Gender and labor supply in Cameroon: an analysis in terms of job satisfaction

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Gender and labor supply in Cameroon: an analysis in terms of job satisfaction

https://doi.org/10.21203/rs.3.rs-2002558/v1

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The main objective of this article is to analyze, according to gender, job satisfaction in the labor market in Cameroon. To do this, we use data from the fourth Cameroon Household Survey (ECAM4) carried out in 2014 by the National Institute of Statistics. To estimate the determinants of job satisfaction, we use the estimation of a simple logit model.

The results of the logit model regressions and robustness tests show that for the same income, the probability of a man being satisfied at work is less than that of a woman. Moreover, the estimate of the marginal effects shows overall that it is more likely for a woman who works part-time to be satisfied with her job than for a woman who works full-time or for a man.

JEL Classification: J16-J28-C25-C13

Gender

job satisfaction

multinomial logit

marginal effects

The existence of debates on the satisfaction of workers in the labor market as a dimension of the quality of employment restores all its usefulness in taking this concept into account, especially when it is related to gender, because this issue helps to identify the different stereotypes that exist between individuals. This importance also comes from the fact that job satisfaction, which is a measure of the well-being of individuals, makes it possible to measure their productivity, but also to control the problems of absenteeism and turnover of the workforce. Thus, Locke (1970) defines job satisfaction as a pleasant or positive emotional feeling that results from the evaluation of one's work or professional experiences. Following the same logic, Clark (1997) highlights two strong reasons for the need to investigate job satisfaction. Firstly, it represents a measure of individual well-being, the distribution of which is one of the most central in economics, and secondly, job satisfaction is one of the best predictors of work performance (departures, absenteeism and productivity). Therefore, although a subjective concept subject to fundamental criticism, job satisfaction has been proven to be meaningful and complementary to objective indicators of well-being (Stiglitz et al. 2009).

The difference between men and women in the labor market has existed for a long time. This phenomenon, which far from disappearing in recent years, has been redefined. Today, the differences are mainly reflected in two areas, job quality and job satisfaction. The quality of employment, which was traditionally understood as the level of wages, has evolved into a multidimensional concept taking into account the opinion of the worker on his job.

Previous studies have shown that gender differences can be reflected in many aspects of job quality. Women are more likely to be associated with low-wage jobs, part-time, temporary contracts; while, men are more likely to have higher salaries, more chances of promotion. In some gender studies, the job satisfaction gap in favor of women has been attributed to their lower expectations and aspirations at work (Roubaud, 2012). The more or less pronounced disadvantage in the labor market, forces women to lower their job expectations, and other studies indicate that men and women have different values regarding import factors in their jobs. .

The remainder of the article is divided into four sections, of which Section 2 discusses the literature on the relationship between gender and job satisfaction. In section 3, we present the methodology used to estimate this relationship in order to see whether women are satisfied with the jobs they hold. The results are discussed in Section 4 and we conclude in Section 5.

Even though the issue of gender is subject to many controversies in the social sciences, the relationship between gender and job satisfaction has received much attention in the labor economics literature. Indeed, research conducted in Western countries shows that female workers are more satisfied than their male counterparts. This phenomenon has been called the paradox of the satisfied woman (Mueller and Kim 2008; Phelan 1994) because women hold jobs that on average are lower than those of men, and moreover women access low-wage jobs with fewer benefits, less authority, and more limited opportunities for promotion (Ridgeway, 2011).

Several theoretical explanations have been used to explain this phenomenon. First of all, men and women can value a job differently by making trade-offs between their contractual situations and their family lives. Also, women can focus on their role as housewives, rather than participating in the labor market. Additionally, women may have expectations and use different comparison groups to evaluate their jobs. For example, they can compare their jobs to those of other women (from the same reference group) rather than to men, or to women engaged only in the household, and are therefore more easily satisfied with their jobs compared to men.

In general, theoretical and empirical explanations of the personal and professional differences between men and women on the existence of the job satisfaction paradox have shown correlations with a number of factors, such as age, education, health, union membership, company size and working hours.

To explain the paradox of the satisfied woman, researchers have considered many possibilities. Among the explanations, Clark (1997), suggests that women's greater job satisfaction is due to their positions in the labor force in relation to their expectations. Clark reached this conclusion after assuming that the difference between male and female expectations was smaller for younger workers than for more educated workers. In his empirical work, he found no difference in job satisfaction between the youngest men (16-19) or the most educated workers in his sample, but differences for other ages and education levels. Based on this work, the author therefore assumes that the difference in job satisfaction is temporary and lasts only until expectations are adjusted so that there is no gender difference in rewards. Compared to expectations. As soon as more women are exposed to better jobs or contexts that allow them to go beyond their beliefs, they will revise their expectations upwards and the job satisfaction gap would disappear.

Still to provide an explanation for this paradox, ( Sloane and Williams, 2000; Bender et al. 2005) suggests that women are more satisfied at work in female-dominated occupations because they place greater value on other aspects of work such as flexibility, social ties, etc., although these jobs are lower in terms of wages and poorer working conditions. According to this reasoning, the bundle of characteristics associated with female jobs of benefits appreciated by women is sufficient to compensate for the fall in wages. Thus, Bender (2005) finds that taking into account the flexibility between professional responsibilities and the home seems to have greater value for women.

In an essentially descriptive study, Sousa- Poza and Sousa- Poza (2000) analyze gender satisfaction gaps in 21 countries and find that women have higher levels of job satisfaction in Britain, the United States, Hungary and New Zealand, while in the other countries the difference is not statistically significant. They indicate that in countries where the job satisfaction gap between men and women exists, women achieve higher job outcomes such as job security.

3.1-Data source

To assess the job satisfaction gap between men and women, the statistical data used were taken from the fourth Cameroon household survey (ECAM4) conducted by the National Institute of Statistics (INS) in 2014. This survey covered the entire national territory and all categories of assets. Although ECAM 4 is the most recent, it comes up against numerous shortcomings linked sometimes to the degree of detail of certain variables, sometimes to the absence of information on others. Nevertheless, this survey is more suitable for our study, because it contains in section 4 (economic activities and income of household members) comparable detailed information on income, hours worked, individual and professional characteristics, which are all necessary to carrying out the study. Our total sample includes 638 people of working age (15 to 64). All of these enter into the sample for estimating the satisfaction gap between men and women, which include workers with dependent employment and also self-employed workers. The proportion of men is much higher than that of women. In the estimation sample there are approximately 29.78% of women and 70.22% of men), due to the higher proportion of men among the self-employed and the unemployed, which compensates for the low level of activity of women in the labor market.

Job satisfaction is measured through a binary variable that indicates whether the individual is satisfied or not. This binary measure does not provide a better understanding of all levels of satisfaction, which limits the analysis. A Laeken scale would have been more appropriate. In addition to gender, we use a large set of control variables to account for gender differences in individual and occupational characteristics, including: working hours, age, marital status, level of education, socio-professional category, sector of employment, social protection, social capital, type of contract (permanent or temporary), institutional sector and sector of activity.

3.2-Measurement of variables

By retaining a threshold of 25% to have the maximum number of variables in the modeling, it appears that the variables which are linked to our variable of interest are the living environment, the age group, the marital status, the level of education, socio-professional category, institutional sector, employment channel and social security (the p-values for each of these variables are less than 0.25). However, some may be interrelated. It is therefore important to also analyze the link between them.

Take the case of the living environment variable: the column presenting the p-values of the chi-square test between living environment and the other variables shows that living environment is linked to marital status, level of education, socio-professional category, and sector of activity, institutional sector and type of contract. However, the variables sector of activity and type of contract are not linked to satisfaction and therefore are not of interest at the moment.

Among the remaining variables (socio-professional category, institutional sector, employment channel and social security), we note that the socio-professional category and the employment channel are linked therefore, to eliminate the effect of collinearity, we eliminate the channel of use of modeling because less linked with the variable of interest. We therefore emerge from this stage with the variables age group, socio-professional category, institutional sector, and social security.

Note that the last three variables are not related to each other. However, by proceeding in the same way as before with the age group, we would have to rule out the institutional sector variable. To avoid this, we decided to include the age variable in its continuous form. Note also that the sex variable will be included in the model.

The modeling will make it possible to estimate the influence of socio-demographic variables and labor market variables on the probability for an individual to be or not to be satisfied with his job.

3.3- Econometric model and estimation strategy

It is used to model the probability for an individual (i) to be satisfied. The structure of the model is as follows:

Before presenting the results of the econometric estimates in Table 2, we present and interpret the descriptive statistics of the variables used in Table 1 below.

Table 1: Descriptive statistics on the variables

*Variables*	*Terms*	*Workforce*	*Frequencies (in %)*
*Environment*	Rural	108	16.93
*Environment*	Urban	530	83.07
*Sex*	Feminine	190	29.78
*Sex*	Male	448	70.22
*Marital status*	Single	361	56.58
*Marital status*	Married	277	43.42
*Level of education*	Primary	153	23.98
	Secondary	348	54.55
	Superior	137	21.47
*age range*	15 and 24 years old	142	22.26
	25 and 34 years old	262	41.07
	35 and 44 years old	135	21.16
	>= 45 years old	99	15.52
*Socio-professional categories*	Frames	54	8.46
	Skilled employees	373	58.46
	maneuvers	211	33.07
*Search channel* *use*	Recruitment office private	51	7.99
	Personal Relations	575	90.13
	Public employment service	12	1.88
*Type of Contract*	CDD	60	9.4
	CDI	262	41.07
	Others	316	49.53
*Social Security*	Yes	233	36.52
*Social Security*	No	405	63.48
*Full-time*	Yes	510	79.94
*Full-time*	No	128	20.06
*job satisfaction*	Yes	377	59
*job satisfaction*	No	261	41

Source : author

The analysis of descriptive statistics shows that more than 83% of individuals live in an urban environment against barely 17% in a rural environment. The population is predominantly male as only 29.78% are female. There is also a strong presence of single people compared to married people who are estimated at 43.42%. Regarding the age group, more than 40% are between 25 and 34 years old while only 15.52% are over 45 years old. Note that the variable age at departure had an average of 33 years; the youngest being 15 years old and the oldest 79 years old. The distribution of the level of education shows that more than 54% of the individuals surveyed stopped their studies in secondary school, followed by 23.9% who stopped in primary school and 21.47% who continued to higher education. . In addition, most individuals are skilled employees (nearly 60%), followed by laborers (33%) and managers (8.5%).

The characteristics of the labor market of the population show that the channel most used to obtain employment is that of personal relations; more than 90% of jobs are obtained through this channel, against only 7.9% via private recruitment offices and barely 2% from specialized public services such as the National Employment Fund. As regards the type of contract or the stability of employment, almost 42% are permanent contracts and only 9.4% fixed-term contracts. Nearly 80% work full time, ie at least 40 hours per week and more than 63% do not benefit from social security.

A general model (model6) including the explained variable and the variables retained after the Chi-square test was estimated. However, in order to interpret the possible differences between men and women with respect to certain criteria, and in view of the collinearity problems encountered when estimating a single model, six models were estimated in cross-referencing the sex control variable with certain variables (education, socio-professional category, contract, sector of activity and full-time), in order to better understand the influence of sex on job satisfaction.

Thus, models 1, 2, 3, 4 and 5 are those which include the sex variable crossed respectively with level of education, socio-professional category, type of contract or job stability, sector of activity and full time.

To improve the quality of the estimates, certain variables have been grouped together. This is the variable "employment channel" which initially contained 3 modalities and which was reduced to 2 modalities by combining the modalities "Private recruitment office" and "Public employment service" to make a new "Other ". Similarly, the contract variable, which initially contained 3 terms, was reduced to 2 terms by combining the “CDD” and “CDI” terms to make a new “Contract”.

Table 2: Results of the econometric estimations

*SATISFIED*	*Model 1*	*model 2*	*model 3*	*model 4*	*model 5*	*model 6*
*SATISFIED*	*odds* *ratio*	*odds ratio*	*odds ratio*	*odds ratio*	*odds ratio*	*odds ratio*
*Age2*	1.0002** (0.0017)	1.000087 (0.0010)	1.000127 (0.0001)			1.000071 (0.0001)
*Income2*	0.561** (0.172)	1.0077*** (0.0025)	1.0045 (0.0027)	1,059*** (0.188)	1,077*** (0.188)	1.0187*** (0.004)
*SEX*
*Male*						1.077 (0.206)
*ENVIRONMENT*
*Urban*						0.748 (0.181)
*SACTIV*
*Industry*						0.755 (0.210)
*Primary sector*						1,107 (0.463)
*Services*						0.8553 (0.214)
*matt stat*
*Married*						0.9189 (0.174)
*Contract2*
*Others*						0.912 (0.158)
*CSP*
*Skilled employees*			0.5201** (0.159)	0.675 (0.249)		0.663 (0.2116)
*maneuvers*			0.349*** (0.113)	0.457** (0.187)		0.441** (0.1500)
*institutional sector*
*Public_and_IO*		1,369 (0.483)	1,532 (0.543)			1,209 (0.411)
*Informal private*		1,147 (0.2152)	1,283 (0.247)			(0.411)
*Social Security*
*Yes*	0.745** (0.132)	0.728** (0.126)	0.770 (0.141)	0.8100 (0.144)		0.765 (0.1402)
*Full-time*
*Yes*						0.927 (0.202)
*Job channel*
*Personal relationships*	0.561** (0.1724)					0.648 (0.183)
*Educational level*
*Secondary*		0.609** (0.127)		0.649** (0.137)	0.6506** (0.1348)	0.650 (0.137)
*Superior*		0.393*** (0.112)		0.384*** (0.110)	0.459*** (0.124)	0.392*** (0.114)
**SexEducation level***
**Femalesecondary***	22.986** (63.3748)
**Femininehigher***	0.2804** (0.148)
**Maleprimary***	35,273 (95,761)
**Masculinesecondary***	30,080 (81.9005)
**Masculinesuperior***	22,986 (63.3748)
**GenderCSP***
**Femaleexecutives***		1,427 (0.866)
**Femalemaneuvers***		0.551** (0.197)
**Maleexecutives***		2.187** (0.968)
**Malequalified employees***		0.975 (0.223)
**Masculinemaneuvers***		0.585** (0.143)
**GENDERSECTOR OF ACTIVITY***
**Womanindustry***				0.921 (0.561)
**Femaleprimary***				2,581 (3,262)
**Womenservices***				0.708 (0.336)
**Masculinecommerce***				35,614** (66.56)
**Masculineindustry***				27,990** (52,560)
**Maleprimary***				45,147** (85,322)
**Masculineservices***				36.936** (69,129)
**SEXFULL TIME***
**Female no***					1,678 (0.594)
**maleno***					27,416** (3,842)
**maleyes***					27,272** (53.99)
**Sexcontract***
**Female contract***			1.773** (0.533)
**malecontract***			1,297 (0.331)
**Male others* Number of observations = 638 Pseudo R 2 = 0.0328**			1.643** (0.421)

Source: author Notes: standard deviation in brackets, ***P<0.01;**P<0.05;*P<0.1

The results show that income squared, socio-professional category and social security influence the probability that an individual is satisfied with his job. Indeed, those who are registered in a social security system are less likely to be satisfied with their job compared to those who have no social security. Similarly, laborers (respectively skilled employees) are 2.6 times (1/0.38) (respectively 1.8 times) less likely to be satisfied with their work compared to managers;

The results of model 1 (where sex and level of education were crossed) show that at the 10% threshold, monthly income, social security and job search channel among others have an influence on satisfaction. At work, regardless of the individual's gender. The influence of social security is practically the same as before. With regard to the job search channel, it can be said that compared to those who seek work through private recruitment offices or public employment services, those who have obtained their employment through are almost half as likely to be satisfied. Also, the higher the age and the income, the higher the job satisfaction. Income influences satisfaction more (nearly twice) than age. With regard to the level of education, at the 10% threshold, we can say that compared to women who have a primary level of education, women who have stopped studying in secondary school or higher have less chance of be satisfied with their jobs. This result is visible through the analysis of the marginal effects, which clearly shows that it is more likely for a woman with primary education to be satisfied with her job, compared to women with another level of education. Studies or to men of the same level. On the other hand, the satisfaction of a man of secondary level or higher is slightly higher than that of a woman of the same level.

With regard to sex and socio-professional category, we note that compared to qualified women, women laborers are nearly twice as likely to be satisfied with their job, male executives are twice as likely to be satisfied. Being and laborers 1.7 times less likely to be. Regarding gender and type of contract, compared to women who have a job that is not very stable, women who have a CDD or CDI contract are almost twice as likely to be satisfied with their job, a little less (1.6) than men with not very stable jobs.

By increasing gender by sector of activity and then gender by income, the results show that compared to women traders, men in general are more satisfied with their jobs regardless of the sector of activity. Indeed, shopkeepers (respectively men working in the primary sector) are 35 times (respectively 45 times) more likely to be satisfied with their job compared to shopkeepers. Also, we can say that at the same income, the probability that a man is satisfied at work is less than that of a woman. By looking at working time, the crossing of this variable with gender shows that compared to women who work full time, men who work full time are 27 times more likely to be satisfied with their job, and the same applies to those who do not work full time but part time. Overall, the analysis shows that it is more likely for a woman who works part-time to be satisfied with her job than for a woman who works full-time or for a man.

Job satisfaction, as a subjective measure of well-being, was concretely captured by a binary variable that takes the value 1 if the worker is satisfied with his job and 0 otherwise. In this case, the methodology adopted consisted of using a simple logit model. In addition, the analysis was initially carried out on the total sample before being disaggregated so as to take into account sector specificities and those linked to gender.

The results show that the paradox of the satisfied woman is verified in the labor market in Cameroon. At the same income, women are more satisfied with their jobs than men. This can be explained by the fact that women have lower expectations. Estimates at the aggregate level reveal an insignificant effect of marital status on job satisfaction. At this level, the variables that affect satisfaction are mainly education, socio-professional category, working time, income and job stability. With regard to the job search channel, it can be said that compared to those who seek work through private recruitment offices or public employment services, those who have obtained their employment through are almost half as likely to be satisfied. Also, the higher the age and the income, the higher the job satisfaction. With regard to the level of education, at the 10% threshold, we can say that compared to women who have a primary level of education, women who have stopped studying in secondary school or higher have less chance of be satisfied with their jobs. Finally, it is more likely for a woman who works part-time to be satisfied with her job than for a woman who works full-time or for a man.

The public authorities must put in place institutional arrangements to increase the economic capacities of women. Particular emphasis should be placed on increasing the positions of responsibility assigned to them. Finally, we plead in favor of taking into account the issue of worker satisfaction in national statistical surveys because those present do not really make it possible to capture the reasons for employee satisfaction or dissatisfaction.

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I testify on behalf of all co-authors that manuscript submitted to International Review of Economics:

.Title: Gender and labor supply in Cameroon: an analysis in terms of job satisfaction

All authors:

.this manuscript is original and has not been published in whole or in part elsewhere;

.the manuscript is not currently being considered for publication in another journal;

.all authors have been personally and actively involved in substantive work leading to the manuscript, and will hold themselves jointly and individually responsible for its content;

.no funding was received for conducting this study;

.no financial or conflict of interests in any material discussed in this manuscript

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Gender and labor supply in Cameroon: an analysis in terms of job satisfaction

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3. Methodology

4. Results Of The Econometric Estimations

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