Xavier Sala-i-Martin
Columbia University
April 2010
1.
2.
Aid has some positive effects on growth (Jeffrey
Sachs 2004) and needs to be multiplied.
Aid has effect on growth, only under some
circumstances (conditional aid)
1. Conditional on Policies (Craig, Burnside and Dollar
(2000), Dalgaard and Tarp (2004))
2. Conditional on type:
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))
Source: Easterly (2003), JEP
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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
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Early studies found positive correlations
(Papeneck 1973, Levy 1988).
But then came Peter Boone (1994): Aid and
growth are not correlated, period.

Then a very Influential paper was written
by Burnside and Dollar (2000):
   0  1 X   2 AID   3 AID * GoodPolicies  

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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.
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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

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.

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.

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.

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)
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Svensson (1999), aid works for democracies only.
Collier and Dehn (2001), and Guillaumont and Chauvet
(2001), aid is helpful in countries experiencing sharp price
drops for key commodity exports
Collier and Hoeffler (2004), aid works only in countries that
are emerging from civil war and have good policies
Dalgaard, Hansen, and Tarp (2004), aid works only outside
the tropics (because countries outside the tropics have good
institutions):
◦ Note on this paper: outside the paper, there were 4 countries:
Botswana, Jordan, Egypt and Syria. Do we think Botswana has grown
because of aid? Do we think Jordan has grown because of aid (Jordan
received a lot of aid during the previous oil boom, from oil producers
in the region; AT THE SAME TIME, it opened up the economy for these
nice neighbors? Was growth caused by aid or by the opening?

Roodman (2007) shows that all these papers suffer from the
same problems Burnside and Dollar does: not robust to
simple specification changes.

Clemens, Radelet, and Bhavnani (2004)
argue that if separate aggregate aid into
 (i) emergency and humanitarian aid,
 (ii) growth that affects growth only in the long run (e.g. aid
that supports democracy, health, environment or education)
 (iii) aid that may increase growth over the short run [ie, 4
years] (infrastructures and aid for productive sectors and
agriculture and aid that helps deal with balance of payments
problems),
 then there is a positive association between aid and growth.
◦ Problem: not clear that development process is to help
economies out of 4 year recessions (balance of payments
problems) o agriculture…
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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.

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
1.
2.
Savings trap (savings rate is close to zero
for poor countries for subsistence reasons
and then shuts up as income increases)
Nonconvexity in the production function
(there are increasing returns for some range
of k)
Stable
Stable
δ+n
Unstable
s(k)f(k)/k
3.
Demographic trap (impoverished families
choose to have lots of children)
- www.gapminder.org
Stable
s(k)f(k)/k
Unstable
Stable
δ+n
k
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The main implication is that a country that is
“stuck” in a poverty trap (the low income steady
state) that receives aid in the form of capital
that is less than the distance between its initial
position and the next steady state, converges
back to the low steady state.
Hence, the fact that aid has not worked in the
past does not prove that it is ineffective.
In fact, the poverty trap implies that the total
amount of aid must be increased enough to put
countries over the unstable steady state
IMPORTANT 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!)

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))

If there is technological progress, the savings
trap automatically disappears!

If there is technological progress, the savings
trap automatically disappears!
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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
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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
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
Per Capita Growth
Poorest Fifth in 1950
Other Countries
Source: Easterly 2005
1950-2001 1950-1970 1975-2001
1.60%
1.90%
0.80%
1.70%
2.50%
1.10%
Defenders of Poverty Trap theory show that poor countries have grown less
after 1975.
But how do we explain positive growth (1.9% per year) between
1950 and 1970?
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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

Define take off as a period of large sustained
growth (more than 1.5%) following a long period
of zero growth (defined as -0.5% to +0.5%)
Source: Easterly 2005

Easterly, Kremer, Pritchett, and Summers
(1993)
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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
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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

Not very robust (Kremer, Onatski and Stock
show that it depends on one or two data
points)
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There is capital in the developing world but it is
not invested in the developing world
Correlation between Aid and Growth is zero
(more on this later).
Is it Poverty Traps or Corruption?
◦ Countries with low scores on “corruption” tend to grow
1.3% less than other countries (Easterly 2006)
◦ Multiple regression: holding constant “corruption”, the
“level of poverty” does not matter (Easterly 2006)
◦ 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?
My Girlfriend

The Many Players (International Cartel of Good
Intentions):
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 …
◦
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Well Intended people... But good intentions are NOT
enough

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

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

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!

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)

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

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!
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One way to “solve” the principal agent problems has
been to use “conditionality”.
Problem with conditionality is that we don’t know
what works so we move according to the latest “fads”
◦ 1950s and 1960s: state-led growth
◦ 1970s: basic human needs
◦ 1980s: macroeconomic stability, trade reforms, and
privatization
◦ 1990s: governance and corruption
◦ 2000s: Institutions
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Too many conditions… that are substituted by more
conditions (those who criticize fiscal austerity, want
more health and education)
Samaritan’s dilemma: if conditions are not met, aid
institutions do not have the credibility of moving out.
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No: the debate should not be on whether to increase
the amount of AID but HOW AID should be spent?
More disaggregated studies seem to show more
positive correlations than macro studies
◦ Michaelova and Weber (2006) and Dreher, Nunnenkamp, and
Thiele (2007): aid education raises primary enrollments.
◦ Mishra and Newhouse (2007): health aid reduces infant mortality
◦ World Bank Independent Evaluation Group (2006) says that only
22.5% of WB projects in Africa (2001-05) had unsatisfactory
outcomes and only 20.1% had no long-run benefits (the
outcomes were usually measured through surveys to own World
Bank employees!).
Maybe we can learn more from micro episodes but
(i) even these micro studies may be “too general” (or too
disaggregated)

◦ We know “IN GENERAL” what needs to be done: education, health,
sanitation, entrepreneurship, and so on.
◦ But when it comes to actual ACTIONS, we do not know how to do it.
◦ Examples:
 Human capital is good. But do we build schools, pay higher salaries to
teachers, pay salaries to students?
 Health is good. But should we do treatment or vaccines?
 Entrepreneurship and job creation is good. But is it about lack of capital
(microcredits), or lack of skills (skill transmission), or bad institutional
environment, or lack of social capital (trust)?

Within each categories, we do not know how to do it?

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Business school collaboration?
NGO training
Mentoring / Angels
(ii) The way some of these studies are conducted may be fundamentally
flawed.

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.

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.”

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
1.
2.
There is confusion of correlation and causation
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)
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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)


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)

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!

Better than “lab experiments” with students
because stakes are large and the field
experiment is about real life event (not a
game played by a student)

…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.
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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:

Results:

DECISION:
◦ The impact of the program on learning was measured by testing children at the
end of the school year.
◦ 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.
◦ Based on the pilot, the NGO decided NOT to scale up and use the money for
something else!

Problem: Schools are plagued by high teacher absenteeism.
Paper: Duflo and Hanna (2005)
NGO: Seva Mandir
Program:
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Measuring Outcomes:

Results:
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◦ 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
◦ School attendance of teacher
◦ 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
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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

Outcome:

Result:
◦ 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.
◦ The program was subsequently implemented in MANY countries around
the world

Problem: low teacher attendance in Kenya
Program:

Measuring outcomes:

Results:

◦ 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
◦ Teacher attendance and students’ grades
◦ 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)

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:

Measuring Outcomes:

Results:

◦ 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
◦ 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)
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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.

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?


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 group-liable. Groups are
chose randomly.
Results:
a) 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!




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.

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,…?)



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
1) 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)
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)


Measure of success: teenage pregnancy (a proxy
for HIV).
Results:
1) Teaching had no effect on teenage childbearing
2) 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.
Main lesson: surprise!




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.

Results:

Conclusion: Another surprise! Deworming affects
schooling!
◦ 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)



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
End
The World Bank Group
http://www.worldbank.org/tenthings/

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, communitydriven 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.

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 faithbased 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.

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.

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.

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.

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.

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 cutting-edge 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.

While most people in the 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.

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.

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.

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.



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!!!



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, 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-CS-100407
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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