INTRODUCTION
Problem statement
As of 2010, approximately 1.22 billion people worldwide, or 21% of the global population, lived on less than $1.25 a day, according to the World Bank Addressing poverty reduction and improving living conditions has become a significant focus of public policy globally In Vietnam, the poverty rate has seen a remarkable decline in recent years, dropping from 15.5% in 2006 to 10.7% in 2010, as reported by the General Statistics Office (GSO) However, the GSO's 2010 report highlighted a stark contrast between rural and urban poverty levels, with rural areas experiencing a poverty rate of 13.2%, compared to just 5.1% in urban areas A key challenge remains in effectively distributing the benefits of economic growth, particularly to rural communities.
Therefore, rural economy deserves more attention and support to reduce inequality between rural and urban area
Lack of access to funding for working capital and investment significantly contributes to poverty in developing countries (McCarty, 2001; Pham & Lensink, 2002) Addressing credit constraints for poor rural households is a key focus of poverty alleviation strategies in nations like Vietnam.
Farmers require access to credit to boost productivity and improve their living standards due to the seasonal nature of their work and associated uncertainties (Ololade & Ologunju, 2013) Microcredit is recognized as an effective tool for alleviating poverty and enhancing living conditions (ADB, 2000a; Morduch and Haley, 2002; Khandker, 2003) Agricultural credit is crucial for sustainable development globally, with the microfinance sector experiencing significant growth, especially after receiving the Nobel Peace Prize in 2006 (Tra Pham and Robert Lensink, 2007) Research has consistently highlighted the role of microfinance in poverty reduction and its positive impact on household welfare In Vietnam, the demand for rural credit surged following the decollectivization of agriculture in 1986, leading to a proliferation of microfinance options and regulatory reforms that have been instrumental in combating poverty The Vietnamese government has implemented various microcredit programs through multiple channels, including banks, credit funds, money lenders, and input providers, to cater to diverse client needs.
Access to adequate financial services is crucial for poor families to enhance their productivity and living standards However, commercial banks often overlook the poor due to their lack of viable collateral, leading to limited credit opportunities In response, governments in developing countries have implemented credit programs over the past four decades to improve rural households' access to formal credit Unfortunately, the lending mechanisms and credit markets remain heavily regulated by government interventions, such as interest rate controls and credit quotas, which hinder their effectiveness.
Robinson (2001) and Gonzalez Vega (2003) highlight that many microfinance institutions in developing countries struggle with sustainability Agricultural development banks, created by commercial banks to offer credit to rural households deemed uncreditworthy, often provide subsidized interest rates Unfortunately, most of these credit programs have not achieved their goals of being sustainable financial providers or effectively serving the poor (Adams, Graham, and von Pischke 1984; Adams and Vogel 1986; Braverman and Guasch 1986).
Risk management and transaction costs associated with Asymmetric information are the most problematic features facing by lenders and borrowers (Pham & Lensinnk,
In Vietnam's financial market, various credit sources cater to different borrower groups, yet many low-income households struggle to access these resources due to credit rationing, which often excludes them from formal lending sectors (Stiglitz & Weiss, 1981) The coexistence of formal, semi-formal, and informal lenders highlights the challenges faced by the poor in securing credit To mitigate information asymmetry between borrowers and lenders, government microcredit programs collaborate with local People's Committees to enhance the lending process and support the microcredit market's functionality.
Improving the efficiency and effectiveness of the microfinance system is a key challenge for policymakers and program organizers, particularly in narrowing the gaps in service delivery and target beneficiaries.
With data collected from The Vietnam Access to Resources Household Survey
The Vietnam Access to Resources Household Survey (VARHS) 2012 builds upon the Vietnam Household Living Standards Survey (VHLSS) by conducting repeated surveys of the same households, gathering comprehensive data on income, expenses, land, agriculture, assets, investments, migration, climate change, and social welfare Supported by institutions such as the University of Copenhagen, CIEM, ILSSA, and IPSARD, this research utilizes econometric techniques to analyze the factors influencing access to both formal and informal credit.
Research objective
This thesis aims to empirically examine the factors that affect households' access to various credit sectors, while also exploring the interplay between participation in formal and informal credit systems.
Additionally, marginal effects of each independent variable on the choice of credit source are also shown in this research.
Research questions
This research is to answer two central questions:
What are determinants affecting the probability of household accessing to different types of credit sector?
Is there any evidence of a correlation between participating in formal credit and participating in informal credit at the same time?
Research Structure
This dissertation is structured into six chapters Chapter one outlines the problem statement and research objectives In chapter two, relevant concepts are discussed, including demand credit theory and credit rationing theory, along with the introduction of explanatory variables Chapter three explores the history of microfinance, the government's role in it, and provides an overview of Vietnam's credit market structure Chapter four details the research framework, data description, and methodology employed Chapter five presents the empirical models and estimated results Finally, chapter six concludes with policy recommendations and discusses the study's limitations.
LITERATURE REVIEW
Concept of credit
There are several and various definitions regarding the word credit as follows:
Credits are referred as loans which permit consuming in the present, in exchange for an agreement to make repayment at sometimes in the future (Pischie et al., 1983)
Obtaining credit was considered as the process of controlling over the use of money, goods and services based upon a promise to repay at a future day (Adegeye &
According to Ololade & Ologunju (2013), credit is a mechanism for the temporary transfer of assets to individuals or organizations that do not possess them This process necessitates evidence of a debt obligation in exchange for a loan, although transactions between friends or relatives based on trust are typically exempt from this requirement.
Microcredit which is a component of microfinance provides small loan to the poor for self –employment That generates income, helping them care for themselves and their family (The Microcredit Summit, 1997)
Microcredit aims to enhance income levels and elevate living standards in semi-urban and urban areas by offering small-scale financial services, including savings and credit, to rural households.
Microfinance is defined variably, but it generally refers to providing small financial assistance to low-income individuals (CGAP) This assistance can take various forms, such as savings, money transfers, payments, remittances, and insurance, aimed at supporting those in need (Christen R.P., 1997) The primary objective of these services is to alleviate poverty (Khan & Rahama, 2007).
Microfinance is also defined as a development approach, which is composed of financial and social intermediation to benefit for the poor (Legerwood, 1999)
Microfinance institutions (MFIs) offer more than just credit; they also facilitate group formation, enhance self-confidence, and provide training in financial literacy and management skills for group members.
Microcredit, often confused with microfinance, specifically refers to the provision of loans to low-income individuals In contrast, microfinance encompasses a wider range of financial services, including loans, savings, and insurance, aimed at supporting the financial needs of the poor.
Theory of demand for credit
According to the life cycle model proposed by Franco Modigliani in 1966, individuals struggle to maintain consistent consumption levels amid family size changes and future uncertainties To optimize lifetime utility, income must be allocated over time, as noted by Morduch in 1995 Consumers can finance their purchases through savings from past or present income, or by utilizing credit, which enables them to make inter-temporal choices By borrowing, individuals gain immediate spending power while committing to repay the loan and interest in the future, as highlighted by Soman and Cheema in 2002.
In 1986, Modigliani explored the inter-temporal model of the life cycle hypothesis and the permanent income hypothesis, which elucidate individual consumption behavior His model assumes that borrowers have the opportunity to access loans in a perfect market.
According to the model proposed by Chen and Chiivakul (2008), a household's consumption level is influenced not only by its current income but also by its lifetime characteristics, particularly its behavior regarding participation in the credit market.
Current consumption is influenced by expectations of future consumption, as consumers assess their long-term affordability This evaluation is closely tied to their savings and demand for loans, highlighting the interconnectedness of financial planning and spending behavior.
The Cobb-Douglas production function, represented as Y = AL^α K^β, emphasizes capital as a vital input factor in production, where profit is influenced by both labor (L) and capital (K) alongside existing technology (Zellner et al., 1966) This model illustrates the relationship between capital and labor in determining production output and its subsequent impact on income distribution (Felipe and Adams, 2005) Additionally, capital can be sourced from various credit avenues, each offering different interest rates that affect the overall cost of capital.
Credit rationing theory
In 1981, Stiglitz and Weiss based on two assumptions to introduce theory of credit rationing:
(1) There is unable to differentiate level of risk associated with safe and risky borrower, and
(2) Loans are subjects to the ability of repayment ability of borrower at the end of investment period
In the microcredit market, two significant issues arise from asymmetric information: adverse selection and moral hazard Adverse selection pertains to the screening process, where varying transaction costs, such as interest rates, differentiate between reliable and unreliable borrowers Conversely, moral hazard emerges during monitoring, as lenders are aware that the risk is collectively borne, leading to potential irresponsible behavior by borrowers (Pham & Lensink, 2007).
In general, lenders judge borrower’s creditworthiness basing on a set of information that they have
The Stiglitz and Weiss (1981) model posits that while the expected returns of projects (E i) are uniform across borrowers, the likelihood of success (𝑝i) varies In this framework, E i s and E i f denote the returns from projects in the event of success and failure, respectively The bank provides loans (B) to all borrowers at a uniform interest rate (r).
A project's success yields a return greater than the repayment amount, represented as (1+r)*B, while failure results in a return that falls short of what the lender receives The project's feasibility hinges on the expected return surpassing the opportunity cost (C i) (Stiglitz & Weiss, 1981) The expected return of the project can be calculated accordingly.
Additionally, in the case of success, the return to borrower is higher than opportunity cost: π(𝑝 i , r) = 𝑝 i [ E i s – (1+r)*B] ≥ C i (2.2) From (2.1) and (2.2), (2.3) is obtained: π(𝑝 i ,r) = E i – E i f + 𝑝 i [E i f – (1+r)*B] ≥ C i (2.3) Taking derivative of the function (2.3), (2.4) is obtained:
The first derivative of the function is negative, indicating that the expected return function is decreasing This suggests that as the probability of success increases, the expected return of the project diminishes.
From equation (2.2), using the implicit function theorem to differentiate r respects to p i:
Equation (2.5) shows that an increase in interest rate charged by lender leads to a decrease in probability and vice versa
Marginal borrowers are individuals anticipating a zero return on their projects, represented by π(𝑝 i , r * ) = 0 When the interest rate (r) exceeds r *, these borrowers exit the market, leading to an increase in interest rates for new marginal borrowers This phenomenon illustrates the impact of rising interest rates on marginal borrowers.
Figure 2.1 Probability of success and expected returns to borrowers
Figure 2.1 demonstrates how interest rates and opportunity costs influence expected returns for borrowers E1 represents the expected returns for a marginal borrower at the probability of success, denoted as 𝑝 i m The analysis indicates that an increase in these factors significantly impacts the expected returns for marginal borrowers.
Borrowers with 𝑝 i m’ ≤𝑝 i ≤𝑝 i m out of the market
Marginal borrowers exit the market when the cost of borrowing, denoted as C i, falls below the opportunity cost The expected return for new marginal borrowers, represented as E 3, is influenced by a success probability, p i m’, which is lower than the previous probability, p i m.
According to Quach (2005), there is the same effect of opportunity cost on expected return
From the perspective of the bank, the expected return is: κ(𝑝 i, r)= 𝑝 i (1+r)B + (1-𝑝 i )E i f (2.6) Differentiating function (2.6) with respects to p i :
The first derivative of the function is greater than zero It implies that an increase in probability leads to an increase in expected return to the bank
If the interest rate increases:
(1) The value of component (1+r)*B increase
(2) The value of p i decrease (according to function 2.5 shown above), which leads to lower expected return as result of withdraw of lower-risk borrowers (Stiglitz &
Stiglitz and Weiss (1982) introduced the concept of a critical equilibrium interest rate, which plays a vital role in banking profitability When the current interest rate is below this critical level, banks can raise their rates without losing many borrowers, resulting in increased income However, if the interest rate surpasses the critical equilibrium, lower-risk borrowers may exit the credit market, leading to a decline in the lender's profits Consequently, banks allocate credit at the critical equilibrium interest rate to maintain stability.
Figure 2.2 illustrates the relationship between lender returns and the interest rate (r), highlighting the critical equilibrium point (r ra) where the supply of funds aligns with the demand for funds At this equilibrium interest rate, the bank's expected return is maximized without any rationing of funds.
Figure 2.2 Return to the bank
Determinants of participation in microcredit programs
When households face insufficient income and wealth to support their purchases, they often resort to borrowing money to finance their consumption (Kirchler et al., 2008) The loan acquisition process consists of two stages: initially, households seeking credit submit applications for a specific loan amount from their chosen credit sector.
Second, the providers choose which applicants are met requirements for loan based on household’s information and availabilities of the lenders
Stiglitz and Weiss (1981) showed that behavior of accessibility to credit is explained by demand theory (demand side) and rationing process of credit (supply side)
In Northern Nicaragua, access to credit is influenced by the interplay between household characteristics on the demand side and the attributes of financial institutions on the supply side, as noted by Vaessen (2002) Similarly, Duong Pham (2002) highlights the importance of these factors in shaping credit accessibility.
E r focused on characteristics of both demand side and supply side in determining factor that influence on accessibility of rural credit in Vietnam
This analysis emphasizes the demand-side characteristics that influence the likelihood of accessing credit Key determinants of microcredit participation include factors such as age, gender, marital status, education, household size, income, savings amount, land size, agricultural activity, social networks, and geographic location.
According to the life circle hypothesis, the age is negative relationship with the decision to get loan It is also confirmed in research of M Ajugam and C
Research indicates that access to credit resources diminishes with age, as highlighted by Ramasamy (2007), Okurut (2006), and Mohamed (2003) Younger individuals are more inclined to borrow compared to older adults, primarily due to varying levels of personal risk (Fabbri and Padula, 2004; Zeller, 1994; Magri, 2002; Abdul-Muhmin, 2008).
Del- Rio and Young, 2005); additionally, the young tend to spend more on a variety of activities while the old maybe less (Mpuga, 2008)
Several studies indicate that access to credit tends to increase with age For instance, Tinh (2010) found a significant positive correlation between the age of the household head and the likelihood of obtaining a loan, a finding that was further supported by research conducted by Tang et al in 2010.
Banerjee et al (2010) Bruno and Cre1pon et al (2011) prove that there are a majority proportion of male borrowers from the microcredit programmes
Moreover, Nwaru (2011) and Bendig et al (2009) also proven that demanding in loan negatively related with being female
However, contrary to mentioned studies, Owuor George (2009) stated that being a female headed household increases probability of joining financial activities
Numerous studies indicate that married individuals are more likely to secure loans compared to their unmarried counterparts, primarily due to differing levels of financial needs (Kamleitneir and Kirchler, 2007; Bridges et al., 2004; Chen and Jensen, 1985; Duca and Rosenthal, 1994).
A study by Kenya National Fin Access (2009) revealed that married individuals have the highest likelihood of participating in credit programs In contrast, single households face challenges in accessing credit due to limited social networks (Ferede, 2012).
2.4.4 Level of household head’s education
Education significantly impacts access to microcredit programs, as highlighted by Tang et al (2010) Quach (2005) found a positive correlation between education level and loan demand Additionally, higher education levels enhance awareness of the financial market system, making it a key determinant for accessing microcredit programs (Yehuala, 2008; Okunade, 2007; Vaessen, 2001).
Research by Khandker (2001, 2005) indicates that households led by individuals with higher education are less likely to engage with microcredit programs Supporting this, Cuong H Nguyen (2007) found that in Vietnam, heads of households with advanced education have reduced access to the credit sector, which predominantly serves borrowers with only primary or lower secondary education.
The influence of education on access to credit sources varies significantly, as highlighted by Bendig et al (2009) Their research indicates that individuals with higher education levels are more likely to utilize formal financial institutions Conversely, there is an inverse relationship between education and the likelihood of obtaining informal loans.
The ideas of relationship between household size and accessibility of credit are different from studies to studies.
Research by Schreiner & Nagarajan (1998), Vaessen (2001), Ho (2004), and Quach (2005) demonstrates a significant positive relationship between family size and household borrowing Larger households tend to demand more loans, indicating that credit allocation increases with the number of family members This finding is consistent with Nguyen's research, which supports the notion that larger family sizes lead to higher borrowing needs.
Bendig et al (2009) found that larger household sizes negatively affect the likelihood of obtaining credit, as families with more dependents, such as children and elderly members, tend to allocate a significant portion of their income to support these individuals, increasing the risk of default (Tang et al., 2010).
However, household size did not effect on getting loan in Greece (Mitrakos and Simiyiannis, 2009)
Income is a key factor in defining poverty, with the World Bank indicating that households with higher incomes are not classified as poor Microcredit primarily aims to assist those in poverty by providing them with loans to boost their income Consequently, individuals with higher incomes have a reduced likelihood of securing loans This relationship is supported by research from Pham & Lensink (2007) and Li et al (2011), which demonstrates a negative correlation between household income and the probability of participating in credit programs, including microcredit.
It is also explained that marginal utility of consumption of poor household is high, leading to more demand for credit of the poor (Ferede, 2012)
Research indicates that high-income households are more likely to access loans compared to low-income households (Crrok, 2001; Lin and Yang, 2005; Jappelli and Pistaferri, 2007) This disparity is attributed to the assumption that high-income households typically hold mortgages (Ambrose et al., 2004).
Savings serve as collateral, mitigating adverse selection and moral hazard in the lending process between borrowers and lenders Higher savings provide greater liquidity, allowing applicants with larger savings to access credit more easily This indicates a positive correlation between household savings and the likelihood of obtaining credit.
OVERVIEW OF MICROFINANCE SYSTEM
The role of government in microfinance
The state's influence on the microfinance sector yields varying outcomes, including potential drawbacks like restricting private sector service distribution However, it is widely acknowledged that the state plays a crucial role in the sector's development This role can be categorized into three main functions: the protector role, the provider role, and the promotional role.
The protector role is crucial as it fosters confidence among low-income households and highlights the disparity between local credit recipients and financial institutions The state employs a legal framework to safeguard savers and regulate credit institutions, addressing unfair competition To enhance the effectiveness of this legal environment, it is essential for the state to adopt a flexible approach rather than a rigid one.
The government plays a direct role in providing microfinance services to impoverished individuals; however, this involvement can lead to unfair competition for credit institutions by offering preferential credit To mitigate this issue, the state should focus on supporting credit institutions and participate in payment services or savings programs instead of directly providing preferential credit.
The state plays a crucial role in promoting the microfinance industry through both direct and indirect methods Indirectly, policies and investments serve as tools to create benefits for the sector, although they may not prioritize fair competition or the strengthening of payment systems Conversely, direct promotion occurs through the development of a comprehensive microfinance strategy, which includes establishing wholesale organizations and offering essential financial and technical assistance.
Overview of credit market in Vietnam
Like other countries, credit system of Vietnam comes from three main sectors including formal, semi-formal and informal sector (Meyer and Nagarajan, 1992)
The integration of the formal, informal, and semi-formal financial sectors enhances credit accessibility for the poor while fostering competition among various credit providers in rural markets Notably, these sectors exhibit significant differences in interest rates and lending practices, as highlighted by Pham and Lensink (2007).
Figure 3.1 Microfinance Systems in Vietnam
The Vietnam Bank for Social Policies
Vietnam Bank for Agriculture and Rural Development ((VBARD)
The People’s Credit Funds (PCF)
Mi cr oc redi t S ys te m in V ie tn am
The formal sector in Vietnam includes state-owned organizations such as the Vietnam Bank for Social Policy (VBSP) and the Vietnam Bank for Agriculture and Rural Development (VBARD), along with the People Credit Funds (PCF), as reported by the World Bank in 2002.
According to the World Bank (2002), formal sector lending constitutes 73.5% of total lending in Vietnam, targeting the overall rural credit market This sector's strength lies in its extensive network and strong ties with the Local People Committee, which is crucial to Vietnam's financial system However, despite its advantages, the formal sector has not fully met its established objectives (Phan, 2012) One key player in this landscape is the Vietnam Bank for Social Policies (VBSP).
The Vietnam Bank for the Poor (VBP), established in 1995, was officially renamed the Vietnam Bank for Social Policies (VBSP) following the Prime Minister's Decision No 131/2002/QD-TTg on October 4, 2002, and the subsequent government decree.
Established under Decision 78/NP-CP on October 4, 2002, the Vietnam Bank for Social Policies (VBSP) aims to assist the poor and other policy beneficiaries by providing low-interest loans and accessible financial services Operating as an effective tool for the Vietnamese government in the fight against poverty, VBSP seeks to bridge the gap for low-income households However, despite its theoretical goal of serving these households, the bank has struggled in practice; in 2001, only 50% of its clients were classified as poor households VBSP functions under the supervision of the State Bank of Vietnam and is supported by the Vietnamese government (Izumida, 2003).
The fastest method for delivering loans to the impoverished in Vietnam involves four major organizations: the War Veterans Union, the Farmers Union, the Youth Union, and the Women’s Union These organizations are tasked with creating savings and credit groups, certifying eligible poor households, and overseeing the proper use of loans by borrowers.
As of July 2003, six programmes are offered loan by VBSP:
(1) Loan to low-income households
(3) Loan for job generation promotion (Resolution No.120/HDBT, dated April 11,1992)
(4) Loan for foreign migrant worker
(5) Loan for difficult development areas
The outstanding loan of VBPS reached the amount of 72,660 billion VND with 7.5 million borrowers were provided (VBSP, 2009) b The Vietnam Bank for Agriculture and Rural Development (VBARD)
The Vietnam Bank for Agriculture and Rural Development (VBARD) which is known as the Vietnam Bank for Agriculture was established in the year 1988
VBARD is the state policy bank and the leading channel proving credit to rural households in all types of agriculture activity in rural areas (BWTP, 2008)
VBARD requires collateral, including residential properties, movable assets, goods, or land rights, as part of its lending process The bank serves both rural households and low-income households (LIHs) Notably, the percentage of rural households accessing VBARD rose significantly from 9% in 1992 to 30% in 1994, as reported by Wolf (1999).
VBARD had become the largest commercial bank by the end of the year 2001 with more than 2,300 branches nationwide serving 50% of the poor that access to financial services (BWTP, 2003)
VBARD primarily focuses on lending to state-owned enterprises (SOEs), viewing them as low-risk clients due to government backing While it has been a significant source of credit in rural areas, VBARD's lending efforts do not fully address the needs of the entire rural credit market, especially for impoverished households.
Bias in risk assessment of complicated lending process is the main reason of undeveloped VBARD’s operation
There are three functions of VBARD:
(1) To provide credit to rural farmers and entrepreneurs with collateral
(2) To reduce transaction costs as well as increase its coverage of rural households, group lending methodology is used
VBARD offers opportunities for borrowers who cannot provide collateral by utilizing the brokerage services of mass organizations In this system, loan repayments to VBARD are guaranteed by the members of these organizations Additionally, the People's Credit Funds (PCF) play a significant role in supporting these initiatives.
In the late 1980s, following the collapse of Vietnam's rural credit cooperatives, the government established People’s Credit Funds (PCFs) to reform and support the country's banking system The primary goal of PCFs is to restore public confidence in the formal rural credit system (Putzeys, 2002) Modeled on principles of self-help, self-organization, and self-management, PCFs specifically target farmers and small entrepreneurial households as their clients (Putzeys, 2002).
By the end of 2004, over 900 PCFs were established across 53 provinces, according to BWTP However, these facilities were primarily concentrated in economically prosperous areas with better infrastructure, resulting in limited coverage in poorer regions.
3.3.2 The semi-formal credit market
The semi-formal credit sector, which includes organizations like Women's Unions, Farmer's Associations, and Youth Unions, is primarily funded by international and national donors (Phan, 2012) While these semi-formal credit programs lack a nationwide network, they offer distinct advantages over formal lenders by targeting the poor and providing subsidized credit NGOs, in particular, are recognized as effective channels for credit delivery, especially through group lending and social intermediation, leveraging successful practices from international experiences (Dao, 2002).
McCarty,2001) Moreover, semi-formal sector is considered as an important channel of many international programs for combat poverty (World Bank, 2000, p.110)
Informal financial system consists of relatives, friends, neighbor, unregistered private money lenders, traders and rotating savings and credit associations (ROSCAs) (Quach, 2005) a Relatives, Friends and Neighbors
This type of credit is typically interest-free and comes with various loan terms—monthly, quarterly, semi-annually, or annually—based on the borrower's extra income and the relationship with the lender Friends, relatives, and neighbors often provide this credit without requiring collateral or formal loan agreements, leveraging their personal connections However, this form of credit is primarily intended to assist in emergencies or personal expenses such as medical costs, funerals, and weddings, rather than for financing agricultural production.
Private money lenders offer various credit terms, including permanent, seasonal, and daily options, primarily in rural areas where affluent individuals have substantial funds available for lending However, the exact number of money lenders and the extent of credit usage remains unquantified due to the informal nature of transactions between borrowers and lenders.
(2002), money lenders provide credit and negotiate for a rate about 3-10% per month c Rotating Savings Credit Associations (ROSCAs)
Rotating Savings Credit Associations (ROSCAs) serve as an informal credit channel for rural households in Vietnam, relying on social connections to provide credit across generations Despite their significance, ROSCAs lack regulation under credit laws due to weak social sanctions and inadequate screening processes (Phan, 2012) In Vietnam, these associations are known as "Hui" in the South and "Ho" in the North (Pham and Lensink, 2007) Members typically share common professions, such as farmers, traders, or war veterans, or belong to the same hamlet, fostering a strong sense of community.
Two main forms of ROSCAs are applied to rotate the fund to participants:
RESEARCH METHODOLOGY
Research process
Research process includes six steps as presented in Figure 4.1
The initial step in the research process involves recognizing the significant role of microcredit in global development strategies, while also addressing the existing challenges that need improvement in its implementation This focus serves as the central point of this study.
The second step in the research process is the literature review, where relevant literature pertaining to the research problem is examined This review highlights how previous studies were conducted and summarizes their findings, providing a comprehensive understanding of the problem statement.
The third step in process of research is to present model of research This section presents the frame of research with all variables in econometrics analysis
The fourth step in process of research is to collect data for variables related to econometrics model from VARHS source
The fifth step in process of research is to mention about econometrics method used
The bivariate probit model is utilized to analyze the factors influencing access to credit in both the formal and informal sectors This section presents the findings of the research, highlighting the key determinants affecting credit accessibility.
The last step in process of research presents conclusion from empirical evidence after data analysis and recommends policy for improving operation of credit marker.
The data
The research data originates from the 2012 Vietnam Access to Resources Household Survey (VARHS), conducted with the collaboration of the University of Copenhagen, CIEM, ILSSA, and CAP-IPSARD This comprehensive data collection utilized a meticulously designed questionnaire that gathered extensive information on household characteristics, expenditures, income, wealth, demographics, education levels, employment, financing, and production activities The insights derived from this data are invaluable for policymakers, supporting both Vietnamese and international research, particularly in the context of rural development.
Different claim-type combinations are presented by the binary triples as Table 4.1
In the analysis, each element of the triplet represents a dummy variable for distinct credit sectors: the Formal sector, Semi-formal sector, and Informal sector A value of 1 indicates that a participant is involved in a specific sector, while a value of 0 signifies non-participation.
Table 4.1 and Figure 4.2 provide a comprehensive overview of household participation in the three credit sectors Out of 1,522 households engaged in the rural credit market, 724 accessed formal credit, representing 47.57% of the sample In contrast, the participation rates for the semi-formal and informal sectors are significantly lower, at 4.47% and 27.14%, respectively.
Table 4.1 Summary of Participation in different credit sectors
(Formal, Semi-formal, Informal) Frequency Joint probability
Figure 4.2 Participation in credit sector
The estimations of unconditional and conditional for rural credit resources are calculated from Table 4.1 are presented in Table 4.2
Table 4.2 Conditional and Unconditional Credit Participation Probabilities
The marginal probability of the semi-formal sector is the lowest among the three sectors, accounting for only 8.55% of the sample In contrast, the formal sector exhibits the highest marginal probability at 67.61%, while the informal sector stands at 46.39% Notably, the probability of observing a combination of all three sectors is a mere 1.17%.
Formal onlySemi-formal onlyInformal onlyFormal & SemiformalSemi-formal & InformalFormal & InformalAll
The analysis reveals significant correlations among the three credit sectors Notably, 66.67% of the sample engages in formal credit, while the likelihood of households utilizing formal resources decreases to 38% and 40% for those participating in semi-formal and informal sectors, respectively Furthermore, a household's involvement in either the semi-formal or informal credit sectors diminishes if they are already participating in another sector This indicates that the probability of engaging in any particular credit sector declines with the presence of alternatives, highlighting the interconnectedness of credit participation choices.
This research examines various variables including age, gender, marital status, education, household size, income, savings amount, land size, agricultural activities, social networks, and location Summary statistics for these explanatory variables are presented in Table 4.3.
Variables Mean Std Dev Min Max
Data Analysis Method
Households often engage with multiple credit sectors, indicating that their choices are interconnected rather than exclusive Consequently, estimating participation in different credit sectors using separate probit equations may be inefficient, as this approach overlooks the potential relationships between error terms.
Both multivariate probit regression model and multinominal logit regression model can estimate the influence of independent observations on different categories of dependent observations with unordered multiple choices (Gbetibouo,2009)
The former approach is favored over the latter due to its ability to simultaneously analyze the impact of various explanatory variables on different choices, as well as the interrelationships between these decisions Furthermore, this method permits the error terms, representing unmeasured factors, to be freely correlated (Lin, Jensen & Jen).
2005) There may be negative correlation which will be presented by the negative value of rho if two outcomes are substitutability, complementarity otherwise
The multivariate probit model differs from the univariate probit model by acknowledging the potential relationships among various credit sources and the correlation of unobserved disturbances in adoption functions Since households in the study share similarities across equations and exhibit potential substitutability or complementarity among sectors, it is likely that the error terms, which represent unobservable factors in these estimations, will be correlated.
From the literature review, multivariate probit model is employed to investigate which determinants affecting on demand for different credit sources
This research focuses on the factors influencing access to formal and informal credit sectors, as only about 10 percent of households engage with the semi-formal credit sector, indicating its minimal impact on overall lending Consequently, the study examines the relationship between the formal and informal credit sectors exclusively.
This study examines the relationship between formal and informal sector participation, treating them as two correlated dependent variables within a multivariate biprobit model To analyze the factors influencing access to credit, two binary variables are created, representing formal lenders (F) and informal lenders (IF).
Multivariate Probit regression utilizes Maximum Likelihood Estimation (MLE) along with Likelihood Ratio (LR) and Wald tests to identify the key determinants influencing household participation in various credit sectors.
There are two equations for this estimation and each equation is a binary choice model; for simplicity, same explanatory variables are employed in estimation which is given as below:
X ’ presents for explanatory variables including age, gender, marital_stt, edu, hhsize, income, savingamount, landsize, agriculture_act, network and location
F * , IF * are latent variables for the participation in formal credit sector and informal credit sector
F and IF which received value of 1 if these respective latent variable are greater than 0 and zero otherwise
In probit estimation, the indicator variable IF equals 1 if IF is greater than 0, and 0 otherwise The error terms, εF and εIF, are assumed to follow a multivariate normal distribution with a mean of zero and a covariance of one, as the specific variance for each error term cannot be determined (Greene, 2003).
E(ε F ) = E(ε IF ) = 0 Var(εF) = Var(εIF) = 1 Cov(ε F, ε IF ) = ρ = ρ F,IF = ρ IF,F
There are four possible outcomes in bivariate probit model:
P 11 = Pr[F=1,IF=1] = ∫ −∞ β F X ′ ∫ −∞ β IF X ′ ϕ 2 (β F X ′ , β IF X ′ , ρ)d(β F X ′ )d(β IF X ′ )
(β F X ′ , β IF X ′ , ρ) = exp {−0.5[(βFX’)2 + ((βIFX’)2 + ρ(βFX’)( βIFX’)]}/(1 – ρ2)
RESULTS AND DISCUSSION
Estimation of determinants of microcredit participation
The model is estimated by carring out in Stata presented in Table 5.1
Table 5.1 Determinants of accessibility to formal and informal credit sector
Variables formal credit informal credit
Coefficient Std Error Coefficient Std Error
***significant at 1 percent, **significant at 5 percent, * significant at 10 percent Source: Author’s calculation
The study identifies seven key factors influencing a household's likelihood of engaging with formal credit: age, education, household size, income, savings amount, land size, and location In contrast, the determinants for accessing informal credit differ significantly from those associated with formal credit.
Estimation result indicates seven variables (age, gender, marital_stt, hhsize, income, savingamount, network, location) are statistically significant determinants of participation in informal credit source
The relationship between age and the likelihood of obtaining formal credit is statistically significant at the 5% level, with older individuals having a higher probability of access, which declines as age decreases In contrast, informal lenders are more inclined to provide loans to younger borrowers rather than the elderly, aligning with studies that indicate a negative correlation between age and access to rural credit One reason for this trend is that younger individuals often lack the accumulated assets necessary for securing loans from formal lenders, leading them to seek informal sources of credit, such as loans from friends or relatives, which typically do not require collateral Conversely, older individuals usually possess sufficient assets to meet the collateral requirements of formal lending institutions.
Younger individuals often have stable incomes, which lowers the risk for informal lenders Furthermore, informal lending provides opportunities for young family members, who represent the most productive generation, to advance in their careers.
Gender may not be a reliable indicator of formal credit borrowing likelihood due to government policies aimed at reducing gender inequality in rural credit markets, ensuring equal rights for all applicants However, gender discrimination persists in informal lending, with a statistically significant negative impact associated with the gender of the household head Research indicates that male-headed households are more frequently served by informal lenders compared to female-headed households, corroborating Wabei's (2012) findings that women often lack collateral compared to their male counterparts.
Estimation results indicate a positive relationship between marital status and the likelihood of experiencing credit rationing from the informal sector Specifically, single household heads face a lower probability of accessing informal credit, which Ferede (2012) attributes to their lack of social networks This trend has been supported by various researchers, including Kirchler, Kamleitneir, and Kirchler (2007), as well as Bridges et al (2004) and Magri (2002).
Research indicates that education does not influence the likelihood of obtaining informal credit However, in the formal credit sector, educational attainment significantly impacts demand, with a 1% level of significance This suggests that individuals with higher educational qualifications have an increased probability of accessing formal credit options.
Research indicates that demand for credit tends to be lower among borrowers with fewer years of education (Khandker, 2001; Cuong H Nguyen, 2007) Higher education is often associated with increased income and access to more productive jobs, leading formal lenders to favor educated borrowers due to their reduced risk of default.
Research indicates that larger families have a positive impact on accessing formal credit, while negatively affecting participation in the informal credit sector at a 1% significance level This is attributed to government policies aimed at supporting larger families with more dependents, as they are more susceptible to economic shocks.
Informal lenders typically do not require collateral, relying instead on informal agreements between lenders and borrowers They assess the borrower's ability to repay the loan by considering family size and dependent members; families with fewer children and elderly individuals are seen as lower-risk borrowers Consequently, informal lenders prefer to lend to those with fewer dependents to mitigate the risk of default at the loan's maturity date.
Household income significantly influences credit allocation in both formal and informal sectors, though the impact varies between the two Specifically, the coefficient of household income for formal credit sources differs in sign from that of informal credit sources, highlighting the distinct dynamics at play in each sector.
Research indicates that income significantly influences household demand for informal credit, with a 5% significance level suggesting that higher household income increases the likelihood of borrowing from the informal credit sector This finding aligns with studies conducted by Crrok (2001), Lin and Yang (2005), Jappelli and Pistaferri (2007), and Ambrose et al.
A study from 2004 indicates that high-income households have greater access to loans compared to low-income households In contrast to the informal credit sector, which shows a negative relationship between income and credit constraints in the formal sector, the findings suggest that low-income households are more inclined to borrow due to the higher marginal utility of consumption associated with their limited income The literature on rural credit highlights that microfinance primarily aims to provide capital to the poor for improving their living conditions (World Bank, 2010), resulting in lower chances of credit approval for high-income applicants These findings align with previous research by Pham & Lensink (2007), Li et al (2011), and Ferede.
The estimation results indicate that the amount saved significantly influences credit participation in both the formal and informal sectors at a 1 percent significance level In the informal sector, the negative coefficient for the savings variable suggests that households with higher savings are not perceived as poor Conversely, in the formal sector, there is a positive correlation between savings and credit participation This aligns with existing research, which highlights that formal credit often requires collateral; thus, borrowers with larger savings are more likely to secure loans from formal credit sources The findings of this study are consistent with the literature on this topic.
The results presented in Table 5.1 demonstrate a statistically significant relationship between rural credit participation and land size at the 1 percent level Larger land sizes serve as collateral for credit borrowing, facilitating access to credit markets, particularly formal ones that require collateral to mitigate risk This finding aligns with the research conducted by Vu (2002) and Zeller (2001).
In 2003, research by Ravi and others highlighted that households with extensive land holdings require increased credit for agricultural production However, it was found that borrowing from the informal sector is significantly negatively correlated with land size at the 1% significance level This suggests that households with larger land sizes are often not perceived as poor, according to Quach.
Estimation of conditional marginal effects
Table 5.2 Marginal effects for conditional probability of formal sector participation
Variables dy/dx Std Err P>|z| X
The likelihood of a household engaging in the formal sector is estimated at 40.18 percent, based on the assumption that it is already involved in the informal sector, as indicated by the calculation Pr(formal=1|informal=1) = 0.40 in Table 4.2.
Command predict(pcond1) is employed to compute conditional marginal effects for each independent variable on formal sector participation with given informal participation sector
An additional year of education for the household head increases the likelihood of accessing the formal credit sector by 1.7%, assuming all other variables remain constant.
In term of marital_stt, the opportunity to get credit from formal source for married household head is 18.2 percent higher compared to that for unmarried household head
The location of a household significantly influences its access to formal credit lending, particularly for those engaged in the informal credit sector Research indicates that households situated in rural areas are predicted to experience a 31.6 percent increase in the likelihood of receiving credit from formal lenders, all other factors being equal.
The derivative dy/dx for the income variable is -0.0002, indicating that when controlling for other variables at their average levels, the likelihood of accessing formal credit decreases by 0.02 percent for every increase of 1,000,000 VND in household income, assuming the household is already engaged in the informal sector.
Savings serve as formal collateral for credit borrowing, establishing a positive correlation with access to formal credit Notably, for households engaged in the informal credit sector, each increase of 1,000,000 VND in savings raises the likelihood of obtaining formal credit by 0.1 percent.
The likelihood of a household engaged in the informal sector gaining access to the formal sector increases by 0.3% with an additional 1000m² of land ownership, assuming other variables remain constant.
Similarly, marginal effects for conditional probability of informal participation sector with given formal participation sector, Pr(informal=1|formal=1) are shown as Table 5.3
Table 5.3 Marginal effects for conditional probability of informal sector
Variables dy/dx Std Err P>|z| X
The likelihood of a household engaging in the informal sector is estimated at 27% for those already participating in the formal sector, as indicated by the calculation Pr(informal=1|formal=1) = 0.27, shown in Table 4.2.
Command predict(pcond2) is employed to compute conditional marginal effects for each independent variable on informal sector participation with given formal participation sector
The age of a household head negatively impacts participation in informal credit when the household is already engaged in the formal sector Specifically, for each additional year in age, the likelihood of accessing informal credit decreases by 0.4 percent, assuming all other variables remain constant.
The marital status of the household head significantly influences the likelihood of seeking informal credit services Specifically, for households engaged in the formal credit sector, married households are 18.7 percent more likely to approach informal providers compared to their unmarried counterparts, all other factors being equal.
Research by Yehuala (2008), Okunade (2007), and Vaessen (2001) indicates that a one-year increase in education level correlates with a 0.9 percent rise in the likelihood of engaging with informal credit sources, assuming other variables remain constant.
The coefficient for household size is -0.02, indicating that, when controlling for other variables, the likelihood of informal participation decreases by 0.2% for each additional member already engaged in the formal credit sector.
Social networks and geographical location significantly influence household borrowing from the informal credit sector According to the estimated results, households engaged in the formal credit sector will see an increase of 5.7% and 6.1% in their access to informal credit as the independent variable shifts from zero to one.
CONCLUSION
Research Findings
Many households seek loans, but some are excluded due to concerns about their ability to repay and lack of adequate collateral As a result, these borrowers often resort to the informal credit sector, where they face significantly higher interest rates compared to traditional lending options.
This research aims to explore the relationship between the use of formal and informal credit providers, addressing a gap in existing studies that typically examine these sectors separately By investigating the determinants of household participation in both credit sectors, this study seeks to provide a comprehensive understanding of how formal and informal credit systems interact with one another.
This research utilizes a bivariate probit model to analyze the factors affecting household access to both formal and informal credit sectors, while also exploring the relationship between the two The findings reveal that the determinants for utilizing the formal credit sector differ significantly from those for the informal sector Additionally, the study indicates that participation in the informal credit sector negatively impacts the likelihood of engaging with the formal credit sector, and conversely, involvement in formal credit reduces the probability of informal credit participation.
Seven key factors influence formal credit participation: age, education, household size, income, savings amount, land size, and location The analysis suggests that formal lenders are more likely to extend credit to households that possess better financial stability, often requiring collateral such as land size or savings amount.
Policy implications
Based on the research results, in order to improve the smooth of microfinance system operation, some policies are suggested:
Firstly, network of commercial bank should be expanded at village level
Additionally, administrative process for lending should be more simplified
Accessing credit source is necessary for the poor improving living standard
Access to formal credit for the poor in Vietnamese rural areas is essential yet challenging Developing a banking system in these regions is crucial to bridging the gap between low-income individuals and financial institutions Additionally, simplifying administrative processes is necessary to facilitate timely borrowing for the poor Research indicates that education level significantly impacts access to formal credit; thus, complicated procedures can hinder low-educated households from effectively choosing suitable credit sources.
The lending assessment criteria in microfinance should prioritize the actual needs of impoverished households rather than relying on the lender's confidence or traditional collateral measures Credit providers must focus on the characteristics of households that indicate poverty, rather than their savings or land size, as those with greater savings or larger landholdings often possess more capital and better livelihood opportunities.
To align interest rate policies in rural areas with market capitalism, it is essential to gradually move away from preferential interest rates The Vietnam Bank for Social Policies (VBSP) currently provides loans to the poor at these preferential rates, but this approach has unintended consequences that diverge from the fund's original objectives Firstly, the high default risk associated with lending to low-income individuals makes it challenging for them to secure loans, prompting banks to favor lending to households with collateral, such as savings or land Secondly, low interest rates can lead to inefficient use of credit, as they may encourage investments in poor-quality projects due to reduced capital costs.
To effectively harness the potential of informal credit sources in rural poverty reduction, it is essential to establish a suitable management system Informal loans play a crucial role in the lives of the poor, representing a significant portion of available credit Enhancing the functionality of the informal credit sector is vital; however, it is important to note that these loans often come with exorbitant interest rates, particularly in remote areas where formal credit options are limited Therefore, implementing partial controls on the formal credit sector is necessary to mitigate the risk of trapping the poor in a cycle of poverty.
Limitations
The limited number of households engaging in semi-formal credit within the sample restricts this research from analyzing the relationship between the semi-formal credit sector and both the formal and informal credit sectors.
One of the key factors influencing household credit demand is the distance from the borrower's location to the lender However, this study lacks specific data on the distance to the chosen lender, as only the distance to the nearest credit institution is available Consequently, this research is unable to determine the impact of proximity to credit providers on microcredit participation.
Research indicates that factors influencing credit participation stem from both demand and supply side characteristics (Vaessen, 2002; Duong Pham, 2002) This study specifically concentrates on the demand side, focusing exclusively on household characteristics.
These limitations mentioned are suggested for further research
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Appendix 1: Statistics variables savingamount 1522 22.41842 58.27375 0 820 landsize 1522 10.99167 16.30339 035 181.2 income 1522 88.22605 157.5349 2.005 4500 agricultur~s 1522 6708279 470067 0 1 marital_stt 1522 8554534 3517587 0 1 network 1522 5611038 4964154 0 1 informal 1522 4638633 4988563 0 1 semi_formal 1522 0854139 2795885 0 1 formal 1522 6760841 4681222 0 1 location 1522 7943495 404309 0 1 gender 1522 8390276 3676263 0 1 edu 1522 8.339685 3.226634 1 13 material 1522 8554534 3517587 0 1 age 1522 47.13272 12.3758 18 93 hhsize 1522 5.754928 3.423013 1 25 Variable Obs Mean Std Dev Min Max summarize
Likelihood-ratio test of rho=0: chi2(1) = 480.84 Prob > chi2 = 0.0000 rho -.8135035 0208197 -.850499 -.7684985 /athrho -1.137302 0615582 -18.48 0.000 -1.257954 -1.01665 _cons 744359 2261336 3.29 0.001 3011452 1.187573 location -.33206 0821836 -4.04 0.000 -.493137 -.1709831 network 1535791 071354 2.15 0.031 0137279 2934304 agriculture_activities -.0047116 0754129 -0.06 0.950 -.1525181 1430949 landsize -.0024476 0022311 -1.10 0.273 -.0068204 0019252 savingamount -.0022606 0007772 -2.91 0.004 -.0037839 -.0007373 income 0006474 0002615 2.48 0.013 0001349 0011599 hhsize -.0544963 0104913 -5.19 0.000 -.0750589 -.0339337 edu 0063396 0105358 0.60 0.547 -.0143102 0269893 marital_stt 4704264 1384631 3.40 0.001 1990436 7418092 gender -.2400197 1285607 -1.87 0.062 -.4919939 0119546 age -.012557 0029225 -4.30 0.000 -.018285 -.0068289 informal
_cons -1.161806 2348165 -4.95 0.000 -1.622038 -.7015737 location 8583475 082791 10.37 0.000 6960802 1.020615 network -.0449324 0745282 -0.60 0.547 -.1910049 1011402 agriculture_activities 0551399 0783177 0.70 0.481 -.0983599 2086397 landsize 0076207 0025141 3.03 0.002 0026931 0125483 savingamount 0025958 0009093 2.85 0.004 0008136 0043779 income -.0008037 0002655 -3.03 0.002 -.001324 -.0002834 hhsize 0185429 0107979 1.72 0.086 -.0026207 0397064 edu 0289365 0110286 2.62 0.009 0073209 0505521 marital_stt 0680732 1387999 0.49 0.624 -.2039696 340116 gender 151997 1284223 1.18 0.237 -.0997061 4037001 age 0075118 0030486 2.46 0.014 0015367 0134869 formal
Coef Std Err z P>|z| [95% Conf Interval]
Log likelihood = -1626.0435 Prob > chi2 = 0.0000 Wald chi2(22) = 244.13Seemingly unrelated bivariate probit Number of obs = 1522
Appendix 3: The marginal effects for Pr(formal=1, informal=1)
Appendix 4: The marginal effects for Pr(formal=1, informal=0)
The analysis of marginal effects reveals significant insights into the relationships between various variables and their impact on the probability of being formal or informal The dummy variable shows a substantial change in probability (dy/dx = 0.1144) with a highly significant p-value (0.000), indicating a strong correlation Additionally, the variable "network" has a marginal effect of 0.0349, approaching significance (p = 0.063), while "agriculture" shows minimal impact (dy/dx = 0.0121, p = 0.541) The variable "land size" exhibits a marginal effect of 0.0011 (p = 0.095), suggesting a potential but weak influence Notably, "household size" has a significant negative effect (-0.0118, p = 0.000), while education positively influences the outcome (0.0090, p = 0.002) The marital status variable shows a strong positive effect (0.1322, p = 0.000), whereas gender has a negligible effect (-0.0336, p = 0.342) Age also contributes negatively (-0.0019, p = 0.015), highlighting its relevance in the model Overall, these findings underscore the importance of demographic and social factors in determining formal and informal employment probabilities.
The analysis of marginal effects shows that the change in the dummy variable from 0 to 1 results in a dy/dx value of 0.2090319, indicating a significant positive impact with a p-value of 0.000 Notably, the network variable has a marginal effect of -0.0507289, approaching significance at p=0.057 The variable for agricultural services shows minimal impact (0.0075307, p=0.789), while land size has a marginal effect of 0.0015651, with a p-value of 0.059, suggesting a near-significant positive influence Savings demonstrate a significant positive effect (0.0009607, p=0.001), whereas income has a negative marginal effect of -0.0002817, significant at p=0.004 Household size positively influences the outcome with a dy/dx of 0.0183539 and a p-value of 0.000 Education shows no significant impact (0.0012581, p=0.750) The marital status variable negatively affects the outcome (-0.1078536, p=0.028), while gender has a marginal effect of 0.0885767, nearing significance at p=0.062 Finally, age positively influences the outcome with a dy/dx of 0.0045826 and a significant p-value of 0.000.
Appendix 5: The marginal effects for Pr(formal=0, informal=1)
Appendix 6: The marginal effects for Pr(formal=0, informal=0)
The analysis reveals the marginal effects of various variables on the likelihood of being in the informal sector A significant decrease in the probability is observed with increasing land size (dy/dx = -0.0021, p = 0.004) and savings (dy/dx = -0.00085, p = 0.001) Conversely, income shows a positive effect (dy/dx = 0.0002593, p = 0.001), while household size and education negatively impact the probability of informal employment (dy/dx = -0.0098, p = 0.003; dy/dx = -0.0065, p = 0.055, respectively) Gender and marital status do not significantly influence the outcome, with p-values of 0.146 and 0.230, respectively Age is also a significant factor, indicating a negative relationship with the probability of being informal (dy/dx = -0.0031, p = 0.001) Overall, the results suggest that land size, savings, household size, education, and age are key determinants in the transition between formal and informal employment sectors.
The analysis reveals that the marginal effect of the dummy variable transitioning from 0 to 1 is significant, with a coefficient of -0.0772 (p < 0.001), indicating a strong negative impact The network variable shows a negative effect of -0.0101, though it is not statistically significant (p = 0.177) The variable for agricultural status presents a negligible negative effect of -0.0057, which is also not significant (p = 0.472) Land size has a small but significant negative effect of -0.0006 (p = 0.024) Savings and income variables show minimal impacts, with coefficients of -0.00006 and 0.00002, respectively, both lacking significance Household size positively influences the outcome with a coefficient of 0.0033 (p = 0.003) Education has a significant negative effect of -0.0038 (p = 0.001), while marital status shows a significant negative impact of -0.0713 (p = 0.004) Gender and age variables do not demonstrate significant effects, with p-values of 0.545 and 0.188, respectively Overall, the analysis highlights significant factors influencing the probability of formal versus informal employment, particularly emphasizing the importance of marital status and household size.
Appendix 7: The marginal effects for the marginal probability of outcome 1 Pr(formal=1)
Appendix 8: The marginal effects for the marginal probability of outcome Pr(informal=1)
The analysis of marginal effects indicates that the discrete change of the dummy variable from 0 to 1 yields a dy/dx value of 0.3234715, with a statistically significant p-value of 0.000 Other variables such as network and agricultural services show no significant impact, while land size and savings demonstrate positive effects on the probability of being formal, with p-values of 0.002 and 0.004, respectively Conversely, income has a negative effect on formal status, with a p-value of 0.002 Household size and education also influence the outcome, with education showing significance at p=0.009 Age appears to have a positive effect with a p-value of 0.014, while marital status and gender do not significantly affect the probability of being formal Overall, the results highlight the importance of land size, savings, education, and age in determining formal employment status.
The analysis reveals significant marginal effects on various variables influencing the probability of being informal Specifically, a discrete change in the dummy variable yields a dy/dx of -0.1319, indicating a substantial impact The network variable shows a positive effect of 0.0608, significant at the 0.031 level Conversely, the agricultural sector exhibits negligible influence with a dy/dx of -0.0019 Land size and savings also reflect minor effects, with values of -0.0010 and -0.0009, respectively Income contributes positively at 0.0003, while household size has a notable negative effect of -0.0216, both statistically significant Education shows no significant effect, while marital status positively influences the probability of informality at 0.1791 Gender presents a marginally significant negative effect of -0.0955, and age negatively impacts the probability with a dy/dx of -0.0050, reinforcing the importance of demographic factors in this context Overall, these findings underscore the complex interplay of socio-economic variables affecting informal sector participation.