Factors Affecting UK Wages: Highest Qualification and Gender Differences

Work has a substantial influence on the personal life of an individual, their economic status, social class, worth, self-esteem, perception, political perspectives, self-efficacy, and empowerment. The level of education, ability, interest, and personality influences the nature of the job that one performs (Miki & Yuval, 2011). Despite high participation by women in the labor force over the last three decades, there are differences in productivity and pay across various jobs and sectors. Many women around the world seem to be captured in a productivity trap that imposes substantial costs on their welfare and economic empowerment. Such conditions discourage investment in women for the future work environment. The differences in productivity and wages for men and women can be attributed to their differences in economic activities. This article investigates the influence of gender and highest qualifications in the UK wage. 

Literature 

The gender pay gap measures the earnings of both men and female where the later average pay is calculated as a percentage of the former. The differences in the compensation have widely been studied and various factors identified as contributing to the disparity. Some of the studies have human capital differences, part-time working, travel patterns, occupational segregation and workplace segregation as some of the factors that have contributed to the differences. In other studies, the pay gap has been attributed to rational choices made by the two groups and not as a result of discrimination. 

There are differences in the jobs that men and women could take in different sectors, industries or even occupation or job type. Such differences evolve with economic development yet structural changes in employment are inadequate to address segregation in employment. Women all over the world seem to be working in low productivity jobs, work or run small firms, are overrepresented as unpaid family workers and are mostly based in the informal sector. According to Blundell, Joyce, Keiller & Ziliak (2017) the UK has recorded a substantial increase in the earnings inequalities for a long time. The increases in the last two decades can be attributed to a secular decline in the number of hours worked by men in the lower wage percentile. The inequality in the hourly wage for men in the 5 th and 95 th percentile has remained substantially the same. The UK women have recorded an increase in the minimum wage, therefore, realizing a decline in the inequalities. The trend, however, has not been sufficient to stop the rising wage inequalities. 

According to Leaker (2008), historical differences in the qualification of both males and females contribute to the pay gap. The review points out that despite the existence of the variation, men and women who hold higher education qualifications are on the increase. According to the labor force survey, the level of skill has an insignificant impact on the gender pay gap. In its findings, the pay gap for full-time employees is the smallest for individuals with GCSE as their highest qualification which was 12.7%. The most senior pay difference exist for individuals educated to A-level followed by degree holders or its equivalent both having 19.3% and 18.6% respectively. 

A few studies on the impact of education and gender wage gap have assumed that schooling contributes only to human capital. Education is therefore associated to explain nondiscriminatory wage differences. Education also works as a labor market signal and might reduce discrimination, thus, acting as proof to the future commitment of women to their career (Livanos & Núñez, 2012). Despite there being policies that address wage differences, there are still differences on the wage in UK. According to the author, the wages of female could be affected by low commitment, physical weakness, caring nature and lesser need for income. Such stereotypes change the perception of the employers who think that the female employee would prefer to take care of the family rather than career development. Such situations encourage employers to train male employees whose future revenues are perceived to be sure. The employment of women including their pay could be affected by their skill and preference. 

According to a report by the UK Commission for employment and skills (2015), 73.7% of male and 61.7% of females were employed. According to the report, the unemployment rates, especially for women, have fallen in the last ten years. Women recorded a 9.4% decline compared to 5.7% for their male counterparts. The report further notes that there was a drop in the employment rate for both genders along the qualification profile and especially for those with low qualifications. The male/female differential in employment is highest for individuals with low qualifications. Women in the workplace continue to earn less than men. On average, females earn 19.1% less in an hour excluding part-time (Bosworth & Kersley, 2015; EU, 2013). 

Many women than men in the UK work part time, and the discount for part-time indicates that the differential per hour for the full-time worker is 9.4%. The report, however, does not show the causes of the differences. The hourly rate for full-time employees exceeds that of the part-timers. In 2014, the difference between full time and part time was 12.31 and 8.44 pounds for women and 13.59 and 8.0 pounds for men. Such statistics show that full-time pay is higher for the males than females despite the fall over time. The part-time rate is lower for the two genders, and this negatively affects women in the workplace because a fifth of them work part-time compared to 6% of their male counterparts (Bosworth & Kersley, 2015; EU, 2013). 

Theory 

Literature review has identified the impact of highest educational qualification and gender on the UK wage. It is evident that the two variable coupled with others affect the wages in the UK. Despite recorded improvements in the salaries of men and women, inequalities still exist where men in the same job earn higher than their female counterparts. Different factors have been identified as contributing to the variation in the wages between men and women, but additional studies need to be conducted to determine whether there are other underlying issues regarding wages and gender. There are several reasons for investigating the involvement of women in employment. The labor market has experienced a substantial increase in the number of women underemployment yet there are differences in the wages that they receive compared to men. In the UK, gender can be used to explain the differences in wages for people with the same skills, education, and experience. Despite improvements in academic qualifications and on the job experience, women are still paid less than men, are promoted slowly and find it difficult to reach the top levels in employment. The use of part-time jobs has substantially contributed to the wage differential in men and women in the UK. It is necessary to identify the factors that contribute to the success of women in the workplace to understand the processes and structures that facilitate or prohibit equal opportunity for them. 

Methodology and data 

Primary data for this study was obtained from a database that incorporated different variables that affect wage in the UK. Two variables; highest qualification and gender were selected for this article. A total of 10539 samples were analyzed to determine the relationship between the two variables and wage levels in the UK. Secondary data for the study was obtained from journal articles on educational qualifications, gender, and wages. Similarly, reports from the UK Commission for Employment and skill, Institute of Fiscal Studies and the European Commission were used to obtain secondary information for the literature review. 

The two variables are instrumental in understanding the concept under study. It is possible to determine the impact of highest educational qualifications and gender on the UK wage, therefore, enabling further studies to inform on future studies and policy issues. The highest academic qualifications were recorded for the sample under investigation which was grouped into males and females. In doing so other factors like minimum working hours, average wage per hour and part-time jobs considered as dummy variables. Despite having a huge sample that was representative of the UK workforce, it was difficult working on the data due to its volume. Similarly, this study assumed that the two variables are the primary determinants of the UK wage. 

From the sample, degree or equivalent, higher education, GCE A level or equivalent, GCSE grades A* - C or equivalent, other qualifications and no qualifications were used to group highest education qualifications. The sample included 5503 females and 5036 males constituting 52.2% and 47.8% respectively. The two variables are directly related to the hypothesis of the study. By understanding them as factors that affect the wages in the UK, it is possible to establish the link between the two and others that have not been dealt with in this article. 

Concept	Variable	Hypothesis	Literature
Demographics	Gender	Males are likely to earn more than females	Bosworth & Kersley, (2015).
Human capital	Highest qualification	High education increases the wages in the UK workforce	Bosworth & Kersley, (2015).

Method 

Data on the two variables were coded and analyzed using SPSS and Gretl statistic software. The descriptive statistics including means, standard deviation, median, range, and skewness. Highest qualifications were grouped into eight groups while gender was grouped into male and female. Other statistics included correlation and regression analysis. Slope dummy variables of 0-1 were used in regression analysis to indicate the presence or absence of any relationship between the variables and UK wage. 

Results 

From the analysis, of the 10539 samples, the mean for the highest educational qualification was 2.80. The standards error was 0.015. The median was three while the mode was 1. The standard deviation from the mean was 1.570 while the variance was 2.466. The skewness is 0.385, and the standard error of skewness is 0.024. The range is six, and the 25, 50 and 75 quartiles are 1,3 and four respectively. The above statistics show that majority of the respondents both men and women had some form of academic qualifications. From the highest qualification, degree or its equivalent had the highest frequency at 32.5% followed by GCE A level or equivalent at 23.2% then GCSE grades A* - C or equivalent 20.5% higher education 9.9%, other qualifications 8.0% don't know 7% and 5.2% did not qualify. 47.8% of the respondents were male while 52.2% were females. 

Dependent Variable: Highest educational qualification
Equation	1	2
Constant	2.89	2.99
	2.7945**	2.987**
Human capital
Highest qualification	1.539	1.5585
	1.51955**	1.5585**
Gender
	(0.0596)	(0.60)
	(0.1385)***	0.0192***
R-squared	0.00036	0.00036

** Significance at 5% 

*** Significance at 1% 

Most of the respondents had university qualifications implying that the majority of the employees in the UK workforce are well educated. The second and third grouping shows employees with mid-level academic qualification probably working in middle level to low-level employment and therefore earning low wages. The last three categories represent individuals who find it hard to climb the employment ladder due to their academic qualifications. Such individuals are likely to earn low wages which are adequate to meet basic needs. The Pearson correlation for the data is -0.019 indicating that there is a weak relationship between gender and highest qualifications. The relationship between the two indicates that as one variable increases the other declines but at a slow pace. The standard error of the estimate is 1.570. It shows the accuracy in which the sample represents the entire UK workforce. It indicates that there is less spread in the data and therefore there are high chances of the occurrence of the sample mean than the population means. The degree of freedom is 1indicating that only one variable was used in the calculation of the estimate. The significant F change is 0.052 which was added to improve the prediction. The value is tested severally to determine the most effective for the prediction. The regression equation is Y = 2a -0.6X + 1.509. It can be used to determine the regression line. The 0.6x is the explanatory variable and while Y is the dependent variable. Using highest qualifications as the dependent variable and sex as a predictor, the sum of squares for the regression is 9.347 which indicate how best the model represents the analyzed data. It is the summation of the squared differences of the data from the mean. The mean square or the square root of the squared mean is 9.347. The F is 3.791 and indicates whether the mean of the two variables is significantly different. 

Conclusion and recommendation 

The UK has experienced an increase in wage differential over the last three decades. Despite increased women participation in employment and equal academic qualifications, there is still a significant difference in the wages of both males and females. The country has seen a drastic change in its employment regarding the composition of men and women. There has been a decline in employment across gender and educational qualification. Similarly, significant changes have been reported across margins where male workers have experienced a drop in the average working hours. Such observable changes have been most active in the low educated and pronounced in the lower quartile of the wage distribution. The highest level of academic qualification has a significant effect on the wages received by an individual indicated by the results that supports the literature. 

According to Miki Yuval, (2011) women pursue higher education due to the differences in the wage structure and use their educational qualification to compensate against the discrimination that they face in the job market. According to the author, women use the extra education to reduce the wage gap between them and their male counterparts. There are high chances that women with higher education will be paid higher wages than those who have not attained such qualifications. Women who pursue higher education are likely to concentrate more on their career than on parenting which ultimately improves their wages. 

As a way of improving the wages of both genders, individuals should enroll in high educational institutions to improve their chances of enhanced wages. Doing so will ensure that they have a higher chance of gaining meaningful employment and have higher chances of being promoted to better positions. The government can take corrective measures to protect women from the influences of the discriminatory wages through tax measures while increasing the incentives for women to be employed. Similarly, the government can devise a standard wage structure for the two genders to eliminate the chances of wage discrimination. The government can also tighten its stance on low wages to eliminate any discriminatory practices by employees against women. Women can enroll in high education to compensate for the discrimination while ensuring that they take .less of the lowly paid part-time jobs. The academic fraternity can study this area further to establish strategies for improving the wages paid to women. 

References 

Blundell, R., Joyce, R., Keiller, A., & Ziliak, J. (2017). Income inequality and the labour market in Britain and the US.  Institute Of Fiscal Studies. Economic And Social Research Council . 

Bosworth, D., & Kersley, H. (2015, November). Opportunities and outcomes in education and work: Gender effects. UK Commission for Employment and Skill. Retrieved January 08, 2018, from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/477360/UKCES_Gender_Effects.pdf 

Eu, (2013). The current situation of gender equality in the United Retrieved January 6, 2018, from http://ec.europa.eu/justice/gender-equality/files/epo_campaign/130911_country-profile_united_kingdom.pdf 

Livanos, I. & Núñez, I. (2012). The effect of higher education on the gender wage gap . International Journal of Education Economics and Development, Vol. 3, No. 1, pp.33–47. 

Miki, M., & Yuval, F. (2011). Using education to reduce the wage gap between men and women.  The Journal of Socio-Economics ,  40 (4), 412-416. http://dx.doi.org/10.1016/j.socec.2011.04.006 

Appendices 

Statistics
		Highest qualification (detailed grouping)	Sex of respondent
N	Valid	10539	10539
	Missing	0	0
Mean		2.80	1.52
Std. Error of Mean		.015	.005
Median		3.00	2.00
Mode		1	2
Std. Deviation		1.570	.500
Variance		2.466	.250
Skewness		.385	-.089
Std. Error of Skewness		.024	.024
Range		6	1
Percentiles	25	1.00	1.00
	50	3.00	2.00
	75	4.00	2.00

Frequency

Highest qualification (detailed grouping)
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Degree or equivalent	3427	32.5	32.5	32.5
	Higher education	1044	9.9	9.9	42.4
	GCE A level or equivalent	2441	23.2	23.2	65.6
	GCSE grades A*-C or equivalent	2164	20.5	20.5	86.1
	Other qualification	839	8.0	8.0	94.1
	No qualification	549	5.2	5.2	99.3
	Don't know	75	.7	.7	100.0
	Total	10539	100.0	100.0

Sex of respondent
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Male	5036	47.8	47.8	47.8
	Female	5503	52.2	52.2	100.0
	Total	10539	100.0	100.0

Correlations
		Highest qualification (detailed grouping)	Sex of respondent
Highest qualification (detailed grouping)	Pearson Correlation	1	-.019
	Sig. (2-tailed)		.052
	N	10539	10539
Sex of respondent	Pearson Correlation	-.019	1
	Sig. (2-tailed)	.052
	N	10539	10539

Non parametric correlation

Correlations
			Highest qualification (detailed grouping)	Sex of respondent
Spearman's rho	Highest qualification (detailed grouping)	Correlation Coefficient	1.000	-.016
		Sig. (2-tailed)	.	.094
		N	10539	10539
	Sex of respondent	Correlation Coefficient	-.016	1.000
		Sig. (2-tailed)	.094	.
		N	10539	10539

Regression

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics
					R Square Change	F Change	df1	df2	Sig. F Change
1	.019 a	.000	.000	1.570	.000	3.791	1	10537	.052
a. Predictors: (Constant), Sex of respondent

ANOVA a
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	9.347	1	9.347	3.791	.052 b
	Residual	25981.613	10537	2.466
	Total	25990.960	10538
a. Dependent Variable: Highest qualification (detailed grouping)
b. Predictors: (Constant), Sex of respondent

Coefficients a
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B		Correlations
		B	Std. Error	Beta			Lower Bound	Upper Bound	Zero-order	Partial	Part
1	(Constant)	2.891	.049		58.924	.000	2.794	2.987
	Sex of respondent	-.060	.031	-.019	-1.947	.052	-.120	.000	-.019	-.019	-.019
a. Dependent Variable: Highest qualification (detailed grouping)

Output from Gretl 

Model 3: OLS, using observations 1-10539 

Dependent variable: Highest Qualifications 

Coefficient std. error t-ratio p-value 

------------------------------------------------------- 

Constant 2.89064 0.0490568 58.92 0.0000 *** 

SEX −0.0596193 0.0306219 −1.947 0.0516 * 

Mean dependent var 2.799886 S.D. dependent var 1.570479 

Sum squared resid 25981.61 S.E. of regression 1.570271 

R-squared 0.000360 Adjusted R-squared 0.000265 

F(1, 10537) 3.790617 P-value(F) 0.051567 

Log-likelihood −19708.90 Akaike criterion 39421.79 

Schwarz criterion 39436.32 Hannan-Quinn 39426.70