Term Project

# Term Project - Wages and Wage Earners 1 Wages and Wage...

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Unformatted text preview: Wages and Wage Earners 1 Wages and Wage Earners: EC 315 Insert Name Term Project Report Fall 1 2010 Wages and Wage Earners 2 TABLE OF CONTENTS DICUSSION OF RESULTS 3 SUMMARY 11 REFERENCES 12 APPENDIX 13 Wages and Wage Earners 3 Wages and Wage Earners: Discussion of Results In this model (figure 1), our objective is to find out the impact of industry, occupation, years of education, race, gender, work experience, marital status, age and union on annual wages. We run a linear regression to find the relationship. Variables Y =Annual wage in dollars x1=Industry (1=Manufacturing, 2 = Construction, 0= Other) x2 =Occupation (1=Mgmt., 2=Sales, 3=Clerical,4=Service, 5= Prof.,0=Other) x3=Years of education x4 =Southern resident (1=Yes, 0=No) x5 =Non-white (1=Yes, 0=No) x6= Hispanic (1=Yes, 0 =No) x7 =Female (1 =Yes, 0 =No) x8 = Years of Work Experience x9= Married (1=Yes, 0=No) x10 =Age in years x11 =Union member (1 =Yes, 0=No) 30 observations Figure 1 Here, the impact of any independent variable on the dependent variable can be measured by the p value of the coefficient of the variable. Lower the p value, higher will be the impact of the variable on the independent variable. “The dependent variable annual wage in dollars is determined by variables years of education, Hispanic, gender, and age.” Here, the primary independent variable is number of education years. This is used as a screening tool by the employers. Highly education people gets white collared jobs and earns higher wages than others. This is why, this particular variable is important. In this case, we are using the model Wages and Wage Earners 4 annual wage=b1(industry)+b2(occupation)+b3(no of years of education)+b4(southern resident)+b5(nonwhite)+b6(Hispanic)+b7(female)+b8(years of work experience)+b9(married)+b10(age)+b11(union member). Here, the dependent variable is annual wages. This is measured in terms of dollars. The independent variables are given as: X1: industry. This is a dummy variable, recorded as: Industry=1 if manufacturing =2 if construction =0 otherwise This is supposed to affect wages positively as usually, workers in construction and manufacturing sectors earn higher than others. X2: occupation. This is also a dummy variable recorded as: Occupation=1 if management =2 if sales =3 if clerical =4 if service =5 if professional =0 otherwise Here, the expected sign is positive as wages varies across occupation. X3: years of education. This is measured in terms of years of schooling. This is expected to affect wages positively as people with higher number of educational years usually earns higher than others. X4: southern resident. This is a dummy variable recorded as: Wages and Wage Earners 5 Southern resident =1 if yes =0 other wise Here, the expected sign is uncertain as we don’t have any idea whether southern residents earn higher wage or not....
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## This note was uploaded on 08/29/2011 for the course EC 315DLB taught by Professor Randallbarcus during the Fall '10 term at Park.

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Term Project - Wages and Wage Earners 1 Wages and Wage...

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