The Economics of Poverty
and Development
Alessandro Tarozzi
February 13, 2016
% of population living on less than $2 per day
Based on the 2009 UN Human Development Report
2
Literacy
% population age 15 and above who can, with understanding, read and write a short, simple
statement on their everyday life.
Source: 2009-2013, From World Bank Development Indicators
For updates and many more indicators see http://data.worldbank.org/indicator/
3
Total Fertility Rate (TFR)
# children that would be born to a woman if she were to live to the end of her childbearing
years and bear children in accordance with current age-specific fertility rates.
Source: 2009-2013, From World Bank Development Indicators
4
% Births attended by skilled health staff
Source: 2009-2013, From World Bank Development Indicators
5
Access to Sanitation
% of the population using improved sanitation facilities (flush to piped sewer system, septic
tank, pit latrine or ventilated improved pit latrine, or pit latrine with slab, or composting toilet.)
Source: 2009-2013, From World Bank Development Indicators
6
Malaria in 2014 (# reported cases)
Source: World Malaria report 2014
7
But progress has been made
8
HIV Prevalence among 15-49
Source: 2009-2013, From World Bank Development Indicators
9
Food Insecurity
Source: The State of Food Insecurity in the World 2013, FAO
“A total of 842
million people
in 2011–13, or
around
one in eight
people in the
world, were
estimated to be
suffering from
chronic hunger,
regularly not
getting
enough food to
conduct an active
life.”
FAO 2013, p. 2
10
Children U5, % Stunting
Children U5 with “low height” given age and gender
Source: 2009-2013, From World Bank Development Indicators
11
Children U5, % Underweight
Children U5 with “low weight” given age and gender
Source: 2009-2013, From World Bank Development Indicators
12
Missing Women
Source: Anderson and Ray, Review of Economic Studies (2010) 77, 1262–1300
13
Not clear that things will improve with economic
development
Source: The Hindu
14
Child Mortality (# per 1,000 live births)
Source: 2009-2013, From World Bank Development Indicators
15
Life Expectancy at Birth and National Income
(The Preston Curve) - 2008
Source: Deaton (2013): The Great Escape
16
Is it just money?
17
How can we change this?
• Economic theory
– Endogenous growth models
• Schooling (“human capital”)
• R&D
– Trade
– Public Economics (Tax systems)
• However, it may not be so easy to use this for policy:
– How do we improve schooling?
•
•
•
•
•
More schools?
More teachers?
Better teachers?
More scholarships?
Free education?
18
How can we test if a policy will work?
• Theory always a good starting point
– E.g. contract theory can tell us what to expect if we
change teachers’ incentives
• But needs to be grounded in reality
– E.g. a model that assumes that teachers can be fired
and their performance can be observed may not be
useful in some situations
• Data can be used to test theories empirically
• But this can be very hard
19
0
.1
.2
.3
.4
.5
“Endogenous” Program Placement
100
200
300
# Bednets distributed
Do bednets increase malaria risk?
400
500
20
Endogenous Program Take-up
Yes
0
20
40
60
80
100
No
Did household borrow from microfinance?
Are micro-loans good for business?
21
Counterfactual are not... factual
• Ideally, we would like to observe reality with
and without a given policy
• But in reality, counterfactuals are not observed
• Massive literature in econometrics to address
this problem
• In recent years, in Development Economics
(and in other applied fields as well) an “old”
methodological tool has become prominent
22
Randomized Controlled Trials
• In general, correlation between X and Y does not imply
causation
• But what if you can assign X randomly?
• E.g. suppose you want to see if a program that changes
incentives to teachers improves schooling outcomes
23
Randomized Controlled Trials
• Carry out the new teacher program only in some schools, while
others continue with usual business
• New Program: Treatment
• Status Quo: Control
• If assignment is truly random, the program should be the only reason
why outcomes different between Treatment and Control schools
24
Impact Evaluation with RCT
4
3.5
Average outcome
3
2.5
2
2.1
2
1
0
Before
After
Control
Treatment
• Difference-in-Differences
• (3.5-2.1) – (2.5-2) = 1.4 – 0.5 = 0.9
25
In a nutshell...
1.
2.
3.
4.
5.
6.
Come up with a bright idea
Arm yourself with patience and determination...
Find an implementation partner
Seek (possibly a lot of) funding
Obtain ethical approvals!
Make sure that Treatments and Controls are truly
comparable before your program starts (baseline
data)
7. Implement the program (interim data)
8. Evaluate the program (end-line data)
9. Publish the results (often years after 1.)
10. Policy outreach if results are promising
26
• Advantages of RCTs
– If implemented well, rigorous and simple
– Allows to interpret the results causally
– Well-tailored to answering specific questions
• Drawbacks
– Not always possible
• e.g. Macro policies
• Ethical issues
– Will not tell you why the results where what they were
• Results often not so easy to interpret
– Not so simple to implement
• Non-compliance
• Attrition
– External validity
•
•
•
•
Are results useful in other places/times?
General equilibrium effects?
Long-term effects?
“Partner selection bias”
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Examples (1)
malaria & demand for bednets
• Caused by Plasmodium spp,
(Pf the deadliest), transmitted by
Anopheles mosquitoes
• Huge global burden, in 2005 300660m Pf cases/year, 80m in India,
• huge health/econ costs,
no vaccine
• Many RCTs showed intensive
distribution/use of ITNs can
substantively lower malaria
28
Examples (1)
malaria & demand for bednets
• Adoption of preventive health products in LDCs often
low and demand highly elastic, even at very low prices
– Liquidity/credit constraints often cited among key factors
– Free provision/subsidies often advocated, e.g. WHO
• Externalities often key
• However, cost-recovery often important for proponents of
sustainability of welfare programs
• May help targeting & usage
• 1st RCT evaluating demand of health-protecting
products offered on credit vs. free or control
conditions
29
Examples (1)
malaria & demand for bednets
• 141 villages randomly selected from list of 900 served by microlender Bharat Integrated Social Welfare Agency (BISWA)
• ~2,000 households, 10,000 individuals.
30
What did we find?
• More than 50% of households purchased one
bednet or more
• However, mixed evidence of improvements in
malaria indices
• Interpretation:
– Loans may help increasing demand, however:
• Not everyone will buy, reducing benefits of
“externalities”
• Do not address low usage rates
• Policy implications
– Loans can help sustainability, but at the expense
of effectiveness
31
Example 2: Microcredit
• The poor often lack access to formal credit
– Interest rates from moneylenders often very high, 40-200% year
common
• “Moral hazard” and “Adverse selection” make it very hard for formal
credit sector to operate in poor village economies
• Perhaps the poor could start new businesses and get out of poverty if
only they could borrow now, and repay once their business is
successful
– Self-sustainable in best-case scenario
– But, especially in the past, formal credit sector did not lend to the poor
• Subsidized, public programs of lending often perform poorly
– High default rates
– Elite capture
– Political motivation
32
Example 2: Microcredit
• In a nutshell, use local networks to reducing
monitoring costs, leading to lower interest rates,
high repayment rates, more access to credit
• Potential to provide an escape from poverty
• First proponent: M Yunus (Nobel Peace Prize 2006)
• # of very poor families with a microloan
– About 8 million in 1997, and ~140 million in 2010.
• Does it really work?
• Hard to evaluate
– Selective program placement (where do MFI operate?)
– Selective borrowing (who borrows?)
33
Example 2: Microcredit
• Recent collection of papers evaluate MC in a wide set
of contexts
• Common methodology: randomly determine who is
offered micro-loans
• American Economic Journal: Applied Economics
2015, 7(1): 1–21
–
–
–
–
–
–
Rural and urban Sonora state, Mexico
Rural Mongolia
Bosnia
Hyderabad, India
Rural Morocco
Amhara and Oromiya, rural Ethiopia
34
Findings
• Uptake significantly less than universal
– Take-up hard to predict
• Overall, no evidence of generalized ‘transformative power’ of
MF, but systematic evidence of some improvements for some
groups
• Wide range of responses in ‘social outcomes’, perhaps due to
varying emphasis chosen by different MFIs
• Also, no evidence of perverse effects due to vicious cycles due
to over-borrowing or wasteful spending
• Statistical power often limited: hard to reject even large
impacts (both positive and negative) for many outcomes
Looking ahead in Development Economics
• Very hot field in Economics
• Great data now available to answer lots of
very important questions...
• ...and opportunities to make you own data
when needed
• Lots of progress, but still more to do...
36