General instructions: You may do this project in a group of up to three (3) students. Commands from Excel menus are indicated as follows: File > Open means you should click on File and then select Open from the menu. Variable names are in all upper-case letters. Something you actually have to hand in is indicated in italics.  In this project you will examine data from the March 2000 Current Population Survey (CPS), contained in the following file: earn2000.xls Random sample of U.S. adults (18+) in the labor force who worked full-time, year-round in 1999 and were not self-employed.

I have attached a brief description of the CPS and a listing of the variables in our data set and their definitions.

What you hand in should be typed, and all tables and figures should be numbered or labeled for easy identification when you discuss them in the text of your answers. 

Click on Data sets

2. To download, right-click on the data set you need (earn2000), and you should then be given the option of saving the file somewhere. Save it to your hard disk or a jump.

In Excel, click on Tools. Near the bottom of the menu you should see an option for Data Analysis. If it is there, you are all set. If it is not there, you need to click on Tools > Add-Ins. You will the be able to add the Analysis ToolPak, which has what you need.

1. Open the data set (earn2000.xls) in Excel. Variable names are across the top row, and the variables are in columns. If you forget what a variable is, see the definitions attached to this handout (also on the web site).

2. Scroll to the bottom of the data. How many observations (N) are there in the sample?

3. Take a look at observation (person) #15. Describe this person. Report her or his:

gender
age
race
region of residence
educational attainment
earnings from wages and salary
occupation
marital status

4. Now obtain some basic statistics for the following three variables: age, school, and wagesal.

Throughout this assignment, we will use wagesal (annual wage and salary income) as our measure of earnings. For each of these three variables, do the following:

• Generate means, etc. using Tools > Data Analysis > Descriptive Statistics. Note that you must specify the Input Range (cells that contain all the data). Include the variable name in the input range and check Labels in First Row.

•   Generate percentiles using Tools > Data Analysis > Rank and Percentile.

Note: When given the option for where to put the output, it is convenient to place it on a new worksheet in the same workbook.

• Print out your results using the Print button, and hand in the printout with your assignment.

• For each of the three variables, compare the mean and median. If they are different, what is the significance of one being bigger than the other?

• For each of the three variables, find the 10th, 25th, 50th, 75th, and 90th percentiles. Examine the extremes of the distribution (top and bottom few values). Are there any obvious "outliers"?

5. Use the same descriptive statistics procedure to answer the following questions. (Hint: the proportion of Asians in the sample is given by the mean of the asian variable.) You do not need to hand in the actual printouts for these questions, just the number for each.

What percentage of individuals are white? Black? Asian? Latino?
What percentage of individuals are female?
What percentage of individuals are married with spouse present?
What percentage of individuals graduated from high school but had no further formal education?
What percentage of individuals have more than a high-school education?

6. Now examine the subsample of women. The best way to do this in Excel is sort all your data by female , using either Data > Sort or Data > Filter, then copy the observations with female = 1 into a new sheet.

Using this restricted (female) sample, answer all parts of questions 4 and 5 over again for females only.

7. Repeat all of #6 for the subsample of males only.

8. Using your separate results for men and women (#7 and #8), compare the men and women in the sample. In terms of the variables you have examined, in what ways are men and women similar? In what ways are they different?

1. Using the earnings data (full data set), create an XY scatter plot of earnings (vertical) against years of school (horizontal). To do this, it helps to have the X variable (school) in one column and the Y variable (wagesal) in the next column immediately to the right. Then use the chart wizard to create an XY scatter.

2. Estimate a simple regression with earnings (wagesal) hours as the dependent (Y) variable and schooling (school) as the independent or X variable. To do this, use Tools > Data Analysis > Regression and proceed as we did in class. Make sure to use the variable labels. Check off the boxes for residuals and residual plots. Put the output on a new worksheet.

• Write out the formula for the equation you have estimated.

• Interpret the R-squared.

• Interpret the ANOVA table and the F-statistic.

• Calculate a 95% confidence interval for the slope coefficient. Is the slope significantly different from zero? Could you reject the hypothesis that an additional year of schooling increases wage and salary income by $1000? By $2000? Explain.

3. Now examine the residual plot from this regression. Does the residual have any systematic relationship to the variable school? Are there outliers? Would you say the residual appears random?

4. Repeat question #2, but this time try to remove the obvious outlier(s) from the data. Do your regression results change much?

We now predict earnings using school and exper, where exper = age - school - 6. This variable is often called "years of potential work experience."

1. Run a regression in which the dependent variable is wagesal and the independent variables are school and exper. (Note that all the X-variables (regressors) must be in adjacent columns.) Print out the results.

• Check the general validity of the regression by examining and discussing the R-squared, F-statistic, and residuals.

• Interpret the coefficients on school and exper. Are these coefficients statistically significant?

2. Now run a similar regression, but this time use as the dependent variable the natural log of wage and salary earnings (do NOT take the logs of school or exper.) To do this you need to generate a new column of numbers with the variable name lwagesal (the Excel function LN(..) produces the natural log). Print out the results.

• Again, check the general validity of the regression by examining and discussing the R-squared, F-statistic, and residuals. Do the residuals appear to be more randomly distributed? Do you prefer this regression or the regression from #1? Explain.

• Interpret the coefficients on school and exper. Are these coefficients statistically significant?

For the remainder of this Data Analysis Project, continue to use the log of earnings (lwagesal) as your dependent variable.

3. To your regression from #2, now add as a regressor a dummy variable for female (include school and exper as regressors too). Print out the results. Controlling for schooling and experience, does the evidence suggest that women earn more than, less than, or about the same as men?

4. To your regression from #3, add two interaction terms: the interaction between female and school, and the interaction between female and exper (include all the regressors you used in #3). This requires creating two new columns of numbers. For instance, the interaction of female and school could be femsch = female*school. Print out and carefully interpret the results:

• Draw a diagram representing the relationship between schooling and log earnings implied by your regression, with separate lines for men and women (assume that exper = 10 years in each case). Is there evidence that the returns to schooling are significantly different between men and women?

• Draw a diagram representing the relationship between work experience and log earnings implied by your regression, with separate lines for men and women (assume that school = 14 years in each case). Is there evidence that the returns to experience are significantly different between men and women?

• Use an F-test to test the hypothesis that the coefficients on female and the two female interactions are all equal to zero (that is, test the general hypothesis that there are no significant gender differences in the earnings equation estimated here). See the handout on F-tests for details.

5. Be prepared to discuss your findings in class: No formal presentations, but think about how your findings for questions #3-4 might be explained.

The Current Population Survey (CPS) is a monthly survey of about 50,000 households conducted by the Bureau of the Census for the Bureau of Labor Statistics. The survey has been conducted for more than 50 years.  The CPS is the primary source of information on the labor force characteristics of the U.S. population.  The sample is scientifically selected to represent the civilian noninstitutional population. Respondents are interviewed to obtain information about the employment status of each member of the household 15 years of age and older. However, published data focus on those ages 16 and over. The sample provides estimates for the nation as a whole and serves as part of model-based estimates for individual states and other geographic areas.

Estimates obtained from the CPS include employment, unemployment, earnings, hours of work, and other indicators. They are available by a variety of demographic characteristics including age, sex, race, marital status, and educational attainment. They are also available by occupation, industry, and class of worker.  Supplemental questions to produce estimates on a variety of topics including school enrollment, income,  previous work experience, health, employee benefits, and work schedules are also often added to the regular CPS questionnaire.

CPS data are used by government policymakers and legislators as important indicators of our nations's economic situation and for planning and evaluating many government programs. They are also used by the press, students, academics, and the general public.

citstat citizenship status: 1= Native-born US, 2= Native-born US outlying areas, 3= Native-born US abroad US parent, 4= Foreign born-naturalization, 5= Not a US citizen

marital 1=married, spouse present, 3=married, spouse absent, 4=widowed, 5=divorced, 6=separated, 7=never married

05 .Administrative support, incl..clerical Service

09 .Precision production, craft &.repair Operators, fabricators & laborers

10 .Machine operators, assemblers & .inspectors

11 .Transportation & material .moving