The Open Philanthropy Blog

Note: This post originally appeared in the monthly farm animal welfare newsletter written by Lewis Bollard, our program officer for farm animal welfare. Sign up here to receive an email each month with Lewis’ research and insights into a farm animal advocacy research topic. We decided to cross-post this one because we thought it was especially interesting and wanted to make people aware of Lewis’ newsletter, but note that the newsletter is not thoroughly vetted by other staff and does not necessarily represent consensus views of the Open Philanthropy Project as a whole.

When we think of farm animals, we likely don’t think of carp. But this family of freshwater fish — which includes the three most populous farmed fish species in the world: crucian carp, silver carp, and catla — is likely the most numerous farmed vertebrate animal in the world, with an estimated 25-95 billion farmed every year. (About 62 billion chickens are farmed every year, but each is farmed for just 5-8 weeks, whereas carp are farmed for 12-14 months, meaning far more carp are alive at any given time.)

Fish are the forgotten farm animal. Of the more than 100 undercover investigations that U.S. animal advocates have done to expose abuse of farm animals, just one focused on fish. For a long time scientists questioned if fish could feel pain, though our internal investigation suggests there’s about as much evidence for some fish being able to feel pain as there is for birds.

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A little over a year ago, the HistPhil blog put up a post by Tamara Mann Tweel about a now-published report we commissioned her to work on, regarding the Clinton Health Access Initiative (CHAI)’s role in global price drops for antiretroviral drugs (which can be crucial in treating HIV/AIDS).

The HistPhil post states:

Antiretroviral drugs (ARVs) went down from 10,000 – $15,000 per person per year to $140 per person per year between 2000 and 2005. This price drop inspired governments and international bodies to purchase ARVs and administer therapy to millions of individuals stricken with HIV/AIDS.

While the Clinton Foundation often receives credit for the entirety of the ARV price drop, my report affirmed scholarship that claimed the price drop actually occurred in three stages. The first, from $15,000 per person per year to approximately $1000 per person per year in specific cases, can be attributed to activists persuading pharmaceutical companies to offer philanthropic prices to discreet pilot projects; the second price drop, from approximately $1000 per person per year to approximately $350 per person per year, can be attributed to the active creation of an international generic drug market; and the final drop, from $350 to $140, can be attributed to deliberate market interventions into the generic market by the Clinton Health Access Initiative (CHAI).

As discussed in the full report, this three-stage price drop corresponded to a massive increase in the purchases of antivirals (especially by governments and nonprofits); we haven’t specifically estimated the deaths averted by this development, but feel confident that it qualifies as the sort of hit we’re interested in.

We don’t feel fully confident that any particular funder or nonprofit was crucial to the price drop. The key things we learned from the report were that:

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Last year and the year before, we published a set of suggestions for individual donors looking for organizations to support. This year, we are repeating the practice and publishing updated suggestions from Open Philanthropy Project staff who chose to provide them.

The same caveats as in previous years apply:

  • These are reasonably strong options in causes of interest, and shouldn’t be taken as outright recommendations (i.e., it isn’t necessarily the case that the person making the suggestion thinks they’re the best option available across all causes). Note that interested staff wrote separately about where they personally donated, in this post.
  • In many cases, we find a funding gap we’d like to fill, and then we recommend filling the entire funding gap with a single grant. That doesn’t leave much scope for making a suggestion for individuals. The cases listed below, then, are the cases where, for one reason or another, we haven’t decided to recommend filling an organization’s full funding gap, and we believe it could make use of fairly arbitrary amounts of donations from individuals.
  • Our explanations for why these are strong giving opportunities are very brief and informal, and we don’t expect individuals to be persuaded by them unless they put a lot of weight on the judgment of the person making the suggestion.
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As part of getting started in science funding, we’ve explored several different methods of finding high-impact giving opportunities, including scanning published research, networking in fields of interest, and considering proposals sent to us by people we know. We recently announced four grants totalling $10.8 million that represent another approach: piggybacking on a government grant program designed to find transformative research.

The approach, in brief:

  • The National Institutes of Health has a program specifically for higher-risk, high-impact research.
  • The NIH has been able to fund only a small portion of proposals received through that program. Some projects considered worthy by peer review were ultimately rejected.
  • The NIH sent out a notice on our behalf to all unfunded 2016 applicants, and more than half re-submitted their applications to us. We received 120 proposals in three weeks.
  • We viewed this RFP as a way to both identify high-risk, high-reward projects and to test our hypothesis that high-risk, high-reward research is underfunded in general.
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For this post, some Open Phil staff members wrote up the thinking behind their personal donations for the year. Staff are listed in order of their start dates.

You can click the below links to jump to a staff member’s entry:

Holden Karnofsky

I front-loaded my giving last year, and consistent with that, I am not giving this year.

Alexander Berger

This year, I’m planning to follow the same allocation I did last year:

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We divide our scientific research funding into two categories: neglected goals and basic research. We believe that some research areas are underfunded because achieving the relevant research objectives is underrated by the “broad market” (according to our values). We call such research objectives “neglected goals.”

In 2014, we set a goal to be in a position to identify focus areas in science by the end of 2016. This post explains our initial plan for this work, our original hopes and expectations, what we have done so far, and our plans for work in this area going forward. In brief:

  • Our initial plan was to identify focus areas using a series of shallow and medium-depth investigations, analogous to the process we used to identify focus areas in U.S. policy and global catastrophic risks.
  • We found that our investigations took longer than expected and we felt that they gave us an inadequate basis to declare focus areas and hire specialist program staff to lead our work in those areas. Moreover, we could not envision investigations with acceptable time costs that would form an adequate basis for making such decisions.
  • However, our investigations did, in multiple cases, result in our science advisors’ identifying “standout” giving opportunities: giving opportunities that seemed unusually promising by the standards of the field they were investigating, and strong compared to giving opportunities we’ve seen generally.
  • We decided to pivot to a model in which generalist scientific advisors are given a broad mandate to opportunistically identify standout giving opportunities within about a dozen areas. Rather than investigating each area in depth and choosing a few as focus areas, they investigate one at a time, looking primarily for standout opportunities, and choose which area to investigate based on their subjective estimate of the odds of finding standout opportunities. We’re very excited by the giving opportunities that the science team is finding under this model, and it’s unclear whether it would have been better to use our previous model and hire staff specializing in just a couple of program areas.
  • A spreadsheet summarizing our list of priorities and cause-specific progress so far (listed in alphabetical order) is here.

We are likely to give a separate, shorter update on basic research in the future.1

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This post aims to give blog readers and followers of the Open Philanthropy Project an opportunity to publicly raise comments or questions about the Open Philanthropy Project or related topics (in the comments section below). As always, you’re also welcome to email us at info@openphilanthropy.org if there’s feedback or questions you’d prefer to discuss privately. We’ll try to respond promptly to questions or comments.

You can see our previous open thread here.

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This is the first in a series of posts summarizing the Open Philanthropy review of the evidence on the impacts of incarceration on crime. The full report is available in PDF, Kindle, and ePub formats.

About when Chloe Cockburn joined Open Philanthropy to spearhead our grantmaking for criminal justice reform, I was tasked with reviewing the research on whether reducing the number of people in American jails and prisons might actually increase crime. In effect, we at Open Philanthropy asked ourselves: what if we’re wrong? What if our grantees win reforms that cut the number of people behind bars, and that pushes the crime rate up? How likely is that? And how likely is it that any increase would be large enough to overshadow the benefits of decarceration, which include taxpayer savings and expanded human freedom?

It may seem strange to launch a grantmaking program even as we question its empirical basis. But Open Philanthropy had already invested significant time in studying criminal justice reform as a cause. And practical decisions must always be made in the face of incomplete information, forcing people and organizations to exercise what Herbert Simon called “bounded rationality.” It can be boundedly rational to act on the information gathered so far, even as we gather more.

The final report reaches two major conclusions:

  • I estimate, that at typical policy margins in the United States today, decarceration has zero net impact on crime outside of prison. That estimate is uncertain, but at least as much evidence suggests that decarceration reduces crime as increases it. The crux of the matter is that tougher sentences hardly deter crime, and that while imprisoning people temporarily stops them from committing crime outside prison walls, it also tends to increase their criminality after release. As a result, “tough-on-crime” initiatives can reduce crime in the short run but cause offsetting harm in the long run.
  • Empirical social science research—or at least non-experimental social science research—should not be taken at face value. Among three dozen studies I reviewed, I obtained or reconstructed the data and code for eight. Replication and reanalysis revealed significant methodological concerns in seven and led to major reinterpretations of four. These studies endured much tougher scrutiny from me than they did from peer reviewers in order to make it into academic journals. Yet given the stakes in lives and dollars, the added scrutiny was worth it. So from the point of view of decision makers who rely on academic research, today’s peer review processes fall well short of the optimal.

The rest of this post elaborates on those conclusions.

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This is the second in a series of posts summarizing the Open Philanthropy review of the evidence on the impacts of incarceration on crime. The full report is available in PDF, Kindle, and ePub formats.

As I explain in the intro post, in thinking about how incarceration affects crime rates, it is useful to distinguish between “before,” “during,” and “after” effects. The “before” effects of incarceration are deterrence: the prospect of jail or prison time may dissuade people from committing crime. Surely this must happen to some extent, but how much at current policy margins, is a question for research. The experimental and quasi-experimental studies I read and reproduced mostly said: not much.

Below, I review research on:

  • Laws criminalizing driving under the influence of alcohol;
  • a mass prison sentence suspension in Italy;
  • whether young people commit less crime as they obtain the age of criminal majority, when they first face the risk of adult-level sanctions;
  • California’s severe “Three Strikes and You’re Out” sentencing law;
  • laws adopted in many states to increase minimum sentences for various crimes, or lengthen sentences for crimes involving guns.

For the last two, I obtained the data and computer code for the relevant studies and analyzed them afresh.

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This is the third in a series of posts summarizing the Open Philanthropy review of the evidence on the impacts of incarceration on crime. The full report is available in PDF, Kindle, and ePub formats.

In my deterrence post, I explained why, in my reading, the research says that stiffer sentencing hardly deters crime in this country today.

In this post, I move from the “before” of incarceration to the “during,” what criminologists call “incapacitation.” Does putting more people in prison markedly reduce crime outside prison walls—at least while those people are still in prison? I think that in writing my full report, I approached the research on this question with just as much skepticism as I did with deterrence. Yet the incapacitation research better withstood my scrutiny. I am convinced that decarceration on the scale proponents hope for measurably increases crime in the short run. (It may do the opposite in the long run, by reducing exposure to the potentially criminogenic influences of prison; my next post investigates that possibility.)

I found six studies that met my criterion of exploiting an experiment or a strong natural experiment. One takes place in Italy, one in the Netherlands, and the rest in the United States. I will briefly describe four, and say more about the two U.S. ones whose data and code availability allowed for replication and reanalysis.

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