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Buddha’s Grace Illuminates All: Temple Destruction, School

Construction, and Modernization in the 20th Century China

Shaoda Wang† Boxiao Zhang‡

October 2015

Preliminary Version, Please Do Not Cite or Circulate

Abstract

This study documents a novel natural experiment in the early 20th century China called the

“Temple Destruction Movement (TDM),” where local governments were required to take

over the assets of Buddha and Taoist temples to support local modern schooling. We show

that before the TDM, the prior stock of average temple assets was not correlated with

various measures of human capital and economic development; while after the TDM

started, regions with higher prior stock of average temple assets constructed more schools,

enrolled more students, evolved more modern elites, and also experienced faster

urbanization. Moreover, we find that the impacts of the TDM persist in the 21st century:

regions with higher prior stock of temple assets before the TDM have higher human

capital and better economic performance in 2000.

Keywords: Temple Destruction Movement; Human Capital; Economic Development

JEL: O15; O18; N35

† Department of Agricultural and Resource Economics, U.C. Berkeley. ‡ Guanghua School of Management, Peking University.

We are indebted to Noam Yuchtman and Se Yan for their generous support. We would also like to thank Alain de Janvry, Elisabeth Sadoulet, Debin Ma, Yu Hao, Kris Mitchener, Jean-Laurent Rosenthal, Aprajit Mahajan, Ethan Ligon, Jeremy Magruder, Zhiwu Chen, Paul Katz, Yang Xie, Xi Lu, Chicheng Ma, John Loeser, Ye Wang, Xuenong Zhou, and seminar participants at the NEUDC conference, the All-UC group Frontiers in Chinese Economic History conference, Berkeley ARE development workshop, and the third International Symposium on Quantitative History for their helpful comments. Financial support from the Berkeley Economic History Lab (BEHL) is gratefully acknowledged. Any remaining errors are solely ours.

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1 Introduction

For over 1,300 years, especially in the late imperial period (1368-1911), China’s civil society

in general, and its traditional education system in particular, were fundamentally shaped by

the civil service exams (Elman, 2000). At various levels, tutors, schools, and academies

trained students with Confucian classics, which were the official materials of the exams.

To prepare for these exams, students had to devote themselves mainly into writing poems

and eight-legged essays, which partly led to the academic tradition of valuing humanities

over science and technology (Lin, 1995; Huff, 2003).

In the late 19th and early 20th centuries, China experienced consecutive military defeats

against the western powers and Japan, which led to the abolishment of the civil service

exams, and the explorations of ways to generate modern human capital and new elites.

Among the numerous attempts made by the government, not surprisingly, constructing

modern schools was the most important and persistent one. Since the central government

faced severe debts and deficits, the provision of education funding was almost entirely

undertaken by lower-level governments and local elites.

To help the local elites raise adequate amount of funding for modern schooling, the

central government implemented a novel policy aimed to close most of China’s total

200,000 Buddha\Taoist temples and take over most of their assets to finance modern

education, which was known as the “Temple Destruction Movement (TDM).” The TDM

was first proposed and approved in 1898, but due to political and ideological reasons never

actually implemented until 1912.

In this paper, making use of a unique prefecture-level panel dataset on modern

education in the late Imperial and early Republican eras of China, we first show that before

the movement started, stock of average temple assets (measured by the average number

of temples in 1820) was orthogonal to various kinds of indicators for human capital and

economic prosperity, and was not correlated with either the levels or the trends of average

number of schools and students. However, after the movement started, places with higher

stock of temple assets ex ante constructed more schools, enrolled more students, and later

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evolved more elite human capital (measured by average number of top university alumni

and oversea students), and also experienced faster economic growth (measured by

urbanization rate). Furthermore, we provide evidence that the increase of human capital

and its positive effects on economic growth persists until the 21st century: places with

higher prior stock of average temple assets in 1820 have higher levels of human capital

and economic performance in 2000. Our study speaks to several strands of literature.

First, we add to the increasing literature on the formation and persistence of human

capital through time. Economic historians have long argued that formation of modern

human capital is essential for a society’s transition towards a modern economy (Yuchtman,

2014), but there is few empirical evidence documenting how exactly does modern human

capital evolve, our paper fills in this blank. Also, there have been papers showing that mass

human capital investment in history could have persistent effects on today’s educational

levels and investments (Huillery, 2009; Wantchekon et al., 2015; Nunn, 2011). Different

from these works, we also study how elite human capital could evolve from investment in

mass human capital, and also estimate the effects of human capital on economic

development in the mid-run and in the long-run.

Second, our paper contributes to the literature on the relationship between human

capital and economic development. There exists a huge literature on whether human

capital could boost economic development, some using cross-country comparison (Hall

and Jones, 1999; Glaeser et al., 2004; Acemoglu et al., 2014), others using cross-regional

analysis (Acemoglu and Dell, 2009; Gennaioli et al., 2013). But as pointed out by many

researchers, investment in education is an endogenous decision affected by many factors,

such as institutions (North and Thomas, 1973, Acemoglu et al., 2002, Acemoglu et al.,

2014, Engerman et al., 2012, Galor et al., 2009), political structure (Lindert, 2004), fiscal

capacity (Bardhan and Mookherjee, 2006), ethnicity and culture (Easterly and Levine, 2012,

Fernandez, 2011, Peri, 2012), which will also affect economic outcomes. Therefore,

estimating the causal effects of human capital on development remains challenging. Some

of the more recent works adopt historical natural experiments to identify this causal

relationship (Ferraz et al., 2015; Hornung, 2014; Becker et al., 2011; Squicciarini and

Voigtlaender, 2015), basically showing that human capital has positive impacts on

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development, and such effects could persist in the long run. Our paper reassures these

findings in the context of 20th century China using a comprehensive historical dataset. We

also differentiate from these recent papers by studying and documenting the formation of

elite human capital in addition to only mass human capital, which is believed to be crucial

to the transition and upgrade of an economy (Cantoni and Yuchtman, 2014).

Third, our paper is also related to the large literature on the effects of religion on

economic growth. Since Max Weber (2002), whether Protestantism could actually

encourage hard work and in turn contribute to economic prosperity has led to heated

debates and mixed evidence in the empirical literature. Cantoni (2013) finds no relationship

between Protestantism and growth, many papers find evidence supporting Weber’s

proposed channel (Stulz and Williamson, 2003; Barro and McCleary, 2003), but others

argue that it is not religion itself, but the human capital and other by-products of it, that

contribute to growth (Glaeser and Sacerdote, 2008; Becker and Woessmann, 2009). In the

context of China, Bai and Kung (2015) find that Protestantism promoted growth in China

through building hospitals and schools, Chen et al (2015) find similar results, but also

suggest that this human capital channel is not exclusive. To our best knowledge, despite

the large literature on Protestantism, there has been few studies on the social and economic

effects of Buddhism and Taoism, which are highly important religions in the context of

China, and many other Asian countries as well. Our results suggest that these two religions

contributed to modernization significantly, but likely only through the channel of their

assets being appropriated to support modern schooling, and not through shaping social

preferences or any other channels.

The remainder of this paper is organized as follows. Section 2 will document the

historical background of the formation of modern human capital in China and the Temple

Destruction Movement. Section 3 discusses the data and presents descriptive statistics.

Section 4 will present the effects of the TDM on both mass and elite human capital

formation, and the effects on mid-run economic development. Section 5 presents evidence

of the persistence of human capital and its effects on economic growth in the 21st century.

Section 6 discusses the robustness of our results. Section 7 concludes.

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2 Historical Background

Fundamentally shaped by the civil service exam, China’s traditional education system were

constituted by tutors, traditional schools and academies. At the junior level, there were

several kinds of public or private traditional schools, including community schools (shexue),

charitable schools (yixue) and clan schools (zuxue). According to imperial edict, those

schools’ objective was jiaohua, can be translated as “civilization” or “enculturation.” They

trained students to learn basic reading and writing skills, and to recite Confucian classics,

including the Four Books and Five Classics (Sishu Wujing), which were the standard

textbooks of the civil service exam. For exam participants, they usually needed to hire

tutors or attend academies to obtain senior education afterwards, including writing poems

and eight-legged essays (Baguwen).

In the late 19th and early 20th centuries, after consecutive military defeats against the

western powers and Japan, Chinese government and elites realized the challenges they

faced in technology, as well as in culture and education. In 1902, China issued the first

comprehensive plan of building its own modern education system, and began to construct

modern primary schools, secondary schools and universities. Instead of Confucian classics,

students were required to learn modern knowledge about science, technology and

humanities in those schools.

During 1904-1905, Japan succeeded in the Russo-Japanese War, which was the first

major military victory in the modern era of an Asian power over a European nation. Japan's

success was attributed to the Meiji Restoration and Modernization, which set an example

for the Qing Dynasty and facilitated a new round of education reform, including the

abolishment of the civil exam system as well as the traditional education system, and the

further construction of modern schools (Franke, 1960).

According the experiences of western countries, public provision of modern

education requires consistent financial support. However, in late Qing Dynasty, both

central and local governments faced severe debts and deficits, and were unable to build

and sustain a modern education system. In 1895, after the Sino-Japanese war, the deficit

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of Qing government reached 15 million tael of silver.1 In 1910, the number increased to

80 million (Zhang, 1996). To raise adequate funding to support modern schooling, a novel

policy was prompted, which aims to activate traditional temple assets to finance local

education, and is often referred to as the “Temple Destruction Movement (TDM).”

Early in 1898, one of the most important Chinese politicians, Zhang Zhidong, wrote

a famous proposal (Quanxue Pian) to the Emperor Guangxu and the Empress Dowager

Cixi, suggesting that the government should take over the assets of those Buddha temples,

and use the money to build primary schools in the local communities, so that China will

cultivate more literate youngsters and generate modern human capital to compete against

the western countries. As part of the tremendous 1898 reform plan (Wuxu Bianfa), the

government accepted this advice, and planned to change 70% of the country’s temples

into primary schools, and take over 70% of these temples’ assets to use as education budget.

However, due to political and ideological reasons, the 1898 reform plan only lasted for 104

days, before most of the innovative policies were terminated by the Empress Dowager

Cixi, and 7 of the major designers of the reform were sentenced to death, even the emperor

himself was imprisoned in the Forbidden City (Xu 2008). As a result, the TDM was also

banned by the government. However, under the pressure of raising enough fund for local

education, some local governments implemented this policy secretly at smaller scales.

Realizing such cases, in 1905, the central government re-emphasized that the TDM was

illegal.

In 1912, the Qing government was overthrown, and the Republican of China was

founded. In the same year, President Yuan Shikai issued the “31 rules of temple

administration,” which formally urged the local governments to take over all (rather than

70%) of the temple assets to construct modern schools. Soon after that, the TDM thrived

across the whole country, and persisted with local authorities even though the centralized

state power disappeared again after Yuan Shikai died in 1916. Nearly twenty years later, in

the 1930s, the Kuomintang reestablished the centralized state power and formally

1 Tael is a Chinese traditional unit of weight, 1 tael ≈ 37.8 g.

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facilitated the movement once again. Therefore, the TDM persisted for more than 40 years,

until the establishment of the PR China (Katz 2012, 2013).

Buddha and Taoist temples had accumulated large amount of assets throughout the

history, including buildings, land, and cash. While there is no official survey data, some

studies estimate temple assets using historical documents. Xu (2010) summarizes that, in

late Qing Dynasty, there were about 2 million Buddha and Tau temples, and they totally

owned about 16 million houses, 13,000 square kilometers’ land and “millions tael of silver.”

As a matter of fact, the imperial China actually had a long-standing historical tradition that

governments solve their fiscal crisis by seizing temple assets. Following that tradition, it

was natural that the Qing and Republican governments regarded temple assets as viable

sources of funding for modern education. During the TDM, local governments and elites

took over tremendous amount of wealth from temples, and used it to support the

construction of a modern education system.

With adequate funding collected from temple assets, modern education thrived

quickly across the country. As shown in figures 1 and 2, during the first half of the 20th

century, the development of modern education has been boosted twice2. In 1910s and

1930s, both the amount of schools and students had increased greatly, which are consistent

with the periods that the TDM was conducted and facilitated.

[Insert Figure 1 and 2 about here]

Historical studies also find that the Temple Destruction Movement had huge

destructive effects on Buddhism and Taoism in China. Before the Temple Destruction

Movement, in Chinese history, there were four similar movements of seizing temple assets,

summarized by the Buddhists as “four greatest disasters” (Sanwu Yizong Fanan). However,

none of them can be compared to the TDM. The most common form of destruction was

to use temples’ houses as schools. From existing historical evidences, more than 70% of

schools were transformed from temples. In addition, some local governments also

regulated temples’ land as part of schools’ assets, or imposed high “religion tax” on temples

(Xu, 2010).

2 The data we use in the figures will be introduced in section 3.

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3 Data and Key Variables

The data for our analysis is collected from assorted sources, and constructed as several

datasets. First, we have a prefectural-level panel dataset, with information on modern

education, temple and other control variables in the early 20th century (1907-1930), which

will be used to investigate the effects of the TDM on school construction. Second, we

obtain personal information of top university alumni and oversea students before the

establishment of the PR China, and use the average number of students as the measure of

elite human capital from the 1890s to the 1940s. Third, we use a county-level cross-

sectional dataset, with information on urbanization rate and other control variables in 1920

and 1964, to identify the effects of school construction on economic development in the

mid-run. Finally, we collect county-level average income and other indicators of human

capital in 2000, and try to investigate the TDM’s positive effects on long-term economic

growth.

3.1 Prefectural-level Panel Data of Modern Education and Temple Assets

Modern Education Data

The original education data basically includes three historical periods, from the late Qing

Dynasty to the Republic of China. It records detailed information on the number of

schools, number of students, types of schools and education budget at the county-level.

In the first period, this database includes three years’ education data in the late Qing

Dynasty, which is the year of 1907, 1908 and 1909. In the second period, this database

includes two years’ education data in the beginning of the Republic of China, which is the

year of 1915 and 1916. The above five years’ survey data was respectively complied as the

first, second, third, fourth and fifth time of the “Statistical Chart of Education” (Jiaoyu

Tongji Tubiao). In 1905, during the political reform in the late Qing Dynasty, the Ministry

of Education was established for modern education reform. Between the year of 1907 and

1909, the Minister of Education was Zhang Zhidong, who was well-known for his progress

of sponsoring modern education in Hubei. Since the modern education reform had been

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conducted in China for nearly ten years at that time, Zhang Zhidong carried out those

surveys to investigate and evaluate the reform in nationwide.

However, after the third survey in 1909, the nationwide survey of modern education

was interrupted by the 1911 Revolution. Till the year of 1915 and 1916, in the early

Republic of China, the Ministry of Education successfully conducted the fourth and fifth

surveys again under the comparatively stabilized atmosphere. Afterwards, the national

survey of modern education was once again interrupted by conflicts and wars.3

The third period is much longer than the first two periods, which is between the year

of 1919 and 1935. From 1920s to 1930s, the national surveys of modern education were

missing because of the unstable political atmosphere. Therefore, the county-level

education data comes from several provincial surveys conducted by some local education

authorities. This dataset includes five provinces’ county-level education data in 1920s, and

nine provinces in 1930s. The above surveys provide by far the best and the most complete

national county-level education data, which has been cited as credible historical data in the

history literature (Shang, 2001; Su, 2007). The specific sources of education data can be

specifically traced and is summarized in table A1 in Appendix.

Based on the original county-level education data with multiple years, we then do two

important adjustments to reorganize it into a prefectural-level panel data with five years.

In the first period, we observed that the data of 1907, 1908 and 1909 is basically complete

with little missing values. Therefore, we keep all the 1907, 1908 and 1909’s data in the first

period. However, in the second period, we observed that the data of 1915 has a complete

coverage, while the data of 1916 has many missing values. We then only use the 1915’s

data to represent the second period and drop the data of 1916. As for the third period, we

set 1930 as the representative year, and for each available province, we choose data whose

survey year is closest to 1930. In summary, we now have the adjusted data with five

representative years, for each period. A tricky problem is how to append them into a panel

data. From the late Qing Dynasty to the Republic of China, the changes of county-level

administrative divisions were frequent and complicated. Furthermore, we are lack of any

3 The detailed process of the five times’ nationwide education survey has been fully studied by several historians, see Guan (1999), Ma and Lu (2002), and Wang (2010).

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GIS map to handle the changes. Luckily, most of the changes are within prefectures, and

we can avoid the tricky problem by summarizing the data from county-level to prefecture-

level. Based on 1820’s map from CHGIS,4 we are finally able to construct a prefectural-

level education panel data with years of 1907, 1908, 1909, 1915 and 1930.

The modern education data has 264 prefectures in 18 provinces in 1907, 1908, 1909

and 1915, and 135 prefectures in 10 provinces in 1930. We use the number of primary

schools and students as the main indicator of modern education development.

Temple and Traditional School Data

The data for temple assets is documented in the “Imperial Encyclopedia of the Qing

Empire” (Daqing Yitong Zhi), which includes prefecture-level information on the important

local Buddha and Taoist temples. This book was compiled by Qing government for more

than 150 years, and was final-edited in 1842. For each prefecture, we obtain the total

number of important temples as the indicator of temple assets before the TDM. Our data

includes 264 prefectures in 18 provinces, which can be perfectly matched to 1820’s map

from CHGIS.

Furthermore, we also collect the number of traditional schools in 1820, and include

it in our regression as a control variable.

Population, Area and Jinshi Data

We also include other important socioeconomic information in our study. We have

collected prefectural-level population data for six years, including 1776, 1820, 1850, 1880,

1910 and 1953. We have also collected area of each prefecture.

In Qing dynasty, there are more than 26,000 successful candidates of the civil exam

(Jinshi). From the “Official Records of Jinshi in Qing Dynasty” (Qingdai Jinshi Timing Lu),

we identify each exam successful candidate’s prefecture of origin and construct a

prefectural-level data of the total number of civil exam graduates.

4 The China Historical Geographic Information System provide a prefectural-level GIS map in 1820.

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We match the aforementioned datasets to the panel dataset of education. Except for

the number of primary schools and students, all the other variables are time-invariant.

3.2 Prefecture-level Panel Data of Top University Alumni and Oversea Students

From assorted historical archives and alumni books, we obtain personal information of

top university alumni and oversea students. We identify 16,439 students who graduated

before 1950, from the top 2 universities in China, Peking University and Tsinghua

University, both located in Beijing. We divide the era into eight 5-year periods, and for

each prefecture, we summarize the number of students enrolled in the top 2 universities

in each period. For oversea students, we identify 9,001 individuals who were born between

1890s and 1940s. Similarly, we divide the birth era into 10-year periods, and for each

prefecture, we count the number of future oversea students born in each period.

By above method, we construct two prefecture-level panel data of the number of top

university alumni and oversea students. Furthermore, we match the datasets to the panel

datasets of education, population and area, especially the variable of average temples.

3.3 County-level Data of Urban Population and Urbanization Rate

The data of urban population and urbanization rate in 1920 are obtained from “The

Christian Occupation of China” (Stauffer, 1922), which contains population of all counties

and urban population of cities with more than 25,000 residents. Therefore, we compute

the proportion of population living in urban areas as a measure of urbanization in 1920.

We also collect population data in 1964 from the “The Complied Statistics of the

Second National Population Census” (National Bureau of Statistics, 1982). The data

includes total population, male population and the number of households. In the Census

data, we also know in each county how many people are registered as “urban residents,”

and how many as “rural residents.” The residents were largely classified by their types of

occupation: urban residents typically participate in non-agricultural production, and vice

versa. Since in the 1960s migrations across regions were banned in most cases, the ratio of

urban residents in a region is an accurate measure of its level of urbanization. Therefore,

we can obtain urbanization rate and average family size in 1964.

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We merge the above two datasets together, and construct a county-level dataset of

urbanization rate. Furthermore, we identify the prefecture each county belongs to, and

combine the data with prefectural-level variables. In the end, we have information for 1,700

counties from 18 provinces in our data.

3.4 County-level Contemporary Data of Economic Growth and Human Capital

To study the long-term impacts of the TDM, we assemble contemporary socioeconomic

indicators of Chinese counties. We obtained county-level data on economic growth and

human capital in 2000, including aggregate GDP, government subsidies on education and

culture, from the Statistical Materials of Public Finance of Cities and Counties; and data on

population, literacy rates and years of schooling from the Fifth National Population

Census (2000).

One of the challenges faced by our study is that because of the long historical span,

political regimes changed and territorial borders of many counties also shifted. We address

this challenge by first comparing the GIS data for both 1820 and 2000 and then converting

prefecture-level variables in the early 20th century to those in 2000 using overlapping area

as weights. To achieve those ends, we obtain historical GIS data from the China Historical

Geographic Information System (CHGIS). Then we construct a data set that combines

historical education and temple data, and contemporary socioeconomic data.

3.5 Key Variables

All variables used in this study are summarized in table 1, containing their definition,

sources and statistics. In the following, we discuss several key variables.

Average Temple Assets

Ideally, to construct a proxy for temple assets, we would like to have detailed information

on the number of temples in each prefecture, along with measurements of scale and wealth

for each temple. However, as discussed in section 3.1, the “Imperial Encyclopedia of the

Qing Empire” only lists those important temples, and doesn’t include the small ones.

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Therefore, we could only proxy for the “stock of temple assets” using the “number of

important temples” in each prefecture.

For this to be a good proxy, we need to assume that the (unobservable) overall temple

asset is positively correlated with the number of important temples, which seems to be

reasonable. Also, since whether a temple is listed as “important” or not is a subjective

judgement of the authors of the “Imperial Encyclopedia of the Qing Empire,” the

standards may be inconsistent across regions, and lead to measurement errors. We need to

assume that the measurement error is not correlated with local education conditions, which

means that the authors do not change their criterion for temples according to the local

education levels, which is also likely to be satisfied.

We use 1820 population as denominator to calculate the average number of important

temples, and obtain the proxy variable of average temple assets.

Average Schools\Students

We use the education data and population data described in section 3.1 to calculate the

average schools and average students.5 For modern schools\students in 1907, 1908, 1909

and 1915, we use the population of 1910 as denominator. For schools\students in 1930,

since we only have population data for 1910 and 1953, we use 1910 population as

denominator, but also try to use 1953 and the simple average of 1910 and 1953, for all the

three cases, the results are largely the same.

Average Top University Alumni and Oversea Students

With the student data in section 3.2, combined with population data, we calculate the

average number of enrolled top university students and born oversea students in every

period. Similarly, for periods after 1920s, we use 1910 population as denominator, but also

try to use 1953 and the simple average of 1910 and 1953, for all the three cases, the results

are largely the same.

5 Average Schools= Schools/Population, Average Students= Students/Population.

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Urbanization Rate and Schools’ Differences

In the county-level data, we have urbanization rate in 1920 and 1964. We define the

∆Urban as the difference between two rates.6 We also have average schools in 1909 and

1915, and can define ∆School and ∆ln(School) in the same way.7

Contemporary Economic Development and Human Capital

In our contemporary county-level data, we have average income (GDP per capita) and

urbanization rate as the indicator of economic development level in 2000. To investigate

the mechanisms, we use average years of schooling, literacy rate and culture and education

expenditure per capita to measure the level of human capital.

[Insert Table 1 about here]

4 Empirical Results: Short- and Mid-Terms

4.1 TDM and Mass Education

As argued above, prefectures with more temple assets ex ante, thanks to the TDM, had

easier access to education funding during the Republican era, and were thus more likely to

invest in modern education, which would lead to better economic development later on.

To formalize this logic, we first adopt a DID model, and test whether the places with more

temple assets ex ante indeed constructed more schools during the movement.

We define the prefectures with temples more than the median value as “treated,” the

others as “control.” The information of schools is available in years 1907, 1908, 1909, 1915,

and 1930. As discussed above, the Temple Destruction Movement really started to peak

after 1912, so we can regard the changes between 1907 and 1909 as “pre-trends,” and the

changes between 1909 and 1930 as “post-trends.”

[Insert Figure 3 about here]

Figure 3 shows the trends in the average number of schools for treated and control

groups. In 1907, both groups had essentially the same amount of schools; before the TDM

6 ∆Urban= urbanization rate in 1964 - urbanization rate in 1920 7 ∆School= average primary schools in 1915 - average primary schools in 1920. ∆ln(School)= ln(average primary schools in 1915) – ln(average primary schools in 1909).

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was introduced, the school construction for both groups followed very similar trends, and

their difference in levels were also almost negligible. Differences between the prior trends

is statistically insignificant, so the identical trends assumption holds, which makes our

Difference-in-Differences approach likely to be a valid one. Moreover, before the

treatment, difference in levels in the two groups is also statistically insignificant, which

provides further credibility to our identification strategy. During the post-treatment period

(1909-1930), the number of schools in the treated group (prefectures with temples above

the median value) increased much faster than that in the control group. Moreover, the

differences between trends enlarged between 1915 and 1930, a naïve but intuitive

interpretation for this fact is that the control group started to face increasing marginal costs

of taking over temple assets, while the treated group, having more temples, did not face

such problem.

In figure 4, we show the trends in the number of students for treated and control

groups in years 1907, 1908, 1909, 1915 and 1930. We see that both the pre-trends and the

post-trends of average primary students are very similar to that of the schools.

[Insert Figure 4 about here]

We quantify the observations from the graphical analysis using a DID method:

𝑆𝑐ℎ𝑜𝑜𝑙𝑖𝜏 = ∑ 𝛼𝜏𝜏∈{1908−1930} ∗ 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 ∗ 𝑌𝑒𝑎𝑟𝜏 + 𝑋𝑖𝜏′ ∗ 𝛽 + 𝜌𝜏 + 𝜇𝑖 + 𝜀𝑖𝜏 (1)

where 𝑆𝑐ℎ𝑜𝑜𝑙𝑖𝜏 is defined as the number of primary schools per 10,000 people in

prefecture i, 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 is defined as the number of temples per 10,000 people in

prefecture i in 1820, 𝑌𝑒𝑎𝑟𝜏 is the time dummy. 𝑋𝑖𝜏 is a set of control variables that vary

both across units and time, 𝜌𝜏 is a time effect common to all prefectures in period 𝜏, 𝜇𝑖

is a time-invariant effect unique to prefecture i, and 𝜀𝑖𝜏 is a prefecture time-varying error

distributed independently of 𝜇𝑖 and 𝜌𝜏. The year of 1907 is left as a comparison.

[Insert Table 2 about here]

As shown in table 2, the interactions of temples and 1908 and 1909 are not statistically

significant, which is consistent with the “parallel trends” assumption. The interactions

“temple*1915” and “temple*1930” are both statistically significant at 1% level, which

indicates that the prefectures with more temples have a faster speed of school construction

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after the movement started. We try to control for different variables, including the time-

variant impacts of ln(Population), ln(Area), Average Traditional Schools and Average Jinshi

Number, in column 2 and 3, and the prior results are highly robust. Since nearly half of

prefectures have missing values in 1930’s data, in column 4, we only include the data of

1907, 1908, 1909 and 1915, and the prior results remain the same. In column 5, we exclude

prefecture fixed-effects, and include the interaction term temple*1907, to test whether

regions with different number of temples have different number of schools in 1907. As

we can see, the coefficient is statistically insignificant, indicating that temples are unlikely

to be correlated with number of schools in 1907.

We also estimate the effect of TDM on primary school students using the same DID

method:

𝑆𝑡𝑢𝑑𝑒𝑛𝑡𝑖𝜏 = ∑ 𝛼𝜏𝜏∈{1908−1930} ∗ 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 ∗ 𝑌𝑒𝑎𝑟𝜏 + 𝑋𝑖𝜏′ ∗ 𝛽 + 𝜌𝜏 + 𝜇𝑖 + 𝜀𝑖𝜏 (2)

where the dependent variable has been changed as 𝑆𝑡𝑢𝑑𝑒𝑛𝑡𝑖𝜏, which is defined as the

number of primary schools per 10,000 people in prefecture i. As shown in table 3, patterns

the results are very similar to table 2. Before the TDM, parallel trends are satisfied, but

after the TDM, places with higher stock of average temples enrolled significantly more

students. Results are highly robust to the inclusion of various control variables, column 5

indicates that the initial level of average students is also uncorrelated with average stock of

temples.

[Insert Table 3 about here]

4.2 TDM and Elite Education

As shown in section 4.1, the TDM led to construction of more schools and enrollment of

more students, indicating that the TDM has very likely contributed to massive education.

However, did the TDM affected only mass human capital, or did it also contribute to the

formation of elite human capital?

In this section, we measure elite human capital using “average number of alumni

from top universities” and “average number of oversea students,” and provide evidence

that before the TDM, the formation of elite human capital was uncorrelated with the stock

17

of average temples; but after the TDM, regions with more temples cultivated significantly

more modern elites. We provide suggestive evidence that higher education expenditure per

student due to the TDM might contributed to elite human capital.

Alumni of Top Universities

For top 2 university alumni, we aggregate the individual level data to the prefecture level

and match it with the stock of average temples and other prefecture level variables. We

also aggregate the yearly data to 8 five-year periods, so that the change of alumni for each

prefecture becomes smooth. Our results are not sensitive to different methods of

aggregation.

As shown in section 4, the TDM started to peak after 1909. Considering the regular

time of primary and middle schools education, we would expect that the stock of average

temples had and only had effects on top university enrollment after the 1920s. Therefore,

we define a dummy variable 𝑃𝑜𝑠𝑡𝜏, which equals one if period 𝜏 is after 1920, and zero

otherwise. We then estimate the following equation:

𝐴𝑙𝑢𝑚𝑛𝑖𝑖𝜏 = 𝛼 ∗ 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 ∗ 𝑃𝑜𝑠𝑡𝜏 + 𝑋𝑖𝜏′ ∗ 𝛽 + 𝜌𝜏 + 𝜇𝑖 + 𝜀𝑖𝜏 (3)

where 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 is the stock of average temples in prefecture i in 1820, 𝑋𝑖𝜏′ is a vector

of prefecture level control variables that varies with time. 𝜌𝜏 is time fixed effect, 𝜇𝑖 is

prefecture fixed effect, 𝜀𝑖𝜏 is the error term that varies both with time and prefecture.

As shown in the first three columns of table 4, a 10% increase in the stock of average

temples would result in about 2% increase in average top university alumni after the

movement.

[Insert Table 4 about here]

In order to present the dynamic effects and test for pre-trends, we also explore a

slightly different specification where the stock of average temples is interacted with each

period, and the coefficients and confidence intervals are plotted in figure 5. As we can see,

there is no differences in trends for the pre-periods, and significantly different trends in

the post-periods, suggesting that our diff-in-diff approach is likely to be valid.

[Insert Figure 5 about here]

18

Oversea Students

We do almost exactly the same exercise with oversea students as outcome variable. We

aggregate the individual data to prefecture level, and aggregate the yearly data into 6 ten-

year periods. The record of oversea students document their birth year rather than year of

going abroad, so we define periods according to year of birth. Since the TDM started in

1912, we would expect that only individuals born after 1900 could benefit from the stock

of average temples. Therefore, in this specification, 𝑃𝑜𝑠𝑡𝜏 equals one for periods after

1900, and zero otherwise.

We estimate the following equation:

𝑂𝑣𝑒𝑟𝑠𝑒𝑎𝑖𝜏 = 𝛼 ∗ 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 ∗ 𝑃𝑜𝑠𝑡𝜏 + 𝑋𝑖𝜏′ ∗ 𝛽 + 𝜌𝜏 + 𝜇𝑖 + 𝜀𝑖𝜏 (4)

where 𝑂𝑣𝑒𝑟𝑠𝑒𝑎𝑖𝜏 is defined as the average number of oversea students in prefecture i in

period 𝜏, other variables are defined same as before.

As shown in table 4, previous stock of temple assets would strongly affect the ratio

of oversea students after the TDM started. A 10% increase in average temple assets would

lead to 1.2% increase of average oversea students.

Same as for top university students, we run another specification that interact average

temples with each period, and plot the coefficients and confidence intervals. As shown in

figure 6, temples were not correlated with change in oversea students before the movement,

while places with more temples had significantly more oversea students after the

movement started.

[Insert Figure 6 about here]

4.3 Mid-Term Impacts on Economic Development

As discussed in sections 4.1 and 4.2, because of the TDM, regions with more temples

accumulated higher human capital, at both mass and elite levels. A natural question would

then be, whether the advantage in human capital later transformed in to a margin in

economic development?

Before the Temple Destruction Movement

As shown before, human capital was uncorrelated with temples in both levels and trends.

Ideally, we would like to conduct same tests for our economic outcomes. However, there

19

are very few economic indicators that have multiple periods of data available before the

TDM started. So instead, we do the following two tests.

First, we collect cross-sectional data on average tax revenue before the TDM, and run

an OLS regression of tax on temples. Since average tax revenue is a good indicator for

local economic prosperity, and our hypothesis is that temples are orthogonal to economic

development before the TDM, we should expect to see that the stock of temples have no

effects on average tax revenue. As we can see from columns 1-3 in table 5, this is exactly

the case.

Second, while we do not have multiple periods for most economic indicators, we do

have a cross-sectional dataset on the distribution of modern factories across the country

in 1916. We first argue that since the TDM only started in 1912, and education should need

longer time to influence industrialization, the establishment of factories before 1916

should not be affected by the TDM. Furthermore, since there were very few modern

factories before the self-strengthen movement (1861), we could actually interpret the level

of factories in 1916 as the trends of factory establishment between 1861 and 1916. This

way, by running a regression of the 1916 factories on 1820 temples, we are actually also

testing whether the stock of temple assets affected the trend of industrialization in the

late-Qing era (pre-TDM). As shown in columns 4-6 in table 5, temples are uncorrelated

with factories, indicating that the parallel trends assumption is also likely to hold for

economic development.

[Insert Table 5 about here]

After the Temple Destruction Movement

Since information for China’s regional economic outcomes before the 1980s is largely

missing, for economic outcomes, we follow the literature and specify the degree of

urbanization as a proxy for economic development, given that this indicator is believed to

be highly and positively correlated with per capita income (Acemoglu et al 2005; Bai and

Kung, 2014).

We estimate the following equation:

20

∆𝑢𝑟𝑏𝑎𝑛𝑖 = 𝛼 ∗ 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 + 𝑋𝑖′ ∗ 𝛽 + 𝜇𝑝𝑟𝑜𝑣 + 𝜀𝑖 (5)

where ∆𝑢𝑟𝑏𝑎𝑛𝑖 is the speed of urbanization during 1920-1964, 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 stands for the

average number of temples in 1820 in the prefecture that county i belongs to, 𝑋𝑖′ is a

vector of county-level control variables, 𝜇𝑝𝑟𝑜𝑣 is province fixed effect, 𝜀𝑖 is the error

term.

As shown in columns 1 and 2 of table 6, counties with higher stock of average temples

in 1820 experienced faster urbanization during 1920-1964. While exclusion restriction

could not be directly tested, we provide suggestive evidence by exploring the channels

through which temples might affect growth: if temples affect economic outcomes only

through human capital, once including measures of human capital as control variables in

the regressions, temples should no longer have significant impacts on economic outcomes.

As shown in columns 3 and 4 of table 6, once control for mass education, the

magnitude and significance of the coefficient for temples drop sharply; when we further

control for elite education in in columns 5 and 6, the magnitude further drops and the

significance totally disappears. These results suggest that human capital accumulation is

likely to be the source of economic development we observed in the data.

[Insert Table 6 about here]

4.4 Mechanisms

4.4.1 Mechanisms for Elite Human Capital Formation

Since temples and elite human capital are uncorrelated for periods before the TDM, it is

likely that the parallel prior trends assumption is satisfied: places with more temples and

places with fewer temples did not have significant differences in human capital

accumulation. To further test this hypothesis, we regress the average number of Jinshi on

the average number of temples. As shown in table 7, there is no statistically significant

relationship between Jinshi and temples, this reassures that the TDM is an appropriate

natural experiment to study the effects of human capital accumulation.

[Insert Table 7 about here]

Why might the TDM improve elite human capital formation? One straight-forward

channel is that since more schools were constructed, more students were enrolled in

21

modern schools, and thus more elites were selected out of a larger pool. In addition to this

explanation, since temple assets were used to not only construct schools, but also finance

students, it could be possible that the TDM also cultivated elites by improving the quality

of education. We test this hypothesis by regressing the average education fund per student

on the average amount of temples using a fixed effect model. As shown in table 8, places

with more temples had higher education fund per student, which suggests that the quality

of education was also better. Considering the large heterogeneity of education quality in

the Republican era, it is reasonable that places with more sufficient funding could provide

much better education, and their students will be more likely to stand out and become

elites.

[Insert Table 8 about here]

4.4.2 Mechanisms for Economic Development

As shown in section 4.3, by exploiting the TDM as a natural experiment, we find significant

positive effects of education investment on economic development. However, remaining

concerns include the following two:

1. It might be the case that the TDM added to the budget of local governments where

there were more temples, and not 100% of the added budget was on school

construction, or by adding new education budgets, they could reallocate the original

government education budget for other purposes. If that is the case, then the observed

effects on urbanization could also be explained by “resource reallocation” and “general

government spending,” rather than just increased investment in education.

2. During the TDM, thousands of temples were shut down, as a result, many monks

decided to return to laity (Katz, 2013). It might be the case that these “monks returned

to laity” added to the local labor supply, and directly contributed to local development.

In this section, we discuss these two concerns respectively. Qualitative evidence and

placebo tests will be provided to address the concerns.

Resource Reallocation

Both government documents and anecdotal evidence suggest that during the late Imperial

22

and early Republican eras of China, massive primary education was supported exclusively

by the local gentries, and the governments at different levels were really hands-off on such

issues (Shang, 2001; Su, 2007). Given that the local governments did not have any budget

prepared for massive education before the TDM, we may not need to worry too much

about the possibility that by using temple assets to build schools, they can then reallocate

the original education budget in other ways, and boost the economy.

However, even though the TDM requires that the temple assets should be used only

for massive education, we may still be concerned that some governments would simply use

the temple assets in other ways, for instance, invest in basic infrastructures, and thus

contribute to the local economy. If that is the case, temples may affect the economy

through channels other than education, then we could no longer interpret findings of

section 4.3 as a causal relationship between human capital and economic development.

However, as we discussed in section 4.3, once human capital is controlled for, temples

no longer affect growth. This indicates that it is unlikely that temples boosted the economy

in ways other than education, which is inconsistent with this hypothesis. In addition,

qualitative evidence suggests that local governments hardly had any budget prepared for

modern education other than raising from local elites and temples, and education budget

was almost the only legitimate reason to ask for their donations. As a result, using these

funds to serve other purposes is difficult for the local governments, and could easily cause

severe social unrest and even local conflicts (Xu, 2010; Katz, 2013). Therefore, both the

data and the historical evidence seem to be inconsistent with this alternative hypothesis.

Increased Labor Supply

As shown in section 4.3, temples have no effects on growth after controlling for human

capital, so it is unlikely that the temples, in addition to boosting investment in education,

had also contributed to the economy by adding the “monks returned to laity” to local labor

supply.

To further address this concern, we carry out a falsification test. We have shown that

the treatment group (prefectures with temples above the median value) and the control

group (prefectures with temples below the median value) had largely comparable

23

socioeconomic conditions before the TDM, and we have also shown that the TDM was

happening very rapidly between 1909 and 1930. So if it was the case that many “monks

returned to laity” joined the labor force due to the TDM, given that the monks did not

have their own land and were thus unlikely to participate in agricultural production, it is

likely that we would observe the treatment group had a higher worker/population ratio in

the 1930s. From the “Chinese Industry Report,” we collect total numbers of industry

workers in 135 counties in 1934. However, as shown in table 9, the average temple assets

has little impact on the ratio of workers in 1934, meaning that the “monks returning to

laity” concern may not be a severe one.

[Insert Table 9 about here]

5 Empirical Results: Long-Term

In section 4, we have shown the effects of the TDM on mass and elite human capital, and

also the effects of such human capital growth on economic development. In this section,

we would like to further explore the persistence of these effects.

As explained in section 3, we matched a county level socio-economic dataset with our

historical prefecture level panel dataset. Using this merged dataset, we could estimate the

following equation:

𝑌𝑖 = 𝛼 ∗ 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 + 𝑋𝑖′ ∗ 𝛽 + 𝜇𝑝𝑟𝑜𝑣 + 𝜀𝑖 (6)

where 𝑌𝑖 is the outcome of interest for county i in year 2000, 𝑇𝑒𝑚𝑝𝑙𝑒𝑖 stands for the

average number of temples in 1820 in the prefecture that county i belongs to, 𝑋𝑖′ is a

vector of county-level control variables, 𝜇𝑝𝑟𝑜𝑣 is province fixed effect, 𝜀𝑖 is the error

term.

Persistence of Human Capital

We first test whether the positive effects of the stock of average temples persisted into the

21st century.

As shown in table 10, counties with higher stock of temples in 1820 have significantly

higher average years of schooling, literacy rate, and per capita culture and education

expenditure in 2000. On average, a one standard deviation increase of average temples in

24

that prefecture would lead to the average years of schooling in that county to increase by

0.1 years, the illiteracy rate to drop by 6%, and the per capita culture and education

expenditure to increase by about 4%.

These results are highly robust to various types of control variables. Specifically, if

one concerns that the TDM has direct impact on religious practices in different regions,

which would in turn affect the outcomes, we show that including other measurements of

religious practices (average vicars, average churches) does not change the result at all, which

could arguably reduce the concern.

[Insert Table 10 about here]

Persistent Impacts on Economic Development

We now examine whether the persistence of human capital would contribute to economic

development in the long run.

As shown in table 11, counties with higher stock of temples in 1820 have significantly

higher per capita GDP and urbanization rate in 2000. On average, a one standard deviation

increase of average temples in that prefecture would lead to per capita GDP to increase

by about 300 Yuan (about 10% of the mean), and urbanization rate to increase by about

6%.

To justify the causal effects of human capital on these economic outcomes, we

explore the potential channels through which temples could affect the economy: if temples

affect economic outcomes through ways other than human capital, by including measures

of human capital as control variables in the regressions, temples should still have

significant impacts on economic outcomes.

As we can see from columns 3 and 6 of table 11, after including three measures of

human capital (average years of schooling, literacy rate, average culture and education

expenditure) as control variables, the positive effects of average temples totally disappear.

Therefore, it is unlikely that temples affect the economy through channels other than

human capital, and thus the results of table 11 could likely be interpreted as causal evidence

that human capital accumulation contributed to economic growth in the long run.

[Insert Table 11 about here]

25

6 Robustness

As shown in the previous tables, our results are not sensitive to the inclusion of various

kinds of control variables. In addition to that, in this section, we explore several different

datasets and specifications to further justify the robustness of our previous results.

Alternative Measurement of Temple Assets

The key variable for our analysis is the “stock of average temples,” which is used to proxy

for the average stock of temple assets in that local area. In our main analysis, we used

prefecture-level data digitized from the “Imperial Encyclopedia of the Qing Empire”

(Daqing Yitong Zhi), which documents the most important temples across the country.

According to historical records, China had more than 200,000 registered temples, but only

about the most important 1.5% were documented in our data.

For our measurement to precisely reflect the distribution of temple assets, we then

need to make the assumption that the total stock of temple assets in a region is highly and

positively correlated with its stock of large temples. How reliable is this assumption, or

how sensitive are our results with respect to it? We explore another source of temple data,

a county-level record digitized from the “Imperial Encyclopedia of Gazeteer” (Gujin Tushu

Jicheng). It also documents the most important temples across the country in 1776, basically

in a close period to the first source. Since there is no county-level population data available,

we could not conduct our analysis at the county-level using this new dataset. Instead, we

aggregate it to the prefecture level, so that we have a different measure of the stock of

temple assets, which covers about the most important 15% of the temples in China. We

first plot a scatter graph between the two different measures, as shown in figure 7, they are

very highly and positively correlated, just as we expected. Furthermore, we re-produce

figures 1 and 2 using the new data, as shown in figures 8 and 9, the patterns are almost

26

identical. We re-run all the main specifications using the new dataset, all the results go

through8.

Therefore, we conclude that our main findings are unlikely to be sensitive to the

measurement of temples.

Inclusion of Primary Schools in 1820

Another concern about our results is that when testing for pre-trends of school

construction and students enrollment before the TDM started, while we had three periods

(1907-1909), they are too adjacent to each other, and that may reduce the power to detect

potential significant differences in such a short period. If that is the case, our evidence for

“parallel trends” will be weakened.

To address this issue, we make use of the 1820 data of traditional primary schools.

While both referred to as “primary schools,” the traditional ones in 1820 are different in

many ways from the modern ones in our latter data, so we did not include this period in

our panel dataset for main analysis. However, for the sake of testing parallel trends and

balanced initial levels, the 1820 data could be helpful: change of primary schools between

1820 and 1909 could reflect the trend of human capital investment in the long-run, which

could provide us with substantial power to detect any potential differences caused by the

differences in the stock of temples.

Including 1820 data in our sample, we reproduce the figures of trends in school

construction. As we can see from figure 10, prefectures with average temples above and

below median level had almost exactly the same number of average schools in 1820

(statistically indistinguishable); and there is no statistically significant differences in the

trends of school construction in the following 89 years. These results further suggest that

temples are not correlated with initial human capital stock, nor are they correlated with the

prior trends of human capital formation9.

These results further convince us of the “parallel trends” and the “balanced initial

levels” assumptions.

8 Tables are available upon request. 9 Including 1820 data would not change the results of our main analysis, tables available upon request.

27

Excluding Treaty Ports

Past research has shown that the treaty ports experienced significantly faster development

throughout the 20th century (Jia, 2014). In addition, some of the coastal areas have more

Buddhists than other regions. Therefore, one might be concerned that our results of

temples on economic growth could be partly driven by the development of the treaty ports.

To reduce this concern, we re-run our main results excluding the treaty ports10. As

shown in table 12, the results are very similar to what we had using a full sample. Therefore,

our results are unlikely to be driven by a spurious correlation between treaty ports and

Buddhists.

7 Conclusion

In this paper, we combine various sources of historical datasets and document a novel

natural experiment in the early 20th century China, the Temple Destruction Movement.

We show that before the TDM, the stock of average temple assets is not correlated

with various measures for human capital and economic development; but after the TDM

started, regions with higher stock of temple assets obtained both higher mass human

capital (constructing more schools, enrolling more students), and higher elite human

capital (cultivating more top university alumni and oversea students). In the mid-run, our

results suggest that places invested more in human capital due to the TDM also

experienced faster urbanization in the later periods.

In the long-run, we present evidence that human capital created by the TDM

persisted into the 21st century: regions with higher temple stocks in 1820 have higher

average years of schooling, literacy rate, and per capita expenditure on culture and

education in 2000. Furthermore, our results suggest that the economic impacts persisted

as well: regions with higher temple stocks in 1820 also have higher per capita GDP and

urbanization rate in 2000.

10 We present here the results of human capital on economic growth using the restricted sample. Other results are available upon request.

28

Our results are robust to various types of different specifications and use of

alternative datasets. Several alternative channels are also discussed and ruled out.

The findings of this paper suggest that investment in modern education could have

significant and persistent positive effects on human capital accumulation, which could

contribute to regional economic growth, both in the mid-run and in the long-run. In

addition to highlighting the importance of education, another potentially important policy

lesson from the documented Temple Destruction Movement is that: for many developing

countries with rich under-used cultural assets in the religious sector, and meanwhile weak

stock of human capital to sustain modern economic growth (e.g., Nepal, Mongolia, etc.),

finding proper ways to activate these cultural assets to serve for human capital growth

could receive tremendous positive effects, which would persist even in the long-run.

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Figure 1. Total Number of Primary Schools between 1907 and 1946

Figure 2. Total Number of Primary Students between 1907 and 1946

37

Figure 3. Average Primary Schools between 1907 and 1930

Figure 4. Average Primary School Students between 1907 and 1930

38

Figure 5. The Dynamic Impacts of Average Temple Assets on Top University

Alumni (1900-1950)

Figure 6. The Dynamic Impacts of Average Temple Assets on Oversea Students

(1890-1940)

39

Figure 7. Correlation between Two Measures of Temple Assets

40

Figure 8. Average Primary Schools between 1907 and 1930

(With Robust Measure)

Figure 9. Average Primary School Students between 1907 and 1930

(With Robust Measure)

41

Figure 10. Average Primary Schools between 1820 and 1930

(With Traditional Primary Schools in 1820)

42

Table 1. Statistics Summary

Variables Variable Definition Sources Obs. Mean S.D.

Prefectural-level Panel Data of Modern Education and Temple Assets

Primary Schools # of primary schools between 1907 and 1930 1 1150 314.67 517.68

Primary Students # of primary students between 1907 and 1930 1 1162 11844.90 25066.60

Temple Assets # of temples in 1820 2 264 12.31 10.49

Traditional Schools # of traditional schools in 1820 2 264 12.31 8.27

Jinshi Number # of civil exam graduates 3 264 95.50 152.57

Average Primary Schools # of primary schools / pop in 1910 1, 4 1150 2.57 4.41

Average Primary Students # of primary students / pop in 1910 1, 4 1162 90.14 172.92

Average Temple Assets # of temples / pop in 1820 2, 4 264 0.14 0.13

Average Traditional Schools # of traditional schools / pop in 1820 2, 4 264 0.13 0.09

Average Jinshi Number # of civil exam graduates / pop in 1910 3, 4 264 0.63 0.88

1820 Population 10,000 4 264 143.68 128.69

ln(1820 Population) 4 264 4.53 1.05

1910 Population 10,000 4 264 154.32 141.54

ln(1910 Population) 4 264 4.60 1.05

1953 Population 10,000 4 264 196.78 181.08

ln(1953 Population) 4 264 4.85 1.01

Area square km 5 264 15914.78 19272.74

ln(Area) 5 264 9.35 0.78

Prefecture-level Panel Data of Top University Alumni and Oversea Students

Average Top University Alumni Top University Students / population 6 2,420 0.03 0.05

Average Oversea Students Oversea Students / population 7 1,268 0.02 0.06

County-level Data of Urbanization Rate

1964 Urbanization Rate urban population / population 8 1,701 0.10 0.14

1920 Urbanization Rate urban population / population 9 1,701 0.06 0.08

∆Urban 1964's urban rate - 1920's urban rate 8, 9 1,701 0.04 0.15

∆School 1915's schools - 1909's schools 2 1,701 2.14 2.30

∆ln(School) ln(1915's schools) - ln(1909's schools) 2 1,701 2.94 4.43

Family Size population / # of households 8 1,701 4.40 0.89

1964 Population 10,000 8 1,701 33.90 31.02

1964 Male Population 10,000 8 1,701 17.38 15.93

Contemporary County-level Data of Economic Development and Human Capital

Average Years of Schooling 12 1,742 7.22 1.01

Literacy Rate (%) 12 1,742 89.18 8.06

CE Expenditure per Capita Culture and Education Expenditure per Capita 11 1,742 94.37 45.83

Average Income GDP per Capita 11 1,742 5406.43 3937.03

2000 Urbanization Rate urban population / population 12 1,742 27.36 21.52

Data Sources: 1: Statistical Chart of Education (1907-1934). 2: Imperial Encyclopedia of the Qing Empire (1842). 3: Jiang, Qingbai (2007), Official Records of Jinshi in Qing Dynasty. 4: Ge, Jianxiong

(2000) China Population History. 5: Harvard Yenching Institution (2007), CHGIS, Version 4. 6: Books of Peking University and Tsinghua University. 7: Archives of Oversea Students. 8: National

Bureau of Statistics (1982), The Complied Statistics of the Second National Population Census. 9: Stauffer (1922), The Christian Occupation of China. 10: Liu, Dajun (1934) Chinese Industry Report.

11: Statistical Materials of Public Finance of Cities and Counties (2000). 12: National Bureau of Statistics (2000), The Complied Statistics of the Fifth National Population Census.

43

Table 2. DID Model: Effects of TDM on Primary School

Dependent Variable: Average Primary School

(1) (2) (3) (4) (5)

Average Temple Assets*1907 -1.756

[1.560]

Average Temple Assets*1908 0.799 0.600 0.736 0.736 -0.744

[1.686] [1.830] [1.875] [0.997] [1.457]

Average Temple Assets*1909 0.764 0.571 0.669 0.669 -0.889

[1.686] [1.830] [1.875] [0.996] [1.455]

Average Temple Assets*1915 6.138 5.218 4.938 4.938 3.832

[1.686]*** [1.830]*** [1.875]*** [0.996]*** [1.455]***

Average Temple Assets*1930 37.988 29.759 26.559 28.594

[2.302]*** [2.610]*** [2.680]*** [2.332]***

ln(Population)*Year FE NO YES YES YES YES

ln(Area)*Year FE NO YES YES YES YES

Average Jinshi Number*Year FE NO NO YES YES YES

Average Traditional Schools*Year FE NO NO YES YES YES

Year FE YES YES YES YES YES

City FE YES YES YES YES NO

R-squared 0.622 0.643 0.657 0.536 0.556

Num. of Prefecture 264 264 264 264 264

Obs. 1,150 1,150 1,150 1,027 1,150

Notes: This table reports estimation results of equation (1). Coefficients are reported with standard errors in brackets. ***, **,

and * indicate significance at 1%, 5% and 10% levels.

44

Table 3. DID Model: Effects of TDM on Primary Student

Dependent Variable: Average Primary Student

(1) (2) (3) (4) (5)

Average Temple Assets*1907 -33.462

[54.063]

Average Temple Assets*1908 18.764 10.782 7.393 7.393 -14.748

[63.286] [69.631] [69.477] [29.978] [50.242]

Average Temple Assets*1909 19.675 13.076 7.861 7.861 -16.769

[63.265] [69.628] [69.471] [29.975] [50.180]

Average Temple Assets*1915 155.915 121.696 104.333 104.333 100.568

[63.265]** [69.628]* [69.471] [29.975]*** [50.180]**

Average Temple Assets*1930 1,636.907 1,429.753 1,204.461 1,202.466

[85.394]*** [98.588]*** [98.649]*** [82.070]***

ln(Population)*Year FE NO YES YES YES YES

ln(Area)*Year FE NO YES YES YES YES

Average Jinshi Number*Year FE NO NO YES YES YES

Average Traditional Schools*Year FE NO NO YES YES YES

Year FE YES YES YES YES YES

City FE YES YES YES YES NO

R-squared 0.693 0.703 0.729 0.583 0.662

Num. of Prefecture 264 264 264 264 264

Obs. 1,162 1,162 1,162 1,027 1,162

Notes: This table reports estimation results of equation (2). Coefficients are reported with standard errors in brackets. ***, **,

and * indicate significance at 1%, 5% and 10% levels.

45

Table 4. Effects of TDM on Elite Human Capital

Dependent Variable: Top 2 University Alumni Oversea Students

(1) (2) (3) (4) (5) (6)

ln(Average Temple Assets)*Post -0.029 0.211 0.096 -0.077 0.123 0.042

[0.034] [0.038]*** [0.036]*** [0.049] [0.055]** [0.049]

ln(Population)*Year FE NO YES YES NO YES YES

ln(Area)*Year FE NO YES YES NO YES YES

Average Jinshi Number*Year FE NO NO YES NO NO YES

Year FE YES YES YES YES YES YES

City FE YES YES YES YES YES YES

R-squared 0.578 0.773 0.803 0.342 0.636 0.714

Num. of Prefecture 242 242 242 212 212 212

Obs. 2,420 2,420 2,420 1,268 1,268 1,268

Notes: This table reports estimation results of equation (3) and (4). Coefficients are reported with standard errors in brackets. ***, **, and *

indicate significance at 1%, 5% and 10% levels.

46

Table 5. Effects of TDM on Average Tax Revenue and Factories

Dependent Variable: Average Tax Revenue Average Factories

(1) (2) (3) (4) (5) (6)

Average Temple Assets -0.056 0.005 0.001 -0.056 0.010 0.008

[0.050] [0.062] [0.068] [0.053] [0.064] [0.065]

Population 0.000 0.000 0.000 0.000

[0.000] [0.000] [0.000] [0.000]

Area -0.030 -0.030 -0.000 -0.000

[0.037] [0.037] [0.000] [0.000]

Average Traditional

School 0.015 0.028 0.027

[0.108] [0.046] [0.046]

Province Dummy NO YES YES NO YES YES

R-squared 0.001 0.081 0.081 0.004 0.151 0.152

Obs. 264 264 264 264 264 264

Coefficients are reported with standard errors in brackets. ***, **, and * indicate significance at 1%, 5% and 10%

levels.

47

Table 6. Effects of TDM on Mid-term Economic Development

Dependent Variable: ∆Urban = 1964 Urban Rate - 1920

(1) (2) (3) (4) (5) (6)

Average Temple Assets 0.086 0.109 0.089 0.073 0.051 0.048

[0.033]*** [0.039]*** [0.035]** [0.039]* [0.035] [0.039]

Average Jinshi Number -0.000 -0.000 0.000 0.000

[0.000] [0.000] [0.000]*** [0.000]

1964 Population 0.006 -0.032 0.003 -0.027

[0.007] [0.009]*** [0.007] [0.009]***

1964 Household -0.000 0.001 0.000 0.001

[0.000] [0.000]** [0.000] [0.000]***

Area 0.000 0.000 -0.000 0.000

[0.000] [0.000] [0.000] [0.000]

Mass Education NO NO YES YES YES YES

Elite Education NO NO NO NO YES YES

Province Dummy NO YES NO YES NO YES

R-squared 0.004 0.089 0.007 0.108 0.038 0.125

Obs. 1,700 1,700 1,700 1,700 1,700 1,700

Notes: This table reports estimation results of equation (5). Coefficients are reported with standard errors in brackets. ***,

**, and * indicate significance at 1%, 5% and 10% levels. For mass education, we control variables of the difference of

primary schools, students and expenditure between 1909 and 1915, as well as the amount of primary schools, students and

expenditure in 1915. For elite education, we include variables of the total number the average number of top university

students and oversea students into regression.

48

Table 7. Average Jinshi Number on Average Temple Assets

Dependent Variable: Average Jinshi Number

(1) (2) (3) (4)

Average Temple Assets 0.048 0.035 0.198 0.224

[0.404] [0.441] [0.471] [0.476]

Population -0.056 -0.048

[0.084] [0.087]

Area 0.082 0.065

[0.072] [0.083]

Average Traditional School -0.301

[0.727]

Province Dummy NO YES YES YES

R-squared 0.000 0.236 0.240 0.241

Obs. 264 264 264 264

Coefficients are reported with standard errors in brackets. ***, **, and * indicate

significance at 1%, 5% and 10% levels.

49

Table 8. DID Model: Effects of TDM on Expenditure of Primary Student

Dependent Variable: Average Expenditure of Primary Student

(1) (2) (3)

Average Temple Assets*1915 8.110 7.074 4.112

[4.537]* [4.936] [4.959]

Average Temple Assets*1930 11.793 17.463 15.243

[6.221]* [6.790]** [6.811]**

Average Jinshi Number*1915 2.612

[0.818]***

Average Jinshi Number*1930 1.848

[0.870]**

Year FE YES YES YES

ln(Population)*Year FE NO YES YES

ln(Area)*Year FE NO YES YES

City FE YES YES YES

R-squared 0.219 0.361 0.382

Num. of Prefecture 264 264 264

Obs. 586 586 586

Notes: Coefficients are reported with standard errors in brackets. ***, **, and * indicate significance

at 1%, 5% and 10% levels.

50

Table 9. Mechanism II: Increased Labor Supply

Dependent Variable: Ratio of Workers

(1) (2) (3) (4)

Average Temple Assets 0.017 0.019 0.002

[0.023] [0.026] [0.032]

ln(Average Temple Assets) -0.002

[0.003]

Average Jinshi Number 0.000 0.001 0.001

[0.001] [0.001] [0.001]

Population 0.000 0.000 0.000

[0.000] [0.000] [0.000]

Area -0.000 -0.000 -0.000

[0.000] [0.000] [0.000]

Province Dummy NO NO YES YES

R-squared 0.004 0.022 0.090 0.094

Obs. 135 135 135 132

Coefficients are reported with standard errors in brackets. ***, **, and * indicate

significance at 1%, 5% and 10% levels.

51

Table 10. Effects of TDM on Long-term Human Capital Accumulation

Dependent Variable: Average Years of

Schooling Literacy Rates (%)

Culture and Education

Expenditure per Capita

(1) (2) (3) (4) (5) (6)

Average Temple Assets 0.010 0.010 0.055 0.066 0.451 0.369

[0.002]*** [0.002]*** [0.020]*** [0.015]*** [0.097]*** [0.097]***

Population in 1820, 1910, 1953 YES YES YES YES YES YES

Area YES YES YES YES YES YES

Average Jinshi Number YES YES YES YES YES YES

Population in 2000 NO YES NO YES NO YES

Average Primary School NO YES NO YES NO YES

Prov FE YES YES YES YES YES YES

R-squared 0.515 0.504 0.438 0.446 0.573 0.615

Obs. 1,742 1,616 1,742 1,616 1,742 1,616

Notes: This table reports estimation results of equation (6). Coefficients are reported with standard errors in brackets. ***, **, and * indicate significance at

1%, 5% and 10% levels.

52

Table 11. Effects of TDM on Long-term Economic Development

Dependent Variable: Average Income ln (Urbanization Rate)

(1) (2) (3) (4) (5) (6)

Average Temple Assets 30.107 29.853 6.779 0.006 0.005 0.000

[9.583]*** [10.022]*** [9.329] [0.002]*** [0.002]*** [0.001]

Average Years of Schooling 275.729 0.781

[151.358]* [0.023]***

Literacy Rates (%) 100.247 -0.042

[21.248]*** [0.003]***

Culture and Education

Expenditure per Capita

37.386 0.001

[2.388]*** [0.000]***

Population in 1820, 1910, 1953 YES YES YES YES YES YES

Area YES YES YES YES YES YES

Average Jinshi Number YES YES YES YES YES YES

Population in 2000 NO YES YES NO YES YES

Average Primary School NO YES YES NO YES YES

Prov FE YES YES YES YES YES YES

R-squared 0.432 0.454 0.540 0.301 0.333 0.635

Obs. 1,742 1,616 1,616 1,742 1,616 1,616

Notes: This table reports estimation results of equation (6). Coefficients are reported with standard errors in brackets. ***, **, and * indicate significance at

1%, 5% and 10% levels.

53

Table 12. Effects of TDM on Mid-term Economic Development

(Robustness Check without Treaty Ports)

Dependent Variable: ∆Urban = 1964 Urban Rate - 1920

(1) (2) (3) (4) (5) (6)

Average Temple Assets 0.075 0.091 0.086 0.069 0.060 0.048

[0.030]** [0.035]*** [0.031]*** [0.035]** [0.031]* [0.035]

Average Jinshi Number 0.000 0.000 0.000 0.000

[0.000] [0.000] [0.000]* [0.000]

1964 Population 0.007 -0.017 0.002 -0.015

[0.006] [0.008]** [0.007] [0.008]*

1964 Household -0.000 0.001 -0.000 0.001

[0.000] [0.000] [0.000] [0.000]

Area 0.000 0.000 0.000 0.000

[0.000]* [0.000] [0.000] [0.000]

Mass Education NO NO YES YES YES YES

Elite Education NO NO NO NO YES YES

Province Dummy NO YES NO YES NO YES

R-squared 0.005 0.077 0.018 0.094 0.045 0.117

Obs. 1,308 1,308 1,308 1,308 1,308 1,308

Notes: This table reports estimation results of equation (5). Coefficients are reported with standard errors in brackets. ***,

**, and * indicate significance at 1%, 5% and 10% levels. For mass education, we control variables of the difference of

primary schools, students and expenditure between 1909 and 1915, as well as the amount of primary schools, students and

expenditure in 1915. For elite education, we include variables of the total number the average number of top university

students and oversea students into regression.

54

Appendix

Table A1. Sources of Education Data (1907-1934)

Year Region Source

1907 Nationwide 《光绪三十三年第一次教育统计图表》

1908 Nationwide 《光绪三十四年第二次教育统计图表》

1909 Nationwide 《宣统元年第三次教育统计图表》

1915 Nationwide 《中华民国第四次教育统计图表》

1916 Nationwide 《中华民国第五次教育统计图表》

1919 Jiangsu 《江苏六十县八年度教育状况表》《民国教育统计资料汇编 16》

1921 Guangdong 《广东教育统计图表民国十年度》

1922 Guangdong 《广东教育统计图表民国十一年度》

1923 Guangdong 《广东教育统计图表民国十二年度》

1923 Zhili 《直隶教育统计表民国十二年度》《民国教育统计资料汇编 10》

1925 Zhili 《直隶教育统计表民国十四年度》《民国教育统计资料汇编 10》

1924 Shanxi 《山西省第九次教育统计民国十三年》《民国教育统计资料汇编 12》

1925 Zhejiang 《中华民国十四年度浙江省教育统计图表》

1927 Zhejiang 《中华民国十六年度浙江省教育统计图表》

1929 Zhejiang 《中华民国十八年度浙江省教育统计图表》《民国教育统计资料汇编 23》

1928 Hebei (Zhili) 《河北省各县普通教育概览》《民国教育统计资料汇编 11》

1929 Hebei (Zhili) 《河北省教育统计概要民国十八年》《民国教育统计资料汇编 11》

1930 Fujian 《福建省教育统计民国十九年度》《民国教育统计资料汇编 30》

1931 Henan 《民国二十年河南省教育统计图表》

1931 Anhui 《安徽省教育统计民国二十年度》《民国教育统计资料汇编 25》

1932 Anhui 《安徽省教育统计民国二十一年度》《民国教育统计资料汇编 25》

1933 Anhui 《安徽省教育统计民国二十二年度》《民国教育统计资料汇编 25》

1934 Anhui 《安徽省教育统计民国二十三年度》《民国教育统计资料汇编 26》

1932 Jiangxi 《江西省教育统计民国二十一年度》《民国教育统计资料汇编 28》

1933 Jiangxi 《江西省各县教育概况二十二年度》《民国教育统计资料汇编 28》

1932 Jiangsu 《民国二十一年度江苏省教育经费统计图表》

1933 Jiangsu 《民国二十二年度江苏省教育经费统计图表》

1933 Guangxi 《广西省教育概况统计民国二十二年》《民国教育统计资料汇编 29》

1934 Zhejiang 《浙江省二十三年度教育统计》

1934 Guangdong 《广东省二十三年度教育概况》《民国教育统计资料汇编 30》

1935 Hebei (Zhili) 《河北省教育概况民国二十四年》《民国教育统计资料汇编 11》