Health and health policies in developing countries
Table of Contents
1. The economic burden of malaria ..................................................................................................... 1
1.1 Malaria and poverty ................................................................................................................... 1
1.2 A case of multiple equilibria? .................................................................................................... 3
1.3 The economic effect of eradication : The Indian case ............................................................... 4
2. Reducing the incidence of malaria : Free distribution or partial cost recovery? ............................. 8
2.1 The debate .................................................................................................................................. 8
2.2 Results from a randomized experiment ..................................................................................... 8
Bibliography ...................................................................................................................................... 13
1. The economic burden of malaria
1.1 Malaria and poverty
The incidence of malaria in the world is largely correlated with poverty, as shown in the
comparison between Figure 1and Figure 2.
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Figure 1: malaria incidence around the world
Source : Gallup Sachs 2001
Figure 2: Poverty incidence around the world
Source : Gallup Sachs 2001
Table 1 shows the result of a regression run over GDP per capita considering its usual determinants
and the incidence of malaria. Table 1 illustrates the fact that the effect of Malaria remains
significant even after controlling most of the income determinants, including the geographical
position of the country.
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Table 1: Income and malaria incidence
Source : Gallup Sachs 2001
1.2 A case of multiple equilibria?
Basis of Gallup and Sachs basic argument (2001) states that malaria creates a vicious circle: it
maintains populations in poverty, which itself contributes to weaken them against disease (for all
sorts of reasons), and so on. Malaria has been eradicated from India (22% incidence in the score
less than 1% in 1965 after DDT campaign), but progress has been very uneven at the global level,
and particularly low, precisely, in the infected zones (tropical and subtropical), as shown in Table 2:
Table 2: Initial level and subsequent decrease in malaria incidence, by climate zone
Source : Gallup Sachs 2001
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Why did campaigns failed in the most infected areas? The following quote, taken from the same
paper, explains the causes of the failure of many eradication campaigns in infected areas essentially physical rather than economic:
“In response to the failure of WHO vector control projects in Cameroon, Nigeria, and elsewhere in
Africa in the 1960s, the WHO sponsored an intensive malaria control and research project in the
district of Garki, Nigeria.19 No resources, manpower, or institutional support were spared. Over the
course of 7 years, WHO and the Nigerian government spent more than $6 million to try to eliminate
malaria in 164 villages and compare the changes to control villages. Insecticide spraying of every
hut at least every 10 weeks during the course of the study had an average coverage of 99%. A third
of the villages were also given mass drug administration as a prophylaxis against malaria.
The man-biting rate of mosquitoes in the Garki villages was reduced by 90% from their prestudy
level, but despite this huge reduction in mosquito density, there was no significant change in the
parasite rate among the villagers.
[…] The intensity of malaria transmission in Garki was ‘‘very high indeed.’’19 During the wet
season, a person in this district would be bitten on average 174 times per night by the Anopheles
gambiae s.l. malaria vector and 94 times per night by the Anopheles funestus vector. Such high
biting rate estimates are not unusual. Robert and others20 estimate that a person in the Kou Valley in
Burkina Faso sleeping without mosquito protection—as most do—receives 158 bites by Anopheles
gambiae per night, with total mosquito bites of 35,000 per year. The vectorial capacity, or the
transmission rate of malaria between people through the vectors, reached 2,000 times the critical
value required to maintain endemic malaria, with a range of 18–145 malaria-transmitting bites per
person per year in the 8 villages studied.20 In lay terms, everyone was constantly reinfected with
malaria. […] The control efforts were defeated by the vectorial capacity of the mosquitoes, which
vastly exceed what was required to maintain transmission of malaria.” (Gallup Sachs 2001, p. 4)
1.3 The economic effect of eradication : The Indian case
How to measure the effect of eradication campaigns, and in particular if there is negative
correlation between effectiveness of campaigns and initial impact, beyond anecdotal
evidence? Cutler and al.(2010) consider an equation of impact of eradication in DID on Indian
data. (i) is the individual observation unit and the outcome variable, yit , is not the reduction in the
incidence of malaria but a variable of economic performance, in order to explore the idea of a
vicious cycle disease povertydisease.
o Either the educational achievement (literacy or having completed high school)
o Or the household income, approximated by log consumption per person in the household (selfconsumption included, the majority of households being agricultural)
Let d i  be the residence district of the individual i. The explanatory variable of interest is the
interaction of two variables: Pi , is equal to one if the individual was born after the period of
eradication (completed in 1965), and M d i  the initial incidence of malaria in the district of residence
of the individual i. The equation to estimate is:
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yit  0  1M d  2 Pi  3  Pi  M d   t  Xitβ  uit
(1)
Where X it is a vector of individual characteristics observed at time t. Alternatively, one can
replace M d by a fixed effect by district.
The coefficient sign over the interaction of the dummy variable about the date of birth Pi and the
initial infestation variable M d is graphically interpreted in the Figure 3. For the individuals born
before eradication, a larger local infestation ( M d is bigger) reduces future economic performance
(y), this explains why the red curve descends along the M axis. On the other hand, for individuals
born after, there is no effect (since the eradication has already taken place), this explains why the
second red curve is horizontal. The group P = 1 is used as control group, while the group P = 0 is
the treatment group, in which we should observe a negative relationship between economic
achievement and local infestation. The interaction term, should be positive, measuring the slope of
the grey surface when we go from left to right. Phew, if you understood, congratulations.
Figure 3: Initial infestation and individual birth date: The experience of Cutler et al. (2010)
y (economic outcome)
y(M | P = 1)
Birth date (preor posteradication)
P = 0 (born
before
eradication)
P = 1 (born
after
eradication)
y(M | P = 0)
M (initial malaria infestation)
The geographical distribution of the variable describing the initial impact, M d is illustrated in the
map below:
Figure 4: Initial incidence of malaria in India, by district
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Source : Cutler et al. (2010)
Taking education as an outcome variable (the idea is that a reduced incidence of malaria encourages
school attendance and increases the success rate), the results are displayed in Table 3.. The variable
describing the initial impact interacts with the treatment variable is actually a vector of three
dummy variables for three levels of impact (highest malarious, potential epidemic the average level,
and omitted low incidence). The hypothesis to be tested is
 2 yit
0
M d Pi
(2)
I.e. a negative effect of M d smaller in absolute terms (larger in algebraic value) when the individual
is born after eradication ( Pi largest). Therefore one should, if this hypothesis were true, have
positive and significant coefficients on the Post ×High initial impact variable. This is not what
emerges from the regressions.
Table 3: Treatment effect on educational achievement, interacted with initial incidence
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Source : Cutler et al. (2010)
For "income" (i.e. the expenditure), the results are shown in Table 4.
Table 4: Treatment effect on household income, interacted with initial incidence
Effet du traitement sur le revenu des ménages, interagi avec l’incidence initiale
Source : Cutler et al. (2010)
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In brief, the results of Cutler et al. do not confirm the conjecture of the "vicious circle" in which the
incidence of malaria reduces the accumulation capabilities of human capital and perpetuates
poverty which itself promotes malaria.
2. Reducing the incidence of malaria : Free distribution or partial cost
recovery?
2.1 The debate
The debate that raged a few years ago, focused on the mode of intervention-distribute insecticidetreated nets (Insecticide-Treated bed net gold ITNs) or sell them?
The ITN substantially reduce infant mortality by malaria (mortality reduced by 20% for a child who
sleeps under an ITN). Their cost is high ($5-$7 to the PPP; less at the exchange rate current but still
very expensive for African households). Because of strong externalities, free distribution can be an
effective way to fight the disease:
o Reduction in the mosquito population (less meat to stitch!)
o Higher mortality among mosquitoes due to contact with insecticide-treated nets (MI)
o Reduction of malaria transmissible parasites
Thus Gimnig et al. (2003) showed that the infant mortality, the incidence of anaemia and malaria
were reduced similarly by the MI among users and non-users within a radius of 300 meters.
Externalities are maximized with a coverage rate of 50%, which is, in practice, very rare. Rates
have rising significantly (19.8% of Kenyan households had at least one in 2003, 65% in 2008 - but
we are talking about a single MI per household) but it is still far from optimal coverage.
Sachs (2005) argued that given the effectiveness of the MI, they should be distributed for free. On
the other hand, according to Easterly (2006), distribute them for free produces only waste. Selling
them to $0.50, "it gets the net to those who both value them and need them" (p. 13).The argument is
twofold:
o A selection effect: the price of 'filter' users, because only those who most need are willing to pay
for it.
o A psychological effect (or " sunk cost "): the fact of having paid for it gives value to the object
and discourages waste.
2.2 Results from a randomized experiment
Trying to tie the conjectures on the basis of empirical evidence, Cohen and Dupas (qi 2010)
conducted randomized experiment 20 prenatal care clinics in Kenya (545 pregnant women). The
conjecture to be tested were the following:
1. A positive price reduces the demand?
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2 If Yes, can be observed that the applicants are women who most need it?
3 Women who have paid for their net use it more intensely or more carefully?
Experience design
o 4 clinics serve as a control group
o 3 clinics sell nets at Ksh 40.00 ($0.60 - 10% of the cost and 10Ksh below the current price)
o 3 sell at Ksh 20.00 (5% of the cost)
o 5 sell at Ksh 10.00 (2.5% of the cost)
o 5 distribute them for free
Each clinic receives a bonus of $75 per month (the experience lasts 3 months during the rainy
season) if it cooperates (management of the distribution of the MI according to the Protocol, with
random checks)
The experience involves two steps to separate the selection from the sunk costs effect:
o First step: the price is randomized between clinics;
o Second step: in the clinics where the IM are sold (not in those who distribute them for free), we
proposed a lottery in a sub-sample of women drawn randomly; lots reimburse a portion of the price
paid (so all women were willing to pay the MI, but some eventually receive it for free)
226 women drawn randomly received monitoring visits on the use of the MI 3 to 10 weeks after the
purchase, to see if the MI were suspended (in "use position") and if the women were regularly using
it.
Results
First question: is demand decreasing in price? The answer is in the Table 5. In columns 1 and 2, the
price is reported as a categorical variable. Coefficient is negative and significant at 1% in the two
specifications (which differ because of the choice of fixed effects). In columns 3 and 4, the price is
entered in the form of a vector of dummies. The coefficients are decreasing (increasing in absolute
value) with price. The evidence is therefore clear: the more the MI are expensive, the less women
will buy them, which is quite reasonable for an economist...
Table 5: Effect of the price of ITNs on their demand
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Source : Cohen et Dupas (2010)
Second question: When ITNs are sold for a price (between 10 and 40 KSH), is it true that women
who purchase them are more exposed to malaria compared to the average users of the 4 control
group clinics? In other words, is it true that those who buy ITNs are those who most need it—
Easterly’s selection effect?. The malaria exposure criterion is the anemia measured by hemoglobin
rate in the blood. Suppose that hemoglobin rates are distributed across the population according to
the black curve in Figure 5. Then, among ITN buyers, as one raises the price (i.e. as one moves
from clinics selling ITNs for 10 shillings clinics selling them for 20, then 40 shillings), the
distribution of hemoglobin rates should get worse, as shown in Figure 5.
Figure 5: The selection effect of a positive price
10
40-Ksh
buyers
20-Ksh
buyers
10-Ksh
buyers
Control group:
density function
Hemoglobin rate in blood
Control group:
cumulative distribution
function
Hemoglobin rate in blood
Is this true? Figure 6 shows the cumulative distribution of hemoglobin rates for the control group
(empty circles) and the group of ITN buyers at 40Ksh (the highest price). Contrary to what the
selection conjecture suggests, the two distributions are almost identical. There seems to be no
selection effect.
Figure 6: Is there a selection effect?
Source : Cohen et Dupas (2010)
Finally, third question – the most important one: Are women who received their ITN for free using
them more recklessly? This is what Easterly called the “sunk cost” effect, i.e. the idea that when
someone buys something, he/she takes more care of it than if he/she got it for free. Cohen and
Dupas attempted to identify this effect through a sub-experiment that “filtered out” the selection
effect. That is, they tested the conjecture only on the sub-sample of women who were ready to buy
the ITN. Among those, they gave out lottery tickets whose prize would be the ITN’s
reimbursement. Thus, the winners were willing to buy their ITN, but they actually got it free. The
results is shown in Figure 7, where the vertical axis measures the proportion of ITNs seen visibly
hanging over the bed during post-experiment visits. Under the sunk-cost conjecture, that rate should
be particularly low for women who got their ITN free thanks to the lottery ticket. Again, the
conjecture is wrong. It is actually higher than for women who paid 10 shillings.
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Figure 7: ITN use against their price
Source : Cohen et Dupas (2010)
Results are confirmed by the regression in Table 6.
Table 6: The sunk-cost effect
Source : Cohen et Dupas (2010)
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Finally, a cost-benefit analysis, taking into account externalities suggests a massive effect of free
distribution (Table 7) which gives the cost per child saved the least in most cases, in particular
intermediate cases in terms of assumptions on externalities and the effectiveness of the MI. We
noticed that the maximum cost is frequently the one that corresponds to a pay distribution but with
minimum price, where moral hazard seems maximum (Figure 7).
Table 7: Cost of a life saved
Note: Each block corresponds to a hypothesis concerning the rate of use where the externality (of users to non-users)
occurs. In block one, there is the lowest rate of use, the externality is therefore the most powerful. Inside each block,
each column corresponds to a hypothesis on the effectiveness of the MI; in columns (1), (4) and (7), the efficiency is
maximum. Going from left to right the table, weaken both, the strength of externalities and the effectiveness of the MI,
which decreases the number of lives saved.
Source: Cohen and Dupas (2010)
Bibliography
Cohen, Jessica, and P. Dupas (2010), “Free Distribution Or Cost-Sharing? Evidence From A
Randomized Malaria Prevention Experiment” Quarterly Journal of Economics CXXV, 1-45.
Gallup, John Luke, and J. D. Sachs (2001), “The Economic Burden Of Malaria”; American Journal
of Tropical Medical Hygiene 6485–96.
Cutler, David; W. Fung, M. Kremer, M. Singhal, and T. Vogl (2010), “Early-life Malaria Exposure
and Adult Outcomes: Evidence from Malaria Eradication in India”; American Economic Journal:
Applied Economics 2, 72–94.
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