ABSTRACT

This study pursues to identify the potential covariates of total factor productivity from the existing literature models of total factor productivity by using encompassing and LASSO techniques. The annual time series data of Pakistan are collected for the period 1982-2020. In this study we have considered the covariates of total factor productivity provided in literature in case of Pakistan. The final model is found by encompassing and LASSO techniques, which are TFPMEncompassing and TFPMLASSO. Then we chose the final model for total factor productivity by ranking both models according to their standard errors. It has been observed that TFPM Encompassing has the minimum standard error and encompasses the TFPMLASSO. Therefore, TFPMEncompassing is considered as parsimonious model for the covariates of total factor productivity in Pakistan. The results of parsimonious model suggest that the major covariates of total factor productivity are expenditure on education, foreign direct investment, unemployment rate, inflation, imported machinery, residential patents, and nonresidential patents of Pakistan. This is considered superior because it contains all those regressors, which are not collectively present in one model in the existing literature. ARDL bound test has been applied to find the long run cointegrating relationships among the covariates of total factor productivity. In the long run the expenditure on education, Inflation, unemployment rate and residential patent of Pakistan have negative and significant, whereas the imported machineries and nonresidential patents have positive and significant impact on total factor productivity of Pakistan. While in the short run, imported machineries, unemployment, foreign direct investment, residential and nonresidential patents have positive and significant effects on total factor productivity of Pakistan. Whereas, Inflation have negative and significant effect on total factor productivity both short and long run. Therefore, it is recommended that the Government should need to improve the technique of production by importing machinery, more investment and that will lead high productivity in every sector.