ISSN (Print) 0473-453X

Discussion Paper No. 1126

ISSN (Online) 2435-0982

DO ELITE COLLEGES MATTER?
THE IMPACT ON
ENTREPRENEURSHIP DECISIONSAND CAREER DYNAMICS
Naijia Guo
Charles Ka Yui Leung

March 2021

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Do Elite Colleges Matter? The Impact on Entrepreneurship Decisions and Career Dynamics

Naijia Guo, Chinese University of Hong Kong. Email: guonaijia@cuhk.edu.hk
Charles Ka Yui Leung, City University of Hong Kong. Email: kycleung@cityu.edu.hk

This version: March 2021

Abstract
Elite college attendance significantly impacts students' entrepreneurship decisions and career dynamics.
We find that an elite college degree is positively correlated with entrepreneurship (i.e., owning an
incorporated business) but not with other self-employment forms. Our overlapping generations model
captures self-selection in education and career choices based on heterogeneous ability and family wealthendowments over the life-cycle. Our estimates show that (1) entrepreneurs and other self-employedindividuals require different types of human capital, and (2) elite colleges generate considerably morehuman capital gain than ordinary colleges, particularly for entrepreneurs. Distinguishing between eliteand ordinary colleges improves our prediction of entrepreneurship decisions. Providing subsidies for elitecolleges is more efficient than subsidizing their ordinary counterparts to encourage entrepreneurship,enhance intergenerational mobility, and enhance welfare. In contrast, although start-up subsidy increases
entrepreneurship, it does not improve their performance, and it is inferior to education subsidy ingenerating efficiency, equality, and intergenerational mobility.
JEL Classification: D15, I20, J24
Keywords: entrepreneurship, elite college, intergenerational transfer.

Acknowledgement: We are grateful to (in alphabetical order) Grey Gordon, Jeremy Greenwood, Eric Hanushek,
Chih-Sheng Hsieh, Minchung Hsu, Fatih Karahan, Ed Lazear, Michio Naoi, Phuong Ngo, Masao Ogaki, Paul Oyer,
Elena Pastorino, Ponpoje Porapakkarm, Christopher Taber, Xuan Song Tam, Cheng Wang, Kuzey Yilmaz,
Donghoon Yoo, seminar participants at Chinese University of Hong Kong, City University of Hong Kong,
Hitotsubashi University, Hong Kong Baptist University, Jinan University, Keio University, Monash University,
National Graduate Institute for Policy Studies (GRIPS), National Taiwan University, and University of Technology
Sydney, and conference participants at the Asian Meeting of the Econometric Society, China Meeting of the
Econometric Society, EALE-SOLE-AASLE World Conference, World Congress of the Econometric Society for
their helpful comments on previous versions. Insights and suggestions from Peter Arcidiacono (editor) and two
anonymous referees significantly improve this paper. This project is supported by the Early Career Scheme of theHong Kong Research Grant Council (project code: 2191108). Part of this research was conducted while Leung wasa visitor at the Hoover Institution, whose hospitality is gratefully acknowledged. Leung’s travel has been supportedby the Center for Research in Econometric Theory and Applications (Grant No. 109L900201) from The Featured
Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of
Education (MOE) in Taiwan, and by the Ministry of Science and Technology (MOST), Taiwan under Grant No.
MOST 109-2634-F-002-045, the Joint Usage/Research Center at ISER, Osaka University and Grant-in-aid for
Research Activity, Japan Society for the Promotion of Science (15H05728, 20H05631), and the International Joint
Research Promotion Program of Osaka University. Chuantao Cui, Joe Ng, and Shuangxin Wang, Shumeng Zhang
provide excellent assistance. All errors are our own.

1

Introduction

Do elite colleges matter? The ongoing lawsuit between the Students for Fair Admissions
against Harvard University, and the related discussion, suggest that the public believes that
elite colleges matter.1 The large amount of bribery involved in the recent elite college admission scandal may even suggest that a “premium” is placed on graduating from an elite college
over an ordinary one.2
However, calculating the “elite college premium” is not straightforward, as elite college
students are positively selected in terms of their ability and family background (Chetty et al.,2020). Dale and Krueger (2002) argue that there is no earning differential between elite college
graduates and ordinary college graduates after controlling for college selectivity. Thus, theimplied elite college premium is negative, as elite colleges charge much higher tuition fees
than ordinary colleges. Numerous studies debate these findings (e.g., Black and Smith, 2004,
2006, Dale and Krueger, 2014, Hoxby, 2009, Ge et al., 2018).3
In this paper, we analyze the effect of attending an elite college on lifetime income, focusing
on students’ entrepreneurship decisions and career dynamics. We need to overcome several
empirical challenges. As elite college attendance is an endogenous choice, becoming and when
to become entrepreneurs are also endogenous choices. Furthermore, casual observations do not
answer whether elite college attendance would facilitate entrepreneurship.4 On the one hand,elite colleges may increase students’ entrepreneurial human capital. On the other hand, elitecolleges’ high tuition fees may deter potential entrepreneurs due to financial constraints. As
a result, smarter and richer individuals are more likely to attend elite colleges and become
successful entrepreneurs.
Thus, to account for selection in terms of ability and wealth, we develop an overlapping generations life-cycle model that unifies the seminal work of Keane and Wolpin (1997),
which focuses on life-cycle education and career choices, and a series of works by Cagetti and1

On November 17, 2014, Students for Fair Admissions (SFFA) filed a lawsuit in the federal district court
against Harvard University for race-based discriminatory admission practices. On September 30, 2019, the district
court found no evidence of any intentional discrimination. On February 25, 2020, SFFA filed an appeal. For moredetails of the SFFA vs. Harvard case, see court document Case 1: 14-cv-14176-ADB, Document 672 (filed
09/30/2019) and Case: 19-2005, Document: 00117556565 (filed 02/25/2020).
2
According to McLaughlin and DeGeurin (2020), the Federal prosecutors have charged around 50 parents. On
top of the expensive tuition, the average bribery is about $500,000.
3Black and Smith (2004) use a matching method to show that the often-used linear specification can lead tobiased results. Black and Smith (2006) compare four econometric methods and find that the literature probably
underestimates the effect of college quality. Hoxby (2009) argues that elite colleges enable their students tomake massive human capital investments and become more competitive with their resources. Dale and Krueger(2014) extend their earlier work by examining the returns to the college of a more recent cohort and over a longer
time horizon. They argue that the college effects on wages are concentrated in certain subgroups, such as African
American and Hispanic students. Ge et al. (2018) find that elite college attendance has significant marriage market
benefits, especially for women.
4
Zimmerman (2019) shows that for male students from expensive private high schools, attending an elitebusiness-focused degree program in Chile significantly enhances the probability of attaining a top corporate position. Such differences are not found for female students or male students from other types of high schools.

1

De Nardi (2006, 2009), which emphasize entrepreneurship decisions.5 In particular, we model
how agents self-select different educational and career options after receiving intergenerationaltransfers of wealth and acquiring multi-dimensional abilities. Hence, our model evaluates (a)the contributions of different types of education (elite college, ordinary college, or no college)
to the accumulation of different kinds of human capital and (b) the production technologies,
riskiness of the income stream, and human and physical capital requirements of various career choices (employment, entrepreneurship, and other self-employment). Our model captures
the diversity in education choices, subsequent career dynamics (switching from one career toanother), and intergenerational mobility observed in our panel dataset. Our assessment of therelative importance of different factors in the variation of lifetime incomes and career choices
contributes to the nature versus nurture debate. Furthermore, our simulation and counterfactual
exercises shed light on the importance of elite college attendance to entrepreneurship decisions.
Our analysis proceeds in several steps. First, we show that in terms of mean, median, andstandard deviation, entrepreneurs’ income profile (incorporated business owners) is different
from employees’ income profiles and other self-employed individuals (unincorporated business owners).6 A restricted access dataset from the Panel Study of Income Dynamics (PSID)
allows us to identify the college at which each respondent studied. Then we show that elitecollege graduates are more likely to become entrepreneurs than to engage in other forms ofself-employment. Moreover, entrepreneurs earn more than employees on average, but they also
experience more volatile income streams, while other self-employed individuals are similar to
those employees. These findings suggest that it is essential to distinguish between entrepreneurs
and other self-employed individuals (Levine and Rubinstein, 2017). As different career paths
deliver various average income and different income volatilities, we consider it critical to explicitly model career dynamics (the transitions between being an employee, an entrepreneur,and other self-employed). Therefore, our modeling strategy complements previous studies on
wage dynamics which either focus on employees only (Juhn et al., 1993, Low et al., 2010, Yamaguchi, 2010), or combining employees with self-employed (Dale and Krueger, 2002, 2014).
In this paper, we highlight the impact of elite college attendance on entrepreneurship.
Next, we construct an overlapping generations life-cycle model of education and career
choices. Education and career choices are typically not random. For example, more able andwealthier individuals are more likely to enroll in elite colleges and become entrepreneurs. In
our model, agents inherit multi-dimensional abilities (defined as general ability, unincorporated
ability, and incorporated ability) and wealth from their families. They make educational choices(high school, ordinary college, or elite college) and career decisions (employees, entrepreneurs,
or other self-employed). Education improves general, unincorporated, and incorporated human5

We directly model the education and career decisions. Assuming the model is correctly specified, we recover
the causal results of elite colleges on entrepreneurship.
6
Carrington et al. (1996) also find that the self-employed’s annual earnings are more volatile than the employees. Throughout this paper, the terms “other self-employed individuals” and “unincorporated business owners”
are used interchangeably.

2capital, and these human capital gains potentially differ between elite and ordinary colleges.7
We estimate our model using the PSID and generate several sets of results. First, our lifecycle model captures both education and career decisions. We match the income level of different career paths. We also mimic the career and income dynamics in the data, such as theconditional probabilities, as well as the income correlations of switching from one career toanother.8 Our structural model also provides estimates of intergenerational links, such as theconditional probability of a son’s educational or career choice given the father’s decision. This
unified framework for studying educational alternatives, career dynamics, and intergenerational
links is new to the literature to the best of our knowledge.
Second, we estimate the effect of elite college attendance on the accumulation of humancapital. Our model predicts that agents born with the higher general ability and financial capacities are more likely to enroll in elite colleges. After controlling for selection in terms ofability and wealth, elite colleges still deliver higher gains for general, unincorporated, and incorporated human capital than ordinary colleges; the increase in incorporated human capitalis the largest. The average elite college premium (discounted lifetime utility gains from goingto an elite college compared with an ordinary college, net of tuition) is positive and equivalent
to $16,712 in 2011 dollars, which justifies people’s willingness to attend elite colleges despite
their high tuition fees.
Third, we show that incorporated and unincorporated businesses operate with very different
human and physical capital requirements, which justifies our decision to treat them separately
in the model. Incorporated businesses use general and incorporated human capital, whereas
unincorporated businesses mostly use unincorporated human capital. Moreover, incorporated
businesses have an entry cost of $58,000, while the corresponding figure for unincorporated
firms is only $8,000 (all in 2011 dollars). Consequently, individuals who are high in generaland incorporated ability self-select into incorporated businesses, while individuals who are low
in general ability but high in unincorporated ability sort into unincorporated businesses. Initial
wealth increases the chance of owning an incorporated business but does not affect the prospectof owning an unincorporated business.
Fourth, we evaluate the effect of elite colleges on entrepreneurship by conducting decomposition and simulation exercises. Compared with our full model, which includes differences inabilities, wealth, and schooling at age 20, excluding variation in education reduces the model’s
explanatory power for the entrepreneurship decision (measured by the conditional variance) by
5.4 percentage points (ppt). In contrast, the explanatory power for the self-employed decision
7

In this paper, human capital is different from ability. In broad terms, human capital is equal to the sum ofability endowment, human capital gain from school, and human capital gain from experience. We provide details
in later sections.
8
The distribution of entrepreneurial returns is known to be skewed, and it is not easy to match precisely. Hall
and Woodward (2010) find that almost three-quarters of venture-backed entrepreneurs receive nothing at the firm
exit while a few earn more than a billion dollars. Kartashova (2014) finds that the private entrepreneurial premiumis positive when data from more recent years are included. Our model matches several moments of the distributionof entrepreneurial returns observed in the data.

3

is unaffected.9 Moreover, when we group elite and ordinary colleges, the explanatory power ofeducation for the entrepreneurship decision is much smaller (only 2.6 ppt), suggesting that distinguishing elite and ordinary college graduates is vital to understanding their entrepreneurial
decisions. We further simulate the changes in career choices by comparing individuals assigned to elite and ordinary colleges. Transferring elite college graduates to ordinary colleges
leads to a substantial drop in the probability of becoming an entrepreneur by 5.6 ppt (45.5%).
The chance of becoming other self-employed only declines by 0.9 ppt (6.6%). Moreover, oursimulation shows that elite colleges’ effect on entrepreneurship is concentrated for the individuals with high incorporated ability and low initial wealth. These exercises jointly suggest thatconsidering elite college attendance is essential to understand entrepreneurship decisions.
Our last set of results comes from two counterfactual experiments: subsidies for college
attendance and business start-up. We find that subsidizing elite college students increases thenumber of entrepreneurs and their income indirectly, reduces the age of first entrepreneurship,
and increases entrepreneurship duration. These effects are more extensive than those for ordinary college subsidies. Besides, providing tuition subsidies to elite colleges is more efficient in
improving social welfare and intergenerational income mobility. However, these subsidies also
increase income inequality.
On the other hand, start-up subsidies for entrepreneurs could substantially increase the fraction of entrepreneurs. The age of first entrepreneurship would decline, but the average income(excluding subsidies) and entrepreneurship duration would decrease. It also has limited effects
on education decisions. We also find that subsidies to unincorporated businesses are more efficient in improving social welfare than incorporated businesses. These results reinforce the ideathat distinguishing ordinary and elite colleges in the education stage and distinguishing incorporated and unincorporated business in the career stage is valuable in understanding life-cycle
choices and policy considerations.
The remainder of this paper proceeds as follows. The formal model is presented in Section
2, followed by a description of the data used for the estimation in Section 3. We explain theidentification and estimation strategies in Section 4. The estimation results are presented in
Section 5, where we discuss the model fit of the targeted and untargeted moments, the elite college premium, and the effects of abilities and initial wealth on education and career decisions.
Section 6 analyzes elite colleges’ effect on entrepreneurship through decomposition analysis
and a simulation exercise. Section 7 presents the counterfactual analysis of providing subsidies
to different college students and different business start-ups. Section 8 concludes the paper.
We relegate supplemental materials, including the literature review and additional empiricalresults, to the Appendix.
9

While excluding the ability differences reduces the model’s explanatory power for the entrepreneurship decision by more than half, excluding the variation in initial wealth does not significantly impact the model’s explanatory power. Among different abilities, the incorporated ability is the most crucial factor.

4

2

Model

2.1

Model Setup

Economic environment Single-individual dynasties populate the economy. Each life lasts
for at least 65 years and at most 100 years. Each period is five years. For the first four periods(20 years) of an individual’s life, the individual is a part of his parent’s household and does
not make any economic decisions. At age 20, the young individual moves out of his parent’s
house and forms his household. He decides whether to enroll in college and, if so, what type ofcollege to attend. There are three levels of education attainment, high school, ordinary college,and elite college, which are denoted e ∈ {hs, nc, ec}, respectively.10
Individuals not in school choose between being an employee, an entrepreneur (i.e., an incorporated business owner), or other self-employed (i.e., an unincorporated business owner),
which are denoted j ∈ {em, ib, ub}, respectively. All individuals decide how much to consume(c) and save (k). Also, those who own a business choose an investment level of kj . Workers
must retire at 65, but self-employed individuals can continue to run their business after 65 ifthey owned a business in the previous period.
At age 30, each individual has a child. Individuals are altruistic towards their offspring. A
child’s expected lifetime utility enters the parent’s value function with weight ω ∈ [0, 1]. Children inherit abilities from their parents. When children leave home and begin their households,
parents have the option of giving them a one-time gift of liquid assets, denoted by R.11 Thiscan be motivated by the observation that many parents help their children pay for college or
finance their businesses.12
Human capital Each person is born with three types of ability (A = {Aem , Aib , Aub }). (Aem )is the general ability to produce out of labor. Self-employed abilities (including incorporated
ability and unincorporated ability, Aib and Aub ) capture the capacity to produce with capital. We
use Aib to capture the non-routine skills, cognitive and non-cognitive, required by incorporated
businesses and Aub to capture the manual skills that are needed by unincorporated businesses.13
The child’s initial ability is broadly defined to include genetics, family culture, motivation, andknowledge acquired from parents. We assume the three abilities are uncorrelated. Abilities
are assumed to be log-normally distributed and imperfectly transferred from parent to child10

We focus on whether individuals graduate from college instead of college enrollment and dropout decisions.
College dropouts are treated as high school graduates in our model. We assume that each period is five years
because it takes four to five years to get a college degree.
11
In this paper, we focus on father-son intergenerational linkage in terms of education, income, and career
choice. We abstract from other important decisions and intergenerational channels, such as fertility and parental
time allocation, that other authors have explored. Among others, see Gayle et al. (2018), Lee and Seshadri (2019).
12
Empirical studies confirm the existence of inter vivos transfers for college and other investments. See Hurdet al. (2011) and Haider and McGarry (2018).
13
Levine and Rubinstein (2017) show that entrepreneurs engage in activities demanding a high degree of nonroutine skills. At the same time, other self-employed individuals perform tasks requiring relatively strong manual
skills.

5

according to an AR(1) process according to14
log Acj = θj log Apj + ψj

for j ∈ {em, ib, ub}(1)where Acj is the child’s ability, Apj is the parent’s ability, and ψj ∼ N (0, (σja )2 ) for j ∈(σ a )2j{em, ib, ub}. The variance of ability Acj is σj2 = 1−θ2.
jIn this model, the ability is inherited, but human capital can be enhanced. Agents canimprove the in-born general human capital (hem ) by attending college and through learningby doing. How much general human capital a person has depends on his general ability (Aj ),education (e), and potential experience (x) according to2
log hem = log Aem + µem
e + γ1 x + γ2 x(2)where µem
is the general human capital gained through education. We allow human capitalegains to differ by school type e and career type j. We normalize the human capital gains
from high school µjhs ∈ {em, ib, ub} to zero. Potential experience x is determined by age andwhether a person is attending college.
For both incorporated and unincorporated human capital (hib and hub ), its evolution depends
on the corresponding ability (Aib /Aub ) and education (e).15
log hj = log Aj + µje

for j ∈ {ib, ub}(3)where µje is the incorporated/unincorporated human capital gained through college-type e withthe human capital gained from high school µjhs again normalized to zero.
College choice Elite and ordinary colleges charge different tuitions and provide different
levels of financial aid. Net tuition isTe − fe (k p , Aem )for e = nc, ec

where Te is college tuition and fe is financial aid. Financial aid is a function of education type(e), family assets (k p ), and general ability (Aem ).16 Our formulation embeds both need-based
14

There is increasing evidence that “general ability” and “self-employed abilities” are indeed different andtransferred between generations. See Kerr et al. (2018), Hartog et al. (2010), and Schoon and Duckworth (2012).
15
We assume away learning by doing for incorporated/unincorporated human capital. We already have thediminishing return to investment ν that plays a similar role in capturing the hump shape in the life-cycle income
profile. Also, we assume that incorporated/unincorporated businesses use incorporated/unincorporated humancapital and general human capital, and the latter has learning by doing. The empirical evidence for the correlation
between entrepreneur experience and performance is controversial. Toft-Kehler et al. (2014) and others proposethat such a correlation depends on the type of entrepreneur.
16
We assume that financial aid does not depend on incorporated ability or unincorporated ability because theseabilities are difficult for universities to observe. Most studies find that financial aid is a function of SAT scores
or IQ test scores, which are good predictors of employee performance. See Schmidt (2000), Schmidt and Hunter(1998, 2004).

6and merit-based financial aid.
In addition to the difference in price, the two types of colleges also have different admission rates. We assume that colleges cannot directly observe students’ general ability, but only
students’ SAT scores, which signal their general ability. Colleges select their students based on
their SAT scores, which are a function of general ability and noise.
SAT = κAem + ε(4)where ε ∼ N (0, σε2 ).17 Ordinary colleges are not selective, and elite colleges select students
based on their SAT scores, with p(SAT ) being the admission rate.18 Therefore, when elite college is the best choice for students, the chance of being admitted is p(SAT ), which is exogenous to students. If students are not admitted to elite colleges, they will go for their second-best
choice, which could be ordinary college or high school. The modeling of ability requirementsallows us to incorporate the selectivity and capacity constraints of elite colleges.
Technology In our model, entrepreneurs and other self-employed individuals operate theirfirms, so their production technologies generate their income. Employees provide their labor
to representative firms, which then combine labor with capital to produce income.
Entrepreneurs and other self-employed individuals have similar income structures. Their
income depends on 1) the productivity of the business technology (Pj ), 2) their incorporated
or unincorporated human capital (hj ), 3) their general human capital (hem ), 4) their physicalcapital investment in the incorporated/unincorporated business (kj ), 5) an idiosyncratic productivity shock (j ), and 6) the entry cost of opening an incorporated/unincorporated business(Cj ≥ 0) if they were not incorporated/unincorporated business owners in the previous period(j−1 6= j). Formally, it means that
Ij = Pj hj (hem )ρj (kj )vj ej − Cj 1{j−1 6= j}j ∈ {ub, ib}(5)where j ∼ N (0, ξj2 ), j ∈ {ub, ib} are serially uncorrelated.19 To capture the fact that business
investment is risky, we assume that j , j ∈ {ub, ib} is unknown to individuals before they
make their career choices. The parameters ρj and νj , 0 ≤ ρj , νj ≤ 1 are the rates of returnto general human capital and physical capital, respectively. We assume that all self-employedindividuals are one-person firms that only use the business owner’s human and physical capitalfor investment.20
17

We assume that SAT scores do not depend on initial wealth (k0 ) or incorporated/unincorporated ability conditional on general ability due to identification concerns. See more discussions in Section 4.
18
Fu (2014) shows that the admission rates for ordinary colleges are close to one, and the admission rates of
elite colleges vary little by family income.
19
In our PSID sample, the log of total income (the sum of labor income and business income) of incorporated
business owners has a skewness of -0.049, and that of unincorporated business owners has a skewness of -1.16. It
seems reasonable to assume that the productivity shocks follow normal distributions.
20
According to Kochhar et al. (2015), only 24% of self-employed individuals had at least one paid employee in
2014. Modeling hiring workers’ decisions for entrepreneurs is not easy, as the entrepreneurship decision affects

7

Agents who do not operate their own firms earn their living as employees in the employee
sector. The income process for employees is
Iem = whem eem

(6)where w is the market wage rate (per efficiency unit), hem is the general human capital, and em
2
a serially uncorrelated idiosyncratic productivity shock with em ∼ N (0, ξem).21 The labor of
employees (measured in efficient labor units, i.e., human capital) is aggregated to the market
supply of labor Lem , soZ

hem eem dh.

Lem =

(7)

h∈S emThe employee sector production function Fem combines the aggregate capital Kem (which is
explained further later) and Lem to produce goods according toαFem (K, L) = Pem Kem
L1−α
em .

(8)

The production function Fem has constant returns to scale. With the competitive input markets,aggregate labor’s marginal product determines the wage rate w.
Leverage Following Evans and Jovanovic (1989), Kiyotaki and Moore (1997), we assume
that entrepreneurs and other self-employed individuals can borrow up to a λ proportion of theirassets k, so(kj − k) ≤ λk

forj ∈ {ib, ub}(9)where λ is the leverage ratio with λ ∈ [0, 1]. The maximum leverage ratio, defined as the ratio
between the maximum amount of investment and equity, kj /k, is (1 + λ).22
We assume there is no borrowing constraint for college students because many studies
find that borrowing constraints do not bind for most U.S. college students (e.g., Heckman andMosso, 2014, Cameron and Taber, 2004, Carneiro and Heckman, 2002, Cameron and Heckman, 2001). College students can get federal loans that cover their tuition and minimum living
expenses and borrow commercially.
However, the total amount of debt, which is the sum of student loans and business loans,salary workers’ wage rate through an equilibrium effect. The value of entrepreneurship and the value of workers
would depend on how many people choose to become entrepreneurs in equilibrium, which makes it very difficult
to solve in a heterogeneous agent model.
21
Guvenen (2007, 2009) estimated the autoregression coefficient of labor income to be 0.821 using PSID. Since
one period is five years in our model, the five-year autoregression coefficient is 0.373 (= 0.8215 ). Therefore, we
assume that income shocks are serially uncorrelated.
22
We assume that employees do not face a borrowing constraint, following Cagetti and De Nardi (2006, 2009).
In our PSID sample, the average debts (excluding mortgage) of employees are $16,093 in 2011 dollar, while theaverage debts of business owners are $78,170.

8

cannot exceed that leverage ratio of λ. Therefore, anyone who takes out a student loan forcollege effectively reduces the business loan in the future. Thus, student loans somehow discourage financially constrained students from enrolling in an elite college for entrepreneurs.
Preferences Every individual has the utility function
c1−σ+ bib 1{d = ib} + bub 1{d = ub}
1−σ+ bnc 1{d = nc} + bec 1{d = ec}u(c, d) =(10)

where bd ∼ N (0, (ηd )2 ) with d ∈ {ib, ub, nc, ec} are shocks to the consumption value of entrepreneurship, other self-employment, ordinary college, and elite college, respectively. These
shocks affect the non-pecuniary utility of career or school choices and they are i.i.d. acrossindividuals and over time.23 Households discount the future at the rate β.
A household’s lifetime utility is given by
U=

17
X

β t−1 ζ(t)u(t) + β 6 ωU c .

(11)

t=1

An individual can live for up to 17 periods (from age 20 to 100 with one period equal to five
years). A child’s utility U c enters his parent’s utility function when the parent is 50 years old(period 7) with weight ω. ζ(t) is the survival rate and we assume ζ(t) = 1 before age 65, andζ(t) < 1 after 65.24
2.2

Individual Problem in Recursive Form

Before introducing the mathematical formulation of our model, it is instructive to provide a
descriptive overview. Agents go through different stages of life, starting at age 20. Age 20 is
the schooling stage when agents decide whether to attend an elite college, an ordinary college,
or no college. Given their educational achievement, agents are in their working stage between
ages 20 and 65. On top of the standard consumption-saving decisions, individuals choose theircareer path, choosing between being an employee, entrepreneur, or other self-employed. At age
50, agents can make a one-time transfer to their offspring. Starting at age 65, employees retireand face a chance of death. Conditional on surviving, self-employed individuals can choose
between continuing the business and retirement after 65.
Retirement stage Let Wj represent the expected life-time utility for different career choices:
retirement (j = re), entrepreneurship (j = ib), and other self-employment (j = ub). The statevariables Ω include age t, education type e, abilities A = {Aem , Aib , Aub }, capital k, last period
career type j−1 , and “consumption shocks” for incorporated businesses bib and unincorporated
23

Empirical studies support the view that there are consumption values to college and entrepreneurship. See
Benz and Frey (2008), Astebro et al. (2014), Jacob et al. (2018), and Gong et al. (2018). These shocks help to fit
the schooling choice and career choice in the data that cannot be explained by pecuniary returns alone.
24
We assume that once people die, the government gets their wealth.

9businesses bub , which are the non-pecuniary utility individuals would receive if they become
business owners.
Employees retire at age 65 and decide how much to consume (c) and save for the next
period’s capital (k 0 ). The value of retirement isWre (Ω) = maxu(c, em) + βζ(t)V (Ω0 )
0
c,k

s.t.

c + k 0 = k(1 + r) + p,

(12)

c>0

where r is the interest rate and p is the pension received by retired person. Following Cagetti
and De Nardi (2006), we assume pension to be a φ fraction of the average income before
retirement. The next period’s state variables are Ω0 = {t + 1, e, Aem , Aib , Aub , k 0 , em}.25
The value function for a business owner (incorporated or unincorporated) is
Wj (Ω, j ) = maxu(c, j) + βζ(t)EV (Ω0 )0(13)c,k ,kjs.t.

v

jc + k 0 = (1 − δ)kj + Pj hj hρem
kj j ej − Cj 1{j−1 6= j} − (1 + r)(kj − k)(kj − k) ≤ λk,

c > 0,

for j ∈ {ub, ib}

where δ is the capital depreciation rate. Ω0 = {t + 1, e, Aem , Aib , Aub , k 0 , j, b0ib , b0ub }.
When agents reach retirement age, they are only allowed to continue their career paths ifthey were self-employed in the last period; otherwise, they must retire.
(
V (Ω) =

max{Wre (Ω), EWj−1 (Ω, j−1 )} if j−1 ∈ {ib, ub}

Wre (Ω)

if j−1 = re

The expectations are taken over j−1 because individuals do not observe productivity shocks
when making their career choices.
Working stage without intergenerational transfers Except for intergenerational transfers,
self-employed individuals’ maximization problem is the same as before and after age 65. For
employees, the forward-looking maximization problem in the working stage is denoted by
Wem , which is different from (12) as employees are paid a salary during these stages. The
salary changes over time as employees accumulate human capital and experience different
productivity shocks in each period. Formally, it isWem (Ω, em ) = maxu(c, em) + βEV (Ω0 )0c,ks.t. c + k 0 = k(1 + r) + whem eem ,

(14)

c>0

Given that retired workers cannot be self-employed, b0ib and b0ub do not affect their value functions. Therefore,
the next period’s state variables do not include b0ib , and b0ub .
25

10

where Ω0 = {t + 1, e, Aem , Aib , Aub , k 0 , em, b0ib , b0ub }.
An agent can freely change his career at the beginning of each period, but he does notobserve the productivity shocks em , ib , and ub .
V (Ω) = max{EWem (Ω, em ), EWib (Ω, ib ), EWub (Ω, ub )}(15)

Working stage with intergenerational transfer At age 50, parents can give a one-timetransfer to their offspring. The value function of an “employee parent” isWem (Ω, em ) = maxu(c, em) + βEV (Ω0 ) + ωEJ(Φ̃|Aem , Aib , Aub )0c,k ,Rs.t.

c + k 0 + R = k(1 + r) + whem eem ,

(16)

c>0

where J (.) is the value function of the child and Φ̃ = {A˜em , A˜ib , A˜ub , R, k 0 , b̃nc , b̃ec }. The
expectation is taken over the child’s abilities (A˜em , A˜ib , and A˜ub ) and shocks to the consumption
value of college for children (b̃nc and b̃ec ). The child’s abilities are correlated with the parent’s
abilities but are not observed by parents at the time of the transfer.
Similarly, the value function of an “business-owner parent” at age 50 isWj (Ω, j ) = maxu(c, j) + βV (Ω0 ) + ωEJ(Φ̃|Aem , Aib , Aub )0c,k ,kj ,R

s.t.

(17)

v

j
c + k 0 + R = (1 − δ)kj + Pj hj hρem
kj j ej − Cj 1{j−1 6= j} − (1 + r)(kj − k)c > 0,(kj − k) ≤ λk,

for j ∈ {ub, ib}

Schooling stage We now define the value function of the offspring, J (.). At age 20 (t = 1),
an agent decides whether to attend an elite college, an ordinary college, or work.
J(Φ) =XDe He (Φ) + Dec [p(SAT )Hec (Φ) + (1 − p(SAT )) max{Hhs (Φ), Hnc (Φ)}]

e=hs,nc(18)
where De = 1 if school e is the agent’s best choice. When elite college is the best choice(Dec = 1), the chance of being admitted is given by p(SAT ). If not admitted to elite colleges,
the agent will go for his second choice. Φ = {Aem , Aib , Aub , k, k p , bnc , bec }. k is the initial
wealth, the monetary transfer individuals receive from their parents. k p is parent’s wealth,which affects the financial aid.
The value function of high school graduates who do not attend college is
Hhs (Φ) = EV (1, hs, Aem , Aib , Aub , k, em, bib , bub )(19)High school graduates directly enter the labor market at age 20. Like others, they need to pay
entry costs if they want to become a business owner. Therefore, we set t = 1 and j−1 = em.
11

The expectation is taken over bib and bub because we assume individuals do not observe theirconsumption shocks to career choices when they make their schooling decision.
The value functions of individuals attending ordinary or elite colleges take the form
He (Φ) = maxu(c, e) + βEV (Ω0 )0where e ∈ {nc, ec}

c,k

s.t.

c + k 0 = (1 + r)(R − Te + fe (k p , Aem )),

(20)

c>0

where Te is college tuition, fe is financial aid, and Ω0 = {2, e, Aem , Aib , Aub , k 0 , em, b0ib , b0ub }.
We assume that college students cannot work part time when they are in school and they enter
the labor market at age 25 (t = 2).
2.3

Equilibrium

In equilibrium, the wage w and interest rate r in the non-self-employed sector are such that• each agent’s consumption, investment, capital use, education choice, and occupation
choice are optimal,• the capital market clears (i.e., the total capital from all agents’ savings equals the capitaldemand by both self-employed and non-self-employed individuals) so thatZ

Z

Z

bub dh + Kem

bib dh +

kdh =
h∈Sem

h∈Sib

(21)

h∈Sub

where h is the household index, Sem , Sib , and Sub are the sets of households who choose
to be employees, entrepreneurs, and other self-employed, respectively, and bj = kj −k forj ∈ {ib, ub} denotes the amount of borrowing by entrepreneurs and other self-employedindividuals, and• the labor market clears (i.e., the total labor in efficient labor units supplied by employees
equals the labor demanded by the non-self-employed sector) so thatZ

hem eem dh.

Lem =

(22)

h∈S em

3

Data

3.1

Data Source and Data Construction

Our primary data source is the Panel Study of Income Dynamics (PSID) of The United States.
This longitudinal project began in 1968 with a nationally representative sample of over 18,000
individuals living in 5,000 families. The PSID tracks these individuals and their descendants,
even after they form new families so that we can track the education and life-cycle career
choices of parents and children. We focus on white males aged 25-60 with a father identified
12

in the PSID. We also obtain restricted access data on school identifiers, which can be linked to
the Integrated Post-secondary Education Data System (IPEDS) to provide rich information on
the quality of the colleges that respondents attended.
Because we focus on the impact of elite college attendance on entrepreneurship and career
dynamics, it is important to identify which colleges are considered to be elite. We follow Blackand Smith (2006) in using factor analysis to construct the college quality index
Index = 0.096 ∗ faculty-student ratio + 0.137 ∗ rejection rate + 0.257 ∗ retention rate
+ 0.245 ∗ faculty salary (in millions) + 0.385 ∗ mean of reading and math SAT (in 100s).
The top 100 universities, according to this index, are defined as elite.26 Elite colleges include 15 flagship public universities. Therefore, not every state has an elite flagship public
university, according to our definition. Students living in states without a flagship public university must pay out-of-state tuition (which is much higher than in-state tuition) to go to anelite flagship public university. 41% of students surveyed in the PSID attending an elite flagship public university pay out-of-state tuition. Appendix Table C1 provides summary statistics
of elite and ordinary colleges. Elite colleges have higher faculty-student ratios, higher rejection
rates, higher retention rates, higher faculty salaries, and higher SAT scores. They also chargehigher in-state and out-of-state tuition. We define an individual as having an “elite college”(“ordinary college”) education if he/she graduates from an elite college (ordinary college) andnot simply if he/she attended an elite college (ordinary college). That is, education is definedby whether the individual receives a college degree.27
We determine the primary occupation every five-years as the occupation that lasts thelongest time during the period. Given that our sample focuses on prime-age males, a tiny
fraction of them (around 5%) have non-employment as their primary status. We drop theseobservations in our analysis, so our sample is restricted to employed and self-employed individuals over the observed periods. The income is measured as the average annual income ofthe primary occupation over five years, including labor income and business income.
Since the PSID tracks all family members, we can match individuals with their fathers evenafter moving out of the original sampled household. Therefore, we observe the education andlife-cycle career paths of sons and fathers, and we can calculate the intergenerational transfer
in education and career. We also obtain the parental monetary transfer information from the
Rosters and Transfers Parent/Child File of 2013 PSID, which surveys transfers to children. We
combine three questions on parental transfer: how much you pay your child to help to pay
for school, buying a home, and other expenses. The average amount of parental transfer is$12,581, with 24% receive zero transfer from parents. We obtain individuals’ SAT scores from
26

Appendix Table C2 shows our list of elite colleges. Our list is comparable to other rankings, such as the U.S.
News Top 100 Colleges, and it does not change much over time. The current list is based on 2016 data.
27
From now on, “elite/ordinary college attendance (go to an elite/ordinary college)” and “elite/ordinary college
completion (receive an elite/ordinary college degree)” are used interchangeably.

13

the Transition into Adulthood module of PSID, which is available biannually from 2005 to
2013, and surveys individuals aged 18 to 28 years old.
We make the following sample restrictions: 1) keep while males aged 25–60; 2) keep individuals with information on father’s education and career; 3) drop non-employed individuals
and high-school dropouts; 4) trim individuals with top 1% and bottom 1% income. Therefore,
our sample has 1,817 individuals with 19,475 individual-year observations, born between 1949
and 1988.
3.2

Summary Statistics

We now present some summary statistics. The upper panel of Table 1 shows that 35% of
individuals in our sample have a college degree. Among them, 17% graduated from elite colleges. Elite college graduates are 8 ppt and 5 ppt more likely to become entrepreneurs thanhigh school graduates and ordinary college graduates, respectively. However, the chance of being other self-employed is similar across the three education groups. On average, elite college
graduates earn 137% and 52% more than high school graduates and ordinary college graduates,
respectively. We also show that elite college graduates come from more affluent families – fathers of elite college graduates earn 89% and 36% more than fathers of high school graduatesand ordinary college graduates, respectively.
Table 1: Summary Statistics by Career
High school
graduates

Ordinary college
graduates

Elite college
graduates

Other self-employed
Entrepreneur
Age
Income (mean)
Income (std)Father’s income

12.8%
4.0%
35.7
47,01845,70355,842

12.4%
7.0%
36.4
73,387
68,25477,556

11.4%
12.3%
37.6
111,416
154,135
105,281

Observations
Population share

12,639
64.9%

5,706
29.3%

1,130
5.8%

Employee

Other self-employed

Entrepreneur

Ordinary college
Elite college
Age
Income (mean)
Income (std)Father’s income

28.3%
4.7%
35.4
60,31456,61863,095

28.3%
5.3%
37.3
58,54264,426
65,777

37.1%
15.2%
39.5
117,360
149,760
87,013Observations
Population share

15,930
81.8%

2,454
12.6%

1,091
5.6%

Notes: Father’s income is measured as the average annual income between age 40 to 50. Eachindividual-year is one observation.

The lower panel of Table 1 shows that 18.2% of individuals in our sample do not work14

as employees.28 Among them, 31% are entrepreneurs (i.e., own an incorporated company),
and 69% are other self-employed (i.e., own an unincorporated business).29 Also, employees
and entrepreneurs are quite different in their education, income level, and family background.
Entrepreneurs are 9 ppt and 10 ppt more likely to graduate from ordinary and elite colleges,
earn 95% more than employees, and their fathers earn 38% more than fathers of employees.
In contrast, other self-employed individuals have similar education levels, own income, andfathers’ income compared to employees. These findings are consistent with the literature (e.g.,
Hamilton, 2000, Levine and Rubinstein, 2017, Moskowitz and Vissing-Jørgensen, 2002), andalso justify our modeling approach to distinguish between different types of self-employment.
Table 2 shows the intergenerational relationships in education and career choices. The upper panel demonstrates the intergenerational persistence in education. Compared with individuals whose fathers have an ordinary college degree, those whose fathers have an elite college
degree are 11.3 ppt more likely to graduate from an elite college. They are 18.8 ppt more
likely than those whose fathers have a high school degree. The bottom panel shows a similarintergenerational persistency in career choice. Offsprings of individuals who have owned anincorporated business have the highest probability of owning an incorporated company, 10.1
ppt higher than a son whose father ever owned an unincorporated business but never own anincorporated business, and 10.5 ppt higher than those with a devoted employee father.
Table 2: Intergenerational Persistency in Education and Career Choices
Education choice
Son \ father

High school

Ordinary college

Elite college

High school
Ordinary college
Elite college

77.5%
20.0%
2.7%

51.3%
38.5%
10.2%

41.5%
36.9%
21.5%

Career choice
Son \ father

Employee

Entrepreneur

Other self-employed

62.7%
14.1%
23.2%

49.6%
24.6%
25.8%

54.9%
14.5%
30.6%

Employee
Entrepreneur
Other self-employedNotes: This table shows the probability of sons choosing a given education level or career
conditional on father’s education level or career. Father’s education or career choices are shown incolumns and son’s are in rows.

To further elucidate the relationship between elite college attendance, career choices, andincome, we run some simple regressions, shown in Table 3. Based on the sample of college
28

86% of the “business owners” in the PSID data spend some time on their business. It suggests that the majorityof them participate in the management of their business.
29
The top 3 industries for entrepreneurs are construction industry (17%), retail trade (13%), and financial services (11%). Medical, dental, and health services only account for 6%. Among other self-employed individuals,
the top 3 industries are the same (accounting for 19%, 14%, and 10% of all such individuals, respectively). The
top 3 occupations for entrepreneurs are executive, administrative, and managerial occupations (47%), professional
specialty (18%), and sales (11%). The top 3 occupations for other self-employed individuals are executive, administrative, and managerial occupations (23%), professional specialty (16%), and sales (13%).

15

graduates and controlling for father’s education and career, Column (1) shows that graduatingfrom an elite college is associated with a 4.2 ppt higher probability of being an entrepreneur
than ordinary college graduates. In comparison, graduating from an elite college does not significantly affect the chance of being other self-employed, as shown in Column (3). Column(5) shows that elite college graduates have 31% higher income than ordinary college graduates.30 The even columns of Table 3 further explore the heterogeneous effects of elite college
by parental income. We find that elite college’s impact on entrepreneurship is smaller for individuals from affluent families (those with higher father income), which suggests that elitecolleges play a role in mediating intergenerational elasticity in career choices. The effect ofelite college on lifetime income does not vary by family income.
Table 3: Effects of Elite College on Career Choice and Income

Elite college degree
Log father’s average income at age 40-50
Elite college * log father’s average income
Graduate school degree
Father has high school degree
Father has ordinary college degree
Father has elite college degree
Father ever runs unincorporated business
Father ever runs incorporated businessAge
Age squareConstant
Observations

Entrepreneur(1)(2)
0.0416∗∗∗0.0442∗∗∗(0.0084)(0.0048)0.0397∗∗∗0.0421∗∗∗(0.0060)(0.0063)-0.0179∗∗∗(0.0045)0.0035-0.0038(0.0062)(0.0062)
0.0662∗∗∗0.0674∗∗∗(0.0093)(0.0094)
0.0650∗∗∗0.0656∗∗∗(0.0102)(0.0102)0.0809∗∗∗0.0795∗∗∗(0.0119)(0.0139)0.01140.0112(0.0080)(0.0080)
0.0655∗∗∗0.0658∗∗∗(0.0074)(0.0074)
0.0117∗∗∗0.0116∗∗∗(0.0031)(0.0031)-0.0001∗∗-0.0001∗∗
0.00000.0000-0.6369∗∗∗-0.6615∗∗∗(0.0861)(0.0884)
8152
8152

Other self-employed(3)(4)-0.0025-0.0045(0.0099)(0.0098)
0.0056-0.0031(0.0071)(0.0074)
0.0063(0.0172)-0.0023-0.0025(0.0074)(0.0074)
0.01280.0172(0.0110)(0.0111)0.00310.0051(0.0121)(0.0121)0.0047-0.0004(0.0164)(0.0165)0.0680∗∗∗0.0686∗∗∗(0.0095)(0.0095)
0.0394∗∗∗0.0382∗∗∗(0.0087)(0.0087)0.0207∗∗∗0.0209∗∗∗(0.0036)(0.0036)-0.0002∗∗∗-0.0002∗∗∗0.00000.0000-0.3953∗∗∗-0.3041∗∗∗(0.1018)(0.1044)8152
8152

Log income(5)(6)
0.3059∗∗∗0.3168∗∗∗(0.0229)(0.0474)0.1502∗∗∗0.1611∗∗∗(0.0164)(0.0173)0.0204(0.0394)
0.0914∗∗∗0.0912∗∗∗(0.0171)(0.0171)-0.0056-0.0108(0.0257)(0.0258)
0.0349∗∗∗0.0624∗∗∗(0.0102)(0.0282)
0.0587∗∗∗0.0451(0.0183)(0.0385)-0.1334∗∗∗-0.1340∗∗∗(0.0220)(0.0220)0.1039∗∗∗0.1024∗∗∗(0.0203)(0.0203)0.1909∗∗∗0.1908∗∗∗(0.0083)(0.0083)-0.0021∗∗∗-0.0021∗∗∗(0.0001)(0.0001)5.2700∗∗∗5.1536∗∗∗(0.2360)(0.2428)8152
8152

Notes: We use a linear probability model. The dependent variable for the first column is whether the respondent owns an incorporated business, thedependent variable for the second column is whether the respondent owns an unincorporated business, and the dependent variable for the third column
is log annual total income. The sample is restricted to white males aged 25–60 with college degree or above. Each individual-year is one observation.
Standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

One possible channel through which elite college attendance could affect lifetime income
is better access to graduate schools. Using the PSID, we find that having a graduate school
degree does not increase the chance of being an entrepreneur, as shown in Column (1) of
Table 3. Perhaps it is because professional jobs (such as dentist, physician, accountant, or
lawyer) account for less than 10% of entrepreneurs. Likewise, the income gains from attending
graduate school are much smaller than that of elite college attendance (9% vs. 31%), as shown
30

Appendix Table C3 shows that having an elite college degree is associated with a higher income for all
individuals. The premium is the largest for entrepreneurs.

16

in Column (5) of Table 3. Hence, we focus on the choice between elite and ordinary college
attendance and abstract away from graduate school attendance.
To summarize, we find that (1) elite college graduates have a higher chance of becoming
an entrepreneur, and (2) there is intergenerational persistence in education and career choices.
Hence, the positive correlation between elite college attendance and entrepreneurship is subject to a selection bias. In the next section, we will explain how we identify and estimate amodel with endogenous education and career choices to identify the real effect of elite college
attendance on entrepreneurship.

4

Identification and Estimation

This section explains how we identify and estimate the model parameters. We fix a few parameters in our model and estimate the rest of the parameters using the simulated method of
moments (SMM). Appendix Table C4 shows the fixed parameters, including the discount rate,
survival rate, utility function parameter, pension, budget constraint, college tuition, and college
financial aid. These parameter values are relatively standard in the literature. Because eachperiod is five years, we set the discount rate to 0.821, equivalent to a 0.95 annual discount rate.
The capital depreciation rate is assumed to be 0.266, equal to a 6% yearly depreciation rate. The
survival rate is less than one after age 65 and calibrated using survival data from the Health andRetirement Study from 2011; the details are shown in Appendix Table C5. Following Cagetti
and De Nardi (2006), we assume that a pension is 40% of average income before retirement,
and the utility function parameter σ is set to 1.5.
We set our collateral constraint parameter λ to 1.22 to mimic the stylized fact that, in the
Kauffman Firm Survey, the start-up firms’ total equity accounts for 45% of their total capital(Robb and Robinson, 2014).31
Since the PSID does not have information on respondents’ financial aid or the chances ofbeing admitted to elite colleges, we use the estimates of Fu (2014) to calibrate financial aid
and elite-college admission rate. Fu (2014)’s estimates are based on the NLSY97 data.32 Ourfinancial aid formula is
Financial aid of college = D(e) − 32.5 × family wealth in thousands− 7432 × SAT score at bottom 1/3 + 6875 × SAT score at top 1/3
31

The Kauffman Firm Survey is a longitudinal survey of new businesses in the United States. It collects annual
information on 4,928 firms that started in 2004. Total equity includes owner equity, insider equity, and outsider
equity, and total debt includes owner debt, insider debt, and outsider debt. Total capital is the sum of total equity
and total debt. Recall that our collateral constraint is kj ≤ (1 + λ)k. When it holds with equality, capital/equity =kj /k = (1 + λ). When we set k/kj = 0.45, λ is approximately 1.22.
32
School Identifier is restricted access data in the NLSY97 and is available only to researchers within the U.S.,
so we rely on the estimates from Fu (2014). Fu (2014) uses a slightly different list of elite colleges from us; shedefines the top 30 private universities, top 20 liberal art colleges, and top 30 public universities as elite. Our elitecollege list is based on Black and Smith (2006). The difference between our list and the list used by Fu (2014) is
minimal.

17where D(nc) = 13, 901 and D(ec) = 20, 224. According to these formulas, students frompoorer families and higher SAT scores (a signal of general ability) receive more financial aid
when they attend college. On average, elite colleges charge higher tuition on the one hand andprovide more generous financial aid than ordinary colleges on the other hand.
We also calibrate elite colleges’ admission rates based on the estimates of Fu (2014), which(1) shows that admission rates only depend on SAT scores but not family income, and (2)reports the admission rates of elite private and elite public colleges separately.33 We take aweighted average of the admission rates of private and public elite colleges. The weights are
the number of students enrolled in each type of college in the PSID.34 The weighted admission
rates of elite colleges are 0.209 for students with SAT scores at the bottom 1/3, 0.559 for the
middle 1/3, and 0.756 for the top 1/3.
We calculate the average tuition at elite and ordinary colleges using the Integrated Postsecondary Education Data System (IPEDS) data in 2003, roughly when the NLSY97 cohorts
entered colleges. On average, elite colleges charge $33,046 (in 2011 dollars) and ordinary
colleges charge $12,761.
Appendix Table C6 shows the parameters that remain to be estimated and the moments used
to identify these parameters. Recall that individuals make education choices according to theirarray of abilities and wealth in our structural model. Hence, specific moments from the data
allow us to identify the selection in abilities and human capital gain from an elite college. For
instance, the wage gap between elite and ordinary college students reflects the ability difference
between elite and ordinary college students and human capital gain from elite colleges. Withpanel data, we can also use income correlation for stayers (those who do not change jobs)
and switchers (those who change jobs between two adjacent periods) to identify the ability
distribution. Below, we provide a more detailed discussion of our identification strategy.
First, we track the individuals over time and calculate changes in their income when they
stay in the same career and switch careers. The standard deviation of general ability (σem ) andthe standard deviation of productivity shocks for employees (ξem ) are jointly identified from theincome variation of employees and the income correlation between two periods for individuals
who are employees in both periods. If the dispersion of general ability is large relative to thatof the productivity shocks, more of the employee income variation is driven by general ability variation. We should observe a high-income correlation between two adjacent periods for
employees.35 The income variation for entrepreneurs and other self-employed individuals can
33

In Appendix Table A9 of Fu (2014), the admission rates of elite private colleges are 0, 38.5%, and 61.7%
for students with SAT scores lower than 800, between 800 and 1200, and above 1200, and the corresponding
admission rates of elite public colleges are 53.8%, 80.0%, 92.8%.
34
Unfortunately, we cannot observe the type of colleges students applied for in the PSID, so we have to use theenrollment numbers as weight.
35
The correlation of earnings between two periods for employee stayers is not exactly mapped to the dispersionof general ability because entry and exit of employment are endogenous. Therefore, we also use observed changes
in earnings following entry or exit to estimate the returns to paid employment while controlling for selection on
individual time-invariant effects. Keane and Wolpin (1997) adopt a similar identification strategy.

18

be decomposed into three parts: general ability variation and the contribution of general ability to entrepreneur income (ρib /ρub ), incorporated/unincorporated ability variation (σib /σub ),and the dispersion of productivity shocks (ξib /ξub ). To identify the σ’s, ρ’s, and ξ’s, we use
the income variation and the income correlation between two periods for individuals who areentrepreneurs/other self-employed in both periods along with the income correlation between
two periods for individuals who switch between being employees and entrepreneurs/other selfemployed. If the σ’s are large, we should observe a strong income correlation between two
adjacent periods for individuals who remain in the same career. If the ρ’s are large, we shouldobserve that individuals who have high earnings as employees also have high incomes whenself-employed.
Once we recover the ability distribution, we can identify the standard deviations of the consumption shocks to the value of ordinary and elite colleges (i.e., ηnc and ηec ) and the human capital gains from ordinary and elite college attendance (i.e., µje for e ∈ {nc, ec}, j ∈ {em, ib, ub})with the following equations. The first set of equations are the education decision.
P r(Φ ∈ Π)p(SAT ) = P r(e = ec)
P r(Φ ∈ Ψ) + P r(Φ ∈ Π0 )(1 − p(SAT )) = P r(e = nc)
where Φ = {Aem , Aib , Aub , k, k p , bnc , bec } are the initial conditions when young adults make theschooling decision, including abilities, own wealth, parent’s wealth, and consumption shocks
to colleges. Π is the set of students whose first choice is elite colleges, Π0 is the set of students
whose first choice is elite colleges and the second choice is non-elite colleges, and Ψ is the setof students whose first choice is ordinary colleges. The observed share of students graduatingfrom an elite college equals the share of students whose first choice is elite colleges multipliedby the share of students being admitted. The share of students graduating from a non-elitecollege equals the share of students whose first choice is non-elite colleges and the share of
students denied by elite colleges and admitted by non-elite colleges instead.
The second set of equations are for the average human capital after college for employees,
entrepreneurs, and other self-employed individuals with either an elite or an ordinary college
degree.
em
E[log Aem |Φ ∈ Π̃] + µem
ec = E[log fec ]emibE[log Aib |Φ ∈ Π̃] + µib
ec + ρib (E[log Aem |Φ ∈ Π̃] + µec ) = E[log fec ]emubE[log Aub |Φ ∈ Π̃] + µub
ec + ρub (E[log Aem |Φ ∈ Π̃] + µec ) = E[log fec ]em
E[log Aem |Φ ∈ Ψ̃] + µem
nc = E[log fnc ]emib
E[log Aib |Φ ∈ Ψ̃] + µib
nc + ρib (E[log Aem |Φ ∈ Ψ̃] + µnc ) = E[log fnc ]emub
E[log Aub |Φ ∈ Ψ̃] + µub
nc + ρub (E[log Aem |Φ ∈ Ψ̃] + µnc ) = E[log fnc ]19

where fej denotes the average human capital of individuals with e ∈ {nc, ec} education andj ∈ {em, ib, ub} career type when they finish college. Π̃ is the set of students who graduate
from an elite college, and Ψ̃ is the set of students who graduate from an ordinary college. Using
the panel data, we run income regressions and get individual fixed effects, which are equivalent
to fej because fej does not change after an individual finishes his education.
Take the second equation as an example, we have two components on the left hand side:emE[log Aib |Φ ∈ Π] + µib
ec and ρib (E[log Aem |Φ ∈ Π] + µec ). Note that we have recovered
the distributions of general, incorporated, and unincorporated abilities (σem , σub , σib ) and thecontribution of general ability to entrepreneur income (ρib ). With the structural model, we canpredict what type of individuals will choose to attend elite college and become entrepreneurs,
so we recover E[log Aib |Φ ∈ Π] and E[log Aem |Φ ∈ Π]. Therefore, the only unknowns are µibecem
and µec in this equation.
ib
ub
em
ib
ub
In total, we have eight equations and eight unknowns (ηnc , ηec , µem
ec , µec , µec , µnc , µnc , µnc ),so we can identify the effects of ordinary and elite college attendance on general, incorporated,
and unincorporated human capital.
The identification of the other parameters is standard. The average incomes of employees,
entrepreneurs, and other self-employed individuals are used to identify the technologies of thenon-self-employed sector, incorporated businesses, and unincorporated businesses (Pem , Pib , Pub ).
The life-cycle income profiles of employees, entrepreneurs, and other self-employed individuals identify the return to potential experience for employees (α1 , α2 ) and the diminishing returns
to investment for entrepreneurs and other self-employed individuals (νib , νub ). The standard deviations of consumption shocks for entrepreneurs and other self-employed individuals (ηib , ηub )are identified by the fraction of incorporated and unincorporated business owners. The transition rates between being an employee and being an entrepreneur/other self-employed pin down
the costs of opening incorporated/unincorporated business (Cib /Cub ). If Cib /Cub is high, fewer
employees will open incorporated/unincorporated businesses. Intergenerational correlations
in careers identify the intergenerational transfer in general, incorporated, and unincorporated
abilities (θem , θib , θub ). Parental monetary transfers as a proportion of parental wealth identify a parent’s weight on the offspring’s welfare. We standardize the SAT score, so it has amean of zero and a standard deviation of one. The joint distribution of SAT scores and initial
wealth at age 20 identifies the relationship between general ability and SAT scores (κ) because
Cov(SAT, k0 ) = κCov(Aem , k0 ). The variance of SAT scores identifies the distribution of thenoise, because V ar(SAT ) = κ2 V ar(Aem ) + σε2 .
We estimate the model by the simulated method of moments (SMM). A weighted squared
deviation between sample aggregate statistics and their simulated analogs is minimized withrespect to the model’s parameters. The weights are the inverse values of the estimated variances of the sample statistics—the estimation proceeds in two steps. First, we make an initial
guess of the joint distribution of initial wealth and parent generation abilities. We then simulate 5,000 individuals by drawing their initial wealth and abilities from the distribution and20their idiosyncratic shocks to the non-pecuniary utility of education and career choices and the
productivity shocks to career choices according to the parameters. We solve the overlapping
generations model by iterating until we reach a steady state with the parent generation having
the same distribution of initial wealth, general ability, incorporated ability, and unincorporated
ability as the offspring generation. The model predicts (1) the education and career decisions
and their income and wealth over the life-cyles, and (2) the children’s abilities and the monetary transfers from parents to children. Thus, the model shows how wealth and abilities aretransferred across generations.36
Second, we compute the simulated moments using the PSID sample and compare themto the sample aggregate statistics, which include: 1) education choice; 2) career choice byeducation and age; 3) mean and variance of income by education, career, and age; 4) correlation
between incomes in period t and t + 1 by career type; 4) career transitions in period t andt + 1; 5) intergenerational mobility in education and career; 6) parental monetary transfers as afraction of parental wealth; 7) correlation between SAT scores and initial wealth at age 20; and8) variance of SAT scores.37

5Estimation Results

5.1

Parameter Estimates and Model Fit

Table 4 shows the parameter estimates with standard errors in parentheses. In general, themodel fits education choices, career choices by education, and average and standard deviationof income by education and career, as shown in Table 5. Moreover, Appendix Figures C1and C2 show that the model can fit the career choice and average income by career type overthe life cycle. The model predicts that the chance of owning an incorporated or unincorporated
business increases with age, as individuals accumulate their physical capital in their early career
to overcome the borrowing constraint. The model also predicts the hump-shape income profile
of employees and self-employed individuals over the life cycle. The diminishing return tophysical capital investment drives the hump-shape income of self-employed individuals.
Our model also addresses the following topics: (1) income correlation and career transition,(2) intergenerational persistence in education, career, and income, (3) return to elite colleges,and (4) the choice of an incorporated or unincorporated business. Furthermore, we analyze
how abilities and initial wealth affect subsequent education and career choices in Appendix B.
36
With the distribution of the offspring generation’s initial wealth and abilities, we simulate the children’s lifecycle decisions and predict the intergenerational transfer of money and abilities for the grandchildren generation.
We continue to iterate until the joint distribution of initial wealth and abilities converges.
37
Since we use NLSY97 to calibrate financial aid, admission rates, and college tuition, we should restrict our
PSID sample to cohorts born in the 1980s. However, our sample size will shrink by 75%, with only 27 elite-college
graduates and 27 individuals who have been entrepreneurs. Therefore, we have to use the full PSID sample toincrease the statistical power of our results. When we construct moment (7), we keep only individuals with SAT
scores. When we construct other moments, we keep individuals with and without SAT scores.

21

Table 4: Parameter Estimates

Productivity (P )Return to ordinary college (µnc )Return to elite college (µec )Return to potential experience (γ1 )Return to experience squared (γ2 )Return to capital (ν)
Contribution of EM human capital to EN (ρ)
Std of productivity shock (ξ)
Entry cost (C)Std of consumption shock (η)
Std of ability (σ a )Intergenerational correlation in ability (θ)

Employee

Entrepreneur

Other
self-employed2005 (526)0.25 (0.09)
0.47 (0.17)
0.32 (0.08)-0.032 (0.01)
0.66 (0.19)
0.38 (0.14)
0.47 (0.16)

4.1 (0.5)
0.28 (0.06)
0.56 (0.19)
0.75 (0.22)
0.15 (0.06)
0.73 (0.31)
58000 (22500)
0.0003 (0.0001)
0.38 (0.15)
0.41 (0.12)

20.8 (8.3)
0.20 (0.05)
0.35 (0.16)
0.58 (0.20)
0.03 (0.01)
0.59 (0.19)
8000 (2300)
0.0008 (0.0002)
0.32 (0.12)
0.38 (0.05)

Std of consumption shock for college (η)
0.020 (0.006)/0.018 (0.004) (NC/EC)
Weight on offspring’s welfare (ω)
0.040 (0.009)
Output elasticity of capita (α)
0.246 (0.082)
Mapping from general ability to SAT scores (κ) 2.050 (0.571)
Std of noise in SAT scores (σε )0.627 (0.092)
Notes: This table presents the parameter estimates and the standard errors of the estimates are shown in parentheses. EM: employee,
EN: entrepreneur, IB: incorporated business owner, UB: unincorporated business owner, NC: ordinary college, EC: elite college.
Employee ability refers to general ability and employee human capital refers to general human capital.

5.2

Income Correlation and Career Transition

Economic agents change careers and hence their level of income over their life-cyle. The firstpanel of Table 6 shows that our model mimics the empirical career dynamics (i.e., transitions
between two adjacent periods). For example, 87.0% of employees in our data remain employees in the next five-year period, with the model predicting 88.7%. Our data show that 53.0%(52.0%) of entrepreneurs (other self-employed individuals) are still in business five years later,while the model predicts 56.6% (53.3%). More than a third of the self-employed become employees five years later. The five-year transition rate from entrepreneurs to employees is 34.3%
in the data and 30.4% in the model. The five-year transition rate from unincorporated businessowner to employee is even higher, 38.7% in the data and 40.2% in the model. Consequently, thetransition rates between entrepreneurs and other self-employed individuals are low. The fiveyear transition rate from other self-employed individuals to entrepreneurs is 9.3% in the data
and 6.5% in the model. The five-year transition rate from entrepreneurs to other self-employedindividuals is 12.7% in the data and 13.0% in the model.
Our model also fits the income correlation between periods for stayers and switchers (between career types), as shown in the second panel of Table 6. For stayers (those who remainin the same career over the five years), the empirical income correlations are 0.71, 0.70, and0.41 for employees, entrepreneurs, and other self-employed individuals, respectively, while themodel counterparts are 0.69, 0.73, and 0.51. For people who move from being an employee tobeing an entrepreneur (other self-employed), the income correlation is 0.60 (0.49) in the data

22

Table 5: Model Fit: Targeted Moments

Education choiceHigh school graduates
Ordinary college graduates
Elite college graduates
Career choice by education
High school: Employee
High school: Other self-employedHigh school: Entrepreneurs
Ordinary college: Employee
Ordinary college: Other self-employed
Ordinary college: Entrepreneurs
Elite college: Employee
Elite college: Other self-employed
Elite college: Entrepreneurs
Average income by education and career
High school employees
High school other self-employedHigh school entrepreneurs
Ordinary college employees
Ordinary college other self-employed
Ordinary college entrepreneurs
Elite college employees
Elite college other self-employed
Elite college entrepreneurs
Income standard deviation by education and career
High school employees
High school other self-employedHigh school entrepreneurs
Ordinary college employees
Ordinary college other self-employed
Ordinary college entrepreneurs
Elite college employees
Elite college other self-employed
Elite college entrepreneurs
OthersParental transfer as a share of parent’s five-year income
Correlation between SAT scores and initial wealth at age 20
Variance of SAT scores (standardized)

Data

Model

64.9%
29.3%
5.8%

64.9%
29.6%
5.6%

83.3%
12.8%
4.0%
80.6%
12.4%
7.1%
76.4%
11.4%
12.3%

82.8%
12.8%
4.4%
80.3%
12.7%
7.0%
74.1%
12.5%
13.4%

48,502
46,291
86,28572,43673,381
121,670
106,891
99,778
186,503

50,35747,251
83,827
73,10570,055120,106
113,613
95,395
186,646

27,35743,03098,85752,67070,210156,013
147,351
129,042
206,590

30,44245,789110,88157,00170,055
139,100
149,942
130,452
188,903

0.041
0.255
1.000

0.039
0.274
0.992

Notes: We standardize SAT scores so it has a mean of zero and a standard deviation of one.

and 0.56 (0.43) in the model.38
Our model also matches well with some untargeted moments on income transitions. The
first panel of Table 7 shows that our model fits the average starting age of entrepreneurship andthe average duration of entrepreneurship. On average, individuals start their first incorporated
business at age 37 and the business lasts for 10 years. The model can also fit the income
transitions for stayers and switchers. The average employee income for those who remain
employees for two consecutive periods is $54,582 in the data and $52,926 in the model. The
38

Our findings are in line with the related studies, such as Karahan et al. (2019). Our contribution is to highlightthe differences in income correlation between different career paths (employees, entrepreneurs, and other selfemployed).

23

Table 6: Model Fit: Targeted Moments (cont’d)

Career transitions
Employee - employee
Employee - entrepreneur
Employee - other self-employed
Entrepreneur - employee
Entrepreneur - entrepreneur
Entrepreneur - other self-employed
Other self-employed - employee
Other self-employed - entrepreneur
Other self-employed - other self-employed
Income correlation by career transitions
Employee - employee
Employee - entrepreneur
Employee - other self-employed
Entrepreneur - employee
Entrepreneur - entrepreneur
Entrepreneur - other self-employed
Other self-employed - employee
Other self-employed - entrepreneur
Other self-employed - other self-employedIntergenerational persistency in education choices
High school - high school
High school - ordinary college
High school - elite college
Ordinary college - high school
Ordinary college - ordinary college
Ordinary college - elite college
Elite college - high school
Elite college - ordinary college
Elite college - elite college
Intergenerational persistency in career choices
Employee - employee
Employee - entrepreneur
Employee - other self-employed
Entrepreneur - employee
Entrepreneur - entrepreneur
Entrepreneur - other self-employed
Other self-employed - employee
Other self-employed - entrepreneur
Other self-employed - other self-employed

Data

Model

87.0%
3.3%
9.8%
34.3%
53.0%
12.7%
38.7%
9.3%
52.0%

88.7%
2.5%
8.8%
30.4%
56.6%
13.0%
40.2%
6.5%
53.3%

0.710
0.602
0.493
0.530
0.697
0.090
0.567
0.483
0.410

0.691
0.559
0.427
0.634
0.731
0.189
0.391
0.398
0.512

77.5%
20.0%
2.7%
51.3%
38.5%
10.2%
41.5%
36.9%
21.5%

70.2%
26.4%
3.4%
59.2%
31.3%
9.5%
49.7%
32.1%
18.2%

62.7%
14.1%
23.2%
49.6%
24.6%
25.8%
54.9%
14.5%
30.6%

64.6%
17.2%
18.2%
50.9%
27.5%
21.6%
55.6%
17.9%
26.5%

Notes: The career transition panel presents the career transitions from period t to period t + 1, where one
period is five years. The income correlation by career transitions panel presents the correlation between
incomes in period t and period t + 1 by career transition types. The intergenerational persistency ineducation/career choices panel presents the probability of sons choosing a given education level/career
conditional on father’s education level/career.

average employee income for those who become entrepreneurs five years later is $75,482 in
the data and $76,920 in the model, suggesting that entrepreneurs have much higher salaried
earnings as employees before they start an incorporated business. However, those who are
current employees and become unincorporated business owners five years later have an average
income of $54,745 in the data and $51,693 in the model, suggesting that these individuals have
similar earnings as employees before opening an unincorporated business to those who remain

24employees (Levine and Rubinstein, 2017).39 For entrepreneurs in the current period, stayers
have the highest income, while those with the lowest income become unincorporated businessowners. For the current unincorporated business owners, stayers have a medium-income, while
those with the most insufficient income become employees.
Table 7: Model Fit: Untargeted Moments

Age of first entrepreneurship
Duration of entrepreneurship
Lagged income by career transitions
Employee - employee
Employee - entrepreneur
Employee - other self-employed
Entrepreneur - employee
Entrepreneur - entrepreneur
Entrepreneur - other self-employed
Other self-employed - employee
Other self-employed - entrepreneur
Other self-employed - other self-employedIntergenerational income elasticity
Whole sample
Both father and son are devoted employees
Father has worked as non-employee; son is devoted employee
Father is devoted employee; son has worked as non-employee
Both father and son have worked as non-employee

Data

Model

37.6
10.0

37.3
10.2

54,58275,482
54,745
109,868
123,262
87,82455,01788,54759,58752,92676,92051,693
115,724
119,371
82,84852,42981,301
63,497

0.39
0.51
0.32
0.39
0.31

0.42
0.56
0.37
0.41
0.34

Notes: The lagged income by career transitions panel presents the income in period t by career transition fromperiod t to period t + 1. The intergenerational income elasticity panel presents the income elasticity conditional
father’s and son’s career types. Intergenerational income elasticity is calculated by regressing son’s average
income between ages 30 and 50 on father’s average income during the same age range.

5.3

Intergenerational persistence

We also estimate the intergenerational transmission of abilities. As shown in Table 4, the intergenerational correlation of ability for Aem , Aib , and Aub are 0.47, 0.41, and 0.38, respectively.
These estimates are similar to the estimates in Grönqvist et al. (2017) where they find an intergenerational correlation of 0.42 – 0.48 for cognitive skills and 0.42 for non-cognitive skills.
The last two panels of Table 6 show that our model explains a large share of the intergenerational persistence in education and careers. The data show that 78% of the offspring ofhigh school graduates are also high school graduates, while the model predicts 70%. Similarly, the persistence in receiving an ordinary college degree is 39% in the data and 31% in themodel. The persistence in receiving an elite college degree is 22% in the data and 18% in themodel. Our model mimics the intergenerational persistence in careers. 63% of the individuals
whose fathers are devoted employees (i.e., individuals who never own a business throughout
their lifetime) are also devoted employees themselves. The model predicts 65%. Similarly,
entrepreneurship (those who own an incorporated business at some point) is also intergener39

Note that entrepreneurs and other self-employed individuals are older than employees on average (Table 1);
we do not observe a significant income difference between stayers and would-be-other-self-employed individuals.
Hence, age cannot be the driving factor for the income difference between stayers and would-be-entrepreneurs.

25ationally persistent. It is 25% in the data and 28% in the model. The counterpart for otherself-employment (i.e., unincorporated but not incorporated business ownership) are 31% in thedata and 27% in the model.
The second panel of Table 7 sheds light on the intergenerational income elasticity between
fathers and sons. It is another set of untargeted moments. We calculate the intergenerational
income elasticity by regressing the average income of sons aged between 30 and 50 years(as a proxy for their permanent income) on the average income of fathers in the same age
range.40 The intergenerational income elasticity is 0.39 in the data and 0.42 in the model. The
model reproduces the fact that income persistence differs across different types of families.
The persistence is highest when both the father and the son are employees. It is followed byfamilies in which either the father or the son is an employee. Families in which both the father
and son are self-employed have the lowest income persistence because the income variation ismore substantial for non-employees (entrepreneurs and other self-employed individuals) thanfor employees. These results suggest that career choices may affect intergenerational income
elasticity.
5.4

Human capital gain from elite colleges

Consistent with our discussion of the potential self-selection bias, the upper panel of Table 8shows how people with different combinations of abilities and initial wealth sort into various
education. Recall that abilities are normalized to have zero mean. Elite college graduates havea much higher general ability (0.782) than graduates from ordinary college graduates (0.492)
and high schools (-0.306). Financial aid is positively related to the SAT score, which is, inturn, positively associated with general ability. Other things being equal, students with higher
general abilities would face lower tuition and more incentives to enroll in colleges. Comparedwith the intense sorting in general ability, the sortings in incorporated and unincorporated abilities are less noticeable. There seems to be a weak positive sorting in incorporated ability anda weak negative sorting in unincorporated ability. Elite college graduates have a slightly higher
incorporated ability (0.022) than graduates from ordinary colleges (0.008) and high schools(-0.012). The unincorporated ability of elite college graduates is the lowest (-0.010), followedby ordinary college graduates (-0.003) and high school graduates (0.008). In addition to theselection of abilities, we find robust sorting in terms of initial wealth. The last column of Table
8 shows that elite college students have much higher initial wealth than the other two types ofstudents. On average, elite college graduates have $77,758 at age 20, while ordinary college
and high school graduates only have $23,488 and $16,447, respectively.41 Appendix B visualizes some sorting to shed light on how individuals with different abilities and initial wealth sort
into different education types.
40

Haider and Solon (2006) find that the income earned around the age of 40 is the best proxy for permanent
income.
41
This finding is consistent with Chetty et al. (2020), who also find that the degree of segregation by parental
income is very high across colleges, and selective colleges have few students from less privileged backgrounds.

26

Table 8: Average Ability and Wealth at Age 20 by Education and Career
Generalability

Incorporated
ability

Unincorporated
ability

Wealth atage 20

By education typeHigh school
Ordinary college
Elite college-0.3060.492
0.782-0.012
0.008
0.022

0.008
-0.003-0.01

16,44723,48877,758

By career type
Employee
Entrepreneur
Other self-employed

0.001
0.162
-0.182-0.0761.451-0.055-0.127-0.1730.966

20,31528,767
23,621

Notes: This table presents the average ability and initial wealth at age 20 by education and career types. Average ability
is normalized to be zero. Initial wealth is in 2011 dollars.

Considering the students’ self-selection, elite colleges still offer more significant gains ingeneral, incorporated, and unincorporated human capital than ordinary colleges do. Table 4shows that graduation from elite college leads to an increase in the general/incorporated/unincorporated
human capital by 47%/56%/35%, while graduation from ordinary college leads to a 25%/28%/20%
increase. Among the three types of human capital, return to an elite college is the largest forincorporated human capital (28 ppt) than the other two types of human capital (22 ppt for general human capital and 15 ppt for unincorporated human capital). Thus, ignoring elite colleges’
effect on entrepreneurship may underestimate the returns from attending an elite college.
Appendix Table C7 goes further and shows how people with varying combinations of abilities and initial wealth sort into diverse education and career paths. The average incorporated
ability of entrepreneurs is lower for elite college graduates (1.001) than ordinary college graduates (1.360) and high school graduates (1.535). In this sense, select college attendance lowers
entrepreneurship’s entry barrier, while individuals with lower education levels need to be genuinely talented to start an incorporated business. Likewise, elite college graduates who own
an unincorporated business have the most inferior unincorporated ability among the three education levels. Thus, elite colleges facilitate people transiting from being employees to selfemployment.
5.5

Incorporated vs. Unincorporated Businesses

This section discusses how economic agents choose between the two forms of self-employment.
Table 4 shows that the contribution of general human capital is 0.15 for incorporated businesses, whereas the corresponding number to unincorporated firms is only 0.03. It means thatincorporated businesses combine general human capital and incorporated human capital, while
unincorporated businesses mostly use unincorporated human capital. Thus, our results are consistent with Lazear (2004, 2005), that entrepreneurs need to be “jacks-of-all-trade.”
The bottom panel of Table 8 further shows that individuals with high general ability but low
entrepreneurial ability choose to become employees. Meanwhile, those with the high generalability and high incorporated ability own incorporated businesses. Those with low general27

ability but high unincorporated ability become unincorporated business owners. Appendix B
provides more discussions on how individuals with different abilities and initial wealth sort into
different career types.
The ability mix is not the only determinant of a career. The entry cost of starting a business is also essential, but it is not directly observable by econometricians.42 Fortunately, ourstructural estimation can recover them. More specifically, we find that the cost of opening
an incorporated business is $58,000, while it is only $8,000 for unincorporated business (allin 2011 dollars).43 Thus, our estimates are consistent with the observation that incorporated
business owners tend to be wealthier and older.
The fact that the two types of businesses use different mixes of human capital and havedifferent entry costs explain why transitions between the two types of companies are rare, as
shown in the first panel of Table 6 and also documented in Levine and Rubinstein (2017).

6

Effect of Elite Colleges on Entrepreneurship

We have shown that abilities and initial wealth affect individuals’ education decisions and theirsubsequent career choices. In this section, we evaluate the importance of different factors using
two approaches. We first decompose the variation of lifetime income and career choices into
the variations of abilities, initial wealth, and schooling. We then simulate the career choices andincome over the life-cycle when individuals are assigned to different types of colleges while
holding the other variables constant.
6.1

Decomposition analysis

In the spirit of Lee and Seshadri (2019), we explain lifetime outcome differences with the~ a vector“state variables” at age 20. The “state variables” include 1) individual abilities, A,
that consists of three types of abilities, Aem , Aub , and Aib ; 2) wealth transfers received fromone’s parents at age 20, k0 ; and 3) education type, e (high school graduate, ordinary college
graduate, or elite college graduate). The outcome variables are an individual’s career choices(employee, incorporated, or unincorporated business owner) and lifetime income (defined as
the present-discounted sum of earnings at all ages up to retirement).
We compute the fractions of career choices and lifetime income that can be attributed tovarious combinations of these initial conditions by calculating the conditional variances.44 We
first examine the degree to which abilities, wealth, and schooling at age 20 can jointly explain
self-employment and income. We then drop these initial conditions one by one to assess therelative importance of each.
The upper panel of Table 9 shows the decomposition results of our baseline model. First, we
42

Entry cost includes both the direct costs of incorporation, such as annual fees and the preparation of moredetailed financial statements and the indirect agency costs associated with the separation of ownership and control.
43
To put things in perspective, $58,000 in entry costs would be equivalent to 1.8 years of elite college tuition.
44
To compute the conditional variances, we regress the outcome variables on the initial conditions. We divide
each dimension of the initial conditions into small groups and use group dummies in the regressions to increase
flexibility. We have seven groups for ability, eight groups for initial wealth, and three groups for education.

28

analyze how the initial conditions affect career choices. The first two rows of the upper panelof Table 9 present the decomposition results on entrepreneurship and other self-employment.
Column (1) shows that abilities, wealth, and education at age 20 can explain 44.4% of thedecision to be an entrepreneur and 39.6% of choice to be unincorporated business owners. This
result suggests a lot of uncertainty in people’s career paths as different people value alternative
career paths differently and experience further shocks to productivity over their life-cycles.
Table 9: Decomposition: Variance conditional on individual state at age 20
Baseline
Variance explained by (%):

(1)
~ k0 , e)(A,(2)~ k0 )
(A,

(3)
~ e)
(A,

(4)
(k0 , e)(5)
(Aub , Aib , k0 , e)(6)
(Aem , Aib , k0 , e)(7)
(Aem , Aub , k0 , e)Ever be an entrepreneur
Ever be other self-employed
Lifetime income

44.4
39.6
53.2

38.9
39.6
45.9

43.9
38.8
52.5

13.9
11.4
20.8

33.5
37.7
29.9

42.3
13.2
50.9

19.5
36.9
46.6

(1)
~ k0 , e)(A,(2)~ k0 )
(A,

(3)
~ e)
(A,

(4)
(k0 , e)(5)
(Aub , Aib , k0 , e)(6)
(Aem , Aib , k0 , e)(7)
(Aem , Aub , k0 , e)

41.8
39.6
50.1

38.9
39.6
45.9

41.2
38.8
49.0

11.5
11.4
15.2

31.0
37.7
24.1

39.7
13.2
47.8

17.0
36.9
43.5

Combine elite and ordinary colleges
Variance explained by (%):
Ever be an entrepreneur
Ever be other self-employed
Lifetime income~
Notes: This table presents the variance of career choices and lifetime income conditional on different combinations of initial states at age 20. Initial states include abilities A,~ includes general ability Aem , unincorporated ability Aub , and incorporated ability Aib .
initial wealth k0 , and schooling e. A

Among the three state variables, schooling has a pronounced effect on becoming selfemployed, especially on entrepreneurship choice. Comparing column (2) with column (1),
we find that excluding the variation in education reduces the conditional variance of being anentrepreneur by 5.5 ppt but does not affect the conditional variance of being an unincorporated
business owner. Thus, the education level has more influence on being an entrepreneur. This
result is consistent with the reduced form estimation in Table 3 that the probability of being anunincorporated business owner is similar across education groups.
In column (3), we leave out initial wealth, which is the transfer an individual receives fromhis or her parent at age 20, and surprisingly, it barely affects the conditional variance. The
conditional variance only declines by 0.5 ppt for entrepreneurship and 0.8 ppt for other selfemployment. This result may arise because education and abilities fully capture the explanatory
power of initial wealth for career choices. In contrast, we find that abilities play an importantrole. In column (4), we leave out abilities, and the explanatory power of the model dramaticallydeclines by 30.5 ppt and 28.2 ppt for entrepreneurship and other self-employment, respectively.
To understand the relative importance of general, unincorporated, and incorporated abilities,
columns (5) to (7) further exclude each of the three abilities one by one. In particular, excluding general ability reduces the conditional variance of entrepreneurship by 10.9 ppt, but only
by 1.9 ppt for other self-employment. Recall that incorporated businesses demand employee
human capital but not unincorporated businesses. Not surprisingly, leaving out unincorporated
ability reduces other self-employment’s conditional variance by 26.4 ppt but barely changes
entrepreneurship’s conditional variance. Similarly, leaving out incorporated ability reduces
entrepreneurship’s conditional variance by 24.9 ppt but hardly affects other self-employment.
Overall, career choice decisions are mainly driven by career-specific ability. In particular, thedecision to become an entrepreneur is driven by general ability and schooling.
29

Next, we analyze the explanatory power of abilities, wealth, and education at age 20 on
lifetime income. Despite the life-cycle uncertainty (the shocks on the productivity and consumption value of different careers), the initial conditions explain a sizable portion (53.2%) of
the lifetime income variance (column 1).45 When we exclude education in the initial conditions, the conditional variance of lifetime income declines from 53.2% to 45.9% (by 7.3 ppt),
as shown in column (2). This result is in contrast to Lee and Seshadri (2019): they find thatcollege choice only reflects selection, as the college choice margin can be explained almost entirely by the other variables. Our model distinguishes between elite and ordinary colleges andallows the two types of colleges to affect the accumulation of various kinds of human capital(general, unincorporated, and incorporated) differently. Different career paths demand alternative combinations of human capital and deliver very diverse income processes. Therefore, it isvital to distinguish between elite versus ordinary colleges and between different career paths.
Like career choices, we find that leaving out the initial wealth barely affects the conditionalvariance of lifetime income (0.7 ppt decline, as shown in column (3)), while leaving out abilities
has a significant impact. Removing the three abilities reduces the explanatory power by 32.4
ppt, as shown in column (4). This result is consistent with Lee and Seshadri (2019), who alsofind a sizable explanatory power of ability but a small one of wealth.46 In columns (5) to (7),we re-examine the model’s explanatory power by excluding the three abilities one by one. We
find that general ability explains more of the lifetime income than the other two abilities.
Lastly, we re-perform the above analysis by grouping elite and ordinary colleges and report
the results in the bottom panel of Table 9. Comparing the upper and bottom panels reveals theimportance of distinguishing elite and ordinary colleges. Column (1) shows that when we do
not differentiate between elite and ordinary colleges, the initial conditions’ capacity to explain
the entrepreneurship decision drops from 44.4% to 41.8%. At the same time, that for other selfemployment is not affected. Moreover, the fraction of variance in lifetime income explainedby all initial conditions drops from 53.2% to 50.1%. Recall that when we differentiate the two
types of colleges, excluding education reduces the conditional variances of entrepreneurshipand lifetime income by 5.5 ppt and 7.3 ppt, respectively. When we combine the two types ofcolleges, excluding education only reduces the conditional variances of entrepreneurship andlifetime income by 2.9 ppt and 4.2 ppt, suggesting that the explanatory power of education forentrepreneurship and lifetime income drops 47% and 42%, respectively. Therefore, considering elite college attendance is critical to understand entrepreneurship decisions and lifetime
income.
As pointed out by Lee and Seshadri (2019), one caveat of this analysis is that it cannot
reveal the exact contribution of each state variable at age 20 because they are intercorrelated.
45

Lee and Seshadri (2019) can explain 74% of the lifetime income. Our model explains a smaller fraction
because we do not model the pre-college human capital investment by parents. Our model also allows for different
productivity shocks on employees, unincorporated and incorporated business owners, which increases lifetime
income uncertainty.
46
Lee and Seshadri (2019) only allow for one-dimensional ability.

30

Appendix Table C8 shows that abilities and initial wealth are positively correlated. Education
decision is also affected by abilities and wealth. The next section provides a simulation exercise
to quantify elite colleges’ effects on career choice and lifetime income.
6.2

Simulation Analysis

To quantify the significance of elite college education on entrepreneurship decisions and lifetime income, we conduct two simulations in this section. First, we study the changes in elitecollege graduates’ (ordinary college graduates’) career choices and lifetime income if they attended ordinary college (elite college) instead. Second, we compare individuals’ career choices
and lifetime incomes with different combinations of abilities and initial wealth if assigned toelite colleges with those assigned to ordinary colleges.47 The results on career choices areshown in Table 10, and the impacts on lifetime income are shown in Table 11.
6.2.1

Effect on career choice

If elite college graduates attend ordinary colleges, their chance of becoming entrepreneurs
drops significantly, falling by 6.5 ppt (48.5%), from 13.4% to 6.9%, as shown in the first column of Table 10. However, their likelihood of engaging in other forms of self-employment
only declines slightly, by 0.1 ppt (0.8%), from 12.5% to 12.4%. Suppose ordinary college
graduates attend elite colleges, their chance of becoming entrepreneurs increases by 2.8 ppt(19.6%), while their likelihood of being other self-employed does not change. Again, theseresults confirm that elite colleges have an enormous impact on entrepreneur decisions but notother self-employment decisions.
The above analysis mimics the average treatment effects of elite colleges on elite college
graduates and ordinary college graduates. To address the potential heterogeneity in the “treatment effect” of elite college education, we conduct additional simulations. Specifically, we
simulate individuals’ career choices and income with given levels of abilities and initial wealthover the life-cycle, assuming that all of them attended elite colleges or attended ordinary colleges. We compare the differences between these two simulations, which shed light on theimportance of elite college attendance for a given group of individuals. We repeat this exercise
for individuals with different combinations of abilities and initial wealth. For each of the threeabilities (employee, unincorporated, and incorporated), the low (high) type is defined as one
standard deviation below (above) the mean. For the initial wealth, the low type has $10,000 atage 20, while the high type has $30,000.
We find that individuals with low incorporated ability have little chance of becoming anentrepreneur. The effect of elite colleges on entrepreneurship for that group of people is quite
limited. The impact of elite college on entrepreneurship is most significant among individuals
with high incorporated ability, low abilities in the other two dimensions, and low initial wealth(denoted by(L, L, H, L)). Moving these people from elite colleges to ordinary colleges reduces
47

To facilitate the comparison, we conduct the simulations conditional on the same set of simulated consumption
and productivity shocks of each career choice, with and without an elite college degree.

31

Table 10: Simulation: Effect of elite colleges on career choiceElite college graduates
Ordinary college graduates(L, L, L, L)(L, L, L, H)(H, L, L, L)(H, L, L, H)(L, H, L, L)(L, H, L, H)(L, L, H, L)(L, L, H, H)(H, H, L, L)(H, H, L, H)(L, H, H, L)(L, H, H, H)(H, L, H, L)(H, L, H, H)(H, H, H, L)(H, H, H, H)

Elite
college

Entrepreneur (%)
Ordinarycollege

13.4
9.8
0.0
0.0
0.0
0.0
0.0
0.0
21.8
14.8
0.0
0.0
7.1
8.2
31.1
33.6
22.6
25.1

6.9
7.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8.0
0.0
0.0
0.0
6.9
11.8
15.3
8.3
10.6

Diff
6.5
2.8
0.0
0.0
0.0
0.0
0.0
0.0
21.8
6.8
0.0
0.0
7.1
1.3
19.3
18.3
14.3
14.5

Other self-employed (%)
Elite
OrdinaryDiffcollegecollege
12.5
12.7
0.5
0.5
2.0
2.0
10.6
12.8
0.0
0.0
16.8
18.3
7.9
8.2
0.9
0.9
15.9
17.4

12.4
12.7
0.0
1.4
1.5
1.8
4.1
8.5
0.0
0.4
14.8
16.8
5.8
6.4
1.1
1.4
14.6
14.9

0.1
0.0
0.5
-0.9
0.5
0.2
6.5
4.3
0.0
-0.4
2.0
1.5
2.1
1.8
-0.2
-0.5
1.3
2.5

Notes: We simulate the career choice and earnings over the life-cycle when individuals attend elite colleges and when theyattend ordinary colleges. The first two rows present the results of elite college graduates and ordinary college graduates,
respectively. The following rows present the results of individuals with a fixed level of initial abilities and wealth. The four
elements in the parentheses refer to general ability, unincorporated ability, incorporated ability, and initial wealth, respectively.
Low abilities refer to one standard deviation below the mean and high abilities refer to one standard deviation above the mean.
Low wealth represents an initial wealth of 10,000 USD at age 20 and high wealth represents 30,000 USD initial wealth. The
first three columns present the probability of being an entrepreneur if the individual attended elite colleges, that if he attended
ordinary colleges, and their difference. The last three columns present the probability of being other self-employed if theindividual attended elite college, that if he attended ordinary college, and their difference.

the probability of becoming an entrepreneur by 21.8 ppt. The effect drops to 6.8 ppt when thesame individuals have high initial wealth (denoted by (L, L, H, H). For individuals with highunincorporated and incorporated abilities but low general ability (denoted by (L, H, H, L) and(L, H, H, H)), the effects of elite college on entrepreneurship are also much more extensive forthe poor than the rich (7.1 ppt vs. 1.3 ppt). Elite colleges (1) enhance entrepreneurial humancapital and (2) improve employee salaries so that potential entrepreneurs accumulate wealthfaster to open a business. The differential effects of elite colleges between the poor and richsuggest that (2) serves as an essential mechanism in affecting the entrepreneurship decision,consistent with the reduced form findings in Table 3.
Furthermore, for individuals with high general and incorporated abilities (denoted by (H, ·, H, ·)),
elite college significantly improves the probability of becoming an entrepreneur (by 14 - 19ppt), regardless of initial wealth and unincorporated ability. In contrast, the effect of elitecollege on other self-employed is almost zero. Elite college barely affects the probability ofengaging in unincorporated businesses, except for students with low general ability, high unincorporated ability, and low initial wealth ((L, H, L, L) and (L, H, H, L)).
In sum, graduating from an elite college increases the chance of becoming an entrepreneur
but not an unincorporated business owner. Such effects are concentrated on individuals with32

the high incorporated ability and low financial capacity.
6.2.2

Effect on lifetime utility

Next, we analyze the effect of elite colleges on lifetime income. While consumption values
partly drive people’s education choices in our model, our elite college premium is incomebased. In particular, we define the elite college premium as the difference between the discounted present value (DPV) of lifetime income (including tuition) at age 20 for an individual
who chooses to attend an elite college and the DPV of lifetime income attending an ordinary
college. The calculation includes tuition expenditure but not the consumption value of colleges.
The first row of Table 11 shows that moving elite college graduates to ordinary colleges
leads to a substantial decline in their lifetime income, which is reduced by 21%. It translates toan elite college premium of $147,290 (in 2011 dollars, net of tuition). Although elite collegescharge much higher tuition fees ($81,140 more over four years) than ordinary colleges, they
provide higher returns in terms of the general, incorporated, and unincorporated human capital.
Therefore, the net return of going to an elite college is positive. When we move ordinary college
graduates to elite colleges, we observe an increase in lifetime income by 12%, as shown in thesecond row of Table 11.48
Table 11: Simulation: Effect of elite colleges on lifetime income

Elite college graduates
Ordinary college graduates(L, L, L, L)(L, L, L, H)(H, L, L, L)(H, L, L, H)(L, H, L, L)(L, H, L, H)(L, L, H, L)(L, L, H, H)(H, H, L, L)(H, H, L, H)(L, H, H, L)(L, H, H, H)(H, L, H, L)(H, L, H, H)(H, H, H, L)(H, H, H, H)

Baseline Model

Forced to be
employees

Difference

21.45
14.50
15.04
14.16
16.92
14.28
19.53
11.05
25.28
12.00
16.22
18.05
23.34
12.60
24.17
29.91
23.40
26.37

16.88
12.22
14.65
13.83
16.52
13.93
18.74
10.26
19.96
9.13
15.26
17.71
20.68
10.24
18.89
23.35
18.79
22.66

4.57
2.28
0.39
0.33
0.40
0.35
0.79
0.79
5.32
2.87
0.96
0.34
2.66
2.36
5.28
6.56
4.61
3.71

Notes: The first column presents the elite college premium (percentage change in the lifetime
income if the individual’s education changed from ordinary to elite colleges), allowing for freecareer choice. The second column shows the elite college premium if we force individuals to
become employees. The third column presents the difference in the elite college premium between
the first two columns. The first two rows present the results of elite college graduates and ordinary
college graduates, respectively. The following rows present the results of individuals with a fixed
level of initial abilities and wealth. For details, please refer to the footnote of Table 10.

Next, we analyze how elite college premium varies by abilities and initial wealth, as shown48

Some of them may have elite colleges as their first choice but get rejected by elite colleges and have to go toordinary colleges.

33

in the third to last row of Table 11. Overall, elite college significantly improves the lifetime
income for almost all types of individuals. The improvement is more extensive for individuals
with high employee abilities or high incorporated abilities. The effect of elite college on lifetime income is similar across individuals from high- and low-income families, consistent withthe reduced form findings in Table 3.
We further analyze how elite college premiums would change if the self-employment possibility vanishes, i.e., we force everyone to be employees in the simulation. For elite college
graduates, the estimated elite college premium declines from 21% to 17%, as presented in thefirst row of Table 11, suggesting that ignoring the self-employment option will underestimate
the elite college premium. Moreover, this downward bias is tremendous for individuals withhigh incorporated abilities (potential entrepreneurs), as elite colleges bring more massive human capital gain for entrepreneurs than employees.
In sum, attending an elite college increases the chance of becoming an entrepreneur and improves lifetime income but does not affect an unincorporated business owner’s likelihood. Sucheffects are concentrated on individuals with the high incorporated ability and low financial capacity. Modeling self-employment decision avoids under-estimation of elite college premium.

7

Counterfactual Analysis

Entrepreneurship is believed to drive economic growth since Schumpeter (1934). Entrepreneurs
are subsidized in many ways in different countries and the effects are mixed (Lerner, 2009,
Lerner and Schoar, 2010). This section considers two types of subsidies: indirect subsidies toelite or ordinary college students and direct subsidies to incorporated or unincorporated businesses.
7.1

Subsidies to elite or ordinary college students

We first analyze how an education subsidy would affect entrepreneurship and other aggregate
variables. We separate the cases of subsidies to elite versus ordinary college students. We consider a subsidy rate from 0 to 1, with the subsidy covering all tuition when the rate reaches 1.
Labor income tax finances the subsidy and keeps the government budget balanced. While theprevious section’s simulation exercises take prices as given, we study the individual and aggregate outcomes in the new stationary equilibrium with new prices.49 We keep the admission rateof elite colleges unchanged in this counterfactual, and we assume that elite colleges do not facecapacity constraints.
Figure 1 shows the impact on the fractions of ordinary college graduates, elite college graduates, entrepreneurship, and other self-employment for the two experiments at different tuition
subsidy rates. For instance, when the elite college subsidy is 50%, the annual effective elite college tuition is $16,523, which is slightly higher than that of non-elite colleges ($12,761). The
fraction of elite college graduates increases from 5.5% to 16.7% (by 11.2 ppt), and the frac49

However, we do not take into account the transitional costs incurred when we move from the old steady-state
to the new one.

34

tion of ordinary college graduates reduces from 29.2% to 26.0% (by 3.2 ppt). Some ordinary
college students would switch to elite colleges for higher gains to general, unincorporated, andincorporated human capital. Others still prefer ordinary colleges due to the tuition difference
and preference shocks.
Figure 1: Counterfactual: Subsidy to Elite/ordinary College Students
fraction of non-elite-college graduates

.25

.05

.1

.3

.15

.35

.2

.4

.25

fraction of elite-college graduates

0

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

0

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

fraction of other self-employed.126 .128.13.055 .06 .065 .07 .075 .08.132 .134

fraction of entrepreneurs

.1

0

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

0

elite college subsidy

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

non-elite college subsidy

The same subsidy rate for ordinary college students increases the fraction of ordinary college graduates by 5.6 ppt and does not affect the fraction of elite college graduates. Non-elitecollege subsidies cannot attract elite-college students to substitute away from elite colleges.
Notice that elite-college students mainly include those with high general ability and those whocome from affluent families. The former receive generous financial aid from elite colleges andpay low net tuition; the latter care more about the elite-college human capital gains than the expensive tuition. Non-elite college is sub-optimal for these two groups even if non-elite colleges
are 100% subsidized. Ordinary college subsidies mostly encourage high school graduates toswitch to ordinary colleges.
The lower left and right figures of Figure 1 present the effects of subsidies on career choices.
Consistent with the previous section’s findings, elite college subsidies have a more considerable
impact on the number of entrepreneurs and other self-employed individuals than ordinary college subsidies. Also, elite college subsidies on entrepreneurship are more pronounced than onother forms of self-employment. A 50% subsidy to elite college students increases the fractionof entrepreneurs from 5.5% to 6.8% (by 1.3 ppt) and the fraction of other self-employed individuals from 12.7% to 12.9% (by 0.2 ppt). The same subsidy rate for ordinary college students
35

only increases the fraction of entrepreneurs by 0.3 ppt and the fraction of other self-employedindividuals by 0.2 ppt.
The three figures at the top of Figure 2 show the effects of subsidies on entrepreneur income
and dynamics. Providing college subsidies has two effects on entrepreneur income and dynamics. First, individuals who go to an elite or ordinary college can acquire more human capital,
which increases the chance that they enter and stay in business. Second, college subsidies encourage those with relatively low incorporated ability to become entrepreneurs. They become
entrepreneurs at an older age because they need more time to accumulate physical capital, andtheir business is unpromising. Therefore, the net effect of college subsidies on the entry andexit of entrepreneurship is ambiguous. Our counterfactual analysis shows that elite and ordinary college subsidies encourage more people to become entrepreneurs and allow them to enter
earlier and stay longer, suggesting that the first channel dominates the second.
Figure 2: Counterfactual: Subsidy to Elite/ordinary College Students (Cont’d)

10.35

36.6

10.2

36.8

10.25

37

10.3

37.2

150
140
130
120
110

duration of entrepreneurhsip
10.4

age of first entrepreneurship37.4

entrepreneur income (in K)

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

IGT income elasticity

welfareincome gini

.505
.5
.495

1

.35

1.05

.36

1.1

.37

.38

1.15

.39

.51

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

1.2

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

elite college subsidy

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

non-elite college subsidy

Moreover, elite college subsidies are more efficient than ordinary college subsidies to improve entrepreneurial performance. A 50% subsidy to elite college students increases entrepreneur income by 18.6%, reduces the age of beginning entrepreneurship by 0.37 years, andincreases the average duration of entrepreneurship by 0.07 years. However, the same subsidy
rate to ordinary college students only increases entrepreneur income by 2.2%, reduces the ageof starting entrepreneurship by 0.04 years, and increases the average duration of entrepreneurship by 0.04 years.
The bottom three figures in Figure 2 present the aggregate effects on society, including36

intergenerational income elasticity, welfare, and the income Gini coefficient.50 Intergenerational income elasticity declines as the subsidy rate increases for both types of subsidies, andthe effect is more substantial for elite college subsidies. A 50% subsidy to elite and ordinary
college students reduces the intergenerational income elasticity by 3.0 ppt and 0.9 ppt, respectively. The intuition is straightforward. College subsidies (particularly elite-college subsidies)encourage more students from low-income families to enter college, which weakens the links ofintergenerational persistence (in abilities and wealth). This result echoes the finding of Chetty
et al. (2020) that removing the segregation in parental income across colleges can significantly
reduce intergenerational income persistence.
Furthermore, both subsidy types improve social welfare, which is optimized at the 100%
subsidy rate in both cases. This finding is consistent with Abbott et al. (2019), who also findthat more generous financial aid is welfare improving. A 50% subsidy to elite college students
improves social welfare by 15.1%, while a 50% subsidy to ordinary college students improvessocial welfare by 7.1%. Figure 2 demonstrates that elite college subsidies provide more considerable welfare gains than ordinary college subsidies at all levels of subsidies. The extensive
welfare gains of elite college subsidies mainly come from the relaxation of the business owners’
borrowing constraints. Individuals with high entrepreneur ability from disadvantaged families
will bear a hefty student loan when elite-college tuition is high, hindering them from making further loans to finance their business. Hence, some may not enroll in elite colleges at all.
Therefore, elite-college subsidies encourage would-be entrepreneurs from low-income families
to enroll in elite colleges and start an incorporated business.51
Although elite-college subsidies may reduce such distortions from borrowing constraints
and improve social welfare, they increase income inequality because they mostly benefit individuals with high ability. A 50% subsidy to elite college students increases the Gini coefficientby 0.8 ppt, whereas a 50% subsidy to ordinary college students only increases the Gini coefficient by 0.1 ppt.
In sum, elite college subsidies are more efficient than their ordinary college counterpart inincreasing the number of entrepreneurs, improving entrepreneurs’ income, reducing the ageof starting entrepreneurship, and extending entrepreneurship duration. Relative to ordinary
college subsidies, elite college subsidies more significantly reduce intergenerational income
persistence and improve social welfare. However, elite college subsidies magnify income inequality.
One caveat of this counterfactual experiment is that we keep the admission rates of elitecolleges unchanged. However, when elite college subsidies encourage more students to enroll
in elite colleges, the number of admitted students may exceed the colleges’ capacity constraints.
Therefore, we perform an additional counterfactual experiment in which the number of students
50

Welfare is the summation of the expected value of the discounted sum of utility of each agent.
Note that these would-be entrepreneurs primarily come from the top of the ability distribution. Hence, elitecollege subsidy could represent a transfer from the less-able to the more able.
51

37

enrolled in elite colleges is kept unchanged while the subsidy on elite college tuition increases.52
Figure 3 presents the effects of elite college subsidies on education and career choices after
changing its admission rate and comparing them with the impact of ordinary college subsidies.
With a constant fraction of elite college graduates, the fraction of entrepreneurs increases withthe subsidy, and the magnitude is larger than the ordinary college subsidy counterpart. Suchrise in entrepreneurs is due to the composition change among elite college students. Appendix
Table C9 shows that, with elite college subsidies increase, the average general ability and initial wealth of elite college graduates decline, while the average incorporated ability of elitecollege graduates increases.53 It is because elite college subsidies reduce elite colleges’ costs,
attract more students with relatively lower general ability and wealth, and relax the borrowing
constraints that prevent potential entrepreneurs from attending elite colleges, increasing the average incorporated ability of the applicants. 54 Therefore, the number and incorporated abilityof elite college graduates can increase with a fixed number of elite college graduates.

fraction of non-elite-college graduates.3 .32 .34 .36 .38 .4

fraction of elite-college graduates

-.945

1.055

Figure 3: Counterfactual: Subsidy to Elite/ordinary College Students (Adjusted for Admission
Rate)

0

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

0

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

.055

.06

.127 .128 .129 .13 .131 .132

fraction of other self-employed.065

fraction of entrepreneurs

.1

0

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

0

elite college subsidy

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

non-elite college subsidy

Figure 4 further shows that the elite college subsidies are superior to the ordinary college counterpart in increasing entrepreneur income, reducing the age of first entrepreneurship,
52

Hence, we scale down the admission rates of elite colleges at all score levels proportionally to keep thenumber of elite college graduates constant. At the same time, we assume there is no capacity constraint forordinary colleges.
53
The average ability and initial wealth of elite college graduates have similar patterns when we do not adjust
the admission rate.
54
There is also a small increase in the average unincorporated ability of elite college graduates.

38

increasing the duration of entrepreneurship, and reducing intergenerational income elasticity.
However, the difference between the two subsidies gets smaller under elite college capacity
constraints. Moreover, elite college subsidies are not better than the ordinary college counterpart in terms of social welfare.55 Both elite college subsidies and ordinary college subsidies
moderately increase income inequality in a similar magnitude. Thus, we should use elite college subsidies with caution when there exists a capacity constraint.56
Figure 4: Counterfactual: Subsidy to Elite/ordinary College Students (Cont’d, Adjusted for
Admission Rate)age of first entrepreneurship

10.6
10.5
10.4
10.3

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

IGT income elasticity

welfareincome gini
.5
.499
.497
.496

1

.32

.34

1.05

.36

.498

1.1

.38

.4

1.15

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

elite college subsidy

7.2

duration of entrepreneurhsip

10.2

114

37

116

37.1

118

37.2

120

122

37.3

entrepreneur income (in K)

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

non-elite college subsidy

Subsidies to incorporated or unincorporated businesses

This section considers start-up subsidies for the self-employed. According to Caliendo (2016),
many Western countries have some form of start-up subsidies. While formats and effects
vary across nations, business subsidies typically involve a lump-sum subsidy given to the selfemployed when the business is started. Therefore, we study the impact of a start-up subsidy as
55

At lower levels of subsidies, elite college subsidies with adjusted admission rates have similar welfare gains
as ordinary college subsidies. At a higher level of subsidies, elite college subsidies’ welfare gain gets flattened out
and is exceeded by that of ordinary college subsidies.
56
In Appendix Figures C3 to C6, we present the impact of providing elite college subsidies and ordinary college
subsidies, taking wage rate and interest rate as fixed. In the partial equilibrium setup, the effects of subsidies oncollege enrollment and entrepreneurship dynamics are similar. The welfare effect gets slightly larger. Overall, we
still find that elite college subsidies are better than ordinary college subsidies to encourage more entrepreneurs
and improve their performance. Elite college subsidies provide a more considerable welfare gain than ordinary
college subsidies if there is no capacity constraint of elite colleges; with capacity constraint (adjusted admissionrate), elite college subsidies become less efficient.

39

a lump-sum transfer.
In this model, since there is a fixed cost to start a business, the start-up subsidy is equivalent
to reducing the entry costs. The subsidy ranges from 5,000 to 50,000 dollars, which is 8.6%
to 86% of the incorporated business’s entry costs and 4.5% to 45% of entrepreneurs’ annual
income. We use a labor income tax to finance the subsidy and allow wage rate and interest rates
to adjust to the new stationary equilibrium.57
As shown in Figure 5, a $50,000 incorporated business subsidy leads to a substantial increase in the fraction of entrepreneurs, from 5.5% to 8.1% (47% increase). However, incorporated business subsidies do not affect the fraction of elite college graduates, ordinary college
graduates, or other self-employed. Although entrepreneur subsidies relax the credit constraint
for potential entrepreneurs who want to attend colleges, the entrepreneur subsidy may not belarge enough to get them into colleges.58
Figure 5: Counterfactual: Subsidy to Incorporated/unincorporated Businesses

0

0

.1

.2

.1

.3

.4

.5

fraction of non-elite-college graduates.2

fraction of elite-college graduates

0

10000

20000 30000
subsidy

40000

50000

0

20000 30000
subsidy

40000

50000

fraction of other self-employed

.12

.14

.16

.18

.055 .06 .065 .07 .075 .08.2

fraction of entrepreneurs

10000

0

10000

20000 30000
subsidy

40000

50000

incorporated business subsidy

0

10000

20000 30000
subsidy

40000

50000

unincorporated business subsidy

Given that the subsidy reduces the entry barrier of an incorporated business, individuals
with lower incorporated human capital can enter the business. Therefore, the age of first entrepreneurship declines with the average entrepreneur income (excluding subsidy) and the duration of entrepreneurship due to the selection effect, as shown in the upper panel of Figure
57

Appendix Figures C7 and C8 present the results when fixing the prices. The findings of partial equilibrium
are similar to those of general equilibrium.
58
The average age of starting an incorporated business is 37. Hence, a $50,000 entrepreneur subsidy at age 37
discounted to age 20 is only $24,970, which is relatively small compared to four-year college tuitions ($132,000
for elite colleges and $51,000 for non-elite colleges).

40

6. Thus, start-up subsidies contrast with elite college subsidies, which increase entrepreneur
income and entrepreneurship duration.
Figure 6: Counterfactual: Subsidy to Incorporated/unincorporated Businesses (Cont’d)age of first entrepreneurship

10

duration of entrepreneurhsip

01000020000300004000050000
subsidy

8
7

34

100

35

105

36

9

110

37

115

38

entrepreneur income (in K)

010000 20000 30000 40000 50000
subsidywelfareincome gini

1.04

.495

1.01
1

.38

.385

.5

.39

1.02

.505

1.03

.4
.395

0

.51

IGT income elasticity

10000 20000 30000 40000 50000
subsidy01000020000300004000050000
subsidy01000020000300004000050000
subsidy

incorporated business subsidy

01000020000300004000050000
subsidy

unincorporated business subsidy

We then compare incorporated business subsidies with unincorporated business subsidies,
ranging from 5,000 to 50,000 dollars, accounting for 62.5% to 625% of unincorporated business’ entry costs. Not surprisingly, unincorporated business subsidies encourage more people
to engage in other self-employment and have little impact on entrepreneurship, as shown in
Figure 5 and the upper panel of Figure 6. A $50,000 unincorporated business subsidy increases
the share of other self-employment from 12.7% to 18.8% (48%).
The bottom panel of Figure 6 suggests that both subsidies to incorporated and unincorporated businesses increase income inequality. Subsidies for unincorporated enterprises (butnot incorporated businesses) reduce intergenerational income mobility. Moreover, subsidies tounincorporated businesses are more efficient in improving welfare compared to subsidies toincorporated counterparts. In sum, distinguishing the two types of self-employment provides
the opportunity to consider policies targeting different forms of businesses and uncovers theirdifferentiating welfare implications.
7.3

Subsidies to students or businesses? A graphical approach

The previous sections have shown the consequences of both education subsidies (both for thecase of elite and ordinary colleges) and start-up subsidies (both for the case of incorporated
and unincorporated businesses). This section asks which type of subsidy would a government
41

prefer. A fiscally-constrained government needs to know whether (and how much) to invest
in different forms of subsidies. To our knowledge, there is no consensus in the literature on
how to compare different policy regimes in a unifying framework. Different authors adopt
different approaches depending on the context (Chari and Kehoe, 1999, Krusell et al., 1996).
We, therefore, follow a relatively intuitive approach from the literature, proposed by Hanushek
et al. (2003, 2014). The idea is to consider efficiency, equality, mobility as “social goods”
and consider society to have a different “social goods production frontier” under each policy
regime. We can then ask the following question: Which regime can attain the highest possiblelevel of efficiency? Equality? Mobility? What is the trade-off among different social goods
under each regime? Hanushek et al. (2003, 2014) address these questions graphically.
In Figure 7, the horizontal axis is the welfare, which is a proxy for “efficiency.” We normalize the level of welfare under zero-subsidy as unity. The vertical axis is (1-Gini), where
Gini is the Gini coefficient. (1-Gini) increases with the level of income equality and is a proxyof “equality.” For any given regime, we vary the subsidy rate, the tax rate, and wage rates sothat the government balances her budget and the markets clear at any subsidy rate. Hence, weconstruct a production frontier for social goods for each regime. With this interpretation, thefurther away is the locus from the origin, the better is the regime. It means that for a given
level of efficiency, that regime can generate a higher level of equality and vice versa. Someobservations are in order. First, there is a trade-off for all regimes. It means that we must bearwith more inequality for any given policy regime to increase efficiency. Second, the businessstart-up subsidy regimes, both incorporated and unincorporated businesses, are inferior to theeducation subsidy regimes. Third, although the elite college subsidy regime has a lower frontierthan the ordinary college subsidy counterpart, it can generate a higher efficiency level when thesubsidy rate is close to unity.
In the same spirit, we also plot Table 8. The horizontal axis is the welfare, a proxy for “efficiency.” The vertical axis is now (1- intergenerational income elasticity), a proxy of “mobility.”
The idea is that a society with high mobility should have low intergenerational income elasticity. Some observations are in order. First, while we need to trade-off efficiency and mobility
under the unincorporated business subsidy regime, we can increase efficiency and mobility simultaneously by providing college tuition subsidies. Second, for a ranger of efficiency, theordinary and elite college subsidy regimes deliver a similar mobility level. However, whether
we adjust the admission rates or not, the elite college subsidy regime gives a significantly higher
mobility level after some threshold level of efficiency than the ordinary college counterpart.
These results in this section are consistent with the previous sections. Education subsidy
regimes are “better” because they encourage more people to attend college and improve individuals’ human capital. The college students receive human capital gains and have their lifetime
income increase. On the other hand, a start-up subsidy does not relax the financial constraints
for pre-college students. It hence has a limited effect on the aggregate and distribution of human capital in society. The elite college subsidy can do better than the ordinary college subsidy
42

.49

.495

1 - Gini.5.505

Figure 7: Counterfactual: Aggregate Expected Utility and Inequality

1

1.05

1.1
Aggregate expected utility

1.15

elite college subsidy

ordinary college subsidy

incorporated business subsidy

unincorporated business subsidy

1.2

elite college subsidy (adjusted admission rates)

Notes: The horizontal axis is the welfare, a measure of efficiency. The vertical axis is (1-Gini coefficient), ameasure of equality. The further away is the locus from the origin, the better is the regime. It means that for agiven level of efficiency, that regime can generate a higher level of equality and vice versa.

in some cases because it would enable people who have high abilities but limited financial
resources to become entrepreneurs.

8

Conclusion

Central to the discussion surrounding elite colleges are two questions: does elite college attendance matter? and if so, why? This paper addresses these questions by constructing andestimating an overlapping generations life-cycle model that captures the selection into different types of education and careers based on abilities and wealth inherited from parents. Ourmodel allows for three different human capital types (general, unincorporated, incorporated)and distinguishes between elite and ordinary colleges, which could deliver distinct human capital accumulation packages. Our model also allows for different career paths (employee, entrepreneur, and other self-employed) that require different types of human capital. To estimate
such a model, we use the PSID panel data with restricted accessed school information.
We find that elite colleges contribute more than ordinary colleges to accumulate different
kinds of human capital, particularly entrepreneurs’ human capital. Consequently, elite college
attendance increases the likelihood of becoming entrepreneurs. We estimate that the (average) elite college premium is positive and significant, which means that elite college attendance generates positive income gains. Our decomposition analysis shows that education has
sizable power to explain self-employment decisions, predominantly the decision to pursue en43

.6

1 - IGT income elasticity
.62
.64
.66

.68

Figure 8: Counterfactual: Aggregate Expected Utility and Intergenerational Mobility

1

1.05

1.1
Aggregate expected utility

1.15

elite college subsidy

ordinary college subsidy

incorporated business subsidy

unincorporated business subsidy

1.2

elite college subsidy (adjusted admission rates)

Notes: The horizontal axis is the welfare, a measure of efficiency. The vertical axis is (1- integenerational income
elasticity), a measure of mobility. The further away is the locus from the origin, the better is the regime. It meansthat for a given level of efficiency, that regime can generate a higher level of mobility and vice versa.

trepreneurship. Our simulation exercise further shows that moving elite college graduates toordinary colleges would significantly reduce their chance of becoming entrepreneurs but would
have little impact on their opportunity to engage in unincorporated business ownership. All
these results confirm that while elite college enhances different kinds of human capital, theelite college premium is tied tightly to entrepreneurship.
Our counterfactual analysis contrast subsidies to students in elite college with that in ordinary college and find that the former has many merits. Suppose the elite colleges have nocapacity constraint. In that case, elite college subsidies are more efficient than ordinary colleges in (1) increasing the number of entrepreneurs, (2) improving the income of entrepreneurs,(3) reducing the age of entering entrepreneurship, (4) increasing the duration of entrepreneurship, (5) reducing intergenerational income persistence, and (6) bringing a more considerable
increase in social welfare. The only drawback is that elite college subsidies increase income
inequality. If the elite colleges are tightly constrained in capacity, elite college subsidies canstill change elite college students’ composition and produce more entrepreneurs than ordinary
colleges. We also investigate start-up subsidies for both entrepreneurs and unincorporated business owners. We show that education subsidy regimes are superior to business start-up regimes
to generate efficiency, equality, and intergenerational mobility. Overall, our paper suggests that
elite colleges are essential engines for producing more successful entrepreneurs but that hightuition fees and borrowing constraints prevent some would-be entrepreneurs from attending
44

elite colleges.
We ignore potentially relevant elements for tractability. For instance, Dyrda and Pugsley(2018) study how tax reforms change the composition of incorporated businesses between Ccorporations and S-corporations. Unfortunately, the PSID data do not distinguish between thesetwo kinds of corporations. Future work could further explore how tax policies affect career
choices. Lazear (2016) explores a model with different career paths with errors in individuals’
performance estimates. He suggests that overconfidence is more prevalent in occupations withnoisier estimates of ability, such as entrepreneurship. Dillon and Stanton (2017) and Hincapié
(2020) also consider the initial uncertainty in entrepreneur earnings and continuous learning
about the entrepreneurial earnings process. We abstract from the signal extraction considerations to keep the model simple as we attempt to integrate insights from the human capital andentrepreneurship literature. We also abstract from the reality that many students do not finish
their college education (Hanushek et al., 2003). Despite the rich modeling on the student-side
sorting, we take a simple approach to model the institutional-side sorting, and our model takes
the supply side of colleges as given.59 Given that we do not have data on applications or admissions, it is not easy to distinguish between the selection on the student side and the institution
side. Future work should explore how these issues would affect the parameter estimation andcorresponding policy implications.
Another limitation is our negligence of the trends in college attendance rates and the college
wage premium. This is rooted in the overlapping generations model, which assumes a steadystate (Abbott et al., 2019 and Lee and Seshadri, 2019). While the distributions of abilities canbe stable over time, elite colleges’ costs and benefits may change. The distribution of initial
wealth may also vary across generations. Therefore, we can treat the current estimates as anaverage on the cohort born in the 1950s to 1980s.60 Future work may collect more data for the
more recent cohort and examine whether the policy advice may need to be refined.

59

There is a debate on whether elite colleges face a capacity constraint in the literature. For instance, Che andKoh (2016) report that “1,415 freshmen accepted Yale’s invitation to join its incoming class in 1995–96, although
the university had aimed for a class of 1,335. In the same year, Princeton also reported 1,100 entering students, thelargest number in its history. Princeton had to set up mobile homes in fields and build new dorms to accommodate
the students unincorporated ability of elite college graduates” (p.1296). Fu (2014) argues that “Expanding college
capacities has very limited effects on college attendance...” (p.261).
60
Given that the tuitions, financial aids, and admission rates in the baseline model are calibrated for the 1980s
cohorts, the policy counterfactual regarding college subsidies is relative to a benchmark that approximates thestate of the world for the 1980s cohorts.

45

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50

APPENDIX
Appendix A

Literature Review

This paper builds on the insights of many authors. Having discussed the literature on elitecolleges in the introduction, we now focus on the literature on self-employment.1
Several authors explore the individual characteristics, including income, wealth, and education, that affect the probability of an individual’s becoming self-employed (Blanchflower and
Oswald, 1998, Dunn and Holtz-Eakin, 2000, Evans and Jovanovic, 1989, Evans and Leighton,
1989, Holtz-Eakin et al., 1994, Hurst and Lusardi, 2004, Lindquist, Sol, Van Praag, and Vladasel,
Lindquist et al.). In particular, these studies produce mixed evidence of the relationship between
education and self-employment. Some studies do not find a significant effect (Dunn and HoltzEakin, 2000, Evans and Jovanovic, 1989), while others observe a significant impact (Parker
and Van Praag, 2006, Samaniego and Sun, 2019). Blanchflower (2000) examines OECD data
and finds “evidence that self-employment is more prevalent among groups at the two ends ofthe education distribution and especially so for the least educated.” Thus, several competing
factors, such as human capital accumulation, opportunity cost, and financial constraints, affectthe choice of education and self-employment.
The family may also affect the self-employment decision. Nicolaou and Shane (2010) use
data on identical (MZ) and fraternal (DZ) twins in the U.S. to confirm the existence of a genetic
component of the intergenerational transfer of self-employment. Using Swedish adoption data,
Lindquist et al. (2015) compare individuals living with adopted parents with those living with
their biological parents and find that post-birth factors are more critical than pre-birth factors.
Using Norwegian data, Hvide and Oyer (2018) find that most male self-employed individuals
start a business in an industry the same as or closely related to that of their fathers.
Our paper is also related to the recent literature, highlighting the introduction of selfemployed economic agents, hence the fact-matching in macro models (Bassetto et al., 2015,
Cagetti and De Nardi, 2006, 2009, De Nardi and Yang, 2014, and Quadrini, 2009). Samaniego
and Sun (2019) introduce endogenous education choices to the Cagetti and De Nardi framework and find that the higher labor earnings of college graduates allow them to mitigate credit
constraints and become self-employed. They also find that subsidizing education’s welfare benefits is greater than removing financing constraints on education because subsidies facilitate theaccumulation of physical capital and loosen the credit constraints on would-be entrepreneurs.2
Some dynamic equilibrium models of self-employment do not contain a life-cycle structure(Glover and Short, 2010, Michelacci and Schivardi, 2017). Kwark and Ma (2021) incorporate
entrepreneurial choice in a dynamic general equilibrium model with both aggregate and idiosyncratic shocks and show that their model can replicate the income transition matrices over
occupational choices. Following Vereshchagina and Hopenhayn (2009), Choi (2018) develops
a dynamic occupation choice model. It shows that self-employed individuals with better outside options as paid workers tend to take more business risks and thus exhibit higher firm exit
rates, more growth dispersion, and faster growth conditional on survival.
Our paper is also related to an emerging literature that differentiates between entrepreneurs
and other self-employed individuals. Glover and Short (2010) documents that incorporated
1

Please refer to Astebro et al. (2014), Hanushek and Woessmann (2015), Kerr et al. (2018), Oreopoulos andSalvanes (2011), Oreopoulos and Petronijevic (2013), and Van der Sluis et al. (2008) for surveys of the literature
on self-employment and education.
2
Samaniego and Sun (2019) do not distinguish between elite and ordinary colleges and combine unincorporated and incorporated business owners as entrepreneurs. They calibrate their model and assume that entrepreneur
human capital follows a simple Markov process with only two values. We estimate our model using individual
panel data, and we allow for a more flexible human capital accumulation process.

1

entrepreneurs operate larger businesses, accumulate more wealth, and are, on average, moreproductive than unincorporated entrepreneurs. Levine and Rubinstein (2017) shows that two
types of self-employed have distinct cognitive and non-cognitive abilities, and Levine and Rubinstein (2018) analyzes how abilities and liquidity constraints have different effects on thelikelihood of selecting entrepreneurship and other self-employment. Hincapié (2020) develops
and estimates a dynamic Roy model of occupational choice, where individuals choose between
white-collar, blue-collar, unincorporated business, incorporated business, and not working.3
We contribute to the literature in several ways. First, we build a life-cycle model in which
different agents have different abilities and monetary endowments inherited from their families
and make their education and career decisions accordingly. Hence, we can estimate the effectof education, particularly elite college education, on self-employment decisions controlling forthe impact of wealth and ability. Our model mimics the observed intergenerational persistencyin education, career, and income. Second, we show that the differences between incorporated
and unincorporated business ownership are substantial. Specifically, these two types of selfemployment have different technologies and risks and require different types of human capitaland entry costs. Our structural model recognizes the differences between these two types ofself-employment and explains life-cycle career decisions. Third, we conduct two counterfactual experiments, subsidies to elite and ordinary college students and subsidies to incorporated
and unincorporated business startups. We evaluate their micro effects on entrepreneurs’ decisions and performance and the aggregate impact on welfare, inequality, and intergenerational
mobility.

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Insights from behavioral economics. Journal of Economic Perspectives 28(3), 49–70.
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3

Hincapié (2020) quantify the relative importance of determinants of whether to become an entrepreneur andwhen to become an entrepreneur using a dynamic Roy model with experience accumulation, risk aversion, andimperfect information about ability. However, the model is silent on (1) the modeling of education decisions, (2)how human capital obtained from the school, and (3) wealth accumulation after school affects entrepreneurshipdecisions.

2

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4

Appendix B

Additional Results on the Effects of Abilities and Initial Wealthon Education and Career Decisions

This paper sheds light on how individuals with different abilities and initial wealth sort into
different education and career types. In this section, we present some visualizations of the sorting. To illustrate how abilities and initial wealth jointly affect education and career decisions,
we divide individuals’ initial wealth into three groups: the bottom 1/3, the middle 1/3, and the
top 1/3. Abilities are standardized and range from +2 to -2 standard deviations.
Figure B1 shows how general ability and initial wealth jointly affect decisions about college
attendance and self-employment. The upper-left panel shows that the chance of graduatingfrom an elite college increases with a better general ability and initial wealth. Individuals withbelow-average ability are unlikely to enroll in an elite college because they do not want toapply for elite colleges. Elite colleges charge high tuition, and the return to an elite college isrelatively low for low-ability individuals. Besides, the admission rates of elite colleges are low
for individuals with low SAT scores. Based on our estimation, the general ability is mappedon to SAT scores according to SAT = 2.050Aem + ε and the noise ε has a standard deviationof 0.627. For individuals with high general abilities, they are more likely to apply for elitecolleges. Elite colleges reject around one-quarter of the applicants, and hence those applicants
have to attend ordinary colleges or directly enter the labor market. The likelihood that anindividual with high general ability (above one standard deviation) graduates from an elitecollege is 15% for the bottom initial wealth group, 18% for the middle group, and 23% for the
top group. The pattern that low- and middle-income students “undermatch” to elite collegesis also found in Chetty et al. (2020), who show that at any given level of SAT/ACT scores,
children from higher-income families attend more selective colleges. This is mainly because
low-income students are deterred by the high tuition fees.
The upper-right panel of Figure B1 shows that the likelihood of graduating from an ordinary college increases with the general ability and is highest for the top initial wealth group.
Individuals with ability below one standard deviation are unlikely to attend an ordinary college
because the return to ordinary college is relatively small compared to the tuition. The lower-leftpanel shows that conditional on general ability, the chance of owning an incorporated businessincreases with initial wealth. In contrast, the lower-right panel shows that the opportunity to
own an unincorporated business contingent on general ability does not vary by initial wealth.
These relationships result from entrepreneurship being more capital intensive than other selfemployment forms because entrepreneurship has an enormous entry cost. It is also possiblethat initial wealth does not play a direct role, but serves as a proxy of incorporated ability.
Moreover, we find that conditional on initial wealth, the chance of becoming an entrepreneur
increases with general ability, whereas the chance of becoming other self-employed declines
with general ability.
Figure B2 demonstrates the combined effects of incorporated ability and initial wealth on5education and career choices. The upper-left and upper-right panels show that conditional
on incorporated ability, individuals from high-income families are more likely to attend elitecolleges and ordinary colleges, respectively. We find no apparent sorting behavior in terms ofincorporated ability in either graph. The bottom two panels show that holding initial wealthfixed, incorporated ability increases the likelihood of being an entrepreneur but reduces thepossibility of being other self-employed. Moreover, conditional on incorporated ability, theinitial wealth is positively associated with the probability of being an entrepreneur but has no
impact on the likelihood of being other self-employed.
Figure B3 presents the interaction between unincorporated ability and family wealth foreducation and career choices. The upper-left panel shows that conditional on unincorporated
ability, the probability of having an elite college degree is much higher for individuals from the
top initial wealth group. However, we do not find stable sorting behavior in the unincorporated
ability for all three initial wealth groups. The upper-right panel shows the fraction of ordinary
college graduates. Positive sorting in unincorporated ability is evident for the high initial wealthgroup but not for the other two groups. The lower-left panel shows that the likelihood of
being an entrepreneur declines with unincorporated ability. In contrast, the lower-right panel
shows that the probability of being other self-employed increases with unincorporated ability.
Moreover, conditional on unincorporated ability, the initial wealth is positively associated withthe likelihood of being an entrepreneur but has no impact on the possibility of being otherself-employed.
In sum, we find sorting behaviors in education and career choices — individuals with abetter general ability and initial wealth sort into elite colleges. Individuals with a high generalability and incorporated ability are more likely to own an incorporated business. In contrast,
individuals with low general ability and high unincorporated ability are more likely to own
an unincorporated business. Initial wealth increases the chance of owning an incorporated
business but does not affect the prospect of owning an unincorporated business.

6fraction of non-elite college graduates
.1
.2
.3
.4
.5

.25

0

fraction of elite college graduates
.05
.1
.15
.2
0
-2

0
employee ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealth2-2

middle 1/3 initial wealth0
employee ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealthmiddle 1/3 initial wealth0.1fraction of entrepreneurs
.05
.1
.15

fraction of other self-employed
.15
.2
.25

.3

(b) Fraction of ordinary college graduates.2(a) Fraction of elite college graduates

2

-2

0
employee ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealth2-2

middle 1/3 initial wealth0
employee ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealth(c) Fraction of entrepreneurs

middle 1/3 initial wealth(d) Fraction of other self-employed

Figure B1: Education and career choices by general ability and initial wealth72fraction of non-elite college graduates
.25
.3
.35

.1
fraction of elite college graduates
.02
.04
.06
.08

.2

0
-2

0
incorporated ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealth2-2

middle 1/3 initial wealth0incorporated ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealthmiddle 1/3 initial wealth

-.2

.05

fraction of entrepreneurs
0.2.4

fraction of other self-employed
.1
.15

.2

(b) Fraction of ordinary college graduates.6(a) Fraction of elite college graduates

2

-2

0
incorporated ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealth2-2

middle 1/3 initial wealth0incorporated ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealth(c) Fraction of entrepreneurs

2

middle 1/3 initial wealth(d) Fraction of other self-employed individuals

Figure B2: Education and career choices by incorporated ability and initial wealth8fraction of non-elite college graduates
.25
.3
.35

.09
fraction of elite college graduates
.05
.06
.07
.08

.2

.04
-2

0
unincorporated ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealth2-2

middle 1/3 initial wealth0unincorporated ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealthmiddle 1/3 initial wealth(b) Fraction of ordinary college graduates

00

fraction of entrepreneurs.05.1

fraction of other self-employed
.2
.4

.6

.15

(a) Fraction of elite college graduates

2

-2

0
unincorporated ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealth2-2

middle 1/3 initial wealth0unincorporated ability (standardized)
bottom 1/3 initial wealth
top 1/3 initial wealth(c) Fraction of entrepreneurs

2

middle 1/3 initial wealth(d) Fraction of other self-employed individuals

Figure B3: Education and career choices by unincorporated ability and initial wealth9Appendix C

Supplementary Tables and Figures

Table C1: College Characteristics of Elite and Ordinary Colleges

Elite college
Ordinary college

Share

Facultystudent
ratio

Rejection
rate

Retention
rate

FacultysalarySAT
score

In-state
tuitionOut-ofstate
tuition

16.5%
83.5%

0.13
0.07

0.66
0.32

0.94
0.74

92,85959,928

1,356
1,035

29,068
14,11530,893
17,104

Notes: To define elite colleges, we follow Black and Smith (2006) in using factor analysis to construct a college quality index as afunction of the faculty-student ratio, rejection rate, retention rate, faculty salary, and mean of reading and math SAT scores.

10Table C2: List of Elite Colleges
Ranking Institution name
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100

California Institute of Technology
Franklin W Olin College of Engineering
Harvard University
Yale University
Princeton University
Harvey Mudd College
Massachusetts Institute of Technology
Pomona College
Washington University in St Louis
Dartmouth College
Stanford University
Swarthmore College
Columbia University in the City of New YorkDuke University
Brown University
University of Pennsylvania
Amherst College
University of Chicago
Williams College
Tufts University
Rice University
Northwestern University
University of Notre Dame
Claremont McKenna College
Carleton College
Cornell University
Georgetown University
Vanderbilt University
Haverford College
Carnegie Mellon University
Johns Hopkins University
Wellesley College
Bowdoin College
Emory University
Washington and Lee University
Reed College
Wesleyan University
Middlebury College
Vassar College
University of Southern California
Cooper Union for the Advancement of Science and Art
Colby College
Brandeis University
Scripps College
Davidson College
Oberlin College
Barnard College
Grinnell College
College of William and MaryColgate University
Jewish Theological Seminary of America
Macalester College
Boston College
New York University
University of California-Berkeley
Kenyon College
Whitman College
University of Rochester
Rensselaer Polytechnic Institute
Wake Forest University
Wheaton College
Connecticut College
Georgia Institute of Technology-Main Campus
University of Michigan-Ann Arbor
Bucknell University
Lehigh University
SUNY College at Geneseo
University of Virginia-Main Campus
Colorado College
New College of Florida
Bryn Mawr College
St Olaf College
University of North Carolina at Chapel Hill
University of California-Los Angeles
Kalamazoo College
Trinity College
Case Western Reserve University
Gettysburg College
University of Illinois at Urbana-Champaign
Trinity University
Lafayette College
Thomas Aquinas College
Occidental College
University of RichmondVillanova University
George Washington University
Beloit College
Rose-Hulman Institute of Technology
University of Miami
Dickinson College
Worcester Polytechnic Institute
United States Air Force Academy
Tulane University of Louisiana
Knox College
University of Maryland-College Park
Furman University
United States Coast Guard Academy
United States Naval Academy
Boston University
Illinois Institute of Technology

11

Quality index

Public

6.31
6.20
6.18
6.16
6.14
6.14
6.11
6.01
6.00
5.99
5.99
5.98
5.94
5.93
5.91
5.91
5.90
5.88
5.86
5.83
5.82
5.81
5.79
5.79
5.77
5.77
5.76
5.74
5.73
5.73
5.73
5.72
5.72
5.72
5.71
5.71
5.71
5.68
5.67
5.64
5.64
5.60
5.60
5.59
5.59
5.58
5.57
5.56
5.56
5.56
5.54
5.53
5.52
5.50
5.49
5.49
5.48
5.48
5.48
5.46
5.45
5.45
5.44
5.43
5.43
5.43
5.42
5.42
5.41
5.41
5.38
5.37
5.36
5.35
5.35
5.34
5.33
5.31
5.31
5.31
5.31
5.31
5.31
5.30
5.30
5.30
5.29
5.27
5.27
5.27
5.26
5.26
5.26
5.26
5.25
5.25
5.23
5.23
5.23
5.21

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
1
0
0
0
0
0
0
0
1
1
0
0
1
1
0
1
0
0
1
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
1
1
0
0

Table C3: Regression on Log Total Income

Elite college degree
Graduate school degree
Log father’s average income at age 40-50
Father has high school degree
Father has ordinary college degree
Father has elite college degree
Father ever runs unincorporated business
Father ever runs incorporated businessAge
Age squareConstant
Observations(1)
Employee0.3077∗∗∗(0.0233)
0.0438∗∗∗(0.0170)
0.1091∗∗∗(0.0160)
0.0065(0.0257)
0.0970∗∗∗(0.0283)
0.0378(0.0386)-0.1018∗∗∗(0.0218)-0.0495∗∗(0.0203)
0.1655∗∗∗(0.0085)-0.0017∗∗∗(0.0001)
6.2167∗∗∗(0.2351)
6,468(2)
Entrepreneur
0.4987∗∗∗(0.0796)0.1495∗∗∗(0.0575)
0.2824∗∗∗(0.0603)-0.1901∗∗(0.0892)-0.0405(0.1000)
0.2044(0.1286)-0.3711∗∗∗(0.0715)-0.3699∗∗∗(0.0691)
0.1560∗∗∗(0.0290)-0.0017∗∗∗(0.0004)
4.7090∗∗∗(0.8253)
959(3)
Other self-employed0.2713∗∗∗(0.0800)
0.1068∗∗∗(0.0278)0.4003∗∗∗(0.0712)0.1687∗(0.0959)0.1297(0.0997)-0.1283(0.1370)0.1558(0.1039)-0.3087∗∗∗(0.0806)
0.2125∗∗∗(0.0341)-0.0025∗∗∗(0.0004)
2.3512∗∗(1.0541)692

Notes: We use an OLS model. The dependent variable for all three columns is annual income. The sample includes allwhite males with college degree or above. The first column restricts the sample to employees, the second column
entrepreneurs, and the third column other self-employed.
Standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table C4: Fixed Parameters
Parameter

Meaning

Calibration

β
δ
ζt
σ
φ
λ
Te
fe (k p , Aem )pediscount rate
capital depreciation rate
survival rate after age 65
utility function parameter
pensionbudget constraint
college tuitioncollege financial aid
admission rates of elite colleges

0.821
0.266
See Table C5 (Health and Retirement Study)
1.5 (CDN2006)
40% of average earnings (CDN2006)
1.22 (RR2014)
33,046 for elite and 12,761 for ordinary (PSID)
See Section 4 (Fu2014)
0.209 if SAT scores below 800
0.559 if SAT scores between 800 and 1200
0.756 if SAT scores above 1200 (Fu2014)

Notes: CDN2006: Cagetti and De Nardi (2006); RR2014: Robb and Robinson (2014) Fu2014: Fu (2014).

12

Table C5: Survival Rate by Age
Age

65

70

75

80

85

90

95

100

Survival rate

95%

93%

89%

83%

73%

57%

38%

21%

Notes: Data source is Health and Retirement Study.

Table C6: Estimated Parameters
Parameter

Meaning

Target moments

Pem , Pib , Pubµje , e ∈ {nc, ec}, j ∈ {em, ib, ub}
γ1 , γ2
νib , νub
ρib , ρubζj , j ∈ {em, ib, ub}
Cib , Cub
ηib , ηub
σja , j ∈ {em, ib, ub}θj , j ∈ {em, ib, ub}
ηnc , ηecωαEM, IB, and UB technology
return to education
return to experience
return to capital
contribution of EM human capital to IB/UB
std of the productivity shock
cost of IB and UB
std of consumption shocks to IB and UB
std of EM, IB, and UB abilities
intergenerational ability transfer
std of consumption shocks on NC and EC
weight on offspring’s welfare
output elasticity of capital

average income of EM, IB, and UB
income by education and career type
EM income by age
IB and UB income by age
income correlation for switchers between EM, IB, and UB
income std of EM, IB, and UBtransitions between IB and UB
fraction of IB and UB
income correlations of stayer in EM, IB, and UB
intergenerational correlations in education and career
fraction of NC and EC
parental monetary transfer as a fraction of parental wealthinterest rate

Notes: EM: employee, EN: entrepreneur, UB: incorporated business owner, UB: unincorporated business owner, HS: high school graduate, NC: ordinary college
graduate, EC: elite college graduate.

Table C7: Average Ability and Wealth at Age 20 by Education and Career
Employee

Entrepreneur

Other self-employed

Total

General ability
High school
Ordinary college
Elite college
Total-0.275
0.494
0.750
0.001

-0.136
0.631
1.049
0.162

-0.5630.426
0.673-0.182-0.306
0.492
0.782
0.000

Incorporated ability
High school
Ordinary college
Elite college
Total-0.067-0.084-0.116-0.076

1.535
1.360
1.001
1.451

-0.058-0.045-0.097-0.055-0.012
0.008
0.022
0.000

Unincorporated ability
High school
Ordinary college
Elite college
Total-0.134-0.115-0.135-0.127-0.169-0.210-0.052-0.173

0.997
0.931
0.755
0.966

0.008
-0.003-0.0100.000

Wealth at age 20
High school
Ordinary college
Elite college
Total

15,97622,343
69,177
20,315

17,93026,21293,43928,767

16,95624,16777,446
23,621

16,44723,48877,75821,758Notes: This table presents the average ability and initial wealth at age 20 by education and career types. Average ability
is normalized to be zero. Initial wealth is in 2011 dollars.

13

Table C8: Distribution of Initial Conditions
Meanlog(Aem )log(Aub )log(Aib )
k0
s

0
0
0
22670
0.319

Variance

Correlation withlog(Aem ) log(Aub ) log(Aib )

0.41
0.32
0.38
39810
0.466

1
0
0
0.210
0.267

1
0
0.079
-0.003

1
0.266
0.079

k0

1
0.202

Notes: We normalize the means of log abilities to be zero and assume that thecorrelations between abilities are zero. Education is treated as a continuous variable
here, where 0 equals high school graduates, 1 equals ordinary college graduates, and2 equals elite college graduates.

Table C9: Counterfactual: Average Ability and Wealth at Age 20 of Elite College Graduates
for Different Levels of Elite College Subsidies
Subsidy

Generalability

Incorporated
ability

Unincorporated
ability

Wealth at age
20

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1

0.782
0.771
0.727
0.683
0.661
0.654
0.648
0.632
0.631
0.629
0.628

0.022
0.051
0.085
0.119
0.148
0.172
0.208
0.241
0.270
0.311
0.342

-0.010-0.009-0.008-0.007-0.006-0.005-0.005-0.004-0.004-0.003-0.00377,75877,34976,952
76,67776,17575,84575,59675,09174,78774,35173,976

Notes: This table presents the average ability and initial wealth at age 20 for elite college graduates
under different level of elite college subsidies with adjusted admission rates. Average ability isnormalized to be zero. Initial wealth is in 2011 dollars.

14

0

0.2

0.4

0.6

0.8

1.0

Figure C1: Career Choice by Age

25

30

35

40
age

employee data
unincorporated data
incorporated data

45

50

55

employee model
unincorporated model
incorporated model

0

50000

100000150000Figure C2: Average Income by Career and Age

25

30

35

40
age

employee data
unincorporated data
incorporated data

15

45

50

employee model
unincorporated model
incorporated model

55

Figure C3: Counterfactual: Subsidy to Elite/ordinary College Students (Partial Equilibrium)
fraction of non-elite-college graduates

.25

.05

.1

.3

.15

.35

.2

.4

.25

fraction of elite-college graduates

0

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

0

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

fraction of other self-employed.055 .06 .065 .07 .075

.127 .128 .129 .13 .131 .132

fraction of entrepreneurs

.1

0

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

0

elite college subsidy

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

non-elite college subsidy

Figure C4: Counterfactual: Subsidy to Elite/ordinary College Students (Cont’d, Partial Equilibrium)

10.5
10.3

36.6

10.2

36.8

37

10.4

37.2

150
140
130
120
110

duration of entrepreneurhsip
10.6

age of first entrepreneurship37.4

entrepreneur income (in K)

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

IGT income elasticity

welfareincome gini
.515
.51
.505
.5
.495

1

.36

1.05

.37

1.1

.38

1.15

.39

1.2

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

elite college subsidy

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

non-elite college subsidy

16

Figure C5: Counterfactual: Subsidy to Elite/ordinary College Students (Adjusted for Admission Rate, Partial Equilibrium)
fraction of non-elite-college graduates

.3

.35

.4

.053 .054 .055 .056 .057

fraction of elite-college graduates

0

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

0

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

fraction of other self-employed

.055

.06

.065

.07

.127 .128 .129 .13 .131 .132

fraction of entrepreneurs

.1

0

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

0

elite college subsidy

.1

.2

.3

.4 .5 .6
subsidy rate

.7

.8

.9

1

non-elite college subsidy

Figure C6: Counterfactual: Subsidy to Elite/ordinary College Students (Cont’d, Adjusted for
Admission Rate, Partial Equilibrium)

10.4
10.2

114

36.9

37

10.3

122
120
118
116

duration of entrepreneurhsip
10.5

age of first entrepreneurship37.1 37.2 37.3 37.4

entrepreneur income (in K)

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

IGT income elasticity

welfareincome gini
.5
.499
.497
.496

1

.36

.37

1.05

.498

.38

1.1

.39

1.15

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

elite college subsidy

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
subsidy rate

non-elite college subsidy

17

Figure C7: Counterfactual: Subsidy to Incorporated/unincorporated Business (Partial Equilibrium)

0

0

.1

.2

.1

.3

.4

.5

fraction of non-elite-college graduates.2

fraction of elite-college graduates

0

10000

20000 30000
subsidy

40000

50000

0

20000 30000
subsidy

40000

50000

fraction of other self-employed

.12

.14

.16

.05 .055 .06 .065 .07 .075

.18

fraction of entrepreneurs

10000

0

10000

20000 30000
subsidy

40000

50000

incorporated business subsidy

0

10000

20000 30000
subsidy

40000

50000

unincorporated business subsidy

Figure C8: Counterfactual: Subsidy to Incorporated/unincorporated Business (Cont’d, Partial
Equilibrium)age of first entrepreneurship

10

duration of entrepreneurhsip

01000020000300004000050000
subsidy

7

34

95

35

100

8

105

36

9

110

37

115

38

entrepreneur income (in K)

00

welfare10000 20000 30000 40000 50000
subsidy

income gini

.505
.5
.495

1

.38

.385

1.02

.39

1.04

.395

1.06

.4

.51

1.08

IGT income elasticity

10000 20000 30000 40000 50000
subsidy01000020000300004000050000
subsidy01000020000300004000050000
subsidy

incorporated business subsidy

01000020000300004000050000
subsidy

unincorporated business subsidy

18