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Updates

It’s been a while since that last post and a lot has changed in the interim.

I am pleased to announce I have taken up my position at the Research School of Economics at the Australian National University, after visiting Stanford. The position also carries with it the title of Inaugural Wealth and Wellbeing Fellow, and there’s no teaching until 2018.

The three research projects I have been working on that I am most excited about I unfortunately can’t talk about yet publicly. Hopefully, I will be able to resume blogging later in the fall or winter.

I recently participated in EAGxMelbourne and EA Global, as well as seminars at the University of Melbourne and UNSW.

As for future travel plans, I have some things scheduled in North America (Berkeley, NYC, Princeton, Chicago) and, later on, Hong Kong and Singapore. Let me know if you are nearby one of those areas and interested in meeting up.


Research funding committees

I am glad to be part of the Social Science Meta-Analysis and Research Transparency (SSMART) review committee for its next round of projects. A total of $230,000 is available in grants of up to $30,000.

I am also excited to be on an oversight committee put together by ACE to assist their newly hired research officer Greg Boese in making decisions about which research to fund. TJ Mather and Maxmind have pledged $1,000,000 over the next three years to investigate the most effective ways to help animals, an area in which very little research exists. The people on the committee are very impressive and I look forward to working with them!

Research committees can be fun, because you get to stay apprised of all the new and exciting projects before they happen. They are also a great form of effective altruism; 80,000 Hours often recommends the somewhat similar work of a foundation grantmaker.

I am very excited to see what comes out of these initiatives.


Using machine learning for meta-analysis

AidGrade is starting to use machine learning to help extract data from academic papers for meta-analysis. This is a big deal – meta-analyses tend to go out of date quickly because data extraction is such a time-intensive process and new studies are constantly coming out at an ever-increasing rate.

AidGrade will use its existing database of impact evaluation results to help build and validate models. For each extracted piece of information, it will also generate a probability that the information is correct.

At the very minimum, this will reduce the amount of time it takes to identify key characteristics of studies, such as where they were done and which methods they used. It is also the only way to ensure that meta-analyses are perpetually updated as new studies come out. Given that the methods should be scalable to much of economics, education, and health (think of a ScienceScape for meta-analysis – they have catalogued 25 million studies, a number which one would definitely need machine learning to process!), it will build this tool in a general way so that its results can be used to inform policy even in developed countries.

To support this, AidGrade has a new crowdfunding campaign. Please share and contribute.


How much does an impact evaluation improve policy decisions?

Thanks to excellent feedback, I’ve extended my generalizability paper to include discussion of how much an impact evaluation improves policy decisions.

Results, in a nutshell: the “typical” impact evaluation (of a program with a small effect size, compared to an outside option that also has a small effect size) might improve policy decisions by only about 0.1-0.3% (of a small amount). If the outside option is much different (say an effect size of 0) and it is one of the earliest impact evaluations on a topic, this can go up to 4.6%.

There are a lot of caveats here, chief among them that an impact evaluation provides a public good and many people can use its results.

Nonetheless, personally, I find this sobering. I don’t think we’re usually in that best case scenario. These aren’t the results I want, but they are the results I get.


Veganism, Part 2: Psychological biases and food choice

Part 1 here.

I’m vegan for all the usual reasons. If you think you have an objection, I encourage you to look it up and see if someone’s addressed it here — they have a lot of common responses.

That’s not the topic of this post. This post is about how to make it easier, building from the psychology literature. I suspect the main thing holding people back from going all the way (or going vegetarian) is not that they don’t agree it’s a good thing, but that they feel it is hard. It would be silly to think that our food choices are immune to psychological biases, so it’s worth exploring why it might be easier than you think.

First, I think projection bias plays a large role here, in that people don’t realize how much their tastes will change, and tastes are mostly endogenous. There’s a reason that if you ask people what their favourite foods are, they will often point to food they ate when they were a child or food from their hometown. For most people, the example of switching between 2% and skim milk also seems to resonate: whichever one you were previously drinking, if you switch it will taste disgusting at first, but in time you get used to it, and in fact if you were to try to switch back you would find your original choice disgusting.

Recognizing that every choice you make is not just a choice about what to eat today but a choice about what you will want to eat in the future helps. It provides a very motivating and positive framing.

Another bias people have is that they think of all the things they know that they like to eat, and they have a hard time picturing what they would eat if they went vegan/vegetarian. A nice analogy I heard: people go around with a box their whole lives, and every time they find something they like, they put it in the box. When they get to the point of considering veg*nism, they compare their box of stuff with a new, empty box of what they would eat if they were veg*n. But it’s not a fair comparison, because they have had a long time to accumulate all those things in their box, and if they went veg*n, in time they would find other things to add to the veg*n box (although there would be a temporary cost).

Another thing that I think can be helpful psychologically is reframing the action so that instead of reducing meat, one adds vegetables/etc. If you add enough good, tasty stuff, you’ll be more likely to crowd out the rest.

The bigger problem, my fellow vegan economist friend Josh suggests, is information aversion. Also, the tendency for people to be inconsistent and biased when it would help them avoid cognitive dissonance. People are less likely to assign farm animals moral weight when they are hungry or after they were served meat. Josh, Emiliano and I are planning more research in animal advocacy.


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