The Data Section
Gathering and arranging your data is often the hardest part of this project.
The paper is microeconomic in nature, some useful sources of data are: ∙
National Longitudinal Study of Youth
∙Panel Study of Income Dynamics
∙Current Population Survey
∙General Social Survey
A useful portal to find data is: http://www.oswego.edu/~economic/data.htm. It includes dozens of links to data sources.
In getting your dataset in order, try to do all your work in one do-file. This do-file should include everything from loading your original datasets, and the commands for making any alterations to them.
Some useful commands are:
∙destring (this command converts a text variable to numeric. This is especially useful if your data originally came in Excel format, and you copy-and-pasted into Stata.) ∙ xpose (transpose. It rotates your dataset so that columns become rows and rows become columns)
∙merge (to splice together two different datasets.)
∙collapse (helps you calculate averages and sums. For example, you might have data for 100 countries, each with 10 years. This lets you calculate the 10-year average for each
country.)
∙reshape (useful if you have panel data and you want to switch between long and wide format)
It is common to provide a table of summary statistics, too. (Remember the summarize
command in Stata.) If you do this, remember that readability is important for your reader: keep the number of decimal places to the necessary minimum
If data are categorical, or ordinal, explain this. For example, property rights are often reported on a 1-5 scale. Be sure to mention this, and also explain whether a score of 5 means that there are good property rights or poor ones. Age is probably a continuous variable. But sometimes it is reported in brackets. If so, let your reader know
Keep the decimal points to a minimum. Three decimal places are usually more than enough.
It is often useful to provide a scatterplot of the main variables that you will be focusing on. For example, your paper might focus on the effects of property rights on per capita GDP. You will, of course, have to control for other variables, such as education, political rights, or whatever. But presuming the main variables you are interested in are property rights and pcGDP, it is not a bad idea to provide a scatterplot, and even maybe a simple regression line overlaid on the scatterplot.
A similar and powerful way of making the case that X affects Y is to provide two summary tables that compare two groups. For example, suppose that X is TaxRates and Y is GDP. Then it might be presumed that higher tax rates correlate to lower GDP. So, split your dataset into two groups, one with low tax rates and another with higher ones, and report summary statistics for each. You should be able to tell at a glance whether GDP is higher in the low tax country. You will also be able to tell how high- and low-tax countries differ regarding all the other X variables, too. As always, use as templates the articles you’ve read in your literature review.
And remember, this is an important part of your paper. Your tables should be formatted to look like ones in the academic journals you are reading. You’ll have to cut and paste from Stata into Excel, and then format the tables before they are presentable. Tables should have titles and be numbered (Table 1: Description of the Data; Table 2: Data Sources,etc…).you don’t have to report regression results at this stage. That comes later.The Data Section portion of your paper should be approximately 2-4 pages long. Not much writing is involved, but be sure to include a revised introduction
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