The Long-Term Effects of Protestant Activities in China
Yuyu Chen
Guanghua School of Management
Peking University
[email protected]
Hui Wang
Guanghua School of Management
Peking University
[email protected]
Se Yan
Guanghua School of Management
Peking University
[email protected]
November 2013
Does culture, and in particular religion, exert an independent causal effect on long-term
economic performance, or is it merely a reflection of the latter? We explore this issue by studying
the case of Christianity spread in China during the late 19th and early 20th century. Even though
Christianity never become a prevalent religion in China, its spread, especially among the less
developed and isolated regions, open a new window for the local people to see the outside world,
and encourage them to become more open-minded. Such cultural interactions generate persistent
and profound impacts on regional development. Combining county-level data on Protestant
presence in 1920 and socioeconomic indicators in 2000, we find that the spread of Protestantism
has generated significant positive effects in promoting long-run regional economic prosperity. To
better understand if the relationship is causal, we exploit the fact that missionaries actively
conducted disaster relief to gain trust from local people and use frequencies of historical disasters
as instruments for Protestant distribution. Our IV results confirm and enhance the OLS results. We
further investigate the transmission channels between history and today, and find that
improvements in education and health account for about half of the total effects of missionaries’
work on today’s economic outcomes, and the other half are possibly the contributions from the
changes in culture and people’s mindset.
Keywords: Christianity, Economic Growth, Education, Foreign Direct Investment, China
JEL Classification Numbers: I20, N15, N35, O11, O43, Z12
Preliminary; For Seminars and Conferences only; No Dissemination or Citation Please
1
“The missionaries …have been among the pioneers of civilization.”
--- President William McKinley, 19001
1. Introduction
The causal effects of Christianity, in particular Protestantism, on economic
performances are controversial. In his famous work, “The Protestant ethic and the
Spirit of Capitalism”, Max Weber suggested that Reformed Protestantism, by
nurturing stronger preferences for hard work and thriftiness, has led to greater
economic prosperity (Weber 2001). Recent empirical studies of the role of religion in
economic growth have found mixed results. While Cantoni (2010) argues that there
was no effect of Protestantism on economic growth, other studies find significant
positive effects, but suggest different mechanisms through wich religion may have
contributed to economic performances. Some of these studies suggest similar
channels to Weber’s argument that religion beliefs may have fostered certain moral
codes or social values that are conducive to economic growth (Lipset and Lenz, 2000;
Stulz and Williamson, 2003; Barro and McCleary 2003, Barro 2004; McCleary and
Barro 2006, Arruñada, 2010). But other studies argue that it is not religious beliefs but
the side effects that religious activities brought in that have contributed to economic
growth (Glaeser and Glendon, 1998; Glaeser and Sacerdote 2008; Becker and
Woessman 2009).
In this paper, we use the lens of history to better understand the long-run effects
of religion on economic growth. In particular, we want to find out whether and how
the spread of Protestantism in the late nineteenth and early twentieth centuries
generated persistent impacts on China’s economic achievements today. We construct a
data set mapping historical Protestant presence and contemporary economic outcomes
at county level, and find that the diverse economic performances across different
counties in 2000 are positively correlated with the intensity of Protestant presence at
the beginning of the twentieth century. To our knowledge, our paper provides the first
empirical evidence on the long-term effects of religious activities on economic
outcomes in China.
However, such correlation might be subject to endogeneity issue due to
unobservable county-level heterogeneities. It might be possible that more developed
counties attracted more Protestant activities in the past and continued to perform
better at present. We try to identify the causal effects of Protestant presence on
contemporary economic outcomes in three ways. First, historical archival records
suggest that missionaries were usually more passionate to go to less developed areas.
Second, we proxy historical economic prosperity by population density and land tax
revenues of Chinese counties, and find no positive correlation between historical
1
The president address for the Ecumenical Missionary Conference in the Carnegie Music Hall, New York City,
April 21, 1900.
2
economic prosperity and Protestant presence. Third, we notice that missionaries
actively conducted disaster relief in order to gain trust from Chinese people. In light
of this historical fact, we use frequencies of droughts and floods in the early twentieth
century as the instruments for the intensity of Protestant presence across counties. Our
IV coefficients are positive and significant, suggesting that more Protestant activities
in 1920 resulted in better economic outcomes in 2000.
We further investigate the precise channels through which the Protestant
activities in 1920 survived the political turbulences in China persisted to the present.
Our empirical tests suggest that higher intensity of Protestant presence results in better
education and health conditions in 2000. Historical evidences also corroborate our
findings. While going to remote hinterland counties to spread the God’s message,
missionaries also help local people build modern education, health systems and
diffuse western science and technology. Such efforts may have contributed to
accumulation of human capital in local communities and reshaped the social values of
local people.
There are two reasons why studying the long-term effects of Protestantism in
China is valuable. First, China is a large country with homogeneous culture and
institutions but large variations in local economic performances. China in history was
long dominated by Confucianism. Christianity was prohibited until in the late
nineteenth century when China was defeated by western powers and forced to open
up. Since then, foreign missionaries self-selected to go to different counties and
spread the God’s message. Therefore, the transplant of Protestantism to a large and
peripheral country such as China was to a large extent a quasi-natural experiment,
which allows us to examine the within-country variations in religious activities and
economic performances.
Second, since 1978, China’s economy has been keeping a stunning growth spurt
with an almost double-digit annual GDP growth rate. People tend to attribute this
so-called “China miracle” entirely to the Reform and Open-up, a radical institutional
change beginning in 1978, and try to define a Chinese model” of growth or to
establish a “Beijing consensus” of development-enhancing policies (e.g. S. Philip Hsu,
Yu-Shan Wang and Suisheng Zhao, 2011). Such thinking assumes that China’s
economic, political, and social circumstances sufficiently resemble prevailing
conditions in other low-income nations so that application of Chinese policies may
produce something akin to recent Chinese outcomes. But the deep historical roots
surrounding important features of China’s current institutions and the central role of
China’s legacy of human capital undermine this approach. We argue in this paper that
China’s Reform and Open-up since 1978 was to a large extent a continuation of the
modernization movement since the mid nineteenth century, but was disrupted by wars
and revolutions. Missionaries were in fact pioneers of the modernization movement.
They disseminated western science, technology and ideology to even the most remote
counties in China, fostered the accumulation of certain types of human capital that
were conducive to modern economic growth. Such human capital was suppressed
during the Cultural Revolution, but revived quickly since 1978, and paved the way for
3
the institutional changes and rapid economic growth. On this regard, our research
contributes to a large body of literature of that study the persistent effects of historical
events on current economic developments, such as Acemoglu, Johnson and Robinson
(2002; 2005) and Nunn (2009).
In the next section, we provide some historical background on the spread of
Christianity during the late 19th and early 20th century. Section III describes the data
we use to study the long-term effects of the spread of Christianity. Section IV
provides OLS estimations to assess the relationship between the spread of Christianity
and today’s economic performances and then use an instrumental variable approach to
address the issue of causality. Section V explores the possible mechanisms for the
long-term effects. The final section discusses the implications of our findings and
concludes.
2. Historical Background
Christianity was never a major religion in China, a Confucianism-dominated
country. It began to spread in China as early as in the seventeenth century. However,
Pope Clement XI forbade Chinese Christians to engage in Confucius-related activities
in 1704, and this outraged Emperor Kangxi. As a result, he completely banned
Christian activities in 1720. This policy was reinforced by the successive emperors.
Since the early nineteenth century, the trade between Europe and China reached a
historically high record, and Christian missionaries attempted to penetrate China
again. But as Christian activities were still banned by the Qing court, Christian
presence was still negligible up to the 1840s.
China was forced to open up to the western powers when China was defeated by
Britain in 1842. As a result, missionaries as well as other foreigners were allowed to
live in China in 1846. Defeated again in 1860, the Qing government signed the
Convention of Peking which granted freedom of religion in China and allowed the
missionaries to own lands and build churches. A large number of Protestant
missionaries came to different parts of China from Europe and America. By 1900,
there were more than 80 thousand Protestant converts (Wang, 1991).
Rapid expansion of Christianity accumulated mistrust and opposition between
missionaries and local residents. There are three reasons of mistrust and opposition.
First, the monotheism of Christianity was not accepted by Chinese people who
believe the polytheism and worship ancestors. Second, the egalitarian tradition of
Christianity clashed with the entrenched hierarchical tradition in the Chinese society.
Lastly and more generally, western customs and lifestyle brought to China with the
expansion of Christianity were drastically different from Chinese culture. Mistrust
and opposition led to frequent conflicts, and these conflicts culminated in the Boxer
Rebellion in 1900. In this tragic xenophobic conflict, more than 20 thousand
Christians were killed, and three fourth of churches were destroyed. This rebellion led
to a war between the Qing government and the Eight Powers. China was defeated
4
again and was forced to sign the Boxer Protocol in 1901 in which the government
guaranteed safety of the missionaries. Protestant missionaries and activities in China
were well protected since then.
The first two decades of the twentieth century witnessed an extraordinarily rapid
expansion of Christianity in China. 1,500 foreign Protestant missionaries arrived in
China in 1900. The number climbed to 3,445 in 1905 and 8,000 in 1927. There were
about 80 thousand Protestant converts in 1900, and 130 thousand in 1904. In 1922 this
number soared to 402,539. By the time of 1920, there were more than 120 Protestant
denominations in China, and church activities penetrated nearly 70 percent of Chinese
counties (Wang, 1991).
The unprecedented expansion of Protestantism in China in the early twentieth
century was not purely a consequence of improved security condition. The changed
missionary strategy after 1900 was a major cause of surging Christian activities in
China. Learning the lesson from the tragedies in the Boxer Rebellion, many Christian
organizations came to realize that the hostility toward Christianity was largely caused
by their condescending way of conducting missionary work. As a result, almost all the
major denominations agreed to hold a historical conference, the Centenary Missionary
Conference in 1907. In this conference, many missionaries resolved to take measures
to localize themselves and gain trust from local people, and agreed that an effective
way of building trust was disaster relief.2 Therefore, the conference officially honored
the past disaster relief services, and for the first time appealed for concerted efforts in
the ongoing relief work.3 Since this conference, disaster relief became one of the
central strategies in Protestant missionary work in China.
China is a country frequently inflicted by natural disasters. In the long history of
fighting with disasters, the Chinese government had developed a set of quite
sophisticated disaster relief mechanisms. However, as the central empire was falling
apart in the late nineteenth century, the government’s disaster relief mechanisms
gradually collapsed. Noticing the absence of government support, missionaries began
to raise fund and bring food and other resources to people in drought-stricken areas. A
catastrophic drought hit five provinces of northern China between 1876 and 1879,
followed by a famine in which about one million people died of hunger. Over 100
missionaries went to those areas and helped those in need (Gu, 2004, p289). This was
one of the earliest famine relief operations by missionaries in China. Inspired by the
relief work in this famine, some missionaries began to sporadically engage in other
famines as well. After the Centenary Missionary Conference in 1907, the disaster
relief work became much more organized. Different denominations set up committees
2 For example, a renowned missionary, F. Harmon, said in the conference that “At the end of the famine relief
operations, it was felt that the great spirit of inquiry and readiness to listen to the Gospel, which was manifested,
and should be taken advantage of” (China Centenary Missionary Conference, Shanghai, 1907, p526). Timothy Lee,
a famous missionary then, said that disaster relief was “an ideal way to reduce prejudices and prepare the ways for
the Chinese to accept Christianity” (Gu, 2010).
3
For example, the conference records noticed that “the whole world is being called upon to send food and money
to relieve the famine stricken millions in Central China. This conference is being appealed to almost daily for
volunteers to go to aid in the distribution of relief. No one questions the Christlikeness of this work” (China
Centenary Missionary Conference, Shanghai, 1907, p493).
5
to coordinate their relief operations in the major disasters in 1907, 1910-1912 and
1917-1918. In 1920, northern China was struck by another severe drought. In order to
coordinate international disaster relief efforts, missionaries set up a nation-wide
organization, China International Famine Relief Commission and raised over 30
million US dollars for the famine (Gu, 2004, p294).
Due to lack of modern medical knowledge and functioning public health system,
cholera, plague and other epidemics decimated thousands of people in the disasters.
Missionaries made great efforts to establish hospitals and public health system in
those areas trying to control diseases and save life. As Christian missionaries became
more deeply engaged in local communities in disaster relief, they reported that many
government officials and gentry elites were experts of Confucius classics, but ignorant
of modern science and technology. This ignorance was a big impediment of effective
disaster relief. As a result, they tried hard to persuade Chinese government to build
modern education system. Moreover, many missionaries put the idea of education
reform into practice, and established a large number of elementary schools, middle
schools, and even universities. Many of these Christian schools soon became
exemplary models that secular schools learned from. These achievements improved
the human capital of local people, and might also have changed the mindset and social
values of local people. Such effects survived the political turmoils in the ensuing
years and persisted to the present, and might have significantly contributed to the
economic performances today.
.
3. Data
To examine the impacts of Protestant activities on long-run economic
performances, we assemble three county-level data sets: Christian activities and
socioeconomic conditions in 1920; socioeconomic conditions in 2000; and
geophysical conditions of all the counties in the data. In order to match historical data
with contemporary data, we have to handle the issue of changing county borders with
GIS method. We explain the details in this section.
3.1 Historical Data
Our data of Christian activities in China in 1920 are obtained from a published
statistical report: The Christian Occupation of China: A General Survey of the
Numerical Strength and Geographical Distribution of the Christian Forces in China
("COC" afterwards). The report was compiled by the China Continuation Committee,
the central organization of Christian churches in China. One main task of this
organization was to coordinate and promote more effective evangelization in China.
For that purpose, beginning from 1918, the Committee spent three years in conducting
a county-level survey on the status quo of Christian activities in China and published
the survey results in “COC”. “COC” reports various measures on Christian activities
such as numbers of missionaries, converts, churches, and Christina schools and
6
hospitals in Chinese counties. It also collects and publishes other important
socioeconomic information of surveyed counties from county gazetteers and various
survey materials by local governments, such as political orientation, population, and
education condition. The Committee made special efforts to ensure the quality of
survey data in order to deliver a reliable and objective description of the situation of
Christian influence in Chinese counties. Therefore, COC is considered to be a good
source to study socioeconomic conditions in Chinese counties in the early twentieth
century.
[TABLE 1 ABOUT HERE]
In this paper we measure the intensity of Christian activities in each county by
three indicators: the numbers of Christian converts, churches and vicars for every one
thousand people. The summary statistics are reported in Table 1. On average there
were 0.74 converts, 0.016 churches, and 0.024 vicars per 1,000 people in each
Chinese county in 1920. Figure 1 shows the density of Christian converts across
counties in our sample. Darker grids are the counties with higher densities of
Christian converts. Although many coastal counties had higher density of Christian
converts, the figure suggests that there were also active Christian presence in quite a
number of inland counties. Therefore, our data suggest that the different intensity
levels of Christian activities were not simply driven by the distances of these counties
to the coastline.
[FIGURE 1 ABOUT HERE]
3.2 Contemporary Data
We assemble contemporary socioeconomic indicators of Chinese counties to
study the long-term impacts of Christian activities. The original sources of these data
are various surveys conducted by the National Bureau of Statistics of China. The 2000
county-level GDP, educational and health expenditure data, are obtained from the
Statistical Materials of Public Finance of Cities and Counties. Demographic variables
(e.g., population and children mortality rate4) and variables of education outcome (e.g.,
years of schooling and literacy rate) are drawn from the Fifth National Population
Census (2000). The main dependent variable in our paper is GDP per capita in 2000.
The mean of GDP per capita is 5,584 yuan (about $800). Figure 2 reports GDP per
capita of all the counties in our data set, and each grid represents each county. The
darker the grids are, the higher GDP per capita the counties have. Similar to Figure 1,
it is evident that not all rich counties are coastal ones. Quite a number of inland
counties also have high GDP per capita.
[FIGURE 2 ABOUT HERE]
3.3 Climate Data
Our historical climate data are drawn from the Distribution Gallery of Droughts
4
2000 population census ask each women between age 16 and 60 about the total number of children they give
birth to, and the total number of children that passed away. “Children morality rate” is defined as the ratio of the
latter over the former.
7
and Floods in the Past Five Hundred Years of China. This gallery was compiled by
the Institute of Synoptic Meteorology and Climatology under the administration of
China Meteorology Administration (“CMA” hereafter). The original sources of
historical climate data are county gazetteers and official archives. Combining
historical and contemporary climate data, this gallery allows us to build a data set of
droughts and floods for 120 stations from 1470 to 2000. For each station in each year,
the gallery uses a 5-scale Drought-Flood Index (“DF-index” hereafter) to categorize
local climate: 1 for serious flood, 2 for flood, 3 for normal condition, 4 for drought, 5
for serious drought.5 This data set has been widely used in various studies, and its
consistency and reliability have been carefully examined and confirmed by many
meteorologists (Yao, 1982; Ronberg and Wang, 1987).
In our paper, we count the incidence rate of DF-index=1 or DF-index=2 for a
station over our sample periods and use it as a measure of flood frequency.6 Similarly,
we construct drought frequency by counting the incidence rate of DF-index=4 or
DF-index=5.7
Our climate data are collected from only 120 stations. We use a conventional
approach, “inverse-distance-weighted method” (IDW), to convert station-level
drought and flood frequencies into county-level variables. It assumes that a county’s
climate is an average outcome of the climates of all nearby stations, weighted by the
distances from this county to nearby stations. We report the details of processing the
climate data in Appendix 1.
3.4 Geophysical Data
We control for county-level geophysical characteristics in our multivariate
analyses. These data are drawn from the Surface Meteorological Database constructed
by the CMA. The database contains annual precipitation and temperature for 754
meteorological stations between 1990 and 2000. We use the same IDW method to
convert station-level variables to county-level ones.
3.5 County Matching
A big challenge in our study is that the territories of many counties changed
because of regime changes over this long historical period. We solve the problem by
comparing the GIS data for both 1920 and 2000 and converting county-level variables
in 1920 to those in 2000 using overlapping area as weights. Historical GIS data are
obtained from the China Historical GIS Project by Harvard University and Fudan
University.8 County GIS data in 2000 (including longitudes and latitudes) are
obtained from the ACASIAN Data Center at Griffith University in Brisbane, Australia.
Average altitudes of these counties are obtained from the SRTM 90m Digital
Elevation Data (Jarvis et al., 2008). As a result, we build a data set combining
5
See Zhang and Crowley (1989) for detailed description of this categorization method.
In different specifications we test our results for three periods: 1800-1840, 1900-1920, and 1978-2000.
7
Using incidence rate of DF-index=1 for flood and incidence rate of DW-index=5 fordrought does not alter the
main results.
8
The historical county-level GIS data can be downloaded at http://www.fas.harvard.edu/~chgis/.
6
8
historical Christian intensity data, historical and contemporary climate data,
contemporary socioeconomic data and geophysical data for 1,743 counties in China
proper.
4. Empirical Results
4.1 Baseline Correlations
We first study the simple correlation between county-level Christian intensities in
1920 and economic performances in 2000. Figure 3 shows the correlation between log
numbers of converts per 1,000 people in 19209 and log per capita GDP in 2000 for all
these counties. It is evident that these two variables have a strong positive correlation.
[FIGURE 3 ABOUT HERE]
We further examine this relationship with OLS regressions controlling for other
county-level characteristics. The baseline specification is given by:
ln(pcGDPi)= α + β‧ln(convertsi/populationi) + Xi'‧γ+ εi ,
(1)
where ln(pcGDPi) is the natural log of GDP per capita in county i in 2000;
ln(convertsi/populationi) is the natural log of Christian converts per 1,000 people in
1920; Xi is a vector of control variables including county locations, climates, and
other geophysical characteristics; and εi is the error term.
Table 2 reports the results. Column (1) contains only the variables of interest.
This result reflects the relationship shown in Figure 4. In Column (2) we include a set
of regional fixed effects.10 While the magnitude of the marginal effect is reduced, the
correlation between Christian activities and GDP per capita remains positive and
significant at 1% level. In Column (3), we control for a set of county geophysical
variables, including longitudes, latitudes and altitudes, and distances to provincial
capitals. We further control county climates by adding in precipitation and
temperature in column (4), and contemporary frequencies of extreme weather
(1978-2004) in column (5). The coefficients on Christian activities remain
significantly positive.
[TABLE 2 ABOUT HERE]
4.2. Establishing Causality: Instrument Variables Results
The positive correlation between Christian activities in 1920 and contemporary
9
For counties with zero convert (272 such counties in our data), their natural logs are not defined. Instead, we use
ln(1/populationi), pretending that these counties have 1 convert - the lowest value of the non-zero sample. We also
try alternative methods to deal with the logarithm transformation of the zeros, including (1) assuming
converti/populationi = 0.001 for zero-convert counties – the lowest value of converti/populationi for among the
non-zero sample, and (2) conducting the analyses excluding the zero-convert counties. The main results remain
robust.
10
China proper is divided into 7 regions: North China includes Beijing, Tianjin, Hebei, Shanxi; East China
includes Shanghai, Jiangsu, Zhejiang, Shandong, Anhui; Northeast China includes Liaoning, Jinlin, Heilongjiang;
Middle China includes Hubei, Hunan, Henan, Jiangxi; Southern China includes Guangdong, Guangxi, Hainan,
Fujian. Southwest includes Sichuan, Chongqing, Guizhou, Yunnan. Northwest includes Shanxi, Gansu, Xinjiang,
Qinghai, Ningxia.
9
economic performances in 2000 reported in Table 2 does not guarantee a causal
relationship. One major concern for causality is that selection of counties for
preachment in 1920 was not random. It is possible that missionaries preferred to
preach in richer counties in 1920 and these counties are still richer today. In this
subsection we study the location choices of Christian preachment before 1920 from
different perspectives.
First, historical evidence suggests that a considerable amount of missionaries
actively worked in the less developed hinterland of China. Hudson Taylor, a famous
British missionary, believed that, facing life hardship, people in poor and remote
regions were more likely to respond to the calling of God (Wang, 1997). Many
missionaries shared the same view and volunteered to live and work with local people
in the poor and isolated counties. A good example is China Inland Mission (“CIM”
hereafter), a Protestant denomination founded by Taylor in 1865. The goal of CIM
was to conduct missionary work in all the inland provinces. It was the first Protestant
denomination arriving in Shanxi (in 1876), Sichuan (in 1877), Guizhou (in 1877) and
Yunnan (in 1877) (Broomhall, 1901).11 CIM’s goal was supported by many
missionaries. It became the largest Protestant denomination in the early twentieth
century. More than 1000 missionaries joined CIM and spread the God’s gospel in
remote villages. Many other denominations followed CIM, and left footprint in almost
every part of this big county.
To empirically examine if missionaries were more active in the less developed
counties, one needs the economic indicators of those counties in 1920. However,
direct measures of historical economic performances are unavailable. Instead, we use
two proxies for historical economic conditions: population density and per capita land
tax revenue. Population density is a commonly used, although quite crude, proxy for
historical economic prosperity.12 Per capita land tax revenue can also largely reflect
local economic conditions.13 Since both these two measures are relatively crude, we
only consider the results here suggestive. Figure 4 plots these two measures against
the number of churches and vicars per 1,000 people for Chinese counties in 1920.14
All figures show significant negative correlations, suggesting that Christians were
more active in less developed regions. Given this evidence, it is unlikely that the
observed positive relationship between Protestant activities and current economic
performance is completely driven by selection. In fact, selection tends to bias the OLS
results towards zero.
[FIGURE 4 ABOUT HERE]
11
Actually it is the only Protestant mission in province Guizhou and Yunnan until 1900.
12
See Acemoglu, Johnson and Robinson (2002) for example. Our measure of county-level population also comes
from the COC.
13
Prefectural-level land tax data in 1820 are obtained from Liang (2008). Land tax in the Qing dynasty was
calculated based on size and quality of arable lands multiplied by a fixed tax rate. After normalized by population,
it reflects the development of local agriculture sector, which to a large extent indicates overall economic conditions
in a traditional society.
14
We use the number of missionaries and the number of churches in each county because we want to measure the
location choices of Christian missionaries before 1920. As a robustness check, we also use the number of converts
and find similar results.
10
In order to rigorously examine the causality between historical Christian
activities and contemporary economic performances, we use instrumental variable
technique to formally address the potential selection problems. As described in
Section II, since the beginning of the twentieth century, Christian organizations
decided to use disaster relief as one of the main methods to gain trust from Chinese
people and better conduct missionary work. In light of this fact, we construct
county-level incidence rate of droughts and floods between 1900 and 1920 and use
them as instruments for intensity of Christian activities.
[TABLE 3 ABOUT HERE]
We report the 2SLS estimates of equation (1) in Table 3. Column (1) reports the
results without any control variables. Column (2) includes regional fixed effects.
Column (3) adds in county level geophysical characteristics. Column (4) and (5)
further include contemporary climates and contemporary frequencies of droughts and
floods.
The second-stage estimation results are reported in Panel A. Coefficients on
ln(convert) remain positive and significant across different columns, ranging from
0.16 to 0.19. These results are significant not only statistically but also economically.
The standardized beta coefficients reveal that a one-standard-deviation increase in
ln(convertsi/populationi) leads to 0.44 to 0.52 standard deviation increase in log GDP
per capita. According to the estimates in column (5) with full control, a county with
the mean of income at 5,584 yuan, one more convert per 1,000 people can lead to an
increase of 1168 yuan (about 180 US dollars) in per capita GDP. Moreover, estimates
of β in Table 3 though 2SLS are significantly higher than those in Table 2, which
confirms the underestimation of impacts of historical Christian activities in OLS
regressions.
The first-stage results are reported in Panel B. The coefficients on both flood and
drought frequencies are significantly positive, suggesting more Christian activities in
the counties with more disasters. The F-statistics of the instrument variables is higher
than the critical value suggested by Stock and Yogo (2005), which rules out concerns
for weak instruments. In an unreported placebo test, in addition to the disaster
frequency in 1900-1920, we also control for the disaster frequency during the adjacent
periods: 1860-1900 and 1920-1960. It turns out that only 1900-1920 variables have
positive and significant coefficients. The coefficients on disaster controls in other
periods are mostly insignificant. The evidence here is consistent with the historical
fact that Protestant churches actively involved in disaster relieves in China as a
strategy to gain trust from Chinese society.
One might worry about the validity of our instruments because disasters in
history might correlate with disasters today and adversely affect today’s economic
outcome. In fact, this channel is unlikely because of the following reasons. First, all of
our specifications pass Sargan over-identification test at conventional statistical levels.
Second, we find that the weather between 1900 and 1920 and that between 1920 and
1940 are highly correlated, but their correlations with the weather between 1980 and
11
2000 are very weak.15 Third, we control for the current disaster frequencies in
column (5) of Table 3, and the effect of historical Christian activities remains robust.
Moreover, this specification delivers a much higher P-value in the over-identification
test than other columns, which strongly supports the exclusive restrictions of our
instruments.16
4.3. Robustness Checks
In Table 4 we conduct a number of robustness checks for our estimates. Column
(1) of Table 4 addresses the concerns of potential outliers by omitting potentially
influential observations.17 Compared with our preferred specification in Table 3, the
coefficient on convert density is unaffected.
[TABLE 4 ABOUT HERE]
Coastal counties were easier to reach than hinterlands in the past. Furthermore,
coastal regions usually enjoy more preferred policies in today’s China. Therefore, one
might wonder if the long-term impacts of Christian activities are merely driven by
coast-hinterland disparity. In column (2) we examine this issue by taking out all
counties in coastal provinces. The effects of Protestant activities become even
stronger for the hinterland sample, suggesting that the mechanisms through which
missionary work affected long-run economic outcomes were more effective for inland
counties.
A small number of counties in our full sample are in fact big cities. For both
geographical accessibility and economic policy reasons they could be positively
correlated with both Christian activities and present economic performance. In
column (3) we drop the urban counties and focus only on the rural ones. Our
estimation results on the long-term effects of Christianity are barely changed.
In addition to the application of instrument variables to tackle the selection issue,
in column (4) of Table 4, we also add in control for historical economic
heterogeneities across counties. We use land tax per capita to proxy for initial
economic development of each county.18 The effects of Christian activities become
slightly bigger than those in the main specification (column 5 in Table 3), which is
reasonable due to the nature of selection bias discussed in Subsection 4.2.
Catholic missionaries were also quite active in China during the period we study.
However reliable and detailed data on Catholic activities are not available. As a result
we focus on the effects of Protestantism instead of Catholicism in this paper. As a
15
The correlation coefficients of droughts and floods between the period of 1900-1920 and the period of
1980-2000 are only 0.19 and 0.095 respectively, but the correlation coefficients between the period of 1900-1920
and the period of 1920 and 1940 are 0.4 and 0.5. The weak correlation between disasters in history and today
might be resulted from improved irrigation systems.
16
We should also keep in mind that, different from one hundred years ago, China’s economic performances today
are less sensitive to weather shocks because the main sector of Chinese economy today is manufacture instead of
agriculture. In addition, with vastly improved communication, transportation and weather forecasting techniques,
the impacts of extreme weather on economic outcome today are much smaller.
17
“Potentially influential observations” are identified using Cook’s distance, which measures how much each of
the estimated coefficients change when each observation is deleted. If an observation has a Cook’s distance greater
than 4/N, where N=1742 is our sample size, it is excluded from regression in Column (1) of Table 4A.
18
Using population density in 1920 as a proxy for economic development delivers similar results.
12
robustness check, we include the number of prefectural-level Catholics mission
stations as a control variable in order to rule out the possible effects of Catholic
activities.19 The result is reported in column (5) of Table 4. The coefficient on the
Protestant converts is reduced but remains positively significant.
In the main specification we use the number of converts instead of churches or
vicars to measure the intensity of Christian activities in each county. We do so
because the number of converts captures the outcome of Christian preachment, while
the numbers of churches and vicars captures the inputs. As robustness checks, in
columns (6) and (7) of Table 4 we report the 2SLS results using numbers of churches
and vicars, and find that our main results are not affected. We also find that the
coefficients of churches and vicars are bigger than that of converts. This is because
one additional church or vicar should yield much larger effects than one additional
convert.
5. The Effects of Protestant Activities on Education
Many historians notice that, while conducting disaster relief, Protestant
missionaries were actively engaged in helping local Chinese establish modern
education and medical system in the early twentieth centuries.20 In the next two
sections we discuss how their efforts in promoting education and medicine generated
profound and long-lasting effects in the Chinese society.
Exactly as John K. Fairbank noted, “in the end the Christian influence was
probably strongest in education” (Fairbank, 1974, p13). China completely rebuilt the
education system in the early twentieth century, and missionaries played a critical role
in this rapid transformation. Christian missionaries soon realized the fundamental
flaw of traditional Confucius education after they came to China in the early
nineteenth century.21 In the process of disaster relief, missionaries found that a main
obstacle of their work was lack of modern science and technology among many
Chinese people, including some elites, and the main cause was believed to be
traditional Confucius education. Therefore, building modern education system
became an imperative task. For instance, a well-known missionary, Timothy Lee,
wrote a proposal to the governor of Shanxi Province when he was conducting disaster
relief in 1884, emphasizing the importance of reforming China’s education system.
He said, “Education is the first priority for China. As the western countries keep
developing their education day-to-day, China will lose his chance to overtake them in
19
We thank Ying Bai and James Kung for generously providing us with the data on Catholic mission stations. The
original information was shown in a crude map included in the appendix of Stauffer (1922), which contains no
prefecture boundary information. Bai and Kung (2011) locate the data to all the prefectures by their relative
positions in the original map. Given the way of constructing the data, the results with this measure of Catholic
activities is only suggestive.
20
For example, see Cohen, 1978, p548.
21
E. C. Bridgman was believed to be the first American missionary working in China. As early as in 1830, he
began to criticize the drawbacks of the traditional education and examination system and call for the establishment
of a modern education system (Barnett and Fairbank, 1985, p. 100).
13
10 years. In conclusion, education is the most significant and urgent thing for China”
(Lee, 1889).
Christian missionaries determined to build new school system in China for the
purpose of not only educating Chinese, but also converting Chinese. They believed
that western education was crucial to dissipate mistrust and misunderstanding of
Chinese people toward Christianity. Alvin Pierson Parker, chairman of the
Educational Association of China, explicitly expressed this opinion in 1896, “as a
Christian educators’ association, we should play a dominating role in China’s
education reform and satisfy the interests of Christianity” (Educational Association of
China, 1896).
Driven by the motives of educating and converting Chinese, missionaries made
big achievements in revamping China’s education. The number of Protestant schools
rose sharply from 347 in 1877 to 7,382 in 1922, almost tripling every twenty years
(Gregg, 1946, p.16-17). Among these Christian schools, 6,599 (86%) were elementary
schools, 291 (7%) were middle schools, 16 (0.2%) were colleges, and 75 (1%) were
vocational schools (China Educational Commission, 1922, p. 416).22
Primary schools are the foundation of modern mass education. To empirically test
the effects of Christian activities on the development of modern primary schools, we
regress, at the county level, the number of students per 1,000 people in Christian
primary schools on the density of Christian converts,23 the latter of which was
instrumented by the frequencies of historical droughts and floods. We report the
results in column (1) of Table 5. Because our dependent variable is censored at zero,
we estimate a Tobit model instead of OLS.24 The IV result in panel A indicates a
statistically and economically significant effect of Christian activities on the
enrollment rate of the Christian primary school: transforming the coefficient into
marginal effect (evaluated at the sample mean) suggests that one standard deviation in
convert per 1,000 people increases the enrollment rate of Christian primary school by
0.92 standard deviations.
[Table 5 is about here]
Christian missionaries were not only building more schools, but more critically,
different schools. They introduced new curriculums which put a big emphasis on both
natural sciences, such as mathematics, physics and chemistry, and social sciences,
such as law and business. Practical subjects such as foreign language studies and
engineering were also included. English, for example, was offered to 58% of the
students in the 4th or 5th grade (Stauffer, 1922, p. 1075). Class lectures were
combined with practices and laboratory experiments. As a result, these new schools
enjoyed big advantage over traditional schools in training students qualified for
booming industrial and commercial sectors.
22
The rests are special institutions, including orphanages and schools for the blind or deaf.
If one assumes that portion of school-aged children is constant across counties, then this result can be viewed as
clear evidence for the casual effects of Christian activities on the enrollment rate of the school-aged children in
Christian elementary schools.
24
The OLS results are not significantly different from the Tobit ones.
23
14
Another big advantage of Christian schools was funding. According to a report on
the status of Christian schools in China, over half of the funding of Christian schools
came from foreign Protestant organizations, which were usually quite stable and
sufficient (Stauffer, 1922, p. 1094).25 As a sharp contrast, because of frequent
political turbulences and civil wars, public schools were often plagued by insufficient
financial support from the government. For example, in 1911, the education budget
accounted for merely 1.5% of the total government spending (Stauffer, 1922, p.
1068).26 In the early 20th century, teachers and administrators in the public schools,
and even staffs of the Ministry of Education frequently protested for arrears in salary
(China Educational Commission, 1922, p. 22).
Better schools educate better students. Take student promotion rate as an example.
In 1920, 21% of the students in Christian junior elementary schools entered senior
elementary schools (as opposed to 10% for the public school students), and 10% of
the students in Christian senior elementary schools entered middle schools (as
opposed to 3.4% for the public school students) (Stauffer, 1922, p. 404). Moreover,
Christian schools educated a large amount of professionals who were urgently needed
by the society. Stauffer (1922, p. 409) reports a surveyed on 5,500 high school
graduates in 1918: 30% of them continued their study in college; 30% of them worked
for churches; 20% of them went to teach in other schools; and the other 20% went for
business or other professions such as doctor, nurse and lawyer. By contrast, 70% of
the graduates from public middle schools had difficulty in finding jobs (China
Educational Commission, 1922, p. 19).
As Christian schools became so successful, they set an exemplary model for
Chinese public schools. Wang (1997) documents three spillover effects of Christian
schools to China’s education system. First, combining western origins with Chinese
reality, most textbooks edited by missionaries were well customized to suit the needs
of Chinese students. Therefore, their textbooks were widely used in many public
schools (Wang, 1983, p112). Second, a large number of graduates from Christian
schools became teachers in public schools and taught subjects that were in urgent
need of those schools.27 Third, both the teaching and the organization methods of
Christian schools greatly influenced Chinese officials and educators, who put similar
models into practices later.28 As being said in one of the reports from a missionary
education association of China, “by perfecting and strengthening this arm of the
service (Christian schools), we increase the probability that the future governmental
educational system of China will be largely influenced and molded by such superior
examples.” (Silby, 1902, p 621)
Empirically, we corroborate the spillover effects of Christian schools in China
with two pieces of statistical evidence. First, we investigate, within a county, whether
25
31% came from tuition fees, and the rest of 18% came from domestic donations.
60% of the government budget was diverted to military purpose and war indemnity.
27
For example, graduates from Tengchow College, a famous school established by the American Missionary
Calvin Mateer, showed high competiveness in their job markets among Chinese schools (Wang, 1983).
28
For example, when Zhang Zhidong, the Huguang Governor, planned to establish modern schools in the early
1900s, he sent many of his officials to Boone Memorial School, a famous Christian academy established by
American Episcopal Church in 1871, to study its education and management model (Wang, 1983, p113).
26
15
more intensive Christian activities induced higher enrollment rate for public
elementary schools. The results in column (2) of Table 5 support this conjecture. In
particular, our IV Tobit coefficient implies that one standard deviation in convert per
1,000 people increases the enrollment rate of public elementary school by 0.70
standard deviation. In other words, 1 more convert per 1,000 people increases the
enrollment rate of public elementary schools by 0.58% (the mean value is 1.19%).
Next, we examine, across counties, whether the enrollment rate of public
elementary schools in one place was influenced by the Christian activities in the
nearby counties. Specifically, consider the following specification:
ln(Student/pop1920i)= α +β1 ln(Christianityi)
+β2 ln(Christianity_Contiguousi) + Xi'γ+ εi
,
(2)
where the enrollment rate of elementary schools in county i (Student/pop1920i) is
regressed on both the Christian activities in its own location (Christianityi) and the
Christian activities in the neighboring locations (Christianity_Contiguousi). The latter
is constructed by averaging the convert densities in all counties contiguous to county i,
weighted by the distance of each of these counties to county i. The instrument for this
variable, which is the average frequency of droughts and floods in the nearby counties,
can be constructed in a similar fashion.
Column (3) in Panel B reports the OLS results of β1 and β2. They are both
significantly positive, and the effects of Christian activities in the own counties are
larger than those in nearby counties (β1>β2). However, once we estimate the model
with instruments (column (3) in Panel A), the neighboring effects become
insignificant. We further investigate this issue by dividing the overall sample into two
groups: counties with Protestant converts and counties with no converts. Their results
are reported in column (4) and (5) of Table 5, respectively. Notice that in column (5),
only β2 can be estimated due to the construction of the subsample (Christianityi=0).
The results show that the influences of Protestantism in neighboring counties are not
significant for the counties with their own Protestant activities, but significant for the
counties without Protestant presence. Specifically, for the latter counties, their
elementary school enrollment rates increase by 0.97 standard deviation if the convert
population density in their contiguous counties increases by 1 standard deviation. That
is to say, increasing 1 convert per 1,000 people leads to the increase in the enrollment
rate by 1.61% (the mean value is 1.06%).
6. The Effects of Protestant Activities on Health Care
6.1 Hospitals and Clinics
Herbal therapy was the main form of medical treatment in China until western
medicine was introduced in China in the nineteenth century. Christian missionaries
played a critical role in the introduction of western medicine into China. Peter Parker,
16
an American missionary, built up the first modern hospital in China in 1837. More
than 2,000 patients received treatment in the first year of its operation (Bush, 1879).
Since then modern medical system gradually flourished in China. By 1889, 61
Christian hospitals and 44 clinics were operating in China.29 This number was more
than doubled in the first two decades of the twentieth century, reaching 326 and 244
respectively (Stauffer, 1922, p96). The annual number of inpatients soared to nearly
150 thousands and that of outpatients over one million (Stauffer, 1922, p623). By
1937, over 300 Christian hospitals were established in China, providing with more
than 20 thousand beds. These hospitals were not only located in coastal provinces, but
more in hinterlands. Many hospitals offered free medical services to the poor and the
needed. John K. Fairbank, a renowned historian, commented on this, “Modern
Western medicine in China was to an important degree a consequence of missionary
demonstration and instruction” (Fairbank, 1983).
Figure 5 presents the correlation between Christian activities and the numbers of
hospitals and pharmacies at provincial level.30 The positive correlations are very
strong: 1% increase in the number of converts is associated with 1% greater in the
number of modern hospital and 0.4% greater in the number of modern pharmacies.
[Figure 5 about here]
With strong support from Christian organizations, these Christian hospitals were
usually equipped with advanced facilities and were well financed. Dr. Harold Balme,
the dean of the School of Medicine in Shandong Christian University, surveyed the
situation of 165 Christian hospitals in 1919. His study found that most of the surveyed
hospitals owned medical laboratories, 75 hospitals had the capability to conduct
laparotomy, and 24 had been equipped with X-ray machines (Balme and Stauffer,
1920).
6.2 Public Health
What the missionaries brought to China was not only western hospital system, but
more importantly the idea of western public health. Whenever large epidemics broke
out in disaster-struck areas, missionaries were usually the foremost in organizing
effective remedial and preventive measures. For example, in 1872, cholera struck
Tianjin. Missionaries provided medical aid to people who were infected. In
1911-1912, a highly deadly pneumonic plague broke out in northeast China. A group
of missionaries set up the Anti-Plague Bureau to fight with the plague.
In frequent disaster reliefs, Christian missionaries found that lack of public health
knowledge and consequent outbreaks of epidemics were main reasons of large death
toll in the disasters. Disseminating knowledge of disease control became a priority in
the disaster relief. The prevention measures included sterilization of medical
29
Records of the General Conference of the Protestant Missionaries of China, held at Shanghai, May 7-20, 1890.
(pp. 733), published by General Books LLC.
30
The main reason we cannot conduct this analysis at the finer geographical level is that the number of hospital in
1920 is only available at the provincial level.
17
equipment and tools, disinfection of food and drinking water, control of flies and
mosquitoes, etc. Missionaries enforced quarantine in disaster struck areas and
effectively controlled expansion of contagious diseases. According to a study by Dr.
Balme in early twentieth century, 69 Christian hospitals (42% of total) were equipped
with quarantine facilities (Balme and Stauffer, 1920). A good example is control of
leprosy. Local governments and missionaries collaborated to enforce quarantine lepers,
and the number of leprosy cases reduced drastically in the early twentieth century
(Stauffer, 1922, p437-438).
Health conditions of women and children were critical in public health.
Missionaries worked hard to help improve health conditions of women and children.
Female missionaries played a critical role in disseminating knowledge of obstetrics
and gynecology among Chinese women. They made regular visits to pregnant women
and provided medical services in case of necessity.31 Because of enormous efforts of
female missionaries, obstetricians, midwives and nurses began to practice in many
places in China, resulting in significant decline of women and infant mortality in the
early twentieth century.
6.3 Medical Education
Missionaries were also pioneers in promoting modern medical education in China.
They believed that “scientific medicine in China must not continue indefinitely to be a
‘foreign doctrine’” and “the medical profession of China must become national if it is
to be universally accepted” (Mac Alister, 1921). As a result, Christian organizations
had established 116 medical education institutions by 1920. Among these institutions,
10 were medical colleges, which accounted for one third of all the medical colleges in
China (Stauffer, 1922, p.425). Most of these colleges continued to operate today and
trained a large amount of excellent doctors. The other 106 institutions were nurse
schools (China Educational Commission, 1922, p. 416). Usually these schools were
larger and equipped with better facilities than public medical schools. In fact, these
Christian medical schools provided an advanced model of medical education that
public schools were trying to follow.
Setting up schools was not the only way that missionaries promoted medical
education. They translated a large number of western medical books to Chinese. A
famous missionary, John Glasgow Kerr, published the first medicine journal in China
in 1868.32 A few years after that, the Medical Missionary Association of China, set up
31
For example, as early as in 1888, seven Canadian missionaries conducted a comprehensive
survey of health conditions of women and children in Zhangde Prefecture, Henan Province.
Meanwhile, with the fund raised in the home country, they set up gynecological clinics and
provided free medical services to local women and children. They also provided monthly training
class to women on the knowledge of obstetrics and gynecology. See Song (2008, p. 31) for details.
32
John Glasgow Kerr (1824 – 1901) was a Presbyterian medical missionary. He came to China
with the American Presbyterian Mission in 1854, and soon became the head of the Ophthalmic
Hospital in Canton and later the Guangzhou Boji Hospital (The Canton Hospital). He worked
there for 47 years and treated about 1 million patients. Besides his outstanding contribution in
medical care, he was also famous for his student, Dr. Sun Yat-sen.
18
in 1886, published its own journal in 1888 with a focus on transmitting western
medical knowledge to the Chinese. Missionaries published the first Chinese medicine
dictionaries in 1908, which standardized the Chinese medical terms (China Mission
Year Book. 1912, pp. 267-268).
7. Persistent Impacts of Protestant Activities
The status of Christianity overturned since the People’s Republic of China (PRC)
was established in 1949. The government regarded missionaries as a form of “western
imperialism”.33 Consequently, the central government established its official church
system and severed their connections with foreign organizations. By 1952, all foreign
missionaries left China. During the Cultural Revolution (1966-1976), all religious
activities, including the government-regulated churches, were banned. Christianity
revived in China since the Reform and Open-up in 1978. However, it is still under
tight regulation by the government. Religious services can only be practiced in the
government-sanctioned churches, and these churches do not maintain any official
connection with foreign churches.
Soon after the Communist Party took power in 1949, the administration of
Christian schools and hospitals was handed over to the government. However, many
of these schools were still functioning, trained a large number of students, and
persistently contributed to local economic development. Most of Christian hospitals
were also functioning and benefiting local communities. Furthermore, the
missionaries’ endeavors before 1949 promoted the consciousness of education and
public health of Chinese people, and were greatly conducive to accumulation of
human capital in the years to come.
In Table 6 we empirically examine the long-run effects of historical Protestant
activities on the education and health outcome in 2000. Columns (1)-(3) report the
results for three education measures: county-level educational expenditure normalized
by population, average years of schooling and literacy rate. Similar to the previous
section, we use frequencies of droughts and floods from 1900 to 1920 as instruments
for county-level numbers of converts in 1920. The first stage results are not reported
here because they are the same as that of the Column 5 in Table 3.
[TABLE 6 ABOUT HERE]
Panel A reports the second stage of the IV estimates.34 The results in Columns
(1)-(3) confirm that the past Christian activities have positive and significant effects
on a county’s long-run education achievements. Take years of schooling as an
example, evaluating at the mean, 1 standard deviation increase in converts per 1,000
people in 1920 leads to 0.92 standard deviation increase in average years of schooling
33
This point was stressed many times by the Premier Zhou Enlai in the meetings with Chinese Christian leaders.
For the same reason discussed in Section 4.2, 2SLS coefficients on Christian activities have higher magnitude
than OLS coefficients (reported in Panel B).
34
19
in 2000.
Christian organizations help local communities build hospitals, introduce modern
medical technologies, and improve public sanitation. We examine whether such
endeavors generated long-lasting effects in current health conditions, measured by
children mortality rate in column (4) and public health expenditure in column (5).
Regression results show that historical Christian activities had significantly negative
effects on current children mortality rate. 1 standard deviation increase in converts per
thousand people decreases the children mortality rate by 0.86 standard deviations.
When evaluating at the sample mean, this amounts to 37% decrease in children
mortality rate. In addition, counties with more intensive Christian activities tend to
have more government spending on public health, but the effects are not statistically
significant.
8. Other Possible Channels of Causality
Missionaries conducted a lot of undertakings in the early twentieth century. We
wonder if, aside from education and health care, there was any other channel that their
undertakings persistently affected today’s economic outcome. We control for current
education and health outcome in the GDP regression and examine whether the effects
of Protestant activities remain. In principle, we would like to estimate the following
model:
ln( pcGDP2000 )   2   2 ln(converts / population1920 )   2Edu 2000
  2Health2000  X 2   2 ,
(3)
where ln(converts/population) is instrumented by historical drought and flood
frequency during 1900 to 1920. In this test, we experiment all the combinations of
different education and health measures and report the results in Table 7. As expected,
the coefficients of these controls are positive and significant. For example, column (2)
of Table 7 reveals that 1% increase in years of schooling is associated with 0.71%
increase in GDP per capita. This result is comparable to existing studies using
cross-country variations.35
More importantly, Table 7 shows that, after controlling for education and health
measures, the effects of Christian activities are lower than the results in our baseline
model (Table 3), but remain positive and significant. For example, in column (8)
when both years of schooling and children mortality rate are included, the coefficient
of converts is reduced by 38% compared with the last column of Table 3.
[INSERT TABLE 7 HERE]
35
For example, Mankiw et al (1992) conduct cross-country analysis and find that 1% increase in years of
schooling is associated with 0.66% increase in GDP per capita.
20
A potential problem of estimating equation (3) is that current education and
health conditions may also be endogenous. Unobservable county-level characteristics
affecting education and health status can also affect economic outcomes, and there is
no instrument variable for them in our context.
Following Becker and Woosman (2009), we bound the effect of Christian
activities on GDP per capita net of contemporary education and health outcomes. The
bounding procedure involves two steps. First, we run an OLS regression with
equation (3). The results of this auxiliary regression are reported in Table A2. We
choose years of schooling as a measure of education development and children
mortality rate as a measure of health outcome. Using other measures delivers similar
results.
In the second step, we take out education and health outcomes, and estimate the
net effects of historical Protestant intensity on today’s GDP in the following
specification:
ln( pcGDP2000 )  Edu2000   Health2000   2   2 ln(converts / population1920 )  X 2   2
,
(4)
where  and 
are based on the estimate values of  2 and  2 obtained from
Step 1 (column 5 of Table A2), and adjusted for potential biases reported by other
well identified studies in the literature.
Card (1999) reviews the literature on the return to education, and concludes that
the upward bias due to ignorance of ability accounts for about 10% of the OLS
estimates. Moreover, studies using institutional changes to create exogenous variation
in schooling actually receive estimates 20-40% higher than those of OLS.36 In light
of this, we bound the range of estimates of the economic return to years of schooling
(  ) from 80% to 140% of its OLS estimate (  2 ).
Studies evaluating the effects of health improvement on economic performance
based on regional variations are not quite conclusive (Jack and Lewis, 2009). They
always suffer from omitted variable bias and reverse causality (Mankiw, 1995,
p303-304). Gallup and Sachs (2001), and Bloom et al (2004) are examples of very
few studies that construct instrumental variables to solve these endogeneity issues.
But their identification strategies are still in debate, and their 2SLS results are not
significantly different from the OLS ones. As a result, we conservatively choose a
wide range for the economic return of improvement in children mortality rate(  ),
from 80% to 200% of its OLS estimate (  2 ).
36
The main reason is that institutional improvements usually affect people with low education outcomes, who
tend to have higher marginal return to schooling. Similar downwards biases in OLS may apply to our context as
well.
21
Each cell in Table 8 reports an estimate of  2 in equation (4), where Christian
activities in 1920 are instrumented by historical disaster frequencies. The titles of
each column and row indicate the values we choose for 
and  . In cell [1,1],
when both  and  are set to 0, we obtain the point estimate of Christian effect in
Table 3.
[INSERT TABLE 8 HERE]
Column (1) reports the results when we only take out the education channel in the
dependent variable of the regression in equation (4). The coefficients on Christian
activities range from 0.1 to 0.14 depending upon the values we choose for  , and are
both statistically and economically significant. Such results suggest that education
improvements account for about 26-47% of the total effect of Christian activities.
Row (1) reports the results when we only take out the health channel. With a wide
range of  we choose, the coefficients on Christian activities vary from 0.15 to 0.18,
which are also statistically significant. Such results suggest that long-run health
improvements contribute to about 5-21% of the total effects of Christian activities on
GDP per capita today.37
With almost all the combinations of possible values of education and health
coefficients, the effects of Christian activities are significant, ranging from 0.07 to
0.18. Insignificant results only appear when the true effect of return to education is 40
percent higher than that of the OLS estimate and the true effect of return to health is
more than 120% of the OLS estimate (Row 7 and Columns 4-7). Even in these cases,
the magnitudes of β2 still have significant economic meanings.
The above analysis shows that improvements in education and health account for
about 50% of the long-run effects of Protestantism on economic outcomes. That is to
say, there are other unquantifiable channels through which historical Protestantism
affect today’s GDP per capita, and our studies find that they may contribute to another
50% of the total effects. Indeed, aside from education and health care, Protestantism
may have generated a variety of profound effects on the Chinese society. For example,
more Protestant activities may have resulted in more open attitudes toward new ideas
and technologies, better work ethics, or more entrepreneurship.
An implication of this argument is that the long-run impact of Protestantism may
have been most conspicuous since China began Reform and Open-up in 1978.
Although most effects of Protestantism on attitudes and ethics were suppressed during
the Revolution era, they revived and became indispensable for economic success in a
37
In fact, it is possible to calculate the threshold value of

and

beyond which β starts to become
insignificant. The  value for β to lose significance at 5% level is 1.8 times of χ2, and the corresponding
value is 4 times of δ2.
22

market-oriented environment. To test this, in Figure 6 we plot average GDP per capita
between 1950 and 2010 for two groups of our sample provinces. Group 1 consists of
the 11 provinces with the lowest measures of ln(convert/population) in 1920, and
Group 2 consists of the 12 provinces with the highest measures of
ln(convert/population) in 1920.38
[INSERT FIGURE 6 HERE]
Two patterns are noticeable here. First, provinces with higher intensity of
Christian activities are on average richer than those with lower intensity. More
interestingly, the gap between these two groups widens rapidly in the late 1970s. The
GDP per capita in Group 1 almost doubled that in Group 2. These patterns are
consistent with our hypothesis that the unquantifiable effects of Protestantism on
attitudes, ethics, mindset, and so on might have mattered in a central planned
economy, but mattered much more in the era of Reform and Open-up since 1978.
9. Conclusion
In this paper, we construct a data set mapping Protestant presence in Chinese
counties in 1920 and socioeconomic indicators for those counties in 2000. By
exploring this large within-country variation, we find that the spread of Protestantism
has generated significant positive effects in promoting long-run regional economic
prosperity.
One major identification challenge is that, if counties with better geographical, or
socioeconomic conditions were more attractive to missionaries in the past, and these
counties continue to have better economic performance in the present, we might
obtain a positive correlation between past Christian activities and present economic
outcomes, even though the former do not have any causal effect on the latter. We
pursue several strategies to tackle this challenge. First, historical and statistical
evidence suggests that missionaries were actually inclined to preach in less developed
areas. Therefore, if there is any selection effect, our OLS regression should have
underestimated rather than overestimated the true effects of Christian activities on
economic development. Second, in light of the fact that Christian churches actively
conducted disaster relief to gain trust from local communities, we use historical
disaster frequencies as instruments for Christian activities. Our 2SLS estimates
confirm and further enhance the OLS results of the positive effects of Christian
activities. Our results are robust to different subsamples, different measures of
Christian presence, and different geographical and climate controls.
We then investigate the channels through which the effects of missionaries’ work
persisted over time. While spreading Protestantism in China, missionaries took active
part in building modern education and medical system. They set up a large number of
38
The Provincial-level GDP data are obtained from China Compendium of Statistics 1949-2008. We use the
provincial-level data because county-level GDP data before 1993 are not available.
23
western-style schools and hospitals, and helped build public health system in China.
Tens of millions of Chinese people benefited from their pioneering work. Such efforts
might have contributed substantially to the accumulation of human capital in China in
the past century.
However, we realize that the effects of the spread of Protestantism could be
complex and profound. It is possible that missionaries’ work induced the changes in
work ethics, attitudes toward western culture, entrepreneurship, and so on. Such
changes might also have boosted long-run economic growth. Our empirical results
suggest that improvements in education and health account for about half of the total
effects of missionaries’ work on today’s economic outcomes, and the other half are
possibly the contributions from the changes in culture and people’s mindset.
24
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28
Data Appendix
1. Mapping historical information with current administrative units
Refer to Figure A1 as an illustrative example, where squares represent 1999’s
county boundaries and circles represent 1920’s county boundaries. For the latter we
have measures of Christian activities (Xi1920, i=1,…,5), including number of converts,
vicars, and churches. The corresponding 2000 county level measures (X1999), shown
as the grey area, is calculated by
xk 

k  A~ E
x1999
k
where
 x11920 
x1999
A
sA
s1
...
 x51920 
x1999
E
sE
s5
Figure A1. An illustrative map
The implicit assumption is that the church activities in 1920 are equally
distributed with a county.
2. Convert station level information into county level
Given the climate measures at the station level (Yj, j=1,…,J), the county level
measures (Yc) are constructed by
Yc 
Y
j 1, J
j
 wij
Yc 

j 1,..., J
Y j  wcj
where wij is the weight, which is constructed using Shephard’s method (Shepard,
1968):
wcj 
distcj 2

k 1,.., J
29
distck 2
Figure 1.Converts per 1,000 People in 1920
Convert per 1k people
(by quantile)
.00 - .01
.50 - .80
.01 - .10
.80 - 1.00
.10 - .20
1.00 - 1.50
.20 - .30
1.50 - 2.50
.30 - .50
2.50 - 25.00
Note: This map shows the distribution of converts (normalized by population) in year 1920 among counties
in our sample. Each grid is a county with 2000 boundary. Converts information at the 1920 county level was
transform into 2000 county level. Refer to Appendix for details. Darker color in the map means higher
density of Christian converts per 1,000 people.
1 / 14 Figure 2. GDP per capita in 2000
GDP per capita in 2000
(by quantile, in 1k yuan)
.00 - 1.80
4.20 - 5.00
1.80 - 2.50
5.00 - 5.80
2.50 - 3.00
5.80 - 7.20
3.00 - 3.60
7.20 -10.00
3.60 - 4.20
10.00 - 35.00
Note: This map shows the distribution of GDP (normalized by population) in year 2000 among counties in
our sample. Each grid is a county with 2000 boundary. Darker color in the map means higher GDP per
capita.
2 / 14 6
7
GDP per capita (log), 2000
8
9
10
11
Figure 3. Correlation between Historical Christian Activities and Current Economic Performance
-7
-5
-3
-1
1
Converts per capita (log), 1920
3
(coef = .13, s.e. = .01, N = 1742, R2 = .14)
Note: This figure plots logarithm of converts per capita in 1920 against logarithm of GDP per capita in 2000.
Each dot is a county in our sample. The solid line is the fitted regression line with slope equal to 0.13 and t
statistics equal to 16.92. The correlation coefficient between these two variables is 0.38.
Code:
D:\Religion\OLS\Tables_20131010 \ T1_SumStats.do
3 / 14 Figure 4. Correlation between Historical Economic Conditions and Historical Christian Activities
in 1920
Population Density (log), 1920
0
2
4
6
-2
-2
Population Density (log), 1920
0
2
4
6
8
Panel B
8
Panel A
-8
-6
-4
-2
Churches per 1000 people (log), 1920
0
-6
coef = -.21, se = .029
-4
-2
Vicars per 1000 people (log), 1920
0
coef = -.117, se = .029
Tax per capita (log), 1820
-8
-6
-4
-10
-10
Tax per capita (log), 1820
-8
-6
-4
-2
Panel D
-2
Panel C
-8
coef = -.211, se = .029
-6
-4
-2
Churches per 1000 people (log), 1920
0
-6
-4
-2
Vicars per 1000 people (log), 1920
0
coef = -.118, se = .029
Note: These figures plot Christian church activities in 1920 against historical economic performance.
Historical economic performance is measured by the population density (normalized by geographic area) in
panel A and B, and the land tax per capita in 1820 in panel C and D. Christian church activities is measured
by number of churches per 1,000 people in panel A and C, and number of vicars per 1,000 people in panel B
and D. All values are in logarithm. In each figure, the dot represents a county. The solid line is the fitted
regression line with its slope and standard error report reported below the figure. The shaded area represents
the 95% confident interval.
4 / 14 Figure 5. Correlation between Historical Christian Activities and Historical Western Medicine
Development in China
4
Panel A. Number of Western Hopital
FujianGuangdong
ln(# of Hospitals), 1920
1
2
3
Jiangsu
Shangdong
Hubei
Hunan
Zhili
Zhejiang
Henan
Anhui
Jiangxi
Guangxi
0
Yunnan
Guizhou
8.5
9
9.5
10
ln(# of Converts), 1920
10.5
11
beta coef = 1.01, t-stat = 5.05, N = 14, R2 = 0.68.
Panel B.Number of Phamacies
1.5
ln(# of Phamacies), 1920
2
2.5
3
3.5
Shangdong
Jiangxi
Hunan
Guangdong
Jiangsu
Henan
ZhejiangFujian
Yunnan
Hubei
Guizhou
Zhili
Anhui
1
Guangxi
8.5
9
9.5
10
ln(# of Converts), 1920
10.5
11
beta coef = .39, t-stat = 2.05, N = 14, R2 = 0.27.
Note: These figures plots provincial level number of converts against historical Western medicine
development in China, measured by number of western hospitals in 1920 (Panel A) and number of pharmacies
in 1920 (Panel B). All values are in logarithm. In each figure, the solid line is the fitted regression line with its
slope and standard error report reported below.
Figure 6. Path of Economic Development across Provinces since 1950
5 / 14 Average real per capita GDP (000s)
.5
1
1.5
2
2.5
0
1950
1960
1970
1980
Year
1990
2000
2010
Provinces with high convert/population in 1920
Provinces with low convert/population in 1920
Note: In this figure, 23 provinces in our sample are divided into two groups: one includes 12 provinces
with higher converts/population in 1920; the other one includes 11 provinces with lower
converts/population in 1920. The figure plots the weights average per capita GDP across years for each
of the group. The weights are the provincial population in each year. Nominal values are deflated to
2000 price level.
6 / 14 7 / 14 Table 2. Relationship between Historical Church Activities and Current Economic Performance
Dependent variable is log GDP per capita in 2000
(1)
(2)
(3)
(4)
(5)
ln(converts/population)
0.13
0.09
0.05
0.06
0.05
[0.01]***
[0.01]***
[0.01]***
[0.01]***
[0.01]***
Longitude
0.04
0.04
0.04
[0.01]***
[0.01]***
[0.01]***
Latitude
-0.01
-0.01
-0.01
[0.01]
[0.01]*
[0.01]*
Distance to Provincial Capital
-0.15
-0.15
-0.15
(in logs)
[0.02]***
[0.02]***
[0.02]***
Altitude
-0.10
-0.11
-0.11
(in logs)
[0.02]***
[0.02]***
[0.02]***
Temperature
-0.29
-0.38
(in logs)
[0.11]**
[0.12]***
Rain
0.10
0.20
(in logs)
[0.11]
[0.12]
Current Flood Frequency
-1.14
(1980-2000)
[0.37]***
Current Draught Frequency
-0.61
(1980-2000)
[0.28]**
Regional FE
NO
YES
YES
YES
YES
R-sq
0.14
0.26
0.36
0.36
0.37
N
1742
1742
1742
1742
1742
Notes:
OLS estimates of euqation (1) are reported. Observationas are at the 2000 county level. The dependent
variable is the logarithm of GDP per capita in 2000, lny. The Chrisitian activities variable
ln(converts/population) is the logarithm of converts of each county in 1920 normalized by population.
Regional fixed effects are indicator variables for the 7 regions in China proper: north, east, northeast,
middle, southern, southwest, and northwest China.
Coefficients are reported with standard errors in brackets. ***, **, and * indicate significance at 1%, 5%
and 10% levels.
8 / 14 Table 3. Relationship between Historical Church Activities and Current Economic Performance
(1)
(2)
(3)
(4)
Panel A. Second Stage. Dependent variable is log GDP per capita in 2000
ln(converts/population)
0.20
0.20
0.21
0.21
[0.03]***
[0.06]***
[0.05]***
[0.05]***
Location Controls
NO
NO
YES
YES
Climate Controls
NO
NO
NO
YES
Current Natural Disaster
NO
NO
NO
NO
Reg. FE
NO
YES
YES
YES
N
1742
1742
1742
1742
(5)
0.20
[0.05]***
YES
YES
YES
YES
1742
Panel B. First Stage. Dependent is historical Christian activities, ln(converts/population)
Flood Frequency
1.41
1.64
1.86
2.12
2.04
(1900-1920)
[0.51]***
[0.50]***
[0.49]***
[0.49]***
[0.50]***
Draught Frequency
5.84
3.52
4.26
4.13
4.27
(1900-1920)
[0.55]***
[0.57]***
[0.56]***
[0.56]***
[0.58]***
Location Controls
NO
NO
YES
YES
YES
Climate Controls
NO
NO
NO
YES
YES
Extreme Weather Frequency
NO
NO
NO
NO
YES
(1980-2000)
Regional FE
NO
YES
YES
YES
YES
F-stat on IV
66.73
18.93
29.35
27.06
27.38
Sargan Test (p-value)
0.22
0.40
0.10
0.36
0.45
Notes:
This table reports estimates of equation (1), where historical Chiristian activities are instrumented by
historical frequency of extreme weather. Observationas are at the 2000 county level. Panel A reports
estimates of the second stage. The dependent variable is the logarithm of GDP per capita in 2000, lny. The
Christian activities variable ln(converts/population) is the logarithm of converts of each county in 1920
normalized by population. Panel B reports estimates of the first stage. The dependent variable is the
endogenous variable, ln(converts/population). Instruments include the flood and draught frequency
between 1900 to 1920.
Coefficients are reported with standard errors in brackets. ***, **, and * indicate significance at 1%, 5%
and 10% levels.
9 / 14 10 / 14 Table 5.
Relationship between Historical Church Activities and Development of Modern Education
Enrollment Rate
(in logs)
ln(converts/population)
Christian
Primary
Schools
(IV
Tobit)
(1)
Public
Primary
Schools
1.47
[0.19]***
0.39
[0.08]***
Primary Schools in Total (2SLS)
(2SLS)
(2)
ln(converts/population in
the neighboring counties)
Controls
N
F-stat on IV
Sargan Test (p-value)
ln(converts/population)
ln(converst/population in
the neighboring counties)
YES
1742
26.56
0.00
YES
1742
26.56
0.00
1.33
[0.04]***
0.12
[0.01]***
Overall
Convert/Pop1920>0 Convert/Pop1920=0
Sample
(3)
(4)
(5)
Panel A. Second Stage
0.54
0.58
[0.32]*
[0.30]*
-0.12
-0.15
0.42
[0.20]
YES
1701
[0.17]
YES
1444
[0.19]**
YES
250
0.00
0.00
Panel B. OLS Estimates
0.12
0.16
[0.01]***
[0.02]***
0.00
0.06
0.04
0.08
[0.02]***
[0.02]***
[0.04]**
Notes:
This table examines the effects of historical Christian activities on development of modern educational
system in the early 1920s of China. Observations are at the 2000 county level. The Christian Activities
variable log (converts/population) is the logarithm of converts of each county in 1920 normalized by
population. The across county spillover effects are captured by the weighted average of converts/population
in 1920 in its contiguous neighboring counties (in logs).
All specifications are estimated through 2SLS, where ln(converts/population) is instrumented by historical
frequency of extreme weather, including the flood and drought frequencies between 1900 and 1920. Christian
activities in the neighborhood counties are instrumented by the weighted average of historical Flood/Draught
frequency in the neighborhood counties. Column (1) is Tobit regression since 30% of the county has zero
number of primary student from Christian schools. Column (3)-(5) exclude counties with no converts and no
converts in their neighborhood.
Coefficients are reported with standard errors in brackets. ***, **, and * indicate significance at 1%, 5% and
10% levels.
11 / 14 Table 6. Effects of Church Activities on Current Education and Health Outcomes
(1)
(2)
Panel A. Second Stage
Educational
Expenditure
(per Capita)
Dep. Var. (in logs)
Years of
Schooling
(3)
(4)
(5)
Literacy
Children
Mortality
Health
Expenditure
(per Capita)
ln(converts/population)
0.05
0.07
0.05
-0.38
0.05
[0.03]*
[0.01]*** [0.01]*** [0.06]***
[0.05]
Location Controls
Yes
Yes
Yes
Yes
Yes
Climate Controls
Yes
Yes
Yes
Yes
Yes
Current Natural Disaster
Yes
Yes
Yes
Yes
Yes
Reginal FE
Yes
Yes
Yes
Yes
Yes
Number of Observations
1742
1742
1742
1742
1742
Sargan Test (p-value)
0.00
0.02
0.00
0.00
0.00
Panel B. OLS Estimates. Dependent Variable is Log GDP per Capita in 2000
ln(converts/population)
0.02
0.01
0.00
-0.04
0.03
[0.01]***
[0.00]***
[0.00]
[0.01]*** [0.01]***
Notes:
The Christian Activities variable ln(Convert/Pop1920) is the logarithm of converts of each
county in 1920 normalized by population. Coefficients are reported with standard errors in
brackets. ***, **, and * indicate significance at 1%, 5% and 10% levels. The Hansen's J statistic
is used for the test of overidentifying restrictions in the presence of heteroskedasticity. The joint
null hypothesis is that the instruments are valid instruments, i.e., uncorrelated with the error
term, and that the excluded instruments are correctly excluded from the estimated equation.
12 / 14 Table 7. Alternative Channels
(1)
(2)
(3)
(4)
(5)
(6)
Panel A. Second Stage. Dependent Variable is Log GDP per Capita in 2000
ln(converts/population)
0.15
0.12
0.13
0.17
0.17
0.11
[0.04]*** [0.05]** [0.05]*** [0.06]*** [0.05]*** [0.06]*
ln(Education exp. per capita)
0.59
[0.05]***
ln(Years of schooling)
0.71
0.72
[0.16]***
[0.16]***
ln(Literacy rate)
0.82
[0.13]***
ln(Children mortality rate)
-0.03
-0.03
[0.03]
[0.03]
ln(Health exp. per capita)
0.26
[0.02]***
Location Controls
Yes
Yes
Yes
Yes
Yes
Yes
Climate Controls
Yes
Yes
Yes
Yes
Yes
Yes
Current Natural Disaster
Yes
Yes
Yes
Yes
Yes
Yes
Reg. FE
Yes
Yes
Yes
Yes
Yes
Yes
N
1742
1742
1742
1742
1742
1742
Panel B. First Stage. Dependent Variable: ln(Converts/Pop1920)
1900-1920 Flood Freq.
2.15
1.75
2.01
2.09
2.22
1.74
[0.52]*** [0.51]*** [0.52]*** [0.51]*** [0.52]*** [0.52]***
1900-1920 Draught Freq.
4.35
4.04
4.4
3.97
4.38
3.08
[0.59]*** [0.58]*** [0.59]*** [0.59]*** [0.59]*** [0.60]***
F-stat on IV
27.53
24.92
28.46
22.79
27.83
13.43
Over Identification
0.03
0.99
0.95
0.52
0.03
0.87
(p-value)
Panel C. OLS Estimates. Dependent Variable is Log GDP per Capita in 2000
ln(converts/population)
0.04
0.04
0.05
0.05
0.04
0.04
[0.01]*** [0.01]*** [0.01]*** [0.01]*** [0.01]*** [0.01]***
Notes:
This table reports estimates of equation (3), where historical Chiristian activities are instrumented by the
flood and draught frequency between 1900 to 1920. Observations are at the 2000 county level. Panel A
reports estimates of the second stage. The dependent variable is the logarithm of GDP per capita in
2000. The Christian activities variable ln(converts/population) is the logarithm of converts of each
county in 1920 normalized by population. Panel B reports estimates of the first stage. The dependent
variable is the endogenous variable, ln(converts/population). Panel C reports the corresponding OLS
specifications of Panel A without instruments. Control Variables include location controls like
longitude, latitude, (log) distance to the capital city and (log) altitude, climate variables like temperature
and precipitation, current extreme weather frequency and regional fixed effect.
Coefficients are reported with standard errors in brackets. ***, **, and * indicate significance at 1%, 5%
and 10% levels.
13 / 14 Table 8. Effect of Christian Activities on Economic Performance after Adjusting for Education and Health:
Bounding Analysis
(1) 0
ln(Years of schooling)
(2) 80% χ2
(3) 90% χ2
(4) 100% χ2
(5) 110% χ2
(6) 120% χ2
(7) 140% χ2
(1)
0
0.19
[0.05]***
(2)
80% δ
0.18
[0.05]***
ln(Children Death Rate)
(3)
(4)
(5)
100% δ
120% δ
150% δ
0.17
0.17
0.16
[0.05]***
[0.05]***
[0.05]***
0.14
0.12
0.12
0.11
0.11
0.1
0.1
[0.05]***
[0.05]***
[0.05]***
[0.05]**
[0.05]**
[0.05]**
[0.05]**
0.13
0.11
0.11
0.11
0.1
0.1
0.09
[0.05]***
[0.05]**
[0.05]**
[0.05]**
[0.05]**
[0.05]**
[0.05]**
0.12
0.11
0.1
0.1
0.09
0.09
0.08
[0.05]***
[0.05]**
[0.05]**
[0.05]**
[0.05]**
[0.04]**
[0.04]*
0.12
0.1
0.1
0.09
0.09
0.08
0.08
[0.05]**
[0.05]**
[0.05]**
[0.05]**
[0.04]*
[0.04]*
[0.04]*
0.11
0.09
0.09
0.09
0.08
0.07
0.07
[0.05]**
[0.05]**
[0.05]**
[0.04]*
[0.04]*
[0.04]*
[0.04]
0.1
0.08
0.08
0.07
0.07
0.06
0.06
[0.05]**
[0.04]*
[0.04]*
[0.04]
[0.04]
[0.04]
[0.04]
(6)
180% δ
0.16
[0.05]***
(7)
200% δ
0.15
[0.05]***
Notes:
This table reports estimates of equation (4). Each cell reports the results of a separate regression. They are the 2SLS
estimates of the coefficients on ln(converts/population) in 1920 that is instrumented by frequency of flood and
drought during 1900-1920. The dependent variable is ln(GDP per capita) in 2000. The returns to education and
health stem from OLS coefficients in an auxiliary regression of the ln(GDP per capita) on years of schooling,
children mortality rate, ln(converts/population), and the control variables (as reported in column (5) of Table 2). To
adjust for the potential bias of the OLS estimates, coefficient on years of schooling is multiplied by factors
indicated in titles of each row, and coefficient on children mortality rate is multiplied by factors indicated in titles
of each column.
Coefficients are reported with standard errors in brackets. ***, **, and * indicate significance at 1%, 5% and 10%
levels.
14 / 14