Data publikacji: 18 paź 2021
Zakres stron: -
Przyjęty: 26 kwi 2021
DOI: https://doi.org/10.2478/izajole-2021-0004
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© 2021 Henry Hyatt et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Recent empirical research has documented a multi-decade decline in the fluidity of the U.S. economy, including a fall in the business start-up rate, declining flows of workers between primary jobs, and, most notably, a decline in the flows of workers to and from jobs that are short-term or secondary sources of earnings (Hyatt and Spletzer, 2013; Davis and Haltiwanger, 2014; Decker et al., 2014). This declining fluidity has troubling implications for the aggregate economy, since productivity tends to rise when new businesses enter and more productive businesses expand (Foster et al., 2001). It also matters for the outcomes of individual workers, since they benefit from opportunities to increase their earnings when they find better jobs (Topel and Ward, 1992). Moreover, there is a strong empirical relationship between labor reallocation rates and aggregate wage growth (Faberman and Justiniano, 2015).
Over the course of these same decades, the number of business income (BI) recipients has increased dramatically. This is in part due to the rise of the “gig economy” where individuals work through an online platform rather than in a traditional employer–employee relationship (Abraham et al., 2018b; Katz and Krueger, 2019). Recent studies have also documented a steady shift away from self-employment being an individual's primary source of earnings, as workers increasingly use it to supplement their earnings from wage and salary work (Jackson et al., 2017; Collins et al., 2019).
In this paper, we explore the connection between these two phenomena – specifically, the decline in hires and separations of wage and salary workers and the rise in the share of the workforce generating earnings through self-employment. We find that a small but nontrivial amount of the missing labor market fluidity can be found among the self-employed, who are omitted from most reallocation series. This relationship is especially important among marginal jobs that are short in duration or provide a secondary source of income. Our results suggest that the “gig economy” provides many entry-level and side jobs through which workers transition rapidly. The omission of the self-employed from most published labor reallocation statistics is therefore a significant limitation.
Using a combination of U.S. survey data and administrative tax records, we document the rise of self-employment in the U.S. in recent decades. We find that nearly one-in-five income recipients in the U.S. received BI in 2014 – underscoring the importance of the growing literature on self-employment and the rise of the “gig” economy. We also use these administrative tax records to document that the share of income recipients who receive BI rose 15% between 1991 and 2016, which is consistent with the earlier work by others, including Jackson et al. (2017) and Abraham et al. (2018a), on the rising share of the population with BI.
By linking information at the worker-level on wage and salary jobs and self-employment, we are able to follow workers as they flow between different employers. We also track their movements between wage and salary jobs and self-employment, and as they take on or discontinue stopgap or secondary work. We use our linked data to show that self-employed workers tend not to receive wage and salary income from their owned businesses. This finding has two important implications. First, there is a small literature, starting with Agarwal et al. (2016) and Kerr and Kerr (2017), that attempts to identify business founders, owners, and entrepreneurs as a firm's top wage and salary earners (using matched employer–employee records). Our finding that self-employed individuals rarely receive wage and salary income from their owned business cautions against such an interpretation. Second, our result that BI recipients are mostly absent from U.S. matched employer–employee data indicates that the self-employed are, by and large, omitted from existing studies of labor market fluidity including, e.g., Hyatt and Spletzer (2013) and Davis and Haltiwanger (2014).
Next, we use these linkages to compute the rates of entry into and exit from BI receipt and show how the dynamics of self-employment jobs compare to flows in and out of wage and salary work. We find that transition rates into and out of self-employment have been lower than hire and separation rates for wage and salary jobs, implying that self-employment is more stable than the typical wage and salary job. In addition to documenting that the self-employed are largely omitted from the existing literature measuring the decline in labor market fluidity (Hyatt and Spletzer, 2013; Davis and Haltiwanger, 2014), we also corroborate the general findings of this literature as we are the first to document the slowdown in labor reallocation among wage and salary workers using individual-level U.S. income tax records.
These facts provide new insight into the dynamics of this growing segment of the U.S. workforce and imply that incorporating BI receipt into labor reallocation measures should lead to smaller measured declines in recent decades. We find that incorporating self-employment transitions into our labor reallocation measures has a substantial impact on hire and separation rates, reducing the measured declines by 1.3 and 1.4 percentage points respectively (accounting for 8.3% and 8.7% of the observed declines using only wage and salary earnings). However, this reduction primarily works through dampening the measured high volatility among wage and salary jobs observed before the Great Recession and especially before 2000. Measures of hire and separation rates post 2007 are largely unaffected by the inclusion of transitions to and from self-employment. We find much smaller offsetting effects (in the range of 0.1 to 0.2 percentage points) for employer-to-employer transitions as well as transitions into and from nonemployment. This implies that the decline in labor reallocation measured by a broad range of studies including Hyatt and Spletzer (2013) and Davis and Haltiwanger (2014) is a real phenomenon, and not the result of these studies’ omission of the dramatic rise in self-employment over this period.
We further find that most of the offsetting effect of including BI recipients in hire and separation rates can be attributed to jobs that are secondary sources of income, or short in duration. Among such jobs, inclusion of BI recipients offsets 1.1 and 1.2 percentage points of the measured decline in the incidence of hires and separations, respectively. Given that these secondary and short-duration jobs had the most dramatic measured declines among measures of labor reallocation (Hyatt and Spletzer, 2017; Hall and Schulhofer-Wohl, 2018; Pries and Rogerson, 2019), this finding suggests that the increase in the population receiving BI has played a role in the long-run decline in marginal wage and salary jobs.
We next explore the relationship between BI receipt and the number of wage and salary jobs worked. We find evidence that self-employment jobs displace wage and salary jobs, but this occurs primarily for workers who derive the majority of their earnings from self-employment. Self-employment jobs that supplement a worker's wage and salary earnings do not appear to displace secondary wage and salary jobs for these same workers. Further, these differing degrees of crowding out have remained fairly constant over time.
We also explore to what extent the rising prevalence of self-employment may be thought of as true reallocation of labor as opposed to reclassification of work. Put differently, we estimate how much of the growth in self-employment represents a redefinition of firm boundaries, as the tasks are reassigned from wage and salary employees to self-employed contractors, vs. a reallocation of labor to new tasks, as self-employed workers take up tasks that are distinct from the work they had previously done as wage and salary employees. To do this, we identify the number of distinct industries in which an individual works in each year (whether wage and salary or self-employment). To the extent that self-employment represents only a redefinition of firm boundaries, we can expect that the number of industries in which an individual works in a given year will be unaffected by whether the individual is a wage and salary employee or self-employed. Instead, we find that for years in which individuals received BI, they tended to work in 0.43 to 0.44 more industries in that year relative to years they only received wage and salary income (a 34% increase in the number of industries worked in the year). This finding suggests that BI recipients tend to work in new industries rather than the same industries from which they receive wage and salary payments. This result is broadly consistent with the findings of Collins et al. (2019), who suggest a limited role for firms reclassifying workers in the rise of the number of self-employed workers.
There are no standard definitions for the terms “self-employment” and “business ownership.” A wide spectrum of work arrangements may appropriately be captured by these labels, and nuanced language is needed to communicate delineations between some of these types of jobs. This lack of uniformity poses a challenge for surveys that seek to elicit information about the nature of these work arrangements from respondents. For this reason, workers who receive only secondary self-employment earnings are more likely to not report these earnings on surveys. Workers who receive payments from a business as a contract worker but in all other ways appear similar to other wage and salary workers are more likely to misclassify these earnings in survey responses (Abraham et al., 2020).
We use tax reports of self-employment and business ownership earnings to measure participation in these non-traditional work activities for the U.S. In this section, we identify the tax (administrative records) data sources we use to count these jobs and to explore the relationship between BI and labor market fluidity. Using these data, we demonstrate that earnings from the vast majority of these “jobs” are not present in administrative records covering wage and salary jobs and are thus not included in conventional measures of labor market fluidity. In this section, we also define the employment and fluidity measures we use throughout this paper.
Each year, each tax-paying U.S. business reports annual earnings associated with each wage and salary worker on Form W-2, Wage and Tax Statement. For this analysis, we use the universe of W-2 earnings records in each year beginning in 2005. Among business owners and other self-employed, however, the scope of covered activity and the type of information collected each year by the U.S Internal Revenue Service (IRS) varies with the legal form of organization of the business entity. This information is summarized in Table 1.
Administrative records data on U.S. wage and salary and BI
| Employee | W-2 Wage and Salary Statement | N/A | Social Security Administration MEF, DER extract, 1991–2015 | W-2 Records 2005–2015 |
| Sole proprietorship | 1040 Schedule C Profit or Loss from Business | If total self-employment income >$400 | Social Security Administration MEF, DER extract, 1991–2015 | Census nonemployer and employer Business Registers 2007–2015 |
| Partnership | Schedule K1: Partner's Share of Income, Deductions, Credits, etc. | If total self-employment income >$400 | Social Security Administration MEF, DER extract, 1991–2015 | Schedule K1 filings 2007–2015 |
| S corporation | Schedule K1: Partner's Share of Income, Deductions, Credits, etc. | No | N/A | Schedule K1 filings 2007–2015 |
| C corporation | No | N/A | N/A |
DER, Detailed Earnings Record; MEF, Master Earnings File; SE, Self-Employment Tax.
The vast majority of self-employed (whether or not they consider themselves business owners) operate as sole proprietors. In addition to owners of conventional businesses, this legal form of organization covers freelancers, contract workers, “gig” workers, etc. Each year, these sole proprietors report their receipts, expenses, and profits on IRS Form 1040 Schedule C Profit or Loss from Business. Information from these Schedule C filings is maintained at the U.S Census Bureau in the Nonemployer and Employer Business Registers. Using these data, we are able to identify business owners beginning in 2007.
Owners of partnerships and S corporations are issued an information report by the business called a Schedule K1 that identifies the owner's ownership share of business profits in a given year and thus their associated ownership earnings. These pass-through entities covered by Schedule K1 may have multiple owners. We include information on these entities linked to their owners for years 2007 through 2015.
In each year, sole proprietors and partners who receive (from all combined ownership activity) net annual earnings above $400 are also required to file a Schedule Self-Employment Tax (SE) with the IRS. The Master Earnings File (MEF) database maintained by the Social Security Administration contains information on BI from Schedule SE filings as well as information on wage and salary earnings from each Form W-2 a person has received during the year. Note that the MEF lacks identifiers for the businesses that Schedule SE filers own. Therefore, the only distinction we can make is whether or not an individual receives BI, not the dynamics associated with the particular businesses that they own. The many different types of legal form suggest the question of why all businesses are not of the same type. One reason that there are so many Schedule C businesses is that this is the only legal form for which one does not need an Employer Identification Number (EIN). It is therefore the default legal form for individuals doing freelance work or working as an independent contractor. It is also the default legal form for those who participate in the “gig economy” through online platforms.
We measure the relationship between BI and labor market fluidity using two administrative records datasets. First, we use survey responses linked with administrative records data. Specifically, we use Current Population Survey (CPS) records enhanced with longitudinal administrative records providing information on years 1990–2016. These administrative records data cover wage and salary jobs as well as self-employment spells for sole proprietors and partners. A sole proprietorship is an unincorporated business owned by a single individual, where the individual is legally liable for the debts and obligations of the business. A partnership is a business jointly owned by a set of individuals (or companies), where each owner is legally liable for the debts and obligations of the business – although limited liability partnerships can differentiate the degree of liability across owners. An S corporation is an incorporated business owned by a restricted number of individuals, where the incorporation shields the owners from some legal liability for the debts and obligations of the business. For income tax purposes, every year, employers in the U.S. are legally required to file W-2 forms with the IRS, where these forms report the wage and salary earnings of each individual employed during the year by the employer. Because these filings are mandatory for the employer, they provide a nearly comprehensive record of wage and salary income in the U.S.
Our first dataset is the Annual Social and Economic (ASEC) Supplement to the CPS linked with administrative records on wage and salary earnings, as well as BI. In each year, sole proprietors and partners who receive (from all combined ownership activity) net annual earnings above $400 are required to file a Schedule SE with the IRS. The MEF database maintained by the Social Security Administration contains information on BI from Schedule SE filings as well as information on wage and salary earnings from each Form W-2 a person has received during the year. Our analysis uses an extract (referred to as the Detailed Earnings Record [DER]) that includes MEF records for each CPS respondent for whom a Protected Identification Key (PIK), a person-level longitudinal identifier, is available. Note that the CPS-DER lacks identifiers for the businesses that Schedule SE filers own. Therefore, the only distinction we can make is whether or not an individual receives BI, not the dynamics associated with the particular businesses that they own.
By using the sample weights from the CPS-ASEC in combination with DER administrative information for the same year, our dataset can be used to estimate population-level characteristics of Schedule SE recipients as well as W-2 jobholders in each year. It is worth noting that this linked dataset provides information (including weights) for only those CPS respondents who have received a PIK and are therefore linkable with administrative records data sources. Because not all respondents receive a PIK, we create sample weights that adjust for the likelihood of receiving a PIK. We use the base weights for the CPS multiplied by the inverse of the estimated probability of receiving a PIK from a linear probability model. Observable characteristics used to estimate this probability are age categories, education categories, gender, race group indicators (black alone, white alone, and any other race), marital status, a foreign born indicator, and indicators for state of residence.
We construct our second dataset from universe-level sources of wage and salary earnings and BI for several legal forms of organization. We use the universe of W-2 records for wage and salary jobs starting in 2005. We obtain sole proprietor ownership spells from the U.S. Census Bureau nonemployer and employer business registers. For partnerships and S corporations, which are required to file with the IRS an annual Schedule K1 form for income tax purposes, we obtain the universe of Schedule K1 data filings from 2007 through 2015. See Garcia-Perez et al. (2013) for further description of the sole proprietor data. See Goldschlag et al. (2017) for further descriptions of the Schedule K1 data and linkages. One potential concern with using administrative tax records for measuring the prevalence of different types of self-employment activity is that some changes in the prevalence in particular types of tax records could reflect changes in filing requirements or incentives as opposed to changes in real activity. Our time series excludes the two most significant changes to the tax treatment of different legal forms of organization. Our analysis starts 4 years after the Tax Reform Act of 1986 and ends two years before the 2017 Tax Cuts and Jobs Act. These two pieces of legislation strongly influenced the composition of U.S. businesses by legal form; see Dyrda and Pugsley (2019).
Our universe-level dataset offers several advantages over the CPS-DER. First, it is universe-level data rather than a sample of the population. Second, it includes S corporations and distinguishes between sources of BI (by legal entity). Third, by obtaining ownership information directly from tax reports, we gain a wealth of information on the entities they own and operate. If an individual has multiple businesses, each with a distinct EIN, then ownership is considered separately for each business. This contrasts with the CPS-DER, which pools together all sole proprietor and partnership earnings for the person in a calendar year and so does not allow this distinction. Fourth, we obtain information on sole proprietors and owners of partnerships who did not file a Schedule SE. Finally, the business identifiers in our universe-level data, along with the universal coverage of wage and salary payments for these businesses, allow us to assess the extent to which business owners receive wage and salary payments.
To determine whether BI payments are indeed omitted from wage and salary records, we now examine how often business owners receive wage and salary payments from the businesses they own. To do so, we merge our universe-level business ownership data with the W-2 wage and salary records based on the EIN of the employer and the PIK of the owner. A match on EIN and PIK indicates that, in addition to the BI, the owner also receives wage and salary payments from the owned business.
As shown in Table 2, relatively few business owners received wage and salary income from their businesses. Only 7.4% of sole proprietors and 1.1% of the owners of partnerships received wage and salary income from their businesses. The owners of S corporations were more frequently employees of the businesses that they own, with 40.4% of the owners of S corporations receiving wage and salary income from their owned business. These results indicate that most business owners are not present in employer-reported wage and salary records – and thus BI recipients are generally omitted from labor reallocation statistics. This is because during our time series, sole proprietors and partners were legally prohibited from receiving wage and salary payments. In contrast, the owners of S and C corporations who perform substantial services for their businesses were legally required to receive wage and salary compensation.
Share of business owners who receive wage and salary payments
| Owners of EIN firms in W-2 (%) | 7.4 | 1.1 | 40.4 |
The results of this exercise have two important implications. First, these findings have important implications for the small literature that, starting with Agarwal et al. (2016) and Kerr and Kerr (2017), assumes that business owners can be found among the higher-earning employees in U.S. matched employer–employee data. For additional discussion of the legal requirements or prohibitions regarding wage and salary payment to business owners; see Appendix C. There we also more closely replicate the approach of Agarwal et al. (2016) and Kerr and Kerr (2017), who label an individual as a business founder if the individual was one of the three highest earning employees in the year that the business hired its first employee; see Appendix Tables C1 and C2. These results provide additional reasons that researchers should not assume that business owners are usually present in U.S. matched employer–employee data.
Second, these results demonstrate that BI recipients are omitted from most U.S. labor reallocation statistics. Most such measures are derived from employer reports of their payroll data. For the Business Employment Dynamics (BED) and Job Openings and Labor Turnover Survey (JOLTS), the reference point is employees who are on their payroll in the pay period for the week containing the 12th of the month. The Quarterly Workforce Indicators (QWI) and Job-to-Job (J2J) are derived from payroll tax data that serves as matched employer–employee data.
We construct individual-level annual measures of employment, hires, and separations using the concepts developed by Abowd et al. (2009). See Appendix A for formal definitions. Note that, following conventional estimation of labor reallocation rates, we focus on the presence or absence of particular income sources. The volatility of self-employment income is an important subject for future research.
Following Hyatt et al. (2014), we also construct annual measures of worker transitions into and out of employment, as well as transitions between dominant employers from one year to the next. We identify a worker's dominant employer at the start of a given year as the employer that: (1) paid the worker positive earnings in both the current and previous year (indicating that the worker was employed at the start of the current year), and (2) paid the highest cumulative earnings, over those two years, among the set of firms that employed the worker at the start of the current year. If an individual's dominant start-of-year employer changes from one year to the next, then we infer an employer-to-employer transition. If a person is employed at the beginning of a year but not at its end, we infer a employment-to-nonemployment transition. Finally, if a person is employed at the end of a year but not at its beginning, we infer a nonemployment-to-employment transition.
We now measure how prevalent BI receipt is in the U.S., and how this has evolved over time. To do so, we consider all income recipients, and what share of them receives BI. The results in this section show that a substantial and growing share of the U.S. population are BI recipients.
We categorize workers into one of three types of employment based on their receipt of wage and salary income and/or BI in a given year. Our first category of income recipients consists of workers who only have wage and salary income. Our second category consists of individuals who only receive BI. Our third category consists of those individuals who receive both BI and wage and salary income. Our calculations here exclude the nonemployed, so these three shares sum to 100% in each year and are exhaustive of the population with positive wage and salary income or BI.
We measure the trends in BI receipt over time. Figure 1A shows that the share of individuals receiving any BI in the CPS-DER records rose 15% between 1991 and 2016. The share with only wage and salary income fell from 90.1% in 1991 to 88.5% in 2016, which corresponded with an increase in the share with any BI from 9.9% to 11.5%.
Figure 1
Shares of the population with income in a calendar year, by income source.
A larger fraction of the population received BI in the universe-level data than in the CPS-DER. As shown in Figure 1B, according to the universe-level W-2 and BI records, 19.6% of income recipients received some BI in 2014. Furthermore, BI was the sole source of income for 10.9%. In the CPS-DER (see Figure 1A), a smaller share (11.5%) of income recipients had BI in 2014. The higher share of the population with positive BI in the universe-level data is likely due to the fact that our CPS-DER sample only includes sole proprietors and owners of partnerships who file a Schedule SE. The CPS-DER thus excludes owners of S corporations as well as sole proprietors and owners of partnerships who earn less than $400 in BI or who file a 1040 Schedule C (Profit of Loss from Owned Business) but who do not report self-employment earnings on Schedule SE for any other reason.
Many new BI recipients rely on this income exclusively and the majority of self-employment jobs have since 1991 been workers who have no other source of earnings. This group has grown modestly over time, especially during times of economic downturn. This pattern is consistent with workers who may be detached from or who may not be engaging in traditional wage and salary work, finding stopgap or entry-level work more readily from self-employment. It is worth noting that, following the recession in the early 2000s as well as the great recession, the rise in the share of workers who receive only BI rises and does not return to pre-recession levels. Overall, however, this group has declined as a proportion of all self-employment (the share of individuals with only BI grew from 6.0% of all income recipients in 1991 to 6.5% in 2016 but declined as a proportion of all self-employment from over 60% in 1991 to about 54% in 2014). As shown in Figure 1(b), the universe-level administrative records data indicate that the share of individuals with only wage and salary income declined from 81.3% in 2007 to 80.4% in 2014. The ordinal ranking of the shares of BI categories was similar in both the CPS-DER sample and the universe-level sample. In the universe-level administrative records data, 9.5% of income recipients had only BI in 2007, while 10.9% did in 2014.
The broad result of increasing BI receipt echoes earlier findings of Abraham et al. (2018b) and Katz and Krueger (2019). It is important to note that these seemingly modest changes of a couple of percentage points in the share of the population that receives BI reflect important changes in employment and the number of business entities in the U.S. in recent decades. Each percentage point increase in the share of this population corresponds with more than one million new BI recipients, and roughly as many new business entities. To see this, note that the employed population in the U.S. was 157 million in June of 2019, according to the U.S. Bureau of Labor Statistics (2019). We also explore this further in Appendix Figure B1, which presents totals for the data sources we consider. The number of individuals with BI in the CPS-DER increased from 13.0 million in 1996 to 19.4 million in 2016. In the universe-level administrative records data, it increased from 16.7 million in 2008 to 18.9 million in 2015.
We now consider the transition dynamics of wage and salary employees as well as BI recipients. This is an important first step because this paper is the first to use federal income tax records to measure labor reallocation rates. We calculate hire and separation rates following the definitions in Abowd et al. (2009). We also calculate entry and exit rates for BI recipients, and this is the first study to consider how these entry and exit rates evolve over time. As described above in Section 2.4, these are defined analogously to hire and separation rates. We present hire and separation rate time series that are consistent with published estimates. We also show that BI receipt is more stable and less cyclical than the typical wage and salary job.
We measure transition rates into and out of BI receipt, and compare these with hire and separation rates for wage and salary employees in Figure 2. The hire and separation rates in Figure 2A show the “stair-step” pattern noted by Hyatt and Spletzer (2013), with strong declines during and after the 2001 and 2007–2009 recessions, and only a modest recovery in the expansions that follow them. Additional evidence on the hire and separation rates from W-2 data is shown in Appendix Figure E1 and the associated discussion. Note that the W-2 hire and separation rates that we report in Figure 3 range from 41% to 69%. These hire and separation rates are much higher than most published series because our series are calculated on an annual basis, whereas most other series are published on a monthly or quarterly basis. In Appendix Figure E2, we compare the annual hire and separation rates with other available data by summing monthly or quarterly rates for any given year. The estimates in Figure Figure 3 are within the range of other available data sources.
Figure 2
Hire and separation rates vs. BI entry and exit rates.
Figure 2A shows three facts which indicate that incorporating BI receipt into our measures of hire and separation rates should result in series that are lower in magnitude, exhibit less cyclicality, and have less of a trend decline. First, prior to the 2007–2009 recession, the wage and salary hire and separation rates were consistently higher than BI entry and exit rates, and this difference peaked at 24.8 percentage points immediately prior to the 2001 recession. Second, BI entry and exit rates were less cyclical than wage and salary hire and separation rates. Third, BI entry and exit rates did not show evidence of trend declines. The entry rate was virtually unchanged from 45.9% in 1991 to 44.6% in 2016. The exit rate increased slightly from 42.9% in 1990 to 46.7% in 2015.
Using universe-level BI records, we find that the rate of BI entry and exit differed substantially by legal form of organization. Figure 2B shows that sole proprietor businesses had entry and exit rates between 38% and 52%, whereas the BI entry and exit rates for partnerships and S corporations were about two to four times lower (ranging from 9% to 19%). Goetz et al. (2017) present entry and exit rates for sole proprietors in 2009. The entry and exit rates for BI for sole proprietors are broadly consistent with, but somewhat lower than, the entry and exit rates calculated for Schedule SE filers from the CPS-linked sample. Only sole proprietors earning more than $400 from the business are required to file a Schedule SE.
These findings provide guidance on how omitting the self-employed and business owners affects measured labor reallocation rates. The hire and separation rates derived from our W-2 data are similar to most published data series and therefore are consistent with the findings of Hyatt and Spletzer (2013) and Davis and Haltiwanger (2014) on changes in the hire and separation rates in recent decades. Our findings on stable BI entry and exit rates contrast with the evidence presented by Decker et al. (2014) and Pugsley and Şahin (2018) on the declining entry and exit rates of specifically employer businesses. Overall, our results indicate that including BI transitions should lower the magnitude and cyclicality of labor reallocation rates. We explore this in the following section.
We now assess the implications of excluding BI recipients from measures of labor market fluidity. To do so, we first compute standard measures of labor reallocation using wage and salary payments alone. We then recompute these labor reallocation measures treating the receipt of BI as though it were a wage and salary job. To the extent that BI receipt captures labor conducted by the recipient on behalf of the owned business, this allows us to measure the effect of using more inclusive definitions of labor reallocation. This is the antecedent upon which the interpretation of our exercises in this Section depends. If many BI recipients are performing labor, then it makes sense to include them in a labor reallocation measure. If they rather take no active role in the business other than providing investment or capital, then it makes less sense to include them. It is likely that the income of sole proprietors is more indicative of labor than that of the owners of partnerships or S corporations.
The following exercises demonstrate that increasing BI receipt explains a small but nontrivial share of declining hires and separations. We further show that this offsetting effect occurs mainly among jobs that are secondary sources of income, or short in duration. The employer-to-employer transition rate is relatively unaffected by including or excluding BI recipients. These findings imply that self-employment, including that associated with the rise of the “gig economy,” is an increasingly important opportunity for temporary employment.
We now include BI recipients when we calculate hire and separation rates. For comparison, we also show hire and separation rates using W-2 data alone. Figure 3 shows results using the CPS-DER. Because BI recipients constituted only 6–9% of consecutive-year employment in the CPS-linked records, the more inclusive hire (Figure 3A) and separation (Figure 3B) series mimic those created using only W-2 records of wage and salary income. For a time series of the BI share of consecutive-year employment, as well as its share of dominant (maximal income) consecutive-year employment; see Appendix Figure D1. The universe-level dataset shows that including BI entrants and exits in hire and separation rates results in both a more substantial lowering of the hire and separation rates and smaller cyclical fluctuations in these labor reallocation measures. See Appendix Figure F1.
Figure 3
Hires and separations.
Because BI entry and exit rates were lower than wage and salary hire and separation rates up until the 2007–2009 recession, the more inclusive hire and separation rates tended to be lower than those that relied on the W-2 wage and salary records only. The differences between these hire and separation rates were largest in the 1990s, peaking in 2000 at 1.8 percentage points. As the wage and salary hire and separation rates fell during the 2001 and the 2007–2009 recessions, the relative acyclicality of the BI entry and exit rates caused the gap between the combined and W-2 only hire and separation rates to shrink – such that from 2008 onward the difference never exceeded 0.6 percentage points. Including BI receipt in the calculation of hire and separation rates also reduces the measured decline from 1990 to 2016, since the large gap observed in the 1990s between the BI entry and exit rates vs. the W-2 hire and separation rates had largely disappeared by 2009. This dampening effect was largest for the separation rate – with the separation rate calculated using both BI and wage and salary income falling 1.2 percentage points less from 1990 to 2015 relative to the separation rate calculated using only wage and salary records (the combined separation rate fell 13.0 percentage points, whereas the wage and salary separation rate fell 14.2 percentage points over this period). The combined hire rate, on the other hand, fell by 5.3 percentage points from 1991 to 2016, which was 0.2 percentage points less than the 5.5 percentage point decline in the hire rate measured using only wage and salary income.
In Figure 4, we consider how including BI can affect the employer-to-employer transition rate, as well as the rate at which workers transition into and out of nonemployment. We find that including BI has little effect on these reallocation rates. Additional detail on our employer-to-employer and transition series is shown in Appendix Figure E3 and the associated discussion. In Appendix Figure E4, we benchmark these transition rates against the similar estimates from the CPS, JOLTS, and J2J. The CPS-ASEC also asks how many non-overlapping employers a worker had in the previous year; see Molloy et al. (2016) and Hyatt et al. (2018). In Appendix Figure E5, we compare the CPS-DER W-2 employer-to-employer transition rate with responses to the CPS-ASEC. In Appendix Figure F2, we present analogous results using our universe-level data.
Figure 4
Employer-to-employer and nonemployment transitions.
The employer-to-employer transition rate evolved in a basically identical manner whether we include or exclude the self-employed, as shown in Figure 4A. In the CPS-DER, differences between the employer-to-employer transition rate using W-2 data alone, relative to that which includes BI recipients, were at most 0.2 percentage points. As a result, the long-term evolution of the employer-to-employer transition rates including and excluding BI recipients are also virtually identical. Both series increase by about 2.0% from 1991 to 2015.
Results for employment-to-nonemployment transitions are shown in Figure 4B. Again, the rates are similar whether including or excluding BI recipients. In the CPS-DER, transition rates including only wage and salary employment to nonemployment were consistently higher than those same transition rates that include BI recipients, by 0.1 to 0.5 percentage points. Such evidence is consistent with transitions between wage and salary work and self-employment being omitted from employer-reported administrative records. Using employer-reported administrative records alone, such moves may appear to be into or from nonemployment.
Results for nonemployment-to-employment transitions, shown in Figure 4C, tell a broadly similar story. Transition rates excluding BI recipients were almost always higher than those that include them, by as much as 0.6 percentage points (the exception is 2009, when they were lower by less than 0.1 percentage point).
The decline in labor reallocation rates in recent decades was concentrated among jobs that provided secondary income or were short in duration. Hire and separation rates capture transitions into and out of all wage and salary jobs. The dominant employer-based measures of worker reallocation, on the other hand, capture only transitions to or from an individual's dominant employer as of the start of the year. Thus, these dominant employer-based measures of worker reallocation omit both of the following: (1) coincident, but lower earning jobs (“secondary jobs”); and (2) jobs that start and end within the same calendar year (“short-duration jobs”). As formalized by Hyatt and Spletzer (2013), the residual between the hire and separation rates and these dominant employer-based measures are precisely these secondary and short-duration jobs. In addition to Hyatt and Spletzer (2013), a few other papers have considered the role of short-duration jobs in declining labor market fluidity. Pries and Rogerson (2019) showed the importance of short-duration jobs for labor market fluidity using the QWI. Hyatt and Spletzer (2017) explored this using the LEHD microdata which underlie those measures. Hall and Schulhofer-Wohl (2018) explored declining short-duration jobs using data from the CPS.
We plot these transitions in Figure 5, both using W-2 data alone, as well as using data on BI. The results are similar to those in Figure 3. Secondary and short-duration jobs followed the “stair-step” pattern noted by Hyatt and Spletzer (2013). These jobs declined during the 2001 recession, were stable between 2002 and 2006, and then declined again during the 2007–2009 recession. Secondary and short-duration jobs were the main driving force in the stair-step pattern in overall hires and separations, consistent with the findings of Hyatt and Spletzer (2017). Including BI recipients in the calculation of secondary and short-duration hires and separations lowers labor reallocation rates by nearly a percentage point in the 1990s, but by only 0.2 to 0.3 percentage points in more recent years. This differential shows that the inclusion of BI recipients has a substantial offsetting effect on the measured decline in the hire and separation rates for secondary and short-duration jobs.
Figure 5
Secondary and short-duration job hires and separations.
The inclusion or exclusion of business owner income affects the empirical measures that are used to assess changes in labor market fluidity and the extent of its decline. We summarize our findings in Table 3, which considers our results from the CPS-DER for the years 1994 and 2014. We choose these particular years to assess the trend decline in labor market fluidity for three reasons. First, 1991 was a recession year, and so its low transition rate is likely due to cyclical factors rather than any trend change. Second, 2015 is the last year which can be calculated given available data, and so is most likely to be subject to revision with updates to the administrative records data. Third, the increase in the transition rates from 1993 to 1994 was the largest in most of our labor reallocation time series. These large increases were also coincident with a redesign of the CPS, and so may not have been driven by economic conditions.
Measured decline in reallocation (percentage points) including vs. including self-employed: 1994 vs. 2014
| Hires | 15.7 | 14.4 | 1.3 | |
| Separations | 16.1 | 14.7 | 1.4 | |
| Employer-to-employer | 0.7 | 0.6 | 0.2 | |
| Employment-to-nonemployment | 1.8 | 1.7 | 0.1 | |
| Nonemployment-to-employment | 1.3 | 1.1 | 0.2 | |
| Secondary/short-duration hires | 13.9 | 12.8 | 1.1 | |
| Secondary/short-duration separations. | 13.8 | 12.6 | 1.2 |
Because BI entry and exit rates were stable over time, and, during the 1990s, much lower than wage and salary hire and separation rates, their inclusion lowers the total measured decline in the hire (separation) rate by 1.3 (1.4) percentage points. The inclusion of BI receipt therefore offsets 8.3% (8.7%) of the decline in the hire (separation) rate. The inclusion of BI receipt also offsets some of the measured declines in the employer-to-employer, employment-to-nonemployment, and employment-to-nonemployment transition rates, but the magnitude of these declines was smaller in magnitude (only offsetting 0.1 to 0.2 percentage points change in each measure). The residual comparing these measures with each other is secondary and short-duration jobs, which (mechanically) end up being the dominant mechanism by which rising BI receipt offsets the measured declines in hire and separation rates. Inclusion of BI receipt changes the measured decline in the secondary and short-duration job hire rate by 1.1 percentage point (from 13.9% to 12.8%) and its separation rate by 1.2 percentage points (from 13.8% to 12.6%).
These findings highlight the importance of increasing BI receipt on understanding changes in labor market fluidity, both on the underlying economic phenomena as well as the measurement issues. Declines in measured labor market fluidity are concentrated among hires and separations into and from jobs that either provide relatively little income, or do not last very long. Secondary and short-duration BI receipt have become more prevalent in the U.S., while over these same decades wage and salary jobs have disappeared. The magnitudes of these changes are, however, very different. Therefore, the offsetting effect is about an order of magnitude smaller than the decline in hire and separation rates.
The preceding section provides evidence that the inclusion or exclusion of BI affects the measured decline in labor reallocation rates. In this section, we follow workers as they transition between wage and salary work and BI receipt to further refine our understanding of the economic significance of these flows.
We first explore the extent to which BI receipt displaces workers’ wage and salary jobs; as well as how this has changed over time. In some cases, BI receipt may indicate that workers switched their labor activity from wage and salary jobs to self-employment, resulting in workers having fewer wage and salary jobs in years when they receive BI. Alternatively, when engaging in self-employment work, workers may tend to retain their existing jobs while expanding into new sources of earnings.
We seek to assess whether transitions between wage and salary employment and BI receipt represent efficiency-enhancing labor reallocation. For example, if such a transition occurs because a company changed the legal structure of the employment agreement with a worker from being a wage and salary employee to being a self-employed contractor, then this transition does not improve the efficiency of the labor market. We therefore explore to what extent the self-employment activity takes place within the same broad industry sector as the wage and salary job(s). For example, to the extent that ridesharing platforms encourage wage and salary taxi drivers to switch to being self-employed drivers on the ridesharing platforms, then these transitions may be thought of as within-industry reclassification rather than efficiency-enhancing labor reallocation. If, instead, BI receipt tends to diversify the industry composition of the work performed by an individual, then the self-employment activity is more likely to represent efficiency-enhancing labor reallocation.
The results that follow suggest that rising self-employment represents reallocation. This is especially true for the volatile tail of secondary and short-duration jobs. We show that when self-employment is a person's dominant source of income, it displaces about one wage and salary job. However, we find a much smaller relationship among secondary jobs. We further find that BI recipients increase the number of industries in which they work. Both of these findings suggest that self-employment is an increasingly important component of productivity-enhancing labor reallocation.
To examine the degree of displacement of wage and salary jobs by BI receipt, we conduct a regression analysis. Our specifications exploit within-person, over-time variation in self-employment status. Our outcome variable of interest is the number of wage and salary jobs held in a given calendar year. If movement into self-employment represents an expansion of the intensity of individuals’ labor activity, then we would expect less than a one-for-one fall in wage and salary jobs. Our data do not permit us to observe workers holding more than one self-employment job in any given year. Instead, we can observe only the presence and amount of total combined self-employment earnings. Thus, it is possible that BI reported on an individual's Schedule SE may represent activities that would have generated multiple W-2s for a wage and salary worker. For example, in previous decades a particular worker might have received multiple W-2s for short stints as a taxi driver and as an office temp worker, whereas in today's “gig economy,” that same worker may instead work short stints via a ridesharing platform and a clerical services platform. If this is the case, then our accounting method used in the previous section, which allows at most one Schedule SE or sole proprietor job (and hence business owner entry and exit) per person per year, may understate the true amount of worker reallocation. In this case, our estimate of the offsetting effect of self-employment dynamics on labor reallocation would be a lower bound.
Results are shown in Table 4. We regress the number of wage and salary jobs held by an individual on an indicator for whether the individual is self-employed in that year. We report estimation results for regressions performed both with and without worker-specific fixed effects. We consider all individuals who worked in wage salary or self-employment, and include an indicator as to whether a worker ever received self-employment income. In a typical year, workers who are
Number of wage and salary jobs worked regressed on BI
| Intercept | 1.420*** (0.000) | 1.409*** (0.000) | ||
| Ever BI | 0.074*** (0.000) | 0.081*** (0.001) | ||
| Ever BI* BI | −0.143*** (0.001) | −0.101*** (0.001) | −0.088*** (0.001) | 0.013*** (0.001) |
| Ever BI* BI*BI dom | −1.002*** (0.001) | −0.818*** (0.001) | −1.010*** (0.002) | −0.817*** (0.001) |
| Worker fixed effects | N | Y | N | Y |
| 0.096 | 0.446 | 0.151 | 0.652 | |
*, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
In years with positive earnings, workers received earnings from almost one and a half employers if they received only wage and salary income. Specifically, they worked at 1.409 to 1.420 employers if never self-employed, and 1.490 (=1.409+0.081) to 1.494 (=1.420+0.074) if ever self-employed. Self-employment income displaced wage and salary jobs, but there were substantial differences depending on whether it constituted a worker's dominant form of income. If self-employment provided secondary income, it displaced at most a small fraction (–0.013 to 0.143) of a wage and salary job. In contrast, if self-employment was a worker's dominant form of income, it displaced about one (0.804 to 1.145) wage and salary job. This suggests that the secondary self-employment generates increased labor activity. In Appendix Table G1 we consider the relationship between self-employment and total jobs worked without distinction on whether it constituted a worker's dominant form of income. We find that, overall, self-employment income is associated with 0.313 to 0.883 fewer wage and salary jobs. This suggests that there is less than one-to-one displacement of wage and salary jobs when workers receive self-employment income.
Has the relationship between BI receipt and wage and salary work changed over time? To address this question, we estimate a regression specification in which we allow for the effect of the presence of BI to vary by year. Separately by year, we estimate the effects of BI receipt, as well as the BI being the individual's dominant source of income. We additionally control for year effects. Results of this specification are presented in Figure 6.
Figure 6
Regression point estimates over time.
A substantial change occurred in the relationship between BI receipt and the number of wage and salary jobs if the BI was the individual's secondary source of income. In the 1990s, secondary self-employment income was associated with 0.085 to 0.136 fewer wage and salary jobs. But in the years that followed the 2007–2009 recession, the presence of secondary self-employment income was associated with a reduction of only 0.032 to 0.039 fewer wage and salary jobs. Secondary income from self-employment never displaced a substantial number of wage and salary jobs and that this small degree of substitution has lessened over time. In contrast, if the BI was a person's dominant source of income, then BI receipt is associated with an additional eight-tenths fewer wage and salary jobs, and this differential was stable over time.
We now explore the extent to which self-employment is associated with individuals working in a more diverse set of sectors. If individuals work in more industries when receiving BI, this suggests that transitions into self-employment represent efficiency-enhancing labor reallocation. Specifically, we assess the extent to which BI receipt displaces wage and salary jobs where the outcome variables are both the total number of jobs held during the year (wage and salary plus self-employment) and total number of unique North American Industry Classification System (NAICS) supersectors in which the person worked in the year. The key explanatory variables are variables indicating whether the person is receiving BI and whether the BI is the person's dominant (highest) source of earnings in the year. We have NAICS supersector information for the years 2002–2015, so we run regressions on this subsample of the CPS-ASEC respondents with wage and salary earnings or self-employment earnings in the CPS-DER. The linked dataset includes individuals who are age 16 or older at the time of the CPS survey and who have received a PIK. Individuals are included in this sample for all years during 2002–2015 in which they are aged 16+ and have (1) wage and salary earnings with at least one job with non-missing NAICS code in Business Register, or (2) self-employment earnings with non-missing NAICS supersector.
Results of this exercise are presented in Table 5. Workers were employed in 1.258 industries, on average, when working in wage and salary employment. Overall, the presence of BI was associated with 0.431 to 0.444 additional industries worked. We further distinguish the effect of BI on the number of industries worked based on whether it provided an individual's main source of income. Secondary self-employment income is associated with 0.384 to 0.392 more total industries. When self-employment was a person's dominant source of income, that person worked in 0.508 (= 0.392 + 0.116) to 0.547 (= 0.384 + 0.163) additional industries.
Number of industries worked regressed on BI
| Intercept | 1.258*** (0.000) | 1.258*** (0.000) | ||
| BI | 0.444*** (0.001) | 0.431*** (0.001) | 0.384*** (0.001) | 0.392*** (0.002) |
| BI*BI dom | 0.163*** (0.002) | 0.116*** (0.002) | ||
| Worker fixed effects | N | Y | N | Y |
| 0.053 | 0.406 | 0.055 | 0.407 | |
*, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
These findings suggest that self-employed individuals, whether self-employment is their dominant or secondary source of earnings, tend to work in new industries rather than the same industries as their wage and salary employment. More broadly, these findings provide further evidence that the observed rise in self-employment has led to reallocation of work across broad sectors of the economy and has been an important contributor to labor market fluidity.
Our regression evidence implies that BI receipt is associated with increased labor reallocation. Side jobs from self-employment do not displace wage and salary jobs. When workers are BI recipients, they work in more industries. This evidence suggesting that increasing BI receipt is not mere reclassification echoes findings from Collins et al. (2019). This earlier study demonstrated that the number of recipients of 1099 forms (which are sent by online platforms to “gig” workers) has not kept pace with the rise in Schedule C filings. Our results not only provide additional evidence that increasing BI receipt is not mere reclassification but also indicate that workers are moving into this work as demand conditions indicate. This evidence suggests that increasing BI receipt plays an increasingly important role in productivity-enhancing labor reallocation.
Our paper explores the relationship between the rising share of the U.S. population receiving BI and the declining dynamism of the U.S. economy, specifically through the lens of labor reallocation. We show that most business owners do not receive wage and salary payments from their owned businesses; and therefore they are largely omitted from current studies of labor reallocation. We find that including BI recipients in measures of labor market fluidity reduces the measured decline in the rate of hires and separations from 1994 to the early 2014 by 1.3 to 1.4 percentage points (about 8.3–8.7%), primarily among jobs that were secondary sources of income or short in duration.
Complimenting recent studies that show a growing prevalence of self-employment (such as Abraham et al. (2018a) and Jackson et al. (2017)); this paper documents similar increases in BI receipt using new administrative tax records for the U.S. Remarkably, we find that one-in-five income recipients in the U.S. received BI in 2014. We show that in the second decade of our sample (between 2004 and 2014), the rise in self-employment largely represents an expansion of new supplemental earnings opportunities, as these self-employment jobs are increasingly held by workers who derive the majority of their earnings from traditional wage and salary work.
Our paper relates to the literature on the role of legal protections in fostering business activity. Our finding that transitions into and out of self-employment differ based on the legal structure of the self-employment activity indicates a potential link between legal protections for self-employment activity and the stability of that activity. We find that self-employed individuals who identify as sole proprietors (and thus are personally liable for the self-employment activity) enter into and exit from self-employment at rates that are almost as high as wage and salary workers’ hire and separation rates. When, however, the self-employed individuals use more complex legal structures that better shield themselves from legal liability, we find that the self-employment activity is far more stable (with transition rates about 75% lower than those of sole proprietors).
Our findings on labor reallocation, in conjunction with the rising prevalence of self-employment, highlight many open questions that we hope future research can address. First, as we have shown with labor reallocation, labor market dynamics, both over the business cycle and in the long run, can change when the self-employed are included in our labor market statistics. Identifying which measures of labor market dynamics are sensitive vs. robust to the inclusion of the self-employed will further our understanding of the labor market. The importance of ensuring that labor market statistics accurately capture these dynamics is underscored by the fact that many of these measures of labor market dynamics inform current fiscal and monetary policy.
Second, we observe both of the following: (1) the growth in self-employment is more pronounced for individuals where it is a secondary source of income; and (2) including the self-employed mitigates more of the declines in hire and separation rates for short-duration and secondary jobs. These findings underscore the need for further analysis of the role of self-employment in helping individuals compensate for slack labor demand. This need is most acute for studies of individuals who are marginally attached to the labor market, as they are more likely to hold multiple jobs or be employed in short-duration jobs.
Third, there is the question of the relationship between the changing dynamics of employer and nonemployer businesses. As we have noted, the steady increase in BI recipients is in stark contrast to contemporaneous trends in employer business activity as considered by Decker et al. (2014) and others. It is therefore plausible that these trends are related, and that the increasing dynamism of nonemployer businesses represents economic activity that was formerly the purview of employer businesses. While this remains an important question for future research, our results provide some guidance on the nature of this shift. Our results on the relationship between BI receipt and the number of jobs and industries worked in Section 6 suggest a limited role for firms reclassifying wage and salary workers as independent contractors. These results echo the findings of Collins et al. (2019), who show that Form 1099 filings have not kept pace with the number of Schedule C sole proprietor businesses. Therefore it is unlikely that this shift is the direct action of larger, employer businesses. We therefore suggest that future research do more to systematically characterize the increase in nonemployer businesses that has occurred in the U.S. in recent decades.
Finally, our research adds to the growing body of work that documents the considerable and increasing diversity among self-employed workers. As we show in this paper, workers who derive all of their earnings from self-employment, though on the rise during economic downturns, are a declining share of the self-employed overall as are owners of partnerships and S corporations. Jackson et al. (2017) document a rise in the share of self-employed workers with low levels of deducted business expenses, suggesting that the self-employed are increasingly composed of workers supplying primarily labor. Exploring possible differences in economic status and differences in fluidity across these different groups remains an opportunity for future research. Abraham et al. (2018b) document substantial change in the demographics of self-employed workers in taxi and limousine service sector (NAICS, 4853), coinciding with the expansion of ridesharing. Abraham et al. (2018b) and Glasner (2021) both provide evidence that workers are more likely to transition to self-employment following displacement or other shocks to the traditional employment environment (a minimum wage hike, for example). An open question is to what extent certain segments in the broad population of self-employed face a diminished propensity to exit self-employment and return to traditional covered employment. If this is the case, then self-employment may have become an increasingly separate informal labor market for certain groups of the self-employed. Further research is needed to understand the heterogeneous impact of the rising self-employment on labor market outcomes of different subpopulations.