ANALYSIS OF WAGE DISCRIMINATION AGAINST WOMEN AND FACTORS INFLUENCING WAGE DETERMINATION IN MUNTAN ORGANIZATION
INTRODUCTION
BACKGROUND
The international trade industry has grown so fast that many organizations have been demanding more and eloquent workers. Several theories assume that with the expanding levels of international trade, call for more untrained staff is going to increase and hence reducing pay divergence among laborers. Descriptions of pay imbalances among gender never been clarified despite the several studies that have conducted on the same. Most people argue that discrimination on gender influences the labor market. (DAVID J.MAUME, 2004). Women have considered themselves as the most affected group by this kind of discrimination. The continued inequality frame of reference says that the feminine gender faces obstacles in gaining uniformity with their male colleagues despite their career qualifications.
Organizations managers have accused of encouraging this discrimination practices that they favor the male gender and leave out the females with no one to look into their wage discrimination claims. However, the managers have defended these claims by lamenting that this wage determination approach is determined by several factors to leave alone the gender perspective. Inequality of negotiation power between the worker and the employer and other shortcomings of the worker such as insufficient training, immobility of workers could lead to wage difference among workers since those with good bargaining power, and other attributes attract more wages than those who haven’t (Lindblom, 1948)
Men have considered receiving a bit more payments than women on average. The believed to be encouraged by the practical differences that exist in me and absent in women in terms of the work rate and other added advantages such as level of education and previous work experience. Sometimes employers come up with job allocation setups within particular jobs in the organization, and most of the time, women assigned to jobs that pay low wages, this strategy considered to encourage wage discrimination in an organization (Bruce J. Chapman, 1985). Don't use plagiarised sources.Get your custom essay just from $11/page
With the increasing competition in the field of international trade, women find it challenging to cope up with the growing pressure from their employers; this gives men a considerable opportunity to occupy all the demanding tasks in the organization. This would encourage employers to reward male workers with high wages than the female workers, and hence wage discrimination practice comes into place.
Objectives of the research
This study had one general intention that;
- To describe and understand the features that aid in wage decision
Our specific goals were to;
- Determine the comparison of gender and the other elements of wage decision
- Ascertain gender influence on wage decision.
Research questions
This research aimed at handling the following questions regarding wage decision technic;
- What are the individual features of the employees at the organization?
- Are men favored in terms of wage values against women?
- Which specific features contribute to wage payment policies at the organization?
The hypothesis below guided in answering the above questions,
- Ho: there is no discrimination against women since wage for men and women is not statistically different
Ha: there is discrimination against women due to the difference in salaries between men and women
- Ho: salary is not determined by either age, gender, education, and race of an employee
Ha: wage is statistically contributed by age, gender, education, and race
The main reason for carrying out this study
There are complaints from our female employees that they feel like they discriminated in terms of payment approaches. This concern forced us to conduct to come up with the correct information about these allegations. Workers are essential in almost all departments in this organization; thus, to maintain their confidence in us, we wanted to come up with an appropriate wage determination strategy that will be equal to both our male and female staff.
Research assumptions
Our study based on the analysis of a regression model to come up with the contribution of each factor on the unique feature (dependent variable). Therefore, we assumed that our data tested for regression assumptions, and thus it was ready for use. The ordinary least square concluded that the response variable is linear, and therefore the relationship between the features (variables) is direct.
DESCRIPTION OF DATA AND METHODOLOGY
This part of our project explains the regression model used to regress the features illustrating wage deviation among workers of our company. It also includes a descriptive summary of the data.
Data source
We randomly sampled 100 employees directly from our firm and extracted the following information from them.
- Experience the experience possessed by a worker in the job market in years
- Wage: the salary value of each individual in our sample of 100 employees
- Education the years each worker spent in his/her education
- The gender we indicated one if the employer was a female and 0 if a male
- Color for white employees we indicated 0, and for nonwhite, we labeled 1
The analysis was done using Excel
Theory description of the multiple model
It is a model that takes the form of a probability function in explaining the possibilities of occurrence. It includes typically two or more independent variables. Mathematically it is presented as:
Where β0, β1, β2 ………. βn are regression coefficients of predictors’ and is the error term.
The coefficients show any amount of change in M corresponds to a unit change in a predictor of interest when the other predictors are kept constant.
ANALYSIS RESULTS AND INTERPRETATION
The analysis here was basing on our research objective, and interpretations were as follows.
- We began by exploring objective one that: description of data and understanding the features that aid in wage decision.
Descriptive analysis was performed on the features for wage determination to draw an understanding of their measurements.
From the above table, the descriptive analysis of the continuous features was as shown. The average salary per employee was 30833. On average, an employee spent 13 years in school. The average years of experience of a worker were 20 years. The organization’s workers had an average of 39 years old. The employee received a little salary went home with 9879 while the highest-paid worker received 83601 pounds. A worker who spent fewer years in school spent only four years, and the most educated staff spent 18 years studying. The most inexperienced member of staff in our firm had 0 years of experience, while the most experienced had 50 years of experience in the job industry. Our youngest employee was 18 years, and the oldest employee was 64 years of age.
Pie charts produced to describe the characterization of the categorical determinants, age, and nonwhite.
The pie chart below tells that our organization has many white workers than nonwhite workers.
Another pie chart generated to illustrate the number of male and female workers we have in our firm.
From the chart, our firm had more men than women percentages being 53% and 47% respectively.
The next step was to analyze our objective 2, in that we were required to determine if there exists wage discrimination against the female gender in our firm and comparing the relation of gender with other features to assess significance.
Two-sampled t-test applied to find significant divergence between salary for men and women. These analyses were done on SPSS and outputs are shown and illustrated below.
Analysis of wage disparities of men and women.
The following table gives the independent sample t-test, which will explain any existence of wage divergence between the two genders.
The result of the above table, the test statistic is less than the significance level at 99%; thus, we conclude that there is evidence of wage difference between men and women in our firm.
Gender against education t-test table
It was clear from the table that differences in education between the male and female workers did not exist hence any significance disparity.
Gender against experience
The following t-test table explains any significant difference in gender and experience factors
The test statistics are extensive compared to the significance level at 99% implies the significant difference between the two features.
Gender against age
The above illustration in the table proves further that there wasn’t any difference in age between our female workers and male workers.
About the results obtained, it is proven that higher salaries given to male workers than female supported by the evidence of differences in individual features between the two genders. Thus we ascertain that low wage to ladies portrays discrimination on gender basis.
Our third party of the analysis based on objective number 3, where our main task here was finding out the specific features that contribute to the wage determination technic in our firm.
The multiple linear model analysis
Two regression models generated in this section to model the relation between the dependent features of the study against the independent elements to monitor the contribution of each independent feature on the outcome feature. These two models were the general one, which constituted all the study variables and the revised model termed as the final model that consists of only the variables of interest.
The general model
This model comprises of all the variables, and theoretically, it presented as;
The analysis results presented and interpreted, as shown.
From the model summary table, the model predicted 61% of the value observed on the data set; the R-value explains this on the table. About 37.2% of the variation of wages is focused by the independent features.
Focusing on the ANOVA table, the test statistics are significant at a 95% confidence interval; thus, the independent factors predict the dependent variable.
The coefficients table below gives the estimates of the general model. All the independent variables are statistically significant, explaining the wage at a 95% confidence interval except for nonwhite features.
The relationship between education and age against wages was positive, with a unite increase in knowledge and age improve the pay by 2746 and 381, respectively. Gender and nonwhite (color) relate negatively to wage. The exclusion of the experience factor in the model to be lack of sufficient degrees of freedom.
Final regression model
Our research entailed determining wage disparities between gender, experience, and education. It is for this reason that we came up with another different model that would explain the relationship of these determining factors of interest. This central idea in formulating this model was to leave out the variables that were unnecessary according to our discrimination allegations in our firm. After controlling the unnecessary features, we came up with a final model that presented as shown.
The results after analysis were as follows
From the summary table, 59% of the observations in the data explained by the model. However, to determine the effectiveness of our revised model, we formulated a hypothesis for the coefficients of regression that;
Ho: all factors of the regression must be zeros
Ha: not all ratios of regression analysis must be zeros
The ANOVA table obtained from the study of the final model answered the above hypothesis, as illustrated below.
As shown from the above table, the null hypothesis was neglected in favor of the alternative hypothesis since the reference statistic was lower than the significance level at 95% confidence interval. We proved that at least one of the regression coefficients is more than zero.
The following table shows the regression estimates of our final model.
The three factors, education, experience, and gender, are all statistical significance in elaborating their influence on wages. Knowledge and expertise positively influence the salary of an employee with a unit increase in experience and education to escalate wage by 3160 and 396 pounds, respectively. Gender influences a negative relation with payment; this implies that women are affected negatively in wage shares hence facing discrimination of about -11149 pounds in salaries as compared to men.
CONCLUSION
Our study aimed at determining whether the allegations of wage discrimination against females in our organizations were true. We achieved this by analyzing the features we generated from our 100 employees and regressing these features to understand their correlation. We carried out several hypotheses to test wage discrimination practices in our firm. According to the results from our analysis, it dictated that gender indeed influence wage values in our firm with high salaries paid to men staff than women staff despite their similarities in other factors such as education and experience.
Like any other study, our research faced some challenges in coming up with these final results. Our final model did not include all the variables explaining the influence of the wage decision approach. We did not consider the age and color of an employee in our final model even though these two variables could influence wage discrimination in industries.
RECOMMENDATION
After analyzing all the factors, we recommend that our firm should consider gender during wage determination strategies to avoid discrimination against women in terms of wage distribution among workers.
References
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