LABOR ECONOMICS ASSIGNMENT

Labor Economics Assignment

Labor Economics Assignment

Answer a

Ln (Hourly Wage) = 0.4353 + 0.0924 S + 0.0587 X - 0.0010 X 2

From the above given equation, it is found that there is positive relationship of years of schooling and years of experience with the natural logarithm of hourly wage. The basis of this statement is that the beta values for these variables are in positive. Besides it, it is also observed that there is negative relationship of years of experience squared with the natural logarithm of hourly wage, as the beta value is in negative. For that reason, it can be said that as the years of schooling and years of experience of the participants of the study increases then the natural logarithm of hourly wages will also increase; on the other hand, as the years of experience of the participants of the study increases then the of hourly wage will decrease, which reflects the presence of inverse relationship between the variables.

Keeping this in view, it can be said that the coefficient on X2 indicating the years of experience squared is expected to be negative because it is found that there is inverse association between the variables that are the years of experience and natural logarithm of hourly wage.

Answer b

In relation to the main potential sources of bias which take place in the estimation of the coefficient on the years of schooling (S), very often in practice, researchers meet the need to investigate the spatial variability of estimation of the coefficient. Documentation and the current needs of operators of resources required to collect data on a variety of parameters minerals. Studies of this kind are always very expensive. In order to at least partially reduce these costs is often used regression and correlation analysis. Correlation analysis uses correlation model can evaluate the expected value of a random variable based on a single representation of another random variable (correlated with the first variable). It is important to note that the correlation should be of a cause and effect (Azen & Traxel, 2009).

According to the past studies, correlation and regression analysis is a branch of statistics dealing with the study of relationships and dependencies between the distributions of two or more of the studied traits in the general population. The term refers to the shape regression relationships between features. It is divided into the linear regression analysis and non-linear. In the case of nonlinear analysis, graphical representations of the correlation curves are higher such as a parabola. The concept of correlation applies to the strength of the test dependencies. Regression and correlation analysis may involve two or more variables (analysis manifold). At this point, we will only deal with the simplest case of two variables regression straight. The most important measure of the strength of the relationship between two straight measurable characteristics of a Pearson correlation coefficient of linear correlation coefficient is vital for the estimation of the coefficient. Pearson's correlation coefficient refers to the strength and direction of the relationship ...