Imagine you’re offered a job at The Humane League – one of the most cost-effective animal charities – should you take it?
THL’s has an annual budget of under $100,000. With $50,000, THL is able to save about 700,000 animals from suffering on factory farms.
A 10% improvement to THL’s efficiency means saving an extra 70,000 animals from over 18,000 years of suffering. So, a small improvement to THL’s efficiency is a big deal. The same would go for someone who could fundraise an extra 10% for THL.
Whether you could increase THL’s impact by 10% is a tough question, and given the organization’s extreme efficiency, we have reason to be skeptical. But one issue that bears on the question is the following: how much do different employees typically differ in output?
There have been many studies looking at this very question, across a wide range of jobs, which are summarised in a meta-study by Hunter, Schmidt and Judiesch (1). Output is measured in a variety of ways. For salespeople, it’s the dollar value of what they sell. For doctors, it could be the number of patients seen and treated. Other studies have been done with standardised tests, supervisor ratings and many other metrics (2). I should flag that it’s not clear these metrics correlate with the real value produced by jobs, but I’ll run with them for now.
What they found is that in low complexity jobs, workers’ outputs do not vary much, and the best worker is usually not much better than the average worker. As the jobs become more complex however, there’s more and more variation, and the difference between the best worker and the average grows. For example, in low-complexity jobs the top 10% of workers produce 25% more than the average, and 75% more than the bottom 10%. For high-complexity jobs, such as professional and sales jobs, the difference is much larger. The top 10% of workers produce 80% more than the average, and 700% more than the bottom 10% (3).
Taking these findings literally suggests that the bottom 3% of workers in professional jobs have negative output. Hunter, Schmidt and Judiesch believe this is unlikely, and interpret it to mean that the distribution is not a perfect bell-curve, but instead stops around zero output (it’s probably log-normal instead). I agree that it’s unlikely that someone’s direct output could be negative for long before they were fired. Imagine if a doctor killed more patients than he treated. But if we think about all the indirect effects someone can have on an organisation, like decreasing team morale and consuming lots of supervisor time, then it’s not implausible that some people have an overall negative contribution.
The methods used to measure output often don’t take these indirect effects into account. They tend to focus on what each employee did directly, e.g. the number of insurance contracts sold or the number of patients treated. This would suggest the studies significantly understate the differences in output that really matter.
The crudeness of the metrics will be another reason why the figures might tend to underestimate the true variability in output. For instance, ‘the number of patients treated’ gives some indication of the output of different doctors, but some treatments are better than others. Curing 100 patients of sore throats is not as good as curing 100 patients of cancer. So, there an important type of variation that’s being missed.
So, among charity campaign managers and fundraisers, it would be normal for a good one to be about twice as good as an average one, and many, many times better than a bad one. Being 10% better than your replacement at the top of a charity, therefore, could be quite achievable. And very high impact.
(1) Hunter, J. E., Schmidt, F. L., Judiesch, M. K., (1990) “Individual Differences in Output Variability as a Function of Job Complexity”, Journal of Applied Psychology http://psycnet.apa.org/index.cfm?fa=buy.optionToBuy&id=1990-15949-001
(2) Note that the study does correct for the fact that we can’t perfectly measure output, because we can only take a limited sample of what the worker does
(3) More accurately, output follows a normal distribution (though sometimes with positive skew). For low-complexity jobs, the standard deviation of the distribution is 15%. For high-complexity jobs it raises to 45%. The most variation is found in life insurance sales, where output has a standard deviation of 110%!
Excellent! However the fundraising link doesn’t seem to work