Background: The first, third and fourth goals of SDG are concerned with ending poverty in all its forms everywhere, ensure healthy lives and promote well-being for all at all ages and ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, respectively. Nepal is committed to ensuring all children with access to free, compulsory, and good-quality basic and secondary education in Nepal. Objectives: This study aims to compare the average time to reach education centers and health facility centers by poverty status and ecological region. Materials and Methods: This study is based on NLSS 2011 data. In this study two major factors of access to facility namely education and health are considered. Four variables on access to education and three variables on access to health are used. Mean vectors, correlation matrices, and covariance matrices have been computed. The Multivariate Analysis of Variance is used to compare the mean vectors and to check the homogeneity of the variance-covariance matrix using Box's M test. Four tests namely Wilk's Lambda, Lawley- Hoteling trace, Pillai trace and Roy's largest root have been used to compare the mean vectors. Results: This study has shown that the average time to reach each nearest education center of poor households is higher than non-poor households in each ecological region. The average time to reach the primary school is lowest and highest to reach secondary school in each poverty status and ecological region. The average time to reach the nearest health post is lowest among different health facility centers in each poverty status and ecological region. The p-values of each Box's M and Pillai trace tests are less than 0.0001. Conclusion: The mean vectors of time to reach the nearest education center are significantly different between poor and non-poor households in each ecological region. The average time to reach the health facility centers is also significantly different in poor and non-poor households within each ecological region.

Blindness and visual impairment remain leading causes of disability in the world, and also considered one of the major eye problems in Nepal. This study was planned to identify the significant risk factors of visual impairment and blindness based on hospital data. The analysis is based on 2000 patients' information with age ≥40 years retrospectively. Logistic regression model is used to identify the risk factors of visual impairment. Altogether 710(36%) subjects were having visually impaired (low vision) and very few 29(1.45%) were having blindness. The proportion of having visual impairment and blindness each is found higher in the females as compared to males( p = 0.026). The prevalence of having visual impairment in the age group of 40 -49 years with 95% C.I. is 13.11 %( 10.76% -15.75%) and it reaches to very high (89.00%; 84.89% -92.30%) in the patients having age 70 years or more. The odds of having visual impairment is 8 times more(OR = 7.99, 95% C.I.: 3.99 -15.98, p < 0.001) in 60 -69 age group of people as compared with those of 40 -49 age group based on visual acuity after correction. Risk of developing visual impairment is found to be increasing exponentially with increasing age.

Background: Poverty has been in existence for many years and continues to exist in a large number of countries. Poverty is “pronounced in wellbeing” where wellbeing (and poverty) in broader term, focuses on the capability of the individual to function in society and poor people often lack key capabilities, they may have not adequate income, education, or be in poor health or feel powerless or lack of political freedoms. In Nepal, despite the decreasing trend in poverty incidence, still the current prevalence is very high with the comparison of other countries. Objective: To identify, compare and decomposition of different poverty measures by rural urban area and ecological belt in Nepal. Materials and Methods: Data set of Nepal Living Standard Survey (NLSS) conducted by Central Bureau of Statistics in 2011 consisting of various variables related to food, non-food consumption, income, demographic, socioeconomics, etc., have been used for analysis. In order to measure the poverty, different measures such as head count ratio, poverty gap, poverty severity, Watts index and Sen-Shorrocks-Thon index were used. The comparisons of different poverty measures across different variables were attempted including use of appropriate poverty curves. The decomposition of poverty indices by consumption components using the Shapley value along with Lump-Sum Targeting approach has been applied. Results: Average per capita consumption is 34186.5, the head count index, poverty gap and poverty severity of Nepal are 0.2518, 0.0545 and 0.0182, respectively. The poverty measures of rural area are higher than the urban area, and the incidence of poverty is highest in mountain ecological belt. Food and non-food component allows to 46.39% & 28.42% of the total population to be non-poor of headcount index, 60.19% & 34.34% for poverty gap index and 59.96% & 38.20% for poverty severity, respectively. Conclusion: For both within and overall population, rural area has the higher impact than urban area and each measure of poverty in mountain is significantly higher than hill and terai. To reduce within group headcount index and poverty gap, policymakers should give more focus to rural area and mountain region.

This study examined the impact of non-monetary factors (training, organisational support, promotion, job security, co-worker incivility and job rotation) on employee performance in Internet Service Provider (ISP) firms. The study used an inferential research approach to analyse the impact of independent variables on dependent variable. A standardised questionnaire was surveyed on a sample of 200 ISP employees from the Kathmandu Valley. The results demonstrate that job security and co-worker incivility have statistically significant effects on employee performance, but the rest of the independent variables do not. Based on finding, it is recommended that more the job security, more the employee performance in company and reduction of co-worker incivility around organisation environment will help boost employee confidence and performance. Along with this, this research also showed area for refurnishing other variables like available training, organisational support, promotion facility and application of job rotation for creating more impact of it on employee performance.

This study aims at computing, comparing and decomposing the different inequality indices by rural and urban areas, sex of household head and ecological belt, so that policy maker can make the policy to reduce the inequality in Nepal. This study is based on the raw data taken from the 3rd Nepal Living Standard Survey-2011 conducted by the Central Bureau of Statistics (CBS). The study has used real consumption as the main variable to measure the inequality. In most of the cases five measures of inequality; Coefficient of variation (CV), Quantile Ratio Index, Gini Index, Generalised Entropy Index with parameter 0 and 1 were computed. The Gini index, Theil’s L and Theil’s T indices are 0.328, 0.175 and 0.194, respectively. The study has found no significant difference in inequality between male- and female-headed households; and the inequality in urban areas is higher than that in the rural areas. By ecological belts, the inequality is highest in hills and lowest in mountains. The country should place focus on urban areas and hilly belt to reduce inequality.