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Family planning programs and the reduction in fertility in South and Southeast Asia

Tey Nai Peng, University of Malaya, Malaysia

Introduction

The fertility level in Asian countries has been declining since the launching of family planning

programs about five decades ago, albeit at different pace. Some countries have already attained

below replacement or close to replacement level fertility, as contraceptive use has become more

widespread concomitant with rapid social changes. The implementation of family planning

programs had accelerated fertility decline that was already under way in some countries, and set

the stage for the on-set of fertility decline in others.

The total fertility rate (TFR) in Asia dropped from 5.8 in the early 1950s to 5.0 in 1970-75, and

then fell more sharply to 4.0 in 1975-80, and o 2.3 by 2005-2010. The decline in fertility has not

been uniform across regions and countries. Fertility transition began earliest in East Asia, with

the TFR falling from 5.6 in 1950-55 to 4.4 in 1970-75, 2.8 in 1975-80, reaching replacement

level by early 1990s and it is now at about 1.6. In comparison, the other three sub-regions have

experienced a later and more gradual decline in fertility. Starting at about the same level of about

6 children per woman in 1950-55, the TFR is now highest in Western Asia at 3.0, followed by

South Central Asia at 2.6 and South-eastern Asia at 2.1 (UN 2012).

As of mid 1990s, a total of 6 Asian countries had attained replacement level, and this number

increased to 17 by 2009. A few Asian countries and Territories– Korea, Japan, Singapore,

Taiwan, Hong Kong and Macau have the lowest level of fertility in the world, with a TFR of

below 1.4. On the other hand, the number of countries with a TFR of 5 and above dropped from

33 in the mid 1970s to 21 in mid 1980s, 12 in mid 1990s, and to just 3 by 2009 (UN 2012)...

In most countries, fertility decline can be attributed to rising age at marriage and increased

contraceptive use. Abortion and other proximate determinants of fertility such as long duration of

breastfeeding have also contributed to fertility decline. Contraceptive prevalence rate (CPR) is

around 50 percent in many Asian countries, exceeds 60 percent in Bangladesh, Indonesian, and

Singapore, and reaches around 80 percent in China, Thailand and Vietnam (UN, 2012). The

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singulate mean age at marriage for women remains rather low in Bangladesh (18.6 years), Nepal

(19.9 years), India (20.2 years), Lao (21.7 years), Cambodia (22 years), Pakistan and Thailand

(both at 22.7 years). In contrast the singulate mean age at marriage is around 28-30 years for

women in Singapore, Republic of Korea and Japan (UN 2012). A number of these countries have

undergone rapid fertility decline despite early and universal marriage, due to widespread

contraceptive use, following the launching of family planning program.

The effectiveness of the family planning programs in reducing the fertility rate varies across and

within countries, according to their strengths (as measured by family planning efforts and

contraceptive prevalence rate) and socio-cultural conditions. Contraceptive use and other

proximate determinants of fertility are in turn affected by socio-economic development. Many

countries have made significant progress in raising the standard of living. The human

development index (HDI), a composite measure of income, education and health has been rising

steadily in most countries. More and more women are working in the modern sector of the

economy. About two thirds of Asian population resides in urban areas as compared to about a

quarter in 1970. All these changes have significant impact on the acceptance of family planning

and fertility level.

This paper focuses on four South Asian countries (Bangladesh, India, Nepal and Pakistan) and

four Southeast Asian countries (Cambodia, Indonesia, Philippines and Vietnam). India was the

first developing country to adopt a national family planning program in 1952. Although family

planning services in Vietnam started as early as 1961, a complete national policy was adopted

only in 1993. The family planning program in Cambodia began as recently as 1994. The other

five countries launched their program in the mid- 1960s-1970. Notably, the Indonesian family

planning program (with a policy to engage the private sector) has been recognized as one of the

world’s greatest demographic success stories in the 20th

century.

The fertility level in the eight countries has been declining amidst socio-economic development.

The HDI has increased by 0.8 percent to 2.9 percent per annum in these countries. In 2007, with

the exception of Cambodia, the HDI of the other Southeast Asian countries (Indonesia,

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Philippines and Vietnam) at 0.73-0.75 is significantly higher than that of the South Asian

countries, which ranged from 0.61 in India to 0.54-0.57 in the other three countries (Appendix

1). All the eight countries are classified as medium human development index countries by the

United Nations.

Indonesia and Bangladesh have experienced the most rapid rate of urbanization. In 2010,

Indonesia and the Philippines had the highest urbanization level among the 8 countries, and

Nepal and Cambodia the lowest (see Appendix 2).

The educational level has been rising in the eight countries. Among married women, the

proportion with at least secondary education among the younger cohorts is much higher than the

older cohorts. With the exception of Cambodia, more women in the other three Southeast Asian

countries have at least secondary education as compared to South Asian women. While the

proportion of women with no schooling has declined significantly over successive birth cohorts,

it remained at a high level among the 1990s birth cohorts in Pakistan (about two thirds) and India

(about one third) (Appendix 3).

Women in the eight countries, especially from Bangladesh, India and Nepal are marrying at very

young age. Since 1980, the singulate mean age at first marriage has only increased by 1 to 2

years and actually recorded a small decrease in Vietnam and Cambodia. Teenage marriage is still

very common in these countries (see Appendix 4).

Theoretical Focus and Objective

In the past, economic development was seen as a precondition for fertility decline, and there was

a marked demarcation of developed low fertility countries and less developed high fertility

countries. Within the developing countries, those who could least afford to have more children

were having large family size, because of lack of access to family planning information and

services. This perpetuated the disadvantaged position of the poor. However, since the 1970s,

successful family planning programs have resulted in rapid fertility decline even in resource poor

countries, such as Bangladesh and Cambodia. A recent study by Jain and Ross (2012) and

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several earlier studies show that fertility levels were lowest in the presence of both good social

settings and strong program (Tsui, 2001; Blanc and Grey 2000; Bongaarts, Mauldin and Phillips,

1990; Freedman and Freedman, 1992; Lapham and Mauldin, 1984; Shi, 1990). Jain and Ross

also found that fertility was positively associated with infant mortality and negatively associated

with female education, but not associated with poverty.

The relationship between contraceptive use and fertility has to be analyzed with caution, bearing

in mind the causality of these two variables. At the individual level users are more likely than

non-users to have large family size, as those with larger family size are more likely than those

with no children or few children to use a method. However, the effects of contraceptive use on

fertility can be analyzed using country level or macro level data, such as by place of residence,

educational level and wealth index. The groups that have higher contraceptive prevalence rate

will have lower fertility.

Using macro and micro level data, this paper seeks to add to the literature on the causes of

fertility decline. The main purpose is to demonstrate that with a strong family planning program,

even low-resource countries can reduce the fertility and control population growth, to allow more

resources to be channeled for socio-economic development, and raising the standard of living.

The paper also examines the fertility differentials for policy intervention.

Data and Research Methods

The data sources for this paper are taken mainly from databases of the United Nations

Population Division, UN Human Development Reports and published work based on research

of Futures Group International (for data on family planning efforts). Time series data will be

used for an analysis of the changing trends and patterns of contraceptive use (including method

mix) and fertility and the inter-relationships between these variables. Data from recent

Demographic and Health Surveys (DHS) for the eight countries will also be used to replicate the

cross-country analysis for the various sub-groups of the population in terms of women’s

education, urban-rural residence, wealth index, age at first marriage and number of children at

first use of contraception.

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Scatter plots and simple regression analysis will be produced to examine the relationship

between family planning efforts, contraceptive prevalence rate and fertility across the countries.

Similar analyses will be carried out to examine such inter-relationships across the various sub-

groups of the population within each country. Multiple Classification Analysis will be run to

examine the combined and independent effects of socio-economic variables and age at marriage

on fertility differentials in individual countries.

Findings

Family planning programs, contraceptive use and fertility decline – a cross-countries analysis

In the forty years between 1970 and 2010, the total fertility rate (TFR) has been falling

continuously with few exception (an increase between 1970 and 1980 in Cambodia) between

2000 and 2010). The TFR had fallen by as much as 75 percent in Vietnam, 66.7 percent in

Bangladesh, 60.4 percent in Indonesia between 53-56 percent in India, Nepal and Cambodia, and

close to 50 percent over percent in Pakistan and the Philippines. Interestingly, the pace of

fertility decline in South Asia accelerated only after 20-30 years of family planning program. In

contrast, the impact of family planning program on fertility decline was more immediate in the

Southeast Asian countries.

Fertility level in Bangladesh and Pakistan declines in contrasting style. The two countries had

about the same level of fertility in the 1960s, but Bangladesh is now reaching replacement level

fertility, with a contraceptive prevalence rate (modern methods) of 52 percent, while the fertility

level of Pakistan remains substantially higher, on account of low level of contraceptive use at

19.3 percent. The effects of a later age at marriage among women in Pakistan as compared to

Bangladesh were not sufficient to counter the lower contraceptive use in fertility reduction.

Another interesting comparison is between Vietnam and the Philippines – Vietnam has attained

below replacement level fertility though it started out with higher fertility than the Philippines,

which now has a fertility level substantially higher than replacement level. The more gradual

fertility transition in the Philippines is reflective of the low family planning effort score of 29.8

as compared to 71.1 in Vietnam. In the Philippines, religious opposition prevented the

implementation of a stronger and sustained program that involved the use of modern

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contraceptives (Herrin, 2007). Indonesia, well known for the success story of family planning,

has also achieved replacement level fertility by 2010. The relaxation of family planning program

following decentralization resulted in the stalling of fertility at 2.6 children per woman between

2003 and 2007 (Rahayu, Utomo and McDonald, 2009). Efforts in revitalizing the family

planning program saw the resumption of fertility decline to 2.1 in 2010. Cambodia provides

another excellent case for the study of the impact of family planning on fertility. The total

fertility rate in Cambodia plummeted from 5.4 in the period just before the family planning

program to 2.6 in 2010.

.Table 1: Year family planning program started and trend in total fertility rate

Year fp

started 1970 1980 1990 2000 2010

Bangladesh 1965 6.9 6.0 4.1 2.9 2.3

India 1952 5.7 4.5 3.7 3 2.6

Nepal 1965 6.1 5.7 5 3.7 2.7

Pakistan 1965 6.6 6.4 5.7 4 3.4

Cambodia 1994 5.5 7.0 5.4 3.4 2.6

Indonesia 1967 5.3 4.1 2.9 2.4 2.1

Philippines 1971 6 4.9 4.1 3.7 3.1

Vietnam 1961/93 7.2 4.9 3.2 1.9 1.8

1970-80 1980--1990 1990-2000 2000-2010 1980-2010

Bangladesh 1965 -13 -31.7 -29.3 -20.7 -66.7

India 1952 -21.1 -17.8 -18.9 -13.3 -54.4

Nepal 1965 -6.6 -12.3 -26 -27 -55.7

Pakistan 1965 -3 -10.9 -29.8 -22.5 -48.5

Cambodia 1994 27.3 -22.9 -37 -23.5 -52.7

Indonesia 1967 -22.6 -29.3 -17.2 -12.5 -60.4

Philippines 1971 -18.3 -16.3 -9.8 -16.2 -48.3

Vietnam 1961/93 -31.9 -34.7 -40.6 -5.3 -75

Sources: Data for earlier years – UN Population Division. Data for 2010 from World Bank with

the following links: http://www.tradingeconomics.com/vietnam/fertility-rate-total-births-per-woman-

wb-data.html

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Contraceptive prevalence rate of modern method has been increasing phenomenally in all

countries following the launching of family planning programs, but it was leveling off towards

the end of 2000 in India, Pakistan, Indonesia the Philippines and Vietnam at different level.

Being a late starter in family planning, the CPR in Cambodia continues to increase rapidly. The

proportion of users relying on traditional methods is highest in Cambodia (30.9 percent),

followed by Pakistan (28.5 percent), the Philippines (25.8 percent) and Vietnam, and much lower

in Indonesia (6.5 percent) and India (12.4 percent), Nepal (13 percent) and Bangladesh (Table 2).

A study by Ross and Smith (2010) shows that Vietnam has the highest score (71.1) in family

planning efforts, and the Philippines the lowest (29.8), followed by Pakistan (45.7) and the rest

scored between 53.5 (India) and 59.9 (Indonesia). Family planning effort is closely associated

with contraceptive prevalence rate and unmet need for contraception (Figures 1 and 2), and

fertility level is strongly associated with CPR for modern methods (Figures 3-4). CPR of modern

methods explains as much as 87 percent of the variation in fertility, and 68 percent in the

variation in fertility decline across countries.

Table 2: Family planning efforts, contraceptive prevalence rate and unmet need for contraception

CPR Modern method

Any

method

Unmet

need

Family

planning

effort

score 1970 1980 1990

2000 2007-

2011

2007-

2011

2007-

2011

Bangladesh 56.4 7.0 18.1 38.1 47.4 52.1 61.2 16.8

India 53.5 9.5 34.0 40.0 46.0 48.0 54.8 12.8

Nepal 56.8 2.4 11.0 25.0 38.7 43.2 49.7 24.6

Pakistan 45.7 3.9 7.6 15.8 21.0 19.3 27.0 24.9

Cambodia 55.8 na na 11.5 22.8 34.9 50.5 25.1

Indonesia 59.9 18.2 40.5 52.3 55.7 57.4 61.4 9.1

Philippines 29.8 16.2 28.6 34.2 36.3 48.9 22.3

Vietnam 71.1 na 37.7 52.5 63.5 59.8 77.8 4.8

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Figure 1: Scatter plots of CPRwith family planning efforts score

Figure 2: Scatter plots of unmet need for contraception with family planning efforts score

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Figure 3: Scatter plots of TFR with CPR

Figure 4: Scatter plots of percent decline in TFR with CPR

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Urban-rural and regional differentials in CPR and Fertility

Few respondents in the DHS have mentioned lack of the knowledge or access to family planning

services as reasons for nonuse, and this suggests accessibility to services is not a main barrier. Be

that as it may, in all the four South Asian countries, rural women were much less likely than

urban women to use a method and to have large family size. In Vietnam and Indonesia where

the CPR has reached a high level, rural women were just as likely to use a method as urban

women. In contrast, Pakistani (both urban and rural) had the lowest contraceptive prevalence

rate and largest family size (Figure 5).

Bangladesh U

Bangladesh R

India U

India R

Nepal U

Nepal R

Pakistan UPakistasn R

Cambodia U

Cambodia R

Indon U

Indon RPhilippines U

Philippines R

Vietnam U

Vietnam R

y = -0.032x + 4.6152R² = 0.7597

2.0

2.5

3.0

3.5

4.0

20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

% currenlty using a contraceptive method

CEB

Figure 5: Scatter plots of number of children ever born and percent currently using a

contraceptive method, by place of residence and country

Vast regional and state differentials in contraceptive use and fertility level can be observed in

each of the countries. In the case of India, contraceptive prevalence rate was lowest in low HDI

states (HDI of less than 0.4) at 28.3 percent in Meghalaya, 33 percent in Nagaland, and 38.3

percent in Bihar and 39.3 percent in Jharkhand. In contrast, states with high or medium HDI had

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higher CPR and these include Kerala, Himachai Pradesh and West Bengal, with CPR of around

70 percent. Figure 6 shows close association between CPR and number of children ever born –

states with high CPR have fewer children per woman and conversely states with low CPR have

large family size (Figure 6).

3.0

HP2.5

2.9 2.8

Delhi

RJ

Uttar PradessBihaar

Sikkim

AR

Nagaland

Manipur2.9

2.4

Mehhalaya

AsamJharkhand

Orisa

3.0 3.0Gujarat

2.42.42.4

GoaKerala

Tamil Nadu

y = -0.0244x + 4.1063R² = 0.4838

1.5

2.0

2.5

3.0

3.5

4.0

22.0 32.0 42.0 52.0 62.0 72.0 82.0

Percent using a method

CEB

Figure 6: Scatter plots of number of children ever born and percent currently using a

contraceptive method, by states, India

Socio-economic status (as measured by wealth index) and educational differentials in fertility

After many years of family planning programs, wide socio-economic differentials in

contraceptive use and fertility still persist. In each country, the poorest segments of the

population had the lowest CPR and the largest family size, while the richest segment of the

population had the higher CPR and smallest family size (Figure 7). Women’s educational level is

positively related to contraceptive use and negatively related to family size (Figure 8).

Although few women mention service related reasons for nonuse (as will be shown later), the

observed income and educational differentials are probably due to other factors such as

misconception of the side effects of methods, and other socio-cultural barriers.

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Figure 7: Scatter plots of number of children ever born with CPR by wealth index

Poorest

PoorerMiddle

Richer

Richest

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

40 42 44 46 48 50 52 54 56

CEB

CPR

Cambodia

Poorest

Poorer

Middle

RicherRichest

1.7

1.9

2.1

2.3

2.5

2.7

2.9

3.1

50.0 52.0 54.0 56.0 58.0 60.0 62.0 64.0

CEB

CPR

Indonesia

Poorest

Poorer

Middle

Richer

Richest2.0

2.5

3.0

3.5

4.0

4.5

37.0 42.0 47.0 52.0 57.0

CEB

CPR

Philippines

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Figure 8: Scatter plots of number of children ever born with CPR by women’s education

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Multivariate analysis of fertility

A multitude of socio-economic variables may be used to explain the fertility differentials. These

variables are inter-related and have confounding effects on fertility. Hence, multivariate

techniques are often used to ascertain the independent effects and combined effects of these

variables. Multiple classification analysis (MCA) is used in the following analysis using 5

independent variables – women’s education, place of residence, wealth index, age at first

marriage, and number of children at first use. The last of these variables is chosen instead of

current status of contraceptive use, because of the inverse causation – high parity women are

more likely to use a method to stop childbearing.

MCA outputs show the unadjusted mean (i.e. the same as in bivariate analysis) and the adjusted

means (i.e. controlling for all factors and covariates in the model). The beta values show the

relative importance of each variable in the multivariate context, and the R square values show the

proportion of variance explained.

In all countries in this study, women’s education, place of residence the place of residence have

relatively smaller effects on the number of children ever born as compared to age at first

marriage and the number of children at first use of contraception. In all these countries, urban

women have more children than rural women, but the differentials are relatively small. The wide

variations in the mean number of children across educational groups and wealth index at the

bivariate level become much smaller, after taking into account age at marriage and the timing of

the initiation of contraceptive use.

In all countries except Pakistan (where data is not available), the number of children at first use

is the most significant variable in explaining the differentials in the mean number of children.

Controlling for other variables, women that started to use a method before having any children

have significantly fewer children as compared to the rest, and those that started only after four or

children have the largest number of children. Interestingly the never users also have small family

size, which may be explained by the lack the need for family planning. More detailed cross-

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tabulations show that the never users were mainly those at the beginning or at the end of

reproduction, and many had reported sub-fecundity as reason for not using a method.

As expected, women marrying at younger age would have more children than those marrying

later. The differentials remain very significant even after controlling for other variables and age

of women, and this is true for all countries.

A multitude of socio-economic variables may be used to explain the fertility differentials. These

variables are inter-related and have confounding effects on fertility. Hence, multivariate

techniques are often used to ascertain the independent effects and combined effects of these

variables. Multiple classification analysis (MCA) is used in the following analysis using 5

independent variables – women’s education, place of residence, wealth index, age at first

marriage, and number of children at first use. The last of these variables is chosen instead of

current status of contraceptive use, because of the inverse causation – high parity women are

more likely to use a method to stop childbearing.

MCA outputs show the unadjusted mean (i.e. the same as in bivariate analysis) and the adjusted

means (i.e. controlling for all factors and covariates in the model). The beta values show the

relative importance of each variable in the multivariate context, and the R square values show the

proportion of variance explained.

In all countries in this study, women’s education, place of residence the place of residence have

relatively smaller effects on the number of children ever born as compared to age at first

marriage and the number of children at first use of contraception. In all these countries, urban

women have more children than rural women, but the differentials are relatively small. The wide

variations in the mean number of children across educational groups and wealth index at the

bivariate level become much smaller, after taking into account age at marriage and the timing of

the initiation of contraceptive use.

In all countries except Pakistan (where data is not available), the number of children at first use

is the most significant variable in explaining the differentials in the mean number of children.

Controlling for other variables, women that started to use a method before having any children

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have significantly fewer children as compared to the rest, and those that started only after four or

children have the largest number of children. Interestingly the never users also have small family

size, which may be explained by the lack the need for family planning. More detailed cross-

tabulations show that the never users were mainly those at the beginning or at the end of

reproduction, and many had reported sub-fecundity as reason for not using a method.

Table 3: Multiple classification analysis of children ever born by selected socio-demographic

characteristics in South Asian countries

Bangladesh India Nepal Pakistan

n/ beta Unadj Adjusted n Unadj Adj. n Unadj Adj. n Unadj Adj.

Education 0.1 0.12 0.05 0.05

No education 3054 3.9 3.0 34078 3.6 3.0 5070 3.7 3.1 6333 4.4 4.0

Primary 3040 3.0 2.8 13540 2.8 2.6 1413 2.3 2.9 1298 3.4 3.8

Secondary 3216 1.9 2.6 32450 2.1 2.6 1520 1.7 2.9 1306 2.8 3.7

Higher 830 1.5 2.5 7781 1.5 2.4 241 1.4 3.0 643 2.3 3.5

Residence 0.02 0.01 0.02 0.01

Urban 3799 2.6 2.7 38348 2.5 2.8 2177 2.7 3.0 3645 3.8 3.9

Rural 6341 2.9 2.8 49501 2.9 2.7 6067 3.2 3.1 5935 4.0 3.9

Wealth 0.06 0.14 0.13 0.07

Poorest 1600 3.3 3.0 11133 3.4 3.2 1745 3.6 3.5 1872 4.2 4.1

Poorer 1837 3.0 2.9 13407 3.2 3.0 1593 3.3 3.2 1939 4.1 4.1

Middle 1950 2.9 2.8 16778 2.9 2.8 1550 3.1 3.0 1846 4.1 4.0

Richer 2068 2.7 2.7 20592 2.6 2.7 1673 2.9 2.9 1930 3.8 3.8

Richest 2685 2.3 2.7 25939 2.2 2.4 1683 2.4 2.7 1993 3.3 3.5

Marriage

age 0.14 0.19 0.17 0.32

Before 17 7186 3.1 2.9 33992 3.4 3.1 4238 3.4 3.3 3531 4.7 4.8

17-19 2053 2.1 2.6 28396 2.7 2.8 2709 2.8 3.0 2903 3.9 4.0

20-22 595 1.8 2.2 14810 2.2 2.5 923 2.5 2.6 1738 3.3 3.4

23+ 306 1.4 1.7 10651 1.7 1.9 374 2.1 1.8 1408 2.6 2.1

Started fp 0.32 0.34 0.28

No

children 2209 1.4 2.3 5621 1.6 2.2 603 1.0 2.5 na na na

1 3067 2.4 2.6 16053 2.1 2.6 1377 2.1 2.9

2 1303 3.4 2.9 15035 2.5 2.5 1213 2.8 2.8

3 809 4.3 3.3 11598 3.4 2.9 1028 3.8 3.1

4+ 958 5.9 4.6 11342 5.2 4.2 1266 5.8 4.4

Never used 1794 2.3 2.4 28200 2.2 2.4 2757 2.5 2.7

R square 61.5 51.1 62.4

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Table 4: Multiple classification analysis of children ever born by selected socio-demographic

characteristics in Southeast Asian countries

Cambodia Indonesia Philippines Vietnam

n/beta Unadj. Adjusted n/beta Unadj. Adj. n/beta Unadj. Adj. n/beta Unadj. Adj.

Education 0.05 0.02 0.09 0.13

No education 2397 3.8 3.2 1963 3.9 2.7 176 5.5 4.3 331 3.7 3.2

Primary 6001 3.1 2.9 13324 3.0 2.6 2197 4.2 3.3 1422 3.0 2.7

Secondary 2838 2.1 2.9 13273 2.1 2.6 3722 2.9 3.0 3376 2.3 2.5

Higher 203 1.4 2.9 2273 1.8 2.5 2468 2.3 3.0 206 1.6 2.3

Residence 0.005 0.003 0.006 0.09

Urban 3321 2.5 3.0 12234 2.3 2.6 3838 2.7 3.1 1204 2.1 2.3

Rural 8118 3.1 3.0 18599 2.7 2.6 4725 3.4 3.1 4131 2.7 2.6

Wealth 0.17 0.12 0.18

Poorest 2176 3.4 3.5 7826 3.0 2.9 1971 4.0 3.7

Poorer 2091 3.3 3.2 6170 2.7 2.6 1856 3.4 3.3

Middle 2015 3.1 3.0 5612 2.5 2.5 1728 3.0 2.9

Richer 2212 2.8 2.8 5580 2.3 2.4 1609 2.6 2.8

Richest 2945 2.4 2.5 5645 2.3 2.3 1399 2.2 2.5

Marriage

age 0.3 0.23 0.26 0.23

Before 17 2071 3.7 3.8 7290 3.3 3.1 1220 4.2 3.9 373 3.5 3.2

17-19 4292 3.2 3.3 9461 2.7 2.8 2500 3.5 3.5 1952 2.8 2.8

20-22 2866 2.7 2.8 7244 2.3 2.4 2207 3.0 3.1 1732 2.5 2.5

23+ 2210 2.2 1.9 6838 1.9 1.9 2636 2.3 2.3 1278 2.0 2.0

Started fp 0.32 0.32 0.35

No

children 1301 1.3 1.9 440 1.2 2.3 259 1.2 2.0

1 16521 2.2 2.4 2838 2.3 2.8 2301 2.0 2.3

2 3708 3.1 2.8 1497 3.3 3.2 1257 2.8 2.6

3 1757 4.1 3.3 846 4.3 3.8 538 3.7 3.0

4+ 1793 5.8 4.7 883 6.2 5.0 464 5.3 4.2

Never used 5753 2.1 2.2 2059 2.5 2.6 516 1.6 2.0

R square 56.6 51.6 56.1 62.8

As expected, women marrying at younger age would have more children than those marrying

later. The differentials remain very significant even after controlling for other variables and age

of women, and this is true for all countries.

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Reasons for not intending to use contraception among non-users

Socio-economic differentials in contraceptive use could be due to a variety of reasons. In the

seven countries where data on reasons for not intending to use contraception among non-users,

the most often cited reasons were fertility related reasons such as wanting to have children

among the younger women and menopausal, sub-fecundity and infrequent sex among older

women. These reasons accounted for about half of the reasons cited by Filipino and Indonesian

women to about three quarters among Bangladeshi women. Apart from fertility related reasons,

method-related reasons were the most prominent barriers to contraceptive practice in the

Philippines and Indonesia and to a lesser extent Nepal and Vietnam. Health concerns and fear of

side effects were the main method-related reasons. In Pakistan, Bangladesh and India, opposition

to use was the most important reason for non-use besides fertility related reasons. Respondent’s

opposition to family planning and that of the husband were the main obstacles to contraceptive

use. In Pakistan where opposition to use was cited by 23 percent of those who did not use a

method, religious opposition accounted for about 5 percent and the remaining 18 percent were

mainly self-imposed. Lack of knowledge and access to contraceptive supply were mentioned by

only a small proportion of women for not using a method. More detailed tabulations of reasons

for nonuse show no systematic differentials by wealth index and educational level.

Table 5: Reasons for not intending to use contraception or nonuse of contraception

\

Fertility

related

Opposition

to use

Lack

knowledge

Method-

related Others Total

Bangladesh 74.1 12.1 0.6 8.1 5.1 100

India 65.5 15.2 1.8 11.5 6 100

Nepal 65.4 11.9 0.7 17.3 4.7 100

Pakistan 57.4 23 3 12.2 4.4 100

Indonesia 50.8 4.8 1.3 35.5 7.6 100

Philippines 49.9 9 0.6 39.3 1.2 100

Vietnam 66.7 2.5 3 14.2 13.6 100

Sources: Demographic and Health Survey Report from each country. No data on reasons for

non-use in Cambodia

19

Discussion and conclusions

This paper shows significant associations between family planning program efforts,

contraceptive use and fertility level, both at the country level and sub-groups of the population

within country. In Southeast Asian countries, the fertility decline accelerated soon after the

launching of family planning programs, but there was time lag between the launching of family

planning programs and accelerated fertility decline in South Asian countries. Recent trends show

that some countries in South and Southeast Asia have already reached replacement or below

replacement fertility, and UN projections show that most of the countries in these two regions

will have replacement level or below replacement level fertility within the next one to two

decades, with widespread use of contraception (both from the public and private sources), and

rising age at marriage.

The eight countries selected for this study have undergone different pace of fertility transition,

which could be attributed to the different strength of family planning programs, social settings

and within country differentials. As in previous studies, this study found strong evidence that a

strong family planning program can reduce fertility even in the resource poor countries, and

substantial differentials exist within country. Women’s education and their status in the family

are important determinants in contraceptive use and fertility differentials. Hence, in accordance

to the Plan of Action adopted at the 1994 International Conference on Population and

Development, it behooves all governments to take actions to strengthen the family planning

program and improve the status of women.

20

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21

Appendix 1: Changes in HDI, 1980-2007

1980 2007

Average annual

rate of change

(%)

Bangladesh 0.328 0.543 2.4

India 0.427 0.612 1.6

Nepal 0.309 0.553 2.9

Pakistan 0.402 0.572 1.6

Cambodia 0.515 0.543 0.8

Indonesia 0.522 0.734 1.5

Philippines 0.652 0.751 0.6

Vietnam 0.56 0.725 1.3

Cambodia base year 2000

Vietnam base year 1985

Source: UN, 2011 Human Development Report 2011

:

Appendix 2: Changes in urbanization level, 1970-2010

Sources: UN, 2012

22

Appendix 3: Percent of married women with at least secondary schooling by age

Age group 20-24 25-29 30-34 35-39 40-44 45-49 Total

Bangladesh 52.6 42.5 32.1 28.0 20.4 17.5 38.2

India 52.4 52.3 48.3 41.6 39.2 34.8 45.8

Nepal 41.8 28.0 20.4 12.7 7.1 4.5 29.7

Pakistan 24.2 27.5 24.0 16.1 14.5 11.4 20.3

Cambodia 38.2 28.1 26.0 28.3 23.9 10.6 26.6

Indonesia 61.2 58.5 56.3 52.7 39.3 27.3 50.4

Philippines 81.1 79.5 76.5 69.1 67.1 59.7 72.3

Vietnam 59.8 69.6 70.1 70.5 66.3 62.3 67.2

Source: Author’s own computation using the DHS data

Appendix 4: Changes in singulate mean age at first marriage

Source: UN 2012