Censoring Due to Death, cont'd, & A Visit To Harvard 

 Censoring, cont'd 
John F. Friedman 

 Continuing from the most recent post, for the economist, perhaps a more interesting incidence of this statistical problem is not researchers making this error within the literature but consumers making misjudgments in the marketplace.  (Since most people approach problems in their lives with less rigor than a statistician, perhaps this is not surprising).  In particular, once consumers make these inference mistakes, economic theory suggests that firms will take advantage.  Edward Glaeser wrote at length on this phenomenon in 2003 in "Psychology and the Market." 

 One classic example of this phenomenon - as specifically related to censorship by death - is the mutual fund industry.  Most brochures for management companies aggressively tout the high past returns that have accumulated in their funds.  Consumers then extrapolate these historical earnings into the future, usually choosing managers based on past performance.  Of course, their reasoning is tainted by the same statistical problem; companies will shut down those mutual funds which have poor past performance, leaving only their winners for customers to admire.  (Another problem with this line of reasoning is that there is virtually no evidence that strong past performance predicts of strong future performance.  In this sense, perhaps the greater error is to pay attention to past returns at all!)  This problem is compounded in the market by the fact that any firm which attempts to educate consumers about their mistakes is unlikely to capture the value-added from that effort.  The now-savvy consumers have no reason to invest at the firm that provided the information, and, even if they did, these firms make the most money from naive consumers rather the smart ones, who would now make up the clientele.  See David Laibson and Xavier Gabaix (2004) for more on this phenomenon.  Since no firm has an incentive to educate the public, the entire industry becomes geared towards taking advantage of naive consumers, obfuscating costs, and selectively presenting information. 

  

   
A Visit To Harvard 

 Anton Westveld (Visiting from University of Washington Statistics Department) 

 This past week I had the opportunity to visit with Kevin Quinn, one of my main Ph.D. advisors, at for the Center for Government and International Studies at Harvard. Kevin and Gary King asked if I would provide a brief description of my recent visit. 

 I was fortunate enough to arrive in time to work in the new buildings for the Center. The new space has a modern design that is quite beautiful and utilitarian. 

 Currently we are working on developing statistical methodology for longitudinal social network data. Social network data consist of measured relations occurring from interactions within a set of actors. This type of data allows for the empirical investigation of the interconnectivity of the actors, which is a cornerstone of social science theory. The methodology focuses on data generated from the repeated interaction of pairs of actors, including temporal dyadic data resulting in an outcome for each actor at each time point (e.g. the level of exports from Canada to Japan in a given year). The methodology incorporates structure to account for correlation resulting from interactions as well as the repeated nature of the data. In particular, a random effects model is employed which accounts for five different types of network dependencies.  These five dependencies are then correlated over time through the assumption that the random effects follow a weakly stationary process. 

 Kevin and I spent the last few days discussing appropriate methodology and writing C++ code. We also spent some time discussing the relationship between social network models and statistical game theory models, both of which seek to gain an understating of social phenomena by examining social interaction data.  Due to the Center’s collegial environment, I also had opportunities to discuss my work with Gary King and Jake Bowers.