Purpose 

In the case of wages, one of the important determinants which can be identified is the level of education. The purpose of the report is to establish a relation between wages of an employee and the level of education which such employees are able to attain. An assumption is made that education level of an employee is directly related to the wage level of the employee.

Background

  One of the major aspects which help an individual to develop in physical, mental and character terms is education. Education can help an individual to acquire necessary skills for the purpose of developing in professional life. The level of education for an individual determines the intellectual skills which the individual possess (Alsulami 2018). From the perspective of a nation, quality education is essential for development of human capital in the country. Education allows an individual to reach new heights in terms of skills and in professional environment, these aspects are important for estimating the wages which the individual would get in the market. It is to be further noted that the productivity of the employee enhances as more and more experience is gained in the labor market. As the experience of an employee increases, so does the wages of the employee and also opportunities in different companies (Di Stasio, Bol and Van de Werfhorst 2016). However, the primary factor which has a direct relation with the wage rate of the employee is the level of education which is attained. As per the human capital theory, there is a positive relation between productivity, wages and education level of the employee. This also means that the employee who has attained better education would have more skills and would be more productive in the business. This is one of the reasons that businesses offer more compensations as wages to such competent employees. As per empirical studies, companies considers employees who have more educational degree to be more competent and qualified for a designation and better pay in the company (Abaida, Lakrari and Abdouni 2017). One other study shows that private and social return from education at all levels decline depending on per capita income of countries. Therefore, it can be said that different researchers have different opinions regarding the relation between wage rate and education level of the employee.

Method

The main objective of the research paper is to establish a relation between education and wage rate of an employee. In order to accurately analyze the relation between wage and education level, a sample size of 100 variables is considered. In case of measuring wages, earnings per hour of employees are considered while years of education are the measuring unit for education of an individual. The analysis would involve application of quantitative method of data analysis on data once the same is appropriately collected. In case of quantitative data analysis, statistical and numerical analysis would be applied so that proper results are derived from the analysis (Autor 2014). The statistical techniques would comprise of descriptive statistics, scatter plot and regression analysis so that data set is properly analyzed.  The descriptive statistics involves measures such as mean, standard deviation, and maximum, minimum, median; mode and some other measures as well. The scatter plot shows graphical representation of the relation between wage and education level of an employee. Finally, regression analysis would show accurate results regarding statistical relation between the targeted variables based on which appropriate conclusion can be drawn from the data set.

Results 

The analysis of data initiates with two variables which are represented in the data set and results of the analysis is appropriately shown below:

Table 1: Descriptive statistics of wage

Table 2: Descriptive statistics of education

The above table appropriately shows that the mean wage for 100 samples is 22.31. The mean wage which is computed indicates that the average earning rate for the sample is 22.31. The standard deviation for the sample variable is shown to be 14.02. The comparison between standard deviation and mean indicate that wages do not vary much for the sample size. The sample size which is considered shows maximum wage is 76.39 while reported minimum wage is 4.33. In terms of education, mean is estimated to be 13.76 which is a clear result that people spent average 14 years on education. In the context of education, the standard deviation is computed to be 2.72. The results comparison for mean and standard deviation for education indicate that mean years of education is much more than standard deviation which also means less fluctuations is there. The maximum and minimum results in case of education are 21 years and 6 years respectively.

The key feature of a scatter diagram is that it effectively portrays the dependent and independent variable. The dependent variable is wage rate while the independent variable is education in years. The scatter plot for the tow variables is presented below:

Graph 1: Scatter diagram

The scatter diagram above shows that there are no clear association patterns for education and wages. The graph shows that the scatter plots are scattered highly. In order to check the relation further with more clarity, a trend is added which then shows a positive relation between wage and level of education. This in simple terms means that people who have attained a higher level of education tend to get more in terms of wages for their skills and knowledge. It is to be noted that the scatter diagram presents a weak relation between the variables even though a positive relation is established.

The results which are obtained from the scatter diagram can be further corroborated with the help of regression analysis. The technique allows an individual to establish a proper relation between two variables. The wages which an individual gets is dependent on the skills and knowledge which is acquired through education. This makes wages a dependent variable while education becomes an independent variable. Therefore, earning per hour is the dependent variable and education years are the independent variable. The model which is used is simple linear regression model as only one independent variable is involved. The regression equation which is used for the purpose is

Y is dependent variable that is earning per hour. X is the independent variable that is years of education.  and  are the respective intercept and slope coefficient of the regression.

Table 3: Regression Result of wage on education

The slope coefficient of the regression measure unit change in earning per hour with per unit change in education. The slope coefficient is 2.124. The slope coefficient which is computed shows that there exists a positive relationship between earnings per hour and education years. The analysis shows that an increase in 1 year of education results in increase in earning per hour by 2.12 units.

The significance level which is considered for the analysis is considered to be 5% and in the same context P value of the coefficient is 0.000. This means that the P value is smaller than the level of significance which indicates that ample evidence is not available to accept null hypothesis. Null hypothesis states that there is no statistically significant association between earning per hour and years of education. Therefore, from the result conclusion can be drawn regarding significant relation between wage and education.

R square in the regression model measure goodness of fit of 0.1706. R square value is used as a measure of goodness of fit of a regression model. The analysis reveals an R square value in the regression analysis which reveals that years of education only account for 17% variation in earning per hour.  As it is revealed that fitted model education has a very small proportion in terms of variation of wages, the model is not a good fit model.

Using the regression equation, hourly wage rate can be predicted with 12 and 14 years of education.

People having 14 years of education therefore earn 4.25 additional hourly wage compared to with 12 years of education.

Discussion

The main purpose of this report is to establish a relation between wages and education for which different statistical tools have been applied. The results shows that for a sample size of 100, the average years in education is 14 years while average wages is 22.31. The scatter diagram approach shows a weak positive relation between the two variables.  Finally, the analysis shows that regression techniques further confirms a positive relation between wages and education.

The strengths in the research paper, which can be identified, is the simplicity of the data analysis techniques utilized and the large sample size considered. The data analysis is through and it adequately helps in meeting the research objectives and questions. The conclusion is drawn based on regression analysis between the two variables, which is computed accurately (Bloom et al. 2014). The sample size itself is sufficient to provide a reliable result and help in drawing a conclusion.  The main limitation of the research is that only education is considered as a factor affecting the wages (Chassamboulli and Gomes 2019). The regression analysis also considers only the education level as the independent variable and therefore shows low variations (Kolstad and Wiig 2015). There are other factors as well which affects the wages which are age, gender, years of experience, location and consideration of such factors would only improve the model fitness.

The research paper suggests that there is a positive relation between wages and education level. Therefore, it can be stated that with better educational facilities, a nation can improve incidence of inequality of wages and income. The paper however opposes empirical findings that do not support the positive link between wage and education.

Recommendations 

On the basis of the results of the paper, the following recommendation is made:

• The government should be actively involved in enhancing educational facilities and ensure that there is basic education available for all class of people.
• The poor people should be encouraged for education of their children by ensuring scholarships and allowing subsidize education based on merit.
•  In addition to this, the government should also invest in development of skills so that ample opportunity is available to everyone.

Reference List 

Abaida, A., Lakrari, Y. and Abdouni, A., 2017. An examination of the relationship between competences and wages of higher education graduates: Evidence from Morocco. Tuning Journal for Higher Education, 5(1), pp.75-100.

Alsulami, H., 2018. The effect of education and experience on wages: the case study of Saudi Arabia. American Journal of Industrial and Business Management, 8(1), pp.129-142.

Autor, D.H., 2014. Skills, education, and the rise of earnings inequality among the “other 99 percent”. Science, 344(6186), pp.843-851.

Bloom, D.E., Canning, D., Chan, K.J. and Luca, D.L., 2014. Higher education and economic growth in Africa. International Journal of African Higher Education, 1(1), pp.22-57.

Chassamboulli, A. and Gomes, P., 2019. Public-sector employment, wages and education decisions. discussion paper.

Di Stasio, V., Bol, T. and Van de Werfhorst, H.G., 2016. What makes education positional? Institutions, overeducation and the competition for jobs. Research in Social Stratification and Mobility, 43, pp.53-63.

Kolstad, I. and Wiig, A., 2015. Education and entrepreneurial success. Small Business Economics, 44(4), pp.783-796.