Technology Shocks, Relative Productivity, and Son Preference:
The Long-Term Impact of Historical Textile Production
Meng Xue
George Mason University
Abstract
This paper seeks to better understand the historical determinants of son
preference among Han Chinese. I test the main hypothesis that historical textile
production increased women’s labor productivity, improved their social status, and
affected the evolution of son preference. I exploit county-level variation in textile
production, following a technology shock in the 13th Century, to identify the causal
impact of historical textile production. I find that places with historical textile
production have lower son preference today. The findings are robust to alternative
measures of gender attitudes.
Keywords: Culture, textile production, son preference, historical persistence
I Introduction
Son preference is observed in many developing countries. With the advent of
sex-selective abortion methods, people have become able to manipulate the sex of
newborns. In recent years, son preference has resulted in phenomenal sex ratio
imbalances (SRI) in South Korea, Taiwan, Mainland China and India. To examine the
causes of son preference, scholars have looked at various socio-economic
determinants including per capita income, education, female labor participation, total
fertility and culture. In studying sex selection among East Asian countries, culture is
pivotal: Confucianism, the common cultural factor, is likely to be responsible for sex
imbalances in South Korea, Taiwan and Mainland China’s SRI (Gupta 2003). Today’s
China features high levels of SRI, which reflects the intensity and persistence of son
preference that has survived the persistent communist rule aiming to eliminate gender
differences.
Figure 1 China’s Sex Ratios for Age 0
Source: Fifth National Population Census (2000)
In fact, China’s SRI has been the most severe in East Asia in the past decade. The
state’s one-child policy limits the family’s ability to produce more children to
accomplish the goal of having a son, and naturally, demand for sex-selective
technology has increased. In the 2000 Census, the national average sex ratio for Age 0
is 118:100, which means every 118 boys are born with only 100 girls. Yet, a wide
range of sex ratios (from 81:100 to 196:100) are found in different regions (Figure 1).
This paper proposes that regional differences in son preference can be partially
explained by historical textile production during the 13th and 16th century, which has
marginally shifted the gender culture towards being more daughter-preferring.
An exogenous textile technological innovation dating back to 13th century China
provides us a unique opportunity to study the evolution of gender culture and its
impact on the rest of society. In the Yuan Dynasty (1271-1368), Huang Dao Po, a
Shanghai-born lady (1245—1330), made a breakthrough on both spinning and
weaving technology, after she traveled back from a remote island south of mainland
China – Hainan Island. She made the most advanced textile tool of the time, a pedal
spinning wheel with three spindles, which resembles Spinning Jenny, the
British-version multi-spindle spinning machine invented 1764-1770. She acquired the
technology initially from Li People, an ethnic group residing in Hainan Island.
Huang’s innovation made cotton fabrics cost-effective first time in history (Bray 1997:
215; Kuhn 1998: 212). The decision to begin textile production for different regions
was most affected by technology accessibility and geo-climatic suitability. 1There is
1
Seeing the merits of cotton fabrics, Government encouraged cotton cultivation and textile production (Zurndorfe
2009) throughout the Yuan and Ming Dynasty. The founding emperor of the Ming Dynasty even made it a law that
little evidence showing the decision on textile production was constrained by
pre-existing gender culture. Textile production that took place in private homes gave
women in all possible places a good opportunity to help their family, no matter
whether they were educated or not educated, with or without children, had or had no
rights to participate in public activities. According to Pomeranz, textile production
was an ancient-Chinese version of “womanly skills” (Pomeranz 2003). (Women in
rural areas were the bulk of producers.) Textile production was a favored economic
activity, precisely because it continued to confine women at home.
One advantage of this paper is it exploits variations in historical textile
production on a relatively homogenous sample. Using data from one country can
avoid the complication of international comparability of data.2 Furthermore, I focus
on areas that have been historically inhabited by Han Chinese, as those are the areas
affected by the technology innovation. By restricting my attention to Han-Chinese
regions, I can minimize the impact of unknown observables and unobservables on the
question studied in this paper. As no massive migration has taken place between
textile regions and non-textile regions since the inception of textile production, this
approach is credible.
Literature on China’s historical textile production has been mostly on the impact
of textile production on the pre-modern Chinese economy (Pomeranz 2005, Huang
1990, Goldstone 1996, Li 1998 & Elvin 1999) during late Ming and Qing Dynasty
(after 1600), as part of the attempt to explain why an industrial revolution did not
each household should cultivate at least half a mou1 of cotton out of ten mou of land or beyond—this policy later
proved to be unrealistic for places unsuitable for cotton cultivation.
2
This issue has been pointed out by many authors, including Barro(1991)
happen in China. The mainstream view along those lines is women in China worked
too much at too low a cost, leading to a stagnant economy featuring little technology
advances; unlike Britain, where textile production was mechanized in less than thirty
years after the invention of Spinning Jenny, China’s textile manufacture was not
mechanized until five hundred years after its equivalent of Spinning Jenny. This
“work too much, too cheap” development path is the so-called “involution”, a concept
first created by Philip Huang. Contrary to this view, Pomeranz points out that in the
18th Century, women's earning power was much closer to their husband than were
English women of the same period, based on his calculation of the rice-buying power
of cotton cloth (Pomeranz 2005:243). He also argues women in textile production
were able to make more contribution to their family than they previously did, working
alongside their husband in the fields. Based on Pomeranz’s view, I make a further
claim that rapidly rising earning power can affect the well-being of women and
change people’s son preference.
In examining historical origins of existing cross-cultural differences in beliefs and values
regarding the appropriate role of women in society, Alesina, Giuliano and Nunn (2013)
point out that traditional agricultural practices influenced the historical gender division of
labor and the evolution of gender norms. This paper argues that change in gender relative
productivity could direct the evolution of gender culture in different directions.
This paper consists of six sections. The second section lays out the conceptual
framework. The third section lists data sources. The fourth section discusses the main
empirical strategy. The fifth section summarizes results and robustness tests. The sixth
section concludes the paper.
II Conceptual Framework
I examine the effect of the positive productivity shock generated by the textile
technological innovation on the value of women, their importance to the family, and
their social status. It is hypothesized that the value of women increased as their
earning power grew, inducing a change in gender culture. This hypothesis predicts
women’s participation in textile production Yuan/Ming Dynasty is negatively
correlated with son preference in contemporary China.
An increase in women’s productivity can simultaneously increase the value
placed on daughters and on wives. One of the most important implications of
Pomeranz's research is the value placed on daughters: a family's capacity to survive
and to profit from its work relied upon "an optimal mix of family members of
particular ages and sexes" (Pomeranz 2005:249). Holding the cost of having
daughters constant, the additional benefit of making daughters work before they are
married increases the willingness to have daughters. For a woman’s parents, her
pre-marriage textile work can turn her into a winning asset; she would otherwise be a
losing asset, as in historical China, her value to her parents would go down to zero on
the date of marriage. In the case of marriage, women’s higher productivity provides
higher future incomes for the family, making acquisition of a wife closer to an
investment than mere consumption. Absent the exit option to divorce, I cannot really
apply a household bargaining model to Ming-Dynasty China3, which provides a
3
In a bargaining model with divorce being the threat point, the wage increase of a woman can clearly affect her
bargaining power within the marriage by improving her welfare at the threat point.
mechanism through which women improve their intra-household status. Nevertheless,
women’s ability to make their own living, when marriage ends for irresistible reasons,
does open up more options to them. It was possible for widows in areas where textile
production was prevalent to continue to live a decent life. As remarriage was overall
discouraged after Song Dynasty, textile production allowed women to ensure their
materialistic well-being even in the absence of the marriage institution, while not
corrupting their morals4.
I postulate specific aspects of gender culture, i.e. son preference, can change in
response to a major shift in relative productivity, and once it changes, it tends to
persist against small, random economic and social changes over the time. Bishi
(2001)’s studies on the economics of cultural transmission demonstrate how exactly
values pass down generations; this paper, however, mainly focuses on cultural
evolution and historical persistence.
III Data
To study the long-term impact of historical textile production, I link historical
textile production with current outcomes of interest, measured at the county level. In
this research, I rely on China Historical GIS’s time-series province and prefecture
data. As longitudinal county-level data are only available for a small geographic area
(no more than a few southern provinces) and a short time frame (starting from 1911),
I use the place name lookup system to extend my research to more areas and earlier in
the history by manually matching the historical and current presence of each county.
4
The "three submissions" and the "four virtues" promoted by the Confucian school.
The historical documents I use, mostly prefecture-level chronicles, contain
information on produces and manufactured products of a great number of counties,
among which a smaller number of counties have cotton and/or cotton cloth as their
local specialties. I construct an indicator variable to reflect textile production: the
variable equals one if cotton cloth is a major product, and zero otherwise.
I primarily use the county-level Fifth National Population Census (2000),
prefecture-level Chinese City Statistical Yearbook (CCSY, from 1996 to 2000) and
NASA's Ocean Biology Processing Group’s distance to nearest coast dataset5 to
construct the outcome variable and covariates. Sex ratios for all age groups are
available, but sex ratio for Age 0 is of special interest to this paper, as it closely
measures son preference realized by sex-selective abortion. To overcome the potential
underreporting issues of newborn girls in 2000, I also extract sex ratios for this
specific cohort from 2005 1% National Population Sample Survey and from Sixth
National Population Census (2010). The Census contains information on education
attainment, urban/rural household registration, percentage of agriculture in total GDP,
total fertility rate, household size, ethnic population and migration, all on the county
level. Per capita GDP is measured on the prefecture level, obtained from 2000 CCSY.
Distance to coast measures the distance from the geographic center of a prefecture to
the nearest coast.
From a total of 3279 counties, I select a sample of 1856 counties, 227 prefectures
and 17 provinces or directly governed city regions. I exclude all provinces with no
5
http://oceancolor.gsfc.nasa.gov/DOCS/DistFromCoast/, original 0.04-degree data set
historical local chronicles, and I include neither non-territory nor non-Han regions in
the Ming Dynasty. Because I focus on Han-Chinese regions in this paper, my sample
excludes all ethnic autonomous provinces, prefectures and counties. After applying
the aforementioned rules of exclusion, I do include all other prefectures and counties,
whether a local government existed in that area back in Ming Dynasty. According to
geographic proximity and cultural similarities, I divide seventeen provinces into eight
regions: North (Hebei, Shandong & Henan), East (Shanghai, Jiangsu & Zhejiang),
Mid-South(Hunan & Hubei), Sichuan & Guizhou, Fujian, Shaanxi, Shanxi &
Guangdong.
To complement my empirical strategy, I perform an instrumental variable
analysis with the use of climate data. I mainly use 30-year monthly average relative
humidity across 10’ *10’ space (CRU CL 2.0) from high-resolution gridded climate
datasets obtained from the Climate Research Unit, University of East Anglia, UK.
IV Empirical Strategy
I derive both OLS and IV estimates of the effect of historical textile production
on today’s son preference. The baseline model is
, where
is an outcome of interest, i denotes counties,
is my measure of the historical textile production for County i, and
is a
vector of socioeconomic characteristics.
Running OLS does not help to exclude reverse causality, for instance, past
gender culture may well determine the choice of textile production as well as today’s
son preference. Little evidence suggests that is true; textile production has been
viewed quite harmless by traditional Chinese gender culture. Prior to the introduction
of new spinning and weaving technology, women had always been working at home
performing sericulture.6 Though gender culture is unlikely to directly block women’s
participation in textile production, the decision to adopt textile production might still
be correlated with unobservables, such as technology diffusion, location accessibility
and backwardness; ability to invest in the textile machinery, past income levels and
son preference; women’s talent and capability; and other endowments that might have
an effect on today’s son preference.
To overcome potential endogeniety, I perform an instrumental variable analysis.
I use various geo-climatic variables to construct instruments for textile production.
Ideally, I want to build a comprehensive measure of textile suitability comprising a
wide range of geo-climatic variables. In practice, I focus myself on studying the most
critical condition to textile production—relative humidity in the environment. Upon
research into textile production, I find cotton processing requires a very humid
atmosphere (60%-70%) so that fibers are pliable enough to be twisted into thread. In
the process of weaving, desirable relative humidity (78-80%) is even higher (“Textile
World Record,”1908). Areas without high enough relative humidity usually make no
cloth or cloth of very poor quality. As the 14th century technology constrains the
ability to artificially increase indoor humidity, relative humidity in the natural
environment is a deciding factor on textile production. Closeness to ideal relative
6
Silk was officially part of in-kind tax payments for many hundreds of years, until it was eventually partly
replaced by cotton cloth. No explicit cultural barriers seemed to exist to block textile production; moreover, the
working environment of textile production was highly compatible with culturally favored female seclusion and
foot-binding.
humidity, therefore, makes a good instrument for adoption of textile production. To
create a textile suitability instrument, I measure and aggregate the differences between
monthly average relative humidity and optimal relative humidity for spinning and
weaving. Based on the knowledge that ideal minimum relative humidity for weaving
is 75%, a higher-than-75% monthly-average relative humidity will be recorded as
“excellent”, receiving a score of “1”. Once below 75%, going down every 5% will
increase the value by 1, all the way up to 57. By adding up the monthly scores over 12
months, a county will receive a total score ranging from 12 to 60. I, therefore, build an
index of textile production geo-climatic suitability, in which a low score (closeness to
ideal relative humidity) translates into high suitability. For the convenience of
interpretation, I take the negative value of all scores in the actual IV regression, so the
sign of instrument will be positive, when more suitable climate predicts textile
production. The logic behind this instrument is the closer distance between actual
relative humidity and ideal relative humidity, the more suitable the climate is for
textile production, and the more likely textile production will emerge and endure.
Hence, relative humidity at the right level, via the channel of textile production,
should predict a higher son preference. To satisfy the exclusion restriction, an
instrument needs to be uncorrelated with error term, i.e. relative humidity should
influence son preference only through the channel of textile production. If relative
humidity is negatively correlated with son preference, for example, IV will
overestimate the effect of textile production. However, little evidence suggests
7
-20~0=1, "Excellent", 0.01~5=2, "Good", 5.01~10=3, "OK", 10.01~15=4, "Poor", 15.01~=5, ”Bad”.
humidity helps women to improve social status. In fact, son preference is much higher
in southern provinces, which are on average more humid, than in northern provinces.
V Main Results
Table I provides summary statistics. Table II reports the results of OLS estimates.
The first column summarizes the baseline estimates, which suggest historical textile
production lowers today’s sex ratio by 2.9 boys per hundred girls. Adding a squared
term of per capita income will increase the coefficient of historical textile production
to 0.031, i.e 3.1 boys per hundred girls. In the second column, ethnic is added to
control for the presence of non-Han Chinese, most of whom have no tradition in son
preference. The negative sign is consistent with the common belief that gender culture
is more egalitarian within ethnic minorities. 1% increase in percentage of ethnic
population decreases sex ratio by 0.1 boy per hundred girls, significant at 1% level.
The third column includes two controversially endogenous variables: total fertility
rate and household size. I use these two variables as a proxy for traditionalism. A high
total fertility rate signifies being at the early stage of demographic transition; a large
household size could indicate the presence of an extended family, which facilitates the
transmission of traditional values. Results show household size is positively
correlated with sex ratio, but total fertility rate is not a statistically significant
influence on sex ratio. The disadvantage of using these two variables are both may
well be the outcome of son preference, rather than the cause: high total fertility and
household size can entirely be a pure outcome of conducting high-parity births in
order to pursue male decedents. For this reason, Column III is the only regression that
includes these two variables, and it appears that inclusion of these two variables does
not significantly change the partial correlation of historical textile production with son
preference.
The last two columns deal with heterogeneity across subsamples. In the fourth
column, I simply drop the counties with a relatively large ethnic population (more
than 10%). Not too surprisingly, exclusion of ethnically diverse counties increases the
size of coefficient in front of historical textile production. The fifth column tackles
migration. Counties located in a prefecture where massive recent in-migration takes
place are excluded from the sample in this regression. The deciding rule is if a
prefecture has more than 10% of the total residents being so-called “floating residents”
who are not locally registered, any counties in that prefecture will not be included in
the sample. Again, this restriction of the sample further increases the size of the
coefficient of my variable of interest.
In all five columns, the estimates show that in counties with historical textile
production, any forms of sex selection are less likely to happen, i.e. daughters are
more likely to be born. All standard errors are robust standard errors. Coefficients are
negative and statistically significant at 1% level across all regressions. The estimated
coefficients for the control variables are generally as expected: sex ratio decreases
with women’s attainment of education, and decreases with men’s. In all specifications,
higher per capita income lowers sex ratio. Percentage of non-agricultural household
registration is negatively correlated with sex ratio, consistent with the belief that sex
selection happens more frequently to high-parity births which are more permissible
among people with rural household registration 8 . The sign of provincial capital
dummy is not well understood. It is unclear why being a provincial capital predicts a
higher sex ratio, at least in some specifications. Percentage of agriculture in total GDP
is controlled on a quartile level in the same way as distance to coast is controlled. The
general tendency is the more reliance on agriculture an economy has, the higher the
sex ratio is, reflecting the demand for male labor in a pre-industrial society. Distance
to coast is more complicated to interpret, since a big part of the inland China is
culturally non-Han. Counties that are farthest away from the coast (4th quarile)
actually have the lowest son preference, but otherwise coast to coast increases son
preference.
Alternative sex ratios are used to test the hypothesis; results are not included here
for brevity. The results are somewhat striking: historical textile production shapes the
sex ratio in the total population as well. A statistically significant relationship exists,
though on a much lower magnitude, between historical textile production and sex
ratio in the total population, suggesting that the “missing women” phenomenon
possibly existed before the enforcement of one-child policy and use of sex-selective
abortion. In fact, it is a well-documented fact that both female infanticide and neglect
for female infants are practiced to achieve the goal of having more boys and fewer
girls (Goodkind 1996).
One obvious concern for this paper is whether the total variations are driven by
only a small number of the counties. This concern arises from the superiority of the
8
This is the so-called 1.5-child policy among rural households: a second birth is permitted when the first child is a
girl.
economic environment in the Lower Yangtze Delta Region (Jiangsu Province &
Zhejiang Province) throughout the history. This is a valid concern, since Zhejiang &
Jiangsu (Jiang-Zhe) was indeed the center of the textile production with abundance of
skilled labor; both provinces also supplied the national market with highest-quality
cloth. One can argue that Jiang-Zhe is a special case: Jiang-Zhe was first exposed to
the textile technology innovation; Jiang-Zhe also had high-quality land, intelligent
and hard-working women, and likely more fair gender attitudes both in the past and at
the present. Jiang-Zhe’s combination of many nice qualities behind its top textile
industry also raises the question how exogenous the adoption of textile production
was. To address such concerns, my first step is to re-estimate the main equation
without all the counties in Jiang-Zhe. It turns out the estimates are roughly the same,
and my model does not lose its appeal in the absence of Jiang-Zhe.
To cope with the endogeniety concerns posed by the special case of Jiang-Zhe,
and other potential endogeniety problems, I introduce relative humidity as an
instrument. Table III summarizes the IV estimates. Panel A of Table III reports estimates
of the specifications from Table II, but historical textile production as the dependent variable
and the textile suitability as an additional explanatory variable. The estimates show that the
adoption of textile production is positively correlated with an environment suitable for the
textile production. Panel B of Table III reports second-stage 2SLS estimates. Instrumented
historical textile production continues to lower today’s sex ratio. That is to say, having a
textile-friendly environment is associated with lower son preference today.
The magnitude of the IV coefficients is consistently greater than the OLS estimates. A
possible explanation for this is reversal of fortune of less populous areas in the
industrialization of China. Treaty ports that emerged in the 19th century, which later became
the most economically advanced areas in China, were intentionally built outside old towns to
facilitate new urban development. A more recent example is five special economic zones built
from the scratch in the 1980s. Industrialization and modernization has taken place in areas
which had low level of development (including textile production) back in Ming Dynasty.
With more wealth accumulation, higher female education attainment, and a large service
industry, treaty ports and special economic zones have developed social conditions in favor of
women today. Selection introduces a positive relationship between historical textile
production and sex ratio today, biasing the negative OLS estimates towards zero.
Industrialization and modernization certainly complicates my study. As historical persistence
is more of the case in a relatively stable environment, needless to say, historical textile
production is most accurate in predicting son preference in pre-industrialized China. Though a
big part of China remains agrarian and pre-modern, there is no denying that the economic
miracle in the past few decades has led to fundamental changes to social values, some of
which might happen to be working in the reverse direction of the influence of historical textile
production.
VI Conclusion
I find current differences in son preference among Han Chinese are indeed
shaped by historical textile production which increased women’s productivity relative
to men’s in the past. Historical textile production marginally advanced the position of
women in recent seven hundred years. By exploiting a technology shock to textile
production in the 13th Century, I isolate the impact of textile production on the
evolution and persistence of gender culture. By estimating a simple OLS equation, I
confirm the hypothesis that historical textile production lowered today’s son
preference. To overcome potential endogeniety, I perform an instrumental variable
analysis, as well as historical analysis, to demonstrate the decision to participate in
textile production was independent of past gender culture.
Other than its contribution to the literature on the evolution of gender culture,
this paper complements the literature on pre-modern Chinese economy. My findings
provide evidence that men and women in rural China were ready to adopt new
technology and active in making decisions what to specialize. Counter to stereotypical
views on women being suppressed in pre-modern China, this paper suggests women’s
position in textile regions improved over the time.
Table I Summary Statistics
Variable
Sex ratio
Historical textile
production
Men's years of
education
Women's years of
education
Per capita GDP
% ethnic population
% non-agricultural
household
registration
Provincial capital
dummy
% agriculture in
total GDP
Region dummy
Distance to coast
% floating
population
Observation
2218
2218
Mean
1.18003
.0852119
Std. Dev.
.1362603
.2792597
Min
.8088479
0
Max
1.9316
1
2218
8.182074
1.088603
2.48
12.49
2218
7.083084
1.287191
.86
11.28
2149
2218
2218
8585.385
7.576794
26.17538
9650.489
20.01744
23.28466
2282.008
0
2.36
133304.6
99.28
98.91
2181
.1137093
.3175307
0
1
2218
60.96193
28.24991
.05
96.59
1888
2174
2181
3.353284
379.9389
.0175009
2.251933
317.3284
.3005426
1
.0866663
-.2932602
8
1175.69
4.732937
Table II: Country-level OLS estimates
(1)
(2)
(3)
(4)
(5)
Historical textile production
-0.0297***
(0.00738)
-0.0293***
(0.00737)
-0.0280***
(0.00734)
-0.0305***
(0.00747)
-0.0371***
(0.00940)
Men's years of education
0.0405***
(0.0107)
0.0352**
(0.0109)
0.0422***
(0.0110)
0.0437***
(0.0120)
0.0400***
(0.0113)
Women's years of education
-0.0160+
(0.00955)
-0.0230*
(0.00986)
-0.0223*
(0.00998)
-0.0358***
(0.0102)
-0.0169+
(0.0102)
Log of per capita GDP
-0.0450***
(0.00724)
-0.0452***
(0.00728)
-0.0370***
(0.00764)
-0.0447***
(0.00752)
-0.0594***
(0.00977)
% non-agricultural household
registration
-0.00157***
-0.00118***
-0.00113***
-0.00109***
-0.00160***
(0.000247)
(0.000272)
(0.000279)
(0.000299)
(0.000302)
0.0161+
(0.00915)
0.0192*
(0.00923)
0.00924
(0.00928)
0.0230*
(0.00949)
0.0349**
(0.0133)
-0.00103***
(0.000243)
-0.00115***
(0.000263)
Provincial capital dummy
% ethnic population
Total Fertility Rate
-0.0152
(0.0154)
Household size
0.0599***
(0.0110)
Quartiles of % agriculture in
total GDP
Quartiles of distance to coast
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region Dummies
Yes
Yes
Yes
Yes
Yes
1856
0.317
0.310
35.62
1856
0.332
0.324
33.25
1688
0.350
0.343
37.97
1658
0.289
0.281
31.10
Observations
1856
R2
0.308
Adjusted R2
0.301
F
35.87
Standard errors in parentheses
+
p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001
Table II: County-level 2SLS estimates.
(1)
Closeness to ideal humidity
F-stat
Traditional textile production
Hausman test (p-value)
Hanson J
Ethnic counties
In-migration counties
TFR & Household Size
Socioeconomic Controls
Geographic Controls
Obversations
(2)
(3)
(4)
Panel A. First stage 2SLS estimates. Dependent variable: Historical textile production
(5)
0.00439***
(0.00107)
10.21
0.00439***
0.00443***
0.00477***
(0.00107)
(0.00107)
(0.00116)
10.40
9.486
9.934
Panel B. Second-stage 2SLS estimates
0.00264*
(0.00103)
5.067
-0.446**
(0.147)
0.0003
0.000
Included
Included
Excluded
Yes
Yes
1856
-0.448**
(0.147)
0.0003
0.000
Included
Included
Excluded
Yes
Yes
1856
-0.738*
(0.329)
0.010
0.000
Included
Excluded
Excluded
Yes
Yes
1658
Standard errors in parentheses
+
p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001
-0.294*
(0.132)
0.025
0.000
Included
Included
Included
Yes
Yes
1856
-0.485**
(0.149)
0.000
0.000
Excluded
Included
Excluded
Yes
Yes
1688
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