MISSING WOMEN AND THE PRICE OF TEA IN CHINA:
THE EFFECT OF SEX-SPECIFIC EARNINGS ON SEX
IMBALANCE*
Nancy Qian
Economists have long argued that the sex imbalance in developing countries is
caused by underlying economic conditions. This paper uses exogenous increases in
sex-specific agricultural income caused by post-Mao reforms in China to estimate
the effects of total income and sex-specific income on sex-differential survival
of children. Increasing female income, holding male income constant, improves
survival rates for girls, whereas increasing male income, holding female income
constant, worsens survival rates for girls. Increasing female income increases
educational attainment of all children, whereas increasing male income decreases
educational attainment for girls and has no effect on boys' educational attainment.
I. INTRODUCTION
Many Asian populations are characterized by severe male
biased sex imbalances. For example, whereas 50.1% of the cur
rent populations in western European countries are female, only
48.4% are female in India and China.1 Amartya Sen (1990, 1992)
referred to this observed deficit as "missing women." Most of the
world's missing women are in China and India, where an es
timated thirty to seventy million women are missing, but the
phenomenon cannot be dismissed as a problem of the past or
as one that is isolated to poor countries. Rich Asian countries
such as South Korea and Taiwan have the same sex imbalance
as their poorer neighbors, China and India. Figure I shows that
in China, for cohorts born during 1970-2000, when the economy
grew rapidly, the fraction of males increased from 51% to 57%.
*I am grateful to the editors and two anonymous referees for their helpful
comments. I thank my advisors Josh Angrist, Abhijit Banerjee, and Esther Du
flo for their guidance and support; Daron Acemoglu, Ivan Fernandez-Val, John
Giles, Ashley Lester, Steven Levitt, Sendhil Mullainathan, Dwight Perkins, Mark
Rosenzweig, Seth Sanders, and David Weil for their suggestions; the Michigan
Data Center, Huang Guofang, and Terry Sicular for invaluable data assistance;
and the participants of the MIT Development Lunch and Seminar, the Applied
Micro Seminar at Fudan University, the SSRC Conference for Development and
Risk, the Harvard East Asian Conference, and the International Conference on
Poverty, Inequality, Labour Market and Welfare Reform in China at ANU for use
ful comments. I acknowledge financial support from the NSF Graduate Research
Fellowship, the SSRC Fellowship for Development and Risk, and the MIT George
C. Schultz Fund. All mistakes are my own.
1. Source: 2005 WDI Indicators, available at http://go.worldbank.org/
6HAYAHG8HO.
? 2008 by the President and Fellows of Harvard College and the Massachusetts Institute of
Technology.
The Quarterly Journal of Economics, August 2008
1251
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1252 QUARTERLY JOURNAL OF ECONOMICS
0.57 i-j
0.56
0.55 $
0-54 -
E
? 0.53 - j
S ^X j -? i982~l1990
05 " j 2000 I
0.49 J-.-,-,-,-,-,-i-.-,-,-,-,-.-,-.-,-i-.-.-,-,-.-,-,-,-,-,-.-,-.?
1970 1974 1978 1982 1986 1990 1994 1998
Birth year
Figure I
Sex Ratios by Birth Year in Rural China
Source. The 1% sample of the 1982 and 1990 China Population Censuses and
the 0.05% sample of the 2000 China Population Census.
The observed sex imbalance may be achieved in a variety of ways,
from sex-selective abortion to neglect or even infanticide.
This paper explores whether changes in relative female in
come (as a share of total household income) affect the relative
outcomes for boys and girls. Previous work on this subject has
been impeded by identification problems: areas with higher fe
male income may have higher income precisely because women's
status is higher for other reasons, which makes it difficult to esti
mate the effect of female income on boys and girls.2 I address this
omitted variable bias problem by taking advantage of two post
Mao reforms in China. During the Maoist era, centrally planned
production targets focused on staple crops. In the early reform era
(1978-1980), reforms increased the returns to cash crops, which
2. Empirical studies by Ben-Porath (1967, 1973) and Ben-Porath and Welch
(1976), Rosenzweig and Schultz (1982a, 1982b), Das Gupta (1987), Thomas,
Strauss, and Henriques (1991), Clark (2000), Burgess and Zhuang (2001), Du
flo (2003), Foster and Rosenzweig (2001), and Rholf, Reed, and Yamada (2005)
have shown that female survival rates are correlated with relative adult female
earnings. A relatively new strand of the literature has argued over whether the
observed sex imbalance can be partially explained by biological factors completely
unrelated to cultural or economic conditions. See studies by Norberg (2004), Oster
(2005), and Lin and Luoh (2006). And a recent study by the Lin, Liu, and Qian
(2007) investigates the effect of access to sex-selective abortion on sex ratios at
births and sex-specific survival rates.
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1253
included tea and orchards. Women have a comparative advan
tage in producing tea, whereas men have a comparative advan
tage in producing orchard fruits. Therefore, areas suitable for tea
cultivation experienced an increase in female-generated income,
whereas areas suitable for orchard cultivation experienced an in
crease in male-generated income. This makes it possible to use
a differences-in-differences (DID) strategy to identify the causal
effect of an increase in sex-specific income on outcomes for boys
and girls.
To estimate the effect of a change in sex-specific incomes, I
compare sex imbalance for cohorts born before and after the re
forms, between counties that plant and do not plant sex-specific
crops, where the value of those crops increased because of the
reform.3 I first estimate the effect of an increase in adult female
income on sex imbalance (holding adult male income constant)
by comparing the fraction of males born in counties that plant
tea to counties that do not, between cohorts born before and af
ter the price increase. Then I repeat the same strategy using
orchard production to estimate the effect of an increase in rela
tive male income (holding adult female income constant). These
estimates together allow me to distinguish the effects of increas
ing sex-specific (relative) incomes from the effects of increasing
total household incomes. Finally, using the same strategy with
educational attainment as an outcome, I estimate the effects of
increasing sex-specific incomes on the educational attainment of
boys and girls.
The results show that an increase in relative adult female
income has an immediate and positive effect on the survival rate
of girls. In rural China, during the early 1980s, increasing annual
adult female income by US$7.70 (10% of average rural annual
household income) while holding adult male income constant in
creased the fraction of surviving girls by one percentage point and
improved educational attainment for both boys and girls by ap
proximately 0.5 years. Conversely, increasing male income while
holding female income constant decreased both survival rates and
educational attainment for girls, and had no effect on educational
attainment for boys. These results show that the effect of an in
crease in the value of sex-specific crops is due to the change in the
3. This identification strategy is similar to Schultz's (1985) study of Swedish
fertility rates in the late nineteenth century, which used changing world grain
prices to instrument for changes in the female-to-male wage ratio.
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1254 QUARTERLY JOURNAL OF ECONOMICS
relative share of income between men and women rather than the
change in total household income. This is consistent with the addi
tional finding that an increase in the value of all cash crops, most
of which do not especially favor male or female labor, had no effects
on either sex-specific survival rates or educational attainment.
The empirical results have several theoretical implications
for household decision making. The effects on survival can be
easily explained by either a model of intrahousehold bargaining
or a unitary model of the household in which parents view chil
dren as a form of investment. The results on education favor a
nonunitary model of household decision making. The implication
for policy makers is straightforward: factors that increase the eco
nomic value of women are also likely to increase the survival rates
of girls and to increase education investment in all children.
This study has several advantages over previous studies. A
number of potentially confounding factors were fixed in China
during this period. Migration was strictly controlled, little tech
nological change occurred in tea production, sex-revealing tech
nologies were unavailable to the vast majority of China's rural
population (Zeng et al. 1993; Diao, Zhang, and Somwaru 2000),
and stringent family planning policies largely controlled family
size.
The paper is organized as follows. First, I describe the empir
ical strategy and policy background. Second, I discuss the concep
tual framework. Third, I present the empirical results. Fourth, I
interpret the results. Finally, I offer concluding remarks.
II. Empirical Strategy
This paper uses the value of tea to proxy for female wages
and the value of orchards to proxy for male wages. Tea is picked
mainly by women in China.4 Data on labor input by sex and
crop from the 1990 Population Census are not available for ex
amining sex specialization directly. Instead, I use household-level
survey data from the Ministry of Agriculture's RCRE National
Fixed Point Survey (NFS) from 1993 to examine the correlation
between the fraction of female laborers and the amount of tea
sown.5 Table I, columns (l)-(4), shows that the amount of tea
4. See Lu (2004) for a detailed anthropological analysis of the historical role
of women in tea picking.
5. Please see De Brauw and Giles (2006) and Padro-i-Miquel, Qian, and Yao
(2007) for detailed descriptions of the RCRE data.
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TABLE I ^
The Correlation between Sex Ratios of Adult Laborers and Tea and Orchard Production
Dependent variabl
Tea land sown Tea land/total Fru
(mu = 1/15 hectare) arable land (m
(1) (2) (3) (4) (5) (6)
No. male/No. total -0.115 -0.086 -0.040 -0.01labor in HH (0.056) (0.055) (0.021) (0.022) (0
Village fixed effects N Y N YNYN
Observations 3,488 3,488 3,457 3,457 3,488
R2 0.00 0.14 0.00 0.18 0.00 0
Notes. Coefficients of the fraction of males amongst adult labor
Agricultural Census. Data Source: RCRE 1993 Household Survey.
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1256 QUARTERLY JOURNAL OF ECONOMICS
sown per household and the fraction of arable land that is de
voted to tea per household are both negatively correlated with
the fraction of male laborers within households. Tea bushes are
approximately 2.5 ft (0.76 ms) tall. Picking requires the careful
plucking of whole tender leaves. This gives adult women absolute
and comparative advantages over children and men. For China,
women's specialization in tea picking may have been increased by
strictly enforced household grain quotas that forced every house
hold to plant grain. This means that in households that wished
to produce tea after the reform, men continued to produce grain
while women switched to tea production. Moreover, the monitor
ing of tea picking is made difficult by the fact that the quality and
value of tea leaves increases greatly with the tenderness of the
leaf. This decreases the desirability of hired labor.6 In contrast,
height and strength yield a comparative advantage for men in
orchard-producing areas.7 Columns (5)-(8) in Table I show that
the amount of orchards sown per household and the fraction of a
household's arable land devoted to orchards are positively corre
lated with the fraction of male laborers within a household. In the
1982 Population Census, 56% of laborers in tea production (which
includes picking, pruning, and drying) are male, whereas 62% of
laborers in orchard production are male.8 Since female compara
tive advantage is in picking, this six-percentage-point difference
should be interpreted as a lower-bound estimate of female compar
ative advantage in tea-picking. The magnitude of the advantage
does not affect the internal validity of the empirical strategy.9
6. Agricultural households in general rarely hired labor from outside the fam
ily. In 1997, 1 per 1,000 rural households hired a worker from outside of the im
mediate family (Diao, Zhang, and Somwaru 2000). Because migration and labor
market controls were more strict in the 1980s, it is most likely that the households
studied in this paper hired even fewer nonfamily members. Plentiful cheap adult
labor also would reduce the demand for child labor.
7. Adult men have a comparative advantage in orchard production during
both sowing and picking periods. Sowing orchard trees is strength-intensive, as it
requires digging holes approximately 3 ft (0.91 ms) deep. The height of the trees
means that adult males have advantages, both in pruning and picking, over adult
females and children.
8. This is the sample of adults who report living in rural areas and working
in agriculture in the provinces of this study between the ages of 15-60. The data
do not report hours worked. Due to problems of under-reporting girls at young
ages due to the One Child Policy (1979/80), I cannot use the 1982 Census for the
analysis in this paper.
9. The magnitude of the advantage will affect the interpretation of the elas
ticity of demand for girls with respect to relative female earnings that underlies
the reduced form effects estimated in this paper. For a given estimate of the effect
of increasing tea prices on female survival, a smaller female advantage implies a
larger elasticity.
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1257
A simple cross-sectional comparison of the fraction of males
in counties that plant no tea to counties that plant some tea shows
that the latter have one percentage point fewer males, or one per
centage point more females (see Table II). But these estimates do
not prove that planting tea, or higher relative female earnings, has
a positive causal effect on female survival. The main confounding
factor is that regions that choose to plant tea may be regions with
weaker boy preference. In this case, the cross-sectional compari
son will not be able to disentangle the effect of planting tea from
the effect of the underlying boy-preference. To address this, I take
advantage of two post-Mao reforms that increased the value of
planting tea and orchards relative to staple crops. Hence, in ad
dition to the cross-sectional comparison of the fraction of males
between regions that produce tea and regions that do not, I can
examine the second difference between cohorts born before the
reform and those born afterward (i.e., differences-in-differences).
The two reforms of interest to this paper are the increases in
procurement prices of cash crops such as tea and orchards rela
tive to staple crops and the Household Production Responsbility
System (HPRS), which allowed households to take advantage of
the price increases. Before 1978, Chinese agriculture was charac
terized by an intense focus on grain production, allocative ineffi
ciency, lack of trade, lack of incentives for farmers, and low rural
incomes due to suppressed procurement prices (Perkins 1966; Lin
1988; Sicular 1988b). Central planning divided crops into three
categories. Category 1 included crops necessary for national wel
fare: grains, all oil crops, and cotton. In Category 2 were cash
crops, including orchard products and tea (Sicular 1988a). Cate
gory 3 included all other agricultural items (mostly minor local
items). This last group was not under quota or procurement price
regulation. The central government set procurement quotas for
crops in Categories 1 and 2 that filtered down to the farm or col
lective levels. Quota production was purchased by the state at
very low prices. These quotas were set so that farmers could re
tain enough food to meet their own needs but leave very little in
surplus (Perkins 1966). Nongrain producers produced grain and
other foodstuffs they needed for their own consumption.
Reforms in the post-Mao era (1978 and afterwards) focused
on raising rural income, increasing deliveries of farm products
to the state, and diversifying the composition of agricultural pro
duction by adjusting relative prices and profitability. Two sets
of policies addressed these aims. The first set gradually reduced
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1258 QUARTERLY JOURNAL OF ECONOMICS
TABLE II
Descriptive Statistics
I. Counties that plant no tea II. Counties that plant some tea
Mean Mean
Obs. (Std. error) Obs. (Std. error)
A. Demographic variables
Fraction male 41,665 0.51 10,101 0.52
(0.0003) (0.0007)
Age 41,665 14.00 10,101 14.00
(0.0410) (0.0833)
Han 41,665 0.95 10,101 0.88
(0.0009) (0.0027)
Decollectivized 41,665 0.99 10,101 0.99
(0.0002) (0.0004)
Household size 41,665 5.22 10,101 5.16
(0.0132) (0.0261)
Married 23,641 0.62 7,164 0.62
(0.0002) (0.0004)
Years of education 32,785 6.63 7,996 6.38
(0.0095) (0.0205)
(Female) 37,653 4.70 9,465 4.39
(0.0082) (0.0148)
(Male) 37,618 6.01 9,465 5.69
(0.0072) (0.0130)
Father's education 40,647 6.17 10,043 5.82
(0.0067) (0.0127)
Mother's education 40,655 4.53 10,054 4.33
(0.0082) (0.0146)
School enrollment 40,781 0.24 10,009 0.22
(female) (0.0018) (0.0036)
School enrollment 40,636 0.27 9,977 0.25
(male) (0.0019) (0.0038)
B. Industry of occupation of hou
Agricultural 41,665 0.94 10,101 0.94
(0.0006) (0.0013)
Industrial 41,665 0.04 10,101 0.04
(0.0005) (0.0009)
Construction 41,665 0.01 10,101 0.00
(0.0001) (0.0002)
Commerce, etc. 41,665 0.01 10,101 0.01
(0.0001) (0.0002)
C. Agricultural production and land use (mu =
Farmable land per 23,018 4.87 10,101 4.06
household (0.0150) (0.0211)
Rice sown area 23,018 1.66 10,101 2.55
(0.0106) (0.0106)
Garden sown area 23,018 0.23 10,101 0.34
(0.0029) (0.0047)
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1259
TABLE II
(continued)
I. Counties that plant no tea II. Counties that plant some tea
Mean Mean
Obs. (Std. error) Obs. (Std. error)
Tea sown area 41,665 0.00 10,101 0.15
(0.0000) (0.0034)
Orchard sown area 23,018 0.20 10,10
(0.0029) (0.0034)
Notes. The matched data set of the 1% sample of the 1990 popula
agricultural census. Sample of those born in during 1962-1990. Da
China Agricultural Census. Observations are birth year x county
planning targets and represented a retu
used procurement price as an instrume
tion (Sicular 1988b). Although Category 1
price increases, the increase in prices w
from Category 2. The second set of po
enacted in 1980. It devolved all produc
responsibilities to individual household
ing a collective responsibility, and effe
to take full advantage of the increase
expanding production to cash crops w
Johnson 1966).10 The two reforms con
of agricultural production, greater re
less extensive grain cultivation (Sicula
Although agricultural households may
cific reform as permanent, they were
overall regime shift as permanent. Conse
this initial regime shift as plausibly ex
Figure Ha shows that the reforms
tea and orchards relative to income
crops.11 It also shows that income from
10. During the period of this study, there was
or selling land. Agricultural land is allocated to f
characteristics such as the number of household m
village to farmers (Carter, Liu, and Yao 1995; J
and Liu 1997; Rozelle and Li 1998; Benjamin an
Rozelle 2002; and Burgess 2004). There is no ev
systematically differed between tea- and non-tea
11. I use yearly data from the Ministry of Agric
standard day of labor by crop and procurement pr
that there are 257 labor days in a year and calcul
yearlyinc = outputperday x 257 x pric
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1260 QUARTERLY JOURNAL OF ECONOMICS
8
"S 6.0, ,2.70
? -o-OrchardProd j\ j?*" 25?| U-Tea I . c 5.0- ?Orchard Price \\JT/ ov, _
2 t0 ?Orch Jl $ . Melon Price JJv %2 h-CaMi /\ g [--MelonProd j /"M 210 *
I / \ v 40 A///' *
i8 Vs h mv-^i1 / II 3.0 rjH 170 I
| / |? # 1.50 ?
I 4 / y^ J
i ^? *-^ ^^_ 1 -^^a
Ol .*..'.* *. * "..-,. - .,?,?,-,-,1970 1974 1978 1982 1986 ? 1970 1974 1978 1982 1986 1990
Year ? Year
(a) (c)
1.6 30 |
. Rice .
* 1.4 j- -W
9 1 2 / ,/\ __
I 0.8 ^?/" , I ?- f > jf . /
| 0.6 ,/ " | 10 ^V /X^^^^
8 1970 T<m^ 1978 1982 1986 ^990 051970 1974 ,975 1982 1986 1990a Year Year
(b) (d)
Figure II
(a) Gross agricultur
Source. FAO and Min
reflect years for whe
grains. Source. FAO. (
procurement prices.
FAO.
from orchard pr
Category 2 crops
in their output
that although out
change in the ra
increase for Cate
12. This addresses t
not linear. An incre
household income as
effect of an increase
of orchard crops to d
to relative female inco
such that there exists
affect sex ratio, then
income from orchard
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1261
accelerated after the reform, following the increases in procure
ment prices. Similar increases can be observed in Figure lid for
tea.
The main effect of post-Mao reforms on tea production was
increased picking, since most tea fields were sown during a rapid
expansion program during the 1960s. The 50% increase in pro
curement price in 1979 was followed by extensive tending, prun
ing, and picking (Etherington and Forster 1994). Figures lie and
lid show the sudden increase in procurement prices and the cor
responding increases in tea yield (output per hectare) and orchard
production.
I estimate the effect of an increase in female labor on rel
ative female survival by exploiting the variation in the price of
tea caused by the post-Mao agricultural reforms. The reform in
creased the value of adult female labor in tea-producing regions.
Hence, the intensity of treatment is positively correlated with the
amount of tea sown. The increase should only affect individuals
born close to and after the reform.13 The date of birth and whether
an individual is born in a tea-planting region jointly determine
whether she was exposed to the sex-specific income shock. I com
pare the fraction of males between counties that do and do not
plant tea for cohorts born before and after the reform. Comparing
the sex imbalance within counties across cohorts differences out
time-invariant community characteristics. Comparing the sex im
balance within cohorts between tea-planting and non-tea-planting
communities differences out changes over time that affect these
regions similarly. I estimate the effect of increasing the value of
adult male labor by exploiting the variation in prices of orchard
fruits caused by the reforms. Finally, I estimate the effect of an
increase in total household income without changing the relative
shares of sex-specific incomes by exploiting the variation in prices
for all cash crops where the vast majority of the crops are not
known to favor either male or female labor.
Identification is based on the increase in the value of
Category 2 crops relative to Category 1 crops, for which prices con
tinued to be suppressed, and Category 3 crops, which were never
regulated. Therefore, the effect of Category 1 and Category 3 crops
13. The exact timing of the response in sex ratios to the reform depends on
the nature of sex selection. If sex selection was conducted via infanticide, then the
reform should only affect sex ratios of cohorts born after the reform. However, if
sex selection was conducted via neglect of young girls, then the reform also can
affect sex ratios of children who were born a few years before it.
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1262 QUARTERLY JOURNAL OF ECONOMICS
on the fraction of males should not change after the reform. I test
this by estimating the effect of Category 1 and Category 3 crops
on the fraction of males by regressing the fraction of males on
the interaction terms between the amount of Category 1 crops
sown and birth year dummy variables and the interaction terms
between the amount of Category 3 crops sown and birth year
dummy variables:
1990 1990
sexjc = Yl (catlj x dfif
Z=1963 Z=1963
(1) +TJ,i + Yc+eiC9
The fraction of males in county i
the interaction terms between eatl
crops planted for each county i, an
if a cohort is born in year I; the inte
the amount of Category 3 crops pl
ethnically i, and dj; Han;c, the frac
yt, county fixed effects; and \j/c, coh
variable for the 1962 cohort and all of
Figure Ilia plots the vector of coeff
that the effects of planting Category
close to zero before and after the ref
The validity of the identification st
assumption that only women pick t
earnings. If men or children picked t
male income would exceed actual rel
the strategy would underestimate the
income on sex ratio. If there are an
cultural reasons that cause women t
ative desirability of female children,
enced out by comparing cohorts born
For the DID estimate, I restrict the
during 1970-1986 and estimate the
postc is a dummy variable that equa
after 1979:
sexjc = (tea; xpostc)/? + (orchard; xp
(2) + Han;c? + a + fa + yc + ?ic
The fraction of males in county i
of the interaction terms between te
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1263
| 0.03 -|
?* 0.02-C >
5 E
i| ?01
w ^ 0 - - V^"(|' > V^>-^^1 ,,, + ?,>-.E ?
C CO
.2 75 -0.01 o
?
co -Q
s%
- ca -0.02 -<D >
Z -^ Cat 1 -o- Cat 3o
*i -0.03 i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?
o 1964 1968 1972 1976 1980 1984 1988
Year
(a)
0.54 -i
| 0.52 k t\ I VX// \ /
V ? No Tea ? Tea
0.49 H?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i?i
1962 1966 1970 1974 1978 1982 1986 1990
Birth Year
(b)
Figure III
(a) The effect of Category 1 and 3 crops on sex ratios. Coefficients of the
interactions birth year amount of Category 1 crops planted and birth year
x amount of Category 2 crops controlling for year and county of birth FE.
(b) Fraction of males in counties that plant some tea and counties that plant no
tea. Source. 1% sample of 1990 population census. Note. Tea counties are defined
as all counties that plant some tea.
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1264 QUARTERLY JOURNAL OF ECONOMICS
for each county i, and postc, a dummy variable that indicates if
an individual is born after 1979; the interaction terms between
orchard;, the amount of orchard planted for each county i, and
postc; the interaction terms between cashcrop;, the amount of all
cash crops planted for each county i, and postc; Han;c, the fraction
that is ethnically Han; /;, county fixed effects; and \jrc, cohort
fixed effects. The reference group is composed of individuals born
during 1970-1979. It and all of its interaction terms are dropped.
If the increase in value of tea improved female survival, then it
should be reflected in a decrease in the fraction of males born
after the reforms, (5 < 0. Conversely, if an increase in the value of
orchards worsened female survival, we would expect 8 > 0.
One pitfall of the DID approach is that it may confound the
effects of the reform with the effects of other changes that may
have occurred during the pre- or postreform period. For example,
tea-producing regions may have been experiencing different pre
trends in sex ratios relative to other regions, which may cause the
DID estimate to be capturing differences between tea and non
tea areas besides the increase in tea value. An illustration of the
DID estimate shows that this is not the case. Figure Illb plots
the fraction of males in each birth year cohort for tea-planting
and non-tea-planting counties. The vertical distance between the
two lines shows that prior to the reform, tea counties had more
males, whereas after the reform, tea counties consistently had
fewer males. The DID estimate will be the difference in the aver
age vertical distance before and after the reform. The figure shows
clearly that before the reform, tea areas had more boys than non
tea areas, whereas after the reform, there were consistently fewer
boys in tea areas. Hence, the DID estimate will not be captur
ing differences in prereform trends in sex ratios between tea and
non-tea regions.
I can examine whether the effect of planting tea on sex ratios
occured for the birth years close to the reform more rigorously by
regressing the fraction of males by county and year of birth on the
interaction terms of the amount of tea sown in the county of birth
and birth year dummy variables for all birth years:
1990 1990
sex;c = J2 (tea* x <4)jfy
Z=1963 Z=1963
1990
(3) + Y. (cashcrop; x di)pt + Han;Z=1963
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1265
The fraction of males in county i, cohort c is a function of the
interaction terms between tea;, the amount of tea planted for each
county i, and di, a dummy variable which indicates if a cohort is
born in year I; the interaction terms between orchard;, the amount
of orchard planted for each county i, and ck; the interaction terms
between cashcrop;, the amount of all cash crops planted for each
county i, and dk; Han;c, the fraction that is ethnically Han; yt,
county fixed effects; and \f/c, cohort fixed effects. The dummy vari
able for the 1962 cohort and all of its interactions are dropped. pt
is the effect of planting tea on the fraction of males for cohort I.
If increasing the price of tea improved female survival, then pt
should be constant until approximately the time of the reform,
after which it should become negative. Similarly, Si is the effect of
planting orchards on the fraction of males for cohort I. If increas
ing orchard prices worsened female survival, then 81 should be
constant until approximately the time of the reform, after which
it should become positive.
Another problem of the empirical strategy is that if, at the
time of the reforms, there is a change in the attitudes that drive
sex preference in tea-planting counties, then the estimate of the
effect of planting tea will capture both the relative female income
effect and the effect of the attitude change. Or, if the increase in
the value of tea changed the reason for women to pick tea, then the
prereform cohort will not be an adequate control group. Although
I cannot resolve the former problem, the latter is addressed by
instrumenting for tea planting with time-invariant geographic
data.14
Tea grows under very particular conditions: on warm and
semihumid hilltops, shielded from wind and heavy rain. There
fore, hilliness is a valid instrument for tea planting if it does not
have any direct effect on differential investment decisions and is
not correlated with any other covariates in equation (5). I check
this assumption by estimating the impact of planting tea on sex
ratios for a sample containing only tea counties and those non-tea
counties that share a boundary with tea counties. Hilliness varies
gradually. County boundaries are straight lines drawn across spa
tial areas. The results for this restricted sample are similar to the
estimate for the whole sample, although the precision is reduced
14. I also find that planting tea had no effect on sex ratios for nonagricultural
households living in tea planting counties. This suggests that between-county
comparison is unlikely to capture spillover effects between agricultural and nona
gricultural households.
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1266 QUARTERLY JOURNAL OF ECONOMICS
due to the smaller sample size. This adds to the plausibility of
the identification strategy, unless potentially confounding factors
change discretely across county boundaries. Note that because
the amount of orchards sown is also an endogenous regressor, the
2SLS specification does not separately control for it. The follow
ing equation estimates the first-stage effects of hilliness on tea
production after the reform:
tea; x postc = (slope; x postc)A + (cashcrop x postc)<p
(4) + Han;cf + a + V; + POstcy + ?ic.
The second-stage regression is as follows:
sex;c = (tea; x postc)/J + (cashcrop x postc)<p
(5) 4- Han;c? + a + V; + postcy + ?ic,
III. Conceptual Framework
Since prenatal sex-revealing technology was not available for
the most relevant period of this study, the observed sex imbalance
was caused by differential neglect of girls or in some cases, female
infanticide. The probability of a girl surviving increased with the
desirability of girls relative to boys, and also with the cost of sex
selection.
Regarding relative survival rates for girls, increasing the
price of tea can operate through four channels. First, it can in
crease the relative desirability of having a girl by increasing par
ents' perceptions of daughters' future earnings relative to that of
sons. Second, the increase in total household income can increase
the relative desirability of girls if for some reason daughters are
luxury goods relative to sons. Third, increasing female-specific in
come can improve mothers' bargaining powers. This will increase
relative female survival rates if mothers prefer girls more than
fathers. Finally, increasing the value of adult female labor can
raise the cost of sex selection since pregnancies must be carried
to term before the sex of the child is revealed. The first, second,
and last explanations are consistent with both the unitary and
nonunitary models of household decision making. The third ex
planation is most likely to be consistent with nonunitary model of
the household.15
15. See Bourguignon et al. (1994) and Browning and Chiappori (1998) for
a detailed theoretical discussion about models of collective household decision
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1267
The unitary model makes the strong prediction that an in
crease in income should have the same effect on household con
sumption regardless of which member of the household brings
home the additional income.16 Hence, the second explanation
can be ruled out if an increase in the price of orchard prod
ucts does not have the same effect on female survival rates as
an increase in the price of tea. The number of potential expla
nations can be further refined by comparing the effects of in
creases in the prices of tea and orchard products on the rela
tive educational attainment of girls. In this case, the opportunity
cost of the mother's time is not applicable. The first explanation
of a unitary household with investment motives requires that
the effect of increasing the relative value of women's labor on
girls' educational attainment be symmetric to the effect of in
creasing the relative value of men's labor on boys' educational
attainment.
In short, the empirical results will be able to shed light on
several joint hypotheses. I test the joint hypotheses that house
holds are unitary and parents view children as a form of con
sumption by examining whether an increase in tea prices has the
same effect on sex imbalance as an increase in orchard product
prices. I test the joint hypotheses that households are unitary
and parents view children as a form of investment by examin
ing whether an increase in tea prices has the same effect on
educational attainment for girls as the effect of an increase in
orchard product prices on educational attainment for boys. The
latter test relies on the assumption that returns to education
are positive in tea- and orchard-producing areas and that the
relationship between adult female wages and the returns to ed
ucation for girls is the same as the relationship between adult
male wages and the returns to education for boys. The data used
in this study do not allow a direct test of the opportunity cost
hypothesis. Instead, I present two sets of indirect evidence later
in the section on Interpretation that suggest that opportunity
cost is not likely to play a large role in explaining the empirical
results.
making. See Thomas (1994), Duflo (2003), Park and Rukumnuaykit (2004), and
Ashraf (2006) for empirical evidence on nonunitary households.
16. For simplicity, I assume that members of a unitary decision-making house
hold pool their income. See Browning, Chiappori, and Lechene (2004) for a detailed
discussion of the conditions for which household members do not pool their incomes
and are still unitary.
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1268 QUARTERLY JOURNAL OF ECONOMICS
IV. The Data
The analysis of the sex imbalance uses the 1% sample of the
1997 Chinese Agricultural Census, the 1% sample of the 1990
China Population Census, and GIS data from the Michigan China
Data Center.17 The data are aggregated and matched at the birth
year-county level. The number of individuals in each county
birth year cell is retained so that the regression analyses are
all population-weighted. The sample includes all 1,621 counties
from all fifteen provinces of southern China. Any province that
produced any tea in the 1997 Agricultural Census is included.18
The 1990 Census contains data on sex, year of birth, educational
attainment, sector and type of occupation, and relationship to
the head of household. Because of the different family planning
policies and market reforms experienced by urban and rural areas,
I limit the analysis to rural households. The sample used in the
empirical analysis contains individuals born during 1962-1990,
who were living in a rural area in 1990.
To avoid confounding the estimates with the effects of migra
tion, I further restrict the data to individuals who report having
lived in the same county for over five years. The data do not include
the county of birth. The main analysis assumes that the county
of birth is the county of residence for those who report having
been there for five years or longer. This is consistent with studies
on migration, which find that strict migration controls were well
enforced until the late 1990s.19 Entire families and children did
not migrate because they had no access to government-controlled
food rations, housing, schools, and medical care once they left their
registered homes. The first wave of rural migration did not occur
until the 1990s, during the urban construction boom, and most of
those migrants were young adult men. Consequently, it is highly
unlikely that the results of this paper, which mainly examines
individuals who were children in 1990, are confounded by migra
tion. Using data from RCRE's NFS for 1986-1990,1 find that the
probability of having a household member work away from the
17. This section describes the 1% sample of the 1990 Population Census. The
analysis of sex ratios uses only the 1990 Census and the analysis of education
uses only a 0.05% sample of the 2000 census, described in Appendix LB. The
organization of the censuses is similar.
18. These include Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong,
Henan, Hubei, Hunan, Guangdong, Guangxi, Sichuan, Yunnan, and Shaanxi.
19. See West and Zhao (2000) and De Brauw and Giles (2006) for a detailed
description of migration policies and outcomes in China.
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1269
home village is very low and similar for regions that produce tea
and regions that do not produce tea. There are almost no migrants
under the age of 20, which is consistent with the finding from other
studies that there is little migration of children. For robustness, I
deliberately overestimate the number of migrants from tea areas
to calculate the lower bound of the absolute value of the effect of
planting tea on sex imbalance.
The empirical strategy compares the fraction of males for co
horts born before and after the reform. Hence, the identification
for this paper comes from changes in the fraction of males over
time (across cohorts). One cross section would not be able to dis
tinguish between the two hypotheses: (1) variation in the cross
section is driven by differences across age groups?for example,
there are sex-differential mortality rates during childhood such
that more boys are born and higher mortality rates for boys cause
the fraction of males to be negatively correlated with age (age
effect); and (2) variation in the cross section is driven by differ
ences across birth cohorts?for example, the fraction of boys born
is increasing each year (cohort effect). Figure I plots the fraction of
males by birth year from China's 1982,1990, and 2000 Population
Censuses. It shows that the fraction of males for each birth cohort
is stable over time. Hence, the cross-sectional variation in the frac
tion of males by age in the data for this study can be interpreted
as increases in the fraction of males over time.20
Figure I also addresses concerns that there might be
underreporting of female births due to the One Child Policy.
Comparing hospital-level data to the census data, Zeng et al.
(1993) find misreporting to be present only for extremely young
children, who are easy to hide from the authorities. This is con
sistent with Figure I, which shows that the fraction of males by
birth year is stable over time. For the DID estimates, I use only
data for children four years of age and older.
Reliable data for procurement prices and output are not avail
able for this period at the county level. For the sake of scope, accu
racy, and consistency between areas, this study uses county-level
agricultural data on the sown area from the 1% sample of the
1997 China Agricultural Census. Using 1997 agricultural data
to proxy for agricultural conditions in the early 1980s introduces
20. Ideally, these three censuses could be linked so that these two effects could
be separately identified. However, changes in the geographic identifiers make
linking at the county level difficult.
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1270 QUARTERLY JOURNAL OF ECONOMICS
measurement error. It is also possible that the counties that pro
duced tea in 1997 are the counties that had stronger preferences
for girls prior to the reform. In this case, comparing the frac
tion of males in counties that planted tea in 1997 to the frac
tion of males in counties that did not plant tea in 1997 will
confound the effect of planting tea with the effect of underly
ing preferences for girls. However, as discussed earlier, the gov
ernment emphasis on tea planting during the Cultural Revolu
tion meant that the main determinant of whether a region had
tea fields was geographic suitability, not sex preferences. Specifi
cally, tea grows best on warm and humid hilltops. The population
density of the Chinese countryside and the distribution of hills
throughout southern China mean that counties that plant tea
should not be very different in other respects from their neigh
boring counties that do not plant tea. For robustness, I address
these problems by instrumenting for tea planting with natural
conditions.
To assess whether counties that do not plant tea are good
control groups for counties that do plant tea, I look for system
atic differences between the treatment and control groups. The
average demographic characteristics and educational attainment
shown in Table II, Panel A are very similar between counties
that plant any tea and counties that plant no tea. The difference
in ethnic composition is controlled for in the regression analy
sis. The descriptive statistics for sector of employment in Panel
B show that in both types of counties, 94% of the population is
involved in agriculture. Panel C shows that households in tea
counties farm less total land on average, devote more land to rice
and garden production, and devote less land to orchards. Agri
cultural households have very little farmable land, with an av
erage of only 4.06-4.85 mu (0.20-0.32 hectares) per household.21
Households in counties that plant tea average only 0.15 mu (0.02
hectares) of land for tea. Figure IVa shows the counties that
plant tea. Darker shades correspond to more tea sown. It shows
that tea-producing counties are geographically dispersed, which
helps to alleviate concerns that they are systematically different
from the control group along unobservable characteristics (e.g.,
culture).22
21. The mu, the Chinese unit for measuring area, is l/15th of a hectare.
22. Similar geographic dispersion can be observed for orchard-producing
counties.
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1271
. J *} ^ A
r-?\ i\ 4 "J
(a)
(b)
Figure IV
(a) Tea planting counties in China: darker shades correspond to more tea
planted per household, (b) Hilliness: darker shades correspond to steeper regions.
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1272 QUARTERLY JOURNAL OF ECONOMICS
TABLE III
OLS and 2SLS Estimates of the Effect of Planting Tea and Orchards on Sex
Ratios Controlling for County Level Linear Cohort Trends
Dependent variables
Fraction of males Tea x post Fraction of males
(1) (2) (3) (4) (5) (6)
OLS OLS OLS 1st IV IV
Tea x post -0.012 -0.013 -0.012 -0.072 -0.011
(0.007) (0.006) (0.005) (0.031) (0.007)
Orchard x post 0.005
(0.002)
Slope x post -0.002 0.26
(0.002) (0.057)
Linear trend No No Yes Yes No Yes
Observations 28,349 37,756 37,756 37,756 37,756 37,756
Notes. Coefficients of the interactions between dummies indicating whether a cohort was born post-reform
and the amount of tea planted in the county of birth. All regressions include county and birth year fixed effects
and controls for Han, and cashcrop x post. All standard errors are clustered at the county level. In column
(1), the sample includes all individuals born during 1970-1986. In columns (2)-(6), the sample includes all
individuals born during 1962-1990. Post = 1 if birthyear > 1979. Data for land area sown are from the 1997
China Agricultural Census.
V. Empirical Results
V.A. Results for Survival Rates
The difference-in-differences estimates from equation (2) are
shown in column (1) of Table III. It shows that planting one addi
tional mu of tea decreased the fraction of males by 1.2 percentage
points; planting one additional mu of orchards increased the frac
tion of males by 0.5 percentage points; and planting cash crops in
general had no effect. The estimates for planting tea and orchards
are statistically significant at the 10% and 5% level, respectively.
The estimates for pt, 8i and pt from equation (3) are shown in
Appendix I.A. The coefficients for fa and <$/ are plotted in Figure V.
They show that for cohorts born prior to the reform, the effects of
planting tea and orchards on the fraction of males were similar
to each other and constant across cohorts. The effects diverge for
cohorts born around the time of the reform, when planting tea is
associated with fewer males, while planting orchards is associated
with more males. The differential effects persist over time. These
results lend credibility to the interpretation that the effect of tea
and orchard production on the fraction of males is attributable
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1273
| 0.04 -|
.g -o- Orchard -*- Tea
? I 0.03 - p
$ i 0.02 - . A, /
o ? 0.01 - [\K>~~\ A / \ /\ // \r \/y/
o
*? -0.03 -I?i?i?i?i?i?i?i?i?i?i?i?i?
O 1963 1967 1971 1975 1979 1983 1987
Birth year
Figure V
The Effect of Planting Tea and Orchards on Sex Ratios
Coefficients of the interactions of birth year x amount of tea planted and birth
year x amount of orchards planted controlling for year and county of birth FEs.
to the post-Mao agricultural reforms and not to other changes in
these regions.
Cohort fixed effects control for variation across cohorts that
do not also vary across counties. They cannot control for county
varying cohort trends that may have occurred over the 29 years
of this study. I address this issue by controlling for linear cohort
trends at the county level (e.g., interaction terms of county dummy
variable with linear time trends). In order to make the estimates
comparable to the 2SLS estimates, I restrict the sample to only
counties for which there is geographic data and estimate the same
specification as the second stage of the 2SLS. This differences-in
differences specification does not explicitly control for orchards
because planting orchards is likely to be endogenous. Column (2)
in Table III shows the basic fixed effects estimates. Column (3)
shows the estimate when I control for county-level cohort trends.
The point estimates are similar. They show that planting tea de
creased the fraction of males by 1.3 and 1.2 percentage points.
Estimates from both specifications are statistically significant at
the 5% level. Thus, the OLS estimates are robust to differential
linear changes across cohorts between counties.
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1274 QUARTERLY JOURNAL OF ECONOMICS
Two problems motivate the use of instrumental variables.
First, using 1997 agricultural data to proxy for agricultural con
ditions in earlier years introduces measurement error that may
bias the estimate toward zero. Second, the OLS estimate will suf
fer from omitted variable bias if families that prefer girls switch to
planting tea after the reform. In this case, the OLS estimate will
overestimate the true effect of an increase in the value of female
labor because it will confound the aforementioned effect with the
sex-preferences of households that switched to planting tea. I ad
dress both problems by instrumenting for tea planting with the
average slope of each county.
Figure IVb shows the slope variation in China, where the
darker areas are steeper. The predictive power of slope for tea
planting can be seen by comparing the tea planting counties in
Figure IVa with the hilly regions in Figure IVb. I use the GIS
data pictured in Figure IVb to calculate the average slope for each
county and estimate the following first-stage equation, where both
the amount of tea planted and the slope is time-invariant.
Column (4) of Table III shows the first-stage estimate from
equation (4). The estimate for the correlation between hilliness
and planting tea, X, is statistically significant at the 5% level. Col
umn (5) shows the 2SLS estimate from equation (5). The estimate
is larger than the OLS estimate and statistically significant. Col
umn (6) shows the 2SLS estimate after controlling for county-level
cohort trends. The estimate is similar in magnitude to the OLS es
timate, but no longer statistically significant. The estimates with
and without trends are not statistically different from each other.
The estimate without trends is larger but also less precisely esti
mated. The 2SLS estimate in column (6) shows that conditional on
county-level cohort time trends, the OLS estimate is not biased.
Furthermore, the OLS and 2SLS estimates in columns (3) and (6)
are almost numerically identical to the initial OLS estimate in
column (1). These results give confidence to the robustness of the
initial OLS estimates.
V.B. Results on Educational Attainment
This analysis uses county-birth year level data from a 0.05%
sample of the 2000 Population Census. The sample is selected
based on the same criteria as the main sample from the 1990
Population Census. To confine the sample to children who had
completed their education, I restrict it to cohorts born between
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1275
1962 and 1982. Individuals in the sample should not be af
fected by the Cultural Revolution because disruptions to schools
generally were isolated to urban areas.23 I use cohorts that
were born before 1976 and thus had not yet reached public
preschool age at the beginning of the reforms as the prereform
control.24
The empirical strategy is the same as before. I estimate equa
tion (2) with years of education as the dependent variable to ex
amine the effect of planting tea, orchards, and all Category 2 cash
crops on educational attainment for all individuals. I repeat the
estimation for the sample of girls, the sample of boys, and the
difference in education between boys and girls. This is first done
with dummy variables indicating whether any tea, orchards, or
cash crops are planted in a county, and then with continuous vari
ables for the amount of each crop that is planted. The estimates
in Panel A of Table IV show that planting any tea at all increased
all, female, and male educational attainment by 0.2, 0.25, and
0.15 years, respectively. On the other hand, planting any orchards
at all decreased female educational attainment by 0.23 years and
had no effect on male educational attainment. These estimates are
statistically significant at the 1% level. Planting orchards had no
effect on male educational attainment. The estimates in column
(4) show that planting tea decreased the male-female difference in
educational attainment, whereas planting orchards increased the
difference. The latter is statistically significant at the 1% level.
The sample size for the estimate in column (4) is smaller than
the sample size for the estimate in column (1) because not ev
ery county-birth year cell contains both males and females. The
estimates for all category 2 cash crops are close to zero and sta
tistically insignificant.
I repeat this estimate with continuous variables for the
amount of tea and orchards planted in each county i. Columns
(5)-(8) of Table IV show that the estimates have the same signs
as the estimates with the dummy variables in columns (l)-(4). The
estimates show that one additional mu of tea planted increases fe
male educational attainment by 0.38 years and male educational
23. For robustness, I repeat the experiment on a sample of cohorts born af
ter 1967 who did not begin primary school until after 1974, when schools were
reopened. The results are similar and statistically significant.
24. Children entered public preschools at age 4 or 5 in China during this
period. Public nursery schools, targeted at children aged 1-4, are not available to
most rural populations.
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TABLE IV
The Effect of Planting Tea, Orchards, and Category 2 Cash C
A. Dummy variable for crops sown B. Cont
_ _(l)All (2) Female (3) Male (4) Diff (5) All (6
Tea x post 0.199 0.247 0.149 -0.069 0.449
(0.043) (0.057) (0.049) (0.063)
Orchard x post -0.124 -0.226 -0.029 0.174 -0.0(0.037) (0.050) (0.040) (0.056) (0.056)
Cat2 x post -0.036 -0.024 -0.037 -0.020 -0.065 -0
(0.026) (0.032) (0.028) (0.040) (0
Observations 68,522 33,538 34,984 58,314 6R2 0.37 0.48 0.34 0.14 0.37 0.48
Notes. Coefficients of the interactions between dummies indicating whether a cohort wa
of birth. Dependent variable: years of education. All regressions include controls for Han, co
level. Post = 1 for cohorts born after 1976.
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1277
? 0.15 - _0.10*_* -o- Orchards -*-Tea A
? 0 10 - / \ ^
s f--/
' 0.05- o / V S
o o.oo - ,^_/\\ a /
"5^-0.05- \^\//^^e > V^^sr \ Va /
2| -0-10- ^vX-I -m* N( \ ---0.90 1 |~ -D -0.15 - A * <j,
1 -0-20- \ / - -Lie 2i -0-25- \ / I | \ y5 ---1.30 so -0.30- \f o
? -0.35 J-,-,- -.-,-,-,- -,-,-,-r-U- -,- -.-L -1.50" 1964 1968 1972 1976 1980
Birth year
Figure VI
The Effect of Planting Tea and Orchards on Girls' Educational Attainment
Coefficients of the interactions birth year x amount of tea planted and birth
year x amount of orchards planted controlling for year and county of birth FEs.
attainment by 0.5 years, whereas one additional mu of orchards
decreases female educational attainment by 0.12 years and has
no effect on male educational attainment.
To observe the timing of the effect of tea on educational attain
ment, I examine the effect of planting tea by birth year. I estimate
equation (3) with years of education as the dependent variable
to examine the effect of planting tea by birth year. The dummy
variable for the 1962 cohort and all its interactions are dropped. I
plot the three-year moving averages for the estimated coefficients
for each cohort / in vectors fa and 8i in Figure VI. This shows
that female educational attainment was similar between tea and
orchard areas until 1976, after which it increased in the former
and decreased in the latter.
V.C. Robustness
Family Planning Policies. If the enforcement of family plan
ning policies is systematically varied between tea-planting and
non-tea-planting regions, the empirical strategy will confound the
effects of planting tea with the effects of family planning poli
cies. Family planning policies in China began with a four-year
birth spacing law in the early 1970s. The One Child Policy was
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1278 QUARTERLY JOURNAL OF ECONOMICS
introduced in 1979/1980. Enforcement in rural areas was phased
in during the early 1980s. The One Child Policy applied to all
healthy parents of the Han ethnicity, which comprises 92% of
China's total population.25 Qian (2006) shows that for rural ar
eas, the four-year birth spacing law combined with the One Child
Policy meant that the unanticipated One Child Policy was, in
practice, binding for cohorts born in 1976 and later. Hence, the
effective date of the One Child Policy does not coincide with the
increase in the price of tea in 1979.1 can also investigate the cor
relation between tea and orchard production and family planning
policies directly. The 1989 China Health and Nutritional Survey
(CHNS) reports data on local enforcement of family planning poli
cies. Matching this data to the data used in this study, I find no
correlation. The sample of matched counties are too few to use for
statistical analysis controlling for family planning laws.
I perform two additional robustness checks using the fact
that non-Han ethnic minorities are largely exempt from family
planning restrictions. First, I control for the interaction term be
tween the fraction that is Han and birth year dummies. Next,
I reestimate equation (2) using a sample containing only ethnic
minorities. In both cases, the estimates are similar to the main
results, suggesting that they are not confounded by family plan
ning policies. These estimates are not reported in the paper for
the sake of brevity.
Migration. If migration patterns differed significantly be
tween tea and non-tea areas, then the OLS estimates could be
capturing the effects of migration rather than those of income
changes. In particular, suppose that females born before 1979 are
disproportionately likely to leave tea-planting areas relative to
non-tea areas, whereas outmigration patterns are the same in
both areas for later cohorts. Then the empirical strategy would
incorrectly attribute changes in the sex imbalance to changes in
sex-specific survival rates rather than changes in migration. The
same reasoning would apply to changes in male migration from
orchard areas.
To address migration more directly, I deliberately overesti
mate the number of female migrants from tea areas. Recall that
the DID estimate presented earlier used a sample of individu
als who were four to twenty years old in 1990. Using the 2000
25. See Qian (2006) for a detailed description of the One Child Policy, exemp
tions, and later relaxations.
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1279
Population Census, which reports whether an individual is cur
rently living in his/her county of birth, I assume that all individu
als under 20 years old living away from their counties of birth are
females born in tea-planting regions before 1979 (even, for exam
ple, fifteen-year-old men). Then I add these migrants into the 1990
data and reestimate equation (2). This is even more conservative
than it first appears, since migration rates were approximately an
order of magnitude greater in 2000 than they were in 1990. Even
with this extremely conservative approach, the resulting DID esti
mate scarcely changes. These results are not reported in the paper.
VI. Interpretation
This section discusses the empirical results and their the
oretical implications. The results for survival rates show that
the increase in the value of tea improved female survival. Data
on agricultural income by crop are not widely available for the
time period of this study. If the data on agricultural income
used by Etherington and Forster's (1994) anthropological study
of Chinese tea plantations are representative of the average tea
planting household, then the findings imply that augmenting an
nual household income by 10%, and giving it all to women, in
creases the fraction of girls by 1.3 percentage points. This would
increase educational attainment for boys and girls by approxi
mately 0.2 years. Roughly speaking, this suggests that increasing
household income by 20% and giving it all to women would have
brought China's sex imbalance in the early 1980s to about the
level of Western Europe. These calculations, provided for illustra
tive purposes, assume that the elasticity of demand for girls rela
tive to boys with respect to sex-specific earnings is linear, whereas
it is likely to be highly concave in reality.
The empirical results have several theoretical implications.
The findings for both survival and education reject the joint hy
potheses that households are unitary (under the assumption that
unitary households are income-pooling) and parents view girls as
luxury goods relative to boys. An alternative explanation for the
results within the unitary framework is that parents view chil
dren as a form of investment. This is consistent with the results
for survival. However, if parents view returns from having chil
dren in the same way as returns from children's education, then
this hypothesis cannot easily explain the results for educational
attainment. To see why the empirical results favor a nonunitary
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1280 QUARTERLY JOURNAL OF ECONOMICS
framework, I consider the two scenarios. First, if there are zero
returns to education, the explanation obviously cannot be about
investment motives. Second, if there are positive returns to edu
cation, then the results on education are only consistent with a
unitary model with investment motives if there are positive re
turns to producing tea for boys and girls, no returns to producing
orchards for boys, and negative returns for girls. It is difficult to
think of why this would be the case. A model with intrahousehold
bargaining provides a simple alternative explanation. If moth
ers value education more than fathers and face a higher cost of
neglecting children of either sex, then increasing mothers' bar
gaining power will lead to the equalization in treatment of boys
and girls, which will in turn be reflected in the data as an increase
in relative female survival rates.
The empirical findings cannot directly test the hypothesis
that female survival is increasing because the opportunity cost of a
woman's time is increasing. However, there are several reasons to
think that this is not the primary explanation. First, following the
strategy used in Pitt and Sigle (1998), I investigate the possibility
that fertility decisions are responsive to the cost of women's time
by using data on the month of birth to examine whether fertility
decisions are timed according to planting or harvesting seasons.
I find no evidence of fertility timing. Second, I examine the effect
of planting tea on female survival during the late 1980s, when
ultrasound B, the technology that enables prenatal sex detection,
began to diffuse through China, making sex-selective abortion
possible. There is no reason to believe that the technology dif
fused differentially through tea and non-tea areas. The cost of
sex selection should have decreased in all areas. If the effect of
the increase in tea prices was to improve female survival mainly
by increasing the opportunity cost of sex selection in tea areas
relative to other areas, then the decrease in the cost of sex selec
tion would have diminished this relative difference in opportunity
cost. Consequently, we should observe the effect of planting of tea
decreasing in magnitude in the late 1980s. Figure V shows that
the effect of planting tea persisted over time. In another study, I
investigate the impact of a reduction in the cost of sex selection
on sex imbalance by examining the effects of the introduction of
sex-selective abortion.26
26. Lin, Liu, and Qian (2007) examine the impact of sex-selective abortion on
sex ratios at birth.
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1281
VII. Conclusion
This paper addresses the long-standing question of whether
economic conditions affect outcomes for girls relative to boys.
Methodologically, it addresses the problem of joint determina
tion in estimating the effects of changes in adult income on the
survival rate of girls. It does this by exploiting changes in sex
specific incomes caused by post-Mao reforms in rural China dur
ing the early 1980s. The empirical findings provide a clear affir
mative answer: both sex imbalance and educational attainment
respond quickly to changes in sex-specific incomes. In addition,
increasing total household income without changing the relative
shares of female and male income has no effect on either survival
rates or education investment. Several past studies have found
that China's gender wage gap increased by over 100% from 1976
to 1984.27 The findings of this paper suggest that this increase in
the female wage disadvantage could be an important source of the
growth of missing women in China during this period. Similarly,
the increase in the gender wage gap may be one of the causes of
the observed decline in rural school enrollment during the early
1980s.28 The policy recommendation from these results is clear.
One way to reduce excess female mortality and to increase over
all education investment in children is to increase the relative
earnings of adult women.
Appendk I.A
The Effects of Tea, Orchards, and Cash Crops on Fraction of Males
Tea Orchards Cat. 2 cash crops
(1) (2) (3)
Coeff. Coeff. Coeff.
Birth year (Std. error) (Std. error) (Std. error)
1963 -0.005 0.001 0.000
(0.016) (0.009) (0.002)
1964 0.019 0.015 -0.001
(0.026) (0.010) (0.002)
1965 -0.013 0.012 -0.003
(0.016) (0.009) (0.002)
27. Many studies estimate China's gender wage
100% since 1976. Before the reform, compensation
to education, experience and skill. There was no o
sexes (Rozelle et al. 2002; Cai, Park, and Zhao 200
28. See Hannum and Park (2005) for a description
in rural China during the early reform period.
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1282 QUARTERLY JOURNAL OF ECONOMICS
Appendix LA
(continued)
Tea Orchards Cat. 2 cash crops
(1) (2) (3)
Coeff. Coeff. Coeff.
Birth year (Std. error) (Std. error) (Std. error)
1966 0.000 0.011 -0.001
(0.016) (0.009) (0.002)
1967 -0.015 0.002 0.000
(0.018) (0.009) (0.002)
1968 -0.014 0.003 -0.003
(0.017) (0.009) (0.002)
1969 0.013 0.011 -0.001
(0.018) (0.009) (0.002)
1970 -0.013 0.001 -0.004
(0.019) (0.010) (0.002)
1971 0.008 0.016 -0.002
(0.014) (0.011) (0.002)
1972 -0.003 0.002 -0.003
(0.014) (0.010) (0.002)
1973 -0.001 0.003 -0.004
(0.013) (0.010) (0.002)
1974 -0.003 0.014 -0.003
(0.017) (0.010) (0.002)
1975 -0.021 -0.012 -0.002
(0.016) (0.011) (0.002)
1976 0.003 -0.002 -0.002
(0.023) (0.012) (0.002)
1977 0.001 0.006 -0.002
(0.021) (0.009) (0.002)
1978 -0.008 0.008 -0.004
(0.016) (0.009) (0.002)
1979 0.009 0.015 -0.001
(0.014) (0.010) (0.002)
1980 -0.014 0.014 -0.004
(0.017) (0.009) (0.002)
1981 0.003 0.022 -0.004
(0.018) (0.010) (0.002)
1982 -0.014 0.017 0.000
(0.014) (0.010) (0.002)
1983 -0.021 0.009 -0.002
(0.018) (0.008) (0.002)
1984 -0.016 0.012 -0.005
(0.021) (0.009) (0.002)
1985 -0.006 0.017 -0.003
(0.019) (0.009) (0.002)
1986 -0.016 0.006 -0.004
(0.017) (0.009) (0.002)
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MISSING WOMEN AND THE PRICE OF TEA IN CHINA 1283
Appendlx I.A
(continued)
Tea Orchards Cat. 2 cash crops
(1) (2) (3)
Coeff. Coeff. Coeff.
Birth year (Std. error) (Std. error) (Std. error)
1987 -0.005 0.014 -0.001
(0.018) (0.009) (0.002)
1988 -0.025 0.008 -0.005
(0.015) (0.009) (0.002)
1989 -0.015 0.019 -0.005
(0.022) (0.009) (0.002)
1990 -0.013 0.029 -0.002
(0.023) (0.011) (0.002)
Observations 49,082
R2 0.14
Notes. Coefficients of the interactions between dummies indicating birth year and the amounts of tea,
orchards, and Category 2 cash crops planted in the county of birth controlling for year and county of birth
FEs. Dependent variable: fraction of males. All regressions include controls for Han, and county and birth
year fixed effects. Standard errors clustered at county level.
Appendk I.B
Descriptive Statistics of the 0.05% Sample of the 2000 Population Census
Counties that plant no tea Counties that plant some tea
Obs Mean Std. error Obs Mean Std. error
Fraction of male 81,774 53.31% 0.0017 25,290 53.56% 0.0031
Fraction of Han 81,774 93.47% 0.0008 25,290 86.05% 0.0019
Years of education 81,774 7.14 0.0110 25,290 6.89 0.0198
Male-female 58,590 0.55 0.0071 18,034 0.55 0.0141
education
Fraction with tap 81,441 31.39% 0.0012 25,182 37.60% 0.0021
water
Notes. Sample of individuals born 1962-1986. Observations are birth year x county cells.
Department of Economics, Brown University
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