Economic of Education Quantitative Analyse - Myassignmenthelp.Com

Question: Discuss about the Economic of Education Quantitative Analyse. Answer: Introduction In largest sense, education can be termed as an act of experience that has determinative effect on the mind, character and physical activity of person. Addition to that, Education dealt as a given process by which society gets transmission of collected knowledge, skill and values from one generation to another (Maxwell 1994). Furthermore, Education plays an important role as it help in preparing individuals for entering into labour forces as well as equipping them with the required skills as it engages in future learning experiences. Therefore, Educational accomplishment generally raises ones income (Martins and Pereira 2004). After completing formal education, young people should be able to make a successful change from school to work with the acquired skills and awareness subsequently. Wage differentials have to do with the variability in wages that accrue to different jobs and altered groups of labour in the labour marketplace. The consistency of educational career controls whether wages for this occupation are going to be low or high and will consequently be a source of wage differences. Purpose: The purpose of the study is to predict the statistically significant association between years of education and amount of daily wages. We would like to figure out what amount of daily wages is estimated with the help of years of education. Background: Economists are eager to find the association between years of education and amount of daily wages. Concisely, wages are predominant features in almost all markets particularly of capitalist economies (Budra and Moro-Egido 2008). In recent times, economists have noted wage differentials and asked to clarify them. Addition to that, their empirical studies prove that education plays an important role in defining wages and consequently a basis of wage differentials. The two factors have cause-effect relation according to our pre-assumption. In this research report, we are focusing to verify the relation between these two variables with sampled 100 data. We are seeking to verify and equalize the proven results. Method: The data file contains 100 observations for each of the variables that are wage and educ. Both the variables are numeric in nature. Wage refers earnings per hour and Educ. indicates years of education. The data is analysed with the help of MS Excel. The Data analysis toolpack is installed from analysis toolpack option. We used the Data analysis tool and executed descriptive statistics as well as linear regression equation with the help of given data sets. Descriptive Statistics: Descriptive Statistics wage educ Mean 22.3081 Mean 13.76 Standard Error 1.4021437 Standard Error 0.272704 Median 19.39 Median 13 Mode 38.45 Mode 12 Standard Deviation 14.021437 Standard Deviation 2.727044 Sample Variance 196.60071 Sample Variance 7.436768 Kurtosis 2.6065006 Kurtosis 1.317333 Skewness 1.4858281 Skewness 0.440879 Range 72.06 Range 15 Minimum 4.33 Minimum 6 Maximum 76.39 Maximum 21 Sum 2230.81 Sum 1376 Count 100 Count 100 The descriptive statistics of wage shows that mean and standard deviation of wage is 22.3081 and 14.021437. The amount of wage has minimum value 4.33 and maximum value 76.39. The range of wage is 72.06. The descriptive statistics of education shows that mean and standard deviation of years of education is 13.76 and 2.727044. The years of education has minimum value 6 and minimum value 21. The range of years of education is 15. Scatter plot: This is a scatter plot of education vs. wages. Here, years of education are an independent variable and wage is a dependent variable. The years of education is plotted in the x-axis and wage is plotted in the y-axis. The trend line is fitted in the scatter plot. The scatter diagram indicates that the two variables are not well correlated (Neter et al. 1996). The data points are not also well concentrated. Simple Linear Regression: The simple linear regression determines the linear relationship between two or more variables. One variable must be dependent or response variable and predictor or independent variables are one or more than one in number. The simple linear regression model is stated as Y = 0 + 1*X (Zou, Tuncali and Silverman 2003). Here, Y = dependent/ response variable X = independent/ predictor variable 0 = intercept of the regression model 1 = slope of the regression model / coefficient of the predictor SUMMARY OUTPUT Regression Statistics Multiple R 0.413051559 R Square 0.17061159 Adjusted R Square 0.162148443 Standard Error 12.83441505 Observations 100 ANOVA df SS MS F Significance F Regression 1 3320.693589 3320.6936 20.15936 1.94674E-05 Residual 98 16142.77655 164.72221 Total 99 19463.47014 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -6.914787841 6.633894418 -1.0423422 0.299818 -20.07953508 6.249959394 educ 2.123756384 0.473005701 4.4899171 1.95E-05 1.185091988 3.06242078 The estimated intercept of the model is (0 = -6.914787841). It means that if the year of education were 0, then the daily wage would be (-6.914787841) (Montgomery, Peck and Vining 2012). The estimated slope of the model is (1 = 2.123756384). It means if the education level increase or decrease by 1 year, the amount of daily wage is increased or decreased by 2.123756384 units. The estimated linear regression model is- Wage = (-6.914787841) + 2.123756384*educ. The calculated Multiple R (Correlation Coefficient) of the model is 0.413051559. It indicates a moderately positive correlation between these two variables. The value of multiple R-square is 0.17061159. Multiple R-square is also known the coefficient of variation. Years of education can explain only 17.06% variability of amount of daily wage. The linear association is not strong and significant. The value of multiple R-square (17.06%) refers that the fitting of the linear regression model is not good. The F-statistic is 20.15936 with significant p-value 1.94674E-05 (0.0). The p-value is less than 0.05 when chosen level of significance is 5%. Hence, we reject the null hypothesis of statistically significant linear relationship between the dependent variable (wage) and independent variable (education) with 95% probability. We can conclude that there is no significant effect of years of education on the amount of daily wage. Prediction education wage 12 18.5703 14 22.8178 Difference 4.2475 For the years of education 12, the amount of daily wage is predicted as 18.5703. For the years of educational 14, the estimated daily wage is 22.8178. The difference of daily wage is 4.2475 units for the difference of two years of educations. Discussion: In this research report, the result does not match with outcomes of data analysis executed by economists. The key strength of the research is that the collected data is primarily surveyed and authentic. The limitation of the data analysis of the research is that the size of the surveyed data is small. Therefore, the outcome significantly fluctuated from the previous results. The method of data collection and sampling are similar to other studies. However, the chosen target population may have lots of homogeneity. The outcome is inconsistent in comparison to the other studies. The findings do not have clear policy implications. It is just based on primarily collected data. Recommendations: We should recommend the data collector to collect more data for presenting the true scenario of association between two variables that are years of education and daily wages. The large sample would definitely provide better outcome. References: Budra, S. and Moro-Egido, A.I., 2008. Education, educational mismatch, and wage inequality: Evidence for Spain.Economics of Education Review,27(3), pp.332-341. Martins, P.S. and Pereira, P.T., 2004. Does education reduce wage inequality? Quantile regression evidence from 16 countries.Labour economics,11(3), pp.355-371. Maxwell, N.L., 1994. The effect on black-white wage differences of differences in the quantity and quality of education.ILR Review,47(2), pp.249-264. Montgomery, D.C., Peck, E.A. and Vining, G.G., 2012.Introduction to linear regression analysis(Vol. 821). John Wiley Sons. Neter, J., Kutner, M.H., Nachtsheim, C.J. and Wasserman, W., 1996.Applied linear statistical models(Vol. 4, p. 318). Chicago: Irwin. Oja, H., 1983. Descriptive statistics for multivariate distributions.Statistics Probability Letters,1(6), pp.327-332. Zou, K.H., Tuncali, K. and Silverman, S.G., 2003. Correlation and simple linear regression.Radiology,227(3), pp.617-628.