china's regional agricultural productivity growth in 1985-2007: a multilateral comparison ...

AGRICULTURALECONOMICS

Agricultural Economics 44 (2013) 241–251

China’s regional agricultural productivity growth in 1985–2007:A multilateral comparison1

Sun Ling Wanga,∗, Francis Tuanb, Fred Galec, Agapi Somwarud, James Hansene

aAgricultural Economist, Economic Research Service, USDA, 355 E Street SW, Washington, DC 20024-3221, USAbLecture Professor, School of Agricultural Economics and Rural Development, Renmin University of China, 59 Zhongguancun Street, Haidian District, Beijing

100872, P. R. of ChinacSenior Economist, Economic Research Service, USDA, 355 E Street SW, Washington, DC 20024-3221, USA

dChief Economist, East Asia Economic Consulting, 607 Hawkesbury Lane, Silver Spring, MD 20904, USAeSenior Economist, Economic Research Service, USDA, 355 E Street SW, Washington, DC 20024-3221, USA

Received 18 April 2012; received in revised form 9 October 2012; accepted 15 October 2012

Abstract

In this study, we estimate total factor productivity (TFP) growth as well as multilateral TFP index for 25 contiguous China provinces over the1985–2007 period. Agricultural output growth for each province was decomposed into TFP growth and input growth, where input growth wasfurther disaggregated into contributions from growth of labor, capital, land, and intermediate goods. Over the study period, TFP growth contributed2.7 percentage points to output growth annually, which was slightly higher than the input growth contribution of 2.4 percentage points per annum.On average, the annual rate of productivity growth peaked during 1996–2000, at 5.1%. It slowed in 2000–2005 to a rate of 3.2% per annum anddeclined in the most recent years (2005–2007) to −3.7%. Differences in productivity among regions persisted over the entire period. The tendencytoward faster TFP growth in relatively well-off coastal regions may imply a widening of regional inequality.

Keywords: China agricultural productivity; Total factor productivity (TFP); Tornqvist-Thiel (TT) index; China agricultural policy; Multilateral comparison

1. Introduction

Since late 1978, China has implemented a series of market-oriented reforms including deregulating market, commercializ-ing production, facilitating agricultural trade, and much more.As a result, China’s growth in real gross domestic product(GDP) averaged around 10% per annum from 1978 to 2007,the highest in the world. Reforms also led to rapid transforma-tion in rural China. The expansion of China’s agricultural outputsince 1979 is remarkable. China’s grain output increased from305 million metric tons (mmt) in 1978 to over 500 mmt in 2007,an annual growth rate of 2.5%. Such growth is much faster thanits population growth rate of 1% per annum. The value addedin agriculture rose at an even higher annual rate of 4.8%. Thisgrowth formed the basis for China’s broader macroeconomicgrowth during the ensuing decades. However, the gains frompast reforms seem to be distributed unequally among regions(Fan and Zhang, 2002).

∗Corresponding author: Tel.: (202)694-5460; fax: (202)245-4847.E-mail address: [email protected] (S. L. Wang’s).

1The views expressed herein are those of the authors, and not necessarilythose of the U.S. Department of Agriculture.

Many earlier studies, using various methods, found rapid pro-ductivity growth in China’s agricultural sector following 1978reforms, and attributed it to China’s institutional changes (Lin,1992; McMillan et al., 1989; Zhang and Carter, 1997). Yet, Fan(1991) asserted that the institutional changes must be accompa-nied with technological change for further production growth.Although Stone (1998) indicated that increased input use suchas fertilizer and other inputs also contributed to China’s fastfarm output growth, Brown (1995) also pointed out the limita-tion of contribution from input growth in China’s agriculturalproduction. A number of studies focused on the contributionof public research investments in China’s productivity growthhave found high returns to such investments (Fan, 2000; Fanand Pardey, 1997; Hu et al., 2007; Huang et al., 2002; Rozelleet al., 2005). Notwithstanding, it is not clear whether that growthwas sustained in later decades.

Most studies of China agricultural productivity were fo-cused on specific commodities, such as crops (Colby et al.,2000), livestock (Rae et al., 2006), or individual products (Jinet al., 2010). Such studies may fail to capture the overall gainsin China’s agricultural productivity. There is also an impor-tant geographic dimension to China’s aggregate productivity

Published 2013. This article is a US Government work and is in the public domain in the

USA. DOI: 10.1111/agec.12008

242 S. L. Wang et al./Agricultural Economics 44 (2013) 241–251

growth, and yet there is little productivity analysis at China’sprovincial level. Studies using data from either a single region(Carter et al., 2003) or the entire agricultural sector (Coelliand Rao, 2005; Fuglie, 2008; Nin-Pratt et al., 2010; Zhao andCheng, 2011, among others) cannot provide information onChina’s intraregional development issues.

Among a few provincial productivity studies, Fan and Zhang(2002) employed the Tornqvist-Theil index methodology tomeasure regional as well as national productivity growth forChina agricultural sector. Yet, their estimates as now dated sincethey included data only through 1997. Using data from more re-cent years, Tong et al. (2012) employed a Malmquist index anda stochastic production frontier approach to measure provinciallevel productivity growth during the 1993–2005 period. How-ever, their aggregate output was measured in constant 1993prices. Fan and Zhang (2002) pointed out that constant pricesmay not be appropriate weights in aggregating total output be-cause the growth rates calculated from these constant pricesmay be seriously biased, especially when relative prices havechanged. In addition, neither of these studies showed how therelative productivity levels varied from province to provincenor did they show how output and input composition shiftedthrough years and across regions.

This study provides a new assessment of China’s agriculturalproductivity growth based on provincial data from 1985–2007,a longer time series that includes the early years following re-forms as well as more recent decades that were not available inearlier studies. The study includes broader coverage of outputsand inputs than previous studies which often focused on grainsor a limited scope of commodities. In addition, this study pro-vides geographic perspective by utilizing provincial data thatmay reveal important regional differences in resource endow-ments and access to capital and technology. It is also the firststudy of productivity in China to apply multilateral compari-son techniques to construct a longitudinal spatially linked totalfactor productivity (TFP) panel. Our estimates indicate that thecomposition of both output and input changed over time. Live-stock output grew faster than crops, and intermediate goods ac-counted for a greater share of input growth. The results also in-dicate little change in regional inequality; ranking of provincesby productivity level changed little over the study period.

2. Brief review of China’s agricultural policies

In the 1960s and 1970s China’s agriculture grew marginallyduring years of political upheaval. In the late 1970s China be-gan dismantling the collective agricultural system by adoptingthe “household responsibility system” that allocated land pro-duction rights to individual households and established a moremarket oriented agriculture. The new system gave farmers moreincentive to increase their production. The government gradu-ally liberalized prices and allowed partial privatization of agri-cultural markets in the 1980’s. This series of rural reforms hasmotivated farmers to adopt cost reducing practices and newtechnologies (Colby et al., 2000; Lohmar et al., 2009).

In the mid 1990s Chinese authorities abandoned urban food-rationing, and adopted a “governors’ grain-bag responsibilitysystem,” which required each province to maintain an overallbalance of grain supply and demand, to ensure food securitywhereas it regulated local markets. In the late 1990s the gov-ernment also encouraged farmers to produce high value-addedagricultural products that could bring higher returns per unitof land, such as vegetables, fruits, livestock, aquatic products,medicinal herbs, and flowers.

China’s joining the World Trade Organization in 2001brought reduction in protection policies and increased importsof a few commodities—notably soybeans and cotton (Huanget al., 2004). In 2004, China began a nationwide push to phaseout agricultural taxes, and the taxes on farm households weretotally eliminated in 2006. The policies implemented in recentyears were aimed at increasing farmers’ income and provid-ing incentives to produce food grains for food security purpose(Lohmar et al., 2009).

3. Measures of output, input, TFP, and data sources

3.1. Total factor productivity

TFP is a measure that takes account of the use of all inputs tothe production process. It is defined as the ratio of total output(Y) over total input (X). The TFP measurement in this studyis a nonparametric index number approach. The measure oftechnical change is based on the total differential of the loga-rithmic production function. The growth rate of real output canbe expressed as the summation of inputs growth rates weightedby their output elasticities, and the growth rate of the Hick-sian efficiency index, or so called Solow residual. The outputelasticities cannot be directly observed, yet they can be sub-stituted with the value of their marginal products when eachinput is paid the value of its marginal product. This can convertthe output elasticities into observed income shares. Therefore,the TFP measure is a true index number in the sense that itcan be computed directly from prices and quantities (Hulton,1973, 2001). We adopt the Tornquist-Theil (TT) index as an ap-proximation of the Divisia index to capture the TFP changes be-tween two discrete points in time, using the average shares fromtwo time points as the weighs for inputs or outputs. Thus, thequantity estimate using a TT index is based on the rollingweights that can accommodate any substantial changes in rela-tive prices over time.

Based on the growth of aggregated output and input, theTFP growth between two subsequent periods of time can beexpressed as the difference between these two indexes:

ln

(TFPt

TFPt−1

)=

∑i

1

2∗ (Ri,t + Ri,t−1) ∗ ln

(Yi,t

Yi,t−1

)

−∑

j

1

2∗ (Wn,t + Wn,t−1) ∗ ln

(Xn,t

Xn,t−1

),

(1)

S. L. Wang et al./Agricultural Economics 44 (2013) 241–251 243

where ln TFP is the log of the TFP index; Ri’s are the sharesof output i in total revenue and Wn’s are the shares of input nin total cost at time t and t-1, respectively; Yi’s and Xn’s are thequantities of output i and input n at time t and t-1, respectively.

Caves et al. (1982) proposed a methodology of multilateralcomparisons on outputs, inputs, and productivity using superla-tive index numbers. Under their settings the translog multilateraloutput, input, and productivity indices are all transitive. There-fore, we can construct a normalized multilateral TFP indexusing any region as the base region. The expression of the TFPindex between region k and the base region l can be expressedas follows:

ln

(TFPk

TFPl

)= 1

2

∑i

(Rk

i + Ri

) ∗ ln

(Y k

i

Yi

)

−1

2

∑i

(Rli + Ri) ∗ ln

(Y l

i

Yi

)

−1

2

∑n

(Wk

n + Wn

) ∗ ln

(Xk

n

Xn

)

+ 1

2

∑n

(Wl

n + Wn

) ∗ ln

(Xl

n

Xn

), (2)

where a bar indicates the arithmetic mean and a tilde indicatesthe geometric mean, Ri is the revenue share for output i, Wn

is the cost share for input n. In this study, we construct themultilateral input, output, and TFP index for 25 contiguousprovinces in China using Anhui province as the base provincefor the period 1985–2007.

3.2. Output, input, and data sources

We compiled annual data on agricultural output and inputfor 25 China provinces (we use the term “provinces” for allof the provinces and autonomous regions hereafter),includingAnhui, Fujian, Gansu, Guangdong, Guangxi, Guizhou, Hebei,Heilongjiang, Henan, Hubei, Hunan, Inner Mongolia, Jiangsu,Jiangxi, Jilin, Liaoning, Ningxia, Qinghai, Shaanxi, Shandong,Shanxi, Sichuan (Chongqing municipality is combined withSichuan), Xinjiang, Yunnan, and Zhejiang. We excluded threeprovince-level municipalities (Beijing, Tianjin, and Shanghai),one island province (Hainan) and one autonomous region (Ti-bet) as their output is small and data are not available for allyears. The time period for this research begins in 1985 sincethat was the first year that some critical data needed for thestudy were available.

3.2.1. OutputWe use prices from major crops2 including corn, cotton,

peanut, rice, soybeans, and wheat, and major livestock includ-

2Data on vegetable and fruits as well as some minor crops and livestockcommodities, such as tobacco leaf, tea, wool, silk and others, are not availableat provincial level. We assume that prices for those commodities move closelywith the major crops or livestock included in our estimates at each province orregion. The price indices can be improved in future studies if more commodityprices are available.

ing milk, pork, beef, mutton, chicken, and eggs to constructmultilateral price indices using 1994 as the base year and An-hui as the base province. The estimates will not be affectedby which base province was chosen as the results are transi-tive across provinces. Aggregate output is then measured as alongitudinal implicit quantity panel using total value deflatedby the multilateral price indices. We exclude fishery productionfrom our estimates because of lack of detailed data on its outputand inputs. Data for the values of aggregate output, crops, andlivestock are drawn from the China Rural Statistical Yearbook(National Bureau of Statistics, 1985–2009) and the China Sta-tistical Year Book (National Bureau of Statistics, 1985–2009).During the 1990s, both the level and growth in livestock inven-tories and output reported in Chinese statistics were believed tobe substantially exaggerated (Lu, 1998; Ma et al., 2004; Zhong,1997). After China’s first agricultural census in 1996, statis-tics were revised downward. Although the statistical problemsmay have overstated both the level and growth of livestock out-put before 1996, we have no means of adjusting the livestockstatistics. We used data obtained directly from China StatisticalYearbooks (National Bureau of Statistics, 1985–2009) as thereis lack of alternative livestock data at provincial level. To avoidthe bias in TFP measurement because of the downward live-stock adjustment in 1996, we excluded the TFP growth rate of1996 in our annual TFP growth rate estimate.3 Individual cropprices are from the National Agricultural Product Cost andRevenue Survey data books (Ministry of Agriculture, 1985–2009). Individual livestock prices are from the China AnimalHusbandry Yearbook (Ministry of Agriculture, 1990–2009) andChina’s Livestock Husbandry Statistics, 1949–1988 (Ministryof Agriculture, 1990).

3.2.2. Intermediate goodsWe construct the price index for intermediate goods based on

the Tornqvist index method using the prices and cost shares forfertilizer, pesticides, energy, seed, and feed.4 The total values ofintermediate goods as well as quantities of fertilizer, pesticides,and energy are drawn from China Rural Statistical Yearbooks.Because the Yearbooks only report the price index for theseinputs, we use the published cost information for some specificyears5 from the China Rural Statistical Yearbooks to impute theaverage prices of these inputs by dividing the cost with quanti-ties for those specific years. We then chain link the prices withthe price indices reported in the China Statistical Yearbooks todevelop the fertilizer, pesticides, and energy price series.

3The TFP growth rate showed a sudden drop in 1996 as livestock was adjusteddownward and arable land area was adjusted upward based on agriculturalcensus and land survey individually in 1996.

4The total value of intermediate goods in our study also included plasticcovers, irrigation fee, and other materials. Yet, since the price and quantity datafor those inputs were not available at provincial level we used data on fertilizer,pesticides, energy, feed, and seed to calculate the price index for intermediategoods.

5The data are only available for some specific years.


For some years, the average seed prices can be estimatedusing the seed cost6 divided by amount of seed used per sownarea. After chain linking these prices with constructed cropprice indices from each province we develop a seed price seriesfor each province. For feed prices we use the feed crop price asa proxy. The implicit quantities of feed are calculated based ona percentage of the implicit quantity of livestock.

3.2.3. LandIn China, there is no private ownership of land and therefore

no reported land value. Data on land rents are not availableeither.7 Following Fan and Zhang (2002) we use the residualof total revenue to impute rental costs. The rental rate is thetotal rental cost divided by the arable land area. The officialarable land area data were believed to be underestimated inthe past. The first China agricultural census has shown that thecultivated land area statistics have a discontinuity in 1996 withnearly 40% (130 million hectares) more than previous reported(Lin and Ho, 2003).8 As Lin and Ho (2003) indicated “much ofthe “discovered” farmland was located in the hilly and moun-tainous regions where the quality of land is low.” Because weare unable to make adjustments to pre-1996 land statistics, weuse the official land data reported in China Statistical Year-books (various issues) in our estimates. Yet, we exclude theTFP growth rate of 1996 from our estimate of average annualTFP growth rate because the dramatic increase in land areacould cause a sudden drop in TFP in that year.

3.2.4. CapitalIn this study, we apply the perpetual inventory method (PIM)

to construct the capital stock series for each province. Thequantity and price of capital input are the rental rate and theservice flow of capital stock. The capital input includes threetypes of assets: structures, equipment, and draft animals. ThePIM cumulates investment data measured in constant prices intoa measure of capital stocks. The measurement can be shown asthe following equation:

Kt =∞∑

τ=0

dτ It−τ (3)

where Kt is the capital stock at the end of time t, dτ is the relativeefficiencies of capital goods at age τ , It−τ is the investment attime t-τ . There are three kinds of relative efficiency patterns: thedeclining balance pattern, the one-hoss shay, and the straightline pattern. Little evidence is available to suggest a precisevalue for the curvature parameter used in the relative efficiency

6The cost of seed, feed, and other inputs are reported for some specific yearsbut not every year.

7During the period covered by this study, most land rentals were informalarrangements among neighbors, often with no cash rent.

8Before 1996 arable land statistics were based on an administrative reportingsystem that may understate arable land area due to nonstandard land measures,under-reporting of land-based tax collections, and other factors.

patterns. Following Jorgenson (1973), Fraumeni (1999), Sunand Ren (2008) we adopt a declining balance pattern with geo-metrically declining efficiency for the investment.9 Therefore,Eq. (3) can be expressed as the following equation in discretetime:

Kt = Kt−1 + It − δKt−1, (4)

where δ is the depreciation rate and is equal to the rate ofreplacement. In this case, we need data on capital stock bench-mark, capital investment, investment price deflator, and the de-preciation rate for each type of capital.

Because there is no reported agricultural capital stock bench-mark data at the provincial level to our knowledge, we estimateda steady state capital benchmark for 1984 (the year beforeour study period) following Harberger’s (1978) method. Thismethod has been applied often in the literature when the capitalbenchmark is unavailable (Martin and War, 1994; Sun et al.,2007, among others). We first assume a steady-state relationbetween the steady-state investment (I ∗) and the steady-statecapital stock (K∗):

I ∗ = (g + δ) K∗, (5)

where gis the growth rate of real investment, and δ is the depre-ciation rate. The initial capital stock can be retrieved with thefollowing equation

K∗ = I ∗/(g + δ). (6)

Then by adding investment during the previous period and de-ducting depreciation we can rebuild the capital stock series. Thecapital expenditure is mainly drawn from China Rural StatisticsYearbooks. The data is allocated to three categories—buildings,machinery, and draft animals based on the composition of theagricultural assets from each province.

Under the geometric efficiency decline pattern, the deprecia-tion rate is equal to the rate of replacement. Following Sun andRen (2008), we apply 8% depreciation rates for structures and17% depreciation rate for equipment and draft animals. For thespecial case dτ = δ(1-δ)τ−1 (Ball et al., 2008; Jorgenson, 1963,1973), the rental rate can be shown as

c = w (r + δ), (7)

where dτ is the relative efficiency of the capital goods at timeperiod τ , c is the rental price of capital service, w is the purchaseprice (new investment) of the asset, and r is the real rate ofreturn of this investment (opportunity cost of this investment)calculated as the nominal rate of loans to state and industriesless the inflation rate measured with the GDP deflator in China.The rates of loans and GDP deflators are drawn from the IMFdatabase.

9Ball et al. (1993) applied and compared different efficiency decay patternsin capital stock construction. They found that the annual growth rate of capitalstock varied slightly: −0.54% for one-hoss shay, −0.57% for hyperbolic decay,−0.6% for straight line, and −0.62% for geometric decay pattern. We appliedtheir findings in our estimates and found that with small cost share of capitalservice flow, the estimated TFP growth rates declined by 0.001 to 0.002 per-centage points when using other efficiency decay patterns in our capital stockconstruction.


Table 1Sources of output growth (1985–2007)

Region Province Output Sources of output Input growthgrowth growth decomposition

TFP Input Labor Capital Land Intermediategrowth growth growth growth growth goods growth

Northeast Heilongjiang 5.3% 1.9% 3.5% 0.4% 0.2% 0.1% 2.7%Jilin 5.2% 2.4% 2.8% 0.2% 0.2% −0.1% 2.4%Liaoning∗ 6.0% 2.5% 3.5% 0.1% 0.2% −0.1% 3.3%

North Hebei 6.8% 3.5% 3.3% 0.0% 0.2% −0.1% 3.2%Inner Mongolia 5.6% 2.4% 3.2% 0.1% 0.2% 0.2% 2.7%Shanxi 2.7% 0.5% 2.3% 0.1% 0.2% 0.0% 2.0%

Middle Henan 5.8% 2.3% 3.4% 0.2% 0.2% −0.1% 3.1%Hubei∗ 4.3% 2.5% 1.8% −0.3% 0.1% −0.2% 2.2%Hunan 4.7% 2.3% 2.4% 0.0% 0.1% 0.0% 2.3%

East Anhui 3.6% 1.5% 2.1% −0.1% 0.1% −0.1% 2.2%Fujian∗ 5.6% 3.3% 2.3% 0.1% 0.1% −0.2% 2.3%Jiangsu∗ 4.1% 2.8% 1.3% −0.7% 0.1% −0.2% 2.0%Jiangxi 4.6% 2.6% 2.0% −0.2% 0.2% −0.1% 2.1%Shandong∗ 5.3% 2.0% 3.3% −0.2% 0.5% −0.1% 3.1%Zhejiang 3.2% 3.7% −0.5% −1.8% 0.2% −0.3% 1.3%

South Guangdong∗ 4.0% 3.5% 0.6% −0.2% 0.1% −0.6% 1.3%Guangxi∗ 6.0% 3.3% 2.7% 0.1% 0.1% 0.0% 2.5%

Southwest Guizhou 3.7% 1.6% 2.2% 0.3% 0.1% 0.0% 1.8%Sichuan 6.0% 3.4% 2.6% 0.1% 0.0% 0.0% 2.5%Yunnan 5.8% 2.8% 3.0% 0.7% 0.1% 0.0% 2.3%

Northwest Gansu 5.2% 1.9% 3.4% 0.5% 0.1% −0.1% 2.8%Ningxia 6.0% 3.8% 2.2% 0.4% −0.5% 0.0% 2.3%Qinghai 4.1% 4.0% 0.1% 0.1% −1.5% 0.0% 1.5%Shaanxi 5.4% 2.9% 2.5% 0.1% 0.2% −0.3% 2.5%Xinjiang 6.6% 2.6% 4.0% 0.2% 0.2% 0.1% 3.5%

National average 5.1% 2.7% 2.4% −0.1% 0.2% −0.1% 2.5%

Note 1: “∗” indicates the coastal province.Note 2: National average is the weighted average of provincial estimates using revenue shares from each province as the weights.

4. Sources of China’s agricultural output growthand input growth

Over the 1985–2007 period, China’s agricultural outputgrowth averaged 5.1% per annum. According to the estimates(see Table 1), the 5.1% annual output growth can be decom-posed into 2.4% of input growth and 2.7% of TFP growth. Theroughly equal contributions of inputs and TFP contrasted withthe recent experience of developed countries. Although industri-alized countries, such as the United States and many Europeancountries, have experienced declining or negative input growth,leaving TFP growth as the major driver of agricultural outputgrowth (Ball et al., 2010), China’s input growth still played animportant role over the last two decades. Input growth in Chinareflects a substitution of industrial inputs and capital for laborand land. Among the four input categories, labor and land de-creased and contributed negatively to output growth, reflectingthe crowding-out effect from competing uses of labor and landin nonfarm sector along with fast economic growth. The releaseof surplus labor to rural industry and urban employment alsoraised the production efficiency of labor remaining in agricul-tural sector. The use of intermediate goods (such as pesticides,

fertilizer, fuel, and other materials) began from a low base, andgrew at a rapid 6.43% annual rate (see Table 2), offsetting thedecline in labor and land, and has contributed 2.5 percentagepoints to annual output growth (see Table 1). Capital growth,with a 3.5% annual growth rate (see Table 2), only contributed0.2 percentage points to annual output growth (see Table 1)because capital’s cost share remained much smaller than that ofother inputs.

Productivity growth accounted for more than half of the out-put growth on average. Yet, differences across provinces are no-ticeable. Some provinces relied more on input growth whereasothers attributed most of their output growth to TFP growth.For example, TFP growth accounted for almost entirely out-put growth in Zhejiang, Guangdong, and Qinghai where inputgrowth was negative or smaller than 1%. In general, most North-east and North provinces relied more on input growth whereasmost East and South provinces (many of them near the east orsouth coast) depended more on productivity growth. Althoughthe growing economy has pulled production resources awayfrom the farm sector into nonfarm industrialized sectors, the de-velopment of nonfarm sectors may have benefited the farm sec-tor by generating funds for investment in public infrastructure,


Table 2Inputs growth among regions (1985–2007)

Region Province Labor Capital Land Intermediategoods

Northeast Heilongjiang 2.12% 12.07% 0.15% 6.62%Jilin 1.02% 6.64% −0.17% 6.69%Liaoning∗ 0.53% 4.80% −0.43% 7.19%

North Hebei −0.22% 3.14% −0.47% 8.43%Inner Mongolia 0.61% 3.62% 0.45% 7.77%Shanxi 0.54% 5.72% −0.68% 5.00%

Middle Henan 0.60% 3.00% −0.45% 7.88%Hubei∗ −1.03% 0.45% −0.70% 6.19%Hunan 0.01% 1.25% −0.04% 6.63%

East Anhui −0.30% 2.30% −0.56% 5.41%Fujian∗ −0.38% 2.97% −0.47% 6.20%Jiangsu∗ −2.83% 1.48% −0.50% 4.51%Jiangxi −0.53% 4.65% −0.39% 5.66%Shandong∗ −0.87% 4.36% −0.62% 7.32%Zhejiang −5.07% 2.87% −0.91% 3.72%

South Guangdong∗ −0.51% 2.00% −2.92% 4.59%Guangxi∗ 0.13% 4.55% −0.02% 7.45%

Southwest Guizhou 0.60% 4.45% −0.34% 4.94%Sichuan −0.78% 3.69% −0.77% 6.46%Yunnan 1.16% 4.92% −0.25% 7.24%

Northwest Gansu 1.20% 2.93% −0.47% 6.74%Ningxia 1.05% 1.90% −0.08% 7.47%Qinghai 0.35% −2.65% 0.12% 4.57%Shaanxi 0.23% 3.71% −1.51% 5.75%Xinjiang 1.43% 5.00% 0.20% 9.02%

National average −0.40% 3.50% −0.60% 6.43%

Note 1: “∗” indicates the coastal province.Note 2: National average is the weighted average of provincial estimates usingrevenue share from each province as the weight.

science and technology. Relaxed restrictions on foreign tradeand investment may also have enhanced local agricultural pro-ductivity growth through international technology spill-in andincreased market access.

Table 2 shows growth in the use of individual inputs amongprovinces. Ten of the 25 provinces experienced negative growthin labor, including all six East provinces and five out of sevencoastal provinces. It seems that the higher opportunity cost oflabor in the richer regions on the east coast may have inducedfarmers to substitute other inputs for labor. Every provincehad negative growth in land use except Heilongjiang, InnerMongolia, Xinjiang, and Qinghai, which are all relatively land-abundant provinces located far from the coast. On the otherhand, capital growth is around 3–5% in general, except forHeilongjiang at a 12% annual growth rate. It may have re-flected higher capital intensity of state-owned farms in thatregion (Woodward, 1982).

The emergence of a commercial livestock sector (Fuller et al.,2002) was an important contributor to increased productivity.From Table 3 we can find that, during the 1985–2007 period,livestock output grew at an average rate of 5.45% per annum,compared with crops’ 4.53%. The growth in livestock also ex-ceeded crops’ growth in 17 out of 25 provinces, implying that

Table 3Outputs growth among regions

Region Province Crops Livestock

Northeast Heilongjiang 4.61% 6.78%Jilin 3.83% 7.91%Liaoning∗ 4.86% 7.09%

North Hebei 6.11% 8.09%Inner Mongolia 5.82% 6.48%Shanxi 2.72% 3.80%

Middle Henan 5.09% 7.43%Hubei∗ 3.90% 4.59%Hunan 4.48% 4.96%

East Anhui 3.20% 4.00%Fujian∗ 5.54% 4.36%Jiangsu∗ 3.84% 3.26%Jiangxi 3.84% 5.34%Shandong∗ 4.66% 5.83%Zhejiang 3.35% 2.09%

South Guangdong∗ 4.22% 3.13%Guangxi∗ 6.40% 5.53%

Southwest Guizhou 3.99% 3.58%Sichuan 3.43% 6.87%Yunnan 5.07% 5.62%

Northwest Gansu 5.75% 3.81%Ningxia 5.58% 6.73%Qinghai 4.07% 4.21%Shaanxi 4.96% 6.10%Xinjiang 6.88% 5.76%

National average 4.53% 5.45%

Note 1:“∗” indicates the coastal province.Note 2: National average is the weighted average of provincial estimates usingrevenue share from each province as the weight.

China’s agricultural output growth reflects a change in productmix toward higher-valued products along with its fast economicgrowth.

5. Trends and multilateral comparison of China’s regionalTFP growth

According to Table 1, the increased input use explains onlypart of China’s agricultural output growth. TFP growth hasplayed a significant role in promoting high output growth inChina’s agricultural sector. Table 4 presents the TFP annualgrowth rate for each of the 25 provinces in five subperiods. TheTFP growth rate for1995–1996 was excluded because revisionsin land and livestock data after the 1996 agricultural censuscreated discontinuities in the data. The average annual TFPgrowth rate for1985–2007 was 2.7%. Although it is higher thanthe national annual estimates of 1.1%–2.3% from Zhao andChen (2011),10 it is close to the results estimated by Fan andZhang (2002) in the overlapping periods. The weighted average

10Zhao and Chen (2011) have made adjustment for the livestock series basedon their assumption. Since there is no other reliable provincial livestock datasource, we use data directly from China statistical yearbook. The official live-stock series has been adjusted based on 1996 and 2006 census data.


Table 4Total factor productivity growth among regions

Region Province 1985– 1990– 1995– 2000– 2005– 1985–1990 1995 2000 2005 2007 2007

Northeast Heilongjiang 2.8% 1.8% −0.1% 5.9% −6.4% 1.9%Jilin 3.9% 0.6% 5.5% 4.7% −8.8% 2.4%Liaoning∗ 2.2% 3.8% 4.0% 4.3% −7.3% 2.5%

North Hebei 3.4% 3.8% 5.7% 4.5% −3.3% 3.5%Inner Mongolia 2.6% −0.5% 7.1% 5.7% −8.5% 2.4%Shanxi 2.6% 0.6% 1.6% 1.5% −10.1% 0.5%

Middle Henan 2.8% 3.8% 3.0% 3.2% −6.1% 2.3%Hubei∗ 2.3% 4.8% 2.4% 1.6% −0.4% 2.5%Hunan −1.4% 4.6% 4.5% 4.4% −3.9% 2.3%

East Anhui 0.8% 2.5% 4.7% 0.6% −3.4% 1.5%Fujian∗ −0.4% 6.7% 8.7% 1.4% −2.6% 3.3%Jiangsu∗ −0.1% 6.3% 3.1% 3.4% −0.8% 2.8%Jiangxi 2.2% 1.3% 7.2% 2.6% −2.1% 2.6%Shandong∗ 1.6% 3.7% 4.3% 0.8% −3.1% 2.0%Zhejiang −1.2% 5.0% 9.0% 5.8% −2.5% 3.8%

South Guangdong∗ −0.5% 4.5% 5.4% 4.5% 4.4% 3.5%Guangxi∗ 4.4% 2.7% 8.0% 2.3% −5.3% 3.3%

Southwest Guizhou −1.2% 2.4% 7.6% 2.3% −8.2% 1.5%Sichuan −0.2% 3.4% 8.7% 5.3% −7.4% 3.0%Yunnan 3.3% −1.6% 11.5% 2.4% −3.9% 2.8%

Northwest Gansu 3.2% 2.9% 4.2% 0.5% −5.1% 1.9%Ningxia 6.4% 1.0% 7.0% 3.1% −0.4% 3.8%Qinghai 7.9% 2.3% 5.3% 5.9% −8.7% 4.0%Shaanxi 1.6% 2.4% 7.3% 2.5% 0.1% 2.9%Xinjiang 4.4% 12.0% −6.4% −0.7% 0.7% 2.6%

National average 1.5% 3.7% 5.1% 3.2% −3.7% 2.6%

Note 1: “∗” indicates the coastal province.Note 2: We exclude the estimate of 1996 from the estimate of period “1985–2007.”Note 3: National average is the weighted average of provincial estimates usingrevenue share from each province as the weight.

annual TFP growth rate for 1985–1990 and 1990–1995 periodsin this study were 1.5% and 3.7%, and the average of Fanand Zhang’s (2002) two national TFP estimates were 1.7% and3.7%, respectively.

Estimated TFP growth rates for the periods 1985–1990,1990–1995, and 1996–2000 indicate accelerating TFP growth,with rates of 1.5, 3.7, and 5.1%, respectively (Table 4). TFPgrowth slowed to 3.2%in the 2000–2005 period, still a rapidrate. As a newly opened economy, the fast productivity growthin the China’s agricultural sector can be taken as a catch-upeffect from relatively low productivity levels compared to otherdeveloped countries. Although the possible technical catch-upeffect as well as the efficiency improvement effect through therelocation of surplus inputs from agricultural sector to nonfarmsector may have played an important role in China’s agriculturalproductivity growth, China’s agricultural policies could alsohave enhanced productivity advancement in the post-reformperiod. However, during 2005–2007, 22 out of 25 provincesexperienced negative productivity growth. Although it may re-flect a disease-related decrease in pork production in 2007, thereason is still unclear.

Table 5Rankings of annual TFP growth among provinces (1985–2007)

Province Annual growth rate Ranking

Qinghai 4.0% 1Ningxia 3.8% 2Zhejiang∗ 3.8% 3Hebei∗ 3.5% 4Guangdong∗ 3.5% 5Guangxi∗ 3.3% 6Fujian∗ 3.3% 7Sichuan 3.0% 8Shaanxi 2.9% 9Yunnan 2.8% 10Jiangsu∗ 2.8% 11Jiangxi 2.6% 12Xinjiang 2.6% 13Liaoning∗ 2.5% 14Hubei 2.5% 15Inner Mongolia 2.4% 16Jilin 2.4% 17Henan 2.3% 18Hunan 2.3% 19Shandong∗ 2.0% 20Gansu 1.9% 21Heilongjiang 1.9% 22Guizhou 1.5% 23Anhui 1.5% 24Shanxi 0.5% 25

Note 1: The average annual growth rate does not include the year 1996.Note 2: “∗” indicates provinces that are along the coast.

Table 5 shows the rankings of TFP growth among provinces.We find that five out of the seven top TFP growth provincesare located on China’s coast (see Fig. 1): Zhejiang, Hebei,Guangdong, Guangxi, and Fujian. However, the top two TFPgrowth provinces, Qinghai and Ningxia, are poor provinces inwestern China. Those two provinces were also among the threelowest-ranked provinces in multilateral TFP level in 1985 (seeTable 6). It implies a productivity catch-up effect for these twoprovinces whereas other coastal provinces may be benefitedmore from intensive public infrastructure investment and spill-in effects from trade openness, as discussed in the previoussection.

One unique contribution of this study is the multilateralcomparison of the TFP levels among regions. We construct aspatial-linked TFP index for 25 Chinese provinces following themethodology proposed by Caves et al. (1982). Table 6 presentsthe TFP rankings for the years 1985 and 2007. The top fiveprovinces in 1985 were Guangdong, Hunan, Guangxi, Sichuan,and Zhejiang, in order. Those five provinces are still the top fivein 2007 whereas Zhejiang took Sichuan’s place and became thefourth highest province in TFP level. The six provinces withthe lowest TFP level in 1985 remained at the bottom of therankings in 2007. These six provinces, Shanxi, Shaanxi, Xin-jiang, Qinghai, Gansu and Ningxiaare located in the Northwestor North regions (see Fig. 2). Although Qinghai and Ningxiaexperienced the fastest TFP growth of any provinces during the


post-reform period, they still lagged behind other provinces.Shanxi and Gansu had low TFP levels and slow TFP growth inthe study period. Weak investment and access to technology forthese poorer provinces may have hindered their ability to keepup with other provinces.

Fujian, Hebei, and Yunnan improved their rankings the most,by moving up three or four places. They all demonstrated highTFP growth with rates higher than 2.8% per annum (see Tables 5and 6), but they do not have much in common. Although Fujianand Hebei are both located in the coastal area, Yunnan is arelatively poor province located in the Southwestern region.

Although input growth accounts for most of output growthfor private business sectors (Jorgenson et al. 1987), Ball et al.(2010) showed that agriculture is one of the few exceptions ina study of U.S. and EU agricultural productivity. Our studyshows that for a developing country, China, which has ex-perienced tremendous economic growth after its 1978s eco-nomic reform, both input growth and productivity growth haveplayed important roles in agricultural output growth. Still, TFPgrowth contributed slightly more than input growth to agricul-ture output growth at a rate of 2.7%–2.4% during the studyperiod.

6. Did China’s agricultural policies have an impacton TFP growth?

In this section we perform statistical tests in an attempt to gaininsight into the effects of agricultural policies on TFP growth in

an ex post mode. We apply nonparametric paired T-tests on TFPgrowth rates among five subperiods. Rejection of the null hy-pothesis implies agricultural policies implemented in differentperiods may have generated different impacts on TFP growthamong provinces during the study subperiods. The advantage ofnonparametric methods over econometric methods is that non-parametric methods do not require specification of a functionalform (Conover, 1980; Daniel, 1978) and account for more com-plicated nonlinear relationships (Chiappori and Salanie, 2000).Two nonparametric tests–the Kruskal–WallisX2and the Van derWaerden Scores (Normal) test–are performed to investigatechanges in TFP growth among 25 provinces in the five sub-periods. The Kruskal–Wallis test statistic is given by:

H = 12

N (N + 1)

k∑i=1

T 2i

ni

− 3(N + 1), (8)

where N is the sample size, Ti is the sum of ranks for the ithgroup, and ni is the number of bservation in the ith group. Thetest statistic H approximately follows a chi-squared distributionwith k-1 degrees of freedom, where k is the number of groupsor populations. The Van der Waerden Scores are the quantilesof a standard normal distribution and are computed as:

a(Rj ) = �−1

(Rj

n + 1

), (9)

where � is the cumulative distribution function of a standardnormal distribution. These scores are powerful for normal dis-tributions.

Sources: See Table 2.

Fig. 1. Agricultural TFP growth in China.


Table 6Rankings of TFP level among provinces

Province Ranking_1985 Ranking_2007 Ranking Changes1

Guangdong∗ 1 1 0Hunan 2 2 0Guangxi∗ 3 3 0Sichuan 4 5 −1Zhejiang∗ 5 4 1Hubei 6 7 −1Jilin 7 6 1Shandong∗ 8 9 −1Anhui 9 14 −5Henan 10 10 0Fujian∗ 11 8 3Guizhou 12 17 −5Jiangsu∗ 13 12 1Liaoning∗ 14 15 −1Hebei∗ 15 11 4Heilongjiang 16 19 −3Yunnan 17 13 4Jiangxi 18 16 2Inner Mongolia 19 18 1Shanxi 20 23 −3Shaanxi 21 20 1Xinjiang 22 22 0Qinghai 23 21 2Gansu 24 24 0Ningxia 25 25 0

Note 1: The negative numbers in ranking changes indicate deterioration in theranking.

Table 7Results of the paired–t test on the impacts of agricultural policies on TFP growthamong subperiods

Period Mean TFP Kruskal–Wallis statistic Van der Waerden statisticgrowth rate (Wilcoxon mean score) (Mean score)

1985–1990 2.15% 59.40 −0.221990–1995 3.23% 71.12 0.111996–2000 5.18% 94.28 0.932000–2005 3.14% 72.20 0.002005–2007 −4.12% 18.00 −0.82Asymptotic 60.32 (X2) 33.21(X2)

statisticsP values <0.0001 <0.0001

Table 7 presents the test results. The Kruskal–Wallis X2 statis-tic rejects the null hypothesis at 1% significant level imply-ing that policies implemented in each subperiod had differentimpacts on provincial TFP growth. The Van der Waerden X2

statistic also rejects the null at the 1% significance level. Theseresults show an ex post mode evidence that China’s agriculturalpolicies during our study period affect TFP growth.

Besides the paired T-test we also conduct group mean testsfor each of two successive periods. Table 8 shows that the groupmeans tests are significant from period to period except for thefirst two periods. It seems that from 1985 to 2000, the nation’sfarm sector experienced accelerating productivity growth un-der the impact of a mixed whereas continuously changing setof agricultural policies. Note that the agricultural productiv-ity growth slowed down after 2000. This may imply a slowing

Sources: See Table 6.

Fig. 2. China agricultural TFP level rankings in both 1985 and 2007.


Table 8Paired t-test of mean annual TFP growth by subperiods among the 25 provinces

Compared Mean Cochranperiods t value

First Second Differenceperiod (A) period (B) B − A

1985–1990 vs.1990–1995

2.1% 3.2% 1.1% −1.52

1990–1995 vs.1996–2000

3.2% 5.2% 1.9% 2.18∗∗

1996–2000 vs.2000–2005

5.2% 3.1% −2.0% 2.52∗∗

2000–2005 vs.2005–2007

3.1% −4.1% −7.3% −8.95∗∗∗

Note: ∗∗indicates significant at 5% level, ∗∗∗indicates significant at 1% level.

technical catch-up as China’s productivity level has gained greatadvancement over earlier years. Negative productivity growthfor the 2005–2007 period needs to be monitored as it maybe just a transitory shock caused by animal disease or otherreasons.

7. Summary and conclusions

This study estimates TFP growth as well as multilateral TFPindex for 25 contiguous China provinces over the 1985–2007period. Agricultural output growth was decomposed into TFPgrowth and input growth. Input growth was further disaggre-gated into growth of labor, land, capital, and intermediate goods.Our analysis shows that TFP growth and input growth bothplayed significant roles in China’s agricultural output growth.Over the study period, productivity growth contributed 2.7 per-centage points to output growth annually, which was slightlyhigher than the input growth contribution of 2.4 percentagepoints per annum. Average productivity growth accelerated inthe 1990–1995, and 1996–2000 periods. It slackened during2000–2005 and became negative in 2005–2007. Although theearly years’ high TFP growth may reflect a technical catch-upeffect, China’s agricultural policies could also have enhancedproductivity advancement in the post-reform period. The re-cent productivity slowdown in China’s farm sector should beinvestigated further and monitored in future research.

The implications of our results for regional inequality aremixed. We found strong growth in both inputs and TFP in all re-gions. Yet, differences in productivity among regions persistedover the entire period. Two poor western provinces displayedrapid growth in TFP but remained near the bottom in a produc-tivity ranking. Some other poor provinces also had slow growthin TFP. The tendency toward faster TFP growth in relativelywell-off coastal regions may imply a widening of regional in-equality.

A more thorough evaluation is needed if we want to gainmore insights about the sources of China’s agricultural produc-tivity growth. Overall, the fast productivity growth in China’sfarm sector reflects a mixed result of agricultural reforms, open

policy, increasing investments in agricultural research, rural in-frastructure, and education. Changes in farmer’s right of landuse could also have increased efficiency and productivity ofChina’s farm sector. In addition, this study was not able to ac-count for the contribution of water as a separate input in TFPgrowth accounting because there is a lack of appropriate data.Given the growing concern on water scarcity and competitionfor water resources, including water as a separate input in futureproductivity studies is important as the results could shed lighton future water management policy.

Acknowledgments

The authors are grateful to Bart Van Ark, Harry Wu, ErwinDiewert, and Eldon Ball for their helpful comments and sug-gestions on an earlier version of this article presented at the2011 NBER/CRIW Workshop. We also thank the editor (Ger-ald Shively), and three anonymous reviewers for their helpfulcomments. The authors retain responsibility for any remainingerrors and limitations.

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