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Textiles and the Historical Emergence of GenderEquality in China*
Melanie Meng Xue
UCLA Anderson School of Management
This Version: November 2015
Abstract
This paper tests the hypothesis that pre-modern textile production leads to greatergender equality. I exploit variation in pre-modern textiles at the county level toidentify their effects on gender equality since 1300. I find despite the the lack of ben-efits of working outside home, places where women were more productive in textileproduction developed more favorable views towards working women and witnessedrising gender equality. Pre-modern textile production is negatively correlated withprenatal sex selection, and positively correlated with wife heading the householdand with share of women with political and managerial roles. My results are robustto using various subsamples, sex ratios from other cohorts, other gender outcomes,alternative hypotheses, an instrumental variable analysis, and a micro-level analy-sis. I also investigate the effects of large political and economic shocks after 1850,and find the effects of pre-modern textile production to be overall highly resilient. Idocument an intermediate effect of textiles on women’s probability of survival uponwidowhood dating back to 1500. In addition, I show that women exposed to pre-industrial textile production were more likely to participate in the labor force as earlyas at the onset of China’s industrialization in the 20th century. Using survey data, Ishow that in contemporary China, individuals in counties with a pre-modern textilehistory indeed have more progressive gender moderns and weaker son preference.
Keywords: Culture, historical persistence, gender norms, son preference, relative productivityJEL Codes:J16 N35 O33 O53
*I would like to thank Sascher Becker, John Brown, Joyce Burnett, Shuji Cao, Latika Chaudhary, DanielChen, Bill Collins, Lena Edlund, James Fenske, Philip Hoffman, Paola Giuliano, Remi Jedwab, Saumitra Jha,Noel Johnson, Mark Koyama, Timur Kuran, James Kung, Nan Li, Debin Ma, John Nye, Omer Ozak, NancyQian, Thomas Rawski, Gary Richardson, Eric Schneider, Yan Se, Nico Voigtlander, Bin Wong and audiencesat ASREC 2013, CES 2014, “Deep Causes of Economic Development” at Utrecht (2014), EHA 2014, EHS2014, GMU-AU Economic History Workshop (2012), HKEA (2014), the International Workshop on EconomicAnalysis of Institutions at Xiamen University (2013), NEUDC(2015), Shanghai University of Economics andFinance (2014), World Congress of Cliometrics Society (2013), WADES (2014), and WEHC (2015). All theremaining errors are the fault of the author.
I Introduction
Gender discrimination is extremely common across the world. Going beyond traditional research
on the economic status and political rights of women, recent work has paid increasing attention
to the cultural determination of gender attitudes (Fernandez, 2007; Fernandez and Fogli, 2009;
Fortin, 2005; Jayachandran, 2015). In particular, Alesina, Giuliano, and Nunn (2013) and
Hansen, Jensen, and Skovsgaard (2015) have focused on the pre-modern roots of gender norms,
highlighting historical agriculture in shaping gender roles and gender norms. This paper builds
on this strand of literature to investigate pre-modern roots of gender equality. It does so by
focusing on textile production—the main economic activity of women in most preindustrial
societies.
Alesina, Giuliano, and Nunn (2013) explore how traditional agricultural practices influenced the
historical gender division of labor and the modern levels of gender equality across countries.1
They find that plough cultivation in pre-industrial periods led to an important belief that
the natural place for women is within the home. In contrast to Alesina, Giuliano, and Nunn
(2013), this paper explores a setting where women works inside home, but the productivity of
women varies. I distinguish between two channels through which pre-modern production could
have shaped gender norms and beliefs: working inside or outside home, versus women’s role in
providing for family. Pre-modern China offers an ideal testing ground because the predominant
ethnic group, Han Chinese, used the plough; however, women’s status varied tremendously
within that ethnic group. The plough was operated in the fields, mainly by men; textiles were
produced at home, mainly by women. Types and quantities of textiles varied greatly across
regions. Women in some places were far more productive than women elsewhere, partly due
to more favorable climatic and geographic conditions. I hypothesize that women doing highly
productive work to provide for her family, even at home, can increase their status.
The extent of variation in women’s status in China provides an ideal setting to study the
historical and modern causes of gender inequality. In contemporary China, a leading indicator
of gender inequality is sex ratio at birth. Across China a tremendous amount of variation is found
in the degree of male bias in this measure. Some counties have maintained close-to-normal sex
ratios at birth, whereas in other parts, sex ratio imbalances have become increasingly more severe
since sex-selective technology became available in the late 1980s. According to 2000 Chinese
Population Census, among all 2876 counties, in Jincheng, 102 boys were born per 100 girls;
whereas in Erzhou, 170 boys were born per 100 girls. The amount of male bias in sex ratio at
birth is not a simple function of economic development. As Jayachandran (2015) points out, the
problem of the male-skewed sex ratio at birth cannot be explained by the process of development:
sex ratio imbalances have been intensifying, not lessening, with economic development. Sex ratio
at birth more reflects long-standing prejudice against girls. Hence I use sex ratio at birth as a
1For surveys of field see Guiso et al. (2006); Bisin et al. (2011) and Nunn (2012).
1
main outcome variable to test the impact of pre-modern textiles on gender equality.
To identify the causal effect of pre-modern textile production on modern outcomes, including
sex ratio at birth, I employ a variety of empirical strategies. First, I collect information on pre-
modern textile production collected from thousands of local gazetteers. I end up with a sample of
1513 counties in my main analysis. Second, I link this data to contemporary measures of gender
equality. My outcome variables include sex ratio at birth, women’s education, wife heading the
household among married couples, share of women with political roles and managerial roles,
gender-role attitudes and son preference.
Examining variation across counties and individuals, I find a strong positive relationship between
pre-modern textiles and gender equality today. The baseline estimates suggest that pre-modern
textile production is associated with a reduction in sex ratio at birth by a quarter of its standard
deviation. Pre-modern textiles are negatively correlated with sex ratio imbalances at birth, and
positively correlated with wife heading the household among married couples and with share
of women with political roles and managerial roles. Pre-modern textiles predict more favorable
views about women’s natural ability relative to men. People in areas with pre-modern textiles
are more likely to believe that women should be as career-focused as men.
To account for historical and modern factors that might also play a role in modern gender
outcomes, I include controls for a number of historical characteristics of each county, such as
agricultural suitability, proximity to the Grand Canal or the Yangtze River, treaty port status,
as well as a set of geographic controls, such as latitude, longitude, ruggedness and distance
to coast. I also control for current per capita GDP, share of agricultural workforce, share
of non-agricultural household registration, share of ethnic population, and provincial capital.
Region and province fixed effects are included in all specifications. In running various robustness
checks and considering alternative hypothesis, I also analyze the role of tea production, rice
production, level of development prior to cotton textile production, historical postal routes,
Christian missionary interventions, present-day textile manufacturing, educational attainment,
and migration.
My results are robust to an instrumental variable approach. Farnie (1979) points out that
relative humidity played a key role in textile production.2 Humidity makes cotton fibers more
pliable and reduces the chance of breakages in the yarn. Humidity is positively correlated with
the quality of final products until the effect flattens out approximately at 80%. Hardly any cotton
textiles can be produced when relative humidity drops below 60%. This motivates the use of
county-level relative humidity as an instrument for pre-modern textile production. I aggregate
monthly average relative humidity of each county, based on their contribution to a suitable
environment for spinning and weaving, into a humidity-for-weaving index. This rationale behind
this index is to approximate the number of months available for cotton textile production with
2Fairbank (1978) discusses the relationship between a relative humid climate of Jiangsu and the greatertensile strength and evenness of yarn.
2
a gradient to quality and efficiency. I obtain IV estimates that are comparable to the OLS
estimates providing further confidence that these coefficients can be causally interpreted.
To explain the mechanisms through which pre-modern textiles affect modern outcomes, I review
the history of widow survival in China, examine a number of outcomes of textile production in
the past, and carefully consider the likely effects of several large political and economic shocks
on the persistence of gender norms. I first show that pre-modern textiles predict higher survival
rates of widows as early as the Ming period, suggesting that textiles were already empowering
women historically. I go on to demonstrate female labor participation pattern in the earliest
stages of China’s industrialization. I find women in regions with pre-modern textiles were far
more likely to be represented in the labor force in the early 20th century. I carefully evaluate the
effect of adopting western institutions, the influence of missionary activities, the formation of the
Chinese communist state, and recent economic reforms in China. Little evidence is found that
any of the large political and economic shocks in modern China have severely confounded the
estimates I obtain on pre-modern textiles. Many shocks could have had a level effect on gender
norms, but they are rarely correlated with pre-modern textiles. Even when they are correlated,
they do not seem to affect my main results. I find the persistence effects pre-modern textiles are
overall highly resilient, despite some suggestive evidence that large political or economic shocks
could temporarily or even permanently weaken the persistence. Before I conclude, I use Chinese
Social General Surveys to provide direct evidence on pre-modern textiles shaping gender-role
attitudes and son preference in contemporary China.
Previous studies have showed increased women’s earnings lead to female empowerment (An-
derson and Eswaran, 2009; Ashraf, Karlan, and Yin, 2010; Deininger, Goyal, and Nagarajan,
2010). In studying the determinants of female autonomy, Anderson and Eswaran (2009) find
earned income contributes more to women’s autonomy relative to unearned income, and that
only employment outside their husbands’ farms contributes to women’s autonomy. The relation-
ship between sex-specific income and survival of daughters in East Asia has been documented
by Qian (2008). Qian interprets the finding that excess female mortality is decreasing in female
earnings in terms of women’s increasing bargaining power in the household. My paper shows the
shift in women’s relative earnings can change gender norms in the long run. I provide evidence
that women enjoyed greater autonomy and higher social status in areas with pre-modern textile
production; relative outcomes of women continue to be better even when pre-modern textile
production is long out of the picture. I argue that pre-modern textiles shaped cultural beliefs
about the women’s capability and their proper role in providing for a family.
However, there are also alternative arguments that suggest that part of the long-term impact
arises because pre-modern textile production promoted the development of formal institutions,
gender-specific economic opportunities and overall wealth that may favor women. To rule out
the first channel, I rely on within-country, within-region, and within-province variation, where
3
formal institutions are largely identical and policies are enforced to a similar extent.3 Also,
unlike the west, the Chinese state standardized family practices across regions, classes and
dialect groups by the late imperial period, with far fewer time and space variation in inheritance
practices, marriage rates, naming practices and patrilocality (Ebrey, 1990; Ropp, 1994). After
1949, pervasive reforms in favor of gender equality took place. Formal institutions were created
to guarantee female employment. Due to a high degree of centralization in legislation, official
labor laws or laws on sex discrimination hardly varied from place to place. All of these create
beneficial conditions for me to identify the impact of historical determinants on present-day
gender outcomes. To account for the second channel, I control for sectoral composition today,
including scale of textile production and agricultural workforce. To best account for the third
channel, I control for both current per capita GDP. I show that overall wealth alone hardly
explains the large and systematic differences in sex ratio at birth as documented in this paper.
This paper contributes to the literature on the historical determinants of cultural norms and
beliefs. Many of these document the persistent impact of a negative shock on current cultural
values such as Nunn and Wantchekon (2011)’s work on the effects of the trans-Atlantic slave
trade on corruption and trust today and Voigtlander and Voth (2012)’s study of the persistence
of antisemitic beliefs in Germany. My study is most closely related to those papers that study
how economic factors have shaped contemporary gender norms such Grosjean and Khattar
(2014) who examine conservative gender norms and its origins in historical marriage market
conditions in Australia.4
This paper also contributes to the literature on parental gender bias and sex ratio imbalances
by identifying an important source of differentials in sex ratios. Edlund (1999) explicitly models
sex ratios in relation to son preference, indicating several factors that contribute to unbalanced
sex ratios. Jayachandran (2015) confirms the crucial importance of cultural factors in sex ratio
imbalances. Daul and Moretti (2008) finds evidence for parental gender bias in the U.S. that
parents favor boys over girls. Others have studied son preference, sex-selective abortions, and
changes in sex ratios in non-western countries (Gupta, 2014; Li and Lavely, 2003).5 In particular,
Chung and Gupta (2007) suggests income levels play a key role in unbalanced sex ratios and
that sex ratios can change in nonlinearity through different stages of development.6 Besides,
3Despite the highly centralized law making process, policies can be implemented by local governments withgreater latitude.
4Other relevant studies include Jha (2013) who shows that a cities in India that were medieval trading portsexperienced significantly less religious riots between Muslims and Hindus in the period after 1850. Grosjean(2011) examines the persistence of a culture of honor among Americans of Scots-Irish descent. She finds thatthis culture of honor results in higher homicide rates among Scots-Irish in the US South and Mountain West butnot elsewhere and argues that this culture has only persisted where formal institutions are comparatively weak.
5Contrary to the conclusions of the above studies, Oster (2005) finds evidence that that much of sex ratioimbalances in developing countries can be attributed to Hepatitis B.
6Almond et al. (2013) find positive incomes shocks from land reforms increased sex ratios. Finally, theeconomic consequences of sex ratio imbalances have also attracted scholarly attention in recent years. Wei andZhang (2011) links sex ratio imbalances to differential saving rates across China.
4
in reviewing the history of widow survival in China, this paper also adds to the literature on
“missing unmarried women” (Anderson and Ray, 2015; Miguel, 2005; Oppong, 2006; Sossou,
2002).
The third literature this paper draws on is the economic history literature studying the impact
of textile production on the pre-modern Chinese economy in the context of the Great Divergence
(Huang, 1990; Goldstone, 1996; Li and Li, 1998; Ma, 2005; Pomeranz, 2009; Wong, 2002). Several
scholars have argued that the 17th and 18th centuries were a golden period for the Yangtze Delta,
one of the major textile regions. Pomeranz and Li, in particular, have argued that China’s textile
industry remained highly productive and profitable through the 19th century (Li and Li, 1998;
Pomeranz, 2009), in face of tough competition from British textile manufactures. These studies
substantiate my claim that women’s textile production was highly productive, marketized and
immense in total size.
China is usually considered as having historically conservative gender norms, due to its Confucian
heritage. Severe son preference is often cited as evidence for historically conservative gender
norms and linked to deep cultural determinants such as patrilocality. However, China also
accounts for two-thirds of the richest self-made women in the world.7 Meanwhile, its neighboring
country, Korea, also with a Confucian heritage, has up to 34% CEOs being women, compared
to 9% in Western Europe and 5% in the US. This suggests women in those societies might have
gained status in spite of patrilocality. By exploiting variation in gender gap given the presence
of Confucian heritage, this paper fills in the gap to illustrate the sources of gender equality in
China.
This paper is organized as follows. The second section explains the historical background and
lays out the conceptual framework. Section III discusses data sources and variable constructions.
Section IV summarizes my baseline results, a number of robustness checks, an instrumental
variable analysis and tests of alternative hypotheses. Section V demonstrates that similar effects
can be found using an alternative micro-level datasets. In Section VI, I explore how the effects
of pre-modern textile production first emerged and how they persisted after the decline in
traditional textile production in the late nineteenth century. Section VII concludes the paper.
7http://qz.com/529508/china-is-home-to-two-thirds-of-the-worlds-self-made-female-billionaires/http://qz.com/529508/china-is-home-to-two-thirds-of-the-worlds-self-made-female-billionaires/http://qz.com/529508/china-is-home-to-two-thirds-of-the-worlds-self-made-female-billionaires/
5
II Historical Background and Conceptual
Framework
A Historical Background
A.1 Women in Pre-modern China
Similar to other patrilineal societies, women in premodern China were assigned different roles
to men and took on different responsibilities in both family and society. Chinese society was
also shaped in important ways by Confucian values.
Confucianism has a twofold impact on attitudes on to women. On the one hand, Confucian
tradition strongly disfavored women. Folk wisdom held that a family would suffer economically
from the birth of a daughter.8 This cultural belief was consistent with economic reality prior to
the emergence of the textile industry: daughters could not work outside home due to concern for
women’s “purity”, and had to rely on family resources to survive.9 And unlike sons, a daughter
would not be able to support her own parents once she became married as they had to move into
the homes of their husband’s family and became an official family member there. As a result of
the cost of dowries, too many daughters could cause serious financial distress to the household
(Harrell, 1995; Watson and Ebrey, 1991). For the reasons described above, parents had an
incentive to control the total number of daughters. Excess female mortality during infancy and
childhood was widely observed.10
On the other hand, Confucianism celebrated the virtues of hard work. It therefore elevated
individuals who worked hard to provide for their family, including hard working women. This
provided a path for women to earn the respect due to them for their contribution to the house-
hold. Diligent productive manual labor was seen as the virtue for all women, regardless of class
(Mann, 1997). Qing China (1644–1911) enjoyed a relatively high degree of social mobility, and
it was a society where it was conceivable for individuals to gain social status based on higher
productivity.
A.2 The Cotton Revolution
After agriculture, textile production was the most important economic activity in premodern
societies. In China as in much of the preindustrial world, textile production was carried out
by women “who spent every available moment spinning, weaving, and sewing.” (Barber, 1991).
8In a play Qujiang Chi from early Yuan, the heroine refers to herself as pei qian huo, which literallymeans a money-losing proposition. The term is still used in Mainland China, Singapore, Malaysia, Taiwan,Macau and Hong Kong today. In 2007, the Yahoo dictionary in Taiwan was caught giving the English-languagetranslation of the Chinese term pei qian huo as a. “a money-losing proposition” and b. “a girl; a daughter”(http://news.tvbs.com.tw/entry/305992 ).
9Chow (1991) regards non-western women’s “purity” or “chastity” as both sexual and nationalistic.10Historian James Z. Lee and sociologist Cameron D. Campbell (2007) discovered that girls between ages one
and five had a 20 percent higher mortality than boys.
6
Spinning and weaving were perceived to be a womanly skill. “Men farms and women weave”
as a form of division of labor, became formalized under the state tax system dating back to
300 AD. Under the state tax system, each household was required to pay in-kind taxes in both
grain and textile products. Male labor was occupied by grain production, something women did
not have the comparable physical strength for. Just like other societies, women were far more
productive in making cloth than in agriculture. The fiscal policies of the state solidified this
gender division of labor.
In early times, silk and hemp were two main fabric for clothing. Silk was the most valued of
all fabric, and used in more expensive clothes. Hemp was the predominant fiber for day-to-day
clothes. After 1300 new spinning and weaving technologies for processing the cotton became
available. Cotton textile production was made economically viable for the first time in history
(Bray, 1997; Kang, 1977). The new technologies did not develop endogenously; rather, they
were borrowed from outside of the mainland. Huang Dao Po, a Shanghai native (1245–1330),
learned those technologies from an ethnic group Li residing on the Hainan Island.11 On the
spinning side, new technologies involved a pedal-spinning wheel with three spindles, similar to
the multi-spindle design used in the Spinning Jenny invented in 18th Century England. Rather
than maneuvered by hand only, new spinning machines were maneuvered by both foot and hand.
Prior to the innovation, every person’s workload of weaving has to be matched by three to four
people’s workload of spinning. In comparison, new spinning technologies dramatically increased
the efficiency of spinning.12 Following the technological breakthrough, cotton textiles rapidly
expanded in the following two centuries. Cotton quickly gained popularity over hemp in the
production of day-to-day clothes.
The introduction of new weaving and spinning technology allowed women to increase their
productivity. They sold the surpluses to the regional and national market. By Ming times
(1366–1644), the textile sector was increasingly commercialized and specialized. Cotton textiles
accounted for a large portion of in-kind taxes, second only to grain. Huang (1964a) estimates
that in the early 17th century, at least 1 million bolts of cotton cloth were transported through
the Grand Canal as tax payments to Ming Government.13 The passage of the Single Whip Law
in 1580 further promoted domestic trade and increased the market size for cotton textiles.14
Even after the mid-nineteenth century, as a significant part of spinning began to be displaced
by foreign yarn, weaving remained competitive.15 By 1933, handicraft industry still made up
11Li people today still use those technologies for textile production. The production scene is an importantpart of tourist attractions for the Hainan Island.
12Other technology improvements include new techniques in cotton fluffing and crushing, and weaving insightsin mixed cotton fabrics, colored fabrics and fabrics with mixed warp and weft fibers.
131 bolt of cotton cloth is 33.33 meters in lengh. 1 million bolts of cotton cloth were worth half a million tealsat the time.
14The Single Whip Law was initiated in the early 16th century, and promoted to the entire empire in 1580by Zhang Juzheng (Flynn and Giraldez, 1995).
15Rural China kept using handicraft cloth due to its lower prices and less wear and wear. Foreign merchantsand consular officials in the late nineteenth-century China complained about difficulty of penetrating the Chinese
7
for 61% of the total industry output (Fairbank, 1978, pp.15-28).
Women’s earnings from cotton textile production were substantial. Li (1997) shows a woman’s
textile work around the year was enough to feed 2.7 people. Pomeranz (2002) provides an even
more optimistic estimate:a female’s income could be four times as much as an adult male’s.
Allen’s (2011) wage regressions indicate that textile workers earned a wage premium compared
with workers in construction or agriculture. Women who had the skills to weave artisan cloth
could earn an even higher income.16 Women in textile regions became able to earn enough to
support a household independently for the first time.
B Conceptual Framework
Textile production following the cotton revolution, represented an new opportunity for women to
earn monetary income, and contribute to household income. As the return to producing textiles
was sufficiently high, women were induced to switch away from solely performing non-market
domestic work or producing other fabrics at low quantities mainly for home use.17
Specialization in cotton textile production was possible because of the existence of high devel-
oped goods markets. Shiue and Keller (2007) shows the performance of markets in China and
Western Europe overall was comparable in the late 18th century. Textile production at the
time, shared many similarities with work in the proto-industry in other advanced pre-modern
economies such as 18h century England. The following key differences deserve emphasis. (a.)
Chinese households typically owned the machines rather than renting them. Households occa-
sionally owned more than one machine and hired help. But the scale was very limited.(Fairbank,
1978) (b.) Few concurrent technology shocks occurred during the time frame (1300-1840). The
cotton revolution took place in an agrarian economy, and the economy remained largely agrar-
ian for the next sixth centuries. (c.) Though the goods market was both dense and highly
sophisticated, the labor market was far from being a free labor market. Emperors in Ming and
Qing institutes strict laws on labor mobility. The clan system continued to keep individuals tied
to their extended families (as discussed in Greif and Tabellini, 2010, 2015). This constrained
women’s ability to relocate to regions suitable for cotton textile production. (d.) Only a small
number of regions had the geo-climatic conditions suitable for spinning and weaving, and es-
pecially, for weaving. Cotton textile prices, for a long time, hovered at a level that generated
enough income for a skilled textile worker to support a family of four.18 (e.) A higher per-
centage of Chinese households owned land than British households involved in the putting-out
market, especially in the interior provinces.16The production of artisan cloth was backed up by popular demand of weddings and funerals in pre-modern
China. Its production requires both higher skills of weavers and longer hours.17This can be understood in the context of Pomeranz’s research on economics of respectability. In describing
the role of daughters in a family, he notes that a family’s capacity to survive and to profit from its work reliedupon “an optimal mix of family members of particular ages and sexes” (Pomeranz, 2005).
18Allen (2009) shows one day’s work by a weaver in the late 17th century produced 7,684 calories, which wasadequate to support a family.
8
system in the 18th century. Despite periodic increases in land concentration ratio, China had
no equivalent of the movement of enclosure Britain experienced (1600-1850). The vast majority
of Chinese households where women were weavers had their own land on which men had to field
work. Together (b.), (c.) and (d.) ensured that prices of cotton textiles stayed reasonably high,
whereas (a.) and (e.) led women to reaping most of the benefits from this revolution.
Chinese women had been doing productive work prior to the cotton revolution. But cotton
textile production enabled woman to take on a new role as their household’s major income
earner. By the late Ming period, both unmarried and married women in many textile regions
became predominantly producing for the market, and in many cases their income became the
main source of family incomes. Pomeranz (2005) argues that women became more respectable
due to their highly productive manual labor in textile production.19 And it is highly plausible
that from the perspective of parents, it became less mentally stressful and financially costly
to accept a daughter into the family as women had a potential role as productive members of
the economy in their own right. In fact, women’s ability to support themselves was frequently
tested in the case of widowhood: remarriage was discouraged in Ming and Qing China. However,
women in textile regions found themselves well capable of remaining solvent, in the absence of
their husband’s incomes.
Together this evidence suggests that the rise of the textile industry since the fourteenth century
constituted a significant shock to the level of women’s engagement in market activities and
greatly increased their economic independence. This shock could lead to the breakdown of
prior cultural beliefs concerning women’s role in providing for a family. (Bertrand et al., 2015)
discusses the importance of a gender identity norm—the view that a husband should earn a
higher income than wife—in marital formation and chances of divorce. They find that in fear of
making more money than her future husband does, wife is more likely to give up on work in order
to start a marriage. Meanwhile, dissolution of marriage is far more likely among couples who
violate this important gender identity norm. Their discussion of this gender identity norm helps
to understand the unique nature of the case of female empowerment driven by advanced cotton
textile technologies. Due to the appearance of a new technology and the presence of both a well-
functioning market and historically-specific government institutions, women in textile regions
had unparalleled earning opportunities by the standard of past agrarian societies. As a result of
this change, a large number of women in certain regions of China began to earn comparable or
higher incomes than most men did. In those regions it is plausible that the gender identity norm
that husband should always make more money than women broke down under the pressure of
this large relative income shock. This breakdown in traditional gender identity norms could
lead to the emergence of more favorable beliefs about women and a more optimistic assessment
19Man (2011) provides a summary of depictions of female breadwinners being tough and dependable inhistorical accounts. Her sources include: (Chen et al., 1991; Gu, 1995; ?). In ?, Xu’s wife proudly proclaimsthat she single-handedly supports the family and is a ‘strong woman’, ‘she-husband’. Apparently, her husbandis just passionate about literary writing and paintings, and clueless to how to make both ends meet.
9
of the fates of prospective daughters.
The evolution of gender norms following a long period of taking a more prominent role in
providing for a family, can play a crucial role in gender gap and women’s well-being in present-
day China. Viewing general norms as a complex of nexus of different beliefs and attitudes
concerning the relative status of women, it is sufficient to note that gender norms are both
perpetuated from generation to generation (as shown theoretically by Bisin and Verdier (2001)
and as discussed empirically by numerous studies in sociology and economics (Moen et al.,
1997; Vella and Farre, 2007) and also shaped by the attitudes of others in society (Burda et al.,
2007). Both mechanisms generate cultural persistence and can explain why cultural values, once
established, can be difficult to dislodge.
Inherited gender attitudes shape a wide range of outcomes today. The most important one I
focus on in this paper is sex ratio at birth. When parents today make a decision as to whether
to have a boy or a girl, they do not have complete information on the future prospects of a
boy or a girl in contemporary world. They instead resort to general beliefs about whether boys
or girls are morel likely to thrive in society and to favor the family.20 Sex ratio imbalances
are an important indicator for differential values being assigned to each sex and thus a good
indicator for women’s status in society.21 These cultural beliefs are particularly important under
a one-child policy regime and can be exercised at low costs given the availability of sex-selection
technology.
To sum up, I argue that pre-modern textile production has a strong, persistent impact on
gender roles, gender norms and gender equality today. In particular, I hypothesize a relationship
between pre-modern textile production and gender equality in contemporary China. I theorize
that women’s use of a productive technology increases their social status and the desirability of
daughters. Women’s increasing relative contribution to household income generated new norms
about women’s role in the household as a main breadwinner. In textile regions people developed
the belief that women can protect families from destitution and allowed them to pay their taxes
just as effectively as men. The reformed gender norms and beliefs can persist even though the
economy has moved out of traditional cotton textile production.
20Altruistic parents who care about whether or not their children have fruitful lives will prefer to have boysif they live in a society where women are treated less well.
21President’s Commission for the Study of Ethical Problems in Medicine and Biomedical and Behavior Re-search of the United States in 1983 states that there is no evidence that amniocentesis is being sought widely todetermine fetal sex. Surveys of parents and prospective parents indicate, however, a preference for sons (espe-cially as the first-born child). If it became an accepted practice, the selection of sons in preference to daughterswould be yet another means of assigning greater social value to one sex over the other and perpetuating thehistorical discrimination.
10
III Data
I construct my main variable, pre-modern cotton textile production, from thousands of local
gazetteers published in historical times. I also construct contemporary measures of gender norms
and gender equality, historical and contemporary county characteristics from a large number of
modern censuses, historical sources and GIS files, and climatic and geographic characteristics
from the Climate Research Unit of University of East Anglia, FAO and NASA. From the digital
world map collection of Harvard University, CHGIS, I obtain shape files that contain historical
characteristics for the counties within China. For modern outcome variables, I use the county-
level National Population Censuses (1990, 2000, 2010) and the 2004 Industrial Census from
the China Geo Explorer, the Chinese City Statistical Yearbooks, individual-level census data
(1982, 1990) from IPUMS-International, and Chinese General Social Surveys (2005, 2010). To
construct large political and economic shock variables as well as past outcome variables, I tap
into local gazetteers and make use of economic censuses and statistics complied by missionaries
in the early twentieth century.
In this section, I mainly focus on data used in my county-level analysis, where a total of 1535
counties, 198 prefectures, 15 provinces, and 8 regions are used. Data sources for other historical,
geographic and contemporary variables can be found in the data appendix.
A Explanatory Variable: Pre-Modern Cotton Textiles
Based on local gazetteers between 1368 and 1840, I construct an indicator variable on pre-modern
cotton textile production at a county level. Local gazetteers were published by prefecture gov-
ernments and county governments, containing information on local produces and manufactured
products. I go through county-level and prefecture-level gazetteers to extract information on
cotton textiles.22
It is possible a county that started textile production first would see a larger impact of textile
production in shaping values and beliefs. However, the way local gazetteers were organized
does not allow me to pinpoint the starting point of production by county. Due to similar data
limitations, I cannot examine the quantitative dimension of textile production by county. I
also do not have the full knowledge of the quality of cotton textiles at the county level. As
quantity produced and quality can be potential sources of heterogeneity in the treatment effect,
the estimates should be interpreted as average treatment effect of historical textile production.
To obtain an estimate of the distribution of then textile-producing counties and prefectures
across China today, I map historical locations of cotton textile production into a map of China
in 2000. Due to name and boundary changes of historical counties and prefectures, I resort
to time-series maps of Chinese counties and prefectures to first determine historical locations
22In the data collection process, I mainly refer to the section on local specialties (shi huo zhi) for evidence ofcotton textile production.
11
of cotton textile production. To be specific, I match county names in the source with county
names in a point shape file comprising times-series counties, prefecture names in the source with
prefecture names in a a polygon shape file comprising time-series prefectures. Finally, I spatially
join both times-series maps with the county shape file corresponding with the 2000 population
census to obtain a county-level estimate of cotton textile production. Figure 1 displays the
location of pre-modern textile production.
Figure 1: Explanatory Variable: Pre-Modern Cotton Textiles
B Main Outcome Variable: Sex Ratio at Birth
I use sex ratio at birth from the 2000 Census.23 Data on sex ratios at birth are available at
the county level. My main outcome variable is sex ratio at birth.24 As the distribution of raw
sex ratios is entirely skewed towards the left, I use a common data transformation technique to
create z cores of raw sex ratios.
There is considerable variation in the extent of sex ratios. In 2000, at the county level, sex ratios
at birth range from 81:100 to 196:100. With the exclusion of five autonomous regions, I still
23The reason for mainly using sex ratio at birth in the 2000 census is that (a.) sex selection technologies werewidely available (Chen et al., 2013; Ebenstein, 2010). (b.) regional variation in one-child policy was limited tourban-rural differences. After 2000, some counties began to be experiment a two-child policy for parents thatwere both only-child. (c.) marriage rates were still high. Voluntary infertility was relatively rare. (d.) dataquality of the 2000 census is reportedly higher.
24Alternatively, I can derive a measure of logged form of the deviation of sex ratio at birth from the normalsex ratio, and I get very similar results using the alternative measure.
12
find a wide range of sex ratios (92:100 to 193:100) across counties. Figure 2 shows sex ratio at
birth in seven quantiles.
Figure 2: Main Outcome Variable: Sex Ratio at Birth
A male-biased sex ratio is a crucial indicator of gender bias. China has had the most unbalanced
sex ratios in East Asia for the past decade. In the 2000 Census, the national average sex ratio at
birth is 118:100, i.e. every 118 boys were born to every 100 girls. Prior to the one-child policy,
people resorted to higher-parity births to ensure male offspring. A major problem for identifying
the magnitude of gender bias in this setting is that the characteristics associated with low fertility
are often correlated with characteristics associated with gender equality. Though stopping rules
can distort sex ratios, the distortions become smaller as number of children increases. As a
result, places with high fertility and high levels of gender inequality may not necessarily have a
more distorted sex ratio than a place with lower fertility but low levels of gender inequality.
When levels of fertility are imposed rather than chosen, the relationship between gender bias
and sex ratio not only becomes more pronounced, but also more comparable across China. In
the 1980s, the state first initiated its one-child policy. Families have since lost much of their
autonomy to ensure male offspring through the channel of higher-parity births.25 As sex-selective
technology became widely available after late 1980s, families started to rely on ultrasound and
other technology to secure a son in their first birth.26 The strategy of sex selection at a lower-
25One-child policy was finally phased out on October 29, 2015. http://www.bbc.com/news/world-asia-34665539
26Depending on the household registration status, urban Chinese were allowed to have one child only, whereas
13
parity birth causes sex ratios within a family to be artificially chosen, contributing to sex ratio
imbalances on a much larger scale at the aggregate level (Ebenstein, 2010).
C Baseline Controls
In the baseline regression, I control for contemporary variables including current per capita
GDP, share of agricultural workforce, share of non-agricultural household registration, share of
ethnic population and provincial capital, historical variables including agricultural suitability,
proximity to the Grand Canal or the Yangtze River and treaty port, as well as a set of geographic
variables, such as latitude, longitude, ruggedness and distance to coast.
I obtain most of contemporary variables from the 2000 Census. The current per capita GDP
is based on the map “2000 county GDP” at the digital map collection of Harvard University.
Historical controls include agricultural suitability, proximity to the Grand Canal or the Yangtze
River and treaty port status. Agricultural suitability data are downloaded from the FAO web-
site, used to proxy a county’s agricultural productivity. Rice and tea suitability used to test
the competing hypotheses are also from FAO. Proximity to the Grand Canal or Yangtze is con-
structed from CHGIS files.27 Pre-1300 commercial tax quota and historical courier routes used
to test competing hypotheses are similarly from the digital map collection of Harvard University.
Distance to the nearest coast and ruggedness are constructed from NASA data. To account for
cultural and endowment differences across regions, I use Skinner socioeconomic macroregions
(Skinner and Berman, Skinner and Berman) as region fixed effects.
D Descriptive Statistics
I construct my data set as follows. I exclude five autonomous regions, as well as autonomous pre-
fectures and counties in other provinces, that historically comprise ethnic minorities. Descriptive
statistics for the county-level analysis can be found in Table 1.
Table 1 gives an overview of the key variables in the main sample. A total of 1513 counties
are included. Sex ratio at birth is for Year 2000. Most modern variables are for 2000 as well,
unless otherwise noted. About 40% of the counties had some form of cotton textile production
before 1840. Average sex ratio at at birth for 2000 is 118.9 boys per 100 girls, with a standard
deviation of 14.2. Roughly 10% of the counties are on a major trade network (Grand Canal or
Yangtze). An average county has a value of 4.398 on the humidity-for-weaving index, with a
standard deviation of 2.277.
rural Chinese were allowed to have a second child if the first-born was a girl27 Due to lower transportation costs, a good number of counties located near the Yangtze River and the
Grand Canal produced textiles in pre-modern China. Huang (1964b) emphasizes the importance of the GrandCanal in Ming China, confirming that many counties famous for textile production were located in the GrandCanal area, and the size of trade was considerable. This could pose a challenge to my identification strategy,which I will discuss in the next section.
14
Table 1: Summary Statistics: County-Level Analysis
Variable Mean Std. Dev. Min. Max. NSex ratio at birth 119 14.273 91.622 193.16 1535Pre-modern textiles 0.42 0.494 0 1 1535
Logged share of ethnic minorities -0.88 1.608 -4.605 4.544 1533Logged per capita GDP 13.246 1.117 3.472 18.154 1530Logged per capita GDP2 176.704 27.184 12.056 329.583 1530Logged share of agriculture workforce 3.922 0.985 -2.408 4.57 1535Logged share of non-agriculture registration 2.845 0.701 1.105 4.531 1535Provincial capital 0.102 0.302 0 1 1535Treaty port 0.123 0.329 0 1 1535Agricultural suitability -4.309 1.915 -8 -1 1535On the Grand Canal or Yangtze 0.089 0.285 0 1 1535Log (ruggedness+1) 1.193 0.792 0.078 3.083 1535Logged distance to coast 5.395 1.319 0.083 7.07 1535Latitude 31.805 4.801 20.42 41.773 1535Longitude 114.224 4.285 101.584 122.391 1535
Humidity-for-weaving index 4.423 2.283 1.562 8.333 1535
Yangtze Delta 0.173 0.378 0 1 1535Net in migration 0.012 0.296 -0.293 4.733 1535Sex ratio, aged 5-9 115.15 10.103 95.035 165.582 1535Sex ratio, aged 1-4 121.9 16.352 91.655 204.068 1535Sex ratio, aged 1-4, 2010 119.046 11.472 92.004 161.656 1461Sex ratio at birth, 2010 117.65 10.813 86.400 176.744 1461Women’s years of schooling 7.072 1.023 4.48 11.28 1535Men’s years of schooling 8.164 0.856 6.09 11.91 1535Years of schooling 7.632 0.92 5.520 11.48 1535Women’s years of schooling, 2010 8.427 1.141 5.560 12.85 1461Men’s years of schooling, 2010 9.326 0.951 6.07 13.39 1461Women’s illiteracy rate, 1990 0.343 0.108 0.095 0.748 1126Men’s illiteracy rate, 1990 0.138 0.062 0.018 0.422 1126
Rice suitability 5.937 1.739 1 9 1503Tea suitability 88.131 132.13 0 287 1503Log (#textile companies+1) 2.79 1.626 0 7.87 1535#courier routes 0.434 0.841 0 6 1535Logged (Commercial tax+1), 1077 2.631 3.746 0 10.837 1535Log (communicants per 10,000+1) 1.53 1.095 0 5.254 1038
15
IV County-Level OLS estimates
Having constructed county-level measures of pre-modern textile production, I can examine the
relationship between pre-modern textiles and gender equality in present-day China. I begin by
examining variation at the county level. My outcome variable is sex ratio at birth. I test my
hypothesis by estimating the following equation:
Sex ratio at birth = α + βPre-modern textilesc + XHc Ω + XG
c Λ + XCc Π + εp , (1)
where c denotes a county. Pre-modern textilesc is my measure of pre-modern textile production
at a county level. XHc is a vector of historical controls, and XG
c and XCc are vectors of geographical
and contemporary controls respectively, each measured at the county level.
XGc and XH
c are intended to capture geographic and historical characteristics that may have
been correlated with pre-modern textiles and may still affect present-day outcomes. I control
for whether the county is on the Grand Canal or the Yangtze River, the major trade networks
at the time, as how pre-modern textiles were located was likely influenced by access to market.
To account for geographic differences across counties that may be correlated with openness to
trade, I include in XGc logged distance to coast and logged (ruggedness+1). As historical China
was an agrarian economy, I include in XGc agricultural suitability as a proxy for agricultural
productivity as well as latitude and longitude. To account for an external intervention in re-
cent history–the establishment of treaty ports by western powers—I also include “treaty port
status” as a control. To deal with norms such as patrilocality and concern for women’s purity
(Jayachandran, 2015) and other differences across regions, I include regional fixed effects cor-
responding to Skinner’s Socioeconomic Macroregions. Skinner’s Socioeconomic Macroregions
capture deep-rooted differences across regions, and bisect provincial boundaries in many cases.
The contemporary control variables XCc include the natural log of a county’s per capita GDP
measured in 2000 and its squared term, share of agriculture workforce, share of non-agricultural
household registration, share of ethnic population, governance status of the prefecture contain-
ing the county, governance status of the county, and provincial capital status.28 I use share
of non-agricultural household registration to capture an important source of variation in the
one-child policy.29 Governance status of the prefecture containing the county, governance status
28The logic behind the inclusion of the squared term of log per capita GDP can be found in Chung and Gupta(2007). Chinese prefectures and counties underwent institutional reforms after 1982. Level of political central-ization started to vary across prefectures and counties. I use two categorical variables to characterize governancestatus and degree of centralization of a prefecture or a county. Governance status of the prefecture takes thevalue of one when the prefecture has no centralized government; zero when it has a centralized government.Governance status of the county takes one when it is governed by the prefecture-level government, two when itis self-governed, and three when it is governed by the province-level government.
29While the one-child policy was strictly enforced among Chinese citizens on non-agricultural registrationstatus, a more relaxed version of the one-child policy was enforced among those on agricultural householdregistration status.
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Table 2: Pre-Modern Textiles and Sex Ratio Imbalances: OLS Results
Sex ratio at birth(1) (2) (3) (4)
Pre-modern textiles -3.311∗ -4.117∗∗ -3.404∗ -3.555∗
(1.754) (1.706) (1.691) (1.686)Log per capita GDP 2.064∗
(0.988)Log per capita GDP2 -0.144∗∗
(0.0487)Logged share of agriculture workforce 0.736 0.508
(0.685) (0.686)Logged share of non-agriculture registration -5.595∗∗∗ -5.653∗∗∗
(1.494) (1.510)Provincial capital 3.060 2.009 0.764
(2.058) (2.090) (1.986)Historical controls Yes Yes Yes YesGeographic controls Yes Yes Yes YesRegion FE Yes Yes Yes YesProvince FE No Yes Yes Yes
Observations 1528 1535 1533 1535Adjusted R2 0.360 0.284 0.353 0.310
Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: The table reports the impact of pre-modern textiles on sex ratio imbalances. Theunit of observation is a county in 2000 Census. The dependent variable is sex ratio atbirth. Column 1 reports estimates with all controls along with region and province effects.“Historical controls” are treaty port status, agriculture suitability, and whether a countywas on the Grand Canal or the Yangtze River (major trade network). “Geographic con-trols” are log of ruggedness plus 1, log of distance to coast, latitude, longitude and theirinteraction. Column 2 shows estimates when all contemporary controls are dropped. Col-umn 3 excludes log per capita GDP on the grounds of potential endogeniety. Column 4drops most contemporary variables such as share of ethnic population, share of agricultureworkforce and share of non-agriculture registration but keeps provincial capital. Robuststandard errors are clustered at the province level.
17
of the county, provincial capital is intended to capture differences in enforcement of one-child
policy, political control and government-led growth. Both share of non-agricultural household
registration and governance status could have an effect on sex ratios through one-child pol-
icy (Ebenstein, 2010). In addition, as there is a clear ethnic and cultural component in son
preference, I control for share of ethnic minority population to reduce composition bias.30
OLS estimates of equation (1) including above controls are reported in Table 2. Column 1
reports estimates with all controls along with region and province fixed effects. Column 2, 3
and 4 reports specifications with potentially endogenous variables excluded. Column 2 shows
estimates when all modern controls are excluded, Column 3 shows estimates when per capita
GDP and its squared terms are excluded from the specification, and Column 4 shows estimates
when only provincial capital is included as a contemporary control.
The estimates show that in counties with pre-modern textile production, fewer girls are missing
today. The coefficient estimates are both statistically significant and economically meaningful.
Based on the estimates from Column 1, one unit increase in pre-modern textile production is
associated with a decrease of sex ratio at birth by 1.62 boys per hundred girls (1.62=3.31*0.49).
As I drop all modern controls, the size of the coefficient further increases to -4.117, suggesting
that some of the modern controls are themselves outcomes of pre-modern textile production.
Column 3 suggests the strength of the coefficient for textile production does not depend on
whether I control for per capita GDP.31 In Column 4, I drop most of modern controls but keep
provincial capital status. The coefficient is rather similar to what is in Column 1-3. This will
be the baseline model for the rest of the county-level analysis .
A Robustness checks
A.1 Subsamples
I first check the robustness of my results to the use of alternative samples. Motivated by the
fact that the Yangtze Delta is of special importance to Chinese economy both historically and
contemporarily, I test to see if my results are robust to the control or the omission of three
provinces (Jiangsu, Zhejiang & Shanghai) from the sample.
Next, I look at counties with different rates of migration. Historically, labor mobility was low
due to the control of the clan system. In modern China, the speed of migration has picked
up. Gender norms in the less developed regions of China could have been strengthened if
individuals with more progressive gender norms are more likely to move to developed areas for a
better life. Hence my results could be biased if textile locations are correlated with unobservable
30Autonomous counties and prefectures, which are predominately resided and governed by ethnic minorities,are already excluded from the main sample.
31Though large sex ratio imbalances are a relatively new phenomenon in China, per capita GDP could stillhave already been affected that the sex ratio, i.e. it is possible that per capita GDP is partly endogenous to sexratio imbalances (Wei and Zhang, 2011).
18
characteristics of counties that attract many modern migrants. For robustness, I control for net
in-migration, or omit counties with positive net in-migration.
Table A.1 in the appendix summarizes the results. The coefficient estimates are relatively stable
across the columns. Coefficient estimates are greater for counties outside of Yangtze Delta or
areas that have no net in-migration.
A.2 Sex Ratio at Birth from More Years
In the main analysis, I focus on sex ratio at birth in the 2000 Census. A natural question is
whether the same pattern holds for years within the close range of Year 2000. Based on 2000,
2010 census data, I construct two additional variables from the 2000 Census: sex ratio for aged
1 to 4, sex ratio for aged 5 to 9; and two variables from the 2010 Census: sex ratio at birth,
sex ratio for aged 1-4. I find coefficients of pre-modern textiles in Table A.2 are fairly close
to coefficients in Column 5 of Table 2. The coefficient of sex ratio at birth for 2010 is much
smaller and not statistically significant. I attribute this to the data reporting procedures used
in the 2010 Census.32 There is no reason to believe that the true sex ratio at birth suddenly
dropped for the 2010 cohort, but not for cohorts 2005-2009. Overall, I find pre-modern textile
production affects the cohorts born after 1990 in a highly consistent way. The relationship
between pre-modern textiles and sex ratio at birth revealed by the 2000 census is unlikely just
a fluke.
A.3 Other Gender Outcomes
Table A.3 examines alternative gender outcomes. I find pre-modern textile production also
predicts women’s educational attainment. In 2000, pre-modern textile production is associated
with an increase of 0.167 years of schooling for women. The coefficient is reduced to 0.106
after controlling for men’s education. Coefficients are very similar for 2010. Pre-modern textile
production is also associated with a reduction of 3.1 percentage points in female illiteracy rates,
or 1.14 percentage points when male illiteracy rate is controlled for with a p-value of 0.11.33
B IV Estimation
A potential concern with the OLS estimates is that the counties that were textile producers
may have a higher likelihood of adopting textile technologies. It is possible that counties that
were economically more developed were more likely to have adopted textile technologies, and
counties that were closer to the market or transportation routes were more likely to sustain its
production and make greater profits. If these counties were more commercial and had “modern”
32Due to a booming migrant population, the 2010 Census resorted to the recording method of “recordingevery individual encountered”. Double counting of the 200-million floating population became highly probableevents. News sources suggest this method might have opened the door to data manipulation by lower-levelgovernments, due to the political pressure on “keep the sex ratio at birth below 120” emerging after 2000.http://blog.people.com.cn/article/1354459332295.html
33For the 1990 Census, though data on women’s education are available by educational level, they are notaggregated to the “year of schooling” variable.
19
gender norms, this would bias the OLS estimates away from zero. Though a set of variables
(mainly overall agricultural suitability, distance to the major trade network, distance to the
coast, ruggedness and regional fixed effects) have been included in the main specification and
its variants, I am unable to address likely issues caused by unobservable characteristics, such
as attitudes towards women prior to textile production. Besides, due to imperfect data on pre-
modern textile production, some of the coefficient estimates can suffer attenuation bias due to
measurement error.
To derive an exogenous source in determining the location of textile production within China I
use climate data. An important determinant for the location of textile industry is geo-climatic
conditions. Among all contributing factors, scientists, engineers and industry experts highlight
the importance of relative humidity in producing textiles. In a report on the textile industry in
China (1909), the word “humidity” occurs more than 100 times, suggesting the pivotal role of
humidity in the textile industry.
Climatic conditions were a crucial determinant in the geographic variation in spinning and
weaving in pre-modern China. Spinning and weaving far more likely to occur in humid counties.
Even when spinning and weaving were carried on relentlessly, less humid counties often failed
to produce as high quality cotton fabrics. A lack of humidity severely hampered a county’s
performance in the high-ended markets, as top-notch cotton cloth could only be weaved when
relative humidity was close to or greater than 70%.34 Apart from average relatively humidity,
variance in humidity could also affect production decisions. Textiles could be produced much
more efficiently during parts of the day, and parts of the year that were comparatively humid. For
places that experience greater variance in humidity within the day, the number of hours available
for textile production could be quite limited, regardless of the average relative humidity. A textile
machine represented a large fixed cost. For a family the decision to own textile machinery the
total number of hours possible for textile production would be a key consideration.
The Climate Research Unit of University of East Anglia provides 30-year monthly average
relatively humidity data across 10 arc-minute by 10 arc-minute grid cells globally. I extract
relative humidity values on the basis of x, y coordinates. I construct a relative humidity variable
at the county level by averaging over all relative humidity values within a polygon that represents
a county. I then construct a humidity-for-weaving index as follows. First, every county receives a
score ranging from 1 to 5 for each month, based on the distance between actual relative humidity
for the month and suitable humidity for weaving.35 Second, I add up monthly scores. I get a
34Gazetteer data suggest that a relatively small number of counties were able to produce cotton textiles inpre-modern China. By 1840, up to 40% of the counties participated in textile production, varying in quantityand quality.
35In the absence of modern humidification facilities, hardly any textiles can be produced when relative humid-ity drops below 60%, and that the benefit of moist air starts to wear off once relative humidity exceeds 80%, i.e.there is a non-linear relationship between relative humidity and suitability for textile production. To account fornon-linearity in the impact of relative humidity on historical textile production, I set the lower-bound relativehumidity for feasible production to be 60%, and make it take a value of ”5” if actual relative humidity is below
20
number ranging from 12 to 60 for each county, with 12 being the most suitable, and 60 being the
least suitable. Third, I take the inverse of the total score to build an humidity-for-weaving index
where suitability increases in its value. This index can be seen as approximating the number of
months available for production with a gradient to quality and efficiency. Figure 4 shows the
distribution of humidity-for-weaving index at a county level. Darker shades represent higher
relative humidity and hence, higher weaving suitability. Missing values are shaded white.
Figure 3: IV: Humidity-for-Weaving Index
Ideally, this variable would represent the hours available for textile production. In practice,
data do not exist on the relative humidity for any particular day, let alone variance within a
day. Hence I restrict my focus on the number of months humid enough for textile production as
demonstrated above. A benefit of having a relative humidity index specific to weaving is that
I can extract useful information from relative humidity while avoiding additional biases. Table
3 shows the constructed instrument is correlated with pre-modern textile production, and yet,
uncorrelated with factors normally associated with relative humidity, such as overall agricultural
suitability, tea suitability and tea suitability.
I begin my IV estimation by testing the relationship between my humidity-for-weaving index
and pre-modern textile production. As my textile variable is a binary treatment, I opt for a
Probit-2sls that uses a Probit model for the first stage. Panel A of Table 4 shows the estimates
from the first stage: humidity-for-weaving index is positively correlated with pre-modern textile
the cutoff. Once above 60%, a county will be scored on a lower number as its relative humidity increases, i.e.”4” for 61%-65%, ”3” for 66%-70%, ”2” for 71%-75%, ”1” for 76% or above.
21
Table 3: The Impact of Humidity-for-Weaving on Other Outcomes
(1) (2) (3) (4) (5) (6)Logit Logit OLS Logit OLS OLS
Pre-modern Provincial Agricultural On the major Tea Ricetextiles capital suitability trade network suitability suitability
Humidity-for-weaving 0.152∗∗ -0.118 0.00328 0.0989 3.275 -0.00851index (0.0595) (0.0728) (0.0676) (0.0869) (4.540) (0.0755)Baseline controls Yes Yes Yes Yes Yes Yes
Observations 1524 1524 1535 1045 1503 1503Adjusted R2 0.650 0.800 0.660Pseudo R2 0.420 0.246 0.296
Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: The table reports falsification tests of humidity-for-weaving index. The unit ofobservation is a county in the 2000 Census. The dependent variables are pre moderntextiles, provincial capital status, agricultural suitability, distance to the grand Canal orYangtze (on the major trade network), tea suitability and rice suitability. All controls inColumn 4 of Table 2 are included, with the exception of the one that happens to be thedependent variable in the very specification. Region and province fixed effects are includedin all specifications. Robust standard errors are clustered at the provincial level.
production. Second-stage results are reported in Panel B. Column 1 contains my OLS estimates.
Column 2 report my IV estimate with humidity-for-weaving index being the instrument. My IV
estimate is that a one-standard deviation increase in pre-modern textiles leads to a reduction of
sex ratio at birth by 3.8 boys per hundred girls (3.8=7.61*0.49). This is slightly greater than the
OLS estimate. The increase in coefficient estimates can be partly explained by the IV estimate
by the removal of attenuation bias due to the use of better measured data.
C Competing Hypotheses
C.1 Tea and Rice
Tea and rice production were two other economic activities in which women often participated.
Locations of tea and rice production, in some occasions, overlapped with textile production.
Qian (2008) finds a short-term increase in tea prices can enhance women’s household bargaining
power and increase the share of surviving girls. Perhaps either tea or rice production could
affect gender norms and shape attitudes towards women in the long run and also happen to be
correlated with textile production? I include tea and rice suitability in Column 1 and 2 of Table
A.4 and find no significant change in the coefficient of pre-modern textiles. Rice suitability, in
fact, is positively correlated with sex ratio at birth, confirming the negative role of agricultural
production in women’s status (Hansen et al., 2015).
22
Table 4: Pre-Modern Textiles and Sex Ratio at Birth: Instrumental Variable Analysis
First StageDependent variable: pre-modern textiles
(1) (2)
Humidity-for-weaving index N/A 0.081 ∗∗
(0.036)Baseline controls Yes YesRegion FE Yes YesProvince FE Yes Yes
Second StageDependent variable: sex ratio at birth(1) (2)
Pre-modern textiles -3.555∗ -7.617∗∗
(1.686) (3.993)Same controls as in the first stage Yes Yes
Observations 1535 1524
Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: The table reports IV estimates. The unit of observation is a county in the 2000Census. Humidity-for-weaving index is used as an instrument. The dependent variableis sex ratio at birth. Baseline controls are the same as in Column 1 and 2 of Table 2.Column 1 contains OLS estimates. Column 2 report IV estimates. Region and provincefixed effects are included in all specifications. Robust standard errors are clustered at theprovince level.
23
C.2 Pre-1300 Commerce
Overall economic development and commercialization, prior to the cotton revolution, could have
both promoted cotton textile production and given rise to progressive gender norms. I include
the pre-1300 commercial tax quota in Column 3 of Table A.4 and find only a small increase in
the size of coefficient of pre-modern textiles. Coefficients of pre-1300 commercial tax and its
square term are insignificant. Coefficient signs indicate a likely non-linear relationship between
past commerce and women’s status.36 In contrast to Bertocchi and Bozzano (2013), I do not
find past commerce per se to be consistently conducive to the status of women.
C.3 State Presence
The history of pre-modern textiles suggests that the state played a key role in the relationship
between cotton textile production and women. As I have noted the default norm that women
worked in textile production was partly due to long-standing state policies that required in-kind
taxes and which were most consistent with the paradigm of “men farm and women weave”. In
addition to being used in lieu of other goods for taxation purposes, cotton textiles were important
for military purposes, and were widely used to clothe the imperial army. Also, the state could
have also influenced the local adoption of new textile technologies by state promotion. Despite
the ubiquitous nature of today’s Chinese state, one might still suspect there is a degree of
persistence in state presence. A strong state presence could mean better enforcements of major
state policies, such as the one-child policy or the compulsory education reform. Therefore,
past state presence can simultaneously influence locations of pre-modern textile production and
gender outcomes in contemporary China. Historically, courier routes in China were used and
managed by the central government for conveyors and messengers to fast deliver high-profile
news (Yang et al., 2006). I include historical courier routes in Column 4 of Table A.4 as a proxy
for state presence. I find state presence is negatively correlated with sex ratio at birth. Greater
state presence does improve relative outcomes of women. However, the coefficient of pre-modern
textiles barely changes when historical courier routes are controlled for, suggesting that the two
effects are most likely independent of each other.
C.4 Industrial Persistence
An obvious hypothesis is that pre-modern textiles could shape gender outcomes through the
persistence in sectoral composition. With the availability of modern humidification technologies,
there is little reason to think textile companies continue to locate only in humid areas. However,
being in a naturally humid area might still attract modern textile companies due to cost-saving
considerations. In addition, human capital accumulated in pre-modern production might find its
36This is not overly surprising given the well-documented U-shaped female labor force function (Goldin andSchultz, 1995). Rich merchant families were more able to sacrifice labor incomes of daughters’ or wives’. Foot-binding, for example, started from the very well-off families as female members of those families had no need towork.
24
ways into modern textile production, attracting companies to locate close to where talents are.37
In Column 5 of Table A.4 , I include number of textile companies as a control. I find the scale
of modern textile production indeed reduces sex ratio imbalances. The size of the coefficient
of interest falls by a quarter, but remains highly significant. This suggests pre-modern textile
production does not affect women’s status in contemporary China solely through the channel of
modern textile production. Other explanations would be required.
V Micro-Level Analysis
I turn to a micro-level analysis that examines variation in women’s representation in high-
powered professions and women’s position in the family across individuals, using the 1990 Pop-
ulation Census available at the IPUMS - International. The time frame of the 1990 Census
allows an investigate gender norms just before the planned economy era reached an end. An
additional feature of this census is that they are particularly suitable for studying gender out-
comes prior to mass sex selection.38 The 1990 Census also has quite high data quality in the
sense that the size of the floating population was negligible at the time.
I construct the following two outcomes of interest: holding political or managerial positions
and head of the household.39 I construct a binary variable for “holding political or managerial
positions”. The variable takes the value of 1 for “legislators, senior officials and management”, 0
for other occupations. Missing values are assigned in the case that an individual is listed as “NIU
(not in universe)”. The “head of the household variable” takes the value of 1 for individuals
listed as “head of the household”, 0 individuals listed as “spouse”.
Table A.5 in the appendix describes my sample based on the 1990 Census.40 For an average
prefecture, 41% of its population live in places with pre-modern textile production. 1.7% of
the individuals hold political or managerial positions. 56% of the individuals are head of the
household; the rest 44% are listed as a spouse.
37 In this case, current textile production is an intermediate outcome of pre-modern textile production, a.k.aan endogenous variable.
38In the mass sex selection era, as part of the gender bias has already been reflected in sex selection, outcomesof the survivors—a pre-selected group— might not reflect the full extent of gender inequalities in society. Linet al. (2014); Hu and Schlosser (2015)
39The US Census used to have the “Head of the household” and an accordant variable “Relationship to headof the household”. But it has now switched to “Person 1” and “Relationship to first person listed on the question-naire”. https://www.census.gov/history/www/throughthedecades/indexofquestions/1980population.html I in-terpret wife heading the household for a currently married couple to be an indicator of her power in the family.
40Geographic coverage of my 1990 Census sample is comparable to that of the 2000 Census sample used in mycounty-level analysis. More than 98% of the individuals within the geographic coverage are Han Chinese, higherthan the national average of 93%. This is to be expected as five autonomous regions and autonomous countiesare not included in my analysis. In my micro analysis, I further restrict the sample to Han Chinese individuals.
25
My estimation equation is
yi,p =α + βPre-modern textilesp + θFemalei + ζPre-modern textilesp × Femalei
+ XHp Ω + XG
p Λ + XCp Π + XI
iΓ + εi,p ,(2)
where p denotes a prefecture.41 My outcome variables are “holding political or managerial
positions” and “head of the household”. Pre-modern textilesp is my prefecture-level measure of
pre-modern textile production.42 My variable of interest is the interaction term between pre-
modern textiles and female. If pre-modern textiles were effective in shaping gender norms in
favor of women, I should see the interaction term being significant. XHp , XG
p and XCp are the
same controls as in the county-level analysis.43 XIiΓ denotes current individual-level controls:
age group, marital status, employment status and literacy. Robust standard errors are clustered
at the prefecture level for all specifications.
Estimation results based on logit regressions are reported in Table 5. Coefficient estimates of
pre-modern textiles interacted with female are statistically significant for all columns. This is
consistent with the hypothesis that pre-modern textile production enhances women’s position
in society and at home. Coefficients of pre-modern textiles suggest pre-modern textile produc-
tion has little effect on men’s probability to take political or managerial positions, suggesting
that there is no systematic difference between pre-modern textile production and availability
of political or managerial positions in general. In all specifications, women are far less likely
to either take political or managerial positions, or be the head of the household. This suggests
that despite the socialist laws in favor of gender equality and the rich set of political and eco-
nomic tools available to the state during the planned economy era, women’s position in society
and at home was still not fully equitable with men’s. In Column 1 and 2, I find pre-modern
textile increases women’s probability of holding political or managerial positions. As I restrict
the sample to individuals living in a households with at least one married couple and who are
currently married in Column 3 and 4, the finding should be interpreted as a wife heading the
household rather than denoting female-headed households comprising women who have never
married or divorced women.
41In the IPUMS 1990 census data, individual residence is only recorded at the prefecture level.42 I aggregate the county-level indicator of pre-modern textile production to the prefecture level weighted
by county population. A prefecture unit is constructed from counties belonging to the sample used in thecounty-level analysis.
43XCp are county-level census data aggregated to the prefecture level weighted by county population. For XC
p
most controls from the census year 2000 are replaced with controls from the census year 1990.GDP per capita2000 is replaced by GDP per capita 1989.
26
Table 5: Pre-Modern Textiles and Status of Women : 1990 Census
Political or Managerial Position Head of the Household(1) (2) (3) (4)
Pre-modern textiles -0.00767 -0.0130 -0.313 -0.314(0.0913) (0.0798) (0.195) (0.194)
Female -1.991∗∗∗ -2.018∗∗∗ -5.890∗∗∗ -5.794∗∗∗
(0.0776) (0.0795) (0.232) (0.233)Pre-modern textiles 0.218∗ 0.232∗ 0.629∗ 0.648∗
× Female (0.118) (0.122) (0.375) (0.372)Individual controls No Yes No YesContemporary controls Yes Yes Yes YesHistorical controls Yes Yes Yes YesGeographic controls Yes Yes Yes YesRegion FE Yes Yes Yes YesProvince FE Yes Yes Yes Yes
Observations 2666125 2666125 1815655 1815655Pseudo R2 0.172 0.195 0.685 0.686
Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: The table reports the impact of pre-modern textiles on women’s position in societyat home. The unit of observation is a individual in the 1990 Census. The dependentvariable is binary. All estimates are based on logit regressions. Contemporary, historicaland geographic controls are the same as in Table 2, but contempoary controls here arefrom the census year 1990 instead. Individual controls include age group, marital status,employment status and literacy. Only married individuals are included in the sample forColumn 3 and 4. Robust standard errors are clustered at the prefecture level.
27
VI The Emergence and Persistence of Gender
Equality
A The Emergence of Gender Equality
A.1 Widow Survival: Changing Notions of Women’s Role in Society
Unlike the Europe Marriage Pattern (De Moor and Van Zanden, 2010; Voigtlander and Voth,
2013), pre-modern China featured universal marriage and early marriage. Unmarried and mar-
ried women alike had limited opportunities to participate in society on their own. However,
widows were given a certain amount of autonomy in making economic decisions for the house-
hold, despite the overall conservative family and property ownership laws in pre-modern China
(Afeng, 2002). However, prior to the cotton revolution, women typically lacked the means to
support themselves after their husband’s death. Remarriage was quite common. Things took
another turn after the 11th century. Influenced by Song-Ming Neo-Confucianism first developed
in the Song Dynasty (960–1279), on one hand, inheritance laws became more unfriendly to
women, creating barriers for women to inherit wealth from their deceased husband; on the other
hand, remarriage became stigmatized. Both changes greatly limited options available to widows.
The difficult circumstances faced by widows are not unique to pre-modern China. Widows in
developing countries today continue to face more or less similar problems: widows not only lose
the main breadwinner of the household, but also are restricted access to economic resources
due to property ownership laws and employment norms. Many studies document the role of
widowhood in excess mortality for unmarried adult women (Anderson and Ray, 2015; Miguel,
2005; Oppong, 2006; Sossou, 2002).
The cotton revolution in the fourteenth century greatly improved the prospect of widows. Ming
and Qing China witnessed an unprecedented number of widows who participated in a wide range
of economic and social activities. The precondition to widows’ participation in economic and
social activities is survival. Stable incomes derived from textile production played a conducive
role in widows’ survival. Relying on those incomes, widows not only survived, but had the
financial latitude to support their children and in-laws (Zurndorfer, 1998; Sommer, 2000; Elvin,
1984). Another aspect of textile incomes was economic independence. A strong financial position
critically shaped a widow’s status in the family of her deceased husband’s, and sometimes, in
her natal family. Between 1300 and 1850, the improvement of widow well-being, which was
closely related to cotton production, contributed to the broadening of women’s space in society.
(Pomeranz, 2004; Bray, 1997; Pomeranz, 2005; Zhao, 2015).
Data on the number of widows and their mortality in historical times are hard to come by. But
the records counties and prefectures kept on “virtuous women”—a state-sponsored historical
institution to commemorate widows with high morals—can be illuminating on the topic of widow
survival. In the spirit of Song-Ming Neo-Confucianism, women were praised for maintaining
28
female chastity after their husband’s death. Those women were called “virtuous” women and
often documented in local gazetteers for their glorified deeds. Before 1300, among all “virtuous”
women, half of the women were “chaste windows” who provided for her in-laws and children
for an number of decades, the other half were “heroic widows” who committed suicide upon
their husband’s death to demonstrate their exemplary character (Jiazun, 1979). After 1300,
cotton textiles began to financially empower women. The percentage of women who chose
chaste widowhood over suicide likely increased. I hypothesize that cotton textiles tilted women’s
decision towards chaste widowhood from suicide, as availability of financial means was key to
widow survival.44 All else equal, women with no financial means would be at a higher risk to
commit suicide.
To test the relationship between cotton textile production and widow suicide, I search local
gazetteers for records on “virtuous” women. To circumvent the problem of varying local stan-
dards of awarding “virtuous women” status, I focus on women awarded imperial testimonials of
merit (jingbiao) by the central government. To have a sense of the timing of transition—from
when cotton textiles began to positively shape women’s life trajectories—I start with records of
jingbiao from the Ming Dynasty (1368-1644). Because I perform the search on prefecture-level
gazetteers available on zhongguo fangzhi ku (China’s Gazetteer Database), Series I, I restrict the
sample to prefectures that had at least one prefecture-level gazetteer from the Ming Dynasty
available in the database. Based on the above criteria, a total of 89 prefectures are included in
the sample. Table 6 shows that 41 out of 89 prefectures have pre-modern textile production.
Table 6: jingbiao: Summary Statistics
Variable Mean Std. Dev. Min. Max. NWidow suicide 0.438 1.588 0 10 89Chase widowhood 1.815 4.617 0 34 89jingbiao 2.253 5.411 0 38 89Pre-modern textiles 0.466 0.461 0 1 89Pre-modern textiles, binary 0.461 0.501 0 1 89Latitude 30.395 4.73 19.193 40.015 89Longitude 115.129 4.331 104.048 121.409 89
These numbers supply qualitative evidence that suggests that pre-modern textile production
decreased the share of “virtuous” women committing suicide. In other words, women in regions
where cotton textile production was more important had a greater chance of survival, holding
constant their “virtuous women” status. In Column 3, A one-standard-deviation increase in
44To be awarded “chase widow” status, a long wait is required. According to Qing regulations, to be eligibleto the title of “ chase widow”, a woman either had to remain widowed since before the age of 30 years old tothe age of 50 years old, or had been widowed for ten years or more but died before reaching 50 (Mann, 1987).The long time frame required to be eligible to the “chase widow” status heightened the importance of havingfinancial resources at one’s disposal. Here I do not try to argue that having financial means was the single keyfactor in widows’ decision making; I acknowledge that many factors could be at play (Theiss, 2005; Ropp et al.,2001).
29
pre-modern textile production is associated with a decrease of 0.16 (0.32*0.51) search records
on widow suicide, or 37% of the mean of widow suicide records. Cotton textiles enabled women
to maintain a livelihood in the absence of their husband. From the perspective of parents, a
daughter’s ability to support herself under adverse circumstances reduces their mental and finan-
cial exposure in a world characterized by uncertainty.45 I argue that the image of a financially
empowered, capable and dependable unmarried adult woman, can lead to a notion that women
can be productive and independent members of society.
B Gender Norms and Female Labor Force Participation: Evidence from the Early Twentieth
Century
From 1840 onwards, China began to industrialize at a slow pace. Manufacturing jobs emerged
and they typically required workers to work outside home. Conservative gender norms and
concern for “purity” of women would predict few women would take manufacturing jobs. In
reality, female labor force participation was extremely uneven across regions.The presence of
women in industrial plants was much more common in Jiangsu, Zhejiang and Shanghai, where
women even outnumbered men. Women working outside the home was extremely rare in Zhili,
Shanxi and Shaanxi. Would pre-modern textiles influence women’s decision to participate in the
labor force? Ideally, I would like having disaggregated data at the very onset of industrialization
to examine how women’s initial responses to industrial job opportunities differed. Unfortunately,
a still largely pre-modern and agrarian Chinese state at the time, did not possess the capacity
to collect detailed labor statistics. One of the earliest censuses available that can help answer
this question is the 1916 Economic Census. The census documents number of male and female
workers working in a factory by province and industry, excluding household production workers.
Table ?? provides summary statistics. As seen in the table, in an average province-industry
pair, roughly 19% of the total workers were female, but with high variance.
Table 7: Summary Statistics: Share of Female Workers in 1916
Variable Mean Std. Dev. Min. Max. NPre-modern textiles 0.464 0.33 0.007 0.921 181%Female 0.189 0.252 0 1 181#workers 3839.652 11057.267 7 123127 181Log(#workers+1) 6.506 2.015 2.079 11.721 181
To investigate the role of pre-modern textile production in female labor force participation,
I regress share of female workers on pre-modern textiles. Table 8 suggests that in provinces
with a higher percentage of population exposed to pre-modern textiles, a large share of women
worked in factories. A few possible explanations are (a.) Persistence in specific skills. Women
45Bossler (2000) finds evidence for a continued relationship between a married woman and her natal family.While a woman became a member of her husband’s extended family upon marriage, her natal family could stillbe involved in times of crisis. This includes cases in which a widowed woman in poverty imposed a financialburden on her natal family.
30
Table 8: 1916 Economic Census: Pre-modern Textile and Share of Female Workers
Share of Female Workers(1) (2) (3)
Pre-modern textiles 0.156∗∗∗ 0.124∗∗∗ 0.0844∗
(0.0525) (0.0450) (0.0448)Log(#workers+1) 0.0445∗∗∗
(0.0134)Industry dummies No Yes Yes
Observations 181 181 181Adjusted R2 0.0362 0.266 0.338
Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes:The table reports the impact of pre-modern textiles on share of female workers in theearly 20th century. The unit of observation is an industry within a province. The dependentvariable is share of female workers. #workers referes to the total number of workers in anindustry within a province. Robust standard errors are used in all specifications.
who understood household production of textiles had an advantage in industrial production
of textiles. (b.) Persistence in physical mobility. Pre-modern textiles provided women with
opportunities to engage in market exchange. Women likely enjoyed a higher level of physical
mobility than their counterparts in other places. (c) Persistence in the role of bread-winning
females. Families used to incomes generated by women had to adapt to new economic realities
that manufacturing jobs were better jobs for women to support a family.
I rule out (a.) being the only explanation by showing that a larger share of female workers was
found not only in textile manufacturing plants, but also in other industries. In fact, pre-modern
textile production is positively correlated with the share of female workers in most industries,
except for fur making. Figure 4 illustrates this point with a scatter plot and a fitted regression
line for each of cotton textile manufacturing, knitting, dyeing, and match manufacturing. To
further distinguish between (b.) and (c.) I would need higher quality data. Either (b.) or
(c.) would be consistent with the hypothesis that pre-modern textile production generated
gender norms in favor of women and influenced a range of later outcomes through the channel
of reformed gender norms.
These results could suggest an alternative mechanism for pre-modern textile production to affect
modern-day outcomes: places that had more equal gender norms may have had more rapid
industrialization aided by abundant female labor.46 Once the economy began to industrialize,
growth and development can become proximate causes of better relative outcomes for women. To
deal with, in the next section, I explicitly control for early industrialization as well as investigate
its impact on the persistent effects of pre-modern textiles.
46Previous studies have shown the effect of proto-industry on the locations of modern industries (Farnie, 1979)
31
Figure 4: Share of Female Workers in 1916
C The Persistence of Gender Norms after 1840
C.1 Resilience to Large Political and Economic Shocks
Early Industrialization I try to address potentially uneven effects of the industrialization
and modernization processes across counties. China began to industrialize from the 19th century
onwards, first in treaty ports. Jia (2014) shows that treaty ports had a long-lasting impact on
local economies. This is a potential source of bias if pre-modern textile locations overlapped
with areas that experienced early industrialization, as gender norms might be affected by such
drastic economic and social change. Historical evidence suggests that this should not be a
major concern as industrialization in China was extremely limited and highly isolated (Fairbank,
1978).47 Column 1 and 2 of Table 9 shows coefficient estimates of textile production are robust to
controlling for or omitting treaty ports. Treaty port status has an independent effect on reducing
sex ratio imbalances. The interaction term between treaty port and pre-modern textiles is close
to zero and insignificant, suggesting pre-modern textile production has no differential impact on
modern sex ratio imbalances by treaty port status. The coefficient size of pre-modern textiles
increases slightly when all treaty ports are excluded in Column 2.
47During the late Qing and Republican China era, much of the rural and hinterland China continued toperform household production and their traditional lifestyles.
32
Missionary Influence Missionaries came to China to spread Christian religions in the 19th
Century. They built churches, schools and hospitals. While only a small percentage of Chinese
populations were converted, some of them might have had a disproportional influence on the
rest of society. Since Christianity emphasizes the value of life, and specifies different gender
norms from traditional Chinese religions, missionary activities might have changed local gender
norms. To check this, I first include logged number of communicants per 10,000 plus 1 in the
regression in Column 5. As expected, the percentage of believers in the population is positively
correlated with a more normal sex ratio. The coefficient size of pre-modern textiles increases
by roughly one-fifth, suggesting some of the effects of pre-modern textile production might
have been previously masked by missionary activities in counties with no history of cotton
textiles. I then include both logged number of believers per 10,000 plus 1 and its interaction
with pre-modern textiles in Column 6. The positive sign of the interaction term hints at the
possibility that the effect of pre-modern textile production is less persistent where the percentage
of believers is high, but its coefficient is insignificant.
33
Tab
le9:
Per
sist
ence
sin
ce18
40:
Inte
rmed
iate
Sh
ock
s
Sex
rati
oat
bir
th(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
Ear
lyM
issi
onar
yS
tate
Eco
nom
icA
llN
oin
du
stri
aliz
atio
nin
flu
ence
soci
alis
mre
form
ssh
ock
ssh
ock
Pre
-mod
ern
texti
les
-3.5
17∗∗
-3.7
79∗∗
-3.7
48∗∗
-5.5
83∗∗
-3.7
43∗∗
-3.5
13∗
-4.2
07∗∗
-4.7
04∗
∗-6
.123∗∗
∗-3
.842∗∗
(1.6
21)
(1.5
19)
(1.6
00)
(1.8
97)
(1.7
16)
(1.8
32)
(1.7
23)
(1.7
58)
(1.8
76)
(1.6
23)
Tre
aty
por
t-4
.796
∗∗∗
-3.3
00-3
.536
-5.1
29-5
.580
-5.0
87-8
.428∗
∗-3
.378
-3.6
13
(1.4
43)
(3.2
98)
(3.0
91)
(3.1
54)
(3.3
30)
(3.0
36)
(3.2
45)
(2.2
52)
(3.3
89)
Pre
-mod
ern
texti
les
-0.3
71-0
.406
×T
reat
yp
ort
(4.5
90)
(4.3
90)
Pro
vin
cial
cap
ital
0.78
5-0
.725
0.12
80.
151
-0.4
730.
833
0.4
01
-2.1
07
0.0
645
(2.1
13)
(1.6
82)
(2.7
55)
(2.7
05)
(1.7
21)
(1.9
01)
(1.9
09)
(3.0
25)
(2.7
31)
Pre
-mod
ern
texti
les
2.03
13.6
79
×P
rovin
cial
cap
ital
(2.6
43)
(4.4
00)
Ch
rist
ian
ity)
-0.8
86-1
.333
∗∗-1
.330∗∗
(0.5
53)
(0.5
86)
(0.5
77)
Pre
-mod
ern
texti
les
1.14
30.9
82
×C
hri
stia
nit
y(0
.996
)(0
.938)
On
the
coas
t-4
.097
-4.3
64
(2.4
36)
(2.9
92)
Pre
-mod
ern
texti
les
4.33
0∗
4.2
64
×O
nth
eco
ast
(2.1
87)
(3.4
95)
Bas
elin
eco
ntr
ols
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Reg
ion
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Pro
vin
ceF
EY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
Ob
serv
atio
ns
1535
1346
1038
1038
1535
1379
1535
1308
1038
1038
Ad
just
edR
20.
310
0.33
10.
305
0.30
60.
310
0.30
70.
312
0.3
01
0.3
06
0.3
03
Sta
nd
ard
erro
rsin
par
enth
eses
∗p<
0.10,∗∗
p<
0.05,∗∗
∗p<
0.01
Not
es:
Th
eta
ble
rep
orts
the
imp
act
ofin
term
edia
tesh
ock
son
sex
rati
oim
bal
ance
sas
wel
las
on
the
per
sist
ent
effec
tsof
pre
-moder
nte
xti
les.
Th
eu
nit
ofob
serv
atio
nis
aco
unty
in20
00C
ensu
s.T
he
dep
end
ent
vari
able
isse
xra
tio
at
bir
th.
Base
lin
eco
ntr
ols
are
thos
eu
sed
inC
olu
mn
4of
bas
eres
ult
s.C
hri
stia
nit
yis
mea
sure
dby
log
(com
mu
nic
ants
per
10,0
00+
1).
“on
the
coast
”re
fers
toa
cou
nty
wit
hin
50kil
omet
ers
ofth
eco
ast.
Col
um
n2
dro
ps
all
trea
typ
orts
.C
olu
mn
6d
rop
sal
lp
rovin
cial
cap
itals
.C
olu
mn
8d
rop
sal
lco
asta
lco
unti
es.
Col
um
n9
incl
ud
eal
lin
tera
ctio
nte
rms.
Col
um
n10
incl
ud
eson
lyb
asel
ine
contr
ols
an
du
ses
the
sam
esa
mp
leas
Col
um
n9.
As
par
tof
the
bas
elin
eco
ntr
ols,
trea
typ
ort
stat
us
and
pro
vin
cial
cap
ital
are
contr
oll
edfo
rin
all
spec
ifica
tion
s.R
ob
ust
stan
dar
der
rors
are
clu
ster
edat
the
pro
vin
cele
vel
.
34
Post-1949 State Socialism China has been promoting gender equality through laws, policies
and institutions for over half a century (Johnson, 2009). As gender equality has an important
role in the communist ideal, China passed the marriage law in 1950 to grant women the right
to free marriage and divorce, inherit property, and control of their children. The role of Chinese
women changed from a “family private person” of traditional society to a “social person”, and
Chinese women gained the same legal status as men. The Constitution of the People’s Republic
of China enacted in 1954 expressly stated that women and men enjoy equal rights. China also
mandated equal entry to the labor market and instituted equal pay for equal work for men and
women (Entwisle and Henderson, 2000; Hannum and Xie, 1994; Johnson, 2009; Yang, 1999),
and those equalization policies worked relatively well during the planned economy era. Despite
uneven economic growth, contemporary China has kept most formal institutions that guarantee
gender equality.
Post-1949 socialist policies have undoubtedly shrunk the absolute size of gender gap and trans-
formed gender norms to a large extent. However, it is less clear why post-1949 socialist polices
should be more intense in places with pre-modern textile production. There is little local vari-
ation in formal institutions of China because of its high degree of political centralization.48 A
potential source of variation in the enforcement of socialist policies is whether a county is in a
provincial capital. If this is true, the effects of pre-modern textiles could be less persistent in
provincial capitals where political shocks were larger. I drop provincial capitals in Column 4 and
find the coefficient stays almost the same. In column 3, I include the interaction term between
pre-modern textiles and provincial capital and find it to be slightly positive but not significant.
Recent Economic Reforms In the midst of post-1979 economic reforms, the state has re-
laxed part of its control on the economy and society. Previously hidden gender inequality has
since surfaced (Li and Lavely, 2003). On one hand, there remains very little variation in ei-
ther labor laws or maternity leave law at a local level. On the other hand, the recent thirty
years of growth in China could have led some regions to develop temporary rules or measures
that increase or decrease gender equality as a byproduct of economic growth. I am not overly
concerned with recent manifestations of already existing gender bias, as this could just be en-
dogenous to pre-modern textiles. To address the effect of heterogeneous economic growth and
economic institutions in recent years, I control for per capita income in county-level regressions
(Column 1 and 2 of Table 2), along with other contemporary controls. To see if economic re-
forms are associated with differential persistent effects of pre-modern textiles, I interact coastal
counties with pre-modern textiles in Column 7.49 I find that large economic shocks appearing
in the coast area might indeed weaken the effect of pre-modern textiles. Yet, the insignificant
48Urban authorities in China have little or no ability to shape labors laws and policies at a local level. RuralChina does have more policies generated through democratic processes at the local level (O’brien and Li, 2000).
49China’s export-led economy in the past 30 years has rendered coastal regions a tremendous growth advan-tage. Yangtze Delta and Pearl River Delta are home to million of exporters.On the side of policy interventions,the central government created the earliest special economic zones all on the coast too.
35
coefficient means that no reliable interpretation can be inferred from these results.50 In Column
8 I simply drop all coastal regions, and find the coefficient size of interest increases by roughly a
third. This suggests that pre-modern textile production has a larger effect of reducing sex ratio
imbalances in the non-coastal region than in the coastal region.
I include controls for large political and economic shocks and interacted them with pre-modern
textiles in Column 9. This results in a sample of 1038 counties. I find pre-modern textiles
reduces sex ratio imbalances by 6.1 boys per hundred girls in places with no exposure to any
of the large political and economic shocks, when those shocks are taken as exogenous. Within
the same sample, in Column 10, pre-modern textile production reduces sex ratio imbalanced by
3.8 boys per hundred girls. The analysis of those shocks suggests that the persistent effects of
pre-modern textile production are highly resilient.
D Gender-Role Attitudes and Son Preference in Contemporary China: Evidence from CGSS
Thus far I have reviewed the history of pre-modern textile production advancing women’s posi-
tion and shaping gender norms, and examined a number of outcomes closely related to gender
norms but not studied gender attitudes themselves. Now I directly turn to direct evidence on
gender norms. CGSS 2010 (Chinese General Social Surveys) provides an unprecedented op-
portunity to examine gender-role attitudes among the Chinese. The survey contains questions
regarding beliefs and attitudes associated with women. In addition, the CGSS includes informa-
tion on age group, gender, urban/rural site, marital status, education attainment, party member
status and household registration status.
The first measure of beliefs about women is constructed from each respondent’s view of the
following question: “Do you agree with the following statement: men are naturally more capable
than women?” The second measure comes from the question: “Do you agree with the following
statement: men should focus on career; women should focus on family?” The respondent can
choose from a scale of 1 to 5 ranging from “completely disagree” to “completely agree”. In
addition, I create a measure from two questions on the subjective assessment of how many sons
and daughters one wants to have. For those who answer they want more sons than daughters, son
preference takes on the value of 1. For those who are indifferent between sons and daughters, or
want the same number of sons and daughters, or want more daughters than sons, son preference
takes on the value of 0. I take a subsample of CGSS to match the geographic coverage of the
main sample used in Section IV. Summary statistics are available in Table A.6.
50Besides, heterogeneity in the treatment itself can not ruled out as an alternative interpretation here. Whileemperors between 1300 and 1850 banned ocean trade periodically between 1300 and 1850, the treatment ofpre-modern textiles differ in magnitude and intensity for coastal regions.
36
Tab
le10
:G
end
er-r
ole
Att
itu
des
and
Son
Pre
fere
nce
inC
onte
mp
orary
Ch
ina:
Evid
ence
from
CG
SS
Men
nat
ura
lly
mor
eca
pab
leW
omen
focu
son
fam
ily
Son
pre
fere
nce
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
OL
SL
ogit
Pre
-moder
nte
xti
les
-0.1
69∗∗
-0.1
99∗∗
∗-0
.190
∗∗∗
-0.0
883
-0.1
25∗
-0.1
17∗
-0.3
07∗
-0.3
21∗
-0.3
24∗
(0.0
663)
(0.0
646)
(0.0
677)
(0.0
734)
(0.0
675)
(0.0
664)
(0.1
76)
(0.1
84)
(0.1
85)
Age
grou
pN
oY
esY
esN
oY
esY
esN
oY
esY
esF
emal
eN
oY
esY
esN
oY
esY
esN
oY
esY
esE
duca
tion
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Fem
ale×
Educa
tion
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Indiv
idual
contr
ols
No
No
Yes
No
No
Yes
No
No
Yes
Con
tem
por
ary
contr
ols
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
His
tori
cal
contr
ols
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Geo
grap
hic
contr
ols
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Reg
ion
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvat
ions
5587
5585
5578
5594
5591
5584
5535
5533
5525
Adju
sted
R2
0.01
70.
093
0.09
80.
035
0.10
90.
116
Pse
udoR
20.
034
0.04
20.
045
Sta
nd
ard
erro
rsin
par
enth
eses
∗p<
0.10,∗∗
p<
0.05,∗∗
∗p<
0.0
1
Not
es:
Th
eta
ble
rep
orts
the
imp
act
ofp
re-m
od
ern
texti
les
onge
nd
er-r
ole
atti
tud
esan
dso
np
refe
ren
ce.
Th
eu
nit
of
ob
serv
ati
on
isa
surv
eyre
spon
den
tin
CG
SS
2010
(Ch
ines
eG
ener
alS
oci
alS
urv
eys)
.C
onte
mp
orar
y,h
isto
rica
lan
dgeo
gra
ph
icco
ntr
ols
are
the
sam
eas
inT
able
2.R
obust
stan
dar
der
rors
are
clu
ster
edat
the
cou
nty
level
.
37
I regress pre-modern textile production on beliefs about women and son preference with the
same controls used in Column 1 and 2 Table 2 as well as in Table 5. Robust standard errors
are clustered at the county level for all specifications. Table 10 summarizes the results. Column
1 through 6 report OLS results for beliefs about women. Column 7 through 9 focus on son
preference. Columns 1, 4, 7 contain no individual controls; Columns 2, 5, 8 include basic
individual controls such as age group, gender and education attainment; Columns 3, 5, 9 contain
a full set of individual controls. Individuals in counties with pre-modern textile production are
more likely to disagree with the statement that men are naturally more capable, or the statement
that women should focus on family, and less likely to favor sons over daughters. Pre-modern
textile production is systematically correlated with more progressive gender norms and weaker
son preference.
VII Conclusion
Women’s productivity in textile production varied from society to society historically, and it
often depended on the technology and local geo-climatic conditions. Anthropologists posit that
the shift from flax to wool in the ancient Middle East led to a decline in the status of women
as linen was cultivated on a small scale by women and children while wool production relied
on male management of sheep herds (McCorriston, 1997). In historical China, the switch from
linen to cotton empowered women (Bray, 1997).
This paper provides evidence that a portion of the variation in gender norms and gender inequal-
ity in modern day China can be accounted for by pre-modern textile production. It suggests
that gender norms can be shaped by large and long-lasting relative productivity shocks.
I use both OLS and IV to estimate the impact of pre-modern textile production on today’s sex
ratio imbalances. The results are robust to the exclusion of Yangtze Delta, a region famous
for pre-modern textile production and a number of features associated with a highly developed
historical economy. I also extend my analysis to include other variables more commonly discussed
in the context of gender equality, such as literacy and education. My finding from county-level
regressions cannot be explained by past tea or rice production, commerce prior to the period of
cotton textile production, state presence, current textile industry, or modernization hypothesis.
My micro-level analysis lends support to my county-level analysis, and generates additional
insights into how gender bias worked itself into economic and social outcomes during the planned
economy era. I find that pre-modern textile production helps to explain women’s position even
in the socialist period of China. In both 1982 and 1990 census, pre-modern textile production
is positively associated with share of women with political or managerial positions and wife
heading the household.
In addition to analyzing gender norms in contemporary China, I also look at the impact of
pre-modern textiles on the gender norms and gender equality in the past. I find evidence
38
for an adaptation in gender norms to the cotton revolution at latest by the end of the Ming
Dynasty (1366-1644). Pre-modern textile production likely reduced the rate of widow suicide
and increased quality of life, self-esteem and social status of widows at the time. Based on
the fact that widows, compared to married women, were more poised to making independent
decisions and as well as being visible in public space, I conclude the elevated status of widows
might have contributed to a more positive evaluation of women’s ability and their role in society.
Another historical period I examine is the onset of industrialization in modern China. I find pre-
modern textile production is positively associated with share of female workers in manufacturing
jobs. I interpret this as a reflection of more relaxed gender norms in places with pre-modern
textile production that allowed more women to work outside home to take advantage of new
economic opportunities.
I examine a number of large political and economic shocks and their impact on the transmission
of gender norms since China began to modernize in 1850. After acknowledging the role each of
those shocks in gender bias reflected in sex ratio imbalances, I find coefficients of pre-modern
textile production remain significant and similar in magnitude to baseline estimates. The persis-
tence effects of pre-modern textiles are overall highly resilient; coefficients of pre-modern textiles
interacted with those shocks do provide suggestive evidence that those intermediate shocks might
have served to temporarily or permanently weaken the persistence effects of pre-modern tex-
tiles on gender norms. Had there been none of those shocks, pre-modern textile production
might have reduced sex ratio imbalances by six boys per hundred girls rather than five boys per
hundred girls.
Finally, I investigate gender-role attitudes Chinese people hold in contemporary China, and I
find pre-modern textile production is systematically correlated with more progressive gender
norms and weaker son preference.
39
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A Appendix
Table A.1: Robustness Check: Subsamples
Sex ratio at birth(1) (2) (3) (4)Yangtze Delta Net in-migration
Pre-modern textiles -3.555∗ -4.497∗∗ -3.968∗∗ -5.238∗
(1.686) (1.703) (1.726) (2.779)Yangtze Delta -11.25∗
(6.252)Net in migration -3.379∗∗∗
(0.926)Baseline controls Yes Yes Yes YesRegion FE Yes Yes Yes YesProvince FE Yes Yes Yes Yes
Observations 1535 1361 1535 888Adjusted R2 0.310 0.311 0.288 0.299
Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: See Table 2. Baseline controls are the same as in Column 4 of Table2. YangtzeDelta provinces are Jiangsu, Zhejiang & Shanghai. Column 1 directly controls for YangtzeDelta. Column 2 omits all yangtze Delta provinces. Column 3 directly controls for netin-migration. Column 4 omits all counties with positive net migration. Robust Standarderrors are clustered at the province level.
44
Table A.2: Robustness: Sex Ratio at Birth after 1990
Sex ratio at birth
(1) (2) (3) (4) (5)Aged 5 to 9 Age 1 to 4 At birth Age 1 to 4 At birth
2000 2010
Pre-modern textiles -2.967∗∗ -5.085∗∗ -3.555∗ -3.132∗ -1.193(1.295) (1.913) (1.686) (1.527) (1.610)
Baseline controls Yes Yes Yes Yes YesRegion FE Yes Yes Yes Yes YesProvince FE Yes Yes Yes Yes Yes
Observations 1535 1535 1535 1461 1461Adjusted R2 0.272 0.364 0.310 0.385 0.245
Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes:The table reports the impact of pre-modern textiles on sex ratio imbalances amongyoung cohorts. The unit of observation is a county in the 2000 or 2010 Census. Baselinecontrols are the same as in Column 4 of baseresults. Column 4 and 5 only include countieswith no administrative code change between 2000 and 2010. Robust standard errors areclustered at the province level.
Table A.3: Robustness: Other Gender Outcomes
(1) (2) (3) (4) (5) (6)Women’s years of education Women’s Illiteracy2000 2010 1990
Pre-modern textiles 0.167∗∗∗ 0.106∗∗ 0.163∗∗∗ 0.130∗∗∗ -0.0314∗∗ -0.0113(0.0488) (0.0394) (0.0522) (0.0304) (0.0116) (0.00674)
Baseline controls Yes Yes Yes Yes Yes YesControl for male outcomes No Yes No Yes No YesRegion FE Yes Yes Yes Yes Yes YesProvince FE Yes Yes Yes Yes Yes Yes
Observations 1535 1535 1461 1461 1126 1126Adjusted R2 0.592 0.932 0.651 0.946 0.418 0.783
Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes:The table reports the impact of pre-modern textiles on women’s education andwomen’s illiteracy. The unit of observation is a county in the 2000, 2010 or 1990 Cen-sus. Baseline controls are the same as in Column 4 of baseresults. Column 2 and 4 controlfor men’s years of schooling. Column 6 controls for men’s illiteracy rate. Robust standarderrors are clustered at the province level.
45
Tab
leA
.4:
Com
pet
ing
Hyp
oth
eses
Sex
rati
oat
bir
th(1
)(2
)(3
)(4
)(5
)R
ice
Tea
Pre
-130
0Sta
teIn
dust
rial
com
mer
cepre
sence
per
sist
ence
Pre
-moder
nte
xti
les
-3.4
90∗
-3.3
94∗
-3.5
62∗
-3.4
31∗
-2.9
19∗
(1.6
82)
(1.7
22)
(1.6
87)
(1.6
73)
(1.5
53)
Tea
suit
abilit
y0.
0053
1(0
.005
10)
Ric
esu
itab
ilit
y0.
615∗
(0.3
07)
Pre
-130
0co
mm
erce
0.41
4(0
.825
)P
re-1
300
com
mer
ce2
-0.0
463
(0.1
04)
#his
tori
cal
couri
erro
ute
s-0
.766
∗
(0.3
63)
#te
xti
leco
mpan
ies
-0.8
21(0
.473
)B
asel
ine
contr
ols
Yes
Yes
Yes
Yes
Yes
Obse
rvat
ions
1503
1503
1535
1535
1535
Adju
sted
R2
0.31
70.
318
0.30
90.
311
0.22
8
Sta
nd
ard
erro
rsin
pare
nth
eses
∗p<
0.10,∗∗
p<
0.0
5,∗∗
∗p<
0.01
Th
eta
ble
rep
orts
the
resu
lts
ofte
stin
gco
mp
etin
ghyp
oth
eses
.T
he
un
itof
obse
rvat
ion
isa
cou
nty
in2000
Cen
sus.
Th
ed
epen
den
tva
riab
leis
sex
rati
oat
bir
th.
Bas
elin
eco
ntr
ols
are
thos
eu
sed
inC
olu
mn
4of
bas
eres
ult
s.P
re-1
300
com
mer
ceis
mea
sure
dby
log
(com
mer
cial
tax
qu
ota
in10
77+
1).
#h
isto
rica
lco
uri
erro
ute
sre
fers
tonu
mb
erof
cou
rier
rou
tes
pass
ing
aco
unty
his
tori
call
y.R
obu
stst
and
ard
erro
rsar
ecl
ust
ered
atth
ep
rovin
cele
vel.
46
Table A.5: Summary Statistics: 1990 Census
Variable Mean Std. Dev. Min. Max. NPre-modern textiles 0.446 0.444 0 1 140
Head 0.560 0.496 0 1 2596827Political or Managerial Position 0.017 0.13 0 1 3415654
Age—0 to 4 0.101 0.302 0 1 5828835
5 to 9 0.085 0.279 0 1 582883510 to 14 0.083 0.276 0 1 582883515 to 19 0.102 0.302 0 1 582883520 to 24 0.111 0.314 0 1 582883525 to 29 0.093 0.291 0 1 582883530 to 34 0.075 0.264 0 1 582883535 to 39 0.079 0.27 0 1 582883540 to 44 0.058 0.233 0 1 582883545 to 49 0.043 0.204 0 1 582883550 to 54 0.04 0.196 0 1 582883555 to 59 0.038 0.191 0 1 582883560 to 64 0.031 0.174 0 1 582883565 to 69 0.025 0.156 0 1 582883570 to 74 0.017 0.13 0 1 582883575 to 79 0.01 0.101 0 1 582883580+ 0.007 0.085 0 1 5828835
Marital status—Single/never married 0.449 0.497 0 1 5828835Married/in union 0.501 0.5 0 1 5828835Separated/divorced/spouse absent 0.004 0.062 0 1 5828835Widowed 0.046 0.21 0 1 5828835
Employment status—Employed 0.802 0.399 0 1 4259043Unemployed 0.006 0.077 0 1 4259043Inactive 0.192 0.394 0 1 4259043
Literate 0.804 0.397 0 1 5139572Han 0.982 0.134 0 1 5828835# own children under age 5 0.191 0.491 0 5 5828835# own family member 4.476 1.869 1 20 5828835At least one married couple 0.873 0.333 0 1 5828835
47
Table A.6: Summary Statistics: CGSS
Variable Mean Std. Dev. Min. Max. NPre-Modern Textiles 0.554 0.5 0 1 74
“Men are naturally more capable” 2.982 1.265 1 5 6699“Women should focus on family 3.62 1.226 1 5 6706Son preference 0.684 0.465 0 1 6634
Han ethnicity 0.976 0.154 0 1 6705Female 0.518 0.5 0 1 6718Urban site 0.61 0.488 0 1 6718Communist 0.173 0.379 0 1 6709Age–Less than 20 0.018 0.134 0 1 549821 to 30 0.116 0.321 0 1 549831 to 40 0.184 0.387 0 1 549841 to 50 0.252 0.434 0 1 549851 to 60 0.197 0.398 0 1 549861 to 70 0.134 0.341 0 1 549871 to 80 0.078 0.268 0 1 549881 to 90 0.02 0.14 0 1 549890+ 0.001 0.023 0 1 5498Marital status–Unmarried 0.081 0.272 0 1 5498Cohabited 0.002 0.045 0 1 5498Married 0.815 0.389 0 1 5498Separated 0.004 0.065 0 1 5498Divorced 0.015 0.121 0 1 5498Widowed 0.083 0.277 0 1 5498Educational attainment–Less than primary completed 0.162 0.368 0 1 5496Primary completed 0.012 0.109 0 1 5496Secondary completed 0.539 0.498 0 1 5496University completed 0.174 0.379 0 1 5496Household Registration–Registered as rural 0.613 0.487 0 1 5498Registered as urban 0.335 0.472 0 1 5498Registered as other 0.052 0.222 0 1 5498
48
