FORECASTING WAGES INEQUALITY IN RESPONSE OF TRADE OPENNESS IN PAKISTAN: AN ARTIFICIAL NEURAL NETWORK APPROACH

Irfan Ullah, Xuefeng Qian (), Muhammad Haroon Shah (), Alam Rehman (), Sher Ali () and Zeeshan Ahmed ()
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Irfan Ullah: Reading Academy, Nanjing University of Information Science and Technology, P. R. China
Xuefeng Qian: School of Business Administration, Zhongnan University of Economics and Law, P. R. China
Muhammad Haroon Shah: School of Finance, Zhongnan University of Economics and Law, P. R. China
Alam Rehman: Faculty of Management Sciences, National University of Modern Languages, Islamabad, Pakistan
Sher Ali: Department of Economics, Islamia College University, Peshawar, Pakistan
Zeeshan Ahmed: Lahore Business School, University of Lahore, Gujart Campus, Pakistan

The Singapore Economic Review (SER), 2023, vol. 68, issue 06, 1875-1890

Abstract: Pakistan liberalized its trade in different regimes and the recent trade reforms is CPEC project which is expected to reduce wages inequality of skilled and unskilled labor. This study forecasts wages inequality as a result of trade openness in Pakistan by using artificial neural network approach for the period 1991–2017. The empirical outcomes revealed that trade liberalization is influential factor for reducing wages inequality in Pakistan, and the forecasting results for 2019–2026 show a dynamic trend of wages inequality in the response to trade liberalization; however, in many of the years, the positive implication has been witnessed for the inequality.

Keywords: Trade openness; wages inequality; an artificial neural network; Pakistan (search for similar items in EconPapers)
JEL-codes: D60 E24 E27 F14 F16 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0217590820500058

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