On the Implications of Essential Heterogeneity for Estimating Causal Impacts Using Social Experiments
Randomized control trials are sometimes used to estimate the aggregate benefit from some policy or program. To address the potential bias from selective take-up, the randomization is used as an instrumental variable for treatment status. Does this...
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| Language: | English |
| Published: |
2012
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| Online Access: | http://www-wds.worldbank.org/external/default/main?menuPK=64187510&pagePK=64193027&piPK=64187937&theSitePK=523679&menuPK=64187510&searchMenuPK=64187283&siteName=WDS&entityID=000158349_20110921143338 http://hdl.handle.net/10986/3568 |
| Summary: | Randomized control trials are sometimes
used to estimate the aggregate benefit from some policy or
program. To address the potential bias from selective
take-up, the randomization is used as an instrumental
variable for treatment status. Does this (popular) method of
impact evaluation help reduce the bias when take-up depends
on unobserved gains from take up? Such "essential
heterogeneity" is known to invalidate the instrumental
variable estimator of mean causal impact, though one still
obtains another parameter of interest, namely mean impact
amongst those treated. However, if essential heterogeneity
is the only problem then the naïve (ordinary least squares)
estimator also delivers this parameter; there is no gain
from using randomization as an instrumental variable. On
allowing the heterogeneity to also alter counterfactual
outcomes, the instrumental variable estimator may well be
more biased for mean impact than the naïve estimator.
Examples are given for various stylized programs, including
a training program that attenuates the gains from higher
latent ability, an insurance program that compensates for
losses from unobserved risky behavior and a microcredit
scheme that attenuates the gains from access to other
sources of credit. Practitioners need to think carefully
about the likely behavioral responses to social experiments
in each context. |
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