On the Nature of Entrepreneurship

This paper uses a novel longitudinal administrative dataset drawn from U.S. Internal Revenue Service (IRS) and Social Security Administration (SSA) records to characterize income dynamics and the determinants of entrepreneurial entry for pass-through business owners — sole proprietors, partners, and S corporation owners — who collectively account for over 50 percent of all U.S. business net income. The sample covers 2000–2015 and includes up to 1.3 billion person-year observations for individuals aged 25–65. The authors construct balanced panels using birth cohorts 1950–1975, impute education (college attainment) and skill (cognitive, interpersonal, manual) via machine-learning classifiers trained on CPS and O*NET data, and estimate life-cycle income profiles using a three-component model that separates individual fixed effects, group-specific time effects, and group-cohort-specific age effects.

The paper’s central departure from prior work is coverage of the full income distribution, including the high-earning right tail that household surveys such as the CPS misrepresent due to top-coding and small samples. When the IRS and CPS samples are compared on a consistent classification basis, median self-employment income is lower in the IRS data at all ages, consistent with the survey literature’s emphasis on the “typical” self-employed individual. However, mean incomes diverge sharply: the IRS shows mean self-employment income rising from $23 thousand at age 25 to $93 thousand at age 55, whereas the CPS (with incorporated owners reclassified) shows a rise from only $41 thousand to $73 thousand. Roughly 80 percent of self-employment income in the IRS data accrues to individuals above the $100 thousand threshold, compared to 42–53 percent in the CPS. The IRS-CPS gap is dominated by the right tail and concentrated in professional services and health care. For paid-employed individuals, the IRS and CPS medians and means are close at all ages, confirming the discrepancy is specific to self-employment.

The life-cycle estimation finds that individuals who have “tried self-employment” — a group earning virtually all self-employment income — start at similar average incomes to primarily paid-employed peers at age 25 but reach $134 thousand by age 55, compared with $79 thousand for paid-employed peers with the same observable characteristics. Age effects for the self-employed are 63 percent higher than for the paid-employed at age 26 and remain elevated until age 55. Time effects show dramatically greater cyclical volatility for the self-employed: income growth declined by $9,655 (2008) and $8,785 (2009) for the self-employed versus $373 and $1,583 for paid-employed in the same years, concentrated in real estate and construction.

On the determinants of entry, the paper finds: (i) no evidence that house-price appreciation raises entry rates, contra collateral-constraint hypotheses; (ii) most entrants have lower asset incomes than future entrants with the same characteristics, arguing against a liquid-wealth precondition; (iii) most entrants have higher prior labor income than future entrants, consistent with entry being driven by on-the-job experience rather than fallback from low-paid work; (iv) almost all founders report positive individual tax income in their first year of operation despite negative business net income and no external debt financing. Self-employed income growth exhibits greater dispersion — a 10th-to-90th percentile range roughly 2.5 times wider than for the paid-employed — and a Kelly skewness about 0.1 higher. A standard consumption-risk model calibrated with household-finance estimates of risk aversion rationalizes the patterns if individuals are insured against the most adverse downside shocks. Entry and exit rates are stable across the sample period, including the Great Recession, and the entrepreneurship share does not decline.

The subgroup congruent with non-pecuniary motivation — primarily self-employed individuals earning less than paid-employed peers with matching characteristics — comprises roughly 57 percent of primarily self-employed by count but earns only 16 percent of total self-employment income.

Q1: Why do IRS and CPS data give such different pictures of self-employment income? The CPS suffers from top-coding of high incomes and small samples that underrepresent high earners in key industries. The IRS-CPS mean income gap for the self-employed is dominated by the right tail: in the main IRS sample, individuals above the $100 thousand threshold earn roughly 80 percent of all self-employment income, versus 42 percent in the comparable CPS sample. The average income of top earners above $100 thousand is $355 thousand in the IRS versus $218 thousand in the CPS. The gap is concentrated in professional services and health care and persists across all income thresholds and sample definitions tested. No analogous discrepancy exists for paid-employed individuals, where IRS and CPS medians and means are close at all ages.

Q2: What does the comparison look like at the median versus the mean? At the median, IRS self-employment income is lower than both CPS samples at all ages, with the gap largest for younger owners and those with incorporated businesses — a pattern consistent with the survey-based “self-employment discount” narrative. At the mean, the IRS shows much higher income at older ages: by age 55, IRS mean self-employment income is $93 thousand versus $73 thousand in the CPS sample that includes reclassified incorporated-owner wages. The divergence arises because the mean is sensitive to the right tail, which the CPS systematically underrepresents.

Q3: How does the paper estimate life-cycle income profiles while separating age, time, and cohort effects? Individual income is decomposed into an individual fixed effect (permanent latent ability and preferences), a group-specific time effect (business-cycle fluctuations common to a group), and a group-cohort-specific age effect (life-cycle income growth). Identification exploits the overlapping cohort structure of the 16-year panel: age effects are assumed equal across cohort bins of size at least two, allowing time and age effects to be separately identified. The model is estimated in levels rather than logs to accommodate business losses. Groups are defined as a Cartesian product of 32,256 subgroups based on education, three skill dimensions, industry (21 two-digit NAICS codes), demographics (gender, cohort, marital status, children), and employment-status history.

Q4: What are the headline life-cycle income profile findings for self- versus paid-employed? Among the “primarily employed” group, those who have tried self-employment and those who are primarily paid-employed have similar average incomes at age 25. By age 55 the self-employed reach an estimated $134 thousand (2012 dollars) versus $79 thousand for paid-employed peers with identical observable characteristics. The estimated age effect for the self-employed is 63 percent higher than for the paid-employed at age 26 and remains higher through age 55. These gaps would widen further if incomes were adjusted upward for the BEA-estimated net misreporting rates of 46 percent for unincorporated owners and 14 percent for S corporation owners.

Q5: How large is the group consistent with non-pecuniary motivation, and how much income does it earn? The non-pecuniary subgroup — primarily self-employed individuals (at least 12 years in self-employment) who earn less on average than primarily paid-employed peers matched on gender, education, skills, and other characteristics — is numerically larger, comprising approximately 57 percent of primarily self-employed by count. However, this group earns only 16 percent of total self-employment income. Adjusting for paid-employed fringe benefits and self-employed income misreporting can change the group’s size but does not alter the finding that it accounts for a small income share. The paper concludes that non-pecuniary motives may guide occupational choice for many individuals but are not the driver of the typical dollar earned in self-employment.

Q6: How does idiosyncratic income risk compare between self- and paid-employed? Self-employed income changes are substantially more dispersed: the 10th-to-90th percentile range of income growth is roughly 2.5 times wider for the self-employed than for the paid-employed. Income changes for the self-employed are also more right-skewed, with a Kelly skewness difference of approximately 0.1. When a standard consumption-risk model — augmented with a lower bound on consumption growth to allow for external insurance — is parameterized with risk-aversion estimates from the household finance literature, the observed patterns are rationalized if individuals are insured against the most adverse downside shocks, i.e., the attractive aspect of self-employment is large potential upside with insured downside.

Q7: What happened to self-employed income and exit rates during the Great Recession? Time effects show steep income growth declines for the self-employed of -$9,655 in 2008 and -$8,785 in 2009, compared with much more modest declines of -$373 and -$1,583 for paid-employed peers. The aggregate income declines are concentrated in cyclically sensitive self-employed subgroups in real estate and construction, with their paid-employed counterparts experiencing only modest declines. Despite these large income shocks, exit rates from self-employment showed little change during the Great Recession, either in aggregate or in the cyclically sensitive sectors. Entry rates were likewise stable, and the share of entrepreneurs in the population did not decline over the full sample period.

Q8: Does the evidence support collateral constraints as a binding barrier to entrepreneurial entry? No. The paper tests the hypothesis, standard in the liquidity-constraints literature, that entry rates should be higher for homeowners experiencing house-price appreciation (which raises collateral value). The IRS data do not support this prediction. Separately, comparing asset incomes (interest, dividends, capital gains) of current entrants and future entrants with the same characteristics, the paper finds that most current entrants have lower asset incomes and less liquid wealth than those who switch later, which also argues against a liquid-wealth precondition for entry.

Q9: What does prior labor income reveal about why people enter self-employment? Current entrants have higher prior labor income than matched future entrants with the same characteristics, indicating they enter with accumulated on-the-job experience rather than being pushed into self-employment as a fallback after failure in paid work. This is consistent with self-employment being a deliberate, experience-driven career transition for most entrants rather than a last resort for low earners. The paper interprets this as positive evidence for the role of experience-based human capital in driving entrepreneurial choice.

Q10: How do founders finance startup costs if most have negative business net income in early years? Almost all founders in the sample report positive income on their personal (individual) tax form in the first year of operation, even though most report negative business net income and carry no external debt financing. This pattern suggests founders rely on personal income sources — prior savings, part-time paid employment, or spousal income — to cover startup costs rather than external debt, implying that formal credit-market financing constraints are not the primary barrier to entry for most entrants in the sample.

Q11: What are the scope conditions and key limitations? The sample covers pass-through owners (sole proprietors, partners, S corporation owners) and excludes C corporation shareholders, whose entrepreneurial income does not flow to individual returns until distributed. Income measures exclude most employer fringe benefits; capital gains are excluded from self-employment income, and the authors note their inclusion would strengthen the main findings. The analysis covers 2000–2015 for cohorts born 1950–1975, and income is reported before taxes and transfers. Baseline estimates are not adjusted for misreporting, though BEA-implied adjustments of 46 percent for unincorporated owners and 14 percent for S corporation owners would widen the income gaps further.

Pass-through business owner: An individual who owns a sole proprietorship, partnership, or S corporation, such that business net income flows directly onto the owner’s personal tax return; excludes C corporation shareholders whose income appears only upon dividend or capital-gains distributions.

Tried self-employment: The paper’s primary self-employed comparison group within the “primarily employed” category — individuals with any years in self-employment (including frequent switchers and those with most years in self-employment) — who collectively earn virtually all self-employment income.

Group-specific age effect: The paper’s estimate of how individual income changes with age within a defined subgroup (determined by education, skill, industry, demographics, and employment history), identified by exploiting overlapping birth cohorts in the 16-year panel and separated from individual fixed effects and business-cycle time effects.

Primarily employed: Individuals with at least 12 of 16 sample years in either self- or paid-employment, with at most one intermediate year of non-employment; the paper’s main analytical focus for life-cycle income comparisons.

SOI Databank: The Statistics of Income Databank, a de-identified balanced panel combining SSA demographic records with IRS tax filing data for all living U.S. individuals with a Social Security number over 1996–2015; the paper’s primary data source providing Schedule C, K-1, W-2, and related filing information.

Kelly skewness: A robust measure of distributional asymmetry used by the paper to characterize income growth; the paper reports that Kelly skewness of self-employed income changes exceeds that of paid-employed by approximately 0.1, indicating greater right-skewness in self-employment income dynamics.

Non-pecuniary motivation subgroup: Primarily self-employed individuals who earn less on average than primarily paid-employed peers matched on observable characteristics, taken by the paper as consistent with non-wage job amenities (autonomy, flexibility) driving occupational choice; found to be 57 percent of primarily self-employed by count but earning only 16 percent of total self-employment income.