Entrepreneurship and individual remuneration. Does it pay to be an entrepreneur?

Term Paper, 2016

34 Pages, Grade: 1,0



List of Abbreviations

List of Figures

List of Tables

List of Equations

1 Introduction

2 State of Affairs in 2007

3 Negative Returns
3.1 Returns to Ability
3.2 Within-twin-analysis

4 Positive Returns
4.1 Limited Liability Company Entrepreneurs
4.2 Income Underreporting

5 Comparison of Results

6 Conclusion


This literature overview examines five studies investigating the monetary returns to entrepreneurship compared to wage employment. It starts with a summary of findings up to 2007 by observing a meta study and follows by analyzing four recent empirical analyses. These apply various approaches to discover the link between entrepreneurship and individual remuneration: investigating returns to ability, comparing twins, using a broad definition of entrepreneurship, and correcting for underreporting. The results do not indicate a clear direction. Negative, positive and similar returns for entrepreneurs compared to employed individuals are found. However, if one assumes the problems of a restrictive definition of entrepreneurship and of income underreporting to be generally relevant, higher earnings of entrepreneurs seem plausible.

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

List of Figures

3.1 Percent earnings differential between entrepreneurs and employees for different levels of general ability

4.1 Earnings differential between entrepreneurs and employees by income percentiles over time

List of Tables

5.1 Results

5.2 Definitions

List of Equations

3.1 Extended Abilities Equation

3.2 Within-Twin Analysis

4.1 Estimating Underreporting

1 Introduction

Over the past two decades, the understanding of entrepreneurship and its impact on the economy has vastly increased. Numerous authors have shown a positive effect on job creation (Carre and Thurik, 2008, Mueller et al., 2008, Van Stel and Suddle, 2008), a contribution to innovation (Arvanitis, 1997, Czarnitzki and Kraft, 2004, Lowe and Ziedonis, 2006), and a boost to economic growth and productivity (Brouwer et al., 2005, Disney et al., 2003, Robbins et al., 2000) by entrepreneurial firms. En­trepreneurship was shown to have a significant positive value for the economy as a whole.

Hence, it is important to understand what it is like to work as an entrepreneur. How does founding and running a business of one's own compare to being an employee? To assess this question, one aspect of a person’s employment status is central: How high are the earnings? In this seminar paper, I will address individual remuneration of entrepreneurs by comparing their incomes with employee wages. To that effect, I will em­phasize income levels. Do entrepreneurs earn more than the employed? Do they earn less or the same? However, my analysis will not leave out a glance at income volatility and at returns to specific aspects of human capital.

My overview of the existing literature considers five studies. The first one is a meta study of Van Praag and Versloot (2007), which represents major findings until 2007. The four other articles I cover reflect more recent research, reaching up to 2014. Their partly contradicting results allow to divide them into two categories: negative and positive returns to entrepreneurship. Their different foci produce a balanced mix of in­vestigations with regards to methods and content.

Unfortunately, my overview cannot yield an answer as clear as the stylized facts about entrepreneurship mentioned above. The literature provides different results for the question of entrepreneur remuneration.

The pre-2007 findings show both positive and negative returns to en­trepreneurship, just like the more recent results. However, the recent studies indicating positive results rest their conclusions on methods which could justify doubts about the negative returns findings. If one acknowl­edges that the latter are prone to the risks explained by the positive returns studies, i.e. a restrictive definition of entrepreneurship with­out limited liability companies (LLCs) and underreported income, higher earnings of entrepreneurs compared to employees seem plausible.

2 State of Affairs in 2007

The meta study of Van Praag and Versloot (2007) does not show a clear direction. It observes both negative, positive and similar returns to en­trepreneurship compared to employment.

Van Praag and Versloot examine the value of entrepreneurship for the economy as a whole. The authors investigate the impact of entrepreneurs on four different aspects of industrialized economies, one of them being the influence on individual utility levels. The latter include, among job satisfaction, income and income volatility.

The articles about entrepreneur income covered by the authors all de­fine entrepreneurship as either self-employment or owning-managing an incorporated company. This means that some studies observed by Van Praag and Versloot only use the self-employment definition, e.g. Hamil­ton (2000). The authors’ definition is established practice, notwithstand­ing the self-employed do not necessarily create new firms, which the au­thors state. The authors define remuneration in two parts: income levels and income volatility. Income levels are defined in three ways, according to Hamilton (2000): ”(i) net profit; (ii) a periodic wealth transfer from the firm to the entrepreneur, much like a regular wage, labeled ’draw’, and (iii) draw plus changes in the firm’s equity value.” (Van Praag and Versloot, 2007, p. 372).

Van Praag and Versloot selected their sample of studies with regard to representativeness for relevant research and quality of information. Therefore, they included studies published in journals classified by the Tinbergen Institute Research School as A or AA and added two en­trepreneurship as well as three management journals. In order to embrace recent research methods and the current macroeconomic situation, stud­ies published from 1995 to March 2007 were considered. Furthermore, only research on industrialized economies was taken into account, as the properties of entrepreneurship in developing economies are not compa­rable (Van Stel et. al, 2005, Sternberg and Wennekers, 2005). Finally, the covered studies investigate outcome variables quantitatively and test them empirically. For the topic of individual remuneration, these crite­ria yield eight studies. I have excluded the working paper of Hartog et al. (2007) from these eight, as it later became the study of Hartog, van Praag, and van der Sluis (2010), which I cover separately. The remaining seven studies use cross-sectional and panel data from the United States, ranging in sample size from 3,000 to 29,000 and from the observation year 1968 to 2001.

The results stated by Van Praag and Versloot (2007) do not indicate a clear direction. Hamilton (2000) and Kawaguchi (2002) both find that entrepreneurs earn less than employees, their median income is up to 35% less after 10 years of being an entrepreneur (Hamilton, 2000). According to the authors, such a large differential could be attributed to lower abil­ities of entrepreneurs or to their affiliation with less profitable industries, but Hamilton proves both of these hypotheses wrong. By contrast, Rosen and Willen (2002) show higher median earnings for entrepreneurs and Fairlie (2005) finds that average net entrepreneur profits of disadvantaged male youth in the US exceed their income as employees. This suggests disadvantaged young job-seekers might encounter significant rigidities on the employed labor market, which do not allow them to unfold their po­tential. Holtz-Eakin et al. (2000) point to higher income mobility for entrepreneurs, at least for low-earners: their income increases faster be­ing self-employed rather than employed. For high-earners, Holtz-Eakin et al. found the contrary to be true. Van Praag and Versloot interpret this to point to the importance of a person’s starting point income for the evaluation of the profitability of self-employment. No differential income effect of working as an entrepreneur was found by Van der Sluis et al. (2007). Carrington et al. (1996) conclude that entrepreneurial income reacts stronger to fluctuations in GNP and the unemployment rate, than wages. This result is supported by Van der Sluis et al. (2007) and Rosen and Willen (2002). It could be due to a high dependence of entrepreneurs on investments, which correlates strongly with the business cycle.

Van Praag and Versloot show how ambiguous entrepreneurial remu­neration is from a viewpoint in 2007. Starting with the next section, I will consider more recent findings.

3 Negative Returns

After examining an important meta study from a few years ago, I will investigate two recent studies in this section. Both find negative financial rewards to entrepreneurship using different methods.

3.1 Returns to Ability

Hartog et al. (2010) not only observe differences in employee wages and entrepreneur earnings, but explicitly analyze links between individual abilities and labor market returns. They observe the returns to a person’s general skill set, the income gain from specific abilities, and the effect of the variance of individual skills on earnings. Their Mincer-style income equations indicate higher returns to several abilities for entrepreneurs, which nonetheless do mostly not outweigh the overall negative income premium of entrepreneurship they find.

The authors define entrepreneurship in line with established prac­tice, as self-employment or owning-managing an incorporated company.

Farmers and individuals working less than 300 hours per year as an en­trepreneur are not seen as such, as well as persons being an entrepreneur for less than six months. An employee is defined as an individual whose principal occupation is a paid job by the authors, while entrepreneurial income is the gross hourly income (yearly income divided by hours worked).

Hartog et al. use panel data from the National Longitudinal Sur­vey of Youth (NLSY), which is conducted by the U.S. Bureau of Labor Statistics since 1979. It observes 6,111 persons representative of the U.S. population and aged 14 to 22 years in 1979. The survey participants were interviewed every year until 1994 and biannually after that. The researchers use a data set from 1979 to 2000 consisting of employees and entrepreneurs (defined above), resulting in an average sample size of 4,500 individuals per year. For one person, they obtain eleven yearly observations on average.

The data on abilities the authors use comes from NLSY participants' scores in the Armed Services Vocational Aptitude Battery (ASVAB)[1], assessed in 1980, when they were 15 to 23 years old. In order to mini­mize correlations between abilities, the study uses four of the ten abili­ties measured by the ASVAB: (i) verbal ability; understanding written texts; (ii) mathematical ability; applying logical thinking and performing mathematical calculations; (iii) technical ability; understand physics and mechanics; and (iv) clerical ability; quickly processing information. The non-cognitive ability the authors use is social ability; forming social con­tacts; which was measured by the NLSY in 1980 by asking participants how shy or outgoing they were at age six. Their inclusion of this skill mirrors recent research trends (e.g. Borghans et al., 2008; Heckman et al., 2006, Mueller and Plug, 2006).

Due to interdependence between ability and education (Hansen et al., 2004) Hartog et al. use two sets of ability measures. One only controls for age in 1980, and therefore overestimates ability, while the other controls for age and education at the time of the ASVAB test (1980), leading to underestimated ability measures. Thus, the authors receive upper and lower limits of actual ability. The general ability measure used is computed from the five individual ability measures mentioned above via factor analysis, reflecting psychological findings (Thurstone and Thur­stone, 1941; Carrol, 1993). The spread in abilities, the authors’ final variable, is the coefficient of variation of a NLSY participant’s selected and corrected abilities.

The relationship of ability and education (Roberts et al., 2000, Hansen et al., 2004, Heckman et al., 2006) also affects the authors’ income esti­mations. In order to take into account a positive omitted variable bias, occuring without including education, and an underestimation, when in­cluding education (Angrist and Pischke, 2008), Hartog et al. follow four procedures to handle the interaction between ability, education and in­come: income estimates with and without corrected abilities as well as with and without education.

Another issue Hartog et al. encounter relates to the measurement of ability. While the employment status and income of each individ­ual were recorded repeatedly since 1979, abilities were only measured in 1980. This forces the authors to use a random effects (RE) model, which assumes any unobserved characteristics to be uncorrelated with the ex­planatory variables and the latter to be uncorrelated with themselves. As a person’s career choice could be influenced by certain returns to ability, this might not be the case, however, the authors state. To overcome this issue, Hartog et al. use a diff-of-diff regression in addition to the RE approach. This allows them to exclude unobserved influences which do not vary over time, such as individual motivation to be entrepreneurial.

Hartog, van Praag, and van der Sluis (2010) use variations of the following equation to estimate their different income equations (it is never used in this specification):

Abbildung in dieser Leseprobe nicht enthalten

y is the gross hourly income, A is the composite measure of ability, whereas SA comprises of the five specific abilities. AD is ability disper­sion, S is schooling and X is a vector of other personal characteristics not changing over time[2]. E is the dummy variable indicating entrepreneur­ship and Z represents a vector of control variables changing over time[3]. c is a fixed effect, θ a random effect and t is a random variable. The subscript denotes individual i at time t.

The equation is modified in three ways for each of the authors' re­search questions. First they compute a GLS random effects baseline model (without separating the effect of ability for entrepreneurs and em­ployees), secondly they apply a GLS random effects model to answer the question and thirdly they reassess the results with a diff-of-diff approach.

Hartog et al. mostly find higher returns to ability for entrepreneurs. With their analysis of general ability, they show that for entrepreneurs, one standard deviation more ability on average leads to 14-23%[4] more income. This means that entrepreneurs profit 19-31% more from general ability, than employees, which clearly suggests that the self-employed can value their skills better than employers. The authors’ diff-of-diff estimates confirm these results. However, the income premium general ability pays is not relevant for many entrepreneurs. As figure 3.1 shows, an individual needs to have a general ability 1.72-1.92 standard deviations above average to earn more than an employee, which is the case for only 7.3-7.8%[5] of observation units. This is due to the fact that the
authors find entrepreneur income levels to be on average 8-9%[6] lower than employee incomes.

Abbildung in dieser Leseprobe nicht enthalten

Figure 3.1: Percent earnings differential between entrepreneurs and employees for different levels of general ability.

Notes: Figure from Hartog et al. (2010, p.977). The level of general ability is represented by an individual’s position in the distribution of general ability, in terms of standard deviations. The four lines represent the different estimates with regard to education controls.

With regard to specific abilities, Hartog et al. find positive as well as negative returns to being an entrepreneur. They notice the highest pos­itive premium for technical ability, which leads to 10-12%4 more income per standard deviation for entrepreneurs, possibly reflecting the impor­tance of technical innovations in entrepreneurs’ careers. The lowest neg­ative premium results from clerical ability, which generates 8-11%4 less gross hourly income for persons with entrepreneurial status. This sug­gests the self-employed can choose the speed they work at themselves. The difference in returns to verbal ability is not significant. Social and mathematical ability return 3%4 more income per standard deviation for entrepreneurs, whereas the results for mathematical ability are not sig­nificant when education is controlled for in the income equation. The premium to social skills could show the importance of pitches for en­trepreneurs. On the other hand, the answers of 15-23 year olds to how shy they were at age six might not be a convincing indication for social ability. Both a possibly distorted remembrance and a likely develop­ment of social skills with age point to a low information content of this measure. The diff-of-diff estimate again confirms the results.

Similarly to general ability (see figure 3.1), the higher returns to spe­cific abilities do not make an earnings difference overall for the vast ma­jority of entrepreneurs, Hartog et al. explain. The only exception is technical ability, which leads to higher earnings for the top 19-30%5 of the technical ability distribution. The effect of ability dispersion is not significant for employees. For entrepreneurs, a variation of abilities in­creased by one standard deviation reduces income by 2-3%6, indicating that entrepreneurs profit from being a jack-of-all-trades, as Lazear (2005) suggests. Here too, the diff-of-diff estimate is almost identical to the ran­dom effects model.

Hartog et al. shed light on the importance of abilities for remu­neration and show how the self-employed profit from far above average skills. For most people, however, they cannot state higher returns to entrepreneurship.

3.2 Within-twin-analysis

Hyytinen et al. (2013) take a new approach to measuring entrepreneur earnings by comparing twins. They find that this method comes clos­est to the true remuneration of entrepreneurship, which they find to be significantly lower than for employees.

The authors argue that earlier cross-sectional or panel data investiga­tions face two major problems. Cross-sectional analyses cannot identify unobserved factors which are different from one observation unit to the other. Panel data solves this problem, but introduces another bias: the dynamics of starting or ending work as an entrepreneur, which could be particularly unappealing, influence returns. Therefore, standard panel data estimates can be either over- or underestimates of the true returns. According to the authors, within-twin (WT) estimates offer higher re­liability, as they allow to control for unobserved heterogeneity and to compare different persons at the same time. To test these hypotheses, they conduct estimates of entrepreneurial earnings using four methods: ordinary least squares (OLS), WT, FE, and first-differencing (FD).

To generate their data set of interest, Hyytinen et al. matched twin data with employment data. The original twin data they use is the Finnish Twin Cohort Study with 11,927 same sex twin pairs born before 1958. It includes answers to numerous life- and work-related questions the respondents were asked in 1975, 1981, and 1990. The other data set used is the Finnish Longitudinal Employer-Employee Data (FLEED) from Statistics Finland, years 1988 to 2004. FLEED registers all labor market participants in Finland and, among others, their employment status. The matching returns 7,187 male[7] genetically identical (monozy­gotic, MZ) twins. Women are not included to leave the estimations un­affected by gender pay gaps, which seems sensible as women earn signif­icantly less than men in Finland (Eurostat, 2014).

For a robustness check, the authors also use dizygotic (DZ, on average 50% genetically identical) twins from the Finnish Twin Cohort Study.

Hyytinen et al. (2013) use the FLEED data’s official definition of entrepreneurship: ’’earnings over a minimum limit, entrepreneurship has lasted at least for four months, the person owns alone at least 30% of the firm or at least 50% together with his/her family” (Hyytinen et al. 2013, p. 60). Analogously to the authors above, farmers are not included. The authors use the ’annual sum of wages and salaries, entrepreneurial income, and capital income” (Hyytinen et al. (2013), p. 60) to de­fine remuneration. It is an extensive definition including pay for any job and income from business activity, copyright, dividends and interest payments.

Corresponding to their study design, the authors encounter several robustness issues. For the ordinary least squares (OLS) model, no cor­relation of unobserved heterogeneity, such as ability, with earnings is assumed, which is not plausible (Hartog et al., 2010). According to the authors, the FD and FE estimators may describe especially unfortunate states of being an entrepreneur or an employee, and therefore be mislead­ing. While the FD method only examines the time right before and after an employment status change, the FE approach takes into account all observations. However, it also only investigates individuals who changed from entrepreneur to employee status, or vice versa, therefore potentially only covering persons who were in unfortunate occupations. The authors' analyses of the mean earnings of the observation units investigated by FD and FE estimates show that income in the last year of being an employee before becoming an entrepreneur as well as in the first year after being an entrepreneur are lower than usual. Also, income in the first year as an entrepreneur is especially high. This points to an upward bias of the FD estimator. Similarly, it imperils the FE assumption that earnings do not influence the decision to be or not to be an entrepreneur (strict exogeneity).

The authors’ most robust estimator, the twin differencing WT ap­proach, uses an estimation of the following equation:

Abbildung in dieser Leseprobe nicht enthalten

Where y is the log of annual income, ENT indicates entrepreneurial status, X is a vector of control variables, and vj is an error term. The subscript denotes individual i from twin pair j at time t. γ is the coeffi­cient indicating the difference in entrepreneurial and employee income.

Hyytinen et al. find that their hypothesis of a downward biased OLS
estimator and upward biased FE and FD estimators, the WT estimator ranging in the middle, for the monetary returns to entrepreneurship, is confirmed by their estimations: yols < Ywt < γfe < Yfd. Multiple variations of the model, among others different control variables, a larger sample size, and dropped outliers, do not affect this ranking. The OLS estimate yields an entrepreneurial annual income on average 44%[8] smaller than employee wages. The second least returns result from the WT approach, which states on average 23%8 less earnings. The FE estimator shows 13.3% less returns, while the FD method estimates a premium of 13.3% for entrepreneurs, but both turn out statistically insignificant. Importantly, the lower returns to self-employment found with the WT estimate are almost three times as low as the average negative premium in the results of Hartog et al. (2010). As the latter use RE and diff-of- diff (FE) estimators, this is in line with the findings of Hyytinen et al. Compared to their FE estimate of -13.3%, the -8 to -9% of Hartog et al. do not seem too far off.

With different approaches, Hartog et al. and Hyytinen et al. both find that entrepreneurs earn less than their employee counterparts. In the next section, I will consider two studies indicating the opposite.

4 Positive Returns

This section observes two recent studies pointing to positive income pre­miums for entrepreneurship compared to wage employment. One uses an unconventional definition while the other focuses on mismeasurement.

4.1 Limited Liability Company Entrepreneurs

Berglann et al. (2011) take an unconventional approach to defining en­trepreneurship. They include employee-owners of LLCs and thus find significantly higher returns to entrepreneurship compared to wage em­ployment.

As most studies define entrepreneurship as being self-employed (Hamil­ton, 2000, van der Sluis, 2008), they do not consider employed owners of LLCs. Berglann et al. (2011) argue that this approach does not allow the central aspect of entrepreneurship to be taken into account: ”[...] the dual role of employing both human and financial capital into a business activity.” (Berglann et al, 2011, p. 180). According to them, the latter covers both investors who work for the company they invested in, as well as employees who bear the risks a firm faces. Therefore, Berglann et al. define entrepreneurs as either self-employed persons or as individuals who are employees at the LLC they significantly invested in[9]. A significant investment means owning at least 30% or at least 10% while also being a board member. This two-part definition significantly increases their sample compared to the conventional definition of self-employment, as their data set includes as many LLC employee-owners as self-employed persons. Further, the authors distinguish between individuals who were employed right before (proactive), and who were unemployed right be­fore (reactive) their switch to entrepreneurship. This distinction allows them to consider potential benefits of being employed before, such as R&D knowledge (Bhide, 2003). Regarding remuneration, Berglann et al. apply a broad definition, they include business earnings, wages, and capital income.

The data the authors use is compiled from three different Norwegian administrative register data sets. They combine data on the economic activities of all residents [10], a register of most Norwegian firms’ account­ing data, and a list of owners of all LLCs in the country. This list allows the authors to track indirect ownership, i.e. holding stakes of one com­pany through other companies, which occurs often in their data. By matching the three data sets for October 1 five years in a row, they obtain entrepreneur and employee panel data from 2000-2005. The au­thors observe entrepreneurs who became such in 2001, therefore allowing to compare their prior employment characteristics to the control group. The latter is compiled via propensity score matching, which leads to a very similar earnings path of treatment (entrepreneurs) and control (em­ployees) group before the proactive entrepreneurship decisions in 2001. The reactive entrepreneur matching is not as successful, see figure 4.1. The authors observed 10,546 proactive and 1,637 reactive entrepreneurs. For the earnings profile, data from 1997 to 2006 is used.

Abbildung in dieser Leseprobe nicht enthalten

Figure 4.1: Earnings differential between entrepreneurs and employees by income percentiles over time.

Notes: Figure from Berglann et al. (2008, p.189). The sharp decrease in differences before 2006 is probably due to the introduction of a new dividend tax in that year, which was announced in 2004.

As figure 4.1 (a) indicates, Berglann et al. find that proactive en­trepreneurship leads to a median income premium of 16.4%n from 2001 to 2005. Also, they show how earnings dispersion increases sharply for entrepreneurs. This, according to the authors, might reveal higher in­come risk or a higher importance of individual skills, the latter of which would support the results of Hartog et al. It is important to note that the high dispersion is not equally distributed for negative and positive financial premiums of entrepreneur status, instead, the higher percentiles of the entrepreneurial income distribution gain much more than the lower percentiles lose, suggesting that unsuccessful entrepreneurs acknowledge [11] their limits. Further, Berglann et al. show that proactive LLC en­trepreneurs earn significantly more than the self-employed, their pre­miums are higher and losses smaller. The authors even demonstrate that proactive entrepreneurs who did not survive in business until 2005 on av­erage profited financially from being an entrepreneur for a while. These results are in sharp contrast to the findings of Hartog et al. and Hyytinen et al., as well as to some of Van Praag and Versloot’s observations, which state negative returns between -8 and -35% for the self-employed.

Due to a much smaller sample size for reactive entrepreneurs, the in­sights about the latter are limited. Nonetheless, Berglann et al. find that reactive entrepreneurs gain a median income premium of 8.3% compared to employees. Remarkably, they do better than employees in any income percentile. Coming from unemployment, entrepreneurship seems to offer benefits only compared to wage employment. Regarding returns for LLC entrepreneurs compared to the self-employed, the reactive do not show a difference. For non-surviving reactive entrepreneurs, Berglann et al. find that the period of entrepreneurship is slightly rather profitable than not on average.

The extended definition Berglann et al. use leads them to positive returns to entrepreneurship. Being owner-employee of an LLC seems especially rewarding.

4.2 Income Underreporting

Astebro and Chen (2014) discuss various explanations for the mostly[12] observed more volatile and not higher earnings of entrepreneurs, defined as full-time self-employed persons, compared to employees (Hamilton, 2000, Kawaguchi, 2002, Hyytinen et al., 2013, Hartog et al., 2010). These include ignorance of expected income, the lack of accurate ability com­pensation through wages, or motivation through non-monetary criteria, e.g. job satisfaction. Nevertheless, they investigate another possible, and potentially easier to measure, reason for these findings, underreporting of income by entrepreneurs.

The latter has been investigated by various authors, many of whom identify significant amounts of underreported entrepreneurial income (Feld­man and Slemrod ,2007, Pissarides and Weber, 1989, ). However, Tedds (2010) finds underreporting amounts to only $3000 a year on average. Due to these ambivalent findings, Astebro and Chen engage in their own empirical analysis of underreporting. Their data originates from 15 years of observations from the Panel Study of Income Dynamics (PSID), a rep­resentative sample of households in the U.S. It includes 7,371 heads of households observed from 1980 to 1987 and 1990 to 1996, of which 13% were entrepreneurs. To estimate how much entrepreneurs are underre­porting, Astebro and Chen examine the association of household food expenditures and income for otherwise equal entrepreneurs and employ­ees. The authors use the following equation:

Abbildung in dieser Leseprobe nicht enthalten

The dependent variable ln ci is the log of food expenditures, ln yi is the log of yearly income, and the dummy variable D has the value one for a household head with entrepreneurship status. Xi is a vector of control variables, ei describes the influence of unobserved factors on the household’s food expenditures, τ the influence of different years, and 6 specifies the influence of different industries. In the subscript, k denotes the household employment status, i the household, t the year, and m the industry. 1 — exp(-γ/ß) yields the fraction of income underreported. Astebro and Chen estimate an OLS and an instrumental variable (IV) model, the latter of which uses education as an instrument for income to correct for possible income mismeasurement.

Hurst et al. (2014) state three central assumptions for the proce­dure described above: (i) underreporting of food expenditures must be


[1] ASVAB results show a strong correlation with other frequently used intelligence measures (Frey and Detterman, 2004) and are well received by vocational psycholo­gists (Ryan Krane and Tirre, 2005).

[2] The authors include gender, ethnicity, parental education levels and birthyear dummies.

[3] These are age and year dummies, health status, marital status, urbanization and region.

[4] The coefficients are significant on a significance level of 1%.

[5] According to the authors, these fractions are based on a 95% confidence interval.

[6] The coefficients are significant on significance levels between 5% and 1%.

[7] The authors intend to leave their estimates unaffected by gender pay gaps and gender-related work supply differences.

[8] The coefficient is significant on a significance level of 1%.

[9] Farmers are not included here either.

[10] This data set includes a variety of demographic characteristics.

nTo leave out possible distortions due to gender pay gaps, I only cover the results Berglann et al. found for men.

[12] As explained above, e.g. Fairlie (2005) and Rosen and Willen (2002) find positive income premiums for the self-employed.

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Entrepreneurship and individual remuneration. Does it pay to be an entrepreneur?
Humboldt-University of Berlin  (Chair of Entrepreneurial and Behavioral Decision Making)
Innovative Entrepreneurship
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entrepreneurship, remuneration, returns to entrepreneurship
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Justus Kirchhoff (Author), 2016, Entrepreneurship and individual remuneration. Does it pay to be an entrepreneur?, Munich, GRIN Verlag, https://www.grin.com/document/371176


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