Essays on financial activities and industrial change the study case in vietnam

GENERAL INTRODUCTION

Background of the study

The Agenda for Development (A/RES/51/240) defines development as a process aimed at enhancing the quality of life for all individuals Key elements for achieving sustainable development include economic, social, and environmental progress Furthermore, the Sustainable Development Goals (SDGs) emphasize the importance of empowering domestic financial institutions to broaden access to banking, insurance, and financial services for everyone Consequently, this study examines sustainable economic development in Vietnam.

Rural development plays a crucial role in alleviating poverty in Vietnam, where the government has implemented various programs, including microcredit initiatives and agricultural extension services, to support households Once one of the poorest countries in the mid-1980s, Vietnam has successfully transitioned to a middle-income nation (Banker & Ungor, 2019) In recent years, the country has transformed its economic structure through industrialization and modernization, capitalizing on the comparative advantages of its industries and services.

Objectives

This study aims to achieve two main objectives:

Between 2000 and 2015, Vietnam experienced significant changes in its economic structure, with a focus on identifying key sectors that drove this transformation This period saw a shift towards industries such as manufacturing and services, highlighting their pivotal roles in the country's economic development Understanding these changes is essential for analyzing Vietnam's growth trajectory and future economic strategies.

This article explores the financial dynamics of small and medium-sized enterprises (SMEs) and rural households in Vietnam It aims to identify the relationship between corporate finance, specifically financial leverage, and investment decisions within Vietnamese SMEs Additionally, the study evaluates how membership in farmer unions affects the financial outcomes of rural households, highlighting the significance of these organizations in enhancing economic stability and growth.

Contribution of the study

This study enhances the current understanding of rural development in Vietnam by examining the impact of social organizations in rural areas Furthermore, it adds valuable insights into the interplay between corporate finance and investment decisions within emerging markets.

This study uses several methodologies to address the problem such as causal inferences, input-output decomposition analysis which have received much attention in the current research literature.

Structure of the study

This study explores Vietnam's economic development, beginning with an overview in Chapter 1 Chapter 2 analyzes the significance of the food supply chain in Vietnam from 2000 to 2015 through a decomposition Input-output approach Chapter 3 examines the relationship between corporate finance and investment decisions of Small and Medium Enterprises in Vietnam, employing various econometric techniques, including Logit, Tobit, and Fractional logit models Finally, Chapter 4 provides an empirical assessment of how farmer union membership influences household production and credit volume, utilizing the Propensity Score Matching approach.

HOW IMPORTANT A FOOD SUPPLY CHAIN IN VIETNAM

Introduction

Many economists, such as Kuznets (1979), Lin and Monga (2011), Uy et al

Economic restructuring plays a crucial role in a country's development and growth, as highlighted by Vu and colleagues In recent years, Vietnam has undergone significant transformation through industrialization and modernization, leveraging its comparative advantages in various industries and services After three decades of renovation, Vietnam has achieved remarkable progress, successfully transitioning from a low-income to a middle-income country, according to Barker and Ungor.

A significant body of research has explored changes in economic structure, with key contributions from scholars such as Leontief (1941) and Feldman et al (1987) Many studies have adopted a macroeconomic perspective, analyzing aggregate indicators like employment and GDP growth (Hacks, 1989; Skolka, 1989) For instance, Pham Quang Ngoc & Mohnen (2004) utilized multisectoral models to link Vietnam's economic growth to structural changes, examining input-output tables from 1989, 1996, and 2000 K.M V (2017) also highlighted the positive impact of structural change on GDP growth While often overlooked, the industrial approach is crucial for understanding structural change This study aims to employ the industrial approach to identify key sectors and analyze the economic structure of Vietnam using input-output tables from the year 2000 onward.

2015 Backward-forward linkages and the decomposition approach are applied to

4 analyze the IO tables The major findings are used to explain the role of certain subsectors and reveal the trends in the economic structure of Vietnam’s economy

This paper contributes significantly to the literature on structural economic change by analyzing the Vietnamese economy through industrial analysis It decomposes output changes into technological advancements and final demand for each sector, allowing for an investigation of the key factors influencing total input changes Additionally, this study is the first to utilize four Vietnamese input-output data tables to compare the economy's structure across three distinct periods, providing insights into Vietnam's structural economic transformation over 15 years, from 2000 to 2015.

Vietnam has undergone significant structural economic changes, as reported by the General Statistical Office (GSO) in 2016 The country has reduced its reliance on agriculture, with the agricultural sector's contribution to GDP decreasing by 6.2% from 23.24% in 2000 to 17% in 2015 Meanwhile, the industrial and construction sector also saw a decline of 4.88%, dropping from 38.13% to 33.25% In contrast, the services sector experienced growth, increasing its share of GDP by 1.1%, from 38.63% to 39.73%.

Figure 1 illustrates the share of four main sectors in Vietnam’s GDP from

Between 1986 and 2009, significant shifts occurred in various economic sectors, particularly agriculture and manufacturing The agricultural sector's contribution to GDP decreased from 34% in 1986 to 17% in 2009, while the manufacturing sector saw growth from 17% to approximately 25% during the same period The services sector emerged as the dominant force, increasing its share of GDP from over 46% in 1986 to 54% in 2009 In contrast, the mining and quarrying sectors remained relatively stable, contributing less than 6% to GDP throughout these years.

Figure 1: Shares of GDP by Sectors, 1986–2009

The food supply chain is essential for rural development, producing vital raw materials for various industries (Marsden et al., 2000; Kastrinaki & Stoneman, 2011) It encompasses all processes from production to distribution, highlighting the agricultural sector's crucial role in this chain (Aramyan & Van Gogh, 2014) In Vietnam, agriculture remains the primary supplier of raw materials, significantly supporting sectors like food and textiles, with the food sector heavily reliant on agricultural inputs (Dieu TTM, 2006).

Input-output (IO) analysis examines the interconnections between industries, households, and government, providing valuable insights into a country's economy Numerous studies, including those by Skolka (1989), Franke and Kalmbach (2005), and Marconi et al (2016), have utilized IO analysis to explore economic dynamics Additionally, research by Hongsakhone and Ichihashi (2019) has focused on the interdependencies among households In Vietnam, several studies, such as those conducted by T Bui et al., have analyzed changes in the nation's economic structure using IO tables.

In their studies, T Bui and Kobayashi (2012) analyzed interindustry linkages between manufacturing and nonmanufacturing sectors using input-output (IO) tables from 1989, 1996, and 2000 Their findings revealed that the manufacturing sector exhibited stronger internal linkages compared to nonmanufacturing sectors Subsequent research by Phong N.V (2013), Nguyen P Thao (2014), and Tran et al (2016), as well as Ha, N.H.P & Trinh, B (2018), further explored these dynamics, contributing to a deeper understanding of industry interactions over time.

Current research highlights the importance of the Input-Output (I-O) decomposition method; however, there is a lack of studies utilizing four I-O tables simultaneously Most existing literature typically compares data over extended periods, usually five years, which may not accurately reflect changes in economic structures that can occur in shorter timeframes.

Our analysis reveals that the agriculture, hunting, forestry, fishing, food and beverage, and tobacco product sectors saw significant output gains due to shifts in final consumption demand Additionally, the machinery and equipment sector experienced substantial growth, driven by changes in final demand for investment and exports, as well as technological advancements in intermediate inputs Conversely, the wholesale and retail trade and repair sector faced a dramatic decline in final demand for consumption, investment, and exports over the past five years (2010-2015).

This paper is structured into six sections: Section 1 provides an overview of the research statement and background on Vietnam's economy Section 2 details the methodology employed in the study Section 3 describes the data utilized for analysis Section 4 presents the empirical results, while Section 5 discusses the implications of these findings Finally, Section 6 offers concluding remarks on the research.

Methods and Models

To identify the key sectors in the Vietnamese economy and investigate its structural transformation 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

Where X is total output; [𝐼 − (𝐼 − 𝑀)̂ 𝐴] −1 is the Leontief inverse matrix;

A is the input coefficient matrix; I is the 34x34 identity matrix; 𝑀̂ is a 34 x 34 diagonal matrix with diagonal elements 𝑚 𝑖

𝑀)̂ is self-sufficient rate matrix; 𝐹 𝑑 is vector of domestic final demand; 𝐸𝑋 is vector of total export

Backward and forward linkages, introduced by Rasmussen in 1956, are essential for identifying key sectors within an economy Backward linkage refers to the connections a sector has with others from which it purchases inputs, illustrating how a one-unit increase in sector i affects other sectors Conversely, forward linkage describes the relationships a sector maintains with those to which it sells its outputs Both linkages can be quantified using specific equations, as outlined by Miller and Blair in 1985.

Where: bij are the inverse matrix elements [𝐼 − (𝐼 − 𝑀)𝐴̂] −1 , n is the number of sector (n4)

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 expressed as:

The change of output can be expressed in the following ways:

⏟ (7) Technology change Final demand change

Where: 𝑩 = [𝑰 − (𝑰 − 𝑴 ^ )𝑨] −𝟏 is the Leontief inverse matrix, F is the vectors of final demand

This study employs the structural decomposition method established by Dietzenbacher & Los (1998), which analyzes total output changes as a sum of shifts in final demand and technological advancements Subsequently, we further dissect the alterations in final demand by examining its components, including consumption (C), investment (I), and exports (EX).

Data

This study utilized Vietnam's Input-Output (IO) tables from 2000, 2005, 2010, and 2015, as published by the Organization for Economic Co-operation and Development (OECD) The IO tables reflect the transactions of goods and services across 34 sectors in current prices (USD million) for the years 2000, 2005, and 2010, while the 2015 table includes 36 sectors To maintain consistency in our analysis, the 2015 IO table was adjusted to align with the 34 sectors used in the earlier years Table 1 below presents the selected 34 sectors for this research.

Table 1: Sectors selected for the study

1 Agriculture, hunting, forestry and fishing

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 fuel

10 Other non-metallic mineral products

14 Computer, electronic and optical equipment

16 Motor vehicles, trailers and semi-trailers

19 Electricity, gas and water supply

21 Wholesale and retail trade; repairs

27 Renting of machinery and equipment

30 Public administration and defense; compulsory social security

33 Other community, social and personal services

34 Private households with employed persons

Results

Table 2 displays the output structure based on the 34 sectors The total output of the Vietnamese economy $66,545.9 million in 2000 and $570,059.6 million in

Between 2000 and 2015, the agricultural sector's contribution to the economy declined significantly from 21.15% to 13.07% Similarly, the wholesale, retail, trade, and repairs sector saw a decrease from 11.56% to 5.02% In contrast, the food products, beverage, and tobacco sector experienced growth, increasing its share from 11.21% to 12.42% during the same period, marking a notable rise of 3.72%.

10 share of the textiles, textile products, leather, and footwear sector to Vietnam GDP over the 15-year period 2000-2015

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

Agriculture, hunting, forestry and fishing

Food products, beverages and tobacco

Textiles, textile products, leather and footwear

Wholesale and retail trade; repairs 7690.2 11.56 13873 9.87 30033.9 10.48 28630.4 5.02

Coke, refined petroleum products and nuclear fuel

Renting of machinery and equipment

Electricity, gas and water supply 1363.5 2.05 2761.7 1.97 5673.5 1.98 12531.3 2.20

Motor vehicles, trailers and semi- trailers

Other non-metallic mineral products 1351.1 2.03 3836.3 2.73 7036 2.45 9715.3 1.70

Pulp, paper, paper products, printing and publishing

Wood and products of wood and cork 297.5 0.45 1382.6 0.98 2258.2 0.79 6791.4 1.19

Other community, social and personal services

Private households with employed persons

Table 3 presents the backward and forward linkage results for 34 industries across the years 2000, 2005, 2010, and 2015, calculated using equations (2) and (3) These linkages assess the intersectoral connections of each industry with others, where backward linkages demonstrate how an industry impacts others, while forward linkages reflect how it is influenced by external industries (Chenery and Watanabe, 1958).

According to Table 3, the agriculture, hunting, forestry, and fishing sector is the leading sector in the Vietnamese economy, as both its backward and forward

The analysis reveals that linkages between agriculture, hunting, forestry, and fishing with other sectors consistently exceed 1 across all periods, indicating strong interdependencies in both input demand and output supply Additionally, the pulp and paper sector remains significant throughout the examined years Notably, the food products, beverages, and tobacco sector underwent considerable transformation over the 15-year period; initially reliant on interindustry supply in 2000, it has since emerged as a vital component of the Vietnamese economy since 2005.

BL FL BL FL BL FL BL FL

Wood and products of wood and cork

Pulp, paper, paper products, printing and publishing

Other non-metallic mineral products

Motor vehicles, trailers and semi- trailers

Electricity, gas and water supply

Renting of machinery and equipment

Wholesale and retail trade; repairs

Public administration and defence; compulsory social security

Private households with employed persons

Using equation (7), we analyzed the total output change by breaking it down into technological advancements and final demand shifts across three distinct periods: 2000-2005, 2005-2010, and 2010-2015 The findings, presented in Table 4, indicate that the agriculture, hunting, forestry, fishing, and food, beverage, and tobacco product sectors underwent significant changes during these years.

Between 2000 and 2015, the machinery and equipment sector experienced significant technological advancements, leading to substantial output growth In contrast, the wholesale and retail trade and repair sector faced a dramatic decline in total output during the 2010-2015 period.

Between 2000 and 2005, the food sector experienced the most significant technological advancements, while the mining and quarrying sector led from 2005 to 2010, and the textiles sector took the lead from 2010 to 2015 Despite this shift, the food sector maintained the second-highest level of technological change in the most recent period Additionally, the agriculture sector demonstrated the largest change in final demand across all three periods, followed by the food, wholesale and retail, textiles, and construction sectors.

2015), there was a drastic decline in the 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 analyzed the changes in final demand, breaking it down into consumption, investment, and exports, as shown in Table 5 The agriculture sector exhibited the most significant change in consumption across all three periods, followed closely by the food and wholesale sectors In contrast, the textiles sector led in export changes during the same periods, with the agriculture and food sectors also ranking high in exports Specifically, the agricultural sector recorded the third-largest export change between 2000-2005 and 2015-2010, and the second-largest in 2005-2010 The food sector ranked fifth, fourth, and second in export changes for 2000-2005, 2005-2010, and 2010-2015, respectively Additionally, construction showed the largest change in investment over the entire 15-year period, while the machinery sector's growth in the last five years was largely attributed to changes in final investment and export demand.

Some main findings are clear from the tables First, the agricultural sector and food sector played important roles throughout the 15 years considered The

The significant changes in total output in the textile and machinery sectors of Vietnam's economy are largely attributed to shifts in final consumption demand The textile sector has emerged as a key player, bolstered by rising export levels In contrast, the machinery sector experienced notable growth from 2010 to 2015, driven by increased final investment, export demand, and advancements in technology for intermediate inputs However, the wholesale and retail sector faced a considerable decline in final investment and export consumption demand during the same five-year period.

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

Food products, beverages and tobacco 40716.8

Textiles, textile products, leather and footwear 37785

Agriculture, hunting, forestry and fishing 30579.7

Textiles, textile products, leather and footwear 4959.9

5379.91 Food products, beverages and tobacco

Coke, refined petroleum products and nuclear fuel 7475.96

Renting of machinery and equipment 6200.10

Wholesale and retail trade; repairs 16423.7

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)

Motor vehicles, trailers and semi-trailers 1094.89

Other non- metallic mineral products 928.99

Computer, Electronic and optical equipment 4784.90

Motor vehicles, trailers and semi-trailers

Discussion

Between 2000 and 2015, the agriculture sector remained a cornerstone of Vietnam's economy, undergoing significant reforms since the Doi Moi policy began in the 1980s The shift from collective to household-based farming, initiated by Decree No 10 in April 1988, allowed farmers greater autonomy in managing their production While agricultural cooperatives improved rural infrastructure, they fell short in providing equitable income for members In the 1990s, further reforms reduced government oversight, allowing for freer trade in rice, increased export quotas, and lower agricultural taxes These changes enhanced farmers' access to production inputs and enabled them to market their products more freely, leading to a notable transformation in both consumption and exports in the agriculture sector from 2000 to 2005.

Under Decree 5 from the 4th Party Congress, the Vietnamese government aims to shift the agricultural economic structure towards large-scale commodity production that integrates with the processing industry and market demands Agricultural activities play a crucial role in Vietnam's food supply chain, supplying essential inputs to food processing enterprises Analysis of four Input-Output (IO) tables reveals that the agriculture sector primarily sells its products to the food products, beverages, and tobacco sectors (2015).

61%, 2010 – 46%, 2005 – 46%, 2000 – 38%) Additionally, the food sector mostly purchases inputs from the agriculture sector (approximately 50%)

Vietnam's textile sector has experienced rapid growth, largely due to free trade agreements Between 2000 and 2015, it emerged as one of the fastest-growing industries globally, achieving an annual growth rate of around 6%, positioning Vietnam as a leading exporter in the textile market.

Vietnam has emerged as the second-largest supplier of textiles and garments to the United States, European Union, and Japan (Pertiwi & Sukmawani, 2017) Prior to its independence in 1975, the Vietnamese government fully controlled the textile and garment industries, with exports primarily directed to the Soviet Union (A.N Tran, 1996) Post-independence, many textile firms became self-managed, leading to a significant boost in exports after Vietnam signed a bilateral free trade agreement with the US in 2000 The country’s accession to the WTO in 2007 further opened markets in the US, EU, and China, yielding numerous advantages for the textile and garment sector (CIEM, 2010) Subsequent trade agreements with Australia, South Korea, and Japan under the ASEAN framework have also enhanced exports Recent agreements, including the EU-Vietnam Free Trade Agreement (EUVFTA) and the Trans-Pacific Partnership (TTP), are expected to further benefit this vital industry.

In Vietnam, state-owned corporations maintain significant control over key industries, such as the Vietnam Coal and Minerals Industries Corporation (Vinacomin) in mining, the Vietnam Tobacco Corporation (VINATABA) in tobacco, and the Vietnam Food Association (Vinafood 1 and Vinafood 2) in rice products Despite this dominance, some textile enterprises, like Vinatex and Garco10, are increasingly becoming independent from government influence Consequently, the country faces ongoing challenges in its industrial development.

23 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

Our study aligns with previous research by Dang et al (2019) and Ha and Trinh (2018), which analyzed Vietnam's economic structure using input-output (IO) tables from 2012 and 2016 They found that the agriculture, food, oil and gas production, and manufacturing sectors significantly influence the input demands of other industries Despite shifts in the economic landscape from 2000 to 2015, agriculture and food sectors remained vital to Vietnam's economy However, challenges persist, as economies reliant on primary industries like agriculture can become vulnerable due to decreasing returns to scale (DRS) The need for new technologies and innovations is critical for expanding production and avoiding stagnation Consequently, sectors such as machinery or textiles may emerge as leading industries in the future.

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

Textile industry Garment Industry Footwear

Vietnam National Textile and Garment Group (VINATEX)

Pou Yuen Vietnam Limited Liability Company

Duc Quan Investment and Development Joint Stock Company Viet Tien Garment Corporation

TaeKwan Vina Industrial Limited Liability Company

Dam San Joint Stock Company Garco 10 Corporation

Hwaseung Vina Limited Liability Company

Phu Bai Spinning Mill Joint Stock

Chang Shin Viet Nam Limited Liability Company

Hoa Tho Textile Garment Joint Stock Company

Pou Sung Vietnam Limited Liability Company

Saigon Beer-Alcohol-Beverage Corporation (Sabeco)

Acecook Viet Nam Joint Stock Company

Vietnam Tobacco Corporation (Vinataba) Saigon Tobacco Limited Company

Heineken Vietnam Vinh Hoan Joint Stock Company Vinataba Thang Long Hanoi Beer, Alcohol and Beverage

Bien Dong Seafood Limited Liability Company

Hanoi Liquor Joint Stock Company Minh Phu Seafood Corporation

Binh Tay Wine Joint Stock Company

VIETNAM AGRIBUSINESS Limited liability Company

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 Tân Thạnh An Limited Liability

Bien Dong Seafood Limited Liability Company An Cuong Woodworking materials Kien Giang Import and Export Joint

Shing Mark Vina Limited liability Company

Tan Dong Tien Joint Stock Company Hung Vuong Corporation – HVG DONGWHA Corporation

Hoa Phat Joint Stock Company Thaco Group Kubota Tractor Corporation Gang Thép Thái Nguyên Joint Stock

Company Toyota CLAAS KGaA GmbH

VISCO Joint Stock Company Honda

Vietnam Engine and Agricultural Machinery Corporation (VEAM)

Dana Joint Stock Company Ford Thaco Corporation

Viet Duc Joint Stock Company GM Vietnam Truong Hai Auto Corporation (THACO)

Vinacomin Masan resources Nui Phao Thach Khe (Vinacomin)

The Vietnamese Agricultural Machinery Market is projected to grow from USD 425 million in 2025 to USD 560 million by 2030, reflecting a CAGR of 11.5% Agriculture, contributing approximately 14% to Vietnam's GDP, faces labor shortages that drive the adoption of mechanized solutions, especially in rice farming The shift towards modern machinery, such as combine harvesters, enhances efficiency and reduces reliance on manual labor As domestic and export demands for crops like rice and coffee rise, farmers increasingly utilize advanced equipment to meet quality standards The market is moderately consolidated, with key players like CLAAS, Kubota, and Yanmar leading the industry, focusing on innovation and strategic partnerships to expand their presence.

Conclusions

Identifying leading industries is crucial for poverty reduction and economic development in developing countries For instance, the IT sector in the United States, the automobile industry in Japan, and the electronics sector in South Korea serve as prime examples of this concept Despite Vietnam's rapid growth over the past 15 years, it still qualifies as a developing nation This paper aims to pinpoint the key industries driving Vietnam's economic development by utilizing the decomposition method on a series of input-output tables from 2000 to 2015.

From 2000 to 2015, the agricultural sector and food products consistently ranked among the top five of 34 sectors, highlighting their crucial role in supporting the Vietnamese economy Additionally, textiles and mining emerged as significant manufacturing industries during this period Notably, the machinery sector experienced rapid growth in the last five years, indicating its potential as a future driver of the Vietnamese economy.

The 2015 Leontief inverse analysis reveals that both food products and the agricultural sector exhibit significant backward and forward linkage effects, each exceeding 1 This indicates a high demand for outputs from these sectors by others, as well as their substantial impact on various sectors through the purchase of intermediate goods Notably, food products emerged as the most influential sector in terms of backward linkage effects in the years 2000, 2005, and 2010, while the agricultural sector was the most sought after for forward linkage effects during the same period.

A decomposition analysis revealed that significant output growth in food products and the agricultural sector was primarily driven by shifts in final consumption demand Additionally, the textiles industry saw an enhanced role in the Vietnamese economy due to rising final export demand Conversely, the machinery sector experienced rapid growth over the last five years of the analyzed period.

Over the past five years, the wholesale and retail sector has faced a significant downturn in final consumption, investment, and export demand In contrast, the overall changes in the economy were influenced not only by fluctuations in final investment and export demand but also by technological advancements in intermediate inputs.

The Vietnamese economy heavily relies on food products and agriculture, which are vital sectors; however, this dependence poses significant challenges for economic development The primary industry, particularly agriculture, is often characterized by diminishing returns to scale (DRS), making the country vulnerable to economic stagnation Many developing nations face similar issues, as reliance on the primary sector can hinder progress and innovation To foster sustainable growth, Vietnam should diversify its economy by investing in manufacturing industries such as machinery and textiles, thereby enhancing its economic resilience and development potential.

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

THE LINK BETWEEN FINANCIAL LEVERAGE AND

Introduction

The relationship between capital structure and investment decisions is a critical focus in corporate finance, with extensive research exploring both theoretical and empirical aspects Key macroeconomic factors influencing investment decisions include the real exchange rate, inflation, and capital flows (Binding and Dibuasu 2017; Atella 2003; Chen Fei et al 2019) Additionally, firm-level factors such as accounting quality, financing constraints, management characteristics, and capital structure significantly impact investment choices (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, defined as the ratio of total debt to total assets, plays a crucial role in corporate investment strategies, especially in incomplete markets characterized by transaction costs and asymmetric information (Aivazian et al 2005) This study explores the relationship between capital structure, indicated by financial leverage, and investment decisions among Vietnamese SMEs, utilizing a comprehensive unbalanced panel dataset from 2011 to 2015 Employing econometric techniques such as Logit, Tobit, and Fractional Logit models, the analysis reveals significant insights into the correlation between financial leverage, investment choices, and financing sources.

Financial leverage is a key indicator of capital structure and significantly impacts investment decisions in corporate finance (H.T Trinh et al 2017) Established financial theories suggest that financial leverage may be irrelevant (Modigliani & Miller 1958) or inversely related to a firm's investment (Myers 1977; Lang et al 1996; Aivazian et al 2005; Gome, 2001).

Modigliani and Miller's (1958) capital structure theory posits that in a perfect market, a company's capital structure does not influence its market value, which is instead determined by profitability, cash flow, and net worth However, real-world market imperfections, such as moral hazards and information asymmetry, challenge this notion (Jensen 1986; Lang et al 1991; Myers and Majluf 198) Their original theory overlooked critical factors like taxes, transaction costs, and bankruptcy costs (Frank & Goyal 2009) Subsequent theories, including the trade-off theory (Myers 1984) and pecking order theory (Jensen & Meckling 1976; Ross 1977; Myers & Majluf 1984), argue against Modigliani and Miller, suggesting that firms have distinct preferences for various financing types Myers (1984) emphasized that companies weigh the tax advantages of debt against the potential costs associated with bankruptcy.

The empirical literature challenges the leverage irrelevance theory and supports the pecking order theory, suggesting that firms prioritize financing sources based on cost and asymmetric information For instance, Lang et al (1996) found that companies with higher debt ratios are less likely to seize growth opportunities compared to those with lower debt, indicating that high debt firms tend to invest less despite available growth potential Additionally, Aivazian (2005) highlighted a significant negative impact of financial leverage on investment decisions in Canadian publicly traded companies, particularly in firms with low growth opportunities However, these findings primarily stem from developed countries, raising questions about their applicability to developing nations.

A study by Xuan Vinh Vo (2018) examined Vietnamese SMEs listed on the Ho Chi Minh Stock Exchange from 2006 to 2015, revealing that high levels of debt significantly limit corporate investment opportunities.

Phan Q T (2018) examined the relationship between firm investment and debt financing using data from the Ho Chi Minh and Ha Noi Stock Exchanges from 2010 to 2016 The study found that higher levels of debt negatively affect firm investment, indicating that increased debt in a company's capital structure correlates with reduced investment However, this research primarily focused on larger listed firms, leaving a gap in understanding the impact on small and medium-sized enterprises in Vietnam.

Our research highlights a significant positive correlation between financial leverage and investment decisions among SMEs in Vietnam, aligning with H.T Trinh's 2018 findings We discovered that higher financial leverage facilitates access to external financing, which is crucial for new investments, as Vietnamese SMEs often struggle with limited internal funding and restricted access to external sources, primarily bank loans Consequently, firms with elevated financial leverage are better positioned to secure credit for investments compared to their lower-leverage counterparts This study enhances understanding of the interplay between corporate finance and investment strategies in emerging markets, offering valuable insights for SMEs and policymakers to improve credit access through strategic planning, diversified funding, and reduced information asymmetry with financial institutions.

This paper is structured into several sections: Section 2 focuses on small and medium-sized enterprises (SMEs) in Vietnam, Section 3 outlines the data and empirical methodology used in the study, Section 4 discusses the empirical results and their implications, and Section 5 concludes with final remarks.

Data and Methods

This study examines the connection between financial leverage and investment decisions using quantitative surveys of small and medium-sized enterprises (SMEs) conducted by the Central Institute for Economic Management (CIEM) in 2011, 2013, and 2015, along with insights from the Institute of Labour Science.

The survey, conducted by the Institute for Labour Market and Social Affairs (ILSSA), the Development Economics Research Group (DERG) at the University of Copenhagen, and the United Nations University World Institute for Development Economics Research (UNU-WIDER), involved extensive data collection across various demographics.

A survey conducted across 2,500 enterprises in nine provinces of Vietnam, including Ho Chi Minh City, Long An, Khanh Hoa, Lam Dong, Nghe An, Quang Nam, Hanoi, Hai Phong, and Phu Tho, revealed significant insights (UNU-WIDER, 2018) The questionnaire utilized for this survey remained largely consistent throughout the study.

Between 2011 and 2013, a comprehensive questionnaire was developed, comprising 132 questions focused on various aspects of enterprises, including employment, operations, costs, revenues, production, credit, loans, and environmental expenses In 2015, the questionnaire underwent a slight revision, particularly in the areas of credit and finance It aimed to gather detailed information on firm characteristics and performance, encompassing owner attributes, workforce size, revenue and cost analysis, input usage, economic constraints, and investment strategies.

The surveyed enterprises span 18 sectors, including food processing, fabricated metal products, and wood manufacturing, with selections made from data provided by the General Statistics Office of Vietnam (GSO) The firms encompass private, collective, limited liability, joint stock enterprises, and partnerships that are officially registered under provincial laws To ensure comprehensive representation, the survey utilized a stratified sampling technique, incorporating various types of enterprises from each province.

This analysis utilizes an unbalanced sample of 6,057 micro enterprises, gathered across three survey rounds: the first in 2011 with approximately 2,512 enterprises, the second in 2013 with 2,542 enterprises, and the final round in 2015 comprising 2,648 enterprises It is important to note that certain variables were excluded, and some observations were removed due to missing information, resulting in the final total of 6,057 enterprises in the dataset.

Table 7 presents a summary of the survey data statistics and the variables utilized in our study The primary focus of our research is financial leverage, defined as the ratio of debt to total assets of the enterprises from the previous survey round Additionally, we incorporated other influential variables that may affect firm investment behavior, including firm size, revenue growth, and profitability.

In this study, we analyze 33 physical assets and ownership, drawing from previous research (H.T Trinh et al 2017; Dang 2011) Key firm-level variables, including total assets (SIZE) and physical assets (FIXED), serve as collateral and reflect borrowing capacity from financial institutions Additionally, we incorporate growth potential (GRR) and profitability (GROPF) as crucial determinants influencing investment decisions and financing choices Ownership status (OWN) is also examined, as it may impact management decisions, particularly in family-owned firms.

Table 7: Definition and summary statistics of the variables

Code Variables Description/Calculation method Number of

Dummy variable for new investment (=1 if the firm made a new investment during the past two years; 0 otherwise) 6,057 0.5418 0.4982

Share of external financial sources for new investment financed by bank loans and other sources that charge interest 3,282 0.412 0.4466

Share of internal financing sources or borrowing from family and friends without interest 3,282 0.5851 0.4473

Share of internal financing sources for new investment financed by borrowing from family and friends without interest 3,282 0.192 0.3462

Share of internal financing sources for new investment financed by retained earnings 3,282 0.393 0.44

Ratio of total debt to total assets at the end of the year of the previous survey round 6,057 0.0714 0.1797

SIZE Size Log of the total assets at the end of the year of the previous round 6,057 7.1718 1.7712

Growth of revenue=log of revenue in the second year minus the log of revenue in the first round of the survey 6,057 -0.0159 0.2763

Log of gross profit/revenue at the end of the year of the previous survey round 6,057 5.3614 1.4506

Ratio of physical assets (such as plants and machinery) to total assets at the end of the year of the previous survey round 6,057 1.4124 27.1834

OWN Ownership 1 if family ownership; 0 otherwise 6,057 0.6217 0.4849

ND LOCATION 1 if the enterprise is located in North

CD LOCATION 1 if the enterprise is located in

SD LOCATION 1 if the enterprise is located in South

Table 7 indicates that 54% of SMEs engaged in new investments over a three-year period, with external financing sources accounting for 41% and internal financing sources, including borrowing from family and friends and retained earnings, making up 59% Notably, there was only a slight variation in the financing source ratios across the three periods (see Appendix 1) In a survey conducted in 2011, internal financial resources emerged as the primary source for investment.

In a survey conducted in 2013, internal financing sources, including retained earnings and borrowing from family and friends, constituted 62% of total investments, while external financing sources made up 36% However, by 2015, the trend shifted, with internal financing sources surpassing external sources by approximately 20%, indicating that enterprises increasingly favored investing with their own capital over bank loans.

This study aims to explore two primary objectives: first, to assess how financial leverage influences the investment decisions of small and medium-sized enterprises (SMEs) in Vietnam, and second, to evaluate the impact of financial leverage on the financing sources utilized for new investments.

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:

In the context of small and medium-sized enterprises (SMEs), the dependent variable $ \text{INV}_{i,t} $ indicates whether SME $ i $ has implemented a new investment during survey round $ t $, with a value of zero otherwise Additionally, $ \text{LEV}_{i,t-1} $ represents the financial leverage or debt ratio, calculated as the total debt divided by total assets at the end of the previous year, providing insight into the firm's financial health and investment capacity.

𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑒𝑡 𝑖,𝑡−1); 𝑋 𝑘,𝑖 refers to a vector of other control variables expected to affect the decision to invest; and 𝜀 𝑖 is the error term

In our analysis, we utilize a logit model to estimate the binary dependent variable, aligning with methodologies from prior research (Hall et al 2000; H.T Trinh et al 2017; Dang 2011) We incorporate a range of enterprise characteristics, including revenue growth, profitability, ownership structure, physical asset ratio, and location, to effectively assess the investment decisions of SMEs.

The coefficient of financial leverage (LEV) in our model is crucial as it indicates how leverage influences an enterprise's investment decisions Additionally, the variable SIZE is determined by taking the logarithm of the total assets at the end of the period.

In the previous survey round, GROWTH measures the revenue growth by calculating the logarithmic difference between the second and first year's revenue PROF indicates the firm's profitability, while FIXED reflects the ratio of physical assets to total assets Additionally, OWN denotes whether the enterprise is family-owned.

ND, CD, and SD represent location dummies indicating North Vietnam, Central Vietnam and South Vietnam

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

Table 8: Investment decisions of SMEs in Vietnam (2011-2015)

Note: Values reported in parentheses are the robust standard errors (SE);

*,**, and *** indicate significance at the 10%, 5% and 1% levels, respectively

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

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

The analysis of Tables 8 and 9 reveals a significant positive correlation between financial leverage (LEV) and investment decisions, indicating that SMEs with higher financial leverage are more inclined to pursue new investments Specifically, a one-unit increase in LEV corresponds to a 7.56 increase in the log odds of seeking new investments from 2011 to 2015 While this finding challenges traditional finance theories and prior empirical research, it aligns with H T Trinh's 2017 study This phenomenon can be attributed to the limited internal financing options available to Vietnamese SMEs, which forces them to rely on external financing However, these firms often face challenges in securing external funds due to insufficient collateral or asymmetric information Consequently, financial leverage serves as an indicator of a firm's creditworthiness, suggesting that SMEs with greater financial leverage are more likely to attract investment opportunities.

Revenue growth (GRR) and profitability (GRPROF) are significant factors influencing investment decisions in SMEs A positive relationship indicates that SMEs with higher revenue growth are likely to invest more, although this trend was less pronounced in 2013 Additionally, profitable firms tend to allocate more resources to fixed assets While the correlation between total assets (SIZE) and investment decisions was initially unclear, strong evidence emerged when considering the location of SMEs, particularly in South Vietnam, where total assets significantly impact investment choices.

The study revealed a negative relationship between the fixed asset ratio (FIXED) and investment decisions, which contradicts earlier findings by H.T Trinh et al (2017), although this relationship was less pronounced in 2011 Additionally, the collateral demanded by credit institutions negatively impacts investment decisions Furthermore, a negative correlation exists between ownership (OWN) and firms' investment decisions, indicating that SMEs owned by households are less motivated to pursue 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

In Vietnam, firms in North regions, including Ha Noi, Phu Tho, and Hai Phong, show a higher propensity for new investments compared to those in Central and South Vietnam, which encompasses areas like Quang Nam, Quang Ngai, Nghe An, Ho Chi Minh City, Khanh Hoa, and Lam Dong The analysis reveals a significant positive coefficient for new investments (ND) in North Vietnam, while the coefficient for South Vietnam (SD) is significantly negative.

This study's second estimation explores the impact of financial leverage on a firm's decision to utilize either internal or external financing for new investments The findings, presented in Tables 10-13, detail the estimated outcomes of Tobit and fractional logit models for external financing sources (EXT) and internal financing sources (INT) based on surveys from 2011, 2013, and 2015, as well as the complete sample.

The findings indicate that financial leverage (LEV) positively influences access to external financing sources (EXT) while negatively affecting internal financing sources (INT) across both models Specifically, firms with higher LEV are more likely to seek funding from external sources, such as banks or credit institutions, for new investments, whereas those with lower LEV tend to depend on internal financing This underscores the notion that elevated LEV enhances a firm's ability to secure external financial support.

Financial leverage significantly influences the financing decisions of SMEs in two key ways Firstly, credit institutions may hesitate to extend additional loans to SMEs exhibiting high financial leverage due to perceived financial risks Consequently, these SMEs are deterred from seeking further credit, limiting their growth opportunities.

High financial leverage in small and medium-sized enterprises (SMEs) often leads them to seek external financing sources, whereas those with lower leverage tend to rely on internal financing Our findings indicate that the reliance on internal financing is more common, aligning with the pecking order theory proposed by Myers and Majluf (1984) This theory suggests that when SMEs evaluate their financing options, they prioritize internal resources over external ones.

Small and medium-sized enterprises (SMEs) prioritize internal financing for investments, utilizing these resources first Once internal sources are exhausted, they turn to debt financing Finally, when further debt issuance becomes impractical, they resort to equity financing.

The study reveals a significant positive relationship between total assets (SIZE) and external financing sources, while indicating a significant negative relationship between total assets and internal financing sources across both models This finding aligns with previous research by Bhaird and Lucey (2010) and Sogord-Mira (2005) Total assets serve as collateral for firms, enhancing their likelihood of securing bank credit Consequently, larger firms tend to favor external financing, whereas smaller firms primarily depend on internal financing sources.

The coefficient of profitability (PROF) shows a significant positive association with external financing sources and a negative association with internal financing sources in the full sample and the 2011 survey, challenging the pecking order theory and earlier findings; however, these associations are not significant in the 2013 and 2015 surveys Previous research indicates a negative link between external financing and firm profitability, suggesting that more profitable firms tend to prefer internal financing Contrarily, our findings imply that firm profitability significantly influences the choice of external financing, as firms continue to seek external funds despite high profits Additionally, there is no substantial evidence connecting revenue growth (GRR) or family ownership (OWN) to external financing choices, as their coefficients are statistically insignificant Nonetheless, a significant positive correlation exists between revenue growth and retained earnings, alongside a significant negative correlation with alternative financing sources in the 2011 and 2015 surveys and the overall sample.

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

Tobit Flogit Tobit Flogit Tobit Flogit Tobit Flogit

Note: Standard errors are shown in parentheses

*** represents significance at the 1% significance level, ** represents significance at the 5% significance level, * represents significance at the 10% significance level

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

Conclusion

This study examines the impact of financial leverage on investment decisions and financing choices among small and medium-sized enterprises (SMEs) in Vietnam, utilizing survey data from 2011 to 2015 Contrary to established theories by Myers (1977) and Gomes (2001), as well as empirical findings by Lang et al (1996) and Aivazian et al (2005), which suggest that a high debt ratio correlates with limited growth opportunities and reduced investment likelihood, our results indicate a positive relationship between financial leverage and investment decisions in Vietnamese SMEs This suggests that firms with greater financial leverage are more inclined to pursue new investments for expansion, challenging Modigliani & Miller’s theory and highlighting that companies may have distinct preferences when making investment choices.

Firms with higher financial leverage tend to rely more on external financing sources while reducing their use of internal financing for new investments This trend is attributed to high-leverage companies having better access to external funding due to their positive credit histories In the context of Vietnamese SMEs, there is a noticeable shift from internal to external financing sources (H.T Trinh et al 2017) Conversely, low financial leverage limits access to external financing, leading companies to primarily utilize internal funds for expansion As businesses successfully grow, their access to external financing improves, subsequently increasing their financial leverage.

Nevertheless, in contrast to previous studies (H.T.Trinh et al 2017), our results show that firm profitability has a significant impact on the choice of external financing

48 sources because firms still rely on external financing sources even if they make high profits, suggesting that profitable enterprises invest more in fixed assets

To effectively stimulate new investment in SMEs, it is essential to reduce credit constraints and enhance financial leverage Policymakers should prioritize improving credit access, alleviating information asymmetry, and directly providing subsidies to support SMEs Additionally, the government can facilitate better awareness of financial institutions that offer investment opportunities and establish information-sharing channels between investors and SMEs Reducing trade facilitation costs can also attract more foreign investment, while promoting SME participation in global networks is crucial for their growth and sustainability.

Our study acknowledges specific limitations while offering valuable insights for future research It concentrated solely on small and medium-sized enterprises (SMEs) in Vietnam, a representative nation within emerging economies Consequently, conducting comparative studies in other developing countries would enhance the understanding of SMEs in diverse contexts.

INFLUENCE OF FARMER UNION MEMBERSHIP ON THE

Introduction

In low-income countries, rural development plays a crucial role in poverty reduction, as a significant portion of impoverished individuals reside in rural areas where household income largely depends on farming activities like rice production, livestock, and agro-forestry However, these individuals often face challenges such as low education levels, limited skills, and restricted access to essential services, including financial and healthcare resources Research indicates that organizing poor farmers into groups or cooperatives can help them overcome these barriers and improve their livelihoods.

Agricultural cooperatives play a crucial socioeconomic role by assisting family farms in overcoming challenges related to external economies of scale and enhancing market competitiveness, as highlighted by Valentinov (2007) and supported by Nuhanovic et al.

Agricultural cooperatives have been shown to significantly alleviate poverty among farmers and communities, as highlighted by research in 2017 and Deriada (1995), who emphasized their role in rural poverty reduction These organizations also enhance individual negotiating power and lower transaction costs, according to studies by Bernard and Spielman (2009), Francesconi and Ruben (2012), Markelova et al (2009), and Valentinov (2007) However, some research, such as Fischer and Qaim (2012), indicates that agricultural cooperatives may underperform in developing countries This paper aims to explore the effects of farmers’ union membership on rural households in Vietnam, drawing comparisons with similar cases in other developing nations.

Since the doi moi renovation in 1986, numerous international and local organizations, including community-based groups and Vietnamese nongovernmental organizations, have emerged in Vietnam The farmers’ union serves as a compelling case study for assessing the impact of social networks on farm households due to Vietnam's unique context, characterized by a single-party government that sponsors mass organizations with strong grassroots ties and extensive memberships Since doi moi, these organizations, particularly the farmers’ union, have gained independence and now play a vital socioeconomic role by supporting impoverished individuals through improved education, healthcare, living conditions, and access to financial services.

The farmers' union is crucial in facilitating the lending process of Vietnam's rural credit program, the Vietnamese Bank for Social Policy (VBSP), which aims to provide credit to low-income individuals lacking collateral Modeled after Bangladesh's Grameen Bank, the VBSP operates on a group lending scheme, where village heads and commune leaders form borrower groups and oversee loan monitoring This approach not only fosters accountability through credit groups but also reduces transaction costs by addressing asymmetric information Research by Hong Sun et al (2018) highlights the significant impact of social capital, particularly kinship and friendship, on the borrowing behavior of rural households in China, with the farmers' union being a key player among mass organizations However, the existing literature connecting social networks to farm households remains limited.

This study explores the significant role of farmers' union membership in enhancing economic performance and financial stability, particularly in developing countries like Vietnam Previous research, such as Giannakis et al (2018) in Cyprus, demonstrated that farmer union membership positively influences off-farm work decisions Similarly, Newman et al (2014) found that high-quality network membership correlates with increased savings Furthermore, Takashi (2009) highlighted the farmers' union's crucial involvement in microcredit programs in Vietnam By providing empirical evidence on the impact of social networks, specifically farmers' unions, this study contributes to the existing literature on rural household production and credit volume.

This article is organized into several sections: Section 2 provides a literature review and an overview of the background of farmers' unions in Vietnam Section 3 details the data and variables utilized in the study Methodological approaches for analysis are outlined in Section 4, while Section 5 presents the empirical results and their interpretations Finally, Section 6 concludes the paper.

Context and Literature Review

The Vietnam Farmers' Union (VNFU), founded on October 14, 1930, is a sociopolitical organization led by the Communist Party of Vietnam, focusing on the interests of farmers Open to individuals over 18 from various backgrounds in agriculture and related sectors, membership requires a voluntary commitment to the union's principles The primary motivation for farmers to join the VNFU is to gain support for developing their agricultural enterprises Recently, the VNFU has enhanced its role by helping farmers access credit through the Vietnam Bank for Social Policies (VBSP), aiming to provide collateral-free loans and promote savings and credit opportunities for rural development.

The VNFU offers 52 groups dedicated to aiding in repayment management and actively engages in national initiatives focused on job creation, agricultural extension, and vocational training Membership in the VNFU can yield significant economic advantages that extend beyond the organization's primary goals.

The first successful microcredit program was initiated by Professor Muhammad Yunus at Grameen Bank in Bangladesh in 1976, paving the way for similar initiatives in other developing countries to provide loans without collateral to impoverished individuals, particularly women This program has enabled over seven million Bangladeshis to access credit and has been recognized as a vital tool for poverty alleviation and sustainable economic improvement However, recent studies present mixed findings regarding the impact of microfinance on household income For instance, research by Al-Shami et al (2017) indicates a positive effect on women's income in Malaysia, while Cuong (2008) highlights significant benefits from Vietnam's credit program on household income and expenditures Moreover, studies by Mohammad et al (2017) and Thu and Goto (2020) suggest positive correlations between microfinance and health service usage, as well as increased education expenditures for minority students Conversely, Angelucci et al (2014) found no transformative impact of microcredit on household income, noting that loans were primarily used for investments Luan and Bauer (2016) acknowledged a positive effect of credit on nonfarm income but found no influence on farm income, attributing this to potential farming shocks.

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

In Vietnam, 53 organizations play a crucial role in facilitating consultative meetings to select and recommend candidates for credit programs Among these, the Farmers’ Association is identified as the most significant contributor to bank loans, particularly following the establishment of the VBSP loan, which led to a notable increase in its membership While empirical analysis supporting this claim was not conducted, research by Newman, Tarp, and Broeck (2014) highlights that membership in organizations like the Farmers’ Association and Women’s Union significantly influences household formal savings in rural areas.

Materials and Methods

4.3.1 Study sites and survey approach

The Vo Nhai district, known for being one of the most rural areas in Vietnam, was chosen for this study due to the implementation of a rural credit program and its distinction as having the highest poverty rate in the northern mountainous region of the country.

Thai Nguyen Province, located in Vietnam's northern mountainous region, spans 3,533.2 km² and had a population of 1,190,600 in 2015 Renowned for its tea industry, the province dedicates 16,000 hectares to tea production However, it faces economic challenges, with a poverty rate of 11.1% in 2014, rising to over 40% in Vo Nhai District As of 2016, 66.7% of households in the province were engaged in agriculture, forestry, livestock, and aquaculture, significantly higher than the national average of 53.7% Notably, Vo Nhai District exhibits the highest concentration of farm households at 83.15%.

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

The study initially included 401 households, but 59 were excluded due to missing information, resulting in a final sample size of 342 households In Vo Nhai district, Thai Nguyen Province, 111 households were members of a farmer union while 81 were non-members The analysis focused solely on Thai Nguyen Province, excluding Bao Hieu district from the main estimation.

192 farm HHs, which is 0.5% of the total number of farm households in the selected area The primary data were collected through an HH survey conducted in 2015-2016

In our study, households (HHs) without farmers’ union membership were designated as the control group, while those with membership constituted the treatment group The HH survey encompassed various aspects, including demographic characteristics, access to credit, and economic performance Respondents provided information about the HH head’s age, education level, gender, occupation, ethnicity, and poverty status Additionally, the survey assessed HH access to resources, credit, and primary production activities For credit analysis, participants detailed their loan transaction history from any sources over the past decade.

This study utilized the propensity score matching (PSM) method to assess the effects of farmers' union membership on various outcome variables, including household economic performance, which encompasses average livestock and crop production, total income, and expenditures, as well as the average credit volume obtained from the VBSP program.

This study's selection of household (HH) variables is informed by a literature review (Caliendo and Kopeinig, 2008; Becker and Ichino, 2002) We identified several potential covariates related to HH characteristics that may influence both treatment and outcome variables A summary of the assumptions regarding the control variables and the variables of interest is provided in Table 14.

Table 14: Description of selected variables

Family size Total members in the household Members

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

Age of household head Age of household head Years

Education of household head The number of years in school of the household head Years Occupation of household head

Dummy variable for occupation of the household head; 1 for working full-time on farm, 0 otherwise

Credit dummy Dummy variable for credit status of HH; 1 = has borrowed money from the VBSP, 0 otherwise Total agricultural and forestry land holdings

Total lands owned by the HH 1000 m 2

Livestock production contributes significantly to the household's monthly income, with total livestock production valued at millions of VND In addition, crop production plays a crucial role, also amounting to millions of VND The overall monthly income of the household reaches millions of VND, while total expenditures for the same period are also in the millions Furthermore, the household's credit volume from the Vietnam Bank for Social Policies (VBSP) reflects the total borrowing amount, which is measured in millions of VND.

Analyzing the causal effects of farmers' union membership on outcome indicators is crucial to address endogeneity bias It is essential to account for both observable and unobservable characteristics when randomly assigning individuals to treatment groups (Wossen et al., 2017) This study utilizes Propensity Score Matching (PSM) to effectively control for these factors.

Propensity Score Matching (PSM) addresses endogeneity bias by pairing treated households with similar untreated households, as outlined by Rosenbaum and Rubin (1983) The core principle of PSM involves constructing counterfactual outcomes for those in the treatment group based on their propensity scores derived from a set of covariates By calculating the differences in outcomes between matched observations, we can estimate the treatment effect Following the methodology of Tran and Goto (2019), we implement a two-step process to determine the average treatment effects (ATEs) and average treatment effects on the treated (ATETs) for the outcome variables.

In the first step, the propensity score was estimated by applying a probit model, which is expressed as follows:

Additionally, 𝐷 𝑖 =1 if an HH is a member of the farmers’ union, and 𝐷 𝑖 =0 if an HH is not a member of the farmers’ union

𝑋 𝑖 is a vector of HH characteristics

𝛷 represents the standard normal cumulative distribution function

To achieve optimal matching quality and ensure a balanced sample concerning the chosen covariates, we employed various models for estimating the propensity score, each incorporating a distinct set of variables A detailed overview of the selected variables can be found in Table 3.

The propensity score was estimated using a probit model, followed by one-to-one nearest neighbor matching with a 0.01 caliper Treated units lacking control units within this caliper were excluded from the analysis Additionally, each control observation may be utilized for matching multiple treated units.

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) suggest that effective matching should yield pseudo-ATETs that are close to zero and statistically insignificant, indicating the plausibility of the un-confoundedness assumption The model demonstrating the best balancing properties will be selected for estimating the Average Treatment Effects (ATEs) and Average Treatment Effects on the Treated (ATETs) in the subsequent analysis.

In the second step, each selected model will be used to estimate the ATEs (equation

2) and ATETs (equation 3) as follows:

ATET = E(𝑌 1 − 𝑌 0 |𝐷 = 1) = 𝐸(𝑌 1 |𝐷 = 1) − 𝐸(𝑌 0 |𝐷 = 1) (3) where 𝑌 1 and 𝑌 0 are potential outcomes, and 𝐷 denotes a treatment indicator

In our dataset, we can only observe 𝐸(𝑌 1 |𝐷 = 1), while 𝐸(𝑌 0 |𝐷 = 1) remains unobserved This unobserved term is counterfactual and is estimated by averaging the outcomes from the matched control group.

We utilized Propensity Score Matching (PSM) due to its effectiveness in adjusting for confounding variables in observational studies PSM offers advantages over simple regression by forming treatment and control groups with comparable propensity scores, unlike regression, which estimates outcomes for unmatched households Additionally, PSM reduces self-selection bias linked to observable covariates, as noted by Rosenbaum and Rubin (1983).

To ensure the reliability of our findings, we utilized additional matching techniques, including the inverse probability weighted (IPW) method and regression adjusted (RA) methods While propensity score matching (PSM) aligned treatment and comparison group observations, IPW estimators were employed to calculate probability weights for determining weighted averages of outcomes across different treatment levels.

Results and discussion

Table 2 presents the mean differences between selected variables for FU member households and non-member households Notably, household heads who are members of FU tend to have a higher level of education compared to those who are non-members.

Younger and predominantly female heads of households (HH) are more common among members compared to nonmembers Additionally, the control group possesses a greater amount of farmland than the treatment group, indicating an imbalance in selected covariates and suggesting potential self-selection issues.

The treatment households (HHs) exhibit higher average values in livestock production, expenditure, and credit volume compared to the control HHs, with the credit volume being 4.3 million VND greater However, there are no significant differences in mean values for livestock and crop production, and the total income is lower for the treatment units than for the control units Additionally, other covariates show no statistically significant differences between the treatment and control groups.

Participation in farmers' unions often varies among households due to differing self-selection processes This variation can result in mean differences in outcome variables between treatment and control groups, potentially skewing conclusions about the treatment effect While Propensity Score Matching (PSM) does not eliminate self-selection bias, we have identified several sets of data to address this issue.

60 covariates to estimate the Propensity score then test the consistency of the impacts results by using different models and different matching algorithms

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

Variables Treatment Group Control Group Difference (T-C)

Obs Mean S.D Obs Mean S.D Mean S.E

Age of the HH head 111 45.585 0.964 81 48.283 1.251 -2.698 * 1.554

Educational level of the HH head

Total agricultural and forestry land holdings

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)

To assess the impacts of FU’s membership, we utilized the methodology outlined in section 3.3 According to Caliendo and Kopeinig (2008), propensity score modeling serves to balance selected covariates rather than predict individual selection into treatment and control groups Our analysis identified two out of eight models that achieved the most balanced samples post-matching, as detailed in Table 3 The mean differences in covariates, including the age and educational level of the household head and occupation dummies, were significantly different prior to matching but became nonsignificant afterward Most covariates showed a mean difference that approached zero, with the exception of total agricultural and forestry land holdings, which remained nonsignificant These findings indicate an improvement in sample balance.

Table 16: Balance checking before and after matching

Mean SE Mean SE Mean SE

Age of the HH head -2.698* 1.554 0.337 1.55 1.411 0.343 Educational level of the HH head 1.065*** 0.343 0.000 0.302 -0.011 0.327

Total agricultural and forestry land holdings -1.774 5.753 7.638 8.691 3.362 6.687

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

The analysis of Average Treatment Effects (ATEs) and Average Treatment Effects on the Treated (ATETs) reveals that farmers’ union membership positively influences livestock production, with member households producing over 9.121 million VND (11.556 million VND in model 2) Additionally, member households access higher credit volumes compared to non-member households, suggesting that union membership facilitates greater credit availability for production enhancement Consistent results across various models and matching methods further affirm the significant impact of farmers’ union membership on both livestock production and credit volume This study underscores the importance of social factors, such as union membership, in supporting rural households, aligning with Takashi's (2009) findings on the critical role of farmers' unions in microcredit loans and highlighting the increased loan volume and livestock production following the introduction of VBSP loans in 1996.

Additionally, the ATET estimator for the outcome variables reveal that farmer’s union membership has a positive effect on livestock production and credit volume

The findings indicate that farmers' union membership plays a significant role in household production, similar to the ATE results However, there is an inconsistency between the two models: while model 2 shows a significant effect of membership on livestock production, model 1 does not Additionally, the ATET results reveal a significant impact of membership on credit volume in model 1, but this impact is not observed in model 2 Furthermore, the ATET results vary when different matching methods are applied, highlighting the need for careful interpretation.

We found no evidence of an impact of FU membership on other potential outcomes such as crop production, total income and HH expenditures

Our analysis reveals that both Propensity Score Matching (PSM) and linear regression consistently indicate a statistically significant impact of farmers' union membership on household livestock production and credit volume, even after accounting for various covariates.

The study reveals that membership in farmers' unions significantly influences household credit volume and livestock production, while its impact on crop production is inconsistent The positive correlation between union membership and livestock production, as well as credit access, highlights the economic advantages for union members and their households It is important to note that this does not imply that union members are unfairly leveraging their position for credit allocation; rather, their higher educational levels contribute to their perceived creditworthiness Additionally, the relationship between education and union membership can further affect credit utilization in household agricultural production.

Table 17: The results of PSM estimation

ATE SE ATE SE ATET SE ATET SE

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

Table 18: Results of ATE estimation: Impact of membership on HHs

Membership (1 vs 0) PSM NN-MATCH IPW RA

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

Table 19: Results of ATET estimation: Impact of membership on HHs

Membership (1 vs 0) PSM NN-MATCH IPW RA

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

Conclusion

This study investigates the role of farmers' union membership in enhancing production and access to microcredit for farm households in rural Vietnam, contrasting with previous literature that primarily focused on microcredit's impact on household welfare Conducted in Vo Nhai district, Thai Nguyen province—an area with a notably high poverty rate in northern Vietnam—the research utilized household surveys and employed Propensity Score Matching (PSM) alongside other methods and linear regression to ensure result consistency The findings reveal that membership in farmers' unions significantly boosts livestock production and increases the credit volume available to 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

Households (HHs) that are members of farmers’ unions experience higher credit volumes and increased livestock production compared to nonmember HHs, highlighting a significant impact of union membership on these factors The results indicate that financially better-off HHs are more inclined to join farmers’ unions, suggesting that these organizations offer substantial benefits to farm households While this finding may seem apparent, it has not been extensively addressed in previous research Notably, union member HHs demonstrate a more pronounced increase in livestock production, reinforcing the idea that rural households pursue specific goals through collective action.

The difference in the results originates from a difference in the matching method and the different 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

Membership in farmers' unions offers 69 benefits that extend beyond the organization's primary goals, demonstrating a significant positive impact on household production This research contributes to the existing literature on the role of social organizations, particularly farmers' unions in Vietnam, and provides valuable insights for policymakers regarding the effects of local organizations on rural development.

The farmers' union should strengthen its initiatives to improve access to resources, particularly financial services Additionally, exploring the interactions between local organizations and rural households across various regions can inform effective policies and support social organizations in Vietnam's rural development efforts.

SUMMARY OF THE STUDY

Tiêu đề	Essays on Financial Activities and Industrial Change: The Study Case in Vietnam
Tác giả	Nguyen Thi Bich Ngoc
Người hướng dẫn	Prof. Ichihashi Masaru
Trường học	Hiroshima University
Thể loại	doctoral dissertation
Năm xuất bản	2020
Thành phố	Hiroshima

Định dạng
Số trang	116
Dung lượng	1,98 MB