I am working on a story problem for a project.
"You work for a small environmental foundation that wants to analyze fuel consumption. Your boss (in the year 2002) has asked you to help her analyze the 2001 data on highway fuel consumption to find out what a change in the tax rate does to fuel consumption. She has provided a datafile (labelled
Fuel2001) that has data on the following variables:
Drivers Number of Licensed drivers in the state
FuelC Gasoline sold for road use (1000s of gal.)
Income Per capita personal income (year 2000)
Miles Miles of Federal-aid highway miles in the state
MPC Estimated miles driven per capita
Pop Population age 16 and over
Tax Gasoline state tax rate, cents per gallon
She wants you to produce an analysis that does the best job of helping her argue that higher tax rates lead to lower fuel consumption. But her initial analysis of the data (simply including all of these variables in a naive regression model) did not find that tax rates had a statistically significant effect. Is there anything that can be done to find out if there is a relationship? Write her a short memo on that subject including your analysis, including specific estimates of the relationship."
I'm supposed to present her with the best case possible to support her cause (raising gas tax to reduce fuel consumption), but I should also include the best model, for her information. I have transformed all the skewed variables by taking their natural log. I ran a bunch of different regressions in Stata until I found one in which tax was statistically significant. When I ran the same regression robust tax was no longer statistically significant.
What would you do and show (graphically, including tables or graphs)? What does it mean that the robust regression makes tax insignificant?
I know it would be helpful to have the data, but I can't figure out how to upload it.
Thanks in advance for any input.