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@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@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

 

On Mon, Jul 7, 2014 at 9:36 PM, Ben Hoen <bhoen@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@lbl.gov

http://emp.lbl.gov/staff/ben-hoen

 

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