@article{Khan_Ghauri_Siddiqui_Iqbal_2023, title={Causality between Economic Development and Unemployment: Using ARDL Model}, volume={3}, url={https://ijsse.salmaedusociety.com/index.php/ijsse/article/view/104}, DOI={10.58661/ijsse.v3i1.104}, abstractNote={<p><em>The meditation of this study is to realizeunemploymentin Pakistan with the perspective of macroeconomic factors, including FDI,private investment,exports and government expenditure.Annual dataused in this study from 1985 to 2019was obtained from different issues of economic surveys and official website of the Pakistan’s Central Bank. The prerequisite required to exploretime series model is to check the stationarity, we apply ADF and PP tests to identify stationary series among our macroeconomic series. All variables are used log transformation in order to smooth the series. The results reveal that at level only unemployment is stationary while remaining series are stationary at first difference. Therefore,the appropriate time series model for different stationary series is Autoregressive Distributed Lag (ARDL) model. We apply four different ARDL models to detectthe long-run connection between unemployment and other macroeconomic series employed in this paper. Out of four, three models confirm that the relationship amoung variables is characterized long-run between unemployment and government expenditures.The empirical results reveal that FDI and exports(model (1)) help to reduce unemployment while government expenditure(model (2)) has no impact. Moreover, private investment (model (3)) has aninverse relationship with unemployment. The results also show a long run connection of foreign direct investment, exports, government expenditures and private investment with unemployment if all variables are in one model (4). More focus must be paid towards increasing the FDI which opens the door of employment opportunities. Exports increases production in the exising settings and help reduce unemplolyment.</em></p>}, number={1}, journal={International Journal of Social Science & Entrepreneurship}, author={Khan, Muhammad Irfan and Ghauri, Saghir Pervaiz and Siddiqui, Ammar Ahmed and Iqbal, Athar}, year={2023}, month={Jan.}, pages={466–488} }