**4. Econometric results**

At the level of the non-parametric approach, a first analysis is made by distinguishing according to the age group in order to capture the specificities that could exist between young people and adults. The parametric approach is discussed later.

#### **4.1 Non-parametric approach**

Kaplan-Meier estimators of the non-parametric approach for survival functions by age group show that adult unemployment durations are generally shorter than those of youth (see **Figure 1**). It also appears that the unemployment durations of young women are longer than those of young men (see **Figure 2**). Their survival function above that of young men confirms their difficulties in entering the labor market.

If we combine the age criterion with that of gender, we can, in relation to the level of the rates, constitute two groups of young people. The youngest (14–24 years old), and particularly young women, can be classified as the most group. Young men over the age of 24 are significantly less exposed to unemployment than those in the first group.

#### **4.2 Parametric approach**

**Table 3** (annexed) presents the results of risk function estimates taking into account the heterogeneity between individuals. It traces the explanatory factors of the duration of unemployment among young people by sex.

Among the factors that expose workers to long-term unemployment, the analyses tend to highlight individual characteristics. Among these characteristics, it is common to distinguish "demographic" individual characteristics (sex, age and


that belonging to the group of individuals aged 14–24 rather than being a young adult (25–35 years old) increases the chances of getting out of unemployment. This indicates that in Côte d'Ivoire, adults are leaving unemployment more quickly. They "survive" less time in the unemployment state. The age of the end of studies being around 24 years (short cycle of vocational training type), a possible explanation would be that at the exit of the education system, these young people

*A Gender Analysis of the Determinants of Youth Unemployment in Côte d'Ivoire*

Beyond age, it also seems that long-term unemployment would affect more young graduates. However, the results obtained with the level of education contrast this reasoning. It appears that it is young people with no education who are less

This gives an idea of the type and quality of the job, regardless of the sex of the applicant. Thus, young women or young men with no education are those who have a high probability of getting out of unemployment relatively quickly. One possible explanation lies in the relocation or closure of many companies caused by the military-political crisis unleashed in 2002. What fuels the precarious job of workers without expertise or professional training? The informal sector, especially domestic ones, occupies the majority of young people. The formal private sector offers only a small proportion of jobs [44]. This situation is the result of a strong mismatch between training and employment. Policies in favor of the decline in unemployment would be more effective by favoring a revision of the Ivorian education system that would integrate the concerns of businesses and professions of the future

Other factors may play in favor of long-term unemployment, sometimes just as important: this is the fact of residing in the economic capital supposed to reduce the probability of leaving unemployment. However, among young women, residing in Abidjan increases the chances of staying longer unemployed. The economic capital is therefore an area with fewer job opportunities for them. Among young men,

Age group 14–24 years 56.16 43.84 100.0

Place of residence Abidjan 53.71 46.29 100.0

Educational level None 69.58 30.42 100.0

Marital status Single 46.63 53.37 100.0

Ensemble 56.18 43.82 100.0

*Source: Authors' calculations based on 2012 AGEPE data.*

*Proportion of unemployed youth by gender (%).*

**Woman Man Ensemble**

25–35 years 60.51 39.49 100.0 36 years and + 48.91 51.09 100.0

Other urban areas 57.17 42.83 100.0 Rural areas 59.15 40.85 100.0

Elementary school 64.71 35.29 100.0 High school 43.64 56.36 100.0 Higher education 40.69 59.31 100.0

Married 66.99 33.01 100.0 Widowed/divorced 83.64 16.36 100.0

have a strong employability.

*DOI: http://dx.doi.org/10.5772/intechopen.85287*

likely to be unemployed.

(**Table 4**).

**Table 4.**

**207**

*Source: Authors' calculations based on 2012 AGEPE data.*

*\*Significant at 10%.*

*\*\*Significant at 5%.*

*\*\*\*Significant at 1%.*

#### **Table 3.**

*Determinants of the duration of youth unemployment by gender* Weibull with correction of heterogeneity *(*Gamma*).*

family situation) and individual socio-economic characteristics (initial education and level of education) [43].

An examination of the results of the estimates for the entire young population reveals that young men are more likely to leave unemployment than young women. The positive significance of the coefficient of this variable confirms those obtained with the non-parametric approach. Unemployed young women aged 14–24 have the same characteristics as young men in the same age group, with the difference that young women enter the labor market earlier. Their greater difficulty in accessing the labor market may be due to their low level of education and the weight of tradition relegating the role of women to household chores.

If young people are unemployed longer compared to adults (see **Figure 1**), the results highlight particularities within the young population. It should also be noted

#### *A Gender Analysis of the Determinants of Youth Unemployment in Côte d'Ivoire DOI: http://dx.doi.org/10.5772/intechopen.85287*

that belonging to the group of individuals aged 14–24 rather than being a young adult (25–35 years old) increases the chances of getting out of unemployment.

This indicates that in Côte d'Ivoire, adults are leaving unemployment more quickly. They "survive" less time in the unemployment state. The age of the end of studies being around 24 years (short cycle of vocational training type), a possible explanation would be that at the exit of the education system, these young people have a strong employability.

Beyond age, it also seems that long-term unemployment would affect more young graduates. However, the results obtained with the level of education contrast this reasoning. It appears that it is young people with no education who are less likely to be unemployed.

This gives an idea of the type and quality of the job, regardless of the sex of the applicant. Thus, young women or young men with no education are those who have a high probability of getting out of unemployment relatively quickly. One possible explanation lies in the relocation or closure of many companies caused by the military-political crisis unleashed in 2002. What fuels the precarious job of workers without expertise or professional training? The informal sector, especially domestic ones, occupies the majority of young people. The formal private sector offers only a small proportion of jobs [44]. This situation is the result of a strong mismatch between training and employment. Policies in favor of the decline in unemployment would be more effective by favoring a revision of the Ivorian education system that would integrate the concerns of businesses and professions of the future (**Table 4**).

Other factors may play in favor of long-term unemployment, sometimes just as important: this is the fact of residing in the economic capital supposed to reduce the probability of leaving unemployment. However, among young women, residing in Abidjan increases the chances of staying longer unemployed. The economic capital is therefore an area with fewer job opportunities for them. Among young men,


#### **Table 4.**

*Proportion of unemployed youth by gender (%).*

family situation) and individual socio-economic characteristics (initial education

*Determinants of the duration of youth unemployment by gender* Weibull with correction of heterogeneity

**Explanatory variables Woman Man Ensemble**

Man – – 1.591 (4.71)\*\*\* 14–24 years old 1.266 (1.41) 1.190 (1.22) 1.240 (1.99)\*\*

No 3.050 (3.36)\*\*\* 2.070 (3.42)\*\*\* 2.347 (4.69)\*\*\* Primary 1.673 (1.57) 1.314 (1.29) 1.438 (0.04)\*\* Secondary 1.150 (1.45) 0.231 (0.60) 1.144 (0.75)

Abidjan 0.836 (0.93)\* 1.020 (0.14) 0.950 (0.44) Other urban 0.684 (1.34) 0.908 (0.63) 0.823 (1.61) Rural *Ref. Ref. Ref.*

Single 1.451 (0.73) 1.436 (0.49) 1.150 (0.35) Married 1.083 (0.016) 1.822 (0.81) 1.169 (0.40) Widower/divorced *Ref. Ref. Ref.* **Constante** 0.280 (2.12)\*\* 0.270 (1.73)\* 0.267 (3.09)\*\*\* *Ln(p)* 0.482 (10.52)\*\*\* 0.454 (0.454)\*\*\* 0.461 (15.95)\*\*\* *Ln(θ)* 1.320 (12.48)\*\*\* 1.360 (23.28)\*\*\* 1.579 (36.32)\*\*\* *p* 2.3368 1.575 1.586 *σ=1/p* 0.61746 0.635 0.630 *θ* 6.169488 0.227 4.855 **Number of observations 2 368 2 530 4 898**

weight of tradition relegating the role of women to household chores.

An examination of the results of the estimates for the entire young population reveals that young men are more likely to leave unemployment than young women. The positive significance of the coefficient of this variable confirms those obtained with the non-parametric approach. Unemployed young women aged 14–24 have the same characteristics as young men in the same age group, with the difference that young women enter the labor market earlier. Their greater difficulty in accessing the labor market may be due to their low level of education and the

*(01)* 1023.86 1701.12 2100.95

*Prob* > *chi2* 0.000 0.000 0.000 *Log likelihood* 4243.682 7246 8923

If young people are unemployed longer compared to adults (see **Figure 1**), the results highlight particularities within the young population. It should also be noted

and level of education) [43].

*Source: Authors' calculations based on 2012 AGEPE data.*

**Other characteristics**

*Regional Development in Africa*

**Level of education**

**Middle of residence**

**Marital status**

*Wald chi<sup>2</sup>*

*\*Significant at 10%. \*\*Significant at 5%. \*\*\*Significant at 1%.*

**Table 3.**

*(*Gamma*).*

**206**

there are no statistically significant differences in the chances of getting out of unemployment between those living in rural areas and those living in urban areas.

As we pointed out in Section 1, several initiatives have been undertaken to combat unemployment in Côte d'Ivoire. But to our knowledge, there are still no accompanying measures geared specifically to the long-term unemployed. This practice could significantly reduce the duration of long-term unemployment.
