ANALYSIS OF THE EFFECT OF SOCIO-ECONOMIC

AND PROXIMATE DETERMINANTS OF FERTILITY

ON BIRTH INTERVAL AND CHILDREN EVER BORN

TO WOMEN IN PAKISTAN

ASIFA KAMAL

2008-2011

17-GCU-Ph.D-STAT-2004

DEPARTMENT OF STATISTICS

GC UNIVERSITY LAHORE

ANALYSIS OF THE EFFECT OF SOCIO-ECONOMIC AND PROXIMATE DETERMINANTS OF FERTILITY ON BIRTH INTERVAL AND CHILDREN EVER BORN

TO WOMEN IN PAKISTAN

Submitted to GC University Lahore In partial fulfillment of the requirements

for the award of degree of

Doctor of Philosophy

IN

STATISTICS

By

ASIFA KAMAL

2008-2011

17-GCU-Ph.D-STAT-2004

DEPARTMENT OF STATISTICS GC UNIVERSITY LAHORE

DECLARATION I Ms. ASIFA KAMAL Roll No. 17-GCU-PhD-Stat-2004 student of Doctor of Philosophy

in the subject of Statistics session 2008-11, hereby declare that the matter printed in the

thesis titled Analysis of the Effect of Socio-Economic and Proximate Determinants of

Fertility on Birth interval and Children Ever Born to Women in Pakistan is my own work

and has not been printed, published and as research work, thesis or publications in any

form in any University, Research Institution etc. in Pakistan or abroad.

--------------------------- Signature of Deponent

Dated ---------------------

RESEARCH COMPLETION CERTIFICATE Certified that the research work contained in this thesis titled Analysis of the Effect of

Socio-Economic and Proximate Determinants of Fertility on Birth interval and Children

Ever Born to Women in Pakistan has been carried out and completed by Ms. ASIFA

KAMAL Roll No. 17-GCU-PhD-Stat-2004 under my supervision.

---------------------------- Supervisor

---------------------- Date Submitted Through Prof. Dr. Muhammad Khalid Pervaiz

------------------------------ Controller of Examination GC University, Lahore.

Chairperson Department of Statistics GC University, Lahore.

ACKNOLEDGEMENT

By the grace of Allah, the Almighty, the Beneficent and the most Merciful, I have been

able to produce this thesis, under the able guidance of Dr. Muhammad Khalid Pervaiz

Chairperson, Department of Statistics, Government College University, Lahore. I am

sincerely indebted to Dr. Khalid without whose inspiring help this task would not have

been successfully completed. Again, I have to express my deep gratitude to him who has

not only been source of inspiration to me, but also ingrained in me sense of dedication to

this research work and as a motivator he exerted his own efforts far beyond the usual.

I am also thankful to our PhD coordinators Mr. Sharoon Hanook and Mr. Masood Amjad

Khan for their cooperation throughout the entire period of PhD. Valuable suggestions of

Mr. Masood Amjad Khan helps me in improving the thesis.

I am further respectfully grateful to Dr. Muhammad Hafeez, Chairman, Sociology

Department, Punjab University Lahore, for providing valuable suggestion to use data

PDHS 2006-07 for this topic. He also helped in accessing data and report of PDHS 2006-

07. I also extend my special thanks to Mr. Fatehuddin, programmer at NIPS for guidance

towards use of data.

Since, however, it remains to be a fact that no one can produce any research work in

isolation. As such I express my revered acknowledgement, in the first place to all of my

colleagues at LCWU, Lahore, faculty members Statistics Department of GCU, Lahore

and also my Ph.D. class mates who provided encouragement in moments of anxiety while

working on the thesis.

With affectionate sincerity I pay my gratitude to my following dear ones particularly Dr.

Asma Kamal, sister, who prayed day in and day out which eventually answered with

blessing of completion of my thesis. Again, Ahmad Shehryar, brother, who helped me in

compiling the research papers. Further, Ahmed Mansoor Kamal who provided me with

transport facility that helped me in completing my work on time.

Special thanks to my parents for their efforts, prayers and encouragement that fruitfully

bore results for me making them so proud today.

My colleague and dependable friend Mahnaz Makhdum, AP, who is always found on

hand for very helpful discussions that succeeded inspirational going on with the research

work by me.

Last but not the least, I owe to Mr. Muhammad Nawaz, attendant who frequently and

promptly served us with much needed tea during meetings with supervisor so as to

recoup the stress of overwork from time to time.

i

TABLE OF CONTENTS

Preliminaries of Terms related to Study ix Abbreviations xvii Abstract xiii 1 Introduction 1 1.1 Problem of Rapid Population Growth 1 1.2 Definition of Fertility 3 1.3 Measurement of Fertility 3 1.3.1 Aggregate Measurement of Fertility 4 1.3.2 Measurement of Individual Fertility 6 1.4 Fertility Control 6 1.4.1 Natural Fertility Restraints 6 1.4.2 Behavioral Restraints of Fertility 7 1.5 Types of Fertility Limiting Behaviors 7 1.6 Factors and Covariates Affecting Fertility of Individual (Stopping

Behaviour) 8

1.7 Birth Interval Analysis (Spacing Behaviour) 22 1.8 An Economic Theory of Fertility 28 1.9 Effects of Child Bearing on Parents Life Style 30 1.10 Effect of Interpersonal Interaction and Communication on the

Fertility Norms 31

1.11 Problem of Over Population 32 1.12 Demographic Profiles of Region (Pakistan) 34 1.13 Drafts for Execution of Study 36 1.13.1 Objectives 36 1.13.2 Study Plan 37 1.14 Utilization of Research 38 2 Literature Review 40 2.1 Factors Affecting Children Ever Born [Stopping Behavior of Fertility] 40 2.2 Birth Interval Analysis [Spacing Behavior of Fertility] 57 2.3 Main Findings of Literature Review keeping in view the Methodology 65 2.4 Main Findings of Literature Review keeping in view the Factors 68 2.5 Regression Analysis with Missing Values 70 3 Research Methodology 71 3.1 Sample Design 71 3.2 Questionnaire 73 3.3 Response Rate 73 3.4 Missing Data 73 3.4.1 Analysis with Missing Observations 74 3.5 Description of Covariates and Factors 75 3.5.1 Limitations of Data 81

ii

3.6 Bivariate Analysis 82 3.6.1 Test for Independence of Attributes 82 3.6.2 The Likelihood Ratio Test 83 3.6.3 Odds Ratios (OR) 83 3.7 Conditional Independence 84 3.7.1 Cochrane-Mantel-Henszel Tests 84 3.7.2 Testing Homogeneity of Odd Ratios 86 3.8 Binary Regression Model 86 3.8.1 Complementary Log-log Regression (cloglog) 89 3.8.2 Estimation of Parameters for Binary Regression Model 89 3.8.3 Wald Test 90 3.8.4 Model Diagnostics 90 3.8.5 Choice of Link Function 93 3.8.6 Assessing Strength of Association of Model 94 3.8.7 Quality of Prediction 94 3.9 Regression Modeling for Count Data 95 3.9.1 Poisson Regression Model 95 3.9.2 Poisson PMLE 96 3.9.3 OverDispersion Tests 97 3.9.4 The Hurdle Model 98 3.10 Survival Analysis (For Birth Interval Analysis) 99 3.10.1 Survival Function 99 3.10.2 Hazard Function 99 3.10.3 Product Limit Survivorship Function 99 3.10.4 Long Rank Test 100 3.10.5 Cox Proportional Regression Model 100 4 Statistical Analyses of Factors Affecting Children Ever Born 102 4.1 Descriptive Analysis 103 4.2 Bivariate Analysis 109 4.2.1 Testing Conditional Independence after Confounding Region 111 4.3 Binary Models for Factors affecting Children Ever Born 121 4.3.1 Model Diagnostics 122 4.3.2 Decision of Link Function 126 4.3.3 Computations of Complementary loglog Regression Model for Factor

Affecting Children ever Born 127

4.3.4 Interpretation of Coefficients of Cloglog Model 129 4.3.5 Assessing Strength of Association 134 4.3.6 Assessing Quality of Predictions 134 4.4 Poisson Logit Hurdle Model for Children Ever Born (For Incomplete

Fertility Data) 136

4.4.1 Over Dispersion Test 137

iii

4.5 Poisson Regression Model for Children Ever Born (with Completed Fertility Data)

140

4.5.1 Ovedispersion Test 140 4.5.2 Interpretation of Factors Contribution 143 4.6 Analysis of Factors affecting CEB using the Multiple Imputations 159 4.6.1 Testing Randomness of Missing Observations 160 4.6.2 Testing Pattern of Missing Observations 162 4.7 Comparisons of Models 166 5 Statistical Analyses of Birth Intervals 168 5.1 Marriage to First Birth Interval 169 5.1.1 Descriptive Analysis for Marriage to First Birth Interval 169 5.1.2 Non Parametric Analysis (Kaplan Meier Product Limit Estimate of

Survival Time) for Marriage to First Birth Interval 172

5.1.3 Comparison of Survival Distribution for Marriage to First Birth Interval

175

5.1.4 Univariate Analysis (Marriage to First Birth Interval) 182 5.1.5 Multivariate Analysis: Cox Regression Model for Marriage to First

Birth Interval 184

5.1.6 Prediction Model for Marriage to First Birth Interval 195 5.1.7 Interpretation of Relative Risk of Prediction Model (Marriage to First

Birth Interval) 195

5.1.8 Covariate Survival Curve for Marriage to First Birth Interval 196 5.2 Analysis of Higher Order Birth Intervals 197 5.2.1 Problem of Censoring and Selectivity 198 5.2.2 Descriptive Analysis for Higher Order Birth Intervals 198 5.2.3 Non Parametric Analysis (Kaplan Meier Product Limit Estimate of

Survival Time) for Higher Birth Intervals 202

5.2.4 Univariate Analysis for Higher Order Birth Intervals 215 5.2.5 Multivariate Analysis: Cox Regression Model for Higher Order Birth

Intervals 217

5.2.6 Prediction Model for Higher Order Birth Intervals 231 5.2.7 Interpretation of Relative Risk 232 5.2.8 Covariate Survival Curve for Higher Order Birth Intervals 233 5.3 Study of Change in Socio-economic Variables after Inclusion of

Biological Factors for Higher Order Birth Intervals 233

5.3.1 Change in the Effect of covariates of Socio-economic Model after inclusion of Biological Factors

235

5.4 Comparison between the Two Models (Marriage to First Birth Interval and Model of Higher Order Birth Intervals)

237

6 Conclusions 240 6.1 Limitations of Study 248 6.2 Recommendations for Future Research 248

iv

6.3 Policy Implication of Study 249 A Appendix 251 A.1 List of Surveys related to Fertility 251 REFRENCES 259 BIBLIOGRAPHY 285

LIST OF TABLES Table: 1.1 Comparisons of Important Demographic Indicators of Provinces 34 Table 3.1 Missing value codes in Demographic and Health Surveys 74 Table 3.2 Factors and Covariates for the Analyses of Children Ever Born 75 Table 3.3 Categorization of Covariates 78 Table 3.4 Description of Response Variable in the Analyses of Children Ever

Born 79

Table 3.5 Description of Covariates and Factors for Birth Interval Analysis 79 Table 3.6 Contingency Table (2 2)× 82 Table 3.7 Contingency Table for ( )CEB ContrceptiveUse× 83 Table 3.8 Partial Table (4 2 2)× × 84 Table 3.9 Choice of Link Function 93 Table 4.1 Percentage distribution of respondents (according to socio-

economic, demographic characteristics and family size) 106

Table 4.2 Association of family size versus demographical and socio-economic factors

110

Table 4.3 Conditional Association between CEB and Socio-economic/ Demographic Factors after Confounding Region

111

Table 4.4 Conditional (Stratified) Odd Ratios between CEB and Socio-economic/ Demographic Factors after Confounding Region

114

Table 4.5 Conditional Independence (Cochrane-Cochrane-Mantel-Haenszel Test): (2×2×4) Tables

116

Table 4.6 Collinearity Diagnostics 122 Table 4.7 Outliers Detection 125 Table 4.8 Testing Correctness of Link Function 127 Table 4.9 Parameter Estimates of Cloglog Regression Model 127 Table 4.10 Pseudo R Square 134 Table 4.11 Percentage of Correct Classification for Full Model 134 Table 4.12 Percentage of Correct Classification for Null Model 135 Table 4.13 Stratum Numbers with Singleton PSU 137 Table 4.14 Testing Overdispersion Assumption 137 Table 4.15 Parameter Estimates for Poisson Logit Hurdle Model for CEB 138 Table 4.16 Testing Overdispersion Assumption 140 Table 4.17 Poisson Regression Model for CEB (Testing Significance of

Model) 141

Table 4.18 Parameter Estimates for Poisson Regression Model for CEB to Women Aged 40-49

142

Table 4.19 Frequencies of missing observations 160 Table 4.20 Separate Variance t Tests for missing and non missing groups 161 Table 4.21 Pattern of missing observations 162 Table 4.22 List of the nesting rule that describe the pattern 163 Table 4.23 Frequencies of complete, incomplete and imputed observations 163 Table 4.24 Comparison of models with and without imputation (completed

fertility data) 164

v

vi

Table 4.25 Comparison of Mean and Standard Deviations for without Imputation and Imputed Data

165

Table 5.1 Percentage Distribution of Marriage to First Birth Interval by Different Socio-economic and Demographic Characteristics

171

Table 5.2 Kaplan Meier Estimates of Mean and Percentiles by Socio-economic and Demographic Characteristics for Marriage to First Birth Interval

173

Table 5.3 Product Limit Estimates of the Proportion of Women in Pakistan not having Marriage to First Birth by Different Socio-economic and Background Characteristics

177

Table 5.4 Comparison of Survival Distribution using Log Rank Test for Marriage to First Birth Interval

182

Table 5.5 Cox Proportional Hazard Model when Socio-economic and Demographic Variables are inserted one at a Time for Marriage to First Birth Interval

183

Table 5.6 Overall Test of Proportionality for Marriage to First Birth Interval 186 Table 5.7 Multicollinearity Diagnostics for Marriage to First Birth Interval 189 Table 5.8 Cox Regression Model for Marriage to First Birth Interval 191 Table 5.9 Cox Regression Model for Marriage to First Birth Interval

(Prediction Model) 195

Table 5.10 Percentage Distribution of Higher Order Birth Intervals by Different Socio-economic and Demographic Characteristics

200

Table 5.11 Kaplan Meier Estimates of Mean and Percentiles of Survival Time by Socio-economic and Demographic Characteristics for Higher Order Birth Intervals

204

Table 5.12 Product Limit Estimates of the Proportion of Women in Pakistan not having Next Birth by Different Socio-economic and Background Characteristics for Higher Order Birth Intervals

207

Table 5.13 Comparison of Survival Distributions for Higher Order Birth Intervals

214

Table 5.14 Cox Proportional Hazard Model when Biological, Socio-economic and Demographic Variables are inserted one at a Time for Higher Order Birth Intervals

215

Table 5.15 Overall Test of Proportionality for Higher Order Birth Intervals 217 Table 5.16 Multicollinearity Diagnostics for Higher Order Birth Intervals 223 Table 5.17 Cox Hazard Model for Higher Order Birth Intervals 224 Table 5.18 Cox Hazard Model for Higher Order Birth Intervals (Prediction

Model) 231

Table 5.19 Comparison of Socio-economic, Biological and Combined Model for Higher order Birth Intervals

234

A.1 Trends in Fertility 252 A.2 Poisson Regression Model for CEB (Testing Significance of

Model) 253

A.3 Parameter Estimates from Poisson Regression Model 253 A.4 Parameter Estimates from Poisson Logit Hurdle Model 254

vii

A.5 Cox Proportional Hazard Model for Socioeconomic and Variables (Higher Order Birth Intervals)

256

A.6 Cox Proportional Hazard Model (Biological Model for Higher Order Birth Intervals)

258

viii

LIST OF FIGURES Figure 1.1 Relationship between Socio-economic and Biological

(Intermediate Fertility Variables) factors 9

Figure 1.2 List of Intermediate Fertility Variables 9 Figure 1.3 Diagram Showing Effect of Social Interactions on Fertility Norms 32 Figure 3.1 Sample Design (PDHS, 2006-07) 72 Figure 3.2 Stratification of Population (PDHS, 2006-07) 72 Figure 4.1 Histogram showing Children Ever Born to Women 104 Figure 4.2 Graph showing Leverage Values vs Case ID 123 Figure 4.3 Graph showing Standard Residual vs Case ID 124 Figure 4.4 Graph showing Cook’s D vs Case ID 125 Figure 4.5 Simple Bar Chart for CEB 126 Figure 4.6 Multiple Bar Charts showing consanguineous and Age of

Women vs Total CEB 133

Figure 4.7 Multiple Bar Chart showing Actual Number of Children Born to Women vs Age

155

Figure 4.8 Multiple Bar Chart showing Ever Use of Contraceptives vs Age

155

Figure 5.1 Histogram for Length of Marriage to First Birth Interval 170 Figures 5.2-5.12 Graphs of Survival Functions for Women Background

Characteristics and Marriage to First Birth Interval 180

Figure 5.13 Graph of Martingale Residual vs Linear Predictor for Marriage to first Birth

185

Figures 5.14-5.17 Smoothed Residual Plots for Linearity (Marriage to First Birth Interval)

186

Figures 5.18-5.21 Dfbeta Plots for Detection of Outliers (Marriage to First Birth Interval)

188

Figure 5.22 Survival Curve (Marriage to first Birth Interval) 196 Figure 5.23 Histogram for Length of Higher Order Birth Intervals 199 Figure 5.24-5.41 Graphs of Survival Functions for Women Background

Characteristics and Higher Order Birth Intervals 211

Figure 5.42 Graph of Martingale Residual vs Linear Predictor for Higher Order Birth Intervals

218

Figures 5.43-5.51 Smoothed Residual Plots for Linearity (Higher Order Birth Intervals)

219

Figures 5.52-5.60 Dfbeta Plots for Detection of Outliers (Higher Order Birth Intervals)

221

Figure 5.61 Survival Curve for Higher Order Birth Intervals 233

ix

Preliminaries of Terms Related to Study

Abortion: Abortion is the termination of a pregnancy

Abstinence: Aabstinence is the practice of voluntarily refraining from some thing here it

means refraining from sexual activity.

Agrarian: Related to agriculture or farming

AIC (Akaike's information criterion): It is a measure of the goodness of fit of an

estimated statistical model. It is grounded in the concept of entropy, in effect offering a

relative measure of the information lost when a given model is used to describe reality

and can be said to describe the tradeoff between bias and variance in model construction,

or loosely speaking that of accuracy and complexity of the model. The AIC is not a test

of the model in the sense of hypothesis testing; rather it is a test between models - a tool

for model selection.

Amenorrhea: It is the absence of a menstrual period in a woman of reproductive age.

Anthropological: It is the study of humanity. Anthropology's basic concerns are ", "Who

are the ancestors of human beings", "What are humans' physical traits?", "How do

humans behave?", "Why are there variations and differences among different groups of

humans?", "How has the evolutionary past influenced its social organization and

culture?" and so forth.

Anemia: Anemia means lack of blood. It decreases red blood cells. It’s sign is less

quantity of hemoglobin in blood.

CAIC (Consistent Akaike Information Criterion): AIC, which does not directly

involve the sample size, n, has been criticized as lacking properties of consistency. A

popular alternative to AIC that does incorporate sample size is BIC. Consistent AIC is

called close kin of BIC.

Closed birth interval: As seen from the vantage of that census or survey, the intervals

between the recorded successive births are called closed birth intervals.

x

Coital frequency: The sexual union of male and female. A term used for human beings

only.

Comb’s IN scale: It is based on the conjoint measurement and the unfolding theory of

preferential choice. The position of respondents is located on the IN continuum scales by

asking a series of questions preferences.

Demographic Transition: Demographic transition is defined as “transition from high

birth and death rates to low birth and death rates as a country develops from a pre-

industrial to an industrialized economic system” (Thompson, 1929). Developed countries

fall in stage 3 and 4 while developing in 2 and 3.

Discrete Time Hazard Model: Two extensions of the proportional hazards model to

discrete time, starting with a definition of the hazard and survival functions in discrete

time and then proceeding to models based on the logit and the complementary log-log

transformations.

EC Framework or Synthesis Framework : The “EC Framework” is an alloy of two

generally two contradictory approaches to the study of household fertility behaviour-the

“Household Demand” approach developed by Becker (1960); Mincer (1963); Willis

(1973); Schultz (1976, 1980) and many others, and the “Socio-biological” approach

propounded by Davis and Blake (1956); Bongaart (1978;1982); Bongaart and Potter

(1983) and Bongaart and Menken (1983). The framework has proved tenable in both less

developed and developed countries. The EC framework has served as the conceptual

framework for the National Academy of Science study and for the fertility determinants

in the developing countries.

ESCWA (Economic and Social Commission for Western Asia): Established in order

to fulfil the economic and social goals set out in the United Nations Charter by promoting

cooperation and integration between the countries in each region of the world.

GDP (Gross Domestic Product): It is a measure of a country's overall official economic

output. It is the market value of all final goods and services officially made within the

xi

borders of a country in a year. It is often positively correlated with the positive standard

of living though its use as a stand-in for measuring the standard of living has come under

increasing criticism and many countries are actively exploring alternative measures to

GDP for that purpose. Gross domestic product comes under the heading of national

accounts, which is a subject in macroeconomics.

Extended Family: It consists of nuclear family and other blood relatives.

Fecundability: It refers ability to reproduce

Fixed effects Model: In econometrics and statistics, a fixed effects model is a statistical

model that represents the observed quantities in terms of explanatory variables that are all

treated as if those quantities were non-random. This is in contrast to random effects

models and mixed models in which either all or some of the explanatory variables are

treated as if they arise from the random causes.

Frequency of Intercourse: Sexual union between male and female

Gamma Heterogeneity Model: Model with gamma distribution of error term is called

unobserved heterogeneity model with gamma distribution.

Gestational age: Gestational age is the age of embryo or fetus (or new born infant)

Induced Abortion: Intentional termination of a pregnancy

Infecundability: It refers inability to reproduce

Jarque-Bera test: In statistics, the Jarque–Bera test is a goodness of fit measure of

departure from normality, based on the sample kurtosis and skewness. The statistic JB

has an asymptotic chi-square distribution with two degree of freedom and can be used to

test the null hypothesis that the data are from a normal distribution. The null hypothesis is

a joint hypothesis of the skewness being zero and the excess kurtosis being 0, since

samples from a normal distribution have an expected skewness of 0 and an expected

excess kurtosis of 0 (which is the same as a kurtosis of 3).

xii

Lacatational Amenorrhea: It is the absence of a menstrual period in a woman of

reproductive age due to breastfeeding

Lactational Infecudability: It refers to inability to reproduce due to breastfeeding

Life Expectancy: This entry contains the average number of years to be lived by a group

of people born in the same year, if mortality at each age remains constant in the future.

The entry includes total population as well as the male and female components. Life

expectancy at birth is also a measure of overall quality of life in a country and

summarizes the mortality at all ages. It can also be thought of as indicating the potential

return on investment in human capital and is necessary for the calculation of various

actuarial measures.

Likelihood Ratio Test: In statistics, a likelihood ratio test is used to compare the fit of

two models, one of which is nested within the other. Both models are fitted to the data

and their log-likelihood recorded. The test statistic (usually denoted D) is twice the

difference in these log-likelihoods:

Listwise Deletion: SPSS will not include cases (subjects) that have missing values on the

variable(s) under analysis. If you are only analyzing one variable, then listwise deletion is

simply analyzing the existing data. If you are analyzing multiple variables, then listwise

deletion removes cases (subjects) if there is a missing value on any of the variables.

Median Age: This entry is the age that divides a population into two numerically equal

groups; that is, half the people are younger than this age and half are older. It is a single

index that summarizes the age distribution of a population.

Menstruation: Menses or Periods

Miscarriage: End of a pregnancy at a stage where the embryo or fetus is incapable of

surviving, generally defined in humans at prior to 24 weeks of gestation.

Modernization variables: This term is used to denote the collective set of the process of

economic development and social change operating in the country. There are five factors

xiii

that bring shift to modern conditions of child bearing. These are (1) innovations in public

health and medical care (2) innovations in formal schooling (3) urbanization (4) the

introduction of new goods and (5) the establishment of a family planning programme.

Neoclassical theory of fertility by Becker: In line with the neoclassical theory he

suggests that children can be viewed as durable good, yielding primarily psychic income

to parents. Household fertility decisions are determined by female wage and family

income, which are supposed to measure time costs of raising children and earnings

potentials. It is assumed that fertility decision is reflected by the time cost of raising

children and the quantity- quality interaction.

Nuclear Family: It consist of husband, wife and unmarried children.

Open Birth Interval: The interval between a birth and a fixed date, such as that of a

census or survey is called an open birth interval.

Pairwise Deletion: SPSS will include all available data. Unlike listwise deletion which

removes cases (subjects) that have missing values on any of the variables under analysis,

pairwise deletion only removes the specific missing values from the analysis (not the

entire case). In other words, all available data is included e.g. conducting a correlation on

multiple variables, and then SPSS will conduct the bivariate correlation between all

available data points, and ignore only those missing values if they exist on some

variables. In this case, pairwise deletion will result in different sample sizes for each

correlation. Pairwise deletion is useful when sample size is small or missing values are

large because there are not many values to begin with, so why omit even more with

listwise deletion.

Parity: Number of live born children a woman has delivered

Patriarchal: It is a social system in which male is the head of household

Per Capita GDP: An approximation of the value of goods produced per person in the

country, equal to the country's GDP divided by the total number of people in the country.

xiv

Postpartum Insusceptibility: When period of postpartum amenorrhea and postpartum

abstinence is combined it is called period of post partum insusceptibility

Postpartum period: Period beginning after the birth of child usually a period of six

week

Premature Rupture of Membrane: It is a condition that occurs in pregnancy when

there is rupture of membrane (rupture of the amniotic sac and chorion) more than an hour

before the onset of labor.

Premature: Preterm usually for preterm birth

Principal Component Analysis: Principal component analysis (PCA) involves a

mathematical procedure that transforms a number of possibly correlated variables into a

smaller number of uncorrelated variables called principal components. The first principal

component accounts for as much of the variability in the data as possible, and each

succeeding component accounts for as much of the remaining variability as possible

Pseudo Coefficient of Determination: Pseudo R-squares is used for regressions of

categorical outcome variables because simple R-square is not available. It measures

explained variability. It shows improvement in null model with inclusion of regressor.

High value is recommended. It looks like R-square and its value range from 0-1.

Qausi-Maximum Likelihood: A quasi-maximum likelihood estimate (QMLE, also

known as a "pseudo-likelihood estimate" or a "composite likelihood estimate") is an

estimate of a parameter θ in a statistical model that is formed by maximizing a function

that is related to the logarithm of the likelihood function, but is not equal to it. In contrast,

the maximum likelihood estimate maximizes the actual log likelihood function for the

data and model. The function that is maximized to form a QMLE is often a simplified

form of the actual log likelihood function. A common way to form such a simplified

function is to use the log-likelihood function of a miss specified model that treats certain

data values as being independent, even when in actuality they may not be. This removes

any parameters from the model that are used to characterize these dependencies. As long

xv

as the quasi-likelihood function that is maximized is not overly simplified, the QMLE (or

composite likelihood estimate) is consistent and asymptotically normal. It is less efficient

than the maximum likelihood estimate, but may only be slightly less efficient if the quasi-

likelihood is constructed so as to minimize the loss of information relative to the actual

likelihood.

Quantum of Fertility: The tempo of fertility is measured by time it takes to make the

transition for those women who continue reproduction.

Random effects Model: In statistics, a random effect(s) model, also called a variance

components model is a kind of hierarchical linear model. It assumes that the dataset being

analyzed consists of a hierarchy of different populations whose differences relate to that

hierarchy. In econometrics, random effects models are used in the analysis of hierarchical

or panel data when one assumes no fixed effects (i.e. no individual effects).

Robust regression model: Robust regression is a form of regression analysis designed to

circumvent some limitations of traditional parametric and non-parametric models. In

particular, least square estimates for regression models are highly non-robust to outliers.

One instance in which robust estimation should be considered is when there is a strong

suspicion of heteroskedasticity.

Role-egalitarian: Equality or egalitarianism is a political doctrine that holds that all

people should be treated as equals from birth. In marriage it means that wife and husband

are equal. No one have authority on other.

Role-segregation: It is the influence of husband and wife in making certain decisions

e.g. making decisions about fertility and contraceptive use.

Sex Ratio: It is the ratio of males to females in a population.

Spontaneous Intra Uterine Mortality:

Ssterility: The quality or state of being unable to reproduce; of being infertile.

Tempo of Fertility: The quantum of fertility is indicated by the proportion of women

who move to the next higher parity.

Tertiary Education: It is referred to as third stage, third level, and post-secondary

education. It is further education or higher education.

Total Fertility Rate: It is the average number of children that would be born to a woman

over her lifetime

Unobserved Heterogeneity Term: It is denoted by “ε ”.

Unobserved Heterogeneity: Unobserved heterogeneity amounts to observations being

conditionally different in terms of their hazards in ways that are unaccounted for in the

systematic part of our models due to unavailability of important covariates.

xvi

xvii

Abbreviations

DHS Demographic and Health Survey

OECD Organization for Economic Cooperation and Development

PPA Postpartum Amenorrhea

PDHS Pakistan Demographic and Health Survey

TFR Total Fertility Rate

UNICEF United Nations International Children Emergency Fund

USAID United States Agency for International Development

WHO World Health Organization

xviii

Abstract

In Pakistan rapid population growth is a great hindrance in the economic growth of

country. Many policies were implemented in the past to control rapid population growth.

Pakistan has entered into the early transitional period of fertility. To get immediate results

and for the formulation of future population policy, it is essential to understand the

dynamics of fertility behavior. Fertility level of any population varies due to variability in

the number of children ever born to women and birth interval length. Individual fertility

can be assessed by these two methods. The identification of magnitude and direction of

effect of socio-economic and demographic factors on children ever born and birth

interval analysis helps in population policy implications. The results will entail to control

those factors which effect stopping behavior of fertility (children ever born) and spacing

behavior of fertility (birth interval).

Following the family planning policy of Pakistan that women should have two children,

children ever born is dichotomized at two. The effect of socio-economic and

demographical factors is investigated which motivate couple to have more than two

children (above replacement level of fertility). Women age, husband age, women age at

marriage, husband education (no and secondary), women current work status, wealth

index (poorer, poorest and middle), ideal number of children, number of children died,

ever use of contraceptives, cousin marriage and region (Punjab) has significant impact on

size of family (two family norm). Positive relationship is found between women age,

husband education, women who is currently not working or for those who never worked,

women whose husband desire more children than her, ideal number of boys, ideal

number of children, contraceptive knowledge, contraceptive users and number of children

died with size of family. Age at marriage, husband age, women education, women

current work status, wealth index, women whose husband desire same number of children

than wife, polygamous marriage, cousin marriages and urban residents have negative

association with family size.

xix

Poisson regression model for completed and incomplete fertility data is also run on

children ever born. The analysis is carried without categorization of children ever born to

understand general fertility behaviour. Age of women, age at marriage, education of

women (secondary and higher level), wealth index (richer and richest), husband desire for

children, ideal children, ever use of contraceptives, child mortality and polygyny

contribute significantly on fertility. Factors which have positive effect on fertility of

women are age of women, lack of agreement among spouses on number of children, high

fertility intentions, son preference, contraceptive use and consanguineous marriages. Age

of women at marriage, age of husband, education of wife, education of husband

(secondary and Higher), wealth index (richer and richest), polygyny and urban residents

have inverse effect on fertility.

Birth interval analysis exposed the length of interval between subsequent births which is

helpful in understanding the reproductive behavior. Factors affecting the spacing

behavior of Pakistani women are also studied. It is studied with the help of analysis of

two birth intervals i.e. marriage to first birth interval and higher order birth intervals. It

can be concluded after observing prediction model that in marriage to first birth interval,

age at marriage and age at first birth has played vital role in its determination. It is

evident from prediction model (preferred model) of higher order births that major

contribution towards subsequent birth interval is due to proximate determinants of

fertility or biological variables (age of women, age at marriage, period of breastfeeding,

period of amenorrhea and period of abstinence). Some socio-economic, demographic or

cultural variables have also shown significant results even in the presence of biological

factors in higher order birth intervals. These are region (Punjab) and birth order. The

significance of these factors may be due to non inclusion of some important proximate

determinants such as frequency and duration of contraceptive use etc.

Finally it is concluded that urbanization and modernization factors are playing its role in

declining the fertility through control of stopping behavior (children ever born to women)

but not spacing behavior (birth intervals). Attitudinal factors through stopping behaviour

(lack of agreement between spouses about fertility desires, fertility intention, son

xx

preference and contraceptive use) are causing increase in the fertility. Biological factors

for both stopping and spacing behavior have expected universal effect. There is need to

lower fertility by changing the attitudes of couples towards fertility. Family planning

programs should be revised to get the replacement level of fertility by changing the

behaviours of both couple and family.

CHAPTER 1

Introduction

Size and composition of population is based on the progression of fertility, mortality and

migrations. Fertility is most important factor, essential for continuation of human life on

earth. Fertility analysis is important for policy makers to get guidance for family planning

programs. It helps in evaluation of these programs on the basis of outcomes of analysis.

Fertility analysis is equally important for both economically developed and not developed

nations. Its study is important for those developed countries where fertility has reached

below replacement level. It is generating the problem of lack of manpower in the labour

market. On the other side many developing as well as under developed nations are facing

the problem of rapid population growth which is above the replacement level of fertility.

These countries are confronted with the problem of scarce resources to meet the growing

demand of population. In Pakistan this rapid population growth is creating a great

hindrance to the economic growth of country. Problems of rapid population growth are

discussed in this chapter within the perspective of Pakistan. Biological and behavioral

mechanism of fertility is illustrated briefly. Only brief over view of techniques for

measurement of period and cohort measure of fertility rates are given. Measurement of

individual fertility is discussed in detail. Various factors which can affect individual’s

fertility are chosen from international and national studies and are elaborated after

extensive search from literature. Objectives of the study with policy implications are also

given. All aspects of study are indexed with the proper references.

1.1 Problem of Rapid Population Growth

Many developing countries are facing the problem of rapid population growth. Pakistan

is in the early stage of demographic transition from the past two decades (Ali & Buriro,

2008). Mortality rate has turned down rapidly. Decline in infant mortality rate was 20%

in the past sixteen years i.e. from 1990 to 2006 (Bhutta, Cross, Raza & Zahir, 2008). But

decline in fertility was sluggish before 90’s and increased after 90’s. Total fertility rate

had declined from six children (1992-96) to four children (2004-06) per woman (Ali &

Buriro, 2008). Increase in adolescent population and reduction in dependency ratio

1

exhibit that phase of population transition has started. The average of more than six

children per woman has begun to turn down in late 1980’s (Arnold & Sultan, 1992;

Feeney & Alam, 2003). High population growth rate is creating great hindrance to social

and economic progress of country. Realizing the adverse consequences of this rapid

population growth, a first Population Welfare Programme was launched in early 1960’s.

But Pakistan is still far away from success. Currently Government is facing the severe

shortage of resources in energy sector (gas, electricity) caused load shedding. Major

portion of resources is consumed to fulfill the requirements of ever growing population

and less is left for productivity purpose. Economic activity has reduced in the past few

years. Shortage of wheat has risen to its crucial level. Siddiqui (2004) has pointed out that

agriculture production has decreased even in those areas where it was abundant in the

past. Agriculture growth has declined from 6.5% in 2004-06 to 2% in 2009-10

(Economic Survey, 2009-10). Livestock production has also decreased. The growth in

livestock sector has declined from 15.8% in 2005-06 to 4.1% in 2009-10 (Economic

Survey, 2009-10). With this rapid population growth, it will be hard to meet the growing

demand of food. Water crisis is also creating clashe among the provinces. Decline in

population growth is national to sustain the socio-economic development. Pakistani

society is in the process of revolution which includes urbanization and influence of mass

media in creating awareness (National Population policy, 2010). Though in past,

Governments had put their efforts to lower population growth but advancement was slow.

Government is now trying to commence and regulate various birth control programs.

Implementation of these programs requires information about those factors which can

influence fertility behaviours. Effect of socio-economic and cultural factors on fertility is

of diverse nature in different populations (Park, 1978).

Investigation of factors which have caused increase in population is very important. It is

only possible if impact of social, cultural, demographical and attitudinal factors on

individual’s fertility is estimated through scientific study. Government can formulate

policies on the basis of these results. Pakistan has reliable demographic data to be

analyzed (Ali & Buriro, 2008). Data on many fertility related factors and covariates is

also available in these surveys to model the factors which affect individual’s fertility. The

concept of fertility can be illustrated within the medical and demographical framework.

2

1.2 Definition of Fertility

Medical definition of fertility is given as:

The ability to conceive and have children, the ability to become pregnant through normal

sexual activity (MedicineNet.com, Sep., 2009).

Fertility, fecundity and fecudability are frequently used terms in the demographic studies.

Demographical definition of fertility is given as:

Fertility indicates the product or output of reproduction’ rather than ability to have

children. The physiological ability to have children, that is manifest roughly in the period

between monarchy and menopause in women, is termed fecundity. Fecundability is the

probability of becoming pregnant or the likelihood of exposure to the possibility that

depends on the pattern of sexual and pregnancy preventive behavior (Frank, 2008).

Regassa (2007) had defined the human fertility in the following possible ways.

a. Potential fertility or fecundability: - It is determined by possible number of children born to a woman in her entire reproductive period.

b. Natural Fertility: - If none of fertility control method is opted such as contraceptives, abortions, extensive period of lactation and abstinence (If husband

is away from home for long time) then it is called natural fertility (Coale &

Trussell, 1975).

c. Controlled fertility: - It is controlled by contraceptive usege, prolong breastfeeding and sterility etc.

Measurement of fertility is always given special attention in demography to assess total

increase or decrease in population size. Many statistical techniques have been also

developed to serve the purpose. Few of these are given as following.

1.3 Measurement of Fertility

Quantitative measurement of human fertility is a multifarious task. The phenomenon is

not straight forward due to quantification of various factors that are hard to measure.

Enclosure of these factors in statistical study is really hard nut to crack. Fertility can be

measured collectively (for total population) or at an individual level (Pressat, 1972).

3

Collective measurement of fertility is called aggregate fertility. Measures which are

popular in demography to estimate aggregate fertility are given in the following

subsection.

1.3.1 Aggregate Measurement of Fertility

Overall trend of fertility can be measured through rates. These rates comprised of period

and cohorts measures of fertility. “Period” is cross sectional study conducted in the

period of one year while “cohort” refers to the study about the same group of people over

a long period. Rates can also be computed with age specification e.g. age specific fertility

rates and marital fertility rates. Combination of these rates produces synthetic indices of

fertility i.e. total fertility rate and gross reproduction rate. These indices like rates can

also be age specific. Following are the important period measures of fertility.

a. Crude Birth Rate (CBR): - It is the number of live births per 1,000 persons alive at the middle of that year. It provides an easy procedure to measure fertility using

total births and total population only. It is useful in case of limited data. It is

affected by the age structure of population. (Pollard, A. H.,Yousaf and Pollard,

G.N., 1990)

b. General Fertility Rate (GFR): - It is improved version of CBR and considers also age and sex distribution of population. It is the number of births per 1,000

women aged 15-44. General fertility rates make improvement in CBR which

occur due to abnormal sex ratio or age distribution in population. (Pollard, A.

H.,Yousaf and Pollard, G.N., 1990)

Child women ratio and Coale’s index of fertility are also period measure of fertility. Both

are devised for historical research. Following are cohort measures of fertility.

c. Age-Specific Fertility Rate (ASFR): - It is obtained by dividing the number of births to mothers of each age group by the number of females of that age group in

population per 1’000 women. Commonly used age group consists of period of

five year. (Pressat, 1972)

d. Total Reproduction Rate (TFR):- Total fertility rate is defined as the aggregate of the age-specific birth rates of women during her entire child bearing period, in

4

a given years. Total fertility rate consider rate of children to women instead of

daughters to women. It is the finest measure which can be used for comparison of

fertility of different populations. If total fertility rate is greater than two it means,

couple has on the average two children. Total fertility rate also ignores age

structure of a population so it is “synthetic “measure. It does not provide

information about the number of births women actually have during her

reproductive span. Total fertility rate assumes no mortality during the child

bearing span. (Pressat, 1972)

e. Gross Reproduction Rate (GRR): - It is defined as “Average number of daughters that would be born to women during her lifetime if she passed through

her child-bearing years conforming to the age specific fertility rates of a given

year”. (Pressat, 1972)

The only difference between the gross reproduction rate and total fertility rate is

that gross reproduction rate considers only birth of daughters and is true

representative of “reproduction”. Like total fertility rate it also ignores mortality

aspect.

f. Net Reproduction Rate (NRR):- “The net reproduction rate is a measure of the number of daughters that a cohort of newborn girl babies will bear during their

lifetime assuming a fixed schedule of age-specific fertility rates and a fixed set of

mortality rates”. Net reproduction rate measures reproduction by accounting both

fertility and mortality. Net reproduction rate is more reasonable measure of actual

reproductive capacity of particular population. It eliminates age structure effect,

which can seriously distort the rates of population growth or decline. It is often

considered as “early warning sign” of fertility. (Pressat, 1972)

NRR=1 indicates that each generation of mothers is exactly replaced by

their daughters. It is called replacement level of fertility.

NRR=0.7 70% of generation of mothers is replaced by their daughters

NRR=0.3 It indicates decline in population from mother to daughters

generation

5

1.3.2 Measurement of Individual Fertility

Other than aggregate measurement of fertility there is also possibility to assess individual

fertility. Individual fertility can be measured by using number of children ever born to a

woman during her reproductive life span or by analyzing the birth intervals. Novel

statistical modeling techniques are available to quantify the impact of factors influencing

these measures of fertility. These include Regression Modeling, Survival Analysis, and

Life Table Analysis etc.

Fertility can be limited with the help of various methods. These are illustrated in

subsection 1.4.

1.4 Fertility Control

Fertility can be controlled through different methods. These are called fertility restraints.

Fertility restraints are decomposed into two broad categories. One comprised of

biological factors and other is about behavioral factors. Both of these are described as

below.

1.4.1 Natural Fertility Restraints

The potential reproductive span of women is 35 years so number of births should also be

35. It is called total fecundity or potential to have a child (Frank, 2008). But due to

biological restrictions, the number of births is limited to 15 instead of 35. These natural

restraints are also called biological restraints. The possible duration period of these

restraints is given as:

• Pregnancy duration. It is usually period of nine months.

• Period of post partum infecundability. It is a period of 1.5 months.

• Waiting time to conception is 7.5 months.

• Period of intra-uterine mortality is two months.

• Sterility can be age specific sterility or it can be pathological

Along with natural restraints there are also certain behavioral restraints of fertility. These

behavioral restraints are given as following:

6

1.4.2 Behavioral Restraints of Fertility

The attitudes and behaviours of couple can also control fertility. Following methods can

be used to control fertility deliberately.

• Marital duration

• Breastfeeding duration

• Use of contraceptives

• Tempted abortions

These factors had reduced the fertility up to two children in industrially developed

countries and 5-8 children in African populations (Frank, 2008).

Prior to discussion of factors that can influence individual fertility, it is necessary to

understand different behaviors through which fertility can be limited or controlled. These

are discussed in section 1.5.

1.5 Types of Fertility Limiting Behaviors

Knodel (1987) has presented the idea of three fertility inhibiting behaviors during early

transitional period of fertility. These are starting, spacing and stopping behaviors of

fertility. These are elaborated as below:

a. Starting Behavior of Fertility: Size of family depends on starting behavior of fertility which is measured through time of initiation of reproduction.

b. Spacing Behavior of Fertility: Intentional long birth spacing also limit child bearing and called as spacing behavior of fertility. Delay in marriage determines

spacing behavior.

c. Stopping Behavior of Fertility: It is to cease births after having an intended number of children. Age at marriage is also regarded as a gauge of stopping

behavior.

Both spacing and stopping behavior complement each other. In European countries,

stopping behavior had played significant role in early transitional period of fertility than

spacing behavior particularly. Demographers have focused on the analysis of stopping

7

behavior rather spacing behavior of fertility due to its easy measurement indices (Bavel,

2004). Spacing and stopping behaviors can also be used to analyze fertility at individual

level. Event history analysis had got attention to measure individual’s fertility behaviour

(Gutmann & Alter, 1993). Logistic regression model or count regression models with

parity, age and rest of covariates can be used to study stopping behavior. Similarly

duration models can be used to study spacing behavior. Many demographers have used

both ideas for the assessment of fertility (Bavel, 2004). On the basis of this idea the

present study is designed to investigate the factors influencing both stopping and spacing

behavior of fertility in Pakistan.

Factors and covariates which can affect number of children ever born to a woman

(stopping behaviour) and birth interval analysis (spacing behaviour) are discussed below.

1.6 Factors and Covariates Affecting Fertility of Individual (Stopping

Behavior)

Identification of factors affecting fertility is the important feature for the analyses of the

family size or total number of children ever born to a woman. Children ever born to

women serve as a measure of individual’s fertility. Biological factors which contribute

towards fertility are breastfeeding practice, deliberate fertility control through

contraception, coital frequency, abortions and reproductiveness (Baschieri & Hinde,

2007). There are several socio-economic and demographic factors which affect the

women’s fertility. Davis and Blake (1956) have defined some biological and behavioral

factors through which social, economic and cultural factors influence fertility. The socio-

economic and cultural factors were termed as background variables due to their indirect

effect on fertility. Biological variables were named as intermediate variables and their

effect on fertility is direct. Biological factors are also recognized as proximate

determinants of fertility. The risk of conception in case of no deliberate control of fertility

is chance phenomenon, which depends on biological and behavioral factors. Davis and

Blake (1956) have defined relationship between socio-economic and biological or

intermediate fertility variables. This relationship is shown in the diagram on the next

page.

8

Figure 1.1: Relationship between Socio-economic and Intermediate Fertility Variables

Davis and Blake (1956) have listed the intermediate variable. Bongaarts (1978) has

grouped these factors or variables into three broad classes, given in Figure 1.2:

Figure 1.2: List of Intermediate Fertility Variables

Both biological factors and individual choice determine family size. Biological factors

change human fertility through social or cultural norms. Choice of family size depends

upon many social, economic and demographic factors. The effect of biological factors

which are called proximate determinant by Bongaarts (1982) are exhaustive and only

9

difference between actual and estimated fertility is due to measurement error (Bascheire

& Hinde, 2007). The survey data on all proximate determinants of fertility is impossible

to collect. Social, economic and demographic factors can be used alternatively to estimate

fertility along with available biological factors. Knowledge and assessment of their

impact on fertility can help government to formulate policies for population control.

Socio-economic and demographic factors operate in an explicit system. Suppose wealth

index has negative effect on fertility. The mechanism working behind this result may be

due to delay in marriage, due to education, or increase in the status of women or

awareness about family planning methods etc.

Park (1978) categorized the factors affecting fertility into three broad classes i.e.

demographic, attitudinal and social-economic-residential. These three categories are more

or less inter related e.g. age is considered as biological factor but at the same time age is

also treated as demographic factor. The effect of these factors may also vary e.g.

breastfeeding period is usually found to be no longer in case of educated women which

can result in high fertility but educated women have delayed marriage and more

awareness about contraception which can result in lowering fertility. The example of

effect of education on fertility is also helpful in understanding the indirect impact of

education on intermediate factor i.e. breastfeeding.

United Nations (1973 as cited in Park, 1978) had abridged statistical studies conducted in

various countries for analysis of association between socio-economic factors and fertility.

The indicators documented in these studies were place of residence, educational status,

ation, employment status of women, religion and ethnicity.

ling techniques. Elaboration of socio-

economic and demographic factors is given below in the context of fertility measurement

ver born is used as a fertility measure.

economic status, occup

Marital status, age and age at marriage were taken as demographic variables. Knowledge

of contraception, use of contraceptives and ideal number of children were usually treated

as attitudinal or motivational factor. Many demographic models were proposed to

measure the relationship between fertility and intermediate variables (Bongaarts, 1978;

Davis & Blake, 1956). But impact of social, cultural and demographic factors can also be

studied using sophisticated statistical mode

of women. Total number of children e

10

a. Age of Women

Women’s age is most important biological and as well as demographical determinant of

fertility (Children ever born). Moon (1973) investigated through his study that 67% of

variation in family size was due to age of women. The chances of female to become

pregnant decline with increase in age. A study conducted in France had proven that

percentage of reduction in fertility is striking after age 30. The decline becomes more

severe after age 40 (Sherman & Silber, 1991).

b. Age of Women at Marriage

Marriage is the social and religious agreement of entering into sexual union. In Islamic

society there is no concept of premarital sex. Coale (1975 as cited in Hakim, 1994)

associated higher fertility with early age at marriage. Early marriages increase the

reproductive span of women. Inverse relationship is usually expected between age at

marriage and fertility. Legal age at marriage for a woman in Pakistan is 16 years and

above. But there is also evidence of childhood marriages. Hakim (1994) concluded in his

study that mean number of children ever born were 5.1 for women married at age less

hose age at marriage was 16-19, 3.6 for 20-24 and 2.5 for those

women who had married after the age of 25. Decline in fertility of women becomes sharp

for the effect of husband’s age on fertility. In many societies especially in patriarchal

than 16, 4.1 for women w

when women’s age at marriage crosses the 19 years and she is not willing to compress

her fertility period (Agarwala, 1967 as cited in Hakim, 1999). Age at marriage has shown

strong influences on the fertility in societies where contraceptives are not popular as

compared to those societies where fertility is controlled deliberately. Many studies had

proven negative association between age at marriage and fertility (Alam & Karim, 1986

as cited in Hakim, 1994). It is potential factor of fertility in Pakistan due to less

prevalence of fertility control methods.

c. Age of Husband

It is misconception that fertility of man does not decline with age. But it is not true as

proved by French study. Fertility of man also decreases with age but decline is moderate

(Sherman & Silber, 1991). Other than biological reason there is also behavioural reason

11

societies like Pakistan husband enjoys absolute supremacy in the household as a decision

maker. If the age difference between both spouses is large then it causes hurdle in

mu omen could not stop child bearing without husband

consent and if husband is older than wife, this decision becomes more difficult due to

com nicating the fertility desires. W

lack of communication between the two (Zafar, 1996).

Hassan and Killick (2003) provided empirical evidence of reduction in fertility due to

increase in the age of husband. They concluded that if age of husband is above 45, then

wife’s pregnancy has five times more chance of being delayed. The result holds even

after confoudung the effect of women’s age. But some studies had shown insignificant

association between age of husband and fertility (Sichona, 1993).

d. Education of Both Spouses

Education is recognized as fundamental factor for family formation in any society.

Education generally results in improvement of individual’s status in the society in the

form of better health facilities, employment status, awareness etc. (Cochrane, 1979 as

cited in Hakim, 1994). Many fertility studies have documented negative relationship

between fertility and education (United Nation, 1983 as cited in Hakim, 1994). But Ware

(1981 as cited in Hakim, 1994) had documented positive association between fertility and

education for poor countries. These are four Sub Sahran countries of Africa and analysis

was at macro level. No or negative relationship of education and fertility was found in

four these countries (Diamond, Newby & Varle, 1999). Breastfeeding period is short for

educated women which increases the chances of next conception. Female education has

more obvious impact on fertility as compared to male education. This fact is evident from

many relevant studies conducted in Pakistan (Hakim, 1994). Education changes the

standards, plans, behaviors and desires of couples regarding reproductive behavior.

Women’s education shifts their dimensions to other interests such as jobs. It changes the

marriage patterns which can affect fertility (Sathar, Crook, Callum & Kazi, 1988).

ucat about contraceptive use and methods (Oheneba-Sakyi, Ed ion also creates awareness

1992; Uddin, Kabir, Choudhury, Ahmad & Bhuyan, 1985). This knowledge has proven

effective in limiting family size. Highly educated or professional women usually prefer

delay in conception (Alsaawi & Adamchak, 2000). The total fertility rate is 4.8 for

12

illiterate women and 2.3 for secondary educated women in Pakistan (Ali & Burriro,

2008). Hussain and Qamar (2008) reported that educational level of women is low in

Pakistan i.e. no education (65%), primary (14.2%), middle (6.3%), secondary (8.1%),

higher education (6.4%).

e. Work Status of Women

Relation between women’s work status and fertility is considered interdependent. Child

rearing requires mother’s time and finances which effect women’s reproductive

decisions. Reason of fertility decline in western industrial countries is also associated

with women’s work in the labour market (Goldin, 1990; Mincer, 1985). In many fertility

related studies conducted in developed countries negative relationship was found between

paid work and fertility. This is because working women have to sacrifice her time and

money for upbringing of child. This phenomenon is described as theory of opportunity

cost. Women’s participation in economic activity is indicator of their status in making

household decisions including reproductive decision. Women who work outside the

home are better aware as compared to those who do not work. In developing countries

ure can be positive (Lloyd, 1991). Minor association was found

between women’s work and fertility in Pakistan (Syed, 1978 as cited in Hakim, 1994).

nat of relationship

But this minor association is attributable to the fact that women’s participation in labor

force market is not ample to make judgment about its association with fertility. Pakistani

women who are currently engaged in any economic activity are 25.9%. Only 2.1 % of

women continue their jobs after marriage (Hussain & Qamar, 2008). It becomes difficult

for them to cope with domestic responsibilities along with job after marriage.Pakistani

women need not to work because male members are responsible to bear the financial

burden of household.

f. Wealth Index

Income of household is one of the most important correlate of fertility. According to the

classical Malthusian theory of fertility, higher income is associated with higher fertility

(Micevska, 2001). Nature of relationship between income and fertility may be positive or

negative. In short run it is anticipated as a positive while in the long run it is negative.

Income is the outcome of economic activity which causes decline in the demand of

13

children. According to theory of opportunity cost of time, inverse relationship is expected

between income and fertility. Thus negative relationship of income was found in many

fertility related studies (Dominda, & Nicholas, 2002; Handa, 2000; Kaur, 2000). Kaur

(2000) had also reported indirect link of income and reproduction i.e. with increase in

income age at marriage and education increases which indirectly effect reproductive

decisions. Income distribution also indirectly affects fertility. Sometimes fertility decision

of couples is not only affected by their own socio-economic status but also due to other

members in the society. An increment of 10% in income had caused 1% decline in

fertility in Kenya (Schultz, 2005). It is difficult to get exact income of household. So

wealth index is used as an indicator of socio-economic status. This index has gained

popularity in various economic studies (Rutstein, 1999). Wealth index is a reliable

measure of consumption expenditure and income (Rutstein, 1999). Index is calculated

using possession of all durable consumer items like television, car or motorbikes etc.

household e.g. flooring, sanitation facilities etc.

Each asset is taken as a variable and factor scores are calculated using Principle

lued than wife’s. Knowledge of husband’s desire for planning the family is

along with characteristics of residence of

Component Analysis. These scores are standardized (Gwatkin, Rutstein, Jhonson, Pande,

& Wagstaff, 2000). Household is ranked after standardization from lowest to highest

using sum of scores for each asset (Pakistan Demographic and Health Survey 2006-07).

Ali and Buriro (2008) reported in Pakistan Demographic and Health Survey that the

lowest wealth quintile has highest value i.e. 5.8 children and highest wealth quintile has

lowest total fertility rate (3.0) in Pakistan. This factor is discussed in more detail in

section 1.8 under the topic of an economic theory of fertility.

g. Husband’s Desire for Children

Marital fertility is based on desired reproductive targets of both spouses which generally

comprised of ideal family size, timings and sex composition. Husband’s desire for more

children as compared to wife’s desire creates barrier in making decision about birth

control which consequently affects number of children ever born (Saleem & Pasha,

2008). Lack of mutual understanding between both spouses about decision of number of

children causes barrier in the use of fertility control methods. Moreover husband’s desire

is more va

14

also essential. Most of women do not have even knowledge about husband’s desire for

t of

iew about nature of relationship between actual and intended family size. Intended

umber of children can be used successfully as good predictor for actual number of

children in developed counties but not in developing countries (Ali and Rukanuddin,

992). Intended family size is a popular predictor of actual family size and is used by

ers (Schoen, Astone, Kim, Nathanson & Fields, 1999). Fertility

family size. Consequently this lack of interspousal discussion discourages women to

control fertility. Patriarchal societies force the women to accept husband’s decisions.

Husband’s desire for children can be a potential predictor for number of children born to

women. Sometimes with small number of living children, husband’s desire is found to be

more dominating while with large number of children this dominance is shifted towards

women (Bankole, 1995). Husband’s desire as a regressor, depicts the agreement between

husband and wife on reproductive decision and measure its impact on number of

children.

h. Fertility Intention (Ideal Number of Children)

Desire for ideal family size is not only based on couple’s individual decision but also

depends on the family norms. Involvement of family in this decision may results in

difference between actual and intended number of children. If desires and plans of ideal

family size are closely related to actual family size then there is no need of analyzing its

effect. Desire of couple about family size can result in various attitudes of fertility i.e.

coital frequency, contraception use or planning to conceive after certain period. To

remain childless is usually not by choice. Demographer’s have contradictory poin

v

n

1

several social demograph

preferences may vary with time and may also change at different parities. American’s

ideal family size was found to be two or three children (Hagewen & Morgan, 2005).

According to data of PDHS 2006-07, 70% of women desired to have at least four

children. Those who have stated ideal family size with two children were only 13%.

15

i. Son Preference

Effect of son preference in any society can be observed through their bahaviours e.g. less

education, more malnutrition, more mortality among girls and not to give them share in

inheritance. Son prefrence has also shown strong effect on fertility. Son preference

affects demographic composition of household. Sex composition of family especially

desirable number of sons is an important predictor of family size. Couples stop child

bearing after the birth of desired number of sons. Gender biasedness is very common in

Asian countries including Pakistan. It affects the decision of couple about number of

children. Gender discrimination was found high in Punjab (India) where sex ratio at last

birth was 84 (Leone, Mathews, & Zuanna, 2003). It means 100 boys against 84 girls.

Reason for this preference is the financial support which male children can offer in future

and girls are regarded as dependent member. Son preference was also found in Pakistani

society (Khan & Sirageldin, 1977 as cited in Mahmood & Ringheim, 1996). High

mortality among female infants and children is indicator of sex preference in Pakistan

(Arnold, 1992 as cited in Okun, 1996). Pakistan had evidence of higher son preference

than India and Bangladesh (Nag, 1991 as cited in Mahmood & Ringheim, 1996). Son

preference affects the family size only in a natural period of fertility (Niraula & Morgan,

ood & Ringheim, 1996). In societies 1994; Rahman & De Vanzo, 1993 as cited in Mahm

where fertility transition has near to start it depends upon parity. Contraceptives are

usually initiated after the birth of desirable number of sons. Impact of gender preference

on fertility was observed in Africa, Middle East (Arnold, 1992 as cited in Okun, 1996)

and Jordan, Syria (Cleland, Verrall, & Vaessen, 1983 as cited in Okun, 1996). Moderate

gender preference is found in Egypt, Morocco, Algeria, Tunisia and Lebanon

(Williamson, 1976 as cited in Okun, 1996). No strong effect of son preference on fertility

is found in Korea and Taiwan (Arnold & Kuo, 1984; Arnold, 1985 as cited in Okun,

1996).

j. Awareness of Contraceptives and its Use

Contraceptives are used to reduce the risk of conception. It is used for deliberate fertility

control. Adoption of contraceptive is not popular in Pakistan due to desire of large family

size. Other common hindrance is lack of accessibility to contraceptives particularly in the

16

rural areas of Pakistan. Religious beliefs also prohibit the use of contraception. Son

preference and lack of women’s autonomy in decision amking affect its use. In spite of

Government efforts for contraceptives promotion its prevalence level is still unattained.

Bangladesh, Indonesia and India have almost the same socio-economic condition, but

thay had higher contraceptive rate as compared to Pakistan. The high prevlance rate of

contraceptives in these countries had resulted in lower fertility (Mahmood & Ringheim,

n

e s vel of knowledge about contraceptive methods. Women’s

education and wealth quintile has positive link with the knowledge of contraceptives.

1996). The reason for high prevalence rate of contraceptives in Bangladesh is its

accessibility. In Bangladesh, contraceptives are free and accessible to women irrespective

of their education or urban rural status due to effective family programs. Polygamous

marriages are not allowed in Hindu religion and there is no religious prohibition on the

use of contraceptives which has resulted in low fertility in India. In India female

sterilization is successfully promoted contraceptive, which is a permanent method. On

the other side in Pakistan family planning program is not working effectively particularly

in rural areas. Regassa (2007) measured the impact of future intention to use

contraceptives on fertility.

Modern methods of contraception which are used in Pakistan are female sterilization,

male sterilization, the pills, intrauterine device (IUD), injectables, implants, male

condoms and emergency contraceptives. The traditional methods used are rythem and

withdrawl. Two social marketing brands are working in Pakistan i.e. Greenstar and Key.

These are not only suppliers of contraceptives but also provide information. They are

creating awareness in public through media campaigns. In PDHS 2007, urban wome

hav lightly higher le

Among currently married women, only 49% have ever used contraceptives. The

prevalence of contraceptives in Punjab, Sindh, KPK and Baluchistan is 33, 27, 25 and 14

percent respectively. Sindh and Punjab have more contraceptive prevalence rate than

KPK and Baluchistan. This difference is attributable to literacy ratio and status of women

in the province. The sequence of women literacy ratio in Punjab, Sindh, KPK and

Baluchistan is 35.10, 34.78, 18.82 and 14.09 respectively. Contraceptive use is higher

among women with higher education as compared to illiterate. (Ahmad & Eskar, 2008)

17

k. Child Mortality

Infant or child mortality is also one of the major determinants of fertility. It can affect

fertility in two ways. Higher mortality decreases the population. But it may cause

increase in fertility. Couples sometimes produce more children for the replacement of

tho hildren whose c have died. The replacement effect of child mortality is very strong.

equently affects total children ever born (Al-Qudsi, 1998). Association between

fertility and child mortality is generaly strong. Death of children in early reproductive

t societies polygyny was widely practiced (White,

creases the chances of

Woman with positive attitude towards child death produces two more children as

compared to woman who has not experienced child loss (Syamala, 2001). Zhang (1990)

had presented the idea of hoarding of children in case of possible deaths in future.

Breastfeeding is terminated after the death of child which results in short birth intervals

and cons

span affects women’s potential to produce children in the later age. Women, whose

perception about child death is strong, usually desire more children (Balakrishnan, 1978).

l. Polygyny

Polygyny is anthropological term. Schwimmer (2003) defined polygyny as a family term.

It is marriage of single man with more than one woman. Having more than one wife is

acceptable in 75% of known societies of world. The opposite of polygyny is monogamy

i.e. having a single wife. In ancien

1988).

Three models are found in literature to describe the nature of relationship between

polygynous marriage and fertility. The first model is called “Sexual Competition Model”.

It is based on biological reasoning of fertility differential between polygynous and

monogamous women. Less coital frequency lowers the fertility in polygynous marriage

as compared to monogamous marriage (Anderton & Emigh, 1989 as cited in Alnuaimi &

Poston, 2009). Division of time between the wives also de

conception (Musham, 1956 as cited in Alnuaimi & Poston, 2009). Separate residence is

also reason of less sexual contacts.

Secondly “Favoritism model” also caused decline in fertility. Some wives are less

preferred by husband, so result is lower fertility (Muhsam, 1956; Garenne and Van de

18

Walle 1989 as cited in Alnuaimi & Poston, 2009). Preference is sometimes based on

order of marriage. Third model illustrates “Male Demand for Progeny”. This model

resulted in higher fertility but not for all wives. If husband’s desire for particular number

of children is fulfilled from first wife then it may results in lower fertility for second wife

(Anderton & Emigh, 1989 as cited in Alnuaimi & Poston, 2009)

Polygyny is legal in Islam and there is permission of having four wives at one time

(Quran, Surah An-Nisa 4:3). Polygyny has resulted in insignificant effect on the number

of children ever born in Ghana (Sichona, 1993). The reason of positive relationship

between polygyny and fertility as described by Ezeh (1997) is that women in high

polygyny areas usually enter into conjugal relation latterly and they desire more children.

In Cameron and Central Africa, women in polygynous marriage had depressed the

fertility as compared to monogamous marriage (Wasao, 2001). Pakistani law also allows

tion of permission from former wife. Frequency of

rate is higher for those couples who are relatives. Elevation in fertility is reported

polygynous marriage with the restric

existence of Polygynous marriages has increased more than 2% since 1990-91 PDHS.

Prevalence of polygynous marriage is highest among the age group of 15-19 years.

Women in polygynous relation are approximately 7 percent as reported by PDHS 2006-

07. Polygyny is more common in no education and in poor class. In Baluchistan these

marriages are twice as compared to Punjab (Sultan & Baqai, 2008). Low educational

level and tribal system is responsible for this provincial difference. It is the perception

that keeping more than one wife may caused increase in fertility of Pakistani women

(Bukhari, 2010).

m. Consanguineous Marriages

Cousin marriage tradition is very popular in Christians and Muslims societies. Pakistan is

one of those countries where it is practiced widely. It has biological effect on human

inbreeding. A first cousin marriage is also found as a significant determinant of fertility

(Tuncbilek & Koc, 1994). Bittles (1991) reported that in Egypt, Morocco and Sudan total

fertility

in many fertility related studies (Bai, John, & Subramaniam, 1981; Phillipe, 1974, Reddy

& Rao, 1978; Rao & Inbaraj, 1979; Schull, Furusho, Yamamoto, Nagano, & Komatsu;

1970a as cited in Khlat, 1988). In Beirut, couples who are relatives have also elevated

19

fertility (Khlat, 1988). Shami, Schmit and Bittles (1990, as cited in Hussain and Bittles,

1999) have found positive association between number of pregnancies and cousin

marriage. This positive association declined when number of pregnancy losses was

included in the analyses. Consanguineous marriage reduces the fertility of couple due to

pre and post neonatal mortalities (Ansari & Sinha, 1978; Reid, 1976 as cited in Khlat,

1988).

Besides biological reasons, there are some social reasons affecting fertility due to cousin

marriages. Hussain and Bittles (1999) pointed out that in cousin marriages couples got

married in early age with less education so use of contraceptive is not common among

them. This may result in more number of pregnancies in cousin marriages. In non

consanguineous marriages girls are usually highly educated and engaged in white collar

jobs. Sometimes it becomes difficult to find suitable mate for them in a family (Sathar &

Kazi, 1988 as cited in Hussain and Bittles, 1999). The consequences might be delayed

marriages and thus number of children born to a non consanguineous married woman

decline.

Cousin marriages are most common in Pakistani society as compared to rest of world.

ord marriage between first and second cousin is 61 percent Acc ing to PDHS-2006-07,

(32% from father side and 21 % from mother side). Cousin marriage is more popular in

rural area, in no education group and among poor class. The percentage of cousin

marriage in Punjab, Sindh, KPK and Baluchistan is 53, 56, 43 and 52, respectively

(Sultan & Baqai, 2008). Cousin marriages are more common in Punjab and Sindh as

compared to KPK. The reason is to avoid distribution of land ownership outside the

family in case of non consangunious marriages. Total fertility rate in both urban and rural

areas is found slightly higher due to cousin marriages in Pakistan (Afzal, Ali, & Siyal,

1994).

n. Region of Residence

Regional disparities in fertility levels are usually due to cultural/ social values and norms

of the ethnic groups residing in those regions. Santos-Silva and Covas (2000) used region

as a factor affecting fertility in their study. Pakistan is country of diversified cultures and

norms. Heterogeneity in population characteristics can be observed in the four provinces

20

(Punjab, Sindh, KPK and Baluchistan) of Pakistan. Alam and Shah (1986) found

variation in fertility decline in the four provinces. The total fertility rate in Punjab, Sindh,

KPK and Baluchistan is 3.9, 4.3, 4.3 and 4.1, respectively (Ali & Buriro, 2008).

Diversification of culture is an important feature of Pakistani people. This difference is

e, race, dresses and behaviours. Women social status is also

different in these provinces which can affect fertility behaviour (Hakim, 1994). Pakistan

e

ersity of Indonesia, 1974 as cited in Hakim, 1994) and Egypt

(Omran, 1973 as cited in Hakim, 1999) revealed high fertility in urban areas. Low