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.
Changes in group size and tracking: Berkeley High School (BHS) tackled within-school segregation and friending bias by assigning students to small, intentionally diverse ‘houses’ or ‘hives’ in the ninth grade. This approach focuses on the way students are tracked and the size of their groups to encourage more inclusive interactions.
New domains for interaction: Programs and venues promoting cross-SES interactions can help reduce friending bias. An example is the Boston gym Inner City Weightlifting, which recruits personal trainers from lower-SES backgrounds to coach affluent clients. This approach flips power dynamics, bridges social capital, and fosters genuine inclusion. Peer mentoring programs and internship opportunities can also contribute to reducing friending bias.
Restructuring of space and urban planning: Lake Highlands High School in Texas identified its building architecture as a barrier to cross-SES interaction. A large-scale construction project created a single cafeteria and more spaces for all students to interact, encouraging encounters between students from different social groups. Architecture and urban planning can play a role in reducing friending bias outside schools through social infrastructure, public parks, and public transit.
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?
For other outcomes [other than increasing economic mobility], other social capital indices that we construct here may be stronger predictors. For example, differences in life expectancy among individuals with low income across counties are more strongly predicted by network cohesiveness measures (clustering coefficients and support ratios) than EC [economic connectedness].
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.
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.
Changes in group size and tracking: Berkeley High School (BHS) tackled within-school segregation and friending bias by assigning students to small, intentionally diverse ‘houses’ or ‘hives’ in the ninth grade. This approach focuses on the way students are tracked and the size of their groups to encourage more inclusive interactions.
New domains for interaction: Programs and venues promoting cross-SES interactions can help reduce friending bias. An example is the Boston gym Inner City Weightlifting, which recruits personal trainers from lower-SES backgrounds to coach affluent clients. This approach flips power dynamics, bridges social capital, and fosters genuine inclusion. Peer mentoring programs and internship opportunities can also contribute to reducing friending bias.
Restructuring of space and urban planning: Lake Highlands High School in Texas identified its building architecture as a barrier to cross-SES interaction. A large-scale construction project created a single cafeteria and more spaces for all students to interact, encouraging encounters between students from different social groups. Architecture and urban planning can play a role in reducing friending bias outside schools through social infrastructure, public parks, and public transit.
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.