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Special

issue

,7

A1\

Vsl.

11

Angela

BESANA

CLUSTER ANALYSIS

OII

USA UNIVERSITIES

AS REVENUE

DIVERSIFIERS

DURING

THE

FINANCIAL

CRISIS

Abstract

For more than ten years, _{Universities have experienced an increase in the} complexity of their work and the competitive scenario that is very crowded, un-stable and rather unforeseeable.

on

one side, audiences.

are

multip,re and arways changing in their esti_

rnates

of

relationships

with

universities.

On

the other side, uiiversities

are

al-'"'rays looking for diversified revenues, as public ano private'giant_mat<ers _suffer

cf budgetary cuttings.

., The first paragraph is focused on the nonprofit entrepreneur and the

uni-versity as

a

_{nonprofit entrepreneur who diversifies}

r."r"nu"..

The secono para-9199n

is

mainly concerned _,on the presentation

of

revenue jiversification _{in 85}

USA _{universities in 2008-2009, their performances affected}

nylne

financial

cri-]i:

Th:

third paragraph is-a. _{cruster anarysis}

-

ward

_{and K-means}

-

of the sam_

ple _{as for performances of these univeriities}

who have

,or"tr,nu.

exaggerated the role of _{investments as a source of income.}

The

anarysis

wiil give

evidence _{rhat revenue diversificafion through in_}

vestments caused heavy losses.

Key

words:

University, performance, cluster.

O Angela Besara, 2012. Besana Angela, Associate professor

Losina

PURNASTUTI,

Paul

MILLER,

Ruhul

SALIM

ECONOMIC REIURNS TO

SCHOOLING

IN A

LESS DEVELOPED COUNTRY:

EVIDENCE

FOR

INDONESIA,

Abstract

The purpose

of

this study

is

to

provide

an

update

of

the

empirical

_{evi_}

dence

on

the private return

to-schooring

in rndonesia'usin! sampte

_data

from

rn_

donesian Famiry

Life

survey

4

(|FLS +1.

ftre

augmented Mincerian

_moder

is

utir-ised

to

quantify the private retum

to

schoolingl Tne main

ru.rlt

oot"ined

indi-cates thai the return to schooling in Indonesiais relatively low-compare to other

Asian and Less

Developed

Countries. lt is

_also

found tnai return to schooling

_for

females are

significanfly

different

_{from those of males.}

Key words:

Earnings,

experience, returns

io

schooting.

O Losina Purnastuti, Paul

Miller,

Ruhul Salim, 2012. Purnastuti Losina, Ms, Curtin University.

,0uR,tIt

0f EUn0PEIit

EC0il0fitY

Special issue

-

2012

'1.

lntroduction

There

is

a

quiet

wide

literature

on the

empirical estimation

of

Mincerian

wage return

to

schooling

in

less developed countries.

ln

terms

of the

empirical

findings from developed countries, there

has

been an ongoing debate

concern-ing

even

the

magnitude

of the

returns

to

schooling.

some

studies, for

example,

provide evidence

of

a

relatively low private return

to

schooling

in

developing

countries,

whereas there

are

numerous

other empirical studies that find that the

return

to

schooling

is quite

high. Some studies provide evidence

that

return

to

schooling

for

females are higher than those

of

males, and some find

the

return to

schooling for females are

_{lower than}

those

_{of males.}

Despite the voluminous

empirical

literatures on the returns to schooling

in

less

developed countries,

to

date

there

have

been only

a limited number

of

stud-ies

based

on

lndonesian data. This

study

therefore

has

the potential

to

fill

a

ma-jor

gap in

the

literature.

The paper is organized as follows. Section

2

focuses on literature

review.

Section

3

describes

the data.

Section

4

briefly discusses

the

empirical

frame-work. Section

5

presents

the

estimate results.

section

6

draws some

conclu-sions,

2.

Brief Literature

Review

There is a vast body of

research

on the

labour

market benefits

associated with

education. The

human

capital

modeland

the

signalling/screening

model

are

widely used

to

explain

the

relationship between education

and

labour

market

outcomes. Human capital

theory

emphasizes

that

education provides

informa-tion

and skills

to

enhance

the

productive

capacities of individuals. lndividuals

_will

invest

in

education through schooling

to

acquire skills and

productivity. These

skills

and productivity

raise

the

value of individuals to

employers

and

thus

lead to higher

wages

being offered

(schulE

1961, Ridell 2006). Further,

okuwa

eao4)

states

that education

ls an

essential determinant

of

earning

in

market

econo-mies.

The

higher

an

individual's educational attainment,

the

higher that

individ-ual's expected starting

wage and the

steeper

the rise

in earning capacity over

time.

The estimation of causal links between schooling and earnings has

been

puzzling

labour

economists

for

several

decades.

one of

the

major

_questions

about

the

relationship between

education

and earning is, then, how much is

the returns

to

education? Many methodologies have been proposed in the

literature

l0slna Purnastutl, Paul illllet,

nunIl 3r]

Economic Retutns to Schooling in a Less Developed Evidence

for

empirical research

is

human capital earning

function,

proposed by (1974), which reveals how wages are related to schooling and work experierEa

Chiswick (2002,

p.22-23)

states that the human capital earning

funch

introduced by Mincer has several distinct characteristics that make it

partiatffi

attractive: First, the functional form is an equation based on the optimizing

h-haviour of individuals and represents the outcome of

a

labour market procaa" Second,

it

converts the monetary cost

of

the investment

in

human capital

it

years

of

Schooling

and

years

of

labour market experience.

ln

other words.

I

tonverts <immeasureblas> into (measurebles>. Third, the function is

adapffi

to inclusion of other variables that affect earnings. Fourth, it allows

comparism

across time and demographic groups, since the coefficients

of

the regressin

equation have economic interpretations.

3.

Data

The data set used in the empirical analysis is the lndonesian Family

Lib

Survey 4 (|FLS4).lFLS4 is a nationally representative sample comprising

13,536-households and 50,580 individuals, Spread across provinces on

the

islands

of

Java, Sumatra, Bali, West Nusa Tenggara, Kalimantan, and Sulawesi. Together these provinces encompass approximately 83 percent of the lndonesian

popula-tion and much of

its

heterogeneity. The lndonesia Family Life Survey is a

con-tinuing longitudinat socioeconomic and health survey. The survey collects data

on

inaividual respondents, their families, their households, the communities in which they live,

and the

health

and

education facilities

they use.

IFLS4 was

fielded in iate 2OA7 and early 2008. 1FLS4 was a collaborative etfort by RAND, the Center for Population and Policy Studies (CPPS) of the University of Gadjah Mada, and Survey Meter.

For purposes

of

the empirical analysis for this paper, an extract

of

data

was created from the IFLS4 data base. To create the extract, data frorn the

indi-vidual-level files and household-level fites had

to be

merged.

As

noted above, persons in the individualfile who were aged less than 15 and more than 65 were excluded from the sample. ln addition, only individuals who provided full

informa-tion about their education, employment, and family background were included in

the

sample. Besides that, persons

in

school

or

the

military during

the

survey week were omitted.

The extract contains both IFLS4 variables and derived variables for each person. The variables contained in each person's record are as follows: unique identifiers for individuals and their household, years of schooling, highest level of education obtained, age, potential work experience, gender, marital status, area (rural-urban), amount of earnings by month, and household size.

r0uarlt

0t tuB0Pt AI

rG0]r0HY

Special issue

-

2012

individuar

in

their

job.

The

unit

of

measurernell

i.

jypL?!

(Rp)

(us$1

was

ap_

::Hflirlr.

":::l

i:-p:.jo^o,o

3t,l!"

time

or

tne

zodtriobdl,=u*"rrt

Monthry

earnings

are

used instead

of an

hourry

earnings indicator,

u"."rr"Jir.,i;TJ';nJ

figure respondents were

expricifly

asked to

,up[ty.

whire an

_rrourrv

wage

_{meas_}

ure could

be

constructed,

_carcuration

of

_trouriy

wages

_wouto

iequire

using

an_

other variable, hours worked in

the

reference

mont6, which

is

in turn subject

_to

measurernent error.

H"19"

the

monthry

wage data

"r"

"rgr"d'io

oe tess'frone

to

measurement

error.

.There.is

arso a preflrence tor tne'use

of

monthry earn_

ings based

on the fact that

in

tndoneiia

_{emproyer/emproyee}

agreements

are

generally based

_{on monthly wages.}

There

is

one

_{independent variable that needs}

to

_be

constructed from other

information

in the data.set,

_{namery potentiar}

work

eiperi"*".'Measures

of

ac-tual

labour

force

experience,

an

important variable

in'the

study

of

earnings

de_

termination,

are

absent

from

the

IFLS4 oata

sets.

H"ril;;

potentiai

_rabour

force experience variable can

oe catcutated

from the information available.

_Most

empirical studies usually use the following

basic

forrnula to

deiive a

measure

of

potential

work

_experience

-

_ag.e

minus

ylap

or

schoorinj

,inu.

officiar age

_to

start primary schoor

q0 o1

{)

r]owever,

ior the

purposes

6t

catutating

potentiar

work experience

in

tirls

sjuo.v _{the fortowing forrnura}

wi[

_be

,."J,

"g"

minus

years

of

schooling minus age

firsiattended priirary

_schoor.

The

aim

of

using this

for_

mula

is

to obtain more precise data on

_potentiat

wo*

expeiience since the

age

individuals

first

attended primary

schooivaries

"ppr".iiilly.'iir"ng".

from

5

to

14

years.

'.

-

----'r'

''

'

.

.fhu_.yrmary

statistics

for the

main variabres used

in this study are

re-ported in Tabre

1

Th9

mean

monthry

earnings

are

Rp1,+io,rig

for

_mare

work-ers and Rp1,066,059 for

femare

worliers. ThJ mean

yurr"

oi

schooling are

rera_

tively low,.specificaily

_10.61

years for

_males

ano 10.ti3

y""r"

f*

femares, or

just

one year higher

than.the.9.

years

of

_computsory

study.

rnu

*oik"r.

in the

sam-ple

have

mean

potentiar_

wo1k

experience

(iob

tenure)

or

approximatery

18'04(7.89)

years and

.17..23

(7.78) years

for

miles

anO

femates,

respectively.

The

Table

'l

data reveal that male

ino

_remate

workers have

bioadly

similar

lev-els

of

schooling, potentiar

rabour

market experience, age and

job

tenure.

_They

dffer

appreciably in terms of

_{earnings, where}

the mean"for

malls

(1 _,476, 1 1g) is

38.46 percent above

the

mean

foriemales

(1,066,059)

w","trrn

to this

_issue

below.

,

lpproximately

67.62 percent (3,10g

individuars)

of the

mare sampre come

from

urban area. About 73.02 perceni

1t,t1B

individuarsl

oi

tne

femar!

il;i;

live in urban area. Based on the

maritaisiatus,

3,065 individuals or about

_5g.g4

percent

of

the male

sample

are

married,

and

l,2zz

inaividuals

or

about

g0.14

losln0 Pulntslutl,

paul illltcr, nuntl

Sr

Economic Retums to Schooling

in

a Less Developed Evidence

for

Table 1

Summary Statistics of Variables

Source: Author's calculation based IFLS4 data set.

4"

Empirical

Framework

The

specification

of the

earnings equation

used below

is

based on

tle

human capital model

developed

by

Mincer

(1974).

This model assumes

thd

(i)

the

only costs

of

schooling are the forgone earnings,

and

(ii) each

individud

starts working immediately after completion of school. The model shows that the

natural logarithm of earnings can be expressed as a function of years of

school-ing, post

schooling

labour

market experience

and its quadratic term.

Further-more, this

relationship provides

a

direct

measure

of the

returns

to

schooling through the coefficient

of the

years

of

schooling variable in

the

earnings

regreJ

sion.

To

_{provide more detailed evidence on the returns to education}

in

lndone-sia,

the

_{basic earnings equation is augmented with other variables}

that

_may

in-fluence earnings. The

first

such variable is tenure. This

variable

represents the

work

experience

in the

present

job. current job tenure

is

_usually

viewed as

_a

measLlre of firm-specific training and knowledge.

The

second variable

is

marital

status. Marital status

is

typically associated

with

household specialisation. The specialisation hypothesis arEues that a married couple can engage in

specialisa-tion of their household tasks. Then male workers are able to focus their time and

elfort on labour market activities (Gray, 1gg7), and females, having relatively low

market wages, allocate proportionally more time

to

home duties. Therefore,

be-lng

married

most

likely affects the

wages

of

males positively

while

having the [image:7.595.116.546.64.519.2]

do-t0unilEl

0r EUn0Pt[il tc0[0ilY

Special issue

-

2012

mestic tasks.

The

last variable

is

a

residential

dummy (rural

versus

urban),

which is

intended

to control for

the

earnings differential between urban and

rural

areas. Hence, the equations

with

these

controlvariables

become:

(1)

where earningsi

is monthly earnings

for

individual

i,

yrschyri

is

years of

schooling

for individual

i,

expri is

a

measure of work experience

for

individual

i,

ez.priz is

experience squared for individual

i,

tenttrei

represents

the

job

tenure

for

indi-vidual

i,

terunef

is tenure squared for individuali, marriedi denotes the dummy

for

marital

status

for

individual

i,

and

uhaniis

a

residentialdummy

(urban

versus

rural)

for

individual

i,

and

p

;

is

a

disturbance

term representing other factors

which cannot be explicitly

measured, and whicfr are

assumed to be independent

of

yrschyr;

and expri.

According

to human

capital

theory

F.t

= t,

and

so the

esti-mated regression coefficient

_B1

is interpreted as the average private rate

of

re-turn

to

one

additional

year of schooling.

It

is

frequently

argued

that

in

the

case

of

femates' returns to education

de-rived

from standard Mincerian models may be biased because the females that

participate in the labour force are not representative

of

all females.

ln

order

to

correct

for such

a

potentialselectivity

bias, a

two-step Heckman

(1979) selection

correction approach can be adopted.

A

probit model of the labour force

partici-pation probability of a female is estimated in the first step. Then, in the

second

step,

the derived inverse Mills

ratio

(A)

is included

in

the earnings function as

an

additional explanatory variable.

5"

Results

5.1. Return

to

Schooling

loslna Purnastull, Paul illll0r,

Iuhul Salln

Economic Returns to Schooling in a Less Developed Country: Evidence for Indonesia

Table 2

OLS Estimates of Augmented Mincerian Earnings Functions

and the Selectivity Bias Corrected Earnings Equations

Notes: Robust standard errors in parentheses. *, ** and *** denote statistical significance at the 10 percent, 5 percent and 1 percent levels, respectively.

The results suggest that an additional year of education is associated with

an annual 4.6, and 5.4 percent increase in earnings maie, and female workers,

respectively. These estimates

of

the return

to

schooling

in

lndonesia are

sub-stantially smaller than the Psacharopoulos (1981) average estimate

of

14 per-cent for Less Developed Countries, and the Psacharopoulos (1994) average

es-timate

of

9.5 percent for Asian countries. However these results are

in

agree-ment

with some

empirical studies,

for

example: Jamison

&

Gaag

(1987) in

China, Flanagan (1998) in the Czech Republic,

Weielal.

(1999) in China, Fazio

& Dinh (2004) in china, Aromolaran (2006) in Nigeria, and Astam, Bari &

King-don (2010)

in

Pakistan.

A

relatively low rate

of

return to schooling is generally

faced

by

countries experiencing economic transition,

such

as china

and the

former Russian countries. Typically, the return to schooling in such countries is

low in the

early stage

of the

economic transition process,

then

gradually

in-creases after market oriented economic reform is implemented. The lndonesian

economy shifted from a controlled economy to a market driven economy in '1966

(Ananta & Arifin 2008). Referring to the general pattern of the return to schooling

in economic transition countries, the low return

to

schooling in lndonesia in the late 2000s invites a question. At this period, where the economic reform process had already reached the market driven economy stage, the return to schooling is

expected

to

be

higher

than the

estimates described above. Moreover, Duflo (2001) using data from

a

1995 intercensalsurvey of lndonesia, found estimates

of economic returns to schooling ranging from 6.8 to 10.6 percent. This suggests

that the relatively low return

to

schooling in the current study of data lor

2007-2008 is triggered by some other source. A likely candidate in this regard is a

de-'I

I I

I I

i

I I

{ {

I

G

I

,it

a

fi ru

il

il

s

(a) (b) c

Variables Males Females

Constant 5.24319 (0.03406)*** _{4.95'l 18} (0.04849)**'

Years of Schoolinq 0.04586 (0.00221)*** 0.05429 (0.00321)***

Exoerience 0.00734 (0.00294)*** _{0.00795 (0.00392)*}

Exoerience' -0.00412 (0.0000a*. -0.00015 (0.00009).

Tenure 0.01139 (0.00260)*** _0.02432 (0.00388)***

Tenure' -0.00017 (0.00009). -0.00052 (0.00009)"*'

Married o.03276 @.021321 -0.05018 rc.o24991"*

Urban 0.09994 (0.01556)*'* 1 541

R. 0.2152 0.3032

Chow test (F-test) _37.43

o-value

ial issue

-

2012

0unil[[

T

tUBO

,

0

S

PErlt

tc0[0HY

cline in the _{quarity of} sclo?r _{and a significant increase in the suppry of educated}

worker in the labour market, due to aiombination _or

evenii

_{sJcn as the massive}

school construction program

in

_{1g73 and 1g74}

"no

in"

"orpursory

education program

in

_{1994. Both.explanations, though particutarry}

ine'iatter,

featured in

accounts of the decline in the return to scho-oling in the U's

in1n" rszo..

The estimates

of the

return

to

schooring

for

femares

(5.4

percent) _are

higher than that

fot.*1!-":

(4.6 percent). The

i+est confirns

_{that these}

differ-ences are statistically different, indicatinq. that schooling

is

more financialty re-warding in the _{labour market for females ihan for}

mates.ihis

_{resutt is consistent}

with the findings of many empirical studies, such as Deoralikar (1gg1) in lndone_

sia, MilrerJ\4u!vey,

lMa.rtil

11997) in Austraria, Hanagan

(isga)

in the czech Repubric, Bruneilo,

ggr!

& Lucifoia (2000) in itary,

Ar*"ioizciorl

i"-rrll"*Lo,

and Asaduttah _{(2006) in Bangladesh.}

The coefficients on the potential labour market experience variable and its

squareci term have the exRe:ted signs, and portray

tn!

usuJ

concavity

of

the experience-earnings profile. The increase in

earninir

..ro"iri"d

with an extra year of potential labour market experience is given

ai:

dlneaaraos

^

@--

p.*ZB"erpr,

where

p,

is tne _{estimated coefficient on the experience variable, and}

,F" is the estimated coefficient

ol

_{lhe experience squared variable. Thus this payoff varies}

with the level of _{potential work experience. Also of interest is the level}

of

experi-:nce 1t

which

the

predicted experience-earnings profire

p".t..

This is

where

F.+zfl,expr

= 0 .

This occurs when potentjal work

erp"iien"e

reaches 33.5g and 26.50 for male, and female

sample

respectivety (see figure 1).

The next variable

to

consider

b

job

_{tenure. This measure}

is

_{incruded in} the modelalong with work experience. tiy doing so. it is

poislre

to obtain an in_

dication on the relative importance of general a-no firm

"i""in"

nrran

capital for

earnings deterrnination. The _{increase-in eamings associated witn an}

extialear

of tenure is given as:

?It srninos.

-#=F.tzf-terune.

whe-r_e

f.

is tne estimated _{coefficient on the tenure variable, and}

f,

_{is the estimated} coefficient on the tenure squared variabre. Thus this

prrc#

rrriSi

_{with the rever of}

tenure. _{All the specifications show that tenure has a larg6r partiaienect than}

exferi-ence and age over much

of

the early parts

oj

the _{ex'perience-earnings ano}

age-earnings profiles. For example, in estimition using mate (temaie) sampres, the

coef-ficient for _{potential work experience is 0.00734 (0:0079s1,}

,nJ

tnl

"oefficient for ten-ure.is 0.01139 (0.02432). This suggests that seniority in'terms or

lu

t"nrr"

is reta_

tively more important than potentiil work experience among

tnoid

in their first year in the labour force or in

-their cunent job. Tl'ris pattern holdJ over much of tne darrv

career' For exampre,

?1",

10 years

of

seniority

an

additionat year in the

job

l0slna pu1n08tItl, poul iltltcl,

RInul srllr

Economic Returns to Schooling in a Less Developed

Countrr

Evidence for Indonesl

of work experience an additional year of experience increases earnings by approxF mately 0.494 (0.495) percent for males (femates).

The estimates also suggest that, on average, residents of urban areas

re-ceive significantly higher earnings than individuals living in the rural areas. The coefficient

of

the urban dummy variable is 0.09994 and 0.13455

for

male and

females samples, respectively. Thus the relative effect on earnings is 0.f 05 (exp

(0.09994) -1

=

1.105105

-

1) for male workers and 0.1441exp (O.tS+55)

-'t

₌

1144a22

-

1), and these imply that the male (female) workers from urban areas

earn 10.5( 14.4) percent more than workers from rural areas, with the difference

being significant at

the 1

percent significance

level.l

comparing the male and

female samples, the coefficieni

of the

urban dummy variable

is

nigner

for

fe

males than males. This gender differential in estimates of the partial effects of

urban area residence in the earnings equation, where the effect is larger for

fe-males, is consistent with the evidence in relation to schooling and job tenure.

The marital status variable

is

significant only for females. Being married

has a positive, though statistically insigniflcant, effect on earnings for males but

leads

to

around

5

percent lower earnings for female workers, presumably

be-cause of the extra home duties they undertake and child bearing/rearing

activi-ties. ln other words, being married is most likely to have little effect on the wages

of male workers while it has a negative effect on the wages of female workers.

[image:11.595.49.558.76.574.2]

5.2.

Experience-Earnings

and

Tenure-Earnings

Profiles

Figure 1 _{compares the experience-earnings and the tenure-earnrngs}

pro-files. Panel A _{(B) presents the experience-earnings and tenure-earnings} profiles

for male (female) sample. The experience-earnings and the tenure-earnings

pro-files

display rapid. initial- earnings growth,

and then

decline

after

reaching a

maximum point. For both specifications (Panels

A

and B), the tenure-earnings

profiles lie above the experience-earnings profiles and have

a

steeper shap! This pattern reinforces the comment above, to the effect that tenure is

a

more

important determinant

for

earnings

than

potential tabour market experience

among bolh males and females. That is, employers value seniority in ierms of

job tenure more than potentialwork experience.

Panel

C

compares

the

experience-earnings profiles

by

gender. Male

workers reach their experience-earnings peak at 30.6 years of experience, while

female workers reach

their

experience-earnings

oeak

earlier

than their

male

counterparts, which is at 26.5 years of experience. Thus for a male leaving

edu-cation at age 16, this peak would be at age 47 and for females teaving school at

age 16 this peak would occur at 43 years.

1

For detailed explanation how to calculate relative effects from dummy variable

fe0[0tilv

l0uBil[1

,T

TUDOPETII

Special issue

-

2012

n

E;

6

I

I

6a

tB

Ed

[image:12.595.79.471.191.725.2]

9a

Figure

I

Experience

-

Earnings

and Tenure

-

Earnings Profiles

troot,e

twoal1p",r ro. r.l3 r.,," q"$

i*;EM *;;l

01020J0u60

tott$throfi EF t te Ctral

@

+ Ap€d.E.'ifil6 6 Erpai.rcOIcmis + TanuE-,rAht + TenuE-fmrlet

I

9n

a6 E ;v;

C

Males and Females

3'e

Bq

,!-!<t 5

Aa

s-':b

6-!'d E fs

{*Eexlr-*;*l

M ales and FEmalss

l0slDs Ptrrnastutl, Paul lilll0r.

BuhEl

Srll

Economic Rehrrns to Schoolingia a Less Developed CcEolEc Evidence

for

Comparing

the

tenure-earning profiles

for

females

and

males Panel D), there are

two

main points

of

interest. First, the

gap

between

fu

tenure-earnings profile

and

that

of

males initially narrows

with

years in the

it

However, after females' tenure-earnings profile reaches its peak, the

gendergl

gets wider.

Second,

the

peak

of

the

tenure-earnings

profile,

u,lc

fir+zf"ur*r'e=0,

for

females

(24

years) occurs before

that

for

n*

(35.9 years). This also means that male workers get to their

experience-eani$

peak earlier than their

tenure-earnings

peak.

ln

the case

of

female

wodqr

however,

the

tenure-earnings peak comes earlier than

the experience-eamilgf

peak.

Panel E

compares

the

experience-earnings and tenure-earnings

prffi

for male and female samples. Both the experience-earnings and

tenure-eamigr

profiles

of

female workers

lie

below those

of

male workers

and

have

a

steeperr

shape. Females reach the turning point earlier than males in the case of both

tr:l

experience-earnings and tenu re tenu re-earnings profiles.

5.3.

Selectivity

Bias

It

is

frequently argued

that the

returns

to

education

for

females derived

from either the

standard

or

augmented Mincerian

models may be

biased

be

cause the females that participate in the labour force are not representative of

d

females. This is known as sample selection bias, and it is generally regarded as

a

potentially important, though difficult to address, econometric issue in this type of applled research.

ln

order

to

correct

for

such

a

potential setectivity

bias,

a

two-step

Heckman (1979) selection correction approach

can be

adopted.

This

two-step

approach re-casts the sample selectlon problem as an omitted variable problem, and

so

provides, in principle at least, a tractable means

of

addressing

the

issue-A

probit model of the

labour

force

participation probability

of

a

female is

esti-mated in

the

first step. Then, in

the

second step, the derived inverse Mills ratio (A) is included in

the

earnings function as

an

additional explanatory variable. ln

addition

to

years

of

schooling

or the

dummies for education level, potential ex-perience

or age,

experience squared or

age

squared, marital status

and

urban

area

of

residence, household size, a dummy variable _{for the existence of a} child

younger than five years old in the household, a dummy variable for religion, and

a dummy variable for the existence of either a father or mother in the household

are included in the probit model. These four variables

are

included in

the

model of the decision of whether females participate in labour market because it is pre.

sumed that they are some

of

the factors that directly influence whether females

join

the

_{labour market} and which

do

not affect market earnings.

lt

is argued that

household

size, the

presence

of

children younger than

five

years

old,

and the

presence of the father or mother in the household influence females' decision to

,

0

S

0unflrl

I tut0

Pt[]t rc0il0tilY

issue

-

2012

terms of the amount of domestic duties and time that

has

to

be devoted

to

their

family.

The

religion

of the

respondent

(lslam)

is

included

in the

probit

modet

since religious/lslamic values are of critical importance in many paris

of

lndone-sia. Many believe that female Muslims are

not

supposed to

join

the labour

mar-ket. Given

this

model

of

labour

force

participation, and

the

earnings equations

used previously,

it is

seen

that the

dummy variable

for the

existence

of

a

chitd

younger

than five

years

old

in the

household,

the

dummy variable

for

religion,

the

dummy

variable for the existence

of

father of other

in

the

household and

the

variable

for

household

size

are used,

along

with

the

non-linearity

of

the

sample

selectivity

(A)

term,

for

identification

purposes.

Table 3

Estimates

of

the

Selectivity

Bias Corrected Earnings Equations

Notes: Standard errors in parentheses.

",

** and

"*

denote statistical significance at the 10 percent, 5 percent _and 1 percent levels, respectively.

The Heckman model estimates are reported

in

Tables 3. All the variables

in the probit

labour

force

participation model have

the

expected signs

and

are

statistically

significant. All

the

identifying

variables have strong,

negative impacts

on the participation probability.

Each of

these effects

is

highly statistically

signifi-cant, which suggest

that there

should

not be any

rnajor rnulticolliniearity

[rob-lems following

the

inclusion

of

the

A

term

in the

earnings equation.

However,

when the inverse Mills ratio,

A,

is included in the earnings function

it

turns out

to

Probil Mincerian Constant _0.35173 (0.11293)*"* _5.a2265 (0.06938)*** Year of Schoolinq -0.01695

(0.00584y*

_0.05499

(0.0031n.""

Elperience _{0.03595 (0.00539)*"* 0.0060't} (0.00378) Experiencez -o.00112 (0.00012)*** -0.00009 (0.00009)

Tenure _{o.02422 (0.00369)""*}

Tenure' _-0.00052 (0,00012)*"*

Married _{-0.63521 (0.05638)*". -0.01260} (0.03679) Urban _{0.43750 (0.03760)-*. o.1',1234}

(0.02765)*

Household size -0.03515 (0.00662)**r

Child under 5

-o34992

(0.0396A*"*

Muslim -4.43157

{0.05521)**

Father/mother lives in the same house -2.00666

(0.1'1293)*

A _{-0.06906 (0.04528)}

Adj

R'

_0.1198

Observations 7911 7911

Censored observation _{6380 6380}

[image:14.595.48.469.342.580.2]

t0slns Purn0stutl. PrIl illll0r,

nuhIl srllr

Economic Returns to Schooling in a Less Developed Countryr: Evidence for Indonesia

be statistically not significant. Therefore, it can be argued that the corresponding

estimates for this model reported in Table 2 column (c) do not suffer from

selec-tivity bias.

6"

Conclusion

This paper uncovers evidence

of

returns

to

schooling

in

lndonesia and

highlights some important points. ln this study, males and females estimates for

separating the causal effect of education on earnings between these two

gen-ders

have

been

compared.

This

study employs

OLS

as

methodotogical

ap

proach to measure

ihe

retum to schooling. ln order to correct the possibility

d

bias selectivity, two step Heckman's model is adopted. The results suggest

thd

there is no selectivity bias. This confirms that the conventional estimates without correcting selectivity are valid and acceptable.

The estimation

of

the augmented Mincerian earnings functions revealed

that the return for an extra year of schooling is positive and significant. Employ-ing the augmented Mincerian model that includes the control variables for tenure

and

its

squared, marital status,

and

urban

area

of

residence,

the

return to

schooling are

4.6,

and 5.4

for

male, 'and female samples, respectively. These results confirm that the returns to schooling in lndonesia are low in comparison

with the return

to

schooling in many other countries, particularly Asian and

d+

veloping countries. Furthermore, it is clearly shown that the returns to schooling

are higher for females than for males, which is in agreement with the findings of

other

studies,

e.

g.

Deolelikar (1991)

in

lndonesia, Miller, Mulvey,

&

Martin-(1997) in Australia, Flanagan (1998) in the Czech Republic, Brunello, Comi, &

Lucifora (2000)

in

ltaly, Avecedo (2001)

in

Mexico, and Asadullah (2006) in Bangladesh.

The results show that statistical control for tenure and its squared term

b

more important than potential work experience. Marital status has a positive

im-pact on earnings for males but it has a negative impact on earnings for females.

These results support

the

household specialisation hypothesis. The estimates

also

suggest

that, on

average, residents

of

urban areas receive significanfiy higher earnings than individuals living in the rural areas.

The estimates

of the

return

to

schooling based

on

additional years of

schooling using the OLS method presented in this paper provide some valuable

information

on the

lndonesian education

sector.

However,

these

empirical

analyses may have limitations.

ln

particular, in this chapter the endogeneity of

schooling was not taken into account. Devoting a separate study to it may shed

lndonesia-l0unil[r

1.

tf tu[0Pt[[

tc0x0my

Special issue

-

2012

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ltr

.t

F,Til

-lsE

charactertl

trade

flq

geographtst

tation _of

B(

BoU

namic

sFil

tropy

of

ft

interprebd

I

genotypbril

greater

the(

economic

{

naturally

bl

nomic

sy#

to the

enad

nature of

sfl

variance

S-S

nst< level

df

tation

of

ed

flows, we

gd

dynamic

ed

an

eventudf

tem correspq{

*ii

,r!

@ Bruno G-

I;j

Ruettimann _BniC