INTRODUCTION
Problem statement
The "Doi Moi" renovation process in Vietnam, launched in 1986, marked a significant transition from a centrally planned economy to a dynamic market economy over the past two and a half decades A key reform was the legalization of private economic activities, which empowered households and businesses while removing price controls on nearly all goods and services These reforms particularly impacted the rural sector, granting farmers greater freedom in production choices and gradually reducing price distortions As a result, agriculture, which is vital for the livelihoods of the majority of Vietnam's population, has benefited greatly, especially for those reliant on small-scale agricultural self-sufficiency in rural areas (Benjamin and Brandt, 2004).
Vietnam has transformed into the second largest rice exporter globally, achieving higher yields in rice and other crops without increasing rice cultivation area or reducing domestic consumption, a significant shift from its status as a rice importer in the mid-eighties.
Since 2000, Vietnamese farmers have embraced new agricultural opportunities, leading to the cultivation of diverse crops like pepper and rubber This shift has propelled Vietnam to become the world's second-largest coffee producer, while the production and export of fruits and vegetables have seen significant increases This income growth can be largely attributed to the diversification of agricultural practices.
-= activities such as aquaculture, livestock, and non-farm activities with substantial tructural changes towards more industry and services
The significance of various income sources in rural income growth influences policy and public investment strategies When rural income growth is primarily driven by technological advancements that enhance crop yields, prioritizing investments in agricultural research and extension services becomes essential Conversely, if income growth is largely attributed to crop diversification, it is crucial to concentrate on improving agricultural credit, transportation, and access to market information to support this transition Additionally, if poverty reduction is mainly linked to diversification into non-farm activities, the focus should shift towards providing training, electrification, and commercial credit to promote non-farm employment opportunities.
Non-farm activities in rural areas generate more jobs and higher incomes compared to agricultural activities, positively impacting the agricultural sector by addressing market failures, particularly in credit and insurance However, the significance of non-farm income cannot be viewed in isolation from agriculture, as both sectors are interconnected through investment, production, human resources, and consumption decisions, forming complex livelihood strategies for rural households To enhance household income through non-farm employment, it is essential to overcome existing constraints.
The significant transition towards non-farm incomes has led to a clear distinction among rural households, separating those who remain solely as farmers from those who adopt a mixed approach of agriculture and non-farm activities This situation prompts an intriguing inquiry into whether market signals, despite their limitations in a transitioning economy, have favored individuals with inherent traits that enhance their farming capabilities over those who have chosen to diversify away from agriculture.
The objectives of study
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 in Vietnam.
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?
Structure of thesis
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 outlines the methodology employed in this study, while Chapters 4 and 5 present the data and explain the statistical results The thesis will conclude with a summary of the findings and some final remarks.
LITERATURE REVIEW
Definitions
Income diversification encompasses various concepts, with its patterns differing based on specific definitions This article reviews studies on income diversification, emphasizing the importance of household-level factors in understanding why families pursue multiple income-generating activities One key definition highlights income diversification as an increase in the number of income sources or a more balanced distribution among them For instance, a household with two income sources is considered more diversified than one with only one, and a household where each source contributes equally to the total income is more diversified than one where one source dominates.
The term 'non-farm' encompasses activities outside of primary agriculture, forestry, and fisheries, yet it includes the trade and processing of agricultural products, even when conducted on farms through micro-processing Non-farm employment can be categorized into wage work, which includes agricultural jobs, and self-employment that is not related to agriculture The rural non-farm sector typically comprises manufacturing, trade, construction, transportation, communications, and various services.
Barrett and Reardon (2001) emphasize that the definition of economic activities is structured according to national accounting systems, which categorize them into primary, secondary (manufacturing), and tertiary (service) sectors The classification is independent of the location, scale, or technology involved in the activities It's important to differentiate 'non-farm' from 'off-farm'; the latter typically refers to activities conducted away from a household's own farm and is often associated with agricultural labor on another's land, as noted by Ellis (1998) Therefore, 'off-farm' activities do not align with the standard definition of 'non-farm.'
Factors effect to income diversification
Income diversification differs from livelihood diversification; the latter involves households developing a varied portfolio of activities and social support systems to enhance living standards and mitigate risks.
Income generation is one of the components of livelihood strategies (Ellis 1998)
Livelihood diversification also encompasses the social institutions, gender relations, property rights, and other non-income support systems that sustain a living
Household motives for diversification vary widely among different settings and income groups, highlighting a crucial distinction between two primary types: (1) diversification aimed at wealth accumulation, primarily influenced by attractive "pull factors," and (2) diversification focused on risk management and resilience, often in response to shocks or the decline of agricultural viability.
Barrett et al (2001) identify two primary sets of motives influencing agricultural decisions The first set, known as "push factors," includes elements such as risk reduction, responses to diminishing returns in family labor due to land constraints from population pressure and fragmented landholdings, and reactions to crises or liquidity constraints These factors often lead households to self-provision various goods and services, especially when faced with high transaction costs The second set, termed "pull factors," encompasses the realization of strategic complementarities between activities, such as integrating crop and livestock production, as well as specialization based on comparative advantages derived from superior technologies, skills, or resources.
Households also use income diversification for pre-risk management or to cope with shocks that have occurred (Reardon, Delgado, and Malton 1992; Reardon et al 1998)
In developing countries, most households do not rely solely on a single source of income due to economic uncertainty Research indicates that to ensure livelihood sustainability, these households actively seek to diversify their income streams, avoiding prolonged dependence on just one or two income sources.
There are, in fact, several factors responsible for observed income diversification at the household level According to Barrett el al (200 1 ), these include:
(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, particularly in rural areas, the shift towards non-farm income sources has significantly increased, now representing a substantial portion of household earnings Research by Reardon et al (1998) reveals that from the 1970s to the 1990s, non-farm income comprised an average of 42% of total income in Africa, 40% in Latin America, and 32% in Asia Numerous studies in rural Africa highlight a positive correlation between non-farm diversification and improved household welfare Consequently, development agencies, including the World Bank and various NGOs, have widely endorsed policies promoting non-farm employment as a vital strategy for enhancing rural livelihoods (Delgado and Siamwalla 1999).
Household non-farm activities
The World Development Report (2008) highlights the ongoing redefinition of agriculture, manufacturing, and services in the economic development process It identifies two key trends in structural transformation: initially, agriculture contributes significantly to gross domestic product (GDP) and employment, with shares reaching up to 50% and 85% in low-development countries, but these figures decline as economies grow This pattern has been historically observed in developed nations and is currently evident in developing countries experiencing economic growth Despite agriculture's diminishing role, it remains crucial in poorer nations, particularly in Sub-Saharan Africa, where it accounts for an average of 34% of GDP and 64% of employment In countries with a GDP per capita between $400 and $1,800, particularly in Asia, agriculture represents about 20% of GDP and employs 43% of the labor force.
These ratios decline to 8 percent and 22 percent, respectively, in countries in the
The GDP per capita in many Eastern European and Latin American countries ranges from $1,800 to $8,100 Integrating both forward and backward links to agriculture, known as extended agriculture, can significantly boost its economic contribution, often increasing the share by 50% or more, particularly in middle-income nations.
The decline in agriculture's GDP share is attributed to the significant increase in the manufacturing sector, particularly in Malaysia and Thailand, where manufacturing's share notably doubled While the service sector in Malaysia has been substantial, it has primarily supported rather than led the transformation, unlike in India, where the service sector's GDP share rose from 42% to 52%, largely fueled by the information technology industry This structural transformation also altered export compositions, with agricultural exports decreasing as a percentage of total exports, while manufacturing exports saw a substantial rise.
Figure 2-1: Share of labor and GDP in agriculture
Share of labor and GOP in agriculture
• Share of labor in agriculture !1990-2005, average)
+ Smre of GOP from agriculture (1990-2005, averaoel -Trajectories of the share ot labor in agriculture 1961-2003
GOP per capita, constant2000US$ flog scale I
Solllt'e:WOR 2D08tnm band on dat~ from World Sank 200ov
Nôo: The list o!l-!4nor coon and tho coontr!qs thty rtpruent can bolound on P"'l• xviiL ource: World Development Report (2008)
Non-farm activities play a crucial role in enhancing household economies, particularly by providing employment during agricultural off-seasons Research by Haggblade et al (1989) indicates that between 5-65% of farmers engage in secondary non-farm work, with 15-40% of total family labor hours allocated to these income-generating activities As countries develop, non-farm tasks become increasingly commercialized, reflecting a rise in non-farm employment statistics The demand for non-farm products and services is driven by advancements in agricultural technology and management, leading to higher incomes for landowning households These households, in turn, invest their increased earnings into labor-intensive goods and services produced by small-scale non-farm enterprises Consequently, households involved in non-farm activities experience higher incomes, prompting them to expand their operations or encourage neighbors to participate This decision to hire additional labor for the non-farm sector aligns with behavioral models of labor supply, where households aim to maximize earnings while managing resource constraints and minimizing risks The diversification into non-farm activities involves interdependent choices regarding labor supply and capital investments, highlighting the strategic nature of these economic decisions.
(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 with limited agricultural employment opportunities, income diversification through non-farm activities has become increasingly important A significant factor influencing this trend is land distribution, particularly in land-scarce, labor-rich countries like China and India, where inadequate access to land can drive poorer households away from agriculture and into the non-farm sector This shift can positively affect inequality and poverty levels Research by Adams (1995) in Pakistan and Chinn (1979) in Taiwan demonstrates that non-farm income contributes to reducing 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.
On the other hand, studies in Africa have generally produced very different results
Research by Collier et al (1986) in Tanzania and Matlon (1979) in Nigeria indicates that non-farm income negatively affects rural income distribution, primarily benefiting large landowners Conversely, in land-rich, labor-scarce nations like those in Africa, greater access to land may encourage the majority to remain in agriculture, with only wealthier households transitioning into the non-farm sector.
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 tend to reduce income inequality The study highlights that in rural areas with limited connectivity to urban centers, established agricultural power dynamics enable individuals with higher farm incomes to capitalize more effectively on non-farm income opportunities.
With better access to urban markets, they suggest that opportunities for non-farm employment are less dependent on these power structures and are therefore more equalizing of income
A study by Kinsey, Burger, and Gunning (1998) analyzed 400 resettled households in rural Zimbabwe over 13 years, revealing that income diversification serves as a coping mechanism during droughts However, the alternative income sources available are often low-return activities, including day jobs and agricultural piecework.
Empirical studies on income diversification in Zimbabwe reveal significant limitations, particularly regarding the urban context, where urban poor households face similar risks as rural ones, including labor return variability and market failures A major challenge in these studies is the reliance on the share of non-farm income as a proxy for income diversification, which is difficult to measure accurately and less relevant for urban areas Comparative studies in Ethiopia, Tanzania, and India also highlight that household composition plays a crucial role in diversification strategies Specifically, larger household sizes positively influence the likelihood of agricultural diversification, while a greater male labor force increases the probability of local off-farm diversification and migration, suggesting that larger households benefit from economies of scale in managing household chores, allowing more members to pursue non-farm activities.
Credit constraints significantly influence household decisions regarding diversification, serving as one of the most critical factors Limited access to credit greatly diminishes the likelihood of households pursuing diversification strategies, whether in farming or non-farming activities This finding aligns with existing empirical literature, which indicates that barriers to entry often hinder activity diversification, highlighting the necessity of financial resources or credit access to fund initial investments in new ventures.
Barrett et al., (200 1 ); Abdulai & CroleRees, (200 1 ); and W oldenhanna & Oskam, (2001))
In Latin America, non-farm wage earnings typically surpass self-employment earnings, particularly in countries like Brazil, Chile, Colombia, Mexico, and Nicaragua Conversely, in nations such as Ecuador, Honduras, and Peru, self-employment plays a more significant role, especially in poorer regions This disparity is also evident within specific areas of a country; for instance, research by Berdegue et al (2001) indicates that the share of wage employment in rural non-farm enterprises is substantially higher in more favorable zones compared to less advantageous ones.
In northern Honduras, areas with better infrastructure and higher rural town density experience significantly higher non-farm wage income compared to self-employment income Conversely, in the southern region, where infrastructure is lacking and town density is lower, self-employment plays a 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 (Bertini et al., 2006;
The non-farm economy plays a crucial role in poverty reduction by providing alternative income sources and stimulating agricultural growth through increased productivity from reduced agricultural labor Additionally, policies that promote the non-farm economy can help curb rural-to-urban migration, a significant issue in many transition economies Studies across seven African households reveal that in four cases—Botswana, Kenya, Malawi, and Zimbabwe—non-farm wage income is nearly twice as significant as self-employment, while the remaining three cases—Rwanda, Ethiopia, and Sudan—show the opposite trend Generally, the share of non-farm wage earnings rises near urban areas, whereas part-time self-employment is more prevalent in remote, rural regions.
Alain de Janvry, Elisabeth Sadoulet, and Nong Zhu (2005) utilized household survey data from Hubei province to analyze the potential impacts on rural households' incomes, poverty, and inequality in the absence of non-farm income sources Their findings indicate that without non-farm employment, rural poverty levels would be significantly higher and more severe, accompanied by increased income inequality Key factors such as education, proximity to urban areas, and neighborhood and village effects play a vital role in enabling certain households to access non-farm opportunities Additionally, they discovered that farmers who remain solely in agriculture possess unobservable traits that enhance their productivity, suggesting a positive selection bias Participation in non-farm activities also generates beneficial spillover effects on agricultural production, aligning with the growing literature highlighting the significance of the rural non-farm sector in developing nations.
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 correlation between employment in the non-farm sector and the agricultural wage rate in rural India In the 1980s, non-farm incomes constituted a significant portion of household earnings, with approximately 40 million new jobs created, predominantly in the farm sector However, between 1993/4 and 2004/5, non-farm employment growth surpassed that of agriculture, with 60% of new rural jobs arising from the non-farm sector The most substantial increase in non-farm employment occurred from 1999/0 to 2004/5.
There is a considerable variation across quintiles and across major Indian states
Access to non-farm occupations is influenced by factors such as education, wealth, caste, agricultural conditions, and population density While the non-farm sector can contribute to poverty reduction, its impact may be limited for the poor who lack assets Research indicates that growth in specific non-farm sub-sectors correlates with increased agricultural wage rates Although individual and household participation in rural non-farm activities has been well-studied, there is a lack of literature on how trade reforms and other policy measures affect rural households' decisions to engage in these activities.
Mukesh Eswaran, Ashok Kotwal, Bharat Ramaswami, and Wilima Wadhwa (2005) analyze the effects of liberalization in the 1980s and 1990s on earnings and gender disparity in India Their findings indicate that the non-farm sector significantly contributes to increasing educational attainment among the population by creating job opportunities for literate individuals and younger cohorts, encouraging them to transition away from agriculture While non-farm employment does not directly benefit women, it often leads to men taking jobs in these sectors, resulting in women stepping in to manage agricultural tasks Additionally, advancements in agricultural productivity through technical innovations have been crucial in raising agricultural wages, thereby enhancing women's earnings as agricultural productivity improves.
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 hese sectors could be due to both the push and pull factors The gender wise istribution gives a clear impression of distress-driven employment increase The urvey revealed that although linkages between the farm and non-farm sectors in rural ndia were multifarious and strong, yet there were examples of a vibrant non-farm ector that was emerging without the support of the agricultural sector The scenario as a whole make a believer of the role of both the demand and distress -pull as well as external factors in generation of non-farm employment That most of the non-farm activities took place in 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) researched non-farm household enterprises, identifying them as a significant pull factor for income generation, income inequality reduction, and income volatility mitigation Their findings indicate that while these enterprises enhance income and decrease between-household inequality, their influence has waned in Vietnam from 1993 to 2002 Consequently, the authors argue that untargeted policies promoting non-farm household enterprises are increasingly unjustifiable, whereas targeted, export-oriented policies may be warranted if policymakers possess adequate information for effective targeting and if market failures necessitate support for these enterprises over the formal sector.
A study by Remco H Oostendorp, T.Q Trung, and N.T Tung (2009) aligns with Thai Hung Pham's findings (2007), utilizing data from the Vietnam Household Living Standards Survey across 1993, 1998, and 2002, analyzed through a Multinomial Logit Model regression The research highlights a significant shift in the rural labor force towards non-farm employment, establishing the non-farm sector as the primary source of employment for rural populations beyond agriculture Key individual-level factors influencing non-farm diversification include gender, ethnicity, and education Interestingly, land ownership, a crucial physical asset for rural households, negatively impacts non-farm employment, as increased landholdings tend to reinforce agricultural concentration Furthermore, both physical and institutional infrastructures play vital roles in determining individual participation in the non-farm sector.
RESEARCH METHODOLOGY
Model specification-dependent variable
Efforts to quantify income diversification have primarily focused on rural areas, emphasizing the proportion of non-farm income in total household earnings Studies suggest that a higher percentage of non-farm income correlates with increased diversification and reduced vulnerability to weather-related shocks, a significant risk in agricultural livelihoods However, using the share of non-farm income as a measure of diversification presents challenges, such as treating households with one versus multiple income sources equally in terms of risk mitigation Additionally, accurately measuring this indicator requires comprehensive accounting of income from both farm and non-farm activities Furthermore, this measure is less applicable in urban settings, where most income sources are inherently non-farm.
The Shannon equitability index will serve as an effective measure of household diversification levels Households often pursue multiple income sources to mitigate income risks, particularly those stemming from macroeconomic policies that can lead to job losses, such as the reduction of public-sector employment experienced in Vietnam during the 1990s.
Income of households in Vietnam is not balanced at the number source of income
Urban and rural areas exhibit significant differences in livelihood strategies, with rural households having a more diversified income base; only 3.55% rely on a single income source compared to 6.21% in urban areas In 2008, over 65.39% of rural households had three or four income sources, while 48.60% of urban households had at least three Following economic shocks, both areas experienced a decline in income source diversification, but rural areas were more adversely affected Notably, around 93% of households in both settings received money transfers, primarily from pensions and domestic remittances, highlighting the importance of these financial support systems.
The criticism of using the number of income sources as a measure of diversification stems from several factors Firstly, households with more economically active adults tend to have more income sources, reflecting labor supply decisions rather than a genuine desire for diversification This concern emphasizes the importance of considering per capita income sources and the demographics of household members in empirical analyses Secondly, discrepancies arise when comparing households that derive varying proportions of income from similar activities; for instance, a household earning 99% of its income from farming is treated the same as one earning 50% from farming and 50% from wage labor without proper adjustments To address this, actual incomes from different sources can be estimated, and weights can be assigned using the Shannon equitability index, which offers advantages over merely counting income sources This index is straightforward to measure and provides a more nuanced understanding of income distribution within households.
Households that derive 50 percent of their income from farming and 50 percent from wage labor exhibit greater income diversification compared to those that rely on over 50 percent of their income from farming The income diversification index, which ranges from zero to 100, indicates the percentage of actual income diversity relative to the maximum potential diversity To measure overall income diversity, the Shannon equitability index is utilized, adapted from the Shannon index commonly used for assessing species diversity (Magurran, 1988).
The Shannon index of income (Hincome) measures the diversity of income sources within a household, considering both the number of income streams (S) and the distribution of income shares (incsharei) from each source This index increases as the diversity of income sources grows, indicating a more equitable distribution Additionally, the Shannon equitability index (E) is derived from the Shannon index, further reflecting the household's income diversity.
The income diversification index measures how concentrated or scattered household income is across various sources, indicating the level of income diversification Households with the most diversified income streams will have a higher index value (E), while those reliant on a single income source will have a lower value The index ranges from a minimum of 0, representing the least diversified households, to a maximum of 100, indicating households with equal income from four different activities A greater number of income sources and a more balanced distribution of income shares result in a higher value of E.
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 minimal change, with most values ranging from 30 to 60 This trend indicates that households prefer not to expand their income sources or equalize their income types Instead, they are transitioning from quantitative to qualitative income diversification, focusing on 2-3 specialized activities that enhance product competitiveness and increase profitability.
According to FAO (1998), a household's decision to engage in economic activities is influenced by two primary categories of factors: those affecting the relative returns and risks of agricultural production, and those determining the capacity to participate in non-farm activities, such as education and access to credit Alain de Janvry, Elisabeth Sadoulet, and Nong Zhu (2005) suggest that these factors are shaped by the household's physical and human capital endowments, as well as the local environment Key determinants of choice include variables related to human and social capital, household composition, household assets, and characteristics of local institutions and villages.
When analyzing household-level choices, key human and social capital factors include the age and education level of the household head, along with the educational attainment of household members Additionally, household composition is assessed by its size, while household assets are evaluated based on per capita land holdings Furthermore, local institutions and village characteristics are considered, incorporating aspects such as village density and the distance from the village to the provincial center.
Basing on de Janvry and Sadoulet (200 1 ), assuming that individual decisions are not independent across members of a given household
3 Econometric Model ased on the above research and the data of Vietnam Living Standards Survey 2008 hich conducted by World Bank (WB) and the General Statistic Office of Vietnam, ualitative and quantitative analysis are applied; in which, qualitative analysis is used o describe current status of household income and role of non-farm income in total ousehold income; quantitative analysis is used to find which factors are most effect o diversification trend by using Two Stage regression First, probit regression for ousehold with 1 income resource (Diversification Index equal to 0) and more than 1 ind of income recourse (remaining household), then Least Square regression will be
• se for household with more than 1 kind of income resource, model for both stage is uggested as follow:
Diversification Index = f (Gender ; Age ; Age square ; Education ; Education
; Training ; Landholding per capita ; Household size ; Dependency ratio illage Density; Non-farm percent; Distance from urban center) ariable descri tion:
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
2 Age, Age square: age of household head Age has a differential impact on participation in agricultural and non-agricultural, which might potentially be explained by different physical fitness requirements across sectors Manual agricultural labor is often harder than work in other sectors, so that older people are at a disadvantage
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
5 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 m non-farm activities It had same effect as Education in households
Econometric Model
Based on the Vietnam Living Standards Survey 2008 conducted by the World Bank and the General Statistics Office of Vietnam, this study employs both qualitative and quantitative analyses to assess household income dynamics The qualitative analysis describes the current status of household income and the significance of non-farm income in the overall income structure In contrast, the quantitative analysis identifies key factors influencing income diversification trends through a Two-Stage regression approach Initially, a probit regression is applied to households with a single income source (Diversification Index equal to 0) versus those with multiple income sources, followed by a Least Squares regression for further insights.
• se for household with more than 1 kind of income resource, model for both stage is uggested as follow:
Diversification Index = f (Gender ; Age ; Age square ; Education ; Education
; Training ; Landholding per capita ; Household size ; Dependency ratio illage Density; Non-farm percent; Distance from urban center) ariable descri tion:
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
2 Age, Age square: age of household head Age has a differential impact on participation in agricultural and non-agricultural, which might potentially be explained by different physical fitness requirements across sectors Manual agricultural labor is often harder than work in other sectors, so that older people are at a disadvantage
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
5 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 m 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 enhance a household's ability to engage effectively in agriculture Conversely, lower per capita landholdings result in insufficient agricultural income to meet household expenses, leading to financial strain Consequently, families often seek additional income through non-farm activities.
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 would be expected to push People out of agriculture and may stimulate non- farm activities (through lower transactions costs, economies of agglomeration, etc.)
11 Non-farm Percentage of the labor force employed in non-farm activities: capture the strength of clustering of non- farm activities, and access to the specific infrastructure necessary to promote non-farm 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 analysis of household income structure in Vietnam is derived from the 2008 Vietnam Household Living Standard Survey (VHLSS 2008), which includes data from 9,189 rural and urban households across eight regions Out of these, 5,967 households provided community data The survey is divided into eight categories and addresses various topics, making it ideal for examining the relationships between different household income sources, household assets, characteristics, and public assets.
Table 4-1: Structure offamily income in the 2008 survey
Planting Livestock Services for agriculture Aquaculture Forest and hunting Wage
Share of total family income % of income
Total family income, Thousand VND per year 500,413,635
Per household income, Thousand VND per year 27,583
Table 4.1 gives the breakdown of "Structure of family income in the 2008 survey"
The VHLSS data reveals eight distinct income sources categorized into four mutually exclusive types: agriculture income, wage income, non-farm income, and money transfer income Agriculture income, which encompasses five sources—planting, livestock, agricultural services, aquaculture, and forest hunting—can vary significantly based on the type of crops or livestock cultivated Households engaged in agriculture may be self-employed or operate businesses, with income stability influenced by the diversity of their agricultural activities Non-farm income includes both earned and unearned sources, such as remittances from family members who have migrated to urban areas, welfare subsidies, pensions, and interest from money transfers, as well as income from the rural non-farm economy This latter sector aligns with national accounting definitions, incorporating secondary sectors like manufacturing and construction, and tertiary sectors such as transport and services Non-farm income can be further divided into five specific 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 trade, agriculture, forestry, and aquaculture It also includes small business ventures such as shopkeeping and artisan crafts, like tailoring, home appliance repair, and shoe repair.
(5) Other- includes property benefit, gift, remittances, welfare, pensions, interest
I come from (1), (2) and (3) are consider as wage income, (4) still keep name as non- f:~rm income and (5) is namely money transfer category So there are 4 different i 1come sources considered
Data from the VHLSS 2008 indicates that agriculture remains a primary income source for rural households, contributing to 56.70% of their total family income This agricultural income is comprised of 27.71% from various farming activities.
~ gricultural (activity that includes revenue from sales of farm products and value of
In rural households, a significant portion of income is generated from various sources, with 3.49% coming from aquaculture and other agricultural activities such as livestock, forestry, and hunting Notably, non-agricultural income accounts for 20.07% of family earnings, while salaries contribute 28.67% Additionally, financial transfers play a crucial role in supporting many rural families.
Approximately 3.13% of households receive transfers, including pensions, unemployment benefits, gifts, and other social benefits, which constitute 14.23% of total family income The significant number of recipients highlights the large proportion of seniors within these households.
~oldier among the rural population Although planting and livestock income contribute relatively little to total family income, they are relative large number of households
~ngage in these activities For most households, family income is quite diversified
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.
Descriptive statistics
Variable Obs Mean Std Dev Min Max age 6504 49.46817 13.72242 16 97 age2 6504 2635.376 1485.211 256 9409 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_p_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
According to the VHLSS 2008 data, the average household size in the surveyed sample is four, aligning with the national average Approximately 24.49% of households are headed by women The mean educational attainment for adults is 6.9 years, surpassing the national average of 5.5 years (UNDP, 2010) The average age of household heads is 49.4, with an unexpectedly low mean of 1.3 years of schooling, indicating that many individuals lacked educational opportunities during and immediately after the Vietnam War, relying instead on life experience Additionally, the average landholding per capita is 1,512 m², and households are located at a considerable distance from the nearest urban center.
On average, residents in rural areas travel 49.2 km, taking about one hour to reach the market by motorcycle The annual per capita income is approximately 11,850.7 thousand VND, equivalent to around 650 USD, reflecting a reasonable average income level in Vietnam.
Household's income diversification
Table 4-3: Structure of employed population by kind of economic activity
Mining and quarrying Manufacturing Construction Wholesale and retail trade; repair of motor vehicles, motor cycles and personal and household goods
Hotels, restaurant Transport, storage and communications Wage activity
Public administration and defence; compulsory social security
Education and training Health and social work Other activity and money transfer
Real estate, renting and business activities
(Source: General Statistics Office of Vietnam, 2009)
Vietnam, with a population of nearly 86 million in 2009, ranks as the 13th most populous country globally Although the economy has evolved since the introduction of petroleum in the mid-1980s, the share of agriculture, livestock, forestry, and fishing in GDP decreased from 27.76 percent in 1996 to 22.10 percent in 2008, with agriculture contributing only 18.14 percent Despite this decline, the agricultural sector remains a vital source of employment and livelihood for nearly half of the population Data from the VHLSS 2008 indicates that typical rural families earn income from two to three different sources, highlighting that income diversification is positively correlated with higher household income However, families must navigate potential entry barriers and constraints, as identified by Lapar et al (2003), to optimize their income sources.
Figure 4-1: Family income and the number of family 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 examines income diversification across various sectors and timeframes at the household level, utilizing per capita income quintiles to highlight differences among households in different income categories While per capita income is a straightforward indicator, expenditure is considered a more accurate reflection of household welfare Heavily indebted households with substantial current earned income may appear to have a high standard of living, despite their financial challenges.
Households may experience low consumption levels due to income being allocated to debt repayment Additionally, savings can enable families to maintain high consumption rates despite temporary declines in their current income.
I owever, culturally people in rural Vietnam tend to rely on subsistence behavior and tpeir expenditure does not reflect exactly their economic condition
Table 4-4: Trends in income diversification by the number of income sources
Mean SD Mean SD Mean SD
~easuring diversity in the number of income sources, table 4-4 displays the average
~umber of income sources of rural households is conditional on household per capita ncome-based quintiles n 2004, each household had 2.68 income sources, on average while the most income
~iversified households had 4 sources of income Over time, there is limited variation n the number of sources and between the end-points of the period under consideration
2004 - 2008), the size of increase in overall income sources is small (0.0 1 ), indicating
The analysis reveals a consistent increase in income diversification throughout the observed period Notably, the level of income diversity remains relatively uniform across different income quintiles According to Table 4-4, households in the "poorest quintile" have an average of 2.41 income sources, while those in the "highest quintile" average 2.56 sources.
"ghest for those in the "middle quintile" (2.80) in 2004 This indicates that the rich d the poor are not much different in terms of the level of diversity in income sources
2004 This cross-sectional pattern of diversity remains unchanged in the period
According to Minot et al (2006), income diversity exhibits an inverted U-shape, with households in the middle quintiles having the most income sources, while those at both ends of the distribution are less diversified Table x illustrates this trend, showing that the average number of income sources fluctuates most for households in the "richest quintile." This higher diversification among wealthier households supports the "pull-distress diversification" strategy Data from table 4-4 reveals a clear pattern of income diversification from 2004 to 2008, with an overall increase in income sources across quintiles; however, the richest households experienced the most significant growth (0.06), while the "poorer quintile" saw a decrease (-0.03) This suggests that income diversification in Vietnam may reflect a combination of demand-pull and distress-push strategies.
3.2 Measurement of income share diversity
Between 2004 and 2008, households showed notable trends in income diversification, as detailed in Table 4-5 In 2004, agricultural income was the primary source, constituting 37.30% of total household income, followed by wage employment at 28.05% and money transfers at 18.07% By 2008, agricultural income's share rose to 40.82%, influenced by a significant increase in food prices, particularly rice This 3.5 percentage point increase in agricultural income may reflect both price fluctuations and household decision-making processes Conversely, contributions from non-farm businesses and money transfers declined, while wage income remained relatively stable.
The growth of non-farm income sources is steadily increasing each year, reflecting a greater diversity in income streams In 2008, the share of income from money transfers decreased to 15.77%, a drop of 2.55%, while non-farm income remained relatively stable at 15.5%, with a smaller decline of just 1.21%.
Table 4-5: Trends of income diversification, by income shares
As shown in Table 4-5, in 2004 the pattern of income shares across quintiles is quite clear in terms of the share of income from non-farm business and wage employment
Households in the poorest quintile rely the least on non-farm activities for their income, with a minimal share compared to wealthier groups In contrast, the richest quintile sees a significant increase in non-farm income, accounting for 24.5 percent of their total earnings This highlights the disparity in income sources, as the wealthiest households benefit the most from non-farm businesses.
Between 2004 and 2006, households across different income quintiles showed a shift away from traditional agricultural income sources The poorest households relied heavily on agriculture for 50.21% of their income, while the richest households derived only 21.61% from agricultural wages Notably, as households became poorer, their reliance on wage income increased The poorest quintile reduced their agricultural income reliance by 3.26 percentage points, whereas the second and fourth quintiles saw smaller reductions of 0.3 and 0.25 percentage points, respectively Overall, there was a decline in reliance on agricultural income and a rise in non-farm income sources across all quintiles This trend suggests that while the motivations for diversification may differ between rich and poor households, the outcome of balancing income from various sources remains consistent.
Roles of non-farm activities in Vietnam's rural household economy
In Vietnam, the rural labor force is expanding quickly, yet employment opportunities are not keeping up Annually, Vietnamese farmers lose approximately 74,000 hectares of farmland to housing, industrial parks, and infrastructure development, resulting in an annual farmland loss rate of about 1% due to urbanization and climate change Consequently, the per capita farmland in Vietnam is decreasing significantly, with the average agricultural land area per person at just 1,224 square meters, compared to global averages.
The reduction of farmland in Vietnam poses significant challenges to social development, leading to an increase in landless farmers and social differentiation The share of agriculture and forestry in total employment decreased from over two-thirds in 1990 to approximately 48.87 percent in 2008, resulting in more than 6 million farmers losing their jobs and a high unemployment rate in rural areas With limited land available for agricultural expansion, it is crucial to develop non-farm sectors to enhance rural employment, stimulate economic growth, improve income distribution, and alleviate poverty Expanding job opportunities outside of agriculture can also mitigate rural-to-urban migration, which exacerbates urban social issues Given the slow pace of industrialization, rural non-farm sectors must absorb the excess labor force, promote economic diversification, and create sustainable income sources.
In 2008, over 36% of households earned non-farm income, with 32.16% in rural areas and 49.87% in urban regions, reflecting a 3% increase since 2006 Households with non-farm income reported a per capita income of 15,838 thousand VND, which is 1.24 times higher than the 12,717 thousand VND of those without non-farm income A significant disparity exists in farm income, where households with non-farm income earned only 16,275 thousand VND annually, compared to 22,417 thousand VND for those without This indicates that families pursue non-farm income not as a supplementary source, but as a vital means to meet their basic needs due to insufficient farm income.
Table 4-6: Income of household with and without non-farm income
Average income from Without non-farm With activities Farm
Wage Non-farm Money Transfer Total
An analysis of income structures for non-farm households indicates that smaller salaries and farm income are supplemented by non-farm activities Households engaged in non-farm income earn salaries that are, on average, 5,788 thousand VND lower than those without such income Additionally, non-farm self-employment contributes an extra 29,765 thousand VND to their overall earnings.
Data from Table 4-7 indicates that non-farm activities primarily focus on providing supply services to local communities, with only 9% dedicated to producing commercial products or handicrafts for external markets, such as wood and textile goods The majority of non-farm activities, accounting for 33.63%, stem from retail sales and repair services for household items The remaining income sources are evenly distributed among hotels, restaurants, transportation services (road, railroad, and pipeline), as well as food and beverage production.
Table 4-7: Detailed non-farm activities of household
Types ofNon-farm activities Whole country Rural areas Retail sale, repair family applicants 33.63% 33.27%
Road, railroad and pipeline transport 7.93% 6.91%
Whole sale and agent sale 5.73% 6.20%
Wood processing and production of wood,
Sale services to local rural residents 3.60% 2.84%
The potential for families to earn non-farm income is influenced by the structure and quality of their human capital Research indicates that higher levels of education among family members are associated with increased likelihood of employment in non-farm sectors.
Table 4-8: Status ofTraining and Education of household
Highest training degree Percent Highest learning degree Percent
Short-term Training 3.71% Primary School 37.70%
Professional High Upper Secondary school 3.94% school 21.29%
With 89.82% of household members do not attain any professional training and over 73.57% just attain secondary school or lower levels So, the way to get more income is wage and non-farm income Young people could move to urban areas and join industries and services and get wage income but middle-aged and old people are not able do so since they have low education level and lack of training in professional work, cannot change their normal life and they want to stay in rural area and work in non- farm activities The reduction in the importance of agricultural activities and the importance of non-farm activities is a main feature of economic development.
Econometric evidence
Table 5-1 presents the probit regression results for the diversification index, revealing that at the household level, human and social capital variables have a minimal influence on income diversification decisions Notably, the age of the household head significantly affects diversification choices, while the education level of the household head shows a non-significant impact on the decision to pursue additional activities.
The education level of household members plays a crucial role in diversification decisions, as investing in education for the next generation opens up opportunities for income-generating activities beyond traditional roles This can include wages from non-farm jobs and remittances from family members working in urban areas Additionally, household size positively correlates with diversification, indicating that each additional member increases the likelihood of pursuing diverse income sources by 0.09.
Table 5-1: Probit Regression results of diversification index
Robust [95% none diver Coef Std Err z P>lzl Con f Interval] age 0.065174 0.011991 5.44 0 0.041673 0.088675
~istance -0.00175 0.000936 -1.87 0.061 -0.00359 8.04E-05 edu -0.02551 0.03272 -0.78 0.436 -0.08964 0.038621 edulev 0.032327 0.015563 2.08 0.038 0.001825 0.062829 numedu -0.0489 0.044223 -1.11 0.269 -0.13557 0.037778 lg_ender -0.06102 0.072228 -0.84 0.398 -0.20258 0.080548 dependency -0.21442 0.094159 -2.28 0.023 -0.39897 -0.02987 house size 0.106196 0.024204 4.39 0 0.058756 0.153635 land _p_ c -6.74E-06 4.72E-06 -1.43 0.154 -1.6E-05 2.52E-06 training 0.283806 0.106385 2.67 0.008 0.075294 0.492317 vi !!age dens 4.79E-05 6.17E-05 0.78 0.438 -7.3E-05 0.000169 region
Second, household with members has training in non-farm work is associated with a much higher probability of diversification index with the factor of 0.28 These results
~an be explained by increasing returns to scale in household chores for households with a larger size and more labor availability that makes it easier for them to let some
!members engage in others activities Studies of Dercon and Krishnan ( 1996) in Ethiopa and of Tanzania and Micevska and Rahut (2008) in India find similar results
The presence of older household members significantly decreases the likelihood of migration, as a higher dependency ratio limits labor availability Additionally, the decision to diversify agricultural production is positively influenced by the amount of arable land per adult Generally, local non-farm decisions are primarily influenced by the household's asset position rather than by human or social capital or household composition For detailed insights, refer to the results of the linear regression on the diversification index presented in Table 5-2 (see appendix).
Table 5-2: Liner Regression results of diversification index
Robust [95% diver index Coef Std Err t P>lti Con f Interval] age 0.496044 0.132329 3.75 0 0.236635 0.755454 age2 -0.0039 0.001229 -3.18 0.002 -0.00631 -0.00149 distance -0.03639 0.00843 -4.32 0 -0.05292 -0.01986 edu -0.28528 0.306865 -0.93 0.353 -0.88684 0.316281 edulev 0.682239 0.155369 4.39 0 0.377663 0.986816 numedu 0.237208 0.349843 0.68 0.498 -0.4486 0.92302 gender 0.407125 0.720861 0.56 0.572 -1.00601 1.820259 dependency -2.10355 0.907021 -2.32 0.02 -3.88162 -0.32548 house size 0.393301 0.183138 2.15 0.032 0.034288 0.752315 land p c -0.00062 0.00012 -5.15 0 -0.00085 -0.00038 training 4.976478 0.791587 6.29 0 3.424696 6.52826 village dens 0.000159 0.000419 0.38 0.705 -0.00066 0.000981 regiOn
(1) Demographic factors: Household s1ze has a positive effect on the diversification index The larger household size, diversify trend of household
The gender of the household head does not significantly impact the diversification index, indicating no notable correlation In contrast, the age of the household head is significant; older heads tend to possess greater life experience, which contributes to increased income resources for the household.
Education significantly influences household diversification, with higher education levels correlating to increased diversification trends Studies by Corral and Reardon (2001), Yunez and Taylor (2001), and de Janvry and Sadoulet (2001) show that more educated households typically earn greater overall income, although not necessarily more from farming This positive impact of education on income is substantial, as higher education levels lead individuals to seek better job opportunities in cities or engage in non-farm activities Consequently, increased education translates to higher total income and access to better-paying jobs Datt and Jolliffe (2005) emphasize that education is a strong determinant of living standards in both rural and urban settings, revealing a substitutability between education and land ownership Their findings also indicate that adult education positively affects household welfare across various environments.
(2003) find that education is the factor that mostly affects households' escape from poverty
Per capita landholding significantly impacts the diversification index, with very small landholdings leading to reduced diversification Additionally, higher village population density correlates with an increased diversification index, as residents seek alternative income sources when agricultural land is limited Although the distance from the nearest urban center does not show a significant effect, it suggests that households farther from urban areas tend to experience lower income levels.
CONCLUSIONS AND RECOMMENDATIONS
Conclusions and recommendations
This study reviews the diversification of household income in Vietnam, focusing on the role of non-farm income It reveals that one-third of households have not engaged in economic diversification, yet both wage and non-farm activities significantly boost average total income The findings indicate a growing number of income sources, particularly among wealthier households, highlighting the importance of non-farm activities in rural economies, especially in land-scarce mountainous regions Non-farm income is increasingly vital for household income, surpassing agriculture as the primary income source Households adopt two strategies to enhance income: increasing the number of income sources through self-employment and diversifying into different sectors of the rural economy Success hinges on improving access to non-farm activities and well-functioning labor markets, with education playing a crucial role in enhancing wage income and non-farm earnings Despite progress, Vietnam lags behind its Asian neighbors in rural education investment and skill levels, underscoring the need for government intervention to reduce education and training costs Better-educated individuals secure higher wage jobs, and training enhances income from non-farm activities, contributing to improved social well-being Additionally, urban areas must provide better access to skilled jobs for migrants to maximize the benefits of education and training, potentially drawing more educated individuals away from agriculture.
This thesis acknowledges several limitations, particularly in its analytical components The income diversification index, a central variable in this study, is not widely utilized in existing income diversification research Additionally, while the dataset includes social capital variables such as access to credit, it fails to address the ease of access to these resources The analysis primarily seeks to enhance our understanding of the fundamental assumptions, strengths, and weaknesses inherent in these approaches.
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APPENDIX summarize age age2 edu edulev numedu gender dependency ouse_size land_p_c training village_dens
Std Dev Min distance diver_index
-+ - age 6504 49.46817 13.72242 16 97 age2 6504 2635.376 1485.211 256 9409 edu 6504 1.337946 1.345635 0 12 edulev 6504 6 986716 2 966165 0 12 numedu 6504 8085793 1.085226 0 6
-+ - gender 6504 2021833 4016591 0 1 dependency 6504 2946816 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
'~ probit none_diver age age2 distance edu edulev numedu gender dependency hquse_size land_p_c training village_dens i.region , vee
I eration 4: log pseudolikelihood = -933.83116 Pfobit regression
Lbg pseudolikelihood = -933.83116 none_diver I Coef
-+ - age 1 0651739 0119907 5.44 0.000 0416725 0886753 age2 1 -.0006555 0001089 -6.02 0.000 -.0008689 -.0004421 distance I -.0017534 0009356 -1.87 0.061 -.0035871 0000804 edu I -.025508 0327197 -0.78 0.436 -.0896374 0386214 edulev I 0323269 0155627 2.08 0.038 0018245 0628293 numedu I -.0488981 0442234 -1.11 0.269 -.1355743 0377781 gender I -.0610157 0722278 -0.84 0.398 -.2025795 0805482 dependency I -.2144217 0941592 -2.28 0.023 -.3989703 -.0298731 house_size I 1061956 0242044 4.39 0.000 0587558 1536354 land_p_c I -6.74e-06 4.72e-06 -1.43 0.154 -.000016 2.52e-06 training I 2838056 1063852 2.67 0.008 0752944 4923169 illage_dens I 0000479 0000617 0.78 0.438 -.0000731 0001688 region
, l , , ' regress diver_index age age2 distance edu edulev numedu gender dependency hquse_size land_p_c training village_dens i.region if
Linear regression Number of obs
Robust Std Err t P>ltl [95% Conf Interval]
-+ - age 4960444 1323285 3.75 0.000 236635 7554537 age2 -.0039031 0012292 -3.18 0.002 -.0063128 -.0014933 distance -.0363908 0084304 -4.32 0.000 -.0529173 -.0198642 edu -.2852792 3068649 -0.93 0.353 -.8868399 3162814 edulev 6822394 1553687 4.39 0.000 3776633 9868156 numedu 2372084 3498427 0.68 0.498 -.4486035 9230203 gender 4071246 7208607 0.56 0.572 -1.00601 1.820259 dependency -2.103547 9070205 -2.32 0.020 -3.881619 -.3254753 house_size 3933013 183138 2.15 0.032 0342879 7523147 land_p_c -.0006172 0001199 -5.15 0.000 -.0008524 -.0003821 training 4.976478 7915866 6.29 0.000 3.424696 6.52826 illage_dens 0001589 0004191 0.38 0.705 -.0006627 0009805 region