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Research ArticleThe Effects of Declining Fertility on Household SocioeconomicConditions in Tanzania A Comparative Study of Urban versusRural Areas of Kwimba District Mwanza Region

George Felix Masanja Emmanuel Lwankomezi and Chrisant Emmanuel

Department of Geography St Augustine University of Tanzania PO Box 307 Mwanza Tanzania

Correspondence should be addressed to George Felix Masanja grgmasanjayahoocouk

Received 13 May 2016 Accepted 10 July 2016

Academic Editor Jonathan Haughton

Copyright copy 2016 George Felix Masanja et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

This study examined the effects of declining fertility on household socioeconomic and health conditions in Tanzania using acomparative survey of urban versus rural areas of Kwimba District in Mwanza region Cross-sectional cum causal-comparativeresearch design was adopted for the study The target population is comprised of all females of the childbearing age residing inKwimba District The study utilized a stratified random sampling technique to pick two areas in the district while disproportionaterandom sampling technique was used to select 196 respondents A questionnaire was used to elicit information from therespondents Multivariate analyses were adopted to answer the three research questions of the study The findings of this studyrevealed that women of the childbearing age from the two study sites in the district exhibited a small difference regardingfertility inequalities and socioeconomic and health conditionsThis study also discovered a significant relationship between criticalsocioeconomic variables and womenrsquos improved socioeconomic status in the communities These findings therefore providean explanation for the onset of fertility decline which has consequently led to some demonstrated demographic dividend at ahousehold levelThe paper recommends enhancement of social transformation and women empowerment in both rural and urbanenvironments for sustained improved living conditions

1 Introduction

Current population statistics in Sub-Saharan Africa appearto imply a gradual decline in fertility see for example [1ndash6] The 1991-92 Tanzania Demographic and Health Survey(TDHS) based on a sample of 9238 women presented aTotal Fertility Rate (TFR) of 63 for the years 1989ndash92 [7]As reported by the 1996 TDHS which interviewed 8120women the TFR obtained had decreased to 58 for the 1993ndash96 period [8] The 1999 Tanzania Reproductive and ChildHealth Survey (TRCHS) interviewed a smaller sample of4029 women It reported a TFR for the period from 1996through 99 of 56 [8] The 2012 census preliminary resultspresent a decline in the level of fertility from 65 in 1988 to52 in 2012 [9] The consistency and harmony indicated bythe estimates from these different sources are convincing andsatisfactory It seems fairly clear that there was no decline in

Tanzanian fertility before the early 1980s but since then therehas been a modest decline in Tanzania especially during the1990s

An array of socioeconomic and cultural factors eitherseparately or in combination has been proposed by previousstudies to explain patterns of fertility transition see forexample [10 11]Themajor determinants of declining fertilityin Tanzania include nuptiality patterns postponement offirst marriage marital disintegration through divorce orwidowhood and low remarriage rates which are associatedwith low levels of fertility

Economic hardship of which many studies have raisedin Sub-Saharan countries (see eg [3 12]) had been fun-damental in starting Tanzaniarsquos fertility decline Tanzaniansbegan to experience grave economic problems during the1970s and early 1980s It is widely believed that economichardship resulted in couples seeking to defer or prevent

Hindawi Publishing CorporationInternational Journal of Population ResearchVolume 2016 Article ID 4716432 14 pageshttpdxdoiorg10115520164716432

2 International Journal of Population Research

further childbearing especially at parities above three or four(see eg [12 13])

Despite the literature carrying an extensive debate andcontroversy over the past twenty years on fertility declinea better understanding of the socio-economic effects andimplications on family structures emerging among presentAfrican households as a result of such a decline is yetto be fully understood To our knowledge few studieshave empirically examined the interrelationships betweenthe socio-economic factors womenrsquos autonomy (empow-erment) and fertility in Tanzania using field survey dataThe specific objectives of this study include examiningthe observed fertility behaviour among rural versus urbanhouseholds determining the socioeconomic and culturalfactors contributing to the observed fertility patterns amongrural versus urban households and assessing the observedfertility behaviour patterns in light of demographic divi-dend or deficit changes among rural versus urban house-holds

The theoretical framework guiding this study combinesthree prominent theories advanced to explain the fertilitytransition in Sub-Saharan Africa These are the supply-demand framework innovation-diffusion theory and wealthflows theory

(1) Supply-Demand Framework The supply-demand frame-work as advanced by [14] extracts its concept mainly fromthe domain of neoclassical economics The supply-demandframework suggests that people will eventually realize thatlower mortality has produced a situation in which morechildren are going to survive than can be afforded and at thatpoint fertility will decline

The benefits derived from children which served as abasis for increase in fertility are no longer the case currentlySustained economic growth over 90 years after theWorldWarII has led to children being increasingly sent to school foreducation and to health services when sick The rising costof education and providing healthcare for children is a majorproblem for most couples now and has caused a decrease inthe demand for children which instead has contributed to agradual decline in fertility in the African subregion

(2) Innovation-Diffusion Theory The most important ideabehind the diffusion theory is that social interaction is amajor mechanism through which the endorsement of newtechnologies ideas and behaviours takes place Further thediffusion theory posits that theories relying on individualrational decision-making in response to economic or struc-tural change could not explain at best the observed fertilitytransitions in many areas of the world The outcomes of thetwo major research efforts completed in the mid-1980s thePrinceton European Fertility Project and the World FertilitySurvey led certain researchers to conclude that structural andeconomic changes alone provide an incomplete explanation(see [15ndash17])

Connecting this theory to the explanation of fertilitytransition in Sub-Saharan Africa it can be deduced thatcertain lifestyle practices which were characteristic of thedeveloped world have now been diffused into the African

culture consequently leading to a decrease in fertility in theAfrican subregion

(3) Caldwellrsquos Wealth Flows Theory Caldwellrsquos theory [18 19]states that the level of fertility is essentially controlled by thedirection of the net wealth flows among parents and childrenwhich include all the expected benefits over a lifespan Theend result of this economic justification is either maximumor zero fertility but this is adjusted by the impact of personalsocial and physiological reasons The controlling principleunderlying the direction of intergenerational wealth flowsis the social organization of the society especially familystructures Caldwell argues that in all traditional societiesthe net wealth flow has been from younger to older gener-ations which means that economic motives promote highfertility This flow will only be reversed if the economic anddomination of a mental state popularly known as ldquoemotionalprimacyrdquo is withdrawn from the bond of broader family tiesand is focused on the conjugal family The nucleated familyis therefore a necessary condition for low fertility and thetransition from high to low fertility is a product of socialchange with economic implications rather than economicchange alone

From these three theories it has been noted that contactwith western societies influenced the economy urbanizationmigration education and even factors such as oppressionand inequality of women and hegemony and patriarchywhich influence womenrsquos position in society in many Africansocieties and cultures Such a contact has altered the customsand practices which in turn have led to gradual changeinfertility behaviour

2 Materials and Methods

21 Study Area Characteristics The study area chosen toexamine the research problem is Kwimba District of Mwanzaregion Figure 1 presents the map of the district and thestudy sitesThedistrict lies between latitude 2∘591015840 to 3∘3101584043210158401015840south of equator and also longitude 33∘141015840 to 33∘2110158403610158401015840 eastof Greenwich Kwimba District is one of 7 districts of theMwanza Region of Tanzania Kwimbarsquos population is 406509[20] The district headquarters is located in Ngudu towncentre

22 Study Design The study applies a cross-sectional andcausal-comparative design It utilizes data gathered froma recent household survey to examine whether economicdevelopment and social change might have created support-ive conditions in which fertility has modestly declined inTanzania

23 Population Sample and Sampling Procedures The pop-ulation for this study comprised all females from KwimbaDistrict whose number amounts to 206093 [9] Out ofthis population a sampling frame comprising all females ofthe childbearing age residing in Ngudu urban and Kwimbarural was constructed Kwimba District had a total of 87076females of childbearing age living in 149725 households [9]

International Journal of Population Research 3

2∘45

998400

2∘55

998400

3∘05

998400

3∘15

998400

3∘25

998400

2∘45

998400

2∘55

998400

3∘05

998400

3∘15

998400

3∘25

998400

32∘50

99840033

∘00

99840033

∘10

99840033

∘20

99840033

∘30

998400

32∘50

99840033

∘00

99840033

∘10

99840033

∘20

99840033

∘30

998400

Kwimba District

N

0 5 10 20

(km)

Scale 1 100000 20142015

RoadsF_CODE_DES

RoadTrailRiver

NguduNyamikoma

Figure 1 Map of Kwimba District showing study areas SourceKwimba Lands office 2015

From the sampling frame the sample size was determined bythe statistical equation [21]

119899 =

1199112

119901119902119873

1198902

(119873 minus 1) + 1199112

119901119902

(1)

where119873 is population of 87076 119890 is 002 (since the estimateshould be within 2 of true value) and 119911 is 2005 (as per tableof area under normal curve for the given confidence level of955) 119902 = 1 minus 119901 119901 = taken to be 002 based on the result ofthe pilot study

The result of the computation was 19653888 as a samplesize The study adopted a probability sampling design Sevenage groups (15ndash19 to 45ndash49) were taken and subjected todisproportionate stratified sampling to obtain sample sizesfor the different strata Results were distributed to studyunits (households) found in the two selected study sites Themain sample drawn was equally divided into two separatesubsamples of 98 respondents each and these represented thetwo study areas for comparison purposes

The study sites chosen were Nyamikoma village of SumveWard and Ngudu urban centre which is the district seatThe rationale for choosing these sites considered the notablevariations in population distribution and environmentalconditions both ofwhich formed a basis for varying responsesto fertility issues Specifically Nyamikoma village was chosenlargely on the basis of its remote rural location which was

thought to result in relatively low out-migration of mothersIn addition the areawas expected to have relatively highmor-tality levels due to the general level of underdevelopmentThepopulation of the study area comprises mainly the Sukumatribe although some traders and government employeesfrom other tribes have immigrated in small numbers fromneighbouring districts and towns to settle The vast majorityof the populationmakes their living fromagriculture on smallland holdings growing maize cotton cassava millet andrice The extended family culture is still very strong in ruralareas of Kwimba District Having children especially manychildren is regarded as a source of pride accomplishmentand future security by young persons Nevertheless changeshave started to penetrate into this traditional family structureThe younger generations have in some cases started to moveout of this arrangement by creating their own economicbases The disintegration of the traditional family structuregained momentum especially during the last two decadeswhen other means of obtaining land (ie through thevillage government) were made possible This variation hasimplications for the labour force and reproductive capacityof the population A primary consideration in the selection ofNgudu for the studywas its urban locationwhichwould resultin relatively low fertility as expected in urban areas owing tofactors suggested by a variety of literatures which includesresidents being better educated secularized and workingin industrial or service sectors Interviews aimed at tryingto understand the magnitude of the difference in fertilitytransition with respect to place of residence in the districtwere conducted in Nyamikoma village of Sumve Ward andNgudu urban centre

24 Data Collection Methods A complete listing and map-ping of all households in each sampled study areawere carriedout prior to the survey to stratify households accordingto the age status of women A household was deemedeligible for interview if it had a mother whose age fellinto the category of 15ndash49 Detailed socioeconomic anddemographic characteristics of 197 women of reproductiveage (ie 15ndash49 years) residing in Ngudu and Nyamikomawere collected during the second quarter of 2015 using astructured pretested individual womenrsquos questionnaire Thisdata collection tool was divided into four parts Part 1 col-lected individual mothersrsquo information in a household Part 2solicited information on factors argued to be responsible forthe current declining fertility scenario Such factors includedparity progression postpartum insusceptibility modernitycost of living women empowerment and social transforma-tion Others focused on perceptions of education leading tochanges in womenrsquos fertility preferences Part 3 inquired onimplications of reduced fertility at a household and individuallevel Inquiries concentrated on whether improvements ineducation empower women in other areas of life such asparticipation in decision-making control of resources andlabour force participation and whether reduced fertilityspurs income growth and increases womenrsquos empowermentPart 4 gathered information on net economic benefits fromchildren that is amount provided to children minus amountreceived by parents


25 Analytic Framework Consistent with our study objec-tives our analyses examined the effects of an observed fallingtrend of fertility on household socioeconomic conditionsamong Tanzanian rural versus urban households based ontwo major areas of measurement The first one assessedthe observed fertility decline in Kwimba District usingpredictors The study identified two main variables inde-pendent and dependent The independent variables includedparity progression postpartum insusceptibility the cost ofliving social transformation andwomen empowermentThedependent variable (fertility decline) was measured usinga bifurcated test of whether a woman had had a biggernumber of living children than her reported desired numberof children by subtracting her reported desired number fromher reported actual number If the difference was greater thanzero she was coded as having had more children than herstated desired number

251 Womanrsquos Parity We sought to determine whether theimpact of norms (modernity versus traditional) differed forwomen with no children women with 1-2 children andwomen with 3-4 children Consequently this variable wasstratified into two categories for analysis

252 Postpartum Infecundability The index of postpartuminfecundability was used to measure the fertility-inhibitingeffect of breastfeeding or postpartum abstinence in the studycommunities The paper [22] contends that ldquoin the presenceof breastfeeding andpostpartumabstinence the average birthinterval equals approximately 185 months (75 + 2 + 9) plusthe duration of postpartum infecundability which is definedby length of postpartum amenorrhea and abstinencerdquo Theindex is calculated as

119862119894=

20

185

+ 119894 (2)

where 119894 is average period of postpartum infecundability(postpartum insusceptibility) produced by breastfeeding andpostpartum abstinence

253 Social Transformation This variable is among thesocioeconomic and cultural factors contributing to theobserved fertility patterns among rural versus urban house-holds It was measured using two factors namely moder-nity and traditional norms Modernity norms include theproportions of women desiring a small family size of 3-4 orfewer children and the proportion ofwomenwho approved ofnatural family planning Modernityrsquos contribution to fertilitydecline was therefore captured by asking women about theirperceptions of how many women in their community usednatural family planning Responses were coded none (1)some (2) most (3) and all (4) Traditional norms on the otherhand focused on womenrsquos perceptions about age at marriageeducation ofwomen and support for natural family planningWomenwere askedwhether they believed age atmarriage hasincreased and whether education has changed their mindseton large family sizes and approval of natural family planningTheir responses were coded dichotomously for approval (1)and disapproval (2)

254 Cost of Living Respondents were asked whether thecost of living had a fertility-inhibiting effect Based on theresponses participants were classified as modern women ortraditional Hence these variables were coded dichotomouslyas 0 for agreeing and 1 for disagreeing

255Womenrsquos Empowerment Thiswas an independent vari-able Measures of womenrsquos empowerment are usually usedin different contexts to carry multiple meanings The paper[23] defines womenrsquos empowerment as the ldquoability to makechoicesrdquo Women were asked whether they ever discussedwith their spouses about household decision-making andlabour participation For measuring womenrsquos empowermentamong rural versus urban mothers this study employed theWomen Empowerment Index (WEI) formula adapted andmodified from [24] Since various decision-making variablesare important in a household for different purposes the studyassigned the values as shown in the formula Each valuewas rated A decision-making indicator rated 1-2 indicateslow empowerment while 3-4 indicates high empowermentTherefore the average scoring value of a particular indicatorfor all respondents in households of the study area became theaverage of the value119870119870 is any rating value of each indicatorThe higher the index score the more empowered the woman

This study considered four intrahousehold decision-making indicators which are related to the fertility domainThese are

1199091 cash management (income expenditure and

investment for earning)1199092 travel and recreations (mobility to outside home

for marketing visiting relatives etc)1199093 childrenrsquos education (school enrollment expendi-

ture on books uniforms tuitions etc)1199094 family planning (freedom for family size prefer-

ence)

The Women Empowerment Index (WEI) index is calculatedas

WEI (f t) =(sum4

119894=1

119909119894)

4

(3)

where WEI (ft) is Womenrsquos Empowerment Index for fertilityper respondent 119909 is value of decision-making

WEI indices are as follows 1 = husband alonemakes deci-sions 2 = husband dominates in decisions 3 = husband andwife make joint decisions 4 = wife dominates in decisionsand 5 = wife alone makes decisions even in the presence ofthe husband

Low and high empowerment scores became the finaldichotomous variables that divided respondents whoreported having any say in all four household decisionsWe performed Ordinary Least Square (OLS) regression toexamine the relationship of these two variables

256 Demographic Dividend To measure emerging eco-nomic growth that can result from shifts in populations age


composition mainly when the proportion of the working-age population (15 to 64) is greater than the nonworking-age proportion of the population (14 and younger and 65and older) we computed the youth dependency ratio and thechildren under 15 per household surveyed in Ngudu urbanand Nyamikoma rural These measures would help us seewhether many women have now started to enter the labourforce and whether this period has led to bearing smallerfamilies and rising income among households in both studyareas It would help us investigate the degree to which thedemographic dividend is realized at the household level Ourmethodological technique applied logistic regression withinteraction terms Interaction termswere used to examine thecross-sectional relationship between household wealth andage structure in each study area

Control Variables They included age education residenceand wealth For assessing nonlinear trends age in yearswas squared and used as a predictor Education was mea-sured as a categorical variable ranging from ldquononerdquo toldquohigher educationrdquo Wealth indices were computed basedon 9 household assets adapted from the 2010 TDHS Eachhousehold asset was assigned a weight produced throughprincipal component analysis and the resulting scores wereregulated in relation to the standard normal distributionwith a mean of 0 and a standard deviation of 1 [25] Thescores were summed for each household and ranking wasdone Respondents were divided into quintiles from lowestto highest All statistical analyses were executed using SPSSVersion 16 with significance level set at 119901 lt 005

The second area of measurement required connecting thetheoretical framework of the study with observable changesin Kwimbarsquos fertility behaviour All the three theories offertility decline apparently converge on economic benefitsobtained from children during parentsrsquo old age and socialinteraction factors of which Caldwellrsquos wealth flow theoryappears to consider both of them

The measurement undertaken by this study thereforerequired testing the relevance and applicability of Caldwellrsquostheory in Kwimbarsquos fertility decline by calculating the neteconomic benefits from children It entails calculating theamount provided to children minus amount received byparents in a high fertility society The measure used to assessthe economic contribution of children to their parents is theinternal rate of return (IRR)This rate is the discount rate thatmakes the net present value of investment flows zero In thisstudy children are described as an investment by parents whomight bemotivated to provide old age security It was decideduseful to compare the rate of return of children with otherinvestments The internal rate of return for a parentrsquos birthcohort was computed using the following formula

NPV =119879

sum

119905=1

119862119905

(1 + 119903)119905

minus 119862119900 (4)

where 119862119905is net cash inflow during the period 119905 119862

119900is total

initial investment costs 119903 is discount rate and 119905 is number oftime periods

Results of Caldwellrsquos theory in Kwimbarsquos fertility declinesurvey are reported in relation to the emergent life historytheory and parental investment advocated by Lawson andMace

3 Results

31 Sociodemographic Characteristics The majority of re-spondents (908) were between the ages 20 and 34 yearswith a mean age at marriage 1887 (SD plusmn2615) years Themean duration of marriage was 1296 (SD plusmn8217) The mar-ried respondents were in themajority (817) A considerableproportion of the women (32) were married before theage of 16 and 945 were in monogamous relationships Amajority of the women lived in a rural area (85) Some 45of women reported that they had an occupation Howeveronly about one out of ten women (12) of those who livedin the rural area were engaged in nonagricultural sectorsFifteen percent had no education while 76 had a primaryor secondary education and 9 had more than a secondaryeducationThemajority of women (68) lived in a householdwith a low standard of living the remainder in a householdwith a medium (24) or high (8) standard of living Onaverage women had had 38 births (not shown)

The principal resulting variable in this analysis is thefertility level elucidated by the children ever born (CEB)by women before surpassing the age of 50 years The studypopulation divided into three parity groupsmdashwomenwith nochildren (weighted 119899 = 29) women with 1-2 children only(119899 = 100) and women with 3-4 children (119899 = 67)mdashis shownin Table 1

Results indicate that the sociodemographic characteris-tics norms and behaviours that have contributed to thereduction of fertility varied to some extent among the groupsNot surprisingly women at higher parity were older Com-pared to the other two groups women with 3-4 children weresignificantly more likely to have had less education Cross-residential area distributions across female parity groupsindicate that the sample of women was more or less even

32 Differentials in Education Education was found to beassociated with fertility inhibition Women who had sec-ondary education primary education and no formal educa-tion respectively had 116 138 and 152 times more childrencompared to those who had completed higher educationIrrespective of this variation individual education was nota significant predictor in both communities studied and itseffectwas larger in the community than at the individual levelA one standard deviation increase in individual educationwas associated with an 11 reduction in fertility (95 CI(minus017 minus005) Wald 119885-test 119901 lt 0001) when considering theaverage effect across both communities Independent of indi-vidual background factors a one standard deviation increasein average education in the community was associated with a15 reduction in individual fertility (95 CI (minus019 minus008)Wald 119885-test 119901 lt 0001) This result means that a onestandard deviation increase in education at the communitylevel therefore had 12 times larger effect than a comparable


Table1Descriptiv

edatafor

weightedsampleo

fwom

enacrossstu

dycommun

ities

inKw

imba

distric

t

Ngudu

urban

Nyamikom

a

Wom

enwith

nochild

ren

(119899=12)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=48)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=38)

or

mean(SD)

pvalue

(chi-squ

ared

or119905-te

st)lowast

Wom

enwith

nochild

ren

(119899=17)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=52)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=29)

or

mean(SD)

119901value

(chi-squ

ared

ort-test)lowast

AgeM

(SD)

234(57)

263(62)

349(73)

lt0001

176(46)

203(49)

289(72)

lt0001

Education(

)lt0001

lt0001

Non

e1982

1676

3667

173

1376

1757

Prim

ary

944

716

966

857

859

823

Second

ary

1221

1205

1113

1043

1046

1113

Higher

4321

5212

955

4425

2612

4479

Wealth

()

lt0001

lt0001

Lowest

1521

1221

1967

1265

1145

1122

Second

2129

1484

1886

1634

1543

1312

Third

1832

1872

2023

1734

1665

1622

Fourth

2261

2517

2103

2012

2113

1921

Highest

2257

2907

2022

2112

1923

1723

Resid

ence

lt0001

lt0001

Postp

artum

insusceptib

ility

M(SD)

183(059)

177(048)

182(054)

gt005

176(044)

178(041)

169(035)

gt005

Costofliving(

)9356

182(058)

9550

lt0001

9033

178(056)

9233

lt0001

Socia

ltransform

ation

192(074

)219

(073)

226

(067)

lt0001

188(067)

179(062)

176(066)

lt0001

lowast

Com

parin

gdifferences

acrossthethree

paritygrou

ps119899=12119899=48and119899=38forN

gudu

and119899=17119899=52and119899=29forN

yamikom

aSourceK

wim

bafertilitysurvey2015


increase at the individual level This analysis therefore showsthat independent of their own characteristics relative toothers women in the most educated community (apparentlyin Ngudu urban centre) were predicted to have fewer than aquarter as many children (157 plusmn se 028) as those in the leasteducated community (216 plusmn se 017)

33 Wealth Differentials The distribution of wealth var-ied significantly across the three groups with single-paritywomen having the greatest wealth The mean value of anindex of wealth was 213 with a standard deviation of 141Theapportionment of the wealth index was bimodal with rangevalues falling between 15 and 22 Immense differentials wereobserved in the wealth index by community residence edu-cation of a woman and her labour force participation Therewere fairly vast differences in the value of the wealth indexby age age-at-first marriage education occupation andparity The uppermost mean index of wealth was recordedfor women in Ngudu urban and the lowest in Nyamikomarural Computed standard deviations showed that the wealthinequality was highest among Nyamikoma rural womenWith an increment in a womanrsquos education level therewas an increase in the wealth index that is women withpostsecondary education had nearly twice the average levelof wealth Women in nonagricultural occupations (mainlyfound in Ngudu urban) had higher-than-average values forthe wealth index while women in agricultural occupations ofNyamikoma are relatively poor Wealth tends to rise with theage of the woman the increment being more conspicuous at25 years and then turning moderate With the rise in age-at-first marriage there was an increase in the index of wealththat is women who married at a relatively youthful age wereconsiderably less wealthy than those who married at 25 yearsor older Women with parity 1-2 recorded the highest indexof wealth Those with a parity of 3-4 had a noticeably lowermean value for the index of wealth

34 Fertility Differentials Fertility variations among studyareas were obtained utilizing a variety of selected comparisonvariables such as wealth index age at marriage place ofresidence education labour force participation and socialtransformation The percentage of women with children wasnoticeably lower for women in the wealthiest section (20)but differed little among women in the other four quintilesof the distribution of wealth index Analysis of the currentfertility differentials by the value of the wealth index ismade more complex by the value differences of the wealthindex by other variables that are likely to affect the womanrsquosfertilityTherefore it was necessary to perform a multivariateanalysis including controls for the effects of confoundingvariables Parameter estimates in the logit model are givenin Table 3 As to the core finding the wealth index had a5 percent significant negative effect on marital fertility Themultiplicative factor showed that a unit increase in the wealthindex cut down the odds log of a woman who gave birth inthe last 12 months by 00036 for Ngudu urban and 00033 forNyamikoma rural

Analysis by place of residence shows that women living inthe rural area had a slightly higher fertility thanwomen living

in the urban environment Computed odds ratios showedthat women living in Nyamikoma rural area were 114 timesmore likely (OR = 1122) to give birth than Ngudu urbanwomen However after controlling for other variables therural and urban odds ratios were not significant [119901 = 0286(rural) and 119901 = 0626 (urban)] Table 2 presents the statistics

Overall the effect of rural-urban residence has continuedto be prominent till the last decade but the results from oursurvey show that the effect has faded away

It has been observed during the field study inNyamikomarural and Ngudu urban areas data that womanrsquos age atmarriage has increased and the proportion married at anearly age has fallen substantially in the last two decadesIncrease in level of education enhances age at marriage andhence reduces the reproductive span of women and curbsfertility During the field survey it has been observed thatrespondents have knowledge on the legal age at marriage inTanzania However the ideal age at marriage in rural areas isstill lower than that of urban areas but the gap has narrowed

Education is the sole factor that has significant effect overtime and only those with higher education show distinctlyvery low fertility Results from our survey indicate that thereis increase in the educational level of women It is felt thatdifferences between the urban educated and uneducatedare not large due to exposure Moreover rural educatedwomen are enjoying more autonomy and are taking activepart in decision-making which is an indicator of womenempowerment

The analysis of Period Parity Progression Ratios showsthat almost all women move for the first child in the districtThe proportion of women with progression from first tosecond child differs with place of residence The pace ofdecline is faster among Ngudu urban women with higherparities In Nyamikoma rural about a third of womenmovedfrom third to fourth child but this occurrence is rare inNguduurban

Contrary to expectation no significant rural-urban dif-ference in ideal family size is seen in the two study communi-ties in the district especially when influences of other factorsare controlled in multivariate analysis Thus the observeddifferences are primarily not net effects

Zero-order correlations were performed across all vari-ables in both study areas The main point of examinationis to establish the significance of the degree of associationamong the test variables and to assess overlapping varianceTable 3 presents the zero-order correlation matrix of all theten test variables Results show that almost all demographicvariables social transformation and women empowermentvariables were significantly associated with fertility declineIn all but nine cases the magnitude of correlation coefficientsis significant at the 005 level

Age also correlates positively with number of childrenSurprisingly the relationship between residence and cost ofliving is very low and not significant although the result is inthe predicted direction Education correlates inversely withnumber of children Nevertheless the magnitude of corre-lation coefficient for education and women empowermentis far higher Residence however correlates positively withsocial transformation but it is also inversely correlated with


Table2Parameter

statisticso

flogisticmod

elof

whether

awom

angave

birthin

last12

mon

ths

Independ

entvariable

Ngudu

Nyamikom

aParameter

estim

ate

Standard

error

Odd

sratio

Parameter

estim

ate

Standard

error

Odd

sratio

Intercept

minus48904

10985

1000

0minus37543

10657

1000

0Indexof

wealth

minus00036

00016

0995lowastlowast

minus00033

00452

1000

0Stud

yarea

(place

ofresid

ence)

000

0010

0010

000

014

1234

1232

Highestlevelofedu

catio

nNoeducation

02013

02533

1225

02311

02201

1435

Prim

ary

02969

02238

1346

02322

02152

1321

Second

ary

minus00214

02014

0979

00012

01222

0636

Higher

000

0001534

1000

0000

0001233

0734

Participationin

labo

urforce

Not

working

000

000231

1000

000011

02361

02332

Agriculturalw

ork

minus05536

0184

0575lowastlowast

minus044

3300345

06578

Non

agric

ulturalw

ork

minus015

02257

0682

016663

02135

044

62Age-at-fi

rstm

arria

ge01624

006

6212

08lowast

02433

00744

0110

1Age

01345

00597

1144lowast

01343

00221

00231

ParityProgression

011458

01702

3127lowastlowastlowast

01212

01553


1to2

06344

01121


06755

01233


3to

4000

0001222

1000

0000

0001322

2252

Socialtransfo

rmation

01316

00496


01322

01551

3212lowastlowast

119873=196(98forN

gudu

)and

(98forN

yamikom

a)

lowast

Sign

ificant

at005

lowastlowast

Sign

ificant

at001

lowastlowastlowast

Sign

ificant

at0001

SourceK

wim



Table 3 Zero-order Pearson correlation coefficients matrix for analyzing the relationships between test variables (119873 = 197)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Age 100 016lowastlowastlowast 013lowastlowastlowast minus013lowast minus004lowast minus047lowastlowastlowast 039lowastlowast minus022lowastlowast minus002 015lowastlowast

Education 100 061lowastlowastlowast minus012lowastlowastlowast 11lowastlowastlowast minus019lowastlowastlowast 003lowastlowastlowast 007lowastlowastlowast 010lowastlowastlowast 031lowast

Occupation 100 003lowastlowastlowast minus003lowastlowastlowast minus007lowastlowastlowast 011lowastlowastlowast 006lowastlowastlowast 011lowastlowastlowast 001Wealth 100 003lowastlowastlowast minus002lowastlowast 001 00 002lowast 018lowastlowastlowast

Residence 100 026lowastlowast 002lowastlowastlowast 001 012lowastlowast 013lowastlowastlowast

Postpartum insusceptibility 100 22lowastlowastlowast 001 002 001Number of children 100 006lowastlowastlowast 004 009lowastlowastlowast

Cost of living 100 003 008lowastlowastlowast

Social transformation 100 012lowastlowast

Women empowerment 100lowast

119901 lt 005 lowastlowast119901 lt 001 and lowastlowastlowast119901 lt 0001Source Kwimba fertility survey 2015

number of children An inverse relationship is observed insettings of economic crisis or personal hardship Wealth isinversely correlated with number of children (001) In bothstudy communities of Kwimba District the monetizationof the economy increases awareness of the costs of raisingchildren regarding food clothes health and education

35The Effect of Postpartum Insusceptibility It was necessaryto calculate the index of postpartum insusceptibility todetermine its influence on fertility reduction Results indicatethat postpartum insusceptibility is the principal inhibitorof fertility in both study communities of Kwimba DistrictIts role is slightly higher in Ngudu urban (047) than inNyamikoma (043) These low indices signal a decreasingfertility trend

36 Cost of Living Theamount of variance in household con-sumption expenditure explained by the number of childrencontrolling for other covariates indicates that despite thelower coefficient of determination in general it is statisticallysignificant for both Nyamikoma rural and Ngudu urbanhouseholds showing that the variables (number of childrenand equivalent consumption) are well fitted to the OrdinaryLeast Square regression model For the Ngudu urban sub-sample an additional child leads to an increase of per capitaconsumption expenditure and adult equivalent consumptionexpenditure by 91 and 115 respectively while for theNyamikoma rural subsample an additional child leads to anincrease of per capita consumption expenditure and adultequivalent consumption expenditure by 79 and 102respectively on average Percentages also show that theoverall consumption expenditure is higher for urban relativeto rural households

In connection with this finding most of the respondentsin Ngudu urban and amongNyamikoma rural young womencited economic factor as the main reason to choose a smallfamily size They mentioned the high cost of child rearingOlder urban women also mentioned that high living costs inurban areas motivated them to have a small family Womenmentioned that educational cost of children has increasedand increased number of educated youth in the job market

required quality education which is expensive Thereforequality of children becamemore important to parents insteadof quantity of children

37 Social Transformation To determine whether modernityvalues (proportion of women desiring a small family sizeand the proportion of women who approve family planning)intervened between background characteristics and familyplanning adoption first-order partial correlations were com-puted In each case the effect of a particular modernityvalue was held constant when determining the relationshipbetween background characteristics and adoption Resultsindicate that inmost cases the value of the partial correlationwas substantially reduced thereby providing evidence that theparticular modernity values in question did have an inter-vening role For example the proportion of women desiringa small family size correlated positively and significantly(119903 = 023 119901 lt 0001 119903 = 19 119901 lt 0001) with familyplanning adoption for Ngudu urban and Nyamikoma ruralrespectively

As regards traditional norms correlation coefficientsbetween womenrsquos perceptions about age at marriage andfamily planning adoption controlling for place of residencewealth and education produced values of 014 and 012 at 119901 lt001 for Ngudu urban and Nyamikoma rural Education andfamily planning adoption had values of 024 and 021 at 119901 lt001 for Ngudu urban andNyamikoma ruralThe coefficientsindicate that motherrsquos education and family adoption is in apositive direction

Correlation between age at marriage and adoption offamily planning had coefficient values of 04 and 02 at119901 lt 001 when holding place of residence wealth andeducation constant Such a finding shows that age at marriageand adoption of family planning is negatively correlatedsuggesting that young mothers are quicker at approvingfamily planning as opposed to older mothers

38 Women Empowerment An examination of the effectsof womenrsquos empowerment on the ideal number of childrenwas performed after controlling for background variables AnOrdinary Least Squared (OLS) regression helped to interpret


Table 4 Summary of the effects of control variables on the idealnumber of children and womenrsquos empowerment

Ngudu (high) Nyamikoma (low)Age minus (lowastlowast) + (lowast)Residence minus (lowastlowast) ndash (lowast)CEB + (lowastlowast) + (lowastlowast)Age lowast CEB minus (lowastlowast) + (lowast)Husbandrsquos education minus minus

Husbandrsquos occupation(agriculture) minus minus

Labour force participation + + (lowastlowast)Factors

Education factor minus (lowastlowast) minus (lowastlowast)Household decision-makingfactor + minus (lowastlowast)

lowast significant at 119901 value lt 005lowastlowast significant at 119901 value lt 001OLS regression modelSource Kwimba fertility survey 2015

results which give initial impressions on how the variablesbehave The controlled variables were set at womenrsquos ageresidence the number of children ever born and husbandrsquossocioeconomic and demographic characteristics

Table 4 indicates that most of the control variablesof the womenrsquos demographic characteristics are statisticallysignificant across both study areas It is worth taking intoaccount the husbandrsquos characteristics while exploring theideal number of children since the decision about childrenis usually a joint decision Interestingly husbandsrsquo back-ground characteristics do not seem to be influential infertility preference The effect of control variables on thetypes of husbandrsquos occupation is not consistent across thetwo study areas and husbandrsquos education has absolutelyno significant effect on any study community The resultshave consistently found three factors including womenrsquoslabour force participation womenrsquos education and womenrsquoshousehold decision-making that affect individual womenrsquosempowerment Womanrsquos engagement in paid employment(mostly found in Ngudu urban centre) was found to increasethe odds of favouring the ideal number of children by 62relative to Nyamikoma by 48 The emerging result is thatlabour force participation factor is statistically associatedwitha lower ideal number of children

The correlation between household decision-making fac-tor and the ideal number of children is negatively significantin Ngudu and Nyamikoma which shows that women in thecategory of higher level of household decision-making prefersmaller ideal numbers of children

39 Demographic Dividend

391 Cross-Sectional Relationship between Household Wealthand Age Structure Table 5 displays the results of this esti-mation in our sample We show two main specifications Incolumns 1 and 3 we take youth dependency ratio definedas the number of children under the age of 15 per adult of

working age in the household being a dependent variableand regress it on the wealth quintiles In columns 2 and 4we repeat the regressions from columns 1 and 3 but use thenumber of children under 15 as a dependent variable

According to the results the estimated coefficients incolumns 1 and 3 imply that the wealthiest Ngudu urbanhouseholds have on average a 0265 lower dependency ratioand support on average 047 children less than the pooresthouseholds while for Nyamikoma rural the correspondingvalues are 0158 against 0270

Results displayed in columns 2 and 4 suggest that thedecline in the number of dependent children was 001 amonghouseholds in the lowest quintile 003 in the second quintile007 in the third quintile 015 in the fourth and 047 in thetop wealth quintile for Ngudu urban Values for Nyamikomarural were 002 003 003 017 and 027 respectively Theseresults appear to suggest that the poor in Kwimbamight havestarted the process of transition trying to catch up with therichThismodest change for them accrues from the reductionof their family sizes from five to four compared to the four tothree or even lower reduction by the rich

The testing of Caldwellrsquos wealth flow transfer to assess itsapplicability in Kwimba District demanded to compute theaverage economic costs paid by a parent and the economicbenefits received by a parent Benefits of children are the nettransfers received by parents during old age Net transfersreceived by a parent were measured relative to consumptionof the parentThese costs and benefits were analyzed over thelifecycle of a parent by tracking their birth cohortsThe studyprovides results which show that the net familial transfersof the average parent over the entire parental lifecycle aresuch that parents of high cohort fertility (3-4 children)receive positive net familial transfers from children The rateof return from investment in children ranged from 5 to6 percent The average rate of return from investment byparents of low cohort fertility (lt3 children) ranged from 3 to4 percent An interesting observation is that despite parentsof low cohort fertility also receiving net familial transfersfrom children the data trend indicates that children arenet economic costs to young parents with fewer children asopposed to the old with many children in the current periodof time

4 Discussion

Findings of this comparative study suggest that both studyareas have indicated a modest fertility decline despiteNgudu urban having a higher rate of decline compared toNyamikoma rural Results also indicate that all three womenrsquosempowerment factors show significant effects on womenrsquosfertility measured by the ideal number of children Back-ground characteristics such as age urban residence religionand the current number of children ever born of the womenhave significant effects on her fertility preference Householddecision-making and labour force participation have demon-strated to be associated with lower fertility preference in bothcommunities These results conform to the findings of [26]

Results from field study have also shown that older ruralwomen want to have one additional child compared to their


Table 5 Ordinary Least Square (OLS) regression results for household wealth and age structure Estimated coefficients

Dependent variable

Youth dependencyratio

(Ngudu urban)(1)

Children under 15per household(Ngudu urban)

(2)

Youth dependency ratio(Nyamikoma rural)

(3)

Children under 15 perhousehold

(Nyamikoma rural)(4)

First quintile (lowest) minus0101lowast(0475)

minus00117(00261)

minus00009(0143)

00221(003422)

Second quintile minus0114lowast(00570)

minus00250(00203)

minus000154(0156)

00305(00568)

Third quintile minus0126lowastlowastlowast(00305)

minus00684lowastlowastlowast(00230)

minus0205lowastlowast(00800)

minus00318(00584)

Fourth quintile minus0266lowastlowastlowast(00396)




Fifth quintile (wealthiest) minus0265lowastlowastlowast(00334)




119901-value 000 000 013 000Robust standard errors in parentheseslowastlowastlowast

119901 lt 001 lowastlowast119901 lt 005 and lowast119901 lt 01Source Kwimba fertility survey 2015

urban counterparts and the trend remained the same inrecent years Moreover older women in rural areas desiredto have a larger family and in contrast their knowledge onfertility regulation has been found to be low Young ruralwomen on the other hand prefer to have at least two childrenand also aspire to provide quality education to their children

The desire for fewer children in Kwimba District mightbe a consequence of emerging population pressure on thecultivable land and a lack of labour opportunities outsideagriculture which negate any current or future benefitsfrom childrenrsquos work Instead children are seen as a burdenin terms of extra mouths to feed extra outlay on schoolfees clothes and healthcare These findings confirm theassumption that desired fertility is the outcome of parentsrsquoassessment of the costs and benefits of their offspring Suchresults conform to conclusions of [14]

Drawing from the impact of householdwealth on fertilityresults have shown a notice able gap in fertility rates betweenwomen in the highest wealth quintile and the lowest quintileWhile the lowest quintiles in both communities have youthdependency ratios of one or higher the highest quintilesrange between 06 (Ngudu urban) and 075 (Nyamikomarural) The national youth dependency ratio stands at 084[27] Our results are consistent with a small but growing bodyof the literature that asserts fertility and family resources arenegatively linked See for example [28]

As regards social transformation the results stronglysuggest that transmission of norms and attitudes from highly(Ngudu urban centre) to less educated women (Nyamikoma)increases the pace of fertility decline the moment a crit-ical mass of educated women is reached as has hap-pened in Nyamikoma The principal mechanism driving thiseffect appears to be an increased frequency of interactionamong the two communities This exposure may changethe expectations that less educated women have about themost appropriate behaviour in their local community whilealso legitimizing their reproductive preferences within their

private social networks This can then contribute to a fasterfertility decline in the community

With respect to Caldwellrsquos wealth transfer theory findingshave shown that both rates of return from parentsrsquo depositsand children are declining Rates of return from childrenare dropping faster than the parentsrsquo deposits Physicalinvestments are turning out to be more promising thaninvestment in children If parents take into account theprevailing financial opportunity costs economic returns ofchildren are still positive for the elderly parents The returnsare negative however for the young parents since the returnsfrom children are slowly declining

The new theory about rising investment costs of rearingsocially and economically competitive offspring has triggereda debate and has been supported by advocates of parentalinvestment and the optimisation of human family size Theproponents of this life history theory and human reproduc-tive behaviour view fertility limitation through a reallocationof resources to parental investment which ultimately can rep-resent an adaptive strategy to ensure offspring success [20 2629ndash34]They further believe that a trade-off between quantityand ldquoqualityrdquo of offspring is fundamental to human lifehistory Additionally this investment-relatedmodel identifieseducation as a primary attribute involved in the increase inpersonal or parental investment in modern economies [35]

Kaplan and Lancaster [29] and Kaplan et al [30] whoextended the life history theory and the economics of thefamily to explain how quality-quantity trade-off leads to lowfertility behaviour stated that a fertility decline in a givensociety begins when parents perceive the benefits and costs ofchild schooling Their main argument is that modernizationserves to escalate relationships between parental investmentand childrenrsquos success eliciting evolved mechanisms of fer-tility regulation to value offspring quality over quantityFollowing this stance they assert that a decline in fertilitymayalso be construed as a strategic move from high fertility tohigh investment in fewer offspring


While this argument provides another explanation forfertility decline it should be noted that this phenomenon isnot of developed countries alone where rewards to fertilitylimitation fall selectively on relatively wealthy individualsLawson and Mace [20] who have studied at length oncontemporary British families demonstrate that offspringwith an investing mother tend to have an investing father aswell leading to a visible range of measures of child health andeducational successThis finding indicates that richer womenare likely to have lower fertility than poorer householdsSuch a result is in line with fertility models that speculatethis negative relationship arguing that families with higherincome and status tend to substitute quantity of children withquality [36ndash38]This shift in focus fromhavingmany childrento increased consciousness about their ldquoqualityrdquo could be oneway to explain the inverse relationship between wealth statusand fertility

In developing countries fertility limitation resulting fromquality-quantity trade-offs has also been evident The resultof the study by [16] shows that exogenous increase in fertilitydecreases child quality and suggests that a decrease in familysize brought about by exogenous factors increases the qualityof children Li et al [27] also find supporting evidenceto the quality-quantity trade-off by examining the effectof family size on child educational achievement in ChinaUsing educational achievement and school achievement as ameasure of child quality the study finds a negative correlationbetween family size and child quality providing an empiricalsupport to the quality-quantity model of fertility

Turning back to the findings of this study the structuralchange in socioeconomic conditions in the Tanzania andKwimba District in particular has for the past two decadeshelped to make the difference between them smaller andmotivate rural women to adopt small family The diffusion ofideas of small family and change in aspirations about childrenfrom urban to rural area has contributed to a modest declinein rural fertility rate Alongside with this scenario the fallin the number of children in African families frees capitalavailable to elevate child quality For example the intangibleeconomic activities that children provide to their parentsmaybe defined as the product of the number of children and theiraverage quality [16 39]

Further investigation on this fertility change has revealedthat parents in the two study areas are highly concernedabout the schooling of their children owing to the internalefficiency of the educational system in the city being quitelowThough education is free at primary and secondary levelsin Tanzania some amount of parentsrsquo money is occasionallypaid for school uniform textbook services extra curriculaactivities and the like This situation holds true in KwimbaDistrict and this study suggests that economic crisis is ina position to bring about fertility decline through time bychanging the behaviour of the young generation who is partlysurvivors of the current economic squeeze

5 Conclusions

The onset of the fertility transition in Kwimba Districthas been demonstrated by the tested data of this study

Similarly initial signals of the demographic dividend at alocal scale have also started to emerge The available cross-study areasrsquo evidence suggests that the decline in fertilitytriggered during the demographic transition is associatedwith lower dependency ratios increased education andincreased women empowerment The associations betweenincome and dependency ratios have on average directed thisstudy to conclude that households with higher incomes inKwimba District have fewer children to support

Despite the strong association between wealth and agestructure at the household level the implications of thedemographic transition on inequalities between the urbanand rural women in Kwimba District have not been obviousfrom a perspective characterized by continuous change Thisis because the benefits of the demographic transition interms of lower dependency ratios accrued almost to allsocioeconomic groups though at a different scale

Although children in Africa are still net economic bene-fits in high fertility cohorts they are at the same time turningout to be net economic costs in low fertility cohorts Resultsof the Kwimba study conclude that the internal rate of returnof children decreases over a parentrsquos birth cohort such thata falling trend is observed for parents who have low cohortfertility (lt3 children) Additionally results for the NguduandNyamikoma study confirm the theoretical prediction thathaving a higher number of children has an adverse effect onthe consumption expenditure of a given household

Drawing from the analysis emanating from the datapresented the findings of this study are consistent withthose of many nonevolutionary studies on the demographictransition However data analysis of this study also indicatesthat smaller numbers of surviving children per woman arealso related to increased investment in mothers and theirchildren (measured here by formal education) and thereforeunderlines the potential relevance of a combination ofmodelsin bringing about fertility transition

Based on the findings this study therefore recommendspolicies and programmes targeted at regulating high fertilityto continue suppressing entrenched patriarchal values andgender inequalities which fuel stalling of fertility decline inTanzania and many parts of Sub-Saharan Africa

Competing Interests

The authors declare that they have no competing interests

Authorsrsquo Contributions

George Felix Masanja is the major contributor in this paper

Acknowledgments

The authors would like to thank St Augustine University ofTanzania for the financial support to this study

References

[1] E Van de Walle and D Meekers ldquoMarriage drinks andKola nutsrdquo in Nuptiality in Sub-Saharan Africa Contemporary


Anthropological and Demographic Perspectives C Bledsole andG Pison Eds pp 57ndash73 Clarendon Press Oxford UK 1994

[2] A Hinde and A J Mturi ldquoRecent trends in Tanzanian fertilityrdquoPopulation Studies vol 54 no 2 pp 177ndash191 2000

[3] A Mturi and A Hinde Fertility levels and differentials inTanzania Population Division Department of Economic andSocial Affairs UNPOPPFD20018 httpwwwunorgesapopulationpublicationsprospectsdeclinemturipdf

[4] P Makinwa-Adebusoye Socio-Cultural Factors Affecting Fertil-ity in Sub-Saharan Africa The Nigerian Institute of Social andEconomic Research (NISER) Lagos Nigeria 2001 httpwwwunorgesapopulationpublicationsprospectsdeclinemakin-wapdf

[5] B Malmberg ldquoDemography and the development potential ofsub-Saharan Africardquo Current African Issues 38 2008 httpmercuryethzchserviceengineFilesISN92313ipublication-document singledocument33e93ac7-4383-4cb3-aa0f-81471311e250en38pdf

[6] A Agwanda and A Amani ldquoPopulation Growth Structureand Momentum in Tanzaniardquo Dicussion Paper Economic andSocial Research Foundation Dar es Salaam Tanzania 2014httpwwwthdrortzdocsTHDR-BP-7pdf

[7] SNgallaba SHKapiga I Ruyobya and J T BoermaTanzaniaDemographic and Health Survey 19911992 Bureau of StatisticsDar es Salaam Tanzania Macro International Calverton MdUSA 1993

[8] National Bureau of Statistics and Macro International Inc Tan-zania Demographic andHealth Survey 1996 Bureau of Statisticsand Calverton Dar es Salaam Tanzania Macro InternationalInc National Bureau of Statistics National Bureau of Statisticsand Macro International Inc Maryland Md USA 2000

[9] National Bureau of Statistics (NBS) Office of Chief Govern-ment Statistician (OCGS) and Zanzibar The 2012 Populationand Housing Census Basic Demographic and Socio-EconomicProfile Key Findings NBS and OCGS Dar es Salaam Tanzania2014

[10] S Madhavan ldquoFemale relationships and demographic out-comes in sub-Saharan Africardquo Sociological Forum vol 16 no3 pp 503ndash527 2001

[11] B Bigombe and G M Khadiagala ldquoMajor Trends AffectingFamilies in Sub-Saharan Africardquo Major Trends Affecting Fam-ilies A Background Document Report for United NationsDepartment of Economic and Social Affairs Division for SocialPolicy and Development Program on the Family 2003 httpwwwunorgesasocdevfamilyPublicationsmtbigombepdf

[12] N Rutenberg and I Diamond ldquoFertility in botswana the recentdecline and future prospectsrdquo Demography vol 30 no 2 pp143ndash157 1993

[13] J Schaller ldquoFor richer if not for poorer Marriage and divorceover the business cyclerdquo Journal of Population Economics vol26 no 3 pp 1007ndash1033 2013

[14] R A Easterline and E N CrimminsThe Fertility Revolution ADemand-Supply Analysis University of Chicago Press ChicagoIll USA 1985

[15] J Cleland and C Wilson ldquoDemand theories of the fertilitytransition an iconoclastic viewrdquo Population Studies vol 41 no1 pp 5ndash30 1987

[16] A Cigno and F C Rosati ldquoWhy do Indian children work andis it bad for themrdquo Discussion Paper 115 IZA Bonn Germany2000

[17] J Bongaarts and S C Watkins ldquoSocial interactions and con-temporary fertility transitionsrdquo Population and DevelopmentReview vol 22 no 4 pp 639ndash682 1996

[18] J C Caldwell ldquoToward a restatement of demographic transitiontheoryrdquo Population and Development Review vol 2 no 3-4 pp321ndash366 1976

[19] J C Cadwell Theory of Fertility Decline Academic Press NewYork NY USA 1982

[20] D W Lawson and R Mace ldquoParental investment and theoptimization of human family sizerdquo Philosophical Transactionsof the Royal Society B Biological Sciences vol 366 no 1563 pp333ndash343 2011

[21] C R Kothari Research Methodology Methods and TechniquesWishwa Prakashan New Delhi India 2003

[22] J Bongaarts and E G Potter Fertility Biology and Behavior AnAnalysis of the Proximate Determinants Academic Press NewYork NY USA 1983

[23] N Kabeer ldquoThe conditions and consequences of choicesreflections on the measurement of womenrsquos empowermentrdquoUNRISD Discussion Paper 108 United Nations ResearchInstitute for Social Development (UNRISD) GenevaSwitzerland 1999 httpwwwunrisdorg80256B3C005BC-CF9(httpAuxPages)31EEF181BEC398A380256B67005B720A$filedp108pdf

[24] M L Bose A Ahmad and M Hossain ldquoThe role of gen-der in economic activities with special reference to womenrsquosparticipation and empowerment in rural Bangladeshrdquo GenderTechnology and Development vol 13 no 1 pp 69ndash102 2009

[25] D Gwatkin S Rutstein K Johnson R Pande and AWagstaff Socio-Economic Differences in Health Nutrition andPopulation World Bank Washington DC USA 2000 httpsiteresourcesworldbankorgINTPAHResourcesIndicator-sOverviewpdf

[26] H Kaplan ldquoA theory of fertility and parental investment intraditional and modern human societiesrdquo Yearbook of PhysicalAnthropology vol 39 pp 91ndash135 1996

[27] H Li J Zhang and Y Zhu ldquoThe quantity-quality trade-off of children in a developing country identification usingchinese twinsrdquo Demography vol 45 no 1 pp 223ndash243 2008httpwwwncbinlmnihgovpmcarticlesPMC2831373

[28] D E Bloom D Canning G Fink and J E Finlay ldquoFertilityfemale labor force participation and the demographic divi-dendrdquo Journal of Economic Growth vol 14 no 2 pp 79ndash1012009

[29] H Kaplan and J Lancaster ldquoThe evolutionary economics andpsychology of the demographic transition to low fertilityrdquo inAdaptation and Human Behavior An Anthropological Perspec-tive L Cronk N Chagnon and W Irons Eds pp 283ndash322Aldine de Gruyter Hawthorne NY USA 2000

[30] H Kaplan J B Lancaster W T Tucker and K G AndersonldquoEvolutionary approach to below replacement fertilityrdquo Ameri-can Journal of Human Biology vol 14 no 2 pp 233ndash256 2002

[31] D W Lawson and R Mace ldquoSibling configuration and child-hood growth in contemporary British familiesrdquo InternationalJournal of Epidemiology vol 37 no 6 pp 1408ndash1421 2008

[32] D W Lawson and R Mace ldquoTrade-offs in modern parentinga longitudinal study of sibling competition for parental carerdquoEvolution andHuman Behavior vol 30 no 3 pp 170ndash183 2009

[33] D W Lawson and R Mace ldquoOptimizing modern familysize trade-offs between fertility and the economic costs ofreproductionrdquo Human Nature vol 21 no 1 pp 39ndash61 2010


[34] R Mace ldquoThe evolutionary ecology of human family sizerdquoin The Oxford Handbook of Evolutionary Psychology R I MDunbar and L Barrett Eds pp 383ndash396 Oxford UniversityPress Oxford UK 2007

[35] B S Low C P Simon and K G Anderson ldquoAn evolutionaryecological perspective on demographic transitions modelingmultiple currenciesrdquo American Journal of Human Biology vol14 no 2 pp 149ndash167 2002

[36] R J Quinlan ldquoGender and risk in a matrifocal Caribbeancommunity a view from behavioral ecologyrdquoAmerican Anthro-pologist vol 108 no 3 pp 464ndash479 2006

[37] R J Quinlan ldquoHuman parental effort and environmental riskrdquoProceedings of the Royal Society B Biological Sciences vol 274no 1606 pp 121ndash125 2007

[38] R J Quinlan andM BQuinlan ldquoParenting and cultures of riska comparative analysis of infidelity aggression amp witchcraftrdquoAmerican Anthropologist vol 109 no 1 pp 164ndash179 2007

[39] F Tadese Socio-economic determinants of fertility in urbanEthiopia [MS thesis] School of Graduate Studies of AddisAbaba University Addis Ababa Ethiopia 2008

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further childbearing especially at parities above three or four(see eg [12 13])

Despite the literature carrying an extensive debate andcontroversy over the past twenty years on fertility declinea better understanding of the socio-economic effects andimplications on family structures emerging among presentAfrican households as a result of such a decline is yetto be fully understood To our knowledge few studieshave empirically examined the interrelationships betweenthe socio-economic factors womenrsquos autonomy (empow-erment) and fertility in Tanzania using field survey dataThe specific objectives of this study include examiningthe observed fertility behaviour among rural versus urbanhouseholds determining the socioeconomic and culturalfactors contributing to the observed fertility patterns amongrural versus urban households and assessing the observedfertility behaviour patterns in light of demographic divi-dend or deficit changes among rural versus urban house-holds

The theoretical framework guiding this study combinesthree prominent theories advanced to explain the fertilitytransition in Sub-Saharan Africa These are the supply-demand framework innovation-diffusion theory and wealthflows theory

(1) Supply-Demand Framework The supply-demand frame-work as advanced by [14] extracts its concept mainly fromthe domain of neoclassical economics The supply-demandframework suggests that people will eventually realize thatlower mortality has produced a situation in which morechildren are going to survive than can be afforded and at thatpoint fertility will decline

The benefits derived from children which served as abasis for increase in fertility are no longer the case currentlySustained economic growth over 90 years after theWorldWarII has led to children being increasingly sent to school foreducation and to health services when sick The rising costof education and providing healthcare for children is a majorproblem for most couples now and has caused a decrease inthe demand for children which instead has contributed to agradual decline in fertility in the African subregion

(2) Innovation-Diffusion Theory The most important ideabehind the diffusion theory is that social interaction is amajor mechanism through which the endorsement of newtechnologies ideas and behaviours takes place Further thediffusion theory posits that theories relying on individualrational decision-making in response to economic or struc-tural change could not explain at best the observed fertilitytransitions in many areas of the world The outcomes of thetwo major research efforts completed in the mid-1980s thePrinceton European Fertility Project and the World FertilitySurvey led certain researchers to conclude that structural andeconomic changes alone provide an incomplete explanation(see [15ndash17])

Connecting this theory to the explanation of fertilitytransition in Sub-Saharan Africa it can be deduced thatcertain lifestyle practices which were characteristic of thedeveloped world have now been diffused into the African

culture consequently leading to a decrease in fertility in theAfrican subregion

(3) Caldwellrsquos Wealth Flows Theory Caldwellrsquos theory [18 19]states that the level of fertility is essentially controlled by thedirection of the net wealth flows among parents and childrenwhich include all the expected benefits over a lifespan Theend result of this economic justification is either maximumor zero fertility but this is adjusted by the impact of personalsocial and physiological reasons The controlling principleunderlying the direction of intergenerational wealth flowsis the social organization of the society especially familystructures Caldwell argues that in all traditional societiesthe net wealth flow has been from younger to older gener-ations which means that economic motives promote highfertility This flow will only be reversed if the economic anddomination of a mental state popularly known as ldquoemotionalprimacyrdquo is withdrawn from the bond of broader family tiesand is focused on the conjugal family The nucleated familyis therefore a necessary condition for low fertility and thetransition from high to low fertility is a product of socialchange with economic implications rather than economicchange alone

From these three theories it has been noted that contactwith western societies influenced the economy urbanizationmigration education and even factors such as oppressionand inequality of women and hegemony and patriarchywhich influence womenrsquos position in society in many Africansocieties and cultures Such a contact has altered the customsand practices which in turn have led to gradual changeinfertility behaviour

2 Materials and Methods

21 Study Area Characteristics The study area chosen toexamine the research problem is Kwimba District of Mwanzaregion Figure 1 presents the map of the district and thestudy sitesThedistrict lies between latitude 2∘591015840 to 3∘3101584043210158401015840south of equator and also longitude 33∘141015840 to 33∘2110158403610158401015840 eastof Greenwich Kwimba District is one of 7 districts of theMwanza Region of Tanzania Kwimbarsquos population is 406509[20] The district headquarters is located in Ngudu towncentre

22 Study Design The study applies a cross-sectional andcausal-comparative design It utilizes data gathered froma recent household survey to examine whether economicdevelopment and social change might have created support-ive conditions in which fertility has modestly declined inTanzania

23 Population Sample and Sampling Procedures The pop-ulation for this study comprised all females from KwimbaDistrict whose number amounts to 206093 [9] Out ofthis population a sampling frame comprising all females ofthe childbearing age residing in Ngudu urban and Kwimbarural was constructed Kwimba District had a total of 87076females of childbearing age living in 149725 households [9]


2∘45

998400

2∘55

998400

3∘05

998400

3∘15

998400

3∘25

998400

2∘45

998400

2∘55

998400

3∘05

998400

3∘15

998400

3∘25

998400

32∘50

99840033

∘00

99840033

∘10

99840033

∘20

99840033

∘30

998400

32∘50

99840033

∘00

99840033

∘10

99840033

∘20

99840033

∘30

998400

Kwimba District

N

0 5 10 20

(km)

Scale 1 100000 20142015

RoadsF_CODE_DES

RoadTrailRiver

NguduNyamikoma



119899 =

1199112

119901119902119873

1198902

(119873 minus 1) + 1199112

119901119902

(1)










119862119894=

20

185

+ 119894 (2)










ence)


WEI (f t) =(sum4

119894=1

119909119894)

4

(3)










NPV =119879

sum

119905=1

119862119905

(1 + 119903)119905

minus 119862119900 (4)


119900is total



3 Results






Table1Descriptiv

edatafor

weightedsampleo

fwom

enacrossstu

dycommun

ities

inKw

imba

distric

t

Ngudu

urban

Nyamikom

a

Wom

enwith

nochild

ren

(119899=12)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=48)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=38)

or

mean(SD)

pvalue

(chi-squ

ared

or119905-te

st)lowast

Wom

enwith

nochild

ren

(119899=17)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=52)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=29)

or

mean(SD)

119901value

(chi-squ

ared

ort-test)lowast

AgeM

(SD)

234(57)

263(62)

349(73)

lt0001

176(46)

203(49)

289(72)

lt0001

Education(

)lt0001

lt0001

Non

e1982

1676

3667

173

1376

1757

Prim

ary

944

716

966

857

859

823

Second

ary

1221

1205

1113

1043

1046

1113

Higher

4321

5212

955

4425

2612

4479

Wealth

()

lt0001

lt0001

Lowest

1521

1221

1967

1265

1145

1122

Second

2129

1484

1886

1634

1543

1312

Third

1832

1872

2023

1734

1665

1622

Fourth

2261

2517

2103

2012

2113

1921

Highest

2257

2907

2022

2112

1923

1723

Resid

ence

lt0001

lt0001

Postp

artum

insusceptib

ility

M(SD)

183(059)

177(048)

182(054)

gt005

176(044)

178(041)

169(035)

gt005

Costofliving(

)9356

182(058)

9550

lt0001

9033

178(056)

9233

lt0001

Socia

ltransform

ation

192(074

)219

(073)

226

(067)

lt0001

188(067)

179(062)

176(066)

lt0001

lowast

Com

parin

gdifferences

acrossthethree

paritygrou

ps119899=12119899=48and119899=38forN

gudu

and119899=17119899=52and119899=29forN

yamikom

aSourceK

wim
















Table2Parameter

statisticso

flogisticmod

elof

whether

awom

angave

birthin

last12

mon

ths

Independ

entvariable

Ngudu

Nyamikom

aParameter

estim

ate

Standard

error

Odd

sratio

Parameter

estim

ate

Standard

error

Odd

sratio

Intercept

minus48904

10985

1000

0minus37543

10657

1000

0Indexof

wealth

minus00036

00016

0995lowastlowast

minus00033

00452

1000

0Stud

yarea

(place

ofresid

ence)

000

0010

0010

000

014

1234

1232

Highestlevelofedu

catio

nNoeducation

02013

02533

1225

02311

02201

1435

Prim

ary

02969

02238

1346

02322

02152

1321

Second

ary

minus00214

02014

0979

00012

01222

0636

Higher

000

0001534

1000

0000

0001233

0734

Participationin

labo

urforce

Not

working

000

000231

1000

000011

02361

02332

Agriculturalw

ork

minus05536

0184

0575lowastlowast

minus044

3300345

06578

Non

agric

ulturalw

ork

minus015

02257

0682

016663

02135

044

62Age-at-fi

rstm

arria

ge01624

006

6212

08lowast

02433

00744

0110

1Age

01345

00597

1144lowast

01343

00221

00231

ParityProgression

011458

01702


01212

01553


1to2

06344

01121


06755

01233


3to

4000

0001222

1000

0000

0001322

2252

Socialtransfo

rmation

01316

00496


01322

01551

3212lowastlowast

119873=196(98forN

gudu

)and

(98forN

yamikom

a)

lowast

Sign

ificant

at005

lowastlowast

Sign

ificant

at001

lowastlowastlowast

Sign

ificant

at0001

SourceK

wim






































4 Discussion





Dependent variable


(Ngudu urban)(1)


(2)


(3)




minus00117(00261)

minus00009(0143)

00221(003422)


minus00250(00203)

minus000154(0156)

00305(00568)




minus00318(00584)
























5 Conclusions







Competing Interests




Acknowledgments


References



















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal






2∘45

998400

2∘55

998400

3∘05

998400

3∘15

998400

3∘25

998400

2∘45

998400

2∘55

998400

3∘05

998400

3∘15

998400

3∘25

998400

32∘50

99840033

∘00

99840033

∘10

99840033

∘20

99840033

∘30

998400

32∘50

99840033

∘00

99840033

∘10

99840033

∘20

99840033

∘30

998400

Kwimba District

N

0 5 10 20

(km)

Scale 1 100000 20142015

RoadsF_CODE_DES

RoadTrailRiver

NguduNyamikoma



119899 =

1199112

119901119902119873

1198902

(119873 minus 1) + 1199112

119901119902

(1)










119862119894=

20

185

+ 119894 (2)










ence)


WEI (f t) =(sum4

119894=1

119909119894)

4

(3)










NPV =119879

sum

119905=1

119862119905

(1 + 119903)119905

minus 119862119900 (4)


119900is total



3 Results






Table1Descriptiv

edatafor

weightedsampleo

fwom

enacrossstu

dycommun

ities

inKw

imba

distric

t

Ngudu

urban

Nyamikom

a

Wom

enwith

nochild

ren

(119899=12)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=48)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=38)

or

mean(SD)

pvalue

(chi-squ

ared

or119905-te

st)lowast

Wom

enwith

nochild

ren

(119899=17)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=52)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=29)

or

mean(SD)

119901value

(chi-squ

ared

ort-test)lowast

AgeM

(SD)

234(57)

263(62)

349(73)

lt0001

176(46)

203(49)

289(72)

lt0001

Education(

)lt0001

lt0001

Non

e1982

1676

3667

173

1376

1757

Prim

ary

944

716

966

857

859

823

Second

ary

1221

1205

1113

1043

1046

1113

Higher

4321

5212

955

4425

2612

4479

Wealth

()

lt0001

lt0001

Lowest

1521

1221

1967

1265

1145

1122

Second

2129

1484

1886

1634

1543

1312

Third

1832

1872

2023

1734

1665

1622

Fourth

2261

2517

2103

2012

2113

1921

Highest

2257

2907

2022

2112

1923

1723

Resid

ence

lt0001

lt0001

Postp

artum

insusceptib

ility

M(SD)

183(059)

177(048)

182(054)

gt005

176(044)

178(041)

169(035)

gt005

Costofliving(

)9356

182(058)

9550

lt0001

9033

178(056)

9233

lt0001

Socia

ltransform

ation

192(074

)219

(073)

226

(067)

lt0001

188(067)

179(062)

176(066)

lt0001

lowast

Com

parin

gdifferences

acrossthethree

paritygrou

ps119899=12119899=48and119899=38forN

gudu

and119899=17119899=52and119899=29forN

yamikom

aSourceK

wim
















Table2Parameter

statisticso

flogisticmod

elof

whether

awom

angave

birthin

last12

mon

ths

Independ

entvariable

Ngudu

Nyamikom

aParameter

estim

ate

Standard

error

Odd

sratio

Parameter

estim

ate

Standard

error

Odd

sratio

Intercept

minus48904

10985

1000

0minus37543

10657

1000

0Indexof

wealth

minus00036

00016

0995lowastlowast

minus00033

00452

1000

0Stud

yarea

(place

ofresid

ence)

000

0010

0010

000

014

1234

1232

Highestlevelofedu

catio

nNoeducation

02013

02533

1225

02311

02201

1435

Prim

ary

02969

02238

1346

02322

02152

1321

Second

ary

minus00214

02014

0979

00012

01222

0636

Higher

000

0001534

1000

0000

0001233

0734

Participationin

labo

urforce

Not

working

000

000231

1000

000011

02361

02332

Agriculturalw

ork

minus05536

0184

0575lowastlowast

minus044

3300345

06578

Non

agric

ulturalw

ork

minus015

02257

0682

016663

02135

044

62Age-at-fi

rstm

arria

ge01624

006

6212

08lowast

02433

00744

0110

1Age

01345

00597

1144lowast

01343

00221

00231

ParityProgression

011458

01702


01212

01553


1to2

06344

01121


06755

01233


3to

4000

0001222

1000

0000

0001322

2252

Socialtransfo

rmation

01316

00496


01322

01551

3212lowastlowast

119873=196(98forN

gudu

)and

(98forN

yamikom

a)

lowast

Sign

ificant

at005

lowastlowast

Sign

ificant

at001

lowastlowastlowast

Sign

ificant

at0001

SourceK

wim






































4 Discussion





Dependent variable


(Ngudu urban)(1)


(2)


(3)




minus00117(00261)

minus00009(0143)

00221(003422)


minus00250(00203)

minus000154(0156)

00305(00568)




minus00318(00584)
























5 Conclusions







Competing Interests




Acknowledgments


References



















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal









119862119894=

20

185

+ 119894 (2)










ence)


WEI (f t) =(sum4

119894=1

119909119894)

4

(3)










NPV =119879

sum

119905=1

119862119905

(1 + 119903)119905

minus 119862119900 (4)


119900is total



3 Results






Table1Descriptiv

edatafor

weightedsampleo

fwom

enacrossstu

dycommun

ities

inKw

imba

distric

t

Ngudu

urban

Nyamikom

a

Wom

enwith

nochild

ren

(119899=12)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=48)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=38)

or

mean(SD)

pvalue

(chi-squ

ared

or119905-te

st)lowast

Wom

enwith

nochild

ren

(119899=17)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=52)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=29)

or

mean(SD)

119901value

(chi-squ

ared

ort-test)lowast

AgeM

(SD)

234(57)

263(62)

349(73)

lt0001

176(46)

203(49)

289(72)

lt0001

Education(

)lt0001

lt0001

Non

e1982

1676

3667

173

1376

1757

Prim

ary

944

716

966

857

859

823

Second

ary

1221

1205

1113

1043

1046

1113

Higher

4321

5212

955

4425

2612

4479

Wealth

()

lt0001

lt0001

Lowest

1521

1221

1967

1265

1145

1122

Second

2129

1484

1886

1634

1543

1312

Third

1832

1872

2023

1734

1665

1622

Fourth

2261

2517

2103

2012

2113

1921

Highest

2257

2907

2022

2112

1923

1723

Resid

ence

lt0001

lt0001

Postp

artum

insusceptib

ility

M(SD)

183(059)

177(048)

182(054)

gt005

176(044)

178(041)

169(035)

gt005

Costofliving(

)9356

182(058)

9550

lt0001

9033

178(056)

9233

lt0001

Socia

ltransform

ation

192(074

)219

(073)

226

(067)

lt0001

188(067)

179(062)

176(066)

lt0001

lowast

Com

parin

gdifferences

acrossthethree

paritygrou

ps119899=12119899=48and119899=38forN

gudu

and119899=17119899=52and119899=29forN

yamikom

aSourceK

wim
















Table2Parameter

statisticso

flogisticmod

elof

whether

awom

angave

birthin

last12

mon

ths

Independ

entvariable

Ngudu

Nyamikom

aParameter

estim

ate

Standard

error

Odd

sratio

Parameter

estim

ate

Standard

error

Odd

sratio

Intercept

minus48904

10985

1000

0minus37543

10657

1000

0Indexof

wealth

minus00036

00016

0995lowastlowast

minus00033

00452

1000

0Stud

yarea

(place

ofresid

ence)

000

0010

0010

000

014

1234

1232

Highestlevelofedu

catio

nNoeducation

02013

02533

1225

02311

02201

1435

Prim

ary

02969

02238

1346

02322

02152

1321

Second

ary

minus00214

02014

0979

00012

01222

0636

Higher

000

0001534

1000

0000

0001233

0734

Participationin

labo

urforce

Not

working

000

000231

1000

000011

02361

02332

Agriculturalw

ork

minus05536

0184

0575lowastlowast

minus044

3300345

06578

Non

agric

ulturalw

ork

minus015

02257

0682

016663

02135

044

62Age-at-fi

rstm

arria

ge01624

006

6212

08lowast

02433

00744

0110

1Age

01345

00597

1144lowast

01343

00221

00231

ParityProgression

011458

01702


01212

01553


1to2

06344

01121


06755

01233


3to

4000

0001222

1000

0000

0001322

2252

Socialtransfo

rmation

01316

00496


01322

01551

3212lowastlowast

119873=196(98forN

gudu

)and

(98forN

yamikom

a)

lowast

Sign

ificant

at005

lowastlowast

Sign

ificant

at001

lowastlowastlowast

Sign

ificant

at0001

SourceK

wim






































4 Discussion





Dependent variable


(Ngudu urban)(1)


(2)


(3)




minus00117(00261)

minus00009(0143)

00221(003422)


minus00250(00203)

minus000154(0156)

00305(00568)




minus00318(00584)
























5 Conclusions







Competing Interests




Acknowledgments


References



















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal










NPV =119879

sum

119905=1

119862119905

(1 + 119903)119905

minus 119862119900 (4)


119900is total



3 Results






Table1Descriptiv

edatafor

weightedsampleo

fwom

enacrossstu

dycommun

ities

inKw

imba

distric

t

Ngudu

urban

Nyamikom

a

Wom

enwith

nochild

ren

(119899=12)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=48)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=38)

or

mean(SD)

pvalue

(chi-squ

ared

or119905-te

st)lowast

Wom

enwith

nochild

ren

(119899=17)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=52)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=29)

or

mean(SD)

119901value

(chi-squ

ared

ort-test)lowast

AgeM

(SD)

234(57)

263(62)

349(73)

lt0001

176(46)

203(49)

289(72)

lt0001

Education(

)lt0001

lt0001

Non

e1982

1676

3667

173

1376

1757

Prim

ary

944

716

966

857

859

823

Second

ary

1221

1205

1113

1043

1046

1113

Higher

4321

5212

955

4425

2612

4479

Wealth

()

lt0001

lt0001

Lowest

1521

1221

1967

1265

1145

1122

Second

2129

1484

1886

1634

1543

1312

Third

1832

1872

2023

1734

1665

1622

Fourth

2261

2517

2103

2012

2113

1921

Highest

2257

2907

2022

2112

1923

1723

Resid

ence

lt0001

lt0001

Postp

artum

insusceptib

ility

M(SD)

183(059)

177(048)

182(054)

gt005

176(044)

178(041)

169(035)

gt005

Costofliving(

)9356

182(058)

9550

lt0001

9033

178(056)

9233

lt0001

Socia

ltransform

ation

192(074

)219

(073)

226

(067)

lt0001

188(067)

179(062)

176(066)

lt0001

lowast

Com

parin

gdifferences

acrossthethree

paritygrou

ps119899=12119899=48and119899=38forN

gudu

and119899=17119899=52and119899=29forN

yamikom

aSourceK

wim
















Table2Parameter

statisticso

flogisticmod

elof

whether

awom

angave

birthin

last12

mon

ths

Independ

entvariable

Ngudu

Nyamikom

aParameter

estim

ate

Standard

error

Odd

sratio

Parameter

estim

ate

Standard

error

Odd

sratio

Intercept

minus48904

10985

1000

0minus37543

10657

1000

0Indexof

wealth

minus00036

00016

0995lowastlowast

minus00033

00452

1000

0Stud

yarea

(place

ofresid

ence)

000

0010

0010

000

014

1234

1232

Highestlevelofedu

catio

nNoeducation

02013

02533

1225

02311

02201

1435

Prim

ary

02969

02238

1346

02322

02152

1321

Second

ary

minus00214

02014

0979

00012

01222

0636

Higher

000

0001534

1000

0000

0001233

0734

Participationin

labo

urforce

Not

working

000

000231

1000

000011

02361

02332

Agriculturalw

ork

minus05536

0184

0575lowastlowast

minus044

3300345

06578

Non

agric

ulturalw

ork

minus015

02257

0682

016663

02135

044

62Age-at-fi

rstm

arria

ge01624

006

6212

08lowast

02433

00744

0110

1Age

01345

00597

1144lowast

01343

00221

00231

ParityProgression

011458

01702


01212

01553


1to2

06344

01121


06755

01233


3to

4000

0001222

1000

0000

0001322

2252

Socialtransfo

rmation

01316

00496


01322

01551

3212lowastlowast

119873=196(98forN

gudu

)and

(98forN

yamikom

a)

lowast

Sign

ificant

at005

lowastlowast

Sign

ificant

at001

lowastlowastlowast

Sign

ificant

at0001

SourceK

wim






































4 Discussion





Dependent variable


(Ngudu urban)(1)


(2)


(3)




minus00117(00261)

minus00009(0143)

00221(003422)


minus00250(00203)

minus000154(0156)

00305(00568)




minus00318(00584)
























5 Conclusions







Competing Interests




Acknowledgments


References



















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal






Table1Descriptiv

edatafor

weightedsampleo

fwom

enacrossstu

dycommun

ities

inKw

imba

distric

t

Ngudu

urban

Nyamikom

a

Wom

enwith

nochild

ren

(119899=12)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=48)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=38)

or

mean(SD)

pvalue

(chi-squ

ared

or119905-te

st)lowast

Wom

enwith

nochild

ren

(119899=17)

or

mean(SD)

Wom

enwith

1-2child

ren

(119899=52)

or

mean(SD)

Wom

enwith

3-4

child

ren

(119899=29)

or

mean(SD)

119901value

(chi-squ

ared

ort-test)lowast

AgeM

(SD)

234(57)

263(62)

349(73)

lt0001

176(46)

203(49)

289(72)

lt0001

Education(

)lt0001

lt0001

Non

e1982

1676

3667

173

1376

1757

Prim

ary

944

716

966

857

859

823

Second

ary

1221

1205

1113

1043

1046

1113

Higher

4321

5212

955

4425

2612

4479

Wealth

()

lt0001

lt0001

Lowest

1521

1221

1967

1265

1145

1122

Second

2129

1484

1886

1634

1543

1312

Third

1832

1872

2023

1734

1665

1622

Fourth

2261

2517

2103

2012

2113

1921

Highest

2257

2907

2022

2112

1923

1723

Resid

ence

lt0001

lt0001

Postp

artum

insusceptib

ility

M(SD)

183(059)

177(048)

182(054)

gt005

176(044)

178(041)

169(035)

gt005

Costofliving(

)9356

182(058)

9550

lt0001

9033

178(056)

9233

lt0001

Socia

ltransform

ation

192(074

)219

(073)

226

(067)

lt0001

188(067)

179(062)

176(066)

lt0001

lowast

Com

parin

gdifferences

acrossthethree

paritygrou

ps119899=12119899=48and119899=38forN

gudu

and119899=17119899=52and119899=29forN

yamikom

aSourceK

wim
















Table2Parameter

statisticso

flogisticmod

elof

whether

awom

angave

birthin

last12

mon

ths

Independ

entvariable

Ngudu

Nyamikom

aParameter

estim

ate

Standard

error

Odd

sratio

Parameter

estim

ate

Standard

error

Odd

sratio

Intercept

minus48904

10985

1000

0minus37543

10657

1000

0Indexof

wealth

minus00036

00016

0995lowastlowast

minus00033

00452

1000

0Stud

yarea

(place

ofresid

ence)

000

0010

0010

000

014

1234

1232

Highestlevelofedu

catio

nNoeducation

02013

02533

1225

02311

02201

1435

Prim

ary

02969

02238

1346

02322

02152

1321

Second

ary

minus00214

02014

0979

00012

01222

0636

Higher

000

0001534

1000

0000

0001233

0734

Participationin

labo

urforce

Not

working

000

000231

1000

000011

02361

02332

Agriculturalw

ork

minus05536

0184

0575lowastlowast

minus044

3300345

06578

Non

agric

ulturalw

ork

minus015

02257

0682

016663

02135

044

62Age-at-fi

rstm

arria

ge01624

006

6212

08lowast

02433

00744

0110

1Age

01345

00597

1144lowast

01343

00221

00231

ParityProgression

011458

01702


01212

01553


1to2

06344

01121


06755

01233


3to

4000

0001222

1000

0000

0001322

2252

Socialtransfo

rmation

01316

00496


01322

01551

3212lowastlowast

119873=196(98forN

gudu

)and

(98forN

yamikom

a)

lowast

Sign

ificant

at005

lowastlowast

Sign

ificant

at001

lowastlowastlowast

Sign

ificant

at0001

SourceK

wim






































4 Discussion





Dependent variable


(Ngudu urban)(1)


(2)


(3)




minus00117(00261)

minus00009(0143)

00221(003422)


minus00250(00203)

minus000154(0156)

00305(00568)




minus00318(00584)
























5 Conclusions







Competing Interests




Acknowledgments


References



















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal



















Table2Parameter

statisticso

flogisticmod

elof

whether

awom

angave

birthin

last12

mon

ths

Independ

entvariable

Ngudu

Nyamikom

aParameter

estim

ate

Standard

error

Odd

sratio

Parameter

estim

ate

Standard

error

Odd

sratio

Intercept

minus48904

10985

1000

0minus37543

10657

1000

0Indexof

wealth

minus00036

00016

0995lowastlowast

minus00033

00452

1000

0Stud

yarea

(place

ofresid

ence)

000

0010

0010

000

014

1234

1232

Highestlevelofedu

catio

nNoeducation

02013

02533

1225

02311

02201

1435

Prim

ary

02969

02238

1346

02322

02152

1321

Second

ary

minus00214

02014

0979

00012

01222

0636

Higher

000

0001534

1000

0000

0001233

0734

Participationin

labo

urforce

Not

working

000

000231

1000

000011

02361

02332

Agriculturalw

ork

minus05536

0184

0575lowastlowast

minus044

3300345

06578

Non

agric

ulturalw

ork

minus015

02257

0682

016663

02135

044

62Age-at-fi

rstm

arria

ge01624

006

6212

08lowast

02433

00744

0110

1Age

01345

00597

1144lowast

01343

00221

00231

ParityProgression

011458

01702


01212

01553


1to2

06344

01121


06755

01233


3to

4000

0001222

1000

0000

0001322

2252

Socialtransfo

rmation

01316

00496


01322

01551

3212lowastlowast

119873=196(98forN

gudu

)and

(98forN

yamikom

a)

lowast

Sign

ificant

at005

lowastlowast

Sign

ificant

at001

lowastlowastlowast

Sign

ificant

at0001

SourceK

wim






































4 Discussion





Dependent variable


(Ngudu urban)(1)


(2)


(3)




minus00117(00261)

minus00009(0143)

00221(003422)


minus00250(00203)

minus000154(0156)

00305(00568)




minus00318(00584)
























5 Conclusions







Competing Interests




Acknowledgments


References



















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal









































4 Discussion





Dependent variable


(Ngudu urban)(1)


(2)


(3)




minus00117(00261)

minus00009(0143)

00221(003422)


minus00250(00203)

minus000154(0156)

00305(00568)




minus00318(00584)
























5 Conclusions







Competing Interests




Acknowledgments


References



















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal







Dependent variable


(Ngudu urban)(1)


(2)


(3)




minus00117(00261)

minus00009(0143)

00221(003422)


minus00250(00203)

minus000154(0156)

00305(00568)




minus00318(00584)
























5 Conclusions







Competing Interests




Acknowledgments


References



















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal










5 Conclusions







Competing Interests




Acknowledgments


References



















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal






















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal





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