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Making Ends Meet: The Role of Informal Work in Supplementing Americans’ Income

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Data from the Survey of Household Economics and Decisionmaking indicate that, over the course of a month, more than one-quarter of adults engage in some informal work outside of a main job. Of these, about two-thirds say that they do informal work to earn money and about one-third say that informal work is an important source of household income. Informal work plays a particularly important role in the household finances of minorities, the less educated, those experiencing financial hardship, those who work part time involuntarily, independent contractors, and the unemployed. Aggregate earnings from informal work are modest but help many households to make ends meet. Informal work cannot compensate, however, for the lack of benefits typical of part-time and contractor work.
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Katharine G. Abraham is professor of economics and survey methodology at the University of Maryland. Susan
N. Houseman is vice president and director of research at the W. E. Upjohn Institute for Employment Research.
© 2019 Russell Sage Foundation. Abraham, Katharine G., and Susan N. Houseman. 2019. “Making Ends Meet:
The Role of Informal Work in Supplementing Americans’ Income.” RSF: The Russell Sage Foundation Journal
of the Social Sciences 5(5): 110–31. DOI: 10.7758/RSF.2019.5.5.06. We are grateful to Lillian Vesic- Petrovic
for excellent research assistance and to Erica Groshen, Harry Holzer, two anonymous referees, and partici-
pants in the conference on Improving Employment and Earnings in Twenty- First Century Labor Markets for
valuable suggestions on an earlier draft of this paper. Direct correspondence to: Katharine G. Abraham at
kabraham@umd.edu, 1218 Lefrak Hall, University of Maryland, College Park, MD 20742; and Susan N. House-
man at houseman@upjohn.org, 300 S Westnedge Ave., Kalamazoo, MI 49007.
Open Access Policy: RSF: The Russell Sage Foundation Journal of the Social Sciences is an open access journal.
This article is published under a Creative Commons Attribution- NonCommercial- NoDerivs 3.0 Unported Li-
cense.
Making Ends Meet:
The Role of Informal
Work in Supplementing
Americans’ Income
 .   . 
Data from the Survey of Household Economics and Decisionmaking indicate that, over the course of a
month, more than one- quarter of adults engage in some informal work outside of a main job. Of these, about
two- thirds say that they do informal work to earn money and about one- third say that informal work is an
important source of household income. Informal work plays a particularly important role in the household
finances of minorities, the less educated, those experiencing financial hardship, those who work part time
involuntarily, independent contractors, and the unemployed. Aggregate earnings from informal work are
modest but help many households to make ends meet. Informal work cannot compensate, however, for the
lack of benefits typical of part- time and contractor work.
Keywords: informal work, gig work, independent contractors, income adequacy
  
In recent years, widespread media reports have
trumpeted the rise of the so- called gig econ-
omy, characterized by a workforce increasingly
composed of independent contractors, consul-
tants, freelancers, and others in nonemployee
arrangements. Workers in these arrangements
typically provide services for short durations
to clients or customers. The attention focused
on the gig economy echoes a similar interest
in the temporary or so- called contingent work-
force that emerged in the late s and s.
Although some may value the flexibility or
other attributes of nonemployee work arrange-
ments, such workers are not eligible to receive
employer- provided benefits, are not covered by
social insurance programs such as unemploy-
ment insurance and workers’ compensation,
and are not afforded protections under em-
ployment and labor laws. Consequently, there
has been widespread concern that such ar-
rangements put workers at significant risk rel-
ative to those in a more traditional employee
relationship.
Given the widely held belief that the tradi-
rsf: the russell sage foundation journal of the social sciences
   
tional employee- employer relationship is in de-
cline, many were surprised by the findings from
the  Contingent Worker Survey supplement
(CWS) to the Current Population Survey (CPS)
released in June  by the U.S. Bureau of La-
bor Statistics (BLS). The BLS developed the
CWS, first fielded in  and repeated on sev-
eral subsequent occasions, to learn more about
the arrangements under which Americans
work. Earlier findings reported by Lawrence
Katz and Alan Krueger () had suggested
that the prevalence of alternative work arrange-
ments as measured in the CWS grew signifi-
cantly between  and , though more re-
cent work by the same authors concludes that
any increase was much smaller than they had
initially estimated (Katz and Krueger ). The
new CWS data show no increase between 
and  in the prevalence of any of the alterna-
tive work arrangements the supplement mea-
sures—independent contractors, on- call work-
ers, temporary agency employees, and contract
firm employees. In fact, the CWS data show a
slight decline over those twelve years in the
prevalence of independent contractor arrange-
ments, captured by asking survey respondents
whether they worked as an independent con-
tractor, independent consultant, or freelance
worker. This finding was especially surprising
to many, given evidence from tax data and other
financial data suggesting nonemployee work
arrangements have become more common
(see, for example, Farrell and Greig a,
b; Jackson, Looney, and Ramnath ;
Abraham et al. b; Farrell, Greig, and Ham-
oudi ).
A central reason for the apparent discrep-
ancy between the CWS findings and other ev-
idence is the focus of the CWS on the main
jobs held by people categorized as employed
in the basic monthly CPS. Studies using tax
data and other financial data have found that
work done as an independent contractor, con-
sultant, or freelancer oen supplements other
sources of income rather than represents a pri-
mary source of income (for example, Farrell
and Greig a, b; Jackson, Looney, and
Ramnath ; Abraham et al. a, b;
Farrell, Greig, and Hamoudi ; Koustas
; Katz and Krueger ). The CWS was
not designed to capture information about
nonemployee work activity that supplements
a primary job.
Reflecting the perspective that a worker’s
well- being depends primarily on the character-
istics of his or her main job, some have charac-
terized the CWS findings as showing that any
changes in the prevalence of gig and other non-
employee work arrangements are of little sig-
nificance and do not merit the large amount of
attention they have received. Lawrence Mishel
(), for example, describes the new CWS
data as providing “the best measure of indepen-
dent contracting” and throwing “cold water on
those hyping the explosion of freelancing and
the rapidly changing nature of work.” Other re-
search concludes that the growing prevalence
of independent contractor, consultant, and
freelancer work has led to only a modest in-
crease in nonemployee earnings as a share of
total earnings (see, for example, Mishel and
Wolfe ).
Arguably, however, growth in the share of
people who supplement earnings from a main
job or other sources of income with nonem-
ployee work is itself an important development.
Such growth, which by design the CWS will not
capture, may indicate underlying problems
with workers’ primary jobs. In addition, even
in cases in which informal work is a person’s
only work activity, if respondents do not think
of what they are doing as a job, they may not
report it when answering the standard CPS
questions and thus may never be asked the
CWS questions about their work arrangements
(Abraham and Amaya ; Bracha and Burke
). This is an additional reason the picture
painted by the CWS may be incomplete. As doc-
umented in ethnographic studies of low-
income communities, even a relatively small
amount of money from nonemployee work ac-
tivity can make a critical difference to a low-
income household trying to make ends meet
(see, for example, Edin and Lein ; Seefeldt
and Sandstrom ). The value of informal
work to the households engaging in it could be
considerable even if the aggregate amount of
income it generates is modest.
The primary contribution of this article is to
present new evidence on the role of informal
work as a source of income for individuals and
households with different characteristics. Our
    
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analysis uses data from the Survey of House-
hold Economics and Decisionmaking (SHED),
a large household survey sponsored by the
Board of Governors of the Federal Reserve Sys-
tem. In  and , the SHED included a spe-
cial module with detailed questions about var-
ious types of informal work done outside a
person’s main job (Federal Reserve , ).
Given the extensive information the survey col-
lects on demographic characteristics, financial
situation, and employment status, these data
are especially well suited to examining who is
involved in informal work and the role that
earnings from informal work play in household
incomes. We also exploit the limited panel
structure of the survey to examine the persis-
tence of informal work from one year to the
next.
Although the SHED data do not allow us to
make statements about how the prevalence of
informal work has changed over time, they
imply that more than one- quarter of adults
age eighteen and older participated in infor-
mal work for pay during the survey reference
month. Two- thirds of those reporting informal
work say that their motivation is to earn money;
more than one- third say that the money earned
from informal work over the previous twelve
months was a very or somewhat important
source of household income; and just under
one- third say that it usually accounts for  per-
cent or more of their household’s monthly in-
come. Although there is reason to suspect that
the overall incidence of informal work is higher
among respondents to the SHED than in the
population as a whole, informal work nonethe-
less appears to be an important source of in-
come for many who are doing it.
The share of people reporting that they do
informal work to earn money varies consider-
ably across groups based on their demographic,
financial, and employment characteristics. A
disproportionate share of respondents who are
less educated, minority, low- income, unem-
ployed, or financially distressed report working
in informal jobs to earn money. Informal work
to earn money also is more prevalent among
workers who are part time, sole proprietors,
contractors, or consultants on their main job
or who have unpredictable work schedules.
Moreover, informal work appears to be more
persistent and important to household income
among those with these same characteristics.

Despite a widespread perception that nonem-
ployee work has become more common, data
from standard household surveys such as the
Current Population Survey and the American
Community Survey show no upward trend in
self- employment in recent decades. In contrast,
substantial growth in the number of people
with income from nonemployee work is appar-
ent in tax data (Katz and Krueger ; Jackson,
Looney, and Ramnath ; Abraham et al.
b). Based on an analysis of data for a sam-
ple of respondents to the Annual Social and
Economic (ASEC) supplement to the CPS linked
to tax records, one study concludes that roughly
one- third of the growth in self- employment be-
tween  and  captured in tax data but
missing from the CPS- ASEC occurred among
people for whom secondary self- employment
was not captured in the CPS- ASEC and roughly
one- third among people for whom no work-
related income was reported in the CPS- ASEC
(Abraham et al. b).
Findings such as these have contributed to
fears that the questions asked on standard
household sur veys may be missing informal
work activity. Katz and Krueger () report on
responses from a sample of subjects recruited
via Amazon’s Mechanical Turk. They first asked
subjects the standard CPS employment ques-
tions and then asked additional questions to
probe for whether the subjects had done any
work on small paid jobs that they had not in-
cluded in their previous responses. In their
sample,  percent of those not categorized as
multiple job holders (based on their responses
to the CPS questions) acknowledged that they
had done so. Katharine Abraham and Ashley
Amaya () report similar findings, also based
on a sample of respondents recruited via Ama-
zon’s Mechanical Turk. In their study, respon-
dents were asked to report for themselves and
for others in their households. Both for self-
reports and for proxy reports, probing uncov-
ered substantial amounts of informal work ac-
tivity not reported in response to the standard
CPS questions.
The periodic Contingent Worker Survey sup-
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plement to the CPS collects information about
work arrangements to augment the informa-
tion collected in the basic monthly CPS. As
noted, however, the CWS asks only about the
arrangements on individuals’ main jobs as re-
ported in the basic monthly CPS. If informal
work activity is reported on the monthly CPS
but not considered to be a subject’s main job
or is not reported in response to the standard
CPS employment questions, the CWS does not
ask about it. Even if people report informal,
nonemployee work as their main job in the
CPS, they may not consider themselves to be
independent contractors, independent consul-
tants, or freelance workers, and thus not be
captured by the CWS question used to identify
the independent contractor group.
The possibility that informal work is under-
reported in existing household surveys has gen-
erated considerable interest in new approaches
to measuring its prevalence. In a series of in-
novative papers, researchers at the JPMorgan
Chase Institute have used data on deposits
from online platform companies into the
checking accounts of Chase banking customers
to measure trends in online platform work.
Their latest estimates incorporate payments
originating from  separate platforms. Diana
Farrell, Fiona Greig, and Amar Hamoudi ()
report that, in March of , . percent of
JPMorgan Chase checking accounts received
deposits that originated with an online plat-
form company, up from a little over percent
in March of  and less than . percent in
March of .
The JPMorgan Chase data, however, may be
missing some online platform payments and
thus understating to some unknown extent the
share of households with online platform in-
come. First, though lengthy, the list of online
platform companies considered in compiling
the data is not exhaustive. Second, some online
platform payments may not flow through re-
cipients’ checking accounts. The largest share
of online platform payments is for transporta-
tion ser vices. In , Ly introduced its Ex-
press Pay option; Uber followed in  with
Instant Pay. Both services allow drivers to trans-
fer money they have earned instantly to a debit
card rather than have it deposited at regular
intervals into their checking account. Other
platforms’ payment arrangements vary, with
some offering deposit to a checking account as
the only option, others offering multiple pay-
ment options that include deposit to a checking
account, and still others not having deposit to
a checking account as an option.
Although interest in the prevalence and
growth of online platform activity has been
considerable, work mediated through online
platforms represents only a subset—and quite
likely a small subset—of all informal work.
Other researchers seeking to measure the over-
all prevalence of informal work activity have
carried out household surveys designed spe-
cifically for that purpose. The Federal Reserve
Bank of Boston’s Survey of Informal Work Par-
ticipation (SIWP) has been fielded several times
since  as a supplement to the Survey of
Consumer Expectations (SCE). The SCE is a ro-
tating online panel with participants who may
remain in the sample up to twelve months. Re-
spondents to the January and December 
SIWP were given a list of different types of in-
formal work activity and asked to indicate those
in which they were “currently engaged.” Based
on these responses, using a broad definition of
informal work, Anat Bracha and Mary Burke
() estimate that . percent of household
heads age twenty- one and older were currently
engaged in one or more types of such activity.
The estimated share participating in informal
work activities exclusive of selling or renting
property is . percent.
The Enterprising and Informal Work Activi-
ties (EIWA) Survey sponsored by the Federal Re-
serve Board was administered online to the GfK
KnowledgePanel in October and November of
 (Robles and McGee ). Like the SIWP,
the EIWA contained a battery of items asking
respondents about different informal income-
generating activities, but with a six- month ref-
erence period. The EIWA estimates indicate
that about  percent of the U.S. population age
eighteen and older engaged in at least one of
these activities during the six- month reference
period. This includes people who earned in-
come by selling new or used goods or renting
out property. Focusing more narrowly on labor
service activities, the EIWA estimates are that
. percent of the adult population earned
income by housecleaning, house sitting, yard
    
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1. The SHED’s focus on informal work outside a main job is dierent from the focus in the SIWP and EIWA, both
of which asked about all informal work activity.
2. Response rates for the EIWA and the 2016 and 2017 SHED are under 5 percent; no response rate is reported
for the SIWP, but based on the description of how the survey sample was constructed, it likely is similarly low.
Although the relationship between response rates and nonresponse bias is not monotonic (Groves and Peytcheva
2008), very low response rates may exacerbate concerns about sample representativeness.
work, or other property maintenance tasks and
that . percent did so by babysitting or pro-
viding childcare services.
The  SHED, also administered online
via the GfK KnowledgePanel, contained a sin-
gle question about whether a respondent was
currently engaged in informal work activity.
This question focused on informal work that
was not part of a job the respondent had al-
ready reported or, in the case of a respondent
with more than one job, not part of their main
job. In , the SHED adopted the more de-
tailed set of questions about informal work ac-
tivity developed for the EIWA and a one- month
reference period, again focusing specifically on
work that was not part of an already reported
job or main job. SHED respondents were told
to exclude taking GfK surveys when answering
these questions. According to our tabulations
of pooled data from the  and  SHED,
described more fully later in this article, .
percent of adults age eighteen and older re-
ported participating in informal work outside
of a main job during the survey reference
month; excluding activities that involved sell-
ing or renting property, that figure is . per-
cent.
The SIWP, the EIWA, and the SHED are con-
sistent in estimating high prevalence rates for
informal work activity. All three are based on
online panels weighted to match the demo-
graphic characteristics of the adult population
as a whole. A possible concern is that the type
of people who are willing to participate in an
online panel also might be more likely than
others with similar observable characteristics
to participate in informal work activity. In our
analysis of the  and  SHED data, we
have attempted to assess the extent to which
the nature of the sample may have affected the
prevalence of informal work activity among
SHED respondents, but this is difficult to do,
and some uncertainty unavoidably remains.
There is no obvious reason, however, to doubt
our findings regarding the correlates of partic-
ipation in informal work.
Ethnographic research suggests that, at
least in certain populations, income from in-
formal work is an important supplement to
households’ income from other sources. In one
early study, for example, Kathryn Edin and
Laura Lein () examined the household bud-
gets of low- income mothers in four cities, doc-
umenting the multiple sources of income these
mothers drew on to make ends meet. Among
mothers in their sample who were on welfare,
about  percent engaged in informal work that
was not reported to their caseworkers; about 
percent who were not on welfare engaged in
informal work in addition to their primary job.
In a more recent example, Kristin Seefeldt and
Heather Sandstrom () studied mothers in
Los Angeles and southeastern Michigan who
were neither working at a regular job nor receiv-
ing cash welfare benefits. They too find evi-
dence of substantial reliance on informal work,
though they observe that the amounts of
money earned from such work can be quite un-
stable. Focus groups conducted by one of us in
connection with a related project also yielded
evidence of substantial reliance on a variety of
types of informal work in economically de-
pressed areas of southwestern Michigan.
A limitation of the findings from qualitative
research is that they cannot readily be general-
ized. Research using tax data has established
that, in the population as a whole, a consider-
able share of self- employment activity supple-
ments income from a primary wage and salary
job (Jackson, Looney, and Ramnath ; Abra-
ham et al. a, b). Farrell and her col-
leagues find that income from work mediated
through online platforms supplements earn-
ings from other sources and compensates for
fluctuations in income from individuals’ pri-
mary jobs (Farrell and Greig a, b; Far-
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3. For additional details about the 2016 and 2017 SHEDs, see Federal Reserve 2017, 2018.
rell, Greig, and Hamoudi ). Similarly, in a
study of the earnings of Uber drivers based on
data obtained from a large online personal fi-
nancial management service, Dmitri Koustas
() finds that earnings from driving smooth
fluctuations in earnings from a main job and
thus smooth consumption spending.
Related to how informal work is being used
is whether informal work activity tends to be
short term or persistent. Studies of participa-
tion in online platforms have found that many
participants do not remain on the platforms for
long. Cody Cook and his colleagues, for exam-
ple, analyze records for Uber drivers who
started driving between January  and
March  (Cook et al. ). More than 
percent of new drivers were no longer active on
the platform six months later, they report; a
driver was considered active if he or she made
at least one trip within twenty- six weeks aer
a given date. Farrell and Greig (b) report
that turnover in the online platform economy
as a whole is high. In their study, they identify
online platform participants from deposits to
bank accounts and find that more than half
exited within twelve months of entry. Relatively
little is known, however, about the persistence
of participation in informal work more gener-
ally.

The Survey of Household and Economic Deci-
sionmaking is sponsored by the Board of Gov-
ernors of the Federal Reserve System. It has
been conducted annually since , and de-
tailed questions about informal work have been
included on the survey since . GfK, a con-
sumer research firm, has administered the sur-
vey using its online KnowledgePanel. The cu-
mulative survey response rate—reflecting the
response rate to the invitation to join the
KnowledgePanel, the response rate to an initial
profiling survey carried out as part of the pro-
cess of developing the sample for the SHED,
and the response rate to the SHED itself—was
about . percent in  and . percent in
. These rates are quite low relative to those
for the surveys underlying official labor- market
statistics but fairly typical for probability- based
online survey panels.
We use information about the demographic
characteristics of SHED respondents, their
household incomes, and their employment
situation. In the employment section of the
SHED questionnaire, respondents are asked
whether at any point during the prior month
they were employed for someone else, self-
employed, temporarily laid off from a job, or
not employed. An individual may report mul-
tiple statuses. Additional employment- related
information also is collected, including infor-
mation about the main job of those who report
being employed. Everyone—regardless of
whether they report employment during the
prior month—then is asked whether they have
engaged in any of eleven () or twelve ()
types of “occasional work activities or side
jobs” during the month. Those who previously
reported working during the month are in-
structed not to include activities on their main
job. Thus the survey is designed to capture in-
formal work activities that the respondent may
not have considered when answering the initial
employment questions or that are secondary to
a primary job.
The survey groups informal activities into
three broad categories: personal services, on-
line activities, and offline sales and other ac-
tivities. Within each category, respondents are
asked about three or four more specific types
of work. Personal services include babysitting,
childcare services, dog walking, or house sit-
ting; disabled adult or elder care services;
house cleaning, house painting, yard work, or
other property maintenance work; and provid-
ing other personal services such as running er-
rands, helping people move, and so forth. On-
line activities include completing paid online
tasks, such as those on Amazon Services, Me-
chanical Turk, Fiverr, Task Rabbit, or You Tube;
renting out property online, such as a car or
residence; selling goods online through eBay,
Craigslist, or other websites; driving using a
ridesharing app such as Uber or Ly ( sur-
vey only); and other online paid activities. Re-
spondents are instructed not to include taking
    
rsf: the russell sage foundation journal of the social sciences
4. GfK maintains a modest incentive program to encourage panel members to participate in surveys. In addition
to the standard GfK incentives, those completing the SHED received the equivalent of $5 through the GfK re-
wards system, in the form of points that could be used for online purchases from participating merchants.
GfK surveys in reporting their online activities.
The final category includes selling goods or ser-
vices at flea markets, garage sales, or other tem-
porary locations; selling goods at consignment
shops or thri stores; and any other paid activ-
ity that the respondent had not previously men-
tioned.
Individuals who report having engaged in
informal work during the prior month are
asked additional questions about their reasons
for doing so, allowing us to identify those
whose primary motivation is to earn money. In
addition, the survey asks questions about the
importance of informal work to household in-
come and the amount of time that the respon-
dent usually devotes to informal work activity.
The SHED questionnaires are available for
download from the survey website. Several
changes were made to the work- related ques-
tions between  and . For example, al-
though obtaining essentially the same informa-
tion, the sequence of questionnaire items used
to collect the information for determining a
person’s employment status was modified; a
question was added to allow those working part
time voluntarily to be distinguished from those
working part time who would have preferred
full- time work; and, in the question about in-
formal work activity, driving for Uber, Ly, or
another ridesharing company was added as an
explicit response option and minor changes
were made to the wording of several other re-
sponse options. We have created a data set that
harmonizes the two years’ responses.
Responses to the  SHED, fielded in Oc-
tober, totaled , and to the  SHED,
fielded in November and December, ,, for
a grand total of , responses. GfK has cre-
ated survey weights for use in analysis con-
structed so that the characteristics of the
weighted sample match those of the population
age eighteen and older based on the March Cur-
rent Population Survey with respect to age, gen-
der, race, ethnicity, education, census region,
metropolitan area status, and household in-
come. Among those interviewed for the 
SHED, , were reinterviewed in . GfK
also has created weights suitable for use with
this smaller panel sample.
Most of the results we report are based on a
sample created by pooling the  and 
responses, treating the two years’ data as inde-
pendent cross- sections. We drop  cases that
were missing values for variables needed for
our analysis, reducing the usable sample from
, to , cases, a loss of . percent. Our
analysis of the smaller panel interviewed in
both  and  focuses either on the 
people who reported being engaged in informal
work in the  SHED or on the  people in
that group who said their reason for doing in-
formal work in  was to earn money. We
drop ninety-one cases from the first group (.
percent of the sample cases) and eighty-one
from the second group (. percent of the sam-
ple cases) owing to missing values for variables
of interest, leaving us with  and  usable
cases, respectively. All reported tabulations of
sample distributions make use of the survey
weights constructed by GfK.
Informal Work: Evidence from the SHED
The detailed information about informal work
collected on the SHED together with the rich
set of demographic, financial, and employment
variables also available on the survey make it
well suited to exploring who performs informal
work and their reasons for doing so. The
smaller panel subsample allows us also to use
these data to examine the persistence of infor-
mal work.
Incidence of Informal Work Activities
Tables  and  show the incidence of informal
work activities by the respondent’s demo-
graphic characteristics, income and finances,
and employment status and job characteristics,
based on pooled data from the  and 
surveys. The first column of each table shows
the percentage of the population with various
characteristics. Column  shows the percentage
engaged in any informal work activity during
the last month, while columns  through  dis-
play the percentages engaged in each of the
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   
5. Each measure of informal work shown in tables 1 and 2 diers by demographic group (age, gender, race and
ethnicity, and education) and by financial and job characteristics (household income, financial well- being,
monthly income changes, employment status, and work schedule status) at the .001 level of significance.
three categories of informal work. Column
shows the percentage who report being en-
gaged in two or more informal work activities
during the month.
Overall, . percent of respondents report
being engaged in some type of informal work
activity during the previous month: . per-
cent engaged in personal services, . percent
in online activities, and . percent in offline
sales or other activity. Among all respondents,
. percent—or about  percent of those re-
porting any informal work activity—report be-
ing engaged in at least two types of informal
activities during the month. As noted earlier,
our definition of informal work includes those
who rent property or sell goods online—catego-
Table 1. Percent with Informal Work Outcome by Type of Arrangement and Demographic
Characteristics
Percent of
Population

Any
Informal
Work in
Past
Month

Of Which Percent
with 
Informal
Arrange-
ments

Personal
Services

Online
Tasks

Oline
Sales and
Misc.
Activities

All      
Age years
–      
–      
–      
–      
–      
–      
 plus      
Gender
Male      
Female      
Race-ethnicity
White      
Black      
Hispanic      
Multiracial      
Other      
Education
High school or less      
Some college      
College plus      
Source: Authors’ calculations based on SHED data (Federal Reserve 2017, 2018).
Note: Tabulations based on SHED data pooled for the years 2016 and 2017 and weighted using GfK
weights designed to make the sample representative of the U.S. population eighteen and oolder.
N = 18,560.
    
rsf: the russell sage foundation journal of the social sciences
Table 2. Percent with Informal Work Outcome by Type of Arrangement and Financial and Job
Characteristics
Percent of
Population

Any
Informal
Work in
Past Month

Of Which Percent
with 
Informal
Arrange-
ments

Personal
Services

Online
Tasks

Oline
Sales and
Misc.
Activities

Household income
Less than       
 to       
 or more      
Financial well-being
Diicult to get by      
Just getting by      
Doing okay      
Living comfortably      
Monthly income changes
Often varies      
Sometimes varies      
Roughly the same      
Employment status
Full-time employee      
Part-time employee      
Self-employed or partner      
Consultant or contractor      
Not employed, looking      
Not employed, not looking      
Part-time preference
, N 
Voluntary part time      
Involuntary part time      
Work schedule status
employees, consultants,
contractors, N 
Varies at own request      
Employer determines
Less than  week’s notice      
 to  weeks’ notice      
 plus weeks’ notice      
Normally the same hours      
Source; Authors’ calculations based on SHED data (Federal Reserve 2017, 2018).
Note: Tabulations based on SHED data pooled for 2016 and 2017 and weighted to represent the U.S.
population age eighteen and older. Information to distinguish voluntary and involuntary part time available
only for 2017; results for just those two employment statuses shown for that restricted sample. Questions
on work scheduling asked only of those identifying themselves as employees, consultants, or contractors.
N = 18,050 unless otherwise noted.
rsf: the russell sage foundation journal of the social sciences
   
ries that are sometimes excluded because they
may largely reflect returns to capital. The share
of those doing any informal work in the SHED,
however, remains high if these categories are
dropped. Excluding those whose only informal
work activities in the prior month involve rent-
ing property or selling goods online, the esti-
mated incidence of any informal work is .
percent and of online informal work is . per-
cent.
Table  shows the incidence of informal
work by demographic characteristic. Informal
work declines monotonically with age, though
a sizable minority of older adults report some
type of informal work activity in the preceding
month (. percent among those age sixty- five
to seventy- four and . percent among those
seventy- five and older). The relative importance
of various types of informal work activities also
varies systematically by age. The most common
form of informal work among the youngest
age group, eighteen through twenty- four, per-
haps not surprisingly, is personal services,
which includes childcare, elder care, and home
maintenance work. Among prime-age working
adults—those age twenty- five to fiy- four—on-
line tasks are the most common form of infor-
mal work; among those age fiy- five and older,
the incidence of informal work is relatively
evenly distributed across the three categories.
The percentage of those engaged in two or
more types of informal work activities also de-
clines with age: . percent of respondents age
eighteen to twenty- four but only . percent of
those age seventy- five and older report more
than one type of informal work activity.
The incidence of informal work activity var-
ies little by gender. Minority groups generally
are only somewhat more likely to report work-
ing in an informal arrangement than whites,
but the mix of types of work activities varies
considerably more by race and ethnicity than
the overall incidence. African Americans and
Hispanics are much more likely than whites to
provide personal services and to have engaged
in two or more types of informal work activity.
Interestingly, the incidence of informal work
activity is, if anything, slightly higher among
those who are more educated. Those with a
bachelor’s degree are about  percentage points
more likely than those with a high school edu-
cation or less to report doing informal work in
the last month (. versus . percent). The
patterns for the overall incidence of informal
work, however, mask considerable heterogene-
ity in the patterns by type of activity. The share
of people providing personal services declines
sharply with education level; among those with
a four- year college degree, the proportion pro-
viding personal services is only about half (.
percent) that among those with a high school
education or less (. percent). In contrast, the
proportion engaging in online work activities
rises sharply with education, with college-
educated individuals about  percent more
likely to engage in online activities (. per-
cent) than those with a high school education
or less (. percent). College- educated respon-
dents also are somewhat less likely than less-
educated respondents to report having engaged
in two or more informal work activities in the
last month.
Table  reports the incidence of informal
work activities by three measures of the house-
hold’s or respondent’s finances—household
income, a subjective assessment of financial
well- being, and variability of the respondent’s
income. Annual household income is reported
in categories, and in table  we report three ag-
gregated groupings that correspond roughly to
household income terciles—less than ,,
, or more but less than ,, and
, or more. The overall incidence of in-
formal work is similar across the household in-
come terciles, but as with race and education,
the composition of that informal work varies
greatly across the categories. Most striking,
those in the bottom tercile are more likely to
provide personal services (. percent) than
those in the middle (. percent) and top ter-
ciles (. percent). Those in the bottom tercile
also are somewhat more likely to report work-
ing in more than one informal arrangement
(. percent) than those in the second (.
percent) or third terciles (. percent).
In addition to reporting their household in-
come, respondents provide a subjective assess-
ment of their financial well- being, answering
that they find it “difficult to get by,” that they
are “just getting by,” that they are “doing okay,
or that they are “living comfortably.” Compared
with those who report living comfortably, those
    
rsf: the russell sage foundation journal of the social sciences
6. Although we label part- time workers who say they would have preferred full- time work as involuntary part
time, this measure does not correspond exactly to the measure of involuntary part- time employment in the Cur-
rent Population Survey. The CPS measure requires not only that individuals working part time prefer full- time
work but that they were available during the survey reference week to work longer hours.
7. How taking into account previously unmeasured informal work activity aects the labor- force participation
rate and unemployment rate will depend on whether those participating in such activity had previously been
categorized as unemployed or as out of the labor force.
8. Employees accounted for 97 percent of the respondents who were asked the survey’s work scheduling ques-
tions, and for simplicity we refer to the whole group as employees.
who report finding it difficult to get by are 
percentage points more likely to have worked
in an informal arrangement (. versus .
percent) and almost  percentage points more
likely to have worked in two or more arrange-
ments (. versus . percent).
Respondents also are asked about the stabil-
ity of their monthly income. About percent
indicate that it oen varies from month to
month,  percent that it is mostly the same but
sometimes varies, and about  percent that it
varies little. Those who report that their
monthly income oen varies are more than 
percentage points more likely to report having
engaged in informal work activities in the last
month (. percent) than those whose income
varies little (. percent). They also are nearly
twice as likely to have worked two or more side
jobs than those with stable incomes (. versus
. percent). These statistics of course are de-
scriptive; the higher incidence of informal work
could be a response to unstable income from a
main job or periodic spells of unemployment,
or the higher variability of income could be a
consequence of periodically having side jobs.
Table  also shows the incidence of informal
work arrangements by employment status and,
among employees, contractors, and consul-
tants, by how the individual’s work schedule is
determined and by its variability. The preva-
lence of informal work exceeds  percent
among those who are self- employed, sole pro-
prietors or partners, those who are consultants
or contractors, and those who are not employed
but looking for work. These numbers are  to
 percentage points higher than among full-
time employees. In , part- time employees
were asked whether they preferred part- time or
full- time hours; we find a similarly high preva-
lence of informal work among those stating
they would have preferred full- time work, a
group we call involuntary part time. The inci-
dence of working multiple side jobs also is
quite high in each of these groups, ranging
from about  to  percent. The prevalence of
informal work is lowest among those who are
not employed and not looking for work, but
even in this group, about one in five reports
having engaged in some informal work activity
in the prior month.
The relatively high reported prevalence of
informal work during the past month among
those who report not being employed at any
point during the month is notable. Some re-
searchers have suggested that those engaged in
informal work for pay may not think of these
activities as regular jobs and so may fail to re-
port them in response to the questions about
employment on government household sur-
veys. To the extent this occurs, it will lead to an
understatement of the employment to popula-
tion ratio and potentially to an understatement
of the labor- force participation rate and an
overstatement of the unemployment rate (see,
for example, Bracha and Burke ; Abraham
and Amaya ).
Although not the focus of
this article, the descriptive statistics reported
in table  suggest that underreporting of em-
ployment that consists of informal work may
indeed be a significant problem in official sta-
tistics.
The final variable in table  describes work
scheduling among full- time employees, part-
time employees, and consultants or contrac-
tors. Three- fourths of employees normally
work the same hours each week. For about one
in six (. percent), the schedule varies at the
employer’s request; within this group, about
two- thirds (. percent of all employees) usu-
ally receive less than one week’s notice from
their employer about their upcoming work
schedule, and another  percent (. percent
rsf: the russell sage foundation journal of the social sciences
   
9. For each measure of informal work incidence and importance shown in tables 3 and 4, dierences by demo-
graphic characteristics (age, gender, race and ethnicity, and education) and by financial and job characteristics
(household income, financial well- being, monthly income changes, employment status, and work schedule
status) are statistically significant at the .001 level.
of all employees) usually receive only one to two
weeks’ notice. Work schedules vary at the em-
ployee’s request for  percent of employees.
Relative to that among employees with a fixed
schedule, the incidence of informal work is
to  percentage points higher among employ-
ees who receive short notice about their sched-
ules from their employer (two weeks or less) or
whose schedule varies at their own request. For
the former, the high rate is consistent with in-
dividuals using informal work to supplement
hours and income. For the latter, however, the
direction of causality may be reversed, with em-
ployees choosing variable hours to accommo-
date informal work activities.
Importance of Informal Work to Income
For policy analysis, what matters is not simply
who has informal work arrangements but their
reasons for engaging in these casual work ac-
tivities. Some may engage in these activities as
a hobby or a way of making social connections,
but the tabulations reported in tables  and
show that informal work is especially prevalent
among those who are economically disadvan-
taged or work in nonstandard arrangements.
This suggests that economic motivations also
are likely to play an important role.
Tables  and  provide descriptive evidence
that bears more directly on this issue. The
SHED asks respondents who had done infor-
mal work in the previous month their main rea
-
son for this activity. Column  of tables  and 
repeats information from tables  and  on the
percentage of respondents reporting any infor-
mal work activity during the previous month.
Column reports the percentage indicating
that their goal is primarily to earn income, and
columns  and  the percentages for whom in-
formal work either is their primary source of
income or supplements their income or their
family’s income.
Although the questions about participation
in informal work pertain only to activities in
the preceding month, those who report such
work also are asked about its importance to
their income and the intensity of such work
over a longer period. Column  shows the per-
centage indicating that the work was an impor-
tant source of household income over the pre-
vious year. Column reports the percentage
indicating that such activities usually account
for at least  percent of their household in-
come. Column  shows the percentage indicat-
ing that they usually spend at least twenty
hours per month on informal work activities.
As in table , the top row of table  reports
statistics for all respondents and subsequent
rows report breakouts by demographic charac-
teristics. Table  reports on financial and job
characteristics. Eighteen percent of all respon-
dents, or about  percent of those who re-
ported working in an informal arrangement in
the preceding month, say they did so primarily
to earn money. Of those who give earning
money as the main reason,  percent (. per-
cent of all respondents) say that they work side
jobs to supplement their income or assist fam-
ily members; the other  percent (. percent
of all respondents) say that informal work ac-
tivities are their primary source of income.
Among all respondents, . percent say that
informal work activities were an important
source of household income during the previ-
ous twelve months, . percent that such earn-
ings usually constitute at least  percent of
their household income, and . percent that
they usually spend at least twenty hours per
month on informal work activities.
Large differences in the importance of in-
come from informal work and the hours spent
in these activities are apparent across some de-
mographic groups. The importance of infor-
mal work as an income source declines sharply
with age. Nonetheless, . percent of respon-
dents age twenty- five to thirty- four and .
percent of those age thirty- five to forty- four re-
garded income from informal work as an im-
portant source of household income over the
previous year. Minorities generally appear
more reliant than whites on income from in-
formal work. Among blacks, for example, .
    
rsf: the russell sage foundation journal of the social sciences
percent indicate that informal work arrange-
ments are their primary source of income, .
percent that they were an important source of
household income during the previous twelve
months, and . percent that they usually ac-
count for at least  percent of their income.
These rates are  to  percent larger than
those for whites. Table  also shows that less-
educated individuals are considerably more
likely than those with a bachelor’s degree to
say that informal work is their primary source
of income and to consider it an important
component of their household income over the
previous year.
With respect to the respondent’s financial
situation, the various indicators of reliance on
informal work for income decrease with house-
hold income, decrease as respondents’ sub-
jective assessment of their financial well- being
improves, and decrease as monthly income be-
comes less volatile (table ). Notably, among
those who report finding it difficult to get by,
. percent report being engaged in informal
work to earn money, . percent that such
Table 3. Percent with Informal Work by Reason and Intensity of Use and Demographic Characteristics
Any
Informal
Work in
Past
Month

Any
Informal
Work to
Earn
Money in
Last
Month

Important
Source of
House-
hold
Income

Usually 
Percent
or More
of House-
hold
Income

Usually
Do  or
More
Hours per
Month

Of Which
Primary
Source of
Income

Supple-
ments
Income

All       
Age years
–       
–       
–       
–       
–       
–       
 plus       
Gender
Male       
Female       
Race-ethnicity
White       
Black       
Hispanic       
Multiracial       
Other       
Education
High school or less       
Some college       
College plus       
Source: Authors’ calculations based on SHED data (Federal Reserve 2017, 2018).
Note: Tabulations based on SHED data pooled for years 2016 and 2017 and weighted using GfK weights
designed to make sample representative of the U.S. population eighteen and older. N = 18,560.
rsf: the russell sage foundation journal of the social sciences
   
Table 4 Percent with Informal Work by Reason and Intensity of Use and Financial and Job Characteristics
Any
Informal
Work in
Past
Month

Any
Informal
Work to
Earn
Money in
Last
Month

Important
Source of
House-
hold
Income

Usually 
Percent
or More
of House-
hold
Income

Usually
Do  or
More
Hours per
Month

Of Which
Primary
Source of
Income

Supple-
ments
Income

Household income
Less than        
 to        
 or more       
Financial well-being
Diicult to get by       
Just getting by       
Doing okay       
Living comfortably       
Monthly income changes
Often varies       
Sometimes varies       
Roughly the same       
Employment status
Full-time employee       
Part-time employee       
Self-employed or partner       
Consultant or contractor       
Not employed, looking       
Not employed, not looking       
Part-time preference
, N 
Voluntary part time       
Involuntary part time       
Work schedule status
employees, consultants,
contractors, N 
Varies at own request       
Employer determines
Less than  week’s notice       
 to  weeks’ notice       
 plus weeks’ notice       
Normally the same hours       
Source: Authors’ calculations based on SHED data (Federal Reserve 2017, 2018).
Note: Tabulations based on SHED data pooled for 2016 and 2017 and weighted to represent the U.S. population
age eighteen and older. Information to distinguish voluntary and involuntary part time available only for 2017;
results for just those two employment statuses shown for that restricted sample. Questions on work scheduling
asked only of those identifying themselves as employees, consultants, or contractors. N = 18,050 unless otherwise
noted.
    
rsf: the russell sage foundation journal of the social sciences
10. Breakouts for part- time workers who want and do not want full- time work are only available in the 2017 data
and therefore are not included in the regressions.
work is their primary source of income, .
percent that informal work had been an impor-
tant income source during the prior year, .
percent that they usually earn at least  per-
cent of their income from informal work, and
. percent that they usually work at least
twenty hours per month on informal jobs.
A strong correlation also exists between an
individual’s employment status and working in
informal jobs to earn money. The data in table
 show that sizable minorities of part- time em-
ployees, particularly those who would prefer
full- time work, and of those who are not em-
ployed but are looking for work rely signifi-
cantly on income from informal work arrange-
ments to supplement their income. Use of
informal work arrangements to earn money is
strikingly high among those in nonemployee
arrangements as well. More than  percent of
those who say that they are self- employed, a
sole proprietor, a partner, or a consultant or
contractor report doing informal work outside
their main job to earn income in the last month.
More than  percent of those in these groups
report that this income was an important
source of their household’s income during the
preceding year; and more than  percent also
indicate that at least  percent of their house-
hold’s income usually comes from such side
jobs. Among those working under the same set
of employment arrangements, more than 
percent report usually spending at least twenty
hours a month on informal work activities.
Similarly, the data indicate that a large minor-
ity of those with unpredictable work sched-
ules—employees, contractors, or consultants
who are given two weeks or less notice regard-
ing their schedule—rely on income from infor-
mal work.
Many of the variables measuring demo-
graphic characteristics, financial well- being,
and job characteristics are highly correlated
with each other. This makes it difficult to know
from the descriptive statistics presented in ta-
bles  through  whether these variables have
any independent relationship with individuals’
propensity to work in informal jobs and rely on
income from these jobs over the short and me-
dium term. To partially address this issue, we
estimate five linear probability models in which
the dependent variables alternately indicate
. the respondent had informal work in the
past month,
. the respondent had informal work to earn
money in the past month,
. informal work was an important source of
household income in the last twelve
months,
. informal work usually accounts for  per-
cent or more of the respondent’s house-
hold income, and
. the respondent usually spends twenty
hours or more per month on informal
work activities.
We include all of the demographic, financial,
and job characteristic variables from tables
though  that are available for both  and
 as explanatory variables. Table  reports
selected coefficient estimates from these de-
scriptive regressions.
Controlling for other factors, those in the
lower- and middle- income terciles, those who
report being under some level of financial
stress, and those with variable monthly in-
comes are significantly more likely to indicate
not only that they worked in side jobs to earn
income in the last month but also that such
jobs have been an important source of income
over a longer period and that they spend sig-
nificant time working in side jobs. For exam-
ple, relative to those who report being finan-
cially comfortable, those who are finding it
difficult to get by are  percentage points
more likely to have worked a side job in the last
month to earn money,  percentage points
more likely to report that income from side
jobs has been important to household income,
and  percentage points more likely to report
both that income from these jobs usually ac-
counts for at least  percent of their house-
hold income and that they usually spend at
least twenty hours per month in informal work
activities.
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   
Table 5. Selected Coeicient Estimates from Linear Probability Models of Informal Work Outcomes on
Demographic, Financial, and Job Characteristics
Any
Informal
Work

To Earn
Money

Important
Source of
Household
Income

Usually 
Percent of
Household
Income

Usually 
Hours per
Month

Household income
Less than  ** ** ** ** **
    
 to  ** ** ** ~ **
    
Financial well-being
Diicult to get by ** ** ** ** **
    
Just getting by ** ** ** * **
    
Doing okay ** ** ** ~ 
    
Monthly income changes
Often varies ** ** ** ** **
    
Sometimes varies ** ** ** ** **
    
Employment status
Part-time employee ** ** ** ** **
    
Self-employed or partner ** ** ** ** **
    
Consultant or contractor ** ** ** ** **
    
Not employed, looking ** ** ** ** **
    
Not employed, not looking – –** –** – –
    
Work schedule status
Varies at own request ** ** ** * **
    
Less than two weeks’ notice * *   ~
    
R    
Source: Authors’ calculations based on SHED data (Federal Reserve 2017, 2018).
Note: Each column represents a separate regression with the indicated dependent variable. Standard
errors clustered on individual and reported in parentheses. Controls for demographic characteristics
(age, gender, race-ethnicity, education) included but not reported. Reference categories for each set of
variables as follows: household income $100,000 or more; living comfortably; monthly income
generally the same; full-time employee; and work schedule mostly the same or three plus weeks’
notice. N = 18,560.
**p<.01; *p<.05; ~ p<.10 level
    
rsf: the russell sage foundation journal of the social sciences
Even aer controlling for other factors, an
individual’s employment status on their main
job continues to be an especially strong pre-
dictor of working in informal activities to earn
income and of the intensity and economic im-
portance of that work. Relative to full- time em-
ployees, those who are self- employed, a sole
proprietor, a partner, or a consultant or con-
tractor are  to  percentage points more
likely to have worked in one or more informal
activities in the last month to earn money,  to
 percentage points more likely to view in-
come from these side jobs as having been im-
portant to household income in the last year,
and  to  percentage points more likely to
report that side jobs usually account for at
least  percent of their household income.
They also are to  percentage points more
likely to report that they usually spend at least
twenty hours per month working in such jobs.
Although the heterogeneity in self- employment
arrangements is considerable, for a sizable mi-
nority of the self- employed, informal work ap-
pears to be an important supplement to in-
come from the primary job. In all models, the
coefficient estimates for those who are not em-
ployed but are looking for work are generally
similar in magnitude to estimates for those in
nonemployee arrangements, indicating heavy
reliance on income from informal work during
unemployment spells. As noted, these findings
suggest that government surveys may not fully
capture casual work, raising the possibility
that employment, labor force, and unemploy-
ment statistics are biased.
Among employees, contractors, and consul-
tants, having a variable or unpredictable work
schedule also is associated with a higher inci-
dence of working informal jobs to earn income
and with various measures of the importance
of those earnings to income and the intensity
of that work. For example, compared with those
with stable work schedules or considerable ad-
vance notice of their work schedules, those
whose hours vary mainly at their own request
and those who typically receive two weeks or
less notice about their schedule from their em-
ployer are  and  percentage points more
likely, respectively, to work an informal job to
earn income. Particularly for the latter group,
informal work may be a way to supplement in-
come from a job characterized by unpredict-
able and variable hours and earnings.
Persistence of Informal Work
Although people participate in informal work
activities or side jobs for a variety of reasons,
the evidence presented in the preceding section
indicates that individuals who have relatively
low earnings, are in precarious or nonstandard
work arrangements, or are unemployed fre-
quently use casual work arrangements to help
make ends meet. The policy implications of
these findings depend in part on whether ca-
sual work is typically a short- term fix for indi-
viduals who are temporarily in financial diffi-
culty, or something that people rely on over a
longer period, whether because they experience
frequent spells of nonemployment or because
their main job provides inadequate or unreli-
able income.
Here we present evidence regarding the per-
sistence of informal work based on the subsam-
ple of SHED respondents who were interviewed
in both  and . The first column of table
shows, conditional on reporting informal
work during the prior month in the  survey,
the percent who reported informal work during
the prior month in the  survey. Column
indicates the percentage of those who reported
doing informal work to earn money in the 
survey who gave the same response in the 
survey. As in previous tables, we report these
statistics for all respondents and by selected
demographic, financial, and employment or
job characteristics. Because the sample sizes
for these tabulations are considerably smaller
than those underlying earlier tabulations—
for the column  percentages and  for col-
umn  percentages—we have aggregated cate-
gories for some variables. The weights devel-
oped by GfK for the – panel sample
were used in preparing these tabulations.
Among those who reported informal work
during the prior month in , exactly half did
so for the prior month in , just over a year
later. Among those reporting in  that they
worked a side job primarily to earn money, .
percent gave the same response in . Al-
though some of the cell sizes are quite small
once the data are broken out by demographic,
financial, and employment characteristics, the
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   
Table 6. The Persistence of Informal Work
Informal Work in /
Informal Work in Percent

Informal Work to Earn Money
in /Informal Work to
Earn Money in Percent

All  
Age years
–  
–  
–  
 plus  
Gender
Male  
Female  
Race-ethnicity
White  
Hispanic  
Other  
Education 
High school or less  
Some college  
College plus  
Household income 
Less than   
 to   
 or more  
Financial well-being 
Diicult to get by  
Just getting by  
Doing okay  
Living comfortably  
Monthly income changes 
Often varies  
Mostly same, sometimes varies  
Roughly the same  
Employment status 
Full-time employee  
Part-time employee  
Self-employed/contractor  
Not employed  
Work schedule status 
employees, consultants,
contractors, N  and 
Varies at own request  
 or fewer weeks’ notice  
 plus weeks’ notice  
Source: Authors’ calculations based on SHED data (Federal Reserve 2017, 2018).
Note: Sample includes individuals interviewed in both 2016 and 2017 SHED. Unless otherwise
indicated, N = 517 for first column and N = 314.
    
rsf: the russell sage foundation journal of the social sciences
data in table  are generally consistent with the
findings reported earlier. For example, despite
no clear pattern in the persistence of informal
work activity by level of education overall, con-
ditional on having done informal work to earn
money in , those with a high school educa-
tion or less were  percentage points more
likely to be doing informal work to earn money
in  (. percent) than those with a bache-
lor’s degree (. percent). Similarly, condi-
tional on having a side job to earn money in
, those whose household income fell below
, in that year were  percentage points
more likely to still be working a side job to earn
money in  (. percent) than those with
household incomes of , or more (.
percent). Those who reported finding it diffi-
cult to get by or said they were just getting by
in  were more than  percentage points
more likely to report still having a side job to
earn money in  (. and . percent, re-
spectively) than those who in  reported liv-
ing comfortably (. percent). The year- over-
year persistence rate in working a side job to
earn income is also somewhat higher for those
who worked in part- time jobs or who were not
employees in  (. and . percent, re-
spectively) than for full- time employees (.
percent).
Are the SHED Estimates Biased?
A natural concern about these findings is
whether the SHED respondents are typical of
the overall population in regard to their par-
ticipation in informal work activities. A possi-
ble concern is that, even among those with sim-
ilar observable characteristics, someone who is
willing to participate in an online panel also
might be more likely to participate in other in-
formal work activity.
One strategy for assessing the potential for
this sort of bias is to compare estimates of in-
formal work activity from the SHED to esti-
mates from other sources. The SHED estimates
of the overall prevalence of informal work activ-
ity are quite similar to those from the SIWP and
EIWA, but because the data for all three of these
surveys are collected in a similar fashion, this
finding is unsurprising.
We also can compare the  SHED esti-
mate of the share of people who had been paid
within the past month for “driving using a ride-
sharing app such as Uber or Ly” and the JPM-
organ Chase estimate of the share of house-
holds with income in a given month from a
transportation platform. The estimate based on
the  SHED, for which data were collected in
November and December, is that . percent of
individuals had driving income during the
prior month; the JPMorgan Chase estimate is
that, in March , deposits from online trans-
portation platforms were recorded for . per-
cent of checking accounts. Although not an
apples- to- apples comparison, the order of mag-
nitude of the two estimates is similar. More-
over, the JPMorgan Chase estimate, which is
lower than the SHED estimate, does not cap-
ture certain payments, including transfers di-
rectly to debit cards, and thus may understate
the prevalence of participation in online driv-
ing platforms.
Another approach to assessing the sensitiv-
ity of our results to possible selection bias is to
exclude online activity from our measures of
participation. The rationale for doing this is
that participants in the online GfK panel may
be more likely than is typical to take on other
online work and, if so, estimates that exclude
online work may more closely approximate the
prevalence of informal activity in the popula-
tion. In the same spirit, we also go further and
construct estimates that exclude all informal
activity carried out by anyone in the SHED sam-
ple who reports any online activity. Not surpris-
ingly, restricting the set of informal work ac-
tivities considered in this way substantially
reduces the estimated prevalence of informal
work activity. Our baseline estimate is that .
percent of adults age eighteen and older en-
gaged in informal work activity over the prior
month; excluding those who were involved only
in online activities reduces this to . percent;
and dropping anyone who did any online work,
even if they also were involved in other types of
informal work, reduces it to . percent. Al-
though clearly lower—indeed, perhaps too
low—these numbers still imply a substantial
level of participation in informal work activi-
ties. The online tables mirror the information
provided in tables  and  for these two other
rsf: the russell sage foundation journal of the social sciences
   
11. The online appendix is available at https://www.rsfjournal.org/content/5/5/110/tab-supplemental.
definitions of informal work. As can be seen
in these tables, the basic patterns apparent in
our baseline estimates hold up aer excluding,
first, all online work and, second, all informal
work done by anyone who participated in any
online work. Groups that are relatively disad-
vantaged (by race, by education, by financial
circumstances, or by employment status) are
far more likely to rely on informal work to earn
money and, moreover, to report that informal
work is an important source of income. Al-
though members of the SHED sample may be
more likely than those in the population at
large to participate in informal work, the pat-
terns of reliance on informal work we have doc-
umented seem unlikely to be an artifact of is-
sues with the representativeness of the SHED
sample.
   
According to the SHED estimates for  and
 presented in this paper, as many as  per-
cent of adult Americans engaged in informal
work activities outside their main job during
the survey reference months. Although infor-
mal work is common regardless of race, ethnic-
ity, education, and household income, the rea-
sons individuals hold side jobs and the extent
to which they rely on them for income differ
systematically across groups. Minorities, the
less educated, those with lower incomes or ex-
periencing financial stress, those in nonstan-
dard work arrangements, and the unemployed
are far more likely to work side jobs to earn
money. They also are more likely to report that
earnings from these jobs were important to
household income over the prior year, that
these earnings usually make up at least  per-
cent of their income, and that they usually
spend at least twenty hours or more per month
in these activities.
Reliance on informal work for income also
varies strikingly by work arrangement. Relative
to full- time employees, part- time employees—
particularly those who would prefer full- time
work—and those who are sole proprietors or
partners, are contractors or consultants, or are
in some other self- employment arrangement
are considerably more likely to hold side jobs
to earn money and to indicate that informal
work is an important source of income over
short and longer time horizons. Among em-
ployees, contractors, and consultants, those
with unstable or unpredictable schedules are
considerably more likely to have informal jobs
to earn money. The relative importance of in-
formal work to supplement income among
those in part- time or other alternative work ar-
rangements may be a symptom of the inade-
quate or unstable hours and earnings oen as-
sociated with these forms of work.
For most people, informal work accounts for
a relatively small share of income. Yet, consis-
tent with evidence from ethnographic studies,
the SHED estimates suggest that informal work
plays an important role in helping the econom-
ically vulnerable and those in alternative work
arrangements make ends meet.
Informal work is not, however, a panacea.
Those most likely to hold informal jobs to sup-
plement income are the least likely to work in
arrangements that provide critical benefits
such as sick pay, health insurance, and retire-
ment plans. According to data from the U.S.
Bureau of Labor Statistics (), whereas 
percent of full- time employees were offered
employer- provided health- care benefits,  per-
cent were offered employer- provided retire-
ment benefits, and  percent were offered
paid leave, the corresponding figures for part-
time employees were just  percent,  per-
cent, and  percent. Workers in contract and
consultant arrangements generally are treated
as self- employed and so, like sole proprietors
and others in nonemployee arrangements, are
not eligible for employer- provided benefits. Be-
cause informal work generally is treated as self-
employment as well, it rarely comes with em-
ployee benefits. Thus, while informal jobs may
boost earnings, they do not help workers ac-
cess benefits, which are an important compo-
nent of the compensation package for most
full- time employees. Lacking benefits such as
health insurance or a pension during retire-
ment is a common source of financial hard-
ship.
    
rsf: the russell sage foundation journal of the social sciences
The perceived growth in independent con-
tractor and other nonemployee arrangements
has focused considerable policy attention on
increasing access to benefits among these so-
called gig workers. Recent proposals at the fed-
eral and state levels primarily target large plat-
form companies, such as Uber and Ly, that
help connect workers providing services with
customers. Although the specifics vary, the pro-
posed legislation typically would enable or re-
quire such companies to provide workers’ com-
pensation or to contribute to benefit plans that
are portable across jobs (Fitzpayne and Green-
berg ; Maxim and Muro ). Yet available
evidence suggests that workers in these ar-
rangements typically use them to supplement
income from a main job. Moreover, the evi-
dence presented shows that, although work
done online or through mobile apps accounts
for a significant share of informal work, tradi-
tional types of informal work are more com-
mon among the economically vulnerable pop-
ulations most dependent on this work for
income. A more comprehensive approach for
addressing the lack of benefits among workers
in part- time and nonemployee arrangements
is therefore needed.

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To monitor trends in alternative work arrangements, the authors conducted a version of the Contingent Worker Survey as part of the RAND American Life Panel in late 2015. Their findings point to a rise in the incidence of alternative work arrangements in the US economy from 1995 to 2015. The percentage of workers engaged in alternative work arrangements—defined as temporary help agency workers, on-call workers, contract workers, and independent contractors or freelancers—rose from 10.7% in February 2005 to possibly as high as 15.8% in late 2015. Workers who provide services through online intermediaries, such as Uber or TaskRabbit, accounted for 0.5% of all workers in 2015. Of the workers selling goods or services directly to customers, approximately twice as many reported finding customers through off-line intermediaries than through online intermediaries.
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The 1996 welfare reform law sought to reformulate single mothers’ income package, replacing cash welfare checks with paychecks. However, many single mothers have not been able to do that and have neither earnings nor cash assistance. Among a sample of single mothers in Los Angeles and southeast Michigan, we find that when single mothers lose jobs and do not receive cash assistance, they package income from a variety of sources (such as other public assistance programs and informal child support), find others in their social networks to pay their bills, or move in with others. However, their income packaging strategies are fraught with challenges. Benefits from certain public programs are difficult to secure; financial assistance from friends and family members can quickly vanish, particularly if a partner is deported or jailed; and doubling up with others often leads to living in crowded and unsafe conditions.
Driving the Gig Economy
  • Katharine G Abraham
  • C John
  • Kristin Haltiwanger
  • James R Sandusky
  • Spletzer
Abraham, Katharine G., John C. Haltiwanger, Kristin Sandusky, and James R. Spletzer. 2018a. "Driving the Gig Economy." Unpublished paper, University of Maryland, College Park.