China Economic Review 62 (2020) 101504
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China Economic Review
journal homepage: www.elsevier.com/locate/chieco
The changing role of agriculture with economic structural change –
The case of China
Yumei Zhanga, Xinshen Diaob,
a
b
T
⁎
Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, China
International Food Policy Research Institute, USA
ARTICLE INFO
ABSTRACT
Keywords:
Structural change
Agriculture
General equilibrium model
China
We analyze the implication of structural change to the evolving role of agriculture using China as
an example. By combining a growth decomposition exercise with Input-Output (IO) and CGE
model analyses using China's seven input-output tables (IOTs) in 1987–2017, the evolving role of
the agriculture is quantitatively measured. The growth decomposition analysis shows that between 1978 and 2017, China doubled the size of its total labor force, while the absolute number
of agricultural workers falls in this period. Rising labor productivity in agriculture has led to
rapid agricultural growth without increasing agricultural employment, allowing agriculture to
indirectly contribute to the economywide productivity growth through structural change.
The measurement of economic integration using an IO approach helps to explain why China's
rapid structural change has been accompanied by similar rapid productivity growth within each
sector. The general equilibrium effect of structural change on the evolving role of agriculture is
further assessed using two CGE models representing the initial (1987) and end (2017) years of a
period of 30 years. Similar agricultural productivity shock induces a smaller economywide gain
in 2017 than in 1987 in the CGE models, while the gap in the general equilibrium gain between
these two years is much smaller than the difference in agriculture's size of the economy in the two
years because of stronger linkages between agriculture and the rest of the economy in 2017.
About 0.5 unit additional nonagricultural value-added is associated with a unit agricultural
value-added increase in 1987, while additional gains in nonagricultural value-added rise to 2.7
unit in 2017.
Our analysis of economic integration and implication of structural change to the evolving role
of agriculture emphasizes the supply side role for sustainable growth in which agriculture continues to play an important but different role from the past when the demand side effects were
stronger. Policies to strengthen supply side linkages have been emphasized in the recent years in
China. Exploring further integration between agriculture and the rest of the economy should be
part of the new growth strategy.
1. Background
It is well-known that share of agriculture in an economy measured by gross domestic products (GDP) and in a country's total
employment is falling during economic growth, that is, growth is commonly accompanied by structural change. What is the implication of structural change to the role of agriculture? Besides agriculture's important role in reducing poverty particularly among
⁎
Corresponding author at: Development Strategy and Governance Division, IFPRI, 1201 I Street, NW, WA 20005-3915, USA.
E-mail addresses: zhangyumei@caas.cn (Y. Zhang), x.diao@cgiar.org (X. Diao).
https://doi.org/10.1016/j.chieco.2020.101504
Received 16 January 2020; Received in revised form 3 May 2020; Accepted 9 June 2020
Available online 11 June 2020
1043-951X/ © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/BY-NC-ND/4.0/).
China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
rural households and improving inequality, does the role of agriculture differ over time in economic growth with structural change?
This paper evaluates the changing role of agriculture from a lens of structural change. China is an excellent example for this purpose
because of its unprecedent growth accompanied by rapid structural change in the past four decades. By combining a growth decomposition exercise with Input-Output (IO) analyses and computable general equilibrium (CGE) model simulations, the analytical
narrative of the paper emphasizes that in the process of economic transformation and when domestic economy becomes more
integrated, the agricultural sector has become a more integral component of the whole economy. The role of the agriculture in a more
integrated economy differs from an agricultural dominant and less developed economy. While the share of primary agriculture and
hence its direct contribution to economic growth declines with economic integration, the share of the broadly defined agri-food
system (AFS) in the economy may decline much modestly. Within the AFS and measured as shares of the economy or total employment, off-farm components are possibly relatively stable with structural change. Finally, based on the simulation analysis using
two CGE models for the Chinese economy in 1987 and 2017, the paper shows that because of rapid change in Chinese economic
structure during this period of 30 years, the role of the agriculture becomes more dominant by its indirect contribution either through
the AFS or through enhanced linkages with the rest of the economy in the recent years.
2. Introduction
Agricultural economists emphasize the important role of the agricultural sector in economic development. For many low-income
developing countries, agriculture is the largest sector in the economy and in total employment.1 Large size often matters in explaining
the role of agriculture. Because of this, agriculture can serve as a catalyst to economic growth (Dorward, Kydd, Morrison, & Urey,
2004; Eicher & Staatz, 1998; Johnston & Mellor, 1961; Mellor, 1995; Schultz, 1964, 1968), create jobs (Christiaensen, 2019; World
Bank, 2017), in addition to undoubtable outcomes of poverty reduction and improvement in food and nutrition security
(Christiaensen, Demery, & Kuhl, 2011; DFID, 2005; Ivanic & Martin, 2018; Kraay, 2006; Ravallion & Chen, 2003, 2007).
The concept of structuralism in development economics, on the other hand, dates back to the founding of the Economic
Commission for Latin America (ECLAC) in 1948. The central tenet of structuralism is that developing countries differ qualitatively
from developed ones. Further, if these differences are not recognized, policies designed to stimulate growth and poverty reduction in
the developing world are doomed to fail. The intellectual foundations of structuralism are attributed primarily to Raul Prebisch the
founding director of ECLAC. A key insight of Prebisch which remains highly relevant today had to do with the important role of
industrialization in the developing world. Prebisch (1950) along with Singer (1950) argued forcefully that the price of primary
commodities relative to those of manufactured goods were bound to decline over time dooming poor countries to poverty unless they
industrialized.
Emphasizing the importance of agriculture or emphasizing the importance of structural change led growth can lead to different
growth strategy and policy priorities. Policies advised by “agriculture-first” camp emphasize investment in agriculture that demonstrated high rates of social and economic returns and the reductions in poverty (Alston, Chan-Kang, Marra, Pardey, & Wyatt,
2000; Byerlee & Alex, 1998; Fan, Zhang, & Zhang, 2001; Hazell & Haddad, 2001; Ruttan, 1997; World Bank, 1982). Strategies
proposed by structuralists emphasize industry policies in the now famous period of import substituting industrialization (ISI) in Latin
America as well as in many developing countries in Africa and Asia. While such strategies may work in the initial years of development in some countries, in the longer run they slowed development and often ended up in failure among many other countries
(Timmer, 1998). In most developing countries, a large part of the population has continuously to rely on agriculture in a much longer
period than structuralists expected and neglect of agriculture has made many of them trapped in low productive part of the economy
and left out of the development process (Huang, Otsuka, & Rozelle, 2008).
It should point out that the view toward the role of agriculture has changed dramatically in the past several decades. Unlike in the
1950s and 1960s when agriculture was traditionally thought of an inferior partner in development from which labor could be
costlessly moved into industry (Lewis, 1954) and investment should be channeled primarily to industrialization (Hirschman, 1958),
modern development economists today mostly agree that the role of agriculture and rural development is an integral part of healthy
development. While agriculture definitely continues to play the role in supplying surplus labor to manufacturing and services,
contributing to foreign exchange earnings, and raising rural incomes, will its role differ today compared with the past when a
majority of population depended on agriculture for livelihood? In studying the relationship between agriculture and economic
structural change, however, the focus seems to be mainly on the impact of agriculture in structural change. Structural change out of
agriculture through labor push and labor pull has be widely studied empirically (e.g. Alvarez-Cuadrado & Poschke, 2011), and there
is also increasing concern for rural households lagging behind with structural change. Productivity growth among smallholder
agriculture is often slower than productivity growth in modern industrial sector. Together with falling prices for agricultural raw
materials and staple foods due to the Engel's Law, many rural households whose livelihood depend on subsistence agriculture have
hardly benefited from structural change led economic growth (Huang et al., 2008). While understanding the impact of agriculture in
structural change is important for healthy development, the implication of economic structural change to the roles of agriculture is
equally important and deserves more research.
This paper focuses on the implication of structural change to the evolving role of agriculture. We assess this role by understanding
1
According to the World Development Report 2008, agriculture is a source of livelihoods for an estimated 86% of rural people. Of the developing
world's 5.5 billion people, 3 billion live in rural areas, and of these rural inhabitants an estimated 2.5 billion are in households involved in
agriculture, and 1.5 billion are in smallholder households (World Bank, 2008).
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China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
the integration of agriculture with the rest of the economy in structural transformation. Studies on interdependence of agriculture and
industry can date back to Peigang Zhang's thesis “Agriculture and industrialization” in 1949 (Zhang, 1949), and literature for
agriculture's linkage effects is also rich, many of which are highly relevant to this paper (see, for examples, Hazell & Haddad, 2001;
Vogel, 1994). Difference from the early agriculture linkages literature is that in this paper we analyze the interlinkages between
agriculture and the rest of the economy from the lens of structural change and focus on the dynamic process and the evolving role of
agriculture in economic development.
China is used as an example for this purpose in the paper. Why China? With its unprecedent growth rate in the past four decades,
the Chinese economic structure has rapidly changed. Emphasizing the importance of agriculture is a consistent policy for the Chinese
government while the country has been rapidly industrialized in this period. Thus, China provides us a natural experiment in
understanding the evolving role of agriculture with structural change.
There are many approaches that can be chosen to evaluate the implication of structural change to the role of agriculture. For
example, using microeconomic farm-level data Cao and Birchenall (2013) examine the role of agricultural productivity as a determinant of China's post-reform economic growth and structural change. In this paper, we emphasize the changing role of agriculture
with structural change using an economywide approach. While similar as in Cao and Birchenall (2013) the CGE model simulation
section of the paper assesses the economywide contribution of agricultural productivity shock, our analysis focuses primarily on the
differential impact channels of agricultural productivity shock because of the difference in economic structure over time.
The advance of studying the implication of structural change to the role of agriculture using China as an example is also due to the
available data in China, which allows us not only to measure the structural change in the broad economy, but also to assess the level
of economic integration and changing patterns of interlinkages between agriculture and the rest of the economy. China systematically
publishes disaggregated Input-Output tables (IOTs) based on frequently conducted industrial or economic census/surveys. IOTs detail
the sectoral relationship in the economy and report the consumption patterns of rural and urban households as well as trade patterns.
Using multiple rounds of IOTs in combination with a structural change analysis for the broad economy allows us to discover some
stylized facts that cannot be seen either in the structural change analysis or in the linkages effect analysis alone. These stylized facts
indicate that structural change has led to the increased integration of the economy and changing patterns of interlinkages between
agriculture and the rest of the economy. We found that such linkages were often dominated by consumption-to-production linkages
coming directly from agricultural growth in the early stage of development when agriculture was a dominant sector. Overtime with
income growth and changing structure of the economy, the stronger linkage effects from agricultural growth come from production
side as the economy becomes more integrated.
Based on these stylized facts, two economywide CGE models are developed for 1987 and 2017 representing the initial and end
years of a period of 30 years. The two models are used to assess the differential role of agriculture with significant different economic
structure over time. The simulations of the CGE models further confirm the stylized facts observed from the IO analysis, and the
indirect gain in the broad economy from agricultural productivity shock coming through economic linkages is much larger in 2017
than in 1987 as the economy becomes much more integrated in 2017.
Our analysis of economic integration and implication of structural change to the evolving role of agriculture emphasizes the
supply side role for sustainable growth in which agriculture continues to play an important but different role from the past when the
demand side effects were stronger. Policies to strengthen supply side linkages have been emphasized in the recent years in China
(Chen & Groenewold, 2019). Exploring further integration between agriculture and the rest of the economy should be part of the new
growth strategy.
The structure of the rest of the paper is as follows. Section 2 documents the patterns of structural change in the broad economy in
the past four decades using a growth decomposition approach developed by McMillan and Rodrik (2011). Section 3 focuses on
measuring economic integration and the integration of agriculture and the rest of the economy over time based on multiple rounds of
IOTs in 1987–2017. We develop two CGE models for the initial (1987) and end (2017) years of a period of 30 years in Section 4 to
assess the differential role of agriculture when Chinese economic structure has changed significantly over time. Section 5 concludes.
3. Structural change and economic growth in China
China's phenomenal economic success in the recent four decades has been widely documented and become a commonly recognized world miracle that is not necessary to be repeated here. While the fact that China has transformed from an agricultural
dominant economy to an industry giant is well known, few have measured such structural change quantitatively for the past four
decades. This section fills this gap using a growth decomposition approach developed by McMillan and Rodrik (2011).
3.1. A conceptual framework
The growth decomposition analysis in this section is guided by a conceptual framework best articulated in Dani Rodrik (Huang &
Ding, 2016; Rodrik, 2014). Essentially, Rodrik identifies three sources of economic growth. One source of growth is the normal
productivity gains that arise within sectors as a result of the accumulation of fundamental capabilities, such as better institutions,
healthier and better-educated workers, improved technologies, and more enabling policies. Two other sources of growth arise from
structural changes in the economy. There are potential gains from growth in modern manufacturing, which unlike other sectors, can
more easily converge to the high levels of productivity observed in leading export countries, even when starting from relatively low
levels of fundamental capability. The final source of growth arises from the movement of workers from low to higher productivity
sectors.
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Y. Zhang and X. Diao
Rodrik's conceptual framework has its roots in development economics dated back to the work of Lewis (1954), who draws a
sharp distinction between the traditional and modern sectors of the economy; accumulation, innovation, and productivity growth all
take place in the modern sector while the traditional sector remains technologically backward and stagnant. Thus, economywide
growth depends largely on the rate at which resources—principally labor—can migrate from the traditional to the modern sector. The
Lewis's theory is still important in the context of developing countries today after more than six decades, because the economies of
today's poor countries are still very much characterized by structural dualism. The implication of this dualism is that there are
potentially large payoffs to moving workers out of the traditional sector and into the modern sector.
The rapid growth of China in the past four decades is undoubtably seen as a successful story consistent with Rodrik's conceptual
framework. In fact, in Rodrik (2014), China is documented as part of the successful East Asian model that involves a growth strategy
to heavily invest in the rise of modern, highly productive industries like export manufacturing, and shifting workers from low
productivity sectors like traditional agriculture into the modern industrial sector. This structural change led East Asian model has
displayed visible short to medium term benefits as well as setting the stage for longer-term growth.
3.2. Stylized facts of structural Dualism in China
The evidence of structural dualism in China four decades ago can be displayed by the comparison of labor productivity across
sectors. A database produced by researchers at the Groningen Growth and Development Center (GGDC) is used for the comparison.2
Fig. 1 shows the productivity gaps across sectors in 1978, the first year China started her economic reform, are enormous. Each
bin in the figure corresponds to one of the nine sectors in the dataset, with the width of the bin corresponding to the sector's share of
total employment, and the height corresponding to the sector's labor productivity level as a fraction of average labor productivity in
the economy. Agriculture, at 60% of average productivity, had the lowest productivity in 1978. Furthermore, the figure makes
evident that roughly 70% of Chinese labor force was in the most unproductive agricultural sector in 1978. Based on this figure, it
appears that the potential for structural change to contribute to labor productivity growth was large when China started economic
reforms four decades ago.
3.3. Measuring structural change led growth
To quantitatively measure the contribution of structural change to growth in China, we apply the growth decomposition approach
developed by McMillan and Rodrik (2011), who express two components of labor productivity growth using the following equation:
t k
i
yt =
i
yit +
i
yit
t
i,
where yt and yit refer to economywide and sectoral labor productivity levels, respectively, and θit is the share of employment in sector
i. The Δ operator denotes the change in productivity or employment shares between t-k and t and t > k. The first term on the
righthand side of Equation in the decomposition is the weighted sum of productivity growth within individual sectors, where the
weights are the employment share of each sector at the beginning of the period. As in McMillan and Rodrik (2011), we call this the
“within” component of productivity growth. The second term captures the productivity effect of labor reallocations across different
sectors. It is the inner product of productivity levels (at the end of the period), with the change in employment shares across sectors.
We call this second term the “structural change” term.
The decomposition approach clarifies how partial analyses of productivity performance within individual sectors (for example,
manufacturing) can be misleading when there are large differences in labor productivities (yit) across economic activities. In particular, a high rate of productivity growth within a sector can have ambiguous implications for overall economic performance if the
sector's share of employment shrinks rather than expands. If the displaced labor ends up in activities with lower productivity,
economywide growth will suffer and may even turn negative.
The above equation is applied to the updated GGDC data for the quantitative decomposition of economywide labor productivity
growth. Year one of the analysis is 1978, which is the first year when China started the economic reforms. The final year in GGDC
data is 2011 and we further updated it to 2017 using published data from China's National Bureau of Statistics (NBS, 2019). We break
down the time series of updated GGDC data into four decades as four sub-periods: 1978–1987, 1988–1997, 1998–2007, and
2008–2017. Fig. 2 displays the decomposition result of annual labor productivity growth rate in these four sub-periods. The annual
labor productivity growth rate is reported along the horizontal axis, ranging from 6.7% in the first two periods to 8–9% in the last two
periods. The bars are coded according to how much of labor productivity growth comes from structural change and how much comes
from within-sector labor productivity growth in agriculture, in manufacturing, in other industries, which combines the three industrial sectors in the data, mining, urban utilities and construction, and in services, which is the combination of trade, transport,
business services, public services and personal services in the data.
The first finding for the period of 1978–1987 is the impressive contribution of agricultural productivity growth within-sector to
the broad economic growth. As shown in Fig. 2, contribution from within agricultural labor productivity growth is 2 percentage
2
The most recent version of GGDC data which were last updated in January 2015 (Timmer, de Vries, & de Vries, 2015) is used here. The GGDC
dataset consists of 10 economic sectors' aggregate employment and real value-added statistics for 30 developing countries including China and 9
high-income countries covering the period up to 2010.
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Y. Zhang and X. Diao
Sector-relative labor productivity, the economywide
labor productivity = 100
1,000
961
Agriculture (70.5%)
Manufacturing (13.2%)
Transport services (2.1%)
Trade services (3.2%)
Utilities (0.2%)
900
800
700
Personal services (1.6%)
Mining (1.8%)
Construction (2.1%)
Business services (0.9%)
600
500
386
305
250
242
219
400
300
200
149
100
103
61
0
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
Share of total employment (%)
Fig. 1. Relative Labor Productivity and Share of Employment across Sectors.
(Source: Authors' calculation using data from Groningen Growth and Development Center (GGDC).)
Within agriculture
Within manufacturing
Within O. industry
Within services
Structural change
2008-2017
1998-2007
1988-1997
1978-1987
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
Fig. 2. Labor Productivity Growth within Agricultural and Nonagricultural Sectors and Due to Structural Change in Different Periods in China
(percentage annual average growth rate).
Sources: Authors' calculation using data from Groningen Growth and Development Center (GGDC) until 2011 and data from China's NBS for
2012–2017.
points per year, i.e., one-third of economywide labor productivity growth (of 6.7% annually) is the direct outcome of productivity
growth within agricultural sector. This notable contribution from agriculture can be explained by the rural focus of China's reforms in
this period. Between 1978 and early 1980s, China gradually dismantled the collective farming system and replaced it by the
Household Responsibility System nationwide (Lin, 1987) and increased the above quota price, the payment farmers received for
voluntary sales beyond the mandatory deliveries (Sicular, 1988). Both institutional and price reforms produced market-based incentives for farmers to invest in and improve the land they worked on to increase productivity. The clear causal relationship between
the early rural reforms and agricultural productivity growth in the 1980s has been well assessed by scholars (see, for examples, Lin,
1987, 1988, 1992; McMillan, Whalley, & Zhu, 1989; Fan, 1991; Huang & Rozelle, 1996; Fan & Pardey, 1997).
Structural change is an equally important driver to the economywide productivity growth in the period of 1978–1987, and it
explains about 30% of economywide productivity growth. With agricultural productivity growth and small farmland holding size,
rural Chinese households increasingly looked for employment opportunities outside farming in various rural nonfarm activities or
migrated to urban sectors. Labor movement from agriculture to the nonagricultural sector created productivity growth economywide,
because labor productivity in any nonagricultural sectors is higher than agriculture (Fig. 1). Between 1978 and 1987, agricultural
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Y. Zhang and X. Diao
share of total employment fell from more than 70% to about 60%, while employment share increased in manufacturing, construction,
trade and transport sectors, whose labor productivity was 2–3 times of that in agriculture in this period (Fig. 1).
The pace of economywide labor productivity growth is similar in the second decade of 1988–1997 as in the first period
(1978–1987), while the driving forces of the growth differ significantly between these two periods. As shown in Fig. 2, within-sector
labor productivity growth in the manufacturing sector becomes the most important driver in 1988–1997, accounting for 3.3 percentage points per year and about 50% of labor productivity growth economywide. This pattern of growth can also be explained by
the economic reforms, which had moved from the rural to urban areas in early 1987 (China State Council, 1986). Urban reforms
provided more effective profit-sharing mechanisms for both state-owned and collectively owned firms that released entrepreneurs'
animal spirits and made existent manufacturing firms much more productive. In this period, manufacturing share of total employment did not increase much, i.e., few new manufacturing firms were created, which explained why structural change led growth is
quite modest in the second period, while within-sector productivity growth in the manufacturing sector is the dominantly driving
force to economywide labor productivity growth.
The economywide labor productivity growth rate rose to more than 9% per year in the period of 1998–2007, a subperiod with the
highest annual productivity growth in 1978–2017. Two important factors contributed to such growth. First, the reforms of stateowned industrial enterprises had been deepened since 1998 (Shao, 2014), which led to the closedown of many low productive
industrial firms. Laid-off low skilled factory workers slowed down the contribution from structural change to labor productivity
growth in this period, while the remaining firms became more productive, leading to high within-sector productivity growth in the
nonagricultural sectors. The second factor is that China became a WTO member country in December 2001. The global market gives
China opportunities to rapidly expand her labor-intensive and export-oriented manufacturing, and the competition in the world
market and rising foreign direct investment lead to continuously rapid productivity growth within all nonagricultural sectors.
We also observe that besides the first subperiod, contribution of structural change to economywide productivity growth is modest
and explains about 10% of productivity growth in each of the recent three decades. The institutional barriers including the Hukou
system and land tenure insecurity had impeded the free movement of rural labor forces from rural areas to urban areas and hence
from the agricultural to nonagricultural sectors (Deininger, Jin, Xia, & Huang, 2014; Wang, Akgüҫ, Liu, & Tani, 2020). Without such
barriers, size of agricultural employment in the Chinese economy should have been much smaller than what we observed in the data,
implying that productivity gains from structural change have not be fully realized with such institutional barriers.
Comparing decomposed productivity growth across the four sub-periods, not only the manufacturing sector that increasingly
becomes a more important driver of economywide labor productivity growth, but also the services particularly in the most recent
period. The WTO access benefits not only China's manufacturing sector. When manufacturing is export oriented, productivity in
trade, transport and business services that provide logistics services to international trade has grown hand-to-hand with manufacturing expansion (Liao, 2020). Rapid growth also raises wealth of the majority of Chinese, whose increased spending on domestic
services creates positive demand-side effects on growth.
Consistent with productivity measure using different approaches (e.g., Jin, Ma, Huang, Hu, & Rozelle, 2010; Shen, Baležentis, &
Ferrier, 2019; Wang, Herzfeld, & Glauben, 2007), the direct contribution of within-sector productivity growth from the agricultural
sector is impressive in China for the past four decades. Such sustainable growth requires technological changes and adoption of
modern inputs through public investments in research, development and extensions (Ito, 2010; Jin, Huang, Hu, & Rozelle, 2002), in
additions to institutional and policy reforms emphasized above. After the extremely high labor productivity growth within agriculture in the early years as an outcome of the rural reforms, agricultural productivity contribution to the economywide growth
remains positive but relatively modest in the subsequent three sub-periods, mainly because share of agriculture in total employment
falls, from more than 70% in 1978 to 27% by the end of the studied period in 2017. Between 1978 and 2017, China doubled the size
of its total labor force. However, all new employment opportunities are created in manufacturing and services and the absolute
number of agricultural workers falls in this period. Rising labor productivity in agriculture has led China's agricultural production and
value-added to grow rapidly without increasing agricultural employment. This allows agriculture to indirectly contribute to the
economywide productivity growth through structural change, a less obvious factor in assessing the role of agriculture on broad
economic growth.
4. Economic integration with structural change
Literature for agriculture's linkage effects is rich and many of it are highly relevant to this paper. IO, Semi-IO, SAM and CGE
approaches are broadly used for measuring such linkage effects, while most of the analyses are based on countries' one-year IO/SAM
that captures the countries' economic structure in a given year. Some of these studies assess the differential linkage effects by
comparing IOs/SAMs of a several countries with different level of development. This allows their authors to partially capture the
relationship between different economic structure and the magnitude of the linkage effects (e.g., Kubo, Robinson, & Syrquin, 1986;
Haggblade, 1991; Vogel, 1994). However, IOs for different countries in different years are still hard to properly quantify the relationship between structural change and the linkage effects over time. In this section, we rely on China's seven IOTs in 1987–2017 to
measure the relationship between structural change and economic integration as well as the increased linkages of agriculture with the
rest of the economy over time.
An input-output table (IOT) describes the sale and purchase relationships between producers and users/consumers within an
economy, and it is often constructed from industrial or economic census/survey that properly captures the economic structure in the
survey year. Using multiple rounds of IOTs over time allows us to observe the changing relationship across sectors in the economy
and between agriculture and many nonagricultural subsectors through input-output linkages as well as the changing patterns of
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Y. Zhang and X. Diao
consumer consumption and trade. This IO analysis in combination with the structural change analysis in the previous section helps us
better understand the integration of the economy with structural change.
A matrix in an IOT presents inter-industrial flows of goods and services (both domestically produced and imports), and additional
columns outside this matrix in an IOT present the flows from producers to final demanders including households, government,
investment and exports. The economic integration and the interlinkages of agriculture and the rest of the economy are directly
measured using the IOT data. China constructed the economy's first IOT in 1978 and has published seven IOTs since 1991 for the
period of 1987 to 2017 (NSB 1991, 1996, 1999, 2005, 2009, 2015, 2019). These seven IOTs have different numbers of production
sectors as well as agricultural subsectors. We aggregate their sectors to make the seven IOTs consistent in sector structure for the
analysis discussed in this section.3 The analysis first highlights the broad integration of the economy and then focuses on the changing
relationship between agriculture and the rest of the economy in 1987–2017.
4.1. Economic integration between agriculture and the rest of the economy
Economic integration is often referred to closer economic relationship among states from unification of economic policies between states. We borrow this concept to describe the closer relationship among sectors in a domestic economy and use the share of
total intermediate inputs or GDP in national gross output as a measure for the level of overall economic integration. A sector's
intermediate input demand is the difference between this sector's gross output and its value-added. Change in a sector's input-output
technology often leads to increased demand for intermediate inputs that are produced by many other sectors. When many modern
sectors in the economy increase their demand for intermediate inputs, that is, when the input-output technological relationship
becomes denser across sectors, it is shown up as reduction in the value added share of the economy and increased demand for
intermediate inputs (Kubo et al., 1986). At the extreme, when the technology is quite primal and production of individual sectors
relies mainly on primary inputs such as labor and land/capital, GDP value is close to the national gross output and value for
intermediate inputs is close to zero in the gross output. In this economy, there are little sectoral linkages, which represents the least
integrated economy in our definition (still, economic sectors can be affected by each other through competition in labor and other
factor markets). Structural change is measured by the movement of labor from such traditional sectors with low productivity to the
modern sectors with high productivity, while production process of the modern sectors becomes more complicated than the traditional sectors, as modern sectors rely much more on intermediates that are produced by many other modern sectors than traditional
sectors. With more labor being employed in the modern sectors, we expect that the economy becomes more integrated, and share of
GDP in national gross output falls and share of intermediate inputs rises.4
We focus on the relationship between agriculture and the rest of the economy for assessing economic integration and using the
components agricultural goods consumed as intermediates, final consumption and exports to measure such relationship. Fig. 3
displays these components as shares of gross agricultural output in the seven years' IOTs. The figure also includes agricultural goods
used in the processed food sector, a sector using such agricultural primary products intensively. Primary agricultural goods include
products of crops, livestock, forestry and fishery.
Fig. 3 clearly shows that when structural change leads to the economy being more integrated, primary agricultural products are
increasingly used as intermediates and less so as final consumer goods. About 47% of agricultural output was used as intermediates in
1987, and the share increased to 77% in 2017. On the other hand, 34% of primary agricultural products were consumed directly by
rural households that produced them in 1987, while this share fell to only 8% in 2017. Share of agricultural products consumed by
urban households changes relatively modestly, while it increased between 1987 and 2017 because of rapid urbanization that has
raised the urban share of total population significantly in the last thirty years (Deng, Huang, Rozelle, & Uchida, 2008).
Three factors can explain the changes in the use of agricultural products. The first factor is the structural change discussed in the
previous section. The second factor is agriculture's commercialization and specialization. The third factor comes from the demand
side associated with growth in rural nonfarm income and rapid urbanization.
The previous section indicates an increasing role of manufacturing in the structural change led growth particularly in the last two
sub-periods. There are two manufacturing subsectors in which agricultural products used more intensively as intermediates – food
processing and textile/footwear industries, and expansion in production and increases in interdependency of these two sectors are the
main reasons for agricultural products being increasingly used as intermediates. While both food processing and textile/footwear
industries have expanded rapidly in China, the driving forces behind their growth differ significantly. The expansion of textile/
footwear industry is led by exports, while growth in food processing is led by domestic demand.
China has become the world largest exporter of clothing and footwears since joining WTO. Finishing textile and footwear products
are processed from other textile products such as fiber, cloth and leather, which, in turns, are processed from primary agricultural
products such as cotton, silk, linen, wool, hides and furs. When exports of finishing textile and footwear products grow rapidly,
together with the deepening in the interdependency of their supply chains within Chinese economy, demand for agricultural intermediates also rises rapidly.
3
Number of the sectors in the seven IOTs are described in Appendix Table A1.
Economic integration also results from specialization in manufacturing and services that causes many activities within a single factory unit to
become independent and separate production/service units, and vertically or horizontally integrated supply chains are part of economic integration.
Such type of economic integration is often associated with technology change outside manufacturing sector such as new technologies in communication, information, transportation and other logistic services.
4
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China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
80
70
1987
1992
1997
2002
2007
2012
2017
60
50
40
30
20
10
0
Total intermediates
In processed food
Rural
Intermediates
Urban
Final demand
Fig. 3. Share of Agricultural Outputs Used as Intermediates and Final Consumption (Agricultural gross output value = 100 in each year).
Source: Authors' calculation using data from China's IOTs for 1987–2017 (NBS 2019).
Processed food products are much less tradable internationally for Chinese producers, whose products are mainly for the domestic
market. Growing demand for processed foods and rapidly declines in primary agricultural consumption are led by two interlinked
factors: commercialization of agriculture and urbanization. With rapid industrialization, millions and millions rural migrants moved
from rural to urban areas working in urban nonagricultural sectors (De Brauw, Huang, Rozelle, Zhang, & Zhang, 2002; Gong, Kong,
Li, & Meng, 2008; Cai & Wang, 2010; Li, Huang, Luo, & Liu, 2013). Among the remaining farm households, some become highly
commercialized, specializing in producing one or a few agricultural products (Rae, Ma, Huang, & Rozelle, 2006; Bi, Huang, & Rozelle,
2007; Wang, Huang, & Rozelle, 2017; Huang & Ding, 2016; Yin, Sun, You, & Müller, 2019). For those nonspecialized small farm
households, agriculture becomes a less important income source and they depend on either rural nonfarm income or remittances sent
by their family members working in the urban sector (Démurger, Fournier, & Yang, 2010; Zhao & Barry, 2014; Zhu & Luo, 2008).
These two factors have completely transformed rural household consumption patterns. Most rural households have transferred from
subsistent farmers that consume foods mainly produced themselves to purchasing foods from markets, of which most are processed
products.
In the meantime, China has rapidly urbanized and the majority of population now live in urban areas. Food consumption for
urban households is predominantly as processed products. More importantly, with rapid income growth, Chinese households' total
consumption expenditure increased five time with annual growth rate about 10% in Yang, 1997–2017 (World Bank, 2019). High
income growth itself can significantly change food consumption patterns from primary food dominant to more processed products.
Fig. 4 displays the shares of primary and processed foods in both rural and urban total expenditure in the seven IOTs in
50
1987
45
1992
1997
2002
2007
2012
2017
40
35
30
25
20
15
10
5
0
Rural consumpon
Urban consumpon
Primary agriculture
Rural consumpon
Urban consumpon
Processed foods
Fig. 4. Share of Primary Agricultural and Processed Food Products in Household Total Expenditure (Household total expenditure = 100 in each
year).
Source: Authors' calculation using data from China's IOTs for 1987–2017 (NBS 2019).
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China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
0.700
0.600
0.500
0.400
agric
nagri
0.300
0.200
0.100
0.000
1987
1992
1997
2002
2007
2012
2017
$320
$390
$750
$1,110
$2,510
$5,930
$8,650
Fig. 5. Input-output Coefficients in the Nonagricultural sector.
Source: Authors' calculation using data from China's IOTs for 1987–2017 (NBS 2019); the GNI per capita, Atlas method in current US$ along x-axis is
from the World Development Indicator (World Bank, 2019).
1987–2017. In 1987, an average rural household spent 45% of her income on primary agricultural foods, and only 14% on processed
food items, while in 2017, the share for the former fell to 13% and rose to 25% for the latter. A similar pattern of change in primary
food consumption also occurred for urban households, and the share for primary agricultural food in urban households' total expenditure fell from more than 24% in 1987 to less than 7% in 2017. However, share of processed foods also declined for the urban
households, from 24% in 1987 to 18% in 2017, which can be explained by the Engel law, as nonfood items particularly housing
become much dominant in urban total expenditures with income growth.
It should point out that while from the agricultural sector point of view, more agricultural products are used by nonagricultural
sectors as intermediates, it does not mean that agriculture becomes more important for the nonagricultural sector's production.
Therefore, it is necessary to check whether the use of agricultural products as intermediates increases disproportionally, i.e., whether
the integration of agriculture and nonagriculture contributes to the declined share of value added in manufacturing's gross output.
Fig. 5 displays the input-output (IeO) coefficients of agricultural and nonagricultural goods used in nonagricultural production in
1987–2017, and the nonagricultural sector's gross output is one. The figure also includes per capita gross national income (GNI) in
current USD in each year along the x-axis, a measure the World Bank uses to classify countries with low-, middle- and high-income
statuses. In fact, ratio of agricultural as intermediates in nonagricultural gross output fell from 0.071 to 0.033 between 1987 and
2017. On the other hand, the IeO coefficient for the nonagricultural goods used as intermediates in nonagricultural production
constantly rises over time, from 0.54 in 1987 to 0.66 in 2007 and fell slightly to 0.61 in 2017. This finding is not surprising given that
the integration of the economy is primarily driven by manufacturing development that uses much more intermediates than other
sectors, of which most are produced by various subsectors within manufacturing.
Another way to assess the integration between agriculture and the rest of the economy is to use the input-output multiplier
approach, which takes into consideration of circulate effect among sectors. Following the linkages effect literature (see for example
Hazell 1991 and Vogel, 1994), we calculate the backward and forward input-output linkages multipliers for the agricultural sector.5
The backward linkage multiplier measures the increased demand for nonagricultural goods as intermediates used in the agricultural
sector when production of agricultural sector increases by one unit (e.g., 1 million USD in constant price), while the forward linkage
multiplier measures the increased demand for agricultural goods as intermediates by the nonagricultural sector when production of
nonagricultural sector increases by one unit. The multipliers are calculated using the 1987–2017 IOTs discussed above. Fig. 6 displays
the result.
As clearly shown in Fig. 6, the backward linkage multiplier effects are much higher than the forward linkage effects in agriculture.
More importantly for our study, the figure shows that the backward linkage effects become stronger with economic growth, i.e., the
higher the income level, the more demand for nonagricultural products used as intermediate inputs by the agricultural sector.
Between 1987 and 2017 in 30 years, the backward linkage multiplier increases from 0.44 in 1987 to 0.96 in 2007 and remains at 0.84
in 2017 (the dots in Fig. 6). This implies that for producing a same 1 million USD agricultural goods, the agricultural sector creates 96
thousand USD intermediate demand for the nonagricultural goods in 2007 and 84 thousand USD in 2017, while it created only 44
5
We did not consider the economywide linkages effect to calculate full SAM multipliers that take into consideration of consumption linkage
effects as in Vogel (1994) and focus only on the input-output multipliers. The assumptions of unconstrained resources, exogenous prices and fixed
consumption patterns for the full SAM multipliers calculation are unrealistic, and we prefer to use a CGE model for assessing such economywide
linkage effects in the next section. Given that our input-output multipliers are calculated from the actual IOTs of 1987–2017, a Leontief technology
assumption for the use of intermediate inputs is acceptable similar as in a CGE model analysis.
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China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
1.00
0.90
0.80
0.70
0.60
forward linkages - ag as inputs used in nonag
0.50
Backward linkages - nonag as inputs used in ag
0.40
0.30
0.20
0.10
1987
1992
1997
2002
2007
2012
2017
$320
$390
$750
$1,110
$2,510
$5,930
$8,650
Fig. 6. Input-output Multiplier for the Agricultural sector.
Source: Authors' calculation using China's IOTs for 1987–2017 (NBS 2019); the GNI per capita, Atlas method in current US$ along x-axis is from the
World Development Indicator (World Bank, 2019).
thousand USD in 1987.6
On the other hand, while the forward linkage effects are weaker than the backward linkage effects, we observe a modestly
declining trend between 1987 and 2017. In 1987 one million USD of expansion in nonagricultural production creates 19 thousand
USD demand for agricultural goods, and it falls to 10 thousand in 2017 (the squares in Fig. 6).
While the trends are comparable between direct measurement of the IeO coefficients and the input-output linkage multipliers, the
magnitude of the multipliers is 2.6 to 3.5 time of the IeO coefficients.
4.2. Assessing the evolving role of agriculture – a CGE model analysis
Based on the stylized facts analyzed in the two previous sections, we develop two CGE models to further assess the evolving role of
the agriculture with structural change. Various CGE models have been developed for China and applied to analyze different economic
development and policy issues (see, for examples, Latorre, Yonezawa, & Zhou, 2018; Qi & Zhang, 2018; Diao, Zhang, & Chen, 2012;
Horridge & Wittwer, 2008; Garbaccio, 1995; Xu, 1994). The two CGE models we developed in this section are used to quantitively
measure the interlinkages in the Chinese economy with different economic structure over time, paying particularly attention to the
agri-food systems (AFS) in such measures.
4.2.1. Measuring the agri-food systems (AFS)
The agri-food systems (AFS) concept has been broadly adopted by agricultural economists with various definitions in the past two
decades. A recent book titled “Sustainable Food and Agriculture – An Integrated Approach” edited by Campanhola and Pandey
(2019) systematically describes the systems and the subsectors that make up agri-food systems (see Chapter 33 –Agrifood Systems in
Campanhola & Pandey, 2019). In the literature, the structures and functions of AFS are described and pathways of or policies to
develop sustainable AFS are discussed (see, for example, Thompson et al., 2007), while few have given quantitative measures of the
system as well as its role in the broad economies or employment.
We adopt the measurement developed by Thurlow (2019) in this section and developed seven social accounting matrices (SAMs)
using China's IOTs 1987–2017 discussed intensively in the previous section. The detail description of the SAMs can be found in
Appendix I. These seven SAMs are first used to measure the changing patterns of AFS in China with structural change in the past three
decades. Thurlow (2019) measures different components of AFS as shares in a country's total GDP and total employment, and the
following sectors/subsectors or part of some nonagricultural sectors/subsectors are considered as components of AFS: (a) The traditional primary agricultural sector including all crops, livestock, forestry and fishing; (b) Processing food that is a manufacturing
subsector and some non-food manufacturing subsectors that directly use agricultural raw materials as intermediates such as yarn and
natural fibers, and wood and timber products; (c) Production of inputs used directly by farmers and agro-processors (e.g., fertilizer
and banking services). Inputs produced by farmers and processors themselves are excluded to avoid double-counting. Only the
portions associated with local input producers are part of the AFS; (d) Domestic transportation and trade activities (retailing and
wholesaling) associated with the movement of agri-food products between farms, firms and final points of sale (markets); Finally, (e)
food services sector and a portion of hotel and accommodation sector calculated based on the share of agri-food inputs in these
6
While the locus of the multipliers in Fig. 8 is very similar as in Fig. 2 of Vogel (1994), Vogel's analysis is based 27 countries' SAMs of various
years. The graphs in Vogel are the fitted values of these SAMs' multipliers against real per capita income.
10
China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
(a) 40.0
Primary Ag
Food processing
Nonag used by AFS
Trade&transport for AFS
Food processing used in hotel & restaurant
35.0
30.0
% of national total GDP
25.0
20.0
15.0
10.0
5.0
0.0
(1987)
(1992)
(1997)
(2002)
(2007)
(2012)
(2017)
$320
$390
$750
$1,110
$2,510
$5,930
$8,650
Year & GNI pc in current US$
(b)
70.0
Primary Ag
Food processing
Nonag used in AFS
Trade&transport for AFS
Food processing used in hotel & restaurant
60.0
% of national total employment
50.0
40.0
30.0
20.0
10.0
0.0
(1987)
(1992)
(1997)
(2002)
(2007)
(2012)
(2017)
$320
$390
$750
$1,110
$2,510
$5,930
$8,650
Year & GNI pc in current US$
Fig. 7a. Value-added along AFS with Different Levels of Income Over Time. Source: Authors' calculation using SAMs constructed from China's IOTs
for 1987–2017 (NBS 2019); the GNI per capita, Atlas method in current US$ along x-axis is from the World Development Indicator (World Bank,
2019). Employment along AFS with Different Levels of Income Over Time.
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China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
sectors' total input purchases.
Figs. 7a and 7b display, respectively, the shares of value-added and employment in national GDP and total employment for
different components of China's AFS in the seven SAMs between 1987 and 2017. Because the textile sector is rather aggregated in
China's IOTs and yarns and natural fibers cannot be identified as a separate textile subsector, we exclude the whole textile sector from
the AFS.7 We also exclude wood and timber products as they cannot be separated from wood products in the IOTs. As one of the world
largest importers for wood and timber products, domestically produced wood and timbers might have played rather modest role in
China's booming exports of furniture and other wood products that use wood raw materials as one of important intermediates. The
figures also include per capita gross national income (GNI) in current USD in each year along the x-axis. With GNI per capita at $320
to $750, China was classified as a low-income country in 1987–1997. The country became a lower middle-income country in 2002
and an upper middle-income country since 2012. With China moving up the income ladder, Fig. 7a clearly shows a declining trend in
the share of AFS's value-added in national GDP over time, while the pace of the declines in the trend slows down after China's per
capita income reached $2510 in 2007. Further investigating in the components of the AFS in Fig. 7a indicates that the declines in the
share of AFS in GDP is driven by the falling share of primary agricultural value-added in GDP, while shares of other components of
AFS beyond agriculture are relatively stable with modestly rising in some components over time. The AFS has become more
dominated by the components outside primary agriculture with income growth. More than 40% of AFS value-added comes from
components beyond primary agriculture since 2002, reaching 50% in 2017, while primary agriculture that used to account for 75% of
the value-added of AFS in 1987 becomes just a half in 2017.
Source: Authors' calculation using SAMs constructed from China's IOTs for 1987–2017 (NBS 2019); the GNI per capita, Atlas
method in current US$ along x-axis is from the World Development Indicator (World Bank, 2019).
The declining trend of AFS employment in total employment in Fig. 7b is similar as the trend for the value-added in Table 7(a).
Employment share of AFS in 2017 is only about a half of that in 1987, falling from 64% in 1987 to 33% in 2017. While the declines in
AFS employment are solely driven by the falling share of agricultural employment, agricultural employment still dominates the AFS,
and employment beyond primary agriculture accounts for 18.3% of AFS total employment in 2017, and it is only 6.4% in 1987.
Putting Figs. 7a and 7b together, it shows that labor productivity is much higher in the AFS beyond agriculture. Thus, the movement
of labor from agriculture to other activities within AFS is expected to further improve productivity of the whole AFS and contribute to
broad economic growth.
4.3. Assessing the evolving role of agriculture using CGE models
We developed two comparable and consistent CGE models for China in 1987 and 2017 to shed light on the differential role of
agriculture with economic integration and different economic structure over time. The models are constructed consistently with the
neoclassical general equilibrium theory. Similar to other single country CGE models, China is assumed to be a small open economy in
the sense that the international prices for goods exported and imported are exogenous. However, in contrast to a theoretical small
open economy model in which prices are exogenous for the domestic economy and imports/exports are the excess demand/supply, in
the CGE model, domestic prices for products produced and consumed domestically are endogenous. More description of our CGE
models can be found in Appendix I.
There are 10 production sectors same in the two CGE models, which are aggregated from the more disaggregated sectors in the
1987 and 2017 IOTs. One aggregate sector is agriculture. To assess the role of AFS in the economy, we keep processing food as a
standalone sector and aggregate other industrial sectors into four: textile, clothing, other manufacturing and other industry. There are
four aggregate service sectors including three services related to the AFS, i.e., trade, transportation, and hotel and restaurants, and a
highly aggregate service subsector – other services – to cover all other services that are not directly associated with the AFS.
In the CGE models, we posit a Stone-Geary type utility function so that demand patterns will be non-homothetic with declining
budget share of agricultural product. In one of the sensitivity tests in Appendix II, we also relax this assumption on preferences to test
the magnitude of demand side effects in explaining the model results.
The SAMs for 1987 and 2017 represent initial and end points for a period of thirty years. The differences in economic structure in
the two SAM reflect such long-term evolution in economic structural change. With similar model structures and same elasticities in
production and trade functions in the two CGE models, if the comparative statics exercises from a similar shock result in different
outcomes between the two models for 1987 and 2017, we can argue that such difference is driven by the different economic
structures that are captured in the two SAMs representing the initial and end years of a period of thirty years.
Specifically, the shock in the comparative statics exercises is a 10% increase in agricultural labor productivity in the two CGE
models and the new equilibriums resulted from the shocks are compared with the initial SAMs for the two years. We then check
whether changes from the initial two SAMs are different between 1987 and 2017. Since our focus is to assess the impact of structural
change on the role of agriculture using the historical data that informs the differences in economic structure in the SAMs for 1987 and
2017, we should avoid the use of a dynamic CGE model as in Diao et al. (2012). The dynamic CGE model is usually used to simulate a
possible economic growth path ex ante from an initial data point representing the current position of the economy. In fact, structural
7
Excluding the relevant textile subsector from the calculation of AFS may underestimate the scale of AFS in the recent years given that China has
become the world largest exporter of finishing textile products since mid-2000s after China joins WTO. Some of the exported and domestically
consumed textile products do use yarn and natural fibers produced domestically. However, the changing patterns of AFS are still clearly observed
with the exclusion of this component of textile sector in 1987–2017 as demonstrated in Figs. 9(a) and 9 (b).
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Y. Zhang and X. Diao
Table 1
China 1987 and 2017 CGE model results.
Calculated from the SAMs
Variables
Unit
Per capita GDP
2010 US$
Share in GDP
%
Agriculture
AFS beyond agriculture
Other industry
Other services
Share in household consumption %
Agriculture
Processing food
Employment
Million
Share in employment %
Agriculture
AFS beyond agriculture
Other industry
Other services
Due to 10% ag productivity shock
1987
2017
634
7308
28.0
10.0
40.4
21.5
7.9
8.3
35.1
48.7
36.6
18.3
528
8.2
19.6
776
60.0
4.3
19.5
16.2
27.0
6.1
24.6
42.3
Variables
Change in GDP from the SAMs
% of gain in GDP
Agriculture
AFS beyond agriculture
Other industry
Other services
Change in household consumption %
Agriculture
Processing food
Unit
%
%
Change in employment
Agriculture
AFS beyond agriculture
Other industry
Other services
Million
1987
2017
1.35
0.84
65.6
12.3
18.5
3.6
26.9
23.5
25.8
23.7
4.5
0.8
2.0
2.6
−6.2
1.3
3.4
1.6
−8.4
1.9
2.8
3.7
Source: China 1987 and 2017 SAMs and CGE model simulation results.
change is unlikely to be captured by a dynamic CGE model of which the theorical foundation is the neoclassical growth theory. As
argued in Rodrik (2014), for developing countries, rapid structural change should be understood by the combination of neoclassic
growth theory and the dual economy theory.
For conducting a proper comparative statics analysis, the factor supply accounts should be fixed, and such accounts include labor,
land and capital in the CGE model. Moreover, to avoid the number of outputs (production sectors) to be larger than the number of
inputs (factors) that would cause a dimensional problem at equilibrium, we further fixed capital at the sector level. The land is a
portion of capital in the original SAM and it is separated from capital only for the single agricultural sector. That is, land used in
nonagricultural sectors is still part of sectors' capital. Since both capital and land are fixed in comparative statics, the assumption that
land is used only in the agricultural sector does not affect the model result. The only mobile factor is labor, which is assumed mobile
across sectors. However, we consider labor market equilibrium in a manner that allows for structural misallocation in the economy.
This assumption is particularly important for China's agriculture in the presence of institutional impediments including the Hukou
system and land tenure insecurity to the free movement of factors (Deininger et al., 2014; Jin & Deininger, 2009; Meng, 2012; Wang
et al., 2020; Whalley & Zhang, 2007; Yang, 1997; Zhang, 2010). In particular, we assume there are a set of wedges with different
value across sectors that prevent the equalization of the value marginal products of labor among the sectors. Since the value of the
marginal product of labor is assumed to be lower in the agricultural sector than in manufacturing and service sectors in equilibrium,
the economy has more workers in the agricultural sector. Structural change – a movement of workers from agricultural to nonagricultural sectors – would increase economywide labor productivity.
Intuitively, with different size of agriculture in the Chinese economy in 1987 and 2017, a same 10% shock on agricultural
productivity is expected to positively affect the total economy differentially. For the Chinese economy in 1987 when agriculture
accounts for 28% of GDP, gain in total GDP is expected to be more than that in the recent year of 2017 when agriculture becomes just
8% of GDP. The model results support this intuitive expectation. As shown in Table 1, the 10% agricultural productivity shock results
in 1.35% more GDP in 1987 and only 0.84% in 2017, both compared with these two years' GDP in the respective SAMs. However, we
further notice that agriculture's share of GDP in 1987 is 3.5 times of that in 2017, while 10% agricultural productivity shock leads to
only 60% more GDP in 1987 than in 2017. Obviously, there are other general equilibrium effects affecting the simulation results,
which are our interests for designing the simulations.
Agriculture is a less tradable sector in China with 1–2% of output exported each year. With a commonly used imperfect substitution assumption between exports and production for domestic markets in the CGE models, the model simulations are expected to
capture endogenous price effects at the new equilibriums.8 Consequently, the productivity shock in agriculture is to create an excess
supply of the agricultural good (and excess demand for nonagricultural goods). The relative prices for the nonagricultural goods will
therefore have to rise to encourage additional production of nonagricultural goods, leading to more consumption of agricultural
product among producers using it as an intermediate input. As we discussed in the previous section, when the economy becomes
more integrated in 2017 than in 1987, more primary agricultural products are used as intermediates and less consumed directly. For
consumers, more processed food and less primary agricultural products are consumed in 2017 than in 1987. Thus, we should expect
that the linkage effects from agricultural products as intermediates used to increase production of other nonagricultural sectors are
stronger in 2017 than in 1987, while the direct final consumption effects due to lowering relative price of agricultural good are
8
We also test whether different elasticities of substitution between trade and domestic demand in the trade functions affect the model results and
how large the magnitude of such differences is. The sensitivity tests are discussed in Appendix II.
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China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
stronger in 1987 than in 2017.
We use sectoral contribution to GDP gain after the shock to assess the economic integration effects and evolving role of agriculture
in such integration. The top block of second panel in Table 1 displays the model results. In 1987, 65.6% of GDP gain after the shock
comes directly from increased agricultural production, while the share falls to 26.9% in 2017. On the other hand, 12.3% of GDP gain
is from the AFS beyond agriculture in 1987, while such contribution almost doubles, rising to 23.5% in 2017. The industrial and
service sectors that are not directly associated with the AFS also benefit more from agricultural productivity shock when the economy
is more integrated. In 1987, 18.5% and 3.6% of GDP gain are from industrial and service sectors outside the AFS, respectively, while
their contribution rises to 25.8% and 23.7% respectively in 2017.
Consumer demand also increases with an agricultural productivity shock through relative price and income effects, while demand
side effects become more modest particularly for primary agriculture at higher level of income in 2017. The middle block of the first
panel in Table 1 reports the shares of primary agriculture and processed food in consumers' total expenditure in 1987 and 2017 and
the middle block of the second panel reports changes in their consumption resulted from 10% agricultural productivity shock in both
years. In 1987, 36.6% and 18.3% of consumers' total expenditure are for primary and processed food products respectively, while the
share falls to 8.2% for primary agricultural food and rises slightly to 19.6% for processed food in 2017. Responding to the lowered
price for agriculture and higher income due to 10% agricultural productivity shock, primary agricultural consumption increases by
4.5% but the increase for processed food is only 0.8% in 1987. On the other hand, primary agricultural consumption increases
modestly by 2.0% and more (2.6%) for the processed food with the same shock in the model for 2017.
The CGE models also generate positive correlation between the sector components of labor and agricultural productivity investigated in Section 2. With fixed supply of total labor, a positive agricultural productivity shock in the model leads to the agricultural sector to release labor to other sectors. The bottom block of the second panel in Table 1 reports such labor movement in the
simulations. 60% of labor employed in agriculture in 1987, while the share falls to 27% in 2017 (see the bottom block of the first
panel in Table 1). With 10% agricultural productivity shock, about 6.2 million workers are released from agriculture in 1987 and 8.4
million in 2017. 1.3 million of released labor (i.e. 21%) is employed by the AFS beyond agriculture in 1987 and 1.9 million (23%) in
2017. Outside the AFS, the manufacturing sector was much more important in Chinese economy than the services in 1987 when 40%
value-added is generated from industrial sectors that are not directly associated with the AFS, while in 2017, services become more
important. Excluding services associated with the AFS, other services generate 49% total GDP in 2017. Because of this, the same 10%
agricultural productivity shock leads to industrial sectors employing more released agricultural labor in 1987, 3.4 million, about
54%, while it is the services that employ more released agricultural labor in 2017, 3.7 million, about 44%.
5. Conclusions
It is well-known that share of agriculture in an economy and in total employment is falling during economic growth and structural
change. We analyze the implication of structural change to the evolving role of agriculture using China as an example. The unprecedent growth in China has led to rapid structural change in the past four decades. By combining a growth decomposition exercise
with Input-Output (IO) and CGE model analyses using China's seven IOTs in 1987–2017, the evolving role of agriculture is quantitatively measured in the paper. The IO and CGE analyses pay particularly attentions to the agri-food system (AFS) that includes
many economic activities beyond traditional agriculture.
The conceptual framework of Rodrik (2014) and the growth decomposition approach developed by McMillan and Rodrik (2011)
are applied for the structural change analysis. The analysis shows that the direct contribution of productivity growth within agricultural sector to the broad economic growth is impressively high in China particularly in the early years when the Chinese reforms
focused on the rural areas. Over time, such contribution remains significant but becomes relatively modest because of rapid falling of
agricultural shares in the economy and total employment. However, it is worth to emphasize that between 1978 and 2017 China
doubled the size of its total labor force, while all new employment opportunities have been created in manufacturing and services and
the absolute number of agricultural workers falls in this period. Rising labor productivity in agriculture has led to rapid agricultural
growth without increasing agricultural employment. This allows agriculture to indirectly contribute to the economywide productivity
growth through structural change, confirming the arguments of many development economists who emphasize the impact of agriculture on structural change in the relationship between agriculture and structural change. The structural change exercise of our
paper also reveals the importance of within sector productivity growth in both manufacturing and services. More than 40 years'
sustainable growth in China has come from both within-sector productivity growth in all economic sectors and rapid structural
change.
The measurement of economic integration using an IO approach helps to explain why China's rapid structural change has been
accompanied by similar rapid productivity growth within each sector. The structural change is accompanied by economic integration
and different sectors in Chinese economy become more interdependent on each other. Technological change begets technological
change across sectors more easily, and productivity gains in each sector benefit other sectors via the stimulated spillovers and
technological transfers.
Looking beyond agriculture for the whole agri-food system (AFS) can help to explain why economic integration is important for
understanding the evolving role of agriculture when the primary agriculture becomes a small component of the whole economy. The
CGE model analysis further quantifies such evolving role. The two CGE models represent the initial and end years of a period of
30 years in which the structure of Chinese economy has changed significantly. Because of this, the models show that the agriculture's
direct contribution becomes obviously less important with structural change when agriculture is a much smaller component of the
whole economy. On the other hand, over time economic integration enhances the linkages between agriculture and the rest of the
14
China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
economy, generating more economywide gain indirectly from such linkages.
While the total effect of a same agricultural productivity shock is smaller in 2017 than in 1987 in the CGE analysis, the gap in the
gain between the two models is much smaller than the difference in agriculture's size of the economy in the two years. Measured by
the share of national GDP, size of agriculture in the Chinese economy in 1987 is 3.5 times of that in 2017, while the same 10%
agricultural productivity shock leads to the gain in GDP only 60% more in 1987 than in 2017. Through the general equilibrium
effects, about 0.5 unit additional nonagricultural value-added is associated with a unit agricultural value-added increase in 1987,
while additional nonagricultural value-added rises to 2.7 unit in 2017.
Part of such linkage effects occur within the AFS that includes nonagricultural components directly integrated with primary
agriculture through economic activities. Linkages within AFS are even stronger than the economy as whole when the economy
becomes more integrated. One unit increase in agricultural value-added leads to 1.19 unit increase in AFS value-added (including
that unit from the primary agriculture) in 1987, and it leads to 1.87 unit increase in AFS value-added in 2017.
The demand side effects through consumption-to-production linkages become less important when agriculture becomes more
integrated with the rest of the economy and size of agriculture becomes much smaller. Consumers enjoy the economic benefit
generated from agricultural productivity mainly from their increased consumption of non-primary agricultural products.
The CGE models also generate positive correlation between the sector components of labor and agricultural productivity investigated in Section 2. As the economy becomes more integrated, positive productivity shock in agriculture releases more labor from
agriculture to more-productive sectors of the economy including those as part of the AFS.
Our analysis on economic integration and evolving role of agriculture with structural change emphasizes the supply side role for
sustainable growth in which agriculture continues to play an important but different role from the past when the demand side effects
were stronger. Policies to strengthen supply side linkages have been emphasized in the recent years in China. Exploring further
integration between agriculture and the rest of the economy should be part of the new growth strategy.
Acknowledgement
The research for this paper is funded by the National Natural Science Foundation of China (NSFC), (Grant number:
NSFC71761147004), the Chinese Academy of Agricultural Sciences (CAAS), the Agricultural Science and Technology Innovation
Program of CAAS (Grant number: ASTIP-IAED-2020-05), and the CGIAR Research Program on Policies, Institutions, and Markets
(PIM) led by the International Food Policy Research Institute (IFPRI). The authors thank Institute of the Agricultural Economics and
Development, CAAS, and China Academy for Rural Development, Zhejiiang University for organizing the two workshops titled GAMS
and Its Use in Agriculture and Economic Modeling in Bejing on May 13-14, 2019 and Hangshou on May 15-18, 2019, respectively,
where we had opportunities to present the early findings of our research and received feedback and comments from the workshops’
participants. The authors also thank the two anonymous reviewers for their extremely helpful comments and suggestions on the early
version of this manuscript.
Declarations of Competing Interests
None.
Appendix A. Discussion of China SAMs and CGE models
Description of the detail social accounting matrixes (SAMs) for China
A SAM is a square matrix typically representing a national economy structure in a given year. Each account in the SAM is
represented by a row and a column. Each cell shows the payment from the account of its column to the account of its row (Lofgren
et al., 2002). According to double-entry accounting, for each account in the SAM, the totals for corresponding row and column are
equal, such that the total revenue is equal to the total expenditure. The standard SAM usually includes six types of accounts, and they
are activity, commodity, factor, investment, domestic institutions (firms, households and government) and the rest of the world.
Seven detailed SAMs for China are constructed for this study using China's IOTs from 1987 to 2017 (1987, 1992 Yang, 1997, 2002,
2007, 2012 and 2017) published by National Bureau of Statistics (NBS). To keep all detail information in IOTs, the number of
production sectors in the seven SAMs are the same as those in the IOTs presented in Appendix Table A1. Macro and trade data are also
required for SAM construction including data from national accounts, government budgets, and balances of payments. Data for
household incomes by sources, number of employments by sectors, and wage rate are from China's NBS publications including China
Statistical Yearbooks.
Table A1
Number of sectors and agricultural sectors in China's IO tables 1987–2017.
Year
Total number of the
sectors
The agricultural sectors
The manufacturing sectors (#)
The other industrial sectors (#)
The service sectors
(#)
1987
1992
33
33
1 sector
1 sector
19
19
5
5
8
8
15
(continued on next page)
China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
Table A1 (continued)
Year
Total number of the
sectors
The agricultural sectors
The manufacturing sectors (#)
The other industrial sectors (#)
The service sectors
(#)
1997
124
72
12
35
2002
122
72
10
35
2007
135
81
9
40
2012
139
84
13
37
2019
149
5 sectors: crop, forestry, livestock, fishing, other
agriculture
5 sectors: crop, forestry, livestock, fishing, agricultural services
5 sectors: crop, forestry, livestock, fishing, agricultural services
5 sectors: crop, forestry, livestock, fishing, agricultural services
5 sectors: crop, forestry, livestock, fishing, agricultural services
86
13
45
Source: China NBS (1991, 1996, 1999, 2005, 2009, 2015, 2019).
In each IOT, there are two aggregate households, rural and urban households, and their expenditure by commodities. Calculation
of income sources for the two household groups rely on data for rural and urban employment, wage rates for different types of labor
categories, and government transfers, which are all from China Statistical Yearbooks. Returns from capital is distributed to rural and
urban households considering their total expenditures. Government expenditure for different commodities are in IOTs. Government
revenues from different taxes, government transfers to different households, and the income flows between the government and the
rest of the world are from the national fiscal budgets published by Chinese Ministry of Finance. Trade data are included in the IOTs
and the remittance incomes received from the rest of the world by the two households are from the data of balances of payments.
Savings for the two household groups and for the government are the differences between their incomes and total expenditures. With
data coming from various sources, a cross-entropy estimation technique is used to balance the SAMs.
Discussion of the structure of the two CGE models
The two CGE models have identical model structure, and both are static single country CGE model similar as in Lofgren, Harris
and Robinson (2001). Because two models are based on two years' SAMs, they represent China's economic structure in the initial
(1987) and end (2017) years of a period of 30 years. The CGE models are based on the neoclassical general equilibrium theory, and
consumers (households) and producers are individual economic agencies with optimization behaviors.
Behavior functions for consumers (the two aggregate households)
The representative consumers maximize their welfare (represented by a utility function) facing a budget constraint. Using a StoneGeary utility function, the consumer problem can be presented mathematically as follows:
Maxi. Uh =
(c h , i
h, i )
h, i
i
s. t .
i
pi ch, i = (1
sh )(1
yth ) Yh
In the utility function ch,i is the level of consumption for good i by household h, which are rural and urban in our case, γh,i is the
subsistence level of consumption for good i by household h, βh,i is the marginal budget share for good i by household h. In the budget
constraint function, pi is the price for good i faced by consumers, sh the saving rate of household h, yth an income tax rate, and Yh
household h's total income.
The demand functions that are derived from maximizing the above utility function are explicitly defined in the CGE models,
which are known as linear expenditure system (LES) in the CGE model:
c h, i =
h, i
(1
sh )(1
yth ) Yh
Pi
j
Pj h, j
+ h, i
(1)
Behavior functions for producers
The producers are defined at the production sector level, and production functions are constant returns to scale in technology.
Accordingly, a CES production function is defined for each sector as follows:
(
)
1
Xi = i f i, f Vi, f i i , f ∈ F
(2)
where Xi is the output of sector i, Λi a shift parameter reflecting total factor productivity (TFP) of sector i, αi the parameter for
factor f (land, labor, and capital) employed in the production of commodity i, Vi,f the factor demand, and ρ a parameter to capture the
1
substitution relationship between factors, which transfers the elasticity of substitution in the following way: 1 + = i and σi is the
i
elasticity of substitution between factor inputs.
Following the neoclassical general equilibrium theory for producers' behavior of profit maximization, the rearranged first order
condition of maximizing profits PAiXi − ∑fWfVi, f subjecting to the technology defined in (2) provides a system of factor demand
functions that are explicitly defined in the CGE model as follows:
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China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
Vi, f =
i
i
1+ i
(1 tvai ) PAi
i, f
wf wdistf , i
1
1+ i
Xi
(3)
where PAi is the value-added component of the producer price i, Wf wage rate or capital/land rental rate, and tvai value added tax for
sector i. wdistf , i represents a set of wedges on Wf across sectors due to factor market distortions discussed in Section 4. Since capital is
assumed to be sector specific in the model, wdistk, i captures the shadow returns to sector i's capital different from the average capital
rent, Wk, which is fixed at one. For labor and land, wdistf , i are fixed, and Wf are endogenous variables.
Intermediate inputs are also used in the production process and the Leontief technology is assumed for the relationship between
the use of intermediate inputs and the production output. Thus, demand for intermediates are determined by the fixed input-output
coefficients, ioi,j between commodity i employed in the production of output j. The complete producer price is then defined as
PXi = PAi +
j
ioj, i Pj
(4)
Factor market equilibrium and household income
Labor is assumed to be fully employed and mobile across sectors, land is used only in the agricultural sector, and capital is sector
specific. Thus, the model only requires an equilibrium condition for labor:
i
Vi, lab = VSlab
(5)
VSlab is the total supply of labor. This equation determines the average wage rate, Wlab.
Assuming all factors are owned by households,9 household income Yh is determined by
Yh =
h, f Wf wdistf , i Vf , i
f ,i
(6)
where δh, f is a coefficient matrix to determine the income distribution from factor earnings to individual households.
Behavior functions for imports and exports
The Armington function and constant elasticity of transformation (CET) function are applied to define the relationship between
domestic and imported or exported parts of the same commodity, and the imperfect substitution is assumed between the goods
produced and consumed domestically and the same good exported or imported in the CGE model:
Ci = Ai (µi Di
i + (1
µi ) Mi
i
1
i
(7)
and
(8)
Pi Ci = PDi Di + PMi Mi
(7) is the Armington function defining the substitution relationship between imports and domestically produced same good. Ci
represents the combination of imported and domestically produced same good, Di the domestically produced part of Ci and Mi the
imported part of Ci. PDi is price for Di, which is endogenous, while PMi = (1 + mti) x PWMi is price for imports and is exogenous at
the given tariff rate, mti and world price, PWMi.
1
Xi = Bi ( i Di i + (1
(9)
i ) Ei i ) i
and
(10)
PXi Xi = PDi Di + PEi Ei
(9) is the CET function defining the substitution relationship between exports, Ei, and domestic consumption, Di, of the same good
produced in China. PEi = (1 - eti) x PWEi, is the exogenous price for exports in which eti is export tax rate and PWEi world price for
exports. Rearranging the first order condition of maximizing PiCi − PDiDi − PMiMi subjecting to (7), an equation to define the
equilibrium level of the ratio between Di and Mi used in the CGE model is as follows:
1
µi PMi 1 + i
Di
=
Mi
1 µi PDi
(11)
Similarly, rearranging the first order condition of minimizing PXiXi − PDiDi − PEiEi subjecting to (9) gives the following ratio of
Di and Ei that is used in the CGE model:
Di
=
Ei
1
i
PDi
i PEi
1
i 1
(12)
Ci is determined by the aggregation of individual households' demand, investment demand, government demand and
9
Part of returns to capital is owned by the government in the model. To simplify the equation discussion, we ignore such ownership at this
moment.
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China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
intermediate demand:
h
ci, h + ciINV + ciGOV +
j
ioi, j Xj = Ci
(13)
where ciINVis investment demand for good i, and ciGOVis government demand for good i, which are all exogenous in the model.
A General Equilibrium without Foreign Income Flow and Current Account Imbalance
With ciINVand ciGOV being given and with world prices, PWMi and PWEi, together with total labor and land supply and sector
specific capital that are all exogenously given, the general equilibrium of the model is defined by simultaneously solving above 13
equations for 13 endogenous variables, which are Yh, ch,i, Xi, Vf,i, Ci, Di, Mi, Ei, Pi, PAi, PXi, PDi, Wf for labor and land and wdistk, i for
sector capital. Given that the net foreign income flow is an exogenous variable, to include it in the model through introducing a
current account balance equation will not affect the key system of equations defined.
Current Account and Macro Closures in the CGE Model
The current account imbalance has to be taken into account in the CGE model as they do affect the real side of the economy
through the relationship between exports and imports, and between the savings and investment. We start from this well-known
identity equation that links current account balance to national savings and investment:
CA = E
M
NFI
S total
I total =
(14)
NFA,
where CA is the current account balance, E = ∑iPWEiEi and M = ∑iPWMiMi are total exports and imports of goods and services, and
P c GOV ) is national savings, and NFA is the foreign
NFI is the net foreign incomes from abroad. Stotal = h sh (1 yth ) Yh + (Y GOV
i i i
assets. This identity states that a country is running a current account surplus whenever the sum of its trade balance subtracting NFI is
positive, in which case national savings exceed national investment.
The macroeconomic balance in a static CGE model is handled exogenously by the following macroeconomic closure rule: ∑hyrowh
+ yrowGOV = NFI, where yrowh is net foreign income (e.g., foreign remittances, which can also be negative) received by household h,
and yrowGOV government foreign assets holding.
If there is trade surplus, i.e., if total exports are greater than total imports, CA > NFI, while if the country runs trade deficits,
CA < NFI. In a static CGE model, the trade surplus or deficit is exogenously fixed, while both total exports and imports can change
but they have to change simultaneously.
A.1. Sensitivity Tests for the CGE Model Results
Given that many elasticities are used in the CGE models, it is necessary to assess whether the model results discussed in Section 4
are sensitive to the choice of these elasticities. Such sensitivity test is a common practice for the CGE modeling exercise. There are
three sets of elasticities used in our models, and we test them separately.
Income elasticity in households' demand functions
We make specific assumptions on consumer preferences in the models and assume that demand is non-homothetic with a declining budget share for agricultural product. The marginal budget shares are estimated for rural and urban households separately
using changes in average budget shares over changes in per capita total expenditure across commodities over time. The income
elasticity of demand for each commodity is calculated as a ratio of marginal budget share over average budget share for the same
commodity. The income elasticity for the 1987 model is averaged from 1990 to 1995 and the income elasticity for the 2017 model is
averaged from 2012 to 2017. As expected, income elasticity of demand for agricultural product declines over time and its value
becomes much lower in 2017 model than in 1987 model and increases slightly for processed food in the same period (see Table A2).
Table A2
Income elasticities for demand in the CGE models.
1987
Agriculture
Processed food
Textile
Clothing
Other manufacturing
Other industries
Trade
Transportation
Hotel and restaurant
Other services
2017
Rural
0.85
1.11
0.77
0.77
1.45
1.45
1.04
1.04
1.04
1.04
Urban
0.82
1.05
1.13
1.13
1.14
1.14
1.04
1.04
1.04
1.04
Rural
0.44
1.13
0.72
0.72
0.89
0.89
1.15
1.15
1.15
1.15
Urban
0.29
1.06
0.63
0.63
0.82
0.82
1.15
1.15
1.15
1.15
Source: Authors' estimation using household expenditure data from China NBS.
A sensitivity test is designed to assess the effect of non-homothetic assumption and income elasticity on the model results. The test
tries to avoid income elasticity effect by assuming homothetic preferences and using Cobb-Douglass demand functions to replace the
demand functions derived from non-homothetic utility function in the models (i.e. the linear expenditure functions of Eq. (1) in
18
China Economic Review 62 (2020) 101504
Y. Zhang and X. Diao
Appendix I). Column two of Table A3 reports the results. Switching from non-homothetic to homothetic in consumer demand lowers
the general equilibrium effects of agricultural productivity shock on GDP in both 1987 and 2017, implying that income elasticity
matters in a general equilibrium model. Without a declining budget share for agricultural product, an agricultural productivity shock
is expected to lead to more consumption of agricultural good when relative prices for agricultural product falls. This lowers the
linkages effects and hence the general equilibrium gains in total GDP from the shock. While the reduction in GDP gain is modest in
both years in the test, the reduction is more in 2017 than in 1987. This is expected as at higher level of income in 2017 the marginal
budget share for agricultural good is much lower in 2017 than in 1987 as shown in Table A2. With modest differences in the changes
for various model variables after the shock, findings discussed in Section 4 hold with little concern.
Elasticity in the trade functions
The second and third sensitivity tests focus on the possible effects of choice of elasticity in the trade functions. Following CGE
model's tradition, two-way trade is allowed for any production sector and both exports and imports are imperfectly substitutable with
consumption of the same commodities produced and consumed domestically in the model. Thus, the model requires elasticities of
substitution in the trade functions between exports and domestic sales of the same commodities and between imports and demand for
domestically produced same products. Estimation of such trade elasticity is impracticable, and we have to assign value to such
elasticity arbitrarily. Specifically, in the model we assigned trade elasticity being 2 for the less tradable sectors and doubled it to 4 for
the three highly tradable manufacturing sectors: textile, clothing and other manufacturing. We then increase and decrease the trade
elasticity by 30% in the sensitivity tests (see Table A3, columns 1–3). Columns three and four of Table A4 report the test results. As
shown in Table A4 and compared with the sensitivity test for consumer preferences, the choices of trade elasticity seem to have much
modest effects on the model results in both years. This is because both agriculture and processed food are less tradable in China, and
domestic economy instead of trade is the driving force to explain the model results at the new equilibrium after the agricultural
productivity shock.
Table A3
Elasticities in trade and production functions in the model and sensitivity tests.
Elasticity in trade functions
Agriculture
Processed food
Textile
Clothing
Other manufacturing
Other industries
Trade
Transportation
Hotel and restaurant
Other services
Elasticity in production functions
In the model
30% higher
30% lower
in the model
30% higher
30% lower
2.0
2.0
4.0
4.0
4.0
2.0
2.0
2.0
2.0
2.0
2.6
2.6
5.2
5.2
5.2
2.6
2.6
2.6
2.6
2.6
1.4
1.4
2.8
2.8
2.8
1.4
1.4
1.4
1.4
1.4
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.53
0.53
0.53
0.53
0.53
0.53
0.53
0.53
0.53
0.53
Source: Authors' choices for the 1987 and 2017 China CGE model.
Elasticity in the production functions
The last two sensitivity tests focus on the possible effects of choice of elasticity in the production functions. The CES production
functions instead of Cobb-Douglas function are used in the models, which implies that the substitution between factor inputs is less
elastic. Again, the estimation for elasticity in the CES production functions is impracticable and we assign the elasticity at value 0.75
for all sectors. Similar as sensitivity tests for the trade elasticity, we increase and demand the elasticity in the CES production
functions by 30% in the tests (see Table A3, columns 4–6). The last two columns of Table A4 report the results. Again, the new
equilibrium results after the shock are similar as in the Section 4 and changes are modest for all variables discussed in Section 4.
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