Regression Analysis On Relationship Between Education And Wages

Background

As stated earlier, education tends to enrich the lifestyle of a person and increases the capability. It is often believed that skilled and educated labor is more productive than the unskilled one. Hence, policy makers often tend to emphasize on the importance of education to increase labor productivity. It is often considered that a labor with higher education will produce higher and thus have a higher wage (Reardon 2013).

However, previous research on the same topic in the African region and surroundings state that every worker will receive the same pay. A similar research conducted in the other parts of the world also state that not much relationship has been found between the two given factors. People with higher education have been getting similar wages like those with lower wages (Siegel 2016). People who have a higher educational are often employed in areas with low wages in an area o establishment.

In order to examine the relationship between the education of a person and his wages the approach that will be used is the Regression Analysis.  The education will be taken as the `X` variable which is the independent variable and the wages will be taken to be the `Y` variable which is the dependent variable (Jaggia et al., 2016). The education is taken to be the independent variable because educational level has an impact on the wages and income received. Therefore, the variables have been taken accordingly.

Regression Analysis can be described as a set of statistical processes, which can be used to estimate the relationship among a variety of variables. There are several techniques, which may be used to model and analyze the various variables wherein the focus lies on the dependent variable and the independent variable. Specifically, the regression analysis helps to analyze and observe the change in the dependent variable based on the changes in the independent variable. The independent variable is believed to remain fixed (Draper and Smith 2014). The analysis is primarily used for prediction and forecasting the future or even a relationship between two variables.

Hence, in this report, the education has taken to be the independent variable and the wages are taken to be the dependent variable. The Regression Analysis will be conducted using Microsoft Excel Data Analysis tools after which the results shall be interpreted.  

The scatter diagram given above describes the relationship between the education and the wages received. It can be observed that the relationship between the two variables is extremely weak and that education has not impact on the wages received by a person (Chatterjee and Hadi 2015). The R square is almost zero, which reflects that the explanatory power of the Regression is zero.

Method

In simple terms, it can be stated that the scatter diagram reflects no relationship between the dependent variable that is the wages and the independent variable that is the education.

Y=0.0803X+11.967

From the data analysis, it can be seen that the resultant Regression equation is given as above. In the given regression equation, the line has been given in the form of y=mx+c, where m is the slope of the line. In the given equation, it can be observed that the slope of the given regression line and analysis is 0.0803 which is equivalent to zero. According to Darlington and Hayes (2016), when the slop of a line is equal to or approximately 0 then the line is a vertical line and there stands no relationship between the two given variables.

In the given scenario, according to the result, there lies no relationship between the education of a person and the wages received.

In the given scenario, as per the calculation, the value of Multiple R is 0.41305, which is not even one percent. The value of Adjusted R square is 0.1 per cent .This proves that it is not even one percent. Adjusted R square explains the explanatory power of the regression, which is not even one percent with respect to the given dependent variable, which is wages. The chosen variable is therefore not a good choice.

Hence, it can be stated that according to the given criteria and results that were obtained after the regression analysis had been performed, there lies no relationship between the dependent and the independent variable.  Hence, it can be stated that education qualification of a person does not reflect the person`s wage income.

The regression equation is Y=0.0803X+11.967

It did not provide a good fit as the slop of the line is extremely role thus defining and stating the fact that there is no relationship between the two variables

For a person with 14 years of education

Therefore, as x is education

X= 14

Wages(y) = 0.0803X+11.967

= 0.0803(14) +11.967

=1.1242+11.967

=13.0912

Hence, according to the regression equation obtained the predicted wages of a person with 14 years of education is 13.0912/hr.

For a person with 12 years of education

X= 12

Wages(y) = 0.0803X+11.967

= 0.0803(12) +11.967

=0.9636+11.967

=12.9306

Hence, according to the regression equation obtained the predicted wages of a person with 12 years of education is 12.9306/hr.

The difference between their hourly wage is approximately 0.1606 wages is not even a unit. Hence, this reflects the results that the educational level of a person has no effect on one`s wages

Therefore, from the given analysis it can be stated that there lies no relationship between the educational level of the person and the wages the person receives. The regression line obtained from the study showed an extremely low slope and this reflects that there lies no relationship between the two variables or the sample size is too small to come to a conclusion.

Other studies made by various authors reflect a similar finding. According to Patrick et al. (2014), similar results were observed with a sample taken from workers in Ghana.  The research is often conducted by the authors because the logic suggests that people with more education should be earning higher, but practical studies tell a different story.

These findings imply that the government should adopt policies with the help of which the highly educated people get jobs according to their criteria. In the present scenario, the people with different educational backgrounds are performing the same job because of the dearth of employment. This should not be the case.

The following recommendations can be made:

• The government must try to increase the enrolment in schooling. These programs need to be effectively monitored.
• The investments to education and other relevant learning activities should be subsidized by the government in order to make sure that the low-wagers can improve their standard of living from their income.
• Women must be encouraged to pursue higher education.
• The workers should be provided with on-the job training to improve their skills.

References

Anderson, D., Sweeney, D. and Williams, T., 2014. Modern business statistics with Microsoft Excel. Nelson Education.

Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D. and Cochran, J.J., 2014. Essentials of statistics for business and economics. Cengage Learning.

Chatterjee, S. and Hadi, A.S., 2015. Regression analysis by example. John Wiley & Sons.

Darlington, R.B. and Hayes, A.F., 2016. Regression analysis and linear models: Concepts, applications, and implementation. Guilford Publications.

Draper, N.R. and Smith, H., 2014. Applied regression analysis. John Wiley & Sons.

Jaggia, S., Kelly, A., Beg, A.B.M., Leighton, C., Olaru, D., Salzman, S. and Sriananthakumar, S., 2016. Essentials of business statistics: communicating with numbers. McGraw-Hill Education.

Patrick, E., Hagan, E., Ahouandjinou, E. and AttahObeng, P. 2014. Relationship between Education and Wage differentials in Ghana: A Case Study of Accra – a Suburb of greater Accra Region,International Journal of Academic Research in Business and Social Sciences January 2014, Vol. 4, No. 1 ISSN: 2222-6990

Psacharopoulos, G. ed., 2014. Economics of education: Research and studies. Elsevier.

Reardon, S.F., 2013. The widening income achievement gap. Educational Leadership, 70(8), pp.10-16.

Siegel, A., 2016. Practical business statistics. Academic Press.

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