Stochastic Demographic Dynamics and Economic Growth: An Application and Insights from the World Data

Abstract

This research has two broad objectives: First, to model population growth in a stochastic framework such that the effects of possible non-mean convergent shocks could be studied theoretically on long-run economic growth and planning. Second, an empirical strategy for modelling stochastic population growth over time is provided. Forecasting exercise has been rigorously carried for population growth and income by embedding the stochastic growth feature of population. For modelling purpose, a long-memory mechanism for population growth is suggested so that the classical economic growth assumption of constant and/or non-stochastic population growth in economic growth models appear as a limiting case. The analytical results show that embedding the stochastic features of population growth helps in explaining the economic growth volatility. In particular, it is found to be a formidable cause of the presence of long-memory in output. The empirical analysis shows that unless the stochastic feature of population growth is taken into empirical growth models, we will not be able map out the significant effects of demographic variables consistently over time. It is also shown that how corroborating the information of stochastic shocks of population alters our forecast vision by impacting significantly on the precision of the estimates.