| Abstract: |
Replication is a critical component of scientific credibility as it increases
our confidence in the reliability of the knowledge generated by original
research. Yet, replication is the exception rather than the rule in economics.
In this paper, we examine why replication is so rare and propose changes to
the incentives to replicate. Our study focuses on software code replication,
which seeks to replicate the results in the original paper using the same data
as the original study and verifying that the analysis code is correct. We
analyse the effectiveness of the current model for code replication in the
context of three desirable characteristics: unbiasedness, fairness and
efficiency. We find substantial evidence of “overturn bias” that likely leads
to many false positives in terms of “finding” or claiming mistakes in the
original analysis. Overturn bias comes from the fact that replications that
overturn original results are much easier to publish than those that confirm
original results. In a survey of editors, almost all responded they would in
principle publish a replication study that overturned the results of the
original study, but only 29% responded that they would consider publishing a
replication study that confirmed the original study results. We also find that
most replication effort is devoted to so called important papers and that the
cost of replication is high in that posited data and software are very hard to
use. We outline a new model for the journals to take over replication post
acceptance and prepublication that would solve the incentive problems raised
in this paper. |