ECON 442/Powers/Fall 2022
Instructions for Lightning Presentations
YOU MUST BE PRESENT ON BOTH MONDAY & WEDNESDAY TO EARN CREDIT FOR THE LIGHTNING
PRESENTATION.
You have no more than 5 minutes to present. Upload your slides onto the Moodle. Be sure to get them
up before class time. Please name the file ùourlastname]_lightning.pptxŠPrepare slides:
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Explain the topic
The research
o If primary research: your methods & data
o If you largely relied upon secondary research: the sources you relied upon, the logic of
your arguments
o If you did a literature review: The articles you read and what you learned from them.
Your major findings
Sum up: What is the !ke-away-essage?/What did you learn?/ What did you conclude?
I find I can talk about 30 seconds per slide, so I would make around 5-8 slides if I were doing this project.
Some General Term Paper Draft Comments
11/26/2022
Style:
Do not quote mundane statements.
Citation should be scholarship-style rather than ï and so saysJSome papers have noticeable repetition. Be succinct.
Number your figures and tables and refer to those figure and table numbers in the narrative (e.g.,
©gure 1 shows; (e regression coefficients are in Table 1i. Put your tables and figures in a
separate section after the references.
There is often a tendency to write in first person (e.g, `then did å known Avoid first person.
Content:
Some papers devote much of the narrative to presenting knowledge directly from the course (e.g., a
long discussion of what we learned about the GWG in class, or a multi-paragraph explanation of what
convex compensation is). Focus on applying this knowledge, not restating it.
A note on working with GWGs:
For those estimating gender wage gaps, there is some apparent confusion about the residual gender
wage gap. If you use the predicted wages from an OLS wage equation, you will just get back the mean
earnings. The RESIDUAL GWG is the part of the GWG that is NOT explained by the observed factors, or
GWG-hat = GWG-actual b. This is the measure of the wage gap that holds observed factors constant.
Another thing you can do with wage regressions is predict womenàwages if they had menÊcharacteristics. Suppose you estimated a wage equation for men and another for women. The simplest
thing you can do is to use the coefficients from the wage equation for women, but plug in the average
characteristics of the men. Then you would see how much the women would make if they had menÊcharacteristics. A more complete take on this type of exercise is to use the Oaxaca decomposition. This
breaks out the observed difference between menà& womenàearnings into the portion due to the
difference in observables and the portion due to the difference in coefficients. Wikepedia provides a
wonderfully clear, quick, & easy-to-understand explanation.

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