The Supply and Demand Side
Impacts of Credit Market
Information
Discussion
Atif Mian, Chicago GSB
Broad Question
• The paper poses a fascinating question: How does the
availability of public credit registry information alter
lending relationships?
– We have now a large crowd emphasizing the importance of stuff
like “law”, “property rights”, “institutions” etc.
– We understand them at an abstract level, but what do they mean
operationally?
– One way to think about this question is the mechanism design
view. For example, institutions are mechanisms that societies
are able to design and implement in order to achieve some
common good.
– A very important example of such mechanism design is
availability of “credit history” at individual and corporate levels.
Many countries still do not have such effective systems, even
though they are quite simple in terms of technology and
investment.
– So how important are credit histories? And how exactly do they
work to improve financial market efficiency? This is my read on
the contribution of this paper.
Quick Outline
• They have loan level data from a large microfinance
bank in Guatemala.
• Staggered entry of credit registry across the 39 branches
from March 2002 to January 2003
• However, borrowers had no knowledge of this. They
were then randomly given this knowledge between June
and November 2004. (only done with groups, not
individuals)
• Exploit staggered entry (I think) to show that there large
screening in and out of borrowers, which improves loan
outcomes.
• Informing borrowers, improves payment behavior.
Main Comment
• I already love the question, and it appears the authors
have the basic ingredients to write a very good paper.
– But there is a lot of confusion right now in the paper (hopefully
no big worries hiding behind this confusion)
• There are a lot of tangential discussions, and “chatting”
in the paper. And a theory which does not really seem
essential.
– For example, insurance discussion, claim that nothing is lost of
sharing bad information on your clients, “Hirshleifer effect” etc.
– I would suggest keep the initial question as sharp and simple as
possible, then go directly into data and tests, carefully describing
where identification of coefficients is coming from, and then
finally some discussion of what the results mean, interpretation,
welfare question etc.
• On the other hand, the paper lacks some firstorder information. For example, there is no data
description section! Also a lack of careful
description of identification assumptions.
– This makes my job a bit difficult as a discussant.
• Some basic questions:
– What is the frequency and time-frame of the full data
that you have access to?
– Do you also observe registry information?
– What are typical loan contracts like in terms of interest
rates, amount, maturity, roll-over propensity,
renegotiation, etc.
• Table 1: “measure changes in the lending contracts
observed on first loans which were issued before and
after bureau.”
– Is this the identification strategy? I thought they were going to
exploit the “staggered” nature of entry and essentially do a diffin-diff?
– I think table 1 is exploiting the staggered entry (because
otherwise time dummy absorbs everything). However, none of
this is explained in the paper.
• Why should we see an effect at the time of entry of
bureau? Does entry mean all past information? Or does
it mean they will start sharing from that point on? If it
includes past information then how long is the history?
• What are the means of dependent variables?
Try log size? What is “ITE” doing here?
• Why not difference-in-difference over time at the branch
level? This will also address the “weighting issue”. This
result should be given along side the loan level results.
(and in later tables too)
• Table 2: The result that loan volumes increase on existing borrowers
is mechanical I think. We do not observe guys with 0 (-ve) loan
demand, so conditioning on guys that continue to receive, it is not
too surprising. In any event, the correct model should be a Tobit
here. (again a careful discussion of where identification is coming
from will help clarify the thoughts here)
• The authors try to rationalize the result of a negative impact of
bureau on default by “mean reversion”. But if that were the case it is
easily testable using “placebo treatments”.
• Why can’t we observe bureau data and directly test the selection /
de-selection process of the bank?
• Puzzle: Bureau can only help the MFI if the lender already had some
other lending relationship. Given the magnitude of “in” and “out”
screening, is it believable that all these guys were borrowing from
multiple sources? Do you have that information?
– When are all the other MFIs implementing this bureau and do they
inform the borrowers?
– Keeping track of above is critical since (a) bureau by definition only
effect multiple guys initially, and (b) competition effects might even
make you lose your best guy.
• It is a bit surprising that borrowers do not know
about credit registry.
– Isn’t that illegal?
– Isn’t it in the bank’s own interest to tell borrowers
about it?
• Perhaps worth doing a welfare gain through
moral hazard and adverse selection gains (will
need a structural model …. Perhaps this is
where theory can really help)