Monitoring Service Delivery in the Financial Crisis
Markus Goldstein(Interpreted by Jishnu Das)
The findings, interpretations and conclusions expressed in this presentation are entirely those of Markus Goldstein. They do not necessarily represent the views of Jishnu Das and/or his family.
Predictions• Lots of predictions that financial crisis will hit HD
outcomes
• This is almost certainly correct for health (Baird, Friedman and Schady, 2007)
Predictions
• And probably true for education in low-income countries
Source: Ferriera and Schady (2008)
Sources of the hit: Households
• Lots of micro-evidence that shocks to households (especially when they are correlated) lead to declines
Sources of the hit: Public Spending
• Some evidence that public spending declines with macro-shocks (Peru)
• Source: Paxson and Schady (2005)
Sources of the hit: Public Spending
• But this can be compensated for by donor spending (Indonesia)
Two Unknown Unknowns
• Will drops in public spending affect outcomes? If so, how?
• Do we have monitoring tools to capture household responses with sufficient speed to engender policy responses?
Remainder of presentation
• How new M&E tools are changing the way we think of the relationship between public spending & outcomes
• How these can be used for policy responses to the crisis
• A Proposal for monitoring in the crisis
Spending ≠ outcomes: In X-sections
& time-series
Source: WDR 2004
Why?Evidence from new studies
New Evidence: Results from PETSWhat you get is not what was
sanctioned
Across CountriesPercent of 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 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.
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
New Evidence from QSDS: Public expenditures crowd-out
private expenditures
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)
Evidence experimentally replicated in India, with identical substitution coefficients
New Evidence: Absenteeism Surveys
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
As a consequence: Evidence from Quality Studies
Spending ≠ outcomes
• In health unqualified private sector doctors in Delhi provide better care than qualified public sector doctors
• In education the cost per percentage correct in tests is 3 times higher in public compared to private schools in Pakistan
So…
• There is plenty of room for improvements• BUT• There are good reasons to believe that we
don’t know enough– Can’t find any studies of the effects of
spending cuts that are causal (usually strongly correlated with economy wide problems)
Proposal based on lessons learnt
• Sentinel Surveillance Sites for Service Delivery (S4D)
IDEA
• High Frequency Surveys in limited sites (villages/neighborhoods)
• Collect information using a variety of different tools
• Household Surveys• Facility surveys (education health)• Outcomes (for instance, learning)
Precursors• ICRISAT• More recently
– Financial Diaries in Bangladesh, India and South Africa (Bi-weekly for one year)
– Morbidity and Health Seeking behavior in Delhi, India (Weekly and Monthly for 2 years)
– LEAPS in Pakistan (Annually for 4 years)
• All these surveys are yielding valuable (and new) information on household and provider behavior—particularly in relation to shocks
The S4D Outline
• Work with limited sampling sites in a number of countries at higher frequency than usual
• Collect periodic information from– Households (Main outcomes, labor, wages)– Facilities (Absenteeism, PETS, Quality)– Higher administrative units to track budgets
(Funds availability and flows)• Fixed Reporting Formats + Public Data
Summary
• Financial Crisis will potentially lead to worse HD outcomes
• But• This is also an opportunity
– To address inefficiencies in the delivery of services
– To set up a M framework that can provide real-time information for real-time policy
• Deepen our understanding of HD processes
Additional slides for discussion
Ethiopia
MozambiqueMalaw i
Eritrea
Burundi
Tanzania
Sierra Leone
Nepal
Niger
Burkina Faso ChadGuinea-Bissau
Rw anda
Sudan
Madagascar
Nigeria
Mali
Cambodia
Vietnam
Yemen, Rep.
Uganda
Bangladesh
Central African Republic
Togo
KenyaGambia, The
Benin
Lao PDR GhanaIndia
Haiti
Mongolia
ZambiaMauritania
Pakistan
Uzbekistan
Lesotho
Angola
China
Senegal
Guinea
Azerbaijan
Tajikistan
Zimbabw e
Cameroon
Sri Lanka
Honduras
Albania
Cote d'Ivoire
GeorgiaSyrian Arab Republic
Bolivia
ArmeniaMoldova
Indonesia
Papua New GuineaCongo, Rep.
Kyrgyz Republic
Egypt, Arab Rep.Philippines
Ukraine
Morocco
Guatemala
Iran, Islamic Rep.
Bulgaria
West Bank and Gaza
Kazakhstan
Romania
Ecuador
Algeria
Jordan
Dominican Republic
El Salvador
Turkmenistan
Paraguay
Jamaica
Tunisia
Lithuania
Peru
Namibia
Colombia
Latvia
Macedonia, FYR
Belarus
ThailandLebanon
Russian Federation
Turkey
Panama
Poland
Mexico
Costa Rica
Venezuela, RB
Mauritius
Slovak Republic
Estonia
South Africa
Malaysia
Brazil
Trinidad and Tobago
Croatia
Chile
Hungary
Czech Republic
Oman
Uruguay
Saudi Arabia
Argentina
SloveniaKorea, Rep.
PortugalGreeceSpain
Kuw ait
Israel New ZealandIreland ItalyUnited KingdomCanadaAustraliaFinland FranceNetherlands
Belgium
Sw eden
United States
Austria Germany
Norw ay
DenmarkJapan Sw itzerland
-150
-100
-50
0
50
100
150
-150 -100 -50 0 50 100 150
Per capita public spending on health 1990s average (Log difference between actual and predicted by GDP per capita x100)
Und
er-5
mor
talty
rate
200
0(L
og d
iffer
ence
bet
wee
n ac
tual
and
pre
dict
ed b
y G
DP
per
cap
ita x
100)
And the same for health…
…and vastly different changes in spending can be associated with similar changes in outcomes.
Figure 1: Official vs. Effective Expenditures by Regional Health Delegations
• CHAD: On average, RHDs received only 26,7% of their official non-wage budgetary expenditures from the MoH
0
100
200
300
400
500
600
700
800
900
1000
Mill
ions
of C
FA F
ranc
s
0
5
10
15
20
25
30
35
40
45
Perc
enta
ge R
ecei
ved
Initial Allocation
Resources Received
Percentage
Average
Percent of time present, and percent of time teaching
Beyond absenteeism: Effective supply of teaching
0
20
40
60
80
100
Tunisia Egypt Morocco Yemen Pernambuco(Brazil)
Lebanon Ghana
Potential time Presence time Time on task
Sources: Lane and Millot (2002); Abhadzi, Millot and Prouty (2006).
Private MBBS Public MBBS Private, No MBBS
Health: What they know, what they do40% of essential questions asked
Perc
enta
ge o
f Ess
entia
l Tas
ks C
ompl
eted
Source: Das and Hammer (2008)
Education: Its not the money
260
184
259
75 6882
0
50
100
150
200
250
Cos
t for
Eve
ry P
erce
nt C
orre
ct (i
n R
s.)
Government Private
Cost of Schooling
English Math Urdu
Source: Pakistan, The LEAPS Report
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