the weather factor and variability in china's grain supply

The Weather Factor and Variabilityin China’s Grain Supply1

Colin A. Carter and Bin Zhang

University of California, Davis, California 95616

Received July 22, 1997; revised June 5, 1998

Carter, Colin A., and Zhang, Bin—The Weather Factor and Variability in China’s GrainSupply

The variability of China’s grain output due to uncertain weather conditions is quantifiedusing disaggregate county-level data. Most studies of agricultural productivity growth inChina focus on output growth in response to input growth and policy change; littleattention has been given to the weather factor. A model is estimated to examine produc-tivity growth, controlling for the impact of weather. The model is applied to five grainproduction regions in China. We study the slowdown in growth of China’s grain produc-tion in the mid-1980’s and find that it was due to less favorable weather conditions andto a loss in production efficiency after 1985.J. Comp. Econom.,September 1998,26(3),pp. 529–543. University of California, Davis, California 95616.© 1998 Academic Press

Journal of Economic LiteratureClassification Numbers: 047, 053, Q11.

1. INTRODUCTION

China is the world’s largest producer and consumer of grain. It is also a large(but erratic) grain trader, exporting rice and corn and importing wheat. China’srole in world grain trade means that fluctuations in its massive grain supply arepotentially destabilizing to world markets (Wan and Anderson, 1990). Althoughpolicy error was largely responsible for the most recent famine in China,catastrophic weather also contributed to the poor harvest in 1959–1960 (Walker,1984; Stone and Zhong, 1989). A repeat of the 1959–1960 famine is unlikelybecause China has opened to world trade and substantial technological advanceshave been made in China’s grain production through seed breeding, watercontrol, and fertilizer-using technologies (Stone, 1993). However, these techno-logical advances have not reduced weather related fluctuations in China’s grainoutput and Mother Nature still plays a significant role in the harvest. Grain yields

1 We are grateful to Josef Brada, John Bonin, and two anonymous reviewers for helpful comments.

JOURNAL OF COMPARATIVE ECONOMICS26, 529–543 (1998)ARTICLE NO. JE981543

529 0147-5967/98 $25.00Copyright © 1998 by Academic PressAll rights of reproduction in any form reserved.

remain highly correlated among provinces and year-to-year variations in nationalgrain output have increased (Stone and Zhong, 1989). For instance, 1984 wasconsidered to be a good weather year in China, when damage to the grain cropdue to either flood or drought was minimal with weather-related losses estimatedto be only about 4% of total grain production (SSB, 1993). In contrast, 1991 wasbelieved to be a bad weather year when damage to the grain crop due to flood anddrought was estimated to have reduced total production by more than 10% (SSB,1993). Perkins (1988) has argued that the decline in China’s grain output growthrate in 1983–1986 was partly due to the fact that 1985 and 1986 were poor yearsfor grain due to unfavorable weather.

In China, the weather’s influence on grain production varies across regions andamong crops. China’s maize is grown in risky areas from a production stand-point, subject to damage from frost, drought, heat, and waterlogging (Stone andZhong, 1989). A large percentage of the wheat crop is irrigated in China, butsurface irrigation does not necessarily mean greater protection against drought. Alack of rain usually means no river flow. For instance, in 1997 large areas northof the Yangtze river were hit by the worst drought since the 1970’s and thedrought was so severe that the supply of irrigation water for seven northernprovinces was affected adversely (China Daily,October 20, 1997). In fact, it hasbeen argued that expanded irrigation has served to destabilize grain production inChina (Wan and Anderson, 1990).

From 1978 to 1984, grain output in China grew rapidly at 4.4% annually,following the introduction of a package of economic reforms affiliated with thehousehold responsibility system (HRS). Output then subsequently slowed to anaverage growth rate of 1.0% over the 1984 to 1992 period, largely due to aslowdown of yield growth rates. There are two competing explanations for thedecline in growth rates after 1984 (Watson, 1994). The first stresses the largecontribution that the HRS made to the successful growth in China’s agriculturalproductivity (Lin, 1987, 1992; Nolan, 1988; McMillanet al., 1989). The HRSresulted in one-time productivity gains and thus subsequent agricultural growthdepended on technical change, which was not forthcoming. The second school ofthought emphasizes the relative importance of marketing policy reform and pricesignals (Lardy, 1983; Sicular, 1992). These policy developments had a positiveeffect in the early reform period but then a negative one after 1984, when thegovernment unexpectedly reversed its efforts to liberalize the grain markets andreasserted mandatory procurement quotas (Sicular, 1995).

The objective of this paper is to contribute to this literature by estimating theweather’s impact on China’s grain output.2 The role of weather is an issue thathas been subject to substantial debate. Tang (1980) argued that weather has a

2 In a related paper, Lin and Wen (1995) tested the hypothesis of whether the deviation from, orreturn to, regional self-sufficiency in grain played a decisive role in slowing down or acceleratingChina’s grain output growth rate. They compare the commune period with the reform period and find

CARTER AND ZHANG530

rather modest influence on national agricultural output in China, because Chinais such a large and diverse country and weather impacts tend to cancel oneanother across regions. Alternatively, Kueh (1984, 1995) studied the effects ofweather on aggregate grain yields in China and concluded that weather fluctu-ations have a significant impact on grain yields. Stone and Zhong (1989) alsoreported that the effects of weather on the variability of grain output in China aresignificant. McMillanet al. (1989) examined agricultural productivity growth inChina; they acknowledged that weather conditions may cause substantial fluc-tuations in agricultural output.

Most of the past empirical work on the weather’s impact has used eithersubjective national weather “indexes” (Tang, 1980) or the Chinese government’sofficial disaster area statistics (Kueh, 1984, 1995); both approaches are highlyproblematic. The index used by Tang was simply based on a subjective classi-fication of each year as a “good,” “average,” or “poor” weather year. The dataused by Kueh on natural disasters, obtained from the State Statistical Bureau ofChina, are based on reports that come in after the harvest. Due to ex-postreporting, these data could be biased because local officials prefer to blame badharvests on weather conditions. We use county-level data for temperature andprecipitation, which is unlikely to be subject to such reporting bias.

2. DATA

This study utilizes disaggregate county-level data. Grain production datainclude aggregate output and five inputs: area sown to grain, labor, farm ma-chinery, irrigation, and fertilizer.3 The weather data include monthly meantemperatures and total monthly precipitation. Weather data were obtained fromthe World Meteorological Organization (WMO) through the National Center forAtmospheric Research in Boulder, Colorado. This data base consists of monthlyweather records between 1980 and 1990 from 249 weather stations, located in249 different counties that are distributed in 22 main grain producing provincesin China. The corresponding grain production data for each selected county were

that the relaxation of the self-sufficiency policy during the reform period was not a major factor inexplaining productivity changes.

3 Like all related studies on China’s agriculture, we had to work with imperfect data and makecertain assumptions when compiling the data. Whenever data problems exist, this could lead topotential bias in estimation. In assembling our data, we took steps to ensure that any such potentialbias was minimized. The State Statistics Bureau county-level data does not include grain-specificinput variables. Labor and fertilizer grain input data were generated by using labor and fertilizer inputshares from provincial cost and production survey data (SPB, 1988 to 1990) and applying thoseshares to the county-level data. For machinery and irrigation, the county-level data reported the totalacreage plowed by machinery and the total acreage under irrigation (for all crops). We convertedthese numbers to percentages by using grain’s share of total acreage for each county. We assumedthat the level of irrigation and mechanization was the same for all crops in each county.

WEATHER AND CHINA’S GRAIN SUPPLY 531

drawn from Statistical Summaries of China’s Rural Economy by Counties,published by the State Statistics Bureau of the People’s Republic of China (SSB).This cross-sectional county-level data set covers 6 years: 1980, 1985, and1987–1990. We used these county-level data for the purposes of estimating fiveregional weather–production models. A longer, 1978–1992, time series provin-cial data set was used in conjunction with the production models for the purposesof studying policy implications through growth accounting.

According to the National Committee for Agricultural Regionalization ofChina (NCAR, 1984), grain production regions in China can be divided into fivemajor hydrometeorological regions. They are northeast China, Huang-Huai-HaiRiver Basin (HHH), middle and lower Yangtze River Valley (MLY), southwestChina, and south China. Each region has unique hydrometeorological conditionssuch as temperature and precipitation. NCAR further divides each hydrometeo-rological region into subregions to capture differences in river systems, mountainranges, and cropping systems within a region. Table 1 reports the definition of theregions, the number of subregions, and the number of sampled counties in eachregion.

3. CHINA’S CLIMATE

China is on the southeastern sector of the Eurasian continent toward the PacificOcean and air masses of either continental or maritime origin affect its climate.Most of China is situated in the mid-latitudes, so the climate is mostly temperate.Winter (December–February) and summer (June–August) are generally regardedas the dominant seasons in China, whereas spring (March–May) and autumn(September–November) are only transitional seasons in which weather condi-tions are often related closely to those in winter and summer (Xu and Peel, 1991).

Table 2 lists monthly mean temperatures and precipitation for the five majorgrain production regions, between 1980 and 1990. Two attributes of temperatureare of importance. First, temperatures generally decrease from the south to the

TABLE 1

Grain Production Regions and Number of Sampled Counties

Region name Region no. AcronymNo. of

subregionsNo. of sampled

counties

Northeast China I Northeast 4 37Huang-Huai-Hai River

Basin II HHH 4 40Middle-Lower Yangtze

River Valley III MLY 6 80Southwest China IV Southwest 5 60South China V South China 3 32

CARTER AND ZHANG532

north4 in China’s grain production regions across all months. The differencebetween the south and north is larger in winter than in summer. Second, thepattern of changes in temperature from month to month is quite similar acrossregions. For example, for all regions, the temperature starts to rise in February,reaches a maximum in July, and then declines to a minimum in January. Centeredon July, there exists an almost symmetric relationship between temperatures inmarch through June and August through November.

Three points regarding the monthly precipitation data merit brief discussion.First, the data show that, in general MLY has the highest rainfall for all months,followed by south China and the southwest. The two northern regions (HHH andthe northeast) have the least rainfall. The average annual rainfall is about 1500mm in MLY, 1300 mm in south China, 1000 mm in the southwest, and onlyabout 600 mm in HHH and the northeast. Second, compared with temperature,the differences in rainfall across regions are larger and they are more apparent inthe first half of the year (January to June). This phenomenon is due to theinfluence of the southeast Pacific monsoon (Xu and Peel, 1991). Finally, theseasonal distribution of rainfall in the south is relatively uniform. Spring andsummer each account for one-third of total annual rainfall, and autumn andwinter share the remaining one-third. In contrast, the rainy season in the north isshort. Often one-half to two-thirds of annual rainfall takes place in summer,while droughts are not unusual in the spring.

4 The term “north” is used to represent the two northern regions, the northeast and the HHH,whereas the term “south” stands for the three southern regions, the MLY, the southwest, and southChina.

TABLE 2

Monthly Mean Temperature (Degrees Centigrade) and Precipitation(Millimeters) by Region: 1980–1990

Region Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. Sum

NortheastTemp. 215.4 210.4 21.7 7.9 15.4 21.2 23.6 23.3 16.0 8.821.9 211.7Precip. 7.0 7.6 12.1 33.8 46.8 93.5 173.2 131.4 73.7 27.2 14.4 7.2 627.9

HHHTemp. 0.0 2.1 7.2 15.0 20.6 25.4 27.7 26.9 22.3 16.8 9.1 2.1Precip. 16.9 17.8 31.6 31.2 73.5 91.5 165.7 135.0 62.8 43.3 17.1 6.8 693.2

MLYTemp. 7.4 7.9 11.5 17.7 22.7 26.6 30.0 29.1 25.1 20.6 15.1 9.1Precip. 68.0 111.3 140.7 155.4 204.4 205.3 151.7 182.8 145.3 74.6 62.0 22.6 1524.0

SouthwestTemp. 7.5 8.4 12.1 17.5 21.7 24.1 25.9 25.6 22.1 18.1 13.7 8.9Precip. 20.6 26.9 42.8 69.3 136.9 180.7 180.4 170.3 121.0 81.9 37.3 13.1 1,081.3

South ChinaTemp. 13.5 14.2 17.4 21.3 25.2 27.2 28.1 28.0 26.0 23.1 19.0 14.7Precip. 30.0 60.3 69.8 120.6 198.0 187.5 206.2 203.5 143.2 72.3 43.5 16.3 1,351.2

Note.Data from the WMO, 1990.


To capture the weather factor, this study combines the temperature andprecipitation data and uses a monthly aridity index. The index is defined as

Wij 5 Pij /~1.07Tij!, (1)

whereWij is the aridity index for monthj ( j 5 1, . . .,12) and for countyi; Pij

is the total precipitation for monthj (in mm); andTij is the mean temperature formonth j (in °C). This index was developed by Oury (1965). Although simple, itis based on sound agronomic and meteorological concepts.

The aridity index is essentially rainfall normalized with respect to temperature.The higher the index, the more “temperature-adjusted water” is available for cropgrowth (Oury 1965). When the index lies below 20, drought is implied; when thevalue falls below 10, drought is said to be “desert-like.” The mean aridity indexfor each month and each region (and subregion) is reported in Table 3. A fewpoints from Table 3 warrant mentioning. First, Table 3 shows that, on average,there were no desert-like weather conditions in China’s grain production regionsduring the 1980’s. Second, for a given year, there are more months in which theindex is below 20 in the north (6 for the Northeast and 9 for HHH) than there arein the south (1 for MLY, and 4 for the Southwest and South China). Thisindicates that the north is much more arid. Third, the index values in all regionsare relatively high between April and September, which coincides with the rainyseason. Finally, for a given month within each region, there is large variation inthe subregional aridity indexes. For example, the April indexes in the twonorthern subregions of MLY (III1 and III2) are about one-half of the Aprilindexes for the two southern subregions (III5 and III6). This underscores theimportance of including the subregional variables in our models.

4. THE MODEL

This paper divides the postreform period into two subperiods: 1978–1984 and1985–1992. There are two principal motivations for this division. First, paststudies on the effects of economic reforms on China’s agricultural production,such as Lin (1992) and McMillanet al. (1989), used the 1978–1984 time period.Hence this periodization makes our analysis comparable to that of others.Second, both the make-up and the pace of policy reform differed in the two timeperiods. In the first period the government grain policies focused on transformingthe old communal farming system into the HRS and raising procurement pricesfor farm products. The policies in the second period were geared more toreforming the marketing system (Sicular, 1995). This is not to say that the secondperiod policies were fully successful, and in fact they were largely reversed bythe government during the second period.

The weather and production data from 249 counties over the 6 years (1980,1985, and 1987–1990) were used to estimate five separate weather-production

CARTER AND ZHANG534

models, one for each of the five hydrometeorological regions.5 These modelsprovided us with grain production elasticities for the inputs and the weatherindex, which, in turn, were used for growth accounting. For purposes ofgrowth accounting, additional time series grain production data at the pro-vincial level were used. These data were drawn from several issues of theStatistical Yearbook of China.It is a continuous time series, covering a longerperiod than the county-level data and is used for productivity growth ac-counting for China’s grain production over the two subperiods: 1978 –1984and 1985–1992.

5 Estimating five weather–production models, instead of one aggregate model, helps control forinterregional effects of weather differences. We thank Josef Brada for pointing this out.

TABLE 3

Monthly Mean Aridity Index by Region: 1980–1990

Region Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec.

I1 16.9 10.3 10.7 16.8 12.3 23.3 37.6 27.2 32.4 10.5 12.0 19.6I2 17.2 16.3 11.7 16.7 18.1 21.5 33.8 27.8 23.8 18.7 14.7 18.3I3 28.5 21.0 18.9 32.9 21.6 30.7 51.4 35.6 29.0 21.2 30.5 24.0I4 13.8 12.4 13.1 16.8 17.6 20.8 29.8 26.9 22.7 12.4 9.3 6.9Northeast 20.3 15.9 14.2 21.0 17.6 24.0 37.9 29.5 26.8 16.0 17.2 16.5II1 10.6 13.2 18.1 9.9 14.0 12.6 22.4 22.1 12.7 12.1 7.3 4.3II2 11.2 9.1 13.3 11.2 15.7 15.7 28.7 21.7 15.4 10.4 7.4 4.4II3 25.6 26.7 29.6 13.1 26.6 20.2 27.9 20.4 12.8 20.9 13.5 6.7II4 21.5 14.0 20.2 14.4 22.2 23.3 31.8 31.4 19.4 15.6 10.0 9.0HHH 17.8 16.3 20.6 12.1 19.6 17.7 27.6 23.7 15.0 14.9 9.8 6.2III1 38.2 62.6 65.6 37.4 36.3 34.0 31.4 31.1 29.1 20.2 23.5 14.9III2 28.2 51.6 52.6 32.4 36.6 31.1 26.9 27.5 16.8 23.9 22.0 8.7III3 48.7 80.6 74.7 50.8 44.7 38.5 17.5 24.3 18.7 25.2 25.5 12.9III4 58.5 88.0 80.1 58.3 49.3 38.3 17.1 18.7 20.7 22.3 23.4 14.7III5 42.4 65.9 69.2 60.7 58.1 39.2 19.3 32.8 43.3 14.5 24.2 13.0III6 42.4 65.9 69.2 60.7 58.1 39.2 19.3 32.8 43.3 14.5 24.2 13.0MLY 43.9 69.9 69.3 50.6 47.5 36.9 21.7 27.7 28.8 20.0 24.0 13.0IV1 8.3 9.9 17.5 20.8 26.7 29.0 31.2 30.0 30.9 21.1 11.5 4.8IV2 11.7 12.7 17.4 21.4 29.0 35.0 45.0 41.5 35.4 26.1 16.1 8.2IV3 21.1 27.6 34.2 37.4 40.7 40.0 28.6 26.6 22.5 29.8 23.1 10.3IV4 19.5 22.3 23.9 23.6 43.1 44.3 26.1 26.8 22.0 25.2 16.3 9.2IV5 5.4 9.4 9.2 10.0 29.8 42.1 40.4 39.5 37.9 26.3 10.5 5.1Southwest 13.2 16.2 20.1 22.8 34.2 38.2 34.0 32.6 29.4 25.9 15.8 7.6V1 13.9 33.0 31.6 53.9 48.6 30.2 34.2 31.6 21.1 7.6 10.9 8.3V2 17.0 34.7 25.9 33.1 43.3 27.8 30.0 35.6 27.5 14.4 12.0 5.9V3 5.8 9.6 12.4 10.3 25.6 34.0 35.2 31.3 29.2 21.7 13.7 3.6South China 12.9 24.9 23.3 31.0 39.2 32.4 33.6 33.3 26.8 16.4 13.0 6.5

Note.Data from the WMO, 1990.


The production model we use is a standard production function with weatheradded as one of the arguments. The production function can be written as

Qit 5 f~Nit, Lit, Mit, I it, Fit, W, D!, (2)

wheret is the year index for 1980, 1985, and 1987–90;i is the county index;Qit

is grain output in thousand metric tons;Nit is number of labor days in grainproduction in ten thousands;Lit is area sown to grain in 10,000 mu6; Mit is thepercentage of machine-plowed cultivated land, a proxy for the level of mecha-nization in grain production;Iit is the percentage of irrigated land, a proxy forwater availability in grain production;Fit is chemical fertilizer usage in grainproduction in metric tons;W is a vector of monthly aridity indexes, defined asdeviations from the intertemporal sample mean for each county; and finally,D isa vector of subregion dummy variables7 to capture the influence of weatherconditions and other factors (e.g., technical efficiency and soil qualities) affectinggrain production across counties but within a region.

The Cobb–Douglas functional form was used in several previous studies ofChina’s agriculture (Fan, 1991; Lin, 1992; Dong and Dow, 1993; Chow, 1993).We chose this same approach and expressing (2) in Cobb–Douglas form, weobtain

lnQit 5 Oi

b iDi 1 Ok

5

eklnXkit 1 Oj

12

t jlnWjit 1 m it, (3)

whereX is a vector of the five inputs,bi are coefficients for the subregions,e areinput elasticities, andt are (monthly) weather index elasticities. All othervariables are the same as defined in (2). We estimated Eq. (3) for each of the fiveregions in Table 1, using a random effects model estimation method (Greene,1990, pp. 482).

5. RESULTS

Assuming that the model is correctly specified, the regression results reportedin Table 4 are encouraging. First, the adjustedR2s are between 0.89 and 0.95,suggesting that the estimated models do a good job of explaining variation inChina’s grain production in the five regions over the sample period. As expected,60 to 70% of the production fluctuation for a given region is explained byintercounty variations, as measured by the “between”R2 in Table 4. About 30 to

6 15 mu5 1 hectare.7 We followed the NCAR in dividing both regions and subregions. Their major criteria for regional

and subregional dividing lines include river systems, mountain ranges, cropping systems, hydrom-eteorological conditions, and so on. Names of weather stations and the names of provinces andcounties that are included in each of five major grain production regions and their subregions areavailable upon request.

CARTER AND ZHANG536

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40% of the fluctuation is explained by intracounty variations. This findingindicates that our disaggregate approach is more efficient than a more aggregateapproach, since the latter would likely miss 30 to 40% of the variation in theestimated productivity growth.

Third, the signs of the coefficients are consistent with prior expectations. Thenegative signs on the aridity indexes in August and September for the Huang-Huai-Hai, the middle-lower-Yangtze, and the south China regions are not sur-prising because excessive rainfall is concentrated in this period, when cropdamaging floods most often occur. In the northeast region, from October toJanuary, the weather is found to have no significant effect. This is no surprise dueto the fact that there is little farming activity in this northern region during thewinter. Finally, the significant estimates for the subregional dummy variablessuggest that differences in the weather, technical efficiency, soil qualities, andother social and economic factors among counties within a region are importantin determining China’s grain productivity.

Using the functional form in Eq. (3) and taking its partial derivative withrespect to timet, the growth rate of grain production in a given region can bedecomposed using

lnQit

t5 O

k

5

ek

lnXkit

t1 O

j

12

t j

lnWjit

t1

m it

t. (4)

The first term in (4) measures the effect of changes in inputs on productiongrowth; it is the sum of growth rates in inputs (labor, land, machinery, irrigation,and fertilizer) weighted by the relevant production elasticities. The second termcaptures the effects of weather on production growth, which is the sum of thepercentage changes in deviations in the aridity indexes from their sample means,weighted by the corresponding production elasticities of the monthly aridityindexes.

The last term in Eq. (4) is the residual, which is used to reflect the effects ofeconomic reforms (i.e., efficiency gains) on production growth. The method ofestimating production efficiency is a stylized application of Solow’s (1957)residual measure of a region’s movement toward or away from the productionsurface (i.e., the residual is equated with agricultural production efficiency ortotal factor productivity). Our maintained hypothesis is that the reform of thecommune system improved individual incentives and resource allocation, andthereby increased productivity (production efficiency). This is the same approachused by Kawagoe and Hayami (1985), Wong and Ruttan (1988), Johnson (1988),McMillan et al. (1989), and Wen (1993).8

8 It should be noted that productivity gains measured this way may overestimate the effects ofreform on productivity growth since the residual may also contain other elements such as biasedtechnological change. Potential overestimation of productivity growth is the major shortcoming of

CARTER AND ZHANG538

Using provincial level data from 1978–1992, growth accounting was con-ducted for the five grain production regions for the two subperiods: 1978–1984and 1985–1992. The results are presented in Table 5. Four points merit discus-sion. First, the higher output growth in the first period was attained with lowerinput growth compared to that seen in the second period. Hence, our resultssuggest that the slower growth in grain production in the second period (1985–1992) was not due to a decline in input use.9 This is contrary to some commonbeliefs (for instance, see Yao, 1994, p. 81). The average growth rate in aggregateinputs during the first period was 1.6%, compared with 2.4% in the secondperiod.10

Second, weather plays an important role in determining grain production butthe effects of weather on grain production were quite different in the two periods.Weather was found to contribute about 1.3% to the growth in grain productionin the first period (1978–1984). However, it contributed only 0.4% in the secondperiod (1985–1992). This suggests that about 0.9% of the decrease in the growthrate of grain production in the second period was due to less favorable weatherthan in the first period.

According to official Chinese statistics, an acre of disaster-affected crop leadsto a 30 to 40% lower-than-normal yield in that area. TheStatistical Yearbook ofChina (1993) reported that the average amount of disaster-affected area wasabout 17 million ha annually in the first period 1978–1984, but it was over 23million ha in the second period. This supports our finding that the weather wasmore favorable, on average, in the first period than in the second period.

Third, netting out the growth rate in aggregate inputs and the effects of weatherfrom total production growth, the residual is the growth in total factor produc-tivity (TFP). The growth in TFP, in this case, is due either to advances intechnology or to increases in production efficiency, which includes both alloca-tive and technical efficiencies. In the first period (1978–1984), the gains inproduction efficiency were estimated to be 2.3%. For the second period, theaverage gain in production efficiency was estimated to be20.6%, suggesting aloss in efficiency in China’s grain production.

this methodology. However, because one of our major findings is negative efficiency gains from 1985to 1992, the potential overestimation does not present a problem.

9 However, it is worthwhile to note that the growth rate in chemical fertilizer use in the first periodwas 13.5% compared with 8.7% in the second period. The slower growth rate in the latter period wasdue to rapidly rising prices for chemical fertilizers and changes in fertilizer distribution policy (Yeand Rozelle, 1994).

10 Wen (1993) studied agricultural input growth and found that, in the first subperiod, 1978–1984,there was a decline in grain sown area and a slowdown in the growth rate of farm labor and farmmachinery. The only input that increased was chemical fertilizer. These trends are consistent with ourresults in Table 5. Wen provides a comparison of weighted input indexes from five different studies(in his Table 10).


TA

BLE

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1978

–198

419

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in

MLY

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Sou

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est

Sou

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Nat

ion

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rage

grow

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5.4

4.6

6.1

45.

35.

16.

91.

80.

73.

12.

42.

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puts

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2.4

Labo

r0.

80.

22

0.5

0.6

1.3

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0.7

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CARTER AND ZHANG540

There are considerable differences in the estimated gains in production effi-ciency across regions. In the first period, the MLY River Basin and the southChina achieved the highest efficiency gains in production (3.8 and 3.2%, respec-tively), while the HHH River Basin had the lowest gains (1.2%). In the secondperiod, the northeast enjoyed large efficiency gains, while gains for the HHHRiver Basin were modest (0.5%). All other regions suffered a loss in efficiencyin the second period.

Why were the largest first-period efficiency gains experienced in the southernpart of China rather than in the north? We believe that this is due mainly to laborproductivity gains because the HRS raised the effective labor input (McMillanetal., 1989) and labor was relatively more abundant in the south. Over the period,the ratio of grain-sown area to labor in the northeast and the HHH River Basinwas 0.9 ha and 0.23 ha, respectively; in comparison, it was only 0.16 ha in theMLY River Basin, 0.15 ha in the southwest, and 0.12 ha in south China.

Our results also indicate that the efficiency gains in production in the twonorthern regions (northeast and HHH River Basin) were generally higher than inthe three southern regions (MLY River Basin, southwest, and south China)during the second period (1985–1992). We believe that this is partly due tohigher economies of size in the northern regions, since the gains in this regionwere not fully realized until after grain marketing reforms were instituted in themid-1980’s.

6. CONCLUSION

In future years, China will undoubtedly continue to experience both goodweather years and bad weather years. Weather conditions would not be animportant issue for national agricultural output if the consequences of weathervariability across regions canceled out because China is such a large and diversecountry geographically. Instead, we have found that weather variability hasmeasurable effects on year-to-year national grain output. Our results suggest thatthe weather played a role in both the grain output growth spurt that began in thelate 1970’s and then its subsequent slowdown during the mid 1980’s. This isconsistent with the finding in Stone and Zhong (1989) that China’s grain yieldsremain highly correlated among provinces and year-to-year variations in nationalgrain output have increased.

Our results have implications for the world grain market. Prior to implement-ing its open door policy, China absorbed most of its grain supply shocksinternally. This has changed and the world markets will presumably absorb futuregrain supply shocks originating in China. This study highlighted the weatherfactor and its role in China’s grain supply. In future work, it would be interestingto study the interactions between weather and technological innovations, such asnew plant varieties and new farming practices. Kueh (1995) hypothesized thatweather has become less important as a factor in national crop supply fluctuations


in recent years. It would be worthwhile to test this hypothesis with actualmeteorological data.

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the weather factor and variability in china's grain supply

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