Management Analytics

Student ID: M00692002

Table of Contents Introduction 3 Descriptive Analysis 3 Regression Analytics 10 Managerial Interpretations and Implications 16 Conclusion 17 Reference List 18

Introduction

Attempt to estimate impact of education on wage demands attention on other important factors including experience of employees. In this matter, though education holds significant impact in directing salary margin of employees, gender and level of experience are also crucial to influence wage discrimination (Jara, 2015). Inequality in education level signifies inability of an employee to possess enough skill and knowledge to perform a task. Apart from this, lack of traditional level of experience is also liable to affect scope of employability and can also fail to utilise potential of a worker (Cochrane, Pacheco and Li, 2017). Regarding managerial decision making, education has been considered as a major factor that contributes to determine salary scale of employees.

This report aims to explore managerial decision in a fictional company Beta Technologies through evaluating data obtained from 204 employees of this company. Hence, the research question can be formulated as-

Does education significantly impacts salary margin of employees?

As this study has focused on investigating impact of education in determining salary scale or influencing wage discrimination in selected company Beta Technology, this section is based on presenting frequency tables of identified variables in following manner-

Table 1: Descriptive Table on Number of Employees

Figure 1: Number of Employees

Table 2: Descriptive Table on Gender of Employees

Figure 2: Gender

Table 3: Age of Employees

Figure 3: Age of Employees

Table 4: Frequency Table of Prior Experience of Employees

Figure 4: Prior Experience of Employees

Table 5: Frequency Table on Beta Experience of Employees

Figure 5: Beta Experience of Employees

Table 6: Frequency Table on Education Level of Employees

Figure 6: Education Level of Employees

Table 7: Frequency Table on Annual Salary of Participants in USD

Figure 7: Annual Salary of Employees in USD

In order to evaluate managerial decision making issues in Beta Technologies in regards with evaluating impact of education on salary scale or wage discrimination, regression analysis has been performed in this section.

Table 8: Model Summary of Regression Model on Education and Annual Summary

The regression table shows values of R and R2 to be respectively 0.54 and 0.29. In this case, value of R being 0.54 that is greater than 0.05 (0.54>0.05) signifies that there is medium degree of correlation between the dependent variable (Annual salary) and independent variable (education). In addition, R2 denotes how much variation in dependent variable can be explained or regressed by independent variable and hence, value of R2 being 0.29 indicates that only 29% data can be explained by which is a small number.

Table 9: ANOVA Table of Education and Annual Summary

Analysis of above-presented ANOVA table indicates that value located in ‘Significance F’ column of ‘Regression’ row is 0.00 which is less than 0.05 (0.00<0.05). Hence, it can be interpreted that the regression model fits the data and can predict outcome variable which makes it statistically significant.

Table 10: Coefficient Analysis of Education and Annual Summary

As education level has been selected as the independent variable and annual salary as dependent variable to measure impact of education in wage discrimination in Beta Technology, p value of Annual Salary (USD) is 0.00. Now, this being less than 0.05 (0.00<0.05) signifies that there is statistically significant relationship between the identified variables. Hence, the regression equation can be explained as-

Annual Salary (USD) = 29359.94 + 9760.93 [Education]

This regression equation indicates that level of education holds statistically significant impact in controlling salary margin in above-mentioned company. Moreover, it can also be interpreted that increase in education level is likely to ensure hike in salary margin that readily promotes discrimination in wages.

Figure 8: Normal Probability Plot of Regression Model on Education and Annual Salary

Apart from this, the non-probability plot presented above also signifies that possessing lower level of education is likely to reflect in salary scale of employees while higher level of education promotes salary margin. Hence, this can be counted as a major factor irrespective of gender and experience to control salary scale.

Table 11: Model Summary of Regression Model on Gender and Annual Summary

The values of R and R2 are respectively 0.21 and 0.04. Now, value of R being 0.21 (0.21>0.05) indicates that there is high degree of positive correlation or there is highly positive correlation between the dependent variable (Annual salary) and independent variable (gender). Moreover, R2 being 0.04 shows that only 4% data can be explained which is a very small number. Hence, it can be interpreted that very small amount of variation in dependent variable can be explained by independent variable.

Table 12: ANOVA Table of Gender and Annual Summary

The ANOVA table shows that value located in ‘Significance F’ column of ‘Regression’ row is 0.00 that is less than 0.05 (0.00<0.05). Thus, the regression model can predict the outcome variable and it is statistically significant and fits the data.

Table 13: Coefficient Analysis of Gender and Annual Summary

In this particular regression model gender has been identified as the independent variable and annual salary is dependent variable. Hence, p value of Gender being 0.00 (0.00<0.05) implies that there is statistically significant relationship between the dependent and independent variables. Now, the regression equation can be developed as-

Annual Salary (USD) = 78784.71+ -12874.62 [Gender]

The regression equation shows that gender statistically significant impacts on annual salary in Beta Technology. Hence, reflection of gender differentiation on salary margin also indicates towards managerial issues in decision making.

Figure 9: Normal Probability Plot of Regression Model on Gender and Annual Salary

Multiple Regressions

Table 14: Model Summary of Multiple Regression Model

The values of R and R2 are respectively 0.56 sand 0.32. This R value being greater than 0.05 shows that there is high degree of positive correlation or there is highly positive correlation between the dependent variable (Annual salary) and independent variables (gender and education). Moreover, R2 being 0.32 shows that 32% data can be explained.

Table 15: ANOVA Table of Multiple Regressions

The ANOVA table shows that value located in ‘Significance F’ column of ‘Regression’ row is 0.00 that is less than 0.05 (0.00<0.05). Hence, the regression model fits the data and its is statistically significant.

Table 16: Coefficient Analysis of Multiple Regressions

In this particular regression model gender has been identified as the independent variable and annual salary is dependent variable. Hence, p value located in both gender and education row is 0.00 which is less than 0.05 (0.00<0.05). This signifies that that there is statistically significant relationship between the dependent and independent variables. The multiple regression equation would be-

Annual Salary (USD) = 36436.64 + -10377.91 [Gender] + 9522.71 [Education]

Hence, the multiple regression equation implies that both gender and education holds statistically significant impacts on annual salary in Beta Technology.

Figure 10: Normal Probability Plot of Multiple Regression Model

Attempt to estimate impact of education on wage demands attention on other important factors including experience of employees. In this matter, though education holds significant impact in directing salary margin of employees, gender and level of experience are also crucial to influence wage discrimination (Jara, 2015). Inequality in education level signifies inability of an employee to possess enough skill and knowledge to perform a task. Apart from this, lack of traditional level of experience is also liable to affect scope of employability and can also fail to utilise potential of a worker (Cochrane, Pacheco and Li, 2017). Furthermore, assessment of impact of other factors such as gender and field experience is also essential to determine salary scale. For instance, knowledge and experience on Beta has been advantageous for employees to secure higher margin of salary while lack of this has led to low pay scale.

As the productive labour market of UK is liable to deliver opportunity for both experienced and apprentices to test knowledge and skills, discrimination in wage has been a major issue for business entities including Beta Technology to provide equal chances (Takeuchi et al. 2017). This has also promoted centralised economy while hinting toward issues of managerial decision making. Extensive research on impact of education on salary scale has presented that in a number of cases, employees having high potential and field experience are liable to receive low salary due to lack of educational qualification (Castellano, Musella and Punzo, 2019). This signifies lack of efficacy in HR practices and weak management and employment structure. In this context, focus on earning mobility and improving employment and wage structure is an essential concern for business entities (Lee, Green and Sissons, 2018).

In case of evaluating impact of education, it is also necessary to focus on gender discrimination that has been repeatedly reported to signify management decision making issues (Silva et al. 2018). Assessment of obtained data of 204 employees of Beta Technology presents that male employees of this companies are getting higher salaries than women despite of having similar level of education. In this regard, it is important to mention that apart from education, gender has played significant role in affecting HR practices and management decisions (Fidrmuc and Tena, 2018).

Hence, it can easily be interpreted that lack of focus on strengthening management structure and salary scale to reduce instances of discrimination. Beban and Trueman (2018) have pointed that availability of facility for students to work while pursuing educational courses has directed a great number of students to work as professionals in business corporations. In such cases, discrimination in salary scale on grounds of education level has often traced to indicate towards issues of managerial decisions. Hence, focus on reducing this wage discrimination and considering all factors including experience and practical knowledge of employees would be effective to resolve these issues and ensure holistic growth of business.

The findings of this report are premised on exploring impact of education on controlling wage discrimination. Concrete evaluation of this report presents that education has been a major factor in determining potential of workers.

Training and Education Programmes – Attempt to incorporate training and educational programmes through improving performance of Human Resource Management (HRM) department would be effective for Beta Technologies to reduce chances of discrimination in salary margin. It would also help to enhance potential of employees through improving level of productivity (Rienzo, 2017). Besides, adoption of improved HR practices can also ensure equal distribution of workload and would support to limit wage discrimination (Sarkar, 2017).

Following Legislative Instruction – Apart from developing effective HR practices, focus on compliance with legislative instructions would be effective for aforementioned company to reduce discrimination in wages. Focus on following guidelines of National Minimum Wage Act 1998 would be beneficial for this company to deliver equal opportunity to apprentices and skilled employees to utilise their potential (Legislation.gov.uk, 1998).

Journals

Beban, A. and Trueman, N., 2018. Student workers: The unequal load of paid and unpaid work in the neoliberal university. New Zealand Sociology,  33(2), pp. 99-131.

Castellano, R., Musella, G. and Punzo, G., 2019. Exploring changes in the employment structure and wage inequality in Western Europe using the unconditional quantile regression. Empirica,  46(2), pp. 249-304.

Cochrane, B., Pacheco, G. and Li, C., 2017. Temporary-Permanent Wage Gap: Does Type of Work and Location in Distribution Matter? Australian Journal of Labour Economics,  20(2), pp. 125-147.

Fidrmuc, J. and Tena, J.D., 2018. UK national minimum wage and labor market outcomes of young workers. Economics,  12(5), pp. 1-28,28A.

Jara, H.X., 2015. The Effect of Job Insecurity on Labour Supply. Australian Journal of Labour Economics,  18(2), pp. 187-204.

Lee, N., Green, A. and Sissons, P., 2018. Low-pay sectors, earnings mobility and economic policy in the UK. Policy and Politics,  46(3), pp. 347-369.

Rienzo, C., 2017. Real wages, wage inequality and the regional cost-of-living in the UK. Empirical Economics,  52(4), pp. 1309-1335.

Sarkar, S., 2017. Employment polarization and over-education in Germany, Spain, Sweden and UK. Empirica,  44(3), pp. 435-463.

Silva, F., Vieira, J., Pimenta, A. and Teixeira, J., 2018. Duration of low-wage employment: a study based on a survival model. International Journal of Social Economics,  45(2), pp. 286-299.

Takeuchi, D.T., Dearing, T.C., Bartholomew, M.W. and Mcroy, R.G., 2018. Equality and Equity: Expanding Opportunities to Remedy Disadvantage. Generations,  42(2), pp. 13-19.

Website

Legislation.gov.uk, 1998. National Minimum Wage Act 1998. Viewed on 16/04/2019 <http://www.legislation.gov.uk/ukpga/1998/39/contents>

Prior Experience

Beta Experience

Education Level

Annual Salary

Normal Probability Plot

Sample Percentile

Annual Salary (USD)

Normal Probability Plot

Sample Percentile

Annual Salary (USD)

Normal Probability Plot

Sample Percentile

Annual Salary (USD)

Number of Employees

Gender of Employees

Age of Employees

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