A Malthusian Quagmire? Maize and Population
Growth in China, 1550–1910
†
Shuo CHEN and James Kai-sing KUNG
Hong Kong University of Science and Technology
This version, March 2012
†
Direct all correspondence to James Kai-sing KUNG, Division of Social Science,
Hong Kong University of Science & Technology, Clear Water Bay, Hong Kong.
Email: [email protected]. We thank Oded Galor, Debin Ma, Robert Margo, Nathan Nunn,
Dwight Perkins, Louis Putterman, Nancy Qian, Tom Rawski, David Weil, Jeffrey
Williamson, Noam Yuchtman and seminar participants at various universities for
helpful comments and suggestions. We alone are responsible for any remaining errors.
Abstract
We examine the effect of the introduction of maize—a New World crop—on
population density and economic development in China, an important part of the Old
World, in the 1550–1910 period. By exploiting the regional variation in maize
diffusion and using an interaction term that accounts for distance to the point of
adoption and climate across prefectures over time as instrument, one decade of maize
planting resulted in a 3.3% increase in population, or an overall 7.9% for the period
1776–1910. For the longer 1550–1910 period, extrapolation based on the above
estimate gives an increase that ranges from 11.2% to 20.5%. This result is robust to
the inclusion of a number of controls and exclusion restriction tests. Unlike the
introduction of the potato in Europe, however, the relationship between maize
planting and economic growth is found to be negative. These findings corroborate
narratives of a stagnating Chinese economy and China’s failure to industrialize at a
time when Europe was experiencing sustained growth in per capita income, thereby
enabling its people to escape from the Malthusian trap.
Keywords: Malthus, Maize, Population Density, Economic Growth, China.
JEL Codes: J1, N1, N5, O14, O53.
1. Introduction
Until the 18th century the world was basically trapped within a Malthusian
quagmire, as resources generated through technological progress and land expansion
were confined primarily to an increase in the size of the population but not income per
capita, which for several millennia had remained basically unchanged (Clark, 2007;
Galor and Weil, 2000; Galor, 2005; 2010; 2011). Since then, and for the first time in
human history, a decisive transition from an epoch of stagnation to an era of sustained
economic growth occurred thanks to technological progress.
A major source of technological progress for the Old World, according to
Nunn and Qian (2011), came from the biological exchange of food crops between the
Old World and the New World.1 Specifically, they find that the potato—a crop that
originated from the New World—contributed between 25-26% and 27-34% of the
increase in Old World population and urbanization, respectively, between 1700 and
1900. These effects came about as a result of the improvements in caloric and
nutritional intake over previously existing staples; a finding that corroborates the
historical narrative demonstrating the significance of the transfer of food crops
between the two continents not just for the population explosion in the Old World but,
more importantly, also for the Industrial Revolution (Crosby, 1989).
However, this remarkable transformation, or more specifically the rise in the
standard of living, was vastly uneven across societies (Galor, 2011). Of the countries
in the Old World (the entire Eastern Hemisphere), only (Western) Europe experienced
an industrial revolution; Asia and Africa failed to do so (Maddison, 2001). Among
the latter group of countries China’s contrast to Western Europe is especially striking.
With an increase in population from 13 million in 1500 to 400 million in 1900 (Cao,
1
Defined as “the exchange of diseases, ideas, food crops, and populations between the New World and
the Old World following the voyage to the Americas by Christopher Columbus in 1492” (Nunn and
Qian, 2010: 163), the Columbian Exchange has inspired a series of research endeavors examining the
long-term effects of history on economic development. Well-known examples in this exciting body of
literature include attempts to examine the effects of European contact or colonial rule on the colonies
(Acemoglu, Johnson, and Robinson, 2001; Engerman and Sokoloff, 1997; La Porta, Lopez-de-Silanes,
Shleifer, and Vishy, 1997, 1998) and the effects of trade with the colonies on Europe (Acemoglu,
Johnson, and Robinson, 2005; O’Rourke and Williamson, 2002).
1
2000; Ho, 1959; Perkins, 1969)—1.3 times the increase seen in Western Europe—
China alone accounted for approximately one-third of the population increase seen in
the entire world between 1500 and 1900 (Maddison, 2001), and yet it fell significantly
behind its European counterparts over the period, helping contribute to the “great
divergence” from the 18th century onwards (Landes, 2006; Lucas, 2002; Pomeranz,
2000; Pritchett, 1997; Voigtländer and Voth, 2006, among others).
While China did not adopt the potato to the same extent as Europe, it similarly
adopted several New World crops during this period—chief among them maize and
sweet potatoes, and allegedly had resulted in a distinct increase in the overall
production of food and accordingly population growth. As one eminent historian of
China, Ping-ti Ho (1959: 184), remarks, “the increase in national food production
(and accordingly the) continual growth of population” would not have been possible
in the absence of the adoption of these New World crops (see also Perkins, 1969;
Shiva, 1991; and Zhao, 1985).2 Yet, if Maddison’s (2001) estimates are not widely off
the mark, then per capita GDP in China remained stagnant at the subsistence level of
roughly US$600 (in adjusted terms) for as long as seven centuries (i.e., from the Ming
Dynasty to the 1930s), which is consistent with another estimate that real wages in
China were for centuries sufficient only to maintain a family at the subsistence level
(Allen et al., 2011).3
All of this evidence stacks up to support the claim that, although the adoption
of New World crops in China led to population growth, it clearly failed to contribute
to economic growth in per capita income terms. Moreover, as Figure 1 suggests,
China’s population may already have reached its peak by the late 18th century (around
1770 according to Perkins [1969]), whereas the Old World continued to experience
rapid and sustained population growth after 1750. Not only that, but there is evidence,
albeit scattered, to suggest that China’s urbanization rate—measured in terms of the
share of the urban population—actually declined slightly, from 7.4% in 1776 to 7.1%
2
As Ho (1959) graphically illustrates with what he calls the “second agricultural revolution”: “(the) dry
hills and mountains of the Yangzi region and north China were still largely virgin about 1700 but had
since been turned into maize and sweet potato farms” (p. 184, see also p. 192).
3
Perkins (1969), for example, remarks that China could not have done “much more than keep up with
population growth” (pp. 31-32). The assessments made by Elvin (1973) and Ho (1959) are similarly
Malthusian in tone.
2
in 1896 (Cao, 2000), indicating that, unlike Europe, China was still operating under a
Malthusian regime at the end of the 19th century.
Figure 1 about here
Our finding of a new agricultural technology that increases available calories
and leads to population but not economic growth raises the important question of
whether growth in Europe vis-à-vis stagnation in China may be due to a
heterogeneous treatment effect; that is, that some new crops that increase population
also lead to economic growth (such as the potato), whereas others are confined to only
population growth (as in the case of maize). And if so why? We focus, in this paper,
on this second issue, by seeking answers to the following questions.
We begin with a set of questions concerning why China adopted maize (and to
a lesser extent sweet potatoes) instead of the potato—a crop that arguably provides
greater calories and nutrition over the other New World crops? To what extent was
maize diffused in China? Did it replace the traditional Chinese staple crops of mainly
rice and wheat, or was it a mere supplement to the long established Chinese diet
primarily in times of bad weather? A second and more important goal is to establish
reasonable estimates on the effects of its diffusion on population and economic
growth. The China case that we examine sheds light on the aforementioned growth
puzzle in a unique way because, in the absence of an industrial revolution and other
qualitatively important economic changes that occurred in Europe, it represents a
“clean” case for testing the effects of increasing calories on population as well as
economic growth.
By analyzing a new dataset uniquely constructed for this purpose, we seek to
establish robust empirical evidence on the relationship between the adoption of
maize—the most popular New World crop in China in terms of cultivation
suitability—on the one hand and population growth (and to a lesser extent economic
growth) on the other. Work of this nature has long been hampered by the paucity of
systematic data on the precise timing on the diffusion of maize across China, which
explains why the few existing studies—excellent as they are—have not gone beyond
mere narratives (e.g., Cao, 2000; Ho, 1959; Lee, 1982; Wang, 1994). We endeavor to
undertake this somewhat herculean task by collecting the pertinent information
3
systematically from thousands of local gazetteers during the Ming and Qing dynasties.
The resulting unique dataset that we have constructed from this and a variety of other
historical sources allows us to estimate the effect of the duration and extent of maize
planting on population outcomes and economic growth—the latter using urbanization
and real wages as proxies—in China for the 1776 to 1910 period.
Various issues inevitably come to the fore in estimating the causal relationship
between the diffusion of maize and population density over time. The first concerns
the possibility of reverse causality. According to the theory of “endogenous
technological change,” growing population pressure is likely to induce technological
progress (Boserup, 1965; Aghion and Howitt, 1992; Grossman and Helpman, 1991),
and China arguably falls into this category (Perkins, 1969; Elvin, 1973).4 Then, there
is the concern over possible omitted variable bias—most notably the varying quality
of local governance—which is likely to affect the adoption of maize and population
growth simultaneously. Finally, as we rely on the year of publication of the gazetteers
in which maize was first mentioned, which may differ from the actual time of
adoption, the resulting discrepancy, if any, is likely to result in problems of
measurement error.
We deal with the aforementioned estimation issues by exploiting both the
regional variation in maize planting and the variation in climate across prefectures
over time. The underlying rationale for employing this interaction term as our
instrumental variable is premised on the following assumptions. First, the more
proximate a prefecture was to the point of initial maize adoption the likelier it was to
adopt maize. Second, given the drought-resistant nature of maize, prefectures were
more likely to adopt it in times of drought, which precluded the cultivation of rice or
wheat.
4
The endogenous nature presumed in the relationship between population growth and technological
change permeates throughout the pertinent literature on China. For example, for Perkins (1969) the
“major engine generating this rise in yields was population growth” (p. 23). Similarly, Lee and Wang
(1999) see the adoption of American food plants as the “product, not the cause, of population pressure”
(p. 38). Operating under the same logic, Elvin (1973) attributes the increases in productivity resulting
from multiple cropping, wet farming, extensive application of fertilizer and intensive labor investment
all as the result of the increases in population.
4
Our instrumented evidence suggests that maize planting in China led to a
significant increase in population growth: one decade of maize planting contributed to
an approximately 3.3% increase in population. For the shorter 1776–1910 period for
which we have data on population density, this New World crop alone accounted for
approximately 7.9% of the entire increase in population growth. Extrapolating the
contribution of maize to population growth for the longer period of 1550–1910 yields
the conservative estimate of 11.2% although the figure could be as high as 20.5%.
Our results are robust to the inclusion of a variety of controls and specification checks,
most notably other possible channels of agricultural innovation such as the multiple
cropping of rice and the adoption of other New World crops such as the sweet potato
and Irish potato, internal migration, and other factors that are likely to affect
population dynamics, such as wars and epidemics. Moreover, to completely eliminate
the possibility that maize was brought to China to alleviate the growing population
pressure (a demand side shock), we also analyze the effect of maize adoption on grain
prices for the period 1738 through 1910, and confirm that maize did have a significant
and negative effect on prices.
Although the adoption of maize (and other New World crops such as the sweet
potato) in China led to population growth, its magnitude was comparatively smaller
than that of the potato’s contribution, probably because the Chinese treated maize as
purely a supplementary staple crop—something they turned to only when the main
staple crop ran out. Moreover, it failed to result in economic growth. Indeed, in sharp
contrast to what has been found for Western Europe, maize planting in China is
negatively correlated with the urbanization rate and real wage. Our finding that maize
planting led to modest population growth but not to economic growth is consistent
with narratives on the stagnating state of the Chinese economy in at least the last 150
years of its dynastic rule, narratives that identify growing population pressure and the
resulting small agricultural surplus as the predominant barriers to China’s
industrialization (e.g., Elvin, 1973; Perkins, 1969). It is also consistent with a more
recent interpretation of why Europe was able to embark upon an industrial revolution,
whereas China remained trapped within the Malthusian world (Landes, 2006; Lucas,
2002; Pomeranz, 2000; see also Hersh and Voth, 2009). Additionally, this study also
provides direct evidence to support the empirical claim that before 1800 the entire
world was basically still subject to the Malthusian quagmire (Clark, 2007), as well as
5
the theory that places special emphasis on the singular importance of technological
advancement to sustained, qualitative improvements in living standards (Galor and
Weil, 2000; Galor, 2005, 2010, 2011; Ashraf and Galor, 2011). Why different parts of
the Old World ended up with radically different economic outcomes despite their
invariable adoption of New World crops, however, remains an open question awaiting
the progress of future research.
The remainder of this paper is organized as follows. In Section 2, we account
for why maize was the most popular New World crop in China and for its temporal
diffusion across the country. In Section 3, we introduce our data and their sources and
the variables employed in the empirical analysis. Our empirical strategies and their
results, including a host of robustness checks, are discussed in detail in Section 4,
followed by a more tentative discussion of the relationship between maize planting
and economic growth in Section 5. Section 6 provides a few brief concluding remarks.
2. Maize and its Diffusion in China, 1550 to 1900
In subsection 2.1 we account for why it was maize and not the potato that
became the predominant New World crop in China from the middle of the 16th
century onwards. Our research suggests that the choice made by the Chinese was
premised primarily on suitability of soil characteristics and to a lesser extent dietary
preference. The diffusion of maize across China between the mid-16th and early 20th
centuries is the subject of section 2.2, in which we explain how we document this
process of diffusion based on the rich historical information compiled in the local
gazetteers.
2.1 Why Maize?
Of the three New World crops (maize, potato and sweet potato) adopted by the
Chinese during the 16th to 18th centuries, it was maize that experienced the most rapid
and widespread diffusion, and thus likely contributed the most to population growth
(more below on the geographic spread of maize). By the late Qing Dynasty (around
the 1900s), maize was established as the most popular staple of the Chinese after only
6
rice and wheat—the traditional staples for thousand of years (Zhou, 2007).5 Although
sweet potato was also introduced into China at around the same time as maize, it was
not as popular.6 Specifically, our archival evidence suggests that by 1776 slightly over
half, or 55.8%, of our sample (267) prefectures already had records of planting maize,
whereas roughly only half of them, or 28.56%, claimed to have planted sweet potatoes.
More importantly, this trend continued into the late 19th century. And whereas the
incidence of prefectures planting sweet potatoes did increase over time to 47.23% by
1810 and 74.26% by 1880, the corresponding percentages for maize were 73.03% by
1810 and 90.63% by 1880.7 In the case of potato, although there were scattered
accounts of its plantation in the early 1800s (Cao, 2005a, 2005b; Lee, 1982), its wider
diffusion on the plains had to wait until the latter half of the 19th century (Wang, 1994,
pp. 1023; see also Sun, 1984, pp. 91-94).8
An interesting question at this juncture is why, unlike the Old World, which
had adopted potato—the crop with the greatest nutritional and caloric values among
the three—to be its new staple (Nunn and Qian, 2010, 2011), China opted for maize
instead. It is not that the Chinese were unconcerned with nutritional values, but there
are two plausible reasons, speculated below, of why maize was the preferred choice of
crop for the Chinese and which accounted for its widespread diffusion in China.
The predominant reason has to do with suitability of cultivation. Figure 2,
which depicts the varying percentages of the soil suitable for planting the three New
World crops, maize, potato and sweet potato, shows that only about 10% of the land
5
Maize continues to assume great importance in modern-day China and the world at large. In China,
maize is now the second largest grain output after only rice. For the entire world maize ranked as the
number three crop in terms of both daily consumption and output harvested and second in terms of
annual production as of the turn of the 21st century (Nunn and Qian, 2010, Table 1). In terms of the
world’s total output China is now the second largest maize producer globally (FAO, 2010).
6
With an output of more than 10 million metric tons produced on 6.93 million hectares, maize was
already the third largest staple crop in 1936 after rice and wheat (Wang, 1994), whereas sweet potato
was still the number four crop (after maize) in 1978 (Chinese Academy of Sciences, 1980).
7
Our evidence is thus consistent with Warman’s (2003) claim that maize “never lost momentum,
growing at a faster rate than other cereals and basic foodstuffs” (p. 44).
8
As a crop originated from the Andes, the sweet potato was better suited to cultivation in hilly regions.
Like maize, it was a crop that helped the Chinese farmers to expand the extensive margin, whereas rice
was better for the intensive margin (of multiple cropping).
7
in China was suitable for cropping the potato (Panel A), compared to 20% for the
sweet potato (Panel B), but over 55% for maize (Panel C). Moreover, as shown in
Panel A, the northeast was the only region where the potato was most suitable for
cultivation. However, owing to historical-cum-political reasons this territory formerly
known as Manchuria did not open up for the ethnic Han Chinese (migrated primarily
from the North China plain) to settle and farm until after the latter part of the 19th
century (Kung and Li, 2011). Conversely, maize could be easily grown in the entire
basin surrounding the Huai River, the middle and the lower Yangzi region, the North
China plain (especially Shandong Province),9 and the valleys in the populous,
southwestern province of Sichuan.10 In sum, the suitability of soil characteristics was
an important reason why maize and not the other two crops were positively selected
by the Chinese. The Irish experience provides a contrastingly comparative perspective
that supports our view. In Ireland, the soil characteristics were suitable for planting
only potato but not the other two New World crops (Connell, 1962; Gaez, 2002).
Figure 2 about here
Dietary habits may have been the second reason. Prior to the adoption of New
World crops the Chinese had, for hundreds of years to say the least, been accustomed
to having rice (primarily in the south) and wheat (in the north) as their main staples
(Wang, 2006).11 The Chinese thus did not start with a clean slate when seemingly
presented with a menu of choice between the potato, sweet potato and maize. While
the potato may be capable of providing additional nutrition and calories, nonetheless
9
Compared to sweet potato, for example, maize is more resistant to cold weather, which thus also
favored its diffusion in north China (Zhang and Hui, 2007; see also Ho, 1959). Moreover, the ease of
storage, transport, and ready conversion into food represent additional advantages of maize over the
other two crops (Warman, 2003, p. 44).
10
Ho (1959) finds that “many mountainous districts of the southwest depended on maize as a primary
food crop” (p. 185).
11
According to Song Ying-xing (1637), an expert in agricultural technology in the Ming Dynasty, the
prevailing crop mix (output) then was approximately 70% of rice, 15% of wheat and 15% of mixed
cereals.
8
the Chinese consider it (and also sweet potato) rather bland in taste (Ho, 1979;
Perkins, 1969).12
2.2 The Diffusion of Maize in China
Brought to the Old World by Columbus in 1492 and spread rapidly in the
subsequent two centuries, maize was the traditional crop in today’s Mexico (refer to
Appendix 1 for a detailed account of the global expansion of maize during the 16th
and 19th centuries). In China, maize was also introduced around the middle of the 16th
century via three routes. The first was the Silk Road from central Asia and the Pamir
Mountains into Gansu, a province in northwest China. Second, it was also brought to
the southwestern province of Yunnan via India and Myanmar; and, third, the
Portuguese also brought this crop to the coastal province of Fujian in the south (Cao,
1988; Tong, 2000, p. 18).
The ideal way to examine the effect of maize adoption on population growth is
to collect data on the precise timing and acreage of maize cultivation. Unfortunately,
such data are not available. As a second best measure we employ the publication year
of the local gazetteer (difangzhi) that first mentioned maize in a prefecture to proxy
for the year in which maize was first adopted. This is a reliable method because local
governments in China had had a very long tradition of documenting in detail the
affairs of their economies, societies and culture and publishing them in the (typically)
voluminous gazetteers, which may explain why local gazetteers are often regarded as
local encyclopedia. Moreover, given that in the great majority of instances various
levels of government (province, prefecture and county) published their own gazetteers,
cross referencing them allows us to verify the reliability of the reported data. To
gauge when maize was adopted by a given prefecture (if it was adopted at all) we
reviewed all gazetteers published by a prefecture to ascertain which of these
12
As a matter of fact, the potato was not even mentioned in the Chinese text until 1847 in the Almanac
of Plants [zhiwu mingshi tukao] by Wu Qi-jun. It did not appear, for example, in the Complete Treatise
on Agricultural Affairs (nongzheng quanshu), which is the first agricultural encyclopedia ever
published in China (Xu, 1639).
9
publications first mentioned the planting of this crop.13 For example, suppose that a
prefecture had published its gazetteers in 1500, 1600, 1700 and 1800, respectively,
and maize was first mentioned in the 1700 publication, we assume that 1700 is the
year in which maize was adopted. Based on this information, we provide a graphic
account of the geographic diffusion of maize in China between 1600 and 1900 (Figure
3). For instance, while maize was adopted in only a handful of prefectures in Henan
and Jiangsu provinces in 1650, it was already planted in more than a third of the
region north of the Yangzi River by 1700.14 And by 1750, maize became popular also
in the south, where the crop could be found planted in roughly half (approximately
47%) of the prefectures in which it was adopted. It appears that it was only a matter of
time that maize was eventually spread to the entire country, when about 80% of China
were found to have adopted this crop by 1800 before it was adopted virtually
everywhere by the turn of the 20th century.15
Figure 3 about here
Bearing in mind that maize is a drought-resistant crop, it would most likely be
adopted in areas not suitable for the cultivation of rice and wheat (as the latter
requires water). Indeed, the spread of maize in China had, by the account of a Chinese
historian, led to substantial increases in cropping acreage and output—4.3 times
between 1380 and 1900 (Wang, 1973), resulting in a steep rise in productivity from
under 140 catties per mu of land in 1368 to about 240 catties by the middle of the 19th
century (Perkins, 1969, pp. 16-17). Given that up to 55% of the output increase (of
13
For the few prefectures that had either not published their own gazetteers or failed to keep them, we
looked for similar publications at the county level (one prefecture typically consisted of several
counties). Where maize planting was reported by more than one county in a prefecture, we employ the
report with the earliest publication date to proxy for the year of adoption for that particular prefecture.
14
Our findings are thus consistent with Ho’s (1959) narrative that by the 18th century maize had been
spread to the Lower Yangzi when the region became congested: “The hills and mountains along other
tributaries of the Yangzi were likewise turned into maize fields” (p. 185).
15
Ho (1959) notes that between 1904 and 1933 maize accounted for 17% (up from 11%) of the farm
acreage in North China, at the expense of barley, millet and sorghum. The same trend was observed for
Manchuria. In addition to its drought-resistant property, maize was rendered popular in China because
of its higher productivity (of around 180 jin or 90 kg. per Chinese mu of land, one mu is equivalent to
0.067 hectare or 0.16 acre) vis-à-vis other “mixed” cereals in China such as barley and sorghum by 5 to
15 times (Perkins, 1969).
10
roughly 55 million tons of grain) over this long period of time came from the
expansion in cropping acreage (Perkins, 1969, p. 26 and pp. 31-32),16 small wonder
why the adoption of maize in China has been hailed by Ho (1959) as the second
“agricultural revolution” (see also Lan, 2002, p. 227).17 Indeed, there are abundant
archival evidences to suggest that officials of varying seniority—the emperor
included—had enthusiastically promoted the planting of maize.
The popular adoption of maize across China implies, importantly, that it had
established as a supplementary staple item for the Chinese alongside other major grain
crops of rice and wheat (Perkins, 1969). Indeed, the growing importance of maize is
reflected in the evidence on the declining share of rice over time—from accounting
for roughly 70% of the total agricultural output (Song, 1637) in 1637 to only 36% in
1931-1937 (Ho, 1959, p. 192).18
3. Data and Measurement
In order to conduct our empirical analysis we construct a unique panel dataset
based on a number of historical sources. There were altogether 18 (out of 23)
provinces in China in the period covered by our analysis (Figure 4), out of which (317)
there were a total of 267 prefectures (“fu” in Chinese), which is equivalent to the
administrative level between the province and county in today’s China.19 There are
two reasons for choosing the prefecture as our unit of empirical analysis. The first is
data availability: each prefecture published its own gazetteer, which contains a rich
16
According to Perkins (1969) the remainder of the increase, approximately 45%, was accounted for
by a rise in yields achieved via increasing the cropping index and improving traditional agricultural
inputs rather than the adoption of a qualitatively new farming technology (see also Elvin, 1973;
Goldstone, 2003; Huang, 1990; and Li, 1998).
17
The first agricultural revolution refers to the introduction of Champa rice from Champa (the middle
and southern parts of today’s Vietnam) during the Song dynasty.
18
The extent to which maize was consumed depended on regions. In regions where few, if any, acreage
was cropped in rice or wheat (especially in the hilly areas), maize made up nearly 80% of the
inhabitants’ daily food consumption (Song, 2007: 67).
19
Located primarily in the remote northeastern and northwestern corners, the five provinces dropped
were all populated by the ethnic minorities, which covered less than 10% of the total population around
the 1820s.
11
gamut of information pertaining to the demographic, social and economic conditions
of the time. Second, China was so enormous in size that comparisons at the level of
the provinces would likely conceal the highly heterogeneous nature that possibly
exists within a single province. In fact, our data clearly reveal that the number of
years between the earliest year and the latest year in which maize was adopted for a
province ranged from a minimum of 119 years to a maximum of 321 years (Appendix
2), with an average of 280 years.
A slight technical difficulty we have to deal with using the prefectural level
data though is that, since our analysis spans two dynasties (the Ming and the Qing),
the transition of which had resulted in changing demarcation of the boundaries across
some prefectures. To ensure temporal consistencies in the spatial units we analyze, we
have to re-aggregate the counties based on the new classification of the Qing
administration.
Figure 4 about here
3.1 Definition of variables
Population Density. Our dependent variable is population density of the
following years: 1776, 1820, 1851, 1880 and 1910, obtained from “A History of
Population in China”, compiled by Shu-ji Cao (2000). This is the first attempt to
systematically construct population data at the prefectural level of the Qing dynasty
based on more than 3,000 local gazetteers, whose validity has been verified by the
1953 census survey and the scrutiny of such eminent China scholars as Ho (1959),
Perkins (1969), and Skinner (1977). To further verify that Cao’s population data are
not widely off the mark, we perform a correlation test with five other sources that are
relevant albeit less systematic (in the sense that they are either cross sectional or
available only at the provincial level). The resulting correlation matrices, reported in
Panel A of Appendix 3, show that all six sources are significantly correlated at the 1%
level of significance.
For the entire nation as a whole (and for all years), average population density
is 120 (persons) per square kilometer, which for a country as diverse as China surely
masks significant regional variations. For instance, at the one extreme was Taicang fu
12
(of today’s Suzhou city) in Jiangsu Province, which had 671 people per square
kilometers, whereas at the other extreme was Anxizhou fu in Gansu province, where
the comparable density was a mere 0.4.
Duration of maize planting. As mentioned earlier we do not have precise
information on the diffusion of this New World crop (exact timing and actual acreage),
as a compromise we employ the year of publication of that local gazetteer in which
maize was first mentioned to proxy for the year in which maize was adopted in that
prefecture. The difference between the actual year when maize was adopted and the
subsequent chronological time point of population density provides us with a measure
of duration—our key independent variable. For instance, suppose a given prefecture
adopted maize in 1700, 76 years earlier than our first data point on population density,
then the value of duration for that prefecture for the year 1776 would be 7.6 (when
measured in decade terms).
War and Epidemics. Wars and epidemics are the conventional “positive
checks” in Malthus’ theory, it is thus necessary that we control for their possible
effects on population density for the period covered by our analysis. As the largest
civil war with the most severe casualty in Chinese history, the Taiping Rebellion of
1851-1864 provides a good example (Cao, 2000; Ho, 1959; Perkins, 1969).20
Although we have information on the annual incidence of warfare for each prefecture,
we are constrained by our dependent variable of having only five time points (see
above). Given this limitation, we employ the average frequency of war between the
five time intervals in which we have information on population density, in our
regressions. In our entire sample there were altogether 896 wars fought during the
period 1776 to 1910. For example, there were altogether eight wars being fought
between 1851 and 1880 in Caozhou fu, a prefecture located in the southwest
Shandong Province, the average frequency of war incidence is 0.27 (8 out of a total of
30 years).
20
Estimates of the casualty are between 50 million (Perkins, 1969) to 73 million (Cao, 2000). The
demographic impact of this rebellion is well illustrated by Perkins: “Were it not for the Taiping
Rebellion, rising population in the late nineteenth and early twentieth centuries might have outstripped
the ability of Chinese agriculture to provide adequate food supplies” (p. 29).
13
We employ the same method for computing epidemics. According to Mark
Elvin (1973), China experienced “two most widespread and lethal epidemics in her
recorded history” (p. 310). In 1588, for instance, 92 prefectures or counties in as many
as 13 provinces were affected, and 99 localities in 10 provinces were badly affected in
1641. Unlike the data on war, however, those on the epidemics are available at the
level of the province only.
Weather Extremities Last, but not least, it is necessary to control for the direct
effect of extreme weather on the loss of human lives. To do so we include in our
estimations the share of extreme droughts and floods. The data on weather conditions
for both Ming and Qing dynasties and beyond (circa 1470–1979) are obtained from a
total of 120 weather stations, compiled by the Chinese Academy of Meteorological
Science (1981) based on a variety of historical sources including the Veritable
Records of the Ming and Qing Dynasties (ming shilu; qing shilu), the History of the
Ming Dynasty (mingshi) and Draft History of the Qing (qing shigao). Our task, quite
simply, is to pair up the 120 weather stations located across the 267 prefectures based
on proximity; which means that the data on weather for each prefecture is obtained
from its nearest weather station. On average, one weather station roughly covers two
prefectures. Given that the yearly weather variations were measured using an ordinal
scale ranging from 1 (indicating extreme droughts) to 5 (indicating extreme floods),
we simply compute the share of a prefecture having experienced such extremities
(both droughts and floods) in the five intervals where data on population densities are
available. Table 1 summarizes the descriptive statistics of all the variables of interests.
Table 1 about here
3.2 Descriptive evidence
Before we proceed with our empirical analysis we first reveal some
descriptive evidence on the relationship between maize planting and population
density in China for the period 1776 to 1910. Reported in Table 2, the results clearly
show that population density was 55% higher in the maize-planting regions (the
difference is significant at the 1% level) than in regions that failed to cultivate this
crop. It is important to emphasize that not only is the observed difference significant
across all five periods under examination, the magnitude of the difference increases
14
steadily over time (Column 8). Other than the colossal loss of human lives afflicted by
the Taiping Rebellion of 1851–1864, whose impact was reflected in the year 1880,
basically there had been no obvious changes in population outcomes in the nonadoption areas (Column 2), whereas the adoption areas had clearly experienced a
stable increase over time (Column 5).
Table 2 about here
4. Estimation Strategy and Empirical Results
In what follows we first introduce our baseline estimation based on the
Ordinary Least Squares Method (OLS) and discuss the pertinent results in subsection
4.1, followed by those of the Two-stage Least Squares Method (2SLS) in subsection
4.2. In subsection 4.3 we perform a number of robustness checks required for
exclusion restriction tests ranging from other possible sources of technological
progress in Chinese agriculture (4.3.1), other New World crops (4.3.2), to regional
migration (4.3.3), and an alternative estimation method based on Nunn and Qian
(2011) (4.3.4), respectively. Finally, to verify that the adoption of maize in China was
largely a supply side shock we test the effect of the adoption on grain prices in 4.3.5.
4.1 OLS
We begin our baseline estimation with the OLS method based on the
specification in Equation (1):
popdenit = α 1 M it + X 'it α 2 + ∑ β f I i + ε it
(1)
f
Where i indexes a prefecture (fu), popden stands for the population density of
prefecture i in the respective years of 1776, 1820, 1851, 1880 and 1910, and M
denotes the duration of maize planting in prefecture i up to time t. The term X it
denotes a vector of control variables, which include the share respectively of extreme
droughts and floods. As befits a fixed-effect model, the term ∑ β f I i captures the
f
time-invariant regional characteristics for prefecture i that may be associated with the
15
duration of maize planting. We do not however control for the temporal effects in our
estimation because the duration of maize planting is cumulative over time. But we
will employ two measures just to ensure that the effect of maize planting on
population density is not simply due to the effect of the time trend. We defer the
discussions of this particular issue to a later section as they are based on estimations
using the IV-2SLS method. Finally, in Equation (1), ε it is the disturbance term for
absorbing the effects of other random sources of differences in the dependent variable.
We present the OLS results in Table 3. Consistent with our hypothesis, the
relationship between maize planting and population outcome is significant and
positive. In terms of magnitude, population density in the maize-planting areas is
roughly 22% higher than in areas that did not plant maize (Column 1). Using the
alternative measure of planting duration, we find that one decade of maize planting
results in an increase in population density by roughly 1.4 to 1.5% at the 1% level of
significance (Columns 2 through 3). To gauge the overall contribution of maize to
population growth we multiply change in the duration of planting maize (239.45%)
by the pertinent coefficient of 0.15 (the upper bound);21 this yields a magnitude of
3.59% for the 1776–1910 period. Based on this estimate we can extrapolate the
contributions of maize planting to population growth for the longer period of 1550–
1910. If we limit our estimate to consider the duration of maize planting of only those
prefectures having adopted maize (the conservative estimate), the contribution of
maize to population growth would be 1.49% before 1776 (3.59%*[4.35/10.45]) and
5.08% (3.59%+1.49%) for the entire period of 1550–1910.22 However, if we do not
differentiate between prefectures by whether they had planted maize and simply count
the number of years for the entire 360-year period (1550–1910), we obtain a much
higher estimate of 9.64% (3.59%*[360/134]), which may be regarded as the upper
bound of our extrapolation.
Table 3 about here
21
Since we know when maize was adopted in the 267 prefectures, we calculate the average duration
that this crop had been planted for the two periods, 1550–1776 (4.35 decades) and 1550–1910 (14.8
decades), respectively, which yields a change of 239.45% ([14.8/4.35]-1).
22
We know that average duration of maize planting for the periods 1550–1776 and 1776-1910 is 4.35
and 10.45 (14.8-4.35) decades, respectively.
16
Turning to the remaining control variables, we find that only the share of
extreme floods has a directly negative and significant effect on population density
(Column 4), suggesting that local officials in Ming and Qing dynasties had likely
developed effective mechanisms in coping with disasters induced by droughts but not
floods.23
4.2 IV-2SLS
While by extending our dimensions of control the OLS estimates are better
able to deal with problems pertaining to the omitted variable bias, it is unable to deal
with the problem of reverse causality and measurement error. For these reasons we
employ an instrumental-variable approach using the Two-Stage Least Square (2SLS)
Method to re-estimate the relationship of interest.
In this context, a valid instrumental variable should be correlated with the
adoption of maize or, more specifically, the duration of maize planting, on the one
hand, but not correlated with population density on the other. What determined the
probability of a prefecture adopting maize? Two potentially determining factors come
to mind. One pertains to the spherical distance of a prefecture to the nearest point of
one of three original points of adoption, whereas the other is the prevailing climatic
conditions (at the time of adoption). More specifically, we expect that the shorter the
distance between a given prefecture and the nearest, initial point of adoption and the
more severe the climate (in terms of dryness) the greater the likelihood of maize
adoption. The underlying rationale for employing this interaction term is as follows.
First, distance is a popular instrumental variable that is frequently employed to
identify the causal effect of institutions on economic performance (e.g., Becker and
Woessmann, 2009; Dittmar, 2011; Hall and Jones, 1999; Gallup et al., 1998;
McArthur and Sachs, 2001). Second, and more importantly, climate provides us with
the temporal variations with which to gauge the marginal effect of weather on the
23
This empirical result is in fact consistent with the historical finding that the military strengths of
ethnic Han Chinese would be severely weakened by the levee breech of the Yellow River or simply
floods in their two thousand year-long warfare with the nomads (Bai and Kung, 2011).
17
probability of adoption among prefectures that are more or less equal in distance to
the same point of initial adoption.
As noted earlier, our data on the historical weather conditions are taken from
the Chinese Academy of Meteorological Science (1981). Although data on weather
variations are available on a yearly basis, we are constrained by the limitations of our
dependent variable of population density, which contains only five time points. As a
compromise, we employ the “average” of the weather measure by first summing up
the yearly values of each prefecture and then dividing this average by the number of
years between two consecutive time intervals.24
But why would weather conditions in 1880–1910 matter to prefectures that
had already adopted maize in 1776–1820? Here weather variability is intended to
capture not just the effect of when a prefecture was likely to initially adopt maize
(duration), but, equally important, whether a prefecture had expanded its cultivated
acreage over time in response to bad weather that occurred after the initial adoption.
Historical evidence from north China indeed suggests that farmers did expand the
acreage cropped in maize as weather turned worse long after its initial adoption (Li,
1994; Cao, 2000).25
In employing distance as part of our identification strategy we are mindful that
distance to the nearest points of diffusion will not simultaneously pick up any
unobserved effect conferred by economic opportunities other than those of maize.
Specifically, differential trade effects may be the cause of variations in population
density (Galor and Mountford, 2008). Prudence thus requires that we check whether
the three initial adoption points were indeed more commercialized than their
24
There are two reasons why “average” is preferred to extreme weather. First, extreme weather may
have a direct (negative) effect on population density, which violates the condition of exclusion
restrictions. Second, as each of our period intervals (i.e. between 1820 and 1851 in one instance and
1851 and 1880 in another) spans an average of roughly 30 years, the “average” climate in each period
would better predict the probability of maize adoption (and its expansion over time).
25
For example, the largest estate in Qufu Prefecture, Shandong Province in north China, expanded its
acreage sown to maize from 13.4% in 1791 to 34.7% in 1824 (Li, 1994), during which time the weather
index changed from three to two, which indicates a drier climate on our scale. Cao (2000) finds that the
same had occurred in another prefecture of the same province.
18
neighboring prefectures from the Ming Dynasty onwards, and we are relieved to find
that they were indeed not. For example, while Liangzhou fu was part of the Silk Road
and as such had enjoyed substantial economic prosperity before the Tang Dynasty
(618–907 AD), its economic status declined rapidly as trade along the Silk Road
eventually dwindled.26 Another adoption point, Quanzhou fu, shared a similar destiny
when Zhu Yuanzhang, the first emperor of Ming Dynasty, banned all maritime trade
when he came to power in 1371 (Tsai, 2001). Moreover, to further ensure that these
initial adoption points were really not more developed economically than their
neighboring prefectures, we perform the following checks. The first and more
straightforward check shows that there were not any differences in population density
between these three prefectures and their neighbors in 1550—the year of our “initial
conditions”. Second, we conduct a falsification test to ascertain whether the effects on
population density were really due to the adoption of maize and not to other
unobserved economic opportunities, by replicating the 2SLS estimation on those
prefectures whose administrative seat was closest to these three initial adoption points.
Because these neighboring prefectures did not seem to enjoy the same economic
opportunities as the three famous prefectures did, this approach allows us to
distinguish the effect due potentially to economic advantages while holding constant
the effect of distance. We obtain similar results and hence do not report them.
Using the interaction term between distance and weather as the pertinent
instrument, the first-stage of the 2SLS setup assumes the following specification
(Equation 2):
M it = γ 3 Disi *Weatherit + X 'it γ 4 + ∑ β p I ip + ε it (2)
p
where Disi * Weatherit is the interaction term between minimum distance to the closest
point of diffusion and weather variations. In addition to the list of control variables,
the vector X also includes the main effects of average weather conditions and
distance to the point of adoption.
26
The decline of the Silk Road was triggered by the defeat of the Tang Empire by Abbasid the
Caliphate in the battle of Talas (751 AD), which resulted in the severing of ties between China and
Europe (Bai, 2003).
19
The pertinent results are reported in Table 4. With the exception of Column 1,
which is a Probit model (because the dependent variable is a dummy variable
indicating whether a prefecture had planted maize), the rest of the regressions are
OLS. With the expected negative sign, the significance of this variable confirms our
hypothesized reasoning that the probability of planting maize depends on both
proximity to one of the three original points of adoption and dry weather conditions.
The pertinent point estimate is -0.071 and standard error is 0.027 in the Probit model.
We obtain broadly similar results in the estimation that replaces the dummy variable
of maize adoption by planting duration (Columns 2 and 3). To ensure that our 2SLS
regressions yield consistent estimates, it is essential that the instrument in the firststage provides sufficiently precise fit. Following Staiger and Stock (1997), who
suggest that, as a rule of thumb the F-value (which tests the joint significance of the
explanatory variables) obtained in the first-stage cannot be smaller than 10. From
Table 4 it can be seen that the pertinent numerical values are all bigger than 30,27
suggesting that our second-stage IV-2SLS results are unlikely to suffer from the weak
instruments problem, one which can bias our IV estimates toward OLS estimates
(Bound and Jaeger et al, 1995; Staiger and Stock, 1997).28
Table 4 about here
In the second stage, we regress population density on the predicted values of
maize planting based on the following specification (Equation 3):
popdenit = ϕ3 Mˆ it + ϕ 4 X it + ∑η p I ip + ν it
(3)
p
The pertinent results are summarized in Table 5. While the key explanatory
variable of maize adoption (or planting duration) remains highly significant (at the 1%
level of significance), the size of the pertinent coefficients are three times larger than
those of the OLS estimates, suggesting that our earlier estimates do suffer from the
27
In model 3, for instance, the F-value is [-0.504/0.088]2 = 32.801 .
28
As a further test of weak instruments, we employ the Cragg-Donald minimum eigenvalue statistics.
With a value of 33.69 (Column 2 Table 4), it is virtually identical to the F-statistics just discussed, and
it is larger than the critical value as suggested by Stock and Yogo (2005), thus we can conclude that our
instruments are not weak instruments.
20
problem of measurement error (see Griliches and Hausman, 1986). The results in
Column 1, for example, suggest that population density in the maize-planting areas is
1.26 times higher than that in the non-adoption areas (the comparable magnitude in
the OLS estimates is a mere 0.55 in Table 2 and 0.23 in Table 3, respectively).
Likewise, estimations using the alternative proxy of planting duration similarly show
that one decade of maize planting increases population density by 5.6% (e.g., Column
2), compare to a mere 1.5% in the OLS estimates.
To rule out the possibility that the significant effect of maize planting on
population density is not simply due to the effect of the time trend we re-estimate
Model 2 using the pooled OLS method and obtain basically similar results (hence we
do not report them separately). Another approach to ascertain that our results are not
merely driven by the time tend is to regress population density of the five time points
separately on the duration of maize planting. As shown in Figure 5, while all the
pertinent coefficients are significantly positive, the trend is clearly not one that
increases monotonically over time. In fact, after peaking at 0.029 in 1820 (from 0.026
in 1776), the coefficient drops to a mere 0.015 in 1880 and further to 0.014 in 1880.
These results lend solid empirical support to our estimation results.
Figure 5 about here
Given that extreme weather conditions—both droughts and floods—can have
a direct effect on population density, we include them in our control (Columns 3
through 4).29 The pertinent results show that floods have a direct impact on population
density, whereas droughts do not.30 Most important though is that inclusion of these
factors in the estimations does not undermine the significance of our key independent
variable; maize planting continues to have a positive effect on population density.
29
By employing the method proposed by Gruber and Saez (2002), we also control for the lagged shares
of extreme weather conditions and the results are trivially different (hence we also do not report them
separately).
30
This finding is in fact consistent with the results showing that in historical China conflicts were
typically affected by droughts (Bai and Kung, 2011; Jia, 2011); their effect on population density was
thus indirect at best.
21
Other population determinants such as war, which affects population outcomes
by increasing mortality, has a significant and negative effect on population density: an
additional war incidence reduces population density by roughly 36.9% (Column 3).
While simple, this result powerfully supports the view that in the historical context of
China war was indeed a major determinant of the population dynamics (Ho, 1959; Liu,
1977; Perkins, 1969). To ensure that our estimates are not driven by outlier provinces,
we exclude Jiangsu and Zhejiang—the two provinces with the densest population in
China, and obtain basically the same results. Likewise, we exclude Gansu, the
sparsest populated province, and Sichuan, the province that experienced the most
dramatic population change in the Ming and Qing dynasties due to war and migration,
and the results do not change either.
With regard to the effect of epidemics, both Ming (1586–89) and Qing (1639–
1644) had experienced two major catastrophes that allegedly resulted in casualties of
between 20 and 40% of population in one and 50% in another (Elvin, 1973).31 To test
the effect of epidemics we include this variable in Column 4 of Table 5. The result
shows that epidemics have an even more devastating effect than that of war on
population outcome: an additional epidemic, for example, reduces population density
by a hefty 50%—approximately 30% higher than that caused by war. Together, the
results in Columns 3 and 4 support the Malthusian view that, in pre-industrial China,
war and epidemics were indeed the two main mechanisms of “positive checks”.
Table 5 about here
4.3 Robustness Checks and Remaining Estimation Issue
To ensure that our estimations are not contaminated by variables whose
omission may affect population density via the instrumental variable, we perform the
following robustness checks. First we will examine the possible impact of
technological progress of other crops—most notably paddy rice—on population
density. We then employ the share of the soil characteristics suitable for planting
maize as an alternative instrument to exclude the possibility that the effect was due to
31
Further details of these casualties can be found in Cao (1995, 1997) and McNeil (1976).
22
the other two New World crops. Given that maize planting may be correlated with
regional migration we will examine this issue as well. Finally, to check the robustness
of our estimates we test our results using the estimation method employed in Nunn
and Qian (2011). The remaining estimation issue concerns whether the adoption of
maize represented essentially a supply side shock. If it did, we should expect it to
have a negative effect on grain price. We examine this by comparing grain prices
before and after the adoption of maize using a difference-in-differences (DID)
approach.
4.3.1 Other Technological Progress
In view of the fact that up to 45% of the increase in grain output in China
between the 14th and the early 20th centuries came from technological progress
associated with the traditional crops of rice and wheat (Perkins, 1969), this particular
source of technological progress may potentially violate the exclusion restrictions
condition. Given that the main source of technological innovation came from the
intensification of the cropping index, which was the result of adopting an earlyripening seed variety, the ideal measure employed to proxy such progress is changes
in the cropping index both over time and across space. Unfortunately, such data are
available only after John L. Buck conducted his monumental survey of farms across
China in the 1930s (Buck, 1937). It is fortunate, however, that since technological
advancement in Chinese agriculture came primarily from the adoption of earlyripening rice, it is feasible to perform the following natural experiment.
Although the early-ripening variety shortens the crop cycle by as much as onethird or 40 days—from 120 to 80 days, its adoption is constrained by geography; it
can be adopted only in areas with sufficient daylight. Specifically, this implies that,
when planted in North China, the early-ripening variety will take as long a time to
harvest as its middle- or late-ripening counterparts. An increase in latitude to the north
by one degree is roughly equivalent to an increase in distance of 112 square
kilometers in the same direction; this has the effect of shortening the amount of
daylight and results in a lengthening of the crop cycle by two additional days. Simply
stated, in areas north of 33 degree North latitude the adoption of early-ripening rice is
23
technically not feasible (Zhang, 1996). 32 Thus, by restricting our sample to those
prefectures located in the north of 33 degree North latitude we can safely exclude the
possible effect of early-ripening rice on population density. We repeat the same
instrumented regression exercise on this subsample and report the results in Column 1
of Table 6. The estimate of 3.3% is only somewhat smaller than the instrumented
result of 5.5% in Column 2 of Table 5.
4.3.2 Other New World Crops
The other two major crops imported to China from the New World were,
respectively, the Irish potato and sweet potato. Unlike in Ireland, where the potato had
quickly replaced oats as the only staple (Connell, 1962), insofar as the Chinese were
concerned the Irish potato had not become a major staple item. Moreover, it was not
adopted in China until the mid-19th century, not to mention that it occupied a
relatively small acreage compared to maize and sweet potato (Sun, 1984; Wang,
1994). Hence the neglect of the Irish potato should not pose a threat to our estimation
results. The same cannot be said of sweet potato, however, as it was introduced to
China at more or less the same time as maize (Gao, 2003), which therefore led to the
speculation that it may also have contributed significantly to population growth (Bray,
1984; Jia, 2011). Although sweet potato is, like maize, also resistant to drought and
may therefore also be correlated with that part of our instrumental variable that
concerns climatic variations, fortunately its geographic diffusion differed radically
from that of maize in terms of the points of initial adoption.33 This implies that our
instrumental variable will unlikely pick up the effect of sweet potato, and hence
should be orthogonal to the diffusion of other New World crops, particularly the
sweet potato.
Even so, to ensure that our previous estimates do not violate the condition of
exclusion restrictions, we employ an alternative instrument to test whether we may
have overestimated the effect of maize on population density. To do so we instrument
our key independent variable (of planting duration) with the percentage of land
32
The crop cycle of early-ripening rice is approximately less than 80 days, and from there add another
20 days for the normal cycle and another 40 days for the late-ripening variety.
33
Sweet potato was first brought to Guangdong province in 1582 by Xia Chen-yi, an overseas Chinese,
from Vietnam (Li, 1998).
24
suitable for cultivating maize as measured by the soil characteristics. Given that this
alternative instrument is time-invariant, we have to convert our panel data into crosssectional data by collapsing the mean of all five periods into a single measure and run
the regressions again.
Reported in Column 2 of Table 6, the results show that the key variable of
interest is not only significant, the pertinent coefficient (of 3.5%) is strikingly similar
in magnitude to that in Column 1 (of 3.3%) of the same table. The consistency
between the two estimates is strong proof that, even after correcting for various
possible omitted variable biases, our estimates remain robust. Another way of viewing
these results is that they may be seen as representing the lower bound estimates for
the entire category of New World crops.34 The 2SLS result suggests that each decade
of maize planting would lead to an increase in population by 3.3%, which is more
than double the upper bound of the OLS estimate (0.015). Repeating the same
exercise of calculating the contribution of maize to population growth results in 7.9%
(239.45%*0.33) for the period 1776–1910. Based on the same method of
extrapolating the contribution of maize to population growth using the OLS results
(section 4.1), the corresponding lower- and upper-bounds of the contribution for the
entire period of 1550-1910 are 11.18% and 20.54%, respectively. If a crop’s
contribution to overall grain output is proportional to its cultivated acreage, Perkins’
(1969, p. 47) estimate of maize in the overall share of acreage cropped in grain of
10.8% in 1957 suggests that its contribution to overall grain output should be in the
neighborhood of 11%. Considering, however, the fact that maize was typically grown
in hilly regions where the standard of living was distinctly lower (Li, 2000), it is
reasonable to expect maize’s contribution to population growth to be somewhat larger
in magnitude than it was to output growth.35 In any case this is clearly much higher a
magnitude than the 4.9% contribution based on the OLS estimates. Given the biased
nature of the OLS estimate, it is more reasonable that we employ the result of the
2SLS estimates when interpreting the magnitude of the contribution of maize to
population growth.
34
Although we have controlled for prefecture fixed effects, we also run a regression that excludes those
provinces where sweet potato had a disproportionate presence (namely Fujian, Guangxi, Jiangxi and
Shandong [Cao, 2005a, 2005b]), and the results stay the same (and hence not reported).
35
We thank Dwight Perkins for this observation.
25
4.3.3 Regional Migration
To the extent that the adoption of maize was associated with migration, i.e.,
that population pressure forced some people to move from areas with a strong
preference for the conventional crops (of rice and wheat) to areas that are only
suitable for cultivating maize, our estimates of the effect on population density at the
prefectural level due to maize planting would have been spurious by the omission of
this variable. While we do not have detailed data on internal migration for the period
covered by our analysis, given that the only large-scale, cross-province migration
occurred before 1776 (Cao, 2000), we can safely assume that the majority of regional
migration that occurred in the period of our interest took place more or less within the
same province.36 By aggregating our prefectural observations to the province level
and run the regressions again, we can thus obtain an unbiased estimate of the effect of
maize adoption on population density, given that migration at the sub-provincial level
should have no effect on a province’s population density. The results of using the
province-level data are reported in Column 3 of Table 6. Despite the considerably
smaller sample size (of 90 province-level observations), the effect of maize planting
on population density remains significant, albeit at a lower level of significance.37
36
For the period that concerns us there were two major waves of migration across provinces. The first
occurred in the early Ming (the so-called “hongwu migration wave”, ended in 1393), and the second in
the early Qing (which ended in 1776). The magnitude of the former amounted to approximately 11
million or 15.7% of the entire population of 70 million, whereas the latter, while involving more people
in absolute terms (15.67 million), constituted a much smaller percentage—5.7% of 275 million people
(see Cao, 2000).
37
To further rule out the concerns that the relative decline of the Lower Yangzi region during the 16th
to 18th centuries was compensated for by demographic expansion (including migration) in the Middleand Upper Yangzi-regions, and the resettlement of lands caused by the destruction of the Taiping
Rebellion (Cao, 2000), we removed from our sample those prefectures located along the Yangzi region
(altogether 48 prefectures) and in the (Taiping-afflicted) provinces of Jiangsu, Anhui and Zhejiang
(altogether 37 prefectures), and those located in provinces afflicted most by the Taiping havoc,
respectively, and re-estimated Model 2 of Table 5. The results remain the same. Similarly, to eliminate
the concern that the relative decline of the Lower Yangzi region was due to migration to the Manchuria
commencing from the 1860s, we removed 1910 from our data and ran the regression again, in light of
the historical fact that migration to the northeastern provinces (from primarily North China) had been
on a significantly smaller scale prior to the 1920s (Gottschang and Lary, 2000; Kung and Li, 2011).
26
Table 6 about here
4.3.4 Alternative Estimation Method
In order to compare our results with those of Nunn and Qian (2011), who
exploit both the time variation arising from the introduction of potatoes as a field crop
in the Old World using 1700 as the cutoff point, and the variation arising from the
differences in countries' suitability for cultivating potatoes, we replicate their method
by exploiting the variation in suitability for planting maize across the 267 Chinese
prefectures and interacting it with the specific period that maize was adopted in each
prefecture. Reported in column 4 of Table 6, the pertinent point estimate is 0.018 and
the corresponding standard error is 0.009. Using the same counterfactual calculation
employed in Nunn and Qian (2011), the introduction of maize can explain
approximately 17% of the observed population increase in China between 1776 and
1910, which falls within our range of estimates of 11.18% to 20.54%.
4.3.5 The Effect of Maize Adoption on Grain Price
An important assumption underlying our empirical strategy is that the
introduction of maize had the effect of increasing the supply of grain over time, thus
lowering grain price. However, it is possible that maize was brought to China to
alleviate the growing population pressure, in which case the direction of our implied
causality would have been reversed. We would like to completely eliminate this
possibility, even though our instrumental variable approach has satisfactorily dealt
with the potential endogenous problem of reverse causality, and ascertain that the
initial adoption of maize in China was not a demand side shock.38 To do so requires
data on long-term grain prices. Under the Qing regime local governments were
required to keep systematic records on grain prices on a monthly basis, and so the
pertinent data are available for the period 1738–1910 for the 267 prefectures that we
Moreover, in terms of soil characteristics the northeast was better suited for cultivating the potato rather
than maize (see Figure 2). The results do not change either. We thank Tom Rawski for alerting us to
these issues.
38
We thank Oded Galor for this suggestion.
27
have henceforth employed in our analysis. To allow comparability among various
grain crops grown in different regions, we convert grain output into standardized
kilocalories,39 after which we adjust prices according to the exchange rate of U.S.
dollar to silver in 1900 based on the price deflators compiled by Peng (2006).
The result of this long-term price trend is plotted in Figure 6, which shows a
secular rise over time. For example, whereas 10,000 kilocalories of grain only cost
0.0131 USD in 1770 (in 1900 purchase power), the cost rose by more than double to
0.0264 USD in 1900. This overall price trend, however, conceals the substantial
variations across space. For instance, the price equivalence of 10,000 kilocalories of
food in Songfan fu in Sichuan was 0.033 USD, 6.5 times the price in Anshun fu in
Guizhou. This vast regional difference in the price of grain suggests that the adoption
of maize may have a significant effect on grain price variations across regions,
depending on the extent to which maize was adopted in a prefecture.
Figure 6 about here
For this reason we seek to ascertain the effect of the introduction of maize on
grain price by pooling together our observations and conducting a difference-indifferences (DID) analysis. If grain price turns out to be systematically lower after the
adoption of maize, we can safely confirm our hypothesized reasoning concerning the
benign effect of this New World crop on grain price, and vice versa. Our econometric
specification assumes a panel data regression of the form:
GPit = α 1 + α 2 Maizeit + α 3Wit + Pi + yeart + ε it
(4)
where GPit is the grain price in prefecture i in year t, Maizeit is the key variable
indicating the year when maize was adopted in prefecture i, Wit is the effect of
extreme weather, Pi is prefecture fixed effect whereas yeart is year fixed effect. The
data cover the same 267 prefectures for each year for the period from 1738 to 1910.
The results are reported in Table 7. To account for the time-varying reasons for the
adoption of maize among different prefectures, we control for the time trend at the
39
Conversion is based upon sources compiled by the Institute of Nutrition and Food Safety, Chinese
Center for Disease Control and Prevention (2002).
28
provincial level (Model 1) and the prefecture level (Models 2 and 3). Model 2 and 3
are more robust as they allow the time trend of the prefectures within the same
province to vary. In this case, the coefficient of the DID estimates indicates whether
the adoption of maize leads the grain price of the prefectures under study to deviate
from the time trend. In addition, we also seek to ascertain whether bad weather over a
sustained period may lead to higher grain prices and in turn induce the supply
response, we include the variable extreme weather in Model 3.
The negative coefficient of the maize adoption dummy shows that the
adoption of maize did indeed have the anticipated effect of lowering grain price.
Specifically, grain price was 3.4–3.6% lower after maize was adopted. Given that a
(hired) farm worker in the mid Qing period (circa 1740–1840) could afford to
purchase 200 kilograms of rice annually (Chen and Kung, 2012), the price difference
of 3.4–3.6% implies that a peasant family could purchase an additional 6.8 to 7.2
kilograms of rice per annum. The fact that average family size at the time was about 6
to 7 (Liang, 2008) meant that the lower grain price allowed a family to add another
member to it in less than 4.5 years. In Model 3, where extreme weather is included,
we find that it does have the expected effect of inflating grain price by a magnitude of
roughly 3%, but the negative coefficient of the maize adoption dummy remains
negative and significant (albeit with a smaller magnitude).
Table 7 about here
5. Population Growth without Economic Development
The estimates performed by Nunn and Qian (2011) find that in the Old World
the potato contributed not only to population growth but also to economic growth
(measured by urbanization rates). To see whether or not the adoption of maize in
China led to the same outcome we regress the change in economic development,
measured by either urbanization rates or per capita real wages on maize adoption.40
The data on China's urbanization are obtained from two sources. The first is Cao
40
Information on the regional distribution of income in China for the period of analysis is not available.
Hence, following Acemoglu, Johnson and Robinson (2002, 2005), we use the degree of urbanization to
proxy economic outcome, given that it is positively and significantly correlated with per capita income.
29
(2000), who meticulously estimates urbanization rates at the province level for the
years 1776 and 1893 using various scattered population sources from a variety of
local gazetteers—including province, prefecture and county. The other source comes
from the monumental survey of Christianity in China conducted by Milton Stauffer
(1922) between 1918 and 1921, which contains detailed population statistics on cities
of sizes larger than 25,000 for the year 1920; this allows us to compute the share of
urban population in the overall population as a measure of urbanization at the
provincial level.41 Data on real wages are compiled by Chen and Kung (2012), which
contains detailed real wage records for the period 1735 through 1842.
Cao's estimates suggest that overall urbanization rate or the share of urban
population in China's overall population had in fact dropped slightly from 7.4% in
1776 to 7.1% in 1893, whereas Stauffer's (1922) survey shows that overall
urbanization rate by the 1920s was a mere 4.3%.42 For the regressions, we employ all
three time points, namely 1776, 1893 (Cao, 2000) and 1910 (Stauffer, 1922).43 Per
capita real wages for the period 1740 through 1840 averaged about 5000 wen,44 which,
when translated into purchasing power amounted to approximately 200 kilograms of
rice, which barely enabled a peasant family to subsist. There was no distinct rise in
real wages throughout the entire period.
We report the pertinent regression results in Table 8. The dependent variables
are, respectively, urban population share in Models 1 and 2, and real wage per capita
in Model 3. Regardless of model choice (Columns 1 and 3 are the full model and
Column 2 is the model using the sample restricted to the north of the 33° north
latitude line), the negative effect of planting maize on economic development is
41
The reason we do not include Skinner (1977) here is because his data include only eight “macro-
regions” with no clear demarcation of provinces. As shown in Panel B of Appendix 3, however, his
data are significantly correlated with those of the other sources employed to check our data reliability.
42
Granted, these overall percentages must have masked the huge differences between regions. Two of
the most advanced provinces in China, Jiangsu and Zhejiang in the southeast, for instance, were far
more urbanized than the rest—14.3% in the case of Jiangsu and 13.7% in the case of Zhejiang (Cao,
2000; see also Li, 2000).
43
The population variable is for the year 1910 but the urbanization variable, based on Stauffer's (1922)
figure, is from 1920.
44
Wen is the currency unit of the Qing Dynasty.
30
confirmed: for each decade of maize planting the urbanization rate and real wages
drop by 0.07% and 0.04%, respectively.45
Our results differ from those of Nunn and Qian (2011), who find that the
potato’s introduction to the Old World contributed not only to population growth but
more importantly to economic growth as well. Given that China was an important part
of the Old World and that it accounted for a considerable proportion of the world’s
population growth in the 300 or more years leading up to the early 20th century,
clearly the positive welfare effect of the potato on the Old World was confined to
mainly Europe, which had the highest adoption rates of this particular crop. The
comparatively smaller magnitude of maize’s contribution to population growth in the
Chinese context may be explained by the fact that, unlike the potato in Europe, maize
did not replace rice and wheat as the main staple crop; it was essentially a crop to
which people resorted in times of hardship. Rather than maximizing nutritional and
caloric values, they were likely more concerned that the new crop would, while
capable of providing additional nutrition and calories, supplement their long
established tradition of a rice-wheat diet. Thus, unlike the Irish, for whom the switch
from a mix of oats and dairy products to the potato was a much more decisive break
(Connell, 1962; Cullen, 1968; Mokyr, 1981; Mokyr and Ó Gráda, 1984),46 for the
Chinese the potato and sweet potato were unlikely to replace rice and wheat as the
main staples (Cao, 2005a, 2005b). That urbanization rates and per capita real wages
were lower in areas where maize was grown more extensively may have been because
the crop was increasingly grown in newly settled areas and the people were relatively
adequately fed and so had weaker incentives to leave the farms.47 The above evidence
45
To ensure that our estimations are not contaminated by the lack of comparability in measurement
between Cao (2000) and Stauffer (1922) we replicate the same exercise using only Cao’s data and the
results (not shown here) are strikingly similar.
46
However, citing Schivelbusch (1992), Hersh and Voth (2009) observe that even in Europe consumers
remained skeptical of the appeal of the potato for a long time after its introduction, who “only ate it
when no other source of calories was available”, and that “potato consumption may not have improved
the quality of life by much” (p. 2).
47
It is also possible that maize has a “rural bias”; unlike the potato, which can be grown on small plots
in urban areas, maize is typically grown on marginal land in the hilly countryside. Hence the more a
county was cropped in maize the less progress in urbanization as a result. We thank Nathan Nunn for
this observation.
31
suggests that, unless we are concerned with the very long run—like from one million
B.C. to 1990 (Kremer, 1993), it is unlikely that modern economic growth would result
directly from population increases. Given that the relationship between the adoption
of maize and economic development is assessed at only the provincial level, the result
must be treated with great caution. Further, more conclusive evidence should be
obtained.
Table 8 about here
6. Concluding Remarks
The empirical claim of a causal link between the adoption of the potato—a
New World crop—and rapid population and economic growth in the Old World has
obscured a subtle historical fact, namely, that not everywhere in the Old World
benefited from this process equally. Although it was an important part of the Old
World, and a country that accounted for more than one-third of the world’s total
population increase between roughly the 16th and 20th centuries, China failed to catch
up with Europe (the other major part of the Old World) in terms of modern economic
growth or growth in per capita income. Indeed, the economic history of China is
replete with narratives of this great divergence.
By exploiting the variation in the timing of maize adoption across prefectures
in China between 1550 and 1910, we are able to establish a causal link between such
adoption and population growth using the interaction of the distance to the nearest
initial point of adoption and local weather conditions as the pertinent instrument. Our
empirical evidence suggests that the introduction of maize was responsible for a
significant proportion of China’s population increase in this period. More specifically,
one decade of maize planting is estimated to have increased population density by
around 3.3%. For the period as a whole, the conservative estimate of maize’s
contribution to population growth was 11.2%, but could be as high as 20.5%. Unlike
the potato, an originally New World crop that was adopted primarily in Europe and
found to have contributed not merely to population growth but, more importantly, also
to economic growth, our evidence, albeit limited, suggests that the same really cannot
be said for maize in China.
32
Focusing our study on China alone has a clear advantage—it avoids the
potential confounding effects of those other variables (institutions, non-agricultural
technology, trade and so forth) that simultaneously affected growth and population
outcomes. Thus it represents a “clean” case for examining the effect of the
introduction of a new agricultural technology on population and economic growth.
The China case that we examine suggests that different crops and the varying extent
of their adoption may have radically differing growth consequences.
Our speculation of this divergence in economic outcome between China and
Europe is premised on the reasoning that in Europe the potato effectively replaced a
variety of mixed cereals as the main staple crop, whereas in China maize and other
New World crops merely supplemented/augmented the people’s diet as and when rice
and wheat were in short supply. Additionally, the fact that maize was typically grown
on marginal lands (in particular the hilly areas) and that it exhibits a “rural bias” may
also have contributed to the negative relationship between maize planting and
urbanization. A more rigorous answer, however, can only be sought from further
research.
By providing the first empirical account of the causal relationship between the
adoption of maize and population expansion in late imperial China, our study is
consistent with the various corpuses of literature on China’s economic history whose
narratives tell us both of China’s failure to industrialize and of the great divergence
between Europe and Asia. Our findings are also in accordance with the claim that
human existence prior to 1800 was basically subject to the Malthusian trap and
demonstrate the singular importance of technological advancement in lifting those
(non-European) old civilizations out of the Malthusian quagmire.
33
References
Acemoglu, Daron, Johnson, Simon and Robinson A. James. 2001. “The Colonial
Origins of Comparative Development: An Empirical Investigation.” American
Economic Review. 91 (5): 1369-1401.
———. 2002. “Reversal of Fortune: Geography and Institutions in the Making of the
Modern World Income Distribution.” Quarterly Journal of Economics. 117 (4):
1231–94.
———. 2005. “The Rise of Europe: Atlantic Trade, Institutional Change and
Growth.” American Economic Review. 95: 546–79.
Aghion, Philippe and Howitt, Peter. 1992. “A Model of Growth through Creative
Destruction.” Econometrica. 60: 323–52.
Allen, Robert, Bassino, Jean-Pascal, Ma De-bin, Moll-Murata, Christine, and van
Zanden, Jan Luiten. 2011. “Wages, Prices, and Living Standards in China, Japan,
and Europe, 1738–1925.” Economic History Review. 64 (1): 8–38.
Alvin, Mark. 1973. The Pattern of the Chinese Past. London: Methuen Publishing Ltd.
Bai, Shouyi. 2003. A History of Chinese Muslims (Vol.2).(zhongguo huihui minzushi,
xiace) Beijing: Zhonghua Book Company.
Bai, Ying and Kung, James Kai-sing. “Climate Shocks and Sino-nomadic Conflict.”
The Review of Economics and Statistics. 93 (3): 970-981.
Becker, O. Sascha and Woessmann, Ludger. 2009. “Was Weber Wrong? A Human
Capital Theory of Protestant Economic History.” Quarterly Journal of Economics.
124 (2): 531–96
Boserup, Ester, 1965. The Conditions of Agricultural Growth: The Economics of
Agrarian Change under Population Pressure. London, UK: George Allen & Unwin
Ltd.
Bound, John, Jaeger, A. David, and Baker, M. Regina. 1995. “Problems with
Instrumental Variables Estimation When the Correlation between the Instruments
and the Endogeneous Explanatory Variable is Weak.” Journal of the American
Statistical Association. 90 (430): 443–50.
Bray, F. 1984. Science and Civilization in China, Volume 6; Biology and Biological
Technology. Part II: Agriculture. Cambridge: Cambridge University Press.
Buck L. John. 1937. Land Utilization in China. Chicago: University of Chicago Press.
Cao, Ling. 2005a. “Influence on Diet Structure of Chinese People by the Introduction
of American CerealCrops” (meizhouzuowu de chuanru dui woguo renmin
yinshishenghuo de yingxiang). Agricultural Archaeology (nongye kaogu). (3): 176–
81.
Cao, Ling. 2005b. “Influence of the Introduction of American Cereal Crops on
Agricultural Production and Social Economy of China.” (meizhouzuowu de
chuanru dui woguo nongyeshengchan he shehuijingji de yingxiang). Ancient and
Modern Agriculture (gujin nongye). 3: 78–88.
Cao, Shu-ji. 1988. “The Re-exploration of the Introduction of Maize and Sweet Potato
into China” (yumi he fanshu chuanru zhongguo luxian xintan). The Journal of
Chinese Social and Economic History (zhongguo shehui jingjishi yanjiu). 4: 62–74.
———. 1995. “Geographical Environment and Infectious Diseases in Song and Yuan
Dynasties” (dilihuanjing yu songyuanshidai de chuanranbing). Historical
Geography (lishi dili). 12: 183–92.
———. 1997. “Plague Prevalence and Social Change of North China: 1580–1644”
(huanjing yu ren: shuyi liuxing yu huabei shehui bianqian, 1580-1644). Historical
Research (lishi yanjiu). (1): 17–32.
34
———. 2000. History of Population in China, Volume 5. Shanghai: Fudan University
Press.
Chen, Shuo and Kung, James Kai-sing. 2012. “Peasant Rebellions as 'Positive Check':
Empirical Evidence from Qing China.” HKUST Working Paper.
Chinese Academy of Sciences (Research Institute of Geography).1980. General
Instruction of Agricultural Geography in China (Zhongguo nongye dili zonglun).
Beijing: China Science Press.
Chinese Academy of Meteorological Science. 1981. Yearly Charts of
Dryness/Wetness in China for the Last 500 years Period (zhongguo jin wubainian
hanlao fenbu tuji). Beijing: SinoMap Press.
Clark, Gregory. 2007. A Farewell to Alms: A Brief Economic History of the World.
Princeton: Princeton University Press.
Connell, K.H. 1962. “The Potato in Ireland.” Past and Present. 23: 57–71.
Crosby, W. Alfred. 1989. “Reassessing 1492.” American Quarterly. 41(4): 661–69.
Cullen, L. M. 1968. “Irish history without the potato.” Past and Present. 40: 72–83.
Dittmar, J. 2011. “Information Technology and Economic Change: The Impact of the
Printing Press.” The Quarterly Journal of Economics. 126 (3): 1133-1172.
Elvin, Mark. 1973. The Pattern of the Chinese Past. Chicago: Stanford University
Press.
Engerman, L. Stanley and Sokoloff, L. Kenneth. 1997. “Factor Endowments,
Institutions, and Differential Paths of Growth among New World Economies: A
View from Economic Historians of the United States.” In Stephen Haber edt. How
Latin America Fell Behind. Stanford: Stanford University Press, 260-304.
FAO (Statistics Division, Food and Agriculture Organization of the United Nations).
2010. FAOSTAT at http://faostat.fao.org/.
Gaez (FAO’s Global AgroEcological Zones). 2002.
http://fao.org/Ag/AGL/agll/gaez/index.htm.
Gallup, L. John and Sachs, D. Jeffrey. 1998. “The Economic Burden of Malaria.”
Unpublished manuscript, Harvard Center for International Development.
Galor, Oded. 2005. “From Stagnation to Growth: Unified Growth theory.” In Aghion,
P. and Durlauf, S. N. (eds.), Handbook of Economics Growth (Vol. 1A).
Amsterdam: Elsevier Science, North-Holland.
_____. 2010. The 2008 Lawrence R. Klein Lecture: “Comparative Economics
Development: Insights from Unified Growth Theory, International Economics.”
Providence, RI: Brown University.
_____. 2011. Unified Growth Theory. Princeton: Princeton University Press.
Galor, Oded, and Andrew Mountford. 2008. “Trading Population for Productivity.”
Review of Economic Studies. 75: 1143–79.
Galor, Oded, and Weil, N. David. 2000. “Population, Technology, and Growth: From
Malthusian Stagnation to the Demographic Transition and Beyond.” American
Economic Review. 90 (4): 806–28.
Gao, Xue-yuan. 2003. “On the Introduction of Sweet Potato in China” (fanshu
chuanru zhongguo sibaiyishi zhounian ji). Grain Issues (liangshi wenti). 4: 58–58.
Goldstone, A. Jack. 2003. “Comparative Historical Analysis and Knowledge
Accumulation in the Study of Revolutions,” in Dietrich Reuschemeyer and James
Mahoney, eds. Comparative Historical Analysis. Cambridge: Cambridge
University Press.
Gottschang, Thomas and Lary, Diana. 2000. Swallows and Settlers: The Great
Migration from North China to Manchuria. Michigan: The University of Michigan.
35
Griliches, Zvi and Hausman, A. Jerry. 1986. “Errors in Variables in Panel Data.”
Journal of Econometrics. 31 (1): 93–118.
Grossman, Gene and Helpman, Elhanan. 1991. Innovation and Growth in the Global
Economy. Cambridge: MIT Press.
Gruber, Jon and Saez, Emmanuel. 2002. “The Elasticity of Taxable Income: Evidence
and Implications.” Journal of Public Economics. 84 (1): 1–32.
Hall, E. Robert and Jones, Charles. 1999. “Why Do Some Countries Produce So
Much More Output Per Worker Than Others?” Quarterly Journal of Economics.
114 (1): 83–116.
Hersh, J. and H. Voth. 2009. “Sweet Diversity: Overseas Trade and Gains from
Variety after 1492.” CEPR Discussion Paper No. DP7386.
Ho Ping-ti. 1959. Studies on the Population of China, 1368–1953. Cambridge:
Harvard University Press.
_____. 1979. “The Introduction and Spread of New World Crops and Its Impact on
Grain Production in China” (beimei zuowu de yinjin chuanbo jidui zhongguo
liangshi shengchan de yingxiang). World Agriculture (shijie nongye). (4): 34–41.
Huang, P. 1990. The Peasant Family and Rural Development in the Yangzi Delta,
1350-1988. Stanford: Stanford University Press.
Institute of Economics, Chinese Academy of Social Science. 2010. Grain Prices Data
during Daoguang to Xuantong of the Qing Dynasty. Guilin: Guangxi Normal
University Press.
Institute of Nutrition and Food Safety, Chinese Center for Disease Control and
Prevention. 2002. China Food Composition. Beijing: Peking University Medical
Press.
Jia, Rui-xue. 2011. “Weather Shocks, Sweet Potatoes and Peasant Revolts in
Historical China.” IIES Working Paper, Stockholm University.
Jin, Enhui. 1996. Chinese Local Histories: A Comprehensive Annotated Catalog
(Zhong guo difangzhi zongmu tiyao). Taipei: Hanmei Tushu Ltd.
Kremer, Michael. 1993. “Population Growth and Technological Change: One Million
B.C. to 1990. ” Quarterly Journal of Economics. 108 (3): 681–716.
Kung Kai-sing, James and Nan Li. 2011. “Commercialization as Exogenous Shocks:
The Effect of the Soybean Trade and Migration in Manchurian Villages, 1895–
1934” Explorations in Economic History. 48 (4): 568–89.
La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert Vishny.
1997. “Legal Determinants of External Finance.” Journal of Finance. 52: 1131–50.
_____. 1998. “Law and finance.” Journal of Political Economy. 106: 1113–55.
Lan, Yong. 2002. Chinese Historical Geography (zhongguo lishi dili xue). Beijing:
Higher Education Press.
Landes, S. David. 2006. “Why Europe and the West? Why Not China?” Journal of
Economic Perspectives. 20 (2): 3–22.
Lee, James. 1982. “Food Supply and Population Growth in Southwest China, 1250–
1850.” Journal of Asian Studies. 41: 711–46.
Lee, James and Wang, Feng, 1999. One Quarter of Humanity: Malthusian Mythology
and Chinese Realities, 1700–2000. Cambridge: Harvard University Press.
Li, Bo-zhong. 2000. Early Industrialization in the Yangzi Delta, 1550–1850 (jiangnan
de zaoqi gongyehua, 1550-1850). Beijing: Social Sciences Academic Press.
Li, Ling-fu. 1994. “The Spatial Feature of Crops in Shangdong Province in Ming and
Qing ” (Mingqing Shandong liangshi zuowujiegou de shikongtezheng). Journal of
Chinese Historical Geography. 1: 159–74.
36
Li, Tian-xi. 1998. “Exploration on the Role of Oversea Chinese in Introducing Sweet
Potato into China.” (Huaqiao yinjin fanshu xinkao). Journal of Agricultural History
of Chinese. 1: 107-11.
Liang, Fang-zhong. 2008. Historical Statistics on Hukou, Land and Land Tax of
China (Lidai hukou, tudi,tianfu tongji). Beijing: Zhonghua Book Company.
Liu, P. K. and Hwang, Kuo-shu. 1977. “Population Change and Economic
Development in Mainland China since 1400.” In Hou, Chi-ming and Yu, Tzongshian (eds.), Modern Chinese Economic History: Proceedings of the Conference on
Modern Chinese Economic History. Taipei, Taiwan: Academia Sinica.
Lucas, R.E. 2002. The Industrial Revolution: Past and Future. Cambridge: Harvard
University Press.
Maddison, Angus. 2001. The World Economy: A Millennial Perspective. OECD.
McArthur, W. John and Sachs, D. Jeffrey. 2001. “Institutions and Geography:
Comment on Acemoglu, Johnson and Robinson (2000).” NBER, Working Paper
No. 8114.
McNeil, William. 1976. Plague and People. New York: Anchor Books.
“Military History of China” Writing Group. Chronology of Warfare in Dynastic
China (Zhongguo Lidai Zhanzheng Nianbiao). Beijing: China PRC Press.
Mokyr, Joel. 1981. “Irish History with the Potato.” Irish Economic and Social History,
8: 8–29.
Mokyr, Joel and Ó Gráda, Cormac. 1984. “New Developments in Irish Population
History, 1700-1850.” Economic History Review. 37(4): 473–88.
Nunn, Nathan and Qian, Nancy. 2010. “The Columbian Exchange: A History of
Disease, Food, and Ideas.” The Journal of Economic Perspectives. 24 (2): 163–88.
Nunn, Nathan and Qian, Nancy. 2011. “The Impact of Potatoes on Old World
Population and Urbanization.” The Quarterly Journal of Economics. 126 (2): 593–
650.
O’Rourke, H. Kevin and Williamson, G. Jeffrey. 2002. “After Columbus: Explaining
Europe’s Overseas Trade Boom, 1500–1800.” Journal of Economic History. 62 (2):
417–56.
Peng, Kai-qiang. 2006. Grain Price since Qing Dynasty, a Interpretation and Reinterpretation from Historical Perspective. (qingdai yilai de liangjia: lishixue de
jieshi yu zaijieshi). Shanghai: Shanghai People Press.
Perkins, H. Dwight. 1969. Agricultural Development in China 1368–1968. Edinburgh:
Edinburgh University Press.
Pomeranz, K. 2000. The Great Divergence: China, Europe, and the Making of the
Modern World Economy. Princeton: Princeton University Press.
Pritchett, Lant. 1997. “Divergence, Big Times.” Journal of Economic Perspectives. 11
(3): 3–17.
Schivelbusch, Wolfgang. 1992. Tastes of Paradise: A Social History of Spices,
Stimulants, and Intoxicants. Trans. David Jacobson NY: Vintage.
Shiva, Vandana. 1991. The Violence of the Green Revolution: Third World
Agriculture, Ecology, and Politics. New York: Zed Books.
Skinner, G. William. 1977. The City in Late Imperial China. Stanford. Stanford:
Stanford University Press.
Song, Junling. 2007. “Studies on Spreading and Growthing and Influence of Crops
Originated in American during Ming and Qing Dynasties: Focusing on Maize,
Sweet Potato and Tobacco.” PhD Thesis, Henan University.
Song Ying-xing. 1637. Exploitation of the Works of Nature (tiangong kaifu).
37
Song, Zhenghai. 1992. The Collection of Natural Disasters and Unusual Years in
Historical China (zhongguo gudai zhongda ziranzaihai he yichangnianbiao zongji)
Guangzhou, Guangdong: Education Press.
Staiger, Douglas and Stock, H. James. 1997. “Instrumental Variables Regression with
Weak Instruments.” Econometrica. 65: 557–86.
Stauffer, Milton T. 1922. The Christian Occupation of China: A General Survey of
the Numerical Strength and Geographical Distribution of the Christian Forces in
China. Shanghai: China Continuation Committee.
Stock, J. H., and Yogo, M. 2005. “Testing for Weak Instruments in Linear IV
Regression.” In Andrews, D. W. K. and Stock, J. H. (eds.), Identification and
Inference for Econometric Models: Essays in Honor of Thomas Rothenberg, pp.
80–108. New York: Cambridge University Press.
Sun, Jia-shan. 1984. “The Agriculture and Agricultural Book in Ming Dynasty and
Qing Dynasty” (Mingqing liang dai de nongye yu nongshu). In History of Science
in China (zhongguo kexueshi), pp. 91–94.
Tong, Bing-ya. 2000. History of Maize Science and Technology of China (zhongguo
yumi kejishi). Beijing: China Agricultural Technology Press.
Tsai, Henry Shih-shan. 2001. Perpetual Happiness: The Ming Emperor Yongle.
Seattle: University of Washington Press.
Voigtländer Nico and Voth Hans-Joachim. 2006. “Why England? Demographic
Factors, Structural Change and Physical Capital Accumulation during the Industrial
Revolution.” Journal of Economic Growth. 11 (4): 319–61.
Wang, Bao-qing. 2006. “Influences of the Introduction and Extension of American
Crops on Economic and Social Development in China since the Ming and Qing
Dynasties—Taking Shandong as an Example (1368–1949)” (mingqing yilai
beimeizuowu de yinzhongtuiguang dui jingjishehui fazhan de yingxiang: yi
shandong weili, 1368–1949). Agricultural History of China (zhongguo nongshi). 25
(3): 48–61.
Wang, Qi-zhu. 1994. The Origin and Development of Agriculture in China: Primary
Exploration of Agricultural History (Volume 2) (zhongguo nongye qiyuan yu
fazhan: zhongguo nongyeshi chutan). Taipei: National Translate and Edit Bureau
Press.
Wang Yeh-chien, 1973. Land Taxation in Imperial China, 1750–1911. Cambridge:
Harvard University Press.
Warman, Arturo. 2003. Corn and Capitalism: How A Botanical Bastard Grew to
Global Dominance. Translated by Nancy L. Westrate. Chapel Hill: The University
of North Carolina Press.
Wu Qi-jun. 1847. Research on Reality and Titles of Plants (zhiwu mingshi tukao).
Xu, Guang-qi. 1639. Complete Treatise on Agricultural Administration (nongzheng
quanshu).
Yang, Zi-hui. 1995. China historical data and the Relevant Studies (zhongguo lishi
renkou tongji ziliao yanjiu). Beijing: China Reform Publishing House.
Zhou, Bang-jun. 2007. “The Spread of Maize in Sichuan Province and the Related
Issues” (yumi zai qingdai Sichuan de chuanbo jiqi xiangguan wenti). Ancient and
Modern Agriculture (gujin nongye). (4).
Zhao, Gang. 1985. The Study of Crop Acre Yield in Qing Dynasty (qingdai liangshi
muchanliang yanjiu). Beijing: Chinese Agricultural Press.
Zhao, Wen-lin and Xie, Shu-jun. 1988. The History of Chinese Population (zhongguo
renkou shi). Beijing: People’s Press.
38
Zhang, Pi-xian. 1996. Change in Chinese Historical Climate. (zhongguo lishi qihou)
Jinan: Shandong Science and Technology Press.
Zhang, Xiangwen and Hui, Fuping. 2007. “An Analysis on the Motivation for Maize
Planting in Mountainous Regions in Middle-late Qing” (qingdai zhongwanqi
shandi guangzhong yumi zhi dongyi). Journal of Historical Science (shixue yuekan).
10: 34–39.
39
Figure 1. Population in China and the World
0
Population Size (millions)
500
1000
1500
Population in China and the World, 980-1910
950
1150
1350
Year
Chinese Population
1550
1750
1950
World Population
Source: Cao (2001); Maddison (2001)
40
Figure 2. Share of Soil Suitability for Various New World Crops
A. Share of Soil Suitability for Maize
B. Share of Soil Suitability for Potato
C. Share of Soil Suitability for Sweet Potato
Source: GAEZ (2002).
41
Figure 3. The Diffusion of Maize in China
1600
1750
1650
1800
1700
1850
1900
42
Figure 4. Sample Prefectures of China
Note: Map of China in 1820
Source: “CHGIS, Version 4” Cambridge: Harvard Yenching Institute, January 2007.
43
Figure 5: The Effects of Maize Planting on Population Density, 1776-1910
.03
The effect of maize planting in five periods
0.029***
0.026***
.015
coefficient
.02
.025
0.028***
0.015***
1776
1820
1851
1880
0.014***
1910
Note: The coefficients are extracted from the regression based on Model 2 of Table 5, in which we
regress population density of each of the five time points on maize planting.
44
0
Grain Price (1900 USD/10,000 Kilocalorie)
.01
.02
.03
.04
.05
Figure 6: Grain Price from 1738 to 1910
1740
1760
1780
1800
1820
1840
1860
1880
1900
1920
Note: Data on grain price is obtained from “Qing Dynasty’s Price of Food Database,” Institute of
Modern History, the Academia Sinica, Taiwan (http://140.109.152.38/DBIntro.asp), and “Grain Prices
Data during Daoguang to Xuantong of the Qing Dynasty”, Institute of Economics, Chinese Academy
of Social Science (2010). The data were originally kept by the Qing government. Local officials
reported grain prices to the central government each month. Given that cropping patterns were different
across regions, to ensure comparability we convert one dan of grain (of various kinds) into the
standardized kilocalories. Conversion is based upon sources compiled by the Institute of Nutrition and
Food Safety, Chinese Center for Disease Control and Prevention (2002). The dan is the unit of weight
employed at the time. Each dan equals 83.5 kg. The standard calories of various crops are obtained
from Yang et al. (2002). We then calculate the yearly average price. Finally, we adjust the price
according to purchasing power parity, which is 1,900 USD. The deflator is obtained from Peng (2006).
45
Table 1. Summary of Descriptive Statistics
Population density (person/km2)
Maize planting duration (decade)
Average weather index (1: drought; 5: flood)
Minimum distance to point of adoption (ln)
Share of extreme drought (%)
Share of extreme flood (%)
Population density in 1550 (household/km2)
Year of earliest Local Gazetteers available
Frequency of war
Frequency of epidemic
Data
Sources
A
B
C
D
C
C
E
F
G
H
Observations
1,330
1,310
1,322
1,330
1,335
1,335
1,055
1,335
1,335
90
Mean
124.979
9.522
2.898
10.592
7.5
6.8
4.966
1,589
0.943
0.375
Standard
Deviation
127.305
9.272
0.286
17.003
7.6
6.8
11.978
194.468
2.077
0.559
A: Cao, Shuji. 2000. History of Population in China (zhongguo renkou shi). Volume 5. Shanghai:
Fudan University Press.
B: Various Local Gazetteers
C: Chinese Academy of Meteorological Science. 1981. Yearly Charts of Dryness/Wetness in China for
the Last 500 years Period (zhongguo jin wubainian hanlao fenbu tuji). Beijing: SinoMap Press
D: “CHGIS, Version 4” Cambridge: Harvard Yenching Institute, January 2007
E: Liang Fangzhong. 2008. Historical Statistics on Hukou, Land and Land Tax of China (Lidai hukou,
tudi,tianfu tongji). Beijing: Zhonghua Book Company.
F: Jin, Enhui. 1996. Chinese Local Histories: A Comprehensive Annotated Catalog (Zhong guo
difangzhi zongmu tiyao). Taipei: Hanmei Tushu Ltd.
G: “Military History of China” Writing Group. Chronology of Warfare in Dynastic China (Zhongguo
Lidai Zhanzheng Nianbiao). Beijing: China PRC Press.
H: Song, Zhenghai. 1992. The Collection of Natural Disasters and Unusual Years in Historical China
(zhongguo gudai zhongda ziranzaihai he yichangnianbiao zongji) Guangzhou, Guangdong:
Education Press.
46
Table 2. Simple Comparison between Non-adoption Areas and Adoption Areas
Non-adoption areas
Population
density (ln) in
1776
(adoption)(non-adoption)
Adoption areas
mean
3.925
S.D.
1.071
Obs.
118
mean
4.321
S.D.
1.185
Obs.
148
1820
3.927
1.124
72
4.532
1.069
194
1851
3.959
1.332
40
4.591
1.036
226
1880
3.579
1.352
25
4.326
1.072
241
1910
N
N
0
4.446
1.086
266
Total
3.897
1.156
255
4.448
1.087
1075
mean
0.396***
(0.14)
0.605***
(0.149)
0.632***
(0.186)
0.746***
(0.231)
0.551***
(0.077)
Note: *, **, and *** denote significance at the 90%, 95%, and 99% levels respectively.
47
Table 3. Maize Planting and Population Density (OLS)
Dependent Variable: Population Density (ln)
Adoption dummy
FE
FE
FE
(1)
(2)
(3)
0.015***
(0.002)
0.015***
(0.002)
0.0009
(0.0021)
-0.005**
(0.002)
4.265***
(0.030)
Yes
1,330
0.914
0.226***
(0.033)
Duration
Share of extreme drought (%)
Share of extreme flood (%)
Constant
Prefectural fixed effects
Observations
R-squared
4.160***
(0.028)
Yes
1,330
0.920
4.241***
(0.025)
Yes
1,330
0.912
Note: *, **, and *** denote significance at the 90%, 95%, and 99% levels respectively.
48
Table 4. Weather, Distance and Maize Planting (First-Stage of 2SLS)
Dependent Variable: Duration of Maize Planting (by Decade)
Variable
Weather*distance
Weather
Adoption Dummy
Duration
Duration
Probit
FE
FE
(1)
-0.071***
(0.027)
-0.027
(0.036)
(2)
-0.508***
(0.087)
-0.239**
(0.104)
(3)
-0.504***
(0.088)
-0.242**
(0.104)
-0.002
(0.003)
0.003
(0.003)
19.046***
(1.638)
Yes
1302
0.819
Share of extreme drought (%)
Share of extreme flood (%)
Constant
Prefectural fixed effects
Observations
R-squared
2.670
(0.527)
No
1,322
19.151***
(1.627)
Yes
1,302
0.818
Note: *, **, and *** denote significance at the 90%, 95%, and 99% levels respectively.
49
Table 5. Maize and Population Density (IV-2SLS)
Dependent Variable: Population Density (ln)
Explanatory
variable
Adoption dummy
Share of extreme drought (%)
Share of extreme flood (%)
Frequency of war
Epidemics
FE
FE
FE
FE
(1)
(2)
(3)
(4)
1.262***
(0.441)
Duration
Weather
War
0.013
(0.011)
0.056***
(0.015)
0.028***
(0.010)
0.041***
(0.012)
0.024***
(0.009)
0.002
(0.002)
-0.006**
(0.002)
-0.369***
(0.056)
0.055*
(0.033)
-0.010
(0.014)
0.0004
(0.0004)
-0.0010**
(0.0004)
Frequency of epidemic
-0.516**
(0.212)
Constant
3.307***
3.797***
4.019***
4.963***
(0.354)
(0.156)
(0.123)
(0.402)
Prefectural/provincial fixed effects
Yes
Yes
Yes
Yes
Observations
1,322
1,302
1,302
90
R-squared
0.854
0.887
0.909
0.889
Note: *, **, and *** denote significance at the 90%, 95%, and 99% levels respectively.
50
Table 6. Maize and Population Growth (Robustness Check, IV-2SLS)
Dependent Variable: Population Density (ln)
Soil Suitability for Maize (ln)
*Post Adoption Dummy
Duration
Share of extreme drought (%)
Share of extreme flood (%)
Sample
restricted to
the north of
33° latitude
(north)
Prefectural
cross
sectional data
Provincial
data
Nunn and Qian
(2011)
(1)
(2)
(3)
(4)
0.018**
(0.009)
0.033**
(0.017)
0.001
(0.004)
-0.007
(0.005)
0.035**
(0.014)
-0.0001
(0.0002)
-0.0004***
(0.0001)
Frequency of war
Constant
Prefectural/provincial fixed effects
Observations
R-squared
4.552***
4.455***
(0.346)
(0.171)
Yes
No
436
262
0.879
0.871
0.049*
(0.029)
0.0003
(0.0003)
-0.0010**
(0.0004)
-0.559***
(0.201)
5.332***
(0.338)
Yes
90
0.901
Yes
1350
0.728
Note: In Model 4, controls for year fixed effect, prefecture fixed effect and prefecture-specific time
trends are added. *, **, and *** denote significance at the 90%, 95%, and 99% levels respectively.
51
Table 7. Maize Introduction and Grain Price (Difference-in-Difference)
Dependent Variable: Grain Price (ln)
(1)
-0.034**
(0.005)
(2)
-0.036**
(0.014)
(3)
Maize Adoption Dummy
-0.030**
(0.016)
Extreme Weather
0.030***
(0.003)
Constant
-1.950***
-1.987***
-0.424*
(0.204)
(0.195)
(0.220)
Year Fixed Effect
Yes
Yes
Yes
Yes
Yes
Yes
Prefecture Fixed effect
Province-specific Time Trend
Yes
No
No
Prefecture-specific Time Trend
No
Yes
Yes
Observations
40107
40107
32121
R-squared
0.16
0.22
0.42
Note: *, **, and *** denote significance at the 90%, 95%, and 99% levels respectively.
52
Table 8. Maize Planting and Economic Development (IV-2SLS)
Urban population
Urban population
Real Wage (ln)
Share (%)
Share (%)
(1)
(2)
(3)
Duration
-0.071*
-0.092*
-0.038*
(0.039)
(0.048)
(0.021)
Constant
7.692***
8.255***
8.442***
(0.403)
(0.578)
(0.176)
Prefecture fixed effects
Yes
Yes
No
Observations
770
262
645
Note: Models 1 and 3 employ the full sample; Model 2 uses the sample restricted to the north of the
33° north latitude line.*, **, and *** denote significance at the 90%, 95%, and 99% levels respectively.
Dependent Variable:
53
Appendix 1: The Global Expansion of Maize
Russia
about 1650
Spain
Mexico
1492
1560
China
Mecca
about 1520
Congo
1634
1572
India
1563
Philippines
about 1550
Java
about 1700
1601
Australia
54
Appendix 2. The Timing of Maize Adoption (Earliest and Latest) Among
Prefectures
Province
Zhili
Jiangsu
Anhui
Zhejiang
Jiangxi
Fujian
Henan
Shandong
Shanxi
Hubei
Hunan
Shaanxi
Gansu
Sichuan
Guangdong
Guangxi
Yunnan
Guizhou
Earliest Adoption Year
1670
1558
1511
1609
1673
1575
1535
1600
1612
1669
1733
1597
1522
1686
1579
1733
1563
1722
Latest Adoption Year
Time Difference
1890
1879
1820
1800
1872
1850
1835
1890
1900
1800
1852
1865
1766
1900
1900
1900
1890
1890
220
321
309
191
199
275
300
290
288
131
119
268
244
214
321
167
327
168
55
Appendix 3. Correlation Matrices of Population Sources
Panel A: Population Density
Cao (2000)
Liang (2008)
Yang (1995)
Ho (1959)
Skinner (1977)
Zhao and Xie (1988)
Cao
(2000)
Liang
(2008)
Yang
(1995)
Ho
(1959)
Skinner
(1977)
1
0.887***
0.684***
0.864***
0.782***
0.821***
1
0.596**
0.970***
0.876***
0.825***
1
0.623***
0.592**
0.758***
1
0.901***
0.743***
1
0.678***
Stauffer
(1922)
Skinner
(1977)
1
0.729***
1
Panel B: Urban Population Share (%)
Cao
(2000)
Cao (2000) 1
Stauffer (1922) 0.875***
Skinner (1977) 0.706***
Zhao and
Xie (1988)
1
Note: *, **, and *** denote significance at the 90%, 95%, and 99% levels respectively.
56