Wheres my justice shooting victim wonders as former Mari.docx

'Where's my justice?' shooting victim wonders as
former Marine gets probation
By DALE VINCENT
New Hampshire Union Leader
MANCHESTER — “Where’s my justice? Where’s my justice?”
shooting victim
Josephine Otim cried Tuesday as she stood outside the
courtroom where the former
Marine who pulled the trigger was placed on probation for five
years.
Hillsborough County Superior Court North Judge Gillian
Abramson said she didn’t
believe the New Hampshire State Prison system could give
Thomas Landry, now 27,
the treatment he needs for his Post Traumatic Stress Disorder
(PTSD). She gave him
suspended sentences of 7 1/2 to 15 years and 3 1/2 to seven
years for the random
shooting in which Otim was injured.
Abramson acknowledged that Otim, the mother of a young
child, had come to this
country from war-torn Sudan, “expecting and deserving
sanctuary and security.”
But on the night of July 15, 2013, Otim was shot as she sat in
friend Shaquwan’da
Allen’s car on Somerville Street after completing a double shift
as an LNA at a Bedford

nursing home.
Landry, who police said had been drinking the night of the
shooting and was on various
medications — 26 prescription bottles bearing his name were
found at his residence —
walked up to Allen’s car, so close that if she had opened the
door it would have hit him.
It was then he fired the Sig Sauer P229 he carried in a back
waist holster and hit Otim in
the leg. After two surgeries, she still walks with a limp.
“I’ve been through wars ...,” Otim said Tuesday in court. “You
took me to the worst
nightmare of my life ... I can’t trust anyone because of you.”
Landry pleaded guilty to felonies of first-degree assault and
criminal threatening.
The medical director for the New Hampshire State Prison
System, Dr. David Potenza,
told Abramson Tuesday there were programs, including
medications, for veterans and
other men with PTSD at the state prisons. But Landry’s new
Massachusetts clinical
psychologist, Dr. William Newman, said that his methods could
“cure” Landry of his
PTSD.
He said his treatment includes hypnosis, yoga and tai chi,
meditation and mindfulness,
but no medications; he has been seeing Landry twice a week
since September.

“I’m optimistic about his prognosis,” Newman said.
Newman told Abramson he doesn’t believe Landry would be
helped in prison. “They
give medication and I know that’s not going to work,” he said.
Abramson said she was impressed with Landry’s reported
progress and that he entered
a plea that eliminated the need for a trial that would further
traumatize Otim and Allen.
Saying the two women may not agree with her sentence,
Abramson said: “I ask that you
trust me.”
If there is any violation of probation, she promised, Landry will
go to prison.
Conditions of the suspended sentences include 500 hours of
community service within
18 months, continued in- and out-patient counseling, alcohol
and drug screening,
restitution to his victim and the victim compensation fund and a
letter of apology to Otim
and Allen.
Landry, who lived near the shooting scene at the time but now
lives in Massachusetts,
is barred from any contact with the two women and is barred
from possessing firearms.
Abramson ordered review hearings every 90 days and said any
violation of probation,
which will include random urinalysis to ensure Landry isn’t
using alcohol or marijuana,
will result in a termination of probation and imposition of the

jail sentences, which were
suspended for 10 years and would be consecutive if imposed.
Assistant Hillsborough County Attorney Charlene Dulac, who
had requested the prison
sentences, said the night of the shooting Landry “lied to protect
himself and avoid
responsibility.” She said there’s no question he has a mental
illness, but his “random
and unprovoked attack ... created another PTSD victim.”
Both the prosecution and defense can request sentence review
by a three-judge panel.
'Where's my justice?' shooting victim wonders as former Marine
gets probation
The Fertility Transition in Africa
Author(s): Ezekiel Kalipeni
Source: Geographical Review, Vol. 85, No. 3 (Jul., 1995), pp.
286-300
Published by: Taylor & Francis, Ltd.
Stable URL: https://www.jstor.org/stable/215274
Accessed: 06-04-2020 22:24 UTC
JSTOR is a not-for-profit service that helps scholars,
researchers, and students discover, use, and build upon a wide
range of content in a trusted digital archive. We use information
technology and tools to increase productivity and
facilitate new forms of scholarship. For more information about

JSTOR, please contact [email protected]
Your use of the JSTOR archive indicates your acceptance of the
Terms & Conditions of Use, available at
https://about.jstor.org/terms
Taylor & Francis, Ltd. is collaborating with JSTOR to digitize,
preserve and extend access to
Geographical Review
This content downloaded from 107.77.201.20 on Mon, 06 Apr
2020 22:24:36 UTC
All use subject to https://about.jstor.org/terms
THE FERTILITY TRANSITION IN AFRICA*
EZEKIEL KALIPENI
ABSTRACT. Some African countries may be going through the
initial stages of
the fertility transition. In this article multivariate analysis
based on country-level
data from 1980 and 1993 assesses spatial variations and
changes in fertility rates.
Demographic and socioeconomic factors such as education,
rural or urban resi-
dence, status of women, and use of contraceptives are
important factors in
determining the onset of the fertility transition. Over the long
term, fertility will
decline to acceptable levels as Africa continues to experience
socioeconomic
and cultural changes. Of special importance in the transition is

the status of
women in society. Key words: Africa, contraceptive
prevalence, female autonomy,
fertility transition, infant mortality rates, population growth.
D uring the past twenty years apparent high growth rates and
their
implications for the environment have been important emphases
in population studies. A growing popular consensus assumes
that high
population growth rates in Africa are adversely affecting the
environ-
ment (Mott and Mott 1980; Goliber 1989; Jolly 1994; Kalipeni
1994;
Shapiro 1995). According to this consensus, the increasing
pressure of
population on limited resources results in destruction of the
carrying
capacity and hence in declining standards of living. Other
interpretations
contend that in an efficient market a growing population can
encourage
innovation and the development of advanced technologies
(Boserup
1981; Simon 1983; Shipton 1989).
These debates have determined how scholars study the
dynamics of
fertility in Africa and have influenced policies about fertility.
According
to the consensus that high fertility rates and rapid population
growth are
the major factors in environmental degradation and a declining
quality
of life, investment of large financial resources in family-

planning activi-
ties is justified. The study of fertility patterns has emphasized
why the
rates remain high despite a substantial decline in both adult and
infant
mortality rates since the 1950s (Okore 1987; Omideyi 1987;
Udjo 1987;
Mhloyi 1987).
This article adds the largely neglected spatial dimension to the
discus-
sion of African fertility through a geographical or spatial-
temporal frame-
work for the precepts of the demographic transition theory.
Data from
demographic and health surveys (DHS) indicate that fertility
levels in
Africa have begun to decline, and research supports the thesis
that some
* I thank Ellen Kraly and Eliya Zulu for insightful comments
on a draft of this article. I am very grateful
to Zhen Hou for her untiring research efforts. A grant from the
University of Illinois Research Board
funded the research.
DR. KALIPENI is an assistant professor of geography at the
University of Illinois, Urbana,
Illinois 61801.
Copyright ? 1996 by the American Geographical Society of
New York
2020 22:24:36 UTC

FERTILITY TRANSITION IN AFRICA
African countries-Zimbabwe, Botswana, Kenya, and Nigeria,
for exam-
ple-may be in the initial stages of an irreversible fertility
transition (van
de Walle and Foster 1990; Cross, Obungu, and Kizito 1991;
Caldwell,
Orubuloye, and Caldwell 1992).
The central objective of this article is to examine the
geographical
variation in fertility rates by country throughout Africa in order
to iden-
tify some of the underlying influences. Quantitative techniques
are used
to provide confidence in any inferences drawn from the
analyzed data.
Cultural and socioeconomic factors are hypothesized to be
important
spatial correlates in the variation of fertility and consequently
the onset
of the fertility transition now under way.
DATA AND METHODS OF ANALYSIS
Multivariate analysis explains the observed patterns of African
fertil-
ity. Country-level data from 1980 and 1993 are used in this
analysis to
assess the spatial variation and change in fertility rates.
Socioeconomic
and demographic factors include education, rural or urban

residence,
income levels, status of women as measured by the HDI, infant
mortality
rates, and contraceptive prevalence. Pairwise t-test for means,
analysis of
variance, correlation analysis, and stepwise multiple-regression
tech-
niques are employed to determine the levels of regional
variations and to
account for the observed spatial variation of fertility rates. The
continent
is divided into five regions-north, west, east, middle, and
south-as
suggested by the Population Reference Bureau. These regions
exhibit
different sociocultural and economic characteristics.
Initially, the spatial relationships among fourteen independent
vari-
ables and six measures of fertility were examined (Table I).
The choice of
variables was dictated by the availability of data; therefore, the
inde-
pendent variables may not necessarily be the best. Also, the
quality of
data depends on their source. However, data from other
sources, espe-
cially the recently completed demographic and health surveys,
show
similar trends and corroborate the findings of this study.
The choice of variables was also based on the causal model of
demo-
graphic transition. Proponents of this model generally agree
that socio-

economic development is the basic cause of fertility decline
(Beaver 1975).
The model highlights at least four phenomena that are
interrelated via
social, economic, and psychological mechanisms: urbanization,
educa-
tion, nonkinship institutions, and consumption levels or
standards of
living. Social structure includes the relaxation of gender-role
restrictions
on women, the decreased predominance of extended kinship
systems,
and the reduced value of children. Furthermore, the model
accounts for
the effect of other demographic variables such as infant
mortality and
technological advances on contraceptive availability and use.
To opera-
tionalize this model, six multivariate linear models stressing
the nature
287
2020 22:24:36 UTC
THE GEOGRAPHICAL REVIEW
TABLE I-DEPENDENT AND INDEPENDENT VARIABLES
Dependent variables
Crude birthrate 1980
Crude birthrate 1993

Percent change in crude birthrate 1980-1993
Total fertility rate 1980
Total fertility rate 1993
Percent change in total fertility rate 1980-1993
Pairwise difference in crude birthrate 1980-1993
Pairwise difference in total fertility rate 1980-1993
Independent variables
Socioeconomic
Gross national product 1980
Gross national product 1993
Primary-school enrollment ratio 1980
Percent urban population 1980
Percent urban population 1993
Percent change in urban population 1980-1993
Human development index for females
Demographic
Infant mortality rate 1980
Infant mortality rate 1993
Percent change in infant mortality 1980-1993
Overall program effort score and family planning
Policy concerning fertility reduction
Prevalence of modem contraceptives
Governmental view of fertility levels
and direction of the relationship between the chosen
demographic and
socioeconomic variables were formulated. Their form is:
Y = a + PIX1 + -2X2 + p3X3 ... E
where Y is the dependent variable, a measure of fertility; a is

the Y-inter-
cept or constant; p is the regression coefficient; X is the
independent
variable associated with fertility; and e is the error term.
A FACILE DEMOGRAPHIC TRANSITION?
A World Bank study in 1994 noted the dramatic improvement
of health
in sub-Saharan Africa during the previous two decades (Shaw
1994). The
infant mortality rate had declined by 33 percent, from a high of
145 infant
deaths per 1,000 live births in 1970 to 104 per 1,000 live births
in 1992. The
mortality rate for children under the age of five fell 17 percent
between
1975 and 1990. In low-income Africa, between the late 1970s
and the late
1980s mortality for the under-five age cohort declined 41
percent in
Botswana, 32 percent in Burundi, 31 percent in Mali, and 33
percent in
Senegal. Declines have been more rapid in North Africa, with
mortality
of the under-five age cohort dropping by 50 percent in Egypt
and by
288
2020 22:24:36 UTC

around 40 percent in Morocco and Tunisia over the same period
(Sullivan
1991). Life expectancy at birth is currently estimated to be 52
years for
men and 55 years for women and has increased approximately 4
years
every decade since the 1950s (African Development Bank
1992; Popula-
tion Reference Bureau 1993). These gains are remarkable, but
the chal-
lenges of providing accessible health care are immense. The
risk of death
remains markedly higher at all ages in Africa than it does in
other major
world regions.
It has been contended that sub-Saharan Africa has not yet
experienced
a genuine demographic transition and that it is doubtful
whether the
continent will achieve the transition in a timely fashion
(Teitelbaum 1975).
The main factor in achieving the transition is socioeconomic
develop-
ment. The demographic transition postulates a necessary,
causal link
between modernization and fertility reduction. It explains
fertility and
population growth solely in socioeconomic terms: the
consequences of
widespread preference for fewer children that is consequent to
industri-
alization, urbanization, increased literacy, and declining infant
mortality.

Although mortality has declined rapidly in sub-Saharan Africa
over the
past fifty years, the declines have occurred not because of
socioeconomic
development but mainly because of the importation of medical
technolo-
gies from the industrialized world. It therefore can be argued
that the
declining infant mortality rates and crude death rates on the
African
continent are due largely to superficial demographic and
epidemiologi-
cal social changes. Sustainable socioeconomic development has
yet to
take root on the continent. In an examination of the available
data on the
main causes of death, infectious, parasitic, diarrheal,
respiratory, and
nutritional diseases are prominent, an indication that Africa is
still in the
age of famine and pestilence, as postulated by the
epidemiological
transition. In any population the main causes of death are
related to the
levels of economic and institutional development. The changes
in health
problems that come with economic and social advancement or a
shift in the
most common causes of death in a society as it accumulates
wealth are often
called the mortality or epidemiological transition. Pessimists
contend that
deep-rooted cultural forces may prevent Africa from ever
achieving the

fertility transition. Counterarguments can also be offered,
especially when
one takes into consideration that the demographic transition
does not give
a time framework and that European countries took more than a
century
and a half to go through the various stages of the transition.
Mortality
declines in Africa began only about fifty years ago, so, given
more time, the
transition may not be out of reach.
FERTILITY LEVELS AND REGIONAL VARIATIONS
By world standards fertility levels on the African continent are
still
very high. If one excludes the islands of Reunion, Seychelles,
Sao Tome,
289
2020 22:24:36 UTC
and Principe, total fertility rates in 1993 ranged from 4 to 7
children per
woman for most countries. Crude birthrates are also still very
high,
ranging from a low of 25 births per 1,000 persons in Tunisia to
a high of
52 births per 1,000 persons in Niger (Fig. 1). In 1980 only

eight African
countries had crude birthrates of between 33 and 43 births per
1,000
persons. All other countries on the continent had crude
birthrates of 43
or higher. Of the eight countries with crude birthrates of less
than 43, four
were in the well-documented infertility belt in central Africa
(Doenges
and Newman 1989). Although no single overriding factor has
been iso-
lated to explain this pattern, gynecological disorders such as
pelvic
inflammatory disease and the presence of sexually transmitted
diseases
have been implicated. The other countries with lower than
expected
fertility rates in 1980 were Egypt, Tunisia, South Africa, and
Lesotho.
Egypt began to experience a sustained fertility transition as
early as 1960
(Omran 1973). Low fertility in South Africa can be explained
by its high
socioeconomic development.
By 1993 fertility had declined considerably in most parts of
Africa,
with regional patterning in the changes. Northern and southern
Africa
stand out as regions that have experienced the greatest declines
in fertility
rates. On the other hand, countries in central Africa
experienced slight
increases in their fertility rates, which is also true for Sahelian
countries
of western Africa. Analysis of variance for the difference

between re-
gional means yields a regional patterning for crude birthrates
statistically
significant at the 5 percent level in 1993 and a regional
difference in means
for total fertility rates significant at the 10 percent level for the
same year
(Table II). Paired comparisons of t-tests for the change in
fertility rates
between 1980 and 1993 indicate statistically significant
declines at the
continental level, especially for northern and southern Africa
(Table III).
The eastern and western regions experienced declines that were
statisti-
cally insignificant. On the other hand, central Africa
experienced a slight
but insignificant increase in crude birthrates. A similar analysis
of total
fertility rates yields more or less the same results (Table IV).
At the country level, fertility declines of varied magnitude
affected
most countries (Table V and Fig. 2). Data from demographic
and health
surveys and other published works indicate that fertility levels
in most
African countries have indeed begun to decline. For example,
in Bo-
tswana the total fertility rate declined from 7 to 5, or 30
percent, between
1981 and 1988 (Lesetedi, Mompati, and Khulumani 1989).
Kenya, which
has long experienced the highest population growth rate in
Africa-more
than 4 percent annually-appears to be on the way to a fertility

transition
(Kelley and Nobbe 1990). By 1989 the fertility rate for Kenya
had declined
by 17 percent to a low of 6.7 (Kenya Ministry of Home Affairs
and
National Heritage 1989; Cross, Obungu, and Kizito 1991).
Between 1984
and 1988 total fertility rates in Zimbabwe had declined from
6.5 to 5.5
290
2020 22:24:36 UTC
FERTILITY TRANSITION IN AFRICA 291
Computation CBR, 1the author;data from Population
Refere980nCBR 19931993
3 3300 to43. 085
40.90 to 44.7 10
43.00 to 47.00
47.00 to 49.00
PercFIG. Crude birrates for 1980 and 1993 and percentage
change in crude birrates. Sources:
(Meekers 1991). In addition, the fertility transition may also
have started
in some areas of other countries, including Nigeria, Ghana,

Senegal,
Malawi, and Tanzania (Ghana Statistical Service 1989;
Ngallaba and
others 1993; Kalipeni and Harrington 1995). The DHS survey
data also
0.67 to 4.7.
4.78O0o 23.65
indicate that feercentility appeahave increased in some
countries.
Computation by the author; data from Population Reference
Bureau 1980, 1993.
indicate that fertility appears to have increased in some
countries.
In Namibia the total fertility rate is 5, and fertility in this
country has
been declining gradually over the past fifteen years (Katjiuanjo
and
.s
2020 22:24:36 UTC
TABLE II-ANALYSIS OF VARIANCE FOR FERTILITY
RATES

IN AFRICA, BY REGION, 1980 AND 1993
MEAN CRUDE MEAN CRUDE MEAN TOTAL MEAN TOTAL
REGION BIRTHRATE 1980 BIRTHRATE 1993 FERTILITY
RATE 1980 FERTILITY RATE 1993
North 42 34 6.2 4.88
West 49 46 6.8 6.71
East 48 47 6.6 6.77
Middle 45 46 6.0 6.46
South 39 35 5.2 4.57
F-ratio 1.13 4.61 0.72 2.45
P-value 0.3529 0.0032a 0.5825 0.0590b
Sources: Computation by the author; data from Population
Reference Bureau 1980, 1993.
a Statistically significant at the .05 level.
b Statistically significant at the .10 level.
TABLE III-PAIRED COMPARISONS T-TEST FOR
DIFFERENCES IN CRUDE BIRTHRATE
MEAN DIFFERENCE IN
REGION CRUDE BIRTHRATE STANDARD ERROR T-
STATISTIC P-VALUE
North -9.12 1.62 -5.62a 0.0049
West -0.870 .95 -0.91 0.3767
East -1.36 1.64 -0.83 0.4265
Middle 1.38 1.16 1.19 0.2736
South -4.07 1.38 -2.96a 0.0161
Africa -2.04 0.70 -2.92a 0.0052

a T-statistic is significant at the .05 level.
TABLE IV-PAIRED COMPARISONS T-TEST FOR
DIFFERENCES IN TOTAL FERTILITY RATE
MEAN DIFFERENCE IN
REGION TOTAL FERTILITY RATE STANDARD ERROR T-
STATISTIC P-VALUE
North -1.82 0.40 -4.50a 0.0108
West 0.06 0.14 0.39 0.7013
East -1.36 0.36 -0.25 0.8056
Middle 0.25 0.17 1.50 0.1778
South -0.59 0.20 -2.89a 0.0179
Africa -0.25 0.13 -1.89 0.0646
a T-statistic is significant at the .05 level.
others 1993). In Malawi the total fertility rates declined from 8
in 1987 to
7 in 1992, for a decrease of 12 percent in just five years
(Malawi National
Statistical Office 1992). In Zambia total fertility rates declined
from a high
of 7 in the 1980 census to 6.5 in 1992 (Gaisie, Cross, and

Nsemukila 1993).
292
2020 22:24:36 UTC
TABLE V-TOP 15 AFRICAN COUNTRIES WITH GREATEST
DECUNES IN CRUDE BIRTHRATES
FROM 1980 TO 1993
PERCENT
PERCENT CHANGE IN PERCENT
CHANGE IN INFANT PERCENT CHANGE IN MODERN
CRUDE CRUDE CRUDE MORTALITY CHANGE IN URBAN
CONTRACEPTIVE
BIRTHRATE BIRTHRATE BIRTHRATE RATE GNP
POPULATION PREVALENCE
COUNTRY 1980 1993 1980-1993 1980-1993 1980-1993 1980-
1993 IN 1993
Algeria 48.0 34.0 -29.17 -57.04 60.31 -9.82 31
Botswana 51.0 36.9 -27.65 -54.02 317.74 110.83 32
Morocco 43.0 31.3 -27.21 -56.84 53.73 10.71 36
Mauritius 27.0 20.7 -23.33 -46.86 191.57 -6.82 46
Tunisia 33.0 25.4 -23.03 -65.60 58.95 18.40 40

Sao Tome & Principe 45.0 35.0 -22.22 -12.34 -28.57 76.25
Rwanda 50.0 39.5 -21.00 -13.46 44.44 35.00 13
Egypt 38.0 30.8 -18.95 -37.78 55.00 -0.23 44
Kenya 53.0 45.2 -14.72 -12.89 6.25 136.00 18
Malawi 51.0 44.0 -13.73 -21.42 27.78 74.72 7
Zimbabwe 47.0 40.6 -13.62 -54.26 29.16 30.14 36
Lesotho 40.0 35.4 -11.50 -24.32 107.14 372.50
Libya 47.0 41.9 -10.85 -47.69 -15.30 26.33
Ghana 48.0 43.0 -10.42 -25.65 2.56 -10.00 5
Nigeria 50.0 44.8 -10.40 -46.31 -48.21 -19.50 4
Even in the predominantly Muslim, northern African countries
of
Morocco, Tunisia, and Egypt, fertility declined by 18 percent
or more.
Libya, which has been noted for its persistent high fertility
rate, experi-
enced a modest decline of 10 percent. These declines may not
be dramatic,
but they indicate that unusually high fertility rates in Africa are
not static.
REGRESSION ANALYSIS
To account for the observed geographical variations in fertility
levels
on the continent, six stepwise regression models were

generated with the
dependent and independent variables (Table VI). As indicated
by the
F-ratio and the associated R-square values, all six models were
statisti-
cally significant at the 5 percent level. The first model
concerned the
variation of total fertility rates in 1980. Only one independent
variable
met the criterion for entry into a stepwise regression method.
GNP per
capita for 1980, the variable chosen, explained a modest 15
percent of the
variation in total fertility rates for that year. The second model
used total
fertility rates in 1993 as the dependent variable. Three
independent
variables-total fertility rates in 1980 included as a control
variable, the
HDI for females in 1990, and modern contraceptive prevalence
in 1993-
were selected for entry into the model by the stepwise
procedure in the
SAS statistical software package. All standardized regression
coefficients
of the three independent variables were statistically significant
at the 5
percent level. The signs of the standardized regression
coefficients are
293
2020 22:24:36 UTC

Mali -
-C-- ------ -- ^ - i TFR 1990
Uganda . .1
Malawi j TFR 1980
i- _ .
Burundi
Kenya
Liberia
TaZambia
- I
Z Senegal
Togo
Ghana
Tanzania
Nigeria
Zimbabwe
Namibia
Botswana

1 24 5 6 8
TOTAL FERTILITY RATES (TFR)
FIG. 2-Total fertility rates for selected DHS countries 1980 and
1990. Sources: Computation by the
author; data for 1980 from Population Reference Bureau 1980,
and for 1990 from various DHS
publications.
also in the hypothesized direction. In other words, the greater
the HDI,
the smaller the total fertility rate, a negative relationship; the
greater the
percentage of population using modem contraceptives, the
smaller the
fertility rate, a negative relationship; and the greater the total
fertility rate
in 1980, the larger the fertility rate in 1993, a positive
relationship. This
model explained 71 percent of the variation in fertility rates for
1993.
The third and fourth models used crude birthrates in 1980 and
1993 as
independent variables. The results were similar to those
obtained for total
fertility rates. GNP per capita in 1980 and percentage of urban
population
accounted for 29 percent of the variation in crude birthrates in
1980, with
the standardized coefficient for GNP statistically significant at
the 5 percent
level. Both variables chosen for entry into the model for crude

birthrates
in 1993 had statistically significant regression coefficients.
Once again
the HDI and the prevalence of modern contraceptives proved to
be
powerful explanatory variables for crude birthrates in 1993.
The fifth and sixth models used the percentage change in
fertility rates
between 1980 and 1993 as the dependent variable. The fifth
model shows
that 51 percent of the change in crude birthrates between 1980
and 1993
294
2020 22:24:36 UTC
TABLE VI-STANDARDIZED PARTIAL REGRESSION
COEFFICIENTS*
DEPENDENT VARIABLES
Change in
Change in Total
Crude Fertility
Total Total Crude Crude Birthrate Rate
Fertility Fertility Birthrate Birthrate 1980-1993 1980-1993

INDEPENDENT VARIABLE Rate 1980 Rate 1993 1980 1993
(%) (%)
GNP per Capita 1980 -0.3857a -0.3993a 0.3129a
Total Fertility Rates 1980 0.3957a
Percent Urban
Population 1980 -0.3993 -0.2080 -0.2267
Infant Mortality Rate 1980 0.5949a 0.5188a
Human Development
Index for Females 1990 -0.2868a -0.3326a
Modem Contraceptive
Prevalence 1993 -0.5582a -0.5620a
Change in Infant
Mortality Rates
1980-1993 (%) 0.3434a 0.2603a
r2 0.15 0.71 0.29 0.64 0.51 0.41
F-ratio 6.29a 30.9a 7.27a 34.8a 8.40a 7.87a
N 37 41 41 41 37 37
* Empty cells are variables not selected for entry into model by
stepwise procedure.
a Statistically significant at the .05 level.
can be explained by four independent variables-GNP in 1980,
percent-
age urban population in 1980, level of infant mortality rate in
1980, and
change in infant mortality rates between 1980 and 1993. In the
sixth
model, 41 percent of the change in total fertility rates between
1980 and
1993 was accounted for by three variables: percentage urban

population,
infant mortality rates, and change in infant mortality rates. For
all of the
models in which percentage urban population was entered, the
stan-
dardized coefficient was not significant at the 5 percent level
but was so
at the 10 percent level.
POLICY IMPLICATIONS
This overview of regression results highlights the importance
of both
socioeconomic and demographic variables in fertility
reduction. Im-
provements in social, economic, and demographic conditions
are cru-
cial to the realization of an irreversible, sustainable fertility
transition in
Africa. Clearly, contraceptive prevalence and the status of
women as
measured by the HDI as well as reductions in infant mortality
rates are
central to the ongoing fertility declines in many parts of Africa.
My
discussion concentrates on these three variables and their
implications
for a fertility transition on the continent.
295
2020 22:24:36 UTC

THE HUMAN DEVELOPMENT INDEX AND STATUS OF
WOMEN
The United Nations Development Programme formulated the
HDI as
a composite measure of economic and social welfare. Unlike
other meas-
ures of socioeconomic development, this index gives equal
weight to
longevity, educational attainment, and utility derived from
income
(UNDP 1990). A score is derived for each of these, from 0 for
the lowest
achieved by any country to 1 for the highest. Averaging the
three indica-
tors gives a HDI value between 0 and 1. The Human
Development Report
1990 (UNDP 1990) gives the index by sex for each country in
Africa, and
by extracting the data I compiled a HDI for females as a
surrogate
measure of the status of women across the continent. In the
regression
analysis the HDI was a strong explanatory variable for the
spatial pat-
terns of fertility in 1993 as measured by total fertility rates and
crude
birthrates. Countries that had a high HDI exhibited a lower
fertility rate.
The importance of status and autonomy of women in the
attainment
of fertility reductions cannot be overemphasized. If a woman is

the main
controller of her reproductivity, she is more likely to use
contraceptives
and to limit the ultimate number of children born than if the
decision is
left to a man, because women assume most of the physiological
and
child-care burdens of frequent childbearing. One reason for the
persist-
ence of high fertility in sub-Saharan Africa is the minimal
involvement of
women in decision making about childbearing.
The main policy implication of this finding is to reiterate the
call for
upgrading the status of African women. This variable
decisively shows
that in countries in which the status of women has improved,
declines in
fertility have been dramatic during the past decade. Fertility
decreases as
the education of females increases.
INFANT MORTALITY
A good indicator of health conditions in a country is its infant
mortal-
ity rate: the number of deaths of children under the age of one
per 1,000
live births annually. Studies during the past twenty years or so
indicate
that a very significant relationship exists between high levels
of infant
and child mortality and low levels of maternal education
(Caldwell 1979;

Cleland and Van Ginneken 1989; Bicego and Fegan 1991;
Kalipeni 1993).
The education of females seems to correlate highly with infant
mortality
rates. Even if incomes are low, educated women tend to make
better
decisions about their children than do uneducated women.
Lower levels of infant mortality have been shown to be
strongly
correlated with lower fertility rates. Because of comparatively
high child-
hood mortality throughout the developing world, many families
have felt
the need to have more children to ensure that some will survive
to
adulthood. This pattern is further reinforced by the need for
sons as social
security in old age.
296
2020 22:24:36 UTC
The importance of infant mortality in any fertility transition is
high-
lighted by the situation in Kenya, Zimbabwe, and Botswana,
each of
which has an infant mortality rate below 70 per 1,000 live
births

(Caldwell, Orubuloye, and Caldwell 1992). No other countries
in sub-
Saharan Africa record a level below 80, and most have rates
above 100,
which suggests that the attainment of this level of infant
mortality …

Wheres my justice shooting victim wonders as former Mari.docx

Recommended

Recommended

More Related Content

Similar to Wheres my justice shooting victim wonders as former Mari.docx

Similar to Wheres my justice shooting victim wonders as former Mari.docx (14)

More from jolleybendicty

More from jolleybendicty (20)

Recently uploaded

Recently uploaded (20)

Wheres my justice shooting victim wonders as former Mari.docx