Effect On Black-White Wage Differences In Quantity - Free Samples

Question: Discuss about the Effect On Black-White Wage Differences In Quantity. Answer: Introduction: Education in the largest sense is a curriculum of experience that has significant impact on the mind, character and physical activity of any human being. Education is the method by which society gets communication of collected knowledge, skill and values from one generation to another (Maxwell 1994). Education plays a key role to prepare individuals to step into labour forces as well as maintaining them with the skills to appoint in lifelong learning experiences. Educational attainment usually raises ones income and life styles as well (Martins and Pereira 2004). After finishing formal education, young people should be able to build a successful transition from school to work with the achieved skills and knowledge consequently. Wage variability has to do with the changeability in wages that accumulate to different jobs and various groups of labour in the labour market. The constancy of educational career establish whether wages for this occupation are going to be low or high and will therefore be a source of wage variability. Purpose: The purpose of the study is to estimate the statistically significant relationship between years of education and amount of daily wages. We would like to find out what amount of daily wages is predicted with the help of years of education. Background: Economists are keen to find the relationship between years of education and amount of daily wages. in brief, wages are rampant features in almost all markets especially of capitalist economies (Budra and Moro-Egido 2008). In current days, economists have distinguished variability of wage and trying to explain them. Their pragmatic studies show the evidence that education plays an important role to determine wages and therefore a cause of wage variability. The two factors have cause-effect association in accordance to pre-assigned assumption. In this research report, we are highlighting to validate the link between these two variables with collected 100 samples. We are looking to verify and equalize the evident outcomes. 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 indicates earnings per hour and Educ. refers years of education. The data is analysed with the help of MS Excel. The Analysis toolpack is installed from analysis toolpack option. We utilised the data analysis tool and incorporated summary statistics as well as linear regression equation with the help of given data sets. Results: Summary 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 (Oja 1983) The summary statistics of wage indicates that average and standard deviation of wage is 22.3081 and 14.021437. The amount of wage has lowest value 4.33 and highest value 76.39. The range of wage is 72.06. The summary statistics of education indicates that average and standard deviation of years of education is 13.76 and 2.727044. The years of education has lowest value 6 and maximum 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 are 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 refers that the two variables are uncorrelated (Neter et al. 1996). The plotted data are not also well concentrated. Linear Regression Model: The linear regression model determines the linear relationship between two or more variables. One variable must be dependent that is known as response variable and predictor or independent variables are one or more than one in number. Independent variables explain the dependent variable. That is why these are also known as explanatory variables. The linear regression model is Y = a + b*X Here, Y = dependent/ response variable X = independent/ predictor variable a = intercept of the regression model b = slope of the regression model / coefficient of the predictor (Zou, Tuncali and Silverman 2003). 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 predicted intercept of the model is (a = -6.914787841). It explains that if the years of education were 0, then the daily wage would be (-6.914787841) (Montgomery, Peck and Vining 2012). The predicted slope of the model is (b = 2.123756384). It means if the education level enhance or reduce by 1 year, the amount of daily wage is increased or decreased by 2.123756384 units. The pedicted linear regression model is- Wage = (-6.914787841) + 2.123756384*educ. The Multiple R (Correlation Coefficient) of the model is calculated as 0.413051559. It refers 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 weak and insignificant. The value of multiple R-square (17.06%) indicates 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 0.05. Therefoere, 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. Estimation 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 outcome mismatches with results of data analysis incorporated by previous economists. The strength of the research is that the gathered data is preliminarily surveyed and valid. The limitation of the data analysis of the research is that the size of the surveyed data is not large. Therefore, the result significantly has fluctuated from the previous outcomes. The process of data collection and sampling are similar to other studies. However, the selected target population may have lots of homogeneity. The outcome is not consistent compared to the other studies. The findings do not have clear policy implications. It is just based on primarily collected data. Our executed analysis definitely have bias. Recommendations: We should recommend the data collector to gather more data for representing the true scenario of relationship between two variables that are years of education and daily wages. The large sample would certainly provide better result. 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.