econ2P91_Lab3_SOLUTIONS_Winter2011

# econ2P91_Lab3_SOLUTIONS_Winter2011 - ECON 2P91 Business...

This preview shows pages 1–2. Sign up to view the full content.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: ECON 2P91: Business Econometrics with Applications Winter 2011 Lab3 SOLUTIONS The objective of this week’s lab is to demonstrate the use of binary variables in econometrics. As indicated in class, binary variables can be used in the analysis of earnings discrimination cases. Consider the model i i i u AGE AHE + + = 1 β β where AHE and AGE denote average hourly earnings (in dollars) and age (in years), respectively. As noted in class, econometricians often use age as a proxy for experience owing to the difficulties is obtaining data on experience. This choice of age as the proxy variable presupposes that older workers have greater experience. Is there evidence of gender discrimination in earnings? To answer this question, we will use the Excel data file Earnings.xls . For this data set, FEMALE is a binary (or dummy) variable that takes the value 1 if the person is a female and 0 otherwise (recall that a binary variable only takes 2 possible values, 0 or 1). AHE and AGE are as defined above. (a) Run the regression i i i u AGE AHE + + = 1 β β and report the results in standard format (i.e., including 2 R and SER). Note that by running the regression in this form you are presupposing that the intercept and slope coefficients are the same for both females and males (the reference group or benchmark group ). In econometrics, the reference/benchmark...
View Full Document

{[ snackBarMessage ]}