Comments on: Difference in Difference Estimation

By: Todd

Todd — Fri, 27 Jun 2008 19:47:03 +0000

What if you have multiple policy changes over a number of years?

My treatment group is Medicare patients with a control of non-Medicare patients, but I have several policy changes (1 each year over 5 years).

By: Anna

Anna — Wed, 19 Dec 2007 22:27:05 +0000

Depends on whether the trend is linear or not. you can test the two methods and compare them using a likelihood ratio test. If the categorical model is significantly “more explanatory” then you need the “more complicated” categorical model. If it is not, the linear model is sufficient. If you use the categorical model, keep in mind that the coefficient will compare that time period to time 0. the coeffecient for T1 represents the difference between T1 and T0, the coefficient for T2 represents the difference between T2 and T0, etc.

If generally you the values of the coefficients getting progressively larger (or smaller), then you might have a linear trend. If they go up and down, you’ll likely need the categorical model.

By: Healthcare Economist · Midwifery-Promoting Public Policies and Health Outcomes

Tue, 16 Oct 2007 05:27:58 +0000

[...] Miller is that these laws are exogenously enacted; this creates a natural experiment and allows a difference-in-difference [...]

By: chocobo

chocobo — Wed, 23 May 2007 09:20:49 +0000

What if I have more than two years data, say 4 years.
Should T= 0, 1, 2, 3？ or use T1=0,1 T2=0,1 T3=0,1?

By: Matt

Matt — Thu, 05 Apr 2007 06:09:07 +0000

Should be year1, T=0 and year2, T=1. If your statistical package knows what it is doing, however, if you went with the two variable misspecification you mentioned, it would just wind up dropping one of them and you are left with the 1 variable! Same thing applies with the Wisconsin/CA dummies.

The WI in his formulation is the Wisconsin dummy. You decided you liked CA better, so you have a CA dummy. If those are your only two states, then you cannot have dummies for both. You would have a situation of perfect collinearity. Basically, the vector of ones for the intercept would equal the sum of the CA and WI vectors. So you only use 1 or the other, not both.

As a rule of thumb, when you run a regression you are not allowed to have a complete set of dummy variables unless you get rid of the constant term…for example, you cannot have variables for male and female, but rather just one or the other. If you have age bands for under 18, 19-39, 40-64, and 65+, you can only use three of those dummies, but not all four.

Also, the original post does have one problem, in that he says “Y is the percentage of firms offering health insurance in each state in each time period.” Clearly if you did that, you would have exactly 4 observations. CA year 1, CA year 2, WI year 1, and WI year 2. No degrees of freedom. You need matched data from individual firms within each state both before and after the policy change. i.e. say the policy change happened in 2004. You would want to have the insurance provision data for 50 CA firms, both in 2003 and 2005, as well as the insurance provision data for 50 WI firms, both in 2003 and 2005. And since you are likely to be working with binary data on the LHS (did the firm offer health care?), you’d want to run a probit or logit in this case rather than simple OLS.