The relationship between the agricultural trade and productivity in vietnam case

UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM – NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

THE RELATIONSHIP BETWEEN
AGRICULTURAL TRADE AND PRODUCTIVITY
IN VIETNAM CASE

A thesis submitted in partial fulfillment of requirements for degree of Master of
Arts in Development Economics

By
NGUYEN HOANG DIEP

Academic supervisor
Dr. TRAN TIEN KHAI

HO CHI MINH CITY, JANUARY 2015

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ACKNOWLEDGEMENT

So many persons that I want to say thanks to them. I write these
acknowledgments to express my gratitude to them who helped me directly and
indirectly to finish this thesis.
First of all, Dr. Tran Tien Khai who is my supervisor, he helped me to
complete my ideas. Furthermore, he also gave me advices to writing logical for this
thesis, and already gave his hands when I needed.
The other person that is Dr. Tran Khanh Nam, who gave me advice from
beginning when I wrote the thesis design. In addition, he provided me the VARHS
data set to apply in my thesis.
Important to me is to thank the Vietnam-Netherlands program and all of people
and lecturers who supported and gave me their knowledge.
Finally yet importantly, with all my respect for my parents who gave me a
change to study in this program. They have encouraged me to continue my studying.

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ABBREVIATION

FAOSTAT

The Food and Agricultural Organization Corporate
Statistical Database

VARHS

The Vietnam Access to Resources Household Survey

TFP

The Total Factor Productivity

OLS

The Ordinary Least Square

GDP

Gross Domestic Product

DF

Dickey-Fuller Test

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ABSTRACT

This thesis tries to find out whether trade has an effect on agricultural productivity in
Vietnam case. There are two models to estimate relationship between trade and
productivity including national-level model and farm-level model. The time series

data of nine agricultural commodities, which is taken from FAOSTAT, is used in
national-level model. Farm-level model uses cross section data from VARHS 2010
including 1449 farm household in nine provinces. In both models, yield refers as
agricultural productivity. The both models support for a strong positive relationship
between trade and yield. The result also indicates that the land, irrigation, human
capital, cost of production might be necessary for improve productivity.

Key words: Agricultural productivity, international trade, cross-section, time series.

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TABLE OF CONTENT
Page
ACKNOWLEDGEMENT.......................................................................................... 2
ABBREVIATION....................................................................................................... 3
ABSTRACT................................................................................................................ 4
TABLE OF CONTENT.............................................................................................. 5
LIST OF TABLE ........................................................................................................7
LIST OF FIGURE ...................................................................................................... 8
Chapter 1: INTRODUCTION..................................................................................... 9
1.1

Problem statement ............................................................................................9

1.2

Research objective ........................................................................................ 10

1.3

Thesis structure.............................................................................................. 11

Chapter 2: LITERATURE REVIEW........................................................................ 12
2.1. Theory.................................................................................................................12
2.1.1. Agricultural productivity..........................................................................12
2.1.2. Agricultural trade.....................................................................................15
2.2. Empirical study................................................................................................... 17
2.3. Conceptual framework....................................................................................... 19
Chapter 3: RESEARCH METHODOLOGY....................................................... 21
3.1. Agricultural productivity measurement.............................................................. 21
3.2. The tradability index........................................................................................... 22
3.3. Empirical model................................................................................................. 23
3.3.1. The national level model..........................................................................23
3.3.2. The farm level model...............................................................................24
3.4. Data.....................................................................................................................25
3.5. Analytic method................................................................................................. 28
3.5.1. Test for multicollinearity..........................................................................29
3.5.2. Test for heteroscedastiscity ......................................................................30
3.5.3. Test for autocorrelation............................................................................30
3.5.4. Test for stationary....................................................................................31
Chapter 4: RESULT AND DISCUSSION................................................................ 32
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4.1. Overview Vietnam agricultural trade................................................................. 32

4.1.1. Agricultural production in Vietnam...........................................................32
4.1.2. Land........................................................................................................34
4.1.3. Irrigation..................................................................................................35
4.1.4. Fertilizer..................................................................................................35
4.1.5. Farm machinery.......................................................................................36
4.1.6. Human capital..........................................................................................36
4.1.7. Trade.......................................................................................................37
4.1.7.1.

Export...........................................................................................37

4.1.7.2.

Import ...........................................................................................38

4.2. Econometric result.......................................................................................39
4.2.1. The product tradability index and national level response..........................39
4.2.2. The farm tradability index and farm level response...................................42
Chapter 5: CONCLUSION....................................................................................... 47
5.1. Conclusion.......................................................................................................... 47
5.2. Future research................................................................................................... 48
REFERENCE............................................................................................................ 49
APPENDIX............................................................................................................... 53

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LIST OF TABLE

Page
Table 3.1: The description of each variable and expected sign.

27

Table 4.1: Production quantity of selected crops

33

Table 4.2: Annual growth rate of quantity of selected crops

33

Table 4.3: Evolution of crop production value per ha

33

Table 4.4: Water use in Vietnam in 2005.

35

Table 4.5: The mechanization rate in agricultural production activities

36

Table 4.6: Labor force in Vietnam by resident region in selected years
(thousand people)

36

Table 4.7: Structure of employed population by industrial sector
2000-2013 (percentage)

37

Table 4.8: The ADF test result for yield of nine commodities.

40

Table 4.9: The ADF test result of tradable index of nine commodities.

40

Table 4.10: Results of national-level analyses (dependent variable =
yield per each commodity between 1961-2010)

41

Table 4.11: Summary statistics of variables in farm level model.

42

Table 4.12: The correlation among coefficients in farm level model

43

Table 4.13: The correlation result of each variable (VIF, TOL=1/VIF)

43

Table 4.14: Production function result of the farm-level analyses
(dependent variable = yield of an important crop in a farm)

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LIST OF FIGURE
Page
Figure 2.1: The linkage between agricultural productivity and
international trade.

20

Figure 4.1: The value of agricultural production.

34

Figure 4.2: The land of annual and perennial crops 1990-2012 in Vietnam

34

Figure 4.3: Export value of main agricultural commodities since 1990-2011.

38

Figure 4.4: The import value of main agricultural commodities in Vietnam

during period 1990-2011

39

Figure 4.5: The relationship between maize yield and tradable index over time

42

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Chapter 1
INTRODUCTION

1.1

Problem statement
Trade helps people, regions, and countries exchange what they have and what

they need. In food security side, world population might be reach at 9 billion people in
2050; the question is asked how to meet this great demand of food with limited
producers, scare land and water resources. The answer is productivity, and open trade
may encourage farmers to increase quantity of agricultural products to meet a
requirement of food in the world.
The core of role of agriculture can summary in three issues: “raising
productivity, providing market, and generating saving for economic diversification”
(Johnson,

2009).

However,

traditional

farmers

have

suspicions

about

commercialization process that generate new demand, output or more competition.
An increase agricultural productivity has attracted many economists studying
about role of it in development economics in years (Matsuyama, 1992; Machicado et
al., 2008). In addition, agricultural productivity has an essential role in
industrialization and development economics. That means country improves its
productivity with using less labors in agriculture, and access labors in agriculture
transfer into manufacturing. Furthermore, when producers in agriculture sector
increase their incomes thanks to efficient production, demand for manufacturing
increases to meet more demand. On the other hand, some argue that agricultural
productivity has a negative relationship with industrialization. Field (1978) and
Wright (1979) indicate labor force can be a main fight between manufacturing and
agriculture sector because of comparative advantage. When agriculture has low
productivity, manufacturing has an abundant supply of labor with cheap wage.
Matsuyama (1991) shows explanation about these conflicting debates may be
relative to opened economy. He debates that in closed economy an increase
agricultural productivity might make agricultural labors shift to manufacturing and

contribute to economic growth. However, in open economy “high productivity and
output in the agriculture sector may, without offsetting changes in relative price,
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squeeze out the manufacturing sector and the economy will de-industrialize over time,
and in some case, achieve a lower welfare level.”
From Doi Moi 1980, Vietnam agriculture has improved from production and
especially exporting (Nguyen, 1998). Agriculture sector shifts from self-sufficient to
commercialization to supply both domestic and export markets. Vietnam became the
second biggest rice exporter in the world, and production of coffee, pepper, rubber,
cashew nut, fruit and vegetable have increased in quantity and export value. During
1980-1990, agricultural export value increased from 339 US$ million to 2,404 US$
million, and total value of trade increased 3.89 times. Quantity of export rice went
rapidly up from 1 million tons in 1990 to 3 million tons in 1997. Coffee increased
from 90,000 tons to 404,000 tons, cashew increased 27,400 tons to 99,000 tons. When
Vietnam joined World Trade Organization (WTO), the agricultural export coffee went
up 1,256,400 tons, and 178,500 tons for cashew nut, respectively. These evidences
may support for advantage effects of opening trade in agricultural Vietnam.
Furthermore, the question is asked that how trade affects productivity in individual
farm household.
Correspondingly, this study tends to analyze the linkage between agricultural
trade and productivity in Vietnam, contributes to debate above and tries to answer
whether international trade (import and export) in agricultural commodities is related
to agricultural productivity at national and farm level. This paper applies the product
tradable index measurement to represent international trade. In order to analysis of
productivity, country and farm level analyses are implemented with time series and
cross-section analysis in Vietnam.

1.2

Research objective
This thesis concentrates on studying and evaluating effect of trade on

agricultural productivity in Vietnam. Some questions, which this thesis tries to
answer: whether international trade increases agricultural productivity in Vietnam?
In order to answer these questions, this thesis uses two different levels of
analysis from overview to detail to understand clearly how trade influences
agricultural productivity.

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i)

To evaluate the effects of international trade on some main commodities’
productivity at national level, and

ii)

To analyze the relationship between trade and agricultural productivity at
farm household level.

1.3

Thesis structure
Thesis includes five chapters. This chapter introduces the effects of trade on

agricultural productivity, and research objectives for this thesis. Chapter 2 provides
the review of theories and empirical studies relative to effects of trade on agricultural
productivity. Chapter 3 describes the measurement of tradability index, and other
control variables may need in farm level model in detail. The econometric model for
national level and farm level are also introduced in this chapter. Data for each model
and method to estimate each model will be provided. In chapter 4, the overview of
Vietnam agriculture will be represented. The next in this chapter, it shows the results
of two models, and discusses how trade influence agricultural productivity in
Vietnam. Basing on outcome in chapter 4, chapter 5 summarizes these conclusions
and gives the possible future research can support for this thesis.

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Chapter 2
LITERATURE REVIEW

2.1. Theory
2.1.1. Agricultural productivity
2.1.1.1.

What is productivity?

Productivity is defined as “a measure of how efficiently inputs are combined to
produce outputs” (Gray, Jackson, & Zhao, 2011). Productivity contributes to the
economy growth without using additional physical inputs. For agricultural sector,
agriculture has an important role in rural areas and for economic growth. Almost the

poor people concentrates in rural areas and are farmers. Improved agricultural
production will increase food security, farmer’s income, employment, and contributes
to GDP at that country. The challenge for increase agricultural production is the scarce
resources such as lack of water, uncultivated land. Therefore, yield refers as an
increasing quantity with same cultivated land, combination of effective inputs,
applying new technology that may solve this problem (Mundlak, Butzer, & Larson,
2008).
2.1.1.2.

Factors affect agricultural productivity

There are several methods to increase agricultural productivity. Firstly,
increasing output and inputs as well, however, proportionate of increasing output is
larger than inputs. Secondly, an increase output with constant inputs. Thirdly,
decreasing output and inputs with inputs decreasing more. Finally, decrease inputs
with remain output (Adewuyi, 2006). Using effective inputs lead to increase output
method requires technical progress and inputs quality. For example, applying new
technology in production, investment in machinery, applying new technical method,
irrigation system, use of fertilizer and pesticides, etc. must be considered to increase
productivity. The recent discuss papers emphasize in role of technology and its
changes over time (Mundlak, 1992).
The concept of technical efficiency rises due to scarce inputs in agricultural
production. The question of technical efficiency is that how farmers utilize given
inputs to increase output. According to Farrell (1957) divided productive efficiency
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into its technical, allocative and scale components. The level of technical efficiency is

measure as a gap between actual and optimal potential production. Allocative (price)
efficiency refers as the capacity of farmers to choose inputs in minimize the cost of
production. Scale efficiency is defined that an increasing productivity due to an
increasing farm size. Both parametric and non-parametric methods are used to
measure productivity efficiency, while the stochastic frontier model is used widely.
Odhiambo & Nyangito (2003) had a review of factors determine productivity
involving resources inputs, fertilizer use, market access, extension services, farm size,
biophysical factors, and land tenure. While the market access ability of farmers must
be considered in agricultural process. The commercialization in agriculture influences
on productivity through specialization and intensification. This point of view will be
discussed in next part in detail to get the idea of effects of trade on agricultural
productivity.
2.1.1.3.

Agricultural productivity measurement

Agricultural productivity is measure in different ways, and it can determine in
physical term or value term. In economics, agricultural productivity is described as the
ratio of outputs to inputs/land and uses labor productivity or yield to measure
productivity that also called the partial measures productivity. Dharmasiri (2009)
applies the Average Productivity Index (API), which examines the different views of
productivity consist of productivity of land, labor and capital. In many researches,
they use total factor productivity (TFP) as a measurement of agricultural productivity
(Teweldemedhin& Van Schalkwyk, 2010).
Land is an important factor in agriculture. Productivity of land can increase
through increasing of seeds, fertilizer, chemical pesticides, and labor. Farmers can
diversify crops in agricultural land to increase productivity. Labor plays an essential
role in livelihood of people relative to agricultural production. It often is measured by
hours/days of work needed to produce a unit of product. Labor productivity is
described as the total output per unit of labor. Capital (purchase of land, investment in

land, drainage, irrigation system, seeds, agricultural equipment, etc.) is a priority
factor to increase agricultural productivity. Human capital also is concern in measure

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productivity. Correspondingly, agricultural productivity is impacted by physical,
socio-economic and technology (Dharmasiri, 2009).
The partial productivity is measured as output divided by a single input;
therefore, this measurement has many formulas depending on a particular input such
as labor productivity, capital productivity, and land productivity. The commonly used
partial measure is output per unit of land or crop yield for short. Crop yield is usually
used as a comparison among locations/countries or among periods. However, the
partial measurement has some limitations. The measurement is meaningful if other
factors are unchanged. Because land and labor productivity may increase by
increasing of other factors such as fertilizers, tractors, technology, management of
water, human capital, etc. Therefore, the multifactor productivity, which is also called
the Total factor productivity (TFP) is applied in order to solve these problems.
TFP is usually used to measure agricultural productivity. The traditional
measurement of TFP assumes the output is technical efficiency, by contrast, the recent
approaches allow inefficiency of productivity. There are four methods to estimate
TFP: estimation of aggregate production function, TPF index, Data Envelopment
Analysis (DEA) and Stochastic Frontier Analysis (SFA).
Aggregate production function approach assumes that there is no technical
inefficiency. The growth of TFP only includes technological change (Solow, 1957).
Scale change may be included in some researches. The technological change is
estimated by adding time trend variable into aggregate production function or using
the growth accounting method (Solow, 1957). Scale change is estimated as the sum of

elasticities of inputs. Aggregate production function is popular usage, however, it do
not reflect the technical efficiency change.
TFP index was introduced by Hicks (1961) and Moorsteen (1961). It measures
as ratio of growth rate of output to growth rate of total inputs. Measuring TFP index is
simple and does not need a complex estimation technique. In addition, the TFP index
needs the output and input prices, which usually is not available in most countries.
DEA method applies the linear technique to estimate the production frontier
based on dataset. This method does not require any information about output and input

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prices or any a specific form of production function. However, it depends on a
combination of efficient production, hence, it is sensitive to outliers.
SFA is a method of estimating the stochastic production function in which the
error term is decomposed into random noise and technical inefficiency. Darku, Malla
& Tran (2012) applied a SFA approach and decomposed TFP into scale effects,
technical efficiency change and technical change.
2.1.2. Agricultural trade
In general, economists state that the integration into world market will make an
increased

productivity

of

firm

through

increasing

competition

between

countries/industries, market share, innovation and technology, and spillover (Wong,
2009). When country joins world market, it has to face the competition from other
countries, which may be higher quality of goods with cheaper prices. To respond more
competition, firms have less competitive capacity may not survive in competitive
market. However, domestic firms/industries can get benefits from lower tariff barriers
such as they can access higher technology from higher developed countries with lower
price, lower price of material of production processes, the higher skilled labors from
abroad. Moreover, firms/industries with higher capacity to compete in world market,
they have an incentive to export their goods with higher prices.
David Ricardo introduced the comparative advantage theory comparing to the
absolute advantage theory in 1817. The theory answers the question why private
individuals or firms engage in trade, governments favor it and economists defend it. A
person has a comparative advantage at producing something if he can produce at a
lower cost in comparison with another. However, we consider the case that two
countries produce two products. The question is how to know which country should
specify which product if one country have a lower cost at both products. The answer is
“opportunity cost” which refers as the cost of a good we give up to produce another
product. The country has a comparative advantage in one product which has a lower
opportunity cost. Accordingly comparative advantage, if farmers increase their
productivity, their comparative advantage in agricultural production increase.
Therefore, farmers in that region can export more these products into the world
market.

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The challenges effecting farmer’s decisions are the price of inputs, price of
output, markets, and how to increase productivity. International trade will contribute
to balance agricultural price in both parties. For the importing country, opening trade
will push the market price lower and domestic consumers gain from free-trade. By
contrast, domestic producers lose in free-trade because the price of products decrease
as well as quantity supplied. For the exporting country, the shift from no trade to free
trade increases the price. Therefore, the producers make the profit from selling
product with higher price, and consumers lose well-being. On the other hand,
international trade will bring the benefit for domestic country such as advanced
technology, better inputs quality, and managerial skills (Wong, 2006).
The project for agriculture commercialization and trade (PACT) in Nepal wants
to help farmers access to new markets and build a strategic to improve productivity
and quality. PACT’s target is improving of competitive ability of farmers and
agribusiness within selected commodities value chain. The consequences of four years
implementation PACT was increasing volume, sales, and productivity of selected
goods.
Term of international trade is usually measured as nominal imports plus
exports relative to nominal GDP (Alcala & Ciccone, 2004). They debated that the
nominal value will make a misleading about effects of trade. Therefore, the “real
value” is considered to measure international trade that is imports plus exports in
exchange rate relative to GDP in purchasing power parity. The “real value” is
implemented due to eliminate the distortion of different prices in different regions.
There are many studies about the role of international trade in development
economics as general and productivity as particular. Consequently, many papers show
the positive relationship between international trade and productivity. Some

researchers use cross-country or time-series analysis to estimate the trade and
productivity linkage (Teweldemedhin& Van Schalkwyk, 2010). While another use
specific country data for analyzing international trade and productivity. Most of paper
uses the total factor productivity (TFP) to measure the level of productivity
(Shevtsova, 2010); however, some others use openness as trade measurement (Alcala
& Ciccone, 2004; Wong, 2006). According to paper of Fleming & Abler (2012), they
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applied the definition of a product-specific trade exposure index as measurement of
trade on agricultural products in particular year in Chile case. The trade exposure
index is calculated import plus export quantity divided agricultural production.
2.2. Empirical study
Alcala and Ciccone (2004) emphasize that the real openness which refers as
import plus export relative to purchasing power parity GDP is better than openness
only to measure trade because the real openness reflects the real value of non-tradable
goods in different countries. As a result, paper proves that the effect of international
trade on productivity in cross-countries is positive significant and economically. The
paper also provides evidence that the productivity have a positive significant with size
of country when international trade accounts.
Sara A. Wong (2009) investigates how trade effects on manufacturing
productivity at that country, especially at Ecuador when country opens their market to
global market. The study emphasizes on how export-import sector responds to opened
trade. Consequently, the result of study found that trade openness positively affects
productivity in export-oriented manufacturing industries in Ecuador.
Shevtsova (2010) uses the micro-data to estimate the relationship between
export and productivity at firm-level in manufacturing and services sectors during
period from 2000 to 2005. The study tests two hypotheses. First is self-selection effect

which estimates productivity effect before entering export market and result shows
that the firm with higher TFP has higher incentive to enter export market. Second is
learning by exporting effect which estimate productivity effect in entry export-market
period and result is the same with first hypothesis; however, some industries show that
no productivity gain after entry export-market.
For the agriculture sector, the efficiency of international trade may bring
benefits for increasing agricultural productivity. Barbara Coello (2007) analyses the
impact of international trade on export of agricultural commodities in rural Vietnam
during 1993-1998. The study uses price of commodities as trade measurement on
local prices, and then it affects on household income, productivity and profits. As a
result, international trade affects farmers in different way depending on product

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specification and geographical location such as in Southern region of Vietnam
tradable agricultural goods are more sensitive to international prices.
Hassine et al. (2010) recently imply Computable general equilibrium (CGE)
model to estimate the relationship between trade liberalization, agricultural
growth/productivity, and poverty alleviation. The study tends to examine the effects of
trade openness on agricultural productivity and analysis how agricultural productivity
contributes to poverty reduction in Tunisia.
“Does agricultural trade affect productivity?” is the title that Fleming & Abler
(2012) used to estimate whether international trade influences agricultural
productivity. The commodity trade exposure index, which is applies in cross-section
model with 70,000 individual household extracted from Chile agricultural census,
refers as the proportion of imports and export to total agricultural production. In
addition, they analysis two groups: one focuses on traditional products, another

focuses on traditional and non-traditional products. In addition, they used the
production function to show the relationship between trade and yield. The results
support for hypothesis of trade effecting yield. However, the effect of trade on
households planting traditional and non-traditional crops is larger than households
planting only traditional crops.
Nirodha et al. (2013) provides an evidence of effects of trade liberalization
policies on agricultural production growth in Sri Lanka. This paper applies the
Ordinary Least Square and multiple regression models with series data from 1960 to
2010. The analysis divides into two periods before and after liberalization: 1960-1977
and 1977-2010. The econometric result indicates that the trade openness, investment
are statistical significant positive with agricultural growth and eventually lead to
increase agricultural productivity in Sri Lanka.
The question is how trade liberalization can impact to poverty through
increasing agricultural productivity that is the main purpose of Nadia’s paper (2008).
The paper provides the link between trade and agricultural productivity, and then
estimates the link between agricultural productivity and poverty reduction. It applies
panel data for Mediterranean regions, and uses the talent class stochastic frontier
model to estimate Technical Efficiency and Total Factor Productivity. The data
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consists of 36 agricultural commodity and inputs variables such as labor, fertilizer,
agricultural land, irrigation, and machinery. The results indicate that trade openness
has a positive influence to agricultural productivity though technology and help to
reduce poverty.
There are many researches that evaluated the effects of agricultural trade on
poverty reduction through increase income. Nadia et al. (2010) also provide an
evidence of linkage between trade and productivity, and then they applied a

Computable General Equilibrium (CGE) in order to associate the trade-productivity
linkage with poverty in Tunisia. The results support an increasing agricultural
productivity thanks to opening trade through the transfer of technology from advanced
countries. It also provides the inverse impact of policies on agricultural productivity.
The contribution of other control variables such as education, farm size, technology,
political stability, control of corruption, and government effectiveness must be
consider in agricultural productivity growth.
2.3. Conceptual framework
At the household level, the factors effect directly on agricultural productivity
including farmer characteristic (education, age, gender, experience, health, etc.),
farming characteristics (farm size, type of land, tenure, etc.), inputs (fertilizers,
pesticides, capital of investment, seed, human capital), technical process, market
access, biophysical factors (soil type, climate, rainfall, etc.). Many researches
determines factors effect on productivity such as land, capital, and labor (Yair et al.
1997; Majeed and Afaf 2001); human labor, farmyard manure, chemical fertilizers,
water for irrigation, transportation, electricity, diesel fuel, and machinery (Banaeian &
Zangeneh, 2011). Seed, fertilizers, manure, human labor, animal labor, machine,
pesticides, irrigation and land (Elumalai, 2011).
As mention above, the method to increase productivity is an increase output with
reduce/given level of inputs. And technology change may a result of effective inputs
usage.
At the macro level, international trade affects the price of products. The export
country will obtain more profit when sells its product with higher price in
international markets. Therefore, producers have an incentive to increase their product
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though increasing productivity. In agricultural sector, price of agricultural products is

influenced by trade, farmers are encouraged to improve productivity. Farmers apply
new technique to use resource inputs efficiently such as mechanization, new
technology for increasing quality of inputs. While international trade openness an
opportunity for domestic country though technology transfer. Figure 2.1 describes the
linkage among factors can influence agricultural productivity based on the theories
and empirical studies which are discussed above.
Figure 2.1: The linkage between agricultural productivity and international trade.
Agricultural productivity

Outputs

Inputs use
Technical efficiency
Allocative efficiency
Scale efficiency
Technology

Price

International Trade

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Chapter 3
RESEARCH METHODOLOGY

Many studies apply diversified approaches to evaluate effects of trade on

agricultural at many respects of economics and society. Generally, most of studies use
Total Factor Productivity (TFP) as to measure productivity in agricultural sector
(Teweldemedhin & Van Schalkwyk, 2010). However, this study considers crop yields
represented agricultural productivity in Vietnam. Crop yields are not perfect to
measure productivity but it reflects productivity and available variables. The reason
why crop yield is measured as agricultural productivity will be present in below
section.
Trade variable commonly measures as nominal imports plus exports relative to
nominal GDP or also called “openness”. However, if we use nominal variables when
measure trade, it will make misleading in evaluation. Therefore, “real openness”
replaces “nominal openness” to estimate international trade. This study uses the same
method to estimate capacity of agricultural goods trading. Thus, trade variable is
described as the share of imports plus exports in the total production of a specific
commodity, referred to as a product tradable index (TI).
In order to overall view about the effect of trade on agricultural productivity,
this study provides two models at national and farm levels. For national level, model
tries to find out how trade effects on each commodity in 1961-2010. For farm level,
model goes into detail how farms respond effects of trade through agricultural yields.
For each model, this study uses a specific trade variable, which will be described in
detail in next section.
3.1. Agricultural productivity measurement
Agricultural productivity is usually measured as a ratio of output quantities to
input quantities. In general, there are two main types to measure agricultural
productivity, distinguished by their handling of inputs: partial productivity and
aggregate productivity. The ratio of output to a single production factor (input) is
called the partial productivity; in other hand, the ratio of output to all production
factors is called the aggregate or multifactor productivity.
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The partial productivity is measured as output divided by a single input;
therefore, this measurement has many formulas depending on a particular input such
as labor productivity, capital productivity, and land productivity. The commonly used
partial measure is output per unit of land or crop yield for short. Crop yield is usually
used as a comparison among locations/countries or among periods. However, the
partial measurement has some limitations. The measurement is meaningful if other
factors are unchanged. Because land and labor productivity may increase by
increasing of other factors such as fertilizers, tractors, technology, management of
water, human capital, etc. Therefore, the multifactor productivity, which is also called
the Total factor productivity (TFP) is applied in order to solve these problems.
TFP is a ratio of an index of agricultural output to an index of aggregate
agricultural inputs. However, TFP differs from Partial productivity in that they use a
value-weighted sum of output and input to measure index of output or inputs. The
TFP includes other factors may effect on productivity as land, labor, fertilizer,
pesticide, physical capital, irrigation, etc. Although TFP has an advantage in
measuring productivity, TFP is much more complex than partial productivity. Some
researchers used yield to measure productivity (Fleming & Abler, 2010; Banaeian &
Zangeneh, 2011; Dharmasiri, 2009). Yield is not perfect to represent agricultural
productivity, however the underlying data is often easily available in over years.
Moreover, farmers in Vietnam trend to increase land of agricultural crops which have
a high price in order to increase their profit. For example, in 1999 the land for pepper
was 15,000 hectare and increased 45,000 hectare in 2003. Maize increases from
730,000 hectare to 1,156,000 hectare during 2000-2012. Therefore, yield is used to
estimate agricultural productivity in Vietnam for this thesis. In addition, in farm level
model the other variables as irrigation, human capital, labor, cost of production,
machinery are used as the control variables.
3.2. The tradability Index
In general, the tradability index is measured as formula below

𝑇𝐼𝑖𝑗 = (𝐸𝑥𝑝𝑜𝑟𝑡𝑖𝑗 + 𝐼𝑚𝑝𝑜𝑟𝑡𝑖𝑗 )/𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛𝑖𝑗 (1)
Where 𝑇𝐼𝑖𝑗 is the tradability index of commodity i in year j, and TI is
calculated as the summation of exports and imports quantity over the total local
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production of commodity i in year j. The result responds as zero in calculation; it
means that commodity consume in the country. TI is infinite when the products are
imported completely into the country.
This thesis uses two models at different level to evaluate the effects of trade on
agricultural productivity: national level, and farm level. At national level, the TI is
used as equation (1) and bases on the FAOSTAT database to calculate the TI for each
commodity.
At farm level, trade variable is denoted as FTI for Farm tradability index and is
calculated by dividing the total sale for the total production quantity.
𝐹𝑇𝐼𝑖 =

𝑆𝑎𝑙𝑒𝑖
𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛𝑖

(2)

Where 𝐹𝑇𝐼𝑖 is the farm-level TI of commodity i.
3.3. Empirical models
In order to examine the impact of international trade on agricultural
productivity, Fleming and Abler (2013) apply a cross-sectional analysis of crop yields
on Chilean farms. In order to evaluate the impacts of international trade on
agricultural productivity, this paper uses national-level and farm-level analyses.

3.3.1. The national level model
National-level analysis is used in this thesis to examine whether international
trade affects agricultural productivity in general. This analysis tries to prove the
hypothesis that is: the country faces the higher the tradable index in agricultural
commodity, its yield is higher over time. To examine the correlation between trade
and agricultural productivity FAOSTAT database is used in this examination.
𝑌𝑖 = 𝛼0 + 𝛼1 𝑇𝐼𝑖 + 𝑒 (3)
Where Yi is yield of commodity i, 𝑇𝐼𝑖 is tradable index of commodity i, and e
is an error term. In this model, TI is estimated following equation (1). The analysis
concentrates on nine commodities: rice, pepper, vegetable, cassava, cashew nuts, tea,
fruit, maize, and coffee. Because these commodities are traded the most in Vietnam.
For example, in 2011 rice exported 7.72 million tons over 27.15 million tons of total
production. Philippines, Malaysia, and Indonesia are the main imported markets of

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Vietnam. This model is estimated for each commodities during the period 1961-2010
in order to understand that the changes of yield may come from trade or not.
3.3.2. Farm-level model
Farm-level analysis has the same purpose with national-level analysis,
however, it expresses in individual producer. Farm model analysis will base on
production function 𝑌 = 𝑓(𝑋1 , 𝑋2 , 𝑋3 , … , 𝑋𝑛 ). Where Y is a quantity of output and Xs
are vectors of inputs. In addition, production function can be express as the
relationship between one output and one input or multiple inputs. A production
function do not have a specific form of function, it is depend on what combination of
inputs produce which output. There are many form of production function, however
the Cobb-Douglas production function is frequently used in estimate productivity. The

Cobb-Douglas model was represented firstly in 1928 by Charles Cobb and Paul
Douglas in the journal American Economic Review. The study tested the model of the
growth of American economy during 1899 – 1992. The model showed the production
output depended only two inputs which are capital and labor, while there are many
other factors effect on production output.
This farm level model applies a Cobb-Douglas production function to express
the relationship between dependence and independence variables. Y= 𝑎𝑋1𝑏 𝑋2𝑐 … while
a is total factor productivity; b, c are the output elasticity of inputs, respectively. The
Cobb-Douglas function can be expressed as a translog function:
ln(𝑌) = ln(𝑎) + 𝑏 ln(𝑋1 ) + 𝑐𝑙𝑛(𝑋2 ) …
The production function is constant return to scale when b + c + ... = 1,
meaning that the double of using inputs lead to double output as well, b + c + ... > 1 is
increasing return to scale, and b + c + ... < is decreasing return to scale.
I replace Y as crop yield, and X1, X2, ... consist of irrigation, labor, machinery,
amount of production capital, sex, education. In addition, the objective of this analysis
is that farm’s productivity depends on trade. Hence, the farm tradable index will be
added in equation (2) and model assumes to follow a constant return to scale, we have
formula as below:
ln(𝑌𝐿𝐷) = 𝛽0 + 𝛽1 (𝐹𝑇𝐼) + 𝐵2 𝐿𝑛(𝑇𝐿𝑎𝑛𝑑) + 𝛽3 𝐼𝑅 + 𝛽4 ln(𝐿) + 𝛽5 (𝑑𝑀 ) +
𝛽6 𝑙𝑛(𝐾) + 𝛽7 (𝑑𝑆𝐸𝑋 ) + 𝛽8 𝐿𝑛(𝐸𝐷𝑈) + 𝑒 (4)
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Where YLD is yield of an important crop, FTI is farm tradable index, Tland is
total cultivated land of important crop, IR is farm area covered by irrigation, L is a
number of household member relative to agricultural production, dM is a dummy
variable of presence of use of machinery, K is amount of production capital, dSEX is
the sex of farmer, EDU is the average level of education of farmers who join in

agricultural production, e is error term. These variables will be described in table 3.1
involving their units, sources, expected sign, and definition.
3.4. Data
This study is divided two models in order to analysis the effects of trade on
agricultural productivity in macro and micro levels.
The first model represents a national level, and for this analysis, material
source is taken from FAOSTAT database. The FAOSTAT database consists of
agricultural data of over 200 countries and data is available from 1961 to present. For
this national analysis, this study uses data of export and import quantity, production
quantity, and yield per hectare in Vietnam in period 1961 and 2010. Data has 27
agricultural commodities, however I emphasizes main agricultural commodities have
available data including rice, pepper, vegetable, cassava, cashew nuts, tea, fruit,
maize, coffee, which are traded in other countries.
The second model represents a household level and uses dataset extracted from
The Vietnam Access to Resources Household Survey 2010 (VARHS). VARHS is
implemented by the University of Copenhagen, CIEM, ILSSA, and CAP-IPSARD.
This survey supplies detailed information of agricultural households. It consists of
questions as in VHLSS and additional questions about land, agriculture, income,
spending, assets, investments, market linkages, etc. VARHS has implemented since
2006, 2008, 2010 and 2012. For this study, VARHS 2010 is used in farm model. In
general, VARHS 2010 reports 3,202 household’s data with 2,200 panel households at
twelve provinces including Ha Tay, Lao Cai and Phu Tho, Lai Chau, Dien Bien, Nghe
An, Quang Nam, Khanh Hoa, Dak Lak, Dak Nong, Lam Dong, Long An. Because
farms may not produce one crop but many crops, the farm model uses yield of a
specific commodity, which has highest proportion of yield among crops in one
household. In collecting data process, household sample size remains 1450
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