I'm finishing a PhD in Sweden this year and seeking a post-doc in the United States (atmospheric remote sensing). It is clear that a job in academia is not a nine to five job; nor do I want it to be. I often work late in the evening when I'm on a productive spur. When an important dead-line is coming up, one needs to work harder, and there is no overtime paid. I accept that. However, no matter how much I'm interested in my research, I do enjoy and need a reasonable amount of spare time, too; relaxing on the weekends, occasionally a long weekend away, and sometimes a longer trip, such as three weeks in the wilderness.
Regarding the normal work ethos for early-career academics in the United States, I have a hard time judging what is normal and what is excessive. Some examples:
- Erick Carreira letter warning post-docs that he expects all of the members of the group to work evenings and weekends.
- My apparent naïveté in believing that a sabbatical means not working, despite Wikipedia describing it as a rest from work, or a hiatus, often lasting from two months to a year. I was thinking of my friend, who spent a year between his PhD and his first post-doc travelling from France to Mongolia mostly on foot.
- Someone commenting I am not a good role model, but when I don't work on a Sunday, I count that as a vacation day.
- NASA postdocs having no employment-related benefits such as paid vacations, sick leave, or unemployment compensation.
- Question How do we end the culture of “endless hours at work”?
We don't have such a work-ethos where I'm at. I belief that working too hard risks stress and burn-out, and does not increase productivity in the long run, nor human well-being. I want to do science. Doing science makes me happy, but having time to relax while not doing science is important for me.
Does the selection of examples I gave above represent a normal situation in U.S. early-career academia? Should I expect an attitude where asking for a 3-week holiday during the summer is considered as being not serious, or is the situation in practice usually not as bad as the examples above make it sound? How hard to early-career academics in the United States work, really?
Answer
TL; DR: 51 work hours a week with 12 vacation days a year
It is very difficult to assess how hard someone works. It is relatively easy to quantify the input (number of hours worked and the number of vacation days taken) and the output (papers and grants). In 2003 the Sigma Xi post doc society in the US began collecting data from 7600 post docs across 46 different US institutes about a number of issues including hours worked, vacation days taken, papers published and grants submitted. A summary report Doctors without Orders is available. Aggregate data is available via the Wayback Machine. I believe this is the best data set available to answer questions regarding the input and output.
The self reports suggest 12 days of vacation a year and 51 hours a week on average with 25% taking less than 7 days and working over 60 hours. The self report of the publication rate is around 3 with one grant application.
While self reports of hours work and publications are potentially biased, they might be a better measure of the perceived "hardness" of work than the actual hours worked. Obviously it would be nice if the publication and work rates were objectively verified. Obviously, publication rate and hours worked may not be the best metrics of how hard someone is working. This study found that alcohol consumption was negatively correlated with output. It is not clear if high alcohol consumption is positively or negatively correlated with how hard one works, but it might be relevant. Finally, Forbes has a report that University professors have the least stressful job, so maybe despite the hours, we don't actually work that "hard".
The comments to the questions suggest that understanding the input/output function would be desirable. This should be possible from the raw data of the Sigma Xi study, but not from the aggregate data, to determine if the inputs (hours worked) predict the outputs (papers and grants). I would be surprised if there was not a strong correlation, but also wouldn't be surprised if there were a number of outliers (i.e., lots of input and little output and little input and lots of output). Now, publication rate may not be the best measure of output as it doesn't consider quality. A psychology study found that impact factor is not correlated with publication rate suggesting that quantity and quality, as dubiously assessed by impact factor, are not correlated.
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