Could not display document, please try refreshing this page a few times.

Please contact support if you are unable to view this document.

## Counterfactual in Policies

- Lecture number:
- 26
- Pages:
- 2
- Type:
- Lecture Note
- School:
- Cornell University
- Course:
- Econ 3120 - Applied Econometrics
- Edition:
- 1

**Unformatted text preview: **

Lecture 27 Outline of Last Lecture I. Difference in Differences Outline of Current Lecture II. Counterfactual in Policies Current Lecture Effect of Worker Compensation Laws on Weeks out of Work A seminal study of worker’s compensation laws by Meyer, Viscusi, and Durbin (1995) examined the effects of the increase in a cap in weekly earnings covered by workers’ compensation on time spent out of work. The change affected high-income workers, but not low-income workers. Thus, we can assign workers to treatment and control groups based on income, and examine outcomes before and after the policy change. The estimating equation is log(duratit) = β0 +β1highearni +β2a f chnget +β3highearni ∗ a f chnget +uit 5 In this case, the critical assumption is that the average change in duration for the low-income group equals the average change for the high-income group if the policy hadn’t been implemented. This equation was estimated as: log(ddurat) = 1.126 (0.031) +0.256 (0.047) highearn+0.0077 (0.0447) a f chnge +0.191 (0.069) a f chnge ∗ highearn What are the conclusions from this analysis? 3.2 Difference in Differences Using Panel Data Suppose we are evaluating a program in India that provides a random set of primary schools in India with additional teachers to teach remedial skills to lower-performing students. We collect data before and after the program, so we thus have a panel dataset on children’s test scores. That is, we have two observations for all children. As before, we can run a difference in differences model: yit = β0 +β1treati +β2 postt +β3treat ∗ post +uit (1) where our outcome yit represents child i’s test score at time t, treat indicates inclusion in the treatment group, post indicates that the observation is from after the program, and uit is an individualspecific error component. Note that one problem with equation (1) is that we almost certainly have violated the serial correlation MLR assumption if we run this regression using OLS. Because each individual has 2 observations, the errors of the same individual will likely be correlated. There are some pretty straightforward ways to deal with this, but we won’t have time to cover them in this class. For our purposes, we can use the OLS estimates as unbiased and consistent, but we have to take the standard error estimates less seriously. As before, we can write β3 as: β3 = E(yit|treat = 1, post = 1)−E(yit|treat = 1, post = 0) −[E(yit|treat = 0, post = 1)−E(yit|treat = 0, post = 0)] If we have a “balanced” panel, that is, if each individual has exactly two observations, this is equivalent to β3 = E(yi,post −yi,pre| treat = 1)−E(yi,post −yi,pre|treat = 0) 6 An alternative way to specify the differences-in-differences model is by running the model where the observations are at the individual level: yi,post −yi,pre = β0 +β3treat +uit (2) Note here that β3 = E(yi,post −yi,pre|treat = 1)−E(yi,post −yi,pre|treat = 0) which is equivelent to β3 in the panel model (1) above. Using equation (2), we can see ...

View Full Document