The Poverty-Economic Growth-Health Triangle
Cyrine Hannaa and Christophe Mullerb
a Aix-Marseille
University, Aix-Marseille School of Economics (AMSE), CNRS & EHESS, GREQAM (UMR-CNRS 7316), Centre de la Vieille
Charité, 2 rue de la Charité, 13236 Marseille cedex 02, France, +33 753740139, [email protected]
b Aix-Marseille
University, Aix-Marseille School of Economics (AMSE), CNRS & EHESS, GREQAM (UMR-CNRS 7316), Château La Farge,
Route des Milles 13290 Les Milles, France, +33 442935960, [email protected]
Abstract
We identify the dynamic interactions existing between Poverty, Economic Growth and Health
using country-level panel data from developing countries.
We treat endogeneity and the
selectivity issues coming from unavailability of poverty indicators.
We develop innovative
econometric methods that we apply to an original set of indicators at country level. We nd
that the data generation through household surveys depends on crucial socio-economic factors
and that our new method of selectivity correction has a massive impact on our estimation
results casting doubt on previous ndings in the literature and changing fundamentally the
picture and the interpretation of development process.
Keywords:
Poverty, Economic growth, Health, Developing countries, Incomplete panel
data, Simultaneous equations, Sample selection.
JEL Codes:
C33, I19, I32, O11, O43...
1
1
Introduction
Eradicating poverty and improving health conditions are considered among the main goals of
the Millennium development goals and challenges by international organizations, government
and other development specialists. Indeed, the proportion of people living on less than 1,25$
a day in the developing world dropped from 50% to 14% between 1990 and 2015 and the
under ve mortality rate declined by more than half from 90 to 43 deaths per 1000 live births
at this same period (The Millenium Development goal report 2015).
Economic growth, as an indicator of a country's standard of living, plays a major role in
improving the living conditions of populations, then in eradicating poverty. In fact, economic
growth allows the government to reduce its debts and spend more on the health services, education, infrastructure, social programs..., improving the country's standard of living and
creating greater equality. The more productive rms tend to employ more workers reducing
unemployment. Moreover, the trickle down theory (Aghion and Bolton (1997)) admits that
there is a transfer from the rich to the poor if they get richer by raising capital accumulation
and then creating new opportunities for poor people to borrow and to invest.
These ideas were given by several economists studying the impact of economic growth on
poverty but the approaches were dierent because many economists insist also on the role
played by Inequality. One rst trend of the literature claims that growth is good for the
poor. According to Bhalla (2002) and Sala I Martin (2002), economic growth is sucient to
reduce poverty and it had declined over time whatever was the level of distribution. Dollar
and Kraay (2002,2013) showed that incomes of the poor on average rise equiproportionately
with average incomes considering the average incomes of the poorest fth of a country. Furthermore, they showed that the same factors impacting economic growth impact also poverty.
The second approach insists on the role played by the inequality factor as a constraint to
poverty reduction through growth. Ravallion (2005) claims that Inequality is bad for the
poor. Hence, he estimates a rate of poverty reduction through growth and which depends
strongly on the inequality level.
Ravallion and Datt (1991), Ravallion (2001) and Bour-
guignon (2004) insist on both economic growth and inequality changes generating poverty
reduction. Besides, many economists introduced the concept of pro-poor growth which has
an absolute denition where poor people benet from economic growth whatever is the level
of distribution or inequality.
The relative denition is based on the fact that the growth
rate of incomes of the poor exceeds that of average incomes, it means that economic growth
benets more for the poor than the rich. Ravallion and Chen (2008) constructed a pro-poor
growth indicator for China. We have also Kakwani and Pernia (2000) with an application to
developing countries and Klasen (2006) who expanded the pro-poor growth concept to non
income indicators and computed it using economic growth and its determinants.
On the other hand, poor people generally care more for the nutrition of their children
and neglect education.
Consequently, there would be a lack of intellectual people able to
run the country and this can impact negatively human capital accumulation and technologies development. Another fact is that poverty can decrease savings then investment in the
country decelerating economic growth but also can increase emigration of population searching for a better area where they can aord their basic needs.
Besides, they can resort to
violence in their countries and in the two cases, this would be harmful for economic growth
and country's development.
In fact, Ravallion (2012) showed that the initial poverty rate
has sizeable negative impact on the growth rate at any given initial mean consumption in
developing countries. Furthermore, in another work entitled "too poor to grow", Lopez and
2
Serven (2009) showed that there is a negative impact of poverty on economic growth and
which is robust to all considerations.
For the same reasons previously cited, Health also benets from growth by improving
sanitation and nutrition conditions but also investing more in the health sector. On the other
hand, a good child health has a positive eect on his learning abilities and leads to better
educational outcomes then greater investment in eduction and lower fertility. Furthermore,
good health is accompanied with higher productivity, more creativity and better adapatation
to technologies (Bloom,Canning and Sevilla (2004), Aghion Howitt and Murtin (2010)...). In
fact, there is an extensive literature addressing the relationship between economic growth and
health varying in the two directions but also introducing many health indicators. Bhargava,
Jamison, Lau and Murray (2001) studied the two way causality existing between economic
growth and health using adult survival rate for health in 92 countries for the period 1965-1990
and showed that the impact of health on economic growth is positive for low income countries
but negative for high income ones. Furthermore, They didn't nd any evidence about the
impact of economic growth on health. Younger (2001) conrmed this weak relationship based
rather on infant mortality and also Granados and Ionides (2007) but using life expectancy
at birth.
However, Pritchett and Summers (1993) in their article entitled "Wealthier is
healthier", showed that income is a determinant factor in reducing infant mortality but
found less important impact on life expectancy at birth. The same result was found also by
Hanmer, Lensink and White (2003) who insisted also on the role played by health conditions
and educational variables to explain infant mortality.
For the reverse relationship, we have as main reference Acemoglu and Johnson (2006,2013)
whose results were a subject of debate for many economists without nding any evidence
about the impact of health on economic growth.
These results were criticized by many
economists, mainly by Bloom and Canning (2014). In a precedent work published in 2004,
they used a production function approach and showed that one year improvement of life
expectancy leads to an increase of 4% in the output.
Furthermore, Aghion, Howitt and
Murtin (2011) tried separately and jointly to introduce initial level of life expectancy at
birth and changes in this same indicator and showed that the impact of health on economic
growth is larger when combining the two but no evidence about the impact when eliminating
the initial level of life expectancy.
Moreover, Lorentzen, McMillan and Wacziarg (2008)
used adult and infant mortality in order to identify the channels through which health can
impact growth and found that physical investment and fertility are these major channels
in the sense that high adult mortality rates induce co-agents to invest less, and high infant
mortality ones increase fertility which is harmful for economic growth. Taking into account
the demographic transition, Cervellati and Sunde (2011) showed through a theoretical model
of the relationship between life expectancy at birth and income, that the impact depends on
whether the country trigged or not the demographic transition. In fact, based on a panel
database of 47 countries over the period 1940-1980, they found positive impact for poor and
middle income countries and a negative one for the higher income countries.
On the other hand, people suering from poor health have less gains because they work
less hours and need to buy medicines and to have regular health checks so they have lower
savings and are more likely to fall in poverty.
Besides, poverty can be harmful for health
and mainly for child health with causing malnutrition and diseases because of the lack of
basic needs in foods and the poor access to sanitation and a healthy climate. Furthermore,
poor people neglect education leading to a weak comphension and conscience about diseases.
3
In fact, these relationships between health and poverty were studied by some economists in
the literature considering the two way causality. These studies are generally done in South
Asia or Sub Saharan Africa suering more from these two problems. Indeed, Godlonton and
Keswell (2005), basing on the South African integrated survey, used the body mass index
for health and probit models in order to explain poverty and health status based on many
poverty lines. They found a positive association between health and poor status and that
households that contain more unhealthy individuals are 60% more likely to be income poor
than those that contain fewer unhealthy individuals.
Moreover, other studies focused on
education in order to explain this impact and also the long term relationship. For instance,
Khan and Hamidani showed a strong long term impact of health, education and sanitary
conditions on poverty in Pakistan.
For the reverse relationship, Rajan, Kennedy and King (2013) showed for 17 states of
India that the impact of poverty on health changes with changing the poverty indicators
and the control variables.
In fact, they found that low poverty and high literacy rates
impact strongly health when introducing the poverty gap indicator, the number of deaths
and under ve mortality rates but with a greater impact of literacy. Moreover, Klasen (2007)
showed that there is no econometric evidence about the impact of poverty on health when
introducing economic growth as an additional determinant of health using undernourishment
rates, childhood underweight and under ve mortality for a panel of developing countries over
the period 1990-2000. Furthermore, Pe, Wall and Perrson (2000) examined the association
between poverty, social inequity and maternal education with infant mortality and showed
that absolute level of poverty increased the risk of infant mortality in Nicaragua from 1988
to 1993.
However, these relationships existing between the factors of interest were treated separately using the same standard econometric methods. Hence, we study the poverty-economic
growth-health triangle in developing countries by constructing a model that considers simultaneously the three dimensions and identies the two way causalities. The contribution of
this work is mainly in the econometric part through which we develop a system of simultaneous equations model encountering at the same time many econometric issues existing in
the resolution of such problematic and not focused on in the literature.
The rst problem faced is data availability for poverty indicators in developing countries.
In fact, the works done until now about poverty have tried to exploit the poverty data available from household surveys and which are done each ve or ten years and even nonexistent
for some countries, without asking about the causes of this unavailability causing selectivity
problems.
Hence, we make a big step and explain this data unavailability which can't be
random and take into account the selection bias existing from and correct it in the basic modeling. Furthermore, because of this data unavailability, the construction of the database is
also a crucial step and makes us resort to incomplete panel data techniques in our modeling.
The second main issue of such modeling is the endogeneity problems existing. In fact,
introducing the three factors in a simultaneous way needs to search for adequate instrumental
variables for each one which can't have interactions with the others. However, it's not evident
to nd factors that can impact poverty or health but not economic growth and vice versa.
Hence, nding such instruments has been the focus of considerable interest in our work and
which was not deeply developed in recent ones.
The rest of this paper proceeds as follows. Section 2 describes our empirical framework
4
and the econometric methodology followed. Section 3 presents our core results from the tests
done. Section 4 concludes.
2
Empirical strategy
2.1
Database construction
For developing countries, poverty indicators are generally based on primary household survey
data obtained from government statistical agencies and which are done each ve or ten years
and even nonexistent for some countries. Hence, we exploit all the data existing for developing
countries for these indicators even if observations are not available in a uniform way neither
have the same number for each country. Our incomplete panel data is gathered mainly from
World Development Indicators for the period 1980-2011 for the 139 developing countries
from all regions of the world. In fact, the choice of the period of study depends also on the
data availability for poverty indicators for which we begin to have observations at this date.
Table 1 summarizes the representativity of regions in our sample and those of the poverty
observations available for each one. For instance, Sub Saharan Africa represents 37,65 % of
the developing world with only 19,25 % available observations for poverty data from the total
number of available ones.
Besides, taking into account the fact that household surveys can't be run each year because
of the complexity of its process, we divide the period of the study into periods of the same
interval as many econometricians did. Our criteria for the division is the average countries'
frequencies of poverty indicators' observations. More precisely, we compute the mean of the
gap between observations for each country and then calculate it for all countries and obtain
a mean gap of 4 years. Hence, the database is divided into 8 periods of 4 years with dierent
number of observations between countries ranging from 0 to 8. After some data cleansing,
our nal sample includes 113 countries with a total number of observations of 818. In fact,
in our new sample, Sub Saharan Africa represents 38,26 % of the whole sample with 27,21 %
available observations for poverty data from the total number of available ones. Furthermore,
we obtain also a better representativity of the other regions after the division made.
2.2
Econometric model
We now discuss our econometric strategy for specifying the poverty-economic growth-health
equations in developing countries for a dynamic incomplete panel. However, selecting only
available observations for poverty indicators in our sample creates a form of a selection bias
that needs to be corrected. This unavailability can't be random and conducts us to search
for the social and economic factors behind it, thing that has never been done before in the
literature. To do so, we construct in a rst step a model that explains this selectivity problems
existing from poverty data unavailability and the factors behind. We answer in a second step
the basic problematic with correcting for this selection bias existing and handling the other
econometric issues (simultaneity,endogeneity,instrumentation ...).
5
2.2.1
First step (Selection model):
We rst explain data unavailability of the poverty headcount index with 1.25 $ a day as
poverty line. This is without loss of generality since any kind of poverty indicator would be
computed from the same source. In fact, we introduce this indicator with this poverty line in
order to consider the most severe level of poverty. It's evident that, in this part, we exploit
the complete panel having observations or not for our poverty indicator in our sample divided
into 8 periods of 4 years. In fact, we construct a dummy variable
y it
that takes the value 1
or 0 if we have available observation or not. In what follows, we nd the factors considered
in order to explain poverty data availability.
•
Dynamic model:
First, we introduce dynamics when explaining
yit
basing on two hypotheses. The rst one
admits that conducting a household survey in a given period may facilitate the process of
conducting another one in the next period. However, this same fact may lead on the contrary
to a postpone of the household survey in the period after since it is costly to run.
To do
so, we refer to the quadratic exponential model developed recently by Bartolucci and Nigro
(2010) who propose a quadratic exponential approximation in order to capture the unobserved
heterogeneity between the panel data subjects in a dynamic framework for a discrete choice
model.
This approach is easy to run and imposes less hypotheses compared to the other
estimators of the dynamic logit model proposed in the literature notably Chamberlain (1985),
Honoré and Kyriazidou (2000), Carro (2007)... but also performs better in terms of eciency.
In fact, this model is the same as that of the dynamic xed eects logit one but is rather
based on an additional term measuring the eect of the present choice on the expected utility
of the next occasion. We provide details of the econometric model in the Appendix.
•
Endogeneity:
Returning to our basic model, we admit that the richer countries among developing ones have
more resources to conduct surveys. Hence, we introduce economic growth as an independent
variable in the survey generation equation and precisely the GDP per capita as indicator
of country's wealth. Moreover, democracy can inuence the data availability and the motivation to run household surveys since poverty is a delicate topic especially in dictatorial
regimes. Furthermore, we add geographical shocks that can hamper the survey process such
as geographical disaster, social and political shocks like conicts. Because of this multiplicity
of interconnected variables, we must have a close look at endogeneity problems.
Particularly, in this model, the introduction of economic growth in explaining data availability can't be exogenous, mainly with the presence of the other factors presented. That's
why, we refer to advanced techniques that can handle endogeneity problems and test it in
our quadratic exponential logit model for binary panel data. The technique used is a control
function approach presented by Wooldridge and Papke (2008) based on two steps. In the rst
one, we estimate the reduced form of the endogenous variable by an adequate instrument
adding the other exogenous variables introduced in the basic model. The second step consists
on computing the errors from this rst step and introducing them in the basic equation in
order to test for the endogeneity. Then the whole model takes this form:
0
0
yit∗ = αi + xitExog β1 + xitEndog β2 + yi,t−1 γ + e∗t (αi , Xi ) + Residendogit δ + it ,
6
where
αi
are the individual specic intercepts,
exogenous and endogenous covariates.
xitExog
Residendogit
and
xitEndog
are respectively the
are computed from the following rst
equation estimated by the xed eects model :
0
0
xitEndog = ηi +xitExog λ+zit δ +ξit ,
where
ηi
is the individual specic intercepts and
zit
is an exogenous instrument for the
endogenous variable.
In fact, the choice of instruments is a crucial step in our work. In this rst step model,
we should introduce exogenous factors that can impact economic growth but not necessarily
the probability of conducting a household survey in a given country. After several attempts
tried, we retain those that can be related to natural resources and precisely the price of oil
multiplied by a positive or negative sign depending on whether the country is exporter or
importer of oil.
Finally, we compute the inverse mills ratio
λit
from this estimation in order to correct
the selectivity bias in our basic model expanded in the second step. We refer to the standard two step estimation of Heckman (1979) based rather on a normal distribution of the
selection equation error terms (Probit model). However, this method was developed later by
Lee (1983) who generalised the selection bias correction procedure in the case of the nonnormal distribution. Precisely, Dubin and McFadden (1984) considered the multinomial logit
model to explain the residential demand for appliance then that of electricity. Bourguignon,
Fournier and Gurgand (2007) developed also the multinomial logit case with many Monte
Carlo comparaisons. Moreover, in order to explain immigrant earnings in the U.S labor market, Miller and Chiswick (2002) referred to a logit selection equation determining the eects
of the English skills and correct the bias existing from the interactions between earnings and
language.
Besides, considering panel data models, Wooldridge and Semykina (2013) explained how
to handle selectivity issues for dynamic panel data with probit models but there is no development of the selection correction when considering the logit model for panel data neither,
the dynamic one. Furthermore, no study tried to handle the same issue in the case of simultaneous equations.
2.2.2
Second step (Simultaneous system):
Now, we keep only the observations available for poverty indicators, which yields an incomplete panel database of 75 countries and 231 observations.
Besides, for the variables
chosen, we introduce the standard indicators cited before for poverty and economic growth
and consider infant and child mortality rates but also life expectancy at birth as main health
indicators usually used by most researchers.
We resolve a system of simultaneous equations for dynamic incomplete panel data with
sample selection and endogeneity problems. We explain simultaneously each variable of the
triangle by its lag value and the two other lagged variables of the triangle. Considering the
selection bias correction, we introduce the same inverse mills ratioλit in level considering the
poverty equation, and with its lagged value in the two other equations in which poverty is
introduced as explicative variable with its lagged value as follows:
P overtyit = β0i1 + β11 P overtyi(t−1) + β21 Ec.Growthi(t−1) + β31 Healthi(t−1) + γλit +it1
7
Ec.Growthit = β0i2 +β12 P overtyi(t−1) +β22 Ec.Growthi(t−1) +β32 Healthi(t−1) +γλi(t−1) +it2
Healthit = β0i3 + β13 P overtyi(t−1) + β23 Ec.Growthi(t−1) + β33 Healthi(t−1) + γλi(t−1) +it3
where
β33
β0i1 , β0i2 , β0i3
are the xed eects intercepts,
are the coecients to estimate and
•
it1 , it2 , it3
β11 , β21 , β31 , β12 , β22 , β32 , β13 ,β23 ,
are the residuals from each equation.
Econometric approach:
As we mentioned before, the relationships between the factors of the triangle were treated
separately in the literature and almost no studies tried to consider in a simultaneous way
these relationships. We can refer to the empirical work done by Gupta and Mitra (2004) in
fteen states of India in which they used three stage least squares for structural equations and
showed that economic growth and health are positively linked for the two way relationships
leading to poverty reduction.
In fact, this econometric method were also used by other
researchers such as Bhargava, Jamison, Lau and Murray (2001) who treat the two way
causality existing between economic growth and health using adult survival rate for health in
92 countries for the period 1965-1990. Besides, the same standard models are used by most
econometricians mainly GMM (Dollar and Kraay (2002,2013), Lopez and Serven (2009),
Acemoglu and Johnson (2006,2013)...), Fixed and random eects model (Aghion, Howitt
and Murtin (2011), Rajan, Kennedy and King (2013) ...)and even OLS for panel data.
In our work, we make a big step and treat the three dimensional system with the most
developed econometric methods.
More precisely, we use the xed eects three stage least
squares for dynamic panel data with endogenous regressors (Baltagi and Deng (2012)) but
applied in our context to incomplete dynamic panel data and with a specic instrumentation.
We perform in the estimation the Hausman test for the estimator's convergence. Indeed, it
holds in each case the xed eects model and not the random eects one (Error component
three stage least squares EC3SLS). Moreover, we combine this model with the incomplete
panel data techniques and a particular instrumentation of the lagged dependent variable
and the two other variables of the triangle considered as endogenous. We construct our own
program in the software Stata, gathering all the technical support available in order to handle
the econometric issues existing in such modeling and almost neglected in the literature.
The xed eects three stage least squares with incomplete panel and endogeneity problems
is computed as follows:
h 0
i−1 h 0
i
P
P
−1
−1
0
0
Z̃ H̃ (H̃ 0 ( v ⊗Q)H̃ ) H̃ 0 Ỹ
β̂F E3SLS = Z̃ H̃ (H̃ ( v ⊗Q)H̃ ) H̃ Z̃


Z̃1 0 0
where Z̃= 0
Z̃2 0 is the matrix of explicative variables
0
0
for each equation with
Z̃3
the within transformation.

Y˜1
Ỹ = Y˜2  is the matrix of dependent variables for each equation with the within transY˜3

formation.
8

2
2
2
σv11
σv12
σv13
2
2
2

 σv21
σv23
σv22
v=
3
2
2
σv31 σv32 σv33

P
σij2
is the covariance matrix of the residuals.
are computed from the residuals of the FE2SLS (Fixed eects two stage least squares)
estimation of equation i (ei ) and that of equation j (ej ) as follows:
0
2
σvij
= √
tries,
ki,j =
0
ei ∗ej
(N −n−ki )∗(N −n−kj )
where N=total number of observations, n=Number of coun-
Number of parameters in the equation i, j.
Furthermore, Referring to Baltagi and Chang (2000), we have:
Q : Matrix of within transformation for incomplete panel with :
Q = IN − P , P = diag(J¯Ti ), J¯Ti = T1i (JTi ), JTi is a matrix of ones of dimension Ti .


H̃1 0
0
H̃= 0 H̃2 0  : Matrix of instruments for each equation with the within trans0
0 H̃3
formation.
•
Instrumentation approach:
A strong ingredient of our econometric method is the specication of instrumental variables.
First, we refer to Arellano and Bond (1991), Blundell and Bond (1998) in order to instrument
the lagged dependent variable in each equation. This instrumentation is based on the use of
the matrix having a specic form of the past observations of the dependent variable ranging
from lag order 2 to more.
The matrix of instruments for the lagged dependent variable
introduced in each equation takes this form for each country i:

xi1 0
0 ... 0
 0 xi1 xi2 . . . 0
Mi =
 ... ... ... ... ...
0
0
0 . . . xi1
Mi

...
0

...
0

...
... 
. . . xi(Ti −2)
0 M j
is adjusted to the size of the longest matrix
nal instruments' Matrix is
0
0
0
M = M1 , M2 , ..., MN
of individual j with adding 0 then the
. The moment conditions for each country
are written as follows:
0
E(Mi ∗ ∆˜
i ) = 0
where
∆˜
i = (i3 , ..., iTi )0 .
Second, beyond lags, we use other information as instrumental variable for each equation.
There is a gap in the literature since major studies resort simply to instrumentation considering the lags of the endogenous variables (Dollar and Kraay (2002,2013), Lopez and Serven
(2009), Bloom and Canning (2004),...). This is not necessarily reliable because we can have
interactions with more than one order. Moreover, few studies introduce other instruments
in order to handle endogeneity issues. For instance, Younger (2001) used a trade indicator
as an instrument for the usual GDP per capita in order to study the impact of economic
growth on health.
Pritchett and Summers (1993) used the same instrument with adding
investment. Furthermore, Acemoglu and Johnson (2006,2013) constructed a predicted mortality rate based on interventions for diseases since 1940 in 75 countries from all regions of
the world as instrument for changes in life expectancy at birth when studying the impact
of health on economic growth. They showed that there is no evidence about the impact of
9
health on economic growth after checking the robustness of their instrument constructed.
These results were criticized by Bloom and Canning (2014) who showed the weakness of
the instrument used by Acemoglu and Johnson with introducing the initial level of life expectancy and which made the impact become positive. Furthermore, Lorentzen, McMillan
and Wacziarg (2008) used adult and infant mortality and instrumentation with the malaria
ecology index in order to identify the channels through which health can impact growth.
Besides, Godlonton and Keswell (2005) tried to identify the relationships between health and
poor status with introducing depression index and access to sanitation as instruments for
health.
Returning to our approach, we keep the same instrument constructed in the rst step for
economic growth considering the fact that the price of oil aects strongly economic growth
whatever is the country importer or exporter but can't have a direct economic link with
poverty or health.
For health, the main instrument retained is the foreign aid devoted directly to health
from the AIDDATA. This database gathers all sources of foreign aid mentioning the country
receiving the aid, donors and all details of the aid. Hence, we tried to select deeply those
which are directly devoted to the health sector, epidemics, health care...and which are not
related to the living conditions or any other subject susceptible to have a relationship or
direct interaction with poverty, neither with economic growth.
However, this instrument
may be problematic and endogenous in the sense that there is two way causality existing
with health because these aids are given based on the level of health conditions or health
shocks in the recipient country. Hence, we introduce exogenous shocks on this foreign aids
and more precisely, we consider nancial crisis that have faced the donor countries which
can impact their foreign aid devoted to the recipient country. Many tests were done with
interacting the foreign aid for health with nancial cricis of the donor countries or considering
only these cricis.
Besides, the most crucial step is to nd instruments for poverty that can't have direct
link with economic growth or health. We retained female headed household which represents
the percentage of households with a female head.
In fact, households whose the head is
female have more risk to fall into poverty because there are less chances and opportunities
for women to work especially in our context of developing countries which is still suering
from women discrimination.
Another consideration is in the same direction as foreign aid
for health and which is the foreign aid for low cost housing. This aid is devoted mainly to
people suering from bad housing conditions, to disaster victims, homeless people...But we
have the same problem of endogeneity that we correct with exogenous shocks considering the
nancial cricis of the donor countries.
3
3.1
Results
Selectivity equation:
This part is of great interest in our work.
In fact, it allows us to take into account the
selectivity issues that are corrected in the basic econometric model. On the other hand, it
makes us investigate the factors behind the availability of poverty indicators using the most
developed techniques for binary panel data.
10
The results are reported in Table 2. The two rst columns present those of the endogenous variable explanation and which is the GDP per capita explained by the other exogenous
variables introduced in the basic model with adding the instrument considered for this variable, using the xed eects model. The coecient of the instrument introduced is signicant
and negative at 1%. Hence, an increase in the price of oil decreases the level of economic
growth in developing countries noting that most countries of our sample are importers of oil.
The residuals of this rst estimation, noted Residendog are computed to be introduced
in the basic model presented in the second part of the table in which we aim to explain the
binary indicator of poverty data availability. In fact, there is no signicance of the coecient
associated to the residuals computed at the level under 12% indicating the exogeneity of our
economic growth variable suspected to be endogenous in our model. Futhermore, the lagged
dependent binary variable appears with a positive coecent signicant at 5 % indicating
that the probability to conduct a household survey in a given period is increased when we
conduct a household survey in the period before. That may be due to the fact that there are
many steps in the process of surveys that are already done and people conducting them are
familiar with many issues faced before. Besides, we have a positive coecient for the lagged
GDP per Capita signicant at 10% conrming the hypothese that richer countries among
developing ones have more resources to conduct surveys. Moreover, the coecient associated
to democracy is positive and signicant at 5%.
In fact, in the democratic context, there
is more freedom and then reliability of the statistics and transparency of the government
institutions.
Finally, conicts appear with no signicant coecient in our model. However, we have
positive coecient for geographical shocks as natural disasters signicant at 1%. This sign
doesn't correspond to what we expected but this positive coecient makes us say that potentially, countries suering from geographical shocks arouse more the interest and the motivation to conduct surveys in.
The last column of the table reports the results from a standard logit xed eects model
estimation. In fact, we obtain reversed results when considering the coecient associated to
the lagged GDP per Capita which becomes no signicant and that of the lagged dependent
variable which appears with a negative sign. This shows how much the results are sensitive to
the econometric approach followed and the great interest of considering the dynamic aspect
and endogeneity.
3.2
Simultaneous system:
The tables (3A), (4A) and (5A) report the estimations of the three dimensional system,
respectively for poverty, economic growth and health equations.
In fact, there are three
versions for each one corresponding to several instruments' choices.
Instrumentation (1)
includes the donor of foreign health aid countries' cricis as instrument for health and those
of the low cost housing aid donor countries for poverty.
We keep for all instrumentations
the indicator of price of oil as instrument for economic growth.
Instrumentation (2) adds
the interaction with the amount of aid for both health and poverty instruments included in
instrumenation (1) by multiplying the dummy for donor countries' cricis with these amounts.
Instrumenation (3) includes just the amount of foreign aid for health as health instrument
and female headed households as poverty instrument.
Furthermore, the results from the
three instrumentations are given with changing also the health indicator.
11
Moreover, considering instrumentation 3, tables (3B), (4B) and (5B) report estimations
of the same equations using the basic Fixed eects model in the rst comumn of each table,
that with the two stage least squares without taking into account simultaneity in the second
one. Finally, the last column of each table reports the estimations of the Fixed eects three
stage least squares model without taking into account selectivity.
Beginning with the results reported in Table (3A), the most relevant result is the high
signicance of the inverse mills ratio's coecient and which is robust to changes in the health
indicator and instrumentation.
This conrms the great interest of correcting selectivity
neglected in the current literature.
However, the lagged dependent variable for poverty
gives several results changing both with the health indicator and the instrumentation in
terms of signicance and sign. In fact, the coecient associated to this variable is negative
and signicant at 5% considering the three combinations of instruments when using the
life expectancy at birth.
However, it is positive and signicant at 10 % for the two rst
instrumentations with introducing the infant mortality rate under one year and no signicant
for the other considerations. These results don't let us make conclusions about the impact of
the lagged dependent variable for poverty or rather let us say that there is no consensus about
its impact. Besides, considering the impact of economic growth on poverty, the coecients
associated to the lagged GDP per Capita are no signicant with the two rst instrumentations
considering both infant mortality under one and ve years as health indicators. However, it
is in this case of instrumentation signicant and negative at 5% with life expectancy at birth.
This make us suspect about the instrumentation with the donor countries' nancial cricis
interacted with the amount of aid for health and that for low cost housing or without. In
other words, these nancial cricis that have experienced the donor countries can potentially
impact the level of economic growth for the countries receiving the aid because usually, these
aids are given under some commitments and conventions as trade exchanges or they are given
in the form of debt that's why these instrumentations are suspicious. However, considering
the third instrumentation with the amount of aid for health and female headed household as
instrument for poverty, we obtain negative and signicant coecients at the level of 5 and
1% with a greater impact considering life expectancy at birth. In fact, in this last case, an
increase in the level of GDP per Capita by 1% decreases poverty by 2,69%.
Coming to the impact of health on poverty, this impact is so relevant and robust to all
considerations. In fact, for the three instrumenations, we have positive and signicant coecients at the level of 1% for both infant mortality rates under one and ve years with almost
the same value.
For instance, an increase of the infant mortality rate by 1% leads to an
increase in the level of poverty by almost 2,3% considering the rst and second instrumentation. However, we obtain high values of the negative coecients associated to the impact of
life expectancy at birth considering the three instrumentations. These results are suspicious
and due potentially to the fact that the computation of this indicator is based on estimations
using mortality rates one century before the birth so the use of this standard indicator for
health can't be totally a reliable task.
Furthermore, when we compare these results with those reported in Table (3B), we see
clearly the dramatic changes when changing the econometric approach. In fact, the correction
of endogeneity and the introduction of simultaneity reverses the signicance and even the
signs of some coecients mainly that of the lagged dependent variable for poverty. Besides,
when we look at the last column in which we don't correct for selectivity, we see clearly how
much this correction emphasizes the impact of health on poverty with increasing the value of
coecients, changes the signicance of the coecient related to the lagged dependent variable
12
for poverty but also that of economic growth when considering infant mortality under one
year.
Table (4A) exposes the results of the second equation of economic growth and which are
almost the same varying the instrumentation and the health indicators. In fact, we have the
signicance of the inverse mills ratio and a high level of signicance for the lagged dependent
variable. Besides, the results show no impact of poverty on economic growth at any of the case
considered. However, the impact of health is so relevant and robust to all considerations. For
instance, an increase by 1% of the level of infant mortality under one or ve years leads to a
decrease in the GDP per Capita by almost 0,5% for the three instrumentations. Furthermore,
we have a stronger impact considering the life expectancy at birth appearing with greater
coecients and with changing the instumentation.
In fact, an increase in the level of life
expectancy at birth by 1% can increase GDP per Capita by 3% on average. These results
are in contradiction with those found by Acemoglu and Johnson (2007) admitting that there
is no evidence about this impact and join those found in the other studies cited before.
Furthermore, comparing the results with those in Table (4B), we see that the results
are almost the same in terms of signicance and signs. Besides, the correction of selectivity
emphasizes the impact of some variables on economic growth.
Table (5A) gives the results of the health equation.
Beginning with the inverse mills
ratio, the associated coecients are signicant for the three instrumentations when using life
expectancy at birth as health indicator.
This is not really important since we are mainly
interested to test it in the rst equation. Besides, we have positive coecients signicant at
1% for the lagged dependent variable for all the indicators introduced.
For the impact of
poverty on health, we have signicant and negative coecients for the three instrumentations
only when introducing life expectancy at birth but with small impacts. This result joins that
found by Klasen (2007) when adding the level of GDP per Capita in the same equation and
using infant mortality rates.
When we observe the impact of economic growth on health,
we found reverse results for this impact with negative and signicant coecients at 10 %
considering infant mortality rates for the two rst instrumentations but no impact considering
the third one. For instance, an increase by 1% of the GDP per Capita leads to an decrease
of infant mortality rates by approximatively 0,18%.
Besides, when comparing the results with those in table (5B), the coecient relative to
the impact of economic growth on health looses its signicance when introducing simultaneity
and correcting for selection bias. The other results are almost the same in terms of signicance
and signs.
4
Discussion
The aim of this study is mainly to explain the interactions existing between the three factors of the triangle Poverty-Economic Growth-Health in developing countries considering the
econometric issues existing in such problematic. We combine all the technical support existing in the recent econometric literature in order to do so. We correct for the selectivity bias
existing from poverty data unavailability which makes us obtain more precision and relevance
in the results with amplifying the impact of some variables but also shows how much they are
sensitive to this consideration with even reversing the sign of coecients. Furthermore, this
consideration allows us to investigate extra relationships when explaining the poverty data
13
unavailability as rst step emphasizing the high impact of economic growth but also that of
democracy and disasters which play a major role in determining the motivation to conduct
surveys in developing countries.
Besides, the use of the method developed by Bartolucci
and Nigro and combined with that of Wooldridge makes us encounter at the same time the
heterogeneity existing between the countries of the sample, the dynamic aspect in binary
panel data but also endogeneity.
Besides, we can say that the choice of instruments and the indicators introduced imply
dierent results changing squarely with this choice. First, the results show that there is no
consensus about the impact of the past level of poverty on the current one. Furthermore,
the impact of economic growth on poverty was sensitive to the choice of the instruments
introduced but also that of the econometric model estimated.
Moreover, we have several results considering the impacts of poverty and economic growth
on health and which depend strongly on the health indicator introduced. In fact, we have
a great impact of poverty on health but not that of economic growth when considering life
expectancy at birth and the reverse result when considering the infant mortality rates.
Nevertheless, we have other relationships which are robust to all considerations in our
study mainly those related to the dynamic aspect for economic growth and health. Furthermore, we have the impact of health on both poverty and economic growth and which seems to
be so relevant in all the cases considered but no evidence about that of poverty on economic
growth.
5
Conclusion
Finally, we can say that the study of the triangle Poverty-Economic Growth-Health in developing countries was of great interest making us investigate many econometric issues that we
encounter at the same time but also made us realise how much the results can be sensitive
to many choices considered in the econometric strategy.
Hence, the results obtained from
this study makes us conclude that maybe economic growth can have a direct impact on both
poverty and health but when it is based on equity and improvement of the essential needs
in order to improve the situation of the poor and that of people or children suering from
poor health, in other words this impact is relevant under some conditions. However, there is
a great deal of evidence about the strong role played by the health factor indicating in one
way, how much poor health people and children can be vulnerable to poverty and in another
way the importance of human capital in general in the process of economic growth. However,
we neglected in our study the role played by inequality and also education which can have
relevant interactions with the factors of the triangle as it was shown by many researchers.
14
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18
Appendix
xit the vector of strictly exogenous covariates, the model assumes that :
0
∗
yit =¶{yit ≥0} , yit∗ = αi + xit β1 + yi,t−1 γ + e∗t (αi , Xi ) u it ,
the error terms it have standard logistic distribution and are independent, αi are the
Considering
individual specic intercepts
∗
For t < T, et (αi , Xi )
= log
e*T (αi , Xi ) = φ + xiT β2
h
i
0
1+exp αi +xi,t+1 β1 +e*t+1 (αi ,Xi )+γ
0
1+exp[αi +xi,t+1 β1 +e∗t+1 (αi ,Xi )]
,
0
This additional term measures the eect of the present choice on the expected utility of
the next occasion (t+1).
Among the basic assumption
of the model, we have:
P
0
0
exp yi+ αi + yit xit β1 +yiT (φ+xiT β2 )+yi∗ γ
t
.
p(yi |αi , Xi , yi0 ) = P P
0
0
exp z+ αi + zt xit β1 +zT (φ+xiT β2 )+zi∗ γ
z
The
P
t
includes all possibe binary response vectors
0
z =(z1 , . . . , zT ) , z+ =
Pz
P
P
yi0 z1 + zt−1 zt , yi+ = yit , yi∗ = yi,t−1 yit .
t
t
P
zt , zi∗ =
t
t
yiu represents a set of sucient statistics for αi
αi given P
Xi , yi0 and yiu , we have:
p(yi+ |αi , Xi , yi0 ) =
p(yi = z|αi , Xi , yi0 )
z(yiu )


P 0
0
exp(yi+ αi ) P
= µ(α
exp  zt xit β1 + zT (φ + xiT β2 ) + zi∗ γ .
i ,Xi ,yi0 )
Proving that
because
yi
is conditionally
independent of
t
z(yi+ )
Then the conditional distribution is written
as follows :


P
0
0
exp yit xit β1 +yT (φ+xiT β2 )+yi∗ γ 
p(yi |α, Xi , yi0 , yi+ ) =
p(yi |αi ,Xi ,yi0 )
p(yi+ |αi ,Xi ,yi0 )
t
=
P
exp
t
z(yi+ )
depend on
αi
P
0
and which does not
0
zt xit β1 +zT (φ+xiT β2 )+zi∗ γ
then becomes:


0
0
P

exp
yit dit β1 uyiT (φuxiT β2 )uyi∗ γ 
t>1
p(yi |Xi , yi0 , yiu ) =
t
"
P
exp
z(yiu )
P
t>1
0
0
zt dit β1 uzT (φuxiT β2 )uzi∗ γ
# with
The conditional maximum likelihood estimator of
mizing the conditional log-likelihood
P
0
0
θ = (β1 , β2 , φ, γ)0
is obtained by maxi-
l(θ) using a simple iterative algorithm of Newton Raph-
son where :
l(θ) =
dit = xit − xi1 , t = 2, ..., T .
¶{0 < yiu < T } log [pθ (yi |Xi , yi0 , yiu )].
19
Table 1: Sample composition by region
First initial sample
Sample after division
Region
% in the sample
% Poverty data available
% in the sample
% Poverty data available
East Asia and Pacic
15.20
13.67
10.88
11.93
Europe and Central Asia
13.92
21.97
15.89
21.48
Latin America and Carribean
18.63
35.84
19.07
24.11
Middle East and North Africa
8.50
4.95
9.66
8.11
South Asia
6.11
4.32
6.23
7.16
Sub Saharan Africa
37.65
19.25
38.26
27.21
Table 2: Selection Equation
Fixed eects model
Bartolucci and Nigro model
Logit xed eects
Lag_GDP/capita (Endog variable regression)
s for selection : dummy variable
s for selection
Explicative variable
Coecient
Explicative variable
Coecient
Coecient
lag_conict
-0,074**
lag_conict
-0,218
-0,453
lag_democracy
0,008**
lag_democraty
0,055**
0,168***
lag_disasters
-0,060*
lag_disasters
1,098 ***
1,088**
lag_IVGDP
-0,135***
Lag_GDP/capita
2,182*
0,675
cnst
6,661***
Residendog
-1,715
Lag_s
0,455 **
-0,446**
The Conict indicator is a dummy variable constructed from the Uppsala Conict Data Program (UCDP), 2012. Democracy indicator is from the
Polity IV Project 2010 (Center for Systemic Peace). Disasters are from (EM-DAT).The other indicators are from WDI. IVGDP : Price of oil* (±1)
if the country is exporter or importer of oil. The total number of observations is 818, with 113 countries. * denotes signicance at the 10 % level,
** denotes signicance at the 5% level, *** denotes signicance at the 1% level.
20
Table 3A: Second step Estimation (Poverty equation)
FE3SLS
FE3SLS
Instrumentation 1
FE3SLS
Instrumentation 2
Instrumentation 3
Explicative Variable
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Ln(Lag.Poverty1,25)
0,161*
0,117
-0,202**
0,181*
0,143
-0,208**
0,074
0,021
-0,214**
Ln(Lag.Gdp/Capita)
-0,3
-0,462
-2,532***
-0,318
-0,421
-2,203***
-0,796**
-1,036***
-2,697***
Ln(Lag.Mortinf1)
2,471***
Ln(Lag.Mortinf5)
2,481***
2,278***
Ln(Lag.Life_exp)
Lag.Inverse-Mills ratio
2,147***
2,320***
1,918 ***
-11,129***
4,645***
5,128***
5,537***
-12,273***
4,707***
5,175***
5,231***
-10,150***
4,749***
5,172***
5,759***
Table 3B: Second step Estimation (Poverty equation)
FE model (no endogeneity)
FE2SLS model (no simultaneity)
FE3SLS model (no selectivity correction)
Instrumentation 3
Instrumentation 3
Instrumentation 3
Explicative Variable
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Ln(Lag.Poverty1,25)
0,409***
0,408***
0,431***
0,395
0,396
0,415
-0,267***
-0,367***
-0,691***
Ln(Lag.Gdp/Capita)
-0,244
-0,254
-0,727
0,063
0,050
-0,527*
-0,463
-0,814**
-2,895***
Ln(Lag.Mortinf1)
0,766***
Ln(Lag.Mortinf5)
Ln(Lag.Life_exp)
1,139***
0,687***
1,999***
1,019***
-1,635*
1,681***
-5,7***
Lag.Inverse-Mills ratio
21
-4,425**
Table 4A : Second step estimation (Economic growth equation )
FE3SLS
FE3SLS
Instrumentation 1
FE3SLS
Instrumentation 2
Instrumentation 3
Explicative Variable
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Ln(Lag.Poverty1,25)
0,115
0,108
0,097
0,106
0,104
0,130
0,042
-0,006
-0,004
Ln(Lag.Gdp/Capita)
0,783***
0,799***
1,156***
0,865***
0,879***
1,196***
0,817***
0,835***
1,136***
,
Ln(Lag.Mortinf1)
-0,589***
Ln(Lag.Mortinf5)
-0,521***
-0,545***
Ln(Lag.Life_exp)
Lag.Inverse-Mills ratio
-0,435***
-0,494***
3,613***
-0,870*
-1,048**
-1,901**
-0,404***
3,097***
-1,089*
-1,275**
-1,744**
3,75***
-1,122**
-1,287**
-2,125***
Table 4B : Second step estimation (Economic growth equation )
FE model (no endogeneity)
FE2SLS model (no simultaneity)
Instrumentation 3
FE3SLS model (no selectivity correction)
Instrumentation 3
Instrumentation 3
Explicative Variable
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Ln(Lag.Poverty1,25)
0,006
0,007
-0,003
0,133
0,115
-0,023
0,042
0,042
0,052
Ln(Lag.Gdp/Capita)
0,775***
0,780***
0,882***
0,663***
0,647***
0,632***
0,691***
0,704***
0,949***
,
Ln(Lag.Mortinf1)
Ln(Lag.Mortinf5)
Ln(Lag.Life_exp)
-0,246***
-0,498***
-0,218***
-0,432***
-0,447***
1,001***
-0,387***
3,195***
Lag.Inverse-Mills ratio
22
2,015***
Table 5A: Second step Estimation (Health Equation)
FE3SLS
FE3SLS
Instrumentation 1
FE3SLS
Instrumentation 2
Instrumentation 3
Explicative Variable
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Ln(Lag.Poverty1,25)
0,036
0,045
-0,015**
0,042
0,053
-0,014*
0,024
0,033
-0,017**
Ln(Lag.Gdp/Capita)
-0,164*
-0,187**
0,014
-0,162*
-0,183*
0,015
-0,132
-0,143
0,014
Ln(Lag.Mortinf1)
0,963***
Ln(Lag.Mortinf5)
0,962***
0,938***
0,967***
0,947***
Ln(Lag.Life_exp)
0,948***
1,009***
Lag.Inverse-Mills ratio
0,348
0,357
-0,188**
0,991***
0,401
0,434
-0,176**
1,024***
0,223
0,230
-0,214***
Table 5B: Second step Estimation (Health Equation)
FE model (no endogeneity)
FE2SLS model (no simultaneity)
Instrumentation 3
FE3SLS model (no selectivity correction)
Instrumentation 3
Instrumentation 3
Explicative Variable
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Coe(1)
Coe(2)
Coe(3)
Ln(Lag.Poverty1,25)
-0,010
-0,010
0,001
-0,140*
-0,167*
0,008
0,008
0,016
0,004
Ln(Lag.Gdp/Capita)
-0,125***
-0,142***
0,008
-0,507***
-0,588***
0,064***
-0,149*
-0,168*
0,01
Ln(Lag.Mortinf1)
0,957***
Ln(Lag.Mortinf5)
Ln(Lag.Life_exp)
0,835***
0,943***
0,944***
0,816***
0,896***
0,922***
0,676***
Lag.Inverse-Mills ratio
23
0,875***