Monday, July 25, 2011

Playing the Trust Game in Rural Cameroon

Hi Everyone,

I recently read "Does trust extend beyond the village? Experimental trust and social distance in Cameroon" in Experimental Economics and had some reservations about their use of the term "social distance" to describe their primary findings. My thoughts on this are in detail below.

I think the paper is interesting and well done. The authors use the trust game to look at how people respond to partners from their own or a neighboring (but not very close) village. Their main result is that the first movers send significantly more money to their anonymous partner when that partner is in the same village than when the partner is in a different village. It is a between comparison. This is not entirely surprising, but still a nice result and the experiment and analyses are well done.

But I was surprised to find no discussion or even mention of how the role that norms of money lending and informal insurance may influence behavior in the laboratory. There are large bodies of literature on informal lending and insurance that suggest how much influence such institutions have in how people perceive and use money in rural villages where formal credit and insurance markets are incomplete. The trust game in many ways may simply cues participants, who in turn respond as their village norms dictate. In that case, the authors' results on trust differing according to village membership would be confounded by this norm response. In fact, the norms of behavior outside the lab setting, which I argue are invoked in the game, may even be capturing more than just trust (with expected reciprocity), but within village norms of giving, sharing and insurance. This is particularly true if players are aware of each others' village locale. Thus the paper's measure is not a response to social distance but a measure of norms. That the design includes people from both villages paired with others from the opposing village helps, but only nullifies my comment if the villages are incredibly different with very different norms of money usage.

In that sense, I do not think that the experimental design (and accompanying survey data) allow the authors to unequivocally identify the difference in giving by village membership as being a social distance effect. Thus, the interpretation that social distance explains the difference in trusting may not in fact explain what people are responding to in your treatment. Rather, they may be responding according the the norms in their villages (i.e. how people respond to the money allocation events).

As the results show, whether or not the recipient is in the village certainly matters in terms of average amount given in the trust game, but WHY it matters may not be (primarily) social distance interacting with individual decision making. With informal insurance and credit markets playing such an important role in how rural communities in many developing countries use money, I would expect certain rules or norms to be in place for how people respond to the in village/out village money allocation events. One example of a rule or norm would be "when someone in our village asks for money, we always give". I realize that this is not the appropriate rule/norm for their data, since they have such high rates of giving overall and thus cannot look at the play/do not play decision. But I think it conveys the idea that people may not be responding to the incentives and the expectation of immediate reciprocity (i.e. choosing to trust), but rather responding to the cue of it being a money allocation event -- norms of how people respond to such an event likely differ according to whether the other guy is from one's village. To their credit the authors do include ROSCA membership in their regressions. This begins to get at this issue, as they allude to but discuss only briefly. And the coefficient on RSOCA membership is large (larger than their coefficient on the variable of interest) and highly significant. That ROSCA membership is so important (but length of time in a ROSCA is not) suggests there is something more going on here and that village differences is capturing more than just social distance.

I posed the following questions to the authors:
- Have you tried including in the regression an interaction term of ROSCA membership and A and B in the same village?
- Another comment in terms of your future research, why trust game transfers so high among women?
- What about being a women in these villages in Cameroon would contribute to this behavior?
- In line with my above statement on village norms-could it be that women face a greater expectation of giving/sharing and insurance within a village? So that again, it's not trust per say that you're capturing through gender, but the intensity with which female villagers are subject to these financial and familial obligations?

Tuesday, July 19, 2011

Data Tools

In my all our data work I've found that:

R produces great graphics. Like ellipsical confidence intervals and such. But it's statistical syntax has little logic to me. It's not matrix algebra, and it's not one line code. Perhaps it's good for object oriented programmers.

SQL is best for data aggregation, especially biggish data. Like if your data are in relational schema, don't merge them in a statistical tool. Do it directly from SQL.SQL won't let you merge datasets when unique id's don't match, unless you allow for left and right joins. STATA can often produce messy merges-dropping stuff, or lots of missings. Don't ask me why.

SAS is good for data manipulation-transposing, reshaping.

STATA is good for built in stats. It just takes way fewer commands to run an estimation in STATA than in anything else. Hands down.

MATA and MATLAB are good for coding up your own estimators. If you want to change a maximum likelood estimator for some particular distribution, your best bet is to find it in MATA(STATA's Matlab) or MATLAB code and alter it. R would be a bitch. So would SAS.

SPSS just sucks. Don't use it. Who wants to only click their way through life??

Thursday, July 7, 2011

A Natural RCT on Healthcare

Aside from a short sting working for Ray Fisman, this is interesting:

Basically, people were randomly selected to opt into medicare, so the random instrument was "option to opt in".

1) The most noteworthy result to me is "The only type of care with no statistically discernible increase—or decrease—was emergency room use," which seems to account for the bulk of medical expenses by the uninsured (READ!!!

Results by Gruber&Finkelstein (MIT): We find that in this first year, the treatment group had substantively and statistically significantly higher health care utilization (including primary and preventive care as well as hospitalizations), lower out-of-pocket medical expenditures and medical debt (including fewer bills sent to collection), and better self-reported physical and mental health than the control group.”

Goldsten's comments:

2) Can anyone tell me "family-wise error adjusted p-value based on step-down resampling by Westfall and Young" is?

Saturday, July 2, 2011

Logarithmic Distribution of Leading Numbers in any Data??

This is unusual.

Benfor'ds Law, also called the first-digit law, states that in lists of numbers from many (but not all) real-life sources of data, the leading digit is distributed in a specific, non-uniform way. According to this law, the first digit is 1 about 30% of the time, and larger digits occur as the leading digit with lower and lower frequency, to the point where 9 as a first digit occurs less than 5% of the time. This distribution of first digits is the same as the widths of gridlines on the logarithmic scale.

The shape isn't surprising, given that a lot of stuff follows a bell if the sample is big enough. But 1's have the highest frequency.

Why would that be?