1
Analysis of Child Gender Discrimination Based on Adults’ Consumption
Patterns: Microdata Evidence from China
Koohi-Kamali, F., R. Liu, and Y. Liu
Abstract
The applications of the Rothbarth model of inferring child gender discrimination
from the variations in parental living standard have consistently failed to uncover
evidence for bias from surveys in countries with some of the world’s worst welfare
outcomes for girls. This paper demonstrates the importance of the remedies
required for an effective implementation of that model with an application to a
survey from urban China. The paper obtains econometric evidence for the
presence of child gender bias for the survey by non-parametric and semiparametric methods in addition to the standard parametric estimates. The results
reported for three categories of adult goods all suggest bias against girls, in
contrast to those reported in earlier applications of the model to China. The
additional probit estimates of the probability of having a second child conditional
on the gender of the first child provide further support for our findings. The test
results cast doubts on the previous findings that claim the Rothbarth model of
gender discrimination to be ineffective in identifying evidence of bias from
consumption patterns.
2
Preliminary Draft
Please Do Not Quote
Analysis of Child Gender Discrimination Based on Adults’ Consumption
Patterns: Microdata Evidence from China
Koohi-Kamali, Feridoon, Ran Liu, and Yichun. Liu, 2014
Department of Economics, Franklin & Marshall College1
1. Introduction
A large quantity of material is available on the effects of parental gender
discrimination by welfare outcomes on health and education for Asian children; yet the
direct evidence on gender inequality in intra-household resource allocation has been
relatively rare. This paper tests micro-data from China for the presence of such
inequality. Gender discrimination has been a prevalent phenomenon in China
throughout history. Recently, with the rapid growth of its economy and its opening to
the outside world, one would expect gender discrimination to become less severe.
However, some researches using aggregate data still show strong evidence for the
existence of son preference in Chinese families, with evidence such as a high ratio of
boys to girls, the evidence of different abortion and contraception decisions between
those families who have a boy and those who do not, and evidence of gender
differentials in school enrollment; see for example, Arnold and Liu (1986), Zeng (1988),
Zeng et al. (1993). We expect these conditions to leave traces on expenditure
patterns within households. However, no study has demonstrated successfully the
existence of unequal parental treatment of daughters compared with sons as the
Yichun Liu wishes to thank Franklin & Marshall College for a summer Hackman scholarship
that enabled them to carry out her research for this paper.
1
3
outcome of unequal resource allocation within the household; indeed the evidence on
intra-household child gender inequality has been lacking even for countries with some
of the worst welfare outcomes for girls. This is not for the lack of an effective approach
to analyzing inequality in intra-household resource allocation due to gender of children.
In particular, the Rothbarth (1943) model, which is most suitable for this purpose, see
Gronau (1988), has consistently failed to uncover such bias in all its applications
undertaken over a long period, see Deaton (1997).
This study applies the approach to the Rothbarth model advocated in Koohi-Kamali
(2008) to a household survey from an urban Chinese east coast province. The paper is
an attempt to explain why many earlier studies using the Rothbarth model have failed
to obtain evidence of child gender discrimination in expenditure patterns, and to
demonstrate the effectiveness of that model in revealing child gender bias in parental
consumption patterns. An important feature of the paper is its employment of
distribution-free non-parametric and semi-parametric econometric methods in its
application of the model. The robustness of such procedures greatly enhances the value
of the empirical evidence obtained; and unlike earlier studies, the outcomes suggest the
presence of intra-household child gender discrimination in both countries. The paper
contributes to the accumulation of a very limited existing body of evidence on the
effectiveness of that model in highlighting an important dimension of inequality.
In section 2, we will discuss the Rothbarth model, examining a large number of
studies based on it and the failure of almost all of them to obtain evidence of gender
discrimination, as well as the issues required for an effective application of the
Rothbarth method to an analysis of child gender bias in intra-household allocations.
Section 3 deals with econometric non-parametric, semi-parametric, and parametric
specifications of the Rothbarth model in this paper; section 4 discusses with the data
and the types of truncations employed; section 5 examines the results in two separate
4
sub-sections, first for China, then for Vietnam. A final section summarizes the findings
and a few of the remaining outstanding issues
2. Inferring Child Gender Bias from Adult Consumption:
Theory and Evidence
The approach examined and applied in this paper is based on the variation in
the share of total household income, or expenditure, allocated to the household’s adults.
Suppose we divide consumption goods into those consumed jointly by all household
members, adults and children, such as food, and those consumed exclusively by adults,
for example tobacco or adult clothing. In this case, the level of expenditure on such
adult goods can be an indicator of the adults’ living standard. Rothbarth (1943) was the
first to suggest that changes in adult goods expenditure level across households with
different numbers of children provides a measure of the cost of children, and Deaton
(1987 and 1989) was the first to attempt to extend that model to testing discrimination
against girls, inferred from adults’ consumption patterns across households with
varying numbers of boys and girls. If the fall in the level of adult goods is larger when
there are more boys than girls in the family, then one can infer that parents as decisionmakers are willing to sacrifice more of their living standards for the welfare of boys,
other things being equal. This approach has many attractive features as a model of intrahousehold inequality when compared to other alternatives and yet, surprisingly, almost
none of its many attempted applications over a lengthy period have been able to obtain
evidence of child gender bias from consumption patterns, even though some of the
countries examined have records of severe discrimination against girls as measured by
welfare outcomes. However, Koohi-Kamali (2008) has compared the Rothbarth model
of intra-household child gender discrimination to other alternatives, including the large
number of studies on child gender bias based on that model, and has demonstrated the
5
problems in these studies. There are essentially two major shortcomings in these earlier
studies. First, they ignore the impact of extended family observations on the results
obtained; such households usually contain adult earners apart from the household head
and spouse, and their consumption is unlikely to be influenced by the gender of the
children of the household head. The average non-nuclear household size is likely to be
significantly larger because of a larger number of adults, and usually greater adult
employment, and yet the model pre-supposes that expenditure patterns reflect the
preferences of the parents as the sole decision-makers, see Deaton and Muellbauer
(1986); it is a model of internal allocation in a nuclear family. 2 Inclusion of nonnuclear households in almost all previous studies must have contaminated evidence of
child gender bias. Second, the earlier applications seek evidence of child gender bias
by the age of children without ever offering a hypothesis, or systematic evidence, on
how bias is related to age. However, parents with strong preferences for boys who have
girls early in their fertility cycle, are likely, in the absence of prenatal screening
technology, to continue having more children until they meet their targeted sex
composition of children; see for example Muhuri and Preston (1991). This suggests that
child gender bias can plausibly be related to the number of children. However, an
additional female birth may be regarded by some pro-son families as a disadvantage
large enough to dissuade the discriminating parents from moving to a higher parity,
even though that decision would be based on son preference, 3 see Clark (2000).
2
The extended family is a major household type in most developing countries, and in
many is likely to constitute the principal unit of consumption. For example, Lanjouw and
Ravallion (1993, table 1) for rural Pakistan contains demographic groups labelled according to
the number of adults within them. If we assume that there are no extended families in groups
with up to three adults and that households with four or more are exclusively of non-nuclear
types, then the extended families account for 56% of their sample.
3
Suppose a couple with two girls and no boy most prefers to have two boys and one girl.
The couple might consider (an approximate) 50% risk of a third girl sufficiently high to stop
6
Therefore, a sensible approach for obtaining child gender evidence would be through
an examination of household internal allocation conditional on gender and other
demographics, namely using the model of adult goods consumption adopted in this
study. This suggests that the earlier studies mis-specified the demographic variables of
the model: the correct specification of the child gender indicators must be defined over
the number of children, not their age. Accounting for these two requirements, KoohiKamali (2008) shows statistically significant effects of child gender on adults’
consumption obtained from parametric applications of the Rothbarth model for
Ethiopia. However, as we argue in the next section, it is highly desirable to obtain such
evidence without imposing an arbitrary functional form, whether linear or quadratic, on
the key explanatory variable of the model, namely, total expenditure. In this respect, we
should mention Gong et al. (2005), which represents the model as applied to China,
employing non-parametric and semi-parametric econometric methods applied to a
sample of nuclear households. We shall also present our child gender evidence
employing the same methods in order to display the robustness of our findings and to
contrast them with the absence of child gender effects in Gong et al. (2005).4
3. Econometric Specification of the Model
Total expenditure is the principal variable in any Engel curve analysis, and its most
common specification is to enter it into a budget share as linear and quadratic terms of
the logarithm of per capita total expenditure. However, if the relationship is not
correctly captured by such functional forms, then the parameter estimates will be biased.
We can employ functional-form free methods to overcome this problem. Such methods
allow the data themselves to reveal the shape of the relationship between two key
having more children.
4 Burgess and Zhuang (1998) represents an earlier parametric application of the Rothbarth
model to China; neither of the two studies found any evidence of child gender effect on
consumption patterns.
7
variables without imposing any particular functional form on them a priori.
The basic idea of nonparametric regression is for each xi point to average the yi
corresponding to xi in an interval around x, to form 𝑚
̂=
∑𝑛
𝑖=1 𝑦𝑖
𝑛
. Given the equation
error term u,
𝑚
̂ =𝑚+
∑𝑛
𝑖=1 𝑢𝑖
𝑛
. And E(ui)=0 as n → ∞ ;
⏞ being a consistent estimator of m. Expressed in terms of the kernel
this results in 𝑚
regression for 𝑓̃(𝑥), yi is added accumulatively; weighted by its kernel weight, that is
𝑥−𝑥𝑖
𝑚
̂ = ∑𝑛𝑖=1 𝑦𝑖 𝐾 (
ℎ
𝑥−𝑥𝑖
)⁄ ∑𝑛𝑖=1 𝐾 (
ℎ
).
(1)
Note that dividing the denominator for 𝑚
̂ by a value h close to zero makes the
regression function very imprecise at the tails with low densities. For this reason, the
kernel estimators with a fixed bandwidth are truncated at the tails, see Silverman (1986),
and for various non-parametric applications, Deaton (1997, pp. 170-80). We employ
the Gaussian kernel function to examine the relationship between the (logarithm) total
expenditure and budget share of several adults goods; since this function is based on
the normal distribution and does not use discrete bands; it is, therefore, more suitable
for a continuous variable such as total expenditure.
However, the main attraction of non-parametric regression is its distributionfree method, allowing the data themselves to choose the parameter estimates and the
shape of the curve that is best suited to the sample at hand. But there is a cost to this
advantage: the major shortcoming of non-parametric statistics is that it cannot handle
analysis involving more than a few dimensions; indeed as the number of variables
increases beyond two or three, the sample size required to minimize the mean integrated
squared error jumps from 67 for three variables to close to a million with ten variables,
see Silverman (1986, p. 94, table 4.2). This is the so-called cure of dimensionality of
non-parametric analysis, a serious limitation for the type of regression required for
8
household budget surveys, as these typically include more, sometimes many more, than
ten variables. The development of semi-parametric econometrics is a response to this
limitation.
Semi-parametric econometrics combines the advantages of the non-parametric
approach with the capabilities of multivariate regression by retaining the linear
parametric structure but applying non-parametric methods to a small subset of the
variables, usually just one. In the analysis of household expenditure surveys, the typical
non-parametric component of a multivariate regression is its key variable, namely, total
expenditure. A well-known method is the one developed by Robinson (1988), the
partially linear semi-parametric estimator. The Robinson approach obtains the
expected value of the dependent variable regressed non-parametrically on the key
variable with unknown functional form, and, in addition, regresses each of the
parametric variables on that key variable, once more non-parametrically, to obtain
similar expected values for each. It performs as many non-parametric regressions as the
number of independent variables in the equation, plus an additional one with the
dependent variable. The effects of the non-parametric component in the equation are
then controlled by subtracting the expected values from each of the corresponding
variables. The new equation with differenced variables thus defined is then estimated
by OLS without the bias resulting from the effect of misspecification, see KoohiKamali (2013). An example is the budget share equation with the flexible WorkingLeser functional form specification y=α+ ϒ.ln x +β.z +ε.
The semi-parametric version is written as
y= F(ln x) +β.z +ε
(2)
where x represents total expenditure and z is a vector of all other variables, such as
demographics or regions, and F is the unknown function for x. In the first step the
conditional means of E(y|ln x) and each E(z|ln x) are estimated non-parametrically. In
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the second stage, E(y|ln x), and each E(z|ln x) are subtracted from both sides of the
equation, thus giving
Y - E(y|ln x) = {z- E(z|ln x) }.β +ε.
(3)
(3) is the Robinson estimator, with the left hand variable, a differenced variable,
regressed on a vector of differenced z variables. The OLS can now be applied to this
equation to obtain unbiased parameter estimates; Robinson demonstrates that the
resulting estimator is consistent and asymptotically (in large samples) normal. This
paper follows broadly similar non-parametric and semi-parametric estimation
employed in two applications of Robinson’s estimator to expenditure share equations
in China and Pakistan that tested for the presence of child gender bias in adult
consumption patterns, see Gong et al. (2005) for an application to China, and Bhalotra
and Attfield (1998) for a similar estimation method for Pakistan and for econometric
details. Robinson’s multiple non-parametric application can be burdensome when the
equation to be estimated contains a large set of variables. An alternative is provided by
the differencing approach developed by Yatchew (1997, 2003). In this approach, the
data on the variable with the unknown functional form are sorted in increasing order so
that x1 ≤ x2 ≤…≤ xT. Yatchew suggests first differencing the data to obtain:
Yt – yt-1 =(zt – zt-1).β+ [F (xt) – F(xt-1)] + (ε- εt-1), t=2, …,T .
(4)
As sample size increases, the (xt – xt-1) differences shrink at a rate of 1/T so that F(xt-1)
tends to cancel F (xt) and the OLS estimator of β is asymptotically consistent in large
samples. This method thus applies non-parametric regression just once compared to the
multiple applications of the Robinson method. However, the Yatchew method tends to
produce larger standard errors.
It must be noted, however, that parametric application of the Rothbarth model
requires the gender indicator to enter the equation expressed as a proportion of the total
number of children, after controlling for child age separately. This is a key difference
10
between our model specification and the earlier studies, including Gong et al. (2005).
A full semi-parametric specification (5) is therefore written as
w j= F[ln(
4
ng
n
xh
)]  ln nc +  g
   a a  . +  z + 
nc a 1 nc
nh
(5)
where wj denotes budget share of adult goods j, xh is household total expenditure , nh is
household size, na/nc indicates the proportion of children in different age groups a over
the total number of children nc, ng / nc is the proportion of girls in the household and
constitutes our child gender index. z is now the vector of additional variables, such as
regional dummies; variables specific to the analysis of child gender bias, such as work
status of adults, dummies for the mother’s age and her educational level; as well as their
interactive terms; and  is a normally distributed error term. We also provide similar
estimates by a corresponding parametric specification. A well-known specification that
has a record of providing a good fit in a variety of contexts is the Working-Leser semilogarithmic flexible functional form augmented with a quadratic term for a curvature
effect of the logarithm of per capita total expenditure.
w j=  0 +  1 ln(
4
ng
n
xh
x
)+  2 [ln( h )]2  ln nc +  g
  a a +  z + 
nc a 1 nc
nh
nh
(6)
If parents spend less on their own living standards when they have more boys than girls,
then the model predicts that the parameter estimate on the child gender index, the
proportion of girls, should have a statistically positive sign; that is, if a boy is replaced
by a girl in (5) or (6), controlling for all additional influences, one would expect higher
adult goods consumption levels, resulting in an increase in the budget share of adult
goods, i.e. in a positive coefficient estimate for child gender. We provide non-parametric
and parametric evidence for the key relationship between the budget share of each
category of adult good and the log of per capita total expenditure for both urban China
and Vietnam, and semi-parametric and parametric evidence on child gender bias with
11
the fully specified models (5) and (6) for China, and finally, additional parametric
evidence obtained by an instrumental variable estimator for the consumption effects of
child gender for Vietnam.
4. Data and Sample Truncations
We employ a random 2002 sample survey of 1,288 households from a very
prosperous east coast province, conducted by the Chinese Government’s Statistic
Authority.5 In the light of the discussion above of household types, in this study we
focus exclusively on nuclear famlilies. We define nuclear households as consisting of
two parents and at least one child under the age of 16. Table 1 below presents the
percentage for different types of households. The nuclear family accounts for 33.62%
of the total household types in the survey. The first truncation is to exclude all the
extended families. In addition, the number of rural households was too small to be
included in the sample; therefore the 433 households in the final sample are entirely
urban.
Three categories of pure adult goods are available from this sample: alcohol,
tobacco, and adult clothing. As a result, the alcohol, tobacco, and adult clothing shares
of total household expenditure are the three dependent variables for (7) and (8) that we
test for child gender effect. As in all previous applications of this model, the dependent
variables are defined to include households that have positive expenditure on these
goods and those with zero expenditure. This definition captures gender effects more
fully than one based on positive expenditure alone, see Deaton (1988). Table 2
demonstrates the incidence of purchase and non-purchase of each selected adult good
in the survey. Finally, table 3 presents the means and standard deviations of the main
variables employed in the Engel budget share equations for the sample of nuclear
5 A code of confidentiality prevents us from revealing the province by name.
12
households.
In order to test if the results are robust with respect to age, we shall examine one
more issue, namely, the sensitivity of the age definition of children to the results
obtained. We will repeat the regression with a new sample with a child defined as no
older than 15 years old, while the definition/delineation of age groups will remain
unchanged and the age group of 13-15 will be the reference group.
5. Results
Graphs-1-6 for China show the non-parametric regression line for adult clothing,
tobacco, and alcohol obtained with the Gaussian kernel with 95% confidence intervals;
with 2% trimming at the tails, and a band-width default selected at 0.8. In order to
provide an initial idea about child gender effects on adult consumption, the graphs are
drawn separately for each adult goods category for households with more boys than
girls, and those with more girls than boys. As discussed earlier, intrahousehold
discrimination against gilrs should result inter alia in higher budget share of adult goods
consumption for the girl-dominant households. The graphs indicate that the girldominant Engel curve for alcohol tends to be above the boy-dominant curve, with its
pick values notably higher for the girl group both at the bottom and towards the top of
curve. For tobacco, the curve of girl-dominant group is above that for the boy-dominant
group for the bottom part of the tobacco Engel curve, and tends to raise at the top end
as the curve for the boy-dominant group displays a decline. Finally, with adult clothing,
both curves tend to increase with total expenditure. However, the rise in the Engel curve
of adult clothing is sharper (above a budget share of 0.04) for the girl-dominant, but it
also declines more sharply towards the top end of the curve. For a clearer evidence on
child gender effects on consumption, we must rely on semi-parametric functional-formfree evidence capable of controlling for a variety of critical influences.
13
An important issue must be addressed before turning to the full semi-parametric
and parametric results. Detection of child gender bias from consumption patterns by (5)
and (6) assumes the right-hand variable demographics are conditioning, exogenous
variables. The widespread use of gender-selective abortion makes this assumption less
plausible for China. A cross-section sample cannot provide a solution to this problem,
but some insight into the likelihood of having more children, conditional on having a
girl or a boy already present in the household, can be obtained from a probit analysis
of fertility behavior in the sample.
Table 5 presents the probit estimates of the probability of having a second child
conditional on a dummy for the first child being a boy, with additional controls for the
log of total expenditure, age, and education of the parents, age of children, and regions.
A second set of probit estimates provides the same probability conditional on interactive
variables between the first boy dummy and each region dummy in order to obtain some
evidence of variation in pro-son preference across the province. The main point evident
from this table is negative coefficient estimate on the first child being a boy in both
models. Moreover, the coefficient is significant at 1% in the first model, and for its
interactive term with the delta region in the second model. Consistent with sonpreference bias, these values suggest a fall in probability of having a second child if the
gender of the first is male. Father’s age has a significant positive impact on the
probability of having the second child if the first is a boy, as does mother’s if aged 3549. In terms of the explanatory ability of the models, we also note that both can
successfully predict the correct outcome in 87-88 percent of cases. These estimates
provide some evidence of pro-son gender-selectivity preference in fertility behavior in
this province of China.
Let us now turn to the results obtained with the semi-parametric model (6). Since
there seem to be significant curvature effects of the total expenditure per capita for adult
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clothing and alcohol, we add a quadratic term of the log of total expenditure per capita
to the parametric regression when estimating adult clothing and alcohol, while for
tobacco we choose the standard semi-log function. The key results of interest to this
paper on direct evidence of child gender bias are summarized in table 6 below. The
estimates indicate the presence of gender effects, and all are in the positive female child
discrimination direction (first row in bold).
A comparison of results from using parametric techniques and semi-parametric
methods in table 6 indicates that they do not vary much from each other, thereby further
confirming the likelihood of the robustness of the evidence on gender discrimination
against girls in Chinese households.6
The coefficient estimates for the log of household size (number of children) are
insignificant as expected, given the small variation in the number of children in China.
We can observe a significant negative effect on tobacco consumption in families with
children in the age group of 7-9 years, and the presence of children in the household
exerts significantly negative effects on adults’ consumption of adult clothing, increasing
with child age.
Finally, table 7 presents the main results of the effects of gender bias on adult good
consumption with the new sample that defines a child as no older than 15, in order to
test the sensitivity of the results to the precise age definition of the children. The
estimates are very similar to those in table 6, thereby indicating the insensitivity of the
earlier findings to the age definition of a child.
Summarizing, based on our results, we can conclude that all three coefficients of
the proportion of girls for different adult goods categories are significantly positive,
suggesting that gender bias significantly influences the consumption of adult goods.
The full evidence for such a conclusion should come from a specification test comparing
the two. Such a test, see Gong et al. (2005), is beyond the scope of this paper.
6
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6. Conclusion
This research has aimed to demonstrate the effectiveness of the Rothbarth model
of intra-household child gender discrimination in view of the current consensus of
opinion on its poor performance. We offered evidence from an urban Chinese survey
from a prosperous east coast province which suggest that female child discrimination
evidence is present, and that it is evident in all the three different categories of adult
goods examined (alcohol, tobacco, and adult clothing). The procedure followed was
that of Koohi-Kamali (2008). We employed samples of strictly nuclear households for
both countries and examined the key relationship between the budget share of adult
goods and the logarithm of per capita total expenditure non-parametrically for China.
Both our partially linear semi-parametric and least squared parametric evidence for
China and our IV parametric estimates for Vietnam employed a gender specification
based on the number of children rather than their age, unlike almost all previous
applications of the model. The results add to a small but accumulating set of evidence
that highlights the poor applications of the model in earlier studies as an explanation
for their failure to uncover evidence of child gender discrimination from parental
consumption. However, several caveats should be stated. Our Chinese sample is from
a rich province. While it is not necessarily a disadvantage to record the evidence of
child gender bias among the rich, it is desirable to show a similar pattern holding with
more representative surveys. With respect to our Chinese findings, the limitation of a
study on child gender bias with a cross-section survey must also be stressed, although
our probit analysis suggests the gender of a child can predict fertility behavior of sonpreferring households. Since there is little evidence for the use of gender-selective
technology among Vietnamese households, we regard the results from the Vietnam
survey as additional support for the specification of the intra-household model
16
advocated, and we believe that our approach to that model has opened up a research
area of great interest to public policy.
17
Non-parametric regression for Engel curves of adult goods on log of per capita total expenditure based
18
on the Gaussian kernel with 2% trimming at low densities and a default band width equal 0.8.
Table 1. Distribution for Different Types of Households
Extended Households
(855) 66.38%
Nuclear Households
(433) 33.62%
All Household Types
(1,288) 100%
Table 2. Incidence of Purchase for Each Selected Adult Good in the Survey
Adult Clothing
(1,119) 86.88%
Alcohol
(1,017) 78.96%
Tobacco
(661) 51.32%
19
Table 3- Mean and St. Dev. of the Principle Variables—Final Sample
Variables
Mean
St. Dev
Budget Share of Adult Clothing
0.033
0.02325
Budget Share of Alcohol
0.00298
0.0056
Budget Share of Tobacoo
0.008
0.018
Log of pc Total Expenditure 9.147
0.598
Log
of pc
Total Expenditure Sq. 84.015
Capital
(lx_n)
Log
of Number of Children (ln) 0.1
(lx_n2)
11.047
Proportion
of
Children
0-2 0.0238
0.148
Proportion
(ch0_2)
Proportion
(ch3_6)
of
Children
3-6 0.168
0.362
of
Children
7-9 0.195
0.385
0.25
Proportion
(ch7_9) of Children 10-12 (ch 0.1913
Proportion
of Girls (Girlp)
0.5206
10_12)
0.372
0.478
Regional Dummies
1- North
0.093
0.291
0.143
0.35
3-West (ywest)
0.126
0.333
4-Delta Area (pdelta)
0.6372
0.481
2- East
(ynorth)
(yeast)
Sample Size
419
Table 4-Vietnam- Sample Incidence of Purchase of Key Adult Goods
Purchase (%)
Non-purchase (%)
Alcohol (beer/liquor)
23.2
76.8
Tobacco
35.0
65.0
Cosmetics
100.0
0 .0
20
Table 5. Probit Estimates for Urban China of Decision To Have a 2nd Child Conditional on
Gender of the 1st Child
Model 1
Model 2
Variable
Coefficient
z-value
Coefficient
z-ratio
st
1 child boy
-1.6231
-2.59
Log total expen
-0.9428
-4.45
-0.9816
-4.86
Girl_proportion
-1.7628
-2.70
-1.1026
-2.23
East dummy
1.3944
4.13
1.7917
3.28
Delta dummy
0.5107
1.65
1.2040
2.29
North dummy
0.4039
1.04
0.5621
0.91
1stboy*East
-0.7321
-1.35
1stboy*Delta
-0.4592
-1.12
1stboy*North
-1.3140
-2.44
Mo30-34
-0.4329
-0.76
-0.3917
-0.70
Mo35-39
0.9412
1.61
1.0232
1.80
Mo40-49
-0.0755
-0.13
0.0220
0.04
Fa30-34
0.5537
0.90
0.5742
0.92
Fa35-39
1.4285
2.78
1.2621
2.53
Fa40-49
0.9783
1.96
0.8279
1.69
Mother’s educ.
-0.1754
-0.75
-0.1413
-0.60
Father’s educ
-0.2799
-1.42
-0.2497
-1.33
Mother_selfemp
0.5553
1,63
0.4929
1.52
Child0-2
0.9513
1.30
1.0977
1.54
Child3-6
-0.1363
-0.37
-0.1861
-0.51
Child7-9
-0.2103
-0.69
-0.1184
-0.38
Child10-12
-0.4119
-1.55
-0.3514
-1.27
Constant
7.3263
3.66
6.5223
3.64
Log Likelihood
-114.1452
-116.0401
2
Pseudo R
0.3564
0.3458
%correct predic
0.89
0.88
Notes: Sample of 419 households with at least one child (defined as under 16 years of
age). Dependent variable: 1 if children >= 2; 0 if child=1. Omitted groups: South for
region, 13-15 years for child age; proportion of boys for child gender.
21
Table 6. Effects of Child Gender Bias on Adult Goods (Absolute t-ratio in Brackets)
Varia
Child Age≤16
Adult Clothing
Alcohol
Tobacco
Semiparam
bles
Girlp 0.0092(4.18)
iccricci
lx_n
(4.1(4.18)
rp
Parametr
Semiparam
Parametr
Semiparam Parametr
.0088(4.06)
.0025 (4.10)
.0024 (3.57)
.0055 (2.84)
.1484 (4.83)
-
.1938 (2.33)
-
-.0032(1.72)
lx_n2
-.0077(4.68)
-
-.0010(2.32)
-
-
-
ccricc
ccicicca
cccric
iccarra
.0057 (2.87)
metric
Ln
.0033 (0.68)
.0034 (0.84)
.0007 (0.49)
.0009 (0.64)
-.0027(0.65)
.0030 (0.69)
Ch0_
-.0093 (1.12)
-.0087(0.95)
.0012 (0.48)
.0014 (0.02)
.-.0063(0.87)
-.0051(1.31)
Ch3_
2
Ch7_
6
-.0097 (2.30)
-.0097(1.96)
.0015 (1.06)
.0015
.0004 (0.10)
.0001 (0.03)
-.0115 (3.12)
-.0119(3.45)
.0005 (0.48)
.0004
(0.001)(0.38)
-.0064(2.05)
-.0063 (2.33)
Ch10
9
-.0115 (3.46)
-.0113(3.32)
-.0008(0.85)
.0007 (0.74)
-.0037(1.31)
-.0041(1.50)
Ywes
12
Yeast
t
-.0392(1.55))
-.0381(1.91)
.0005 (0.36)
.0002 (0.26)
-.0113(3.38)
-.0110(4.51)
-.0503(1.94)
-.0537(2.54)
-.0137(1.45)
-.1360(1.83)
.0027 (0.94)
.0029 (1.00)
Ynort
.1016(3.30)
1.1017
.0018(1.14)
.0016 (0.86)
-.0002 (0.02)
-.0001(0.00)
Const
h
2
R
.
RMS
-
0.5786
(2.93)
-
0.0704
-
0.00431
0.3367
0.3784
0.2063
0.2054
0.1989
0.2042
0.0195
0.0198
0.0054
0.0054
0.0171
0.0173
419
419
419
Samp
E
Notes: Semi-parametric estimates are obtained using applications of Robinson (1988), the
le
partially linear model, with 2% trimming for low densities based on equation (7). Parametric
regressions employ equation (8) based on the Working-Leser flexible functional form, linear
for tobacco and quadratic for adult clothing and alcohol; with heteroscadesticity-consistent
standard errors. The sample consists of two-parent nuclear families with at least one child.
Gender index (“girlprp” in the first row) is defined as the proportion of the number of children
with expected positive sign for discrimination against girls. The regressions include regional
dummies and other controls for parental age, education, and work status, as well as their
interactive variables
Table 7. Parametric Effects of Child Gender Bias on Patterns of Adult Goods
Consumption with Child Age <= 15 (t-ratio in Brackets)
Adult Clothing
Alcohol
Tobacco
0.0095 (3.96)
0.0029 (3.73)
0.0054 (2.45)
22
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