Theory of Wages and Profits - Essay Example

Microeconomic decisions and policy making are economic factors that have been debated upon from the wage differentials and human capital framework points of view. In his theory of wages and profits, Adam Smith stated that labor wages do vary with the labor skills, and ease or hardship of the work done (Smith, Edwin & Lerner). The human capital, as an investment, requires a cost and, at the same time, payment for any return over the monetary or non-monetary lifetime. According to Harmon et al (2003), extensive research works and literature have been conducted to establish the functions of human capital earnings and its impacts on the decomposition of the periodic earnings into linear function forms, in which the log of earnings serve as both the function of schooling measure in terms of years of experience in work and other factors representing heterogeneity within a cross-section sample of people observed, and dependent variables. This simple statistical model has however been faced by some limitations, i.e., biasness in the estimation of returns to education and endogeneity in the schooling variables. As a positively correlated function of the unobserved variable of ability, people with varied ability to work and learn are in most instances a better position to school for longer periods; hence this enhanced ability will have a reflection of higher wages within their occupations. Additionally, existence of a symmetric correlation between any of the independent variables such as schooling and the error term in an OLS regression can consequently lead to bias in the estimates. In this case, effects of ability/heterogeneity have to be random in the sample to avoid positive correlations. Card (1999) explored the causal relationship of education on earnings, and explicitly the study analysed the heterogeneity between schooling of twins in contrast to their earnings. The assumption in the study was that twins would have the same ability and other external influences so that differences in wages could be more accurately associated with differences in education. Card used the pooled sample of men and women standing up to 198,075 aged from 16 – 66 during the years 1994 to 1996. In this given time frame, the study targeted the differences between individuals having 10, 12 and 16 years of schooling to their returns. An OLS regression analysis was used to inspect the human capital earning with variety of hourly, weekly and annual earnings. The study findings explored an interesting impact of an instrumental factor family background (Altonji) on the schooling and earning of their children which had 30% variation in the earnings, similarly college education differences and location near college or university had some significant influence over schooling and hence earnings. Birth season was only an insignificant instrumental factor (Card 1999). The OLS estimator results by Card suggested a difference of around 10% in the earnings of twins that could be explained by educational differences. Furthermore the earnings of certain subgroups that had external structural influence on their schooling were higher than average individuals as a whole. Dearden et al. (2002) applied econometric model using OLS estimation on returns to academic as well as vocational qualification in Britain. This study is a longitudinal using sample data of National Child Development Study (NCDS), 1991 and Labour Force Survey (LFS) of 1998 has been used. Her study examines the varying aspects of gender, ability, qualification and family background too. This longitudinal study develops a framework where NCDS results were compared with LFS statistics while after some selection criteria a final sample size of 6867 individuals (3007 Male & 3860 Female) was processed through estimation. Estimation results identified that males with O-Levels/ GCSE earn 13 to 23% premium to their qualification while for A-levels they earn 18% and for a degree they yield 11 to 32% premium. In contrast to men, women with O-levels/ GCSE earn 11 to 21% premium and with A-levels it adds to 20% and for degree women return 30% premium to the qualifications in a year. The study confirmed that along with academic qualifications, if individuals acquire vocational qualifications their premium may rise by 40% but vocational qualifications alone have reported fewer premiums (Dearden, McIntosh and Myck). Apart from returns on qualifications, the research identified a difference between male and female premiums to their qualifications where female earn less to their schooling. Further continued study analyses to the same domain were presented in McIntosh, 2006. He used the sample data of Labour Force Survey during the period 1996 to 2002. His study also applied econometric model using OLS estimator after reducing biases and error. The sample size stood at 15,000 individuals. His study aimed to focus the changing patterns of return to schooling. The results revealed an identical premium return to both males and females. Yet the variance of qualification confirmed heterogeneous returns among O-levels, A-levels and degree level labour force (McIntosh). Investing in human capital is a private decision which affects the return to schooling; so, every additional qualification adds to the return (Dickson and Sarah). Harmon et al., 2003 conducted microeconomic empirical investigation on the returns to schooling. They concluded that every investment reflects the size of returns. Harmon used a comparative approach to analyse cross sectional returns on education and furthermore, applied OLS using a dataset acquired from International Social Survey Program (ISSP). The results revealed relative differences among the twenty five countries mostly from European Union (EU) such as Ireland, UK, Italy, Germany, Greece, Norway and Switzerland based on their microeconomic wage circumstances. The overall average return to schooling through OLS statistics was concluded around 6 to 11% international. Conclusion     The concerns of the scholars that bias or endogeneity existed in the estimation of returns to schooling did not prevent them from carrying out their regression analyses. One approach to avoid endogeneity is to use instrumental variables. These are variables that are correlated with ability but not schooling for example. By including these, the Mincer regression model is extended to include some proxy measure for ability. The typical regression model for estimating returns to education for individuals is (Harmon, 2003) Lnwi=β0+β1Educi+β2expi+β3exp2i+Xβ+εi Where i represents each person in a cross section such as the BHP survey data; lnw = the logarithm of wage for person i; educ = is the variable measuring years of schooling or higher education; exp = years of experience X is a vector of other characteristics describing heterogeneity in the sample that might be correlated with earnings such as gender, sex, social class, marital status, and ε is the error term in the regression equation. The hypothesis is that β1 is positive and significant which represents the returns to edu Read More