essays on health determinants in kenya - semantic scholar · iii preface this thesis is organized...

ESSAYS ON HEALTHDETERMINANTS IN KENYA

Japheth Osotsi Awiti

A Thesis Submitted in Partial Fulfilment of the Requirements for the

Degree of Doctor of Philosophy in Economics in the University of Nairobi

2013

i

Declaration

ii

Acknowledgements

Generous financial assistance from the African Economic Research Consortium

(AERC), both towards the coursework component of my PhD studies under the Col-

laborative PhD Programme in Economics (CPP) and towards the research portion of

the PhD is acknowledged and appreciated. I am grateful for comments and guidance

from my supervisors, Professor Germano Mwabu and Professor Jane Kabubo–Mariara.

I would also like to recognize and appreciate the efforts of all the lecturers who taught

me in the coursework component of the PhD, both at the University of Dar es Salaam

and at the Joint Facility for Electives (JFE) in Nairobi. I would also like to thank my

classmates with whom I held very fruitful discussions. I am grateful for comments from

the resource persons and participants at AERC biannual research workshops where the

proposal and the work–in–progress for this thesis were presented. I am also grateful for

comments from participants at the School of Economics seminar where this work was

presented. Last but not least, I would like to appreciate the sacrifices made by my family

during the entire period of the PhD studies and especially during the thesis writing stage.

iii

Preface

This thesis is organized around three essays dealing with health determinants in

Kenya. The first essay investigates the effect of prenatal care use on the probability of

delivering a low birth weight infant. The second essay investigates the effect of preceding

birth interval length on the probability of a mother experiencing a miscarriage, stillbirth,

or an abortion. The third and final essay investigates the effect of smoking on self–rated

health status. The three essays give us an idea about the important health determinants

in Kenya.

The key contribution of the thesis to knowledge and policy is a demonstration that

failure to control for unobserved mother–specific effects, for instance via a multi–level

modelling framework, leads to an overstatement of the beneficial effects of prenatal

care use on birth outcomes. The second main contribution is a demonstration that

preceding birth intervals of length 36 to 59 months only improve maternal health after

the endogeneity of preceding birth interval has been controlled for. The third contribution

is showing that failure to control for sample selection bias, endogeneity of smoking and

unobserved heterogeneity leads to an understatement of the negative effects of smoking

on self–rated health status by about 50%.

The thesis is organized around 5 chapters. Chapter 1 introduces the thesis, Chapter

2 presents the first essay, Chapter 3 the second essay, Chapter 4 the third essay, while

Chapter 5 summarizes the thesis.

iv

Abstract

We investigate the effect of prenatal care use on infant health, the effect

of preceding birth interval length on maternal health, and the effect

of smoking on general health. We employ an estimation strategy that

controls for potential endogeneity of the key covariates, potential un-

observed heterogeneity, and potential sample selection bias. We obtain

three main results. First, models that do not control for unobserved

mother–specific effects overstate the beneficial effects of prenatal care on

infant health. We particularly find that after controlling for unobserved

mother–specific effects, adequate use of prenatal care decreases the prob-

ability of delivering a low–birth weight infant by 0.036, holding other

factors constant. Without such control, however, the corresponding

reduction would have been 0.26. Second, preceding birth interval is

an endogenous determinant of maternal health. In particular, we find

that preceding birth intervals of length 36 to 59 months can only be

shown to improve maternal health after we control for the endogeneity

of preceding birth interval. Third, failure to control for sample selection

bias, endogeneity of smoking and unobserved heterogeneity leads to an

understatement of the negative effects of smoking on self–rated health

status by about 50%. In particular, we find that after controlling for

sample selection bias, endogeneity of smoking and unobserved hetero-

geneity, the probability of individuals who smoke rating their own health

as “Poor” compared to their age–mates is higher than that of those who

do not smoke by 0.018, holding other factors constant. Without such

controls, however, the corresponding difference in probabilities is only

0.009. Our results imply that policies that promote adequate use of

prenatal care services, those that promote adequate spacing of births,

and those that discourage smoking should be pursued so as to improve

the health of the Kenyan people.

Contents

Declaration i

Acknowledgements ii

Preface iii

Abstract iv

List of figures vii

List of tables viii

1 Introduction 1

1.1 Definition and Importance of Health . . . . . . . . . . . . . . . . . . . . 1

1.2 Measurement of Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Health Determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 Key Health Indicators for Kenya . . . . . . . . . . . . . . . . . . . . . . . 7

1.5 Purpose and Objectives of the Thesis . . . . . . . . . . . . . . . . . . . . 7

1.6 General Modelling Framework for the Thesis . . . . . . . . . . . . . . . . 9

1.7 Contributions of the Thesis to Knowledge and Policy . . . . . . . . . . . 12

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2 Prenatal Care and Infant Health: A Multilevel Analysis 22

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.3 Purpose and Objectives of the Study . . . . . . . . . . . . . . . . . . . . 29

2.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.5 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.7 Summary, Conclusions and Policy Implications . . . . . . . . . . . . . . . 60

v

Contents vi

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

3 Preceding Birth Interval Length and Maternal Health 74

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74



3.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80


3.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

4 Smoking and General Health 109

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109



4.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113


4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

5 Summary, Conclusions, and Policy Implications 144

5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

5.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

5.3 Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

List of figures

1.1 A General Framework for Analyzing Health Determinants . . . . . . . . 9

2.1 A Conceptual Framework for Analyzing the Effect of Prenatal Care Use

on Infant Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.2 An Example of a Multilevel Data Structure . . . . . . . . . . . . . . . . . 42

3.1 A Conceptual Framework for Analyzing the Effect of Preceding Birth

Interval Length on Maternal Health . . . . . . . . . . . . . . . . . . . . . 80

4.1 A Conceptual Framework for Analyzing the Effect of Smoking on General

Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

vii

List of tables

1.1 Indicators of Infant and Child Health at the Individual Level . . . . . . . 3

1.2 Indicators of Maternal Health at the Individual Level . . . . . . . . . . . 5

1.3 Key Health Indicators for Kenya for Selected Years . . . . . . . . . . . . 7

1.4 A Comparison of Kenya’s Key Health Indicators with Selected Regions . 8

2.1 Low Birth Weight Newborns (%) for Selected Countries and Regions . . 24

2.2 Indicators of Prenatal Care . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.3 WHO Recommended Minimum Prenatal Care Visits for Developing Countries 26

2.4 Maternal Risk Factors for Low Birth Weight . . . . . . . . . . . . . . . . 27

2.5 Summary of Studies Showing Positive Effects of Prenatal Care on Birth

Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.6 Factors Influencing Prenatal Care Use . . . . . . . . . . . . . . . . . . . 30

2.7 Variable Definitions for Prenatal Care and Infant Health Models . . . . . 47

2.8 Reporting of Birth Weight by Year of Survey . . . . . . . . . . . . . . . . 48

2.9 Descriptive Statistics for the Variables in Prenatal Care and Infant Health

Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.10 Low Birth Weight Status by Year of Survey . . . . . . . . . . . . . . . . 50

2.11 Prenatal Care Use by Skill of Provider . . . . . . . . . . . . . . . . . . . 50

2.12 Adequacy of Prenatal Care Use, Analytic Sample for Prenatal Care Use . 51

2.13 Low Birth Weight and Adequacy of Prenatal Care Use . . . . . . . . . . 51

viii

List of tables ix

2.14 Average Marginal Effects for Sample Selection and Prenatal Care Probit

Models, Robust Z Statistics in Parentheses . . . . . . . . . . . . . . . . . 52

2.15 Average Marginal Effects from Single–Level Low Birth Weight Models,

Robust Z Statistics in Parentheses . . . . . . . . . . . . . . . . . . . . . 56

2.16 Average Marginal Effects from Multi–Level Low Birth Weight Models,

Robust Z Statistics in Parentheses (Number of Observations = 7331) . . 57

2.17 Average Marginal Effects from Our Chosen Low Birth Weight Models,


3.1 Stillbirth rate for Selected Countries and WHO Regions, 2009 . . . . . . 75

3.2 Distribution of Preceding Birth Intervals in Kenya . . . . . . . . . . . . . 76

3.3 Determinants of Maternal Health . . . . . . . . . . . . . . . . . . . . . . 77

3.4 Risk Factors for Short Birth Intervals . . . . . . . . . . . . . . . . . . . . 79

3.5 Variable Definitions for Maternal Health Models . . . . . . . . . . . . . . 91

3.6 Descriptive Statistics for Maternal Health Models . . . . . . . . . . . . . 92

3.7 Distribution of Maternal Health Status by Survey Year, Percentages in

Parentheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

3.8 Distribution of Maternal Health Status by Preceding Birth Interval Length,

Percentages in Parentheses . . . . . . . . . . . . . . . . . . . . . . . . . . 93

3.9 Average Marginal Effects from Sample Selection and Preceding Birth

Interval Length Models, Robust Z Statistics in Parentheses . . . . . . . . 94

3.10 Average Marginal Effects for Maternal Health Status Models, Robust Z

Statistics in Parentheses . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

4.1 Some Facts about Tobacco Use and its Consequences in Kenya . . . . . . 111

4.2 Determinants of Smoking . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

4.3 Variable Definitions for Smoking and General Health Models . . . . . . . 122

4.4 Descriptive Statistics for Smoking and General Health Models . . . . . . 123

List of tables x

4.5 Distribution of Self–Rated Health Status in the Dataset . . . . . . . . . . 124

4.6 Distribution of Self–Rated Health Status by Smoking Status in the Dataset124

4.7 Average Marginal Effects for Sample Selection and Smoking Status Models,


4.8 Average Marginal Effects for Probability of Rating Own Health as “Poor”,


4.9 Average Marginal Effects for Probability of Rating Own Health as “Satis-

factory”, Robust Z Statistics in Parentheses . . . . . . . . . . . . . . . . 129

4.10 Average Marginal Effects for Probability of Rating Own Health as “Good”,


4.11 Average Marginal Effects for Probability of Rating Own Health as “Very

Good”, Robust Z Statistics in Parentheses . . . . . . . . . . . . . . . . . 131

“And for man to look upon himself as a capital good, even if it did not impair his freedom,

may seem to debase him... by investing in themselves, people can enlarge the range of

choice available to them. It is one way free men can enhance their welfare. ”

— Theodore William Schultz (1902 – 1998)

Chapter 1

Introduction

1.1 Definition and Importance of Health

Health can be viewed as comprising physical, mental, and social well–being (Thomas and

Frankenberg, 2002; World Health Organization (WHO), 2006a; Mwabu, 2008; Strauss

and Thomas, 2008). Economists’ main interest in health is motivated by the fact that

good health is a component of human capital (Brock, 2002; Becker, 2007; Mwabu, 2008)

and, therefore, a key factor in wealth creation (Pritchett and Summers, 1996; Mwabu,

2001). Good health has also been shown to have positive effects on economic growth

(Arora, 2001; Bhargava, Jamison, Lau and Murray, 2001; Mayer, 2001; Bloom, Canning

and Sevilla, 2004; Qureshi and Mohyuddin, 2006).

We can look at health from a population point of view (in which case we talk about

population health) or from an individual point of view (in which case we talk about

individual health). From a policy perspective, sometimes we are specifically concerned

with the health of specific population groups (such as infants, children, adolescents, or

mothers) as opposed to general health. We can view infant health as referring to the

health of persons below the age of 1 year and child health as referring to the health of

persons aged 1 to 5 years. We can also view maternal health as the health of women

during the period when they are pregnant, at childbirth and the period immediately

following childbirth (Lule et al., 2005).

A policy maker’s interest in infant and child health is motivated by the fact that many

health problems that affect adults can be traced to early periods of one’s life (Hertzman

and Power, 2004; Currie, Stabile, Manivong and Roos, 2010). Research also shows that

infant health has effects on long–run adult health and other outcomes such as adult

1

Introduction 2

height, intelligence, education outcomes, and labour force outcomes (Black, Devereux

and Salvanes, 2007; Oreopoulos, Stabile, Walld and Roos, 2008; Al-Saleh and Renzo,

2009).

Policy makers have an interest in maternal health because research shows that good

maternal health increases labour supply, productive capacity, and economic well-being of

communities (Lule et al., 2005; Meyerhoefer and Sahn, 2010). Maternal health is also

important for infant and child health since neonatal and infant deaths can result from

poor maternal health (United Nations Population Fund (UNFPA), 2004).

1.2 Measurement of Health

The health status of a particular individual or population refers to all forms of the

individual’s or population’s health (Lopez et al., 2006). True health status is typically

unobservable but it can be approximated by using observable indicators (Stein, 2004),

which typically focus on the identification and assessment of negative deviations from what

would otherwise constitute normal life (Patrick and Bergner, 1990). The indicators help

improve health when they are applied in advocacy, accountability, system management,

quality improvement, and research (Etches, Frank, Di Ruggiero and Manuel, 2006).

Health status indicators can be constructed for individuals or entire populations. We

can broadly classify the indicators into three categories: indicators of infant and child

health, indicators of maternal health, and indicators of general health.

Indicators of Infant and Child Health

At the population level, the indicators of infant and child health status include neonatal

mortality rate, postneonatal mortality rate, infant mortality rate, birth weight distribution,

gestational age distribution (see, for example, Ruhm, 2000; Fayissa, 2001; Bhargava,

2003; Buitendijk et al., 2003; Alves and Belluzzo, 2004; Mwabu, 2009), child mortality

rate (see, for example, Fayissa, 2001), and under-five mortality rate. The most commonly

reported ones are, however, the birth weight distribution, the infant mortality rate, and

the under-five mortality rate.

For birth weight distribution, the emphasis is mainly on the percentage of children

with low birth weight. A child is considered to have low birth weight if his/her weight

Introduction 3

Table 1.1: Indicators of Infant and Child Health at the Individual Level

Indicator Literature source

Child survival Mosley and Chen, 1984; Schultz, 1984; Rutstein, 2000;

Kabubo–Mariara, Karienyeh and Kabubo, 2012.

Birth weight Rosenzweig and Schultz, 1982; Schultz, 1984;

Almond, Chay and Lee, 2005;

Oreopoulos, Stabile, Walld and Roos, 2008;

Mwabu, 2009;

Mbuya, Chideme, Chasekwa and Mishra, 2010.

Apgar score Almond, Chay and Lee, 2005;

Oreopoulos, Stabile, Walld and Roos, 2008;

Mwabu, 2009.

Gestation Oreopoulos, Stabile, Walld and Roos, 2008.

Nutritional status indicators Mosley and Chen, 1984; Rutstein, 2000;

Mukudi, 2003; Nakabo–Ssewanyana, 2003;

Alves and Belluzzo, 2004;

Kabubo-Mariara, Ndenge and Mwabu, 2009;

Kodjo, 2009; Case and Paxson, 2010;

Mbuya, Chideme, Chasekwa and Mishra, 2010;

Adewara and Visser, 2011.

at birth is less than 2,500 grams (Zegers–Hochschild et al., 2009; WHO, 2011a). The

infant mortality rate is the number of children who die aged under 1 year per 1,000 live

births (WHO, 2011a). The probability, subject to current age–specific mortality rates, of

a child born in a specific year dying before reaching age 5, expressed as a certain number

per 1,000 live births is referred to as the under–five mortality rate (WHO, 2005).

Table 1.1 summarizes the various indicators that have been used in the literature for

measuring infant and child health at the individual level.

The nutritional status indicators (such as height-for-age, weight-for-height, and weight-

for-age) are typically calculated using the World Health Organization (WHO)’s growth

standards (De Onis, Garza, Onyango and Martorell, 2006). Nutritional deficiencies in

childhood are associated with poor adult health outcomes, such as shorter adult height

(Victoria et al., 2008).

Introduction 4

The main indicators of malnutrition in children include underweight (having a weight

for age score that is less than −2 standard deviations of the WHO child growth standards

median), stunting (having a height for age score that is less than −2 standard deviations

of the WHO child growth standards median), wasting (having weight for height score

that is less than −2 standard deviations of the WHO child growth standards median) and

overweight (having a weight for height score that is greater than 2 standard deviations

of the WHO child growth standards median) (WHO, 2010). Underweight children are at

an increased risk of mortality, stunted and wasted children are at an increased risk of

illness and death, while obese children are at an increased risk of suffering from most

non–communicable diseases (Caulfield, De Onis, Blossner and Black, 2004; WHO, 2010).

Indicators of Maternal Health

At the population level, the typical indicators of maternal health status include maternal

mortality ratio (see, for example, McCarthy and Maine, 1992; Alexander et al., 2003;

WHO, 2006b) and maternal morbidity (Alexander et al., 2003; WHO, 2006b). Maternal

mortality ratio refers to the number of maternal deaths per 100,000 live births (WHO,

2011a). The death of a woman is said to be a maternal death if it results from causes

related to pregnancy or exacerbated by pregnancy or the management of the pregnancy

(WHO, 2011a). Maternal morbidity refers to illness and injury related to pregnancy and

childbirth (UNFPA, 2004).

Other indicators of maternal health at the population level include percentage of

women experiencing a live birth or a stillbirth following fertility treatment, distribution

of timing of first antenatal visit, and distribution of place of birth (Wildman et al., 2003).

Table 1.2 on page 5 shows the indicators of maternal health at the individual level

that have been used in the literature.

Other indicators of maternal health at the individual level include contraceptive use,

visit to obtain antenatal care in the first trimester, and safe delivery (Saikia and Singh,

2008).

Introduction 5

Table 1.2: Indicators of Maternal Health at the Individual Level

Indicator Literature source

Pregnancy outcome Kramer, 2003; Villamor and Cnattingius, 2006.

Pregnancy complications Koblinsky, 1995; Anderson et al., 2008;

Say, Souza and Pattinson, 2009.

Maternal morbidity Gandhi, Welz and Ronsmans, 2004.

Maternal mortality Mace and Sear, 1996.

Nutritional status indicators Koblinsky, 1995; Saikia and Singh, 2008.

Indicators of General Health

At the population level, there are several indicators of general health status which can be

broadly classified into two groups: health expectancies and health gaps (Murray, Salomon

and Mathers, 2002). Health status indicators that estimate the average time (in years)

that an individual could live in particular health states are generally referred to as health

expectancies (Mathers, 2002). Examples of health expectancies are disability–free life

expectancy and health–adjusted life expectancy (HALE) (Mathers, 2002).

Health status indicators that involve the quantification of differences between actual

health and some stated norm for health are referred to as health gaps (Murray, Mathers,

Salomon and Lopez, 2002). Examples of health gaps are disability–adjusted–life–years

(see, for example, Murray and Acharya, 1997; Mathers, Lopez and Murray, 2006) and

healthy–life–years (see, for example, Hyder, Rotllant and Morrow, 1998).

At the individual level, the main indicators of general health status include self-

reported general health status (where individuals rate own health in one of several

discrete categories compared to some reference group, such as age–mates); self-reported

morbidity, illness and normal activity (where individuals report whether or not they were

sick or injured during a reference period, such as four weeks; or the number of days the

individual failed to engage in some usual or “normal” activity due to sickness or injury);

self-reported physical functioning (where individuals report whether or not they had

difficulties in performing activities of daily living); nutrition–based indicators (such as

nutrient intakes, and anthropometrics which typically include height, weight, and body

mass index); haemoglobin levels (which can be used to compute the prevalence rates

of anaemia in both children and adults); and whether one is suffering from an acute or

chronic disease (Strauss and Thomas 1998, 2008; Mwabu, 2008).

Introduction 6

The most commonly used anthropometric measure for adults is the Body Mass Index

(BMI), typically computed by dividing an individual’s weight (in kilograms) by the square

of the individual’s height (in meters). The measure is used to classify an individual as

either underweight, normal, overweight or obese. An individual is considered underweight

if he/she has a BMI of less than 18.5, normal if he/she has a BMI of between 18.5 and

24.9, overweight if he/she has a BMI of between 25 and 29.9, and obese if he/she has a

BMI of 30 and above (National Institutes of Health (NIH), 1998; Hiza, Pratt, Mardis and

Anand, 2000). BMI values of above 25 are unhealthy and have been shown to increase

the risk of chronic diseases such as high blood pressure, diabetes, heart disease, stroke,

certain types of cancer, arthritis, and breathing problems (Hubert, Feinleib, McNamara

and Castelli, 1983; Colditz, Willet, Rotnitzky and Manson, 1995; Giovannucci et al.,

1995; Hochberg et al., 1995; Walker et al., 1996).

1.3 Health Determinants

Individual health outcomes are typically influenced by various health inputs such as

use of preventive and curative health services, health behaviours such as smoking and

alcohol consumption, and habits (Rosenzweig and Schultz, 1983; Mosley and Chen, 1984;

Schultz, 1984; McCarthy and Maine, 1992; Norris et al., 2003; Contoyannis and Jones,

2004; Maitra and Pal, 2007; Strauss and Thomas, 2008).

The actual mechanics for producing health at the individual level is likely to be

influenced by the individual’s personal socio-economic characteristics such as age, gender,

marital status, education, occupation, income, and ethnicity (Gilleskie and Harrison,

1998; Marmot, 2002; Fuchs, 2004; Strauss and Thomas, 2008; Cutler and Lleras-Muney,

2010); aspects of the individual’s family background such as the health of the individual’s

parents and his or her genetic endowment; and various environmental characteristics

such as the disease environment, accessibility of health facilities1 and the quality of care

(Mosley and Chen, 1984; Schultz, 1984; Strauss and Thomas, 2008).

1For a comprehensive discussion of access to health care, see Gulliford and Morgan (2003).

Introduction 7

Table 1.3: Key Health Indicators for Kenya for Selected Years

Indicator 1990 2000 2008/2009*

Life expectancy at birth (years) 61 54 60

Neonatal mortality rate (per 1,000 live births) 30 32 27

Infant mortality rate (per 1,000 live births) 64 66 55

Under – five mortality rate (per 1,000 live births) 99 105 84

Adult mortality rate (per 1,000 population) 253 421 319

Maternal mortality ratio (per 100,000 live births) 380 560 530

* Data for all other indicators is for 2009 while that for Maternal mortality is for 2008.

Source: WHO (2011b).

1.4 Key Health Indicators for Kenya

Table 1.3 summarizes the key health indicators for Kenya in the recent past. From

this table we can observe that Kenya’s health indicators are not very impressive. The

table shows, for example, that as of 2009, life expectancy at birth was 60 years, having

increased from 54 years in 2000. We can further see from the table that infant mortality

rate was 55 per 1,000 live births as of 2009, an improvement from a value of 66 in 2000.

The table also shows that as of 2008, maternal mortality ratio was 530 per 100,000 live

births, an improvement from a value of 560 in 2000.

Table 1.4 on page 8 compares Kenya’s health indicators with those for Africa, Europe

and the whole world. From the table, we can observe that although Kenya’s life expectancy

at birth (60 years) is higher than the average for the African region (54 years), it is still

lower than that for Europe (75 years) and the world average (68 years). This is also true

for all the other indicators shown in the table.

1.5 Purpose and Objectives of the Thesis

A careful look at the various health indicators for Kenya (such as those presented in

Table 1.3 and Table 1.4) leads to the conclusion that health issues in Kenya for infants,

children, mothers, and other adults still require urgent attention. People also generally

invest in various activities aimed at improving their health such as seeking adequate

Introduction 8

Table 1.4: A Comparison of Kenya’s Key Health Indicators with Selected Regions

Indicator Kenya Africa Europe World

Life expectancy at birth (years) 60 54 75 68

Neonatal mortality rate (per 1,000 live births) 27 36 7 24

Infant mortality rate (per 1,000 live births) 55 80 12 42

Under – five mortality rate (per 1,000 live births) 84 127 13 60

Adult mortality rate (per 1,000 population) 319 383 146 176

Maternal mortality ratio (per 100,000 live births)* 530 620 21 260

* Data for all other indicators is for 2009 while that for Maternal mortality is for 2008.

Source: WHO (2011b).

prenatal care when pregnant, spacing births optimally, quitting smoking, exercising, and

seeking health care from health facilities when sick (see, for example, the discussions in

Grossman, 1972 and Becker, 2007). People do not know a priori whether these activities

do actually improve their health. For us to be able to recommend actions on the part of

individuals and policy–makers that improve health (which could be indicated by better

health indicators), we need evidence that such actions do indeed improve health. It is

on this basis that this thesis investigates the effects of some specific health inputs and

behaviours on health outcomes for infants, mothers, and the general adult population in

Kenya.

Its specific objectives include:

1. Determining the effect of the adequacy of prenatal care use on infant health in

Kenya.

2. Determining the effect of preceding birth interval length on maternal health in

Kenya.

3. Determining the effect of smoking on general health in Kenya.

4. Drawing appropriate policy implications from the findings.

Introduction 9

Figure 1.1: A General Framework for Analyzing Health Determinants

Observed Variables Unobserved Variables

Preferences

Biological endowments/Genetics

Individual /Household Socio – economic Characteristics

Community Characteristics /Environmental factors

Health Inputs / Health Behaviours

Health Outcomes

Source: Author’s Own Construction Based on Schultz (1984).

1.6 General Modelling Framework for the Thesis

Conceptual Model

Following Schultz (1984), we can develop a general conceptual framework for analyzing the

effects of the various health inputs and behaviours on health outcomes. The framework

is illustrated in Figure 1.1. The framework shows how health outcomes are linked to

observable health inputs and behaviours, and unobservable biological endowments. It

further shows that the health inputs and behaviours are in turn influenced by unobservable

preferences and observable individual and household demographic and socio-economic

characteristics, community characteristics and environmental factors.

We use this framework in each of the essays only that in the essays we have a specific

health input/health behaviour and a specific health outcome.

Introduction 10

Theoretical Model

Our theoretical modelling framework for each of the essays follows Rosenzweig and Schultz

(1982, 1983) and Mwabu (2009). In this framework, individuals (or their representatives)

are assumed to maximize the utility, U , obtained from the consumption of goods and

services that have no direct effect on health, X, those goods that yield utility directly

but also affect individual health, Y , and the individual’s health status, H. For a typical

individual i, we can write this utility function as follows:

Ui = U(Xi, Yi, Hi). (1.1)

Since health cannot be purchased from the market, it has to be produced using

both marketed and non–marketed inputs (Grossman, 1972). We, therefore, assume that

the individual’s health status is influenced by purchased inputs into health, such as

health care, Z, other factors, Q, and unobservable biological endowments, µ. Thus, the

individual’s health status function (or health production function) is given by:

Hi = H(Zi, Yi, Qi, µi). (1.2)

The individual is assumed to maximize the utility function subject to the above health

production function and a budget constraint.

Estimation Issues

When estimating the models in each essay, we control for the following key issues:

potential sample selection bias, potential endogeneity of some of the covariates, and

potential unobserved heterogeneity.

Sample Selection Bias

In general, a selected sample is a non–random sample obtained from a restricted portion

of the population of interest (Wooldridge, 2002). Sample selection bias, in general, arises

whenever we only observe the variable of primary interest for a selected sample and there

is correlation between the unobservable factors determining inclusion in the sample and

the unobservable factors influencing the variable of primary interest (Vella 1992, 1998).

Introduction 11

The literature proposes several approaches to correcting for sample selection bias (see,

for example, Heckman, 1979; Olsen, 1980; Lee, 1982; Garen, 1984; Vella 1992, 1993). In

this thesis, we employ the approach suggested by Vella (1992) in the first essay found in

chapter 2, that suggested by Olsen (1980) in the second essay found in chapter 3, and

Heckman’s approach (Heckman, 1979) in the third essay found in chapter 42.

Endogeneity

An econometric model is said to suffer from the problem of endogeneity if there is a

correlation between the error term in the model and one or more of the regressors included

in the model (Stock and Watson, 2011). Endogeneity can result from the omission of

confounder variables from the model, reverse causality between the dependent variable

and the endogenous regressor, and measurement errors in the regressors (Cameron and

Trivedi, 2010). When a regression model suffers from the problem of endogeneity, the

estimated regression coefficients are inconsistent, and we can also not infer causality

between the dependent variable and the independent variables in such a model (Cameron

and Trivedi, 2010). Zohoori (1997) demonstrates that controlling for endoegeneity matters

in empirical studies. Terza, Basu and Rathouz (2008) discuss two Instrumental Variable

(IV)-based approaches to correcting for endogeneity bias in nonlinear models: two–stage

residual inclusion (2SRI) and two–stage predictor substitution (2SPS), and show that, in

non–linear models, the 2SRI approach is consistent while the 2SPS approach is not. We

consequently use the 2SRI approach in estimating our models since they are non–linear.

Unobserved Heterogeneity

Unobserved heterogeneity is said to occur in an econometric model if there is a non-

linear interaction between unobservable factors and the endogenous covariate which

causes the effect of the endogenous covariate on the variable of interest to differ among

population subjects (Zohoori and Savitz, 1997). The standard procedure for controlling

for unobserved heterogeneity is the control function approach (Florens, Heckman, Meghir

and Vytlacil, 2008; Mwabu, 2009; Wooldridge, 2010). We employ this approach in our

essays.

2The different approaches to correcting for sample selection bias are used to illustrate the differentmethods available in the literature for dealing with this problem.

Introduction 12

1.7 Contributions of the Thesis to Knowledge and

Policy

The thesis makes the following contributions to knowledge and policy using Kenyan data:

1. It shows that failure to control for unobserved mother–specific characteristics may

lead to an overstatement of the effects of adequate prenatal care use on reducing

the probability of low birth weight. This provides the rationale for using methods

that control for unobserved mother–specific effects in this kind of analysis, such as

multi–level modelling.

2. It shows that preceding birth interval length is an endogenous determinant of

maternal health. In particular, it shows that preceding birth intervals of length

36 to 59 months only improve maternal health after this endogeneity has been

controlled for. As such, failure to control for the endogeneity of preceding birth

interval may make preceding birth intervals of length 36 to 59 months to appear

to worsen maternal health. Models that link preceding birth intervals to maternal

health must, therefore, control for the endogeneity of preceding birth interval. The

finding that preceding birth interval improves maternal health provides evidence for

pursuing programmes aimed at increasing birth spacing.

3. It shows that failure to control for sample selection bias, endogeneity of smoking

and unobserved heterogeneity leads to an understatement of the negative effects

of smoking on self–rated health status by about 50%. For example, when we

do not control for sample selection bias, endogeneity of smoking and unobserved

heterogeneity, we find that smoking increases the probability of rating own health as

“Poor” compared to age–mates by 0.009, holding other factors constant. When we,

however, control for sample selection bias, endogeneity of smoking and unobserved

heterogeneity, we find that smoking increases the probability of rating own health

as “Poor” as compared to age–mates by 0.018, holding other factors constant.

Similarly, without controlling for sample selection bias, endogeneity of smoking and

unobserved heterogeneity, smoking reduces the probability of rating own health as

“Very Good” as compared to age–mates by 0.033, holding other factors constant.

However, after controlling for sample selection bias, endogeneity of smoking and

unobserved heterogeneity, we find that smoking reduces the probability of rating

own health as “Very Good” as compared to age–mates by 0.069, holding other

factors constant.

REFERENCES 13

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http://www.who.int/whr/2005/whr2005_en.pdf

http://www.who.int/governance/eb/who_constitution_en.pdf

www.who.int/making_pregnancy_safer/.../birth_spacing.pdf

http://whqlibdoc.who.int/publications/2010/9789241599955_eng.pdf

http://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf

http://www.who.int/whosis/whostat/EN_WHS2011_Full.pdf

REFERENCES 21

[103] Zohoori, N. and Savitz, D. A., 1997. Econometric approaches to epidemiologicdata: relating endogeneity and unobserved heterogeneity to confounding. Annalsof Epidemiology 7 (4), pp.251–257.

Chapter 2

Prenatal Care and Infant Health: A

Multilevel Analysis

2.1 Introduction

As pointed out in Chapter 1, one of the indicators of infant and child health status

is birth weight (see, for example, Mwabu, 2009). Birth weight refers to the weight of

the newborn at birth, typically obtained within the first hour of birth for live births

(WHO, 2011). It is one of the most important predictors of infant health outcomes

(McCormick, 1985; Kramer, 1987; Pollack and Divon, 1992; Ohlsson and Shah, 2008).

It has been demonstrated to be a good summary measure of a child’s health at birth

(see, for example, Mwabu, 2009). The World Health Organization (WHO) defines low

birth weight as a weight at birth of less than 2,500 grams (Zegers–Hochschild et al., 2009;

WHO, 2011). In this chapter, we measure infant health using birth weight.

There are several undesirable health and other outcomes associated with low birth

weight such as an increased risk for illnesses and deaths among infants and neonates,

an increased risk for chronic non–communicable diseases in later life, and poor school

performance signalled by grade repetition (Institute of Medicine (IOM), 1985; Barker

and Osmond, 1986; Chaikind and Corman, 1991; Hack, Klein and Taylor, 1995; Paneth,

1995; Corman and Chaikind, 1998; UNICEF and WHO, 2004; Reyes and Manalich, 2005;

Ohlsson and Shah, 2008). Further, it is more expensive to care for low birth weight

infants as compared to caring for normal weight infants (Lewit, Baker, Corman and

Shiono, 1995; Ohlsson and Shah, 2008). Reducing the number of low birth weight babies

could have substantial economic benefits for low–income countries, such as increases

22

Prenatal Care and Infant Health: A Multilevel Analysis 23

in labour productivity and reduction in costs associated with infant illness and death

(Alderman and Behrman, 2006).

Low birth weight, being an indicator of inadequate foetal growth, may be a consequence

of either premature birth (birth before a gestation period of 37 weeks), intrauterine

growth retardation (IUGR), or both (IOM, 1985; Kramer, 1987; Kallan, 1993; Paneth,

1995; Ohlsson and Shah, 2008; UNICEF and WHO, 2004).

Table 2.1 on page 24 shows the percentage of newborns that are low birth weight

for Kenya as compared to other countries and the various World Health Organization

(WHO) regions for the periods 2000 – 2002, and 2005 – 2010. The table shows that the

percentage of low birth weight newborns in Kenya declined from 11% over the 2000 – 2002

period to 8% over the period 2005 – 2010. The table further shows that the percentage

of low birth weight newborns for Kenya were lower as compared to those of Uganda and

the United Republic of Tanzania both over the 2000 – 2002 period and over 2005 – 2010

period. Rwanda has, however, lower percentages compared to Kenya for both periods.

According to the table, the percentage of newborns who are low birth weight for Kenya

are also lower than the averages for Africa, South–East Asia, and Eastern Mediterranean

regions. Although this might appear impressive, it is still important to find out what

can be done to reduce the percentage of low birth weight newborns in Kenya to as low

levels as those in Rwanda or even lower.

Prenatal care, also called antenatal care, refers to the medical attention provided

to an expectant mother throughout the period of pregnancy but excluding labour and

delivery (Garrido, 2009). In the ideal scenario, prenatal care should involve the following:

provision of appropriate advice on health matters such as nutrition, hygiene, newborn

care and safer sex; identification of expectant women at risk of experiencing pregnancy

complications through appropriate screening and diagnosis; and either the treatment of

identified pre–existing illnesses and conditions or, where treatment is not possible at the

particular health facility, referral to an appropriate health facility that can deal with

the identified conditions (Berg, 1995). Prenatal care can further benefit both expectant

mothers and their unborn children through identification of expectant mothers at risk

of delivering low birth weight infants or experiencing complications during delivery and

reducing such risks by providing appropriate psychosocial, nutritional, and medical

interventions (Kramer, 1987; Alexander and Korenbrot, 1995).


Table 2.1: Low Birth Weight Newborns (%) for Selected Countries and Regions

2000 – 2002 2005 – 2010

Kenya 11 8

Uganda 12 14

United Republic of Tanzania 13 10

Nigeria 14 12

Rwanda 9 6

Africa 14 13

Americas 9 8

South–East Asia 26 24

Europe 8 7

Eastern Mediterranean 17 21

Western Pacific 8 5

Source: WHO (2006, 2012).

Several indicators have been used in the literature to measure prenatal care use. Table

2.2 on page 25 gives examples of such indicators.

The World Health Organization (WHO) recommends a minimum of four prenatal

care visits at particular intervals, to skilled health personnel (doctors or nurses), for

expectant women in developing countries (Berg, 1995). The recommended timing for

each visit is shown in Table 2.3 on page 26. According to the table, for example, the first

prenatal care visit should occur within the first 16 weeks of pregnancy while the third

visit should occur at 32 weeks of pregnancy. There are further detailed recommendations

on what should be done at each visit (see Berg, 1995). Garrido (2009) shows that

the recommendations of WHO regarding prenatal care use in developing countries are

appropriate. In this chapter, we construct a prenatal care utilization index based on

WHO’s recommendations.

The rest of the chapter is structured as follows: Section 2.2 reviews the literature,

Section 2.3 discusses the purpose and objectives of the study, Section 2.4 discusses the

methodology of the study, descriptive statistics are discussed in Section 2.5, the results

are presented in Section 2.6, and Section 2.7 gives the summary, conclusions and policy

implications.


Table

2.2:

Ind

icat

ors

ofP

renat

alC

are

Indic

ator

Exam

ple

sof

Stu

die

sU

sing

the

Indic

ator

Num

ber

ofpre

nat

alca

revis

its

LaV

eist

,K

eith

and

Guti

erre

z,19

95;

Rou

s,Jew

ell

and

Bro

wn,

2004

;

Eva

ns

and

Lie

n,

2005

;N

azim

and

Fan

,20

11.

Num

ber

ofpre

nat

alca

revis

its

War

ner

,19

95.

adju

sted

for

pre

gnan

cyle

ngt

h

Whet

her

pre

nat

alca

rew

asev

erin

itia

ted

Jew

ell,

2009

.

Auth

or-c

onst

ruct

edqual

ity

Naz

iman

dF

an,

2011

.

index

for

typ

eof

care

rece

ived

Tim

ing

offirs

tpre

nat

alca

revis

itF

rank,

Str

obin

o,Sal

keve

ran

dJac

kso

n,

1992

;

Gol

den

ber

g,P

atte

rson

and

Fre

ese,

1992

;

LaV

eist

,K

eith

and

Guti

erre

z,19

95;

War

ner

,19

95;

Joy

ce,

1999

;C

onw

ayan

dK

uti

nov

a,20

06;

Weh

by

etal

.,20

09a;

Weh

by

etal

.,20

09b.

Kes

sner

index

ofad

equac

yof

Kot

elch

uck

,19

94a;

Joy

ce,

1994

;

pre

nat

alca

rere

ceiv

edL

aVei

st,

Kei

than

dG

uti

erre

z,19

95;

Del

gado-

Rodri

guez

,Gom

ez–O

lmed

o,B

uen

o–C

avan

illa

san

dCalv

ez–V

arga

s,19

97;

Con

way

and

Kuti

nov

a,20

06;

Jew

ell,

2009

.

Adeq

uac

yof

pre

nat

alca

reuti

liza

tion

index

Kot

elch

uck

,19

94a;

Kot

elch

uck

,19

94b.

Index

esbas

edon

WH

OB

erg,

1995

;G

arri

do,

2009

.

reco

mm

endat

ions

for

dev

elop

ing

countr

ies


Table 2.3: WHO Recommended Minimum Prenatal Care Visits for Developing Countries

Visit Recommended timing (Pregnancy weeks)

First ≤ 16

Second 24 – 28

Third 32

Fourth 36 – 38

Source: Berg (1995).

2.2 Literature Review

IOM (1985), Kramer (1987), Kallan (1993), Guyatt and Snow (2004), Mitgitti et al.

(2008), Ohlsson and Shah (2008), and Darling and Atav (2012) summarize the literature

on the determinants of low birth weight. They identify a number of maternal risk factors1

for low birth weight. The factors are summarized in Table 2.4 on page 27.

According to the table, for example, an adolescent mother is at an increased risk of

delivering a low birth weight infant. The table also shows that mothers with a previous

history of preterm/low birth weight births are at an increased risk of giving birth to a

low birth weight infant.

Since absent or inadequate use of prenatal care is a risk factor for low birth weight

(IOM, 1985), we would expect prenatal care use to improve birth weight. Although many

studies show that this is in fact the case (see, for example, the studies cited in Table 2.5

on page 28), Kramer (1987) cites studies that fail to find an effect of prenatal care on

reducing the risk of low birth weight. Other studies (see, for example, Grossman and

Joyce, 1990; Currie and Grogger, 2002) only find weak influences of prenatal care on the

health of infants. Why is this the case? One answer suggested by Conway and Deb (2005)

is that studies that find prenatal care to be ineffective in birth weight determination

typically combine “complicated” and “normal” pregnancies. Conway and Deb (2005)

proceed to demonstrate the effectiveness of prenatal care on “normal” pregnancies.

Table 2.5 on page 28 summarizes the key findings from some of the studies that find

prenatal care to be effective in lowering the risk of low birth weight.

1These are factors whose possession or presence is associated with an increased probability of givingbirth to a low birth weight infant (IOM, 1985; Lopez et al., 2006).


Table 2.4: Maternal Risk Factors for Low Birth Weight

Main Factor Detailed Breakdown

Historical factors Short or long birth interval

Previous history of preterm/low birth weight births

Maternal history of being low birth weight

Demographic factors Adolescent mothers

Minority race

Acculturation

Unmarried/Cohabiting

Parity (0 or more than 4)

Nutritional factors Iron deficiency

Lack of fish oil in diet

Anthropometric factors Low Body Mass Index

Medical and pregnancy–related conditions Anatomical factors

Uterine factors

Placental factors

Infections

Malaria

Human Immuno–deficiency Virus (HIV)

Bacterial vaginosis

Trichomoniasis

Syphilis

Gonorrhea

Urinary tract infection

Periodontal infection

Others

Multiple pregnancy

Psychosocial factors Adverse psychosocial factors

Lifestyle–related factors Tobacco use

Heavy alcohol use

Cocaine use

Narcotic use

Environmental factors Environmental tobacco exposure

Violence/Maternal abuse Violence/abuse

Maternal trauma

Infertility and in vitro fertilization (IVF) treatment

Health care risks Absent or inadequate prenatal care

Source: IOM (1985, Table 1, p.7); Kramer (1987), Kallan (1993), Guyatt and Snow (2004), Mitgitti etal. (2008); Ohlsson and Shah (2008, Table 16.1, p.259).


Table

2.5:

Su

mm

ary

ofS

tud

ies

Sh

owin

gP

osit

ive

Eff

ects

ofP

ren

atal

Car

eon

Bir

thW

eigh

t

Asp

ect

ofP

renat

alC

are

Eff

ect

onB

irth

Wei

ght

Exam

ple

sof

Stu

die

s

Num

ber

ofpre

nat

alca

revis

its

Incr

ease

Joy

ce,

1999

;K

orou

kia

nan

dR

imm

,20

02;

Rou

s,Jew

ell

and

Bow

n,

2004

;L

in,

2004

;

Jew

ell

and

Tri

unfo

,20

06;

Raa

tika

inen

,H

eisk

anen

and

Hei

non

en,

2007

;

Naz

iman

dF

an,

2011

.

Qual

ity

ofpre

nat

alca

reIn

crea

seM

wab

u,

2009

;N

azim

and

Fan

,20

11.

Del

ayin

seek

ing

pre

nat

alca

reD

ecre

ase

Weh

by

etal

.,20

09a.

Ear

ly(fi

rst

trim

este

r)in

itia

tion

ofpre

nat

alca

reIn

crea

ses

Fra

nk,

Str

obin

o,Sal

keve

ran

dJac

kso

n,

1992

;

War

ner

,19

95;

Weh

by

etal

.,20

09a.

Inad

equat

euse

ofpre

nat

alca

reD

ecre

ases

IOM

,19

85;

Bar

ros,

Tav

ares

and

Rodri

gues

,19

96;

Kot

elch

uck

,19

94b;

Gol

dan

i,B

arbie

ri,

Silva

and

Bet

tiol

,20

04;

Raa

tika

inen

,H

eisk

anen

and

Hei

non

en,

2007

.

Pre

nat

alca

revis

its

lost

earl

yin

the

pre

gnan

cyD

ecre

ase

Eva

ns

and

Lie

n,

2005

.


The literature identifies several factors that influence prenatal care use. Table 2.6 on

page 30 summarizes some of these factors.

2.3 Purpose and Objectives of the Study

Looking at the literature, we can conclude that the issue concerning the effectiveness of

prenatal care in reducing adverse pregnancy outcomes such as low birth weight is not yet

settled. This is evidenced by the fact that although many studies have demonstrated the

effectiveness of prenatal care in reducing the occurrence of adverse pregnancy outcomes,

some studies (see, for example those cited in Kramer, 1987) find it to be ineffective. A

further closer look at the literature also reveals that there have been studies in Kenya

investigating the effect of particular types of prenatal care, such as immunization of

the mother against tetanus, on birth weight in Kenya (see, for example, Mwabu, 2009).

Other studies (see, for example, Simiyu, 2004; Were and Bwibo, 2009) simply analyze

illnesses and deaths of low birth weight infants. Yet other studies (see, for example,

Magadi, Diamond, Madise and Smith, 2004; Brown et al., 2008) simply examine the

effect of antenatal care on birth outcomes. To the best of our knowledge there are no

studies so far that link the adequacy of prenatal care use to low birth weight in Kenya.

Furthermore, most of the existing studies also use a single–level modelling framework.

It is important to link the adequacy of prenatal care received to birth outcomes

because some mothers may attend clinics for insufficient number of times or may not

get the prenatal care from qualified providers, making it difficult for us to assess the

effectiveness of prenatal care received from skilled providers.

It is for this reason that this chapter contributes to the literature by examining the

effect of the adequacy of prenatal care use on the probability of delivering a low birth

weight infant in Kenya. The chapter further adds to the literature by using a multilevel

modelling framework in its analysis.

Specifically, the chapter pursues the following objectives:

1. Determining the effect of the adequacy of prenatal care use by the mother on infant

health, where infant health is measured by whether or not the infant has low birth

weight.

2. Determining the factors that influence the adequacy of prenatal care use.


Table

2.6:

Fac

tors

Infl

uen

cin

gP

ren

atal

Car

eU

se

Fac

tor

Exam

ple

sof

Stu

die

s

Typ

eof

faci

lity

vis

ited

Ob

erm

eyer

and

Pot

ter,

1991

.

(that

is,

whet

her

pri

vate

orpublic)

Mat

ernal

dem

ogra

phic

,G

olden

ber

g,

Pat

ters

onan

dF

rees

e,19

92;

soci

o–ec

onom

ic,

situ

atio

nal

,D

elga

do-

Rodri

guez

,Gom

ez–O

lmed

o,B

uen

o–C

avan

illa

san

dGa

lvez

–Var

gas,

1997

;

and

psy

chol

ogic

alfa

ctor

sC

elik

and

Hot

chkis

s,20

00;

Lin

,20

04;

Chan

dhio

k,

Dhillo

n,

Kam

bo

and

Sax

ena,

2006

;B

row

net

al.,

2008

;

Weh

by

etal

.,20

09a;

Jew

ell,

2009

.

Rac

eL

aVei

st,

Kei

than

dG

uti

erre

z,19

95.

Mat

ernal

hea

lth

stat

us

Weh

by

etal

.,20

09a.


Figure 2.1: A Conceptual Framework for Analyzing the Effect of Prenatal Care Use on InfantHealth

Observed Variables Unobserved Variables

Maternal / Household

Preferences

Biology/Genetics/Maternal Health

Maternal /Household Demographic and Socio – economic Characteristics

Community Characteristics /Environmental factors

Prenatal Care

Infant Health

(Birth Weight)

Source: Author’s Own Construction Based on Figure 1.1 and Schultz (1984)

3. Drawing appropriate policy implications from the findings.

2.4 Methodology

Conceptual Framework

Based on the general framework presented in Figure 1.1 and Schultz (1984), we can

develop the conceptual framework shown in Figure 2.1 for the analysis of the effect of

prenatal care use on infant health.

According to the framework (also called the conceptual model), infant health (as

measured by birth weight) is influenced by prenatal care use and unobservable bio-

logical endowments of both the mother and the child, including true maternal health

status. Prenatal care use, in turn, is influenced by maternal/household demographic and


socio–economic characteristics, community characteristics or environmental factors, and

unobservable maternal/household preferences. Prenatal care use is also influenced by

unobservable biological endowments of the mother and the unborn child.

Theoretical Framework

Following the general modelling framework discussed in Chapter 1, which is based on

Rosenzweig and Schultz (1982, 1983) and Mwabu (2009), we assume that an expectant

mother, j, maximizes the utility, Uj, obtained from her consumption of various goods

and services that have no impact on the health of her unborn child, Xj, and the health

status of her unborn child, Hj . We can represent the expectant mother’s utility function

as follows:

Uj = U(Xj, Hj). (2.1)

We assume that the health status of the unborn child, Hj, is in turn influenced by

the adequacy of prenatal care use, Zj, that affects health directly, other factors, Y , and

unobservable biological endowments, µj. The health production function of the unborn

child can, therefore, be represented by the following:

Hj = H (Zj, Y, µj) . (2.2)

The mother is assumed to maximize her utility function subject to the above health

production function and a budget constraint given by:

Ij = PxXj + PyY + PzZj (2.3)

where I is exogenous mother’s/household’s income, Px is the unit price of X, Py is a

vector of the unit prices for the respective components of Y , and Pz is the unit price of

Z.

Mwabu (2009) shows that manipulation of the above equations leads to the following

input demand equations:

Xj = X(Px, Py, Pz, Ij, µj) (2.4)

Zj = Z(Px, Py, Pz, Ij, µj) (2.5)


As observed by Mwabu (2009), these equations show that commodity prices are

correlated with infant health.

Estimation Issues

As mentioned in Section 1.6, when estimating our models, we need to worry about

potential sample selection bias, potential endogeneity of some of the covariates, and

potential unobserved heterogeneity.


Our discussion of sample selection bias follows closely that by Vella (1998) on the same

issue. In this chapter, we only observe the birth weight of a child if it (the birth weight) is

reported in our dataset. Not all children in the dataset, however, have their birth weights

reported. We are concerned with determining the effect of prenatal care on the birth

weight of the children with reported birth weights so as to draw conclusions regarding

the effect of prenatal care on the birth weight of all children. Whether or not we have

sample selection bias depends on the difference between children whose birth weights

are reported and those for whom birth weights are not reported. If the sub–sample

of children with reported birth weights is randomly drawn from the population of all

children, selectivity bias will not arise if we were to just study this particular sub–sample.

If, however, non–reporting of birth weight is non–random, then the sample of children

with reported birth weights is also non–random. This implies that the children with

reported birth weights and those whose birth weights are not reported have different

characteristics. Sample selection bias will arise when some of the factors influencing the

reporting of birth weight also determine birth weight. If these factors are observable,

they can be included in the birth weight equation as conditioning variables. In such a

case there will be no sample selection bias.

If, however, the unobservable factors affecting the decision to report the birth weight

of the child are correlated with the unobservable factors affecting the birth weight itself,

then a relationship exists between reporting of birth weight and the process determining

the birth weight (Vella, 1998). In such a case, it is not enough to control for the observable

factors when explaining birth weight because the process determining whether or not

birth weight is reported also influences birth weight. If these unobservable factors (from


the birth weight reporting equation) are correlated with the observable factors in the

birth weight equation, then failure to include an estimate of the unobservable factors in

the birth weight equation leads to sample selection bias.

As mentioned in Section 1.6, although several approaches to correcting for sample

selection bias have been proposed in the literature (see, for example, Heckman, 1979;

Olsen, 1980; Lee, 1982; Garen, 1984; Vella 1992, 1993), we use the approach suggested

by Vella (1992) since in our case the dependent variable is discrete.

This approach makes use of the generalized residuals from a probit regression (for

a discussion of generalized residuals, see Gourieroux, Monfort, Renault and Trognon,

1987). The approach typically involves identifying the selection factor and estimating a

probit model of the selection factor. Generalized residuals of the resulting model are then

included together with the selection factor in the equation of primary interest. Statistical

significance of the coefficient of the generalized residuals in the equation of primary

interest is then indicative of the presence of selection bias. If the estimated coefficient

of the generalized residuals is not statistically significant, however, there is no selection

bias.

In our case the selection factor is the reporting of birth weight. We, therefore,

estimate a model relating the decision to report birth weight to household and individual

characteristics, and some instruments. We then include in the equation of primary

interest (our infant health model) generalized residuals from this model together with a

variable indicating whether or not birth weight is reported.

Endogeneity

In our infant health model, we suspect that the covariate measuring the adequacy of

prenatal care use is endogenous. We suspect that this endogeneity is due to the presence

of unobservable factors in the infant health equation that are correlated with the adequacy

of prenatal care use chosen by the mother. If this endogeneity is not corrected for, the

resulting estimate of the coefficient of prenatal care use in our infant health model would

be inconsistent (Guevara and Ben–Akiva, 2008).

We employ the Two–Stage Residual Inclusion (2SRI) method (Terza, Basu and

Rathouz, 2008) in an attempt to correct for this endogeneity. For simplicity, we assume

that this is the only endogenous covariate in our model. Following Bollen, Guilkey

and Mroz (1995), we test for the endogeneity of the adequacy of prenatal care use in


the infant health production equation by testing for the statistical significance of the

generalized residuals from the prenatal care equation. If the coefficient of the residuals

is significantly different from zero, then the adequacy of prenatal care use variable is

endogenous; otherwise, it is exogenous.


Unobserved heterogeneity will exist in our infant health model if there are some unobserv-

able factors that interact non-linearly with the adequacy of prenatal care use causing the

effect of prenatal care use on infant health to differ amongst children in the population

(Zohoori and Savitz, 1997). As mentioned in Section 1.6, the standard procedure for

correcting for unobserved heterogeneity is the control function approach (see, for example,

Florens, Heckman, Meghir and Vytlacil, 2008; Mwabu, 2009; Wooldridge, 2010). We

adopt this approach for our model.

This approach involves including in our equation of primary interest (in our case, the

infant health equation) interactions between the residuals and the endogenous explanatory

variable. If the coefficient of the resulting interaction term is statistically significant,

there is unobserved heterogeneity in our infant health model. If the coefficient is not

statistically significant, there is no unobserved heterogeneity in our infant health model.

Model Identification

Our discussion in this subsection follows that in Mwabu (2009) on the issue of model

identification. So as to properly interpret the estimated parameters of our infant health

model, it is important that infant health effects of the endogenous covariate (in our

case, the adequacy of prenatal care use) and of the sample selection rule be identified.

Because we have one endogenous variable in our model, identification requires at least two

exclusion restrictions since we have a situation that requires the simultaneous solution of

two equations. A minimum of one instrument is required for the endogenous covariate

and another minimum of one exogenous variable is also required for the determination of

selection of observations into the sub–sample to be used for estimation purposes (Mwabu,

2009).

The variables chosen as instruments should be uncorrelated with the stochastic error

term in the infant health equation (i.e. they should be valid or exogenous), should be


correlated with the endogenous variable in the infant health equation (i.e. they should

be relevant, or rather, their effects on the endogenous explanatory variable in the infant

health equation should be statistically significant), and should be excluded from the infant

health equation (Wooldridge, 2002; Murray, 2006; Mwabu, 2009; Brookhart, Rassen and

Schneeweiss, 2010). Guevara–Cue (2010) discusses various tests that can be used to

check the validity of instrumental variables in discrete choice models.

In our case, therefore, the variables we use as instruments for prenatal care use should

first, affect prenatal care use or be associated with prenatal care use; second, they should

be unrelated to mother or household characteristics; and third, they should be related to

birth weight only through their association with prenatal care (Brookhart, Rassen and

Schneeweiss, 2010).

Examples of variables that have been used as instruments for prenatal care in the

literature include number of prenatal care clinics or providers per capita (see, for example,

Wehby et al., 2009a), distance from residence to prenatal care clinics (see, for example,

Wehby et al., 2009a; Wehby et al., 2009c), population per hospital bed (see, for example,

Wehby et al., 2009a), unemployment rate (see, for example, Wehby et al., 2009a), rate of

uninsured females (see, for example, Wehby et al., 2009a; Wehby et al., 2009c), price of

prenatal care (see, for example, Wehby et al., 2009c), bus strikes (see, for example, Evans

and Lien, 2005), whether mother cohabits with father of child (see, for example, Conway

and Deb, 2005), and mother’s income (see, for example, Conway and Deb, 2005).

We use the “average distance to the nearest health facility” and the “health facilities

per 100,000 of population” as instruments in our models. Our models are, therefore,

exactly identified (Murray, 2006). The choice of distance to the nearest health facility as

an instrument is based on the assumption that distances to health facilities are correlated

with prenatal care use. Since mothers have other uses for their time (such as engaging in

paid work, housework, and child care), they must optimally allocate the time available

to them amongst the various uses (Becker, 1965; Agenor and Canuto, 2012). The longer

the distance to the nearest health facility, the higher the opportunity cost to the mother

of visiting the facility for prenatal care. Research (see, for example, Arcury et al., 2005;

Fortney et al., 2005; Kosimbei, 2005; Qian et al., 2009) actually shows that distance

to the health facility significantly influences the utilization of health care services. We

would, therefore, expect mothers’ utilization of prenatal care to be limited the longer the

distance to the nearest health facility. Consequently, we expect mother’s utilization of

prenatal care to be inadequate the longer the distance to the nearest health facility.


One argument in the literature against the use of distance to the nearest health

facility as an instrumental variable is that mothers can choose to live near health facilities

because of their health–related problems or preferences (Rosenzweig and Schultz, 1982;

Garrido, 2009). This then undermines the argument that the distances are exogenous.

To overcome this possibility, we use provincial level averages for the distance to the

nearest facility. This is because, even though an individual mother may choose to live

near a health facility because of health issues or preferences, all the women in a province

are unlikely to make this decision simultaneously every time they are pregnant. As such,

an individual woman’s decision may not immediately affect the average distance to the

nearest health facility in a province. Furthermore, if the relocation of a mother is from

one area of the province to another area of the province, this does not change the average

distance to the nearest health facility in the province.

The health facilities per 100,000 of population is aimed at indicating the overall

accessibility and availability of health care in a particular region. We expect that the

higher the number of health facilities per 100,000 of population, the more the health

care, including prenatal care, is accessible and available for use. Consequently, we expect

that the higher the number of health facilities per 100,000 of population, the higher the

probability of adequate prenatal care use.

We test the validity of our instruments using a procedure due to Guevara–Cue (2010).

This procedure involves four steps. In the first step, we estimate a model of prenatal

care in which we include both instruments. We then obtain the generalized residuals

of this model. In the second step, we estimate a model of infant health in which the

residuals from the prenatal care model are included as an additional regressor. Call this

“Model 1”. The third step involves a re–estimation of the infant health model estimated

in the second step but including one of the instruments as an additional regressor. Call

this “Model 2”. In the final step, a likelihood ratio test is conducted comparing the two

models. A failure to reject the null hypothesis is indicative of instrument validity.

Empirical Model

We need to formulate an empirical version of the infant health model. Since we are

measuring the infant’s health status by considering whether or not the infant has low

birth weight, our dependent variable is binary and the appropriate model in our case is the

binary regression model (Long, 1997; Long and Freese, 2006). A comprehensive discussion

of the binary regression model can be found in Long (1997), Long and Freese (2006),


Wooldridge (2002, 2010), and Greene (2008, 2012). We formulate both a single–level

model and a multilevel model of infant health.

Single–level Model

Long and Freese (2006, p.132) discuss the three methods that can be used to derive the

binary regression model: assuming that there is an unobserved variable that is linked

to the observed outcome through a measurement equation, constructing the model as a

probability model, and generating the model as a random utility model. We adopt the

latent variable method because of its appeal to intuition.

To formulate the single–level model, we let Hi be the observed health status of the

ith infant which is defined as follows:

Hi =

1 if infant i has low birth weight,

0 otherwise.(2.6)

We then assume that there is an unobserved or latent continuous variable H∗i (repre-

senting, for example, a true infant health status), which is linked to the observed infant

health status via the following equation:

Hi =

1 if H∗i > 0,

0 otherwise.(2.7)

The latent variable is, in turn, linked to covariates by the following equation:

H∗i = β1 + β2Zi + β3Y + ε1i (2.8)

where Z is an indicator of the adequacy of prenatal care use, Y is a vector of controls,

and ε1 is a stochastic error term.

For given values of Z and Y , it can be shown (see, for example, Long and Freese,

2006; Cameron and Trivedi, 2010) that


Pr (Hi = 1 | Z, Y ) = Pr (H∗i > 0 | Z, Y ) (2.9)

where Pr stands for “probability”.

From Equations (2.8) and (2.9), it can be shown that

Pr (Hi = 1 | Z, Y ) = Pr (−ε1i ≤ β1 + β2Zi + β3Y | Z, Y ) = F (β1 + β2Zi + β3Y )

(2.10)

where F (·) is the cumulative density function of −ε1i, which is the same as that of ε1i if

ε1i is symmetric.

If we assume that ε1i follows a logistic distribution, we end up with the logit model;

while if we assume that ε1i follows the standard normal distribution, we end up with the

probit model (Rabe–Hesketh and Skrondal, 2008). We adopt the probit model because

of its popularity in the literature.

In our case, the relevant probit model is given by:

Pr (Hi = 1 | Z, Y ) = F (β1 + β2Zi + β3Y ) = Φ (β1 + β2Zi + β3Y ) (2.11)

where Φ(·) is the standard normal cumulative distribution function.

Because Z is potentially endogenous in Equation (2.8), we have to control for this

potential endogeneity. To use the Two–Stage–Residual–Inclusion method to control for

this potential endogeneity, we estimate a model for the adequacy of prenatal care use,

obtain generalized residuals from the estimated model (Gourieroux, Monfort, Renault

and Trognon, 1987), and then include these generalized residuals together with the

adequacy of prenatal care variable in our structural equation of interest.

The adequacy of prenatal care use variable is constructed based on the WHO recom-

mendations (Berg, 1995). In particular, we consider prenatal care use to be adequate

if it is obtained from a skilled provider, the total number of visits is at least four, and

the first visit occurs within four months of pregnancy. The adequacy of prenatal care


variable is defined as follows:

Zi =

1 if mother sought adequate prenatal care while pregnant with infant i,

0 otherwise.

(2.12)

Using the latent variable formulation, we can define a latent variable Z∗i that is related

to Zi via the following equation:

Zi =

1 if Z∗i > 0,

0 otherwise.(2.13)

This latent variable is linked to the covariates using the equation

Z∗i = α1 + α2Y + α3Q+ ε2i (2.14)

where Y is a vector of controls, Q is a vector of instruments, and ε2 is a stochastic error

term.

Assuming a standard normal distribution for ε2 leads to a probit model given by:

Pr (Zi = 1) = Φ (α1 + α2Y + α3Q) (2.15)

We estimate this model, obtain its generalized residuals, and include the generalized

residuals as an additional variable in the structural equation of interest.

To control for possible non–random selection of the estimation sample, we also estimate

a sample selection equation. Let selection into the sample be given by the following

Ii =

1 if infant’s birth weight is reported,

0 otherwise.(2.16)


We assume that there is a latent variable linked to the reporting of birth weight and

related to the covariates via the following equation:

I∗i = γ1 + γ2Y + γ3Q+ ε3i (2.17)

where Y is a vector of controls, Q is a vector of instruments, and ε3 is a stochastic error

term.

Assuming a standard normal distribution for ε3 leads to a probit model given by:

Pr (Ii = 1) = Φ (γ1 + γ2Y + γ3Q) (2.18)

We also include the generalized residuals from the estimation of the above model in

our structural equation of interest.

To control for potential unobserved heterogeneity, we include the interaction of the

adequacy of prenatal care with the generalized residuals from the prenatal care equation.

Equation (2.8) is, therefore, extended as follows:

H∗i = β1 + β2Zi + β3Y + β4Ii + β5ε2i + β6ε3i + β7Zε2i + ε1i (2.19)

where Z is an indicator of the adequacy of prenatal care use, Y is a vector of controls, I

is an indicator of whether or not birth weight is reported, ε2 are generalized residuals

from the prenatal care model, ε3i are generalized residuals from the sample selection

model, and ε1 is a stochastic error term.

Consequently, we end up with the following single–level probit model

Pr (Hi = 1) = Φ (β1 + β2Zi + β3Y + β4Ii + β5ε2i + β6ε3i + β7Zε2i) . (2.20)

Multilevel Analysis

Sometimes economists use data collected at different levels of analysis, for example, most

household surveys collect data at the individual, household and even community levels.

Another example is where we may have data collected on the characteristics of children,

their mothers, and the regions they live in. Such data could easily be organized in a

hierarchical manner where, for instance, an individual is linked to a particular household


Figure 2.2: An Example of a Multilevel Data Structure

Level 2: Mother Mother 1 Mother 2

Child 1 Child 2 Child 3 Child 4 Level 1: Child

Mother 3

which is in turn linked to a particular community. Data organized in such a manner

where units at one level are linked to more larger units at a higher level is referred to as

multilevel data (Steenbergen and Jones, 2002)2. The analysis of multilevel data is referred

to as multilevel analysis (Snijders and Bosker, 1999). For a discussion of substantive and

statistical motivation for using multilevel analysis, see Steenbergen and Jones (2002).

Figure 2.2 shows an example of a multilevel data structure. In the figure, children

are the level 1 units while mothers are level 2 units.3 Children are nested in mothers.

Notice from the figure that it is possible for a mother to have more than one child, as is

the case with mother 2 in the figure, but it is not possible for a child to have more than

one biological mother.

In multilevel analysis, we aim at explaining the variability in a predicted variable

obtained at the lowest level in the data structure based on data gathered at all the levels

of nesting (Snijders and Bosker, 1999; Steenbergen and Jones, 2002). For example, for

the data structure shown in Figure 2.2, multilevel analysis would aim at explaining the

2In general, multilevel data may consist of several levels of analysis that can be related to one anotherin some hierarchical order (Steenbergen and Jones, 2002).

3It is conventional in multilevel analysis to refer to the lowest level of analysis as level 1, the nexthighest level of analysis as level 2, and so on (see, for example, Steenbergen and Jones, 2002).


variability in a predicted variable measured at the child level, such as birth weight, based

on data gathered from both children and mothers.

Multi–level Model

The multilevel model is obtained by breaking the stochastic error terms in our single–

level models into two parts, a mother–specific component, ζ, and an infant–specific

component, ε. The mother–specific component, ζ, controls for unobservable mother–

specific characteristics that affect the dependent variable of interest (e.g. infant health,

adequacy of prenatal care use, reporting of birth weight), and is assumed to remain

unchanged across infants born to the same mother but independent across mothers (Rabe–

Hesketh and Skrondal, 2008). The infant–specific component, ε, varies between infants

as well as mothers but is assumed to be independent across both infants and mothers

(Rabe–Hesketh and Skrondal, 2008). It is also further assumed that ζ is independent of

ε (Rabe–Hesketh and Skrondal, 2008)

The multilevel counterparts of our binary outcomes are as follows:

Hij =

1 if infant i from mother j has low birth weight,

0 otherwise.(2.21)

Zij =

1 if mother j sought adequate prenatal care when pregnant with infant i,

0 otherwise.

(2.22)

Iij =

1 if the birth weight for infant i from mother j is reported,

0 otherwise.(2.23)

For the multilevel case, the binary outcomes are related to the respective latent

continuous responses via the following equations:

Hij =

1 if H∗ij > 0,

0 otherwise.(2.24)


Zij =

1 if Z∗ij > 0,

0 otherwise.(2.25)

Iij =

1 if I∗ij > 0,

0 otherwise.(2.26)

The multilevel latent responses are given by:

H∗ij = β1 + β2Zij + β3Y + ζ1j + ε1ij (2.27)

Z∗ij = α1 + α2Y + α3Q+ ζ2j + ε2ij (2.28)

I∗ij = γ1 + γ2Y + γ3Q+ ζ3j + ε3ij (2.29)

where

Y is a vector of controls,

Q is a vector of instruments,

ζ1j, ζ2j, ζ3j are random intercepts that control for unobservable mother–specific char-

acteristics,

ε1ij, ε2ij, ε3ij are infant–specific stochastic error terms.

We assume that ζ1j ∼ N (0, ψ1), ζ2j ∼ N (0, ψ2), and ζ3j ∼ N (0, ψ3). ε1ij, ε2ij, ε3ij are

all assumed to follow the standard normal distribution.

The corresponding multilevel probit models are given by:

Pr (Hij = 1) = Φ (β1 + β2Zij + β3Y + ζ1j) (2.30)

Pr (Zij = 1) = Φ (α1 + α2Y + α3Q+ ζ2j) (2.31)

Pr (Iij = 1) = Φ (γ1 + γ2Y + γ3Q+ ζ3j) (2.32)


To control for potential endogeneity of prenatal care, potential sample selection bias

and potential unobserved heterogeneity, we extend Equation (2.27) as follows:

H∗ij = β1 + β2Zij + β3Y + β4Iij + β5ε2ij + β6Zij ε2ij + β7ε3ij + ζ1j + ε1ij (2.33)

where ε2ij are generalized residuals from the multilevel prenatal care model, and ε3ij

are the generalized residuals from the multilevel sample selection model.

The resulting multilevel probit model is given by:

Pr (Hij = 1) = Φ (β1 + β2Zij + β3Y + β4Iij + β5ε2ij + β6Zij ε2ij + β7ε3ij + ζ1j) . (2.34)

We can quantify the dependence among the various dichotomous responses for the

same mother by using the conditional intraclass correlation, ρ, of the latent responses

H∗ij (Rabe–Hesketh and Skrondal, 2008). For our models, this is given by:

ρ =ψ

ψ + 1. (2.35)

To check whether multilevel modelling is appropriate for our study, we perform a

Likelihood Ratio test for ρ = 0, which is equivalent to testing whether ψ = 0 (Rabe–

Hesketh and Skrondal, 2008). If we reject this hypothesis then the implication is that a

multilevel model is appropriate; a failure to reject the hypothesis would indicate that the

multilevel model is not appropriate (Rabe–Hesketh and Skrondal, 2008).

We estimate our models using Stata 11 software (StataCorp, 2009). The multilevel

models are estimated using the gllamm command (Rabe–Hesketh, Skrondal and Pickles,

2002; Rabe–Hesketh, Skrondal and Pickes, 2004; Rabe–Hesketh, Skrondal and Pickles,

2005; Rabe–Hesketh and Skrondal, 2008) that runs in Stata 11 (StataCorp, 2009).

Data

The main data set we use are the Demographic and Health Survey (DHS) data sets

for Kenya collected in 1993, 1998, 2003, and 20084. A good guide to Demographic and

Health Survey (DHS) data sets can be found in Rutstein and Rojas (2006). Demographic

4More information on Demographic and Health Surveys can be obtained by visiting http://www.

measuredhs.com/What-We-Do/Survey-Types/DHS.cfm

http://www.measuredhs.com/What-We-Do/Survey-Types/DHS.cfm



and Health Surveys are nationally representative household surveys that provide a wide

range of household level data on child and maternal health.

Data on distances to the nearest health facilities comes from the 2005 Kenya Integrated

Household and Budget Survey (KIHBS), conducted by the Kenya National Bureau of

Statistics. Data on health facilities per 100,000 of population was obtained from the

Ministry of Health.

Table 2.7 on page 47 shows the variable definitions for the various variables found in

our models.

2.5 Descriptive Statistics

The overall sample pools together observations for 1993, 1998, 2003, and 2008. A key

observation of our dataset is that some children in the dataset do not have their birth

weights reported. Table 2.8 on page 48 summarizes, for our pooled sample, information

about reporting of birth weight by year of survey.

From the table, we can observe that about 55% of the children in the pooled sample

do not have information on birth weight. As explained in Section 2.4 above, this is likely

to lead to sample selection bias and is dealt with using the approach suggested by Vella

(1992).

Our analytic samples differ depending on the model being estimated due to missing

values on some of the other variables. For instance, our sample selection model uses

18,974 observations, our adequacy of prenatal care use model (the demand model for

adequate prenatal care) uses 15,713 observations while our birth weight model uses 7,331

observations.

Table 2.9 on page 49 presents the descriptive statistics for some of the variables in

our models, based on the pooled sample.

Table 2.10 on page 50 shows that only 533 (or 7.3%) of the children in the analytic

sample are low birth weight children. We can note from the table, however, that the

proportion of children who are low birth weight declined steadily from about 8.5% in

1993 to about 5.4% in 2008.


Table 2.7: Variable Definitions for Prenatal Care and Infant Health Models

Variable Definition

Low birth weight 1 if child’s weight at birth is less

than 2,500 grams, 0 otherwise.

Birth weight reported 1 if child’s birth weight is reported,

0 otherwise.

Adequate prenatal care 1 if prenatal care use is sought from a

skilled provider (doctor or nurse), the total number of visits is

at least four, and the first prenatal care

visit occurs within four

months of pregnancy; 0 otherwise.

Mother’s age at birth of child mother’s age at time of birth of child in years.

Mother’s education Mother’s years of education.

Parity Number of previous births to the mother.

Number of living children Number of living children born to mother .

Mother wanted pregnancy 1 if Yes, 0 otherwise.

Urban residence 1 if area of residence is urban, 0 otherwise.

Male child 1 if sex of child is male, 0 otherwise.

Twin or multiple birth child 1 if child is twin or from a multiple birth, 0 otherwise.

First born child 1 if child is first born, 0 otherwise.

Asset index Household’s asset index, ranges from 0 to 10.

1993 1 if survey year is 1993.




Average distance to nearest health facility Provincial level average distance to nearest

health facility in kilometres.

Health facilities per 100,000 Number of health facilities

per 100,000 of population, measured at provincial level.

Health facilities per 100,000 squared Square of number of health facilities

per 100,000 of population.

Selection residual Generalized residuals from the selection model

Prenatal care residual Generalized residuals from

the prenatal care model.

Interaction of prenatal care with residual Interaction of adequacy of

prenatal care with its residual.

Table 2.11 on page 50 shows prenatal care use by skill of provider for the analytic

sample for the adequacy of prenatal care model. According to the table, in about 92% of

the cases, prenatal care was sought from a skilled provider (that is, either a doctor or a


Table 2.8: Reporting of Birth Weight by Year of Survey

Year of Survey Reported Birth Weight Total

No Yes

1993 3,450 2,665 6,115

(56.4%) (43.6%) (100%)

1998 2,008 1,523 3,531

(56.9%) (43.1%) (100%)

2003 3,271 2,678 5,949

(55%) (45%) (100%)

2008 3,199 2,880 6,079

(52.6%) (47.4%) (100%)

Total 11,928 9,746 21,674

(55%) (45%) (100%)

Note: Percentages are shown in parentheses.

nurse). Table 2.12 on page 51 shows the adequacy of prenatal care use by year of survey.

According to the table, about 74% of the observations are associated with inadequate

use of prenatal care. Table 2.13 on page 51 shows the distribution of low–birth weight

status by adequacy of prenatal care use for our analytic sample for the birth weight

model. From the table, about 68% of low birth weight observations also had inadequate

use of prenatal care.

2.6 Results

First–Stage Models

We estimate our models in two stages. In the first stage, we estimate sample selection

models and prenatal care models. In the second stage, we estimate the low birth weight

models.

One important question we may want to answer after the estimation of our models is

how changes in the explanatory variables affect the probabilities of a positive outcome.


Table 2.9: Descriptive Statistics for the Variables in Prenatal Care and Infant Health Models

Variable Number of Mean Standard Minimum Maximum

Observations Deviation

Low birth weight 9,746 0.076 0.265 0 1

Birth weight reported 21,674 0.450 0.497 0 1

Adequate Prenatal Care 17,617 0.262 0.440 0 1

Mother’s age at birth of child 20,070 26.305 6.50 12 49

Mother’s education 21,672 6.212 3.99 0 23

Parity 21,674 0.355 0.577 0 5

Number of living children 21,674 3.563 2.266 0 17

Mother wanted pregnancy 21,674 0.555 0.497 0 1

Urban residence 21,674 0.194 0.395 0 1

Male child 21,674 0.507 0.50 0 1

Twin or multiple birth child 21,674 0.031 0.173 0 1

First born child 21,674 0.230 0.421 0 1

Asset index 20,500 3.476 1.905 0 10

Average distance to nearest health facility 21,674 8.043 4.422 3.11 22.64

Health facilities per 100,000 21,674 17.900 4.736 12 26

Health facilities per 100,000 squared 21,674 342.855 175.354 144 676

This question can be answered by reporting the marginal effects of the respective covariates

(Long, 1997). The marginal effect is computed by taking the partial derivative of the

estimated probability model with respect to the variable of interest (Long, 1997). As

shown by Long (1997, p.72), the resulting partial derivative is a function of all the

variables and the estimated parameters in the model and assumes the same sign as the

estimated coefficient.

This partial derivative can either be evaluated at the means of the various variables,

leading to what is called the marginal effect at the means, or it can be computed for

each observation and then averaged over all observations, leading to what is referred to

as the average marginal effect (Long, 1997; Bartus, 2005; Greene, 2008; Cornelissen and

Sonderhof, 2009). The average marginal effects are preferable to the marginal effects

at means (Bartus, 2005; Greene, 2008). We, therefore, compute and report the average

marginal effects for the variables in our models.

For a dummy explanatory variable, the marginal effect is given by the differences in

the probabilities when the variable assumes the value of 1 and when it assumes the value

of 0 (Long, 1997).


Table 2.10: Low Birth Weight Status by Year of Survey

Year of Survey Child Low–Birth Weight Total

No Yes

1993 2,260 210 2,470

(91.5%) (8.5%) (100%)

1998 1,312 115 1,427

(91.9%) (8.1%) (100%)

2003 1,495 110 1,605

(93.1%) (6.9%) (100%)

2008 1,731 98 1,829

(94.6%) (5.4%) (100%)

Total 6,798 533 7,331

(92.7%) (7.3%) (100%)

Note: Percentages are indicated in parentheses.

Table 2.11: Prenatal Care Use by Skill of Provider

Year of Survey Provider of Prenatal Care Total

Unskilled Skilled

1993 262 5,808 6,070

(4.3%) (95.7%) (100%)

1998 253 3,256 3,509

(7.2%) (92.8%) (100%)

2003 547 3,404 3,951

(13.8%) (86.2%) (100%)

2008 425 3,648 4,073

(10.4%) (89.6%) (100%)

Total 1,487 16,116 17,603

(8.4%) (91.6%) (100%)


Table 2.14 on page 52 shows the results for the sample selection model and the

adequacy of prenatal care model. We show the results for the multilevel model and those

for the single level model, for comparison purposes. The single level model results are


Table 2.12: Adequacy of Prenatal Care Use, Analytic Sample for Prenatal Care Use

Year of Survey Adequacy of Prenatal Care Use Total

Inadequate Adequate

1993 4,272 1,793 6,065

(70.4%) (29.6%) (100%)

1998 2,585 928 3,513

(73.6%) (26.4%) (100%)

2003 3,081 877 3,958

(77.8%) (22.2%) (100%)

2008 3,068 1,013 4,081

(75.2%) (24.8%) (100%)

Total 13,006 4,611 17,617

(73.8%) (26.2%) (100%)


Table 2.13: Low Birth Weight and Adequacy of Prenatal Care Use

Low Birth Weight Adequacy of Prenatal Care Use Total

Inadequate Adequate

No 4,701 2,097 6,798

(69.2%) (30.8%) (100%)

Yes 361 172 533

(67.7%) (32.3%) (100%)

Total 5,062 2,269 7,331

(69%) (31%) (100%)


Source: Author’s Own Computation.

shown in columns (1) and (3) of the table while the multilevel model results are shown

in columns (2) and (4) of the table.

Columns (1) and (2) in the table show the results for the sample selection model

while columns (3) and (4) show the results for the adequacy of prenatal care model. A

careful look at columns (1) and (2) indicates that mothers who are older, more educated,

have fewer living children, wanted their pregnancies, reside in urban areas, have twin


Table 2.14: Average Marginal Effects for Sample Selection and Prenatal Care Probit Models,Robust Z Statistics in Parentheses

Variable Sample Selection Model Prenatal Care Model

(Report birth weight =1) (Adequate prenatal care =1)

(1) (2) (3) (4)

Mother’s age at birth of child 0.011 0.025 0.009 0.012

(2.54) (2.88) (2.09) (2.25)

Square of mother’s age at birth of child -0.000048 -0.00010 -0.000097 -0.00013

(-0.65) (-0.70) (-1.26) (-1.42)

Mother’s education 0.026 0.058 0.005 0.006

(24.41) (18.29) (4.47) (4.74)

Parity -0.013 0.008 0.033 0.038

(-1.97) (0.72) (3.57) (3.94)

Number of living children -0.027 -0.062 -0.014 -0.016

(-9.64) (-9.86) (-4.61) (-4.54)

Mother wanted pregnancy 0.039 0.078 0.056 0.064

(5.84) (6.00) (7.91) (8.05)

Urban residence 0.163 0.347 0.031 0.036

(16.50) (15.88) (2.98) (2.90)

Male child 0.013 0.019 0.005 0.007

(2.06) (1.63) (0.72) (0.98)

Twin or multiple birth child 0.095 0.182 0.037 0.038

(4.56) (4.19) (1.49) (1.16)

First born child 0.097 0.201 0.008 0.008

(9.04) (9.86) (0.69) (0.68)

Asset index 0.050 0.112 0.015 0.016

(23.56) (18.03) (6.43) (6.12)

1998 -0.070 -0.140 -0.045 -0.048

(-7.09) (-7.45) (-4.59) (-4.80)

2003 -0.087 -0.184 -0.106 -0.105

(-9.98) (-10.14) (-10.35) (-12.09)

2008 -0.057 -0.124 -0.080 -0.081

(-6.55) (-6.82) (-7.92) (-8.72)

Average distance to nearest health facility 0.010 0.022 -0.003 -0.004

(9.53) (8.77) (-2.78) (-2.73)

Health facilities per 100,000 -0.057 -0.124 0.032 0.033

(-8.26) (-7.91) (4.23) (3.77)

Health facilities per 100,000 squared 0.002 0.004 -0.001 -0.001

(10.35) (9.69) (-4.92) (-4.45)

ψ 2.324 1.166

ρ 0.70 0.54

Likelihood Ratio Test for ρ = 0

χ21(P − value) 1221.84 (0.00) 296.78 (0.00)

Number of observations 18974 18974 15713 15713


or multiple births, or are members of wealthy households are more likely to report the

infant’s birth weight, holding other factors constant. Further, first born children are

more likely to have their birth weights reported, holding other factors constant.

Columns (3) and (4) present the prenatal care use model. From the two columns, we

can observe that, holding other factors constant, the longer the average distance to the

nearest health facility, the lower the probability of seeking adequate prenatal care. This

is in line with our expectations. As expected, also, more health facilities per 100,000 of

population increase the probability of seeking adequate prenatal care, if other factors are

held constant.

We can also observe from Columns (3) and (4) in the table that the older the mother

at the time of birth of the child, the higher the probability of seeking adequate prenatal

care, holding other factors constant. This result is consistent with the findings from

the literature (see, for example, McDonald and Coburn, 1988; Ribeiro et al., 2009).

Consistent with the literature (see, for example, Raghupathy, 1996; Celik and Hotchkiss,

2000), we find that holding other factors constant, highly educated mothers are more

likely to seek adequate prenatal care.

The results also indicate that, holding other factors constant, the probability of using

adequate prenatal care is higher if the mother wanted the pregnancy as compared to a

case where the pregnancy is unwanted by the mother. This finding is consistent with the

finding in the literature (see, for example, Delgado–Rodriguez, Gomez–Olmedo, Bueno–

Cavanillas and Galvez–Vargas, 1997; Eggleston, 2000; Magadi, Madise and Rodrigues,

2000).

Our results also indicate that urban residence is associated with a higher probability

of seeking adequate prenatal care as compared to rural residence. The results in the

literature are mixed. Some studies (see, for example, Celik and Hotchkiss, 2000) do not

find such a relationship while others (see, for example, Baldwin et al., 2002) support our

finding.

In the table, the likelihood ratio test for ρ = 0 is a test of the null hypothesis that

the variance of the random intercept is zero. From Table 2.14 on page 52, we can see

that this hypothesis is rejected in both the sample selection and prenatal care models.

We, therefore, conclude that the multilevel versions of our models are appropriate.

A closer look at the results shows that, in most cases (but definately in not all cases),

the average marginal effects are higher for the multilevel model than for the single–level


model, in absolute terms. This implies that, in general, the single–level model understates

the effect of the respective covariate on the variable of interest.

Infant Health Models

Table 2.15 on page 56 shows the results for the single–level low birth weight models.

The columns showing the results have been labelled (1), (2), (3) and (4). Column (1) of

Table 2.15 shows the basic model; column (2) shows the model controlling for sample

selection bias; column (3) shows the model controlling for both sample selection bias

and endogeneity of prenatal care use; while column (4) shows the model controlling for

sample selection bias, endogeneity of prenatal care use and unobserved heterogeneity.

Since the selection residual is statistically significant at the 5% level of significance

in column (2), we conclude that the model in column (1) does suffer from selection

bias. Looking at the model in column (3), we can conclude that prenatal care is an

endogenous determinant of infant health since the coefficient of the prenatal care residual

is statistically significant at the 5% level of significance. Notice also that the average

marginal effect of the adequate prenatal care variable is now negative unlike the case

of the models in columns (1) and (2). The model in column (4) shows that there is no

unobserved heterogenity in our model since the coefficient of the interaction of prenatal

care with its residual is not statistically significant. In the single–level case, therefore,

the model in column (3) is the most appropriate. Comparing the model in column (3) to

the basic model in column (1), we note the following observations which point to the

importance of controlling for the endogeneity of prenatal care use in models of infant

health:

i. In column (1) of Table 2.15, the average marginal effect associated with adequate

prenatal care use is positive, implying that adequate use of prenatal care is likely to

worsen infant health. The result in column (3), however, indicates that adequate use

of prenatal care improves infant health. This means that if we had not controlled

for the endogeneity of prenatal care use, we would have concluded that adequate

use of prenatal care worsens infant health. Controlling for endogeneity, therefore,

helps us arrive at the conclusion that adequate use of prenatal care improves infant

health.

ii. In column (1) of Table 2.15, the average marginal effect of adequate use of prenatal

care is statistically insignificant but in column (3) it is statistically significant at


the 5% level of significance. Thus, failure to control for endogeneity of prenatal

care use makes it (prenatal care) appear to have no effect on infant health. Once

we control for this endogeneity, however, we find that adequate use of prenatal care

improves infant health.

Table 2.16 on page 57 shows the results for the multi–level birth weight models. The

column of results are also labelled as (1), (2), (3) and (4). Column (1) of the table shows

the basic model; column (2) shows the model that controls for sample selection bias;

column (3) shows the model that controls for sample selection bias and endogeneity of

prenatal care use; while column (4) shows the model that controls for sample selection

bias, endogeneity of prenatal care use and unobserved heterogeneity. The multi–level

results shown in column (2) of Table 2.16 imply that there is no sample selection bias since

the coefficient of the selection residual is not statistically significant. From column (3) of

Table 2.16, we can conclude that prenatal care use is endogenous since the coefficient

for prenatal care residual is statistically significant. Based on the results in column (4),

we can conclude that there is no unobserved heterogeneity because the coefficient of the

interaction of prenatal care with its residual is not statistically significant. The best

multi–level model, therefore, appears to be the model in column (3) since the model in

column (4) does not improve the results obtained in column (3).

We conclude from column (3) of Table 2.16 that, just as was the case in the single–level

model, prenatal care use is endogenous even in the multilevel model. We can, therefore,

conclude that prenatal care use is an endogenous determinant of infant health measured

by birth weight. The result that prenatal care use is endogenous is consistent with the

findings in the literature (see, for example, Rous, Jewell and Brown, 2004; Conway and

Kutinova, 2006). Based on a Likelihood Ratio test for instrument validity for the model

in column (3) of Table 2.16, not explicitly shown in the table, we conclude that the

instruments are valid (χ21 = 0.07, p− value = 0.798).

We show in Table 2.17 on page 58 the single–level results based on the model in

column (3) of Table 2.15 on page 56 and the multi–level results based on the model in

column (3) of Table 2.16 on page 57, for comparison purposes. We focus our attention

on the results from the multilevel model, since we have already demonstrated that the

multilevel model is an appropriate model for studying the effect of prenatal care on low

birth weight.


Table 2.15: Average Marginal Effects from Single–Level Low Birth Weight Models, Robust ZStatistics in Parentheses

Variable Low Birth Weight = 1

(1) (2) (3) (4)

Adequate prenatal care 0.003 0.004 -0.260 -0.298

(0.44) (0.66) (-2.40) (-2.33)

Mother’s age at birth of child -0.008 -0.008 -0.006 -0.005

(-2.16) (-2.30) (-1.44) (-1.24)

Square of mother’s age at birth of child 0.00016 0.00016 0.00014 0.0001

(2.38) (2.39) (2.05) (1.94)

Mother’s education -0.003 -0.004 -0.006 0.0001

(-2.88) (-3.41) (-0.28) (0.05)

Parity -0.005 -0.004 0.004 0.006

(-0.61) (-0.51) (0.46) (0.60)


(-0.82) (-0.00) (-1.65) (-1.70)

Mother wanted pregnancy -0.003 -0.006 0.013 0.017

(-0.50) (-0.90) (1.29) (1.38)

Urban residence 0.014 0.007 0.031 0.036

(1.80) (0.83) (2.22) (2.15)

Male child -0.012 -0.013 -0.009 -0.009

(-1.98) (-2.11) (-1.58) (-1.48)


(8.08) (7.58) (7.58) (7.17)

First born child 0.035 0.029 0.039 0.041

(3.43) (2.81) (3.34) (3.31)

Asset index -0.003 -0.007 0.002 0.004

(-1.67) (-2.51) (0.45) (0.68)

1998 -0.005 -0.0009 -0.019 -0.023

(-0.65) (-0.11) (-1.68) (-1.73)

2003 -0.017 -0.012 -0.049 -0.057

(-1.99) (-1.34) (-2.79) (-2.55)

2008 -0.031 -0.028 -0.055 -0.061

(-3.60) (-3.11) (-3.68) (-3.37)

Selection residual -0.038 0.013 0.022

(-1.98) (0.45) (0.67)

Prenatal care residual 0.157 0.199

(2.44) (1.99)

Interaction of prenatal care with residual -0.027

(-0.55)

LR Test for Instrument Validity

χ21(P − value) 0.15 (0.696)

Number of observations 7331 7331 7331 7331

Note: An explanatory variable indicating whether or not birth weight was reported was included in

models controlling for selection bias but it was automatically dropped by the software during estimation.


Table 2.16: Average Marginal Effects from Multi–Level Low Birth Weight Models, Robust ZStatistics in Parentheses (Number of Observations = 7331)


(1) (2) (3) (4)

Adequate prenatal care 0.002 0.002 -0.036 -0.035

(0.56) (0.55) (-2.22) (-2.14)

Mother’s age at birth of child -0.005 -0.005 -0.005 -0.005

(-2.14) (-2.12) (-1.97) (-1.96)

Square of mother’s age at birth of child 0.0001 0.0001 0.0001 0.0001

(2.30) (2.30) (2.22) (2.21)

Mother’s education -0.002 -0.002 -0.001 -0.001

(-2.68) (-2.40) (-1.97) (-1.96)

Parity -0.003 -0.003 -0.002 -0.002

(-0.53) (-0.63) (-0.32) (-0.32)


(-0.70) (-0.68) (-1.01) (-1.02)

Mother wanted pregnancy -0.002 -0.002 0.0002 0.0002

(-0.65) (-0.42) (0.05) (0.06)

Urban residence 0.009 0.010 0.012 0.012

(1.92) (2.02) (2.36) (2.36)

Male child -0.007 -0.007 -0.007 -0.007

(-1.87) (-1.86) (-1.78) (-1.78)


(6.51) (6.66) (6.57) (6.58)

First born child 0.019 0.020 0.020 0.020

(3.19) (3.26) (3.29) (3.29)

Asset index -0.002 -0.002 -0.001 -0.001

(-1.52) (-1.21) (-0.72) (-0.71)

1998 -0.003 -0.003 -0.006 -0.006

(-0.58) (-0.65) (-1.04) (-1.05)

2003 -0.010 -0.011 -0.016 -0.016

(-1.86) (-1.92) (-2.58) (-2.58)

2008 -0.019 -0.019 -0.023 -0.023

(-3.18) (-3.23) (-3.63) (-3.62)

Selection residual 0.007 0.004 0.005

(0.74) (0.50) (0.51)


(2.42) (1.78)

Interaction of prenatal care with residual -0.004

(-0.14)

ψ 0.835 0.818 0.832 0.831

ρ 0.46 0.45 0.45 0.45

LR Test for ρ = 0: χ21(P − value) 18.09 (0.00) 17.80 (0.00) 18.01 (0.00) 17.94 (0.00)

Note: An explanatory variable indicating whether or not birth weight was reported was included in

models controlling for selection bias but it was automatically dropped by the software during estimation.


Table 2.17: Average Marginal Effects from Our Chosen Low Birth Weight Models, Robust ZStatistics in Parentheses


Single Level Model Multi–Level Model

Adequate prenatal care -0.260 -0.036

(-2.40) (-2.22)

Mother’s age at birth of child -0.006 -0.005

(-1.44) (-1.97)

Square of mother’s age at birth of child 0.0001 0.0001

(2.05) (2.22)

Mother’s education -0.0006 -0.001

(-0.28) (-1.97)

Parity 0.004 -0.002

(0.46) (-0.32)

Number of living children -0.006 -0.002

(-1.65) (-1.01)

Mother wanted pregnancy 0.013 0.0002

(1.29) (0.05)

Urban residence 0.031 0.011

(2.22) (2.36)

Male child -0.009 -0.007

(-1.58) (-1.78)

Twin or multiple birth child 0.363 0.104

(7.58) (6.57)

First born child 0.039 0.020

(3.34) (3.29)

Asset index 0.002 -0.001

(0.45) (-0.72)

1998 -0.019 -0.006

(-1.68) (-1.04)

2003 -0.049 -0.016

(-2.79) (-2.58)

2008 -0.055 -0.023

(-3.68) (-3.63)

Selection residual 0.013 0.004

(0.45) (0.50)


(2.44) (2.42)

ψ 0.832

ρ 0.45

LR Test for ρ = 0: χ21(P − value) 18.01 (0.00)

LR Test for Instrument Validity: χ21(P − value) 0.15 (0.696) 0.07 (0.798)

Number of observations 7331 7331


The results show that adequate use of prenatal care reduces the probability of having

a low birth weight infant, holding other factors constant. This actually implies that

prenatal care use is effective in preventing low birth weight. This finding is consistent

with the findings from the studies in the literature that find prenatal care use to be an

effective determinant of birth weight (see, for example, Conway and Deb, 2005; Jewell

and Triunfo, 2006).

A comparison of the results from the multi–level model and the single–level model

shows that the single–level model overstates the effect of adequate use of prenatal care

on reducing the probability of low birth weight. In the single–level model, holding other

factors constant, adequate use of prenatal care reduces the probability of delivering a low

birth weight infant by 0.26. In the multilevel model, however, the corresponding reduction

is only 0.036. Consequently, when we do not control for unobserved mother–specific

characteristics, we are likely to end up overstating the effect of adequate prenatal care

use in reducing the probability of giving birth to a low birth weight infant.

The results from the multilevel model also show that older mothers are less likely to

give birth to low birth weight infants, holding other factors constant. This is consistent

with the finding in the literature of a positive association between mother’s age at birth

of the child and the infant’s birth weight (see, for example, Fraser, Brockert and Ward,

1995; Roth, Hendrickson, Schilling and Stowell, 1998; Borja and Adair, 2003; Mwabu,

2009). Consistent with the findings in the literature (see, for example, Reichman and

Pagnini, 1997; Delpisheh, Brabin, Attia and Brabin, 2008; Swamy et al., 2012), there

appears to be a U−shaped relationship between maternal age at birth of the child and

the probability of delivering a low birth weight infant, holding other factors constant.

Our results also indicate that maternal education reduces the probability of low birth

weight, holding other factors constant. This result is consistent with some of the findings

in the literature (see, for example, Chevalier and O’Sullivan, 2007; Siza, 2008; Muula,

Siziya and Rudatsikira, 2011). There are, however, studies in the literature that fail to

find a significant relationship between maternal education and the incidence of low birth

weight (see, for example, Lawoyin and Oyediran, 1992).

The results further indicate that urban residence is associated with an increased

probability of delivering a low birth weight infant, holding other factors constant. This

result is not surprising since there is evidence in the literature that living in urban areas

does not necessarily guarantee better birth and infant health outcomes (see, for example,

Hillemeier, Weisman, Chase and Dyer, 2007; Simonet et al, 2010).


According to the results, male infants have a lower probability of having low birth

weight as compared to their female counterparts, holding other factors constant. This is

consistent with the findings in the literature (see, for example, Mondal, 1998; Mwabu,

2009).

Infants from twin or multiple births have a higher probability of having low birth

weight as compared to singletons, holding other factors constant. This finding is consistent

with the findings in the literature (see, for example, Adamson, 2007).

Consistent with the findings in the literature (see, for example, Magadi, Madise and

Diamond, 2001; Mwabu, 2009), the results also indicate that first born infants have a

higher probability of having low birth weight than non–firstborns.

The results of the Guevara test show that our instruments are valid.

2.7 Summary, Conclusions and Policy Implications

Summary

In this chapter we investigate the effect of prenatal care on infant health in Kenya using

a multilevel modelling framework. Although there are several indicators of infant health,

we use birth weight because it has been demonstrated to be a good summary measure of

infant health at birth (Mwabu, 2009). There is still controversy in the literature regarding

the effectiveness of prenatal care in preventing adverse birth outcomes and there are also

few studies done in Sub–Saharan Africa that investigate the effect of prenatal care on

birth weight. Although there have been previous attempts to study the effectiveness of

particular types of prenatal care on improving birth outcomes in Kenya (see, for example,

Mwabu, 2009), or antenatal attendance in Kenya (see, for example, Magadi, Diamond,

Madise and Smith, 2004; Brown et al., 2008), no systematic attempt has so far been

made to link adequacy of prenatal care use to low birth weight in Kenya. Previous

studies also mainly used the single–level modelling framework. This chapter adds to the

literature by studying the effectiveness of adequacy of prenatal care use in lowering the

probability of giving birth to a low birth weight infant. It also extends the literature by

using a multilevel modelling framework (see, for example, Steenbergen and Jones, 2002).

Based on WHO definition (see, for example, Zegers–Hochschild et al., 2009; WHO,

2011), we classify an infant’s birth weight as either low or not low. Following WHO


recommendations (Berg, 1995), we construct an index for the adequacy of prenatal care

that classifies prenatal care use as either adequate or inadequate. We then estimate a

model of the effect of the adequacy of prenatal care use on the probability of having a

low birth weight infant, controlling for other factors. The estimation strategy adopted

controls for potential sample selection bias by using an approach suggested by Vella (1992),

potential endogeneity of prenatal care by using the Two–Stage–Residual–Inclusion method

(Terza, Basu and Rathouz, 2008), and potential unobserved heterogeneity by using the

control function approach (Florens, Heckman, Meghir and Vytlacil, 2008; Mwabu, 2009;

Wooldridge, 2010). In the estimation of our models, provincial level average distances to

the nearest health facility and health facilities per 100,000 of population in each province

are used as instruments. The main data set used to estimate our models are the Kenya

Demographic and Household Survey (KDHS) data sets collected in 1993, 1998, 2003

and 2008. The average distances to the nearest health facilities are obtained from the

community data set of the 2005 Kenya Integrated Household and Budget Survey. Health

facilities per 100,000 of population are obtained from the Ministry of Health.

Our results indicate that prenatal care is an endogenous determinant of birth outcomes

but sample selection bias is not an issue in our models. When we do not control for

the endogeneity of prenatal care, adequate use of prenatal care appears to be ineffective

in lowering the probability of low birth weight. Our main finding, after controlling

for the endogeneity of prenatal care, is that adequate use of prenatal care reduces the

probability of delivering a low birth weight infant, holding other factors constant. This

result is consistent with the findings of some of the studies in the literature (see, for

example, Conway and Deb, 2005; Jewell and Triunfo, 2006). The results further indicate

that models that do not control for unobserved mother–specific characteristics overstate

the effect of adequate prenatal care use by the mother in reducing the probability of

delivering an infant with low birth weight. Regarding the determinants of adequate use of

prenatal care, we find that it is significantly influenced by accessibility of health facilities,

availability of health facilities, mother’s age at birth of child, mother’s education, parity,

number of living children born to mother, whether or not mother wanted pregnancy, and

whether or not mother resides in urban areas.


Conclusions

Based on the findings from this chapter, we can draw four main conclusions. First,

adequate use of prenatal care improves birth outcomes, in particular birth weight.

Prenatal care is, however, considered to be adequate if it is obtained from a skilled

provider (either a doctor or a nurse), the mother makes at least four visits to the provider,

and the first visit is made within the first 16 weeks of pregnancy (see, Berg, 1995). The

implication here is that before we judge prenatal care to be ineffective in influencing birth

outcomes, we have to confirm whether it was adequate or not. Second, prenatal care is

an endogenous determinant of birth weight. Prenatal care appears to be ineffective in

influencing birth outcomes in models that do not control for this endogeneity. Attempts

to investigate the effectiveness of prenatal care in influencing birth outcomes must, control

for its endogeneity if they are to arrive at credible conclusions. One way of controlling for

this endogeneity is by using the Two–Stage–Residual–Inclusion method (for details about

this method, see Terza, Basu and Rathouz, 2008). Third, the effect of prenatal care on

birth weight is overstated in models that do not control for unobserved mother–specific

effects. To obtain the accurate effect of prenatal care on birth outcomes, therefore, one

has to control for such effects. One way of doing that is by estimating a multilevel rather

than a single–level model (for details on multilevel modelling, see Steenbergen and Jones,

2002). Fourth, adequate use of prenatal care is significantly influenced by community

characteristics (such as accessibility and availability of health facilities), socio–economic

characteristics of the mother (such as her age at birth of the child, her level of education,

her parity, whether or not she resides in urban areas), household characteristics (such as

wealth status), and maternal psychological factors (such as whether the pregnancy was

wanted or not).

Policy Implications

Since our study shows that adequate use of prenatal care improves infant health, the

implication is that policies for promoting adequate use of prenatal care should be pursued.

Some of these policies include:

i. Encouraging expectant mothers to only seek prenatal care from skilled providers

(that is, doctors and nurses).

ii. Encouraging the expectant mothers to make at least four visits to their prenatal

care provider.


iii. Educating the expectant mothers on the importance of making their first prenatal

care visit within the first 16 weeks of pregnancy.

iv. Improving accessibility and availability of prenatal care services by, for example,

ensuring health facilities are not very far from where expectant women live and

there are enough nurses and doctors to provide the care when it is sought.

v. Discouraging early pregnancies among women.

vi. Investing in girl/maternal education.

vii. Discouraging unwanted pregnancies among women by, for instance, encouraging use

of contraceptives.

viii. Encouraging family planning to reduce the number of children that mothers have

to take care of.

REFERENCES 64

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Chapter 3

Preceding Birth Interval Length and

Maternal Health

3.1 Introduction

As explained in Chapter 1, maternal health includes the health of women when they

are pregnant, during delivery and the period immediately following delivery (Lule et

al., 2005). As also pointed out in Chapter 1, among the maternal health indicators are

pregnancy outcomes (see, for example, Kramer, 2003; Villamor and Cnattingius, 2006).

Adverse pregnancy outcomes (such as stillbirths1, miscarriages2, and preterm births3)

may be indicators of underlying maternal health problems (see, for example, Villamor

and Cnattingius, 2006). We can, therefore, investigate maternal health indirectly by

investigating whether or not a woman experiences an adverse pregnancy outcome.

In this chapter, we measure maternal health using information on whether or not

a woman reports to have experienced an adverse pregnancy outcome, in particular a

miscarriage or a stillbirth.4

1A stillbirth, also referred to as a fetal death, is death of a foetus before birth at or after a gestationalage of 20 completed weeks (Zegers–Hochschild et al., 2009).

2A miscarriage, also known as a spontaneous abortion, is the loss of a foetus of less than 400g or theloss of a clinical pregnancy before a gestational age of 20 completed weeks (Zegers–Hochschild et al.,2009).

3A preterm birth refers to a birth after a gestational period of more than 20 but less than 37 completedweeks (Zegers–Hochschild et al., 2009).

4The survey question, however, asked the women whether they had ever had a pregnancy that miscarried,was aborted, or ended in a stillbirth. There could, therefore, be women in our dataset reporting anabortion which, if not done due to maternal health conditions, is strictly speaking not an adversepregnancy outcome. More information on Demographic and Health Survey (DHS) questionnaires canbe found at http://www.measuredhs.com/What-We-Do/Survey-Types/DHS-Questionnaires.cfm

74

http://www.measuredhs.com/What-We-Do/Survey-Types/DHS-Questionnaires.cfm

Preceding Birth Interval Length and Maternal Health 75

Table 3.1: Stillbirth rate for Selected Countriesand WHO Regions, 2009

Country/Region Stillbirth rate (per 1,000 total births)

Select Countries

Kenya 22

Namibia 15

Mauritius 9

United Kingdom 4

United States of America 3

WHO Regions

Africa 104

Americas 34

South – East Asia 78

Europe 27

Eastern Mediterranean 74

Western Pacific 37

Source: WHO (2012).

Table 3.1 shows the stillbirth rate for selected countries and WHO regions as of 2009.

From the table, we can observe that the stillbirth rate for Kenya for 2009 was 22 per

1,000 total births. This is higher than that of the other countries shown in the table.

Kenya’s rate is, however, better than the averages for the various WHO regions.

There are several risk factors for adverse pregnancy outcomes. These include short

interpregnancy intervals (see, for example, Lule et al., 2005; Conde–Agudelo, Rosas–

Bermudez and Kafury–Goeta, 2006), long interpregnancy intervals (see, for example,

Conde–Agudelo, Rosas–Bermudez and Kafury–Goeta, 2006), maternal obesity (see, for

example, Cnattingius, Bergstrom, Lipworth and Kramer, 1998; Rosenberg, Garbers,

Lipkind and Chiasson, 2005), maternal morbidity (see, for example, Scannapieco, Bush

and Paju, 2003; Rosenberg, Garbers, Lipkind and Chiasson, 2005) and smoking (see, for

example, Walsh, 1994). In this chapter, we mainly focus on the interpregnancy interval

or generally on birth intervals.

Although there are several different measures of birth spacing (see, for example,

Davanzo et al., 2004 and WHO, 2006 for a detailed discussion), following DaVanzo et


Table 3.2: Distribution of Preceding Birth Intervals in Kenya

Months Since Preceding Birth % of Mothers

2003 2008–2009

7 – 17 9.4 9.1

18 – 23 13.5 13.5

24 – 35 36.5 34.2

36 – 47 16.9 17.9

48 – 59 9.8

48 and above 23.7

60 and above 15.4

Total 100.0 100.0

Note: The figures may not sum to 100 due to rounding.

Source: Opiyo (2004, Table 4.7); Munguti and Buluma (2010, Table 4.7).

al. (2004) and Rutstein (2005), we use preceding inter–birth interval as a measure of

birth spacing. Birth interval length in our case, therefore, refers to the time, in months,

between the birth of the child in whom we are interested and the immediate previous birth

to the mother (Rutstein, 2005). Rutstein (2005, Table 1) summarizes the distribution of

preceding birth intervals in months for a select group of developing countries and shows

that majority of the birth intervals are of length 24 – 29 months.

Table 3.2 shows the distribution of preceding birth intervals for Kenya based on the

data gathered from the Kenya Demographic and Health Surveys in 2003 and in 2008 –

2009. The table shows that for both periods, the majority of the mothers had a preceding

birth interval length of 24 – 35 months.

The rest of the chapter is organized as follows: Section 3.2 reviews the literature while

Section 3.3 gives the purpose and objectives of the study. The methodology is discussed

in Section 3.4, the descriptive statistics are discussed in Section 3.5 , Section 3.6 presents

the results and Section 3.7 gives the summary, conclusions and policy implications.


Table 3.3: Determinants of Maternal Health

Determinant Example of Studies

Intermediate or Proximate Determinants

Maternal health status prior to and during a pregnancy McCarthy and Maine, 1992.

Maternal reproductive characteristics McCarthy and Maine, 1992;

(such as age, pregnancy order, birth spacing, etc) Gertler, Rahman, Feifer and Ashley, 1993;

Thaddeus and Maine, 1994; Lule et al., 2005.

Access to health services McCarthy and Maine, 1992.

Health care behaviour/use of health services McCarthy and Maine, 1992;

Thaddeus and Maine, 1994.

Unknown or unpredicted factors McCarthy and Maine, 1992.

Distant determinants

Women’s status in family and community McCarthy and Maine, 1992; Raghupathy, 1996;

Mistry, Galal and Lu, 2009.

Family’s status in community McCarthy and Maine, 1992.

Community’s status McCarthy and Maine, 1992.

Source: Author’s Own Construction Based on McCarthy and Maine (1992, Figure 2).


In general, a number of factors influence maternal health. These factors are called

intermediate determinants if their influence is direct and distant determinants if their

influence is indirect (McCarthy and Maine, 1992). Some of these factors are summarized

in Table 3.3.

Birth intervals can influence the health of both the mother and her unborn child (Rosen-

zweig and Schultz, 1987; Miller, 1991; Ikamari, 1998; Whitworth and Stephenson, 2002;

Bhargava, 2003; Norton, 2005; Rutstein, 2005; Razzaque et al., 2005; Conde–Agudelo,

Rosas–Bermudez and Kafury–Goeta, 2006; WHO, 2006). For example, compared to

women with inter–pregnancy intervals of 27–50 months, the risk of suffering from several

different maternal morbidities such as pre–eclampsia and high blood pressure is higher

among women having inter–pregnancy intervals of less than 6 months or 75 or more

months (Razzaque et al., 2005). Further, longer birth intervals are associated with a

lower risk for neonatal, infant and child mortality (Rutstein, 2005).

Demographic and medical literature suggests that the best or optimal, in terms of

not compromising both infant and maternal health, birth interval length is between


36 to 59 months (Conde–Agudelo and Belizan, 2000; Setty–Venugopal and Upadhyay,

2002; Razzaque, et al., 2005; Rutstein, 2005; Conde–Agudelo, Rosas–Bermudez and

Kafury–Goeta, 2007). Interval lengths outside this range are associated with an increased

risk of adverse maternal health outcomes such as increased risk of pre–eclampsia and

eclempsia, increased risk for labour dystocia, increased risks of uterine rupture and

uteroplacental bleeding disorders, increased risk for maternal death, and increased risk

for anaemia (Conde–Agudelo and Belizan, 2000; Conde–Agudelo, Rosas–Bermudez and

Kafury–Goeta, 2007).

A variety of factors influence a mother’s spacing of births such as the health status of

the previous child, maternal demographic and socio–economic characteristics, practices

such as breastfeeding and postpartum abstinence, and cultural norms (Setty–Venugopal

and Upadhyay, 2002). Table 3.4 on page 79 summarizes the risk factors for short birth

intervals. Some studies (see, for example, Rafalimanana and Westoff, 2001), however,

show that in some countries women prefer longer birth intervals than they actually have.


Previous studies concerning birth intervals in Kenya have either examined the link

between birth intervals and child health (see, for example, Ikamari, 1998; Kosimbei, 2005;

Mustafa and Odimegwu, 2008) or the gap between preferred and actual birth intervals

(see, for example, Rafalimanana and Westoff, 2001). To the best of our knowledge, no

study has so far attempted to link preceding birth interval to maternal health in Kenya

or to analyze the determinants of preceding birth interval lengths in Kenya. This study

fills this gap.

The study contributes to the literature by examining the effect of preceding birth

interval length on maternal health in Kenya. Specifically, the objectives of the study

include:

1. Determining the effect of preceding birth interval length on maternal health, where

maternal health is measured by whether or not a woman has ever had a pregnancy

that was terminated, miscarried, or ended in a still birth.

2. Determining the factors that influence the preceding birth interval length chosen by

the mother.


Table 3.4: Risk Factors for Short Birth Intervals

Factor Example of Studies

Infant death Zenger, 1993; Bohler, 1994; Mturi, 1997;

Udjo, 1997;

Grummer–Strawn, Stupp and Mei, 1998;

Gyimah, 2002.

Poor infant health Bereczkei, Hofer and Ivan, 2000;

Setty–Venugopal and Upadhyay, 2002.

Young maternal age Suwal, 2001;

Setty–Venugopal and Upadhyay, 2002;

Rasheed and Al–Dabal, 2007.

No formal education for mother Setty–Venugopal and Upadhyay, 2002.

Having first birth later in life Upadhyay and Hindin, 2005.

Having fewer children Setty–Venugopal and Upadhyay, 2002.

Low maternal socio–economic status Isvan, 1991;


Non–employment of mother Setty–Venugopal and Upadhyay, 2002.

Little maternal decision–making autonomy Upadhyay and Hindin, 2005.

Rural residence Mturi, 1997;


Social pressure for women to prove fertility Setty–Venugopal and Upadhyay, 2002.

Short breastfeeding duration Millman and Cooksey, 1987; Mturi, 1997;

Setty–Venugopal and Upadhyay, 2002;

Hajian–Tilaki, Asnafi and Aliakbarnia–Omrani, 2009.

Not practising postpartum abstinence Setty–Venugopal and Upadhyay, 2002.

Son preference Mace and Sear, 1997;

Graham, Larsen and Xu, 1998;


Short previous birth intervals Trussell et al., 1985.

Non–contraceptive use Yeakey et al., 2009.

3. Drawing appropriate policy implications from the study findings.


Figure 3.1: A Conceptual Framework for Analyzing the Effect of Preceding Birth IntervalLength on Maternal Health

Unobserved Variables

Maternal /Household Demographic , Reproductive and Socio – economic characteristics

Preceding Birth Interval Length

Maternal Health Outcome (Miscarriage /Stillbith/ Abortion)

Characteristics of the Previous Child

Maternal/ household preferences

Community Characteristics /Environmental Factors

Maternal Health Endowment

Observed Variables

Source: Own Construction Based on Figure 1.1. and Schultz (1984).

3.4 Methodology

Conceptual Model

Based on the general framework shown in Figure 1.1 of Chapter 1, we can develop a

conceptual framework for analyzing the effect of birth interval length on maternal health.

Such a conceptual framework is shown in Figure 3.1.

According to the figure, the maternal health outcome measure (in our case, whether

or not a woman has ever experienced a pregnancy termination, miscarriage, or stillbirth),


is influenced by the preceding birth interval length through a maternal health production

function. It is also influenced by maternal health endowment (the component of maternal

health due to either genetic or environmental conditions uninfluenced by maternal

behaviour but partly known to the mother or her family, but not to the researcher).

The preceding birth interval length, in turn, is influenced by unobservable mater-

nal/household preferences, and unobservable maternal health endowment (for example,

unreported pre-existing maternal health conditions). It is further influenced by observ-

able maternal demographic and socio-economic characteristics (such as age, number of

children, education, place of residence, etc), characteristics of the previous child (i.e.,

whether the child is alive or dead, and whether the child is sickly or not sickly), household

characteristics such as household wealth, and community characteristics (such as cultural

norms/practices and health infrastructure).

Theoretical Model

Our theoretical model follows closely that by Rosenzweig and Schultz (1982, 1983) and

Mwabu (2009). We assume that a multiparous mother derives utility, U , from the

consumption of various goods and sevices, X, and from her own health, H. We can

therefore represent the utility function of a typical mother as follows:

U = U (X,H) . (3.1)

The mother’s health, however, is influenced by the preceding interval between one

child and the next, Z, other inputs, Y , and unobservable biological endowments, µ. We

can, therefore, represent the mother’s health production function as follows:

H = H (Y, Z, µ) . (3.2)

The mother faces the following budget constraint:

I = PxX + PyY + PzZ (3.3)

where I is exogenous household income, Px is price per unit of X, Py is price per unit of

Y , and Pz is price per unit of Z.


Even though Z is not directly purchased from the market, it depends on other goods

that are purchased from the market.

The mother’s standard economic problem relates to maximizing her utility function

subject to her health production function and budget constraint (Rosenzweig and Schultz,

1982). The solution to the mother’s utility maximization model leads to the following

reduced input demand equations.

X = X (Px, Py, Pz, I, µ) (3.4)

Y = Y (Px, Py, Pz, I, µ) (3.5)

Z = Z (Px, Py, Pz, I, µ) . (3.6)

Following Mwabu (2009), it can be shown, by the technique of total differentiation,

that price effects on maternal health depend on the effects of changes in prices on the

demand for health production inputs as well as on the marginal products of these inputs in

the production of maternal health. The implication of this is that we must simultaneously

estimate the maternal health production function and input demand parameters, so as

to be able to predict the effect of changes in the prices of the various health inputs on

maternal health (Mwabu, 2009).

Following Rosenzweig and Schultz (1983) and Mwabu (2008), we can formulate a

hybrid maternal health production function that combines equations (3.2) and (3.5) in

an effort to causally link Z to changes in maternal health status. Such a hybrid function

is given by:

H = h (Z, Px, Py, I, µ) . (3.7)

In this equation, Z is potentially endogenous since it may be affected by the initial

health status of the mother (Mwabu, 2008). It can also be shown that the fact that we

cannot observe µ creates complications in interpreting the coefficient of Z as its marginal

product, if not controlled for (see Rosenzweig and Schultz, 1982). This means that the

estimation procedure we adopt for Equation (3.7) should take into account the fact that

Z is potentially endogenous and the fact that µ is unobservable (Mwabu, 2008).


Estimation Issues

As explained in Section 1.6 of Chapter 1, when estimating our models, we need to worry

about potential sample selection bias, potential endogeneity, and potential unobserved

heterogeneity.


Our discussion on sample selection bias follows closely that in Vella (1998). In this

Chapter, we measure the health status of a mother by asking her whether or not she

experienced an adverse pregnancy outcome such as a miscarriage, a stillbirth or an

abortion. We are, therefore, only able to observe the health status of a mother if she

reports it in the dataset. For mothers who do not report their health status, we are

unable to analyze the effect of the preceding birth interval length on their health status.

We are, however, interested in determining the effect of the preceding birth interval

length on maternal health for all mothers, not just those who report their health status.

If the unobservable factors that influence a mother’s reporting of her health status

are correlated with the unobservable factors that influence her reported health status,

then our estimates will be biased if we do not include in our maternal health model

an estimate of these unobservable factors (Vella, 1998). An issue of sample selection

bias will, therefore, arise. We must therefore control for potential sample selection bias

in our estimation by including an estimate of these unobservable factors. We use the

method proposed by Olsen (1980) to control for this selection bias. An advantage of the

Olsen approach compared to the Heckman (1979) approach is that, unlike the Heckman

approach which requires an iterative probit in the first step, the Olsen approach only

requires Ordinary Least Squares (OLS) regression techniques in the first step (Olsen,

1980).

The Olsen approach (Olsen, 1980) is implemented as follows:

i. Estimate a linear probability model of the selection equation. There should, however,

be at least one regressor in the linear probability model that is not in the main

model of interest.

ii. Obtain the predicted probability of selection into the sample. Call this predicted

probability, say, P .


iii. Construct the variable(P − 1

). This variable, referred to as the selection term,

is the additional variable that should be included in the substantive model as a

correction for sample selection bias. If the coefficient of this variable is significantly

different from zero, then we reject the null hypothesis of absence of selection bias.

Endogeneity

In our model, the covariate measuring the preceding birth interval length is potentially

endogenous. To some extent, the preceding birth interval length is a choice variable

(chosen by the mother either singly or jointly with other household members). If

the unobservable factors that influence maternal health status are correlated with the

preceding birth interval length chosen by the mother, then the estimate of preceding

birth interval length in the maternal health production function is inconsistent (Guevara

and Ben–Akiva, 2008) and cannot be given a causal interpretation (Cameron and Trivedi,

2010). We control for this potential endogeneity by using the Two–Stage–Residual–

Inclusion (2SRI) method (Terza, Basu and Rathouz, 2008). This approach involves

including as an additional regressor in our maternal health equation, the generalized

residuals from the preceding birth interval length model (for a discussion on how the

generalized residuals are computed, see Gourieroux, Monfort, Renault and Trognon,

1987). The preceding birth interval length is said to be endogenous if the coefficient of

the preceding birth interval length residuals is statistically significant in the maternal

health status equation (Bollen, Guilkey and Mroz, 1995).


Unobserved heterogeneity will exist in our maternal health status model if there are

some unobservable factors that interact non-linearly with the preceding birth interval

length causing the effect of the preceding birth interval length on maternal health to

differ amongst mothers in the population (Zohoori and Savitz, 1997). To control for

unobserved heterogeneity, we include in our maternal health equation, the interaction

of the preceding birth interval length with its residuals, as an additional regressor (see,

for example, Mwabu, 2009). Unobserved heterogeneity will exist in our model if the

coefficient of this additional regressor is statistically significant.



We follow Mwabu (2009) in our discussion of model identification. For us to properly

interpret the estimated coefficients of our maternal health model, the maternal health

effects of the preceding birth interval length (the potentially endogenous input) and

of the sample selection rule, should be identified (Mwabu, 2009). We assume that the

preceding birth interval length is the only potentially endogenous input. Consequently,

identification requires a minimum of two (not one) exclusion restrictions; one for the

endogenous covariate and another one for determining the selection of mothers into the

estimation sample (Mwabu, 2009).

The chosen instruments should be exogenous (that is, they should be uncorrelated

with the error term in the maternal health equation), relevant (that is, they should

significantly affect the preceding birth interval length), and should be excluded from

the maternal health equation (Murray, 2006; Mwabu, 2009; Brookhart, Rassen and

Schneeweiss, 2010).

Some of the instruments that have been used in the literature include miscarriages (see,

for example, Buckles and Munnich, 2012), muslim religion (see, for example, Makepeace,

2006), duration of breastfeeding (see, for example, Maitra and Pal, 2005), sex composition

of previous children (see, for example, Maitra and Pal, 2008), and contraception use (see,

for example, Maitra and Pal, 2008).

We assume exact identification for our models and use two instruments in our

estimation strategy (Murray, 2006). The instruments we use are the average breast

feeding duration, computed from our dataset at the provincial level; and the proportion

of women in each province reporting to have ever used contraception, referred to here as

contraceptive prevalence, also computed from our dataset at the provincial level.

The average duration of breastfeeding is chosen because of the association between

duration of breastfeeding and birth spacing (see, for example, Mturi, 1997; Hajian–Tilaki,

Asnafi and Aliakbarnia–Omrani, 2009; Singh, Singh and Narendra, 2010). It has also

been demonstrated in the literature (see, for example, Millman and Cooksey, 1987) that

lack of breastfeeding increases the risk of conceiving.

The proportion of women using contraceptives is chosen because there is evidence in

the literature that contraceptive use is associated with longer birth intervals (see, for

example, Rafalimanana and Westoff, 2001; Yeakey et al., 2009). Although contraceptive

use at the level of the individual woman has beneficial effects on maternal health (see,


for example, Ahmed, Qingfend, Liu and Tsui, 2012), there is no reason to expect the

prevalence of contraceptive use in a particular region to have effects on the health of an

individual woman.

We expect both our instruments to be positively related to preceding birth interval

length. We expect contraceptive prevalence to be positively related to preceding birth

interval length because the higher the prevalence of contraceptive use in a particular area,

the more accessible or available contraceptives are in that area and consequently the

higher the chances that any individual woman who wants to use them will be accessible

to them in that area. We expect higher average breastfeeding in an area to impact on

the duration of breastfeeding of an individual woman through peer effects.

We use provincial averages to remove the likelihood of the instruments being choice

variables by the mother. We argue that although an individual woman can choose the

duration to breastfeed, she is unlikely to influence the average breastfeeding duration in

the entire province where she lives. Similarly, although an individual woman can choose

whether or not to use contraceptives, she is also unlikely to influence the proportion of

women in the entire province using any type of contraception at any given point in time.

Empirical Model

We measure maternal health using a binary variable. For a typical mother i, we define

the following health status measure:

Hi =

1 if mother i experienced a miscarriage, stillbirth, or abortion,

0 otherwise.(3.8)

where Hi is the observed health status of mother i.

Being a binary variable, the appropriate model for maternal health status is the

binary regression model (Long, 1997; Long and Freese, 2006).

We assume that underlying this observed maternal health status is a continuous latent

variable, H∗i that is related to the observed health status for mother i via the following


equation:

Hi =

1 if H∗i > 0,

0 otherwise.(3.9)

The latent variable is in turn linked to the covariates via the following equation:

H∗i = β0 + β1Y + β2Zi + ε1i (3.10)

where Y is a vector of controls, Z is preceding birth interval length and ε1 is the stochastic

error term.

When we have values of Y and Z, it can be demonstrated that (see, for example,

Long and Freese, 2006; Cameron and Trivedi, 2010):

Pr (Hi = 1 | Y, Z) = Pr (H∗i > 0 | Y, Z) (3.11)

where Pr stands for “probability”.

Substituting Equation (3.10) into Equation (3.11) and performing further manipula-

tions gives:

Pr (Hi = 1 | Y, Z) = Pr (−ε1i ≤ β0 + β1Y + β2Z | Y, Z) = F (β0 + β1Y + β2Z) (3.12)

where F (·) is the cumulative density function of −ε1i, which is the same as that of ε1i if

ε1i is symmetric.

Assuming a standard normal distribution for ε1i leads to a probit model given by:

Pr (Hi = 1 | Y, Z) = F (β0 + β1Y + β2Z) = Φ (β0 + β1Y + β2Z) (3.13)

where Φ(·) is the standard normal cumulative distribution function.

Since Z is potentially endogenous, the 2SRI method requires that we obtain its

generalized residuals and include them as an additional regressor in our model. We must,

therefore, also formulate a model for Z that can be estimated.

Recall from Section 3.2 that the best birth interval, in terms of enhancing maternal

and infant health, is 36 to 59 months. Based on this observation, we define our birth


interval measure as follows:

Zi =

1 if the preceding birth interval is 36 to 59 months,

0 otherwise.(3.14)

where Zi is the observed preceding birth interval length for reference child i.

We assume that this observed preceding birth interval length is linked to an unob-

servable continuous variable Z∗i via the following equation.

Zi =

1 if Z∗i > 0,

0 otherwise.(3.15)

We can link Z∗i to the various covariates using the equation

Z∗i = α1 + α2Y + α3Q+ ε2i (3.16)

where Y is a vector of controls, Q is a vector of instruments, and ε2 is the stochastic

error term.

Assuming a standard normal distribution for ε2 leads to the following probit model:

Pr (Zi = 1) = Φ (α1 + α2Y + α3Q) . (3.17)

We estimate this model, obtain the generalized residuals for Z, and then include the

generalized residuals as an additional regressor in our structural model of interest to

control for endogeneity. Unobserved heterogeneity is controlled for by including in our

structural model of interest, an interaction between preceding birth interval length and

its generalized residual as an additional regressor.

The variable for inclusion of mothers in the sample is defined as follows:

Ii =

1 if mother i reports her health status,

0 otherwise.(3.18)


Following Olsen (1980), we formulate the following sample selection equation:

Ii = γ1 + γ2Y + γ3Q+ ε3i (3.19)

where Y is a vector of controls, Q is a vector of instruments, and ε3 is the stochastic

error term.

The Olsen approach requires that we estimate this model, obtain the predicted

probabilities for inclusion into the sample, P , construct the selection term, P − 1, and

include this selection term as an additional regressor in our structural equation of interest.

To control for potential endogeneity of birh interval length, potential sample selection

bias, and potential unobserved heteorgeneity, Equation (3.10) is extended as follows:

H∗i = β0 + β1Y + β2Zi + β3ε2i + β4

(Pi − 1

)+ β5Ziε2i + ε1i (3.20)

where Y is a vector of controls, Z is the preceding birth interval length,(P − 1

)is the

sample selection term, ε2i are the generalized residuals from the preceding birth interval

length model, and ε1 is the stochastic error term.

Assuming a standard normal distribution for ε1 leads to the following probit model,

which is our structural equation of interest:

Pr (Hi = 1) = Φ(β0 + β1Y + β2Zi + β3ε2i + β4

(Pi − 1

)+ β5Ziε2i

). (3.21)

Our equations are estimated using Stata 11 software (StataCorp, 2009).

Data

Our main dataset comes from the Kenya Demographic and Health Surveys (KDHS)

conducted in Kenya in 1998, 2003, and 2008.5 These are nationally representative

household surveys that collect a wide range of household level data on child and maternal

health. In Kenya, they are carried out by the Kenya National Bureau of Statistics

(KNBS) in collaboration with various international organizations.

5More information on the Demographic and Health Surveys can be obtained at http://www.

measuredhs.com/What-We-Do/Survey-Types/DHS.cfm. The 1993 dataset for Kenya does not con-tain information on adverse pregnancy outcomes.




The analytic sample consists of women aged between 15 and 49 years who reported

whether or not they had experienced an adverse pregnancy outcome (specifically either a

miscarriage, stillbirth, or an abortion). The unit of observation is a multiparous mother,

aged 15 - 49 years, who reports whether or not she had experienced an adverse pregnancy

outcome.

Table 3.5 on page 91 shows the definition of the variables used in our models.


Descriptive statistics are shown in Table 3.6 on page 92. There are variations in the

number of observations for the various variables because some of the variables are missing

for some of the observations. The statistics show, for instance, that the youngest mother

gave birth at the age of 12 years while the oldest mother gave birth at the age of 48

years. We can also see from the table that contraceptive prevalence in our dataset varies

from 0.22% to 59.07%. Average breastfeeding duration, on the other hand, varies from

about 13 months to about 18 months.

Table 3.7 on page 92 shows the distribution of the maternal health status indicator

by year of survey. According to the table, about 10% of the women in our overall sample

report ever having experienced a miscarriage, a stillbirth or an abortion.

Table 3.8 on page 93 shows the distribution of maternal health status by preceding

birth interval length in our overall sample. The table shows that about 27.3% of the

women who had preceding birth interval lengths of 36–59 months did not experience

adverse pregnancy outcomes. The table also shows that about 71.2% of the women who

experienced adverse pregnancy outcomes had birth interval lengths outside the 36–59

months bracket.

Preceding Birth Interval Length and Maternal Health 91Table

3.5:

Var

iab

leD

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itio

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for

Mat

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alH

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Var

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Defi

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Table 3.6: Descriptive Statistics for Maternal Health Models

Variable Number of Mean Standard Minimum maximum


Maternal health status 15,554 0.103 0.304 0 1

Preceding birth interval length 11,773 0.274 0.446 0 1

Report health status 15,559 0.9997 0.0179 0 1

Primary education 15,554 0.588 0.492 0 1

Secondary education 15,554 0.183 0.387 0 1

Higher education 15,554 0.041 0.197 0 1

Currently married 15,554 0.797 0.403 0 1

Male reference child 15,554 0.509 0.500 0 1

Mother’s age at birth 14,424 26.235 6.466 12 48

Number of living children 15,554 3.406 2.163 0 13

Urban residence 15,554 0.227 0.419 0 1

Asset index 14,472 3.576 1.966 0 10

1998 15,554 0.227 0.419 0 1

2003 15,554 0.382 0.486 0 1

2008 15,554 0.391 0.488 0 1

Contraceptive prevalence 15,554 32.502 13.125 0.22 59.07

Average duration of breastfeeding 15,554 16.169 1.571 13.04 18.58

Square of duration of breastfeeding 15,554 263.897 50.151 170.042 345.216

Table 3.7: Distribution of Maternal Health Status by Survey Year, Percentages in Parentheses

Year of Survey Ever Experienced Miscarriage, Stillbirth or Abortion Total

No Yes

1998 3,249 279 3,528

(92.1%) (7.9%) (100%)

2003 5,287 661 5,948

(88.9%) (11.1%) (100%)

2008 5,416 662 6,078

(89.1%) (10.9%) (100%)

Total 13,952 1,602 15,554

(89.7%) (10.3%) (100%)


Table 3.8: Distribution of Maternal Health Status by Preceding Birth Interval Length, Per-centages in Parentheses

Ever Experienced a Miscarriage, Preceding Birth Interval Length 36 – 59 months Total

Stillbirth or Abortion No Yes

No 7,561 2,833 10,394

(72.7%) (27.3%) (100%)

Yes 982 397 1,379

(71.2%) (28.8%) (100%)

Total 8,543 3,230 11,773

(72.6%) (27.4%) (100%)

3.6 Results


We estimate our models in two stages. In the first stage we estimate the sample selection

equation together with the preceding birth interval equation. In the second stage we

estimate the maternal health production functions.

As indicated in Chapter 2 Section 2.6, we report the average marginal effects for our

models. Table 3.9 on page 94 shows the results of the sample selection model and the

preceding birth interval model. The results from the sample selection model are shown

in the column labelled (1) while those from the preceding birth interval length model are

shown in the column labelled (2).

Focusing on the preceding birth interval model in column (2), we observe that the

coefficients on the instrumental variables have the expected signs. In particular, the

higher the contraceptive prevalence rate in the province in which the mother lives, the

higher the probability of having a preceding birth interval length of 36 to 59 months,

holding other factors constant. Similarly, the higher the average duration of breastfeeding

in the province in which the mother lives, the higher the probability of having a preceding

birth interval of length 36 to 59 months, holding other factors constant.

A careful look at the results for the preceding birth interval model reveals that

significant determinants of the 36 to 59 months preceding birth interval include secondary


Table 3.9: Average Marginal Effects from Sample Selection and Preceding Birth IntervalLength Models, Robust Z Statistics in Parentheses

Variable Sample Selection Model Preceding Birth Interval Model

(Report health status =1) (36 – 59 months =1)

(1) (2)

Primary education 0.00025 -0.012

(0.67) (-1.12)

Secondary education 0.00065 -0.035

(1.87) (-2.61)

Higher education 0.00043 -0.0067

(1.81) (-0.23)

Currently married 0.00018 -0.044

(0.45) (-4.14)

Male reference child -0.00030 -0.014

(-0.99) (-1.97)

Mother’s age at birth 0.000069 0.009

(1.55) (10.80)

Number of living children -0.0002 -0.015

(-0.88) (-5.67)

Urban residence -0.00010 0.028

(-0.14) (2.40)

Asset index -0.00008 -0.003

(-1.16) (-1.25)

2003 0.00089 -0.024

(1.93) (-2.57)

2008 0.0011 -0.0017

(1.86) (-0.18)

Contraceptive prevalence 0.00001 0.0009

(1.43) (2.29)

Duration of breastfeeding 0.0015 0.046

(1.49) (1.71)

Square of duration of breastfeeding -0.00005 -0.0014

(-1.69) (-1.70)

Number of observations 13,417 14,692


education, marital status, sex of the reference child, mother’s age at birth, number of

living children, and type of residence (that is, whether rural or urban).

According to the results, mothers whose highest level of education is secondary have a

lower probability of having preceding birth intervals of 36 – 59 months compared to those

without formal education, holding other factors constant. The results also show that

currently married mothers have a lower probability of having preceding birth intervals of

length 36 – 59 months compared to their not currently married counterparts, holding

other factors constant.

The results further indicate that the probability of the preceding birth interval being

36 – 59 months is lower if the reference child is male as opposed to female, holding other

factors constant. This finding is similar to the finding in the literature (see, for example,

Mace and Sear, 1997). This would tend to suggest that mothers looking out for male

children have shorter preceding birth intervals. There are, however, other studies in the

literature (see, for example, Chakraborty, Sharmin and Islam, 1996) that do not find the

sex of the reference child to be a significant determinant of birth interval length.

Mother’s age at birth is shown to positively affect the probability of having a preceding

birth interval of length 36 – 59 months, holding other factors constant. This result is

consistent with the findings reported in the literature (see, for example, Chakraborty,

Sharmin and Islam, 1996; Hajian–Tilaki, Asnafi and Aliakbarnia–Omrani, 2009). The

results also indicate that the higher the number of living children born to a mother,

the lower the probability of having preceding birth intervals of length 36 – 59 months,

holding other factors constant. Further, we observe from column (2) of the table that

mothers residing in urban areas have a higher probability, as compared to those living in

the rural areas, of having a preceding birth interval of length 36 to 59 months, holding

other factors constant.

Maternal Health Models

The estimated versions of the maternal health production functions are shown in Table

3.10 on page 96. The table shows the estimation results of four maternal health status

models. In column (1) of the table we present the results of the maternal health status

model that does not correct for sample selection bias, endogeneity of preceding birth

interval length or unobserved heterogeneity. The results in column (2) are for a model

that corrects for sample selection bias. In column (3) we correct for both sample selection


Table 3.10: Average Marginal Effects for Maternal Health Status Models, Robust Z Statisticsin Parentheses

Variable Miscarriage, stillbirth, or abortion =1

(1) (2) (3) (4)

Preceding birth interval length 0.008 0.008 -0.325 -0.325

(1.16) (1.15) (-104.38) (-104.44)

Primary education 0.012 0.001 -0.022 -0.031

(1.27) (0.08) (-1.57) (-1.92)

Secondary education -0.008 -0.029 -0.082 -0.10

(-0.08) (-1.63) (-4.55) (-4.56)

Higher education 0.014 -0.002 -0.028 -0.037

(0.60) (-0.08) (-1.30) (-1.78)

Currently married 0.029 0.026 -0.029 -0.057

(3.55) (2.87) (-1.49) (-1.73)

Male reference child 0.0015 0.010 0.011 0.011

(0.24) (1.15) (1.24) (1.28)

Mother’s age at birth 0.006 0.004 0.009 0.012

(7.84) (2.22) (4.14) (3.68)

Number of living children 0.003 0.008 0.002 -0.001

(1.49) (1.98) (0.40) (-0.24)

Urban residence -0.013 -0.011 0.022 0.037

(-1.51) (-1.23) (1.58) (1.88)

Asset index -0.0013 0.002 0.007 0.009

(-0.65) (0.67) (2.03) (2.33)

2003 0.047 0.021 -0.045 -0.071

(5.04) (1.09) (-1.83) (-2.18)

2008 0.045 0.013 -0.040 -0.061

(4.94) (0.54) (-1.54) (-1.95)

Selection term 29.212 82.869 105.216

(1.42) (3.33) (3.36)

Preceding birth interval length residual 0.599 0.911

(3.74) (2.88)

Preceding birth interval interacted with residual -0.142

(-1.13)

Number of observations 10,233 10,233 10,233 10,233

bias and potential endogeneity of preceding birth interval length. Column (4) shows

results where sample selection bias, potential endogeneity of preceding birth interval

length and potential unobserved heterogeneity are controlled for.


In column (2) we notice that the coefficient of the selection term is not statistically

significant. This indicates that there is no sample selection bias in our model. Preceding

birth interval length is indeed endogenous in our model as indicated by the statistical

significance of the coefficient of the preceding birth interval residuals in column (3).

Notice from column (3) that, after controlling for the endogeneity of preceding birth

interval length, we now notice that there is actually sample selection bias in our model

and failure to control for it would bias the results. From the results in column (4), we can

conclude that their is no unobserved heterogeneity in our model since the coefficient of

the interaction between preceding birth interval length and the preceding birth interval

length residuals is not statistically significant. Our preferred model is, therefore, the one

shown in column (3). We, therefore, focus our attention on the results in column (3).

We can observe from column (3) that preceding birth interval length is a significant

determinant of maternal health status. Specifically, the results show that having a

preceding birth interval length of 36 – 59 months reduces the probability of reporting

an adverse pregnancy outcome (a miscarriage, stillbirth, or an abortion) by 0.325. This

indicates that optimal birth spacing is beneficial to maternal health. This finding is

consistent with the findings reported in the literature where birth intervals outside 36

– 59 months are found to be associated with an increased risk of stillbirths (see, for

example, Williams et al., 2008), where short or long birth intervals are found to be

detrimental to maternal health (see, for example, Conde–Agudelo and Belizan, 2000;

Conde–Agudelo, Rosas–Bermudez and Kafury–Goeta, 2007; Stamilio et al., 2007), and

where long interpregnancy intervals are found to be associated with an increased risk of

stillbirths (see, for example, Stephansson, Dickman and Cnattingius, 2003).

Other significant determinants of maternal health status include secondary education,

mother’s age at birth, and household wealth (as measured by the asset index). Mothers

whose highest level of education is secondary have a lower probability of experiencing

a miscarriage, stillbirth, or an abortion, compared to those without formal schooling,

holding other factors constant. This finding is supported by the findings in the literature

where stillbirth rates are found to be higher for mothers with lower levels of education

(see, for example, Luque–Fernadez, Gutierrez–Garitano and Bueno–Cavanillas, 2012;

Auger, Delezire, Harper and Platt, 2012), and where education is shown to have beneficial

effects on maternal health (see, for example, McAlister and Baskett, 2006; Karlsen et al.,

2011).

Although marital status is not statistically significant in our model, the sign of the

coefficient implies that currently married mothers are less likely to experience adverse


pregnancy outcomes compared to those who are not currently married, holding other

factors constant. This suggests that being married has beneficial effects on maternal

health. This finding is consistent with the finding in the literature (see, Gunilla, Haglund

and Rosen, 2000) that lone mothers are at a higher risk of premature death than mothers

with partners. Other studies (see, for example, Osborn, Cattaruzza and Spinelli, 2000)

show that unmarried women have an increased risk for spontaneous abortion.

The older the mother at the time of birth, the higher the probability of experiencing

an adverse pregnancy outcome, holding other factors constant. This result is consistent

with the finding in the literature that women who give birth at an advanced age have a

higher risk of a stillbirth (see, for example, Miller, 2005; Huang et al., 2008; Reddy, Ko

and Willinger, 2006), and the finding that women aged 35 and above are at a higher risk

of experiencing a miscarriage (De la Rochebrochard and Thonneau, 2002).

According to the results, mothers from wealthy households are less likely to experience

adverse pregnancy outcomes, holding other factors constant. This result is consistent

with the finding in the literature that poor women are at a higher risk of having stillbirths

(Ha et al., 2012).


Summary

This chapter examines the effect of preceding birth interval length on maternal health

in Kenya. We measure maternal health using an indicator of whether or not a woman

has ever had a pregnancy that was terminated, miscarried, or ended in a still birth

since adverse pregnancy outcomes may point towards some underlying maternal health

problems (see, for example, Villamor and Cnattingius, 2006).

The literature identifies key risk factors for adverse pregnancy outcomes as short

interpregnancy intervals, long interpregnancy intervals, maternal obesity, maternal mo-

bidity, and smoking (see, for example, Walsh, 1994; Cnattingius, Bergstrom, Lipworth

and Kramer, 1998; Rosenberg, Garbers, Lipkind and Chiasson, 2005; Conde–Agudelo,

Rosas–Bermudez and Kafury–Goeta, 2006).

According to the literature, birth interval lengths of between 36 to 59 months do

not compromise both infant and maternal health (see, for example, Setty–Venugopal


and Upadhyay, 2002; Conde–Agudelo, Rosas–Bermudez and Kafury–Goeta, 2007). Key

factors that influence birth intervals include the health status of the previous child,

mother’s demographic and socioeconomic characteristics, contraceptive use and cul-

tural/traditional practices such as breastfeeding and postpartum abstinence (see, for

example, Setty–Venugopal and Upadhyay, 2002; Yeakey, et al., 2009).

Earlier studies concerning birth intervals in Kenya (see, for example, Ikamari, 1998;

Rafalimanana and Westoff, 2001; Kosimbei, 2005; Mustafa and Odimegwu, 2008) do not

link preceding birth interval to maternal health. Our study fills this gap.

We adopt an estimation strategy that controls for potential endogeneity of preceding

birth interval length, potential sample selection bias and potential unobserved hetero-

geneity. In the estimation, the proportion of women who have ever used any type of

contraceptive in each province and the average duration of breastfeeding in each province,

are used as instruments. We estimate the model using the Demographic and Health

Survey (DHS) datasets for Kenya for 1998, 2003 and 2008.

Our main finding is that preceding birth interval lengths of 36 to 59 months reduce

the probability of a mother experiencing a miscarriage, stillbirth, or an abortion, holding

other factors constant. This result is consistent with the findings in the literature (see,

for example, Williams et al., 2008). We further find that preceding birth interval length

is an endogenous determinant of maternal health. Maternal health is also shown to be

significantly influenced by secondary education, mother’s age at birth, and household

wealth (as measured by the asset index). We also find that preceding birth interval

lengths are influenced by secondary education, sex of reference child, mother’s age at

birth of reference child, number of living children and mother’s region of residence (either

urban or rural).

Conclusions

We can draw a number of conclusions from the findings in this Chapter. First, consistent

with the findings in the literature, preceding birth intervals of length 36 to 59 months

improve maternal health. Second, preceding birth interval is, however, an endogenous

determinant of maternal health. This endogeneity must be controlled for in models that

attempt to link preceding birth interval length to maternal health. Failure to control

for the endogeneity may lead to results showing that preceding birth intervals of length

36 to 59 months worsen maternal health. Finally, preceding birth interval length is


significantly influenced by maternal characteristics (such as her level of education, her

age at birth of the child, her marital status, whether she resides in urban or rural areas,

and number of living children), child characteristics (such as the sex of the child), and

community/cultural characteristics such as contraceptive prevalence and duration of

breastfeeding.

Policy Implications

Our findings imply that policies that encourage mothers to maintain a preceding birth

interval of 36 – 59 months should be pursued. This can be done by, for instance, making

family planning services easily accessible to mothers, and sensitizing mothers on the

health–benefits of adequate spacing of births. Mothers should also be encouraged to

breastfeed their children for longer periods of time. Investment in maternal education is

also a plausible policy in this case.

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[84] Williams, E. K., Hossain, M. B., Sharma, R. K., Kumar, V., Pandey, C. M. andBaqui, A. H., 2008. Birth interval and risk of stillbirth or neonatal death:findings from rural India. Journal of Tropical Pediatrics 54 (5), pp.321–327.

[85] World Health Organization (WHO)., 2006. Report of a WHO technicalconsultation on birth spacing. [pdf] Geneva: World Health Organization.Available at: http://cdrwww.who.int/maternal_child_adolescent/documents/birth_spacing.pdf [Accessed 3 January 2010].

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[89] Zenger, E., 1993. Siblings’ neonatal mortality risks and birth spacing inBangladesh. Demography 30 (3), pp.477–488.




Chapter 4

Smoking and General Health

4.1 Introduction

As discussed in Chapter 1, one of the indicators of general health status at the individual

level is self–reported health status. In studies where the health status of individuals

is measured by their own self–assessments, the individuals are typically asked to rate

their own health, relative to some reference group (e.g., peers), in one of several ordinal

categories (Krause and Jay, 1994).1 Several empirical studies of individual health have

used self-assessed health status as the indicator for health (see for example, Damian,

Ruigomez, Pastor and Martin–Moreno, 1999; Zimmer, Natividad, Lin and Chayovan,

2000; Breidablik, Meland and Lydersen, 2008; Kyobutungi, Ezeh, Zulu and Falkingham,

2009; Yen, Shaw and Yuan, 2010; Kodzi, Gyimah, Emina and Ezeh, 2011; Giron, 2012;

Hanibuchi, Nakaya and Murata, 2012).

The main weakness of self–rated health measures is that even though they may reflect

the individual’s actual health status, they could be dependent on the specific individual

perceptions concerning health (e.g., whether health concerns physical functioning, suf-

fering from particular health problems or practising certain health behaviours) and the

information available to the individual concerning health (Krause and Jay, 1994; Strauss

and Thomas 1998, 2008; Bailis, Segall and Chipperfield, 2003).

Despite this weakness, self-rated health status has been shown to be a predictor

of mortality, morbidity, and disability (see, for example, Idler and Benyamini, 1997;

Burstrom and Fredlund, 2001; Mansson and Rastam, 2001; Osler et al., 2001; Benyamini,

Blumstein, Lusky and Modan, 2003; Ahmad, Jafa and Chaturvedi, 2005; Ford, Spallek

1The frequently used categories are: poor, fair, good, and excellent (Krause and Jay, 1994).

109

Smoking and General Health 110

and Dobson, 2008; Jylha, 2009; McFadden et al., 2009). Other studies (see, for example,

Delpierre et al., 2009) show that the self–reported health status is closer to the true

health status amongst individuals who are highly educated. In this chapter, we use

self–rated health status as an indicator of individual general health.

Active smoking is harmful to health because it is associated with several diseases such

as cancers, cardiovascular diseases, and respiratory diseases (USDHHS, 2004; USDHHS,

2010). Research (see, for example, Strachan and Cook, 1997; Chen, Dales, Krewski and

Breithaupt, 1999; Adab, McGhee, Hedley and Lam, 2005) shows that active smoking is

generally associated with poor health status.

Involuntary or passive smoking2 also has several health implications such as its

association with premature deaths and disease among children and non–smoking adults

(USDHHS, 2006). Other effects of passive smoking include reduced health–related quality

of life (see, for example, Bridevaux et al., 2007) and increased risk of cognitive impairment

in non–smoking women (see, for example, Chen et al., 2012).

Table 4.1 on page 111 summarizes some key facts about tobacco use and its conse-

quences in Kenya. The table shows that about 3% of the male deaths in 2004 can be

attributed to tobacco. The table further shows that about 23% of adult males smoke

while under 1% of adult females smoke. Smoking in Kenya is, therefore, mainly a male

phenomenon. The table further shows that about 11% of the boys aged between 13 and

15 years smoke and about 5% of the girls aged between 13 and 15 years smoke.

The rest of the chapter is organized as follows: Section 4.2 presents a review of

the literature while Section 4.3 discusses the purpose and objectives of the study. The

methodology is discussed in Section 4.4 while Section 4.5 presents some descriptive

statistics. The results are presented and discussed in Section 4.6 while Section 4.7 gives

the summary, conclusions and policy implications.


Factors influencing an individual’s rating of his or her own health include socio–demographic

factors (such as age, gender, and marital status), socio–economic factors (such as educa-

tion and income), psychosocial factors, social capital (measured by interpersonal trust

2Involuntary or passive smoking refers to the exposure of non–smokers to tobacco combustion productsin the indoor environment (USDHHS, 2006).


Table 4.1: Some Facts about Tobacco Use and its Consequences in Kenya

Indicator Value

Tobacco use consequences

Estimated male deaths due to tobacco (2004) 2.9%

Tobacco use

Male cigarette use (age 18 years and above, 2009) 22.5%

Female cigarette use (age 18 years and above, 2009) < 1%

Male smoking any tobacco product (age 18 years and above, 2009) 25.5%

Female smoking any tobacco product (age 18 years and above, 2009) 1.5%

Boys’ current cigarette use (ages 13 – 15) 11.2%

Girls’ current cigarette use (ages 13 – 15) 5.2%

Cigarettes per person 144

Youth exposed to secondhand smoke in homes (ages 13 – 15) 24.7%

Adult smokeless tobacco use 1.7%

Smoking prevalence of health professional students 9.8%

Source: Eriksen, Mackay and Ross (2012).

and social participation), health care access, and lifestyle factors (such as, smoking) (Cott,

Gignac and Bradley, 1999; Kawachi, Kennedy and Glass, 1999; Lima–Costa, Barreto,

Firmo and Uchoa, 2003; Abu-Omar, Rutten and Robine, 2004; Subramanian, Kim and

Kawachi, 2005; Murata et al., 2006; Molarius et al., 2006; Poortinga, 2006; Giordano and

Lindstrom, 2010; Omariba, 2010; Kodzi, Gyimah, Emina and Ezeh, 2011; Prus, 2011;

Delpierre et al., 2012; Giron, 2012; Wang et al., 2012).

Individuals typically choose to actively smoke (Audrian-McGovern et al., 2004; Heikki-

nen, Patja and Jallinoja, 2010). Table 4.2 on page 112 summarizes some of the deter-

minants of smoking. According to the table, smoking is mainly influenced by cigarette

prices, demographic characteristics of the individual, socio–economic characteristics,

psychological factors, addiction, anti–smoking sentiments, and anti–smoking restrictions.

Research shows that those individuals who actively smoke are more likley to rate

their own health as poor compared to non–smokers (see, for example, Cott, Gignac

and Bradley, 1999; Kawachi, Kennedy and Glass, 1999; Birch, Jerret and Eyles, 2000;

Johnson and Richter, 2002; Ho, Lam, Fielding and Janus, 2003; Langhammer et al.,

2003; Kirkland, Greaves and Devichand, 2004; Birch et al., 2005; Nakata et al., 2009;


Table

4.2:

Det

erm

inan

tsof

Sm

okin

g

Det

erm

inan

tE

xam

ple

sL

iter

atu

reS

ou

rce

Cig

aret

tep

rice

sC

halo

up

ka,

1999;

Ch

alo

up

kaan

dW

arn

er,

2000;

Ross

an

dC

halo

up

ka,

2003;

Caw

ley,

Mark

owit

zan

dT

au

ras,

2004;

Ch

enan

dX

ing,

2011;

Non

nem

ake

rand

Farr

elly

,2011.

Dem

ogra

ph

icch

arac

teri

stic

sag

e,ge

nd

er,

mar

ital

stat

us,

and

are

aof

resi

den

ceR

ap

tou

,M

att

as

an

dK

atr

akil

idis

,2009.

Fato

ye,

2003.

Soci

oec

onom

icch

arac

teri

stic

socc

up

atio

n,

edu

cati

on,

reli

gion

,and

inco

me

Jon

esan

dK

irig

ia,

1999;

Cow

ell,

2006;

Vir

tan

enet

al,

2007;

Ken

kel,

Lil

lard

an

dL

iu,

2009;

Rap

tou

,M

att

as

an

dK

atr

akil

idis

,2009;

Fra

nce

scon

i,Jen

kin

san

dS

iedle

r,2010;

Kje

llss

on

,G

erd

tham

an

dL

ytt

ken

s,2011.

Psy

chos

oci

alfa

ctor

sfa

mil

ym

emb

ers

wh

osm

oke,

Pow

ell,

Tau

ras

an

dR

oss

,2005;

Van

Loon

,T

ijhu

is,

Su

rtee

san

dO

rmel

,2005.

frie

nd

sor

pee

rsw

ho

smok

e,fe

elin

gst

ress

ed,

Kem

pp

ain

enet

al.

,2006;

ciga

rett

ead

ver

tisi

ng,

infl

uen

ceby

cigare

tte

off

erin

gs,

Kro

snic

ket

al.

,2006;

Naka

jim

a,

2007;

wea

kfa

mil

yb

ond

s,H

arr

isan

dLop

ez-V

alcarc

el,

2008;

bel

iefs

abou

tth

eh

ealt

hri

sks

of

smokin

gR

ap

tou

,M

att

as

an

dK

atr

akil

idis

,2009;

Goh

lman

n,

Sch

mid

tan

dT

au

chm

an

n,

2010;

Lou

reir

o,

San

z–d

e–G

ald

ean

o,

2010;

Fle

tch

er,

2010.

Ad

dic

tion

Yen

an

dJon

es,

1996;

US

DH

HS

,2010.

Anti

–sm

okin

gse

nti

men

tsD

eCic

caet

al.

,2008;

Rap

tou

,M

att

as

an

dK

atr

akil

idis

,2009.

Sm

okin

gre

stri

ctio

ns

DeC

icca

etal.

,2008;

Rap

tou

,M

att

as

an

dK

atr

akil

idis

,2009.


Darviri, Artemiadis, Tigani and Alexopoulos, 2011; Giron, 2012; Wang et al., 2012).

Other studies, however, find that heavy smokers are more likely to rate their own health

as excellent (see for example, Yen, Shaw and Yuan, 2010). Further, individuals exposed

to second–hand smoke have poorer self–rated health status compared to those individuals

not exposed to second–hand smoke (see, for example, Nakata et al., 2009).


Previous studies on self–rated health in Kenya (see, for example, Kyobutungi, Ezeh,

Zulu and Falkingham, 2009; Kodzi, Gyimah, Emina and Ezeh, 2011) have not explicitly

examined the effect of smoking on health. The studies mainly examine the relationship

between HIV/AIDS and self–rated health (Kyobutungi, Ezeh, Zulu and Falkingham,

2009), or the relationship between religious involvement and social engagement and self–

rated health (Kodzi, Gyimah, Emina and Ezeh, 2011). Our study, therefore, contributes

to the literature by documenting the effect of smoking on self–rated health in Kenya.

Specifically, the objectives of the study include:

1. Determining the effect of smoking on self-rated health status in Kenya.

2. Determining the factors that influence an individual’s decision on whether or not to

smoke.

3. Based on the study findings, drawing some policy implications.

4.4 Methodology

Conceptual Model

Figure 4.1 on page 114 shows a conceptual framework for analyzing the effect of smoking

on general health. The framework is based on the general framework presented in Figure

1.1, that is in turn based on Schultz (1984).

According to the framework, the general health of an individual (as measured by the

individual’s self–rated health status) is influenced by whether the individual smokes or

not and unobservable biological endowments of the individual. However, an individual’s

smoking status is, in turn, influenced by unobservable biological endowments of the


Figure 4.1: A Conceptual Framework for Analyzing the Effect of Smoking on General Health

Unobserved Variables

Individual /Household Demographic, Psychological and Socio – economic characteristics

Smoking

Health Outcome

(Self – Rated Health Status)

Individual/ household preferences

Community Characteristics /Environmental Factors

Individual Health Endowment

Observed Variables

Source: Author’s Own Construction Based on Figure 1.1 and Schultz (1984).

individual; individual’s demographic, socio–economic, and psychosocial characteristics;

and the characteristics of the community or the environment in which the individual

lives.

Theoretical Framework

Following the general modelling framework presented in Chapter 1 that was based on

Rosenzweig and Schultz (1982, 1983) and Mwabu (2009), we assume that a typical

individual derives utility from the consumption of various goods and services that have

no direct effect on his/her health, Y , his/her smoking status (which also affects health

directly), S, and his/her health status, H. The individual’s utility function can, therefore,


be written as follows:

U = U (Y, S,H) . (4.1)

The health status of the individual is, in turn, influenced by the individual’s smoking

status, S, other inputs, X, and unobservable biological endowments, µ. The individual’s

health production function can, therefore, be expressed as follows:

H = H (S,X, µ) . (4.2)

The individual faces a budget constraint given by:

I = PyY + PsS + PxX (4.3)

where I is exogenous household income, Py is unit price of Y , Ps is unit price of S, and

Px is unit price of X.

The individual maximizes his or her utility function subject to the health production

function and budget constraint. As shown by Mwabu (2009), this process leads to the

following reduced–form input demand equations.

X = X (Px, Py, Ps, I, µ) (4.4)

Y = Y (Px, Py, Ps, I, µ) (4.5)

S = S (Px, Py, Ps, I, µ) . (4.6)

Following Rosenzweig and Schultz (1983) and Mwabu (2008), we can formulate a

hybrid general health production function by combining Equations (4.2) and (4.4). The

resulting hybrid health production function is given by:

H = H (S, Px, Py, I, µ) . (4.7)

In this equation, S is potentially endogeneous and the inability to observe µ creates

further estimation problems (Rosenzweig and Schultz, 1982; Mwabu, 2008).


Estimation Issues

As discussed in Section 1.6 of Chapter 1, we need to worry about potential sample selection

bias, potential endogeneity, and potential unobserved heterogeneity when estimating our

models.


We are only able to obtain information on self–rated health status of a particular

individual if the individual reports it. However, information on self–rated health status

is missing for some observations in our dataset. If the unobservable factors that influence

the reporting of self–rated health status are correlated with the unobservable factors that

influence the reported self–rated health status, then a selection issue arises and failure

to control for it may bias our results (Vella, 1998). We use the Heckman procedure

(Heckman, 1979) to control for the sample selection bias.

The Heckman procedure typically involves the estimation of two equations: an

equation of primary interest, and the selection equation. An example of an equation of

primary interest is the self–rated health status equation in this chapter. Mwabu (2009)

summarizes the steps involved in the Heckman procedure. We extend this summary and

provide the following steps for implementing the Heckman procedure.

i. Estimate the selection equation using the probit formulation.

ii. Obtain the predicted probit estimates from the estimated equation.

iii. Construct a probability density function for each observation.

iv. Construct a cumulative density function for each observation.

v. Construct the inverse Mill’s ratio by taking the ratio of the probability density

function to the cumulative density function, for each observation.

vi. Include the inverse Mill’s ratio as an additional regressor in the equation of primary

interest.


Endogeneity

As observed in Section 4.2 above, people typically choose to start smoking (see, for

example, Audrian-McGovern et al., 2004; Heikkinen, Patja and Jallinoja, 2010). We

therefore expect the variable measuring smoking in our model to be potentially endogenous.

We use the Two–Stage Residual Inclusion (2SRI) method (Terza, Basu and Rathouz,

2008) to control for this potential endogeneity. In line with this approach, we include in

our self–rated health status model the generalized residuals3 from the smoking status

model, as an additional regressor. If the coefficient of these generalized residuals is

statistically significant, then smoking would indeed be endogenous in our model (Bollen,

Guilkey and Mroz, 1995).


Unobserved heterogeneity will exist in our self–rated health status model if some un-

observable factors interact non–linearly with the smoking status variable causing the

effect of smoking on self–rated health to differ amongst the various individuals in the

population (Mullahy, 1997). We use the control function approach, as applied by Mwabu

(2009), to control for unobserved heterogeneity in our model.


Since we have one endogenous variable in our model and a sample selection rule, we

need two instrumental variables4 for estimating our first–stage models (Mwabu, 2009).

Common variables used as instruments for smoking in the literature include an encour-

agement to stop smoking versus no encouragement (see, for example, Permutt and Hebel,

1989), observed cigarette price per region (see, for example, Leigh and Schembri, 2004),

random assignment to the intervention or control group (see, for example, Eisenberg and

Quinn, 2006), and per–pack excise tax on cigarettes (see, for example, Mullahy, 1997).

We do not have information on the kinds of instruments that have been used in the

literature in our dataset. This calls for innovation on our part for the type of variables

to serve as instruments. We, therefore, use two instruments. The first is the smoking

3Gourieroux, Monfort, Renault and Trognon (1987) provide a good discussion on how to computegeneralized residuals for various non–linear models.

4More information on the application of instrumental variables in health research can be found inMartens et al. (2006).


prevalence in each district. Using the information on whether an individual in our data

set smokes or not, we construct a smoking prevalence indicator for each district, which

is simply the proportion of individuals in our dataset from the district who reported

that they smoke. We do not expect any single individual in our dataset to influence the

smoking prevalence of the district in which he or she lives. We expect smoking prevalence

in the district where the individual lives to be correlated with the smoking behaviour of

the individual, but not directly related to the self–rated health status of the individual

(since the individual may not be smoking after all or may not be exposed to second–hand

smoke). We further expect that the higher the smoking prevalence in the district where

the individual lives, the higher the probability that the individual smokes, holding other

factors constant. This is because, a high smoking prevalence rate signals the ease with

which individuals can access cigarettes in the particular district.

Another instrument that we use is the average number of cigarettes smoked in the

province where the individual lives. Using information from the male dataset of the

Kenya Demographic and Health Survey of 2003, we are able to construct a variable of the

average number of cigarettes smoked by individuals in the last 24 hours in each province.

We do not expect any particular individual to influence the average number of cigarettes

smoked in the province in 24 hours. We expect the average number of cigarettes smoked

to be correlated with the individual’s decision to smoke but we do not expect this average

number of cigarettes smoked in the province to influence the individual’s rating of his

or her own health. Consequently, we expect that, holding other variables constant, the

higher the average number of cigarettes smoked in the province where the individual

lives, the higher the probability of the individual smoking. A higher number of cigarettes

smoked in any province also indicates the affordability and accessibility of the cigarettes

in the province.

Empirical Model

We measure general health status using the individual’s self–rated health status. In our

dataset, individuals were asked to rate their own health as compared to age–mates in

one of four categories: poor, satisfactory, good, and very good. Using these ratings, we


construct the following observed health status indicator variable:

Hi =

1 if inividual i rates own health as “Poor”,

2 if inividual i rates own health as “Satisfactory”,

3 if inividual i rates own health as “‘Good”,

4 if inividual i rates own health as “Very Good”.

(4.8)

Since this variable is ordinal, the appropriate model for it is the ordered choice

regression model (Long, 1997; Long and Freese, 2006).

Following Long (1997) and Long and Freese (2006), we assume that there is a

continuous latent variable, H∗i , that is related to the observed health status indicator via

the following equation:

Hi =

1 if τ0 = −∞ ≤ H∗i < τ1,

2 if τ1 ≤ H∗i < τ2,

3 if τ2 ≤ H∗i < τ3,

4 if τ3 ≤ H∗i < τ4 =∞,

(4.9)

where τ1, τ2, and τ3 are cutpoints (or thresholds) to be estimated.

The latent variable is in turn related to the various covariates via the equation:

H∗i = β1 + β2S + β3Q+ ε1i (4.10)

where S is the smoking status of individual i, Q is a vector of controls, and ε1 is a

stochastic disturbance term.

The probability of observing a given outcome for given values of the independent

variables is given by:

Pr (Hi = m | S,Q) = Pr (τm−1 ≤ H∗i < τm) (4.11)

where m = 1, 2, 3, 4.


Substituting Equation (4.10) into Equation (4.11) and simplifying leads to the following

formula for predicted probabilities of the observed outcomes:

Pr (Hi = m | S,Q) = F (τm − β1 − β2S − β3Q)− F (τm−1 − β1 − β2S − β3Q) , (4.12)

where m = 1, 2, 3, 4, and F is the cumulative distribution function for ε1. If we assume

a standard normal distribution for ε1 we obtain the ordered probit model, which we

estimate in our case.

Since the smoking status of the individual, S, is potentially endogenous, we need to

obtain its generalized residuals to include them as an additional regressor in our health

status model so as to correct for this potential endogeneity. We must, therefore, estimate

a smoking status model. In our dataset, individuals indicate whether or not they smoke

cigarettes or pipe. The observed value of S for a typical individual i is, therefore, given

by:

Si =

1 if individual i smokes,

0 otherwise.(4.13)

We formulate the smoking status model as a binary regression model because smoking

status is a binary variable (Long, 1997; Long and Freese, 2006).

We assume that the observed smoking status indicator for individual i, Si, is linked

to a latent continuous variable, S∗i , via the equation

Si =

1 if S∗i > 0,

0 otherwise.(4.14)

S∗i is, in turn, linked to the covariates by the following equation:

S∗i = α1 + α2Q+ α3R + ε2i (4.15)

where Q is a vector of controls, R a vector of instruments, and ε2 is a stochastic disturbance

term. We assume a standard normal distribution for ε2 and consequently estimate the

probit model. The probit model, in this case, is given by:

Pr (Si = 1) = Φ (α1 + α2Q+ α3R) . (4.16)


The generalized residuals from our smoking status model are included as an additional

regressor in our structural model of interest to control for the potential endogeneity of

smoking status. We control for potential unobserved heterogeneity by including the

interaction of smoking participation with its generalized residuals, as an additional

regressor in our model.

Following Heckman (1979), the selection equation for individuals in our sample is

given by:

Ii =

1 if individual i reports self–rated health status,

0 otherwise.(4.17)

Based on this equation we use the procedure described in the subsection on Model

Identification above to construct the inverse Mill’s ratio (IMR) that is then included in

our model of interest as an additional regressor.

To control for potential endogeneity of smoking status, potential unobserved hetero-

geneity, and potential sample selection bias, Equation (4.10) is therefore extended as

follows:

H∗i = β1 + β2Si + β3Q+ β3ε2i + β4IMR + β5Siε2i + ε1i (4.18)

where Si is the smoking status of individual i, Q is a vector of controls, ε2 are generalized

residuals from the smoking status model, IMR is the inverse Mill’s ratio, and ε1 is a

stochastic disturbance term.

Substituting Equation (4.18) in Equation (4.11) and assuming a standard normal

distribution for ε1, we obtain the model that we estimate.5

We estimate all our equations using Stata 11 software (StataCorp, 2009).

Data

The dataset we use comes from a nationally representative household Health Expenditure

and Utilization survey carried out by the then Ministry of Health in 2003. The survey

5The model is given by:

Pr (Hi = m | S,Q) = F (τm − β1 − β2S − β3Q− β3 ε2i − β4IMR− β5Si ε2i)− F(τm−1 − β1 − β2S − β3Q− β3 ε2i − β4IMR− β5Si ε2i

),

where m = 1, 2, 3, 4, and F is the cumulative distribution function for ε1.


Table 4.3: Variable Definitions for Smoking and General Health Models

Variable Definition

Self–rated health status Rating of own health compared to age–mates.

1 = Poor, 2 = Satisfactory, 3 = Good,

4 = Very Good.

Report self–rated health status 1 if individual reported self–rated health status,

0 otherwise.

Currently smokes 1 if individual currently smokes, 0 otherwise.

Male 1 if individual is male, 0 otherwise.

Age Individual’s age in completed years.

Primary education 1 if individual’s highest level of education is

primary, 0 otherwise.

Secondary education 1 if individual’s highest level of education is

secondary, 0 otherwise.

University education 1 if individual’s highest level of education is

university, 0 otherwise.

Currently married 1 if individual is currently married, 0 otherwise.

Wealth index 1 = Poorest, 2 = Second, 3 = Middle,

4 = Fourth, 5 = Richest.

Smoking prevalence Percentage of individuals in the sample who smoke.

Measured at the district level.

Average number of cigarettes smoked Average number of cigarettes smoked in

the last 24 hours. Computed at the provincial level.

Inverse Mill’s ratio Inverse Mill’s ratio.

Smoking residuals Generalized residuals from the smoking status model.

Smoking interacted with residuals Interaction of smoking status with smoking residuals.

collected a wide range of information including health related expenditures, self–rated

health status, smoking status, among other variables. In addition to this dataset, we

obtained information on the number of cigarettes smoked in the last 24 hours from the

Kenya Demographic and Health Survey male dataset of 2003. This information was then

used to calculate the average number of cigarettes smoked in the past 24 hours for each

province.

Table 4.3 shows the variable definitions.


Table 4.4: Descriptive Statistics for Smoking and General Health Models

Variable Number of Mean Standard Minimum Maximum


Self–rated health status 37,765 3.040 0.706 1 4

Report self–rated health status 38,121 0.991 0.096 0 1

Currently smokes 37,400 0.056 0.230 0 1

Male 38,020 0.494 0.5 0 1

Age 38,042 22.742 18.072 0 121

Primary education 37,901 0.471 0.499 0 1

Secondary education 37,901 0.153 0.360 0 1

University education 37,901 0.014 0.118 0 1

Currently married 37,458 0.328 0.469 0 1

Wealth index 38,121 2.734 1.398 1 5

Smoking prevalence 37,647 5.650 3.146 0.91 18.43

Average number of cigarettes smoked 38,121 8.083 2.085 5.02 12.46


The descriptive statistics are shown in Table 4.4. This table shows for each variable,

the number of observations, its mean, its standard deviation, its maximum value, and

its minimum value. The number of observations differ across variables since the values

of certain variables are missing for some of the individuals. The table shows that, for

instance, the average smoking prevalence is 5.65%, the minimum smoking prevalence is

0.91%, while the maximum smoking prevalence is 18.43%. Similarly, the table shows

that the mean age in our sample is about 23 years, the youngest individual in the sample

is aged less than a year, while the oldest individual is aged 121 years.

Table 4.5 on page 124 shows the distribution of self–rated health status in our dataset,

while Table 4.6 also on page 124 shows the distribution of self–rated health status by

smoking status in our dataset. From Table 4.5, we can observe that 61.48% of the

individuals in our sample rate their own health as good compared to age–mates. Only

3.85% of the individual rate their own health as poor compared to age–mates. From

Table 4.6, we can observe that majority of the individuals (about 94%) in our data set

do not smoke. We can also further observe that a large majority of the individuals rate

their health as good compared to age–mates, even if the individuals are smokers.


Table 4.5: Distribution of Self–Rated Health Status in the Dataset

Self–rated health status Number of observations Percent (%)

Poor 1,453 3.85

Satisfactory 4,343 11.50

Good 23,217 61.48

Very good 8,752 23.17

Table 4.6: Distribution of Self–Rated Health Status by Smoking Status in the Dataset

Self–rated health status Currently Smokes Total

No Yes

Poor 1,298 129 1,427

Satisfactory 3,954 306 4,260

Good 21,694 1,245 22,939

Very good 8,195 411 37,232

Total 35,141 (94.4%) 2,091 (5.6%) 37,232 (100%)

4.6 Results


We employ a two–stage estimation strategy. In the first stage, we estimate the sample

selection model and the smoking status model. The results of these estimations are

shown in Table 4.7 on page 125. The second stage involves estimation of the self–rated

health status model.

We note from Table 4.7 that with respect to the smoking status model, our instruments

have the expected signs and are statistically significantly different from zero. We can

also observe that, according to the table, smoking status is influenced by the sex of the

individual, age, education level, marital status and wealth.

Specifically, the table indicates that male individuals have a higher probability of

smoking as compared to female individuals, holding other variables constant. This

finding is supported by the observation in the literature that the smoking behaviour

of men is different from that of women (see, for example, Gilmore et al., 2004; Bauer,


Table 4.7: Average Marginal Effects for Sample Selection and Smoking Status Models, RobustZ Statistics in Parentheses

Variable Sample Selection Model Smoking Status Model

(Report self–rated health status =1) (Currently smokes =1)

(1) (2)

Male 0.00078 0.089

(0.82) (32.30)

Age 0.00014 0.002

(3.00) (22.34)

Primary education 0.0083 0.028

(7.08) (9.57)

Secondary education 0.0035 0.030

(2.22) (8.26)

University education 0.0054 0.005

(1.16) (0.60)

Currently married 0.000082 0.036

(0.05) (11.86)

Wealth index -0.00063 -0.004

(-1.69) (-4.05)

Smoking prevalence -0.00022 0.007

(-1.38) (23.48)

Average number of cigarettes smoked 30.002 0.002

(7.51) (3.13)

Number of observations 36,705 36029

Gohlmann and Sinning, 2007) and that males are more likely to smoke than females (see,

for example, Rachiotis et al., 2008; El Ansari and Stock, 2012).

The results also indicate that older individuals are more likely to be smokers than

younger ones, holding other factors constant. This finding contradicts the findings

reported in some of the studies in the literature that show that elderly individuals have

a lower probability of smoking (see, for example, Pomerleau et al., 2004) and also the

findings from studies that show that young women are more likely to be smokers than

older women (see, for example, Lu, Tong and Oldenburg, 2001).

According to the results in the table, individuals whose highest level of education is

primary and those whose highest level of education is secondary have a higher probability

of smoking as compared to those individuals without formal education. This result

contradicts the findings from some studies in the literature that show that individuals


with low levels of education have higher probabilities of being smokers (see, for example,

Hosseinpoor, Parker, d’Espaignet and Chatterji, 2011).

The results also indicate that currently married individuals are more likely to smoke

than their counterparts who are not currently in marital relationships, holding other

factors constant. This result is contradicted by findings from some studies in the literature

that show that the probability of smoking is higher among divorced, separated or widowed

individuals (see, for example, Mwenifumbo, Sellers and Tyndale, 2008).

Self–Rated Health Status Models

We estimate the self–rated health status model using ordered probit. We report the aver-

age marginal effects of the probabilities of rating own health as either poor, satisfactory,

good, or very good. The results are presented in Table 4.8 on page 127, Table 4.9 on

page 129, Table 4.10 on page 130, and Table 4.11 on page 131. In each table we show

the baseline model; the model controlling for selection bias; the model controlling for

selection bias and endogeneity of smoking; and the model controlling for selection bias,

endogeneity of smoking, and unobserved heterogeneity. We mainly interpret the results

in Table 4.8 and in Table 4.11, the two extremes.

Table 4.8 on page 127 presents the results of four models. Column (1) of the table

shows the results for the model that does not control for potential sample selection bias,

potential endogeneity of smoking, and potential unobserved heterogeneity. Column (2)

shows the results of the model that controls for potential sample selection bias. Column

(3) presents the results of the model in which both potential sample selection bias and

potential endogeneity of smoking are controlled for. Finally, column (4) shows the results

of the model that controls for potential sample selection bias, potential endogeneity of

smoking, and potential unobserved heterogeneity.

The results in column (2) suggest the presence of selection bias that biases the results

presented in column (1). The results in column (3), on the other hand, indicate that

smoking status is endogenous in the model for self–rated health status. The results

presented in column (4) also indicate the presence of unobserved heterogeneity which

biases the results in models that do not control for it. Consequently, our preferred model

is the model that controls for all the three potential problems, which in our case is


Table 4.8: Average Marginal Effects for Probability of Rating Own Health as “Poor”, RobustZ Statistics in Parentheses

Variable Self–rated health = Poor

(1) (2) (3) (4)

Currently smokes 0.009 0.009 0.022 0.018

(3.94) (4.05) (3.04) (2.35)

Male -0.004 -0.003 -0.004 -0.005

(-4.02) (-3.40) (-3.95) (-4.33)

Age 0.001 0.001 0.001 0.001

(22.50) (22.72) (21.25) (20.00)

Primary education -0.010 -0.004 -0.004 -0.004

(-8.80) (-2.82) (-2.98) (-3.10)


(-12.62) (-10.78) (-10.88) (-10.97)

University education -0.039 -0.035 -0.034 -0.034

(-9.29) (-8.25) (-8.14) (-8.13)

Currently married -0.013 -0.013 -0.013 -0.014

(-9.29) (-9.09) (-9.29) (-9.44)

Wealth index -0.004 -0.004 -0.004 -0.004

(-10.46) (-11.18) (-11.17) (-11.19)

Inverse Mill’s ratio 0.349 0.347 0.347

(7.55) (7.50) (7.50)

Smoking residuals -0.007 -0.015

(-1.89) (-2.53)

Smoking interacted with residuals 0.012

(1.69)


the model shown in column (4). We, therefore, concentrate on the results presented in

column (4).

According to the results in column (4) of Table 4.8, smoking increases the probability

of rating own health as “Poor” compared to age–mates by 0.018. This means that,

holding other factors constant, the probability of individuals who smoke rating their

own health as “Poor” is higher than that of the individuals who do not smoke rating

their own health as “Poor” by 0.018. Consequently, smoking increases the probability

of rating own health as “Poor” compared to age–mates, holding other factors constant.

This result is consistent with the finding in the literature that individuals who smoke

are more likely to rate their own health as “Poor” (see, for example, Meurer, Layde and

Guse, 2001; Osler et al., 2001; Ahmad, Jafa and Chaturvedi, 2005; Asfar et al., 2007;


Lim et al., 2007; Demirchyan and Thompson, 2008). Comparing the effect of smoking on

self–rated health shown in column (1) with that shown in column (4), we notice that the

result appearing in column (4) is twice that appearing in column (1). We can, therefore,

conclude that failure to control for sample selection bias, endogeneity of smoking and

unobserved heterogeneity leads to an understatement of the effect of smoking on the

probability of rating own health as “Poor” compared to age–mates.

The results in column (4) of the table also indicate that males have a lower probability

of rating their own health as “Poor” compared to females. In particular, holding other

factors constant, the probability of males rating their own health as “Poor” is lower than

that of females by about 0.005. This result is consistent with the findings from studies in

the literature that show that women have a higher probability than men of rating their

own health as “Poor” (see, for example, Ahmad, Jafa and Chaturvedi, 2005; Asfar et

al., 2007; Lim et al., 2007; Montazeri, Goshtasebi and Vahdaninia, 2008; Shin, Shin and

Rhee, 2012). The results further indicate that the probability of rating own health as

“Poor” increases with age. This result is consistent with the findings of some studies in

the literature that indicate that older individuals are more likely to rate own health as

“Poor” (see, for example, Asfar et al., 2007; Lim et al., 2007; Shin et al., 2012).

The results also indicate that individuals whose highest level of education is either

primary, secondary, or university are less likely to rate their own health as “Poor”

as compared to individuals without formal education, holding other factors constant.

Although some studies in the literature (see, for example, Lim et al., 2007) find no

relationship between educational attainment and self–rated health status, other studies

(see, for example, Demirchyan and Thompson, 2008; Montazeri et al., 2008) do find such

a relationship and report that highly educated individuals are more likely to perceive

their own health as being worse. We can also observe that the probability of rating own

health as “Poor” is lower for currently married individuals as compared to those who are

not currently married, holding other factors constant. This result contradicts the finding

in the literature that married individuals are more likely to rate own health as “Poor”

(see, for example, Asfar et al., 2007).

As mentioned earlier, Table 4.11 on page 131 shows the results of the models for the

probability of rating own health as “Very Good” compared to age–mates. We show the

average marginal effects. The results in column (1) do not control for potential sample

selection bias, potential endogeneity of smoking, and potential unobserved heterogeneity.


Table 4.9: Average Marginal Effects for Probability of Rating Own Health as “Satisfactory”,Robust Z Statistics in Parentheses

Variable Self–rated health = Satisfactory

(1) (2) (3) (4)


(3.96) (4.06) (3.04) (2.35)

Male -0.007 -0.006 -0.009 -0.010

(-4.03) (-3.41) (-3.95) (-4.34)

Age 0.002 0.002 0.002 0.002

(25.92) (26.41) (24.33) (22.47)


(-9.03) (-2.83) (-2.99) (-3.11)


(-13.27) (-11.19) (-11.30) (-11.40)


(-9.45) (-8.36) (-8.25) (-8.23)


(-9.45) (-9.24) (-9.44) (-9.59)

Wealth index -0.007 -0.008 -0.008 -0.008

(-10.72) (-11.52) (-11.50) (-11.53)


(7.68) (7.63) (7.62)


(-1.89) (-2.53)


(1.69)


Those in column (2) only control for potential sample selection bias. The ones in column

(3) control for both potential sample selection bias and potential endogeneity of smoking,

while those in column (4) control for potential sample selection bias, potential endogeneity

of smoking, and potential unobserved heterogeneity.

Looking closely at the results in column (2) of Table 4.11 on page 131 shows that

sample selection bias is present and failure to control for it biases the results. A closer

look at the results in column (3) of the same table also shows that smoking status is

endogenous and this endogeneity should be controlled for. The results in column (4) of

the same table also imply that there is unobserved heterogeneity. Thus, the best model

for our purposes is the one shown in column (4) of the table. We will, therefore, only

interpret the results of this model.


Table 4.10: Average Marginal Effects for Probability of Rating Own Health as “Good”, RobustZ Statistics in Parentheses

Variable Self–rated health = Good

(1) (2) (3) (4)


(3.91) (4.01) (3.01) (2.33)

Male -0.003 -0.003 -0.004 -0.005

(-3.99) (-3.38) (-3.89) (-4.26)

Age 0.001 0.001 0.001 0.001

(18.86) (19.28) (18.55) (17.74)


(-8.53) (-2.80) (-2.96) (-3.07)


(-11.81) (-10.23) (-10.31) (-10.38)


(-8.98) (-8.03) (-7.94) (-7.92)


(-8.86) (-8.69) (-8.83) (-8.95)

Wealth index -0.003 -0.004 -0.004 -0.004

(-10.23) (-10.92) (-10.91) (-10.93)


(7.49) (7.44) (7.44)


(-1.88) (-2.51)


(1.69)


The results indicate that individuals who smoke are less likely to rate their own health

as “Very Good” as compared to age–mates, holding other factors constant. Specifically,

the results imply that the probability of individuals who smoke rating their own health

as “Very Good” is lower by 0.069 compared to that of individuals who do not smoke,

holding other factors constant. Consequently, smoking reduces the probability of rating

own health as “Very Good” compared to age–mates, holding other factors constant. This

result is consistent with the finding in the literature that the probability of individuals

who smoke rating their own health as “Very Good” or “Excellent” is very low (see,

for example, Kirkland, Greaves and Devichand, 2004; El Ansari and Stock, 2012). If

we compare the results in column (4) to those in column (1), we notice that even

though in column (1) smoking reduces the probability of rating own health as “Very


Table 4.11: Average Marginal Effects for Probability of Rating Own Health as “Very Good”,Robust Z Statistics in Parentheses

Variable Self–rated health = Very good

(1) (2) (3) (4)

Currently smokes -0.033 -0.034 -0.085 -0.069

(-3.96) (-4.07) (-3.04) (-2.35)

Male 0.015 0.013 0.017 0.021

(4.04) (3.41) (3.95) (4.34)

Age -0.004 -0.004 -0.004 -0.004

(-26.64) (-27.25) (-25.01) (-23.04)

Primary education 0.036 0.014 0.015 0.016

(9.03) (2.82) (2.99) (3.10)

Secondary education 0.079 0.0698 0.070 0.071

(13.28) (11.18) (11.29) (11.38)

University education 0.149 0.133 0.131 0.131

(9.51) (8.40) (8.29) (8.27)

Currently married 0.048 0.048 0.050 0.052

(9.47) (9.26) (9.45) (9.61)

Wealth index 0.015 0.016 0.016 0.016

(10.85) (11.67) (11.65) (11.68)

Inverse Mill’s ratio -1.331 -1.322 -1.321

(-7.72) (-7.66) (-7.66)

Smoking residuals 0.028 0.058

(1.89) (2.53)

Smoking interacted with residuals -0.044

(-1.69)


Good” compared to age–mates by 0.033, that in column (4) shows the corresponding

reduction in probability to be 0.069. This means that in models where sample selection

bias, endogeneity of smoking and unobserved heterogeneity are not controlled for, the

effect of smoking on rating own health as “Very Good” as compared to age–mate is

underestimated.

We can also observe that males are more likely than females to rate their own heath as

“Very Good”, holding other factors constant. This result is consistent with the findings in

the literature (see, for example, McCullough and Laurenceau, 2004). The results also

indicate that rating of own health as “very good” declines with age, holding other factors

constant.


We can further observe that those individuals whose highest level of education is

primary, secondary or university are more likely to rate their own health as “Very Good”

as compared to individuals without formal education, holding other factors constant.

These results are consistent with the finding in the literature that individuals with higher

levels of education are more likely to report “Good” or “Very Good” self–rated health

(see, for example, Phillips, Hammock and Blanton, 2005; Von dem Knesebeck and Geyer,

2007).

We can further observe that currently married individuals are more likely than their

non–married counterparts to rate own health as “Very Good” compared to age–mates,

holding other factors constant. The findings from the literature are mixed with some

studies finding no relationship between marital status and self–rated health (see, for

example, Phillips, Hammock and Blanton, 2005) while others finding married individuals,

especially women, to be more likely to rate own health as “Poor” (see, for example, Asfar

et al., 2007).


Summary

This chapter investigates the effect of smoking on general health status in Kenya. General

health status is measured using self–rated health status. Previous studies have shown

that self–rated health status can predict mortality, morbidity, and disability (see, for

example, McFadden et al., 2009). An individual’s rating of own health may, however,

be influenced by the individual’s perceptions about health and the health information

available to the individual (see, for example, Strauss and Thomas 1998, 2008).

The literature shows that self–rated health status is influenced by socio–demographic

factors, socio–economic factors, psychosocial factors, social capital, health care access,

and lifestyle factors (for example, smoking) (see, for example, Delpierre et al., 2012;

Giron, 2012; Wang et al., 2012).

Most studies show that both active and passive smoking are associated with an

increased probability of rating own health as “Poor” (see, for example, Wang et al., 2012)

although there are a few exceptions where those who smoke are reported to rate their

own health as “Excellent” (see, for example, Yen, Shaw and Yuan, 2010).


This study fills a gap in the literature created by the failure of previous studies

on self–rated health status in Kenya to explicitly investigate the relationship between

smoking and self–rated health (see, for example, Gyimah, Emina and Ezeh, 2011). Using

household level data collected by the Ministry of Health in 2003, we estimate an ordinal

model linking smoking to rating of own health. The estimation strategy adopted for

the study controls for potential sample selection bias using the approach suggested

by Heckman (1979), potential endogeneity of smoking using the Two–Stage–Residual–

Inclusion (2SRI) method (Terza, Basu and Rathouz, 2008), and potential unobserved

heterogeneity using the control function approach, as applied by Mwabu (2009).

The main finding is that smoking increases the probability of rating own health as

“Poor” but reduces the probability of rating own health as “Very Good”, holding other

factors constant. This result is consistent with the findings reported in the literature

(see, for example, Demirchyan and Thompson, 2008). The study also demonstrates that

when we do not control for sample selection bias, endogeneity of smoking and unobserved

heteorgeneity we end up underestimating the effect of smoking on self–rated health.

Conclusions

We can draw three main conclusions from the findings in this Chapter. First, smoking

is harmful to health, consistent with the findings in the literature. Second, failure to

control for sample selection bias, endogeneity of smoking and unobserved heterogeneity

leads to an understatement of the negative effect of smoking on self–rated health. Third,

the risk factors for smoking include male gender, older ages, having primary education,

having secondary education, low wealth and being currently married. Smoking is also

influenced by availability of cigarettes.

Policy Implications

Our findings point towards policies aimed at discouraging smoking so as to improve

general health. An example of such a policy is restrictions on the availability and

accessibility of cigarettes to high risk groups.

REFERENCES 134

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Chapter 5

Summary, Conclusions, and Policy

Implications

5.1 Summary

Health encompasses physical, mental, and social well–being (see, for example, Strauss

and Thomas, 2008). Good health is of interest to economists mainly because it is

a component of human capital that contributes to wealth creation (see, for example,

Mwabu, 2001; Mwabu, 2008). It may be sometimes informative to study the health of

specific population groups, such as infants and mothers, alongside general health.

Health can be investigated at the individual or population level, which leads to

population and individual measures of health. This thesis is concerned with individual

health.

We cannot observe an individual’s true health status but can approximate it with the

help of one or several observable indicators (Stein, 2004). Examples of the indicators of

individual health include birth weight (for infants), pregnancy outcomes (for mothers),

and self–reported general health status (for general health) (see, for example, Strauss

and Thomas 1998, 2008; Villamor and Cnattingius, 2006; Mwabu 2008, 2009).

Individuals can be thought of as producing health that maximizes the utility they

derive from health and other goods (see, for example, Rosenzweig and Schultz 1982,

1983; Mwabu 2008, 2009). The health must, however, be produced with the help of

various inputs (see, for example, Mosley and Chen, 1984; Schultz, 1984; Fuchs, 2004).

144

Summary, Conclusions, and Policy Implications 145

The individuals do not, however, know a priori which inputs are health enhancing and,

therefore, need this information. This thesis aims at contributing to this knowledge.

The thesis investigates the effects of various health inputs on health outcomes in

Kenya. It does this in three essays. The first essay, discussed in Chapter 2, investigates

the effect of the adequacy of prenatal care use on infant health. In this essay, birth

weight is used as a measure of infant health (for a justification, see , for example, Mwabu,

2009). Following the WHO classification, infants are classified as either having low birth

weight or normal weight (Zegers–Hochschild et al., 2009; WHO, 2011). The birth weight

of infants is then linked to a measure of adequacy of prenatal care use, constructed

based on WHO recommendations (Berg, 1995) and other individual and socio–economic

characteristics. Single–level and multilevel (see, Steenbergen and Jones, 2002) models

are then estimated controlling for potential sample selection bias, potential endogeneity

of prenatal care, and potential unobserved heterogeneity using data from the Kenya

Demographic and Household Surveys (KDHS) collected in 1993, 1998, 2003 and 2008.

We find that adequate use of prenatal care improves infant health. In particular,

adequate use of prenatal care lowers the chances of delivering a low birth weight infant.

The literature (see, for example, Conway and Deb, 2005; Jewell and Triunfo, 2006)

supports this finding. The multilevel model results show that if we do not control for

unobserved mother–specific effects, then we run the risk of overstating the effect of

prenatal care on infant health.

The second essay, contained in Chapter 3, investigates the effect of preceding birth

interval length on maternal health. Maternal health is measured in the essay using an

indicator of whether or not a woman experienced an adverse pregnancy outcome (in

particular a miscarriage, a stillbirth or an abortion) since adverse pregnancy outcomes

may be pointers towards the existence of some underlying maternal health problems (see,

for example, Villamor and Cnattingius, 2006). Previous studies on birth intervals in

Kenya (see, for example, Ikamari, 1998; Rafalimanana and Westoff, 2001; Kosimbei, 2005;

Mustafa and Odimegwu, 2008) have not linked preceding birth intervals to maternal health

and the essay fills this gap. We construct a model that links maternal health to preceding

birth interval and other variables. The estimated model controls for potential sample

selection bias, potential endogeneity of preceding birth interval length, and potential

unobserved heterogeneity. We use data from the Kenya Demographic and Health Survey

(KDHS) collected in 1998, 2003, and 2008 to estimate our model. Consistent with the

literature (see, for example, Conde–Agudelo and Belizan, 2000; Stephansson, Dickman

and Cnattingius, 2003; Conde–Agudelo, Rosas–Bermudez and Kafury–Goeta, 2007;


Stamilio et al., 2007; Williams et al., 2008), we find that preceding birth intervals of

length 36 – 59 months improve maternal health. This conclusion is, however, only arrived

at when we control for the endogeneity of preceding birth interval.

The third essay, presented in Chapter 4, looks at smoking and general health. General

health, in this essay, is measured using self–rated health status. Studies show that

self–rated health status is a predictor of actual health status (see, for example, Ahmad,

Jafa and Chaturvedi, 2005; Ford, Spallek and Dobson, 2008; Jylha, 2009; McFadden et

al., 2009). In the literature, both active and passive smoking are shown to be harmful

to health (see, for example, USDHHS, 2004; Adab et al., 2005; USDHHS, 2006; Chen

et al., 2012). Since previous studies on self–rated health in Kenya (see, for example,

Kyobutungi, Ezeh, Zulu and Falkingham, 2009; Kodzi, Gyimah, Emina and Ezeh, 2011)

have not explicitly examined the effect of smoking on self–rated health status, this

essay fills this gap. We estimate a model linking the individual’s smoking status to

the individual’s self–rated health status using data mainly from the National Health

Expenditure and Utilization Survey collected by the Ministry of Health in 2003. We

control for potential sample selection bias, potential endogeneity of smoking, and potential

unobserved heterogeneity in the estimation process. The results confirm the findings

from the literature of a negative effect of smoking on health (see, for example, Montazeri,

Goshtasebi and Vahdaninia, 2008; El Ansari and Stock, 2012; Shin, Shin and Rhee, 2012).

We particularly find that the probability of rating one’s own health as “Poor” compared

to age–mates is higher among smokers than among non–smokers, holding other factors

constant. This probability is, however, underestimated in models that do not control

for sample selection bias, endogeneity of smoking and unobserved heterogeneity. We

also find that the probability of rating one’s own health as “Very Good” is lower among

smokers as compared to non–smokers, holding other factors constant. This probability is

also underestimated in models that do not control for sample selection bias, endogeneity

of smoking and unobserved heterogeneity.

5.2 Conclusions

We can draw several conclusions based on the findings in this thesis. First, prenatal care,

when adequately used, improves infant health. The beneficial effects of prenatal care on

infant health are, however, overstated in models that do not control for unobserved mother–

specific effects. Second, preceding birth intervals of length 36 to 59 months improve


maternal health. However, preceding birth interval is an endogenous determinant of

maternal health and its positive effect on maternal health is only observed after controlling

for this endogeneity. Third, smoking is harmful to health. The harmful effects of smoking

on health are, however, underestimated in models that do not control for sample selection

bias, endogeneity of smoking and unobserved heterogeneity. There is need to carry

out further research that seeks to shed more light on the barriers to adequate use of

prenatal care, barriers to optimal birth spacing, and the determinants of smoking among

different population groups. Such research will help in designing policies that promote

health–enhancing behaviours and actions.

5.3 Policy Implications

The findings from the thesis have several implications for policies that could improve health

in Kenya. First, policies that promote adequate use of prenatal care are likely to improve

infant health by, lowering the number of low birth weight infants, and ultimately infant

and child mortality. This will help towards the attainment of Millennium Development

Goal (MDG) No. 4 on reducing child mortality (United Nations, 2010). Second, policies

that make it easier for mothers to optimally space their births should be pursued.

Such policies are likely to contribute to the attainment of MDG No. 5 of improving

maternal health (United Nations, 2010). Third, policies that discourage active and

passive smoking would lead to an improvement in general health. Examples of such

policies are documented in the World Health Organization’s International Framework

Convention on Tobacco Control (WHO, 2003), which Kenya has already ratified.

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