**INTRODUCTION**

Tourism comprises the activities of persons traveling to and staying in places outside, their usual environment for not more than one consecutive year for leisure, business another purposes. Over the past several decades international tourism has gained distinct importance around the globe. World tourism recovered strongly in 2010 even exceeding the expectations. The tourists’ arrivals grew by 6.7 percent in 2010 against the 4.0 percent decline in the previous year – the year hardest hit by the global economic crisis. Similarly, tourism receipt remained at US $ 852 billion

in 2009 (UNWTO, 2010). In Nepal, despite the belated start of formal tourism after the restoration of democracy in 1952, it gained remarkable growth over the years. In 1962, 6,179 tourists1 travelled Nepal. Nowadays, Nepal caters more than half million tourists and earns foreign currency equivalent of about NRs. 16,825 million. The sector provides employment for about 20 percent of economically active population and contributes about 3.0 percent on gross domestic product (GDP).Tourism is one of the productive business activities directed for the production of the goods and services. It provides goods and services to the customers (visitors, generally foreigners) and employment and income to the locals. With this tourism business, enterprises and the people (related directly or indirectly) generate earnings from the operation of the tourism business activities. Further, tourism as an economic activity produces various direct, indirect and induced impacts in the economy. It ultimately increases the foreign exchange earnings, generates employment opportunity and increases income. Again, the resultant income flows being circulation in the economy, encourages for other economic activities to take place inducing many rounds of income. It also stimulates for the income and employment in other sectors of the economy. Tourism has various economic, social, cultural and environmental effects on tourism Destinations and the effect can be both positive and negative. The primary purpose of this article is to examine the relationship between tourism and economic growth of Nepal. It attempts to determine the relationship between earnings from tourism.

**REVIEW OF LITERATURE**

Tourism has burgeoned worldwide in the last two and half decades and outshined traditional industries to become one of the world’s largest and fastest growing economic activities. It emerged with a general consensus that it not only increases foreign exchange earnings but also creates employment opportunities. It also stimulates growth of the various industries and business and by the virtue of this triggers overall

economic growth. Despite of increasing importance of tourism, it has attracted relatively little attention in the literature in general and economic impact analysis in particular.

There are several models and software tools such as Multiplier model. Multipliers measure the effect of expenditures spent into an economy or the final change in output in an economy relative to the initial change in visitor expenditure. Tourism multipliers are used to determine changes in output, income, employment, balance of payment due to change in the level of tourism expenditures in the area. They are particularly used to capture the secondary economic (indirect and induced) effects of tourism activity. In mathematical terms, the multiplier effect can be calculated as:

Multiplier = 1 / (1 – C + M) ; where C = Marginal propensity to consume and NRB ECONOMIC REVIEW, M = Marginal propensity to imports.

There are some common multipliers such as income multiplier, employment multiplier and government revenue multiplier to measure the extra income, employment and revenue respectfully created by an extra unit of tourism expenditures.

Meanwhile, the Tourism Satellites Account (TSA) is also used to measure the contribution of tourism in the national economy. It has a link to the existing System of National Accounts (SNA) and is developed as an extension or satellite of the I-O framework of the SNA. It provides an estimate of overall value added through tourism and thus ascertain the extent of tourism’s contribution to gross domestic product. Though there are various economic impact models none of them can capture all dimensions and changes in the tourism industry and its actual impact in the overall economy. The choice of suitable model requires good judgment and considerable modification in the model. The causal relationship between tourism earnings and growth in developing economies has been of considerable interest among contemporary economists because of its tremendous policy implications. Despite the increasing importance of tourism to achieve the national economic goal, economic analysis has attracted relatively little attention in the Nepalese studies.

**METHODOLOGY**

To examine the role of tourism earnings on economic growth it is necessary to investigate whether tourism receipt causes economic growth or not. The model is specified as follows:

YR t = α 0 + ER t + ύ t ; ………. (1)

where, YR represents level of real GDP at time t, ER refers to the level of real foreign exchange earnings from tourism at time t, ύ t is the error term and t indicates the time period.

First of all, unit root test has been carried out to each series individually in order to provide information about the data being stationary. Non-stationary data contain unit root. The existence of unit root makes the results of hypothesis test unreliable as it create the problem of spurious. There are various methods such as Dickey Fuller (DF), Dickey Fuller (ADF), Durbin Watson test (CRDW), and Phillip-Perron (PP) to conduct unit root test. Here, Augmented Dickey-Fuller Test (ADF) has been applied to test for the existence of unit root and to determine the degree of differences in order to obtain the stationary series of GDPR and RFXET. The result is derived using Johansen Cointegration Test.

Johansen’s methodology takes its starting point in the vector autoregression (VAR) of order *p *given by **yt **= **μt **+ **At yt-1 **+ ……….+ **Ap yt-p **+ **εt **

where **yt **is an *n*x1 vector of variables that are integrated of order one – commonly denoted I(1) – and **εt **is an *n*x1 vector of innovations. This VAR can be re-written as **:**

**Δ yt = **Σ−

=

Γ

1

1

*p*

*i*

**i Δ yt-i + Πyt-p + μt + εt **………. (2)

Where, **Π **= Σ=

Α − Ι

*p*

*i*

*i*

1

and **Γt **= – Σ

+ =

Α

*p*

*j i*

*j*

1

In this test, the null hypothesis of *r *co-integrating vectors is tested against the alternative of *r*+1 co-integrating vectors. Thus, the null hypothesis *r *= 0 is tested against the alternative that *r *= 1 against the alternative *r *= 2, and so forth. Johansen proposes two different likelihood ratio tests of the significance of these canonical correlations and thereby the reduced rank of the Π matrix: the trace test and maximum eigenvalue test, as follows :

Jtrace ) / ( *p r *= –*T *( ) Σ

+ =

−

*n*

*i r*

*i*

1

ln 1 λˆ

Jmax (*r */ *r *+1) = -T ( ) 1

ln 1 ˆ + − *r*

λ

Where, *T *is the sample size and λˆ is the *i*:th largest canonical correlation. It is also to note that the co-integration tests are very sensitive to the choice of lag length. Following Cartavella-Jorda and Shamin and et. al. after confirmation of the existence of co-integration between the variables in the equation, the Granger Causality test has been performed.

The traditional practice in testing the direction of causation between two variables is the Granger causality test. According to Granger, X causes Y if the past values of X can be used to predict Y more accurately than simply using the past values of Y. In other words, if a past value of X improves the prediction of Y with statistical significance, then we can conclude that X “Granger Causes” Y. The Granger causality test consists of estimating the following equations:

*t*

*n*

*t i*

*t*

*n*

*t*

*t i t i YR *= +Σ *YR *+Σ *ER *+*U*

=

−

=

− 2 1

1

0 1 β β β ………. (3)

2 The error in DF test might be serially correlated. The possibility *Tourism and Economic Growth in Nepal*

23

*t*

*n*

*t i*

*t*

*n*

*t*

*t i t i ER *= +Σ *ER *+Σ *YR *+*V*

=

−

=

− 2 1

1

0 1 α α α ………. (4)

Where Ut and Vt are uncorrelated and white noise error term series. Causality may be determined by estimating Equations 3 and 4 and testing the null hypothesis that

Σ=

*n*

*i *1

ß1i =

0 and Σ=

*n*

*i *1

α 1i = 0 against the alternative hypothesis that

Σ=*n*

*i *1

β1i ≠ 0 and Σ=

*n*

*i *1

α 1i ≠ 0

for equations (3) and (4) respectively. If the coefficient of α 1i is statistically significant but β1i is not statistically significant, then YR is said to have been caused by ER (unidirectional). The reverse causality holds if coefficients of β1i are statistically significant while α 1i is not. But if both β1i and α 1i are statistically significant, then causality runs both ways (bi-directional).

The evidence of co-integration allows using a vector error correcting modeling of the data to formulate the dynamic of the system. If both variables YR and ER are co-integrated then there is a long run relationship between them. Of course, in the short run these variables may be in disequilibria, with the disturbances being the equilibrating error. The dynamics of this short run disequilibrium relationship between these two variables can be described by an error correction model (ECM). According to Engle and Granger, the Error Correction Model can be specified as follows

for any two pairs of test variables.

Δ *YRt *= + *p*1 *Zt–*1 + α 1 Δ FR*t *+ *U*1*t *………. (5)

Δ *FRt *= + *p*2 *Zt–*1 + ß1 Δ *YRt *+*U*2*t *………. (6)

Statistical significance tests are conducted on each of the lagged Zt term in Equations (5) and (6). The coefficients of the Zt reflect the short run disequilibrium in the model. The parameters, p1 and p2, are the speed adjustment parameters in equation (5) and (6) when there is a discrepancy from long run equilibrium.

**CONCLUSION**

The analysis about the relationship between tourism earning and economic growth exhibited the significant relationship between the variables. Using the concepts and methods of the unit root test, co-integration, Granger causality test and error correction method, the study confirms that there exists short-term dynamic relationship as well as long-run cointegrating relationship between tourism income and GDP.

In addition, the evidence seems to verify the notion that tourism growth granger causes economic growth and vice versa indicating a bi-directional causality between economic growth and tourism growth. It is clear that tourism growth increases economic activities and economic growth also facilitates for the expansion of tourism activities in the country. It is concluded that policy should be focused to develop tourism sector in order to achieve high economic growth