Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 
DOI 10.1186/s13561-016-0085-7
RESEARCH Open Access
Socioeconomic determinants of use of
reproductive health services in Ghana
Gordon Abekah-Nkrumah* and Patience Aseweh Abor
Abstract
Background: The study examines trends in the consumption of reproductive health services (use of modern
contraceptives, health facility deliveries, assisted deliveries, first trimester antenatal visit and 4+ antenatal visits) and
their determinants using four rounds of Ghana Demographic and Health Surveys (1993, 1998, 2003 and 2008) data.
Methods: The study uses cross-sectional and pooled probit and negative bionomial regressions models to estimate
the determinants of use of the above listed reproductive health services for the period from 1993 to 2008.
Results: Summary statistics suggest that the above-listed reproductive health services have consistently improved
from 1993 to 2008. However, use of traditional methods of contraception increased in urban centers between 2003
and 2008, although the reverse was the case in rural areas. Regression results suggest that place of residence,
access to and availability of health services, religion, and birth order are significant correlates of use of reproductive
health services. Additionally, the study suggests that the number of living children has the largest effect on use of
modern contraception. The effect of a partner’s education on use of modern contraception is higher than that of
the woman, and a much stronger correlation exists between household wealth and use of reproductive health
inputs than expected.
Conclusion: The study associates the increasing use of traditional contraceptives in urban centers and the much
stronger effect of household wealth with urban poverty and the increasing indirect cost of health services, and
argues for interventions to improve quality of service in public facilities and reduce inequities in the distribution of
health facilities. Finally, the study advocates for family planning-related interventions that involve and target
partners given the importance of partner education in the use of modern contraception.
Keywords: Socioeconomic, Determinants, Reproductive Health
Background To improve mother and child health, the World Health
Mother and child health constitute a major challenge in Organisation (WHO) formulated the Mother Baby Package,
many developing countries. For example, it is estimated based on four principles of safe motherhood: (1) Family
that 99 % of all maternal deaths in 2008 were in develop- Planning – to ensure that individuals and couples have the
ing countries [1], with Sub-Saharan Africa (SSA) having information and services to plan the timing, number and
the highest maternal mortality rate (MMR) of 640/100,000 spacing of pregnancies. (2) Antenatal Care – to prevent
live births. In addition, statistics available for under-five complications where possible and ensure that pregnancy-
mortality and morbidity suggest that developing countries related complications are detected early and treated appro-
shoulder a higher burden compared to developed coun- priately. (3) Clean/Safe Delivery – to ensure that all birth
tries [2, 3]. Thus, a major objective of primary health care attendants have the knowledge, skills and equipment to
programmes in several developing countries is to improve perform a clean and safe delivery, together with postpartum
mother and child survival through improved utilization of care for mother and baby. (4) Essential Obstetric Care –
preventive reproductive and childcare services [4, 5]. to ensure that essential care for high-risk pregnancies and
complications is made available to all women who need it.
Following the implementation of the Safe Motherhood
* Correspondence: gabekah-nkrumah@ug.edu.gh
Department of Public Administration and Health Services Management, programme in many developing countries, and an emphasis
University of Ghana Business School, P. O. Box 78, Legon, Accra, Ghana
© 2016 Abekah-Nkrumah and Abor. Open Access This article is distributed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link
to the Creative Commons license, and indicate if changes were made.
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 2 of 15
on investments in reproductive health inputs as a channel of the five listed reproductive health inputs through pooled
to reducing mother and child-related morbidity and mor- regression estimates. As already indicated, the added value
tality, policy makers and academics have become very inter- of the current study lies in the fact that the use of four
ested in the factors that determine the use of reproductive rounds of survey data makes it possible to examine changes
health inputs/services. Thus, over the last two to three in the use of reproductive health inputs across time both at
decades, substantial research efforts have been directed the national, and rural and urban level. Although our regres-
towards identifying and understanding the factors that sion estimates are based on pooled data, the inclusion of
influence the use of reproductive health inputs. This not- time dummies in the regression model makes it possible to
withstanding, coverage of reproductive health services identify a time effect on the use of reproductive health in-
(especially contraception use and delivery assistance) con- puts. Thirdly, the disaggregation of use of contraception is
tinues to be low, even when MMR and pregnancy-related important in helping us improve our understanding of the
malnutrition and complications continue to be high in nuanced nature of contraception usage and its determinants.
many SSA countries [1, 6]. For example, the 2008
estimated average MMR for SSA was 640/100,000 live Methods
births compared to 85/100,000 for Latin America and Data source
the Caribbean (LAC). Although Ghana’s MMR of The study uses four rounds (1993, 1998, 2003 and 2008)
350/100,000 live births is deemed to be one of the of the GDHS datasets. The Ghana Statistical Service,
lowest in SSA, especially when compared to the supported by OR/IFC Macro and IFC International
1200/100,000 in Chad. Ghana’s figure is nevertheless Company, collected all four rounds of the GDHS data-
high compared to 310/100,000 in Bolivia and 17/ sets. The GDHS is nationally representative and based
100,000 in Chile, the highest and lowest respectively in on a two-stage probability sampling strategy. Females
LAC for the same period. The high levels of MMR in the aged 15–49 years are interviewed from the selected house-
mist low coverage of reproductive health services suggest holds. In addition, men aged 15–59 years from a sub-
the need to revisit the use of reproductive health inputs, sample of a second or third of total households selected
especially with the availability of more recent datasets. are also interviewed. The survey also collect information
Although the existing health literature on Ghana on children aged between 0 and 59 months. Information
abounds in studies that have examined the determinants collected by the GDHS survey relevant to the study in-
of reproductive health inputs [7–10], majority of them cludes: background characteristics of women and their
are either based on a single reproductive health input or husbands/partners, reproductive histories, current use of
on a single cross-sectional dataset. This makes it difficult contraceptive methods, antenatal visits, delivery assistance
to see at a glance the changes in the consumption of re- and health facility deliveries. For the purposes of estimat-
productive health inputs over time and the influence of ing the socioeconomic determinants of use of reproduct-
policy-relevant covariates on several reproductive health ive health services the different waves (1993, 1998, 2003
inputs. In addition, existing studies have mostly looked and 2008) are pooled. In the case of the descriptive statis-
at contraception use from an aggregate perspective (i.e., tics, however, the individual waves are analyzed separately.
whether a woman uses contraception or not, and whether
a woman uses modern contraception or not). We argue Variable definition and measurement
that further disaggregation of an input like contraception Modern contraceptives, delivery care and antenatal care
may be more important in eliciting further information are used as indicators of reproductive health services
for policy targeting. For example, it is not unreasonable to (dependent variables). These three are selected on the basis
assume that the effect of socioeconomic factors on the use that they are part of the four services constituting the pack-
of contraception will depend on the type of contraception age of services under the Safe Motherhood programme.
(modern contraception, condoms only, or all other mod-
ern contraception other than condoms). Current contraceptive usage
Thus, the current paper pools four rounds of Ghana In the survey, women are asked about their current contra-
Demographic and Health Surveys (GDHS) data (i.e., 1993, ceptive use, with the first answer being no use of contra-
1998, 2003 and 2008) and uses that to examine the socioeco- ception at all, up to use of about 13 other methods of
nomic determinants of use of reproductive health inputs contraception, that are either modern or traditional. This
(use of modern contraception, timing of first antenatal visit, variable is recoded into three dummy variables (use of
number of antenatal visits, health facility delivery and deliv- modern contraception, use of other modern contraception,
eries assisted by health professionals). Specifically, the study that is, all other modern methods excluding condoms and
first examines changes in the use of the above-mentioned re- use of only condoms). The three dummy variables are
productive health inputs across the four surveys. Secondly, coded 1 where the relevant method is in use, otherwise 0.
the paper examines the socioeconomic determinants of use Traditionally, contraceptive models have been formulated
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 3 of 15
as use of modern or non-modern methods. This is on the and (Vj = 0) if otherwise. X represents a vector of ex-
basis that non-modern methods are known to be ineffect- planatory variables, and β are coefficients to be esti-
ive and therefore could be likened to a situation of not mated. Consistent with the extant literature, (see for
using contraceptives at all. Thus, the decision to disaggre- example: [13, 14], X is carefully selected to include indi-
gate the variable into the three distinct dummies is to vidual level factors of the women (i.e., age, birth order/
enable us to examine the nuanced nature of the use of the number of living children, level of education and that of
different contraceptive categories (modern, condoms, and her partner, marital status, religion and ethnicity), house-
other modern methods). hold factors (i.e., household wealth index and number of
elderly women in the household) and Community fac-
Delivery care tors (i.e., place of residence and availability and accessi-
Two dummy variables are used to capture delivery care for bility to health facilities). Unfortunately, the GDHS data
the last birth preceding the survey. These are deliveries does not contains variables (distance to health facility,
assisted by health professionals (doctors, nurses and mid- category of health personnel, and health infrastructure)
wives) and deliveries occurring in a health facility (private that have commonly been used as proxies to capture
or public). The variables are coded 1 if delivery took place availability and accessibility to health facilities [8, 15, 16].
in a health facility or was assisted by any of the three health Thus we follow prior authors [17–19] to compute the
professionals, otherwise the variable is coded 0. The choice non-self cluster proportion of households with access to
of the two variables is on the basis that they give a woman good water (NSCPHGW), a non-self cluster proportion
in labour, access to professional delivery services and emer- of households with flush toilets (NSCPHGS), and a non-
gency obstetric care (EOC) where necessary. self-cluster proportion of children with complete vacci-
nations (NSCPCCV) as proxies for accessibility and
Antenatal care availability of health services.
The antenatal visits variable captures the number of With Equation 1, we are assuming that all dependent
antenatal visits made by the pregnant woman (i.e. count variables are binary, including antenatal visits as discussed
form 1,2,3…n). However, WHO recommends at least 4 in Section 2.2. Although the paper’s focus is examining
antenatal visits for a pregnant woman to be deemed pro- the determinants of use or otherwise of reproductive
tected from pregnancy-related risk and complications health services (i.e. binary form), we additionally model
[11, 12]. Based on this recommendation, we assume that the determinants of antenatal care visits in an ordered and
any number of antenatal visits fewer than 4 is as risky as count form via an Ordered Probit (OP) and a Negative
not going at all. Thus, the variable is coded as binary Bionomial Model (NBM). The use of OP and NBM makes
(1 if a woman had 4+ visits, or else 0). In addition, it possible to examine the marginal effect of each add-
antenatal visit is used in an ordered and count form itional visit to the threshold of 4+ (in the case of Ordered
to enable us to examine whether the determinants of Probit) or the maximum number of visits (in the NBM).
the intensity of use of antenatal services differ from For the Ordered Probit, antenatal visits are deemed to
the determinants of the decision to use or not to use be in an ordered discrete choice form (1, 2, 3….4+).
antenatal services. The definition and summary statistics Thus, the probability that a mother chooses any of the
of the remaining variables (i.e., both dependent and inde- alternatives will increase with utility derived. Assuming
pendent variables) used are captured in Table 1. there are I possible outcomes or antenatal choices facing
a mother, a set of threshold coefficients or cut points
Statistical estimation {K1, K2,…, KI − 1} is defined for K0 = −∞ and K0 =∞, and
As indicated in Section One, the object of the study is the choice of antenatal care for the Jth mother may be
examining the determinants of a woman’s decision to use generalized as:
reproductive health services or not in Ghana. Framing the 
  
 
question in this form reduces the woman’s decision into a Pr V j ¼ i ¼ Pr Ki−1 < Xjβþ uj < Ki : ð2Þ
binary choice set (i.e. using or not using reproductive health
services). If the two alternatives are generalized as J, and an Where the probability that individual j will choose out-
indirect utility derived from choosing any of the two alterna- come i depends on the attributes of antenatal care and
tives as V, then the probability that a woman will use or not those of the individual/households and community (Xjβ)
use reproductive health services can be expressed as below. falling between (i − 1). X represents a vector of explanatory

  
  variables, also defined in Table 1, and β are the coefficients
Pr V j ¼ 1 ¼ Pr Xjβþ εj > 0 : ð1Þ to be estimated. The cut-points for the antenatal healthcare
choices are based on ordering the number of visits made
Where, for instance, (Vj = 1) if reproductive healthcare to the health centre, i.e., ranging from 0 visits, 1 visit… to
is used based on the definition of the variables in Table 1, the maximum number of visits which according to the
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 4 of 15
Table 1 Summary statistics for use of reproductive health inputs − pooled data: 1993, 1998, 2003, 2008
Variables Contraceptive models Variables Antenatal and delivery
N Mean SD N Mean SD
Modern contraception 8,270 0.150 0.357 Delivery assistance 8,261 0.476 0.499
Use of condoms only 8,270 0.025 0.155 Health facility 8,259 0.458 0.498
Other modern contracep 8,270 0.125 0.331 Woman’s age
Woman’s age 15–19 (Ref) 8,261 0.051 0.221
15–19 (1 = base) 8,270 0.051 0.221 20–24 8,261 0.205 0.404
20–24 = (2) 8,270 0.205 0.404 25–29 8,261 0.256 0.436
25–29 = (3) 8,270 0.256 0.436 30–34 8,261 0.208 0.406
30–34 = (4) 8,270 0.208 0.406 35–39 8,261 0.158 0.365
35–39 = (5) 8,270 0.158 0.364 40–44 8,261 0.086 0.281
40–44 = (6) 8,270 0.086 0.281 45–49 8,261 0.036 0.186
45–49 = (7) 8,270 0.036 0.187 Birth order
Woman’s education One child (Ref) 8,261 0.208 0.406
No educ (1 = Base) 8,270 0.418 0.493 Two children 8,261 0.195 0.396
Primary = (2) 8,270 0.276 0.447 Three children 8,261 0.158 0.365
Secondary = (3) 8,270 0.293 0.455 Four and above 8,261 0.438 0.496
Tertiary = (4) 8,270 0.013 0.115 Woman’s education
Partner education No educ (Ref) 8,261 0.417 0.493
No educ (1 = Base) 8,270 0.328 0.469 Primary 8,261 0.276 0.447
Primary = (2) 8,270 0.168 0.374 Secondary 8,261 0.294 0.455
Secondary = (3) 8,270 0.400 0.490 Tertiary 8,261 0.013 0.115
Tertiary = (4) 8,270 0.063 0.243 Partner education
Marriage dummy 8,270 0.896 0.306 No educ (Ref) 8,261 0.327 0.469
Muslim dummy 8,270 0.324 0.468 Primary 8,261 0.168 0.374
Ethnicity Secondary 8,261 0.400 0.490
Akan (1 = Base) 8,270 0.424 0.494 Tertiary 8,261 0.063 0.243
Ga/Dangme = (2) 8,270 0.061 0.239 Missing Husb. Dummy 8,261 0.042 0.200
Ewe and Guans = (3) 8,270 0.145 0.352 Muslim dummy 8,261 0.324 0.468
North ethnicities = (4) 8,270 0.328 0.470 Ethnicity
Others = (5) 8,270 0.042 0.200 Akan (Ref) 8,261 0.424 0.494
Household wealth Ga/Dangme 8,261 0.061 0.240
Poorest (1 = Base) 8,270 0.288 0.453 Ewe and Guans 8,261 0.145 0.352
Poorer = (2) 8,270 0.217 0.412 North ethnicities 8,261 0.328 0.469
Middle = (3) 8,270 0.183 0.387 Others 8,261 0.042 0.200
Richer = (4) 8,270 0.171 0.377 Number of elderly 8,261 1.382 0.716
Richest = (5) 8,270 0.141 0.348 Household wealth
Ecological zones Poorest (Ref) 8,261 0.288 0.453
Southern belt (1 = Base) 8,270 0.253 0.435 Poorer 8,261 0.217 0.412
Capital city = (2) 8,270 0.092 0.288 Middle 8,261 0.183 0.387
Middle belt = (3) 8,270 0.356 0.479 Richer 8,261 0.171 0.377
Northern belt = (4) 8,270 0.299 0.458 Richest 8,261 0.141 0.348
Rural dummy 8,270 0.708 0.454 Ecological zones
NSCPHGW 8,270 0.391 0.393 Southern belt (Ref) 8,261 0.253 0.435
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 5 of 15
Table 1 Summary statistics for use of reproductive health inputs − pooled data: 1993, 1998, 2003, 2008 (Continued)
NSCPHFT 8,270 0.058 0.165 Capital city 8,261 0.092 0.288
NSCPCCV 8,270 0.717 0.179 Middle belt 8,261 0.357 0.479
No. of living children Northern belt 8,261 0.299 0.458
No child (1 = Base) 8,270 0.003 0.052 Rural dummy 8,261 0.708 0.455
One child = (2) 8,270 0.233 0.423 NSCPHGW 8,261 0.391 0.393
Two children = (3) 8,270 0.214 0.410 NSCPHFT 8,261 0.058 0.165
Three children = (4) 8,270 0.170 0.376 NSCPCCV 8,261 0.717 0.179
Four and above = (5) 8,270 0.380 0.486 Year
Year 1993 dummy 8,261 0.219 0.413
1993 dummy (1 = Base) 8,270 0.219 0.413 1998 dummy 8,261 0.256 0.436
1998 dummy = (2) 8,270 0.256 0.436 2003 dummy 8,261 0.291 0.454
2003 dummy = (3) 8,270 0.291 0.454 2008 dummy 8,261 0.235 0.424
2008 dummy = (4) 8,270 0.235 0.424 Sample dummy
Timing of 1st antenatal 7514
No. antenatal visits 8083
Source: Authors’ calculations. Calculations take account of sample weights. Note that the models on timing of 1st antenatal visits and number of antenatal visits
are based on slightly different samples per the sample dummy. NSCPHGW, NSCPHFT and NSCPCCV are the non-self-cluster proportion of households with good
water, non-self-cluster proportion of households with flush toilets, and non-self cluster proportion of children under five with complete vaccination, respectively.
The values in parentheses next to the variables are the definitional codes. Note, partner’s education includes a 5th category (missing husbands), which is excluded
from the table. This was added to cater for women who do not have partners and would otherwise have been dropped from the regressions
WHO standards is 4+ for appropriate antenatal care. Thus, contraception may be attributed to the urban decline in
Equation 1 is used to estimate all the binary dependent var- the use of modern contraception.
iables, while Equation 2 is used to estimate the determinant In addition to the use of modern contraception, the re-
of antenatal visits in an ordered form. In the case of the in- sults in Table 2 suggest that health facility deliveries and de-
tensity of use of antenatal visits to the maximum number, liveries assisted by health professional have been increasing
an NBM is used and the model specification is attached as gradually in Ghana. Even when the data is disaggregated
Appendix 2, with both the estimates of the Ordered Probit into urban and rural areas, health facilities and assisted de-
and NBM contained in Table 5 in Appendix 1. liveries continue to show gradual increases, except for the
large gap between rural and urban areas. Antenatal care
Results (i.e., antenatal visit in first trimester and 4+ visits) also im-
Descriptive results proved across years, both at the national and disaggregated
In this section, we present trends in the use of the three level (rural/urban). The results in Table 2 equally suggest a
reproductive health inputs at the national and rural/urban marginal rural/urban difference in whether the first ante-
areas. Figures 1 and 2 present contraceptive usage at the natal visit occurred in the first trimester, whereas for 4+
national level for all women, and women below 34 years antenatal visits, the rural/urban gap remains large.
of age, respectively. Figures 1 and 2 suggest that the use of Although consumption of contraceptives declined for
modern contraceptives (i.e., any modern method, con- the period 2003 to 2008, the general trend has been that
doms only and other modern methods) have been improv- consumption of reproductive health inputs has been bet-
ing gradually over the years, except in the case of ter in Ghana compared to many other African countries.
traditional methods where, as expected, usage is on the For example, Ghana’s percentage of women making 4+
decline. Condoms seem to be the least used method of antenatal visits, delivering in a health facility and using
contraception, although the rate of use among women modern contraception and condoms in 2008 is relatively
34 years and below is higher than the average among all better than respective figures in Liberia (66 %, 36.9 %,
women. What is, however, surprising is the fact that apart 11.7 % and 3.5 %), Nigeria (44.8 %, 35 %, 10.5 % and
from traditional methods, use of all other methods of 4.7 %), Sierra Leone (56.1 %, 24.6 %, 8.2 % and 1.1 %),
contraception declined between 2003 and 2008. The fig- Madagascar (49.3 %, 35.3 %, 23 % and 1 %) and Kenya
ures in Table 2 suggest a 19.7 % drop in the use of modern (47.1 %, 42.6 %, 28 % and 2.6 %). Notwithstanding this, it
contraceptives between 2003 and 2008. Given that rural is also the case that Ghana’s performance compares un-
consumption continued to increase for the same period, favourably to other developing countries such as Bolivia
the national level drop in the use of all forms of modern (72.1 %, 67.5 %, 24 % and 3.6 %), Paraguay (90.5 %, 84.6 %,
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 6 of 15
Fig. 1 Trends in contraceptive usage - all women (%)
52.4 % and 16.3 %) and Jamaica (87 %, 97.6 %, 52 % and consumption model could be endogenous based on re-
19.4 %) [20]. verse causality. A standard correction to this challenge is
the implementation of instrumental variable (IV) pro-
Regression results cedure. However, it is very difficult to find appropriate
As earlier indicated, the determinants of use of repro- instruments for endogenous NLC from the DHS data. In
ductive health services are estimated using probit the absence of an IV procedure, an alternative is drop-
models. However, in the case of antenatal visits, add- ping the NLC from the model. However dropping the
itional models; Ordered Probit and Negative Binomial NLC from the model could potentially result in endo-
Models (NBM) were used to estimate the marginal effect geneity bias arising from omitted variables, especially
of every additional visit from 1, 2, 3 and 4+ and 1, 2, 3, given the fact that NLC has the highest effect on con-
4….n visits respectively. Although the coefficients of the sumption of contraceptives (see Table 3). For the avoid-
Ordered Probit and NBM were slightly different from ance of doubt, we have re-estimated the model without
that of the probit model, the direction of correlation and NLC. The results (not shown) remain generally the same
level of significance are generally the same. Thus, we as including it in terms of direction of correlation and
present the results of the probit models. In addition, level of significance, but with a drop in the goodness of
number of living children (NLC) in a contraception fit of the model. Thus, we argue that removing the NLC
Fig. 2 Trends in contraceptive usage - Women under 35 (%)
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 7 of 15
Table 2 Trends in the use of reproductive health inputs in Ghana general, not much emphasis is often placed on the quasi
Reproductive health inputs 1993 1998 2003 2008 R-square in a probit model.
National estimates As per the results in Table 3 (see estimates for sample
Contraceptive usage Traditional 90.73 89.34 84.73 86.51 of all women), age does not have a significant effect on
use of modern contraceptives, although women in the
Modern 9.27 10.66 15.27 13.49
20–24, 35–39 and 40–44 age brackets are more likely to
Place of delivery Home 57.84 54.65 52.06 39.81 use other modern contraceptive methods (i.e., modern
Health facility 42.16 45.35 47.94 60.19 contraceptive methods other than condoms). Where
Professional delivery assist No 56.22 53.78 50.80 36.49 modern contraceptives is redefined to mean only con-
Yes 43.78 46.22 49.20 63.51 doms, all the coefficients on women’s age, with the ex-
Antenatal visit in 1st trimester No 61.28 55.89 50.08 42.62 ception of women in the 20–24 age bracket, become
significant with a change in sign from positive to nega-
Yes 38.72 44.11 49.92 57.38
tive. This suggests that compared to younger woman,
4+ antenatal visits No 39.95 35.14 28.00 20.00 relatively older women are less likely to use condoms as
Yes 60.05 64.86 72.00 80.00 contraceptives. Even where the model is re-estimated
Urban estimates using a sample of women below 34 years of age, the re-
Contraceptive usage Traditional 87.50 87.27 82.59 86.02 sults generally remain the same. Besides contraceptive
Modern 12.50 12.73 17.41 13.98 use, age has a positive correlation with pregnancy-related
reproductive health services (i.e., whether the first ante-
Place of delivery Home 20.32 22.54 20.69 16.59
natal visit occurred in the first trimester, 4+ antenatal
Health facility 79.68 77.46 79.31 83.41 visits, health facility deliveries and deliveries assisted by
Professional delivery assist No 18.55 21.91 19.73 14.25 health professionals – See Table 4). However, it is import-
Yes 81.45 78.09 80.27 85.75 ant to note that as per the size of the coefficients, the ef-
Antenatal visit In 1st trimester No 55.15 50.88 42.95 37.43 fect of age on consumption of pregnancy-related
Yes 44.85 49.12 57.05 62.57 reproductive health services increases with age, reaches a
peak around 40–44 and declines from age 45 and beyond.
4+ antenatal visits No 16.88 18.49 10.95 9.73
Except for first trimester antenatal visits, women and
Yes 83.12 81.51 89.05 90.27 partners’ education and household wealth are positively
Rural estimates and significantly correlated with all the dependent vari-
Contraceptive usage Traditional 92.68 90.50 86.74 86.98 ables for contraception use, antenatal and delivery care.
Modern 7.32 9.50 13.26 13.02 Although both women and partner’s education are signifi-
Place of delivery Home 72.91 65.96 69.52 55.44 cant and positive, the coefficients of partners’ education
are slightly higher than that of women’s education in the
Health facility 27.09 34.04 30.48 44.56
contraception model. The reverse is true for the antenatal
Professional delivery assist No 71.32 65.01 68.08 51.47 and delivery care models. In addition to the fact that the
Yes 28.68 34.99 31.92 48.53 effect of household wealth is significant and positive, the
Antenatal visit in 1st trimester No 64.16 57.80 54.41 46.23 size of the coefficients increases as one moves from a
Yes 35.84 42.20 45.59 53.77 lower to a higher wealth category. Compared to unmar-
4+ antenatal visits No 49.14 40.86 37.25 26.88 ried women, married women are more likely to use any
form of modern contraception, although the probability of
Yes 50.86 59.14 62.75 73.12
use reduces in the case of condoms. In addition, the
Source: Authors’ calculation
Note: Calculation takes account of sample weight results also suggest that compared to other religions,
Muslim women are less likely to use any form of modern
from the contraception model will equally lead to an contraception, have their first antenatal visit within the
endogeneity bias, in addition to compromising the good- first trimester of pregnancy, have 4+ antenatal visits, de-
ness of fit of the regression model. Thus, the contracep- liver in a health facility or to deliver with the assistance of a
tion model uses the NLC as one of the covariates. health professional. Whereas women who have more living
It is important to caution that the probit estimates children are more likely to use different forms of modern
should be interpreted with care given the potential endo- contraceptives, women with 2nd to 4th order births are less
geneity of number of living children in the contraception likely to use antenatal or delivery care. In the case of birth
models. It is also important to acknowledge that our order, the size of the coefficients increase as a woman
quasi R-square is low. However, this in itself is not a moves from a lower order birth to a higher order birth.
challenge given that most relevant variables used in the The ecological zone and rural dummies are not signifi-
literature are included in our model and the fact that in cant in the contraception models. However, rural women
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 8 of 15
Table 3 Socioeconomic determinants of contraception use in Ghana
Variables Sample of all women Sample of women 34 and below
Use of modern Use of condoms Use of other Use of modern Use of condoms Use of other
contraception only modern methods contraception only modern methods
Beta SE Beta SE Beta SE Beta SE Beta SE Beta SE
Woman’s age
20–24 0.0156 [0.0218] −0.0058 [0.0036] 0.0437* [0.0262] 0.0161 [0.0212] −0.0078 [0.0049] 0.0416* [0.0239]
25–29 0.0035 [0.0219] −0.0077** [0.0035] 0.0326 [0.0253] 0.0086 [0.0216] −0.0108** [0.0051] 0.0362 [0.0231]
30–34 0.0091 [0.0238] −0.0070* [0.0040] 0.0383 [0.0282] 0.0205 [0.0240] −0.0099* [0.0056] 0.0493* [0.0268]
35–39 0.0100 [0.0257] −0.0129*** [0.0029] 0.0543* [0.0316]
40–44 0.0158 [0.0278] −0.0130*** [0.0024] 0.0643* [0.0352]
45–49 −0.0341 [0.0276] −0.0133*** [0.0020] 0.0107 [0.0357]
Woman’s
education
Primary 0.0547*** [0.0129] 0.0097** [0.0046] 0.0429*** [0.0117] 0.0535*** [0.0152] 0.0121* [0.0064] 0.0398*** [0.0135]
Secondary 0.0654*** [0.0144] 0.0126*** [0.0048] 0.0507*** [0.0133] 0.0739*** [0.0169] 0.0187*** [0.0071] 0.0531*** [0.0152]
Tertiary 0.0723* [0.0392] 0.0067 [0.0110] 0.0731* [0.0388] 0.1120** [0.0539] 0.0138 [0.0186] 0.1114** [0.0547]
Partner education
Primary 0.0816*** [0.0183] 0.0096 [0.0070] 0.0732*** [0.0174] 0.0660*** [0.0211] 0.0116 [0.0096] 0.0573*** [0.0196]
Secondary 0.0537*** [0.0140] 0.0130** [0.0058] 0.0392*** [0.0124] 0.0554*** [0.0171] 0.0217** [0.0085] 0.0340** [0.0146]
Tertiary 0.0959*** [0.0259] 0.0379** [0.0156] 0.0531** [0.0222] 0.0871*** [0.0311] 0.0550** [0.0233] 0.0335 [0.0250]
Missing husband 0.0302 [0.0332] 0.0287 [0.0200] −0.0008 [0.0280] 0.0285 [0.0355] 0.0359 [0.0252] −0.0048 [0.0281]
dummy
Women in union 0.0519*** [0.0129] 0.0087*** [0.0030] 0.0375*** [0.0121] 0.0473*** [0.0154] 0.0093** [0.0047] 0.0329** [0.0142]
Muslim dummy −0.0416*** [0.0100] −0.0001 [0.0034] −0.0388*** [0.0091] −0.0397*** [0.0116] −0.0017 [0.0046] −0.0351*** [0.0103]
Ethnicity
Ga/Dangme 0.0113 [0.0172] 0.0058 [0.0057] 0.0035 [0.0167] 0.0166 [0.0198] 0.0113 [0.0092] 0.0036 [0.0183]
Ewe and Guans 0.0292** [0.0139] 0.0053 [0.0039] 0.0201 [0.0128] 0.0153 [0.0139] 0.0073 [0.0052] 0.0047 [0.0125]
Northern 0.0298* [0.0161] −0.0021 [0.0040] 0.0290* [0.0149] 0.0233 [0.0179] 0.0009 [0.0060] 0.0188 [0.0160]
ethnicities
Others 0.0218 [0.0232] 0.0109 [0.0096] 0.0106 [0.0217] 0.0107 [0.0259] 0.0124 [0.0130] 0.0008 [0.0233]
Household wealth
Poorer 0.0258** [0.0127] 0.0129** [0.0064] 0.0162 [0.0110] 0.0245* [0.0142] 0.0148* [0.0085] 0.0130 [0.0120]
Middle 0.0408*** [0.0154] 0.0193** [0.0080] 0.0253* [0.0132] 0.0329* [0.0178] 0.0184* [0.0096] 0.0183 [0.0151]
Richer 0.0784*** [0.0190] 0.0255** [0.0102] 0.0538*** [0.0165] 0.0661*** [0.0221] 0.0300** [0.0130] 0.0383** [0.0185]
Richest 0.1229*** [0.0264] 0.0354** [0.0148] 0.0874*** [0.0237] 0.0873*** [0.0278] 0.0423** [0.0183] 0.0475** [0.0233]
Ecological zones
Capital city 0.0000 [0.0169] 0.0103* [0.0062] −0.0199 [0.0151] −0.0325* [0.0170] 0.0062 [0.0072] −0.0439*** [0.0147]
Middle belt 0.0167 [0.0113] −0.0001 [0.0028] 0.0163 [0.0106] −0.0004 [0.0117] −0.0024 [0.0037] 0.0023 [0.0108]
Northern belt 0.0180 [0.0179] 0.0017 [0.0050] 0.0141 [0.0162] 0.0146 [0.0201] 0.0039 [0.0074] 0.0089 [0.0177]
Rural dummy −0.0069 [0.0130] −0.0042 [0.0033] −0.0002 [0.0115] −0.0188 [0.0146] −0.0062 [0.0045] −0.0096 [0.0126]
NSCPHGW −0.0162 [0.0149] −0.0054 [0.0040] −0.0081 [0.0139] −0.0063 [0.0174] −0.0076 [0.0056] 0.0030 [0.0153]
NSCPHFT 0.0024 [0.0234] −0.0042 [0.0060] 0.0114 [0.0224] 0.0116 [0.0271] −0.0050 [0.0083] 0.0200 [0.0250]
NSCPCCV 0.0780*** [0.0266] 0.0045 [0.0064] 0.0700*** [0.0248] 0.0940*** [0.0296] 0.0049 [0.0090] 0.0851*** [0.0273]
No. of living
children
One child 0.9087*** [0.0125] 0.6103*** [0.0479] 0.8706*** [0.0165] 0.8702*** [0.0166] 0.5315*** [0.0479] 0.8131*** [0.0221]
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 9 of 15
Table 3 Socioeconomic determinants of contraception use in Ghana (Continued)
Two children 0.9194*** [0.0112] 0.5551*** [0.0567] 0.9020*** [0.0128] 0.9004*** [0.0144] 0.5112*** [0.0573] 0.8713*** [0.0174]
Three children 0.9320*** [0.0078] 0.6198*** [0.0579] 0.9244*** [0.0095] 0.9298*** [0.0094] 0.6298*** [0.0610] 0.9177*** [0.0125]
Four and above 0.8787*** [0.0152] 0.4386*** [0.0448] 0.8328*** [0.0178] 0.9316*** [0.0101] 0.6870*** [0.0545] 0.9094*** [0.0141]
Year
1998 dummy 0.0596*** [0.0162] 0.0014 [0.0041] 0.0598*** [0.0156] 0.0368** [0.0174] −0.0039 [0.0050] 0.0453*** [0.0166]
2003 dummy 0.1252*** [0.0173] 0.0069 [0.0044] 0.1160*** [0.0173] 0.1129*** [0.0191] 0.0067 [0.0059] 0.1045*** [0.0187]
2008 dummy 0.1035*** [0.0181] −0.0005 [0.0040] 0.1067*** [0.0179] 0.0801*** [0.0191] −0.0031 [0.0049] 0.0880*** [0.0187]
Number of 8270 8270 8270 5955 5955 5955
observations
Pseudo R2 0.074 0.116 0.069 0.074 0.105 0.069
P-value 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Source: Authors’ calculations
Note: *** is significant at p < 0.01, ** is significant at p < 0.05, * is significant at p < 0.10. NSCPHGW, NSCPHFT and NSCPCCV are the non-self cluster proportion of
households with good water, non-self cluster proportion of households with flush toilet and non-self cluster proportion of children under five with complete
vaccination, respectively
Partner’s education includes a fifth category (missing husbands) but this is excluded from the table. It was added to cater for women who do not have partners
and would otherwise have been excluded from the regressions
are less likely to have 4+ antenatal visits, deliver in a evident from the data, urban expansion arising from
health facility and have professionally assisted deliveries. rural–urban migration may provide a plausible explan-
In addition, women in the capital city and middle belt ation. The implications of rural–urban migration may be
are more likely to have 4+ antenatal visits, deliver in a an increased number of urban dwellers who have char-
health facility and have professionally assisted deliveries, acteristics (education and household wealth) similar to
compared to women in the southern belt. The results rural dwellers. In addition, such migrants often live at
also show that women from Northern Ghana are signifi- the peripheries/fringes of the city or in urban slums
cantly more likely to have 4+ antenatal visits compared where access to health facilities or services are highly
to women from Southern Ghana, but less likely to go for constrained. Given such constrained access to health
antenatal visits in the first trimester (p < 0.10), deliver in services, lower levels of education and income, it is rea-
a health facility or use delivery assistance from health sonable to argue that women needing contraceptives
professionals (p > 0.10). Also, NSCPCCV is significantly may turn to available substitutes such as traditional
positively correlated with modern contraceptives and methods. Indeed, recent evidence from the Multiple
other modern contraceptives. In addition, NSCPCCV, Cluster Indicator Survey [21] suggests a decline in urban
NSCPHGW and NSCPHFT are significantly positively health facility deliveries at a time when health facility de-
correlated with 4+ antenatal visits, health facility delivery liveries in rural areas are increasing. Finally the descrip-
and professionally assisted deliveries. tive results show a large rural urban gap in 4+ antenatal
Finally, the coefficients of the year dummies suggest visits. This gap may be explained by a variety of factors,
that women were more likely to use modern contracep- including poor road infrastructure, longer average dis-
tion or any other modern contraception in 1998, 2003 and tance to health facilities in rural areas, and the skewed
2008, respectively, compared to 1993. In the case of ante- distribution of health facilities and health personnel in
natal and delivery care, however, the results suggest that favour of urban centres, therefore making it difficult, if
women in 1998 and 2003 were less likely to have 4+ ante- not impossible, for women to have access to and con-
natal visit, deliver in a health facility and have profession- sume reproductive health services even when available.
ally assisted deliveries compared to women in 1993. In the case of the regression results, the effect of a
woman’s age on use of condoms and other modern
Discussion of results contraception is not unexpected. The finding that women
The descriptive results suggest that condoms are popular above the age of 25 are significantly less likely to use con-
among women 34 years and below compared to all other doms compared to women below 20 may be explained by
women. This may be due to the fact that such women the fact that younger women who may not have started
are more likely to find alternative contraceptive methods bearing children are afraid that use of other modern contra-
such as pills, injectables, and implants as intrusive and ceptives (such as injectables, pills and implants) create in-
stigmatizing. Additionally, the descriptive results suggest fertility problems and may therefore not be willing to use
a decline in urban consumption of modern contracep- them [22, 23]. Conversely, women who are 25 years of age
tives. Although reasons for the decline are not directly or older are more likely to be married and may need the
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 10 of 15
Table 4 Socioeconomic determinants of antenatal care and delivery care in Ghana
Variables Antenatal care Delivery care
1st trimester antenatal visit 4+ antenatal visits Health facility deliveries Delivery assistance by health professional
Beta SE Beta SE Beta SE Beta SE
Woman’s age
20–24 0.0684** [0.0302] 0.0327 [0.0232] 0.0399 [0.0311] 0.0620** [0.0304]
25–29 0.1529*** [0.0329] 0.1155*** [0.0236] 0.1382*** [0.0343] 0.1630*** [0.0331]
30–34 0.1480*** [0.0342] 0.1303*** [0.0247] 0.1586*** [0.0384] 0.1875*** [0.0374]
35–39 0.1491*** [0.0375] 0.1461*** [0.0238] 0.1769*** [0.0390] 0.2031*** [0.0385]
40–44 0.1634*** [0.0398] 0.1504*** [0.0227] 0.1948*** [0.0411] 0.2044*** [0.0401]
45–49 0.0974** [0.0430] 0.1250*** [0.0271] 0.1612*** [0.0460] 0.1836*** [0.0455]
Birth order
2nd birth order −0.0188 [0.0206] −0.0637*** [0.0195] −0.1489*** [0.0207] −0.1436*** [0.0207]
3rd birth order −0.0517** [0.0230] −0.1230*** [0.0235] −0.1786*** [0.0232] −0.1935*** [0.0233]
4th plus birth order −0.1211*** [0.0241] −0.1316*** [0.0218] −0.1956*** [0.0260] −0.2085*** [0.0270]
Woman’s education
Primary 0.0280 [0.0182] 0.0625*** [0.0139] 0.0545*** [0.0181] 0.0501*** [0.0180]
Secondary 0.0103 [0.0194] 0.1180*** [0.0168] 0.1461*** [0.0197] 0.1412*** [0.0192]
Tertiary 0.1916*** [0.0601] 0.2217*** [0.0455] 0.2352** [0.0929] 0.2668*** [0.1021]
Partner education
Primary −0.0057 [0.0213] 0.0465*** [0.0171] 0.1100*** [0.0226] 0.1081*** [0.0220]
Secondary 0.0003 [0.0202] 0.0656*** [0.0165] 0.1448*** [0.0200] 0.1401*** [0.0198]
Tertiary 0.0406 [0.0304] 0.1820*** [0.0206] 0.2955*** [0.0324] 0.2871*** [0.0315]
Missing husband dummy −0.0480 [0.0349] −0.0690** [0.0338] 0.0480 [0.0387] 0.0507 [0.0399]
Muslim dummy −0.0509*** [0.0168] −0.0881*** [0.0146] −0.0759*** [0.0189] −0.0833*** [0.0183]
Ethnicity
Ga/Dangme −0.0420 [0.0307] −0.1263*** [0.0306] −0.0333 [0.0338] −0.0374 [0.0330]
Ewe and Guans 0.0174 [0.0176] −0.0217 [0.0194] 0.0072 [0.0242] −0.0119 [0.0248]
Northern ethnicities 0.0565** [0.0244] 0.0401* [0.0218] 0.0345 [0.0295] 0.0315 [0.0288]
Others 0.1067*** [0.0342] 0.0398 [0.0292] 0.1055*** [0.0357] 0.0872** [0.0350]
No. of elder women HH 0.0028 [0.0082] 0.0062 [0.0082] 0.0183* [0.0108] 0.0256** [0.0112]
Household wealth
Poorer 0.0370** [0.0182] 0.0396** [0.0157] 0.0354* [0.0199] 0.0341* [0.0192]
Middle 0.0412** [0.0201] 0.0494*** [0.0164] 0.1075*** [0.0211] 0.1057*** [0.0205]
Richer 0.0961*** [0.0238] 0.1376*** [0.0178] 0.2099*** [0.0258] 0.2051*** [0.0252]
Richest 0.1806*** [0.0293] 0.1951*** [0.0195] 0.2976*** [0.0308] 0.3051*** [0.0301]
Ecological zones
Capital city −0.0426 [0.0282] 0.0647** [0.0271] 0.1215*** [0.0393] 0.1034*** [0.0377]
Middle belt −0.0156 [0.0170] 0.0554*** [0.0157] 0.1355*** [0.0199] 0.1354*** [0.0200]
Northern belt −0.0460* [0.0265] 0.0995*** [0.0227] −0.0117 [0.0313] −0.0109 [0.0311]
Rural dummy 0.0306 [0.0199] −0.0766*** [0.0188] −0.2226*** [0.0235] −0.2289*** [0.0217]
NSCPHGW 0.0319 [0.0255] 0.0492** [0.0245] 0.0829*** [0.0291] 0.0637** [0.0286]
NSCPHFT 0.0038 [0.0432] 0.0333 [0.0596] 0.1221* [0.0664] 0.1601** [0.0651]
NSCPCCV 0.0290 [0.0401] 0.2363*** [0.0362] 0.1228** [0.0500] 0.1514*** [0.0488]
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 11 of 15
Table 4 Socioeconomic determinants of antenatal care and delivery care in Ghana (Continued)
Year
1998 dummy 0.0551** [0.0240] −0.0143 [0.0201] −0.0469* [0.0263] −0.0603** [0.0264]
2003 dummy 0.1040*** [0.0246] 0.0278 [0.0197] −0.0543* [0.0290] −0.0564** [0.0284]
2008 dummy 0.1917*** [0.0229] 0.0944*** [0.0179] 0.0941*** [0.0291] 0.1231*** [0.0279]
No. of observations 7514 8083 8263 8261
Pseudo R2 0.041 0.145 0.277 0.278
Alpha 985.1906
Chi2 463.5 0.0000
P-value 0.0000 0.0000 0.0000
Source: Authors’ calculations
Note: *** is significant at p< 0.01, ** is significant at p< 0.05, * is significant at p< 0.10. NSCPHGW, NSCPHFT and NSCPCCV are the non-self cluster proportion of households
with good water, non-self cluster proportion of households with flush toilet and non-self cluster proportion of children under five with complete vaccination, respectively
Partner’s education includes a 5th category (missing husbands) but this is excluded from the table. It was added to cater for women who do not have partners
and would otherwise have been excluded from the regressions
consent of their partner to use condoms, which are more sector and are outside the domain of mainstream clinical
likely to interfere with sexual relations. Thus, the use of service providers. Thus, family planning consumables
other forms of modern contraception, seen as less interfer- such as condoms, pills and injectable are sold on the
ing sexually, may appeal to such women much more than market at slightly subsidized prices, making access to re-
condoms. In addition, the inverted U-shaped relationship sources/wealth an important determinant [35, 36]. In
between age and pregnancy-related-reproductive health in- the case of antenatal and delivery care, the positive effect
puts may be due to the fact that pregnancy complications of household wealth is somewhat surprising, especially
increases with age, leading to increased consumption of re- when one considers the fact that such services are free
productive health inputs among relatively older pregnant in public facilities and also covered by the National
women [7, 24]. However, given that reproductive activity Health Insurance Scheme. Perhaps the indirect cost of
reduces at older ages (35–44), it is reasonable to assume these inputs (distance to health facility and the opportunity
that consumption of reproductive health inputs will decline cost of visiting a health facility) may be as important as fees
among women of such age group [25, 26]. paid at the point of service. Alternatively, the poor quality
Women’s education may be a proxy for women’s auton- of service at some public facilities may mean that some po-
omy; an important determinant of women’s ability to make tential users turn to private providers, who charge market
strategic life choices [27, 28]. These include decisions to use prices and thereby make household wealth an important
contraceptives, visit the hospital for antenatal care and de- determinant. The fact that the private health sector in
liver in a health facility [29]. Similarly, educated women are Ghana (Private For Profit Providers, PFPP; Faith-Based Pro-
likely to be more efficient (through access to and use of viders, FBP; and Private Non-Profit Providers, PNPP) ac-
health-related information) in the production of health com- counts for around 55 % of health services [37] lends some
pared to their uneducated counterparts [8, 14, 30, 31]. The credence to this suggestion. In addition, an analysis of data
difference in the size of the women and partners’ education from the GLSS 4 (1998/99) and GLSS 5 (2005/06) suggests
coefficients, although marginal, is still important. As indi- that the proportion of the respective survey sample who
cated earlier, education may influence household decision- had medical problems and sought help from public facilities
making and, possibly, control of the choice or consumption dropped from 48 to 45 %, while those who sought help
of reproductive health services. Thus, it may be the case that from PFPP and PNPP increased marginally from 47 to
on matters of contraception, partners have greater control 49 %, and 6 to 8 %, respectively [38]. Descriptive results
over decision-making [32–35]. Hence, partners who are ed- from the GLSS 4 and 5 suggest that 51 and 48 % of the
ucated and understand the benefit of contraception use are sample, respectively, in the lowest income quintile used ser-
more likely to exert such influence in the decision to use vices of private providers against 48 and 49 % for those in
modern contraception. The higher effect of women’s educa- the richest quintile. Similarly, 48 and 51 % of the sample
tion on the pregnancy-related reproductive health inputs from rural areas used the services of private providers,
compared to her partner’s education may be a reflection of against 50 and 47 % of those from urban centres.
access to resources rather than control of decision-making. In addition, the positive effect of being located in the cap-
The positive effect of household wealth on the use of ital city or the middle belt reflects the resource-rich nature
modern contraception is expected. In Ghana, family of these zones as well as the concentration of social services
planning products are generally controlled by the private such as schools and health facilities, thereby improving
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 12 of 15
access relative to the southern belt. To the contrary, the important implications for reproductive and child health
negative effect of the Northern belt and women living in policy formulation. First, the increased use of traditional
rural areas reflects a high prevalence of poverty and inad- methods of contraception in urban areas is worrying. It may
equate infrastructure such as health facilities in rural areas. therefore become important for policy makers to revisit the
For example, four rounds of the GLSS – 1991/92, 1998, rural–urban equity narrative in the face of high levels of
2005/06 and 2014 – have consistently cited the Northern rural–urban migration, as indicated earlier. The existing nar-
belt (Northern, Upper East and Upper West regions) to be rative that tends to emphasize the fact that rural dwellers are
the most poverty endermic zone in Ghana. Also, the finding worse off compared to their urban counterparts may lead to
that rural women are less likely to use reproductive health resource concentration in rural areas in some cases. For ex-
inputs compared to urban women may be due to the fact ample, the desire in Ghana to bridge the rural–urban gap in
that in Ghana, as in many developing countries, social infra- the use of reproductive health services, and for that matter
structure such as health, water and sanitation facilities tend reduce MMR in rural areas, led to the adoption and imple-
to be clustered around urban centres. Thus, urban dwellers mentation of the Community Health Planning and Services
are more likely to be closer to such facilities and therefore (CHPS) programme in 2003. After about a decade of being
to use them compared to rural women [26, 39, 40]. implemented, evidence from GDHS 2008 and MICS 2011
The fact that married women and women with more liv- suggests that the use of some reproductive health inputs
ing children are more likely to use contraceptives is straight- (modern contraception and health facility deliveries) have
forward and consistent with the existing literature [22, 23]. improved in rural areas at a time when usage is declining in
In addition, the size (largest) of the coefficient of NLC on urban centres. Although this paper is not suggesting that the
use of modern contraception is significant: the NLC a rural–urban consumption difference in the said reproductive
woman has is the single most important decision point for health inputs is due to the presence or otherwise of the
the use of contraceptives. This may have undesired implica- CHPS programme, it will be equally important for policy
tions for population control, especially in a society like makers to relook at how to balance rural–urban resources
Ghana where cultural pressures favour relatively large family distribution in a manner that responds to current needs.
sizes. For example, the 2008 GHDS suggests that on the Secondly, the fact that the probability of using reproduct-
average, a Ghanaian women desires to have four children. ive health inputs increases with the level of wealth is critical
For birth order, the negative correlation may be due to the for policy intervention and targeting. As indicated earlier,
fact that first time/early births are more likely to be associ- antenatal and delivery care are generally free and catered
ated with pregnancy and birth-related complications. This for under the National Health Insurance System. Thus, the
may explain first timers’ use of more reproductive health in- strong correlation between household wealth and use of
puts compared to women with later order births. It may antenatal and delivery care suggests that costs other than
also be the case that first-timers/women with early order the direct cost of the services rendered may be very import-
birth may be responding to recommendations from health ant. Thus, policy makers may need to revisit the discourse
workers to use reproductive health inputs to reduce the on reducing the indirect cost of accessing reproductive
level of risk normally associated with first-time pregnancies health services, which in Ghana is more likely to be associ-
[41]. The negative effect on the Muslim dummy, is perhaps ated with the average distance to health facilities and the
an indication that where beliefs associated with the Muslim opportunity cost of visiting the health facilities.
religion conflict with the demands of modern medicine such In addition, the fact that the number of living children
as reproductive healthcare, Muslim women may opt not to has the largest effect on the probability of using modern
use it [10, 42]. Not surprisingly, prior authors have found contraceptives in a country where, on the average, women
that in Ghana, Muslim woman are less likely to use repro- desire to have four children should be an issue for policy at-
ductive health inputs compared to Christian women tention. The development literature suggest that the desire
[7, 9, 10]. The positive effect of the health accessibility and for large family sizes in developing countries is normally
availability proxies (NSCPHGW, NSCPHFTand NSCPCCV) driven by the need for farm hands and sometimes insur-
confirms the existing literature [8, 43] that social infrastruc- ance/pensions in old age. Thus, policy measures to
ture such as health facilities and health personnel are crucial modernize agriculture with improved access to subsidized
to the consumption of reproductive health inputs. and cheap agricultural technology and inputs, together with
appropriate pension schemes especially for rural dwellers,
Policy implications and conclusion will reduce the desire for large families. Other than the is-
This study set out to examine the changes in the use of sues of increasing use of traditional contraception in urban
reproductive health inputs (use of modern contraception, areas, the effect of household wealth, partner’s education
and antenatal and delivery care) over time, from 1993 to and number of living children, the effect of the other covar-
2008, as well as the socioeconomic determinants of use of iates on the use of reproductive health inputs is standard
reproductive health inputs. The findings of the study have and supported by the findings of existing studies.
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 13 of 15
Appendix 1: Ordered Probit and Negative
Binomial Estimates
Table 5 Socioeconomic determinants of antenatal visits and hospital facility delivery in Ghana
Variables Ordered antenatal visits Number of antenatal visits
Beta SE Beta SE
Woman’s age
20–24 −0.0105* [0.0063] 0.3821** [0.1742]
25–29 −0.0329*** [0.0062] 1.0020*** [0.1930]
30–34 −0.0351*** [0.0062] 1.1870*** [0.2275]
35–39 −0.0399*** [0.0055] 1.6063*** [0.2515]
40–44 −0.0373*** [0.0051] 1.5979*** [0.2821]
45–49 −0.0336*** [0.0055] 1.5225*** [0.3803]
Birth order
2nd birth order 0.0244*** [0.0068] −0.4571*** [0.1006]
3rd birth order 0.0423*** [0.0092] −0.6750*** [0.1155]
4th plus birth order 0.0437*** [0.0074] −0.9199*** [0.1264]
Woman’s education
Primary −0.0204*** [0.0036] 0.5812*** [0.1126]
Secondary −0.0360*** [0.0046] 0.8647*** [0.1262]
Tertiary −0.0499*** [0.0052] 1.0138*** [0.2673]
Partner education
Primary −0.0162*** [0.0043] 0.4080*** [0.1506]
Secondary −0.0201*** [0.0045] 0.5296*** [0.1140]
Tertiary −0.0455*** [0.0040] 1.1103*** [0.1888]
Missing husband dummy 0.0282** [0.0121] −0.2587 [0.2042]
Muslim dummy 0.0332*** [0.0051] −0.5588*** [0.0977]
Ethnicity
Ga/Dangme 0.0319*** [0.0104] −0.6683*** [0.1517]
Ewe and Guans 0.0090 [0.0064] −0.4239*** [0.1209]
Northern ethnicities −0.0134** [0.0061] 0.2082 [0.1521]
Others −0.0088 [0.0089] 0.1398 [0.2300]
No. of elder women HH −0.0014 [0.0027] 0.0949 [0.0580]
Household wealth
Poorer −0.0118*** [0.0044] 0.2170 [0.1349]
Middle −0.0166*** [0.0043] 0.5920*** [0.1424]
Richer −0.0387*** [0.0043] 1.0628*** [0.1635]
Richest −0.0504*** [0.0047] 1.8244*** [0.2110]
Ecological zones
Capital city −0.0154** [0.0073] 0.5230*** [0.1651]
Middle belt −0.0178*** [0.0045] 0.3527*** [0.1131]
Northern belt −0.0210*** [0.0065] 0.2901 [0.1893]
Rural dummy 0.0255*** [0.0052] −0.5817*** [0.1191]
NSCPHGW −0.0093 [0.0075] 0.6315*** [0.1586]
NSCPHFT −0.0149 [0.0184] 0.1153 [0.2191]
NSCPCCV −0.0788*** [0.0128] 1.6288*** [0.3052]
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 14 of 15
Table 5 Socioeconomic determinants of antenatal visits and hospital facility delivery in Ghana (Continued)
Year
1998 dummy 0.0074 [0.0068] 0.1156 [0.1627]
2003 dummy −0.0116* [0.0064] −0.0212 [0.1523]
2008 dummy −0.0307*** [0.0051] 0.4506*** [0.1550]
No. of observations 8083 8083
Pseudo R2 0.098
Alpha 0.1533
Chi2 1111.4 1514.0
P-value 0.0000 0.0000
Source: Authors’ calculations
Note: *** is significant at p < 0.01, ** is significant at p < 0.05, * is significant at p < 0.10. NSCPHGW, NSCPHFT and NSCPCCV are the non-self cluster proportion of
households with good water, non-self cluster proportion of households with flush toilet and non-self cluster proportion of children under five with complete vac-
cination, respectively. Partner’s education includes a 5th category (missing husbands) but this is excluded from the table. It was added to cater for women who do
not have partners and would otherwise have been excluded from the regressions
Appendix 2: The negative Binomial Model use the negative binomial model (NBM) to correct for
The negative binomial model improves the efficiency of over-dispersion. The NBM follows from the Poisson
the Poisson model by adding a parameter, and an error model but introduces an additional parameter, an error
term to the mean function of the Poisson distribution. term εj, to the mean function of the Poisson distribution
In the Poisson model, the number of antenatal visits y, as in Equation A3.
as in the current case, for a woman j, has a Poisson dis-   
tribution with a mean μ conditioned on some covariates μ~j ¼ E y j xj ¼ exjβþεj ; :yj ¼ 0; 1; 2……… ðA3Þ
x as below:   With the introduction of the error term, the NBM al-μ xjβj ¼ E yj xj ¼ e ; :yj ¼ 0; 1; 2……… ðA1Þ lows for cross-sectional heterogeneity, given that an unob-
served individual effect can be taken into the conditional
where β is the a parameter to be estimated, and the mean function. Following from this, and assuming that
probability of y given the vector of covariates x exp(ej) has a distribution with a mean of 1 and a variance
expres sed as in equation (A2) below : of α , the conditional mean of yj will continue to be μj.  y Thus as α approaches 0, yj becomes a Poisson distribution. ¼ e−μjμ jj ð Þ On the other hand, if exp(e ) is assumed to have a gammaPr y x jj j ; A2y ! :yj ¼ 0; 1; 2;…… ::j distribution with a gamma function Γ(), then the condi-
tional probability function yj will be the negative binomial
In practice, count data often show over-dispersion distribution as in Equation A4 below.
with the effect that the variance becomes more than the
mean and therefore violates the principal Poisson as-
sumption that the variance should be equal to the mean. 
  
 
Γ y þ α−1  α−1 
¼ j α
−1 μ yj
Apart from the over-dispersion, another challenge of the Pr yj xj y !Γðα− ;1Þ α−1 þ μ α−1 þ μ
Poisson model is the issue of excess zeros, which is often j
associated with count data such as use of antenatal visits. yj ¼ 0; 1; 2………
To overcome the two challenges, alternative approaches ðA4Þ
(such as the Negative Binomial, Zero-Inflated Poisson and
Negative Inflated Binomial models) have been used in the Equation A4 was used to estimate the intensity of use
literature to account for over-dispersion and excess zeros. of antenatal visits in the count form.
In the current case, excess zeros don’t seem to be a chal- Competing interests
lenge given that the percentage of zeros (i.e., those not The authors declare that they have no competing interest.
using) is 12.94 %, 11.20 %, 7.92 % and 3.93 % for 1993,
Authors’ contributions
and 1998, 2003 for 2008, respectively. In addition, the PAA conceived the idea and was responsible for writing the background
variance of antenatal visits is less than the mean in all and conclusion section of the paper as well as proof reading and formatting
years. Nonetheless, the lrtest that alpha = 0 (i.e., the Pois- the final manuscript. GA was responsible for acquiring the relevant data,
carrying out modeling and estimation and consequently writing the
son model is an appropriate fit for the data) is rejected at methods, results and discussion. Both authors read and approved the final
p < 0.001 (see column 3 Table 5 in Appendix 1). Thus we manuscript.
Abekah-Nkrumah and Abor Health Economics Review  (2016) 6:9 Page 15 of 15
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