Evidence In Panel Error Correction Model -Myassignmenthelp.Com

Question: Discuss About The Evidence In Panel Error Correction Model? Answer: Introduction There have been several contradicting beliefs on how the level of education influences the incomes of individuals. there have been varied opinions on the subject with those insinuating a measurable relationship between the two variables of level of education and operations while others suggesting that there is no direct correlation between the two (whether positive or negative )( Easterbrook, Kuppens, Manstead, 2016). We, therefore, aim to clarify this aspect through data analysis to explicitly verify if there is a correlation or not and if there is then what form is it. Background In recent decades especially in advanced countries, the value of education has risen effectively putting a higher wage premium on education. this because higher education is often associated with a higher understanding and mastery of the subject as well as higher skill level and cognitive abilities (Goldin Katz, 2009). Education is a right that enables people to develop themselves, improve productivity and therefore living conditions as well (Mart Linares, 2015). Since the development of human capital theory, the study of effects of education in the field of economics has been of great interest .the level of education influences the type of jobs and therefore wages (Machlup, 2014).). For instance, in certain fields, the amount of specialized education directly translates to higher earnings eg. The health sector where specialists almost earn double what the general practitioners earn. (Philippon, Reshef, 2012) Mean Earnings by Highest Degree Earned, $: 2009 (SAUS, table 232) All amounts are in real terms; Statistical Abstract of the United States (SAUS) published by the US Census Bureau. The table clearly shows a direct positive correlation between wages and time spent in education. Education is a key aspect of the economic growth of any country. International evidence shows that there is a direct, permanent and positive relationship between education and salaries. This is to mean that holding all factors constant the higher the worker's education the higher the wages or earnings. The theory of human capital (mincer, 1974) presents a dynamic model for wage determination, it focuses on the various life stages of with respect to income. In this type of model, Mincers proposal is to complete the basic model incorporating variables measuring time in weeks worked per year and post-schooling education investment (Apergis, Dincer. Payne, 2014.). A world report and US news conducted surveys and reported that holders of bachelors, masters, doctoral and professional degrees earned about $2.27, .67, 3.35 and 3.65 million dollars in their lifetime. The report also showed that degree holders earned slightly more than college or diploma degrees (US news and world report, 2011). Mincer also proposes the formulae expressing income according to years spent in education. The wage logarithm is used in the equation to impose a constant ratio effect on the variables of wage. Therefore under this method, the natural log of income is not separable from education hours and other variables such as gender and experience. This can be shown in the equation Log (w) = 0+1S+2X+3X2+ Where 0,..., 3 are regression parameters, w is the worker's wage and S Are years of education. However, this proposal does not separate between the cause and effect of education on increased wages. This may be because of growth of productivity from education. In these terms, we observe that Mincers equation is consistent with the human capital theory (Yin, 2015) Discussion Looking back at our data, of the 100 entries of wages per hour and education hours we obtain a mean of 22.3081 for wages and average education years of 13.76. The wages have a standard deviation of 13.951154 while years of education have a standard deviation of 2.7133743. wage mean Educ mean ds wage ds educe wage min wage max educ min educ max median wages median educ hrs 22.3081 13.76 13.9512 2.71337 4.33 76.39 6 21 19.39 13 The wage standard deviation is very high (13.951154) about the same value as the mean, this shows high variance/dispersion in the wage entries among different levels of education hours. This is further enhanced by the huge margin between the minimum wage value (4.33) and the max (76.39) bringing a very high range of 72.06. The standard deviation for the education hours is relatively lower at 2.71 while the min and max are 6 and 21 respectively. This show a minimal variance but still a high range of 15. Graph showing relationship between wages and education hours The scatter marketing does show a general upward trend of increase of wages with an increase in education time. The scatter shows that majority entries for education time are around the mean at about 13, with clustering between times 12-17. The trend line indicates a progressive growth, increase in education hours leading to increasing in wages. The bar graph especially shows a fairly constant rate of wages at around 12. However, the trend line is at Y=2.1238x -6.9148 R2= 0.1706 The catter plot has a slope coefficient of 2.1238 which implies the y-intercept. This indicates that for every additional educational wage unit there is a corresponding 2.1238 increase in education hours. The scatter plot indicates a positive correlation between the two variables. The trend line, however, does not provide for a good fit as most of the entries are outliers and clustered along certain education time points that the line only cuts across leaving majority of entries as outliers .this predicts data bias across different points in the plot The p-value for the wage and education hours are 0.29981763 and 1.9467E-05 respectively. The value for the wage is relatively higher than the significance level and this implies that there is insufficient evidence to assume a non-zero correlation. The p-value of education time I however significantly low indicating the data is statistically significant. For a person with 12 years of education, the wage would be Y=2.1238x -6.9148 Y (12) =2.1238(12)-6.9148= 18.5708 For a person with 14 years of education, predicted wages would be Y (14) =2.1238(14) -6.9148=22.8184 The difference in hourly rate is 22.8184-18.5708=4.2476 Conclusion This method of data analysis is a simple and straight-forward with minimal chance of error, the method allows for analysis of one data set while the ANOVA allows comparison of different data sets but with homogeneity. The data analysis presentations are in line with previous studies. Our data show a correlation coefficient of R2 = 0.1706 .this denoted a positive correlation though to a small extent. It denotes a small variability of the data from the mean, a variation of 17% .however this results vary in different disciplines and professions. For more precision, the data should be collected in the different fields separately and analyzed separately to see a clearer picture of this scenario. The methodology, however, is efficient and simple for any researcher. References Goldin, C. D., Katz, L. F. (2009). The future of inequality: The other reason education matters so much. Mart Linares, R. M. (2015). An empirical examination of the relationship between wages and education. Mincer, J. (1974). Schooling, Experience, and Earnings. Human Behavior Social Institutions No. 2. Schultz, T. W. (1960). Capital formation by education. Journal of political economy, 68(6), 571-583. Philippon, T., Reshef, A. (2012). Wages and human capital in the US finance industry: 19092006. The Quarterly Journal of Economics, management, 1551-1609. Yin, R. K. (2015). Qualitative research from start to finish. Guilford Publications Machlup, F. (2014). Knowledge: Its creation, distribution and economic significance, Volume III: The economics of information and human capital (Vol. 3). Princeton University Press. Apergis, N., Dincer, O., Payne, J. E. (2014). 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