To help communicate this, it may be helpful if organisations published typical ratios of applicants to hires to let people plan accordingly.
This would be helpful indeed! Although I suspect organizations would be reluctant to do this as it might put off applications. On the other hand, it might turn off applications that would not be good anyway and save a lot of work. This data would also be useful for job seekers to decide whether they should apply mostly within EA, or focus efforts on impactful opportunities outside EA (like me). If there are 1,000 applicants for 50 positions yearly, I should not bet too much on getting into an EA org. I think this should especially encourage applicants who don’t have the right skills or knowledge yet to apply outside EA to skill up, instead of hanging around in EA limbo.
I think it is worth thinking more about how the application processes of EA orgs can create more value (for the community). I think it is an opportunity for the organization to help the wider community: reviewing hundreds of applicants is a unique data set. One concrete action is publishing or sending general feedback about what differentiated top applicants from non-top applicants, which skills/backgrounds were in wide supply and which were rare and useful. These can also be referred to for later hiring processes for similar roles. There’s more discussion about this topic in this Facebook post.
it may be helpful if organisations published typical ratios of applicants to hires to let people plan accordingly
While in general I’m a big fan of more data, I worry that in this particular case it will shed more heat than light. I suspect that “ratios of applicants to hires” will be a really poor proxy for how competitive a position is. For example, Walmart in DC has something like a 2.6% acceptance rate.
Further, I don’t think there’s a good way to provide actually useful statistics on applicant quality in a privacy-conscious way. Eg, you can’t just use the mean or the median. It doesn’t matter if the median candidate has <1 year of ops job experience if the top 10 candidates all have 15+.
This would be helpful indeed! Although I suspect organizations would be reluctant to do this as it might put off applications. On the other hand, it might turn off applications that would not be good anyway and save a lot of work. This data would also be useful for job seekers to decide whether they should apply mostly within EA, or focus efforts on impactful opportunities outside EA (like me). If there are 1,000 applicants for 50 positions yearly, I should not bet too much on getting into an EA org. I think this should especially encourage applicants who don’t have the right skills or knowledge yet to apply outside EA to skill up, instead of hanging around in EA limbo.
I think it is worth thinking more about how the application processes of EA orgs can create more value (for the community). I think it is an opportunity for the organization to help the wider community: reviewing hundreds of applicants is a unique data set. One concrete action is publishing or sending general feedback about what differentiated top applicants from non-top applicants, which skills/backgrounds were in wide supply and which were rare and useful. These can also be referred to for later hiring processes for similar roles. There’s more discussion about this topic in this Facebook post.
While in general I’m a big fan of more data, I worry that in this particular case it will shed more heat than light. I suspect that “ratios of applicants to hires” will be a really poor proxy for how competitive a position is. For example, Walmart in DC has something like a 2.6% acceptance rate.
Further, I don’t think there’s a good way to provide actually useful statistics on applicant quality in a privacy-conscious way. Eg, you can’t just use the mean or the median. It doesn’t matter if the median candidate has <1 year of ops job experience if the top 10 candidates all have 15+.