Dynamic effects of health status’ drivers: Evidence from Burkina Faso

Ousmane Traoré

Department of Economics (UFR/SEG), Université Ouaga II (UO2), 12 P.O BOX 417 Ouagadougou 12. Burkina Faso.

ORCID 0000-0003-2911-5429

Abstract

The study analyses the dynamic relationships of life expectancy and under-five infant

mortality to their determinants in Burkina Faso, from the 1971-2009. Shock on physicians and

nurses produces a decrease in life expectancy, statistically significant in the long run for

nurses. Shock on income and carbon dioxide emission produces an increase of life

expectancy. Infant mortality decreases after the same shocks and shock on it does not affect

life expectancy in the short run, but a significant high increase since the medium run. The

results indicate that social responsibility values, life expectancy and infant mortality policies

must be simultaneously reinforced.

Keywords: population health; life expectancy at birth; infant mortality; dynamic simulation

JEL Classification: I18, C15

This research did not receive any specific grant from funding agencies in the public,

commercial, or not-for-profit sectors.

1. Introduction

Tel : +226 70 15 21 00 ; +226 77 80 01 01 ; +226 79 54 95 96E-mail: oustraor@yahoo.fr

1

Improvements in population health status may be as important as improvements in income,

with regard to enhancing development and human welfare (Bloom & Canning, 2008). In spite

of the remarkable improvements in the health conditions and status worldwide, sub-Saharan

African countries still suffer from some of the worse health problems (Akinlo & Sulola,

2019). Owing to its direct or indirect effects on a population’s capacity to produce and

consume valuable goods and services, health is a fundamental economic factor. Improvements

in the global health status can be measured by life expectancy gains and preventable death

reductions (Sen & Bonita, 2000). In the context of Burkina Faso, during a period of ten years

(1993-2003), the overall mortality decreases from 193 ‰ to 184 ‰. In term of percentage,

this represents just a decrease of 5%. Specifically, for youth mortality, only infant mortality

registered a decrease and this decrease has been drawn only by neonatal mortality (INSD,

2004). Thus, it is obvious that life expectancy at birth experiences little change in the country.

From a policy perspective, understanding the evolution over the short, medium and long

runs of the population’s health status, both in terms of life expectancy at birth and mortality

rate, may become a crucial tool not only in the determination of public health priorities and in

the allocation of resources in the health sector but also in the evaluation of the success of

political programs and in the organization of social security system policies (Laranjeira &

Szrek, 2016). Bourgeois-Pichat (1966) indicates that there is a close relationship between life

expectancy at birth and mortality, which is not deterministic as a simple mathematical

function but can be empirically analyzed from the available data on a given population. It is,

therefore, important to understand how wider changes in socioeconomic conditions impact

health inequalities or health status to shape the design of an effective set of public policies

meant to tackle the aforementioned issue (Allanson & Petrie, 2013).

In Burkina Faso, as in most developing countries, government intervention in the health

system mainly includes the construction of hospitals, with an emphasis on the increase of

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health personnel. The main objective of these supply side health policies is to promote the

access of the population to health services. In this context, several studies focus only on cost

analysis as well as issues relating to the sustainability of health policies and, by extension, the

viability of the health system. However, the impact of public health choices on the overall

health of the population is rarely studied. Government decisions affect the way in which

resources are allocated between health and other goals, between medical services and other

determinants of health, and among the various types of medical services. Thus, if such

allocation decisions are to be optimal, it is necessary to know the returns concomitant to each

possible use (Auster, Leveson, & Sarachek, 1969). So, evaluating and understanding the

evolution of the population’s health status is a compulsory tool in the determination and

assessment of public health policies.

2. Background

It is generally assumed that increasing health services (i.e., more physicians and/or hospital

beds) will provide a corresponding increase in the health status of a population, as the result

of increased accessibility (Kim & Moody, 1992). Changes in the global health status are

contributed by both public-health professionals and some underlying driving forces, many of

which lie outside traditional public health work (Sen & Bonita, 2000). Empirically, high

levels of population health go hand in hand with high levels of national income. Higher

incomes promote better health through improved nutrition, better access to safe water and

sanitation, and increased ability to purchase more and better‐quality health care services.

Access to education also contributes greatly to the epidemiological transition (WHO, 2003).

Auster, Leveson, and Sarachek (1969) find that medical services, combining the service of

physicians, paramedical personnel, capital, and drugs, decrease the age-adjusted death rate. In

addition, high education is associated with relatively low death rates, while high income,

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however, is associated with high mortality when medical care and education are controlled.

Indeed, environmental factors are potential health drivers. The World Health Organization

(WHO, 2015) emphasized that energy and fuel used by residential sector drive about 18% of

the total global carbon dioxide (CO2) emissions. However, carbon dioxide is the most

prominent greenhouse gas that deteriorates the environment and impacts human health

(Wang, Asghar, Zaidi, & Wang, 2019). Using health expenditure as a health indicator,

Apergis, Jebli, and Youssef (2018) find that in the long run, there is a unidirectional causality

running from renewable energy consumption to health expenditures and bidirectional

causality between health expenditures and CO2 emissions.

This paper address the following central research question: to what extent do medical

service allocation, as opposed to environmental and socioeconomic factors, contribute to

changes in the health of the population of Burkina Faso? Previously, Auster, Leveson, and

Sarachek (1969) have mentioned that the answers to such a basic question were not given in a

satisfactory way. Indeed, empirically, once analysts select and estimate a model, inferences

are typically limited to short-run effects and interpretations follow that of a static model: “a

unit change in X (at t-s) leads to an expected change in Y (at time t)” (De Boef & Keele,

2008). As such, analysts frequently fail to compute and interpret quantities such as long-run

impacts of the exogenous variables, the mean, and the median lag lengths of effects (De Boef

& Keele, 2008). Williams and Whitten (2012) show that simulating the quantities of interest

over longer periods and across theoretically interesting scenarios draw much richer inferences

compared to conventional studies, which emphasise statistical significance and other standard

inferences drawn from single coefficients over a one-time period. As an alternative solution,

dynamic simulations offer an alternative to the hypothesis testing of model coefficients by

conveying the substantive significance of the results through meaningful counterfactual

scenarios (Jordan & Philips, 2018).

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The purposes of thes paper are as follows: to estimate, using a dynamic simulation

approach, the sensitivity of health status in Burkina Faso with respect to medical services,

environmental aspects, and socio economic factors over the period 1971-2009. In these

regards, this paper contributes by applying a recently developed dynamic simulation method

(Jordan & Philips, 2018) in an empirical analysis to address the following hypotheses: higher

levels of education and medical services are dynamically associated with better population

health status, while a poor quality of environment (CO2 emissions) dynamically deter a

population’s health status. Higher life expectancy at birth is dynamically associated with

lower under-five infant mortality (hereafter called infant mortality).

3. Methods

3.1. Analytical framework of dynamic simulation on time series data

Studies using time series data must deal with the econometric specification choices. Recent

work in the time series literature has stressed the importance of testing for unit roots as well as

the existence of long-run relationships or cointegration between variables. Since the

presence/absence of each of these characteristics ultimately determines the appropriate model,

the failure to perform such pretesting makes spurious inferences more likely (Philips, 2018).

In this paper, we used pretesting strategies developed by Philips (2018) to formulate better

econometric model specifications and conducted dynamic tests to verify the formulated

hypotheses. Philips (2018) provides a step-by-step guide for adequate model specification,

which we have synthesized as follows:

(i) The first level concerns the preliminary checks compulsory for the next levels. The

steps in this level include the following:

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Step 1. Unit root testing of the dependent variable. The result may be I(1), in order to allow

Step 3.

Step 2. Ensure that no independent variable has an order of integration higher than I(1).

(ii) The second level entails the implementation of the bound test, in the following

steps:

Step 3. Estimate the ARDL in the error correction form.

For this purpose, one may use the information criteria to aid lag specification in the model.

The general form of the ARDL error correction model can be specified as follows:

(1), where is the dependent variable and is a variable among independent variables.

represents the maximum lag of both independent and dependent variables. Notably, the

maximum lag may differ between variables. is a white noise error term.

Step 4. Test the joint significance of all lagged variables appearing in levels using a Wald/F-

test, and proceed to the bound test.

If the bound test suggests cointegration, all variables appearing in levels appear to be I(1) and

have a cointegrating relationship with the dependent variable.

(iii) The third level is the dynamic simulation of the effect of independent variables and

the analytical calculation of the hypothesis testing.

3.2. Models specification

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In this article, we focus on life expectancy at birth and infant mortality as indicators of the

health status of a population. Therefore, in order to have a uniform sense of the desirable

variations in these two indicators, we choose to use the inverse of the under-five mortality.

This lets us avoid some confusion in the interpretations and also facilitate comparisons. Based

on the analytical framework, that leads to the ARDL general specification in equation (1), we

formulate three empirical models. The first model, the equation (2), analyses life expectancy

at birth ( ) as a dependent variable. The second model, equation (3) specifies the inverse of

infant mortality rate, namely , as the dependent variable. In these two models, the

independent variables are summarized in the vector . Based on the literature we retain as

the group of socioeconomic factors, the gross domestic product per capita used as the income

indicator and the levels of school enrolment rate. For the environmental variable, the CO2

emission is used. Medical services comprise the population strength of physicians and the

paramedical personnel per 1000 inhabitants. Medical services in our study refer only to health

professionals under the assumption that capital and drugs are supposed to remain an

insignificant channel compared to the improvement of physicians and the impact of

paramedical personnel on population health. Hence, instead of combining these dimensions of

medical services together, we chose to analyze the performance attributed to health

professionals, ceteris paribus. Certainly, in Burkina Faso, we can highlight the upsurge (in the

last decade) of diseases often individually called “the disease of kings” or “rich man’s

disease,” such as diabetes, cancer, or kidney failure, that require both technical and

professional treatment. However, these pathologies have attracted global concern and are

projected to become the leading cause of deaths worldwide. However, in the specific context

of Burkina Faso, the major concern remains the treatment of the so-called tropical diseases

and the provision of primary health care, which require the support or action of healthcare

professionals in emergency hospital and outpatient care.

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To test our hypothesis regarding the relationship between life expectancy and infant

mortality, we use equation (4). In this equation, infant mortality appears to be a driver of life

expectancy (see the vector of covariates ), as shown in the literature. To account for the

endogeneity or the reverse causation problem that such specifications would lead to, we use

an Instrumental Variables (IV) estimation procedure implemented using the Two-Stage Least

Squares (2SLS), which can solve problems related to both reverse causality and measurement

error (Filmer & Pritchett, 1999). In the model (4), a valid instrument for infant mortality

would be needed to satisfy several econometric conditions. First, the instrument would need

to be correlated with infant mortality. Second, the measurement error in the instrument would

need to be uncorrelated with the measurement error in infant mortality. Third, the instrument

should not be correlated with the endogenous variable (life expectancy at birth). Following all

these conditions, in equation (4), neo-natal mortality is used as an instrument to calculate the

infant mortality rate.

(2),

(3),

where

(4),

8

4. Empirical results

4.1. Variables, descriptive characteristics, and pretesting

We use the time series data drawn from the World Bank dataset and the health statistics of the

Burkina Faso National Institute of Statistics and Demography. Table 1 presents a definition of

variables, the unit root test, and the maximum lag test of these variables. Philip Perron’s unit

root test and the Schwarz and Akaike information criteria procedure are used to test for

stationarity and determine the maximum lag for each variable, respectively. These preliminary

tests lead to the conclusion that all variables are I(1), except the undergraduate nurses per

1000 inhabitants, which is I(0) (p < 0.01). Thus, our independent variables do not have an

order of integration higher than I(1) (see step 2 of the analytical framework). The maximum

lag of dependent variables (life expectancy at birth and infant mortality) is two, while all the

independent variables have one lag.

9

Table 1: Stationarity check and maximum lag of main variables

Variables Definitions P value InterpretationsBound test Inferences

Maximum Lags

Dependent

Life Life expectancy at birth 0.4703 I(1) I(1) 2

Infmort Mortality rate, infant (per 1,000 live births) 0.9921 I(1) I(1) 2

Independent

GDP (income) Gross domestic product per capita 0.3458 I(1) I(1) 1

qco2 CO2 emissions (metric tons per capita) 0.4351 I(1) I(1) 1

Physis Physicians per 1000 inhabitants 0.0122 I(1) I(1) 1

Gradnurse Graduate nurses per 1000 inhabitants 0.1251 I(1) I(1) 1

Ungradnurse Undergraduate nurses per 1000 inhabitants 0.0000 I(0) I(1) 1

Midwife Midwives per 1000 inhabitants 0.1420 I(1) I(1) 1

schoolprim Gross school enrolment rate primary 0.9768 I(1) I(1) 1

schoolsecond Gross school enrolment rate secondary 0.8530 I(1) I(1) 1

schooltertiary Gross school enrolment rate tertiary 0.1022 I(1) I(1) 1

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We use the stata programs (Jordan & Philips, 2018) to test for cointegration based on

Pesaran, Shin, and Smith’s (2001) cointegration test. Table 2 concludes the cointegration at

the 1% level for the models (2), (3), and (4), since the F-statistic for each model is above the

I(0) critical value. The bound test serves as a robustness check for the degree of integration of

the variables (see step 4 of the analytical framework). Thereby, we conclude that all

independent variables are I(1) and have a cointegration relationship with the dependent

variables.

Table 2: Pesaran, Shin, and Smith cointegration test results

F-tests

Thresholds I(0) I(1)

10% 2.260 3.534

5% 2.676 4.130

1% 3.644 5.464

Models F-statistics

(2) 687.00039

(3) 155.00037

(4) 584.00069

4.2. Results of ARDL dynamic simulation

Given the pretesting results, we make dynamic simulations on the ARDL models (2), (3), and

(4) using the dynardl command of the stata dynamac program, developed by Jordan and

Philips (2018). Table 3 presents the numeric results of the simulation. We can observe that the

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number of physicians and the secondary school enrolment rate have significant and negative

effects on life expectancy at birth (equation (2)). Instead, the number of midwives, the

income, the CO2 emission and the primary school rates have positive and significant effects.

For the equation (3), we observe that only the number of physicians and the secondary school

enrolment rate significantly affect the infant mortality rate. A reduction in infant mortality

(the inverse of infant mortality) seems to positively affect life expectancy at birth, as per

equation (4). The estimated coefficient of the instrument variable is significant (p < 0.01).

This indicates that infant mortality is indeed strongly influenced by a “genetic” component of

neonatal mortality (Rutstein, 1984).

Table 3: Simulation results of equation (2), (3), and (4)

(2) (3) (4)

VARIABLES

–0.331***

(0.0398)

–0.201***

(0.0507)

0.103

(0.166)

0.264***

(0.0781)

–0.0150***

(0.00396)

–0.0253*

(0.0146)

–0.0123***

(0.00347)

–0.00141

(0.00253)

0.00452

(0.00935)

–0.00238

(0.00224)

–0.00127

(0.00154)

0.00671

(0.00544)

–0.00108

(0.00133)

12

0.0246***

(0.00607)

–0.000458

(0.0229)

0.0210***

(0.00527)

0.00956**

(0.00457)

–0.0230

(0.0188)

0.00886**

(0.00392)

0.00918***

(0.00304)

0.0132

(0.0151)

–0.000452

(0.00381)

0.0662***

(0.0122)

0.116

(0.0779)

0.0145

(0.0192)

–0.0280***

(0.00763)

–0.0806**

(0.0295)

0.00178

(0.0112)

0.00406

(0.00321)

-0.00541

(0.0111)

–0.000203

(0.00312)

1.113*** –0.601*** 0.776***

(0.152) (0.138) (0.163)

37 37 36

0.888 0.657 0.925

20.65 4.97 26.82

0.0000 0.0005 0.0000

Note: Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

In order to attempt the best interpretation of the substantive of the results, the output of the

command helps visualize the effect of a counterfactual change in one regressor at a single

point in time, holding all other elements as equal, using stochastic simulation techniques.

Graph 1 shows the dynamic simulation results of the impacts on life expectancy (model 2).

For physicians, nurses, and secondary school enrollment rates, a +1% shock at t = 10 on these

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determinants produces a small decrease in life expectancy, statistically significant in the short

run but not in the long run for physicians and secondary school enrollment. The result for

medical personnel, aligned with the findings of Ronny et al. (2019), can challenge the

inexperience of new professional staff and, to some extent, the lack of social responsibility

values in schools and training centres for healthcare professionals. This results can further be

supported by those of Griffiths, et al. (2019) who find that high nursing assistant staffing was

associated with increased mortality.

For midwives, income, CO2 emissions, and primary and tertiary school enrolment rates, a

+1% shock at t = 10 produces an increase in life expectancy, which is significant in both short

and long runs, with a predicted increase value of about 4.1% for primary school enrollment,

3.98% for midwifes, and 3.935% for both income and carbon dioxide emissions.

Graph 2 shows that a 1% shock at t = 10 produces a small decrease in infant mortality (i.e.,

a small increase in the inverse of infant mortality), statistically significant in the short run for

all the independent variables except primary school enrolment. This decrease in infant

mortality is deeper and statistically significant in the long run for all variables, remaining the

deepest for primary school enrolment (i.e., the highest increase in the inverse of infant

mortality (about 6.5%) and income (highest increase in the inverse of infant mortality, by

about 5.25%).

Graph 3 shows that a shock on infant mortality at t = 10 has no impact on life expectancy

in the short run but a significantly high increase in the medium run. This increase remains

significant in the long run, with a predicted value of about 5.125%.

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Graph 1: Dynamic simulation of ARDL model (2): Effects on life expectancy

15

physician gradnurse

ungradnurse midwife

gdp qco 2

schoolprim schoolsecond schooltertiary

Note: Dots show the average predicted value. Shaded lines show (from darkest to lightest) the 75%, 90%, and 95% confidence intervals.

Graph 2: Dynamic simulation of ARDL model (3): Effects on inverse of infant mortality

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physician gradnurse

ungradnurse midwife

gdp qco2

schoolprim schoolsecond schooltertiary

Note: Dots show the average predicted value. Shaded lines show (from darkest to lightest) the 75%, 90%, and 95% confidence intervals.

Graph 3: Dynamic simulation of ARDL model (4): Impact of decrease in infant mortality on

life expectancy

Note: Dots show the average predicted value. Shaded lines show (from darkest to lightest) the 75%, 90%, and

95% confidence intervals.

5. Discussion

Our study outlines the unintended impacts of certain health drivers in Burkina Faso, as

formulated across our hypotheses. First, we find that the general assumption associated with

the increase in health service attainment requires more physicians to the increase the health

status of a population (Kim & Moody, 1992), which is partially true for Burkina Faso. We

obtain some evidence on the negative impact (that is significant in the short run but not in the

long run for physicians) of an increase in the numbers of physicians, graduate nurses, and

undergraduate nurse on life expectancy at birth. Only an increase in the number of midwives

lead to an increase in life expectancy. At this level, our article does not pose a radical

17

challenge to the theoretical relationship between input and output in the field of health, but it

particularly offers complementary knowledge with respect to the existing results on the nature

of the link between health resources and the health status of a population. Indeed, Kim and

Moody (1992) find that health resources, as a whole, do not make a significant contribution to

the health of a population, and this contribution is really rather small in comparison to the role

of socioeconomic resources. The results also indicate that socio-economic and environmental

factors contribute more to the health of a population as compared to medical resources. Thus,

the results confirm and, under certain conditions, can even shed light on the theoretical scope

of the results of Kim and Moody (1992), particularly due to the law of decreasing marginal

returns. However, this situation, in our case, cannot be explained exclusively by the number

of healthcare professional ceteris paribus; here, one has to consider (above all) the fact that

the purpose of increasing the existing number of healthcare professionals may be pursued

even to a point where the detriment of the quality (an important objective) of healthcare

professionals is witnessed. Therefore, a set of vices, including low determination, poor

performance, and failure to consider the values of social responsibility in the provision of

healthcare, can prove harmful to the health of a population. Hence, under these conditions, a

gradual accumulation of healthcare professionals will undermine or even negatively affect the

health capital of a particular population. As a result, health production technology becomes

ineffective.

Second, unlike many studies (Wang, Asghar, Zaidi, & Wang, 2019 ; Apergis, Jebli, &

Youssef, 2018) on the socio-economic effects of CO2 emissions, our results highlight a

positive effect of CO2 emissions on the health status of the population in Burkina Faso. In

theory, this result raises questions about the externalities generated by activities that are

polluting, which can be mixed in nature, i.e., positive and negative. Thus, in accordance with

the growth model of Mohtadi and Roe (1992) that focuses on pollution, the positive effect of

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CO2 emissions reveals that the level or nature of polluting activities in Burkina Faso is such

that the negative externality, due to the harmful effects of pollution on health, remains lower

than the positive externality. This result may have a scope in light of pollution reduction

policies by providing a new argument on the financing or compensation of certain economies

with respect to the implementation of international commitments aimed at reducing

greenhouse gas emissions.

Third, in terms of education, our results, contrary to the generally expected effects of

school enrollment levels, indicate that primary school enrollment is likely to have a greater

effect on the health of a particular population as compared to other levels of education. This

result corroborates the arguments that already support the contribution of primary education

to development in developing countries. As for income, the results seem to confirm our

expectations. Income has the greatest effect on health, after primary education. Put together,

these results are indeed contrary to those of Auster, Leveson, and Sarachek (1969), who find

that higher education is associated with relatively low death rates and higher incomes, albeit

also being associated with high mortality. In view of the effect of income in our case, we can

conclude that the impacts of adverse factors associated with the growth of income, like

unfavorable diets, lack of exercise, psychological tensions etc. are lower to the beneficial

effects of increases in the quantity and quality of care. Therefore, policies that aim to grow the

per capita gross domestic product and primary school enrolment rates deeply impact the

population’s health in Burkina Faso.

Our hypothesis regarding the dynamic relationship between life expectancy at birth and

infant mortality is confirmed in this paper. Indeed, a decrease in infant mortality leads to an

increase in life expectancy in Burkina Faso, in both medium and long runs. This is in the

alignment with Bourgeois-Pichat’s (1966) assertion. Given this result, the objectives of

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reducing infant mortality and improving life expectancy at birth must be pursued

simultaneously.

This research has a number of limitations. First, the simulation analysis may be limited by

the number of observations on the time series data. A more detailed dynamic evolution and

more precise inferences could be obtained using a lengthier time series. This study is also

limited by it use of only medical services (healthcare professionals) as a driver of health. In

the context of the recrudescence of the so-called “rich diseases,” which often requires

evacuations outside Burkina Faso for care and treatment, further studies could combine and/or

dissociate medical services and the medical capital, in addition to other drivers, to analyze the

production of health in Burkina Faso.

6. Concluding remarks

This paper contributes to the existing knowledge on the impacts of population health drivers.

First, it conducts an investigation of life expectancy at birth and infant mortality as health

indicators. Second, by dynamically investigating the evolution of population health status,

which is marked by life expectancy at birth and infant mortality, this work comprises the first

application of a dynamic simulation analysis on health indicators. Thus, it contributes to our

understanding of the dynamic evolution of the population health under the effect of its

determinants. For this purpose, our research may aid the implementation and assessment of

health policies. To reduce infant mortality and to improve life expectancy at birth, some major

policy implications can be draw from our finding.

The finding that an increase in the numbers of physicians, graduate nurses, and

undergraduate nurse lead to a decrease of life expectancy at birth that is significant in the

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short run but not in the long run for physicians show the inefficiency of health care

professionals in Burkina Faso. This result highlights a lack of motivation in care provision.

This requires from government to strengthen the involvement of medical professionals in their

job. To achieve this objective, several policies may be undertaken. First, in the short run, the

government in Burkina Faso must regulate the training school of graduate and undergraduate

nurses that has been dominated by private sector. Without strong regulation, the main

objective of these private schools is essentially pecuniary with a consequence of a decrease in

the quality of health care professionals who have just to pay for the entry in these private

schools instead to pass a selectivity test for the entry. Many of them, each year integrate the

public sector of health system. Second, a major concerns of health care professionals are their

working condition that must be improved by the government. These conditions are the

weakness of wage of health professional and the luck of materials in the health system. The

government of Burkina Faso has adopted in the end of 2019, the policy of “fonction publique

hospitalière” after long discussion with the union of health system workers. This policy

having for objective to improve working conditions in the health system must be seriously

implemented and strengthened in the long term. Third, to perform the competences of health

care professionals in the short and long runs, social responsibility values must be introduced

in each level (public or private) of training in order to avoid corruption in the provision of

health service and to improve the determination of health care providers.

Another policy inference of our result is that the increase of income lead to a higher

increase of life expectancy at birth and higher decrease of infant mortality in the short and

long runs. This result means that health is a luxury good in Burkina Faso that requires higher

involvement of government in health care financing. Growth policies must be improved in the

main sectors of the economy in order to boost the income of the population and also the

public spending in the health sector.

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The results highlight that carbon dioxide emissions have a positive and significant effect

on life expectancy at birth and a negative and significant effect on infant mortality in the both

short and long runs. This finding do not give any support to polluting growth activities but

provide new argument for compensation of less developed economies like the Burkina Faso

with respect to the international commitments aimed at fighting against greenhouse gas

emissions. Countries characterized by low life expectancy and aiming to improve the living

conditions of their population through the structural transformation policies of their

economies based on industrialization, must therefore benefit from support for clean energy

use. These countries must, for example, benefit from technical and financial support for the

development of solar energy.

For the education variables, as the result show a negative effect of secondary school

enrollment on life expectancy at birth, civic and sportive educations must be strengthened in

the secondary school to prevent adolescent to serious damage like cigarette consumption, drug

and alcohol dring, abortion and accidents. These are social scourges that affect many students

from secondary school in several developing countries like Burkina Faso. Policies aiming to

limit of avoid cigarette, drug and alcohol consumption must be reinforced and any activity

that consist to promote of sell these products must be forbidden near the schools. Over, social

responsibility of the parents, in general of households is also required.

Finally, the positive association between reduction of infant mortality and improvement of

life expectancy at birth implies that policies aimed to improve life expectancy and those

aimed to decrease infant mortality are complements in the medium and long runs so must be

simultaneously reinforced since the short run in Burkina Faso. Thus national policies of infant

vaccine and the free of fee care of infant adopted since 2018 by the government in Burkina

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Faso must not have a crowding out effect on the financing of any development policy that

aims to improve well-being of population in particular the elder population.

Conflict of Interest: The author declares that he has no conflict of interest.

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