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Browsing Statistics by TU Institute "Institute of Science & Technology"

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    Impact of tourism in socio-economic development of nepal: A multivariate approach
    (Faculty of Statistics, 2018) Dhakal, Basanta Kumar
    Tourism being one of the major foreign exchange earnings and job providing sectors in local level is a growing service industry in Nepal. It significantly plays an important role in social and economic development of nation. Keeping in view of this reality, the objectives of this study are to examine the relationship between tourism benefits towards economic development process of the nation by using VEC model, to assess the residents’ attitudes towards economic impact of tourism in Nepal, and to assess the residents’ perceptions towards social impact of tourism in Nepal using EFA. This study is an attempt to apply different statistical methods/ models using two different sets of data namely secondary time series data and primary data. Vector error correction (VEC) model has been applied to analyze the secondary data from the period of 1990/91 to 2014/15 tourism data of Nepal provided by Ministry of Tourism and Civil Aviation for examining the relationship between tourism benefits towards economic development process of the nation. For analyzing the residents’ attitudes and perceptions towards economic and social impacts of tourism respectively, Exploratory Factor Analysis (EFA) has been used based on primary data through face to face field survey of 601 respondents from three tourist destinations with response rate 91.76%. A set of questionnaire was developed to collect the data, and the respondents’ level of agreement has been measured by five point Likert scale. In order to investigate the long run relationship, VEC model has been used, and it indicated that the role of average length of stay towards increasing GDP is greater than number of international tourist arrival in Nepal. The results of Granger causality analysis have also illustrated that the increasing average length of stay of tourist plays positive role to increase GDP and vice versa (p value <0.001) and large number of international tourist plays the affirmative role to increase their average length of stay (p value <0.001). Similarly, in order to look into Nepal's foreign exchange earnings through tourism with an analysis of the international tourists’ arrival and the duration they spent in Nepal. The empirical result from the VEC model has concluded that the role of average length of stay towards increasing earnings from tourism is greater than number of international tourist. The findings from Granger causality analysis have also demonstrated the large number of international tourist and their average length of stay play positive role to increase foreign exchange earnings (p value <0.001). Similarly, the large number of international tourist plays the affirmative position to expand their average length of stay and vice versa (p value <0.001). Likewise, in order to explore long run relationship between number of international visitors and their length of stay towards their average expenditure in Nepal, the result of VEC model has indicated that the role of average length of stay towards increasing expenditure per tourist is greater than number of international tourists’ arrivals in Nepal. The results of Granger causality analysis have depicted that the increasing average length of stay of tourist takes part in affirmative position to increase expenditure of visitor and vice versa (p value <0.001). The large number of international tourist plays the positive role to increase their average length of stay (p value <0.001). In order to examine long run relationship of foreign exchange earnings from tourism and average expenditure of international tourists towards share of GDP of Nepalese tourism, the result of VEC model has shown that the role of average expenditure per visitor towards increasing GDP is greater than foreign exchange earnings from tourism. The results of Granger causality analysis have also depicted that increasing expenditure per visitor plays positive role to increase GDP and vice versa (p value <0.001). Similarly, foreign exchange earnings also facilitate the expansion of GDP (p value <0.001). The EFA found that 67.84% total variance has been explained by positive economic factors of tourism and 59.39% total variance has been explained by negative economic factors of tourism illustrating both positive and negative impacts of tourism from the respondents. Tourism, apart from being perceived as an economic factor, is also a social component and it prevails subjectively and intangibly in the community. It is found that 56.3% total variance has been explained by positive social factors of tourism and 60.4% total variance has been explained by negative social factors of tourism indicating the both negative and positive perceptions towards social impacts of tourism from respondents. It shows that tourism industries of Nepal are not still well planed and controlled but it has great potentiality for further development. So, effort should pay critical and sustained attention towards promoting cultural and natural resources, improving the infrastructure of tourism industry and employing the tourism marketing skills to optimize the economic benefits and social betterment for the quality of life of people through the tourism development.
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    Optimizing multiple regression model for rice production forecasting in Nepal
    (Institute of Science and Technology, Statistics, 2015) Dhakal, Chuda Prasad
    This research, testing the possibility of use of probable predictors, has optimized multiple regression model to be used for rice production forecasting in Nepal. Fifty years (1961-2010) time series data were divided into training sample (a sample which is used to build the model) (n=35), and test sample size of 15 through which the built model was cross validated for its reliability in forecasting. This research has explored and used all the underlying principles of linear regression model building and its application in forecasting the production, mainly crop production such as rice. The model sustained with the three principle predictors: harvested area, rural population and price at harvest whereas these variables could explain 93% variation in production; the forecast variable. The model as such was parsimonious and as well the good fit with minimal (5%) mean absolute percentage error in its forecast. It therefore, for this fit, was concluded that multiple regression model could be scientifically used in forecasting, and the concerned stakeholders could thus be benefited from the this model especially for the enhanced ease, and efficiency for rice production forecasting to be used for planning purpose at national level. Future work might consider to increase the precision of the model in any aspects like making it more parsimonious and reliable than which have been purposed in this study.
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    Risk Factors Affecting Poverty in Nepal: Statistical Modeling Approach
    (Institute of Science & Technology, 2023-07) Acharya, Krishna Prasad
    Poverty is one of the main problems of developing countries, like Nepal and its reduction is a central issue. The identification of its determinants to reduce the monetary poverty is one of the key issues. According to previous studies, log-binomial regression model (LBRM) is a good option to logistic regression model (LRM) for common outcomes, mostly used in the analysis of clinical and epidemiological data. However, the use of LBRM and the comparison with LRM for data on poverty has not been discussed yet. The objectives of this study are to identify the important risk factors, to compare the LRM and LBRM in identifying the risk factors and estimating their effects on poverty in Nepal, and to assess the stability of the model through bootstrapping method. The data used for the analysis is the cross-sectional household level data (n = 5988) of Nepal Living Standard Survey 2010/11. All the data required for this study are not available in the provided household level data file of 5,988 households but are available in the individual level data file of 28,670 individuals. The individual level data are converted into household level data in order to generate the data on a number of variables, and merged into the main data file. With the support of rigorous review of literature and the availability of the variables in the dataset, seven possible independent variables have been considered for both the LRM and LBRM. They are: sex of household head (female / male), literacy status of household head (illiterate / literate), status of remittance recipient of household (no / yes), status of land ownership (no / yes), household with access to nearest market center (poor / better), number of children under 15 years (more than two / at most two), and number of literate members of working age population (WAP) (none / at least one). The response variable is household poverty (poor / non-poor). Implementing the stepwise forward and backward selection procedure with all these seven variables for the development of each final multiple regression model, only six variables except sex of household head has come out statistically significant at 5% level of significance. The LRM has yielded the odds ratio (OR) and LBRM has yielded risk ratio (RR) with 95% confidence interval estimate (CIE) for each covariate. Diagnostics of the model, the goodness of fit test, a risk assessment based on the presence of variables, and the stability of each model has been carried out. The classification and discrimination of the LRM has been also assessed. LRM and LBRM have been compared with respect to different criteria such as selection of covariates, effect size and its precision. The model's good fit test using and test of model's diagnostics criteria has also been compared. Further, the comparisons have also been made in risk assessment on the bais of factors present in the model, stability of the model and convergence failure problem. The effect size in terms of OR and in RR of six factors in each final model namely illiterate household head (OR: 2.20, 95% CIE: 1.86 – 2.61, p < 0.001; RR: 1.68, 95% CIE: 1.49 – 1.89, p < 0.001), remittance non recipient household (OR: 1.90, 95% CIE: 1.64 – 2.20, p < 0.001; RR: 1.45, 95% CIE: 1.33 – 1.59, p < 0.001), household with no land holdings (OR: 1.53, 95% CIE: 1.31 – 1.78, p < 0.001; RR: 1.22, 95% CIE: 1.11 – 1.34, p < 0.001), household with poor access to market center (OR: 1.77, 95% CIE: 1.52 – 2.07, p < 0.001; RR: 1.51, 95% CIE: 1.34 – 1.69, p < 0.001), household having > 2 children aged under 15 (OR: 4.69, 95% CIE: 4.06 – 5.42, p < 0.001; RR: 2.96, 95% CIE: 2.66 – 3.28, p < 0.001) and household not having literate members of WAP (OR: 1.29, 95% CIE: 1.07 – 1.56, p < 0.001; RR: 1.16, 95% CIE: 1.05 – 1.29, p < 0.001) are significantly associated with the likelihood of poverty. For each covariate, the OR is overestimated than that of RR. There is narrower 95% CIE of RR than that of OR for each covariate. It shows that RR is more precise than OR. Greater elevation in risk in LRM compared to LBRM varies from 13% to 173%. In each model, there is no convergence issues have been countered, where both the models are equally stable as assessed by bootstrapping procedure. Almost all variables are repeated 100% times among 1000 times repetition. The visual assessments of diagnostics of each model are reasonably satisfactory. There is considerable acceptable discrimination of LRM (AUC: 0.78) and model correct classification values of 67.15%. The good fit of the model is satisfied by LRM [ with 8 d.f.= 6.05, p = 0.53] but not satisfied by LBRM [ with 8 d.f.= 28.60, p = 0.0004]. Since the LRM satisfied the majority of requirements of model performance instead of some limitations, this model seems to be better than the LBRM for this data set. Nevertheless, the LBRM is an option for the LRM since it has better accuracy and avoids overestimating effect size. The findings of this study are expected to be useful for researchers and policy makers in the relevant field.