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Understanding the Drivers of the Youth Labour Market in Kenya

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Disadvantaged Workers

Part of the book series: AIEL Series in Labour Economics ((AIEL))

Abstract

This article identifies the macro and microeconomic determinants of youth unemployment and inactivity rates. It finds that although the size of the youth cohort does have significant implications for the status of youth in the labour market, aggregate labour market conditions have a greater influence. The article also finds a large gap between the youth and the overall employment elasticities in the country. This implies that fostering economic growth and ensuring economic sustainability, important as these factors are, will not be sufficient to address youth challenges. Efforts will need to be focused on improving the youth employment content of growth. In this regard, results from the microeconometric analysis find that boosting tertiary school attendance and providing targeted vocational training to young people (particularly women) would be the most effective measures for improving youth employability in the country.

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Notes

  1. 1.

    Although the international definition of youth comprises individuals aged 15–24, a detailed analysis of different labour market variables by age cohort shows that the group of people aged 25–34 sometimes presents clearly distinctive patterns, which are interesting to consider. Thus, the analysis in this article will take into account, whenever possible, an enlarged sample of youth (15–34), differentiating always between the two youth cohorts. The enlarged group also complies better with the official definition of youth used by the Government of Kenya, which includes individuals between the ages of 15 and 30 years.

  2. 2.

    This figure takes into account the new youth entering the working-age but also the people that, having exceeded 65 years of age, fall outside the working-age range.

  3. 3.

    Due to the unavailability of information by age group, it is not possible to include figures on employment and employment-to-population for the youth aged 15–34.

  4. 4.

    The inactivity rate for people aged 15–34 attained 42.4 % in 2011, which is 1.8 percentage points above the 2000 figure (40.6 %). Although this figure is significantly lower than that of youth aged 15–24, it is still 24.4 percentage points above the adult (35 years or more) inactivity rate.

  5. 5.

    Lack of data in Kenya, did not allow for the direct analysis of the number of youth who are discouraged (those who are not participating in the labour force but would rather be working) or that are not in employment, education or training (NEET). Figure 10.4, however, illustrates the importance of discouragement through the difference in trends in youth inactivity and enrolment in tertiary education.

  6. 6.

    In 2006 (latest year for which information on working poor exists), the proportion of employed young workers living in extreme poverty (i.e. below the international poverty line of $1.25 PPP per day) was 16.8 %. This is close to 2 percentage points more than among their adult counterparts (15 %). Meanwhile, the percentage of employed young workers living in poverty (i.e. below the international poverty line of $2 PPP per day), was 35.6 %, which is 2.6 percentage points higher than the adult working poverty figure.

  7. 7.

    Due to the lack of clarity of the wage-youth unemployment relationship on the literature, this variable has not been considered in this analysis.

  8. 8.

    Given that the youth labour challenge in Kenya includes both, young people in unemployment and young people who have fallen into inactivity, the analysis of determinants will focus on “youth not in employment” which includes both components.

  9. 9.

    Given the potentially high multicollinearity that might be affecting the first estimation, the second estimation was also constructed with views to check the robustness of the results of the first estimation.

  10. 10.

    Previous studies have found similar results, showing that the relative cohort size variable is largely unaffected by adding the youth enrolment rate as a control (Korenman and Neumark 1997). These results seem intuitively correct for the Kenyan case, where low employment-to-population ratios do not seem to be explained by education since the tertiary school enrolment rate (which is the most direct substitute for youth employment) has remained persistently low, at 4 % in 2009.

  11. 11.

    These output-employment elasticities have been estimated through the long-term relationship between output and employment during the last 20 years (from 1991 to 2011). Employment data were gathered from KILM database (ILO 2011) and real GDP data from the WEO database of the IMF (IMF 2012). It is important to note that while elasticities provide an idea of the employment response to growth, they do not account for the quality of jobs created in the economy—they cannot distinguish between formal and informal sector jobs or between part-time and full-time employment.

  12. 12.

    The simulations presented in this section were constructed by applying the output-employment elasticity to the GDP growth scenarios defined in Figure 10.5. Figures make reference to the number of jobs needed by 2015 to absorb the growing working-age population, estimated by UNDESA (2011).

  13. 13.

    Data collection for the KIHBS 2005/2006 was undertaken during a period of 12 months starting on May 16, 2005. The survey was conducted in 1,343 randomly selected clusters across all districts in Kenya, comprising 861 rural and 482 urban clusters. The database contains information for 13,430 households and 66,709 household members (KNBS 2007).

  14. 14.

    The individuals without available information regarding their main activity during the past 7 days (47 missing values in the sample) were excluded from the analysis.

  15. 15.

    The literature on youth unemployment has also stressed the importance of household income in explaining the labour market status of individuals (Rice 1987). Due to the inaccessibility to information related to household income, we were unable to include this variable in the analysis. However, given the importance of this observation of the literature, we have controlled for the employment status of other members in the household, which can be interpreted as a proxy of how much the individual needs a job (Rees and Gray 1982).

  16. 16.

    People in education account for 42 % of inactive individuals in our sample. In order to ensure that this factor does not influence the difference in the probability of being inactive between youth and adults, two controls were carried out: first, a dummy variable capturing school attendance was included in the model; second, the model was estimated separately for the whole sample and for each group of age excluding people in education. All coefficients of the variables of interest remained highly significant and the absolute sizes of the estimated effects changed relatively little between the two estimation techniques.

  17. 17.

    “Gender discrimination” captures the differences in employment status between men and women that are not explained by educational attainment, family responsibilities and the effect of having a network. However, it is important to bear in mind, that other than discrimination, the residual effect could be linked to factors such as health conditions, for which the model did not control due to lack of information.

  18. 18.

    These two factors affect less the probability of older youth (24–35) of being inactive. Indeed, tertiary education reduces the probability of being inactive by 15.6 % for this age group and vocational training by 5 %.

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Acknowledgment

The authors would like to thank Steven Tobin for valuable comments in different versions of the article. We also thank two anonymous referees for their helpful suggestions on this article. Research assistance by Cecilia Heuser in the latest version of this article is gratefully acknowledged. The results of the empirical analysis developed in this article have been summarized in Chap. 3 of the report Kenya: Making quality employment the driver of development, produced by the Research Department (previously, International Institute for Labor Studies, IILS) of the ILO (2013).

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Correspondence to Verónica Escudero .

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Escudero, V., Mourelo, E.L. (2014). Understanding the Drivers of the Youth Labour Market in Kenya. In: Malo, M., Sciulli, D. (eds) Disadvantaged Workers. AIEL Series in Labour Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-04376-0_10

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