Greater Accessibility Education Inequality -Myassignmenthelp.Com

Question: Discuss About The Greater Accessibility Education Inequality? Answer: Introducation People try to get higher level of education to obtain higher wage rate per hour. It is believed that higher level of education makes skilled worker (Yirmiyahu, Rubin and Malul 2017). Moreover, a skilled worker gets higher amount of wages compare to an unskilled or semi-skilled worker. Economists also consider this relation as interesting. The government of a country can impose any policy to promote higher education. On the other side, skilled workers work efficiently as they get higher wages compare to others. This further helps a country to increase its gross domestic product (GDP). People with higher level of education generally enjoy a good standard of living. Hence, for developing and developed countries, education is an important factor (Verger, Altinyelken and Novelli 2018). It further helps a country to develop. Therefore, education is an important factor for a countrys economy. Hence, economists try to establish a relation between education and wage rate of a country. This fu rther helps to implement a theory. Moreover the government of a country can apply different policies, based on the relation. Method: Here, education is considered as an independent variable and wage is considered as a dependent variable. To obtain higher level of education, a person has spent many years. In this report, education is measured by this number of years. Moreover, wage is measured as the earning per hour of a worker. To understand an association between education and wage, a statistical tool will be used. The simple linear regression analysis is used to analyse this relation (Fox 2015). Furthermore, a scatter plot will help to draw a simple linear regression analysis. To analyse further, a trending line will help to plot this regression line by covering maximum scattered points. By analysing this regression line, one can understand that whether education has any impact on wage or not. It will also describe that whether both variables have positive or negative relations. Results: This section will summarise various outcomes of both education and wage by doing statistical analysis. In this section of the report, descriptive analysis related to education is done. In figure 1, a scatter diagram shows the relation between education and wage. Education is taken as independent variable. This scatter diagram is roughly showing a positive relation between education and wage. However, it does not follow any exact relation between number of years of education and wage per hour (Fox 2015). At a same education level, some persons are getting high amount wage per hours and some are getting comparatively low wages per hour. Hence, wage discrimination is sharply found among different workers with same years of educational experience. The estimated regression equation is y= 2.123x 6.914. Here, y is the independent variable, that is, education. On the other side, x represents wage, that is, dependent variable. Slope coefficient measures the steepness of a regression line between education and wage. Here, slope is 2.123. As the equation gives a positive value of the slope, it indicates a positive relation between these two variables (Mooi, Sarstedt and Mooi-Reci 2018). When education increases by 1 unit, wage increases by 2.123. In this equation, P value is 0.0000. P-value of 5% or less that indicates statistically significant. Here, P value is less than 0.5. This will reject the null hypothesis that the coefficient has no effect (Walsh et al. 2014). Hence, this shows a statically significant association. The regression equation does not provide a good fit. Best-fitted line represents the best approximation of all given data. Here, a trend line has drawn. There are many points, which are above the line and below the line. Here, the value of r-square is 0.1706. This indicates that wage explains an estimated 17% of the variation in education (D'Agostino 2017). This value is very low. Using the equation y= 2.123x 6.914, predicted wage rate will be calculated. Here, x is the year of education of a worker and y is the predicted wage rate of that worer. When a person has 12 years of education, the predicted wage will be 18.562. On the other side, when a person has 14 years of education, the predicted wage rate will be 22.808. Hence, as a person has two years of extra education, he can earn extra 4.246 wage per hour. Description: The result does not provide any sharp relation between education and wage. However, this research has done based on only 101 data. As the sample size is small, the outcome cannot be predicted exact outcome. Hence, the linear regression is showing a rough image of the whole data set. The outcome of this analysis is not consistent. People believe that higher studies will help them to attain higher amount of wage. This is one of the chief reasons behind higher education. However, in this report the outcome does not present this concept accurately (Fox 2015). This wrong outcome further negatively affects higher education. There are various points, which are situated far from the regression line. Moreover, at the same level of education, some people are earning higher amount of wage per hour. This will not help the government of a country to implement a particular policy. Hence, this report will note properly help any researcher to further. However, number of education does not indicate any particular stream of education. Different people chose different streams of education. Hence, with same educational year with different educational stream affect wage rate. Recommendation: To implement a proper recommendation, a proper association between education and wage is needed. The government or private sector will offer higher wage for those people, who have higher years of educational experience. Wage discrimination of workers with same educational level is not wanted. Educated people with more years of experience want to earn more compare to other people, who have low level of education. This will further help those workers to work efficiently. If two persons with same educational level will earn different amount of wages, then it will adversely affect the efficiency level of them. Furthermore, both private and government sectors will increase their hourly wage rate to attract more workers. On the other side, government should promote higher level of education so that people can earn higher amount of wage. This will further help a country to operate efficiently. Education helps a country to grow and develop further. Hence, perfect policy related to education is important. Moreover, only number of years of education is mentioned. However, various streams of education are not mentioned. Engineers or doctors earn more wages compare to other people. Hence, those data do not provide any detail information related education. This will further mislead researches. Hence, with number of education, types of jobs should be mentioned, References: D'Agostino, R., 2017.Goodness-of-fit-techniques. Routledge. Fox, J., 2015.Applied regression analysis and generalized linear models. Sage Publications. Mooi, E., Sarstedt, M. and Mooi-Reci, I., 2018. Regression Analysis. InMarket Research(pp. 215-263). Springer, Singapore. Verger, A., Altinyelken, H.K. and Novelli, M. eds., 2018.Global education policy and international development: New agendas, issues and policies. Bloomsbury Publishing. Walsh, M., Srinathan, S.K., McAuley, D.F., Mrkobrada, M., Levine, O., Ribic, C., Molnar, A.O., Dattani, N.D., Burke, A., Guyatt, G. and Thabane, L., 2014. The statistical significance of randomized controlled trial results is frequently fragile: a case for a Fragility Index.Journal of clinical epidemiology,67(6), pp.622-628. Yirmiyahu, A., Rubin, O.D. and Malul, M., 2017. Does greater accessibility to higher education reduce wage inequality? The case of the Arab minority in Israel.Studies in Higher Education,42(6), pp.1071-1090