This study examines the impact of several important macroeconomic variables such as quality of education, infrastructure development, foreign direct investment inflow, and green energy transitions on economic growth. We analyzed annual time series data sample for estimation of the above macroeconomic indicators during 1990 to 2020. We use nonlinear auto-regressive distributive lag model (NARDL) approach to detect the short-term and long-term effects of undermentioned macroeconomic variables on economic growth of Pakistan. The results primarily reveal that the quality education, foreign direct investment inflow, and infrastructure development are playing a significant positive role in the economic growth of Pakistan. Similarly, in short term the foreign direct investment inflow, infrastructure, and green energy transition coefficients are significantly positive related to sustainable development goals. However, the education found as unsubstantial as contributive as other variables. Moreover, the Granger causality and structural break estimations are employed to estimate the causal association between the selected parameters and unexpected change over the economy. The estimated outcomes find the unidirectional causality from education and green energy transition towards economic growth, where education is found within relation to infrastructure. Additionally, bidirectional causal relationship is found between FDI and infrastructure towards economic growth which shows that the increase in foreign investment has the potential to boost the economic growth. Finally, all the estimated indexes are considered as important sources towards the economic growth.
Keywords: Economic growth; Pakistan; Sustainable development goal indexes; Time series analysis; VECM, NARDL.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.