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How Credible is the Credibility Revolution?

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When economists analyze a well-conducted RCT or natural experiment and find a statistically significant effect, they conclude the null of no effect is unlikely to be true. But how frequently is this conclusion warranted? Kevin Lang discusses.

Image courtesy of interviewee. August 1, 2024

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This video is a transcription of an interview discussing the "credibility revolution" in economics and how it relates to evaluating the effectiveness of policies. The key points covered include:

1. **Credibility Revolution**: This video movement aims to improve the methods used in econometric studies to better compare different groups and isolate the effects of specific variables, such as military service on earnings. Techniques like utilizing random assignment and draft lotteries enhance the reliability of causal inferences.

2. **Null Hypothesis Testing**: Economists often start with a null hypothesis, which states that there is no effect from a policy or intervention. They analyze data to determine if they can reject this hypothesis. The confidence in rejecting the null hypothesis hinges on the statistical significance of the findings, typically assessed through p-values.

3. **Findings on False Discoveries**: The research suggests that a significant proportion of studies published in leading economics journals may contain false discoveries. Specifically, 41% of rejected null hypotheses might be erroneous, and for studies with marginal p-values around 0.05, the false discovery rate could soar to 65%.

4. **Implications for Policy**: The findings prompt a cautious approach to policy implementation based on economic studies. Economists must be more discerning about the robustness of evidence before endorsing policies, especially when the consequences of incorrect conclusions could be substantial.

5. **Future Research Directions**: The interviewee expresses a commitment to focus on development economics and plans to explore further the accuracy of findings in that area, emphasizing the need for more rigorous replication studies in the field.

Overall, the discussion highlights the importance of scrutinizing research methods in economics and the need for a cautious interpretation of results, especially when formulating public policy.

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Here are some key quotes extracted from the video you've :

1. **On Traditional Economic Analysis:**
"Historically, when economists tried to figure out whether something had an effect... they would compare people who went to Vietnam with people who did not go to Vietnam."

2. **On the Credibility Revolution:**
"The credibility revolution tried to find ways to make that comparison better."

3. **Using the Draft Lottery Example:**
"One of the ways that we did that was to take advantage of... the draft lottery."

4. **On the Null Hypothesis:**
"The way economists normally think about this is to begin with what we call the null hypothesis."

5. **On Statistical Significance:**
"We generally say that the result is significant if the probability of the event happening by chance is less than 5%."

6. **Questioning the Null Hypothesis:**
"The real question is how likely is the null hypothesis to be true?"

7. **On False Discoveries:**
"41% of all rejected null hypotheses in these leading economics journals are false discoveries."

8. **On Narrow Rejections:**
"If I look at very narrow rejections, ones where the probability is right around 0.05... then we're talking about 65% of those being false discoveries."

9. **Reflection on Empirical Research:**
"I think we have to be cautious. We have to have a lot less confidence in calling so-called significant results as clear policy implications."

10. **Concern Over Evidence:**
"I got worried that we fund a lot of really good studies... but that means we're sometimes gonna find that policies work when they really don't work."

These quotes encapsulate the core ideas presented in the speech about economic analysis, the relevance of statistical testing, the nuances of the credibility revolution, and the concerns regarding the reliability of findings in empirical economics.

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Historically, when economists tried to figure out whether

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something had an effect, say whether, going

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to Vietnam had an impact on your eventual earnings,

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what they would do is they would compare people who went

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to Vietnam with people who did not go to Vietnam,

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and they would try to look at people who looked similar.

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So maybe what they would do is to,

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look only at white people, who went to Vietnam

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and who didn't go to Vietnam,

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or maybe even white high school dropouts who went

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to Vietnam versus white high school dropouts who didn't.

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But we were never very good at being convincing

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that we had found all of the things that were different

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between the people who went to Vietnam and those who didn't.

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And so what the credibility revolution tried

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to do is to find ways to make that comparison better.

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So they tried to find somebody who looked even more similar

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to somebody who went to Vietnam.

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And one of the ways that we did that was to take advantage

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of something like in the case of Vietnam,

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the draft lottery.

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So the draft lottery was based

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on the day that you were born.

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So it might be that if you were born on January 5th,

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you had a low lottery number, which meant

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that you were very likely to be drafted.

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But if you were born on January 6th, you happened

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to have a very high lottery number

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and were unlikely to be drafted.

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And there's really almost no difference between the people

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who were born on January 5th and people born on January 6th.

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So that seems like a pretty good, pretty good comparison.

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And we could look at what was the difference in the

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proportion of people who went

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to Vietnam based on their birthdate

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and what were the differences in their outcome,

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and figure out the impact of going to Vietnam.

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Now, that example is a little bit more precise than we've

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ever been able to do, but it's the basic idea,

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the credibility revolution basically popularized a set

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of techniques that compare people in situations

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where they seemed like they were pretty

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nearly randomly assigned.

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In the best cases, they actually were,

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they actually ran experiments just the way physicians do.

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Um, so we might in a situation where

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the training program was oversubscribed, randomly assigned,

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who of the people wanted to go into the training program,

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got into the training program, compare outcomes.

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There, there are a bunch of other

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techniques in the interest of time.

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I don't think I should go into them,

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but basically it's about it finding ways to do a better job

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of comparing apples to apples into apples oranges.

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The critical issue that I wanted to address is,

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let's take one of these very well

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executed credibility revolution studies,

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and it says that, you know, some policy is effective, say,

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I don't know, for example,

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maybe giving students more tutoring has a significant effect

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on their earnings after they leave high school

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or secondary school to be more international.

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Um, how confident should we be

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that the policy actually works?

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That's the question I want to answer.

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Now, the way economists normally think about this is

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to begin with what we call the null hypothesis, right?

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In this case, the null hypothesis is at the effect

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of giving the extra tutoring on later earnings this year,

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and it has no effect on outcomes, right?

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And normally what the economist is going to say is,

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suppose I do find an effect.

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How likely is it that if there were no effect,

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I would actually find an effect that the, that's this large.

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So to give a a simple example, you know,

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think about tossing a coin a hundred times.

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I won't always get 50 heads.

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Sometimes I got 48 heads, sometimes I get 52 heads, right?

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The same thing can happen if you're tutoring people.

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Sometimes kids you happen to randomly sign to get tutored

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better than the ones that you happen to randomly sign not

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to get tutoring, or they just happen to get lucky on the,

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on the test, they guess some multiple

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choice questions, right?

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Um, and so what we wanna ask is how likely,

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if there really was no difference, would I find an effect

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that's this large, that thing's called the P value?

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And we generally say that the result is significant

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if the probability of the event happening by chance

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is less than 5%, right?

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So in that case, we would say that the effect that we found

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of tutoring, that we found a probability

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that it has no effect is sufficiently unlikely

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that we would reject the hypothesis that it has no effect.

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And as a result, since the alternative to having no effect

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is that it has an effect, we implicitly,

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or I would say often explicitly conclude

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that the tutoring did have an effect, right?

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And that in turn would point to supporting a policy

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of implementing tutoring.

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I wanna be careful here, right?

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There are other considerations like cost, right?

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Uh, but now at least we know that tutoring is effective.

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So, so the question that I ask is how likely is it

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that tutoring is effective?

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And notice that's a different question, right?

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Normally what we ask is,

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suppose tutoring doesn't have an effect.

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How likely is my result? Right?

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We never ask, assume that the tutoring does have an effect.

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How unlikely is my result?

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And we need to know both of those, right?

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So the real question is

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how likely is the null hypothesis to be true?

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And let me see if I can give you an extreme example.

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Uh, but I think a helpful one in terms of the intuition.

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Suppose I have a coin, right?

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And I know, or at least I believe

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that it's either a fair coin produced by the US mint,

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or it's a fake coin with two heads.

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Right? Now, if it's produced by the US mint,

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and if I flip it 50% of the times, it'll give heads 50%

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of the time it'll give me tails.

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Of course, if it's a fake coin, I always get two heads.

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But let's not worry about that yet.

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Now, as an economist, I start with a null hypothesis

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that it's a two headed coin, right?

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So I flip it some number 20 times, let's say,

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and I get, let's say 17 hits.

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Um, if the coin's truly fair,

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that's a really unlikely outcome, right?

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And since my null hypothesis is that the coin is fair,

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I have to reject it, right?

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Because the outcome that I've observed is unlikely under

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the null hypothesis.

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And since my only choices are either that it's a fair coin

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or it's a two-headed coin under the traditional way

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of thinking about it, I reject the null

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and accept the alternative, which is

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that it's a two-headed coin.

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And that, of course is ridiculous

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because I couldn't possibly get even one tail,

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let alone three, if it were a two headed point.

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So the question I wanna ask is how likely

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is the outcome under the alternative versus

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how likely it is under the null?

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And we also have to ask, you know,

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originally when I started out,

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how likely did I think the alternative and the,

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and the null were right?

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Because if I were totally convinced that the coin

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was fair, right?

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It would take a lot of evidence to to change my mind, right?

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So if I thought it was pretty likely that it was unfair,

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then it wouldn't take so much evidence.

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So I also have to take that into account

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and putting those two things together, I can calculate

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the probability that the null hypothesis is true.

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There are two things that I need to know in order

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to decide whether null hypothesis is true or not.

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That's what I was just talking about, right?

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How likely is the outcome

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that I observed not only under the null,

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but also under the alternative,

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and also how likely was the null a priority?

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What did I think it was, right?

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There's a formula for putting those things together,

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and it's called the base there.

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So the goal of my research is to take the body

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of empirical research

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that broadly fits into the credibility revelation

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and ask how likely is it that a rejected null

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that's been published in a good journal

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should not have been rejected?

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Another way of putting it is what's the false discovery

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rate, right?

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And what particularly what is the false discovery rate

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and empirical economics

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that draws on the credibility revolution?

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So the way I proceed is I think of economists

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as producing results from two different situations.

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In the first situation, they're actually testing

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hypotheses that are true.

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And in that case, I'm gonna use our most common measure of

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how likely the finding is under the null, which is

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what we call the T statistic.

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And in the situation where the null is true,

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the t statistic has the T distribution.

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So it's very easy. And for those people who are not familiar

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with the T distribution, you can think of it

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for most purposes as having a normal distribution

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centered on zero and a standard deviation of one.

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So that's, that's some fraction

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of the null hypotheses that are being tested.

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And then there are the null hypotheses that are false.

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And the average t statistic we're going

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to find in those cases is gonna depend on

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how large the true effect is

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and how precise our experiment is.

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So the more data we have, everything else equal,

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the higher the T statistic will tend to be,

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and that t statistic turns out we'll have something

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that's called the non-central T distribution.

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So what I then do is I assume a fairly general

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raw distribution from which those expected Ts

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that are the central point

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of the non-central T distribution, like the, the

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distribution from which they are drawn.

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And then my objective is to measure the proportion

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of hypotheses that economists study that are true.

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In other words, the ones that should not be rejected,

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and the distribution of TT statistics, the false nulls

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that should be rejected.

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Okay? So to do this,

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had my research assistant collect data from a set

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of leading journals, I have to give her credit,

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they read the paper sufficiently carefully

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to identify the core null hypothesis.

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And, and that's important, right?

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'cause we didn't include robustness, checks,

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heterogene analysis

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unless they were core to the paper, right?

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And we then took those data

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and found the parameters of the model

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that best fit the empirical distribution

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of the two statistics.

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Now, I've kind of simplified things.

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The final model is a little more complicated than

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what I just described.

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For example, I allow the null hypothesis

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to be only approximately true.

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So that if you know, the null is zero is true, even if the,

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it's just a little bit different from zero.

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Uh, but I think I've described

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what capture the spirit of what I do.

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This work has been done,

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I think in a less fully convincing way,

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but in a similar way in the medical literature.

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Um, and there they find a pretty high false discovery rate,

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but they're not finding kind of numbers

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that I I'm about to tell you about.

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Um, so the best estimate

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after I've kind of generalized the model is that 41%

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of all rejected null hypotheses

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in these leading economics journals are false

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discoveries, right?

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I'm talking about studies that use

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credibility revolution methods.

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I'm talking about leading economics journals.

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I should not, I should add, okay, it's important,

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that I'm excluding some extreme T statistics.

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So ones with the T statistics that go for 10,

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and I exclude them because in most cases,

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those really aren't trying to test the all hypothesis.

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They're looking at trying

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to get a more precise parameter

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estimate or something like that.

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Now, even worse is, if I look at very narrow rejections,

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ones where the probability is right around 0.05 i

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where there's about a 5% chance of getting something

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that's extreme or larger, then we're talking about 65%

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of those being false discoveries.

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And first, to re really only have a 5% chance

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of a false discovery,

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we need a much larger T than we've been using.

12:58.675 --> 13:01.175
We would need a tee of about 5.48,

13:01.785 --> 13:04.005
and that would eliminate over 80%

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of the published rejected nulls.

13:06.265 --> 13:08.885
So I wanna be careful here, right?

13:09.765 --> 13:13.685
I I'm not suggesting there's any malpractice on the part

13:13.685 --> 13:15.525
of any economist or economist, right?

13:16.335 --> 13:19.625
This happens because a lot of economists think of a lot

13:19.625 --> 13:22.825
of interesting male hypotheses which they hope to reject,

13:23.245 --> 13:26.675
but most of which are true or approximately true, right?

13:27.215 --> 13:29.555
And when we reject, that's often the

13:29.555 --> 13:30.635
most interesting finding.

13:31.135 --> 13:34.295
So those are the ones that get published in the journals

13:34.295 --> 13:37.255
and we publish a lot of false discoveries, right?

13:37.555 --> 13:40.595
So one thing I don't find is that a lot

13:40.595 --> 13:42.955
of false discoveries are due to what's called P hacking.

13:44.005 --> 13:46.445
P hacking refers to the case where, well,

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you try your estimates a certain way

13:48.865 --> 13:51.685
and you don't get quite a, the T statistic

13:51.755 --> 13:54.285
with an absent value of above 1.96.

13:55.735 --> 13:58.235
So you don't quite make the 0.05 cutoff.

13:58.695 --> 14:00.515
So, which we like to use, you say, well,

14:00.515 --> 14:02.155
maybe I should limit the sample in this way.

14:02.865 --> 14:04.355
That doesn't quite get you there either.

14:04.815 --> 14:06.795
And you say, well, maybe instead

14:06.795 --> 14:08.995
of looking at the difference in years of education,

14:09.715 --> 14:11.355
I should look at the difference in

14:11.355 --> 14:12.595
high school graduation rates.

14:13.095 --> 14:14.635
And eventually you find something

14:14.635 --> 14:16.035
that's statistically significant.

14:16.615 --> 14:18.755
And if that happens, you would get a lot

14:18.755 --> 14:20.395
of false discoveries, right?

14:20.655 --> 14:23.315
But there are actually a bunch of tests out there,

14:23.315 --> 14:26.075
in the literature for looking at p hacking and I,

14:26.075 --> 14:28.875
and I find them, and I don't find any evidence of p hacking.

14:29.335 --> 14:31.675
Uh, in fact, I find some evidence that the shortage

14:31.695 --> 14:34.395
of T statistics just above 1.96.

14:35.195 --> 14:37.015
And frankly, I think that makes sense.

14:37.675 --> 14:39.775
Uh, a lot of the times we're going to be skeptical

14:39.795 --> 14:43.095
of our ability to reject an all hypothesis and formally

14:43.235 --> 14:45.935
or informally we're skeptical of findings

14:45.935 --> 14:47.775
that are barely statistically significant.

14:48.625 --> 14:51.245
And so those papers are less likely to get published.

14:51.545 --> 14:54.685
And so we actually find a shortage of two statistics just

14:54.685 --> 14:55.845
above 1.96.

14:55.945 --> 14:57.125
Let me start by explaining

14:57.125 --> 14:58.525
how I got interested in the question.

14:58.925 --> 15:01.985
I think that'll help. So somewhat surprisingly,

15:01.985 --> 15:03.665
because I'm not a development communist,

15:03.815 --> 15:06.745
I've served on a review panel for grants in policy

15:07.265 --> 15:08.705
oriented development economics

15:08.765 --> 15:10.585
for quite a number of years now.

15:11.945 --> 15:15.765
And the funder is very interested when we find something

15:15.765 --> 15:18.125
that seems to work in figuring out how

15:18.125 --> 15:19.245
to get people to use it.

15:20.505 --> 15:25.085
And so what happened is I got worried that we fund a lot

15:25.085 --> 15:26.525
of really good studies,

15:27.185 --> 15:30.235
but that means we're sometimes gonna find

15:30.235 --> 15:32.435
that policies work when they really don't work.

15:33.315 --> 15:35.335
And I want it to be able to tell the funder

15:35.435 --> 15:39.415
how confident should we be about promoting policies based on

15:39.415 --> 15:40.575
the studies that we funded.

15:41.965 --> 15:44.625
And so I think the implication is that we have to be

15:45.825 --> 15:49.795
much more careful about how strong the evidence has to be

15:50.765 --> 15:52.225
before we encourage people

15:52.445 --> 15:54.585
to adopt some policy or intervention.

15:55.215 --> 15:58.945
Okay? Now again, that's not only going to depend on

15:58.945 --> 16:01.865
how likely the result is to be a false discovery, right?

16:02.285 --> 16:04.585
It also depends on the cost of implementing the policy,

16:05.755 --> 16:08.245
both when it works and when it doesn't work,

16:08.465 --> 16:09.605
and how well it works.

16:10.415 --> 16:12.475
So, you know, if something is virtually costless

16:13.055 --> 16:15.035
and we have very weak evidence that it works,

16:15.735 --> 16:17.155
why not go ahead and do it?

16:18.265 --> 16:22.365
Um, but it would be great if we could figure out how to fund

16:22.585 --> 16:24.165
and value more replication.

16:24.165 --> 16:25.245
Studies and economics

16:25.825 --> 16:27.325
and economics to rewards

16:27.325 --> 16:29.245
for replications are usually pretty low.

16:30.695 --> 16:35.475
And it's also a case, excuse me, that by its very nature,

16:36.255 --> 16:39.075
the credibility revolution often favors studies

16:39.665 --> 16:42.315
that cannot be replicated in any fundamental sense.

16:43.335 --> 16:46.845
We can look at the effect of military service in other wars,

16:47.225 --> 16:48.645
but there's only one Vietnam.

16:50.005 --> 16:54.005
And even when we use randomized controlled trials, many

16:54.005 --> 16:57.725
of them are sufficiently expensive that we're not going

16:57.725 --> 16:59.365
to do many of them right?

16:59.535 --> 17:01.605
After a lot of digging, we found that moving

17:01.605 --> 17:04.725
to opportunity had positive effects on young children.

17:05.585 --> 17:07.685
Now, I, I will say, my gut tells me that

17:07.685 --> 17:08.965
that result is probably right,

17:09.955 --> 17:12.935
but the important point is we're not likely to get a bunch

17:12.935 --> 17:15.375
of other moving opportunity experiments,

17:15.715 --> 17:16.815
in the near future.

17:18.115 --> 17:20.375
So, so mostly I would say the implication

17:20.375 --> 17:21.495
is that we have to be cautious.

17:22.075 --> 17:25.095
We have to have a lot less confidence in calling.

17:25.565 --> 17:27.855
So-called significant results

17:28.755 --> 17:31.255
as clear policy implications

17:31.955 --> 17:34.855
and not being fooled by our own rhetoric, including

17:34.855 --> 17:36.535
by the term credibility revolution.

17:37.255 --> 17:39.635
Uh, I wanna be clear, I'm a big fan

17:39.635 --> 17:40.795
of the credibility revolution,

17:41.495 --> 17:44.115
but I think it's led to an ex excessive degree

17:44.115 --> 17:45.395
of confidence in our results

17:46.455 --> 17:48.835
and moving to the future, I'm working

17:48.895 --> 17:52.045
Right now hoping to get funding for it,

17:52.525 --> 17:54.165
focusing on development economics

17:54.365 --> 17:56.485
'cause that's what got me most interested.

17:56.905 --> 17:58.565
And it's an area where we have a lot

17:58.785 --> 18:00.925
of randomized controlled trials.

18:01.625 --> 18:04.405
So it will be interesting to see if these issues

18:04.405 --> 18:07.165
that I'm raising are more important

18:07.625 --> 18:09.965
or less important, in that area.

18:10.265 --> 18:12.125
But I don't have any answers for that yet.

"Evaluating Economic Impact: The Methodology of Comparison and the Credibility Revolution"

00:00:00 - Historically, when economists tried to figure out whether something had an effect, say whether, going to Vietnam had an impact on your eventual earnings, what they would do is they would compare people who went to Vietnam with people who did not go to Vietnam, and they would try to look at people who looked similar. So maybe what they would do is to, look only at white people, who went to Vietnam and who didn't go to Vietnam, or maybe even white high school dropouts who went to Vietnam versus white high school dropouts who didn't.
00:00:44 - that we had found all of the things that were different between the people who went to Vietnam and those who didn't. And so what the credibility revolution tried to do is to find ways to make that comparison better. So they tried to find somebody who looked even more similar to somebody who went to Vietnam. And one of the ways that we did that was to take advantage of something like in the case of Vietnam, the draft lottery. So the draft lottery was based
00:01:23 - So it might be that if you were born on January 5th, you had a low lottery number, which meant that you were very likely to be drafted. But if you were born on January 6th, you happened to have a very high lottery number and were unlikely to be drafted. And there's really almost no difference between the people who were born on January 5th and people born on January 6th. So that seems like a pretty good, pretty good comparison. And we could look at what was the difference in the proportion of people who went to Vietnam based on their birthdate and what were the differences in their outcome, and figure out the impact of going to Vietnam. Now, that example is a little bit more precise than we've ever been able to do, but it's the basic idea,
00:02:09 - of techniques that compare people in situations where they seemed like they were pretty nearly randomly assigned. In the best cases, they actually were, they actually ran experiments just the way physicians do. so we might in a situation where the training program was oversubscribed, randomly assigned, who of the people wanted to go into the training program, got into the training program, compare outcomes. There, there are a bunch of other techniques in the interest of time. I don't think I should go into them, but basically it's about it finding ways to do a better job of comparing apples to apples into apples oranges.
00:02:50 - let's take one of these very well executed credibility revolution studies, and it says that, you know, some policy is effective, say, I don't know, for example, maybe giving students more tutoring has a significant effect on their earnings after they leave high school or secondary school to be more international. how confident should we be that the policy actually works? That's the question I want to answer. Now, the way economists normally think about this is to begin with what we call the null hypothesis, right? In this case, the null hypothesis is at the effect of giving the extra tutoring on later earnings this year,
00:03:32 - And normally what the economist is going to say is, suppose I do find an effect. How likely is it that if there were no effect, I would actually find an effect that the, that's this large. So to give a a simple example, you know, think about tossing a coin a hundred times. I won't always get 50 heads. Sometimes I got 48 heads, sometimes I get 52 heads, right? The same thing can happen if you're tutoring people. Sometimes kids you happen to randomly sign to get tutored better than the ones that you happen to randomly sign not

"Evaluating the Significance of Tutoring Effects: Understanding P-Values and Null Hypotheses"

00:04:13 - on the test, they guess some multiple choice questions, right? and so what we wanna ask is how likely, if there really was no difference, would I find an effect that's this large, that thing's called the P value? And we generally say that the result is significant if the probability of the event happening by chance is less than 5%, right? So in that case, we would say that the effect that we found of tutoring, that we found a probability that it has no effect is sufficiently unlikely
00:04:54 - And as a result, since the alternative to having no effect is that it has an effect, we implicitly, or I would say often explicitly conclude that the tutoring did have an effect, right? And that in turn would point to supporting a policy of implementing tutoring. I wanna be careful here, right? There are other considerations like cost, right? but now at least we know that tutoring is effective. So, so the question that I ask is how likely is it that tutoring is effective? And notice that's a different question, right?
00:05:32 - suppose tutoring doesn't have an effect. How likely is my result? Right? We never ask, assume that the tutoring does have an effect. How unlikely is my result? And we need to know both of those, right? So the real question is how likely is the null hypothesis to be true? And let me see if I can give you an extreme example. but I think a helpful one in terms of the intuition. Suppose I have a coin, right? And I know, or at least I believe that it's either a fair coin produced by the US mint, or it's a fake coin with two heads.
00:06:15 - and if I flip it 50% of the times, it'll give heads 50% of the time it'll give me tails. Of course, if it's a fake coin, I always get two heads. But let's not worry about that yet. Now, as an economist, I start with a null hypothesis that it's a two headed coin, right? So I flip it some number 20 times, let's say, and I get, let's say 17 hits. if the coin's truly fair, that's a really unlikely outcome, right? And since my null hypothesis is that the coin is fair, I have to reject it, right? Because the outcome that I've observed is unlikely under the null hypothesis.
00:06:56 - or it's a two-headed coin under the traditional way of thinking about it, I reject the null and accept the alternative, which is that it's a two-headed coin. And that, of course is ridiculous because I couldn't possibly get even one tail, let alone three, if it were a two headed point. So the question I wanna ask is how likely is the outcome under the alternative versus how likely it is under the null? And we also have to ask, you know, originally when I started out, how likely did I think the alternative and the, and the null were right? Because if I were totally convinced that the coin
00:07:38 - It would take a lot of evidence to to change my mind, right? So if I thought it was pretty likely that it was unfair, then it wouldn't take so much evidence. So I also have to take that into account and putting those two things together, I can calculate the probability that the null hypothesis is true. There are two things that I need to know in order to decide whether null hypothesis is true or not. That's what I was just talking about, right? How likely is the outcome that I observed not only under the null, but also under the alternative, and also how likely was the null a priority? What did I think it was, right? There's a formula for putting those things together,

"Assessing the False Discovery Rate in Empirical Economics: An Analysis of Credibility Revolution Research Findings"

00:08:20 - So the goal of my research is to take the body of empirical research that broadly fits into the credibility revelation and ask how likely is it that a rejected null that's been published in a good journal should not have been rejected? Another way of putting it is what's the false discovery rate, right? And what particularly what is the false discovery rate and empirical economics that draws on the credibility revolution? So the way I proceed is I think of economists as producing results from two different situations.
00:09:01 - hypotheses that are true. And in that case, I'm gonna use our most common measure of how likely the finding is under the null, which is what we call the T statistic. And in the situation where the null is true, the t statistic has the T distribution. So it's very easy. And for those people who are not familiar with the T distribution, you can think of it for most purposes as having a normal distribution centered on zero and a standard deviation of one. So that's, that's some fraction of the null hypotheses that are being tested. And then there are the null hypotheses that are false.
00:09:42 - to find in those cases is gonna depend on how large the true effect is and how precise our experiment is. So the more data we have, everything else equal, the higher the T statistic will tend to be, and that t statistic turns out we'll have something that's called the non-central T distribution. So what I then do is I assume a fairly general raw distribution from which those expected Ts that are the central point of the non-central T distribution, like the, the distribution from which they are drawn. And then my objective is to measure the proportion of hypotheses that economists study that are true.
00:10:28 - and the distribution of TT statistics, the false nulls that should be rejected. Okay? So to do this, had my research assistant collect data from a set of leading journals, I have to give her credit, they read the paper sufficiently carefully to identify the core null hypothesis. And, and that's important, right? 'cause we didn't include robustness, checks, heterogene analysis unless they were core to the paper, right? And we then took those data and found the parameters of the model that best fit the empirical distribution
00:11:07 - Now, I've kind of simplified things. The final model is a little more complicated than what I just described. For example, I allow the null hypothesis to be only approximately true. So that if you know, the null is zero is true, even if the, it's just a little bit different from zero. but I think I've described what capture the spirit of what I do. This work has been done, I think in a less fully convincing way, but in a similar way in the medical literature. and there they find a pretty high false discovery rate, but they're not finding kind of numbers that I I'm about to tell you about.
00:11:49 - after I've kind of generalized the model is that 41% of all rejected null hypotheses in these leading economics journals are false discoveries, right? I'm talking about studies that use credibility revolution methods. I'm talking about leading economics journals. I should not, I should add, okay, it's important, that I'm excluding some extreme T statistics. So ones with the T statistics that go for 10, and I exclude them because in most cases, those really aren't trying to test the all hypothesis. They're looking at trying to get a more precise parameter

"Assessing the Reliability of Economic Findings: The Challenge of False Discoveries and Statistical Significance"

00:12:31 - Now, even worse is, if I look at very narrow rejections, ones where the probability is right around 0.05 i where there's about a 5% chance of getting something that's extreme or larger, then we're talking about 65% of those being false discoveries. And first, to re really only have a 5% chance of a false discovery, we need a much larger T than we've been using. We would need a tee of about 5.48, and that would eliminate over 80% of the published rejected nulls. So I wanna be careful here, right?
00:13:13 - of any economist or economist, right? This happens because a lot of economists think of a lot of interesting male hypotheses which they hope to reject, but most of which are true or approximately true, right? And when we reject, that's often the most interesting finding. So those are the ones that get published in the journals and we publish a lot of false discoveries, right? So one thing I don't find is that a lot of false discoveries are due to what's called P hacking. P hacking refers to the case where, well, you try your estimates a certain way and you don't get quite a, the T statistic
00:13:55 - So you don't quite make the 0.05 cutoff. So, which we like to use, you say, well, maybe I should limit the sample in this way. That doesn't quite get you there either. And you say, well, maybe instead of looking at the difference in years of education, I should look at the difference in high school graduation rates. And eventually you find something that's statistically significant. And if that happens, you would get a lot of false discoveries, right? But there are actually a bunch of tests out there, in the literature for looking at p hacking and I, and I find them, and I don't find any evidence of p hacking. in fact, I find some evidence that the shortage of T statistics just above 1.96.
00:14:37 - a lot of the times we're going to be skeptical of our ability to reject an all hypothesis and formally or informally we're skeptical of findings that are barely statistically significant. And so those papers are less likely to get published. And so we actually find a shortage of two statistics just above 1.96. Let me start by explaining how I got interested in the question. I think that'll help. So somewhat surprisingly, because I'm not a development communist, I've served on a review panel for grants in policy oriented development economics for quite a number of years now. And the funder is very interested when we find something
00:15:18 - to get people to use it. And so what happened is I got worried that we fund a lot of really good studies, but that means we're sometimes gonna find that policies work when they really don't work. And I want it to be able to tell the funder how confident should we be about promoting policies based on the studies that we funded. And so I think the implication is that we have to be much more careful about how strong the evidence has to be before we encourage people to adopt some policy or intervention. Okay? Now again, that's not only going to depend on
00:16:02 - It also depends on the cost of implementing the policy, both when it works and when it doesn't work, and how well it works. So, you know, if something is virtually costless and we have very weak evidence that it works, why not go ahead and do it? but it would be great if we could figure out how to fund and value more replication. Studies and economics and economics to rewards for replications are usually pretty low. And it's also a case, excuse me, that by its very nature, the credibility revolution often favors studies

"Reevaluating Confidence in Economic Research and Policy Implications: Insights from the Credibility Revolution"

00:16:43 - We can look at the effect of military service in other wars, but there's only one Vietnam. And even when we use randomized controlled trials, many of them are sufficiently expensive that we're not going to do many of them right? After a lot of digging, we found that moving to opportunity had positive effects on young children. Now, I, I will say, my gut tells me that that result is probably right, but the important point is we're not likely to get a bunch of other moving opportunity experiments, in the near future. So, so mostly I would say the implication
00:17:22 - We have to have a lot less confidence in calling. So-called significant results as clear policy implications and not being fooled by our own rhetoric, including by the term credibility revolution. I wanna be clear, I'm a big fan of the credibility revolution, but I think it's led to an ex excessive degree of confidence in our results and moving to the future, I'm working Right now hoping to get funding for it, focusing on development economics 'cause that's what got me most interested. And it's an area where we have a lot of randomized controlled trials.
00:18:10 - that I'm raising are more important or less important, in that area. But I don't have any answers for that yet.

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