international development aid

International Development Aid

Xavier Sala-i-MartinColumbia University

March 2009

1. 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))

2. Conditional on type:1. Infrastructures [Clemens, Radelet, and Bhavnani (2004)]2. Education [Michaelova and Weber (2006) and Dreher,

Nunnenkamp and Thiele (2007)]3. Health [Mishra and Newhouse (2007)]

3. Aid has NO effect on growth or may even undermine it (Peter Bauer (1972), Bill Easterly (2006))

Heated Debate

Evidence: Caution with Simple Figures

Source: Easterly (2003), JEP

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

Causality

Early studies found positive correlations (Papeneck 1973, Levy 1988).

But then came Peter Boone (1994): Aid and growth are not correlated, period.

Empirical Evidence

Empirical Evidence Then a very Influential paper was

written by Burnside and Dollar (2000):

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.

esGoodPoliciAIDAIDX *3210

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 Sachs-Warner 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.

Empirical Evidence

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.

Instrumental Variables

Source: Rajan and Subramanian (2005)

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.

Other papers arguing that aid matters “conditionally”

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…

Final point about Evidence

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.

Despite the evidence that Aid does not work (or may hurt)

Start with Fundamental Equation of “Solow-Swan”:◦ Δk=sf(k) - (δ+n) kor◦ 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

δ+n

s(k)f(k)/k

StableStable

Unstable

3. Demographic trap (impoverished families choose to have lots of children)

- www.gapminder.org


http://www.gapminder.org/

Demographic Trap

s(k)f(k)/k

δ+n

Stable

Stable

Unstable

k

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!)

Poverty Traps: Implications

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 1: Savings Trap

If there is technological progress, the savings trap automatically disappears!

Problem 2: Savings Trap

If there is technological progress, the savings trap automatically disappears!

Problem 3: Demographic Trap

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 3: Fertility Behavior

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 non-convexities exist, but need to argue that non-convexities cannot be “convexified” by averaging production from below the convex and above area

This is a lot harder

Problem 4: Non-Convexities Trap

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-4%

-2%

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2%

4%

6%

8%

$100 $1,000 $10,000 $100,000

Annu

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ate

1970

-200

6

Per Capita GDP in 1970

β-Convergence

Problem 5: Poor Countries did not grow less than others

Per Capita Growth1950-2001 1950-1970 1975-2001

Poorest Fifth in 1950 1.60% 1.90% 0.80%

Other Countries 1.70% 2.50% 1.10%Source: Easterly 2005

Problem 5 (cont): Poor Countries did not grow less than others

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) between1950 and 1970?

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 6: Evidence of Conditional Convergence suggests that “fundamentals” explain low income

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%)

Problem 7: Little Evidence of “Take Offs”

Source: Easterly 2005

Problem 8: And take offs are not related to Aid

Easterly, Kremer, Pritchett, and Summers (1993)

Problem 9: Growth is NOT persistent across decades

Problem 10: Quah (1996): Seems Evidence in favor of Traps

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

Forecasting the future of the WDI (by country)

Not very robust (Kremer, Onatski and Stock show that it depends on one or two data points)

Problem

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)

Additional Problems

◦ 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?

Interesting questions:

Let me introduce...

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 …

Well Intended people... But good intentions are NOT enough

The World of International Development Aid

Markets◦ Suppliers need to listen to customers◦ Responsibility/Accountability if don’t supply

what’s wanted Why?

◦ Customer has something the supplier wants (money)

Mechanisms that Work

Firms Customers

Information

Products

Money

Liberal Democracy◦ Listen to “customers”◦ Responsibility/Accountability

Why?◦ Customer has something the supplier wants

(votes)

Mechanisms that Work

Politicians Voters

Information

Policies

Votes

AfricanCitizens

AfricanBureaucrats

WBBureaucrats

Donors

X?

The Aid World

A strange sequence ofPrincipal-Agent problemsWith misaligned incentives

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

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: Citizens of RICH World (Donors)

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: The Local Intermediaries

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!

IDA: NGOs

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

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.

Conditionality?

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!).

Should we STOP IDA?

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?

Business school collaboration? NGO training Mentoring / Angels

(ii) The way some of these studies are conducted may be fundamentally flawed.

Evaluation and Learning

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 vs. 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

Correlation and Causation

1. There is confusion of correlation and causation2. 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)

All three examples

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)


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 non-

random reasons, a problem known as attrition.

Randomized Field Experiments…

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 1: Teacher Absenteeism in India:

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 2: Teacher Absenteeism (second try)

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

RFE 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

Example 3: Salaries for Students

Problem: low teacher attendance in Kenya 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 4: Teacher Absenteeism in Kenya.

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 Results:

◦ 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)

RFE Example 5: Children Immunization

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?


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.

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 7: Teaching Entrepreneurship in Peru

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 programs1) 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)

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)


Measure of success: teenage pregnancy (a proxy for HIV).

Results: 1) Teaching had no effect on teenage childbearing2) 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.

RFE 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!

RFE Example 9: Education and … Worms!

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

In Sum

Ten Things You Never Knew About the World Bank

The World Bank Grouphttp://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, 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.

The World Bank’s Priorities Have Changed Dramatically

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.

1. We are the World’s Largest Funder of Education

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.

2. We Are the World’s largest External Funder in the fight against AIDS

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.

3. We are the leader in the fight against corruption worldwide

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.

4. We Strongly Support Debt Relief

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.

5. We are one of the largest international funders of biodiversity projects

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.

6. We work in partnership more than ever before

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.

7. We are helping to bring clean water, electricity and transport to poor people

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.

8. Civil society plays a larger role in our work

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.

9. We help countries merging from 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.

10. We are responding to the voices of poor people

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

In Sum

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

Emmanuel Kuadzi

\\SFILE\CDN\CWB\CEO's without borders-JLL-CS-100407

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

http://www.gtz.de/

international development aid

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