INTRODUCTION
Problem statement l 2 The objectives of study
The "Doi Moi" renovation process in Vietnam, officially launched in 1986, has transformed the country from a centrally planned economy to a dynamic market over the past two and a half decades Key reforms included legalizing various forms of private economic activities and removing price controls on most products and services These changes significantly impacted the rural sector, granting farmers greater freedom in production choices and gradually reducing price distortions, ultimately benefiting the majority in agriculture.
Vietnam's population whose livelihoods are closely dependent on small-scale agricultural self-sufficiency in rural areas (Benjamin and Brandt, 2004).
Vietnam has transformed into the world's second-largest rice exporter, achieving higher yields in rice and other crops without increasing agricultural land or decreasing domestic consumption, a remarkable shift from its status as a rice importer in the mid-1980s.
In the year 2000, farmers gained the freedom to choose their agricultural production, leading to the cultivation of diverse crops like pepper This shift created a divide between traditional farmers and those who adopted mixed agriculture by incorporating non-farm activities This scenario prompts an intriguing inquiry into whether market signals, despite their imperfections in a transitioning economy, favored farmers with inherent qualities that made them more proficient compared to those who diversified away from agriculture.
The significance of various income sources in rural income growth influences policy and public investment strategies When technological advancements that boost yields are the primary driver of rural income growth, prioritizing investments in agricultural research and extension is essential Conversely, if crop diversification is the main contributor to income growth, efforts should concentrate on enhancing agricultural credit, transportation, and market information Additionally, if income growth or poverty reduction is mainly linked to diversification into non-farm activities, the focus should shift towards training, electrification, and providing commercial credit to foster non-farm employment opportunities.
Non-farm activities have the potential to generate more employment opportunities and higher incomes for rural communities compared to traditional agricultural practices Additionally, these non-farm incomes can positively influence agricultural activities by addressing market failures and fostering economic growth in the region.
In rural areas, the growth of non-farm income is intricately linked to agricultural activities, as both sectors influence investment, production, and consumption decisions within the rural economy These interconnections are essential for the complex livelihood strategies employed by rural households To enhance household income through non-farm employment, it is crucial to address existing constraints that hinder this growth.
This thesis aimed at investigating the role of rural non-farm activities on household income by analyzing the result of a Vietnam household living standard survey in
2008 More specifically, the objectives of the study are:
(1) First, reviewing current status of diversification level of household income.
(2) The second objective is to examine the role of non-farm income in increasing household income and how to increase non-farm income for household inVietnam.
Research question
In order to research the importance of income diversification and the role of non-farm income in total income of household during 2002 — 2008 the following research questions are raised:
What is current status of household’s income diversification?
Does income diversification affect household’s income? What factors do affect household income diversification?
The thesis will begin with Chapter 1, the introduction will present In the next, a review of existing empirical research related to diversification and non-farm economy.
Chapter 3 will outline the methodology, followed by the presentation of data and statistical results in Chapters 4 and 5 The thesis will conclude with a summary of the findings and final remarks.
LITERATURE REVIEW
Definitions
Income diversification encompasses various concepts, with its patterns varying based on the definitions applied This article examines several studies on income diversification, highlighting the importance of household-level factors in understanding why families pursue multiple income-generating activities One key definition emphasizes the increase in the number of income sources or the balance among them; for instance, a household with two income sources is more diversified than one with only one Moreover, a household where both income sources contribute equally to the total income is considered more diversified than one where one source dominates, illustrating the nuanced nature of income diversification (Joshi et al 2002; Ersado, 2003).
Non-farm activities encompass all operations outside of primary agriculture, forestry, and fisheries, including the trade and processing of agricultural products, even if conducted on the farm These activities can be categorized into wage work, which includes agricultural jobs, and self-employment outside of agriculture The rural non-farm sector typically comprises manufacturing, trade, construction, transportation, communications, and various services According to Barrett and Reardon (2001), this definition aligns with national accounting conventions, distinguishing between primary production, secondary manufacturing, and tertiary service activities, regardless of the location or scale of the operation.
The term "non-farm" should not be mistaken for "off-farm," as the latter typically refers to activities conducted away from a household's own agricultural land Some scholars, like Ellis (1998), specifically define "off-farm" as labor performed on another person's land, which does not align with the conventional understanding of "non-farm."
Factors effect to income diversification
Income diversification should not be confused with livelihood diversification, which involves households creating a varied portfolio of activities and social support systems to enhance living standards and mitigate risks According to Ellis (1998), income generation is just one aspect of broader livelihood strategies Livelihood diversification also includes essential elements such as social institutions, gender relations, property rights, and non-income support mechanisms that contribute to sustaining a household The motivations for diversification, as well as the available opportunities, vary significantly across different contexts and income levels, highlighting a key distinction between diversification aimed at accumulation—primarily driven by "pull factors"—and diversification intended to manage risks, cope with shocks, or move away from stagnating or declining agricultural practices.
Barrett et al (2001) identify two primary sets of motives influencing household behavior The first set, known as "push factors," includes risk reduction, responses to diminishing returns in family labor supply due to land constraints, and reactions to crises or liquidity constraints Additionally, high transaction costs often lead households to self-provision essential goods and services The second set of motives, referred to as "pull factors," involves the realization of opportunities that attract households to new options.
5 of strategic complementarities between activities, such as crop-livestock integration or milling and hog production, specialization according to comparative advantage accorded by superior technologies, skills or endowments, etc.
Households often engage in income diversification as a strategy for pre-risk management and to navigate economic shocks (Reardon, Delgado, and Malton 1992; Reardon et al 1998) In developing countries, few households rely heavily on a single income source, as research indicates that most prefer to avoid prolonged dependence on one or two income streams (Reardon 1997; Bryceson 1999; Ellis 2000) Various factors contribute to this trend of income diversification at the household level (Barrett et al 2001).
(1) Self-insurance against risk in the context of missing insurance markets (e.g., Kinsey, Burger, and Gunning 1998);
(2) An ex post coping strategy (e.g., Reardon, Delgado, and Malton 1992), with extra individuals and extra jobs taken on to stem the decline in income;
(3) An inability to specialize due to incomplete input markets;
(4) A way of diversifying consumption in areas with incomplete output markets;
(5) Simple aggregation effects where the returns to assets vary by individual or across time and space.
In developing countries, rural households are increasingly diversifying their income sources beyond farming, with non-farm income now representing a significant portion of total household earnings An analysis by Reardon et al (1998) reveals that from the 1970s to the 1990s, non-farm income constituted an average of 42% of household income in Africa, 40% in Latin America, and 32% in Asia Numerous studies in rural Africa indicate a positive correlation between non-farm diversification and improved household welfare.
Recent findings have highlighted the importance of promoting non-farm employment in rural areas, a policy that has garnered significant support from development agencies, including the World Bank and various non-governmental organizations (Delgado and Siamwalla 1999).
Household non—farm activities
3.1 Declining a share of agriculture in GDP and labor
The World Development Report (2008) highlights that economic development involves a continuous redefinition of agriculture, manufacturing, and services It notes two key trends in this structural transformation: initially, agriculture constitutes a significant portion of gross domestic product (GDP) and employment, with shares reaching up to 50% and 85%, respectively, but these figures decline as countries progress in their development.
Historically, structural transformation patterns have been evident in developed nations and are now emerging in developing countries experiencing growth As these countries develop, agriculture's crucial yet diminishing role in economic growth becomes apparent In many impoverished nations, particularly in Sub-Saharan Africa, agriculture still constitutes a significant portion of the economy, averaging 34% of GDP and 64% of employment In countries with a GDP per capita between $400 and $1,800, predominantly in Asia, agriculture represents about 20% of GDP and 43% of the labor force, with these figures declining to 8% and 22% in more advanced economies.
The GDP per capita in many Eastern European and Latin American countries ranges from $1,800 to $8,100 Incorporating extended agriculture through forward and backward linkages can significantly enhance its economic contribution, often increasing the share of agriculture in the economy by 50% or more, particularly in middle-income nations.
+ Share of GOP from agriculture (I9KJ-20tE, aver8gel
The decline in agriculture's contribution to GDP, as noted by Cleinens et al (2008), is largely attributed to the significant rise in the manufacturing sector, particularly in countries like Malaysia and Thailand, where manufacturing's share has notably doubled While Malaysia's service sector is substantial, it has primarily supported rather than led the transformation, contrasting with India, where the service sector's GDP share surged from 42% to 52%, largely due to the booming information technology industry This structural transformation has also altered export compositions, with agricultural exports decreasing in proportion to total exports, while manufacturing exports have seen substantial growth.
Figure 2-1 Share of labor and GDP in agriculture
Sha re d bbor and GU' in agriculture
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Non-farm activities play a crucial role in enhancing household economies, particularly by offering employment during the off-peak periods of the agricultural cycle By analyzing farm management surveys and time allocation studies, it becomes evident that these activities not only supplement income but also contribute to economic stability for farming families.
Research by Haggblade et al (1989) on African farm households reveals that between 5% and 65% of farmers engage in secondary employment within the non-farm sector, with non-farm activities accounting for 15% to 40% of total family labor hours dedicated to income generation.
‹countries develop, more of these tasks are commercialized and more non-farm employment appears in the statistics.
The rising demand for non-farm products and services is driving technological advancements and improved management practices in agriculture As agricultural productivity increases, landowning households experience higher incomes from their land Consequently, these households invest their newfound income in labor-intensive goods and services produced by small-scale firms in the non-farm sector.
Households engaged in non-farm activities tend to have higher incomes, leading them to expand their current ventures or invite neighbors to join in these activities When a household decides to hire additional labor for the non-farm sector, it reflects a targeted application of behavioral models related to factor supply, particularly in the context of labor.
The economic model of labor supply and capital investment is driven by incentives and capacity variables Households aim to maximize their earnings while navigating constraints from limited resources and balancing the desire to minimize risk Consequently, households must make informed choices regarding their economic activities.
The labor supply and capital investment decisions in this context involve diversification into non-farm activities This choice can be broken down into five interconnected and simultaneous decisions.
In land-rich, labor-scarce countries like those in Africa, the abundance of land often results in a majority of the population remaining engaged in agriculture, while only wealthier households transition into the non-farm sector.
(1) Non-farm participation: choice of farm sector activity or non-farm activity.
(2) Level of non-farm activity.
(3) Sectored choice: manufacturing or services.
(4) Location: whether to undertake it locally or elsewhere.
(5) Form: whether to undertake self-employment or wage-employment.
In developing rural economies, limited employment opportunities in agriculture have led to an increasing reliance on non-farm activities for income diversification A significant factor influencing this shift is land distribution; in land-scarce, labor-rich countries like China and India, inadequate access to land often forces poorer households to transition from agriculture to the non-farm sector This transition can positively affect poverty levels and inequality, as evidenced by studies from Adams (1995) in Pakistan and Chinn (1979) in Taiwan, which show that non-farm income helps reduce rural income inequality Adams (1995) further highlights that non-farm income is particularly beneficial for the poor, as it tends to decrease with larger land ownership and total rural income.
Research in Africa reveals contrasting findings regarding non-farm income and its effects on rural income distribution Studies by Collier et al (1986) in Tanzania and Matlon (1979) in Nigeria indicate that non-farm income negatively impacts income distribution, primarily benefiting large landowners Additionally, these studies utilize non-farm income share as a proxy for income diversification, a metric that poses measurement challenges due to the complexity of accurately accounting for income sources This measure also lacks relevance for urban-rural comparisons, as non-farm income plays a different role in urban settings.
Empirical studies in Ethiopa and Tanzani by Dercon and Krishnan (1996) and in India by Micevska and Rahut (2008), find similar results Household composition seems
Empirical Literature
Research by Piesse, Simister, and Thirtle (1998) indicates that non-farm income sources contribute to increased income inequality in remote areas, while in regions with better access to urban markets, they help reduce income inequality In less connected rural areas, agricultural power dynamics enable those with higher farm incomes to take greater advantage of non-farm income opportunities Conversely, in areas with improved access to urban markets, non-farm employment opportunities become less influenced by these power structures, leading to a more equitable distribution of income.
Kinsey, Burger, and Gunning (1998) conducted a study on 400 resettled households in rural Zimbabwe over 13 years, revealing that income diversification serves as a coping strategy during droughts; however, the additional income sources are often low-return activities like day jobs or agricultural piecework While empirical studies on income diversification in Zimbabwe provide valuable insights, they tend to overlook the urban context, where poor households face similar risks as those in rural areas, including fluctuating labor returns, market failures, and various economic uncertainties.
In Ecuador, Honduras, and Peru, self-employment plays a more significant role than non-farm wage employment, especially in poorer areas This trend varies within countries, as shown in Chile, where rural non-farm enterprises have a higher wage employment share in more favorable zones Additionally, household size positively influences the likelihood of diversifying agricultural activities; each extra household member increases the chances of on-farm diversification Furthermore, a larger male labor force enhances the probability of both local off-farm diversification and migration, attributed to increased efficiency in household chores that allows more members to engage in non-farm activities.
Credit constraints significantly impact households' decisions to diversify their assets, as limited access to credit greatly diminishes the likelihood of pursuing both farm and non-farm diversification strategies This finding aligns with existing empirical literature, which indicates that financial resources and access to credit are essential for overcoming entry barriers and funding the initial investments required for new activities (Barrett et al., 2001; Abdulai & CroleRees, 2001; Woldenhanna & Oskam, 2001).
In Latin America, non-farm wage earnings typically surpass self-employment income levels Specifically, countries such as Brazil, Chile, Colombia, Mexico, and Nicaragua demonstrate a significant proportion of non-farm income derived from wage employment.
12 towns while part-time self-employment looms largest in remote, rural areas.
Alain de Janvry, Elisabeth Sadoulet, and Nong Zhu (2005) conducted a study using household survey data from Hubei province to analyze the impact of non-farm income sources on rural households Their findings indicate that, in the absence of non-farm employment, rural poverty levels would significantly increase, and income inequality would also rise Additionally, the research highlights the importance of factors such as education, proximity to urban areas, and both neighborhood and village effects in enabling certain households to access these vital income sources.
Research from 2004 indicates that in northern Honduras, particularly in areas with better infrastructure and higher rural town density, non-farm wage income significantly exceeds self-employment income Conversely, in the southern region, where infrastructure is less developed and town density is lower, self-employment plays a much more crucial role in the local economy.
Non-farm economy still is the key concept for both researchers and policy makers in promoting and implementing rural development strategies (Bernini et at., 2006;
The non-farm economy plays a crucial role in poverty reduction by providing alternative income sources and stimulating agricultural growth, as a decrease in agricultural labor can enhance productivity and increase family incomes Additionally, policies that promote the non-farm economy can help mitigate rural-to-urban migration, a significant challenge in many transition economies Studies conducted on seven African households, including those in Botswana, Kenya, and Malawi, support these findings.
Zimbabwe show non-farm wage income nearly twice as important as self-employment while the other three cases e.g Rwanda, Ethiopia, and Sudan suggest the reverse
(Reardon, 1997) In all regions, the wage share of non-farm earnings increases near
Access to non-farm occupations is influenced by factors such as education, wealth, caste, agricultural conditions, and population densities While the non-farm sector's direct contribution to poverty reduction may be limited due to the lack of assets among the poor, growth in specific non-farm sub-sectors is linked to increased agricultural wage rates Research has primarily focused on individual and household factors driving participation in the rural non-farm sector, yet there is a lack of insight into the impact of trade reforms and other policy opportunities Additionally, farmers who remain solely in agriculture tend to possess unique, unobservable traits that enhance their productivity, indicating a positive selection bias Furthermore, engaging in non-farm activities can positively influence household farm production, highlighting the growing significance of the rural non-farm sector in developing countries.
Peter Lanjouw, Abusaleh Shariff, and Dil Bahadur Rahut (2007) pay attention to the significance of the non-farm sector in the rural Indian economy since the early 1970s.
Research indicates a strong link between non-farm employment growth and agricultural wage rates in rural India In the 1980s, non-farm incomes represented a substantial share of household earnings, with approximately 40 million new jobs created, predominantly in the agricultural sector However, from 1993/94 to 2004/05, the growth of non-farm employment surpassed that of agriculture, with six out of every ten new rural jobs arising in the non-farm sector.
India were generated in the non-farm sector The largest increase in incremental employment attributed to the non-farm sector took place between 1999/0 and 2004/5.
There is a considerable variation across quintiles and across major Indian states.
14 measures) on the decision making process by rural households to participate in non- farm activities.
Mukesh Eswaran, Ashok Kotwal, Bharat Ramaswami, and Wilima Wadhwa (2005) examine the effects of liberalization in the 1980s and 1990s on earnings and gender disparity in India Their findings indicate that the non-farm sector has significantly contributed to increasing educational attainment among the population by creating job opportunities for literate individuals and younger generations transitioning away from agriculture While non-farm sectors do not directly benefit female employees, they often lead to men finding employment, which results in women taking on agricultural roles in their absence Additionally, advancements in agricultural productivity through technical innovations have been crucial in raising agricultural wages, subsequently improving women's earnings as agricultural productivity rises.
S Ranjan (2007) agrees that there are trends in the level and nature of employment in the rural non-farm sector The rise in male workers was larger than the rise in female workers and the manufacturing units in the non-farm sector continued to absorb the highest number of workers The demand-pull factors at work are the expansion of employment in sub-sectors-construction, trade-hotels, restaurants, transport and communications sectors hold promise of employment opportunities The expansion in these sectors could be due to both the push and pull factors The gender wise distribution gives a clear impression of distress-driven employment increase The survey revealed that although linkages between the farm and non-farm sectors in rural India were multifarious and strong, yet there were examples of a vibrant non-farm sector that was emerging without the support of the agricultural sector The scenario
The findings emphasize the significant impact of both demand and distress-pull factors, along with external influences, in the creation of non-farm employment Additionally, it is noted that the majority of non-farm activities occur within the unorganized sector.
T.Q Trung and N.T Tung (2008) using data from Vietnam Household Living Standards Survey in 1993, 1998, 2002 to analyze multiple indirect effects of trade liberalization on performance and business behaviors of non-farm household enterprises in the context of economic environment change during the transition period in Vietnam As focus on trade liberalization, they found that Vietnamese economy has experienced high economic growth rate but the total non-farm household enterprises income in the selected industries affected by trade liberalization increased not much The reason is the entry and exit rates of non-farm household enterprises are quite high in comparison with other international findings Vietnamese non-farm household enterprises also faced with many constraints in terms of low competition, differentiation and value added chain of products; weak marketing; poor and obsolete technology; weak entrepreneurial skills and low qualifications of non-farm entrepreneurs; insufficient business and market information; and shortage of capital and of skilled laborers, limited access to credit.
Remco H Ostendorp, T.Q Trung, and N.T Tung (2009) studied non-farm household enterprises, identifying them as a key factor for income generation, income inequality reduction, and income volatility mitigation Their research revealed that these enterprises enhance income levels and decrease inequality among households However, they also noted a decline in the significance of the non-farm household enterprise sector in Vietnam between 1993 and 2002 Consequently, the authors concluded that promoting non-farm household enterprises through untargeted policies may become increasingly unjustifiable.
RESEARCH METHODOLOGY
Model specification-dependent variable
Efforts to quantify income diversification have primarily focused on rural areas, estimating the proportion of non-farm income within total household income Studies suggest that a higher percentage of non-farm income indicates greater diversification and reduced vulnerability to weather-related shocks, a significant risk in agriculture-dependent regions However, using the share of non-farm income as a proxy for diversification presents challenges, as it treats households with varying numbers of income sources equally Additionally, accurately measuring this share necessitates comprehensive accounting of all income streams, and its relevance diminishes in urban areas, where most income is typically non-farm.
The Shannon equitability index will be utilized to measure household diversification levels, reflecting the pursuit of multiple income sources as a strategy to mitigate income risk associated with macroeconomic policies, such as public-sector job losses experienced in Vietnam during the 1990s Additionally, there is an imbalance in household income sources in Vietnam, with significant differences in livelihood strategies observed between urban and rural areas.
In 2008, 18 areas exhibited a diversified income base, with only 3.55% relying on a single income source, compared to 6.21% of urban households Over 65.39% of rural households had three or four income sources, while 48.60% of urban households had at least three However, following economic shocks, all areas experienced a decline in income diversification, with rural areas facing a more significant reduction Notably, approximately 93% of households in both urban and rural regions received money transfers, primarily driven by pensions and domestic remittances, highlighting their critical role in household income stability.
The number of income sources as a measure of diversification may be criticized on several grounds First, a household with more economically active adults, all things
Households with a greater number of income sources tend to have higher overall income, reflecting both labor supply decisions and a desire for diversification To analyze this, the study incorporates per capita income sources and considers household members' age, sex, and education However, discrepancies arise when comparing households with varying income shares from similar activities For instance, a household deriving 99% of its income from farming is counted the same as one with a 50-50 split between farming and wage labor unless adjustments are made By utilizing the Shannon equitability index, which accounts for differences in income shares and is straightforward to measure, the analysis can provide a more accurate representation of income distribution among households.
19 while calculating the non-farm income share involves accounting for the actual household income from various sources By doing so, for example, a household with
Households with a balanced income distribution, such as those earning 50 percent from farming and 50 percent from wage labor, demonstrate greater income diversification compared to those relying heavily on farming for over 50 percent of their income The income diversification index, which ranges from zero to 100, reflects the actual income diversity relative to its maximum potential This measure employs the Shannon equitability index, originally designed to evaluate species diversity, to assess overall income diversity The adapted formula considers the number of income sources and their respective shares in total household income, represented by S and incshare The Shannon index (H) increases with greater income diversity, while the Shannon equitability index (E) is derived from it, further quantifying the distribution of income sources within households.
The index evaluates the concentration of household income across different sources, reflecting the level of income diversification Households with greater income diversification will exhibit a higher index value, while those with less diversified income will have a lower value.
The diversification of income sources significantly impacts the E index, which ranges from a minimum of 0 for households reliant on a single income to a maximum of 100 for those with equal income from four different activities A higher E value indicates a greater number of income sources and a more balanced distribution of income shares, highlighting the benefits of financial diversification for households.
Figure 3-1.’ Distribution of the diversification index
The diversification index, calculated using the Shannon equitability index method, measures the diversification level of rural households in Vietnam Between 2004 and 2008, the index showed only slight changes, with most values ranging from 30 to 60 This trend indicates that households are less inclined to expand their income sources and are focusing on achieving a balance among various income types Instead of pursuing quantitative diversification, they are now emphasizing qualitative diversification by specializing in 2-3 key activities, which enhances product competitiveness and increases profitability.
MOdel specification-independent variables ccording to FAO (1998), there are two major categories of factors determine a ousehold’s decision to participate in economic activities: first, the factors that affect
The relative return and risk of agricultural production, along with factors influencing participation in non-farm activities—such as education and access to credit—are critical elements in understanding economic dynamics According to Alain de Janvry, Elisabeth Sadoulet, and Nong Zhu (2005), these factors are largely determined by a household's physical endowment.
The determinants of household decision-making are influenced by various factors, including human and social capital, household composition, assets, and local institutions Key human and social capital variables encompass the age and education level of the household head, as well as the education levels of other members Household composition is represented by the size of the household, while household assets are assessed through per capita land holdings Additionally, local institutions and village characteristics, such as population density and the distance from the village to the provincial center, play a significant role According to de Janvry and Sadoulet (2001), it is important to recognize that individual decisions within a household are interdependent.
The research utilizes data from the Vietnam Living Standards Survey 2008, conducted by the World Bank and the General Statistics Office of Vietnam, employing both qualitative and quantitative analysis Qualitative analysis describes the current status of household income and the significance of non-farm income within total household earnings In contrast, quantitative analysis identifies key factors influencing income diversification trends through a two-stage regression approach Initially, probit regression is applied to households with a single income source (Diversification Index equal to 0), followed by least squares regression for households with multiple income sources.
22 ise for household with more than 1 kind of income resource, model for both stage is suggested as follow:
Diversification Index = f (Gender ; Age ; Age square ; Education ; Education Level ; Training ; Landholding per capita ; Household size ; Dependency ratio ;
\/illage Density ; Non-farm percent ; Distance from urban center)
1 Gender: Dummy for gender of household head Using dummies for gender differences instead of estimating separate equations by gender in order to directly compare differences by gender rather than differences among men and women When household header is women, she is tendency stable income and do not like risk when invest in new activities Men normally will accept the risk and using family resource into other activities Since an economy that is composed of households which interact as collective units, rather than one in which individuals interact as purely independent agents, the differences among households as defined by the gender of their head can reveal a lot about different economic experiences.
The age of the household head significantly influences participation in both agricultural and non-agricultural activities This impact can be attributed to varying physical fitness demands in each sector, as manual agricultural labor tends to be more physically demanding than jobs in other fields Consequently, older individuals may face disadvantages when engaging in agricultural work.
3 Education: Number of years of schooling of the household head It has positive impacts on income While schooling does not seem to be important for agricultural wage laborers, it significantly increases the probability of finding work in non-agricultural sectors.
4 Education Level: The average number of years of schooling of household members 15 years old and above Households with higher education level engage more in non-farm activities, and that human capital has an important effect on the level of non-farm income achieved.
Number Education: Number of people in household had pass Lower Secondary school degree Higher people number, the family will have more income from wage and non-farm activities.
6 Training: Dummy variable if member of household trained in non-farm activities It had same effect as Education in households.
Econometric Model
The article utilizes data from the Vietnam Living Standards Survey 2008, conducted by the World Bank and the General Statistics Office of Vietnam, to explore household income dynamics It employs both qualitative and quantitative analyses; qualitative analysis highlights the current state of household income and the significance of non-farm income within total household earnings In contrast, quantitative analysis identifies key factors influencing income diversification trends through a Two-Stage regression approach This involves first applying probit regression for households with a single income source (Diversification Index equal to 0) and then using Least Squares regression for households with multiple income sources.
22 ise for household with more than 1 kind of income resource, model for both stage is suggested as follow:
Diversification Index = f (Gender ; Age ; Age square ; Education ; Education Level ; Training ; Landholding per capita ; Household size ; Dependency ratio ;
\/illage Density ; Non-farm percent ; Distance from urban center)
1 Gender: Dummy for gender of household head Using dummies for gender differences instead of estimating separate equations by gender in order to directly compare differences by gender rather than differences among men and women When household header is women, she is tendency stable income and do not like risk when invest in new activities Men normally will accept the risk and using family resource into other activities Since an economy that is composed of households which interact as collective units, rather than one in which individuals interact as purely independent agents, the differences among households as defined by the gender of their head can reveal a lot about different economic experiences.
The age of the household head plays a crucial role in determining participation in agricultural versus non-agricultural activities As age increases, the physical demands of manual agricultural labor can become more challenging, putting older individuals at a disadvantage compared to younger workers This age-related disparity highlights the varying physical fitness requirements across different sectors.
3 Education: Number of years of schooling of the household head It has positive impacts on income While schooling does not seem to be important for agricultural wage laborers, it significantly increases the probability of finding work in non-agricultural sectors.
4 Education Level: The average number of years of schooling of household members 15 years old and above Households with higher education level engage more in non-farm activities, and that human capital has an important effect on the level of non-farm income achieved.
Number Education: Number of people in household had pass Lower Secondary school degree Higher people number, the family will have more income from wage and non-farm activities.
6 Training: Dummy variable if member of household trained in non-farm activities It had same effect as Education in households.
7 Landholding per capita, is the total areas of cultivated land used for agriculture production divided by total member of household, measured by square meters per person For a rural household, land is the main form of physical capital. Larger per capita landholdings also equip a household better to engage in agriculture Lower landholding per capita, income from agriculture is not enough for household’s expenditures It makes pressure in household budget and they tend to be doing anything in non-farm activities to get more income.
8 Household size: The size of the household: land ownership might proxy wealth and contacts, and thereby provides some indication of the extent to which individuals are better placed to take advantage of opportunities in the non-farm sector.
9 Dependency ratio: The percentage of family members engaged in cultivation activities, proxy a latent demand to diversify out of agriculture (and thereby reduce exposure to agriculturally related risk)
10 Village Density: The population density in the village (total village landholdings divided by the village population) a high population density
24 would be expected to push People out of agriculture and may stimulate non- farm activities (through lower transactions costs, economies of agglomeration, etc.)
The non-farm employment percentage reflects the concentration of non-farm activities within the labor force and highlights the importance of specific infrastructure in fostering these activities.
12.Distance from urban center: measured distance (km) from the village that households are living to the nearest urban center.
DATA ANALYSIS AND DISCUSSIONS
Data description
The 2008 Vietnam Household Living Standard Survey (VHLSS 2008) provides a comprehensive analysis of household income structures, encompassing 9,189 rural and urban households across eight regions of Vietnam With data from 5,967 households at the community level, the survey is categorized into eight distinct sections, making it ideal for exploring the relationships between various income sources, household assets, characteristics, and public resources.
Table 4-1 Structure offamil y income ff7 the 2008 survey
- Services for income % of income
Total family income, Thousand VND per year 500,413,63 5 Total family survey, household 9189. Per household income, Thousand VND per year 27,583 tsource.’ VHLSS 2008j
Table 4 1 gives the breakdown of “SeacIere of family income in the 2008 survey ”.
There are 8 income sources that can be gleaned from the VHLSS data, which form
There are 26 mutually exclusive income categories, including agriculture income, wage income, non-farm income, and money transfer income Agriculture income, which encompasses five distinct sources—planting, livestock, agricultural services, aquaculture, and forest and hunting—can be generated by household members who are self-employed in agriculture or who own agricultural enterprises.
Agricultural households experience income variability due to their reliance on crop cultivation, which can involve a single crop, multiple crops, or a combination of crops and livestock This diverse approach to farming carries distinct implications for income stability.
On-farm income encompasses both earned and unearned income, including remittances from family members who migrate to cities, welfare subsidies, pensions, interest from money transfers, and activities within the rural non-farm economy To define the 'non-farm' sector, it is beneficial to adhere to the national accounting system, which categorizes all secondary sectors—such as manufacturing, processing, and construction—and tertiary sectors—like transport, trade, finance, rent, and services—as non-farm Additionally, certain primary sub-sectors, including mining, are also classified as non-farm Non-farm income can be categorized into five distinct sources.
(1)Government employment — includes wages from all government and public sector service;
(2)Private sector — includes wages from private sector companies;
(3)Unskilled labor — includes wages from any unskilled non-farm activity, such as construction, brick-making and ditch digging;
Self-employment encompasses profits and earnings derived from various activities, including production trade, agriculture, forestry, aquaculture, shopkeeping, and artisan services such as tailoring, home appliance repair, and shoe repair.
(5)Other - includes property benefit, gift, remittances, welfare, pensions, interest.
I icome from (1), (2) and (3) are consider as wage income, (4) still keep name as non- firm income and (5) is namely money transfer category So there are 4 different i icome sources considered.
According to data from the VHLSS 2008, agriculture remains a primary source of income for rural households, accounting for 56.70% of total family income in the surveyed areas This income is derived primarily from agricultural activities, which contribute 27.71% through revenue generated from the sale of farm products and their associated value.
Household income sources include 3.49% from aquaculture and other agricultural activities such as livestock, forestry, and hunting Notably, 20.07% of family income comes from non-agricultural sources, while an additional 28.67% is generated from various other activities.
Money transfers play a crucial role for many rural households, with 13% of them receiving various forms of transfers, such as pensions, unemployment benefits, gifts, and other social assistance, which account for 14.23% of their total income This high rate of recipients is largely due to the significant presence of seniors and ex-soldiers in the rural population While income from agriculture and livestock is relatively low, a considerable number of households participate in these activities, leading to a diversified income structure for most families.
Over 3.04% of households are classified as "non-diversifiers," relying solely on a single source of income In contrast, over 75.81% of rural households benefit from a combination of non-farm or salaried income alongside agricultural earnings.
Variable Obs Mean Std Dev Min Max age 6504 49.46817 13.72242 16 97 age2 6504 2635.376 1485.211 256 9409
28 edu 6504 1.337946 1.345635 0 12 edulev 6504 6.986716 2.966165 0 12 numedu 6504 0.808579 1.085226 0 6 gender 6504 0.202183 0.401659 0 1 dependency 6504 0.294682 0.316826 0 1 distance 6504 39.25969 35.70457 0 446 diver index 6504 41.94158 23.47788 0 99.85677 house size 6504 4.198493 1.681861 1 15 land c 6504 1897.226 4340.304 0 126675 training 6504 0.164668 0.370909 0 1 village dens 6504 661.8885 749.9889 0.3461 15661.4
4.2 provides data on a summary of surveyed household characteristics derived the sample The average household size is four people and this indicator is with the national average household size About 24.49% of the households headed by women The mean year of schooling (of adults) is 6.9 years and higher that of the national statistic (on average 5.5 years) (UNDP, 2010) The mean age head is 49.4 and their years of schooling unexpected low, 1.3 years This showing that before and right after of Vietnam war, not much of people had a to go to school, their experience help them in real life The average per capita is 1,512m2 The distance to the nearest urban center is quite far on average, so people in rural area need about 1 hour to go to market by Income per capita is approximately 11.850,7 thousand VND per year over all income sources, translating to about 650 USD per capita This is appears reasonable average income of Vietnam.
(Source General Statistics Office of Vietnam, 2009)
Vietnam, with a population of nearly 86 million in 2009, is 1largestth world’s most populous country The economy is still basically, even if since the advent of
Table 4-3 Structure of employed population by kind of economic activity
In the mid-1980s, the contribution of agriculture, livestock, forestry, and fishing to GDP was 27.76 percent, with agriculture alone representing 24.17 percent However, by 1996, this share had decreased to 22.10 percent, indicating a decline in the agricultural sector's relative importance in the economy.
- Wholesale and retail trade; repair of motor vehicles, motor cycles and personal and household 10.36% 10.84% 11.46% 11.80% II.96% goods
Public administration and defence; compulsory social 1.00% l 11% 1.29% 1.65% 1.93% security
Other activity and money transfer
- Real estate, renting and business
In 2008, the agriculture sector in Vietnam accounted for only 18.14% of the economy, yet it remained the primary source of employment and livelihood for nearly half of the population According to the VHLSS 2008 data, typical rural families earned income from 2 to 3 different sources, including money transfers, with income diversification positively correlated to higher household earnings Families must navigate potential entry barriers and constraints that affect specific household types, as highlighted by Lapar et al (2005) The distribution of rural households by income sources further illustrates this diversity.
Figure 4-1 Family income and the number offamily income sources
Figure 4-2 Number of households and number of income sources
> 2008 ource VHLSS 2004, 2006, 2008(at constant price year2000)
This section explores income diversification at the household level by examining cross-sector and temporal patterns among various income sources Utilizing per capita income quintiles, it highlights the differences between households in distinct income categories While per capita income is a common indicator, the article argues that expenditure serves as a more effective measure for both descriptive and analytical purposes in understanding income diversification.
3 i accurate picture of household welfare Heavily indebted households with a large a nount of current earned income may be thought of as having high living standard
In rural Vietnam, households often experience low consumption levels due to prioritizing debt repayment over spending Additionally, savings play a crucial role, enabling families to maintain higher consumption rates despite temporary declines in income This reliance on subsistence behavior reflects the cultural practices prevalent in these communities.
I ieir expenditure does not reflect exactly their economic condition.
Table 4-4 Trends in income diversification by the number of income sources
Table 4-4 illustrates the diversity of income sources among rural households, highlighting that the average number of income sources per household was 2.68 in 2004 Households with the highest income diversification reported an average of four income sources Throughout the analyzed period, there was minimal variation in the number of income sources across households, indicating a consistent trend in income diversification.