this study use the dependent variable as the annual aggregate international tourist arrivals, collected from the Sri Lanka Tourist Board for the sample period between 1966 and 2015 (Ceylon Tourist Board, 1975; SLTDA, 2012). These data were plotted in Figure 1. Following figures have been used for selecting dummy variable to represent the tourism arrivals in absence of the war.

Table 3 Tourism Growth during the War Peace Episodes

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Source: Based on Sri Lanka Tourist Board data for 1970 to 2013.

Table 3 presents average annual growth rates in international tourist arrivals during different episodes of peace which have generated the upward trends in Figure 5. These upward trends can be identified as a high level of tourist arrivals due to the absence of war and the implementation of tourism promotional campaigns. The lower part of Table 3 indicates that periodical growth rates in international tourist arrivals during the political violence and war which have generated the downward trends in Figure 5.

Most tourist flows have been for holiday and pleasure purposes, and therefore the determinants of demand for holiday trips are assumed to be the same for total tourist flows. Therefore, in order to examine the magnitude of the effects that these variables are likely to have on overall demand, the independent variables have been used as suitable variables in consistenc with previous studies, specially Selvanathan (2006). First, we use a dummy variable to capture the effect of peace and tourism growth during the past 46 years. The dummy variable for peace variable [illustration not visible in this excerpt] is defined as 1 in the years when there was a peace in Sri Lanka and 0 otherwise. In addition, other independent variables, the exchange rate [illustration not visible in this excerpt] the tourism price index [illustration not visible in this excerpt] for Sri Lanka, and per capita world income [illustration not visible in this excerpt] for the period 1966 - 2015 are the other variables in this model. While the average annual exchange rate published by the Central Bank (The Central Bank of Sri Lanka, 2013) is used in this study, the tourism price index published by the Tourism Development Authority (SLTDA, 2012) is used for the price index. The word per capita income is used as a proxy for disposable income.

First, the variables for the tourism demand model were selected based on the theory of demand. Second, the variables were tested for multicollinearity using the collinearity test. As shown in Table 2 LARRI is strongly correlated with the [illustration not visible in this excerpt]. The model dropped the [illustration not visible in this excerpt] due to the high collinearity with the [illustration not visible in this excerpt] and included latter only as is more appropriate proxy for the changes in inflation affecting tourists. The correlation between [illustration not visible in this excerpt] is not significant, therefore, shows no high multicollinearity problem. Insignificant collinearity among the all the independent variables included as regressors in the tourism demand model, passed the multicollinearity test.

Table 4 Correlation Matrix

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Source: Authors’ Calculations

In order to examine the relationship between the number of tourist arrivals and the above variables, and to evaluate the targets specified in the Sri Lankan TDS, the following simple regression model is used in this study:

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LTOURt is the numbers of tourist arrivals; LEXRt is the exchange rateǢ

TPRICEt is the tourism price indexǢ WPERCAP is the world per capita;

PEACEt is the dummy variable for peace variable.

The estimated model is given below and the detailed results, including those associated with various diagnostic tests, are shown in Table 5.

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As can be seen from Table 5 all three independent variables are statistically significant at the one per cent level. The exchange rate and tourist arrivals are positively related demonstrating, as expected, that depreciation of the exchange rate is good for tourism. This indicates that a depreciating currency serves as an important variable to improve the competitiveness of Sri Lanka as a major tourist destination in the Asian region. This is important in the context of the depreciation of the Sri Lankan rupee in early 2012 after the Central Bank of Sri Lanka gave up the maintenance of an

overvalued exchange rate. Our results strongly suggest that this recent depreciation will have a positive impact on the Sri Lankan tourist industry.

Table 5.OLS Regression Estimation Results

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Source: Authors’ Calculations

Notes: *** are significant at the 0.01 level

The empirical results also suggest that there is a negative relationship between increases in the tourism price index and the demand for tourism by international visitors. This demonstrates that the importance of managing the Sri Lankan tourism boom properly in order to avoid an escalation of cost in the tourism industry as it absorbs the projected expansion in tourism demand. Finally, the results strongly demonstrate that (with the lag of the dummy variable constructed to capture the impact of peace and war on tourism) tourist arrivals to Sri Lanka would increase by 23 per cent per year if a peaceful environment can be maintained. It is a necessary condition that Sri Lanka is to maintain the political stability and to accelerate the reconciliation process in order to make a peaceful environment as a tourism driving force of post-war economic development. The world per capita income shows a positive relationship with the demand for tourism in Sri Lanka.

The highly significant F-statistics as well as the high adjusted R2 shown in Table 6 demonstrate that the estimated model has excellent explanatory power. In addition, the first order lag term for the dummy variable eliminates the autocorrelation which would otherwise exist in our time series model.

4.2 Evaluation of the Targets

Using the estimated results of the econometric model in the previous section we can evaluate the feasibility of achieving the tourist arrival target of the TDS. According to TDS, it is clear that government’s growth strategy is to consolidate first and then, to target exponential growth which means that for the first four years the Government expects 12.9 per cent to 28.6 per cent per year growth rates. Thereafter the growth rate is anticipated as accelerating up to 48.1 per cent in 2015 and finally to be stabilised at around 25 per cent growth per annum in 2016 (see Figure 6 for more details).

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Figure 6. Expected tourist arrivals and growth 2010 to 2016.

Source: Based on tourism development plan 2011 - 2016 (Ministry of Economic Development, 2011) and Sri Lanka Tourism Annual Report (SLTDA, 2012)

According to Figure 6, actual tourist arrivals have been more than the expected against the target set for each of the past five years until 2014 and could not achieve the target in 2015. The average annual growth rate from 2009 to 2015 was 23 per cent and this is in line with our econometric estimations. It is also possible to compare our estimation with the government targets incorporated in the TDS for the 5-year period 2012-2016. Table 6 illustrates TDS targets and our estimation according to respective year on year (YoY) growth rates.

Table 6 Projected tourism arrivals to Sri Lanka according to the TDS and the model

illustration not visible in this excerpt

Source: Based on tourism development plan 2011 - 2016 (Ministry of Economic Development, 2011)and authors’ calculation

If other factors remain favourable (such as peace and political stability, exchange rates, price competitiveness, and the international environment) our empirical results suggest that TDS’s target of achieving 2.5 million tourism arrivals by 2016 is problematic. Although the targets are statistically feasible until 2014, it is important to evaluate the reasons of the TDS’s target in 2015 which was expecting 48% increase than the previous year.

The above data indicate that the Sri Lankan tourism industry was in a favourable position to achieve the targets until 2014 but the lack of political stability and international pressures to delays in reconciliation process have been affected to missing the targets in 2015 and it is a challenge to achieve of 2.5 million tourism arrivals by 2016.

5. Issues in tourism Sector in Sri Lanka

Our econometric analysis suggests that tourist arrivals to Sri Lanka would increase by 23 percent per year if peaceful environment exists in Sri Lanka that would be 2.3 million tourist arrivals in 2016. This is below with the targets set out in the TDS without systematic modelling work. Although the target would not be achieved properly, anticipated tourist arrivals would be real challenge in tourism sector in Sri Lanka (Fernando, 2016). A growing sector in an economy is always facing challenges and therefore, tourism sector as a growing sector in Sri Lanka is no exception. There are a number of impediments to achieving such targets and need to be addressing them by both tourism industry and government. Analysing tourism sector is more complicate as tourism differs from many other economic activities in that it makes use of a diverse range of facilities across a large number of industrial sector (Fernando, 2015). Comprehensive and reliable statistics are essential for policy- makers to make evaluate the efficiency and effectiveness of management decisions to support tourism development. It needs to be a solid data base, however, available data have a number of shortcomings even tourism arrivals in Sri Lanka.

Firstly, accommodation is more important for tourism and it should be fulfilled international tourism standards. The Sri Lankan hotel industry comprises tourist hotels that are graded establishments, along with other establishments such as guest houses and inns registered with the Sri Lanka Tourism Development Authority (SLTDA, 2012). According to the Sri Lanka Tourism Annual Report, the lodging establishments registered with the SLTDA amounted to 783 which provided 20,609 rooms as at 2012 and the annual hotel room occupancy rate was 70.1% (see Table 7 for more details).

Table 7: Accommodation Capacity

illustration not visible in this excerpt

Source: Annual statistical report of Sri Lanka tourism - 2015

The Sri Lankan tourism industry needs around 45,000 hotel rooms to accommodate the estimated 2.5 million tourists by 2016 (according to TDS). This represents a massive challenge (Fernando et al., 2016). The existing accommodation capacity in the Sri Lankan tourism sector is seems to be inadequate to cater anticipated tourism demand or target, since it is a difficult task to build a large number of hotel rooms in a very short period of time. However, numbers of arrivals are confusing for example, current tourism arrival figures are calculated according to the definition of tourist who spend 24 hours or more, these include many Sri Lankans with foreign passports, foreigners working with local organisations and spend time with business partners all of whom come from limited period less than one year that categorised tourist, however, they may not occupy in hotels room as a tourist guest. If all these are factored in, according to SLTD annual report 2015, only around two third of tourist arrivals are needed hotel rooms for their accommodation and others especially Sri Lankans those who have foreign passports, around 25 per cent of total tourism, most probably accommodate with their relations. In addition, the government has initiated some other community-based alternative accommodation development programs such as the ‘home stay’ program. However, higher-quality accommodations are necessary for Sri Lanka to attract quality high spending tourists. According to the SLTDA, only about 6,000 of the currently available rooms are of medium to high quality. Refurbishment of existing hotel rooms is one option to meet the requirements of the expected number of high-quality tourists.

Secondly, the tourism industry needs to be improved transport infrastructure facilities in order to meet the needs of the anticipated increase in tourism numbers. Despite the recent implementation of massive infrastructure development projects, Sri Lanka has a long way to go to catch up with other favoured Asian tourist destinations like Singapore and Thailand. The country is still lagging behind in terms of road and rail transport. Moving tourists from one location to another location within the country is still time-consuming due to poor infrastructure. Although Sri Lanka is an island, it is yet to have the facilities needed to promote and accommodate significant overseas tourism arrivals by sea. However, tapping into sea travellers (such as the cruise ship market) is at embryonic state. The port infrastructure in the North and the East was severely damaged by the war and rebuilding is necessary in order for tourism potential to be fully realised.

Thirdly, The TDS has identified two types of human resource gaps: the gap in the accommodation industry itself, and the gap in related services. The industry is facing a shortage of trained workers because of decades of neglect in training tourism workforce due to civil disturbances. As highlighted in an industry report, the tourism sector needs five times of the current workforce to cater for 2.5 million tourists by 2016 (Fernando et al., 2016). The hospitality related education and training facilities are not sufficient to train such high numbers of workers or to train workers at the level needed to compete effectively in the high end of the tourism sector.

Fourthly, according to the Tourism Development Strategy 2011 - 2016, the industry is expected to move towards premium prices with greater value addition attracting higher spending tourists. It is important that the country moves away from low-cost tourism and focuses on high-end tourism (Ministry of Economic Development, 2011). Although the country expects to move away from the low-cost tourism and focuses on high-end tourism, attracting quality tourists has been the main issue. There have been growing number of South Asian tourists and members of Sri Lankan diaspora compared with tourists from rich western countries. According to some recent estimates, 20 per cent of recent tourist arrivals are members of the huge Sri Lankan diaspora who are visiting friends and relatives (see Miththapala, 2012). According to the same source, only 82 percent of international arrivals in 2010 were “real tourists” who stayed in hotels.

Finally, rapidly increasing accommodation costs represent another constraint on meeting tourism targets. According to some recent reports, (Clearer Skies, 2011), the pricing of hotel accommodation is not competitive and Sri Lankan hotel accommodation is over-priced for its quality compared with its rivals. While Sri Lanka is more expensive than many other countries for four-star and five-star accommodation, it is more competitive in terms of the price of three-star rated beach resort hotels. These are generally not of a standard that is attractive to international tourists. The room-rates in Sri Lanka have gone up because of the post-war tourism boom such that comparable room rates in other tourist destinations like Thailand, Indonesia, Vietnam and Kenya are cheaper than Sri Lanka. Over the last few years, hotel charges have gone up by about 50 per cent (Clearer Skies 2011). Some believe that the government regulation of these charges represents an unhealthy intrusion into the sector and that it is important to allow rates to be determined by the market (The Nation, 2011). The tourist price index estimated by the Sri Lanka Tourism Development Authority (2010) shows that the index has increased by 5.3 per cent in 2010. Sri Lanka competes with other countries in South Asia and the Asia-Pacific region. According to our OLS regression results shows that there is a 10 per cent negative impact of the tourist price index on the demand for tourism in Sri Lanka. However, the overall tourist price index showed an increase of 5.3 per cent, when compared with the previous year. In absolute terms, it increased by 1,510 points from 4,940 in the 2009/2010 season, to 6,450 in the 2012/2013season (SLTDA, 2012). Prices of the accommodation sector increased by 3.9 per cent while the food & beverage sector increased by 6.1 per cent and the transport sector increased by 7.7 per cent (SLTDA, 2012).

In order to maintain competitiveness with other countries in South Asia and the Asia-Pacific region, Sri Lankan government has introduced a comprehensive policy framework for tourism development. Several key initiatives have been proposed such as setting up an Aquaculture park in Batticaloa, promote sale of gem and jewellery, encourage MICE tourism by establishing necessary infrastructure, encourage spending by tourists, transform and upgrade tourist attraction sites and local tourism zones, encourage theme parks and removal of tax for water sport equipment yachts etc. & introduce hovercrafts and other water based sports for tourists, tax holidays for investors With the view of improving operational efficiency and to facilitate investment a new organisation will be formed under the name “Agency for development”. Tourism branding plan, training and development for tourism youth, registration of tourist hotels based on quality standards etc.

As such Sri Lanka Tourism is confident that the new strategic direction will move the country forward making it the most sought-after travel destination in Asia while all stakeholders of the industry reap its benefits. It is very clear that within the next few years to come, tourism industry will become a sustainable sector in the Sri Lankan economy being the top GDP contributor within next few years. Sri Lanka will adopt a concerted and coordinated approach linking private and public sector and all stakeholders, to assess policies that govern future industry development and provide knowledge to guide successful and sustainable Travel & Tourism strategy for the country.

6. Conclusion

In the first econometric study of this paper, data on monthly tourism arrivals to Sri Lanka was used to model volatility of tourist arrivals for the first time in the case of Sri Lanka as a contribution to the tourism literature in Sri Lanka. As generally accepted, and in common with many other tourist destinations, the empirical results of this study demonstrate that the Sri Lankan tourism industry is very sensitive to political violence, exchange rate changes, and seasonal variations. The analysis suggests that significant increases in political violence lowered tourist arrivals in Sri Lanka and created a substantial amount of volatility in tourism demand.

Secondly, Sri Lankan tourism has managed to recover quickly since the end of war in 2009. After recognising the role of post-war tourism, the Sri Lankan government has launched the TDS for the period 2011-2016. The second study evaluated the post-war tourism boom and the targets of Sri Lankan government’s TDS. We empirically explored whether the targets set in the TDS by the Sri Lankan government are achievable or realistic by using a simple econometric model. A number of policy inferences can be drawn from this study. Given the limited resources available to the Sri Lankan government and the competing claims on these resources, development of strategies for and active promotion of public-private partnerships aimed at creating new tourism related infrastructure (hotel resorts, cruise line facilities, road transport upgrades, etc.) are recommended. There is should be a consideration for promoting the country as a price competitive and safe tourism destination. Finally, the country has to maintain political stability and work towards reconciliation process with improved governance and maintaining rules of law in order for the development strategy to be fully realised.

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Title
Demand, volatility and post-war tourism in Sri Lanka
College
University of Kelaniya
Author
Year
2016
Pages
25
Catalog Number
V370703
File size
584 KB
Language
English
Notes
Sriyantha Fernando is a Senior Lecturer in Department of Commerce and Financial Management, University of Kelaniya, Sri Lanka. He holds a Ph.D. from Griffith University, Australia, and Bachelor of Commerce and Master of Commerce from University of Kelaniya, Sri Lanka.
Tags
demand, lanka
Quote paper
Dr. Sriyantha Fernando (Author), 2016, Demand, volatility and post-war tourism in Sri Lanka, Munich, GRIN Verlag, https://www.grin.com/document/370703

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