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Of maize and men: the effect of a New World crop on population and economic growth in China


We examine the question of whether China was trapped within a Malthusian regime at a time when Western Europe had all but emerged from it. By applying a difference-in-differences analysis to maize adoption in China from 1600 to 1910, we find that cultivation of this New World crop failed to raise per capita income. While maize accounted for a nearly 19 % increase in the Chinese population during 1776–1910, its effect on urbanization and real wages was not pronounced. Our results are robust to different sample selection procedures, to the control of variables pertinent to Malthusian “positive checks”, to different measures of economic growth and to data modifications. Our study thus provides rich empirical support to the claim that under the conditions in eighteenth- and nineteenth-century China, new agricultural technologies led to the Malthusian outcome of population growth without wage increases and urbanization.

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  1. 1.

    While the “quality-quantity tradeoff” has often been associated with Europe’s escape from the Malthusian regime (Clark 2007; Galor and Weil 2000; Galor 2011; Lucas 2002), the fact that it did not occur until the mid-nineteenth century, whereas the divergence had already begun in 1700 when Western Europe had higher per capita incomes than China (Broadberry and Gupta 2006; see also Jones 1981; Landes 2006), suggests that explanation for the divergence lies in the period before 1800.

  2. 2.

    While sweet potato was the other popular New World crop China adopted, data on its diffusion are available only at the provincial level (see Jia 2014).

  3. 3.

    However, China’s failure to industrialize at an important historical juncture was not the result of a small and dwindling agricultural surplus being consumed entirely by a growing population (e.g., Elvin 1973; Perkins 1969); rather, growing population pressure represented more of an endogenous outcome of a rise in agricultural productivity, thanks—at least in part—to the adoption of maize.

  4. 4.

    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 Qijun. It did not appear, for example, in the Complete Treatise on Agricultural Affairs (Nongzheng Quanshu)—the first agricultural encyclopedia ever published in China in the seventeenth century (Xu 1639).

  5. 5.

    The Chinese consider the potato bland in taste (Ho 1959; Perkins 1969).

  6. 6.

    The GAEZ database provides an index ranging from 0 (very suitable) to 8 (very unsuitable) on the suitability of all major staple crops cultivated in China. For each prefecture, we take the index of maize to be our measure of land suitability. See Nunn and Qian (2011) for an application, and in the Chinese context Kung and Ma (2014).

  7. 7.

    Compared to sweet potato maize is more resistant to cold weather, which thus also favored its diffusion in North China (Zhang and Hui 2007).

  8. 8.

    Gansu Province is located west of Henan Province.

  9. 9.

    These southern provinces are Sichuan, Hunan, Hubei, Guizhou, Guangxi, Zhejiang, and Jiangxi (Guo 1986). These trends are thus highly consistent with Ho’s (1959) observations that by the eighteenth century “[t]he hills and mountains along other tributaries of the Yangzi [Yangtze] were likewise turned into maize fields” (p. 185), and that by 1750 maize could be found planted in roughly half (approximately 47 %) of the prefectures in which it would eventually be adopted.

  10. 10.

    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 the three northeastern Manchurian provinces, which, while especially suited for cropping maize, did not do so until the late nineteenth century, upon the Qing government’s eventual removal of the erstwhile restrictions placed upon the ethnic Han to migrate and live there (Gottschang and Lary 2000; Kung and Li 2011). These provinces are not included in our analysis.

  11. 11.

    By the end of the Qing Dynasty (around the 1900s), maize was established as the most popular staple of the Chinese after only rice and wheat—the traditional staples for thousands of years (Zhou 2007; see also Wang 1994). Not only was maize 5 to 15 times more productive (around 180 jin or 90 kg per Chinese mu of land; one mu is equivalent to 0.067 hectare or 0.16 acre) than other “mixed” cereals in China such as barley and sorghum (Perkins 1969), of the three New World crops it also provided the most calories (106 kcal/100 g compared to 99 for sweet potato and 76 for the potato).

  12. 12.

    Maize consumption varied from region to region. 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, p. 67; see also Ho 1959, p. 185).

  13. 13.

    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.

  14. 14.

    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.

  15. 15.

    A prefecture is equivalent to the administrative level between a province and a county in today’s China.

  16. 16.

    Except for Cao (2000), who provides population data at the prefecture level, and Gu (1998), who provides county-level population data circa 1600, all other sources are at the province level. For example, in addition to constructing a series of county population estimates based on Gu (1998), Liang (1980) also provides population estimates for 12 years after 1600 (beginning from 1661 and ending in 1912) but at the province level, which we adopt here for comparison. By the same token, we disaggregate Skinner’s (1977) data on what he refers to as “macro-regions” into provinces—with those belonging to the same region sharing the same population density (e.g., Zhejiang, Jiangsu, and Anhui all belong to the Yangtze region). Doing so enables us to compare the population data of various sources at the province level.

  17. 17.

    The provinces had published 368 volumes; the counties 6777. Together with the 1119 published by the prefectures the total amounts to 8264.

  18. 18.

    Typically, a gazetteer contains the following sections: a general introduction to the prefecture, its geography, population figures and culture, local dignitaries, local produce, major (historic) events, and so forth.

  19. 19.

    To be sure there are altogether 20 prefectures that had either not published their own gazetteers or failed to keep them. To overcome this issue, we looked for gazetteers 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. Also, to ensure that our estimations are robust, we re-estimated column 3 (our baseline regression) of Table 3 by dropping these 20 prefectures and obtained similar results (hence not separately reported).

  20. 20.

    These four prefectures are Weihui and Shanzhou of Henan Province, Jining of Shandong Province, and Jingdongting of Yunnan Province.

  21. 21.

    Estimates of the casualty range from 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).

  22. 22.

    Although we have information on the annual incidence of warfare for each prefecture, we are constrained by the limited data we have on our dependent variable. Given this limitation, we can only employ the average frequency of war between the six time points for which we have information on population density, in our regressions. For example, altogether eight wars were fought between 1851 and 1880 in Caozhou fu, a prefecture located in the southwest Shandong Province. The average war incidence is thus 0.27 wars per year (8 wars in 30 years).

  23. 23.

    Although we have controlled for period fixed effects in our regressions, concern about systematic bias between the two data sources nonetheless obligates us to employ only Cao’s data to re-estimate Tables 3 and 4 for the shorter period of 1776–1900, for which we obtained broadly similar results (hence not separately reported).

  24. 24.

    The p values of the bootstrap t-statistics of 500 and 1,000 times are respectively 0.047 and 0.048.

  25. 25.

    Conley (1999) standard errors adjust for potential spatial interdependence of observations. Typically, spatial independence is assumed to decrease in the distance between two observations and, considering the fact that the prefecture is not a very large spatial unit, there is complete independence for prefectures that are two degrees apart. But we also tried other cutoff values (1, 3, 4 and 5 degrees) and the results stayed the same (Table A4 in the Online Appendix).

  26. 26.

    As a special form of Generalized Method of Moments (GMM) for models with spatially interdependent variables, this approach uses exogenous factors and their spatial lags as instruments for the endogenous regressor of maize adoption. The estimators of GS2SLS are considered to be consistent and asymptotically normal (Kelejian and Prucha 2004), and are not subject to the influence of the “omitted common factors” in the spatial interdependence (Das et al. 2003; Kelejian et al. 2013).

  27. 27.

    Given that maize adoption did not begin in earnest until 1700 and that population explosion in China occurred around the mid-1700s (not to mention that the population data on 1600 are based on a different source), it makes better sense to calculate maize’s contribution to population growth for the shorter period of 1776–1910.

  28. 28.

    In 1776, 51.8 % of our prefectures adopted maize. The corresponding magnitude was 98.5 % in 1910. 0.467 is thus the rate of change in maize adoption between 1776 and 1910.

  29. 29.

    As maize accounted for just 5 % of the total crop acreage during 1914–1918 (Perkins 1969), it raises the concern of whether this New World crop was in fact able to support a substantial 18.77 % increase in population. Based on the assumptions that the acreage sown with maize had been doubled during 1776–1910 from 31.4 to 62.8 million Chinese mu and with the estimated yield of 130 catties per mu, China should be able to produce approximately 4,095 million metric tons of maize. Whether or not this would be sufficient to support an 18.77 % growth in the overall population can be gauged from the following back-of-the-envelope calculation. We know that population had increased by over 100 million people between 1776 and 1910 (104.88 million to be exact, Lin 1996), so 18.77 % would be 19.68 million. Given the life expectancy of 33 or 12,045 days back then (Lin 1996), a male adult required a daily caloric intake of 1,900 calories for work and survival, averaging 22.9 million calories for each person in his lifetime (Liu and Hwang 1977). For a total of 19.68 million people, the amount of calories required would be 450, 557 billion. Given that 100 grams of maize could yield 106 calories, 4,096 million metric tons of maize would translate into 434,006 billion calories—a shortfall of just 3.67 %.

  30. 30.

    As mentioned earlier, up to 55 % of the output increase (of roughly 55 million tons of grain) over this lengthy period of time came from the expansion in cropping acreage (Perkins 1969).

  31. 31.

    The crop cycle of early-ripening rice is approximately less than 80 days. Add another 20 days for the normal cycle and another 40 days for the late-ripening variety.

  32. 32.

    The baseline result, which represents the average treatment effect, should lie somewhere in between the two.

  33. 33.

    As omitted variables, war and epidemics may conceivably reduce maize adoption via one of the following channels: (a) it would be difficult to experiment with new crops during wartime and when disease is spreading, and (b) war and epidemics may reduce population pressure and thus the need to adopt maize.

  34. 34.

    We are unable to do the same with epidemics as that variable is available only at the province level.

  35. 35.

    We thank an anonymous referee for alerting us to this concern.

  36. 36.

    Conversion is based upon sources compiled by the Institute of Nutrition and Food Safety, Chinese Center for Disease Prevention (2002).

  37. 37.

    Quan (1972), for instance, refers to the secular rise in the price of rice during seventeenth century-China as the “price revolution”. Curiously, this was a period when grain prices in the rest of the world went south (Latham and Neal 1973). The reason why grain prices in China went up can be attributed to its exceedingly low level of grain imports. For instance, if we take Perkins’ (1969) estimate of China’s annual grain production of approximately 110 million metric tons during the mid-to-late nineteenth century as benchmark, its import of no more than 55,000 tons per annum amounted to at most a mere 0.1 % of its total output (Hsiao 1974; Zhou 1937). In other words, China failed to take advantage of the falling secular trend of world grain prices to compensate for its own deficits.

  38. 38.

    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).

  39. 39.

    We can check if that is indeed the case by bounding the effects of the underlying causes of growing populations net of the causal effects of maize adoption. By plotting the pre-adoption population growth trends along the vertical axis in Figure A7 prior to maize adoption (set at the 0 vertical line), it is indeed the case that, after subtracting the projected population changes from the estimated effects of maize on population growth (to the right of the 0 vertical line), prefectures having experienced higher population growth prior to maize adoption did subsequently experience a slowdown—a finding confirming the Malthusian predictions.

  40. 40.

    As suggested by Bairoch (1988) and De Vries (1976) and popularly adopted by Acemoglu et al. (2002, 2005) and Nunn and Qian (2011), urbanization rate is employed to proxy for economic prosperity prior to the Industrial Revolution, before reliable GDP figures became available. While doubts might be cast over the appropriateness of using urbanization rate in the Chinese context, if urbanization in Europe meant increases in manufacturing, whereas non-agricultural goods were produced outside of cities in China—a form of proto-industrialization (Greif and Tabellini 2010), evidence suggests that China’s urbanization rates were indeed closely related not only to the level of commercialization but also to exports (Skinner 1977; Xu and Wu 2014), and to regional specialization in the production of silk, porcelain and other exports in late imperial China (Fu 1989; Li 2000; Liu 1996; Skinner 1977; Xu and Gao 2009). The appropriateness of comparing the urbanization rates between China and Europe is perhaps validated by a recent exercise that endeavors to directly compare the GDP and productivity across various economic sectors between an economically developed Chinese county in the Yangtze delta and the Netherlands in 1820 (Li and van Zanden 2012).

  41. 41.

    Granted, these overall percentages must be masking huge inter-regional differences. 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).

  42. 42.

    The population variable is for the year 1910 but the urbanization variable, based on Stauffer’s (1922) figure, is from 1920.

  43. 43.

    Unlike casual or day laborers, long-term agricultural laborers were hired to work for landlords on a year-round basis. Receiving a fixed wage, their duties entailed a wide variety of farm tasks as demanded by seasonal needs. See Huang (1985) for a more detailed description of this type of farm labor.

  44. 44.

    Wen is the currency unit of the Qing Dynasty.

  45. 45.

    Against the lack of an upward trend in real wages, the secular rise in grain prices suggest that there was likely a shift in income distribution towards farm households with net surplus of food to sell—notably the landlords. The widening income gap between landlords and landless laborers during eighteenth- and nineteenth-century China was indeed a concern for many historians of China (e.g., Fang 1994, 2004; Li 1991; and Yao et al. 2007, among others). For Huang (1985), the growing disparity in land wealth was a critical precondition precipitating the Communist revolution.

  46. 46.

    Table A7 in the Online Appendix presents the data from Figs. 689, and 11, as well as the pre/post adoption coefficients.

  47. 47.

    The significance of the interaction term between adoption and Grand Canal suggests that the impact of adoption on real wage is significantly larger in prefectures located near the canal than those outside of it. However, the main effect of adoption remains insignificant in that specification. To find out if adoption may be significant in the more advanced economies of Jiangsu, Zhejiang and Anhui we restrict our sample to these three provinces but the result remains unchanged (hence results not separately reported).

  48. 48.

    As Voigtländer and Voth (2006) show, under a Malthusian regime productivity growth will be transformed much more quickly into population growth, thereby keeping per capita income low. Additional and broader supportive evidence are provided by Ashraf and Galor (2011). They find that in a large sample of countries, the effect of technology on population in the year 1–1500 is an order of magnitude higher (approximately 10 times larger) than its effect on income per capita.

  49. 49.

    For population data we have chosen the data points of 1776, 1850 and 1893, which are either exactly identical or very close to those on urbanization (1776 and 1880) and real wage (which ends in 1842).


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We are most grateful to four anonymous referees of this journal whose insightful suggestions have helped improve this paper tremendously. We also thank Oded Galor, Paola Giuliano, Debin Ma, Robert Margo, Nathan Nunn, Kaixiang Peng, Dwight Perkins, Louis Putterman, Nancy Qian, Tom Rawski, David Weil, Nico Voigtländer, Romain Wacziarg, Jeffrey Williamson, Noam Yuchtman and seminar participants at Brown University, UCLA, and Xiamen University for helpful comments and suggestions, and to Yiming Cao and Zhitao Zhu for excellent research assistance. We alone are responsible for any remaining errors. James Kung acknowledges the financial support of a GRF grant (642711) and from the Yan Ai Foundation.

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Chen, S., Kung, J.Ks. Of maize and men: the effect of a New World crop on population and economic growth in China. J Econ Growth 21, 71–99 (2016).

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  • Malthus
  • New World crops
  • Maize
  • Population density
  • Economic growth
  • China

JEL Classification

  • J1
  • N5
  • O11