st: st: Understanding Heckman ML vs Heckman two step for selection

["Clifton Chow" <clifton_chow@post.harvard.edu>]

I am modeling lnWage using Heckman for selection on a dataset of individuals with disability and I have some basic questions about the difference between Stata's Heckman full Maximum Likelihood and Heckman two-step efficient estimates.  

1. I was under the impression that Heckman two-step is also estimated using maximum likelihood, but I see now it is not so as the coefficients, standard errors, lambda and even the sample in the analysis (both full and censored) are different.  How is Heckman two-step estimated?

2. Both Heckman full ML and Heckman two-step include the estimate for lambda.  Is the IMR part of the main equation in Heckman ML as well?

3. In the literature Heckman two-step is considered more robust and preferred.  Why is this so and if that is the case, why bother with Heckman ML at all?  What is essential advantage of Heckman ML vs. Heckman two-step

If anyone can refer me to the literature that explain these differences well, that would be much appreciated. 

Many Thanks

  
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