Article Type : Research Article
Authors : Athanase Iyakaremye, Atul Pati Tripathi, and William Mudahemuka
Keywords : Fertility; Life; Life expectancy; Mortality
This study investigated
the relationship between fertility rates and life expectancy in Rwanda, a
country undergoing significant demographic changes. With a high fertility rate
of 4.2 children per woman and concerning maternal mortality rates, Rwanda faces
challenges exacerbated by complex health issues like HIV/AIDS. However,
initiatives supported by USAID are aiding in combatting these challenges and
improving overall healthcare. The study delved into the intricate interplay
between fertility patterns and life expectancy dynamics and acknowledged the
scarcity of research on this topic specific to Rwanda. Employed quantitative
research methods including descriptive, correlation, and causal-comparative
analysis. Using data spanning from 1965 to 2020, the study covered significant
findings regarding the relationship between fertility and life expectancy.
Results revealed an inverse correlation between fertility rates and life
expectancy, highlighting the importance of interventions to reduce fertility
rates to enhance overall population health and well-being. In conclusion,
Rwanda's strides in healthcare infrastructure and social welfare since the
devastating genocide have led to a notable increase in life expectancy.
Recommendations include continued investment in healthcare systems, focused
vaccination programs, targeted interventions to reduce fertility rates,
promotion of gender parity and women's health, alignment with Sustainable
Development Goals, partnerships with international organizations, and establishing
robust monitoring and evaluation mechanisms for evidence-based decision-making.
These recommendations aim to further improve healthcare and social welfare,
ultimately enhancing life expectancy and fostering sustainable development in
Rwanda.
Rwanda is an African country with the most densely
populated nation, the fertility rate in Rwanda is high at 4.2 children per
woman and maternal mortality is unacceptably high at 210 per 100,000 births.
Rwanda is also a challenging complex VIH/AIDS epidemic with a prevalence of 3%
among the general population but as high as 50% among the most at-risk
populations. However, USAID is supporting the Government of Rwanda to fight
VIH/AIDS and malaria, increase the quality and use of family planning and
reproductive health services, improve maternally newborn and child health,
promote the increase in access to clean water and sanitation, improve nutrition
and strengthen to overall health sectors (“Global Health | Rwanda | U.S. Agency
for International Development,”) [1]. Health improvement is a key determinant
of economic development, Hence, the impact of life expectance on economic
growth is affected by the level of poverty in any country. Many kinds of
research have been conducted on health and economic growth. However, the role
of poverty reduction and the threshold of health influence the life expectancy
of humankind [2]. Using data from 157 countries, Heath and Lorentzen found that
age-specific fertility in women aged 15 to 19 was negatively correlated with
life expectancy and positively correlated with infant mortality [3].
Leutscher report similar results for a sample of 98
countries: Nations with longer life expectancies have higher ages at first
birth and lower age-specific fertility [4]. Although earlier reproduction is
associated with poorer long-term health outcomes, many factors favour earlier
reproduction versus delayed reproduction [5]. All else being equal, individuals
who reproduce earlier leave more offspring, while those who delay reproduction
leave fewer offspring. Delayed reproduction is therefore associated with lower
long-term fitness and a greater likelihood of lineage extinction [6]. Mortality
is expected to affect fertility patterns even more in humans than in other
mammals because human offspring have very long periods of dependency. As a
result, adult mortality will reduce an individual's fitness, not only depriving
that individual of future reproductive opportunities but also endangering the
survival of existing young children. Human life histories may have been
selected to respond to local mortality pressures and to adjust sexual behaviour
and fertility in adaptive patterns to maximize future reproductive success [4].
Rwanda, undergoing a swift demographic transition, is
witnessing notable changes in its population dynamics, particularly regarding
fertility rates and life expectancy (NISR) [7]. Despite the crucial role
fertility plays in shaping life expectancy, this aspect remains relatively
unexplored within Rwanda's demographic context [8]. Although advancements in
healthcare, socioeconomic conditions, and public health initiatives have
contributed to a consistent rise in life expectancy, the precise influence of
fertility on this trend remains ambiguous [9]. Recognizing the significance of
understanding the correlation between fertility and life expectancy,
policymakers, healthcare practitioners, and researchers are urged to develop
targeted interventions aimed at enhancing population health and fostering
sustainable development [10]. Nevertheless, the available literature on this
subject is scant, necessitating comprehensive studies to unravel the intricate
interplay between fertility patterns and life expectancy dynamics specific to
Rwanda [11]. This research endeavour endeavours to offer valuable insights to
assess the role played by fertility on life expectancy in Rwanda, with the
ultimate goal of informing evidence-based policies and interventions designed
to enhance population health outcomes and promote sustainable development
initiatives within the nation.
The effect on economic growth for countries undergoing
the transition in the period of observation is insignificant although it tends
to be negative and larger in absolute value and size than the average effect
for the full of pre-transition countries [12]. The increase in population
growth is the largest for countries shifting from low-income and middle-income
transition. This has the largest negative population effect on the income per
capita [13]. Concerning the economic transition in crude birth rates, the
effect of life expectancy on lower fertility, and lower population growth is
positive but insignificant over the shorter horizon, but positive and
significant over the longer horizon (Bowser and Hill) [14]. The overall
increase in life expectancy represents a significant increase in the
probability that a nation undergoes fertility and demographic transition.
Improving health, such as reducing child and maternal mortality, will increase
the population. These are believed to reduce fertility, stabilize population
growth, and, over the long term, generate a demographic dividend through
reduced youth dependency. On the contrary, another argument for this may be the
increase in population, particularly in sub-Saharan Africa where a large
population is a problem. The gains from better health could be offset by
falling per capita income if the economy is unable to accommodate the growing
population. On the other hand, diseases that do not lead to death but severely
affect a person's health and productivity have a negative impact on
productivity. Unless proper attention and care are given, HIV AIDS is an
example of the previous statement because the disease can make a patient
dependent on others. But when people living with HIV/AIDS receive adequate
medical treatment and adequate nutrition, they can work, perform and produce.
As prepared by Bloom [15].
Adult life expectancy, for example, by encouraging
parents to go to school, causes a significant increase in the productivity of
workers and a notable decline in fertility. These results are consistent with
recent empirical studies by Hansen and Lonstrup, which show that the causal
effect of life expectancy on per capita income growth is small and negative
before the demographic change, but afterward is very positive [16]. The model
follows the approach used in the literature, which explains the decline in fertility
by parents replacing the number of children with quality, see, and inter alia
[17]. However, this paper makes a clear distinction between the quality
provided to children and formal schooling, which in this model is only acquired
by parents. Sede and Ohemeng combine time allocation and the trade-off between
quantity and quality in a framework for fertility decisions [18]. Children are
seen as goods that are both consumed and produced by their parents. Parents
must decide how to spend their limited resources on the number and quality of
children and other goods and services. The production of children is limited by
consumer technology, the income potential of men and women, and the endowment
of women with time and their non-labour income. However, unlike the latter, the
possibility of a negative value is also taken into account. The impact of the
opportunity cost of childcare is considered to be negative on fertility. The
birth and care of children are very time-consuming, so the wages or value of women
in other non-market jobs should be considered as an opportunity cost of having
children. Because higher income is associated with a higher value of female
time, a negative relationship between income and fertility is expected. The
framework has been further expanded to accommodate a dynamic environment in
which decisions about fertility are made at multiple points in time. Muda have criticized the previously presented
neoclassical fertility frameworks. These consumer demand attitudes are not
appropriate for fertility because fertility is not always controlled. Actual
fertility may be higher or lower than desired fertility [19,20].
A framework that analyzes fertility should keep this
in mind. Another approach to fertility is through theories that discuss the
relationships between fertility, population growth, and income. In Solow's
model of economic growth (1956), an increase in population growth causes
capital per worker to decrease and thus has a negative impact on capital
accumulation and output per worker. High fertility leads to high population
growth and therefore lower income. Contrary to the previously presented models,
income does not affect fertility, but fertility does negatively affect income.
Bleakley combines growth theories and household/labour supply theories to
analyze the relationship between economic growth and fertility [21]. A positive
loop is discussed. An increase in capital per worker raises women's wages
because their productivity is more tied to capital. Increasing women's relative
income reduces fertility because it increases the cost of having children
through the time women have to spend having them. Low fertility leads to
further capital accumulation by the worker and strengthens the process. Low
fertility and income growth thus reinforce each other. Myrskyla discovered an
inversion of the relationship between HDI and TFR [22]. A negative association
was found among low- and medium-HDI countries, as predicted by the
household/labour supply models discussed above. In the more developed
countries, however, this pattern was reversed, and the more developed countries
recorded higher fertility rates. It is proposed to characterize the
relationship between HDI and TFR as a J-shape, with a turning point at an HDI
of 0.86. Jin fined a similar reversal in the relationship between economic
development and birth-robust fertility shift [23]. By decomposing GDP per
capita, it is found that female employment is associated with declines in
fertility. They also point out that fertility rates can only be partially
explained by economic developments and underline the importance of
institutional factors. Population growth and high fertility rates in
resource-poor environments can pose challenges for both society and
individuals. A growing population can affect the well-being of that population
in terms of socioeconomic socio-economic development, environmental
sustainability, sustainability, and resource supply. Resource-poor countries
with growing populations face the challenge of creating jobs for emerging
workers while their governments lack the resources to meet the rising demand
for services and infrastructure (United Nations Population Fund; 2012). The
effects of high fertility are also challenging for individuals. When many
children are born to one mother, it places an economic burden on her household
and increases the likelihood that her family will fall (JAMA 2006,
295:18091823). In families that do not
have sufficient adequate resources for education, nutrition, nutrition, and
health care, children - especially girls - may be forced to drop out of school
and marry early. High fertility also increases the risk of having a child being
born prematurely or with low birth weight and stunting as it grows, and preterm
birth increases the maternal health risk for mothers’ risks (World Bank; 2005).
Demographic transition describes a widely observed
phenomenon where the population transitions from high levels of mortality and
fertility to low levels of mortality and fertility. This transition is marked
by an initial decline in child morale due to improved infrastructure, health
system developments, and socioeconomic improvements, followed years later by a
decline in fertility rates. Rwanda was an exception (Popul Dev Rev) [24]. Rapid
improvements in health systems, infrastructure, and social programs over the
past decade have placed Rwanda in a rapid fertility transition. Between 2005
and 2010, the under-five mortality rate was halved from 152 to 76 deaths per
1000 live births, representing one of the fastest improvements in infant
mortality in human history. This decline in fertility and contraceptive use in
Rwanda coincides with a significant change in government officials' attitudes
toward family planning in the context of economic development policies. Given
that Rwanda has the highest population density of any African country (416
people per square kilometer with an annual population growth rate of 2.6%
(National Institute of Statistics of Rwanda; 2012), smaller families and
limited population growth became priorities for individual well-being and
national progress. Officials then launched widespread campaigns to change
public attitudes toward acceptance of small families, with the informal goal of
bringing the total fertility rate to fewer than 4 children per woman [25].
Following the introduction of compulsory free primary education and in response
to the rising cost of living, the government has launched awareness campaigns
to encourage couples to have only as many children as the family can feed, raise
and support. This was reinforced at the community level with community health
workers and community leaders in monthly Community works meetings. Despite this
major shift in fertility, there are many families in Rwanda who still have
large families; more than 20% of women between the ages of 15 and 49 have
currently had five or more births. Rwanda is undergoing a major demographic
transition that is setting the course for the country's economic development
and bucking trends in a region of slow fertility transition. Understanding
predictors of fertility can support the development of policies and
interventions that both help families achieve their desired fertility and
inform government economic policies and infrastructure development plans.
Findings can also inform fertility policies and programming elsewhere in
sub-Saharan Africa. This article examines some of the determinants of fertility
rates in Rwanda, looking separately at women who have ever been married/living
together and women who have never been married.
Research gaps
The empirical review sheds light on various factors
influencing fertility rates and their implications for economic growth and
population dynamics. However, there is a notable gap in research specifically
addressing the impact of fertility on life expectancy in Rwanda. While existing
studies have explored the relationship between fertility and economic growth,
the focus has primarily been on economic consequences rather than demographic
health outcomes such as life expectancy. Economic transitions, including
improvements in healthcare and education, influence fertility rates,
subsequently affecting economic growth [12]. Despite this, there's limited
research directly examining the impact of fertility on life expectancy within
Rwanda. Moreover, the review underscores the multifaceted nature of fertility
decisions influenced by socioeconomic status, healthcare access, and cultural
norms [14]. While some studies suggest a negative association between fertility
rates and economic development, others indicate a more nuanced relationship,
with factors like women's empowerment and education playing vital roles. The
demographic transition in Rwanda, marked by rapid improvements in healthcare
and infrastructure, offers a unique context for investigating the
fertility-life expectancy relationship. Comprehensive studies focusing on this
relationship are needed to fill the research gap and inform evidence-based
policies for population health and sustainable development in Rwanda [4].
The demographic transition
theory
The Demographic Transition Theory, proposed by Warren
Thompson in 1929, offers a framework for understanding the historical shifts in
population dynamics as societies undergo economic and social development.
According to this theory, societies typically transition through four stages,
each characterized by distinct patterns of fertility, mortality, and population
growth. In the initial stage, known as Stage 1, both birth rates and death
rates are high, resulting in minimal population growth. This stage is typical
of pre-industrial societies where limited access to healthcare, sanitation, and
education contributes to high mortality rates, particularly among infants and
children. As societies progress to Stage 2, improvements in healthcare, sanitation,
and living conditions lead to a significant reduction in mortality rates,
particularly among infants and children. However, birth rates remain high,
resulting in rapid population growth. The transition to Stage 3 occurs as
societies experience further economic development, urbanization, and
improvements in education and access to contraception. In this stage, birth
rates begin to decline as individuals choose to have fewer children due to
increased opportunities for education, employment, and urban living [26].
Finally, in Stage 4, both birth rates and death rates are low, resulting in a
stable population size or even population decline in some cases. This stage is
characterized by advanced healthcare systems, widespread access to
contraception, and a high level of urbanization. The Demographic Transition
Theory suggests that as societies progress through these stages, fertility
declines play a significant role in shaping population age structures and life
expectancy [26].
Research design
The following quantitative research methods were
employed such as descriptive research (it requires a very large sample size and
is used to describe a population), correlation research (it explores the
relationship between two or more variables), and causal-comparative (it seeks
to establish the difference in variables between groups). The methodological
approach adopted the descriptive and econometric approaches. The Gross National
Income (GNI) per capita is presented as a function of life expectancy and other
control variables such as education, mortality, and fertility [27]. The time
series were indulged with the unit root problem that makes the error of the
time series nonstationary. Co-integration test plays a big role in finding the
relationship between variables (Juselius). The vector error correction model
(VECM) was used to investigate the effect of fertility on life expectancy in
Rwanda from 1965 to 2020. The general assumption in the suggested model is that
there is at least one long-run co-integration vector for the variables and the
value of the dependable variable can be meant as a function of past values of
the dependent variable, past values of the independent variable, and error
term.
Population and sample
The life expectancy indicator mostly relies on the
number of years of life expectancy at birth. For instance; among the past
studies conducted, employed the life expectancy at birth, utilized the total
number of years that an individual has to live in a country to gauge the life
expectancy variable. The researcher used the number of years of life expectancy
at birth (total in men and women) to measure life expectancy in Rwanda. To
obtain this measure and annual GDP growth rate, data were collected from the
World Bank Database [28].
Data collection
procedures
The data were retrieved from The World Bank’s World Development Indicators from 1965 to 2020. The data on fertility were used to test the co-integration and causality relationship between life expectancy and fertility in panel data. The researcher used the variable of life expectancy as an indicator of health and employed real per capita GDP as a criterion of economic growth. The study used the annual data and covers the period from 1965 to 2020. The logarithms of variables were employed for empirical analyses. The researcher adopted an empirical specification that allows for different effects of life expectancy on the population. To figure out problems of reverse causality and to investigate the causal effect of fertility on life expectancy. The base sample was relevant to the predicted fertility instrument and life expectancy. In further investigations of the human capital, the channel was tracked based on the population share without schooling and on the average years of schooling in the population of working age constructed by Cohen and Soto [29-32] (Figure 1).
Figure 1: Demographic Transition
Model.
The researcher examined the relationship between fertility
and life expectancy:
Fertility art = ? + ?LEart + ?a
+ ?r + ?t + ?rt + eart, (1)
Where a refers to age group, r refers to Rwanda and r
refers to the year.
Fertility is the number of children born per 1000
women by age group. LEart is the number
of average years an individual is expected to live conditional on surviving to
age -group an in year t in Rwanda r. For
a woman of a particular age group, mortality shocks that eventually affect
older individuals impact her life expectancy but not mortality shocks that
impact younger individuals.
In the early 1990s, Rwanda met the tragedy of a
100-day genocide where a million Innocent Tutsis were killed. This destroyed
all infrastructure and left millions in deeper poverty. In this period, life
expectancy reached a low of 26.2 years in 1993 at the height of the genocide.
However, it has risen in 2018 to 68.7 years.
Rwanda's projection in 2032, life expectancy will be 71.4 years. Many
factors have been put into place to increase life expectancy and social
welfare. Therefore, the government of Rwanda has started to invest in
healthcare systems such as primary healthcare systems, HIV/AIDS healthcare
systems, oncology services, community-based health insurance, and medical
education. The potential increase in vaccination activities has been
dramatically improving Rwandans’ health status. After the genocide against the
Tutsis, at least 25% of Rwandan children have been vaccinated against measles
and polio. In 2022, Rwandan infants received vaccinations against 10 diseases
at the rate of 97%. As Rwanda moves toward development growth, there is a
strong decline in deaths from tuberculosis and malaria as well as maternal and
child mortality. After the Genocide against the Tutsis, Rwanda experienced the
world’s highest rate of child mortality. Hence, in 2022, Rwanda has reached the
global average. The VIH/AIDS case and death rates have potentially slowed down.
The external funds have improved Rwandans ‘health. In the year of 1995, Rwanda
received $0.50 per person for healthcare, less than any other country in the
continent of Africa. Many organizations like Partners in Health (PIH) played in
the increase of the population’s access to healthcare and supported Rwanda to
rebuild community health systems (Figure 2). Visually presents Rwanda's life
expectancy at birth, quantifying the average number of years a newborn is
anticipated to live if prevailing mortality trends persist. This metric serves
as a crucial indicator of overall societal health and underscores the urgency
of targeted interventions to improve longevity and well-being. By providing a
snapshot of life expectancy dynamics, Figure 2 offers policymakers and
stakeholders valuable insights into the effectiveness of current health
initiatives and the areas requiring further attention to ensure sustainable
improvements in life expectancy for Rwanda's population.
OLS between life
expectancy and fertility
Table 1 highlights how fertility (P=0.0000) is
negative and statistically significant on life expectancy at 5%. The
coefficient term tells the change in life expectancy for a unit change in
fertility this means that if the fertility rises by 1 unit, then life
expectancy decreases by -0.7831935. In other words, we need to implement
interventions to decrease the fertility rate in order to increase the life
expectancy in Rwanda (Table 1).
Vector autoregressive
model
The autoregressive model in Table 2 has shown how each
variable has an equation modelling its evaluation over time. The below equation
includes the variables and their covariates lagged at one year such as life
expectancy (p?=?0.000), fertility (p?=?0.000), mortality (p?=?0.000), Urban
population growth (annual %) (p?=?0.000), and Population density (people per
sq. km of land area) (p?=?0.000). The prior knowledge required is a list of
variables and covariates that can be hypothesized (Null and Alternative
hypotheses) to affect each other over a while (Table 2).
Granger causality Wald
tests for life expectancy and fertility
Following the results below in (Table 3) where I have tested the causal effect of life expectancy on fertility and its covariates lagged at 1 year.
Fertility (p?=?0.000),
Urban population growth (annual %) (p?=?0.000), and Population density (people
per sq. km of land area) (p?=?0.000) do significantly cause the life expectancy
at 5%, while mortality (p?=?0.058) does cause life expectancy at 10%.
Mortality (p?=?0.000),
Urban population growth (annual %) (p?=?0.007), and Population density (people
per sq. km of land area) (p?=?0.000) do significantly cause fertility at 5%.
Fertility (p?=?0.000) and
Population density (people per sq. km of land area) (p?=?0.000) do
significantly cause fertility at 5%.
Life expectancy
(p?=?0.000), Mortality (p?=?0.002), and Population density (people per sq. km
of land area) (p?=?0.027) significantly cause Urban population growth (annual
%) at 5%, while fertility (p?=?0.063) does cause Urban population growth
(annual %) at 10%.
Life expectancy (p?=?0.000) and Urban population growth (annual %) (p?=?0.000) significantly cause Population density (people per sq. km of land area) at 5%.
It's frequently observed that there exists an inverse correlation between life expectancy at birth and the fertility rate.
Figure
2: The
trend of life expectancy.
Figure 3: Scatter plot of birth rate and life expectancy.
This inference stems from the understanding that as
the likelihood of a child's survival increases, parents tend to opt for smaller
family sizes, whereas in situations of higher infant mortality, families may
compensate by having more children. This nuanced relationship between life
expectancy and fertility underscores the intricate dynamics influencing
population growth and demographic trends, informing policymakers and
researchers about the interplay between healthcare access, socio-economic
factors, and reproductive behaviours in shaping societies' demographic
trajectories. It is clear that given the fitted line, (Figure 3) expresses an
inverse relationship between fertility and life expectancy at birth. The demand
for more children also depends on their survival at birth, which is also one of
the indicators of life expectancy at birth. Given this, a society with a low
life expectancy will have a high birth rate to give the lucky ones a chance to
survive, while societies with a long-life expectancy will have a low birth
rate.
OLS between fertility
and GPI
GPI (P=0.0000) is a negative and statistically
significant on fertility at 5% in (Table 4). The coefficient term tells the
change in fertility for a unit change in GPI this means that if the fertility decreases
by 1 unit, then GPI increases by 1.92. In other words, we need to implement
interventions to increase the GPI rate in order to decrease fertility in
Rwanda.
Conclusion
Rwanda's post-genocide journey has been marked by
remarkable progress in healthcare infrastructure and social welfare. Notably,
life expectancy has surged from the depths of tragedy to 68.7 years in 2018,
with projections soaring to 71.4 years by 2032. This upward trajectory owes
much too strategic investments in healthcare systems, robust vaccination
programs, and vital external funding. As a result, mortality rates from
diseases like tuberculosis, malaria, and HIV/AIDS have plummeted, signifying
tangible improvements in public health outcomes. Furthermore, empirical
analyses underscore a significant correlation between fertility rates and life
expectancy, shedding light on the intricate dynamics shaping population health.
Recognizing this, interventions aimed at reducing fertility rates emerge as pivotal
for enhancing overall well-being and sustainability. By empowering individuals
with access to reproductive healthcare and education, Rwanda stands poised to
further bolster its gains in life expectancy and elevate the quality of life
for its citizens. In conclusion, Rwanda's journey towards improved healthcare
and social welfare stands as a beacon of resilience and progress. As the nation
continues to chart its course towards a brighter future, sustained commitment
to inclusive policies and targeted interventions will be essential in ensuring
that the gains achieved thus far pave the way for lasting prosperity and
well-being for all Rwandans.
Recommendations
Based on the findings presented, several
recommendations can be made to further improve the healthcare system and social
welfare in Rwanda to improve the life expectancy for Rwandan economic
development:
Continued
investment in healthcare systems: The government
should continue its investment in healthcare infrastructure, including primary
healthcare systems, HIV/AIDS healthcare services, oncology services, and
community-based health insurance. This will ensure continued access to
essential healthcare services for all citizens.
Focus
on vaccination programs: Given the significant
impact of vaccination activities on improving health outcomes, Rwanda should
maintain and expand its vaccination programs to cover a wider range of
diseases. This will help further reduce child mortality rates and improve
overall population health.
Targeted
interventions to reduce fertility rates:
Implementing interventions to reduce fertility rates can contribute to
increased life expectancy. This could include education and access to family
planning services, as demonstrated by the negative relationship between
fertility rates and life expectancy.
Promotion
of gender parity and women's health:
Enhancing gender parity and improving women's health can have positive effects
on fertility rates, as evidenced by the relationship between Gender Parity
Index (GPI) and fertility. Policies and programs aimed at empowering women,
providing access to education and healthcare, and promoting gender equality
should be prioritized.
Sustainable
development goals alignment: Aligning healthcare and
social welfare policies with the Sustainable Development Goals (SDGs) will
ensure a holistic approach to development. This includes addressing issues such
as maternal and child mortality, infectious diseases, and poverty reduction.
Partnerships
and external support: Continued collaboration
with international organizations and partners, as demonstrated by initiatives
like Partners in Health (PIH), can provide valuable support in strengthening
healthcare systems and improving access to essential services.
Long-term monitoring and evaluation: Establishing robust
monitoring and evaluation mechanisms to track progress and identify areas for
improvement is essential. This will enable evidence-based decision-making and
ensure that resources are allocated effectively.
The researchers express their deepest gratitude to all
those who contributed to the completion of this research study on the
relationship between fertility and life expectancy in the Rwandan economy.
First and foremost, we extend our sincere appreciation to the United States
Agency for International Development (USAID) for their support in providing
information aimed at improving healthcare in Rwanda. Their assistance has been
invaluable in addressing complex health issues, including HIV/AIDS, maternal
mortality, and overall healthcare infrastructure development. The researchers
also acknowledge the National Institute of Statistics of Rwanda (NISR) for
providing access to the necessary data for conducting this study. Their
commitment to data collection and dissemination has been instrumental in
advancing research efforts in Rwanda. Furthermore, the researchers are grateful
to the healthcare professionals and policymakers in Rwanda who are tirelessly
working towards improving population health outcomes and fostering sustainable
development. Their dedication and efforts are reflected in the notable increase
in life expectancy observed in Rwanda in recent years. Finally, the researchers
extend their appreciation to researchers and other institutions for their
guidance, support, and encouragement by their articles, books, and reports that
were used throughout the research process. Together, these contributions have
enabled the researchers to shed light on the critical relationship between
fertility rates and life expectancy in the Rwandan economy and to provide
evidence-based recommendations for further enhancing healthcare and social
welfare in the country.