The report focuses on evaluating the relationship between education and wage. Education of a worker determines the skill level and hence likely to affect wage. Therefore, difference in wage is one factor contributing to wage differential. The purpose of the report is to evaluate the proposed relationship between earnings of workers and education years. The estimated relationship has policy implication for reducing wage or income inequality. The objective is to examine how education level influences wage.
Human capital is one important aspect determining the productive capacity of firms. Education is a qualitative attribute that not only contributes in development of mind but also helps in building a good character. The knowledge acquires from basic and formal education guides individual towards the right direction after joining the labor force. Without education potential skills of an individual cannot be explored fully. It is true that formal education is not sufficient develop all the needed skill. After completing formal education professional training and skill accumulation is required (David 2014.) In modern technical world, without formal education the advanced technology cannot be learnt. These skills by determining productivity influences wage.
Wage is the payment given by firm to the laborers. In general instance, wage is determined in the factor market from balancing labor supply and labor demand. The equilibrium wage thus depends on both demand and availability of workers. Firms always desire for highly productive pool of laborers. In order to have a skilled workforce often wage above the equilibrium is paid (Apple, 2017). It is not possible to judge workers’ productivity at a glance. Education is used as an indicator of deciding productivity and wage.
The likely relation between education and wage attracts the attention of economists in explaining wage inequality and drawing relevant policy prescription.
The paper uses qualitative research methods in exploring relationship between education and wage. To indicate wage level data on hourly wage rate is collected. The level of education is indicated by years spend on education. A sample size of 100 is taken for the analysis purpose. A general description of the collected samples is obtained from their summary statistics. The summary statistics provides information on mean, standard deviation, range, maximum, minimum and others. The pattern of relationship or correlation between the two variables is presented with developing a scatter diagram. In the scatter plot wage is taken as dependent variable and education is taken as independent variable. The relation obtained graphically from the scatter plot is finally confirmed by estimating a linear regression between wage and education. Regression analysis is one of the most important tool used to determine direction and strength of relationship between variables (Chatterjee and Hadi 2015). Regression provides a statistically valid relation between the chosen variables.
The mean hourly wage rate is 22.3081. This implies worker on an average receives a remuneration of 22.31 per hour. The standard deviation from the descriptive statistics is obtained as 14.02. The standard deviation indicates volatility of a distribution. A standard deviation less than average implies a less volatile distribution. Here average wage is greater than standard deviation of the wage. Hence, wage distribution is not much volatile. The maximum and minimum wage is 73.39 and 4.33 respectively.
The average of education year is obtained as 13.76 that is approximately 14 years. The average shows workers on an average have 14 years of education. The maximum education year is 21 while the minimum education year is 6. The standard deviation for education years is 2.72. Here again the standard deviation is far less than mean education years implying a small variation of the distribution.
The fitted trend line in the scatter diagram shows a positive association between hourly wage and education years. As the education level increases, there is an increase in the wage rate. For the education years between 5 and 10 there are only a few observations. Therefore, 5-10 years of education is not so much important. As the education years increases its importance in determining wage increases. The data points are mostly lie between 10 to 15 years and 15 to 20 years.
The regression equation that can be used to predict wage given the level of education is given as follows
Wage = a + (b*education)
Where, a is the intercept and b is the slope co-efficient of education level.
Using results from regression output the estimated regression equation is obtained as
Wage = -6.9148 + (2.1238* education)
The slope coefficient measures the elasticity of dependent variable with respect to independent variable (Darlington and Hayes 2016). The slope education therefore measures the extent of change in wage due to unit change in education. The slope for education is estimated as 2.1238. The positive slope referred to a positive relation between hourly wage and education years.
A statistically valid relation requires the slope to be statistically significant. The null hypothesis for regression slope co-efficient is that there is no significant association between wade and education. The P value of the slop coefficient is 0.0000. Since the P value is less than 5% significance level, the null hypothesis is rejected confirming a statistically significant relation.
A model is said to be a good fit when most of the variation in the dependent variable is explained by the independent variable. This is obtained from the measures of correlation co-efficient or R square (Rumelhart 2017). The R square value for the model is 0.1706. This implies in the wage model where education is used as the independent variable, most of the variation in wage remained unexplained as only 17% variation is explained by education. Therefore, wage model is not fitted good.
Wage = -6.9148 + (2.1238* education)
Using this equation wage can be predicted for any given level of education.
At education years of 12, predicted wage is
Wage = -6.9148 + (2.1238* education)
= -6.9148 + (2.1238*12)
= -6.9148 +25.4856
At education years of 14, predicted wage is
Wage = -6.9148 + (2.1238* education)
= -6.9148 + (2.1238*14)
= -6.9148 +29.7332
Difference in hourly wage
22.8184 – 18.5708
From the regression result, a positive relation is found between education and wage. The positive significant relation implies with increase in education years, wage level increases. The mean hourly wage is 22.61 and that of mean education year is 14. The model though gives a statistically significant result but is not fitted goods having a very small R square.
The quantitative analysis used in the research paper has several advantages. A definite outcome is obtained and the result based on selected samples can be approximate for population. However, the result satisfies the expected positive relationship between education and wage and provides statistically significant result. A relatively small sample size is both the strength and weakness of the study. A very small R square value is obtained making the model a bad fit.
The result of present research is consistent with many other research papers developed in this field. These papers establish education as one of the significant factors causing wage differential (Bárány 2016). Based on the significant association between hourly wage and education policy can be undertaken to improve wage level.
Wage inequality is a problem in most of the developing nation. Government in these nation should promote education as a tool to raise wages to low paid unskilled workers.
In addition to basic education, government should ensure that education is continued for sufficient years. As suggested from the paper, worker should have 10 to 20 years of education.
Educating workers is a better tool to improve wage level than using minimum wage legislation. The policy of minimum wage ends up with unemployment. However, education raises skills and workers’ productivity benefitting both the employer and worker.
Apple, M.W. ed., 2017. Cultural and economic reproduction in education: Essays on class, ideology and the state (Vol. 53). Routledge.
Bárány, Z.L., 2016. The minimum wage and inequality: The effects of education and technology. Journal of Labor Economics, 34(1), pp.237-274.
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.
David, H., 2014. Skills, education, and the rise of earnings inequality among the" other 99 percent".
Rumelhart, D.E., 2017. Schemata: The building blocks. Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics, artificial intelligence and education, 11, p.33.
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