I wanted to figure out where EA community building has been successful. Therefore, I asked Claude to use EAG London 2024 data to assess the relative strength of EA communities across different countries. This quick take is the result.
The report presents an analysis of factors influencing the strength of effective altruism communities across different countries. Using attendance data from EA Global London 2024 as a proxy for community engagement, we employed multiple regression analysis to identify key predictors of EA participation. The model incorporates geographic, economic, linguistic, and social factors, explaining 52.3% of the variance in per capita attendance.
Methodology
Data Source
Per capita attendance at EAG London 2024 for 62 countries
Variables
Geographic proximity (distance from London, UK)
Economic development (GDP per capita)
English language proficiency
Income inequality (Gini coefficient)
Youth population percentage
Social media penetration
Religiosity
Trust in institutions
Philanthropic culture
Analytical Approach
Multiple regression analysis with per capita EAG London 2024 attendance as the dependent variable
Key Findings
Model Performance:
R-squared value: 0.523
The model explains 52.3% of the variance in per capita attendance
Significant Predictors:
GDP per capita (positive relationship)
Distance from London (negative relationship)
Philanthropic culture (positive relationship)
Social media penetration (positive relationship)
English proficiency (positive relationship)
Non-significant Factors:
Gini coefficient
Youth population percentage
Religiosity
Trust in institutions
Top 10 Countries with Strongest EA Communities (based on positive residuals):
Switzerland
Norway
Netherlands
Estonia
Sweden
Singapore
Finland
Czech Republic
Australia
Ireland
Interpretation of Results
Economic Factors: GDP per capita emerges as the strongest predictor of EA engagement, suggesting that economic development plays a crucial role in fostering EA communities.
Geographic Considerations: Distance from London negatively correlates with engagement, which is expected given that the event was held in London. This finding should be interpreted cautiously: a) It may reflect the ease of attending for nearby countries rather than overall EA engagement. b) The strong performance of some distant countries (e.g., Australia, Singapore) suggests that highly engaged EA communities can overcome geographic barriers.
Cultural Factors: A country’s philanthropic culture strongly predicts EA engagement, highlighting the importance of pre-existing charitable inclinations.
Technological Infrastructure: Social media penetration positively correlates with EA engagement, underscoring the role of online connectivity in community building.
Language: English proficiency remains a significant factor, though its impact is less pronounced than economic and cultural variables.
Limitations
Proxy Measure: EAG attendance may not fully capture all forms of EA engagement.
Single Time Point: The analysis is based on one conference in one year, potentially missing temporal variations.
Unmeasured Variables: Other factors not included in the model may influence EA community strength.
Linear Model Assumptions: The analysis assumes linear relationships, potentially missing complex interactions or non-linear effects.
Host Country Bias: The UK’s attendance is likely inflated due to hosting the event, potentially skewing overall results.
I’m surprised that the “top 10” doesn’t include Denmark, Austria, Belgium, and Germany, since they all have more population-adjusted participants than Ireland, are not English-speaking, are more distant from London, and have lower GDP per capita[1]
Are we using different data?
In general, I’m a bit sceptical of these analyses, compared to looking at the countries/cities with the most participants in absolute terms. I also expect Claude to make lots of random mistakes.
But absolute terms isn’t very useful if we’re trying to spot success stories, right? Or am I misunderstanding something?
But yeah, something seems off about Ireland. The rest of the list feels quite good though. David Moss said they have some per capita estimates in the pipeline, so I’m excited to see what they produce!
How does it decide country? Just according the name? I tried to find people from Czechia between EAG participants by asking gpt find Czech names and there was a lot of false positives...
I’m curious if you fed Claude the variables or if it fetched them itself? In the latter case, there’s a risk of having the wrong values, isn’t there?
Otherwise, really interesting project. Curious of the insights to take out of this, esp. for me the fact that Switzerland comes up first. Also surprising that Germany’s not on the list, maybe?
I wanted to figure out where EA community building has been successful. Therefore, I asked Claude to use EAG London 2024 data to assess the relative strength of EA communities across different countries. This quick take is the result.
The report presents an analysis of factors influencing the strength of effective altruism communities across different countries. Using attendance data from EA Global London 2024 as a proxy for community engagement, we employed multiple regression analysis to identify key predictors of EA participation. The model incorporates geographic, economic, linguistic, and social factors, explaining 52.3% of the variance in per capita attendance.
Methodology
Data Source
Per capita attendance at EAG London 2024 for 62 countries
Variables
Geographic proximity (distance from London, UK)
Economic development (GDP per capita)
English language proficiency
Income inequality (Gini coefficient)
Youth population percentage
Social media penetration
Religiosity
Trust in institutions
Philanthropic culture
Analytical Approach
Multiple regression analysis with per capita EAG London 2024 attendance as the dependent variable
Key Findings
Model Performance:
R-squared value: 0.523
The model explains 52.3% of the variance in per capita attendance
Significant Predictors:
GDP per capita (positive relationship)
Distance from London (negative relationship)
Philanthropic culture (positive relationship)
Social media penetration (positive relationship)
English proficiency (positive relationship)
Non-significant Factors:
Gini coefficient
Youth population percentage
Religiosity
Trust in institutions
Top 10 Countries with Strongest EA Communities (based on positive residuals):
Switzerland
Norway
Netherlands
Estonia
Sweden
Singapore
Finland
Czech Republic
Australia
Ireland
Interpretation of Results
Economic Factors: GDP per capita emerges as the strongest predictor of EA engagement, suggesting that economic development plays a crucial role in fostering EA communities.
Geographic Considerations: Distance from London negatively correlates with engagement, which is expected given that the event was held in London. This finding should be interpreted cautiously: a) It may reflect the ease of attending for nearby countries rather than overall EA engagement. b) The strong performance of some distant countries (e.g., Australia, Singapore) suggests that highly engaged EA communities can overcome geographic barriers.
Cultural Factors: A country’s philanthropic culture strongly predicts EA engagement, highlighting the importance of pre-existing charitable inclinations.
Technological Infrastructure: Social media penetration positively correlates with EA engagement, underscoring the role of online connectivity in community building.
Language: English proficiency remains a significant factor, though its impact is less pronounced than economic and cultural variables.
Limitations
Proxy Measure: EAG attendance may not fully capture all forms of EA engagement.
Single Time Point: The analysis is based on one conference in one year, potentially missing temporal variations.
Unmeasured Variables: Other factors not included in the model may influence EA community strength.
Linear Model Assumptions: The analysis assumes linear relationships, potentially missing complex interactions or non-linear effects.
Host Country Bias: The UK’s attendance is likely inflated due to hosting the event, potentially skewing overall results.
I’m surprised that the “top 10” doesn’t include Denmark, Austria, Belgium, and Germany, since they all have more population-adjusted participants than Ireland, are not English-speaking, are more distant from London, and have lower GDP per capita[1]
Are we using different data?
In general, I’m a bit sceptical of these analyses, compared to looking at the countries/cities with the most participants in absolute terms. I also expect Claude to make lots of random mistakes.
But of course, Ireland’s GDP is very artificial
But absolute terms isn’t very useful if we’re trying to spot success stories, right? Or am I misunderstanding something?
But yeah, something seems off about Ireland. The rest of the list feels quite good though. David Moss said they have some per capita estimates in the pipeline, so I’m excited to see what they produce!
How does it decide country? Just according the name? I tried to find people from Czechia between EAG participants by asking gpt find Czech names and there was a lot of false positives...
I’m curious if you fed Claude the variables or if it fetched them itself? In the latter case, there’s a risk of having the wrong values, isn’t there?
Otherwise, really interesting project. Curious of the insights to take out of this, esp. for me the fact that Switzerland comes up first. Also surprising that Germany’s not on the list, maybe?
Thanks!