Schooling and Earning Theories - Essay Example

? Schooling and earning theories The statistics provided every year by the United s Bureau for Census has been able to show that there is a very strong relationship between education and earning. In the year 2008, those workers who had any bachelor’s degree in the US earned sixty-five per cent more compared to workers who had only a diploma from high school (Jacob, 2008). They also earned almost one hundred and thirty per cent more compared to the workers who had no diploma. The difference in income between those workers who have attained a bachelor’s degree and those having only a high school diploma is called “college premium”. This premium has increased since the 1970s. It means that the employers in different sectors value workers who are more educated (Steve, 2006). The set of abilities and skills that different individuals bring to the market is called human capital. Schooling is an investment in the human capital. This is so because it delivers skills that are specialized and boosts the productivity of workers. As a result, levels of education that are higher generally lead to an increased power of earning (Boniface, 1999). Different researches have been able to show that each year one spends in school improves their annual earning by ten per cent (Melvin, 1991). However, one should way the options of attending work to furthering their education. This choice is called the “opportunity cost” or the true price of education. A person who seeks to maximize his future earnings should choose remaining in school to learn more if only the long-run payout in an additional year in school is able to surpass the opportunity cost (Vade, 2012). The impact caused by schooling on the different levels of earning carries a significant weight for the decision making of persons as well as the policy makers. With the theories of education and earning in the United Kingdom and the averaged ten per cent return rate annually, schooling represents among the finest investment any given government can make for the period of financial limitations and scarce resources. Through subsidizing education, the policy creators are able to ensure that education is more affordable and more accessible. It is argued that investment in the sector of education is a sure way for good result of an economy and the standard of living for the people who have low incomes. (Ruben, 1999). The Wage-schooling locus is a theory based on the total amount of earnings that different firms are ready to pay their employees for a given level of schooling. According to the theory, different workers with different educational level have different amount of salaries (Helen, 2005). The more one is educated the more his or her salary per annum. There are certain properties that a wage-schooling locus has. The locus slopes upwards. The upward sloping curve shows that more education is needed in the market and an increase in the level of education has an increasing effect on the salaries of employees (Jackson, 1997). This curve indicates an earnings increase associated with one year additional of education. The wage-schooling locus curve is concave. This indicates that there is a diminishing rate of return to the accumulation of human capital. Each additional year of education yields less knowledge increase and a lower additional earning compared to previous years (Else, 1999). A year increase in education leads to an increase in salaries but at a reducing rate. The wage-schooling locus indicates the salary which a worker would get after completing a certain level of education (Reuben, 1990). If this worker were a high school graduate, he or she would earn $ 21,000 in a year. If the worker advances his or her education for one year in college, his or her returns will increase to $ 24,000, according to this graph. Dollars 24,000 21,000 12 13 14 Years of schooling Mincer function Mincer was able to come up with an earning natural logarithm model. This model is a function of the education years and the years of the experience in the potential labor market. This may be simplified as ones age then subtract the schooling years and lastly subtract six. In the version used frequently of the Mincer’s functions of human capital earnings, log earnings are usually modeled as linear function sums of the years of education as well as a quadratic function of the potential experience years. Logy=logy0+rS+_1X+_2X2 In this model, y represents the earnings and y0 represents the earning levels of the individual who is not educated and does not have any experience. S represents the schooling years while X represents the experience of the potential labor market. This equation is the workhorse of the empirical research on the determination of earnings. It is among the most used models in the empirical economics since it was estimated on a number of data for many countries during a long period of time (Earl, 1988). This equation is popular in the world due to a number of reasons. This equation is based on an investment formal model and it also provides specifications that are able to fit into data very well in most contexts (Jacob, 1989). In Mincer’s early work, he stated that the profile of age-earnings that were resulting were steeper for the more educated employees than for those who are less educated. This means that the log earnings may not be a separable function of age and education (Stone, 1996). He said that there is nothing such as a return rate that is single to education but each age group has different return rates. This means that the return rates are different for different ages as calculated by the equation. Human capital models are usually used in different empirical analyses of the distribution of income to attempt explain level differences, skewness and inequality of the earnings of different workers who have a difference in age and schooling. This is done in order to interpret the shapes of the age-earnings profiles of different individuals. It also helps to explain the distribution of earning differences among countries and regions (Leolel, 1994). Though these studies may be sketchy in some respects, they tend to provide some qualitative consistent interpretations of a variety of income distribution features. Applying the model of schooling in the regression of 1950 log of men’s earnings, of those aged twenty five and sixty four, in a labor force that is experienced on their schooling years, Chiswick was able to find that the determination coefficient varied between ten and twenty per cent in the regions of United States. The coefficients are ten per cent in the non-north regions and eighteen per cent of the earnings of the white men in the southern regions. These coefficients of determinations may be overstated since they are usually based on data that is grouped by schooling intervals and income. An application of this same regression to the white persons who were not students and male aged between fifteen and sixty-four, of the year 1959 earnings in the United States, yield a determination coefficient of seven per cent. The schooling model has not been a great success in its performance. Though it is not successful, this model does not need to be doubtful on its importance and relevance to the analysis of human capital. The schooling model usually presents incomplete specifications of the theory of human capital on the earnings distributions (Lewis, 2005). This model is not able to explain how unequal earnings are among the individuals differing in behavioral characteristics as well as schooling. Though it is not effective enough, it is able to explain how schooling and earning are related (Charles, 1985). Problem There are some problems associated with the empirical study based on return to education. A very important issue is the effect of errors on the survey measurements in schooling (Leonel, 1993). Any error during the survey may affect the whole calculation of the outcomes. In any equation, a change in the given data affects the whole calculation and will yield an answer that is not correct. Other arguments revolve around the assumption that the errors during measurements in schooling are usually uncorrelated with schooling that is true (Jacob, 1994). Because schooling is measured typically as a variable that is discrete having outcomes that range between fixed lower and upper limits, the errors in the reported schooling may be mean-regressive (Jacob, 2005). The Mincer function also has a number of assumptions which may be a problem associated with it. The function does not put into consideration the non- monetary and monetary costs of education. Different researches have shown that there are considerable costs that are involved by furthering education such as equipment, fees and books (Jacob, 1996). The equation does not consider these costs and may affect the interpretation of the relationship between earning and schooling. Conclusion Jacob Mincer came up with an approach which helps in determining the relationship between earning and schooling. He also explained how schooling is usually distributed in the population of a given area. According to his research, education plays a great role in enhancing the earning that an individual gets. As he was doing his research, he came up with an earning natural logarithm model. This model used different factors in calculating the earnings of different individuals. These factors include the level of education that one has and the experience that one has in a market that has potential. 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