Nonetheless, they report and rely on the test statistics derived from this obviously faulty IV regression. Finally, we further examine two aspects of our instrumental variable approach that have been attacked by RM. These are the validity of the exclusion restrictions underlying the use of interactions as instruments and the importance of the “one-half acre” program eligibility rule. We find that identification does not require both of these. Indeed, the results originally reported in the PK paper hold up extremely well in this new analysis.

The final question is the value of continuing this process of “replication” and rebuttal, a process that can be dated back to Morduch’s (1998) replication paper distributed months before the PK paper was first published. In Section 2 of this paper, we briefly reviewed the recent history of this replication/rebuttal sequence. In this paper, we make the same points about the two-stage setup used by RM that we made in 2011 and in 1999, and we cannot imagine that there is anything to be gained by restating them yet again in the future. Upon discovering errors in their work, RM have always come up with new lines of attack but have never got their econometrics and the 2SLS setup right. Perhaps one reason for the continuation of this process consisting of RM’s triumphal announcement of finding devastating problems with PK, followed by rebuttal by PK, is that RM do not believe in non-randomized studies, deeming them “unreliable” and claiming that their complexity hides a form of “obscurantism.”<sup>44</sup> We note that others have also attempted to replicate PK. Duvendack and Palmer-Jones (2012) have published a paper that is both a “replication” of PK and a replication of an earlier replication of PK by Chemin (2008) in the _Journal of Development Studies_ . Pitt’s reply paper (2012), published in the same issue, lists many serious errors in their code and misrepresentations of method in their paper, and concludes that the Duvendack and Palmer-Jones results provide no credible evidence on the validity of PK or Chemin or on the effectiveness of

> 44 On this, Roodman has written:

My main conclusion about non-randomized quantitative studies is that they are obscurantism, however unintentional, and what their complexity hides, ultimately, is a failure to prove the assumptions needed to demonstrate cause and effect. Warren Buffett’s investment rule—don’t buy what you don’t understand— works in microfinance research too. RCTs are easy to understand, so you should buy them more than nonrandomized studies. _<u>(http://blogs.cgdev.org/open_book/2011/06/rcts-are-people-too.php)</u>_ and The confusing math [referring to some equations describing the model of Pitt and Khandker], _nota bene_ , is from a non-randomized study. The math for randomized control trials (RCTs) could hardly be simpler. You flip a coin a bunch of times to decide who is offered a financial service and who isn’t. Then you come back later, and for each of the two groups—those offered and those not—you compute the average outcome (are you still with me?). Then you subtract one average from the other. Got that? Then you’re done. Even my third grader can understand that math. Well, my third grader derived the formula for the area of a regular octagon...but his classmates can understand RCT math too. So let’s not taint the method with obscurantism. _(http://blogs.cgdev.org/open_book/2011/06/rcts-are-people-too.php)_ 29