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EARNINGS OF SELF-EMPLOYED WORKERS AND
PEER EFFECTS AMONG TEENAGERS

presented by

Daiji Kawaguchi

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EARNINGS OF SELF-EMPLOYED WORKERS AND PEER. EFFECTS A.\IONG
TEENAGERS

By

Daiji Kawaguchi

A DISSERTATION

Submitted to
Michigan State University
in partial f1.11fillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Economics

2002

ABSTRACT
EARNINGS OF SELF-EMPLOYED WORKERS AND PEER. EFFECTS AMONG
TEENAGERS
Bv

v

Daiji Kawaguchi

This dissertation contains three essays in applied microeconoinics.

Chapter 1 revisits the empirical results of Lazear and Moore [Edward Lazear and
John Moore (1984) “Incentive, Productivity, and Contract” Quarterly Journal of Eco-
nomics 99]. That paper found that empirical experierice-earnings profiles were flatter for
self-emplrwed workers and argued that. this supported the Lazear contract theory that
claim firms use life-cycle backloaded payment systems to work around principal-agent
problems between firms and workers. This chapter reproduces the Lazear and Moore
result on more modern data, but argues for an alternative interpretation. In particular,
this chapter argues that. self—employed workers face more wage variation but enjoy a
higher return for human capital. A model based on these assumptions can produce flat—
ter experience-earlrings profile. since selflemployed workers start their career with more
human capital and due to opportunity cost, they invest less in human capital on the
job. The chapter develops implications of the model not found in the Lazear contract
model and concludes by developing support for these implications.

Chapter 2 attempts to explain the lower earnings among self-employed workers found
by Hamilton [Barton Hamilton (2000) "Does entrepreneurs]rip pay? An Empirical Anal-
ysis of the Return of Self-Employment” Journal of Political Economy 108]. That paper
found 20% lower ez-u‘nings of self-employed workers with 10 years of business tenure
than comparable salaried workers with 10 years of job tenure. This difference in earn-
ings can in principal be explained by the cmnpensating wage differential theory when
self—en’iployed jobs have attractive uon-numetary aspects. Using the National Longi-

tudinal Survey Youth 79 (NLSY'TQ), this chapter tests whether self-employment is as-

sociated with higher global job satisfaction. By looking at changes in job satisfaction
for individuals over time. I overcome the difficulty of interpreting differences in sub jec-
tive job satisfaction scores across individuals that cross—sectional analysis would require.
Using my estimates, I calculate the monetary value of the non-monetary aspects of self-
employment and find that. one dollar earned while a self-employed worker is equivalent
to as much as three to four dollars earned as a salary or wage worker. Although the
valuation is surprisingly high but the the, directimi of the estimate is consistent with
the compensating wage differential hypothesis. Although job satisfaction is a partial
component of workers" total utility. the value of self-employment in terms of job sat-
isfaction is sufficiently high to support the compensating differential hypothesis as an
explanation for lower earnings among self-employed workers. I also evaluate several
other explanations for the surprisingly high valuation of self-employment.

Chapter 3 attempts to estimate peer effects on substance usage among teenagers.
This chapter first summarizes the problems in the identification of peer effects. The
existence of unobserved characteristics of individuals and endogenous sorting into refer-
ence groups based on unobserved characteristics causes problems in the identification of
peer effects. The solutions for this problem are: 1. To control “imobservaliile” through
including plenty of explanatory variables using rich data set. or using sibling method to
difference out nnol')serval)le. 2. To use natural experimental situation in which reference
group is assigned randomly. 3. To use economic theory to get a prediction that arises
only from peer effect. but. not from contextual or correlated effect. In this (’rl‘iapter, the
method 1 was taken. Significant peer effects were found on substance usage among

teenagers.

ACKNOWLEDGEMENTS

I would first like to thank Professor David Neumark. the chairperson of my disserta-
tion committee. Before taking his class, I was womlering if I can study American labor
markets as well as American researclmrs because of my lack of experience in American
society. When I expressed this concern in a class presentation, he just recommended me
to look at data which is pulflicly avail-able. This open minded attitude of him toward the
study of American labor market largely removed my concern and, consequently, I used
American data for all three chapters in this dissertation to address several problems in
labor economics. He gave me critical connnents on my marmscripts and taught me how
to write papers. In addition, working as his research assistant was a great opportunity of
apprenticeship. He taught me lessons through "teaching by doing” that I am very grate-
ful. Professor Jeff Biddle kindly participate in my dissertation commitee and gave me
well thought comments on my manuscripts in very timely manner. He has been always
generous to take time to address my concerns. in particular, when Professor Nuemark
was on leave in the fourth year of my doctoral study. Professor Jeffrey “fooldridge also
kindly took part in my dissertation connnittee and took his time to discuss econometric
issues in my dissertation. His distinguished approach to econmnetrics enlightened me
and sparked my interest in applied econometrics. The third chapter of this dissertation
is the outgrowth of the term paper that I sul’n‘nitted for his applied econometrics class.
Without help of three members of my dissertation committee, I could never finish this
dissertation. Professor Charles Hadlock from Finance Department. also kindly took part
in the dissertation defense as an outside committee member.

I am also grateful to professors who taught classes and participated in my seminar
presentations. In particular, I would like to thank Professor Gerhard Glomm, Professor
John Strauss, and Professor John Godderris. Professor Glomm taught me neoclassical
economics starting from the definition of “Economy.” I especially learned a lot. through

writing a paper with him and Far-undo Sepulveda. Professm‘ Strauss and Professor God-

iv

derris regularly attended my seminar talk and gave me. comments and encouragements.

During four and half years of my doctoral study, I learned as much from friends as
from professors. In particular. the conversation with Scott Adams, Linda Bailey, Ali
Berker, Rehim Kilic, Jai—Min Lee. Zhehui Luo, Facundo Sepulveda. Jeongseok Song,
Chien-Ho Wan have stimulated my interest in many research fields of economics. I am
also very grateful to all their friendship off campus. I deeply appreciate all participants
of labor lunch workshop who kindly listen to my presentation and comment on my
work. All graduate students from Japan in both economics and agricultural economics
department. in particular, Hirokatsu Asano, Tatsushi Adachi, Kei Kajisa, Yoko Kijima,
Horoki Nakayama. Katsushi Okada. lx'yota Yamada. and Takashi Yamano have made
me feel at home in East Lansing.

My wife, Yukimi. has patiently understood and supported my study at Michigan
State mostly from Japan. The time I could live with her in Ann Arbor for one year
was the most delightful time during my stay in Michigan. I am very much indebted to
my parents for their support in various ways. My parents experience as self-employed
workers largely motivated my research interest in self-employment. I am very grateful
to Yukimi and my parents for their unconditional love to me. I hope my receipt, of PhD.

degree will give great relief for them as well as for me.

\f

Contents

1 Human capital accumulation of salaried and self-employed workers 1
1.1 Introduction .................................. 1

1.2 Replication of Lazear and Moore (198<l)’s results and the wage risk of

self- en'iployed workers ............................ 4

1.2.1 The model and estimation ...................... 4

1.2.2 The issues of measurement error ................... 6
1.3 The model ................................... 8
1.4 Supporting evidence of the model ...................... 11

1.4.1 SE workers have higher human capital ............... 11

1.4.2 Less human capital accumulation among SE workers ....... 12

1.4.3 Both winner and loser select SE? .................. 15
1.5 Conclusion ................................... 15

2 Compensating wage differentials among self-employed workers:

Evidence from job satisfaction index 17
2.1 Introduction .................................. 17
2.2 How Economists Have Used

Job Satisfaction Measures .......................... 21
2.3 Data and Lower Earnings among

Self—Employed Workers ............................ 22
2.4 Job Satisfaction Scores and Descriptive

Analysis .................................... 25

vi

2.5

Compensating Differentials among
Self-Employed \Vorkers ............................
Extensions ...................................

Conclusion ...................................

Peer effects on substance use

3.1
3.2
3.4

among American teenagers
Introduction ..................................
Reflection problem ..............................
The model with perceived peer behaviors ..................
Literature review ...............................
3.4.1 Identificatitm through proxy variable ................

3.6
3.7
3.8
3.9

3.4.2 Identification assuming the absence of the contextual effect. . . . .

3.4.3 Identification through variance covariance structure ........
3.4.4 Identification through dynamic structure ..............
3.4.5 Identificatitm through sibling method ................
3.4.6 Identification through economic theory ...............
Estimatitim ...................................
3.5.1 Data ...................................
3.5.2 The model ...............................
3.5.3 The results ..............................
The (almost) school fixed effect estimation .................
The household fixed effect. estimation using the sibling sample ......
Peer effects and the demogrz-mhie groups ..................
Conclusion ...................................

27
31
37

38
38

41

43
44
46
47
48
49
51
51
52

55
56
7

5

59

List of Tables

Table 1-1: The replication of the Lazear and Moore (1984)’s finding and the

risk of self-employed workers ......................... 66
Table 1-2: School Enrollment by Sectors (1988-1998) .............. 67
Table 1-3: Linear Probability Model of School Enrollment ........... 67

Table 1-4: Training Participation by Type of Training and Cost Bearing (1988-

1998) ...................................... 68
Table 1—5: Linear Probability Models of Training Participation ......... 69
Table 2-1: Sample Construction .......................... 71
Table 22: OLS Regression Coefficients ...................... 72
Table 2—3: Job Satisfaction among Workers .................... 73
Table 2-4: Probability to Change Job by the Next Interview .......... 74
Table 2—5: Job Change Probit. Model ....................... 74

Table 2-6: Transition of Job Satisfaction Associated with Job Change . . . . 75

Table 2-7: The Results of Ordered Probit Estimation .............. 76
Table 3—1: Sample Construction .......................... 77

Table 3-2: Descriptiw Statistics of Substance Use. and Subjective Measure of

Peer’s Behavior by Grade, .......................... 78
Table 3-3: OLS Estimates of Incidence of Substance Use ............ 79

Table 3-4: Incidence of Substance Use using within School Duplication Data. . 80

Table 3—5: Incidence of Substance Use using Sibling Data ............ 81
Table 3—6: ()LS Estimates of Incidence of Substance Use ............ 82

viii

List of Figures

Figure 1-1: Life Time Utility of Each Career Path and Initial Human Capital . 70

Figure 1-2: Distribution of Test Scores among SE and SW W'orkers ...... 70
Figure 3—1: Youth Substance Use. Age 12-17, Current Users .......... 83

ix

Chapter 1

Human capital accumulation of
salaried and self-employed workers

1.1 Introduction

\th‘ker’s wage growth with work experience is one of the most robust empirical findings
in economics. Lazear (1979) explained wage growth among salary / wage workers based
on principal—agent theory. In this model, workers’ behavior is not perfectly observed
and workers’ shirking is detected only by chance. According to Lazear, it is optimal
for an employer to pay his employee less than the worker’s marginal product when the
worker is young and more than the worker‘s marginal product when the worker is old.
This payment system discourages workers from shirking: The worker is fired when the
shirking is detected and thus cannot receive his money back.

Human capital theory also predicts wage growth with work experience because of
workers skill formation. Since Lazear's theory and human capital theory are not mu-
tually exclusive. it is very difficult to attribute the observed wage growth exclusively to
either model.

As an indirect. test of Lazear‘s theory, Lazear and Moore (1984) (LM hereafter) com-
pared the wage growth of salary / wage workers and self-en‘iployed workers. Since self-

eruployed workers do not. have. an incentive to shirk, the wage growth of self-employed
Workers can be attributed to the human capital accumulation. Assuming identical hu-

man capital accuunilation between salary / wage (SW) workers and self-elliployed (SE)

workers, the difference of wage growth between SW workers and SE workers can be at-
tributed to Lazear‘s theory. In fact, LM found a steeper wage-experience profile among
SW workers than SE workers and used the finding as evidence to support Lazear’s
theory.l

There are several other theories that explain wage growth on the job. Salop and Salop
(1976) proposed the model in which firms offer upward tilted wage profiles to screen
desirable workers for firms. In their model, workers have heterogeneous probability to
quit their job and this is 1.)rivate information of workers. It is costly for firms that
workers quit because of necessary training for new workers. In this situation, firms use
tilted up wage profile as a. screening device so that only workers with low probability
to quit apply for the job. According to their theory, self-employed workers have flatter
wage profile than those of salary / wage workers since self-employed workers do not. have
to utilize tilted up wage profile as a screening device. It is important to emphasize
that Salop and Salop's theory and Lazear‘s theory render the same prediction about the
difference between salary / wage and self-employed workers and, accordingly, we cannot
distinguish those two theories only looking at wage profiles of both jobs.

Jovanovic (1979) attempted to explain upward leping wage profile among workers
using the concept. of matching quality. In his theory, workers gradually learn the quality
of job match that. is specific to a worker and a. job‘, and workers who realize bad match
quit their job. As a. result of this self-selection, wage rises among job stayers. Workers
learn the quality of job match only gradually because they cannot distinguish wage
shock due to job match and idiosyncratic shock at. each moment. Using series of wage
realization, workers infer their job match quality. In this setting, average wage growth

1LM in fact estimated wage equation for each occupation and found positive correlation between the
present value of life time earnings and the slope of wage-experience profile among salary or wage workers
but not among self-employed workers. They interpreted this as evidence of productivity enhancing
effects of deferred payment system among salaried workers. However, the life time income was calculated
based on the estimated coefficients of the wage equation that includes experience term. so when slope
coefficient is nearly zero. which were the case for self-employed workers in many occupations in their
study. then the life time income is determined irrelevant of slope coefficient. Thus, the difference of
wage-experience profile is crucial to derive their results.

among job stayers depend how fast. the workers who realizes bad job match change their
job. As several studies indirectly show, self-employed workers tend to invest. in their
own business at the start up.2 If this investn'ient is sunk, then self-employed workers
who realize bad job match are less likely to change the job due to the income flow
generated from the sunk investment. In this sense. self-employed workers are “foot
fixed” workers while salary/ wage workers are "foot lose" workers. Thus Jovanovic’s
theory also potentially explains Lazear and Moore’s finding.

These discussions show the possibility that Lazear’s. Salop and Salops and Jo-
vanovic’s theory can }_)<_)tentie-tlly explain the observed flatter earnings profiles of self—
employed workers than salary / wage workers. Among the alternative explanations for
LM’s findings, the human capital theory is exclusively pursued in this paper since this
theory does not require any type of infm'mational ii‘nperfection. It would be most strik-
ing to obtain the theoretical prediction consistent with LM’s finding assuming perfect
il’lfOl‘IIlathI‘l. Accordingly, the purpose of this paper is to propose a model that. pre-
dicts the flatter earnings-tenure profile among SE workers based only on human capital
theory.

To develop this model, two crucial aspects of self-employment that. differentiate self-
employment from salary / wage job are C‘OIISidGl‘Bt‘l; firstly, the larger variation in income
compared with that experienced by SW workers, secondly, the higher return for human
capital than that of SW workers. Modelling these two characteristics of SE's wage
determination with workers“ risk aversion. workers’ optimal human capital investment
decision produces a steeper wage profile among SW workers, as LM observed. In this
model. the workers choose to be 8“" or SE workers in the first period. Since to be
SE is risky, the worker with high human capital selects SE because the higher return
for human capital compensates for the risk. Under the convexity assumption of human
(Ta-pita] production. workers with higher human capital invest less in their human capital

on the job due to higher opportunity cost. Thus SE workers, who have higher human

 

 

28cc Dunn and Holtz—Eakin (200(1) for example.

capital as a result of self—selection, tend to have a flatter wage profile compared with
SW workers because of less human capital investment on the job.

The structure of the rest of this paper is as follows. The second section examines
the wage risk for self-employed workers since the wage risk of self- employment is a
crucial assumption for the main conclusion. The third section introduces the model of
human capital accmnulation under the income risk. The fourth section provides evidence

consistent with the. model. The fifth section concludes.

1.2 Replication of Lazear and Moore (1984)’s re-
sults and the wage risk of self- employed work-

ers
1.2.1 The model and estimation

In this section, I replicate the results obtained in LM using a different data set and test
whether the wages of SE workers are more volatile than those of SW workers.
Data for the years 1985 through 1998 were taken from NLSY79. The sample is

restricted to white males and is used to estimate the model:
lIl If," 1‘ .X',,.31 + .328” + 8,‘{(‘Y,‘t — 113133; + C,‘ + U“, (1.1)

where tr“ is hourly wage rate.3 and X” is a vector of standard control variables in Mincer

type wage equation and X is a vector of sample mean of each explanatory variable,4 8a

 

3Hourly rate of pay is constructed by the Center for Human Resource Research (CHRR) based on
respondents’ usual earnings (inclusive of tips. overtime, and bonuses but before deductions) per hour,
day, week or month. Then this earnings is divided by the hours worked in the corresponding time
unit. Hours worked might be seriously measured with error among SE workers. Then the wage rate
among SE workers may be subject to larger measurement. error. However, the analysis in the next
section shows that measurement error that is correlated with SE status does not affect the conclusion
in this section. CHRR requests wages/salaries/tips income and business/firm income along with other
income categories from both wage / salary workers and self-employed workers. Thus there is less concern
that self-employed workers earnings contains capital income, in addition to labor income, which is the
main concern for the measurement of income among self-employed when CPS is used. Therefore the
n'ieasurement of earnings for self-employed workers in NLSY 79 is as good as those in SIPP that were
used in Hamilton (2000). Although Hamilton (2000) also used the earnings that include capital gain
as an alternative earnings measure of self-employed workers. I just focus on the labor earnings of
self—employed worker here.
4The variables included in the regression appear in Table 1-1. The variable names are self-

PX{)1 anatory.

is a dummy variable that takes one if the worker is self-employed and c. is individual
heterogeneity. The interaction with the self-employed dummy is included to replicate
LM‘s findings. The model is estimated by both pooled OLS and fixed effects assuming
E [u,—,|X,-. S), c,] = 0, where X) = [Xih Xirl. The estimation results appear in the first
and second column of Table 1—1. The return to experience among SE workers (about
7% for the first year) is almost 00% of SW workers’ (about 11%- for the first year).
In addition, there is almost no return to tenure among SE workers. while SW workers
enjoy a return of 3.2% for the first year of tenure. The. difference of hourly wage between
SW workers and SE workers are evaluated at. various points of experience and tenure.
Although SE workers enjoy SE premium when they start. off their business, but this
SE premium declines due to lower return to experience and tenure. The lower return
to tenure among SE might be because of the Lazear contract or less on-the—job human
capital investn'ient by SE workers. but. lower return to general work experience among
SE worker can be explained by less human capital investment among SE workers but
not by the Lazear theory since it only explains the higher return to tenure among SW
workers due to in‘iplicit incentive contract. Similarly, Salop and Salop"s theory cannot
explain lower return to experience among self-employed workers since the theory only
explains the return to tenure. Moreover. even if self-en‘iployed workers are “locked" in
their jobs due to the investment made at. their business start up. Jovanovic’s theory
explains lower return to business tenure but. does not explain lower return to experience
among self-employed workers. These results were not found in LM since they only used
potential experience as an explanatory variable but did not use tenure because of data
limitation. In sum, SE workers earn higher wages and experience less wage growth.

Using the residual of the previous fired (afl'ects estimation as the dependent variable,
.\ ‘2 ‘ , '
“u = “in + 7'15”. + (1,; + tit. (12)

is estimated.” Since we are interested in the wage variation faced by an individual, I

 

”Essentially this is Breush-Pagan‘s test for heteroscedasticity.

used residuals from fiJ’t—‘fll cflects wage regression to create a measure of wage variation.
The model (1.2) is estimated by both OLS and fixed effects considering the possibility
that workers with high volatility due to intrinsic reason self-selects into SE. We are
interested in if 71 = 0. The results of the OLS and fixed effects estimation appear in
Table 1-1. Column (3) and (4) By using the results of the OLS regression, we can say
that the conditional wage variance among SE workers is about 2.6 times larger than
that of the salaried w<girkers. Also. the results are essentially the same for both OLS and
fixed-effects. From this evidence. we conclude that being self-employed is riskier than

being a SW" worker.
1.2.2 The issues of measurement error

There is a possible flaw in the previous analysis due to measurement. error in the wages
of self-employed workers. Smaller return to experience and tenure among SE workers
may be due to measurement error in the wages of SE that are systematically correlated
with experience or tenure. It is also likely that larger conditional variance of wages
among SE workers is due to measurement error. Joulfaian and Rider (1998) report
that SE workers underreport their income by 18% on average, using the Tax Payer
Compliance Measurement Program data collected by the IRS. Although respondents
have no incentive to avoid taxation by umlerreporting their income in the case of NLSY,
underreporting is still possible because respondents may refer to their tax forms to report
their income.6 The follmving analysis explicitly discuss the issue of measurement error.

The model with measurement error is following:
lll (1‘; = ‘Xugl + (0)28" + Sip(.Y,‘t — (Y)33 + C,‘ ‘i‘ 6’”. (1.3)

In 11.3, = 1n tiff, + (I, + bis” + u”. (1.4)

 

“In addition to the nuaasurement error due to under reporting. measurement. error of hourly rate of
pay can be introduced through the division of (daily, weekly or monthly) earnings by hours worked in
corresponding period. Since hours of work may be more erroneous among SE workers. the measurement
error in hourly rate of pay can be correlated with SE- dummy. However. argument in this section carries
over if the individual tendency to nus-report hours worked is constant while a worker is in the same
type of job.

where d, is an individual specific tendency to misreport regardless of employment status
and b, is an individual specific tendency to misreport wages when the respondent is self-
ernployed. The assun’iption that these individual tendencies are time-invariant is cr1.1cial
in the following discussion. These tendencies (3);. (1;) and errors in equations (en, rm) are
assumed to be independent. The assumptions on error terms. E [eulba ci. (1,, X ,:. 3,] = 0,
E[u,~,|bi. (Ii, (1,. X,, 5,] = 0 and E[e',-2,)b,:. c,-. (1,. X1. St] 2 (1+ 33,.)03 are assumed. To test if
st~>lf-employed workers face the larger risk, we are interested in if (p = 0. The model that

is actually estimated is
In 11'), = Xm31 + 13-3.», + SMXI': — X);33 + c, + c” + (31 + bis” + 21,}. (1.5)
Applying the fixed effects transforination, we obtain
In 113;, —— Iii—w,- = (Xi, —- X1331 + 32(5): —— 5,) + (8”le —fi).;33 + cit + b,(s,;, —.§,,:) +11” (1.6)

In this situation. the fixed effects estimator is not a consistent estimator since j.)li1n."32 =
.32 + b, wl‘iere b : 13(1),). Thus the fixed effects estimator of :32 estimates the lower bound
of ,3? given I) < 0.

Another possible. measurement. error arises from the differential concept. on the in-
come between SW’ workers and SE workers. SE workers may report return for physical
capital of their own business as their wages: they also may subtract the cost of physical
investment in their own business from their wages. It is likely that SE workers invest
in physical capital when they start their businesses and collect the return later. Then
the wages of short. temu'ed SE workers are understated and the wages of long tenured
SE workers are overstated. Due to this measurement error, the return to tenure among
SE workers may be overestimated. Regardless of this possilnlity of upward bias, the
estimated return to tenure among SE workers are almost zero.

The conditional variam':e of measurement error may also depend on self—employn‘rent
status through the effect of bi. Define the error term in (1.6) as he 2 e“ +u,~, +1),(s,t —.§,—).
Then

Elllfr X7381] = (1+ 05‘”)fo + (7?, + (52'! —” giyElbflxri‘ 8'3”

 

A
.—l
“J

V

q
I

The last term tells us that. the job status changer tends to have a larger variance. As-
smning E[I)?|X,:. 5,] = (7,3). regressing the residual of fixed effects wage equation on Si,
and (st, — .97.)"2 renders consistent estimators of e) and 03. The OLS and fixed effects
estimates appear in column (5) and (6) of Table 1-1. Although the estimate of go dimin—
ishes slightly. it is still large and statistically significant. Therefore, we still conclude

that. being a self-employed worker is riskier than being a wage-salary worker.

1.3 The model

Let us suppose that. each worker lives for two periods and that each worker is endowed
with one unit of time for each period. Each worker knows his ability in the first period.

Each worker has the following preference with constant absolute risk aversion:

[/11 = " (“Pl-'iifu'n + “321] (1-8)
where on is the degree of absolute risk aversion of worker 1'. and Hf” is the wage offer for
worker i at time t. The wage offer depends on job choice, meaning whether the worker

is self-enmloyed or salaried worker. The wage offer for job j is
“’in =()J'(1— n.,:,)/1,-, + 6’)”, €in N 1V“). Uf)f01']= SE, SH], (1.9)

where 11., is the human capital of worker i. at period t, 11., E [0.1] is the portion of
time devoted to the human capital accumulation by the worker i at time t. The initial
human capital hi1 is given as an endowment for each worker and includes human capital
accumulated through education and innate ability.7 The human capital for both periods
is assumed to be general across the jobs. The parameter bi: which is exogenously given
by the labor market for the workers. is the unit price of human capital in job j. The
random variable c,”- is a shock to the wage. This model assumes e,“- is independently

distributed across individual, time. and two jobs. Taking expectation of the life-time

 

‘While recognizing the emlogeneity of the educational decision. I have treated this as given since
the main interest of this analysis is on-the-job human capital accumulation.

utility and using the ordinal property of utility function,

ELY =()J'(1— Ili1)}l,‘1—(’7,/2)0’j+()j(1—n,'2)}lig ‘— (Ali/2)”? (1.10)

J

is obtained}8 Each worker has access to the following human capital accumulation
teclimiilogy:

hi? :1?” + (5(Hl'1hl'1)o.0 E (0.1), (1.11)

where 6 is efficiency of human capital investment on human capital accumulz-ition.9 The
parameter 0 represents a workers learning ability. which is assumed to be identical for
all workers. This technology shows that the worker with higher human capital is more
productive in the production of additional human capital, but the effect diminishes,
since a E (0.1).

Two assumptions are made that distinguish SW and SE workers.

. I .) . , . .
AssumpMm 1 (73).); > (raw. 1.e.. the wages of SE workers are more volatile than those of
8“" workers. The empirical evidence supports this assumption as seen in the previous

section.

Assumption. 2 by; > (my. i.e., the return for human capital is higher for self-employed
workers. This assumption is justified by the higher returns to education among SE
workers than SW workers in previous study that utilize Census data.10 For example,
Fairlie and Meyer (1990) found 0.90 as the return to education among SE workers while
they found 0.59 among S“? workers. The data used in this study does not indicate
higher return to education. though.

Under these assumptions, each worker maximizes his lifetime expected utility (1.10)

. . ') . .
by choosing nil and a. career path ( {J};:1) under the constraint of human capital accu-

 

6See Appendix for derivation.

9This functional form of human capital accumulation is standard in the literature. See llecknian
(1976).

U’See Borjas and Bronars (1989) and Fairlie and Meyer (1996). In particular Fairlie and Meyer (1990)
used 1990 Census Public Use Blicrodata 5-percent sample that contains 11881 SE sample and 100-314
SW sample.

mulation technology (1.11).” Since indirect. utility from the career path of SE—SW’ is
always dominated by the indirect utility from other career 1.)athes. we should consider
three possible career paths of SE - SE. S\-\" - SW, and 8“" - SE.

The optimal human capital investment time, n”, is

(in I h—l. for '01) stayers.
I)” Z (I) ) ll 1 _1 J ~ (1.12)
(“fin )1 ~ (do)l “h,1 , for job changers

These solutions show the human capital investment time decreases in the level of initial
- ')
human capital.”
By substituting the optimal 11,1 in the objective function of each career path, we

obtain the following indirect utility functions for each career path for each individual i.

l

. -4 _e_ _1_ . .
l‘j—jU’il-AH) 212(0le —",','(Tj-2+bj()‘ ” (01"0 —(.11 “) for] =3 SE.’SH. (1.13)
l'su'—s1«:(hz‘1~ 7:) = (bsu' +bs‘F31hil " (Vi/21(Ui'u' + (7.31?)
'_” ; n
+b;,,«~b;.,;6n—‘n(afi — 0+») (1.14)

These expressions tell us that the choice of career path depends on each worker's level of
initial human capital and the degree of risk aversion. The relationship between lifetime
utility for each career path and initial human capital is graphed in Figure 1-1 given the
degree of risk aversion. This graph shows the lifetime utility of being a SE worker is
higher than being a SW" worker for the high human capital worker. In addition. the
graph shows that. the worker with a “medium" level of human capital switches jobs in
the middle of his career.

The worker with higher initial human capital selects self-em})loyment. This selection

affect the wage growth of SE and SW workers. The "average" wage growth for each

 

11As a result of optimization. 71,2 = 0 is trivially chosen.

1“’There are two factors related to the initial human capital level and human capital investnu-‘nt. First.
from (1.11) the worker with higher human capital is more 1')roductive in human capital accunnilation.
Hmvever. this effect is diminishing becmrse the term (”uh”) is exponentiated by a E (0.1). Second.
the worker with higher human capital pays more opportunity costs of human capital investment, which
is bjnnhn. The marginal benefit of investment diminishes in hn but the marginal cost is constant
in lid: thus the worker with high [1,1 chooses lower ”,1. The convexity of human capital production
function is the crucial assumption to derive this result.

10

career path is, 1“

 

 

 

Eu,” h” +61/(1—a)na/(1—0) . r
9]-]- — EMU —- hi1_ (SI/(l—HJQI/'(1~n) for j —— SE,SH, (1.15)
. ~ _ Ewes»: _ bse ha + (ll/(1‘0)((bSE/bsn')0')a/(1_0) . (1 16)
9511—.8E —EU‘15-'u' b3”, h” _ (51/(1—0)((bSE/bsw)a)l/(l—Q)- .

If the initial level of human capital is given, the wage profiles of the SE and the SW
workers are identical and it ('lecreases as hi1 increases and converges to one. However,
what. we observe is (£575-51; < ggu-_5u- because the SE worker’s 12,-1 is higher than the
SW worker’s. Thus the observed difference between wage profiles is the result of the

workers" heterogeneity in the initial level of human capital.

1.4 Supporting evidence of the model

1.4.1 SE workers have higher human capital

The theory discussed in the previous section predicts that the worker with higher human
capital selects self-em})loyment. With respect. to the observable characteristics. several
studies report that the worker with high education is more likely to be self—employed.14

It is a stylized fact that the worker who has a self-employed father is more likely to
be a SE worker, even after controlling for inlierita.nce.15 In particular, Dunn and Holtz-
Eakin (2000) emphasize the importance of the intergenerational transmission of human
capital rather than the mitigated liquidity constraint to explain this finding, since they
found a very large effect of the parent's self-employment status on the son's selection
into self-ernployment even after controlling the amount. of the parents” assets. They
also found that the son of a successful self-employed worker is likely to be self-employed.
From this finding. they conclude that the transmission of human capital is the important.

channel to explain the i1itergenerational correlation of self-employnient status. Although

13Although the expected value of a ratio is not a ratio of expected values. this measure gives us a
rough idea.

14See Borjas and Bronars (1989) and Evans and Leighton (1989)

1“SSee Lindh and ()hlsson (1.996) Blanchflowm‘ and Oswald (1998). ”out and Rosen (1999) and Dunn
and Holtz-Eakin (2000). By controlling inheritz‘ince. the researchers try to partial out the effect of
liquidity constraint.

ll

their findings may imply the transmission of human capital that is specific to the SE,
the theoretical discussion made in this paper still holds since the worker with "any kind
of” higher human capital experiences lower wage growth. These results support the
prediction of the model presented here: Self-employed workers experience lower wage

growth because of higher level of initial human capital.

1.4.2 Less human capital accumulation among SE workers

The model predicts less human capital accumulation among SE workers. This predic-
tion can be directly tested through the comparison of recorded human capital investment
behavior by SE and SW workers. School attendance or participation to training while
working are good measure of human capital investment. The i’LSY79 records the par-
ticipation to training in a consistent way after 1988 survey, thus analysis sample in this
subsection is restrictml after 1988. This sample restriction reduced the sample size to
17825 for school enrollment analysis and 17818 for training participation analysis. This
restriction also largely excludes college students who have part-time job since respon-
dents are at least age ‘21 in 1988.

Firstly, school enrollment while working is analyzed. Table 1-2 tabulates any school
enrollment since last interview by the types of job. While 9 percent of SW workers
enrolled in school between last and current interview, only less than 6 percent of SE
workers enrolled. To control the difference in the characteristics of SW and SE workers,
school enrollment was regressed on self-enipltwment dummy as well as observed char-
acteristics by OLS. Fixed effects linear probability model was also estimated to deal
with individual hcterogeneity. Both ()LS and FE results, which appear in Table 1-3,
indicate that SE workers are about. 2 percentage points less likely to enroll in school
while average enrollment rate is about 8.7 percent.

Secondly, participation to training is analyzed. The participation to training is not
straiglitforwardly cmnparable between SE and SW workers since some training that

takes place in SE sector may not be recognized just because it does not take the form

12

of formal training. To work around this proli)lem, Table 1-4 Panel A breaks the partici—
pation to training into types of training that workers participate. On-the—Job—Training
("Apprenticeship Program," “Formal Company Training Run by Employer or Military
Training" or “Seminars or Training Programs at Work Not Run by Employer”) may
not be conmaralfle between SE and SW workers since those training programs are less
likely to exist. in SE sector in formal fashion and consequently actual participation to
comparable inftn'n‘ial In'ograms by SE workers may be Inisclassified as non participa-
tion. 011 the other hand. the participation to Off-the—Jol) Training (“Business School,”
“Vocational or Technical Institute.” "Correspondence Course,” "Seminars or Training
Programs outside of \\-"()rk" and “Vbcational Rehabilitation Center”) is comparable be-
tween SE and SW workers since the definition of these training programs are presumably
identical across SE and SW workers. Table 1-1 Panel A shows that 18.91 percent of SW
workers participate in training while 9.21 percent of SE workers participate in training.
Restricting our interest to Off-the-Job Training (Off-JT hereafter) renders the same
picture, although the difference is largely reduced. While 6.20 percent of SW work-
ers participate in Off-JT, 5.00 1_)ercent of SE workers participate in Off-JT. To control
the difference in observed characteristics between SE and SW workers, liner probability
models with and without. fixed effects are estimated. The OLS result. that appears in
Column (1) of Table 1-5 indicates that SE workers are 9.7 percent points less likely to
participate in training. while FE result that appears in Column (2) of Table 1-5 indi-
cates that SE workers are 5.0 percent points less likely to participate in training. As for
Off—JT, OLS result. that appears in Column (3) of Table 1-5 indicates that SE workers
are 1.2 percent points less likely to participate in Off-JT while FE result (Column (1) of
Table 1-5) indicates 1.0 percent points reduction. All of the results above indicate that
SE workers are less likely to participate in training and accordingly invest less in their
human capital on the job.

The model also predicts that 8“" workers who become SE in the second period invest

more in their general human capital in the first period because of lower opportunity

13

cost of human capital investment and higher return to human capital. To test this
theoretical predicticm. using SW workers as sample, the participation to training is
regressed on future SE dummy and other controls by OLS.l6 The result of estimation
appears in Column (5) in Table. 1—5 but. this result indicates that future SE workers
are less likely to participate in training in general. This result does not necessarily
contradict with the theory since SW" workers who become SE in the future may invest.
less in firm specific human capital. Thus it is important. to disentangle investment in
general human capital from investment in firm specific human capital. Ideally, general
human capital investment should be regressed on future SE dummy and other controls.
As an attempt. to implement this idea. the participation to Off-JT or training whose
cost is paid by workers or government are regressed on future SE dummy and controls.
In particular. the infm‘ination who heard the cost of training is important. since Becker’s
traditional theory predicts that. training that endows workers with general human capital
is paid by workers while the cost of investment in firm specific human capital is shared
by both workers and firms. Although training whose direct cost is paid by employer
might be excrrtually paid by workers through lower wage, training whose direct cost, is
paid by employee or government bounds the minimal set of training that presumably
endows workers with general human capital. The results of regressions of Off-JT and
own/goverment paid training participation on future SE dummy and other variables
appear in Column (6) and (7) of Table 1—5. The participation to Off-JT does not differ
practically among SW workers who stay in firm and who become SE in the future.
However. future SE workers are 1.1 percent more likely to participate in the training
whose cost is paid by his / her own or government. Considering only 3.3 percent of SW’
workers participate in own / government paid training. the difference is large. This result
imply that prospective SE workers invests more in general (portable) human capital

while they are SW workers.

 

1“Future SE dummy varies within individual for those who switch to SE and switch back to SW but
no prospect to be SE again. Since identification based on those observations are. weak. fixed effects
estimation is not illl})lt'lll(_‘llt(’(l.

1 1

1.4.3 Both Winner and loser select SE?

The model predicts a very simple selection rule: The worker with high human capital
selects SE. A sensible criticism for this prediction is that there are two kinds of SE
workers. The first. kind is an eligible entrepreneur and the second kind is the SE worker
who is not. qualified to work for a. firm and is forced to work by himself. If this story
describes the real world. it is not surprising that the latter group experiences less wage
growth since the less eligible worker has less learning ability and experiences less wage
growth.

To examine this possibility. I studied the distribution of ability among self-eiiipltjiyed
and wage—salary workers. If there is a. “two tail selection rule" among SE workers, we
should find bimodal distribution of ability among SE workers that have two peaks at.
high and low ability. As a. proxy for the ability I used the AF QT89 (Armed Force
Qualifying Test) score that is contained in NLSY 79. The result. of the kernel density
estimation of the. distribution of test scores for the SE and SW workers appears in Figure
1—2. Comparing the two distributions. we find bimodal distrilmtions among SW workers
and SE workers. However SW workers have peaks on high sores and low scores but SE
workers have perks on high scores and medium scores . This evidence shows that “two

tail selection rule" is more likely among 8“" workers than among SE workers.

1.5 Conclusion

In this paper. the human capital accumulation by self-employed (SE) and salaried and
wage (SW) workers was ans-ilyzed. Under the assumptions that the wages of SE workers
are more volatile than salaried workers and the wages of SE workers more sharply reflect
their human capital. SE workers invest less in their human capital because of their higher
initial human capital. This ('lifference in human capital investment behavior results in
a flatter wage profile for SE than SW workers. This theory was indirectly supported by

the empirical facts about self-employed workers. In particular. the empirical evidence

15

shows that the SW workers with future SE prospect. accumulate more general human
capital on the job and this evidence is consistent with the model developed in this paper.

The model shows that the Self-(‘1Il[)l()_\'(.?('l workers are not necessarily a good "control”
group to test the Lazear contract. since not only does the incentive effect of the Lazear
contract produce the steeper wage profile of salaried workers. but the difference in human
capital investment has this effect as well. This conclusion does not. deny the existence
of the Lazear contract. nor the results produced by Lazear and Moore (1984). However,
simply attributing the difference of wage profiles to the incentive effect of the Lazear
contract may overt-istimate its importance.

The full-blown test of the theory introduced in this paper is left. for future research.
Appendix

The derivation of (1.10) is as follows:
Eff” : //—expl—7,-(Ir,-1 + 11'.2)](1F(c',-1}-)(IG'(c*i-_)J-)
= ’/9Xl)l7i(f’J‘/H1(1“’ln)+ €iji)l(/F((’U‘1)
-/exp[e,i(bJ/2,~2(1— r1i2)+ eij2)](lG(c,-jg)
= — CX])[—‘},;(f)J-(1 — 11.1)12” — (“H/2M;
+bjhi‘2 —' (’i‘i/‘Zlf’fll.

The independence of error terms across periods derives the second line and the property

0f 10g normal distribution produces the third line. When ln.r ~ .n\¥'(‘172..s‘2). it. is known

that. Er = exp(m + (fl/‘2).s2). In our case, e = 1111' ~ ;\’(Ilz..52) thus Eexp(e) 2 E1:

E‘XD( m + (1/2)82). The ordinal property of utility fimction results in (1.10).

16

Chapter 2

Compensating wage differentials
among self-employed workers:
Evidence from job satisfaction index

2. 1 Introduction

Self-employed wcn‘kers comprised 10.5% of the total US. workforce in March 1996.1
Despite this large share of self-employed workers. self-employment has not attracted
much attention among labor economists until recently. and the workings of the labor
market among self-employed workers are still largely unknown. One of the remaining
puzzles about self-employed workers is their lower earnings, which this paper attempts
to explain with compensating wage differential theory. The compensating differential
theory predicts l(_)wer earnings among self-en'iployed workers when non-earnings aspects
of self-employed job positively affect workers” utility. This paper directly tests the
theory using job satisfaction scores available in the National Longitudinal Survey Youth
79 (NLSY79).

Several studies report lower earnings among self-employed workers as compared with

their salaried and wage-earning counterparts.2 For example. Hamilton (2000) found

 

lFor recent trends of self—employment in the U.S.. refer to Mauser and Picot (1999).

2The analysis in the previous chapter described self-employed sector as the sector where the return
to human capital is higher and consequently workers with higher human capital self-select into self-
en‘iployment. In total. self-employed sector was described as better paid sector. At first glance, this
chapter and the previous chapter may look contradicting. but if the utility from non-monetary aspects
of self-employed job is introduced in the model of the previous chapter. then discussion in the previous
chapter still carry over but we may observe lower earnings among self-employed wm'kers. This can be

17

that self-employed workers earn less than salary / wage workers with similar. observable
qualifications. using several measures of self-employed workers’ earnings available in the
Survey of Income and Program Participation (SIPP) 1984 panel. These lower earnings
are mainly due to a lower growth in earnings among self—employed workers. According to
Hamilton’s findings. on average. self-employed workers with 10 years of business tenure
earn 19% less than salary/ wage workers with the same amount of work experience.
Lower earnings growth among self—employed workers was also found by Lazear and
Moore (1984). Krashinski (2000) found 10 to 30% lower median earnings among self-
employed workers. except for college graduates. after controlling for observable workers’
characteristics using CPS files over the period of 1979 - 1992. Carrington. McCue. and
Pierce (1996) also found similar earning differences using the Current Population Survey
(CPS) March files between 1967 and 1992.

These observed lower median and mean earnings among self-employed workers are
rather puzzling. llC)VV'(‘V€l‘. considering the self—employed workers labor income risk as
reported by Carrington. McCue. and Pierce (1996).3 They found that self-employed
workers labor earnings are three times as sensitive to macro aggregates as salary / wage
workers’ and concluded that the labor earnings of self—employed workers are much more
pro—cyclical to the business cycle. In addition. self—employed workers tend to use their
own assets as capital for their own businesses due to liquidity constraints (Evans and
Jovanovic ( 1989)) and. as a result. their labor earnings and asset income tend to co—move.
This co—movement nrakes it difficult for self-employed workers to insure their future
consumption, compared with wage / salary workers who can insure this by saving in a
safe asset. In addition. I\'Ioskowitz and Vissing-Jorgensen (2001) showed self-employed

workers tend to invest a large portion of their assets in their own businesses. As a. result.

 

easily seen from Figure 1-1. If noanonetary aspects of self-employment generate utility. then indirect
utility of SE - SE or SW - SE career from monetary income shifts down while total indirect utility stay
constant. This shift may create lower average earnings among self-employed workers.

3Labor income risk among self—en‘iployed workers is intuitively appealing. and consequently several
theoretical papers employ it as an assumption that characterizes self-employed jobs. See Kililstrom and
Laffont (1979) and Kanbur (1982) for example.

18

self-employed workers" portfolios are riskier than those of salary/ wage workers, whose
assets are invested in more diversified funds. Despite this risk. the average return on
portfolios held by self-employed workers is almost equivalent to the average return on
portfolios held by salary/ wage workers. To compensate for this total income risk. at
least at the first glance. the average of earnings for self-employed workers should be
higher than that for salary / wage workers. In addition to income risks, self-employed
workers enjoy fewer fringe benefits. such as employer-provided health insurance. than
salary / wage workers. as pointed out. by Hamilton (2000). Considering the negative
aspects of self-emplovmeiit. the lower earnings of self-employed workers is a puzzle.
\Vorkers' negative self—selection into self—employment is one possible explanation for
these observed lower earnings. If workers with negative unobserved characteristics self-
select into self-enmloyrnent. the lower earnings observed among self-employed workers
may be due to these negative traits. To evaluate this possibility, Hamilton (2000)
compared the earnings of two groups of salary / wage workers during a two year period;
the first group consisted of workers who became self-employed workers in the second year,
and the second group consisted of workers who continued to be salary/ wage workers.
He did not find any significant (Llifference in salary / wage earnings for these two groups
and concluded that self-selection does not explain lower earnings among self-employed
workers. Krashinski (2000) did the same exercise using matched CPS data for 20 years
and found no evidence 0 positive or negative selection into self-employment.“1 Borjas
and Bronars (1989) even found positive self-selection into self-employment among white
”13188. using a Heckman-style. self-selection correction method.‘5 To summarize. previous
reSearch shows that negative self-selection into self-employment does not explain the

.___4\
I'{I‘ashinski (2000) used this finding as evidence that self-employed workers are a good control

group for testing the institutional hypothesis to explain wage inequality during the 1980s and 90s
among salary / wage workers because self-employed workers are relatively free from institutional factors
Snell.) as minimum wage restrictions and labor union involvement.

QThe SMSA-level aggregate labor market conditions are used as instruments in the first. stage se-
ecti011 equation. The variables are unemployment rate. population growth rate. crime rate. the level
Pf local government expenditurt—x and the mean of income and education level. These varial')les are
dSSimred to affect the labor market mobility but do not influence the determination of earnings.

19

lower earnings of self-employed workers.

Hamilton (2000) speculated the compensating differential as an alternative expla-
nation. He claimed that self-employed workers enjoy non-earnings aspects of self-
employment. such as being their own boss. and. accordingly. they accept lower earn-
ings. although his claim was not. supported by direct empirical evidence. In one sig-
nificant study. Blanchflower and Oswald (1998) tested compensating differentials for
self-employed workers using the job/ life satisfaction variable available in British Na-
tional Child Development Survey. Using cross section data from 1981 and 1991. they
found that self-employed workers are more satisfied with their job/life. than salary / wage
wm‘kers. However. as authors admitted in their paper. there is a possibility that "self-
employed people may be intrinsically more optimistic.” and higher job/life satisfaction
among self-employed workers might be due to intrinsic characteristics of self-employed
workers. Several psychological studies. in fact. have revealed that. people with positive
attitudes toward life are more likely to be self-employed.6 This problem arises from the
interpersonal comparison aspect of the job / life satisfaction score that. is determined by
subjective perception. This problem can be resolved by considering the change of job
satisfaction associated with changing jobs. because these sz-itisfaction scores are cour-
pared within individuals. This possibility. however. cannot be explored without using
panel data. In addition. contrary to the findings in Blanchflower and Oswald (1998).
Clark and Oswald (1991). using a. medical measure of psychiatric health. found that self-
employed workers are. more highly stressed than salary / wage workers. Considering the
risks that self-employml workers face, this result is not surprising. Thus, the evidence for
the compensating differential among self-employed workers found in Blanchflower and
Oswald (1998) is very informative but it does not decisively support the compensating
differential hypothesis. This paper attempts to overcome the limitation of Blanchflower
and Oswald (1998) by using panel data with a subjective job satisfaction measure (Na-

tional Longitudinal Survey Youth 79). and. in addition. it attempts to calibrate the

 

“See Brockhaus and llorwitz (1986) for review of the literature.

20

monetary value of self—employmelit status.

The rest of this paper is organized as follows: Section ‘2 overviews the use of job
satisfaction measures in economics. Section 3 briefly discusses our data and confirms
the lower earnings of self-etripltiiyed workers. Section 4 discusses job satisfaction scores
in the data and implements a descriptive analysis. Section 5 describes a simple model
of the compensating differential among self-employed workers and estimates the param-
eters of the model. Section 6 extends the analysis in Section 5. relaxing the imposed

assumptions. Section 7 provides a. summary and conclusion.

2.2 How Economists Have Used
Job Satisfaction Measures

Economists often hesitate to use subjective job-satisfaction measures because linking
job satisfaction measures with underlying utility is thought to be (.lifficult.7

In the empirical literature. howevm‘. labor economists have made significant efftn‘ts to
incorporate job satisfaction n‘ieasures into economic analyses of labor market outcomes.
There are roughly two ways in which job satisfaction is used in economic analyses of
labor markets.

The first uses job satisfaction scores as an independent variable to examine the.
effect of job satisfaction on economic outcomes. Freeman (1978) showed that job (dis-
)satisfaction predicts workers’ job quitting behavior fairly well. even after controlling
worker and job characteristics including their wages. C arrington. McCue. and Pierce
(1996) and Clark (2001) obtained similar results using German and British data re-
spectively. These findings establish that job satisfaction is a. very informative economic
variable. The method that is going to be applied to the data. set in this study will help
determine whether job satisfaction is a reliable economic variable. The results of the

analysis will appear in data section of this paper.

 

‘Job satisfaction is regarded as one of most. important concepts by industrial and organizational
psychologists. and textbooks in the field typically devote an entire chapter to job satisfaction and its
effect on job performance. For example. see McCormick and Ilgen (1985) and Siegel and Lane (1987).

‘21

The second type of studies has analyzed the determination of job satisfaction. given
that the job satisfaction variable is a reliable economic variable. The job satisfaction
variable has been widely used to examine the effect of unionism on job satisfaction
(Borjas (1979). Leigh (1979) and Bender and Sloane (1998)). Somewhat surprisingly,
they typically found lower job satisfaction among union as compared with non-union
workers. They explained this counter—intuitive finding as an evidence of an exit-voice
mechanism of labor unions: even though workers are not happy with their jobs. they do
not quit because their ”voice" is heard thrtiiugh labor union.

Several studies have used job satisfz-tction scores to test the relative income concern
ll)'}'.)()lll(".SlS; job satisfaction is not only determined by individual earnings. but also by
relative position of the earnings compared with workers who share similar characteristics
(Hamermesh (1977). Clark and Oswald (1996). Hamermesh (2001)). All of these. studies
found evidence supporting the relative income concern hypothesis.

This review of literature shows how the use of job satisfaction in economics has

extended the area on which economics can shed light.

2.3 Data and Lower Earnings among
Self-Employed Workers

The National Longitudinal Survey Youth (NLSY79) is used in this study. The analysis
sample consists of observations between 1985 and 1998 that was restricted to white male
in order to be consistent with previous studies (Hamilton (2000), Krashinski (2000)).
Iru’lividuals who work for money and are out of school are included in the sample.
Individuals are dropped if their job classifications are unknown. The construction of the
analysis sample is tabulated in Table '2 - 1.

As the first. step of the analysis. the lower earnings of self—employed workers. which
have been observed in previous studies. are replicated with this data. The. following

wage equation is estimated to see the earnings (,lifferentials between self-employed and

salary / wage workers.
lll (1‘); = .30 + I113} 'i‘ {fgb'fffn + Selfulfitjfg + C,‘ + ()1?! , (2.1)

where 21)., is hourly rate of earnings.t4 self,-, is the dummy that. indicates self-employed.
17,-, is the vector of human capital and demographic variables. c.- is unobserved indi-
vidual heterogeneity. and e,-, is idiosyncratic error that satisfies E [eulself a. '17“. Ci] = 0.
The model is estin‘iated through OLS assuming [(.',-|self,-,.:1:,-,] = 0. and this assump-
tion ensures that self selection into self—employment does not occur on the basis of
unobserved characteristics. \‘Vhen the assumption [c.jsel f it. In] = 0 is violated through
self-selection based on unobservables. the OLS estimator is biased. To deal with this
possibility. the model is also estimated through a fixed effects estimation. The fixed
effects estimator is unbiased if e,-, is strictly exogenous (i.e. E [eitlsel f.. 27,-, 0,5] = 0. where
self.- = [self.1.....scl f.-T].;r. = [r.1......r:.T]); thus self-selection into self-employment
based on time—ccmstant unobserved characteristics is allowed.

The differences in earnings between salary / wage and self—employed workers are eval-
uated at 10 years of job market experience and 10 years of job (business) tenure.9 The
point. of evaluation is important. since the life-cycle earnings profile is much flatter among
self-employed workers as pointed out in Lazear and Moore (1984).

The results of estimation and the estimated difference of earnings appear in Table

‘2 - 2. Both the results of OLS and the fixed effects estimation show that the earnings

 

8Hourly rate of pay is constructed by the Center for Human Resource Research (CHRR) based on
respondents usual earnings (inclusive of tips. overtime. and bonuses but before deductions). CHRR
requests wages/ salaries / tips income and business/firm income along with other income categories from
both wage/salary workers and self-employed workers. Thus there is less concern that self—employed
workers earnings contains capital income. in addition to labor income. which is the main concern for
the measurement of income among self-employed when CPS is used. Therefore the measurement of
earnings for self-employed workers in NLSY 79 is as good as those in SIPP that were used in Hamilton
(2000). Although Hamilton (2900) also used the earnings that include capital gain as an alternative
earnings measure of self-employed workers. I just focus on the labor earnings of self-employed worker
here.

9Hamilton (2000) evaluated the differences at 10 years of tenure and ‘20 years of experience. However.
in the sample used in this analysis. only 1.54% of total sample has more than 20 years ofjob experience.
Thus the difference was instead evaluated at 10 years of experience. About half of the observations in
the total sample have more than 10 years of experience and about. 10 “/c of total sample has more than
10 years of job tenure.

23

of self-employed workers are higher without job experience or tenure as we can see
from the positive coefficients for the self-employment dummy. However. the earnings-
experience/ tenure profiles are flatter for self-employed workers than for salary/ wage
workers. Because of the flatter earnings profile. self-employed workers with 10 years
of job experience and 10 years of business tenure earn 111% (t = —1.731) to 23% (t =
1.709) less than salary / wage workers. depending on their educational background and
marital status according to the OLS result. This result almost corresponds to the 19%
self—employment penalty evaluated at 10 years of job (business) tenure and 20 years
of experience found by Hamilton (2000) using labor income as self-employed workers”
earnings.

According to the fixed effects result. however. self-employed workers with 10 years
of job experience and 10 years of business tenure earn almost the same amount as
salary / wage workers on average. This result may seem to imply an upward bias of the
OLS estimator: however. careful examination of the estimated coefficients reveals that
the difference between the OLS and fixed effects results mainly come from the difference
in the estimated coefficient for the interaction of self-e111})loyment and education. The
first (‘lifference estimates of the return to education. however. are not reliable because
the earnings before attent‘ling school may not reveal full individual lieterti)geneity (see
discussion in Card (2000)). In particular. self-employed workers can attend school while
they work less. which results in lower earnings. and then then start to work at their full
capacity after graduating. which ultimately results in higher earnings. Thus the fixed
effects estimate for the return to education among self—enmloyed workers may not be a
reliable estimate and the conclusion instead should rely on OLS estimates.

To summarize the findings. self-emrfloyed workers earn less mainly due to a. lower
return to job experience and job tenure. When the difference is evaluated at 10 years
of job experience and 10 years of job (business) tenure. self-employed workers earn 11%
to 23% less of comparable salary / wage. workers on average. Next I attempt to explain

this earnings difference with the conmensating earnings differential theory.

21

2.4 Job Satisfaction Scores and Descriptive
Analysis

The main survey item used in this study is global job satisfaction. The question reads

How [do/did] you feel about. your job with [name of employer]? [Do/ Did]
you(1) like it very much. (2) like it fairly well. (3) dislike it somewhat. or (1)

dislike it very much'.’ (CODE ONE ONLY.)

The distribution of responses for this question is tabulated in Table 2- 3. The
distribution remains nearly constant over time. Examination of Table 2- 3 reveals that
bout 65% of self-employed workers chose. "like it very much" while only about 45%
of salary / wage workers chose this answer. It is also noTable 2- that only about. 35%
of self-employed workers chose. “dislike,” ("dislike somewhat” and “dislike very much”
combined.) while around 8-10‘fc of salary / wage workers chose this answer. This rough
examination of the distribution clearly indicates that self-employed workers are more
satisfied with their jobs.

Before developing a detailed discussion of the compensating earnings differential
based on job satisfaction scores. I examine whether this score is a meaningful economic
variable. If the job satisfaction score contains meaningful information about. a worker’s
actual job satisfaction. the score should predict the observed worker’s behavior. in par-
ticular. the worker's future job change.

Table 2- 4 tabulates the probability of job change between time t and t— 1 classified
by the job satisfaction at time t — 1. This Table 2- clearly tells us that the worker who
dislikes his/ her job is more likely to change his/ her job.

Since the probability of job change may depend on the worker’s demographic. char-
acteristics that may be correlated with job satisfaction. a probit model in which the
probability of job change depends on demographic variables as well as job satisfaction is
estimated. The results of the probit estimation appear in Table 2 - 5. Column 1. The re-

sults indicate that those who dislike their jobs very much are 22% more likely to change

their jobs than those who like their jobs very much. The inclusion of control variables
hardly changed the results obtained in Table 2- ‘1. I have also tried the specification
that included lagged log wage as an independent. variable, as used in Clark (2001). The
estimated results for this specification appear in Table 2- 5. Column 2. The results
indicate that a 10% increase in hourly earnings decreases the probability of job change
by 0.26%. The size of this effect is relatively small compared with the effect ofjob satis-
faction on job change. For example. changing job satisfaction from “like somewhat" to
“like very much” decreases the job change probability by 10.8% (=0.231-0.l23). This
larger effect. of job satisfaction on job change than wage effect can be explained as fol-
lows. If the job match quality is more significant in job satisfaction determination than
in wage determination. job satisfaction is a more crucial determinant ofjob change than
wage because workers have a greater chance to improve their job satisfaction but. less
chance to improve their wage through job change. This relatively small effect of hourly
earnings on job change compared with job satisfaction was also found in Clark (2001),
although the result in his study was not this extreme. \Ve should also notice that the
size of the coefficients for job satisfactitm dummies hardly changed due to inclusion of
log of hourly earnings.

The results obtained from Table 2- ‘1 and Table 2- 5 clearly indicate that. the job
satisfaction score contains valuable information about the worker's actual satisfaction
in his or her job.

One of the main drawlmcks of using the job satisfziiction score. as noted in the previous
literature. is the difficulty in the interpersonal comparison of subjective measures. This
study attempts to overcome this difficulty by using panel data because panel data t
enables the researcher to examine the change in job satisfactitm associated with job
change. In the follmving analysis. the job satisfaction score is assumed to be comparable
within each individual over time. This assumption is much weaker than the assumption

of inttiu'iwrsonal mmparability of subjective measures.

As a simple way to examine the change in job satisfaction associated with job change,
the transition matrices of job satisfaction for job stayers and job changers appear in Table

2- 6. Findings from these matrices are. smmnarized as follows:

0 Among job stayers. self-employed workers are more likely to stay in the "like very

much" category as compared with wage / salary workers. (Panel A and Panel B)

0 Job changers who move from salary / wage jobs toself-en'iployed jobs are more likely
to experience a positive transition of job satisfaction than job changers who stay
in salary / wage jobs. It is also noTable 2— that job changers’ job satisfaction as
salary/wage workers are originally slightly higher than stayers. This may be

evidence of self-selection. (Panel A and Panel B)

o This positive transition of job satisfaction is less likely to occur among those who
change from self—enmloyed jobs to wage/ salary jobs. .\Ioreover, about 20% of job

changers experience a negative transition of job satisfaction. (Panel F)

These findings suggest the (:(‘inclusion that. self-employed jobs are more satisfying
than salary / wage jobs. Hmvever. other demographic characteristics that also might af-
fect job satisfaction. such as marital status. may vary at the time of job change and this
might result in the findings above. To address this possibility, the effects of workers"
observed and unobserved characteristics on job satisfaction are controlled in the follow-
ing analysis. In addition. I attempt to calculate the monetary value of self-employment

status in terms of job satisfaction.

2.5 Compensating Differentials among
Self-Employed Workers

To test the compensating differential hypothesis among self-employed workers. this sec-

tion attempts to calculate the monetary value of self-employment status in terms of

27

job satisfaction to see if it is large enough to explain the earnings differential between
salary / wage workers and self-employed workers.

A straightforward way to see whether the difference in job satisfaction between self-
employed job and salary / wage job explains the difference in earnings is to estimate the

following equation:
In 11'” = .30 + .1",-,.31 + .i)’-_>sclfa + .13, + :33jsl-t + cf, . (2.2)

and see if H0 : .32 = 0 hold. If the null hypothesis is not. rejected. then we can conclude
that the difference in earnings originated from job satisfaction. not from self-employment
status. H(;)wever this obvious method neglects the fact that job satisfaction is also the
function of earnings. as shown in previous studies. This endogeneity biases the estimates
of .33 upward because j.s,~, and c” are positively correlated. This bias causes a. downward
bias in the estimates of (.33 since job satisfaction and self-employn'iei1t status are positively
correlated. as we saw in the previous section. This downward bias may result in the
false acceptance of the null hypothesis. Thus. this avenue is not pursued in this paper.

Instead, I examine how self-e111plt‘)yment status and earnings affect job satisfaction to
see whether the countiensating earnings (’lifferential explains lower earnings among self-
employed workers. To calculate the monetary value of self-e1nployment status in terms
of job satisfaction, the marginal rate of substitution between self-e1nployment status
and monetary earnings in terms of job satisfaction is calculated. The link between the
job satisfaction score and utility is specified as

iijI. 2 #3.
if [13 > jsf, 2 112,
if [.12 > jsf, 2111,
if [11 > jsft.

(2.3)

JSit :

idioms—x

where jsi, is a catt-rgorical variable indicating worker 1'. at time is response to the job
satisfaction question (1: "Dislike Very Much” - 4:"Like Very Much“ ), whereas js’f, is the
latent continuous variable of job satisfaction and “(It 2 1. 2. 3) are the thresholds of job

satisfaction that determine the answer for the job satisfaction question. Although many

28

factors may affect a workers job satisfaction. to see the tradeoff between self-employment.
status and monetary compensation. those two factors are mainly considered as the
determinants of job satisfaction. To estimate the monetary value of self—e1nployment.
status in terms of job satisfaction, three additional assumptions are imposed.

First. as a shape of job satisfaction function. a linear function is assumed as a first
order a1.)proximation. Several (.lemographic variables are also assumed to affect job sat—
isfaction. Moreover. other factors that may affect job satisfaction are assumed to be
independent of self-emplt)yment status. monetary conmensation. and demographic vari—
ables. and thus these factors are assumed to be normally distributed. This assumption
results in

Assumption 1 (Linear job satisfaction function)
I“; = ()0 + 915;: + 92111 u‘n + rags + (3‘ + f’zt- (“ulna “7m J'ir- ('i N i ,(0‘ 1) i (2'4)

where 8a is the dummy variable for self-e1nployment status. in, is hourly rate of pay. .1',-,
is the vector of a workers attributes. and c,- is individual heterogeneity in utility level.
Specifically .1‘” contains a. marital status dummy; a sex dummy: age. racial or ethnic
group dunnnies: educational backgrmmd: labor market experience: and job (business)
tenure.

The next step is to calibrate the monetary value of self-employment status in terms
of job satisfz-iction. As a. measure of monetary value, we can calculate how much workers
can give up in terms of salary / wage earnings in exchange of one dollar earnings as a self-
enmloyed worker while keeping their job satisfaction constant. The 0 in the following

equation gives this ratio of trade off:

 

02111(c111',,)+.1',-,63+ c,: + (y, = gl + 92111 ”wit + (13,63 + c.- + 6);. (‘2.5)

 

v

js' of a salary/wage worker )5” Of a SGlf-GIIIployfi‘d WOI‘liCI’
When a worker receives 0 dollars of earnings per hour as a salary / wage worker, the
worker has the same. level of job satisfaction when the worker earns one dollar per hour

as a self-employed worker. The solution for the equation is simply, a = exp(01/62). In

‘29

other words. 0 dollars of earnings as a salary / wage worker is equivalent to one dollar of
earnings as a self-employed worker. This value, a, is reported as a. monetary value of
self-employment. status in terms of job satisfaction.

To simplify the econometric i’nodel, two additional assumptions that will be relaxed
later are made:

Assimzptton 2 (Independence of Heterogeneity)

c,~ _L 3;. try. .‘1,',- . (2.6)
where s,- = [5,-1.51-2. ...,.s,-T]. w,- = ['1L',’1,U‘,‘2.....lU,‘T] and .L‘i = [.17i1,.l'7,'2,...,;1f,~7‘]. This as-

sumption assures that individual heterogeneity is independent of observables and that
the heterogeneity does not cause any inconsistency of the pooled, ordered probit esti-
mator.

Assmnptz’o'n. 3 (No feedback from current job satisfaction shock to future self-enmloyment
status)

5,. U',‘..’17i.C,' ~1V(0,1) . (2.7)

 

f’it
This assumption rules out the feedback from current shock on job satisfactimi to future
self—en'iployment status through job change. since if the feedback exists. the distribution
of current 6 depends on future .9.

These three assunmtions result in the pooled. ordered probit model. and the pa-
rameters in (2.4) can be estimated. To estimate the model, I dropped the observations
whose hourly rate of pay were either above the 99 percentile or below the 1 percentile in
each year, and this sample selection results in sample (5) in Table 2- 1. Since those who
earned extremely high wages and were extremely satisfied with their jobs could only
report. “like their job very much" at. the maximum and vice versa for low wage earners.
including those extreme earners would attenuate the coefficient. on hourly rate of pay
toward zero and this would make the estimates of the monetary value of self—employn'ient

status upwardly biased.10 The results of estimation appear in Table 2- 7, Column 1 and

 

“’1 tried several trimming rules. The results were not essentially changed when I applied 5‘7t-95‘7t or

30

Column 2. The result. that appears in Column 1 is the specification that only includes
the self-employment. diunmy and the log of hourly wage as independent variables. The
coefficient for self—employment is 0.486. The size of the coefficient is not large enough to
change the worker's response to the job satisfaction question from ”dislike very much”
to ”like somewhat" or from "dislike somewhat.” to ”like very much,” since the critical
values of the ordered probit are -0.377. 0.360. and 1.891. However, the size of the coeffi-
cient is much larger than the coefficient. for log earnings. The coefficient for log earnings
is 0.248. which is surprisingly small if we compare this value with the critical values. Due
to this small effect of earnings on job satisfaction. one-dollar earnings of self-employed
workers are evaluated as more than seven dollars of salary / wage workers’ earnings. This
value is large enough to compensate for the lower earnings among self-en‘iployed workers
whose earnings are about 20% lower than salary / wage workers‘ when workers have 10
years of job experimrce and 10 years tenure.

When marital status. educational attainment. and job experience are included in
the specification. the (.‘oefficient for earnings becomes even smaller because a part of
high earnings is explained by the added explanatory variables. In this specification.
the estimated monetary value of self—enn.)loyment status l)€('()ll’l(‘S a = 818; one dollar
earnings as a self-employed worker is ermivalent to 88.18 earnings as a salary/wage
worker. Again. this puzzling result is obtained due to the small effect of earnings on job

satisfaction while the effect of self-employment status is large.

2.6 Extensions

A surprisingly large estimate of the monetary value of self-employment. was obtained
in the previous section. Now. I consider several possible reasons why the effect of self-
employment status on job satisfacticm may be overestimated. To do so, I will relax the
assumptions made so far one by one in this section. As partly suggested in the anal-

ysis of transition matrices of job satisfaction, workers who become self-employed seem

 

“IVs—90% rules.

:3 1

to have a positive attitude toward their jobs independent from self-e1nployment status.
If this is the case. the coefficient. for self—employed workers was overestimated in the
pooled probit model. since c,- and s" are positively correlated. In addition. if workers
with high ability have high expectations for their earnings, workers with high ability
are less happy with their current earnings. As a result. unobserved heterogeneity and
current earnings. which is a. proxy for ability. may be negatively correlated and the co-
efficient for hourly rate. of pay may be negatively biased. Considering this heterogeneity
or other possibilities that unobserved heterogeneity in job satisfaction are dependent
upon observable characteristics. the assumption 2 (Interpersonal comparability of job
satisfaction) is replaced with

Assumption 2' (the "Fixed Effects" assumption)
- __ — _ -) ‘ .
(ylsi. ti’,..1',- ~ .\ (ms,- + ”,gln (1',- + 73.13. 0;). (2.8)

This assumption allows depemlence between c,- and 3,: or .r,- in a restrictive way.11

where §,-. lnfuy and f,- are mean of s,-. In 11',- and 1',- respe(’-tively.12 The consistent es-
timators are obtained through a. pooled. ordered probit estimation of the model that.
includes individual means of lll(l("})(’ll(l(‘llt variables. The importance of assumption 3
should be emphasized here. If current. shock to job satisfaction. e”. affects the future
value of self-em])loyment status through job change behavior, e” and .57.- , which is one
of the independent \I’ariables and dependent on the future self-employn'ierit status. is
dependent and a consistent estimator cannot be obtained.

The results of estimation appear in Table '2- 6. Column 3. The coefficient for self-
ernployment decreases as expected from the positive correlation of c,- and 5,1,.At. the same
time, the coefficient. for earnings increased due to negative correlation of ed and In 1.1.3,.
As a. result, the calculated monetary value of self-einplr.)ymeiit status becomes 284% of

earnings.

 

llMundlak (1078) proposed a variant of this assumption in a linear regression framework.
12The analysis under this assumption is called a fixed effect analysis because this assumption allows
dependent unobserved heterogeneity..

As confirmed in the previous section and in previous studies (Freeman (1978). Clark.
Georgellis. and Sanfey (1998) and Clark (2001)). job changes tend to follow low job
satisfaction. In the light of this fact. ruling out the feedback from e.“ to 55,4. u-Sn‘
is a strong assumption. In particular, if a salary/ wage worker experiences low job
satisfaction because of some shock (after conditioning on individual heterogeneity) and
become a self—employed worker. we tend to overestimate the effect of self-employment. on
job satisfaction because a salary / wage workers with current negative shock is more likely
to be self-employed in the following period. To take care of this possibility. feedback
effects are allowed through the assumption.

Assumption. 8° (Feedback effect)
('-,:,|s.-. .1?,:.(1,- ~ .\'(h(s,-,.s.~,r1. H.817). 1). (2.9)

where h(s,~,. sits]. 8,7”) = (5111121.X(l8,-,*T — .9,,+T_1|. lsit+1 — .s,,|). This function in-
dicates that if a change in self-employment status takes place within T years due to
current shock on job satisfaction. the parameter 7' is the maximum lag period of the
feedback from c” on future job change. For example. if 7' = 3 then the feedback is
assumed to take place within 3 years. The cases 7' = 1. 2 and 3 are considered in the
following analysis. The model is estimated with a pooled. ordered probit model with an
individual mean of independent variables and h,(.) as independent variables.

The estimated results of the "fixed effects" probit model with feedback effects appear
in Table 2- 7. Columns (4) - (G). The estimated coefficients for the feedback term. h(.).
are all negative but. they are not significant. in all specifications. Negative coefficients im-
ply that a current negative shock on job satisfaction causes a change in self-ernployment
status through a job change. However. even after considering correlated heterogeneity
and feedback from job satisfacticni to future job changes. we find essentially similar re-
sults as before: The estimated monetary value of self—er’nliiloyinent status is about 390%.
Since the coefficients for the feedback terms are not significantly different from zero, I

take the fixed effects estimate. which is d : 3.387(s.e. = 0.097). as the most preferable

33

estimate for the monetary value of self-e1nployment status in terms of job satisfaction.

Now. the very high valuation of self-employn'ient status is the puzzle that should
be explained. Although self-employed workers with 10 years of job experience and 10
years of business tenure earn about. 20% less than their salaried / wage—earning counter-
parts. the estimated results imply that one dollar of earnings as a self-employed worker
is equivalent. to three to four dollars earned as a salary/ wage worker in terms of job
satisfaction.13 If we consider job satisfaction as equivalent to utility. this value means
that self-ernployed workers do not move to salary / wage jobs even when they are offered
three or four times more than their current earnings. but. this finding is counterintuitive.
However. there are four explanations that may explain these surprising findings.

The first. is the fact that job satisfaction is only a segment of utility function. Sup-
pose the simplest form of utility function. which consists of only consumption and job
satisfaction:

-u(c(zc).js(se. w)) . (2.10)

where c is consumption that is presumably a function of earnings. Then the marginal

rate of substitution between self-e111})loyment status and earnings is

 

 

 

UH ~
. . (l '. ° 18..
4 V _ ()M‘ _ J5 - M
U“. (1.. - (u. + U}. ~Js...

where f_,. denotes the partial derivative of f with respect to .1'. Since (1.. > 0 and cu. > 0.
the monetary value of self—e1nploymmit in terms ofjob satisfaction. which is (j.5'..,.(../j8u.).
overstates the value of self-employment status in terms of utility. Although the mone-
tary value of self-em})loyment status in terms of job satisfaction has been estimated in
this paper. the monetary value of self-e1nployment status in terms of utility should be
estimated to appraise the validity of the (“:ompensating differential hypothesis. However,
calculating the monetary value of self—ernployment status in terms ofjob satisfaction is a.

useful exercise in light of the reality that a numerical measure of utility is not available.

 

13Althorugh I tried several specifications in which the monetary value depended on job experience
and tenure. the result virtually did not change.

The second explanation concerns the overestimation of success among self-employed
workers. Empirical studies have found that self-employed workers are overly confident in
their future success as compared with salary / wage workers (Cooper. “'00. and Dunkel-
berg (1988) and Arabsheibani. de.\Ie/.a.. Maloney. and Pearson (2000)). For example.
Arabsheibani. deMeza. Maloney. and Pearson (2000) report. that self-employed work-
ers expect better financial outcomes in the following year than salary/ wage do, even
though they. in fact. experience worse outcomes. \\'hen self-employed workers expect.
future monetary success. self—employmei1t status has a subjective "option” value for
future earnings and this option value may make self-enmloyed workers more satisfied
with their jobs. Although this effect is attributed to the non—monetary value of self-
employment. status in this study. it instead should be attributed to the mcmetary value
of self-e111})loymeiit status. This overestimation of the non-monetary aspect and un-
derestimation of the monetary aspect result in an overestimation of the non-monetary
aspect of self—emploved jobs in terms of job satisfaction.

The third explanation relates to underreportii1g in self-elliployed workers’ earnings;
if this is the case. the value ofself-e111})loyment is overestimated. since the utility from
underreported earnings is captured through self—employment status. Although NLSY
makes good efforts to collect reliable labor income from self-employed workers. self-
employed workers may refer to their tax forms from previous year in which earnings
were uiiderreported for tax avoidance pin‘poses when they answer earnings questions.
For example. Joulfaian and Rider (1998) show that the underreport rate of self-employed
earnings is about 20% on average. using the Taxpayer Compliance Measurement Pro-
granrl'lAlso. self—employed workers may consume out of business expenses. For example.
they may drive coi‘npairv—owned cars for personal purposes. This also may increase the
monetary value of self-employment status. but it. should not be interpreted as a com-

pensating differential since it. simply captures consumption.

 

HThe Taxpayer Compliance Measurement Program data are stratified samples of individual tax
returns subject to intensive line-by—line exz—uninaticms (.loulfaian and Rider (1998)).

The fourth explanation relates to i heterogeneity in the marginal job satisfaction
from self—employment.. “'hen heterogeneity is explicitly modeled, the utility function
becomes

J“; = 90 + (9115i! + 92111 Ur'u + 1,193 + Ci + Cit-

Z 90 ‘i’ 918,7 + 9211111”); + J'it93 ‘i‘ Ci + (’1'),

THIS”. (1')). .I'); N .‘\Y((91,'— 01)5i(, (TC). (2.12)

hvlai‘ginal job satisfaction and self-em;)loyment 01,-, and .s,-,, are likely to have a positive
correlation since those who gets higher job satisfaction from self-employn'ient are more
likely to be self—employed. consequently. s” and the error term is positively correlated
and plime—li > 91. Thus the pooled. ordered probit estimator overestimates the average
of 61. This positive correlation can be very large for workers who want to be self-
employed at any cost. Thus the calculated monetary value of self—employn’ient from the
pooled probit model can be interpreted as the upper bound of the average evahiation of
self-employmeiit. throughout the population. In addition. the entry to self—employ1nent
continues so long as a workers evahiation of self—employment. status is above the earnings
penalty of self-employed workers. Thus. the earnings penalty of self-employed workers is
determined at the margin. through a market mechanism. It is no surprise to find a higher
average evaluation of self-employment status than the evaluation of self-employment
status by the last, worker who marginally becon‘ies a self-employed worker.

The interpretation of the monetary value of self-employment status becomes more re-
stricted for the fixed effects model. The coefficient 0“ is identified as those who changed
self-employment status during the sample period since if Sit is constant over sample pe-
riod, the variable is perfectly multicolinear with .57,- and only 01i+71 is identified for those
observations. Thus the estimated Incmetary value of self-eniployment status from the
fixed effects model is the average (waluation of self~e1nployment status among workers
who experience a transition between a salary / wage job and self—employed job. As we can

see from Table ‘2- 6. there are more observations that transit from a salary / wage job to

36

a self-employed job. so it is not surprising to find higher evaluations of self-e1nployment
status among workers who become self-employed during the sample period. Those who
leave self-employmeiit might also do so because of financial reasons but not because. of
job satisfaction.

These four considerations may well reconcile the estimated self-eniployment penalty
(20% of earnings for a worker with 10 years of job experience and 10 years ofjob tenure)
with the estimated monetary value of self-employinent jobs (300% - 100%) in term of

job satisfaction.

2.7 Conclusion

Analysis of job satisfaction scores show that. self-employed workers are more satisfied
with their jobs than salary / wage workers. .‘\Ioreo\-'er. one dollar of earnings while a self-
employed worker is equivalent to three to four dollars of earnings while a. salary/wage
worker in terms of job satisfaction. This finding is preserved even when individual het-
erogeneity. which is potentially correlated with self-employinent status, and the feedback
effect. which runs from current job satisfaction to future job change. are considered.

This high valuation of self-emph)yment status in terms of job satisfaction may over-
estimate the actual trade-off between self-employment status and monetary income in
terms of utility. However. even after taking the effect of unavoidable overestimation into
consideration, the value of self-em})loyment status in terms of job satisfaction, which is
300 to 400‘} of other workers' earnings. seems high enough to explain the lower earnings
of self-employed workers. Thus. the results obtained in this paper support. the compen—
sating differential hvpothesis as an explanation for lower earnings among self-employed
workers.

Promising future research would develop a rigorous appraisal of the con'ipensating
wage differential hypothesis by using a better measurement of utility or principals of

revealed preference.

37

Chapter 3

Peer effects on substance use
among American teenagers

3. 1 Introduction

Widesmead use of illicit substances by American teenagers attracts both public attention
and research interest. The changing percentages of substance users during the 1990s are
plotted in Figure 2-1. Although the percentage of alcohol users dropped in the early
1990s, it still remains high. This figure shows the steady trend of cigarette users at a
high level.1 It is also notable that the percentage of marijuana users increased from
4.41% to 9.7% l)(~?t\~\'t‘(‘.ll 1990 and 1997.

These figures have sparked much public interest about the reasons why teenagers
use substances and what policy makers can do to reduce this usage. Besides the price
of substances, peer effects or peer pressure is identified as a. critical (ilet.er1ninant., since
the use of substances is considered to be a. highly social bel‘1a.Vi(;)1‘.“2

Reacting to this interest, economists and sociologists have tried to estimate the ex-
istence and the strength of peer effects. Idei‘itifying peer effects is not. easy since an
observed behavior shared liiiy a teenager and his/ her peer may result from unobserved

factors that group members share instead of peer effects. In addition, identifying peer

 

1On the other hand, Gruber (2000) reports that the percentage of cigarette smokers increased by
one-third between 1991 and 1997. (Based on youth behavior risk survey. for 9th through 12th graders.
the number has increased from 27% to 36% between 1991 and 1997.)

28ee Los-Angeles—Times (1999) for interviews of youth smokers on the reasons why they smoke.

38

effects becomes complex when the average reference groups outcome is used as a mea-
surement. of peers’ behavior. Determining whether a teenager's behavior affects his/ her
peers or vice versa is difficult. Manski (1993) articulated this as the ” reflection prob-
lem.” In addition, both current substance users and backgrounds of group members may
affect individual behaviors. Although both effects are called peer effects, each has dif-
ferent policy implications. Distinguishing between these two effects, however, is known
to be difficult (Manski ((1993)).

This paper ermiloys a critically (.lifferent strategy for identifying peer effects; I identify

peer effects by using teenagers’ subjective perceptions. Manski (1993) wrote:

Given that identificatimi based on observed behavior alone is so tenuous,
ex1.)erin'1ental and subjective data will have to play an important role in

future efforts to learn about. social effects.

Using subjective pt-u‘ceptions of peer behaviors, identifying peer effects is free from the
problems that arise with using an average outcome as a peer variable. According to
Manski (2000), this approach has not been taken seriously since he originally suggested
it in 1993.

In addition, I employ school and household fixed effect estimation to ensure the

rolmstness of my results.

3.2 Reflection problem

Manski (1993) articulated several issues concerning the it'lentification of peer effects using
an average group outcome as a peer variable. A short sketch of the problem follows.
Let y be an outcome of interest. I be attributes characterizing an individual’s reference
group and 3 be attrilmtes that affect the outcome directly. The outcome is characterized
by

y = a + 3E(y(.r) + E(:|.r)7 + :1) + u. E(u|.r, :) = .175. (3.1)

39

If :3 7E 0. then an individual behavior is affected by the mean of the group outcome,
E (ylr). This is the ”endogenous effect.” If 7' 75 0. an individual behavior is affected by
the group mean of the exogenous variable (background of group members); this is the
"' contextual effect.” If 6 ¢ 0, the model exhibits the ”correlated effect.” People in the
same group behave similarly because their shared group characteristics are correlated
with unobservable factors. such as social institutions.

Taking conditional expectation on 2 and 1‘, the model becomes
E(y|.r. :) 2 (1+ .3E(yl.r) + E(z|.1")",r + 2!) + 16. (3.2)

To discuss the identification of parameters, we need to solve for the conditional expec-

tation of y in terms of .r and 2. Using the iterated law of expectations,
E(y|.r) = (r + .3E(y|.r) + E(:|.1‘)‘," + E(Z|.1‘)'I] + 1‘6. (3.3)
Solving the expression for E ( ylr) and substituting it into (3.2), we obtain
E(y|.r. 2) = (,1/(1—3)+ E(:|.l')l(7+;31])/(1—— 3)] +.r6/(1—,3)+ :n. (3.4)

The composite parameters are identified if [1, E (2)17), 1?. z] are linearly independemt.3
Even if the linear independence is assured, the endogenous effect ( 113) is not identified
if the contextual effect. is present. (La 7 7f 0). As Manski (1993) stressed, distinguishing
the endogenous effect from the contextual effect is important. because these two effects
have critically different policy implications. Consider, for example, that a lecture on
the health effects of smoking is provided to one class but not to the other classes in a

particular school. If the lecture effectively makes students quit smoking in the class.

 

3The linear independence of explanatory variables is violated if

1. : is a function of .1: since E(:(.r)i.r) = Z(.‘l'). Consequently E(:|.r) and z are linearly dependent.
This case occurs in the situation when group members share the same exogenous variables.

2. 3 does not vary with .17. Since E(:).r) is constant. E( :|;r) and z are linearly dependent. This case
occurs if average exogenmls variables are identical across groups.

3. E(:l.r) is a linear function of .1: since E(:).r) = 0.1:. This is a theoretical possibility rather than
an actual possibility since .17 is usually an index for a group.

‘10

the effect. propagates to the students in the other classes through the endogenous effect.
If the endogenous effect does not exist, the effect of the lecture is limited to the class
where the lecture is given. \Vhile the endogenous effect implies this ”social multiplier,”
the contextual effect and the correlated effect do not imply that.

Moreover, .\Ianski (1993) warned that, in general. sample correspondence of E (yl.r),
denoted by E,\-(;y|.r). is not identical to E ( glr). As a result, [3 can be calculated in the
sample, even if 3 is not identified in the population. Thus, the successful calculation
of 13 does not. imply anything about the identification. After all, the identification of

the mixture of the endogenous effect and the contextual effect is possible under the

 

assumption of the linear independence of [1, E (: .r).;1:. z]. Distinguishing between the
endogenous effect. and the ccmtextual effect is. l’lUWE’VCI‘. principally impossible.

The difficulty of the identification arises because the mean of the outcome is used
as an explanatory variable. I avoid this complication in this study since the direct
subjective perceptions of peers’ behavior, instead of the average behavior of peers, is
the key explanatory variable. The beauty of this approach is that, once the linear
independence assumption of explanatory variables is assured. the endogenous effect and

the contextual effect. are Scip(l.’l'(lf(:fly identified. In the next section, the model with

subjective perception is discussed.

3.3 The model with perceived peer behaviors

The model with perceived peer behaviors is specified as follows:

y 2 (1+ :3p+ E(:

 

1))" + :57) + 21., (3.5)

where y is an outcome of interest, .r is attributes characterizing an individuals reference
group and 2: is attributes that. affect the outcome directly. The variable [i is subjec-
tive perception of peer behaxv'iors. Once E ( ulp. 2,17) = 0 and linear independence of

[1. p. z, E (2);1:)] are assured,4 the parameters, a. .7 and I}, are identified through OLS.

 

‘JThe assumption of linear indepemlence here is more restrictive than the assumption needed in
Manski (1993). since 1) is newly added to the list of \I'ariables which should be linearly independent.

51 1

Since the group average of observed behaviors is not used as an explanatory variable as
in Manski (1993), no complication of identification arises.

The crucial assumptitm in this model is that perceived rather than actual peer l.)eliav-
iors determine individual behaviors. This assumption is consistent with the conformist
model. Akerlof (1997) argued that individuals get, utility from behaving like an ”aver-
age" person in a reference group. It is natural to assume that perceived peer behaviors
produce the image of the ”average" person. This ”conformist” behavior may be rein-
forced through the formation of group norms. By forming group norms that require
members to engage in similar behaviors, each individual group member enjoys higher
utility. Becker (199(5) considered a utility function (U) that has own norm (N) and norm
of peers (Np) as arguments and its cross derivative to be positive (62b'/(é3i’\701\’p) > 0).
Thus individuals can obtain higher utility through forming their own norms similar to
the group norm. It is natural again to assume that each individual perceives group
norms through perceived peer behaviors. Individuals bel'iave according to their norms.
To summarize, the assumption that. perceived peer behaviors detern'iine individual be-
haviors is a natural one if peer effects operate through ”conformist” weft—arences or the
enforcement. of group norms.

At the same time. there are many other reasons why peer effects exist. For example,
American kids play baseball instead of cricket since other kids know how to play baseball,
and, consequently, it is easier to find playmates. .\~loreover, since playing an actual game
would be much more fun than playing catch. it may be an important goal for kids to find
many playmates easily. In this example, utility obtained from playing baseball depends
on the number of players with whom a kid plays (up to 18 players). By the same token,
teenagers may obtain higher utility from tobacco if they consume it with their friends. In
these situations. actual peer behaviors. rather than perceived ones, affect an individual’s
behaviors. Although this possibility cannot be ruled out, it is simply assumed that, an

individual behavior is influenced only by the perceived peer behaviors.5 Relaxing this

 

"Of course. the actual peer behaviors may determine perceivetl peer behaviors: ll()\\'(‘\'(,‘l‘. here I

42

assumptitm causes complication in identifying the endogenous effect as noted earlier.
Thus, the interpretation of the endogenous effect in this paper should be restricted by

this assumption.

3.4 Literature review

Many papers shed light on peer effects. In this section, the papers are classified by
the identification strategy used. The identification of the endogenous effect is especially
focused because of its unique policy implication of the ”social multiplier.” A thorough
review of the existing literature reveals the strengths and weaknesses of each identifica-
tion strategy. In particular. the limitation of an identification strategy that only uses
observed behavior becmnes clear.6 I also introduce the studies that use economic theory
to help provide additional insights into the identification of the endogenous effect. It
should be noticed that each study does not necessarily fall into a single category since

several identification strategies may be used in any given paper.

3.4.1 Identification through proxy variable

Several studies use variables that represent "peer quality” or ”neighborhood quality.”
The problem of whether the endogenous effect can be identified using these I)I‘(_)Xy vari-
ables is considered. The outcmue of interest (teenage pregnancy and high school drop-
ping out in Evans, Oates. and Schwab (1992) and high school dropping out in Crane

(1991)) is cliaracterized by

y = a + .3E(yl.r) + 211+ '11. (3.6)

 

assume that once perceived peer behaviors are included in the behavioral equation. the equation does
not include actual peer behavior.

6Many of studies surveyed in this section used nonlinear models such as probit. However. we
discuss identification strategies in the context of linear models, since the discussions of identificaticm
fundamentally carry over. Moreover. the identification of parameters should not depend only on the
ncmlinearity assumption. The sizes of peer effects in those studies are discussed in terms of marginal
effects.

"13

Since two of studies above implicitly assume absence of the contextual effect, I also adopt
that assumption. Instead of E (ylr). however, a proxy variable, such as the portion of
students who is eligible for a. school free lunch program (Evans, Oates. and Schwab

(1992)) or a neighbm‘hood occupational structure (Crane (1991)),
([2 E(yl.r) + 't‘ (3.7)
is used in the estimation. The equation actually estimated reduces to
y = o +.3(1+ :I; + u — .31‘ (3.8)

The assumptions E(u|.r. :) = 0 and E(c|.r. :) = 0 are required for the identification
of the endogenous effect. ,3. Assuming the absence of the correlated effect, the first
assumption is assured. ()n the other hand. the second assumption is questionable.
Since q in these studies consists of variables defined on reference groups, q are naturally
functions of .r. As implied by (3.7), '1' is a function of .r accordingly. Thus, E (elm. z) 7é 0.
Although using proxy of E (glr) reveals something about broadly defined peer effects,

identifying the endogenous effect is impossible using a proxy variable.

3.4.2 Identification assuming the absence of the contextual ef-
fect

Many studies of peer effects fall into this category. Case and Katz (1991) looked at the
effect. of neighborhood average incidence on youth behavior, including drug use. Sacer-
dote (2000) looked at the effect of randomly assigned roommates in a college dormitory
on student outcomes such as GPA. Gaviria and Raphael (2000)) looked at peer effects
within schools on students' behavior.7

These studies try to identify the structural parameters in (3.2). As we have seen in
(3.4), the endogenous effect. (.3) cannot be identified without assuming absence of the

contextual effect (i.e. '7' = 0). Consequently, all of these studies assume the absence

 

7The behaviors considered in Gaviria and Raphael (2000) are substance uses, church attendance,
and dropping out of high school

11

8 Replacing the population mean

of the contextual effect except for Sacerdote (2000).
E(y|.r) with the sample average E,\v(,z/|.r), the (3.2) is estimated assuming '7' = O. The
assumption '7 = 0 is not severe if the research interest. lies in examining broadly defined
peer effects. However. if the existence of the "social n'iultiplier” is the main concern,
the assumption is restrictive. since all of the observed peer effects are attributed to the
endogenous effect. by assumption. Thus the identification based on '7 = O is not appro—
priate for deriving policy implications since the impact of policy intervention depends
on the existence of the "social n‘iultiplier.”

Most of the studies mentioned earlier were concerned about the violation of E (u Ir. 2.) =
0 and the consequent bias in the OLS estimator. Unobserved group characteristics (e.g.
teachers” competence in a school) make E (ulr. z) a function of .17 (the correlated effect).
This correlation of .r and it makes the OLS estimator of 3 biased since E ( ylr) is also
a function of 1'. Alternatively, unobserved individual characteristics correlated with ob-
served characteristics make E ( (1.).1'. 2) be. a function of z. This correlation makes the OLS
estimator of I] biased and the bias could be transmitted to the estimator of 3. For the
latter concern, IV estimation implied by (3.3) solves the problem. Under assumption
’7 = 0, E ( 2|.r) is excluded from the behavioral equation of interest. (3.2) but correlated
with E(y|.r) given I] # 0; thus E(:l.r) serves as instruments for ELI/[1). An important
point here is that u is allowed to be correlated with 2 but not with .1:. If u is correlated
with .r, E ( zlr) is correlated with u and cannot be an IV.

Evans, Oates, and Schwab (1992) considered the case in which 11 includes parents’
”willingness" to invest in their children when this willingness affects where the family
resides (at) so that the parents choose peer quality (E(y).r)) endogenously. In this sit-
uation of endogenous sorting, u and .r are correlated. To address this concern, they

9

used varieties of E (2);!) as IVs. As criticized in sulmequent studies, this strategy is

 

25In Sacerdote (2000). additional information of error structure was used to identify :3 and “)- as we.
will see in the next subsection. He found 5., to be negligible. After this finding, he used 7 = 0 to identify
the endogenous effect.

9The metropolitan area unemployment rate. median family income, poverty rate. and the percentage
of adults who completed college were used as 1V s.

dubious because the correlation of u and .17 (caused by endogenous sorting) implies the
correlation of u and E (:|.r). Thus E(:|.r) is not ideal IVs for E (ylr). Accordingly, their
conclusion that peer effects do not exist. after controlling endogenous sorting is dubious
as well.

Sacerdote (2000) used randmn assignment of roommates in a college dorm to avoid
this endogenous sorting. This natural experimental situation effectively rules out. en-
dogenous sorting. Gaviria and Raphael (2000) divided the sample into two sub-samples:
families that. had moved in previous two years and the families that had not moved. They
argued that if endogenous sorting is widespread, estimates of peer effects for families
that had recently moved into a new neighborhood should be larger. Their findings were
mixed.10 Although this method does not capture endogenous sorting that had occurred
more than two years earlier. the impm'tant issue here is whether peer effects are critically
different across groups. This is a clever possible strategy when the random assignment

of peers is not available.
3.4.3 Identification through variance covariance structure

The identification strategies intrmluced so far all assume the absence of the contextual
effect (7 = 0). To the best of my knowledge. the only paper that attempts to relax this
assumption is Sacerdote (2000). Since there are only two students in a dorm room, the
system of interest is

g) 2 (1+ :3”)- + 3.1-c," + 3,!) + u,;, (39)
yj = a + {3.1/1: + 3r," + zJ-I} + “j. (3.10)

where Ire/((1,) = lv'u/(uj) 2 (TS is assumed and E(u,|:,, 31-. uj) = E(uJ~|:,-, 31.11,) = 0 is

assured from the random assignment. Substituting (3.10) into (3.9), the reduced form

(I + (1.3 (1+3)? (1+ 3)!) 11;+._311j

‘U’: 1—.3‘3 2 1—.3'-’ 31—32 1—32

(3.11)

 

‘ . . . o. . I .
l’Different peer effects were found in the two sulrsamples for marijuana usage but not in drinking,
smoking. church attendance or dropping out of high school.

.16

is obtained. Estimating this equation through OLS. three coefficient estimates are pro-
duced. Since there are five parameters (a. 3. 7, r). (73), two more estimates are required
to recover all of them. The remaining two functions used for identifying the parameters
come from the variance and covariance structure of error terms due to the randonmess

of the roommate assignment. Then the variance and covariance structures are

 

u-+.3u- 1+32 .,
l' - ._'___-’. = —— , ‘ 3.12
m( 1_ J") ) (1_,j2)‘20“ ( )
ktlltl
(‘or(lli + .311}. 111- + 311,-) = 2.305 . (3'13)

1— 32 1— .32 (1 - 3‘3)?

Using three coefficients estimatm’s of the OLS and the estimates of variance and co-
variance of residuals. parameters ((1. {37:11.03) are recovered.” Comparing the two
estimates of .3 obtained through this method and (i)btained assuming e,- = 0,12 he found
similarity between the two estimates and concluded that the, peer effect. works through
the endogenous effect rather than the contextual effect (i.e., ’7' = 0 is not restrictive
assumption.) Although this is a path breaking approach, the identification crucially
depends on the homoscedasticity assumption embtxlied in (3.12) and (3.13). It is not
clearly a priori, however, if all of the. students in dormitories share equal variances in
the unobservable characteristics. As in the usual discussion, the identification off the
assumption of error structure is rather tenuous. Results obtained in this study thus are
informative but not definitive. It. should be noted, however, that this aptn'oach is the

best. one can take with a data set that contains only observed behaviors.
3.4.4 Identification through dynamic structure

Manski (1993) introduced some studies that exploit the dynamic structure of peer effects
transmission. The estimated equation is the dynamic version of (3.2):

E,(g|.r. :) = o + .3E,_1(g|.r) + E,_1(:|.r)7 + 2,1} + 1,2) (3.11)

 

11Standard errors of flame estimates are calculated through bootstrapping.

12Even under assumption of A, = t). regressicm of g, on y_, and s,- does not render unbiased estimator
of (r. .3. 7/ and 0?, since Et’u,'g_,. 3,) ,i (1 since y, is a function of 31,11] and u, as shown in (3.11) even if
e, = (1. His discussion is not quite correct in this sense.

17

Since peer effects do not operate conte1npm‘aneously, the estimation is free from the
”reflection problem" and opens the possibility for estimating peer effects.

Studies of Biddle (1991) and Norton. Lindrooth, and Ennett (1998) fall into this
category. Biddle (1991) analyzed peer effects in the demand for personalized license
plates using state-level aggregate data. In his study. 3;, is current demand of personalized
license plate in state .r. E,_1(y|.r. :) is the demand in the last year. 2, includes state
aggregate income and the number of cars in a state. State dummies are included as .r,.
He implicitly assumed "y = 0. He found significant peer effects in the demand.

Norton. Lindrooth, and Ennett (1998) analyzed peer effects in Sixth to ninth graders“
substance uses. In their study. 3], is sixth to ninth graders’ current use of substances, and
E,_.1(y|.17) is average use of substances among students who attended the same primary
school. The vector z, included sets of demographic and regional characteristics. They
imI')licitly assumed 7 = 0. Since peer effects are defined at an earlier schooling level. the
estimation of peer effects is the dynamic version. The result of estimation showed that
3 is in the neighbor of 1.

Although these estimators are immune from .\Ianski (1993)'s criticism, one should
realize that identification of .‘3 crucially depends on the exclusion of contemporaneoi1s
peer effect. by assumptions. Once the assumptions break down, Manski (1993)’s criticism
again applies to these two studies. It is not clear whether the contemporaneous peer

effect can be ruled out in those two studies a. priori.

3.4.5 Identification through sibling method

Aaronson (1998) attempted to address the heterogeneity bias caused by household het-
erogeneity such as parents‘ ”willingness” to invest in their children. He estimated the
effect. of neighborhood quality, measured by poverty rate or average dropout rate. on a

teenagers dropping out of high school. His model specifies outcome as

my, 2 a + E(.’/l'1.)fj."3+ 3,1,1) + ('j + um. (3.15)

‘18

i,t and j index individual. time. and household. respectively. E ( yl'J'), J- is a regional av-
erage and (:j denotes an llll()lf)S€I‘V0(l household heterogeneity. Absence of the contextual
effect was assumed. The sibling method differences out cj but it also differences out
E (y|.r),j if it is time invariant. Thus. the identification of the peer effect, 3, crucially
depends on the time variance of E ( ylr)” along with the time invariance of household het-
erogeneity. cj. To obtain variation in E ( ylr)”, he used a sibling sample from households
that moved. As Aaronson (1998) noted. however. the residential change and change of
family’s unobservable components are likely to be correlated,13 and consequently the
time invariance assumption on cJ- may be violated. .‘VIoreover. the change of :17 should af-
fect individual behavior only through the change of E (ylr) (i.e.. the assumptions ’7' = 0
and E("u|;r. z) = 0 are critical). Regardless of these restrictions, it is informative that
he found significant peer effects even after controlling household heterogeneity, since the
bias caused by the household heterogeneity was critical concerns in previous studies.
The sibling method is also enmloyed in this study to identify the endogenous effect;
however, time invariance of household heterogeneity is no longer needed by using sub-
jective perceptions of peers' l_)ehaviors. since the siblings" perceptions may vary within a

household. Accordingly. c J- can be differenced out without differencing out perceptions.

3.4.6 Identification through economic theory

So far. I have surveyed identification strategies that only use observed behaviors as
information. On the other hand, there are several notable studies that identify peer
effects using prior kntm'ledge suggested by economic the(_)ry.

Neumark and Postlewaite (1998) suggested ” relative income concern" as a factor
to explain the rapid increase of the labor force participation (LF P) rate among US.
married women. C(_)11stru('-ti11g an economic model of ” relative income concern," they
actually estimated peer effects on labor force participation among married American

women. They used a sister-in-law's LFP as the peers" li)ehavior and regressed a woman’s

 

13Negative unobservable shock within a family may make the family move.

19

LFP on her sister-in-laws LFP. They found significant peer effects on the LFP decision.
Moreover, they directly estimated the prediction obtained from their theory. The theory
predicts that. a woman is more likely to be employed if her husbands income is less
than her sister-in-law‘s husbands income and the sister—in-law is not employed. This
is because the woman’s household may "win" in the income race due to the woman's
additional earnings. ()n the other side of coin, a woman is less likely to be employed
if the woman with the lmsband whose income is less than the sister—in-law‘s and her
sister-in-law is employed. This is because the household is unlikely to "beat” the sister-
iii-laws household with the woman's additional earnings. The estimated result of this
” best response function" was consistent. with theoretical prediction.

Munshi (2000) also avoids spurious findings by using predictions from economic
theory. He analyzed the teclmological diffusion during the Green Revolution in India.
He used the teclmological characteristics that the High Yield Variety (HYV) of rice
is more sensitive to unobservable land quality than is the high yield variety of wheat.
As a consequence of this teclmological factor, economic theory predicts stronger peer
(social) effects for the diffusion of wheat. This is because, roughly speaking. an agent
cannot learn much from his/ her neighbors experience when the success of technology
adoption strongly depends on unobserved heterogeneous factors. The data supported
this prediction; he found stronger peer effects in the wheat. HYV diffusion than in the
rice HYV diffusion.

These studies are persuasive because they carefully examined theoretical predictions
and did more than regress the individual outcomes on group outcomes. Accordingly,
these studies are free from .\Ianski (1993)'s criticism. This strategy. however. can only

be applied to the situation in which sharp theoretical predictions are available.

3.5 Estimation

This review of the existing research clearly indicates the limitation of studies that use
only observed behavior to identify the endogenous effect. As has been discussed, eco-
nomic theory can provide precious information for the identification of these effects.
In the context of substance usage by teenagers. however. obtaining such a. theoretical
prediction is difficult since the preferences of teenagers. which are necessary to derive
behavioral predictions theoretically. are largely unknown a. priori. I thus use perceived
peer behaviors. which are potentially error ridden. as additional informaticm to identify
the endogenous effect. The description of data. the econometric model. and the results

of estimations follow.

3.5.1 Data

The data set used in this study is the National Longitudinal Survey Youth 97 CeoCode
file. The sample construction is summarized in Table 3-1. I used the set (10) (N=6356)
as a sample for the cross-sectional studies. and I applied (almost) school fixed effect es-
timation and sibling fixed effect estimation to the set (11) (N26312) and (12) (N22158)
respectively. In Table 3—1, the sample means of outcomes are tabulated. From the
tabulation, we can confirm that the sample selection does not. drastically change the
property of sample in terms of outcomes.

The outcomes are constructed by using questions about substance use in last 30
days. The respondent who smokes / drinks more than or equal to one. cigarette/ drink is
defined as a smoker/tlrinker; similarly. the respondent who uses marijuana more than
or equal to once per month is defined as a marijuana user.

In order to construct the peer variables. res1.)(_)ndents were asked about their subjec-
tive perception of their peers' behavior by the f(.)llowing questions:

”\Vhat percentage of kids (in your grade / in your grade when you were last in

school)

(smoke / smoked ) cigaret tes'.’

(get/got) drunk at. least once a month?

(have / ever)used marijuana. inhalants. or other drugs?"

Respondents were allowed to answer the questions with one of the following five
categories:

1. almost none (less than 10%)

about 25%

9°!0

about half (50%)

about 7.5%

:L“

5. almost all (more than 90% ).

From these categories. I constructed perceived peers’ behaviors in which ”almost
none” was coded as 0. and ”almost all" was coded as 1.

Descriptive statistics of individual substance uses and perceived peer substance uses
are tabulated in Table 3—2. An interesting finding is that the respondents systematically
overestimate peer behaviors and the degree of overestimation is not negligible. It is
worth noting that this HIPHSIII‘PIIIE‘IIT error is not a problem if we assume the variable
that affects the respondents’ behavitn's is the perceived peer behaviors rather than the

”objective" (for econometricians) peer behaviors.”

3.5.2 The model
The substance use by a teemigm' is specified as
y = n + 3’1) + E(ZIJ‘)"; + :1} + u. (3.16)

where y is a binary variable set to one if the individual is a substance user. The variable

I) is perceived peer behaviors. The vector 2 contains a set of student. family, school. and

 

HThe difference between the subjective measure and the objective measure of the peer behaviors
implies that we cannot calculate the social multiplier effect from the size of endogenous effect (3). since
we need to know how individuals formed their perceptions. Only with perceived peer behaviors. how-
ever. can we confirm the existence of the endogenous effect. without assuming al.)sence of the contextual
effect (i.e. '7 = (l). as discussed befm‘e.

C}!
to

regional characteristics that may affect individual substance use. The variable .17 is an
attribute of the group. which is ” schools" in this model. The determination of perceived

behavior is specified as
p = 91E(y|.r) + :92 + E(z|.r)93 + v. (3.17)

I assume E (u|.r. z) = 0 and E (lll z) = 0. Inclusion of several measures of parental
involvement in vector :3 such as participation in PTA meetings and many variables (85
total) that. may affect. a. youths substance use. makes the assumption E (u|.r..z) = 0
plausil.>le.

There are still potential sources of omitted variable bias. For example. state anti-
drug campaigns to reduce. teenager substance use is a. possible omitted variable that
may affect both a respondents and his or her peers substance use (”correlated effect.”
using Manski (1993)’s terminology). In this situation. the violation of E(u|.r. z) = 0 is
likely to occur through E (ulr. z) = f (it). To reduce this possibility, I included many
regional variables in 3 that characterize the county where the teenager lives (e.g., state
cigarettes tax rate. beer tax rates. cormty-level poverty rate. county-level unemployment
rate, the demographic composition of the county. and other characteristics). It is still
fair to say. however. that the assiunption E ( ulr. :) = O can be violated. Thus, I will
later relax this assumption and use fixed effect estimation.

To ensure that the OLS is an unbiased and consistent estimator. I also assume
E('u|.1‘. 2.1) = 0. (3.18)

This means that the error term of the l)(;‘ll'd\'lOl"dl equation (a) is not correlated with the
error term in the equation determining perceptions of peer behaviors (e). Under this
assumption. applying OLS (3.16) renders an unbiased and consistent estimator of ,3. '7

" - ).'as-.
and lecu‘e

E(u|z.p..r) = E(ul.r. z.p(E(y|.r). z. e)) = E(ul.r. z. c) = 0. (3.19)

53

The cell average of 2 within a school is used instead of E(z|;r).15 Since 11 is heteroscedas-
tic due to the binary dependent variable. the variance covariance matrix is calculated
using the White formula.

Since the respondents school i<.le.ntification number (ID) is not available in my data
set. I create the (almost) school ID by matching the respondent’s county ID. school size

and student / teacher ratio. 16

3.5.3 The results

I report the results of the above model in Table 3—3.

For cigarette smoking. the coefficient 0.221 means that. a 10 percentage point increase
in the subjective perception of the peer smoking probability increases the probability of
smoking by 2.2 percentage points. This estimate is statistically significant. For alcohol
drinking. the estimated coefficient is 0.311. For marijuana usage. the OLS estimate is
0.229.

The results clearly show the existence and statistical significance of peer effects.
W hen a teenager’s perception of the 1')e.r('tentage of his/ her peers who use a substance
increases by 10 percentage points. the prt.)bability that he/she will use the substance
increases from 2.2 to 3.1 1.)er('rentage points. Although the difference in identification
strategy prohibits me from serious comparison of the estimates. the estimated peer
effect is comparable to the estimated effect of Gaviria. and Raphael (2000) for smoking

and alcohol drinking (0.150 for smoking. 0.106 for alcohol drinking and 0.254 for drug

 

15Although this first step estimation may change the asymptotic distribution of the OLS estimat(_)rs.
it is known that under the null of H0 : '7 = 0 the asymptotic distribution is not affected.

lbSchool size is classified into five categories: 1-299. 300—499. 500-749. 750-999. and 1000+ students.
Student/teacher ratio is classified into four categories: less than 14. 14-17, 18-21 and more than 22.
The Bureau of Labor Statistics assigns these two variables to each respondent based on the confidential
information held by BLS. Thus. when each school in a county is different in either school size or
student / teacher ratio. I can identify all of the schools. If all of the schools in a county share both the
same school size and student/teacher ratio. I just identify the county. In the worst-case scenario. it
is to assume E (SlJf) is constant within a county. This mis-matching becomes serious if the variation
of E (:31?) is huge within a county. However. the direction of bias in the estimator of 7' caused by this
measurement error is not clear a. p‘r‘iori. since the measurement error is mean reverting (to the county
mean) and not classical. The contextual effect that operates at the county level is, however. at least
captured.

use). The absence of the contextual effect (‘7 = 0) is not rejected through an F -test.
In sun’nnary, the results of the estimation robustly show the existence of peer effects
in the causal sense. .\Ioreover. due to the usage of perceived peer behavior as the key
independent variable. the results show that peer effects work through the endogenous
effect. This implies the existence of the ”social multiplier.”

The causal interpretation critically depends on the assumption E(u|;1:, z. e) = 0. The

multitude of variables in z (82 in total). l‘iowever, makes this assumption realistic.”

3.6 The (almost) school fixed effect estimation

Although the previous section made the best effort to assure the assumption E (izlr. z, c)
0 is correct. the school attribute .r nonetheless may still contain some information which
systematically predicts teenagers" substance use that the regional or school character-
istics included in z fail to capture. In other words, there might be regional and school
factors that encourage the teenagers’ substance use that are not observed. For example,
suppose a cigarette shop is located just in front. of high school A. Moreover. suppose
this ” unobservable” makes a student. in the high school 50 percentage points more likely

to smoke. Then the assumption E(u|.r. :. if) = 0 is violated. because

0.5. if .r = A.

E (11.3.14' = ~ -
i l ) 0. ()therw1se.

(3.20)

This possibility is addressed through school fixed effect estimation, which can be repre-

sented in the equation
LU!) : jlhj + 3071+ E(:..‘r)j72 + (1(I)j +110. (3.21)

Here 2'. is a subscript for an individual and j is a. subscript. for a school. The coefficient 72

is not identified, since E (2|;r) j is invariant within a school. The random variable {1(1)}-

 

17If the instrumental variables that are sufficiently correlated with p but not correlated with u exist. we
can use Hausman (1978)‘s method to test endogeneity of p. In particular. specifying E(u.i.1:. :. I?) = pH,
”0 : p = 0 can be tested given E(ul;r. :) = 0. However. finding such IV is prohibitively difficult.

captures the school specific unobservable which affects the teenagers substance usage,
such as a cigarette shop at the school gate.

Under the assumptions E(u|p. :.a..r) : 0 and E(a|p, 3.1?) = 0, both the random
effects estimator and fixed effects estimator are consistent. However. only E (111 p. z. a) =
0 needs to be true for the consistency of the fixed effect estimator. This allows for a
test of the assumption E ( a] 1). z. .r) = 0 using a Hausman test. Since the random effect
estimator is not efficient due to heteroscedasticity, I use the robust form of the Hausman
test introduced in \Vooldridge (2001). Although it is not rigorously justified, the single
variable Hausman test roughly tests the null E(a|p, 2.51.“) = 0. Under this assumption,

t.18 The results of the random,

the estimator of the coefficient for p is roughly consisten
fixed effect estimation and the Hausman tests appear in Table 3-4. All of the Hausman
tests do not reject the null of E (0| p. z. .r) = 0. Thus I am in favor of using the random
effect estimator because of its relatively efficient property. Peer effects for smoking,
drinking and marijuana usage are 0.22. 0.31. and 0.23 respectively. These numbers are

similar to the OLS results. This is probably due to the fact that the vector :5 already

contains enough ii‘iformation to capture the school "‘ unobservable.”

3.7 The household fixed effect estimation using the
sibling sample

To reinforce the previous results. I estimate the household fixed effect model using sibling
samples. One concern in the previous research (Evans, Oates, and Schwab (1992)) was
the endogeneity of peer quality due to omitted household characteristics. Peer quality
can be endogenous since parents who are willing to invest in their children send their
children to a school with good peers. Parental care can also directly affect a child's
behaviors. This problem can be avoided through controlling household unol.)served

heterogeneity using the household fixed effect. estimation.19

 

1”This discussion is not exactly true as far as p is correlated with the other explanatory variables.
19The sibling method. however. does not necessarily solve the endogeneity. Since between household
difference of outcomes and perceived peer behaviors are wiped away. the identification solely depends on

I estimate the following model:
1],]- 23:31))j + $,‘j";1 ‘i' E(Zl;1'),‘j + (.‘j + Uij (3.22)

where i is a subscript. for an individual. j is a subscript. for a household, and c is a
household unobserved heterogeneity. The. same econometric discussion from the previous
section applies.

The results of random and fixed effects estin‘iation and the Hausman tests appear in
Table 3-5. All of the Hausman tests reject the null of E(c|p. 2,1) = 0. Thus, the fixed
effects estimator is preferred. These results are similar to the previous OLS results that
appeared in Table 3—1. Peer effects for smoking, drinking and marijuana usage are 0.14,
0.27, and 0.21 respectively. The fixed effects coefficient estimates are smaller than those
obtained from the random effects or OLS estimation due to the positive correlation
of the household unobserved heterogeneity and the peer variable. Nevertheless, even
after allowing for the correlation of the household heterogeneity and the peer variable
through the fixed effect model, the estimated peer effects are practically and statistically

significant.

3.8 Peer effects and the demographic groups

Thus far, the existence of peer effects is a robust result. Next, it is interesting to inves-
tigate the strength of peer effects within different demographic groups. With knowledge
of the group in which the endogenous effects are strong, policy makers can effectively
target the policy that is likely to (’liscourage youth substance use within that group,

since he/ she can expect larger policy effects through the larger ”social multiplier” .20 To

 

within household variation of outcomes and perceptions. If each sibling in a household has unol.)served
characteristics that determine both substance use and perception of peer behaviors. the sibling estimator
is still biased. If this within household unobservable plays a more important role than the between
households unobservable. the sibling estimator can be more biased. However. this discussion is unlikely
to apply in the context of substance use. See Bound and Solon (1999) and Neumark (1999) for possible
biases in the sibling estimator of the return to education.

20This discussion assumes. though. that sensitivity to the policy (a part of 1}) is the same across
groups.

57

estimate the difference in the endogeinms effects across groups, I assume the following
model in which the strength of peer effects may depend on the different demographic
groups:

y = (i + .311) + p * 21.32 + :7 + E(:l.1‘)'l] + u. (3.23)

‘) . .
“I Since the exogeneity

where 21 is the part. of 3 that defines demographic groups.
of p was not rejected in the school and household fixed effects estimation, I assume
E(ulp, 3,1“) = 0. Under this assumption, OLS is an unbiased estimator.

The results of the regressions appear in Table 3-6. Some of the estimated coefficients
on the interaction terms are statistically significant and the effects are not. negligible. As
for the marijuana usage, females are less likely than males to be affected by their peers.
For the other two substances. a gender difference was not found. Fewer peer effects
were found among black teenagers. For smoking behavior, peer effects among black
teenagers are one-third of that. found anmng white teenagers. The smaller peer effects
among black teenagers are statistically different from zero for all substances. Hispanics
are also less vulnerable to peer pressure. An expectation is that. minority teenagers
might not obtain as much utility as non-minority teenagers from imitating each other.
Teenagers with both biological parents are less likely to be affected by their peers in
their smoking and marijuana uses. As for drinking, the coefficient on the interaction
term of the peer variable and ”both biological parents" is not statistically significant.
The first two results may imply that. the teenager who does not have both biological
parents present is more likely to depend on his peers to form his behavior. This result is
consistent with Steinberg (1987):}. The result was not. obtained in Caviria and Raphael
(2000), probably because the peer variable only interacts with the single parent dummy,

not with the race dummies.

 

2‘1 also estimated the endogemms effect using a sub—sample of demographic groups. The results
obtained were qualitatively the same as the results obtained in this section.

3.9 Conclusion

The estimation of peer effects on substance usage through perceived peer behaviors
shows the existence of significant peer effects. “hen the perceived peer substance use is
doubled, the probability that. a teenager will use substances increases by forty to sixty
percentage points. .\I<:)reover, the endogenous effect is found to be more important than
the contextual effect when explaining the peer effects on youth substance use. This
finding implies that. current peer behaviors, rather than peer backgrounds, determine
individual behaviors. Thus, if some exogmious shock reduces a groups substance use,
this reduction propagates to other groups of youths through the endogenous effect.
Hence, policy makers can expect. a "social multiplier” effect in policies that discourage
youth substance use.

In my model, the endogenous effect is identified when perceived peer behaviors are
exogenous. To assure this exogeneity assumption, I used a rich set of controls consisting
of parent, neighborlmod and school cltaracteristics. ;\I(;)reover, the robustness of the
results is confirmed through the school and household fixed effect estimations. we also
find that. the strength of peer effect. depends on the demographic group to which a
teenager belongs. Peer effect is found to be large among white teenagers and teenagers
without both biological parents.

Although this paper finds a robust peer effect. this study does not shed enough
light on the mechanism of peer effect itself. Thus this study is still a ” reduced form”
study of peer effects. More direct research on the mechanism of peer effect is left for
future research. Also. peer effects on the several other socioeccmomic behaviors such as
criminal or sexual activities are the topics for the future research. I hope the robust

findings in this study stimulate further investigation in this field.

59

Bibliography

AARONSON. D. (1998): "Using sibling data to estimate the impact of neighborhoods

on children’s educational (:)utco111cs." Journal of Human Resources. 33(1). 915—946.

AKERLOF. G. A. (1997): "Social distance and social decisions," Econometrica, 65(5),

1005—1027.

ARABSHEII'MNI. G.. D. l)ICl\1EZA. J. I\IALO.\IEY. AND B. PEARSON (2000): “And a
Vision Appeared unto them of a Great Profit: Evidence of Self-Deception among the

Self-Employed,” Economics Lcllcrs. 67. 3Al~4l1.
BECKER, G. S. (1996): Accounting for tastcschap. 11. Harvard University Press.

BENDER, K. A., AND P. J. SLOANE (1998): "Job satisfaction. trade unions, and

exit-voice revisited." Imlastrlal and Labor Relations Review. 51(2). 222—240.
BIDDLE. J. (1991): "A bandwagon effect in personalized license plates‘?,” Economic

Inquiry, 29(2). 375 388.

BLANC‘HFLOWER. D. 0.. AND A. J. OswALI) (1998): "What makes an entreprenem‘.’,"‘

Journal of Labor Economics. 16(1). '26 (if).

BQRJAS. G. .l. (1979): ".lol)satisfaction. wages and unions,“ Journal of Human Re—

sources, 11(1). 2140.

BORJAS, G. J.. AND S. G. BRONARS (1989): "Consumer (liscrimiiration and self-

emj')loyment." Journal of Political Economy. 97(3). 581 605.

60

BOUND, J., AND G. SOLON (1999): "Double trouble: on the value of twin-based

estimation of the return to schooling," Economics of Education Review, 18, 169—182.

BROCKHAUS. R. H.. AND P. S. HORWITZ (1986): "The Psychology of Entreprcnem',”
in The Art of Science of Entrcprcnc-urshlp, ed. by D. L. Sexton, and R. W. Smilor.

Ballinger, .\lassacluisetts.

CARI). D. (2000): "The Causal Effect of Education on Earnings," in Handbook of Labor
Economics, ed. by O. C. Ashenfclter, and D. Card, vol. 3A, chap. 30. Elsevier, New
York.

CARRlNGTON. W. J.. K. MCCUE. AND B. PIERCE (1996): “The role of em-

ployer/emj)loyee interactions in labor market cycles: Evidence from the self-

employed.” Journal of Labor Economics. 11(1). 5717602.

CASE. A. C.. AND L. F. KATZ (1991): "The company you keep: The effects of fam-
ily and neighborhood on Disadvantaged youths.” Discussion paper, NBER “forking

Paper Series May.

CLARK, A., Y. Cl£()R(;l£Ll.IS. AND P. SANFEY (1998): “.lol) satisfaction, wage changes,
and quits: Evidence from Germany." Rcsearclz. in Labor Economics, 17, 95-121.

CLARK. A. E. (2001): "What really matters in a job‘.’ Hedonic measurement using quit
data,” Labour Economics.

CLARK, A. E.. AND A. J. OSWALD (1991): "linhappiness and L'nen1}.)loyment,"’ Eco-

nomic Journal. 101(121), 618‘ 59.

(1996): "Satisfaction and cmnparison income,” Journal of Public Economics.

61, 359 381.

Cooeua. A. C.. C. Y. Woo. AND W. C. DUNKELBICRC (1988): "Entrepreneurs’

Percieved Chances for Success." Journal of Business Venturing. 3. 97—108.

(51

CRANE. J. (1991): "The epidemic theory of ghettos and neighborhood effects on drop-

ping out. and teenage childbearing.” American. Journal of Sociology, 96(5), 1226-1259.

DUNN. T., AND D. HOLTz-EAKIN (2000): “Financial capital, human capital, and
the transition to self—employment: Evidence from intergeinérational links,” Journal of

Labor Economics, 18(2). 282 305.

EVANS, D. 8., AND B. JOV’ANOVIC (1989): "An Estimated Model of Entrepreneurial

Choice under Liquidity Constraints," Journal of Political Economy. 97(1), 808—27.

EVANS, D. 8.. AND L. S. LEIGHTON (1989): "Some empirical aspects of entrepreneur-

ship,” Aimrrican Economic Rccicw, 79(3), 519 535.

EVANS. W. N., W. E. OATES. AND R. M. SCHWAB (1992): "Measuring peer group

effects: A study of teenager behavior." Journal of Political Economy, 100(5), 966~991.

FAIRIJE, R. \V., AND B. D. MEYER (1996): "Ethnic and racial self-eniployment (lif-

ferences and possible exj’)la.nations." Journal of Human. Rmources, 31(4), 757—793.

FREEMAN, R. B. (1978): “Job satisfaction as an economic variable," American Eco-

nomic. Rcuicun 68. 135—111.

GAVIRIA. A.. AND S. RAPHAEL (20(10): "School based peer effects and juvenile behav-

ior,” Forthcoming in Review of Economics and Statistics.

CRUISER. J. (2000): “Youth smoking in the 1.7.8.: Prices and policies,” Discussion paper,

NBER \Vorking Paper Series 7506.

HAMERMESII, D. S. (1977): "Economic aspects of job satisfation,"7 in Essays in Labor

Market and Population Analysis, ed. by Ashenfelter, and Oates. \Viley.

(2001): "The Changing Distribution of .101) Satisfaction,” Journal of Human

Resources, 36(1). 1‘30.

HAMILTON, B. H. (2000): "Does Entrepreneurship Pay? An Empirical Analysis of the

Return to Self—E1nployment," Journal of Political Economy, 108(3), 601 631.

HAUSMAN. J. A. (1978): “Specification tests in econometrics," E(0110nltl7u(l 46(6),

1251—1271.

HEC‘KMAN J. J. (1976): ".A life—cycle model of earnings. learning, and consumption,”

Journal of Political Economy, 811(1). 811- 11.

HOUT, l\'I., AND H. S. ROSEN (1999): "Self-employment, family background, and race,”

Discussion Paper 7-133, N BER.

JOULFAIAN, D., AND M. RIDER (1998): "Differential taxation and tax evasion by small

business Vational T111 Jouinal. 51(1), "75-687.

JOVANOVIC, B. (1979): "Job matching and the theon of tulnm e1 Journal of Political

EconomyS 7(5), 972 990.

KANBUR, S. M. R. (1982): "Entrepreneurial risk taking. inequality. and public policy:
An application of inequality 1lecomposition analysis to the general equilibrium effects

of progressive taxation." Journal of Political Economy, 90(1), 1*21.

KlHLSTROM. R. E.. AND J.-J. LAI‘EONT (1979): A general equilibrium en-
trepreneurial theory of firm formation based on risk aversion." Journal of Political

Economy, 81(1). 719718.

KR \SHINSKI H. (2000):) Sele111plm111eIIt and “(we Inequalm P1111(etonLnI\e1s1t\

Mimeo.

LAZEAR. E. P. (1979): "\Vhy is there 111a.ndatory i'eti1'(:‘111elrt‘?.” Journal of Political

Economy. 87(6), 1261- 128 1.

LAZEAR. E. R, AND R. L. MOORE (1981): "Incentives. P1'1_)ductivity. and Labor

Contracts," Quarterly Journal of Economics. 99. 275—295.

63

LEIGH. D. E. (1979): "Do Union Members Receive Compensating \V’age Differential -
Note.” American. Economic Review, 11(1), 21—410.

LINDII, T.. AND H. ORLSSON (1996): "Self-employment and windfall gains: Evidence

from the Swedish lottery.” Economic Journal. 106(139), 1515-1526.
LOS-ANGELES-TIMES (1999): "Smoking among youths continue to rise," January 8.

LUCAS, R. E. (1988): "On the .\11_‘cln~1.11ics of Economic Development," Journal of Mon-

etary Economics. 22(1). 3 12.

MANSER. M. E., AND G. PICOT (1999): "The Role of Self-E1111:)loy1nent in US. and

Canadian .1111) Growth." Monthly Labor Rcci1—‘ttc. 122(1), 107725.

MANSKI. C. (2000): “Econtnnic Analysis of Social Interactions." Journal of Economic

Pcrspcctim-f. 68(3), 115 36.

MANSKI. C. F. (1993): "Identification of endogenous social effects: The reflection

problem," Roiiicin of Economic Studics, 60(3), 531 512.

MCCORMICK. E. J.. AND D. ILCEN (1985): Industrial and Organizational Psychology.

Prentice Hall. New Jersey. eighth edn.

MOSKOWITZ. T. J.. AND A. VISSING-JORGENSEN (2001): "The Private Equity Pre-

mium Puzzle.” Graduate School of Business. University of Chicago.

MUNDLAK, Y. (1978): "On the Pooling of Time Series and Cross Section Data.” Econo-

metrica. 416(1). 691—85.

MUNSIII, K. (2000): "Social leaning in a. heterogeiIeous 1')opulation: Technology diffu-

sion in the Indian Green Revolution." .\Ia.nuscript.

NEUMARK. D. (1999): "Biases in twin estimates of the return to schooling.” Economics

of Education. Recicut 18. 113-118.

64

NEUMARK. D.. AND A. POSTLEWAITE (1998): "Relative income concerns and the rise

in 111arried women’s employment." Journal of Public Economics, 70. 157 183.

NORTON. E. C.. R. C. LINDROOTII. AND S. T. ENNETT (19.98): “Controlling for
the endogeneity of peer substance use on adolescent alcohol and tobacco use," Hcaltli

Economics, 7. 139—7153.

SACERDOTE, B. (2000): "Peer effects with random assignment: Results for Dartmouth

roommates." Discussion paper, NBER \V'orking Paper Series 7-169.

SALOP, J .. AND S. SALOP (1976): "Self-selection and turnover in the labor market,"

Quatcrly Journal of Economics, 90(1). 619627.

SCHILLER, B. R.. AND P. E. CRIM'SON (1997): "Entrepreneurial origins: A longitu-

dinal inquiry." Economic Inquiry. 35(3), 52377531.

SIECEL, I... AND I. M. LANE (1987): Pcrsonncl and Organizational Psychology. Irwin,
Illinois.
STEINBERG. L. (1987): "Single parents. step parents, and the susceptibility of adoles-

cents to antisocial peer pressure," (71 1111 Dccclopmcnt, 58, 269275.

\A'fOOLDRIImE, J. M. (2001): Economctric Analysis of Cross Section and Panel Data.

MIT press. Cambridge. MA.

Table 1-1: The replication of the Lazear and Moore (l984)’s finding and
the risk of self-employed workers.

 

 

 

 

(1 ) (2) (3) (4) (5) (6)
Model OLS F.E. OLS F.E. OLS F.E.
Dependent variable In wage In wage Residual2 Residualz Residual2 Residual2
0H2) 0f(2) of(2) of(2)
education 0.070 — — — — —
(0.003)
experience 0.063 0.1 12 -— — — —
(0.005) (0.008)
experience-7100 -0120 -0.187 .. - _ _
(0.026) (0.019)
tenure 0.052 0.032 — — — —
(0.004) (0.003)
tenure-7100 -0250 0198 _ _ _ _
(0.029) (0.020)
self- employed 0.032 0.086 0.237 0.189 0.201 0.172
(0.033) (0.018) (0.041) (0.038) (0.056) (0.046)
self X (educ- educ ) 70007 7 7 7 7 7
(0.01 1)
self x (8» ex) -0.043 -0.043 — — — —
(0.020) (0.012)
sele(ex2- ex2)/100 0.193 0.146 — — — —
(0.088) (0.049)
self x (1611- ten) -0.054 -0.03l — — — —
(0.016) (0.009)
selfx (tenJ- ten2 )/ I ()0 0243 0'228 7 7 7 7
(0.100) (0.057)
(self- W): _ — — — 0.133 0.059
(0.092) (0.090)
Constant 5.566 — 0.147 — 0.145 —
(0.058) (0.009) (0.088)
Observations 23887 23887 23887 23887 23887 23887
Number of individuals — 2715 — 2715 — 2715
R2 0.31 _ _ _ _ _
Wage of SE — Wage of SW
0 year experience and 0.343 0.357
0 year tenure (0.107) (0.061)
5 years experience and -0.034 0.080
5 year tenure (0.059) (0.032)
10 years experience and -0.l92 -0.01 l
10 year tenure (0.062) (0.035)

 

Notes: ,
l. Standard errors are in parenthesis for coefficient estimates. For OLS estimates, standard errors are corrected
for the panel clustering.

2. Dependent variable in the each regression (3), (4), (5) and (6) is the squared idiosyncratic residual of

regression (2 ).

3. Year dummies are included but coefficients are not reported.

4. Wage of SE - Wage of SW is evaluated at the sample mean of education that is 13.107.

66

Table l-2: School Enrollment by Sectors (1988-1998)

 

 

Salary and Self Total
Wage Employed
Enrollment Rate 9.01 5.60 8.67
N 16021 1804 17825

 

Notes:

1. The workers who were enrolled in school at all between last interview date and
cunent interview date are classified as “enrolled.” This does not necessarily imply '

cunent enrollment.

2. Only observations afier year 1988 are included in the sample. Youngest

respondents are 24 in 1988.

Table 1-3: Linear Probability Model of School Enrollment
Dependent Variable: Enrolled in School (Yes=l, No=0)

 

 

(1) (2)
Mean of Dependent Variable 0.087 0.087
Method of Estimation OLS F.E.
Self-Employed -0.024 -0.019
(0.008) (0.009)
Education 0.026 -
(0.002)
Experience -0.015 -0.040
(0.004) (0.006)
Experience”) 0.045 0.081
(0.015) (0.013)
Tenure -0.012 -0.007
(0.002) (0.002)
remit-e3 0.055 0.039
(0.011) (0.012)
Constant -0. 138 0.508
(0.030) (0.080)
Observations 17825 1 7825
R-squared 0.07 0.02
Number of 1D — 2634

 

Note: The same note applies as Table 2. Standard errors are in parenthesis for
coefficient estimates. For OLS estimates, standard errors are corrected for the panel

clustering.

67

Table 1-4: Training Participation by Type of Training and Cost Bearing (1988-1998)

Panel A: Training Participation by Type of Trainings

 

 

Salary Self— Total
and Employed
Wage
Not Participate in Training 81.09 90.74 82.06
On—the-job Training 1 1.67 3.61 10.85
Apprenticeship Program 0.75 0.28 0.70
Formal Company Training Run by Employer or Military Training 7.35 1.83 6.79
Seminars or Training Programs at Work Not Run by Employer 3.57 1.50 3.36
Off-the -job Training 6.20 5.00 6.07
Business School 0.29 0.33 0.29
Vocational or Technical Institute 1.45 0.67 1.38
Correspondence Course 0.52 0.28 0.49
Seminars or Training Programs outside of Work 3.71 3.55 3.69
Vocational Rehabilitation Center 0.14 0.1 l 0.13
Government Training Program 0.09 0.06 0.09
Other 1.05 0.67 1.01
N 16014 1804 17818

 

Panel B: Training Participation by Type of Cost Bearing

 

 

Salary Self- Total
and Employed
Wage
Not Participate in Training 81.09 90.74 82.06
Training Cost Paid by Self or Family 1.57 3.88 1.81
Employer 15.87 3.60 14.63
Job Training Program Act 0.14 0.1 l 0.14
Trade Adjustment Act 0.02 0.06 0.02
Job Corps Program 0.01 0.00 0.01
Work Incentive Program 0.01 0.00 0.01
Veteran’s Administration 0.02 0.06 0.03
Vocational Rehabilitation 0.09 0.00 0.08
Other 1.17 1.55 1.21
N 16014 1804 17818

 

Notes:
1. The results are based on most recent participating training program that starts
between the last interview date and the current interview date.
2. Among types of training, “Business School,” “Vocational or Technical Institute,”
“Correspondence Course,” “Seminars or Training Programs outside of Work” and
“Vocational Rehabilitation Center” are classified as off-the- job training.

3. Trainings paid by “Self or Family” and govemment programs are classified as
“own cost training.”

68

Table 1.5; Linear Probability Models of Training Participation

 

 

 

(l) (2) (3) (4) (5) (6) (7)
Sample SE and SW SE and SW SW Only
Dep. Var. Training Ofi‘J’I‘ Training Ofi-JT Own/Gov.
Paid
Mean 0.179 0.061 0.179 0.061 0.033
Method of OLS F.E. OLS r15. OLS OLS OLS
Estimation
SE -0097 -0050 -0012 -0010 - - —
(0.009) (0.013) (0.006) (0.009)
Future Self — _ _ - -0057 -0.001 0.014
(0.012) (0.008) (0.006)
Education 0.024 — 0.008 — 0.024 0.006 0.000
(0.002) (0.001) (0.002) (0.001) (0.001)
Experience -0.003 0.000 -0.003 -0.005 -0.003 -0.005 -0.006
(0.004) (0.009) (0.003) (0.006) (0.005) (0.003) (0.003 )
Experience) 0.023 . 0.010 0.020 0.016 0.020 0.025 0.019
(0.018) (0.019) (0.011) (0.013) (0.025) (0.016) (0.012)
Tenure 0.006 -0006 -0.001 -0002 0.008 -0.001 -0005
(0.003) (0.003) (0.002) (0.002) (0.003) (0.002) (0.001)
Tenure’ -0.038 0.025 0.009 0.022 -0049 0.014 0.022
(0.018) (0.017) (0.012) (0.012) (0.025) (0.016) (0.008)
Constant -0.148 0.195 -0035 0.117 -0103 0.000 0.095
(0.032) (0.120) (0.018) (0.079) (0.037) (0.021) (0.017)
Observations 17818 17818 17818 17818 13671 13671 13671
R-squared 0.04 0.01 0.01 0.00 0.03 0.01 0.01
Number of [D - 2634 - 2634 - - -

 

Note: The same note applies as Table 2. Standard errors are in parenthesis for coefficient

estimates. For OLS estimates, standard errors are corrected for the panel clustering

69

Figure 1-1: Life Time Utility of Each Career Path and Initial Human Capital

Life Time Utility (1‘)
A 315— SEW”)

SW - SE (VH)

SW 7 SW (12,.)

 

/ > Initial Human Capital (h,,)

The parameter of absolute
risk aversion (y) is given

 

Note: SE: Self-Employed workers; SW: Salary and Wage workers.

Figure 1-2: Distribution of test scores among SE and SW workers

—a— AFQT Score for SW Workers ——9— AFQT Score for SE Workers

.015 A

.01 "

 

 

l T

100

0—4

. 50
Percentile of AFQT Score in 1989

Note: Bandwidth = 6 and Epanechnikov kernel was used to estimate the kernel density. The
distribution of percentile ranges from -5 to 105 because of the bandwidth = 6, actual distribution
of the percentile of AFQT89 ranges from 1 to 99. Workers who were both SE and SW over their
careers contribute to both populations on a career-weighted average.

70

Table 2-1: Sample Construction

 

 

 

Total Salary Self-
Wage employed
workers workers

Original NLSY79 1985-1998 152232
White 90120
Male 45480
Employed + out of school 24756
Work in private. govemment and self-employed 24580
Valid answer for job satisfaction: Sample ( 1) 24533 22095 2438
Employed + out of school for two consecutive interviews 19893
Valid class + tenure variables 19402
Work in private. govemmcnt and self-employed for two 19298
consecutive interviews
Valid answer for job satisfaction: Sample (2) 19222 17265 1957
Sample (2) + lagged demographic variables are available: 13889 12512 1377
mple (3)
Sample (1) + valid covariate + more than 2 years of 20454 18585 1869
observation: mple (4)
Hourly wage/eamings are between 1 percentile and 99 20065 18328 1737

percentile: amateur

Note:

Tenure variable is used to identify job change. Sample ( 1) is used in the analysis of Table 2-3. Sample (2)

is used in the analysis of Table 2-4. Sample (3) is used in regression analysis.

71

m.

Table 2-2: OLS Regression Coefficients
Dependent variable: log hourly wage
Sample: White Male (Sample (4))

 

(1)

(2)

 

 

OLS Fixed
Effects
Self-employment -0.01 1 0.022
(0.030) (0.016)
Married-Spouse present 0.093 0.056
(0.014) (0.010)
Education 0.078 -
(0.003)
Experience 0.047 0. 1 09
(0.006) (0.010)
Experience: / 100 -0.068 0.115
(0.028) (0.021 )
Tenure 0.046 0.029
(0.004) (0.003)
Tenure2 / 100 .0223 -0.186
(0.028) (0.021 )
Self-employment x Married - Spouse Present -0.059 -0.000
(0.061 ) ( 0.028)
Self-employment x Education -0.0()8 -
(0.012)
Self-employment x Experience -0.020 -().024
(0.023) (0.013)
Self-employment x Experience2 / 100 0.1 15 0.080
(0.095) (0.053)
Self-employment x Tenure -0.050 -0.027
(0.016) (0.009)
Self—employment x Tenure2 / 100 0.221 0.194
(0.102) (0.058)
Constant 5.162 5.923
(0.044) (0.044)
Observations 20454 20454
R—squared 0.30 0.26
Number of individuals 2661 2661
Wage of Self-Employed — Wage of Wage and Salaried Workers
No Experience and No Tenure 0.184 0.209
(0.128) (0.071)
5 Year Experience and 5 Year Tenure -0.084 0.027
(0.064) (0.036)
10 Year Experience and 5 Year Tenure -0.184 -0.018
(0.064) (0.037)

 

Standard errors robust against panel clustering are in parentheses for OLS estimates. Standard errors are in

parenthesis for F .E. estimates.

Table 2-3: Job Satisfaction among Workers
Panel A Job satisfaction among salary/wage workers
Sample: White Male (Sample (1))

 

 

year Like very Like fairly Dislike Dislike very Total
much well somewhat much observation
Row % Row % Row °/o Row “/6
85 45.97 43.27 8.65 2.1 1 1849
86 43.25 48.13 6.13 2.49 1926
87 41.95 50.56 5.97 1.52 2043
88 41.28 48.82 8.20 1.70 2122
89 43.59 46.89 7.43 2.09 2154
90 41.58 49.84 6.23 2.35 2167
91 43.17 47.58 7.41 1.84 1633
92 43.00 48.49 6.51 1.99 1658
93 42.19 48.85 7.69 1.27 1652
94 41.51 50.60 6.39 1.51 1660
96 43.1 1 48.93 6.55 1.41 1633
98 46.12 46.62 5.32 1.94 1598
Total 43.00 48.24 6.89 1.87 22095

 

Panel B: Job Satisfaction among Self-Employed Workers

 

 

year Like very Like fairly Dislike Dislike very Total
much well somewhat much observation
Row “/6 Row "/6 Row % Row %

85 72.61 22.29 3.18 1.91 157
86 63.07 31.25 1.70 3.98 176
87 59.90 36.04 2.54 1.52 197
88 64.71 31.22 3.17 0.90 221
89 64.38 33.48 1.72 0.43 233
90 65.07 31.00 3.06 0.87 229
91 70.05 26.40 2.54 1.02 197
92 63.26 33.95 2.33 0.47 215
93 63.59 33.98 1.94 0.49 206
94 62.56 33.33 3.08 1.03 195
96 74.52 22.60 2.40 0.48 208
98 65.69 29.41 3.92 0.98 204

Total 65.67 30.60 2.63 1.1 1 2438

 

73

Table 2-4: Probability to Change Job by the next Interview
Sample: Sample (3)

 

 

Job satisfaction in the previous interview Salaried and Self-Employed Mean
Wage Worker Workers
Like very much 0.148 0.108 0.143
Like fairly well 0.177 0.207 0.179
Dislike somewhat 0.256 0.383 0.263
Dislike very much 0.388 0.266 0.379
Total 0.173 0.153 0.171
N 12512 1377 13889

 

Note:
Sample is constructed to be consistent with the sample used for the job change regression.

Table 2-5: Job Change Probit Model
Dependent variable: Job Change between t and t—l(’Yes= l; No=0)
Sample: Sample (3)

 

 

( 1) (2)

Lagged job satisfaction: Dislike very much 0.764 0.734
(0.098) (0.099)

Laged job satisfaction: Dislike somewhat 0.434 0.432
(0.051) (0.052)

Lagged job satisfaction: Like somewhat 0.149 0.145
(0.029) (0.030)

Self Employment -0.013 0.006
(0.062) (0.064)

Self Employment x Dislike very much , —0.627 -0.567
(0.308) (0.291)

Self Employment x Dislike somewhat -0.123 -0.103
(0.284) (0.285)

Self Employment x Like somewhat 0.135 0.130
(0.099) (0.102)

Laggcd education -().017 -0.005
(0.005) (0.006)

Lagged experience ' -0.012 -0.005
(0.004) (0.004)

lagged tenure 41.046 -0.047
(0.004) (0.004)

Laggcd log wage -0. 135
(0.026)

Constant -0.545 0.164
(0.080) (0.166)

Log Likelihood .6163 ~5979
Pseudo R2 0.029 0.033
°/o Conectly Predicted 82.92 81.78
Observations 13889 13592

 

Marginal effects of Probit model is presented.

Robust standard errors against panel clustering are in parentheses.
Lagged job satisfaction: like very much is the omitted category.

74

Table 2-6: Transition of Job Satisfaction Associated with Job Change (Percentage)
(SE: Self-Employment Job. SW: Salary and Wage Job)
Sample: White Male (Sample (2))

 

 

     

   

Like Like Dislike Dislike
Very Much Somewhat Fairly Well Very Much
W
W
Egel A: Job Stayers
1 /war - l /w N 13910
Like Very Much 30.17 10.34 0.96 0.19
Like Somewhat 13.11 33.26 2.95 0.52
Dislike Fairly Well 1.03 3.79 1.62 0.29
Dislike Very Much 0.31 0.74 0.45 0.27
Egel B; Job Stags
(SE-SE) N=1327
Like Very Much 54.56 9.87 0.38 0.15
Like Somewhat 13.49 17.71 0.75 0.30
Dislike Fairly Well 0.53 0.75 0.60 0.15
Dislike Very Much 0.08 0.30 0.23 0.15
W
/w - l /w N=2838
Like Very Much 21.71 21.04 3.84 1.41
Like Somewhat 12.37 25.55 5.25 1.27
Dislike Fairly Well 1.30 2.85 1.30 0.42
Dislike Very Much 0.42 0.81 0.25 0.21
' ' vi us (ear
Job gjhanger
‘- =4
Like Very Much 51.22 12.20 2.44 0.00
Like Somewhat 4.88 24.39 2.44 0.00
Dislike Fairly Well 0.00 0.00 0.00 2.44
Dislike Very Much - - - -
wa / ala - N= 89
Like Very Much 40.41 23.09 3.23 0.85
Like Somewhat 7.13 17.15 2.89 0.68
Dislike Fairly Well 1.19 1.87 0.51 0.17
Dislike Very Much 0.17 0.51 0.17 0.00
Eanel F: 19b g; hanger
(SE-glm/wage) [11:51 7
Like Very Much 39.26 12.19 1.74 0.39
Like Somewhat 18.76 20.50 0.58 0.58
Dislike Fairly Well 2.32 1.35 0.39 0.19
Dislike Very Much 0.97 0.77 0.00 0.00

 

75

Table 2-7: The Results of Ordered Probit Estimation
Dependent variable: “Like very much”=4. “like somewhat”=3, “dislike sorrewhat“=2,
“dislike very much =1”

Sample: Sample (5)

 

 

(l) (2) (3) (4) (5) (6)
Pooled Pooled ”Fixed "Fixed "Fixed "Fixed
Ordered Ordered Effect" Effect" Effect" Effect"
Probit Probit Ordered Ordered Ordered Ordered
Probit Probit Probit Probit
with with with
Feedback Feedback Feedback
Self Employed 0.486 0.489 0.4 1 6 0.43 l , 0.43 1 0.428
(0.051) (0.051) (0.047) (0.054) (0.054) (0.054)
Log wage 0.248 0.232 0.341 0.315 0.315 0.315
(0.024) (0.031) (0.033) (0.036) (0.036) (0.036)
Married 0.044 -0.067 -0.071 -0.071 -0.071
(0.026) (0.026) (0.029) (0.029) (0.029)
Education 0.023 0.042 0.039 0.039 0.039
(0.006) (0.030) (0.038) (0.038) (0.038)
Experience 0.014 0.001 0.004 0.004 0.004
(0.005) (0.009) (0.01 1) (0.01 1) (0.01 1)
Mean(self employed) 0.129 0.143 0.156 0.157
(0.086) (0.096) (0.099) (0.102)
Mean(log wage) -0.171 -0.127 -0.127 -0. 127
(0.049) (0.052) (0.052) (0.052)
Mean (married) 0.184 0.185 0.185 0.185
(0.047) (0.050) (0.050) (0.050)
Mean( education) -0.0 16 -0.015 -0.014 -0.015
(0.031 ) (0.039) (0.039) (0.039)
Mean(experience ) 0.014 0.010 0.010 0.010
(0.009) (0.011) (0.011) (0.011)
Fe ck ' s lace
Within 1 year 0033
(0.046)
Within 2 years 0042
(0.046)
Within 3 years 0032
(0.047)
Year dummies? Yes Yes Yes Yes Yes Yes
3rd Cut Point (113) -().377 -0.238 -0.533 -0.478 -0.481 -0.480
(0.165) (0.188) (0.241) (0.258) (0.258) (0.258)
2nd Cut Point (112) 0.360 0.501 0.208 0.282 0.279 0.280
(0.162) (0.185) (0.238) (0.256) (0.256) (0.256)
lst Cut Point (111) 1.891 2.038 1.747 1.823 1.820 1.821
(0.164) (0.188) (0.240) (0.258) (0.258) (0.258)
Mean of predicted latent 1.757 1.903 1.613 1.688 1.685 1.686
variable (u)
Monetary Value of 7.104 8.182 3.387 3.930 3.928 3.893
Self-Employment status (2.596) (4.277) (0.697) (1.045) (1.125) (1.095)
Observations 20065 20065 20065 17344 1 7344 17344
_L-ogLikelihood -19159 -19094 -19066 -16460 -16460 -16460

 

Note: Panel clustering robust standard errors are in parenthesis for pooled ordered probit estimates.

Standard errors are in parenthesis for "Fixed Effects" effect ordered probit estimates. Married dummy is one if married
and spouse present. zero otherwise. Monetary value of self-employment status is calculated by Exp[ (coefficient for
self-employment)/(coefflcient for log wage)]. Standard error for this value is calculated through bootstrapping of500

repetitions.

76

 

Table 3-1: Sample Construction

 

Average incidence (Non weighted)

 

 

N Smoking Drinking Marijuana
use
(1) “711019 sample 8984 0.162 0.185 0.086
(2) All outcomes are ax ailable 8940 0.161 0.185 0.085
(3) Demographic variables are available 8851 0.161 0.185 0.086
(4) Relationships with parent are available 8833 0.161 0.185 0.086
(5) All peer variables are available 8518 0.165 0.190 0.088
(6) School characteristics are available 7498 0.166 0.192 0091
(7) Grade in school is available 7495 0.166 0.192 0.091
(8) Parent‘s 11(1C available 7491 0.166 0.192 0.091
(9) Variables from parent questionnaire are available 6615 0.168 (“93 0.092
(10) Proxy variables are available‘ 6356 0.168 0.195 0.092
(Basic analysis sample)
(1 1) Within school duplicationoccurs’” 6312 0.167 0.196 0092
(School fixed effect analysis sample)
(12) Siblings data are available'“ 2458 0.170 0.192 0.093

(Siblingirxed effect analysis sample)
Note:

*Proxy variables for school quality (If experience threat. if something stolen in school, Feel safe in school.)

and neighborhood quality (if any gang in neighborhood).

"Since school identification number is not available. quasi-school id, which is made out of county

dummy. school size. and student -teacher ratio are used as quasi-school id. Bureau oflabor statistics assigns

last two variables based on the school id number.
"*Siblings are detennined by the identical household id.

77

Table 3-2: Descriptive Statistics of Substance Use and Srbjective Measure of Peer's Behavior by Grade

 

 

 

Grade Smoke last Peer who Drunk last Peer who Use Peer who Number of
30 days smoke 30 days get drunk marijuana use illegal Observation
(Subjective) (Subjective) last 30 days drug
(Subjective)

4 0 0 0 0 0 0 2

5 0.044 0.1 18 0.041 0.077 0 0.068 37
(0.043) (0.055) (0.033) (0.039) (0.038)

6 0.058 0.147 0.039 0.037 0.018 0.084 426
(0.013) (0.013) (0.01 1) (0.007) (0.007) (0.010)

7 0.097 0.273 0.084 0.1 1 1 0.036 0.174 1294
(0.009) (0.009) (0.009) (0.007) (0.006) (0.008)

8 0.153 0.375 0.146 0.202 0.067 0.257 1319
(0.01 1) (0.009) (0.01 1) (0.008) (0.008) (0.009)

9 0.238 0.529 0.269 0.389 0.145 0.426 1416
(0.013) (0.008) (0.013) (0.009) (0.011) (0.010)

10 0.260 0.553 0.329 0.481 0.142 0.476 1204
(0.014) (0.008) (0.015) (0.009) (0.01 1) (0.010)

1 1 0.260 0.553 0.329 0.481 0.154 0.474 624
(0.014) (0.008) (0.015) (0.009) (0.016) (0.010)

12 0.291 0.555 0.371 0.513 0.154 0.487 34
(0.021 ) (0.01 l ) (0.022) (0.012) (0.016) (0.060)

Total 0.190 0.427 0.214 0.303 0.099 0.333 6356
(0.006) (0.004) (0.006) (0.004) (0.004) (0.004)
Note.

1. All statistics are calculated using sampling weight.

2. Standard errors of mean are in parenthesis.

78

Table 3-3: OLS Estimates of Incidence of Substance Use

 

 

 

( 1) (2) (3)
Dependent variable " ‘ "' ‘ ‘ 111W mcidsnmtmamuana
smgking in last 30 @ys rinkin in l 5 use in last 30 days
Method of estimation OLS OLS OLS
Pgr ( Fractign)
Peer smoke 0.218
(0.017)
Peer drunk 0.309
(0.020)
Peer illegal drug 0.228
(0.015)
2 (Other control Yes Yes Yes
variables)
E[z 1x] Yes Yes Yes
(Contextual Effect)
F-statistics for 1.04 1.01 1.16
Contextual effect (0.379) (0.447) (0.163)
R2 0.145 . 0.158 0.128
Sample size 6356 6356 6356
Note.

1. The vector zcontains following variables:

2 (Independent Variables): Dummies if respondent lives with mother, father, biological mother. biological
father, foster mother or foster father. Female dummy, age, black dummy, other minority dummy, Hispanic
dummy, school grade dummies (grade 5 — grade 12), [Middle/junior high] school and high school

dummies, catholic school dummy, private school dummy, student/teacher ratio category dummies (3
categories), School size category dummies (3 categories), Census regional dummies, urban dummy and
proxy variables for unobserved school characteristics ( lf experience thread, if something stolen in school,
Feel safe in school), parents back ground variables (parent born in US, parent speak a language other than
English in home. parent was with both biological parents at age of 14), last year‘s household income
category dummies (less than $20,000, 320,001-340,000. $40.001-S60,000. 560,001-880,000, more than
$80,001, and household income not available). household size. number of household member less than age
18 and less than 6, proxy variables for parent’s involvement in education (Often or sometimes participate in
PTA activity, oflen or sometimes volunteer in school education) dummies for mother’s education and
father’s education, dummy if any gang in neighborhood, county level variables (Share of white population,
black population, Indian population, Hispanic population, share of population under 5 years old, 5-17 years
old, 18-20 years old, 21-24 years old, 25-34 years old. 35-44 years old, 45-54 years old. 55-64 years old,
65-74 years old and 75+ years old share of male in population). State tax rates (cigarettes tax and beer tax).
There are 85 variables in total, counting each dummy of categorical variable as a variable.

2. White (heteroscedasticity-robust) standard errors are in parenthesis.

3. All of the test statistics are robust against heteroscedasticity.

79

Table 3-4: Incidence of Substance Use using within School Duplication Data
(Almost school random & fixed effect estimation.)

 

 

 

(1) (2) (3) (4) £5) 16)
Dependent van'able MW Wild W
W mm mm
Method of estimation Random- F ixed- Random- F ixed- Random- Fixed-
Effect Efi‘ect Effect Effect Effect Effect
mm
Peer smoke 0.218 0.218
(0.018) (0.018)
Peer drunk 0.310 0.305
(0.019) (0.021 )
Peer drug 0.228 0.221
(0.016) (0.016)
2 ( Other control Yes Yes Yes Yes Yes Yes
variables)
Hausman Test 1.42 1.20 1.68
F (35, 1201) (0.014) (0.124) (0.000)
Single variable 0.680 0.740 -0.625
Hausman test
t-statistics
Sample size 6312 6312 6312 6312 6312 6312
Note.

1. School id is not available in data. so the (almost) school id is created out of county id, school size, and
student -teacher ratio. Surveyor assigns last two variables based on confidential school id number.
2. Same control variables as in Table 3-4 were included. Some of the variables that do not vary within

school dropped in fixed effect

3. Heteroscedasticity robust standard errors are in parenthesis for estimated coefficient. For test statistics, p-

values are in parenthesis. All the tests are robust against heteroscedasticity.

80

Table 3-5: Incidence of Substance Use using Sibling Data
(Household random & fixed effect estimation.)

 

 

(1) (3) (3) (4) (5) (6)
Dependent variable Incidence of QM *ttes W W
smoking in last 30 days @nking in last 30 days 111mm
Method of Random- F ixed-Effect Random- F ixed—Efi‘ect Random- Fixed-Effect
estimation Effect Effect Effect
W193)
Peer smoke 0.207 0.122
(0.029) (0.040)
Peer drunk 0.326 0.247
(0.033) ( 0.044)
Peer illegal drug 0.245 0.196
(0.025) (0.034)
2 (Other control Yes Yes Yes Yes Yes Yes
variables)
E[z Ix] Yes Yes Yes Yes Yes Yes
(Contextual Effect)
Hausman Test 1.74 1.69 1.21
F (35, 1201) (0.000) (0.000) (0.0%)
Single variable -3.830 -2.899 -2.944
Hausman test
t-statistics
Sample size 2458 2458 2458 2458 2458 2458

 

Note
1. The same control variables as in Table 3-4 were included. Some of the variables that do not vary within

household dropped in fixed effect.
2. Heteroscedasticity robust standard errors are in parenthesis for estimated coefficient. For test statistics. p-
values are in parenthesis. All the tests are robust against heteroscedasticity.

81

Table 3—6: OLS Estimates of Incidence of Substance Use

 

 

(l) (2) (3)
Dependent variable mm Insidensmf Incidmsmf
~ oigarottos smoking aloohol drinkigg in W
in last ,30 do ys last 30 days lost 30 days
OLS OLS OLS
Poe;
Peer's substance usage (portion) 0.417 0.440 0.352
(0.034) (0.038) (0.030)
Female x peer’s usage -0.014 0.050 -0.059
(0.028) (0.033) (0.026)
Black x pccr‘s usage -0.3 12 -0.236 -0.1 75
(0.032) (0.038) (0.029)
Hispanic x pcer‘s usage -0.l28 -0.060 -0.071
(0.039) (0.044) (0.034)
Both biological parents -(). 122 -0.027 -0.047
x peer‘s usage (0.029) (0.033) (0.027)
2 (Other control variables) Yes Yes Yes
R2 0143 0.150 0121
Sample size 6356 6356 6356

 

Note.
The same note as Table 3-4 applies.

82

Percentage of users

F igure 3—1: Youth Substance Use, Age 12-17, Current Users

——o—— Marijuana and hashish —a—Alcohol
—9— Cigarettes

35—

30—

254

20‘

154

10‘

5—

 

 

1 9190

l l I

(Source: National Household Survey on Drug Abuse, annual.)

83

19257