ABSTRACT
Ghana has experienced an unexpectedly rapid fertility
decline over the past 30 years, which has not been adequately explained in
light of the concurrent persistently low usage of contraception. Factors at
both the individual and contextual level have been investigated for their role
in the determination of fertility levels and differentials in Ghana however,
the relative contributions of the contextual factors compared with the
individual factors to variation in fertility has rarely been studied. This study
investigated how much of the observed district level fertility differentials
are attributable to contextual versus compositional effects using a multilevel
framework.
Data from the second round of the Performance Monitoring and
Accountability 2020 survey were analyzed using a 2-level multilevel framework
with individuals as the first level and districts at the second level. Age,
education, wealth, marital status, history of family planning use and age at
first sex were used as individual-level predictors while urban/rural residence
was included as a district-level explanatory variable. Multilevel multivariate
regression models with interaction terms included were used to determine how
much of the variance was attributable to each level.
Age, education, marital status,
age at first sex and history of family planning were found to significantly
influence cumulative fertility, however, most of the observed effects of these
variables were significantly attenuated when age interactions were included in
the models. The models also found that only 3-4% of the variance in cumulative
fertility could be attributed to contextual effects as opposed to individual
effects.
Cumulative fertility is primarily determined by
individual-level characteristics, and how these characteristics change with age
and over time. Thus, policies aimed at fertility regulation should pay
particular attention to improving the socio-economic circumstances of women.
CHAPTER 1: INTRODUCTION
This chapter introduces the basic
concepts of fertility, and discusses the current levels and differentials in
fertility in Ghana, and what is known about the factors that have contributed
to the current state of affairs. It then identifies gaps in the current state
of knowledge, which are summarized in the form of a problem statement. It goes
on to provide justification for this study in terms of the potential usefulness
of the study results. All this is put in the context of a conceptual framework
that is derived from two current theories of fertility decline. Finally there
is a short discussion of the scope of this study.
BACKGROUND
Fertility decline is one of the
main aims of Ghana’s National Population Policy of 1994. This policy recognizes
“…the crucial importance of a wide understanding of the deleterious effects of
unlimited population growth and the means by which couples can safely and
effectively control their fertility,” (National Population Council, 1994). It
aims to reduce the total fertility rate to 4.0 by 2010 and to 3.0 by 2020;
(Addo, 1987). Achieving sustainable fertility decline across the country
requires a thorough understanding of the factors which influence fertility so
that programs can be designed to modify these factors as needed to achieve the
goal.
Ghana has achieved remarkable
fertility decline in the past four decades. The total fertility rate of Ghana
has fallen from the 6.47 recorded in the World Fertility Survey of 1979
(Cleland & Hobcraft, 1985; Cleland & Scott, 1987) to about 3.7 in 2013
(PMA2020, 2013). While varied reasons have been given for this dramatic decline
(Benefo & Schultz, 1996; Bongaarts, 2006; Boserup, 1985; Bryant, 2007; John
C. Caldwell, Orubuloye, & Caldwell, 1992), fertility transition experienced
in Ghana has not conformed to established models. For example, despite the fact
that increased contraceptive use is thought to be one of the major
drivers of fertility decline across the world, the progress of fertility
decline in Ghana has been achieved without a commensurate increase in the
utilization of contraception (Benefo & Schultz, 1996; Blanc & Grey,
2002)
Researchers investigating the
factors which influence fertility decline have to keep in mind the fact that
the levels of aggregation at which they define their variables have an impact
on the results they obtain (Bliese, 2000; Klein & Kozlowski, 2000)(Bollen
& Van de Sompel, 2006) Population fertility levels result from the
aggregation of the individual reproductive behaviors of the members of the
population and thus, it would appear that variables used to predict fertility
should be defined at the individual level. However, contextual factors such as
social norms on marriage and contraception influence individual reproductive
behavior. Policies and programs aimed at achieving fertility change are often
designed and implemented at population level and these may influence individual
reproductive behavior (Bongaarts, 1994; Pritchett, 1994). Thus, there is a
strong argument to be made for the inclusion of aggregated and population-level
variables in models of fertility (Lloyd & Gage-Brandon, 1994) (Bongaarts,
2001) (John C. Caldwell, 1979).
This interplay between contextual
and individual level variables in the determination of reproductive behavior
and fertility levels has been studied by a number of researchers including Zaba
et al, who in 2004 observed that significant behavioral changes such as
increasing age at first sex could be attributed to “tremendous socio-economic
change resulting in increased levels of education, wealth accompanied by
significant urbanization” and that these were ultimately responsible for the
observed decline in fertility in Ghana. (Zaba, Pisani, Slaymaker, & Boerma,
2004) The determinants of fertility interact with each other not just within,
but also across levels. The complexity of some of these interactions is
illustrated by the interaction between education and urban/rural residence in
their effect on fertility. While urban fertility has been consistently found to
be lower than rural fertility (Muhuri, 1994) (Mboup, 1998) (Lee, 1993) (Alene,
2008), urban/rural residence is also known to be associated with higher levels
of education and literacy (including girl child education) (Zhang, 2006)
(Kravdal, 2002), which are also linked in turn to higher levels of income and
wealth (Barrett, Reardon, & Webb, 2001) (Sahn & Stifel, 2003) higher
usage of contraception, and lower levels of unmet need for contraception (Khan,
Mishra, Arnold, & Abderrahim, 2007) (Adongo et al., 1997) (Ainsworth,
Beegle, & Nyamete, 1996)). The extent to which the observed urban-rural
differentials in fertility are due to differentials in education levels between
urban and rural folk, as opposed to differential distributions of the
determinants of fertility in the urban-rural space has been less studied and is
not clear. Considering the complexity of the determination of fertility, the
population policy of Ghana would appear to be right in its broad based approach
to fertility decline.
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