First, the RCT is a much stronger study. I’m not sure why you’re picking a bone with a correlational paper when there is a causal manipulation that I linked first.
Second, did you actually read the paper? 1B isn’t the graph of productivity; 1C is. You can’t just look at a graph, either–you need statistics.
“For Output, figure 1B, there is no visible monotonic or linear trend, so a seasonal time correction might be more appropriate here. Moreover, average output appears to be slightly lower during WFH.
For Productivity, figure 1C, the graph is more volatile, which is not surprising for a ratio. There is no clear linear time trend before WFH, but some variation from month to month, so a seasonal correction might be more appropriate. Productivity drops visibly during WFH. Finally, figure 1D plots the log of Productivity, which drops considerably after the start of WFH.
To quantify the WFH effect, and to control for employee and team time-invariant variables (via employee and team fixed effects), we now turn to the regression analyses. Informally, the estimates give us average differences in outcomes before and during WFH for the same employee, controlling for team effects (since employees sometimes switch teams) and time trends.
Table 4 reports WFH effect estimates based on OLS regressions for all three outcome variables, plus the natural logarithm of Productivity, in each case with linear and seasonal time trend corrections. All estimates are in line with the visible effects in the raw data in figure 1.
…
Columns 5 and 6 show that both WFH effect estimates on Productivity are negative, but only the estimate with seasonal time trend is significantly different from zero. We prefer that specification, since both the plot and the linear time trend coefficient indicate that a linear trend is not as appropriate. According to this specification, productivity decreased by 0.26 output percentage points per hour worked. Given an average WFO productivity of 1.36, this estimate corresponds to a 19% drop in output per hour worked. This is economically significant: if employees worked a fixed 40 hours per week, this would imply a drop in output of 10.2 output percentage points in a week. In other words, if employees had not increased time worked during WFH, on average they would have completed only 90 of 100 assigned tasks.”
Tar_alcaran@sh.itjust.works 10 months ago
Because I don’t have access to that one. It could be amazing, it could be shit, I don’t know.
Good thing I was talking about output then, and not productivity!
Thanks, I do actually remember a couple of things from when I got my doctorate. And my gripe is with the statistical analysis, or the
One of those things you learn in statistics is that you can’t ignore trends when analysing data. You also can’t just control for external factor just by doing some ordinary least square regression.
Authors are assuming productivity (which is based basically on output / time worked) is seasonal, when the time worked is barely patterned, and output is very clearly not seasonal in the data shown. If it was seasonal, you would expect a roughly similar increase in at the same time each year, say February. What we see is the exact reverse.
There may be periodicity, but seasonality is not at all shown, and there is too little data shown to make any claims about periodicity in output. Say they’re on an 18 month development cycle, and you’re controlling for a winter-dip, you’d be skewing your data hugely for no reason.
In other words, the data wasn’t significant (see emphasis) until they “corrected” for a factor that they haven’t shown exists, and is, in my opinion, completely counterfactual.
And even if we assume the authors are correct, they only show the whole system was less efficient in the first 5 months of WFH, which is hardly a surprise to anyone. They don’t show the cause lies with the workers, even though they constantly use wording that seems to imply that it does. I promise you it wasn’t the coders who were calling for constant meetings.
So to sum up:
This paper completely assumes a demonstrably incorrect time factor, and uses it to create significance where there was none before. And even if you ignore that, they place blame on workers instead of on the system.
canihasaccount@lemmy.world 10 months ago
The RCT is free to access (if you haven’t downloaded more than three NBER papers; if you have, open the page in a different browser). Scroll down on the page I linked and download it via the button.
Statistically, you can control for variables in OLS regression–that’s literally exactly what the model does when you include more than one variable–and, provided that you got your doctorate in anything that uses statistics, I am sure you know that.
Seasonality is one of the more basic economics concepts. The influence of weather and seasonal illness trends on productivity has been shown in a number of studies (e.g., productivity declines during the flu season). The authors didn’t “show” it because it would be like showing gravity in a physics paper. Some things can be assumed. Also, productivity didn’t have a trend, as was stated in the text that I quoted.
You completely ignored the log transformed results, which the authors note were better fit by the regression than the untransformed data, and which showed less productivity in work from home regardless of whether seasonality was controlled.
Personally, I think people should be able to work from home all they want. Productivity isn’t the only important thing in life, nor is it the only important thing to businesses (e.g., retention of top employees is important). I am wholly against WebMD and all other companies requiring employees to return to the office. All I was doing in my comments was trying to clarify the data on WFH and productivity. There are good reasons to continue to allow WFH, but increased productivity is not one.
I’m going to finish my course prep. You can have the final word here; I don’t have time to continue debating anymore.