# Positly covid survey 2: controlled productivity data

_(This continues data from the survey I did the other day on Positly (some other bits [here](https://worldspiritsockpuppet.com/2022/01/14/preliminary-long-covid-survey.html) and [here](https://worldspiritsockpuppet.com/2022/01/18/covid-survey.html).)_

Before asking everyone whether they had had covid, or have ongoing problems from it, or anything else, I asked them about their recent productivity relative to 2019. I was hoping to minimize the influence of their narratives about their covid situation and how it should affect their productivity on their productivity estimates.

The clearest takeaway, I think: people who would later report ongoing post-covid symptoms also reported being way less productive on average than people who didn’t report any covid.

Perhaps stupidly, I asked people about their productivity in one of three randomly selected ways, to better tell if results were sensitive to misunderstandings, wording or other vagaries of survey taking. While I stand by this for higher commitment surveys, here it meant trying to aggregate a bunch of slightly mismatched claims about how unproductive a year people are having, when I could have just been averaging a column of numbers. And people did give different-looking distributions of answers for different questions, and different numbers of people with covid got randomized into different kinds of questions, so combining them is messy.

The different questions were these:

1.  How productive were you this week, relative to your average in 2019?
    *   More than twice as productive
    *   About 1.5x as productive
    *   About as productive
    *   About three quarters as productive
    *   Less than half as productive
2.  How productive were you this week, as a percentage of average for you in 2019?
    
    (slider: 0-200%; instruction: For more than 200%, just put 200%)
    
3.  Were you much less productive in 2021 than in 2019?
    *   Yes
    *   No

Below are the answers, divided up by question type. Note that the numbers of respondents involved are tiny and things are noisy. For example, the category question only got two long covid sufferers, and one of them was the nurse apparently working 200% as much as in 2019 but fatigued to the point of crashing her car. (In general, productivity varying because of demand as well as supply is an obvious-in-retrospect theme, according to my reading of the long answers to later questions.)

|     | 0-200% slider (average %) | Not ‘much less’ productive (fraction of people) | Category choice (average 1st-5th option, higher more productive) |
| --- | --- | --- | --- |
| **Productivity of demographic** |     |     |     |
| All respondents | 106% | 48% | 3.15 |
| No covid | 108% | 54% | 3.09 |
| Covid | 100% | 31% | 3.28 |
| Covid & not long covid | 109% | 29% | 3.26 |
| Long covid | 70% | 33% | 3.50 |
| **n in demographic** |     |     |     |
| Total n | 85  | 54  | 89  |
| Covid n | 18  | 13  | 25  |
| Long covid n | 4   | 6   | 2   |

(Probably I should have given you the ‘people who had covid more than two months ago’ numbers, but I didn’t and I’m short on time for this, sorry! All the data is available at the end.)

It would be nice to combine all these answers. A natural way to do it might be to assign plausible seeming numerical values to all the answers to 1 and 3 , then average all these values.

For instance, let’s use the following conversion:

Question 1

*   More than twice as productive: 200%
*   About 1.5x as productive: 150%
*   About as productive: 100%
*   About three quarters as productive: 75%
*   Less than half as productive: 50%

Question 3

*   Yes (i.e. much less productive): 80%
*   No: 100%

With these we get:

| Respondents | Average productivity relative to 2019* | n   |
| --- | --- | --- |
| All | 106% | 228 |
| Covid | 106% | 56  |
| Long covid | 89% | 12  |
| Covid & not LC | 110% | 44  |
| No covid | 106% | 172 |
|     | _*According to above described questionable quantification procedure (for those of you skipping to the table)_ |     |

A different thing to do is call everything below 80% ‘much less productive’ then convert everything into fraction of people avoiding that fate:

| Demographic | P(not much less productive) | n   |
| --- | --- | --- |
| All | 68% | 228 |
| Covid | 64% | 56  |
| Long covid | 25% | 12  |
| Covid & not LC | 75% | 44  |
| No covid | 69% | 172 |

The ‘covid & not LC’ category is notably more productive than the ‘no covid’ category in both combinings, which seems interesting. Some things that might be going on:

*   Covid is overall negligibly bad for productivity, and the quarter of people whose lives would have gotten worse lately anyway classify it as ‘long covid’, leaving the other covid sufferers unusually productive. (This being the main thing going on seems inconsistent with other evidence to me, but maybe there’s some of that, or maybe my understanding of the other evidence is mistaken.)
*   People who get long covid are disproportionately those whose productivities were on a less uphill trajectory.
*   People who get covid and not long covid have a relatively productive time because they are freed from avoiding covid, whereas people without covid are often paying costs that are a large fraction of the cost of having covid to avoid it.
*   Maybe people’s sense of their own productivity is higher when they had reason that it might be low.
*   Maybe people with covid but no ongoing problems are disproportionately in households with other people who have some kind of problem from covid (including for instance having died, or having lost income from a brief illness), and capable of helping, and so are being more productive by necessity.
*   This data is so meagre and messy that reading anything into it that’s not about a huge effect is a mistake, and this is already too much thought on the topic.

I tend to think it’s the latter, but if anyone wants to actually do statistics, or anything else with this data:

[Data](https://docs.google.com/spreadsheets/d/1RcGe3ZiizCkSnCPtLE7UiYQr7TdNTxF1kdJpoWM8j-A/edit?usp=sharing)