When Hristos Doucouliagos was a young economist in the mid-1990s, he got interested in all the ways economics was wrong about itself—bias, underpowered research, statistical shenanigans. Nobody wanted to hear it. “I’d go to seminars and people would say, ‘You’ll never get this published,’” Doucouliagos, now at Deakin University in Australia, says. “They’d say, ‘this is bordering on libel.’”

Now, though? “The norms have changed,” Doucouliagos says. “People are interested in this, and interested in the science.” He should know—he’s one of the reasons why. In the October issue of the prestigious Economic Journal, a paper he co-authored is the centerpiece among a half-dozen papers on the topic of economics’ own private replication crisis, a variation of the one hitting disciplines from psychology to chemistry to neuroscience.

The paper inhales more than 6,700 individual pieces of research, all meta-analyses that themselves encompass 64,076 estimates of economic outcomes. That’s right: It’s a meta-meta-analysis. And in this case, Doucouliagos never meta-analyzed something he didn’t dislike. Of the fields covered in this corpus, half were statistically underpowered—the studies couldn’t show the effect they said they did. And most of the ones that were powerful enough overestimated the size of the effect they purported to show. Economics has a profound effect on policymaking and understanding human behavior. For a science, this is, frankly, dismal.

One of the authors of the paper is John Ioannidis, head of the Meta Research Innovation Center at Stanford. As the author of a 2005 paper with the shocking title “Why Most Published Research Findings Are False,” Ioannidis is arguably the replication crisis’ chief inquisitor. Sure, economics has had its outspoken critics. But now the sheriff has come to town.

For a field coming somewhat late to the replication crisis party, it’s ironic that economics identified its own credibility issues early. In 1983 Edward Leamer, an economist at UCLA, published a lecture he called “Let’s Take the Con Out of Econometrics.” Leamer took his colleagues to task for the then-new practice of collecting data through observation and then fitting it to a model. In practice, Leamer said, econometricians fit their data against thousands of statistical models, found the one that worked the best, and then pretended that they were using that model all along. It’s a recipe for letting bias creep in.

At about the same time as Leamer wrote his paper, Colin Camerer—today an economist at Caltech—was getting pushback for his interest in reproducibility. “One of my first papers, in the 1980s, has all of the data and the instructions printed in the journal article. Nowadays it would all be online,” Camerer says. “I was able to kind of bully the editor and say, ‘This is how science works.’” Observe, hypothesize, experiment, collect data, repeat.

Over time, things improved. By 2010, the field was undergoing a “credibility revolution,” says Esther Duflo, an economist at MIT and editor of the American Economic Review. A few top journals began to sniff out shenanigans like p-hacking, massaging data for favorable outcomes. They asked for complete datasets to be posted online, and for pre-registered research plans (so investigators can’t change their hypotheses after the fact). To publish in these journals, economists now have to submit the actual code they used to carry out their analysis, and unlike the old days it has to work on someone else’s computer.

Yes, open data, available code, and pre-registration don’t always guarantee reproducibility. “If I pick up Chrissy Teigen’s cookbook, it might not taste the same as it does at her house,” says Camerer, “even though she’s only 10 miles away and was shopping at the same store.” In 2015, economists at the Federal Reserve and Department of the Treasury tried to replicate 67 papers using data and code from the original authors; they were able to do it without calling the authors for help for just 22. It was a little grim.