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Gender Difference and Job Quality: Application in Cameroon

Cosmas Bernard Meka'a1, Silvain Raoul Fotso2*

1Department of Economics and Commerce, University of Douala, Douala, Cameroon

2Department of Economics and Applied Management, University of Douala, Douala, Cameroon

*Corresponding Author:
Silvain RaoulFotso
Department of Economics and Applied Management,
University of Douala,

Received: 06-Jul-2023, Manuscript No. JSS-23-105128; Editor assigned: 10-Jul-2023, Pre QC No. JSS-23-105128 (PQ); Reviewed: 24-Jul-2023, QC No. JSS-23-105128; Revised: 27-Dec-2023, Manuscript No. JSS-23-105128 (R); Published: 04-Jan-2024, DOI: 10.4172/JSS.10.1.005

Citation: Fotso SR, et al. Gender Difference and Job Quality: Application in Cameroon. RRJ Soc Sci. 2024;10:005.

Copyright: © 2024 Fotso SR, et al. This is an open-access article distributed under the terms of the creative commons attribution license, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

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The objective of this article is to examine to what extent gender differences exist in the quality of employment in the labor market in Cameroon. For this, we use data from the fourth Cameroon Household Survey (ECAM4). This survey has the advantage of covering the national territory and providing information on the socio-demographic characteristics of workers as well as on the dimensions of job quality.

In addition to examining gender inequalities in the different dimensions of job quality, a principal component analysis was carried out to construct a composite index of overall job quality. The results establish that the probability for women to hold a better job is lower compared to men.

The only possibilities that women have of accessing quality jobs exist when they have managerial status and when they work in large companies. Inequalities in access to better quality jobs to their disadvantage are more the result of differences in unobservable characteristics rather than differences in individual characteristics.


Gender; Job quality; Multinomial logit; Probability; Index


JEL Rating: J16; J23; C25; C35


Gender inequality in the labor market is an old phenomenon which, far from disappearing in recent years, has been redefined. Early studies focused on income disparities and came to the conclusion that, other things being equal, women hold low paying jobs compared to men. Several explanations have been put forward in the literature. For example, according to the theory of human capital developed by Becker, the differences in investment in human capital, and therefore in productivity between men and women, are the cause of this situation. As a result, women find themselves in sectors of activity offering “poor quality” jobs with low rates of pay [1].

More recent studies on the gender issue focus on inequalities in access to quality employment between men and women. This variable, which was traditionally apprehended only in terms of salary level, has evolved into a multidimensional concept. Several dimensions are highlighted, including: Socio-economic security, skills and training opportunities, working conditions, work-life balance, and opportunities for promotion. Each of these dimensions includes several indicators, for example, socio-economic security is composed of indicators on wages and contracts; working conditions are represented by indicators on work intensity, working time, health risks, etc. Thus, according to studies, women are more likely to be associated with lower paid jobs, part-time work, temporary contracts and less social protection; while men are more often associated with higher salaries, greater chances of promotion and higher category positions [2].

Gender equality in the world of work has progressed over the past 20 years. The participation of women in the labor market has increased considerably, particularly in industrialized countries and in some middle-income countries. The improvement in their level of education has enabled more women to enter the labor market. As a result of globalization, women in developing countries have entered this market in various ways, working in export processing zones or special economic zones, in the agricultural sector and increasingly in as independent workers. However, increasing the number of jobs for women does not always go hand in hand with improving the quality of their working conditions. There are many reasons to be interested in the latter and to seek to improve it. From the workers' point of view, a “good” quality job is associated with better job satisfaction and increased well-being, but it also allows companies to achieve higher performance. At the macroeconomic level, job quality is correlated with higher labor force participation and employment rates [3].

Since the 1980's, Cameroon has initiated several transitions in its development, which are likely to affect the quality of employment and the gender pay gap. After a period of deep and lasting economic crisis due to the deterioration of the terms of trade, Cameroon implemented a structural adjustment policy in 1988 under the aegis of the Bretton Woods institutions. The measures taken during this phase affected jobs both in quantity and quality. Indeed, between 1983 and 1993, there was a double contraction in wages, particularly in public administration, and a decrease in the wage rate in all sectors of activity. The situation has deteriorated in recent years due to the international crises since the subprime crisis in 2008. The fourth Cameroonian household survey (ECAM4) underlines that, in a context marked in particular by demographic growth estimated at 2.5% in 2014, the activity rate of people aged 15 or over fell to 72.1% in 2014 against 76.2% in 2010 and 81.9% in 2007.

During this year 2014, women represented 45.6% of the active population, a slight increase of 0.5% compared to 2001 when the female active population was 45.1%. While the high proportion of employed workers in relation to the population of working age remains a dominant trend, the disparities, on the one hand between men and women and on the other hand between urban and rural dwellers also seem to be maintained over time. The increase in the activity rate among women does not necessarily imply that they hold jobs in favorable settings, because the quantity of employment does not necessarily relate to the quality of employment, especially if the majority of jobs are in the informal sector or are precarious [4].

Our study therefore aims to show that women hold "poor" quality jobs in the Cameroonian labor market compared to men. To achieve this objective, we use microeconomic data from the fourth Cameroon household survey (ECAM IV) conducted in 2014 by the National Institute of Statistics [5].

The remainder of this article is organized as follows. Section 2 presents a review of the literature on gender inequalities in general and that concerning the quality of jobs in the labor market in particular. Section 3 describes the econometric model. Section 4 analyzes the data to estimate the model. Section 5 presents the results and implications of the study. Finally, the conclusion is the subject of section 6.

Literature Review

Previous studies, carried out mostly in developed countries, have shown that gender differences can be reflected in many aspects of job quality, such as pay, type of contract, working time, working conditions, skills and training opportunities, the possibility of promotion and social protection. In these different contents, women are more likely to be associated with low wage and part time jobs. Most of them have temporary contracts and poor social protection. On the other hand, more men have higher salaries, have more chances of promotion, and are in higher job categories [6].

Job quality: A multidimensional concept of labor market analysis

While job quality is a broader and multidimensional concept, the dimensions actually considered vary greatly depending on the study. Green's analysis includes skill level, work effort and intensity, autonomy, wages, risk, level of job security, and worker well-being. The study by Brown et al., retains dimensions close to those of green, namely: Job satisfaction, effort and stress, job security, labor relations within the company, and finally wages and wage inequality. On the other hand, the Canadian index is based on a different approach, of a macroeconomic and sectoral nature: It is constructed from the full-time/part-time ratio, the salaried employment/self-employment ratio, the high-income employment ratio wages/low wage employment and an employment stability indicator [7].

In the standard model, the wage, the preferred variable in economic analysis, is the essential source of employment utility. Under the theory of compensating differences, the utility function is modified to take into account the stability of employment (risks of job loss, for example) and working conditions. Attempts at empirical validation suggest, however, that wage premiums do not entirely compensate for the inconveniences and risks incurred by the worker. In other words, the salary alone cannot sum up the quality of the job. The theory of human capital also points out that training is one of the best means of obtaining a quality job with a high salary and satisfactory working conditions. An international survey of workers, the international social survey program, reveals that job security and job satisfaction are among the most important characteristics of a "good" job and that the intensity of work is one of the causes of dissatisfaction. According to an analysis by the European household panel, wages, job stability and wage mobility are job satisfaction factors in many countries. Furthermore, the balance between work and family life is also a concern of working people: In France, according to the Histoire de life–Construction of Insee identities, four working people out of ten declare that their work (times, place, organization) makes it difficult to organize their family life [8].

Finally, an originality of the European approach appears in the importance given to the "gender" dimension, that is to say both to equality between men and women and to the conditions for reconciling family life and professional. The indicators devoted to these aspects are numerous and relatively precise compared to other dimensions [9].

Gender and quality of employment

The social sciences have long presented a partial view of job quality, with each discipline emphasizing one or two criteria. Sociologists based themselves on socio-professional classes to apprehend the quality of employment, and especially explored the question of autonomy. Psychologists, on the other hand, focused on job satisfaction or stress. Finally, economists made wages the central variable in their analysis, notably through the theory of human capital. This disciplinary orientation of the dimensions of the quality of employment seems to us to be partly dated today, as the lines of fracture and reflections have evolved [10].

Today, economists are increasingly interested in other elements, in particular job satisfaction, and adopt a relatively broad analysis grid. If they continue to alert us to the permanence of social divisions, sociologists also note recompositions of the notion of this concept. They recognize that job quality can vary within an occupation as well as between occupations. We place ourselves in this approach of disciplinary opening which enriches the concept of job quality in order to present our review of the literature [11].

The economic literature on gender pay gaps is very extensive. It includes as explanatory factors wage gaps among others, the taste for discrimination or even statistical discrimination. However, a large part of these wage gaps remains unexplained. To explain these wage gaps between men and women, Salomons highlights the existence of the relational network. According to this author's approach, wage gaps are explained both by the density of the male social network, which is greater than the density of the female social network. This recourse to social capital to explain wage gaps has been the subject of studies such as those of Yogo in the case of Cameroon. In a study entitled social capital and the labor market in Cameroon, the author shows that social capital has a positive and significant effect on wages. Indeed, the use of share capital induces a premium of 3.34% of the average salary [12].

The theory of human capital developed by Schultz then Becker, by placing wages at the center of job quality, stipulates that the higher the investment in human capital, the more we should expect a higher income in the labor market. According to this theory, the unfavorable situation of women compared to men in terms of remuneration is due to the acquisition of less human capital [13].

Fomba et al., in a study entitled Atypical jobs and labor market outcomes in Cameroon, the Democratic Republic of Congo and Chad, highlight the effects of atypical jobs (part-time, written fixed term contract), as well as labor relations based on unwritten contracts (verbal contract, absence of contract) on remuneration (salary and access to social benefits) and mobility in labor markets. They find that in these three countries, employees with a written fixed-term contract and employees engaged under an unwritten contract record lower wages, respectively compared to employees with a written contract of indefinite duration and those engaged under a non-written contract. writing. However, in these three countries no significant difference in salary is highlighted between part-time and full-time employees after selection control, even if being involuntarily hired part-time significantly reduces the probability of access at least one social benefit in these countries [14].

The tendency to make wages the sole determinant of job quality has been criticized. For Rosenthal, taking salary into account alone does not fully reflect the expectations of individuals in relation to the jobs they occupy. The author pleads for the use of a composite indicator making it possible to give an overall account of the quality of employment. More in-depth work gives more consistency to the construction of indicators of the latter [15].

Lemiere and Marc examine the state and evolution of the quality of employment among women and men in France at two times, namely the beginning of the 1990's and the beginning of the 2000's. The authors consider four dimensions of job quality: Pay, hours of work, typical and atypical work schedules and job stability. These authors manage to show that women experience a greater deterioration in the quality of their jobs compared to men, particularly in terms of scheduling, because being a woman multiplies by 7 the probability of being at work. part-time and by 1.7 that of being on a limited-term contract. In total, women were 4 times more likely to be on an atypical contract (either for a limited period or part-time). Although this research does not take into account a large number of dimensions, the originality lies in the fact that these authors have proposed a typology of job quality in 10 classes allowing a more in-depth study of the phenomenon [16].

Lowe studies the characteristics of job quality in Canada. His study takes stock of job quality in Canada based on a number of dimensions and indicators. Thus, the author finds as the main dimension of job quality: Job stability, remuneration, working time, the presence of pension plans, collective insurance and the qualification of workers [17].

Regarding African countries, the study by Roushdy and Assaad on Egypt highlights the dimensions of job quality. These authors use as dimensions of the quality of employment the salary, the existence of social security, medical insurance, training, job stability, full-time employment, working time and the nature of the workplace. They show, after using factor analysis, that social security and health insurance contribute to more than 63% of the total variance of the job quality indicator [18].

In a comparative study between South Korea and Australia on the quality of women's jobs, Hanna Jung and Joonmo Cho using an ordered logit, find that South Korean female workers hold inferior jobs compared to compared to their male counterparts, and the magnitude is even greater in Australia. In both countries, job quality becomes higher with age, but it is higher in South Korea. Examining job quality by education level shows that higher education is associated with higher job quality in South Korea. On the other hand, the effect of the level of education on the quality of work is not significant in Australia. This result can be interpreted by the fact that a high level of education does not guarantee access to good quality jobs in Australia. The main factor that affects job quality in this country is the job characteristic. Thus, women workers occupy better quality jobs in public enterprises compared to private enterprises. Salary, job security and working hours are considered better in state-owned companies compared to private companies in South Korea as well [19].

China, Yang Miao et al., examine gender differences in job quality and job satisfaction of physicians in rural western areas, using principal component analysis and factor analysis. They arrive at the result that gender differences were found only in satisfaction with pay in relation to workload, opportunities for promotion and work environment. On the other hand, while Chinese male doctors are better paid, their female counterparts hold good quality jobs and are generally satisfied with these jobs [20].


This study uses the job quality model proposed by Hanna Jung and Joomo Cho. We take a similar approach to study gender differences in job quality in the Cameroon labor market. Inspired by this modelling, we construct a composite job quality variable made up of 4 dimensions.

The general empirical model is given by the following equation:

QL t=β0+δgri+β1 age 2i+β2 mari+β3 nei+β4 sacti+β 5 sinsti+β 6 cspi+β 7 Tle+ε

(t=1….4; i=1……N)


QL t:Job quality composite variable.
It contains 4 dimensions (employment stability, social protection, working time and social capital).
Gri,=Variable representing sex.
It is coded as 1 depending on whether the individual is male and 0 depending on whether the individual is female. This is the variable of interest of our work. On the labor market, it makes it possible to identify the disparities that exist between men and women.
Age2=Square of the age.

The addition of the squared age makes it possible to model the effect on the independent variable, a relation of linearity or nonlinearity.

Husband represents marital status. It is coded as 1 depending on whether the individual is married and 0 depending on whether he is single. Participation in the labor market is most often influenced by an individual's marital status. For example, when the head of the household (male) is the main earner this can lead to a disincentive for the spouse to participate in the labor market, and even when they decide to participate in the labor market jobs are the most part-time time.
Nei=Represents the level of education.

We have subdivided it into 4 modalities (primary, lower secondary, upper secondary and higher). According to the theory of human capital, the level of education makes it possible to obtain “good” quality jobs. It is one of the most widely used variables in the literature to explain job quality.

Sacti=Represents the business sector. We have subdivided it into 3 sectors (industry, commerce and services). This variable makes it possible to explain the quality of employment in the sense that the jobs occupied in these sectors vary according to whether one is a man or a woman.

Sinsti=Represents the institutional sector. It is subdivided into three sectors (public, formal private and informal private).
Cspi=Represents the socio-professional category. It is subdivided into 4 modalities (informal boss, qualified employee, manager and formal boss).

Tlei=Represents the size of the company. It is an indicator of job quality. We have subdivided it according to the number of employees working in the company (2-5; 6-10; 11-50; 101-500).

As Munoz de Bustillo mentions in their book on job quality, the starting point in the analysis of job quality is the construction of a synthetic indicator. The choice to create such an indicator is justified since it provides a more global picture of the situation in which the worker finds himself, both personally and professionally. Thus, this index may combine low pay, low social coverage, social benefits and atypical working hours, in particular, while another may have good pay, high autonomy in his work, a range of social benefits.

Many studies do not use weights when constructing their indices. Green and Tarek, in their study on job quality trends in Europe, use a synthetic indicator of intrinsic job quality based on 4 dimensions which include 17 indicators in total. A sub-index is calculated for each dimension from the sum of the scores of the indicators concerned. The method then consists in normalizing the results by bringing them back on a scale of 0 to 100. The overall index corresponds to the average of the sub-indices since no weighting of the sub-indices is used. Holman and McClelland for their part, by showing the limits of synthetic indicators of job quality and by not using weighting, opt instead for the so-called method of analysis of relative weights proposed by Lebreton and Tonirandel using regressions. Thus, from five major dimensions with which a series of indicators are associated, the authors construct a synthetic indicator with a relative weight for each indicator. The weights are determined based not on the variance explained by each indicator in the model, but on the variance explained individually by each indicator in relation to three variables which they believe are good determinants of job quality.

The studies that have just been presented use one or more composite indicators, as the case may be, in their analysis of the quality of employment. However, many of them do not use weighting in the construction of their tools. On reading these studies, certain arguments have been presented to justify this methodological choice, but it should also be said that others have instead pleaded for the need to use this weighting approach.

As seen previously, many authors do not seem to be in favour of weighting, since they did not use weights in the creation of their aggregate indicator of job quality. For Holman and McClelland, an unweighted synthetic indicator is problematic insofar as we must admit that each indicator that composes it has a positive, equal and independent contribution to the variable. According to them, by choosing not to weight the indicators, we end up under-representing or over-representing them in the construction of the variable since we do not take into account their real contribution. It is therefore appropriate, according to Holman and McClelland, to use a quantitative (statistical) approach which makes it possible to take into account the variance explained by the indicators forming part of the synthetic indicator. The method generally used is that of Principal Component Analysis (PCA) in the presence of quantitative data or that of Multiple Correspondence Analysis (MCA) in the presence of quantitative and qualitative data.

With regard to the construction of the aggregate indicator in this study, we will use the construction of an unweighted synthetic indicator. Referring to the work of Thierry Penard et al., we analyze the impact of the different explanatory variables on the overall quality of employment. For this we have constructed a composite indicator of job quality. The latter is the sum of the four components mentioned above. We proceed to a coding of this indicator in four (4) modalities 1, 2, 3 and 4.

The quality of the job will be worth:

• When the individual's job will have a job quality component.
• When the job will have two components.
• When the job has three components.
• When the job will have all four components.

This indicator thus provides a job quality variable in 4 categories: 1 for “poor quality”, 2 for “average quality”, 3 for “good quality” and 4 for “very good quality”.


The data used in this study come from the fourth Cameroonian household survey (ECAM4) carried out in 2014 by the National Institute of Statistics. This survey has the advantage of covering the national territory and of having a wide range of information concerning, among other things: employment, education, unemployment, income, health, housing, poverty, land rights, etc. Although ECAM 4 is the most recent, it comes up against shortcomings related to the details of certain variables, or the absence of information on others. The sample includes a total of 46,560 individuals aged 10 and over interviewed in 2014. We considered individuals aged 15 to 64, who correspond to the group of individuals who can be active according to the ILO. Our study focuses both on employees in the public and formal private sector but also on employees and self-employed in the informal private sector.

Results and Discussion

All of the descriptive statistics for the study variables are included in Table 1. The overall sample is made up of individuals aged 15 to 70 years. by observing this Table 1 below, we observe that on average 74.2% of the population has a quality job. With regard to the explanatory variables, the maximum value is 1 while the minimum value is 0. The socio-professional category presents a standard deviation of 1.22, the level of study a standard deviation of 1.27, the height company a standard deviation of 0.790, industry a standard deviation of 1.238, institutional sector a standard deviation of 0.580 and age a standard deviation of 13.99. These statistics show that there are hidden disparities in the various job quality indices (Table 1).

Study variables Average Standard deviation Minimum Maximum
Job stability 0.036 0.188 0 1
Social protection 0.038 0.192 0 1
Share capital 0.032 0.467 0 1
Work time 0.636 0.480 0 1
Cat. socio-professional
Informal boss
formal boss
Qualified employee
Level of education
Lower secondary
Upper secondary
Size of the company
2-5 employees
6-10 employees
11-50 employees
101-500 employees
.Four hundred ninety-seven
Activity sector
Institutional sector
Formal private
Informal private
Age 32.90 13.99 15 70

Table 1. Descriptive statistics of analysis variables.

The results of the multinomial logit model estimation are shown in Table 2 below. Thus, compared to men, the probability for women to hold “good” quality jobs is lower on the labor market in Cameroon. The education level variable has negative and significant coefficients for both women and men. This result means that the level of education is no longer a guarantee of obtaining a “good” quality job in accordance with the results found in the literature and the criticism brought to the theory of human capital. This same result was found in Australia by Hanna Jung and Joonmo Cho.

When looking at the socio-professional category, the results show that female managers and those who have received a qualification have a higher probability of benefiting from better jobs compared to unskilled women and labourers. We thus obtain the same result as that of N'gratier in a study on the determinants of job quality in Côte d'Ivoire. This result is in line with the developments taking place in our African societies in general and on the Cameroonian labor market in particular. It is about the stated desire of the public authorities to provide women with more skills and more representativeness in the administrations and decision-making bodies and on the idea of giving them more autonomy.

Concerning the size of the company, we note that women have a high probability of accessing "good" quality jobs when they are in large companies, because in these large firms, in most in some cases, the conditions of well-being are met to prevent employees from engaging in opportunistic behavior and lower productivity. Our results are thus consistent with the work of Hanna Jung and Joonmo Cho, where the size of the company measured by the number of employees is a determinant of job quality.

Female executives have a better chance of accessing a “good” quality job compared to male executives. With specific regard to the dimensions of job quality taken individually, we note that our results agree to a certain extent with the results of several studies. Indeed, executives are those who benefit the most from in-company training compared to non-executives, especially when it is a woman. In general, it is accepted that executives are design agents (unlike other categories such as workers and other subordinate agents who are executing agents) reflecting on the implementation of a production process. in the company, of a new way of conducting management. As a result, the latter must be constantly on the lookout for structural and technological changes. They must be trained and constantly receive modules allowing them to bring their companies to greater adaptability and resistance.

The managerial socio-professional category is often correlated with the level of education and/or professional experience. Thus we will assume thereafter, all other things being equal, that executives and other assimilated socio-professional categories are those who have proven competence and possess a significant stock of training and experience. Also, the literature on the influence of the level of human capital abounds. From the work of Becker to recent work, including that of Gambiers and Verniers, the importance of the level of training for the individual, the company and society is not disputed, apart from a few paradoxes often linked to the lack of correlation between level of education and employability. The Socio-Professional category of a worker is almost always correlated to his level of training and his professional experience. Individuals with a large stock of training are likely to access decision making positions.

Overall, for women to benefit from better quality jobs, they must have a lot of professional experience and a high level of education even if there is a mixed effect between the level of education and the quality of work. 'job.

Regardless of gender, the public sector is generally associated with good quality jobs relative to other institutional sectors. However, compared to men, the fact that women are in the formal sector (public or private) increases the probability that the job is of good quality. This result is interpreted by the fact that the informal sector generally corresponds to lower quality jobs in the sense that it is the least socially protected and the jobs are not stable.

The larger the size of the company (measured here by the number of employees), the greater the probability of accessing “good” quality jobs. Even if compared to men, women have lower quality jobs, the estimation results show that when they are in large companies, the probability of accessing “good” quality jobs increase.

There is a mixed effect between the level of education and the quality of employment. If the theory of human capital developed by Becker shows that a job of “good” quality (linked to the salary) is correlated with the level of education, our results do not prove it. Indeed, whether for men or women, the level of education does not directly lead to access to “good” quality jobs. Several interpretations explain this result. The social capital measured here by the relational network is a gateway to the labor market used by many workers, therefore depending on one's network and sometimes independently of one's level of education, one can occupy a job of " bad » quality, that is to say an unstable, part-time job which is not socially protected (Table 2).

Job quality Male Woman set                                      
  Coeff. Std.err. P>|z| Coeff. Std.err. P>|z|              Coeff. Std.err.  P>|z|
0 (Reference)
Sex     .0634 .863 0.673
Age2 0.765*** 0.202 0 0.365* 0.17 0.032            -.0006** .0002 0.002
Socio-Professional Category    
Informal boss 0.887*** 0.193 0 1,451*** 0.16 0.000            1.264*** .122 0.000
Qualified employee 0.608*** 0.188 0.001 1,078*** 0.25 0.000            .878*** .144 0.000
Frame -0.0736 0.389 0.85 1.075* 0.495 0.030            .469 .302 0.120
Formal boss 0.675 0.425 0.112 2,401*** 0.725 0.001            1.442*** .356 0.000
Level of education    
Primary -0.716 0.309 0.021* 0.421** 0.156 0.007            .1792 .132 0.175
Lower secondary -0.911*** 0.308 0.003 0.216 0.176 0.221             -.0277 .139 0.843
Upper secondary -1.302*** 0.314 0 -0.196 0.206 0.341             -.465 .152 0.026
Superior -1.193*** 0.346 0.001 -0.194 0.288 0.501             -.4301 .192 0.026
Size of the company    
2-5 employees -0.268 0.178 0.132 -0.422* 0.172 0.014              -.352*** .122 0.004
6-10 employees -0.421* 0.231 0.069 -0.5477* 0.238 0.021               -.482 .164 0.003
11-50 employees -0.296 0.262 0.258 -0.2486 0.311 0.424              -.244 .198 0.218
101-500 employees -1.063*** 0.325 0.001 -1.179** 0.419 0.005              -1.091*** .254 0.000
Institutional sectors    
Audience 0.196 0.377 0.602 -1.055** 0.388 0.007              -.426 .266 0.109
Formal private 1.177** 0.442 0.008 1,490* 0.694 0.032               1.048** .351 0.003
Informal private 0.980*** 0.324 0.002 0.454 0.312 0.145               .639*** .222 0.004
Sector of activity    
Industry 0.224 0.189 0.236 -0.295 0.179 0.100              -.0069 .1300 0.957
Shops -0.301 0.19 0.113 -0.059 0.203 0.769              -.1516 .137 0.271
Services -0.182 0.184 0.323 -0.145 0.187 0.437              -1574 .130 0.228
Job quality Male Woman set
  Coeff. Std.Err. P>|z| Coeff. Std.Err. P>|z| Coeff. Std.Err.  P>|z|
Sex           .4045*** .086 0.000
Age2 0.843*** 0.2 0 0.551*** 0.17 0.001               -.0015*** .0021 0.000
Socio-Professional Category    
Informal boss 1,894*** 0.193 0 1,910*** 0.16 0.000                  1.831*** .123 0.000
Qualified employee 1,096*** 0.187 0 1,575*** 0.247 0.000                  1.193*** .143 0.000
Frame 1,243*** 0.361 0.001 1.905*** 0.467 0.000                  1.385*** .281 0.000
Formal boss 1,962*** 0.416 0 2,739*** 0.724 0.000                  2.125*** .352 0.000
Level of education    
Primary -1,030**** 0.307 0.001 0.152 0.156 0.330               -.1464 .131 0.266
Lower secondary -1.331*** 0.307 0 0.31 0.176 0.857                -.387** .139 0.005
Upper secondary -1.920*** 0.312 0 -0.603** 0.207 0.004               -.1.041*** .151 0.000
Superior -2.206*** 0.344 0 -0.812** 0.286 0.005               -1.324*** .191 0.000
Size of the company    
2-5 employees -0.129 0.176 0.462 -0.375* 0.171 0.029              -.2600 .1218 0.033
6-10 employees -0.267 0.23 0.246 -0.5481* 0.239 0.022              -.4007 .164 0.015
11-50 employees -0.044 0.259 0.863 -0.136 0.309 0.659              -.0784 .196 0.690
101-500 employees -0.464 0.309 0.134 -0.906 0.402 0.024              -.679** .242 0.005
Institutional sectors    
Audience 1,329*** 0.382 0.001 -0.31 0.379 0.413                .405 .262 0.123
Formal private 2,054*** 0.445 0 2,240*** 0.688 0.001               1.74*** .347 0.000
Informal private 1,595*** 0.337 0 0.682* 0.314 0.030              .988*** .226 0.000
Branch of activity    
Industry 0.902*** 0.188 0 -0.265 0.18 0.142             .328 .129 .0012
Shops 0.831*** 0.187 0 0.549** 0.202 0.007             .673*** .136 0.000
Services 0.789*** 0.182 0 0.251 0.187 0.179            .497*** .129 0.000
Job quality Male Woman set
  Coeff. Std.Err. P>|z| Coeff. Std.Err. P>z| Coeff. Std.Err.  P>|z|
Sex   .610*** .120 0.000
Age2 2,071*** 0.26 0 1,671*** 0.334 0.000               -.003*** .004 0.000
Socio-Professional Category    
Informal boss -0.62 0.592 0.295 -0.635 0.8703 0.466               -.618 .482 0.200
Qualified employee 3,023*** 0.371 0 3,941*** 0.53 0.000                 3.22*** .301 0.000
Frame 3,635*** 0.478 0 4,691*** 0.66 0.000                 3.888*** .384 0.000
Formal boss 1.35 0.693 0.051 -13.0368 1988.691 0.995                 1.418 .629 0.024
Level of education    
Primary -0.238 0.426 0.576 0.582 0.579 0.315                .518 .291 0.075
Lower secondary -0.173 0.243 0.683 0.914 0.559 0.102                .682 .289 0.018
Upper secondary -0.373 0.423 0.378 -0.347 0.565 0.539                .291 .291 0.319
Superior -0.805 0.446 0.071 -0.224 0.598 0.707                 -.065 .314 0.836
Size of the company    
2-5 employees -0.418 0.263 0.112 -0.871 0.35 0.803                 -.363 .204 0.076
6-10 employees 0.212 0.295 0.473 0.311 0.375 0.407                 .212 .226 0.348
11-50 employees 0.739 0.297 0.013 0.779 0.387 0.044                 .759*** .232 0.001
101-500 employees 0.78 0.332 0.019 0.644 0.452 0.155                  .682 .263 0.010
Institutional sectors    
Audience 2,583*** 0.655 0 0.907 0.55 0.099               1.66*** .412 0.000
Formal private 2.961*** 0.694 0 2,894*** 0.814 0.000               2.601*** .478 0.000
Informal private 1,316 0.639 0.039 0.682 0.549 0.466              .727 .404 0.072
Branch of activity    
Industry 0.709 0.273 0.01 1,234 0.523 0.018              .563 .226 0.013
Shops 0.698*** 0.312 0.026 1.993*** 0.545 0.000               1.012*** .259 0.000
Services 0.968*** 0.261 0 1,671*** 0.484 0.001               1.03*** .216 0.000
Job quality Male Woman set
  Coeff. Std.Err. P>|z| Coeff. Std.Err. P>|z| Coeff. Std.Err.  P>|z|
Sex    .665*** .177 0.000
Age2 2,187*** 0.345 0 1.97*** 0.559 0.000                -002*** .005 0.000
Socio-Professional Category    
Informal boss 13.74812 1259.433 0.991 -12.203 643.8424 0.985             -12.56 513.38 0.777
Qualified employee 4,308*** 1,024 0 4,307*** 1,076 0.000              4.192 .731 0.502
Frame 4,555*** 1,082 0 5,114*** 1,181 0.000              4.592 .783 0.808
Formal boss 2,951 1,238 0.017 -11.946 2915.247 0.997              0.024 2.813 0.447
Level of education    
Primary -0.802 0.522 0.124 0.937 1,131 0.407             .117 .414 0.777
Lower secondary -0.579 0.515 0.261 0.633 1,115 0.570             .274 .408 .502
Upper secondary -0.904 0.517 0.08 0.528 1,099 0.631             -.099 .408 0.808
Superior -1.191 0.542 0.028 -0.532 1,121 0.635              - .327 .430 0.44
Size of the company    
2-5 employees 0.708 0.427 0.098 1,226 0.594 0.039             .730 .339 0.03
6-10 employees 1.725*** 0.422 0 1.807*** 0.564 0.001             1.649*** 0.330 0.000
11-50 employees 1.987*** 0.416 0 1,758*** 0.561 0.002             1.897*** 0.326 0.000
101-500 employees 2,271*** 0.436 0 1,980*** 0.594 0.001              2.077*** .344 0.000
Institutional sectors    
Audience 1.0698 0.762 0.16 0.2 0.863 0.981            .4712 .566 0.406
Formal private 2,177*** 0.784 0.005 2,129 1.055 0.044            1.97*** .608 0.000
Informal private 0.382 0.743 0.607 -0.513 0.886 0.563      -.401 -560 0.94
Branch of activity    
Industry 1,266*** 0.379 0.001 0.961 0.888 0.279       .988*** .334 0.003
Shops 1,354*** 0.431 0.002 2.21* 0.901 0.014        1.53*** .376 0.000
Services 1.21*** 0.372 0.001 1.522* 0.826 0.066        1.177*** .328 0.000
Number of obs = 8,990
Pseudo R2 = 0.2141
Number of obs = 8.183 Number of obs = 17.173
Pseudo R2 = 0.1455 Pseudo R2= 0.1899

Table 2. Econometric results of gender inequality determinants linked to job quality (multinomial logit).


The issue of job quality has become crucial for developing countries. It is even more alarming when they are considered as a source of inequality. The objective of this work is to analyze gender differences in job quality. Using data from ECAM 4, we identify the factors that affect the probability of accessing “good” quality jobs through a multinomial logit model. We find a significant effect of the sex variable. Women have a very low probability of accessing quality jobs. At the end of this analysis, the major observation is that in Cameroon, the gaps persist when it comes to quality jobs. Specifically, the results show that the level of education does not affect the quality of employment among women. However, it has a significant effect on job stability if we take the job quality index individually. Public authorities should pay attention to the quality of employment because, from the point of view of workers, a quality job is associated with higher job satisfaction and an increase in well-being, it also allows companies to achieve higher performance. In addition, we recommend the establishment of institutional arrangements to increase the economic capacities of women. Particular emphasis should be placed on increasing the positions of responsibility assigned to them.