Quantitative Service Delivery Surveys:Some lessons from schools surveys
Deon FilmerDevelopment Research GroupThe World Bank
Kampala, UgandaJuly 29, 2009
Why carry out a Quantitative Service Delivery Survey?
Outcomes are low …
Starting points: Learning outcomes are low
Inequality in TIMSS 2003 Mathematics test scores
200
300
400
500
600
700
Uni
ted
Sta
tes
Kor
ea
Hun
gary
Lith
uani
a
Sou
thA
frica
Chi
le
Mal
aysi
a
Bot
swan
a
Rus
sian
Fede
ratio
n
Iran
Phi
lippi
nes
Jord
an
Egy
pt
Indo
nesi
a
Quintile 5 Quintile 1 AverageSource: Analysis of TIMSS 2003 database
Starting points: Learning outcomes are low
Inequality in SACMEQ 2000 Mathematics test scores
200
300
400
500
600
700
Sey
chel
les
Mau
ritus
Sou
th A
frica
Bot
swan
a
Nam
ibia
Sw
azila
nd
Leso
tho
Uga
nda
Ken
ya
Moz
ambi
que
Zam
bia
Mal
awi
Tanz
ania
Quintile 5 Quintile 1 AverageSource: Analysis of SACMEQ 2000 database
Why carry out a Quantitative Service Delivery Survey?
Outcomes are low …
… is it a lack of money?
Public spending is not enough to improve outcomesPattern across countries
* Difference in logs (x100) form rate predicted by GDP per capitaSource: WDR 2004
Public spending is not enough to improve outcomesPattern across primary schools in Mauritania
Similar changes in public spending can be associated with vastly different changes in outcomes…
Source: WDR 2004
…and vastly different changes in spending can be associated with similar changes in outcomes.
How to assess the lack of association between spending and outcomes?
Public spending benefits the rich more than the poor– Expenditure incidence analysis of public spending for
diagnosis Lack of demand by households
– Impact evaluation of programs to promote demand Money fails to reach frontline service providers
– Public expenditure tracking surveys (PETS) Poor quality services
– Quantitative Service Delivery Survey (QSDS) e.g., absenteeism, time on task
Public spending benefits the rich more than the poor
0
5
10
15
20
25
30
35
40
Tanzania1993/94
Coted'Ivoire1995
Uganda1992/93
Guinea1994
Brazil1997
Malawi1994/95
Indonesia1998
Kenya1992/93
SouthAfrica2000
Poorest quintile Quintile 2 Quintile 3 Quintile 4 Richest quintile
Expenditure incidence of public spending
Lack of demand by households0
.2.4
.6.8
1P
roba
bilit
y of
enr
ollm
ent
1 2 3 4 5 6 7 8 9 10Decile
Non-recipient Recipient
Impact of a scholarship program on girls’ enrollment in Cambodia:Enrollment probability among recipient and non-recipient girls by economic status decile
Source: Filmer and Schady (2008)
Impact of a conditional cash transfer on girl’s and boy’s middle school enrollment
Impact of demand-side programs
Disbursed public spending on school grants that actually reach schools
Percent
GNI per capita (2000)
GNI per capita PPP
(2000)Ghana 1997/98 51 330 1880Kenya 2004 (secondary school
bursary funds)78 250 810
Madagascar 2002 88 2050 4610Peru 2001 (utilities) 70 / 97 670 2280PNG (2001/2002) 72 / 93 280 510Tanzania 2002-2003 62 270 1250Uganda 1991-1995/2001 <20 /
80Zambia 2001 (discretion/rule) 24 / 90 320 740Ye and Canagarajah (2002) for Ghana; Republic of Kenya (2005) for Kenya; Francken (2003) for Madagascar; Instituto Apoyo and World Bank (2002) for Peru; World (Bank 2004) for
PNG; MOF, Government of Tanzania (2005) for Tanzania; Reinikka and Svensson (2005) for Uganda; Das et al. (2002) for Zambia.
Percent of school grants that actually reach schools
Classic approach to analyzing education outcomes…
Inputs School Outputs / Outcomes
Money
$$
Quality of public services
… QSDS are a way to get inside the “black box” of service delivery at the facility level
What are Quantitative Service Delivery Surveys?
Take the facility as the unit of analysis– Could be complemented with a household/users survey
Collect quantitative information about– Physical infrastructure– Staff characteristics– Income and expenditures– Governance and management– Characteristics/Quality of service provision– Outcomes
Two different sets of surveys
Indonesia:– December 1998: Early days of economic crisis …
were schools feeling any impact– April/May 2000: Longer-run school-level impacts
of the crisis, decentralization looming PNG
– April/May 2002: little knowledge about the status of services in PNG; particular interest in decentralization; explicit concern about expenditure tracking
Some lessons from experience, with a focus on two different sets of surveys
Indonesia:– 600 schools– 5 purposively selected provinces– 15 districts (40 schools per district)
PNG– 220 schools– 8 purposively selected provinces– 2 districts (10 schools per district)
Activity structure
Indonesia:– Close collaboration with research department of ministry of
education.– Ministry staff served as full partners in pilot/questionnaire
development; served as regional survey supervisors.– Gave the survey some legs within the ministry, enabled
substantially lower costs … but cost in terms of capacity and experience.
– Study conceived of as stand alone survey, with Ministry/policymakers as primary audience.
PNG:– Partners with National Research Institute, an independent
agency– Overseen by “working group” with various government,
NGO, and other representatives.– Little hands on input from Ministry of Education.– Study conceived of as a part of WB Poverty Assessment.
What worked well Indonesia
– Trends in enrollments at the school level Non-conventional wisdom result that enrollment impacts were
mainly urban and at the secondary level; and in non-private/non-secular schools.
But difficulty: enrollment levels/trends … not enrollment rates.– Perceptions of impact of crisis
Identified “general impact” and “school functioning” as two main impacts (exploratory PC analysis)
– Status of crisis-relief government programs (scholarship and grant programs)
Schools grants: Coverage; use; interesting substitution between grants and other sources of government (especially local government) sources of funding
Scholarships: Coverage (among students)– Trends in charging of fees
Indonesia 2000: Sources of school funding by grant receipt and public/private status
0
1000
2000
3000
4000
5000
6000
7000
8000
Public-Received
Grant
Public-NoGrant
Private-Received
Grant
Private-NoGrant
Grant Local Central
Primary schools
0
20000
40000
60000
80000
100000
120000
140000
Public-Received
Grant
Public-NoGrant
Private-Received
Grant
Private-NoGrant
Grant Local Central
Junior Secondary schoolsIn public schools, local government spending adjusted in response to grant
No adjustment in private schools
Substitution between grants and local government funding
Zambia 2001: Effect of a 100 Kwacha increase in expected and unexpected school grants on household expenditures on education
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0Expected Unexpected
Kw
acha
Household spending falls by about 45 for each additional 100 Kwacha spent on anticipated grants
Substitution between grants and household spending
Source: Jishnu Das, Stefan Dercon, James Habyarimana, Pramilla Krishnan (2004)
What worked well PNG
– Descriptive status of schools (very little prior information)
– Good documentation of delays in subsidies / teacher pay
– Reasonable assessment of teacher absenteeism (pre-announced window for visit)
– Good data to construct “ghost teacher” estimate (with substantial effort in matching to government payroll records)
Delay in ability to use subsidy: PNG 2001
0
20
40
60
80
100
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
Accessible Remote
Percent who received any subsidy
0
5
10
15
20
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
Accessible Remote
Weeks delay
Note: Q1,Q3=National, Q2,Q4=Provincial
Absence rates among teachers and health workers
Note: Surveys were all fielded in 2002 or 2003. Sources: Chaudhury et al (2006) except for PNG, World Bank (2004) and Zambia, Das et al (2005).
0
10
20
30
40
50
Bangladesh Ecuador India Indonesia Papua NewGuinea
Peru Zambia Uganda
Primary schools Primary health facilities
PNG 2002: Depletion of the effective supply of teachers
Source: PESD 2002.
8572 68
100
0
20
40
60
80
100
Teachers onpayroll
Minus "ghost"teachers
Minus absentteachers
Minus schoolsclosed "for
lack ofteachers"
Results from QSDS:Effective supply of teachers
Percent of time officially allocated to schooling; when a teacher is present; and spent in teaching and learning activities
Beyond absenteeism: Effective supply of teaching
Sources: Egypt, Yemen and Lebanon from Lane and Millot (2002); Tunisia, Pernambuco, Morocco and Ghana from Abhadzi, Millot and Prouty (2006); Cambodia from Benveniste, Marshall and Caridad Araujo (2008); and Laos from Benveniste, Marshall and Santibanez (2007).
Investigating accountability in education service deliveryPNG 2002: Teacher absence declines with parent and community involvement
0
5
10
15
20
25
30
0 1 2 3 4 5
Index of parent and community involvement
Perc
ent a
bsen
t
Source: PNG PESD 2002.
What was harder Indonesia:
– Trends in overall school incomes—never clear we had full picture (what we did have was worrisome, especially for private schools)
– But, incredibly complicated system … is this worth doing when the system is so complex?
PNG:– Complex funding system … but able to track some specific
payments (school subsidies)– But … school financial data very spotty
only about half of the schools had documentation about spending, half about receipts
Only 30% of schools had both expenditures and receipts documentation
Funding education in PNG2001, million Kina
Leave fare
Payroll Salary(284)
Recurrent(153)
Development(91)
Salary Subsidy Q1&Q3
(40)
in kind
Leave fare(1) Subsidy Q1&Q3
Subsidy Q2&Q4 Subsidy Q2&Q4 (21)
Grants Grants (15)
Donors Grants & donationsNGOs (66)
(68)
Provincial Government and Administration
Dept. of Finance
and Treasury
National Dept. of
Education
Non-teaching staff
Parents
Bank account (school/ BOM/ Joint)
Project fees
School fees
School
Bank account
Teachers
Contractors
Source: Based on information collected during the PESD 2002 survey.
Q1,3
Q2,4
What I would think twice about doing again
Enrollment trends (unless have information on universe of schools and on population trends by area)– Hard (time consuming) to collect, hard to interpret
Too many instruments– PNG had 9 instruments, 7 at the school level.
Non-representative/random sample of parents
Survey instruments in PNG:– School (head teacher); – teacher roster; – select teachers; – data appendix; – grade 5 teacher; – board of management member; – parent; – District Education Advisor; – Provincial Education advisor.
I would think (very) hard about what financial data to collect
The more specific the better– But even there, school officials often don’t associate specific
transfers to “official” name Anything more than tracking a clearly defined transfer
is very hard. Even that is hard:– missing information at schools; – missing records at provincial level; – defining the “base”
Official declarations in Government Circulars Budget disbursements School level expectations
What I would never do again
Data entry using a package not designed for that purpose
Sophisticated survey/tracking exercise in a country where policy environment not conducive to use information
Thank you!