Impact of Maternal and Child Health on Economic Growth:
New Evidence Based Granger Causality and DEA Analysis
Final version: March 2013
Arshia Amiria, Ulf-G Gerdthamb,c,d
a
Research assistant, Shiraz, Iran
Department of Economics, Lund University, Lund, Sweden
c
Health Economics & Management, Institute of Economic Research, Lund University, Lund,
Sweden
d
Centre for Primary Health Care Research, Lund University, Lund, Sweden
b
Study commissioned by
the Partnership for Maternal, Newborn & Child Health (PMNCH)
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Table of contests
Executive Summary
1. Introduction
1.1. Background
1.2. Rationale
1.3. Objectives of the study
2. Methodology
2.1. Data
2.2. Empirical strategy
3. Result
3.1. Granger Causality of health outcomes and GDP per capita
3.2. The result of a Barro inspired growth model using DEA method
4. Conclusions and discussion
5. Apendix
5.1. Fixed effect panel data analysis
5.2. Data Envelopment Analysis (DEA)
References
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Executive Summary
Background
The health of women, mothers and children is fundamental to development, as reflected in
Millennium Development Goals (MDGs) 4 (reducing child mortality) and 5 (improving maternal
health and achieving universal access to reproductive health). Significant additional investments
are needed to achieve MDGs 4 and 5 and to improve women’s and children’s health beyond the
MDG target date of 2015. Demonstrating the broader societal returns of investment in women’s
and children’s health can be a critical tool in mobilizing additional resources. Economic
arguments may resonate particularly well with certain stakeholders who influence investment
decisions, such as Ministries of Finance, parliamentarians, bilateral and multilateral donors, and
global and regional development banks.
To support global, regional and national advocacy for increasing resources, demand has been
expressed by members of the Partnership of Maternal, Newborn & Child Health (PMNCH) and
the broader reproductive, maternal, newborn and child health (RMNCH) community for the
synthesis, and if necessary, the generation of evidence on the economic benefits of investing in
RMNCH. To achieve this, a work program has been established under the auspices of PMNCH.
The work program includes a systematic literature review, an econometric study of the
relationship between RMNCH outcomes and economic growth, the development of a
framework/model for estimating the national economic returns of investment in RMNCH, and
technical consultations.
Objectives
The objectives of this study are: (i) to examine whether there are relationships between maternal
and child health outcomes and economic growth in different countries at different income levels,
and, given such relationships, (ii) to estimate the direction and magnitude of these relationships.
Methods
As measures of maternal and child health, we use the under-five mortality rate (the number of
deaths of children under five per 1,000 live births) and the maternal mortality ratio (the number
of deaths per 100,000 live births). Data on mortality in 1990-2010 is taken from the WHO global
data repository (http://apps.who.int/ghodata/) including 180 countries for under-five mortality
and 170 countries for maternal mortality. As a measure of economic growth we use per capita
Gross Domestic Product (GDP) in 1990-2010in 2000 US$ from the World Bank’s World
Development Indicators 2012: http://devdata.worldbank.org/wdi2011.htm.
To examine whether there are relationships between maternal and child mortality and economic
growth we use international country-level panel data and Granger causality analysis to identify
the direction of the relationships between GDP and maternal and child mortality and to estimate
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the rough magnitude of the effects involved. 1 However, because of restrictions in data
availability we are not able to include other related factors in the Granger causality analysis. To
improve the estimate of the effect of reductions in child mortality on GDP, by taking into
account other growth related factors, we follow one of the most influential growth models in
economics proposed by Barro (1990)2 in combination with Data Envelopment Analysis (DEA). 3
Using DEA and the Barro model, we estimate how much a decrease in child mortality may
increase GDP for each country.
Results
Below we report the result of the Granger analysis of the direction of the relationships between
GDP and maternal and child mortality and the results of the DEA analysis of the impact of
reductions in child mortality4 on GDP growth.
A. The Granger analysis of direction of association
i. The under-five mortality and economic growth:
In 105 of 180 (58%) countries, we find bi-directional relationships.5 This indicates that in the
majority of countries, changes in under-five mortality have an impact on GDP and vice versa.
In 49 countries (27%) we find one-way relationships from under-five mortality to GDP. In 14
countries (8%) we find one-way relationships from GDP to under-five mortality. For the
remaining 12 countries (7%), no relationships are found.
ii. Maternal mortality and economic growth:
In 68 of 170 (40%) countries we find bi-directional relationships. One-way relationships from
maternal mortality to GDP are found in 50 countries (29%) and one-way relationships from GDP
to maternal mortality are found in 19 countries (11%). No relationships are found in 33 countries
(19%).
We also find that the magnitude of the effect of reductions in child mortality on GDP in highincome countries (HICs) and upper middle-income countries (UMICs) is larger than lower1
Granger, C.W.J. (1969) Investigating causal relations by econometric models and cross-spectral methods.
Econometrica 37, 424–38.
2
Barro, R.J. (1990) Government spending in a simple model of endogenous growth. Journal of Political Economy
98, 103–125.
3
We use an explicit endogenous growth model (Barro, 1990), in which public expenditure is considered as an input
of the production function. For y the GDP per unit of labor, we have: y = f(k, d) with k, the private capital by unit of
labor, and d, a “productive public expenditure”, see Ventelou, B. & Bry, X. (2006) The role of public spending on
economic growth: Envelopment methods. Journal of Policy Modeling 28, 403–413.
4
In calculating DEA, we made child mortality as the index of child health because of having higher significant
causal relationship with GDP instead of maternal mortality.
5
A bi-directional relationship (H↔Y) implies that variation of H (Y) causes variation of Y (H). A unidirectional
relationship from, for example, H to Y (H→Y) means that variation of H has a significant effect on Y, but the
variation of Y has no effect on H.
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middle-income countries (LMICs) and low-income countries (LICs). However, in contrast, the
magnitude of the effect of GDP on maternal and child health outcomes in LMICs and LICs are
larger relative to HICs and UMICs.
B. Barro growth model / DEA analysis of magnitude
To explore the effects of other growth-related factors in the model, we used DEA analysis in a
Barro framework where in addition to child health we included government spending, population
and (fixed) capital in the model, in order to determine the efficiency rate. The efficiency rate for
each country demonstrates the magnitude of the impact of child health outcomes on GDP. In
Cote d'Ivoire the efficiency rate is 91.5% in 2001 to 2010. This may be interpreted as follows: if
child health increases by one percentage (one percentage point reduction in the under-five
mortality rate), increases GDP by 5% (as an example) in a country with a 100% efficiency rate,
then GDP in Cote d'Ivoire will increase by 4.6% (0.915*5%).
The results of the DEA analysis indicate that reductions in mortality will generally have a large
effect on GDP growth, since the average overall efficiency rates for all countries in the data are
more than 90% (91.1% in 1990-2000 and 92.2% in 2000-2010). As noted above, the results
indicate that a decrease in child mortality would lead to a larger effect on GDP in richer
countries compared with poorer countries, although the difference in the average efficiency rate
between different groups of countries are not statistically significant.
Countries with the highest efficiency rates overall are Bahamas, Canada and Germany. The
lowest efficiency rates overall were found for Madagascar, Paraguay and Singapore. Armenia,
China and the Ukraine have the highest efficiency rates among LMICs, whereas Liberia,
Mozambique and Tanzania have the highest efficiency rates among LICs. Algeria, Guatemala
and Honduras have the lowest efficiency rates among LMIC, whereas Benin, Kenya and
Madagascar have the lowest efficiency rates among LICs.
Discussion
Analysis of the causal direction of the relationships between GDP and maternal and child health
outcomes and the magnitude of the effects is important since the results can provide powerful
arguments for investment in maternal and child health.
We find in general that the relationships between maternal and child health outcomes and GDP
run in both directions, with the majority running from maternal and child health to GDP. We find
evidence that the causal effects of GDP on maternal and child health outcomes are stronger in
LICs and LMICs relative to HICs and UMICs. This may reflect that the effect of marginal health
investments on health outcomes is higher at low levels of GDP, i.e. in countries where the level
of health investments is generally lower.
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In contrast, the causal effect of maternal and child mortality on GDP is generally stronger in
HICs and UMICs. This may be due to the differences between poor and rich countries with
respect to the human capital level or infrastructure. Human capital is the stock of competencies,
knowledge, social and personality attributes, including creativity, embodied in the ability to
perform labor so as to produce economic value. 6 The higher human capital level of richer
countries compared to poorer countries implies that an equal reduction in maternal and child
mortality will cause GDP to increase more in richer countries than in poorer countries.
6
Simkovic, M. (2012) Risk-based student leons. Whasington and lee law review 70, 1.
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1. Introduction
1.1. Background
Reproductive, maternal, newborn & child health (RMNCH) is fundamental to development,
which is reflected in Millennium Development Goals (MDGs) 4 (reducing child mortality) and 5
(improving maternal health and achieving universal access to reproductive health). It has been
demonstrated that significant additional investments are needed to achieve MDGs 4 and 5 and
improve women’s and children’s health beyond the MDG target date of 2015
(http://www.who.int/pmnch/activities/jointactionplan/en/). Developing and presenting economic
arguments that resonate with stakeholders influence investment decisions, such as Ministries of
Finance and Planning, which are critical to mobilize additional resources. These stakeholders
need to be convinced that spending on RMNCH should be seen as an investment, and not simply
a cost.
For a long time the prevailing view among economists was that the link between health and
economic development ran in one direction only, from economic development to investment in
health. This view was articulated in an influential background paper to the World Development
Report 1993 entitled Wealthier is Healthier. It recognized that economic development leads to
improved health outcomes through its impact on indirect pathways to health – such as better
nutrition, water and sanitation, living environment and education – but the reverse direction of
health’s impact on economic development was not fully acknowledged. This paradigm began to
shift about 10 years ago, particularly through the work of the Commission on Macroeconomics
and Health (CMH; http://www.who.int/macrohealth/en/). The CMH demonstrated that the
causality runs in both directions and that "healthier is wealthier". 7 Nevertheless, most of the
evidence presented by the CMH was related to the effects of investments in HIV/AIDS and
malaria.
1.2. Rationale
Two of the major objectives of the Partnership for Maternal, Newborn & Child Health
(PMNCH) are (a) to address evidence gaps and (b) to contribute to raise additional funds to
address MDGs 4 and 5.
In 2009, PMNCH developed an investment case for RMNCH in Asia and the Pacific in
collaboration with an informal network of institutions and analysts concerned with the lack of
progress on MDGs 4 and 5 in the region. 8 An investment case for Africa was developed in 2010
in
collaboration
with
Harmonization
for
Health
in
Africa
(http://www.who.int/pmnch/topics/economics/20110414_investinginhealth_africa/en/).
7
For example, a Commission background study by Bloom and Williamson entitled “Demographic transitions and
economic miracles in East Asia” attributed 30-50% of East Asia’s impressive growth in 1965-1990 to reduced infant
and child mortality, lower fertility rates, and improved reproductive health (see Bloom and Williamson, 1997).
8
MNCH network for Asia and the Pacific (2009) Investing in maternal, newborn and child health – The case for
Asia and the Pacific. Geneva: WHO and PMNCH.
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Literature reviews were conducted to inform the investment cases and it became clear that there
is limited evidence on the economic benefits of investing in RMNCH.
To support global, regional and national advocacy for increasing resources, demand has been
expressed by members of PMNCH and the broader RMNCH community for the synthesis, and if
necessary, the generation of evidence on the economic benefits of investing in RMNCH. To
achieve this, a work program has been established under the auspices of PMNCH. The work
program includes a systematic literature review, an econometric study of the relationship
between RMNCH outcomes and economic growth, the development of a framework/model for
estimating the national economic returns of investment in RMNCH, and technical consultations.
1.3. Objectives of the study
The objectives of this study are: (i) to examine whether there are relationships between maternal
and child health outcomes and economic growth in different countries at different income levels,
and, given such relationships, (ii) to estimate the direction and magnitude of these relationships.
In an econometrics analysis between two variables, two main aims are, firstly, finding the
existence and direction of causal relationships between variables and, secondly, measuring the
magnitude of the effects between variables. To reach the first aim, we analyze the causal
relationships between health outcomes (maternal and child mortality) and income, or rather per
capita gross domestic product (GDP). To define the dimension of effect of the relationships in
the first aim, we calculate the efficiency of the health outcomes on increasing GDP in growth
amounts of variables in a Barro framework. Thus in the analysis we use country-level panel data
(180 countries) and Granger causality analysis to identify the direction of relationships between
the health outcomes and GDP and also to perform an approximate estimate of the magnitude of
the effects involved by employing advanced econometric techniques. We describe this in detail
in the next section. A limitation of our Granger analysis is that we are not able to include any
control variables due to limitations in the available data9. We therefore complement the analysis
by a Data Envelopment Analysis (DEA) 10 which is applied on a Barro (1990) inspired growth
model. By use of the DEA method, we estimate how much an improvement in the health
outcomes will impact on GDP for each country relative to others, which in turn indicates the
economic return in terms of GDP of potential investments in health in various countries.
Efficiency is a key concept in economic analysis. Since the seminal work of Charnes et al.
(1978), some of the major research has focused on DEA over the last three decades (Cook and
Seiford, 2009). In an economic analysis of variables like economic growth, GDP and
productivity, which can be defined as outputs of a production function, it is important to know
9
Since the number of observations in the time dimension is limited in the WHO data set, we are not able to include
control variables in the Granger analysis.
10
We use an explicit endogenous growth model (Barro, 1990), in which the public expenditure is considered as an
input of the production function. For y the GDP per unit of labor, we have: y = f(k, d) with k, the private capital by
unit of labor, and d, a “productive public expenditure” (Ventelou and Bry, 2006).
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that how much these variables can be expected to increase as a result of changes in different
input factor (see Farrell, 1957). In other words, it would be important to find out that what would
be the maximum effect of input variables (like child health) on output (like GDP)? The answer
may be found in DEA. The investigation of efficiency on the relationship between different
health outcomes and GDP would be important to economists in informing policies to improve
the effects of the health outcomes on economic growth.
2. Methodology
2.1. Data
Five variables 11 are available on the WHO data website (http://apps.who.int/ghodata/) as
indicators of child health. In the current study, we use the under-five mortality rate (probability
of dying by age 5 per 1,000 live births), which is a commonly used indicator to measure progress
on child health (and is the indicator of MDG4). In addition, to measure maternal health (MDG5),
we use the maternal mortality ratio (number of deaths per 100,000 live births). As a measure of
economic growth we use GDP in 2000 US prices (World Bank’s World Development Indicators
2012; http://devdata.worldbank.org/wdi2011.htm). Data for 1990-2010 was selected to the
analysis.
We include 42 high-income countries (HIC), 38 upper-middle-income countries (UMIC), 50
lower-middle-income countries (LMIC), and 50 low-income countries (LIC), i.e. 180 countries
in total. We use 5-year pooled data from 1990 to 2010 (1990-2010). For the list of countries
included in our data, see Table 4 below.
For the purpose of testing the efficiency of the health outcomes on GDP growth in a Barro model
framework, the data of GDP growth (annual %), population growth (annual %), and general
government final consumption expenditure (annual % growth) are derived from the World
Bank’s World Development Indicators, 2012, in weighted means of the first and last years of two
periods of 1990 to 2000 and 2000 to 201012, in growth amounts. For the list of available data
during each period see Tables 4 and 5.
2.2. Empirical strategy
In panel data analysis it is possible to classify three main types of approaches. The first one was
pioneered by Holtz-Eakin et al. (1985), which estimates and tests vector autoregression (VAR)
coefficients using panel data by taking the autoregressive coefficients and regression coefficients
slopes as variables. A similar procedure was applied by Hsiao (1986), Holtz-Eakin et al. (1988),
Hsiao (1989), Weinhold (1996), Weinhold (1999), Nair-Reichart and Weinhold (2001) and Choe
11
Infant mortality rate (probability of dying between birth and age 1 per 1000 live births), under-five mortality rate
(probability of dying by age 5 per 1000 live births), the number of infant deaths (thousands), the number of underfive deaths (thousands), and measles immunization coverage among 1-year-olds (%).
12
In many growth models, economists commonly calculate their variables in the period of 10 years.
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(2003). The second approach proposed by Hurlin and Venet (2001), Hurlin (2004a, b), Hansen
and Rand (2004), Judson and Owen (1999) treats the autoregressive coefficients and regression
coefficient slopes as constants using a panel data Fixed Effects (FE) estimator. Adams et al.
(2003) can be treated as the third approach dealing with causality in panel data models. The main
contribution of this approach is proposing a refinement for small data sets (see Adams et al.,
2003, p. 8). However, as stressed by Hoover (2003), the approach by Adams et al. (2003) lacks
rigorous test of invariance that causal inference needs (Erdil and Yetkiner, 2010).
Our study employs the second approach because of its suitability to our data sets, in which we
have relatively a short time dimension but large number of countries. In panel data analysis the
error term uit may be decomposed into country effects μi, time effects £t, and a random term vit.
The country effects represent all country-specific omitted variables and the time effects represent
all omitted variables that have equal effects on all countries. Different ways of modeling these
countries and time-specific terms provide different panel data models. An OLS regression
assumes that μi=0 and £t=0. An FE model assumes that μi and/or £t are fixed constants for each
country and time period respectively, in which an appropriate panel model is OLS with countryspecific and/or time-specific dummy variables. If the FE is the correct specification, but an OLS
is estimated, the estimated effects will be biased if μi is correlated with other explanatory
variables.
To reach the objectives in the study, we use the Granger causality analysis to explore the
direction of the effect of variation between the variables (health outcomes and GDP) using panel
data for individual countries. In testing causality with panel data, it is vital to test heterogeneity
between cross-section units. The first source of heterogeneity is caused by permanent cross
sectional disparities. A pooled estimation without the heterogeneous intercepts may lead to a bias
of the slope estimates and could result in a fallacious inference in causality tests (Hurlin, 2004a;
see Erdil and Yetkiner, 2010). Another basis of heterogeneity caused by heterogeneous
regression coefficients θk is more problematic than the first one, i.e. one should consider the
different sources of heterogeneity of the data generating process. Thus a series of different
causality hypothesis will be tested: two types of homogenous causality hypotheses: 1)
homogenous and instantaneous non-causality hypothesis (HINC) and 2) the homogenous
causality hypothesis (HC) and an overall (homogenous) causality within country income group.
If 1) and 2) are rejected then we test the heterogeneous non-causality hypothesis (HENC). For
more details about panel data analysis and Granger causality tests, see appendix (section 5.1).
If we find causal relationships between health outcomes and economic growth for most
countries, then we assume that that decreases in health outcomes in average increases GDP. For
countries where we cannot identify casual relationships we assume that other factors blurred the
health outcomes-growth relationship. Given that there is in general a significant causal
relationship from health outcomes to GDP, we extend the analysis and calculate the efficiency
rates of health outcomes on GDP using DEA analysis which aims to measure how much GDP
can be increased in different countries at present health outcomes if the efficiency rate of health
10 | P a g e
outcomes on GDP increases. This measure is based on the assumption that there is no room for
further increases in the efficiency rate for those countries at the measured 100% efficiency rate.
DEA analysis is the non-parametric mathematical programming approach to frontier estimation.
The piecewise-linear convex hull approach to frontier estimation, proposed by Farrell (1957),
was considered by only a handful of authors in the two decades following Farrell paper. Authors
such as Afriat (1972) suggested mathematical programming which could achieve the task, but
the method did not receive wide attention until a paper by Charnes et al. (1978) coined the term
DEA. There have since been a large number of papers which have extended and applied the
DEA methodology (Coelli, 1996). For more discussion of DEA see appendix (section 5.2).
3. Results
3.1. Granger Causality of health outcomes and GDP per capita
To reach the objectives in the study, we use the Granger causality analysis to explore the
direction of the effect of variation between the variables (health outcomes and GDP) using panel
data for individual countries. Below we present the results of the Granger causality analysis.
Table 3 shows the values of Wald statistics for testing the two types of homogenous causality
hypotheses: HINC and HC. The results allow us to reject both of the null hypotheses at 1% level
of significance indicating no homogenous causality between GDP and health outcomes (child
mortality and maternal mortality), i.e. the existence of causal relationships appears to differ
across countries. We further test whether the causality is an overall (homogenous) causality for
each country income group or sourced from causality relations for individual countries
(heterogeneous). The test also rejects the existence of a homogeneous causality. The final step is
discovering the existence of causality in the individual countries.
In sum: our results confirm that in the majority of countries there is a feedback (bi-directional)
causal relationship of health outcomes on GDP which indicates that investments in health may
provide returns in terms of higher GDP. We also find that the relationship between under-five
mortality (MDG4) and GDP is often more significant than maternal mortality (MDG5) and GDP.
In the Granger analysis, we find a stronger relationship between health outcomes and GDP in
LIC and LMIC compared to HIC and UMIC which may be due to the level of human capital
dependency and higher marginal effect of health spending on LIC and LMIC. We also find that
the stronger relationships are because the effects of GDP on health are stronger in LIC and LMIC
compared to HIC and UMIC while in contrast it is found that the effects of health on GDP are
stronger in HIC and UMIC compared to LIC and LMIC.
i. Under-five mortality and economic growth:
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In 105 (58%) of 180 countries we find bi-directional13 relationships (see Tables 1 and 3). This
implies that in the majority of countries, changes in GDP have an impact on under-five mortality
and vice versa. In the context of country groups, the shares of bidirectional causality are
observed at 55%, 29%, 66% and 76% for HIC, UMIC, LMIC and LIC, respectively.
In 49 countries (27% from total) we find a one-way relationship from under-five mortality to per
capita GDP. In 14 countries (8%) we find a one-way relationship from per capita GDP to underfive mortality. For the remainder 12 countries, we find no significant relationships (7%). The
shares of under-five mortality to GDP causal relation are 19%, 53%, 24% and 18%, and also
from GDP to under-five mortality are 17%, 3%, 8% and 4% for HIC, UMIC, LMIC and LIC,
respectively.
ii. Maternal mortality and economic growth:
In 68 (40%) of 17014 countries we find bi-directional relationships (see Tables 1 and 4). In the
context of country groups, the shares of bidirectional causality are observed at 31%, 9%, 53%
and 55% for HIC, UMIC, LMIC and LIC, respectively.
A one-way relationship from maternal mortality to GDP and the inverse one (GDP_maternal
mortality) are obtained in 50 (29%) and 19 (11%) countries, respectively. The shares of maternal
mortality to GDP causal relation are 29%, 59%, 26% and 12%, and also from GDP to maternal
mortality are 16%, 0%, 10% and 9% for HIC, UMIC, LMIC and LIC, respectively. No
relationships are found in 33 (19%) countries.
Table 1. The percentage of significant relationships and the average size of effect between
MDG4/MDG5 & GDP
Percentage of significant relationships between MDG5 & GDP
Average of size of effect
Country group
Bilateral
MDG4 to GDP
GDP to MDG4
MDG4 to GDP
GDP to MDG4
HIC
55%
19%
17%
-598.87
-0.0041
UMIC
LMIC
LIC
Totality
29%
66%
76%
58%
53%
24%
18%
27%
3%
8%
4%
8%
-283.99
-174.63
-96.21
-288.42
-0.078
-0.039
-0.21
-0.083
Percentage of significant relationships between MDG5 & GDP
Country group
Bilateral
MDG5 to GDP
GDP to MDG5
Average of size of effect
MDG5 to GDP
GDP to MDG5
HIC
-397.17
13
31%
29%
16%
-0.014
Suppose that we have two variables (H: child health & Y: GDP). If we find a bilateral relationship (H↔Y), it
means that the variation of H causes the variation of Y and our variables have a high effect on each other. If we find
a unidirectional relationship from H to Y (H→Y), it means that the variation of H has a significant effect on Y, but
the variation of Y has no effect on H, and similar for Y→H.
14
In the analysis of maternal mortality (MDG5) and under-five mortality, we exclude 12 countries due to lack of data
availability in WHO data set (Andorra, Antigua and Barbuda, Argentina, Dominica, Kiribati, Marshall Islands,
Monaco, Palau, San Marino, St. Kitts and Nevis, Seychelles, and Tuvalu are excluded). However, we include 2
countries (West Bank and Gaza and Puerto Rico) in analyzing MDG5 whose MDG4 observations are not available.
12 | P a g e
UMIC
LMIC
LIC
9%
53%
55%
59%
26%
12%
0%
10%
9%
-151.38
-117.52
-13.02
-0.10
-0.32
-1.09
Totality
40%
29%
11%
-169.77
-0.38
Notes: In order to compare the size of effect of statistically significant relationships, the average of the coefficients
of first lag of exogenous variable is reported. This amount is a good index for size of effect which is just useful for
comparison, econometrically.
In the statistically significant relationships, interestingly, we also find that the magnitude of the
effect of health on GDP in HICs and UMICs is bigger compared to LMICs and LICs while the
size of the effect of GDP on health in LMICs and LICs are generally bigger compared to HICs
and UMICs. The latter may reflect that the marginal effect of health investments on health
outcomes is more effective in poorer countries. However, at the same time, as indicated by the
former effect, investments in health on GDP may not go in the same direction, for example, since
the quality-improving effect of labor, through better health, on GDP, is higher in richer countries
Over 90% of valid coefficients in the countries are negative, which means that our empirical
results are similar to economic theories that suggest a negative relationship between child
mortality and GDP.
Table 2. Test results for homogenous causality hypotheses
Test
MDG4→ GDP
GDP→ MDG4
MDG5→ GDP
HINC
5.89E+29**
7.17E+27**
5.01E+28**
High-income
HC
5.91E+29**
1.12E+33**
5.42E+28**
HINC
966.69**
3.48**
34.47**
Upper-Middle-income
HC
980.20**
282240.5**
50.13**
HINC
5.92E+27**
2.41E+29**
9.92E+26**
Lower-Middle-income
HC
5.96E+27**
4.55E+32**
9.98E+26**
HINC
32.07**
313.85**
1.32E+27**
Low-income
HC
31.64**
1198923.00**
1.35E+27**
Notes: ** p<0.01%. Most series of GDP and under-five mortality rate contain unit root.
County group
GDP→ MDG5
6.35E+28**
2.66E+32**
2.83**
1850.04**
9.02E+29**
1.30E+33**
174.18**
1185282.00**
Table 3. Test results for heterogeneous causality hypotheses between under-five mortality
(MDG4) and GDP
HICs
Direction
UMICs
Direction
LMICs
Direction
LICs
Direction
Andorra
No
Argentina
Dead-y
Albania
Bilateral
Bangladesh
Bilateral
Antigua and
Barbuda
Bilateral
Belize
Bilateral
Algeria
Bilateral
Benin
Dead-y
Australia
Bilateral
Botswana
y-Dead
Angola
Bilateral
Burkina Faso
Bilateral
Austria
Bilateral
Brazil
Bilateral
Armenia
Dead-y
Burundi
Bilateral
Bahamas, The
y-Dead
Bulgaria
No
Azerbaijan
Dead-y
Cambodia
Bilateral
Bilateral
Bilateral
Bahrain
Bilateral
Chile
Dead-y
Belarus
Bilateral
Central African
Republic
Barbados
y-Dead
Costa Rica
Dead-y
Bhutan
Dead-y
Chad
13 | P a g e
Belgium
Dead-y
Croatia
Dead-y
Bolivia
Bilateral
Comoros
y-Dead
Brunei Darussalam
Bilateral
Dominica
Dead-y
Bosnia and
Herzegovina
Bilateral
Congo, Rep.
Bilateral
Canada
Dead-y
Equatorial Guinea
Bilateral
Cameroon
y-Dead
Cote d'Ivoire
Bilateral
Cyprus
y-Dead
Gabon
Dead-y
China
Dead-y
Eritrea
Bilateral
Czech Republic
Bilateral
Grenada
Dead-y
Colombia
Bilateral
Ethiopia
Bilateral
Bilateral
Gambia, The
Bilateral
Denmark
Bilateral
Hungary
Dead-y
Congo, Dem.
Rep.
Estonia
Bilateral
Kazakhstan
Dead-y
Cuba
Bilateral
Ghana
Bilateral
Finland
y-Dead
Latvia
No
Djibouti
Bilateral
Guinea
Bilateral
Bilateral
Guinea-Bissau
Bilateral
France
Bilateral
Lebanon
Bilateral
Dominican
Republic
Germany
No
Libya
Bilateral
Ecuador
Bilateral
Haiti
Dead-y
Greece
Bilateral
Lithuania
Dead-y
Egypt, Arab Rep.
Dead-y
India
y-Dead
Iceland
Bilateral
Malaysia
Dead-y
El Salvador
Bilateral
Kenya
Bilateral
Ireland
Bilateral
Mauritius
No
Georgia
Dead-y
Korea, Rep.
Bilateral
Italy
Bilateral
Mexico
Bilateral
Guatemala
Bilateral
Kyrgyz Republic
Dead-y
Japan
Dead-y
Montenegro
Dead-y
Guyana
Bilateral
Lao PDR
Bilateral
Kuwait
Bilateral
Oman
Bilateral
Honduras
Dead-y
Liberia
Bilateral
Luxembourg
Dead-y
Palau
Dead-y
Indonesia
Bilateral
Madagascar
Bilateral
Malta
Bilateral
Panama
Dead-y
Iran, Islamic Rep.
y-Dead
Malawi
Bilateral
Monaco
Bilateral
Poland
No
Iraq
Bilateral
Mali
Bilateral
Netherlands
Bilateral
Romania
Bilateral
Jamaica
Bilateral
Mauritania
Bilateral
New Zealand
y-Dead
Russian Federation
No
Jordan
Dead-y
Mongolia
Bilateral
Norway
Bilateral
St. Kitts and Nevis
Dead-y
Kiribati
y-Dead
Mozambique
Bilateral
Portugal
No
St. Lucia
No
Lesotho
Dead-y
Nepal
Bilateral
Qatar
Bilateral
St. Vincent and the
Grenadines
Dead-y
Maldives
Bilateral
Niger
Bilateral
San Marino
Dead-y
Serbia
Bilateral
Marshall Islands
Bilateral
Nigeria
Bilateral
Saudi Arabia
Dead-y
Seychelles
Dead-y
Micronesia, Fed.
Sts.
Dead-y
Pakistan
Bilateral
Bilateral
Singapore
Bilateral
Slovak Republic
Dead-y
Moldova
Bilateral
Papua New
Guinea
Slovenia
y-Dead
South Africa
Dead-y
Morocco
Bilateral
Rwanda
No
Spain
Dead-y
Turkey
Bilateral
Namibia
Bilateral
Senegal
Bilateral
Sweden
y-Dead
Uruguay
Dead-y
Nicaragua
Bilateral
Sierra Leone
Bilateral
Switzerland
Dead-y
Venezuela, RB
Bilateral
Paraguay
Bilateral
Solomon Islands
Dead-y
Trinidad and Tobago
Bilateral
Peru
Bilateral
Sudan
Bilateral
United Arab
Emirates
Bilateral
Philippines
Bilateral
Tajikistan
Dead-y
United Kingdom
No
Sri Lanka
Dead-y
Tanzania
Dead-y
United States
Bilateral
Suriname
Bilateral
Timor-Leste
Bilateral
Swaziland
Dead-y
Togo
Bilateral
Syrian Arab
Republic
Bilateral
Tuvalu
Dead-y
Thailand
y-Dead
Uganda
Bilateral
14 | P a g e
Tonga
Bilateral
Uzbekistan
Dead-y
Tunisia
Bilateral
Vietnam
Bilateral
Turkmenistan
Bilateral
Yemen, Rep.
Bilateral
Ukraine
Bilateral
Zambia
Dead-y
Vanuatu
No
Zimbabwe
Bilateral
Notes: Hurlin (2004a) critical values for Wald statistics for testing causality in micro panels is used to find the valid
coefficients. Cross-section weight is used for having a better determination of our unbalanced observation. Because
of our short available time period we only use one lag of endogenous variables in our Granger analysis.
Table 4. Test results for heterogeneous causality hypotheses between maternal mortality (MDG5)
and GDP
HICs
Direction
UMICs
Direction
LMICs
Direction
LICs
Direction
Australia
y-Dead
Argentina
No
Albania
Bilateral
Bangladesh
Bilateral
Austria
Bilateral
Belize
No
Algeria
Dead-y
Benin
No
Bahamas, The
y-Dead
Botswana
Dead-y
Angola
Bilateral
Burkina Faso
Bilateral
Bahrain
Dead-y
Brazil
Dead-y
Armenia
Dead-y
Burundi
Bilateral
Barbados
Dead-y
Bulgaria
No
Azerbaijan
No
Cambodia
Bilateral
Bilateral
Belgium
Dead-y
Chile
Dead-y
Belarus
Bilateral
Central African
Republic
Brunei
Darussalam
Dead-y
Costa Rica
Dead-y
Bhutan
Dead-y
Chad
Dead-y
Canada
Bilateral
Croatia
No
Bolivia
Bilateral
Comoros
Bilateral
Bilateral
Congo, Dem. Rep.
Bilateral
Cyprus
y-Dead
Equatorial Guinea
Bilateral
Bosnia and
Herzegovina
Czech Republic
Bilateral
Gabon
No
Cameroon
Bilateral
Cote d'Ivoire
Bilateral
Denmark
No
Grenada
Dead-y
China
Dead-y
Eritrea
Bilateral
Estonia
Bilateral
Hungary
Dead-y
Colombia
Dead-y
Ethiopia
Bilateral
Finland
No
Kazakhstan
Dead-y
Congo, Rep.
Bilateral
Gambia, The
Bilateral
France
y-Dead
Latvia
No
Cuba
y-Dead
Ghana
y-Dead
Germany
Dead-y
Lebanon
Dead-y
Djibouti
Bilateral
Guinea
Bilateral
Dead-y
Guinea-Bissau
Bilateral
Greece
No
Libya
Dead-y
Dominican
Republic
Iceland
Bilateral
Lithuania
Dead-y
Ecuador
Bilateral
Haiti
Bilateral
Ireland
Dead-y
Malaysia
Dead-y
Egypt, Arab Rep.
Dead-y
India
Bilateral
Italy
No
Mauritius
No
El Salvador
Bilateral
Kenya
y-Dead
Japan
Dead-y
Mexico
Dead-y
Georgia
No
Korea, Rep.
Dead-y
Kuwait
Bilateral
Montenegro
Dead-y
Guatemala
No
Kyrgyz Republic
No
Luxembourg
Dead-y
Oman
Bilateral
Guyana
y-Dead
Lao PDR
Bilateral
Malta
Bilateral
Panama
Dead-y
Honduras
Bilateral
Liberia
y-Dead
Netherlands
No
Poland
No
Indonesia
Bilateral
Madagascar
Bilateral
New Zealand
y-Dead
Romania
No
Iran, Islamic Rep.
Bilateral
Malawi
Bilateral
Norway
Bilateral
Russian Federation
Bilateral
Iraq
Bilateral
Mali
y-Dead
Portugal
No
St. Lucia
No
Jamaica
Bilateral
Mauritania
Bilateral
Puerto Rico
Bilateral
St. Vincent and the
Dead-y
Jordan
y-Dead
Mongolia
y-Dead
15 | P a g e
Grenadines
Qatar
Bilateral
Serbia
Dead-y
Lesotho
Dead-y
Mozambique
No
Saudi Arabia
Dead-y
Slovak Republic
Dead-y
Maldives
y-Dead
Nepal
y-Dead
Dead-y
Niger
Bilateral
Singapore
y-Dead
South Africa
Dead-y
Micronesia, Fed.
Sts.
Slovenia
Dead-y
Turkey
Dead-y
Moldova
Bilateral
Nigeria
No
Spain
No
Uruguay
Dead-y
Morocco
Bilateral
Pakistan
y-Dead
Sweden
No
Venezuela, RB
No
Namibia
Bilateral
Papua New Guinea
Bilateral
Switzerland
No
Nicaragua
Bilateral
Rwanda
Dead-y
Bilateral
Paraguay
Bilateral
Senegal
Dead-y
Dead-y
Peru
Bilateral
Sierra Leone
Bilateral
Bilateral
Philippines
Dead-y
Solomon Islands
No
Sri Lanka
Dead-y
Sudan
Bilateral
Suriname
No
Tajikistan
No
Swaziland
Dead-y
Tanzania
Dead-y
Syrian Arab
Republic
Bilateral
Timor-Leste
Bilateral
Thailand
Bilateral
Togo
Bilateral
Tonga
Bilateral
Uganda
Bilateral
Tunisia
Bilateral
Uzbekistan
No
Turkmenistan
y-Dead
Vietnam
Bilateral
Ukraine
Bilateral
Yemen, Rep.
y-Dead
Vanuatu
No
Zambia
No
West Bank and
Gaza
Dead-y
Zimbabwe
Dead-y
United Arab
Emirates
United
Kingdom
United States
Note: see table 3.
We conclude from the analysis above that there is evidence that the causal effects in general run
both from GDP to health and from health to GDP, for most countries and for both health
outcomes under study (child mortality and maternal mortality). Below we focus on the causal
relationship running from health to GDP and extend the analysis to a Barro growth model
approach and we also restrict measurement of health to under-five mortality of children which
appeared stronger in the Granger analysis.
3.2. The result of a Barro inspired growth model using DEA method
Numerous factors impact on GDP and there is not any empirical literature in economics which
estimated the exact magnitude of health on GDP. But there are some indexes such as the size of
effect and efficiency rate which are used to compare the magnitudes among countries. This
section presents the results of DEA analysis to measure how far from the frontier different
countries are located, i.e. indicating how much GDP may be increased at the current level of
child mortality. In other words, static efficiency exists at a point in time and focuses on the
maximum potential of GDP which can be increased with the current economic and health
structure of each country in comparison with other countries in the Barro framework. In the first
16 | P a g e
step of our estimation (see above, section 3.1) we tested for causality between under-five
mortality and GDP. Because we find evidence for co-linearity between the lags of our variables,
and because our time period is short (only five times because of availability of data) we are not
allowed to investigate Granger causality between under five mortality and GDP in the Barro
framework or include other variables as control in our causality analysis. However, the aim of
this section is to investigate the efficiency rates of under-five mortality on economic growth in
the Barro model. With respect to the inclusion of other productivity-related factors, we follow
Ventelou and Bry (2006) and apply a DEA analysis in a Barro framework, i.e. we also include
government spending, population and (fixed) capital.
We use an explicit endogenous growth model developed by Barro (1990), in which public
expenditure is considered as an input of the production function. For y the GDP per unit of labor,
we have: y = f(k, d) with k, the private capital by unit of labor, and d, a “productive public
expenditure”. As demonstrated by Barro (1990), this extension of the Solow model allows
generating positive and permanent growth rate for the economy: the law of decreasing returns
(valid for the private capital) could be offset by a continuous flow of public expenditure,
counterbalancing period after period the “falling tendency of the rate of profit” (Ventelou and
Bry, 2006).
To follow Barro, we multiply and include population growth in the Barro function. According to
the result of Granger causality between child mortality and GDP we find a high relationship
between these two, therefore we also include child health data in the Barro model to find
efficiency rate of under-five mortality on increasing economic growth. Then, for y, GDP growth,
we have: y = f(k, l, d, h) with k, the private capital growth, d, government expenditure growth, l,
population growth, and, h, newborn mortality growth15.
Most economists have tested the Barro model in 10-year periods. Therefore we calculate the
Barro model with DEA in 1990-2000, and 2000-2010. Because of fixed growth of private capital
during short periods we do not include capital growth in our model.
In the study, the efficiency rate for each country shows how much increase in child health may
impact on (positive) GDP growth compared to other countries. In Cote d'Ivoire the efficiency
rate is 91.5% in 2001 to 2010. This may be interpreted as follows: if child health increases by
one percentage (one percentage point reduction in the under-five mortality rate), increases GDP
by 5% (as an example) in a country with a 100% efficiency rate, then GDP in Cote d'Ivoire will
increase by 4.6% (0.915*5%).
The results of efficiency rates are available in tables 6 and 7 during the periods of 1990-2000 and
2000-2010, respectively. For the reason of data availability in the World Development
Indicators, we lose some countries in each period. The result of both CRS and VRS models is
15
In linear programming we are not allowed to include negative amounts of variables. Therefore, we multiply
newborn mortality growth to (-1) in the Barro framework.
17 | P a g e
reported. To allow for the possibility of a non-linear path GDP, we mainly rely on the VRS
results. Based on the growth model theories, economists use nonlinear functional forms in order
to describe the path of economic growth, such as, Cobb-Douglas function.
Our result of the DEA analysis indicates that the higher the efficiency rate, the larger effect a
reduction in mortality will have on GDP. The average overall efficiency rates for all countries in
the data are 91.1% in 1990-2000 and 92.2% in 2000-2010. For the period of 1990-2000, the
mean efficiency rates are 91.14%, 94.50%, 89.26% and 90.35% in HIC, UMIC, LMIC and LIC,
respectively. The figures for the later period 2000-2010, are 92.44%, 93.68%, 90.82% and
92.00% in HIC, UMIC, LMIC and LIC, respectively.
In our empirical analysis we find that countries with exceptionally high efficiency are Bahamas,
Canada, Germany, and Trinidad and Tobago in HIC, Bulgaria, Chile, Kazakhstan, Kosovo,
Latvia, and South Africa in UMIC, Armenia, Azerbaijan, Belarus, China, Lesotho, Nicaragua,
and Ukraine in LMIC, Liberia, Mozambique, Tajikistan, Tanzania, and Zambia in LIC. The
lowest efficiency was gained in Brunei Darussalam, Cyprus, Ireland, and Turkey in HIC,
Botswana, Gabon, and Malaysia in LMIC, Algeria, Bangladesh, Bolivia, Colombia, Egypt,
Guatemala, Honduras, Morocco, Namibia, Peru, Philippine, and Syrian in LMIC, Benin,
Gambia, Kenya, and Madagascar in LIC.
Table 5. Summary of DEA result
1990-2000
2000-2010
Highest efficiency rates
HIC
0.91
0.92
The Bahamas
Canada
Germany
Trinidad and Tobago
Brunei Darussalam
Cyprus
Ireland
Turkey
lowest efficiency rates
Mean of efficiency rates
UMIC
LMIC
0.95
0.89
0.94
0.91
Bulgaria
Armenia
Chile
Azerbaijan
Kazakhstan
Belarus
Kosovo
China
Latvia
Lesotho
South Africa
Nicaragua
Ukraine
Botswana
Algeria
Gabon
Bangladesh
Malaysia
Bolivia
Colombia
Egypt
Guatemala
Honduras
Morocco
Namibia
Peru
Philippine
Syrian
LIC
Totality
0.9
0.91
0.92
0.92
Liberia
Mozambique
Tajikistan
Tanzania
Zambia
Benin
Gambia
Kenya
Madagascar
Table 6. Results of efficiency rates during the period of 1990-2000 using DEA method
Countries
CRS
VRS
Countries
CRS
VRS
Countries
CRS
VRS
Albania
0.572
0.997
Gabon
0.724
0.828
Papua New Guinea
0.842
0.89
18 | P a g e
Algeria
0.628
0.814
Gambia, The
0.878
0.913
Paraguay
0.642
0.787
Armenia
0.532
1
Germany
0.746
0.921
Peru
0.752
0.832
Australia
0.791
0.878
Greece
0.751
0.895
Philippines
0.766
0.831
Austria
0.776
0.925
Guatemala
0.726
0.802
Poland
0.844
0.972
Bahamas, The
0.659
0.852
Guinea
0.913
0.913
Portugal
0.78
0.942
Bangladesh
0.806
0.848
Honduras
0.757
0.816
Romania
0.544
0.935
Belarus
0.69
0.943
Hungary
0.694
0.941
Russian Federation
0.536
0.939
Belgium
0.781
0.934
Iceland
0.747
0.877
Senegal
0.834
0.888
Belize
0.822
0.847
India
0.832
0.889
Seychelles
0.938
0.975
Benin
0.813
0.846
Indonesia
0.939
0.966
Singapore
0.762
0.795
Bolivia
0.775
0.837
Iran, Islamic Rep.
0.82
0.872
Slovenia
0.737
0.949
Brunei Darussalam
0.694
0.802
Italy
0.808
0.954
South Africa
0.996
1
Bulgaria
0.847
1
Japan
0.665
0.933
Spain
0.771
0.93
Burkina Faso
0.998
0.999
Jordan
0.883
0.883
Sri Lanka
0.754
0.903
Cameroon
0.801
0.888
Kazakhstan
0.625
1
Sudan
0.808
0.853
Canada
0.848
0.919
Kenya
0.692
0.844
Swaziland
0.733
0.944
Cape Verde
0.859
0.889
Latvia
0.521
1
Sweden
0.806
0.932
Chad
0.951
0.988
Lesotho
1
1
Switzerland
0.721
0.89
Chile
1
1
Luxembourg
0.866
0.915
Syrian Arab Republic
0.85
0.87
China
1
1
Madagascar
0.712
0.787
Tanzania
1
1
Colombia
0.533
0.831
Malaysia
0.956
0.973
Thailand
0.805
0.899
Costa Rica
0.949
0.96
Mali
0.868
0.907
Togo
0.795
0.873
Cote d'Ivoire
0.855
0.902
Malta
0.744
0.936
Trinidad and Tobago
0.949
1
Cuba
0.602
0.874
Mauritius
0.918
0.958
Tunisia
0.806
0.866
Cyprus
0.848
0.881
Mexico
0.804
0.866
Turkey
0.73
0.835
Czech Republic
0.789
0.951
Morocco
0.665
0.833
Uganda
0.965
0.966
Denmark
0.787
0.93
Mozambique
0.904
0.912
Ukraine
0.354
0.97
Dominica
0.739
0.979
Namibia
0.904
0.913
United Kingdom
0.833
0.948
Dominican Republic
0.824
0.869
Netherlands
0.833
0.923
United States
0.89
0.946
Ecuador
0.801
0.866
New Zealand
0.796
0.881
Uruguay
0.857
0.939
Egypt, Arab Rep.
0.792
0.854
Nicaragua
1
1
Venezuela, RB
0.789
0.866
El Salvador
0.914
0.959
Norway
0.839
0.933
Yemen, Rep.
0.908
0.908
Ethiopia
0.621
0.765
Oman
0.852
0.89
Zambia
1
1
Finland
0.809
0.931
Pakistan
0.893
0.931
France
0.764
0.917
Panama
0.963
0.978
Note: The unity figure in the table indicates a 100% efficiency rate.
Table 7. Results of efficiency rates during the period of 2000-2010 using DEA method
Countries
CRS
VRS
Countries
CRS
VRS
Countries
CRS
VRS
Albania
0.705
0.916
Gambia, The
0.587
0.841
Pakistan
0.68
0.933
Argentina
0.664
0.915
Germany
0.546
1
Panama
0.764
0.929
Armenia
0.869
0.975
Greece
0.567
0.91
Paraguay
0.632
0.883
19 | P a g e
Australia
0.624
0.936
Guatemala
0.562
0.828
Peru
0.684
0.847
Austria
0.551
0.945
Guinea
0.793
0.947
Philippines
0.69
0.904
Azerbaijan
1
1
Honduras
0.589
0.844
Poland
0.655
0.936
Bahamas, The
0.556
1
Hungary
0.585
0.953
Portugal
0.492
0.92
Bangladesh
0.635
0.858
Iceland
0.533
0.857
Romania
0.713
0.967
Belarus
1
1
India
0.826
0.939
Russian Federation
0.759
0.956
Belgium
0.536
0.935
Indonesia
0.623
0.89
Senegal
0.671
0.897
Bolivia
0.618
0.873
Ireland
0.528
0.845
Serbia
0.595
0.951
Botswana
0.624
0.846
Italy
0.485
0.934
Singapore
0.721
0.899
Brazil
0.606
0.857
Japan
0.511
0.945
Slovak Republic
0.722
0.945
Brunei Darussalam
0.492
0.874
Jordan
0.783
0.925
Slovenia
0.588
0.909
Bulgaria
0.751
1
Kazakhstan
0.896
0.984
South Africa
0.599
0.9
Cambodia
0.742
0.863
Kenya
0.655
0.884
Spain
0.521
0.89
Canada
0.62
1
Kosovo
0.823
1
Sri Lanka
0.699
0.938
Cape Verde
0.758
0.937
Lao PDR
0.666
0.875
Swaziland
0.553
0.928
Chile
0.645
0.944
Latvia
0.73
1
Sweden
0.593
0.97
China
0.982
0.984
Lebanon
0.762
0.964
Switzerland
0.572
0.97
Colombia
0.64
0.893
Lesotho
0.656
0.926
Syrian Arab Republic
0.569
0.84
Costa Rica
0.688
0.928
Liberia
1
1
Tajikistan
1
1
Cote d'Ivoire
0.515
0.915
Lithuania
0.726
0.984
Tanzania
0.452
0.81
Croatia
0.632
0.981
Luxembourg
0.546
0.853
Thailand
0.645
0.921
Cuba
0.701
0.945
Malaysia
0.569
0.822
Togo
0.599
0.935
Cyprus
0.542
0.84
Malta
0.54
0.935
Tunisia
0.635
0.87
Czech Republic
0.644
0.922
Mauritania
0.727
1
Turkey
0.59
0.808
Denmark
0.498
0.94
Mauritius
0.659
0.948
Uganda
0.836
0.906
Dominican Republic
0.696
0.89
Mexico
0.54
0.904
Ukraine
0.754
1
Ecuador
0.649
0.867
Moldova
0.681
0.985
United Kingdom
0.548
0.936
Egypt, Arab Rep.
0.656
0.837
Morocco
0.694
0.905
United States
0.571
0.978
El Salvador
0.547
0.9
Mozambique
1
1
Uruguay
0.657
0.976
Estonia
0.639
0.942
Namibia
0.633
0.838
Venezuela, RB
0.538
0.874
Ethiopia
1
1
Netherlands
0.516
0.923
Vietnam
0.758
0.909
Finland
0.574
0.97
New Zealand
0.578
0.939
Yemen, Rep.
0.676
0.889
France
0.537
0.964
Nicaragua
0.527
0.867
Gabon
0.556
0.893
Norway
0.517
0.892
Note: see table 6.
20 | P a g e
4. Conclusions and discussion
The analysis of the causal relationship between maternal and child health and GDP and the
magnitude of effect is vital since this indicates potential economic and social returns on
investments. The objectives of this study were to examine if there is a relationship between
maternal and child health on GDP and to estimate the direction and the magnitude of any such
relationships. In the analysis we use panel data Granger analysis based on a simple bivariate
model to provide some initial evidence. After this, the analysis focuses on the causal relationship
on the effect of health (children) on GDP based on a multivariate model in a Barro framework,
using DEA analysis.
We find in general that the relationships between maternal and child health outcomes and GDP
run in both directions, with the majority running from maternal and child health to GDP. We find
evidence that the causal effects of GDP on maternal and child health outcomes are stronger in
LICs and LMICs relative to HICs and UMICs. This may reflect that the effect of marginal health
investments on health outcomes is stronger at low GDP levels, i.e. in countries where generally
the level of health is lower.
However, in contrast, the causal effect of maternal and child mortality on GDP is generally
stronger in HICs and UMICs. This indicates that the improvement of human capital through
health on GDP is more effective in richer countries, i.e. productivity of labor is relatively higher
in a rich country than in a poor country. Human capital is the stock of competencies, knowledge,
social and personality attributes, including creativity, embodied in the ability to perform labor so
as to produce economic value (Simkovic, 2012). The higher human capital level of richer
countries compared to poorer countries implies that an equal reduction in maternal and child
mortality will cause GDP to increase more in richer countries than in poorer countries.
The DEA analysis shows that the efficiency rates of child health (in terms of mortality) on GDP
has increased somewhat over time and also that the efficiency rates tend to be higher in richer
countries, though the differences are small and insignificant over time as well as between
countries at different degrees of development. Thus our results indicate that health investments in
poorer countries may increase GDP and reduce the gap in health between rich and poor
countries. The analysis also indicates that other important factors in driving GDP growth are
investments in human capital and structural factors such as infrastructure.
In sum, this study shows that the efficiency of health investment works through two different
mechanisms which are important to consider in particular in lower income countries. Firstly,
health investments will improve the health level and will reduce the gap in health inequality
among countries and different income levels. Secondly, investments in health in lower income
countries which increase the efficiency of health on GDP will in addition lead to higher GDP
levels even at the existing level of health in lower income countries which will increase growth
in GDP and reduce income inequality in the world. One important limitation of this study,
21 | P a g e
however, is that we are not able to identify the most important factors which reduce the
efficiency of health in GDP and this is therefore an important task for future research.
An important direction of future researches to investigate what factors drive the efficiency rate
(impact) of maternal and child health on GDP and also whether the trend continues in efficiency
rates and across countries. One important limitation of this study is that we had to restrict the
analysis to only two variables in the econometric Granger analysis, i.e. one variable of health and
GDP, without control of other potentially confounding variables, such as education, and without
consideration of other aspects of health. Another limitation is the short nature of the time
dimension. Thus we suggest that, following the recommendations of the Commission on
Information and Accountability for Women’s and Children’s Health, WHO and other relevant
organizations, in collaboration with researchers, should continue to support countries in
collecting and analyzing macro and micro-level data that can be used to further study the
interaction between health and economic development.
22 | P a g e
5. Appendix
5.1. Fixed effect panel data analysis
Following Hurlin and Venet (2001)16, we consider two covariance stationary variables, denoted
by x and y, observed on T periods and on N cross-section units. In the context of Granger (1969)
causality procedure, for each country i from [1, N], the variable x is causing y if we are able to
predict y using all available information on y and x, than if only the historical information on y
had been used. Thus, we use a time-stationary VAR representation, used for a panel data set. For
each country i and time period t, we estimate the following model (Erdil and Yetkiner, 2010):
p
p
k 1
k 0
yi ,t    k yi ,t k   k xi ,t k ui ,t 17
As Erdil and Yetkiner (2010) stated, it is assumed that the parameters β are identical for all
individual countries, while the coefficients θ may have country-specific dimensions. Also, the
residuals are assumed to satisfy the standard properties, i.e. they are independently, identically
and normally distributed and free from heteroscedasticity and autocorrelation. The use of panel
data, that is, pooling cross section and time series data in a panel data framework, has a number
of advantages. First, it provides a large number of observations. Second, it increases the degrees
of freedom. Finally, it reduces the co-linearity among explanatory variables. In sum, it improves
the efficiency of Granger-causality tests (Hurlin and Venet, 2001; see Erdil and Yetkiner, 2010).
In testing causality with panel data, it may be important to pay attention to the question of
heterogeneity between cross-section units. The first source of heterogeneity is caused by
permanent cross sectional disparities. A pooled estimation without the heterogeneous intercepts
may lead to a bias of the slope estimates and could result in a fallacious inference in causality
tests (Hurlin, 2004a; see Erdil and Yetkiner, 2010). Another basis of heterogeneity caused by
heterogeneous regression coefficients θk is more problematic than the first one, i.e. one should
consider the different sources of heterogeneity of the data generating process. Thus a series of
different causality hypothesis will be tested. Our strategy for investigating Granger no-causality
test is presented in Table 8.
Table 8. Strategy of FE Granger non-causality test
Steps
Tests
1.
HINC
2.
HC (for all countries)
16
Direction of null
hypothesis
Rejected
Accepted
Rejected
Accepted
Results and consequences
Go to the 2th step
We face to invalid coefficients and panel set
Go to the 4th step
Go to the 3th step
In order to explain and review the background of FE method we reference some parts of the paper of Erdil and
Yetkiner (2010) as a good empirical work can be found in the special issue of Applied Economics on page 3 to 5.
17
u is normally distributed with ui,t=αi +εi,t, p is the number of lags, and εi,t are i.i.d. (0, σ2); see also Erdil and
Yetkiner (2010).
23 | P a g e
Rejected
Go to the 4th step
Accepted
We face to homogenous data
Rejected
A casual relationship exists
4.
HENC
Accepted
A casual relationship does not exist
Notes: if any of the tests are accepted, the estimations of variables of interest will be biased.
3.
HC (for subgroups)
According to Table 8, if we can finish all steps successfully, then we can analyze Granger noncausality test completely, though if there is homogeneity then our estimation would be biased. If
the coefficients are not different from each other across countries, then this complicates the
analysis since the model must be enlarged with more equations or variables to take into account
the effects of the differences across countries (see Arellano, 2003). Therefore, finding nonhomogenous coefficients in a model is a key standard qualification in the econometrics analysis
(Arellano, 2003). According to Table 8, the first test procedure, labeled as homogenous and
instantaneous non-causality hypothesis (HINC), is directed towards testing to see whether or not
the θk’s of xi,t-k are simultaneously null for all countries i and for all lag k (for more details see
Erdil and Yetkiner, 2010):
H0: all coefficients are equal to zero
H 0 :  k  0, i  [1, N ], k  [0, p]i  j
H1: there is at least one statistically significant coefficient
H1 :  k  0, (i, k )
If the HINC null hypothesis is not rejected then we may face invalid coefficients θk in the above
Granger model, though if HINC is rejected, we turn to the second step in which we test the
homogenous causality hypothesis (HC), i.e. we test whether all of the coefficients θ are identical
for all lag k and are statistically different from zero:
H0: all coefficients are identical
H 0 :  ki   kj , i , j  [1, N ], k  [0, p]
H1: there are at least two coefficient which are not identical
H1 :  ki   kj , (i, j, k )
If the HC hypothesis is also rejected, this indicates that the process is non-homogenous and no
homogenous causality relationships may be obtained (Hurlin, 2004a), i.e. if both HINC and HC
tests are rejected then it may be possible to find meaningful causal relationships in the Granger
causality test. If the HC hypothesis is rejected, we turn to the third step and test whether the HC
hypothesis is also rejected within subgroups of countries to see whether the rejection is an
overall characteristic or whether it is due to composition of subgroups, i.e. perhaps θ coefficients
24 | P a g e
are not equal in the total sample of countries but turn out equal within subgroups and then we
would have to face homogeneity anyway. In order to divide our data set in subgroups there is no
obvious unique definition, but it may be natural to divide our country data set in income
subgroups (HICs, UMICs, LMICs and LICs). According to Table 1, if the null hypothesis of the
overall and subgroup HC tests are rejected in the second and third step we finally turn to the
fourth step in which we test the heterogeneous non-causality hypothesis (HENC):
H0: exogenous coefficient and its lags of each country are equal to zero means that there is not a
causal relationship between exogenous and endogenous variables
H 0 : ik  0, i  [1, N ], k  [0, p]
H1: exogenous coefficient and its lags of each country are not equal to zero means that there is a
causal relationship between exogenous and endogenous variables
H1 : ik  0
In this step we test the nullity of all the coefficients of the lagged explanatory variable x for each
country. These N individual country tests identify countries for which there are no causal
relationships. If the HENC hypothesis is not rejected, this means that for some countries x does
not cause the variable y. The causal relationship is relevant only for a subgroup of countries for
which the HENC hypothesis is rejected.
As using micro-panels, where there are a large number of cross-section units and a small number
of time series observations, the FE estimator of the coefficients of lagged endogenous variables
are biased and inconsistent (Nickell, 1981; see Erdil and Yetkiner, 2010). However, Nickell
(1981) demonstrates a fall in the size of bias on the coefficients of lagged endogenous variables
with the presence of exogenous regressors. Furthermore, Judson and Owen (1999) provide
Monte Carlo evidence and show that the FE estimator’s bias decreases with T. Finally, there is
one more point to note; Wald test statistics do not have a standard distribution under H0 when T
is small (Hurlin and Venet, 2001). Hurlin (2004a) provides exact critical values for Wald
statistics to test causality in micro panels, which we use to carry out the statistics (Erdil and
Yetkiner, 2010).
5.2. Data Envelopment Analysis (DEA)
Charnes et al. (1978) proposed a model which had an input orientation and assumed constant
returns to scale (CRS). The CRS model which follows (Wu et al., 2006)
t
E c  max
u
k 1
k
y rk
s
v
j 1
25 | P a g e
j
x rj
t
Subject to 0 
u
k 1
k
y rk
 1, i= 1,…,n
s
u
j 1
j
x rj
u k ; v j   >0, all k, i.
As Wu et al. stated to get a geometric appreciation for the CRS model, one example from Cook
and Seiford (2009) can represent the above modeling, a form such as pictured in Figure 1. This
figure provides an illustration of a single output single input case. The x variable is an input and
also y is the output of our DEA model. An empirical problem related to economic theories is to
identify which variable is input and which one is output. To make our example simpler we probe
a single input single output case. Here, each decision maker unit (DMU) is like a country in our
study. Imagine that x is government expenditure growth and y is economic growth in our Barro
framework. If we solve the model above for each of the DMUs, this amounts to projecting those
DMUs to the left, to a point on the frontier. In the case of country or DMU #3, for example, its
projection to the frontier is represented by the point 3*.Intuitively, one would reasonably
measure the efficiency of DMU #3 as the ratio A/B = 4.2/6 = .70 or 70%.
Figure 1: Constant returns to scale projection in the single input single output case.
26 | P a g e
Source: Cook and Seiford (2009).
Subsequent papers have considered alternative set of assumptions, such as Banker et al. (1984)
who proposed a variable returns to scale (VRS) model. One form of their VRS model is (Wu et
al., 2006)
t
E c  max
u
k 1
k
y rk
s


 v0   v j x rj 


j 1


t
Subject to 0 
u
k 1
k
y rk
s


 v0   v j x rj 


j 1


 1, i= 1,…,n
u k ; v j   >0; v0 unconstrained in sign.
Where X ij and Yik represent input and output data for the ith country with j ranging from 1 to s
and k from 1 to t, and  is a small non-Archimedean quantity (Charnes and Cooper, 1984;
Charnes et al. 1979). Index r indicates the country to be rated, and there are n countries. When
v0 is set to 0, the assumption of constant returns to scale is imposed, and the model becomes that
of Charnes et al. (1979). Note that Model (2) is a linear fractional program which can be
transformed to a linear program (Wu et al., 2006)
t
Er  max  uk yrk
k 1
s


s.t.  v0   v j xrj   1
j 1


s




u
y

v


k ik
 0  v j xij   0, i= 1,…,n
k 1
j 1


t
u k ; v j   >0; v0 unconstrained in sign,
Therefore, conventional linear programming (LP) methods can be applied to solve a DEA model
in which one seeks to determine which of the n countries defines an envelopment surface that
represents the best practice, referred to as the empirical production function or the efficient
frontier. Efficient in DEA while those countries that do not, are termed in efficient. As Wu et al.
stated, DEA provides a comprehensive analysis of relative efficiencies for multiple inputmultiple output situations by evaluating each country and measuring its performance rather than
an envelopment surface composed of other countries. Those countries are the peer groups for the
27 | P a g e
inefficient units known as the efficient reference set. As the inefficient units are projected onto
the envelopment surface, the efficient units closest to the projection and whose linear
combination comprises this virtual unit form the peer group for that particular country. The
targets defined by the efficient projections give an indication of how this country can improve to
be efficient (source of the DEA methodology is Wu, Yang & Liang, 2006).
In reference to Figure 2, that portion of the frontier from point 1 up to (but not including) point 2,
constitutes the increasing returns to scale portion of the frontier; point 2 is experiencing constant
returns to scale; all points on the frontier to the right of 2 (i.e., the segments from 2 to 3 and from
3 to 4) make up the decreasing returns to the scale portion of the frontier (Cook and Seiford,
2009).In this study, the efficiency rates are calculated with DEAP software (version 2.1), which
calculates the same efficiency rate for all the inputs of each DMU.
Figure 2. The variable returns to scale Frontier.
Source: Cook and Seiford (2009).
28 | P a g e
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