Quantitative Assessment of Unemployment among Youths in Malaysia

Authors

  • Lok Lee Wen School of Quantitative Sciences, Universiti Utara Malaysia 06010 Sintok, Kedah, Malaysia
  • Masnita Misiran Centre for Testing, Measurement & Appraisal, Universiti Utara Malaysia 06010 Sintok, Kedah, Malaysia
  • Hasimah Sapiri School of Quantitative Sciences, Universiti Utara Malaysia 06010 Sintok, Kedah, Malaysia
  • Siti Suzlin Supadi Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
  • Zahayu Md Yusof Institute of Strategic Industrial Decision Modelling (ISIDM), School of Quantitative Sciences for Testing, Measurement & Appraisal, Universiti Utara Malaysia 06010 Sintok, Kedah, Malaysia

DOI:

https://doi.org/10.11113/mjfas.v18n5.2436

Keywords:

Unemployment rate, Pearson correlation, multiple linear regression.

Abstract

The youth unemployment rate is three times greater than the total unemployment rate in Malaysia, also it is moving more fluctuated and growing every year after a significant rise in 2015. Hence this study aims to determine the relationship between the influential factors and the youth unemployment rate and to examine which factors can serve as future indicators of youth unemployment rate in Malaysia. The annual basis of secondary data from 1992 to 2019 and the analysis methods are used Pearson correlation and multiple linear regression. The correlation results show that inflation rate, trade openness and urbanization are negatively correlated with youth unemployment rate, while Foreign Direct Investment (FDI), migration and exchange rate are positively correlated. Also, the regression results indicate only four factors which are FDI, trade openness, exchange rate, and urbanization are significant to the youth unemployment rate. Thus, they can serve as the future indicators of youth unemployment rate in Malaysia. More specifically, FDI and exchange rate are positively significant correlated with youth unemployment rate, whereas trade openness and urbanization are negatively significant related with youth unemployment rate.

References

Akoglu, H. (2018). User’s guide to correlation coefficients. Turkish Journal of Emergency Medicine, 18, 91-93.

Anthony, D. (2018). Young and jobless in Malaysia. DOI:https://www.thestar.com.my/business/business-news/2018/08/06/young-and-jobless-in-malaysia.

Aurangzeb. & Asif, K. (2013). Factors effecting unemployment: A cross country analysis. International Journal of Academic Research in Business and Social Sciences, 3, 219-230.

Azmin, N. A. M., & Affandi, S., Shukur, N. A., & Aumran, N. A. (2017). The determinants of unemployment in Malaysia. International Business Management Conference, 3, 178-186.

Balcerzak, A. and Zurek, M. (2011). Foreign direct investment and unemployment: VAR analysis for Poland in the years 1995-2009. European Research Studies Journal, 14, 3-14.

Choudhry, M. T., Marelli, E., & Signorelli, M. (2010). The impact of financial crises on youth unemployment rate. Quademi del Dipartimento di Economia, Finanza e Statistica, 79, 1-18.

Cohen, E. (2017). Effect of welfare and employment policies on the correlation between migration and unemployment. Economics and Sociology, 10, 246-264.

Croux, C., & Dehon, C. (2010). Influence functions of the spearman and kendall correlation measures. Stat Methods Appl, 19, 497-515.

Dutt, P., Mitra, D., & Ranjan, P. (2009). International trade and unemployment: Theory and cross-national evidence. Journal of International Economics, 78, 32-44.

Gujarati, D. (2012).Econometrics by Example. Macmillan.

Hongfei, D., Ronnel, B. K. and Peilian, C. (2019). Income inequality is detrimental to long-term well-being: A large-scale longitudinal investigation in China. Social Science & Medicine, 232, 120-128.

Idowu, O. (2013). Challenges of urbanization and urban growth in Nigeria. American Journal of Sustainable Cities and Society, 2, 79-95.

Michael, E., & Geetha, C. (2020). Macroeconomic factors that affecting youth unemployment in Malaysia. Malaysian Journal of Business and Economics, 7, 181-205.

Nwosa, P., Keji, S., Adegboyo, S., & Fasina, O. (2020). Trade openness and unemployment rate in Nigeria. Oradea Journal of Business and Economics, 2, 52-62.

Puspadjuita, E. A. R. (2018). Factors that influence the rate of unemployment in Indonesia. International Journal of Economics and Finance, 10, 140-147.

Ramli, S. F., Firdaus, M., Uzair, H., Khairi, M., & Zharif, A. (2018). Prediction of the unemployment rate in Malaysia. International Journal of Modern Trends in Social Sciences, 1, 38-44.

Tan, V. N., Zahayu, M. Y., Masnita, M, & Siti, S. S. (2021). Assessing youth unemployment rate in Malaysia using multiple linear regression. Journal of Mathematics and Computing Science, 7, 23-34.

Todaro, M. P. (1997). Urbanization, unemployment and migration in Africa: Theory and policy. DOI:https://knowledgecommons.popcouncil.org/cgi/viewcontent.cgi?article=1247&context=departments_sbsr-pgy.

Shakur, E. S. A., Aziz, N. H. S., Abdullah, S. S., & Rasid, N. H. A. (2020). Determining unemployment factors among job seeking youth in the east coast of Peninsular Malaysia. Journal of Asian Finance, Economics and Business, 7, 565-576.

Umair, M. and Ullah, R. (2013). Impact of GDP and inflation on unemployment rate: A study of Pakistan Economy in 2000 – 2010. International Review of Management and Business Research, 2, 388-400.

Wooldridge, J. M. (2019). Efficiency of OLS: The Gauss-Markov Theorem. Introductory Econometrics: A Modern Approach (7th ed.). Cengage.

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Published

15-12-2022

Issue

Vol. 18 No. 5 (2022): September-October

Section

Article

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