Targeting children in Cambodia for enhancing school

attainments

Dr. Sarthi AcharyaChief Technical Adviser, Ministry of Planning

Usha MishraChief Social Policy, UNICEF Cambodia

1

Child Poverty and Social Protection Conference 10–11 September 2013

Outline

Methodology

Latest trends in poverty in Cambodia

Determinants of school attainment

Select multiple overlapping deprivation analysis

Considerations for targeting

Policy Implications

Methodology

• Explaining the participation (or lack of it) of children in the educational stream in a Multivariate Framework,

• A set of three regressions has been estimated.

• The first equation tries to explain the dominant reasons for the school attendance, the second the extent of participation, and the third, the spatial variations in the same.

• Multiple regression method has been deployed for estimating the equations, using both, the Logistic Regression Approach (a qualitative response model) and Ordinary Least Squares (OLS).

Fewer children are poor

4

2004 2011

34 3331 29

27

26 24 23

35

30 30

2321 20

15

25

35

45

2004 2005 2006 2007 2008 2009 2010 2011

CSES Measured and CDB Computed Poverty Rates

CDB

CDB: 2004 POVERTY BY DISTRICT National

Poverty

0 - 1010 - 2020 - 3030 - 4040 - 5050 - 55

PVR

KPT

BAT

MKR

KRT

RATSTG

PUR

SRP

KKG

KPC

KSP

OMC

BMC

KCH

PVG

KAM

KDL

TAK

SVR

SHV

PLN

PNP

KEP

CDB: 2011 POVERTY BY DISTRICT National

Poverty

0 - 1010 - 2020 - 3030 - 4040 - 5050 - 42

PVR

KPT

BAT

MKR

KRT

RATSTG

PUR

SRP

KKG

KPC

KSP

OMC

BMC

KCH

PVG

KAM

KDL

TAK

SVR

SHV

PLN

PNP

KEP

1 in 2 child belongs to the poorest 33% of the households, children disproportionately bear the burden of poverty and vulnerability

Distribution of persons (6 to<18) by Monthly Per Capita Expenditure (MPCE)/ source CSES 2010/ Acharya and Mishra/UNICEF 2013

Why education? Persistent Challenges in primary education

86.9 87.0 87.9 88.9 89.087.9

92.193.3

94.4 94.8 95.296.4 97.0

74.8177

84.881.583.6

81.1

85.684

70

75

80

85

90

95

100

2005 2006 2007 2008 2009 2010 2011 2012

CDB.PRIM.NAttR EMIS.PRIM.NER Census2008.PRIM.NAttR CDHS.PRIM.NAttR CSES.PRIM.NAttR

Why education?

18.9

47

52

34.8

32.635.0 35.1

37.8

0

10

20

30

40

50

60

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

LSS NER

TARGET ACTUAL

50

75

61.6

58.1

58.5

5553.6

40

45

50

55

60

65

70

75

80

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

LSS GER

TARGET ACTUAL

UNICEF SITAN 2013

• Still Equity Issues, esp. at District/Commune level and by Wealth Quintile• Continued Low Transition from Primary to Secondary (< 80%) and Continued High Dropout in Secondary

(20%) lead to a loss of 40% of Human Resources• Gap Enrolment and Attendance Impact on Learning and re-inforced by geo/wealth disparities• Lack of Data on actual Learning Outcomes

Thus .. Overall though educational attainment rate are increasing

• ………there still are some 21% of children in the 6-17 age- group out the educational stream, many of whom join the work force

Child labour dimensions

Draft CMDG report 2013

• What is keeping children out of school?

Determinants of School attendance/enrolmentLogistic Regression - Determinants of Child's

Enrolment /Attendance in School

Coefficient S.E. Sig.

Ln (Per capita consumption)Industry of head of household (binary)Worker status (binary)AgeWhether household head been to school (binary)Constant

1.179 .101 .000

.257 .101 .011

-.529 .251 .035

-.185 .013 .000

.274 .107 .010

-10.563 1.208 .000

Dependent variable: Whether a child (age 6-17 years) is in school

Nagelkerke R2 = 0.100; Predicted correct = 82.2%; n = 3,986

Note1: The equation was estimated separately for age- groups 6- 11 and 12- 17 years. There was no observable difference; hence, the sample was pooled.Note 2: Some sociological variables like sex of the child, sex of the head of the household, and ethnicity of the respondent were all found statistically insignificant, and dropped.

Supply side factors are still there….

Determinants of number of years child is in school

Elasticity at mean S.E. z-score

-Ln (Per capita consumption)

-Industry of head of household (binary)

-Worker status (binary)-Age

-Household head been to school (binary)

0.3480 .0145 12.29

0.0389 .0152 2.56

-0.0799 .0378 -2.11

-0.2790 .0020 -14.33

.0414 .0162 2.56

Elasticity Values Calculated from Logistic Regression

Elasticity values in the Table above suggest that the most important factor influencing education is income: A doubling in income could raise enrolment by some 34%.

The other two demand side variables show small, though statistically significant elasticity values.

Spatial targeting through using local/commune level information

Coefficients Std. Error t Elasticity at mean

(Constant) 63.8943 0.9292 68.7636

Distance junior-secondary school (Km) -0.0028 0.0023 -1.2274 -0.0002

Distance senior-secondary school (Km) -0.0034 0.0021 -1.6081 -0.0005

Wet season paddy yield 1.4567 0.2877 5.0639 0.0391

Distance of village to province town -0.0489 0.0069 -7.1378 -0.0272

# Motos to families (%) 0.0159 0.0080 1.9869 0.0106

# Cycles to families (%) 0.0723 0.0044 16.2546 0.0630

# Families living in thatched huts to total

families (%)

-0.1806 0.0111 -16.2717 -0.0422

# Families cultivating land le1ha to total

families (%)

-0.0043 0.0016 -2.6795 -0.0008

# Toilets to families (%) 0.1168 0.0073 16.1083 0.0551

# Families with water connection to total

families (%)

0.0540 0.0045 11.9045 0.0331

Dry season paddy yield 0.3676 0.1463 2.5133 0.0047

R2 = 0.150; F = 192.063; n = 11882

Key Findings: Multiple Deprivation

=8%

Multiple aspects of deprivation affecting educational attainments

Children suffering from all the three deprivations there are about 8% of the total child population

Children suffering from two of the three deprivations in the diagram are some 11% of the total chidren population

Those suffering from just one deprivation add up to about 36%

There are about 46% children who are free from the said deprivations.

Key Findings

• The most important factor influencing education is income: A doubling in income could raise enrolment by some 34%.

• Income, job location and education of the head of the household significantly affect the duration of schooling of children.

• Families educate their children as they get relatively affluent. However, the magnitude of elasticity is rather small

Conclusions

• Unless addressed, a significant proportion of the next generation will remain ill/un educated resulting in perpetuation of poverty through generations.

• Deprived children are not a homogeneous group. Different groups require different policy and programme responses.

Implication for larger policy agenda

• Implication for cash transfer programmes like cash scholarships

• Strong relationship with the labour market and the wage structure and the overall rate of return on investment in education

• Policy measures in social protection and in enhancing educational outcomes can not be taken in isolation of what’s happening in the rest if the economy and the policy/planning paradigm

Challenges for targeting

• How do we identify these children? Spatially? Collapsing of datasets?

• Child labour as entry point?

• A relationship between proxy means test and deprivation analysis?

• Understanding of the issue, obviously deepened but targeting remains a challenge unless combined with the Commune Data Base and other administration data

Terimakasih

Thank you