A Modified Exponential Distribution for Predicting Long Term Unemployment Rate

  • L. M. Kwaghkor Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria .
  • E. S. Onah National Mathematical Center (NMC), Abuja-Lokoja Road, Kwali. FCT, Abuja, Nigeria.
  • T. Aboiyar Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
  • J. A. Ikughur Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
Keywords: Exponential distribution, Modified Exponential Distribution, Unemployment Rate, Continuous – Time Stochastic Process and Labour Force.


The segment of those in the labour force who are keenly looking for work but could not find it at least 20 hours during the reference period are considered to be in unemployment state. This is among the biggest threats to social stability in many countries including Nigeria. Several studies have been conducted on the modelling and forecasting of unemployment rate. But these studies gives only short term predictions. This research work modified the exponential distribution that can give a long – term prediction of unemployment rate of a country. The modified distribution satisfied the condition for a distribution. The result shows that if on the average four million persons entered the unemployment state of Nigeria’s labour market in 2016, then by the modified exponential distribution, 3814918 persons (7.16%) will likely join the unemployment state by 2017, 13693214 persons (25.7%) by 2020, 25969155 persons (48.74%) by 2025, 34440832 persons (64.64%) by 2030, 44313800 persons (83.17%) by 2040, 49013183 persons (91.99%) by 2050 and so on. In order to avoid the negative effect of unemployment on the Nigeria’s economy and even Nigeria as a nation, practical measures must be taken by the government to reduce unemployment to the barest minimum.


Peter, J. W., Garth, J. V. S. & Grant E. M. An optimal investment strategy in bank management. Mathematical Methods in the Applied Sciences. 34 , 1606-1617 (2011).

NBS, Presentation of Labour Statistics Based on Revised Concepts and Methodology for Computing Labour Statistics in Nigeria. www.nigerianstat.gov.ng/pages/download/285. , (2015a).

NBS, Unemployment/Under Employment Watch-Q2. (2015b). www.nigeriastat. ng/report/307.

NBS, Unemployment/Under Employment Watch-Q3. (2016). www.nigeriastat. ng/report/481.

Shang Risk Implications of Unemployment and Underemployment. www.soa.org/.../research-2015-12-risk-implications-unemployment-underemployment.pdf. (2015).

Msigwa, R., Kipesha, E. R. Determinants of youth unemployment in developing countries:Evidence from Tanzania. J. Eco. & Sust. Dev. 4 , 67-76 (2013).

Uddin, P. S. O., Uddin, O. O. Causes, Effects and Solutions to Youth Unemployment Problems in Nigeria. Journal of Emerging Trends in Economics and Management Sciences (JETEMS). 4 , 397-402 (2013).

Asaju, K., Adagba, S. O., Kajang, T. J. The Efficacy of Fiscal Policy in promoting Economic Growth and Poverty Reduction in Nigeria. Research in World Economy. 1 , 33-40 (2014).

ILO., World Employment and Social Outlook Trends 2019 ILO. www.ilo.org/wcmsp5/groups/public/dgreports/dcomm/publ/documents/publication/wcms 670542.pdf. , (2019).

Meron, D., Modeling and Forecasting Unemployment Rate in Sweden using variousEconometric Measures. M.SC. Thesis, Örebro University School of Business, Department of Applied Statistics, https://www.diva-portal.org/smash/get/diva2:949512/ FULLTEXT01 .pdf. , 59-68 (2016).

Nkwatoh, L. S., Forecasting Unemployment Rates in Nigeria Using Univariate Time Series Models. International Journal of Business and Commerce. 1 , 33-46 (2012).

Dritsaki, C., Forecast of SARIMA Models:An Application to Unemployement Rate of Greece. American Journal of Applied Mathematics and Statistics. 4 , 136-146 (2016).

Hall, R. E., A Theory of the Natural Unemployment Rate and the Duration of Employment. Journal of Monetary Economics. 5 , 153-169 (1979).

Adenomon, M. O., Modelling and Forecasting Unemployment Rates in Nigeria Using ARIMA Model. FUW Trends in Science & Technology Journal, www.ftstjournal.com. 2 , 525-531 (2017).

Von, V., Lessons Learned from Germany's 2001-2006 Labor Market Reforms . Inaugural Dissertation. Retrieved from https://opus.bibliothek.uni-wuerzburg.de/les/3538/Schummdiss. pdf. , 78-92 (2009).

Walpole, R.E., Myers, R.H., Myers, S.L.,Ye, L., Probability and Statistics for Engineers and Scientists. 9th Edition, Boston. Pearson Education, Inc. , 89 (2012).

Akinyemi, S., Ofem, I. B., Ikuenomore, S. O., Graduate Turnout and Graduate Employment in Nigeria. International Journal of Humanities and Social Science. 2 , 257-265 (2012).

Saharareporters, 45 Percent of Nigerian Graduates Unemployed: Survey. http://saharareporters.com/2016/01/25/45-percent-nigerian-graduates-unemployed-survey. , (2016).

Amaefule, A., Fabiyi, O., Adepegba, A.,Onuba, I., Four million Nigerians have lost their jobs this year-NBS. https://punchng.com/four-million-nigerians-have-lost-their-jobs-this-year-nbs/ Featured. ibinfovolume , (2017).

How to Cite
Kwaghkor , L. M., Onah , E. S., Aboiyar , T., & Ikughur , J. A. (2020). A Modified Exponential Distribution for Predicting Long Term Unemployment Rate. International Journal of Mathematical Analysis and Optimization: Theory and Applications, 2019(2), 599 - 609. Retrieved from http://jmtcs.unilag.edu.ng/index.php/ijmao/article/view/567