Unemployment and Underemployment in Kenya: A Gender Gap Analysis

Gayline Vuluku; Anthony Wambugu; Eliud Moyi

doi:doi:10.11648/j.eco.20130202.11

| Peer-Reviewed

Unemployment and Underemployment in Kenya: A Gender Gap Analysis

Gayline Vuluku, Anthony Wambugu, Eliud Moyi

Published in Economics (Volume 2, Issue 2)

Received: 19 June 2013 Published: 20 July 2013

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

This paper analyses the gender gaps in open unemployment and underemployment in Kenya, using data from the Kenya Integrated Household Budget Survey 2005/06. Unemployment and underemployment probability functions were estimated separately for men and women, using binary probit specification and the gender gap in each outcome was decomposed to determine what factors explain it. The probit estimates indicate that even after controlling for differences in personal and household characteristics, the women were still more likely than men to be unemployed or underemployed. Observable individual human capital characteristics, marital status, region of residence, non-labour income and individual’s age were significant determinants of unemployment and underemployment. Decomposition results show that most (88.8percent) of the total female-male unemployment probability gap is explained by female-male differences in individual and household characteristics and only 11.2percent is unexplained. In contrast, only 5.4percent of the female-male underemployment probability gap is explained by female-male differences in individual and household characteristics while 94.6percent is unexplained. The key characteristics generating female-male gaps in unemployment and underemployment probabilities in Kenya are region of residence, age, education level, marital status and adverse shocks. This implies that policy interventions that aim to lower gender gaps in unemployment and underemployment should target the most affected age cohorts and locations. Policy should also give priority to interventions that narrow disparities in access to education and those that reduce adverse shocks.

Published in	Economics (Volume 2, Issue 2)
DOI	10.11648/j.eco.20130202.11
Page(s)	7-16
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Unemployment, Underemployment, Gender

References

[1]	Ashenfelter and Ham J. (1979), Education, unemployment, and earnings. Journal of Political Economy 87; pp 99–116.
[2]	Aurora T.(2002), On the link between human capital and firm performance a theoretical and empirical survey, FEP Working Paper no. 121
[3]	Azmat G., Guell M. and Manning A., (2006), Gender Gaps in Unemployment Rates in OECD Countries, Journal of Labour Economics, vol. 24, no. 1. Pp 1-37
[4]	Becker, G. S. (1957). The Economics of Discrimination. Chicago: University of Chicago Press.
[5]	Becker, G, S. (1962), Investing in Human Capital: A Theoretical Analysis, The Journal of Political Economy Vol. 70, No 5, 9-49.
[6]	Burke R. J., (1997),Correlates of under-employment among recent business school graduates, International Journal of Manpower, Vol. 18 Iss: 7 pp. 627 – 635
[7]	Fairlie R. W. (2003); An extension of the Blinder-Oaxaca decomposition technique to logit and probit models. Yale University, Discussion Paper no. 873.
[8]	Fairlie R. W. and Sundstrom W. A. (1997); The racial unemployment gap in long-run perspective. The American Economic Review, Vol. 87, Iss: 2 pp.306-310
[9]	Hirsch, E. (2007), Dual Labour Market Theory: A Sociological Critique, Sociological Inquiry, Vol. 50, No. 2, 133-145.
[10]	Jana S. L. and Terrell K. (2007); What Drives Gender Differences in Unemployment? Comparative Economic Studies Vol. 49, pp128–155
[11]	Kingdon G. and Knight J. (2000), Race and the Incidence of Unemployment in South Africa, Review of development Economics, Vol. 8, No. 2, pp 198 - 222
[12]	Long, S. J. (1997), Regression Models for Categorical and Limited Dependent Variables, SAGE
[13]	Psacharopoulos G. and Zafiris T. (1989), Female Labour Force Participation: An International Perspective. The World Bank Research Observer, Vol.4, No. 2, pp.187-201
[14]	Republic of Kenya (2008), Labour Force Analytical Report 2008, Nairobi: Government Printer.
[15]	Republic of Kenya (2010), Statistical abstract, Nairobi: Government Printer.
[16]	Republic of Kenya (2003), Report of 1998/1999 Labour Force Survey, Government printer.
[17]	Republic of Kenya (2008), The Vision 2030, Nairobi: Government Printer.
[18]	Republic of Kenya (2008), Kenya Integrated Household Budget Survey-2005/06, Nairobi: Government Printer
[19]	Suda C. (2002), Gender disparities in the Kenyan labour market: Implications for poverty reduction. University Of Nairobi, Kenya. Nordic Journal of African Studies Vol.11 (3): Pg 301-321.
[20]	Sackey H. A. and Osei B. (2006), Human Resource Underutilisation in an Era of Poverty Reduction; An analysis of Unemployment and Underemployment in Ghana. African Development Review Vol 18, no. 2, pp.221-247(27).
[21]	United Nations Development Programme (2010), Human Development Report. UN Plaza, New York, NY10017, USA
[22]	Wilkins R. (2006), Personal and Job Characteristics Associated with Underemployment, Australian Journal of Labour Economics, Vol. 9, No. 4, pp 371 – 393
[23]	Wamuthenya R.W. (2010): To what extent can disparities in compositional and structural factors account for the gender gap in unemployment in the urban areas of Kenya? Institute of Social Studies Working Paper No. 502
[24]	Wamalwa F.M. (2009), Youth Unemployment in Kenya: Its Nature and Covariates. Kenya Institute for Public Policy Research and Analysis (KIPPRA) Discussion Paper No. 103

Cite This Article

Plain Text BibTeX RIS

APA Style

Gayline Vuluku, Anthony Wambugu, Eliud Moyi. (2013). Unemployment and Underemployment in Kenya: A Gender Gap Analysis. Economics, 2(2), 7-16. https://doi.org/10.11648/j.eco.20130202.11

Copy | Download

ACS Style

Gayline Vuluku; Anthony Wambugu; Eliud Moyi. Unemployment and Underemployment in Kenya: A Gender Gap Analysis. Economics. 2013, 2(2), 7-16. doi: 10.11648/j.eco.20130202.11

Copy | Download

AMA Style

Gayline Vuluku, Anthony Wambugu, Eliud Moyi. Unemployment and Underemployment in Kenya: A Gender Gap Analysis. Economics. 2013;2(2):7-16. doi: 10.11648/j.eco.20130202.11

Copy | Download

@article{10.11648/j.eco.20130202.11,
  author = {Gayline Vuluku and Anthony Wambugu and Eliud Moyi},
  title = {Unemployment and Underemployment in Kenya: A Gender Gap Analysis},
  journal = {Economics},
  volume = {2},
  number = {2},
  pages = {7-16},
  doi = {10.11648/j.eco.20130202.11},
  url = {https://doi.org/10.11648/j.eco.20130202.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eco.20130202.11},
  abstract = {This paper analyses the gender gaps in open unemployment and underemployment in Kenya, using data from the Kenya Integrated Household Budget Survey 2005/06. Unemployment and underemployment probability functions were estimated separately for men and women, using binary probit specification and the gender gap in each outcome was decomposed to determine what factors explain it. The probit estimates indicate that even after controlling for differences in personal and household characteristics, the women were still more likely than men to be unemployed or underemployed. Observable individual human capital characteristics, marital status, region of residence, non-labour income and individual’s age were significant determinants of unemployment and underemployment. Decomposition results show that most (88.8percent) of the total female-male unemployment probability gap is explained by female-male differences in individual and household characteristics and only 11.2percent is unexplained. In contrast, only 5.4percent of the female-male underemployment probability gap is explained by female-male differences in individual and household characteristics while 94.6percent is unexplained. The key characteristics generating female-male gaps in unemployment and underemployment probabilities in Kenya are region of residence, age, education level, marital status and adverse shocks. This implies that policy interventions that aim to lower gender gaps in unemployment and underemployment should target the most affected age cohorts and locations. Policy should also give priority to interventions that narrow disparities in access to education and those that reduce adverse shocks.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Unemployment and Underemployment in Kenya: A Gender Gap Analysis
AU  - Gayline Vuluku
AU  - Anthony Wambugu
AU  - Eliud Moyi
Y1  - 2013/07/20
PY  - 2013
N1  - https://doi.org/10.11648/j.eco.20130202.11
DO  - 10.11648/j.eco.20130202.11
T2  - Economics
JF  - Economics
JO  - Economics
SP  - 7
EP  - 16
PB  - Science Publishing Group
SN  - 2376-6603
UR  - https://doi.org/10.11648/j.eco.20130202.11
AB  - This paper analyses the gender gaps in open unemployment and underemployment in Kenya, using data from the Kenya Integrated Household Budget Survey 2005/06. Unemployment and underemployment probability functions were estimated separately for men and women, using binary probit specification and the gender gap in each outcome was decomposed to determine what factors explain it. The probit estimates indicate that even after controlling for differences in personal and household characteristics, the women were still more likely than men to be unemployed or underemployed. Observable individual human capital characteristics, marital status, region of residence, non-labour income and individual’s age were significant determinants of unemployment and underemployment. Decomposition results show that most (88.8percent) of the total female-male unemployment probability gap is explained by female-male differences in individual and household characteristics and only 11.2percent is unexplained. In contrast, only 5.4percent of the female-male underemployment probability gap is explained by female-male differences in individual and household characteristics while 94.6percent is unexplained. The key characteristics generating female-male gaps in unemployment and underemployment probabilities in Kenya are region of residence, age, education level, marital status and adverse shocks. This implies that policy interventions that aim to lower gender gaps in unemployment and underemployment should target the most affected age cohorts and locations. Policy should also give priority to interventions that narrow disparities in access to education and those that reduce adverse shocks.
VL  - 2
IS  - 2
ER  -

Copy | Download

Author Information

Gayline Vuluku

School of Economics, University of Nairobi, Nairobi, Kenya
Anthony Wambugu

School of Economics, University of Nairobi, Nairobi, Kenya
Eliud Moyi

Kenya Institute for Public Policy Research and Analysis (KIPPRA), Nairobi, Kenya

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Gayline Vuluku, Anthony Wambugu, Eliud Moyi. (2013). Unemployment and Underemployment in Kenya: A Gender Gap Analysis. Economics, 2(2), 7-16. https://doi.org/10.11648/j.eco.20130202.11

Copy | Download

ACS Style

Gayline Vuluku; Anthony Wambugu; Eliud Moyi. Unemployment and Underemployment in Kenya: A Gender Gap Analysis. Economics. 2013, 2(2), 7-16. doi: 10.11648/j.eco.20130202.11

Copy | Download

AMA Style

Gayline Vuluku, Anthony Wambugu, Eliud Moyi. Unemployment and Underemployment in Kenya: A Gender Gap Analysis. Economics. 2013;2(2):7-16. doi: 10.11648/j.eco.20130202.11

Copy | Download

@article{10.11648/j.eco.20130202.11,
  author = {Gayline Vuluku and Anthony Wambugu and Eliud Moyi},
  title = {Unemployment and Underemployment in Kenya: A Gender Gap Analysis},
  journal = {Economics},
  volume = {2},
  number = {2},
  pages = {7-16},
  doi = {10.11648/j.eco.20130202.11},
  url = {https://doi.org/10.11648/j.eco.20130202.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eco.20130202.11},
  abstract = {This paper analyses the gender gaps in open unemployment and underemployment in Kenya, using data from the Kenya Integrated Household Budget Survey 2005/06. Unemployment and underemployment probability functions were estimated separately for men and women, using binary probit specification and the gender gap in each outcome was decomposed to determine what factors explain it. The probit estimates indicate that even after controlling for differences in personal and household characteristics, the women were still more likely than men to be unemployed or underemployed. Observable individual human capital characteristics, marital status, region of residence, non-labour income and individual’s age were significant determinants of unemployment and underemployment. Decomposition results show that most (88.8percent) of the total female-male unemployment probability gap is explained by female-male differences in individual and household characteristics and only 11.2percent is unexplained. In contrast, only 5.4percent of the female-male underemployment probability gap is explained by female-male differences in individual and household characteristics while 94.6percent is unexplained. The key characteristics generating female-male gaps in unemployment and underemployment probabilities in Kenya are region of residence, age, education level, marital status and adverse shocks. This implies that policy interventions that aim to lower gender gaps in unemployment and underemployment should target the most affected age cohorts and locations. Policy should also give priority to interventions that narrow disparities in access to education and those that reduce adverse shocks.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Unemployment and Underemployment in Kenya: A Gender Gap Analysis
AU  - Gayline Vuluku
AU  - Anthony Wambugu
AU  - Eliud Moyi
Y1  - 2013/07/20
PY  - 2013
N1  - https://doi.org/10.11648/j.eco.20130202.11
DO  - 10.11648/j.eco.20130202.11
T2  - Economics
JF  - Economics
JO  - Economics
SP  - 7
EP  - 16
PB  - Science Publishing Group
SN  - 2376-6603
UR  - https://doi.org/10.11648/j.eco.20130202.11
AB  - This paper analyses the gender gaps in open unemployment and underemployment in Kenya, using data from the Kenya Integrated Household Budget Survey 2005/06. Unemployment and underemployment probability functions were estimated separately for men and women, using binary probit specification and the gender gap in each outcome was decomposed to determine what factors explain it. The probit estimates indicate that even after controlling for differences in personal and household characteristics, the women were still more likely than men to be unemployed or underemployed. Observable individual human capital characteristics, marital status, region of residence, non-labour income and individual’s age were significant determinants of unemployment and underemployment. Decomposition results show that most (88.8percent) of the total female-male unemployment probability gap is explained by female-male differences in individual and household characteristics and only 11.2percent is unexplained. In contrast, only 5.4percent of the female-male underemployment probability gap is explained by female-male differences in individual and household characteristics while 94.6percent is unexplained. The key characteristics generating female-male gaps in unemployment and underemployment probabilities in Kenya are region of residence, age, education level, marital status and adverse shocks. This implies that policy interventions that aim to lower gender gaps in unemployment and underemployment should target the most affected age cohorts and locations. Policy should also give priority to interventions that narrow disparities in access to education and those that reduce adverse shocks.
VL  - 2
IS  - 2
ER  -

Copy | Download