Market Access and Specialization in cash crop:
What are Vietnam’s expected gains from WTO
accession
June 14, 2008
Barbara Coello*
This paper discusses the link between trade liberalization and farmers’ specialization in cash
crops. Farmers are distinguished between subsistence-oriented farmers and export cash crop
producers. Among the latter, using a panel, we can distinguish between export-oriented
farmers in 2002 and 2004, those who quitted the export market and those who entered. An
agricultural trade index is computed that captures the export market access of each
Vietnamese province according to its specialization in cash crops. The decision to enter, quit
or stay in the export market is then related to the trade index, controlling for household and
farm characteristics. The gain in agricultural income due to change in market access abroad is
estimated. Finally the impact of an improvement of market access abroad is then simulated.
JEL Classification numbers: Q17, Q12, F16
Key Words: trade liberalization, agriculture, income gains, Vietnam
*
Paris School of Economics at the Laboratoire d’Economie Appliquée -Institut National de Recherche
Agronomique (LEA -INRA), 48 Boulevard Jourdan, 75014 Paris. +33 143136364. [email protected]
-1-
1. Introduction
Vietnam has experienced since the mid nineties, after the structural reforms of Doi Moi, a
surge in exports of agricultural goods as well as manufactured goods. In the rural sector, new
land use rights were distributed, farmers were given a greater freedom in the choice of their
production, and price distortions were diminished.2 Over the period 1993-2004, overall
poverty has decreased by almost 39 percentage points, to 19% in 2004 (Vietnamese academy
of social sciences, 2006).
Vietnam had a comparative advantage in agricultural products and feared relatively little
competition on the import side as it was still protected from imports.3 Moreover, as Vietnam
opened its borders to farm exports and raw material imports, new commercial opportunities
emerged in domestic and international markets. This has allowed Vietnam to become a major
worldwide exporter and producer, for some agricultural commodities such as rice, coffee and
also cashew, black pepper and tea. This phenomenon is expected to exacerbate in future years
as Vietnam’s accession into the WTO will considerably increase its market potential.4 With
agriculture still weighing more than fifty percent of the employed population, agricultural
trade liberalization is likely to have a strong impact on households incomes. However,
surprisingly little is known on the distribution of export gains across households.
This paper focuses on agricultural households. It tries to identify how market access abroad
has influenced households’ specialization in export crops. We answer the question. What has
been the impact of trade liberalization on Vietnamese farmers? We use the 2002-2004
household panel survey. We define export-oriented households as agricultural households
who grow the main cash crops exported by Vietnam (coffee, pepper, rubber, tea and cashew).
In the panel, we distinguish between households who remained export-oriented during 20022004, those who remained subsistence-oriented, households who began to grow cash crops
and those who stopped growing them. We try to assess the determinants of these transitions
into or out the export market. Among those determinants, changes in the market access
abroad, computed at the provincial level do play a role. We estimate the agricultural income
gains of staying in the export market or entering into it, compared to being subsistenceoriented or quitting the export market, based on a propensity score matching method.5 These
estimations are then used to simulate the impact of an improvement in market access abroad
on Vietnam.
This paper relates to the large empirical literature on the impact of trade liberalization on
wage inequality. Papers focus generally on the effect of trade liberalization on wages in the
import-competing manufacturing sector. Most papers studies Latin America (Feliciano, 2001;
Hanson & al, 1999; Goldberg & al, 2005; Attanasio & al, 2004). In Asia, the pattern of trade
liberalization is somewhat different, with a larger emphasis on agricultural exports. Trade
liberalization was first studied through the price of rice and its variation (Benjamin & al,
2002; Edmonds & al, 2004). However, in recent years, other cash crops besides rice played a
significant role and deserve further study. Our methodology is close to Balat and al (2006)
2
See Paquet, 2004 and Lavigne, 1999 for more details on this economic renovation.
During this period, the government implemented policies that limited imports in competitive sectors (through
ad valorem tariffs and non-tariff barriers, such as quantitative restrictions, duty quotas, prohibitions, licensing
and special regulations). The government also promoted exports with the creation of Export Processing Zones
(EPZ) in 1991, tax exemption for exporters and the elimination of tariffs on imported fertilizers (Auffret, 2003).
4
Vietnam became WTO-s 150th member on January 11 , 2007.
5
See Heckman & al, 1997 for a theoretical description and an empirical application on labor markets of the
different matching method, and more particularly of the Local Linear Propensity score matching.
3
-2-
which investigates the constraints that prevent farmers in Malawi from entering into export
commodity markets. Pham (2007) looks in the case of Vietnamese rural households, at the
impact of trade policy on non-farm employment. These papers however are based on repeated
cross sections. The originality of our work is to look at a household panel.
In the following, section 2 presents the data; section 3 compares export-oriented households
and other households and looks at the determinants of the transitions into or out of the export
market; section 4 estimates the income gains of a specialization in export crops after
households with similar characteristics have been matched. Section 5 simulates the impact of
an improvement in market access abroad for Vietnamese agricultural products; section 6
conducts some robustness analysis and section 7 concludes.
2. Data
a. Dataset
The paper uses two waves of the Vietnam Household Living Standards Surveys (VHLSS) in
2002 and 2004. The first collected information from 30,000 households sample and the
second from 9,000 households with all topics. They both included a household and a
commune module. The household questionnaire includes information on basic demographic
on all household members (age, sex, relationship to head), on household expenditures (by
expenditures purposes: food, education, health, etc.), on household income, employment and
labor force participation, education of household members (literacy, highest diploma, fee
exemption), on health of household members (use of health services, health insurance), on
housing (type of housing, electricity, water source, toilet, etc.), on assets and durable goods
and on participation in poverty programs. The VHLSS 2004 also included an expanded
module on agriculture that include information in agriculture Land: land transaction (renting
in/out in past 12 months, changes in land and land use rights in last 10 years), on Sales of
products, on changes in crops in last 10 years and finally on access to farm extension services.
(Phung &al, 2006)
The surveys include 20,156 agricultural households in 2002 and 6,300 in 2004. Local
information on infrastructure was collected separately in a community questionnaire. In the
following, we focus on the panel subset of 2640 agricultural households.6 An agricultural
household is defined as a household reporting a positive harvest value in any crop in the
VHLSS household questionnaire.7
6
The panel linkage dataset was provided by Brian McCaig as the one provided by the statistical institute (GSO),
show some inconsistency.
More precisely, in the panel, 2640 households stayed in agriculture in 2002-2004, while 169 quitted farming and
224 entered in agriculture.
7
38 crops are reported in the survey. Actually, more crops are grown but they are not identified separately by
their names in the survey.
-3-
Dataset
2002
2004
Households Cross Section
Household Panel
Hslds present each year in farming
Hslds Exiting from farming
Hslds Entering into farming
Total
20,156
6,300
2,640
169
2,640
Household-Crop Panel
Crops present each year
Crops Exiting
Crops Entering
Total
2002
11,164
5,342
2,809
16,506
224
2,864
2004
11,164
6,185
17,349
Table 1. Description of VHLSS 2002 2004 data
The 2,640 farmers in the panel cultivate 16,506 different crops in 2002 and 17,349 in 2004.
Moreover 11,164 crops are cultivated in both years, providing an original panel of householdcrops. This paper uses the panel crop element as well as the 5,342 crops that are abandoned in
2002 and the 6,185 introduced crops in 2004.
b. Cash crop producers
In 1990, rice accounted for 80% of total agricultural exports (table 2). During the 90s, this
share dropped to 32.7% in 2004 and other crops emerged such as rubber, coffee and to a
lesser extent, cashew. Overall trade balance has been positively driven by the primary
products and among them, food products (GSO, 2006).
Share of agricultural products in total nonoil exports
Composition of Agricultural exports:
Rubber
Coffee
Tea
Rice
Cashew
Black pepper
Cinnamon
Groundnut
1990
1995
2000
2004
80
46
25
22
4.7
7.3
0.6
80.2
3.8
3.5
na
na
12
37.4
9.8
40.7
0.8
4.5
na
0.7
9.4
28.4
4
37.8
9.5
8.3
0.3
2.3
20.5
22
3.3
32.7
15
5.2
0.3
0.9
100
100
100
100
Table 2. Composition of Vietnam agricultural exports
(Source: Athukorala & al, 2007 based on GSO)
Crops are identified as other or cash crops, depending on the amount exchanged
internationally, based on COMTRADE and GSO statistics. The “cash crops” are defined as:
tea, coffee, rubber, pepper and cashew. The crops labeled "Other" can be either exported but
with no such specialization in foreign markets or simply subsistence-oriented crops for local
consumption. We leave aside rice, because rice is exported, imported and domestically
consumed. The decision to begin growing rice is thus different from the decision to begin
growing cash crops. Moreover, rice production was extensively studied in other papers. Table
3 shows the classification of the 38 crops in the dataset according to their market orientation.
-4-
Water
morning
glory
Litchi, logan,
Sapodilla
rambutan
(grosse baie)
Custard
apple
Fresh
legumes
(beans)
Jute, ramie
(fibe textile)
Indian Corn
Potatoes
Other leafy
greens
Tomatoes
Suger cane
Tobaco
Mulberry
Oranges,
limes,
mandarins
Apples
Grapes
Jackfruit,
durian
Kohlrabi,
cabbage,
cauliflower
Specialty
rice
Sweet
potatoes
Cassava
manioc
Glutinous
rice
Soy beans
Peanuts
Pineapple
Tea
Other
Cash crops
Table 3.
Plums
Cotton
Coconut
Bananas
Sesame
seeds
Mango
Papaya
Rice
Coffee
Rubber
Black pepper
Cashew
Definition of crops’ trade orientation from VHLSS crop dataset.
Map 1 (see appendix) shows the geographical distribution of cash crop producers across
Vietnam provinces and indicates the average provincial level of agricultural income. Regions
in Vietnam are presented in Map 2 (see appendix). The Southeast provinces have a high
agricultural average income and concentrate cash crop producers. The Central Highlands also
count many cash crop producers but with a lower level of agricultural income, between
2,136,000 Vietnam Dong (VND) per capita (pc) and 1,827,000 VND/pc. The Central Highlands
includes Dac Lak province, where coffee started successfully in the mid-nineties. Mekong
River Delta is the richest region, but not because of cash crops only. Conversely North
Central Coast is the poorest region, with provincial agricultural income between 872,000
VND/pc and 1,827,000 VND/pc, and a equal distribution of cash crop farmers all over the
region. Finally the Northeast is the more heterogeneous region in terms of income and
localization of cash crop farmers.
Table 4 shows the share of the harvest that is actually sold on markets, by types of crops. For
example, in 2004 farmers growing other crops sold on average 29% of their production on
markets. In the case of rice, only a quarter of the production goes to the market, the remaining
being auto-consumed in the farmer household. The share goes up to 78.6% in the case of cash
crop farmers for 2002.
Other
Cash crops
Rice
Table 4.
Average
0.2977
0.7865
0.2461
2002
Std. Dev.
0.3915
0.3793
0.3167
Average
0.3069
0.7967
0.2470
2004
Std. Dev.
0.3944
0.3724
0.3020
Share of the production sold on markets according to crops’ orientation.
This paper examines the specialization patterns in cash crops vs. subsistence orientated. Thus,
households are defined as export-oriented if they produce any of the “cash crops”. But the
definition of a subsistence-orientated household is tricky. As our main objective is to identify
specialization patterns of cash crops farmers we want to be sure that the group we are
comparing it too has not the same orientation. What we want to capture is the gain from trade
liberalization, thus we are looking at cash crop producers for which we are sure that most of
their production is sold on markets. But if we compare the latter with for example a producer
of rice that is also exporting all its production, we would not find any effect as our control
group is also treated. We want to be sure that we are not including such farmers in the non-
-5-
0
.0002
Density
.0004
.0006
.0008
treated (comparable) group. Thus the definition of subsistence-orientated households is i/they
are not cash crop producers (i.e. not growing any of the cash crops), and ii/ they sell on
markets no more than 50% of their total production. In the case of rice for example, a
household who sells less than fifty percent of its total production on local markets is not
affected by the treatment (market access).
According to this definition, households can be either cash crop producers (export-oriented),
labeled as 1, or subsistence oriented, labeled as 0.
Figure 1 plot a kernel density of the variation in agricultural income between 2002 and 2004
according to households’ production. We observe that cash crops and subsistence orientated
producers follow a drastic different distribution. The cash crop growers have enjoyed a higher
increase of their agricultural income, as did the subsistence orientated farmers.
-5000
0
5000
Difference in agricultural income pc between 2002 and 2004
National
Cash crop farmers
10000
Domestic orientated farmers
(1) Gaussian kernel density
Figure 1.
Kernel density distribution of agricultural income gains by type of
farmers (2002-2004) 8
c. The dynamics of crop specialization
We turn now to the change in crop specialization during 2002 and 2004. Out of 2640
households in the panel (Table 5), 347 households stay in cash crops in both years (1 to 1) and
1348 households stay in subsistence oriented crops (0 to 0). 160 households are entering into
cash crop production (0 to 1) and 82 are quitting cash crop production (1 to 0). We will, for
8
The household agricultural income used in this paper has been recomposed based on the agriculture section of
the VHLSS. Physical harvests of each crop are valued by a provincial price computed from unit values at the
household level. The latter are defined as the ratio of values sold on the market over quantities, deflated by a
month and a year deflator. The base period is January 2002. These unit values are then averaged by crop and
province. The, highest and lowest percentile are dropped.
-6-
simplicity, call these farmers respectively cash crops (or exporters), subsistence orientated,
newcomers and quitters. The same classification can be done for each cash crop separately,
except rubber, for which no mobility is observed.9
2002 to 2004 Cash crops
0 to 1
160
1 to 0
82
1 to 1
347
0 to 0
1348
Tea
72
35
127
1292
Coffee
20
23
133
1432
Pepper
43
8
75
1425
Cashew
37
18
64
1438
Table 5. Descriptive statistics on cash crops dynamics
2002 to 2004 Cash crops
0 to 1
6.63
1 to 0
8.21
1 to 1
91.79
0 to 0
93.37
Tea
5.27
17.53
82.47
94.73
Table 6.
Coffee
0.41
6.99
93.01
99.59
Pepper
0.83
0.83
98.68
99.17
Cashew
0.66
5.88
94.12
99.34
Probabilities of transitions
In terms of percentage, the probability of quitting the export market is higher 8.21% than the
probability of entering 6.63% despite the favorable context for exports (Table 6). This is the
case for all cash crops, except pepper.
3. Barriers to entry
A specialization in export crops may be due to different factors. A first set can relate to
households characteristics such as, the number of children, the gender, age and marital status
of household head and his or her education level. Another set of factors relate to the
characteristics of the farm: its size, the use of pesticides, and the type of entitlement
(ownership of a land certification). A third set of factors are not infrastructure per se, but the
ownership of transport facility by the household (car, for instance). A last factor comes
directly from trade policy. Here we consider the market access provided by Vietnam's partner
countries to agricultural exports from Vietnam.
a. Modeling market access
We start from the tariffs faced on world markets by the Vietnamese “cash crops” collected in
UNCTAD TRAINS dataset. We take lagged tariffs variations, because households need a
certain time in order to get the information on market conditions abroad.10 But also because
households are heterogeneous with respect to risk. During a field survey in Binh Phuoc
province11, most farmers reported during the interviews that they “will change their cropping
patterns because someone they know has already done and is doing well”12. In other words,
the first household who enters the export market may be self selected, in terms of risk
aversion and credit constraint. However, we do not want to restrict our study to those
9
Thus, rubber is ruled out from the specific analysis of cash crop (but still included in the cash crops category)
Mainly due to information asymmetry.
11
Conducted by Loren Brandt for a survey on land redistribution (World Bank).
12
In that province they are mostly switching into cashew crop.
10
-7-
particular households. Thus we construct a lagged index of tariffs variation that occurred in
the 1990’s.13
Vietnam as a whole faced of course the same improvement in international market access.
However each province faced a different market access depending on its natural resource
endowment.14 We capture the differential effect of trade liberalization over Vietnam
provinces, based on the chance to grow a certain crop. Thus in the line of Topalova (2005),
we construct an ex-ante crops’ distribution over the country. Then we compare households’
probability to begin producing cash crops, based on provincial natural resource endowment.
Then we create for each of the cash crops an acreage provincial share from 2000 (GSO
data)15. The provincial agricultural trade openness index is defined as follows:
, ,
,
∑
, ,
∆
where ∆ is the average tariffs variation for cash crop e,
province p and year 2000.
, ,
is the acreage of crop e in
b. Modeling participation into cash crop production
We run a probit in order to model the probability of transitions of cash crop farmers16. The
latter are going to constitute our “treatment group”. A household (h) can be in four (i) distinct
situations. It can be an exporter over the whole period (11) or a subsistence orientated farmer
(00). It can also enter into the cash crop sector (01) or leave it (10). We are firstly interested in
comparing households that stay in the same category over the period. In a first specification
(k=1), we estimate the probability to participate in export production relatively to subsistence
orientated farmer: we compare the (11) group to the (00) group. The second specification
(k=2) compares newcomers (01) to subsistence oriented farmers (00). The third (k=3)
specification compare quitters (10) to stable exporters (11).17 The transitional households are
compared to the original group they belong to. The regression includes three sets of
regressors: households’ demographic characteristics (
,), the characteristics of the plot
(
), and the trade index ( , ):
.
.
,
.
(Eq 1)
Index , will take a value of zero in provinces where no acreage was devoted to crop e, in
2000. This will allow us to estimate the probability to be (or become) an export producer
13
The ad-valorem tariffs applied to Vietnam’s cash crops on foreign markets were very erratic through the
period. Moreover some years were missing. Thus we construct a consistent index over all the cash crops and
years based on the variation from 1997-2000 over 1992-1995 average values.
14
By natural resource endowment we mean the type of climate, the altitude and so one. They will determine the
possibility for a province to grow certain type of crop.
15
Unpublished GSO data obtained through the collaboration of the author in a joined project with the World
Bank and the Center of Agricultural Policy (CAP) at IPSARD, Hanoi, Vietnam.
16
The standard errors of the estimators are corrected for the correlation of the residuals between different
observations of the same province (intra-cluster).
17
These three specifications labeled k=1,2 and 3 will represent the column of all the tables.
-8-
based on equal agronomy endowment.18 We encounter some data constraints in the 2002
survey when we want to create detailed characteristics of the plot ( ). In contrast the 2004
survey includes for the first time over the different survey waves, a special module focused on
agriculture. Thus, we could create variables such as the share of land of each household where
the plot is guaranteed with a long term certificate, the quality of the land or the type of
irrigation used by the households Moreover some questions are even retroactive, such as the
past history of long term certificate variables.
c. Results
Table 7 shows that variations in tariffs abroad on Vietnamese cash crops are negatively
correlated with being or becoming an exporter relatively to be or becoming subsistenceoriented.19 In other words, the probability to begin growing a cash crop is higher if market
access abroad for that crop improves (column 1 and 3). This result holds for each crop
separately (table 7) with a substantially larger effect for cashew and coffee. On average and
ceteris paribus, a one percentage point reduction of the agricultural trade index increases the
probability to stay in tea cropping by 1.432 percentage points and to begin tea production by
0.69 percentage point. Conversely, a lower market access abroad pushes household out of the
production of cash crops (column 2).
Table 8 gives the general result for the group of cash crops as a whole. A deterioration of
market access abroad decreases the probability of becoming a cash crop grower. Everything
else being equal, a one percentage point reduction in the agricultural trade index will decrease
the probability of households to participate in the cash crops production by 0.115 percentage
points.
In line with Balat et al (2006), we find that owning a transport facility (such as cars) is
correlated with export orientation. Being part of an ethnic minority (not being a Kinh) plays in
opposite direction depending on the crop: it decreases the probability of becoming a pepper or
cashew producers, while it increases the probability of becoming a tea producer. The
education of the household head has also a mixed impact. If he (she) has a secondary
education, it lowers the probability of beginning to grow coffee and pepper, while it increases
the chance to become a tea grower.
Other factors are correlated with entry into the export market with a non intuitive sign: land
tenure, irrigation and land distance. Tea and coffee are correlated with the use of chemical
pesticides; in addition, coffee producers use also organic chemicals20. More intuitively, tea is
correlated to a high quality of land.
18
We will test the robustness of this hypothesis in section 6.
Pepper in specification 3 did not have sufficient observation to run a probit. Furthermore for a description of
the data used in the regression please refer to Table 15 in Appendix.
20
This might be due to the fact that as the world coffee prices falls, farmers substitute partially organic pesticides
to chemical pesticides.(Ha, 2008).
19
-9-
Trade Index :
Ethnic
Poor
Rich
Gender
Married
technical diploma
secondary & upper
primary
Age squarred
Size of the household
Share of male
Land Tenure
Car
Varicertif
Urban
Organic Pesticides
Chemical Pesticides
Land distance
Quality of Land
Irrigation
Constant
Observations
Pseudo R2
1
exporters
-1.432
Tea
2
newcomers
-0.685
3
quitters
0.889
1
exporters
-3.433
Coffee
2
newcomers
-2.508
3
quitters
0.252
Pepper
1
2
exporters newcomers
-1.91
-1.933
[0.146]**
[0.202]**
[0.275]**
[1.106]**
[0.568]**
[0.202]
[0.458]**
-0.002
0.371
0.528
0.132
0.259
-0.37
-0.427
[0.245]
[0.182]*
[0.312]
[0.258]
[0.287]
[0.379]
[0.242]
1
exporters
-2.148
Cashew
2
newcomers
-3.02
3
quitters
0.468
[0.352]**
[0.358]**
[0.463]**
[0.152]**
-0.948
-0.241
-1.195
-0.458
[0.277]**
[0.200]
[0.345]**
[0.688]
0.047
0.247
0.343
-0.068
-0.154
0.047
-0.145
-0.08
-0.034
-0.01
0.455
[0.178]
[0.209]
[0.325]
[0.141]
[0.153]
[0.280]
[0.144]
[0.145]
[0.207]
[0.223]
[0.493]
0.008
-0.114
-0.487
0.247
0.054
-0.108
0.3
0.4
0.373
-0.908
-0.971
[0.160]
[0.126]
[0.405]
[0.152]
[0.156]
[0.389]
[0.180]
[0.220]
[0.199]
[0.333]**
[0.503]
-0.078
-0.631
0.615
0.527
-0.429
0.43
0.335
0.215
0.198
0.13
-0.838
[0.222]
[0.189]**
[0.463]
[0.286]
[0.261]
[0.778]
[0.283]
[0.351]
[0.451]
[0.420]
[0.682]
0.497
0.17
-0.008
-0.407
-0.01
-0.994
-0.356
-0.253
1.774
1.975
-1.096
[0.207]*
[0.204]
[0.704]
[0.181]*
[0.237]
[0.435]*
[0.253]
[0.374]
[1.219]
[1.055]
[1.052]
0.429
0.553
-0.043
-0.532
-0.027
-0.377
-0.47
-0.633
-2.476
0.755
[0.280]
[0.216]*
[0.769]
[0.372]
[0.578]
[0.271]
[0.447]
[0.472]
[0.958]**
[0.941]
0.37
0.618
-0.106
-0.421
-0.193
0.216
-0.162
-0.65
-0.221
-0.381
-0.379
[0.218]
[0.181]**
[0.417]
[0.188]*
[0.320]
[0.277]
[0.205]
[0.215]**
[0.192]
[0.288]
[0.582]
0.259
0.233
-0.234
0.187
0.435
-0.118
0.25
-0.398
0.176
-0.61
-0.763
[0.165]
[0.176]
[0.364]
[0.164]
[0.233]
[0.284]
[0.189]
[0.211]
[0.193]
[0.282]*
[0.487]
0.025
0.164
0.073
-0.208
-0.063
0.08
-0.091
0.105
0.086
0.063
0.139
[0.139]
[0.131]
[0.237]
[0.123]
[0.098]
[0.239]
[0.123]
[0.142]
[0.145]
[0.183]
[0.556]
-0.064
0.549
-0.325
0.12
0.452
0.583
0.482
0.365
0.01
-0.505
0.312
[0.187]
[0.234]*
[0.336]
[0.107]
[0.288]
[0.328]
[0.216]*
[0.311]
[0.239]
[0.300]
[0.545]
0.425
0.319
0.042
0.299
-0.024
-0.137
-0.06
0.131
-0.081
0.727
2.6
[0.400]
[0.319]
[0.653]
[0.356]
[0.385]
[0.798]
[0.367]
[0.699]
[0.381]
[0.630]
[0.983]**
-0.261
-0.023
0.101
-0.441
-0.327
0.371
-0.414
-0.22
-0.534
-0.286
1.217
[0.171]
[0.195]
[0.331]
[0.273]
[0.260]
[0.293]
[0.194]*
[0.203]
[0.195]**
[0.228]
[0.427]**
0.213
-0.325
0.063
1.152
-0.865
1.272
0.582
0.707
2.117
0.158
[0.460]
[0.499]
[0.964]
[0.261]**
[0.449]
[0.389]**
[0.452]
[0.381]
[0.604]**
[0.735]
-0.206
-0.131
-0.448
-0.099
0.172
0.229
-0.319
-0.193
-0.396
-0.976
0.332
[0.197]
[0.181]
[0.469]
[0.192]
[0.337]
[0.447]
[0.207]
[0.164]
[0.280]
[0.537]
[0.723]
-0.079
0.061
0.348
0.667
0.518
-0.254
0.264
0.441
-0.255
0.983
0.671
[0.369]
[0.273]
[0.534]
[0.280]*
[0.290]
[0.435]
[0.165]
[0.297]
[0.451]
[0.320]**
[0.460]
-1.298
0.503
6.907
5.364
-1.089
-6.009
2.838
3.064
4.179
4.843
-13.888
[2.726]
[2.263]
[4.626]
[2.169]*
[3.801]
[4.498]
[1.962]
[3.017]
[2.502]
[3.486]
[4.809]**
1.432
1.022
-1.079
1.379
0.585
-3.679
0.582
1.905
-0.034
1.693
0.732
[0.601]*
[0.756]
[1.129]
[0.463]**
[1.060]
[1.269]**
[0.566]
[0.628]**
[0.979]
[0.866]
[2.118]
0.039
-0.047
-0.054
0.035
-0.014
-0.025
-0.07
0.153
-0.018
0.062
-0.083
[0.072]
[0.048]
[0.106]
[0.043]
[0.110]
[0.071]
[0.043]
[0.054]**
[0.067]
[0.052]
[0.105]
0.665
0.69
-0.406
0.006
0.256
-0.175
0.067
0.286
0.022
-0.075
0.009
[0.131]**
[0.153]**
[0.435]
[0.136]
[0.266]
[0.243]
[0.229]
[0.177]
[0.267]
[0.246]
[0.322]
-0.661
-0.432
0.412
0.334
-0.048
-0.828
-0.056
0.315
-0.025
-0.618
-0.489
[0.235]**
[0.199]*
[0.321]
[0.133]*
[0.235]
[0.378]*
[0.159]
[0.199]
[0.202]
[0.206]**
[0.519]
-3.537
-4.491
-0.275
-1.15
-2.509
-0.674
-1.565
-4.739
-4.431
-4.579
-0.931
[1.178]**
[1.026]**
[2.147]
[1.003]
[1.030]*
[2.121]
[0.928]
[1.503]**
[1.513]**
[1.761]**
[4.802]
1412
0.36
1357
0.18
162
0.22
1557
0.47
1354
0.29
155
0.15
1493
0.44
1461
0.39
1494
0.56
1468
0.63
81
0.4
Table 7. Determinants of participation into each cash crop
- 10 -
Trade Index :
Chemical Pesticides
Land distance
Quality of Land
Irrigation
Constant
Observations
Pseudo R2
all cash crops
1
2
exporters newcomers
-0.69
-0.565
3
quitters
0.115
[0.163]**
[0.130]**
2.008
0.976
[0.068]
-2.286
[0.502]**
[0.597]
[0.698]**
0.003
-0.031
-0.021
[0.039]
[0.028]
[0.037]
0.406
0.475
-0.009
[0.123]**
[0.136]**
[0.173]
-0.177
-0.158
-0.166
[0.155]
[0.128]
[0.191]
-1.891
-3.036
-1.293
[0.695]**
[0.831]**
[1.118]
1686
0.34
1534
0.2
407
0.11
Table 8. Determinants of participation into any cash crop21
4. Gains from specialization in export crops
a. Inference from observed income
0
.0002
Density
.0004
.0006
.0008
Figure 2 illustrates agricultural income’s gains and losses depending on the evolving
specialization of farmers. The change in the density distribution of newcomers in the cash
crop sector is slightly on the right relative to the others, but this is not so at all points of the
distribution. Some of the farmers who quit the export market loose more than those who
remained export-oriented or become exporters. But this holds only at the lowest part of the
distribution. For upper part of the distribution, some quitters gain more than exporters.
-5000
0
5000
Difference in agricultural income pc between 2002 and 2004
Becoming cash crop farmer
Subsitence farmer
10000
Cash crop farmers
Becoming Subistance farmer
(1) Gaussian kernel density
21
For presentation purpose, only selected variables are shown, but regressions are always run on all variables
listed on table7. Standard errors are in brackets and *refers to a level of significant at 5%; and ** a level of 1%.
- 11 -
Figure 2.
Kernel density distribution of agricultural income gains by type of
transition (2002-2004)
Map 3 (see appendix) displays the average agricultural gain and the number of newcomers by
provinces. Three observations can be underlined. First, most of the provinces that have
newcomers, counted initially cash crop producers22. Second, the Central Highland region has
the greatest increase in income as well as in the number of cash crop producers. Moreover it is
the region that already had the highest level of income and of number of exporters (see map 1
in appendix). Finally, North Central Coast performed second, but contrary to Central
Highland, it was one of the poorest region in 2002.
At this stage, we do not take into account the fact that export and subsistence oriented
households could be different for other reasons that could also explain the differences in
income gains. Thus we turn now to a propensity score matching.
b. Comparing comparables.
Let us start with the agricultural income
of farmers (h). Those involved in cash crops
production (i=1) will have an agricultural income defined as
and those who are
subsistence-oriented (i=0) as:
.We want to estimate the expected income differential of
export-oriented households versus subsistence orientated households23. In other words we
want to measure the average “treatment effect” of being an export-oriented household:
|
|
1
1
|
1
The two incomes are not observed at the same time. In order to compare the two groups of
households, which may differ in their characteristics, we calculate a propensity score. The
score summarizes the households. Export-oriented households are then matched with other
households, based on their propensity score. The method used here is the Kernel matching.
Then all the treated are matched with a weighted average of all controls with weights that are
inversely proportional to the distance between the propensity scores of treated and controls.
The propensity score measures the probability of a household to participate into cash crop
production based on its observables as defined in equation 1 section 3b. A necessary
assumption is that observations with a given propensity score have the same distribution of
observables for households growing cash crops and those growing subsistence crops. Thus we
need to impose a balancing property.24
c. Results
We use the procedure suggested by Dehejia & al, 2002. Most of the crops’ specifications
satisfy the balancing property. The standards errors are bootstrapped. In table 9, we put in
bracket the cases where it is not satisfied. In general, farmers entering into the cash crops
22
This finding reinforces the idea that natural endowment, measured by the provincial share of acreage in 2000,
which is a component of the trade index, do play a role.
23
This specification corresponds to k=1 as defined in section 3b. We will alternatively look at the three
specifications (k=1,2,3).
24
This means that it exist sufficient treated and non-treated households are comparable controlling for all
households covariates.
- 12 -
sector are expected to gain 645,600 VND/pc on average more than comparable subsistence
oriented farmers. This represents 24.5 percent of the average total per capita expenditure of
the panel in 2002. Another interesting comparison is the 2002 poverty line, which is set at
1,916,000 VND per person.25
Situation
2002
0
2004
1
vs.
0
0
1
Tea
Gain in agricultural income (1000 VND)
Coffee
Pepper
Cashew
Cash crops
267.03
103.093
{967.024}
302.2621
-458.04
271.4207
-1122.06
353.8763
256.99
149.2251
{782.1451}
213.566
{751.1573}
365.707
1480.27
1180.284
645.60
150.0269
546.04
614.9614
-496.96
198.3714
{1320.919}
1375.874
405.15
159.0539
0
vs.
1
1
1
1
vs.
0
0
99.80507
358.041
Table 9. Estimated agricultural income gains over 2002-200426
However those results are attenuated if we look at the gain in expenditure per capita (table
10). Newcomers are only earning 70,930 VND /pc more than subsistence oriented farmers and
the difference is not significant any more. Cash crop producers have an average treatment
effect that is only about 158,450 VND /pc in term of expenditure compared to subsistence
oriented farmers. This discrepancy comes from the fact that agricultural income is just one
part of household’s income. Moreover, the Vietnamese have been increasing their rate of
savings.
Situation
2002
0
2004
1
vs.
0
0
1
Gain in total expenditure pc. (1000 VND)
Coffee
Pepper
Cashew
Cash crops
-83.77
113.5906
{-144.5616}
255.9959
103.01
274.504
-553.28
323.9543
-119.99
144.886
{267.4252}
202.7004
{276.7958}
313.1941
25.12
512.7541
70.93
107.0576
-147.40
371.9192
-256.18
130.1842
{150.7766}
557.8921
158.45
136.8423
0
vs.
1
1
1
1
vs.
0
Tea
0
Table 10.
472.79
356.5388
Estimated per capita expenditure gains over 2002 -200427
5. Simulating an improvement in market access abroad
To start with, table 11 gives an overview of the share of cash crops acreage as compared with
other crops28. After cereals, which are mainly composed by rice, perennials are the second
25
At January 2002 1 $US (or 1.12 EUR) is equivalent to 15,000 VND.
Standard Errors are in below in italics
27
Standard Errors are in below in italics
26
- 13 -
most important crops’ category grown over Vietnam, with 9.6% and 10% of total acreage in
2002 and 2004 respectively.
National Share of acreage by type of crop
Cereals
Vegetables & Beans
Annual Industrials
Perennials
Fruits
Total
Table 11.
2002
2004
74.53
73.46
5.46
5.81
5.48
5.37
9.68
10.03
4.85
5.32
100
100
Share of acreage over crops’ type (source GSO, 2006)
In this section we simulate the impact of an improvement of market access for Vietnam cash
crops. Results in table 12 show that a one percentage point decrease in tariff applied to
Vietnam’s exports will on average, ceteris paribus, increase by 12.3% the number of
households involved in cash crop production. It increases by 6.8% the number of households
entering into cash crop production and decrease the number of cash crop producers switching
to subsistence crops by 3.1%. 29
Predicted acreage (ha)
in percentage of the total
Predicted Number of farmers
[1]
2,346,611
17.8%
20,249
[2]
1,588,016
12.0%
13,703
[3]
3,525,792
26.8%
30,424
Improvement of market access
Effect on acreage (ha)
Effect on number of farmers
in percentage of the total
1,619,161
13,972
12.3%
897,229
7,742
6.8%
-405,466
-3,499
-3.1%
Table 12.
Estimated effect of an improvement of market access
6. Robustness of the results
For robustness check, we created an alternative agricultural index based on the VLSS 19971998 commune data. This survey reported the total area by sub-aggregate crops (i.e.
perennials) and by communes. This information was used to compute the share of each
province on the acreage of cash crops. Results are reported in table 13 part 1. The signs and
order of magnitude of coefficients remain unchanged compared to table 8, despite higher
coefficients rate in the alternative trade index.
28
Perennial is a plant that lives for more than two years. Here it includes tea, coffee, pepper, cashew, and rubber
(all cash crops) plus coconut.
29
The predicted acreage is calculated by multiplying the predicted probability (obtained in our estimation of the
propensity score) and Vietnam total acreage (GSO, 2006). The predicted number of farmers is composed by
dividing the predicted acreage over the average farmers’ acreage plot (GSO, 2006). Finally the effect on acreage
of a improvement of market access is obtained by multiplying the marginal effect of one percentage point
reduction of tariffs applied to Vietnam cash crops in the rest of the world (in average levels) with the predicted
acreage. Finally the effect on number of farmers is obtained by dividing over the average Vietnamese plot
acreage and the percentage is the share of the latter over the total number of farms (GSO, 2006).
- 14 -
We also tested the validity of the trade index by including in the regression, the distance from
each province to the nearest maritime port30. This last variable was created by the author, in
order to proxy the provincial distance to international markets as in Nicita (2004). The results
in table 13 part 2 show again the robustness of our agricultural trade index measure. The
coefficient of the distance variable is counterintuitive: the farther a household is from any
maritime port, the higher the probability that it is a cash crop producer or becomes so. This
can be due to the fact that the relatively isolated Central Highland concentrates many cash
crop growers. (map 1 in appendix).
1
exporters
Specification 1
2
newcomers
3
quitters
-8.407
-7.007
0.869
[2.701]**
[1.944]**
[0.999]
On all Cash crops
Trade Index
Trade Index 2
Distances
Chemical Pesticides
Land distance
Quality of Land
Irrigation
Constant
Observations
Rsquared
1
exporters
-0.669
Specification 2
2
newcomers
-0.571
3
quitters
0.102
1
exporters
-0.705
Specification 3
2
newcomers
-0.546
3
quitters
0.149
[0.163]**
[0.132]**
[0.068]
[0.177]**
[0.130]**
[0.062]*
0.097
-0.022
-0.093
[0.048]*
[0.037]
[0.053]
2.505
1.369
-2.291
1.901
0.998
-2.116
[0.643]**
[0.552]*
[0.661]**
[0.494]**
[0.587]
[0.698]**
-0.035
-0.039
-0.008
0.003
-0.031
-0.021
[0.035]
[0.032]
[0.037]
[0.038]
[0.029]
[0.037]
0.464
0.512
0.02
0.396
0.481
-0.004
[0.124]**
[0.135]**
[0.177]
[0.124]**
[0.138]**
[0.169]
-0.192
-0.133
-0.263
-0.166
-0.16
-0.174
[0.162]
[0.127]
[0.205]
[0.155]
[0.128]
[0.186]
-1.709
-2.996
-1.316
-2.389
-2.917
-0.792
-1.322
-2.654
-1.958
[0.778]*
[0.826]**
[1.156]
[0.707]**
[0.792]**
[1.165]
[0.743]
[0.754]**
[1.127]
1626
0.27
1488
0.18
388
0.1
1686
0.35
1534
0.2
407
0.11
1695
0.29
1541
0.17
409
0.06
Table 13.
Robustness of determinants of participation into any cash crop
Finally we also test the exogeneity of our explicative variables and more particularly the
farm’s characteristics as chemical pesticides that seems to have strong impact in our
estimations Table 13 Part 3 shows that our coefficient of interest does not change
significantly. This gives us confidence in the exogeneity of our independent variables. Of
course as we are dropping some of the independent variables, we are loosing some explicative
power retrieve in the smallest Rsquared.
7. Conclusion
Vietnam has considerably increased its agricultural exports which significantly contributed to
the trade surplus. In addition to have a real comparative advantage, agricultural products have
enjoyed a better market access abroad during the last decade. Moreover Vietnam rural
development duty relatively to other development countries, in particular to China, has been
described as a success story; with a drop of poverty and a stable and low inequality.
We explored the impact of the improvement in market accesses abroad for Vietnamese
farmers. We proceeded in three steps. First we identified the determinants of crop
specialization distinguishing between cash crop producers, newcomers, quitters and
subsistence farmers. Based on those results, we matched households on their propensity score
30
We show and describe only the results on all cash crops , but the robustness check was also done at the crop
level, and can be provided by the author on request
- 15 -
to compare gains in agricultural income over 2002-2004 depending on crop specialization.
Finally we simulate the impact on production’s choices of an improvement in market access.
Our results show that farmers had a larger probability to become cash crop producers,
relatively to stay in subsistence crops, when their opportunity to sell abroad increased. Our
study focalizes on the trade policies of Vietnam’s partner market. Other determinants might
also have influenced farmers’ choices such as the price at which they could sell their products
at the national and at the international level. In a previous paper we show that in the nineties
national and local prices converged towards the international prices, increasing considerably
the income of farmers who were already involved in crops that could be exported at the time
they opened to international markets (Coello, 2007).
We found that households’ farm characteristics seem to matter more for crop specialization
than households’ own characteristics. In particular, households with a better quality land and a
high use of chemical pesticides were more likely to be export orientated.
However Vietnam agriculture seems to have being overusing chemical pesticides at levels far
higher than the optimal level for profit maximization (Nguyen & al, 2003). Thus even if in the
short run the use of chemical pesticides may improve yields, it may have the inverse effect in
the long run. Industrialized countries, such as France, for instance have experienced negative
externalities due to chemical pesticides’ overuse during the last decades.31
We also find that agricultural income gains have been larger for newcomers relatively to
subsistence farmers. We are focusing on producers and do not include the effects of trade
liberalization on Vietnamese consumers. We still know that consumers are negatively affected
when agricultural prices reach a threshold, as demonstrated by recent hunger strikes over the
world. In Vietnam a signal of the negative effect on consumers of high agricultural prices is
the export tax on rice that authorities have implemented in April 2008 to ensure food security.
The panel that allowed to track individuals, their cultivations and income gains. However the
time span is too short to see if cash crop farmers faced a greater vulnerability due to their
dependence on international market volatility.
In the last section of the paper, we find that a one percent point decrease in tariff applied to
Vietnam exports to the rest of the world will on average, ceteris paribus, increase the number
of cash crop producers by 12.3%. In a dynamic perspective, it will increase the number of
household entering into cash crop production by 6.8%. A caveat of these results is that there is
no general equilibrium effect here. We could think for example that an increase in the number
cash crop producers could impact the demand of inputs and thus lower the expected income
increase.
This paper highlights the impact of trade liberalization on exports. However, following
Vietnam’s accession to WTO, the action will also move on the import side. For instance
maize producers, who are mainly poor households from ethnic minorities, may have some
difficulties to compete with subsidized maize imports.
Finally, even if we show a positive impact of cash crops production on agricultural income
gains, some long term perspective should also be taken into account. This success may lead to
a shortage of arable land, and negative ecological externalities, as deforestation and soil
erosion. This already happened in 1999, when coffee farmers cleared more than 74,000
hectares of forest in Dac Lak province alone at the time of booming coffee prices. (World
31
One of them is a decrease in quality of land (Nicolino, 2007)
- 16 -
Rainforest Movement, 2001). The current increase in international crop prices could also lead
to such ecological externalities.
- 17 -
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Appendix
Variable
Distances
Size of the household
age of the head
Age squarred
Share of males
Chemical Pesticides
Organic Pesticides
Land distance
Land Tenure
varicertif
Gender
Married
primary
secondary & upper
technical diploma
Ethnic
Land Tenure
Car
Quality of Land
Irrigation
Urban
Trade Index :
Tea
Coffee
Pepper
Cashew
All crops GSO
All crop vlss
Poor
Rich
(Middle class)
Brief description
Log of Distance to the main maritim port
Logarithm of the size of the household
Logarithm of the age of the head
Logarithm of the age squarred
Share of males
Share of the amount spent on Chemical pesticides on total agricultural expenditures
Share of the amount spent on Organic pesticides on total agricultural expenditures
Log of Distance to their land
Share of land owned with a land tenure before 2000
Variation "Land Tenure" between 2000 and 2004
=1 if male,=0 otherwise
=1 if married ,=0 otherwise
=1 if having primary education ,=0 otherwise
=1 if having secondary or upper education ,=0 otherwise
=1 if having technical education ,=0 otherwise
=1 if none-kinh,=0 otherwise
=1 if households do not have a land tenure, =0 otherwise
=1 if households own a car ,=0 otherwise
=1 high quality land, =0 otherwise
=1 if pumps system, =0 otherwise
=1 if urban, =0 otherwise
See Section for more details
All crops with GSO source for acreage provincial data
All crops with 1997comune vlss source for acreage provincial data
Low 30% of the expenditure distribution
Upper 30% of the expenditure distribution
Omitted variable
Table 14.
- 20 -
Variables description
Average
4.549
1.465
3.815
7.630
0.508
0.151
0.008
5.945
0.466
0.227
0.842
0.864
0.264
0.369
0.046
0.198
0.466
0.022
0.575
0.312
0.077
Std. Dev.
1.711
0.377
0.286
0.573
0.187
0.103
0.033
1.873
0.468
0.393
0.365
0.342
0.441
0.483
0.210
0.399
0.468
0.147
0.494
0.463
0.266
-0.166
-0.047
-0.100
-0.116
-0.444
-0.012
0.300
0.300
0.400
0.421
0.180
0.304
0.405
0.996
0.029
0.458
0.458
0.490
Map 1. Agricultural income pc in 2002 and cash crop producers
- 21 -
Map 2. Decomposition of Vietnam by its 8 administrative regions.
- 22 -
Map 3. Agricultural income variation (2002-2004) and newcomers
- 23 -