THE IMPACT OF POLITICAL VIOLENCE ON TOURISM IN NEPAL

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Sidharth Moktan

May 2015

THE IMPACT OF POLITICAL VIOLENCE ON TOURISM IN NEPAL

Sidharth Moktan

May 2015

Economics

Abstract

This study uses the Auto Regressive Distributed Lag (ARDL) framework to investigate the

relationship between tourism and political violence in Nepal in the presence of a structural

break. Using monthly time series data from January 1991 to December 2012, we find

results that suggest the existence of negative short run and long run relationships between

the two variables. The results of a dynamic ordinary least squares (DOLS) model

estimation suggests that our results are robust to differences in model specification. A

Toda-Yamomoto Granger Causality analysis suggests that political violence

Granger-causes tourism in Nepal. The stability of the long run estimates is tested using

CUSUM and CUSUMSQ tests.

KEYWORDS: Maoist, Nepal, Political Violence, Tourism, Terrorism, ARDL,

Cointegration, Time Series, Causality, CUSUM

JEL CODES: L83, C32

ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED

UNAUTHORIZED AID ON THIS THESIS

Sidharth Moktan

Signature

Contents

1 Introduction 1

2 Background 2

2.1 Empirical background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.2 Theoretical background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3 Data and Methodology 7

3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4 Results and Discussion 13

4.1 Preliminary testing and model specification . . . . . . . . . . . . . . . . . 13

4.2 Bounds testing and estimation of long run and short run relationships . . . . 15

4.3 Toda-Yamamoto Granger Causality . . . . . . . . . . . . . . . . . . . . . 18

4.4 Dynamic ordinary least squares . . . . . . . . . . . . . . . . . . . . . . . . 19

4.5 CUSUM AND CUSUMSQ Stability Tests . . . . . . . . . . . . . . . . . . 21

5 Conclusion 23

6 References 24

1 Introduction

Nepal, a small landlocked country situated in between China and India, has long been con-

sidered one of the top tourist destinations in Asia. 1 The country owes its popularity to its

rich flora and fauna, the abundance of Buddhist and Hindu heritage sites, and the Himalayan

range which is home to nine of the ten highest peaks in the world. The country has been

able to capitalize on its popularity and now derives a significant portion of its revenue and

employment from tourism. In 2013, the tourism sector accounted for 8.2 % of the country’s

GDP and employed 7% of the nation’s labor force making it one of the largest economic

sectors in the country (World travel and tourism council, 2014).

It stands to reason that the growth of the country should be strongly tied to the

performance of tourism. The existence of such a causal relationship is supported by Gau-

tam (2011) who uses cointegration analysis to find results that suggest that tourism causes

economic growth in both the short run and long run. Given the importance of tourism to

the welfare of Nepal, we believe that it is crucial for the country to formulate policies that

mitigate factors that are harmful to the industry.

In this paper, we examine the impact of one such factor, namely, political violence

and instability. We focus on the violence that erupted out of the decade long Maoist conflict.

The insurgency, which resulted in the deaths of 15,000 people and the internal displacement

of 200,000 people, was one of the most important events in the history of the nation (INSEC,

2007). The conflict, which mainly stemmed from the poor and marginalized people’s frus-

trations with income inequality, nepotism, favoritism, and discrimination within the political

and social system, played an integral role in transforming the socio-economic and political

structures of the country (INSEC, 2007 and Upadhyay et al., 2011).1Kathmandu, the nation’s capital, recently featured in trip advisor’s ranking of the top 10 places to visit in

Asia.

1

While the conflict was successful in bringing about much needed positive change

in many domains, including the empowerment of the poor and marginalized, it created an

atmosphere that was less than ideal for attracting tourists. Although the Maoists never di-

rectly targeted tourists, their conflicts with the government and other entities led them to be

involved in a number of activities that indirectly affected tourists. For instance, they bombed

and attacked multiple hotels and properties owned by the Ranas and Shahs whom they con-

sidered to be a part of the ruling class that had impeded the equitable growth of the country

(Upadhyay et al., 2011). Although, these bombings weren’t directed at tourists, they were

perceived as threats by foreign nationals who make up a large part of the clientele for these

hotels.

Our paper analyzes the extent to which tourism was affected by the violence per-

petrated by the Maoists. Our results suggest that the conflict had a significant and negative

impact on tourism in the short and long run. Given these findings, we believe that the gov-

ernment should formulate policies that prevent political violence and instability in the future.

We hope that our work can be used to inform such policy decisions.

The remainder of this paper is organized as follows. Section 2 introduces the

theoretical and empirical background, Section 3 explains the empirical methods, Section

4 presents the econometric results along with a discussion of its relevance, and section 5

concludes.

2 Background

2.1 Empirical background

The literature on the impact of violence on tourism has almost exclusively been focused

on the study of the impact of terrorism as compared to other forms of violence such as

2

armed conflicts, revolutions and political instability (Neumayer, 2004). The existing body

of literature provides contradicting information on the relationship between non-terrorist

forms of violence and tourism. Using a panel data approach, Neumayer (2004) finds that

political violence and unrest caused by non-terrorist activities have a significant impact on

tourism. This finding is contradicted by Fielding and Shortland’s (2010) time series study

which finds that local political instability does not significantly affect tourism in Egypt.

Measuring the impact of a slightly more intense form of conflict, Selvanath (2007) finds that

the Tamils Independence war had a significant and negative impact on the inflow of tourists

into Sri Lanka. The lack of studies on the impact of political violence on tourism combined

with the mixed results provided by existing studies highlight the need for more studies on

this topic.

As compared to literature on the impact of political violence on tourism, the studies

on the impact of terrorism on tourism is fairly extensive. The body of literature almost unan-

imously agrees on the significant and negative impact of terrorism on tourism. A number

of these studies employ the Auto Regressive Distributed Lag (ARDL) method of cointe-

gration developed by Pesaran and Pesaran (1997), Pesaran and Shin (1999), and Pesaran et

al. (2000, 2001) to test for the existence of a long run relationship between terrorism and

tourism. Using the ARDL procedure on annual time series data, Raza & Jawaid (2013) find

results that indicate the existence of short run and long run relationships between terrorism

and tourism in Pakistan. Employing the same methodology for annual time series data on

Turkey, Feridun (2013) finds that terrorism has significantly and negatively impacted Turk-

ish tourism in the short and long run. While both of these studies indicate that terrorism

has a negative impact on tourism, they provide very different estimates for the magnitude of

this impact. The presence of such a big difference between estimates from two studies that

3

employ the same methodology suggests that the results of these studies are not generaliz-

able and that country-specific characteristics have a significant influence on how terrorism

impacts tourism.

The significance of country-specific effects is further demonstrated by Drakos and

Kutan (2001). The paper employs the Seemingly Unrelated Regression (SURE) model on

monthly time series data to study the regional effects of terrorism on tourism in Turkey,

Israel and Greece. The authors find that the inflow of tourists into Turkey and Israel is more

sensitive to terrorism than Greece.

One strand of literature attributes these inter-country differences to the character-

istics of the host country and its surroundings. Mansfeld (1996) and Raza & Jawaid (2013)

suggests that a country’s level of involvement in its security situation is correlated with the

inflow of tourists into the country. In addition, Mansfeld (1996), Enders et al. (1992), Son-

mez (1998) and Raza & Jawaid (2013) argue that tourism is not only affected by the security

situation within the country but also by the conditions affecting the neighboring countries.

The factors outlined above could potentially explain the differences between countries since

variations in location, resource endowments and rates of development could lead to differ-

ences in these factors.

Another body of research suggests that these differences could be partially at-

tributed to differences in the characteristics of tourists. Cook and McCleary (1983), D’Amore

and Anuza (1986), and Raza & Jawaid (2013) argue that a tourist’s response to terrorism is

influenced by the tourist’s previous international experience. Furthermore, Mazursky (1989)

and Raza & Jawaid (2013) finds that travel behaviour is affected by the nature of previous

travel. Both of these findings could potentially explain the inter-country differences in the

response of tourists to terrorism since it is reasonable to expect that different countries attract

4

different types of tourists due to variations in location, visa restrictions and travel expenses.

As previously mentioned, there is a gap in the existing literature with regards to

studies that examine the impact of political violence and instability on tourism. Combined

with the contradictory findings made by existing papers, this shortcoming in the literature

provides an opportunity for us to make a contribution by examining the impact of the Maoist

revolution on the inflow of tourists into Nepal.

2.2 Theoretical background

We employ the theoretical framework developed by Enders, Sandler and Parise (1992) to

explain how terrorism affects the rational decision making process undertaken by tourists.

The model assumes a two stage budgeting process in which the consumer allocates his in-

come between broad consumption categories in the first stage and then divides the allocated

income in each category into category-specific expenditures in the second stage. For the pur-

poses of this paper, we will only present the sub maximization procedure that corresponds

with the second stage of the budgeting process because the first stage does not shed light on

how political violence affects a consumer’s decision to travel to certain countries. 2

The consumer’s overall budget constraint can be represented as follows.

IA = IT + IO (2.1)

Where IA stands for aggregate income, and IT and IO represent the income allocated to

tourism and non-tourism related activities respectively. Without loss of generality, we can

assume that the consumer can only choose between two tourist destinations. This allows us

to define the budget constraint and utility function for the sub maximization procedure as2The decision to travel to certain countries over others is an example of a consumer dividing her expenditure

within a broad category of consumption i.e., tourism. This decision corresponds with the second stage ofthe budgeting process.

5

follows:IT = QA · PA +QB · PB (2.2)

U = U(QA, QB) (2.3)

Where Q represents quantity, P represents price, and A and B correspond with destinations

A and B respectively. Enders et al. (1992) notes that the price of tourism is dependent on

income, the value of time, and risk factors. We thus define the prices of tourism as follows.

PA = PA(IT , V T,RA) (2.4)

PB = PB(IT , V T,RB) (2.5)

Where V T represents value of time and Rk is a measure of the degree of risk at location

k. Having defined the budget constraint, utility function and price for the sub maximization

procedure, we can now maximize the consumer’s utility as follows.

maxQA,QB

U(QA, QB) subject to IT = QA · PA +QB · PB (2.6)

Using the first order conditions, we derive the following demand functions.

q∗A = qA(PA, PB, IT ) (2.7)

q∗B = qB(PA, PB, IT ) (2.8)

Substituting the values of PA and PB from Equations 2.4 and 2.5 into the demand functions

yields the following.

q∗A = qA (PA(IT , V T,RA), PB(IT , V T,RB), IT ) (2.9)

q∗B = qB(PA(IT , V T,RA), PB(IT , V T,RB), IT ) (2.10)

The existing theory established by Enders et al. (1992) claims that an increase in risk caused

by violent activities will lead to an increase in the perceived price of travel to the country i.e.,

∂P∂R

> 0. Furthermore, the assumption of monotonic utility dictates that price is inversely

6

related to quantity demanded i.e., ∂q∂P

< 0. Combining these relationships, we can show that

an increase in risk caused by violence leads to a fall in quantity demanded in the following

manner.

∂q∗

∂R=∂P

∂R· ∂q

∗

∂P< 0 (2.11)

Having demonstrated the theoretical underpinnings that have been used to describe the rela-

tionship between violence and tourism, we proceed to describe the empirical methodology

that allows us to test the validity of this relationship.

3 Data and Methodology

3.1 Data

This study uses monthly data that spans the 21 year period from January 1991 to December

2012. The sample period captures the 5 years prior to the Maoist conflict, the full 11 years

during the conflict, and the 6 years following the conflict. The information on violence,

denoted by Vt, was collected from the global terrorism database and measures the number

of politically violent acts perpetrated by the Maoist party and its affiliates in a given month.

The information on tourism, denoted by Tt, was collected from the Nepal Tourism Board

and is measured as the total number of foreign nationals that entered the country in a given

month.

7

Figure 3.1: Combined plot of tourism and violence

Jan-91

Jan-92

Jan-93

Jan-94

Jan-95

Jan-96

Jan-97

Jan-98

Jan-99

Jan-00

Jan-01

Jan-02

Jan-03

Jan-04

Jan-05

Jan-06

Jan-07

Jan-08

Jan-09

Jan-10

Jan-11

Jan-12

0

20

40

60

80

100 TourismTrend

Violence

[1] The trend line is calculated as the average inflow of tourism during the 12 preceeding months[2] The unit of measurement for tourism is thousands

A combined plot of the tourism and violence series is presented in Figure 3.1.

The trend line for tourism shows that the inflow of tourists has generally tended to increase

over time. The series briefly departs from this trend for three years starting around 2001.

As depicted by the violence series, this departure coincides with the time period when the

Maoist attacks had just begun to intensify. The joint occurrence of these two events point to

the possible existence of a causal relationship between violence and tourism. Furthermore,

the graph indicates that it took over eight years for tourism to recover to pre-2001 levels.

This suggests that violence might have a long term impact on the inflow of tourists. We

conduct formal analyses to test for the existence of these relationships in the following

sections.

Figure 3.1 indicates that the tourism series might be non-stationary since it pos-

8

sesses a trend, exhibits seasonal variation and seems to have suffered from a structural break

around 2001. The existence of non-stationarity could pose challenges during the estima-

tion process and will most definitely influence the model selection process. The violence

series, on the other hand, seems to be stationary since the violent incidents seem to occur

sporadically. We will formally address these econometric issues in the methods section.

3.2 Methodology

Recent literature suggests that failure to account for structural breaks can produce spurious

results for unit root and cointegration tests (Zivot and Andrews, 1992; Pahlavani, et al.,

2006). As a result, we begin our analysis by using the Zivot-Andrews (ZA) procedure to

formally test for the presence of structural breaks in the tourism series. This procedure

involves testing the null hypothesis that tourism contains a unit-root with drift but does

not contain a structural break against the alternative hypothesis that the series is a trend

stationary process with a one-time break (Jayanthakumaran, 2007). We test the hypotheses

by sequentially estimating the following identity over different values of Tb.

Tt = µ+ θDUt(Tb) + βt+ γDTt(Tb) + αyt−1 +k∑j=1

cj∆yt−j + et (3.1)

WhereDUt(Tb) is a dummy variable that captures a level shift in time period Tb andDTt(Tb)

is a dummy variable that captures a trend shift in Tb. The dummy variables are defined as

follows: DUt = 1 if t > Tb and zero otherwise, and DTt = t − Tb if t > Tb and zero

otherwise. The null hypothesis is rejected if α is significant. Furthermore, the value of Tb

that yields the most significant t-ratio for α, tα, is selected as the point of structural break. If

the ZA procedure identifies a significant structural break in the tourism series, we augment

our cointegration model by including a dummy variable that accounts for the presence of

9

this structural break.

We use the Auto Regressive Distributed Lag (ARDL) method of cointegration, de-

veloped by Pesaran and Pesaran (1997), Pesaran and Shin (1999), and Pesaran et al. (2000,

2001), to test for the presence of a cointegrating relationship and to study the long run and

short run relationships between tourism and violence. We chose this method because of the

advantages it offers over other methods of cointegration. Firstly, unlike other methods that

can only be used when the underlying variables are I(1) or greater, the ARDL method can

be applied when the underlying variables are I(0), I(1), or a combination of both orders of

integration (Pesaran and Shin, 1999). The usefulness of this property will become apparent

when we specify our model. Secondly, the ARDL can be used in the presence of endo-

geneous independent variables (Pesaran and Shin, 1999). This property could potentially

prove to be useful in our estimation since a number of past studies have found the presence

of bi-directional causality between violence and tourism. Lastly, the ARDL model has better

small sample properties (Haug, 2002).

The ARDL approach involves estimating the following Unrestricted Error Correc-

tion Model (UECM) using Ordinary Least Squares (OLS)3.

∆Tt = ω0 +

p∑i=1

αi∆Tt−i +

q∑j=1

αj∆Vt−j + β1Tt−1 + β2Vt−1 + ω1Bi + ω2TRi + εt (3.2)

Where ω0 is the intercept, Bi is a structural break dummy variable that equals one if t is

greater than or equal to break point and zero otherwise, TRi is a trend term, εt is a white

noise error term, Tt is the number of tourists entering Nepal in month t, Vt is the number of

violent activities that occurred in the country in month t, ∆ is the first difference operator,

3In this section, we present the most general version of the ARDL model which includes a drift term, a trend,and a term for structural break. More restrictive versions of this model can easily be derived by setting thecoefficient of these terms to zero. The results of these restricted models will be presented in the resultssection.

10

and p and q are the maximum lags determined by minimizing an information criterion such

as the Schwarz Bayesian Criterion (SBC).

The coefficients on the lagged level terms, Tt−1 and Vt−1, represent the long run

relationship between tourism and violence and the coefficients on the first differenced lagged

terms, ∆Tt−1 and ∆Vt−1, represent the short run dynamics. Before we estimate and interpret

these relationships, we must ensure that a long run cointegrating relationship exists between

tourism and violence. This involves testing the null hypothesis H0 : β1 = β2 = 0 i.e., there

is no cointegrating relationship between violence and tourism. These hypotheses are tested

by comparing the calculated Wald F-statistic to the critical bounds reported by Pesaran et

al. (2001). Pesaran et al. (2001) reports bounds for five cases with different restrictions

on the trend and intercept. Similar to Feridun (2010), we analyze results for three of these

cases. The null hypothesis is accepted if the F-statistic falls below the critical value for

I(0), rejected if it falls above the critical value for I(1), and the result is inconclusive if the

F-statistic falls in between the values for I(0) and I(1). If the null hypothesis is rejected,

we can proceed to calculate the long run and short run coefficients of the model using the

estimated values of the ARDL model.

In order to estimate the long run relationship between tourism and violence, we

assume that the model is in equillibrium in the long run. This allows us to set the coefficients

on the first differenced terms in Equation 3.2 equal to zero. This yields the following long

run expression:

ω0 + β1Tt−1 + β2Vt−1 + ω1Bi + ω2TRi = 0 (3.3)

Which can be rearranged to generate our coefficients of interest as follows.

Tt−1 = −(ω0

β1+β2β1

Vt−1 +ω1

β1Bi +

ω2

β1TRi

)(3.4)

11

The term −β2β1

represents the long run relationship between violence and tourism. If −β2β1

is

negative and significant, we can conclude that a single act of violence significantly reduces

the inflow of tourists into Nepal by an amount equal to the magnitude of the coefficient in

the long run.

Using these long run coefficients, we can estimate the following Error Correction

Model (ECM) and generate the coefficients for the short term.

∆Tt = φ0 +

p∑i=1

φi∆Tt−i +

q∑j=1

φj∆Vt−j + ηECTt−1 + µt (3.5)

Where the lagged error correction term, ECTt−1, is generated as follows:

ECTt−1 = Tt−1 − ˆTt−1 (3.6)

= Tt−1 −(−ω0

β1− β2β1

Vt−1 −ω1

β1Bi −

ω2

β1TRi

)(3.7)

The coefficient φj in Equation 3.5 represents the relationship between violence and tourism

in the short run. If φj is negative and significant, we can conclude that a single act of vi-

olence significantly reduces the inflow of tourists into the country by an amount equal to

the magnitude of the coefficient. The coefficient η on ECTt−1 represents the speed of ad-

justment to the long run equillibrium following a shock in the short run (Raza and Jawaid,

2013). If −1 < η < 0, we can conclude that the model converges to the long run equillib-

rium and that a shock in the current time period is corrected by η in each subsequent time

period until the shock is completely corrected. Alternatively, 0 < η < 1 indicates that the

model is convergent to the long run equillibrium but a shock is reinforced in the following

time periods until it decreases to zero , and −1 > η > 1 indicates that the model does not

converge and that the error is magnified in subsequent time periods until the values explode.

In the following section, we implement the tests outlined above and discuss the

relevance of the findings.

12

4 Results and Discussion

4.1 Preliminary testing and model specification

We start by checking for the presence of a structural break in the tourism series. The result of

the Zivot-Andrews test reported in Table 4.1 suggests that a statistically significant structural

break occurred in the tourism series in April 2001. As a result, we include a dummy variable

in our cointegration model to account for this break.

Table 4.1: Zivot-Andrews test

Break Estimated Asymptotic Critical Valuespoint t-statistic 1% 5% 10%

April 2001 -6.22 ∗∗∗ -5.57 -5.08 -4.82

Notes:[1] The model used to calculate the t-statistic includes a drift and trendterm[2] *** Denotes the null hypothesis of unit root with no structuralbreak can be rejected at the 1% level

We now check the orders of integration of the underlying variables to ensure that

the ARDL model can be used in our analysis. The ARDL model can be used when the

underlying regressors are I(0), I(1) or a combination of both orders of integration. How-

ever, it does not produce reliable estimates when regressors are I(2) or higher. The results

of the Modified Dickey Fuller (MDF) and Phillips Perron (PP) tests reported in Table 4.2

indicate that none of our variables are I(2) or higher. Both the MDF and PP tests indicate

that violence does not contain a unit root and is I(0). This result is consistent with our un-

derstanding of political violence as an activity that does not follow a specific trend and tends

to revert to a stationary mean of zero. The two tests give conflicting results for tourism,

possibly due to differences in the powers of the tests and the somewhat arbitrary nature of

the lag selection process (Bender et al., 2006). The MDF test shows that tourism contains a

13

unit root and is I(1), whereas, the PP test shows that tourism does not contain a unit root and

is I(0). The result of the PP test is a little surprising since Figure 3.1 shows the existence of

seasonality and a time dependent trend in the tourism series. The result of the MDF test is

more consistent with our understanding and expectation of the tourism series. Regardless of

which test produces the more accurate results, we can use the ARDL procedure since both

of our variables are lower than I(2).

Table 4.2: Results for Modified Dickey Fuller and Philips Perron tests

Variables Modified Dickey Fuller (MDF) Philips Perron (PP)I(0) I(1) I(0) I(1)

Tourism 0.97 -2.75 ∗∗∗ -2.57 ∗∗∗ -20.44 ∗∗∗

Violence -5.53 ∗∗∗ -10.99 ∗∗∗ -9.25 ∗∗∗ -24.38 ∗∗∗

Notes:[1] *** denotes that the null hypothesis of a unit root is rejected at the 1% level

Having found results that indicate that our variables possess the required stationar-

ity properties, we proceed to specify the lag structure for our model. We use the Akaike In-

formation Criterion (AIC), Hannan-Quinn Information Criterion (HQIC) and the Schwarz-

Bayesian Information Criterion (SBIC) to inform our lag selection process. The results

reported in Table 4.3 show that all three information criteria are minimized at the first lag.

Therefore, we include all of our variables at the first lag.

Table 4.3: Lag length selection

Lag Order AIC HQIC SBIC0 63.23 63.26 63.311 -145.84 ∗ -145.61 ∗ -145.26 ∗

2 -142.09 -141.76 -141.273 -138.89 -138.46 -137.82

Notes:[1] * Denotes the lag order that minimizes each informationcriterion

14

4.2 Bounds testing and estimation of long run and short run

relationships

As explained in section 3.2, the first step in assessing the relationship between tourism and

violence is to conduct a bounds test to examine whether a cointegrating relationship exists

between the two variables. The results for the bounds test reported in Table 4.4 indicate that

a strong cointegrating relationship exists between tourism and violence. The persistence of

this relationship across the three different models suggests that the result is robust. These

findings provides strong support for our hypothesis that the political violence during the

Maoist conflict had a valid long run impact on tourism.

Table 4.4: ARDL bounds testing

Estimates Pesaran and Shin Bounds2

Model F-stat 1% 5%I(0) I(1) I(0) I(1)

π1 77.27 ∗∗∗ 6.34 7.52 4.87 5.85π2 23.26 ∗∗∗ 5.15 6.36 3.79 4.85π3 4.74 ∗∗ 3.88 5.30 2.72 3.83

Notes:[1] Model π1 contains unrestricted drift and trend terms,model π2 includes an unrestricted drift term and no trend,and model π3 contains no drift or trend terms[2]The table reports bounds for models with two regressors[3] *** Denotes the null hypothesis of no cointegration canbe rejected at the 1% level, and ** denotes that the nullhypothesis can be rejected at the 5% level

Having found support for the existence of cointegration, we proceed to estimate

the long run and short run relationships between tourism and violence. The long run results,

reported in Table 4.5, indicate that Violence has a significant and negative impact on tourism

in Nepal. The coefficient estimate indicates that a single act of political violence reduces

the inflow of tourists by approximately 1,045 people in the long run. We can thus infer that

the 25 counts of political violence that took place in February 2006 led to an approximate

15

reduction in the number of tourists by 26,125 people 4 in the long run, which is equivalent

to 6.8% of the tourists that entered the country in that same year.5 These estimates suggest

that political violence might have caused serious setbacks to the tourism industry of Nepal.

Table 4.5: Long run results using ARDL

Variables CoefficientsConstant 17,366.01 ∗∗∗

(10.18)

Vt−1 -1,045.33 ∗∗∗

(-2.06)

Break -22,305.89 ∗∗∗

(-6.85)

Trend 242.21 ∗∗∗

(10.25)

Adjusted R2 0.30F-test 19.80Durbin’s Test 17.04Engle’s Multiplier 0.68

Notes:[1] *** Denotes significance at the 1%level[2] z-scores are reported in paranthesesbelow the coefficient estimates. Thez-scores were calculated using the deltamethod[3] The standard errors were estimatedusing the newey-west procedure

The coefficient estimate for the structural break term suggests that the structural

break of April 2001 had a significant and negative impact on tourism. This finding supports

our earlier observation of how tourism sharply fell following the rise in the violence perpe-

trated by the Maoists. Furthermore, the coefficient estimate for the trend term suggests that

tourism tends to increase by approximately 242 people over each month.4The number of tourists lost was caluculated as the product of 25 and 1,0455The approximate percentage loss in tourists is calculated as follows: −1045∗25

TY ear06

16

To check the validity of our model, we test for the presence of serial correlation and

Auto Regressive Conditional Heteroskedasticity (ARCH) effects. The results for Durbin’s

Alternative test indicates that our model suffers from serial correlation. We correct for this

by estimating Newey-West standard errors. The results for Engler’s Lagrange Multiplier test

indicates that our model does not contain ARCH effects.

Table 4.6: Short run dynamics

Variables Coefficients

Constant 100.41(0.19)

∆ Vt−1 -236.42 ∗

(-1.77)

ECTt−1 -0.65 ∗∗∗

(-12.57)

∆ Tt−1 -0.42 ∗∗∗

(-8.30)

Adjusted R2 0.31F-score 40.02

Notes:[1] *** Denotes significance atthe 1% level, ** denotessignificance at the 5% level,and * denotes significance atthe 10% level

The estimation results of the short run model are reported in Table 4.6. Similar to

the long run estimates, the short run estimates indicate that violence has a significant and

negative impact on the inflow of tourists in the short run. The coefficient estimate for the

lagged term of violence, ∆Vt−1, shows that a single act of political violence reduces the

inflow of tourists by approximately 236 people in the short run. Once again, we can use

the events of February 2006 to show that the 25 counts of violence that occurred in that

17

month led to an approximate reduction of tourists by 5,900 people6 in the short run, which

is equivalent to approximately 23% of the tourists that entered Nepal in that month.7 On

a percentage basis, these results show that violence has a bigger impact on tourism in the

short run than in the long run. This finding is consistent with our expectations since a recent

event of violence is more likely to deter tourists from visiting a country than an incident that

occurred years in the past.

Furthermore, the results in Table 4.6 show that the error correction term, ECTt−1,

is statistically significant and negative. The estimate indicates that the model corrects a

disturbance felt in period t by 65% in the following period. We can thus conclude that the

model seems to converges relatively quickly to the long run equilibrium following a shock.

4.3 Toda-Yamamoto Granger Causality

While the ARDL procedure outlined above allows us to detect the presence of a cointegrat-

ing relationship between tourism and violence, it does not give us information on the direc-

tion of this long run relationship. We use the Granger Causality test procedure developed by

Toda and Yamamoto (1995) to uncover this direction of causality. The Toda-Yamamoto pro-

cedure involves estimating the following system of equations with the Seemingly Unrelated

Regression (SURE) technique.

Tt = α1 +m+d∑i=1

γ1,i∆Tt−i

m+d∑i=1

γ2,i∆Vt−i + ε1,t (4.1)

Vt = α2 +m+d∑i=1

λ1,i∆Tt−i

m+d∑i=1

λ2,i∆Vt−i + ε2,t (4.2)

Where the error terms ε1,t and ε2,t are white noise error terms, m is the maximum lag length

determined by minimizing an information criterion such as the Schwarz Bayesian Criterion,6The number of tourists lost was caluculated as the product of 25 and 2367The approximate percentage loss in tourists is calculated as follows: −236∗25

TFeb,06

18

and d is the highest order of integration among all of the underlying variables in the model.

The procedure involves testing the two null hypotheses H0 : γ2,i = 0 i.e., violence

does not Granger cause tourism and H0 : λ1,i = 0 i.e., tourism does not Granger cause

violence.

Table 4.7: Results of the Toda and Yamamoto Granger Causality tests

Dependent Variable Wald’s Chi-SquaredTourism Violence

Tourism - 41.99(0.00)

Violence 22.35 -(0.10)

Notes:[1] The lag lengths for Tourism and Terrorism are 15 asper the Schwarz Bayesian Criteria[2] p-values are reported in parantheses below the F statistics

The results of the Toda-Yamamoto Granger causality test, reported in Table 4.6,

indicate that unidirectional Granger causality exists between the two variables and that this

causality flows from violence to tourism. The existence of Granger causality provides ad-

ditional support for the existence of a valid relationship between violence and tourism. The

unidirectional nature of the causal relationship tells us that political violence in Nepal is

not determined by the number of tourists entering the country. This is consistent with our

expectations since the people’s war was not concerned with the terrorization of foreigners

but with the upliftment of the poor and down trodden.

4.4 Dynamic ordinary least squares

We compare the ARDL estimates with estimates generated from the Dynamic ordinary least

squares (DOLS) method, Stock and Watson (1993), to check if our findings are robust to

differences in model specification. The DOLS method involves estimating the dependent

19

variable as a function of the level value of the independent variable and the leads, level and

lags of the first difference of the independent variable. The addition of the leads and lags

corrects for endogeneity and serial correlation (Stock and Watson, 1993). The following

equation is used to estimate the dynamic ordinary least squares model.

Tt = α0 + α1Vt +

p∑i=−p

φpVt−p + α2TRt + α2Bt + εt (4.3)

The results for the DOLS estimation are reported in Table 4.8.

Table 4.8: Dynamic ordinary least squares

Variables Coefficients

Constant 17,910.5 ∗∗∗

(12.53)

V -1,409.64 ∗∗∗

(-3.60)

∆Vt−1 979.62 ∗∗∗

(3.24)

∆V 1,342.02 ∗∗∗

(3.94)

∆Vt+1 129.09(0.43)

Trend 232.22 ∗∗∗

(13.58)

Break -20,441.47 ∗∗∗

(-7.42)

Adjusted R2 0.52F-stat 36.30

Notes:[1] *** Denotes significance at the 1% level[2] t-scores are reported in parantheses below thecoefficient estimates

Although we estimated the model using 2 leads and lags of violence, we only

20

report results for the 1st lead and lag for the sake of brevity. The results indicate that the

estimates of the ARDL procedure are robust to model differences. The DOLS estimates

indicate that violence reduces the inflow of tourists by approximately 1,410 people in the

long run. This estimate is very close to the estimate of 1,045 people made by the ARDL

model. This similarity of results provides further support for the robustness of our findings.

4.5 CUSUM AND CUSUMSQ Stability Tests

We use the CUSUM and CUSUMSQ tests, proposed by Brown et al., (1975), to check the

stability of our parameter estimates over time. It is important to verify the constancy of

parameter estimates because this knowledge allows us to judge the reliability of our findings

for future time periods. This information might be especially useful to policy makers as it

provides information on whether political violence can be expected to have a similar impact

on tourism in the long run.

Figure 4.1: Cumulative sum of recursive residuals

Jan-92

Jan-93

Jan-94

Jan-95

Jan-96

Jan-97

Jan-98

Jan-99

Jan-00

Jan-01

Jan-02

Jan-03

Jan-04

Jan-05

Jan-06

Jan-07

Jan-08

Jan-09

Jan-10

Jan-11

Jan-12

−40

−20

0

20

40

[1] The thick line represents the cumulative sum of recursive residuals[2] The dashed lines represent critical bounds at the 5% significance level

The CUSUM test involves recursively plotting the cumulative sum of residuals

21

against the break points. The coefficient estimates are said to be stable if the CUSUM

statistic stays within 5% significance level (portrayed by two straight lines whose equations

are given in Brown et al. (1975, Section 2.3)). A similar procedure is used to carry out the

CUSUMSQ which is based on the squared recursive residuals. The two tests allow us to

examine different aspects of the stability of parameter estimates. The CUSUM test detects

systematic changes in the regression coefficients while the CUSUMSQ test is used to capture

sudden departure from the constancy of regression coefficients (Khan & Hye, 2013).

Figure 4.2: Cumulative sum of squares of recursive residuals

Jan-92

Jan-93

Jan-94

Jan-95

Jan-96

Jan-97

Jan-98

Jan-99

Jan-00

Jan-01

Jan-02

Jan-03

Jan-04

Jan-05

Jan-06

Jan-07

Jan-08

Jan-09

Jan-10

Jan-11

Jan-12

0

0.5

1

[1] The thick line represents the cumulative sum of squares of recursive residuals[2] The dashed lines represent critical bounds at the 5% significance level

The results of the CUSUM and CUSUMSQ tests are dispayed in Figures 4.1 and

4.2 respectively. Figure 4.1 shows that the CUSUM plot stays within the 5% critical bounds.

This suggests that there are no systematic changes in the regression coefficients during the

time period under study. On the other hand, Figure 4.2 shows that the CUSUMSQ plot does

not stay within the 5% critical bounds. This suggests that the regression coefficients have a

tendency to suddenly depart from the constancy of regression coefficients during this time

period. However, this departure from constancy is only temporary as the CUSUMSQ plot

22

returns to the 5% bands around 2011. This suggests that the short term deviation of the

parameter estimates is only transitory in nature. Combined, these findings imply that the

regression coefficients tend to fluctuate around the constant value in the short term but that

the constant value does not undergo any significant systematic shifts in the long run. This

suggests that our findings might not be suitable for short run forecasts but that they provide

reliable estimates for the long run.

5 Conclusion

Our results indicate that political violence has had a significant and negative impact on

tourism in Nepal. We use the ARDL framework to test for the existence and magnitude of

this effect. Our findings suggest that a single act of violence leads to a reduction in tourism

by 236 people in the short run and 1,045 people in the long run. In addition, the Toda-

Yamamoto procedure indicates that political violence Granger-causes tourism. Our findings

are consistent with Neumayer (2004) and Selvanath’s (2010) findings which indicate that

political violence has a negative and significant impact on tourism.

Given Gautam’s (2011) findings which indicates the existence of a causal rela-

tionship between tourism and economic growth in Nepal, we believe that it is of utmost

importance for the government to formulate policies that eliminate factors that deter tourism

within the country. Our study identifies political violence as one such deterrent. To mini-

mize political violence, we believe that the government should devise non-violent methods

of appeasing and quelling protestors. Furthermore, the government should formulate poli-

cies aimed at alleviating poverty, discrimination, inequality and other conditions that have

historically been known to create breeding grounds for protests and violent revolutions.

The results of our study should be interpreted with caution since there are a num-

23

ber of factors other than political violence that could have affected the inflow of tourists into

the country during the same time period. We leave it to future researchers to identify and

examine the impact of such factors. We believe that the ongoing energy crisis provides a

particularly interesting opportunity for researchers to examine the impact of infrastructural

deficiencies on tourism. Furthermore, the abolition of the 240 year old monarchy in 2008

provides an opportunity to study the impact of a dramatic political regime shift on tourism.

References

[1] Bender, K.A., Neumann, R.M., & Skatun, J.D. (2006). Unemployment and other mea-

sures of labor market inefficiency: a comparison of UK and US labor markets 1931-96.

Economic Inquiry. 44. 629-43.

[2] Brown, R. L., J. Durbin, & J. M. Evans. (1975). Techniques for Testing the Constancy

of Regression Relations over Time. Journal of the Royal Statistical Society, Series B.

37. 149-192.

[3] Cook, R.L. & McCleary, K.W. (1983). Redefining vacation distances in consumer

minds. Journal of Travel Research. 22 (2). 31-34.

[4] D’Amore, L.J. & Anuza, T.E. (1986). International terrorism: implications and chal-

lenge for global tourism. Business Quarterly. 20-29.

[5] Drakos, K. & Kutan, AM. (2003). Regional effects of terrorism on tourism in three

Mediterranean countries. Journal of Conflict Resolution. 47. 621-41.

[6] Enders, W., Sandler, T. & Parise, G.F. (1992). An econometric analysis of the impact

of terrorism on tourism. Kyklos. 45. 531-554.

24

[7] Feridun, M. (2011). Impact of terrorism on tourism in Turkey: empirical evidence from

Turkey. Applied Economics. 43 (24). 3349-3354.

[8] Fielding, D. & Shortland, A. (2010). How Do Tourists React to Political Violence?:

An Empirical Analysis of Tourism in Egypt. Discussion Papers of DIW Berlin 1022.

German Institute for Economic Research.

[9] Gautam B.P. (2011). Tourism and Economic Growth in Nepal. NRB Economic Review,

Nepal Rastra Bank, Research Department. 23 (2). 18-30.

[10] Government of Nepal - Ministry of Culture, Tourism & Civil Aviation. (2013). Nepal

Tourism Statistics 2012. Nepal.

[11] Haug, A. (2002). Temporal aggregation and the power of co-integration tests: a Monte

Carlo study. Oxford Bulletin of Economics and Statistics. 64. 399-412.

[12] Informal Sector Service Centre (INSEC). (2007). Nepal Human Rights Year Book

2006. Kathmandu.

[13] Jayanthakumaran, K., Pahlavani, M. & Neri, F. V. (2007). Trade reforms and break-

points in Australia’s manufactured trade: an application of the Zivot and Andrews

model. International Journal of Applied Econometrics and Quantitative Studies4 (2).

69-78.

[14] Khan R.E.A & Hye Q.M.A. (2013). Financial Liberalization and Demand for Money:

A Case of Pakistan. The Journal of Developing Areas 47(2). 175-198.

[15] Lutkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. New York.

Springer.

[16] Mansfeld, Y. (1996). Wars, Tourism and the "Middle East" Factor. In: Pizam, A.,

Mansfeld, Y. (Eds.), Tourism, Crime and International Security Issues. Wiley, New

York. 265-278.

25

[17] Mazursky, D. (1989). Past experience and future tourism decision. Annals of Tourism

Research. 16. 333-344.

[18] National Consortium for the Study of Terrorism and Responses to Terror-

ism (START). (2013). Global Terrorism Database [Data file]. Retrieved from

http://www.start.umd.edu/gtd

[19] Neumayer, E. (2004). The impact of political violence on tourism : dynamic econo-

metric estimation in a cross-national panel. London: LSE Research Online.

[20] Pahlavani, M. & Harvie, C. (2006). Sources of Economic Growth in South Korea: An

Application of the ARDL Analysis in the Presence of Structural Breaks âAS 1980-

2005. Department of Economics. University of Wollongong.

[21] Pesaran, M.H. & Pesaran, B. (1997). Working with Microfit 4.0: Interactive Econo-

metric Analysis. Oxford University Press, Oxford.

[22] Pesaran, M.H. & Shin, Y. (1999). An autoregressive distributed lag modeling approach

to co-integration analysis. In: Strom, S. (Ed.), Econometrics and Economic Theory in

the 20th Century: The Ragnar Frisch Centennial Symposium. Cambridge University

Press, Cambridge.

[23] Pesaran, M.H., Shin, Y. & Smith, R.J. (2000). Structural analysis of vector error cor-

rection models with exogenous I(1) variables. Journal of Econometrics 97. 293-343.

[24] Pesaran, M.H., Shin, Y. & Smith, R.J. (2001). Bounds testing approaches to the analy-

sis of level relationships. Journal of Applied Economics. 16. 289-326.

[25] Raza, S.A., & Jawaid, S.T. (2013). Terrorism and tourism: A conjunction and ramifi-

cation in Pakistan. Economic Modelling. 33. 65-70.

[26] Selvanathan, S. (2007). The effect of war and other factors on Sri Lankan tourism.

Applied Economics Letters. 14 (1). 35-38.

26

[27] Sonmez, S. (1998). Tourism, terrorism, and political instability. Annals of Tourism

Research. 25 (2). 416-456.

[28] Stock, J. & Watson, M.W. (1993). A simple estimator of cointegrating vector in higher

order integrated systems. Econometrica. 6 (4). 783-820.

[29] Toda, H.Y. & Yamamoto, T. (1995). Statistical Inference in Vector Autoregressions

with Possibly Integrated Processes. Journal of Econometrics. 66. 225-250.

[30] Upadhyay P.K., Muller-Boker.U. & Sharma S.R. (2011). Tourism Amidst Armed Con-

flict: Consequences, Copings, and Creativity for Peace-building Through Tourism in

Nepal. The Journal of Tourism and Peace Research. 1 (2). 22-40.

[31] World Travel and Tourism Council. (2014). The Economic impact of travel and tourism

2014 Nepal.

[32] Zivot E. & Andrews D.W.K. (1990). Further Evidence on the Great Crash, the Oil

Price Shock, and the Unit Root Hypothesis. Cowles Foundation Discussion Papers

944, Cowles Foundation for Research in Economics Yale University.

27