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OCCUPATIONAL SEGREGATION AND GENDER WAGE 
DIFFERENTIALS IN CAMEROON 

 
 

Nadine Zeh 
Faculty of Economics and Management, University of Maroua- Cameroon, PO. Box: 391, Maroua 

 
Abstract: The labor market in Cameroon has faced significant challenges due to economic conditions 
and demographic factors, resulting in a restructuring of the labor force. This has led to deteriorating 
employment conditions, increased precarious work, and the growth of the informal sector. Women in 
Cameroon, in particular, face overrepresentation in low-wage jobs, especially in the informal 
agricultural sector. While there have been advancements in women's participation in the labor 
market, gender disparities persist, with women often occupying lower-paying positions. This paper 
examines the evolving role of women in the Cameroonian labor market, highlighting progress in 
terms of professional equality in top positions, yet acknowledging ongoing challenges such as the 
gender pay gap and rural gender inequalities. The study also emphasizes the economic invisibility of 
women's domestic and reproductive work, which, despite its critical role in maintaining households 
and communities, remains largely unrecognized. Understanding these dynamics is essential for 
addressing labor market inequalities and promoting gender equality in Cameroon. 
Keywords: Cameroon labor market, Gender disparities, Informal sector, Gender pay gap, Rural 
gender inequalities 
 
 
1. Introduction  
In Cameroon, the economic situation and the demographic weight have led to a destructuration of the 
labour market. The employment situation and the availability of social services have considerably 
deteriorated with the development of precarious employment and an expansion of the informal sector. 
Labour market indicators in Cameroon show strong disparities (National Institute of Statistics (NIS), 
2012; Ekamena, 2014; Baye, Epo & Ndenzako, 2016; International Labour Organization (ILO), 2017). 
The informal sector provides the most opportunities for professional insertion; in fact, it currently 
occupies about 90% of workers (NIS, 2016). In Cameroon, women tend to be over-represented in low-
wage jobs and especially in the informal agricultural sector where productivity and farm incomes are 
lower (NIS, 2012). The female population is represented in all sectors of activity, particularly in the 
service sector. However, according to the ILO (2017), informal employments are highly concentrated 
in the service sector.  
Since the 1990s, the behaviour of women in the labour market in Cameroon has evolved. The progresses 
in terms of professional equality in the labour market are reflected in the portion of women in the top 
jobs. Currently, there are eight women ministers and three women secretaries of state in the 
government, a percentage of 17.18%, compared to 11.7% in 2012 and 6.7% in 2002. Today, women 
represent 35% of parliament compared to 13.9% in 2012 and 5.9% in 2002. Despite these progresses, 

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the place of women in senior positions of responsibility remains limited and the gender pay gap is still 
current. In rural zones, gender inequalities are widespread in the labour market, where men and women 
often work in different combinations of employment, as selves-employed farmers, temporary workers, 
or unpaid family workers. Rural women continue to be penalised by the invisibility of their work in the 
home economy. They are strongly engaged in domestic and reproductive functions, which are crucial 
for the maintenance of home, families, parents and communities, but this is seen as an extension of 
family duties, which explains that this important part being economically invisible.  
Despite these effects on gender equality and economic productivity, employment segregation occurs in 
both developed and developing countries. It also depends on social norms and beliefs and local 
constraints on labour supply and demand. This is of particular concern as gender attitudes are 
persistent and continue to hamper access to better opportunities for women in many countries 
(Giuliano, 2018). This is a serious preoccupation for equity, gender equality and the implications of low 
autonomy on the well-being of women and children.   
Effective arguments have been made for policies to improve women's position in the labour market. In 
particular, a growing body of research points to the adverse effect of gender employment gaps on the 
overall productivity and growth potential of emerging economies (Klasen & Lamanna 2009).   
Occupational segregation has significant costs for the economy. Indeed, the limited participation of 
women in leadership and management positions could be trammed to innovation and economic 
growth; it limits efforts to encourage women's participation in the labour market (Das & Kotikula, 
2019). Some studies have shown that there are benefits to having a more diverse workforce as there are 
economic costs associated with gender inequalities in the labour market. According to these 
approaches, the economic benefits of increasing women's labour force participation have beneficial 
effects on productivity and economic growth (Ngai & Petrongolo, 2017; Ostry, Alvarez, Espinoza & 
Papageorgiou, 2018).  
Progresses are being made in several countries to reduce gender gaps in human capital (education and 
health), but these progresses are not always associated with improved conditions for women in the 
labour market. Despite significant improvements in policies related to women's empowerment over the 
past decades, women's participation in the labour market has remained low, even in developed 
countries (Lagarde & Ostry, 2018). Patterns of occupational gender segregation vary depending on 
countries. Globally, women tend to be concentrated in low productivity sectors (Das & Kotikula, 2019).  
Women are found predominantly among the unemployed and family workers. They continue to occupy 
the majority of atypical, informal, temporary and parttime jobs (ILO, 2019). Under these conditions, 
women would not achieve full economic and social autonomy. According to the data in Cameroon, there 
is a persistence of limited access of women to certain jobs, which would also lead to a persistence of 
gender wage gaps. The aim of this study is to characterise the evolution of wage differentials between 
men and women in the labour market, taking into consideration the distribution of men and women 
according to socio-professional categories in Cameroon since 2001. It is up for us to determine whether 
women and men work in different jobs because of their different preferences and attitudes or rather 
because of selection mechanisms in the labour market, thus maintaining wage gaps.  The research for 
explanations, sources and estimates of these gaps is a reason to explain and reduce the persistence of 
employment inequalities.  

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This study will have five sections, after the introduction, in the second section we will make a survey of 
the theoretical contributions, the third section will be devoted to the methodology and in the fourth 
section we will present the results and then the conclusion.  
2. The theoretical literature review  
2.1. Theoretical contributions to occupational segregation  
One theoretical explanation for occupational segregation is Bergmann's “crowding hypothesis” 
(Bergmann, 1971, 1974). According to this model, competition is imperfect in the labour market; the 
dominant group (men) can ration  the entrance into certain types of jobs and thus benefit from a higher 
wage rate in these jobs. In contrast, the disadvantaged group (women) is excluded from these jobs and 
accumulates a limited number of jobs. The increase in the participation rate of women in the labour 
market has not reduced the concentration of female jobs and the occupational segregation of men and 
women.  
2.1.1. Horizontal segregation  
The human capital approach is used to explain the phenomenon of occupational segregation. It predicts 
that women will tend to be specialised in occupations of their preference and where their career 
discontinuities are not penalised, i.e. in rather low-skilled occupations (Polachek, 1981). However, in 
reality, members of both sexes are found in jobs requiring a higher investment in specific human 
capital, which is inconsistent with the theory. Some economists have developed theoretical models that 
explain occupational segregation in the labour market in terms of employer behaviour. For example, 
according to Becker (1957), employers have a taste for discrimination. Arrow (1972) and Phelps (1972) 
argue that employers do not have such preferences, but that women are excluded from certain jobs 
because of the imperfect information employers have about them. According to Arrow (opcit), if women 
know that they will be excluded from certain jobs, they will less invest in human capital.  
However, institutional economists believe that segregation is not due to discrimination, but to the 
structure of the labour market. Killingsworth (1987) bases his approach on labour market favouritism 
towards men.   
It assumes that the market is made up of two types of jobs (A and B) and that employers discriminate 
in favour of male employees in accessing the better-paid B job, even though women are also equally 
productive. He then shows that this discrimination leads to several results that are consistent with 
certain stylised facts observed in the labour market. In particular, the job tenure gap will be larger in 
jobs where women are under-represented (Job B) (Havet & Sofer, 2002).  
2.1.2. Vertical segregation  
Vertical segregation is a limit to women's access to the hierarchical functions. This access seems to be 
limited by an invisible and transparent "glass ceiling". To explain the glass ceiling, we use arguments 
related to the sociology of work and organisations, the sociology of the family, or the sociology of 
professions.  
One argument has to do more with the characteristics of the work organisation in managerial 
occupations, which are essentially adapted to men's strategies and less often compatible with women's 
strategies and aspirations. The search of a balance between professional and family investment leads 
women to give up continuing careers that require time commitment and permanent availability. The 
norm of permanent availability/mobility is one of the conditions for gaining power (Maruani & Nicole, 

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1989). Women managers are more likely to live alone, and when they are in a couple, they are more 
likely to be the partners of managers and therefore have to negotiate their mobility and availability with 
a spouse who is himself preoccupied with his own promotion (Pochic, 2004).  
Another factor is the fundamental role of networks in accessing leadership positions. Women often lack 
the information, connections, and support needed to reach higher positions because they are cut off 
from the networks, both formal and informal, whose support is essential for advancement within the 
company. Because they receive less attention and encouragement from their superiors, they may feel 
less legitimate. This factor works in a loop, as the higher percentage of men at the decision making level 
contributes to maintaining the glass ceiling (Maruani & Nicole, op.cit).  
Another argument that contributes to the creation of the 'glass ceiling' is related to the stereotypical 
conception of the skills required and in the conception of responsibility or qualification. The notion of 
hierarchical responsibility is defined in relation to the number of subordinates under one's instruction, 
and careers are based more on manly principles such as competition or courage. Furthermore, the 
implementation of the competence approach in the new classification systems combines over-valuation 
of the technical dimension and under-valuation of the relational dimension (Sehili, 2000). Relational 
and behavioural skills are often less recognised on the labour market and are not subject to real formal 
learning processes because they are assumed to be 'innate', natural because they are acquired within 
the family socialisation (Daune-Richard, 2001).  
2.2. A theory of job market segmentation   
According to the employment segmentation theory (Doeringer and Piore, 1971), the distribution of 
wages and socio-economic status in the labour market less depends on the distribution of education 
levels than the structure of the labour market. While the proponents of the neoclassical human capital 
theory assert that there are two types of jobs, namely skilled and unskilled, employment segmentation 
theory reprents it differently. The basic assumption of this theory is that the labour market is divided 
into two sectors: the primary sector and the secondary sector. The difference between the two sectors 
has more to do with the quality of the jobs themselves than with the qualifications of the employees. 
Jobs in the primary sector are good jobs, while jobs in the secondary sector are bad jobs.  
The secondary sector is characterised by jobs requiring a very low level of qualification, offering only 
inconstant employment, low pay, poor working conditions and small chance to progress in their career. 
The staff is not unionised and is predominantly dominated by people from disfavored groups: ethnic 
minorities, women and older people, immigrants. On the other hand, the primary sector is 
characterised by jobs that are hierarchical to each other and relatively well paid, on-the-job training, 
clear differences in wages (wage structure), opportunities for promotion, well-defined work rules and 
job stability (Doeringer & Piore, op.cit).  
The primary sector is divided into two tiers: the lower-jobs tier and the upper-jobs tier. The two tiers 
are distinguished by the same differences as between the primary and secondary markets. Compared 
to subordinate jobs, senior jobs are characterised by greater security, higher levels of education, creative 
freedom and relatively higher incomes (Griffin, Kalleberg & Alexander, 1981).  
According to segmentation theory, the valuation of education depends on the type of market in which 
the individual is hired: differences in human capital are not expressed by differences in wage gains if 
one is in the primary market (Granahan & Shakow, 1990). According to Doeringer & Piore (op.cit), the 

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labour market is structured according to the level of technological complexity of production. Individuals 
are recruited for jobs when they have human capital that matches the technical requirements. Wages 
are not determined by the level of education, but by the characteristics of the jobs. This is why, for jobs 
that are technically unsophisticated and belong to the lower primary sector, educational attainment 
does not influence recruitment and pay determination. This approach explains why people are paid 
according to the functions they perform and not according to their education levels.  
3. Methodology   
3.1. Specification of the model  
The occupied job is an important phenomenon that differentiates populations in the labour market and 
thus the allocation of wages. A method for decomposition of the wage differential is provided by Brown, 
Moon & Zoloth (1980). This method is an extension of the Oaxaca-Blinder (1973) decomposition; it 
introduces job occupation differences into the analysis of wage differentials incorporating distinctions 
in wage differentials between job categories, i.e. due to different job structures (inter-category gap) and 
also within the same job categories (intra-category gap). These two types of wage gaps are further 
decomposed in order to distinguish between the justified portion and the portion attributable to 
discrimination. The main idea is to measure how the total gender wage gap is explained by gender 
differences in job allocation (occupational segregation). To determine the Brown & al. (1980) wage 
decomposition equation, we start from the OLS wage equations for men and women respectively, 
expressed as follows:  

 
 

W h j ˆjh X hj et W fj ˆjf X fj (1)        
h f 

 
 

Where j denotes the job category occupied, W j and W j are the logarithm of the wage average of men  

 
h ̂  f are the vectors of the estimated coefficients, X hj and X f j are the matrices of and women respectively, 

ˆj and j 
the individual average characteristics of the workers. If we take Pjh and Pjf , the probabilities of working 
in category j where j=1,...J. It follows that :  
J J 

 
 

 

W hj W fj PWjh h j Pjf W f j (2) j 1 j 1 
By adding and subtracting from the right-hand side of equation (2), we get:  
W h W fJ P f W h W   f J   Pjh Pjf Pjf W hj                (3)  j 

 
 
 

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j 1 j 1  
The first term represents the within-occupational component, which measures the part of the gap that 
is due to wage differences within occupations. The second term corresponds to the inter-occupational 
component: this is the portion of the wage differential is attributable to differences in the distribution 
of male and female workers across job occupation.  
Both terms can be decomposed into explained and unexplained components. The equation of Brown et 
al (1980) can be written:  

 
W J Pj j     J   J f J   h h P jf   Pjf 
 (4) h f f ˆh X hj X fj Pjf X fj hj fj X hj ˆjh Pjh P j X j ˆj 
 

 
 

 
 
W 

j 1 j 1 j 1   j 1   
The first two terms of equation represent the intra-occupational component of the wage gap. The first                       

term is the explained portion which captures the part of the within occupation differential that is due 
to the different levels of labor market characteristics.   
The second term is the unexplained portion, that is, the portion of the within wage differential that 
arises from gender differences in the rates of return to labor market characteristics and that is 
interpreted as wage discrimination.  
The third and fourth terms on the right hand side correspond, respectively, to the explained and  

  f unexplained portions of the inter-occupational component. The parameter P j represents the non-
discriminatory occupational structure for female. The explained portion of the inter-occupational term 
measures the part of the across occupation wage differential that results from differences in male and 
female endowments. The unexplained portion reflects the part of the across occupation wage 
differential that is not explained by differences in the two groups’ characteristics and that is understood 
as employment discrimination (Meurs & Ponthieux, 1999).  
To estimate the wage decomposition using the Brown et al (1980) method, the first step is to predict 
the probability of men's access to job occupations based on a set of individual characteristics. These are 
age, age2 , experience, education and place of living. This requires an estimation of the reduced form of 
the Multinomial Logit model for the sample. The second step is to simulate the distribution of the 
occupations for women as if they had the same employment access structure as men.   
The third step is to estimate the wages of both populations for each occupation category. The six 
categories include: senior managers, skilled workers, manual workers, patrons, family helpers and 
apprentices combined into one category and self-employed. The total gender pay gap is then 
decomposed into different terms.  

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3. 2. Data and Variables of the Study  
In Cameroon, the data sources that gather information to quantify the more recent evolution of wage 
discrimination are the ECAM II, ECAM III and ECAM IV databases, which are respectively the second, 
third and fourth Cameroon Household Consumption Surveys conducted in 2001, 2007 and 2014. 
The limitation of this work is that the employee population is considered homogeneous according to 
the criterion of actual working time during the reference week. However, salaried workers in the urban 
milieu are often subjects to the phenomenon of underemployment because of the importance of 
seasonal jobs. This is all the more important as certain professions (nursing, textiles, etc.) are 
predominantly female and others (fishing, crafts, transport, etc.) predominantly male. It is therefore 
likely that part of the gender wage gap is due to this phenomenon of underemployment, which is not 
apprehended by the Cameroon Household Surveys.  
We restrict our analyses on individuals aged from 15 to 60 who declare themselves to be employed and 
receive a salary. The wage variable is the logarithm of the declared monthly income, either as an amount 
or as an interval. It includes wages, salaries and other earnings in cash from the activity. For the self-
employed it also corresponds to the net business income, i.e. the profit, or to the mixed income for the 
Informal Production Units, as the profit is difficult to calculate in their case.  The variables selected to 
explain income are: age, age squared, gender, level of education, experience, socio-professional 
occupation, place of living.   
4. Results   
Table 1 presents the total wage gaps in 2001, 2007 and 2014 which are 0.9877, 0.6787 and 0.7899 
respectively. We note that the gender wage gap in Cameroon decreased from 2001 to 2007 by 30.89% 
and increased by 10% between 2007 and 2014. We can explain this decrease with the financial crisis of 
2008 which would have had the effect of deteriorating the labour market, women being the first to 
undergo the consequences; the gender wage gap would have increased. This study shows a persistence 
of gender wage differentials between 2001 and 2014. Nevertheless, the results show that efforts have 
been made and continue to be made to reduce gender inequalities in the labour market in Cameroon. 
These results are in line with the findings of a study by the ILO (2017) on the persistence of monthly 
gender wage gaps between 2005 and 2010 in Cameroon.  
Table 1: Total gender wage differential  

Years   2001  2007  2014  

Total 
differential  

0,987745  0,678788  0,789945  

     Source: Author's calculation based on ECAM 2, ECAM 3 and ECAM 4 
The results of the decomposition of these differentials are contained in Table 2 and show each portion 
thereby determined over the years. They show that the intra-category component explains more of the 
wage gap than the total gap. Indeed, 0.7547 (76.41%) in 2001; 0.5639 (83%) in 2007 and 0.6055 
(76.65%) in 2014 represent the intra-category wage gap while 0.2329 (23.59%) in 2001; 0.1148 (17%) 
in 2007 and 0.1844 (23.35%) correspond to the wage gap that results from gender differences in the 
distribution of the jobs. Thus, a larger part of the total wage gap is due to differences in the wages of 
men and women within the same occupations, while a small part of this gap is explained by the different 
distribution of men and women in the same occupations. The results also show that by combining intra- 

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and inter-category effects, the unexplained total attributed to gender discrimination fully justifies the 
gap.  
Table 2: Decomposition of the wage differential on access to socio-professional groups  

  Intra-occupational 
differential  

Inter-occupational 
differential  

Years  2001  2007  2014  2001  2007  2014  

Explained 
portion   

-0,0072  
(-
0,95%)  

-0,0229  
(-4%)  

-0,0807  
(-13%)  

0,0539 
23,16%  

0,0429  
(37,38%)  

0,0681  
(37%)  

Unexplained 
portion  

0,7620 
100,95%  

0,5868  
(104%)  

0,6862  
(113%)  

0,1790 
76,84%  

0,0718  
(62,62%)  

0,1162  
(63%)  

Total  
0,7547  
76,41%  

0,5639  
(83%)  

0,6055  
(76,65 
%)  

0,2329 
23,59%  

0,1148  
(17%)  

0,1844  
(23,35 
%)  

Source: Author's calculation based on ECAM 2, ECAM 3 and ECAM 4   
The analysis of the intra-category component in the explained and unexplained parts shows the 
significant importance of the unexplained part in explaining the differentials in the same occupations. 
Indeed, the unexplained portion totally represents and evens more the intra-category component.  
While 4% in 2007 and 13% in 2014 of this component is explained by differences in labour market 
characteristics between men and women, 104% in 2007 and 113% in 2014 cannot be explained by these 
differences. We notice that in 2001, this difference in labour market characteristics between men and 
women is almost zero (0.95%). Noting that; the explained portion is negative from 2001 to 2014, which 
means that with regard to the differences in individual gender characteristics in the labour market, men 
are disadvantaged. This means that the enhancement of individual characteristics decreases intra-
category gender wage differentials.   
The results therefore show that a large part of the gender wage gap is not explained by individual 
endowment differences, but is due to wage discrimination. As regards the inter-category component, 
23.16% of the gap in 2001, 37.38% of the gap in 2007 while 37% of the gap in 2014 is explained by 
differences in characteristics between men and women on the labour market. While 76.84% in 2001; 
62.62% in 2007 and 63% in 2014 are explained by discrimination against women in access to certain 
employment. This highlights an aspect of discrimination that stems from the over-representation of 
women in lower paying firms and men in higher paying firms. This result points to the existence of 
horizontal occupational segregation as presented by Bergman (1974). 
 
 
 
 
 
 
 

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Table 3: Decomposition of the wage differential per socio-professional occupation 

Socio-
professional 
occupation  

Years  
Intra-occupational 
differential  

Inter-occupational 
differential  

Senior 
manager  

 2001  -0,0047 (-0,62%)  0,2760 (118,48%)  

2007  0,0025 (0,45%)  0,3618 (315%)  

2014  0,0108 (1,79%)  0,3699 (200,59%)  

Skilled 
workers  

 2001  -0,0217 (-2,87%)  0,6482 (278,24%)  

2007  -0,0215 (-3,8%)  1,0814 (942%)  

2014  0,0410 (- 6,78%)  0,8242 (446,91%)  

Manual 
workers  

 2001  0,0362 (4,8%)  0,5752 (246,92%)  

2007  0,0135 (2,4%)  0,3424 (298,27%)  

2014  0,0182 (3,01%)  0,3563 (193,24%)  

Patrons  

 2001  0,0155 (2,06%)  0,1343 (57,68%)  

2007  0,0226 (4%)  0,3314 (288%)  

2014  0,0209 (3,45%)  0,1508 (81,76%)  

Self-
employed   

 2001  0,6668 (88,34%)  -1,3918 (-597,45)  

2007  0,4816 (85,4%)  -2,0013 (-1743%)  

2014  0,5016 (82,84%)  -1,4026 (-760,56%)  

Family 
helpers 
apprentices  

and  2001  0,0626 (8,29%)  -0,0090 (-3,86%)  

2007  0,0651 (11,55%)  -0,0010 (-0,93%)  

2014  0,0128 (2,11%)  -0,1142 (-61,96%)  

Total  

 2001  0,7547  0,2329  

2007  0,5639  0,1148  

2014  0,6055  0,1844  
Source: Author's calculation based on ECAM 2, ECAM 3 and ECAM 4  
The results in Table 3 give the decomposition of the total wage gap per component and per 
socioprofessional category. The decomposition of the intra-category component shows that the same-
employment wage gap is higher in the own-account workers category with a decrease from 2001 to 
2014. Indeed, this category accounts for 88.34% in 2001, 85% in 2007 and 82.84% in 2014 of the total 
gap within the same employments, this gap is totally and even more justified by the unexplained portion 
attributed to wage discrimination against women. 
The decomposition of the inter-category component also shows that own-account workers have the 
highest gap, but in the distribution of the occupations, women are the most favoured. The category 
“selfemployed” has thus contributed over the years in Cameroon to reducing the gap in access to 
employment due to occupational segregation, which often increases the gender wage gap. For manual 

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workers, the intra-category gap was 4.8% in 2001, 2.4% in 2007 and 3.01% in 2014, while the inter-
category gap was 246.92% in 2001, 298% in 2007 and 193.24% in 2014. Both gaps are largely explained 
by wage discrimination on the one hand and by the limited access of women to this occupation on the 
other. However, we see a decrease of discrimination and limited access of female manual workers in 
2014, perhaps women have decided to go over their nature and face the difficulties associated with this 
occupation occupation.   
The percentage of senior managers in the total gap in the same employment is the lowest (it is negative 
in 2001, 0.45% in 2007 and 1.79% in 2014), but it remains sufficiently high in the inter-category 
component. This means that the gender wage differential is more justified by the occupational 
segregation that women experience in this occupation. Furthermore, the negative sign of the 
unexplained part of the intra-category difference shows a persistence and increase of gender pay 
discrimination in the occupation “senior managers” between 2001 and 2014.  
As for the occupation “skilled workers”, the results show that the wage gap is in favour of women. Its 
percentage of the total intra-category gap is small and negative (-2.87%, -3.8% in 2007 and -6.78% in 
2014). This job category therefore contributes to a decrease in the intra-category wage gap. In addition, 
the percentage in the inter-category component is the highest (278.24% in 2001, 942% in 2007 and 
446.91% in 2014) and it is in favour of men as in the occupation “senior managers” and “patrons”. These 
results show that the gender wage gap observed in the labour market is largely explained by women's 
limited access to the best high-wage occupations. This corroborates with the results of Baye & al. (2016) 
who show that women in terms of wages are penalised and that the gender wage gap is explained by 
individual and labour market characteristics.  
These results in theory could be explained by the fact that decision-makers or employers, based on the 
fact that women are not able to occupying high positions of responsibility due to their nature and the 
distribution of roles in the household, will consider the nomination or recruitment of men. Offering 
women positions that allow them time to take care of the home. Moreover, the employer is unable to 
make difference between women who will remain in the labour market in the long term, regardless of 
atypical working hours or often difficult and demanding working conditions, and those who will leave 
quickly. He therefore expects lower productivity on average from women, with a relatively high 
variance, and he will not be prepared to hire them on the same pay terms as men. Similarly, women 
anticipating that they will not have the same capacity as men to occupy certain categories of jobs, will 
make a bad investment in human capital, or will generally choose occupations where working 
conditions are compatible with their family responsibilities.  
Conclusion  
The objective of this study was to determine the evolution of gender wage differentials in the 
Cameroonian labour market between 2001 and 2014. The estimated results of the wage decomposition 
using the Brown et al. method (1980) show that in addition to being discriminated, women are also 
occupationally segregated, which has persisted since 2001. This is mainly due to the fact that women 
are majoritary in collective and informal enterprises where salaries are lowest, while men are majoritary 
in private enterprises and in high-level state positions where salaries are higher. Indeed, the results 
show a clear persistent contrast from 2001 to 2014 between the occupation of “senior manager”, “skilled 
workers” and “employers” and the occupation of “selfemployed workers”, “manual workers” and 

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“apprentices”. The first ones are the place of a marked occupational segregation towards women which, 
added to the discriminations against them, creates a strong wage differential between men and women 
in Cameroon. However, the results also show that in some jobs, male “self-employed” workers are 
occupationally segregated, while male “skilled employees” are discriminated.   
This study highlights the persistence of the gender pay gap. Furthermore, it shows that this persistent 
wage gap is largely a consequence of the structure of the labour market and is linked to the different 
position of women and men in this market. Despite the government's efforts to reduce inequalities in 
the labour market through the promotion of gender equality in certain recruitments, gender equality in 
income is still far from being achieved, and much remains to be done to reduce these gender 
inequalities. Heavy emphasis must be placed not only on the education of the girl child, but also on the 
access of women to the highest paid job categories.  
Furthermore, in Cameroon, the problem of employment is more in terms of underemployment. This 
underemployment mostly affects women, because they are highly represented in informal sector 
activities that are not recognised, registered, protected or regulated by the public authorities. Hence the 
need to act to improve working conditions in the informal sector, with a view to enhancing the value of 
their work and reinforcing their professionalism, which would lead to an aspiration to revise their salary 
conditions. Furthermore, measuring and understanding the phenomenon of occupational segregation 
in Cameroon is more necessary than ever in order to implement effective public action to promote 
gender-equitable governance. 
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