Who Counts? Counting Workers In The Gig Economy

Who Counts? Counting Workers In The Gig Economy


SPEAKER: The following is
a presentation of the ILR School at Cornell University. ILR– advancing
the world of work. [MUSIC PLAYING] LOUIS HYMAN: So good evening. Welcome to the second season
of the future of work, here, at the Cornell
University’s ILR School. I am Louis Hyman I’m a
professor here at Cornell. I am also the director
of the Institute for Workplace Studies,
which is the Center for the Study of the Future
of Work at Cornell University. And like you, I
have many questions about the future of
work that I think should be answered
with something called data, facts, reality. What’s up with that? Like how big is
this gig economy. Who works in it? Why do they work in it? Why is it so hard to know what’s
going on with all those Uber drivers and delivery
people GrubHub, or even how many people
simply work a freelance job? And you think to yourself,
well, doesn’t the government track that? What’s wrong with those people? Why don’t they just tell
us what they’re doing? And so tonight, we’re going
to get to the bottom of it. We’re going to find out
exactly what’s going on. And we have someone
with us tonight who, for labor economists and
for data nerds is a celebrity. Well, for regular people, you
think, oh, Ariana Grande, you think, The Rock. Well, for us it’s Erica Groshen. And we have her here tonight. She was the Commissioner of
the Bureau of Labor Statistics until just a few
months ago, which is that government agency
that is responsible for all of those number that tell us
exactly what the workforce is doing. Or at least, that’s
what they try to do as much as
humanly possible. Now, in her own research
as an economist, she’s worked on all
the important issues like jobless recoveries,
male/female wage differences, wage volatility,
internal career paths, and other similarly
relevant questions about our contemporary economy. So while we call
here a data nerd, she’s actually someone asking
the deepest, most important, most crucial questions
about how we work today. And while she was at the
Bureau of Labor Statistics, she pushed for better numbers
around this workforce. And she’ll explain tonight what
we know, what we don’t know, why we don’t know
what we don’t know, and what we can do about it
in the next couple of years. So please welcome Erica Groshen. [APPLAUSE] ERICA GROSHEN: Well,
thank you, Louis, for that warm introduction. I’m, as many of
you might know, I’m actually visiting the
ILR School this year. So it’s both, an opportunity
to get to know better all of the work that’s taking
place, here in the ILR School, and I really think
it’s going to be an interesting and
productive year. Because you know,
the labor market is the largest, the most
complex market in our economy. 60% of costs in this
country go to labor. So it is the biggest market. And you know it’s
the most complex because an hour of labor is
one of the hardest things to define– what it does and
what it’s worth. And the ILR School,
where you are right now, is really the broadest
and the deepest assemblage of people who try to
understand that, probably, in the entire country. This really is a premier
place for understanding that. And right here,
in New York City, this is the arm
of the ILR School that reaches in to talk to
the people of New York City. So it’s a special place to be. And I’m honored to be
able to be here this year. What I want to do today is
talk to you about this question that people have been asking,
certainly, the whole four years I was at the BLS
people were asking, what about the gig economy? You know, what are you doing
to count the gig economy? How are you doing it? Why do we know? And what I want to go
through is, first of all, you can’t count
something without knowing what you’re counting. So what are the gig jobs? What does that term mean? Let’s define it so that, at
least in our conversation today, we’ll know what
we’re talking about. We’ll then, talk about why it
is we’d want to measure them, what we know so far,
what we’ll know soon. So although, there’s some
things we don’t know yet, we’re going to get some
crucial information very soon, and talk about what
else we need to know. And I’m actually then going
to tell you how you can help. Because there’s an important
role that you can play. So let’s get going. I want to spend a little time
talking about the BLS mission. BLS is the Bureau
of Labor Statistics. And the reason
that I’m doing that is partly because, well,
old habits die hard. I’m used to starting my talks
by telling people about the BLS. But even more than
that, so that you can understand how important
it is to measure things like the gig economy to
satisfy the mission of the BLS. So the BLS is this agency
that was started actually– I often ask this–
anybody know– nobody from the BLS is allowed
to answer this question. I have some my former
colleagues here. But anybody know when
the BLS was created? I’ll give you one clue. It’s the– AUDIENCE: ’25. AUDIENCE: ’20 ERICA GROSHEN: OK. It was the first statistical
agency created in the US. And it was earlier in
1920 or 1925 or that. [INTERPOSING VOICES] LOUIS HYMAN: Don’t worry. You’re not being graded. ERICA GROSHEN: All right. So it’s 1884. And the reason is
because this was a time of huge industrial unrest. Employers and nascent unions
were killing each other in the streets. And the policy-makers
of the time decided that they’d be one
step closer to restoring peace in the country if
the two sides were working from a common set
of gold-standard facts. So the BLS was created to
measure labor market activity, working conditions, price
changes, the kinds of things that employers-employees
were disagreeing about. It collects it, analyzes
it, disseminates this essential
economic information. It started off in the
Interior Department because there was
no Labor Department. Then, when the Commerce
and Labor was created, it went there. And then, when labor and
commerce were split up, it went to the Labor Department. So in some ways, it
is the first part of the Department of Labor. In fact, much of the
Department of Labor was created as
spin-offs from the BLS. Over time, it was appreciated
that the statistical mission had to be very separate
from the policy mission, and that was well
understood early on. But why is it so important? If you’re a data
nerd, sometimes you forget why it is you like
data, why you like it so much. It’s a very simple answer. Because it helps you
make good decisions. That’s why you like it. Because you feel
more in control, and you feel like you’re doing
the right thing for yourself when you have data
you can rely on. And so the mission is to
support public and private decision-making. We, in this country, want
to make good decisions. And we think that people
ought to be able to make good decisions for themselves. But the truth is, they can
only make good decisions for themselves if they
have good information to base those decisions on. So whether it’s families or
companies or policy-makers, they all need this information. So what makes information good? Well, think about data. You’ve got to make
a decision on it. First of all, it’s
got to be Accurate. You’ve got to get it right. It’s got to be Objective,
free from bias. You can’t worry that
somebody is fussing with it. It’s got to be Relevant–
the information you need. It’s got to be Timely–
get to you in time to make those decisions. And it’s got to be Accessible in
the literal sense of the word. You can find it, get
your hands on it. And the figurative
sense of the word– you can understand what
it really means. And if you put
those together, they spell out AORTA, the largest
artery in the human body. Data is the lifeblood
of the economy and, really, a public good. So think about it–
one of the things the BLS does is create
state unemployment rates. Why does it create state
unemployment rates? Well, a lot of money
is doled out to states on the basis of how their
economy is doing or not. And if each state was
in charge of estimating its own unemployment rate, you
might get some funny results, right? And they might not
really be comparable. Social security
benefits– if Congress had to fight every year about
what to do with social security benefits, it’s not
clear they’d even get what little
done that they’re getting done now, right? But this is an
important BLS charge. If the BLS makes a 1/10
of a percentage point mistake in the CPI,
the federal government will overpay or underpay
social security recipients by $1 billion. so that’s the charge of the BLS. Now, let’s turn, then, to the
question at hand tonight– gig jobs. You came here because you have
a sense of what a gig job is. But you probably also
said to yourself, yeah, but what’s in and what’s out
of that bucket, all right? What does it really mean? This is a really vague term. Everybody uses it
because it’s evocative, and because it’s short, but it
has many different definitions. There are many different
components that people might be thinking about– short jobs with many
clients, short assignments from a platform, short
jobs done on a platform, flexible scheduling,
all of these things. I’m going to use it
in a particular way tonight to mean the sum
total of all of that, just nonstandard work, work
that is not wage and salary done for us for a single
employer that’s going to last for a long time. So when you think of what’s
a real job, a standard job, this is everything
that’s not that. And I’m going to
do that, but I’m going to divide it up
in a special way that was devised at the
Bureau of Labor Statistics in the
mid-1990s to try and get a handle on this
nonstandard work. And there are two
basic components that define what it is that
makes work nonstandard. One of them is that you have an
alternative work arrangement. And that’s jargon
that means that you don’t have this single boss. You may have many bosses. You may have no boss. So the alternative
work arrangement is you’ve got something other
than this clear boss who’s paying you a standard
set of benefits in a way that is pretty
standard across the economy. So that’s one part of this. The other part that
people have in mind is that some people
are contingent workers. And the essence of
a contingent worker is that your attachment
to that job is temporary. You do not expect
that you’re going to be doing this for
the foreseeable future. So a temp help job
would be in that. All right. So when you talk about
alternative work arrangements, you’re thinking about
self-employment, independent contractors
being contracted out to a different company. When you’re talking about
a contingent worker, it’s that this may disappear. Now these are not
mutually exclusive. You could be one,
the other, both. When I’m talking about
gig work tonight, I’m going to say you’re
falling into at least one of these categories. When I get to specifics
and start to count people, then I’m going to define
it much more carefully what I’m talking about. But the gig work I’m using is
sort of this catchall category. So why is it that we have
this sense that it’s growing? Well, there are a
number of reasons why people think that this
whole area is growing. One, is that we think
that IT has really facilitated growth
of this kind of work through the matching
ability of IT. Another is we think that
the fast pace of change in our economy
requires employers to be more flexible
than they used to be, more trade, more competition,
more technological change. We also think that this
is a way that employers have found to get around
employment regulations and some of the costs associated with
having regular employees. There’s the story out
there that new generations may prefer gig work. And there’s also just the
growth that there are always some industries where
there was gig work. Those have tended to be growing
faster than the industries that had less gig work. And if I’ve left something
off of this list, I hope you’ll tell me because I
need to keep growing the list. But there are all these
reasons why people have this sense that it’s growing. With the sense
that it’s growing, come the concerns, all right? And the main reason that
people are concerned about this growth in gig
work is because it suggests that we’re entering
a world where risks are being transferred
from employers to workers. So we now have diffused or no
employer responsibility at all to protect workers in ways
that regulation required. So in a standard job,
employers have a responsibility to protect workers by
providing a safe environment, by paying them in a
way that’s regulated, by not discriminating
against them, not harassing them–
things like that. When there’s no employer
or multiple employers, there’s no one taking
that role, so the worker bears the risk of these things. There’s also, there are
concerns about there being less stability for
workers, for their families, for their communities, and
even the financial system when you get more variable
jobs and hours, when you have less social
insurance, like unemployment insurance, workers
compensation, social security. And when you have few or no
employer-provided benefits– health benefits, retirement
benefits, things like that. So it’s a transfer of risk. And also the training role– when there’s a
long-term relationship between the employer
and the employee, then there’s more of
a responsibility borne by the employer for
retraining workers and training them as they
move up in their career. So for all of these
reasons, people are very interested in this
change in the labor market. There are some very
interesting books. The Fissured Workplace
is one of them, but many, many other
books that talk about what it is, these challenges. And then there’s an
interesting paper by two ILR graduates, Seth
Harris and Alan Krueger, saying that maybe we need
another classification for– there are employees, but maybe
there’s another classification. I think they call it
independent workers? LOUIS HYMAN: Yeah. ERICA GROSHEN:
Independent workers, yeah. So what kind of protections
can be offered in that case? All right. So that’s behind, so why do
we want to count gig jobs. This handsome fellow is Baron
Kelvin of thermometer fame– Kelvin degrees, you know? And around the time
that the BLS was formed, he, in much more flowery
words, said something like, if you can’t measure
it, you can’t improve it. He was arguing that we needed
to measure the things that we have to work with. And that’s still true today. So why do we want
to count gig jobs? Because we want to assess
the size of the issue, and because we want to
inform policy and the public about what’s going on. So what do we want to measure? How much there is,
what the trends are, what are the characteristics? So who’s being affected– the
employers, the employees– what kind of impacts,
and, particularly, the terms of employment so
that we really understand them? What’s the compensation–
high-wage jobs, low-wage jobs, hours, benefits, training,
and the implicit and explicit expectations, particularly,
for the duration of work. How long is this
attachment going to be? So that’s, if you’re
going to measure this, what you want to be able
to get your handle on. And I’m happy to say
that the BLS started measuring these sorts of
things beginning in 1995. It was a survey conducted
in a number of years– ’95, ’97, ’99, 2001, 2005– over 100,000 workers each
time, carefully tested questions about their
working arrangements and their satisfaction
with them. And I even show you there, the
cover page of the last one. But you might notice that
2005 is kind of a long time ago, right? That was the last time that BLS
was funded to do this survey. And while I was at
BLS, every year, we asked Congress to fund
BLS to conduct the survey. And Congress decided not to
fund BLS to do the survey. But there’s really a crying
need for this information. So I’m going to talk to
you about the next one. But first, let me tell you
about what we knew in 2005. So the survey focuses on
people’s primary jobs. So if you’re looking
just at primary jobs– now I’m going to
divide up these jobs into those two
kinds of categories that I was talking about– the alternative working
arrangements categories. And say, OK, what percent
of all employed workers fall into these categories? What percent of all
workers, in 2005, were provided by contract firms. And you can see that
it’s about a 1/2%. Work at temporary help
agencies, about 1%, on-call workers closer to
2%, independent contractors were the largest part, 7
and 1/2% of employment. And if you add those
up, which you can, then you get to about
11% of the workforce was, by these alternative
work arrangement measures, part of the gig economy. The other measurement, of
course, is contingent jobs. Now, this is shown
in a different way. Because how
temporary your job is is sort of a continuous
variable, right? It’s not either/or. So depending on how
stringent you want to be, then you can count
more and more people as being temporary
or not temporary. So in terms of all
people who are in jobs that aren’t expected to last– over 4%. But if you want
to narrow it down to people who had the
job for less than a year and expected it to last
less than a year going forward, then you’re getting
down to more like 2%. These numbers aren’t huge,
but they’re not tiny either. That gives you an idea
of where we were in 2005. OK. Also, we have information
on who these folks were. So you look at the
no-boss folks, right, the alternative work
arrangement folks, what you find is that, once
you break those categories up, the demographics
and the satisfaction vary a lot by what
kind of arrangement you’re working under. It’s not the same to be going
an independent contractor as to be a temp help worker. So look at independent
contractors, here, these workers
are more likely to be– than people in
traditional jobs– are more likely to
be older and white. So many of them tend to
be skilled and high-paid. Temporary help agency
workers are much more likely to be young,
female, black, or Hispanic. Independent contractors
like what they’re doing. 82% of them preferred
to a traditional job. Temp help workers–
only one third of them. So very different, depending
on what kind of arrangement you’re talking about. Contingent workers– those
who don’t expect their job to last– are much more likely to be young
than non-contingent workers. And they’re less
likely to be white. If they’re young, they’re more
likely to be enrolled in school than non-contingent workers. So a lot of these contingent
jobs are taken by students. And about 45% actually
kind of like it. They prefer it to a permanent
job, at least, for their needs at the time. So you get this sense
that it’s a mixed bag. But there’s a lot
of good information here, understanding what roles
this is filling in society. Now, 2005, as I’ve said,
is kind of a long time ago. Does that mean we have no
information since then? Not exactly. Every month, the Bureau
of Labor Statistics does the current
population survey. This is a survey that is the
source of the unemployment rate for the country. It’s a survey of 60,000
households every month, asking questions about
the labor market activity. And there are some questions
in this ongoing survey that are not as detailed
and focused on the gig economy as the ones I
just showed you, but have some relationship to gig work. So people are asked, are you
normally a part-time worker? Are you self-employed? Do you hold multiple jobs? All of these
things, you’d think, if there’s a big burgeoning
of the gig economy, you could see pick-up in
those categories, right? So that gives you an idea
of what might be going on. So let me show you
something very interesting. Usual part-time,
multiple job-holder and self-employed
from 2002 to 2017, as a share of
employment in the CPS. Many more people,
part-time workers– so let’s start
with the blue line. That’s the top– starts around
17 and 1/2%, turns down slowly, then the recession
comes, big jump up. And since then, been
trending back down. Certainly, no huge jump. Next is self-employed,
the green line. Around 6%, 7%
trending– not up– down. And multiple-job-holder
essentially flat during this time. Most people find these
lines really surprising. None of them is really
aimed exactly at gig work, although, self-employed we
do consider in that role, but it’s only a part of it. But this is surprising. So what’s going on? We have this perception. These numbers are supporting it. Whoops. Let me go back. That’s right. And that, I was
going to show you something from a different
data source, which is the Current Employment
Statistics Survey. And this is a
survey of employers and asked how many
people do you have employed during
the pay period that contains the 12th of the month. So this is the source
of the jobs counts that we get on the first Friday. If you look just at temporary
help agencies’ employment, you see the big dip
during the recession. You see the recovery afterwards. It looks kind of flat after
that, maybe a minor trend up. But again, no major
explosion of jobs. So how does it square
with our perceptions? There are some explanations
out there for this, and they’re worth going through. I think they’re probably
all partly true. Let me start. First of all, remember
we’re working off a small base for a number
of these categories. The changes– we could
have doubling or tripling. But if you start
from a small base, then the noisiness of
the data could easily be masking what we’re seeing. And it’s really not
that many people yet. So some of these changes just
may be too small to pick up. Another part of the
explanation is certainly that we have more of
this internet matching, but they’re the same jobs. So think of so many of
the Uber and Lyft drivers. They were taxi drivers before. So it was, for many of them,
it was gig work before. Now, it’s for a
different employer, it has an electronic
matching component, but you’re not going
to see a big change in these measures of the job. Because essentially,
it’s not some different. That’s probably
part of the story. Another part is that we’re
looking in the wrong place. I haven’t talked about some of
the other categories of work. And maybe the
growth is in those, like independent
contractors, and also, particularly, workers
that are contracted out to other companies– so the security guard
that works for one company and is placed in a hotel. And then, there’s misreporting
and mismeasurement, that our ways of
gathering this information need to keep up with the times. So let me start with
the misreporting and mismeasurement. There are some
recent studies that suggest that gig work is
underestimated in the CPS. Larry Katz and Alan
Krueger have a study where they took the questions
from the contingent worker survey and from CPS,
and they repeated it in another kind of sample. And then, they probed more to
see whether these questions would pick up gig work. And looking just
at contingent work, they said, well, probably
missing some of the action there. But the real number– our better number–
doesn’t look that much higher than what the
Workers Survey would pick up. And most of the increase there
is actually offline, not. Online and because
it was a small base, it’s really a small
number of people. So not so big on the
temporary job part. But the alternative
working arrangements, they found a much
bigger discrepancy in what the surveys
picked up and what they were able to glean
from their newer questions. And on the basis of their
back-of-the-envelope calculations, they think maybe
over 90% of the post-recession job growth gains were in these
alternative work arrangement jobs. I think that the
CPS is missing a lot of the growth in
self-employment, in particular. Katharine Abraham, former
commissioner of the BLS did a lot of research
in this area, has also been working with
Susan Houseman of the Upjohn Institute, and some people
at the Census Bureau– a very interesting
set of studies. And they’ve been finding out
that many self-employed people report themselves as
employees, and that many fail to report non-employee work
at all, that the questions that are asked don’t get the
answers that we expect and that half of
temp help workers identify themselves as
regular employees in the CPS. So this suggests
that mismeasurement is part of what’s going on. Another part is looking
in the wrong place. So if you go back to
the line I showed you about what was
happening to temp help from the current employment
statistics survey, the Payroll Survey, that’s
just one segment of a larger industry classification called
professional and business services. And an awful lot of
those industries contract work out to other employers. And you can see that this,
if you look at this larger area, here, you see some fairly
rapid growth as a proportion of the economy– from 12 and 1/4%
up to 14 and 1/4% during this period
of 2002 to 2017. So this kind of contracting
for work between companies is clearly a growing
part of what’s going. So I’ve been hinting
that maybe we’re going to be able to get to
some of the bottom of this. The Contingent Workers Survey
was refielded in May 2017. One-time funding was found
by the Department of Labor. Previous administration
found one-time funding for it to be able to do this. This Survey repeated most of
the questions from the past so that there is
continuity, that you can compare what you had
then to what we have now. But two new
questions were added. I’m going to show them
to you in a minute. The results are going to
be available in early 2018. I don’t know exactly when,
but this is the link to it. And as soon as they have
a date for when they’re going to release it,
they’ll, at least, put that up on the website. Let me tell you a little bit– so it’s going to be
very interesting to be able to compare these
things and to look at the two new questions. These two new questions,
which are going to be asked, both about your main job,
and your secondary job are– is that an in-person
job that was obtained through an
internet-based or mobile platform? OK. So to try and get at the
role of computers in this– And then, the
second question is, is your main or
your secondary job a series of online
tasks completed through internet-based
companies. So the first one, an in-person
job is like Uber, that is, mediated through an
electronic matching platform. The second one is
like Mechanical Turk. Any of you can sign up
for Mechanical Turk, and you can get small
amounts of money for complete completing
fairly tedious tasks. So the idea is to
try and measure how much of that
activity there is. Yes So this will be
very interesting. Now, when we get
this survey, we’re going to know a lot more
about the employee side, but we still aren’t
going to know very much about the employer side. And to really understand this
change in the labor market, we need to know about
the employer side. We need to know how their
characteristics relate to their labor supply choices– whether it’s their size, their
industry, their location– how they assess their options. And the reason that we
need to know these things is because, if we want to think
about how policy should react to this change in
the labor market, we need to understand how
it’s operating so that we can improve policy effectiveness,
so that we can understand what the pressure points would
be and any policy change, and how to reduce unintended
consequences so that we do only what we want to do and not
what we don’t want to do. And also, having
information like this will just inform
employers and employees about what’s going
on so that they can make good decisions for
themselves at the same time. And the way to do
this, of course, is to provide
gold-standard data, data that people can trust, that
lives up to the standards that we talked about
in the very beginning. There is, at this point, no
plan for something like this, but there should be. OK. And even on the
worker side, we need to know things that
aren’t going to come out of this version of the
Contingent Worker Survey, right? We need to know more about
people’s non-main jobs than the contingent
workers survey is going to be able to obtain
because it’s really focused mostly on people’s main jobs. We need to be able to
understand better how to elicit full answers about activity. Because I’ve already
told you that we have reason to
think that there are going to be some limitations
on the quality of the data. And we need to understand
what role these jobs are playing in people’s work
lifecycle, their whole work life. This Contingent Worker Survey
is a cross-sectional survey. It doesn’t ask about
people’s past or future. It’s just what are
you doing right now. And why do we need to do this? Because this is very similar
to the kinds of things I was talking about before. We need to improve our incidence
measures to really understand how much of this
there is, and we need to understand
its impact on workers. And doing this is
very straightforward. We need to do the Contingent
Worker Survey regularly. And we need to
have the bandwidth to update it to change the
questions in the way they should be changed and to
be able to merge this data series with other data series
so that we can get at some of the longitudinal questions
and so we can compare this, for instance, with 1099
forms and things like that, so that we can
validate our findings and get more detail
without adding to the burden of
the respondents. So now, here comes your part. [LAUGHING] Statistical agencies don’t
operate in a vacuum, right? I hope everybody here
is thinking about, oh, what is it that I really
need to know about what’s changing in the labor market? And are they asking the
question, the burning question, that’s really important
for my purposes on the job or in my life? When you share those
needs and those questions and those insights with
the statistical agencies, like the BLS, than
they have a better chance of using
limited resources to do the right thing. So it’s this back and
forth, this conversation with the user
community, that helps to ensure that the products do
answer the questions properly. So get to know your
local BLS people. There are five of
them here today. LOUIS HYMAN: Raise your hands. ERICA GROSHEN: Raise your hand. OK. Marty Kohli, raise your hand. Marty is the head of the
New York BLS office, right? Get to know Marty. He’s your new best friend. And he can help
you get the answers to any questions you have, get
you the data that you need. And you can tell him what
it is you need to know. OK. The next thing you
need to do is make sure that the data that
you work with is as good as possible by promoting
participation in BLS surveys. They are voluntary. Every household that’s in the
survey, every company that’s in a survey makes
their own decision about whether to participate. BLS gets the highest response
rates in the business thanks to the work of these
folks here, but it’s not 100%. It’s often in the
80s and the 90%, but it would be even better
if it were closer to 100%. Because every time
there isn’t a response, BLS imputes the answer. BLS is very good at
imputing answers, but the truth is better. You can do something about that. You work for companies. You study in schools. You have friends and family
who get invited to participate in these surveys. You’re a trusted voice. They need to hear from you
that this is really important. Find out if the organization
that you work for participates in BLS surveys. And if they do, thank them,
tell them how important it is, and if they don’t,
get to work on them. Marty can help you with that. It’s really important. A lot of financial institutions
and educational institutions do not participate. Get after them. A very major
educational institution is now participating
again in surveys because I reached
out directly to them and shamed them into
doing it, right? But you can do that too. You can do that from
inside it, right? You’re a trusted voice– do it. OK. LOUIS HYMAN: Was it us? ERICA GROSHEN: I would be
violating a law if I told you. LOUIS HYMAN: Oh, wow. Well, I would never want
you to violate a law, Erica. ERICA GROSHEN: That’s right. LOUIS HYMAN: That’s right. ERICA GROSHEN: OK. LOUIS HYMAN:
Particularly on camera. ERICA GROSHEN: You should
also champion official data whenever you have the chance. If you use the data, then any
time someone impugns that data, they’re impugning your
work and your trust in it. It’s very easy to deflate the
cheap shots, the sloppy work, the nihilism that causes people
to just dismiss statistics. They don’t know what
they’re talking about. They don’t understand
the techniques used. They don’t understand why it
is the data should be trusted. You need to explain
that to them, that this is work done
by career civil servants, it is not subject to
political manipulation. It is state-of-the-art work. It is the best
information out there. You can do more good for
the statistical system by the conversations
you have at the cocktail parties and barbecues
than many of us can do from our lofty perches
because we are not the trusted voices that you are. It’s fun. I guarantee you will enjoy it
because those loudmouths don’t know what they’re talking about. But if you let them
keep on saying it, they’re going to keep on saying
it because it gets the laugh, or it makes them seem smart
or something like that. And if you don’t know
how to defend the data, go to the website. There’s plenty of ammunition. Or talk to your new best friend,
Marty, and he will help you. OK. And then, each of you who
works for an organization that has a leg affairs staff,
and those leg affairs staff are communicating their
needs to the government. You need to make sure that
they understand that you need the data to do the work right. Because they’re used to
going to Capitol Hill and talking about taxes and
talking about regulation. They’re not used to going
there and saying, by the way, don’t cut the budget for BLS. Because if you do,
our company won’t be able to locate its
stores well anymore, or make good financial decisions
anymore, or give good career guidance anymore. They need to hear that from you. It’s actually, probably
the easiest thing you could ever ask them to do. But nobody’s been
telling them to do that. And your CEOs need
to hear it from you too so that it happens. You need to endorse
the budget for the BLS and data-sharing legislation
so that the agencies can share information and make
the statistics even better. And just today at 11:00 AM,
the Paul Ryan-Patty Murray commission issued a
set of recommendations on how to improve
official statistics and evidence-based
policy-making. Every one of those
recommendations needs to be enacted. And it will be the work
of the organizations that you work for
in supporting this that will make a difference. So I hope you’ll find out about
the Ryan-Murray commission, and maybe you’ll have a
session on that one next. LOUIS HYMAN: Maybe we will. ERICA GROSHEN: That’s right. LOUIS HYMAN: I’d love to
have those people here. That would be lovely. ERICA GROSHEN: So you can
do all of these things. I recommend– I actually
tell you, a lot of them are fun to do. I spent four years doing some
of them, and they’re fun. OK. So bottom line– when you
think about the gig economy, make this kind of
division in your mind between the
contingent workers who are in temporary situations. I would maintain some skepticism
about a vast explosion of those kinds of workers. That’s the one that’s hardest
to pick up in the data. On the other hand,
the alternative work arrangements where
there’s no clear boss, that seems like it’s growing
faster, and especially among independent contractors
and contracted out workers. The 2017 Contingent
Workers Survey is going to advance
our understanding of a lot of recent trends. Keep your eyes open for that. But other information
is really still going to be needed on the employer
side, more information on the employees
side, and the ability to match this information
with other information big data sources,
IRS information, so that we can really push
ahead in understanding this. And with that, I want to
thank you for your attention, and I look forward to
all of your questions. [APPLAUSE] LOUIS HYMAN: So thank
you so much, Erica. That was a wonderful talk. And I’m sure we have
lots of questions. I have a question. ERICA GROSHEN: No. LOUIS HYMAN: And since I’m on
the stage, I get to ask first. Then, I’m going to
ask my question. ERICA GROSHEN: Go right ahead. LOUIS HYMAN: Go right ahead. So what’s interesting
to me is that what makes this so
hard to measure doesn’t seem to be the numbers. It’s not math that makes
it hard, it’s words. And it’s words, both,
in how economists and social scientists are
constructing these categories that’s difficult. What is this? It’s so easy to say
do you have a job? And if you have a
normal quote unquote, “full-time permanent
job,” they’re, like, yes, I have a job. But otherwise it’s
much more amorphous. So there’s that
challenge of words. And then, there’s a
challenge of words of people who are actually
working in these ways. So I did this survey last year. And part of the survey had
all these Uber drivers in it. And you look at– they say, I
drive for Uber, I’m unemployed. I drive for Uber, I
have a part-time job. I drive for Uber, I’m a full
time independent worker. And I’m a full-time
employer, who’s my boss? Uber. And part of it is the
words we use to describe ourselves and our identities. And this seems to be one
of the great challenges. How do you think this can
be resolved, going forward with these kinds of survey
methods, which are, themselves, based on a more stable notion
of what these things are, which, right now, things
are very unstable? How do you grapple with
that as a social scientist? ERICA GROSHEN: It’s a
really important question. But it speaks to the
expertise that you find in the social science
community and, in particular, in the statistical agencies. They are experts in
trying to figure out exactly how to ask these
questions so you understand the answer you’re getting. There is an entire process
that they go through to design the questions to make sure that
they’re only measuring what can be measured and that everybody
understands what it is that they’re after, whether it’s– and you need everybody
to understand. You need the respondent
to understand, you need the interviewer
to understand, and then you need
the analyst who gets the data to understand. And so BLS has a
cognitive lab where they– LOUIS HYMAN: –do it. ERICA GROSHEN: Yeah. Oh, yeah. And they go to
all of these steps that you need to
devise questions. So they start out
with focus groups, and then they do testing,
and then they do retesting, and then they’ll run
parallel surveys. LOUIS HYMAN: So this is what
you mean by gold standard. It’s not like when I run a
Google survey that I just mail to my friends from high school. I’m like, hey– ERICA GROSHEN: That’s right. LOUIS HYMAN: Are
you– drive for Uber? ERICA GROSHEN: That’s right. LOUIS HYMAN: Right? Exactly. OK. ERICA GROSHEN: So that’s
one part of the answer that, for it to be
a gold standard, you need that background work. Otherwise, you don’t
know what you’ve got. And even when you
have something, you can’t be sure that it
continues to be relevant if you don’t keep testing it. So that’s part of it. The other part of it
is that along the way, you have to define
what these buckets are. And so that’s some of the work
that the BLS did, and say, when they designed the
Contingent Worker Survey, they did the background
work to say, look, these are the two key buckets– the alternative work
arrangement bucket and the temporary bucket. And we have to talk
about them separately. They are different concepts,
even if we often conflate them in our normal conversation
because they really are separate. LOUIS HYMAN: Because
there’s just so much we’ve talked about,
we’d say, just not normal. ERICA GROSHEN: Right. LOUIS HYMAN: All the
other stuff– not normal– whatever that normal is. We have a lot of questions from
the audience, and some online. So I’m going to take some
questions from the audience. And then, we can go to online. First– yes, thank you. AUDIENCE: Me? LOUIS HYMAN: Yes. And then– just wait a
second for the microphone. AUDIENCE: Thank you. Hi, Erica. Great job. ERICA GROSHEN: Thank you. AUDIENCE: So My name
is Marjorie McFarland. I learned a lot more about
the gig industry, or the gig economy, over the summer in
a program I took at Cornell. And I actually decided
to start a gig company. I landed my first
contract– woo-hoo– a month ago. And we’re charging forward. So my question for you–
in your slide presentation, you mentioned that your surveys
were done starting in ’95, and the last one in 2005. Which industry did you conduct
these surveys in, and why? Thank you. ERICA GROSHEN: This was a
nationally representative sample. So every industry in the economy
is represented in the survey. OK. And that’s intentional to
say where is this happening? LOUIS HYMAN: Another question? Gentlemen back there? AUDIENCE: Hello, Erica. Great job. My name is Jose Torres. I work at the BLS. I’m an economist. I work at the Employment Cost
Index Occupation Requirements Survey as well as the
Employee Benefits Survey. And I love when you mentioned
how 10 basis points in the CPI can cost the US government
$1 billion, yet, to BLS budget is only
$609 million a year. Personally, I see that to be
such a high risk, to only fund the BLS budget by $609 million. How do you feel about that? And what do you think we
can do as American citizens, or as BLS employees, to
let Congress know, listen, we’re taking a big risk by
underfunding this agency and only funding
it by $609 million. Yet, a small issue
with the CPI can lead to such a large expense. And that’s only the
CPI, not to mention the ECI, or the unemployment
numbers, or anything else. How can we emphasize that
to our leaders in Congress? Thank you. LOUIS HYMAN: And the CPI is
the Consumer Price Index, which is the measure of inflation. ERICA GROSHEN: So you
ask a great question. BLS employees, of
course, as citizens, can vote like any other
and have conversations with their friends and families. But officially, they have
to step back from that. However, everybody else here
who is not a BLS employee needs to let their elected
representatives– because they are the ones who
are in charge of transmitting your will to Washington, to make
the decisions on how the budget is allocated– they need to know that these
data are really valuable. The US has not had an
inflationary increase– and it’s had an increase
in prices that it pays and in wages to its workers,
which is a good thing. The prices– wages have
continued to go up, and BLS’s budget has been flat. It’s, in the past seven
years, that’s been a real cut of 14% in BLS’s budget. If it’s flat again
this year, BLS will have to start
cutting programs. It’s already at risk– with certainty, it’s falling
behind in modernization. And the risks of a failure
of the employment situation not coming out on
time, like the CPI not coming out on time because
outdated hardware or software failed, and there isn’t
enough bandwidth to cope with the crisis, is
just rising every day. So these leg affairs folks
who represent your company’s interests need to speak up. And you, as
citizens, need to get in touch with your
congressmen and senators because they are making
those decisions right now. Today, the Senate
Health Committee voted on the market for
the BLS budget for 2018. I actually don’t know
what they decided yet. The House voted
flat funding again. Senate, I don’t know. And too many people
are saying oh, you’re lucky it wasn’t a cut. LOUIS HYMAN: Our
businesses and our markets rely on this information to
make decisions every day. ERICA GROSHEN: They do. And they have not
spoken up for it. They do not speak up for it. And I don’t know why. And if you can come up– that’s another thing you can do. If you can come up, if
you can tell me how, if we can get them to speak up– LOUIS HYMAN: Well, there’s
one more question from online, and then we’re going to have to
break because we’re running out of time. Someone online asked,
the GAO produces estimates of broadly defined
nonstandard [INAUDIBLE] work. In 2015, using a
range of sources estimating about a
third of the workforce. Of course, there’s
other kinds of surveys– the Upwork Freelancers
Union Survey. People talk about this. What would make this
different than those kinds– why is this better,
not to put it in terms that would
be causing you to have a brawl with the GAO. I know there’s a lot
of [INAUDIBLE] and beef between the BLS and the GAO. Why is this better than the
GAO or other kinds of surveys? ERICA GROSHEN: Well, the biggest
difference between the GAO estimate and the
estimates that I’ve shown you is that, in their
view of nonstandard work, they decided to include
all part-time workers as nonstandard. A definition they chose, they’re
very transparent about it. If you add in all
part-time workers, that number gets a lot larger. So that’s the main thing. They chose a very inclusive,
very broad definition. But a lot of part-time workers
have all of the protections that we’ve been talking about. So the BLS decision was
not to include them. But one always could
add them back in. There’s no reason
why you couldn’t. LOUIS HYMAN: So it gets back
to these questions of words and where we draw lines. And we’re still debating that. ERICA GROSHEN: There’s
another subtlety about some of the other
estimates of the size of the– if someone says what’s the
size of the gig workforce, that’s different than what’s
the size of gig employment. So this is a
subtlety about words. But when people estimate the
size of the gig workforce, they want to say,
over the course of the year, what’s
the share of people who never held a gig job? That’s a much larger number
than in any particular week how many people were
working in gig jobs. So one is saying, what share
of the productive activity is kind of flowing
through gig jobs. So that’s more that’s
more the BLS concept. This size of the gig
workforce is one– it’s a legitimate question. But you would ask that if
what you’re interested in is how many people have
an interest in that work. Or if you were,
say, a gig employer, and you wanted to know
how many people you might be able to attract to
do work for your company for small amounts of time
or large amounts of time, then the size of
the gig workforce would be more what
you want to know. LOUIS HYMAN: Well,
thank you so much. This has been fascinating. Before we thank Erica
Groshen for coming tonight, to hopefully welcome you to
the next event, next month. We’re going to have Michelle
Miller who is the co-founder of coworker.org, which is
the first and largest digital worker organizing platform for
bringing together people who work at Starbucks and all
those other kinds of part-time and shift workers that
may or may not be– depending on who you ask– part of this
contingent workforce. But thank you, Erica
Groshen, for coming tonight and explaining to us
the importance of data. I hope we all do reach
out to our representatives to get the truth out
there in the world. And thank you,
everybody, for coming. ERICA GROSHEN: Thank you, Louis. [APPLAUSE] LOUIS HYMAN: And we’re
going to have some more– SPEAKER: This has been a
production of the ILR School at Cornell University. [MUSIC PLAYING]


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