Counterfactual in Policies (2 pages)

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Counterfactual in Policies



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Counterfactual in Policies

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

Unformatted text preview:

Econ 3120 1st Edition Lecture 26 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 0 1highearni 2a f chnget 3highearni 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 0 1treati 2 postt 3treat 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



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