Fahmi Brownbag Revised

Are Private Schools More Effective than PublicSchools?

Mohamad Fahmi∗

Department of Economics, Universitas Padjadjaran, Indonesia

July 27, 2010

Abstract

I attempt to replicate the research carried out in the paper entitled "The Effectiveness ofPrivate Versus Public Schools: Case of Indonesia" the work of Bedi and Garg (2000). Bediand Garg (2000) find that selectivity bias in the earnings estimation reverses the superiorityof public schools over private schools. To confirm the findings, I use the same sample dataused by Bedi and Garg (2000) and a sample data that created from the Indonesia FamilyLife Survey 1. I also discuss the use of school quality proxies by Bedi and Garg (2000) thatmay bias their estimates of the earnings differential. My findings show that the surprisingfindings of Bedi and Garg (2000) are not robust and suggest that public school graduatesearn significantly higher than private non-religious school graduates and imply that thequality of public schools are better than private non-religious schools.

JEL classification: J31Keywords: School effectiveness; Earnings; Indonesia

1. Introduction

While it is generally accepted that public secondary schools are at the highest quality in In-

donesia (Strauss et al., 2004; Newhouse and Beegle, 2006), Bedi and Garg (2000) find that

workers who attended private non religious schools experience a 75 per cent earnings advan-

tage over graduates of public schools. Without correcting for selectivity bias, public school

∗e-mail: [email protected].

1

graduates are found to earn 31 per cent more over private non religious graduates. However,

a negative selection effect for private non religious schools is identified and correcting for this

selection effect results in a large earnings premium of private non religious schools over public

schools. Despite private non religious school graduates generally having lower academic qual-

ifications than public school graduates, it is argued that the private non-religious schools are

more effective than the public schools. Bedi and Garg’s (2000) finding supports Hannaway’s

(1991) claim, “that private schools perform better due to greater school level autonomy and

their responsiveness to the needs of students and parents”. The policy implication of these

findings is to encourage a greater private sector role in Indonesian education since the results

suggest the private sector is more efficient and effective in delivering education.

Along with Bedi and Garg (2000), Newhouse and Beegle (2006) investigate the effective-

ness of private and public lower secondary schools in the Indonesian context, focus on the

relationship between school choice and academic performance rather than school choice and

future earnings. Newhouse and Beegle (2006) found the academic performance of public lower

secondary school students was superior to private school students as measured by national fi-

nal test exam scores (UN1) upon completion of lower secondary school. An important aspect

of the findings by Newhouse and Beegle (2006) is that they are difficult to reconcile with the

earlier findings by Bedi and Garg (2000). In particular, Newhouse and Beegle (2006) suggest

that the resource advantages of public schools are unlikely to be outweighed by any efficiency

benefits of private schools.

In this paper, I re-examine the earnings differential between public and private lower sec-

ondary school students, originally studied by Bedi and Garg (2000). Despite careful attempt to

replicate these earlier results, I am unable to do so. While Bedi and Garg (2000) use the first

wave of the Indonesia Family Life Survey (IFLS1) that issued in 1996 (DRU-1195-CD), my

sample data are obtained from the re-release version of IFLS1 (IFLS1-RR).

In order to explain the differences, I discuss the use of school quality proxies by Bedi

1UN or Ujian Nasional is the newest system of national centralized final examination. In 2008, IndonesianGovernment increased the standard average passing grade from minimum 5.25 to 5.50 for 6 subjects.

2

and Garg (2000). Several variables which identify the condition of the last school attended

confound lower and upper secondary schools. The use of such proxies may bias their estimates

of the earnings differential.

Using the re-release sample data of IFLS1 and with an absence of school quality indicators,

my findings suggest that the surprising findings of Bedi and Garg (2000) are not robust. My

findings suggest that public school graduates earn significantly higher than private non reli-

gious school graduates and imply that the quality of public schools are better than private non

religious schools.

In the next section, I try to replicate Bedi and Garg (2000) data sample. The following

section introduces the empirical strategy to re-estimate the effect of lower secondary schools

quality on earnings differential. Section 4. provides the results of school choice and earnings

decomposition estimates, while Section 5. concludes the paper.

2. Sample Replication

In order to replicate the result of Bedi and Garg (2000), I first try to create an identical dataset,

using the Indonesia Family Life Survey 1 (IFLS1) 1993. The IFLS1 is a large-scale lon-

gitudinal survey of socio economic and health status of the individual and household level.

The IFLS1 sampling scheme was based on provinces, then the sample was randomly selected

within these provinces. For cost-effectiveness reasons the survey focused on only 13 out of

26 provinces on the Island of Java, Sumatra, Bali, West Nusa Tenggara, Kalimantan, and Su-

lawesi. These were selected to represent approximately 83 per cent of the Indonesian popu-

lation. While waves 2, 3, and 4 of the IFLS have been released between 1997 and 2009, the

analysis in Bedi and Garg (2000) focuses on IFLS1. I assume that their study commenced

when only IFLS1 was available, and while IFLS2 was released in 1997, it did not contain

employment data required to extend the analysis.

I created a sample data based on Bedi and Garg’s (2000) guidance (pages 467-468). Fol-

3

lowing the guidance in pages 467-468 of Bedi and Garg (2000), I attempt to replicate their

sample. However, I was unable to exactly reproduce their sample. My initial sample data set

consisted of 7220 respondents who have earnings and are no longer students. The size of the

initial data was almost twice the size of Bedi and Garg’s (2000) initial sample data of 4900

observations. Missing and miscoded data and also sample restrictions then reduced the data set

by 6170 (more than 85 per cent) to 1050 observations. Most of the observations, 5448, were

dropped as respondents had not proceeded beyond primary school, while 274 observations

were dropped since respondents had more than 12 years of education. Moreover, I dropped 13

respondents due to missing information on school type and 9 observations as they had either

99997 or 999997 on total monthly earnings. Finally, I exclude a further 389 observations as

they had some missing information, miscoded class size (41 observations), number of months

in school period per year (45), failed in primary school (1), parents’ education (294), province

where school is located (6) and religion (2). Table 1 presents the full comparison of the exclu-

sion process with results of Bedi and Garg (2000).

4

Table 1: Comparison of Exclusion Process

Item Bedi and Fahmi*

Garg (2000)

Initial income information 4900 7220

Had not proceeded beyond primary education 3391 5448

Had more than 12 years of education 291 274

Lack of information on hours of work 33 37

Missing information on school type 10 13

Reported incomes seemed implausibly high 3 9

Missing information on class size - 41

Attend(ed) school more than 12 months (miscoded) - 45

Missing information on failed in primary school - 1

Missing information on father’s education - 214

Missing information on mother’s education - 80

Missing information on school location - 6

Missing information on religion - 2

Number of remaining observation 1194 1050

5

In order to understand the differences between my sample and the sample extracted by Bedi

and Garg (2000) it is important to note that Bedi and Garg (2000) used the IFLS1 issued by

RAND in 1996 (DRU-1195-CD), while I used the IFLS1 data set called IFLS1-RR (re-release

in 2000) that updates the original IFLS1. Peterson (2000) explains that IFLS1-RR revises and

restructures the original IFLS1 to accompany with IFLS2. The different between IFLS1 DRU-

1195-CD and IFLS1-RR maybe the source of the discrepancy between my sample and that

of Bedi and Garg (2000). Arjun Bedi kindly sent the sample data set, PUBPRIV.DTA2. Bedi

and Garg (2000)create the file on 7 February 1998 which consists of 1527 observations and

231 variables. However, they were unable to provide the code used to construct the sample,

making it impossible to clearly identify the sources of the discrepancies.

2The file PUBPRIV.dta originally consists of 1,527 observations, 231 variables. It is created on 7 February1998. The exclusion of some observations of missing data on earnings information drops the final sample data setto 1194.

6

Table 2: Tracking Process of Mismatch Sample Data

No. Note

Obs.

745 Identical

17 Unidentified

152 Had more than 12 years’ education.

34 - Missing information on period in school in months.

- Bedi and Garg (2000) substitute the missing data by sample mean.

32 - Missing information on class size.

- Bedi and Garg (2000) substitute the missing data by sample mean.

154 - Missing information on father’s education.

- Bedi and Garg (2000) put "0" instead of missing values in three dummy

variables of father’s of education.

- Three variables of father’s education are FATH_PRI, FATH_JH and

FATH_SH.

60 - Missing information on mother’s education.

- Bedi and Garg (2000) put "0" instead of missing values in two dummy vari-

ables of mother’s education.

- Two variables of mother’s education are MOTH_PRI and MOTH_SEC.

I was able to compare my sample (1050) with Bedi and Garg’s (2000) sample (1194) using

survey identification code. It is possible to match 745 respondents precisely. Of the remaining

449 observations, 17 observations are unidentified and 432 are considered as missing informa-

tion. Conversely, my sample contained 305 observations that were not in the sample provided

by Bedi and Garg (2000).

Of the 305 observations with missing data, 34 observations have no information on the

number of months per year of attending school and 32 observations have no information on

7

class size. Bedi and Garg (2000) substitute for the missing data on those observations by using a

sample mean instead of dropping the number of observations. The remaining 214 observations

have no information on either father’s or mother’s education. Bedi and Garg (2000) put "0"

value on those observations rather than dropping them. While the exclusion process is clearly

documented, the substitution process on the 305 observations is not explained in the paper. I

provide the details of my comparison in Table 2. I also present a full complete comparison of

summary statistics between the two sample in Table 3 and the description of all variables in

Table 9 in Appendix 5.. Given the differences in the sample data, I proceed to replicate their

methodology using the sample that I have extracted.

Table 3: Comparison of Descriptive Statistics

Variable Bedi and Garg (2000) Fahmi

Mean Std. Dev Mean Std. Dev

LOGEARN -0.202 1.079 -0.290 1.063

EARN 1.492 2.567 2.030 17.655

AGE 34.66 7.502 34.264 7.321

JUNIOR 0.307 0.462 0.415 0.493

SENIOR 0.521 0.499 0.527 0.500

MALE 0.672 0.469 0.689 0.463

BAHASA 0.404 0.491 0.370 0.483

HIN_BUD 0.066 0.248 0.074 0.262

CHRIST 0.091 0.289 0.092 0.290

PRI_FAIL 0.204 0.403 0.208 0.406

SCHOLAR 0.048 0.215 0.040 0.196

FATH_PRI 0.422 0.494 0.521 0.500

FATH_JH 0.101 0.302 0.113 0.317

FATH_SH 0.085 0.279 0.084 0.277

MOTH_PRI 0.380 0.485 0.470 0.499

Continued on Next Page. . .

8

Table 3 – Continued



MOTH_SEC 0.109 0.312 0.094 0.292

DIRT FLOOR 0.067 0.251 0.044 0.205

CLASS SIZE 36.47 9.301 36.651 8.884

MONTHS 9.459 1.849 9.638 1.710

OTH_PR 0.023 0.148 0.031 0.175

SKALI_ED 0.043 0.204 0.036 0.187

NSUMA_ED 0.106 0.308 0.097 0.296

WSUMA_ED 0.068 0.253 0.049 0.215

SSUMA_ED 0.051 0.220 0.052 0.223

LAMP_ED 0.023 0.151 0.027 0.161

EJAVA_ED 0.120 0.325 0.135 0.342

WJAVA_ED 0.139 0.346 0.131 0.338

CJAVA_ED 0.141 0.348 0.155 0.362

BALI_ED 0.048 0.215 0.058 0.234

NTB_ED 0.042 0.200 0.056 0.230

YOGYA_ED 0.067 0.251 0.065 0.246

SSULA_ED 0.042 0.202 0.038 0.192

JAKAR_ED 0.079 0.270 0.069 0.253

URBAN 0.708 0.455 0.670 0.470

SKALMNT 0.043 0.204 0.050 0.219

NSUMATRA 0.098 0.297 0.084 0.277

WSUMATRA 0.066 0.250 0.045 0.207

SSUMATRA 0.053 0.225 0.057 0.232

EJAVA 0.103 0.304 0.117 0.322

WJAVA 0.131 0.338 0.125 0.331

CJAVA 0.088 0.284 0.098 0.298

BALI 0.054 0.226 0.068 0.251


9




NTB 0.042 0.202 0.057 0.232

LAMPUNG 0.029 0.168 0.034 0.182

YOGKARTA 0.067 0.251 0.065 0.246

SSULAWES 0.042 0.202 0.040 0.196

JAKARTA 0.176 0.381 0.160 0.367

Number of Sample 1194 1050

3. Estimation Procedure and Sample

I use a two-step earnings estimate with selection bias correction and Blinder-Oaxaca Decom-

positions to determine the earnings differential between public school group and private school

groups. The two step earnings estimates that is corrected for selection bias problem is based

on the technique that developed by Lee (1983). I follow Lee (1983) to employ an unordered

multinomial logit (MNL) model in obtaining the selection correction terms and estimating the

lower secondary school choice.

To determine the effect of school quality on earnings differential, I follow Bedi and Garg

(2000) to estimate four separate earnings estimates: public, private non religious, private Is-

lamic and private Christian. According to Kingdon (1996) this method is able to avoid endo-

geneity problem. The earnings determination of individual i who attended school type j may

be written as

10

Yij = βjXij + eij (1)

where Y is earnings, β is parameters of exogenous variable X that consists of personal and

family characteristics, and u is the error terms.

The inclusion of selection correction terms to overcome the selection bias problem modifies

the equation 1 to

Yij = βjXij + θjλij + ηij (2)

where

λij =φ(Hij)

Φ(Hij

(3)

and

Hij = Φ−1(Pij) (4)

θ is the coefficient on inverse Mills ratio λij , ηij is error terms, φ(Hij is the standard normal

density function, Φ(Hij is the normal distribution and Pij is probability of individual i chooses

the type school j.

I follow Bedi and Garg (2000) using the Blinder-Oaxaca decomposition to estimate earn-

ings differential between public school and private school graduates. Bedi and Garg (2000)

use the two-fold decomposition that included some non-discriminatory coefficient vectors to

determine the contribution of the gap in the predictors. According to Reimers (1983), the two

fold decomposition can be written as

lnYk − lnYm = (X̄j − X̄m)[β̂jD + β̂m(I −D)] + (β̂j − β̂m)[X̄j(I −D) + X̄mD] (5)

11

where the subscript k refers to the public schools group and the subscriptm refers to private

schools groups. lnY is the natural logarithm of individual earnings. X is a vector of observed

characteristics and β is a vector of coefficients on observed characteristics. I is the identity

matrix and D is a diagonal matrix of weights.

Now the two-fold decomposition is

R = Q+ U (6)

where R is the earnings difference. The first component, Q, is the earnings differential

that is "explained" by group differences in the predictors. The first difference is also known as

quantity effect. The second part, U is the "unexplained" part. U is the differences caused by

discrimination and unobserved variables.

I also follow Bedi and Garg (2000) to use the mean coefficients between the low and the

high model or D = 0.5 proposed by Reimers (1983). Reimers (1983) believes that the dis-

crimination in labour market could affect the earnings of either the majority or minority group.

Therefore, Reimers (1983) suggests that the diagonal of D (matrix of weights) should equal

0.5 to avoid the inconsistency in decomposition result.

I use several STATA modules to estimate school choice and earning differential. To obtain

the marginal effect of the explanatory of the multinomial logit estimates, I use the STATA

module mfx2 developed by Williams (2006). Williams (2006) claims that mefx2 enhances

the marginal effect computation and post-estimation table formatting. I use the STATA module

selmlog developed by Jann (2008a) to estimate the earnings equation with the selection

correction terms. Using selmlog, I obtain the mean and the coefficient on λ in the first step

and estimate the earnings equation with selection bias corrected in the second step.

To estimate the Blinder-Oaxaca earnings decompositions, I employ the STATA module

oaxaca3 developed by Jann (2008b). The oaxaca’s command is not only capable to calculate

the earnings decompositions but it is also capable to obtain the standard errors and covariances.

3See Jann (2008c) for methods and formulas.

12

The sample data for this study that is obtained from the first wave of the Indonesia family

Life Survey (IFLS1) consists of 1,526 observations. The sample of this study consists of in-

dividuals with minimum lower secondary education and their primary activity during the past

week of the interview is working, trying to work or helping to earn income.

The sample contains 966, 295, 155 and 110 observations from public school, private non

religious, private Islamic and private Christian school groups. The size of initial sample with

earnings and lower secondary education information is 2,104. Dropping observations with

missing values on one ore more variables reduces the sample size to 1,526. I present the sample

means of all variables by school-type in Table 4. I also present the summary of statistics of all

sample in 10 in Appendix 5..

13

Tabl

e4:

Sam

ple

Mea

nsby

Scho

ol-T

ype

Vari

able

Publ

icPr

ivat

eN

RPr

ivat

eIs

Priv

ate

Ch

Mea

nSt

d.D

ev.

Mea

nSt

d.D

ev.

Mea

nSt

d.D

ev.

LO

GE

AR

NIN

GSM

-0.1

131.

077

-0.3

451.

043

-0.2

921.

195

-0.1

261.

13

EA

RN

ING

SM2.

481

19.3

771.

196

1.99

81.

857

4.30

41.

546

1.77

4

AG

E34

.907

7.16

334

.325

7.47

84.

729

7.52

937

.209

6.65

4

SEX

0.73

20.

443

0.69

80.

460.

697

0.46

10.

627

0.48

6

LA

NG

IND

O0.

414

0.49

30.

393

0.48

90.

316

0.46

60.

445

0.49

9

ISL

AM

0.85

70.

350.

780.

415

0.99

40.

080.

473

0.50

2

CIT

Y0.

212

0.40

90.

261

0.44

0.13

50.

343

0.28

20.

452

TOW

N0.

264

0.44

10.

224

0.41

70.

168

0.37

50.

373

0.48

6

VIL

LA

GE

0.52

40.

500

0.51

50.

501

0.69

70.

461

0.34

50.

478

PRIF

AIL

0.19

80.

398

0.24

10.

428

0.22

60.

419

0.19

10.

395

JUN

IOR

0.33

10.

471

0.39

30.

489

0.45

20.

499

0.31

80.

468

SEN

IOR

0.48

30.

500

0.47

50.

500

0.44

50.

499

0.45

50.

500

Con

tinue

don

Nex

tPag

e...

14

Tabl

e4

–C

ontin

ued

Vari

able

Publ

icPr

ivat

eN

RPr

ivat

eIs

Priv

ate

Ch

Mea

nSt

d.D

ev.

Mea

nSt

d.D

ev.

Mea

nSt

d.D

ev.

Mea

nSt

d.D

ev.

WO

RK

SD0.

068

0.25

20.

078

0.26

90.

097

0.29

70.

082

0.27

5

FAT

HPR

I0.

759

0.42

80.

817

0.38

70.

852

0.35

70.

745

0.43

8

FAT

HJH

0.12

80.

335

0.09

20.

289

0.08

40.

278

0.14

50.

354

FAT

HSH

HE

0.11

30.

317

0.09

20.

289

0.06

50.

246

0.10

90.

313

MO

TH

PRI

0.87

10.

336

0.89

50.

307

0.89

70.

305

0.8

0.40

2

MO

TH

JH0.

084

0.27

70.

061

0.24

0.07

10.

258

0.07

30.

261

MO

TH

SHH

E0.

046

0.20

90.

044

0.20

60.

032

0.17

70.

127

0.33

5

Obs

erva

tions

966

295

155

110

15

4. Empirical Results

4.1 Lower Secondary School Choice

For estimation of the lower secondary school choice, I use a multinomial logit (MNL) model.

Following Bedi and Garg (2000), I assume individuals and their parents choose a set of lower

secondary school types that consists of a public school category, private non religious category,

private Islamic category, and private Christian category. I present the results of the school

choice multinomial logit estimates in Table 6 and I also provide marginal effects of explanatory

variables in Table 5.

The results show that individuals with more educated parents are more likely to attend pub-

lic schools. There is evidence that individuals with more educated parents also prefer to study

in private Christian school. However, this finding may be ambiguous since there are contradic-

tory signs on father’s upper secondary and higher education (FATHSHHE) and mother’s upper

secondary and higher education (MOTHSHHE) variables. Individuals with less educated par-

ents have higher probability of attending private non religious schools. There is no evidence

that parents’ education influence the decision of attending private Islamic schools.

As expected, non-Islamic background has a positive effect on enrolment of both private non

religious and private Christian schools. On the other hand, individuals with Islamic background

are more likely attending public and private Islamic schools. Males are more likely to enrol

in public schools, while females prefer to attend private Christian. There is no evidence that

sex affect the probability of access in private non religious and private Islamic schools. These

results are corresponding to Bedi and Garg’s 2000 findings.

Furthermore, individuals who lived in urban area when they were 12 years old have higher

probability of attending private non religious and private Christian schools. On the contrary,

public and private Islamic schools are more likely attended by individuals from rural area.

16

Table 6: Lower Secondary School Choice Estimations

Variable Private NR Private Is Private Ch

Coeff/(SE) Coeff/(SE) Coeff/(SE)

CITY 0.365** -0.671** 0.937***

(0.174) (0.268) (0.279)TOWN -0.095 -0.713*** 0.815***

(0.168) (0.235) (0.255)SEX -0.228 -0.295 -0.431*

(0.149) (0.193) (0.230)LANGINDO -0.124 -0.217 -0.151

(0.147) (0.196) (0.223)ISLAM -0.571*** 3.266*** -2.015***

(0.170) (1.008) (0.219)PRIFAIL 0.237 0.086 0.037

(0.162) (0.213) (0.276)WORKSD 0.115 0.253 0.514

(0.264) (0.307) (0.398)FATHJH -0.410* -0.497 -0.100

(0.239) (0.326) (0.310)FATHSHHE -0.343 -0.549 -0.949**

(0.256) (0.400) (0.421)MOTHJH -0.231 0.204 -0.149

(0.292) (0.379) (0.418)MOTHSHHE 0.165 0.077 1.484***

(0.362) (0.508) (0.404)CONSTANT -0.539*** -4.346*** -0.962***

(0.203) (1.020) (0.300)Pseudo R-Square 0.066N 1526Chi2 157.603***

Note: Standard errors are in parenthesis and heteroscedasticity consistent. * Significance

at 10% level, ** Significance at 5% level and *** Significance at 1% level.

17

Table 5: Marginal Effects of School Choice Estimations

Variable Public Private NR Private Is Private Ch

Coeff/(SE) Coeff/(SE) Coeff/(SE) Coeff/(SE)

CITY -0.069* 0.056 -0.042*** 0.055**

(0.035) (0.030) (0.013) (0.020)

TOWN 0.005 -0.017 -0.040*** 0.052**

(0.030) (0.026) (0.012) (0.018)

SEX 0.061* -0.028 -0.014 -0.019(0.027) (0.024) (0.013) (0.012)

LANGINDO 0.032 -0.016 -0.011 -0.005(0.026) (0.023) (0.012) (0.010)

ISLAM 0.138*** -0.073* 0.111*** -0.176***

(0.034) (0.030) (0.010) (0.026)

PRIFAIL -0.039 0.038 0.002 -0.001(0.030) (0.028) (0.013) (0.013)

WORKSD -0.049 0.008 0.013 0.027(0.049) (0.042) (0.022) (0.027)

FATHJH 0.077* -0.055 -0.022 0.000(0.036) (0.032) (0.015) (0.015)

FATHSHHE 0.094* -0.039 -0.024 -0.031**

(0.040) (0.036) (0.018) (0.011)

MOTHJH 0.025 -0.037 0.018 -0.006(0.049) (0.041) (0.028) (0.018)

MOTHSHHE -0.116 -0.004 -0.007 0.127*

(0.069) (0.055) (0.028) (0.052)

N 1526

Pseudo R2 0.066

Log likelihood -1466.449

Note: Standard errors are in parenthesis and heteroscedasticity consistent. * Significance at

10% level, ** Significance at 5% level and *** Significance at 1% level.

18

4.2 Earnings Equations

In his college choice and earnings model, Strayer (2002) believes that there are two sources of

earnings determination that is influenced by school quality. First, a different type of high school

will generate a different level of earnings. The quality of a type of high school influences the

probability of attending a type college, then the earnings determination from different type of

college could be considered comes from the quality of the high school. Second, a high quality

school directly increases skills of students to gain higher earnings in labour market.

In my model, I assume that earnings differential mainly comes from the indirect effect

of school quality which is determined by different types of lower secondary school. I use

this assumption based on two reasons. First, the proxies of school quality indicators from

IFLS1 that used by Bedi and Garg (2000) may bias the earnings determination. Bedi and Garg

(2000) use three proxy variables: a dummy variable of whether the school has a dirt floor

(DIRT FLOOR), the length of the school term (MONTHS), and the number of students in the

class (CLASS SIZE). In IFLS2 and the later waves, the questions regarding length of school

term and class size are categorized on basis school level. IFLS2 do not provide information

about type of school’s floor in education section (Book 3A). According to the manual book

of IFLS1, type of floor, length of school term, and class size4 provide information about the

school characteristics last attended by respondents. Since my sample consists of individuals

whose range of level education are between lower secondary education and higher education,

information on these proxy variables may bias the estimations. Second, the availability of

standard variables for school quality indicators such as teacher-student ratio in IFLS1 is poor.

I only find 286 of 1530 observations that have teacher-student ratio.

I estimate separate earnings equations for each type of school. The earnings estimates are

estimated by including the selection correction variables that obtained from the multinomial

logit school choice estimates. I focus on the influence of school quality to earnings formation

4The information regarding type of floor, length of school term and class size are provided in the book ofBUK3DL3

19

and I also investigate the effect of academic achievement and attainment, personal and family

characteristics, and parents’ education on earnings determination. The estimated coefficients

on those variables measure the direct effect of these variables to earnings formation.

Table 7 shows the earnings estimates based on type of lower secondary school attended.

The estimated coefficients on personal and family characteristics measure the direct contribu-

tion of these factors to earnings determination. In all four school groups, the education attain-

ment variables (HE and SENIOR) are the most important factors for earnings determination.

Individuals with upper secondary and higher education earn higher earnings than their counter-

part who only graduate from lower secondary schools. The 0.522 log point difference on HE

implies that an individual from the public school group who is educated in a higher education

institution has probability to earn about 68 per cent higher than those with upper secondary

education. Similar condition also occurs in private non religious (46% higher), private Islamic

(51% higher) and private Christian (15% higher) school groups. An individual with upper sec-

ondary education earns about 57% (in public the school group), 46%(private non religious),

38% (private Islamic), and 35% (private Christian) higher than an individual who only attend a

lower secondary school. The significant coefficient on dummy variable PRIFAIL also inform

there is a direct return of early education achievement on earnings. Individuals who failed a

grade in primary education earn about 16 per cent lower if they attend public schools, while

the negative gap increases to about 115 per cent if they attend private Christian schools.

As expected, personal and family characteristics also have a direct effect to earnings for-

mation. Being male increases an individual’s earnings for about 23 per cent and 37 per cent

in public school and private non religious school groups respectively. Living in urban areas

increases individuals’ income for about 28 per cent and 95 per cent in private non religious

and private Islamic school groups respectively. The positive and significant coefficient on

LANGINDO in public (0.177) and private Christian (0.782) school groups implies that indi-

viduals who speak Indonesian language in daily life as they more likely lives in urban areas

enjoy a higher income by 19 per cent and 118 per cent respectively. It is also not surprising that

20

Individuals who live in urban areas and attend private Islamic schools earn 94 percent higher

than their counterpart within the sector.

Table 7: OLS Estimates of Earnings with Selection Correc-

tion



CONSTANT -2.623*** -3.611*** -2.036 -2.103

(0.880) (1.355) (2.844) (3.038)

AGE 0.087* 0.145* -0.088 0.107

(0.040) (0.057) (0.113) (0.152)

AGE2 -0.001 -0.002* 0.002 -0.001

(0.001) (0.001) (0.002) (0.002)

URBAN 0.115 0.249 0.663** -0.064

(0.076) (0.142) (0.224) (0.286)

SEX 0.203* 0.316* -0.081 0.369

(0.084) (0.137) (0.229) (0.213)

LANGINDO 0.177* -0.064 0.341 0.782**

(0.084) (0.154) (0.299) (0.276)

ISLAM 0.179 0.080 1.136 0.093

(0.169) (0.223) (1.411) (0.562)

PRIFAIL -0.152 -0.131 -0.254 -0.731**

(0.084) (0.150) (0.213) (0.241)

SENIOR 0.456*** 0.378** 0.324 0.303

(0.071) (0.124) (0.193) (0.230)

HE 0.978*** 0.761*** 0.737* 0.439

(0.096) (0.190) (0.339) (0.273)

FATHJH 0.082 -0.164 0.462 0.366


21




(0.121) (0.230) (0.369) (0.317)

FATHSHHE 0.238 -0.074 0.503 0.725

(0.150) (0.246) (0.453) (0.501)

MOTHJH -0.184 0.544 0.188 -0.422

(0.121) (0.281) (0.375) (0.459)

MOTHSHHE -0.042 0.276 -0.732 -0.041

(0.182) (0.311) (0.561) (0.593)

LAMBDA 0.072 -0.0715 -0.067 0.523

(0.594) (0.720) (0.666) (0.636)

Adj R2 0.255 0.243 0.339 0.390

N 966 295 155 110

Note:

- The dependent variables is the logarithm of hourly earnings.

- Standard Errors are in parenthesis and heteroscedasticity consistent.

- * Significance at 10% level, ** Significance at 5% level and *** Significance at 1% level.

- Dummy variables of regions of residence and school are included in estimation but not reported.

4.3 The Blinder-Oaxaca Earnings Decompositions

Table 8 highlights the effect of school quality on the earnings decompositions. I use the

Blinder-Oaxaca decomposition to determine the earnings gaps between public and private

schools. The base model is estimated from the earnings estimates in 2. The results on Table

8 suggest there are positive earnings gaps between public and private school groups. With-

out including the selection correction terms, the public school group earn about 26%, 19%

and 1% higher than private non religious, private Islamic and private Christian school group

22

respectively. However, only the public-private non religious gap that is statistically significant.

Using the two-step earnings estimate and including the selection correction term to the

estimates, the positive earnings gap between public and private non religious school group is

corrected or increased to about 45 per cent. This result is contradictory with Bedi and Garg’s

(2000) finding when the selection correction terms reversed the superiority of individuals form

the public school group over the private non religious group.

The positive earnings gap between public and private Islamic school group is also increased

to about 39 per cent. On the contrary, the inclusion of selection bias correction term reverses

the advantage of the public school group over the private Christian school group as the gap is

reversed to about -123 per cent. However, all these gaps are not statistically significant and

provide the weak evidence of the effect of selection bias to earnings decomposition of public

and private school groups.

The academic achievement gap in public-private non religious school and public-private

Islamic decompositions are positive and significant. These results suggest that academic at-

tainments have more effect on earnings for individuals from public school group than those

private school groups. Furthermore, the experiences in labour market are more important for

the private Christian school group than the public school groups since it contributes to a -6

percent of the total earnings gap.

23

Table 8: Two-Folds Decomposition

Private NR Private Is Private Ch

Difference 0.232** 0.178** 0.013

(0.078) (0.088) (0.099)

Adjusted 0.374 0.330 -0.803

(0.971) (1.388) (1.144)

Total Explained: 0.115** 0.066 -0.080

(0.046) (0.113) (0.177)

1. Experience 0.024 0.004 -0.064*

(0.016) (0.025) (0.036)

2. Personal/Familiy 0.015 -0.013 0.065

(0.017) (0.96) (0.142)

3. Academic 0.056** 0.091** -0.022

(0.022) (0.033) (0.028)

4. Parents’ Education 0.005 0.025 -0.002

(0.010) (0.023) (0.032)

5. Other Variables 0.016 -0.041 -0.057(0.030) (0.049) (0.077)

Total Unexplained 0.259 0.264 -0.723

(0.983) (1.467) (1.270)

Note: 50 replication of bootstrap standard errors are in parenthesis. * Significance at 10% level,

** Significance at 5% level and *** Significance at 1% level. Experience: age, age2, Personal and

Family Characteristics: Urban, Sex, Langindo and Islam; Academic achievement and attainment:

Prifail, Senior and HE; Parents’ Education: Fathjh, Fathshhe, Mothjh and Mothshhe.

24

The results that I have presented imply that the lower secondary school quality has an

indirect effect on earnings of individuals. The results show public school graduates earn sig-

nificantly higher than private non religious school graduates and these imply that the quality of

public schools are better than private non religious schools.

5. Conclusion

In this paper, I have tried to replicate the results of Bedi and Garg (2000). Given their use of

an early release of the IFLS data and my use of a re-released IFLS dataset, I was unable to

reproduce the sample they had used. I attempt to replicate their results using the sample that

obtained form the re-release version of IFLS1 (IFLS1-RR). While, I unable to replicate many

of their results, I am also unable to reproduce the large earnings differential of private non

religious school graduates relative to public school graduates.

The use of some proxies as school quality indicators in Bedi and Garg (2000) earnings

model may also bias the results. IFLS1 provides the information of school quality based on

school characteristics last attended by respondents, rather than information of school quality

by level of education. Since some of the respondents attended senior or higher education,

therefore, it may bias the validity of the model. I also obtain insufficient observations from

IFLS1 that have the standard school quality indicator, teacher-students ratio. With the absence

of school quality variables, I assume that different of school quality only affects indirectly to

earnings decomposition.

Using the re-release sample data of IFLS1 and with an absence of school quality indicators,

my findings suggest that public school graduates earn significantly higher than private non

religious school graduates and these imply that the quality of public schools are better than

private non religious schools.

Education attainments and academic achievement are the most important factors for earn-

ings determination. The graduates of public, private non-religious and private Islamic schools

25

who attend higher education institution earn about 50 per cent higher than their counterparts

with upper secondary education. Attending an upper secondary school increases the earn-

ings of graduates of all types of school by about 40 per cent. On the other hand, failing a

grade in primary education reduces the earnings of graduates by 115 and 16 per cent in private

Christian school and public school groups, respectively. These results support the productivity

argument for investing in young children (Heckman et al., n.d.; Heckman, 2006) and suggests

early childhood academic performance increases the individual’s earnings in the future.

26

References

Bedi, A. S. and Garg, A. (2000), ‘The effectiveness of private versus public schools: The case

of indonesia’, Journal of Development Economics 61, issue 2, 463–494.

Hannaway, J. (1991), ‘The organization and management of public and catholic schools: Look-

ing inside the black box’, International Journal of Educational Research 15, 463–481.

Heckman, J. (2006), ‘Skill formation and the economics of investing in disadvantaged chil-

dren’, Science 312(5782), 1900.

Heckman, J., Arbor, A. and Masterov, D. (n.d.), The productivity argument for investing in

young children.

Jann, B. (2008a), ‘The blinder-oaxaca decomposition for linear regression models’, The Stata

Journal 8(4), 453–479.

Jann, B. (2008b), ‘Oaxaca: Stata module to compute the blinder-oaxaca decomposition’, Sta-

tistical Software Components S 456936.

Jann, B. (2008c), A stata implementation of the blinder-oaxaca decomposition, ETH Zurich

Sociology Working Papers 5, ETH Zurich, Chair of Sociology.

Kingdon, G. (1996), ‘The quality and efficiency of private and public education: A case-study

of urban india’, Oxford Bulletin of Economics and Statistics 58(1), 57–82.

Lee, L. F. (1983), ‘Generalized econometric models with selectivity’, Econometrica 51, 507.

Newhouse, D. and Beegle, K. (2006), ‘The effect of school type on academic achievement:

Evidence from indonesia’, Journal of Human Resources 41(3), 529–557.

Peterson, C. E. (2000), Documentation for ifls1-rr: Revised and restructured 1993 indonesian

family life survey data, wave 1, Technical report, RAND.

27

Reimers, C. W. (1983), ‘Labor market discrimination against hispanic and black men’, The

Review of Economics and Statistics Vol. 65(No. 4), pp. 570–579.

Strauss, J., Beegle, K., Dwiyanto, A., Herawati, Y., Pattinasarany, D., Satriawan, E., Sikoki,

B., Sukamdi and Witoelar, F. (2004), Indonesian Living Standards Before and After the

Financial Crisis: Evidence from Indonesia Family Life Survey, Rand Corporation, USA and

Institute of Southeast Asian Studies.

Strayer, W. (2002), ‘The returns to school quality: College choice and earnings’, Journal of

Labor Economics 20(3), 475–503.

Williams, R. (2006), ‘Mfx2: Stata module to enhance mfx command for obtaining marginal

effects or elasticities after estimation’, Statistical Software Components.

28

Table 9: Definitions Variable Used in Bedi and Garg (2000)

Variable Description

LOGEARN Log hourly earnings

EARN Hourly earnings in thousands of rupiahs

AGE Age in years

JUNIOR Completed junior secondary education

SENIOR Completed senior secondary education

MALE Male

BAHASA Indonesian language spoken at home

HIN_BUD Religion Hindu or Buddhist

CHRIST Religion Christian

PRI_FAIL Failed a primary school grade

SCHOLAR Received scholarship at secondary school

FATH_PRI Father has primary education

FATH_JH Father has junior secondary education

FATH_SH Father has senior secondary education

MOTH_PRI Mother has primary education

MOTH_SEC Mother has secondary education

DIRT FLOOR School has dirt floorsa

CLASS SIZE Number of students in class a

MONTHS Length of school terma

OTH_PR Educated in other provincesb

SKALI_ED Educated in South Kalimantan

NSUMA_ED Educated in North Sumatra

WSUMA_ED Educated in West Sumatra

SSUMA_ED Educated in South Sumatra

LAMP_ED Educated in Lampung

EJAVA_ED Educated in East Java


29


Variable Description

WJAVA_ED Educated in West Java

CJAVA_ED Educated in Central Java

BALI_ED Educated in Bali

NTB_ED Educated in Nusa Tenggarra Barat

YOGYA_ED Educated in Yogyakarta

SSULA_ED Educated in South Sulawesi

JAKAR_ED Educated in Jakarta

URBAN Resides in an urban area

SKALMNT Resides in South Kalimantan

NSUMATRA Resides in North Sumatra

WSUMATRA Resides in West Sumatra

SSUMATRA Resides in South Sumatra

EJAVA Resides in East Java

WJAVA Resides in West Java

CJAVA Resides in Central Java

BALI Resides in Bali

NTB Resides in Nusa Tengarra Barat

LAMPUNG Resides in Lampung

YOGKARTA Resides in Yogyakarta

SSULAWES Resides in South Sulawesi

JAKARTA Resides in Jakarta

30

Tabl

e10

:Var

iabl

esD

escr

iptio

nan

dSa

mpl

eM

eans

Vari

able

Des

crip

tion

Mea

nSt

d.D

ev.

LO

GE

AR

NIN

GSM

Log

hour

lyea

rnin

gs-0

.177

1.09

EA

RN

ING

SMH

ourl

yea

rnin

gsin

thou

sand

sof

rupi

ah2.

102

15.5

16

Pers

onal

and

Fam

ilyC

hara

cter

istic

s

AG

EA

gein

year

s34

.942

7.25

2

SEX

Mal

e=1,

Fem

ale=

00.

714

0.45

2

LA

NG

IND

OIn

done

sian

lang

uage

spok

enat

hom

e0.

402

0.49

1

ISL

AM

Rel

igio

nIs

lam

?ye

s=1,

no=0

0.82

80.

377

CIT

YR

esid

esin

anci

tyar

eaw

hen

12ye

ars

old

0.21

90.

414

TOW

NR

esid

esin

anto

wn

area

whe

n12

year

sol

d0.

254

0.43

6

VIL

LA

GE

Res

ides

inan

villa

gear

eaw

hen

12ye

ars

old

0.52

70.

499

UR

BA

NR

esid

esin

anur

ban

area

0.70

580.

456

WO

RK

SDw

orki

ngw

hile

inpr

imar

ysc

hool

0.07

40.

262

Aca

dem

icA

chie

vem

ent

and

Att

ain-

men

t

PRIF

AIL

Faile

da

prim

ary

scho

olgr

ade

0.20

80.

406

JUN

IOR

Atte

ndlo

wer

seco

ndar

yed

ucat

ion

0.35

50.

479

Con

tinue

don

Nex

tPag

e...

31

Tabl

e10

–C

ontin

ued

Vari

able

Des

crip

tion

Mea

nSt

d.D

ev.

SEN

IOR

Atte

ndup

pers

econ

dary

educ

atio

n0.

476

0.50

0

HE

Atte

ndH

ighe

rEdu

catio

n0.

170

0.37

6

Pare

ntsE

duca

tion

FAT

HPR

IFa

ther

has

prim

ary

educ

atio

n0.

779

0.41

5

FAT

HJH

Fath

erha

slo

wer

seco

ndar

yed

ucat

ion

0.11

80.

323

FAT

HSH

HE

Fath

erha

sup

pers

econ

dary

educ

atio

n0.

104

0.30

5

MO

TH

PRI

Mot

herh

aspr

imar

yed

ucat

ion

0.87

30.

333

MO

TH

JHM

othe

rhas

low

erse

cond

ary

educ

atio

n0.

077

0.26

7

MO

TH

SHH

EM

othe

rhas

uppe

rsec

onda

ryed

ucat

ion

0.05

0.21

8

Reg

ions

ofSc

hool

OT

HPR

Edu

cate

din

othe

rpro

vinc

esb

0.13

40.

34

SKA

LIE

DE

duca

ted

inSo

uth

Kal

iman

tan

0.03

50.

185

NSU

MA

ED

Edu

cate

din

Nor

thSu

mat

ra0.

10.

3

WSU

MA

ED

Edu

cate

din

Wes

tSum

atra

0.05

40.

227

SSU

MA

ED

Edu

cate

din

Sout

hSu

mat

ra0.

050.

219

LA

MPE

DE

duca

ted

inL

ampu

ng0.

021

0.14

3

EJA

VAE

DE

duca

ted

inE

astJ

ava

0.14

20.

349

WJA

VAE

DE

duca

ted

inW

estJ

ava

0.13

80.

345

Con

tinue

don

Nex

tPag

e...

32

Tabl

e10

–C

ontin

ued

Vari

able

Des

crip

tion

Mea

nSt

d.D

ev.

CJA

VAE

DE

duca

ted

inC

entr

alJa

va0.

145

0.35

3

BA

LIE

DE

duca

ted

inB

ali

0.05

10.

22

NT

BE

DE

duca

ted

inN

usa

Teng

garr

aB

arat

0.04

90.

216

YO

GYA

ED

Edu

cate

din

Yog

yaka

rta

0.06

60.

249

SSU

LA

ED

Edu

cate

din

Sout

hSu

law

esi

0.04

10.

199

JAK

AR

ED

Edu

cate

din

Jaka

rta

0.07

10.

258

Reg

ions

ofR

esid

ence

SKA

LM

NT

Res

ides

inSo

uth

Kal

iman

tan

0.05

0.21

9

NSU

MA

TR

AR

esid

esin

Nor

thSu

mat

ra0.

086

0.28

WSU

MA

TR

AR

esid

esin

Wes

tSum

atra

0.04

80.

215

SSU

MA

TR

AR

esid

esin

Sout

hSu

mat

ra0.

054

0.22

7

WJA

VAR

esid

esin

Wes

tJav

a0.

132

0.33

9

CJA

VAR

esid

esin

Cen

tral

Java

0.09

10.

288

EJA

VAR

esid

esin

Eas

tJav

a0.

121

0.32

6

BA

LI

Res

ides

inB

ali

0.06

0.23

8

NT

BR

esid

esin

Nus

aTe

ngar

raB

arat

0.05

10.

22

LA

MPU

NG

Res

ides

inL

ampu

ng0.

026

0.16

YO

GK

AR

TAR

esid

esin

Yog

yaka

rta

0.07

10.

257

Con

tinue

don

Nex

tPag

e...

33

Tabl

e10

–C

ontin

ued

Vari

able

Des

crip

tion

Mea

nSt

d.D

ev.

SSU

LA

WE

SR

esid

esin

Sout

hSu

law

esi

0.04

10.

199

JAK

AR

TAR

esid

esin

Jaka

rta

0.16

70.

373

N15

26

34

Table 11: OLS Estimates of Earnings (No Selection Correction)



CONSTANT -2.686*** -3.528*** -1.855 -3.129(0.687) (0.918) (2.056) (2.099)

AGE 0.087** 0.145*** -0.088 0.123(0.040) (0.049) (0.113) (0.116)

AGE2 -0.001 -0.002** 0.002 -0.001(0.001) (0.001) (0.002) (0.002)

URBAN 0.115 0.248* 0.671*** 0.000(0.082) (0.143) (0.222) (0.273)

SEX 0.208*** 0.321** -0.073 0.330(0.075) (0.136) (0.206) (0.208)

LANGINDO 0.180** -0.064 0.347 0.776***(0.080) (0.172) (0.270) (0.293)

ISLAM 0.193 0.093 1.041** -0.338(0.122) (0.181) (0.525) (0.204)

PRIFAIL -0.156* -0.138 -0.255 -0.701***(0.085) (0.134) (0.187) (0.231)

SENIOR 0.456*** 0.379*** 0.326* 0.302(0.075) (0.121) (0.193) (0.229)

HE 0.978*** 0.762*** 0.742** 0.445*(0.088) (0.182) (0.323) (0.235)

FATHJH 0.091 -0.155 0.476 0.366(0.089) (0.227) (0.419) (0.299)

FATHSHHE 0.250** -0.070 0.521 0.500*(0.124) (0.263) (0.356) (0.282)

MOTHJH -0.184 0.553** 0.182 -0.362(0.118) (0.250) (0.369) (0.387)

MOTHSHHE -0.052 0.279 -0.734** 0.340(0.176) (0.309) (0.325) (0.277)

Note: Standard Errors are in parenthesis and heteroscedasticity consistent. * Significance at 10%

level, ** Significance at 5% level, and *** Significance at 1% level. Dummy variables of regions

of residence and school are included in estimation but no reported.

35

Documents

Fahmi Brownbag Revised