A QUALITATIVE RESEARCH ON GENDER DISCRIMINATION

AQUALITATIVERESEARCHON GENDER DISCRIMINATION

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Tableof Contents

Table of figures and tables ii

Introduction 1

1.0 Background study 1

Data Analysis and Findings 2

2.1 Descriptive statistical frequencies 2

2.1.1 Gender Frequency Distribution of the Respondents 2

2.3 Evidence of differential returns to education by gender 3

2.3.1 Crosstabulation on years of schooling and gender 3

2.3.2 Scatter plot of earnings against years of schooling 4

3.0 Work Experience based on gender 5

3.1 Correlation analysis on Work Experience based on gender 5

3.2 Linear regression analysis Work Experience based on gender 5

References 7

Tableof figures and table

Table 1: Gender Frequency Distribution of the Respondents 2

Table 2: gender and Total years of work experience 5

Table 3: linear regression analysis model summary on gender and years of work experience 5

Table 4: linear regression analysis ANOVA on gender and years of work experience 6

Table 5: linear regression analysis model summary on gender and years of work experience 6Y

Figure1: years of schooling *gender Cross tabulation 3

Figure2: scatter plot of earnings against years of schooling 4

AQUALITATIVE RESEARCH ONGENDER DISCRIMINATION

Country X government wanted toinvestigate existence of gender discrimination in the labor market.According to the argument by Women Rights Association, female workersare less educated than their male counterparts. However, theEmployers’ Association attributes any possible gender gap on workexperience where male workers are said to be more experienced thanfemale workers. This is because female workers spend most of theirtime outside the labor market taking care of the children.

  1. Background study

Genderdiscernment is the prejudicial treatment of an individual on accountof gender. Gender discrimination impacts both women and men. It isusually very clear in job circumstances wherein a particular genderis provided preferential intervention or obtains far less pay or jobcommitments resulting from gender bias and unfair practices. Genderdiscrimination furthermore exists in sporting activities,institutions and political establishments. Genderdiscrimination not only happens on gender features but regarding howindividuals are treated inversely due to their sex. Managers whooffer a distinct working environment and promotional prospects formales and females circumvent anti-discrimination legal guidelines.Creditors who present better conditions to a specific gender over theother find themselves a violation of anti-discrimination legaldirectives. Gender discernment is unlawful, and several laws havebeen in place to put a stop to and eradicate discriminatory habits.

DataAnalysis and Findings

2.1Descriptive statisticalfrequencies

2.1.1Gender Frequency Distribution of the Respondents

gender

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

male

1634

57.2

57.2

57.2

female

1224

42.8

42.8

100.0

Total

2858

100.0

100.0

Table1: GenderFrequency Distribution of the Respondents

Table1. Depicts that majority (57.2 per cent) of the target respondentemployees were males while 42.8 percent were females. Thedistribution table yields the impression that males and females werenot equally selected in the participation of this research by a ratioof 5:4.

2.3Evidence of differential returns to education by gender

Accordingto the Women’s Rights Association, the returns to education forfemale workers are significantly lower than for male workers. Thiscan be evidenced by analysis of the years of schooling and tests ofcognitive ability among the workers based on gender.

2.3.1Crosstabulation on years of schooling and gender

Figure1: yearsof schooling * gender Cross tabulation

Figure1 shows the cross tabulation on gender of the respondents withrespect to their years of schooling in the country. The figures showsthat the majority of employees who went school for 12 years were maleworkers followed by women. The graph depicts that despite men goingto school for more years the number of women doing the same decreasedwith the increase of number of years while still being employed. Forinstance, those with 20 years of schooling include 9 female workersand 18 male workers while those with 12 years of schooling include500 female workers and 710 male workers.

2.3.2Scatter plot of earnings against years of schooling

Figure2: scatter plot of earnings against years of schooling

Figure2, shows that earning is directly proportional to years of schooling.This implies that since females have fewer years of schooling thanmales, then their earnings will definitely be lower than for males.Thus women get lower earnings due to less years of schooling ascompared to men.

3.0Work Experience based on gender

Accordingto reports by Employer’s Association, women have less workexperience than male workers. To ascertain this argument, theanalysis was carried out and the results are as shown in the tablebelow.

3.1Correlation analysis on Work Experience based on gender

gender

Total years of work experience

gender

Pearson Correlation

1

-.048*

Sig. (2-tailed)

.011

N

2858

2858

Total years of work experience

Pearson Correlation

-.048*

1

Sig. (2-tailed)

.011

N

2858

2858

*. Correlation is significant at the 0.05 level (2-tailed).

Table2: gender and Total years of work experience

Fromthe correlation table 2 above, the test reveals that there is statistically significant difference between genderand Totalyears of work experiencewhoser(0.48)= 0.011, p &lt 0.05. Thus it was that thereis a positive correlation betweenjob expectation andgenderand Totalyears of work experience.

3.2Linear regression analysis Work Experience based on gender

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.048a

.002

.002

2.1778

a. Predictors: (Constant), gender

b. Dependent Variable: Total years of work experience

Table3: linear regression analysis model summary on gender and Total yearsof work experience

Fromadjusted R square if multiplied by 100, this yield the percentage ofthe variance in the dependent variable or the outcome variable. Thus,0.002*100= 2% of the variance in the Totalyears of work experience can be explained by gender.

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

30.852

1

30.852

6.505

.011b

Residual

13545.667

2856

4.743

Total

13576.519

2857

a. Dependent Variable: Total years of work experience

b. Predictors: (Constant), gender

Table4: linear regression analysis ANOVA on gender and Total years of workexperience

Thesignificant of the model is 0.011 that is less than 0.05 P value, themodel is significant. Therefore, the model significant is F(1, 2856)= 6.505, P = 0.011

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

8.093

.124

65.038

.000

gender

-.210

.082

-.048

-2.550

.011

a. Dependent Variable: Total years of work experience

Table5:linear regression analysis model summary on gender and Total years ofwork experience

Forthe equation for the line y = mx + b, by using the unstandardizedcoefficients, then the slope m = -0.210 and y intercept = 8.093.Therefore the equation of the line y = -0.210x + 8.093. Thesignificant of the model is 0.011 that is less than 0.05 P value, themodel is significant. Therefore, the model significant is t (- 2.550)= -0.48, P = 0.011

AdditionalData

Toprove more on the analysis, a new variable was created namelyGENYEAR,this was the product of years of schooling and gender variables. Theoutcome was analyzed under the linearregression analysis in respect to earnings.The output was as shows in the figure below

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.034a

.001

.001

7.76905

a. Predictors: (Constant), hourly wage (in dollars)

b. Dependent Variable: GENYEAR

Fromadjusted R square if multiplied by 100, this yield the percentage ofthe variance in the dependent variable or the outcome variable. Thus,0.001*100= 1% of the variance in the earningscan be explained by the product of gender and years of schooling.

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

201.759

1

201.759

3.343

.068b

Residual

172382.854

2856

60.358

Total

172584.613

2857

a. Dependent Variable: GENYEAR

b. Predictors: (Constant), hourly wage (in dollars)

Thesignificant of the model is 0.068 that is less than 0.05 P value, themodel is significant. Therefore, the model significant is F(1, 2856)= 3.343, P = 0.011

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

18.721

.286

65.347

.000

hourly wage (in dollars)

.036

.020

.034

1.828

.068

  1. Dependent Variable: GENYEAR

Forthe equation for the line y = mx + b, by using the unstandardizedcoefficients, then the slope m = 0.036 and y intercept = 18.721.Therefore the equation of the line y = 0.036x + 18.721. Thesignificant of the model is 0.068 that is less than 0.05 P value, themodel is significant. Therefore, the model significant is t = 1.828,P = 0.011

Discussionand Conclusion

Theresults of the analysis revealed that male workers fair comparativelybetter than their female counterparts. In terms of years ofschooling, female workers are less compared to male workers while interms of years of experience, male workers appear to be moreexperienced than female workers. The regression analysis illustratesvery weak correlation in years of experience and tests for cognitiveability and a relatively stronger correlation between years ofschooling and tests for cognitive ability. Also, the analysis resultsshowed that many women tend to spend more time outside the labormarket than males.

Theresults of the analysis support the argument by Women RightsAssociation of the female workers being less educated than their malecounterparts. Male workers having more than ten years of schoolingare far too many than the female counterparts. Additionally, theargument by Employer’s Association of the male workers having moreyears of experience than female workers is true. This is wellrevealed from the regression analysis of the number of workers basedon gender against years of experience having a higher coefficient ofdetermination for male workers than for female workers. TheEmployer’s Association also attributed the low number of femaleswho are experienced in their work to the time spent outside the labormarket while taking care of children. Ideally, this can be consideredto be true though the data did not have any provision for womentaking care of the children.

Asthe government of country X, the arguments by Women RightsAssociation and Employers Association on the current state of femaleworkers in the labor market can be considered to be true. Fromstatistics, only 42.8% of the women are in employment compared to57.2% of the male workers in the country. It is critical for thegovernment to understand that years of schooling among female workersappear to be relatively low than in male workers. Also, the countryhas more males with more years of work experience than femaleworkers. It is also imperative for the government to take intoconsideration the time spent by women outside the labor market.Though the Employer’s Association argued that females spent moretime outside the labor market taking care of the children, thevalidity and reliability of this argument is still unclear. First,there statistics revealed no data related to child rearing, andsecond, there are other reasons that can contribute to many femalesspending more time outside the labor market other than just takingcare of the children.

Itis clear from the analysis that female workers are relatively lesscompetitive than their male counterparts in the labor market in termsof education and years of experience. Using years of experience asdependent variable and years of schooling as independent variable,the regression analysis revealed little correlation. This impliedthat years of experience cannot be explained by years of schooling.Ideally, years of schooling and years of experience should have astrong correlation. On the other hand, a regression analysis of yearsof schooling as independent variable and tests of cognitive abilityas dependent variable revealed a relatively stronger correlationimplying that cognitive ability is a function of years of schooling.

Asthe government, there is need to promote education among women aswell as investigate on the issues that make most of them waste a lotof time outside the labor market. Time spent outside the labor marketsignificantly affects the work experience. For a country to remaincompetitive in terms of gender and labor, then the government shouldformulate measures that will ensure that women do not lag behind ineducation and their needs are taken care of in order to reduce thetime spent outside the labor market. This will translate directly toincreased cognitive ability and work experience among women.

Limitationsof the Analysis

Themajor limitation of the current analysis is the fact that the numberof female workers was not equal with the number of male workers.Female workers were 42.8% while male workers were 57.2%, a differenceof 14.4%. Therefore, comparing the female and male workers could havebeen more accurate if the workers representation in terms of genderwas equal. Another limitation is due to the fact that some of thedata given was extremely large to represent effectively using theSPSS. For instance, representing comparative scatter chart fromregression analysis of male and female and getting the coefficient ofdetermination is difficult to get.

References

AgrestiA, Finlay B. Statistical methods for the social sciences. UpperSaddle River, N.J.: Pearson Prentice Hall 2009.