DHS ANALYTICAL 

STUDIES 56

Marriage and Fertility Dynamics:

The Influence of Marriage Age 

on the Timing of First Birth and 

Birth Spacing 

August 2016

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Kerry L.D. MacQuarrie.

DHS Analytical Studies No. 56 

Marriage and Fertility Dynamics: 

The Influence of Marriage Age on the Timing of First Birth 

and Birth Spacing 

Kerry L.D. MacQuarrie1

ICF International 

Rockville, Maryland, USA 

August 2016 

1

 The DHS Program, Avenir Health 

Corresponding author: Kerry L.D. MacQuarrie, The DHS, ICF International, 530 Gaither Road, Suite 

500, Rockville, MD 20850, USA; phone: +1 301-572-0282; fax: +1 301-407-6501; email: 

Kerry.macquarrie@icfi.com 
Acknowledgments: The author extends her gratitude to numerous people who contributed to the 

production of this study in various capacities: Margaret Greene of GreeneWorks for her thoughtful review 

of an earlier draft; Stew Tolnay, Charlie Hirschman, Aimée Dechter, Sara Curran, and Darryl Holman of 

University of Washington for their feedback on the preliminary study idea; Tom Pullum, ICF 

International, for technical and programming guidance on managing birth histories data; Daniel Powers, 

University of Texas, for suggestions for the multivariate decomposition models; and Amy Finnegan, 

Duke University, for assistance organizing relevant literature. 

Editor: Diane Stoy 

Document Production: Natalie La Roche 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#AID-OAA-C-13-00095). The views expressed are 

those of the authors and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. For additional information about The DHS Program, contact 

The DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 

301-407-6500; fax: 301-407-6501; email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

MacQuarrie, Kerry L.D. 2016. Marriage and Fertility Dynamics: The Influence of Marriage Age on the 

Timing of First Birth and Birth Spacing. DHS Analytical Studies No. 56. Rockville, Maryland, USA: ICF 

International.
iii 

Contents 

Tables ................................................................................................................................................................... v

Figures ............................................................................................................................................................... vii

Preface ................................................................................................................................................................ ix

Abstract .............................................................................................................................................................. xi

Executive Summary ......................................................................................................................................... xiii

1. Background ............................................................................................................................................ 1

2. Methods and Data ................................................................................................................................. 5

2.1. Country and Survey Selection ................................................................................................... 5

2.2. Sampling and Sample Weighting .............................................................................................. 6

2.3. Sample Restriction ..................................................................................................................... 7

2.4. Measures .................................................................................................................................... 7

2.5. Scope of the Analysis and Analytic Strategy .......................................................................... 11

3. Trends in Median Age at Marriage and Age at First Birth............................................................. 15

3.1. Marriage and the First Birth in South Asia .............................................................................. 15

3.2. Marriage Age and First Birth in Southeast Asia ...................................................................... 17

3.3. Age at First Sex ....................................................................................................................... 18

4. Characteristics Associated with Age at Marriage ............................................................................ 19

4.1. Marriage Age and Gender Context .......................................................................................... 19

4.2. Marriage Age and Education and Occupation ......................................................................... 26

4.3. Marriage Age and Wealth ........................................................................................................ 30

4.4. Marriage Age and Religion ..................................................................................................... 33

4.5. Marriage Age and Residence ................................................................................................... 34

4.6. Marriage Age and Subnational Region .................................................................................... 36

4.7. Marriage Age and Husbands’ Education and Occupation ....................................................... 39

5. Characteristics Associated with the First Birth Interval ................................................................. 45

5.1. First Birth Interval and Gender Context .................................................................................. 46

5.2. First Birth Interval and Education and Occupation ................................................................. 52

5.3. First Birth Interval and Wealth ................................................................................................ 57

5.4. First Birth Interval and Religion .............................................................................................. 59

5.5. First Birth Interval and Residence ........................................................................................... 61

5.6. First Birth Interval and Region ................................................................................................ 62

5.7. First Birth Interval and Husbands’ Education and Occupation ............................................... 65

6. Association of Marriage Age with the First Birth Interval ............................................................. 71

6.1. Age at Marriage ....................................................................................................................... 71

6.2. Birth Cohort ............................................................................................................................. 74

6.3. Other Covariates ...................................................................................................................... 75

6.4. Underlying Hazard Distribution .............................................................................................. 76
iv 

7. Multivariate Decomposition Results .................................................................................................. 77

7.1. Overall Decomposition Results ............................................................................................... 77

7.2. Age at Marriage and Changes in the First Birth Interval ......................................................... 78

7.3. Gender Context and Changes in the First Birth Interval ......................................................... 79

7.4. Other Women’s Socio-Demographic Characteristics and Changes in the First Birth 

Interval ..................................................................................................................................... 81

7.5. Husband’s Characteristics and Changes in the First Birth Interval ......................................... 81

8. Marriage Age and Subsequent Birth Intervals................................................................................. 85

8.1. Age at Marriage ....................................................................................................................... 85

8.2. Effect of the First Birth Interval and Birth Cohort .................................................................. 88

8.3. Other Covariates ...................................................................................................................... 88

9. Discussion and Conclusions ................................................................................................................ 91

References .......................................................................................................................................................... 95

Appendix Tables ............................................................................................................................................. 101
v 

Tables 

Table 1. Sample parameters for surveys included in the analysis ..................................................... 6

Table 2. Proportion of women age 25-49 excluded from analysis due to never being married 

or experiencing a negative first birth interval ..................................................................... 7

Table 3. Change in the mean age at first marriage among ever-married women age 25-49 ........... 19

Table 4. Mean age at first marriage among ever-married women age 25-49, by women’s 

decision-making ................................................................................................................ 20

Table 5. Mean age at first marriage among ever-married women age 25-49, by attitudes 

toward wife beating .......................................................................................................... 22

Table 6. Mean age at first marriage among ever-married women age 25-49, by spousal age 

difference .......................................................................................................................... 24

Table 7. Mean age at first marriage among ever-married women age 25-49, by level 

of education ...................................................................................................................... 26

Table 8. Mean age at first marriage among ever-married women age 25-49, by respondent's 

occupation ......................................................................................................................... 28

Table 9. Mean age at first marriage among ever-married women age 25-49, by household 

wealth quintile .................................................................................................................. 31

Table 10. Mean age at first marriage among ever-married women age 25-49, by religion .............. 33

Table 11. Mean age at first marriage among ever-married women age 25-49, by place 

of residence ....................................................................................................................... 35

Table 12. Mean age at first marriage among ever-married women age 25-49, by subnational 

region ................................................................................................................................ 36

Table 13. Mean age at first marriage among ever-married women age 25-49, by husband's 

education ........................................................................................................................... 39

Table 14. Mean age at first marriage among ever-married women age 25-49, by husband's 

occupation ......................................................................................................................... 41

Table 15. Change in the mean marriage to first birth interval (in months) among ever-married 

women age 25-49 .............................................................................................................. 45

Table 16. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by women's decision-making .......................................................................... 46

Table 17. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by attitudes toward wife beating ..................................................................... 48

Table 18. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by spousal age difference ................................................................................ 50

Table 19. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by level of education ....................................................................................... 53

Table 20. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by occupation .................................................................................................. 55

Table 21. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by household wealth quintile .......................................................................... 57

Table 22. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by religion ....................................................................................................... 60

Table 23. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by place of residence ....................................................................................... 61

Table 24. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by subnational region ...................................................................................... 63
vi 

Table 25. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by husband's education ................................................................................... 66

Table 26. Mean marriage to first birth interval (in months) among ever-married women 

age 25-49, by husband's occupation ................................................................................. 68

Table 27. Effect of age at marriage on the marriage to first birth interval: Time ratios from 

unadjusted loglogistic hazard models (95% confidence intervals in parentheses) ........... 71

Table 28. Adjusted effects on the duration of the marriage to first birth interval: Time ratios 

from multivariate loglogistic hazard models .................................................................... 72

Table 29. Overall multivariate decomposition of change in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects ........................................................................................................................... 78

Table 30. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects of marriage age.................................................................................................. 79

Table 31. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects of gender context variables ............................................................................... 80

Table 32. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects of husbands' characteristics ............................................................................... 82

Table 33. Effect of age at marriage on the duration of the second birth interval: Time ratios 

from unadjusted loglogistic hazard models ...................................................................... 85

Table 34. Adjusted effects on the duration of the second birth interval: Time ratios from 

multivariate loglogistic hazard models ............................................................................. 86

Appendix Table 1. Observations for mean age at first marriage among ever-married women age 25-49 .... 102

Appendix Table 2. Observations for hazard models predicting the first birth interval ................................. 102

Appendix Table 3. Model diagnostics of the underlying hazard of the first birth interval ............................ 102

Appendix Table 4. Country-specific religion categories and coefficients (time ratios) from adjusted 

loglogistic hazard models of the marriage to first birth interval ..................................... 104

Appendix Table 5. Country-specific regional categories and coefficients (time ratios) from adjusted 

loglogistic hazard models of the marriage to first birth interval ..................................... 105

Appendix Table 6. Comparison of β coefficients from linear regression models and loglogistic 

accelerated failure-time hazard models predicting duration of the first birth interval 

(Discrepancies in direction or significance highlighted in red) ...................................... 106

Appendix Table 7. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects of women's socio-demographic variables: Education ..................................... 112

Appendix Table 8. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects of women's socio-demographic variables: Occupation ................................... 113

Appendix Table 9. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects of women's socio-demographic variables: Household wealth quintile ........... 115

Appendix Table 10. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects of women's socio-demographic variables: Religion quintile .......................... 116
vii 

Appendix Table 11. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects of women's socio-demographic variables: Place of residence ........................ 117

Appendix Table 12. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences 

in effects of women's socio-demographic variables: Subnational region ....................... 118

Appendix Table 13. Observations for hazard models predicting the second birth interval ............................. 120

Appendix Table 14. Model diagnostics of the underlying hazard of the second birth interval ....................... 121

Appendix Table 15. Tercile survival times to first birth in months, ever-married women age 25-49 ............. 122

Figures 

Figure 1a. Trends in the median completed age at key events among women age 25-49 in four 

South Asian countries ....................................................................................................... 16 

Figure 1b. Trends in the median completed age at key events among women age 25-49 in three 

Southeast Asian countries ................................................................................................. 17 
viii 
ix 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be 

useful to policymakers and program managers in low- and middle-income countries. DHS Analytical 

Studies serve this objective by providing in-depth research on a wide range of topics, typically including 

several countries and applying multivariate statistical tools and models. These reports are also intended to 

illustrate research methods and applications of DHS data that may build the capacity of other researchers. 

The topics in the DHS Analytical Studies series are selected by The DHS Program in consultation with 

the U.S. Agency for International Development. 

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

Director, The DHS Program 

xi 

Abstract 

In a context of rising marriage age for women and a compression of first birth intervals, this study uses 

survival analysis, hazard models, and multivariate decomposition techniques to investigate the influence 

of marriage age on the first birth interval over time, and the implications of both marriage age and first 

birth interval on the second birth interval. Secondarily, the study assesses the influence of the gender 

context. The study analyzes these relationships in seven countries—four in South Asia and three in 

Southeast Asia—that have experienced significant change in either age at marriage or the first birth 

interval, or both. Demographic and Health Surveys data from over approximately a decade are used to 

examine changes in these dynamics over time. Significant increases in marriage age and significant 

decreases in the first birth interval (except in Cambodia) are observed, albeit at varying rates. Later 

marriage is associated with shorter first birth intervals but longer second birth intervals. Marriage age 

remains the most consistent influence on the first birth interval after controlling for birth cohort, gender 

context, and women’s and husbands’ characteristics. Compositional shifts toward later marriage 

contributes substantially (38%-89%) to declines in the first birth interval in Bangladesh, India, Nepal, 

Pakistan, Indonesia, and the Philippines, while a change in the effect of marrying later contributes to 

change in the first birth interval in India and Nepal. Marriage age continues to influence the second birth 

interval, after controlling for the length of the first birth interval and other covariates. 

xiii 

Executive Summary 

Marriage age has steadily risen for women in much of South and Southeast Asia, although different 

regions experience variation in average ages at marriage and rates of increase. A relationship between 

nuptiality patterns and certain fertility dynamics, specifically the initiation of childbearing and total 

fertility, is well established and there is an expected shortening of the first birth interval that coincides 

with later marriage. However, there is variation across countries in the first birth interval and its rate of 

change—even when average marriage age is similar. Less known is whether marriage age or other 

factors exert more influence on the first birth interval, whether trends in marriage age and the first birth 

interval are universal or localized among selected subgroups of women, and to what extent compositional 

shifts contribute to changes in the first birth interval and the relative importance of marriage age to birth 

spacing beyond the first birth interval. 

This study uses survival analysis, hazard models, and multivariate decomposition techniques to 

investigate the influence of marriage age on the first birth interval over time, and the implications of both 

marriage age and first birth interval for the second birth interval. In addition, attention is given to 

indicators that describe the gender context. The study addresses these relationships in seven countries—

four in South Asia and three in Southeast Asia—which have experienced significant change in either age 

at marriage, the first birth interval, or both in recent decades. Data from Demographic and Health 

Surveys are used from two points in time over approximately a 10-year period to examine changes in 

these dynamics over time. 

This study identified two distinct marriage patterns based on region: median completed age at marriage is 

low (during adolescent years) in South Asia and older in Southeast Asia. Similarly, the median 

completed age at first birth is lower—and the difference between median age at first birth and marriage is 

longer—in South Asia than in Southeast Asia. 

However, time trends are not contrasted consistently between the two regions, nor are they consistent 

with region. Instead, the pace of change has been variable. Marriage age has increased rapidly in 

Indonesia, Nepal, and Bangladesh, but has been slower elsewhere. The difference between the median 

completed age at marriage and first birth has remained steady in Pakistan, Cambodia, and the Philippines, 

but narrowed elsewhere. 

The mean marriage age has increased significantly in all study countries. With this increase, the first 

birth interval1

 has become significantly shorter in four countries: Bangladesh, Nepal, Pakistan, and 

Indonesia. The decrease has been smaller in Pakistan and Indonesia. Meanwhile, the first birth interval 

has lengthened significantly in India and the Philippines while there has been no change in Cambodia. 

This study examines trends in marriage age across a range of indicators that describe the gender context, 

women’s socio-economic and socio-cultural milieu, and husband’s characteristics. The study found 

universal increases in mean marriage age in South Asia (and Indonesia), across nearly all categories or 

levels of these indicators. However, marriage age increased at differential rates among groups in these 

countries. Increases in marriage age are localized in Southeast Asia within groups where marriage age 

was already higher, which means that differentials in marriage age grew wider by most characteristics in 

most countries. Trends in Indonesia, both in marriage age overall and across subgroups of women, more 

closely resemble those observed in South Asia than in either Cambodia or the Philippines. 

 

1

 As measured by the extended mean. 
xiv 

There is a significant change in the first birth interval over (roughly) the last decade in every study 

country with the exception of Cambodia. While mean marriage age increased significantly in all 

countries, the first birth interval became significantly shorter by 0.5-6.5 months in Bangladesh, Nepal, 

Pakistan, and Indonesia. On the other hand, the first birth interval increased significantly by 0.6 months 

in India and by 2.8 months in the Philippines. 

In general, there has been more variation in trends in the first birth interval across gender context, and 

women’s and husband’s characteristics than in trends in marriage age. As with marriage age, change in 

the first birth interval is observed to be generally universal across subgroups of women in the South Asian 

countries in the study, according to women’s decision-making, spousal age difference, household wealth 

quintile, subnational region, and the husband’s education and occupation. Contrasted with changes in 

marriage age, change in the first birth interval is more often concentrated in specific groups of women 

according to attitudes toward wife beating, women’s education and occupation, religion, and place of 

residence. Change in the first birth interval is almost always localized in just a few select subgroups of 

women in Southeast Asia for all of the characteristics examined in the study. Indonesia again resembles 

the South Asian countries with regard to trends in the first birth interval according to residence and 

region. Disparities in the first birth interval by spousal age difference, wealth, place of residence, and 

husband’s education narrowed over time in most study countries, but increased or remained steady for 

other indicators. 

Nearly all characteristics examined in this study are associated with marriage age in bivariate analyses in 

all study countries. The patterns of association are observed in South and Southeast Asian countries alike, 

with the degree of difference in marriage age across a characteristic varying across countries rather than 

regions. Indicators that describe a more gender equitable context (more women’s decision-making, 

attitudes that reject wife beating, small spousal age difference) are associated with an older marriage age, 

with women’s decision-making appearing to be the least important among the three variables. Women’s 

education, employment in professional or clerical occupations, household wealth, and urban residence are 

positively associated with older marriage age in all study countries. Husband’s education and, to a lesser 

extent, husband’s occupation in professional positions are also associated with marrying later. Women 

who are not working or are employed in agricultural occupations marry at younger ages, as do women 

married to husbands in agricultural occupations. Marriage age varies by subnational region and religion, 

but patterns are inconsistent across countries. This inconsistency points to the significance of local 

context rather than broad regional patterns. 

There is greater homogeneity in marriage age in Cambodia than in other study countries. Differences in 

marriage age across characteristics are usually small and sometimes non-significant (e.g. women’s 

decision-making, women’s occupation, and religion) in Cambodia, whereas they are generally large in 

India and Indonesia (as is the case with regard to education, wealth, place of residence, and husband’s 

characteristics). Compared with marriage age, there are somewhat fewer associations between first birth 

intervals and the characteristics examined in this study. 

This study finds a strong, consistent, and negative association between marriage age and the hazard of the 

first birth interval in bivariate and multivariate analysis in six of the seven study countries, with the 

Philippines the exception. However, the magnitude is modest. First birth intervals are 2-6% shorter with 

each year increase in age at marriage. This relationship is robust with the inclusion of controls, but 

declines to 1-2% shorter intervals with each year increase in age at marriage. A significant positive 

association between marriage age and the first birth interval, initially not apparent in bivariate analysis, 

emerges in the Philippines when controlling for other factors. Marriage age influences the first birth 

interval independent of the effect of birth cohort. 
xv 

Birth cohort also independently influences the first birth interval. Birth cohort has a negative association, 

meaning that the first birth interval declines over time even after controlling for increases in marriage age. 

This birth cohort effect reinforces the influence of marriage age on first birth intervals over time. 

There is substantial variation across countries in other characteristics associated with the first birth 

interval in multivariate hazard models. Two indicators that describe the gender context—spousal age 

difference and women’s decision-making—are generally associated with the first birth interval in most 

South Asian countries, while attitudes toward wife beating are not. Women’s decision-making is 

negatively related to the first birth interval, although not monotonically; there is a small increase with a 

longer first birth interval among women with the greatest decision-making capacity in some countries. 

This association loses significance in Cambodia and is significantly positive in Pakistan when controlling 

for marriage age and other factors. There is no association in Southeast Asia. Spousal age difference is 

curvilinearly associated with the first birth interval, so that the intervals are longest among those with 

very little and very large age differences. Independent of controls, spousal age difference is negatively 

associated with the first birth interval only in Bangladesh and India and positively so in the Philippines. 

Women’s education is associated with shorter first birth intervals in bivariate analysis; this association is 

retained in Pakistan and the Philippines with multivariate controls. However, education has a positive 

association with the first birth interval even after accounting for marriage age and other controls. 

Rural residence is associated with longer first birth intervals in India, Nepal, and Pakistan, whereas urban 

residence is associated with longer first birth intervals in Bangladesh and Indonesia, even after age at 

marriage and other controls. There is no association in Cambodia or the Philippines, which suggests that 

urban residence is more salient in South Asia than Southeast Asia. The first birth interval varies by 

subnational region in three of five countries (India, Nepal, and the Philippines) after controlling for other 

factors, with the greatest regional differences in India and smaller in Nepal. There is no such association 

in Bangladesh or Pakistan. 

Bivariate analysis suggests that women who are not working or are employed in agricultural occupations 

or unskilled manual labor have shorter first birth intervals while those employed in professional and 

clerical occupations have longer first birth intervals. These associations are rarely observed when 

controlling for marriage age and other covariates. Associations with wealth and religion are similarly 

sporadic in multivariate analysis. Attitudes toward wife beating and husband’s characteristics (education 

and occupation) are infrequently associated with the first birth interval. 

Decomposition analysis investigates whether first birth intervals changed as they did over time because 

women marry at later ages, other characteristics changed (composition component), or whether the first 

birth interval changed because the rate these women are subject to has changed (effects component). The 

study finds no regional pattern in changes in composition, changes in rates, or both that drive the change 

in the first birth interval. Changes in the composition component contribute the majority of change in the 

first birth interval in Nepal and Pakistan in South Asia and Indonesia in Southeast Asia, whereas changes 

in the effects component contribute more to the overall change in Bangladesh and India in South Asia and 

the Philippines in Southeast Asia. 

Both India and the Philippines experience significant change in the composition and effects components, 

in opposing directions. Changes in composition would imply shorter first birth intervals and changes in 

rates (effects) would imply longer first birth intervals (if all else had remained the same); the net effect of 

these changes is a longer first birth interval over time. 

The lack of significant change in first birth intervals in Cambodia could have been the net result of 

compositional and rate changes of equal magnitude operating in opposing directions. It is not. Cambodia 
xvi 

has not experienced a significant change in either the overall composition or effects component or in 

either the composition or the effect of marriage age or any other constituent factors. 

In detailed decomposition results, marriage age is a significant influence on declines in first birth intervals 

in the six countries that have experienced change in the first birth interval. More women marrying later 

than they did in the past accounts for 38-89% of the change in the first birth interval in the four South 

Asian countries and -10% of the change in Philippines and 86% of the change in Indonesia. The rate 

(effect) of marriage age has changed over time in India and Nepal, which implies shorter first birth 

intervals at each age of marriage in India and longer first birth intervals at each age of marriage in Nepal. 

With these two exceptions, the effect of marriage age on the first birth interval has not changed over time. 

By and large, neither changes in the composition nor effect of the gender context variables contribute to 

change in the first birth interval. Changes (reductions) in the spousal age difference contribute modestly 

to longer first birth intervals in South Asia, but not in Southeast Asia. 

The two countries where the first birth interval increased rather than decreased over time (the Philippines 

and India) manifest slightly different driving factors. In India, the contributions to a shorter first birth 

interval of more women marrying at older ages are amplified by a shorter first birth interval at all ages of 

marriage. However, the effect of these shifts in both composition and rate is reversed by cumulative 

changes in rates. Women in selected occupations, with secondary and higher education, and Hindu 

women, among others, experience longer first birth intervals and this combination of attributes results in 

slightly longer first birth intervals overall. 

In the Philippines, contributions to shorter first birth intervals of more women marrying at older ages than 

in the past are reversed by cumulative changes in rates over time for certain subgroups. Most notably, the 

first birth interval has become longer among Roman Catholics (contributing 345% to the change in the 

first birth interval), women with any education, women in the National Capital and two other regions, 

women who do not work, and those with husbands in professional occupations. 

As with the first birth interval, marriage age is strongly and significantly associated with the second birth 

interval, although the size of the effect is modest. Marriage age is associated with a 1.3-3.5% longer 

second birth interval in all seven countries. The effect is attenuated slightly with controls except in 

Pakistan, where the association is no longer significant. The result leads to the conclusion that marriage 

age influences not just the timing of the first birth, but also subsequent birth spacing, even after 

controlling for changes in birth cohort, the duration of the first birth interval, and other covariates. 

The duration of the first birth interval is positively associated with second birth interval, except in Nepal. 

This influence is particularly strong for women with first birth intervals in the longest tercile. These 

women experience second birth intervals that are 5-20% longer than women whose first birth intervals 

fall in the shortest tercile. Similarly, women who were born in later birth cohorts experience longer 

second birth intervals than do women born between 1960-69 in all countries except India and Pakistan. 

In contrast, indicators that describe the gender context are poorly and inconsistently associated with 

longer second birth intervals, after controlling for marriage age, birth cohort, first birth interval, and other 

covariates. This finding would suggest that, to the extent that gender context influences birth spacing, it 

does so largely indirectly through these other factors. Husband’s characteristics and women’s place of 

residence are also inconsistently associated with the second birth interval. However, several socioeconomic and socio-cultural indicators (education, wealth, and more occasionally subnational region and 

religion) tend to be independently associated with the second birth interval, net of other factors. 
1 

1. Background 

The previous several decades have seen a general, steady rise in the age at marriage for women around 

the world (Lloyd 2005). Marriage delay is a common feature of the Asian demographic landscape, 

although different regions of Asia experience variation in average ages at marriage and rates of increase 

(e.g. Jones 2007, 2009). Marriage continues to be near universal with more than 95% of women marrying 

by age 40 in each of the South and Southeast Asian countries, while rates of singlehood have increased 

appreciably in some East Asian countries (Jones 2009; Lloyd 2005; Retherford, Ogawa, and Matsukura 

2001). 

Marriage and the initiation of childbearing are two significant milestones that mark a transition from 

childhood or adolescence to adulthood. Early marriage (usually understood as marriage before the age of 

18 or during adolescence) is viewed as a violation of human rights (Nguyen and Wodon 2012b; Nour 

2009; UNICEF 2005). It is also associated with a range of adverse social outcomes (Singh 1998), 

including lower educational attainment (Field and Ambrus 2008; Kim and Stinner 1980; Lloyd and 

Mensch 1999; Lloyd and Mensch 2008; Nguyen and Wodon 2012a; Nguyen, Wodon, and Wodon 2015; 

Tian 2013; Vogelstein 2013; Wodon, Nguyen, and Tsimpo 2016), greater poverty and economic 

insecurity (UNICEF 2005), disempowerment of women (Klugman et al. 2014; MacQuarrie 2009; 

Malhotra et al. 2011; Wodon, Nguyen, and Tsimpo 2016), and gender-based violence (Raj 2010; Speizer 

and Pearson 2011; UNICEF 2005). In countries where marriage age is traditionally low, gender equality 

advocates herald marriage delay for its potential to improve girls’ education, agency, and life options. 

Marriage at young ages is also associated with “too early” pregnancy and poor reproductive health 

outcomes (Bongaarts and Cohen 1998; Godha, Hotchkiss, and Gage 2013; Santhya et al. 2010). This 

includes greater risk of maternal mortality and morbidity (Loaiza Sr. and Wong 2012; Murphy and Carr 

2009) as well as infant mortality (Amin and Bajracharya 2011a; Wachs 2008), low birth weight and child 

stunting outcomes (Lloyd and Mensch 2008; Raj et al. 2010). The World Health Organization has 

estimated that births to adolescents contribute disproportionately to pregnancy and birth-related burden of 

disease (Chandra-Mouli, Camacho, and Michaud 2013). 

 Delayed marriage is often promoted as an effective strategy to delay childbearing, particularly where 

non-marital childbearing is rare (Chandra-Mouli, Camacho, and Michaud 2013; Hindin et al. 2016). 

Public health experts welcome such trends for the potential to avert “too early” pregnancies and closely 

spaced pregnancies, and the associated negative maternal health problems. These concerns form the 

impetus for policy and programmatic actions to delay marriage among girls, which have burgeoned in 

recent years (Chandra-Mouli, Lane, and Wong 2015; Kalamar, Lee-Rife, and Hindin 2016; Lee-Rife et al. 

2012). 

That prevailing nuptiality patterns and fertility are closely associated is well acknowledged in 

demographic literature, as evidenced by the inclusion of marriage age as one of four proximate 

determinants of fertility (Bongaarts 1978, 2015). Demographers have noted both implicitly and explicitly 

that there is a link between marriage timing and the quantum and tempo of fertility (Donaldson and 

Nichols 1978). In populations with higher age at marriage, total fertility is generally observed to be low 

(Abedin 2011; Ertem et al. 2008). This reduction in total fertility with increasing marriage age can occur 

if non-marital fertility is negligible and age-specific marital fertility rates are not substantially different 

from marriage age. Where non-marital fertility is low and marriage universal, birth postponement 

promulgated by delayed marriage may contribute substantially to reduced fertility (Bongaarts 1999; 

Hirschman 1985; Hirschman and Rindfuss 1980; Timaeus and Moultrie 2008). When marriage age 

increases, fertility declines because fewer women are at risk of childbearing and they have shorter spans 

of their reproductive careers. Later marriage in such settings delays entry into childbearing. Furthermore, 
2 

the length of the first birth interval frequently affects the pace of subsequent birth intervals (Rodriguez 

and Trussell 1980; Tsui 1982). In addition, shifts toward later marriage can result in lower fertility in 

settings where later marriage results in higher levels of “voluntary control of marital fertility” through 

contraception and/or abortion (Coale 1992). Thus, increases in age at marriage result in a compression of 

reproductive life span and influence both the tempo and the quantum of total fertility (Padmadas, Hutter, 

and Willekens 2004). 

With the initiation of childbearing, empirical evidence typically observes that increases in the age at 

childbirth are smaller than increases in age at marriage (Bloom and Reddy 1986; Lloyd 2005; Mason and 

Entwisle 1985; Mensch, Bruce, and Greene 1998). That is, declining marital birth intervals accompany 

increasing age at marriage (Christensen 1939; Christensen and Bowden 1952; Dyson and Moore 1983; 

Feng and Quanhe 1996; Hirschman and Rindfuss 1982; Rindfuss and Morgan 1983; Trussell and Reinis 

1989; Tsui 1982). Trussel and colleagues suggest that the first birth interval changes curviliearly with 

marriage age (Trussell, Menken, and Coale 1979). However, trends toward longer first birth intervals over 

time have also been observed (Christensen 1939; Christensen and Bowden 1952). This pattern has two 

implications. First, advocates who seek to move early pregnancies from the teen years and early 20’s are 

likely to see smaller increases in age at first birth for each unit increase in age at marriage than desired. 

Second, increasing age at marriage and changes in the timing of the first birth are not independent trends. 

Trends in the first birth interval are likely to be associated with trends in marriage age. Furthermore, each 

of these trends may be influenced by a common set of social forces that act on both marriage timing and 

the initiation of childbearing. 

The observed association between increasing marriage age and declining first birth intervals has been 

attributed alternately to biological factors as marriage moves from women’s sub-fecund period in the 

early adolescent years to peak fecund years in the 20’s (Amin and Bajracharya 2011b; Kallan and Udry 

1986; Trussell and Reinis 1989); motivations for couples marrying at older ages to compensate for their 

“late start” (a form of “catch-up” fertility) (Basu 1993; Mensch, Bruce, and Greene 1998), or greater 

coital frequency in choice marriages or marriages to a familiar partner as opposed to arranged marriages 

that may coincide with a later age at marriage (Feng and Quanhe 1996; Fricke and Teachman 1993; 

Rindfuss and Morgan 1983). 

Empirical evidence largely bears out the expectation of a shorter first birth interval with increasing 

marriage age. While the available explanations support the general direction of the relationship between 

marriage timing and the first birth interval, they are inadequate in explaining the variation across 

populations. Although commonly co-occurring, the simultaneous trends of increasing age at marriage 

and declining first marital birth interval do not produce a clear and consistent relationship. For example, 

biological explanations of fecundity would lead us to expect shorter birth intervals at higher ages of 

marriage, and to expect birth intervals of similar duration among settings with similarly higher marriage 

ages. Instead, there is great variation in the duration of the first birth interval given an equivalent average 

age at marriage (Amin and Bajracharya 2011b). While all countries in which the median age at marriage 

was below 17 years, the first birth interval was consistently longer than 24 months. However, the birth 

interval in countries with higher median ages at marriage varies considerably. 

This variation indicates that there remains much we do not know about the trends in and relationship 

between marriage timing and timing of the first birth. Little research has focused on this topic since the 

mid-80s. More recent research has examined biological factors or trends in marriage age or first birth 

independently of one another. The sociological mechanisms that underlie this pattern remain underexplored. 

The few sociological explanations suggest that trends toward later age at marriage may be accompanied 

by other changes—erosion of arranged marriage (Feng and Quanhe 1996; Ghimire 2016; Ghimire and 
3 

Axinn 2013; Rindfuss and Morgan 1983), changes in spousal familiarity and coital frequency (Basu 1993; 

Fricke and Teachman 1993; Lavely 2007; Rindfuss and Morgan 1983), changing expectations about 

women’s education and employment (Field and Ambrus 2008; Hirschman 1985; Hirschman and Rindfuss 

1980; Kim and Stinner 1980), and shifts toward greater women’s empowerment and a more equitable 

gender context (Desai and Andrist 2010; Dyson and Moore 1983; Malhotra and Tsui 1996; Mensch, 

Singh, and Casterline 2005; Shrestha 1998). These may also contribute to changes in the first birth 

interval directly or indirectly through their interaction with marriage age. For example, women who 

marry at older ages are more likely to participate in the selection of their marriage partner and have 

greater decision-making capacity upon marriage (Banerji and Vanneman 2011; Hong 2006; Jensen and 

Thornton 2003; Singh and Becker 2012). Such factors may influence the first birth interval directly 

through differences in coital frequency or the ability to enact decisions to control fertility. Whether an 

increase in marriage age is the only change or whether it is accompanied by other significant social 

changes and for whom may determine the degree of the marriage age’s influence on the first birth 

interval. We also do not know how robust the effect of marriage age is on subsequent birth spacing 

beyond the first birth. 

Underexplored questions include (1) for whom marriages occur later and births earlier; that is, are these 

changes experienced universally by all women or are they concentrated among select social groups? It 

has been documented that marriage age varies across socio-economic and socio-cultural factors with, for 

example, women from wealthier families marrying at younger ages and those with more education 

marrying at older ages (Aryal 2007; Jensen and Thornton 2003; Kim and Stinner 1980). Work by Coale 

and colleagues suggests that the age pattern of marriage follows a predictable normal distribution in 

settings where the age of marriageability is low, although as the mean marriage age rises, so does the 

degree of heterogeneity (Coale 1971; Coale and McNeil 1972). It is possible that the increasing 

heterogeneity in the distribution of marriage ages could account for some of the observed variation in first 

birth intervals at older mean marriage ages (Amin and Bajracharya 2011), particularly if increases in 

marriage age are unevenly distributed, for example, across educational or wealth subgroups. 

Other questions that are underexplored are (2) whether births are occurring earlier for all who marry later, 

i.e., if the rate of the first birth interval is evenly distributed; (3) whether compositional shifts in marriage 

age or other social changes explain changes in the first birth interval and subsequent birth spacing; (4) the 

relative importance of marriage age when compared to these other factors in explaining the timing of the 

first birth; and (5) the relative importance of marriage age in explaining the first birth interval and 

subsequent birth spacing across country contexts. 

This study examines the relationship between marriage age and two aspects of the family formation 

process: the initiation of childbearing (timing of the first birth) and subsequent birth spacing (the 

transition from the first to the second birth). To do so, the study uses Demographic and Health Surveys 

(DHS) data from seven countries in Asia: Bangladesh, India, Nepal, and Pakistan in South Asia and 

Cambodia, Indonesia, and the Philippines in Southeast Asia. Each of these countries has experienced a 

statistically significant change in the age at marriage or at first birth or both, while maintaining a norm of 

universal marriage. This study examines trends in marriage age and the first birth interval over time, 

along with shifts in the association of gender, women’s, and husband’s characteristics with marriage age 

and first birth interval. The study assesses the influence of age at marriage on the first birth interval 

through a multivariate hazard model and subsequently, decomposes the change in the first birth interval 

over time into shifts in composition (marriage age and other factors) and changes in effects (marriage age 

and other factors) upon the first birth interval. Finally, the study estimates a second hazard model to 

determine the independent influence of marriage age and the first birth interval on the second birth 

interval. 

5 

2. Methods and Data 

2.1. Country and Survey Selection 

This study uses data from seven countries in Asia: Bangladesh, India2

, Nepal, and Pakistan in South Asia 

and Cambodia, Indonesia, and the Philippines in Southeast Asia. Countries are included in the study if 

(1) marriage is or approaches being universal (≥95% women married by age 40); (2) the majority of 

childbearing occurs within marriage; (3) there has been a statistically significant change in either the age 

at marriage or at first birth or both; (4) there are two or more standard DHS surveys available over a span 

of more than 5 years; and (5) these surveys include the variables of interest. 

DHS surveys are nationally representative, population-based household surveys that produce a broad 

range of demographic and health indicators. The surveys are generally large and enjoy response rates in 

excess of 90%. Standard DHS surveys routinely collect data on women’s birth histories and on the 

timing of marriage; this makes these data well suited to the analysis of a variety of marriage and fertility 

dynamics. The surveys also collect data on a broad range of individual background characteristics and 

gender and women’s empowerment relevant to the analysis of such dynamics. The surveys employ 

standardized questionnaires and modules for household, women’s, and men’s interviews (ICF 

International 2015). As such, they produce indicators that are comparable across countries and within 

countries over time. 

Since The DHS Program began in 1984, it has conducted more than 300 surveys in 90 countries. 

Standard DHS surveys are implemented at intervals of about 5 years to allow for comparisons over time. 

Thirty-three surveys have been conducted in the countries selected for this study. This study uses the 

individual woman’s survey, which is conducted among women of reproductive age (15-49). It uses each 

survey conducted in study countries to describe overall trends in the timing of marriage and first birth. 

However, the majority of the analysis uses either the most recent survey or the most recent survey paired 

with a survey that was conducted approximately 10 years prior (“priority surveys”). Sample parameters, 

including sample sizes and response rates, for the study’s surveys can be found in Table 1. Priority 

surveys are indicated in bold. 

 

 

2

 India DHS surveys are also referred to as the National Family and Health Surveys (NFHS). 
6 

Table 1. Sample parameters for surveys included in the analysis 

Region and 

Country 

Year of 

survey Sample type 

Age 

range 

# of women interviewed 

(unweighted) 

Eligible 

woman 

response 

rate 

South Asia 

Bangladesh 

2014 Ever married women 15-49 17,863 97.9 

2011 Ever married women 12-49 17,842 97.9 

2007 Ever married women 15-49 10,996 98.4 

2004 Ever married women 10-49 11,440 98.6

1999-2000 Ever married women 10-49 10,544 96.9 

1996-97 Ever married women 10-49 9,127 97.8 

1993-94 Ever married women 10-49 9,640 97.4 

India 

2005-06 All women 15-49 124,385 94.5

1998-99 Ever married women 15-49 90,303 95.5

1992-93 Ever married women 13-49 89,777 96.1 

Nepal 

2011 All women 15-49 12,674 98.1

2006 All women 15-49 10,793 98.4 

2001 Ever married women 15-49 8,726 98.2

1996 Ever married women 15-49 8,429 98.2 

Pakistan 

2012-13 Ever married women 15-49 13,558 93.1

2006-07 Ever married women 15-49 10,023 94.5

1990-91 Ever married women 15-49 6,611 96.3 

Southeast Asia 

Cambodia 

2014 All women 15-49 17,578 97.6 

2010 All women 15-49 18,764 97.5 

2005 All women 15-49 16,823 97.5

2000 All women 15-49 15,341 98.7 

Indonesia 

2012 All women 15-49 45,067 95.9

2007 Ever married women 15-49 32,895 96.1 

2002-03 Ever married women 15-49 29,483 98.3

1997 Ever married women 15-49 28,810 98.3 

1994 Ever married women 15-49 28,168 97.8 

1991 Ever married women 15-49 22,909 97.6 

1987 Ever married women 15-49 11,884 98.5 

Philippines 

2013 All women 15-49 16,155 98.3

2008 All women 15-49 13,594 98.3 

2003 All women 15-49 13,633 97.8

1998 All women 15-49 13,983 97.2 

1993 All women 15-49 15,029 98.0 

Note: Analysis is restricted to ever-married women age 25-49. 

2.2. Sampling and Sample Weighting 

The surveys employ multistage, clustered area sampling techniques. In the first sampling stage, the 

country is stratified into major subnational regions from which census-based enumeration areas are 

selected with probability proportional to size. The major regions may or may not coincide with 

administrative units (as in the Philippines) and consist of states (India), provinces or groups of provinces 

(Cambodia, Indonesia, Pakistan), divisions (Bangladesh), or ecological zones (Nepal). Urban areas and 

less populous areas are typically oversampled in the first sampling stage to produce reliable regional 

estimates and rural-urban comparisons of health indicators. A mapping and household listing exercise is 

then conducted in each selected enumeration area. In the second sampling stage, households are 

randomly selected from the household list within each enumeration area. 
7 

Pre-calculated sampling weights are applied; these account for both sampling probability and nonresponse. In addition, the study uses the complex survey (svy) commands available within Stata 14.1 to 

account for the clustered sampling design and to estimate robust standard errors as the basis for 95% 

confidence intervals reported in the following sections. 

2.3. Sample Restriction 

This study’s analysis is restricted to ever-married women age 25-49. This age restriction is imposed 

because the median age at marriage and first birth have not been reached by the start of the preceding age 

group, age 20-24, in all study countries; the median ages at these events have been reached by the start of 

the 25+ age groups. This restriction reduces the selection bias that would be present if early-marrying 

women were over-represented in the data. 

Table 2 shows the attrition in sample size from excluding never-married women. As indicated, the 

proportion of women who are never-married at the most recent survey is sizable in Cambodia and the 

Philippines. Elsewhere, it does not exceed 5% of all women age 25-49. 

While experiencing a birth is not a precondition for inclusion in the analytical sample, the analysis is 

restricted to women who marry prior to their first birth. That is, women who experience a negative first 

birth interval because their first birth occurred prior to marriage are excluded from all analyses. This 

demographically convenient life order sequence facilitates a valid and meaningful measure of the interval 

between marriage and first birth. The proportion of women experiencing a negative birth interval is 

small—less than 3%—in all study countries with the exception of the Philippines (7%), also as seen in 

Table 2. Analysis of the second birth interval is necessarily limited to women who have already 

experienced a first birth. 

Table 2. Proportion of women age 25-49 excluded from analysis due to 

never being married or experiencing a negative first birth interval 

Never married 

Negative first birth 

interval 

 % # (unweighted) % # (unweighted) 

South Asia 

Bangladesh 2014 0.00 0 1.24 165 

India 2005-06 2.44 3,623 1.68 1,307 

Nepal 2011 3.06 232 1.50 113 

Pakistan 2012-13 0.00 0 1.58 166 

Southeast Asia 

Cambodia 2014 7.99 974 2.34 276 

Indonesia 2012 4.66 1,850 2.62 1,006 

Philippines 2013 11.98 1,118 7.01 615 

2.4. Measures 

Century month codes 

In DHS datasets, the dates of several key events are recorded in century month codes (Rutstein and Rojas 

2006). A century month code (CMC) is the number of months elapsed between an event and January 

1900. For example, the CMC corresponding to June 2012 is 1350. Century month codes are used to 
8 

record and calculate intervals between the respondent’s date of birth, date of first union, date of birth for 

each of the respondent’s children, and date of interview. The DHS invests significant effort to ensure that 

dates of these events are accurately reported through multiple data checks and procedures for reconciling 

discrepant reports and imputing missing information. 

Age at marriage 

Age at marriage refers to women’s first marriage. This is calculated as the difference between the CMC 

of the respondent’s date of first union and date of birth. This difference, expressed in months, is then 

divided by 12. Date of first union refers to the time that the respondent first began living with her 

husband. Cohabitation is a better measure of the initiation of marital sexual exposure—and thus of the 

starting point for the period of risk for the first birth interval—than date of formal marriage in settings 

where ceremonial marriages, spousal separation prior to menarche, or return marriage characterize the 

marriage process (Basu 1993; Bhattacharya et al. 1989; Choe, Thapa, and Mishra 2005; Rodriguez and 

Trussell 1980). Unions include both formal, legally recognized marriages as well as non-formal unions in 

which women are living together with a man as if married. 

First birth interval 

The first birth interval is the interval between marriage and the first birth. The length of the first birth 

interval is calculated as the difference between the century month codes for date of first union and date of 

first birth as expressed in months. Negative birth intervals (premarital births) are excluded from the 

analysis. First birth intervals of less than 8 months, which may possibly indicate premarital conceptions 

followed by a marriage and then birth, are retained in the analysis. 

Second birth interval 

Subsequent birth spacing is assessed through analysis of the second birth interval, which is the span of 

time between the first and the second births. The second birth interval is calculated as the difference 

between the century month codes for the second birth and the first birth, and is expressed in months. 

Birth cohort 

The measure that indicates the decade in which women were born is calculated based on the century 

month code for their date of birth. In this calculation, 1960-69 serves as the reference category in 

multivariate analysis. Only the India dataset includes respondents in the 1950-59 birth cohort. All others 

include women born in the 1970s and 1980s, in addition to the referent period. 

Women’s decision-making 

Women’s decision-making is one of three variables used to describe the gender context. The indicator is 

calculated as a simple (unweighted) additive index of the sum of the number of household decisions in 

which women participate. Component items are participation in decisions about: 

1. Healthcare for herself 

2. Major household purchases 

3. Visits to her family or relatives 

This index ranges from 0 to 3 in six of the seven study countries. In the Philippines, it ranges from 0-4 

with an additional item about participation in decisions about purchases for daily household needs. Data 

on women’s decision-making are included in the most recent survey for all countries in this study. 

However, these data were omitted from some earlier surveys in India and Pakistan. Therefore, this 

measure is excluded from analyses of change over time in these countries. 
9 

Attitudes toward wife beating 

Women’s attitudes toward wife beating, the second measure that describes the gender context, are 

assessed by responses to question that ask if respondents think a husband is justified in hitting or beating 

his wife in the following situations: 

1. If she goes out without telling him 

2. If she neglects the children 

3. If she argues with him 

4. If she refuses to have sex with him 

5. If she burns the food 

A dichotomous measure is calculated and set to 0 if the respondent reports wife beating to be acceptable 

in at least one of these scenarios and 1 if she rejects wife beating in all of these scenarios. 

In the Nepal 2011 DHS survey but not the 2001 survey, a filter question was inserted. Respondents 

answering “no” when asked, “In your opinion, should a husband hit or beat his wife for any reason at 

all?” were not asked if wife beating was justified in specific scenarios. This questionnaire change may 

result in a measurement change (decrease) in the prevalence of attitudes accepting of wife beating as well 

as in detecting any associations with this variable. 

Data on women’s attitudes toward wife beating were not collected in Bangladesh DHS surveys. In 

addition, these data were omitted from some earlier surveys in India and Pakistan. Therefore, this measure 

is excluded from the analyses of change over time in these countries. Data on women’s attitudes toward 

wife beating were collected consistently in the three Southeast Asian countries in this study. 

Spousal age difference 

Spousal age difference is the third variable that describes the gender context. This measure is calculated 

as the difference, in whole years, between women’s completed age and that of her husband. In situations 

where women are the same age as or are older than their husbands, this indicator is set to 0. Therefore, 

this measure can be interpreted as the number of years by which husbands’ age exceeds the woman’s age. 

Women’s education 

Women’s education at the time of the survey is categorized into no education, primary, secondary, and 

higher education. No education is the reference category. 

Women’s occupation 

Women’s occupation is an indicator that captures whether women worked in the 12 months preceding the 

interview and, if so, in what occupation. Not working serves as the reference category. Occupational 

categories are: 

1. Agricultural 

2. Professional/technical/managerial (hereafter referred to as “professional”) 

3. Clerical 

4. Sales 

5. Services 
10 

6. Skilled manual labor 

7. Unskilled manual labor 

8. Other 

No data on women’s occupation is available in the Bangladesh DHS. In India, there is no differentiation 

between skilled and unskilled manual labor. In Nepal, there is no separate category for women working 

in sales. These respondents are grouped with those working in “other” occupations. 

Household wealth quintile 

This study uses household wealth quintile as a measure of relative wealth. This measure is calculated 

based on ownership of a range of assets and housing materials. The construction of this measure, now 

standard in DHS surveys, is described in detail elsewhere (Rutstein 2008; Rutstein and Johnson 2004). 

The poorest wealth quintile serves as the reference category. 

Religion 

Women’s religious affiliation is a variable with country-specific response options. As a result, the 

reference religion is country-specific. The reference group is the largest religious group in each country. 

The reference religious group and other religious categories for each country are listed in Appendix Table 

4. Muslim, Christian, Hindu, and Buddhist are common religious classifications found in the study 

countries. No data on religious affiliation is available in the Pakistan or Indonesia DHS surveys. 

Place of residence 

Place of residence captures whether the respondent resides in a rural or an urban area at the time of the 

survey, based on a priori classification of primary sampling units selected for the survey. Rural is the 

reference category. 

Subnational region 

Subnational region is the second country-specific indicator in the analyses. Subnational regions in this 

variable are those used in the first sampling stratum, along with urban/rural place of residence. These are 

divisions in Bangladesh; states in India (26); provinces or small groupings of provinces in Cambodia (19), 

Indonesia (6), and Pakistan (4); ecological zones in Nepal (3); and administrative regions in the 

Philippines (17). Although Bangladesh currently has 7 divisions, Rangpur and Rajshahi divisions were 

sampled as a single region in 2004 and not separately. Therefore, these two divisions were combined to 

ensure compatibility in the analyses of change over time. 

Several pairs of states from the India 2005-06 dataset—Bihar and Jharkhand, Madhya Pradesh and 

Chattisgarh, Uttar Pradesh and Uttaranchal—were combined to be comparable to sampled states in the 

1998-99 dataset. Similar changes and exclusions were made in Indonesia to account for territorial 

changes. In every country, the most populous region serves as the reference group. These are Dhaka, 

Bangladesh; Uttar Pradesh (including Uttaranchal), India; the Terai, Nepal; Punjab, Pakistan; Phnom 

Penh, Cambodia; Sumatera, Indonesia; and the National Capital administrative region, the Philippines. A 

full listing of the regions by country is found in Appendix Table 2. 

Husband’s education 

Husband’s education is taken from women’s reports of their husband’s level of educational attainment. 

This measure is also categorized as no education, primary, secondary, and higher education. There is a 

fifth category that does not exist for women’s education: don’t know. No education is the reference 

category. 
11 

Husband’s occupation 

Husband’s occupation, also taken from women’s reports, is categorized in the same manner as women’s 

occupation: 

1. Agricultural 

2. Professional/technical/managerial (hereafter referred to as “professional”) 

3. Clerical 

4. Sales 

5. Services 

6. Skilled manual labor 

7. Unskilled manual labor 

8. Other 

9. Not working 

Whereas “not working” is the reference category for women’s occupation, agricultural occupation is the 

reference category for husband’s occupation. Agricultural occupation is a category in every survey with 

data on husband’s occupation and is frequently the most prevalent occupation. Indonesia 2012 DHS, 

Cambodia 2014 DHS, and Philippines 2013 DHS do not collect data on husband’s current employment 

status, but categorizes husband’s occupation regardless of current employment status. India 2005-06 

DHS (NFHS-3) and Indonesia 2012 DHS capture husband's current employment status as a separate 

occupational category and categorize husband's occupation only for husbands who are currently working. 

As with women’s occupation, no data on the husband’s occupation are available in the Bangladesh DHS. 

In India, there is no differentiation between skilled and unskilled manual labor. In Nepal, there is no 

separate category for husbands working in sales. These respondents are combined with those working in 

“other” occupations. 

2.5. Scope of the Analysis and Analytic Strategy 

In the following sections, this study describes overall trends in the age at marriage and first birth. This 

depiction of trends uses the median ages at marriage and first birth, as completed age in years, using every 

DHS survey as a data point. Although medians are a more stable measure of central tendency for 

distributions with right-hand skew, they are a more crude measure of the age of these events because they 

are calculated from age in completed years, rather than months (Rutstein and Rojas 2006). The purpose 

of this section is to provide an overview of trends over time in the age at marriage, age at first birth, and 

the interval between marriage and the first birth, as well as to discern any regional patterns in these 

events. 

Different population subgroups may exhibit distinctive patterns in the timing of the first birth. These 

differences may be due, in part, to distinctive patterns in marriage age. In the subsequent two sections, 

therefore, the study extends its analysis of trends over time by initiating an examination into the 

compositional shifts, first in marriage age and secondly, in the first birth interval. This study specifically 

investigates the distribution across background characteristics in the mean age at marriage and mean 

length of the first birth interval, respectively. These two sections present data at two points of time: the 

most recent DHS survey (Survey 2) and a DHS survey approximately one decade earlier (Survey 1). This 

time point is selected for Survey 1 so that it includes as many variables that describe the gender context as 

possible; earlier surveys often exclude these measures. 
12 

Two statistical tests are shown. The first is a test for the significance of the difference in means between 

surveys, disaggregated by each background characteristic. The test detects if change in marriage and first 

birth patterns is occurring in a common manner for all groups of the population, whether changing trends 

are concentrated within certain segments of the population, or whether there are divergent trends in 

opposing directions for the different groups. 

The second statistical test is a test of independence to determine to what extent each background 

characteristic may be associated alternately with marriage age and the first birth interval. Because the test 

is repeated at both Survey 1 and Survey 2, changes in the association of background characteristics with 

these outcomes can also be detected. Sample sizes on which marriage age patterns and trends are 

analyzed are presented in Appendix Table 1. 

With the principal outcome—the first birth interval—the time to event is of interest. Therefore, this is 

analyzed in a survival analysis framework. This approach allows us to use data on all respondents, 

whether or not they have experienced the failure event (first birth) in question, while accounting for right 

censoring beyond the observation period. Therefore, in this section, the means in the survival experience 

are reported. Specifically, an extended mean is computed to account for any underestimation in the 

restricted mean that would occur because the last observed analysis time is censored for those women 

who have not experienced a first birth by the time they were interviewed. Differences in means are 

assessed with a Tarone-Ware test for the equality of survivor functions across groups and survey years 

(Tarone and Ware 1977). Similar to the Wilcoxon test, the Tarone-Ware test is appropriate when hazard 

functions may vary non-proportionally across groups. The Tarone-Ware test gives greater weight than 

the Wilcoxon test to earlier failure times, when more cases are at risk (Cleves et al. 2010; Tarone and 

Ware 1977). Sample sizes, including the number of cases who experience failure (had a first birth) and 

the total person-months of observation contributed by the sample, are presented in Appendix Table 2. 

Next, the study compares the association of marriage age with the first birth interval first in bivariate and 

multivariate hazard models. These models also identify other background characteristics that may be 

associated with the first birth interval, independent of marriage age. Multivariate models control for birth 

cohort to account for period shifts in the timing of marriage and childbearing; gender context (women’s 

decision-making, attitudes toward wife beating, and spousal age difference); socio-demographic 

characteristics (education, occupation, household wealth, religion, place of residence, and subnational 

region); and husband’s characteristics (education and occupation). 

Hazard models are estimated as loglogistic accelerated failure time (AFT) models using data from the 

most recent DHS survey, with time ratios (i.e. exponentiated coefficients) reported for ease of 

interpretation (Allison 1995; Box-Steffensmeier and Jones 2004). The loglogistic distribution was 

selected as the best or second-best fitting distribution in each country, as assessed by the low Akaike 

Information Criterion (AIC) and Bayesian Information Criterion (BIC) from tests for non-nested hazard 

models (Box-Steffensmeier and Jones 2004; Cleves et al. 2010). Results of model diagnostics are 

reported in Appendix Table 3. The loglogistic AFT model is a fully parametric hazard model in which 

hazards are not assumed to be proportional, which prior research has suggested may not be the case with 

covariates of the first birth (Trussell and Bloom 1983). The loglogistic model implies a hazard 

distribution h(t) that is unimodal and not necessarily monotonic, with the scale of the logistic-distributed 

baseline hazard3

 estimated by γ (Allison 1995; Box-Steffensmeier and Jones 2004). When γ approaches 

0.5, as it does in this study’s models, it implies an underlying hazard that quickly increases with time 

 

3

 As such, the log-logistic hazard model is analogous to the logit model for discrete dependent variables. 
13 

upon the onset of risk (marriage) before declining with a long tail as time increases4 (Allison 1995; 

Cleves et al. 2010). 

This study then estimates a multivariate decomposition analysis of the first birth interval. This analysis 

uses data from both Survey 1 and Survey 2 to examine whether changes in the first birth interval over 

time are due to changes in the levels/distribution of marriage age, or whether the influence of marriage 

age on the first birth interval has changed over time. The multivariate models use the same measures to 

describe the gender context, and women’s socio-demographic and husband’s characteristics as in the 

hazard models. This allows analysis of the extent to which changes in the first birth interval are due to 

compositional changes in the population other than changes in age at marriage, or differences in their 

influence on the first birth interval between surveys. 

The multivariate decomposition models are estimated using the mvdcmp commands available in Stata 

14.1 (Powers, Yoshioka, and Yun 2011). This form of decomposition extends Oaxaca-Blinder 

decomposition techniques to non-linear models such as those handling hazard rates as well as observed 

difference in group means or proportions (Blinder 1973; Oaxaca 1973; Powers and Yun 2009). The 

mvdcmp multivariate decomposition procedure has several advantages for this study. The method allows 

for overall decomposition into the composition component (also known as the characteristics or 

endowments component) and the effects component (also known as the coefficients or rate component). 

It also allows for a detailed decomposition of each variable in the model. The second advantage is a 

correction for the problem of path dependence, in which a nonlinear decomposition is sensitive to the 

order in which variables are entered into the model, or to the selection of the reference category of 

dichotomous covariates, by applying a normalized decomposition. 

A piece-wise constant exponential decomposition model is both computationally complex, requiring a 

dummy variable for each person-period of observation (months), and assumes a proportional hazard 

across groups that is inappropriate for these data. Instead, I opt for a linear regression decomposition, 

partitioning the observed group mean into its composition and effects components. The linear regression 

conveniently produces exponentiated coefficients that are similar in interpretation to the time ratios 

presented for the hazard models in this study. However, the coefficients from a linear model may be 

biased if excessive right censoring is present. Fortunately, as shown earlier, the vast majority of observed 

cases fail (have a first birth) during the period of observation, and this mitigates the extent of such bias. 

To assess the extent to which the linear form of the decomposition model is sensitive to any such bias 

present in the data, the β coefficients for a simple linear regression model are compared with those of a 

loglogistic model for each country at Survey 2. The results of this sensitivity analysis are presented in 

Appendix Table 6. The results indicate minor variations in the detected significance level for certain 

categories of selected variables, including several categories of borderline significance (p value close to 

the p≤0.05 threshold) that appear to be significant in one model and non-significant in another. However, 

the failure to detect significant associations or to falsely detect a significant association is not systematic 

across surveys, nor were any discrepancies found in the direction of associations. 

The final analysis presented in this study examines the implications of marriage age on subsequent birth 

spacing. Specifically, the study estimates hazard models of the second birth interval. This analysis is 

based on all women in the analytic samples who have had a first birth. As with the hazard models for the 

first birth interval, the second birth interval is estimated with loglogistic accelerated failure time models 

that report results in time ratios. As with the earlier hazard models, the sample sizes, observed failures, 

and person-months of observation contributed are displayed in Appendix Table 13. Model diagnostics, 

including the AIC and BIC, which were used to select the hazard form, are displayed in Appendix 

Table 14. 

 

4

 This hazard function is remarkably similar to a lognormal distribution in which σ=1. 
14 

These models use age at marriage and the same covariates that are used to estimate the hazard of the first 

birth interval. In addition, a categorical measure that describes the duration of the first birth interval are 

entered into the models. The inclusion of this measure indicates whether marriage age has an independent 

effect on the second birth interval or if it influences it at all, does so only through its effect on the duration 

of the first birth interval. The categorical measure of the duration of the first birth interval aligns roughly 

with terciles in South and Southeast Asian countries in the first birth interval. For South Asian countries, 

the categories are less than 16 months, 16-32 months, and greater than 32 months. In Southeast Asian 

countries, the categories are less than 12 months, 12-22 months, and greater than 22 months. Data on the 

observed terciles for each study country at the most recent survey are available in Appendix Table 15. 
15 

3. Trends in Median Age at Marriage and Age at First Birth 

Figure 1 in this section displays the median completed age at marriage and first birth among women age 

25-49 for the seven countries in this study. In addition to data from the most recent survey, this section 

draws upon data from all DHS surveys conducted in study countries to describe trends. The presented 

trends data cover approximately 15-20 years5

. Figure 1 reflects two separate regional patterns in marriage 

age and first birth. Subsequent sections will present results with study countries grouped regionally. 

3.1. Marriage and the First Birth in South Asia 

The data for Bangladesh, India, Nepal, and Pakistan indicate that marriage in a phenomenon of 

adolescence. In these South Asian countries, the median completed age at marriage ranges from age 15.8 

in Bangladesh to age 19.5 in Pakistan. India and Nepal are similar to one another with a median age at 

marriage of about age 17.5. 

The median completed age at marriage has increased most rapidly in Nepal and Bangladesh, and has 

increased the least—by just 6 months over 13 years—in India. In Bangladesh, the median completed age 

at marriage has increased from age 14.1 in 1993-94 to age 15.8 in 2014, with most of that increase 

occurring in the last 10 years. 

The onset of childbearing is an event that occurs during adolescence for more than half of women in 

Bangladesh and India. However, the median completed age at first birth is in the early 20s in Nepal and 

Pakistan. Until the most recent survey, the median completed age at first birth was less than age 20 in 

Nepal as well. Like the median completed age at marriage, the median completed age at first birth has 

also increased over time although it has increased more slowly than marriage age. The rate of increase 

has been slowest in Nepal, which experienced the most rapid increase in marriage age. Here, the median 

completed age at first birth increased by nearly 5 months over the 15 years. In contrast, the median 

completed age at first birth increased by nearly 11 months over 12 years in Pakistan, keeping pace with 

the increase in age at marriage there. The increase in the median completed age at first birth in 

Bangladesh and India are similar to one another and are between that of Nepal and Pakistan. However, 

the pace of change has increased in Bangladesh between 2004 and 2014; this nearly approaches the pace 

of change in Pakistan. 

 

 

5

 A standard DHS has not been conducted in Cambodia prior to 2000; therefore, one decade of data is presented for 

Cambodia. 
16 

Figure 1a. Trends in the median completed age at key events among women age 25-49 in four 

South Asian countries 

17.5 17.6

18.2

14.1 14.5

15.8

13

14

15

16

17

18

19

20

21

22

23

24

1990 1993-94 1996-97 1999-00 2004 2007 2011 2014

Median age

Bangladesh

19.4 19.4 19.8

16.9 17.1 17.4

13

14

15

16

17

18

19

20

21

22

23

24

1990 1992-93 1998-99 2005-06 2014

Median age

India

19.8 19.9 20.2

16.2

16.7

17.5

13

14

15

16

17

18

19

20

21

22

23

24

1990 1996 2001 2006 2011 2014

Median age

Nepal

First birth First sexual intercourse First marriage

21.3

21.8 22.2

18.6

19.1 19.5

13

14

15

16

17

18

19

20

21

22

23

24

1990-91 1995 2000 2006-07 2012-132014

Median age

Pakistan
17 

Figure 1b. Trends in the median completed age at key events among women age 25-49 in three 

Southeast Asian countries 

The interval between the median completed age at marriage and first birth has narrowed over time (except 

in Pakistan) because the age at marriage has increased more quickly than age at first birth. There is little 

variation across the four South Asian countries in the difference between the median completed ages at 

first birth and marriage. The difference is 2.4 years in Bangladesh and India, and 2.7 years in Nepal and 

Pakistan. 

3.2. Marriage Age and First Birth in Southeast Asia 

In contrast to the four South Asian countries in this study, the median completed age at marriage is older 

than the adolescent years in the three Southeast Asian countries studied here, with ranges from age 20.4 in 

Indonesia to age 22.3 in the Philippines. For two countries—Cambodia and the Philippines—the median 

completed age at marriage has been in the 20s for all survey data points. In Indonesia, the median 

completed age at marriage did not cross this threshold until the most recent survey in 2012. 

Indonesia shows the most rapid increase in median completed age at marriage, not just among the three 

Southeast Asian countries, but among all seven countries. The age has increased from age 17.7 in 1991 to 

20.4 in 2012. The pace of change in Cambodia and the Philippines resembles that seen in India. 

21.9 22.0 22.4

20.0 20.1 20.5

13

14

15

16

17

18

19

20

21

22

23

24

1990 1995 2000 2005 2010 2014

Median age

Cambodia

20.1

21.0

22.0

17.7

19.2

20.4

13

14

15

16

17

18

19

20

21

22

23

24

1991 1994 1997 2002-03 2007 2012 2014

Median age

Indonesia

22.8 23.2 23.5

21.6 22.0

22.3

13

14

15

16

17

18

19

20

21

22

23

24

1990 1993 1998 2003 2008 2013

Median age

Philippines

First birth First sexual intercourse First marriage
18 

The median completed age at first birth ranges from age 22 in Indonesia to age 23.5 in the Philippines. 

The fastest pace of change of all study countries has also occurred in Indonesia, with the median 

completed age at first birth rising approximately two years between 1991 and 2012. In Cambodia and the 

Philippines, the rate of change has been more modest and has kept pace with the increase in marriage age. 

The difference in median completed ages at first birth and marriage among the Southeast Asian countries 

ranges from 1.2 years in the Philippines, where age at marriage is the highest, to 1.9 years in Cambodia. 

This stands in contrast to the difference found in South Asia, which is two and a half years or more. The 

difference between median completed ages at first birth and marriage has remained steady in Cambodia 

and the Philippines. In Indonesia, the slower increase in the median completed age at first birth compared 

to that in the median completed age at marriage results in a narrowing of their difference over time. In 

1991, the median completed age at these events was separated by 2.4 years, which is similar to the 

difference currently observed among the South Asian countries in the study. In 2012, as marriage age 

transitioned from the adolescent years to the 20s, it has narrowed to 1.6 years; this resembles the 

difference observed among the other two Southeast Asian countries studied here. 

3.3. Age at First Sex 

This study does not focus on the timing of first sex in its examination of the dynamics of marriage age 

and the first birth interval. Nonetheless, it may be of interest to examine to what degree marriage 

coincides with sexual exposure. The median completed age at first sex, where available6

, is also 

displayed (in green) in Figures 1a and 1b. These data indicate that, overall, the median completed age at 

first sex tracks very closely with the median completed age at marriage. These ages are almost identical 

in Bangladesh, India, and Nepal, while there has sometimes been a slight difference between these ages in 

Cambodia and Indonesia. In the Philippines, the median completed age at first sex and at marriage have 

coincided up through 2003. However, the median completed age at first sex has been younger than that at 

marriage since 2008. First sex and marriage are separated by nearly 10 months at the most recent survey. 

 

6

 Data on age at first sex were not collected in some early DHS surveys in Bangladesh and India; no data on age at 

first sex have been collected in any Pakistan DHS. 
19 

4. Characteristics Associated with Age at Marriage 

Tables 4-14 in this section show the distribution across background characteristics of the mean age at 

marriage among ever-married women age 20-49 and the results of tests of significance of the association 

between each background characteristic and the age at marriage. These tables show the means and 

associations with background characteristics at two time points for each study country: the most recent 

DHS survey (Survey 2) and a DHS survey approximately 10 years prior7

 (Survey 1). These tables also 

show the positive (increasing age at marriage) or negative difference (declining age at marriage) for each 

background characteristic and results that test the statistical significance of the change. Sample sizes are 

shown in Appendix Table 1. 

Table 3 shows the total change in the mean age at first marriage in the seven study countries. The change 

in the mean age at marriage has been statistically significant in all seven study countries, in South Asia 

and Southeast Asia alike. Among the South Asian countries, mean age at marriage has increased by 0.4 

to 0.9 year between surveys (p=0.000). In Southeast Asia, the increase between surveys has been more 

modest in Cambodia (0.3, p=0.000) and the Philippines (0.2, p=0.013). However, the largest increase is 

in the Philippines, where the mean age at marriage rose by 1.25 years between 2003 and 2013 (p=0.000). 

Table 3. Change in the mean age at first marriage among ever-married women age 25-49 

Survey 1 Survey 2 Difference 

survey 1-

Country Mean CI Mean CI p-value survey 2 1

South Asia 

Bangladesh 2004 2014 

15.28 15.16 15.41 16.21 16.09 16.33 0.93 ***

India 17.39 17.32 17.46 17.76 17.70 17.82 0.4 ***

Nepal 2001 2011

17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 2006-07 2012-13

19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Southeast Asia

Cambodia 2005 2014

20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 2002-03 2012 

19.24 19.08 19.39 20.49 20.34 20.64 1.25 ***

Philippines 2003 2013

21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

1 p-value of significance test for the difference in means between surveys. 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

4.1. Marriage Age and Gender Context 

Three variables describe the gender context and its possible association with age at marriage. These are 

the number of decisions in which women are involved, attitudes toward wife beating, and spousal age 

difference. Data that show the mean age at marriage according to each of these variables, respectively, are 

found in Tables 4-6. India and Pakistan surveys did not collect data on women’s decision-making and 

wife beating attitudes at Survey 1 and, in Bangladesh, neither survey collected data on wife beating 

 

7

 Surveys in India and Pakistan, which are approximately 6 years apart, are the exception. 
20 

attitudes. Thus, change in marriage age according to these characteristics cannot be assessed for these 

countries. 

Table 4. Mean age at first marriage among ever-married women age 25-49, by women’s decision-making 

 Survey 1 Survey 2 Difference 

survey 1-

Country Number of decisions Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia 

Bangladesh 

2004 2014 

*** *** 

0 15.07 14.88 15.26 16.11 15.96 16.26 1.04 ***

1 15.15 14.94 15.35 16.01 15.78 16.25 0.87 ***

2 15.45 15.24 15.66 15.98 15.79 16.18 0.54 ***

3 15.38 15.24 15.53 16.45 16.31 16.59 1.07 ***

Total 15.28 15.16 15.41 16.21 16.09 16.33 0.93 ***

India 

 1998-99 2005-06

*** 

0 na na na 17.37 17.26 17.47 -- --

1 na na na 17.75 17.64 17.86 -- --

2 na na na 17.73 17.64 17.83 -- --

3 na na na 18.04 17.96 18.13 -- --

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.4 ***

Nepal 

2001 2011

ns ns 

0 17.20 17.02 17.38 17.88 17.55 18.21 0.68 ***

1 17.08 16.83 17.34 18.08 17.76 18.41 1.00 ***

2 17.18 16.95 17.42 17.94 17.65 18.23 0.76 ***

3 17.13 16.93 17.32 17.93 17.72 18.15 0.80 ***

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 

 2006-07 2012-13

 ** 

0 na na na 19.95 19.68 20.22 -- --

1 na na na 20.02 19.62 20.42 -- --

2 na na na 19.60 19.27 19.93 -- --

3 na na na 19.52 19.30 19.74 -- --

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Southeast Asia

Cambodia 

2005 2014

ns ns 

0 s s s 20.39 19.48 21.31 2.33 *

1 20.62 19.42 21.82 21.67 20.73 22.61 1.05 ns

2 20.33 19.85 20.81 20.66 20.32 20.99 0.32 ns

3 20.51 20.30 20.72 20.59 20.45 20.72 0.08 ns

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 

 2002-03 2012

*** *** 

0 18.84 18.14 19.54 19.88 19.53 20.23 1.04 **

1 18.69 18.35 19.02 20.06 19.73 20.38 1.37 ***

2 18.93 18.69 19.17 20.11 19.88 20.33 1.17 ***

3 19.39 19.24 19.55 20.73 20.58 20.88 1.34 ***

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 ***

Continued
21 

Table 4—Continued 

 Survey 1 Survey 2 Difference 

survey 1-

Country Number of decisions Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Philippines 

2003 2013

ns ns 

0 22.36 20.95 23.77 22.69 21.22 24.16 0.33 ns

1 21.85 21.35 22.36 21.62 20.52 22.72 -0.23 ns

2 21.82 21.38 22.25 22.35 21.79 22.91 0.53 ns

3 21.64 21.34 21.94 21.86 21.58 22.14 0.22 ns

4 21.76 21.61 21.91 22.06 21.91 22.21 0.30 **

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

na indicates indicator is not available for the survey. No data on women's decision-making were collected in the 1998-99 India DHS (NFHS-2) or the 2006-

07 Pakistan DHS. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 

4.1.1. Decision-making 

The number of decisions in which women participate is significantly associated with marriage age in 4 of 

7 study countries: Bangladesh, India, Pakistan, and Indonesia. There is no such relationship in Nepal, 

Cambodia, or the Philippines. The salience of women’s decision-making to marriage age has remained 

static over time: there was also no association between these variables in Survey 1 in Nepal, Cambodia, 

and Philippines, while the association is detected in both survey waves in Bangladesh and Indonesia8

. 

In general, marriage age rises with participation in more decisions, although this increase is not 

straightforwardly monotonic. In Pakistan 2012-13, the mean age at marriage is highest among women 

who participate in one decision, and slightly lower among women who participate in 2 or 3 decisions. 

Marriage age has increased over time across all levels of decision-making in Bangladesh, Nepal, and 

Indonesia. Significant increases are seen only among women who make four decisions in the Philippines 

(p=0.007). While the average age at marriage rose by about one third of a year (p=0.000) between 2005 

and 2014 in Cambodia, the average age at marriage rose by 2.3 years (p=0.032) among women who 

participate in no decisions here—the largest decadal increase in the study. Observed increases among 

women with more decision-making capability are not statistically significant. While women of all 

decision-making capabilities experienced sizable increases in marriage age in Indonesia, the increases 

among women participating in some (1-3) decisions outpaced that among women making no decisions. 

Indonesia shows the largest increases in marriage age, after the increase among women making no 

decisions in Cambodia. 

4.1.2 Attitudes toward wife beating 

Attitudes toward wife beating are significantly associated with marriage age in five of the six countries9

for which it could be assessed at the most recent survey. Only in Nepal10 is no association detected. Wife 

 

8

 Change in the association over time cannot be assessed in India or Pakistan since these data were not collected in 

Survey 1. 

9

 Data on attitudes toward wife beating were not collected in Bangladesh DHS surveys. 
22 

beating attitudes have become more salient over time in Cambodia; no association with marriage age was 

detected in 2005. 

Table 5. Mean age at first marriage among ever-married women age 25-49, by attitudes toward wife beating 

 Survey 1 Survey 2 Difference 

survey 1-

Country Wife beating attitudes Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia

India 

1998-99 2005-06

*** 

Acceptable in at least one 

scenario 

na na na 17.26 17.20 17.33 -- --

Rejects in all scenarios na na na 18.26 18.17 18.34 -- --

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.4 ***

Nepal 

 2001 2011

ns ns 

Acceptable in at least one 

scenario 

17.11 16.88 17.34 17.05 15.30 18.80 -0.06 ns

Rejects in all scenarios 17.17 17.02 17.33 17.94 17.76 18.12 0.77 ***

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

2012-13

Pakistan 

2006-07

*** 

Acceptable in at least one 

scenario 

na na na 19.12 18.92 19.31 -- --

Rejects in all scenarios na na na 20.13 19.90 20.36 -- --

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Continued 

 10 In the Nepal 2011 survey, but not the 2001 survey, a filter question was inserted and respondents answering “no” 

when asked, “In your opinion, should a husband hit or beat his wife for any reason at all?” were not asked if wife 

beating was justified in specific scenarios. This questionnaire change may result in a measurement change 

(decrease) in the prevalence of attitudes accepting of wife beating as well as in detecting any associations with this 

variable. 
23 

Table 5—Continued

 Survey 1 Survey 2 Difference 

survey 1-

Country Wife beating attitudes Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Southeast 

Asia

Cambodia 

 2005 2014

ns ** 

Acceptable in at least one 

scenario 

20.40 20.18 20.62 20.52 20.36 20.67 0.12 ns

Rejects in all scenarios 20.58 20.22 20.94 20.79 20.63 20.96 0.22 ns

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 

2002 2012

* *** 

Acceptable in at least one 

scenario 

19.03 18.82 19.24 20.01 19.84 20.17 0.98 ***

Rejects in all scenarios 19.32 19.15 19.48 20.73 20.57 20.89 1.42 ***

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 ***

Philippines 

 2003 2013

*** *** 

Acceptable in at least one 

scenario 

20.80 20.59 21.01 20.98 20.70 21.26 0.18 ns

Rejects in all scenarios 22.11 21.96 22.26 22.16 22.02 22.31 0.06 ns

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

na indicates indicator is not available for the survey. No data on women's decision-making was collected in the 1998-99 India DHS (NFHS-2) nor the 2006-07 

Pakistan DHS. 

In the Nepal 2011 survey, but not the 2001 survey, a filter question was inserted and respondents answering “no” when asked, “In your opinion, should a 

husband hit or beat his wife for any reason at all?” were not asked if wife beating was justified in specific scenarios. This questionnaire change may result in a 

measurement change (decrease) in the prevalence of attitudes accepting of wife beating as well as in detecting any associations with this variable. 

In all study countries, mean age at marriage is older among women who reject wife beating in any 

scenario and younger among women who find wife beating is acceptable in at least one scenario. The 

largest differences are found in Indonesia where women rejecting wife beating married 1.2 years older 

(p=0.000) and in India and Pakistan where women rejecting wife beating married about 1 year older 

(p=0.000) than did their counterparts who find wife beating acceptable. In Indonesia, marriage age rose 

from 2002-03 to 2012 among all women, but the increase is particularly pronounced for women who 

reject wife beating (1.4 years, p=0.000). 

4.1.3. Spousal age difference 

Table 6 shows the mean age at marriage according to spousal age difference. Spousal age difference 

refers to the number of years by which the husband is older than the wife. Spousal age difference is 

significantly associated with marriage age in all 7 study countries and this association has been equally 

significant at both points in time. 
24 

Table 6. Mean age at first marriage among ever-married women age 25-49, by spousal age difference 

 Survey 1 Survey 2 Difference 

survey 1-

Country survey 2 

Spousal age 

difference Mean CI p-value1 Mean CI p-value1 p-value2

South Asia 

Bangladesh 

2004 2014 

*** *** 

0-2 years 17.71 17.13 18.28 18.12 17.73 18.50 0.41 ns

3-5 years 15.88 15.64 16.13 16.81 16.59 17.02 0.93 ***

6-10 years 15.24 15.11 15.38 16.16 16.01 16.31 0.91 ***

More than 10 years 14.82 14.69 14.95 15.57 15.46 15.69 0.76 ***

Total 15.28 15.16 15.41 16.21 16.09 16.33 0.93 ***

India 

 

*** *** 

0-2 years 18.43 18.33 18.53 18.92 18.82 19.02 0.49 ***

3-5 years 17.68 17.60 17.75 17.85 17.78 17.93 0.18 **

6-10 years 17.09 17.02 17.17 17.39 17.31 17.47 0.30 ***

More than 10 years 16.40 16.30 16.49 16.74 16.64 16.85 0.35 ***

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.4 ***

Nepal 

2001 2011

*** *** 

0-2 years 17.87 17.67 18.07 18.68 18.45 18.92 0.81 ***

3-5 years 16.75 16.57 16.93 17.62 17.37 17.86 0.87 ***

6-10 years 16.70 16.54 16.87 17.33 17.09 17.57 0.63 ***

More than 10 years 17.09 16.75 17.42 17.47 17.00 17.93 0.38 ns

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 

 2006-07 2012-13 

*** *** 

0-2 years 20.39 20.14 20.65 20.99 20.68 21.31 0.60 **

3-5 years 19.28 19.04 19.51 19.61 19.40 19.82 0.33 *

6-10 years 18.61 18.40 18.81 19.07 18.83 19.31 0.46 **

More than 10 years 17.96 17.64 18.28 18.18 17.89 18.47 0.22 ns

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Continued 
25 

Table 6—Continued

 Survey 1 Survey 2 Difference 

survey 1-

Country survey 2 

Spousal age 

difference Mean CI p-value1 Mean CI p-value1 p-value2

Southeast Asia

Cambodia 

2005 2014

*** *** 

0-2 years 21.08 20.93 21.22 21.50 21.32 21.67 0.42 ***

3-5 years 19.17 18.99 19.35 19.63 19.42 19.85 0.47 ***

6-10 years 19.15 18.88 19.42 19.40 19.10 19.70 0.25 ns

More than 10 years 20.05 19.55 20.55 20.14 19.62 20.66 0.08 ns

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 

 2002-03 2012

*** *** 

0-2 years 21.01 20.80 21.21 22.58 22.40 22.77 1.58 ***

3-5 years 18.98 18.78 19.17 20.09 19.92 20.25 1.11 ***

6-10 years 18.05 17.87 18.24 18.97 18.80 19.14 0.92 ***

More than 10 years 17.57 17.30 17.84 18.22 17.95 18.49 0.65 ***

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 ***

Philippines 

2003 2013

7,377 *** 8,290 *** 

0-2 years 23.01 22.83 23.18 23.16 22.99 23.34 0.16 ns

3-5 years 20.64 20.43 20.85 20.94 20.72 21.16 0.31 ns

6-10 years 20.14 19.85 20.42 20.69 20.44 20.95 0.56 **

More than 10 years 20.50 20.03 20.97 20.40 20.04 20.76 -0.10 ns

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

The universal pattern is a negative association between spousal age difference and marriage age 

(p=0.000). With larger differences between spousal ages, mean age at marriage decreases monotonically. 

The differences in marriage age between those with the largest and smallest spousal age gap are generally 

larger in the Southeast Asian than in South Asian countries. These groups are separated by 4.4 years in 

Indonesia and 2.8 years in the Philippines. Cambodia exhibits a pattern similar to the South Asian 

countries in the study, where the marriage age is 1.2 to 2.8 years younger among women with largest 

spousal age difference. The South Asian countries also generally have a higher proportion of women in 

marriages characterized by a larger spousal age difference as compared to Southeast Asian countries 

where a greater proportion of women have smaller age differences with their spouse (data not shown). 

A significant increase in marriage age over time has been more universal across all levels of spousal age 

difference in the South Asian countries than in the Southeast Asian countries in the study. In Bangladesh, 

India, Nepal, and Pakistan, marriage age has increased among all women except those with 0-2 spousal 

age gap in Bangladesh and those with more than 10 years age difference in Nepal and Pakistan. Within 

this group, the largest absolute differences occur among women with less than 10 years age difference in 

Nepal and more than 2 years age difference in Bangladesh. However, after correcting for the shorter intersurvey time period in India and Pakistan, women with 0-2 years spousal age difference have seen 

increases in marriage age of the same size as in Bangladesh and Nepal. In general, the increase in 

marriage age is more pronounced among women with less spousal age gap in these four countries. 

Like the South Asian countries in the study, Indonesia has seen significant increases in marriage age 

among women of all spousal age differences. These increases are more pronounced among women with 
26 

age differences of 0-2 or 3-5 years than among women with 6-10 or more than 10 years. Here, marriage 

age increased by 1.6 years between 2002-03 and 2012 among women with 0-2 years spousal age 

difference—the largest increase in the study. However, the increase in marriage age is concentrated 

among certain groups in Cambodia and the Philippines. In Cambodia, marriage age increased for women 

having 0-2 or 3-5 years spousal age difference, but not among women with larger spousal age gaps. In 

the Philippines, only women with a spouse 6-10 years their elder experienced an increase in average 

marriage age (p=0.004). The observed increase of nearly one-third of a year between 2003 and 2013 

among women with a spousal age difference of 3-5 years is nearly significant (p=0.052). 

4.2. Marriage Age and Education and Occupation 

4.2.1. Education 

Table 7 shows the mean age at marriage according to level of education in a similar format to the tables in 

the previous section. The data show that education is associated with marriage age at a significance level 

of p=0.000 at both survey points in all 7 study countries. 

Table 7. Mean age at first marriage among ever-married women age 25-49, by level of education

 Survey 1 Survey 2 Difference 

survey 1-

Country Education Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia 

Bangladesh 

2004 2014

*** *** 

No education 14.65 14.53 14.76 15.16 15.01 15.31 0.52 ***

Primary 15.01 14.86 15.17 15.54 15.41 15.67 0.53 ***

Secondary 16.22 16.03 16.41 16.77 16.61 16.93 0.55 ***

Higher 20.97 20.46 21.48 21.04 20.75 21.33 0.07 ns

Total 15.28 15.16 15.41 16.21 16.09 16.33 0.93 ***

India 

 1998-99 2005-06

No education 16.19 16.14 16.23 16.43 16.38 16.48 0.24 ***

Primary 17.14 17.05 17.22 17.22 17.13 17.31 0.08 ns

Secondary 18.98 18.89 19.07 19.15 19.07 19.24 0.17 **

Higher 22.11 21.98 22.24 23.14 23.00 23.29 1.03 ***

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.37 ***

2001

Nepal 

2011

*** *** 

No education 16.88 16.72 17.03 17.05 16.86 17.24 0.17 ns

Primary 17.52 17.27 17.77 17.62 17.36 17.88 0.10 ns

Secondary 18.70 18.37 19.04 19.32 19.07 19.56 0.61 *

Higher 21.63 20.81 22.44 22.55 22.14 22.95 0.92 *

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 

 2006-07 2012-13

*** *** 

No education 18.54 18.37 18.71 18.66 18.46 18.87 0.12 ns

Primary 19.37 19.05 19.68 19.46 19.19 19.73 0.09 ns

Secondary 20.40 20.06 20.74 21.06 20.76 21.35 0.66 **

Higher 22.76 22.35 23.16 23.86 23.56 24.16 1.11 ***

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Continued
27 

Table 7—Continued

 Survey 1 Survey 2 Difference 

survey 1-

Country Education Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Southeast Asia

Cambodia 

2005 2014

*** *** 

No education 20.19 20.00 20.39 19.96 19.66 20.26 -0.23 ns

Primary 20.19 20.05 20.32 20.41 20.26 20.56 0.22 *

Secondary 20.97 20.75 21.18 21.32 21.12 21.52 0.35 *

Higher 24.10 22.54 25.66 23.97 23.49 24.46 -0.13 ns

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 

 2002-03 2012

*** *** 

No education 17.30 16.98 17.61 17.58 17.08 18.07 0.28 ns

Primary 17.89 17.75 18.03 18.41 18.27 18.54 0.51 ***

Secondary 21.17 21.03 21.31 21.55 21.43 21.67 0.38 ***

Higher 24.90 24.61 25.19 25.31 25.07 25.54 0.41 *

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 ***

Philippines 

2003 2013

7,377 *** 8,290 *** 

No education 18.21 17.47 18.94 18.58 17.84 19.32 0.37 ns

Primary 19.95 19.75 20.16 19.79 19.56 20.02 -0.16 ns

Secondary 21.32 21.14 21.49 21.46 21.31 21.61 0.15 ns

Higher 24.38 24.19 24.57 24.40 24.21 24.59 0.02 ns

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution.

In all study countries, there is a monotonically positive relationship between education and marriage age. 

The largest disparities by education are in Indonesia where women with higher education married when 

they were, on average, 7.7 years older than did those with no education in 2012 and in India, where 

women with higher education married 6.7 years older than those with no education in 2005-06. This is 

followed by Bangladesh and the Philippines where the disparity between education groups approaches 6 

years at Survey 2. 

There have been significant increases in average marriage age over time in multiple education 

categories—usually in the more educated groups of women—in six of seven study countries. In 

Bangladesh, however, marriage age has increased among all educational categories except women with 

higher education. In India, marriage age has increased among women with no education alongside 

women with secondary or higher education (but not primary education) while in Cambodia, increases are 

seen only among women with primary or secondary education. There have been no significant changes in 

marriage age in any educational category in the Philippines. 

The biggest increase over time is seen among women with higher education in India, whose mean age at 

marriage increased from age 22.1 in 1998-99 to age 23.1 in 2005-06 (p=0.000). The increases in marriage 

age concentrated among women with secondary or higher education or the increases among women in 

these groups that have outpaced increases among women with lower education means that the disparities 

in marriage age across educational groups have widened over time in several countries. The widening 

disparities are most notable in India, Nepal, and Pakistan. The growing disparities are more modest in 

Cambodia and Indonesia, while the disparities have narrowed slightly in the Philippines and Bangladesh. 
28 

4.2.2. Occupation 

As shown in Table 8, women’s occupation is associated with marriage age in all study countries11 but 

Cambodia at the time of the most recent survey. In Indonesia, occupation has become salient over time; 

there was no significant association with marriage age in 2002-03, but by 2012, this association was 

statistically significant (p=0.000). 

Table 8. Mean age at first marriage among ever-married women age 25-49, by respondent's occupation

 Survey 1 Survey 2 Difference 

survey 1-

Country Occupation Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia

India 

1998-99 2005-06

*** *** 

Not working 17.83 17.77 17.90 18.24 18.17 18.32 0.41 ***

Agricultural 16.10 16.02 16.17 16.48 16.40 16.55 0.38 ***

Professional/technical/ 

managerial 

21.51 21.25 21.78 22.39 22.12 22.67 0.88 ***

Clerical 22.31 21.77 22.85 21.27 20.79 21.74 -1.04 **

Sales 17.36 17.09 17.63 17.93 17.68 18.18 0.57 ***

Services 17.37 16.96 17.77 17.39 17.21 17.57 0.02 ns

Skilled/unskilled manual 17.01 16.88 17.15 17.44 17.31 17.58 0.43 ***

Other 16.73 16.04 17.43 19.47 18.53 20.41 2.74 ***

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.4 ***

Nepal 

 2001 2011 

  

Not working 17.36 17.03 17.69 18.58 18.17 19.00 1.23 ** *

Agricultural 17.04 16.88 17.21 17.43 17.24 17.61 0.39 **

Professional/technical/ 

managerial 

19.29 18.52 20.07 21.38 20.86 21.89 2.09 ***

Clerical s s s 19.37 18.00 20.73 3.37 **

Services s s s 18.63 18.26 19.01 2.87 ***

Skilled manual 18.66 17.98 19.34 18.21 17.73 18.70 -0.45 ns

Unskilled manual 16.24 15.45 17.03 17.15 16.57 17.72 0.91 ns

Other 17.59 17.14 18.05 s s s 3.11 *

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 

2006-07 2012-13 

*** *** 

Not working 19.52 19.35 19.69 20.00 19.82 20.17 0.47 ***

Agricultural 17.78 17.40 18.16 18.33 17.78 18.88 0.56 ns

Professional/technical/ 

managerial 

21.85 21.09 22.60 22.88 22.07 23.69 1.03 ns

Clerical s s s s s s 4.82 ns

Sales 18.13 17.28 18.97 18.47 17.49 19.46 0.35 ns

Services 18.50 18.19 18.82 19.15 18.77 19.54 0.65 **

Skilled manual 18.65 17.88 19.43 18.38 17.79 18.96 -0.28 ns

Unskilled manual 18.22 17.73 18.71 18.58 18.20 18.95 0.35 ns

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Continued 

 

11 Women’s occupation is not available in the Bangladesh DHS surveys included in this study. 
29 

Table 8—Continued

 Survey 1 Survey 2 Difference 

survey 1-

Country Occupation Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Southeast 

Asia

Cambodia 

 2005 2014

ns ns 

Not working 19.57 18.41 20.73 20.95 20.69 21.21 1.38 *

Agricultural 20.19 20.04 20.34 20.18 19.99 20.37 -0.01 ns

Professional/technical/ 

managerial 

22.49 21.77 23.21 22.57 22.10 23.03 0.07 ns

Clerical 22.95 21.48 24.41 22.35 21.13 23.57 -0.60 ns

Sales 20.36 20.15 20.57 20.59 20.37 20.81 0.23 ns

Services 20.56 19.63 21.49 20.84 20.35 21.33 0.28 ns

Skilled manual 20.09 19.39 20.79 20.94 20.61 21.28 0.85 *

Unskilled manual 20.44 20.23 20.64 21.00 20.01 21.98 0.56 ns

Other 20.65 19.64 21.66 20.57 19.21 21.92 -0.08 ns

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 

2002-03 2012

ns *** 

Not working 19.38 19.20 19.57 20.57 20.40 20.74 1.18 ***

Agricultural 17.98 17.77 18.18 18.80 18.58 19.02 0.82 ***

Professional/technical/ 

managerial 

24.04 23.64 24.45 24.39 24.12 24.66 0.35 ns

Clerical 24.68 24.05 25.32 25.16 24.55 25.77 0.47 ns

Sales 18.83 18.58 19.09 20.28 20.07 20.49 1.45 ***

Services 19.63 19.20 20.07 20.39 20.03 20.74 0.75 **

Skilled manual 19.58 19.15 20.00 19.78 19.56 20.00 0.20 ns

Unskilled manual s s s 19.86 19.23 20.49 -1.37 ns

Other s s s s s s 5.16 *

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 *** 

Philippines 

 2003 2013

 ** *** 

Not working 21.64 21.46 21.82 21.85 21.66 22.04 0.21 ns

Agricultural 19.41 19.02 19.80 19.90 19.60 20.20 0.49 *

Professional/technical/ 

managerial 

23.29 22.99 23.60 23.66 23.41 23.91 0.36 ns

Clerical 24.23 23.69 24.77 23.99 23.53 24.45 -0.24 ns

Sales 21.05 20.78 21.31 22.55 22.10 23.00 1.51 ***

Services 21.99 21.35 22.63 21.46 20.95 21.98 -0.53 ns

Skilled manual 21.62 21.07 22.17 21.24 20.73 21.75 -0.38 ns

Unskilled manual 20.58 20.12 21.04 21.21 20.96 21.45 0.63 *

Other s s s s s s -1.55 ns

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

na indicates indicator is not available for the survey. 

India DHS (NFHS-2 and NFHS-3) do not distinguish between skilled manual and unskilled manual. 

Nepal and Pakistan DHS do not include a separate category for sales; women in these occupations are included in "other" occupations. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 

In general, women who employed in professional or clerical occupations and, in some countries in sales 

occupations, marry at older ages than women who are not working. This is also true of women who work 

in sales in the Philippines and in other occupational categories in India, Nepal, and Indonesia. In all study 
30 

countries, women who work in agricultural occupations marry at younger ages than women who are not 

working. The pattern of older marriage age among professionals and younger marriage age among 

agricultural workers relative to unemployed women has been consistent at both survey points. However, 

women working in clerical positions did not marry at older ages in some countries at Survey 1 as they did 

at Survey 2. 

Changes in marriage age over time have been widespread across most or all occupational categories in 

India and Nepal. In contrast, such changes have been clustered in two or three occupational groups in 

Pakistan, Cambodia, Indonesia, and the Philippines. Across all study countries and occupational 

categories where there have been significant changes in marriage age, marriage age has increased over 

time with one exception. Women working in clerical occupations in India married at an age that is one 

year younger in 2005-06 than did their counterparts in 1998-99 (p=0.003). 

Marriage age has increased among women in all other occupational categories in India, except those 

working in services. The largest increases appear among women working in other occupations. In Nepal, 

marriage age increased in all occupational categories except skilled and unskilled manual labor. Women 

working in professional, clerical, services, or other occupational categories had average marriage ages 

that increased by 2 to 3.4 years between 2001 and 2011. 

In Pakistan, significant increases in marriage age in the 6 years between surveys are restricted to two 

occupational groups and were larger among women working in service occupations (0.7 year, p=0.01) 

than among women who were not working (0.5 year, p=0.000). Although occupation is not associated 

with marriage age in Cambodia, marriage age increased between 2005 and 2014 among Cambodian 

women who are not working (1.4 years, p=0.022) and those working as skilled manual laborers (0.9 year, 

p=0.033). Marriage age increased over the decade between surveys among Indonesian women in sales 

(1.4 years, p=0.000), those who are not working (1.2 years, p=0.000), and those working in services (0.8 

year, p=0.008). In the Philippines, increases in marriage age are restricted to women in sales (1.5 years, 

p=0.000) and in unskilled manual labor (0.6 year, p=0.019). 

4.3. Marriage Age and Wealth 

Household wealth quintile is significantly associated with marriage age in all seven study countries. 

Wealth has increased in salience with regard to marriage age over time in Cambodia, the only country in 

which wealth had not been significantly associated with marriage age at Survey 1. As seen in Table 9, 

wealth has a consistently positive association with marriage age in South Asian and Southeast Asian 

countries alike: marriage age rises monotonically with wealth in most countries. In Cambodia, however, 

marriage increase appears to decline slightly over the first two or three wealth quintiles before increasing 

among the richer and richest wealth quintiles, although these differences are small. 
31 

Table 9. Mean age at first marriage among ever-married women age 25-49, by household wealth quintile 

 Survey 1 Survey 2 Difference 

survey 1-

Country Wealth Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia 

Bangladesh 

2004 2014 

*** *** 

Poorest 14.83 14.65 15.01 15.39 15.20 15.57 0.56 ***

Poorer 14.74 14.59 14.88 15.65 15.47 15.82 0.91 ***

Middle 14.99 14.82 15.17 15.82 15.67 15.97 0.83 ***

Richer 15.32 15.13 15.51 16.18 16.01 16.35 0.86 ***

Richest 16.49 16.21 16.77 17.78 17.55 18.01 1.29 ***

Total 15.32 15.13 15.51 16.18 16.01 16.35 0.86 ***

India 

 1998-99 2005-06 

*** *** 

Poorest 16.13 16.05 16.21 16.43 16.34 16.51 0.30 ***

Poorer 16.30 16.22 16.37 16.66 16.58 16.74 0.37 ***

Middle 16.80 16.72 16.88 17.15 17.06 17.24 0.35 ***

Richer 17.75 17.66 17.84 18.07 17.98 18.17 0.32 ***

Richest 19.55 19.43 19.67 19.92 19.80 20.03 0.37 ***

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.37 ***

Nepal 

2001 2011 

*** *** 

Poorest 16.92 16.68 17.15 17.38 17.09 17.67 0.47 *

Poorer 16.79 16.55 17.03 17.34 17.05 17.63 0.55 **

Middle 16.90 16.63 17.17 17.35 17.07 17.63 0.45 *

Richer 17.21 16.96 17.47 17.89 17.58 18.20 0.68 ***

Richest 17.93 17.65 18.21 19.35 19.11 19.59 1.43 ***

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 

 2006-07 2012-13 

*** *** 

Poorest 18.07 17.76 18.38 18.14 17.78 18.51 0.07 ns

Poorer 18.87 18.58 19.16 18.95 18.67 19.23 0.08 ns

Middle 19.01 18.73 19.29 19.29 19.06 19.51 0.28 ns

Richer 19.37 19.12 19.62 20.17 19.87 20.46 0.79 ***

Richest 20.44 20.16 20.73 21.43 21.20 21.67 0.99 ***

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Continued 
32 

Table 9—Continued

 Survey 1 Survey 2 Difference 

survey 1-

Country Wealth Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Southeast Asia

Cambodia 

2005 2014 

ns *** 

Poorest 20.56 20.32 20.79 20.57 20.28 20.86 0.01 ns

Poorer 20.40 20.18 20.62 20.28 20.03 20.52 -0.13 ns

Middle 20.17 19.97 20.38 20.49 20.20 20.78 0.32 ns

Richer 19.99 19.76 20.21 20.50 20.27 20.73 0.52 ***

Richest 20.52 20.29 20.76 21.31 21.11 21.51 0.78 ***

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 

 2002-03 2012 

*** *** 

Poorest 18.56 18.32 18.79 19.33 19.12 19.54 0.78 ***

Poorer 18.22 18.00 18.44 19.63 19.44 19.82 1.41 ***

Middle 18.73 18.49 18.97 19.95 19.76 20.14 1.22 ***

Richer 19.32 19.07 19.56 20.76 20.55 20.97 1.44 ***

Richest 21.15 20.88 21.42 22.36 22.11 22.61 1.21 ***

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 ***

Philippines 

2003 2013 

*** *** 

Poorest 19.97 19.70 20.23 19.94 19.71 20.17 -0.03 ns

Poorer 20.97 20.74 21.19 21.13 20.90 21.36 0.16 ns

Middle 21.55 21.32 21.79 21.72 21.48 21.95 0.16 ns

Richer 22.42 22.15 22.68 22.89 22.64 23.13 0.47 **

Richest 23.55 23.30 23.79 23.88 23.61 24.14 0.33 ns

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

The largest inter-quintile differences occur between the richer and richest wealth quintiles in all countries, 

except the Philippines. In Indonesia, this difference grows with each successive quintile before doubling 

to 1.6 years difference between the richer and richest wealth quintiles in 2012. In Nepal, the mean age at 

marriage is almost identical among the poorest three wealth quintiles before increasing among the richer 

and richest wealth quintiles. The largest disparity (3.5 years, p=0.000) is observed in India; women in the 

poorest wealth quintile in 2005-06 married at an average age of 16.4 compared to age 19.9 among the 

women in the richest wealth quintile. 

Mean age at marriage has increased over time in every wealth quintile in all South Asian countries except 

Pakistan, where there are significant increases seen only among women in the richer and richest quintiles. 

Marriage age has also increased among all wealth quintiles in Indonesia. However, significant increases 

are restricted to women in the richer wealth quintile in the Philippines and the richer and richest quintiles 

in Cambodia. The biggest increases occur among the richest wealth quintile in Nepal and among all but 

the poorest wealth quintile in Indonesia. Disparities in marriage age between women in the richest and 

poorest wealth quintiles have grown in all countries, most prominently in Nepal and Pakistan, where the 

gap is larger by almost 1 year at Survey 2 than at Survey 1. 
33 

4.4. Marriage Age and Religion 

Table 10 shows that religion is significantly associated with marriage age in all three South Asian 

countries for which there is data and in Cambodia—but not the Philippines—in Southeast Asia12. Muslim 

women in Bangladesh and Nepal married at younger ages than did women of other religions, although in 

India they marry at similar ages as Hindu and Buddhist women. Hindu women married at younger ages 

than Christian, Sikh, and Jain women in India, Buddhist and Kirat women in Nepal, and women of 

“other” faiths in both countries. The differences among religious groups are smaller, but nonetheless 

significant in Cambodia. Here, Buddhist and Christian women marry at older ages than do Muslim 

women. While there is approximately four years that separate the average marriage age of Muslim 

women and the religious group with the oldest marriage age elsewhere, these groups are separated by less 

than a year in Cambodia. 

Table 10. Mean age at first marriage among ever-married women age 25-49, by religion 

 Survey 1 Survey 2 Difference 

survey 1-

Country Religion Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia 

Bangladesh 

2004 2014 

*** *** 

Muslim 15.14 15.03 15.26 16.08 15.96 16.19 0.93 ***

Christian 16.23 15.08 17.38 20.25 18.28 22.22 4.02 *

Buddhist s s s 18.53 17.79 19.27 0.94 ns

Hindu 16.45 16.04 16.86 17.04 16.70 17.39 0.59 *

Total 15.32 15.13 15.51 16.18 16.01 16.35 0.86 ***

India 

1998-99 2005-06 

*** *** 

Muslim 16.96 16.84 17.08 17.31 17.17 17.45 0.34 ***

Christian 20.12 19.75 20.49 20.51 20.13 20.88 0.39 ns

Buddhist 17.29 16.63 17.95 17.38 16.88 17.89 0.09 ns

Hindu 17.29 17.23 17.36 17.69 17.62 17.75 0.40 ***

Other 17.51 16.77 18.26 18.10 17.70 18.50 0.58 ns

Sikh 20.00 19.75 20.24 19.80 19.56 20.03 -0.20 ns

Jain 19.08 18.44 19.71 20.41 19.87 20.95 1.33 ***

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.37 ***

Nepal 

2001 2011 

*** *** 

Muslim 15.97 15.66 16.27 16.86 15.51 18.22 0.90 ns

Buddhist 18.47 18.04 18.90 18.84 18.43 19.26 0.37 ns

Hindu 17.02 16.87 17.17 17.84 17.65 18.02 0.81 ***

Other 20.03 19.18 20.88 20.60 19.80 21.41 0.57 ns

Kirat 16.96 16.00 17.93 18.08 17.37 18.78 1.11 Ns

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Continued 

 

12 Data on religious affiliation are not available in DHS surveys for Pakistan and Indonesia. 
34 

Table 10—Continued

 Survey 1 Survey 2 Difference 

survey 1-

Country Religion Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Southeast Asia 

Cambodia 

 2005 2014 

*** *** 

Muslim 18.67 17.97 19.38 19.88 19.13 20.63 0.36 *

Christian 19.99 18.50 21.49 20.35 19.14 21.56 0.31 ns

Buddhist 20.38 20.28 20.48 20.68 20.56 20.81 1.21 ***

Other 18.11 17.52 18.69 18.39 17.63 19.15 0.29 ns

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Philippines 

2003 2013 

ns ns 

Muslim 19.86 19.42 20.30 20.23 19.73 20.73 0.37 ns

Other 21.71 21.15 22.26 21.67 21.24 22.11 -0.03 ns

Roman Catholic 21.87 21.73 22.01 22.14 22.00 22.28 0.27 **

Protestant 21.55 21.02 22.07 21.95 21.41 22.49 0.40 ns

Iglesia Ni Kristo 21.62 21.06 22.17 22.27 21.64 22.90 0.66 ns

Aglipay 22.18 21.37 22.99 22.02 20.68 23.36 -0.17 ns

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

na indicates indicator is not available for the survey. No data on religion was collected in the Pakistan or Indonesia DHS. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases.

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution.

In contrast to other socio-demographic characteristics, marriage age has not increased consistently across 

religious groups in study countries. Rather, the trend toward older age at marriage is concentrated in 

selected groups. In Bangladesh, marriage age has increased from 2004 to 2014 among all religious 

groups (Muslims, Christians, and Hindus) except Buddhists. The most striking increase—of 4 years 

among Christian women—should be viewed with caution because of their small numbers in the sample. 

In contrast to Bangladesh, marriage age did increase significantly among Buddhist women in Cambodia 

(1.2 years, p=0.000). The marriage age increased among Muslim women in India and Cambodia. The 

mean age at marriage increased significantly among the Hindu majorities in India and Nepal, as well as 

among Jain and women of other faiths in India. In the Philippines, only Roman Catholics, the majority 

religious group, experienced an increase in the average age at marriage. 

4.5. Marriage Age and Residence 

In the bivariate analysis presented in Table 11, place of residence is associated with marriage age at both 

time points in all seven study countries (p=0.000). In South Asian and Southeast Asian countries alike, 

marriage age is higher on average among urban residents than among rural residents. The difference 

between these groups of women exceeds one year in Bangladesh, India, and Pakistan and approaches a 

year in Nepal and the Philippines. The largest difference occurs in Indonesia, where urban women 

married at 2.1 years older than their rural counterparts. Even in Cambodia, where the difference is the 

smallest, urban women married when they are nearly three-quarters of a year older than do rural women. 
35 

Table 11. Mean age at first marriage among ever-married women age 25-49, by place of residence 

 Survey 1 Survey 2 Difference 

survey 1-

Country Residence Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia

Bangladesh 

2004 2014 

*** *** ***

Rural 15.07 14.94 15.20 15.88 15.77 15.99 15.88 0.81 ***

Urban 15.97 15.68 16.27 17.02 16.78 17.27 17.02 1.05

Total 0.00

India 

 

 *** *** 

Rural 16.9 16.79 16.91 17.2 17.10 17.22 ***

Urban 18.7 18.61 18.88 19.0 18.85 19.09 0.018

Total 17.39 17.32 17.46 17.76 17.70 17.82 ***

Nepal 

2001 2011 

*** *** 

Rural 17.07 16.91 17.23 17.80 17.59 18.01 0.73 ***

Urban 17.92 17.47 18.38 18.75 18.41 19.09 0.83 **

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 

 2006-07 2012-13 

 *** *** 

Rural 18.97 18.78 19.16 19.19 18.96 19.43 0.22 ns

Urban 19.62 19.39 19.85 20.59 20.35 20.83 0.97 ***

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Southeast Asia 

Cambodia 

2005 2014 

** *** 

Rural 20.26 20.15 20.38 20.51 20.38 20.65 0.25 **

Urban 20.70 20.44 20.96 21.28 21.08 21.49 0.58 ***

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 

 2002-03 2012 

 *** *** 

Rural 18.45 18.29 18.61 19.43 19.27 19.58 0.98 ***

Urban 20.12 19.87 20.37 21.50 21.29 21.71 1.38 ***

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 ***

Philippines 

2003 2013 

*** *** 

Rural 21.06 20.88 21.25 21.55 21.37 21.73 0.48 ***

Urban 22.34 22.17 22.51 22.45 22.25 22.65 0.11 ns

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

Age at marriage has increased among both rural and urban women in Bangladesh, India, Nepal, 

Cambodia, and Indonesia, with the largest increases observed among urban women in Bangladesh and 

Indonesia. However, age at marriage has risen only among urban women in Pakistan and only among 

rural women in the Philippines. Because marriage age is lower among rural women than urban women, 

this means that the disparity has narrowed over time in the Philippines but widened in Pakistan. The 
36 

rural-urban disparity has also widened in Bangladesh, Cambodia, and Indonesia and narrowed somewhat 

in India. 

4.6. Marriage Age and Subnational Region 

There are regional differences in marriage age in all seven countries, although this was not previously the 

case in Bangladesh and Indonesia. Age at marriage is higher than average in Sylhet and lower than 

average in Khulna, Rajshahi, and Rangpur13 in Bangladesh. In India 2005-06, the most populous state 

pair, Uttar Pradesh and Uttaranchal14, have an age at marriage close to the national average, with the state 

of Andhra Pradesh in the Southeast having the youngest marriage age (16.2 years) and Goa in the 

Southwest having the oldest (23 years). Other Southern states (e.g. Kerala, Karnataka, and Tamil Nadu) 

also have older ages at marriage, as do Manipur and Mizoram in the Northeast. 

Table 12. Mean age at first marriage among ever-married women age 25-49, by subnational region

Survey 1 Survey 2 Difference 

survey 1-

Country Region Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia 

Bangladesh 2004 2014 

ns * 

Dhaka 15.32 15.07 15.57 16.37 16.12 16.61 1.05 ***

Barisal 15.27 15.03 15.51 16.08 15.81 16.34 0.80 ***

Chittagong 15.87 15.51 16.24 16.70 16.42 16.99 0.83 ***

Khulna 14.91 14.69 15.14 15.68 15.48 15.88 0.77 ***

Rajshahi & Rangpur 14.79 14.61 14.97 15.57 15.43 15.71 0.78 ***

Sylhet 16.20 15.81 16.59 17.33 16.97 17.70 1.13 ***

Total 15.32 15.13 15.51 16.18 16.01 16.35 0.86 ***

India 1998-99 2005-06

*** *** 

Uttar Pradesh & 

Uttaranchal 

16.54 16.40 16.67 17.27 17.13 17.40 0.73 ***

Andhra Pradesh 18.86 18.39 19.34 16.22 16.03 16.40 -2.65 ***

Arunachal Pradesh 15.83 15.64 16.02 18.38 17.94 18.82 2.55 ***

Assam 18.39 18.09 18.68 19.00 18.61 19.40 0.62 *

Bihar & Jharkhand 16.74 16.60 16.87 16.59 16.42 16.75 -0.15 ns

Delhi 19.47 19.06 19.88 19.43 19.06 19.79 -0.05 ns

Goa 22.08 21.60 22.56 22.99 22.56 23.42 0.91 **

Gujarat 18.22 17.92 18.51 18.43 18.15 18.71 0.22 ns

Haryana 18.21 17.96 18.47 17.87 17.60 18.15 -0.34 ns

Himachal Pradesh 18.99 18.76 19.21 19.37 19.09 19.65 0.39 *

Jammu & Kashmir 18.33 18.10 18.57 19.53 19.26 19.81 1.20 ***

Karnataka 17.48 17.15 17.82 18.29 17.99 18.59 0.81 ***

Kerala 20.29 19.93 20.65 20.85 20.46 21.24 0.56 *

Madhya Pradesh 16.34 16.14 16.53 16.92 16.76 17.07 0.58 ***

Maharashtra 17.12 16.87 17.38 18.02 17.80 18.24 0.90 ***

Manipur 21.19 20.52 21.86 21.59 21.25 21.93 0.40 ns

Meghalaya 19.24 18.83 19.64 19.92 19.60 20.25 0.69 **

Mizoram 21.18 20.83 21.54 20.77 20.42 21.11 -0.42 ns

Nagaland 20.05 19.64 20.46 19.97 19.70 20.24 -0.08 ns

Continued

 

13 Although Rangpur and Rajshahi are now separate divisions, they were sampled as a single region in 2004. The 

two divisions are combined in 2014 so that regional trends between the two survey points can be compared. 

14 Although separate states in the 2005-06 DHS (NFHS-3), the state pairs of Uttar Pradesh and Uttaranchal, Madhya 

Pradesh and Chattisgarh, and Bihar and Jharkhand were sampled as single states in 1998-99 and are combined so 

that regional trends between the two survey points can be compared. 
37 

Table 12—Continued

Survey 1 Survey 2 Difference 

survey 1-

Country Region Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Orissa 17.66 17.45 17.87 18.08 17.83 18.33 0.42 *

Punjab 20.01 19.82 20.20 19.42 19.17 19.67 -0.59 ***

Rajasthan 16.73 16.60 16.86 16.87 16.64 17.10 0.13 ns

Sikkim 19.53 19.14 19.92 19.79 19.41 20.17 0.26 ns

Tamil Nadu 18.80 18.54 19.05 19.17 18.93 19.40 0.37 *

Tripura 18.52 18.10 18.94 18.57 18.10 19.05 0.05 ns

West Bengal 17.34 17.11 17.58 17.57 17.33 17.81 0.23 ns

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.37 ***

Nepal 2001 2011

*** *** 

Terai 16.66 16.44 16.88 17.64 17.34 17.94 0.98 ***

Mountain 17.57 17.17 17.97 17.69 17.41 17.96 0.12 ns

Hill 17.69 17.47 17.92 18.36 18.12 18.60 0.67 ***

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 2006-07 2012-13 

*** *** 

Punjab 19.64 19.43 19.85 20.07 19.79 20.36 0.43 *

Sindh 18.34 18.08 18.61 19.29 19.03 19.55 0.94 ***

Khyber Pakhtunkwa 18.62 18.30 18.94 19.07 18.72 19.42 0.45 ns

Balochistan 19.38 18.98 19.77 18.53 18.15 18.91 -0.85 **

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Southeast Asia

Cambodia 2005 2014 

*** *** 

Phnom Penh 20.61 20.18 21.03 21.41 21.12 21.70 0.80 **

Banteay Mean Chey 20.02 19.53 20.51 20.73 20.22 21.24 0.70 ns

Kampong Cham 20.69 20.28 21.10 20.81 20.29 21.34 0.12 ns

Kampong Chhnang 21.10 20.70 21.50 20.84 20.35 21.34 -0.25 ns

Kampong Speu 19.88 19.64 20.13 20.30 19.78 20.81 0.41 ns

Kampong Thom 20.47 19.94 20.99 20.73 20.32 21.14 0.26 ns

Kandal 20.32 20.03 20.60 21.37 20.93 21.80 1.05 ***

Kratie 20.50 20.06 20.94 20.20 19.67 20.73 -0.31 ns

Prey Veng 20.03 19.67 20.39 20.07 19.68 20.46 0.04 ns

Pursat 19.83 19.43 20.24 20.74 19.89 21.60 0.91 ns

Siem Reap 20.97 20.66 21.28 20.59 20.04 21.15 -0.37 ns

Svay Rieng 20.09 19.68 20.50 20.27 19.79 20.75 0.18 ns

Takeo 20.41 20.09 20.74 20.63 20.24 21.01 0.21 ns

Otdar Mean Chey 19.60 19.31 19.89 20.05 19.62 20.48 0.45 ns

Battambang & Pailin 20.16 19.68 20.64 20.86 20.33 21.39 0.70 ns

Kampot & Kep 20.09 19.72 20.47 20.06 19.64 20.47 -0.04 ns

Preah Sihanouk & Kaoh 

Kong 

20.32 19.73 20.92 20.35 20.03 20.68 0.03 ns

Preah Vihear & Steung 

Treng 

19.40 19.05 19.75 19.93 19.45 20.42 0.54 ns

Mondol Kiri & Rattanak 

Kiri 

18.72 18.20 19.24 19.46 18.88 20.04 0.74 ns

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Continued 
38 

Table 12—Continued

Survey 1 Survey 2 Difference 

survey 1-

Country Region Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Indonesia 2002-03 2012 

ns *** 

Sumatera 20.00 19.76 20.24 20.93 20.77 21.08 0.93 ***

Java 18.81 18.61 19.02 20.33 20.12 20.55 1.52 ***

Bali & Tenggara 20.31 20.04 20.58 21.13 20.82 21.44 0.82 ***

Kalimantan 19.13 18.86 19.41 19.86 19.59 20.13 0.73 ***

Sulawesi, Maluku & 

Papua 

20.02 19.76 20.28 20.54 20.34 20.75 0.52 **

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 ***

Philippines 2003 2013 

7,377 *** 8,290 *** 

National Capital 22.74 22.42 23.06 22.54 22.15 22.93 -0.20 ns

Cordillera 21.37 20.60 22.14 22.44 21.86 23.01 1.07 *

I - Ilocos 22.33 21.65 23.02 22.79 22.35 23.23 0.46 ns

II - Cagayan Valley 21.45 20.86 22.04 21.06 20.48 21.63 -0.39 ns

III - Central Luzon 21.88 21.47 22.29 22.18 21.79 22.57 0.30 ns

IVA - Calabarzon 22.26 21.89 22.62 22.83 22.40 23.26 0.57 ns

IVB - Mimaropa 20.89 20.26 21.53 20.69 20.03 21.36 -0.20 ns

V - Bicol 21.41 20.93 21.89 21.94 21.41 22.46 0.53 ns

VI - Western Visayas 22.00 21.48 22.52 22.25 21.80 22.71 0.26 ns

VII - Central Visayas 21.68 21.23 22.13 22.60 22.05 23.15 0.92 *

VIII - Eastern Visayas 20.67 20.10 21.25 21.70 21.09 22.31 1.03 *

IX - Zamboanga 

Peninsula 

21.16 20.55 21.76 20.84 20.38 21.29 -0.32 ns

X - Northern Mindanao 21.60 21.05 22.14 21.33 20.52 22.15 -0.26 ns

XI - Davao 21.32 20.82 21.82 21.46 20.88 22.04 0.14 ns

XII - Soccskargen 20.90 20.10 21.69 20.85 20.20 21.49 -0.05 ns

XIII - Caraga 21.01 20.50 21.52 21.25 20.60 21.90 0.24 ns

ARMM 19.95 19.43 20.46 20.08 19.65 20.50 0.13 ns

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

In Nepal, women in the hill region have an older marriage age; women in the terai and mountain region 

tend to marry at similar ages. In Pakistan, the age at marriage ranges from 18.5 years in Balochistan to 

20.1 years in Punjab. Marriage age in Phnom Penh is the highest in Cambodia, about 0.8 year higher than 

the national average. Marriage age exceeds 20 years of age in all regions except Preah Vihear and Steung 

Treng (19.9 years) and Mondol Kiri and Rattanak Kiri (19.5 years). Indonesia and the Philippines exhibit 

a similar pattern of generally high ages at marriage across regions. Marriage age ranges from 19.9 years 

in Kalimantan to 21.1 years in Bali and Tenggara in Indonesia, and from 20.1 years in the Autonomous 

Region in Muslim Mindanao (ARMM) to 22.8 years in Calabarzon in the Philippines. 

However, there has been little change in marriage over time in most regions in the Philippines and 

Cambodia. In contrast, Indonesia has seen shifts in marriage age across all regions. Similarly, increases 

in marriage age have been more regionally widespread in Bangladesh, Nepal, and Pakistan. India presents 

more mixed trends. Significant increases in marriage age have occurred since 1998-99 in approximately 

half of the states. The largest increase (2.6 years, p=0.000) is observed in Arunachal Pradesh. Countering 

the broad trend toward higher age at marriage, there is a significant—and sizable—decline in the age at 
39 

marriage in Andhra Pradesh (-2.7 years, p=0.000) and by a lesser margin in the Indian state of Punjab 

(-0.6 year, p=0.000). Marriage age also declined in Balochistan, Pakistan (-0.85 years, p=0.003). 

4.7. Marriage Age and Husbands’ Education and Occupation 

Two variables that describe husbands’ characteristics—education and occupation—and may be related to 

marriage age or the first birth interval are presented in this section. Data on marriage age according to 

husbands’ education is presented in Table 13 and on the husbands’ occupation in Table 14. 

4.7.1. Husband’s education 

Husband’s education is significantly associated with marriage age in all seven study countries at a 

significance level of p=0.000, although in Cambodia, the association only became detectable in the most 

recent survey (2014). As with women’s own education, marriage age exhibits the same monotonic 

increase with increasing levels of husband’s education, except in Cambodia. Here, as was found with 

women’s education, marriage age first declines slightly among women married to husbands with primary 

education as compared to no education, and then rises with increasing levels of husband’s education. 

Table 13. Mean age at first marriage among ever-married women age 25-49, by husband's education

 Survey 1 Survey 2 Difference 

survey 1-

Country Education Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia 

Bangladesh 

2004 2014 

*** *** 

No education 14.64 14.53 14.76 15.27 15.13 15.41 0.62 ***

Primary 15.04 14.89 15.18 15.80 15.64 15.96 0.76 ***

Secondary 15.46 15.30 15.63 16.34 16.19 16.49 0.88 ***

Higher 17.92 17.50 18.35 18.91 18.65 19.18 0.99 ***

Don't know na na na na na na -- --

Total 15.32 15.13 15.51 16.18 16.01 16.35 0.86 ***

India 

 1998-99 2005-06 

*** *** 

No education 16.12 16.06 16.17 16.42 16.35 16.49 0.30 ***

Primary 16.72 16.65 16.80 16.92 16.84 17.01 0.20 ***

Secondary 17.71 17.64 17.78 18.13 18.06 18.20 0.42 ***

Higher 19.68 19.56 19.81 20.78 20.63 20.92 1.09 ***

Don't know na na na 17.28 16.95 17.62 -- --

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.37 ***

Nepal 

2001 2011 

*** *** 

No education 16.69 16.49 16.89 16.84 16.57 17.11 0.15 ns

Primary 17.21 17.02 17.41 17.37 17.14 17.60 0.16 ns

Secondary 17.50 17.29 17.72 18.19 17.99 18.38 0.68 ***

Higher 18.61 18.25 18.97 20.45 20.03 20.87 1.84 ***

Don't know 16.58 16.09 17.07 17.43 16.17 18.68 0.85 ns

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 

 2006-07 2012-13

*** *** 

No education 18.38 18.17 18.58 18.64 18.40 18.88 0.26 ns

Primary 18.81 18.52 19.10 19.07 18.80 19.35 0.26 ns

Secondary 19.73 19.49 19.97 20.01 19.78 20.24 0.28 ns

Higher 20.48 20.20 20.76 21.56 21.27 21.84 1.08 ***

Don't know s s s 21.79 19.56 24.02 2.40 ns

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Continued 
40 

Table 13—Continued

 Survey 1 Survey 2 Difference 

survey 1-

Country Education Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Southeast Asia

Cambodia 

2005 2014

ns *** 

No education 20.57 20.30 20.85 20.49 20.15 20.83 -0.08 ns

Primary 20.15 19.99 20.31 20.26 20.07 20.45 0.10 ns

Secondary 20.36 20.19 20.53 20.81 20.65 20.97 0.45 ***

Higher 21.57 20.95 22.19 22.80 22.43 23.18 1.23 ***

Don't know 20.84 20.17 21.50 21.86 20.21 23.51 1.02 ns

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 

 2002-03 2012 

*** *** 

No education 17.47 17.09 17.85 18.26 17.64 18.89 0.79 *

Primary 17.87 17.73 18.00 18.62 18.48 18.76 0.75 ***

Secondary 20.62 20.47 20.77 21.37 21.23 21.50 0.75 ***

Higher 23.35 22.99 23.71 24.10 23.86 24.33 0.75 ***

Don't know 18.70 17.64 19.75 19.67 18.31 21.04 0.98 ns

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 ***

Philippines 

2003 2013 

*** *** 

No education 19.56 18.86 20.27 18.68 17.92 19.44 -0.88 ns

Primary 20.16 19.97 20.35 20.18 19.97 20.38 0.02 ns

Secondary 21.77 21.57 21.96 21.89 21.72 22.06 0.12 ns

Higher 23.62 23.42 23.82 23.86 23.66 24.06 0.24 ns

Don't know s s s s s s -3.96 ns

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

na indicates indicator is not available for the survey. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases.

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution.

The disparity in marriage age between those married to men with the least education and with the most 

education ranges from a difference of 2.3 years in Cambodia to 5.8 years in Indonesia. The education 

disparity in South Asian countries ranges from 2.9 years (Pakistan) to 3.6 years (Bangladesh). Women 

who do not know their husband’s level of education tend to have married at ages in the middle of the 

range between those married to men with the no education and higher education. 

Marriage age has increased over time among all categories of husband’s education in Bangladesh, India, 

and Indonesia. However, later marriage age is concentrated only among secondary and higher education 

groups in Nepal and Cambodia and the higher education group only in Pakistan. The largest increase 

occurred among women married to men with higher education in Nepal (1.8 years increase from 2001 to 

2011, p=0.000). Even among countries in which the marriage age increased across all educational 

groups, they did so at different rates, so that the disparity by husband’s education has widened in all four 

South Asian countries and in Cambodia and the Philippines. Only in Indonesia has the disparity remained 

steady, although it is the largest among the study countries. 
41 

4.7.2 Husband’s occupation 

Husband’s occupation is significantly associated with marriage age, although somewhat less strongly so, 

in 6 of 7 study countries. There is no detectable association in Nepal in 2011, though there had been one 

in 2001. There had been no detectable association between husband’s occupation and marriage age in 

Survey 1 in Bangladesh or Indonesia. As with women’s own occupation, women married to men in 

professional occupations tend to have married at older ages and those married to men in agricultural 

occupations at younger ages. Women whose husbands are in clerical and sales occupations often tend to 

marry at older ages and those married to husbands in unskilled manual labor at younger ages, although 

these differentials usually are not as large. 

Table 14. Mean age at first marriage among ever-married women age 25-49, by husband's occupation 

 Survey 1 Survey 2 Difference 

survey 1-

Country Occupation Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

South Asia

India 

1998-99 2005-06

ns * 

Agricultural 16.54 16.48 16.60 16.85 16.77 16.92 0.30 ***

Professional/technical/ 

 managerial 

19.51 19.35 19.67 20.25 20.09 20.42 0.75 ***

Clerical 19.15 18.94 19.35 19.21 19.00 19.41 0.06 ns

Sales 18.24 18.11 18.36 18.67 18.54 18.79 0.43 ***

Services 18.01 17.85 18.18 18.02 17.87 18.18 0.01 ns

Skilled/unskilled manual 17.32 17.24 17.40 17.53 17.46 17.61 0.21 ***

Other 17.88 17.53 18.23 17.21 16.72 17.70 -0.67 *

Not working 17.06 16.86 17.26 17.40 17.12 17.67 0.34 *

Total 17.39 17.32 17.46 17.76 17.70 17.82 0.37 ***

Nepal 

 2001 2011 

** ns 

Agricultural 16.94 16.76 17.13 17.55 17.32 17.79 0.61 ***

Professional/technical/ 

 managerial 

17.92 17.57 18.28 19.71 19.30 20.12 1.79 ***

Clerical 17.11 16.85 17.38 17.80 17.32 18.28 0.68 *

Services 17.86 17.40 18.31 18.44 18.22 18.66 0.59 *

Skilled manual 17.30 16.94 17.67 17.33 17.06 17.61 0.03 ns

Unskilled manual 17.15 16.87 17.44 17.29 17.03 17.55 0.14 ns

Other 17.45 17.11 17.79 18.55 17.67 19.42 1.10 *

Not working na na na na na na -- --

Total 17.16 16.99 17.32 17.93 17.73 18.14 0.78 ***

Pakistan 

2006-07 2012-13 

* * 

Agricultural 18.83 18.54 19.12 18.93 18.50 19.37 0.10 ns

Professional/technical/ 

 managerial 

20.33 19.94 20.73 21.08 20.69 21.48 0.75 **

Clerical 19.22 18.67 19.77 20.73 20.01 21.45 1.51 ***

Sales 19.41 19.10 19.72 20.47 20.18 20.77 1.06 ***

Services 19.17 18.78 19.57 19.93 19.59 20.28 0.76 **

Skilled manual 19.36 19.06 19.65 19.98 19.66 20.29 0.62 **

Unskilled manual 18.96 18.72 19.20 19.07 18.82 19.31 0.11 ns

Other s s s s s s -- --

Not working 18.53 17.91 19.15 19.19 18.50 19.88 0.66 ns

Total 19.20 19.05 19.35 19.69 19.49 19.89 0.49 ***

Continued
42 

Table 14—Continued

 Survey 1 Survey 2 Difference 

survey 1-

Country Occupation Mean CI p-value survey 2 1 Mean CI p-value1 p-value2

Southeast 

Asia

Cambodia 

 2005 2014

* * 

Agricultural 20.26 20.12 20.40 20.28 20.11 20.46 0.02 ns

Professional/technical/ 

 managerial 

20.65 20.29 21.00 21.47 21.13 21.82 0.83 ***

Clerical 20.95 20.32 21.58 21.57 20.65 22.50 0.62 ns

Sales 20.36 19.96 20.77 20.91 20.46 21.36 0.55 ns

Services 20.42 20.05 20.78 20.69 20.37 21.02 0.27 ns

Skilled manual 20.40 20.13 20.68 20.88 20.65 21.11 0.48 **

Unskilled manual 20.47 20.08 20.85 21.21 19.94 22.47 0.74 ns

Other 19.52 18.91 20.14 21.23 20.20 22.25 1.71 **

Not working na na na na na na -- --

Total 20.33 20.22 20.44 20.64 20.52 20.77 0.31 ***

Indonesia 

2002-03 2012 

ns * 

Agricultural 18.08 17.91 18.24 19.09 18.91 19.28 1.02 ***

Professional/technical/ 

 managerial 

21.51 21.15 21.87 22.97 22.70 23.25 1.47 ***

Clerical 21.67 21.31 22.03 23.24 22.86 23.62 1.57 ***

Sales 19.80 19.50 20.10 20.67 20.38 20.96 0.87 ***

Services 19.60 19.34 19.85 20.79 20.48 21.10 1.19 ***

Skilled manual 19.70 19.34 20.05 20.21 20.04 20.38 0.51 *

Unskilled manual 19.12 18.83 19.42 22.36 21.77 22.96 3.24 *

Other 20.64 18.20 23.08 21.44 19.27 23.60 0.79 ns

Not working 18.55 18.05 19.04 19.73 19.15 20.31 1.18 **

Total 19.24 19.08 19.39 20.49 20.34 20.64 1.25 *** 

Philippines 

 2003 2013

*** *** 

Agricultural 20.47 20.25 20.68 20.40 20.21 20.59 -0.07 ns

Professional/technical/ 

managerial 

23.29 22.98 23.60 23.66 23.34 23.98 0.37 ns

Clerical 22.97 22.21 23.73 23.40 22.80 24.01 0.43 ns

Sales 21.97 21.54 22.41 22.96 22.18 23.75 0.99 *

Services 22.61 22.11 23.10 23.13 22.68 23.58 0.52 ns

Skilled manual 21.45 21.15 21.74 22.21 22.00 22.42 0.76 ***

Unskilled manual 21.99 21.76 22.22 21.80 21.53 22.07 -0.19 ns

Other 22.68 21.97 23.40 22.74 21.99 23.49 0.06 ns

Not working na na na na na na -- --

Total 21.76 21.63 21.89 22.00 21.86 22.14 0.24 * 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 p-value of association test within each year. 

2 p-value of significance test for the difference in means between surveys. 

na indicates indicator is not available for the survey. 

Indonesia, Cambodia, and Philippines DHS do not collect data on husband's current employment status, but categorizes husband's occupation regardless of 

current employment status. 

India DHS (NFHS) and Indonesia DHS captures husband's current employment status as a separate occupational category and categorizes husband's 

occupation only for husbands who are currently working. 

India DHS (NFHS) do not distinguish between skilled manual and unskilled manual. 

Nepal and Pakistan DHS do not include a separate category for sales; women in these occupations are included in "other" occupations. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 
43 

The differential between women married to men in professional and agricultural occupations—the 

categories with the youngest and oldest age at marriage—exhibit no clear pattern across South and 

Southeast Asian countries. The disparity exceeds 3 years in India, Indonesia, and the Philippines but 

ranges between 1.2 and 2.2 in Nepal, Pakistan, and Cambodia. This disparity, however, has widened over 

time among all countries. 

Age at marriage has increased among women married to men in most occupational groups in South Asian 

countries and Indonesia. Where increases in marriage age have been widespread in South Asia, they have 

nonetheless not occurred among women married to men in agricultural and unskilled labor positions. 

Indonesia has seen increasing marriage age in these and all other occupational groups, with the exception 

of husbands employed in “other” occupations. 

In contrast to this widespread delay in marriage, increases in marriage age are concentrated in just a few 

occupation groups in Cambodia and the Philippines. Compared to a decade earlier, marriage occurs later 

for Cambodian women married to men in professional occupations and those who are not working, and 

for Indonesian women married to husbands in sales and skilled manual labor occupations. 

45 

5. Characteristics Associated with the First Birth Interval 

In a similar format to the tables in the previous section, Table 15 demonstrates the overall change in the 

mean duration of the first birth interval. Tables 16-26 display the distribution across background 

characteristics of the duration of the first birth interval among ever-married women age 20-49 and the 

results of tests of significance of the association between each background characteristic and the first birth 

interval. The duration of the first birth interval is measured in months and calculated as the extended 

mean, an exponential extension of the survival curve, to account for right hand censoring of some 

observations. These tables show the means and associations with background characteristics at two time 

points for each study country and the negative difference (shortened first birth interval) or positive 

(lengthened first birth interval) for each background characteristic and results testing the statistical 

significance of that change. Associations are tested with a Tarone-Ware test of equality of the survival 

curve. 

Table 16 shows that the change in the first birth interval between Survey 1 and Survey 2 is statistically 

significant in six of seven study countries; Cambodia is the exception (p=0.343). However, the direction 

of the change is not consistent across countries or within regions. While age at marriage increased in all 

study countries, the first birth interval became shorter in four countries: Bangladesh, Nepal, Pakistan, and 

Indonesia. The amount of the decrease between surveys ranges from 0.5 months shorter (Indonesia, 

p=0.001) to 6.5 months shorter (Bangladesh, p=0.000). In India and the Philippines, the first birth 

interval lengthened with increasing ages at marriage by 0.6 months (p=0.000) and 2.8 months (p=0.001), 

respectively. 

Table 15. Change in the mean1 marriage to first birth interval 

(in months) among ever-married women age 25-49 

 Survey 1 Survey 2 Difference 

survey 1- 

survey 2 

 

Country Mean Mean p-value2

South Asia 

Bangladesh 2004 2014 

44.76 38.29 -6.47 *** 

India 1998-99 2005-06 

39.53 40.13 0.60 *** 

Nepal 2001 2011 

44.57 39.08 -5.49 *** 

Pakistan 

2006-07 2012-13 

45.25 44.12 -1.13 *** 

Southeast Asia 

Cambodia 2005 2014 

33.02 31.91 -1.11 ns 

Indonesia 2002-03 2012 

34.58 34.04 -0.54 *** 

Philippines 2003 2013 

27.82 30.61 2.78 *** 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean 

that would occur because the last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across 

survey years. 
46 

5.1. First Birth Interval and Gender Context 

This study uses the same three variables to describe the gender context and its possible association with 

the first birth interval: the number of decisions in which women are involved, attitudes toward wife 

beating, and spousal age difference. The mean first birth interval according to each of these variables, 

respectively, are found in Tables 16-18. India and Pakistan surveys did not collect data on women’s 

decision-making and wife beating attitudes at Survey 1 and, in Bangladesh, neither survey collected data 

on wife beating attitudes; thus, change in the first birth interval according to these characteristics cannot 

be assessed for these countries. 

5.1.1. Decision-making 

At the most recent survey, the number of decisions in which women participate is significantly associated 

with the first birth interval in all four South Asian countries in the study and in Cambodia, but neither in 

Indonesia nor the Philippines. As shown in Table 16, the association (and lack of association) in Survey 

2 remains unchanged from Survey 1. 

Table 16. Mean1 marriage to first birth interval (in months) among ever-married women 

age 25-49, by women's decision-making 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Number of decisions Mean p-value2 Mean p-value2 3

South Asia 

Bangladesh 

2004 2014 

** *

0 48.99 36.67 -12.31 ***

1 45.72 37.84 -7.88 ***

2 41.87 34.79 -7.09 ***

3 43.26 36.74 -6.52 ***

Total 44.76 38.29 -6.47 ***

India 

***

0 na 37.92 -- --

1 na 37.39 -- --

2 na 37.60 -- --

3 na 35.79 -- --

Total 39.53 40.13 0.60 ***

Nepal 

2001 2011 

*** ***

0 47.80 40.82 -6.98 ***

1 42.08 38.65 -3.43 ***

2 39.01 36.17 -2.84 *

3 43.17 35.72 -7.45 ***

Total 44.57 39.08 -5.49 ***

Pakistan 

 2006-07 2012-13 

***

0 na 48.15 -- --

1 na 37.92 -- --

2 na 36.63 -- --

3 na 38.24 -- --

Total 45.25 44.12 -1.13 ***

Continued
47 

Table 16—Continued

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Number of decisions Mean p-value1 Mean p-value1 2

Southeast Asia

Cambodia 

2005 2014

* *

0 s 4 40.40

1 22.09 4 27.76 5.68 **

2 29.32 30.66 1.34 ns

3 33.18 29.51 -3.67 ns

Total 33.02 31.91 -1.11 ns

Indonesia 

 2002-03 2012 

ns ns

0 36.16 30.57 -5.59 ns

1 35.71 29.30 -6.41 ns

2 34.83 30.76 -4.07 ns

3 34.14 32.41 -1.73 ***

Total 34.58 34.04 -0.54 ***

Philippines 

2003 2013 

ns ns

0 30.57 26.58 4 -3.99 ns

1 24.00 4 33.25 9.26 ns

2 31.18 26.25 -4.93 ns

3 31.78 33.96 2.18 ns

4 26.62 29.07 2.46 **

Total 27.82 30.61 2.78 *** 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the 

last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years. 

4 Extended mean is unnecessary because all failures are observed. 

No data on women's decision-making were collected in the 1998-99 India DHS (NFHS-2) nor the 2006-07 Pakistan DHS. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 

Although marriage age rose with greater decision-making, in general, women with greater decisionmaking capacity have shorter first birth intervals. However, this relationship is not monotonic. In several 

countries, the first birth interval lengthens among women participating in the most decisions. The largest 

differences by decision-making capacity are found in Cambodia, where women who participate in one 

decision have first birth intervals that are, on average, 12.5 months shorter than women who participate in 

no decisions (p=0.012). The differences are smallest in India where women who participate in all three 

listed decisions have first birth intervals that are only about 2 months shorter than do those who make no 

decisions (p=0.000). 

In both South Asian countries in which trends can be assessed—Bangladesh and Nepal—the first birth 

interval has become shorter across all levels of decision-making over the decade between surveys. The 

decrease is most substantial among women making no decisions. Among these women, the first birth has 

become shorter by one year in Bangladesh (p=0.000) and 7 months in Nepal (p=0.000). 

In contrast, change in Southeast Asia in the first birth interval has not been widespread across women of 

different decision-making capacity. In Cambodia, the first birth interval appears to have lengthened over 
48 

time for most women; however, this change is only statistically significant among women who make 1 

decision (6 months, p=0.003). The first birth interval has shortened by close to 2 months for women 

making 3 decisions in Indonesia (p=0.000) and increased by 2.5 months among women making 4 

decisions in the Philippines (p=0.005). 

In Bangladesh and Nepal, the first birth interval has changed between surveys in such a way that the 

disparity across decision-making has narrowed by roughly 4 months. This same narrowing of the 

disparity in mean duration of the first birth interval according to level of decision-making is not seen in 

the Southeast Asian countries in the study. 

5.1.2 Attitudes toward wife beating 

Although attitudes toward wife beating are significantly associated with marriage age (except in Nepal), 

wife beating attitudes are generally not associated with the first birth interval. Only two countries—

Pakistan and Indonesia—show such an association at the most recent survey; none did at Survey 115. The 

first birth interval is significantly longer among women who think wife beating is not acceptable in any of 

the scenarios described (44.7 months versus 42.5 months, p=0.000) in Pakistan, but in Indonesia, they are 

slightly shorter among these women (33.8 months versus 34.1 months, p=0.001). 

Table 17. Mean1 marriage to first birth interval (in months) among ever-married women 

age 25-49, by attitudes toward wife beating 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Wife beating attitudes Mean p-value2 Mean p-value2 3

South Asia 

India 

 1998-99 2005-06 

ns

Acceptable in at least 

one scenario 

na 39.47 -- --

Rejects in all scenarios na 40.16 -- --

Total 39.53 40.13 0.60 ***

Nepal 

2001 2011 

ns ns

Acceptable in at least 

one scenario 

42.80 37.35 4 -5.45 ns

Rejects in all scenarios 44.75 39.06 -5.68 ***

Total 44.57 39.08 -5.49 ***

Continued 

 

15 Wife beating attitudes were not assessed at Survey 1 in Pakistan and India. 
49 

Table 17—Continued 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Wife beating attitudes Mean p-value2 Mean p-value2 3

Pakistan 

 2006-07 2012-13 

***

Acceptable in at least 

one scenario 

na 42.48 -- --

Rejects in all scenarios na 44.74 -- --

Total 45.25 44.12 -1.13 ***

Southeast 

Asia

Cambodia 

2005 2014 

ns ns

Acceptable in at least 

one scenario 

31.89 31.92 0.03 ns

Rejects in all scenarios 30.73 31.46 0.73 ns

Total 33.02 31.91 -1.11 ns

Indonesia 

 2002-03 2012 

ns ***

Acceptable in at least 

one scenario 

34.32 34.07 -0.25 ns

Rejects in all scenarios 34.62 33.84 -0.77 ***

Total 34.58 34.04 -0.54 ***

Philippines 

2003 2013 

ns ns

Acceptable in at least 

one scenario 

27.29 24.41 -2.89 *

Rejects in all scenarios 27.91 31.87 3.96 **

Total 27.82 30.61 2.78 *** 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the 

last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years. 

4 Extended mean is unnecessary because all failures are observed. 

No data on attitudes toward wife beating were collected in the 1998-99 India DHS (NFHS-2) nor the 2006-07 Pakistan DHS.

In the Nepal 2011 survey, but not the 2001 survey, a filter question was inserted and respondents answering “no” when 

asked, “In your opinion, should a husband hit or beat his wife for any reason at all?” were not asked if wife beating was 

justified in specific scenarios. This questionnaire change may result in a measurement change (decrease) in the prevalence 

of attitudes accepting of wife beating as well as in detecting any associations with this variable. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 

Change in the first birth interval has been isolated only to women who reject wife beating in Nepal and 

Indonesia, among whom the first birth interval shortened by 5.7 months and 0.8 months, respectively 

(p=0.000). In the Philippines, the first birth interval lengthened among women who reject wife beating (4 

months, p=0.007) and shortened among women who accept wife beating (2.9 months, p=0.030). With the 

shortening of the first birth interval limited to women rejecting wife beating in Nepal and Indonesia and 

changing in divergent directions in the Philippines, the disparity in the first birth interval has widened 

over time in these countries, although these differences are nonetheless not statistically significant in 

Nepal and the Philippines. 
50 

5.1.3. Spousal age difference 

Table 18 indicates that spousal age difference is strongly associated with the first birth interval in all 

seven study countries, and has remained so at both survey points. This pattern resembles the pattern 

found with marriage age. 

Table 18. Mean1 marriage to first birth interval (in months) among ever-married women 

age 25-49, by spousal age difference 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country 3

Spousal age 

difference Mean p-value2 Mean p-value2

South Asia 

Bangladesh 

2004 2014 

*** ***

0-2 years 51.82 40.31 -11.51 *

3-5 years 43.37 36.93 -6.44 ***

6-10 years 38.17 34.07 -4.10 ***

More than 10 years 42.53 38.10 -4.43 ***

Total 44.76 38.29 -6.47 ***

India 

 1998-99 2005-06 

*** ***

0-2 years 37.15 36.57 -0.57 ns

3-5 years 36.58 35.59 -0.99 ns

6-10 years 33.50 36.86 3.36 ***

More than 10 years 41.58 40.21 -1.38 ns

Total 39.53 40.13 0.60 ***

Nepal 

2001 2011

* **

0-2 years 39.67 36.15 -3.52 ***

3-5 years 44.07 34.45 -9.62 ***

6-10 years 42.70 36.57 -6.13 ***

More than 10 years 46.23 51.73 5.51 ns

Total 44.57 39.08 -5.49 ***

Pakistan 

 2006-07 2012-13

* **

0-2 years 42.03 42.18 0.15 **

3-5 years 41.65 40.21 -1.43 ***

6-10 years 43.20 40.23 -2.97 **

More than 10 years 43.16 45.81 2.65 ns

Total 45.25 44.12 -1.13 ***

Continued 
51 

Table 18—Continued

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country 3

Spousal age 

difference Mean p-value2 Mean p-value2

Southeast Asia

Cambodia 

2005 2014

*** *

0-2 years 29.35 29.25 -0.10 ns

3-5 years 27.03 28.33 1.29 ns

6-10 years 30.36 29.55 -0.81 ns

More than 10 years 49.10 38.68 -10.42 ns

Total 33.02 31.91 -1.11 ns

Indonesia 

 2002-03 2012 

*** ***

0-2 years 32.02 31.40 -0.62 ns

3-5 years 30.89 29.74 -1.15 ns

6-10 years 31.10 30.28 -0.82 **

More than 10 years 40.91 43.98 3.08 ns

Total 34.58 34.04 -0.54 ***

Philippines 

2003 2013 

*** ***

0-2 years 25.70 30.59 4.89 ns

3-5 years 23.70 25.67 1.97 ***

6-10 years 27.49 31.60 4.11 ns

More than 10 years 33.92 33.77 -0.15 ns

Total 27.82 30.61 2.78 *** 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the 

last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years. 

4 Extended mean is unnecessary because all failures are observed. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 

The general pattern is one of initially decreasing first birth intervals as the spousal age gap increases, 

before it lengthens again among those with even greater spousal age gaps. In most countries, the shortest 

first birth interval is among women with 3-5 years age difference with their husbands. In Bangladesh, 

however, it is women with a 6-10 year age difference who have the shortest birth interval. Here, the 

longest first birth interval appears among women with 0-2 years age difference with their husbands; this 

pattern is observed in both Survey 1 and 2. In all other countries, the longest birth interval appears among 

women with more than 10 years age difference. 

The largest disparity by spousal age gap in the length of the first birth interval (17.3 months) is observed 

in Nepal, followed by Indonesia and Cambodia. In contrast, 4.6 months separates the first birth interval 

of women with more than 10 years age difference and women with 3-5 years age difference. 

Change in the first birth interval has been widespread in Bangladesh, where all groups of women have 

experienced a shortened birth interval, and in Nepal and Pakistan, where the birth interval has changed for 

all but women with the largest spousal age difference. Pakistani women who are closest in age to their 

husbands (0-2 years difference) in Survey 2 experienced a very slight increase in the first birth interval 
52 

compared to their counterparts in Survey 1 (0.15 months, p=0.010). Meanwhile, women in other age 

groups experienced a shortening of the birth interval. 

In India, the first birth interval changed only for women with the 6-10 age difference with their spouse. 

These women experienced a first birth interval that was 3.4 months longer at Survey 2 than among their 

counterparts at Survey 1 (p≤0.001). Change in the first birth interval is similarly limited to a single group 

of women in Indonesia and the Philippines. The first birth interval shortened by less than a month for 

Indonesian women with 6-10 spousal age difference (p=0.010), whereas it lengthened by 2 months among 

Philippine women with 3-5 years age difference (p=0.001). These data highlight that direction and rates 

of change have not been consistent across age groups outside of Bangladesh, Nepal, and Pakistan. 

The pattern—of shorter first birth intervals among those with 3-5 years age difference and longer among 

those with more than 10 years difference—has been consistent at both surveys. The disparity across 

groups of spousal age difference has declined over time in four study countries, most dramatically in 

Cambodia (from about 22.1 months separating those with the longest and shortest first birth interval to 

about 10.4 months,) and Bangladesh (from about 13.6 to 6.2 months difference) but also in India and the 

Philippines. However, this disparity has increased somewhat (from 10 months to 14.2 months) in 

Indonesia and more than doubled in Nepal (6.6 months to 17.3 months) and Pakistan (1.5 months to 5.6 

months). 

5.2. First Birth Interval and Education and Occupation 

5.2.1. Education 

The first birth interval differs significantly by women’s education in all seven study countries. In all 

countries but Cambodia, this association was observed in Survey 1 as well. There is a negative 

relationship between education and the first birth interval, although the birth interval is not always 

monotonically shorter among women with increasing levels of education. In Bangladesh, the first birth 

interval decreases substantially between women with no education and those with primary education 

before increasing somewhat with increasing levels of education. The largest differential in the first birth 

interval by educational level is in Indonesia, where 18 months separates women with no education from 

women with secondary education. Differentials are also sizable—in excess of 10 months—in Pakistan 

(14 months), Nepal (11 months), and the Philippines (11.5 months). 
53 

Table 19. Mean1 marriage to first birth interval (in months) among ever-married women 

age 25-49, by level of education 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Education Mean p-value2 Mean p-value2 3

South Asia 

Bangladesh 

2004 2014 

*** ***

No education 49.17 42.01 -7.16 ***

Primary 40.89 35.07 -5.82 ***

Secondary 37.81 36.39 -1.42 *

Higher 38.70 38.80 0.10 *

Total 44.76 38.29 -6.47 ***

India 

 1998-99 2005-06 

*** ***

No education 43.82 41.63 -2.18 

Primary 37.68 41.70 4.02 ***

Secondary 34.11 36.00 1.89 ***

Higher 30.56 36.23 5.67 ***

Total 39.53 40.13 0.60 ***

Nepal 

2001 2011 

*** ***

No education 46.98 43.15 -3.83 ***

Primary 37.95 34.72 -3.23 *

Secondary 31.18 31.98 0.80 ns

Higher 26.30 41.86 15.55 ns

Total 44.57 39.08 -5.49 ***

Pakistan 

 2006-07 2012-13 

*** ***

No education 48.46 48.27 -0.20 ns

Primary 39.20 41.23 2.03 *

Secondary 33.43 37.75 4.32 ns

Higher 35.38 34.25 -1.13 ns

Total 45.25 44.12 -1.13 ***

Continued 
54 

Table 19—Continued

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Education Mean p-value2 Mean p-value2 3

Southeast Asia

Cambodia 

2005 2014 

ns ***

No education 32.36 30.33 -2.02 ns

Primary 32.80 31.60 -1.20 ns

Secondary 33.83 32.65 -1.18 ns

Higher 34.75 4 35.52 0.77 ns

Total 33.02 31.91 -1.11 ns

Indonesia 

 2002-03 2012 

*** ***

No education 47.22 48.78 1.56 ns

Primary 36.10 35.52 -0.58 *

Secondary 27.44 30.81 3.36 ns

Higher 32.22 32.84 0.62 ns

Total 34.58 34.04 -0.54 ***

Philippines 

2003 2013 

*** ***

No education 36.27 4 37.92 1.65 ns

Primary 27.35 28.05 0.70 **

Secondary 26.63 26.37 -0.26 ns

Higher 28.77 4 37.92 9.15 **

Total 27.82 30.61 2.78 *** 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the 

last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years. 

4 Extended mean is unnecessary because all failures are observed. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases,

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution.

The first birth interval has become shorter over time among most educational groups in Bangladesh, but 

lengthened over time in India. In contrast to this widespread change, change has been concentrated among 

selected educational groups in Nepal (shorter among women with no or primary education), Pakistan 

(longer among women with primary education), and the Philippines (longer among women with primary 

and with higher education). In summary, trends in the first birth interval by education have been neither 

widespread nor consistent in direction across study countries. 

5.2.2. Occupation 

As shown in Table 20, women’s occupation is associated with the first birth interval in all six study 

countries16. In the Philippines, occupation has become salient over time; there was no significant 

association with the first birth interval in 2003. In Cambodia, the level of significance has weakened 

over time.

 

16 Women’s occupation is not available in the Bangladesh DHS surveys included in this study. 
55 

Table 20. Mean1 marriage to first birth interval (in months) among ever-married women age 25-49, 

by occupation 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Occupation Mean p-value2 3 Mean p-value2

South Asia 

India 

 

*** *** 

Not working 36.86 37.69 0.83 ***

Agricultural 43.00 40.88 -2.12 ***

Professional/technical/ 

managerial 

39.32 44.73 5.41 ***

Clerical 33.70 47.72 14.02 ***

Sales 35.66 38.12 2.46 ns

Services 48.82 41.17 -7.64 ns

Skilled/unskilled manual 45.98 43.09 -2.89 ***

Other 46.95 30.14 4 -16.81 ns

Total 39.53 40.13 0.60 ***

Nepal 

2001 2011 

*** *** 

Not working 41.75 39.09 -2.66 ***

Agricultural 45.16 39.79 -5.37 ***

Professional/technical/ 

managerial 

33.70 41.67 7.97 ns

Clerical s 4 25.85 4 -2.93 ns

Sales na na -- --

Services 52.87 35.26 -17.61 ns

Skilled manual s 35.19 ns

Unskilled manual 40.57 4 33.10 -7.47 ns

Other s 102.71 66.28 ns

Total 44.57 39.08 -5.49 ***

Pakistan 

2006-07 2012-13 

*** *** 

Not working 43.86 42.37 -1.49 ***

Agricultural 51.33 51.88 0.55 ns

Professional/technical/ 

managerial 

32.02 43.72 11.70 ns

Clerical s 4 s 57.99 ns

Sales 41.05 46.67 5.62 ns

Services 47.10 41.28 -5.82 ns

Skilled manual 53.82 46.32 4 -7.50 ns

Unskilled manual 39.13 4 47.04 7.91 ns

Other na na -- --

Total 45.25 44.12 -1.13 ***

Continued 
56 

Table 20—Continued

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 

 

p-value Country Occupation Mean p-value2 3 Mean p-value2

Southeast Asia

Cambodia 

2005 2014

*** * 

Not working 54.64 28.96 -25.68 ns

Agricultural 31.95 29.60 -2.35 ns

Professional/technical/ 

managerial 

45.03 31.20 -13.83 ns

Clerical 53.63 48.50 -5.14 ns

Sales 34.77 32.15 -2.62 *

Services 37.24 4 34.21 -3.03 ns

Skilled manual 25.36 4 35.01 9.65 ns

Unskilled manual 29.48 27.81 -1.67 ns

Other 30.67 4 41.49 10.82 ns

Total 33.02 31.91 -1.11 ns

Indonesia 

2002-03 2012

*** *** 

Not working 31.49 30.24 -1.25 **

Agricultural 36.65 34.75 -1.90 ns

Professional/technical/ 

managerial 

33.21 41.13 7.91 ns

Clerical 31.35 35.75 4.40 ns

Sales 33.94 34.20 0.26 ns

Services 45.39 33.32 -12.07 ns

Skilled manual 39.16 36.09 -3.08 ns

Unskilled manual s 4 25.38 8.09 ns

Other s s 4 22.56 ns

Total 34.58 34.04 -0.54 ***

Philippines 

2003 2013

0.140 *** 

Not working 25.24 4 27.60 2.35 ***

Agricultural 26.24 27.25 1.01 ns

Professional/technical/ 

managerial 

33.30 37.40 4.10 ns

Clerical 30.11 35.62 5.50 ns

Sales 28.76 33.15 4.40 ns

Services 18.31 31.36 13.05 ns

Skilled manual 27.61 31.56 3.95 ns

Unskilled manual 26.42 26.68 0.27 ns

Other s 4 s 4 -2.38 ns

Total 27.82 30.61 2.78 ***

 Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the last observed 

analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years.

4 Extended mean is unnecessary because all failures are observed.

na indicates indicator is not available for the survey.

Indonesia, Cambodia, and Philippines DHS do not collect data on husband's current employment status, but categorizes husband's 

occupation regardless of current employment status.

India DHS (NFHS) and Indonesia DHS captures husband's current employment status as a separate occupational category and categorizes 

husband's occupation only for husbands who are currently working.

India DHS (NFHS) do not distinguish between skilled manual and unskilled manual.

Nepal and Pakistan DHS do not include a separate category for sales; women in these occupations are included in "other" occupations.

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution.

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases.

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution.
57 

Overall, women who are not working or who work in agricultural or unskilled manual labor positions 

have shorter first birth intervals while women employed in professional or clerical occupations have 

longer first birth intervals. The pattern is slightly different in Pakistan, where women employed in 

professional occupations have first birth intervals similar in duration to those among women who are not 

working. Women engaged in agricultural occupations have longer birth intervals. 

Outside of India, where the first birth interval has changed significantly for 5 of 8 occupational groups, 

change has not been widespread but concentrated in selected occupations. The first birth interval has 

become significantly shorter over time among women who are not working in Nepal, Pakistan, and 

Indonesia, and nearly significantly so in Cambodia (p=0.052). However, there was a lengthening of the 

first birth interval among non-working women in India and the Philippines. The first birth interval 

became shorter for Indian and Nepali women working in agriculture, and became longer for Indian 

women working in professional and clerical occupations. 

5.3. First Birth Interval and Wealth 

Household wealth quintile is salient to the first birth interval in all study countries but Cambodia. These 

associations have been consistent over both survey times, as seen in Table 21. However, the pattern of 

the association is not consistent across study countries or across South and Southeast Asian regions. 

There is a monotonic negative relationship between household wealth quintile and the first birth interval 

in India, Nepal, and Pakistan. In Indonesia, first birth intervals become shorter with increasing wealth, 

but not monotonically. On the other hand, there is a positive (non-monotonic) relationship in Bangladesh 

and the Philippines. In Cambodia, the first birth interval is shorter among women in the poorest and the 

richer wealth quintile and longer among women in the middle and richest quintiles, although these 

differences are not significant. 

Table 21. Mean1 marriage to first birth interval (in months) among ever-married women 

age 25-49, by household wealth quintile 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Wealth Mean p-value2 Mean p-value2 3

South Asia 

Bangladesh 

2004 2014 

*** ***

Poorest 52.69 37.47 -15.22 ***

Poorer 41.39 36.97 -4.43 ***

Middle 43.93 37.79 -6.14 ***

Richer 44.49 38.43 -6.07 ***

Richest 42.09 39.54 -2.54 ns

Total 44.76 38.29 -6.47 ***

India 

*** ***

Poorest 46.90 43.79 -3.10 ***

Poorer 43.48 43.08 -0.40 ns

Middle 38.60 38.86 0.25 ns

Richer 37.82 38.10 0.28 ns

Richest 35.11 36.43 1.32 ***

Total 39.53 40.13 0.60 ***

Continued 
58 

Table 21—Continued

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Wealth Mean p-value2 Mean p-value2 3

Nepal 

2001 2011 

*** ***

Poorest 45.50 40.37 -5.13 ***

Poorer 48.64 40.32 -8.32 ***

Middle 42.55 39.78 -2.77 ***

Richer 42.11 36.88 -5.23 ***

Richest 41.28 36.89 -4.39 ***

Total 44.57 39.08 -5.49 ***

Pakistan 

 2006-07 2012-13 

*** ***

Poorest 52.84 50.22 4.72 ns

Poorer 46.09 48.22 -0.42 ns

Middle 47.61 43.29 0.74 ***

Richer 45.05 41.49 -0.62 ***

Richest 34.33 39.01 -2.26 ns

Total 45.25 44.12 -1.13 ***

Southeast Asia

Cambodia 

2005 2014 

ns ns

Poorest 30.88 29.93 -0.95 ns

Poorer 30.73 30.24 -0.49 ns

Middle 34.81 32.32 -2.49 ns

Richer 29.77 29.41 -0.36 ns

Richest 35.62 33.53 -2.09 ns

Total 33.02 31.91 -1.11 ns

Indonesia 

 2002-03 2012 

*** ***

Poorest 36.68 35.91 -0.77 ns

Poorer 35.01 34.88 -0.13 ns

Middle 35.59 31.77 -3.83 ***

Richer 31.79 31.28 -0.51 ns

Richest 30.54 33.49 2.95 ns

Total 34.58 34.04 -0.54 ***

Philippines 

2003 2013 

*** ***

Poorest 26.19 25.05 -1.14 ns

Poorer 25.42 24.33 -1.09 ns

Middle 27.31 32.61 5.29 ns

Richer 30.14 32.67 2.53 ns

Richest 29.17 36.47 7.30 *

Total 27.82 30.61 2.78 *** 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the 

last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years. 

4 Extended mean is unnecessary because all failures are observed. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 
59 

The largest differentials between richest and poorest wealth quintiles are observed in the Philippines, 

where women in the richest households have first birth intervals that are 11.4 months longer than women 

in the poorest households, and in Pakistan, where women in the richest households have first birth 

intervals that are 11.2 months shorter than women in the poorest households. 

The first birth interval has become significantly shorter for women of all wealth levels in Bangladesh and 

Nepal. Elsewhere, change has been more concentrated and inconsistent in direction. The first birth 

interval has become shorter among the poorest Indian women and longer among the richest Indian 

women. In Pakistan, change has been significant among the middle and richer quintiles, and approaching 

significance (p=0.052) among women in the richest quintile. However, the first birth interval lengthened 

for the former group and shortened for the latter two groups of women. The first birth interval shortened 

only among the middle wealth quintile in Indonesia and lengthened only among the richest wealth 

quintile in the Philippines. 

In spite of the variation in time trends, the absolute differential between richest and poorest wealth 

quintiles narrowed over time in six of seven countries. In Bangladesh, the differential even reversed 

direction. While women in the richest households had first birth intervals that were 10.6 months shorter, 

on average, than women in the poorest households in 2004, women in the richest quintile had first birth 

intervals that were 2.1 months longer, on average, than women in the poorest households by 2014. 

However, the differential widened in the Philippines, with women in richest households having first birth 

intervals that were 3 months longer than poorest women in 2003 compared to 11.4 months longer in 2013. 

5.4. First Birth Interval and Religion 

Table 22 indicates that religion is significantly associated with the first birth interval only in India, Nepal, 

and the Philippines. There is no association in Bangladesh and Cambodia, while an association could not 

be assessed in Pakistan and Indonesia17. 

 

17 Data on religious affiliation are not available in DHS surveys for Pakistan and Indonesia. 
60 

Table 22. Mean1 marriage to first birth interval (in months) among ever-married women 

age 25-49, by religion 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Religion Mean p-value2 Mean p-value2 3

South Asia 

Bangladesh 

2004 2014

ns ns

Muslim 45.19 38.34 -6.85 ***

Christian 53.19 27.04 -26.15 ns

Buddhist s 26.52 4 -47.49 ns

Hindu 40.15 36.98 -3.17 *

Total 44.76 38.29 -6.47 ***

India 

*** ***

Muslim 37.48 37.15 -0.33 ns

Christian 30.36 30.74 0.38 ns

Buddhist 30.28 35.05 4.77 ***

Hindu 41.10 41.64 0.55 ***

Other 34.63 31.73 -2.90 ns

Sikh 26.62 29.91 4 3.29 ***

Jain 33.09 28.88 4 -4.22 ns

Total 39.53 40.13 0.60 ***

Nepal 

2001 2011

*** **

Muslim 52.76 40.33 -12.42 ***

Buddhist 38.84 37.87 -0.98 ns

Hindu 44.72 38.93 -5.79 ***

Other 33.95 34.11 0.16 ns

Kirat 45.40 36.65 -8.76 ns

Total 44.57 39.08 -5.49 ***

Southeast Asia 

Cambodia 

 2005 2014

ns ns

Muslim 31.94 33.22 1.28 ns

Christian 28.98 38.85 9.87 ns

Buddhist 33.12 31.63 -1.49 ns

Other 31.35 32.98 1.63 ns

Total 33.02 31.91 -1.11 ns

Philippines 

2003 2013

*** ***

Muslim 31.24 28.77 -2.47 ns

Other 27.02 28.64 1.62 ns

Roman Catholic 27.45 30.50 3.04 *

Protestant 22.33 4 36.03 13.70 ns

Iglesia Ni Kristo 32.98 28.26 -4.72 ns

Aglipay 29.25 25.27 -3.98 ns

Total 27.82 30.61 2.78 *** 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the 

last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years. 

4 Extended mean is unnecessary because all failures are observed. 

Pakistan and Indonesia DHS do not collect data on religion. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 
61 

The pattern across religious groups also varies across countries. In Nepal, Muslim women have longer 

first birth intervals than do the Hindu majority or any other faith group. In India, it is the reverse. Hindu 

women have the longest first birth intervals and Muslim women have shorter first birth intervals, although 

not as short as Sikh and Jain women. In the Philippines, Roman Catholic and Protestant women have the 

longest first birth intervals while Aglipay women have the shortest birth intervals. Muslim women in the 

Philippines have neither the longest nor the shortest first birth intervals, and approach the national 

average. 

The first birth interval has changed in duration only among certain religious groups; these birth intervals 

have become significantly longer among Buddhist, Sikh, and (by a small amount) Hindu women in India 

and Roman Catholic women in the Philippines. The first birth interval has also become significantly 

shorter among Muslim and Hindu women in both Bangladesh and Nepal. 

5.5. First Birth Interval and Residence 

Place of residence is associated with the first birth interval in five of seven study countries. Urban/rural 

differences appear to be more salient in South Asia than in Southeast Asia, with Indonesia the only 

Southeast Asian countries of the three in the study to manifest a statistical relationship between residence 

and the first birth interval. The observed patterns of association have remained the same at both time 

points in all seven study countries. 

Table 23. Mean1 marriage to first birth interval (in months) among ever-married women 

age 25-49, by place of residence 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Residence Mean p-value2 Mean p-value2 3

South Asia 

Bangladesh 

2004 2014 

* ***

Rural 44.53 37.09 -7.44 ***

Urban 45.03 40.50 -4.53 ***

Total 44.76 38.29 -6.47 ***

India 

*** ***

Rural 40.70 41.04 0.34 ns

Urban 37.30 38.16 0.86 ***

Total 39.53 40.13 0.60 ***

Nepal 

2001 2011 

*** ***

Rural 45.10 39.77 -5.33 ***

Urban 39.82 36.67 -3.15 ns

Total 44.57 39.08 -5.49 ***

Pakistan 

 2006-07 2012-13 

*** ***

Rural 48.11 47.00 -1.12 *

Urban 40.29 40.74 0.44 ***

Total 45.25 44.12 -1.13 ***

Continued 
62 

Table 23—Continued

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 

pvalue Country Residence Mean p-value2 Mean p-value2 3

Southeast Asia

Cambodia 

2005 2014 

ns ns

Rural 32.69 30.18 -2.51 ns

Urban 32.51 34.43 1.92 ns

Total 33.02 31.91 -1.11 ns

Indonesia 

 2002-03 2012 

*** ***

Rural 35.74 34.00 -1.74 *

Urban 32.11 34.20 2.10 ns

Total 34.58 34.04 -0.54 ***

2013

Philippines 

2003 

ns ns

Rural 25.88 30.16 4.27 *

Urban 29.40 30.82 1.42 *

Total 27.82 30.61 2.78 *** 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the 

last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years. 

4 Extended mean is unnecessary because all failures are observed. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 

First birth intervals are longer among women living in urban areas in Bangladesh and Indonesia 

(p=0.000), although the differences are small in Indonesia. They are shorter among women living in 

urban areas in India, Nepal, and Pakistan (p=0.000). In Pakistan, women in rural areas have first birth 

intervals that are, on average, nearly 7 months longer than women living in urban areas. 

The first birth interval has become significantly shorter among both rural and urban residents in 

Bangladesh (p≤0.001), but has become shorter only among rural residents in Nepal, Pakistan, and 

Indonesia. It has become significantly longer among urban residents in India, Pakistan, and Indonesia, 

and among both rural and urban residents in the Philippines. The urban-rural differential has increased 

between Survey 1 and Survey 2 in Bangladesh, but has diminished in India, Nepal, Pakistan, and 

Indonesia. 

5.6. First Birth Interval and Region 

Regional differences characterize the mean first birth interval throughout most of the study countries. 

Regional differences are most notable in India where 22.6 months separate women in the state with the 

longest first birth interval (Andhra Pradesh) and those in the state with the shortest first birth interval 

(Nagaland). The differential is smallest in neighboring Nepal, where women in the mountain region have 

first birth intervals that are 3.8 months longer than women in the hill region. 
63 

Table 24. Mean1 marriage to first birth interval (in months) among ever-married 

women age 25-49, by subnational region 

Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Region Mean p-value2 Mean p-value2 3

South Asia 

Bangladesh 

2004 2014

*** ***

Dhaka 44.85 38.03 -6.81 ***

Barisal 42.10 34.27 -7.83 ***

Chittagong 39.71 32.55 -7.16 ***

Khulna 43.75 41.29 -2.45 * 

Rajshahi & Rangpur 48.08 40.21 -7.87 ***

Sylhet 47.68 39.24 -8.45 ***

Total 44.76 38.29 -6.47 ***

India 

1998-99 2005-06

*** ***

Uttar Pradesh & 

Uttaranchal 

43.82 36.76 -7.1 *** 

Andhra Pradesh 25.59 49.18 23.6 *** 

Arunachal Pradesh 51.71 30.38 -21.3 *** 

Assam 33.03 35.26 2.2 *** 

Bihar & Jharkhand 43.41 45.58 2.2 ns 

Delhi 29.84 35.33 5.5 *** 

Goa 43.20 47.85 4.6 ns

Gujarat 39.58 40.05 0.5 ns

Haryana 31.61 35.23 3.6 *** 

Himachal Pradesh 29.81 30.84 1.0 ns

Jammu & Kashmir 31.10 30.35 -0.8 ns

Karnataka 39.48 39.75 0.3 ns

Kerala 35.26 39.09 3.8 *** 

Madhya Pradesh 44.09 42.94 -1.1 ns

Maharashtra 44.65 40.71 -3.9 ns

Manipur 22.14 28.65 6.5 *** 

Meghalaya 20.20 4 29.56 9.4 *** 

Mizoram 23.96 26.79 2.8 ns

Nagaland 19.96 26.57 6.6 ns

Orissa 45.95 37.35 -8.6 ns

Punjab 26.05 30.60 4.5 *** 

Rajasthan 45.14 46.25 1.1 ns 

Sikkim 25.09 33.23 8.1 *** 

Tamil Nadu 43.74 42.16 -1.6 ns 

Tripura 32.53 38.55 6.0 *** 

West Bengal 42.73 40.06 -2.7 ns 

Total 39.53 40.13 0.60 *** 

Nepal 

2001 2011

*** ***

Terai 44.90 39.52 -5.4 ***

Mountain 46.05 41.24 -4.8 ***

Hill 42.05 37.41 -4.6 ***

Total 44.57 39.08 -5.49 ***

Pakistan 

2006-07 2012-13

*** ***

Punjab 43.72 44.31 0.6 ***

Sindh 47.63 39.85 -7.8 ***

Khyber Pakhtunkwa 41.24 43.75 2.5 ns 

Balochistan 51.64 46.95 -4.7 ***

Total 45.25 44.12 -1.13 ***

Continued
64 

Table 24—Continued

Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Region Mean p-value2 Mean p-value2 3

Southeast Asia

Cambodia 

2005 2014 

*** ***

Phnom Penh 34.62 34.26 -0.4 ns

Banteay Mean Chey 31.53 28.15 -3.4 ns

Kampong Cham 35.88 32.87 -3.0 * 

Kampong Chhnang 36.56 28.72 -7.8 ns 

Kampong Speu 36.38 32.58 -3.8 *** 

Kampong Thom 34.36 24.06 4 -10.3 * 

Kandal 26.60 31.45 4.9 ns 

Kratie 30.63 35.43 4.8 *** 

Prey Veng 35.32 30.92 4 -4.4 ns

Pursat 28.15 25.69 -2.5 ns

Siem Reap 30.56 26.58 -4.0 ns

Svay Rieng 35.21 29.00 -6.2 ns

Takeo 29.92 36.51 6.6 * 

Otdar Mean Chey 21.61 26.81 4 5.2 *** 

Battambang & Pailin 32.79 26.65 -6.1 ns

Kampot & Kep 32.63 33.15 0.5 ns

Preah Sihanouk & Kaoh 

Kong 

30.21 33.39 3.2 ns

Preah Vihear & Steung 

Treng 

27.83 33.06 5.2 ns

Mondol Kiri & Rattanak 

Kiri 

33.46 29.53 -3.9 * 

Total 33.02 31.91 -1.11 ns 

Indonesia 

2002-03 2012 

*** *** 

Sumatera 30.61 27.87 -2.7 ***

Java 37.98 35.60 -2.4 ***

Bali & Tenggara 33.53 29.90 -3.6 ***

Kalimantan 29.25 32.83 3.6 ns

Sulawesi, Maluku & 

Papua 

39.31 38.84 -0.5 ns

Total 34.58 34.04 -0.54 *** 

Continued 
65 

Table 24—Continued

Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Region Mean p-value2 Mean p-value2 3

Philippines 

2003 2013 

*** ***

National Capital 32.09 31.76 -0.3 ns

Cordillera 22.11 25.39 3.3 ns

I - Ilocos 28.23 27.03 -1.2 ns

II - Cagayan Valley 25.28 25.71 0.4 ns

III - Central Luzon 28.26 30.54 2.3 ns

IVA - Calabarzon 28.43 29.25 0.8 ns

IVB - Mimaropa 29.43 23.11 -6.3 ns

V - Bicol 20.13 4 28.40 8.3 ns

VI - Western Visayas 25.26 29.00 3.7 ns

VII - Central Visayas 27.31 25.42 -1.9 ns

VIII - Eastern Visayas 27.99 30.79 2.8 ns

IX - Zamboanga 

Peninsula 

24.83 32.52 7.7 ns

X - Northern Mindanao 21.47 32.19 10.7 ** 

XI - Davao 22.77 4 23.52 4 0.8 ns

XII - Soccskargen 20.89 30.77 9.9 ns

XIII - Caraga 26.15 36.94 10.8 ns

ARMM 32.54 29.40 -3.1 ** 

Total 27.82 30.61 2.78 *** 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05) 

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur 

because the last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each 

survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years. 

4 Extended mean is unnecessary because all failures are observed. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 

Changes in the first birth interval have been widespread across regions of Bangladesh (all 7 divisions), 

Pakistan (3 of 4 regions), and Indonesia (3 of 5 province groups). The first birth interval has changed in 

duration in 12 of 26 states or state pairs in India. However, change has been restricted to just a few 

regions in Cambodia (6 of 19 regions) and the Philippines (2 of 17 regions). 

5.7. First Birth Interval and Husbands’ Education and Occupation 

Two variables that describe husbands’ characteristics—education and occupation—and their association 

with the first birth interval are presented in Table 25 and Table 26, respectively. 

5.7.1. Husband’s education 

Husband’s education is significantly associated with the first birth in all seven study countries, although 

in Cambodia, the association only became detectable in the most recent survey (2014), as was the case 

with marriage age. In most cases, the first birth interval becomes shorter with increasing levels of 

husband’s education. Bangladesh and Indonesia suggest slight exceptions to this overall pattern. The 

first birth interval becomes longer again among women married to men with higher education in 

Indonesia and with secondary and higher education in Bangladesh.
66 

Table 25. Mean1 marriage to first birth interval (in months) among ever-married women 

age 25-49, by husband's education 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Education Mean p-value2 Mean p-value2 3

South Asia 

Bangladesh 

2004 2014 

*** ***

No education 47.65 39.90 -7.75 ***

Primary 42.98 34.19 -8.79 ***

Secondary 42.67 38.26 -4.42 ***

Higher 41.64 40.34 -1.30 ns

Don’t know na na -- --

Total 44.76 38.29 -6.47 ***

India 

*** ***

No education 45.28 43.12 -2.16 ***

Primary 40.50 39.12 -1.39 ns

Secondary 36.52 38.48 1.96 ***

Higher 35.10 34.91 -0.19 ns

Don’t know na 67.11 -- --

Total 39.53 40.13 0.60 ***

Nepal 

2001 2011 

*** ***

No education 48.85 43.33 -5.52 ***

Primary 44.92 40.55 -4.36 ***

Secondary 36.74 35.67 -1.07 ***

Higher 33.10 34.47 1.37 ns

Don’t know 75.40 50.61 -24.79 ns

Total 44.57 39.08 -5.49 ***

Pakistan 

 2006-07 2012-13 

*** ***

No education 51.50 47.51 -3.99 *

Primary 42.40 44.99 2.59 *

Secondary 42.71 42.29 -0.41 ***

Higher 36.53 38.23 1.69 ns

Don’t know s 4 98.71 55.12 ns

Total 45.25 44.12 -1.13 ***

Southeast Asia

Cambodia 

2005 2014

ns **

No education 33.81 32.14 -1.67 ns

Primary 31.49 30.57 -0.92 ns

Secondary 31.13 31.52 0.39 ns

Higher 40.44 30.68 -9.76 ns

Don’t know 52.44 58.95 6.51 ns

Total 33.02 31.91 -1.11 ns

Indonesia 

 2002-03 2012 

*** ***

No education 50.11 47.63 -2.48 ns

Primary 36.49 35.07 -1.42 ns

Secondary 29.79 30.85 1.06 ns

Higher 26.34 30.33 3.99 ns

Don’t know 81.05 93.39 12.33 ns

Total 34.58 34.04 -0.54 ***

Continued
67 

Table 25—Continued

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 p-value Country Education Mean p-value2 Mean p-value2 3

Philippines 

2003 2013 

*** ***

No education 32.99 37.68 4.69 ns

Primary 26.92 26.78 -0.14 ns

Secondary 26.93 29.20 2.28 *

Higher 29.52 35.25 5.73 *

Don’t know s s 4 -39.12 ns

Total 27.82 30.61 2.78 *** 

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the 

last observed analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years. 

4 Extended mean is unnecessary because all failures are observed. 

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 

The disparity in the first birth interval between those married to men with the least education and with the 

most education ranges from a difference of about 2 months in Cambodia and the Philippines to 17.3 

months in Indonesia. 

The first birth interval has become shorter over time among most educational categories in Bangladesh 

and Nepal, but such change is isolated among women married to men with no education in India 

(p≤0.001) and women married to men with no education (p=0.049) and with secondary education 

(p=0.001) in Pakistan. There has been a significant lengthening of the first birth interval only among 

Indian women married to men with secondary education (p≤0.001) and Pakistani women married to men 

with primary education (p=0.014), and among Philippine women married to men with secondary or 

higher education (p≤0.05). The disparity according to husband’s education has decreased over the two 

survey periods in all seven countries. 

5.7.2 Husband’s occupation 

Husband’s occupation is significantly associated with the first birth interval in all seven study countries, 

although at a lower level of significance (p≤0.05) in Cambodia and the Philippines. There had been no 

detectable association between husband’s occupation and the first birth interval in Survey 1 in these two 

countries. In general, first birth intervals are longer among women married to men who are not working 

or who are employed in agricultural occupations. They are usually shorter among women married to men 

working in professional, clerical, sales, and service occupations. This pattern does not hold in Cambodia 

and the Philippines, where women married to men in professional occupations have relatively long first 

birth intervals and those married to men in unskilled labor positions have relatively short intervals. 
68 

Table 26. Mean1 marriage to first birth interval (in months) among ever-married women age 25-49, 

by husband's occupation 

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 

 

p-value Country Occupation Mean p-value2 3 Mean p-value2

South Asia 

India 

*** *** 

Agricultural 41.60 42.76 1.15 ***

Professional/technical/ 

managerial 

35.30 36.16 0.86 ns

Clerical 34.94 36.63 1.69 ns

Sales 36.03 36.95 0.92 ns

Services 38.90 38.24 -0.65 ns

Unskilled/skilled manual 38.84 38.20 -0.63 ns

Other 41.70 51.83 10.14 ***

Not working 48.47 51.62 3.15 ns

Total 39.53 40.13 0.60 ***

Nepal 

2001 2011 

*** *** 

Agricultural 46.71 39.79 -6.9 ***

Professional/technical/ 

managerial 

38.90 36.51 -2.4 **

Clerical 43.16 35.95 -7.2 ***

Sales na na -- --

Services 42.82 37.82 -5.0 ***

Skilled manual 39.31 38.83 -0.5 *

Unskilled manual 40.50 38.10 -2.4 *

Other 39.55 48.12 8.6 ns

Not working na na -- --

Total 44.57 39.08 -5.49 ***

Pakistan 

2006-07 2012-13 

*** *** 

Agricultural 56.40 54.70 -1.7 *

Professional/technical/ 

managerial 

33.03 39.92 6.9 ns

Clerical 43.10 32.13 -11.0 **

Sales 41.01 32.71 -8.3 ***

Services 45.73 32.92 -12.8 ***

Skilled manual 37.41 43.57 6.2 ns

Unskilled manual 42.74 47.36 4.6 *

Other s 4 s 4 -164.0 ns

Not working 50.34 52.83 2.5 ns

Total 45.25 44.12 -1.13 ***

Continued 
69 

Table 26—Continued

 Survey 1 Survey 2 Difference 

survey 1-

survey 2 

 

p-value Country Occupation Mean p-value2 3 Mean p-value2

Southeast Asia

Cambodia 

2005 2014

ns * 

Agricultural 32.25 30.64 -1.6 ns

Professional/technical/ 

managerial 

33.71 33.98 0.3 ns

Clerical 33.25 26.89 -6.4 ns

Sales 28.68 30.67 2.0 ns

Services 39.30 32.58 -6.7 ns

Skilled manual 29.11 31.20 2.1 ns

Unskilled manual 29.64 23.03 4 -6.6 ns

Other 41.37 32.14 -9.2 ns

Not working na na -- --

Total 33.02 31.91 -1.11 ns

Indonesia 

2002-03 2012 

*** *** 

Agricultural 36.76 36.34 -0.4 ns

Professional/technical/ 

managerial 

27.10 31.33 4.2 ns

Clerical 26.90 29.68 2.8 ns

Sales 32.53 31.48 -1.1 ns

Services 32.29 32.80 0.5 **

Skilled manual 32.71 31.95 -0.8 ns

Unskilled manual 30.89 23.68 -7.2 *

Other 38.16 30.61 -7.5 ns

Not working 50.17 54.17 4.0 ns

Total 34.58 34.04 -0.54 ***

Philippines 

2003 2013

ns * 

Agricultural 25.40 27.45 2.0 **

Professional/technical/ 

managerial 

29.63 37.08 7.4 ns

Clerical 44.56 21.20 4 -23.4 ns

Sales 27.91 35.43 7.5 ns

Services 34.52 28.72 -5.8 ns

Skilled manual 24.24 32.73 8.5 ns

Unskilled manual 25.08 4 24.40 -0.7 ns

Other 31.44 40.55 9.1 ns

Not working na na -- --

Total 27.82 30.61 2.78 ***

Notes: 

p-values *≤0.05, **≤0.01, ***≤0.001, ns=not significant (p>0.05)

1 An extended mean is computed to account for any underestimation in the restricted mean that would occur because the last observed 

analysis time is censored. 

2 p-value of significance of Tarone-Ware test for the equality of survivor functions across groups within each survey. 

3 p-value of significance of Tarone-Ware test for the equality of survivor functions across survey years.

4 Extended mean is unnecessary because all failures are observed.

na indicates indicator is not available for the survey.

Indonesia, Cambodia, and Philippines DHS do not collect data on husband's current employment status, but categorizes husband's 

occupation regardless of current employment status.

India DHS (NFHS-2 and NFHS-3) and Indonesia DHS capture husband's current employment status as a separate occupational category

and categorizes husband's occupation only for husbands who are currently working.

India DHS (NFHS-2 and NFHS-3) do not distinguish between skilled manual and unskilled manual.

Nepal and Pakistan DHS do not include a separate category for sales; women in these occupations are included in "other" occupations.

s indicates that the indicator is suppressed because it is based on fewer than 25 unweighted cases. 

indicators in italics are based on fewer than 50 unweighted cases and should be interpreted with caution. 
70 

Change in the first birth interval has been relatively widespread across husband’s occupation in Nepal and 

Pakistan. Specifically, the first birth interval has become shorter over time in these two countries. The 

first birth interval has changed in only two occupational categories in India: it has become longer among 

women married to men in agricultural positions and “other” positions. There has been almost no 

discernible change in the first birth interval in husband’s occupational groups in Southeast Asian 

countries in this study. Exceptions are Indonesian women married to men in service occupations —

among whom first birth intervals have become slightly longer—and those married to men in unskilled 

manual labor positions—among whom intervals have become shorter. 
71 

6. Association of Marriage Age with the First Birth Interval 

Tables 27 and 28 present the results of loglogistic accelerated failure time models estimating the interval 

from marriage to the first birth. These models are estimated using data from the most recent DHS survey 

(Survey 2). Results are presented as time ratios, the exponentiated coefficient, to ease interpretation so 

that a positive coefficient (TR>1) corresponds to a greater expected time to the first birth and a negative 

coefficient (TR<1) indicates a shorter expected time to the first birth. Observations (unweighted) and 

diagnostics of the model form for the hazard models are available in Appendix Tables 2 and 3. 

Table 27. Effect of age at marriage on the marriage to first birth interval: Time ratios from unadjusted 

loglogistic hazard models (95% confidence intervals in parentheses) 

South Asia Southeast Asia 

 Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

Age at first marriage 0.976*** 0.962*** 0.940*** 0.978*** 0.981*** 0.977*** 1.002 

(0.97 - 0.98) (0.96 - 0.96) (0.93 - 0.95) (0.97 - 0.98) (0.98 - 0.98) (0.97 - 0.98) (1.00 - 1.01)

Constant 33.25*** 45.01*** 77.35*** 34.94*** 28.71*** 27.46*** 14.16***

(29.68 - 37.25) (42.80 - 47.34) (65.32 - 91.59) (29.83 - 40.93) (26.22 - 31.44) (25.52 - 29.55) (12.85 - 15.60)

Gamma (γ) 0.5741 0.5277 0.4132 0.5732 0.3749 0.4401 0.479

(0.56 - 0.59) (0.52 - 0.53) (0.40 - 0.43) (0.55 - 0.59) (0.36 - 0.39) (0.43 - 0.45) (0.47 - 0.49)

Weighted sample size 

(person-months) 418,626 2,496,946 256,292 369,199 275,937 794,976 192,359 

*** p≤0.001; ** p≤0.01; * p≤0.05 

6.1. Age at Marriage 

Table 27 displays the unadjusted time ratios for the effect of age at marriage on the length of the first birth 

interval. In 6 of the 7 study countries, marriage age is significantly associated with the length of the first 

birth interval at the 0.001 level; the Philippines is the sole exception. In each case, marriage age is 

negatively associated with the birth interval, so that with each additional year of age at which women 

marry, women experience a 2-6% shorter time to first birth. In the Philippines, the time ratio is greater 

than one; this suggests a positive relationship between marriage age and the length of the first birth 

interval, were it to be statistically significant. 

Table 28 indicates that this relationship holds for the 6 countries when controlling for other covariates in 

an adjusted hazard model, with modest effects on the time ratios. Women in Bangladesh, India, and 

Nepal experience a first birth interval that is 4-6% shorter for each year of marriage delay. In Cambodia 

and Indonesia in Southeast Asia, and in Pakistan, women’s expected first birth interval is 1-2% shorter for 

each year older at which they marry. 
72 

Table 28. Adjusted effects on the duration of the marriage to first birth interval: Time ratios from 

multivariate loglogistic hazard models 

South Asia Southeast Asia 

 Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

Age at first marriage 0.949*** 0.956*** 0.940*** 0.981*** 0.978*** 0.986*** 1.009**

Birth cohort (ref=1960-69)

1950-59 na 0.899*** na na na na na

1970-79 0.812*** 1.128*** 0.804*** 0.959 0.934** 0.969 1.031

1980-89 0.671*** 0.915*** 0.713*** 0.853*** 0.899*** 0.950** 0.967

Decisionmaking 

# of decisions in which 

respondent participates 1.009 0.981*** 0.958*** 1.011*** 0.969 0.993 0.987 

Wife beating attitudes (ref=acceptable in at least one scenario 

Rejects in all scenarios na 0.994 0.913 1.032 1.007 0.992 0.993

Spousal age difference 

in years 0.987*** 0.988*** 1.000 0.998 1.002 1.003 1.010*** 

Education (ref=no education)

Primary 0.988 1.01 0.958 0.977 1.04 0.997 0.907

Secondary 1.149*** 1.078*** 0.949 0.936 1.029 0.911 0.820**

Higher 1.602*** 1.241*** 1.245*** 0.901* 1.031 0.947 0.778**

Occupation (ref=not working)

Agricultural na 0.950*** 0.903** 0.877 0.998 0.998 0.984

Professional/technical/ 

managerial 1.058* 0.846* 1.122 1.047 1.039 0.945 

Clerical na 1.131** 0.819 1.142 1.029 1.111** 0.941

Sales na 0.957 na 1.052 0.984 1.021 1.038

Services na 0.881*** 0.870*** 1.005 1.056 1.012 0.928

Skilled manual na 0.94 1.032 0.997 1.024 0.902*

Unskilled manual na 0.958* 0.922 0.986 1.017 0.959 0.931**

Other na 1.027 0.916 na 1.316* 3.790** 1.155

Household wealth 

quintile (ref=poorest) 

poorer 1.005 0.963* 1.026 0.925 0.964 0.987 0.940*

middle 0.987 0.959* 0.978 0.841* 0.977 0.967 0.982

richer 1.008 0.939** 0.890* 0.801** 0.999 0.968 1.022

richest 0.998 0.923*** 0.914 0.761** 0.965 0.944* 1.011

Continued 
73 

Table 28—Continued

South Asia Southeast Asia 

 Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

Religion 

Reference religion Muslim Hindu Hindu na Buddhist na Roman Catholic

Muslim na 0.885*** 1.054 na 0.924 na 1.126*

Christian 0.649 0.898*** na na 0.947 na na

Buddhist 1.009 1.011 0.973 na na na na

Hindu 0.998 na na na na na na

Other na 1.109 1.031 na 0.947 na 0.976

Protestant na na na na na na 1.045

Iglesia Ni Kristo na na na na na na 0.888*

Aglipay na na na na na na 0.820*

Sikh na 0.937 na na na na na

Jain na 0.900 na na na na na

Kirat na na 0.929 na na na na

Residence (ref=rural) 

urban 1.074** 0.951*** 0.965 0.918* 1.060* 0.931*** 0.99

Region (ref=region 1) 

Region 1 Dhaka Uttar Pradesh & 

Uttaranchal 

Terai Punjab Phnom Penh Sumatera National Capitol

Region 2 0.979 1.199*** 1.102* 1.059 0.936 1.121*** 0.865*

Region 3 0.881*** 0.967 0.983 0.986 0.939 0.961* 0.915

Region 4 1.028 0.899** 1.039 1.117* 1.045** 0.933

Region 5 0.961 1.023 0.993 1.014 0.928

Region 6 0.964 1.045 1.058 1.044 0.869**

Region 7 0.918 1.166*** 1.068 0.928

Region 8 1.130*** 1.255*** 0.898*

Region 9 1.138*** 1.191*** 0.894*

Region 10 0.898*** 0.963 0.883*

Region 11 0.851*** 1.022 0.941

Region 12 0.909*** 1.144** 0.939

Region 13 0.923*** 1.095 0.986

Region 14 1.099*** 0.993 0.946

Region 15 0.977 1.06 0.911

Region 16 0.874*** 1.112* 1

Region 17 0.98 1.026 1.119

Region 18 0.815*** 1.007

Region 19 0.959 1.021

Region 20 0.962 

Region 21 1.013 

Region 22 1.307*** 

Region 23 0.977 

Region 24 0.907*** 

Region 25 1.026 

Region 26 0.866*** 

Husband's education 

(ref=no education) 

Primary 0.909** 0.995 0.97 1.052 0.991 0.926 0.985

Secondary 0.973 1.022 0.977 1.037 0.981 0.894 0.971

Higher 1.115* 1.069** 0.989 1.094 0.982 0.922 0.939

Don't know na 0.985 0.917 1.242 1.482 1.37 0.664**

Continued 
74 

Table 28—Continued

South Asia Southeast Asia 

 Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

Husband's occupation 

(ref=agricultural) Professional/technical/ 

managerial 

na 1.006 0.970 1.116 0.961 0.918*** 1.088

Clerical na 1.011 1.059 0.988 0.943 0.949 0.940

Sales na 0.979 na na 0.933* 1.002 1.090

Services na 1.038 1.039 1.021 1.002 0.919** 1.067

Skilled manual na 1.000 0.971 1.087 1.013 0.966 1.049

Unskilled manual na na 0.998 1.030 0.942 0.936 1.027

Other na 1.173 1.074 1.000 1.005 1.105 1.206

Not working na 1.014 na 0.978 na 0.952 na

Constant 

73.371*** 60.107*** 129.052*** 38.011*** 32.625*** 27.882*** 16.589***

(60.125 - 89.535) (55.626 - 64.949) (80.251 -

207.528) (29.652 - 48.727) (27.537 - 38.654) (23.408 - 33.210) (12.986 - 21.193)

Gamma (γ) 0.5492 0.5052 0.3955 0.5555 0.3629 0.4265 0.4695

(0.535 - 0.563) (0.499 - 0.511) (0.384 - 0.407) (0.537 - 0.575) (0.352 - 0.374) (0.418 - 0.435) (0.458 - 0.481)

Weighted sample size 

(person-months) 381,297 2,231,385 240,034 342,734 239,300 716,796 175,005 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Hazard models for Bangladesh excludes controls for: wife beating attitudes, occupation, and husband's occupation as these data were not collected in 

the Bangladesh 2014 DHS 

India 2005-06 DHS (NFHS-3) does not distinguish between skilled manual and unskilled manual.

Hazard models for Indonesia and Pakistan exclude religious affiliation as these data were not collected in the Indonesia 2012 DHS or Pakistan 2012-

13 DHS. 

Indonesia 2012 DHS, Cambodia 2014 DHS, and Philippines 2013 DHS do not collect data on husband's current employment status, but categorizes 

husband's occupation regardless of current employment status. 

India 2005-06 DHS (NFHS-3) and Indonesia 2012 DHS captures husband's current employment status as a separate occupational category and 

categorizes husband's occupation only for husbands who are currently working. 

Region names can be found in Appendix Table 5. 

While there was no significant bivariate association in the Philippines, marriage age is significantly and 

positively associated with the first birth interval in the multivariate model. The effect is small (similar in 

size to the other Southeast Asian countries and Pakistan, but in the opposite direction): the expected time 

to first birth is 1% greater for each year older at marriage. 

These relationships are stable to birth cohort and other socio-demographic controls in the multivariate 

hazard model. 

6.2. Birth Cohort 

The decadal birth cohort variable is significant in all countries but the Philippines; this indicates that the 

expected duration of the first birth interval has changed over time, even after controlling for age at 

marriage. The expected time to first birth has become shorter, monotonically, with successive birth 

cohorts since the 1960-69 birth cohort in Bangladesh, Nepal, Pakistan and in Cambodia and Indonesia. 

The changes are the most stark in Bangladesh and Nepal. Compared to the 1960-69 birth cohort, 

Bangladeshi women born between 1970-79 have a 19% shorter expected time to first birth and those born 

between 1980-89 have a 33% shorter expected time to first birth after marriage. In Nepal, these figures 

are 20% and 29%, respectively. In comparison, the expected interval from marriage to first birth is only 

5-10% shorter in Cambodia and Indonesia for women born 1980-89 compared to those born 1960-69. In 
75 

Pakistan, this figure is 15% shorter. The differences in expected time to first birth are not significantly 

different between women born in 1970-79 and 1960-69 in Pakistan and Indonesia. 

Birth cohorts in India manifest a different pattern than the monotonic decrease in the first birth interval 

over time that is seen elsewhere. Women born in the decade before the reference cohort show an 

expected time to first birth that is 10% shorter than those born in 1960-69. Those born in 1970-79 have 

an expected time to first birth that is 13% longer, while those born in 1980-89 have an expected interval 

that is 8.5% shorter than those born in 1960-69. Stated otherwise, the length of the interval from marriage 

to first birth increased among those born in 1960-69 and 1970-79 before becoming shorter again for the 

later cohort. These differences are all statistically significant at the p≤0.001 level. 

6.3. Other Covariates 

Three variables describe the gender context relative to women’s empowerment. These are women’s 

decision-making in the household, attitudes toward wife beating, and the spousal age difference. Of these 

three, wife beating attitudes showed no significant association with the length of the first birth interval in 

any of the study countries. The other two indicate statistically significant associations in three countries. 

India is the only country for which two gender variables—women’s decision-making and spousal age 

difference—are associated with the length of the first birth interval net of each other and the other 

variables in the model. 

The expected time from marriage to first birth decreases by 2-5% with increasing number of decisions in 

which the respondent is involved in India and Nepal. In Pakistan, each additional decision is associated 

with a 1% increase in the first birth interval. There is no association between women’s decision-making 

and the first birth interval in any of the Southeast Asian countries, nor in Bangladesh. 

The spousal age difference is associated with the first birth interval in Bangladesh, India, and the 

Philippines. In the two South Asian countries, Bangladesh and India, larger spousal age differences are 

associated with shorter first birth intervals, although the magnitude of the effect is small, with the 

expected time to first birth from marriage 1.1-1.2% shorter with each year of difference between spouses. 

In the Philippines, the difference is of similar magnitude in the opposite direction. The expected time to 

first birth is 1% longer with each added year difference in spouse’s ages. 

Among socio-demographic controls in the hazard model, women’s education and rural/urban residence 

are most consistently associated with the first birth interval. Other covariates are more variable in their 

associations with the time from marriage to first birth across countries. 

Net of age at marriage, birth cohort, and other controls, higher levels of women’s education is 

significantly associated with a longer duration to first birth in Bangladesh, India, and Nepal. The 

magnitude of the effect is largest in Bangladesh. Women with higher than secondary education have an 

expected time to first birth that is 60% longer than women with no education here. In India and Nepal, 

this is closer to 25% longer. In contrast, women with higher education in the Philippines and Pakistan 

have shorter first birth intervals than women with no education. There is no association in Cambodia and 

Indonesia. 

Urban residence is associated with the length of the first birth interval in five of the 7 study countries, but 

the direction of the effect is variable. In India, Pakistan, and Indonesia, urban residence is associated with 

a shorter birth interval. In Bangladesh and Cambodia, it is associated with a longer birth interval. 

Neither employment status (not shown) nor occupation was consistently associated with the length of the 

first birth interval across study countries. Working in the last 12 months in agriculture, services, or 
76 

manual labor is associated with a shorter birth interval, while being employed in professional or clerical 

sectors is associated with a longer birth interval in India. Like in India, being employed in agriculture or 

services is associated with a shorter birth interval in Nepal, but unlike India, being employed in the 

professional sector is also associated with a shorter birth interval. Being employed in other employment 

in Indonesia and Cambodia is associated with a longer first birth interval, as is being employed in a 

professional position in Indonesia. In the Philippines, being engaged in manual labor is associated with a 

shorter birth interval. There is no association with any occupational category in Pakistan. 

The length of the first birth interval is monotonically shorter with increasing wealth quintiles in India and 

Pakistan. However, household wealth is associated with a shorter birth interval (compared to those in the 

poorest household wealth quintile) only among those in the poorer quintile in Philippines, the richer 

quintile in Nepal, and the riches quintile in Indonesia. 

Religion is seldom associated with differences in the first birth interval. Muslim and Christian women 

have first birth intervals that are 10-11% shorter than Hindu women in India. Muslim women have birth 

intervals that are 13% longer than Catholics in the Philippines, while women belonging to Iglesia Ni 

Kristo and Aglipay religions have a shorter birth interval. 

Subnational region is inconsistently associated with the first birth interval outside of India. In 

Bangladesh, residence in only one of six divisions—Chittagong—is associated with a birth interval that is 

significantly different (shorter) than that in Dhaka. In Nepal, the birth interval is longer in the mountain 

region than in the terai but no differences in the hill region. In Cambodia, birth intervals are longer in 5 

of 18 regions compared to Phnom Penh. In Indonesia, birth interval is significantly different in 3 of 5 

province groups compared to Sumatera. In the Philippines, the first birth interval is significantly different 

(shorter) in 5 of 16 regions compared to the National Capital region. In India, however, 15 of 25 states 

have an expected time to first birth that is significantly different than Uttar Pradesh and Uttaranchal; in 9 

states it is shorter and in 6 it is longer than in Uttar Pradesh and Uttaranchal. A listing of the region 

names corresponding to the time ratios in Table 29 can be found in Appendix Table 2. 

Husband’s education is seldom associated with the length of the first birth interval. In Bangladesh and 

India, women whose husbands have higher education have a shorter expected time to first birth from 

marriage than do women whose husbands have no education. Husband’s occupation is similarly not 

associated with the first birth interval in most countries. In Pakistan, women whose husbands are not 

working or are in “other” employment have shorter birth intervals than do women with husbands 

employed in agriculture. In Cambodia, this is true for husbands working in sales and in Indonesia among 

husbands working in professional or services positions. 

6.4. Underlying Hazard Distribution 

The hazard model for the length of the first birth interval was fit using a loglogistic distribution, based on 

this model having the lowest or second-lowest AIC and BIC in all study countries (Appendix Table 2). In 

the unadjusted model with a single covariate for marriage age (Table 28), the shape parameter gamma (γ)

ranges from 0.3749 in Cambodia to 0.5741 in Bangladesh. These parameters change little with the 

addition of covariates into the multivariate hazard model. In this model (Table 29), γ ranges from 0.3629 

to 0.5492, respectively. The average difference between models is 0.0169. This implies a hazard 

distribution h(t) that quickly increases to near 1 before declining with a long tail as time increases. 
77 

7. Multivariate Decomposition Results 

While the previous section describes associations between indicators and the first birth interval, this 

section explores the effect of age at marriage and other covariates on the change in the first birth interval 

over time. To address this question, the change in the first birth interval is decomposed into two 

components, one representing changes in the distribution of women’s characteristics (the “composition” 

or “endowments” component) and the other representing changes in the effect of those characteristics (the 

“effects”, “rate”, or “coefficients” component). The analysis presented in this section uses data from the 

two time points used earlier in this study, Survey 1 and Survey 2. Survey 2 is the most recent DHS 

survey and Survey 1 is a DHS survey conducted approximately 10 years prior. 

As shown in the first results section, there was imperceptible change in the first birth interval when 

measured as the difference between the median completed ages at marriage and at first birth in Cambodia, 

the Philippines, and Pakistan, but this decreased substantially in Bangladesh, India, and Nepal. However, 

there is a significant change in the (extended) mean duration of the first interval over the last 10 years in 

every study country except Cambodia. Here, the observed decrease in the first birth interval of just over 1 

month is not statistically significant. The significant decline in the duration of the first birth interval 

ranges from 0.5 months (Indonesia) to 6.5 months (Bangladesh). In India, the first birth interval increased 

by 0.6 months and in the Philippines by 2.8 months. 

The results of the decomposition analysis indicate whether the change in the first birth interval is the 

result of changing composition of the population or a change in the effect that each variable has on the 

first birth interval over time. The results present the proportion of the change in the first birth interval 

attributed to the composition component, cumulatively, and to the effects component, cumulatively 

(Table 29). This study also presents the detailed results that show the contribution of compositional 

changes and effect for each covariate to the overall change in the first birth interval in Tables 30-32 and 

Appendix Tables 7-12. The multivariate decomposition models are estimated using age at marriage and 

each of the gender context, socio-demographic characteristics, and husband’s covariates included in the 

hazards analysis of the preceding section. 

7.1. Overall Decomposition Results 

South Asia 

Table 29 indicates that both the composition and effects components are significantly associated with the 

decrease in the first birth interval in Bangladesh and the increase in India. In both countries, a larger 

proportion (63% and 147%, respectively) of the change can be attributed to the effects component than 

the composition component. In India, the composition and effects component operate in opposing 

directions. Changes in the composition of characteristics across the population would have resulted in a 

small decrease in the first birth interval if the effects of those characteristics stayed constant; the change 

in the effects of covariates would have yielded an increase nearly 50% larger than what was actually 

observed if the composition of characteristics remained constant. 

In Nepal and Pakistan, in contrast, there is no observed change in the effects of the covariates. Changes 

in the composition of characteristics account for the majority (97% and 86%, respectively) of the 

observed change in the first birth interval. 
78 

Table 29. Overall multivariate decomposition of change in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences in effects 

Composition component Effects component Total 

n β Percent β Percent β

South Asia 

Bangladesh 2004-2014 20,017 -1.67 *** 36.76 -2.88 *** 63.24 -4.55 ***

India 1998/99-2005/06 139,267 -0.46 *** -46.77 1.43 *** 146.77 0.98 ***

Nepal 2001-2011 13,385 -5.18 *** 97.17 -0.15 2.83 -5.33 ***

Pakistan 2006-2013 17,685 -0.69 *** 85.87 -0.11 14.13 -0.80 

Southeast Asia 

Cambodia 2005-2014 19,455 0.01 -107.99 -0.03 207.99 -0.01 

Indonesia 2002-2012 52,999 -1.69 *** 115.00 0.22 -15.00 -1.47 *** 

Philippines 

2003-2013 

15,783 -0.82 *** -120.5 1.50 *** 220.5 0.68 * 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decision-making (except 

India and Pakistan), attitudes toward wife beating (except Bangladesh, India, and Pakistan), spousal age difference, 

education, occupation (except Bangladesh), household wealth quintile, religion (except Pakistan and Indonesia), 

residence, subnational region, husband's education, and husband's occupation (except Bangladesh). 

Southeast Asia 

As shown in Table 29, Indonesia shows similarities to Nepal and Pakistan. Here, 115% of the change in 

the first birth interval can be attributed to changes in the composition of characteristics across the 

population; there is no significant change detected in the effects of those characteristics. Like India, the 

Philippines also experienced an increase in the mean first birth interval and, like India, the composition 

component and effects component significantly contribute to change in opposing directions. Furthermore, 

a larger proportion of the change (221%) in the first birth interval is attributed to changes in the effects of 

covariates than can be attributed to compositional changes of those characteristics (-121%). In Cambodia, 

the overall change is not statistically significant, nor is either the composition or effects component. 

7.2. Age at Marriage and Changes in the First Birth Interval 

In the four South Asian countries in this study, age at marriage appears to be the most influential 

covariate associated with change in the first birth interval, as indicated in Table 30. Within the 

composition component, age at marriage is significantly associated with a decline in the first birth interval 

in all four countries. If nothing in the model changed between Survey 1 and Survey 2 except the age at 

which women married, the first birth interval would have become shorter by 0.6 months (Pakistan) to 2 

months (Nepal). Between 38% (Nepal) and 89% (Bangladesh) of the change in the first birth interval can 

be attributed to changes in the age at marriage. 

Within the effects component, we see that the effect of marriage age influences the change in the first 

birth interval in India and Nepal, but not in Bangladesh or Pakistan. The change in the effect of marriage 

age accounts for -232% to -437% of the change in the first birth interval in these two countries. In India, 

the first birth interval would be shorter by 4.3 months, on average, in 2005-06 than it was in 1998-99 if 

neither the composition nor effects of the other characteristics in the model changed. However, the first 

birth interval would be 12.4 months longer in Nepal if nothing else in the model changed between 2001 

and 2011. Stated otherwise, women at all ages of marriage have a shorter first birth interval in India and a 

longer first birth interval in Nepal. 
79 

In both Southeast Asian countries where a significant proportion of the change in first birth interval can 

be attributed to the composition component, age at marriage is significantly associated with a decline in 

the first birth interval. Changes in the age at marriage explain 86% of the change in the first birth interval 

in Indonesia and -10% in the Philippines. If all else in the model remained constant, changes in the age at 

which women married would have resulted in an average first birth interval that is 1.3 months shorter in 

Indonesia. The difference is much smaller—a mere 0.1 month shorter—in the Philippines. 

Table 30. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences in effects of 

marriage age 

Composition component Effects component 

n β Percent β Percent 

South Asia 

Bangladesh 2004-2014 20,017 -1.75 *** 38.44 1.51 -33.13 

India 1998/99-2005/06 139,267 -0.87 *** -88.91 -4.27 *** -437.08 

Nepal 2001-2011 13,385 -2.03 *** 38.13 12.37 ** -231.83 

Pakistan 2006-2013 17,685 -0.61 *** 76.76 1.74 -217.66 

Southeast Asia 

Cambodia 2005-2014 19,455 -0.36 2774.1 -8.06 61270 

Indonesia 2002-2012 52,999 -1.26 *** 86.14 0.6 -41.06 

Philippines 

2003-2013 

15,783 -0.07 *** -10.4 1.57 230.51 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decision-making (except India 

and Pakistan), attitudes toward wife beating (except Bangladesh, India, and Pakistan), spousal age difference, education, 

occupation (except Bangladesh), household wealth quintile, religion (except Pakistan and Indonesia), residence, 

subnational region, husband's education, and husband's occupation (except Bangladesh). 

There is no evidence that there is a significant change in the effects of marriage age over time in any of 

the Southeast Asian countries in the study. To the extent that changes in the first birth interval are due to 

changing effects of characteristics (in the Philippines), this is a result of changes in the effects of 

indicators other than age at marriage. 

7.3. Gender Context and Changes in the First Birth Interval 

Table 31 presents the detailed decomposition results for the three variables that describe the gender 

context. In Bangladesh, changes in the level of women’s decision-making between 2004 and 2014 are not 

associated with the shortening first birth interval. However, the effect that women’s decision-making has 

on the first birth interval has changed. If all else in the model were held constant, the effect of women’s 

decision-making would be to lengthen the first birth interval by 1.6 months. The converse is the case in 

Nepal. Here, the effect of women’s decision-making has not changed from 2001 to 2011. However, 

changes in the level of women’s decision-making would have resulted in a first birth interval that is 

shorter by 1 month, with everything else held constant. 

Neither changes in the level nor effect of women’s decision-making has an influence on changes in the 

first birth interval in the Philippines. In Indonesia, both changes in the levels of women’s decisionmaking and in the effects of women’s decision-making contribute to the change in the first birth interval. 

Ceteris paribus, 2012 levels of women’s decision-making would have resulted in a minute lengthening of 

the first birth interval whereas the effect of women’s decision-making would shorten the first birth 

interval by more than 2 months. 
80 

Table 31. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences in effects of 

gender context variables 

Composition component Effects component 

n β Percent β Percent 

Decision-making 

South Asia 

Bangladesh 2004-2014 20,017 0.11 -2.37 1.61 -35.28 

Nepal 2001-2011 13,385 -1.04 *** 19.41 -0.50 9.30 

Southeast Asia 

Cambodia 2005-2014 19,455 -0.09 715.90 -2.31 17537.00

Indonesia 2002-2012 52,999 0.02 * -1.51 -2.40 163.79 

Philippines 2003-2013 15,783 -0.09 -13.10 -1.95 -286.31 

Wife beating attitudes 

South Asia 

Nepal 2001-2011 13,385 

 Acceptable in at least one 

scenario 

-0.48 8.96 0.51 -9.61 

 Rejects in all scenarios -0.48 9.03 -1.32 24.66 

Southeast Asia 

Cambodia 2005-2014 19,455 

 Acceptable in at least one 

scenario 

0.01 -105.82 0.25 -1886.70 

 Rejects in all scenarios 0.02 -128.81 -0.16 1231.60 

Indonesia 2002-2012 52,999 

 Acceptable in at least one 

scenario 

-0.01 0.65 -0.03 2.19 

 Rejects in all scenarios -0.01 0.67 0.09 -5.90 

Philippines 2003-2013 15,783 

 Acceptable in at least one 

scenario 

-0.01 -1.69 0.12 17.49 

 Rejects in all scenarios -0.01 -1.65 -0.32 -47.55 

Spousal age difference in years 

South Asia 

Bangladesh 2004-2014 20,017 0.18 *** -3.94 -1.43 31.38 

India 1998/99-2005/06 139,267 0.15 *** 15.35 -0.05 -4.89 

Nepal 2001-2011 13,385 0.01 -0.17 0.82 -15.45 

Pakistan 2006-2013 17,685 0.07 * -8.71 0.47 -58.30 

Southeast Asia 

Cambodia 2005-2014 19,455 0.01 -44.80 0.87 -6641.20 

Indonesia 2002-2012 52,999 0.03 -1.71 -0.20 13.32 

Philippines 2003-2013 15,783 0.00 0.29 -0.11 -15.75 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decision-making (except 

India and Pakistan), attitudes toward wife beating (except Bangladesh, India, and Pakistan), spousal age difference, 

education, occupation (except Bangladesh), household wealth quintile, religion (except Pakistan and Indonesia), 

residence, subnational region, husband's education, and husband's occupation (except Bangladesh). 
81 

Women’s attitudes toward wife beating are not associated with changes in the first birth interval, in either 

component, in any of the study countries. 

Changes in spousal age difference are associated with lengthened first birth intervals in three of four 

South Asian countries; Nepal is the exception. However, changes in spousal age difference accounts for 

no more than 15% (India) of any change in the first birth interval. Changes in spousal age difference are 

not significantly associated with changes in the first birth interval in any Southeast Asian country in the 

study. There has been no change in the effect of spousal age difference in any study country. 

7.4. Other Women’s Socio-Demographic Characteristics and Changes in the First Birth Interval 

Detailed decomposition results for women’s socio-demographic characteristics can be found in Appendix 

Tables 7-12. Generally, there has not been a change in the effect of education on the first birth interval; 

India is an exception (Appendix Table 7). Changing education levels are associated with changes in the 

first birth interval in South Asia and among secondary and higher education levels in Southeast Asia, 

although the effect of these changes is small. The case is similar with household wealth quintile in most 

countries (Appendix Table 9). However, the effect of being in the poorer wealth quintile in the 

Philippines has changed, and now contributes -52% to all change in the first birth interval. Changes in 

the proportion of women who are not working contributes to changes in the first birth interval in Nepal, 

Pakistan, Indonesia, and the Philippines, however, there are no associations with shifts in women’s 

occupation (Appendix Table 8)18. The opposite is true in India: no change in the first birth interval can be 

attributed to changes in the proportion of women who are not working, but can be attributed to shifts 

among some occupations. The effect of women’s occupation on the first birth interval has not changed 

except in Indonesia, where substantial proportions of the change in the first birth interval can be attributed 

to the changing effects of occupation. 

Although there are a few compositional changes in religion (Appendix Table 10) that are associated with 

the change in the first birth interval, they do not make a large contribution to changes in the first birth 

interval. The effect of religious affiliation remains unchanged. 

Shifts in urban or rural residence are, by and large, associated with changes in the first birth interval, 

although only small proportions of change can be attributed to these shifts (Appendix Table 11). The 

effect of urban versus residence has not changed between the two surveys. 

Changes in the first birth interval can be attributed to compositional shifts in residence in selected 

subnational regions in Bangladesh, India, Nepal, Indonesia, and the Philippines, but the magnitude of 

change attributable to these shifts are quite small (with the exception of residence in Andhra Pradesh in 

India). The effect of living in a given subnational region has changed in India, Nepal, Pakistan, and 

Indonesia. These results are presented in Appendix Table 12. 

7.5. Husband’s Characteristics and Changes in the First Birth Interval 

As seen in Table 32, changes in levels of husband’s education are significantly associated with changes in 

the first birth interval in Bangladesh, Indonesia, the Philippines, and to a lesser extent, in India but not in 

Nepal or Pakistan. The proportion of change associated with shifts in husbands’ education is sizable in 

Indonesia and the Philippines. In the Philippines, the proportion rivals the proportion attributable to 

marriage age, although it is roughly half that in Indonesia. The effect of husband’s education has changed 

 

18 Women’s occupation is not included in the decomposition model for Bangladesh because no data for this indicator 

were collected in 2004. 
82 

over time only in Bangladesh and only for women whose husbands have primary only education or who 

do not know their husband’s education level. 

Table 32. Detailed multivariate decomposition of changes in the interval to first birth, showing 

contributions to the change attributed to differences in composition and to differences in effects 

of husbands' characteristics 

Composition component Effects component 

n β Percent β Percent 

Husbands' education 

South Asia 

Bangladesh 2004-2014 20,017 

No education 0.01 -0.27 0.39 -8.58 

Primary -0.03 *** 0.65 -0.85 *** 18.66 

Secondary -0.04 0.85 -0.22 4.93 

Higher 0.15 *** -3.36 0.39 ** -8.56 

Don't know 

India 1998/99-2005/06 139,267 

No education 0.00 0.17 -0.13 -13.57 

Primary 0.02 * 2.44 0.01 0.82 

Secondary -0.01 -0.66 -0.13 -13.35 

Higher -0.06 ** -5.91 0.15 15.45 

Don't know 

Nepal 2001-2011 13,385 

No education -0.30 5.60 0.04 -0.79 

Primary 0.02 -0.34 0.00 0.02 

Secondary -0.09 1.70 0.46 -8.59 

Higher 0.01 -0.18 -0.10 1.87 

Don't know 0.01 -0.12 -0.01 0.18 

Pakistan 2006-2013 17,685 

No education 0.01 -0.73 0.18 -23.01 

Primary 0.00 -0.37 -0.15 19.33 

Secondary 0.00 0.06 0.18 -22.45 

Higher 0.03 -4.13 0.55 -68.49 

Don't know 0.00 0.03 -0.01 1.04 

Southeast Asia 

Cambodia 2005-2014 19,455 

No education 0.01 -63.45 -0.41 3100.10 

Primary 0.03 -247.53 -1.00 7636.50 

Secondary -0.07 528.63 -0.56 4273.80 

Higher -0.04 289.77 0.01 -50.64 

Don't know -0.02 153.87 0.06 -454.12 

Indonesia 2002-2012 52,999 

No education 0.05 -3.37 0.00 0.08 

Primary 0.60 * -41.03 -2.19 149.33 

Secondary -0.66 * 45.28 -1.56 106.11 

Higher -0.16 * 10.69 -0.38 26.00 

Don't know 0.00 -0.23 0.01 -0.91 

Philippines 2003-2013 15,783 

No education -0.01 -1.73 0.09 13.39 

Primary -0.07 -10.54 0.34 49.93 

Secondary 0.08 * 12.24 0.72 105.96 

Higher 0.01 0.96 0.46 67.76 

Don't know 0.00 *** 0.12 -0.01 -0.93 

Continued 
83 

Table 32—Continued 

Composition component Effects component 

n β Percent β Percent 

Husband's occupation 

South Asia 

India 1998/99-2005/06 139,267 

Agricultural 0.07 7.21 -0.16 -16.45 

Professional/technical/ 

managerial 

0.00 0.12 -0.08 -7.93 

Clerical 0.00 -0.30 -0.03 -3.11 

Sales -0.05 *** -4.70 -0.12 -12.64 

Services 0.00 -0.34 -0.04 -4.09 

Skilled manual -0.07 ** -7.60 -0.15 -15.69 

Other -0.04 -4.20 0.05 5.48 

Not working 0.00 0.28 -0.04 -4.16

Nepal 2001-2011 13,385 

Agricultural -0.28 5.31 0.86 -16.06 

Professional/technical/ 

managerial 

0.00 0.00 0.10 -1.85 

Sales 0.02 -0.35 0.03 -0.56 

Services 0.07 -1.32 -0.15 2.83 

Skilled manual -0.17 ** 3.22 -0.03 0.47 

Unskilled manual -0.02 0.46 -0.01 0.19 

Other -0.12 2.27 0.10 -1.91 

Pakistan 2006-2013 17,685 

Agricultural 0.01 -1.70 3.35 *** -418.05 

Professional/technical/ 

managerial 

0.00 *** 0.54 1.89 *** -235.82 

Clerical -0.04 4.56 0.70 *** -87.27 

Sales 0.00 -0.29 2.61 *** -326.27 

Services -0.01 0.63 1.87 *** -233.93 

Skilled manual 0.02 *** -2.35 3.56 *** -444.86 

Unskilled manual 0.10 -12.11 3.84 *** -479.81 

Other 0.00 *** -0.12 -0.02 *** 2.67 

Not working -0.01 0.84 0.57 *** -70.67 

Southeast Asia 

Cambodia 2005-2014 19,455 

Agricultural -0.08 590.49 1.46 -11080.00

Professional/technical/ 

managerial 

0.00 -8.46 -0.10 777.04 

Clerical 0.02 -185.25 -0.10 780.69 

Sales 0.00 22.44 0.05 -371.04 

Services 0.01 -90.73 -0.02 176.34 

Skilled manual 0.26 -1987.30 0.54 -4121.50 

Unskilled manual 0.13 -950.70 -0.14 1084.00 

Other -0.03 198.95 0.09 -659.32 

Indonesia 2002-2012 52,999 

Agricultural -0.08 5.53 -0.18 12.29 

Professional/technical/ 

managerial 

0.00 -0.21 0.05 -3.42 

Clerical 0.00 -0.23 0.03 -2.28

Sales -0.02 * 1.67 -0.05 3.70 

Services 0.02 -1.47 -0.11 7.45 

Skilled manual 0.19 -12.83 0.03 -2.23 

Unskilled manual 0.12 -7.87 -0.27 18.16 

Other 0.00 -0.30 0.00 -0.24 

Not working 0.00 0.04 0.01 -0.53 

Continued 
84 

Table 32—Continued 

Composition component Effects component 

n β Percent β Percent 

Philippines 2003-2013 15,783 

Agricultural 0.01 1.39 0.05 7.73 

Professional/technical/ 

managerial 

0.01 1.52 0.21 31.25 

Clerical -0.02 -2.24 -0.03 -3.78 

Sales -0.09 -13.90 0.06 8.43 

Services 0.00 0.43 -0.11 -15.89 

Skilled manual -0.07 -10.05 0.01 0.85 

Unskilled manual 0.02 2.77 0.02 2.36 

Other -0.04 -6.37 0.02 2.52 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decision-making 

(except India and Pakistan), attitudes toward wife beating (except Bangladesh, India, and Pakistan), spousal age 

difference, education, occupation (except Bangladesh), household wealth quintile, religion (except Pakistan and 

Indonesia), residence, subnational region, husband's education, and husband's occupation (except Bangladesh). 

Shifts in husband’s occupation are seldom associated with changes in the first birth interval and, when 

they are, they contribute modestly to that change. The effect of husband’s occupation upon the first birth 

interval remains unchanged in all study countries except in Pakistan, where the effect of all occupational 

groups has changed and usually contributes substantially to the change in the first birth interval. 
85 

8. Marriage Age and Subsequent Birth Intervals 

Prior demographic literature suggests that age at marriage can influence both the quantum and tempo of 

fertility, and not just the onset of childbearing. To explore this question, this study uses a multivariate 

hazard model to estimate associations between covariates and subsequent inter-birth intervals. 

Specifically, this study estimates models for the second birth interval. Restricting analysis of subsequent 

birth intervals to the second birth interval carries the advantage of limiting attrition of the sample size in a 

sample of women who have not completed their fertility, particularly in settings with declining fertility 

levels. As in the hazards analysis of the first birth interval, these models specify a loglogistic hazard 

distribution and are estimated with the age at marriage, birth cohort, gender context, and women’s and 

husband’s socio-demographic characteristics as covariates. In additional, a control for the duration of the 

first interval is included in the models. 

Table 33. Effect of age at marriage on the duration of the second birth interval: Time ratios from unadjusted 

loglogistic hazard models 

South Asia Southeast Asia 

 Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

Age at first marriage 1.035*** 1.022*** 1.025*** 1.004 1.015*** 1.013*** 1.030*** 

(1.027 - 1.042) (1.019-1.025) (1.018 - 1.032) (1.000 - 1.008) (1.009 - 1.020) (1.009 - 1.017) (1.025 - 1.035)

Constant 25.913*** 22.134*** 22.265*** 24.271*** 29.202*** 44.853*** 18.189***

(23.102 - 29.067) (21.023-23.304) (19.909 - 24.901) (22.340 - 26.370) (26.292 - 32.434) (41.105 - 48.942) (16.494 - 20.058)

Gamma (γ) 0.4055*** 0.3516*** 0.3233*** 0.3080*** 0.3750*** 0.4607*** 0.4382*** 

(0.397 - 0.414) (0.346-0.357) (0.313 - 0.334) (0.299 - 0.317) (0.365 - 0.385) (0.453 - 0.469) (0.428 - 0.448)

Weighted sample 

size (personmonths) 

667,060 2,873,777 286,577 312,553 463,907 1,832,481 346,881 

*** p≤0.001; ** p≤0.01; * p≤0.05 

8.1. Age at Marriage 

Table 33 displays the unadjusted time ratios for the effect of marriage age on the length of the second 

birth interval. In 6 of the 7 study countries, marriage age is significantly associated with the duration of 

the second birth interval. Pakistan is the exception. In all six countries, marriage age has a positive 

association with the length of the second birth interval. This finding contrasts with the negative 

association detected with the first birth interval. 

Table 34 displays the results of the multivariate hazard models predicting the second birth interval. In all 

six countries (the exception being Pakistan), age at marriage remains significantly and positively 

associated with the second birth interval when controlling for other covariates in the model. The 

magnitude of the effect is not large. Women experience a second birth interval that is on average 1% 

longer with each year of age older at which women marry. However, the magnitude of the effect is 

virtually unchanged with the inclusion of other covariates. 
86 

Table 34. Adjusted effects on the duration of the second birth interval: Time ratios from multivariate

loglogistic hazard models 

South Asia Southeast Asia 

 Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

Age at first cohabitation in years 1.019*** 1.013*** 1.014*** 1.003 1.010*** 1.018*** 1.020***

Duration of 1st birth interval (ref= <16 months)

16-32 months 1.032 1.026** 0.967 1.01

>32 months 1.109*** 1.158*** 0.992 1.073***

Duration of 1st birth interval (ref= <12 months)

12-22 months 0.993 1.022 1.003

>22 months 1.050* 1.161*** 1.209***

Birth cohort (ref=1960-69) 

1950-59 0.959*

1970-79 1.078** 1.009 1.027 1.024 1.053* 1.330*** 1.118***

1980-89 1.207*** 1.028 1.173*** 1.033 1.255*** 1.603*** 1.306***

Decisionmaking 

number of decisions in which 

Respondent is involved 

0.990 0.996 1.003 1.000 0.994 1.001 1.002

Wife beating attitudes (ref=wife beating is acceptable in at least one scenario

Respondent rejects wife beating na 1.008 1.065 1.026 1.053** 1.024 1.004

Spousal age difference in years 0.994*** 1.002 1.001 0.999 1.007** 0.994*** 0.999 

Education (ref=no education) 

Primary 1.032 0.979 1.013 0.992 0.996 1.103* 0.892

Secondary 1.181*** 1.030* 1.093** 0.996 1.031 1.138** 0.911

Higher 1.282*** 1.352*** 1.316*** 1.123** 0.982 1.014 0.991

Occupation (ref=not working) 

Agricultural na 1.002 1.01 1.012 1.015 0.994 0.986

Professional/technical/managerial 1.064 1.043 0.928 1.112 1.065 0.983

Clerical na 1.144* 1.138 0.903 1.033 0.995 1.042

Sales na 0.969 na 0.851* 1.043 1.053* 1.101

Services na 0.967 1.025 0.976 1.035 1.068 1.073

Skilled manual na na 1.112 1.091 1.092* 1.080** 0.974

Unskilled manual na 1.004 1.017 0.98 1.001 1.075 0.958

Other na 1.057 0.654*** na 1.316* 0.458*** 1.155

Household wealth quintile (ref=poorest)

poorer 1.024 1.002 1.043 1.014 1.044 1.190*** 1.061*

middle 1.039 1.006 1.008 0.962 1.055* 1.260*** 1.123***

richer 1.035 1.008 1.052 1.003 1.087** 1.285*** 1.253***

richest 1.066 1.072*** 1.124** 1.017 1.612* 1.245*** 1.367***

Religion 

Reference religion Muslim Hindu Hindu na Buddhist na Roman 

Catholic 

Muslim na 0.930*** 0.945 na 1.120 na 0.779***

Christian 1.078 0.973 na na 0.829** na na

Buddhist 0.956 0.942 1.064* na na na na

Hindu 1.029 na na na na na na

Other na 0.994 1.144** na 0.910 na 0.939

Protestant na na na na na na 1.084

Iglesia Ni Kristo na na na na na na 1.003

Aglipay na na na na na na 0.910

Sikh na 1.006 na na na na na

Jain na 0.909 na na na na na

Kirat na na 0.861** na na na na

Continued
87 

Table 33—Continued

South Asia Southeast Asia 

 Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

Residence (ref=rural) 

urban 1.087*** 1.023 1.051 0.958* 0.99 0.884*** 0.986

Region (ref=region 1) 

Region 1 Dhaka Uttar Pradesh Terai Punjab Phnom Penh Sumatera National 

Capitol 

Region 2 1.022 1.084*** 0.981 1.095*** 1.088 1.435*** 0.916

Region 3 0.828*** 1.086* 1.000 1.085*** 1.033 1.044 0.964

Region 4 1.156*** 1.184*** 1.034 0.920 1.178*** 0.984

Region 5 1.134** 1.088 1.002 0.961 0.95

Region 6 0.995 0.992 1.021 0.921* 0.93

Region 7 0.777*** 1.231*** 0.959 1.047

Region 8 0.995 0.963 0.867**

Region 9 0.940* 1.098 0.957

Region 10 0.948* 0.954 1.001

Region 11 1.039 0.917 0.93

Region 12 1.069** 1.128* 1.08

Region 13 1.201*** 0.964 1.01

Region 14 0.996 1.087 1.087

Region 15 1.046 0.936 1.138*

Region 16 1.074* 1.012 0.984

Region 17 1.110* 0.929 1.135

Region 18 0.977 0.918 

Region 19 0.945 0.984 

Region 20 1.153***

Region 21 0.942*

Region 22 0.985

Region 23 1.160***

Region 24 1.128***

Region 25 1.293***

Region 26 1.291***

Husband's education (ref=no 

education)

 

Primary 1.014 0.994 0.994 1.03 0.967 1.041 0.918

Secondary 1.116*** 1.008 1.024 1.047* 1.001 1.008 0.937

Higher 1.100** 1.055* 0.912 1.074* 1.012 0.904 0.987

Don't know na 0.977 0.868 1.250* 0.633*** 0.982 1.057

Husband's occupation 

(ref=agricultural)

Professional/technical/managerial na 1.0345 0.968 0.967 0.996 0.949 1.008

Clerical na 1.0091 1.105** 0.936 1.122 0.936 1.087

Sales na 0.9835 na 0.956 0.986 0.926** 1.224*

Services na 1.0156 1.065* 1.004 1.071 0.959 1.031

Skilled manual na na 0.998 0.978 1.066** 0.989 0.988

Unskilled manual na 1.015 1.041 1.003 0.938 0.861** 1.005

Other na 1.069 1.049 0.905* 1.016 0.862 1.008

Not working na 1.028 na 0.956 na 0.915 na

Constant 

25.841*** 21.603*** 21.149*** 22.419*** 23.769*** 18.930*** 19.178*** 

(21.437 - 

31.151) 

(20.088-

23.232) 

(15.003 -

29.813) 

(19.948 -

25.197) 

(19.648 - 

28.755) 

(15.954 - 

22.461) 

(15.250-

24.116) 

Gamma (γ) 

0.3710*** 0.3317*** 0.3115*** 0.2965*** 0.3389*** 0.4186*** 0.4099*** 

(0.363 - 

0.379) 

(0.327-0.337) (0.302 -

0.321) 

(0.288 -

0.305) 

(0.330 - 

0.348) 

(0.411 - 

0.426) 

(0.400-0.420)

Continued
88 

Table 33—Continued

South Asia Southeast Asia 

 Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

Weighted sample size (person-months) 603,029 2,873,777 271,254 288,645 391,788 1,678,620 312,180 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Shorter, medium, and longer first birth intervals roughly align with the average tercile among countries in the region. See Appendix Table 15.

Hazard models for Bangladesh excludes controls for: wife beating attitudes, occupation, and husband's occupation, as these data were not 

collected in the Bangladesh 2014 DHS. 

India 2005-06 DHS (NFHS-3) does not distinguish between skilled manual and unskilled manual.

Hazard models for Indonesia and Pakistan exclude religious affiliation because these data were not collected in the Indonesia 2012 DHS or 

Pakistan 2012-13 DHS. 

Indonesia 2012 DHS, Cambodia 2014 DHS, and Philippines 2013 DHS do not collect data on husband's current employment status, but

categorizes husband's occupation regardless of current employment status. 

India 2005-06 DHS (NFHS-3) and Indonesia 2012 DHS captures husband's current employment status as a separate occupational category 

and categorizes husband's occupation only for husbands who are currently working. 

Region names can be found in Appendix Table 5. 

8.2. Effect of the First Birth Interval and Birth Cohort 

Table 33 indicates that there is no statistically significant difference in the second birth interval depending 

on whether women experienced a shorter or a medium-length first birth interval in most countries. India 

is the exception, where women who experienced a medium-length first birth interval experience a longer 

second birth interval than do women who experienced a short first birth interval. However, in all 

countries but Nepal, women who experienced a longer first birth interval also experience a longer second 

birth interval compared to women experienced a shorter first birth interval. The effect appears to be of 

larger magnitude in Southeast Asia than in South Asia, although India and Cambodia do not reflect this 

pattern. The level of the effect ranges from Cambodia, where women with the longest tercile first interval 

experience a second interval that is 5% longer, to the Philippines where women in this tercile experience a 

second interval that is 20% longer. 

Birth cohort is significantly and positively associated with the second birth cohort, such that women in 

successive birth cohorts experience longer second birth intervals, in all countries except India and 

Pakistan. The difference is greatest in Indonesia where women born between 1970-79 experience second 

birth intervals that are 33% longer and women born 1980-89 experience birth intervals that are 60% 

longer than do women born between 1960 and 1969. 

8.3. Other Covariates 

Gender Context 

With one exception, women’s decision-making and attitudes toward wife beating are not associated with 

the second birth interval. In Cambodia, women who reject wife beating in any of the listed scenarios have 

second birth intervals that are 5% longer, on average, than women who think that wife beating is 

acceptable in at least one scenario. 

Spousal age difference is significantly associated with the second birth interval in three of seven study 

countries—Bangladesh, Cambodia, and Indonesia. However, the direction of this association is 

inconsistent. In Bangladesh and Indonesia, women experience a second birth interval that is 1% shorter 

with each additional year difference in age with their spouse. In Cambodia, second birth intervals are 

nearly 1% longer with each additional year difference in age between spouses. 
89 

Women’s Socio-Demographic Characteristics 

Women’s education is significantly and positively associated with longer second birth intervals in all four 

South Asian countries. The effect is monotonically larger with increasing levels of education. In 

Southeast Asia, only Indonesia has a significant association between women’s education and the second 

birth interval. Women who have a primary or secondary education have second birth intervals that are 

10-14% longer than women with no education. There is no statistically significant difference with women 

with higher education. 

Women’s occupation is only sporadically associated with the second birth interval. In India, women who 

are working in professional and clerical occupations have longer second birth intervals than do women 

who are not working. The same is true of women working in skilled manual labor positions in Cambodia 

and Indonesia and sales positions in Indonesia. 

Household wealth is associated with the second birth interval to a greater extent in Southeast Asia than in 

South Asia. In India and Nepal, only women who live in households in the richest wealth quintile have 

second birth intervals than women living in the poorest wealth quintile. In Indonesia, the Philippines, and 

Cambodia, the second birth interval lengthens monotonically with household wealth quintile. 

Religious affiliation is infrequently associated with the second birth interval, with no apparent regional 

pattern. Christian women in Cambodia and Muslim women in the Philippines have shorter second birth 

intervals than the reference group (Buddhist and Catholic women, respectively). In Nepal, Buddhist 

women and women of other faiths have longer second birth intervals than do Hindu women, while Kirat 

women have shorter second birth intervals. In India, Muslim women have shorter second birth intervals 

than do Hindu women. 

Women in living urban areas have longer second birth intervals than do rural women in Bangladesh, but 

shorter birth intervals in Pakistan and Indonesia. There are pervasive differences in the second birth 

interval by subnational region in Bangladesh, India, Indonesia, and Pakistan and intermittent differences 

in Cambodia and the Philippines. 

Husband’s Characteristics 

There are weaker associations with husband’s characteristics than with women’s characteristics. There is 

positive association between husbands with secondary or higher education and longer second birth 

intervals in Bangladesh and Pakistan and with husbands with higher education in India. Women who do 

not know their husband’s education level in Cambodia have shorter second birth intervals than women 

with no education; in Pakistan, these women have longer second birth intervals. 

Husband’s occupation is only sporadically associated with the second birth interval. Women who have 

longer second birth intervals (compared with women married to men working in agriculture) are those 

whose husbands working in clerical and services occupations in Nepal, skilled manual positions in 

Cambodia, and sales positions in the Philippines. In contrast, in Indonesia, women married to husbands 

in sales positions have shorter second birth intervals, as do Indonesian women with husbands in unskilled 

annual positions and those in “other” occupations in Pakistan. 

91 

9. Discussion and Conclusions 

This study uses survival analysis, hazard models, and multivariate decomposition techniques to 

investigate the influence of marriage age on the first birth interval over time, and the implications of both 

marriage age and first birth interval for the second birth interval. Secondarily, attention is given to 

variables that describe the gender context. The study addresses these relationships in seven countries—

four in South Asia and three in Southeast Asia—which have experienced significant change in either age 

at first birth or the first birth interval, or both in recent decades. The DHS data is used from two points in 

time over approximately a 10-year period to examine changes in these dynamics over time. 

This study identified two distinct marriage patterns based on region: median completed age at marriage is 

low (during adolescent years) in South Asia and older in Southeast Asia. Similarly, the median 

completed age at first birth is lower—and the difference between median age at first birth and marriage is 

longer—in South Asia than in Southeast Asia. 

However, time trends are not consistently contrasted between the two regions, nor are they consistent 

with region. Instead, the pace of change has been variable. Marriage age has increased rapidly in 

Indonesia, Nepal, and Bangladesh, but is slower elsewhere. The difference between the median 

completed age at marriage and first birth has remained steady in Pakistan, Cambodia, and the Philippines, 

but narrowed elsewhere. 

The mean marriage age has increased significantly in all study countries. With this increase, the first 

birth interval19 has become significantly shorter in four countries: Bangladesh, Nepal, Pakistan, and 

Indonesia. The decrease has been smaller in Pakistan and Indonesia. Meanwhile, the first birth interval 

has lengthened significantly in India and the Philippines while there has been no change in Cambodia. 

This study examined trends in marriage age across a range of indicators that describe the gender context, 

women’s socio-economic and socio-cultural milieu, and husband’s characteristics. The study found 

universal increases in mean marriage age in South Asia (and Indonesia), across nearly all categories or 

levels of these indicators. However, marriage age increased at differential rates among these groups in 

these countries, while increases in marriage age are localized in Southeast Asia within groups where 

marriage age was already higher. This means that differentials in marriage age grew wider by most 

characteristics in most countries. Trends in Indonesia, both in marriage age overall and across subgroups 

of women, more closely resemble those observed in South Asia than in either Cambodia or the 

Philippines. 

There is a significant change in the first birth interval over the (roughly) last year in every study country 

with the exception of Cambodia. While mean marriage age increased significantly in all countries, the 

first birth interval became significantly shorter, by 0.5-6.5 months, in Bangladesh, Nepal, Pakistan, and 

Indonesia. On the other hand, it increased significantly by 0.6 months in India and by 2.8 months in the 

Philippines. 

In general, there has been more variation in the trends in the first birth interval across gender context, 

women’s, and husband’s characteristics than with trends in marriage age. Change in the first birth 

interval is observed to be more or less universal across subgroups of women in the South Asian countries 

in the study, according to women’s decision-making, spousal age difference, household wealth quintile, 

subnational region, and both husband’s education and occupation. Compared to changes in marriage age, 

change in the first birth interval is more often concentrated in specific groups of women according to 

 

19 As measured by the extended mean. 
92 

attitudes toward wife beating, education, occupation, religion, and place of residence. Change in the first 

birth interval is almost always localized in just a few select subgroups of women in Southeast Asia for all 

of the characteristics examined in the study. Indonesia again resembles the South Asian countries in 

trends of the first birth interval according to residence and region. Disparities in the first birth interval by 

spousal age difference, wealth, place of residence, and husband’s education narrowed over time in most 

study countries, but increased or remained steady for other indicators. 

Nearly all of the characteristics examined in this study are associated with marriage age in bivariate 

analyses in all study countries. The patterns of association are observed in South and Southeast Asian 

countries alike, with degree of difference in marriage age across a characteristic varying country-wise 

rather than regionally. Indicators describing a more gender equitable context (more women’s decisionmaking, attitudes rejecting wife beating, and small spousal age difference) are associated with an older 

marriage age for all three variables, with women’s decision-making appearing to be the least important 

among them. Women’s education, employment in professional or clerical occupations, household wealth, 

and urban residence are positively associated with older marriage age in all study countries. Husband’s 

education and, to a lesser extent, husband’s occupation in professional positions are also associated with 

marrying later. Women who are not working or who work in agricultural occupations marry at younger 

ages, as do women married to husbands in agricultural occupations. Marriage age varies by subnational 

region and religion, but patterns are inconsistent across countries. This points to the significance of local 

context rather than broad regional patterns. 

Differences in marriage age across these characteristics are usually small and sometimes non-significant 

(for example, women’s decision-making, women’s occupation, and religion) in Cambodia, whereas they 

are generally large in India and Indonesia (as with education, wealth, place of residence, and husband 

characteristics). Compared with marriage age, there are somewhat fewer associations between first birth 

intervals and the characteristics examined in this study. 

This study finds a strong, consistent, negative association between marriage age and the hazard of the first 

birth interval in bivariate and multivariate analysis in six of the seven study countries, with the 

Philippines as the exception. However, the magnitude is modest with first birth intervals 2-6% shorter 

with each one year increase in age at marriage. This relationship is robust to the inclusion of controls, but 

declines to 1-2% shorter intervals with each year increase in age at marriage. A significant positive 

association between marriage age and the first birth interval emerges in the Philippines when controlling 

for other factors. Marriage age influences the first birth interval independent of the effect of birth cohort. 

The prominence of marriage age in explaining the first birth interval reinforces findings from previous 

studies (Westoff 1992). 

Birth cohort also independently influences the first birth interval. Birth cohort has a negative association, 

in that the first birth interval declines over time even after controlling for increases in marriage age. This 

birth cohort effect reinforces the influence of marriage age on first birth intervals over time. 

There is substantial variation across countries in other characteristics that are associated with the first 

birth interval in multivariate hazard models. Two indicators describing the gender context—spousal age 

difference and women’s decision-making—are generally associated with the first birth interval in most 

South Asian countries, while attitudes toward wife beating are not. Women’s decision-making is 

negatively related to the first birth interval, although not monotonically; there is a small increase with a 

longer first birth interval among women with the greatest decision-making capacity in some countries. 

This association loses significance when controlling for marriage age and other factors in Cambodia. 

Spousal age difference is curvilinearly associated with the first birth interval, in that intervals are longest 

among those with very little and very large age differences. 
93 

Women’s education is associated with shorter first birth intervals in bivariate analysis, and retains this 

association in Pakistan and the Philippines with multivariate controls. However, education has a positive 

association with the first birth interval when accounting for marriage age and other controls. 

Rural residence is associated with longer first birth intervals in India, Nepal, and Pakistan, whereas urban 

residence is in Bangladesh and Indonesia, even with age at marriage and other controls. There is no 

association in Cambodia or the Philippines, which suggests that urban residence is more salient in South 

Asia than Southeast Asia. The first birth interval varies by subnational region in three of five countries 

(India, Nepal, and the Philippines) after controlling for other factors, with regional differences greatest in 

India and smaller in Nepal. There is no such association in Bangladesh or Pakistan. 

While bivariate analysis suggested that women who are not working or are employed in agricultural or 

unskilled manual labor occupations have shorter first birth intervals and those employed in professional 

and clerical occupations have longer first birth intervals, these associations are rarely observed when 

controlling for marriage age and other covariates. Associations with wealth and religion are similarly 

sporadic in multivariate analysis. Single-country studies elsewhere have found some of these factors to 

be associated with the first birth interval (Alam 2015; Dommaraju 2008; Rindfuss, Palmore, and Bumpass 

1987). Attitudes toward wife beating and husband’s characteristics (education and occupation) are 

infrequently associated with the first birth interval. 

Decomposition analysis investigated whether first birth intervals changed over time because women 

marry at later ages or other characteristics changed (composition component), or whether the first birth 

interval changed because the rate these women are subject to has changed (effects component). The study 

finds no regional pattern in whether it is changes in composition, changes in rates, or both that drive the 

change in the first birth interval. Changes in the composition component contribute the majority of 

change in the first birth interval in Nepal and Pakistan in South Asia and Indonesia in Southeast Asia, 

whereas changes in the effects component contribute more to the overall change in Bangladesh and India 

in South Asia and the Philippines in Southeast Asia. 

Both India and the Philippines experience significant change in the composition and effects components, 

in opposing directions. Changes in composition would imply shorter first birth intervals and changes in 

rates would imply longer first birth intervals; the net effect of these changes is a longer first birth interval 

over time. 

The lack of significant change in first birth intervals in Cambodia could have been the net result of 

compositional and rate changes of equal magnitude operating in opposing directions. It is not. Cambodia 

has not experienced a significant change in either the overall composition or effects component, nor in the 

composition or effect of marriage age or any other constituent factors. 

In detailed decomposition results, marriage age proves to be a significant influence on declines in first 

birth intervals in the six countries experience change in the first birth interval. That more women marry 

later than they did in the past accounts for 38-89% of the change in the first birth interval in the four 

South Asian countries and -10% of the change in Philippines and 86% of the change in Indonesia. The 

effect of marriage age has changed over time in India and Nepal, implying shorter first birth intervals at 

each age of marriage in India and longer first birth intervals at each age of marriage in Nepal. With these 

two exceptions, the effect of marriage age on the first birth interval has not changed over time. 

Neither changes in the composition nor effect of the gender context variables contribute to change in the 

first birth interval. Changes (reductions) in the spousal age difference contribute modestly to longer first 

birth intervals in South Asia, but not in Southeast Asia. 
94 

The two countries where the first birth interval increased rather than decreased over time (the Philippines 

and India) manifest slightly different driving factors. In India, the contributions to a shorter first birth 

interval of more women marrying at older ages are amplified by a shorter first birth interval at all ages of 

marriage. However, the effect of these shifts in both composition and rate is reversed by cumulative 

changes in rates. Women in selected occupations, with secondary and higher education, and Hindu 

women, among others, experience longer first birth intervals than in the past, and this results in slightly 

longer first birth intervals overall. 

In the Philippines, contributions to shorter first birth intervals of more women marrying at older ages than 

in the past are reversed by cumulative changes in rates over time for certain subgroups. Most notably, the 

first birth interval has become longer among Roman Catholics (contributing 345% to the change in the 

first birth interval), women with any education, women in the National Capital and two other regions, 

women who do not work, and those with husbands in professional occupations. 

As with the first birth interval, marriage age is strongly significantly associated with the second birth 

interval, but the size of the effect is modest effect. Marriage age is associated with a 1.3-3.5% longer 

second birth interval in all seven countries. The effect is attenuated slightly when controls are added 

except in Pakistan, where the association is not significant. The result leads to the conclusion that 

marriage age influences not just the timing of the first birth, but also birth spacing, even after controlling 

for changes in birth cohort, the duration of the first birth interval, and other covariates. 

The duration of the first birth interval is positively associated with second birth interval, except in Nepal. 

This influence is particularly strong for women with first birth intervals in the longest tercile. These 

women experience second birth intervals that are 5-20% longer than women whose first birth intervals 

fall in the shortest tercile. Similarly, women who were born in later birth cohorts experience longer 

second birth intervals than do women born 1960-69 in all countries except India and Pakistan. 

In contrast, indicators that describe the gender context are poorly and inconsistently associated with 

longer second birth intervals, after marriage age, birth cohort, first birth interval, and other covariates are 

controlled. This finding would suggest that, to the extent that gender context influences birth spacing, it 

does so largely indirectly through these other factors. Husband’s characteristics and women’s place of 

residence are also inconsistently associated with the second birth interval. However, several socioeconomic and socio-cultural indicators (education, wealth, and occasionally subnational region and 

religion) tend to be independently associated with the second birth interval, independent of other factors. 
95 

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101 

Appendix Tables 
102 

Appendix Table 1. Observations for mean age at first marriage among evermarried women age 25-49 

 Survey 1 Survey 2 

 year weighted n year weighted n 

Bangladesh 2004 7,457 2014 12,454 

India 1998-99 64,739 2005-06 73,665 

Nepal 2001 6,099 2011 7,279 

Pakistan 2006-07 7,955 2012-13 10,601 

Cambodia 2005 9,133 2014 10,485 

Indonesia 2002-03 24,077 2012 30,059 

Philippines 2003 7,377 2013 8,290 

Appendix Table 2. Observations for hazard models predicting the first birth interval 

Total subjects 

Failures among 

subjects Analysis time units Last observed exit 

South Asia 

Bangladesh 12,514 12,160 429,097 422 

India 72,693 69,319 2,276,662 483 

Nepal 7,258 7,034 253,942 457 

Pakistan 9,800 9,120 332,194 426 

Southeast Asia 

Cambodia 10,284 9,770 272,768 422 

Indonesia 28,955 27,372 731,959 472 

Philippines 8,352 7,910 195,019 364 

Appendix Table 3. Model diagnostics of the underlying hazard of the first birth interval 

Model Observations Degrees of freedom AIC BIC 

South Asia 

Bangladesh 2014 

exponential 388,624 25 33649.65 33921.41 

weibull 388,624 26 33513.45 33796.08 

ggamma 388,624 27 32687.24 32980.74 

gompertz 388,624 26 32204.60 32487.23 

lognormal 388,624 26 31781.07 32063.7 

loglogistic 388,624 26 31116.42 31399.05 

India 2005-06 

gompertz 2,029,150 66 228729.7 229556.3 

lognormal 2,029,150 66 220245.8 221072.4 

loglogistic 2,029,150 66 215279.8 216106.4 

exponential 2,029,150 65 194353.0 195167.0 

weibull 2,029,150 66 193250.6 194077.1 

Nepal 2011 

gompertz 236,616 39 18600.94 19005.54 

exponential 236,616 38 17982.10 18376.31 

weibull 236,616 39 17168.00 17572.60 

lognormal 236,616 39 16143.39 16547.99 

ggamma 236,616 38 15688.39 16082.61 

loglogistic 236,616 39 15631.39 16035.99 

Continued
103 

Appendix Table 3—Continued 

Model Observations Degrees of freedom AIC BIC 

Pakistan 2012-13 

gompertz 308,190 38 34986.11 35390.37 

lognormal 308,190 38 34329.57 34733.83 

loglogistic 308,190 38 33552.04 33956.30 

exponential 308,190 37 29939.09 30332.71 

weibull 308,190 38 29930.92 30335.18 

ggamma 308,190 39 28664.39 29079.29 

Southeast Asia 

Cambodia 2014 

gompertz 236,931 56 24470.07 25051.1 

exponential 236,931 55 24038.84 24609.49 

weibull 236,931 56 23174.29 23755.32 

lognormal 236,931 56 20246.95 20827.97 

ggamma 236,931 57 19568.98 20160.39 

loglogistic 236,931 56 19203.31 19784.34 

Indonesia 2012 

gompertz 663,139 41 86996.45 87464.04 

lognormal 663,139 41 78326.18 78793.78 

exponential 663,139 40 77469.09 77925.28 

weibull 663,139 41 77023.72 77491.32 

loglogistic 663,139 41 75637.12 76104.71 

ggamma 663,139 42 66418.39 66897.39 

Philippines 2013 

exponential 178,255 55 22151.98 22706.98 

weibull 178,255 56 22138.82 22703.91 

gompertz 178,255 56 20731.96 21297.06 

ggamma 178,255 57 19365.84 19941.02 

lognormal 178,255 56 18913.64 19478.74 

loglogistic 178,255 56 18336.47 18901.56 

Notes: 

Full hazard models for Bangladesh exclude controls for: wife beating attitudes, occupation, and husband's 

occupation as these data were not collected in the Bangladesh 2014 DHS 

India 2005-06 DHS (NFHS-3) does not distinguish between skilled manual and unskilled manual. 

Full hazard models for Indonesia and Pakistan exclude religious affiliation as these data were not collected in the 

Indonesia 2012 DHS or Pakistan 2012-13 DHS. 

Indonesia 2012 DHS, Cambodia 2014 DHS, and Philippines 2013 DHS do not collect data on husband's current 

employment status, but categorizes husband's occupation regardless of current employment status. 

India 2005-06 DHS (NFHS-3) and Indonesia 2012 DHS captures husband's current employment status as a 

separate occupational category and categorizes husband's occupation only for husbands who are currently 

working. 
104 

Appendix Table 4. Country-specific religion categories and coefficients (time ratios) 

from adjusted loglogistic hazard models of the marriage to first birth interval 

Bangladesh India Nepal Cambodia Philippines 

Ref=Muslim Ref=Hindu Ref=Hindu Ref=Buddhist Ref=Roman Catholic 

Hinduism 0.998 Muslim 0.885*** Buddhist 0.973 Muslim 0.924 Protestant 1.045 

Buddhism 1.009 Christian 0.898*** Muslim 1.054 Christian 0.947 Iglesia 

 Ni Kristo 

0.888* 

Christianity 0.649 Sikh 0.937 Kirat 0.929 Other 0.947 Aglipay 0.820* 

Buddhist/ 

 neo- 

 Buddhist 

1.011 Other 1.031 Muslim 1.126*

Jain 0.900 Other 0.976

Other 1.109

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Hazard models for most surveys control for: age at marriage, birth cohort, decision-making ability, wife beating attitudes, spousal age 

difference, education, occupation, household wealth quintile, rural/urban residence, region of country, husband's education, and husband's 

occupation 

Hazard models for Bangladesh excludes controls for: wife beating attitudes, occupation, and husband's occupation as these data were not 

collected in the Bangladesh 2014 DHS 

Hazard models for Indonesia and Pakistan exclude religious affiliation as these data were not collected in the Indonesia 2012 DHS or 

Pakistan 2012-13 DHS. 
105 

Appendix Table 5. Country-specific regional categories and coefficients (time ratios) from adjusted 

loglogistic hazard models of the marriage to first birth interval 

Region # Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

Region 1 Ref=Dhaka Ref=Uttar Pradesh 

and Uttaranchal 

Ref=terai Ref=Punjab Ref=Phnom Penh Ref=Sumatera Ref=National Capital

Region 2 Barisal 0.979 Andhra 

 Pradesh 

1.199*** mountain 1.102* Sindh 1.059 Banteay 

 Mean 

 Chey 

0.936 Java 1.121*** Cordillera 0.865* 

Region 3 Chittagong 0.881*** Arunachal 

 Pradesh 

0.967 hill 0.983 Khyber 

Pakhtunkwa

0.985 Kampong 

 Cham 

0.939 Bali & Nusa 

Tenggara 

0.961* I - Ilocos 0.915 

Region 4 Khulna 1.028 Assam 0.899** Balochistan 1.039 Kampong 

 Chhnang 

1.117* Kalimantan 1.045** II – Cagayan

 Valley 

0.933 

Region 5 Rajshahi 0.961 Bihar & 

 Jharkhand 

1.023 Kampong 

 Speu 

0.993 Sulawesi 1.014 III - Central 

 Luzon 

0.928 

Region 6 Rangpur 0.964 Delhi 1.045 Kampong 

 Thom 

1.058 Maluku & 

Papua 

1.044 IVA - 

 Calabarzon

0.869**

Region 7 Sylhet 0.918 Goa 1.166*** Kandal 1.068 IVB - 

 Mimaropa 

0.928 

Region 8 Gujarat 1.130*** Kratie 1.255*** V - Bicol 0.898* 

Region 9 Haryana 1.138*** Prey veng 1.191*** VI – Western

 Visayas 

0.894* 

Region 10 Himachal 

 Pradesh 

0.898*** Pursat 0.963 VII – Central

 Visayas 

0.883* 

Region 11 Jammu & 

 Kashmir 

0.851*** Siem reap 1.022 VIII - 

 Eastern 

 Visayas 

0.941 

Region 12 Karnataka 0.909*** Svay rieng 1.144** IX - 

 Zambo- 

 anga 

 Peninsula 

0.939 

Region 13 Kerala 0.923*** Takeo 1.095 X – Northern

 Mindanao 

0.986 

Region 14 Madhya 

 Pradesh 

1.099*** Otdar Mean

 Chey 

0.993 XI - Davao 0.946 

Region 15 Mahara- 

 shtra 

0.977 Battam- 

 bang & 

 Pailin 

1.06 XII - 

 Socc- 

 skargen 

0.911 

Region 16 Manipur 0.874*** Kampot & 

 Kep 

1.112* XIII - Caraga 1 

Region 17 Meghalaya 0.98 Preah 

 Sihanouk

 & Kaoh 

 Kong 

1.026 ARMM 1.119 

Region 18 Mizoram 0.815*** Preah 

 Vihear & 

 Steung 

 Treng 

1.007 

Region 19 Nagaland 0.959 Mondol Kiri

 & Ratta- 

 nak Kiri 

1.021 

Region 20 Orissa 0.962 

Region 21 Punjab 1.013 

Region 22 Rajasthan 1.307*** 

Region 23 Sikkim 0.977 

Region 24 Tamil Nadu 0.907*** 

Region 25 Tripura 1.026 

Region 26 West 

 Bengal 

0.866*** 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Hazard models for most surveys control for: age at marriage, birth cohort, decision-making ability, wife beating attitudes, spousal age difference, education, occupation, 

household wealth quintile, rural/urban residence, region of country, husband's education, and husband's occupation

Hazard model for Bangladesh excludes controls for: wife beating attitudes, occupation, and husband's occupation as these data were not collected in the Bangladesh 

2014 DHS 

Hazard models for Indonesia and Pakistan exclude religious affiliation as these data were not collected in the Indonesia 2012 DHS or Pakistan 2012-13 DHS.
106

Appendix Table 6. Comparison of β coefficients from linear regression models and loglogistic accelerated failure-time hazard models 

predicting duration of the first birth interval (Discrepancies in direction or significance highlighted in red) 

Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

 Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Age at first cohabitation 

in years 

-1.89*** -0.05*** -1.94*** -0.04*** -2.57*** -0.06*** -1.41*** -0.02*** -0.97*** -0.02*** -1.04*** -0.01*** -0.49*** 0.01** 

(-2.14 - -

1.63) 

(-0.06 - -

0.04) 

(-2.04 - -

1.84) 

(-0.05 - -

0.04) 

(-2.93 - -

2.21) 

(-0.07 - -

0.05) 

(-1.66 - -

1.16) 

(-0.03 - -

0.01) 

(-1.13 - -

0.81) 

(-0.03 - -

0.02) 

(-1.19 - -

0.89) 

(-0.02 - -

0.01) 

(-0.64 - -

0.34) 

(0.00 - 0.01)

Birth cohort (ref=1960-69) 

1950-59 na na 4.57*** 0.12*** na na na na na na na na na na

na na (3.31 - 5.83) (0.09 - 0.16) na na na na na na na na na na

1970-79 

-6.82*** -0.21*** -3.89*** -0.09*** -8.29*** -0.22*** -3.50** -0.04 -2.57** -0.07** -1.54* -0.03 -1.13 0.03

(-9.36 - -

4.28) 

(-0.28 - -

0.14) 

(-4.49 - -

3.28) 

(-0.11 - -

0.07) 

(-10.41 - -

6.18) 

(-0.27 - -

0.16) 

(-5.79 - -

1.21) 

(-0.11 - 0.03) (-4.43 - -

0.70) 

(-0.12 - -

0.02) 

(-2.75 - -

0.33) 

(-0.07 - 0.00) (-2.55 - 0.28) (-0.02 - 0.08)

1980-89 

-13.94*** -0.40*** -7.24*** -0.11*** -13.31*** -0.34*** -9.79*** -0.16*** -4.75*** -0.11*** -4.82*** -0.05** -4.26*** -0.03

(-16.30 - -

11.58) 

(-0.47 - -

0.33) 

(-8.14 - -

6.33) 

(-0.14 - -

0.07) 

(-15.83 - -

10.79) 

(-0.40 - -

0.27) 

(-12.02 - -

7.55) 

(-0.23 - -

0.09) 

(-6.44 - -

3.07) 

(-0.16 - -

0.06) 

(-6.05 - -

3.60) 

(-0.09 - -

0.01) 

(-5.79 - -

2.73) 

(-0.09 - 0.02)

Decisionmaking 

number of decisions in 

which Respondent is involved 

0.25 0.01 -0.64*** -0.02*** -1.56*** -0.04*** 0.24*** 0.01*** -1.01* -0.03 -0.58* -0.01 -0.61 -0.01

(-0.90 - 1.41) (-0.02 - 0.03) (-0.90 - -

0.38) 

(-0.03 - -

0.01) 

(-2.25 - -

0.87) 

(-0.06 - -

0.02) 

(0.14 - 0.34) (0.01 - 0.01) (-2.01 - -

0.02) 

(-0.06 - 0.00) (-1.13 - -

0.03) 

(-0.02 - 0.01) (-1.53 - 0.30) (-0.04 - 0.02)

Wife beating attitudes (ref=wife beating is 

acceptable in at least one scenario Respondent rejects wife 

beating in any scenario 

na na -0.01 -0.01 -4.28 -0.09 1.44 0.03 0.72 0.01 0.02 -0.01 -0.15 -0.01na na (-0.61 - 0.58) (-0.03 - 0.01) (-13.00 -

4.43) 

(-0.42 - 0.24) (-0.10 - 2.98) (-0.02 - 0.09) (-0.54 - 1.98) (-0.03 - 0.04) (-0.91 - 0.95) (-0.04 - 0.02) (-1.52 - 1.22) (-0.06 - 0.04)

Spousal age difference in 

years 

-0.41*** -0.01*** -0.38*** -0.01*** -0.06 0 -0.17 0 0.05 0 -0.07 0 0.04 0.01***

(-0.61 - -

0.20) 

(-0.02 - -

0.01) 

(-0.46 - -

0.31) 

(-0.01 - -

0.01) 

(-0.25 - 0.14) (-0.00 - 0.00) (-0.34 - 0.00) (-0.01 - 0.00) (-0.19 - 0.29) (-0.00 - 0.01) (-0.21 - 0.07) (-0.00 - 0.01) (-0.08 - 0.16) (0.01 - 0.01)

Education (ref=no 

education) Primary -1.63 -0.01 -0.03 0.01 0.07 -0.04 -0.63 -0.02 1.61* 0.04 0.26 0 -2.21 -0.1 (-3.70 - 0.44) (-0.08 - 0.05) (-0.90 - 0.84) (-0.02 - 0.04) (-2.49 - 2.62) (-0.10 - 0.01) (-3.12 - 1.86) (-0.10 - 0.05) (0.06 - 3.16) (-0.00 - 0.09) (-3.07 - 3.58) (-0.09 - 0.09) (-6.99 - 2.57) (-0.24 - 0.05)

Secondary 

1.8 0.14*** 1.12* 0.08*** -0.79 -0.05 -1.14 -0.07 0.98 0.03 -0.65 -0.09 -3.4 -0.19**

(-0.37 - 3.96) (0.06 - 0.22) (0.25 - 1.99) (0.05 - 0.10) (-2.97 - 1.39) (-0.12 - 0.01) (-3.76 - 1.48) (-0.14 - 0.01) (-0.82 - 2.79) (-0.03 - 0.09) (-4.11 - 2.82) (-0.19 - 0.01) (-8.16 - 1.35) (-0.34 - -

0.05) 

Higher 

10.10*** 0.47*** 4.99*** 0.22*** 6.62*** 0.22*** 0.34 -0.10* 0.97 0.03 1.79 -0.05 -3.33 -0.24**

(6.74 - 

13.45) 

(0.35 - 0.59) (3.64 - 6.34) (0.17 - 0.26) (3.06 - 10.18) (0.09 - 0.34) (-2.73 - 3.41) (-0.20 - -

0.01) 

(-2.66 - 4.59) (-0.11 - 0.17) (-2.15 - 5.74) (-0.16 - 0.06) (-8.24 - 1.59) (-0.40 - -

0.09) 

Continued 
107

Appendix Table 6—Continued

Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

 Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Occupation (ref=not 

working) Agricultural 

na na -1.75*** -0.05*** -3.73** -0.10** -3.42 -0.13 -0.27 0.00 -0.13 0.00 -2.49** -0.01na na (-2.64 - -

0.86) 

(-0.08 - -

0.02) 

(-6.40 - -

1.06) 

(-0.17 - -

0.03) 

(-7.33 - 0.49) (-0.30 - 0.04) (-1.67 - 1.12) (-0.05 - 0.05) (-1.82 - 1.55) (-0.05 - 0.05) (-4.05 - -

0.92) 

(-0.08 - 0.05)

Professional/technical/ma

nagerial 

na na 0.91 0.06* -3.65* -0.17* 1.85 0.11 1.01 0.05 0.51 0.04 -3.01*** -0.06na na (-0.44 - 2.26) (0.01 - 0.10) (-7.07 - -

0.24) 

(-0.29 - -

0.04) 

(-3.01 - 6.71) (-0.05 - 0.28) (-1.68 - 3.70) (-0.06 - 0.15) (-1.76 - 2.78) (-0.01 - 0.09) (-4.51 - -

1.51) 

(-0.12 - 0.01)

Clerical 

na na 2.93* 0.12** -5.13 -0.2 -15.58*** 0.13 -0.75 0.03 2.84* 0.11** -3.75** -0.06na na (0.05 - 5.80) (0.05 - 0.20) (-15.01 -

4.76) 

(-0.55 - 0.15) (-21.54 - -

9.63) 

(-0.90 - 1.16) (-4.76 - 3.26) (-0.17 - 0.23) (0.18 - 5.51) (0.04 - 0.17) (-6.05 - -

1.46) 

(-0.17 - 0.05)

Sales na na -0.52 -0.04 na na -1.14 0.01 -0.66 -0.02 0.05 0.02 -0.87 0.04

na na (-2.56 - 1.52) (-0.11 - 0.02) na na (-3.88 - 1.60) (-0.09 - 0.10) (-2.16 - 0.84) (-0.07 - 0.04) (-1.07 - 1.17) (-0.01 - 0.05) (-3.25 - 1.51) (-0.06 - 0.14)

Services 

na na -3.13*** -0.13*** -3.93** -0.14*** 1.01 0.03 0.76 0.06 0.45 0.01 -3.30* -0.08na na (-4.61 - -

1.64) 

(-0.17 - -

0.08) 

(-6.53 - -

1.33) 

(-0.22 - -

0.06) 

(-6.33 - 8.35) (-0.22 - 0.29) (-1.94 - 3.46) (-0.03 - 0.15) (-1.55 - 2.45) (-0.05 - 0.07) (-5.91 - -

0.69) 

(-0.17 - 0.02)

Skilled manual 

na na -1.76*** -0.04* -2.87 -0.06 -1.45 -0.01 -0.11 0 -0.45 0.02 -3.90** -0.10*na na (-2.73 - -

0.78) 

(-0.08 - -

0.01) 

(-6.94 - 1.20) (-0.20 - 0.08) (-4.42 - 1.51) (-0.12 - 0.09) (-1.91 - 1.70) (-0.07 - 0.06) (-1.77 - 0.87) (-0.02 - 0.06) (-6.23 - -

1.56) 

(-0.20 - -

0.00) 

Unskilled manual 

na na na na -3.96 -0.08 1.8 0.05 0.2 0.02 -2.45 -0.04 -2.80*** -0.07**na na na na (-10.24 -

2.31) 

(-0.21 - 0.05) (-11.71 -

15.31) 

(-0.40 - 0.50) (-4.60 - 5.01) (-0.15 - 0.18) (-5.49 - 0.59) (-0.14 - 0.05) (-4.26 - -

1.34) 

(-0.12 - -

0.02) 

Other 

na na -0.3 0.03 -12.83** -0.08 na na -0.09 0.27* 26.43* 1.34** -2.19 0.14na na (-7.98 - 7.39) (-0.26 - 0.31) (-21.70 - -

3.96) 

(-0.70 - 0.53) na na (-4.23 - 4.04) (0.01 - 0.53) (2.17 - 50.69) (0.37 - 2.30) (-11.78 -

7.41) 

(-0.46 - 0.74)

Household wealth quintile (ref=poorest)

poorer 

0.56 0 -1.43** -0.04* -0.08 0.03 -4.30* -0.08 -1.33 -0.04 -1.31 -0.01 -1.70* -0.06*

(-2.88 - 4.00) (-0.08 - 0.09) (-2.46 - -

0.41) 

(-0.07 - -

0.00) 

(-2.50 - 2.33) (-0.04 - 0.09) (-8.16 - -

0.43) 

(-0.22 - 0.06) (-3.25 - 0.60) (-0.09 - 0.02) (-3.00 - 0.37) (-0.06 - 0.04) (-3.11 - -

0.29) 

(-0.11 - -

0.01) 

middle 

-1.74 -0.01 -1.35* -0.04* -2.23 -0.02 -5.63* -0.17* -1.34 -0.02 -2.98*** -0.03 -0.53 -0.02

(-4.00 - 0.52) (-0.10 - 0.07) (-2.45 - -

0.25) 

(-0.08 - -

0.00) 

(-5.61 - 1.16) (-0.11 - 0.07) (-10.06 - -

1.20) 

(-0.33 - -

0.02) 

(-3.38 - 0.70) (-0.08 - 0.03) (-4.75 - -

1.21) 

(-0.08 - 0.02) (-2.09 - 1.03) (-0.08 - 0.04)

richer 

-0.6 0.01 -2.55*** -0.06** -7.13*** -0.12* -7.99*** -0.22** -0.94 0 -2.98** -0.03 -0.21 0.02

(-2.83 - 1.62) (-0.07 - 0.09) (-3.72 - -

1.39) 

(-0.10 - -

0.02) 

(-10.60 - -

3.65) 

(-0.21 - -

0.03) 

(-12.66 - -

3.33) 

(-0.38 - -

0.06) 

(-3.01 - 1.13) (-0.06 - 0.06) (-4.90 - -

1.07) 

(-0.08 - 0.02) (-2.13 - 1.70) (-0.05 - 0.10)

richest 

-1.9 0 -2.67*** -0.08*** -6.61*** -0.09 -10.89*** -0.27** -0.84 -0.04 -3.79*** -0.06* 1.06 0.01

(-4.58 - 0.77) (-0.09 - 0.09) (-4.04 - -

1.30) 

(-0.12 - -

0.04) 

(-10.38 - -

2.84) 

(-0.20 - 0.01) (-15.93 - -

5.86) 

(-0.45 - -

0.10) 

(-3.17 - 1.49) (-0.10 - 0.03) (-5.74 - -

1.84) 

(-0.11 - -

0.00) 

(-1.06 - 3.18) (-0.07 - 0.10)

Continued 
108

Appendix Table 6—Continued

Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

 Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Religion 

Reference religion Muslim Hindu Hindu Buddhist CatholicMuslim 

-3.40*** -0.12*** 1.24 0.05 na na -4.95** -0.08 na na 3.30* 0.12*

(-4.37 - -

2.44) 

(-0.16 - -

0.09) 

(-5.53 - 8.00) (-0.11 - 0.22) na na (-8.53 - -

1.36) 

(-0.22 - 0.06) na na (0.52 - 6.08) (0.03 - 0.22)

Christian 

-9.71*** -0.43 -2.13** -0.11*** -0.65 -0.03 na na -1.4 -0.06 na na na na

(-14.70 - -

4.72) 

(-0.94 - 0.07) (-3.60 - -

0.66) 

(-0.16 - -

0.06) 

(-3.70 - 2.40) (-0.10 - 0.05) na na (-9.30 - 6.50) (-0.25 - 0.14) na na na na

Buddhist 2.51 0.01 -0.58 0.01 1.63 0.03 na na -0.63 -0.05 na na na na (-0.74 - 5.76) (-0.12 - 0.14) (-4.79 - 3.63) (-0.16 - 0.18) (-2.96 - 6.22) (-0.11 - 0.17) na na (-4.86 - 3.60) (-0.22 - 0.12) na na na naHindu 0.63 0 na na na na na na na na (-0.97 - 2.23) (-0.07 - 0.06) na na na na na na na naOther 

na na 3.62 0.1 na na na na na na na na -1.43 -0.03na na (-6.19 -

13.44) 

(-0.17 - 0.37) na na na na na na na na (-3.09 - 0.24) (-0.09 - 0.04)

Protestant na na na na na na na na na na na na -0.24 0.04

na na na na na na na na na na na na (-2.46 - 1.97) (-0.04 - 0.13)

Iglesia Ni Kristo 

na na na na na na na na na na na na -2.92* -0.12*na na na na na na na na na na na na (-5.33 - -

0.50) 

(-0.23 - -

0.01) 

Aglipay 

na na na na na na na na na na na na -5.04*** -0.20*na na na na na na na na na na na na (-7.36 - -

2.72) 

(-0.37 - -

0.03) 

Sikh na na -1.51 -0.07 na na na na na na na na na na

na na (-3.39 - 0.36) (-0.14 - 0.01) na na na na na na na na na na

Jain na na -1.86 -0.11 na na na na na na na na na na

na na (-5.24 - 1.52) (-0.21 - 0.00) na na na na na na na na na na

Kirat na na na na -0.73 -0.07 na na na na na na na na

na na na na (-5.90 - 4.43) (-0.21 - 0.06) na na na na na na na na

Residence (ref=rural) 

urban 1.72* 0.07** -0.99** -0.05*** -0.78 -0.04 -2.21 -0.09* 1.28 0.06* -1.61** -0.07*** -0.79 -0.01 (0.24 - 3.19) (0.02 - 0.12) (-1.74 --0.24) (-0.08 --0.02) (-2.59 - 1.03) (-0.09 - 0.02) (-4.49 - 0.06) (-0.16 --0.01) (-0.22 - 2.77) (0.00 - 0.11) (-2.61 - 0.60) (-0.11 --0.04) (-2.01 - 0.43) (-0.06 - 0.04)

Continued 
109

Appendix Table 6—Continued

Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

 Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Region (ref=region 1) 

Region 1 

Dhaka Uttar 

Pradesh & 

Uttaranchal 

terai Punjab Phnom Penh Sumatera National 

Capital 

Region 2 

-1.14 -0.02 3.19*** 0.18*** 2.82 0.10* 1.17 0.06 0.78 -0.07 3.43*** 0.11*** -2.29 -0.14*

(-3.43 - 1.16) (-0.10 - 0.06) (1.62 - 4.75) (0.13 - 0.23) (-0.98 - 6.62) (0.01 - 0.19) (-1.43 - 3.77) (-0.02 - 0.14) (-2.37 - 3.93) (-0.20 - 0.07) (2.45 - 4.41) (0.08 - 0.14) (-5.11 - 0.54) (-0.27 - -

0.02) 

Region 3 

-3.06** -0.13*** 0.56 -0.03 -0.54 -0.02 0.66 -0.01 0.72 -0.06 0.52 -0.04* -2.2 -0.09

(-4.97 - -

1.15) 

(-0.20 - -

0.06) 

(-1.99 - 3.10) (-0.11 - 0.05) (-2.83 - 1.74) (-0.07 - 0.04) (-1.99 - 3.31) (-0.09 - 0.07) (-1.97 - 3.40) (-0.16 - 0.03) (-0.59 - 1.64) (-0.08 - -

0.00) 

(-4.95 - 0.54) (-0.20 - 0.02)

Region 4 

-0.19 0.03 -2.10** -0.11** 1.78 0.04 2.17 0.11* 0.9 0.04** -1.57 -0.07

(-2.36 - 1.98) (-0.04 - 0.09) (-3.67 - -

0.52) 

(-0.18 - -

0.03) 

(-3.00 - 6.56) (-0.11 - 0.18) (-0.14 - 4.48) (0.02 - 0.20) (-0.18 - 1.97) (0.01 - 0.08) (-4.21 - 1.08) (-0.18 - 0.04)

Region 5 

-1.77 -0.04 0.81 0.02 0.47 -0.01 0.22 0.01 -2.25* -0.08

(-4.15 - 0.62) (-0.11 - 0.04) (-0.77 - 2.39) (-0.03 - 0.08) (-1.72 - 2.65) (-0.10 - 0.09) (-0.84 - 1.28) (-0.02 - 0.05) (-4.43 - -

0.06) 

(-0.16 - 0.01)

Region 6 

-2.2 -0.04 0.94 0.04 1.78 0.06 1.68 0.04 -3.56*** -0.14**

(-4.74 - 0.35) (-0.12 - 0.05) (-0.60 - 2.47) (-0.01 - 0.10) (-0.54 - 4.10) (-0.03 - 0.15) (-0.42 - 3.78) (-0.04 - 0.12) (-5.58 - -

1.55) 

(-0.22 - -

0.05) 

Region 7 -1 -0.09 4.53*** 0.15*** 1.71 0.07 -1.97 -0.07 (-3.45 - 1.45) (-0.18 - 0.01) (2.89 - 6.18) (0.10 - 0.21) (-0.40 - 3.81) (-0.03 - 0.16) (-4.71 - 0.77) (-0.19 - 0.04)Region 8 

1.68* 0.12*** 7.35*** 0.23*** -2.69* -0.11*

(0.06 - 3.30) (0.07 - 0.17) (4.22 - 10.47) (0.13 - 0.32) (-5.26 - -

0.12) 

(-0.21 - -

0.00) 

Region 9 

2.21** 0.13*** 5.49*** 0.17*** -2.18 -0.11*

(0.62 - 3.81) (0.07 - 0.18) (2.88 - 8.09) (0.08 - 0.27) (-4.57 - 0.22) (-0.22 - -

0.01) 

Region 10 

-3.52*** -0.11*** 1.61 -0.04 -3.31** -0.12*

(-4.97 - -

2.06) 

(-0.17 - -

0.05) 

(-0.98 - 4.19) (-0.13 - 0.06) (-5.58 - -

1.05) 

(-0.23 - -

0.01) 

Region 11 

-2.70*** -0.16*** 0.34 0.02 -2.47 -0.06

(-4.19 - -

1.20) 

(-0.23 - -

0.09) 

(-1.67 - 2.36) (-0.06 - 0.11) (-5.30 - 0.37) (-0.17 - 0.05)

Region 12 

-1.76* -0.10*** 4.62*** 0.13** -2.85* -0.06

(-3.13 - -

0.38) 

(-0.14 - -

0.05) 

(2.12 - 7.11) (0.04 - 0.23) (-5.38 - -

0.31) 

(-0.18 - 0.05)

Region 13 

-1.70* -0.08*** 5.14*** 0.09 -0.1 -0.01

(-3.00 - -

0.40) 

(-0.13 - -

0.03) 

(2.31 - 7.98) (-0.01 - 0.19) (-3.48 - 3.27) (-0.11 - 0.08)

Region 14 0.6 0.09*** 4.07* -0.01 -1.2 -0.06 (-0.72 - 1.93) (0.05 - 0.14) (0.16 - 7.98) (-0.14 - 0.13) (-4.17 - 1.78) (-0.17 - 0.06)Region 15 -1.06 -0.02 2.07 0.06 -2.68 -0.09 (-2.35 - 0.22) (-0.07 - 0.02) (-0.72 - 4.86) (-0.04 - 0.16) (-5.37 - 0.01) (-0.20 - 0.01)Region 16 

-2.14** -0.14*** 3.90** 0.11* -0.67 0

(-3.44 - -

0.84) 

(-0.18 - -

0.09) 

(1.25 - 6.55) (0.00 - 0.21) (-3.76 - 2.43) (-0.10 - 0.09)

Continued 
110

Appendix Table 6—Continued

Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

 Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Region 17 -2.04 -0.02 2.26 0.03 -0.01 0.11 (-4.10 - 0.01) (-0.09 - 0.05) (-0.24 - 4.76) (-0.06 - 0.11) (-3.53 - 3.50) (-0.01 - 0.24)Region 18 

-3.53** -0.20*** 1.74 0.01

(-5.84 - -

1.22) 

(-0.28 - -

0.12) 

(-0.77 - 4.24) (-0.09 - 0.10)

Region 19 -1.56 -0.04 1.32 0.02 (-3.42 - 0.29) (-0.11 - 0.02) (-1.24 - 3.87) (-0.09 - 0.13)Region 20 -0.78 -0.04 (-2.48 - 0.92) (-0.10 - 0.02)Region 21 -0.65 0.01 (-2.39 - 1.08) (-0.05 - 0.08)Region 22 5.26*** 0.27*** (3.53 - 6.98) (0.21 - 0.32)Region 23 -0.97 -0.02 (-2.94 - 1.01) (-0.10 - 0.05)Region 24 

-2.31*** -0.10***

(-3.67 - -

0.95) 

(-0.15 - -

0.05) 

Region 25 -1.21 0.03 (-2.93 - 0.51) (-0.03 - 0.08)Region 26 

-3.99*** -0.14***

(-5.40 - -

2.58) 

(-0.20 - -

0.08) 

Husband's education 

(ref=no education) 

(-1.39 - 0.40) (-0.03 - 0.02) 

Primary 

-3.01** -0.10** 0.49 0.02 -2.26 -0.03 -0.28 0.05 -0.14 -0.01 -2.91 -0.08 -2.08 -0.01

(-5.13 - -

0.88) 

(-0.16 - -

0.03) 

(-0.36 - 1.34) (-0.01 - 0.05) (-5.35 - 0.83) (-0.09 - 0.04) (-2.71 - 2.15) (-0.04 - 0.14) (-2.27 - 2.00) (-0.07 - 0.05) (-6.66 - 0.84) (-0.19 - 0.04) (-6.67 - 2.51) (-0.16 - 0.13)

Secondary -1.32 -0.03 1.63* 0.07** -0.99 -0.02 0.61 0.04 -0.25 -0.02 -3.37 -0.11 -1.43 -0.03 (-3.12 - 0.48) (-0.10 - 0.04) (0.36 - 2.90) (0.03 - 0.11) (-3.95 - 1.97) (-0.10 - 0.06) (-1.90 - 3.12) (-0.05 - 0.12) (-2.46 - 1.96) (-0.08 - 0.04) (-7.18 - 0.43) (-0.23 - 0.01) (-6.06 - 3.21) (-0.18 - 0.12)Higher 3.05* 0.11* 0.27 -0.01 -0.53 -0.01 1.7 0.09 -0.1 -0.02 -2.51 -0.08 -1.8 -0.06 (0.59 - 5.51) (0.02 - 0.19) (-3.47 - 4.01) (-0.16 - 0.13) (-4.04 - 2.97) (-0.10 - 0.09) (-1.24 - 4.65) (-0.02 - 0.20) (-3.42 - 3.22) (-0.13 - 0.09) (-6.61 - 1.59) (-0.21 - 0.04) (-6.54 - 2.93) (-0.22 - 0.09)Don't know 

na na na na -3.36 -0.09 -0.79 0.22 4.88 0.38 19.28 0.31 -9.66** -0.41**

na na na na (-13.45 -

6.73) 

(-0.35 - 0.18) (-13.94 -

12.35) 

(-0.35 - 0.78) (-4.55 -

14.32) 

(-0.06 - 0.81) (-4.59 -

43.15) 

(-0.34 - 0.96) (-16.58 -

2.74) 

(-0.72 --0.10)

Continued 
111

Appendix Table 6—Continued

Bangladesh India Nepal Pakistan Cambodia Indonesia Philippines 

 Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Linear 

Regression 

β

Loglogistic 

Hazard 

β

Husband's occupation 

(ref=agricultural)Professional/technical/ 

managerial 

0.72 0.01 -0.99 -0.03 -1.23 -0.04 -0.58 -0.09*** 1.99* 0.08*

(-0.53 - 1.96) (-0.03 - 0.04) (-4.17 - 2.19) (-0.12 - 0.05) (-3.37 - 0.91) (-0.11 - 0.03) (-2.36 - 1.20) (-0.14 - -

0.04) 

(0.12 - 3.87) (0.01 - 0.16)

Clerical 

0.42 0.01 2.04 0.06 3.74* 0.11 -2.99* -0.06 0.11 -0.05 -0.55 -0.06

(-0.90 - 1.75) (-0.03 - 0.05) (-0.82 - 4.91) (-0.01 - 0.13) (0.73 - 6.76) (-0.00 - 0.22) (-5.88 - -

0.09) 

(-0.19 - 0.07) (-1.89 - 2.12) (-0.12 - 0.01) (-3.90 - 2.79) (-0.20 - 0.07)

Sales 

-0.58 -0.02 na na 1.44 -0.01 -0.43 -0.07* 1.48 0 3.63 0.09

(-1.58 - 0.43) (-0.05 - 0.01) na na (-3.53 - 6.41) (-0.16 - 0.14) (-2.88 - 2.02) (-0.14 - -

0.00) 

(-0.26 - 3.22) (-0.05 - 0.05) (-0.88 - 8.14) (-0.09 - 0.26)

Services 

1.2 0.04 1.09 0.04 1.89 0.02 -0.47 0 -1.1 -0.08** 0.39 0.07

(-0.19 - 2.59) (-0.00 - 0.08) (-1.56 - 3.74) (-0.02 - 0.10) (-0.82 - 4.60) (-0.08 - 0.12) (-2.62 - 1.67) (-0.08 - 0.08) (-2.75 - 0.55) (-0.14 - -

0.03) 

(-1.56 - 2.34) (-0.02 - 0.15)

Skilled manual na na -1.64 -0.03 3.90** 0.08 1.16 0.01 0.26 -0.03 0.89 0.05

na na (-4.29 - 1.01) (-0.11 - 0.05) (1.16 - 6.65) (-0.01 - 0.18) (-0.38 - 2.69) (-0.03 - 0.06) (-1.12 - 1.63) (-0.07 - 0.01) (-0.41 - 2.19) (-0.00 - 0.10)

Unskilled manual 0.36 0 -0.3 0 2.02 0.03 -2.36 -0.06 -1.43 -0.06 1.28 0.03 (-0.48 - 1.19) (-0.03 - 0.03) (-3.07 - 2.48) (-0.07 - 0.07) (-0.36 - 4.39) (-0.06 - 0.12) (-6.01 - 1.30) (-0.22 - 0.10) (-3.13 - 0.26) (-0.13 - 0.00) (-0.34 - 2.89) (-0.03 - 0.08)Other 7.61* 0.16 1.52 0.07 1.23 0 2.01 0 -1.15 -0.05 3.55 0.18 (0.30 - 14.92) (-0.04 - 0.36) (-3.55 - 6.59) (-0.06 - 0.20) (-1.38 - 3.85) (-0.09 - 0.09) (-3.29 - 7.30) (-0.14 - 0.14) (-4.49 - 2.20) (-0.17 - 0.06) (-0.62 - 7.72) (-0.03 - 0.39)Not working 

-0.56 0.01 na na -1.13 -0.02 na na -2.95 0.09 na na

(-2.47 - 1.36) (-0.05 - 0.08) na na (-5.58 - 3.32) (-0.22 - 0.17) na na (-12.50 -

6.61) 

(-0.52 - 0.71) na na

Weighted sample size 

(person-months) 

11,273 381,297 65,486 2,231,385 6,754 240,034 9,323 342,734 8,930 239,300 26,492 716,796 7,191 175,005 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 Model for Bangladesh excludes controls for: wife beating attitudes, occupation, and husband's occupation as these data were not collected in the Bangladesh 2014 DHS

India 2005-06 DHS (NFHS-3) does not distinguish between skilled manual and unskilled manual.Models for Indonesia and Pakistan exclude religious affiliation as these data were not collected in the Indonesia 2012 DHS or Pakistan 2012-13 DHS.

Indonesia 2012 DHS, Cambodia 2014 DHS, and Philippines 2013 DHS do not collect data on husband's current employment status, but categorizes husband's occupation regardless of current employment status.

India 2005-06 DHS (NFHS-3) and Indonesia 2012 DHS captures husband's current employment status as a separate occupational category and categorizes husband's occupation only for husbands who are currently working.

Regions listed in Appendix Table 4. 
112 

Appendix Table 7. Detailed multivariate decomposition of changes in the interval to first birth, 

showing contributions to the change attributed to differences in composition and to differences 

in effects of women's socio-demographic variables: Education 

 Composition component Effects component 

 n β Percent β Percent 

South Asia 

Bangladesh 2004-2014 20,017 

No education -0.11 2.34 0.07 -1.53 

Primary -0.04 *** 0.83 -0.30 6.66 

Secondary -0.37 *** 8.12 0.14 -3.17 

Higher 0.15 *** -3.35 0.00 -0.01 

India 1998/99-2005/06 139,267 

No education 0.02 1.82 -0.72 *** -73.37 

Primary 0.02 *** 1.86 -0.06 -6.47 

Secondary -0.07 *** -7.23 0.13 13.19 

Higher -0.04 *** -3.91 0.09 9.46 

Nepal 2001-2011 13,385 

No education -0.33 6.12 -0.10 1.91 

Primary -0.04 0.83 0.09 -1.65 

Secondary -0.44 *** 8.24 0.07 -1.34 

Higher 0.12 ** -2.31 -0.02 0.28 

Pakistan 2006-2013 17,685 

No education -0.14 * 17.81 0.54 -66.83 

Primary 0.00 0.15 -0.08 9.76 

Secondary -0.04 * 5.33 0.03 -3.49 

Higher -0.01 1.49 -0.03 3.88 

Southeast Asia 

Cambodia 2005-2014 19,455 

No education 0.02 -141.18 0.86 -6565.10 

Primary -0.03 237.45 3.07 -23314.00

Secondary 0.00 10.05 0.97 -7352.90 

Higher -0.01 100.16 -0.08 634.91 

Indonesia 2002-2012 52,999 

No education -0.04 2.40 -0.13 9.17 

Primary -0.04 2.59 0.51 -34.71 

Secondary -0.20 *** 13.68 0.08 -5.25 

Higher 0.03 -1.81 0.02 -1.56 

Philippines 2003-2013 17,685

No education -0.01 -0.99 -0.07 -10.58 

Primary -0.03 -4.58 0.56 81.55 

Secondary -0.10 * -14.11 0.35 51.32 

Higher -0.03 * -4.65 0.34 49.26 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decision-making 

(except India and Pakistan), attitudes toward wife beating (except Bangladesh, India, and Pakistan), spousal 

age difference, education, occupation (except Bangladesh), household wealth quintile, religion (except Pakistan 

and Indonesia), residence, subnational region, husband's education, and husband's occupation (except 

Bangladesh). 
113 

Appendix Table 8. Detailed multivariate decomposition of changes in the interval to first birth, 

showing contributions to the change attributed to differences in composition and to differences in 

effects of women's socio-demographic variables: Occupation 

 Composition component Effects component 

 n β Percent β Percent 

South Asia 

India 1998/99-2005/06 139,267 

Not working -0.02 -1.87 0.45 45.81 

Agricultural -0.03 * -3.44 0.24 25.06 

Professional/technical/ 

managerial 

0.01 * 0.73 -0.02 -2.49 

Clerical 0.01 ** 0.83 -0.01 -0.64 

Sales 0.00 0.04 0.02 2.13 

Services -0.08 *** -8.20 0.00 -0.16 

Unskilled manual 0.00 * -0.26 -0.06 -6.10 

Other 0.00 0.10 0.00 0.06 

Nepal 2001-2011 13,385 

Not working 0.34 *** -6.33 -0.17 3.18 

Agricultural -0.32 6.08 -0.63 11.86 

Professional/technical/ 

managerial 

0.01 -0.11 0.02 -0.36 

Clerical 0.00 0.05 0.03 -0.56 

Sales na na na na 

Services 0.12 -2.20 0.03 -0.51 

Skilled manual 0.01 -0.18 -0.03 0.63 

Unskilled manual 0.01 -0.19 -0.04 0.66 

Other 0.29 -5.52 -0.35 6.52 

Pakistan 2006-2013 17,685 

Not working -0.01 1.14 1.57 -196.44 

Agricultural 0.03 -3.36 -0.15 19.14 

Professional/technical/ 

managerial 

0.00 0.02 0.08 -10.53 

Clerical 0.00 *** -0.25 -0.01 * 1.06 

Sales 0.00 -0.45 0.01 -0.64 

Services -0.01 0.78 0.19 -23.87 

Skilled manual 0.03 -4.03 0.05 -6.09 

Unskilled manual 0.02 -1.99 -0.01 1.66 

Continued 
114 

Appendix Table 8—Continued

 Composition component Effects component 

 n β Percent β Percent 

Southeast Asia 

Cambodia 2005-2014 19,455 

Not working -0.04 308.56 -0.07 523.01 

Agricultural -0.01 38.30 2.49 -18931.00

Professional/technical/ 

managerial 

0.02 -166.02 0.20 -1548.00 

Clerical 0.00 -11.60 0.03 -245.42 

Sales 0.04 -270.49 0.51 -3885.40 

Services 0.02 -125.27 -0.11 800.64 

Skilled manual -0.08 632.37 -0.04 273.98 

Unskilled manual -0.09 666.30 0.90 -6850.70 

Other 0.00 1.62 0.03 -218.33 

Indonesia 2002-2012 52,999 

Not working 0.33 * -22.74 -1.39 94.76 

Agricultural 0.30 -20.15 -1.01 68.57 

Professional/technical/ 

managerial 

-0.06 4.35 -0.19 13.11 

Clerical 0.00 0.08 -0.04 2.87 

Sales -0.10 6.77 -0.33 22.46 

Services -0.03 2.14 -0.20 * 13.34 

Skilled manual -0.33 * 22.32 -0.17 11.66 

Unskilled manual -0.10 *** 7.13 0.00 0.01 

Other -0.01 0.48 0.01 -0.72 

Philippines 2003-2013 15,783 

Not working -0.15 ** -21.82 0.31 44.84 

Agricultural 0.01 1.41 0.06 8.97 

Professional/technical/ 

managerial 

-0.02 -3.10 -0.14 -21.24 

Clerical 0.00 0.44 -0.07 -10.33 

Sales -0.14 -21.06 0.15 21.55 

Services -0.01 -0.88 0.03 4.78 

Skilled manual 0.01 0.88 -0.11 -16.09 

Unskilled manual -0.01 -1.94 -0.02 -2.73 

Other 0.00 -0.12 0.00 0.50 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decision-making 

(except India and Pakistan), attitudes toward wife beating (except Bangladesh, India, and Pakistan), spousal age 

difference, education, occupation (except Bangladesh), household wealth quintile, religion (except Pakistan and 

Indonesia), residence, subnational region, husband's education, and husband's occupation (except Bangladesh). 
115 

Appendix Table 9. Detailed multivariate decomposition of changes in the interval to first 

birth, showing contributions to the change attributed to differences in composition and to 

differences in effects of women's socio-demographic variables: Household wealth quintile 

 Composition component Effects component 

 n β Percent β Percent 

South Asia 

Bangladesh 2004-2014 20,017 

Poorest 0.00 -0.10 -0.28 6.18 

Poorer -0.01 0.22 -0.14 3.03 

Middle -0.01 0.21 -0.07 1.49 

Richer 0.00 0.09 0.41 -8.91 

Richest 0.00 0.02 0.13 -2.85 

India 1998/99-2005/06 139,267 

Poorest -0.01 -0.59 0.01 1.21 

Poorer 0.00 0.05 -0.16 -16.26 

Middle 0.00 0.01 0.00 -0.10 

Richer 0.00 * -0.33 0.05 5.51 

Richest 0.00 0.08 0.12 12.67 

Nepal 2001-2011 13,385 

Poorest -0.06 1.17 -0.02 0.38 

Poorer -0.01 * 0.16 0.20 -3.68 

Middle 0.01 -0.27 0.25 -4.65 

Richer -0.03 *** 0.59 -0.67 * 12.60 

Richest -0.04 0.67 0.23 -4.28 

Pakistan 2006-2013 17,685 

Poorest -0.02 *** 3.12 0.48 -59.58 

Poorer 0.00 0.08 0.14 -17.57 

Middle 0.00 0.01 -0.20 25.03 

Richer 0.00 * 0.03 -0.44 54.86 

Richest -0.01 *** 1.44 -0.04 5.10 

Southeast Asia 

Cambodia 2005-2014 19,455 

Poorest 0.00 37.16 0.01 -60.55 

Poorer 0.00 13.32 -0.43 3264.40 

Middle -0.01 42.06 0.07 -500.51 

Richer 0.00 16.21 -0.07 545.34 

Richest 0.00 -27.22 0.42 -3194.70

Indonesia 2002-2012 52,999 

Poorest -0.05 *** 3.49 0.15 -10.06 

Poorer 0.00 0.07 0.02 -1.13 

Middle -0.01 0.47 -0.15 10.33 

Richer -0.01 0.77 0.11 -7.46 

Richest 0.00 * 0.21 -0.13 8.92 

Philippines 2003-2013 15,783 

Poorest 0.00 -0.66 -0.06 -8.50 

Poorer 0.03 *** 3.70 -0.35 ** -51.95 

Middle 0.00 0.17 0.08 11.68 

Richer 0.00 0.15 0.15 21.74 

Richest 0.00 ** 0.03 0.21 30.50 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decisionmaking (except India and Pakistan), attitudes toward wife beating (except Bangladesh, India, and 

Pakistan), spousal age difference, education, occupation (except Bangladesh), household wealth quintile, 

religion (except Pakistan and Indonesia), residence, subnational region, husband's education, and 

husband's occupation (except Bangladesh). 
116 

Appendix Table 10. Detailed multivariate decomposition of changes in the interval to first birth, 

showing contributions to the change attributed to differences in composition and to differences in 

effects of women's socio-demographic variables: Religion quintile 

Composition component Effects component 

n β Percent β Percent 

South Asia 

Bangladesh 2004-2014 20,017 

Muslim 0.00 0.04 -0.20 4.43 

Christian 0.05 * -1.09 -0.05 1.10 

Buddhist 0.05 -1.07 0.00 -0.07 

Hindu -0.02 0.36 0.23 -5.15 

India 1998/99-2005/06 139,267 

Muslim -0.02 *** -1.70 -0.11 -11.09 

Christian 0.00 0.06 0.01 1.29 

Buddhist 0.00 0.03 0.02 1.74 

Hindu 0.00 *** 0.07 0.36 36.60 

Other 0.00 0.07 -0.01 -0.64 

Sikh 0.00 0.00 0.00 -0.22 

Jain 0.00 0.04 0.00 -0.13 

Nepal 2001-2011 13,385 

Muslim 0.00 0.00 -0.28 5.30 

Christian 

Buddhist -0.01 0.10 0.07 -1.38 

Hindu 0.00 0.00 -0.75 14.00 

Other -0.01 0.21 0.02 -0.42 

Kirat -0.01 0.20 0.04 -0.67 

Southeast Asia 

Cambodia 2005-2014 19,455 

Muslim -0.01 55.64 -0.02 122.08 

Christian 0.00 -16.40 0.01 -49.92 

Buddhist 0.01 -45.07 -2.46 18676.00 

Other 0.00 -12.84 0.02 -152.59 

Philippines 2003-2013 15,783 

Other -0.01 -1.31 -0.01 -1.12 

Roman Catholilc -0.03 * -4.04 2.35 *** 345.01 

Protestant -0.01 -0.76 0.14 20.34 

Iglesia Ni Kristo 0.00 0.72 -0.02 -3.17 

Aglipay 0.03 *** 4.63 -0.10 *** -14.77 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decision-making (except 

India and Pakistan), attitudes toward wife beating (except Bangladesh, India, and Pakistan), spousal age difference, 

education, occupation (except Bangladesh), household wealth quintile, religion (except Pakistan and Indonesia), 

residence, subnational region, husband's education, and husband's occupation (except Bangladesh). 
117 

Appendix Table 11. Detailed multivariate decomposition of changes in the interval to first birth, 

showing contributions to the change attributed to differences in composition and to differences in 

effects of women's socio-demographic variables: Place of residence 

 Composition component Effects component 

 n β Percent β Percent 

South Asia 

Bangladesh 2004-2014 20,017 

rural 0.04 -0.80 0.08 -1.80 

urban 0.04 -0.82 -0.27 5.93 

India 1998/99-2005/06 139,267 

rural -0.02 *** -2.54 -0.04 -3.76 

urban -0.02 *** -2.25 0.09 9.45 

Nepal 2001-2011 13,385 

rural -0.02 0.32 0.02 -0.30 

urban -0.02 0.31 -0.14 2.70 

Pakistan 2006-2013 17,685 

rural -0.01 * 0.83 -0.32 40.23 

urban -0.01 * 1.08 0.60 -75.50 

Southeast Asia 

Cambodia 2005-2014 19,455 

rural 0.00 -7.11 0.16 -1212.40 

urban -0.01 58.45 -0.83 6275.00 

Indonesia 2002-2012 52,999 

rural -0.02 *** 1.66 -0.21 14.27 

urban -0.03 *** 1.75 0.23 -15.93 

Philippines 2003-2013 15,783 

rural 0.02 3.07 -0.28 -41.73 

urban 0.02 2.89 0.25 36.10 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decision-making 

(except India and Pakistan), attitudes toward wife beating (except Bangladesh, India, and Pakistan), spousal age 

difference, education, occupation (except Bangladesh), household wealth quintile, religion (except Pakistan and 

Indonesia), residence, subnational region, husband's education, and husband's occupation (except Bangladesh). 
118 

Appendix Table 12. Detailed multivariate decomposition of changes in the interval to first birth, 

showing contributions to the change attributed to differences in composition and to differences in 

effects of women's socio-demographic variables: Subnational region 

Composition component Effects component 

n β Percent β Percent 

South Asia 

Bangladesh 2004-2014 20,017 

Dhaka 0.02 -0.41 0.51 -11.20 

Barisal 0.00 0.05 -0.02 0.43 

Chittagong -0.01 *** 0.20 -0.07 1.57 

Khulna -0.02 * 0.36 0.30 * -6.51 

Rajshahi & Rangpur 0.01 -0.26 -0.28 6.22 

Sylhet 0.00 0.03 -0.14 3.03 

India 1998/99-2005/06 139,267 

Uttar Pradesh & Uttaranchal 0.00 0.50 -0.83 *** -85.28 

Andhra Pradesh 0.27 *** 27.34 0.01 *** 0.61 

Arunachal Pradesh -0.02 -1.77 -0.31 *** -31.88 

Assam 0.00 ** -0.19 0.04 * 4.39 

Bihar & Jharkhand 0.00 ** 0.35 -0.10 -10.54 

Delhi 0.00 ** -0.23 0.02 1.89 

Goa 0.00 *** 0.01 0.00 0.09 

Gujarat 0.01 *** 0.67 0.03 3.53 

Haryana 0.00 *** -0.29 0.03 3.26 

Himachal Pradesh 0.00 *** 0.02 -0.01 -0.90 

Jammu & Kashmir 0.00 ** 0.00 -0.02 * -1.79 

Karnataka -0.01 * -0.58 0.03 3.04 

Kerala 0.00 0.48 -0.02 -1.61 

Madhya Pradesh & 

Chattisgarh 

 0.01 *** 1.31 -0.09 -9.31 

Maharashtra 0.00 0.09 -0.11 -11.19 

Manipur 0.00 *** 0.02 0.00 * 0.34 

Meghalaya 0.00 *** 0.09 0.01 ** 0.56 

Mizoram 0.00 *** 0.01 0.00 0.03 

Nagaland 0.00 *** 0.06 0.00 0.31 

Orissa 0.00 0.01 0.00 0.34 

Punjab 0.00 0.08 0.06 * 5.73 

Rajasthan 0.01 *** 0.63 -0.04 -4.45 

Sikkim 0.00 0.01 0.00 0.04 

Tamil Nadu 0.02 *** 1.73 -0.08 -7.69 

Tripura 0.00 0.00 0.00 0.15 

West Bengal 0.00 *** 0.28 -0.38 *** -39.40 

Nepal 2001-2011 13,385 

Terai -0.03 0.48 1.22 * -22.92 

Mountain -0.01 *** 0.23 -0.12 2.24 

Hill 0.01 -0.27 -0.29 5.44 

Pakistan 2006-2013 17,685 

Punjab 0.00 -0.61 0.73 -91.56 

Sindh 0.00 -0.35 0.02 -2.21 

Khyber Pakhtunkwa 0.00 0.05 0.40 ** -49.94 

Balochistan 0.00 0.04 -0.20 *** 25.28 

Continued 
119 

Appendix Table 12—Continued

Composition component Effects component 

n β Percent β Percent 

Southeast Asia 

Cambodia 2005-2014 19,455 

Phnom Penh 0.01 -55.95 -0.13 964.46 

Banteay Mean Chey 0.00 18.33 -0.14 1080.50 

Kampong Cham 0.00 15.81 -0.01 49.37 

Kampong Chhnang 0.00 -2.36 -0.07 539.89 

Kampong Speu -0.05 375.97 -0.23 1747.70 

Kampong Thom 0.00 17.46 0.03 -207.89 

Kandal 0.01 -88.21 -0.12 900.56 

Kratie 0.04 -320.27 0.10 -734.78 

Prey Veng -0.04 329.95 0.14 -1065.50

Pursat -0.01 93.70 0.11 -810.82 

Siem Reap 0.02 -122.98 0.06 -446.22 

Svay Rieng 0.00 -5.54 0.02 -145.50 

Takeo -0.04 312.73 0.26 -1991.00

Otdar Mean Chey 0.01 -58.62 0.08 -576.45 

Battambang & Pailin 0.00 36.59 -0.21 1592.90 

Kampot & Kep -0.01 48.65 0.00 9.88 

Preah Sihanouk & Kaoh 

Kong 

0.00 -3.77 -0.04 341.64 

Preah Vihear & Steung 

Treng 

0.00 -3.94 0.04 -272.99 

Mondol Kiri & Rattanak Kiri -0.01 64.37 -0.10 755.20 

Indonesia 2002-2012 52,999 

Sumatera 0.00 *** -0.31 -0.29 * 19.91 

Java -0.04 *** 2.84 -0.01 0.63 

Bali & Nusa Tenggara 0.00 0.19 0.11 ** -7.41 

Kalimantan 0.00 0.00 -0.03 1.88 

Sulawesi -0.01 0.43 -0.02 1.16 

Maluku & Papua 0.00 *** -0.31 -0.29 * 19.91 

Philippines 2003-2013 15,783 

National Capital -0.01 -1.09 0.16 22.78 

Cordillera 0.00 0.03 -0.03 -4.43 

I - Ilocos 0.00 0.06 0.01 1.19 

II - Cagayan Valley 0.00 0.05 0.02 3.24 

III - Central Luzon 0.00 0.08 0.04 5.25 

IVA - Calabarzon -0.02 * -2.59 -0.19 -27.20 

IVB - Mimaropa 0.00 0.04 0.00 -0.30 

V - Bicol 0.00 0.08 -0.05 -6.83 

VI - Western Visayas 0.00 0.01 0.04 5.23 

VII - Central Visayas 0.02 * 3.30 -0.14 -20.02 

VIII - Eastern Visayas 0.00 0.20 -0.11 -15.47 

IX - Zamboanga Peninsula -0.01 -1.10 -0.14 * -20.13 

X - Northern Mindanao 0.00 0.08 0.12 18.29 

XI - Davao 0.00 0.30 0.11 16.87 

XII - Soccskargen 0.00 -0.44 0.06 8.67 

XIII - Caraga 0.00 0.62 0.06 8.78 

ARMM -0.01 -1.20 0.03 4.63 

Notes: 

*** p≤0.001; ** p≤0.01; * p≤0.05 

Decomposition models are estimated with the following covariates: marriage age, women's decision-making 

(except India and Pakistan), attitudes toward wife beating (except Bangladesh, India, and Pakistan), spousal age 

difference, education, occupation (except Bangladesh), household wealth quintile, religion (except Pakistan and 

Indonesia), residence, subnational region, husband's education, and husband's occupation (except Bangladesh). 
120 

Appendix Table 13. Observations for hazard models predicting the second birth interval 

Total subjects 

Failures among 

subjects 

Analysis time 

units 

Last observed 

exit dropped 

South Asia 

Bangladesh 12,083 10,614 675,450 417 77 

India 68,933 60,823 2,873,777 413 386 

Nepal 6,990 6,288 288,677 391 44 

Pakistan 9,052 8,270 290,566 381 68 

Southeast Asia 

Cambodia 9,710 8,120 450,916 437 60 

Indonesia 27,206 21,623 1,605,630 385 166 

Philippines 7,871 6,632 345,895 354 39 
121 

Appendix Table 14. Model diagnostics of the underlying hazard of the second birth interval 

Model Observations Degrees of freedom AIC BIC 

South Asia 

Bangladesh 2014 

exponential 11,226 27 27376.31 27574.11 

weibull 11,226 28 25148.41 25353.54 

ggamma 11,226 29 21734.75 21947.21 

gompertz 11,226 28 27087.63 27292.76 

lognormal 11,226 28 22021.14 22226.27 

loglogistic 11,226 28 21864.10 22069.23 

Nepal 2011 

exponential 270,391 40 15696.40 16116.70 

weibull 270,391 41 13585.48 14016.29 

ggamma 270,391 42 10782.44 11223.76 

gompertz 270,391 41 15572.59 16003.40 

lognormal 270,391 41 11453.29 11884.10 

loglogistic 270,391 41 11253.95 11684.76 

Pakistan 2012-13 

exponential 270,466 38 21709.57 22108.87 

weibull 270,466 39 19336.59 19746.40 

ggamma 270,466 40 13627.78 14048.09 

gompertz 270,466 39 25140.15 25549.96 

lognormal 270,466 39 18571.61 18981.42 

loglogistic 270,466 39 17942.46 18352.27 

Southeast Asia 

Cambodia 2014 

exponential 384,057 57 20757.30 21376.24 

weibull 384,057 58 18702.13 19331.93 

ggamma 384,057 59 15202.11 15842.76 

gompertz 384,057 58 21694.92 22324.71 

lognormal 384,057 58 16937.03 17566.83 

loglogistic 384,057 58 16698.47 17328.26 

Philippines 2013 

exponential 312,246 57 18012.64 18619.78 

weibull 312,246 58 17356.15 17973.94 

ggamma 312,246 59 14004.83 14633.28 

gompertz 312,246 58 17994.16 18611.95 

lognormal 312,246 58 15124.84 15742.63 

loglogistic 312,246 58 15036.36 15654.15 

Notes: 

Full hazard models for Bangladesh exclude controls for: wife beating attitudes, occupation, and husband's 

occupation as these data were not collected in the Bangladesh 2014 DHS 

India 2005-06 DHS (NFHS-3) does not distinguish between skilled manual and unskilled manual. 

Full hazard models for Indonesia and Pakistan exclude religious afiliation as these data were not collected in the 

Indonesia 2012 DHS or Pakistan 2012-13 DHS. 

Indonesia 2012 DHS, Cambodia 2014 DHS, and Philippines 2013 DHS do not collect data on husband's current 

employment status, but categorizes husband's occupation regardless of current employment status. 

India 2005-06 DHS (NFHS-3) and Indonesia 2012 DHS captures husband's current employment status as a 

separate occupational category and categorizes husband's occupation only for husbands who are currently 

working. 
122 

Appendix Table 15. Tercile survival times to first birth in months, 

ever-married women age 25-49 

25th percentile 

50th percentile 

(median) 75th percentile 

South Asia 

Bangladesh 13 24 42 

India 13 20 36 

Nepal 15 24 41 

Pakistan 12 21 40 

Southeast Asia 

Cambodia 13 19 29 

Indonesia 11 15 26 

Philippines 10 14 24