Why Independent-Worker Data Is So Damn Bad
Try this experiment: Ask a handful of your indie professional friends, “Last week were you employed by government, by a private company, a nonprofit organization, or were you self-employed?”
If your sample goes anything like ours usually do, more than half the respondents will name the organization that was paying them, despite technically working for themselves. Others will ask you to clarify the question before they can answer it at all. “Someone who has worked on a freelance basis for a single corporation could identify themselves as either privately employed or self-employed. So too could a contract worker or a consultant,” write data analysts at NewAmerica.org.
Nevertheless, that black-or-white question is what drives the main reporting about independent work from the Bureau of Labor Statistics, the agency that tracks unemployment rates. Based on responses to it, the March 2016 BLS report counted self-employment at 15.3 million, up 2% over the year before. Yet even our simple experiment—not to mention contradictory survey evidence and explicit critiques—suggest that those numbers are wildly understated, or at least highly questionable.
The government already knows that. As long ago as 2007 it commissioned a “Panel on Measuring Business Formation, Dynamics, and Performance” to study the collection of business data by statistical agencies, with the hope of improving those systems. The panel’s conclusion: “As it stands, the U.S. business data system is inadequate for understanding … the dynamics of firm and job creation … [and] can yield less accurate, potentially misleading, measures of changes in economic activity.”
That’s bureaucratese for “Holy shit, this set-up is screwed.”
Know the saying about how the hardest step toward solving a problem is admitting that there is one? Not true, in this instance. The next step—fixing the data collection system so it provides an accurate picture of economic change and how people work, especially the changing role that indies play—is far harder. It helps to understand a few reasons why.
- The system originated in a different age, for a now outdated purpose. “U.S. government databases are designed to count in ways best suited to measuring the health of big businesses and their macroeconomic impact,” summarize the NewAmerica.org analysts. The assumptions governing data collection in the 1950s weren’t wrong. It’s just that it’s not the 1950s anymore.
- Classification roulette: What the hell’s an “independent,” anyway? Among government data collectors alone, the definitions are a Scrabble game: contingent worker, non-employer business, temp worker, self-employed worker (“incorporated” and “unincorporated”), sole proprietor, day laborer, freelance worker, independent contractor… just for starters. And among non-government survey researchers too, the variations among definitions make apples-to-apples comparisons equally impossible. (How many hours a week of 1099 work qualify you as an indie? Do you count if you’re a hybrid—a part-time jobholder who works solo as well? Each research instrument contains its own unique answers.)
- Again, none of this is news to the data overseers themselves. As the Government Accounting Office explained in relation to just one small obstacle, in this case involving its attempt to tally “contingent workers”: “No clear consensus exists among labor experts as to whether contingent workers should include independent contractors, self-employed workers, and standard part-time workers.”
What statisticians tell you is that “no clear consensus exists” about anything.
- Longitude is beatitude. Sure, the classifications stink—but if you change them you’ll lose the ability to understand changes over time. And what’s economics without an ability to make comparisons? That necessity, more than anything, may be what puts data collectors in a thankless bind. Emergent Research’s Steve King, one of the very best analysts of the indie economy in the non-government camp, told HBR.org’s Justin Fox that despite what you may think, the BLS’s “household survey is really good. I don’t think they’re missing people who are working; they’re just categorizing them using methods they developed in 1950. Changing that survey takes an act of God, because it messes up all the time series.”
Those reasons, of course, help explain why the official data about independent work today are likely so inaccurate. But they also illustrate that there’s nothing sinister or lazy about government efforts to make data collection better and paint a more accurate picture of the evolving economy. It may be that the roadblocks to fixing the government’s statistical system are just too big.
All Workforce Growth Is Indie
In the meantime, those roadblocks suggest why the newer survey-based assessments—such as Edelman Berland’s for The Freelancers Union and Emergent Research’s for MBO Partners—which dig for more detail from respondents about individual work arrangements, experiences, and attitudes, are the most credible current source of insight. (Though even these projects, despite their strengths, require disclaimers, since the organizations that underwrite them tend to have a pro-indie dog in the business-landscape fight.)
We predict that it will be a proliferation of private, independent, non-government data-collection and research sources that will increasingly clarify the changes in the new world of work and their consequences.
That proliferation will happen because, in the end, good numbers matter. Economists and policymakers ultimately rely on them to assess the consequences of existing policies and to decide what new policies to enact. The numbers help tell leaders what levers to pull.
And the numbers matter to whole swathes of the private and social sectors, too. Corporate marketers and entrepreneurs, not to mention social agencies, philanthropic organizations, and educators, are already hungry to understand the emerging population of indies. How many are there? Who are they? What are their needs?
That new taxonomy of work can’t come fast enough.