IMPACT OF RESEARCH AND DEVELOPMENT ON THE VALUE ADDITION OF SERVICES: STRUCTURAL TRANSFORMATION OF PAKISTAN AND INDIA

Abstract

The structural transformation requires that in the long run, the changes in sectoral shares are associated to the economic growth. With developed as well as developing countries transforming themselves into services-oriented economies, innovation and research and development in information communication technology has become inevitable to achieve sustained economic growth and value addition in services sector. Unit root testing, cointegration bound testing, correlation matrices and autoregressive distributive lag model is being implemented in our analysis of impact of research and development on the value addition of services showcasing the long-run impact vis a vis short-run impact on our time series data set. The time series dataset comprises of two South Asian economies (Pakistan and India). Time period taken into consideration is from 1996 to 2020. The unit root testing for all the variables showed us that they are not stationary at level and are integrated of either order I(1) or I(2). Similarly, covariance-correlation showed us a close relationship among explanatory variables. Cointegration bound testing made us aware of the fact that there certainly exists a long-run causal relationship between our outcome variables and explanatory variables. Auto Regressive Distributive Lag model showcased that relationship between research and development and value addition of services is positive and significant and does have long run positive implications on economic output and growth. Similarly, labor force with advanced education and expenditure on education are also taken as proxies for research and development. Both these variables showcased somewhat ambiguous results on value addition of services for both Pakistan and India.with TensorFlow or PyTorch if required. In order to test hypotheses, and inference regarding interrelationships between variables, regression and structural equation modeling (SEM) are suggested as statistical tools. The implications of the study are both