4.3.1 Tourism trend
An exercise
of finding the goodness of fit of the model to annual
data led to apply either the compound or the exponential functions due to the reliability in results. The trend
functions like linear, quadratic, cubic, growth etc. could not reveal significant degrees of
association. Although the degree of freedom has varied considerably between the variables even if in the same variable,
the applicability of exponential form of the function has envisaged consistent character of tourist arrivals at Pokhara
as a
whole and in other different
tourist attracting places of Pokhara
such as Devi’s Fall and
Mahendra Cave.
Table 4.1 Annual
data tourist trend in Pokhara
city
R
|
*significant at 10% level, ** significant at 5% level,
***significant at 1% level. R2: the degree of explanation, 2=Adjusted R2, SEE: Standard error of estimates, F: Statistics for the significance of all the coefficients, DF: Degree of Freedom. The number in ( ) refer t-values.
The results
of the tourism trends as summed up in equation 1.1 vividly
express that total tourist arrivals
in Pokhara (TTAp)
increase at 5.4 percent
per annum during the survey
period of 1976 to 2010.
The most important fact to be noted is the rate that tourists grow at 19.8 percent
(equ.1.2), when the period of 5 years from 2007 to 2011 is considered. With regard
to the tourism spot, Devi’sFall
and Mahendra Cave, when
separately analyzed, the growth rates traced are 7.0 and 19.2 percent, respectively. In all the functions
t-values are significant at 1 percent
level of significance, the explained percent of variation
(R2) and adjusted R2 varies within the
acceptable range of 83 to 92 percent.
Less standard error of the estimates
along with significant F-values further
justifies the reliability of the applied trend functions which may help to estimate the future statistics of tourists in Pokhara
and its surroundings with the help of which the supply aspect can be restructured and more facilities
to the tourist could be provided
so as
to raise tourists’ expenditure. Unless considered more vital
factors for the purpose as stated above, the application of trend functions
only to tourist arrivals
exhibits incomplete picture
of the tourism scenario
of Pokhara. Therefore, the variables
like income from tourists to Devi’s Fall management committee (YDF),
income earned by Mahendra cave (YMC), and income of Pokhara Sub-Metropolitan (YPSM) are further considered
for trend analysis. The results as depicted in Table 4.2 well illustrate
the rate growth of the aforesaid
factors per annum for the period from 2007 to 2011.
Table 4.2 Annual data of
income and population trend in Pokhara
(2007-2011)
R
|
Note: Figures in parentheses and
asterisks confer the same meaning as in Table 4.1. Source: Calculated by the author
|
As one comes across the results of the compound
functions (1.5 - 1.8),
there remains no room for doubt about the appropriate performance of the models, which have exhibited acceptable
degree of the explained percent of variations except
the
equation
1.5,
where
R2 is only 53
percent. However it may be stated reliable because
of the income that the Pokhara Sub-Metropolitan (YPSM) generates
depending largely on grants
by the government on development and regular expenditure, local taxes, services
and fees. As all the necessary statistics are significant, the equation 1.5 should be taken as a grain
of salt which
estimates 17 percent (in 1.85698) growth per annum. The estimated growth rates of the income
earned by Mahendra cave (YMC) and Devi’s Fall are 27.6 and 9.0 percent, respectively, which set a remarkable scenario.
There would be certainly a difference, no doubt, in the growth rates
when more preceding years are added. As a result of incorporating the preceding years up to 2002, the
growth rate of the income of Mahendra cave has come down to
23.89
percent.2 The compound
function as applied
for the income
earned by Devi’sfall estimates that the income increases at 9 per cent per annum. Since all the necessary statistics R2 F-value, and t-statistics seem to be significant, it can be deduced that the rate of growth of
income received from tourists
has grown at a satisfactory, exponential rate. To this extent, most importantly it is to say that Pokhara municipality hitherto
has not levied any tax to the
existing 19 paragliding companies, whose gross
income is estimated a minimum
of 200 million rupees per annum. These companies pay US$ 1200 for getting
operational certificate and thereafter
a renewal charge of US$ 250 per annum. Besides,
0.1 million rupees per company pay annually to Sarangkot Village
Development Committee. Most pilots are foreigners who charge
US$ 50 plus 13 percent vat per 15 days as remuneration. The owners of the companies pay only business
tax which goes to the government revenue.
4.3.2 Effects of tourism at national perspective
At
the outset, it becomes integral to make an overview of
Nepal, the beautiful country, tourist based country, , the beautiful country, tourist based country, , the beautiful country, tourist based country, ’s economy and tourism’s role in the economic development process. Indeed, it would
be meaningful to search the impact of tourism on national perspective.
Having assumed GDP as a dependent variable,
a number of independent variables such as total tourist arrival (TTA), earnings from tourism
(EFT), earnings of trade,
hotels and restaurants (THR), aggregate investment (AI), export (EXP), import (IMP), foreign
aid (FA), population of Nepal, the beautiful country, tourist based country, , the beautiful country, tourist based country, , the beautiful country, tourist based country, (POP),
2 YMC=5.99-209(1.269935)T
and time (TIME) are regressed applying
the multiple linear,
weighted least squares, two-stage
least squares and auto-regressive
models. Although, per capita income,
tax revenue, non-tax revenue, agricultural GDP, non-
agricultural GDP etc., may also be taken into consideration as development
indices (Sharma 2012), only GDP at current price is considered
a prime development index for giving
bird’s eye view to the national economy. Therefore, the results of the multiple
linear regression illustrate that the coefficients of trade, hotels and restaurant (THR), export (EXP) and aggregate investment (AI) are significant at 1 percent
level of significance, whereas foreign aid and import are significant only at 10 percent level. The
other variables, population (POP) of Nepal, the beautiful country, tourist based country, , the beautiful country, tourist based country, , the beautiful country, tourist based country, , total tourist
arrival (TTA) and time have shown
their coefficients as insignificant. With regard to the
weighted least squares, there exists high degree correlation between
total tourist arrival (TTA) in Nepal, the beautiful country, tourist based country, , the beautiful country, tourist based country, , the beautiful country, tourist based country,
and the earnings from tourism
(EFT) and so EFT is dropped
from the model. Assuming population of Nepal, the beautiful country, tourist based country, , the beautiful country, tourist based country, , the beautiful country, tourist based country, as a weight variable, THR, EXP, and AI subsequently, have been found significant at
1
percent level of significance and import at 10 percent. The two-stage least square model too justifies
the validity of the contribution of trade, hotels and restaurants (THR) to the current price
GDP of the country. Total tourist
arrival (TTA), however,
seems to be significant but only at
10
percent level of significance. Further, the above discussed
significant variables, particularly THR, AI, export,
foreign aid and import are shown to the Cochrane-Orcutt auto-regressive model, which explicitly proved the
determinant capacity of all the regressors
except import. Comparing all the results
of the models, the earning
of trade, hotels
and restaurants (THR), generally tourism-based variable, is justified as a prime factor. Over all, THR, EXP, AI, are found as the vital factors
to determine the growth of the
GDP at current prices of
Nepal, the beautiful country, tourist based country, , the beautiful country, tourist based country, , the beautiful country, tourist based country, (Annex-4.1).
4.3.3 Effects of Tourism in Pokhara
Coming to the case of Pokhara
sub-metropolitan city, unavailability of the necessary
time series data for long period, as well as the lacking of the necessary data concerning the development indices and the complete
macro-economic variables that influence the dependents, originated problems for the scientific measurement. Even then the trouble is
mitigated by the small scale survey
for time series and the secondary
source of cross sectional data related with total investment in tourism (TITp), total employment
generated in tourism sector of Pokhara (TETp) and
total number of businesses related
with the tourism
sector of Pokhara
(TNBTp). Regarding the analysis
of the effects of tourism,
unavailability of the aggregate data further led to study separately for some tourist generating places. The income generated by Devi’sfall is assumed
as dependent, and tourist arrival, employment over there and the time
factors as independent. Since employment has remained constant
throughout the period
2007-2011, and high degree of correlation (r2 =
|
0.97) between time and TA ; employment and time have been dropped from the regression model 1.13.
YDF = Ͳ693165+13.17
TADF …………………………………Equ.1.13
(2.36)
5.58**
R2=0.91, Rഥ2=0.88, SEE=157526.5, F=31.1, DͲW=1.77, DF=4
The single equation regression (equ.1.13) explains that the model as a whole is well fitted. All the necessary statistics are satisfactory with no
persistence of auto-correlation. The 91 percent
of explained percent
of variation and t- significant at 5 percent
level of significance justify that the income of Devi’sfall
increases by Rs. 13 per tourists.
About 2 to 2.3 million rupees is provided by the committee to the development of the Chorepatan Higher Secondary School and around 1.3 to 1.4 million
rupees expended for the management of Devi’sfall. To this extent,
for the evaluation of the degree of
elasticity, the regression equation 1.13 is further
tested by transferring the data
into the natural log form.
ln
YDF =Ͳ0.069+1.194
ln
TADF ………………………………Equ.1.14 (0.368)
6.36***
R2=0.93, Rഥ2=0.90, SEE=0.0456,
F=40.48, DͲW=1.746, DF=4
The absence of auto-correlation, 93 explained percent
of variation, significant F-value,
less standard error of the estimate and 1 percent
level of significance of the t-value have proved the reliability and the validity of the model and reveal elasticity coefficient greater than one. This at
means that a slight increase in the
number of tourist arrivals in Devi’s
Fall assists to increase
the income at large. Furthermore, the linear and log- linear regression models are applied for the income
generated and tourist arrivals in Mahendra cave remaining indifferent between international and domestic tourists.
Table 4.3 Annual
data of linear and log-linear regressions (2002-2011)
|
Equs.
|
Dependent
|
Constant
|
Coefficients
|
R2
|
2
|
SEE
|
F
|
D-W
|
DF
|
|
Equ.1.15
|
YMC
|
-258056
|
9.92 TAMC
(1.94)
5.1***
|
0.77
|
0.74
|
498096.8
|
26.1
|
1.17
|
9
|
|
Equ.1.16
|
lnYMC
|
1.513
|
1.04TAMC
(1.99)
5.25***
|
0.78
|
0.75
|
0.375
|
27.6
|
1.49
|
9
|
Note: Figures in parentheses and asterisks confer the same meaning
as in Table 4.1. Source: Calculated
by author
The equations 1.15 and 1.16 both, however, have explicitly shown the
good performance due to the fulfillment of all the necessary statistics. The income generated by the tourism spot, Mahendra Cave, is increased
by tourist arrivals in that locality by around Rs. 10 per tourist. In fact, foreigners are charged Rs.20, Nepal, the beautiful country, tourist based country, , the beautiful country, tourist based country, , the beautiful country, tourist based country, i nationals Rs. 10 and students Rs.5.But only a fixed number
of persons (4) are employed,
some portion of the
earned money is provided to the development programs for Vindyabasini Higher Secondary School. The application of the
regression model in
the natural log form envisages the more elastic character of the income received by Mahendra Cave. Accordingly, the income is highly responsive to a change in the
incoming tourists to the Cave.
It would be highly imperative to disclose the fact that at present
about
19 paragliding companies
are operating from Sarangkot village and these
companies earn more than 200 million rupees a
year.
During registration of
Paragliding Company, an amount of US$ 1200; and US$ 250 was annually
charged for renewal by the Ministry. Besides,
each Paragliding company
pays 0.1 million
rupees to Sarangkot Village Development Committee.
If 5 percent
of the income could be charged, an amount of 10 million
rupees could be collected.
But due to the shaky policies
of the Pokhara
Municipality, till now, it has not implemented a policy for collecting
any more fees from Devi’sFall and Mahendra Cave. Around 0.0329
and
0.027 million tourists
visit annually to Devi’sFall and Mahendra Cave and
generates 3.8
and 2.9 million rupees per annum.
The application of the linear and log-linear regression models by assuming
annual income earned and total employment
generated as dependents, and total investment
(TI) in tourism related business
institutions, and the total
number of business
enterprises as independents, has shown reliable
statistics (Annex:
4.2). The explained percent of variation ranges between
96 to 99 percent
and the adjusted
R2 remains at the range of 96 to 98 percent of variation. Quite less standard error of the estimates and high significant F-values along with significant t -values at 1 percent level of significance, justify the goodness of the fit of the models. Therefore, it is confirmed that if investment made by the enterprises is doubled, it contributes to raise the income by 15 million rupees
a year. In a similar fashion, a 100 percent increase
in the number of businesses, raises
annual income by 7 percent. Contrary to this, equation 1.18 reveals that investment and the business enterprises are the most promising
factors to raise
total employment in the tourism
sector of Pokhara. In fact, there is the possibility of increase in employment by 28.6 percent
and
309 percent
respectively by doubling the investment and opening up of
new business enterprises. In contrast to this, the log linear models have exhibited inelastic coefficients
of
the independents, total investment
(TI) and the total number of business (TNOB). Therefore, the convincing results of the linear models should be taken as granted
and new investment on tourism supply side is to be made. The inelastic responses of the determination factors like total investment and the total number
of business enterprises related to the tourism sector further led to apply
the auto-regressive and weighted-least squares
into the cross-sectional data. The results are shown
as in Annex 4.3. Both the log-linear models are transformed into the auto-regressive forms (1.21 &1.22) and the final parameters have confidently proclaimed less responsive
coefficients of the total
investment (TI) and total number of businesses (TNOB). The fact is that total investment assists the annual income of retail, travel, food and
lodging businesses by 87 percent
and only 13 percent by the total number of businesses (TNOB). Similarly, the employment is increased
by
74 percent, if a hundred percent growth in the number of businesses is realized. Pondering with the necessary
statistics in the aforementioned
auto-regressive models, the confirmation about
the significant level of the coefficients, 97 percent of the degree
of explanation and the absence of auto-correlation ( du < d <4-du or, 1.538<2.1 < 2.462) have proved the
reliability of the models. The most eventful
journey to the application of
the models is the result of the weighted least squares (Equ.1.24) which entails the impact that the investment makes annual income increase by
16.5
percent, but the employment would increase at 82 percent and 238 percent, respectively, if investment and the total number of businesses
were increased at 100 percent.
Regardless of the positive
results of the effects of tourism on income,
employment
and investment due to the growth
of tourists in Pokhara city, bottlenecks still prevail in transportation, electricity, accommodation, trekking,
sight-seeing, paragliding, supplying
the efficient and moral
manpower. Price variation
in trekking equipments, copying of
international trade mark, lack of electricity and irregular water
supply, absence of direct flight abroad,
throat-cutting polices adopted
by hotels, bad roads, absence
of public toilets, inefficient
manpower in the tourism sector are the major problems explicitly
expressed by various tourism related associations of Pokhara
(Upadhyaya and Khatiwada
2012). All the enlisted threats to tourism may be regarded due to the airy-fairy policies of the government authority.
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