Assignment
Topic: The Labour Supply and Earnings of Married Women in the US
You are supplied with a dataset that contains repeated cross-sectional data from the Census and the American Community Survey (ACS) from 1980 to 2016. Years 1980, 1990 and 2000 contain a random 5% sample from the Census, while years 2005, 2010 and 2016 come from the ACS, a survey of a random sample of the population that is administered each year. The data contains information on age, education, number of children, occupation, wage income, hours of work, spousal income and hours of work of non-Hispanic white women aged 18-65 who are married, are not attending school and are not institutionalized. In total you have 2.5 million observations representing 84.8 million women.
I have included several constructed variables for your convenience (such as potential experience and indicators for children of certain age ranges), but you are free to construct your own variables if you wish.
You are tasked with using the dataset to answer the following questions:
1. What effect do children play in determining the labour force participation of married women? How does spousal income affect your results? How does that change over time?
2. What is the role of occupation in determining the wages and hours of work of women? How does that change over time?
Various things you may want to consider for this particular topic:
- You might find it useful to think about married women's earnings in a selection framework, where labour force participation is a binary choice. You should motivate why this is the case and comment on the consequences of ignoring selection.
- You are also free to use an IV framework to answer the questions. However, note that if you go this way you have to justify the use of instrumental variables and also argue that the instrument you found is valid and relevant.
- Variables relating to the presence of children in the household are often used as an explanatory variable in the participation equation.
You should also motivate their use.
- You may want to control for education and potential experience, as in Mincer regressions.
- Often indicators of urban location or regional location are included in wage regressions to capture systematic differences in wages in different locations. You may want to include those in your regressions.
- You should try several models, but ultimately you must select your preferred specification and compare it with 1 or 2 other model candidates.
- Remember to convert current dollars to constant dollars using the variable cpi99.
- If you are unsure about the precise meaning of any variable, please consult the website of the data: https://usa.ipums.org.
- Each observation in the dataset has a corresponding weight, and you should use the weights for your analysis (you must issue estimation commands in Stata specifying frequency weights [fw = perwt])
Format your assignment according to the following formatting requirements:
1. The answer should be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides.
2. The response also include a cover page containing the title of the assignment, the student's name, the course title, and the date. The cover page is not included in the required page length.
3. Also Include a reference page. The Citations and references should follow APA format. The reference page is not included in the required page length.
Some references for Topic:
Blau, F. D., & Kahn, L. M. (2000). Gender Differences in Pay. Journal of Economic Perspectives, 14(4), 75-99.
Blau, F. D., & Kahn, L. M. (2007). Changes in the Labor Supply Behavior of Married Women: 1980-2000. Journal of Labor Economics, 25(3), 393-438.
Blau, F. D., & Kahn, L. M. (2017). The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature, 55(3), 789-865.
Goldin, C. (2014). A Grand Gender Convergence: Its Last Chapter. American Economic Review, 104(4), 1091-1119.
Heim, B. T. (2007). The Incredible Shrinking Elasticities: Married Female Labor Supply, 1978- 2002. Journal of Human Resources, 42(4), 881-918.