EdMathieu

Head of Data & Research @ Our World in Data
1176 karmaJoined Nov 2021Working (6-15 years)Paris, France
edomt.github.io/about

Bio

Head of Data & Research at Our World in Data

GWWC pledge (10%) since 2018

Get in touch: email, LinkedIn

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32

I agree that things could work like this in theory, but I see two significant issues with how you describe it.

First, the process isn't as simple as "charities are created; the ones proven effective easily and regularly get money; the ineffective ones run out of money and disappear". That resembles the perfect competition model in economics: something handy to reason about the world, but that simplifies reality to the point of hiding many complexities. In reality, many ineffective charities survive for decades, while promising ones sometimes struggle to find the funding they need. These imperfections are one of the very reasons why effective altruism was first conceptualized.

Second, even if this ideal model was true, equally-skilled people still respond differently to risk. For example, in practice, there's a significant difference between being able to say to a potential hire:

  • "Right now, we only have money to pay our staff for less than a year, but our charity is provably effective, so there's nothing to worry about."
  • "We have 2-3 years of financial runway. Beyond that, we're confident we'll find more money, though we can't have 100% uncertainty."

It's a recurrent bias within EA to not see much difference between these two statements. EA people tend to be more tolerant to risk in their career decisions, and okay with making big bets that don't always pay out. They also tend to be relatively young and without kids.

But once an organization grows in size, impact, and ambition, it can't rely forever on risk-tolerant twenty-something EAs. It needs more experienced and senior people to join. And with more experience often come various financial commitments (e.g., mortgage, kids); that's where financial stability can make a big difference.
 

Thanks, Lizka!

In an ideal world, we'd be all over these niches ourselves! We're grateful that our articles are well-received, but we know their format (heavy on text and charts) might deter some people.

Videos are a big one. Kurzgesagt and Vox are the ones that come closest in terms of style and quality. (In my dream world, each article we'd publish would have its own Kurzgesagt-style video.) This niche could comfortably accommodate several players if they're ready to meet the high production quality threshold needed.

Regarding books, I'm not so sure about a distinct "niche" as such. I think it comes down to the author's style. Some books (Factfulness is an obvious example, and my colleague Hannah Ritchie's upcoming book will likely fall into this category, too) are essentially doing this already.

For the "tool" niche, I think Tableau, Datawrapper, and the like have it reasonably well covered. There's room for more innovation, but the barrier to entry is higher if you want to offer something genuinely creative and competitive.

The biggest niche I can think of would be "news reporting in the style of OWID". I think there's a great need for this; something that combines OWID's editorial style, high standard of research, and reliance on evidence and data, but applied to current events (like the situation in Ukraine or the migrant crisis).

Vox's reporting style, or what the FT and Economist data teams do, comes somewhat close to this idea. FiveThirtyEight is another comparison, although their focus has primarily been on US politics and sports rather than global issues. (And unfortunately, given recent layoffs there, the future doesn't look too bright for them.) Still, I believe there's a niche for a more prominent, more focused player to emerge in this area.

Within the OWID team, there's a mix of enthusiasm and skepticism about forecasting. Many of us see it as a promising tool for a more evidence-based understanding of the world, while others express reservations. Much of this skepticism stems from the fact that, often, forecasts lack clear justifications. While the raw forecast is presented, many sites and projects fail to thoroughly explain the reasoning behind these projections. To make forecasting more valuable and accessible, we believe this aspect needs significant improvement.

For now, we're not planning to start publishing forecasts ourselves. It's quite a significant and potentially risky step, not to mention it being quite outside our core expertise. It might even be considered off-brand: people primarily come to OWID for our ability to synthesize the state of knowledge around many issues, not necessarily for us to put forth our own speculative hypotheses about future events.

That said, we've recently collaborated with Metaculus and Good Judgment on projects aimed at forecasting OWID charts. These have been really fascinating projects and served as good first experiments for us in forecasting. We're open-minded about further incorporating forecasting in the future without straying too far from our mission and core competencies.

Hi Lizka – thank you for your thoughtful question!

Our direct engagement with policymakers is somewhat limited, but we do have occasional opportunities to present our work to large international organizations like the UN and WHO. And we know from testimonies and occasional public reports that OWID is also considered very helpful by policymakers at the national level. We know that policymakers, or their aides, value the clarity and conciseness of our work. OWID's approach allows them to comprehend the broader picture quickly, which we believe is mainly due to what we now label as "key insights". This overview provides an immediate understanding of a topic without diving into specifics.

When a more detailed analysis is necessary, our platform allows policymakers to drill down into the data, explore specific time series, and interpret detailed data points. This functionality is beneficial when policymakers want to understand what the data implies, or perhaps bring charts to a meeting, without necessarily jumping to conclusions.

As for bottlenecks in evidence-based policy similar to those in forecasting, we've identified "technical text" as a significant challenge. By technical text, we mean all the information that needs to be presented alongside a chart to make sense, be accurately understood, and be placed into a broader context. This could mean explaining key terms, linking to in-depth articles, discussing the data source, the data's age, and its limitations, etc. We strongly believe that many of our charts could be misunderstood or even misleading without this accompanying text. It's in this space that we feel we bring added value, in contrast to chart-catalog websites like Statista or, to some extent, Wikipedia, which provide the raw data but often lack in-depth explanations.

So, while data is indeed powerful, it's the contextual, nuanced information that often determines the effectiveness of data-based approaches in policymaking.

Hey James – great question, thanks!

100% of the content we publish is planned, decided, and created by our team, without direct input from funders or donors.

Generally, we work hard to convince funders to give us unrestricted grants. But some grants we receive are restricted, which means they are tied to a list of deliverables. When we've accepted restricted grants:

  • They've only ever been tied to general, non-specific outputs such as "expanding our work on COVID-19", "producing a Global Health Explorer", "maintaining the content in our SDG Tracker", or "improving our content on democracy". This means funders never tell us how to produce this content, what the data should show, what insights users should learn, what they should think about an issue after reading it, etc.
  • Funders never get to review or influence the deliverables at any point. Grant reports are typically sent once a year, in which we tell funders, "This year, we produced these things as part of the deliverables for this grant", and link to the content live on our site.

The Longview grant was an unrestricted grant allocated to OWID in 2020, which we used for product development across the site (see our 2020 annual report, page 9). Our article on longtermism was published around two years later, and was entirely disconnected from this donation.

(As a slightly pedantic point: in a very vague and indirect way, there's of course a link there: Longview sees OWID as a charity that cares about the long-term flourishing of humanity, and so they gave us money. And because OWID is a charity that cares about the long-term flourishing of humanity, we thought it'd be great to introduce our audience to longtermism. So these things are not entirely disconnected from a sociological point of view. But in terms of money, deliverables, and editorial freedom, we always make sure they're wholly disconnected.)

Better data publishing practices are probably the number 1 answer. My team spends heaps of time importing data that is hard to access and process, poorly documented, or contains obvious mistakes. This applies to virtually every type of data publisher, whether government, big international organizations, NGOs, companies, research teams…

Better data harmonization between governments would also be tremendously helpful. Across many topics, national agencies tend to record and analyze things differently, making the resulting figures hard to compare. Organizations like the UN, WHO, World Bank, and OECD, work hard to bridge the gap between national methodologies. Still, a world where governments would stop reinventing the wheel whenever they need to measure something would be great!

There are categories of data that are indeed still relatively inaccessible. One example is satellite data, which is "gatekept" by technological difficulty, and the existing commercial data is costly. High-quality open-domain satellite data would be an excellent opportunity to measure trends like land use, economic activity, pollution, etc.

Global energy data has also been in a strange situation for the last few years, with the data locked behind a paywall by the International Energy Agency. We've been campaigning publicly for this to change, and there have been encouraging signs from the IEA, but nothing concrete has happened yet.

Hey Ollie – thanks for the question!

I've engaged with a few activist and political communities in the past, primarily around environmental issues and Green politics. My overall take is that I would find it hard today to be part of these communities compared to the ones that interest me today. From what I remember, epistemic practices tended to be very bad, with lots of motivated reasoning, cherry-picking, various biases, etc. It doesn't necessarily mean the people I met were wrong, but how they made up their minds about issues seems very flawed in retrospect. Compared to this, the epistemic quality of Effective Altruism appears to be its main competitive advantage compared to other communities I encountered. Many people in the community are genuinely cause-neutral and truly adopt (or at least try to adopt) a scout mindset.

If anything seems better about these communities, it's the fact that their direct engagement with politics, the media, etc., makes them much more aware of the importance of public relations and not being perceived as bad actors. My perception – reinforced by everything that happened in EA in late 2022 – is that many EAs see public relations as unnecessary (sometimes even bad, when "PR" is used as a derogatory term). I've met quite a few people who seem to think that the way non-EA people perceive EA doesn't matter at all, as long as EA people are saying things that are evidence-based and smart. I believe this is deeply wrong; a community of smart and "very-right" people won't have much impact if it has such a bad image that no one dares involve it in public discussions.

Interestingly, in the case of EA, this dismissive attitude toward image sometimes applies to individuals as well. Both online and at EAG, I've met more people than I expected who seemed to disregard the benefits of social norms, politeness, kindness, etc., and who behaved in a way that seemed to say "I'm too smart to be slowed down by these stupid things". (To be clear, I don't think the majority of EAs are like this at all; but the prevalence of this behavior seems much higher than in the general population.)

Another thing that comes to mind, valued by people outside EA but shrugged off by people inside EA, is institutional stability. From having worked or collaborated with quite a few different companies, political parties, research organizations, NGOs, etc., I think there is genuine value in building institutions on solid foundations. For EA organizations, this relates to many questions people have raised since the FTX debacle: who should run EA organizations? What should their boards look like? What share of board members should be EAs? What share of board members can overlap between very close EA organizations? I think many EAs have shrugged off these questions as boring, but the long-term stability of the overall EA community depends on them.

Funding runway also falls under that category: many EAs reason about funding stability as if every skilled person was happy to work at an organization that could run out of money in less than a year. Again, I don't think this is a good way of planning things out for the long term. This recent post that described NTI as "too rich" for holding more than 1.5 years’ expenditure, is one example of this bad habit.

Thanks for the question, Kei!

When choosing the topics we would ideally cover on OWID, we aim to be quite broad in our approach. Our tagline is that we publish "research and data to make progress against the world’s largest problems" and voluntarily apply a broad definition of the "world's largest problems". We don't try to follow a specific framework or list of questions (compared to how 80,000 Hours defines the highest-priority problems).

But of course, even though we wish we could cover hundreds of important topics, we only have limited resources and must make choices regarding marginal prioritization. Our principles broadly follow EA's ITN framework, although with a slightly adapted version of each concept.

- Importance: is the topic a big problem for the world? Does it kill people, generate suffering (physical or mental), or cause societal instability? Or, on the positive side, does it unlock potential progress for the world, or preserve something valuable?

- Tractability: is there enough quality data on this topic for us to cover it? Given that OWID's mission consists of relying first and foremost on data to explain important issues, we need reliable, accurate, up-to-date data on a topic if we're going to cover it.

- Neglectedness: is the topic accurately covered by other media, publications, or institutions? Do we often spot confusion or misconceptions about it online? Is there good data on a topic ready to be used somewhere, but it's been ignored or misunderstood for lack of good visualizations and presentation?

In deciding how to prioritize our work, I'd say that importance and tractability are filters that make a topic "OWID material" or not. Neglectedness will typically lead us to prioritize something over the rest of our (very long) wishlist.

Hi nalthaus, thanks for the question! Calculating a population-weighted global average for Self-reported life satisfaction is on our backlog of issues, so this will be tackled at some point! We'll most likely add continental averages as well.

Your second suggestion touches on a larger issue that we're often considering: how to give more freedom to users to (dis)aggregate data in a way that we don't want to pre-generate ourselves. "Life satisfaction across Scandinavian countries" is a great example of such a request. We have yet to come up with the right ideas (and resources to implement them!) to solve this problem, but it's on the long-term roadmap of our Product & Design team.

Thanks for the question, Angelina!

The article on longtermism and our content on AI were published in 2022. They've had great success (6-figure page views in both cases). I was particularly happy that we had no negative reaction to either topic, given that both could have seemed outside of our usual coverage for traditional OWID readers.

On longtermism, the reception was very positive. Max Roser's hourglass chart had a Wait-but-Why vibe that made it particularly popular on social media. My (unsubstantiated) impression is that many people remembered that part of the article more than the broader presentation of longtermism. But if we want existential risks to be taken more seriously, getting more people to adopt a broader perspective of humanity's past and future is probably an essential first step, so I'd say the article was very beneficial overall. Another nice aspect is that it was well-received in longtermist circles; no one seemed to think we had neglected or distorted any angle of the topic.

On AI, the impact has been more immediate. We published a new topic page, 5 articles, and 29 charts late last year. We were delighted that we could give a platform to the excellent data published by Epoch and that it was much more widely seen because of it (both on our site and in re-uses, e.g., in The Economist). Reactions to the 5 articles seemed very positive as well; "Technology over the long run" and "The brief history of artificial intelligence" were the most shared among them.

The most significant limitation is that this was all published just a few weeks before the ChatGPT/GPT-4 craze started. If anything, we're even more convinced now than at the time that AI is one of the world's largest problems, and we're working on an interim update of our content.

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