From the couple of decades remittances have been playing an important role in the Nepalese economy. For people of rural areas even for the urban households, remittance is becoming the major source of livelihood. Therefore, this study considers remittance as a focus variable with an aim to assess the link between remittance and growth. Autoregressive distributed lag (ARDL) model is applied to examine the relationship between remittance and growth. All the variables included in the analysis became stationary after first difference. The result of bound test confirms that the variables are cointegrated. It means the variables have long run relationship. The empirical result reveals that one percent increase in remittance increases the GDP by 0.36 percent in the long run. Similarly, the gross fixed capital formation, secondary school enrolment and the trade openness and per capita GDP have positive relationship. It implies that one percent increase in capital, labor and trade openness increases the per capita GDP by 0.82 percent, 0.46 percent and 0.30 percent in the long run respectively.
With the application of Robust Regression Method, this paper attempts to estimate the production function for manufacturing industries of Nepal. In this endeavour, the production function for Furniture and Pharmaceutical industries have been estimated using cross-section data of the Census of Manufacturing Establishment (CME) 2011/2012. The coefficients of log-linear form of Cobb-Douglas (C-D) production function reveal that the selected manufacturing industries are operating with decreasing returns to scales. The labour coefficients of both industries are found to be statistically insignificant. Negative labour coefficient of Pharmaceutical Industry indicates capital intensive nature of the production and minimal contribution of labour inputs. Although positive and significant, capital coefficients indicate both industries were running with decreasing returns to capital inputs. Total Factor Productivity (TFP) representing the state of technology and factors other than labour and capital found to be instrumental and significant for both the industries.
- Year 2020 Year 2020 Volume 32-1
- Year 2019 Year 2019 Volume 31-2
- Year 2019 Year 2019 Volume 31-1
- Year 2018 Year 2018 Volume 30-1
- Year 2018 Year 2018 Volume 30-2
- Year 2017 Year 2017 Volume 29-1
- Year 2017 Year 2017 Volume 29-2
- Year 2016 Year 2016 Volume 28-2
- Year 2016 Year 2016 Volume 28-1
- Year 2015 Year 2015 Volume 27-2