Doctoral Dissertation
Essays on Financial Activities and Industrial Change: The Study Casein Vietnam

NGUYEN THI BICH NGOC

Graduate School for International Development and Cooperation
Hiroshima University

September 2020

Essays on Financial Activities and Industrial Change: The Study Casein Vietnam

D170255NGUYEN THI BICH NGOC

A Dissertation Submitted to
the Graduate School for International Development and Cooperationof Hiroshima University in Partial Fulfillmentof the Requirement for the Degree ofDoctor of Philosophy

September 2020

ABSTRACT
As mentioned in the agenda 21 at the World Summit on Sustainable Development(WSSD), the world is targeting to promoting sustainable agriculture and rural development.
In developing countries, rural development has been considered as a key tool to alleviate
poverty. Studies over the past decades have provided important information on the related
issues in the rural development and poverty alleviation process. The main purpose of thisresearch is to study the sustainable economic development that has been recognized as oneof the important elements in rural development. The specific objectives of this research wereto investigate the economic structural change of Vietnam’s economy and examine thefinancial activities of the small and medium-sized enterprise and rural households in Vietnam.
In recent years, Vietnam’s economy has gained significant achievements in thedevelopment process. Being one of the poor countries before the renovation, Vietnam has
successfully made a transition to a middle-income country. A large and growing body ofliterature has emphasized introducing or identifying leading industries is essential forpoverty reduction and economic development. Besides, structural change analysis is an
important tool to study the economic development and growth of any country. However,much of the literature concerns the macroeconomic approach rather than the industrial
approach. Therefore, the first objectives of this paper are to examine which sectors can beconsidered key sectors in Vietnam over the past 15 years (2000-2015) and to investigate how
structural economic change has occurred. The backward-forward linkages anddecomposition approaches are applied to analyze the IO tables. The major findings are used
to explain the role of some subsectors as well as reveal the trend of the economic structuralchange of Vietnam’s economy.

iii

The role of small and medium-sized enterprises (SMEs) in Vietnam’s economy has
been supported by various literature. According to the general statistics provided by Vietnam(GSO, 2014), the contribution of 415,656 Vietnamese SMEs to the state’s budget isapproximately 217.5 trillion VND, accounting for 41% GDP. In addition, SMEs in Vietnam
provide jobs to 5.6 million people, accounting for 48.3% of total enterprise employment(GSO, 2014). The importance of SMEs to the Vietnamese economy is undeniable.
Nevertheless, SMEs encounter many difficulties, such as the lack of capital, skilled labor,
and financing sources. Therefore, this study conducts an empirical analysis to investigate therelationship between the capital structure as financial leverage and investment decisions byusing the latest Vietnamese SME data of 2011, 2013, and 2015.
Rural households (HHs) are one important stakeholder in the rural development
process. However, rural households often face challenges due to production constraints thathinder the improvement of their livelihood. Previous studies suggest that rural HHs canovercome these constraints if they are organized into groups. Acknowledging thatsustainable rural development requires collaboration with local government, localorganizations, and the people, this paper contributes to the literature by examining theimpacts of farmers’ union membership on the production and credit volume of HHs using
HH-level data from rural areas in Vietnam. Propensity score matching was employed.
The main results of this research are summarized as follows: Using the Vietnam
input-output table at four points and the decomposition method, we found that theagricultural sector and food sector maintained their central roles over the 15 years
considered. These two sectors experienced increased total output, primarily due to changes
in from final consumption demand. In addition, the textile sector also maintained a higher
position in total output in Vietnam because of the increased final demand for exports.
Moreover, the machinery sector grew rapidly over the last 5 years considered, due not onlyiv

to changes in final demand for investment and exports but also to technological change
affecting intermediate inputs. By contrast, the wholesale and retail sector experienced adrastic decline in its final demand over the last 5 years considered due to changes in
consumption, investment, and exports.
By using three Vietnam SMEs data (2011, 2013 and 2015), we found a contrary
result with financial theories and previous studies that show a positive relationship between
financial leverage and investment decision, suggesting that SMEs with higher financial
leverage tend to seek more investment opportunities than SMEs with lower financial
leverage. We also find that firms with higher financial leverage are more likely to choose
external financing sources than internal ones. Our results confirm that financial theories havevarying levels of applicability in the context of an emerging market, such as Vietnam.
By using the household-level data from rural areas in Vietnam, we found that farmer
union membership has a significant impact on the production and credit volume of rural HHs.
In particular, farmers’ union membership HHs can obtain higher credit volume and attain
more livestock production but less crop production than nonmember HHs. Our findings
suggest that the farmer’s union should enhance its activities to provide more resource
accessibility, especially concerning financial services.
The results of this study add to the literature on rural development in Vietnam. Thismay help policymakers gain better insight into the role of different stakeholders in ruraldevelopment and poverty reduction progress.

v

Acknowledgements
Firstly, I would like to express my sincere gratitude to my main supervisor Prof.
ICHIHASHI Masaru for the continuous support of my Ph.D. study and research. He has
inspired me with his patience, motivation and immense knowledge. His tremendous support
and guidance help me in all the time of my research and writing of my doctoral dissertation.
This is a great honor for me to be his Ph.D. student and this research would not havesucceeded without his kind supervision.
Besides my advisor, I would like to thank the rest of my dissertation committee: Prof.
KANEKO Shinji, Prof. YOSHIDA Yuishiro, Prof. MAHARJAN Keshav Lall and Assoc.
Prof. TRAN Dang Xuan, for their insightful comments, suggestion and encouragement
which incented me to widen my research from various perspectives. Without a doubt, myresearch would not have been completed without their valuable support.
Thirdly, I gratefully acknowledge the great support from TAOYAKA Program,during my study at the Hiroshima University. I would like to send great thanks to all staffs
at TAOYAKA Program and the Graduate School for International Development andCooperation (IDEC), my lab mates whose relentless support and willingness to solve myproblems whenever I need. I also would like to thank Thai Nguyen University of Agriculture
and Forestry for allowing me to pursue my Ph.D. study in Japan.
Last but not the least, I am deeply indebted to my family, my parents, my husband
and my children, my brothers and my relatives who are always supporting me spiritually
throughout pursuing this Ph.D. study and my life. I would also like to extend my sincere
thanks to my friends for the sleepless nights we were discussing for my research, and for all
the fun we have had in the last five years.
NGUYEN THI BICH NGOC
September 2020
vi

LIST OF CONTENTS
ABSTRACT........................................................................................................................... i
Acknowledgements ............................................................................................................. vi
LIST OF CONTENTS....................................................................................................... vii
LIST OF TABLES............................................................................................................... x
LIST OF FIGURES............................................................................................................ xi
CHAPTER 1: GENERAL INTRODUCTION.................................................................. 1
1.1. Background of the study............................................................................................. 1
1.2. Objectives ................................................................................................................... 1
1.3. Contribution of the study ............................................................................................ 2
1.4. Structure of the study.................................................................................................. 2
CHAPTER 2: HOW IMPORTANT A FOOD SUPPLY CHAIN IN VIETNAM IS IN
THE PAST 15 YEARS: DECOMPOSITION INPUT-OUTPUT APPROACH (20002015) ...................................................................................................................................... 3
2.1. Introduction................................................................................................................. 3
2.2. Methods and Models................................................................................................... 7
2.3. Data............................................................................................................................. 92.4. Results....................................................................................................................... 11
2.5. Discussion ................................................................................................................. 24
2.6. Conclusions............................................................................................................... 32vii

CHAPTER

3:

THE

LINK

BETWEEN

FINANCIAL LEVERAGE ANDINVESTMENT DECISIONS IN VIETNAM’S SMALL AND MEDIUM-SIZEDENTERPRISES.................................................................................................................. 34
3.1. Introduction............................................................................................................... 34
3.2. Data and Methods ..................................................................................................... 37
3.4. Results and discussion .............................................................................................. 44
3.5. Conclusion ................................................................................................................ 60
CHAPTER 4: INFLUENCE OF FARMER UNION MEMBERSHIP ON THE
PRODUCTION AND CREDIT VOLUME OF HOUSEHOLDS: EVIDENCE FROMFARM HOUSEHOLDS IN VIETNAM........................................................................... 62
4.1. Introduction............................................................................................................... 62
4.2. Context and Literature Review ................................................................................. 65
4.3. Materials and Methods ............................................................................................. 67
4.4. Results and discussion .............................................................................................. 74
4.5. Conclusion ................................................................................................................ 85
CHAPTER 5: SUMMARY OF THE STUDY................................................................. 87
5.1. Main findings ............................................................................................................ 87
5.2. Implication ................................................................................................................ 88
5.3. Limitations of the study ............................................................................................ 90
REFERENCES .................................................................................................................. 91
APPENDICES.................................................................................................................... 99
Appendix 1. Individual report on TAOYAKA Onsite Team project: ............................. 99viii

Appendix 2: A survey under the Onsite Team Project of and with the support from
Hiroshima University TAOYAKA Leading Graduate School Program ....................... 106
Appendix 3: Household survey Questionnaire in Vietnam ........................................... 112

ix

LIST OF TABLES
Table 1: Sectors selected for the study ................................................................................ 10
Table 2: Total output by sectors (unit: millions of US dollars) ........................................... 12
Table 3: Backward linkage and forward linkage of Vietnam economic (2000-2015) ........ 16
Table 4: Top 5 sectors with the greatest change in total output in terms of changes intechnology and final demand in the period 2000-2015 (unit: millions of US dollars) ........ 20
Table 5: Top 5 sectors with the greatest change in final demand in terms of the change in
consumption, investment and exports in the period 2000-2015 .......................................... 22
Table 6: Some representatives large enterprises in the selected industries in Vietnam ...... 28
Table 7: Definition and summary statistics of the variables................................................ 39
Table 8: Investment decisions of SMEs in Vietnam (2011-2015) ...................................... 44
Table 9: Investment decisions of SMEs in Vietnam across three survey rounds (2011, 2013,
and 2015) (cont.).................................................................................................................. 46
Table 10: The relationship between financial leverage and the choice of financing sources(2011-2015) ......................................................................................................................... 52
Table 11: The relationship between financial leverage and the choice of financing sources(2011)................................................................................................................................... 54
Table 12: The relationship between financial leverage and the choice of financing sources(2013)................................................................................................................................... 56
Table 13: The relationship between financial leverage and the choice of financing sources(2015)................................................................................................................................... 58
Table 14: Description of selected variables ......................................................................... 70
Table 15: The mean difference of households without farmers’ union membership(Treatment) and with farmers’ union membership (Control) .............................................. 76
Table 16: Balance checking before and after matching...................................................... 78xTable 17: The results of PSM estimation (unit: millions of VND) ..................................... 82
Table 18: Results of ATE estimation: Impact of membership on HHs (unit: millions of VND)............................................................................................................................................. 83
Table 19: Results of ATET estimation: Impact of membership on HHs (unit: millions of
VND) ................................................................................................................................... 84

LIST OF FIGURES

Figure 1: Shares of GDP by Sectors, 1986–2009 .................................................................. 5
Figure 2: The study sites...................................................................................................... 69xi

CHAPTER 1: GENERAL INTRODUCTION

1.1. Background of the study
The concept of development has been mentioned in the Agenda for Development(A/RES/51/240) that development is a process to bring a higher quality of life to all people.
The important components to maintain sustainable development are economic development,social development, and environmental development. In addition, the Sustainable
Development Goals (SDGs) addresses that “strengthen the capacity of domestic financial
institutions to encourage and expand access to banking, insurance, and financial services
for all”. Therefore, this study focuses on sustainable economic development in Vietnam.
Rural development is one of the key tools to alleviate poverty in Vietnam. The
Vietnamese government has made significant efforts to reduce poverty by implementing
several programs to support a household such as the microcredit programs, providing
agricultural extension services, introducing new technologies, and so on. Being one of thepoorest countries in the mid-1980s, Vietnam has successfully made a transition to a middleincome country (Banker & Ungor, 2019). In recent years, Vietnam’s economic structure has
been transformed into industrialization and modernization by promoting comparativeadvantages of industries and services.
1.2. Objectives
This study aims to achieve two main objectives:(1) Economic structure change in Vietnam: To examine which sector can be consideredas key sectors in Vietnam’s economy from 2000 through 2015 and investigate how
the economic structural change has done.
1(2) Financial activities of small and medium-sized enterprises (SMEs)and rural
households in Vietnam:a. To identify the link between corporate finance (financial leverage) andinvestment decisions in Vietnam’s SMEs
b. To evaluate the impact of farmer union membership on rural household
1.3. Contribution of the study
This study contributes to the existing literature in the field of rural development andregarding the role of social organization in rural areas in Vietnam. Additionally, this study
contributes to the existing knowledge regarding the relationship between corporate financeand investment decision in the context of emerging markets.
This study uses several methodologies to address the problem such as causal
inferences, input-output decomposition analysis which has received much attention in thecurrent research literature.
1.4. Structure of the study
This study is organized as follows: Chapter 1 will briefly introduce the context ofVietnam’s economic development. Chapter 2 investigates how important a food supply
chain in Vietnam is in the period of 2000-2015 using a decomposition Input-output approach.
Chapter 3 contains the analysis of the correlation between corporate finance and investment
decision of Small and Medium Enterprises in Vietnam using several Econometrics
techniques, such as Logit, Tobit, and Fractional logit models. Chapter 4 presents theempirical estimation of the impact of farmer union membership on the production and credit
volume of households using the Propensity Score Matching approach.

2

CHAPTER 2: HOW IMPORTANT A FOOD SUPPLY CHAIN IN VIETNAM IS IN
THE PAST 15 YEARS: DECOMPOSITION INPUT-OUTPUT APPROACH (20002015)

2.1. Introduction
Many economists, such as Kuznets (1979), Lin and Monga (2011), Uy et al. (2013),
and Vu, K.M., have emphasized the importance of economic restructuring in a country’s
economic development and growth. (2017). In recent years, Vietnam’s economic structure
has been transformed through industrialization and modernization by the promotion ofcomparative advantages in industries and services. After 30 years of renovation, Vietnam’s
economy has made significant achievements. Barker and Ungor (2019) noted that Vietnam
has successfully escaped from being a low-income country and advanced to being a middleincome country.
A large and growing body of literature has investigated changes in economic
structure, for example Leotief (1941), Feldman et al. (1987), Bui Trinh et al. (2012), Marconi
et al. (2016), Ha, N. H. P., & Trinh, B (2018), Dempster et al. (2014), Erumban et al. (2019),
Dhahri et al. (2020) and Almmeida et al. (2020). Most previous studies have used amacroeconomic approach to analyze changes in aggregate economic indicators, such as
employment, the growth rate, and gross domestic product (Peter Hacks, 1989; Skolka, 1989;
Schoonbeek, 1990; Pham Quang Ngoc & Mohnen, 2004 and Rahmaddi & Ichihashi, 2013).
For example, Pham Quang Ngoc & Mohnen (2004) applied a multisectoral model toinvestigate how Vietnam’s economic growth is related to structural change by using thecountry’s input-output (IO) tables for 1989, 1996 and 2000. Similarly, K.M. V (2017) notedthat structural change has a significant positive effect on GDP growth. Despite being
relatively neglected in traditional economic analyses of structural change, the industrial
3

approach is also an essential tool for analyzing structural change. This study focuses on theindustrial approach to identify the key sectors and investigate the changes in the economic
structure of Vietnam by analyzing its IO tables for the period 2000-2015. Backward-forward
linkages and the decomposition approach are applied to analyze the IO tables. The major
findings are used to explain the role of certain subsectors and reveal the trends in theeconomic structure of Vietnam’s economy.
This paper makes two major contributions. First, we add to the well-establishedliterature on structural economic change by examining the Vietnamese economy using
industrial analysis. We decompose the changes in output into the change in technology andfinal demand for each sector. This decomposition is used to investigate the major factors
corresponding to the total change in its input. Second, to the best of our knowledge, thispaper is the first to employ four Vietnamese IO data tables to compare the economy’s
structure in three periods. Therefore, the results are expected to reflect the reality ofVietnam’s structural economic change over 15 years (2000-2015).
According to the General Statistical Office (GSO) (2016), Vietnam has experienced
significant structural economic change. In particular, the country has reduced its dependence
on agricultural sectors and witnessed expanded contributions of the industrial and services
sectors. For example, the share of the agriculture sector in GDP decreases by 6.2% (from23.24% in 2000 to 17% in 2015); the share of the industry and construction sector decreased
by 4.88% (from 38.13% to 33.25%); and the share of the services sector increased by 1.1%
(from 38.63% to 39.73%).

Figure 1 illustrates the share of four main sectors in Vietnam’s GDP from 1986 to
2009, namely, agriculture, manufacturing, mining, and services. The agricultural sector and4manufacturing sector exhibit the most dramatic changes. The agriculture sector accounted
for 34 percent of GDP in 1986 and decreased to 17 percent in 2009. However, themanufacturing sector experienced an upward trend, starting at 17 percent in 1986 andclimbing to approximately 25 percent in 2009, which far exceeds that of the agricultural
sector. Moreover, the services sector represented the largest share of GDP. In particular, theservices sector accounted for over 46 percent of GDP in 1986 and 54 percent in 2009. The
mining and quarrying sectors showed the smallest contribution to the GDP, at less than 6
percent throughout the period.
Figure 1: Shares of GDP by Sectors, 1986–2009

Sources: Tobergte et al. (2013)
We focus on the food supply chain, as it is a key part of rural development (Marsden
et al., 2000) that produces a significant amount of basic materials for other industries(Kastrinaki & Stoneman, 2011). Aramyan and Van Gogh (2014) noted that the supply chain
includes processes from production to distribution. The food supply chain comprises allprocesses related to the production and distribution of food and drink (Kastrinaki &Stoneman, 2011). Therefore, the agricultural sector plays an important role in the foodsupply chain. In Vietnam, the agricultural sector continues to be the largest raw material5

provider to other sectors, such as the food sector and textile sector. Moreover, the food sector
buys mostly inputs from the agriculture sector (Dieu TTM, 2006)
In essence, IO describes the interlinkages among industries, households andgovernment. A considerable literature has applied IO analysis to describe a country’s
economy (such as studies performed by Skolka, 1989; Franke and Kalmbach, 2005; andMarconi et al., 2016) or to investigate the interdependency among households (Hongsakhoneand Ichihashi, 2019). In addition, several studies have investigated changes in Vietnam’s
economic structure based on IO tables, such as T. Bui et al. (2012), T. Bui & Phong N.V.
(2013), Nguyen P. Thao (2014), Tran et al. (2916), Ha, N.H.P. & Trinh, B (2018). T. Bui
and Kobayashi (2012) discussed the interindustry linkages among manufacturing industries
and nonmanufacturing industries by using three IO tables (1989, 1996 and 2000). They
reported that the manufacturing sector showed higher internal linkages than thenonmanufacturing sectors. Although much of the current research pays particular attention
to the IO decomposition method, no single study exists that uses four I-O tables. Most of theprevious literature uses a long-time interval (normally 5 years) to compare one period withanother. This might fail to correctly interpret an economic structure, as it does not change
considerably over a short period of time.
Our main findings show that the agriculture, hunting, forestry, fishing; food andbeverage; and tobacco product sectors experienced the largest output gains as a result ofchanges in final consumption demand. We also found a substantial increase in the machineryand equipment sector, which derived from not only changes in final demand for investment
and exports but also technological change in intermediate inputs. In addition, the wholesale
and retail trade and repair sector experienced a dramatic decline in its final demand for
consumption, investment and export over the last 5 years (2010-2015).

6

This paper is organized as follows: Section 1 briefly introduces the researchstatement and some background on Vietnam’s economy. Section 2 presents the methodology.
Section 3 explains the data used in this study. The empirical results are explained in Section
4. Section 5 discusses the findings. Section 6 presents concluding remarks.
2.2. Methods and Models
To identify the key sectors in the Vietnamese economy and investigate its structuraltransformation over the last 15 years, we analyze Vietnam’s IO tables for 2000, 2005, 2010
and 2015. We aggregated the 2015 data into 34 industries to ensure that the IO tables for all
years were comparable.
The basic model is:̂ 𝐴]−1 [(𝐼 − 𝑀)𝐹𝑑 + 𝐸𝑋]
𝑋 = [𝐼 − (𝐼 − 𝑀)(1)

̂ 𝐴]−1 is the Leontief inverse matrix; A is the
Where X is total output; [𝐼 − (𝐼 − 𝑀)
̂ is a 34 x 34 diagonal matrix
input coefficient matrix; I is the 34x34 identity matrix; 𝑀with diagonal elements 𝑚 𝑖 (𝑡ℎ𝑖𝑠 𝑖𝑠 𝑎𝑛 𝑖𝑚𝑝𝑜𝑟𝑡 𝑟𝑎𝑡𝑖𝑜 𝑤ℎ𝑖𝑐ℎ 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒 𝑏𝑦 𝑚𝑖 =𝐼𝑀𝑖
𝑇𝑜𝑡𝑎𝑙 𝑜𝑓 𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑑𝑒𝑚𝑎𝑛𝑑̂ is self-sufficient rate matrix; 𝐹𝑑 is vector of domestic); (𝐼 − 𝑀)final demand; 𝐸𝑋 is vector of total export.

Backward linkage and forward linkage were introduced by Rasmussen (1956) andhas become useful identification to investigate the key sectors of any economy. The
backward linkage indicates the interconnection of a sector to other sectors from it purchases
inputs (Miller and Blair, 1985). In the other words, it says if sector i increase 1 unit, how it
influences to other sectors. The forward linkage shows the opposite direction as it indicates7the relation of a sector to the other sectors that it sells its output (Miller and Blair, 1985).
The backward linkage and forward linkage have been measures by the following equations:

𝐵𝐿 =

𝐹𝐿 =

(2)

∑𝑖 𝑏𝑖𝑗1∑
𝑛 𝑖𝑗 𝑏𝑖𝑗(3)∑𝑖 𝑏𝑖𝑗1∑
𝑛 𝑖𝑗 𝑏𝑖𝑗̂]Where: bij are the inverse matrix elements [𝐼 − (𝐼 − 𝑀)𝐴−1, n is the number ofsector (n=34).
According to the theory of IO model, the equation of output equals the matrix
product of the Leontief inverse (B) and the vector of final demand (F) can be expressedas:
𝑿 = 𝑩𝑭(4)

The change of output can be expressed in the following ways:∆𝑿 = (∆𝑩)𝑭𝟏 + 𝑩𝟎 (∆𝑭)

(5)

∆𝑿 = (∆𝑩)𝑭𝟎 + 𝑩𝟏 (∆𝑭)

(6)

8

So:
𝟏

𝟏

∆𝑿 = 𝑿𝟏 - 𝑿𝟎 = ⏟∆𝑩(𝑭𝟎 + 𝑭𝟏 ) + ⏟∆𝑭(𝑩𝟎 + 𝑩𝟏 )𝟐
𝟐
Technology change(7)

Final demand change

Where: 𝑩 = [𝑰 − (𝑰 − 𝑴^ )𝑨]−𝟏 is the Leontief inverse matrix, F is the vectors of final
demand.
This study followed the structural decomposition method proposed by
Dietzenbacher & Los (1998). In this approach, the change in total output is the summationof the change in final demand and change in technology. In the next step, we further
decompose the change in final demand with respect to the change in its components, such as
consumption (C), investment (I) and exports (EX).

Final demand change =𝟏𝟏
𝟏(𝑩𝟎 + 𝑩𝟏 )∆𝑪 + (𝑩𝟎 + 𝑩𝟏 )∆𝑰 + (𝑩𝟎 + 𝑩𝟏 )∆𝐸𝑋
𝟐
𝟐
𝟐

(8)

2.3. Data
This paper employed Vietnam’s IO tables for 2000, 2005, 2010 and 2015 publishedby the Organization for Economic Co-operation and Development (OECD). Vietnam’s IO
table presents the flows of goods and services transactions in 34 sectors in current prices(USD million). The 2000, 2005 and 2010 IO tables consist of 34 sectors, while the 2015 IO
table consists of 36 sectors. To ensure that we analyzed IO tables of the same size, we
adjusted the 2015 IO table to have 34 sectors. Table 1 below indicates the list of 34 sectors
selected for the study.

9

Table 1: Sectors selected for the study
No.

Sector1

Agriculture, hunting, forestry and fishing

2

Mining and quarrying

3

Food products, beverages and tobacco

4

Textiles, textile products, leather and footwear

5

Wood and products of wood and cork

6

Pulp, paper, paper products, printing and publishing

7

Coke, refined petroleum products and nuclear fuel8

Chemicals and chemical products

9

Rubber and plastics products

10

Other non-metallic mineral products

11

Basic metals

12

Fabricated metal products

13

Machinery and equipment

14

Computer, electronic and optical equipment

15

Electrical machinery and apparatus

16

Motor vehicles, trailers and semi-trailers

17

Other transport equipment

18

Manufacturing; recycling

19

Electricity, gas and water supply

20

Construction

21

Wholesale and retail trade; repairs

10

22

Hotels and restaurants

23

Transport and storage

24

Post and telecommunications

25

Financial intermediation

26

Real estate activities

27

Renting of machinery and equipment

28

Computer and related activities

29

R&D and other business activities

30

Public administration and defense; compulsory social security

31

Education

32

Health and social work

33

Other community, social and personal services

34

Private households with employed persons

2.4. Results
Table 2 displays the output structure based on the 34 sectors. The total output of theVietnamese economy $66,545.9 million in 2000 and $570,059.6 million in 2015. The shareof the agricultural sector dropped from 21.15% in 2000 to 13.07% in 2015. The share of thewholesale, retail, trade, and repairs sector also experienced a decreasing trend, from 11.56%
in 2000 to 5.02% in 2015. In contrast, the share of the food products, beverage, and tobacco
sector steadily increased from 11.21% to 12.42% over the 15 years considered. There was anotable increase of 3.72% in the share of the textiles, textile products, leather, and footwear
sector to Vietnam GDP over the 15-year period 2000-2015.

11

Table 2: Total output by sectors (unit: millions of US dollars)
2000

2005
Share

Sector#(Mill.

to total(Mill.

USD)

output

USD)

(%)

2010

2015

Share

Share

to

to

total
output(Mill.
USD)

(%)

total
outputShare(Mill.

to total

USD)

output(%)(%)

Agriculture,hunting, forestry
114074.6

21.15

22241.2

15.83

43903.9

15.32

74483.6

13.07

7460.2

11.21

15584.7

11.09

30057.4

10.4970774.2

12.42

3636.95.478596.86.1214611.9

5.1052396.9

9.195943.68.9313031.5

9.27

29008

10.12

36622.6

6.424006.9

6.02

8116

5.78

16755.8

5.85

31133.9

5.467690.2

11.56

13873

9.87

30033.9

10.4828630.4

5.02

2010.23.02

3626.42.58

8510.6

2.97

19801.6

3.47

399.4

0.60

2918.42.08

5178.5

1.81

19482.4

3.42

394.6

0.59

976.6

0.69

1712.60.60

16286.9

2.86

and fishing
Food products,
beverages and2

tobaccoTextiles, textile
products, leather

3
4

and footwear
Construction
Mining and5quarrying
Wholesale and6retail trade; repairs
Hotels and7restaurants
Manufacturing;8

recycling
Machinery and9equipment

12Computer,Electronic and
101485.92.23

2641.2

1.88

9467.33.30

15741.1

2.76

91.3

0.14

152.1

0.11

5091.61.78

15097.1

2.65

1449.72.18

3913.12.78

7499.32.6214159.5

2.48

109.6

0.16

194.1

0.14

351.1

0.12

13804.4

2.42

1590.32.39

3268.32.33

6491.92.2713123.8

2.30

1363.52.05

2761.71.97

5673.51.98

12531.3

2.20

567.1

0.85

3255.92.32

6637.72.32

12088.5

2.12

771.1

1.16

2756.21.96

6019.52.10

11978.2

2.10

109.6

0.16

194.1

0.14

351.1

0.12

10988.9

1.93

459.1

0.69

2016.81.443351.61.1710609.6

1.86

607.4

0.91

2639.5

1.88

5559

1.94

10369.2

1.82

1351.12.03

3836.3

2.73

7036

2.45

9715.3

1.70

938.9

1.41

1662

1.18

2566.50.90

8982.1

1.58

optical equipment
Coke, refined
petroleum products

11

and nuclear fuelChemicals and12chemical products
Renting ofmachinery and13equipment
Transport and14

storage
Electricity, gas and15

water supply
Fabricated metal

16

products
Rubber and plastics

17

products
Computer and18

related activities
Motor vehicles,
trailers and semi-

19

trailers

20

Basic metals
Other non-metallic

21

mineral products

22

Education13

Electricalmachinery and
23

0.89

2349.81.67

4402.81.547967.8

1.40

564.7

0.85

999.6

0.71

1812.20.63

7865.51.38

1028.1

1.54

1820

1.30

3593.71.25

7530.8

1.32

851.4

1.28

2200.71.57

3912.51.37

7137.9

1.25

297.5

0.45

1382.60.98

2258.20.79

6791.41.19

1728.32.60

3059.62.18

6257.42.18

5997.8

1.05

858.8

1.29

1520.21.08

3082.81.08

5649.6

0.99

467.2

0.70

827

0.59

1308.60.46

4931.6

0.87

908.1

1.36

3135.62.23

4912.11.71

4469.50.78

2218.23.33

3926.82.79

7101.82.48

1577.8

0.28

482.6

0.73

991.8

0.71

1971.20.69

1168.50.20

apparatus
R&D and other

24

595.4

business activities
Publicadministration anddefence;compulsory social

25

security
Pulp, paper, paper
products, printing

26

and publishing
Wood and products

27

of wood and cork
Financial

28

intermediation
Other community,social and personal

29

services
Health and social

30

work
Other transport

31

equipment
Real estate32

activities
Post and33

telecommunications

14

Private householdswith employed
34

34.4

0.05

60.9

0.04

119.3

0.04

169.9

0.03

66545.9100140530.5

100

286601.3

100570059.6

100

persons
All sectors

Table 3 shows the backward and forward linkage results for the 34 industries in
2000, 2005, 2010 and 2015 calculated from equations (2) and (3). These linkages of anindustry are meant to measure the intersectoral linkages of that industry to other industries.
A backward linkage illustrates how a given industry influences other industries; forward
linkage indicates how a certain industry has been influenced by other industries (Cheneryand Watanabe, 1958).
According to Table 3, the agriculture, hunting, forestry, and fishing sector is theleading sector in the Vietnamese economy, as both its backward and forward linkages are
greater than 1 in all periods. This implies strong connections between agriculture, hunting,forestry, and fishing and other sectors with respect to both input demand and output supply.
In addition, pulp- and paper-related products is also a key sector in all periods. There was asubstantial change in the food products, beverages and tobacco sector over the 15 years
considered. In 2000, this sector depended on interindustry supply; in other words, it
depended on all other industries. Since 2005, this sector has been considered an importantsector of the Vietnamese economy.

15

Table 3: Backward linkage and forward linkage of Vietnam economic (2000-2015)
2000
Sector

#

2005

2010

2015

BL

FL

BL

FL

BL

FL

BL

FL

1.439

0.892

1.483

1.152

1.442

1.060

1.426

1.383

1.390

0.697

1.344

0.967

1.266

0.846

1.318

0.849

Food products,
1

beverages and tobacco
Wood and products of2wood and cork
Coke, refined petroleum
products and nuclear

3

fuel

0.884

0.570

0.819

0.568

1.127

1.082

1.274

1.383

4

Transport and storage

0.848

0.778

0.907

0.717

0.937

0.608

1.251

0.869

1.235

1.162

1.202

1.182

1.190

1.070

1.216

1.286

1.115

3.000

1.106

2.393

1.090

2.190

1.142

1.716

1.036

1.011

0.917

1.149

1.098

1.053

1.102

0.878

Pulp, paper, paper
products, printing and5

publishing
Agriculture, hunting,6

forestry and fishing
Other non-metallic

7

mineral products
Post and8

telecommunications

0.845

0.797

0.829

0.772

0.833

0.789

1.077

0.630

9

Basic metals

1.021

1.095

0.984

1.276

0.925

1.076

1.062

1.014

Chemicals and chemical
10

products

1.031

1.249

0.910

1.422

1.052

1.214

1.058

1.142

11

Construction

1.083

0.688

1.137

1.079

1.140

1.266

1.058

0.867

1.022

0.675

1.223

0.850

1.259

0.740

1.052

1.116

Manufacturing;
12

recycling
Rubber and plastics

13

products

1.204

1.144

1.198

1.406

1.143

1.207

1.044

1.012

14

Hotels and restaurants

1.051

0.803

1.090

0.804

1.077

0.831

1.036

1.091

16

Motor vehicles, trailers
15

and semi-trailers

1.036

0.779

1.158

0.681

1.026

0.681

1.025

0.790

0.768

1.256

0.785

1.173

0.847

1.150

0.997

1.332

Electricity, gas and16

water supply
Other transport

17

equipment

1.053

1.048

1.235

0.782

1.095

0.751

0.958

0.670

18

Health and social work

0.835

0.670

0.816

0.625

0.814

0.640

0.957

0.601

Textiles, textile
products, leather and
19

footwear

0.930

0.781

0.937

0.800

0.792

0.547

0.952

0.833

20

Mining and quarrying

0.837

1.051

0.762

0.896

0.827

1.954

0.951

2.463

0.836

0.714

0.826

0.681

0.841

0.825

0.948

0.765

1.067

1.050

1.190

1.366

1.079

1.273

0.945

1.138

1.099

1.146

1.038

0.880

1.035

0.990

0.936

1.103

0.907

1.192

0.878

0.986

0.897

1.107

0.931

0.796

1.065

0.835

0.910

0.774

0.980

0.733

0.924

1.198

1.063

0.808

1.104

1.060

0.982

0.785

0.921

0.795

0.989

0.900

0.927

0.933

1.007

0.998

0.912

0.851

0.947

2.284

0.976

1.986

0.980

2.023

0.887

1.453

1.067

0.938

0.942

0.850

0.964

0.818

0.840

0.706

Other community,social and personal
21

services
Fabricated metal

22

products
Computer, Electronic23

and optical equipment
Financial

24intermediation
Renting of machinery

25

and equipment
Electrical machinery

26

and apparatus
R&D and other27

business activities
Wholesale and retail28

trade; repairs
Computer and related29

activities

17

Public administrationand defence;compulsory social
30

security

0.937

0.664

0.972

0.653

0.980

0.671

0.837

0.575

31

Education

0.829

0.731

0.851

0.661

0.868

0.666

0.810

0.594

32

Real estate activities

0.873

1.179

0.876

1.170

0.886

1.117

0.798

0.685

0.995

0.754

1.050

0.687

0.887

0.605

0.789

0.849

0.661

0.661

0.619

0.619

0.635

0.635

0.567

0.567

Machinery and33

equipment
Private households with

34

employed persons

In the next step, using equation (7), we decomposed the total output change into thechanges in technology and final demand over three periods: 2000-2005, 2005-2010 and2010-2015. The results in Table 4 show that the agriculture, hunting, forestry, fishing andthe food, beverage and tobacco product sectors experienced the largest output gains in all
three periods. We also found that there was a substantial increase in technological change inthe machinery and equipment sector, which resulted in a large change in its total output from2000 to 2015. The wholesale and retail trade and repair sector experienced a dramatic decline
in total output in the last period (2010-2015).
Furthermore, Table 4 also indicates that the food sector recorded the greatest
technological change over the period 2000-2005, while it was the mining and quarrying
sector over the period 2005-2010 and the textiles sector over the period 2010-2015. However,the food sector still exhibits the second-greatest technological change in the last period.
Moreover, the agriculture sector had the greatest change in final demand in all three periods,
followed by the food sector, the wholesale and retail sector, the textiles sectors and theconstruction sector. In the last 5-year period (2010-2015), there was a drastic decline in the18wholesale and retail sector’s final demand. In contrast, the machinery sector presented a
rapid increase in its total output and final demand.
Using equation (8), we decomposed the change in final demand into the changes in
consumption, investment and exports. This indicates the extent to which changes in thesefactors contribute to the total change in final demand. The results are presented in Table 5.
The agriculture sector had the largest change in consumption in all three periods, followedby the food sector and wholesale sector. However, the textiles sector had the greatest change
in exports in all three periods. The agriculture sector and food sector still ranked highly interms of exports. The agricultural sector had the third-largest change in exports in 2000-2005
and 2015-2010 and the second-largest change in 2005-2010. The food sector had the fifth-,
fourth- and second-greatest change in exports over 2000-2005, 2005-2010 and 2010-2015,
respectively. Regarding the change in investment, construction reported the largest changeover the full 15-year period. The results also indicate that the growth of the machinery sector
over the last 5 years considered derived from its change in final investment and export
demand.
Some main findings are clear from the tables. First, the agricultural sector and foodsector played important roles throughout the 15 years considered. The largest total output
changes in these two sectors were primarily due to changes in final consumption demand.
Second, the textile sector has become a leading sector of Vietnam’s economy, and its higher
position in total output has been driven by increased exports. Third, the total output of themachinery sector witnessed a significant increase over the last 5 years considered (20102015), which derived from both changes in final investment and export demand andtechnological change in intermediate inputs. Fourth, there was substantial decrease in final
investment and export consumption demand for the wholesale and retail sector over the last
5-year period considered.
19

Table 4: Top 5 sectors with the greatest change in total output in terms of changes intechnology and final demand in the period 2000-2015 (unit: millions of US dollars)
2015-20102010-2005

2005-2000

Agriculture, hunting,

Agriculture, hunting,forestry and fishingforestry and fishing

Total Output change = ∆𝑿
Food products,
1 beverages andtobacco40716.8

Textiles, textile
2 products, leather
and footwear

21662.7
Wholesale and retail

Food products,

trade; repairs

beverages and tobacco

37785

Agriculture,

8166.616160.9
Construction

8124.5Construction

3 hunting, forestry
and fishing
430579.7

Machinery andequipment

15976.5
Foodproducts,

7087.9Wholesale and retail

14574.3 beverages and tobacco 14472.7 trade; repairs

Mining andMining and quarrying

5 quarrying

6182.8

Textiles, textile
products, leather and

14378.1

8639.8 footwear

4959.9Technology change = ∆𝑩 ∗ 𝟎. 𝟓(𝑭 𝟎 + 𝑭𝟏)
Textiles, textile

Mining and quarrying 5379.91 Food products,

1 products, leather
and footwear
Food products,
2 beverages andtobaccobeverages and tobacco11363.68

2691.46
Coke, refined

1967.87 Construction

petroleum products
8759.41 and nuclear fuel1981.29

20

Coke, refinedConstruction

1426.27 Fabricated metal

3 petroleum products
and nuclear fuel4

products
7475.96

Mining andquarrying

1011.27
Financial

460.56 Manufacturing;6668.34 intermediation

recycling

Renting of

Other community,

5 machinery andsocial and personalequipment

1001.68442.74 Basic metals

6200.10 services

808.50

Final demand change = ∆𝑭 ∗ 𝟎. 𝟓(𝑩𝟎 + 𝑩𝟏 )Agriculture,

Agriculture, hunting,

Agriculture, hunting,

1 hunting, forestry

forestry and fishingforestry and fishingand fishing37872.66

Food products,
2 beverages andtobacco4 Construction
Machinery and5 equipment

Wholesale and retailtrade; repairs

trade; repairs
16423.73

Food

3 products, leather

8143.92

Wholesale and retail31957.38

Textiles, textile

and footwear

24347.67

products,

6479.96
Food products,

beverages and tobacco

beverages and tobacco 5433.03

26421.32

16052.78

12143.69 Construction

14550.23 Construction

Textiles,textileproducts, leather and11528.08 footwear

65106.61

Textiles, textile
products, leather and9904.127 footwear

4510.79

21

Table 5: Top 5 sectors with the greatest change in final demand in terms of the change in
consumption, investment and exports in the period 2000-2015(unit: millions of US dollars)No.

𝟎. 𝟓 ∗ (𝑩𝟎 + 𝑩𝟏 )∆𝑪

𝟎. 𝟓 ∗ (𝑩𝟎 + 𝑩𝟏 )∆𝑰

𝟎. 𝟓 ∗ (𝑩𝟎 + 𝑩𝟏 )∆𝑬𝑿

2015-2010
Textiles, textile

Agriculture,1

hunting, forestry 23876.06 Construction

8261.20and fishing

beverages andleather and

23003.43

footwearFood products,
2

products,

Electrical
15109.74 machinery andtobaccoFood products,
1315.18

apparatus

beverages and16731.11

tobaccoAgriculture,

3

Computer andrelated activities

9719.67

Manufacturing;
recycling

1215.03hunting,
forestry and

13478.94

fishing

4

Transport andstorage

Motor vehicles,
8077.21trailers and1094.89semi-trailers
Other non-5

Education

6349.82

metallic mineralproducts

928.99Machinery andequipment

Manufacturing;
recycling10223.27

7180.30

22

2010-2005
Agriculture,1

hunting, forestry
and fishing

Construction
12463.21

Textiles, textile
13085.11

products,
leather and10615.84

footwear2

Food products,

Other non-

Agriculture,

beverages andmetallic mineral

hunting,

tobacco8064.93

products

1945.44

forestry and10484.43

fishing
Wholesale and345retail trade;

Wholesale and5621.18

retail trade;

Wholesale and1803.90

retail trade;

repairs

repairs

repairs

Hotels andAgriculture,

Food products,

hunting, forestry 1400.01

beverages andand fishingtobaccoFinancial

Mining andComputer,intermediation

quarrying

Electronic andrestaurants

3399.752605.391306.03

optical8998.637679.114784.90

equipment
2005-2000
Agriculture,1

hunting, forestry
and fishing

Construction
4913.70

Textiles, textile
4741.17

products,
leather and4215.01

footwear23

Food products,2

beverages andOther transport
2997.04

equipment

Mining and2239.61

quarrying4139.39

tobaccoWholesale and3retail trade;
repairs

Motor vehicles,
2039.92

trailers andsemi-trailers

Agriculture,
996.04

hunting,
forestry and3737.31

fishing
Chemicals and45chemical

Wholesale and1594.10retail trade;

Wholesale and747.91

retail trade;

products

repairs

repairs

Real estate

Other non-

Food products,

activities

1227.69

metallic mineralproducts

747.78beverages and3692.122512.49

tobacco2.5. Discussion
The study’s results show that the agriculture sector remained the most important in
Vietnam’s economy over the 2000-2015 period. Since the Doi Moi policy reform began in
the 1980s, the agricultural sector has experienced substantial reform. According to Decree
No. 10 of April 1988, household-based farming replaced collective farming activities (Tarp
Finn, 2017). Although agricultural cooperatives have achieved benefits in terms ofimproving rural infrastructure in rural areas, this collective model was not successful atproviding equal income for its members (Pham Quang Dieu, 2006). Thus, the new policies
introduced under the agricultural reforms have provided farmers with the authority to
manage their own farms and production. In addition, in the 1990s, the Vietnamese
government implemented several agricultural reforms to reduce government control over24

agricultural production, for example reduced restrictions on input trading, free domestic
trade in rice, increased export quotas and reduced agricultural taxes (Tarp Finn, 2017). As aconsequence, farmers had more access to production inputs and were able to sell theirproducts freely. This might be explained by the considerable change in the agriculture sector
not only in consumption but also in exports during the years 2000-2005 after these policy
reforms in the agricultural sector.
Under Decree 5 of the 4th Party Congress, the Vietnamese government proposed atarget to transform the “agricultural economic structure towards large commodity productionthat connects to processing industry with the market”. Undoubtedly, agricultural activities
remain a substantial role in the food supply chain in Vietnam. The agricultural sector
provides significant inputs for food processing enterprises. From the data in four IO tables,
we found that the agriculture sector sells its products mainly to the food products, beverages
and the tobacco sector (2015 – 61%, 2010 – 46%, 2005 – 46%, 2000 – 38%). Additionally,
the food sector mostly purchases inputs from the agriculture sector (approximately 50%).
Regarding the rapid growth of Vietnam’s textile sector, the upward movement
might be attributed, at least in part, to free trade agreements. During the period 2000-2015,
the textile sector in Vietnam was one of the fastest growing industries and main world
exporters, with annual growth of approximately 6% (WTO, 2016). Pertiwi and Sukmawani(2017) noted that Vietnam is in the second main provider of textiles and garments for theUnited States, the European Union and Japan. Furthermore, before independence in 1975,
the textile and garment industries were fully controlled by the Vietnamese government, andthe products were exported mainly to the Soviet Union (A.N. Tran, 1996). After
independence, most textile firms were self-controlled rather than by government. In 2000,
Vietnam signed a bilateral free trade agreement with the US, which significantly increased
the export of textile and garment products to the world market. In addition, after joining the25

WTO in 2007, Vietnam encountered more open markets in countries around the world , suchas the United States, the European Union and China. This membership has provided
numerous benefits to the textile and garment industry (CIEM, 2010). After WTO accession,
Vietnam also signed several trade agreements with Australia, South Korea, and Japan underthe ASEAN free trade framework. These free trade agreements significantly contributed toincreasing the exports of the Vietnamese textile industry, as the results of the present study
shows. These abovementioned sectors are also expected to benefit from recent signings of
new trade agreements, such as the EU-Vietnam free trade Agreement (EUVFTA) and TransPacific Partnership (TTP).

In Vietnam, some corporations owned by the state government continue to exercise
considerable control over certain industries. For example, the Vietnam Coal and Minerals
Industries Corporation (Vinacomin) dominates the Vietnamese mining industry, theVietnam Tobacco Corporation (VINATABA) dominates tobacco products, and the Vietnam
Food Association (Vinafood 1 and Vinafood2) dominates Vietnamese rice products (asshown in Table 6). However, some enterprises in the textile industry are becoming lessreliant on the government (for example, Vinatex and Garco10 corporation). Therefore,challenges remain regarding the development of particular sectors under free trade. Table 6
below provides some examples of the large companies that belong to the aforementioned
sectors in the Vietnamese market.

In summary, our study results are in line with previous studies on changes inVietnam’s economic structure by Dang et al. (2019) and Ha and Trinh (2018). By using two
Vietnamese IO tables, 2012 and 2016, Ha and Trinh (2918) showed that the agriculture
sector, food sector, oil and gas production sector and other manufacturing industries have a26

large impact and meaningful impact on the input demand of other industries in the economy.
In addition, despite the shift in the economic structure in recent decades, the agriculture
sector and food sector remained important industries in Vietnam’s economy over the period
2000-2015. However, it seems that challenges persist in the development of Vietnam’s
economy, as is the case in other developing countries. When a primary industry, such as
agriculture, dominates the economy, a country’s economic development might be vulnerable
because the primary sector typically exhibits decreasing returns to scale (DRS). New
technology or innovation is required to develop products; otherwise, it is difficult to expand
production. Therefore, primary sectors, such as agriculture, can be a main reason thatcountries remain poor (Chang, 2003 and Reinert, 2007). In conclusion, another processing
and manufacturing industry, such as machinery or textiles, might develop rapidly andbecome a leading industry in subsequent years.

27

Table 6: Some representatives large enterprises in the selected industries in Vietnam
Table 6: Some representative large enterprises in selected industries in Vietnam

Textiles, textile products, leather and footwear
Textile industry

Century Synthetic Fiber Corporation

Garment Industry

Footwear

Vietnam National Textile and Garment

Pou Yuen Vietnam Limited LiabilityGroup (VINATEX)

Company

Duc Quan Investment and Development Joint
Stock Company

TaeKwan Vina Industrial Limited
Viet Tien Garment Corporation

Liability Company
Hwaseung Vina Limited Liability

Dam San Joint Stock Company

Garco 10 Corporation

Company
Chang Shin Viet Nam Limited Liability

Phu Bai Spinning Mill Joint Stock Company

Phong Phu Corporation

Phong Phu Corporation

Company

Hoa Tho Textile Garment Joint Stock

Pou Sung Vietnam Limited Liability

Company

Company

Food products

TobaccoFood products, beverages and tobacco
Beverages

28Vietnam Tobacco Corporation (Vinataba)
Saigon Beer-Alcohol-Beverage Corporation

Acecook Viet Nam Joint Stock

Saigon Tobacco Limited Company(Sabeco)Company(Vinataba)Heineken Vietnam

Vinh Hoan Joint Stock Company

Vinataba Thang Long

Hanoi Beer, Alcohol and Beverage

Bien Dong Seafood Limited Liability

Corporation (Habeco)

Company

Hanoi Liquor Joint Stock Company

Minh Phu Seafood Corporation
VIETNAM AGRIBUSINESS Limited

Binh Tay Wine Joint Stock Company

liability Company

Agriculture, hunting, forestry and fishing
Rice products

Fishery and aquaculture

Wood

Southern Food Corporation – VINAFOOD II

Minh Phu Corporation – MPC

Hoa Net Limited liability company

Northern Food Corporation – VINAFOOD I

Vinh Hoan Corporation– VHC

Nitori Furniture Vietnam

Bien Dong Seafood Limited Liability

An Cuong Woodworking materials

Tân Thạnh An Limited Liability Company

Company

Kien Giang Import and Export Joint Stock

International Development andShing Mark Vina Limited liability

Company

Corporation

Company29

Tan Dong Tien Joint Stock Company

Hung Vuong Corporation – HVG

DONGWHA Corporation

Steel

Automobile

Agricultural Machinery

Hoa Phat Joint Stock Company

Thaco Group

Kubota Tractor Corporation

Machinery and equipment

Gang Thép Thái Nguyên Joint Stock
Company

ToyotaCLAAS KGaA GmbH
Vietnam Engine and Agricultural

VISCO Joint Stock Company

Honda

Machinery Corporation (VEAM)

Dana Joint Stock Company

Ford

Thaco Corporation

Viet Duc Joint Stock Company

GM Vietnam

Truong Hai Auto Corporation (THACO)

Mining and quarrying
Major Mineral Producers
Vinacomin
Masan resources Nui Phao
Thach Khe (Vinacomin)
Co Dinh (Vinacomin)
Ta Phoi (Vinacomin)

30Source:https://www.mordorintelligence.com/industry-reports/vietnamese-agricultural-machinery-market
https://www.statista.com/statistics/976550/vietnam-automobile-manufacturers-market-share/
https://sesprofessionals.com/overview-of-vietnams-mining-industry/
https://viracresearch.com/industry/bao-cao-chuyen-sau-nganh-det-may-viet-nam-q1-201931

2.6. Conclusions
In developing countries, introducing or identifying leading industries is essential
for poverty reduction and economic development. For example, the IT industry in the UnitedStates, the automobile industry in Japan and the electronic equipment industry in South
Korea are leading industries in these countries. Vietnam has grown rapidly over the past 15
years but remains a developing country. Our aim in this paper was to identify which
industries are the main drivers of Vietnamese economic development by applying thedecomposition method to a series of IO tables covering 15 years (2000-2015).
We found that as total output changed, food products and the agricultural sector
remained in high positions, representing the top 5 of the 34 sectors considered from 2000 to2015, which indicates that primary industry and closely related industries, such as food andbeverages, still support the Vietnamese economy. As main manufacturing industries, textiles
and mining were also relatively large producers over the period considered. Remarkably, themachinery sector grew rapidly over the last 5 years of the period considered, which showsthat this sector might be a new driver of the Vietnamese economy in the future.
According to the backward and forward linkage effects obtained from the Leontief
inverse of the 2015 table, food products and the agricultural sector have backward andforward effects that are both larger than 1, which means that the outputs of these sectors
were frequently demanded by other sectors and also affected many other sectors by buying
intermediate goods. These two sectors are also central in terms of linkage effects.
Additionally, food products represented the most influential sector in terms of backward
linkage effects in the 2010, 2005 and 2000 tables, and the agricultural sector was the mostdemanded in terms of forward linkage effects in these three years.

32

By performing a decomposition across periods, we found that the large total outputsof food products and the agricultural sector were fundamentally attributable to changes infinal consumption demand. In addition, the higher position of textiles in the Vietnamese
economy was due the increase in final export demand. On the other hand, the machinery
sector grew rapidly over the last 5 years of the period considered, which derived from not
only from changes in final investment and export demand but also from technological change
in intermediate inputs. By contrast, the wholesale and retail sector experienced a drastic
decline in its final consumption, investment, and export demand over the last 5 years
considered.
As mentioned above, we conclude that food products and agricultural sectors
remain key industries in the Vietnamese economy. However, this finding has an adverse
economic policy implication. The result of mainly relying on a primary industry such as
agriculture is that the country remains vulnerable in terms of economic development because
the primary sector typically exhibits DRS. Many developing countries demonstrate that a
reliance on the primary sector can be a cause of remaining poor since it faces serious
limitations in terms of product expansion unless new technological change or productinnovations/inventions emerge 1 . Therefore, the development of another manufacturingindustry such as machinery, textiles or another sector should be pursued to further economic
development in Vietnam.

1

See Chang(2003) and Reinert (2007) on the historical and structural causes of poverty.

33

CHAPTER 3: THE LINK BETWEEN FINANCIAL LEVERAGE ANDINVESTMENT DECISIONS IN VIETNAM’S SMALL AND MEDIUM-SIZEDENTERPRISES

3.1. Introduction
The relationship between the capital structure and investment decisions of enterprises is amajor area of interest in the field of corporate finance. Numerous researchers have examinedthe determinants of investment decisions in both theoretical and empirical studies. For
example, the influencing macroeconomic factors include the real exchange rate, inflationand capital flows (Binding and Dibuasu 2017; Atella 2003; Chen Fei et al 2019).
Furthermore, several attempts have been made to determine the impact of firm-level factors,
including accounting quality, financing constraints, management characteristics and thecapital structure, on investment decisions (Myers 1977; Lang et al. 1996; Gomes 2001;
H.T.Trinh et al. 2017; Xuan Vinh Vo 2018; Shu-Miao & Chih-Liang 2017; Sang-Min Cho& Sun-A Kang 2017).
Financial leverage is measured as the ratio of total debt to total assets. Basically, thegreater the amount of debt, the greater the financial leverage. Hence, financial leverage isviewed as an important corporate investment policy of a company in an incomplete market
due to transaction costs and asymmetric information (Aivazian et al. 2005). This study
investigates the link between the capital structure as measured by financial leverage andinvestment decisions in Vietnam SMEs by using the comprehensive unbalanced panel data
set of Vietnamese SMEs surveyed during the period 2011-2015. Econometrics techniques,
such as Logit, Tobit and Fractional logit models, are applied to analyze the data. The major

34

obtained outcomes are used to scrutinize the correlation between financial leverage andinvestment decisions and the choice of financing sources.
Financial leverage has been considered a measurement of the capital structure, andits influence on investment decisions is a critical issue in corporate finance (H.T. Trinh et al.
2017). Existing financial theories propose that financial leverage is either not relevant(Modigliani & Miller 1958) or negatively related to firm’s investment (Myers 1977; Lang etal. 1996; Aivazian et al. 2005 and Gome, 2001). According to the capital structure theory,
Modigliani & Miller (1958) noted that in a perfect market, investment behavior is irrelevantto the capital structure of a company. These authors concluded that high leverage or a lower
debt component has no bearing on a firm’s market value. Instead, the market value of a firm
depends on the company’s profitability, cash flow and net worth. However, the corporate
world is characterized by many market imperfections due to moral hazards and information
asymmetry (Jensen 1986; Lang et al. 1991l; Myers and Majluf 198). In addition, the original
version of Modigliani & Miller’s theory did not include the important elements consideredby firms in investment decisions, including taxes, transaction cost and bankruptcy cost
(Frank & Goyal 2009). Since the introduction of Modigliani & Miller’s work, numerous
theories have been proposed, including the trade off theory (Myers 1984) and the peaking
order theories (Jensen & Meckling 1976; Ross 1977; Myers & Majluf 1984). These theories
oppose Modigliani & Miller’s theory and suggest that companies have their own preference
for different types of financing. Myers (1984) argued that companies trade off the tax
benefits of debt against the cost of debt (probability of bankruptcy).
The empirical literature has challenged the leverage irrelevance theory and supports
the peaking order theories proposing that firms prioritize their financing source according to
the cost of financing following asymmetric information. For example, Lang et al. (1996)
argued that firms with a higher debt ratio are less likely to exploit growth opportunities than35

those with a low debt ratio; therefore, firms with a large debt tend to invest less despite thefirms’ growth opportunities. Another facet of the leverage and investment relationship isreported by Aivazian (2005), who examined the impact of financial leverage on investment
decisions in Canadian publicly traded companies. Aivazian found that leverage has asignificant negative impact on investment in low growth opportunity firms. However, theseresults were based on data from mostly developed countries, where firms have moreaccessibility to different sources of financing. Whether these findings apply to developing
countries remains unclear.
An empirical study investigating Vietnamese SMEs listed on the Ho Chi Minh Stock
Exchange (Vietstock-South Vietnam) during the period 2006-2015 conducted by Xuan Vinh
Vo (2018) indicated that debt strongly restricts corporate investment. Similarly, Phan Q. T
(2018) tested the relationship between firm investment and debt financing using data fromboth the Ho Chi Minh Stock Exchange and Ha Noi Stock Exchange from 2010 to 2016. Phan(2018) revealed that the level of debt significantly negatively impacted firm investment.
Thus, higher debt in the capital structure is associated with lower investment. Nevertheless,these studies have focused on relatively large sized listed firms. Thus, limited attention has
been paid to small and medium-sized firms in Vietnam.
Our main findings are summarized as follows. First, we find a positive relationshipbetween financial leverage and investment decisions in Vietnam SMEs; this relationship isconsistent with a previous study performed by H.T Trinh in 2018. Second, the results revealthat a high level of financial leverage increases access to external financing sources for new
investment. The possible explanation for our findings is that Vietnam SMEs usually haveinsufficient internal financing sources and limited access to external financing sources. Most
external financing sources involve bank lending. Therefore, firms with high financial
leverage could prove that they already have access to external financing sources. Thus, firms
36with high financial leverage prevail over firms with low financial leverage because they caneasily obtain credit for new investment. This study contributes to the existing knowledge
regarding the relationship between corporate finance and investment decisions in the context
of emerging markets. Our findings provide potential suggestions to both SMEs and policy
makers for improving credit accessibility by enhancing strategic planning, diversifying
funding sources and reducing information asymmetry between SMEs and credit institutions.
The remainder of the paper is organized as follows: Section 2 introduces SMEs in
Vietnam. Section 3 presents the data and empirical methodology. Section 4 provides theempirical results and a discussion of these results. Section 5 provides some concluding
remarks.

3.2. Data and Methods
3.2.1. Data
To investigate the relationship between financial leverage and investment decisions, we relyon quantitative SME surveys conducted in 2011, 2013 and 2015 by the Central Institute for
Economic Management (CIEM), the Institute of Labour Science and Social Affairs (ILSSA),
the Development Economics Research Group (DERG) at the University of Copenhagen andthe United Nations University World Institute for Development Economics Research (UNUWIDER). The survey covered over 2500 enterprises in 9 provinces in Vietnam, including
Ho Chi Minh City, Long An, Khanh Hoa, Lam Dong, Nghe An, Quang Nam, Hanoi, Hai
Phong and Phu Tho (UNU-WIDER, 2018). The survey questionnaire remained roughly
unchanged in 2011 and 2013 and included 132 questions related to enterprise characteristics,
employment, operations, cost and revenue, productions, credit and loans, and environment
costs. In 2015, a slight change was made in the structure of the questions pertaining to credit
and finance. The questionnaire covered information regarding firm characteristics and37performance, such as owner characteristics, size of the workforce, revenues and costs, inputs,
economics constraints and investment.
The enterprises surveyed are distributed in 18 sectors, such as food processing,fabricated metal products and manufacturing of wood products. The firms were selectedbased on data sources from the General Statistics Office of Vietnam (GSO). The included
firms were private, collective, limited liability or joint stock enterprises and partnerships
formally registered under the Law of Enterprises at the province level. The sampling of thesurvey is based on the stratified sampling technique to ensure the inclusion of all types ofenterprises in each province.
For the analysis in this paper, we use an unbalanced sample of 6057 microenterprises. The first survey round was conducted in 2011 and consists of approximately
2512 enterprises. The second round was conducted in 2013 and consists of 2542 enterprises.
The final survey round was conducted in 2015 and consists of 2648 enterprises. Notably,some variables were not used, and some observations were excluded due to missing
information. After reviewing the dataset, the total number of enterprises is 6057.
A summary of the statistics of the survey data and the variables used in our study isprovided in Table 7. Our main variable of interest is financial leverage, which describes theratio of debt to total assets of the enterprises in the previous survey round. Furthermore, we
included other variables of which might influence to firm investment behavior, such as thesize of the firms, revenue growth, profitability, physical assets and ownership, in all
estimation as following previous studies (H.T. Trinh et al. 2017; Dang 2011). These firmlevel variables such as total asset (SIZE), physical asset (FIXED) may illustrate as collateraland capture the capability of borrowing from financial institutions. In addition, the variables
that represented firm growth potential (GRR) and profitability (GROPF) are chosen because
these variables have considered as an important determinant of investment decision and the38choice of financing source. The ownership status (OWN) also included in our estimation as
it might be influenced to the management’s decision if the firm is family owned.
Table 7: Definition and summary statistics of the variables

Code

Variables

Description/Calculation method

Number of

Mean

SD

6,057

0.54180.4982

3,282

0.412

0.4466

3,282

0.58510.4473

3,282

0.192

0.3462

Observations

Dependent variable
Dummy
INV

variable

for

new

New

investment (=1 if the firm madeinvestment

a new investment during thepast two years; 0 otherwise)
Share

EXT

External
financeofexternal

financialsources for new investment
financed by bank loans andothersourcesthatchargeinterest
Share
INT

of

internal

financing

Internal

sources or borrowing fromfinancefamilyandfriends without

interest
Share

FIV

Internal
financeofinternal

financingsources for new investment
financed by borrowing fromfamilyandfriends without

interest

39

RETE

Internal

Share

of

internal

financingsources for new investment

finance

3,282

0.393

0.44

6,057

0.07140.17976,057

7.17181.7712

6,057-0.01590.27636,057

5.36141.4506

6,057

1.412427.1834

6,057

0.62170.48496,057

0.43780.4961

financed by retained earnings

Independent variables

LEV

SIZE

Financial

Ratio of total debt to total assets
at the end of the year of theleverage

Size

previous survey roundLog of the total assets at the endof the year of the previous round
GrowthGRRofrevenue=log

of

Revenuerevenue in the second year

growthminus the log of revenue in thefirst round of the survey
Log of gross profit/revenue at the

PROF

Profitability

end of the year of the previous
survey round
Ratio of physical assets (such as

FIXED

Physicalplants and machinery) to total

asset ratio

assets at the end of the year of
the previous survey round

OWN

ND

Ownership

LOCATION

1

iffamilyownership;0otherwise
1 if the enterprise is located in
North Vietnam; 0 otherwise

40

CD

SD

LOCATIONLOCATION

1 if the enterprise is located inCentral Vietnam; 0 otherwise
1 if the enterprise is located inSouth Vietnam; 0 otherwise

6,057

0.2081

0.406

6,057

0.35390.4782The table 7 shows that 54% of the SMEs made new investments within three years.
The ratio of the external financing sources was 41%, whereas the ratio of the internalfinancing sources was 59% (including borrowing from family and friends and retained
earnings). However, a slight difference was observed in the ratio of financing sources among
the three periods (Refer to Appendix 1). In the survey conducted in 2011, the major source
used for investment was internal financial sources (including retained earnings andborrowing from family and friends), which accounted for 62% of the total, while the externalfinancing sources accounted for 36% of the total. In the survey conducted in 2013, a slightchange in this ratio was observed such that most enterprises invested using an externalfinancing source (51%). In the survey conducted in 2015, using internal financing sources
for investment outweighed the use of external financing sources by approximately 20%.
These figures imply that enterprises might have preferred investing by using their own
capital rather than bank loans.

3.2.2. Methodology
Our study has the following two main research objectives: (1) determine the impact offinancial leverage on the investment decisions of Vietnam SMEs and (2) determine the effectof financial leverage on the financing sources used for new investment.

41During the first stage of the estimation, we attempt to evaluate the effect of financial
leverage on the investment decisions of SMEs by estimating the following equation:

𝐾∞

𝐼𝑁𝑉𝑖,𝑡 = 𝛼0 + 𝛼1 𝐿𝐸𝑉𝑖,𝑡−1 + ∑ 𝛾𝑘 𝑋𝑘,𝑖 + 𝜀𝑖
𝑘=1

(1)

where 𝐼𝑁𝑉𝑖,𝑡 is a dependent variable referring to SME i implementing a newinvestment during given survey round t and zero otherwise; 𝐿𝐸𝑉𝑖,𝑡−1 is financial leverage or
the debt ratio, which is calculated as the ratio of total debt to total assets at the end of the
𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑏𝑡

previous year (𝐿𝐸𝑉𝑖,𝑡−1 = 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑒𝑡 𝑖,𝑡−1 ); 𝑋𝑘,𝑖 refers to a vector of other control variables
𝑖,𝑡−1

expected to affect the decision to invest; and 𝜀𝑖 is the error term.
To estimate equation (1) in the first step of the analysis, we apply a logit model
because the dependent variable is binary. We follow previous studies (Hall et al. 2000; H.T.
Trinh et al. 2017; Dang 2011) and include the various characteristics of the enterprises, suchas revenue growth, profitability, ownership, physical asset ratio and location, as explanatory
variables for SME investment decisions.
We are interested in the coefficient of LEV in the model as it measures the effect offinancial leverage on the decision of the enterprise to invest. Regarding the other explanatory
variables, SIZE is calculated as the log of the total assets at the end of the year of the previous
round. Furthermore, GROWTH captures the degree of revenue growth, which is calculated
as the log of the revenue in the second year minus the log of the revenue in the first year of
the survey round, and PROF represents the profitability of the firm. In addition, FIXED
represents the ratio of physical assets to total assets, and OWN indicates whether theenterprise is owned by a family. Finally, ND, CD, and SD represent location dummies
indicating North Vietnam, Central Vietnam and South Vietnam.
42

The second estimation in this study focuses on the choice of financing sources andhow it is influenced by financial leverage under the condition that SMEs decide to implement
a new investment in the first stage. As previously mentioned, financial leverage is an
important indicator in SMEs considering financing sources for investment. However,Freeman and Le (2007) noted that Vietnam SMEs usually rely on a small number of internalfunding sources due to their low profit and small business scale. Furthermore, a highfinancial asset is immensely acknowledged if it is considered an indication that the SME isable to borrow in credit markets. Thus, such SMEs could obtain extra credit from financial
institutions, such as banks. Hence, our study expects financial leverage to be positively
correlated with the share of external financing sources and negatively correlated with theshare of internal financing sources.
To examine the impacts of financial leverage on the choice of financing source, we
use the following equation:𝐾∞

𝐹𝑈𝑁𝐷𝑖,𝑡 = 𝛽0 + 𝛽1 𝐿𝐸𝑉𝑖,𝑡−1, + ∑ 𝛾𝑘 𝑋𝑘,𝑖 + 𝜀𝑖
𝑘=1

(2)

where 𝐹𝑈𝑁𝐷𝑖,𝑡 is a dependent variable referring to the financing source. We
separately estimate external (EXT) and internal (INT) sources as dependent variables. 𝜀𝑖 isthe error term with standard properties. The other variables in equation 2 are the same as
those in equation 1, but we excluded the location dummy variable.
In equation (2) in the second step, we first apply a Tobit model with an upper boundof 1 and a lower bound of 0 across the sample of SMEs that implemented a new investmentin the first step because the values of the dependent variable range from 0-1. However,notably, our dependent variables represent proportional data, which, by definition, arebounded between 0 and 1. In this case, the effect of the explanatory variables tends to be
nonlinear, and the variance tends to decrease as the mean approaches 0 or 1. To mitigate
43

these problems, we also apply the following nonlinear regression model: the fractional logit
mode.

3.4. Results and discussion
The results of our study regarding the decision to invest and the choice of financing
sources are shown in Tables 8-13.
Investment decisions
Table 8: Investment decisions of SMEs in Vietnam (2011-2015)
INV

2011-2015
Logit estimation

LEV

SIZE

GRR

GRPOF

FIXED

OWN

ND

6.6081***

6.4150***6.0918***(0.7647)(0.0227)(0.7408)0.02400.03790.0663***(0.0230)(0.0227)(0.0234)

0.4453***0.3799***0.4401***(0.1384)(0.1286)(0.1414)0.1626***0.1600***0.2057***(0.0302)(0.0300)(0.0310)-0.0009*-0.0011**-0.0009*(0.0005)(0.0005)(0.0005)-0.1277*-0.115*-0.1253*(0.0724)(0.0720)(0.0733)

0.6043***(0.0548)44

CD

0.2206***(0.0713)SD-0.8806***(0.0625)_cons

Numberofobservations-1.3703***-1.1832***-1.2993(0.1878)(0.1869)(0.1865)

6057

6057

6057

Note: Values reported in parentheses are the robust standard errors (SE);*,**, and *** indicate significance at the 10%, 5% and 1% levels, respectively.

45

Table 9: Investment decisions of SMEs in Vietnam across three survey rounds (2011, 2013, and 2015) (cont.)
2011

2013

2015

Variables
LEV

SIZE

GRR

GRPOF

FIXED

OWN

6.097***

6.062***

5.588***

2.784***

2.648***

2.549***

11.230***10.826***

10.589***(1.18)(1.163)(1.127)(1.082)(1.072)(1.044)(1.539)(1.565)(1.515)-0.0010.01

0.026**

0.076

0.047

0.107*-0.059*-0.0250.002(0.037)(0.036)(0.037)(0.065)(0.064)(0.065)(0.034)(0.035)(0.035)0.040**

0.357*

0.444**

0.467

0.4

0.338

0.569***

0.498***

0.564***(0.186)(0.183)(0.19)(0.313)(0.312)(0.312)(0.27)(0.242)(0.276)0.218***

0.216***

0.168***

0.135***.183***

0.206**

0.181***

0.157***

0.209***(0.049)(0.05)(0.051)(0.073)(0.072)(0.074)(0.047)(0.046)(0.486)

0.006

0.034

0.003

0.809***.768**

0.928***-0.001*-0.001***-0.001(0.07)(0.095)(0.059)(0.327)(0.326)(0.334)(0.112)(0.0005)(0.0005)-0.071-0.043-0.0480.008

0.0001

0.034-0.249*-0.305***-0.269***(0.114)(0.113)(0.1158)(0.183)(0.18)(0.18)(0.115)(0.112)(0.114)46

ND

0.808***.703***

0.379***(0.088)(0.132)(0.086)CD

0.022-0.194

0.593***(0.112)(0.155)(0.116)SD_cons

No. Obs-0.9466***-0.710***-0.874***(0.098)(0.147)(0.1)-1.473***-1.256***-1.429-2.145***-1.948***-2.156***-0.974***-0.976***-1.045***(0.296)(0.305)(0.299)(0.587)(0.577)(0.579)(0.297)(0.287)(0.292)

6057

6057

6057

6057

6057

6057

6057

6057

6057

Note: Robust standard errors are shown in parentheses. *** represents significance at the 1% level. ** represents significance at the 5% level.
* represents significance at the 10% level.

47

The results presented in Table 8 and 9 show that there was a significant positive
correlation between financial leverage (LEV) and investment decisions, suggesting that
SMEs with high financial leverage tend to make new investments. Thus, for every one-unitchange in LEV, the log odd for seeking new investments increased by 7.56 (2011-2015).
Although this result contradicts finance theory (Myers 1977; Jensen 1986) and otherempirical evidence (Lang et al. 1986, Aivazian et al. 2005), it is consistent with a previous
study performed by H T Trinh (2017). This result may be explained by the fact that Vietnam
SMEs have limited internal financing sources and, thus, depend on external financing
sources. However, these firms experience difficulty in obtaining external financing sources
because of the lack of collateral or asymmetric information. Therefore, financial leveragecan be considered the prior credit history of the enterprise. addition, high financial leverage
implies that the firms gained the trust of previous credit institutions by their borrowing
capability. Thus, SMEs with higher levels of financial leverage are likely to make newinvestments.
The other positive and significant variables include revenue growth (GRR) andprofitability (GRPROF). The positive relationship between revenue growth and investment
decisions suggests that SMEs that have high revenue growth tend to invest more, although
this result is less clear in 2013. In addition, the coefficient of SME’s profitability issignificantly positive, suggesting that profitable firms invest more in fixed assets. There isno clear evidence regarding the correlation between the total asset (SIZE) of SMEs andinvestment decisions; however, we found strong evidence of this correlation after adding thelocation dummy variable in the estimation. Thus, the total assets variable is strongly
associated with investment decisions of SMEs located in South Vietnam.

48

The results also indicated that the fixed asset ratio (FIXED) is negatively related toinvestment decisions, contradicting a previous study (H.T.Trinh et al., 2017); however, thisresult is less clear in 2011. Thus, the collateral required by credit institutions for borrowing
is negatively associated with investment decisions. A negative correlation was foundbetween ownership (OWN) and investment decisions of firms. Clearly, SMEs owned byhouseholds have less incentive to seek new investments.
Our study is also interested in the different investment decisions made by enterprises
located in the following three regions of Vietnam: North Vietnam (including Ha Noi, Phu
Tho, and Hai Phong), Central Vietnam (including Quang Nam, Quang Ngai, and Nghe An)
and South Vietnam (including Ho Chi Minh City, Khanh Hoa, and Lam Dong). According
to the results, the estimated coefficient of ND is positive and significant; meanwhile, theestimated coefficient of SD is negative and significant. These results imply that firms locatedin North Vietnam are more likely to make new investments than the firms located in theother two regions.
Choice of financing sources
The second estimation in this study investigates how financial leverage affects whether afirm chooses an internal or external financing source in making new investments. Tables 1013 show the estimated results of the Tobit and fractional logit mod els of external financing
sources (EXT) and internal financing sources (INT) for the surveys conducted in 2011, 2013,
and 2015 and the full sample.
The results show that financial leverage (LEV) has a positive and significant impact
on external financing sources (EXT) and a negative and significant impact on internalfinancing sources (INT) in both models. Generally, LEV can represent the accessibility offirms to external financing sources, such as banks or credit institutions. Therefore, SMEs

49

with higher LEV tend to use external financial sources (banks or credit institutions) to fundtheir new investments, while SMEs with lower LEV usually rely on internal financing
sources. Our results highlight that a high LEV might help improve accessibility to externalfinancial sources.
There are two possible scenarios in which financial leverage impacts the choice offinancing sources of SMEs. On the one hand, credit institutions are unwilling to provide
extra loans to SMEs with high financial leverage if they consider the firms high financial
risk. Thus, these SMEs are less encouraged to acquire additional credit. On the other hand,
it is probable that high financial leverage SMEs acquire external financing sources, while
SMEs with lower leverage must depend on internal financing sources. Our results prove thatthe second scenario predominates the first scenario, supporting the pecking order theory
proposed by Myers and Majluf (1984). According to their theory, when enterprises consider
their sources of financing, internal sources are always considered first, followed by debt andequity. Hence, SMEs tend to use internal financing first for investment; when such sources
are depleted, debt is issued; and when it is no longer sensible to issue any more debt, equity
is issued.

Regarding the other factors, the results show that there is a positive and significant
relationship between the total assets (SIZE) and external financing sources and a negativeand significant relationship between the total assets and internal financing sources in bothmodels; this outcome is consistent with previous studies (Bhaird and Lucey 2010; Sogord Mira 2005). Generally, the total assets can be considered collateral for firms. Owning morecollateral is expected to improve the chances of receiving credit from banks Therefore, larger
firms are more likely to use external financing, while smaller firms rely on internal financing
sources.
50

Surprisingly, the coefficient of profitability (PROF) is significantly positively
associated with the choice of external financing sources and negatively associated with the
choice of internal financing sources in the full sample and the survey in 2011, contradicting
the pecking order theory and previous studies, but these relationships are not significant insurveys conducted in 2013 and 2015. Previous studies have revealed that there is a negative
relationship between the use of external financing and firm profitability. These studies also
show that when firms obtain higher profits, they are more likely to use internal financing
sources than external sources. In fact, our results suggest that firm profitability has asignificant impact on the choice of external financing sources because firms still rely on
external financing sources even if they make high profits. In addition, there is no clear
evidence supporting the relationship between revenue growth (GRR) or family ownership(OWN) and the choice of external financing sources as the coefficients of GRR and OWN
are statistically nonsignificant. However, there was a significant positive correlation between
revenue growth (GRR) and the choice of retained earnings and a significant negative
correlation with the choice of other finance sources (FIV) in the survey conducted in 2011
and 2015 and the full sample.

51

Table 10: The relationship between financial leverage and the choice of financing sources (2011-2015)
EXT

LEV

SIZE

GRR

OWN

RETE

FIV

Tobit

FlogitTobit

FlogitTobit

FlogitTobit

Flogit1.653***

2.776***-1.668***-2.818***-1.708***-4.187***

0.128

0.014

(0.156)(0.309)(0.156)(0.312)(0.152)(0.507)(0.148)(0.166)0.169***

0.189***-0.169***-0.190***-0.131***-0.170***(0.03)(0.031)(0.03)(0.031)(0.027)(0.031)(0.028)(0.037)-0.123-0.146

0.136

0.156

0.468***

0.591***-0.383***-0.604***(0.114)(0.123)(0.114)(0.124)(0.103)(0.135)(0.107)(0.182)0.084**-0.085**-0.083*

0.023

0.029

-0.146***-0.170***(0.034)(0.037)(0.034)(0.037)(0.031)(0.037)(0.033)(0.041)-0.004-0.005***0.004

0.005***

0.034

0.02

-0.089-0.128(0.006)(0.001)(0.007)(0.002)(0.05)(0.065)(0.061)(0.087)

0.01

0.066

-0.0062-0.0630.168**

0.103-0.268***-0.262**(0.084)(0.088)(0.084)(0.088)(0.076)(0.086)(0.081)(0.106)GRPOF 0.086***

FIXED

INT

-0.0001

0.033

52

_cons

-1.885***-2.570***2.864***

2.560***

1.052***

0.929***

0.309

0.003

(0.226)(0.233)(0.226)(0.233)(0.202)(0.239)(0.217)(0.275)

3282

3282

3282

3282

3282

3282

3282

No. Obs 3282

Note: Standard errors are shown in parentheses.
*** represents significance at the 1% significance level, ** represents significance at the 5% significance level, * represents significance atthe 10% significance level.

53

Table 11: The relationship between financial leverage and the choice of financing sources (2011)
EXT

INT

Tobit

FlogitTobit

Flogit2.150*** -1.137 ***-2.224***-1.168***-43.142***

0.172

0.192

(0.182)(0.351)(0.183)(0.358)(0.171)(0.486)(0.155)(0.187)0. 080**0. 119**-0.085**-0.126**-0.065*-0.1040**0.013-0.032(.040)(0.047)(0.040)(0.047)(0.034)(0.045)(0.034)(0.059)-0. 371**-0.482**0.378**

0.492**

0.412***

0.584***-0.089-0.174(0. 175)(0. 225)(0.176)(0.227)(0.145)(0.198)(0.137)(0.348)GRPOF 0. 112**

0.127**-0.106**-0.120**-0.034-0.058-0.093**-0.098(0. 047)(0. 058)(0.048)(0.057)(0.041)(0.056)(0.042)(0.064)0.063

0. 086-0.088-0.065-0.097-0.130

0.015

0.059

(0. 106)(0. 124)(0.113)(0.125)(0.096)(0.128)(0.061)(0.162)

0.090

0.205

-0.088-0.2020.004-0.114-0.222**-0.188(0. 113)(0.136)(0.113)(0.136)(0.096)(0.127)(0.098)(0.163)

SIZE

GRR

FIXED

OWN

Flogit1.110***

Tobit

FIV

Flogit

LEV

Tobit

RETE

54

_cons

-1. 521***(0.313)

No. Obs 1325-

2.495***

2.566***

1.322***

1.412***-0.085-0.798*(0.372)(0.314)(0.372)(0.260)(0.352)(0.264)(0.420)

1325

1325

1325

1325

1325

1325

1325

2.588***

Note: Standard errors are shown in parentheses.
*** represents significance at the 1% significance level, ** represents significance at the 5% significance level, * represents significance atthe 10% significance level.

55

Table 12: The relationship between financial leverage and the choice of financing sources (2013)
EXT

INT

RETE

FIV

Tobit

FlogitTobit

FlogitTobit

FlogitTobit

Flogit1.221***

2.352***-1.208***-2.328***-0.615***-2.158*-1.319***-1.325***(0.256)(0.776)(0.256)(0.772)(0.217)(1.232)(0.165)(0.491)0.221***

0.290***-0.210***-0.276***-0.139***-0.239***-0.137***-0.118(0.059)(0.079)(0.059)(0.079)(0.050)(0.081)(0.047)(0.085)

0.050

0.076

-0.062-0.094

0.200

0.389

0.043

-0.566**(0.203)(0.085)(0.203)(0.264)(0.173)(0.337)(0.162)(0.276)GRPOF 0.074

0.070-0.081-0.094

0.002

0.021

-0.041-0.128(0.062)(0.085)(0.062)(0.264)(0.054)(0.093)(0.051)(0.090)-0.095-0.168

0.123

0.209

0.119

0.067

0.117

0.207

(0.173)(0.258)(0.173)(0.243)(0.146)(0.306)(0.135)(0.244)

0.161

0.227

-0.151-0.215

0.112

0.184

0.107

-0.474**(0.152)(0.188)(0.152)(0.187)(0.130)(0.188)(0.122)(0.215)

LEV

SIZE

GRR

FIXED

OWN

56

_consNo.
Obs-1.871***-2.895***2.785***

2.781***

0.818**

0.776

1.243***

0.595(0.444)(0.558)(0.443)(0.548)(0.359)(0.580)(0.344)(0.600)

699

699

699

699

699

699

699

699

Note: Standard errors are shown in parentheses.
*** represents significance at the 1% significance level, ** represents significance at the 5% significance level, * represents significance atthe 10% significance level.

57

Table 13: The relationship between financial leverage and the choice of financing sources (2015)
EXT

INT

RETE

FIV

Tobit

FlogitTobit

FlogitTobit

FlogitTobit

Flogit4.253***

4.302***-4.251***-4.297***-6.961***-7.695***

1.048**

0.604(0.523)(0.579)(0.523)(0.579)(0.716)(1.045)(0.466)(0.400)0.223***

0.186***-0.219***-0.186***-0.196***-0.183***-0.010-0.004(0.065)(0.054)(0.065)(0.054)(0.063)(0.055)(0.068)(0.061)

0.208

0.136

-0.188-0.1340.642***

0.593***-0.845***-1.013 ***(0.250)(0.197)(0.250)(0.197)(0.242)(0.218)(0.267)(0.312)GRPOF 0.069

0.059-0.072-0.0610.170

0.129**-0.275***-0.287***(0.072)(0.063)(0.072)(0.0633)(0.073)(0.064)(0.080)(0.073)-0.006-0.006***0.006

0.006***

0.016

0.014

-0.222*-0.214*(0.008)(0.001)(0.008)(0.001)(0.087)(0.056)(0.130)(0.127)0.085-0.094

0.089

0.095

0.375**

0.278*-0.368*-0.251(0.184)(0.149)(0.184)(0.149)(0.181)(0.153)(0. 971)(0.183)

LEV

SIZE

GRR

FIXED

OWN

58

_cons-2.439***-2.383***3.421***

2.386***

0.752

0.448

0.789

0.503

(0.499)(0.407)(0.499)(0.407)(0.479)(0.416)(0.527)(0.489)

1258

1258

1258

1258

1258

1258

1258

No. Obs 1258

Note: Standard errors are shown in parentheses.
*** represents significance at the 1% significance level, ** represents significance at the 5% significance level, * represents significance atthe 10% significance level.

593.5. Conclusion
This study analyzes how financial leverage affects investment decisions and the choice offinancing sources in small and medium-sized enterprises (SME) in Vietnam using SME survey
data (2011-2015). Previous theoretical studies have reported that financial leverage can haveboth a positive and negative impact on investment decisions. Our results contradict to thefinancial theory proposed by Myers (1977) and Gomes (2001), as well as empirical studies doneby Lang et al. (1996) and Aivazian et al. (2005). They argued that high debt ratio implies the
low growth opportunity of the firms; therefore, firms with large debt ratio are less likely toinvest. Whilst, our results reveal that financial leverage is positively related to investment
decisions in Vietnam SMEs. Thus, firms with higher financial leverage are more likely to makenew investments to expand their enterprise. Our study opposed Modigliani & Miller’s theory
and suggest that companies have their own preference for making investment decisions.
Our results also showed that firms with higher financial leverage are likely to increase
the use of external financing sources but decrease the use of internal financing sources for their
new investments. This can be explained by the fact that high leverage enterprises might havebetter access to external financing sources by demonstrating their records at credit institutions.
There is a transition from using internal financing sources to external financing source in the
Vietnam SME context (H.T.Trinh et al. 2017). Low financial leverage implies less accessibility
to external financing sources. Thus, to expand their operations, enterprises usually use internalfinancing sources for new investments. After the operation expands successfully, enterprises
60

have improved their accessibility to external financing sources, and the use of external financing
sources increases their financial leverage.
Nevertheless, in contrast to previous studies (H.T.Trinh et al. 2017), our results showthat firm profitability has a significant impact on the choice of external financing sources
because firms still rely on external financing sources even if they make high profits, suggestingthat profitable enterprises invest more in fixed assets.
According to our finding, it seems that reducing credit constraints for new investment
and stimulating new investment indirectly by raising financial leverage of SMEs could be
effective. Additionally, policy makers should consider implementing several policies, such as
improving credit access to SMEs, alleviating information asymmetry, reducing credit
constraints and directly providing subsidies to SMEs. Government can also play a role in
improving knowledge of different financial institutions of which provide investment
opportunities for SMEs. Also, creating an information sharing channel between investors andSMEs. Moreover, reducing the costs of trade facilitation could provide opportunities for SMEs
to attract more foreign investment. Promoting SME participation in worldwide networks should
also be considered.
Finally, our study is subject to certain limitations also gives insights for future research.
The study was limited to focus only on SMEs in Vietnam which is one representative countryin emerging economics. Therefore, it would be beneficial for comparative stud ies in otherdeveloping countries for future research.
61

CHAPTER 4: INFLUENCE OF FARMER UNION MEMBERSHIP ON THE
PRODUCTION AND CREDIT VOLUME OF HOUSEHOLDS: EVIDENCE FROMFARM HOUSEHOLDS IN VIETNAM

4.1. Introduction
In low-income countries, rural development has been considered a key factor in reducing
poverty, as most poor individuals live in rural areas and most household (HH) income comesfrom farming activities such as rice production, livestock production or agro-forestry
production. However, poor individuals typically have little education, limited skills and limited
opportunities to access additional services and resources, such as financial, healthcare, and otherservices (Duong and Izumida, 2002; Duong and Thanh, 2014; Lin, Zhang and Lv, 2019).
Numerous studies have proposed that poor farmers can overcome these constraints if they areorganized into groups or cooperatives (Narrod, et al., 2009;Wollni and Zeller, 2007; Mojo,
Fischer and Degefa, 2017; Tran and Goto, 2019).
Consequently, agricultural cooperatives have been considered organizations that playsignificant socioeconomic roles. Valentinov (2007) argued that agricultural cooperatives are
essential to help family farms overcome the difficulties of realizing external economies of scaleand developing market competitiveness. Nuhanovic et al. (2017) revealed that agricultural
cooperatives have lifted many farmers and communities out of poverty. In addition, Deriada(1995) pointed out that agricultural cooperatives are important tools for dealing with poverty inrural areas. Another viewpoint emphasizes that agricultural organizations help reducetransaction costs and improve the negotiating power of individuals (Bernard and Spielman,
2009; Francesconi and Ruben, 2012; Markelova et al., 2009; Valentinov, 2007. In contrast, there
62are several papers that show the opposite case, that is, the poor performance of agricultural
cooperatives in developing countries (Fischer and Qaim, 2012). In this paper, we investigate theimpact of farmers’ union membership on rural HHs in Vietnam to make comparisons with cases
from other developing countries.
After the doi moi renovation in 1986, a substantial number of international and localorganizations have begun operations in Vietnam, such as community-based organizations, massorganizations, professional associations, and Vietnamese nongovernmental organizations.
Several reasons support the use of the farmers’ union as a specific case study for evaluating theimpacts of social networks on farm households. First, within the unique context of Vietnam(and its single-party government), mass organizations are party-sponsored (i.e., governmentsponsored) and have very strong grassroots links and large memberships. Mass organizations
have become increasingly independent since doi moi. In particular, the farmers’ union is
becoming an important organization insofar as it plays a significant socioeconomic role byproviding a high level of support for poor individuals, such as by facilitating better educationand healthcare services, better living conditions, and increased access to financial services. In
addition, the farmers’ union also plays an important role in the lending process of the rural credit
program called the Vietnamese Bank for Social Policy (VBSP). In Vietnam, the VBSP wasinspired by the success of the Garmeen Bank project in Bangladesh. The main purpose of the
VBSP is to provide credit opportunities for low income people who have difficulty accessing
other forms of credit that require collateral. Under the VBSP, loans are provided based on agroup lending scheme. Village heads and the commune leaders of the four mass organizations
63noted above help form a group of borrowers and monitor loans. Mass organizations alsoorganize borrowers into credit groups to ensure repayment (Khoi, et al., 2013). In addition,groups-based borrowing helps to reduce transaction costs because it effectively ensures
asymmetric information (Quach, 2005). Measuring the impact of both kinship and friendshipon the probability that rural HHs in China will obtain formal and informal loans, Hong Sun etal. (2018) noted that social capital has significantly influenced the borrowing behavior of farmer
HHs. Among these mass organizations, the farmers’ union plays the most important role
(Takashi, 2009). Second, the empirical literature linking social networks to farm households islimited, particularly in developing countries such as Vietnam. Some notable studies, includingthat by Giannakis, Efstratoglou and Antoniades (2018) in Cyprus, have used discrete choicemodel analysis to find that farmer union membership has a positive impact on the decision towork off-farm. With an objective similar to this study but focusing on different union
membership, Newman et al. (2014) found that membership in high-quality networks leads toincreased levels of saving. In addition, Takashi (2009) concluded that the farmers’ union has
played an important role in microcredit programs in Vietnam. Therefore, our study adds to thisliterature by providing empirical evidence of the role of social networks—and, in particular,that of the farmers’ union—in rural household production and credit volume. In this paper, we
focus on the impact of farmers’ union membership on economic performance and financial
issues.
This paper is structured as follows: a literature review and summarizes the background of
farmers’ unions in Vietnam will be presented in section 2. Section 3 describes the data and64

variables used in the study. Section 4 explains the methodology for analysis, and section 5
presents the empirical results and interpretations. The conclusion is presented in section 6.

4.2. Context and Literature Review
The Vietnam farmers’ union (VNFU), established on 14 October 1930, is a sociopoliticalorganization of Vietnamese farmers that is under the leadership of the Communist Party ofVietnam. For many years, the VNFU has played a key and central role in farmer movements
and in building new types of rural areas. Any individ ual over the age of 18, regardless of sex,ethnic group or religious beliefs, who works in agricultural sectors or agricultural services, suchas agriculture, forestry, fishery, salt-making, handicrafts, small industries and other fields, canbecome a VNFU member by voluntarily signing up and accepting the union’s philosophy, rules
and regulations. For farmers, the main motivation for joining the VNFU is to receive assistancein the development of their agricultural enterprises. In recent years, the VNFU has also assisted
farmers in accessing credit from the VBSP. The aim of the VNFU is to guarantee loans forfarmers so that they can borrow without collateral and to facilitate saving and credit groups toassist in managing repayment. The VNFU also participates in national programs related to job
creation, agricultural extension and vocational training. The economic benefits associated with
VNFU membership have the potential to extend beyond the stated objectives of theorganization.
The first successful microcredit program was established by Professor Muhammad Yunus
who worked in the Grameen Bank in Bangladesh in 1976. This program inspired otherdeveloping countries to provide loans for poor people who do not have any collateral. Under
the Grameen Bank project, more than seven million poor Bangladeshian have received credit,
especially woman. Since then, rural credit or microfinancing has been recognized as the key
65tool for reducing poverty and improving sustainable economic wellbeing (Raihan, Osmani and
Khalily, 2017). However, recent studies have shown both positive and negative impacts(Rooyen, Stewart and Wet, 2012) or no impact of rural credit or microfinance on HH income.
On the one hand, Al-Shami, Razali and Rashid (2017) found that microfinance had a positive
impact on the income of women borrowers in Malaysia, and Cuong (2008) showed that theVietnamese government’s credit program had a significantly positive impact on both HH
income and expenditures. In addition, Mohammad et al., (2017) mentioned that microfinanceprogram is positively corelated with the people that used health services. Furthermore, Thu andGoto (2020) indicated that microfinancing has positive impacts on households’ expenditures,
especially the education expenditures of minority student from the northern mountainous areasof Vietnam. Additionally, Lensink and Pham (2011) revealed a positive effect of microcredit
on HH profits. These studies support the welfare effects of microfinancing. In contrast,
Angelucci, Karlan and Zinman (2014) found no evidence of the transformative impact of
microcredit on HH income, as loans seemed to be used mostly for investment. Luan and Bauer(2016) pointed out that credit has a significant positive effect on nonfarm income, but has noinfluence on farm income. He explained that the impact of credit on farm income could be
limited by farming shocks such as pest infestations or crop diseases.
On the other hand, several papers have pointed out that rural credit is influenced by social
institutions or groups. For example, Takashi (2009) mentioned that mass organizations are quite
important in Vietnam, as they are in charge of organizing consultative meetings to select andrecommend candidates for credit programs. According to him, among five organizations, i.e.,
the Farmers’ Association, Women’s Union, War Veterans’ Association, Old Persons’
Association and Youth Association, the Farmers’ Association plays the most important role inbank loans. In addition, the number of memberships in the Farmers’ Association significantly66increased after the establishment of the VBSP loan. However, he did not perform empiricalanalysis to prove his statement. Analyzing memberships in only the Farmers’ Association and
Women’s Union, Newman, Tarp and Broeck (2014) found that group membership has asignificant impact on HH formal savings in rural areas in Vietnam.

4.3. Materials and Methods
4.3.1 Study sites and survey approachThis study covered the Vo Nhai district, one of the most rural areas in Vietnam. The district
was selected because a rural credit program was offered there and because this area has thehighest poverty rate in the northern mountainous region of Vietnam.
Thai Nguyen Province is in the northern mountainous region of Vietnam, having a total
natural land area of 3,533.2 km2 and a population of 1,190,600 as of 2015. Thai Nguyen
Province is well known for its tea industry, with tea production covering a total area of 16,000
ha. The poverty rate in Thai Nguyen Province was 11.1% in 2014, and in the Vo Nhai District,
it was over 40%. According to the general statistics office, in 2016, there were 150.86 thousandhouseholds whose main activities were related to agriculture, forestry, livestock and aquaculture
production, which accounted for 66.7% of total households; in contrast, the equivalent statistic
for the country as a whole was 53.7%. Among all districts in Thai Nguyen Province, Vo Nhai
District has the highest density of farm households (83.15%).

67

4.3.2 Questionnaire survey
In this study, a simple random sampling technique was applied. From the lists of HHs
provided by the district offices, total of 401 households were randomly selected. There were
59 households excluded because missing information, leaving a total of 342 households in thesample size of the study. Of these, 147 households were members of farmer union, 3 were nonmember of farmer union (in Bao Hieu district, Hoa Binh Province). 111 households weremembers of farmer union, 81 were non- member of farmer union (in Vo Nhai district, Thai
Nguyen Province). We excluded Bao Hieu from the main estimation and focus only in Thai
Nguyen Province with a final sample size of 192 farm HHs, which is 0.5% of the total numberof farm households in the selected area. The primary data were collected through an HH survey
conducted in 2015-2016. In our estimation, we assigned HHs that had no farmers’ union
membership to the control group, while the treatment group included membership HHs. The
HH survey covered a variety of topics related to HH characteristics, access to credit and HH
economic performance. To identify demographic characteristics, the respondents were asked
about the HH head’s age, educational level, sex, occupation, ethnicity, and poverty status. HH
access to resources and credit and the HH’s main production activities were also included in thesurvey. In terms of credit analysis, the participants were asked about the history of t heir loantransactions from any sources within the past 10 years.

68Figure 2: The study sites

Source: diva-gis.org

4.3.3. Methodology
This study employed the propensity score matching method (PSM) to estimate the impactsof farmers’ union membership on several potential outcome variables: the economic
performance of HHs (average livestock production, average crop production, total income andexpenditures) and the average credit volume from the VBSP program. In this study, the choiceof variables related to each HH is based on the literature review (Caliendo and Kopeinig, 2008;
Becker and Ichino, 2002). In addition, we selected several potential covariates from the HH
characteristics that can affect both the treatment and outcome variables. A brief description of69

and the assumptions concerning the control variables and variables of interest are presented inTable 14.
Table 14: Description of selected variables
Variables

Explanation

Unit

Family size

Total members in the household

Members

Sex

The gender of the HH head 1= male; 0 = female

Potential covariates

Age of household
head
Education ofhousehold headAge of household headYears

The number of years in school of the household headYears

Occupation of

Dummy variable for occupation of the householdhousehold head

head; 1 for working full-time on farm, 0 otherwise

Credit dummy
Total agricultural andDummy variable for credit status of HH; 1 = hasborrowed money from the VBSP, 0 otherwise
Total lands owned by the HH

1000 m2

Livestock production

The total amount of livestock production

Million VND.

Crop production

The total amount of crop production

Million VND.

Total income

The total income of the HH in 1 monthMillion VND.

Total expenditure

The total expenditure of the HH in 1 monthMillion VND.

Credit volume

The total borrowing amount from VBSP

Million VND.

forestry land holdings
Potential outcomes70

Studying the causal effects of farmers’ union membership on the potential outcome
indicators is important due to endogeneity bias. It is necessary to control for observable andunobservable characteristics in the random experiment of assigning individuals to the treatmentgroup (Wossen et al., 2017). In this study, we employ PSM to control for endogeneity bias bymatching observations in the treated HHs with similar untreated HHs (Rosenbaum and Rubin,1983). The basic idea of PSM is to construct the counterfactual outcomes of those in thetreatment group based on their propensity score using the subset of covariates. Then, thetreatment effect can be estimated by taking the difference in the outcomes of the matchedobservations. We apply a two-step process to estimate the average treatment effects (ATEs) andaverage treatment effects on the treated (ATETs) for the outcome variables following the study
by Tran and Goto (2019).
In the first step, the propensity score was estimated by applying a probit model, whichis expressed as follows:

𝑃𝑟(𝐷𝑖 = 1|𝑋𝑖 ) = 𝛷(𝛽𝑋𝑖 )

(1)
(1)

Here, 𝐷𝑖 denotes a binary variable.
Additionally, 𝐷𝑖 =1 if an HH is a member of the farmers’ union, and 𝐷𝑖 =0 if an HH is not amember of the farmers’ union.
𝑋𝑖 is a vector of HH characteristics71𝛷 represents the standard normal cumulative distribution function.
To find the best matching quality and to obtain a balanced sample with respect to the selectedcovariates after matching, we selected different models to estimate the propensity score. Eachmodel consists of a different set of variables. The details of the variables selected are provided
in Table 3.
Next, the propensity score was estimated by the selected probit model. Then, thesepropensity score will be matched based on the one-to-one nearest neighbor matching technique(0.01 caliper). Treated units that have no control units within the specific caliper are excluded
from the estimation. Furthermore, in our estimation, a control observation can be used formatching more than once.
In the next step, we treat selected covariates as pseudo-outcome variables and estimate
pseudo-ATETs based on them to check the balance of each model following Imbens and Rubin(2015). Good matching is expected to have pseudo-ATETs that close to zero and statisticallynonsignificant, which means that the un-confoundedness assumption is plausible. The modelwith the best balancing property will be chosen to estimate the ATEs and ATETs in the next
step.
In the second step, each selected model will be used to estimate the ATEs (equation 2) and
ATETs (equation 3) as follows:
ATE = E(𝑌1 − 𝑌0 ) = 𝐸 (𝑌1 |𝐷 = 1) − 𝐸 (𝑌0 |𝐷 = 0)

(2)

72

ATET = E(𝑌1 − 𝑌0 |𝐷 = 1) = 𝐸 (𝑌1| 𝐷 = 1) − 𝐸 (𝑌0 |𝐷 = 1)(3)

where 𝑌1 and 𝑌0 are potential outcomes, and 𝐷 denotes a treatment indicator.
However, in our dataset, we can observe only 𝐸 (𝑌1 |𝐷 = 1), and 𝐸 (𝑌0 |𝐷 = 1) is unobserved;
thus, it is a counterfactual term that is calculated by taking the average of the outcomes of thematched control group.

We employed PSM because it is a popular method used to adjust for confounding inobservational studies. PSM has several advantages over simple regression. Whereas the PSM
technique creates groups of treatment and control groups that have similar propensity score,regression estimates the outcomes for unmatched households. Furthermore, PSM mitigates theeffects of self-selection bias that are associated with the observable covariates (Rosenbaum andRubin, 1983).

To confirm the consistency of the results, we employed other matching methods such as
the inverse probability weighted (IPW) method and regression adjusted (RA) methods. While
PSM matched the treatment and comparison group observations based on a propensity score,
IPW estimators were used to estimate the probability weights to compute weighted averages ofthe outcomes for each treatment level. The contrasts of these weighted averages provided theestimates of the ATEs. Meanwhile, RA estimates were used to separate the regression models
73

of the outcomes on selected covariates for each treatment level. Then, the RA was employed tomeasure the averages of the predicted outcomes for each subject and treatment level. The
treatment effect was calculated based on the contrasts of the treatment averages for the specificpredicted outcomes.
4.4. Results and discussion
4.4.1 Descriptive statistics
The mean differences values for the selected variables of FU’s member HHs andnonmember HHs was reported in Table 2. Most HH heads who hold Fu’s membership have ahigher educational level than do the nonmember HH heads. Additionally, member HH heads
are younger and more likely to be female than nonmember HH heads. The HHs in the control
group owned more farmland than those in the treatment group. These differences present an
imbalance in the selected covariates, which suggests self-selection problems.

Regarding the outcome variables, the HHs in the treatment group have higher mean values
in livestock production, expenditure and credit volume than do the control HHs. For instance,
the average credit volume of the treatment HHs is 4.3 million VND higher than that of t hecontrol HHs. However, the treatment units present a no significantly higher mean value forlivestock production and crop production, and a lower mean value for total income than thecontrol units. For the other covariates, there is not statistically significant different among
treatment and control groups.
74

Regarding whether to participate in the farmers’ union, HHs usually have different self selections. The mean difference in the outcome variables between the treatment and control
groups might lead to biased conclusions regarding the treatment effect. Despite the fact that
PSM cannot control for self-selection bias, we selected several sets of covariates to estimate thePropensity score then test the consistency of the impacts results by using different models anddifferent matching algorithms.

75

Table 15: The mean difference of households without farmers’ union membership (Treatment) and with farmers’ union membership (Control)

Variables

Treatment Group

Control Group

Obs

Mean

S.D

Family size

111

4.126

0.128

Sex

111

0.963

Age of the HH head111

45.585

Educational level of the HH

111

Obs

Difference (T-C)

Mean

S.D

Mean

S.E

81

4.283

1.259

-0.157

0.192

0.017

81

0.987

0.012

-0.023

0.023

0.964

81

48.283

1.251-2.698

*

1.554

8.259

0.254

1.065

***

0.343

***

0.051

Potential covariates

head

81
9.324

0.226

Occupation dummy

111

0.792

0.386

81

0.925

0.029

-0.133Credit dummy

111

0.657

0.045

81

0.74

0.048

-0.0830.067

Total agricultural and

111

46.8355.073-1.7745.753

forestry land holdings

81
45.06

3.229

76

Potential outcome
Livestock production (Mill.
VND)

26.9872.467

21.956

2.82

5.030

3.761

Crop production (Mil. VND)

2.559

0.220

2.442

0.250

0.117

0.335

Total income (Mill. VND)

3.692

0.251

3.695

0.28

-0.003

0.381

VND)

3.246

0.16

2.798

0.148

0.448

*

0.225

Credit volume (Mill. VND)

16.3781.56

12.000

1.349

4.378

**

2.156

Total expenditures (Mill.

Data source: The author’s calculations from the Household Survey from 2016 -2017. ***, ** and * represent significance at the 1%, 5% and 10% levels, respectively; 100
USD = 2,233,000 VND (in 2016)77

4.4.2 Impacts of FU’s membershipTo estimate the impacts of FU’s membership we follow the procedure in section 3.3.
Caliendo and Kopeinig (2008) mentioned that propensity score modeling cannot be used to
predict the selection of individuals into treatment and control groups but rather to balance theselected covariates. Therefore, we found two models (out of eight models) had the mostbalanced samples after matching; the results are shown in Table 3. The mean difference in thecovariates, such as the age of the HH head, the educational level of the HH head, and theoccupation dummy, is significantly different before matching and become nonsignificant after
matching. For almost all of the covariates, the mean difference also decreases almost to zero
except for the total agricultural and forestry land holdings, but this difference is still
nonsignificant. This result implies that the balance of the samples is improved.
Table 16: Balance checking before and after matching.
Before matching

After matchingN= 192Model 1Model 2

Mean

SE

Mean

SE

Mean

SE

Family size

-0.157

0.192

-0255

0.287

-0.1670.251

Sex-0.023

0.023

0.023

0.03

-0.0440.030-2.698*

1.554

0.337

1.55

1.411

0.343

1.065***

0.343

0.000

0.302

-0.011

0.327

Age of the
HH head
Educational
level of the
HH head78

Occupation
dummy-0.133***0.051-0.011

0.045

0

0.049

-0.0830.067-0.0460.088-0.0550.083-1.774

5.753

7.638

8.691

3.362

6.687

Creditdummy
Total
agriculturaland forestry
landholdings

Note: In model 1, total agricultural and forestry land holdings was excluded in the propensity score model (probit
model). In model 2, sex was excluded in the propensity score model.
A caliper of 0.01 is applied for one-to-one nearest neighbor matching based on the estimated propensity score.
***, ** and * represent significance at the 1%, 5% and 10% levels, respectively.

After matching, the ATEs and ATETs for the potential outcomes have been calculated
following Eqn. (2) and Eqn. (3). The results are shown in table 4. First, the ATE results showthat farmers’ union membership has positive effects on livestock production. In particular,
membership HHs have production amounting to more than 9.121 million VND (11.556 million
VND in model 2). Second, membership HHs obtain higher credit volume than nonmember HHs.
There is one possible explanation for this result: HHs that have farmers’ union membership canobtain a relatively high volume of credit to improve their production; therefore, the HH
production of member HHs is significantly higher. Our results are consistent across all models.
79

Moreover, the ATE results from the different matching methods shown in Table 5 also confirm
the consistency of the results. In other words, farmers’ union membership has a significant
impact on the livestock production and credit volume of HHs. The findings of the study pointout the importance of related social factors, i.e., farmer union membership, on the rural
households (Giannakis, Efstratoglou and Antoniades, 2018). Our findings provide further
evidence in support of Takashi’s conclusions (2009), wherein he stated that the farmers’ union
plays important roles in microcredit loans. He pointed out that the number of households with
farmers’ union membership changed significantly after 1996 when the VBSP loans began.
Therefore, our results support the notion that the farmers’ union could be effective in increasing
loan volume and livestock production.
Additionally, the ATET estimator for the outcome variables reveal that farmer’s union
membership has a positive effect on livestock production and cred it volume. Similar to the ATE
results, this finding implies that farmers’ union membership significantly contributes to HH
production. However, there is some inconsistency between two models; we found a significant
impact of membership on livestock production in model 2, but a nonsignificant impact in model
1. Nevertheless, the ATET results show a significant impact of membership on credit volume
in model 1 and a nonsignificant impact in model 2. In addition, the ATET results are notconsistent using the different matching methods.
We found no evidence of an impact of FU membership on other potential outcomes suchas crop production, total income and HH expenditures.
80In our analysis, both PSM and linear regression agree on the statistical significance on theimpact of farmers’ union membership on HHs’ livestock production and credit volume after
controlling for several covariates.
In summary, the study estimation results show that farmers’ union membership has asignificant impact on the credit volume (consistently), crop production (inconsistently) andlivestock production (consistently) of HHs. Specifically, the positive impact of farmers’ union
membership on livestock production and credit volume proves that farmers’ union membershiphas important economic benefits for union members and their HHs. We do not intend to say thatmembers of the farmers’ union are using their power to allocate credit favorably to themselves.
Since members of the farmers’ union have a higher level of education, they are generally
regarded as more creditworthy. In addition, there is a certain correlation between level of
education and membership status, which can influence the use of credit in household production.

81

Table 17: The results of PSM estimation (unit: millions of VND)
ATE

ATET

Model 1Model 2Model 1Model 2

ATE

SE

ATE

SE

ATET

SE

ATET

SE

9.121*

4.302

11.556***

4.010

7.502

4.614

10.431**

4.654

Crop production-0.267

0.427

0.190

0.426

-0.345

0.447

0.024

0.477

Total income-0.478

0.442

0.040

0.482

-0.5670.490-0.2690.548

Expenditure

0.278

0.273

0.171

0.286

0.138

0.307

0.258

0.329

Credit volume

6.678***

2.398

4.243*

2.381

6.046**

2.818

3.7

3.245

Livestockproduction

150 matches

157 matches

80 matches

85 matches

A caliper of 0.01 is applied for one-to-one nearest neighbor matching based on the estimated propensity score.
***, ** and * represent significance at the 1%, 5% and 10% levels, respectiv
82

Table 18: Results of ATE estimation: Impact of membership on HHs (unit: millions of VND)

Membership (1 vs 0)

PSM

NN-MATCH

IPW

RA

ATE

S.E

ATE

S.E

ATE

S.E

ATE

S.E

Livestock production

7.098*

4.186

6.593*

3.901

7.310**

3.565

6.162*

3.513

Crop production

0.548

0.346

0.211

0.340

0.339

0.346

0.191

0.322

Total income

0.006

0.430

-0.260

0.405

0.096

0.474

-0.183

0.407

Expenditure

0.126

0.265

0.164

0.3261

0.498

0.340

0.256

0.296

Credit volume

5.235**

2.278

2.094

2.324

3.302*

1.952

4.048**

1.949

Note: In this PSM estimation, all the covariates (family size, sex, age, education level, occupation dummy, credit dummy, tot al land) are included when estimating the propensity
score.
***, ** and * represent significance at the 1%, 5% and 10% levels, respectively.

83

Table 19: Results of ATET estimation: Impact of membership on HHs (unit: millions of VND)

Membership (1 vs 0)

PSM

NN-MATCH

IPW

RA

ATET

S.E

ATET

S.E

ATET

S.E

ATET

S.E

Livestock production

4.717**

4.648

5.123

4.044

8.315**

3.772

5.613

3.562

Crop production

0.777**

0.371

0.671*

0.375

0.554

0.414

0.359

0.351

Total income-0.098

0.51

0.146

0.448

0.378

0.604

-0.053

0.462

Expenditure

0.242

0.332

0.303

0.310

0.498

0.340

0.256

0.296

Credit volume

3.3

2.097

2.827

2.560

3.097

2.056

4.194**

2.049

Note: In this PSM estimation, all the covariates (family size, sex, age, education level, occupation dummy, credit dummy, total land) are included when estimating the propensity
score.
***, ** and * represent significance at the 1%, 5% and 10% levels, respectively.

844.5. Conclusion
Whereas the previous literature has principally focused on the impact of microcredit onhouseholds’ welfare, this study aimed to examine whether farmers’ union membership plays animportant role with regard to the production and the access to microcredit of farm households
in rural areas of Vietnam. A household survey was implemented in Vo Nhai district in Thai
Nguyen province where the poverty rate has been recorded as relatively high in the northern
mountainous region of Vietnam. We use the PSM to examine the impacts of farmers’ union
membership as well as other matching methods and linear regression for checking theconsistency of the results. We found that farmer’s union membership improved the livestock
production and increased the credit volume of rural households.
In particular, the ATE estimator results show that membership in farmers’ unions is
positively impacts to the livestock production and credit volume of member HHs. In other words,
HHs with farmers’ union membership can obtain higher credit volumes and generate greater
livestock production than nonmember HHs. The impact on credit volume is clear and shows asignificant effect. Meanwhile, the ATET estimator results were not consistent. The results
implied that HHs that are better off in terms of both credit volume and production are more
likely to join farmers’ unions. This finding suggests that farmers’ unions bring potential benefits
in certain areas to farm HHs. Even though this result may seem obvious; however, to the best
knowledge, it has not been considered in the previous studies. It can be explained by the factthat the FU’s membership HHs increased their livestock production more significantly than85

nonmember HHs. In fact, this results support for the view that rural households are consideringcertain objectives in response to collective action (Tarrow, 2003).
The difference in the results originates from a difference in the matching method and thedifferent PSM models. This discrepancy might be a limitation of this study’s methodology.
In conclusion, our estimates indicate that farmers’ union membership has significant
impacts on HHs’ production and credit volume. Therefore, the economic benefits associatedwith farmer union membership have the potential to extend beyond the stated objectives of theorganization. Our findings provide empirical evidence to support the influence of farmers’
union membership on HHs production. This finding adds to the previous literature on theimportance of social organizations, especially the farmers’ union in Vietnam. The results of thisstudy may help policy makers gain better insight into the impacts of local organizations in ruraldevelopment. We suggest that the farmers’ union should enhance its activities to provide moreresource accessibility, especially with regard to financial services. Moreover, further
investigating the interaction effects of other local organizations and rural households in different
regions can help to draw a potential policy and supporting policy of social organization in therural development process in Vietnam.

86

CHAPTER 5: SUMMARY OF THE STUDY
5.1. Main findings
This study focuses on sustainable economic development in Vietnam regarding the
economic structural change and financial activities subjects. We relied on the Vietnam IO
data (2000, 2005, 2010, and 2015), Vietnam SMEs data (2011, 2013, and 2015), and ahousehold survey data (conducted in 2016 and 2017) for our analysis.
The results in Chapter 2 revealed that the agricultural sector and food sector are still
important sectors in the period of 2000-2015 which indicates the primary industry and themost related industry like food and beverage are still supporting the Vietnamese economy.
The linkage effects from the Leontief inverse pointed out that the food sector and agricultural
sector have strong linkage effects which implied that these two sectors have becoming key
industries of Vietnam’s economy in 2015. The results also showed that the total outputs offood products and the agricultural sector mainly came from final demand change by
consumption. Meanwhile, both final demand change by investment and export andtechnological change by intermediate inputs were contributing to the change of themachinery sector. We also found that the wholesale and retail sector was dramatically reduce
its consumption, investment, and export in the last 5 years (2010-2015).
The results in Chapter 3 were contradicted to the financial theory proposed byMyers (1977) and Gomes (2001), as well as empirical studies done by Lang et al. (1996) andAivazian et al. (2005). They argued that a high debt ratio implies the low growth opportunityof the firms; therefore, firms with large debt ratios are less likely to invest. Whilst, our results
reveal that financial leverage is positively related to investment decisions in Vietnam SMEs.
87Thus, firms with higher financial leverage are more likely to make new investments to
expand their enterprise. Our study opposed Modigliani & Miller’s theory and suggest thatcompanies have their own preference for making investment decisions. In addition, we also
found that SMEs with higher financial leverage is likely to increase the use of externalfinancing sources but decrease the use of internal financing sources for their new investments.
This can be explained by the fact that high leverage enterprises might have better access toexternal financing sources by demonstrating their records at credit institutions. There is atransition from using internal financing sources to external financing sources in the Vietnam
SME context (H.T.Trinh et al. 2017). Low financial leverage implies less accessibility toexternal financing sources. Thus, to expand their operations, enterprises usually use internalfinancing sources for new investments. After the operation expands successfully, enterprises
have improved their access to external financing sources, and the use of external financing
sources increases their financial leverage.
The result in Chapter 4 emphasized the roles of a social organization in a rural
household in Vietnam. We specifically found that farmer union membership; as arepresentative of the mass organization; have a positive impact on the production and credit
volume of rural households by using a data from the household survey in rural areas of themountainous region of Vietnam.

5.2. Implication
Our results in chapter 2 suggest that food products and the agricultural sector are
still important industries in the Vietnam economy. Nevertheless, this also shows a hinder
problem as an economic policy implication. Since the country’s economy mostly relied to88the agricultural industry, it needs to develop innovation technology to expand production;otherwise, a country might be remained staying poor because of a decreasing return to scaleof production. Thus, like many developing countries, other manufacturing industries such asmachinery, textile, and other sectors might be the next leading industry of the Vietnam
economy.
According to our findings from chapter 3, it seems that reducing credit constraints
for new investment and stimulating new investment indirectly by raising the financial
leverage of SMEs could be effective. Additionally, policymakers should consider
implementing several policies, such as improving credit access to SMEs, alleviating
information asymmetry, reducing credit constraints, and directly providing subsidies to
SMEs. The Government can also play a role in improving knowledge of different financial
institutions which provide investment opportunities for SMEs.

Also, creating an

information-sharing channel between investors and SMEs. Moreover, reducing the costs oftrade facilitation could provide opportunities for SMEs to attract more foreign investment.
Promoting SME participation in worldwide networks should also be considered.
Lastly, our estimates in chapter 4 indicated that farmers’ union membership hassignificant impacts on HHs’ production and credit volume. Therefore, the economic benefits
associated with farmer union membership have the potential to extend beyond the statedobjectives of the organization. Our findings provide empirical evidence to support the
influence of farmers’ union membership on HHs production. This finding adds to theprevious literature on the importance of social organizations, especially the farmers’ unionin Vietnam. The results of this study may help policymakers gain better insight into theimpacts of local organizations in rural development. We suggest that the farmers’ union
should enhance its activities to provide more resource accessibility, especially with regard89to financial services. Moreover, important policy evidence could be generated by further
investigating the interaction effects of other local organizations and rural households in
different regions.

5.3. Limitations of the study

Some limitations have been drawn from this dissertation. First, the study is limited
to the case in Vietnam. It would be more beneficial for comparative studies with othercountries with a similar economic structure for future research. Second, the discrepancy inresults originate from the difference in the matching method in chapter 2 is another limitation.
The PSM might not be a good method to mitigate self-selection bias into farmer union
membership. Thus, several approaches are applied to further checking the robustness of thestudy’s results.

90

REFERENCES
Aivazian, V. A., Ge, Y., and Qiu, J. 2005. “The impact of leverage on firm
investment: Canadian evidence.” Journal of Corporate Finance 11(1-2): 277-291.
doi:10.1016/s0929-1199(03)00062-2.
Aivazian, V.A., Ge, Y., and Qiu, J. 2005. “Debt maturity structure and firm
investment. Financ. Manage.” 34 (4): 107–119.
Al-Shami, S.S.A., Razali, R.M. & Rashid, N., 2017. The Effect of Microcredit on
Women Empowerment in Welfare and Decisions Making in Malaysia. Social Indicators
Research, 137(3), pp.1073–1090.
Angelucci, M., Karlan, D. & Zinman, J., 2014. Microcredit Impacts: Evidence
from a Randomized Microcredit Program Placement Experiment by Compartamos Banco.
Atella, V., Gianfranco, E. A., and Pier, L. B. 2003. “Investment and Exchange
Rate Under Uncertainty.” CEIS Research Paper. 32, Tor Vergata University, CEIS
Becker, S.O. & Ichino, A., 2002. Estimation of Average Treatment Effects Basedon Propensity Scores. The Stata Journal: Promoting communications on statistics and Stata,2(4), pp.358–377.
Bernard, T. & Spielman, D.J., 2009. Reaching the rural poor through rural
producer organizations? A study of agricultural marketing cooperatives in Ethiopia. Food
Policy, 34(1), pp.60–69.
Bhaird, C. M. A. and Lucey, B. 2010. “Determinants of capital structure in Irish
SMEs.” Small Business Economics 35: 357–375.

91

Binding, G. and Dibiasi, A. 2017. “Exchange Rate Uncertainty and Firm
Investment Plans - Evidence from Swiss Survey Data.” Journal of Macroeconomics 51: 127. https://doi.org/10.1016/j.jmacro.2016.11.004
Bouasone Sengsourivong and Masaru Ichihashi, Interdependency throughTransactions in Rural Villages in Developing Countries: Applying the Qualitative Village
Input-Output Table (mimeo)
Caliendo, M. & Kopeinig, S., 2008. Some Practical Guidance for The
Implementation Of Propensity Score Matching. Journal of Economic Surveys, 22(1), pp.31–
72.
Chang, Ha-Joon, 2003, Kicking Away the Ladder, Anthem Press.
Chen Fei, Weiyin Fei, Yayun Rui and Litan Yan. 2019. “International investment
with exchange rate risk.”

Asia-Pacific

Journal of Accounting& Economics,

DOI: 10.1080/16081625.2019.1569539
Cuong, N.V., 2008. Is A Governmental Micro-Credit Program for The Poor
Really Pro-Poor? Evidence From Vietnam. The Developing Economies, 46(2), pp.151–187.
Dang, V. A. 2011. “Leverage, Debt Maturity and Firm Investment: An Empirical
Analysis.” JournalofBusiness

Finance&

Accounting

38(1-2),225-258.

doi:10.1111/j.1468-5957.2010.02215.x
Deriada, A.L., 1995. Assessment of cooperative movement in a developing
country: the Philippine experience. Forum of International Development Studies, 28, pp.81101.
Dietzenbacher, E., & Los, B. (1998). Structural Decomposition Techniques:
Sense and Sensitivity. Economic Systems Research, 10(4), 307–324. doi:
10.1080/09535319800000023

92Duong, P.B. & Izumida, Y., 2002. Rural Development Finance in Vietnam: A
Microeconometric Analysis of Household Surveys. World Development, 30(2), pp.319–335.
Duong, P.B. & Thanh, P.T., 2014. Impact Evaluation of Microcredit on Welfareof the Vietnamese Rural Households. Asian Social Science, 11(2).
Fischer, E. & Qaim, M., 2012. Linking Smallholders to Markets: Determinantsand Impacts of Farmer Collective Action in Kenya. World Development, 40(6), pp.1255–
1268.
Francesconi, G.N. & Ruben, R., 2012. The Hidden Impact of Cooperative
Membership on Quality Management: A Case Study from the Dairy Belt of Addis
Ababa. Journal of Entrepreneurial and Organizational Diversity, 1(1).
Frank, M. Z. and Goyal, V. K. 2009. “Capital structure decisions: Which factors
are reliably important?” Financial Management 38(1): 1–37.
Frank, Murray Z. and Goyal Vidhan K. 2011. "Trade-off and Pecking Order
Theories of Debt". Handbook of Empirical Corporate Finance: Empirical Corporate
Finance. Elsevier. pp. 135–202.
Freeman, N. J. and Le, B. N. 2007. “SME ﬁnance in Vietnam: Reviewing past
progress and scoping future developments.” Working Draft of Technical Report, United
Nations Industrial Development Organization.
Giannakis, E., Efstratoglou, S. & Antoniades, A., 2018. Off-Farm Employment
and Economic Crisis: Evidence from Cyprus. Agriculture, 8(3), p.41.
Gomes, J, F. 2001. “Financing Investment.” American Economic Review 91:
1263-1285.
GSO. 2014. Enterprise Survey 2013, Hanoi: General Statistics Office of Vietnam.
https://www.gso.gov.vn/default_en.aspx?tabid=515&idmid=5&ItemID=13733

93

Ha, N. H. P., & Trinh, B. (2018). Vietnam Economic Structure Change Based on
Vietnam Input-Output Tables 2012 and 2016. Theoretical Economics Letters, 08(04), 699–
708. doi: 10.4236/tel.2018.84047
Hainmueller, J., 2012. Entropy Balancing for Causal Effects: A Multivariate
Reweighting Method to Produce Balanced Samples in Observational Studies. Political
Analysis, 20(1), pp.25–46.
Hall, G., Hutchinson, P. J. and Michaelas, N. 2000. “Industry effects on thedeterminants of unquoted SMEs’ capital structure.” International Journal of the Economics
of Business 7: 297–312.
Huong Thi Trinh, Makoto Kakinaka, Donghun Kim and Tae Yong Jung.
2017. “Capital Structure and Investment Financing of Small and Medium-Sized EnterprisesinVietnam.” Global

Economic

Review

46(3): 325-

349. DOI: 10.1080/1226508X.2017.1340184
Imbens, Guido, and Donald B.Rubin, 2015. Causal Inference for Statistic, Social,
and Biomedical Sciences: An Introduction. Cambridge University Press.
Jensen, M. 1986. “Agency costs of free cash ﬂow, corporate ﬁnance, andtakeovers.” American Economic Review 76: 323–339
Jensen,M. and Meckling,W. 1976. “Theory of the firm: managrial behavior,agency costs, ownership structure.” Journal of Financial Economics 3: 305–360.
Khoi, P.D. et al., 2013. Formal and informal rural credit in the Mekong River
Delta of Vietnam: Interaction and accessibility. Journal of Asian Economics, 26, pp.1–13.
Lang, L., Ofck, E. and Stulz, M. 1996. “Leverage, investment, and firm growth.”
Journal of Finance Economics 40: 3–29.
Lensink, R. & Pham, T.T.T., 2011. The impact of microcredit on self-employment
profits in Vietnam. Economics of Transition, 20(1), pp.73–111.
94

Lin, J., Zhang, Z. & Lv, L., 2019. The Impact of Program Participation on Rural
Household Income: Evidence from China’s Whole Village Poverty Alleviation
Program. Sustainability, 11(6), p.1545.
Luan, D.X. & Bauer, S., 2016. Does credit access affect household income
homogeneously across different groups of credit recipients? Evidence from rural
Vietnam. Journal of Rural Studies, 47, pp.186–203.
Markelova, H. et al., 2009. Collective action for smallholder market access. Food
Policy, 34(1), pp.1–7.
Modigliani, F. and Miller, M. H. 1958. “The cost of capital, corporate ﬁnance andthe theoryof investment.” American Economic Review 48: 261–297.
Mojo, D., Fischer, C. & Degefa, T., 2017. The determinants and economic impactsof membership in coffee farmer cooperatives: recent evidence from rural Ethiopia. Journalof Rural Studies, 50, pp.84–94.
Myers, S.C. 1977. “Determinants of corporate borrowing.” Journal of Finance
Economics 5: 146–176.
Narrod, C. et al., 2009. Public–private partnerships and collective action in highvalue fruit and vegetable supply chains. Food Policy, 34(1), pp.8–15.
Newman, C., Tarp, F. & Broeck, K.V.D., 2014. Social Capital, Network Effects,
and Savings in Rural Vietnam. Review of Income and Wealth, 60(1), pp.79–99.
Nuhanovic-Ribic, S, Tortia, EC and Valentinov, V 2017. Agricultural Cooperatives: A Struggle for Identity. In, Jonathan Michie, Joseph R. Blasi), Carlo Borzaga(Eds.)The Oxford Handbook of Mutual, Co-operative, and Co-owned Business, 158-170.
OECD 2005. OECD SME and Entrepreneurship Outlook: 2005, OECD Paris,
page 17. https://stats.oecd.org/glossary/detail.asp?ID=3123

95

Organisation for Economic Co-operation And Development (2015). Vietnam
Input-Output Tables. [Data File]. Retrieved from http://stats.oecd.org.
Phan, Q. T. 2018. “Corporate debt and investment with financial constraints:
Vietnamese listed firms.” Research in International Business and Finance 46”: 268-280.
doi:10.1016/j.ribaf.2018.03.004
Quach Manh Hao. 2005. Access to Finance and Poverty Reduction – Anapplication to Rural Vietnam. PhD dissertation, the University of Birmingham.
Raihan, S., Osmani, S. & Khalily, M.B., 2017. The macro impact of microfinancein Bangladesh: A CGE analysis. Economic Modelling, 62, pp.1–15.
Reinert, Erik, 2007, How Rich Countries Got Rich … and Why Poor Countries
Stay Poor, Constable.
Rooyen, C.V., Stewart, R. & Wet, T.D., 2012. The Impact of Microfinance in SubSaharan Africa: A Systematic Review of the Evidence. World Development, 40(11),pp.2249–2262.
Rosenbaum, P.R. & Rubin, D.B., 1983. The central role of the propensity score in
observational studies for causal effects. Biometrika, 70(1), pp.41–55.
Ross,G.C. 1977. “The determinants of financial structure: The inventive signaling
approach.” Journalof Economics and Management Science 8: 23 –40
Rudy Rahmaddi and Masaru Ichihashi, The role of foreign direct investment inIndonesia's manufacturing exports, Bulletin of Indonesian Economic Studies Volume 49,
2013 - Issue 3.
Sang-Min Cho and Sun-A Kang. 2017. “The effect of accounting information
quality and competition on investment inefficiency: evidence from Korea.” Asia-Pacific
Journal of Accounting & Economics, DOI: 10.1080/16081625.2017.1392879

96Shu-Miao Lai and Chih-Liang Liu. 2018. “Management characteristics andcorporate investment efficiency.” Asia-Pacific Journal of Accounting & Economics, 25:34, 295-312, DOI: 10.1080/16081625.2016.1266270
Sogorb-Mira, F. 2005. “How SME uniqueness affects capital structure: evidence
form a 1994-1998 Spanish data panel.” Small Business Economics 25: 447–457.
Soulixay Hongsakhone Masaru Ichihashi, Measurement of reciprocity in a village
through social networks
Sun, H. et al., 2018. The Influence of Social Capital on Farm Household’s
Borrowing Behavior in Rural China. Sustainability, 10(12), p.4361.
Takashi Okae, 2009, Rural credit and community relationships in a Northern
Vietnamese village. Japanese Journal of Southeast Asian Studies. 47(1), pp. 3-30.
Thu, V. H., & Goto, D. (2020). Does Microfinance Improve the Household
Welfare of Ethnic Minorities? Evidence from Bac Kan Province, Vietnam. Progress in
Development Studies, 20(1), 65–83. doi: 10.1177/1464993419886230
Tran, D. & Goto, D., 2019. Impacts of sustainability certification on farm income:Evidence from small-scale specialty green tea farmers in Vietnam. Food Policy, 83, pp.70–
82.
UNU-WIDER 2018. The small and medium enterprise survey in Vietnam
https://www.wider.unu.edu/database/viet-nam-sme-database
Valentinov, V. 2007. Why Are Cooperatives Important in Agriculture? An
Organizational Economics Perspective. Journal of Institutional Economics, 3(1), 55-69.
Valentinov, V., 2007. Why are cooperatives important in agriculture? An
organizational economics perspective. Journal of Institutional Economics, 3(1), pp.55–69.
Wassily W. Leontief. (1941). The Structure of the American Economy. Havard
University Press. 1(2), 246. doi: 10.1017/S0022050700053158
97

Wollni, M. & Zeller, M., 2007. Do farmers benefit from participating in specialty
markets and cooperatives? The case of coffee marketing in Costa Rica1. Agricultural
Economics, 37(2-3), pp.243–248.
Wossen, T. et al., 2017. Impacts of extension access and cooperative membershipon technology adoption and household welfare. Journal of Rural Studies, 54, pp.223–233.
Xuan Vinh Vo, Ellis and Craig 2017. “An empirical investigation of capital
structure and firm value in Vietnam.” Finance Research Letters 22(C): 90-94.
DOI:10.1016/j.frl.2016.10.014

98

APPENDICES

Appendix 1. Individual report on TAOYAKA Onsite Team project:I.

Introduction

There are many challenges that not only Kita Hiroshima, but other rural communities arefacing such as depopulation, aging society, the decline of farmland and so on. For many
years, young people tend to flee from rural village and move to big city for education or jobs.
This common trend is happening across all rural areas in Japan recently. Therefore, Japan isslowly becoming unbalance country with majority of the population are in the urban cities
such as Tokyo, Osaka and Nagoya…. In addition, the disappearance of farmland is a big
problem that need to be working on. In some rural regions, the farmland has declining causedby the rural population is shrinking and aging. The remaining farms are run by elderly
farmers and they might stop farming soon when they get older.
In our onsite team project, we try to investigate the current structure of the agriculture
activities as well as the most difficulties that faced by the local farmers and suggest solution
for these problems.
Our project aimed to achieve the following outcomes:

II.

Study area

Kita-hiroshima town is a town in Yamagata District, Hiroshima Prefecture, Japan. Kitahiroshima was formed on February 1, 2005 from the merger of the towns of Chiyoda,
Geihoku, Oasa and Toyohira which are all from Yamagata district.
Kitahiroshima is classified as a mountainous area of an altitude of 200m~1223m located in
the northern part of Hiroshima prefecture with a land area of 646.24 km² consisting 7.6% of99the prefecture. As of 2018 census, Kita-hiroshima houses 18,840 people and 8,591
households (Kitahiroshima Town Official Website, October 2018).

Figure 1: Location of Kita Hiroshima(Source: https://www.town.kitahiroshima.lg.jp/site/teijuu-joho/)

III.

Methodology

Social course is responsible for investigating the current structure of the agriculture activities
as well as the most difficulties that faced by the local farmers by questionnaire survey. After
conducting a survey, we are able to identify the various challenges that local farmers arefacing then propose a potential solution. Understanding the local people willingness to joina new project is also important for the success of the project. After the implementation of
the project, a survey will be conducted to study the impact of the project on the productionof the farm as well as the other aspect of local people’s life.
Figure 1: Data Gathering and Survey Method100

1 . Interview withlocal government
and local people
before introducing
the project

3. Distribution ofsurveyquestionnaires

2 . Meeting withthe local people(introduction of
the team project)

IV.

4. Data analyzing
after theimplementation of
the project

Results

The result in table 1 describes the descriptive statistics of 63 respondents in Kita Hiroshima.
The average age of respondents is relatively high (65 years old). This represent for the typicalaging population in this region. According to the table, more than 65% of respondents areworking as farmer or related to agricultural activities, while 35% of respondents are working
in other sectors.
Table 1: The descriptive statistics of respondent in Kita Hiroshima Prefecture
Number of
Variableobservations

Average

Minimum

Maximum

63

65.31

28

85

(1= male, 0=female)

63

0.90

0

1

Number of HH member

61

2.90

1

6

(1=farmer, 0=otherwise)

60

0.65

0

1

Farmland size (ha)

57

14.96

0.03

174.01Age (years)
Gender

Occupation

101

The questionnaire also asked respondent about the main problem in the community that they
think are important. Overall, aging population was the most fundamental problem followed
by depopulation and no successor of the farm, 24% 22% and 20% respectively. Besides, theattack of wild animals in the farmlands was also a big problem (Figure 2).
In addition, figure 3 presents the most pressing issues of the household. 37% of respondents
were stressed by the low profit from farming. The second most pressing issue was that they
worried no one will take care of their farm after they are getting older (20%), followed by
the loss of their crop due to animal attacked.

Problem in the Community
30
25

%

20
15
10
5
0

1. Ageing
Population

2. Depopulation 3. Attacks of wild
animals in the
farmlands

4. Young
generation isleaving thecommunity

5. No successorsof the farmlands

6. Others

Figure 2: The main problems that community is facing

102

MOST PRESSING ISSUES OF THE HOUSEHOLD
8. Others2%
7. Low profit fromfarming.
27%

6. I have no choice
but to give up
farming.
6%My children are
5.
leaving the town andno one is taking overto manage the farm.
13%

1. I am getting older
and no one is taking
over my farm.
20%

2. I am getting older
and I am not strong
enough to do
farming activities.
13%
3. I am sick and no
one is taking care of
my farm.
4. Wild animals are 1%
destroying my crops
18%

Figure 3: The most pressing issues of the household worries the mostFigure 4 and 5 indicates the main health problems that respondents were experiencing when
they do farming activities. Overall, they responded that their hands are very weak, and it isdifficult for them to carry the heavy things; especially during harvesting season. The othermain physical limitations are either their whole body, back or legs.

103

Physical Limitations when Farming
10

I easily got backpain whenever I do farming…

13
14

My legs are hurting. I can only stand for a…
Others19

My whole body can't stand the cold/hot…

32

My hands are weak. I cannot carry heavy…

0

5

10

15

20

25

30

%
Figure 4: The main physical limitations when do farming

5. Others
11%

1. Hands or arms
21%

2. Upper or lowerback
11%
4. Whole body40%

3. Legs
17%
Figure 5: Which part of the body that most pain when do farming?
Regarding the willingness to try new agricultural assistive tools that are helpful, 61%
respondents agreed to try if it’s affordable and safe (Figure 6). Thus, easy to use andaffordable are two crucial factors when designing any new assistive tools.
104

35

4. No, I want towork manually…

1. Yes, as long as they are
not expensive
12%

2. Yes, as long as they
are safe…

3. Yes, as long as
they are affordable
and safe
61%
Figure 6: If there are new agricultural assistive tools that are helpful for you, would you
want to utilize them?

V.

Conclusion

After analyzing the data, we are able to identify the various challenges that local farmers arefacing such as the physical limitation when doing farming and the attacking of wild animal
to the crops. We proposed a technical solution to solve these challenges that most farmer arefacing. Firstly, providing new and improved assistive tools to improve and reduce chancesof muscle fatigue and stress for farmer. Secondly, developing a detection and tracking device
system to track the movement of wild animals to that farmer can use the data to take anevasive action. These solutions are expected to be used by not only farmer in Kita Hiroshima
but also farmer in other rural areas of Japan.

105Appendix 2: A survey under the Onsite Team Project of and with the support from
Hiroshima University TAOYAKA Leading Graduate School Program

Code: _________________________________[Name: Optional] ______________________________________
Age of Head of the Household (HH): __________
Gender of HH Head: 1. Male 2. Female
Total number of household members: _________________
Occupation of HH head: ____________________________________
No. of managed farmland in hectares: _________________________

Instruction: Check the box that corresponds your answer. You can choose one or more
answers.
1. What are the problems that your community is facing?
□

Ageing population□

Depopulation□

Attacks of wild animals in the farmlands□

Young generation is leaving the community□

No successors of the farmlands□

Others: ____________________________________

2. What is the most pressing issue in your household that worries you the most?
□I am getting older and no one is taking over my farm.
106□I am getting older and I am not strong enough to do farming activities.

□

I am sick and no one is taking care of my farm.

□

Wild animals are destroying my crops (Please proceed to no. 3 and no. 4)□

My children are leaving the town and no one is taking over to manage my
farm.

□

I have no choice but to give up farming.

□

Low profit from farming□

Others: ____________________________________

3. How often you face wild animal invasion□

Every day□During Planting and Harvest Season□

Others ____________________________________________________

4. Does fencing around farmlands stops the animal invasion

□

Yes

□

To some extent□

No

5. I do farming because:□

It is my primary source of income.

□It has been in my family for several years now.

□

I love to farm and it is part of my everyday life.
107

□

Others: ______________________________________

6. Do you think farming is a very important livelihood in your community?

□

Yes, because_________________________________________________________□

No, because__________________________________________________________

7. Until what age do you want to do farming activities?

□

60-65 years old□

65-70 years old□

75-80 years old□

As long as I want to

8. As you age, what certain health problems are you experiencing that hinder yourfarming activities?
□

My hands are weak. I cannot carry heavy things.

□

My legs are hurting. I can only stand for a limited time.

□

My whole body can’t stand the cold/hot weather.

□

I easily get backpain whenever I do farming activities.

□

Others: _________________________________________

9. Have you had injuries while doing agricultural work?

□

YES
108

If yes, what kind? __________________________________________________
NO□

10. Which time is very difficult to do farming?

□

Plantation□

Irrigation□

Fertilizing□

Harvesting□

Others. Please specify _____________________________

11. Until to what extent do you want to use your body for farming?
□

I want my hands to still continue do farming□

I want my legs to be strong so I can continue do farming□

I want my body as healthy as possible so I can still manage my farmland
despite my old age

12. What activities do you do during non-farming season such as winter season?

□

Community assembly□

Join local festival such as : __________________□

Others: Please specify

13. If there are new agricultural assistive tools that are helpful for you, would you want toutilize them?

109

□

Yes, as long as they are not expensive□

Yes, as long as they are safe□

Yes, as long as they are affordable and safe□

No, I want to work manually.

□

No, I want to use the tools that I already have.

□

I don’t know.

14. What are the factors you want to consider if these tools are introduced for farming?

□

Affordable□

Portable

□

Safe

□

Easy to use□

Not heavy□

Others: _________________________

15. Do you think that new agricultural assistive tools can improve your everyday life?

□

Yes, because they ease my workload.

□

No, I don’t think so.

□

Other reason: _______________________
11016. What do you think is the local culture that best describes your community?

□

Rice farming□

Strong community ties□

Other: __________________________

17. Do you think that younger generation needs to know more about farming?

□

Yes, younger generation needs to know about farming.

□

Yes, but younger generation is going out to live in more urbanized area.

□

It depends on their willingness to know.

18. What are the things that still make you proud that you live in Kita-hiroshima?

□

We have a very unique culture.

□

I have strong connection towards my farmland.

□

I have good relationship with other community members.

□

Other: ________________________________

111

Appendix 3: Household survey Questionnaire in Vietnam
Name of village
Household No.
Name of respondent
Gender:Date

Section 1: Household Roster
No
1
2
3
4

Name

Age

GenderEduc. Level

Marital status Occupations Ethnic group

ID Code:Gender: 0 = Male, 1 = Female
Marital Status: 1 = single, 2 = married, 3 = divorced/separated, 4= spouse
died
Education level: 0 = None, 1 = primary school, 2= secondary school, high school, 3= college, 4 = higher
Occupations: 1= government employee; 2= daily wage earner; 3= monthly worker; 4= farmer
5= self-employee; 6 = Not working (61 = seeking and/or available for work; 62 = attended
school;63 = retired person; 64 = remittance receipient; 65 = not able to work due to disability)
Ethnic group: 1= Kinh, 2= Nung, 3= Tay, 4= Dao, 5= Mong, 6= Hoa, 7= San Chay, 8= Other112

SECTION 2: ECONOMIC ACTIVITIES (in the past 3 months)
2.1 SELF and WAGE EMPLOYMENTMember's
Name

What economic
1 Whereactivities did you do?
did you
Beside this. What
do thisother activities did you
activity?
do?

2 What was yourmajor field of
economic activities
you engage?

3 If you were
engaged inagricultural
sector, what
was youremployment
status?

4 If you were engaged in non-agrisector, what was your work status?

ID Code:1 = where did you work? (11= in the village, 12 = outside the village (write thename))
2 = what was your major field of economic activities you engaged in? (21= agriculture, 22 =non-agriculture)
3 = If you were engaged in agricultural sector, what was your employment status? (31= day labourer, 32 = self-employed, 33 =employer, 34 = employee
4 = If you were engaged in non-agricultural sector, what was your work status? (41 =day labourer, 42 = self -employed, 43 =employer, 44 = employee1132.2 DAILY WAGE

Daily wage

Member'sName

1 Wereyou paid
on a dailybasis

2 what was
the daily
wage in cashin the past 3
months3 Did youreceive
payment inkind?

4 What type of
in-kindpayment did
you receive?

5 How
much
did youreceiveper day?

8 What is
6 Whatyour total
type of7 What is
net takeorg
your grosshome
do/didremuneration remunerationyou
per month?
monthly
work
after allfor?
deduction?

9 What is
the total
value ofin kind orother
benefityourreceived
over thepast 3months?

ID Code:1= Were you paid on a daily basis? (1= Yes, 2 = No (goto Q 6))2= What was the daily wage in cash in the past 3 months? (21 = highest, 22 = lowest, 23 =average))
3= Did you receive payments in kind? (31 = Yes, 32 = No (next activity/next person))
4= What type of in-kind payments did you receive? (41 = rice, 42 = wheat/corn, 43 = meal, 44 =other)ID Code:6 = What type of organization do/did you work for?
1= Government
6= NGOs
organization

114

2= Autonomous body
3= Private office
4= Private mill/factory
5= Local government (village office)

7=Household
8=Other (specify)

SECTION 3:
MICROFINANCE STATUS
1. Has anyone in your household taken a loan so far?
2. Which program of VBSP did you borrow moneyfrom?
3. When did you join eachprogram in VBSP?
4. Is anyone in your household taking loan fromother MFIs?
5. Is anyone in your household taking informal
credit/loan?

Yes (Case 1) Go to 2 and 3
No Go
to 4
(code)(code)(Code)Yes (Case 2)
No (Case 3)
Yes (Case 2)
No Go
to Q

115

6. If your household belong to case 1, please answer these questions?
1

Householdmember's
name (ID if
have)

When
did you
start?

2

3

Purpose of loan?
(You can choose
up to 3)2.1

2.2

Amount
(VND)

4

Loan
term
(month)5Interest
rate
(%)

6

Repayment
method?

7

From
which
program?

8

9

Throughwhich
association?
If any?

Do therepayment processnow? (1=
Yes,
0=No)

10

11

12

13

When did you
join thesaving andlendinggroup?

How much
do you haveto deposit
monthly
under thegroup'sregulation?
(if any)How
manytimes doyou
depositper year?

Howmuch doyou
deposit
each
time?

2.3

116

1= Agricultural activity

6. Repayment
method1= monthly

2= Livestock breading

2= quarterly

3= Forestry activity

3= yearly

4= Fishery activity

4= at the end ofloan term

5= Self business

5= other (specify)

2. Purpose of loan

7. VBSP's Programs

6= Safe water and Rural Sanitation Program

8. Association

1= Poor Households Program
2= Near-poor Household
program
3= Disadvantaged student
program

7= Extremely Disadvantaged Ethnic Minority program

1= Farmer association

8= Housing support Program for the Poor

2= Women's Union

9= Others (Specify)

3== War veterans Association

4= Job Creation Program

7= Health5= Program for Business andProduction
Household Living in
Extremely
Disadvantaged Areas and Communes

8= House building

0= informal loan

6= Education

4= Old Person's Association
5= Young Union

9= Buying durable goods
10= Others (specify)117

7. If your household belong to case 2, please answer these question
1aHouseholdmember's
name (ID if
have)

Which MFIs
and othersource did
you borrowmoney?
(code)

2

3

Purpose of loan? (You canchoose up to 3)

2.1

2.2

Amount
(VND)

4

Loan
term
(month)5Interest
rate (%)

6

8

9

Repayment
method?

Throughwhich
association?
If any?

Do therepayment
process
now? (1=
Yes,
0=No)

10
When
did
you
join
the
savingandlendinggroup?

11
How much
do youhave todepositmonthly
under thegroup'sregulation?
(if any)

12

How many
times do you
deposit per
year?

2.3

118

8. If your household belong to case 3, please answer these question

Householdmember's
name (ID if
have)

1b
Have youever taken aloan so far?
(0= No, If
yes, please
specify
code)

2

3

When didyou start
borrowing?

Purposeof
loan?
(Code)

Other lending sources: 1a, 1b

3

Amount
(VND)

4
Loan
term
(month)5Interest
rate (%)

6

7

8

Repayment
method?

Do therepayment
process
now? (1=
Yes,
0=No)

Why don’t
you joinany MFIs/
VBSP
program?
(Code)

8. Main reason

1. Agribank
2. NGO Micro finance institutions (NGOMFIs)

1. Did not meet requirement

3. Other Commercial bank

3. Complicated procedure

4. Private money lender

4. Other

2. Did not need to borrow

5. Friends
6. Relatives
7. Other

119

SECTION 4. Have you joined any association in your place?
13.1 If yes, what is the association and the purpose of this association?

13.2 How do you judge the association activities?
13.3 If not, do you want to join any association?
13.4 What association would you like to join?

(1= Yes, 0= No)(5= Excellent, 4= good, 3= satisfactory, 2= unsatisfactory, 1= very poor)(1= Yes, 2= No)

13.5 What do you expect from joining this association?

120

SECTION 5: HOUSEHOLD LANDHOLDING
5.1. Landownership in 2015-2016

No.

Category

Area
(Ha)

Ownership(Yes=1,No=0)Name of the crop

Main
Production(kg)

Price/kg

1 Forest land
1. Cultivated
2. Uncultivated
3. For Lending
Agricultural
2land
1. Cultivated
2. Uncultivated
3. For Lending
3 Residential land
4 Other, specify
Total land
5
owned

121

SECTION 6: ASSET AND SAVING
1. Type of house(code)
1= concrete, 2= Wood, 3= Clay, 4= Other (specify)
2. Production assets Ownership: 1= Sole ownership, 2= joint ownership, Purchased or gift: If purchased, specify price; if gifted,
encode 012
34No. of units

Ownership(code)

Year of purchased

Purchased or gift(code)

No. of units

Ownership(code)

Year of purchased

Purchased or gift(code)

Truck
Threshing machine
Plough machine
Others3. Durable goods
1
2
3
4
5
6
7

Car
Motorbike
BicycleTelevision
Refrigerator
Gas stove
Cell phone

122

SECTION 7: AGRICULTURE - CROP PRODUCTION (In the past 3 months)
7.1 From agro-forestry and crop production
If Yes, please fill in the table below, if No, (do not fill in the table below)
1
Crop/products

Rice
Corn
Sugarcane
PotatoOnion
GarlicTomatoGreen banana
PapayaChili
Jackfruit
Bean
Pineapple
Melon
Orange2 (3+4+5)

3

Area of
Production

How much intotal of total
production?

Own
use

Ha

Kg

Kg

Quantity Unit price
Kg

4

5

6

Sold

Still
On
stock

7
(2*6)

Price per
unit

Total
value

Kg

VND

VND

To
Place Distance whom(*)(*) At farm =1; Village market =2 outside the village = 3; Others = 4123

7.2 costs for agro-forestry and crop production (In the last 3 months)
Inputs
Seeds
Fertilizers
Pesticides and herbicides
Hired labour
Hired machineTransport
Payment for land rental
Own use inputs
Others (extension fee, etc), specify

Cost (VND)

Total

124

SECTION 8: FORESTRY PRODUCTION (In the past 3 months)
8.1 From forestry production
If Yes, please fill in the table below, if No, (do not fill in the table below)
12
3 (4+5+6)
4

5

products

Sold

Where to collect?

How much
Area ofin total of
Production
1.1
total
(if any)
Forest 1.2Ownershipproduction?
type(code)(code)
Ha
Kg

Own
use

Kg

Quantity

Unit
price

To
Place Distance whom
(*)

Kg

VND

km

6
Still
On
stock

7
Price
per
unit

8 (2*6)

Kg

VND

VND

Gross
value

TimberBambooBamboo ShootMushroom
Firewood
Other, specify

1.1 Forest Type: 1= assigned production forests; 2= Non-assigned production forests, 3= Community forests, 4= Other, specify
1.2 Ownership: 1= Yes, 2= No(*) At farm =1; Village market =2 outside the village = 3; Others = 4125

8.2 Production costs for agro-forestry and crop production
Inputs
Seeds
Fertilizers
Pesticides and herbicides
Hired labour
Hired machineTransport
Payment for land rental
Own use inputs
Others (extension fee, etc), specify

Cost (VND)

Total

126

SECTION 9: LIVESTOCKS PRODUCTION (In the last 3 months)
9.1 From livestock’s Production If Yes, please fill in the table below, if No, (do not fill in the table below)
1
2
3
4
Sold
Livestock
Own
Still On
Unit
To whomuse Quantity Unit
stock
Place Distance
price
(*)
Cow
Meat
kg
Breeder
individual
Buffaloes
Meatkg
Breeder
individual
Pig
Meat
kg
Breeder
individual
Goat
Meat
kg
Breeder
individual
Duck
Meat
kg
Egg
egg
Chicken
Meat
kg
Egg
egg
HorsesMeatkg
Breeder
individual
Other
kg

5

6 (2+3+4)

7 (5*6)

Price perunit

Production

Total
value

(*) At farm =1; Village market =3 outside the village = 3; Others = 31279.2 Production costs for livestock production
1
Breeder
Livestock(VND)
Cow
Buffaloes
Pig
Goat
Duck
ChickenHorses
Other

2

3

4
Input
Purchased Veterinary fromfeed/fodder services
own
(VND)
(VND)
farm
(VND)

5

6= 2+3+4+5

Other
costs
(VND)

Total cost

128

SECTION 10: AQUACULTURE PRODUCTION (In the last 3 months)

Typeof fish

1
Unit
kg
kg
kg
kg
kg
kg

2
Own
use

Quantit
y

Unit
price

SECTION 11: OTHER PRODUCTIONs
Type of income

3
Sold
Plac Distancee

To whom (*)

Total amount received

4
Price per
unit
(VND)

5 (2+3+4)
Total catchor
production

6(4*5)

7

8(6-7)

Gross
value

Costs

Net Income

From Who

Total

129

SECTION 12: HOUSEHOLD MONTHLY EXPENDITURE
Items
I. Cereals/crops
1. Rice
2. Maize
3. Wheat
4. Soybean
5. Groundnut
6. Sesame
7. Others (specify)
II. Fruits and Vegetables
1. All vegetables (chilli, onion, garlic, etc)
2. Fruits (pumpkin, cucumber, etc)
III. Milk and Milk Products
1.Milk
2. Butter
IV. Other Food Items
1.All spices (salt, pepper, etc.)
2. Sweets, biscuits, etc
3. Tea, coffee, sugar, etc
4. Meat/fish/chicken/eggs
5. All types of edible oils (soybean, etc)

Home
Produced

Quantity (Kg)
Purchased

Gifts/Kindwages/loan

Code1

Price/Unit

Total value (vnd)130

V. Non-Food Items
1.Alcohol, Beer, etc
2.Tobacco, cigarettes, etc
3. Minor household articles
4. Kerosene
5. Firewood, etc
6. All types of cosmetics (hair oil, soaps, toothpaste, detergent,etc)
7. Electricity and Water charges
8. Clothing and shoes
9. Furniture (tables, chair, etc)
10. Watch, TV, fan, etc
11. Vehicles (motorbike, car, etc)
12. Medical expenses (fees)
13. Education expenses (fees)
14. Taxes (house, land, vehicles, etc)
15. Travel/Petrol, vehicle maintenance, etc
16. Ceremonies (marriage, donation...)17. Entertainment (telephone bills, drinking, gambling…)
VI. Other purchases
1. Land
2. Livestock
3. Farm implements & machinery repairs
4. Stationery, post, etc
5. Others (specify)
Note: Code 1 - 1= Kind wages, 2 = Gift from agency, 3 = Gift from individual or relative, 4 = Loan

131