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First, the RCT is a much stronger study. I'm not sure why you're picking a fight 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.

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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.

Columns 7 and 8 explain the log of Productivity, which strongly increases the fit of the regression. The WFH effect is negative and significantly different from zero at all significance levels, irrespective of time controls."