Impact of Natural Hazards on Agricultural Economy in China:
Based on a General Equilibrium Analysis1
University of TSUKUBA

Shuai ZHONG※

GENDAI Advanced Studies Research Organization
University of TSUKUBA

Mitsuru OKIYAMA

Suminori TOKUNAGA

1. Introduction
In China, various natural disaster inducing factors with various hazard formative environments,
such as population, properties, natural resources and ecological condition, can result in different extent
of casualties, property and economic losses as well as damage of natural resources and environment.
Overall, drought widely occurred and caused the most serious losses in historical periods examinedwhile flood was the most frequent natural disaster.
2. Hypotheses
With the limitation of water supply at the regional level, this study aims to test the following
hypothesis in the cases of drought and flood happening: hypothesis 1 (H1): All of agricultural
productions will be significantly stressed, thus their domestic output will decrease into a lower level;
hypothesis 2 (H2): All of households will suffer losses in welfare; furthermore, rural household will
become worse than urban household; hypothesis 3 (H3): Food consumption by all households will be
reduced, and the decrease in rural household will be more than in urban household.
3. Modeling framework and data
We construct a computable general equilibrium (CGE) model for above hypothesis testing. Main
production sectors include: i) agriculture and construction requiring inputs containing multi-regionalland, water, and agricultural labor combined with one non-agricultural labor; ii) food and energy
sectors requiring multi-energy inputs; iii) multi-regional water productions (16 regions). Data is basedon a Social Accounting Matrix contributed by J. GE and S. TOKUNAGA (2011)1. We divide watersector into 16 regions combined with relative regional land use for crop productions (see figure 1). It isa static equilibrium model. The domestic prices of imports and exports are in Chinese Yuan (RMB).
The wage of non-agricultural labor is exogenously fixed as the numeraire price index. Domestic
product and export are presented by a constant elasticity of transformation (CET) function; domestic
product and import are regulated by Armington function. Values of elasticity in above functions are
given by previous studies. Consumption behaviors of households (16 regional rural household and one
urban household are described by Stone-Gary utility function with diversity in income elasiticities ofcommodities. Measurement for the changes in welfare is marked by the equivalent variation (EV): if
EV is positive, the simulation increased welfare, and if it is negative, the simulation decreased welfare.
Water supply is fixable at regional level, and regional prices of water are independent of each other.
1

The authors appreciate the help of Professor Ge, Jianping for permitting us to use his multi-regional social
accounting matrix, which was one of his contributions in his Ph. D thesis.
1Output
Value-added
Labor

= .

Intermediate input

𝑽𝑨 =Capital-Land-Water

Non agricultural CapitalMulti-regional
agricultural labor labor=
Region 1 …

== .

Multi-regional land-water=

Regin1: land-water … Regin16: land-water= .
= .

Region 16

Water

Land

Land

Water

Figure 1, production structure of crop sectors
Note:

and

=1 are given from Jianping GE and Suminori TOKUNAGA (2011);

andis from the GTAP-W model defined by A. Calzadilla, K. Rehdanz

and R.S.J. Tol (2011);

andare related to Leontief and Cobb-Douglas

assumptions respectively.

4. Empirical application and simulation results
4.1 simulation design
We set the year in 2007 as benchmark, and then discuss three scenarios as simulations:(1) Baseline scenario (BS): cultivated land increasing in 2008; (2) Drought scenario (S1):cultivated land decreasing in 2008 because of drought; (3) Flood scenario (S2): cultivated landdecreasing in 2008 because of flood (see figure 2).
Change of cultivated land in each case, unit: %OthersShandong

Inner Mongolia
SichuanChongqing
Hubei

Hebei
Heilongjiang

Anhui
Jilin

Henan
Guangxi

YunnanHainanJiangxi
Guangdong

total
0.000
Flood Scenario

0.020

0.040

Drought Scenario

0.060

0.080

Baseline Scenario

Figure 2, change of cultivated land in each case in all regions2

4.2 Hypothesis testing
According to table 1, we can test H1 based on the following finding. Total output of crops will be
reduced in S1, and be increased in S2. Some crops will be better in S1 including corn, oil seed andpotato; some crops will be better in S2 including paddy, wheat, corn, fruit, potato; and there also somecrops which will be better in both S1 and S2 including corn and potato.
Table 1, change in the output of crop productions in each caseunit:%

BS

S1

S2

paddy

-0.006-0.001

0.245

wheat
corn

0.035
-0.056-0.128
0.060

0.161
0.092

vegetable
fruit

0.004
-0.067-0.258-0.049-0.3123.771

oil seedsugarcane

0.357
0.075

0.008
-0.294-0.086-0.050potatosorghum

0.001
0.003

0.004
-0.0060.208-0.008other crops

0.013-0.001-0.157

total

0.013

-0.0380.059

For testing H2, table 2 shows that compared with urban household, rural household will be worse
in S1 but be better in S2. In detail for rural households, some of them will be better in S1: Yunnan,
Henan; and some of them will be worse in S2: Guangxi, Henan, Jilin and Shandong; but some of themwill be worse in both S1 and S2: Guangxi and Jilin.
Table 2, change in the welfare of household in each case
Unit: 0.1 billion yuan

BS

S1

S2

Guangdong
Jiangxi

2.734
0.926

-16.730-5.66721.28710.430HainanYunnan

0.249
1.018

-0.0821.621

0.822
26.409

Guangxi
Henan

0.448
0.334

-2.2103.909-1.587-5.832

Jilin
Anhui

0.639
-1.422-12.370-5.454-9.992
16.495

Heilongjiang
Hebei

0.006
-2.254-1.705-3.3654.140
25.237

Hubei
Chongqing-0.0960.323-1.844-2.81018.96414.008Sichuan

1.072-18.183

33.853

Inner Mongolia-0.302-0.037

7.522

Shandong-0.267-46.772

2.389

Otherstotal change in rural household

6.765
10.173-35.552-147.250

163.756327.901

total change in urban household-5.572

17.121-74.785

multi-regional rural
household

Table 3 shows similar results as table 2 that: compared with urban household, rural household
will be worse in S1 but be better S2; rural household in Yunnan and Henan will be better in S1; andthose in Guangxi, Henan, Jilin and Shandong will be better in S2, however, those in Guangxi and Jilin

3will be worse in both S1 and S2. It means that for rural household, their welfare has close relations
with their food consumption level.
Table 3, change in the food consumption by household in each case
Unit: %

multi-regional rural
household

BS

S1

S2

Guangdong
Jiangxi

0.014
0.007

-0.088-0.055

0.091
0.069

HainanYunnan

0.013
0.009

-0.026
0.003

0.048
0.183

Guangxi
Henan

0.005
0.007

-0.0390.021-0.008-0.088

Jilin
Anhui

0.009
-0.004-0.157-0.043-0.116
0.068

Heilongjiang
Hebei

0.002
-0.009-0.036-0.033

0.032
0.114

Hubei
Chongqing

0.002
0.006

-0.031-0.053

0.083
0.169

SichuanInner Mongolia

0.006-0.001-0.082-0.007

0.117
0.076

Shandong
Others

0.001
0.005

-0.157-0.035-0.009
0.077

0.005
0.002

-0.049-0.001

0.071-0.028

total change in rural household
total change in urban household

Note: “food” in this study includes crops and the products from food industries covering 5 kinds of products:
meat, milk, vegetable oil, gain, sugar, and other food

4.3 Simulation Analysis
4.3.1 Analysis for those crops better in each case
Lower price is the main reason for those increases of crops in domestic and foreign demand: in
S1, domestic demand increases in corn and potato and foreign demand increases in oil seed; in S2,
foreign demand increases only in corn, and both domestic and foreign demand increase in paddy,
wheat, fruit, and potato. Furthermore, the import of vegetable increase in both two cases (see table 4).
Table 4, Change in domestic demand, export and import and their price level in each caseunit: %

S1: drought scenario
Domestic demand
ExportImportS2: flood scenario
Domestic demand
ExportImport

paddy
wheat

-0.001-0.0400.000-1.659-0.003
2.014

0.134
0.067

6.368
1.790

-7.993-2.037

corn
vegetable

0.064-0.101-0.001-6.053

0.087
4.247

-0.027-0.1201.981-7.459-0.7425.309

fruit
oil seed-0.010-0.002-0.4520.025

0.278-0.0060.493-0.13938.7100.000-18.579-0.158sugarcane
potato-0.294
0.005

0.000

4.275
0.008

-0.050
0.036

4.270

-2.233-2.701sorghumother crops

0.004
0.007

-0.025-0.428

0.022
0.306

-0.027-0.0920.027-3.835-0.059
2.560

paddy

Price of composite
commodities-0.008price ofexports-0.007

price ofimports-0.007

Price of composite
commodities-1.679price ofexports-0.034

price ofimports-0.034

wheat

0.446

-0.007-0.007-0.505-0.034-0.034

Price

4

corn
vegetable

0.011
1.711

-0.007-0.007-0.007-0.007-0.5842.105-0.034-0.034-0.034-0.034fruit
oil seed

0.108-0.009-0.007-0.007-0.007-0.007-8.105-0.041-0.034-0.034-0.034-0.034sugarcane
potato1.801-0.006-0.007-0.007-0.007-0.007-0.913-1.137-0.034-0.034-0.034-0.034sorghumother crops

0.000
0.112

-0.007-0.007-0.007-0.007-0.0461.019-0.034-0.034-0.034-0.034Moreover, higher demand will motivate the crop productions to employ more labor, especially
agricultural labor, which is the main reason why some crops will be better in S1 or S2 (see table 5).
Table 5, the change in the employment of crop production in each caseS1: drought
composite
capital-land-water
-0.054

paddy
wheat
corn
veget
able
fruit
oil
seed
sugar
cane
potato
sorgh
um
other
crops

S2: flood

0.006-0.004composite
capital-land-water
-2.8490.709-2.312-1.051-3.304-0.039
0.499

0.581
0.161

1.603
-0.581

0.017
0.194

-0.629-0.5421.058-0.495

1.197

1.303

-0.603

1.476

0.201

-0.1400.188-21.059

14.493-13.661-1.644

0.304

0.056

0.370

-0.165-0.168-0.734-0.311

1.937-1.6850.102-1.154-1.566

0.206

0.040

-0.6630.342-0.7570.030-0.012-0.008-0.1470.016-0.041-0.674-0.001

0.123

0.160

-0.1830.921

composite regional
agricultural labor

non-agricult
ural laborcomposite regional
agricultural labor

non-agricult
ural labor

4.3.2 Analysis for households’ welfare
In S1, for rural households, their welfare and utility level highly depend on the food consumption
level, but urban household do not; for both rural and urban households in S2, decrease in foodconsumption would reduce their welfare and utility, only except rural household in Shandong. For
most rural households except the one in Henan, increasing income will enhance their foodconsumption level (see table 6).
Table 6, Change in income and utility level for households in each case
Unit: %

rural

IncomeS1
Utility level

IncomeS2
Utility level

Guangdong
Jiangxi-0.1100.034-0.401-0.220

0.130
0.046

0.510
0.404

HainanYunnan

0.024
0.078

-0.020
0.069

0.016
0.250

0.204
1.124

Guangxi
Henan-0.002-0.001-0.0830.102-0.0500.028-0.059-0.153

Jilin
Anhui

-0.179-0.008-0.900-0.159-0.2110.137-0.7270.479

Heilongjiang
Hebei

0.037
0.007

-0.098-0.105

0.062
0.239

0.237
0.791

Hubei
Chongqing

0.081
0.044

-0.058-0.221

0.173
0.315

0.592
1.102

5

SichuanInner Mongolia-0.0300.032-0.391-0.003

0.174
0.210

0.728
0.605

Shandong
Others-0.2180.025-0.834-0.118

0.038
0.146

0.043
0.543

0.003

0.008

0.001

-0.036Urban

5. Conclusion
Hypothesis testing 1 (H1) did not pass the test according to the results: in S1, crop outputs would
increase in corn, oil seed and potato due to increasing domestic (corn and potato) or foreign demand(oil seed); to meet that higher demand, agricultural labor would be integrated into above crop
productions; In S2, crop outputs would increase in paddy, wheat, corn, fruit, and potato due to foreign
demand (corn) or both domestic and foreign demand (others); to meet that higher demand, above crop
productions would employ more agricultural labor. The main reason for above increasing in those
crops is the lower price level of domestic and import market, which would motivate domestic demandand foreign demand. According to the results of the employment changing, agricultural labor plays as
the key role for crop productions in the case that drought and flood occur. These results also prove thatfor crop productions, drought was as a more serious problem than flood due to more crops would be
impacted to decline in output.
Hypothesis 2 (H2) were also not found in results: in S1, households in Yunnan and Henan would
be better in welfare; in S2, most of rural households would be better except those in Guangxi, Henan,
Jilin and Shandong. Those rural households with better or worse welfare were marked by the similarchange in their food consumption in drought or flood cases. In other word, more food consumption
would contribute higher welfare for rural households. Compared with urban household, the welfare ofrural household at total level would be worse in S1 but would be better in S2. The reason behind this isthat the income of urban household would be increased more in S1 than in S2 and further its foodconsumption would be reduced more in S2 than in S1.
Furthermore, hypothesis 3 (H3) were still not proved by results: In both S1 and S2, rural
households with higher welfare and utility level were those with more food consumption. That’s
because for those rural households, food consumption accounts for large proportion in within total
consumption, some of them even larger than 30% according to survey. In S2, for most rural household
only except the one in Henan, their higher income might be the main reason for more foodconsumption. And these higher incomes were mainly from the more employment of land andagricultural labor for those crops with more output.
Other findings include: i) for crop productions, corn and potato would be better in both S1 and S2,
because they can employed more agricultural labor in both two case to meet higher demand for them;ii) The import of vegetable would be increased in both S1 and S2 due to higher domestic price andlower import price. Thus the domestic output would be limited.
Reference
GE, Jianping and Suminori TOKUNAGA. 2011. "Evaluating the Effects of Expanding Grain-Based Fuel Ethanol on
Chinese Economy Using a Computable General Equilibrium Model." Studies in Regional Science, 41(1), 195-218

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