Peer Group Effects

8/13/2019 Peer Group Effects

1/35

1Introductionere is a large empirical literature that seeks to measure the importance of peer group influence in determining the

performance of the

individual members of the group. In education, Summers and Wolfe (1977) and

Henderson, ies!"o#s"i, and Sauvageau (197$), for e%ample, have found that, other

things e&ual, students perform at a higher level if their fello# students are highachievers. 'heir findings provide additional support for the strong claims concerning

peer group influence that #ere part of the famous oleman report (oleman et al. 19)

on educational achievement.

Some recent papers in the theor* of local finance, including +ruec"ner and ee (19$9),

de +artolome (199-), and Sch#ab and ates (1991), have e%plored this issue in a

broader conte%t. /ra#ing on the conceptual frame#or" developed in +radford, alt,

and ates (199), these papers ta"e the levels of final outputs of various local public

goods to depend not onl* on local budgetar* inputs but also on the characteristics of thepeople #ho reside in the communit*. 'here is compelling evidence, for instance, that the

level of public safet* in a particular neighborhood depends not so much on the

fre&uenc* of police patrols as on the propensit* of local residents to engage in illegal

activities. 'he argument, more formall*, is that the 0production function for such local

outputs includes boththe standard inputs such as labor and capital (transformed into

police patrols, for e%ample) andthe characteristics of the local population itself.

'he basic issue #e raise in this paper concerns the testing and measurement of these

peer group (or neighborhood) effects. 'he studies that have loo"ed at this issue t*picall*

begin #ith a 0standard model that see"s to e%plain 0output (as measured b*

standardi!ed test scores, for e%ample) in terms of a vector of student and famil*

characteristics, a vector of 0public inputs (measures of pupil2teacher ratios, the training

and e%perience of the teacher, the availabilit* of certain special educational services,

etc.), and a vector of variables that summari!e the characteristics of the other students in


2/35

the class. 'he estimates of the coefficients on this third set of variables and their

standard errors are then used to test for the presence of peer group effects and to

measure their magnitude. an* studies find that these peer group effects e%ist and are

&uite important.i

ur concern is that the 0peer group (or, more generall*, the set of neighbors or local

residents) is often itself a matter of individual choice. 'he e%treme case of the 'iebout

(193) model of local finance ma"es the point. In a 'iebout #orld, individual

households search costlessl* among a #ide variet* of local communities and select as a

4urisdiction of residence a localit* that provides a menu of local outputs that best fits

their tastes. 5lthough the original 'iebout model did not address the issue of peer group

effects, the later #or" cited


3/35

3 JOURNAL OF POLITICAL ECONOMY

above has incorporated these effects into the anal*tical frame#or" for local public

goods. What is important for our purposes is the implication of these later models

that individual households, in ma"ing their locational choice, are also choosing apeer group for man* of the relevant local services, including local public schools.

In such a setting, the peer group becomes an endogenous variable, determined in

part b* household choice. nce this is recogni!ed, it is clear that the estimation of

the 0standard model b* ordinar* least s&uares or other techni&ues that do not

allo# for the endogeneit* of the peer group are inappropriate.

'he direction 6f the bias introduced b* ignoring simultaneit* depends on the

relationship bet#een the unobserved factors that determine the peer group and the

unobservable factors that determine performance. or the problems this literature

has e%amined, #e suspect that simple models are li"el* to overstate peer group

effects. 'o see this in the conte%t of the education e%ample, consider a child #hose

parents devote great effort to the #elfare of their children. We might reasonabl*

e%pect to find that this child does #ell in school for t#o reasons. irst, his parents

#ill see that he attends a school in #hich the peer group is 0better than e%pected

given the famil*8s observed characteristics. Second, he #ill do #ell in school as a

result of factors that cannot be observed but that are under his parents8 control,

such as the time the parents spend #ith the child. 5 singlee&uation model,

ho#ever, #ould mista"enl* attribute this entire increment to the child8s

performance to his superior peer group.ii

'he purpose of this paper is to e%plore these issues at both the conceptual and

empirical levels. 'o this end, #e choose to e%amine peer group effects on teenage

pregnanc* and the decision to drop out of school. 'eenage pregnanc* is

particularl* interesting in this conte%t in that peer group effects are often thought to

be ver* important. 5s one stud* notes, 0probabl* the agent most blamed8 for in:

creases in teen se%ual activit* over the last decade has been the peer group(Hofferth 19$7, p. ;7). a*er (1991), for e%ample, found that the socioeconomic

status of the students in a girl8s high school is a "e* determinant of the probabilit*

that she #ill bear a child #hile still a teen. +ut other studies of teenage pregnanc*

are less clear on this matter, suggesting that peer influences ma* have been

0overrated as a source of increased se%ual activit* among teenagers (Hofferth


4/35


19$7, p. ;7). 5s


5/35


In the course of this stud*, #e have come in contact #ith a substantial bod* of

empirical literature cutting across the various social sciences that attempts to

measure and assess the importance of neighborhood or peer group effects. 'his

literature addresses a #ide range of behavior including school performance, crime,

labor mar"et performance, and se%ual activit*. Some of this research finds that

such neighborhood effects are important determinants of individual behavior.

rane (1991), for e%ample, develops an 0epidemic theor* of social behavior for

#hich he finds supporting evidence from patterns of school and se%ual behavior in

ghetto neighborhoods. Such evidence is consistent #ith the emerging vie# of an

urban 0underclass that is trapped in poor and geographicall* isolated

neighborhoods (Wilson 19$7).iii

Ho#ever, the findings on the importance of peer effects in this literature are mi%ed.


6/35

ost of the data for our stud* #ere dra#n from the Bational ongitudinal Stud* of

Couth (BSC). 'he full BSC sample includes 1;,$ people (,@-D males and

,;$D females) #ho #ere bet#een 1@ and ;1 *ears old #hen the surve* began in

1979. 'he BSC is #ide ranging, providing information on such matters as labor

mar"et e%perience, famil* bac"ground, fertilit* histor*, and drug and alcohol

abuse. It is particularl* #ell suited for our purposes because it included in 1979 a

surve* of the last secondar* school each respondent attended. 'his surve* as"ed

school officials a number of &uestions about the composition of the student bod*.

We use the responses to these &uestions to construct our peer group variables.

We relied on several criteria to select the sample for our stud* from the larger

BSC sample. irst, #e eliminated all cases in #hich the school data #ere not

available.ivSecond, #e considered onl* #omen #ho #ere aged 19 or *ounger

#hen the BSC began. ost of the #omen #ho #ere ;- or ;1 *ears of age lefthigh school D or @ *ears before the school surve* #as ta"en in 1979. 'hird, #e

included onl* teenagers #ho lived in standard metropolitan statistical areas

(SS5s). ourth, #e e%cluded all BSC respondents #ho became pregnant before

the* enrolled in their 1979 school.

ur final sample included 1,@3D cases.v'able 1 presents definitions and summar*

statistics for most of the important variables #e have used in this stud*.

EFGB5B' is a dichotomous variable that has a value of one if the respondent

became pregnant during her teen *ears. nderreporting is often a problem in

pregnanc* studies? #e suspect that it ma* be a particular problem in the BSC

since in a large ma4orit* of the cases at least one parent #as present during the

intervie#. ott (19$D) notes that underreporting in the BSC appears to be greater

among blac"s than #hites. In our sample, ;;.3 percent of the #hite respondents

and D$.; percent of the blac" respondents said that the* had become pregnant at

least once before the* #ere ;- (EFGB5B' J 1). Hofferth (19$7, p. @;-) notes

that studies that do not rel* on surve* methods find that D9.7 percent of #hites and

D.1 percent of blac"s have teenage pregnancies.vi

nderreporting is thus a real concern as a source of measurement error in our

dependent variable. 5t the same time, teenage pregnanc* is an appealing sub4ect

for this stud* since, as #e noted earlier, it is one for #hich peer group effects are

thought to be especiall* important. ur focus on pregnancies, rather than births,


7/35

PEER GROUP EFFECTS 7

has a further advantage. 'he decision to carr* a pregnanc* to term is complicated

b* a host of variables other than the peer group, man* of #hich are also potentiall*

endogenous. or e%ample, rossman and


8/35


such variables as medicaid funding for abortions, the availabilit* of abortion

clinics, and the presence of a spouse. +irths are one more step removed from the

variable that is of primar* interest.

vii

Father than abandon our dependent variable, #e have responded to this issue b*

bringing additional evidence to bear on the problem. ore specificall*, #e have

emplo*ed three different "inds of 0tests to ensure that the results from the overall

pregnanc* e&uations are not misleadingA (1) We repeated much of the anal*sis #e

report here using actual births, rather than pregnanc*, as the dependent variable.

'his avoids some of the underreporting issuesA the fraction of teens in the BSC

#ho report that the* gave birth to a child before reaching age ;- is roughl*

consistent #ith national estimates. ur findings based on this second dependentvariable are similar, as #e shall see, to our results using the pregnanc* variable. (;)

We divided our sample b* race and estimated separate e&uations for blac"s and

#hites, since underreporting is apparentl* less pervasive for the latter group. (D)

We loo"ed at a #holl* different form of behaviorA the decision to sta* in school or

drop out, for #hich underreporting is, #e believe, unimportant. 5s #e shall see, the

results for our dropout e&uations are much li"e those for the pregnanc* e&uations.

'he results from these three additional e%ercises give us some confidence that

measurement error in the dependent variable is not seriousl* distorting ourfindings.

'he conceptual frame#or" #e presented in the Introduction suggests that #e

should consider three sets of determinants of teenage behaviorA individual and

famil* characteristics, publicl* provided inputs, and peer group effects. 'he

literature on teenage pregnanc* has e%amined peer group effects at several

different levels, including the influence of communit* characteristics, peers at

school, and close friends. ur measure of the peer group effect,

(/IS5/=5B: '5G/), is the natural log of the percentage of students in therespondent8s school #ho #ere classified as economicall* disadvantaged.$9'here is

substantial evidence that teens from lo#:income families are more li"el* to be

se%uall* active and to become pregnant. 'his paper as"s #hether this behavior and

the attitudes that lead to this behavior have important effects on other students at

their school.1-


9/35


We have included a number of famil* characteristics in the stud*.11'he variables

G5G HH, S'GE5'HGF, and 'HGF 5IC are a set of dumm* variables

that describe famil* structure? the suppressed categor* includes families in #hich

both biological parents are present. Similarl*, 'HGF /FE', 'HGF

HIH

8 Hogan, 5stone, and Kitaga#a (19$3) concluded that blac" girls 13L19

*ears old living in a povert* area of hicago #ere much more li"el* to be se%uall*

active than blac" teens living outside povert* areas. urstenberg et al. (19$7) found

that blac"s #ho attended a segregated school #ere 1D times more li"el* to have

had se%ual intercourse than #hites from segregated schools but that the differences

bet#een blac"s and #hites from integrated schools #ere much smaller. +ill* and

dr* (19$3) found that friends8 se%ual attitudes and behavior #ere important

elements in decisions of #hite teenage girls to have se%ual intercourse.

9 Schools #ere as"ed to estimate the percentage of students #ho #ere

disadvantaged under Glementar* and Secondar* Gducation 5ct (GSG5) guidelines.

'he federal government disperses GSG5 funds to states to assist areas #ith high

concentrations of lo#:income families. States ma* distribute funds to districts

according to a range of different criteria, and at the school level it is ver* difficult

to determine the financial characteristics of students8 families. 5ccording to the

.S. /epartment of Gducation, the best and most #idel* used measure ofeconomic disadvantage at the school level is the number of students participating

in the school lunch program? these data #ould al#a*s be available to school

principals.

10 We #ould have li"ed to loo" at more direct measures of peer group effects

such as the pregnanc* rate #ithin the school. High School and +e*ond is the onl*

data set of #hich #e are a#are that #ould allo# us to do so. We cannot use High

School and +e*ond because it is not possible to identif* a respondent8s cit* or

metropolitan area in that data set, and, as #e e%plain belo#, #e re&uire that

information in order to estimate our simultaneous e&uation model.

11 We suspect that the average number of siblings in our sample is so large

(though close to the average in High School and +e*ond) for several reasons. irst,

the average number of siblings per child is al#a*s greater than the average number


10/35


of children in families #ith children (minus one). Suppose that all families had

either one child or nine children and that there #ere e&ual numbers of both t*pes of

families. n average, children #ould have 7.; siblings but the average famil*

#ould have onl* five children. Second, as #e noted above, our sample includes

respondents from the BSC crosssection sample as #ell as the supplementalBSC sample of Hispanic, blac", and disadvantaged *outh, atholics are

overrepresented in our sample for the same reasons? in addition, #e restricted our

sample to BSC respondents #ho lived in SS5s, and atholics are more li"el*

to live in urban areas.

SH, and 'HGF SG GG represent mother8s education? the

suppressed categor* is college graduate. We also include a measure of religious

attendance and the number of siblings as e%planator* variables in light of the

results of past research.viii

'he reported measure of famil* income in the BSC is troubling. Income #as not

reported for ; percent of the cases in our sample, either because the respondent

did not ans#er this &uestion or because at the time of the intervie# the respondent

#as living outside of her parents8 home and her ans#er therefore reflected her o#n

income. We used data for those respondents in our sample #ho did report income

to estimate an e&uation that described income in terms of other household

characteristics, and then #e used the estimated values from this e&uation to impute

the missing values for the income variable.ix'his introduces some measurement

error into our estimates. We tried to address this issue b* defining a set of dumm*

variables (65F'IG ;, 65F'IG D, and 65F'IG @) that together

characteri!e a respondent8s place in the income distribution. 'his approach reduces

measurement error because #e are unli"el* to place a famil* in the #rong &uartile

even if #e cannot estimate its income precisel*. 'he procedure comes at some cost,

though, since it suppresses information b* treating ali"e all families #ithin a

&uartile. 'esting suggests that our basic results are not sensitive to our procedure

for constructing the income variable.

We have included a number of measures of publicl* provided inputs. Gducation

variables include #hether or not a respondent has ta"en a se% education course and

the teacher2pupil ratio in the respondent8s school. We also consider the availabilit*


11/35


of famil* planning clinics, contraceptive la#s, and the level of 5/ pa*ments.

Single-Equation Estimation

In this section #e treat peer group effects as an e%ogenous variable in a single:

e&uation probit model. 'his section is thus a direct parallel to the #or" on peergroup effects in the education literature. ur model can be described as follo#s.

et*M represent a teenager8s unobservable propensit* to become pregnant. We

assume that*M is a linear function of observed variables such as famil*

characteristics and pub:

licl* providedinputs %1N a scalar measure of peer group effects

#here @O is the cumulative distribution function of the standard normal

distribution.

Gstimates of alternative versions of this single:e&uation model are set forth in table

;. odel 1 in column 1 is our basic model. 'he "e* variable in these models is

(/IS5/=5B'5G/), our measure of the peer group effect. 'his variable is

significant and has the e%pected sign? ever*thing else e&ual, placing a teenage girl

in a school in #hich a higher proportion of the students are disadvantaged in:

creases the probabilit* that she #ill become pregnant.

We have included in column ; of table ; the derivative of the probabilit* of

pregnanc* #ith respect to each variable and the associated standard error of the

estimate.xonsider a teenage girl #ho moves from a school #ith the mean

percentage disadvantaged to a school in #hich the percentage of students #ho are

disadvantaged is ;3 percentage points higher? this implies that (/IS5/=5B:

'5G/) rises b* .73. ur results indicate that the probabilit* that she #ill become

pregnant #ould rise b* 1.7 percentage points. 'he magnitude of the response is

roughl* e&uivalent to the addition of 1.3 more siblings to the household.

While these estimates suggest that the peer group effect is a statisticall* significant

and nontrivial determinant of teenage pregnanc*, its importance is small in

comparison #ith variables describing famil* structure. 'eenagers from households

in #hich at least one biological parent is absent have a 7L11:percentage:point

greater chance of becoming pregnant than their counterparts #ho have both natural

parents present. 'o ta"e another instance, a teen #hose mother #as graduated from

high school but did not attend college faces a 1-: percentage:point higher chance


12/35


of becoming pregnant in comparison #ith an identical teen #hose mother #as a

college graduate.

5lthough the race variable is significant onl* at the 1- percent level, #e suspect

that this variable ma* understate racial differences given the discussion of

underreporting presented above. With all other factors such as education and

famil* structure held constant, income seems to have no impact on the probabilit*

of pregnanc*. 5ll the results above are consistent #ith the literature (see Hofferth

19$7? undberg and Elotnic" 199-).

odel 1 also points to the important role that se% education courses can pla* in

reducing teen pregnancies. 'he estimates in column ; of table ; impl* that

providing a teenager #ith a course in se% education reduces the probabilit* that she

#ill become pregnant b* 3.$ percentage points. 'his is not a trivial effect? it

suggests, for e%ample, that a teen from a female:headed household #ho has ta"en a

se% education course #ould have roughl* the same probabilit* of becoming preg:

nant as a teen #ho came from a household in #hich both biological parents #ere

present but #ho had not received se% education.

In the remaining columns of table ;, some e%tensions and alternatives to our basic

model are presented. odel ; includes a set of dumm* variables for religious

affiliation (the suppressed categor* is teens #ith no religious affiliation). Bone of

these dummies is significant, and a log li"elihood test sho#s that 4ointl* the* add

no e%planator* po#er to the basic model. undberg and Elotnic" (199-) find that

teens are more li"el* to become pregnant and give birth if #elfare benefits are high

or if it is difficult to obtain contraceptives. odel D includes the natural log of

5/ pa*ments for a famil* of t#o and a dumm* variable, IGBSG, that has a

value of one if the respondent lives in a state in #hich a merchant must have a

license to sell contraceptives. odel @ includes t#o additional measures of

publicl* provided inputsA IBIS (the number of famil* planning clinics per

teenager in the respondent8s count*) and 'G5HGF2EEI (the teacher2pupil ratio

in the respondent8s school). In all these alternative versions of our basic model,

none of the additional variables is statisticall* significant, and the estimates of the

coefficients on the variables in the basic model are ver* similar to the estimates for

model 1. In particular, the estimated coefficient of (/IS5/: =5B'5G/) is

al#a*s positive, significant, and roughl* e&ual to .-$3.


13/35

In all, the findings from our 0standard model suggest that peer


14/35


15/35

PEER GROUP EFFECTS

group effects matter for teenage pregnanc*? regardless of ho# #e specif* the

single:e&uation model, the results consistentl* sho# that placing a teen in a

school in #hich fe#er of her peers come from disadvantaged families

reduces the probabilit* that she #ill become pregnant. Ho#ever, this t*pe of

model assumes implicitl* that the peer group is an e%ogenous variable. 'o

some e%tent, the composition of the peer group is under a famil*8s control?

#e therefore need to ta"e the endogeneit* of the peer group into account. We

present a model in the ne%t section that does so.


16/35

PEER GROUP EFFECTS

($), it is convenient to e%press2(eP e;) in terms of the conditional distribution

for and the marginal distribution for e;A

G&uation (1D) ma"es the nature of the estimation problem clearer. Suppose

for the moment that and e;are uncorrelated and thus p J -. In this special

case, the li"elihood function can be bro"en into t#o separate pieces. 'he

first line in (1D) #ould simpl* be the li"elihood function for our single:

e&uation probit model, and the second #ould be the li"elihood function for

the normal linear least:s&uares model associated #ith *;. 'hus if p J -, there

is no gain in moving to the simultaneous e&uation model. If p -, ho#ever,

then the t#o models #ould lead to different parameter estimates.

'his model re&uires a set of variables (%;in the notation #e have been using)

that are e%ogenous determinants of the characteristics of a teenager8s peer

group but that are not determinants of the probabilit* of pregnanc*. We have

included in %;the metropolitan area unemplo*ment rate, median famil*

income, povert* rate, and the percentage of adults #ho completed college.

'hese variables are li"el* to be correlated #ith the characteristics of a teen8s

school? ever*thing else e&ual, #e #ould e%pect to find that someone #ho

lives in a metropolitan area in #hich the povert* rate is high is more li"el* to

attend a high school in #hich man* of the other students are disadvantaged


17/35


.'he metropolitan area variables are therefore designed to measure the range of choices available to an individualhousehold. We are assuming implicitl* that parents ta"e their choice of metropolitan area as given #hen choosing a

school for their daughter but treat their location #ithin the metropolitan area (and thus their daughter8s school) as a

decision variable.

'ests using this set of instrumental variables #ere reassuring. 'he* are, not surprisingl*, important determinants of

our peer group variable, the log of the percentage of students disadvantaged.131Ho#ever, the* have little

e%planator* po#er in the basic pregnanc* e&uation. When placed in the pregnanc* e&uation, the estimated

coefficient of each of the instrumental variables #as smaller than its estimated standard error, and the instrumental

variables ta"en as a group did not add significantl* to the e%planator* po#er of the e&uation.17

We have estimated the simultaneous e&uation model using a ma%imum li"elihood techni&ue suggested b* +erndt et

al. (197@). olumn ; of table D presents the s*stem estimates of the pregnanc* e&uation. or comparison, #e have

repeated in column 1 of that table the results from the first single:e&uation probit model presented in table ;.

Several important results emerge in table D. 'he correlation measure p is positive and significant, and the error

terms in the e&uation that e%plain (/IS5/=5B'5G/) and the e&uation that describes the probabilit* of

pregnanc* are positivel* correlated. 5 %;test sho#s that the simultaneous e&uation estimates are significantl*

different from the single:e&uation estimates.

When these error terms are correlated, single:e&uation models ma* mista"enl* attribute part of the impact ofunobservable household characteristics to the peer group. 5 comparison of the estimates of the coefficients on

(/IS5/=5B'5G/) in the t#o models suggests

lDWe would argue that this is a sensible assumption given that intrametropolitan mobilit is much more

common than intermetropolitan mobilit! "onsider those families who lived in metropolitan areas in 1#$%

and moved between 1#&' and 1#$%! (wo-thirds of those families moved within the same metropolitan area!

16 (heR)in a first-stage regression of *+,DIS.D/.0(.,ED on the vector 23 and the instruments


18/35

g8o JOURNAL OF POLITICAL ECONOMY

is !)44! (he coefficients standard errors on the S5S.-level variables in the first-stage regression are log of

the percentage of families in povert6 !$)# !1'78 log of median famil income6 9 !#1& !:#'8 log of the

unemploment rate6 !1:$ !1)18 and log of the percentage of adults with a college degree6 !1#& !1&:!

17 (he coefficients standard errors on the S5S.-level variables in the pregnanc probit are log of the

unemploment rate6 9 !%1& !17&8 log of median famil income6 !:': !7$#8 log of the percentage of families

in povert6 9!1$7 !1#:8 and log of the percentage of adults with a college degree6 9!%4) !)1)! (he 9) log

likelihood test statistic that these four variables are ;ointl se of these alternative instruments had little impact on our basic results!

that in this particular e%ample, the problem is &uite serious. 'he estimated coefficient of

(/IS5/=5B'5G/) is positive and significant in the single:e&uation model. In the simultaneous e&uation

model, ho#ever, it has the #rong sign and is insignificant. 'he peer group effect vanishes in this case #hen #e

ta"e into account the endogeneit* of the peer group. In our sample, the entire effect of the peer group in the single:

e&uation model can be attributed to the choices families ma"eA teens #hose parents choose schools in #hich their

daughters associate #ith relativel* fe# lo#:income peers have a lo#er probabilit* of becoming pregnant than #e

#ould have e%pected given their observed characteristics.

ost of the other results are roughl* similar in the t#o models. Bone of the other variables changes sign, and all

the variables that #ere significant in the single:e&uation model are significant in the simultaneous e&uation model.

oreover, the magnitude of most of the coefficients is roughl* the same in both models. 'he one e%ception is the

race variableA its coefficient increases substantiall* #hen #e move to a simultaneous e&uation model.


19/35


I. . Second "ase of ?eer ,roup Effects@

School Dropouts

We have also e%amined a second issue in #hich peer group influences are thought to be importantA school

dropouts. We use the same sample, data, and models to e%plore the dropout issue that #e used above. We define

/FE' as a dichotomous variable that has a value of one if a respondent did not complete high school beforeshe #as ;- *ears old. It appears that, in contrast to pregnanc*, underreporting among dropouts is not a serious

problem in the BSC. In our sample, 1.1 percent of the #hites, ;9.@ percent of the Hispanics, and 1$.- percent of

the blac"s did not finish high school b* the time the* became ;- (/FE' J 1)? .S. /epartment of Gducation

(19$7) reports comparable rates of 1;. percent, ;3.$ percent, and 17.D percent.xi

'able @ summari!es our basic results. 'he structure of the table directl* parallels table D. olumn 1 reports

estimates of a singlee&uation probit model. olumn ; summari!es the probit e&uation #hen estimated as part of a

simultaneous e&uation model that treats the peer group as a choice variable.

'he estimated coefficient of (/IS5/=5B'5G/) in the single:e&uation model is positive and statisticall*

significant. When #e treat the peer group as an e%ogenous variable, #e find that placing a student in a school in

#hich a greater proportion of students are disadvantaged increases the probabilit* that she #ill not complete high

school. oreover, the effect is fairl* large from a polic* perspectiveA our results impl* that moving a teen from a

school in #hich ;;

percent of the students are disadvantaged (the mean in our sample) to one in #hich @7 percent are disadvantaged

increases the chances she #ill drop out of high school b* ;.; percentage points.


20/35

'his result disappears, ho#ever, in the simultaneous e&uation model, #here the estimated coefficient has the #rongsign and is insignificant. 'he correlation coefficient p is positive and significant in the simultaneous e&uation

model. 'he results in our dropout stud* are thus ver* similar to those in our pregnanc* stud*. 'he unobservable

factors that determine a student8s peer group and those that determine #hether or not she #ill drop out of schoolare positivel* correlated. When #e ignore this correlation in a single:e&uation model, #e conclude that the peer

group matters? #hen #e ta"e this correlation into account, the peer group effect vanishes.%ii

ur estimates of the effects of most of the remaining variables are consistent #ith the education literature. In both

the single:e&uation and simultaneous e&uation models, #e find that the estimated coefficients for the famil*

structure variables (G5G HH, S'GE5'HGF, and 'HGF 5IC), the religion variable, famil* si!e, and

mother8s education are significant and have the e%pected sign. 65F'IG D and 65F'IG @ are negative and

significant in both models? the magnitudes of the coefficients on the income &uartile variables suggest that the

probabilit* of dropping out is a decreasing function of income. 'he race variable is negative and significant in both

models, though smaller in absolute value in the simultaneous e&uation model. We thus find that the fact that blac"

girls in our sample are more li"el* to drop out is not a result of racial differences (in fact, racial differences alone

#ould lead us to find that blac"s are less li"el* to drop out) but is instead a result of differences in famil* structure,

parents8 education, and income.

.re +ur Aesults AobustB

inall*, #e have e%plored the sensitivit* of our results to changes in the specification of our model. Some of the

results of these tests are sho#n in table 3. 'hat table presents the peer group coefficient from a single:e&uation

probit model (col. D), the peer group coefficient from a probit estimated as part of a simultaneous e&uation model

(col. @), the correlation bet#een the error terms in the simultaneous e&uation model p (col. 3), and the statistic

re&uired to test the h*pothesis that p J - (col. ). 5ll the e&uations reported in table 3 included all the e%planator*

variables from our basic model in tables ;, D, and @.

'he results reported in table 3 suggest that our basic findings are robust. 'he peer group measure is al#a*s positive


21/35


and significant in a single:e&uation probit model? it is al#a*s negative and insignificant #hen #e treat the peer

group as an endogenous variable in a simultaneous e&uation model.

'he first t#o ro#s of table 3 repeat our estimates of the pregnanc* and dropout e&uations. 'he rest of the table isstructured as follo#s.


22/35


Births.L5s #e noted above, it appears that our pregnanc* variable contains

a great deal of measurement error. ne alternative is to focus on births ratherthan pregnanc*. easurement error in births in the BSC is apparentl* ver*

small. In our sample, 19.1 percent of all #omen reported that the* had given

birth #hile a teen? using birth record data from Vital Statisticsfor a similar

time period, #e calculate that roughl* ;- percent of #omen gave birth

before the age of ;-.xiii

'he dependent variable +IF'H in ro#s D, 3, and 9 of

table 3 has a value of one if the respondent gave birth before she reached the

age of ;-, and !ero other#ise. 'his definition is consistent #ith the #a* #e

defined EFGB5B' (e%cept in those cases in #hich a respondent becamepregnant for the first time and gave birth #hile she #as ;-).

Functional form.L'he peer group literature provides little guidance as to

appropriate functional form. In ro#s @, 3, and #e use the log:odds

transformation Q/IS5/=5B'5G/2(1-- : /IS5/=5B'5G/)R as a

measure of the peer group. 'his transformation is an increasing 0S:shaped

function of /IS5/=5B'5G/A it is concave #hen /IS5/=5B'5G/ P

3- and conve% #hen /IS5/=5B'5G/ O 3-. 'his functional form is

consistent #ith some of rane8s (1991) findings.xiv

In ro# 7 #e use in our

dropout model the proportion of students in a school #ho drop out as an

alternative measure of the peer group (estimates of the proportion of students

in a school #ho become pregnant or give birth are not available in the

BSC).

Race.LWe tried to estimate our models using separate #hite and blac"

subsamples. ur #or" #ith the blac" subsample #as not ver* successfulA

variables #ere rarel* significant and sometimes had the #rong sign even in

single:e&uation probit models.xv

ur results for the #hite subsample sho#n

in ro#s $:1- of table 3 are ver* similar to those for the full sample.

ne additional finding is note#orth*. When #e estimated separate e&uations

for the #hite and blac" subsamples to e%plain the individual dropout


23/35


decisions, #e obtained results similar to those reported earlier in the paper.

'he estimated coefficient on the peer group variable #as significant in the

single:e&uation model and diminished in si!e #hen estimated as part of thes*stem for both subsamples. It is interesting to note, ho#ever, that the

s*stems estimate #as larger


24/35

PEER GROUP EFFECTS g8g

in the blac" than in the #hite e&uation and #as marginall* significant, suggesting perhaps that choice #as more

restricted for blac"s than for #hites and that the associated problem of endogeneit* #as less severe in the blac"subsample.

Alternative estimation techniques.LInstrumental variables is one alternative to our ma%imum li"elihood techni&ue.

sing that approach, #e #ould first estimate e&uation (@) and then use the predicted values of /IS5/=5B'5G/

in the estimation of the probit e&uation. ur instrumental variables estimate of the /IS5/=5B'5G/ coefficient

in a model in #hich the dependent variable is EFGB5B' is L .13, ver* close to the estimate sho#n in ro# 1 of

table 3.

"oncluding Aemarks

'he central point of this paper can be made in terms of a homel* e%ample. onsider an adolescent #ith a

propensit* to use drugs. We might e%pect such an individual to see" out friends in school #ith a similar inclination.

Bo#, suppose that #e #ere conducting a stud* of drug use. If #e had perfect measures of a person8s peer group

(his or her group of friends), #e #ould most certainl* find that the peer group variable had a high degree of

e%planator* po#er, and #e #ould conclude that peer group effects are &uite important. +ut the real circumstances

are clearl* much more comple%, for the group is itself the ob4ect (at least to some e%tent) of the individual8s

choosing. 'his must be recogni!ed e%plicitl* in the modeling and estimation of the relationship (Kandel 197$). 5s


25/35


behavior is the communit* in #hich the person resides, those in the school the person attends, or a select group of

close friends. We are acutel* a#are that the measure #e have chosen for our stud*, #hich #as dictated largel* b*

the availabilit* of data, has its fla#s. We have used a measure of the socioeconomic ma"eup of the high school the

girl attends. 'here ma* be measures of other groups, close friends or neighbors, that #ould capture 0the peer

group effect more full*.

oreover, it is possible that different econometric approaches to this problem might *ield different results.

hristopher


26/35


Feferences

Cerndt6 Ernst A!8 all6 Cronwn !8 all6 Aobert E!8 and ausman6 err .! FEstimation and Inference in

0onlinear Structural 5odels!GAnn. Econ. and Soc. Measurement: +ctober 1#&7@ 4':94'!

Cill6 ohn +! ,!6 and >dr6 ! Aichard! F(he Influence of 5ale and Hemale Cest Hriends on .dolescent

Se=ual Cehavior!GAdolescence)% Spring 1#$'@ )1-:)!

Cradford6 David H!8 5alt6 A! .!8 and +ates6 Wallace E! F(he Aising "ost of *ocal ?ublic Services@ Some

Evidence and Aeflections!GNat. Tax J.)) une 1#4#@ 1$'-)%)!

Crueckner6 an !6 and *ee6 angoh! F"lub (heor with a ?eer-,roup Effect!G Regional Sci. and Urban

Econ.1# .ugust 1#$#@ :##-7)%!

"ase6 .nne "!6 and at


27/35


"oleman6 ames S!6 et al!Equality of Educational Oortunity.Washington@ ,overnment ?rinting +ffice6

1#44!


28/35

"rane6 onathan! F(he Epidemic (heor of ,hettos and 0eighborhood Effects on Dropping +ut and

(eenage "hild Cearing!GAmerican J. Sociology #4 5arch 1##1@ 1))4-'#!

de Cartolome6 "harles .! 5! FEquilibrium and Inefficienc in a "ommunit 5odel with ?eer ,roup

Effects!GJ.!.E.#$ Hebruar 1##%@ 11%-::!

Hurstenberg6 H! H!6 r!8 5organ6 S! ?!8 5oore6 ! .!8 and ?eterson6 ! .! FE=ploring Aace Differences in the

(iming of Intercourse!GAmerican Sociological Re".') .ugust 1#$&@ '11-1$!

,riliches6 Kvi8 all6 Cronwn !8 and ausman6 err .! F5issing Data and Self-Selection in *arge

?anels!GAnn. #$%NSEE&nos! :%9:1 1#&$6 pp! 1:&-&4!

,rossman6 5ichael6 and oce6 (heodore ! F>nobservables6 ?regnanc Aesolutions6 and Cirth Weight

?roduction Hunctions in 0ew Jork "it.'J.!.E. #$6 no! '6 pt! 1 +ctober 1##%@ #$:-1%%&!

anushek6 Eric .! F(he Economics of Schooling@ ?roduction and Efficienc in ?ublic Schools!GL!Econ.

(iterature)7 September 1#$4@ 1171-&&!


29/35


enderson6 /ernon8 5iesse among Clack .dolescents!G +amily !lanning !ersecti"es1& anuarLHebruar 1#$'@

14'-4#!

encks6 "hristopher6 and 5aer6 Susan E! F(he Social "onsequences of ,rowing >p in a ?oor

0eighborhood!G In%nner-ity !o"erty in t*e United States&edited b *aurence E! *nn6 r! and 5ichael ,!

! 5c,ear! Washington@ 0at! .cad! ?ress6 1##%!

andel6 Denise C! Fomophil6 Selection6 and Sociali


30/35

*undberg6 Shell6 and ?lotnick6 Aobert D! F.dolescent ?remarital "hildbearing@ Do +pportunit "osts

5atterBG 5anuscript! Seattle@ >niv! Washington6 1##%!

5aer6 Susan E! Fow 5uch Does a igh SchoolMs Aacial and Socioeconomic 5i= .ffect ,raduation and

(eenage Hertilit AatesBG In T*e Urban Underclass&edited b "hristopher encks and ?aul E! ?eterson!

Washington@ Crookings Inst!6 1##1!

5ott6 Hrank *! FHertilit Aelated Data in the 1#$) 0ational *ongitudinal Surves of Work E=perience of

Jouth@ .n Evaluation of Data Nualit and Some ?reliminar .naltical Aesults!G "olumbus@ +hio State

>niv!6 "enter uman Aesource Aes!6 1#$:!

Schwab6 Aobert 5!6 and +ates6 Wallace E! F"ommunit "omposition and the ?rovision of *ocal ?ublic

,oods@ . 0ormative .nalsis!GL!!ublic Econ.77 5arch 1##1@ )1&-:&!

Summers6 .nita .!6 and Wolfe6 Carbara *! FDo Schools 5ake a DifferenceBG A.E.R.4& September 1#&&@

4:#-')!

(iebout6 "harles 5! F. ?ure (heor of *ocal E=penditures./J.!.E.47 +ctober 1#'4@ 714-)7!


31/35


>!S! Department of Education! T*e -ondition of Education, A Statistical Reort. Washington@ ,overnment

?rinting +ffice6 1#$&!


32/35


Wilson6 William ulius! T*e Truly 0isad"antaged, T*e %nner -ity& t*e Underclass& and !ublic !olicy."hicago@

>niv! "hicago ?ress6 1#$&!httpA22###.4stor.org
http://www.jstor.org/http://www.jstor.org/


33/35


*I0ED "I(.(I+0S

:Page 1 of 1 -

You have rinted the follo!ing article"

5easuring ?eer ,roup Effects@ . Stud of (eenage Cehavior

William B. Gvans? Wallace G. ates? Fobert . Sch#ab

#he $ournal of Political %conomy&=ol. 1--, Bo. 3. (ct., 199;), pp. 9:991.

Stable FA

http@LLlinks!ist!or!orgLsiciBsiciO%%))-:$%$P)$1##)1%P)#1%%P:.'P:"#44P:.5?,E.SP:E)!%!"N

P:C)-.

#his article references the follo!ing lin'ed citations( )f you are trying to access articles from an off-camus

location& you may be required to first logon via your library !eb site to access $S#*R( Please visit your library+s

!ebsite or contact a librarian to learn about otions for remote access to $S#*R(

QHootnotesR
http://links.jstor.org/sici?sici=0022-3808(199210)100%3A5%3C966%3AMPGEAS%3E2.0.CO%3B2-A&origin=JSTOR-pdfhttp://links.jstor.org/sici?sici=0022-3808(199210)100%3A5%3C966%3AMPGEAS%3E2.0.CO%3B2-A&origin=JSTOR-pdfhttp://links.jstor.org/sici?sici=0022-3808(199210)100%3A5%3C966%3AMPGEAS%3E2.0.CO%3B2-A&origin=JSTOR-pdfhttp://links.jstor.org/sici?sici=0022-3808(199210)100%3A5%3C966%3AMPGEAS%3E2.0.CO%3B2-A&origin=JSTOR-pdf


34/35


Social and Environmental Hactors Influencing "ontraceptive >se .mong Clack .dolescents

/ennis E. Hogan? Ban arie 5stone? Gvel*n . Kitaga#a

Family Planning Persectives&=ol. 17, Bo. @. (se .mong Clack .dolescents

/ennis E. Hogan? Ban arie 5stone? Gvel*n . Kitaga#a

Family Planning Persectives&=ol. 17, Bo. @. (


35/35

iSee Hanushek (1986) for an excellent review of the eucation literature.ii!ew "a"ers have looke at the "ossi#le enogeneit$ of the "eer grou". %ase an &at'

(1990) fin that neigh#orhoo effects are i"ortant for socioeconoic outcoes. he$ arguethat their results rule out the "ossi#ilit$ that the neigh#orhoo effects the$ fin are *ust anartifact of the wa$ failies sort theselves into counities.iii+ur stu$ oes not irectl$ aress the unerclass issue inasuch as ghetto neigh#orhoos

are not the focus of our oel or sa"le. Such a stu$ re,uires a fraework an s"ecificationsthat allow for shar"- iscontinuous effects aong ifferent t$"es of neigh#orhoos (e.g.- %rane1991).ivSchool ata were ore likel$ to #e availa#le for teenagers fro wealthier failies- an we

have therefore o"ene u" the "ossi#ilit$ of soe sa"le selection #ias #$ incluing onl$ thosecases in which the school res"one to the surve$.v+ur sa"le inclues res"onents fro the crosssection /S sa"le that is intene to #e

re"resentative of all $ouths 12341 as well as the su""leental /S sa"le esigne tooversa"le His"anic- #lack- an isavantage $ouths. 5e i not use the /S sa"leweights in this "a"er.vihese nu#ers were co"ute with 1981 ata #$ ac,ueline !orrest for the /ational

cae$ of Sciences re"ort on teenage "regnanc$- 9isking the !uture.She #ases her estiateson counts of first #irths re"orte in the :ital Statistics of the ;nite States an a#ortion countsta#ulate #$ the lan

viiiixhis a""roach will lea to isleaing results if- when o#serva#les are hel constant-

those who i not res"on to the incoe ,uestion s$steaticall$ have higher or lowerincoes than those who i. 5e trie to aress this ,uestion #$ estiating a oelsuggeste #$

Documents

Peer Group Effects