Summary
"Applied economists routinely evaluate the effect of economic policies. For instance, what is the effect of raising the minimum wage on employment? Angrist & Pischke (2010) argue that applied economics has recently experienced a ""credibility revolution'': by switching to transparent methods to evaluate policies, applied economists have increased the credibility of their findings, and the impact of their work.
In the first part of this proposal, I show that the credibility revolution is not complete. Two-way fixed effects regression, a policy-evaluation method used in as many as 26% of the most-highly cited papers recently published in the American Economic Review, relies on the assumption that the policy's effect is constant, across units and over time. In most applications, this assumption is not credible. Therefore, I propose a series of new differences-in-differences estimators, that do not rely on this constant effect assumption, and that can be used in virtually all the applications where two-way fixed effects regressions have been used.
In the second part of this proposal, I argue that the credibility revolution's focus on unbiased estimators may be hard to defend, as variance also matters. I explore two ways of trading-off bias and variance. The first amounts to combining an unbiased and a biased but potentially more efficient estimator of the same parameter. The second amounts to deriving the minimax estimator of the policy's effect, under the assumption that this effect cannot be larger than a (potentially large) constant B.
Finally, the third part investigates the potential impact of the ``credibility revolution''. Specifically, I will run a randomized controlled trial to measure the effect of following an online course presenting policy evaluations, in two very different populations: policy makers and members of the general public, focusing on individuals with a low trust in institutions in that second group.
"
In the first part of this proposal, I show that the credibility revolution is not complete. Two-way fixed effects regression, a policy-evaluation method used in as many as 26% of the most-highly cited papers recently published in the American Economic Review, relies on the assumption that the policy's effect is constant, across units and over time. In most applications, this assumption is not credible. Therefore, I propose a series of new differences-in-differences estimators, that do not rely on this constant effect assumption, and that can be used in virtually all the applications where two-way fixed effects regressions have been used.
In the second part of this proposal, I argue that the credibility revolution's focus on unbiased estimators may be hard to defend, as variance also matters. I explore two ways of trading-off bias and variance. The first amounts to combining an unbiased and a biased but potentially more efficient estimator of the same parameter. The second amounts to deriving the minimax estimator of the policy's effect, under the assumption that this effect cannot be larger than a (potentially large) constant B.
Finally, the third part investigates the potential impact of the ``credibility revolution''. Specifically, I will run a randomized controlled trial to measure the effect of following an online course presenting policy evaluations, in two very different populations: policy makers and members of the general public, focusing on individuals with a low trust in institutions in that second group.
"
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101043899 |
| Start date: | 01-11-2022 |
| End date: | 31-10-2027 |
| Total budget - Public funding: | 1 197 632,00 Euro - 1 197 632,00 Euro |
Cordis data
Original description
"Applied economists routinely evaluate the effect of economic policies. For instance, what is the effect of raising the minimum wage on employment? Angrist & Pischke (2010) argue that applied economics has recently experienced a ""credibility revolution'': by switching to transparent methods to evaluate policies, applied economists have increased the credibility of their findings, and the impact of their work.In the first part of this proposal, I show that the credibility revolution is not complete. Two-way fixed effects regression, a policy-evaluation method used in as many as 26% of the most-highly cited papers recently published in the American Economic Review, relies on the assumption that the policy's effect is constant, across units and over time. In most applications, this assumption is not credible. Therefore, I propose a series of new differences-in-differences estimators, that do not rely on this constant effect assumption, and that can be used in virtually all the applications where two-way fixed effects regressions have been used.
In the second part of this proposal, I argue that the credibility revolution's focus on unbiased estimators may be hard to defend, as variance also matters. I explore two ways of trading-off bias and variance. The first amounts to combining an unbiased and a biased but potentially more efficient estimator of the same parameter. The second amounts to deriving the minimax estimator of the policy's effect, under the assumption that this effect cannot be larger than a (potentially large) constant B.
Finally, the third part investigates the potential impact of the ``credibility revolution''. Specifically, I will run a randomized controlled trial to measure the effect of following an online course presenting policy evaluations, in two very different populations: policy makers and members of the general public, focusing on individuals with a low trust in institutions in that second group.
"
Status
SIGNEDCall topic
ERC-2021-COGUpdate Date
09-02-2023
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