The aim of this empirical project is to find out which variables affect the wages of an individual. This will initially be done by describing the variables such as qualitative data, which are data that are described in terms of quality, for example a person is very tall and quantitative data, which are data that are described in terms of quantity, for example the person is 6ft. Later a model with my variables will be constructed and a number of regressions will be run, which will help me to identify which factors in my model affect the wages of an individual. The results from my regressions will be compared to the standard theory of Microeconomic. As the model deals with individuals and households it is microeconomics.

LITERATURE REVIEW

The journal The Racial Wage Discrimination in America: by The Economist explored the differences in the level of earnings of different races in America. The paper states that black workers, men or women earn less than white workers in America. The paper also suggests that the reason for this is that ‘the average black worker has less education and experience than his white counterpart.’ The paper also articulates that firms are ‘one and a half times more likely to interview a person they think is white than one they think is black, even if both have identical qualifications.’

ECONOMIC THEORY OF WAGES

Economic theory suggests that firms will pay high wages to attract the best pool of workers, workers who have the best education, mainly due to the fact that these workers will be more productive in comparison to workers with lower levels of education. As a result of this theory there is a positive relationship between the level of education and the level of wages that you will receive. Economic theory also suggests that firms will pay higher wages to white men with respects to white women in the U.S, and black men are paid higher than black women in the U.S, which helps to show Labour Market Discrimination. There is also a theory of microeconomics which discusses the benefits of good looks. People who are supposed to be better looking usually experience higher wages than those who are not so good looking.

DATA

The data was collected from The Boston College Department of Economics http://ideas.repec.org/s/boc/bocins.html and has a variety of Stata datasets for econometrics. The file downloaded wage 2 was created by Jeffrey M. Wooldridge. My selected data which is already in stata form, is from the 2000 wages survey in Boston. However this survey is outdated and is not the most recent form of data

The data that was collected had 935 observations and is a cross-sectional dataset on wages. Cross-sectional datasets refers to data which has been collected by observing a number of subjects, (for example individuals, age experience etc.) at one period of time, or without regard to difference in time. Analysing this sort of dataset will consist of comparing the difference between each subject.

Below are the variables used:-

Wages:- It is the dependant variable which is affected by the other variables. It shows the grossly monthly earnings of an individual in dollars and is in quantitative form.

Hours:- This is the first of the independent variables showing the average weekly hours which an individual has worked and is also in quantitative form

IQ:- A quantitative variable showing the I.Q of the individual. It shows the score that the individual has received after doing the test.

Age:- This quantitative independent variable shows the age. It shows the age of the individual when the survey was conducted.

Married:- This is the first qualitative independent dummy variable. If the individual is married then the value will be 1, i.e. married = 1, but if the individual is not married at the time the survey is carried out the value will be 0, i.e. not married = 0.

Education:- This quantitative independent variable shows the years of education that the individual has. If the value is high then it shows that the individual has spent longer in education making them more employable.

Black:- This is the second qualitative dummy variable which shows whether the individual is black or not. If the individual is black then there will be a value 1, but if the individual is not black then there will b a value 0. Black = 1 Non Black = 0

Sibs:- This quantitative variable shows the number of siblings the has. If the value for the certain individual is high then that individual has many siblings, and if the value is low or even zero then the individual has 0 or very few siblings.

THE MODEL

The model which I will first use will be the estimated model:-

Y= Weekly Wages X1= Hours Worked X2= IQ X3= Married X4= married X5= Education X6= Black X7= sibs

Number of Data observations = 935

A regression will be run, using the variables stated as this will help me to find the best linear unbiased estimator. A T-Test will be carried out which will show me which is the best estimator. An F-Test will be carried out subsequent to the T-Test which will show which model is preferred. There is also a need to test the data for multicollinearity and will show which variables are positively, negatively or not correlated at all. This test will be subsequently carried out after the F-test. Finally a test for heteroskedasticity will be carried out to show if there is a difference in variances. These tests will be carried out in this specific order as the results from the F-Test will be needed before multicollinearity is carried out and the results from both of these tests will needed to carry out the test for heteroskedasticity.

The F-Test will be run at the 5% significance level as it will compare the restricted model and the unrestricted model to find out which model is better. A correlation matrix will help to test for multicollinearity, allowing me to look at the correlation between the independent variables and the dependant variables. The test for heteroskedasticity will help figure out which estimator should used, and once confirmed then I will need to standardise the heteroskedasticity.

EMPERICAL RESULTS

This is the first regression uses all of my variables from the dataset which was provided:-

reg wage hours IQ age married educ black sibs

SSR= 123332195 R-squared = 0.1924

wage Coef. Std. Err. t P>|t| [95% Conf. Interval]

hours -3.483357 1.667437 -2.09 0.037 -6.755746 -0.2109685

IQ 4.158012 0.9960817 4.17 0.000 2.203176 6.112849

age 19.41802 3.875994 5.01 0.000 11.81128 27.02476

married 174.9624 38.97636 4.49 0.000 98.47022 251.4545

educ 44.10633 6.4159 6.87 0.000 31.51496 56.6977

black -113.9696 39.96556 -2.85 0.004 -192.403 -35.53611

sibs -4.458277 5.581175 -0.80 0.425 -15.41148 6.494926

_cons -675.0723 183.4671 -3.68 0.000 -1035.131 -315.0134

T-TEST VALUE

The critical value for the 5% significance level if 1.96 Ho : βj = 0

If the T-value is greater than the critical value (1.96) we will reject the hypothesis and it would be significant at the 95% confidence level, however if the T-value is less than the critical value (1.96) then I will accept the hypothesis and it would be insignificant at the 95% confidence level.

All variables are significant apart from the sibling’s variable, which is 42.5. This is dramtically higher than 1.96 and therefore sibling will be omited from my model. Due to this a second regression will be run, restricting my model. As you can see from the regression command below, siblings has been omitted from the regression

.reg wage hours IQ age married educ black

SSR= 123417089 R-squared = 0.1919

wage Coef. Std. Err. t P>|t| [95% Conf. Interval]

hours -3.479966 1.667106 -2.09 0.037 -6.751701 -0.2082306

IQ 4.246344 .9897316 4.29 0.000 2.303972 6.188716

age 19.55043 3.871693 5.05 0.000 11.95214 27.14872

married 174.8716 38.96859 4.49 0.000 98.3948 251.3484

educ 44.72156 6.368262 7.02 0.000 32.2237 57.21943