# Availability of the Gig Economy and

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URL: https://www.frixlaw.com/law-library/documents/agency%3Airs%3A6ce2204406dfa944

## Record

- **Collection:** Agency decision
- **Document type:** Agency decision

## Text

Availability of the Gig Economy and
Long Run Labor Supply Effects for the Unemployed
Emilie Jackson *
November 2022

Abstract
A growing number of American workers earn income through platforms in the gig economy
which provide access to fexible work (e.g. Uber, Lyft, TaskRabbit). This major labor market innovation presents individuals with a new set of income smoothing opportunities when
they lose their job. I use US administrative tax records to measure take up of gig employment
following unemployment spells and to evaluate the effect of working in the gig economy on
workers’ overall labor supply and earnings trajectory. To do so, I utilize penetration of gig platforms across counties over time, along with variation in individual-level predicted propensities
for gig work based on pre-unemployment characteristics. In the short run, I show an increase
in gig work following an unemployment spell and that individuals are correspondingly better
able to smooth the resulting drop in income. However, individuals stay in these positions and
are less likely to return to traditional wage jobs. Thus, several years later, prime-age (25-54)
workers’ income lags signifcantly behind comparable individuals who did not have gig work
available. Among older workers (55-69), I fnd an increase in gig work corresponds to a reduction in receipt of Social Security Disability Insurance (SSDI). I shed light on mechanisms at
play and show that either these individuals have extreme values for fexibility, and are outliers
in their preferences, or individuals are perhaps procrastinating searching for a new job and not
fully optimizing.
* Jackson:

Department of Economics, Michigan State University, East Lansing, MI 48824. Email: emiliej@msu.edu. This research and access to tax data was authorized by the Treasury Offce of Tax Analysis and the
IRS under an academic partnership program. The fndings, interpretations, and conclusions expressed in this paper are
entirely those of the authors and do not necessarily refect the views or the offcial positions of the U.S. Department of
the Treasury or the Internal Revenue Service. Any taxpayer data used in this research was kept in a secured Treasury or
IRS data repository, and all results have been reviewed to ensure no confdential information is disclosed. I would like
to thank Mark Duggan, Paul Oyer, Raj Chetty, Petra Persson, and Gopi Shah Goda for their invaluable guidance and
support throughout this project. I would also like to thank Ran Abramitzky, Nick Bloom, Liran Einav, Andy Garin,
Matt Gentzkow, Matt Jackson, Dmitri Koustas, Adam Looney, Alicia Miller, Luigi Pistaferri, Shanthi Ramnath, Isaac
Sorkin, Heidi Williams, and seminar participants at Colegio de Mexico, Clemson, IE Business School, U. of Michigan, U. of Michigan - Ford, Michigan State, Monash, Notre Dame, Princeton, Stanford, UCSB, UIUC, Brookings,
Lyft, RAND, Treasury - Offce of Tax Analysis, SEA Annual Meeting, MD4SG’20, Young Economist Symposium,
13th Annual All California Labor conference, and 14th Annual EGSC for helpful comments and conversations. I am
grateful to the many people at OTA and IRS who made this work possible. I gratefully acknowledge funding support
from Peter G. Peterson Foundation for a post-doctoral fellowship at NBER, Alfred P. Sloan Foundation Pre-doctoral
Fellowship on the Economics of an Aging Workforce, Laura and John Arnold Foundation, and B.F. Haley and E.S.
Shaw Fellowship for Economics.

1 Introduction
In the last decade, there has been a substantial increase in the number of individuals earning income
through the “gig economy,” by which I mean online platforms such as Uber or TaskRabbit.1 As
seen in Figure 1, the number of individuals with any gig work in the United States (US) increased
from less than one thousand in 2007 to almost two million by 2016. This rapid emergence of the
gig economy represents a potentially major change in the labor market, and presents workers with
more fexible work opportunities.
I focus in this paper on the fact that gig labor market opportunities may be especially relevant
for the unemployed population since they provide short-term, fexible work that permits workers to
recover some of their lost income after job loss. This provides a new and valuable form of insurance
while workers search for a job to re-enter the workforce. However, it is ultimately an empirical
question as to whether short-term work in the gig economy – which may change individuals’ job
search behavior and delay (even indefnitely) their re-entrance into traditional employment – is
benefcial in the longer term.
To investigate this issue, I quantify the take up of gig work following an unemployment spell
and estimate the causal impact of participating in gig work on a workers’ short-run and long-run
earnings, employment, and education decisions. In doing so, I evaluate the trade-off between
smoothing income in the short run and less attachment to traditional work in the long run, measuring the extent to which either or both are present. I utilize the universe of individual Federal
income tax returns for the US and follow a panel of individuals who lose their job, which I measure based on one’s receipt of unemployment insurance (UI).2 I examine earnings, job type (e.g.
gig versus wage employment), post-secondary school attendance, and social insurance receipt (e.g
Social Security Disability Insurance (SSDI) and Social Security retirement) as they evolve before,
during, and after job loss.
My empirical strategy leverages variation in gig platform availability across counties over time
within the US that is driven by the geographic rollout of gig economy platforms. This exploits
the fact that within a given county some individuals will have the additional option of working in
the gig economy depending on the year in which they lose their job (i.e. those who lost their job
after the entry of gig platforms). However, a simple difference-in-differences approach would not
be able to disentangle the effects of gig availability from local labor-market changes happening
differentially in places gig platforms entered earlier. This is potentially the case since platforms’
decisions on where to enter frst were largely driven by population, starting with highly populated
areas, and larger cities may have recovered differentially from the Great Recession during the time
1 The “gig economy” is used colloquially to refer to digital platforms that match consumers and providers. I focus on

gig employment through online platforms rather than contract work more broadly.
2 In the US, unemployment compensation is taxable income and therefore identifable in the tax data.

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period that I examine.3
To deal with this selective gig rollout, I incorporate within-area variation in individuals’ predicted propensity for gig work by splitting individuals into two groups: high and low-gig-propensity.4
In doing so, I account for local labor-market changes that affected all individuals within a countyyear. For instance, high earners (prior to job loss) should not alter their behavior and so by accounting for the differences that they experience when gig platforms are available compared to
when they are not helps me control for the possibility that outcomes are changing differentially
in places where platforms entered earlier. I estimate a probit regression of the decision to ever
participate in gig work on numerous pre-unemployment characteristics for individuals who had
gig platforms available to them at the time of job loss. Utilizing these estimates, I identify comparable individuals who plausibly would have taken up gig work had it been available but simply
did not have it as an option. I refer to these individuals as high-gig-propensity, and the remaining
individuals as low-gig-propensity, excluding the bottom half of the propensity distribution as these
individuals are less similar.
This technique yields a high-gig-propensity group that is 18 percentage points more likely to
engage in gig work than the identifed low-gig-propensity workers, among individuals with the
most gig availability relative to no gig availability. Prior to losing their main job and receiving UI,
essentially no individuals were working in the gig economy to any degree.5 Following job loss, I
document an extensive-margin increase in gig work. Among the overall sample of UI recipients,
about 1-2% of individuals take up gig work following an unemployment shock.
Combining variation in gig availability and individuals’ propensity for gig work, I estimate
a triple-difference specifcation that estimates the impact of gig availability following job loss
(Gruber, 1994). I establish three main fndings. First, high-gig-propensity individuals with the
highest degree of gig availability experience a short-term beneft in income relative to those without
gig availability. Their individual and household income drop by $3,000 less in the year of UI
receipt than those without gig platforms available. However, the income of those without gig
availability catches up the year after UI receipt.
Second, I fnd that despite a smaller drop in income in the short run, two to four years later
the income of prime-age workers who had gig platforms available when and where they lost their
3 Population has an R squared value of 0.75 in predicting the year that gig platforms enter a county.

This R square
comes from a simple regression of the year of gig entry on 2014 county population as a cubic to pick up the curvature
of the relationship.
4 Similar methodology has been employed in other papers, e.g. Banerjee et al. (2018) use within area variation in individuals likelihood of taking up microfnance and compare individuals with high and low propensity for microfnance
in villages that do and do not receive microfnance as an option.
5 This is partly by construction as all of these individuals must have started with a traditional wage job in order to lose
it. However, as I show with co-authors in Collins et al. (2019), the majority of workers in the gig economy overall are
actually individuals who hold a main wage job and gig work is a secondary source of earnings.

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job begins to lag signifcantly behind those who did not have gig platforms available, and this is
driven by lower wage earnings. I show that individuals without gig availability begin to return
to wage jobs, while individuals with gig availability stay in gig positions and are fve percentage
points less likely to return to traditional wage jobs two to four years after UI receipt. This translates into $4,000 lower wage earnings and household income, relative to their high-gig-propensity
counterparts without any gig platforms available.
Third, I establish important heterogeneity across ages by differentiating between prime-age
workers (ages 25-54) and older workers (ages 55-69), as older individuals have been shown to
highly value fexibility (Ameriks et al., 2017) and are especially vulnerable after losing their job.6
Crucially, the implications of entering into gig work depend on the set of relevant and available
options following job loss for each age. Empirically the outside options differ dramatically for
these two age groups. For prime-age workers, gig work crowds out wage jobs that provided higher
earnings as well as important employer-sponsored benefts. In contrast, for older workers, gig
work prolongs labor force participation. An increase in gig work instead reduces receipt of Social
Security Disability Insurance (SSDI) benefts and may delay claiming of Social Security retirement
benefts, which can be fnancially advantageous.
This study helps to distinguish among mechanisms that are consistent with the observed patterns for prime-age workers. First, it may be that individuals learn that they value fexibility after
entering into gig work. Alternatively, individuals may have time-inconsistent preferences, and plan
to search for a new job yet keep procrastinating. Estimates I fnd imply that individuals are willing
to forgo 39% of their earnings in exchange for fexibility. To rationalize the pattern of observed
earnings, individuals would need to have a small discount factor, no larger than 0.86. Together
these estimates suggest that either these individuals have extreme values for fexibility, and are
outliers in their preferences, or individuals are perhaps procrastinating searching for a new job and
not fully optimizing. In fact, both may be at play, and policy intervention would be benefcial to
mitigate behavioral issues if they are at play.
This paper relates to a large theoretical and empirical literature on the behavior of the unemployed. In particular, the gig economy provides a new option for individuals during this time.
The key contributions that I make in this paper are quantifying takeup of gig work following an
unemployment shock, evaluating its ability to help buffer the drop in income, and estimating the
long-run consequences of this takeup for earnings, employment, and skills acquisition.7 This has
6 Ameriks et al. (2017) show that among older workers, willingness to work longer is higher for jobs that offer fexible

schedules and demand-side factors (such as the availability of such fexible positions). Additionally, several papers
examine bridge jobs, which individuals use to partially retire (Maestas, 2010; Rubert and Zanella, 2015; Ramnath et
al., 2017).
7 In particular, there are several key mechanisms that research has examined in the context of unemployment that relate
to this paper: duration (Mofftt, 1985; Katz and Meyer, 1990a; Chetty, 2008), search intensity (Mortensen, 1977),
reservation wages, consumption smoothing (Gruber, 1997), spousal labor response (Cullen and Gruber, 2000) and job

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parallels as well to prior work examining the impacts of experience at temporary help agencies on
subsequent labor market outcomes (Autor, 2001; Autor and Houseman, 2010; Pallais, 2014).
Second, this research builds on an emerging but rapidly growing literature that seeks to understand the recent growth of the gig economy and alternative work arrangements, more generally.8
Relatedly, Katz and Krueger (2017) show with survey data that unemployment is a strong predictor of alternative work transitions. Farrell et al. (2019) document bank account income declining
steadily and involuntary job loss events preceding increases in online platform participation and
revenues. While we now have a grasp on the growth of this type of work, very little is yet known
on the impact of participating in these positions on labor-market outcomes.9 My key contribution
is providing evidence on the causal effects of working in the gig economy following job loss, on
short and long-run labor-market outcomes.
Additionally, this relates closely to many studies that highlight the importance of examining
how the fexibility provided by gig work attracts workers.10 In fact, many drivers cite their preference for fexibility as the reason why they work for Uber (Hall and Krueger, 2018). This paper also
complements Koustas (2018), who shows that rideshare income helps drivers smooth consumption
when facing income fuctuations in their primary job. A key contribution of this paper is to investigate long-term outcomes associated with partaking in gig work, specifcally for those who enter
gig work following an unemployment shock.
This paper proceeds as follows. In Section 2, I provide details on the data and sample construction as well as summary statistics. I then present descriptive evidence on how outcomes evolve
dynamically before and after job loss in Section 3. I explain and motivate my empirical approach
in Section 4. In Section 5, I present my main results for prime-age and older workers. I provide evidence on robustness in Section 5.3. A discussion of these results is included in Section 6. Finally,
I conclude in Section 7.

matches (Acemoglu and Shimer, 1999; Acemoglu, 2001).
8 Early efforts to measure gig work use data from a variety of sources, including: surveys, fnancial institutions, google

trends, and private employers. Estimates indicate that roughly 0.4-1.6 percent of workers are involved in the gig
economy (Harris and Krueger, 2015; Farrell and Greig, 2016; Farrell et al., 2018; Katz and Krueger, 2019), and that
the percentage of workers engaged in alternative work arrangements and contract work is increasing more broadly
(Jackson et al., 2017; Collins et al., 2019; Katz and Krueger, 2019). In earlier work, we show that most of the growth
in self-employment appear primarily to be providing labor services as contractors or freelancers (Jackson et al., 2017)
and is largely being driven the by the growth in gig work mediated through online platforms (Collins et al., 2019).
9 Other studies have examined the effects of Uber or gig availability on the gender gap (Cook et al., 2019b), older
workers (Cook et al., 2019a), auto fnancing (Buchak, 2019), and loan delinquency and credit utilization after being
laid off (Fos et al., 2019).
10 Though many workers are not willing to pay for schedule fexibility, there exists a long tail of workers with high
willingness to pay (Mas and Pallais, 2017). Additionally several papers have examined the value of fexibility as a job
amenity or attribute (Goldin, 2014; Maestas et al., 2018; Wiswall and Zafar, 2018).

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2 Data, Sample Construction, and Summary Statistics
2.1

Individual Income Tax Returns

I use the universe of individual income tax returns fled in the United States from 2005-2017,
which include both income tax returns that are fled by individuals aggregating all of their earnings
and deductions (e.g. Form 1040, Schedule C), and third-party information returns that are fled
by employers or payers on behalf of the payee denoting the amount of money transferred between
the two entities (e.g. W-2s, 1099s). Taken together, these forms contain a wealth of data on:
demographic and economic characteristics, sources of earnings, beneft coverage, and receipt of
social insurance.
Key advantages of these data are the panel nature, which allows me to track individuals over
time, and the comprehensive scope that identifes both the sources and concentration across sources
of an individual’s earnings. Crucially, I am able to differentiate between traditional wage employment and self-employment, and separately identify employers or payers from whom they received
payments.11 As this is administrative population-level data on the self-employed and gig work
population, this addresses many shortcomings of other data sources, which often focus on particular samples, primary employment, or a snapshot in time. This is of particular importance as recent
research has shown that survey-based measures appear to underestimate self-employment Katz and
Krueger (2019); Abraham et al. (2018).
However, these data are designed for tax administration purposes. Thus, the data only contain
the necessary information for an individual to compute and fle their taxes, and for the government
to monitor tax compliance. For example, the data contain aggregate annual earnings from each
employer, but do not decompose the earnings further into hours or a wage rate.
Geography I use counties as the level of geography in my analyses and this is the level for
which I defne the local labor market. The data identify individual addresses including ZIP code
which I map to counties. Individuals typically fle and/or receive multiple tax forms in a given
tax year, each of which do not necessarily contain the same ZIP code. Thus, for the subset of
individuals for whom I identify multiple ZIP codes in a given year, I use the ZIP code that they
denote on their individual tax return (Form-1040) if they fled their taxes.12 For non-flers, I use
the modal ZIP code denoted on all information returns that they received.
Demographics Individual demographics include age and gender, and are retrieved from ta11 It is worth noting that not all self-employed individuals will have work that is frm facing but for those who do, they

would receive a Form 1099 from that frm. However, for those who do not interact with a frm or payer we would not
expect a Form 1099.
12 In cases where the ZIP code on an individual tax return is incorrect or missing, I use the modal ZIP code from other
tax forms fled by an individual (e.g. Schedule C), if valid. Otherwise, I use the modal ZIP code denoted on all
information returns received by an individual.

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bles obtained by the IRS from the Social Security Administration (SSA). More precisely, the data
contain each individual’s date of birth. I construct age as the tax year minus birth year.
Household Structure In years that individuals fle their taxes, for married individuals I can
match them with their spouse with their individual tax return (Form-1040).13 Similarly, I identify
the number of children that individuals have based on the number of child dependents they claim
(on Form-1040) in that year.
Social Insurance In addition to measures of income, tax information returns contain details on
social insurance receipt. Precisely, I identify the receipt of unemployment compensation (1099G), Social Security Disability Insurance (1099-SSA), and Social Security Retirement beneft withdrawal (1099-SSA).

2.2

Measuring Gig Work and Availability

I identify individuals who provide services through online gig platforms based on the receipt and/or
flling of a variety of tax forms, and irrespective of the tax fling status of that individual. More
specifcally, I utilize information returns (Form 1099-MISC and Form 1099-K) that are distributed
from platforms to workers, and returns fled by an individual denoting self-employment income
(Schedule C).14
I identify individuals who work for gig platforms by those who receive a 1099-K or 1099-MISC
from a gig platform Employer Identifcation Number (EIN), and based off their self-described
business professions on Schedule C. It is important to note that Forms 1099-MISC and 1099-K are
not used solely for the online platform economy; similarly, individuals fle Schedule C to report
income earned from a plethora of sources. Appendix B discusses these forms and their uses in
more details.
Based off publicly available lists, I include approximately 50 large online gig platforms for
whom workers provide labor based work to be apart of the “gig economy” defnition.15 As noted
in Farrell et al. (2018), the majority of platform work is made up of transportation platforms. For
each labor platforms, I identify all individuals who receive a 1099-MISC and/or 1099-K from that
frm. I consider these individuals to have gig work. Additionally, individuals reporting compensation related to one of these platforms, or gig work more broadly, are also included for reasons
discussed in Appendix B.

13 This is true if their fling status is "married fling jointly" or "married fling separately".
14 Information returns are sent from platforms to the IRS regardless of whether an individual ultimately fles their taxes.
15 This defnition of online gig platform work can be viewed as an underestimate of the gig economy. Nonetheless, it

provides extensive coverage of individuals working for major platforms in the sector, and thus works well in terms of
capturing individuals propensity for gig work. Further, given that fling thresholds appear to not be binding in practice
during this time period, there are not concerns in censoring of the earnings distribution among gig workers.

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Gig Availability by County
I construct a measure of gig availability at the county-by-year level utilizing the prevalence of gig
work as identifed at the individual level. I aggregate the number of individuals that I observe in
each county-by-year cell with any amount of gig work, and identify the frst year of gig availability
to be the frst year in which I observe at least 30 individuals in that county with gig income.16
Appendix Figure A3 highlights the variation across counties over time in the availability of gig
work as defned by this measure. There are two key takeaways from Appendix Figure A3. First,
each year more counties have gig platforms available as an option, indicated by more polygons
being shaded. Second, gig platforms are also becoming more prevalent within each county over
time, indicated by the shading of each polygon shifting from a light beige towards a darker red.17
Gig platform work may be increasing in prevalence over time, within an area, for many reasons.
For instance, this measure includes multiple platforms and thus once one platforms enter others
likely follow suit. Additionally, supply and demand for platforms will grow with local knowledge
of platforms and their services.

2.3

Sample Construction

Pulling together the components of the data described above, I construct my analysis sample to
consist of individuals experiencing involuntary periods of unemployment as identifed by receipt
of unemployment compensation.18 I draw my sample from the population of individuals with any
positive unemployment compensation in the years 2008-2015. Additionally, I utilize data from
2005-2017 for each individual which provides a sample that is balanced in event time over the
period three years pre- and post-UI receipt (including the frst year of UI receipt).
The fnal analysis sample includes an individual’s frst new UI claim between 2008 and 2015.
UI events are restricted to counties that gig platforms enter by 2015. There are 23 million prime-age
workers and 5 million older workers who experience an UI event in my fnal analysis sample, from
which I draw my stratifed random sample. This leaves 917,128 prime-age workers and 106,243
older workers after stratifcation. See Appendix B.3 for more details on each data restriction and
sampling methodology.

16 I utilize a cutoff of 30 individuals in a county-by-year cell for disclosure reasons at this geographical level.
17 Note that the legend is the same across each sub-fgure to facilitate comparison across years.
18 I only observe eligibility for unemployment compensation conditional on take-up of benefts, and not the full set of

individuals who were eligible but chose to not apply. Given the scope of the tax data, there are limited alternative
ways to identify individuals experiencing a period of involuntary unemployment, and the main alternative would be to
examine mass layoffs. See Appendix C for further discussion of UI takeup.

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2.4

Variable Defnitions

I construct several key outcome variables that together encapsulate the implications for employment, earnings, education, and access to various benefts. All years I use are tax years which
correspond to calendar years.
Gig Work and Gig Earnings Gig work is an indicator for whether or not I identify an individual
with any gig work in a given year, as I described above. Gig earnings represent gross receipts, as
reported by the platforms on Form 1099-MISC and 1099-K, and do not account for the associated
business expenses that an individual deducts. I observe the amount of business deductions that an
individual claims in a given year (on the return Schedule C), and thus also identify self-employment
income net of business deductions. My measure of income that I describe next accounts for all
business deductions claimed by an individual.
Individual and Household Income I use two different measures of income: one at the individuallevel and another at the household-level. I can only identify household-level income for flers when
I can link individuals together with their spouse based off their fling on the Individual Tax Return
(Form 1040). Household-level income I defne as the household’s adjusted gross income (AGI) as
reported on Form 1040. Since I also observe income for non-flers, I construct a second income
measure to incorporate this additional information.19 For individual income, for flers I assign half
of the household’s adjusted gross income for married individuals and all of AGI for non-married
flers, and for non-flers I aggregate income reported on information returns which include wage
earnings (Form W-2), unemployment benefts (Form 1099-G), and social security and disability
benefts (Form SSA-1099).20 As a robustness, for both measures of income I utilize only the
summed information return values rather than AGI.
Labor Force Participation I examine labor force participation across three different types of
employment: traditional wage employment, gig employment, and self-employment more broadly.
For each employment type, I evaluate both the extensive margin, measured as a dummy variable
indicating any amount of employment of that type, and the intensive margin, measured in earnings.
Unfortunately, I do not have data on hours, months or days worked during the year so I cannot break
these earnings apart into an hourly or monthly rate, and thus can only look at aggregate earnings.
SSDI and Social Security Retirement Benefts I construct a variable that identifes the amount
of social security benefts received in a year, as denoted on Form 1099-SSA. I differentiate between
benefts received from the retirement fund versus disability fund.
Post-Secondary Attendance I identify if an individual is a student at a college, vocational
19 Importantly, the individual income measure should be immune to differential changes in fling behavior from gig

availability or take up, that may be present with the household-level income for which I have to restrict to the subsample
of flers.
20 Prior to 2007, due to data limitations, self-employment income cannot be separately allocated to individuals within a
household and thus this allows for consistency over time in the defnition of individual income.

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school or other post-secondary institution by receipt of Form 1098-T in a given year. All institutions eligible for the Department of Education’s student aid programs must issue this form to all
students and the IRS, which identifes all qualifed education expenses.

2.5

Summary Statistics

In Table 1, I present summary statistics for the entire analysis sample described in Section 2.3. An
observation is an individual-year, and summary statistics are presented for pre-UI receipt years in
the balanced sample restricted years —the three years prior to UI receipt.
As seen in Table 1, the majority of my sample are flers, 91%. 30% of the sample is female
and on average individuals are 33 years old. Among the 91% who fled, household AGI is on
average $45,997 and the median household income is $34,500.21 Individual income is on average
$31,920 and almost entirely attributable to wages which are on average $33,204.22 Additionally,
the individual is typically the primary wage earner within the household, as spouses’ wages are on
average $8,362 relative to the individual’s wages, $33,204.
On average over the three years prior to unemployment, 91% held a wage job. As seen in Figure
A1 the share with a wage job is increasing over the three years prior to UI receipt. This is high by
construction, as to receive UI the individual must have frst held a wage job from which to become
unemployed and eligible for UI.23 Additionally, 15% of households fled Schedule C for income
earned in a sole proprietorship —this includes individuals who earn income as an independent
contractor or small business owner, for example. 17% were enrolled at a post-secondary institution.
As a baseline, about 0.30% held a gig position in the pre-UI period.
To help put these numbers in context, compared to the overall wage earning population, these
individuals are on average younger, less likely to be married, and have lower household AGI. For
example, the median household income in the US in 2017 was $61,372, and $55,000 (in 2017
$) back in 2010.24 On the other hand, the median household AGI among this sample, $34,500,
is substantially lower. As a result, a slightly higher number of these individuals, 23%, live in
households that claimed the EITC.
21 I winsorize the top and bottom 1% of income values. Large outlying negative values of AGI typically represent large

claimed losses. The top 1% of wage values are also winsorized.
22 There are a number of reasons why on average wages are slightly higher than individual income. First, a component

of individual income is AGI, which accounts for specifc deductions. Second, is if the household has any reported
business losses then that would reduce the overall AGI. Third, is by the construction of the individual income variable
- if the individual is in a married household and earns the majority of the household income from his or her wages,
then when that is divided by the number of two that may be less than his or her wages.
23 Note, an individual may have lost their job at time -1 or 0, and thus we wouldn’t necessarily expect to have 100%
wage job share in either year.
24 Median household income in 2017 was sourced from https://www.census.gov/library/publications/2018/demo/p60263.html.
Median US household income in 2010 was $49,445 and adjusted to 2017 dollars
(https://www.census.gov/newsroom/releases/archives/income_wealth/cb11-157.html).

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3 Descriptive Evidence on Behavior around UI Receipt
Figures A1 and A2 illustrate how each key outcome variable typically evolves, on average, dynamically relative to the year of UI receipt. These values are restricted to individuals without gig
availability to provide a baseline comparison of magnitudes for subsequent analyses. I describe
outcomes for prime-age workers in Figure A1 and older workers in Figure A2.
Prime-Age Workers
On the extensive margin, measured as indicator for having any gig work in a given year, participation in gig work prior to job loss is effectively zero, which is by construction given that these
individuals at the time of UI receipt live in a county without gig platforms yet available.25 Even
by four years after after UI receipt, only 0.4% of individuals have any gig work compared to 2%
of those who had gig platforms available.
Prior to job loss, household AGI is on an upward trajectory and increases on average from
$40,000 to $49,000. However, households experience a drop of almost $5,000 in the year of UI
receipt and the following year before starting to recover. Similarly, individual income drops from
about $35,000 to $29,000, and begins to recover two years after UI receipt. Underlying these drops
in income are decreases in wage earnings corresponding to the job loss prompting UI receipt.
An individual’s annual wage earnings combine two margins of variation: the extensive margin,
whether an individual holds a wage and salary job, and conditional on having such a position, how
much do they earn in annual earnings. On average, individuals are more likely to hold a traditional
wage and salary position over time prior to the job loss incident of interest. This rate increases
from 87% to 95% of individuals in the years leading up to UI receipt. Recall, by construction,
all of these individuals must have held a wage job at least once in the years prior to the job loss I
identify in order to be eligible for UI. In the year after UI receipt, only 78% of individuals without
gig availability have a wage job, this is a sharp drop of 17 percentage points. Two years following
UI receipt we see an increase of about 5 percentage points in likelihood of holding a wage job,
indicating that roughly one-third of individuals are able to return to the work force. However, this
share stays relatively constant and does not appear to increase substantially over time following
the initial increase.
Corresponding to the job loss timing and the patterns we see with the extensive margin of
holding a wage job, wage earnings increase from about $27,000 to $37,000 prior to UI receipt. In
the year of UI receipt, wage earnings drop to about $27,000 and bottom out at $23,000 the year
25 It is possible that individuals move over time and thus since I have defned gig availability for each individual to be in

the year and county of UI receipt, some individuals may have lived in an area with gig platforms prior to the year of UI
receipt; for this reason, these means are not necessarily precisely zero. However, we see that this is very uncommon,
and individuals appear to have essentially no gig work prior to job loss.

10

following UI receipt before starting to recover on a trajectory similar to that prior to job loss.
The spouse of the individual facing job loss earns about $9,000 prior to the individual’s job
loss and this is relatively stable, though growing slightly, over the years leading up to UI receipt.
Following job loss, we observe a clear spousal labor response given a shift in slope of zero to a positive slope of wage earnings. This indicates that, on average, an individual’s spouse is contributing
more to the household income following the unemployment shock.
On average, about 19% of the individuals are enrolled in post-secondary institutions with eligible tuition payments fve years prior to job loss. This is trending downward leading up to job loss,
as individuals are less likely to still be in school as they age. There is a clear break in trend and
a small increase of about 1 percentage point in schooling in the two years surrounding job loss,
before continuing to decrease on the same trend as prior to job loss.
Older Workers
In Figure A2, there are some key differences in the patterns exhibited among older workers
exhibit as compared to prime-age workers. First, household AGI and individual income are on
average higher at baseline prior to job loss and relatively stable. This is not surprising as these
individuals are older and later in their career. On average, they experience a larger drop in income
and household AGI, $10,000, compared to prime-age individuals whose income dropped by about
$5,000.
Second, there is a substantially larger drop in the share of individuals holding a subsequent
wage job following job loss, almost 30 percentage points. Additionally, unlike the pattern exhibited
by prime-age workers in A1, there is not a small recovery in the rate of individuals holding wage
jobs in the years following UI receipt. Finally, we see an increase in the share of individuals with
the receipt of SSDI benefts that corresponds to the timing of UI receipt. Prior to job loss, the share
with the receipt of SSDI benefts is steady at about 2%. Starting the year of UI receipt, we see
the share with any receipt of SSDI benefts increase from 2% to 10% four years later. There is a
similar pattern with the share of individuals claiming social security retirement benefts; though
this is trending up more prior to job loss and so does not exhibit as pronounced of an increase
following job loss but rather a change in slope.

4 Empirical Approach
The ideal experiment to identify the causal effect of taking up gig work following an unemployment shock would be to randomly assign individuals into and out of gig work at the time of the
unemployment shock. The difference in the outcomes between the two groups would identify
the treatment effect of taking up gig work, as well as the dynamics of these effects. However, in
11

practice there is presumably non-random selection into gig work after an unemployment shock.
For instance, selection might depend on how large or small of an income shock the individual is
faced with, how likely the individual is to get another wage job, which might be a function of their
prior industry or experience, or their ability to recover lost earnings through other responses (e.g.
spousal labor response).
Since the primary objective of this paper is to identify the causal impact of taking up gig work
during spells of unemployment on individual’s outcomes, I need exogenous variation in take up of
gig work to provide a valid counter-factual behavior during unemployment. To address this, my
empirical approach leverages two key sources of variation: the availability of gig platforms and
individual’s propensity for gig work. First, I exploit geographical variation in the availability of
online gig platforms that arises from the rollout of platforms across counties over time. Second,
I introduce within-area variation that permits me to split individuals into two groups: those who
plausibly would even consider gig work, and those unlikely to take up gig work.
These two sources of variation allow me to identify the group of individuals who would have
taken up gig work had it been available to them at the time of unemployment, but happened to
face an unemployment shock in a county prior to the entry of gig platforms. The following two
subsections describe both of these sources of variations in signifcantly more detail.

4.1

Variation in Gig Availability

I leverage variation in the date at which any online gig platform frst enters city to measure the
availability of gig work in a given city in a given year, or on the intensive margin, incorporating the
“intensity” of gig availability based on characteristics such as the number of frms that are present
in a city or how long gig platforms have been present. Crucially, this methodology identifes the
availability of any gig frm rather than relying on the rollout of one specifc gig platform.26 Suppose
that gig platforms frst entered San Francisco in 2010, New York in 2011, and Los Angeles in 2012,
then the thought experiment would be to compare an individual living in San Francisco after 2010
to a similar individual in San Francisco before 2010 as well as to individuals in New York and
Los Angeles where platforms had not yet entered.27 This closely relates to and builds upon several
papers that have exploited variation driven by the launch of specifc gig platforms, most commonly
Uber, across cities(Brazil and Kirk, 2016; Dills and Mulholland, 2017; Berger et al., 2018; Hall
et al., 2018; Koustas, 2018; Buchak, 2019). 28 A simple difference-in-difference exploiting the
rollout variation assumes that the timing of gig frms’ entry into a city is orthogonal to worker
26 Gig availability relies on individuals working for any of the 50 or so large online platforms from publicly available

lists, as mentioned in Section 2.2.
27 This example is for illustrative purposes only.
28 Mishel (2018) estimates that Uber makes up approximately two-thirds of the gig economy.

12

labor supply decisions.29
Figure 2 illustrates the geographical variation in gig availability and prevalence across counties
in 2013 and 2016.30 Each map of the US shows geographical variation by county in the percent
of the working age population, those 15-64, with any earnings from gig work. Darker red shaded
areas indicate a larger percentage of the county working age population have any earnings from
gig work, while lighter beige colors indicate a smaller percentage. Substantial variation in this
measure exists across counties over time. For example in 2013, in the counties surrounding the
Los Angeles area less than 0.14% of working age individuals had any amount of gig work, whereas
in 2016 counties surrounding most major US cities had up to 8.84% of the working age population
with any gig work.
There are two important takeaways from the exhibited variation in the prevalence of gig work.
First, over time more counties have any amount of gig availability as the various platforms rollout
to new geographic markets. Second, the prevalence of gig work continues to increase within an
area following the entry. This is driven by a myriad of factors that include the dissemination of
information regarding a platform’s presence that occurs naturally over time, increased demand for
the services offered by a platform as more consumers become familiar with them, and the entry of
additional platforms as not all necessarily enter a market in the same year.
Approximately 57% of the analysis sample faces an unemployment environment in which gig
platforms were available to them in the year and county in which they received UI. Figure 3 shows
the distribution of the selected UI events across years among those who had gig platforms available
to them at the time of the unemployment shock in the solid gray line with shaded bars, and the
distribution among those who did not have gig platforms available in the dashed black line with
no shading. Not surprisingly, those without gig platforms available are concentrated slightly more
towards the earlier years. As platforms roll out over time, only more individuals will have them
as an option. In Table 2, I show balance on key observable characteristics prior to job loss for
individuals with and without gig availability at UI receipt.
I defne a measure ‘Gig Intensity’ that captures the magnitude of gig availability rather than
a simple indicator for gig work being available as an option. In this measure, I want to capture
any exogenous variation driven by overall trends in how the popularity and availability of these
platforms grow after entering, on average, and exclude variation in the speed of growth arising
from better or worse labor-market outcomes or prospects for workers in that area. Figure A7 plots
the percentage of the working age population in a county-by-year relative to when gig platforms
were frst introduced in that county. A linear approximation appears to roughly ft the average
29 Reiterating Footnote 3, the year of entry of gig platforms in a county is largely predicted by population.

A simple
regression of the year of gig entry on 2014 county population, as a cubic to pick up the curvature of the relationship,
has an R squared value of 0.75.
30 Appendix Figure A3 presents the same map year by year.

13

growth in gig prevalence following the entry of platforms into a county.
I consider the “treatment” of gig availability to occur at the time of UI receipt. Thus, as a
function of the county c in which individual i lives in at the time of UI receipt ti0 , I defne Gig
Intensity as:
Gig Intensityi =

(# Years Gig Available)c(i,t 0 ),t 0
i

i

maxi (# Years Gig Available)c(i,t 0 ),t 0
i

= 19 (# Years Gig Available)c(i,t 0 ),t 0
i

i

i

I rescale this measure to be between 0 and 1 by dividing by 9, the max number of years gig
platforms had been available in the county and year of UI receipt over all individuals. A value
of 1 can be interpreted as becoming unemployed in an environment with the most gig availability
relative to a value of 0 which indicates no gig availability. Among those individuals with any gig
platforms, the median gig intensity value is 13 .
Appendix Figure A6 provides an example of how the gig intensity value varies across areas
and years. Individuals receiving UI in years prior to gig platform entry will have a value of 0.
For example, individuals in County C in Appendix Figure A6 who experience their job loss in the
years 2008-2010 would have a gig intensity value of 0. Gig platforms enter County C in 2012,
those receiving UI in 2012 would therefore have a value of 19 , 2013 would have a value of 29 , and
so on.

4.2

Propensity for Gig Work

Since this time period covers the Great Recession as well as the post recession recovery, and
the timing of entry of frms is correlated with population, it’s plausible that labor-market outcomes in larger cities were recovering at different rates compared to smaller cities. Thus, a simple
difference-in-differences would confound these two effects. Therefore, I employ a third difference
that allows me to incorporate a within-area variation to pick up on local labor market changes.
I utilize pre-UI characteristics to predict an individual’s propensity for gig work. Among treated
individuals, the subset of individuals that had gig platforms available at UI receipt, I observe who
takes up gig work and who does not. Thus, I estimate a probit regression of gig take up on pre-UI
characteristics such as income, wages, EITC claiming, and demographic characteristics. With the
probit estimates I predict a gig propensity for each individual, including those who did not have
access to gig platforms. I split the sample into high-gig-propensity and low-gig-propensity, trying
to capture all the potential gig workers in the high-gig-propensity group and everyone else in the
low-gig-propensity group.
More specifcally, I estimate a probit function where I look at gig take-up post UI as a function
of the following pre-UI characteristics. First, I use demographic characteristics in the year prior
14

to unemployment insurance claiming (i.e. at event time 0). These include a polynomial of an
individual’s age and their gender. I also utilize the zip code in which he or she lived in that
year. Second, I use economic outcomes for the three years leading up to the claiming of UI. This
incorporates a polynomial of wages, income, whether or not an individual had a wage job, any
income from a sole proprietorship, and the share of the household earnings that the individual
contributes, for those fling jointly. Third, I include fling status, marital status and number of
claimed children living in the household.31 Finally, I utilize information about the payer EIN in
the year prior to UI receipt, as this provides additional, otherwise observable, information about the
worker’s characteristics and the likelihood of taking up gig work after losing their job at this frm.32
Though I cannot identify an individual’s exact occupation, I use 3-digit NAICS codes associated
with the frm’s EIN capturing information about the subsector in which that worker used to work.
I split the sample into three groups: high-gig-propensity, the top 1% of the sample in predicted
gig probability; low-gig-propensity, the next 49% of the sample; and those excluded, the bottom
50% of the sample. By dividing the sample, I hope to capture all of the potential gig workers in
the high-gig-propensity group. The low-gig-propensity group will also help to provide additional
within-area variation for identifcation, as I describe in section 4.3. Additionally, I exclude the
bottom 50% in predicted gig probability from the low-gig-propensity individuals to maintain a
group of individuals that are more comparable to the high-gig-propensity individuals. I present the
distribution of gig propensity scores separately by whether or not gig platforms were available in
the county and year when an individual received UI in Appendix Figure A9a. Appendix Figure
A9b shows the distribution of predicted gig propensities among the low group that I retain.
Summary Statistics on High and Low-gig-propensity Individuals
Table 3 presents summary statistics separately for high-gig-propensity and low-gig-propensity
individuals. Relative to the low-gig-propensity sample of UI recipients, high-gig-propensity individuals are slightly more likely to be female (32% versus 30% female), and less likely to be
married (28% versus 29%) or have children (37% vs 40%). On average, high-gig-propensity individuals have a lower household AGI, $40,565 (vs $46,130), and individual income, $27,713 (vs
$32,025). They’re also more likely to have held a gig work position in the pre-UI years, 0.16% vs
0.01%; though both groups have very low baseline values in this regard.
Figure A8 highlights additional variation in individuals’ pre-UI characteristics and how they
relate to the predicted gig propensity measure. For each, I plot the average predicted gig propensity
by binned values for a few of the key predictive variables. Figure A8a demonstrates that younger
31 These measures are conditional on fling, so for non-flers I code these individuals as single and without children, as

done in Yagan (2019).
32 For individuals with multiple W-2s, I use the payer EIN from the W-2 with the largest amount of wages.

15

individuals are more likely to work in the gig economy. This measure peaks around age 25 and,
though not illustrated here, those below 25 have slightly lower propensities on average. Figure
A8b and Figure A8c show that those with lower incomes and wages two years prior to UI receipt
are more likely to participate in gig work. Figure A8d shows that individuals whose wages make
up a larger share of the household’s total wages are more likely to take up gig work, suggesting
that these individuals are more likely to be the primary earner for the household.

4.3

Estimation Approach

I estimate a difference-in-difference-in-differences (DDD) specifcation that leverages variation
in the availability of gig platforms at UI receipt and in individuals predicted propensity for gig
work, as in Gruber (1994). Relative to a standard difference-in-differences, this strategy incorporates additional within-area treatment information. Since the availability of gig platforms should
differentially affect the high-gig-propensity individuals compared to low-gig-propensity individuals, this additional interaction will help control for any other overall changes that coincide with
treatment that affect the outcomes of all individuals, both high and low-gig-propensity. To the
extent that there are other changes that affect all individuals unemployed in an environment with
gig availability relative to no gig availability that are unrelated to the presence of gig platforms,
incorporating low-gig-propensity individuals should account for these changes.
Formally, my estimating equation quantifying work in the gig economy is as follows:
Gigict = αi + β1 Pit + β2 (Pit ∗ Hi ) + β3 (Pit ∗ Gi ) + β4 (Pit ∗ Gi ∗ Hi ) + λct + ηa(i) + ΓXit + εict

(1)

Pit denotes that year t is post UI receipt for individual i, this includes the year of UI receipt. Hi
is an indicator variable that an individual has a high predicted gig propensity. Denote Ei as the frst
year of UI receipt for individual i. Then Gi measures the intensity of gig availability that individual
i faces at event time 0, t = Ei in the county in which they live, ci,t=Ei . I include individual fxed
effects, county-by-year fxed effects (λct ), and single year-of-age fxed effects (ηa(i) ).
With individual fxed effects, the effects are identifed based off within-individual deviations
from their mean outcome value. County-by-year fxed effects allow me to control for local labor
market shocks that affect all individuals. My identifcation is driven by variation across individuals
who do versus do not have gig platforms available to them, accounting for any existing differences
across these counties and years as identifed by differences across these groups among low-gigpropensity individuals. The key identifying assumption is that changes in the difference between
high and low-gig-propensity individuals are not correlated with the intensity of gig availability.
The coeffcient of interest is β4 . Since I have rescaled the gig intensity measure to be between
0 and 1, a value of 1 indicates becoming unemployed in an environment where gig platforms had
16

been available the longest amount of time, among all individuals in my sample. Thus, the interpretation of the coeffcient is the effect for a high-gig-propensity individual who became unemployed
in an environment where gig platforms had been available for the maximum time relative to not being available at all, netting out any differences occurring overall captured by the low-gig-propensity
group. The effect is estimated linearly in the treatment measure of gig availability so scaling the
coeffcient provides the effect size for a given treatment level. So, to get the effect of frst receiving
UI in an environment that had 50% of the maximum gig availability, a county and time combination where gig platforms had been available for half the number of years compared to the longest
available, then you would multiply the coeffcient by one half.
To estimate the effect of working in the gig economy following job loss on labor-market outcomes, I estimate an analogous set of reduced-form regressions with labor-market outcomes, Yict ,
as the dependent variable.
Yict = αi + β1 Pit + β2 (Pit ∗ Hi ) + β3 (Pit ∗ Gi ) + β4 (Pit ∗ Gi ∗ Hi ) + λct + ηa(i) + ΓXit + εict

(2)

Equation 2 estimates reduced-form estimates and identifes the causal effect of gig availability
on unemployment outcomes. Scaling by the frst stage take-up of gig work in Equation 1 would
identify a “treatment on the treated” effect, or the effect of taking up gig work on unemployment
outcomes in this context. This requires stronger assumptions: exclusion of the instrument and
monotonicity (Angrist and Imbens, 1994). Taken together, these assumptions imply that the estimated changes among the high-gig-propensity individuals in earnings, labor force participation,
and schooling, are only due to changes in the those who took up gig work.

5 Effects on Labor Supply and Earnings
I frst present the results graphically with the coeffcient of interest β4 from Equation 2 split into
year by year coeffcients rather than just post. Equation 3 is exactly analogous to Equation 2 above,
but a dynamic version. Event time, in years relative to UI receipt, is denoted by k. In each fgure, I
exclude the year two years prior to UI receipt, and so the coeffcient estimates are relative to event
time −2. Given the structure of the tax data, since I observe unemployment compensation at k = 0
it is possible that job loss occurred in year prior k = −1. Thus, to be conservative, I choose k = −2
to be the excluded year.
 




Yict =αi + ∑ θ1,k Titk + ∑ θ2,k Titk ∗ Hi + ∑ θ3,k Titk ∗ Gi
k=
6 2

k6=2

k6=2



k
+ ∑ θ4,k Tit ∗ Gi ∗ Hi + λct + ηa(i) + ΓXit + εict
k6=2

17

(3)

Titk = 1{t = Ei + k} represents a dummy indicating event time relative to the frst year of UI
receipt for individual i, Ei . The coeffcients of interest in this specifcation are θ4,k .

5.1

Prime-Age Workers

Gig Employment and Earnings
First, I examine extensive margin measures of gig work to quantify to what extent individuals start
working in the gig economy after losing their job. Figure 4a shows an increase of 10.16 percentage
points, among high-gig-propensity individuals, in the year of UI receipt relative to two years prior
for those with the most gig availability relative to no gig availability, netting out any changes
occurring simultaneously among the low-gig-propensity individuals. Among low-gig-propensity
individuals, there is no observed increase in gig work following UI receipt and for all individuals
there is a base of roughly zero gig work in the pre-UI period.
In the year following UI receipt, this extensive margin increase is twice as large, a 19.63 percentage points increase relative to two years prior to UI. This is not surprising if we think individuals are waiting until they exhaust UI benefts before entering, then we would expect a distribution
across months in when individuals exhaust UI benefts. In expectation, only about half of individuals would actually exhaust UI benefts in the same tax year as I frst observe them receiving
unemployment compensation, given a typical state’s UI duration of 26 weeks and assuming UI
recipients are randomly distributed throughout the year. As I only observe the year in which an
individual receives unemployment compensation and not the month, I cannot differentiate between
those starting UI benefts in February versus November.
The next important result from Figure 4a is that gig work increases among high-gig-propensity
individuals following unemployment and then does not decline even four years after UI receipt. If
individuals were using this only for a short period while searching for another job, then we would
expect to see an increase in gig work but then a subsequent decrease when they switched to another
job. However, we can immediately see that individuals enter into these positions and then stay in
them.
In Figure 4b, I present the corresponding results with an intensive margin measure of gig work
—gig earnings (in 2017 dollars).33 The 10.16 percentage points increase in gig work corresponds
to a roughly $588 increase in gig earnings in the year of UI receipt. This implies that each individual working in the gig economy in the year of UI receipt is earning on average $5,800 in that year.
The frst year following UI receipt, high-gig-propensity individuals with the maximum gig availability relative to no gig availability experience an increase in gig earnings of $1,851, implying
annual average gig earnings of roughly $9,400. Implied average annual gig earnings for those with
33 Dollars are adjusted using CPI-U from BLS: https://www.bls.gov/cpi/home.htm.

18

gig work two to four years after UI receipt are roughly $14,000. For perspective, this is roughly
similar to full-time equivalent earnings at federal minimum wage.34
As one of my key objectives in this paper is to disentangle short and long-run labor supply
effects and motivated by the dynamic nature of the effects that I present, I separately estimate
regressions for short- and long-run effects rather than pooling all post years, in Tables 4 and 5,
respectively. In all regressions, I exclude one year prior to UI receipt since it is possible that
job loss occurs in this period and therefore this may be a pre-unemployment period for some
individuals and post-unemployment for others. In all regressions event years, k ∈ [−5, −2] are
considered pre-unemployment years. Short-run estimates present the immediate effect in the year
of UI receipt (k = 0) as the post period of interest, while the long-run estimates consider k ∈ [2, 4]
as the post period of interest. Pooled estimates include the entire post period k ∈ [0, 4].
In Table 4, I show that in the year of UI receipt gig work increases on the extensive margin by
10.51 percentage points and $620.3 in gig earnings, for high-gig-propensity individuals relative to
those without gig platforms available. As seen in Table 5, by two to four years after UI receipt the
increase in gig work is 19.82 percentage points and $2,831 in gig earnings. For completeness, I
also present coeffcients where I pool the short and long-run effects for all outcomes in Appendix
Table A1.
Individual and Household Income
Given the increase in gig work, to what extent does this recover lost income with job loss? Figure
5 highlights the dynamic effects of gig availability at unemployment on individual income. First,
there is a clear short-term smoothing effect. Column 3 of Table 4 shows that in the short run
the income of high-gig-propensity individuals with the maximum gig availability when they lost
their job, relative to that of individuals with no gig platforms available, dropped by $3,118 less.
As illustrated in Figure 5, this advantage fades away rapidly. By the year after UI receipt, those
with and without gig availability at UI receipt have comparable and statistically indistinguishable
changes in income.
Two to four years after UI receipt, the income of high-gig-propensity individuals who had
gig platforms available at UI receipt lags behind comparable individuals who did not have gig
platforms available. In Column 3 of Table 5, I present the coeffcient estimate where I pool all
three long-run post years. The coeffcient -$2,478 indicates that high-gig-propensity individuals
with the maximum gig availability at the time of UI receipt is $2,478 lower two to four years
following UI receipt than comparable individuals who did not have any gig availability.
Appendix Figure A10 exhibits an analogous pattern for household income. Column 4 of Table
5 suggests a larger decrease of about $4,847 when accounting for household income rather than
34 $15,000 is roughly 2,000 hours at the federal minimum wage, $7.25. (https://www.dol.gov/whd/minimumwage.htm)

19

just individual income. Point estimates then drop below zero from one year post UI onwards. Together these results suggest that, among high-gig-propensity individuals, those with gig platforms
available when they lose their job are better able to smooth income in the year of UI receipt. However, two to four years later, their income recovery starts to lag behind. The natural question is
what drives this reversal?
Wage Employment and Earnings
Reversal in income recovery is explained by lower wages earnings, Figure 6a. This is largely a
function of the extensive margin, holding a traditional wage job, as shown in Figure 6b. Examining
the long-run outcomes, Column 5 of Table 5 indicates that annual wage earnings of high-gigpropensity individuals who had the maximum gig availability when they lost their job drop by
$3,951 more than those with no gig platforms available when they lost their job. On the extensive
margin, Column 6 indicates a 4.5 percentage points larger decrease in the probability of holding
a traditional wage job. This is in contrast to the short-run, the year of UI receipt, there is no
differential decrease in holding a wage job or wages in Table 4 Columns 5 or 6.35
Recall in Figure 4a that individuals entered into gig work and stayed in these positions even
a few years later. This does not necessarily preclude the possibility of also holding a traditional
wage job given the fexibility of gig work. However, these individuals are likely working close to
full-time. Though I cannot directly observe hours, using estimates from the literature on typical
hourly wages for Uber drivers —$9.21 (Mishel, 2018) —suggests that these individuals were likely
working close to full-time given that on average they were earning roughly $14,000 annually. On
the one hand, these individuals might be working so intensively on gig platforms because they
have not received a job offer to re-enter a traditional wage position, but are searching. On the other
hand, it is similarly possible that individuals are working so extensively that they do not have time
to search for another job. Finally, it may be that these individuals enter these positions following
job loss, learn they value the fexibility offered by gig work, and choose to stay accepting lower
earnings because they value the fexibility.

5.2

Older Workers

Now I turn to older workers, individuals between the ages of 55 and 69 at the time of UI receipt.
Compared to prime-age workers, older workers are particularly vulnerable in that they have a more
diffcult time of fnding re-employment following job loss and thus typically behave differently
following job loss. Furthermore, they may especially value the fexible nature of gig work as a
35 Note there is a decrease in wages in Table 4 Column 4 that is about one-third of baseline wages in the post period for

all individuals; however, the extensive margin coeffcient is small given that many individuals still receive a W-2 in the
year of UI receipt.

20

bridge to retirement (Ramnath et al., 2017).36
Following my empirical strategy for prime-age workers exactly, I estimate an analogous set
of regressions and fgures using Equations 1, 2, and 3 for the sample of older workers. I present
summary statistics for this older age group in Table 6. Among this sub-population (55-69), approximately 80% are under age 65. The average individual is 56 years old (and the median individual
is 55 years old). 37
Gig Employment and Earnings
Compared to prime-age workers, older workers exhibit a similar increase in gig work following job
loss, Figure 7a. Figure 7a indicates that among the high-gig-propensity individuals who became
unemployed in a county and year with the highest gig availability relative to having no gig availability increased gig work by 15.76 and 31.39 percentage points in the year of and year following
UI receipt, respectively. This corresponds to an increase of $1,248 and $4,430 in gig earnings,
Figure 7b.
The extensive margin increase for high-gig-propensity older workers is almost twice as large
in magnitude as observed for high-gig-propensity prime-age workers. Furthermore, above and beyond the larger extensive margin increase in gig work, the implied gig earnings for each gig worker
on average are also higher. Table 7 and 8 indicate an increase in gig work (and gig earnings) of
16.23 percentage points ($1,233) and 33.09 percentage points ($6,423) in the short-run and longrun, respectively. These estimates imply that each gig worker is earning on average $7,600 in the
short-run and $19,400 in the long-run, and are both approximately 30% larger than we saw for
prime-age workers.
Individual and Household Income
Unlike among prime-age workers, older workers with gig availability do not exhibit the same reversal pattern in income relative to those without gig platforms at the time of job loss (see Appendix
Figure A11). If anything, the point estimates in Appendix Table A2 suggest that individuals with
the maximum gig availability maintain the relative increase of about $5,204 in individual income
and $6,000 in household AGI, though the coeffcients are not statistically signifcant. Again, these
coeffcients are for high-gig-propensity individuals who frst receive UI in an environment with
the maximum gig availability (as a function of county-by-time) relative to comparable individuals
36 While Ramnath et al. (2017) fnd transitions into self-employment as a bridge job between career employment and

retirement less common than expected, this may be due to the fxed costs of entering self-employment, which are
higher than working on a gig platform. Additionally, they examine this in the context of overall workforce transitions
where as I examine individuals facing an unexpected job loss and who are therefore likely not ready to retire.
37 Note that these are averages in the years prior to UI receipt, while the sample is restricted to individuals 55-69 at the
time of UI receipt, these individuals will be a few years younger in the years prior.

21

with no gig availability when they received UI.
Social Security Disability Insurance (SSDI)
Their counterparts, without gig availability, receive SSDI benefts and claim social security retirement benefts rather than returning to the traditional wage workforce, as do the prime-age workers.
Figure 8 highlights a pronounced drop in the receipt of SSDI in the post-UI period. These coeffcients indicate that high-gig-propensity individuals with gig availability receive SSDI benefts at
lower rates than those without gig availability at UI receipt. More specifcally, Table A2 shows this
is a signifcant reduction of 5.9 percentage points in the receipt of SSDI benefts for those with the
most gig availability relative to no gig availability. This suggests that these individuals are on the
margin between working and not. Therefore, this has important fscal implications.
Social Security Retirement
Additionally, the increase in gig participation and earnings through gig platforms postpones withdrawing social security benefts. Figure 9 is noisier and there is not an obvious and large drop
in withdrawing Social Security retirement benefts. However, given the large confdence intervals
due to a small sample, I cannot rule out effects that would be substantial. There is a 1.3 percentage
points reduction in the short run and 4.4 percentage points reduction in the long run (two to four
years after UI) in claiming Social Security retirement benefts among individuals with job loss with
the most gig availability relative to no gig availability. The negative coeffcients indicate that those
with gig availability are less likely to withdraw social security benefts relative to those with no
gig availability. This indicates that gig work is crowding out increases in claiming Social Security
retirement benefts that follow UI receipt.
Since only a sub-group of older workers can actually respond on this margin, those ages 6267, I zoom in on this group for power in Appendix Figure E1 and Appendix Table E1. Among
these individuals, there is a 14 percentage points reduction in Social Security retirement benefts
following UI receipt. This can be fnancially advantageous for two reasons. First, as shown in
Shoven and Slavov (2014), delaying benefts is generally actuarially advantageous. Second, by
working longer, they can only increase their future lifetime benefts by potentially increasing the
value of the highest years in their earnings history or decreasing the number of years with no
earnings that are taken into account when calculating an individual’s beneft.

5.3

Robustness and Placebo Exercises

In this section, I address two potential concerns with my main identifcation strategy. First, I
present two plots showing robustness around my defnition of high and low-gig-propensity. At

22

baseline, I defne high-gig-propensity as the top 1% of the predicted propensity distribution, exclude the lowest 50% of the distribution, and defne low-gig-propensity as the remaining middle
49%. In Appendix Figure A12, I examine two alternative defnitions. First, I present results including all of the bottom 99% of the predicted propensity distribution in the low-gig-propensity group.
Second, maintaining my baseline defnition of low-gig-propensity, I instead alter the defnition of
high-gig-propensity to encompass a broader group of individuals, and include the top 3% rather
than 1% of predicted gig propensities.
As seen in Appendix Figure A12a, incorporating the lowest 50% of predicted propensities in
the low-gig-propensity group, if anything increases the point estimates slightly for gig work. On
the other hand, broadening the defnition of high-gig-propensity dampens the measured effect on
gig work among the high-gig-propensity group. This is not surprising, as increasing the scope of
the high-gig-propensity group means more individuals who are less likely to take up gig work are
included. Appendix Figure A12b shows the corresponding estimates for individual income under
each defnition of high and low. Reassuringly, the patterns of individual income are similarly more
muted for alternative high defnitions with lower estimates for gig work. This provides additional
support that the observed changes in income are driven from those taking up gig work. I have
altered the defnition of high to various other thresholds between 1%-5% and observe qualitatively
similar patterns.
Second, since propensity for gig work closely relates to income and the order of platform entry is correlated with city size, another potential concern might be that higher and lower income
individuals in larger versus smaller areas might have differential recovery in income following unemployment. To address this concern, I run a placebo test where I draw a new random sample
of UI recipients from 2002-2005, prior to the availability of gig platforms. Using the same coeffcients from the probit regression described in Section 4.2, I generate predicted gig propensities
for this placebo sample and similarly split them into high and low-gig-propensity. To simulate gig
availability, I subtract 9 years from the frst year of gig availability by county in order to generate a
placebo gig availability measure that maintains the relative ordering of the platform rollout. I then
calculate the gig intensity measure as a function of these new placebo gig entry dates.
I estimate an analogous regression using Equation 3 for the key outcome variable for primeage workers, individual income. As seen in Appendix Figure A13, there is no clear pattern of
differential changes in income post unemployment. Additionally, there is no longer the inverse
U-shape showing the reversal of relative income as seen in Figure 5. Thus, I fnd this reassuring
that the results I fnd are not driven by differential trends post-unemployment across the different
groups.

23

6 Discussion and Mechanisms
Given that a new choice is being introduced, in this case the gig economy, it is perhaps surprising
that in the long run high-gig-propensity prime-age workers have lower earnings. A natural question
is whether or not this is rational. The following are a few mechanisms that may account for these
results. First, individuals may be learning that they value fexibility and are willing to take an
earnings loss for the fexible amenity. Second, on the other extreme, this could be consistent with
a behavioral story. An individual plans to use the gig economy in the short run to get back on their
feet and search for a new job, however they procrastinate (e.g. O’Donoghue and Rabin (2001)) and
the time they spend working on the gig platforms ultimately crowds out the search effort. Finally,
potential employers might view the time spent working for online gig platforms as undesirable, or
if not disclosed on one’s resume it may look like a longer period of unemployment.38
This study can help distinguish which of these mechanisms may be at play. First, building on
the literature that examines the value of fexibility as a job amenity, I use the regression estimates
differentiating long-term differences in individual income as a way to infer individuals’ value for
fexibility. Precisely, individuals taking up gig work earn approximately 12,502 fewer dollars per
year than their non-gig counterparts in the long-run, or 2-4 years after UI receipt.39 On average,
these individuals earn approximately $32,000 annual in individual income, implying that they are
willing to forgo 39% of their earnings in exchange for fexibility.
This number is very high relative to fndings in earlier literature; for instance, (Mas and Pallais,
2017) fnd estimates of an individual being willing to forfeit up to 20% of earnings, on average, in
the most extreme case where an employer can set their schedule on short notice.40 One consideration is that in prior studies, values of fexibility were considered under different contexts and might
not necessarily refect the same degree of fexibility and hence of valuation. This suggests that
either individuals in this context are either on the extreme end of the distribution in their valuation
for fexible work arrangement, or that there is a behavioral story at play.
Furthermore, we can consider what discount rate would an individual need to have in order to
rationalize the pattern of earnings observed among gig workers, with higher short-term earnings
and lower long-term earnings. These individuals would need to have a discount factor of at most
δ = 0.86 (or an implied interest rate of at least 0.16) to rationalize the choice between the present

38 Adermon and Hensvik (2022) show that in Sweden listing a gig job on one’s resume improves callback rates for

individuals with Swedish sounding names and not Muslim sounding names.
39 Using the estimates from Table 5, 19.82% of high-propensity individuals taking up gig work and earn approximately

2,478 fewer dollars than their non-gig counterparts (-2,478/.1982).
40 Note, that value of fexible work arrangements are also considered in the context of the gig economy, specifcally ride

share, in Chen et al. (2019) and fnd high valuation for fexibility, but in comparison with a non-fexible job in a similar
high wage fuctuation scenario, which differs from the considerations here.

24

discounted value of taking up gig work opposed to not.41 Prior studies suggest this is a low discount
factor (Frederick et al., 2002). 42
Estimates may be consistent with a β −δ type model and time-inconsistent preferences (Strotz,
1956; Ainslie, 1991; Laibson, 1997; O’Donoghue and Rabin, 1999, 2001; Frederick et al., 2002;
Augenblick et al., 2015). Under this model, individuals have a present-bias and may continue to
hold off searching until the next day, and choose to work their gig job today. To the extent this
is occuring suggests the need for a policy intervention to help younger workers exit gig work and
return to traditional work after a short-run recovery. Together these estimates suggest that either
these individuals are have extreme values for fexibility, and are outliers in their preferences, or
individuals are perhaps procrastinating and not fully optimizing. Future work can help shed light
on if either or both of these types of individuals are present.

7 Conclusion
In summary, I document an increase in gig work after job loss (UI receipt). DDD estimates indicate
an increase of 18 percentage points in annual gig-work participation post-UI receipt among the
high-gig-propensity individuals. Correspondingly, the high-gig-propensity individuals with gig
platforms available experience a smaller drop in individual and household income in the year of UI
receipt relative to comparable individuals without gig platform availability. However, this income
smoothing advantage is short-lived, and by two years after UI receipt individual and household
income actually start lagging behind their counterparts without gig availability. This is explained
by a reduction in traditional wage employment and wage earnings.
Crucially, the implications depend on what counterfactual behavior is being crowded out. For
prime-age workers (25-54), it appears to crowd out wage jobs that provide individuals with an upward earnings trajectory, offer important employer-sponsored benefts, and are covered by workplace protections. While for older workers (55-69), the new option of gig work prolongs labor
force participation. In doing so, this reduces receipt of SSDI benefts and postpones claiming
of Social Security retirement benefts. Thus, the effects appear positive in terms of the long-run
implications among older workers.
When examining potential mechanisms at play for the prime-age workers, estimates suggest
one of two things. Either individuals have extreme values of fexibility, as compared to values in
prior studies that examine willingness to pay for fexibility, as well as low discount factors. Alternatively, individuals may be subject to procrastination and obsolescence, and not fully optimizing
41 This compares the present discounted value of high-propensity individuals with gig work available to those without

gig work in years 0-4. A discount factor δ ≤ 0.86 is necessary for discounted earnings to be higher for taking up gig
work.
42 There are experimental studies that have found low discount factors; though this is a non-experimental study.

25

their search behavior. To the extent that this is occurring, suggests the need for a policy intervention to help younger workers search for full employment while in the gig economy. While the
gig positions help individuals cushion job loss in the short-run by providing valuable insurance,
individuals are not shifting back to traditional jobs. Future research can help examine the extent to
which extreme preferences versus behavioral biases are at work, and it is possible that both types
of individuals are present.
Beyond the mechanisms at play, the shift towards gig employment is particularly important because the US systems of beneft coverage and tax administration depend on the employer-employee
relationship. Most Americans receive health insurance coverage, retirement plan coverage, and
related benefts from their employer, in large part because of tax preferences that favor employerprovided coverage. While health insurance coverage is improving for these groups with policies
such as the Affordable Care Act (ACA), gaps in coverage for health and, especially, retirement
benefts remain for this growing group of self-employed (Jackson et al., 2017). Hence, changes
in the employer-employee relationship and shifts toward the non-employee workforce have important consequences for beneft coverage, tax administration, and other labor and tax policy-related
issues. Thus, the implications are more complex than simply measuring changes in income.

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29

Figures and Tables
Figure 1: Number of Gig Workers by Year

Notes: This fgure presents the yearly number of individuals with any gig work as identifed using the universe of
federal individual income tax returns for the US. This includes counts of the universe of individuals who received
Form 1099-MISC or Form 1099-K from a gig platform, as described in Section 2.2, or fled Schedule C denoting
income from one of these platforms.

30

Figure 2: Percent of a County’s Working Age Population (15-64) Partaking in Gig Work
(a) Variation in 2013

(b) Variation in 2016

Notes: Figure 2a and 2b illustrate the geographic variation in gig platform availability at a given point in time across
counties. Second, they illustrate variation within a county over time in the prevalence of gig work, as measured in
the percent of the counties working age population with any amount of gig earnings in that year. “Insuffcient Data”
means that a cell has fewer than 30 observations with any gig work and are suppressed; predominantly, these consist
of zeros rather then suppressed data points.

31

Figure 3: Distribution of Gig Availability Among UI Recipients Across Years

Notes: ‘Gig Available’ denotes the subset of individuals who had gig platforms available in the county and year in
which they frst receive UI benefts and are shown above with a solid blue line with shaded bars. ‘Gig Unavailable’
denotes the subset of individuals who did not have gig platforms available to them in the county and year in which
they frst receive UI and are shown above with a black dashed line and un-shaded bars. The distribution among each
group sums to 1, and 57% of the sample had gig platforms available at UI receipt.

32

Figure 4: Yearly Coeffcients for Gig Work
(Prime-Age Workers)
(a) Gig Work (x 100)

(b) Gig Earnings (2017 )

Notes: Dependent variable in the top panel is an indicator for participating in gig work, expressed in percentage points
(taking the values 0 or 100). Dependent variable in the bottom panel is Gig Earnings (in 2017 $). Coeffcient estimates
for Titk ∗ Gi ∗ Hi are plotted for each year in event time relative to event time k=-2. These estimates are interpreted as the
effect of having the most gig availability at UI receipt compared to no gig availability among high-gig-propensity individuals, differencing out any changes that occur among the low-gig-propensity individuals, and each yearly coeffcient
is relative to two years prior to UI receipt. Sample balanced on event time k ∈ [−3, 2]. Regression includes individual
FEs, county-by-year FEs, and age FEs. Data drawn around UI recipients frst UI event in the period 2008-2015 and
includes observations from 2005-2017. Robust standard errors clustered by individual.

33

Figure 5: Yearly Coeffcients for Individual Income
(Prime-Age Workers)

Notes: Dependent variable is individual income (2017 $), and the top and bottom 1% of values are winsorized.
Coeffcient estimates for Titk ∗ Gi ∗ Hi are plotted for each year in event time relative to event time k=-2. These estimates
are interpreted as the effect of having the most gig availability at UI receipt compared to no gig availability among
high-gig-propensity individuals, differencing out any changes that occur among the low-gig-propensity individuals,
and each yearly coeffcient is relative to two years prior to UI receipt. Sample balanced on event time k ∈ [−3, 2].
Regression includes individual FEs, county-by-year FEs, and age FEs. Data drawn around UI recipients frst UI event
in the period 2008-2015 and includes observations from 2005-2017. Robust standard errors clustered by individual.

34

Figure 6: Yearly Coeffcients for Wage Employment
(Prime-Age Workers)
(a) Wage Earnings (2017 $)

(b) Wage Job (x 100)

Notes: Dependent variable in the top panel is wage earnings (2017 $), and the top 1% of values are winsorized. Dependent variable in the bottom panel is an indicator for wage job (in percentage points 0 or 100). Coeffcient estimates
for Titk ∗ Gi ∗ Hi are plotted for each year in event time relative to event time k=-2. These estimates are interpreted as the
effect of having the most gig availability at UI receipt compared to no gig availability among high-gig-propensity individuals, differencing out any changes that occur among the low-gig-propensity individuals, and each yearly coeffcient
is relative to two years prior to UI receipt. Sample balanced on event time k ∈ [−3, 2]. Regression includes individual
FEs, county-by-year FEs, and age FEs. Data drawn around UI recipients frst UI event in the period 2008-2015 and
includes observations from 2005-2017. Robust standard errors clustered by individual.

35

Figure 7: Yearly Coeffcients for Gig Work
(Older Workers)
(a) Gig Work (x 100)

(b) Gig Earnings (2017 )

Notes: Dependent variable in the top panel is an indicator for participating in gig work, expressed in percentage points
(taking the values 0 or 100). Dependent variable in the bottom panel is Gig Earnings (in 2017 $). Restricted to the
older worker sample, ages 55-69 at the time of UI receipt. Coeffcient estimates for Titk ∗ Gi ∗ Hi are plotted for each
year in event time relative to event time k=-2. These estimates are interpreted as the effect of having the most gig
availability at UI receipt compared to no gig availability among high-gig-propensity individuals, differencing out any
changes that occur among the low-gig-propensity individuals, and each yearly coeffcient is relative to two years prior
to UI receipt. Sample balanced on event time k ∈ [−3, 2]. Regression includes individual FEs, county-by-year FEs,
and age FEs. Data drawn around UI recipients frst UI event in the period 2008-2015 and includes observations from
2005-2017. Robust standard errors clustered by individual.
36

Figure 8: Yearly Coeffcients for Social Security Disability Insurance
(Older Workers)

Notes: Dependent variable is an indicator having received SSDI benefts (in percentage points 0 or 100). Restricted
to the older worker sample, ages 55-69 at the time of UI receipt. Coeffcient estimates for Titk ∗ Gi ∗ Hi are plotted for
each year in event time relative to event time k=-2. These estimates are interpreted as the effect of having the most gig
availability at UI receipt compared to no gig availability among high-gig-propensity individuals, differencing out any
changes that occur among the low-gig-propensity individuals, and each yearly coeffcient is relative to two years prior
to UI receipt. Sample balanced on event time k ∈ [−3, 2]. Regression includes individual FEs, county-by-year FEs,
and age FEs. Data drawn around UI recipients frst UI event in the period 2008-2015 and includes observations from
2005-2017. Robust standard errors clustered by individual.

Figure 9: Yearly Coeffcients for Social Security Retirement Benefts
(Older Workers)

Notes: Dependent variable is an indicator having claimed Social Security Retirement benefts (in percentage points 0 or
100). Restricted to the older worker sample, ages 55-69 at the time of UI receipt. Coeffcient estimates for Titk ∗ Gi ∗ Hi
are plotted for each year in event time relative to event time k=-2. These estimates are interpreted as the effect of
having the most gig availability at UI receipt compared to no gig availability among high-gig-propensity individuals,
differencing out any changes that occur among the low-gig-propensity individuals, and each yearly coeffcient is
relative to two years prior to UI receipt. Sample balanced on event time k ∈ [−3, 2]. Regression includes individual
FEs, county-by-year FEs, and age FEs. Data drawn around UI recipients frst UI event in the period 2008-2015 and
includes observations from 2005-2017. Robust standard errors clustered by individual.

37

Table 1: Pre-UI Summary Statistics

Female
Age
Married
Any Children
Household Filed
Household AGI (2017 $)
Individual Income (2017 $)
Wage Job
Wages (2017 $)
Spouse’s Wages (2017 $)
Schedule C Proft/Loss (2017 $)
Schedule C (HH)
Gig Year
Student
Has SSDI
Has SS Income
Claimed EITC (HH)

Mean Std Dev
0.30
0.46
33
33
0.29
0.45
0.39
0.49
0.91
0.29
45,997 39,667
31,920 25,561
0.91
0.29
33,204 29,286
8,362 19,998
475
3,908
0.15
0.36
0.00
0.01
0.17
0.38
0.0040 0.0627
0.0012 0.0342
0.28
0.45

P10

Median

P90

23

31

45

9,700
4,000

34,500
27,000

98,100
63,900

400
0
0

27,900
0
0

70,200
36,000
0

Notes: Summary statistics are for the three years prior to UI receipt. P10 and P90 represent the 10th and 90th percentile
values of the corresponding variables. All P10, Median, and P90 values are rounded for confdentiality of taxpayer
data. Married is taken from an individual’s fling status. Any Children is an indicator for if a household claimed any
dependents in that year. Household Filed denotes that an individual or their spouse fled an Individual Tax Return
in that tax year (Form-1040). Household AGI is the adjusted gross income drawn directly from the Individual Tax
Return. Individual Income additionally incorporates non-fling earnings by summing information returns (e.g. W-2s,
1099s, etc.) and divides AGI in half for married fling jointly individuals. Wage job is an indicator for receiving a
W-2. Wages is the sum of wages across all W-2 forms received by an individual. Spouse’s Wages is the sum of wages
across all W-2 forms received by an individual; this value is 0 if a spouse has no wages or an individual does not have
a spouse, and is missing for non-flers. Schedule C Proft/Loss denotes the amount of proft/loss claimed on Sch C.
Schedule C (HH) is an indicator for either the individual or spouse having fled Schedule C for income earned through
a sole-proprietorship. Gig Job is an indicator for having an earnings from gig work. Student is an indicator for having
an eligible tuition payment at a post-secondary institution. Has SSDI is an indicator for receiving Social Security
Disability Insurance (Form 1099-SSA). Has SS Income is an indicator for withdrawing Social Security Retirement
Income (Form 1099-SSA). Claimed EITC (HH) is an indicator that a Household claimed the EITC in that tax year.

38

Table 2: Balance Table by Gig Availability

Age
Female
1 Year Prior to UI Receipt:
Wages (’000 $)
Income (’000 $)
Married
Student
Wage Job
HH Wage Share
Tax Filer
Claimed EITC (HH)
Filed Sch C (HH)
2 Years Prior to UI Receipt:
Wages (’000 $)
Income (’000 $)
Married
Student
Wage Job
HH Wage Share
Tax Filer
Claimed EITC (HH)
Filed Sch C (HH)
3 Years Prior to UI Receipt:
Wages (’000 $)
Income (’000 $)
Married
Student
Wage Job
HH Wage Share
Tax Filer
Claimed EITC (HH)
Filed Sch C (HH)
Observations
F-test of joint signifcance

(1)
Gig Unavailable
33.97
0.252

(2)
Gig Available
34.78
0.337

(1) - (2)
P-Value
(0.429)
(0.510)

38.02
47.78
0.346
0.136
0.953
0.888
0.943
0.281
0.163

39.24
48.19
0.286
0.136
0.964
0.910
0.939
0.290
0.153

(0.408)
(0.203)
(0.000)***
(0.027)**
(0.521)
(0.010)**
(0.756)
(0.767)
(0.331)

35.90
45.64
0.339
0.150
0.934
0.884
0.916
0.263
0.162

36.30
45.34
0.279
0.158
0.922
0.906
0.908
0.273
0.156

(0.412)
(0.294)
(0.001)***
(0.014)**
(0.453)
(0.049)**
(0.100)
(0.251)
(0.291)

33.16
43.15
0.327
0.162
0.909
0.883
0.890
0.248
0.163
235,598

33.58
42.55
0.265
0.178
0.885
0.906
0.879
0.255
0.155
499,337

(0.626)
(0.361)
(0.015)**
(0.002)***
(0.364)
(0.156)
(0.965)
(0.288)
(0.070)*
734,935
1.8008

Notes: Columns 1 and 2 present mean values for individuals with UI receipt when gig platforms were and were not
available, respectively. The third column shows the p-values for the difference in sample means controlling for year
FEs and county FEs. Income is individual income as defne in Table 1. Married is taken from an individual’s fling
status. Student is an indicator for having an eligible tuition payment at a post-secondary institution. Wage job is
an indicator for receiving a W-2. HH Wage Share is an individual’s wage earnings as a fraction of the sum of the
individual’s and spouse’s wages. Tax Filer denotes that an individual or their spouse fled an Individual Tax Return in
that tax year (Form-1040). Claimed EITC (HH) is an indicator that a Household claimed the EITC in that tax year.
Filed Schedule C (HH) is an indicator for either the individual or spouse having fled Schedule C for income earned
through a sole-proprietorship.

39

Table 3: Pre-UI Summary Statistics —by Gig Propensity
(Prime-Age Workers)

40

Female
Age
Married
Any Children
Household Filed
Household AGI (2017 $)
Individual Income (2017 $)
Wage Job
Wages (2017 $)
Spouse’s Wages (2017 $)
Schedule C Proft/Loss (2017 $)
Schedule C (HH)
Gig Year
Student
Has SSDI
Has SS Income
Claimed EITC (HH)

Mean
0.32
33
0.28
0.37
0.90
40,565
27,713
0.89
27,459
8,594
600
0.19
0.00
0.19
0.0029
0.0009
0.30

High Gig Propensity
Std Dev P10 Median
0.47
8
23
31
0.45
0.48
0.30
37,278 8,200 29,400
23,386 2,300 22,900
0.32
25,287
0
22,500
21,006
0
0
4,324
0
0
0.40
0.04
0.39
0.0533
0.0298
0.46

P90
45

88,300
56,500
59,200
36,700
1300

Mean
0.30
33
0.29
0.40
0.91
46,130
32,025
0.91
33,347
8,357
472
0.15
0.00
0.17
0.0040
0.0012
0.28

Low Gig Propensity
Std Dev P10 Median
0.46
8
23
31
0.45
0.49
0.29
39,714 9,700 34,700
25,604 4,000 27,200
0.29
29,364
400
28,000
19,972
0
0
3,898
0
0
0.36
0.01
0.38
0.0629
0.0343
0.45

P90
45

98,300
64,100
70,500
36,000
0

Notes: Summary statistics are for the three years prior to UI receipt. P10 and P90 represent the 10th and 90th percentile values of the corresponding variables.
All P10, Median, and P90 values are rounded for confdentiality of taxpayer data. Married is taken from an individual’s fling status. Any Children is an indicator
for if a household claimed any dependents in that year. Household Filed denotes that an individual or their spouse fled an Individual Tax Return in that tax year
(Form-1040). Household AGI is the adjusted gross income drawn directly from the Individual Tax Return. Individual Income additionally incorporates non-fling
earnings by summing information returns (e.g. W-2s, 1099s, etc.) and divides AGI in half for married fling jointly individuals. Wage job is an indicator for
receiving a W-2. Wages is the sum of wages across all W-2 forms received by an individual. Spouse’s Wages is the sum of wages across all W-2 forms received
by an individual; this value is 0 if a spouse has no wages or an individual does not have a spouse, and is missing for non-flers. Schedule C Proft/Loss denotes the
amount of proft/loss claimed on Sch C. Schedule C (HH) is an indicator for either the individual or spouse having fled Schedule C for income earned through a
sole-proprietorship. Gig Job is an indicator for having an earnings from gig work. Student is an indicator for having an eligible tuition payment at a post-secondary
institution. Has SSDI is an indicator for receiving Social Security Disability Insurance (Form 1099-SSA). Has SS Income is an indicator for withdrawing Social
Security Retirement Income (Form 1099-SSA). Claimed EITC (HH) is an indicator that a Household claimed the EITC in that tax year.

Table 4: Short-Run Effects on Labor Supply, Income, and Social Insurance Receipt
(Prime-Age Workers)

VARIABLES

(1)
Gig Year
(x100)

(2)
Gig Earnings

(3)
Individual
Income

(4)
HH AGI

(5)
Wages

(6)
Wage Job
(x100)

Short Run (First Post Year)
0.00469*
(0.00271)
-0.214***
(0.0216)
-0.727***
(0.0664)
10.51***
(0.444)

-0.461**
(0.220)
-7.021***
(2.004)
-46.19***
(6.793)
620.3***
(48.90)

-3,964***
(110.9)
-994.7**
(451.6)
-1,394***
(405.1)
3,118***
(1,039)

-4,932***
(169.5)
208.4
(688.5)
-2,604***
(634.8)
2,573
(1,616)

-11,214***
(132.5)
-770.6
(533.2)
-237.8
(490.6)
1,835
(1,211)

-1.204***
(0.181)
-2.685***
(0.748)
0.236
(0.730)
1.450
(1.847)

Observations (Unweighted)
Observations (Weighted)
R-squared

3,031,317
46,850,202
0.251

3,031,317
46,850,202
0.232

3,031,317
46,850,202
0.811

2,750,404
42,716,803
0.845

3,031,317
46,850,202
0.804

3,031,317
46,850,202
0.442

Pre-Period Dep Var Mean
Pre-Period Dep Var SD

0.01
0.77

0.32
93.24

32,036
25,414

45,178
39,679

33,097
29,275

92.8
25.9

Post
Post x Gig Intensity
Post x High
41

Post x Gig Intensity x High

Notes: Results presented are for the subsample of prime-age workers, those ages 25-54 at the time of UI receipt. Post UI, k ≥ 0, indicate years following (and
including) UI receipt. In these short-run specifcations, Post is restricted to the year of UI receipt. Gig Intensity is a measure, between 0 and 1, of the degree of
gig availability in the county in which an individual lives in at the time of unemployment insurance receipt and is fxed within individual. High is an indicator
variable denoting that an individual is in the high predicted gig propensity sample. Regression includes individual fxed effects, county-by-year FEs, and age FEs.
Data drawn around UI recipients frst UI event in the period 2008-2015 and includes observations from tax years 2005-2017. Top 1% of wages and the top and
bottom 1% of income and AGI are winsorized. All dollar values are infation adjusted to 2017 dollars using CPI-U. Robust standard errors clustered by individual
in parentheses (∗∗∗ p < 0.01,∗∗ p < 0.05,∗ p < 0.1).

Table 5: Long-Run Effects on Labor Supply, Income, and Social Insurance Receipt
(Prime-Age Workers)

VARIABLES

(1)
Gig Year
(x100)

(2)
Gig Earnings

(3)
Individual
Income

(4)
HH AGI

(5)
Wages

(6)
Wage Job
(x100)

Long Run (Two-Four Years Post)
0.00878
(0.0116)
0.0647
(0.0580)
2.090***
(0.140)
19.82***
(0.873)

-2.163
(1.808)
-22.83***
(7.774)
112.9***
(21.88)
2,831***
(167.5)

-7,705***
(165.6)
687.4
(493.0)
432.6
(483.9)
-2,478*
(1,352)

-8,025***
(263.0)
-109.8
(801.5)
-630.2
(814.5)
-4,847**
(2,306)

-13,963***
(194.1)
1,604***
(564.3)
774.6
(589.0)
-3,951***
(1,523)

-14.78***
(0.276)
5.939***
(0.715)
-0.795
(0.829)
-4.542**
(2.252)

Observations (Unweighted)
Observations (Weighted)
R-squared

4,193,859
65,184,888
0.274

4,193,859
65,184,888
0.265

4,193,859
65,184,888
0.721

3,739,463
58,168,178
0.773

4,193,859
65,184,888
0.708

4,193,859
65,184,888
0.406

Pre-Period Dep Var Mean
Pre-Period Dep Var SD

0.01
0.77

0.32
93.24

32,036
25,414

45,178
39,679

33,097
29,275

92.8
25.9

Post
Post x Gig Intensity
Post x High
42

Post x Gig Intensity x High

Notes: Results presented are for the subsample of prime-age workers, those ages 25-54 at the time of UI receipt. Post UI, k ≥ 0, indicate years following (and
including) UI receipt. In these long-run specifcations, Post is restricted to the long-run post years, two to four years after UI receipt. Gig Intensity is a measure,
between 0 and 1, of the degree of gig availability in the county in which an individual lives in at the time of unemployment insurance receipt and is fxed within
individual. High is an indicator variable denoting that an individual is in the high predicted gig propensity sample. Regression includes individual fxed effects,
county-by-year FEs, and age FEs. Data drawn around UI recipients frst UI event in the period 2008-2015 and includes observations from tax years 2005-2017. Top
1% of wages and the top and bottom 1% of income and AGI are winsorized. All dollar values are infation adjusted to 2017 dollars using CPI-U. Robust standard
errors clustered by individual in parentheses (∗∗∗ p < 0.01,∗∗ p < 0.05,∗ p < 0.1).

Table 6: Pre-UI Summary Statistics
(Older Workers)

43

Female
Age
Married
Any Children
Household Filed
Household AGI (2017 $)
Individual Income (2017 $)
Wage Job
Wages (2017 $)
Spouse’s Wages (2017 $)
Schedule C Proft/Loss (2017 $)
Schedule C (HH)
Gig Year
Student
Has SSDI
Has SS Income
Claimed EITC (HH)

Mean Std Dev
0.17
0.37
56
4
0.53
0.50
0.30
0.46
0.94
0.23
65,928 48,402
41,461 29,090
0.90
0.30
43,706 34,507
15,029 25,376
714
5,591
0.28
0.45
0.00
0.01
0.04
0.20
0.01
0.10
0.02
0.14
0.14
0.35

P10

Median

P90

52

55

61

16,500
9,400

53,500
36,300

133,300
79,600

200
0
0

37,700
0
0

89,400
53,500
3300

Notes: Sample is restricted to the older workers subsample, those ages 55-69 at the time of UI receipt. Summary statistics are for the three years prior to UI receipt.
P10 and P90 represent the 10th and 90th percentile values of the corresponding variables. All P10, Median, and P90 values are rounded for confdentiality of
taxpayer data. Married is taken from an individual’s fling status. Any Children is an indicator for if a household claimed any dependents in that year. Household
Filed denotes that an individual or their spouse fled an Individual Tax Return in that tax year (Form-1040). Household AGI is the adjusted gross income drawn
directly from the Individual Tax Return. Individual Income additionally incorporates non-fling earnings by summing information returns (e.g. W-2s, 1099s, etc.)
and divides AGI in half for married fling jointly individuals. Wage job is an indicator for receiving a W-2. Wages is the sum of wages across all W-2 forms received
by an individual. Spouse’s Wages is the sum of wages across all W-2 forms received by an individual; this value is 0 if a spouse has no wages or an individual
does not have a spouse, and is missing for non-flers. Schedule C Proft/Loss denotes the amount of proft/loss claimed on Sch C. Schedule C (HH) is an indicator
for either the individual or spouse having fled Schedule C for income earned through a sole-proprietorship. Gig Job is an indicator for having any earnings from
gig work. Student is an indicator for having an eligible tuition payment at a post-secondary institution. Has SSDI is an indicator for receiving Social Security
Disability Insurance (Form 1099-SSA). Has SS Income is an indicator for withdrawing Social Security Retirement Income (Form 1099-SSA). Claimed EITC (HH)
is an indicator that a Household claimed the EITC in that tax year.

Table 7: Short-Run Effects on Labor Supply, Income, and Social Insurance Receipt
(Older Workers)

VARIABLES

(1)
Gig Year
(x100)

(2)
Gig Earnings

(3)
Individual
Income

(4)
HH AGI

(5)
Wage Job
(x100)

(6)
Has SSDI
(x100)

(7)
Has Soc Sec Ret
(x100)

Short Run (First Post Year)
0.0588***
(0.0155)
-0.375***
(0.0811)
-1.434***
(0.346)
16.23***
(2.205)

3.601**
(1.575)
-25.79***
(7.832)
-112.3***
(34.13)
1,233***
(205.6)

-3,884***
(570.1)
659.8
(1,856)
-2,475
(1,883)
4,174
(4,675)

-4,493***
(896.1)
2,325
(2,960)
-3,425
(2,635)
4,242
(7,776)

-15,024***
(730.2)
-1,280
(2,499)
-111.3
(2,654)
5,778
(7,595)

1.162***
(0.277)
-0.0532
(0.938)
0.640
(1.191)
-2.570
(2.495)

1.540***
(0.468)
1.333
(1.757)
2.774
(2.142)
-1.285
(5.406)

Observations (Unweighted)
Observations (Weighted)
R-squared

165,746
2,697,869
0.303

165,746
2,697,869
0.381

165,746
2,697,869
0.852

154,771
2,547,007
0.880

165,746
2,697,869
0.835

165,746
2,697,869
0.809

165,746
2,697,869
0.746

Pre-Period Dep Var Mean
Pre-Period Dep Var SD

0.01
0.82

0.48
175.88

42,397
28,916

66,580
48,532

44,222
34,283

1.09
10.38

3.20
17.60

Post
Post x Gig Intensity
Post x High
44

Post x Gig Intensity x High

Notes: Sample is restricted to the older workers subsample, those ages 55-69 at the time of UI receipt. Post UI, k ≥ 0, indicate years following (and including) UI
receipt. In these short-run specifcations, Post is restricted to the year of UI receipt. Gig Intensity is a measure, between 0 and 1, of the degree of gig availability
in the county in which an individual lives in at the time of unemployment insurance receipt and is fxed within individual. High is an indicator variable denoting
that an individual is in the high predicted gig propensity sample. Regression includes individual fxed effects, county-by-year FEs, and age FEs. Data drawn
around UI recipients frst UI event in the period 2008-2015 and includes observations from tax years 2005-2017. Top 1% of wages and the top and bottom 1% of
income and AGI are winsorized. All dollar values are infation adjusted to 2017 dollars using CPI-U. Robust standard errors clustered by individual in parentheses
(∗∗∗ p < 0.01,∗∗ p < 0.05,∗ p < 0.1).

Table 8: Long-Run Effects on Labor Supply, Income, and Social Insurance Receipt
(Older Workers)

VARIABLES

(1)
Gig Year
(x100)

(2)
Gig Earnings

(3)
Individual
Income

(4)
HH AGI

(5)
Wage Job
(x100)

(6)
Has SSDI
(x100)

(7)
Has Soc Sec Ret
(x100)

Long Run (Two-Four Years Post)
0.200***
(0.0649)
0.452
(0.285)
1.150*
(0.603)
33.09***
(4.017)

37.00***
(12.91)
51.76
(56.74)
-170.4
(108.0)
6,423***
(768.3)

-10,722***
(852.0)
-1,871
(2,084)
-2,681
(2,030)
6,757
(5,725)

-10,958***
(1,351)
-9,020***
(3,394)
-4,474
(3,185)
10,598
(10,830)

-24,230*** 6.250***
(0.699)
(1,092)
-8.834***
-1,986
(2.095)
(2,586)
-0.759
243.4
(1.624)
(2,716)
-6.227*
11,647
(3.316)
(8,832)

6.238***
(0.868)
6.556***
(2.429)
0.565
(2.427)
-4.390
(6.277)

Observations (Unweighted)
Observations (Weighted)
R-squared

228,361
3,769,690
0.328

228,361
3,769,690
0.326

228,361
3,769,690
0.784

205,439
3,386,606
0.828

228,361
3,769,690
0.752

228,361
3,769,690
0.632

228,361
3,769,690
0.819

Pre-Period Dep Var Mean
Pre-Period Dep Var SD

0.01
0.82

0.48
175.88

42,397
28,916

66,580
48,532

44,222
34,283

1.09
10.38

3.20
17.60

Post
Post x Gig Intensity
Post x High
45

Post x Gig Intensity x High

Notes: Sample is restricted to the older workers subsample, those ages 55-69 at the time of UI receipt. Post UI, k ≥ 0, indicate years following UI receipt. In these
long-run specifcations, Post is restricted to the long-run post years, two to four years after UI receipt. Gig Intensity is a measure, between 0 and 1, of the degree
of gig availability in the county in which an individual lives in at the time of unemployment insurance receipt and is fxed within individual. High is an indicator
variable denoting that an individual is in the high predicted gig propensity sample. Regression includes individual fxed effects, county-by-year FEs, and age FEs.
Data drawn around UI recipients frst UI event in the period 2008-2015 and includes observations from tax years 2005-2017. Top 1% of wages and the top and
bottom 1% of income and AGI are winsorized. All dollar values are infation adjusted to 2017 dollars using CPI-U. Robust standard errors clustered by individual
in parentheses (∗∗∗ p < 0.01,∗∗ p < 0.05,∗ p < 0.1).

FOR ONLINE PUBLICATION:
Availability of the Gig Economy and Long Run Labor Supply Effects for the Unemployed
(Emilie Jackson)

Appendix A

Figures and Tables

46

Figure A1: Summary of Key Outcome Variables by Year Relative to UI Receipt
(Prime-Age Workers)
Gig Job (x 100)

Gig Earnings

HH AGI

Individual Income

Wage Job (x 100)

Wage Earnings

Spouse’s Wage Earnings

Student (x 100)

47
Notes: Averages of each outcome are plotted by year relative to UI receipt for individuals who become unemployed in a county and year where there are no gig
platforms available. Gig Job denotes having any income from gig work in that tax year. Gig Earnings are the sum of all earnings earned in the gig economy.
Household AGI is the adjusted gross income drawn directly from the Individual Tax Return. Individual Income additionally incorporates non-fling earnings by
summing information returns (e.g. W-2s, 1099s, etc.) and divides AGI in half for married fling jointly individuals. Wage job indicates receiving a W-2 in a given
year. Wage Earnings are the sum of all W-2 wages in a given year. Spouse’s Wage Earnings are the sum of all W-2 wages of a spouse and are restricted to flers.
Student is an indicator for having an eligible tuition payment made for post-secondary schooling (Form 1098-T).

Figure A2: Summary of Key Outcome Variables by Year Relative to UI Receipt
(Older Workers)
Gig Job (x 100)

Gig Earnings

HH AGI

Individual Income

Wage Job (x 100)

Spouse’s Wage Earnings

Has SSDI (x 100)

Has SS Ret (x 100)

48
Notes: Averages of each outcome are plotted by year relative to UI receipt for individuals who become unemployed in a county and year where there are no gig
platforms available. Gig Job denotes having any income from gig work in that tax year. Gig Earnings are the sum of all earnings earned in the gig economy.
Household AGI is the adjusted gross income drawn directly from the Individual Tax Return. Individual Income additionally incorporates non-fling earnings by
summing information returns (e.g. W-2s, 1099s, etc.) and divides AGI in half for married fling jointly individuals. Wage job indicates receiving a W-2 in a given
year. Spouse’s Wage Earnings are the sum of all W-2 wages of a spouse and are restricted to flers. Has SSDI is an indicator for receiving Social Security Disability
Insurance (Form 1099-SSA). Has SS Ret Income is an indicator for claiming Social Security Retirement Income (Form 1099-SSA).

Figure A3: Percent of a County’s Working Age Population (15-64) with any Gig Work by Year
2010

2011

2012

2013

2014

2015

[Text truncated at 120,000 characters. The full text is on the page linked above.]

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Source: Frix Law Library, https://www.frixlaw.com/law-library/documents/agency%3Airs%3A6ce2204406dfa944. Public record. Not legal advice.