1. The study compared apples and oranges: a small staff pension fund for employees of the national labor federation (AFLCIO) and a huge Taft-Hartley multiemployer fund for construction workers (members of the Carpenters). The consequences of this problem are not discussed in the study.

2. The paper has a problematic timing problem.
The data are supposed to capture the breaking away of several unions from the AFLCIO into a new federation called Change To Win (CTW). The time period of the study is 2003-2006. But the Carpenters disaffiliated from the AFL-CIO in 2001. So one would expect, if the paper’s thesis is correct (that union pension funds vote in the interests of their members rather than their beneficiaries) that the Carpenters would already, prior to the sampling period, have changed their voting. This too is not mentioned in the paper.

3. One might argue that the relevant change point occurs not when the Carpenters left the AFLCIO but occurs at the time that they affiliated with Change to Win. Problem here is that the affiliation with CTW did not occur until June 2005 and CTW itself was not formally organized until October 2005. Pretty slim temporal pickings, i.e., 14-18 months.

4. Taft-Hartley pension plans are joint union-management funds. They are not controlled by the union. Employers get an equal vote. Why would employers support a union-initiated shift in proxy voting? In fact they did not and do not.

5. Much of the data in the study did not come from the unions but from an asset manager called Marco Associates. Marco votes its clients’ proxies. It provides justification for each of its votes. It is impartial. See this page if you don’t understand this part of the pension industry:
http://www.marcoconsulting.com/2.3.html

Bottom line: The study compares apples and oranges; does not have a sampling framework that is defensible; and fails to recognize that the pension plans it considers to do not vote their own proxies.

Why does the study obtain findings that seem to support the thesis that union proxies support non-fiduciary objectives? In my opinion, one possibility is that the results are a fluke. The other possibility is that there are econometric flaws in the study that were overlooked in the effort to generate an interesting result. I leave it to the econometricians here to look CAREFULLY at the paper and ask themselves if there are flaws. I won’t go into that here. But I contend that, in addition to methodological problems, the econometric framework is problematic.

“The difficulty of replicating this skewed effort at data collection is obvious.”
Ummm, this is Mr. Kaplan’s whole point. If you think the data is skewed or misinterpreted, dig your own ditch – I mean, assemble your own data. Do your own analysis. Then show what the difference is.

Once you have two contradictory sets of data, then is the time to compare votes, companies, and union affiliation data and see who missed or erred in what. There are far too many studies in the Soft Sciences worlds that simply consist of churning the same chamber pot collections over and over. A new study should be built of new logs.

Apparently, Mr. Pedrotty has become certain there are “serious flaws” even before he has had a chance to “review the accuracy” of the model.

Data, on the other hand, takes a long time to clean and assemble, great care needs to be taken to make sure the data represents the correct information you want to analyze. It’s tedious and requires thought, but once someone does it the results are easy enough to replicate. So it makes sense that Mr. Agrawal doesn’t want to hand over hundreds of hours of work–but a good paper always explains how to do assemble the data from the same sources in the same way (takes the thinking part out, so it’s EASY). AFL-CIO can pay for their own data monkeys to follow the instructions laid out in the paper and replicate. Hence, EASY.

Mr. Pedroty’s position is obvious, it is much easier for him to copy Mr. Agrawal’s data set and try to poke holes in it, than it is for him to gather up a representative data set from scratch and apply the analysis himself. And if Mr. Pedroty picks and chooses his data to provide the result he desires, it will become very obvious when the data sets are eventually compared. He’s in a no-win situation, and nitpicking about about “contradictions” such as “easy” vs. “requires effort” is very weak.