Moonlighting and the Transition to Full-Time Entrepreneurship

Moonlighting and the Transition to Full-Time Entrepreneurship | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Moonlighting and the Transition to Full-Time Entrepreneurship Nicholas Graff This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7819449/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper examines the transition from moonlighting to full-time self-employment using panel data from the Survey of Income and Program Participation. Motivations for moonlighting vary—from financial necessity to entrepreneurial experimentation—and these motivations shape the likelihood of transitioning. The paper finds moonlighters with higher moonlighting income and weaker attachment to their primary job are more likely to become self-employed. Access to employer-provided health insurance inhibits transition, and marriage allows for risk-sharing which promotes entry. Understanding the nuanced pathways from moonlighting to self-employment offers critical insight for designing policies that lower barriers and support entrepreneurial growth. Moonlighting Self-employment Entrepreneurship Job-Lock Risk-Sharing I. Introduction The rise of nontraditional work arrangements has blurred the line between wage employment and entrepreneurship, giving renewed importance to the study of hybrid entrepreneurs, individuals who engage in self-employment while retaining a primary job (Folta et al, 2010 ; Thorgren et al, 2016 ). Among them, moonlighters represent a particularly understudied group whose motivations and outcomes vary widely (Kimmel and Conway, 2001). Some pursue side ventures to supplement income from insecure or insufficient primary employment (Block and Landgraf, 2016 ), while others experiment with business ideas before making a full transition to self-employment (Raffiee and Feng, 2014 ). Understanding which moonlighters eventually become full-time entrepreneurs - and why - is crucial for informing policies aimed at promoting successful business formation. This paper investigates the transition from moonlighting to self-employment, emphasizing how the nature of moonlighting income, job characteristics, and personal circumstances predict entrepreneurial entry. People moonlight for different reasons, and these motivations play a key role in whether their side work leads to full-time entrepreneurship (≥ 35 hours/week). This paper contributes to the literature by considering four motivations for moonlighting and the implications for possible self-employment entry. (1) Some individuals engage in moonlighting to supplement income from a stable primary job, with no intention of transitioning into self-employment. For these workers, moonlighting is a financial strategy rather than a career path to self-employment, reflecting constraints on hours or earnings in their main job (Conway and Kimmel, 1998 ). (2) Some individuals engage in moonlighting to supplement income from an insecure or insufficient primary job. For these workers, the secondary job functions as a financial buffer against volatility in their main employment. While they may not initially intend to pursue self-employment, they are more likely to transition if their primary job deteriorates, making entrepreneurship a form of fallback employment rather than a proactive career choice. (3) Some individuals engage in moonlighting as a means of “testing the waters” of entrepreneurship, using the secondary job to acquire information about the viability of a business idea. If the venture yields sufficiently positive signals, individuals may subsequently transition into full-time self-employment (Raffiee and Feng, 2014 ). (4) Some individuals engage in moonlighting primarily for personal interest or enjoyment, with no initial intention of starting a formal business. These “hobby” moonlighters face no financial pressure from their primary employment. However, if income from the hobby grows substantially, it may prompt an unplanned transition into self-employment. Some very successful hobby moonlighters may transition to part-time or full-time entrepreneurship (Pina e Cunha et al, 2024 ). It is important to note that these motivational categories are not mutually exclusive; individuals may engage in moonlighting for multiple reasons simultaneously, and their underlying motivations may evolve over time in response to changes in income, job security, or entrepreneurial opportunities. This study finds that not all moonlighters are equally likely to transition into self-employment. Those who derive a large share of their income from moonlighting are significantly more likely to enter full-time self-employment, suggesting that financial success in a side venture plays a critical role in entrepreneurial transition. In contrast, moonlighters with access to employer-provided health insurance are notably less likely to make this transition, reflecting the influence of job-lock. Moonlighters with weaker ties to their primary job - measured by fewer hours worked - are also more likely to enter self-employment, consistent with transitions driven by employment instability. Meanwhile, factors reflecting household financial responsibility, such as having children or being the sole earner, show little consistent effect. Together, these findings emphasize the importance of both economic signals and that hybrid entrepreneurs still face significant barriers to entering full-time entrepreneurship. The paper concludes with a discussion of how the results of this paper are important for future entrepreneurship policy and research. This paper shows that moonlighting is an important pathway to entrepreneurship. The rise of the gig economy may have changed the way people moonlight – potentially disrupting this pathway to entrepreneurship. II. Theoretical Framework The reason for moonlighting should inform whether the moonlighter has any entrepreneurial intentions. This paper considers four motivations for moonlighting. 1. Moonlighting due to primary job constraints Some individuals moonlight to supplement income from a secure primary job, particularly when hours or earnings in the main job are limited. In this case, moonlighting is not a signal of entrepreneurial intent but rather a response to labor supply constraints in their primary job (Conway and Kimmel, 1998). These individuals are unlikely to transition to self-employment, as their secondary work is a financial strategy aimed at maintaining household income rather than a signal of entrepreneurial intention. 2. Moonlighting for extra income on top of an insecure or insufficient primary job - Moonlighting out of necessity. A second group of moonlighters takes on secondary work to supplement income from an insufficient or unstable primary job. These individuals do not initially seek to become entrepreneurs but moonlight as a form of economic insurance. The secondary job provides additional income to buffer against volatility in wage employment, and in the event of job loss or deterioration in primary employment conditions, self-employment may emerge as a fallback option rather than a proactive career move in the event of a negative shock to the primary job. Kimmel and Conway (2001) find 40% of moonlighters engage in secondary work due to economic hardship, highlighting the prevalence of necessity-driven motives. For these individuals, the transition into full-time self-employment is shaped more by constraints in the labor market than by opportunity or personal aspiration. Accordingly, those with lower wage income or fewer hours in their primary job are expected to face a lower opportunity cost of leaving wage employment and thus a higher likelihood of transitioning. I propose: H1: Moonlighters with lower primary wage income will be more likely to transition into full-time self-employment. H2: Moonlighters with low primary job hours will be more likely to transition into full-time self-employment . Because necessity-driven moonlighters who transition to self-employment are not motivated by opportunity or a strong entrepreneurial orientation, they may be less prepared for the demands of running a business. As a result, one might also expect their post-entry outcomes - such as earnings or business survival - to be worse than those of opportunity-driven entrepreneurs (Block and Sandner, 2009; Baptista et al. 2014). However, the focus of this paper is on understanding entry, rather than performance after entry. 3. Moonlighting to “test the waters” – A Real Options Perspective A third group of moonlighters engages in secondary work not out of financial necessity or labor market constraints, but as a way to explore the viability of entrepreneurship while retaining the safety of a wage job. This behavior aligns with real options theory, which suggests that individuals may pursue hybrid entrepreneurship as a means of acquiring information before committing to full-time business ownership (Folta et al., 2010; Raffiee & Feng, 2014; Petrova, 2012). Moonlighting provides an opportunity to reduce uncertainty in a relatively low-risk environment. Through this process, individuals gain insight along three dimensions: (1) the profitability and scalability of the business idea, (2) their own ability to execute it, and (3) their valuation of the non-pecuniary benefits of self-employment, such as autonomy, flexibility or creative satisfaction. While (1) and (2) reflect the core learning channels emphasized in Raffiee and Feng (2014), the third captures an important component of the learning process. Entrepreneurs may pursue self-employment even with modest financial success if they derive sufficient non-pecuniary benefits. Conversely, even when a venture is financially promising, some may opt to remain in wage employment if they find entrepreneurial work undesirable. One observable indicator of a successful “test” is financial performance. Individuals who earn a substantial share of their total income from moonlighting are more likely to have validated the financial viability of their business idea and are more likely to be able to support themselves through their self-employment income, and will therefore be more likely to transition into full-time self-employment. I propose: H3: Moonlighters with relatively high earnings from moonlighting are more likely to transition to self-employment. Even when moonlighters experience financial success in their side ventures, significant barriers may prevent them from transitioning to full-time self-employment. Financial returns alone may not fully offset the heightened risk and uncertainty associated with entrepreneurial entry. Prospective entrepreneurs must contend with the greater income volatility inherent in self-employment, as well as the substantial uncertainty in their ability to manage a full-scale business. Some operational challenges—such as regulatory compliance, client acquisition, and personnel management—may not be encountered in part-time entrepreneurial activity and thus remain untested. Additionally, moonlighters may be reluctant to forgo valuable non-wage benefits associated with traditional employment, particularly employer-sponsored health insurance. There is mixed empirical support for whether “job-lock” or “entrepreneurship lock” inhibits the transition from wage employment to entrepreneurship by increasing the opportunity cost of leaving a wage job (Holtz-Eakin et al., 1996; Fairlie, Kapur, and Gates, 2011). I propose: H4A: Moonlighters with employer-provided health insurance will be less likely to transition to full-time self-employment. Some of the risks associated with transitioning to full-time self-employment may be mitigated through intra-household risk-sharing (Bertocchi et al. 2011). A spouse’s stable wage income can buffer the financial uncertainty of entrepreneurship, providing a form of informal insurance against income volatility or business failure. In addition, access to employer-sponsored health insurance through a spouse may reduce job-lock concerns, lowering the cost of leaving a wage job. These household-level resources can increase the feasibility of self-employment, particularly for individuals who might otherwise be reluctant to make the transition. I propose: H4B: Moonlighters with a spouse who has employer-provided health insurance will be more likely to transition to self-employment. Concerns about the financial risks and uncertainties associated with entrepreneurship are likely to be more pronounced for moonlighters with substantial household financial responsibilities. Individuals who support dependents or serve as the primary earners in their households may be particularly reluctant to forgo the relative stability of wage employment, as the consequences of business failure are more severe in such contexts. Conversely, moonlighters with greater financial resources—such as high net worth or access to spousal income—may be better positioned to absorb potential losses and thus face fewer barriers to entrepreneurial entry. I propose: H5: Moonlighters with greater financial responsibility will be less likely to transition to full-time self-employment. H6: Moonlighters will greater ability to share risk will be more likely to transition to self-employment. 4. Moonlighting as a hobby. Finally, some individuals engage in moonlighting primarily as a hobby, with no initial intention of pursuing full-time self-employment. These “hobby entrepreneurs” are often motivated by intrinsic rewards such as enjoyment, creative expression, or skill development, rather than financial necessity or entrepreneurial ambition (Pina e Cunha et al., 2024). If their primary employment provides stable and sufficient income, there is no economic pressure to scale their side activity into a formal business. These individuals may treat their moonlighting work as a form of leisure rather than a precursor to entrepreneurial transition. However, hobby-driven ventures may gain unexpected traction leading the individual to consider formalizing the business. While such transitions may be rare, they highlight a potential path to self-employment that emerges not from strategic intent but from the organic growth of a personally fulfilling side activity. III. Data The data used in this paper come from four panels of the Survey of Income and Program Participation (SIPP) – the 1996, 2001, 2004, and 2008 panels. Each panel surveys households for 9 and 16 survey waves. The time between interviews is four months. Taken as a whole, this data begins in 1996 and ends in 2013, before the prevalence of ride-share and food delivery gig work. This data includes two crucial variables for understanding transitions from hybrid entrepreneurship to full-time entrepreneurship – whether a person moonlights and how much that person earns from moonlighting. Specially, the moonlighting question in the survey asks, “People sometimes earn extra money doing work outside of their regular jobs, such as freelancing, consulting, or moonlighting. Did ... do any of that kind of work during the reference period?”. The SIPP does not include any other direct questions about the respondents’ moonlighting activity. Table 1 provides descriptive statistics from the SIPP on a large sample of individuals, comparing moonlighters, non-moonlighters, and the self-employed. While only 2.8% of the full sample moonlight, these individuals are substantially more likely to eventually enter self-employment. Specifically, 17.6% of moonlighters transition into self-employment within the survey time, compared to just 6.8% of non-moonlighters. Similarly, 14.2% of moonlighters transition into full-time self-employment compared to only 6.9% of non-moonlighters. Moonlighters differ systematically from non-moonlighters in ways that may help explain their higher rates of self-employment entry. They earn more overall ( $ 3,875 vs. $ 2,563), are more educated (14.4 vs. 13.4 years), and work longer hours each week (42.5 vs. 34.4). Moonlighters are also more likely to be male and slightly younger than the average respondent. These differences suggest a strong link between moonlighting and future self-employment entry. Table 1 Summary Statistics All Moonlighters Non-Moonlighters Self-Employed Total Personal Monthly Earnings 2599.774 3875.050 2562.868 3942.595 Monthly Earnings from Moonlighting 16.316 580.131 0.000 30.671 Moonlit 0.028 1.000 0.000 0.039 Share of Income from Moonlighting 0.005 0.175 0.000 0.011 Self-Employed 0.082 0.113 0.081 1.000 White 0.817 0.850 0.816 0.882 Female 0.536 0.426 0.539 0.344 # Children Under 5 0.281 0.236 0.282 0.240 Total Weekly Work Hours 34.669 42.542 34.441 49.210 Spouse's Monthly Earnings 2036.195 1839.548 2041.886 2140.763 Age 40.742 39.776 40.770 43.391 Years of Education 13.428 14.404 13.399 13.913 Married 0.669 0.630 0.670 0.750 Ever Switch to Self-Employment 0.071 0.176 0.068 0.144 Ever Switch to Full-Time SE 0.071 0.142 0.069 0.376 Observations 176,175 4,955 171,220 14,439 Note: Table 1 reports mean values for respondents in the first wave of the 1996, 2001, 2004 and 2008 SIPP. Respondents are self-employed if they own a business and work at least 15 hours in that business. Respondents are full-time self-employed if they own a business and work at least 35 hours in that business. IV. Estimation and Results I use SIPP data and linear probability models to examine how moonlighters transition to full-time self-employment, estimating both the likelihood of entry in the next survey wave and the likelihood of switching during the sample period. The models include a set of explanatory variables that capture moonlighters’ economic, demographic, and household characteristics, each corresponding to specific hypotheses derived from the theoretical framework. These variables test the influence of primary job characteristics, moonlighting income, health insurance, and household responsibilities on entrepreneurial entry, reflecting the diverse motivations for moonlighting (e.g., necessity, “testing the waters”, or risk mitigation). The primary job variables include log primary job income and primary job hours. These variables test Hypotheses 1 and 2, which posit that moonlighters with low primary wage income or low primary job hours are more likely to transition to full-time self-employment due to necessity-driven motivations. Lower income or hours reduce the opportunity cost of leaving a wage job, pushing moonlighters toward entrepreneurship as a fallback. Moonlighting income is captured through dummy variables indicating whether moonlighting earnings constitute at least 10% (Moon 10), 25% (Moon 25), or 50% (Moon 50) of total income. These variables test Hypothesis 3 , which predicts that moonlighters with higher earnings from moonlighting are more likely to enter self-employment, as financial success signals the viability of a business venture. Health insurance variables include whether the moonlighter has employer-provided health insurance (Employer HI), and whether their spouse has employer-provided health insurance (Spouse Employer HI). These test Hypotheses 4A and 4B. Hypothesis 4A posits that moonlighters with employer-provided health insurance are less likely to transition to full-time self-employment due to job-lock effects, as they are reluctant to forgo benefits. Hypothesis 4B predicts that moonlighters with a spouse who has employer-provided health insurance are more likely to transition, as spousal coverage mitigates the risk of losing benefits. Household and demographic variables include marital status (married), whether the moonlighter is the only earner in the household (Only Earner), their interaction (Married*Only Earner), whether they have children (Has Kids), and spousal earnings (Spouse’s Earnings). These variables test Hypothesis 5 , which posits that moonlighters with greater financial responsibility (e.g., sole earners or those with children) are less likely to transition due to the higher risks of entrepreneurial failure. The inclusion of a marriage variable and spousal earnings test Hypothesis 6 . Those who are married and have greater spousal earnings are more able to share the risk of an entrepreneurial venture, and thus should be more likely to transition. The strategy deploys two models to capture short-term and long-term effects. Model 1: The “Next Wave Entry Model” estimates the probability of a moonlighter entering full-time self-employment in the subsequent survey wave (four-month period). This leverages the SIPP’s longitudinal structure to track transitions over a short time horizon. $$\:Switc{h}_{i,t+1}=\alpha\:+\beta\:{X}_{i,t}+{\epsilon\:}_{i,t}$$ Model 2: The “Ever-Switch Model” estimates the probability of a moonlighter entering full-time self-employment at any point over the panel duration. This model restricts the sample to those observed in the first wave of each panel and to those who are not already self-employed. $$\:Ever\:Switc{h}_{i}=\alpha\:+\beta\:{X}_{i,1}+{\epsilon\:}_{i}$$ Table 2 reports estimates from Model 1 and Table 3 reports estimates from Model 2. Each table reports results estimated for the full sample, and separate results for men and women. Each specification also includes controls for gender, race, age, educational attainment, and survey year fixed effects. Results Hypothesis 1 posits that moonlighters with lower income from their primary job will be more likely to transition into self-employment, consistent with a necessity-driven motive. However, the results from both models do not support this hypothesis. In five of six specifications, the coefficient on the log of primary job income is positive and statistically significant, indicating that higher primary job income is associated with a greater likelihood of entering self-employment. The coefficient on primary job income is a precisely estimated zero for women in model 1. In model 2, the coefficient on primary job income in the women-only estimation is positive and statistically significant but notably smaller in magnitude relative to the men-only estimate. This suggests that primary job income may not simply capture the opportunity cost of leaving wage work. Instead, higher income could reflect greater financial capacity to launch a business - either through accumulated savings or enhanced access to credit. Additionally, primary job income may be correlated with unobserved entrepreneurial ability, even after controlling for education. Finally, higher income is often correlated with greater wealth, which may reduce financial risk aversion and make entrepreneurial entry more feasible. In sum, the positive relationship between wage income and self-employment entry may be driven by selection on resources or ability, rather than push factors associated with economic hardship. Table 2 Model 1 – Next Wave Entry Model All Men Women Ln(Primary Job Income) 0.00504*** 0.00899*** -0.000108 (0.00191) (0.00281) (0.00244) Primary Job Hours -0.000462*** -0.000518*** -0.000397*** (9.95e-05) (0.000155) (0.000111) Moon 10 0.00118 0.00300 -0.000883 (0.00251) (0.00397) (0.00281) Moon 25 0.00126 0.00451 -0.00277 (0.00352) (0.00583) (0.00393) Moon 50 0.0175*** 0.0273** 0.00526 (0.00675) (0.0117) (0.00638) Married 0.00675* 0.0130** 0.00112 (0.00359) (0.00538) (0.00496) Only Earner 0.00380 0.00953 8.02e-05 (0.00420) (0.00658) (0.00543) Married x Only Earner 1.14e-05 -0.00342 -0.00178 (0.00539) (0.00720) (0.00733) Has Kids -0.00250 -0.00118 -0.00444* (0.00230) (0.00411) (0.00259) Spouse’s Earnings 1.22e-06* 2.90e-06* 5.16e-07 (7.39e-07) (1.65e-06) (7.13e-07) Employer Health Insurance -0.00850*** -0.0131*** -0.00400 (0.00265) (0.00427) (0.00309) Spouse Employer HI -0.00168 -0.00433 0.00103 (0.00288) (0.00448) (0.00344) Constant -0.0208 -0.0773** 0.0288 (0.0275) (0.0373) (0.0398) Observations 13,550 7,128 6,422 R-squared 0.014 0.018 0.013 Note: Estimates from the “Next Wave Entry Model” measure the probability of transition into full-time self-employment in the subsequent four-month survey wave. Explanatory variables include log primary job income, primary job hours, indicators for moonlighting income shares (≥ 10%, ≥ 25%, ≥ 50%), employer-provided and spousal health insurance, marital status, sole-earner status, presence of children, and spousal earnings. All models control for gender, race, age, education, and survey year fixed effects. Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Table 3 Model 2 – Ever-Switch Model All Men Women Ln(Primary Job Income) 0.0354*** 0.0438*** 0.0204* (0.00806) (0.0114) (0.0115) Primary Job Hours -0.00266*** -0.00288*** -0.00240*** (0.000397) (0.000566) (0.000520) Moon 10 0.0157 0.00628 0.0314** (0.0116) (0.0163) (0.0159) Moon 25 0.0225 0.0536** -0.0208 (0.0173) (0.0262) (0.0215) Moon 50 0.0830*** 0.0838* 0.0664* (0.0306) (0.0454) (0.0388) Married -4.00e-05 0.0243 -0.0235 (0.0208) (0.0294) (0.0290) Only Earner -0.00334 0.00117 -0.0117 (0.0220) (0.0325) (0.0293) Married x Only Earner 0.0268 0.0259 0.0111 (0.0274) (0.0374) (0.0401) Has Kids -0.00598 -0.00566 -0.0141 (0.0106) (0.0165) (0.0138) Spouse’s Earnings 3.66e-07 2.56e-06 4.35e-07 (2.04e-06) (4.28e-06) (2.28e-06) Employer Health Insurance -0.0366*** -0.0580*** -0.0184 (0.0129) (0.0205) (0.0159) Spouse Employer HI 0.00845 0.0190 -0.00471 (0.0146) (0.0214) (0.0199) Constant -0.0107 -0.124 0.0871 (0.0927) (0.139) (0.121) Observations 4,051 2,274 1,777 R-squared 0.043 0.042 0.043 Note: Estimates from the “Ever-Switch Model” measures the probability of transition into full-time self-employment at any point during the panel. Explanatory variables are defined as in Table 2 . The sample is restricted to individuals observed in the first wave of each panel who are not already self-employed. All models include controls for gender, race, age, education, and survey year fixed effects. Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Hypothesis 2 proposes moonlighters with fewer hours in their primary job are more likely to transition into full-time self-employment, consistent with necessity-driven motives. The results from both models support this hypothesis: the coefficient on primary job hours is consistently negative and statistically significant. An additional 10 hours of work per week in the primary job reduces the probability of transitioning to self-employment during the sample period by 2.7 percentage points. This indicates that individuals with weaker labor market attachment—measured by fewer hours worked—are more likely to enter self-employment. This pattern suggests that some transitions into self-employment are not driven by opportunity, but rather by instability or insufficiency in wage employment. For these individuals, self-employment may offer a preferable source of income than their primary job. This finding aligns with the notion of necessity entrepreneurship, where individuals are pushed into self-employment due to limited options in traditional wage work. Alternatively, working fewer hours may be a voluntary choice in order to spend more time testing entrepreneurial ideas. In this case, these results could also reflect opportunity-driven motives. Hypothesis 3 posits moonlighters who achieve greater financial success in their side ventures are more likely to transition to full-time self-employment. Results from Model 1 strongly support this hypothesis, with the coefficient on the dummy variable for moonlighters earning at least 50% of their income from moonlighting (Moon 50) being positive and statistically significant using the full sample (e.g., 0.0175, p < 0.01). I find a larger, statistically significant effect in the men-only sample (e.g., 0.0273, p < 0.01), and a positive but statistically insignificant effect in the women-only sample. This indicates that moonlighters with substantial moonlighting income are significantly more likely to enter full-time self-employment, with a notable effect size. The results from Model 2 also support Hypothesis 3 . Moonlighters who earn at least 50% of their income from moonlighting are 8 percentage points more likely to transition to self-employment during the sample period. Estimated effects on the indicator for earning more than 50% of income from moonlighting are large in magnitude, positive and statistically significant for the full sample, and the men and women only samples. These findings align with the "testing the waters" mechanism, in which a moonlighter uses hybrid entrepreneurship to assess the financial viability of their business idea. The large, positive coefficient on Moon 50 suggests that strong financial signals from moonlighting encourage transitions to full-time entrepreneurship, as moonlighters gain confidence in their venture’s potential. This supports the notion that opportunity-driven moonlighters, who derive significant income from their side ventures, are more likely to transition into full-time self-employment after validating their entrepreneurial ideas. Hypothesis A posits that moonlighters with employer-provided health insurance are less likely to transition to full-time self-employment due to job-lock effects, as they may be reluctant to forgo valuable employment benefits. Results from both models support this hypothesis. In Model 1, the coefficient on Employer Health Insurance is negative and statistically significant across in the full sample and the men-only sample (e.g., -0.0085, p < 0.01 for the full sample; -0.0131, p 0.1 for the women-only sample). Similarly, in Model 2, the coefficient is consistently negative and significant aside from the women-only sample (e.g., -0.0366, p < 0.01 for the full sample; -0.0580, p 0.1 for the women-only sample). These findings indicate that employer-provided health insurance significantly reduces the likelihood of transitioning to full-time self-employment. This effect seems to primary impact men as the effect in the women-only sample is not statistically significant and smaller in magnitude. Hypothesis B predicts that moonlighters with a spouse who has employer-provided health insurance are more likely to transition, as spousal coverage mitigates the cost of losing one’s own benefits. Across all specifications, the estimated effect of a spouse having employer provided health insurance is never statistically significant as well as being inconsistently signed. I do not find evidence to support the hypothesis that the presence of a spouse’s employer health insurance mitigates the effect of job-lock. These findings align with the job-lock literature, supporting the notion that employer-provided health insurance acts as a barrier to entrepreneurial entry. However, the lack of support for spousal health insurance as a facilitator highlights the need to explore additional household-level factors influencing risk-sharing dynamics with regards to access to health insurance. Hypothesis 5 posits moonlighters with greater financial responsibilities, such as being the sole earner of a household or having children, will be less likely to transition to full-time self-employment. The results do not provide evidence in support. Across most specifications in each model, “Has Kids” is negative, but is statistically insignificant in every specification aside from the only-women specification in model 1 in which the coefficient is small in magnitude and marginally statistically significant (-0.00444, p < 0.1). The coefficient on “Only Earner” is never statistically significant and is inconsistently signed across specifications. The interaction between “Married” and “Only Earner” is never statistically significant and is inconsistently signed across specifications. Together, these results do not point to a strong relationship between financial responsibility and transition to full-time self-employment, though there may be some support for a negative effect of the presence of children on women’s transition to self-employment. Hypothesis 6 posits moonlighters who have greater ability to share the risk of self-employment will be more likely to transition. The results in Model 1 support this hypothesis. The coefficient on “Married” is positive and statistically significant in the full-sample and in the men-only sample, indicating that married moonlighters are more likely to transition to full-time self-employment. In Model 2, the coefficients on “Married” are not statistically significant. Across models, the coefficient on “Spouse’s Earnings” are positive, but only marginally statistically significant in the full-sample and the men-only specifications of model 1. Overall, these results provide some support for Hypothesis 6 and the notion that hybrid entrepreneurs are more likely to transition to full-time self-employment if they are married, possibly due to the ability to share risk. Overall, I do not find support for hypothesis 1 that moonlighters with lower income from their primary job will be more likely to transition. I do find support for Hypothesis 2 which states moonlighters with fewer ties to their primary job will be more likely to transition. The results support Hypothesis 3 stating that moonlighters who have more financial success will be more likely to transition. I find some support for Hypothesis 4A that states “Job Lock” will inhibit transitions, but do not find support for hypothesis 4B which states the effect of job lock may be mitigated by a spouse having employer provided health insurance. I do not find support for Hypothesis 5 which states greater financial responsibility will inhibit transitions. And I do find some support for Hypothesis 6 which states marriage will increase the probability of transition through risk-sharing. Robustness Checks I conduct a series of robustness exercises to verify that the main results are not an artifact of functional-form assumptions, the discretization of moonlighting income, or a singular definition of self-employment (> 35 hours/week). I re-estimate both models using Probit/Logit rather than a linear probability model (LPM), I separately estimate models 1 and 2 using a continuous variable for portion of income from moonlighting, and separately estimate models 1 and 2 using other definitions of self-employment. All alternative specifications retain the same covariate set and survey-year fixed effects described in the main text. Tables of results from robustness checks are available in the appendix. Re-estimating both models using Logit and Probit with average marginal effects delivers coefficients that closely track the LPM estimates. In the short-run “next-wave” model (Model 1), weaker primary-job attachment remains a strong predictor of entry (negative and highly significant coefficients on primary-job hours across Probit and Logit), the ≥ 50% moonlighting-income indicator stays positive and significant, and employer-provided health insurance continues to dampen entry. In the longer-run “ever-switch” model (Model 2), these patterns also persist under Logit/Probit AMEs: hours are negative and significant, the ≥ 50% moonlighting share is positive and significant, and employer HI is negative and significant. Given the low base rate of next-wave transitions (≈ 1.4% overall), reporting AMEs ensures tight comparability to the LPM magnitudes. Replacing the threshold indicators with a continuous measure of the moonlighting share of income yields results that are directionally identical and statistically significant. In Model 1, the continuous moonlighting share is positive and significant in the full sample and for men (and positive, smaller, and statistically insignificant for women). In Model 2, the continuous share is positive and highly significant for all groups, again reinforcing a monotonic “testing-the-waters” channel. Employer-provided HI remains negative and significant under the continuous specification as well. The results are also robust to redefining the dependent variable. Using “Part-Time,” (> 15 hours/week) “full-time,” (> 35 Hours/week) “only full-time self-employment,” (> 35 Hours/week and no other employment), or “incorporated” (> 15 Hours/week and incorporated) as outcomes, the ≥ 50% moonlighting-income indicator stays positive and significant in the ever-switch framework, while employer-provided HI continues to dampen entry. Across Logit/Probit and continuous-treatment specifications, signs are consistent for men and women, with two recurring nuances: (i) the primary-job-income coefficient is essentially zero for women in the short-run model (but positive in the long-run model), and (ii) the employer-HI “job-lock” effect is smaller and often not significant for women. By contrast, the ≥ 50% moonlighting-income coefficient remains large (though smaller) and significant for women in the ever-switch model, underscoring that high moonlighting share of earnings predicts transitions for both sexes. The spouse’s employer HI variable is consistently small and statistically insignificant across functional forms, aligning with the main-specification conclusion that spousal coverage does not offset job-lock in a measurable way in these data. Taken together, these checks show that the core findings—(i) weaker attachment to the primary job predicts entry, (ii) substantial moonlighting income is a strong precursor to full-time entrepreneurship, and (iii) employer-provided HI inhibits transitions—are not driven by modeling choices, moonlighting measurement, or outcome definition. V. Conclusion and Discussion This paper is motivated by a growing interest in hybrid entrepreneurship and the recognition that moonlighting represents a common, but understudied pathway into self-employment. As traditional employment becomes more uncertain and flexible work arrangements expand, understanding who among moonlighters ultimately transitions into full-time self-employment has become increasingly important. Prior research suggests that individuals moonlight for varied reasons—including economic necessity, entrepreneurial experimentation, and personal fulfillment—and these underlying motivations likely influence whether they eventually become business owners. This study contributes to the literature by examining the diverse motivations for moonlighting and estimating how income composition, job characteristics, and household context predict the likelihood of transition into full-time entrepreneurship. By doing so, it aims to clarify which moonlighters are poised to become future entrepreneurs and identify the barriers that may prevent others from doing so. The analysis reveals that not all moonlighters are equally likely to transition into self-employment, and the nature of their moonlighting activity plays a central role. Moonlighters who earn a substantial share of their income from side work are significantly more likely to enter full-time self-employment, consistent with a “testing the waters” mechanism rooted in real options theory. In contrast, weaker attachment to the primary job, measured by fewer hours worked, is also associated with a greater likelihood of transition, supporting the idea that some moonlighters pursue self-employment as a response to labor market insecurity. These findings highlight the coexistence of opportunity- and necessity-driven motives among moonlighters who transition to full-time self-employment. Other factors play more nuanced roles. Employer-provided health insurance is consistently associated with a reduced likelihood of transitioning to self-employment, lending support to concerns about job-lock. However, contrary to expectations, having a spouse with employer-provided health insurance does not appear to offset this barrier. Additionally, greater household financial responsibility, such as having children or being the sole earner, does not significantly affect entrepreneurial transition. Marriage is positively associated with transition, potentially reflecting intra-household risk-sharing. Taken together, these results show that the decision to move from moonlighting to self-employment depends on both economic signals and institutional frictions, with individual circumstances either enabling or constraining entrepreneurial entry. These findings have important implications for entrepreneurship policy. Prior research has found nascent entrepreneurs pursue hybrid entrepreneurship to “test the waters”. This paper further contributes to the growing literature showing that hybrid entrepreneurship is a critical on-ramp to self-employment. The results in this paper also show there are still significant frictions from “job lock” that inhibit the transition to full-time self-employment among those who are “testing the waters”. This study underscores the robust link between moonlighting and subsequent entry into full-time self-employment, with prior literature highlighting necessity-driven moonlighting as a key motive to supplement income. The rise of the gig economy - encompassing platforms like ride-sharing and delivery services - has significantly expanded access to supplemental work since the data period of this study (1996–2013). Future research should explore how moonlighting has changed over time. Specifically, has the introduction of gig work (Koustas, 2019 ) crowded out other types of moonlighting, specifically moonlighting that more closely resembles hybrid entrepreneurship? If this crowding out is taking place, has it interrupted a critical pathway to full-time entrepreneurship? While this study provides new insights into the transition from moonlighting to self-employment, several limitations remain. The data do not include detailed information on the nature of moonlighting activities, such as industry, business structure, or duration, which limits the ability to distinguish between informal work and scalable entrepreneurial ventures. Additionally, the lack of direct measures of entrepreneurial intent or subjective expectations constrains interpretation of whether transitions are planned or reactive. Future work could address these gaps by incorporating richer data sources, such as longitudinal surveys with attitudinal questions or administrative business data. Further research could also explore post-entry outcomes, such as business survival, growth, or income volatility, to better understand whether different motivations for moonlighting predict long-term entrepreneurial success. Addressing these limitations may prove difficult given the lack of available data on moonlighting. In sum, this paper highlights moonlighting as a meaningful pathway into self-employment and shows that transitions are shaped by both opportunity and constraint. By identifying the factors that predict entrepreneurial entry, the study offers a deeper understanding of who becomes a full-time entrepreneur—and why. Declarations Author Contribution N.G. produced all analysis and wrote all text for this manuscript. References Baptista, Rui, Murat Karaöz, and Joana Mendonça. "The impact of human capital on the early success of necessity versus opportunity-based entrepreneurs." Small Business Economics 42 (2014): 831-847. https://doi.org/10.1007/s11187-013-9502-z Bertocchi, Graziella, Marianna Brunetti, and Costanza Torricelli. "Marriage and other risky assets: A portfolio approach." Journal of Banking & Finance 35.11 (2011): 2902-2915. https://doi.org/10.1016/j.jbankfin.2011.03.019 Block, Jörn H., and Andreas Landgraf. "Transition from part-time entrepreneurship to full-time entrepreneurship: the role of financial and non-financial motives." International entrepreneurship and management journal 12 (2016): 259-282. https://doi.org/10.1007/s11365-014-0331-6 Block, Jörn, and Philipp Sandner. "Necessity and opportunity entrepreneurs and their duration in self-employment: evidence from German micro data." Journal of industry, competition and trade 9 (2009): 117-137. https://doi.org/10.1007/s10842-007-0029-3 Conway, Karen Smith, and Jean Kimmel. "Male labor supply estimates and the decision to moonlight." Labour Economics 5.2 (1998): 135-166. https://doi.org/10.1016/S0927-5371(97)00023-7 Fairlie, Robert W., Kanika Kapur, and Susan Gates. "Is employer-based health insurance a barrier to entrepreneurship?." Journal of Health Economics 30.1 (2011): 146-162. https://doi.org/10.1016/j.jhealeco.2010.09.003 Folta, Timothy B., Frédéric Delmar, and Karl Wennberg. "Hybrid entrepreneurship." Management science 56.2 (2010): 253-269. https://doi.org/10.1287/mnsc.1090.1094 Holtz-Eakin, Douglas, John R. Penrod, and Harvey S. Rosen. "Health insurance and the supply of entrepreneurs." Journal of Public Economics 62.1-2 (1996): 209-235. https://doi.org/10.1016/0047-2727(96)01579-4 Kimmel, Jean, and Karen Smith Conway. "Who moonlights and why? Evidence from the SIPP." Industrial Relations: A Journal of Economy and Society 40.1 (2001): 89-120. https://doi.org/10.1111/0019-8676.00198 Koustas, Dmitri K. 2019. "What Do Big Data Tell Us about Why People Take Gig Economy Jobs?" AEA Papers and Proceedings 109: 367–71 . DOI: 10.1257/pandp.20191041 Petrova, Kameliia. "Part-time entrepreneurship and financial constraints: evidence from the Panel Study of Entrepreneurial Dynamics." Small business economics 39 (2012): 473-493. https://doi.org/10.1007/s11187-010-9310-7 Cunha, Miguel Pina E., et al. "From a Hobby to a Business: Drifting through Paradox While the Business Accelerates." Organizações & Sociedade 31.108 (2024): 88-116. https://doi.org/10.1590/1984-92302024v31n0003EN Raffiee, Joseph, and Jie Feng. "Should I quit my day job?: A hybrid path to entrepreneurship." Academy of management journal 57.4 (2014): 936-963. https://doi.org/10.5465/amj.2012.0522 Thorgren, Sara, et al. "Hybrid entrepreneurs' second-step choice: The nonlinear relationship between age and intention to enter full-time entrepreneurship." Journal of business venturing insights 5 (2016): 14-18. https://doi.org/10.1016/j.jbvi.2015.12.001 Additional Declarations No competing interests reported. Supplementary Files MoonlightingAppendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7819449","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536046134,"identity":"2c775c1a-ed40-4a3c-8387-4b99eddeec7f","order_by":0,"name":"Nicholas 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Introduction","content":"\u003cp\u003eThe rise of nontraditional work arrangements has blurred the line between wage employment and entrepreneurship, giving renewed importance to the study of hybrid entrepreneurs, individuals who engage in self-employment while retaining a primary job (Folta et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Thorgren et al, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Among them, moonlighters represent a particularly understudied group whose motivations and outcomes vary widely (Kimmel and Conway, 2001). Some pursue side ventures to supplement income from insecure or insufficient primary employment (Block and Landgraf, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while others experiment with business ideas before making a full transition to self-employment (Raffiee and Feng, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Understanding which moonlighters eventually become full-time entrepreneurs - and why - is crucial for informing policies aimed at promoting successful business formation. This paper investigates the transition from moonlighting to self-employment, emphasizing how the nature of moonlighting income, job characteristics, and personal circumstances predict entrepreneurial entry.\u003c/p\u003e\u003cp\u003ePeople moonlight for different reasons, and these motivations play a key role in whether their side work leads to full-time entrepreneurship (\u0026ge;\u0026thinsp;35 hours/week). This paper contributes to the literature by considering four motivations for moonlighting and the implications for possible self-employment entry.\u003c/p\u003e\u003cp\u003e(1) Some individuals engage in moonlighting to supplement income from a stable primary job, with no intention of transitioning into self-employment. For these workers, moonlighting is a financial strategy rather than a career path to self-employment, reflecting constraints on hours or earnings in their main job (Conway and Kimmel, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e(2) Some individuals engage in moonlighting to supplement income from an insecure or insufficient primary job. For these workers, the secondary job functions as a financial buffer against volatility in their main employment. While they may not initially intend to pursue self-employment, they are more likely to transition if their primary job deteriorates, making entrepreneurship a form of fallback employment rather than a proactive career choice.\u003c/p\u003e\u003cp\u003e(3) Some individuals engage in moonlighting as a means of \u0026ldquo;testing the waters\u0026rdquo; of entrepreneurship, using the secondary job to acquire information about the viability of a business idea. If the venture yields sufficiently positive signals, individuals may subsequently transition into full-time self-employment (Raffiee and Feng, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e(4) Some individuals engage in moonlighting primarily for personal interest or enjoyment, with no initial intention of starting a formal business. These \u0026ldquo;hobby\u0026rdquo; moonlighters face no financial pressure from their primary employment. However, if income from the hobby grows substantially, it may prompt an unplanned transition into self-employment. Some very successful hobby moonlighters may transition to part-time or full-time entrepreneurship (Pina e Cunha et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is important to note that these motivational categories are not mutually exclusive; individuals may engage in moonlighting for multiple reasons simultaneously, and their underlying motivations may evolve over time in response to changes in income, job security, or entrepreneurial opportunities.\u003c/p\u003e\u003cp\u003eThis study finds that not all moonlighters are equally likely to transition into self-employment. Those who derive a large share of their income from moonlighting are significantly more likely to enter full-time self-employment, suggesting that financial success in a side venture plays a critical role in entrepreneurial transition. In contrast, moonlighters with access to employer-provided health insurance are notably less likely to make this transition, reflecting the influence of job-lock. Moonlighters with weaker ties to their primary job - measured by fewer hours worked - are also more likely to enter self-employment, consistent with transitions driven by employment instability. Meanwhile, factors reflecting household financial responsibility, such as having children or being the sole earner, show little consistent effect. Together, these findings emphasize the importance of both economic signals and that hybrid entrepreneurs still face significant barriers to entering full-time entrepreneurship.\u003c/p\u003e\u003cp\u003eThe paper concludes with a discussion of how the results of this paper are important for future entrepreneurship policy and research. This paper shows that moonlighting is an important pathway to entrepreneurship. The rise of the gig economy may have changed the way people moonlight \u0026ndash; potentially disrupting this pathway to entrepreneurship.\u003c/p\u003e"},{"header":"II. Theoretical Framework","content":"\u003cp\u003eThe reason for moonlighting should inform whether the moonlighter has any entrepreneurial intentions. This paper considers four motivations for moonlighting.\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;Moonlighting due to primary job constraints\u003c/p\u003e\n\u003cp\u003eSome individuals moonlight to supplement income from a secure primary job, particularly when hours or earnings in the main job are limited. In this case, moonlighting is not a signal of entrepreneurial intent but rather a response to labor supply constraints in their primary job (Conway and Kimmel, 1998). These individuals are unlikely to transition to self-employment, as their secondary work is a financial strategy aimed at maintaining household income rather than a signal of entrepreneurial intention.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Moonlighting for extra income on top of an insecure or insufficient primary job - Moonlighting out of necessity.\u003c/p\u003e\n\u003cp\u003eA second group of moonlighters takes on secondary work to supplement income from an insufficient or unstable primary job. These individuals do not initially seek to become entrepreneurs but moonlight as a form of economic insurance. The secondary job provides additional income to buffer against volatility in wage employment, and in the event of job loss or deterioration in primary employment conditions, self-employment may emerge as a fallback option rather than a proactive career move in the event of a negative shock to the primary job.\u003c/p\u003e\n\u003cp\u003eKimmel and Conway (2001) find 40% of moonlighters engage in secondary work due to economic hardship, highlighting the prevalence of necessity-driven motives. For these individuals, the transition into full-time self-employment is shaped more by constraints in the labor market than by opportunity or personal aspiration. Accordingly, those with lower wage income or fewer hours in their primary job are expected to face a lower opportunity cost of leaving wage employment and thus a higher likelihood of transitioning. I propose:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1: Moonlighters with lower primary wage income will be more likely to transition into full-time self-employment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2: Moonlighters with low primary job hours will be more likely to transition into full-time self-employment\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBecause necessity-driven moonlighters who transition to self-employment are not motivated by opportunity or a strong entrepreneurial orientation, they may be less prepared for the demands of running a business. As a result, one might also expect their post-entry outcomes - such as earnings or business survival - to be worse than those of opportunity-driven entrepreneurs (Block and Sandner, 2009; Baptista et al. 2014). However, the focus of this paper is on understanding entry, rather than performance after entry.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;Moonlighting to “test the waters” – A Real Options Perspective\u003c/p\u003e\n\u003cp\u003eA third group of moonlighters engages in secondary work not out of financial necessity or labor market constraints, but as a way to explore the viability of entrepreneurship while retaining the safety of a wage job. This behavior aligns with real options theory, which suggests that individuals may pursue hybrid entrepreneurship as a means of acquiring information before committing to full-time business ownership (Folta et al., 2010; Raffiee \u0026amp; Feng, 2014; Petrova, 2012). Moonlighting provides an opportunity to reduce uncertainty in a relatively low-risk environment.\u003c/p\u003e\n\u003cp\u003eThrough this process, individuals gain insight along three dimensions: (1) the profitability and scalability of the business idea, (2) their own ability to execute it, and (3) their valuation of the non-pecuniary benefits of self-employment, such as autonomy, flexibility or creative satisfaction. While (1) and (2) reflect the core learning channels emphasized in Raffiee and Feng (2014), the third captures an important component of the learning process. Entrepreneurs may pursue self-employment even with modest financial success if they derive sufficient non-pecuniary benefits. Conversely, even when a venture is financially promising, some may opt to remain in wage employment if they find entrepreneurial work undesirable.\u003c/p\u003e\n\u003cp\u003eOne observable indicator of a successful “test” is financial performance. Individuals who earn a substantial share of their total income from moonlighting are more likely to have validated the financial viability of their business idea and are more likely to be able to support themselves through their self-employment income, and will therefore be more likely to transition into full-time self-employment. I propose:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3: Moonlighters with relatively high earnings from moonlighting are more likely to transition to self-employment.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEven when moonlighters experience financial success in their side ventures, significant barriers may prevent them from transitioning to full-time self-employment. Financial returns alone may not fully offset the heightened risk and uncertainty associated with entrepreneurial entry. Prospective entrepreneurs must contend with the greater income volatility inherent in self-employment, as well as the substantial uncertainty in their ability to manage a full-scale business. Some operational challenges—such as regulatory compliance, client acquisition, and personnel management—may not be encountered in part-time entrepreneurial activity and thus remain untested. Additionally, moonlighters may be reluctant to forgo valuable non-wage benefits associated with traditional employment, particularly employer-sponsored health insurance. There is mixed empirical support for whether “job-lock” or “entrepreneurship lock” inhibits the transition from wage employment to entrepreneurship by increasing the opportunity cost of leaving a wage job (Holtz-Eakin et al., 1996; Fairlie, Kapur, and Gates, 2011). I propose:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4A: Moonlighters with employer-provided health insurance will be less likely to transition to full-time self-employment.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome of the risks associated with transitioning to full-time self-employment may be mitigated through intra-household risk-sharing (Bertocchi et al. 2011). A spouse’s stable wage income can buffer the financial uncertainty of entrepreneurship, providing a form of informal insurance against income volatility or business failure. In addition, access to employer-sponsored health insurance through a spouse may reduce job-lock concerns, lowering the cost of leaving a wage job. These household-level resources can increase the feasibility of self-employment, particularly for individuals who might otherwise be reluctant to make the transition. I propose:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4B: Moonlighters with a spouse who has employer-provided health insurance will be more likely to transition to self-employment.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcerns about the financial risks and uncertainties associated with entrepreneurship are likely to be more pronounced for moonlighters with substantial household financial responsibilities. Individuals who support dependents or serve as the primary earners in their households may be particularly reluctant to forgo the relative stability of wage employment, as the consequences of business failure are more severe in such contexts. Conversely, moonlighters with greater financial resources—such as high net worth or access to spousal income—may be better positioned to absorb potential losses and thus face fewer barriers to entrepreneurial entry. I propose:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5: Moonlighters with greater financial responsibility will be less likely to transition to full-time self-employment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH6: Moonlighters will greater ability to share risk will be more likely to transition to self-employment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;Moonlighting as a hobby.\u003c/p\u003e\n\u003cp\u003eFinally, some individuals engage in moonlighting primarily as a hobby, with no initial intention of pursuing full-time self-employment. These “hobby entrepreneurs” are often motivated by intrinsic rewards such as enjoyment, creative expression, or skill development, rather than financial necessity or entrepreneurial ambition (Pina e Cunha et al., 2024). If their primary employment provides stable and sufficient income, there is no economic pressure to scale their side activity into a formal business. These individuals may treat their moonlighting work as a form of leisure rather than a precursor to entrepreneurial transition.\u003c/p\u003e\n\u003cp\u003eHowever, hobby-driven ventures may gain unexpected traction leading the individual to consider formalizing the business. While such transitions may be rare, they highlight a potential path to self-employment that emerges not from strategic intent but from the organic growth of a personally fulfilling side activity.\u003c/p\u003e"},{"header":"III. Data","content":"\u003cp\u003eThe data used in this paper come from four panels of the Survey of Income and Program Participation (SIPP) \u0026ndash; the 1996, 2001, 2004, and 2008 panels. Each panel surveys households for 9 and 16 survey waves. The time between interviews is four months. Taken as a whole, this data begins in 1996 and ends in 2013, before the prevalence of ride-share and food delivery gig work.\u003c/p\u003e\u003cp\u003eThis data includes two crucial variables for understanding transitions from hybrid entrepreneurship to full-time entrepreneurship \u0026ndash; whether a person moonlights and how much that person earns from moonlighting. Specially, the moonlighting question in the survey asks, \u0026ldquo;People sometimes earn extra money doing work outside of their regular jobs, such as freelancing, consulting, or moonlighting. Did ... do any of that kind of work during the reference period?\u0026rdquo;. The SIPP does not include any other direct questions about the respondents\u0026rsquo; moonlighting activity.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides descriptive statistics from the SIPP on a large sample of individuals, comparing moonlighters, non-moonlighters, and the self-employed. While only 2.8% of the full sample moonlight, these individuals are substantially more likely to eventually enter self-employment. Specifically, 17.6% of moonlighters transition into self-employment within the survey time, compared to just 6.8% of non-moonlighters. Similarly, 14.2% of moonlighters transition into full-time self-employment compared to only 6.9% of non-moonlighters. Moonlighters differ systematically from non-moonlighters in ways that may help explain their higher rates of self-employment entry. They earn more overall (\u003cspan\u003e$\u003c/span\u003e3,875 vs. \u003cspan\u003e$\u003c/span\u003e2,563), are more educated (14.4 vs. 13.4 years), and work longer hours each week (42.5 vs. 34.4). Moonlighters are also more likely to be male and slightly younger than the average respondent. These differences suggest a strong link between moonlighting and future self-employment entry.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMoonlighters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Moonlighters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSelf-Employed\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Personal Monthly Earnings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2599.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3875.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2562.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3942.595\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly Earnings from Moonlighting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e580.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.671\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoonlit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare of Income from Moonlighting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e# Children Under 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Weekly Work Hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpouse's Monthly Earnings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2036.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1839.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2041.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2140.763\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.391\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver Switch to Self-Employment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver Switch to Full-Time SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176,175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e171,220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14,439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNote: Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports mean values for respondents in the first wave of the 1996, 2001, 2004 and 2008 SIPP. Respondents are self-employed if they own a business and work at least 15 hours in that business. Respondents are full-time self-employed if they own a business and work at least 35 hours in that business.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"IV. Estimation and Results","content":"\u003cp\u003eI use SIPP data and linear probability models to examine how moonlighters transition to full-time self-employment, estimating both the likelihood of entry in the next survey wave and the likelihood of switching during the sample period. The models include a set of explanatory variables that capture moonlighters\u0026rsquo; economic, demographic, and household characteristics, each corresponding to specific hypotheses derived from the theoretical framework. These variables test the influence of primary job characteristics, moonlighting income, health insurance, and household responsibilities on entrepreneurial entry, reflecting the diverse motivations for moonlighting (e.g., necessity, \u0026ldquo;testing the waters\u0026rdquo;, or risk mitigation).\u003c/p\u003e\u003cp\u003eThe primary job variables include log primary job income and primary job hours. These variables test Hypotheses 1 and 2, which posit that moonlighters with low primary wage income or low primary job hours are more likely to transition to full-time self-employment due to necessity-driven motivations. Lower income or hours reduce the opportunity cost of leaving a wage job, pushing moonlighters toward entrepreneurship as a fallback. Moonlighting income is captured through dummy variables indicating whether moonlighting earnings constitute at least 10% (Moon 10), 25% (Moon 25), or 50% (Moon 50) of total income. These variables test Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which predicts that moonlighters with higher earnings from moonlighting are more likely to enter self-employment, as financial success signals the viability of a business venture.\u003c/p\u003e\u003cp\u003eHealth insurance variables include whether the moonlighter has employer-provided health insurance (Employer HI), and whether their spouse has employer-provided health insurance (Spouse Employer HI). These test Hypotheses 4A and 4B. Hypothesis 4A posits that moonlighters with employer-provided health insurance are less likely to transition to full-time self-employment due to job-lock effects, as they are reluctant to forgo benefits. Hypothesis 4B predicts that moonlighters with a spouse who has employer-provided health insurance are more likely to transition, as spousal coverage mitigates the risk of losing benefits. Household and demographic variables include marital status (married), whether the moonlighter is the only earner in the household (Only Earner), their interaction (Married*Only Earner), whether they have children (Has Kids), and spousal earnings (Spouse\u0026rsquo;s Earnings). These variables test Hypothesis \u003cspan refid=\"FPar6\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which posits that moonlighters with greater financial responsibility (e.g., sole earners or those with children) are less likely to transition due to the higher risks of entrepreneurial failure. The inclusion of a marriage variable and spousal earnings test Hypothesis \u003cspan refid=\"FPar7\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Those who are married and have greater spousal earnings are more able to share the risk of an entrepreneurial venture, and thus should be more likely to transition.\u003c/p\u003e\u003cp\u003eThe strategy deploys two models to capture short-term and long-term effects.\u003c/p\u003e\u003cp\u003eModel 1: The \u0026ldquo;Next Wave Entry Model\u0026rdquo; estimates the probability of a moonlighter entering full-time self-employment in the subsequent survey wave (four-month period). This leverages the SIPP\u0026rsquo;s longitudinal structure to track transitions over a short time horizon.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Switc{h}_{i,t+1}=\\alpha\\:+\\beta\\:{X}_{i,t}+{\\epsilon\\:}_{i,t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 2: The \u0026ldquo;Ever-Switch Model\u0026rdquo; estimates the probability of a moonlighter entering full-time self-employment at any point over the panel duration. This model restricts the sample to those observed in the first wave of each panel and to those who are not already self-employed.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Ever\\:Switc{h}_{i}=\\alpha\\:+\\beta\\:{X}_{i,1}+{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports estimates from Model 1 and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports estimates from Model 2. Each table reports results estimated for the full sample, and separate results for men and women. Each specification also includes controls for gender, race, age, educational attainment, and survey year fixed effects.\u003c/p\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003eposits that moonlighters with lower income from their primary job will be more likely to transition into self-employment, consistent with a necessity-driven motive. However, the results from both models do not support this hypothesis. In five of six specifications, the coefficient on the log of primary job income is positive and statistically significant, indicating that higher primary job income is associated with a greater likelihood of entering self-employment. The coefficient on primary job income is a precisely estimated zero for women in model 1. In model 2, the coefficient on primary job income in the women-only estimation is positive and statistically significant but notably smaller in magnitude relative to the men-only estimate.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThis suggests that primary job income may not simply capture the opportunity cost of leaving wage work. Instead, higher income could reflect greater financial capacity to launch a business - either through accumulated savings or enhanced access to credit. Additionally, primary job income may be correlated with unobserved entrepreneurial ability, even after controlling for education. Finally, higher income is often correlated with greater wealth, which may reduce financial risk aversion and make entrepreneurial entry more feasible. In sum, the positive relationship between wage income and self-employment entry may be driven by selection on resources or ability, rather than push factors associated with economic hardship.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel 1 \u0026ndash; Next Wave Entry Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLn(Primary Job Income)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00504***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00899***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.000108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.00281)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00244)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary Job Hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.000462***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.000518***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.000397***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(9.95e-05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.000155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.000111)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoon 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.000883\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.00397)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00281)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoon 25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00352)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.00583)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00393)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoon 50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0175***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0273**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00526\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00675)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00638)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00675*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0130**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00359)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.00538)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00496)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnly Earner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.02e-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00420)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.00658)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00543)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried x Only Earner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00539)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.00720)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00733)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHas Kids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.00250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00444*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00230)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.00411)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00259)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpouse\u0026rsquo;s Earnings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22e-06*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.90e-06*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.16e-07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.39e-07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.65e-06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(7.13e-07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployer Health Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.00850***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0131***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00265)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.00427)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00309)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpouse Employer HI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.00168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.00448)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.00344)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0773**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0275)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0373)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0398)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eNote: Estimates from the \u0026ldquo;Next Wave Entry Model\u0026rdquo; measure the probability of transition into full-time self-employment in the subsequent four-month survey wave. Explanatory variables include log primary job income, primary job hours, indicators for moonlighting income shares (\u0026ge;\u0026thinsp;10%, \u0026ge;\u0026thinsp;25%, \u0026ge;\u0026thinsp;50%), employer-provided and spousal health insurance, marital status, sole-earner status, presence of children, and spousal earnings. All models control for gender, race, age, education, and survey year fixed effects. Robust standard errors in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel 2 \u0026ndash; Ever-Switch Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLn(Primary Job Income)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0354***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0438***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0204*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.00806)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0115)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary Job Hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.00266***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00288***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00240***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.000397)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.000566)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.000520)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoon 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0314**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0116)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0163)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0159)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoon 25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0536**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0262)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0215)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoon 50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0830***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0838*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0664*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0306)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0454)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0388)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.00e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0294)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0290)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnly Earner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.00334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0220)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0293)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried x Only Earner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0274)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0374)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0401)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHas Kids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.00598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0138)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpouse\u0026rsquo;s Earnings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.66e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.56e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.35e-07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.04e-06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.28e-06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.28e-06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployer Health Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0366***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0580***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0205)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0159)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpouse Employer HI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0146)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0214)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0199)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0871\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0927)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.121)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eNote: Estimates from the \u0026ldquo;Ever-Switch Model\u0026rdquo; measures the probability of transition into full-time self-employment at any point during the panel. Explanatory variables are defined as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The sample is restricted to individuals observed in the first wave of each panel who are not already self-employed. All models include controls for gender, race, age, education, and survey year fixed effects. Robust standard errors in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003eproposes moonlighters with fewer hours in their primary job are more likely to transition into full-time self-employment, consistent with necessity-driven motives. The results from both models support this hypothesis: the coefficient on primary job hours is consistently negative and statistically significant. An additional 10 hours of work per week in the primary job reduces the probability of transitioning to self-employment during the sample period by 2.7 percentage points. This indicates that individuals with weaker labor market attachment\u0026mdash;measured by fewer hours worked\u0026mdash;are more likely to enter self-employment.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThis pattern suggests that some transitions into self-employment are not driven by opportunity, but rather by instability or insufficiency in wage employment. For these individuals, self-employment may offer a preferable source of income than their primary job. This finding aligns with the notion of necessity entrepreneurship, where individuals are pushed into self-employment due to limited options in traditional wage work.\u003c/p\u003e\u003cp\u003eAlternatively, working fewer hours may be a voluntary choice in order to spend more time testing entrepreneurial ideas. In this case, these results could also reflect opportunity-driven motives.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003eposits moonlighters who achieve greater financial success in their side ventures are more likely to transition to full-time self-employment. Results from Model 1 strongly support this hypothesis, with the coefficient on the dummy variable for moonlighters earning at least 50% of their income from moonlighting (Moon 50) being positive and statistically significant using the full sample (e.g., 0.0175, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). I find a larger, statistically significant effect in the men-only sample (e.g., 0.0273, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a positive but statistically insignificant effect in the women-only sample. This indicates that moonlighters with substantial moonlighting income are significantly more likely to enter full-time self-employment, with a notable effect size. The results from Model 2 also support Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Moonlighters who earn at least 50% of their income from moonlighting are 8 percentage points more likely to transition to self-employment during the sample period. Estimated effects on the indicator for earning more than 50% of income from moonlighting are large in magnitude, positive and statistically significant for the full sample, and the men and women only samples.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThese findings align with the \"testing the waters\" mechanism, in which a moonlighter uses hybrid entrepreneurship to assess the financial viability of their business idea. The large, positive coefficient on Moon 50 suggests that strong financial signals from moonlighting encourage transitions to full-time entrepreneurship, as moonlighters gain confidence in their venture\u0026rsquo;s potential. This supports the notion that opportunity-driven moonlighters, who derive significant income from their side ventures, are more likely to transition into full-time self-employment after validating their entrepreneurial ideas.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003eA posits that moonlighters with employer-provided health insurance are less likely to transition to full-time self-employment due to job-lock effects, as they may be reluctant to forgo valuable employment benefits. Results from both models support this hypothesis. In Model 1, the coefficient on Employer Health Insurance is negative and statistically significant across in the full sample and the men-only sample (e.g., -0.0085, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for the full sample; -0.0131, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for the men-only sample; -0.004, p\u0026thinsp;\u0026gt;\u0026thinsp;0.1 for the women-only sample). Similarly, in Model 2, the coefficient is consistently negative and significant aside from the women-only sample (e.g., -0.0366, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for the full sample; -0.0580, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for the men-only sample; -0.0184, p\u0026thinsp;\u0026gt;\u0026thinsp;0.1 for the women-only sample). These findings indicate that employer-provided health insurance significantly reduces the likelihood of transitioning to full-time self-employment. This effect seems to primary impact men as the effect in the women-only sample is not statistically significant and smaller in magnitude.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003eB predicts that moonlighters with a spouse who has employer-provided health insurance are more likely to transition, as spousal coverage mitigates the cost of losing one\u0026rsquo;s own benefits. Across all specifications, the estimated effect of a spouse having employer provided health insurance is never statistically significant as well as being inconsistently signed. I do not find evidence to support the hypothesis that the presence of a spouse\u0026rsquo;s employer health insurance mitigates the effect of job-lock.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThese findings align with the job-lock literature, supporting the notion that employer-provided health insurance acts as a barrier to entrepreneurial entry. However, the lack of support for spousal health insurance as a facilitator highlights the need to explore additional household-level factors influencing risk-sharing dynamics with regards to access to health insurance.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 5\u003c/strong\u003e\u003cp\u003eposits moonlighters with greater financial responsibilities, such as being the sole earner of a household or having children, will be less likely to transition to full-time self-employment. The results do not provide evidence in support. Across most specifications in each model, \u0026ldquo;Has Kids\u0026rdquo; is negative, but is statistically insignificant in every specification aside from the only-women specification in model 1 in which the coefficient is small in magnitude and marginally statistically significant (-0.00444, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). The coefficient on \u0026ldquo;Only Earner\u0026rdquo; is never statistically significant and is inconsistently signed across specifications. The interaction between \u0026ldquo;Married\u0026rdquo; and \u0026ldquo;Only Earner\u0026rdquo; is never statistically significant and is inconsistently signed across specifications. Together, these results do not point to a strong relationship between financial responsibility and transition to full-time self-employment, though there may be some support for a negative effect of the presence of children on women\u0026rsquo;s transition to self-employment.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 6\u003c/strong\u003e\u003cp\u003eposits moonlighters who have greater ability to share the risk of self-employment will be more likely to transition. The results in Model 1 support this hypothesis. The coefficient on \u0026ldquo;Married\u0026rdquo; is positive and statistically significant in the full-sample and in the men-only sample, indicating that married moonlighters are more likely to transition to full-time self-employment. In Model 2, the coefficients on \u0026ldquo;Married\u0026rdquo; are not statistically significant. Across models, the coefficient on \u0026ldquo;Spouse\u0026rsquo;s Earnings\u0026rdquo; are positive, but only marginally statistically significant in the full-sample and the men-only specifications of model 1. Overall, these results provide some support for Hypothesis \u003cspan refid=\"FPar7\" class=\"InternalRef\"\u003e6\u003c/span\u003e and the notion that hybrid entrepreneurs are more likely to transition to full-time self-employment if they are married, possibly due to the ability to share risk.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eOverall, I do not find support for hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e that moonlighters with lower income from their primary job will be more likely to transition. I do find support for Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e which states moonlighters with fewer ties to their primary job will be more likely to transition. The results support Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e stating that moonlighters who have more financial success will be more likely to transition. I find some support for Hypothesis 4A that states \u0026ldquo;Job Lock\u0026rdquo; will inhibit transitions, but do not find support for hypothesis 4B which states the effect of job lock may be mitigated by a spouse having employer provided health insurance. I do not find support for Hypothesis \u003cspan refid=\"FPar6\" class=\"InternalRef\"\u003e5\u003c/span\u003e which states greater financial responsibility will inhibit transitions. And I do find some support for Hypothesis \u003cspan refid=\"FPar7\" class=\"InternalRef\"\u003e6\u003c/span\u003e which states marriage will increase the probability of transition through risk-sharing.\u003c/p\u003e\u003cp\u003eRobustness Checks\u003c/p\u003e\u003cp\u003eI conduct a series of robustness exercises to verify that the main results are not an artifact of functional-form assumptions, the discretization of moonlighting income, or a singular definition of self-employment (\u0026gt;\u0026thinsp;35 hours/week). I re-estimate both models using Probit/Logit rather than a linear probability model (LPM), I separately estimate models 1 and 2 using a continuous variable for portion of income from moonlighting, and separately estimate models 1 and 2 using other definitions of self-employment. All alternative specifications retain the same covariate set and survey-year fixed effects described in the main text. Tables of results from robustness checks are available in the appendix.\u003c/p\u003e\u003cp\u003eRe-estimating both models using Logit and Probit with average marginal effects delivers coefficients that closely track the LPM estimates. In the short-run \u0026ldquo;next-wave\u0026rdquo; model (Model 1), weaker primary-job attachment remains a strong predictor of entry (negative and highly significant coefficients on primary-job hours across Probit and Logit), the \u0026ge;\u0026thinsp;50% moonlighting-income indicator stays positive and significant, and employer-provided health insurance continues to dampen entry.\u003c/p\u003e\u003cp\u003eIn the longer-run \u0026ldquo;ever-switch\u0026rdquo; model (Model 2), these patterns also persist under Logit/Probit AMEs: hours are negative and significant, the \u0026ge;\u0026thinsp;50% moonlighting share is positive and significant, and employer HI is negative and significant. Given the low base rate of next-wave transitions (\u0026asymp;\u0026thinsp;1.4% overall), reporting AMEs ensures tight comparability to the LPM magnitudes.\u003c/p\u003e\u003cp\u003eReplacing the threshold indicators with a continuous measure of the moonlighting share of income yields results that are directionally identical and statistically significant. In Model 1, the continuous moonlighting share is positive and significant in the full sample and for men (and positive, smaller, and statistically insignificant for women). In Model 2, the continuous share is positive and highly significant for all groups, again reinforcing a monotonic \u0026ldquo;testing-the-waters\u0026rdquo; channel. Employer-provided HI remains negative and significant under the continuous specification as well.\u003c/p\u003e\u003cp\u003eThe results are also robust to redefining the dependent variable. Using \u0026ldquo;Part-Time,\u0026rdquo; (\u0026gt;\u0026thinsp;15 hours/week) \u0026ldquo;full-time,\u0026rdquo; (\u0026gt;\u0026thinsp;35 Hours/week) \u0026ldquo;only full-time self-employment,\u0026rdquo; (\u0026gt;\u0026thinsp;35 Hours/week and no other employment), or \u0026ldquo;incorporated\u0026rdquo; (\u0026gt;\u0026thinsp;15 Hours/week and incorporated) as outcomes, the \u0026ge;\u0026thinsp;50% moonlighting-income indicator stays positive and significant in the ever-switch framework, while employer-provided HI continues to dampen entry.\u003c/p\u003e\u003cp\u003eAcross Logit/Probit and continuous-treatment specifications, signs are consistent for men and women, with two recurring nuances: (i) the primary-job-income coefficient is essentially zero for women in the short-run model (but positive in the long-run model), and (ii) the employer-HI \u0026ldquo;job-lock\u0026rdquo; effect is smaller and often not significant for women. By contrast, the \u0026ge;\u0026thinsp;50% moonlighting-income coefficient remains large (though smaller) and significant for women in the ever-switch model, underscoring that high moonlighting share of earnings predicts transitions for both sexes.\u003c/p\u003e\u003cp\u003eThe spouse\u0026rsquo;s employer HI variable is consistently small and statistically insignificant across functional forms, aligning with the main-specification conclusion that spousal coverage does not offset job-lock in a measurable way in these data. Taken together, these checks show that the core findings\u0026mdash;(i) weaker attachment to the primary job predicts entry, (ii) substantial moonlighting income is a strong precursor to full-time entrepreneurship, and (iii) employer-provided HI inhibits transitions\u0026mdash;are not driven by modeling choices, moonlighting measurement, or outcome definition.\u003c/p\u003e"},{"header":"V. Conclusion and Discussion","content":"\u003cp\u003eThis paper is motivated by a growing interest in hybrid entrepreneurship and the recognition that moonlighting represents a common, but understudied pathway into self-employment. As traditional employment becomes more uncertain and flexible work arrangements expand, understanding who among moonlighters ultimately transitions into full-time self-employment has become increasingly important. Prior research suggests that individuals moonlight for varied reasons\u0026mdash;including economic necessity, entrepreneurial experimentation, and personal fulfillment\u0026mdash;and these underlying motivations likely influence whether they eventually become business owners. This study contributes to the literature by examining the diverse motivations for moonlighting and estimating how income composition, job characteristics, and household context predict the likelihood of transition into full-time entrepreneurship. By doing so, it aims to clarify which moonlighters are poised to become future entrepreneurs and identify the barriers that may prevent others from doing so.\u003c/p\u003e\u003cp\u003eThe analysis reveals that not all moonlighters are equally likely to transition into self-employment, and the nature of their moonlighting activity plays a central role. Moonlighters who earn a substantial share of their income from side work are significantly more likely to enter full-time self-employment, consistent with a \u0026ldquo;testing the waters\u0026rdquo; mechanism rooted in real options theory. In contrast, weaker attachment to the primary job, measured by fewer hours worked, is also associated with a greater likelihood of transition, supporting the idea that some moonlighters pursue self-employment as a response to labor market insecurity. These findings highlight the coexistence of opportunity- and necessity-driven motives among moonlighters who transition to full-time self-employment.\u003c/p\u003e\u003cp\u003eOther factors play more nuanced roles. Employer-provided health insurance is consistently associated with a reduced likelihood of transitioning to self-employment, lending support to concerns about job-lock. However, contrary to expectations, having a spouse with employer-provided health insurance does not appear to offset this barrier. Additionally, greater household financial responsibility, such as having children or being the sole earner, does not significantly affect entrepreneurial transition. Marriage is positively associated with transition, potentially reflecting intra-household risk-sharing. Taken together, these results show that the decision to move from moonlighting to self-employment depends on both economic signals and institutional frictions, with individual circumstances either enabling or constraining entrepreneurial entry.\u003c/p\u003e\u003cp\u003eThese findings have important implications for entrepreneurship policy. Prior research has found nascent entrepreneurs pursue hybrid entrepreneurship to \u0026ldquo;test the waters\u0026rdquo;. This paper further contributes to the growing literature showing that hybrid entrepreneurship is a critical on-ramp to self-employment. The results in this paper also show there are still significant frictions from \u0026ldquo;job lock\u0026rdquo; that inhibit the transition to full-time self-employment among those who are \u0026ldquo;testing the waters\u0026rdquo;.\u003c/p\u003e\u003cp\u003eThis study underscores the robust link between moonlighting and subsequent entry into full-time self-employment, with prior literature highlighting necessity-driven moonlighting as a key motive to supplement income. The rise of the gig economy - encompassing platforms like ride-sharing and delivery services - has significantly expanded access to supplemental work since the data period of this study (1996\u0026ndash;2013). Future research should explore how moonlighting has changed over time. Specifically, has the introduction of gig work (Koustas, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) crowded out other types of moonlighting, specifically moonlighting that more closely resembles hybrid entrepreneurship? If this crowding out is taking place, has it interrupted a critical pathway to full-time entrepreneurship?\u003c/p\u003e\u003cp\u003eWhile this study provides new insights into the transition from moonlighting to self-employment, several limitations remain. The data do not include detailed information on the nature of moonlighting activities, such as industry, business structure, or duration, which limits the ability to distinguish between informal work and scalable entrepreneurial ventures. Additionally, the lack of direct measures of entrepreneurial intent or subjective expectations constrains interpretation of whether transitions are planned or reactive. Future work could address these gaps by incorporating richer data sources, such as longitudinal surveys with attitudinal questions or administrative business data. Further research could also explore post-entry outcomes, such as business survival, growth, or income volatility, to better understand whether different motivations for moonlighting predict long-term entrepreneurial success. Addressing these limitations may prove difficult given the lack of available data on moonlighting.\u003c/p\u003e\u003cp\u003eIn sum, this paper highlights moonlighting as a meaningful pathway into self-employment and shows that transitions are shaped by both opportunity and constraint. By identifying the factors that predict entrepreneurial entry, the study offers a deeper understanding of who becomes a full-time entrepreneur\u0026mdash;and why.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.G. produced all analysis and wrote all text for this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaptista, Rui, Murat Kara\u0026ouml;z, and Joana Mendon\u0026ccedil;a. \u0026quot;The impact of human capital on the early success of necessity versus opportunity-based entrepreneurs.\u0026quot; \u003cem\u003eSmall Business Economics\u003c/em\u003e 42 (2014): 831-847. https://doi.org/10.1007/s11187-013-9502-z\u003c/li\u003e\n\u003cli\u003eBertocchi, Graziella, Marianna Brunetti, and Costanza Torricelli. \u0026quot;Marriage and other risky assets: A portfolio approach.\u0026quot; \u003cem\u003eJournal of Banking \u0026amp; Finance\u003c/em\u003e 35.11 (2011): 2902-2915. https://doi.org/10.1016/j.jbankfin.2011.03.019\u003c/li\u003e\n\u003cli\u003eBlock, J\u0026ouml;rn H., and Andreas Landgraf. \u0026quot;Transition from part-time entrepreneurship to full-time entrepreneurship: the role of financial and non-financial motives.\u0026quot; \u003cem\u003eInternational entrepreneurship and management journal\u003c/em\u003e 12 (2016): 259-282. https://doi.org/10.1007/s11365-014-0331-6\u003c/li\u003e\n\u003cli\u003eBlock, J\u0026ouml;rn, and Philipp Sandner. \u0026quot;Necessity and opportunity entrepreneurs and their duration in self-employment: evidence from German micro data.\u0026quot; \u003cem\u003eJournal of industry, competition and trade\u003c/em\u003e 9 (2009): 117-137. https://doi.org/10.1007/s10842-007-0029-3\u003c/li\u003e\n\u003cli\u003eConway, Karen Smith, and Jean Kimmel. \u0026quot;Male labor supply estimates and the decision to moonlight.\u0026quot; \u003cem\u003eLabour Economics\u003c/em\u003e 5.2 (1998): 135-166. https://doi.org/10.1016/S0927-5371(97)00023-7 \u003c/li\u003e\n\u003cli\u003eFairlie, Robert W., Kanika Kapur, and Susan Gates. \u0026quot;Is employer-based health insurance a barrier to entrepreneurship?.\u0026quot; \u003cem\u003eJournal of Health Economics\u003c/em\u003e 30.1 (2011): 146-162. https://doi.org/10.1016/j.jhealeco.2010.09.003\u003c/li\u003e\n\u003cli\u003eFolta, Timothy B., Fr\u0026eacute;d\u0026eacute;ric Delmar, and Karl Wennberg. \u0026quot;Hybrid entrepreneurship.\u0026quot; \u003cem\u003eManagement science\u003c/em\u003e 56.2 (2010): 253-269. https://doi.org/10.1287/mnsc.1090.1094\u003c/li\u003e\n\u003cli\u003eHoltz-Eakin, Douglas, John R. Penrod, and Harvey S. Rosen. \u0026quot;Health insurance and the supply of entrepreneurs.\u0026quot; \u003cem\u003eJournal of Public Economics\u003c/em\u003e 62.1-2 (1996): 209-235. https://doi.org/10.1016/0047-2727(96)01579-4\u003c/li\u003e\n\u003cli\u003eKimmel, Jean, and Karen Smith Conway. \u0026quot;Who moonlights and why? Evidence from the SIPP.\u0026quot; \u003cem\u003eIndustrial Relations: A Journal of Economy and Society\u003c/em\u003e 40.1 (2001): 89-120. https://doi.org/10.1111/0019-8676.00198 \u003c/li\u003e\n\u003cli\u003eKoustas, Dmitri K. 2019. \u0026quot;What Do Big Data Tell Us about Why People Take Gig Economy Jobs?\u0026quot; \u003cem\u003eAEA Papers and Proceedings\u003c/em\u003e 109: 367\u0026ndash;71\u003cstrong\u003e. \u003c/strong\u003eDOI: 10.1257/pandp.20191041\u003c/li\u003e\n\u003cli\u003ePetrova, Kameliia. \u0026quot;Part-time entrepreneurship and financial constraints: evidence from the Panel Study of Entrepreneurial Dynamics.\u0026quot; \u003cem\u003eSmall business economics\u003c/em\u003e 39 (2012): 473-493. https://doi.org/10.1007/s11187-010-9310-7\u003c/li\u003e\n\u003cli\u003eCunha, Miguel Pina E., et al. \u0026quot;From a Hobby to a Business: Drifting through Paradox While the Business Accelerates.\u0026quot; \u003cem\u003eOrganiza\u0026ccedil;\u0026otilde;es \u0026amp; Sociedade\u003c/em\u003e 31.108 (2024): 88-116. https://doi.org/10.1590/1984-92302024v31n0003EN\u003c/li\u003e\n\u003cli\u003eRaffiee, Joseph, and Jie Feng. \u0026quot;Should I quit my day job?: A hybrid path to entrepreneurship.\u0026quot; \u003cem\u003eAcademy of management journal\u003c/em\u003e 57.4 (2014): 936-963. https://doi.org/10.5465/amj.2012.0522\u003c/li\u003e\n\u003cli\u003eThorgren, Sara, et al. \u0026quot;Hybrid entrepreneurs\u0026apos; second-step choice: The nonlinear relationship between age and intention to enter full-time entrepreneurship.\u0026quot; \u003cem\u003eJournal of business venturing insights\u003c/em\u003e 5 (2016): 14-18. https://doi.org/10.1016/j.jbvi.2015.12.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Moonlighting, Self-employment, Entrepreneurship, Job-Lock, Risk-Sharing","lastPublishedDoi":"10.21203/rs.3.rs-7819449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7819449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines the transition from moonlighting to full-time self-employment using panel data from the Survey of Income and Program Participation. Motivations for moonlighting vary\u0026mdash;from financial necessity to entrepreneurial experimentation\u0026mdash;and these motivations shape the likelihood of transitioning. The paper finds moonlighters with higher moonlighting income and weaker attachment to their primary job are more likely to become self-employed. Access to employer-provided health insurance inhibits transition, and marriage allows for risk-sharing which promotes entry. Understanding the nuanced pathways from moonlighting to self-employment offers critical insight for designing policies that lower barriers and support entrepreneurial growth.\u003c/p\u003e","manuscriptTitle":"Moonlighting and the Transition to Full-Time Entrepreneurship","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 09:16:37","doi":"10.21203/rs.3.rs-7819449/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"acd11c46-02c6-4f46-9a96-9aa2376f8dbf","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T14:26:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 09:16:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7819449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7819449","identity":"rs-7819449","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}