Panel data Quiz

[Brian Kipkoech]

Dear Stata Users,

I am working with World Bank data, focusing on countries grouped into four income categories: Low, Lower Middle, Upper Middle, and High income. My analysis aims to examine the relationship between GDP (as the independent variable) and other dependent variable, using panel data from 1961 to 2023.

A challenge I’m facing is that countries can move between income groups over time. Some countries have crossed from Low to Upper Middle or High Income, while others may have moved down. This raises the question of how to properly handle the "mobility" of countries between income groups, especially when analyzing trends in GDP over time.
Given these concerns, I would like to know how to best treat this issue in Stata using panel data techniques. Specifically:

1. Panel Data Techniques: Are there panel data methods that can account for income group mobility and prevent the composition effects from distorting the results?
2. Handling Time Trends: How can I incorporate time trends or adjust for changes in income group membership over time while maintaining the integrity of my analysis?
3. Alternative Grouping Methods: Is there an approach where I can define income groups based on initial income (e.g., 1961 levels) rather than the current classification to avoid composition issues?

[Maarten Buis]

1. Panel data analysis is designed for time changing variables, so that is not a problem.

2. I don't see your problem with the time trend

3. 1961 is arbitrary. Why should that be "initial"? What are you going to do with countries that came into existence after 1961? In short: that does not make sense to me. You are "solving" a non-problem, and thus make everything worse.

[Carlo Lazzaro]

Brian:
welcome to this forum.
As from your description, dependent and independent variables are not that clear.
Assuming that your dependent variable is continuous and you start off your panel data regression with the -fe- estimator, you should be more concerned about time-invariant variables, as, due to demeaning, the -fe- estimator will not return their coefficients.