Comment on WebMD forcing employees back to office. "We aren’t asking or negotiating at this point. We’re informing"

• BlueLineBae@midwest.social ⁨10⁩ ⁨months⁩ ago

  That was a good article with lots of great sources. But my tinfoil hat is telling me that all of these large companies demanding return to office aren’t doing it because “beliefs”. They’re doing it because the people have gained too much power in the last few years and this is the strategy to put them back in their place. Lay people off in hordes while interest is skyrocketing and demand more from them if they want to keep their jobs. Make them afraid and vulnerable so we can control them better. But again… that’s the tinfoil hat talking.

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  • Big_Boss_77@kbin.social ⁨10⁩ ⁨months⁩ ago

    Definitely makes sense...

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• canihasaccount@lemmy.world ⁨10⁩ ⁨months⁩ ago

  Beyond self-reports and perception-based outcomes, most extant studies that I’m aware of have found decreases in real output. For example, a randomized controlled trial by the NBER found that productivity of employees randomly assigned to work from home was 18% lower than employees randomly assigned to work in the office:

  www.nber.org/papers/w31515

  Another study found that output decreased by around 13% when employees worked from home, even though hours worked increased:

  www.journals.uchicago.edu/doi/full/…/721803

  Cognitive performance may also decline in remote settings:

  academic.oup.com/ej/article/132/643/1218/6445994

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  • Tar_alcaran@sh.itjust.works ⁨10⁩ ⁨months⁩ ago

    Another study found that output decreased by around 13% when employees worked from home,

    Ah this paper again.

    If you look at the paper itself (especially graph 1B) and if you look at normal output levels for basically any complex job, you’ll see that this conclusion can easily be reworded as “during WFH, output reduced to levels equal to those of a year before, continuing a trend that had started months before Covid/WFH”.

    It’s basically a useless measurement, and the authors themselves even suggest they should maybe compensate for periodicity, but then don’t do that.

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    • canihasaccount@lemmy.world ⁨10⁩ ⁨months⁩ ago

      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.”

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      • Tar_alcaran@sh.itjust.works ⁨10⁩ ⁨months⁩ ago

        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.

        Because I don’t have access to that one. It could be amazing, it could be shit, I don’t know.

        1B isn’t the graph of productivity; 1C is.

        Good thing I was talking about output then, and not productivity!

        You can’t just look at a graph, either–you need statistics.

        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.

        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.

        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.

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  • FlyingSquid@lemmy.world ⁨10⁩ ⁨months⁩ ago

    Cognitive performance may also decline in remote settings:

    Your claim is you’re stupider when you work from home? And you’re basing that off of online chess tournaments?

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    • canihasaccount@lemmy.world ⁨10⁩ ⁨months⁩ ago

      No, it isn’t; performance != ability, and it’s not clear that cognitive performance declines at all–hence the word “may” mean to you?

      My claim is that

      Cognitive performance may also decline in remote settings

      Nothing more.

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      • Chefdano3@lemm.ee ⁨10⁩ ⁨months⁩ ago

        So your claim is pure speculation and has no real merit. Yes, I agree.

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• SpaceNoodle@lemmy.world ⁨10⁩ ⁨months⁩ ago

  *buyin’ large

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  • synae@lemmy.dbzer0.com ⁨10⁩ ⁨months⁩ ago

    fuckingcapitalists

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