Problem Statement
Income diversification in rural households of developing countries has garnered significant interest from development economics scholars This concept involves the strategic allocation of resources across various income-generating activities, encompassing both on-farm and off-farm endeavors, as noted by Abdulai and Crolerees.
Households diversify their income sources for various reasons, including risk management, ensuring a stable income flow, utilizing surplus labor, and addressing market failures like imperfections in insurance and credit markets (Ellis, 1998).
Governments in developing countries, including Vietnam, are increasingly focused on income diversification as a means to stabilize income and reduce rural poverty, particularly since over 70% of Vietnam's population resides in rural areas Since 1986, the Vietnamese government has implemented various policies aimed at fostering a multi-sector economy, restructuring the economy, and enhancing the socio-economic environment to improve living standards and integrate with the global economy Key objectives for rural development include job creation, increasing agricultural and rural industry income, and promoting services and off-farm activities These initiatives are intended to stimulate income diversification, contributing to Vietnam's notable economic growth and poverty reduction, with an annual growth rate 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 and non-crop activities, including livestock raising and non-farm ventures, as evidenced by the increase in income share from 14.5% in 2008 to 14.23% in 2010 (GSO).
Income diversification is crucial for rural transformation, but its patterns differ across countries and regions, highlighting the need to identify specific determinants in each context Understanding these factors enables governments to formulate effective policies to support rural areas In Vietnam, there is a scarcity of empirical studies addressing income diversification, particularly regarding its impact on household income This paper utilizes data from the Vietnam Household Living Standards Survey 2008 (VHLSS 2008) to explore the factors influencing income diversification among rural households and to assess its effects on their income levels.
Research objectives
This study explores the factors influencing income diversification among rural households in Vietnam, while also assessing how these factors vary across different economic and geographical regions Additionally, the research 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.
Diversification, in its simplest form, refers to the increase in the number of income sources for households (Minot et al 2006) Households with a greater number of income sources are deemed more diversified, and as they accumulate more sources over time, their diversification increases This measure is straightforward to assess and comprehend; however, it primarily emphasizes the quantity of income sources without considering the significance of each source to the household's overall income.
To address the limitations of the previous approach, a more comprehensive indicator has been introduced, considering both the number of income sources and their respective contributions to total household income This concept views income diversification as a process where households strive to increase their income sources and achieve a more balanced portfolio As defined by Ellis (2000) and Minot et al (2006), income diversification encompasses both the quantity and distribution of income sources Building on this concept, Schawarze and Zeller (2005) employed the Shannon equitability index to analyze income diversification among households in Indonesia, taking into account the number and evenness of income sources.
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 through which rural households boost their income from non-farm activities (Barrett and Reardon, 2001) This concept is frequently expressed as the percentage of total household income derived from non-farm sources, as highlighted 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 commercialization in agriculture This type of diversification is commonly assessed through three key measures: crop diversification, agricultural commercialization, and income commercialization Crop diversification indicates the percentage of crop production sold or bartered, while agricultural commercialization refers to the portion of overall agricultural output that is sold or bartered Lastly, income commercialization is evaluated by the share of gross income derived from cash income.
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 share from non-crop activities, and the proportion of income derived from non-farm sources.
In this study, we focus on four key concepts related to income diversification, thoroughly examined in the descriptive analysis For the econometric analysis, we narrow our focus to three specific indicators: the number of income sources, the Simpson index of diversity, and the proportion of non-farm income within total household income.
Theoretical framework
This study is grounded in the Sustainable Livelihood Framework, a concept highlighted by Ian Scoones in 1998, which has gained significance in discussions surrounding rural development and poverty alleviation The term "Sustainable Rural Livelihood" encompasses various issues and has evolved through multiple definitions since its initial introduction by the Brundtland Commission on Environment and Development in 1992 Among these, the Institute of Development Studies (IDS) offers a modified definition of Sustainable Livelihood that reflects its comprehensive nature.
A livelihood encompasses the necessary capabilities, assets, and activities essential for sustaining a means of living It is deemed sustainable when it can withstand and recover from challenges, while also preserving or improving its resources and capabilities without depleting the natural resource base.
The Sustainable Livelihood Framework emphasizes the central role of individuals in navigating various interrelated factors that shape their livelihoods Key to this framework are the livelihood assets—natural, physical, human, social, and financial capital—that individuals can access and utilize The availability of these assets is significantly influenced by contextual factors such as economic and political trends or shocks like natural disasters Additionally, social, institutional, and political environments affect how people leverage their assets to pursue their goals, referred to as livelihood strategies One effective strategy is livelihood diversification, which helps households increase income, reduce income fluctuations, and ultimately enhance their overall well-being.
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 reasons households diversify their income sources into "demand-pull" and "push-distress" factors "Pull" factors enhance wealth accumulation through competitive advantages like superior technologies and skills, while "push" factors arise from challenging circumstances such as adverse weather, policy changes, or market failures These push factors prompt households to engage in non-farm activities for income stability, employing risk management or coping strategies However, Reardon et al (2007) argue that existing literature often overlooks the incentives for diversification and the household capacity variables They propose a framework that emphasizes capital assets, suggesting that the degree of participation in diversification strategies is influenced by various household capacity and incentive variables.
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, it was noted that capitals encompass various assets enabling households to engage in both farm and non-farm activities These capitals are typically classified into four categories: human, physical, financial, and social capital.
The framework established by Reardon et al (2007) and the sustainable livelihoods theory emphasizes the importance of both private and public assets in determining income diversification This capacity can be assessed at various levels, including household, individual, regional, or village Demographic characteristics at the household and individual levels significantly influence the ability to diversify income Meanwhile, regional and village-level factors, particularly physical and institutional infrastructure, are crucial in facilitating income diversification among households Improved access to infrastructure, such as communication and transportation networks, can lower information acquisition costs, reduce transport and transaction expenses, and enhance participation in non-farm activities (Barrett and Reardon, 2001; Davis, 2003; Ellis, 2000; Reardon et al., 2007).
Empirical studies across various countries have demonstrated the significant impact of different asset types on household income diversification According to Barrett, Reardon, and Webb (2001), a key finding in many research papers is that improved education plays a crucial role in enhancing non-farm earnings.
Research indicates that improved physical access to markets enhances non-farm earnings, as demonstrated in Tanzania by 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 factors such as access to public assets (roads, electricity, water) and private assets (education, credit) in influencing households' ability to diversify their income.
Numerous studies have established a positive correlation between income diversification and household welfare For instance, Babatunde and Qaim (2009) highlight that in Nigeria, income diversification significantly enhances total household income, regardless of the measures applied In Zimbabwe, Ersado (2003) examines various indicators of income diversification, such as the number of income sources and the share of nonfarm income, revealing that wealthier households in rural areas tend to have more diversified income streams, while urban households show the opposite trend Additionally, Ersado notes that in rural regions with high rainfall variability, households adopt multiple income sources as a risk management strategy, aligning with existing literature on the topic.
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 VHLSS 2002, 2004, 2006, and 2010 were 22,621, 6,938, 6,882, and 6,753 rural households, respectively To explore the factors influencing income diversification and its relationship with total household income, the research employs cross-sectional data from the VHLSS 2008, which included 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 is derived from two main employment types: wage employment and self-employment Wage employment is further categorized into farm and non-farm sectors, while self-employment encompasses farm activities like crops, livestock, fishery, and forestry, as well as private businesses, both agricultural and non-farm This study classifies household income into eight distinct sources: wage income (from both farm and non-farm), crop income, livestock income, fishery income, forestry income, enterprise income (from private businesses), transfers, and other income.
Calculating income from wage employment involves summing the annual earnings and bonuses of all household members engaged in paid jobs In contrast, income from activities like 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, including gifts and remittances received by household members in the past year, and public transfers from various government programs like social subsidies and poverty reduction initiatives Additionally, other sources of income consist of pensions, lottery winnings, interest from savings and loans, and rental income However, one-off amounts from the sale of assets such as buildings, vehicles, or 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 an income-based approach to examine three key aspects of income diversification: the presence of multiple income sources, the growing significance of non-farm income within total household income, and the commercialization of production activities.
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, a metric utilized by Minot et al., provides insight into the variety of income streams.
The measurement of income sources in households, as discussed by Ibrahim et al (2009), faces criticism for its arbitrariness, particularly since households with more active adults tend to have multiple income sources (Babatunde and Qaim, 2009) Consequently, this indicator is not utilized in isolation but rather in conjunction with the Simpson Index of Diversity (SID) The SID provides a more comprehensive assessment of household income diversification by considering both the number of income sources and their respective contributions to total income Researchers such as Minot et al (2006) and Joshi et al (2003) have employed the SID to analyze the degree of diversification in household income.
In a household with a single source of income, the income share from that activity, denoted as Pi, results in a specialization index (SID) of zero, indicating perfect specialization This means that all income generated comes exclusively from one activity, highlighting the household's reliance on that single income stream.
When a household's income is derived from multiple sources, the proportion of each source relative to the total income diminishes This leads to a decrease in the sum of squared shares, causing the diversification index (SID) to approach 1 A SID value close to 1 signifies that the household has a high level of income diversification.
The non-farm income share (NFS) serves as a key indicator to assess the contribution of income generated from non-farm activities, such as non-farm wage income and enterprise earnings A higher NFS indicates greater household diversification, highlighting the extent to which families are transitioning from agricultural to non-agricultural activities.
This study explores income diversification among households, defining it as the transition from subsistence to commercial production It focuses on two key measures: crop commercialization, which refers to the percentage 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 are sold or bartered.
This research employs various analytical 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 by comparing diversification measures from surveys conducted in various years.
This study aims to identify the determinants of income diversification among households and assess its impact on total household income, utilizing data from the 2008 Vietnam Household Living Standards Survey (VHLSS) To analyze these determinants, we employ regression techniques on three diversification measures: Number of Income Sources (NIS), Source Income Diversification (SID), and Non-Farm Income Share (NFS), alongside independent variables that represent household capital assets Given that the dependent variable in the NIS model is count data, we utilize Poisson regression For the SID and NFS measures, which are censored between zero and one, we apply Tobit regression, following methodologies established by Escobal (2001) in rural Peru and Schwarze and Zeller (2005) in similar contexts.
To examine the effects of income diversification on total household income, three models are employed, treating total income as the dependent variable while incorporating diversification measures as explanatory variables To mitigate endogeneity issues, the analysis utilizes the Instrumental Variables (IV) method, specifically two-stage least squares (2SLS) This approach aligns with the methodology used by Babatunde and Qaim (2009) in their analysis within the Nigerian context.
FINDINGS AND DISCUSSION
Patterns and trends in income diversification
In the analysis of income source diversity based on the Vietnam Household Living Standards Survey (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 income from multiple sources, with an average of 4.08, 4.35, 4.12, 3.50, and 4.28 income sources reported in different regions according to the VHLSS.
Between 2002 and 2010, there was a modest increase in the number of income sources, particularly noticeable in 2004 compared to 2002 However, this was followed by a gradual decline in 2006 and 2008 By 2010, the average number of income sources rose significantly from 3.50 in 2008 to 4.28, indicating a renewed trend of diversity in income sources 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, highlighting the importance of both the number of income sources and their balance Data from the Vietnam Household Living Standards Survey (VHLSS) 2002 supports this observation.
2004, 2006, 2008 and 2010, the value of this index is 0.488; 0.501; 0.484; 0.414; 0.442 respectively
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 shows the least diversity, as indicated by various survey metrics over the years This trend can be attributed to the fact that the poorest households tend to diversify their income more than wealthier ones, a phenomenon observed consistently across all survey years In contrast, the Southeast, being the most urbanized and least impoverished region, reflects a lower level of income source diversity These findings align with the research of Schwarze and Zeller (2005) in rural Indonesia, despite contradicting the results of Abdulai and Croleres (2001) in Mali.
Higher income diversification among poorer households compared to richer ones suggests that these families use diversification 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 crop production, livestock, fisheries, and forestry; however, there has been a noticeable rise in the contribution of non-farm activities to total household income This increase is reflected in the data, showing a growth from 27.4% in 2002 to 37.1% in 2004, indicating a shift towards diversification in income sources over time.
Between 2002 and 2010, the significance of the non-agricultural sector has steadily increased, reflecting a structural transition in the economy This shift is evident as non-farm wage income surged from 13.3% in 2002 to 24.7% in 2010, despite a slight decline in non-farm enterprise share The rise in non-farm income contributions is observed across all household income quintiles, although the extent and pace of this change 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
Source: Analysis of VHLSS 2002, 2004, 2006, 2008 and 2010
The share of non-farm income in total income is significantly lower for poorer households compared to wealthier ones Data from the VHLSS 2002 indicates that the richest quintile receives 40.8% of their income from non-farm sources, while the poorest quintile only sees 15.4% From 2002 to 2008, all income groups experienced an increase in non-farm income share, rising to 23.1%, 35%, 38.9%, 42.6%, and 44.8% for the poorest to the richest groups, respectively However, in 2010, the poorest quintile faced a 5.7% decline in non-farm income share, dropping to 17.4%, with the second quintile also seeing a slight decrease of 1.9% In contrast, the third, fourth, and fifth quintiles enjoyed substantial increases in non-farm income share, rising by 4.8%, 8.7%, and 10.1%, reaching 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 generally experience increased income diversification, particularly in non-farm income, as time progresses However, the degree of this diversification varies significantly across different income quintiles, with poorer households exhibiting much lower levels compared to their wealthier counterparts This disparity can be attributed to the greater challenges faced by the poor in accessing and engaging in non-farm activities.
Diversification, often described as the shift from producing for household consumption to engaging in sales or barter, is analyzed through 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 growing 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 production of crops and agricultural goods in relation to total gross income.
Table 4.4 illustrates the commercialization of crop output across various geographical regions over several years Notably, the North East region has a minimal share of crop sales, with only 30.6% in 2002 and declining to 24.9% in 2010 Other regions with similarly low commercial crop production include the North Central Coast, North West, and Red River Delta, which reported commercial shares of 38.7%, 40.2%, and 41.4%, respectively, according to the VHLSS 2010 In contrast, the Central Highlands, Mekong River Delta, and Southeast regions exhibit a significantly higher marketed proportion of over 80%.
The degree of commercialization in agricultural output varies significantly, with the North East and North West regions showing that nearly 50% of their agricultural products are sold or bartered This percentage has remained relatively stable over the years, despite minor fluctuations.
Red River Delta North East North West
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
According to the VHLSS 2010, the southern regions of Vietnam exhibit a strong 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.
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 steadily increased, with the percentage of crop output marketed rising 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 commercialization rates According to the VHLSS 2010 data, 87.8% of crop output and 80.5% of agricultural output from the highest income group is marketed, compared to only 41.7% and 47.9% for the lowest income group, 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 outlined in Chapter 3, while considering 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 relationship between dependents—individuals under 15 or over 60 for men, and over 55 for women—and the working-age population, which includes men aged 15 to 60 and women aged 15 to 55 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 methodology section outlines the use of Poisson regression for the NIS model, while Tobit regression is applied to the NFS and SID models To address heteroskedasticity in all regressions, the vce(robust) function is utilized for accurate results.
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 compared to ethnic minority households, primarily due to fewer linguistic and cultural barriers This advantage allows them to engage more in income-generating activities and explore opportunities beyond the agricultural sector.
The impact of the age of household heads on diversification indicators is ambiguous Older heads may leverage their accumulated experience to engage in a wider range of income-generating activities Conversely, their expertise in a particular area might lead households to concentrate on that single activity, potentially limiting the number of income sources available.
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
Increased education enhances knowledge and skills, enabling households to engage in a wider range of income-generating activities and professional jobs Consequently, higher education is anticipated to correlate with greater 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.
Larger households with a low dependency ratio tend to acquire a diverse range of skills, facilitating engagement in various economic activities This skill diversity increases opportunities for professional non-farm wage employment, leading to a positive correlation between household size and both the number of income sources and the share of non-farm income Conversely, a higher dependency ratio is anticipated to negatively affect these diversification metrics Additionally, access to electricity and tap water empowers households to establish self-employment non-farm enterprises, suggesting a positive relationship with non-farm income share and income sources However, the influence of these factors on the Simpson index of diversity remains uncertain.
Market access variables, which reflect transaction and transportation costs, significantly influence the operating expenses of enterprises in both agriculture and other sectors Increased distance from daily markets and roads negatively affects the number of income sources and the proportion of income derived from non-farm activities for households Conversely, the duration that a road remains passable shows a positive correlation with income diversification indicators.
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 sources of income 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 indicates a significant positive correlation between the age of the household head and the average education level of household members with the Net Income Score (NIS) This aligns with the expectation that higher education and experience enhance opportunities for wage-earning jobs and improve skills for managing a household business Additionally, while larger household sizes are associated with a greater capacity to engage in income-generating activities, no significant relationship is observed between the dependency ratio and NIS.
The distance from main roads negatively impacts household income sources, aligning with our expectations; however, the opposite is true for proximity to daily markets Specifically, households situated farther from daily markets tend to engage in a greater number 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 significantly linked to the National Income Score (NIS), indicating that households with better access to formal credit markets can engage in a wider range of income-generating activities.
When comparing income source diversification between regions, the North West shows a significant advantage over the Red River Delta, with households in the North West having an average of 0.36 more income sources This highlights the distinct economic differences between these two areas, emphasizing the greater diversity of income sources in the North West compared to the Red River Delta.
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)
The Kinh people exhibit less diversification in their income sources compared to minority groups, resulting in an imbalance among these sources This trend is evident in both models, highlighting that the Kinh primarily engage in non-farm wage employment or self-employment, leading to fewer income sources overall.
The age of a household is significantly correlated with the Sustainable Income Diversification (SID), suggesting that the experience and skills gained over time enable households to effectively implement a strategy of pursuing multiple income sources, as demonstrated in the NIS model This experience also aids in achieving a balance among these various income streams.
The household headed by male is more balanced in income than those headed by female