by Craig Roth | June 9, 2020 | Comments Off on A Field Guide to Interpreting Statistics on Remote Work
The proliferation of statistics on remote work and work-from-home (WFH) has me in a COVID-induced analytic stupor these days. Remote work has received a huge amount of attention during the coronavirus pandemic. The digital workplace vendors I talk to want to know what uptick – and future decline – they can expect. How does this change their total accessible market?
An Informal Guide to Remote Work Terminology
There are many surveys and statistics available, but to properly apply them you need to understand the nuances in how they define what they are measuring. The largest dichotomy is stats produced for policy versus business purposes. Policy-focused stats will include a much larger and more complete picture of work and workers than business stats, which include only people like their customers.
Here are some common contrasting terms and the differences between them:
Remote work vs remote workers: one is the job, the other is the person. Editors and telemedicine psychiatrists are remote workers. Grading papers is remote work for a teaching professor, although the entire job is not designed to be remote. The teaching professor is not normally a remote worker like the telemedicine psychiatrist is.
Remote work vs. working remotely: work designed to be done remotely vs just doing work from somewhere else.
- Remote vs. mobile: This is admittedly splitting hairs, but I have noticed a difference in how these terms are used. Mobile implies moving, so this is often used to refer to workers being able to access bits of information or send quick replies while “on the go”, such as in planes, trains, and automobiles. It wouldn’t be wrong to refer to someone who works in a home office as “mobile”, but it is a less common odd usage of the term.
Remote worker vs. work from home (WFH): “Home” is just one place people can work remotely. Others include coffee shops, libraries, on client sites, or by the pool at a resort (if you’re lucky). So WFH workers will always be a lower figure than remote workers.
Enterprise vs all work: Do the stats include gig workers? Sole proprietors? Cashiers at the corner store? Busboys? Most digital workplace vendors aren’t targeting those types of workers so they focus on surveys of enterprises. But surveys done to guide economic policy need the full scope of employment.
Information work vs. all work: Do police officers count in your survey population? Tradesmen? Surveys for guiding economic policy may be interested in all workers. Those selling remote working technology and services often limit their scope to information workers (where information is the output; admittedly this definition has whiffs of digital elitistism).
Geographic factors: Remote enterprise knowledge work is less common in Asia, so knowing whether the survey includes Asia affects the results. I would expect US, Canada, and Europe stats to be much higher than worldwide stats.
Look for the Fine Print
If you’re reading stats on remote/home/mobile work/workers then look for the fine print explanation since the answers may not be describing the situation you think they are. This is particularly important if you are comparing two sets of statistics. If you’re not careful you may be comparing apple pickers and orange pickers. And if you are publishing such stats I recommend you provide these details if you want your data to be applied correctly:
- Is this a measurement of people, work, or jobs?
- What do you mean by WFH / mobile / remote? Which locales are included: permanent home office, occasional work at home, third places, transitory/mobile places, company owned buildings
- What kind of work / workers are included: information workers, field workers, gig workers, self-employed. For example, are hair stylists, policemen, undocumented nannies, 20 hour per week Uber drivers, migrant farm labor, and active military included?
- Which countries are included?
My takeaway from this ponderous linguistic hair-splitting exercise (other than the fact I seem drawn to ponderous hair-splitting exercises) it is that these terms do not stand alone without explanation. Many authors – even in major news publications – pick the first term that comes to mind without considering if a more accurate term would be preferable. Now that this topic has become of critical commercial and economic importance, I hope the attention paid to the terminology increases accordingly so that we can all form the right conclusions from the mass of data being produced.
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