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DID Missing Women and the Price of Tea in China


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-speci?c agricultural income caused by post-Mao reforms in China to estimate the effects of total income and sex-speci?c 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.

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I. INTRODUCTION Many Asian populations are characterized by severe malebiased sex imbalances. For example, whereas 50.1% of the current 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 de?cit as “missing women.” Most of the world’s missing women are in China and India, where an estimated 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?o 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 useful comments. I acknowledge ?nancial 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.
C 2008 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. The Quarterly Journal of Economics, August 2008

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0.56

0.55

Fraction of males

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0.51 1982 0.5 1990 2000

0.49 1970 1974 1978 1982 1986 Birth year 1990 1994 1998

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 income (as a share of total household income) affect the relative outcomes for boys and girls. Previous work on this subject has been impeded by identi?cation problems: areas with higher female income may have higher income precisely because women’s status is higher for other reasons, which makes it dif?cult to estimate the effect of female income on boys and girls.2 I address this omitted variable bias problem by taking advantage of two postMao 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?o (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-speci?c survival rates.

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included tea and orchards. Women have a comparative advantage in producing tea, whereas men have a comparative advantage 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 increase 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-speci?c income on outcomes for boys and girls. To estimate the effect of a change in sex-speci?c incomes, I compare sex imbalance for cohorts born before and after the reforms, between counties that plant and do not plant sex-speci?c crops, where the value of those crops increased because of the reform.3 I ?rst 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 after the price increase. Then I repeat the same strategy using orchard production to estimate the effect of an increase in relative male income (holding adult female income constant). These estimates together allow me to distinguish the effects of increasing sex-speci?c (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-speci?c 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 increased the fraction of surviving girls by one percentage point and improved educational attainment for both boys and girls by approximately 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 increase in the value of sex-speci?c crops is due to the change in the
3. This identi?cation 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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relative share of income between men and women rather than the change in total household income. This is consistent with the additional ?nding 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-speci?c 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 children 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 economic 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 ?xed in China during this period. Migration was strictly controlled, little technological change occurred in tea production, sex-revealing technologies 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 empirical strategy and policy background. Second, I discuss the conceptual 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 examining 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 (1)–(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 variables Tea land/total arable land (3) ?0.040 (0.021) N 3,457 0.00 0.0002 (0.106) N 3,488 0.00 ?0.010 (0.022) Y 3,457 0.18 (4) (5) (6) 0.065 (0.037) Y 3,488 0.06 Fruit land sown (mu = 1/15 hectare) Fruit land/total arable land (7) 0.005 (0.016) N 3,457 0.00 (8) 0.015 (0.004) Y 3,457 0.05

Tea land sown (mu = 1/15 hectare) (2) ?0.086 (0.055) Y 3,488 0.14

(1)

No. male/No. total labor in HH Village ?xed effects Observations R2

?0.115 (0.056) N 3,488 0.00

MISSING WOMEN AND THE PRICE OF TEA IN CHINA

Notes. Coef?cients of the fraction of males amongst adult laborers per household. Standard errors are clustered at the village level. Data for land sown are from the 1997 China Agricultural Census. Data Source: RCRE 1993 Household Survey.

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sown per household and the fraction of arable land that is devoted 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 household 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 monitoring of tea picking is made dif?cult 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 correlated 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 comparative advantage is in picking, this six-percentage-point difference should be interpreted as a lower-bound estimate of female comparative 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 family. In 1997, 1 per 1,000 rural households hired a worker from outside of the immediate 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 elasticity 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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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 percentage 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 comparison 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 addition 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 relative 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 characterized by an intense focus on grain production, allocative inef?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 welfare: grains, all oil crops, and cotton. In Category 2 were cash crops, including orchard products and tea (Sicular 1988a). Category 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 ?ltered down to the farm or collective levels. Quota production was purchased by the state at very low prices. These quotas were set so that farmers could retain 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 production by adjusting relative prices and pro?tability. Two sets of policies addressed these aims. The ?rst set gradually reduced

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QUARTERLY JOURNAL OF ECONOMICS TABLE II DESCRIPTIVE STATISTICS
I. Counties that plant no tea II. Counties that plant some tea Obs. Mean (Std. error) Obs. Mean (Std. error)

A. Demographic variables 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 household head 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 = 1/15 hectare) 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) Fraction male 41,665

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MISSING WOMEN AND THE PRICE OF TEA IN CHINA TABLE II (CONTINUED)

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I. Counties that plant no tea II. Counties that plant some tea Obs. Tea sown area 41,665 Mean (Std. error) 0.00 (0.0000) 0.20 (0.0029) Obs. 10,101 10,101 Mean (Std. error) 0.15 (0.0034) 0.16 (0.0034)

Orchard sown area 23,018

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Notes. The matched data set of the 1% sample of the 1990 population census and the 1% sample of the 1997 agricultural census. Sample of those born in during 1962–1990. Data for land area sown are from the 1997 China Agricultural Census. Observations are birth year × county cells. Cell size: mean = 89, median = 68.

planning targets and represented a return to earlier policies that used procurement price as an instrument for controlling production (Sicular 1988b). Although Category 1 crops bene?ted from the price increases, the increase in prices was greater for cash crops from Category 2. The second set of policies, the HPRS, was ?rst enacted in 1980. It devolved all production decisions and quota responsibilities to individual households instead of production being a collective responsibility, and effectively allowed households to take full advantage of the increase in procurement prices by expanding production to cash crops when pro?table (Lin 1988; Johnson 1966).10 The two reforms contributed to diversi?cation of agricultural production, greater regional specialization, and less extensive grain cultivation (Sicular 1988b; Johnson 1996). Although agricultural households may not have viewed each speci?c reform as permanent, they were likely to have viewed the overall regime shift as permanent. Consequently, I only interpret this initial regime shift as plausibly exogenous. Figure IIa shows that the reforms increased income from tea and orchards relative to income from Category 1 staple crops.11 It also shows that income from tea did not exceed income
10. During the period of this study, there was no of?cial market for buying or selling land. Agricultural land is allocated to farmers by the village based on characteristics such as the number of household members and land quality by the village to farmers (Carter, Liu, and Yao 1995; Johnson 1995; Kung 1997; Kung and Liu 1997; Rozelle and Li 1998; Benjamin and Brandt 2000; Jacoby, Li, and Rozelle 2002; and Burgess 2004). There is no evidence that the land allocation systematically differed between tea- and non-tea-producing regions. 11. I use yearly data from the Ministry of Agriculture that report output per standard day of labor by crop and procurement price data from the FAO. I assume that there are 257 labor days in a year and calculate for each crop yearlyinc = outputperday × 257 × price.

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FIGURE II (a) Gross agricultural incomes from producing tea and Category 1 crops. Source. FAO and Ministry of Agriculture of China. Note. The missing data points re?ect years for when labor output data are missing. (b) Category 1 production: grains. Source. FAO. (c) Category 2 production: orchard and melon production and procurement prices. Source. FAO. (d) Tea yield and tea procurement price. Source. FAO.

from orchard production.12 The increase in the relative value of Category 2 crops is also re?ected in the disproportionate growth in their output relative to Category 1 crops. Figure IIb shows that although output for Category 1 crops increased, there was no change in the rate of increase. Figure IIc shows that the rate of increase for Category 2 crops such as melons and orchard fruits
12. This addresses the possibility that the effect of income on sex ratio is not linear. An increase in income from tea translates into an increase in total household income as well as an increase in relative female income. I compare the effect of an increase in the value of tea to the effect of an increase in the value of orchard crops to discern whether sex ratios are responding to total income or to relative female income. However, if the income effect on sex ratio is nonlinear, such that there exists some threshold income that must be met before income will affect sex ratio, then this strategy will only work if income from tea does not exceed income from orchard crops.

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accelerated after the reform, following the increases in procurement prices. Similar increases can be observed in Figure IId for tea. The main effect of post-Mao reforms on tea production was increased picking, since most tea ?elds were sown during a rapid expansion program during the 1960s. The 50% increase in procurement price in 1979 was followed by extensive tending, pruning, and picking (Etherington and Forster 1994). Figures IIc and IId show the sudden increase in procurement prices and the corresponding increases in tea yield (output per hectare) and orchard production. I estimate the effect of an increase in female labor on relative female survival by exploiting the variation in the price of tea caused by the post-Mao agricultural reforms. The reform increased 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-speci?c income shock. I compare 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 imbalance 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-speci?c 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. Identi?cation is based on the increase in the value of Category 2 crops relative to Category 1 crops, for which prices continued 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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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: sexic = (1)
1990

l=1963

(cat1i × dl )β l +

1990 l=1963

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(cat3i × dl )δl + Hanic ζ + α

+ ψ i + γ c + εic .

The fraction of males in county i, cohort c is a function of the interaction terms between cat1i , the amount of Category 1 crops planted for each county i, and dl , a variable that indicates if a cohort is born in year l; the interaction terms between cat3i , the amount of Category 3 crops planted in each county that is ethnically i , and dl ; Hanic , the fraction that is ethnically Han; γ i , county ?xed effects; and ψ c , cohort ?xed effects. The dummy variable for the 1962 cohort and all of its interactions are dropped. Figure IIIa plots the vector of coef?cients for β l and δl . It shows that the effects of planting Category 1 and Category 3 crops were close to zero before and after the reform. The validity of the identi?cation strategy does not rely on the assumption that only women pick tea. Tea is a proxy for female earnings. If men or children picked tea, the proxy for relative female income would exceed actual relative female income. Hence, the strategy would underestimate the true effect of relative female income on sex ratio. If there are any unobserved time-invariant cultural reasons that cause women to pick tea and affect the relative desirability of female children, then the effect will be differenced out by comparing cohorts born before and after the reform. For the DID estimate, I restrict the sample to the cohorts born during 1970–1986 and estimate the following equation, where postc is a dummy variable that equals one if individuals are born after 1979: sexic = (teai ×postc )β + (orchardi ×postc )δ + (cashcropi ×postt )ρ (2) + Hanic ζ + α + ψ i + γ c + εic . The fraction of males in county i, cohort postc is a function of the interaction terms between teai , the amount of tea planted

MISSING WOMEN AND THE PRICE OF TEA IN CHINA
Coeff of the int eraction terms fo r cat1*birth year dumm y var and cat3*birth year dummy var

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0.02

0.01

0

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? 0.01 ? 0.02 Cat 1 ? 0.03 1964 1968 1972 1976 Year 1980 1984 1988 Cat 3

(a)
0.54

0.53

Fraction of male

0.52

0.51

0.5 No Tea 0.49 1962 1966 1970 1974 1978 1982 1986 1990 Birth Year Tea

(b)

FIGURE III (a) The effect of Category 1 and 3 crops on sex ratios. Coef?cients of the interactions birth year amount of Category 1 crops planted and birth year × 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 de?ned as all counties that plant some tea.

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for each county i, and postc , a dummy variable that indicates if an individual is born after 1979; the interaction terms between orchardi , the amount of orchard planted for each county i, and postc ; the interaction terms between cashcropi , the amount of all cash crops planted for each county i, and postc ; Hanic , the fraction that is ethnically Han; γ i , county ?xed effects; and ψ c , cohort ?xed 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 re?ected in a decrease in the fraction of males born after the reforms, β < 0. Conversely, if an increase in the value of orchards worsened female survival, we would expect δ > 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 pretrends in sex ratios relative to other regions, which may cause the DID estimate to be capturing differences between tea and nontea areas besides the increase in tea value. An illustration of the DID estimate shows that this is not the case. Figure IIIb 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 average vertical distance before and after the reform. The ?gure shows clearly that before the reform, tea areas had more boys than nontea areas, whereas after the reform, there were consistently fewer boys in tea areas. Hence, the DID estimate will not be capturing 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: sexic = (3)
1990 l=1963

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(teai × dl )β l +

1990 l=1963

(orchardi × dl )δl

+

1990 l=1963

(cashcropi × dl )ρ l + Hanic ζ + α + ψ i + γ c + εic .

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The fraction of males in county i, cohort c is a function of the interaction terms between teai , the amount of tea planted for each county i, and dl , a dummy variable which indicates if a cohort is born in year l; the interaction terms between orchardi , the amount of orchard planted for each county i, and dl ; the interaction terms between cashcropi , the amount of all cash crops planted for each county i, and dl ; Hanic , the fraction that is ethnically Han; γ i , county ?xed effects; and ψ c , cohort ?xed effects. The dummy variable for the 1962 cohort and all of its interactions are dropped. β l is the effect of planting tea on the fraction of males for cohort l. If increasing the price of tea improved female survival, then β l should be constant until approximately the time of the reform, after which it should become negative. Similarly, δl is the effect of planting orchards on the fraction of males for cohort l. If increasing orchard prices worsened female survival, then δl 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. Therefore, 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 spatial areas. The results for this restricted sample are similar to the estimate for the whole sample, although the precision is reduced
14. I also ?nd 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 nonagricultural households.

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due to the smaller sample size. This adds to the plausibility of the identi?cation 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 speci?cation does not separately control for it. The following equation estimates the ?rst-stage effects of hilliness on tea production after the reform: teai × postc = (slopei × postc )λ + (cashcrop × postc )? + Hanic ζ + α + ψ i + postc γ + εic . The second-stage regression is as follows: sexic = (teai × postc )β + (cashcrop × postc )? + Hanic ζ + α + ψ i + postc γ + εic. 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 increase the relative desirability of having a girl by increasing parents’ 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-speci?c income 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 ?rst, second, and last explanations are consistent with both the unitary and nonunitary models of household decision making. The third explanation 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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(4)

(5)

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The unitary model makes the strong prediction that an increase in income should have the same effect on household consumption 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 products does not have the same effect on female survival rates as an increase in the price of tea. The number of potential explanations can be further re?ned by comparing the effects of increases in the prices of tea and orchard products on the relative educational attainment of girls. In this case, the opportunity cost of the mother’s time is not applicable. The ?rst 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 increasing 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 households are unitary and parents view children as a form of consumption 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 examining 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 education 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), Du?o (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 household 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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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 ?fteen 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 migration, I further restrict the data to individuals who report having lived in the same county for over ?ve 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 ?ve years or longer. This is consistent with studies on migration, which ?nd 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 ?rst 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 migration. Using data from RCRE’s NFS for 1986–1990, I ?nd 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 I.B. 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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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 ?nding 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 cohorts born before and after the reform. Hence, the identi?cation for this paper comes from changes in the fraction of males over time (across cohorts). One cross section would not be able to distinguish 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 differences 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 fraction 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) ?nd misreporting to be present only for extremely young children, who are easy to hide from the authorities. This is consistent 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 available for this period at the county level. For the sake of scope, accuracy, 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 identi?ed. However, changes in the geographic identi?ers make linking at the county level dif?cult.

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measurement error. It is also possible that the counties that produced tea in 1997 are the counties that had stronger preferences for girls prior to the reform. In this case, comparing the fraction of males in counties that planted tea in 1997 to the fraction of males in counties that did not plant tea in 1997 will confound the effect of planting tea with the effect of underlying preferences for girls. However, as discussed earlier, the government emphasis on tea planting during the Cultural Revolution meant that the main determinant of whether a region had tea ?elds was geographic suitability, not sex preferences. Speci?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 neighboring 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 systematic 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 analysis. 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. Agricultural households have very little farmable land, with an average 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 1/15th of a hectare. 22. Similar geographic dispersion can be observed for orchard-producing counties.

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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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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 (1) OLS Tea × post ?0.012 (0.007) Orchard × post 0.005 (0.002) Slope × post ?0.002 (0.002) Linear trend No Observations 28,349 (2) OLS ?0.013 (0.006) (3) OLS ?0.012 (0.005) Tea × post (4) 1st Fraction of males (5) IV ?0.072 (0.031) (6) IV

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?0.011 (0.007)

No 37,756

Yes 37,756

0.26 (0.057) Yes 37,756

No 37,756

Yes 37,756

Notes. Coef?cients 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 ?xed effects and controls for Han, and cashcrop × 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 additional mu of tea decreased the fraction of males by 1.2 percentage points; planting one additional mu of orchards increased the fraction 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 signi?cant at the 10% and 5% level, respectively. The estimates for β l , δl and ρ l from equation (3) are shown in Appendix I.A. The coef?cients for β l and δl 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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Coeff of the interaction terms of tea*birth year dummy var and orchard*birth year dummy var

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0.04 Orchard 0.03 0.02 0.01 Tea

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0 –0.01 –0.02 –0.03 1963 1967 1971 1975 Birth year 1979 1983 1987

FIGURE V The Effect of Planting Tea and Orchards on Sex Ratios Coef?cients of the interactions of birth year × amount of tea planted and birth year × 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 ?xed effects control for variation across cohorts that do not also vary across counties. They cannot control for countyvarying 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 speci?cation as the second stage of the 2SLS. This differences-indifferences speci?cation does not explicitly control for orchards because planting orchards is likely to be endogenous. Column (2) in Table III shows the basic ?xed 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 decreased the fraction of males by 1.3 and 1.2 percentage points. Estimates from both speci?cations are statistically signi?cant at the 5% level. Thus, the OLS estimates are robust to differential linear changes across cohorts between counties.

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Two problems motivate the use of instrumental variables. First, using 1997 agricultural data to proxy for agricultural conditions in earlier years introduces measurement error that may bias the estimate toward zero. Second, the OLS estimate will suffer 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 address 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 ?rst-stage equation, where both the amount of tea planted and the slope is time-invariant. Column (4) of Table III shows the ?rst-stage estimate from equation (4). The estimate for the correlation between hilliness and planting tea, λ, is statistically signi?cant at the 5% level. Column (5) shows the 2SLS estimate from equation (5). The estimate is larger than the OLS estimate and statistically signi?cant. Column (6) shows the 2SLS estimate after controlling for county-level cohort trends. The estimate is similar in magnitude to the OLS estimate, but no longer statistically signi?cant. The estimates with and without trends are not statistically different from each other. The estimate without trends is larger but also less precisely estimated. 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 con?dence 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 con?ne the sample to children who had completed their education, I restrict it to cohorts born between

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1962 and 1982. Individuals in the sample should not be affected 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 equation (2) with years of education as the dependent variable to examine 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 ?rst done with dummy variables indicating whether any tea, orchards, or cash crops are planted in a county, and then with continuous variables 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 signi?cant 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 signi?cant 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 every county-birth year cell contains both males and females. The estimates for all category 2 cash crops are close to zero and statistically insigni?cant. 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 (1)–(4). The estimates show that one additional mu of tea planted increases female educational attainment by 0.38 years and male educational
23. For robustness, I repeat the experiment on a sample of cohorts born after 1967 who did not begin primary school until after 1974, when schools were reopened. The results are similar and statistically signi?cant. 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 CROPS ON EDUCATION ATTAINMENT B. Continuous variable for amount of crops sown (4) Diff (5) All (6) Female (7) Male (8) Diff

A. Dummy variable for crops sown (3) Male

(1) All

(2) Female

Tea × post

Orchard × post

Cat2 × post

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Observations R2

0.199 (0.043) ?0.124 (0.037) ?0.036 (0.026) 68,522 0.37

0.247 (0.057) ?0.226 (0.050) ?0.024 (0.032) 33,538 0.48

0.149 (0.049) ?0.029 (0.040) ?0.037 (0.028) 34,984 0.34

?0.069 (0.063) 0.174 (0.056) ?0.020 (0.040) 58,314 0.14

0.449 (0.107) ?0.021 (0.056) ?0.065 (0.032) 68,522 0.37

0.383 (0.133) ?0.119 (0.071) ?0.040 (0.041) 33,538 0.48

0.501 (0.146) 0.054 (0.064) ?0.074 (0.035) 34,984 0.34

?0.097 (0.218) 0.118 (0.086) ?0.012 (0.050) 58,314 0.14

Notes. Coef?cients of the interactions between dummies indicating whether a cohort was born post-reform and the amount of tea, orchards, or cash crops planted in the county of birth. Dependent variable: years of education. All regressions include controls for Han, county ?xed effects and birth year ?xed effects. All standard errors clustered at the county level. Post = 1 for cohorts born after 1976.

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Coeff icients of i ntera cti on terms of orchards*birth year dummy var 0.15
Orchards Tea

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–0.10

0.10
–0.30

0.05 0.00 –0.05 –0.10 –0.15 –0.20 –0.25
–1.30 –0.90 –0.50

–0.70

Coefficients of interaction terms of tea*birth year dummy var

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–1.10

–0.30 –0.35 1964 1968 1972 Birth year 1976 1980
–1.50

FIGURE VI The Effect of Planting Tea and Orchards on Girls’ Educational Attainment Coef?cients of the interactions birth year × amount of tea planted and birth year × 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 attainment, 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 coef?cients for each cohort l in vectors β l and δl 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 planning 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 policies. 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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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 areas, 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. I can also investigate the correlation 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 policies. Matching this data to the data used in this study, I ?nd 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 between 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 planning policies. These estimates are not reported in the paper for the sake of brevity. Migration. If migration patterns differed signi?cantly between 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-speci?c 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 overestimate the number of female migrants from tea areas. Recall that the DID estimate presented earlier used a sample of individuals 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, exemptions, and later relaxations.

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Population Census, which reports whether an individual is currently living in his/her county of birth, I assume that all individuals under 20 years old living away from their counties of birth are females born in tea-planting regions before 1979 (even, for example, ?fteen-year-old men). Then I add these migrants into the 1990 data and reestimate equation (2). This is even more conservative than it ?rst 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 estimate scarcely changes. These results are not reported in the paper. VI. INTERPRETATION This section discusses the empirical results and their theoretical 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 teaplanting household, then the ?ndings imply that augmenting annual household income by 10%, and giving it all to women, increases the fraction of girls by 1.3 percentage points. This would increase educational attainment for boys and girls by approximately 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 illustrative purposes, assume that the elasticity of demand for girls relative to boys with respect to sex-speci?c earnings is linear, whereas it is likely to be highly concave in reality. The empirical results have several theoretical implications. The ?ndings for both survival and education reject the joint hypotheses 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 children as a form of investment. This is consistent with the results for survival. However, if parents view returns from having children 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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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 education, then the results on education are only consistent with a unitary model with investment motives if there are positive returns to producing tea for boys and girls, no returns to producing orchards for boys, and negative returns for girls. It is dif?cult to think of why this would be the case. A model with intrahousehold bargaining provides a simple alternative explanation. If mothers value education more than fathers and face a higher cost of neglecting children of either sex, then increasing mothers’ bargaining power will lead to the equalization in treatment of boys and girls, which will in turn be re?ected in the data as an increase in relative female survival rates. The empirical ?ndings 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 ?nd 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 diffused 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 selection 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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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 determination in estimating the effects of changes in adult income on the survival rate of girls. It does this by exploiting changes in sexspeci?c incomes caused by post-Mao reforms in rural China during the early 1980s. The empirical ?ndings provide a clear af?rmative answer: both sex imbalance and educational attainment respond quickly to changes in sex-speci?c 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 ?ndings 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 overall education investment in children is to increase the relative earnings of adult women.
APPENDIX I.A THE EFFECTS OF TEA, ORCHARDS, AND CASH CROPS ON FRACTION OF MALES Tea (1) Birth year 1963 1964 1965 Coeff. (Std. error) ?0.005 (0.016) 0.019 (0.026) ?0.013 (0.016) Orchards (2) Coeff. (Std. error) 0.001 (0.009) 0.015 (0.010) 0.012 (0.009) Cat. 2 cash crops (3) Coeff. (Std. error) 0.000 (0.002) ?0.001 (0.002) ?0.003 (0.002)

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27. Many studies estimate China’s gender wage gap to have increased by over 100% since 1976. Before the reform, compensation for workers was set according to education, experience and skill. There was no of?cial differentiation between sexes (Rozelle et al. 2002; Cai, Park, and Zhao 2004). 28. See Hannum and Park (2005) for a description of the decrease in schooling in rural China during the early reform period.

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QUARTERLY JOURNAL OF ECONOMICS APPENDIX I.A (CONTINUED) Tea (1) Orchards (2) Coeff. (Std. error) 0.011 (0.009) 0.002 (0.009) 0.003 (0.009) 0.011 (0.009) 0.001 (0.010) 0.016 (0.011) 0.002 (0.010) 0.003 (0.010) 0.014 (0.010) ?0.012 (0.011) ?0.002 (0.012) 0.006 (0.009) 0.008 (0.009) 0.015 (0.010) 0.014 (0.009) 0.022 (0.010) 0.017 (0.010) 0.009 (0.008) 0.012 (0.009) 0.017 (0.009) 0.006 (0.009) Cat. 2 cash crops (3) Coeff. (Std. error) ?0.001 (0.002) 0.000 (0.002) ?0.003 (0.002) ?0.001 (0.002) ?0.004 (0.002) ?0.002 (0.002) ?0.003 (0.002) ?0.004 (0.002) ?0.003 (0.002) ?0.002 (0.002) ?0.002 (0.002) ?0.002 (0.002) ?0.004 (0.002) ?0.001 (0.002) ?0.004 (0.002) ?0.004 (0.002) 0.000 (0.002) ?0.002 (0.002) ?0.005 (0.002) ?0.003 (0.002) ?0.004 (0.002)

Birth year 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986

Coeff. (Std. error) 0.000 (0.016) ?0.015 (0.018) ?0.014 (0.017) 0.013 (0.018) ?0.013 (0.019) 0.008 (0.014) ?0.003 (0.014) ?0.001 (0.013) ?0.003 (0.017) ?0.021 (0.016) 0.003 (0.023) 0.001 (0.021) ?0.008 (0.016) 0.009 (0.014) ?0.014 (0.017) 0.003 (0.018) ?0.014 (0.014) ?0.021 (0.018) ?0.016 (0.021) ?0.006 (0.019) ?0.016 (0.017)

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MISSING WOMEN AND THE PRICE OF TEA IN CHINA APPENDIX I.A (CONTINUED) Tea (1) Birth year 1987 1988 1989 1990 Observations R2 Coeff. (Std. error) ?0.005 (0.018) ?0.025 (0.015) ?0.015 (0.022) ?0.013 (0.023) Orchards (2) Coeff. (Std. error) 0.014 (0.009) 0.008 (0.009) 0.019 (0.009) 0.029 (0.011) 49,082 0.14

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Cat. 2 cash crops (3) Coeff. (Std. error) ?0.001 (0.002) ?0.005 (0.002) ?0.005 (0.002) ?0.002 (0.002)

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Notes. Coef?cients 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 ?xed effects. Standard errors clustered at county level.

APPENDIX I.B DESCRIPTIVE STATISTICS OF THE 0.05% SAMPLE OF THE 2000 POPULATION CENSUS Counties that plant no tea Obs Fraction of male Fraction of Han Years of education Male-female education Fraction with tap water 81,774 81,774 81,774 58,590 81,441 Mean 53.31% 93.47% 7.14 0.55 31.39% Std. error 0.0017 0.0008 0.0110 0.0071 0.0012 Counties that plant some tea Obs 25,290 25,290 25,290 18,034 25,182 Mean 53.56% 86.05% 6.89 0.55 37.60% Std. error 0.0031 0.0019 0.0198 0.0141 0.0021

Notes. Sample of individuals born 1962–1986. Observations are birth year × county cells.

DEPARTMENT OF ECONOMICS, BROWN UNIVERSITY

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