Problem Statement
Income diversification in rural households of developing countries has garnered significant interest from scholars in development economics This concept involves the strategic allocation of resources across various income-generating activities, encompassing both on-farm and off-farm endeavors, as highlighted by Abdulai and Crolerees.
Households often diversify their income sources to manage risks, ensure a steady income flow, utilize surplus labor, and address various market failures, including imperfections in insurance and credit markets (Ellis, 1998).
Income diversification plays a crucial role in stabilizing income and reducing rural poverty, prompting governments in developing countries, including Vietnam, to promote this strategy With over 70% of its population residing in rural areas, Vietnam has implemented various policies since 1986 aimed at fostering a multi-sector economy, restructuring its economic framework, and enhancing living standards while integrating with the global economy Specific objectives for rural development include job creation, increasing income from agriculture and rural industries, and promoting services and off-farm activities These initiatives are designed to stimulate income diversification, leading to significant achievements in economic growth and poverty alleviation, with annual growth rates of 6-8% since the early years of reform.
1990 and the poverty rate falling from 58% in 1993, 29% in 2002, 15.5% in
Between 2006 and 2010, income growth and poverty reduction were significantly influenced by household diversification into higher value crops, livestock raising, and non-farm activities, with income levels rising to 14.5% in 2008 and 14.23% in 2010, according to the GSO.
Income diversification is crucial for the early stages of rural transformation, but its patterns can differ significantly across countries and regions (Ellis, 1998) Identifying the specific determinants of income diversification in various areas is essential, as it enables governments to formulate targeted policies that effectively support rural development.
Despite the limited empirical studies on income diversification in Vietnam, research examining its impact on household income is even scarcer Utilizing data from the Vietnam Household Living Standards Survey 2008 (VHLSS 2008), this paper aims to identify the factors influencing income diversification among rural households and assess how diversification affects their income levels.
Research objectives
This study explores the factors influencing income diversification among rural households in Vietnam, while also assessing the varying impacts of these determinants across different economic and geographical regions Additionally, it investigates the reverse effects of income diversification on household income levels.
LITERATURE
Concepts and measures of income diversification
Income diversification is a key strategy used by households to reduce income variability and maintain a stable minimum income level Empirical studies typically analyze this concept through five distinct indicators of income diversification, each of which is explored in detail.
The first definition is possibly the simplest one that diversification is referred to the increase in the number of income sources of households (Minot et al 2006)
Households with multiple income sources are deemed more diversified, and as the number of these sources increases over time, so does the level of diversification This indicator is straightforward to measure and comprehend; however, it primarily counts the sources equally without considering their significance to the household's total income.
The second approach addresses the limitations of the previous measure by considering both the number of income sources and the contribution of each source to the overall household income.
Income diversification involves households expanding the number of income sources they utilize, aiming for a more balanced distribution among these sources in their financial portfolio (Ellis 2000, Minot et al 2006).
Schawarze and Zeller (2005) utilized the Shannon equitability index to examine income diversification among households in Indonesia, highlighting that the index increases with both the number of income sources and their even distribution.
Similarly, the inverse Herfindahl index is employed by Babatunde and Qaim
(2009) in examining the patterns of income diversification in Nigeria
The third measure focuses on nonfarm employment, which refers to the process by which rural households enhance their income through non-farm activities (Barrett and Reardon, 2001) This concept is frequently represented as the percentage of total household income derived from non-farm sources, as discussed by various authors, including Ellis (2000), Abdulai and CroleRees (2001), and Minot et al (2006).
The fourth definition highlights the transition from subsistence production to commercialized agriculture This type of diversification is typically assessed through three key measures: crop diversification, agricultural commercialization, and income commercialization Crop diversification indicates the percentage of crop production that is sold or exchanged, while agricultural commercialization reflects the share of total agricultural output that is marketed Lastly, income commercialization is evaluated by the proportion of gross income derived from cash sales.
Income diversification, as defined by Minot et al (2006), involves transitioning from low-value crop production to high-value crops, livestock, and non-farm activities Key indicators of this diversification include the percentage of high-value crops, the income proportion derived from non-crop activities, and the share of income generated from non-farm sources.
This study focuses on income diversification by analyzing four key concepts in the descriptive analysis In the econometric analysis, we concentrate on three specific indicators: the number of income sources, the Simpson index of diversity, and the proportion of non-farm income relative to total household income.
Theoretical framework
This study bases on the concept of Sustainable Livelihood Framework
The term "Sustainable Rural Livelihood" has gained significant importance in discussions surrounding rural development and poverty alleviation, as highlighted by Ian Scoones (1998) Initially introduced by the Brundtland Commission on Environment and Development in 1992, its definition has evolved and been refined over time to encompass a variety of related issues.
Among these definitions, IDS’s definition is somewhat a modified one of Sustainable Livelihood as follows:
A livelihood encompasses the necessary capabilities, assets (both material and social), and activities essential for sustaining life For a livelihood to be considered sustainable, it must effectively withstand and recover from various stresses and shocks, while also maintaining or improving its capabilities and assets, without depleting the natural resource base.
The Sustainable Livelihood Framework emphasizes the importance of placing people at the center of various interrelated factors that influence their ability to create livelihoods Key to this framework are livelihood assets, which encompass natural, physical, human, social, and financial capital Access to these assets is significantly affected by contextual factors such as economic and political trends, as well as shocks like natural disasters Additionally, social, institutional, and political environments shape how individuals utilize their assets to pursue their goals, known as livelihood strategies One effective strategy is livelihood diversification, which helps households increase income, reduce income fluctuations, and ultimately enhance their overall livelihood.
Context Livelihood Institutional Livelihood Sustainable
Conditions Resources Procedures and Strategies Livelihood and trends Organizational Outcomes
Contextual analysis of conditions and trends, and assessment of policy setting
Analysis of livelihood resources: trade-offs, combinations, sequences, trends
Analysis of institutional/organizational influences on access to livelihood resources and composition of livelihood strategy portfolio
Analysis of livelihood strategy portfolio and pathways
Analysis of outcomes and trade-off
Figure1: The Sustainable Livelihood Framework (Scoones 1998:4)
Natural capital Human capital Physical capital Financial capital Social capital
1 Increased number of working days created
3 Well-being and Capabilities improved
4 Livelihood adaptation, vulnerability and resilience enhanced
5 Natural resource base sustainability ensured
Determinants of income diversification
Researchers categorize the motivations for households to diversify income sources into “demand-pull” and “push-distress” factors “Pull” factors enable households to accumulate wealth through competitive advantages such as superior technologies and skills, while “push” factors arise from challenging circumstances like adverse weather, policy changes, and failures in credit or insurance markets These conditions compel households to engage in non-farm activities as a means of income smoothing through risk management strategies However, Reardon et al (2007) note that existing literature on income diversification often overlooks the incentives driving this diversification and fails to adequately consider household capacity variables.
The article presents an alternative approach that emphasizes household capacity variables, defined as capital assets This method suggests that the level of participation in diversification strategies is influenced by various factors that reflect the household's available resources and motivations to engage in such activities.
In line with the sustainable livelihoods literature, the ability of households to diversify income highly depends on their access to the different types of capital
It explains why households do not have the same opportunities to participate in non-farm activities, and hence get less diversified income (Abdulai, et all.,
In 2001, various forms of capital were identified, enabling households to engage in both agricultural and non-agricultural activities These capitals are typically classified into four categories: human, physical, financial, and social capital.
In the framework established by Reardon et al (2007) and the broader sustainable livelihoods theory, capital encompasses not only a household's private assets but also its access to public resources This capacity to diversify income can be evaluated at various levels, including household, individual, regional, or village Demographic characteristics at the household and individual levels significantly influence the ability and decisions related to income diversification Meanwhile, at the regional or village level, robust physical and institutional infrastructure is crucial for facilitating income diversification among households Improved access to infrastructure, such as communication networks and roads, can lower information acquisition costs, reduce transport and transaction expenses, and enhance opportunities for households to engage in non-farm activities (Barrett and Reardon, 2001).
Davis, 2003; Ellis, 2000; Reardon, et al 2007)
Empirical studies across various countries have shown that different asset types significantly influence household income diversification Research by Barrett, Reardon, and Webb (2001) highlights that improved education plays a crucial role in enhancing non-farm earnings, underscoring the importance of education in income diversification strategies.
Research indicates that improved physical access to markets significantly boosts non-farm earnings, as demonstrated in Tanzania (Lanjouw et al., 2001) In Southern Mali, Abdulai and Crolerees (2001) found that poorer households face limited opportunities in cash-crop production and non-crop activities, resulting in less diversified incomes primarily due to a lack of capital Similar studies in other developing countries highlight the importance of public assets like roads, electricity, and water, as well as private assets such as education and access to credit, in enhancing households' capacity to diversify their income (Escobal, 2001; Babatunde and Qaim, 2009).
Research consistently demonstrates a positive relationship between income diversification and household welfare For instance, Babatunde and Qaim (2009) highlight that in Nigeria, income diversification significantly boosts total household income across various measures Similarly, Ersado (2003) investigates this relationship in Zimbabwe, utilizing metrics like the number of income sources and the share of nonfarm income The study reveals that in rural areas, wealthier households tend to have more diversified income sources, while the opposite is true in urban settings Additionally, Ersado notes that in rural regions with high rainfall variability, households adopt multiple income strategies as a form of risk management, further supporting the importance of income diversification.
DATA AND RESEARCH METHODOLOGY
Data
This study utilizes data from national household surveys, specifically five Vietnam Household Living Standards Surveys (VHLSS) conducted in 2002, 2004, 2006, 2008, and 2010, to analyze changes in income sources and their contributions to total household income The sample sizes for the VHLSS datasets are 22,621, 6,938, 6,882, and 6,753 rural households for the years 2002, 2004, 2006, and 2010, respectively To explore the factors affecting income diversification and its relationship with total household income, the research focuses on the cross-sectional data from the 2008 VHLSS, which includes a nationwide sample of 45,945 households, comprising 36,756 households in the income survey and 9,189 households surveyed on both income and expenditure (GSO, VHLSS).
As the paper is to examine the income diversification in rural Vietnam, only households in rural areas are included in the research comprising 6,837 households.
Research methodology
3.2.1 Classification and calculation of income sources
According to the Vietnam Household Living Standards Survey (VHLSS), household income derives from two main employment types: wage employment and self-employment Wage employment is further classified into farm and non-farm categories, while self-employment encompasses farm activities such as crop, livestock, fishery, and forestry, as well as private businesses, which include agricultural and non-farm enterprises This study categorizes household income into eight sources: wage income (both farm and non-farm), crop income, livestock income, fishery income, forestry income, enterprise income from private businesses, transfer income, and other income.
Calculating income from wage employment is straightforward, as it involves summing the annual earnings and bonuses of all household members engaged in various jobs In contrast, income from agricultural and other activities—such as crop production, livestock, fishery, forestry, and enterprises—is determined by the net revenue from each activity, which is the difference between total production value and production costs.
Transfers encompass both private transfers, like gifts and remittances received by households in the last year, and public transfers from various government programs, including social subsidies and poverty reduction initiatives Other sources of income consist of pensions, lottery winnings, interest from savings and loans, and rental income However, one-time amounts from the sale of assets such as buildings, vehicles, and gold are not classified as household income according to the Vietnam Household Living Standards Survey (VHLSS).
As discussed above, there are different ways to measure income diversification
This study utilizes the income-based approach to examine three key aspects of income diversification: the presence of multiple income sources, the growing significance of non-farm income in relation to total household income, and the commercialization of production.
Diversification through multiple income sources is analyzed using two key indicators: the number of income sources (NIS) and the Simpson index of diversity The NIS, as utilized by Minot et al., provides insight into the variety of income streams, while the Simpson index measures the diversity of these sources, highlighting the stability and resilience of income portfolios.
The measurement of income sources in households, as discussed by Ibrahim et al (2009), faces criticism for its arbitrariness, particularly noting that households with more active adults tend to have multiple income sources (Babatunde and Qaim, 2009) To address this issue, the Simpson Index of Diversity (SID) is used in conjunction with the initial measure, as it considers not only the number of income sources but also their respective contributions to total income This comprehensive approach allows for a more accurate assessment of household income diversification, as demonstrated in studies by Minot et al (2006) and Joshi et al (2003).
In a household with a single income source, the income share from that activity is represented by Pi, resulting in a zero Specialization Index (SID), which signifies complete specialization in that income-generating activity.
When a household's income is derived from multiple sources, the contribution of each source to the total income diminishes, resulting in a decrease in the sum of squared shares Consequently, the Social Income Diversification (SID) index approaches a value of 1, signifying that the household has a high level of income diversification.
The non-farm income share (NFS) is a key indicator used to assess the contribution of income generated from non-farm activities, such as non-farm wage income and non-farm enterprises A higher NFS indicates greater household diversification, demonstrating the extent to which households transition from farm-based to non-farm activities.
This paper explores income diversification among households, defining it as the transition from subsistence to commercial production It considers two key measures: crop commercialization, which refers to the share of crop production value that is sold or bartered, and agricultural commercialization, encompassing the proportion of value from all agricultural products—including crops, livestock, fishery, and forestry—that is sold or bartered.
This research employs various data analysis methods, including descriptive statistics and econometric techniques, detailed in Chapter 4 The descriptive analysis illustrates income diversification patterns over time and across different household types and geographical regions, comparing diversification measures from surveys conducted in multiple years.
This article analyzes the determinants of income diversification among households using data from the 2008 Vietnam Household Living Standards Survey (VHLSS) and assesses its impact on total household income The study employs regression analysis on three diversification measures: NIS, SID, and NFS, which are evaluated against independent variables representing household capital assets For the NIS model, which uses count data as the dependent variable, Poisson regression is applied In contrast, SID and NFS measures are analyzed using Tobit regression due to their censored nature between zero and one, following methodologies established by Escobal (2001) and Schwarze and Zeller (2005) in similar contexts.
To analyze the effects of income diversification on total household income, we employ three models where total income serves as the dependent variable, and various diversification measures are included as explanatory variables To address potential endogeneity issues, we utilize the Instrumental Variables (IV) method, specifically two-stage least squares (2SLS), to assess the impact of income diversification on household income.
Babatunde and Qaim (2009) use this technique in the similar context in the analysis in Nigeria.
FINDINGS AND DISCUSSION
Patterns and trends in income diversification
In the analysis of income source diversity based on the VHLSS, household income is categorized into eight groups: wage, crop, livestock, fishery, forestry, enterprise, transfer, and other income Table 4.1 illustrates the trends in income diversity among rural households across the country and specific regions, measured by the number of income sources and the Simpson index of diversity Rural households typically derive their income from multiple sources, with an average of 4.08, 4.35, 4.12, 3.50, and 4.28 income sources reported in various regions according to VHLSS data.
Between 2002 and 2010, there was a modest increase in the number of income sources, peaking at 4.28 in 2010 after a decline from 2004 to 2008 This trend of increasing income diversity is consistent across all geographical and economic regions.
The Simpson index of diversity illustrates the trend of income diversification among rural households in Vietnam and its various regions, emphasizing the importance of both the number of income sources and their balance According to the Vietnam Household Living Standards Survey (VHLSS) conducted in 2002, this index provides valuable insights into the income diversification patterns within these communities.
2004, 2006, 2008 and 2010, the value of this index is 0.488; 0.501; 0.484;
Table 4 1 Diversity of income sources by regions across years
Number of income sources (NIS) Simpson index of diversity (SID)
Source: analysis of VHLSS 2002, 2004, 2006, 2008 and 2010
The North East and North West regions of Vietnam exhibit the highest diversity in income sources, while the Southeast region, being the most urbanized and least poor, shows the least diversity This trend suggests that poorer households tend to diversify their income more than wealthier ones Indicators such as NIS and SID consistently increase with household poverty levels, indicating a clear correlation between poverty and income diversification This finding contrasts with Abdulai and Croleres' (2001) results for Mali but aligns with Schwarze and Zeller's (2005) research in rural Indonesia.
Income diversification is more prevalent among poorer households compared to wealthier ones, highlighting its role as a strategy to mitigate risks associated with fluctuations in income from various sources.
Table 4 2 Diversity of income sources by income quintile across years
Number of income sources (NIS) Simpson index of diversity (SID)
Source: Analysis of VHLSS 2002, 2004, 2006, 2008 and 2010
4.1.2 Diversification as a shift to non-farm activities
Agriculture remains a crucial sector, encompassing crops, livestock, fisheries, and forestry; however, there has been a significant rise in the proportion of household income generated from non-farm activities This shift is evident as the share of income from non-farm sources increased from 27.4% in 2002 to 37.1% by 2004, highlighting the growing diversification of income sources in households over time.
Between 2006 and 2010, the significance of the non-agricultural sector increased, reflecting a gradual structural shift in the economy This transition contributed to a rise in non-farm income, with a notable increase in non-farm wages from 13.3% in 2002 to 24.7% in 2010, despite a slight decline in non-farm enterprise share The impact of non-agricultural income on total household earnings is evident across all income quintiles, although the extent and rate of growth differ among them.
2002 2004 2006 2008 2010 s h a re i n to ta l in c o m e wage crop livestock fishery forestry enterprise transfer other
Figure 4 1 Trends in income composition of rural households
Analysis of VHLSS data from 2002 to 2010 reveals a significant disparity in non-farm income shares between different income quintiles In 2002, the richest quintile had a non-farm income share of 40.8%, compared to just 15.4% for the poorest quintile While all income groups saw an increase in non-farm income shares from 2002 to 2008, reaching 44.8% for the richest, the poorest quintile experienced a notable decrease of 5.7% in 2010, dropping to 17.4% The second quintile also faced a slight decline of 1.9% Conversely, the third, fourth, and fifth quintiles saw substantial increases, with non-farm income shares rising to 43.7%, 51.3%, and 54.9%, respectively.
2002 2004 2006 2008 2010 p e rc e n t nonfarm income non-farm wage non-farm enterprise
Figure 4 2 Share of nonfarm income in total income of rural households
Source: Analysis of VHLSS 2002, 2004, 2006, 2008 and 2010
Table 4 3 Share of non-farm income in household’s total income by income quintile across years
Income quintile Share of non-farm income (%)
Source: Analysis of VHLSS 2002, 2004, 2006, 2008 and 2010
Rural households have increasingly diversified their income sources, particularly through non-farm activities, over time However, the degree of income diversification varies significantly across different income quintiles, with poorer households exhibiting much lower levels of diversification compared to wealthier ones This disparity can be attributed to the greater challenges that low-income households face in accessing non-farm opportunities.
As discussed above, diversification is also referred to the transition from production for household’s own consumption to production to sale or bartered
This analysis utilizes two key indicators: crop commercialization and agricultural commercialization Crop commercialization measures the percentage of crop output that is sold or bartered, while agricultural commercialization assesses the increase in the share of various agricultural products—such as crops, livestock, fish, and forest products—that are sold or bartered Together, these indicators reflect the proportion of cash income generated from the total gross income derived from producing crop and agricultural products.
Table 4.4 highlights the commercialization of crop production across various geographical regions over the years surveyed The North East region demonstrates a notably low commercialization rate, with only 30.6% of crop output sold or bartered in 2002, decreasing to 24.9% in 2010 Similarly, the North Central Coast, North West, and Red River Delta regions show modest commercial shares of 38.7%, 40.2%, and 41.4%, respectively, as per the VHLSS 2010 data In contrast, the Central Highlands, Mekong River Delta, and Southeast regions exhibit a significantly higher marketed proportion of crop production, exceeding 80%.
The degree of commercialization in agricultural output varies significantly, with the North East and North West regions showcasing nearly 50% of their agricultural products sold or bartered This percentage remains relatively stable, exhibiting only minor fluctuations over the years.
Red River Delta North East North West
South Central Coast Central Highlands
Share of crop output sold (%)
Figure 4 3 Share of output sold or bartered by region and year
Source: Analysis of VHLSS 2002, 2004, 2006, 2008 and 2010
The southern regions of Vietnam exhibit a high level of market orientation in agriculture, with 81.1% of agricultural output marketed in the Central Highlands, 73.4% in the South East, and an impressive 89.8% in the Mekong River Delta, as reported by VHLSS 2010.
Table 4 4 Measure of commercialization by regions across years
Share of crop output that is sold (%) Share of agricultural output that is sold (%)
Source: Analysis of VHLSS 2002, 2004, 2006, 2008 and 2010
Over time, the commercialization of rural households has been steadily increasing In the country, the percentage of crop output marketed by these households grew from 61.7% in 2002 to 67.6%.
2010 while the proportion of agricultural output sold goes up to 74.1% in 2008 from 71.9% in 2002
The commercialization of agricultural output varies significantly across income levels, with wealthier individuals exhibiting higher rates of commercialization According to the VHLSS 2010 data, the highest income group markets 87.8% of their crop output and 80.5% of their agricultural output, in stark contrast to the lowest income group, which markets only 41.7% and 47.9%, respectively.
Table 4 5 Measure of commercialization by income quintile across years
Share of crop output that is sold (%) Share of agricultural output that is sold (%)
Source: Analysis of VHLSS 2002, 2004, 2006, 2008 and 2010
Econometric results and discussion
The analysis in this section uses the data of 6,837 households in rural areas out of 9,189 households under the Vietnam Household Living Standard Survey (VHLSS) 2008
This section explores the factors influencing income diversification by analyzing three dependent variables previously discussed in Chapter 3, alongside various household characteristics as explanatory variables.
Descriptive statistics for the dependent and independent variables are shown in table 4.6 below
Table 4 6 Descriptive statistics for the dependent and independent variables
NIS Number of income sources 6837 3.50 1.15 1.00 8.00
NFS Share of income from non- farm activities 6837 0.36 0.35 0.00 1.00
SID Simpson Index of 6837 0.41 0.20 0.00 0.84 diversification SID = 1 -
P i : the proportion of income source i in total income
Ethnicity Ethnicity of household head
Age Age of household head
Gender Gender of household head (1
Average education of members in the household (years)
Hhsize Size of household (people) 6837 4.20 1.69 1.00 15.00
The dependency ratio measures the proportion of dependents—individuals under 15 or over 60 for men and over 55 for women—compared to the working-age population, defined as those aged 15 to 60 for men and 15 to 55 for women This ratio is crucial for understanding the economic burden on the productive segment of society.
Farm_size Farm size of household
Electric Electricity in household (1 Yes, 0 = No) 6837 0.96 0.20 0.00 1.00
Tapwater Tap water accessible to 6837 0.10 0.31 0.00 1.00 household (1 = Yes, 0 = No)
Market_dis Distance from household to a daily market (km) 6576 3.55 6.62 0.00 60.00
Road_dis Distance from household to a road (km) 6576 0.48 2.14 0.00 50.00
Road_pass Period that road is passable
(month) 6576 11.44 1.96 0.00 12.00 reg8 Economic Regions of
The Poisson regression is applied in the NIS model, while Tobit regression is utilized for the NFS and SID models To address heteroscedasticity in all regressions, the vce(robust) function is implemented.
4.2.1 Expected sign of determinants of income diversification
This section describes the expected influence of each household characteristic (explanatory variables) on the three measures of income diversification (the dependent variables) The hypotheses are summarized in Table 4.7
Table 4.7 Hypotheses regarding impact of independent variables on measures of income diversification
Number of income sources (NIS)
Non-farm share income (NFS)
Simpson Index of diversity (SID)
Kinh households enjoy greater economic opportunities than ethnic minority households due to fewer linguistic and cultural barriers As a result, they are more likely to engage in income-generating activities beyond agriculture, contributing to their overall economic participation.
The impact of the age of household heads on diversification indicators remains ambiguous While older heads often leverage their accumulated experience to engage in a wider array of income-generating activities, there are instances where their expertise in a particular area may lead them to concentrate solely on that activity This focus can limit the diversification of income sources, ultimately reducing the overall income potential of the household.
On the other hand, higher accumulation of assets over time enables them to participant into more profitable non-farm activities, increasing the share of non- farm income
Higher education equips individuals with greater knowledge and skills, enabling households to engage in a wider array of income-generating activities and professional wage jobs Consequently, it is anticipated that higher education correlates with increased diversification of income sources and a larger proportion of income derived from non-farm activities However, the impact of higher education on achieving a balanced distribution among various income sources remains unclear.
A large household with low dependency ratio is likely to acquire a huger variety of skills, which enable them to pursue more economic activities
Larger household sizes are associated with increased opportunities for members to secure professional non-farm wage jobs Consequently, we anticipate a positive correlation between household size and both the number of income sources and the proportion of non-farm income Conversely, we expect a negative relationship between the dependency ratio and these indicators of income diversification.
Access to electricity and tap water enables households to create self-employment non-farm businesses, leading to a positive correlation with both the share of non-farm income and the diversity of income sources However, the effect of these factors on the Simpson index of diversity remains uncertain.
Market access variables, which encompass transaction and transportation costs, significantly influence the operating costs for enterprises across both the agricultural sector and beyond Increased distance from daily markets and roads adversely affects households by limiting their income sources and reducing the share of income derived from non-farm activities Conversely, the duration that a road remains passable shows a positive correlation with income diversification, highlighting its importance in enhancing economic opportunities for households.
Capital which is partly financed by the formal credit is very vital for the establishment and expansion of enterprises, especially the ones in non- agriculture sectors
4.2.2 Determinants of income diversification (number of income sources)
Surprisingly, when controlling for other variables, Kinh-headed households have, on average, 0.5 fewer income sources than those headed by other minor ethnicities, indicating that ethnicity does not significantly influence income diversification Additionally, as shown in Table 4.8, male-headed households exhibit greater income source diversification compared to female-headed households, even after accounting for other factors.
The analysis in Table 4.8 reveals a significant positive correlation between the age of the household head, average education of household members, and the Net Income Status (NIS), supporting the notion that education and experience enhance the likelihood of securing wage-earning jobs or improving skills for managing a household business Additionally, while larger household sizes are associated with greater participation in income-generating activities, the dependency ratio does not show a significant relationship with the NIS, indicating that larger households are better positioned to engage in multiple income-generating endeavors compared to smaller ones.
The distance to main roads is negatively correlated with household income sources, while the distance to daily markets shows a positive relationship Specifically, households living farther from daily markets tend to engage in a greater variety of income-generating activities, particularly in farming, such as livestock raising, crop growing, and fishing, to meet their daily consumption needs.
Access to formal credit is positively linked to the NIS, indicating that households with greater access to formal credit markets can engage in a wider range of income-generating activities.
When comparing income source diversification, the North West region shows a notable difference from the Red River Delta, with households in the North West averaging 0.36 more income sources than those in the Red River Delta, while controlling for other variables In contrast, households in the Southeast exhibit a different income diversification pattern.
Southeast region has roughly 0.39 sources fewer than household in the Red River Delta
The regression results show no statistically significant difference in the number of income sources across income quintiles
4.2.3 Determinants of income diversification (Simpson index of diversity)
Kinh people exhibit less diversification in their income sources compared to minority groups, resulting in a more imbalanced distribution among these sources This trend is evident in both models, highlighting that Kinh individuals typically rely on fewer income sources, primarily focusing on non-farm wage employment or self-employment.