Transcript Document 7157403

Xavier Sala-i-Martin
Columbia University
June 2013
Heated Debate
Aid has some positive effects on growth (Jeffrey Sachs
2004) and needs to be multiplied.
2. Aid has effect on growth, only under some
circumstances (conditional aid)
1.
Conditional on Policies (Craig, Burnside and Dollar (2000),
Dalgaard and Tarp (2004))
Conditional on type:
1.
2.
1.
2.
3.
3.
Infrastructures [Clemens, Radelet, and Bhavnani (2004)]
Education [Michaelova and Weber (2006) and Dreher,
Nunnenkamp and Thiele (2007)]
Health [Mishra and Newhouse (2007)]
Aid has NO effect on growth or may even undermine it
(Peter Bauer (1972), Bill Easterly (2006))
Evidence: Caution with Simple
Figures
Source: Easterly (2003), JEP
Causality
 Aid could systematically go to countries that are in
trouble (like a natural disaster): if natural disasters
tend to generate low (or negative) growth, this will
tend to generate a negative association between
growth and aid.
 Aid could systematically go to “reward” countries
that did things well in the past. If growth persists,
then there will be a positive association even
though aid does not really cause positive growth.
 In order to solve this problem, econometricians use
“instrumental variables”. IV estimates are supposed
to see the correlation between exogenous aid and
growth
Empirical Evidence
 Early studies found positive correlations
(Papeneck 1973, Levy 1988).
 But then came Peter Boone (1994): Aid and
growth are not correlated, period.
Empirical Evidence
 Then a very Influential paper was written
by Burnside and Dollar (2000):
   0  1 X   2 AID   3 AID * GoodPolicies  
 They find α2 close to zero and α3>0. That is, AID
has a positive effect on growth ONLY if the
country at the receiving end conduct good policies.
 After this paper was published, IFIs and the whole
world demanded more international aid and
conditionality on good policies.
Empirical Evidence
 Problems with the paper: it is NOT robust to the
definition of “aid”, “growth”, or “good policy”
(Easterly, Levine and Roodman (2003)).
 Roodman (2007, “The Anarchy of Numbers”)
also adds that it is not robust to time period
changes
Empirical Evidence
 Definition of Aid:
 Burnside and Dollar use “Grant Aid” (excluding
subsidized loans and debt rescheduling).
 Normal definition (called ODA) includes subsidized
loans and debt rescheduling.
 The two measures are highly correlated (0.933)
 But when Easterly et all use this second measure,
α3 becomes insignificantly different from zero.
Empirical Evidence
 Definition of Good Policy:
 Burnside and Dollar construct a measure which is
an average of inflation, fiscal deficit and a
measure of openness (originally proposed by
Sachs and Warner 1995)
 Easterly et al use TRADE/GDP instead of SachsWarner qualitative measure, they add “Black
market premium” and “financial depth” (ratio of
M2/GDP which is a measure of financial
development) and…
 … the coefficient α3 becomes insignificantly
different from zero.
Empirical Evidence
 Definition of growth
 Burnside and Dollar use 4 year averages
 Easterly et al criticize this because it contains
business cycle noise.
 If use 10-year averages… α3 becomes
insignificantly different from zero
 Roodman (2007) also shows that the paper is
not robust to changes in the sample period of
analysis.
Instrumental Variables
 Burnside and Dollar (2000) use instruments that are based
on “policy quality”.
 The problem is that these variables may be correlated with aid (as
they good policies attract more aid) but may also affect growth
directly (so they are not good instruments)
 Rajan and Subramanian (2005) criticize these instruments
and use “colonial origin” variables and “language” variables
as instruments (France and UK tend to give more aid to their
colonies; if the fact that you have had one colonial power
rather than another does not affect growth, then these are
good instruments)
 Problem: Shleifer et al (various papers) argue that the legal origin
(partly inherited from colonial powers) DOES have an effect on
growth
 Rajan and Subramanian main result: holding constant a
number of RHS variables, the IV correlation between aid and
growth is zero.
Source: Rajan and Subramanian (2005)
Source: Rajan and Subramanian (2005)
Despite the evidence that Aid does
not work (or may hurt)
 Some people ask for more aid: Sachs, Gordon
Brown, etc
 How can they argue that more aid is necessary
if the evidence is that aid does not work?
 POVERTY TRAPS.
Poverty Traps: Theory
 Start with Fundamental Equation of “SolowSwan”:
 Δk=sf(k) - (δ+n) k
or
 Δk/k=sf(k)/k - (δ+n)
 If s and n are constant, and f(.) is neoclassical
(concave with inada conditions), then UNIQUE AND
STABLE STEADY STATE
 Poverty Trap Theory: instead of unique and
stable steady state, THREE STEADY STATES
and Lower and Upper steady states stable and
middle one unstable
Poverty Traps: Theory
1. Savings trap (savings rate is close to zero for
poor countries for subsistence reasons and
then shuts up as income increases)
2. Nonconvexity in the production function
(there are increasing returns for some range
of k)
Savings and Non-Convexities Traps
Stable
Stable
δ+n
Unstable
s(k)f(k)/k
Poverty Traps: Theory
3. Demographic trap (impoverished families
choose to have lots of children)
- www.gapminder.org
Demographic Trap
Stable
s(k)f(k)/k
Unstable
Stable
δ+n
k
Poverty Traps: Implications
 Small amount of aid does not work
 Hence, the fact that aid has not worked in the
past does not prove that it is ineffective.
 However, if the total amount of aid is increased
enough to put countries over the unstable
steady state, countries will converge to the
high-income steady state
 NOTE: this is different from having two savings
lines (if we have two savings lines with two
steady states, then NO amount of aid will work!)
Problem 1: Savings Trap
 Need to have THREE steady states:
 For savings line to cross three times the
depreciation line, you need the savings rate
have to behave in “s” shape:



First low and constant (the savings line declines so it
crosses de depreciation line from above and describes
a stable steady state)
Then s should be raising for intermediate levels of k (so
that the product s(k)*f(k)/k is upward sloping)
Then it should stay constant at a higher level (so that
s(k)*f(k)/k becomes downward sloping again
 In sum, it is NOT enough to argue that “poor
people save less”.
 There is NO evidence that saving rates accelerate
sufficiently rapidly to justify the savings poverty
trap (Kraay and Raddatz (2005))
Problem 2: Savings Trap
 If there is technological progress, the savings
trap automatically disappears!
Problem 3: Demographic Trap
 If there is technological progress, the savings
trap automatically disappears!
Problem 3: Fertility Behavior
 True that fertility declines as income increases...




but population growth is the sum of fertility, minus
mortality, plus net migration
Mortality also declines with capital (and income)
And net migration increases with capital
Hence, need to argue that fertility declines MORE
THAN OFFSET mortality declines, migration
reversals and the diminishing returns to capital so
that the savings and depreciation lines cross three
times
This is empirically unlikely
Problem 4: Non-Convexities Trap
 Normally, non-convexities can be easily
convexified (for example, by using an average of
the two technologies)
 Thus, not only you need to argue that nonconvexities exist, but need to argue that nonconvexities cannot be “convexified” by averaging
production from below the convex and above area
 This is a lot harder
Problem 5: Poor Countries did not grow less
than others
β-Convergence
8%
Annual Growth Rate 1970-2006
6%
4%
2%
0%
$100
$1,000
$10,000
-2%
-4%
-6%
Per Capita GDP in 1970
$100,000
Problem 6: Evidence of Conditional Convergence
suggests that “fundamentals” explain low income
 Holding constant conditioning variables,
the partial correlation between initial
income and growth is negative
 Again: To have poverty traps, we should
have multiple steady states with same
savings and depreciation lines (not that
there are multiple savings lines).
 If there are multiple savings lines, there is
no reason to have increased aid
Problem 7: Quah (1996): Seems
Evidence in favor of Traps
Forecasting the future of the WDI
(by country)
 Quah’s Methology: Based on historical
experience
 Пpp=probability of poor in 1960 staying
poor in 2000
 П pr=probability of poor becoming rich
 П rp=probability of rich becoming poor
 П rr=probability of rich staying rich
Forecasting the future of the WDI
(by country)
 Npoor(2040)=Npoor(2000)* П pp+ Nrich(2000)* П rp
 Nrich(2040)=Npoor(2000)* П pr+ Nrich(2000)* П rr
 Repeat the procedure infinite many times
to get the ergodic (steady-state)
distribution
 Conclusion: depends on Venezuela and
Trinidad-Tobago
Problem
 Not very robust (Kremer, Onatski and Stock
show that it depends on one or two data points)
Interesting questions:
 If it is “corruption” but we increase aid (we
double in the next five years, and double it again
five years later) because we think “poverty traps”,
could we possibly induce more corruption?
 Why doesn’t aid work?
Let me introduce...
The World of International Development Aid
 The Many Players:
 International Institutions (IMF, WB, United Nations, OECD,..
 Development Ministries of rich countries (USAID, Sweden, etc).
 NGOs (non-profit organizations)
 Left-Wing radicals (antiglobalization people)
 Right-Wing radicals (including some churches)
 Great Men and Women: Jeffrey Sachs, Kofi Annan, Desmond
Tutu, Rigoberta Menchu, Subcomandante Marcos, the Pope, the
Dalai Lama
 Great Economists: Angelina Jolie, Bono, Tony Blair, Bob Geldof,
Al Gore …
 Well Intended people... But good intentions are NOT
enough
Mechanisms that Work
 Markets
 Suppliers need to listen to customers
 Responsibility/Accountability if don’t supply
what’s wanted
 Why?
 Customer has something the supplier wants
(money)
Information
Money
Customers
Firms
Products
Mechanisms that Work
 Liberal Democracy
 Listen to “customers”
 Responsibility/Accountability
 Why?
 Customer has something the supplier wants
(votes)
Information
Votes
Voters
Politicians
Policies
The Aid World
Donors
WB
Bureaucrats
A strange sequence of
Principal-Agent problems
With misaligned incentives
African
Bureaucrats
?
X
African
Citizens
IDA
 This means
 We DON’T KNOW what works
 ... and we don’t have incentives to LEARN!
 We don’t have incentives to SATISFY CUSTOMERS
(African citizens).
 We have incentives to SATISFY DONORS (rich
citizens and rich governments)!
 Perverse outcomes!
IDA: Citizens of RICH World (Donors)
 Donors have their own preferences (which may not
coincide with true needs)
 Sharon Stone and Malaria
 Prostitution vs ARVs
 Donors confuse Inputs and Outputs (because they
are satisfied with SPENDING, not getting results)
 Ten things you did not know about the World Bank
 Donors some times don’t know what they are
talking about
 Ashraf, Gine, Karlan (2008)
IDA: The Local Intermediaries
 Aid may lead to
 Corruption (Natural Resource curse)
 Marshall Plan was 2.5% of French and German GDP
 Average African country receives more than 15% of GDP in
Aid.
 Misalocation of Talent
 Culture of dependency and subsidy: Africa is stripped
off its self initiative
 When government revenue does not depend of
economic success (as it is the case, for example, for
countries with government that live of taxation)…
government has less incentive to promote growth.


Government waste and patronage
Lack of interest in the right policies
IDA: NGOs
 Donors are not accountable (unlike firms or
politicians)
 Oxfam and Cashew Nuts
 Bill Gates and Primary care Doctors
 Emmanuel Kuadzi
 Donors only do things that are seen as
“benevolent”
 BUT Maybe the solution is investment, sacrifice,
hard work...
 Maybe Promotion of BUSINESS is the key!
Should we STOP IDA?
 No: the debate should not be on whether to increase the
amount of AID but HOW AID should be spent?
 Learning at the MICRO level: because we know the
GENERAL principles:
 Markets are good
 Rule of law is good
 Education is good
 Innovation is good
 But how do you implement these in a particular country?
Correlation vs Causation
 Example (1): African peasants.
 There was a ebola epidemic.
 Government sent doctors to the worst-affected
areas.
 Peasants observed that in areas with lots of
doctors, there was lots of ebola.
 Peasants concluded doctors were making things
worse.
 Based on this insight, they murdered the doctors.
Correlation vs. Causation
 Example (2): SAT preparation courses in the US.
 In 1988, Harvard interviewed its freshmen and
found those who took SAT “coaching” courses
scored 63 points lower than those who did not.
 One dean concluded that the SAT courses were
unhelpful and “the coaching industry is playing
on parental anxiety.”
Correlation and Causation
 Example (3): In Tanzania, a NPO provides extra
teachers to the schools that want to
participate in their program. The goal is to
reduce the number of classes missed by
students due to teacher absenteeism (a big
problem in Africa).
 One year after the intervention, they evaluate the
grades of the students in the schools were the
program was implemented are higher.
 The NPO concludes that the problem is a success
All three examples
1. There is confusion of correlation and causation
2. Suffer from Sample Selection Bias: “treated” and
“control” (or non-treated) groups were not selected
randomly:
 Ugandan Ebola: doctors were NOT assigned to random
villages but to the worst-off communities; Hence there was
a correlation between number of doctors and disease
(reverse causation).
 Harvard: Students who take prep courses are not random
students but students that are more likely to do worse in
SATs (that’s why they take the course!). (reverse causation)
 NPO: schools that decided to participate were not random
but more likely to have a responsible director or teachers
(spurious correlation: good schools’ desire to improve
teaching causes both the good grades and desire to
participate in the program)
Randomized Field Experiments
 The trial proceeds by taking a group of volunteers
and randomly assigning them to either a
“treatment” group (the group that gets the
intervention), or a “control” group (a group that is
denied the intervention).
 Because it is random, the assignment of the
intervention is not determined by anything about
the subjects.
 As a result, the treatment group is identical to the
control group in every facet but one: the treatment
group gets the intervention.
 Hence, there is no BIAS (the two groups are not
different in any consistent way)
Randomized Field Experiments
 Programs targeted to individuals or local
communities, such as sanitation, education, and
health programs and local government reforms,
are likely to be strong candidates for
randomized evaluations.
 Not all programs are (for example: effects of
central bank independence on inflation may not
be… unless the IMF wants to play God and
experiment with entire countries)
Randomized Field Experiments
 When it comes to analyzing AID, the SELECTION or SAMPLE
BIASES may be large: Generally, individuals who were subjected to
the program and those who were not, are very different:



Programs are placed in specific areas (for example, poorer or richer
areas)
Individuals are screened for participation in the program (for instance,
on the basis of poverty or on the basis of their motivation)
The decision to participate is often voluntary.
◦ Thus, those who were not exposed to a program are often not
comparable to those who were. This is called: SELECTION BIAS
 Hence, unless we do RANDOM trials, we cannot decompose the
overall difference into a treatment effect and a selection bias effect
◦ Ie, we cannot say if the increase in education of an education
program is the result of the program working OR the reflection
that the people who volunteered for the program were more
excited about getting educated!
Randomized Field Experiments…
 …Also present some problems:
 They can be expensive (in the developing world, they




are cheaper, that’s why development economists are
using them more frequently than, for example, public
finance economists)
They can take a long time to complete.
They may raise ethical issues (especially in the
context of medical treatments).
The inferences from them may not generalize to the
population as a whole.
Subjects may drop out of the experiment for nonrandom reasons, a problem known as attrition.
Example 1: Teacher Absenteeism in India




Problem: Indian schools are plagued by high teacher absenteeism.
Paper: Duflo and Hanna (2005).
NGO: Seva Mandir (India)
Program: A second teacher, often a woman, was hired and randomly
assigned to 21 out of 42 schools.
 The hope was to increase the number of days the school was open, to increase
children’s participation, and to improve performance by providing more
individualized attention to the children.
 Teacher and child attendance were regularly monitored in program and
comparison schools for the entire duration of the project.
 Measuring Outcomes:
 The impact of the program on learning was measured by testing children at the
end of the school year.
 Results:
 The program reduced the number of days schools were closed: one-teacher
schools were closed 39 percent of the time, whereas two-teacher schools were
closed 24 percent of the time.
 Girls’ attendance increased by 50 percent.
 However, test scores did not differ.
 DECISION:
 Based on the pilot, the NGO decided NOT to scale up and use the money for
something else!
RFE Example 2: Teacher
Absenteeism (second try)




Problem: Schools are plagued by high teacher absenteeism.
Paper: Duflo and Hanna (2005)
NGO: Seva Mandir
Program:
 120 schools, 60 treated randomly.
 The teacher is given a “tampering proof” camera that registers time and date
of the picture
 Teacher has to take picture of himself WITH students at beginning and end of
day.
 “Valid” day is when beginning and ending times are separated by 5 hours or
more and when there are enough students in the picture
 End of the month salary increases. Salaries in treatment group range from
500 rupees to 1300 rupees depending on valid days. Salaries in comparison
group are 1000 rupees regardless of attendance
 Measuring Outcomes:
 School attendance of teacher
 Results:
 Absence rate was cut from 42% to 22%
 It completely eliminated delinquent behavior (less than 50% attendance)
 it increased “perfect score attendance” (in comparison schools, only 36% of
teachers had perfect record in treatment schools, 90%)
 Test scores of students in treatment schools increased of 0.17 standard
deviations
RFE Example 3: Salaries for
Students
 Problem: Low school and hospital attendance of poor girls in Mexican villages
 Paper: (Gertler and Boyce 2001) and Government of Mexico (Progressa, now
called “Oportunidades”)
 Program: PROGRESA offers grants, distributed to women, conditional on
children’s school attendance and preventative health measures (nutrition
supplementation, health care visits, and participation in health education
programs).
 In 1998, when the program was launched by Ernesto Zedillo (incidentally, an
economist!), officials made a conscious decision to take advantage of the fact that
budgetary constraints made it impossible to reach the 50,000 potential beneficiary
communities of PROGRESA all at once, and instead started with a pilot program in
506 communities.
 Half of those were randomly selected to receive the program, and baseline and
subsequent data were collected in the remaining communities
 Studies take advantage that Progressa was randomly phased to learn lessons
Example 3: Salaries for Students
 Outcome:
 Comparing PROGRESA beneficiaries and nonbeneficiaries, Gertler and
Boyce (2001) show that children had about a 23 percent reduction in the
incidence of illness, a 1 to 4 percent increase in height, and an 18 percent
reduction in anemia.
 An average of a 3.4 percent increase in enrollment for all students in
grades 1 through 8. The increase was largest among girls who had
completed grade 6: 14.8 percent.
 Result:
 The program was subsequently implemented in MANY countries around
the world
RFE Example 4: Teacher
Kenya.in Kenya
Absenteeism
Problem: low teacherin
attendance
 Program:
 International Child Support Africa (ICSA) randomly chooses 50%
of schools to participate in a program.
 They give prizes to teachers monetary prizes to teachers 4th to
8th grades whose students have “best grades” and “most
improved grades”
 Prizes are about ½ of teacher’s monthly salary
 Measuring outcomes:
 Teacher attendance and students’ grades
 Results:
 Teacher attendance in treatment schools was the same as
attendance of comparison group (there is a large fixed cost to
attending school)
 Teachers in treatment schools devoted more time to prepare
their students to pass the tests and NOT more time to
education (teachers respond to incentives)
RFE Example 5: Children
Immunization
 Problem: Mothers do not take their children to the clinic for immunization
(Shockingly, 1% of children are fully immunized at the age of 2)


Surprising given that immunization is free
It is thought that the problem is that clinic is far away and not always open (so cost of long
trip plus uncertainty may not compensate potentially large benefits)
 NPO: Seva Mandir (India)
 Program:
Randomly select 68 of 135 villages and announce one day a month a health worker will be
there for sure (no travel involved for mothers)
 The health worker is given financial incentives to be there
 Of the 68 treatment villages, 34 are randomly selected to give a kilo of lentils to the mothers
that immunize their children under 2 years of age
 Note: if the problem is “travel costs”, the main effect should come from installation camps
and lentils would have no additional effects

 Measuring Outcomes:

Immunization rates



Rates increase only slightly in treatment villages with immunization camp but no lentils
Rates increase DRAMATICALLY in villages where lentils are given.
It turns out that the cost was not the travel cost. The problem is that mothers do not fully
understand the benefits of immunization or have a very high discount rate (so that a small
benefit of immunization today compensates the cost and a lower probability of death 5
years down the road does not)
 Results:

RFE Example 6: Micro-Credits.
Individual or Group Liabilities?
 Karlan and Gine (2006) in Philippines
 Grameen Bank started group liability so everyone followed (group
liability requires members of the group help repay the debt when
other members of the group cannot repay).
 Problem: Is group liability better than individual liabilities?
 Group Liability Advantages
◦ Main: Clients face peer pressures to repay their loans.
◦ Other Advantages:





Clients have incentives to screen other clients so that only trustworthy
individuals are allowed into the program.
Low transaction costs as clients meet and pay at the same time and
location.
Cheaper training costs as clients all gather periodically.
Clients have incentives to market the program to their peers, thereby
helping to bring in more clients.
Group process may help build social and business relationships.
RFE Example 6: Micro-Credits.
Individual or Group Liabilities?
 Group Liability: Disadvantages
 Main: Peer pressure causes tension. This could lead to lower client s satisfaction
and hence higher dropout. ALSO, may destroy social capital so necessary for poor
people with no networks.
 Other:






Older clients tend to borrow significantly more than newer clients. This heterogeneity
often causes tension within the group, because new clients do not want to be
responsible for others’ much larger loans.
Group lending could be more costly for good clients since they are often required to
repay the loans of their peers.
Clients dislike the longer meetings typically required for group lending.
Default rates could be higher because bad borrowers can bring down good borrowers
(i.e., once your peer has gone into default, you have less incentive to pay back the
loan yourself).
Default rates could be higher because clients can “free ride” off of good clients. In
other words, a client does not repay the loan because the client knows that another
client will pay it for them, and the bank will not care because they still will get their
money back.
Villagers with fewer social connections might be hesitant (or even unwelcome) to join
a borrower group.
 Question: Is Group liability better than individual liability?
RFE Example 6: Micro-Credits.
Individual or Group Liabilities?
 Existing Green Bank programs in Philippines: 93 groups
that were receiving group-liable loans are converted to
individual-liable loans. 93 other groups are kept as groupliable. Groups are chose randomly.
 Results:
No change in repayment fraction (so peer pressure seems to
have insignificant effects)
b) Individual-liability centers attract more new clients (so
screening by members by group liable groups is not
superior)
c) Individual-liability centers lose fewer clients to dropouts (so
tensions of group-liability does seem to cause more
dropout).
◦ Conclusion: Benefits of group liability may be overstated!
a)
RFE Example 7: Teaching
Entrepreneurship in Peru
 Problem: Micro-credit recipients often don’t know
how to manage their micro businesses. Question:
Does business training work?
 Karlan and Valdivie (2006)
 NGO: FINCA (a micro finance institution in Peru)
 Experiment:
 Take a 100 banks in Lima and 140 banks in Ayacucho)
and randomly choose 33% who will have a MANDATORY
training, 33% receive VOLUNTARY training, and 33%
receive no training.
 Measures of output:
 Survey each bank before and after, and ask about
business practices, knowledge, incomes and profits.
RFE Example 7: Teaching
Entrepreneurship in Peru
 Results:
 Month after training ends, treatment groups had sales 16% higher than
controls.
 Sales of “worst month” were 28% higher in treatment than in control
groups.

However, despite their larger sales, their profit margins were the same.
 Treatment groups showed superior business knowledge (so the training
was efficient in the sense that knowledge was transmitted)
 Repayment was 3% among treated groups and clients in treated group
were 4% LESS likely to drop out (despite the fact that they complained in
the surveys that the courses were very time consuming: since their
probability of dropping out was lower, this suggests that their perceived
benefits outweight these costs)
 Summary:
 Many of the anticipated beneficial effects did occur. The anticipated cost
(length of tedious classes) was mentioned but outweighted by perceived
benefits.
 Future questions: what is the best way of training (loan officials
are not teachers. Should we have business schools, business
mentors,…?)
RFE Example 8: AIDS and changing
Sexual Behavior
 Duflo, Esther, Pascaline Dupas, Michael Kremer, and
Samuel Sinei (2006), “Education and HIV/AIDS
Prevention: Evidence from a Randomized Evaluation in
Western Kenya”
 Problem: AIDS in Kenya is an epidemic. Can it be
reduced by changing sexual behavior (which is what
worked in the USA)?
 Schools in a Kenyan district are randomly allocated to
one of 4 programs
Classes that teach impact of AIDS (standard HIV-AIDS
curriculum in Kenya, a curriculum that is rarely
implemented)
2) Active student debates about use of condoms (standard
curriculum does not advocate condom use because it is a
controversial issue in Kenya)
1)
RFE Example 8: AIDS and changing
Sexual Behavior
3) Show girls this picture and
explain how dangerous it is
to accept gifts from older
men (gifts are common
part of sexual relationships
in Kenya)
4) Give girls uniforms so that
the cost of going to school
is lower (schooled girls
have a lower probability of
being infected)
RFE Example 8: AIDS and changing
Sexual Behavior
 Measure of success: teenage pregnancy (a proxy
for HIV).
 Results:
Teaching had no effect on teenage childbearing
Debates had no effect on teenage childbearing
(although girls described change in behavior in survey)
3) “Sugar Daddies”: Reduced teenage childbearing by
older men (because girls had fewer relations with older
men, although they had more relations with younger
boys, relations with higher likelihood of using
condoms)
4) Uniforms: reduced dropout rates, reduced teenage
childbearing and marriage. Cost of uniforms: $12.
1)
2)
Main lesson: surprise!
Example 9: Education and … Worms!
 Problem: Four hundred million children of school-age are
chronically infected with intestinal worms. Infected children suffer
listlessness, diarrhea, abdominal pain and anemia. These
parasites are so widespread that some societies do not recognize
infection as a medical problem. Symptoms of worms, such as
blood in the stool, are considered a natural part of growing up. So
even though safe, cheap, and effective oral medication that can
kill 99 percent of worms in the body is available and the World
Health Organization (WHO) recommends mass deworming of
school-aged children, only 10 percent of at-risk children get
treated.
 Research: Kremer and Miguel (2004).
 NPO: ICS Kenya.
 Exercise: 75 schools in Kenya with 30.000 children. Deworming
was phased in randomly. Analysis and tests were done in 2004
and 2007.
Example 9: Education and … Worms!
 Results:
◦ Deworming improved health to the kids treated
◦ Health improved also in neighboring kids (so there is an
externality)
◦ Deworming reduced school absenteeism by 25%
◦ Unlike anemia (which reduces educational achievement),
deworming did not have an impact on test scores.
◦ However, children in treated schools were 52% more likely to
move away from their rural schools to attend a better
secondary school.
◦ 3 years after first study, treated children were taller, heavier
and healthier (disease complementarities)
 Conclusion: Another surprise! Deworming affects
schooling!
In Sum
 One of the tragedies of aid over the last 50 years
is that billions of dollars were spent, the results
were not positive … AND WE DID NOT EVEN
LEARN WHY!!!
 We should redirect our aid efforts in ways that, if
they fail again, at least we learn why they failed
so the mistakes are not repeated.
 It is time to STOP TEACHING and START
LEARNING
The World Bank Group
http://www.worldbank.org/tenthings/
The World Bank’s Priorities Have Changed
Dramatically
 Our work in more than 100 countries is challenging,
but our mission is simple — to help reduce poverty.
Over the past 20 years, our focus has changed and
so has our approach. We are now dealing with
newer issues like gender, community-driven
development and the rights and role of indigenous
people in development. Our support for social
services like health, nutrition, education and
pensions has grown from 5 percent in 1980
to 22 percent in 2003. Today, countries
themselves are coming to us with their own plans
for helping poor people, and we have adopted new
ways of working with them.
1.
are the
World’s
Largest Funder
of Education
 We
Education
is central
to development.
We have
committed
around US$33 billion in loans and credits for education, and we
currently fund 157 projects in 83 countries. We work closely
with national governments, United Nations agencies, donors,
civil society organizations (such as community groups, labor
unions, Non Governmental Organizations and faith-based
groups), and other partners to support developing countries in
their efforts to make sure that all children, especially girls and
disadvantaged children, are enrolled in and able to complete a
primary education by 2015. A good example of our lending in
this area is the India District Primary Education Program, which
specifically targets girls in districts where female rates of
reading and writing are below the national average. Our
support for this program has reached US$1.3 billion and serves
more than 60 million students in 271 districts in 18 of the 29
Indian states. In Brazil, El Salvador and Trinidad and Tobago,
the projects we support have helped local communities increase
their influence on the quality of education for their children by
helping them to assess the performance of local schools and
teachers.
2. We Are the World’s largest External Funder in
the fight against AIDS
 Each day, 14,000 people become infected with the HIV virus.
HIV/AIDS is rapidly reversing many of the social and economic gains
that developing countries have made over the past 50 years. As a
sponsor of UNAIDS (the group that coordinates the international
response to the epidemic), in the past few years we have committed
more than US$1.6 billion to fight the spread of HIV/AIDS around the
world. We have also been one of the largest financial supporters of
HIV/AIDS programs in developing countries. We have promised that
no country with an effective HIV/AIDS strategy will go without
funding. In partnership with African and Caribbean governments, we
launched the Multi-Country HIV/AIDS Program (MAP), which makes
significant resources available to civil society organizations and
communities. Many have developed original approaches to HIV/AIDS,
which others are learning from and adapting to local conditions. The
MAP has made available US$1 billion to help countries in Africa
expand their national prevention, care and treatment programs.
3. We are the leader in the fight against
corruption
worldwide
 Corruption is
the single largest obstacle to development. It
increases wealth for the few at the expense of society as a
whole, leaving the poor suffering the harshest consequences by
taking public resources away from those who need them most.
Since 1996, we have launched hundreds of governance and
anticorruption programs in nearly 100 developing countries.
Initiatives range from requiring government officials to publicly
declare their assets and introducing public spending reforms, to
training judges and teaching investigative reporting to
journalists. Our commitment to fighting corruption has helped
to encourage an international response to the problem. We also
continue to make anticorruption measures a central part of our
analytical and operational work. We are committed to making
sure that the projects we fund are free from corruption, by
setting strict guidelines and providing a hotline for corruption
complaints. So far, about 100 companies have been banned
from participating in projects that we finance. The World Bank
Institute has also developed a major knowledge, learning and
data center on governance and anticorruption.
4. We Strongly Support Debt Relief
 In 1996, with the International Monetary Fund (IMF), we launched
the Heavily Indebted Poor Countries (HIPC) Initiative— the first
comprehensive effort to cut the debts of the world’s poorest,
most indebted countries. Today, 27 countries are receiving debt
relief that will amount to US$52 billion over time. The HIPC
Initiative, combined with other types of debt relief, will cut by
two-thirds the external debt in these countries, lowering their
debt levels to below the overall average for developing countries.
As part of the initiative, these countries are using government
funds freed up by debt relief for programs to cut poverty. For
example, Rwanda has set targets to hire teachers and increase
the number of children who enroll in primary school. Honduras
plans to deliver basic healthcare to at least 100,000 people in
poor communities. Cameroon is strengthening the fight against
HIV/AIDS by, among other things, expanding education to
promote the use of condoms by high-risk groups.
5. We are one of the largest international funders of
biodiversity projects
 Since 1988, we have become one of the largest international
sources of funding of biodiversity projects which protect our
world’s wide variety of animals, plants and other living things.
Even though the loss of biodiversity is an international
concern, people who live in rural communities in developing
countries feel the greatest effects since they are most
dependent on natural resources for food, shelter, medicine,
income, employment and their cultural identity. For this
reason, we have joined Conservation International, the Global
Environment Facility, the MacArthur Foundation and the
Japanese government in a fund that contributes to the
protection of developing countries’ biodiversity hotspots,
which are the Earth’s biologically richest but most threatened
places. Concern for the environment is central to our mission
to reduce poverty. Our environment strategy focuses on
climate change, forests, water resources, pollution
management and biodiversity, among others. Currently,
projects we fund, that have clear environmental objectives,
amount to around US$13 billion.
6. We work in partnership more than ever before
 During the past six years, we have joined a large range of partners
in the international fight against poverty. For example, to help
reduce the effects of global warming, we worked with governments
and the private sector to launch the new BioCarbon Fund and with
the International Emissions Trading Association (IETA) to launch the
Community Development Carbon Fund (CDCF). We are also working
with the World Wildlife Fund to protect forests. With the Food and
Agriculture Organization (FAO) and the United Nations Development
Programme (UNDP), we sponsor the Consultative Group on
International Agricultural Research (CGIAR) which mobilizes cuttingedge science to reduce hunger and poverty, improve human
nutrition and health and protect the environment. Through the
Consultative Group to Assist the Poor (CGAP), we work with 27
other international and donor organizations to provide access to
financial services (such as loans and savings) for the poor, referred
to as microfinance. A partnership to defeat river blindness
throughout Africa has successfully prevented 700,000 cases of
blindness, opened 25 million hectares of arable land to cultivation,
and treats more than 35 million people a year for the disease.

7. We are helping to bring clean water, electricity and
transport
to poor
While
most people
in the people
developed world take infrastructure (for example
clean water, electricity and transport) for granted, it is a dreamed-of luxury
in many parts of the world. Almost 1.4 billion people in developing
countries do not have access to clean water. Some 3 billion live without
basic sanitation or electricity. Infrastructure is not simply about the
construction of large projects. It is about delivering basic services that
people need for everyday life, such as upgrading slums and providing roads
to connect the poorest urban areas. Infrastructure is also an important part
of our efforts to help achieve the Millennium Development Goals. Delivering
safe water has a direct effect on reducing child death rates. Providing
communities with electricity prevents women and children from having to
spend long hours fetching firewood for cooking and heating, and gives
them more time for other activities. Children especially are able to devote
more time to schoolwork. In Morocco, a road project we supported helped
to increase the number of girls who enrolled in schools from 28 percent to
68 percent. Infrastructure also connects communities to the world around
them. A rural electrification project in Ecuador is helping to improve living
standards and broaden opportunities by linking poor communities to
telecommunications, electricity, the internet and business services.
8. Civil society plays a larger role in our work
 The growth of civil society over the past 20 years has been
one of the most significant trends in international
development. Civil society organizations (CSOs) — which
include groups that do not belong to government or the
private sector such as, labor unions, NGOs, faith based
organizations, community groups and foundations — are not
only influential in the international development policy debate
but have become important channels for the delivery of social
services and new development programs. CSO involvement in
projects we have funded has risen from 21 percent of all
projects in 1990 to about 72 percent in 2003. We are also
increasingly supporting CSOs by sharing more information
and offering skills training. We also provide grants to CSOs to
rebuild war-torn communities, provide social services and
support community development. Our civil society staff in
more than 70 offices around the world consult and work with
CSOs on a range of issues from preventing AIDS and
developing microcredit to fighting corruption and protecting
the environment.
9. We help countries merging from conflict
 We are active in 40 countries affected by conflict. We work with
government and non-government partners (local and international)
to help people who have been affected by war, resume peaceful
development, and prevent violence from breaking out again. Our
work deals with a range of needs including jump-starting the
economy, repairing and rebuilding war-damaged infrastructure and
institutions, clearing landmines, helping people who fought in the
conflict and refugees back into society, and targeting programs at
vulnerable people such as widows and children. We have also
developed tools and research to better analyze and understand the
sources of conflict, and to promote economic growth and cut poverty
in a way that reduces the risk of future violence. Among the wide
ranging projects that we have supported are the reintegration of
soldiers who fought in the Great Lakes Region of Central Africa,
rebuilding infrastructure and helping communities in Afghanistan,
dealing with psychological and social trauma in Bosnia and
Herzegovina, rehabilitating street children in the Democratic Republic
of Congo and protecting the property of Colombians who have been
uprooted by conflict.
10. We are responding to the voices of poor people
 Conversations with 60,000 poor people in 60 countries, as
well as our day-to-day work, have taught us that poverty is
about more than inadequate income. It is also about lack of
fundamental freedom of action, choice and opportunity. It is
about vulnerablility to abuse and corruption. We believe that
people who live in poverty should not be treated as a liability,
but as a resource and a partner in the fight against poverty.
Our approach to reducing poverty puts poor people at the
center of development and creates the conditions where they
can gain increased control over their lives through better
access to information and greater involvement in decision
making. Today, we support a variety of community-driven
development projects with funding of more than US$2 billion.
Other ways of supporting poor people include community
managed school programs, judicial reform and access to
justice programs and providing citizens with the ability to rate
basic services, such as access to water, education and health.
In Sum
 Here is an example of an important
institution that prides itself of “Spending
Resources” rather than “Achieving Results”.
 Notice that, even though they explicitly say
in the first slide that their goal is simple:
“To Help Reduce Poverty”, the pamphlet
does not say EVEN ONCE anything about
how the money spent has contributed to the
goal.
 This is what economists would call:
confusing inputs with outputs!!!
In Sum
 Aid agencies have little incentives to achieve
results, because it is not clear what “results”
are, because it is not clear whose “results”
they should satisfy and because it is often
impossible to quantify these “results”.
 Hence, they proudly report the “inputs”
(volume of aid), rather than “outputs”
(results).
 Return
Emmanuel Kuadzi
 Emmanuel, is a Website Developer and a
Designer from Accra (Ghana)
 At age 22, he created “Soft Internet Solutions”.
Employed 25 people...
\\SFILE\CDN\CWB\CEO's
without borders-JLL-CS100407
www.gtz.de
Then came GTZ (a NPO
created by the German
government) and “Soft Internet
Solutions” went out of business
and 25 young entrepreneurs
that were creating wealth lost
their jobs!
IDA: Citizens of RICH World (Donors)back
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without borders-JLL-CS100407