OSIPP Discussion Paper: DP-2012-E-001

The lexicographic preference for a son: evidence from household data
in Vietnam
February 23, 2012

Tien Manh Vu†
Ph.D Candidate, Osaka School of International Public Policy, Osaka University[Key words] Son preference, Sex composition, Birth order, Birth spacing[JEL Codes] J11, J12, J13[Abstract]
This paper examines son preferences by specifying sex composition by birth order of existingchildren as key independent variables. The results indicate a lexicographic preference for a
son by mothers aged 50 years and older. Mothers without a son are also under substantial
pressure to bear more children and shorten their birth spacing. However, once a family
includes a son, parents do not consider sex composition over other decisions on family sizeand fertility timing. It would appear that the preference for a son is relatively stronger forsome birth orders in the northern regions of Vietnam but slightly weaker in the Central
Highlands and South Central Coast. In addition, while women are important in the
Vietnamese labor force, the level of preference for sons does not differ across income atlower birth orders. We also obtain mixed results for son preferences if we include mothers
less than 50 years of age in our analysis.
†Contact: Osaka School of International Public Policy, Osaka University, 1-31 Machikaneyama,
Toyonaka, Osaka 560-0043 Japan. Tel/Fax: +81-(0)6-6850-5656. E-mail: t-vu@osipp.osaka-u.ac.jp.
[Acknowledgments] The author gratefully acknowledges helpful comments and suggestions by
Hisakazu Matsushige and Tsunehiro Ootsuki, Osaka University, and participants at the Labor
Economics Conference held in Awajishima, Japan, September 4-6, 2011 and the Fall Meeting of theJapanese Economic Association held at Tsukuba University, Japan, October 29-30, 2011. The author
also thanks Professor Hisakazu Matsushige, Osaka School of International Public Policy, Osaka
University for permitting to publish this paper.

The lexicographic preference for a son: evidence from household data
in Vietnam1

Tien Manh Vu
Osaka School of International Public Policy, Osaka University

1

An earlier version of this paper is Vu (2011) which employs Vietnamese Households Living Standard Survey
2006.

2

1 Introduction
Recent improvements in health, a low fertility rate, and a high economic growth rate in
Vietnam have challenged the economic roots of the preference for sons. During the lasttwenty years from 1990 to 2008, Vietnamese gross domestic product (GDP) has grown at
approximately 5 percent annually (GSO 2010). At the same time, mortality for children underfive years of age in Vietnam has fallen from 51 per 1,000 live births in 1990 to just 29 in2005 (WHO 2011). Similarly, the annual population growth rate has declined from 1.8
percent in 1990 to only 1.1 percent in 2008, and average life expectancy has increased from65 years in 1990 to 74 years in 2008 (World Bank 2011b, 2001c). Interestingly, however, theparticipation rate of females in the labor force in Vietnam has remained at about 70 percent
throughout this period (World Bank 2011a).
The purpose of this paper is to reexamine the preference for a son in Vietnam using
the Vietnamese Households Living Standard Survey (VHLSS) 2008. Unlike previous studies
in this area, we examine the decision on having additional children up to the fourth birthorder conditional on the sex composition of all previous children. We also compare the levelof son preference by geographic region and household income. In the initial analysis, weemploy logistic models to estimate the effect of the sex composition on decisions concerning
family size. Then, after controlling for the number of children, we apply hazard models tobenchmark the corresponding effect on fertility timing. Lastly, we use the difference indifference (DID) method to examine the level of son preference found.
We obtain four major findings as follows. First, all mothers in Vietnam, especiallythose over 49 years of age, ultimately desire a son. For this reason, mothers without a son
tend to have both more children and shorter birth spacing. However, once we locate a boy (B)in the sex composition of the family's children, parents no longer consider sex composition in
their decision on family size. Second, the son preference appears be stronger in the North3

East (NE), Red River Delta (RRD), North Central Coast (NCC) and North West (NW)
regions of Vietnam, but lower in the South Central Coast (SCC) and Central Highlands (CH).
Third, while females in Vietnam make a significant contribution to the labor force, we findno difference in the level of son preference across income at lower birth orders. Finally, if we
include mothers less than 50 years of age in our analysis, we obtain mixed preferences.
The remainder of the paper is organized as follows. Section 2 details the existing
evidence on gender preference in families, especially in Vietnam. Section 3 describes ourempirical methodology and Section 4 introduces the data used. Section 5 provides thefindings. Section 6 discusses the results and provides our conclusions.

2 Gender preference literature
Research into gender preference, the tendency by families to favor a specific gender for anunborn child, has taken place in both developing and developed countries. For instance, inmany developing countries in South Asia, Southeast Asia, and North Africa, there is evidenceof a strong son preference (Basu and De Jong 2010). A preference for mixed genders also
exists. For example, in the Nordic countries, Danish, Norwegian and Swedish parents have adaughter preference, while Finns exhibit a son preference for their third-born (Andersson etal. 2006). Japanese parents in the 1920-1939 have a lexicographic son preference; however,son preference disappears in the following decades and is replaced with a mixed preferencein parents with two children (Kureishi and Wakabayashi 2011).
The investigation of the evidence attached to gender preference is common inexisting empirical studies. Here, scholars search for behavioral evidence, rather than merely
analyzing the outcome of a specific questionnaire on individual preferences. For example,
Yamaguchi (1989) calculates the average number of boys in a family with a no-son
preference and under both population homogeneity and heterogeneity as to the probability of
having a boy. In this situation, and given a son preference, the average number of boys in a4

family should be higher than the estimated number. Alternatively, Haughton and Haughton(1998) construct eight different tests under limited information circumstances for families
that have completed childbearing. Notably, this line of research suggests specifying the timeinterval between one birth and the next in a hazard model.
In Vietnam, the idea of son preference has been examined by Haughton andHaughton (1995, 1996, 1998) and Bélanger (2002). Using quantitative data on 500 families
in a rural village in Northern Vietnam and qualitative information on 100 individuals,
Bélanger (2002) suggested that people desire to have sons for social, symbolic and economic
value. In earlier work, Haughton and Haughton (1995) employ a sub-survey in the 1992-93
wave of the Vietnamese Living Standard Survey (VLSS) of 2,636 women aged 15-49 years
to argue that while the son preference in Vietnam is relatively strong, it has only a minor
impact on the fertility rate. Later, investigating the birth spacing between the family's second
and third births, Haughton and Haughton (1996) apply two separate Weibull regression
models under the hypothesis of heterogeneous preferences. Their results indicate that half ofthe surveyed parents prefer sons, while for the remainder any gender preference is difficult toconfirm. Later still, from eight different types of tests, Haughton and Haughton (1998)
propose that the simple progression parity model and table are very effective if information
on gender by birth order is available.
Although these existing studies provide a useful foundation for the analysis ofgender preference in Vietnam, they have some key limitations. To start with, parity
progression based on counting the aggregate number of boys and girls in specific types offamilies in the sample does not guarantee all necessary conditions for a son preference inother data sets. In addition, the common assumption by which parents set the family-size
decision in concert with the decision on the number of boys in the family cannot hold. In fact,parents cannot perfectly control the gender of the baby they are expecting. Moreover, using5

overlapping sex composition as an independent variable would cause bias in the estimations.
For example, Lee (2008) specifies(i.e. the first and second children are girls),(the first child has a different gender to the second child) and(the first child is agirl) in the same estimation. Finally, interpretation is less accurate if there is a preference fora certain gender by birth order. For example, the gender birth orders ofgirl and second child is a boy) and(first child is a

(first child is a boy and second child is a girl) may

exhibit conflicting effects relative to the same dependent variables.
By using instead the full sex composition of all previous children as key independent
variables in this analysis, we establish that a son preference exists if the following conditions
hold. First, low-birth-order females have a relatively larger number of siblings thanlow-birth-order males. This is because under a son preference, parents without a son continue
to have children until they get a boy. Subsequently, among parents with the same number ofchildren intending to have one more child, families without the expected number of son(s) are
more likely to be in some hurry and so shorten the spacing of births. This shorter birth
spacing allows mothers to participate in more childbearing trials if they fail to have therequired number of sons.
In addition, we include several other assumptions in our analysis. To start with, there
are many measures aimed at prenatal sex selection, including sex-selective abortion andsperm selection. However, in our analysis we assert that either having more children or
shortening the spacing of births is the most common method for a wide range of people. We
also assume that having more children and altering birth spacing to obtain a preferred genderis representative of the degree of preference for one gender over another. Hence, theinteraction between an independent variable and the gender parents prefer least is also anindicator of the level of gender preference across that variable.

6

3 Empirical models and specifications
This section develops the empirical models we use to test the above two conditions while
controlling for the number of children. We first employ random utility models to estimate theprobability of having more children. We then apply hazard methods to examine the birth
spacing attached to a certain birth order to calculate the probability of having another childevery month after a certain birth order. Finally, we use the DID method to investigate thelevel of son preference by income level and region.
3.1 Random utility models of the probability of having additional children
In the first part of the analysis, we examine the probability of women having more than one,
more than two, and more than three children in their entire lifetime. In general, parents
consider having additional
than the utility

children if the utility of having more childrenof the current

number of children.

=𝑋 ∗𝛽 +𝑊 ∗𝛼 +𝑢 ≥Neithernor

is higher

( > )

(1).

can be calculated. However, it is possible to partly observe thepast decisions of families after mothers complete their fertility at 49 years of age. We specify=

if parents have more than={children and
if
if

=

if parents stop atchildren.

≥
<

(2).

𝑊 is a set ofdummies indicating the sex composition by birth order of all

previous children and 𝑋

are control variables, including parental demographic

characteristics, income and economic geography. More specifically, when
when the first child is female and the base is
𝑊 2 is= , 𝑊 1 iswhen the first child is male. When

when both the first and second child are female,(= ,) when the first child is
7female (male) and the second child is male (female), and the base iswhen both the firstand second child are male. The sex composition of the children is similarly specified when
= . In terms of the control variables, 𝑋

includes the age (in years) of the mother at thebirth, schooling (in years) of the father and the mother, the logarithm of householdincome per capita () and a dummy variable indicating a rural area (𝑢=) if the household resides in an urban area), along with seven dummies identifying the eightmain economic regions in Vietnam as given in the data (see Table 1).
3.2 Right-censored hazard models for fertility timing
In the second part of the analysis, we investigate the time interval or birth spacing in months
between the first and second births, the second and third births, and the third and fourth births
in three separate estimations.
The sample period is the time interval in which we expect the mother to have one
child. The period of our study starts with the birth of themother has the ( + )child and continues until thechild or until the survey ends in 2008, where i ∈ [ , ]. If themother does not have the ( + )child during the study period and is less than 49 years ofage in 2008, we include her as censored data because she could have more children after
2008. Meanwhile, we exclude mothers over 49 years of age by the end of 2008 with a total ofchildren because they are less likely to have additional children after 2008.
Suppose that the time interval for having an additional child follows F(t). The
survival function, indicating the probability a woman does not have additional children attime , is then 𝑆( ) =𝐹( ) = 𝑃(𝑇 > ). 𝑆( ) =function 𝑆( ) decreases steadily through time
The hazard function,at= . Note that the survival

as mothers bear the next child.

( ), is the instantaneous rate of failure or the rate of having

the next child in a small period of time.
8

( )=

∆ →0

𝑃( +∆ >𝑇> |𝑇> )∆𝑓( )= 𝑆( )(3).

The cumulative hazard function is:𝑓(𝑢)𝐻( ) = ∫0 (𝑢) 𝑢 = ∫0 𝑆(𝑢) =(𝑆( ))

We assume the hazard function follows the Weibull hazard distribution
1is the shape parameter estimated from the data. 𝑆( ) =, in which𝐹( ) =

(

) and

( )=

∗

1

∗

(

(4).

()=

∗

(

),

). Overall, the Weibull distribution

deals better with monotone hazard rates that either increase or decrease exponentially overtime.
Generally, we set( , +1 ) = ( , 𝑋𝑘 , 𝑊 𝑘 , 𝛽, 𝛼). Similar to the specification for the

random utility models, 𝑋𝑘 are control variables, parental demographic characteristics andincome. 𝑊 𝑘 is a set of dummies indicating the sex composition of children in the family
until thebirth of motherat time t0. 𝛽 and 𝛼 are the coefficients corresponding to 𝑋

and 𝑊.
3.3 DID for level of son preference
In the third and final part of our analysis, we assert that it would be interesting to identify
whether the degree of son preference differs significantly across each control variable.
Suppose W1 is the sex composition of previous children that is least preferred and 𝑋 is acontrol variable that potentially affects both the probability of having more children and thebirth spacing, such that W1 =if the parents have such a sex composition, otherwise

W1 = . By using β1 ∗ W1 + 𝛿 ∗ 𝑋 + 𝛾 ∗ W1 ∗ 𝑋 , we can estimate DID, 𝛾. If significant,the interaction term 𝛾 suggests that families of the least preferred sex composition behave
substantially differently in terms of the control variable, holding all other factors unchanged.
9In our analysis, we use the eight economic regions of Vietnam and the logarithm ofhousehold income per capita in the set of control variables 𝑋 to test whether the level ofgender preference changes according to the 𝑋 factor.

4 DataThe data we employ in our analysis are from the Vietnamese Household Living Standard andConsumer Price Index Survey 2008, commonly known as VHLSS 2008. The 2008 round of
the VHLSS is generally similar to earlier waves of the survey, technically supported by theWorld Bank and undertaken nationwide by the General Statistics Office of Vietnam (GSO).
The data set is a representative country sample of 45,945 Vietnamese households. We use thesurvey to investigate the fertility of parents, one of whom is the household head, in thesehouseholds. Children included in the survey are children of the household head, and we
obtain this information from a specific question in the survey questionnaire.
To guarantee that the relationship between the child and the household head is truly
that of a blood relative, we select the data using several specific criteria. Specifically, we only
select parents for our sample whose children are all single according to their declared marital
status, and if the household children have siblings, all siblings must have the same familyname. We employ these criteria to prevent the problems arising with our sample observations.
First, the son and his spouse are both identified as ‘children’ of the household head in thesurvey. Second, in Vietnam, a woman may not change her family name if married and she
may continue to live in her spouse's house even if she is widowed. Third, we omit families
where the age of the mother at childbirth is less than 15 or over 49 years to exclude
stepmothers from the analysis. However, the likelihood of errors from including stepmothers
and stepfathers is relatively low, as only 0.24 percent of fathers and less than 0.01 percent ofmothers in the sample are divorced. In fact, 98.65 percent of fathers in the sample are married

10and living with their spouses. The corresponding value is 91.81 percent for mothers, of whichanother 6.09 percent are widows. Altogether, 29,582 households meet these selection criteria.
‘Insert Table 1’

We choose 49 years of age as the milestone for a woman to complete her period of
fertility. Initially, the random utility models we use to estimate the probability of having morechildren require that all mothers complete their fertilities. Some 7,063 households with
12,533 children satisfy this criterion. In the remaining data, 98.9 percent of the eldest
children of the mother are less than 50 years of age, which is far below the life expectancy of
the typical Vietnamese. Therefore, we could consider 12,533 children as being the surviving
children of mothers over 49 years of age. In the subsequent analysis of fertility timing, we
examine the time interval between thewherebirth and the (+ )is an integer from 1 to 3. We exclude all mothers withanalysis. In addition, we omit mothers that are over 49 years old withbirth of the mother,children for eachchildren as theywould not be able to have more children. However, we retain mothers under 50 years of age
withchildren as censored data.

5 Results
5.1 Lexicographic preference for sons and mixed preferences
The analysis indicates a lexicographic preference for a son of mothers over 49 years of age
but an additional mixed preference if we include those mothers less than 50 years of age 2.
Compared with Haughton and Haughton (1995, 1998), in which there is a mixed preferenceofwomen alongside a son preference, our estimates include more detail and covercompleted cases as follows.
2

Additional evidence can be observed from Appendix Figure 1, 2 and 3 using Kaplan-Meier survival estimatesfor birth spacing. As the curves, denoted with only G, are closest to the origin and have the same probability of
having the next baby, mothers with only daughters display the shortest birth spacing.

11‘Insert Table 2’

The results demonstrate a preference for one son by women that have finished theirperiod of reproduction. As shown in analyses (1), (2) and (3) in Table 2, the sex compositions
that do not include any

( ,

and

) are statistically significant and have thegreatest impact on the probability of having additional children than any other sex
composition. Using Ward tests, we reject the null hypotheses that all sex compositions
withoutone (are zero for the cases in columns (2) and (3) in Table 3. Therefore, either having,) or two (as the base) sons does not affect the probability of having a thirdchild. Similarly, having three sons (
son (

,

,

as the base), two sons (,,), or one

) does not influence the probability of having a fourth child. In otherwords, there is a one-son preference. That is to say, parents look forin the sex

composition of all expected children. Once found, they no longer consider sex composition in
their decision on family size.
‘Insert Table 3’

Interestingly, there would appear to be a mixed preference for gender when we
include mothers less than 50 years of age. Parents without

in the sex composition of theirchildren have the greatest probability of shortening their birth spacing, as shown in analyses(4), (5) and (6) in Table 2. However, the preference for only one son does not hold, as theWard tests in (5) and (6) in Table 3 reject the null hypothesis. If we rank the probability of
having more children given a particular sex composition, we can distinguish three groups.
Grou(including

G , ending wi𝑢[(including

and G, ending wi

𝑢

(𝑤

𝑢

)<
G) ∪ (wi ou le er G)] =

<

).
12

As shown in Table 2, all statistically significant sex compositions including

end with,and this has a negative effect on the size decision and birth spacing compared with the base.
The sex compositions including

and ending withequal to zero in a joint Ward test ( 𝐻==

are statistically insignificant and
=

,

( )=

,𝑃

>

). Of these, Group 3 has the greatest probability of having another child, which

we indicate as a strong son preference. In half of Group 2, those without a, there is adaughter preference. However, in the remaining half of Group 2, it is difficult to reach aconclusion, as they could have either a marginal son preference or no gender preference.
Group 1 displays a son and/or marginal son preference.

5.2 Level of son preference by region and household income per capita
The level of son preference by region or DID appears only in the hazard models. As indicatedin Table 5, the Ward tests that all DID coefficients jointly equal zero is rejected in (4), (5)and (6). As shown in Table 4, the level of son preference at some specific birth orders differs
across NE, NW, RRD, NCC, SCC and CH. In addition, NE and RRD exhibit the highest
level of son preference, whereas SCC and CH display the lowest level of son preference.
‘Insert Table 4 and 5’

As shown in Table 6, the level of son preference by income if ever it is statisticallysignificant simply implies the rich can afford a larger family size. The level of son preference
does not differ by income when the birth order is less than three, as the interaction terms in(1), (2) and (4) are statistically insignificant. This is important, because 93.64 percent of thewomen in the sample who are more than 49 years of age have fewer than four children. If alevel of son preference by income exists, it should be evident for all birth orders. Therefore,
the DID by income at the fourth childbirth in (3) suggests that higher-income families with13

three daughters can afford a larger family size. Similarly, the interaction term in (5) suggeststhat higher-income households would be more ready to have their next child.

In addition, if a son preference derives from economic reasons, such as the higher
lifetime value of sons, the poor would have a generally higher level of son preference. In
addition, parents would have as many sons as possible. In fact, as shown in Table 6, just oneof the interaction terms is negative, but even that is statistically insignificant. Therefore, theone-son preference in Vietnam probably arises for reasons other than economic reason and/or
long-term scheme, which is difficult to conclude from the cross section data.
‘Insert Table 6’

6 Conclusion and discussion
After controlling for the number of children, this paper constructs two complementary waysof examining gender preferences in Vietnam. First, random utility models examine whether
the sex composition of existing children in the family affects the decision on having morechildren. Second, hazard models attempt to investigate the relation between sex composition
and birth spacing.

Using the above analysis, we find a lexicographic preference for one son in Vietnam
for mothers that have completed their period of fertility but a mixed preference for sons in thebirth spacing analysis. However, both analyses find that mothers without a son would beunder significant pressure to have another child as early as possible. We also find that thelevel of son preference differs significantly in the northern, SCC, and CH regions acrosssome specific birth orders. However, it is insignificant across the level of income at the firstand second birth orders.

14Sixteen years after Haughton and Haughton (1995) (using VLSS 1992), the last
study in this area in Vietnam, evidence of a son preference remains. In all likelihood, a stableeconomic growth rate, better life expectancy and the lower mortality rate of children underfive years old favor a son preference. This is because parents no longer have to bear as many
children in order to obtain a surviving child. It would also appear that a strong son preference
exists despite the significant role of women in the Vietnamese labor force, with the female
participation rate remaining stable at about 70 percent throughout the period 1990-2006.
Furthermore, son and mixed preferences are in evidence together in our estimations. These
factors, along with the finding that the level of son preference does not differ across the firsttwo birth orders by income, suggest three possible explanations. First, there would be no
economic reason supporting the son preference. It is more likely, therefore, that the lineage ofthe family name, part of Confucian belief, is the cause of a son preference in Vietnam.
Second, probably in long-term scheme, parents need a son as a source for old age support
because it is more likely to see old age people are living in their son’s house than in theirdaughters’ in Vietnam. Third, it could be a combination of both the lineage of the familyname and old age support.

Sex-selective abortion in Vietnam is unlikely to affect our findings. In evidence, UN
World Abortion Policies (2007, 2011) indicates that abortions for women aged 15-44 years in
Vietnam decreased from 35.2 per 1,000 females in 2000 to 18.4 in 2007. In addition, in
Vietnam, the most common reasons given for abortion include a decrease in the preferredfamily size, the reliance on a single method of contraception (the intrauterine contraceptive
device), the poor availability of alternative methods of contraception, the low cost of abortionand the increasing rate of sexual activity among unmarried women (Goodkind, 1994).
Meanwhile, there is no evidence that sex-selective abortion is widespread (Bélanger, 2002),
especially given that 86.31 percent of the children in the sample were born in or before 2001.
15

Even so, the increased incidence of sex-selective abortion would only serve to lower ourcoefficient estimates. Therefore, the actual level of son preference in Vietnam would be evenhigher than that estimated in our study.

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17

Table 1 Descriptive Statistics
Variables

Obs.

Mean

Std. Dev.

Number of children of the household head

29,5822.1

0.986

Age of the mother (years)

28,97142.56610.204

Mother more than 49 years of age (yes/no)29,582

0.239

0.426

Age of the mother when having

child (years)

28,97125.3835.666

Age of the mother when having

child (years)

21,04528.0445.135

Age of the mother when having

child (years)

7,603

30.585

4.891

Years of schooling obtained by the mother

28,971

7.360

4.008

Age of the father (years)

26,80044.68410.063Years of schooling obtained by the father

26,800

8.265

3.942

Annual household income per capita (thousands VND)

29,582

12,162.200

13,766.860

G29,582

0.439

0.496

GG

29,582

0.156

0.363

GB

29,582

0.177

0.381

BG

29,582

0.184

0.387

BB

29,582

0.208

0.406

GGG

29,582

0.039

0.193

BBG

29,582

0.033

0.180

BGG

29,582

0.028

0.165

BGB

29,582

0.031

0.172

GGB

29,582

0.043

0.202

GBG

29,582

0.025

0.156

GBB

29,582

0.027

0.162

Red River Delta

29,582

0.205

0.404

North East

29,582

0.156

0.363

North West29,582

0.050

0.217

North Central Coast

29,582

0.117

0.321

South Central Coast

29,582

0.096

0.295

Central Highlands

29,582

0.070

0.255

Southeast

29,582

0.128

0.334

Mekong River Delta

29,582

0.178

0.383

18

Table 2 Decisions on family size and fertility timing
Logistic
Variables

1

(1)
G

2

(2)

(3)

0.242***(0.0699)

GG

(1,2)

(2, )

( , )

(4)

(5)

(6)

0.214***(0.0194)
0.664***(0.147)-0.130(0.133)-0.0467(0.126)GB
BG
GGG

0.695***(0.0381)-0.146***(0.0418)-0.00977(0.0405)
1.051***(0.322)-0.178(0.260)-0.410(0.293)-0.687**(0.288)-0.251(0.309)-0.0267(0.312)-0.735**(0.291)

BBG
BGG
BGB
GGB
GBG
GBB
Observations

Hazard

5,102

2,620

1,029

0.636***(0.0848)-0.116(0.0954)-0.0228(0.0988)-0.243**(0.101)-0.340***(0.0996)0.0300(0.102)-0.310***(0.109)23,70718,0806,527

The control variables such as mother’s age at latest child’s birth, urban location, education of the mother,logarithm of household income per capita, education of the father and seven dummies indicating the eight maineconomy regions are included but estimates not shown. Robust standard errors are in parentheses (*** p < 0.01,** p < 0.05, * p < 0.1).

19

Table 3 Ward test for H0: All sex compositions including B are equal to zero
Logistic

Hazard

2

(2, )

( , )

(2)

(3)

(5)

(6)

Test value

0.35

6.90

9.94

19.21

𝑃

>

0.55640.22840.00160.0018Conclusion on H0

Accepted

Accepted

Rejected

Rejected

Original estimations are shown in the corresponding columns in Table 2.

Table 4 Level of son preference by region
Logistic
Variables

Red River Delta * All girls
North East * All girls
North West * All girls
North Central Coast * All girls
South Central Coast * All girls
Central Highlands * All girls
Southeast * All girls
Observations

1

Hazard

2

(1,2)

(2, )

( , )

(1)

(2)

(3)

(4)

(5)

(6)

0.101
(0.212)0.271(0.238)0.122(0.467)0.433*(0.241)-0.265(0.250)-0.685**(0.336)-0.205(0.255)5,102

0.966**(0.429)0.846*(0.465)-0.217(0.857)0.423(0.447)0.335(0.482)-0.624(0.600)0.258(0.428)2,620-1.217(0.954)0.348(0.946)0(0)
0.292(0.990)0.860(1.509)0.00445(1.374)-0.810(0.882)
1,026

0.0939(0.0600)
0.114*(0.0641)0.127(0.0918)
0.0389(0.0729)-0.157**(0.0709)-0.0450(0.0783)-0.0520(0.0728)
23,7070.919***(0.106)0.640***(0.111)
0.342**(0.146)
0.422***(0.111)-0.112(0.123)0.0194(0.118)0.116(0.119)18,0801.181***(0.236)0.810***(0.246)-0.00461(0.309)0.609***(0.234)0.158(0.267)0.0390(0.236)-0.0805(0.256)
6,527

The control variables such as mother’s age at latest child’s birth, urban location, education of the mother,logarithm of household income per capita, education of the father and seven dummies indicating the eight maineconomy regions are included but estimates not shown. Robust standard errors are in parentheses (*** p < 0.01,** p < 0.05, * p < 0.1).

Table 5 Ward test on DID by region
Logistic
H0: All DIDs are zero

1

Hazard

2

(3)

(1,2)

(2, )

( , )

(4)

(5)

(6)

(1)

(2)

Test value

16.16

11.67

5.16

21.52

146.19

54.98

𝑃

>

0.02370.11180.523

0.0031

0.000

0.000

Conclusion on H0

Accepted

Accepted

Accepted

Rejected

Rejected

Rejected

Original estimations are shown in the corresponding columns in Table 4.

20

Table 6 Level of son preference by household income per capita
Logistic
Variables

1

2

(1)
G

Hazard
1

(2)

(3)

0.203

(0.935)(0.248)

1

(5)

1.345

-0.362(1.740)(0.403)GGG

lnhhincomepc *All girls(4)-0.331GG

lnhhincomepc

2

( , )

(6)

-5.857-0.439(3.789)(0.787)-0.481***-0.762***-0.723***-0.345***-0.385***-0.338***(0.0722)(0.104)(0.167)(0.0216)(0.0306)(0.0483)

0.0616-0.0737

0.775*

0.001200.119***

0.123(0.101)(0.189)(0.423)(0.0275)(0.0454)(0.0897)

5,102

2,620

1,029

23,70718,0806,527

Observations

The control variables such as mother’s age at latest child’s birth, urban location, education of the mother,logarithm of household income per capita, education of the father and seven dummies indicating the eight maineconomy regions are included but estimates not shown. Robust standard errors are in parentheses (*** p < 0.01,** p < 0.05, * p < 0.1).

0.00

0.25

0.50

0.75

1.00

Appendix Kaplan-Meier survival estimates

0

100

200
analysis timeG

Fig. 1 Birth spacing in months between

300

400

B

and

child by sex composition
21

1.00
0.75
0.50
0.25
0.00
0

100

200
analysis time
GG
BG

400

GB
BB

and

child by sex composition

0.00

0.25

0.50

0.75

1.00

Fig. 2 Birth spacing in months between

300

0

100

200

300

analysis time
GGG
GBG
BBG
GBB

Fig. 3 Birth spacing in months between

GGB
BGG
BGB
BBB

and

child by sex composition22