Hi all,
I'm considering using the cem Stata programme to evaluate the impact of a
welfare-to-work programme in the UK. However, I have never used cem before so I am trying
to understand some basic issues before proceeding with the estimation.
The first thing I'd be interested in understanding is: How does one handle situations
where after running cem the number of matched strata (and units within them) are very
small?
Applying the cem algorithm to data from a previous impact evaluation I get:
Number of strata: 8883
Number of matched strata: 132
0 1
All 8208 1584
Matched 179 141
Unmatched 8029 1443
If I calculate the ATT using cem matched data I get an impact estimate which is positive
(around 5ppts; based on 320 obs only) while using psmatch2 on all data (i.e. not only
those in cem matched strata; around 8,237 obs are used) with kernel weights I get an
estimate of around -5.7ppts. This means I reach opposite conclusions about the impact of
the programme of interest using cem and psmatch2.
I understand the cem-based estimates are based on better matched data (i.e. produce less
biased estimates) compared to my psmatch2 estimate with kernel weights) but this comes at
the expense of external validity: inference on the initial population is made based on a
very small subset of data (estimates based on cem are not statistically significant while
my original estimate was highly significant). Any advice about how one can handle
situations of this type?
Many thanks,
Sergio
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