Hi Ben,
My immediate guess would be the missing data on the county variable, which
may be interacting strangely with the string variables. Maybe try two
things: 1) creating numeric versions of both and repeat the matches and 2)
try dropping the missing county observations and comparing the matches
then.
Cheers,
Matt
On Mon, Jul 7, 2014 at 10:17 PM, Ben Hoen <bhoen(a)lbl.gov> wrote:
Just realized that blockgroup and county are both
strings. See below:
That likely is NOT what cem is looking for is it? Source of the problem?
(And yes, block group variable, which is the census number, is unique
across counties)
Ben
Ben Hoen
LBNL
Office: 845-758-1896
Cell: 718-812-7589
*From:* Matt Blackwell [mailto:m.blackwell@rochester.edu]
*Sent:* Monday, July 07, 2014 10:10 PM
*To:* Ben Hoen
*Cc:* cem(a)lists.gking.harvard.edu
*Subject:* Re: [cem] Understaning CEM's use of a categorical variable and
#0
Hi Ben,
Hm, it definitely should produce more matches when you use county. One
possible issue that I can think of off the top of my head is this: is the
block group variable unique across counties/states? Or do the values of the
block group variable repeat? One thing to check is to see if what happens
if you exact match on both the county and the block group in a single
match.
Hope that helps! If it doesn't, definitely let us know.
Cheers,
Matt
~~~~~~~~~~~
Matthew Blackwell
Assistant Professor of Government
Harvard University
url:
http://www.mattblackwell.org
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On Mon, Jul 7, 2014 at 9:36 PM, Ben Hoen <bhoen(a)lbl.gov> wrote:
Hi all,
I have been using the program cem in Stata (Version 13 MP, with Windows 7
Pro 64 bit), and thought I understood what it was doing well enough but
today something occurred which surprised (read worried) me, in that it
acted as I would NOT have expected it to.
I am trying to match target (i.e,, treated) homes to similar (i.e.,
"comparable") homes that do not have the treatment. In this case, the
"treatment" is whether the home does or does not have a photovoltaic energy
system (pv). I have 100 pv homes (treated), and ~ 5,000 non-pv homes
(comparable).
To match these homes I am using some basic characteristics of the home -
e.g., square feet of living space (sfla), size of the parcel (acres), age
of the home (age), as well as the year in which it sold (sale year) to
ensure the comparable home sold in the same year as the target home and,
finally, a geographic variable (such as the block group) to ensure the
comparable home is located in the same geography. For sale year and the
geogrpahy, they must match perfectly; i.e., the comparable homes must have
sold in the same year as the target (pv) home *and* also be located in
the same geography. For the purposes of this discussion those geographies
could be either the census block group (blockgroup) or the county (county).
All of the block groups fall within the counties, and there are many more
block groups than counties delineated in the data. For example, I have
approximately 30 block groups (each with at least one treated and one
comparable case) and 10 counties (each with at least one treated and one
comparable). In practice, though, in most geographies I have ~ 20-50 times
the number of pv homes available as comparables to match to.
Using the sample data and talking to local experts, I have established
appropriate cut points for my various characteristics and run a command
similar to the following, when blockgroup is used as the geography:
cem sfla(0 1000 2000 3000 5000) age(0 1 10 20 100) acres(0.05 0.15 0.5 1
10) saleyear(#0) blockgroup(#0) , treatment(pv)
And the following, when county is used as the geography:
cem sfla(0 1000 2000 3000 5000) age(0 1 10 20 100) acres(0.05 0.15 0.5 1
10) saleyear(#0) county(#0) , treatment(pv)
So, here's the confusing part:
I will have ~ 70 matching pv homes, and 300 comparable homes if blockgroup
is used, but only 20 matching pv homes, and 100 comparables homes if county
is used. In other words, when I allow a broader geography of comparables to
be drawn from, I get fewer matching cases. i would think the exact opposite
would be the case; if a cast a broader geographic net, I would have more
matches not less.
Any ideas why this would occur?
Thanks, in advance, for any insight you could offer.
Ben
Berkeley Lab
Ben Hoen
Staff Research Associate
Lawrence Berkeley National Laboratory
Office: 845-758-1896
Cell: 718-812-7589
bhoen(a)lbl.gov
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