This study assesses the impact of off-farm employment on people's welfare through the 2016 Vietnam Rural Household Resource Access Survey VARHS dataset is estimated by three factors such
INTRODUCTION
Vietnam's agricultural sector is vital to its economy, producing approximately 43.6 million tons of rice, with 6.5-7 million tons exported, significantly enhancing global food security (Bui & Hoang, 2021) This sector not only meets domestic food demands but also plays a crucial role in economic growth by generating employment and contributing to poverty reduction, particularly for the impoverished population reliant on agriculture (World Bank, 2016) However, challenges such as severe climate change, pest attacks, and unfavorable economic conditions hinder the sector's effectiveness (Duong et al., 2021) Being situated in Southeast Asia, Vietnam is particularly vulnerable to climate change and natural disasters, which threaten rural communities and indirectly impact food security, thereby affecting national stability (Bui & Hoang, 2021).
Many rural households in Vietnam face significant vulnerability to extreme weather events such as storms, floods, and droughts, compounded by economic challenges like high unemployment and price fluctuations.
Many rural households continue to live below the poverty line, making the management of poverty and income shocks from natural and economic crises a primary concern (Duong et al., 2021) As a result, diversifying income through off-farm employment has become essential for these households This engagement not only increases household income but also enables greater spending on essential goods, including clean energy, ultimately improving their overall quality of life.
Numerous studies have explored the effects of employment and non-farm income on different facets of life, particularly in Vietnam and globally Research has delved into the motivations driving rural households to engage in off-farm activities, highlighting the significance of these income sources for economic stability and development.
2001; Escobal, 2001; Reardon et al., 2001; Reardon et al., 2000) These studies explore the factors that drive rural households to engage in off-farm activities, considering both
"Pull" factors, such as improved employment opportunities and reduced risks in non-farm activities, contrast with "push" factors like limited agricultural land and hazardous farming conditions In China, research highlights a notable link between rural off-farm employment and the adoption of clean energy (Zhou et al., 2022) Meanwhile, studies in Mauritius explore how off-farm activities contribute to poverty reduction (Ba et al., 2021) In Bangladesh, income from off-farm jobs has significantly decreased the percentage of households living in poverty (Al-Amin & Hossain, 2019) Similarly, Vietnam has examined the impact of non-farm employment on social welfare, including food consumption and poverty alleviation (Duong et al., 2021).
Previous research has largely focused on the income effects of off-farm employment in rural households, neglecting its broader implications for overall welfare and quality of life This study aims to fill that gap by exploring how off-farm employment influences the welfare of rural Vietnamese households, considering factors such as income, food expenditure, and energy consumption.
LITERATURE REVIEW
Concepts & Theory
Off-farm employment is a vital source of sustenance for rural households in many countries, generating income outside the agricultural sector Due to declining agricultural productivity, limited access to credit, and urbanization, off-farm employment has become increasingly significant for rural households Non-farm income, in this context, refers to earnings from activities unrelated to crop and livestock production, including home-based agricultural processing and hired labor in large factories, excluding agricultural wage-earning activities.
Household expenditure refers to the total amount of money a family spends on goods and services over a specific period, and it can also be expressed as a percentage of the household's income (Sanderford & Koebel, 2014) The concept of welfare varies across different contexts and is defined as a combination of needs, emotions, and health (Broom, 2007) Economically, welfare represents the fulfillment of the needs of a population segment, typically analyzed through various social indicators (Erik, 1976).
Agriculture serves as a vital source of livelihood in many developing countries, yet crop productivity often falls short of potential due to reliance on natural rainfall, limited adoption of advanced production technologies, and inadequate access to agricultural services like extension and credit To address liquidity constraints and declining farm incomes, many small-scale producers are diversifying their production and seeking additional income sources beyond farming This shift highlights the growing importance of non-farm employment in the context of small-scale agriculture in these regions.
2017) Income from non-farm employment is considered a crucial livelihood source for rural households and serves as a means to diversify household income.
Agricultural development is vital for alleviating hunger and poverty in rural areas, but non-agricultural growth also plays a significant role, especially in developing countries facing rapid population growth and limited agricultural resources Small-scale farming households often rely on diverse income sources, with non-agricultural income being a key element Research indicates that non-agricultural income has a substantial impact on poverty levels, as it enhances household income and allows for increased food expenditures, leading to improved access to a variety of higher-quality food and better overall food security Additionally, participation in non-agricultural activities can bolster food security for households that cannot reinvest in agriculture, either by altering their food consumption patterns over time or by reducing the risk of food shortages due to unexpected crop failures.
Energy is vital for all business activities, with many companies depending on non-renewable sources (Cosimo, 2018) Research indicates that non-agricultural employment significantly influences farmers' energy consumption, particularly in adopting clean energy Notably, Ma's (2018) study reveals that clean energy adoption enhances income, reduces income inequality, and improves quality of life The "energy ladder" theory illustrates that as income increases, households transition from biomass to modern energy sources for cooking Additionally, non-agricultural jobs decrease time spent on farming, allowing farmers to opt for clean energy and diminish biomass usage like firewood and straw (Zhang, 2018; Wang, 2019) However, the relationship between non-agricultural employment and energy consumption is complex, varying by employment type and region (Shi, 2009) Overall, non-agricultural employment generally encourages farmers to utilize clean energy more effectively.
Access to clean, affordable energy is vital for human welfare, significantly influencing farmers' health and overall well-being (Ma, 2019) Research indicates that energy consumption patterns vary across cultural backgrounds, affecting health outcomes (Jiang, 2020) The adoption of clean energy for cooking among farmers is crucial for reducing environmental pollution and enhancing health (W Zhou et al., 2022) Improved health can lead to economic benefits and increased welfare (Bloom, 2004) Additionally, the energy consumption revolution in rural areas has been linked to greater farmer happiness (Xu, 2022) However, many rural households still rely on traditional energy sources, which can pose health risks (Olaniyi, 2012) Overall, studies demonstrate that energy consumption has both direct and indirect effects on farmers' health and happiness, highlighting its positive role in enhancing their welfare.
From there, our team proposes the following research model:
Method
Numerous studies have examined the factors influencing welfare improvement in Vietnam, particularly utilizing data from the Vietnam Household Living Standards Survey (VHLSS) and the Vietnam Access to Resources Household.
In Vietnam, people's welfare is assessed through their dynamic poverty status and vulnerability, as highlighted by Imai et al (2011) Key welfare indicators include children's education, household assets, durable goods, and the lost wages of working-age family members (Cuong & Mont, 2012) McKay et al (2018) further emphasize that food consumption, household income, and asset ownership are critical measures of farmers' welfare using the VARHS dataset This article addresses research gaps by exploring three welfare dimensions derived from the 2016 VARHS survey data: food consumption, household income, and the use of clean energy for cooking.
Food expenditure refers to the total money households allocate for various food items and groceries, encompassing a range of categories such as meats (including chicken, beef, and pork), seafood (like fish and shrimp), fruits, dairy products, alcoholic beverages, and dining out.
The income measure is calculated using comprehensive data on total household income from diverse sources, such as agriculture (including crop cultivation, livestock, and aquaculture for profit estimation), wage earnings, income from non-agricultural businesses, various transfers, and shared asset resources.
In studies by Carter et al (2020), Ma et al (2019), and Tian (2020), cooking energy for farmers is categorized into clean energy sources—such as natural gas, biogas, solar energy, and biomass—and non-clean energy sources, including firewood and coal This research designates a value of 0 for the use of solid fuels like firewood and coal as the primary cooking energy, while a value of 1 is assigned when clean energy sources are utilized.
Most analyses employ regression models to explore the relationship between dependent and independent variables (Bui, 2021; Li, 2021; Rajkhowa, 2022) One study utilizes the instrumental variable (IV) method, which involves three steps: first, applying a probit model of non-farm activities (NFi) to identify the instrument (II); second, regressing the probit model (NF) on its predictive outcome and other household characteristics (Xi) (Bui, 2021); and finally, using the derived non-farm activity values in FGLS regression to address variable variance Additionally, a binary probit model estimates the likelihood of household participation in off-farm activities based on observed characteristics (Pham, 2020) Furthermore, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model is employed to select pertinent articles from international journal publications (Duong, 2019).
The instrumental variable method (IV) and control function (CF) are effective estimation techniques for analyzing the endogenous intervention effect model, enabling efficient estimation of binary endogenous variables (Abdullah and Hossain, 2019) A probit model can illustrate the utility difference for farm households between engaging in off-farm jobs and not, reflecting their choice in employment (Li et al., 2019) Additionally, the IV-probit model has been utilized to assess the influence of off-farm employment (LOE) on the adoption of cooking clean energy (CCE) among farmers (Zhou, 2022).
The instrumental variable-based two-stage least squares method (IV-2SLS) was employed to assess the effect of nonfarm income on electricity expenditure, as noted by Ma, Zhou, and Renwick (2019) Additionally, an IV-based Tobit model (IV-Tobit) was utilized to evaluate the influence of nonfarm income on spending for gas and coal Both the IV-2SLS regression model and the IV-Tobit model effectively address the endogenous bias associated with continuous endogenous variables, as highlighted by Burgess et al (2017), Frank et al (2018), and Hassen and Kõhlin (2017).
Numerous studies highlight that off-farm work significantly alleviates poverty by increasing expected food consumption and reducing future food insecurity (Zereyesus et al., 2017) In developing nations such as Vietnam, rural households actively pursue diverse income sources to mitigate unforeseen risks Notably, income from off-farm employment often exceeds net income from agricultural activities for certain rural families (Reardon et al., 2007).
Paid off-farm employment and self-employment significantly enhance the well-being of rural families and contribute to poverty reduction by increasing the use of agricultural inputs for better production (Adjognon, 2017) This additional income allows struggling farmers to adopt innovative agricultural practices, including labor-saving technologies such as herbicides and mechanization (Dedehouanou et al., 2018; Nisrane et al., 2016) In sub-Saharan Africa, off-farm activities are vital for poverty alleviation, providing essential income through heightened labor intensity (Corral & Reardon, 2001; Ackah, 2013) In Nigeria, diversifying from subsistence farming to off-farm work has proven effective in reducing poverty (Timothy, 2011) Although the non-agricultural sector is not the primary focus in Nigeria, it plays a crucial role in creating job opportunities and generating income for rural households.
Research indicates that the likelihood of household heads engaging in off-farm employment, especially wage work, declines with age (Hatlebakk, 2012; Rahman & Akter, 2014; Pham, 2020) Additionally, households with larger farms are less likely to seek off-farm employment opportunities (Winters et al., 2009; Tran et al., 2016; Pham, 2020) The size of a household's farm, which reflects their wealth based on the total arable land they own or manage, leads families with significant land holdings to focus on agricultural production and adopt innovative technologies to boost their agricultural income (Duong & Thanh, 2019; Pham, 2020).
Off-farm employment significantly enhances the adoption of clean cooking energy among farmers, as evidenced by a study indicating that a 1% rise in non-farm employment correlates with a 1.019% increase in the likelihood of using clean energy for cooking This influence operates through three main channels: annual per capita cash income, social networks, and family population structure Furthermore, non-farm income positively affects expenditures on clean energy sources like electricity and gas, highlighting its crucial role in promoting sustainable energy practices in agricultural communities.
Numerous studies have investigated the link between off-farm employment and individual welfare, yet many of these studies face limitations in their data and econometric methods, which may hinder the comprehensiveness of their findings.
Previous studies primarily examined the influence of off-farm employment on household income, neglecting the effects on household welfare and quality of life (Al-Amin, 2019; Ma, 2019; Bui, 2020) Furthermore, the potential benefits of reducing fuel consumption on individuals' welfare have not been adequately explored in the literature The existing research by Ma (2019) and Jiang (2020) has only highlighted the impact on household health.
The Tobit model has been employed in past research to analyze the selection of off-farm jobs among household members (Li et al., 2019) However, it is important to note that, unlike Ordinary Least Squares (OLS) estimators, the coefficients derived from the Tobit model do not represent the marginal effect of a variable on the mean of the observed dependent variable Additionally, estimates obtained from Tobit-type models can be inconsistent and inefficient.
Research gap
Numerous studies have investigated the link between off-farm employment and individual welfare, yet many of these studies face limitations in their data and econometric methods, which may hinder the robustness of their findings.
Previous studies primarily concentrated on the effects of off-farm employment on household income, neglecting to explore the changes in household welfare and quality of life (Al-Amin, 2019; Ma, 2019; Bui, 2020) Furthermore, the potential benefits of reducing fuel usage on individual welfare have not been adequately addressed in existing research While Ma (2019) and Jiang (2020) highlighted the impact on household health, a comprehensive analysis of overall welfare remains absent in the literature.
The Tobit model has been employed in previous research to analyze the selection of off-farm employment among household members (Li et al., 2019) However, it is important to note that, unlike Ordinary Least Squares (OLS) estimators, the coefficients derived from the Tobit model do not represent the marginal effects of regressors on the mean of the observed dependent variable Additionally, estimates produced by Tobit-type models may be inconsistent and inefficient.
The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model is utilized to select relevant articles from published literature; however, it is not suitable for all research types Its application may lead to biased outcomes if significant prior studies are overlooked during the meta-analysis process.
Our research aims to overcome the limitations of previous studies by examining the impact of off-farm employment on individual welfare through a regression analysis of income and expenditure using Ordinary Least Squares (OLS) for improved accuracy and efficiency over the Tobit model Additionally, we will apply a logit regression model to explore how off-farm employment influences energy consumption, while also incorporating robust methodologies to ensure reliable results.
Standard Errors model to account for variable variance in the OLS model, thereby increasing the model’s reliability.
METHODOLOGY
Data
The Vietnam Access to Resources Household Survey (VARHS) 2016 dataset provides valuable insights into how non-agricultural activities influence household income, expenditure, and energy consumption in Vietnam It addresses resource accessibility and the challenges faced by households in managing their livelihoods The dataset encompasses comprehensive demographic details of household members, along with information on assets, savings, credit, insurance, coping strategies, informal safety nets, and social capital (Wainwright & Newman, 2011) Conducted in 12 provinces during the summer, VARHS created a balanced panel dataset of 2,045 households across 161 districts and 456 communes, ensuring consistency by conducting all surveys within the same three-month period each year (Vo).
Empirical models
Previous research has employed linear functions to analyze the connection between non-agricultural employment, household income, and food expenditure (Chang & Mishra, 2008; Mishra et al., 2015; Seng, 2015; Chang et al., 2020) Building on this foundation, we applied descriptive statistical methods to quantify the number of households involved in non-agricultural employment, alongside linear regression techniques to delineate the relationships between non-agricultural employment, total household income, and food expenditure, represented by two key equations.
Household-income = Po + PiOJffann + PiDemo + /^Finance + P4Ỉnfras+ fl
Food-expenditure = po + Pl Of/farm + p2Demo + p^ Finance + pdnfras + fl where:
Offfarm: Variable representing the engagement of each household in non- agricultural employment;
Demo: Variable representing certain characteristics of each household (age, gender, etc.);
Finance: Variable representing certain financial characteristics of each household
Infras: Variable representing certain infrastructure characteristics of each household
(distance to the committee, shocks, etc.). ft: Slope coefficient, estimating the magnitude and direction of the influence of independent variables on the dependent variable.
Xi: Independent variables reflecting the location of each household.
In analyzing the VARHS dataset regarding clean energy usage for cooking, households are classified into two categories: those that use clean energy (coded as 1) and those that do not (coded as 0) Given that the dependent variable is binary, the Ordinary Least Squares (OLS) model is inappropriate for this analysis Instead, the logistic regression model is the most suitable choice for binary dependent variables, as supported by previous research (Isgut, 2004; Akaakohol & Aye, 2014; Dary & Kuunibe, 2012; Lanjouw, 2001) Consequently, our study employed the logistic model to assess the impact of non-agricultural employment on clean energy consumption.
As the above equation is non-linear, it can be linearized by taking the natural log, and the given model is:
The variable "Pi" represents the ratio of the probability of a farmer participating in off-farm employment to the probability of not participating This creates a binary outcome, where a value of 1 indicates that a household is engaged in off-farm work, while a value of 0 signifies that it is not involved in such activities.
Po = constant p] - p]4= logistic regression coefficients
Xil - Xii4 = independent variables e = error term.
EMPIRICAL RESULTS
Descriptive Statistics
Table 1 Descriptive statistics of the characteristics of households by group
Variables Measurement Households with off-farm employment
Households without off-farm employment
Table 1 reveals a significant difference in the average ages of household heads engaged in off-farm employment compared to those without such jobs, with off-farm workers generally being older Additionally, heads of non-farm worker households are predominantly married, averaging 0.938 spouses Moreover, both household heads and their spouses in off-farm employment households demonstrate higher educational attainment than those in households lacking such employment.
Households without non-agricultural employment are generally larger than those with non-agricultural self-employed members, yet both types average 1.8 members participating in social organizations Financially, most households in both categories maintain savings Households involved in off-farm labor manage a smaller farm size of approximately 6,245 hectares, compared to 7,486 hectares for those engaged in non-agricultural labor Additionally, off-farm workers are more vulnerable to natural shocks than their counterparts not involved in off-farm activities There is also a noted disparity in the distance to the People’s Committee between households with non-farm self-employed workers and those not participating in off-farm work.
Table 2 Two-way table of differences between male and female head of household
Data from Table 2 indicates that male-headed households engage more in off-farm activities, with a participation rate of 21.48%, compared to their female counterparts The average annual income for male-headed households stands at 119 million VND, while female-headed households earn approximately 93 million VND per year Additionally, monthly food expenditures are higher for male-headed households at 1.9 million VND, versus 1.6 million VND for female-headed households Furthermore, the use of clean energy for cooking is significantly greater in male-headed households, at 47.70%, which is three times higher than the 15.15% usage rate among female-headed households.
Table 3 Two-way table of differences between household head’s marital status
Table 3 reveals that married household heads are significantly more engaged in off-farm activities, with a participation rate of 24.64%, compared to just 1.62% for those with other marital statuses Moreover, married households enjoy a higher average annual income of 123 million VND, in stark contrast to the 51 million VND earned by unmarried households This increased income results in higher food expenditures for married households, averaging 1.9 million VND per month Additionally, 55.16% of married households utilize clean energy for cooking, while only 7.69% of unmarried households do the same.
• The main characteristics showing the welfare of farmer households are detailed as follows:
- Food expenditure: In terms of food expenditure levels, the majority of households
(652 households) spend between 500,()()() to 1,000,000 VND per month, followed by 504 households spending 1,000,000 to 1,500,000 VND The third-largest group consists of 3 91 households, which spend between 150,000 to 200,000
VND monthly Furthermore, approximately 31% of households have a monthly food expenditure of 2,000,000 VND or more.
Figure 2 Statistical graph of Food expenditure
Income: The study’s findings revealed that the majority of households, totaling
753, reported incomes ranging from 45 to 90 million dong Following closely were households with incomes below 45 million dong, comprising 628 households Subsequently, a substantial number of households, amounting to
In the income range of 90 to 135 million VND, 537 households were reported, while some households experienced no income or negative income Notably, around 16% of all households reported incomes exceeding 180 million VND.
Figure 3 Statistical graph of Income
Only 26% of rural households are involved in off-farm employment, indicating that most rural residents continue to depend on agriculture as their main source of income.
Clean energy consumption: Among rural households, the majority (63%) utilize clean energy, but a significant proportion (37%) still does not adopt clean energy sources.
Figure 4 Statistical graph of Off-farm
Figure 5 Statistical graph of Clean energy consumption
Correlation Matrix
Table 8 (Appendix) presents the correlation matrix, revealing low correlation levels among all independent variables, including off-farm activities, age, marital status, gender, head of household education, spouse's education, household size, per capita savings, overall savings, farm size, shock loss, and development committee involvement The correlation coefficients range from -0.9 to 0.9, indicating a lack of excessive linear relationships and confirming the absence of multicollinearity This allows for the inclusion of these variables in the regression model without concerns of redundancy, facilitating a more accurate interpretation of their individual effects on the dependent variable By integrating all variables into the regression model, we can effectively assess their unique contributions and importance in explaining the variation of the auxiliary variable.
Model diagnostics
MS 2.09 0.477825 edu_spouse 2.08 0.479812 gender 1.59 0.630312 hhsize 1.38 0.726666 age 1.28 0.779823 s_capital 1.17 0.855858 edu_head 1.16 0.858884 farm_size 1.07 0.933968
The research employs the Variance Inflation Factor (VIF) index to assess multicollinearity among independent variables According to Table 4, the highest VIF value recorded is 2.09, which is below the threshold of 5 This indicates that multicollinearity is not a concern in this study's model This finding aligns with Gujarati's (2004) assertion that a maximum VIF index under 5 suggests a low likelihood of multicollinearity.
Both model 1 and model 2 have p = 0.000 < a, thus rejecting hypothesis HO with a 1% So the variance in the regression model has changed.
To address the issue of biased estimated coefficients in the OLS models, we employ a robust standard error model that effectively corrects for variance in both Model 1 and Model 2.
Impact of off-farm employment on household's income and food expenditure
Table 6 reveals that off-farm activities significantly enhance household welfare indicators by increasing total household income Households participating in off-farm work earn an average of 34.2 million VND more annually compared to those who do not This increase in income is logical, as engaging in diverse occupations helps households mitigate economic risks associated with natural events or crop failures, leading to greater stability and higher overall earnings.
Recently, households involved in non-agricultural activities experienced a monthly increase of 201 thousand VND in food expenditure This rise is attributed to higher household incomes, prompting families to invest more in food to enhance the health of their members and boost labor productivity These findings align with previous studies conducted in other developing countries, as noted by Reardon and Berdegue.
Research indicates that engaging in off-farm activities significantly enhances food consumption and expenditures A study in Burkina Faso (2001) demonstrated this positive relationship, while Ersado (2006) found that off-farm diversification also influences food spending Additionally, Owusu et al (2011) revealed that participation in off-farm activities boosts both household income and food expenditures.
Table 6 Regression results show the impact of off-farm employment on household income and food expenditure
MS 32867.4*** 7134.496 328.1*** 108.0142 gender -11923.1** 5492.845 -202.8** 89.26869 edu_head 3601.4*** 618.181 33.50*** 8.816279 edu_spouse 831.9 616.043 10.59 10.66979 hhsize 10820.0*** 1421.173 200.8*** 22.24518 s_capital 1214.8 1567.194 133.8*** 28.41655 saving 21754.3*** 4021.145 301.9*** 72.20768 farm_size 1.324*** 0.3088973 -0.000114 0.0026556
Note: _cons estimates baseline odds; t statistics in parentheses; *p < 1, **p < 05,
The analysis reveals that marital status significantly influences household income, with married heads earning an average of 32.9 million VND more annually than their single counterparts Additionally, households with diverse marital statuses spend 328,000 VND more monthly on food This increase in income and expenditure can be attributed to the larger size of married households Furthermore, the gender of the household head also plays a role, as female-headed households earn 11.9 million VND more per year compared to male-headed households In terms of food expenditure, male-headed households spend 202,000 VND less per month than female-headed ones, aligning with previous research findings (Bui & Hoang, 2021).
Education significantly influences income and food expenditure, with higher education levels in household heads correlating to increased income and spending on food Specifically, for each additional year of education, household income rises by 3.6 million VND annually, while food expenditure increases by 33.5 thousand VND per month These findings align with similar research conducted in China (Li, 2020) and India (Rajkhowa and Qaim, 2022).
Household size significantly impacts both income and food expenditure, with a 1% statistical significance Specifically, each additional household member contributes an increase of 10.8 million VND to annual income and raises monthly food expenditure by 200.8 thousand VND As more members participate in employment, overall income and food consumption rise Furthermore, involvement in organizations and associations also significantly affects food expenditure, with each new member leading to an increase of 133.8 thousand VND per month.
Household savings significantly impact income levels and food expenditure, with a statistically significant correlation at the 1% level Specifically, households that save tend to earn approximately 21.7 million VND more annually and spend an additional 301.9 thousand VND monthly on food compared to those without savings This relationship can be attributed to the interest earned from bank deposits, which boosts household income Consequently, higher income enables households to invest in higher quality and more diverse food options Furthermore, farm size is also statistically significant at the 1% level, indicating that households with larger land areas enjoy higher incomes.
Households facing natural or biological shocks can experience a significant income reduction of approximately 38.6 million VND annually, alongside a monthly food expenditure decrease of 549.6 thousand VND compared to unaffected households Such shocks, including hurricanes and floods, can lead to unemployment for some household members, further diminishing income Additionally, households located farther from the committee tend to have lower income and food expenditures.
Impact of off-farm employment on clean energy consumption
Table 7 presents the findings from a binary logistic regression analyzing how non-agricultural employment and various household characteristics influence clean energy consumption The model's goodness-of-fit is reflected in the last four rows, showing a Log likelihood of -1581.7445, a Prob > chi2 of 0.0000, and a Pseudo R2 of 0.0967, all of which suggest that the logistic regression model is adequately fitting the data.
The estimated logistic regression model indicates that non-agricultural employment positively influences clean energy usage among rural households in Vietnam, with a regression coefficient of 0.518 This suggests that as more household members engage in non-agricultural work, the likelihood of adopting clean energy increases by 51.8% The increase in income from non-agricultural activities enhances farmers' understanding of energy consumption, leading to a shift towards clean energy Additionally, non-agricultural employment alters labor distribution within households, further impacting energy consumption levels This finding is consistent with research by Rahut et al (2016) in Bhutan and Motlaleb et al (2017) in Bangladesh, which also highlighted the positive correlation between non-agricultural income and expenditures on gas and electricity Moreover, studies, including Ma et al (2018), have shown that non-agricultural employment significantly encourages the use of clean energy for cooking in rural households.
Household characteristics significantly influence clean energy expenditure among rural households, as indicated in Table 5 Notably, marital status positively affects clean energy usage, with married household heads having an odds ratio of 2.279006, making them 82.4% more likely to adopt clean energy for the health of children and women Furthermore, the gender of the household head plays a crucial role, with female heads showing a 1% higher likelihood of utilizing clean energy compared to their male counterparts, aligning with findings from Zhou et al (2022).
The education level of the household head is significantly associated with the use of clean energy, with a 1% statistical significance Specifically, for each additional level of education attained by the household head, the likelihood of using clean energy, such as electricity, increases by 8.32% This finding aligns with prior research indicating that higher education levels enhance human capital, which can lead to increased non-farm income and greater affordability of efficient energy sources (Mottaleb et al., 2017).
Table 7 Regression results show the impact of off-farm employment on clean energy use.
MS 0.824*** 2.279006 0.4140664 gender -0.350*** 0.7046932 0.0921732 edu head 0.0832*** 1.08672 0.0160325 edu_spouse -0.00434 0.9956738 0.0132982 hhsize -0.191*** 0.8260817 0.0242621 s_capital 0.0969*** 1.101741 0.0408025 saving 0.439*** 1.551095 0.1910904 farm size 0.9999951 0.00000314
1581.7445 Note: _cons estimates baseline odds; t statistics in parentheses; *p < 1, **p < 05,
Research indicates that an increase in household size negatively impacts energy expenditure, with a 1-person increase leading to a 19.1% decrease in clean energy usage This finding contrasts with Rahut et al (2017), who emphasized the significance of both education and household size in promoting clean energy adoption in rural households.
Participation in social activities significantly boosts clean energy usage among households, with a 1% increase in members involved correlating to a 9.69% higher likelihood of adopting clean energy solutions This trend is attributed to increased awareness and community engagement regarding clean energy consumption (Liu, 2018) Furthermore, the presence of savings accounts in rural households also plays a crucial role, as it reflects an increase in income, further promoting the adoption of clean energy practices.
The "energy ladder" theory posits that with rising income, households shift towards cleaner cooking energy sources, progressing from traditional biomass to commercial energy options (Revell, K et al., 2016).
The estimated model results indicate that natural shocks significantly influence clean energy usage among households at a 1% level Rural households affected by weather events often turn to off-farm activities to enhance their livelihoods, which subsequently increases their clean energy consumption (Duong, 2020) Additionally, location-related factors play a crucial role in clean energy adoption; specifically, distance from the People's Committee has a significant impact, with regression analysis revealing a p-value of 0.002 Households situated farther from the Committee are less likely to utilize clean energy, likely due to reduced access to information and awareness regarding its benefits, a finding that aligns with Ma et al (2019).
DISCUSSION
Off-farm activities play a crucial role in enhancing total household income, particularly for rural households facing severe climate changes and challenging economic conditions To counteract income shocks from natural crises, these households diversify their occupations, reducing risks associated with agriculture Research shows that rural families benefit from off-farm work, achieving higher income and lower risk compared to relying solely on farming While some off-farm employment may not significantly boost household income, it contributes to income stability and consumption Ultimately, engaging in off-farm activities can improve household income and enhance the overall quality of life.
Non-agricultural activities significantly enhance household welfare, as indicated by various studies Increased household income from these activities leads to higher food expenditure, improving family health and boosting labor productivity Research in developed countries supports this, showing that non-farm diversification positively affects food spending (Ersado, 2006) In Northern Ghana, off-farm participation correlates with increased income and food expenditure (Owusu et al., 2011) Similarly, in Burkina Faso, involvement in off-farm activities results in greater food consumption (Reardon et al., 2001).
Research indicates a strong link between off-farm employment and the adoption of clean energy among farmers in Vietnam The increase in family income from off-farm activities encourages rural households to shift towards cleaner cooking energy, as suggested by the energy ladder model This transition reduces reliance on traditional energy sources and enhances overall household income, ultimately leading to improved quality of life for families.
CONCLUSION AND POLICY IMPLICATION
Vietnam's economy is heavily reliant on agriculture, making agricultural development crucial for its growth However, the country's climate presents significant risks of natural disasters that affect farming households To mitigate these risks, many households seek alternative income sources beyond agriculture While previous research has primarily examined how off-farm employment influences household income, there has been limited exploration of its effects on household spending, particularly on food and clean fuel.
Our research examines the influence of off-farm employment on household welfare in Vietnam, utilizing data from a comprehensive household survey Findings indicate that households participating in off-farm work experience notable increases in income, food expenditures, and clean energy use for cooking, reflecting an enhancement in the quality of life for rural farmers Additionally, we analyze various factors impacting income, expenditure, and energy consumption, including age, marital status, education, household size, savings, farm size, environmental shocks, and proximity to community resources, all of which play a crucial role in determining household welfare.
Promoting clean energy in rural areas is crucial for enhancing people's welfare, necessitating government policies that support farming households in adopting renewable energy systems like solar, wind, and biomass This can be achieved through incentives such as promotions, tax breaks, and low-interest loans, making clean energy more accessible Furthermore, fostering collaboration among local farming communities will enhance the adoption of clean energy by enabling the sharing of knowledge, experience, and resources, ultimately optimizing the deployment and management of renewable energy sources.
Increasing rural incomes is a key policy focus in many developing economies, including Vietnam The government should prioritize policies that enhance opportunities for off-farm employment, thereby improving the welfare of rural households Many individuals from rural areas often migrate to larger cities or seek jobs abroad, sending remittances back home, which positively influences local income and welfare To support this, the government must facilitate worker mobility through ongoing vocational training, effective land policies, and the establishment of favorable institutions that promote labor market development.
To enhance household food expenditure and overall well-being in Vietnam, the government should implement policies that promote off-farm employment opportunities for rural households, thereby improving their welfare Additionally, stabilizing food prices and ensuring a consistent food supply is crucial to prevent shortages and price spikes, especially as many households reduce their agricultural involvement Furthermore, fostering collaboration between agricultural families and nutritionists through sharing sessions, forums, and farmers' fairs can facilitate the introduction of innovative products and foods to the market.
Our study, while conclusive, has several limitations Firstly, the dataset on clean energy use lacks diversity, resulting in a limited sample size and variable richness Additionally, our research relies on data from 2016, preventing us from observing changes in the research subjects over time Furthermore, the scope is narrow, as the data is confined to specific provinces in Vietnam To enhance future research, we encourage addressing these limitations and exploring additional factors related to household well-being, such as the use of clean energy for healing, frequency of hospital visits, and expenditures on medicines and dietary supplements, across a broader geographical and temporal context.
Akramov, K and Malikov, E (2015) Rural non-farm employment in Uzbekistan: evidence from household survey data Agricultural and Food Economics, 3(1), 1-20.
Adeoye, I D., Seini, w., Sarpong, D., & Amegashie, D (2019) Effect of off-farm income on multi-dimensional poverty among rural farm households in Nigeria
International Journal of Social Economics, 46(9), 1081-1094.
Al-Amin, A A., & Hossain, M J (2019) Impact of non-farm income on welfare in rural Bangladesh: Multilevel mixed-effects regression approach World Development
Anang, B T., Nkrumah-Ennin, K., & Nyaaba, J A (2020) Does off-farm work improve farm income? Empirical evidence from Tolon district in northern Ghana Advances in
Atamanov, A & Van den Berg, M (2012) Participation and re- turns in rural nonfarm activities: Evidence from the Kyrgyz Republic Agricultural Economics, 43(4), 459
Ackah, c (2013) Nonfarm employment and incomes in rural Ghana Journal of
Awoyemi, T T (2011) Rural non-farm incomes and poverty reduction in Nigeria.
Ba, M., Anwar, A., & Mughal, M (2021) Non-farm employment and poverty reduction in Mauritania Journal of international development, 33(3), 490-514.
Bui, L.K., Hoang, H (2021) Non-farm employment, food poverty and vulnerability in rural Vietnam Environ Dev Sustain 23, 7326-7357.
Bachewe, F N., Berhane, G., Minten, B., & Taffesse, A s (2016) Non-farm income and labor markets in rural Ethiopia (Vol 90) Inti Food Policy Res Inst.
Barrett, c B., Reardon, T., & Webb, p (2001) Nonfarm income diversification and household liveli- hood strategies in rural Africa: Concepts, dynamics, and policy implications Food Policy, 26(4), 315-3 31 https://doi.org/10.1016/S0306- 9192(01)00014-8.
Bui, L K., & Hoang, H (2021) Non-farm employment, food poverty and vulnerability in rural Vietnam Environment, Development and Sustainability, 23(5), 7326-7357.
Burgess, s., Small, D.S., Thompson, S.G., 2017 A review of instrumental variable estimators for Mendelian randomization Stat Methods Med Res 2333-2355.
Carter, E.; Yan, L.; Fu, Y.; Robinson, B.; Kelly, F.; Elliott, p.; Wu, Y.; Zhao, L.; Ezzati, M.; Yang, X.; et al Household transitions to clean energy in a multi provincial cohort study in China Nat Sustain.2020,3, 42-50.
Cuong, N V., & Mont, D (2012) Economic impacts of international migration and remittances on household welfare in Vietnam International Journal of Development
Chang, H., Zhang, J., He, K., & Zeng, Y (2020) Impact of off-farm employment on household clean energy consumption in rural China: A gender perspective
Corral, L., & Reardon, T (2001) Rural nonfarm incomes in Nicaragua World
Do, T L., Nguyen, T T., & Grote, u (2019) Nonfarm employment and household food security: Evidence from panel data for rural Cambodia Food Security, II, 703-718.
Duong TT, Brewer T, Luck J, Zander K (2019) A Global Review of Farmers* Perceptions of Agricultural Risks and Risk Management Strategies Agriculture., 9(1), 10.
Dedehouanou, s F A., Araar, A., Ousseini, A., Harouna, A L., & Jabir, M (2018) Spillovers from off-farm self-employment opportunities in rural Niger World
Development, 105, 428-442. de Janvry, A & Sadoulet, E (2001) Income strategies among rural households in Mexico: The role of off-farm activities World Development, 29(3), 467-480 https://doi.org/10.1016/ S0305-750X(00)00113-3
Do, T L., Nguyen, T T., & Grote, u (2019) Nonfarm employment and household food security: Evidence from panel data for rural Cambodia Food Security', 11, 703-718.
Duong, p B., & Thanh, p T (2019) Adoption and effects of modern rice varieties in Vietnam: Micro-econometric analysis of household surveys Economic Analysis and
Duong, p B., Thanh, p T., & Ancev, T (2021) Impacts of off-farm employment on welfare, food security and poverty: Evidence from rural Vietnam International Journal of Social Welfare, 30(\), 84-96.
Duong, T T., Brewer, T., Luck, J., & Zander, K (2019) A global review of farmers’ perceptions of agricultural risks and risk management strategies Agriculture, 9(1), 10.
Escobal, J (2001) The determinants of nonfarm income diver- sification in rural Peru
World Development, 29(3), 497-508 https://doi.org/10.1016/S0305-750X(00)00104-2.
Frank, A., Gerstlberger, G.W., Paslauski, C.A., Lerman, L.V., Ayala, N.F., 2018 The contribution of innovation policy criteria to the development of local renewable energy systems Energy Policy 115, 353-365.
Impacts of off-farm employment on welfare, food security and poverty: Evidence from rural Vietnam p B Duong, p T Thanh and T Ancev International Journal of Social Welfare 2020 Vol 30 Issue 1 Pages 84-96
Imai, K.S., R Gaiha, and w Kang (2011).‘Vulnerability and Poverty Dynamics in Vietnam’ Applied Economics , 43(25): 3603-18.
Hassen, s., Kốhlin, G., 2017 Does purchase price matter for the waiting time to start using energy efficient technologies: experimental evidence from rural Ethiopia? Energy
Hatlebakk, M (2012) Regional variation in livelihood strategies in Malawi South
Hou, B.; Liao, H.; Huang, J Household cooking fuel choice and economic poverty: Evidence from a nationwide survey in China.
Liu, z Influence of Labor Outflow on Farmers’ Energy Consumption Choice Behavior and Policy Suggestions Master's Thesis, Xi’an University of Science and Technology, Xi’an, China, 2018
LI, s.-p., DONG Y.-q., ZHANG, L.-x., & LIU, c.-f (2021) Off-farm employment and poverty alleviation in rural China Journal of Integrative Agriculture, 20(4), 943-952.
The study by Li et al (2020) investigates the impact of conservation payments and off-farm employment on the welfare of farmers involved in China's "Grain for Green" program, focusing on the Loess Plateau The findings reveal that conservation payments significantly enhance household welfare, while off-farm employment offers additional financial stability The research highlights the importance of integrating conservation initiatives with economic opportunities to support sustainable farming practices and improve the livelihoods of rural communities in China.
Ma, w., Zhou, X., & Renwick, A (2019) Impact of off-farm income on household energy expenditures in China: Implications for rural energy transition Energy Policy,
Ma, w.; Zhou, X.; Renwick, A Impact of off-farm income on household energy expenditures in China: Implications for rural energy transition Energy Policy 2018,127, 248-258 [CrossRef]
McKay, A D., Singhal, s., & Tarp, F (2018) Welfare dynamics in rural Viei Nam:
Learning from regular, high-quality panel data (No 2018/169) WIDER Working
The study by Pham Bao Duong, Pham Tien Thanh, and Tihomir Ancev (2020) explores the effects of off-farm employment on welfare, food security, and poverty in rural Vietnam Published in the International Journal of Social Welfare, the research highlights how diversifying income sources through off-farm work can significantly enhance the living standards of rural households The findings indicate a positive correlation between off-farm employment and improved food security, ultimately contributing to poverty alleviation in the region This evidence underscores the importance of off-farm employment as a strategy for enhancing rural welfare and addressing socio-economic challenges in Vietnam.
Rajkhowa, p & Qaim, M (2022) Mobile phones, off-farm employment and household income in rural India Journal of Agricultural Economics, 73, 789-805.
Reardon, T., Berdegué, J., & Escobar, G (2001) Rural nonfarm employment and incomes in Latin America: Overview and policy implications World Development,
Revell and Stanton (2016) emphasize the importance of altering mental models through effective interface design to encourage responsible energy consumption behaviors in households Their research, presented in "Advances in Ergonomics in Design," highlights how intuitive design can facilitate better energy usage practices, ultimately leading to more sustainable living environments By focusing on user-centered design principles, the authors advocate for innovative solutions that can significantly influence consumer habits and promote energy efficiency at home.
Reardon, T., Taylor, J E., Stamoulis, K., Lanjouw, p., & Balisacan, A (2008) Effects of non-farm employment on rural income inequality in developing countries: An investment perspective Journal of Agricultural Economics, 51(2), 266-288 https:// doi.org/10.1111/j 1477-9552.2000.tbOI 228.x
Szumilas, M (2010) Explaining odds ratios Journal of the Canadian academy of child and adolescent psychiatry, /9(3), 227.
Owusu, V., Abdulai, A., & Abdul-Rahman, s (2011) Non-farm work and food security among farm households in Northern Ghana Food policy, 36(2), 108-118.
Rajkhowa, p., & Qaim, M (2022) Mobile phones, off-farm employment and household income in rural India Journal of Agricultural Economics, 73(3), 789-805.
In their work, Reardon et al (2007) explore the significance of household income diversification into rural non-farm activities, highlighting its impact on economic resilience in developing regions The authors emphasize that engaging in non-farm activities can enhance income stability and reduce vulnerability for rural households They discuss various opportunities and challenges presented by this diversification, underscoring its potential to transform the rural economy This comprehensive analysis is part of a larger discourse on the evolving dynamics of rural livelihoods, as presented in "Transforming the Rural Nonfarm Economy," edited by Haggblade, Hazell, and Reardon.
Tian, Y Research on Rural Energy Policy, Present Situation Evaluation and Development Direction in China China Energy 2020,05, 25-30
Tran, T Q., Vu, H V., & Doan, T T (2016) Factors affecting the intensity of nonfarm participation among ethnic minorities in Northwest Mountains, Vietnam International
Winters, p., Davis, B., Carletto, G., Covarrubias, K., Quinones, E J., Zezza, A., Azzarri, c., & Stamoulis, K (2009) Assets, activities and rural income generation: Evidence from a multicountry analysis World Development, 37(9), 1435-1452.
Zereyesus, Y A., Embaye, w T., Tsiboe, F., & Amanor-Boadu, V (2017) Implications of non-farm work to vulnerability to food poverty-recent evidence from Northern Ghana World Development, 91, 113-124.
Zhou, w., Qing, c., Guo, s., He, J., Deng, X., Song, J., & Xu, D (2022) Labor Off- Farm Employment and Farmers' Cooking Clean Energy Use: Evidence from Rural China Agriculture, 12(1), 972.
Huffman, w E., Hallberg, M., Findeis, J., & Lass, D (1991) Multiple Job-Holding among Farm Families Agricultural Households Survey and Critique, 79-111.
Osarfo, D., Senadza, B., & Nketiah-Amponsah, E (2016) The impact of nonfarm activities on rural farm household income and food security in the Upper East and Upper West Regions of Ghana.
Vo, T T., & Van, p H (2019) Can health insurance reduce household vulnerability? Evidence from Viet Nam World Development, 124, 104645.
Wainwright, F., & Newman, c (2011) Income shocks and household risk-coping strategies: evidence from rural Vietnam Institute for international Integration Studies
Gujarati, D (2004) Basic econometrics 4th Ed., India: Tata McGraw Hill.
Reardon, T., & Berdegue, J (2001) Rural nonfarm employment and incomes in Latin America World Development, 29(3), 28 https://doi.org/10.1016/S0305- 750X(00)00112-1.
Ersado, L (2006) Income diversification in Zimbabwe: Welfare implications from urban and rural areas Policy research working paper—The World Bank, Article No
3964 https://openknowledge worldbank.org/handle/10986/8389.
Owusu, V., Abdulai, A., & Abdul-Rahman, s (2011) Non-farm work and food security among farm house- holds in Northern Ghana Food Policy, 36(2), 108-118 https://doi.org/10.1016/j foodpol.2010.09.002.