Currently, the EA movement tracks human capital and financial capital, using metrics like “number of engaged EAs” or “amount of money pledged toward EA causes”. But other forms of capital seem as important, less understood, and less measured. Plausibly, part of the explanation for the neglect is measurability bias (a streetlight effect).
Network capital
Network capital is the existence and strength of links between people in a social network. Links can have different forms; a very basic one is the ability to get another party’s attention and time, or tacit permission to reach out to them. Other types can include trust, degree of ability to model the other party, and so on.
It would be good to think about what kind of network capital the movement is lacking, what kind of capital will be useful in future, but also how do we find the shortest paths through implicit networks not available as data?
In some sense the standard EA focus on broad career capital, recruitment from elite schools, and elite expertise already builds a lot of network capital. What seems less clear is the ability to effectively use it to do good.
On one occasion, a small group of EAs went through the list of people Dominic Cummings follows on Twitter, and found that we had met or worked with ⅓ of them.
Example: the EA bet on the civil service. A main effect of EAs entering and climbing the civil service is reducing the rest of EA's distance from power centres. Each EA civil servant is then a network capital multiplier for the rest of us. A counterpoint is that this will tend to reduce the distance from x people to 3 people, but in catastrophes it is far better to go from x to 1. (That is, EA → minister.)
Structural capital
Structural capital is the ability of the holder to absorb resources (e.g. people or money) and turn them into useful things. It takes various forms:
- functional and scalable processes,
- competent management,
- suitable legal status and backing,
- good operations support,
- well designed spaces,
- well written code.
On this framing, it may make sense to ask questions like:
- How much of these forms of capital do we have?
- How is it distributed?
- When we are converting between different forms, or substituting one form of capital with another, what are the conversion rates?
- Are we using the different forms of capital efficiently?
This is a part of series explains my part in the EA response to COVID, my reasons for switching from AI alignment work for a full year, and some new ideas the experience gave me. It was co-written with Gavin Leech.
Thanks! Lately I've also thinking about concepts such as network capital or social capital. More specifically, I've been thinking about Chetty's work on social capital and economic mobility. I think this could be useful to help us think about 'impact-mobility'.
What is impact mobility? If economic mobility is the ability of an individual to improve their economic status (usually measured in income); then impact-mobility is the ability of an individual to improve their impact-status (perhaps measured in QALYs achieved or whatever). Presumably, we want our EA communities to have high impact-mobility.
How might we increase impact-mobility?
According to Chetty's research, the share of high socioeconomic status friends among individuals with low socioeconomic status (SES) is among the strongest predictors of upward income mobility identified to date. His team terms this 'economic connectedness'.
I think something similar could be said of the EA community. The share of high impact-status friends among individuals with low-impact status could be one of the strongest predictors of upward impact-mobility. This could be referred to as 'impact-connectedness'.
In a companion paper, Chetty's team analyse the determinants of economic connectedness.
They show that about half of the social disconnection across socioeconomic lines —measured as the difference in the share of high-SES friends between people with low and high SES—is explained by differences in exposure to people with high SES in groups such as schools and religious organisations.
The other half is explained by friending bias—the tendency for people with low SES to befriend people with high SES at lower rates even conditional on exposure. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces.
So, transferring this to EA, we might want to build communities that expose low impact-status individuals to high impact-status individuals (inter-status exposure), and do it in such a way that friending bias is low. This would result in an EA community with high impact-connectedness, and thus high impact-mobility.
How might we increase inter-status exposure and decrease friending bias?
We can look at what increases economic connectedness to help us think about what might increase impact-connectedness.
Regarding inter-status exposure (the socioeconomic composition of the groups to which people belong), Chetty et al cite several policy efforts we might look at: busing programmes aimed at integrating schools; zoning and affordable housing policies aimed at integrating neighbourhoods; and college admissions reforms to boost diversity on campuses. What might the EA equivalents be?
Regarding friending bias (the rate at which cross-SES friendships are formed conditional on exposure), interventions have been studied less frequently. However, Chetty et al do suggest this is shaped by social structures and institutions and can therefore be influenced by policy changes. They list several examples.
What might the EA equivalents be here?
I was originally skeptical of drawing a direct analogy between economic mobility and impact mobility, but after reading the paper I think the mechanisms seem pretty similar: upward income mobility comes from increased inter-economic-status exposure, which increases the exposure of lower-income people to opportunities outside of their communities and ways to attain them – this shapes aspirations and provides access to these opportunities.
This mechanism seems similar to the process I went through to start doing EA work: I met specifically one person who was doing something really cool and impactful and then realised this was something that was achievable for someone like me. Then I met more people, started a project, and now I'm still doing that.
I think EA equivalents for inter-status exposure could be through things like reading groups, fellowships, and conferences; friending bias can be reduced through activities like speed-friending, mentoring, and meet-ups, but I think there could definitely be more programs to introduce "new EAs" to people doing impactful work. For larger groups, perhaps a coffee roulette would do the trick?
Also, this line in the paper caught my eye:
I wonder if there could be a tenuous analogy from a prediction of life expectancy in this study to something like the longevity of engagement with EA. Highly unsure about this – the mechanisms are likely to be very different!
Thanks for highlighting possible similarities between mechanisms, that's an important part I forgot to cover!
Another inter-status exposure intervention I quite like is the use of EA co-working spaces. I only have vibes to back this up, but I think this is where a lot of the value of our Amsterdam space lies.
That's an interesting point about the relationship between network cohesiveness and longevity of engagement with EA, intuitively it feels right.