Technical efficiency of poultry farms in vietnam non parametric and parametric approaches

ABBREVIATION AE Allocative efficiency BCC DEA model as study of Banker, Charnes and Cooper CCR DEA model as study of Charnes, Cooper and Rhodes CRS Constant returns to scale DEA Data Env

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

TECHNICAL EFFICIENCY OF POULTRY FARMS IN VIETNAM NON-PARAMETRIC AND PARAMETRIC APPROACHES

BY

NGUYỄN THỊ NGỌC LINH

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, DECEMBER 2013

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

TECHNICAL EFFICIENCY OF POULTRY FARMS IN VIETNAM NON-PARAMETRIC AND PARAMETRIC APPROACHES

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

BY

NGUYỄN THỊ NGỌC LINH

Academic Supervisor:

DR TRƯƠNG ĐĂNG THỤY

Ho Chi Minh City, December 2013

DECLARATION

This is to certify that the thesis entitle “Technical efficiency of Vietnam poultry farms: non-parametric and parametric approaches”, which is submitted by me in partial fulfillment of the requirement for the degree of Master of Art in Development Economic to Vietnam – The Netherlands Programme The thesis comprises only my original work and due supervision and acknowledgement have been made in the text to all materials used

Nguyễn Thị Ngọc Linh

ACKNOWLEDGEMENT

My appreciation firstly goes to my supervisor, Dr Trương Đăng Thụy, who has made a great effort to support me in this thesis His profound comments have been helpful not only in completing this study but also in improving my knowledge in doing the research

I would like to thank my family, especially my mother I would not complete this thesis, as well as study in this program, without their scarification, encouragement and important support For the love and expectation of my family, which motivate my effort to complete this master degree, my mere expression of gratitude here have never been sufficient

I am very proud to attend this program I am grateful to all lectures in Vietnam – Netherlands Programme for their dedicated instruction and all the courses during the period I studied at the program Besides, I would like to thank all the academic and technical staffs of the Vietnam – Netherlands Programme for supporting me during that time

Moreover, I received the enormous encouragement from my classmates and workmates, especially my special friend who has supported me a lot in the writing process I am very grateful for everything that all of you gave me

ABBREVIATION

AE Allocative efficiency

BCC DEA model as study of Banker, Charnes and Cooper

CCR DEA model as study of Charnes, Cooper and Rhodes

CRS Constant returns to scale

DEA Data Envelopment Analysis

DMU Decision making unit

DPF Deterministic Production Function

FAO Food and Agriculture Organization

GSO General Statistic Office

MLE Maximum likelihood estimate

OLS Ordinary Least Square

PDF Probability density function

SFM Stochastic Frontier Model

ABSTRACT

This study attempts to estimate the technical efficiency as well as determine the factorial effects of technical efficiency level of Vietnam poultry farms under semi-industrial system and traditional system Then, this study employs a two-stage analysis with a household-level dataset in whole country In particular, the first stage estimates technical efficiency level of poultry farms under both systems through non-parametric and parametric approaches, which were represented by Data Envelopment Analysis and Stochastic Frontier Analysis, respectively In the second stage, sources of efficiency will be determined by Tobit regression and least square regression with household-specific characteristics which represent for human capital in qualitative dimension A sample of 3,356 households in VHLSS 2010 is utilized to analyze the broiler poultry production in Vietnam, wherein 820 poultry farms under semi-industrial system and 2,536 poultry farms under traditional system The results from the first stage show that the average technical efficiency which was obtained from SFM is higher than that in DEA; and the TE scores in SFA exhibit the variability lower than TE scores in DEA Moreover, from the analysis in the second stage, it is can be stated that education level

of farmer has significantly effects on the TE differential among poultry farms in the positive way Finally, the results also show that TE scores of poultry farms under both systems located in the Southeast are higher than other agro-ecological regions

Keywords: poultry household, data envelopment analysis, stochastic frontier model, technical efficiency, human capital

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION 1

1.1 Problem Statements 1

1.2 Research Objectives 4

1.3 Research Organization 5

CHAPTER 2: LITERATURE REVIEW 6

2.1 Basic concepts of efficiency 6

2.2 Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) 9

2.2.1 Data Envelopment Analysis (DEA) 10

2.2.2 Stochastic Frontier Model approach (SFM) 12

2.2.2.1 The Production frontier 12

i Deterministic Production Frontier 12

ii Stochastic Production Frontier 13

2.2.2.2 Estimation method 15

i Modified Ordinary Least Squares (MOLS) 15

ii Maximum likelihood Estimation (MLE) 16

2.2.2.3 Measurement of Efficiency 17

2.2.3 Comparison between DEA and SFM approaches 17

2.3 Empirical studies 19

2.3.1 Measurement of technical efficiency of poultry subsector 19

2.3.2 DEA and SFM approaches on measurement of technical efficiency of agriculture sector 22

2.3.3 Impact of human capital on agriculture productivity 24

CHAPTER 3: OVER VIEW OF POULTRY FARMS IN VIETNAM 27

3.1 General characteristics of poultry production in Vietnam 27

3.2 Poultry production system in Vietnam 29

i Traditional extensive household poultry production (non-intensive system) 31

ii Semi-industrial commercial poultry production (semi-intensive) 32

iii Industrial poultry production (intensive system) 32

CHAPTER 4: DATA DESCRIPTIONS AND RESEARCH METHODOLOGY 34

4.1 Data Description 34

4.1.1 Data Source 34

4.1.2 Data Descriptions 34

4.2 Method, Model specification and Variables definition 36

4.2.1 The first stage: Measurement of Technical efficiency 36

4.2.1.1 Measurement of Technical efficiency using Data Envelopment Analysis 36

4.2.1.2 Measurement of Technical efficiency using Stochastic Frontier Model 38 i Production function specification 39

ii Estimation method specification 40

4.2.1.3 Variables Description for the first stage 41

4.2.1.4 Hypothesis testing 44

i Functional form specification test 45

ii Estimating method specification test 45

4.2.2 The second stage: Factorial decomposition of Technical efficiency 46

4.2.2.1 The Technical efficiency model 46

4.2.2.2 Variables description for the second stage 49

4.2.3 Research Hypotheses 53

CHAPTER 5: EMPIRICAL RESULTS AND DISSCUSION 54

5.1 The first stage: The estimation of the Technical efficiency scores 54

5.1.1 Testing null hypotheses for SFM approach in the first stage 54

i The Production Functional form specification test 55

ii The model specification test 57

5.1.2 Discussion on results obtained from the first stage 58

5.1.2.1 Comparing the Technical efficiency scores in SFM and DEA approaches 58

5.1.2.2 The distribution of technical efficiency scores in SFM and DEA approaches 60

5.1.2.3 Technical efficiency varies between agro-ecological regions 63

5.2 The second stage: The determinants of Technical efficiency 65

5.2.1 Testing the significant of coefficients simultaneously for regressions 65

5.2.2 Discussion on the results obtained from the second stage 66

5.2.2.1 The effects of human capital on technical efficiency scores 67

5.2.2.2 The difference of technical efficiency scores between various agro-ecological regions 69

CHAPTER 6: CONCLUSION AND POLICY IMPLICATION 70

6.1 Concluding remarks 71

6.2 Policy implications 72

6.3 Limitations of study and recommendations for future research 73

REFERENCES 74

Appendix 1: Matrix of correlation between variables in Stochastic frontier production function of semi-industrial poultry farms 78

Appendix 2: Matrix of correlation between variables in Stochastic frontier production function of traditional poultry farms 78

Appendix 3: Matrix of correlation between variables in technical efficiency model of semi-industrial poultry farms 79

Appendix 4: Matrix of correlation between variables in technical efficiency model of traditional poultry farms 79

Appendix 5: OLS regression of technical efficiency model of traditional poultry farms 80

Appendix 6: Testing heteroskedasticity for OLS regression of technical efficiency model of traditional poultry farms 80

Appendix 7: Testing heteroskedasticity for OLS-robust regression of technical efficiency model of traditional poultry farms 81

Appendix 8: Summary of Empirical Studies at measuring technical efficiency of poultry sector 82

Appendix 9: Summary empirical studies of comparison DEA and SFM approaches at measuring the technical efficiency 84

LIST OF TABLES

Table 4-1: Summary statistic of broiler poultry productivity of farms between regions

and within systems 35

Table 4-2: Summary statistic of Input and Output variables for Poultry farms under semi-industrial system and traditional system 42

Table 4-3: Statistical description of determinant factors of technical efficiency level 52

Table 5-1: Maximum-likelihood estimates of the Cobb-Douglas and Translog stochastic production frontier models 56

Table 5-2: Tests hypotheses of the production function specification 58

Table 5-3: Summarizing the technical efficiency scores 59

Table 5-4: The technical efficiency distribution in SFM and DEA-BCC 61

Table 5-5: Technical efficiency scores among agro-ecological regions 63

Table 5-6: The factorial effects on technical efficiency scores from SFM and DEA-BCC 66

LIST OF FIGURES Figure 2-1: Technical efficiency and Allocative Efficiency 8

Figure 2-2: Stochastic Production Frontier 14

Figure 3-1: Growth rate of livestock and poultry with base year of 1990 27

Figure 3-2: Annual growth rate of number of poultry with base year of 1990 28

Figure 3-3: Poultry density of Vietnam in 2006 29

Figure 3-4: Average number of birds per household in 2001 29

Figure 3-5: Regional Characteristics of poultry-holding household in 2002 30

Figure 3-6: Proportion of total chicken in three production systems in 2006 and 2009 in Vietnam 33

Figure 5-1: The distribution of TE scores of semi-industrial and traditional systems from SFM and DEA approaches 62

Figure 5-2: Technical efficiency scores between agro-ecological regions 64

CHAPTER 1 INTRODUCTION

1.1 Problem Statements

In the period from 1971 to 1995, the dramatically increasing consumption demand for livestock and production of livestock named “Livestock Revolution” had a remarkable contribution to socio-economic development in developing countries, especially in agriculture sector (Delgado et al., 1999) The benefits from livestock subsector were analyzed in many aspects including ensuring food security, reducing unemployment and alleviating poverty in rural areas, and supplying materials for other sectors both in industry and in agriculture (Delgado et al., 1999; Upton, 2004) Besides, poultry raising subsector has some advantages compared to other livestock subsectors such as the high rate of reproduction and the quick return on capital investment while a large area is not required Therefore, the poor households can keep a dozen of chickens (Sugiyama et al., 2003)

Similarly, in Vietnamese villages, poultry is one of the most common products and becomes more and more popular because of its advantages Poultry production increases more rapidly than other animal raising ones In particular, the annual growth

of head count in poultry is 5% higher than the 2% average growth rate of livestock sector in the period from 1990 to 2010 Furthermore, national poultry population is well developed across the country, mainly in the North regions which comprises around 78% of total poultry population; and the proportion of poultry keeping households rises sharply from nearly 64% in 2001 to 90% of all rural households in 2010 (GSO, 2011)

The popularity of poultry keeping households indicates that poultry production becomes an important resource; it may play a significant role in enhancing living standards such as raising income, creating jobs or improving human nutrition in Vietnamese rural areas However, a remaining puzzle is whether farmers can expand poultry production without an increase in investment or flock size of birds since most poultry keeping households in Vietnam rely on the traditional system In other words, the fact raises a question for farmer and policy maker on how to produce maximal

output at limited resources in their production process Given the foregoing problems, analyzing technical efficiency of poultry farms is helpful to improve rural livelihood

The efficiency concept was first presented by Koopmans (1951) that decision making units (DMU) obtain efficiency point when they produce maximal outputs at a given level of inputs or require the minimal inputs to produce a given level of outputs The aggregate combination of optimal output that can be obtained at a given input level is the production frontier The technical efficiency level of a DMU is estimated by the distance from the observed output to the production frontier

Since then, the technical efficiency concept has become the axiom of latter studies in measuring efficiency level of DMU such as pioneering works of Farrell (1957) The theory of Farrell has been extensively developed by many researchers in two separate lines of research: the non-parametric and parametric approaches In particular, non-parametric approach is a mathematical programming technique represented by Data Envelopment Analysis while parametric approach is an econometric method represented by Stochastic Frontier Model These two methods are leading approaches

in the measurement of efficiency and have their own advantages and disadvantages Nevertheless, using which method to estimate technical efficiency is still in debate, especially in agriculture sector Some authors prefer parametric approach (Alabi and Aruna, 2005; Tung and Rasmussen, 2005; Ike, 2011; Ohajianya et al., 2013) while other prefer non-parametric approach (Heidari et al., 2011, Jatto et al., 2012, Rafiee et al., 2013) Other researchers, possibly for caution, want to compare the results from the two approaches (Wadud and White, 2000; Theodoridis and Psychoudakis, 2008; and Zamanian et al 2013)

There exists an active line of research in analyzing technical efficiency of the agriculture sector in Vietnam However, most of the studies focus on crop subsector while livestock and other subsectors, especially the poultry are somewhat neglected Hanh et al (2007) classified poultry production in Vietnam into three categories based

on the definition of Food and Agriculture Organization consisting of traditional, industrial and industrial system Specifically, proportion of households raising poultry under traditional system is largest and accounts for 75% while the average income is lowest at 1.5million VND per household per year (VHLSS 2010) In contrast, poultry

semi-households under industrial system account for only 0.44% but the average income is over 250million VND per house per year (VHLSS 2010) The large gap of income between traditional, semi-industrial systems and industrial system may be explained by the application of different technologies of poultry keeping households during their production process Apart from that, poultry households located in different ecological regions have various comparative advantages, which also affect poultry farmers’ technical efficiency level

In addition, Tian and Wan (2000) stated that in agriculture sector, there are three main types of factors which affecting the technical efficiency including biological factors, human resources, and socioeconomic conditions Firstly, biological factors are indicated by the specifications of varietal attributes, soil, and characteristics of each agricultural sector such as production system and seasonal crop Moreover, the difference of technical efficiency levels among farms is also explained by human resources Human resources were classified in quantitative and qualitative dimensions

by Schultz (1961) Specifically, the quantitative dimension or the physical factor was represented by number of labor, working day or working hours of labor, whereas the qualitative dimension or the human capital factor was defined by intrinsic attributes of labor which were invested by themselves in their leisure time such as skill, education, experience and other characteristics He argued that the failure of classical production function is that the human resource was treated as a produced means of production regardless of knowledge and skill of labor, while quality of labor force can greatly improve the productivity of firm Finally, socioeconomic conditions also influence the technical efficiency through the status of economic development, policy and institutional setting supporting for farmers In this study, due to data limitations, only the human capital factor was considered as the explanatory factor of technically efficient differentials among farms due to data limitations

In general, the quality of rural labor force tends to suffer due to the massive migration

of farm labors to big cities There is an imbalanced distribution of labor force between urban and rural regions that labor force in rural areas is much higher than in urban areas

in terms of quantity According to GSO, rural population in 2010 is more than 70% of the total population but high-quality labor tends to transition to urban areas because the education level of rural labors is lower Huffman (2001) suggested that when the

environment of industry, including technology and price volatility, is left unchanged, especially in agriculture sector, experience is important, even more important than education in improving the productivity However, agriculture environment always changes over time due to industrial revolution Huffman (2001) also proved the role of human capital in agriculture sector when the environment changes He found that households with at least one member completing primary school education can adopt new seeds while households not capable of adopting new seeds have no member reaching the primary school level Therefore, the improvement of human capital not only leads to more equal redistribution of labor force between urban and rural areas but also potentially improve agriculture productivity

This study aims at measuring the technical efficiency of poultry raising households The study will employ both non-parametric (Data Envelopment Analysis) and parametric (Stochastic Frontier Model) approaches in the first stage for comparing and ensuring the validity of results Furthermore, the technical efficiency was explained by human capital and poultry farm’s socio-economic characteristics In more details, household’s socio-economic characteristics are represented by agro-ecological region and poultry production system which illustrate the difference of TE scores between ecological regions and within poultry production systems in the same region On the other hand, household head’s specifications represent for human capital such as educational level, age and participation to agricultural training course Hence, factorial decomposition of TE score in human capital may contribute to improving rural livelihood policy Data of this study is from the Vietnam Household Living Standards Survey in 2010

1.2 Research Objectives

This study has three main objectives The first is estimating technical efficiency level of poultry farms across Vietnam The second objective is investigating the factors affecting technical inefficiency of poultry raising households in different aspects: human capital and socio-economic characteristics; wherein human capital is considered

to point out the relevant policy implications Finally, this study compares the results from two different approaches of Data Envelopment Analysis and Stochastic Frontier Analysis to ensure the validity of the outcomes

1.3 Research Organization

Literature review of theoretical and empirical studies relevant to this study are presented in the next chapter Chapter 3 provides the overview on poultry sector in Vietnam Data analysis and Methodology are presented in Chapter 4 The results are presented and interpreted in Chapter 5 Lastly, Chapter 6 provides conclusions and limitations of the study

CHAPTER 2 LITERATURE REVIEW

This chapter surveys the literatures on the estimation and determinant of technical efficiency in theoretical and empirical studies It first introduces some definitions and measurements related to the efficiency concept The two approaches of estimating technical efficiency, including Data Envelopment Analysis and Stochastic Frontier Analysis, and their advantages and disadvantages are then discussed The chapter continues with a review of empirical studies on analyzing technical efficiency in agriculture sector and poultry subsector In addition, the impact of human capital on agriculture productivity is also addressed in this chapter Finally, it presents the conceptual analysis of the thesis

2.1 Basic concepts of efficiency

The concept of economic efficiency has become an extensive research topic in recent decades Koopmans (1951) proposed the original definition of efficiency that the producer attains efficiency point if raising one unit of an output requires a reduction of

at least one unit of another output Alternatively, it can be stated that efficiency is the point at which a decrease by one unit of an input requires an increase in another input

to maintain the same level of output, or results in a decrease in an output Therefore, efficient producers could attain higher output level using the same amount of inputs compared to other producers

Economic efficiency has two main components including Technical Efficiency (TE) and Allocative Efficiency (AE also referred to as Price Efficiency) according to the classification of Farrell (1957) In particular, TE component addresses the minimization

of waste input while keeping the output as high as possible Therefore, TE should be analyzed with output-oriented or input-oriented approaches which were introduced in

more detail later in this section While TE is an engineer concept, AE is a behavioral

concept that the production process of producers is under the assumption of

maximizing profit or minimizing cost (Lau and Yotopoulos, 1971)

Farrell (1957) is the pioneer who introduced the theory of measurement of production efficiency He applied the Production Possibility Frontier (PPF) theory as a benchmark

to assess the efficiency of firms in the industry wherein the efficient producer lies on the PPF while those lying below PPF are inefficient

According to Farrell, measurements of efficiency can be defined using the oriented or output-oriented approaches In particular, input-orientated efficiency is the ability to produce a given level of output from the lowest level of inputs Figure 1a shows a simple model producing one output using two inputs The SS’ curve is the iso-quant curve of the firm which can be represented for fully efficient firms, permits the measurement of technical efficiency level (Coelli et al., 2005) Point Q and Q’ lie on the SS’ curve; hence, these points are technical efficiency points while point P indicates the technical inefficiency point The distance QP indicates for amount of waste input which could be reduced without a reduction in output The technical efficiency is explained by the following equation:

input-I

OQ TE

OP

 (2.1)

Where TEI is the technical efficiency score which is measured by the ratio of lowest level of input used (OQ) to total input used in reality (OP) of that firm Value of TEI is bounded from zero to one, where zero and one means minimal and maximal technical efficiency, respectively

With unit cost of inputs, the iso-cost line can be identified and is represented by AA’ The firms at point R and Q’ have the same total cost; however, the output at point R is less than that at point Q’ In addition, the point Q’ demonstrates both the technical efficiency and allocative efficiency combined because it is the tangent point between iso-quant and iso-cost curve On the other hand, the allocative efficiency occurs when that firm produces a given amount of output at the lowest cost The allocative efficiency is denoted by AEI which is commonly expressed in percentage term by the ratio of minimal cost of input to the observed cost of input in reality as follows:

OR AE

OQ

 (2.2)

Figure 2-1: Technical efficiency and Allocative Efficiency

(Source: Coelli et al., 2005)

Meanwhile, output-orientated approach of measuring efficiency is based on the capability of the producing of the maximum level of output from a given set of inputs The measurement of technical efficiency considers the case where production involves two output (q1and q2) and single input (x) using this method is illustrated in Figure 2-1(b) In output-oriented case, ZZ’ represents for the production frontier, and DD’ represents for iso-revenue line Analogous to the previous case, the point B’ is the technical efficiency and allocative efficiency, because it is the tangent point between ZZ’ production frontier curve and DD’ iso-revenue line Technical efficiency and allocative efficiency are defined by the equations:

O

OA TE

Where TE O is represented for technical efficiency level which is obtained by ratio of the observed output of firm (OA) to optimal output level (OB) Equation (2.4) illustrates the measurement of allocative efficiency in case of output-oriented, whereas allocative efficiency is obtained by ratio of observed revenue level (OB) to optimal revenue level (OC)

Based on the idea of Farrell, there have been many frontier models devised later with two main approaches: Parametric and non-parametric approaches Coelli et al (2005) summarized four efficiency measurements: (1) Least square econometric production models; (2) Total Factor Productivity (TFP) indices; (3) Data Envelopment Analysis (DEA); and (4) Stochastic Frontiers Model (SFM) The first two methods are widely used in quantifying the change of technical efficiency based on the assumption that all modelers are technically efficient The two latter methods measure the efficiency score

of firms and have become the principle measurements of production efficiency up to the present time

In general, DEA and SFM are two alternative methods for calculating efficiency index Specifically, DEA is a linear mathematical programming method which employs a non-parametric technique whereas SFM is an econometric method which is inherently parametric As a matter of fact, these two techniques have both advantages and disadvantages which make them complementary to each other

2.2 Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA)

This part provides more detail on the theory of the two different approaches applied in this thesis including Data Envelopment Analysis (non-parametric approach) and Stochastic Frontier Analysis (parametric approach) In general, the application of DEA requires the conditional estimation including Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) while applying SFA requires specifying the production function, estimation method and statistical assumption of the inefficiency term

2.2.1 Data Envelopment Analysis (DEA)

Charnes, Cooper and Rhodes (1978) operationalized the Data Envelopment Analysis (DEA) approach based on Farrell's theory Essentially, they used non-parametric linear optimization to construct PPF from a data set consisting of multi-input and multi-output decision-making units (DMUs) and calculated the efficiency point for each DMU This method was coined CCR model

Suppose that there are n firms or decision-making units (DMUs) which produce s outputs Y using an amount of m different inputs X According to CCR, the

measurement of technical efficiency is a linear programming problem formulated as follows:

0 1 0

0 1

max

s

r r r m

i i i

u y h

m

i ij i

Where h0 is the relative efficiency of jth DMU, u r and v i are positive values and stand

for the intensiveness of the jth DMU to produce maximization level of output y rj using the set of inputs x ij Particularly, u r is the weight of output and v i is the weight of input DMU attains efficiency point when h0 = 1

The CCR model was constructed under the assumptions of constant return to scale that output level is said to be directly proportional to the change in input However, the assumption of CCR model may be inconsistent in some industries which are

predominantly an imperfect market such as agriculture sector, thus firms which belong

to these industries may or may not be operate at optimal scale As a result, the score obtained from CCR model is aggregate efficiency instead of technical efficiency due to

the effect of scale efficiencies (SE)

Banker, Charnes and Cooper (1984) (BCC) proposed a more general model which allows variable return to scale in the calculation of efficiency score By way of that, the

technical efficiency score of a DMU is considered at pure technical efficiency which is

not affected by SE Therefore, BCC model is more powerful than CCR model in the sense that it can be applied for the sector in the imperfect market The CCR and BCC models are illustrated in terms of input-oriented (2.6) and output-oriented (2.7) approaches as follows:

i i

i i

CCR and BCC methods have been widely applied in the field of technical efficiency analysis and have been developed over time Specifically, BCC method is more general with the technical efficiency being decomposed into two separate components: pure technical efficiency and scale efficiency, thus, the technical efficiency score is not distorted Besides, measuring TE by DEA approach assumes that there is no random noise in the production process In practice, this assumption may not be appropriate and hence, may lead to invalid results Many researchers suggest that statistical method can

deal with the random noise problem and it is the topic of next section

2.2.2 Stochastic Frontier Model approach (SFM)

As mentioned in the previous chapter, the statistical method to measure technical efficiency applied in this study is Stochastic Frontier Analysis The requirements of this method are specifying the production frontier and the estimation method Therefore,

this part will provide more detail about the theory of these two concepts

2.2.2.1 The Production frontier

i Deterministic Production Frontier

The definition of a production frontier has been accepted for many decades that the frontier shows maximum possible output obtainable from a given level of inputs with a fixed technology Hence, there have been many studies using the deterministic production frontier to measure technical efficiency such as Afriat (1972), Aigner and Chu (1968) They began with the assumption that the production function gives the maximum possible output at a certain level of inputs Based on the previous studies, Battese (1992) restated the deterministic production frontier model as follows:

and  is the unknown parameter of vector to be estimated The value of y is bounded i

on the deterministic quantity curve, hence the function (2.8) is called the deterministic production function which was illustrated in Figure 2-2 The presence of non-negative random factor i in equation (2.8) indicates the inefficiency effect factor The

inefficiency factor may come from the effect of pure random shock in the production process, variation of technical efficiency and variation of economic efficiency (Aigner

and Chu, 1968)

ii Stochastic Production Frontier

In 1977, Aigner et al introduced their study on a new production frontier called stochastic frontier function with the addition of a disturbance term i  v i u i that yields the model:

of function y i  f x( ; )i  v i The random variable v represents the favorable or i

unfavorable external events such as the influence of climate, luck or machine performance that the firms cannot control while u stands for inefficiency factor The i

symmetric disturbance v is assumed to be independent and follows the normal i

distribution ~N(0, 2

v

 ) The random error term v can be positive or negative; hence, the i

firm’s output varies with deterministic production function (DPF) In general, there are four main types of the distributional assumption of asymmetric disturbance u i

including exponential distribution, gamma distribution, half-normal distribution and truncated distribution The choice of distribution depends on the specification of SFM which was discussed in the section of Production function specification

Battese and Coelli (1995) analyzed the asymmetric disturbance u under non-negative i

truncation distributional assumption as follows:

u  z   w i = 1, 2,…, N (2.10)

The terms on the right hand side of equation (2.4) include observed variables z i

representing specific characteristics of measureable observation ith such as location,

scale, experience, education On the other hand, w is an unobservable random noise i

which has similar characteristic with v described in function (2.9) i

Figure 2-2 illustrates the case of firms A and B with the horizontal axis as the quantity

of input, and the quantity of output is described on the vertical axis Under the effect of statistic noise and without the inefficiency effect (u = 0), output i *

A

Y of firm A varies with DPF by the distance AA’, and output of firm B *

B

Y varies with DPF by the distance BB’ In another case, with the presence of inefficiency factor u , the observed i

outputs of firm A and firm B are A” and B” instead of A and B as the previous case

Generally, frontier outputs tend to lie above or below the deterministic production function because of the effect of symmetric factor On the other hand, observed output tend to be distributed beneath the deterministic production function because of the presence of asymmetric factor or inefficiency factor (Coelli et al., 2005)

Figure 2-2: Stochastic Production Frontier

(Source: Coelli et al., 2005)

Inefficiency effect

Noise effect Noise effect

Inefficiency effect A’

2.2.2.2 Estimation method

As mentioned in the preceding part, Stochastic Frontier Method (SFM) was introduced

by Aigner, Lovell and Schmidt (1977) In this approach, the production function is built based on the average values of observations and the error term indicates the noise and inefficiency factors effect on the production function There are several methods to estimate the stochastic frontier model Mastromarco (2008) summarized two main methods including Modified Ordinary Least Squares (MOLS) and Maximum Likelihood Estimation (MLE)

i Modified Ordinary Least Squares (MOLS)

In the study on Stochastic Frontier Model, Mastromarco (2008) pointed out some drawbacks of estimation methods from many studies In detail, the estimator of the vector will be best linear unbiased and consistent in the Ordinary Least Squares (OLS) method, but it is not consistent for the intercept term In the Corrected Ordinary Least Squares (COLS) method, the problem of intercept term from OLS method seems to be solved With two-step estimation in this method, the intercept term would be corrected

in the second step by maximum value of the OLS residual Then the estimator intercept

in COLS becomes more consistent

Modified Ordinary Least Square (MOLS) was proposed by Afriat (1972) The constant term in MOLS is corrected by the expectation value of the error termi, and OLS is adopted in getting a consistent estimate The model to be estimated is

However, the problem of MOLS technique is the estimates can take values that have no

statistical meaning In other words, in MOLS technique, the structure of “best

practice” production technology is not different from the structure of “central tendency” production technology

ii Maximum likelihood Estimation (MLE)

Maximum likelihood (ML) is applied widely in many studies which can applied many models than other methods The ML estimators have consistency and asymptotic properties The basic concepts of MLE is restated by Davidson & MacKinnon (2004) that it is based on the model specification through probability density function (PDF)

( , )

f y , where y and  stand for dependent variable and vector of parameters, respectively When f y( , ) is evaluated in a given dataset at the n-vector y , the function f y( ,.)is interpreted as the likelihood function of the model for the given dataset instead of PDF Maximizing the likelihood function with respect to parameters

is called maximum likelihood estimate (MLE)

Nonetheless, this method requires stronger assumption of asymmetric distribution than other methods The distributional assumptions of asymmetry in MLE method include exponential distribution, gamma distribution, half-normal distribution and truncated distribution The distributional assumption mentioned in this study is non-negative truncation distribution

Battese and Coelli (1988) put forward the stochastic frontier model with truncated distribution assumption of asymmetric factor 2

symmetric component and the firm is totally efficient The parameter  is the standard normal cumulative distribution function

2.2.2.3 Measurement of Efficiency

Coelli et al., (2005) gave the definition of technical efficiency of firm as a ratio of observed output to the corresponding production level where firm attains the efficiency point The technical efficiency score is bounded from zero to one, wherein at the value

of zero, the firm is totally inefficient and at the value of one, the firm is totally efficient Based on this definition, they constructed the fundamental formula of the technical

efficiency of the ith firm based on the Cobb-Douglas stochastic production function as

2.2.3 Comparison between DEA and SFM approaches

In general, almost all of studies use the two leading methods in measurement of technical efficiency: Data Envelopment Analysis (DEA) and Stochastic Frontier Model (SFM) Particularly, DEA is a linear programming technique that requires the analysis characterized by input or output orientation and returns to scale On the other hand, SPF is an econometric technique, hence, the studies applying it have to specify the

production functional form as well as satisfy specific assumptions on the residual term, which include random shock (or error term) and inefficiency factors Both the DEA and SFM approaches have their advantages and disadvantages There are many studies analyzing the advantages and disadvantages of these two methods both in theoretical and empirical regards such as Alene and Zeller (2005), Cullinane et al (2006) The analysis of Cullinane et al (2006) on the strengths and weakness of two methods was presented below with the same dataset

DEA approach imposes neither specific production function nor any statistical distributional assumptions of the inefficiency term Therefore, it avoids the risk of incorrect statistical specification in production function and inefficiency term Besides these advantages, its drawback is that it does not allow for measurement of random shock or error term It inevitably leads to potential estimation errors because of random shock effects

Compared to DEA approach, the strength of SFM is that it provides for separate measurement of the residual, which decomposes into the error term and inefficiency term Moreover, the estimator is unbiased due to the effects of the error term However, the disadvantage of econometric approaches including SFM is the risk of artificially and perhaps inappropriately constraining the production technology by applying functional form such as Cobb-Douglas, Translog, Quadratic, or other functional forms For instance, choosing inaccurate production functional form makes the estimators invalid In addition, it is difficult to ascertain the distributional specification of error term

In summary, the choice of using appropriate approach depends on whether its assumptions are true for the analyzed sector Particularly, DEA approach may be good for the sector which is characterized by a stable production process; and in the case the production functional form cannot be determined Then, the effect of random noise on technical efficiency seems insignificant On the other hand, SFM is good for the sector which is sensitive to the effect of random noise factor However, identifying exactly the specification of a sector to apply appropriate approach is not trivial Therefore, using non-parametric or parametric method to estimate the efficiency has still been in debate

in several years

2.3 Empirical studies

2.3.1 Measurement of technical efficiency of poultry subsector

A great variety of studies in agriculture sector applied both the non-parametric and parametric approaches in which SFM and DEA were employed to investigate the technical, allocative, cost and scale efficiency However, the efficiency analysis in poultry sector is relatively occasional compared to the other areas

In particular, Alabi and Aruna (2005) estimated that the average technical efficiency of farmers in Niger-Delta is very low at the level of 22%, indicates that poultry farms can reduce 78% of input used without a reducing level of output The technical efficiency model was estimated simultaneously with the stochastic frontier model by maximum likelihood method Data were collected from 116 poultry households with determinants

of household’s income being expenses on feed, medicines and income from other livestock Factorial effects of technical inefficiency are age of farmer, family size, gender and index of innovation adoption The finding is most of farmers’ produced poultry is increasing returns to scale because the elasticity index is greater than one

Tung and Rasmussen (2005) also employed the parametric approach to analyze the production function for poultry smallholders under traditional and semi-commercial poultry production systems in Northern provinces of Vietnam This study was conducted over 360 smallholders in three agro-ecological regions (lowland, midland, highland) with the assumption that poultry production output is contributed by number

of birds, feed, labor, garden size, income level and cost of veterinary Cobb-Douglas production function was estimated by OLS (ordinary least square) method to solve the objectives of this study The results indicate that the production technology is different between three regions, but similar for different poultry production systems in each region Besides, the authors proposed that farmers should increase some main inputs such as number of birds, feed per birds and animal health care to increase level of poultry production

Ike (2011) applied Cobb-Douglas stochastic production function to examine factors affecting poultry production of small-scale household in Enugu State, Nigeria The

sample size of this study was 30 poultry farmers with the set of input variables being farm size (number of birds), capital, initial stock, labor, feed and cost of drugs The difference with others studies is that the authors analyzed factorials of technical efficiency Technical efficiency is affected by farmer’s characteristics such as age, gender, education, experience; household’s characteristics such as household size, poultry production system, extension contact and membership of cooperatives The results showed that most input variables were significant in determining poultry output However, poultry keeping households in increase output level by 38% at the same level

of input And technical efficiency could be improved by using selected breeds and promoting education of farmers

With the same objective and methodology with Ike (2011), Ohajianya et al (2013) measured economic and technical efficiency of poultry farmer in three regions of Imo State, Nigeria The authors used the parametric method stochastic frontier production and maximum likelihood technique with half-normal distributional assumption to estimate technical efficiency of 140 poultry farmers In these regions, the mean of flock size was 772 birds per household, which belongs to semi-industrial poultry production system according to the classification of FAO The Cobb-Douglas function was applied with independent variables: feed, labor, medicine, flock size, capital, management and other inputs The results showed that all input factors were significant with positive signs as the authors expected The technical efficiency score ranged from 0.36 to 0.97 with mean score of 0.75 These figures indicated that on average, farmers in Imo State were not fully economically and technically efficient Farmers could increase poultry output by 25% with their current resources and if technical efficiency improved, economic efficiency could rise substantially

Moreover, some authors prefer non-parametric approach in measuring TE of poultry raising farmers Heidari et al (2011) applied DEA input orientation to determine the economic efficiency of resource utilization in broiler chicken production farms The authors conducted this study with 44 households in 2010 in Yazd province, Iran Surveyed households were raising poultry under industrial poultry production with average capacity and production of 18,142 birds per household and 2,601 kilogram per 1,000 birds, respectively Both CCR and BCC models were applied with the inputs including cost of labors, feed, chick, fuel and electricity, and the output as production

income Technical efficiency scores in two models were quite high, above 90% This indicated that households raising poultry under industrial system had technical efficiency However, they could increase the profit by reducing nearly 10% cost of input used while keeping the level of output unchanged

Jatto et al (2012) employed Data Envelopment Analysis output-oriented to assess technical efficiency level of poultry egg producer This study was conducted in Kwara State of Nigeria with 150 respondents in Ilorin The results showed efficiency producers with TE score ranging from 0.9 to 1 accounted for 32.67%; farmers operating within technical efficiency score from 0.7 to below 0.9 is 26% and TE of other farmers are below 0.7 On average, technical efficiency around 0.74 means farmers can reduce cost of input used 26% when eggs production is unchanged Particularly, the authors suggested that the quantity of inputs that could be reduced are 6.936 number of birds, 91.021 kilogram of feeds and 0.334 working days of labor

Rafiee et al (2013) applied input-oriented DEA to determine whether the inputs were used efficiently in production process of poultry farms in Alburz province, Iran where poultry was one of the most developed sectors of agriculture The data was collected from 40 farms raising poultry for egg production under industrial system There were six farms and nine farms attaining the efficiency point according to the results from CCR and BCC models, respectively In both models, the output collected is egg yield and the inputs include labor, equipment, fossil fuel, electricity, feed and pullet where feed expenditure is highest with the share of 82% followed by fossil fuel with 12% share Although poultry keeping households produce egg under industrial produce system, the inefficiency score was still high On average, these farms could increase their profit by reducing input by 22%

In summary, many studies analyzed technical efficiency of poultry subsector at farm level with two main approaches, that is, non-parametric (DEA) and parametric (SFM) approaches Specifically, DEA is utilized from input-oriented or output-oriented perspective for CCR and BCC models For SFM, stochastic production functions including Cobb-Douglas and Translog function are estimated by maximum likelihood method The proxy of output is the productivity of poultry farms and the proxy of input

is the total cost of input necessary for raising poultry during their production process

such as feed, breeds, medicines, energy, fuel, labor and other direct inputs Most of studies applying parametric approach proved that the majority of inputs have significant impacts on productivity In addition, a common result was found in both parametric and non-parametric approaches that most of poultry raising households were not fully technically efficient, wherein various technical inefficiency levels are due to different agro-ecological regions and different poultry production systems

2.3.2 DEA and SFM approaches on measurement of technical efficiency of agriculture sector

Many papers reviewed above used one of two methods (DEA or SFM) to measure the technical efficiency of poultry households However, choosing which methods to apply has still been controversial Therefore, some studies carried out with both methods not only measured technical efficiency, but also compared performance of these two methods for their caution

Wadud and White (2000) compared technical efficiency (TE) obtained from Data Envelopment Analysis and Stochastic frontier approaches for rice farmers in Bangladesh CRS and VRS models were included in DEA method while SFM had two steps including estimating TE score with Translog model and determining factors affecting TE score The mean of technical TE estimated from VRS-DEA was 0.858, greater than TE estimated from CRS-DEA, 0.789 and TE estimated from SFM, 0.791 Both VRS-DEA and CRS-DEA had variability of TE greater than SFM Although these three estimation methods produced different results, the distribution of TE from CRS, VRS and SFM models were similar which indicates more farms were in the most efficient group Beside, sources of technical inefficiency estimated from SFM approach are socio-economic and demographic factors which are characteristics of farm, non-physical factors and environmental factors

The study of Theodoridis and Psychoudakis (2008) was conducted by the same method with Wadud and White (2000), but gave different results Theodoridis and Psychoudakis (2008) analyzed technical efficiency in farm level in Greece Three employed models are CRS-DEA and VRS-DEA and SFM In contrast to Wadud and White (2000), the technical efficiency scores obtained from VRS-DEA (0.685) and

CRS-DEA (0.634) were lower than TE score obtained SFM (0.812) These TE scores also indicated producing process of farmers in Greece is inefficient Estimators from SFM showed that most farmers operated at mildly increasing returns to scale, while in DEA, farmers operated in increasing and dominantly decreasing returns to scale Technical efficiency scores in both the CRS and VRS DEA measures exhibited greater variability than the stochastic frontier efficiency measures which is similar to the result

of Wadud and White (2000)

Zamanian et al (2013) tested the technical efficiency in agriculture sectors for 21 countries in Middle East and North Africa region (MENA) They applied CCR-DEA, BCC-DEA and SFM method to measure technical efficiency level and compare results from two methods At the country level, the proxy of output used is the quantities of various commodities from agriculture while the set of input used includes five variables: arable land, tractor, labor, livestock and fertilizer By and large, they obtained similar results with Wadud and White (2000), that is, total average technical efficiency scores were in the order: BCC-DEA (0.770) > CCR-DEA (0.744) > SFM (0.479) which indicated the high level of random error in data In addition, results from DEA and SFM method also suggested that for the same rank of countries, Qatar was the most efficient one Moreover, three studies had the same result that TE scores obtained in different methods had high and significant correlations

This section provides a sketch of some empirical studies comparing performance of two approaches for measuring the technical inefficiency of agriculture sector at different levels These studies showed the same results that the variability of TE scores obtained from non-parametric approach is larger than from parametric approach; and the correlation of TE scores estimated from these approaches is high and significant However, the average TE scores calculated from these models vary among studies In particular, Zamanian et al (2013) and Wadud and White (2000) came up with similar results that the mean of TE scores estimated from various models descended in the order: DEA-BCC, DEA-CCR and SFM while average TE scores in study of Theodoridis and Psychoudakis (2008) had the reverse pattern

Briefly, there exist many studies addressing the poultry sector by one of the two approaches reviewed in the preceding section Nonetheless, to the best of my

knowledge, there is no comparative study of parametric and non-parametric approach concerning the poultry sector Therefore, this thesis considers the existing studies as the empirical literature to contribute to the analytical framework of comparison approaches

in poultry sector

2.3.3 Impact of human capital on agriculture productivity

The role of human capital in economic growth has been emphasized in both economic and micro-economic in many studies Specially, the impact of human capital

macro-on productivity of agriculture sector is not clearly like in the industry sector This part addresses the impact of human capital on agriculture productivity through some empirical studies in different aspects: education level, experience, age, gender and agriculture training

Huffman (1977) showed the evidence supporting for the hypothesis that there exists linkage between human capital and allocative efficiency The magnitude of allocative efficiency relates to farmers’ level of education which is a crucial component for increasing allocative performance Therefore, high level of farmers’ education enhances farms’ profit Huffman (1980) did another research on the role of human capital in agriculture He also came to the same conclusion as previous papers that the higher education the farmers can get, the higher the farm output is Huffman (2001) proved the positive impact of education on agriculture production through agriculture functions like gross output/transformation function and profit function He then concluded that these effects of education level are referenced as technical efficiency, allocative efficiency, or economic efficiency aspects

Foster and Rosenzweig (1995) proved that human capital has a significant impact on agriculture output through raising production efficiency They collected data from 4118 households planting the one-year crop from 1968 to 1971 on the areas plated for wheat and rice with new high-yielding seed varieties in India They analyzed the profit function with the proxy of independent variables includes: farmer’s experience, farmer’s education level, equipment, irrigation assets, irrigated land and un-irrigated land Their estimate indicated that imperfect knowledge of households in new seed was

a barrier to the change of their traditional production and accordingly led to difficulty in

improving technical efficiency On the other hand, the experience of farmers had a positive relationship with farm profit

Djomo and Sikod (2012) came to the same conclusion with the studies of Huffman (1977, 1980, and 2001) and Foster et al (1995) that human capital affects the agriculture productivity in a positive way They conducted this study in Cameroon with 4,275 agriculture households in 2007 by ultilizing stochastic frontier model to analyze agricultural productivity level among farms and the impact of human capital on agricultural productivity level was tested simultaneously The results demonstrated that human capital characterized by an additional year of schooling and experience increased agriculture productivity and farmer’s income and reduced inefficiency level

In contrast, Fafchamps and Quisumbing (1999) and Gallacher (1999) proved that education has an insignificant effect on agriculture productivity Particularly, Fafchamps and Quisumbing (1999) analyzed the crop productivity with human capital

as one of the proxy of dependent variables Human capital was represented by age, age squared, year of education, Raven’s test cost (innate ability), height (childhood nutrition) and BMI (current nutrition) They concluded that education and gender have

no effect on crop and livestock production; and high level of education led to increase

in off-farm income and labor force shifting to non-farm sectors in case of Pakistan On the other hand, Gallancher (1999) constructed the research under the hypothesis that the differential technology between decision-making units is a function of available human capital inputs Specifically, the technological change was calculated by increased output per unit of input which excluded nonconventional factors such as human capital

As a result, he rejected the hypothesis that human capital is one of the explanatory variables of TFP differentials

Alam et al (2009) showed the same evidences with Fafchamps and Quisumbing (1999) and Gallacher (1999) that education system did not affect the development of agriculture sector significantly They tested the role of agriculture education and agriculture training on agriculture economics and national development This study was conducted in Bangladesh where the share of labor force in agriculture was about 95% with the assumption that one of the reasons causing production of agriculture less than other industries is low level education of labor force Nonetheless, the authors

suggested that for the sake of sustainable development on agriculture economics and national development, policy makers should consider improving agriculture education and training to adapt new technology for agriculture sector

In short, these studies considered human capital as an input to the technical efficiency model but not an input in production function As a result, these studies gave evidences that the human capital affecting on the agriculture productivity through various channels Specifically, many authors proved the positive impact of human capital on agriculture productivity through education level and experience of farmer (Huffman,

1977, 1980, and 2001; Foster and Rosenzweig, 1995; and Djomo and Sikod, 2012) In contrast, some authors gave contrary conclusions that education level and gender have insignificant effect on agriculture output (Fafchamps and Quisumbing, 1999; Gallacher, 1999; and Alam et al., 2009) Likewise, the impact of human capital is also determined

in the technical efficiency model of some studies (Ike, 2011; Battese and Coelli, 1995; and Alabi et al., 2005) In these studies, the human capital is represented by the age of the farmer and the attendance to the courses of agricultural training However, the effect of these factors on technical efficiency is not clear

CHAPTER 3 OVERVIEW OF POULTRY FARMS IN VIETNAM

This chapter was done in the effort to provide the overview of some main characteristics of poultry raising in Vietnam The characteristics of poultry raising in different eco-logical regions also described in terms of density and flock size of poultry Besides, the classification of three main poultry production systems is also

explained in detail

3.1 General characteristics of poultry production in Vietnam

During the period from 1990 to 2010, the average growth rate of poultry production is more than 5%, which is highest among livestock sectors Figure 3-1 and Figure 3-2 shows the growth of poultry numbers annually from 1990 to 2003 is around 7% However, in 2004, the growth rate decreases dramatically, about 15% due to the impact

of avian flu The avian flu outbreaks occurred in Vietnam from latter 2003 to early

2004 and within two months, the total number of birds destroyed went up to 38million birds with the costs of about a trillion VND The avian flu causes serious damage on many poultry farmers and becomes threat to poultry breeding national center and other provinces and cities Nevertheless, after the avian influenza is controlled, the number of birds was recovered rapidly with the average growth rate of 6% per year from 2005 to

2010

Figure 3-1: Growth rate of livestock and poultry with base year of 1990

(Source: GSO, 2011)

Figure 3-2: Annual growth rate of number of poultry with base year of 1990

(Source: GSO, 2011)

With the long developed history, poultry production is one of the most popular products of Vietnamese agriculture with poultry raising in nearly 90% of total rural households, and proportion of poultry output assumes nearly 20% of total livestock production, ranked second behind swine production in 2010 (GSO, 2011) By region, the share of poultry keeping households located in Northern highland and Mountainous regions is largest, accounting for 35%, Northern Central and Central Coastal is ranked second with proportion of poultry raising population of 22%, Red River Delta 21% while regions in the South assumes 3%, 11% and 8% for Southeast, Mekong River Delta and Highland respectively

Besides, the poultry density is also different between regions Figure 3-3 shows the average poultry density of Vietnam in 2006, which is nearly 650heads/km2 including 450heads/km2 for chicken and 180heads/km2 for duck (Desvaux et al., 2008) According to the report, the regions with highest density of poultry are Red River Delta and Mekong River Delta In the Red River Delta region, the density of poultry is around 4,000heads/km2 and that in Mekong River Delta is 900heads/km2 On the other hand, the lowest poultry density is in the North West and Central Highlands regions with 218heads/km2 and 148heads/km2, respectively

Corresponding to the overall poultry density, the large flock size of poultry-holding household is in large population centers (Figure 3-4) Although the poultry density seems higher in the north, the flock size is larger in the south of country where poultry

producers tend to employ intensive or semi-intensive systems, using industrial feeds and the environment in the south is more market-oriented (FAO, 2003)

Figure 3-3: Poultry density of Vietnam in Figure 3-4: Average number of birds per

2006 (Source: Desvaux, S et al., 2008) household in 2001 (Source: FAO, 2003)

3.2 Poultry production system in Vietnam

In general, there are four main sectors of poultry production system adopted by Food

and Agriculture Organization (FAO) The first sector is the Industrial integrated system which is specified by two main characteristics which are the high level of

biosecurity and commercially marketed products The second and the third sectors are

called Commercial poultry production system In particular, the Sector 2 is

characterized by biosecurity level from moderate to high and products usually marketed commercially; whereas characteristics of Sector 3 include low to minimal biosecurity

and products entering live birds market The last sector is village or backyard production system with the minimal level of biosecurity and products consumed

locally

(FAO Recommendations on the Prevention, Control and Eradication of Highly pathogenic Avian Influenza (HPAI) in Asia, September 2004)

Figure 3-5: Regional Characteristics of poultry-holding household in 2002

(Source: FAO, 2004)

Drawing from Vietnam Household Living Standard Survey (VHLSS) data in 2002, FAO categorizes sectors of poultry system and the share of rural household in poultry production in Vietnam based on the following definitions:

Sector 1: > 2000kg per household

Sector 2: 201 – 2000kg

Sector 3: 51 – 200kg

Sector 4: 1 – 50kg