Keywords: Migration, rural migrant workers, rural household living standards, remittances from migration, PSM... According to Adams and Page 2003, international migration and remittance
OVERVIEW
Necessity of the research
For an extended period, migrant labor has been seen as a crucial component of the nation's economic growth generally and international integration specifically (De Haan,
Migration is crucial for population redistribution and economic restructuring, addressing employment challenges and poverty while contributing to sustainable development plans Additionally, migrant labor significantly influences the social status of migrant households According to the International Organization for Migration Report, substantial changes in migration trends are expected by 2020.
As of now, there are 281 million migrant workers globally, representing 3.6% of the world’s population, with this number projected to exceed 350 million by 2030 This increase in migration is expected to generate remittances totaling 5.4 trillion US dollars, which is twice the GDP of Africa in 2022 In Asia, over 6% of the population migrates within regions, a figure that would rise with the inclusion of short-term and short-distance migration In Vietnam, there are approximately 877.8 thousand domestic migrant workers aged 15 and older, constituting 8.4% of the national workforce, with 55% being women and a significant majority relocating to urban areas Despite this, the migrant population represents only 1.2% of the total population aged 15 and over, with urban migration rates being more than three times higher than those in rural areas.
Reason to choose topic
The relationship between migration and the living standards of rural households is crucial, offering benefits to both immigrants and their new communities Additionally, migration exacerbates the socioeconomic divide between rural and urban areas, as well as between places of origin and destination Research by Adams and Page (2003) highlights that international migration and the remittances it generates positively influence household living standards in developing countries.
Migration from impoverished and developing nations to affluent economies can enhance the income of the poor and contribute to poverty alleviation Research by Stahl (1982) indicates that international migration often incurs significant costs, leading wealthier households to be more inclined to migrate Conversely, internal migration studies in India reveal that it can worsen rural inequality, as affluent migrants are drawn to better opportunities in urban areas or overseas, while those in poverty are compelled to migrate due to dire conditions in rural settings (Lipton, 1980).
Numerous studies have confirmed the significant relationship between remittances from migration and improvements in household living standards and poverty reduction For instance, Adams Jr and Cuecuecha (2013) utilized the Ghana Living Standards Survey and PSM method to demonstrate that remittances are crucial in alleviating household poverty Similarly, Acosta et al (2008) analyzed a large cross-country panel dataset in Latin America and the Caribbean, concluding that while remittances promote economic growth, they also have complex effects on poverty reduction In Guatemala, research by Adams Jr and Cuecuecha (2010) found that remittances positively impact healthcare and education spending, which are vital for enhancing living standards Ziescmer (2012) further emphasized that remittances contribute to GDP per capita growth and educational expenditures In Vietnam, Nguyen Duc Thanh and Hoang Thi Chinh Thon (2012) highlighted that remittances have facilitated poverty alleviation and economic development These findings underscore the critical importance of considering the effects of migration and remittances on rural household living standards in policy-making.
Previous studies have limitations, primarily using remittances to represent migration's impact on household living standards, which may overlook cases where households relocate without receiving remittances This study aims to address this gap by evaluating both migration and remittance factors to provide a comprehensive understanding of their direct correlation with rural household conditions Additionally, while previous research predominantly utilized the Stata tool and the psmatch2 command, this study will incorporate OLS regression and R-Studio for a more detailed analysis of how migration and remittances influence rural household living standards By exploring the effects of rural migrant workers on their hometowns, particularly in Vietnam, this research will offer valuable insights for policymakers and managers, promoting a balanced understanding of agricultural migrant workers’ impacts on their communities.
This article aims to explore the impact of migrant labor on the living conditions of rural households in Vietnam Vietnam was chosen for this research due to the scarcity of studies examining how labor migration affects rural areas, with most existing research focusing on the consequences in destination locations Additionally, Vietnam experiences a high rate of migration from rural to urban areas, leading to significant population imbalances This study seeks to understand how the departure of migrant workers influences the living standards of the households they leave behind, highlighting the pressures faced by these rural communities.
Research objectives
This study aims to analyze the current state of rural job migration in Vietnam and its effects on the living standards of rural households It seeks to evaluate how migration influences socio-economic conditions and to propose targeted policies that address the needs of migrant workers in the context of sustainable development.
The research investigates the impact of migrant labor on rural household living standards to focus on answering a number of main research questions including:
- What factors affect rural job migration?
- How does rural migrant labor affect household income and expenditure?
- How does rural migration impact household health and education spending?
- Can remittances from migrants help improve household living standards?
Scope of the research
This paper focuses on the analysis of migrant workers from rural areas in Vietnam, utilizing data from the 2018 Household Living Standards Survey The primary research subjects are rural migrant workers, with households serving as the unit of analysis.
Research methodology
This study analyzes the impact of rural migration on household living standards in rural areas using data from the 2018 Household Living Standards Survey (VHLSS) and RStudio for data processing It employs three analytical techniques: a probit regression model with marginal effects to assess factors influencing migration decisions, the Propensity Score Matching (PSM) method to compare characteristics of migrant and non-migrant households, and Ordinary Least Squares (OLS) regression to evaluate how migration, remittances, and other factors affect living standards in rural regions.
Research structure
The research is structured into five chapters, beginning with an introduction to the research context, topic selection, objectives, questions, scope, and overall structure The second chapter reviews relevant theories and prior studies The third chapter outlines the analytical framework, detailing the methodology, analytical techniques, and data sources used In the fourth chapter, a statistical analysis of variables is presented, including results from the probit model and marginal effects on labor migration factors, along with a comparison of migrant and non-migrant households using the PSM method The impact of job migration and remittances on household living standards is further examined using the OLS method The final chapter summarizes key findings, draws conclusions, and offers policy recommendations based on the research outcomes.
LITERATURE REVIEW
The basic concepts
Migration is a long-standing concept that varies across different fields and is influenced by various economic factors According to Lee (1966), migration broadly refers to any movement of people within a specific space and time, resulting in a temporary or permanent change of residence Canadian economist Harvey B King, in his book "Labor Economics," emphasizes that migration typically involves relocating to a new residence far enough from the previous one to necessitate a change in permanent residence, which can include moving to another city, province, or country.
In 1958, the United Nations defined migration as the movement of people from one territorial unit to another, which can also include shorter distances This process occurs within a specific migration period and is marked by frequent changes of residence.
As for the law, Clause 7, Article 3 of the 2003 Population Ordinance stipulates that:
Migration refers to the movement of individuals or groups from one country to another or within different administrative units According to the International Migration Law, this term encompasses various forms of population movement, whether across international borders or within a single country It includes refugees, displaced persons, economic migrants, and individuals relocating for reasons such as family reunification Thus, migration is a complex phenomenon that involves diverse motivations and circumstances.
Migration Law is more expansive than the definition provided by the United Nations, as the latter's concept is more constrained in terms of both time and minimum distance.
This article focuses on the United Nations' narrow definition of migration, which refers to the movement between territorial units over a specified time frame The research categorizes migration lasting from six months to two years as temporary, while movements exceeding this duration are classified as long-term migration.
Internal migration encompasses various subcategories, including urban-rural, urban-urban, and rural-rural migration This article primarily highlights the dynamics of rural-urban and rural-rural migration patterns.
Rural migrant workers are individuals who voluntarily relocate from rural areas to urban centers or other rural locations in search of employment opportunities to enhance their family's quality of life This phenomenon represents a distinct blend of migration and labor, highlighting the pursuit of better living conditions through job availability.
2.1.2.1 Group of factors related to household head characteristics a Age of household head
A study on labor migration in Argentina and Paraguay reveals that the average age of migrants is approximately 26 years (Parrado and Cerrutti, 2003) Additionally, rural migrants tend to be younger, with an average age between 20 and 30 years, making up 64.61% of this workforce (Ho Nhut Khuong, 2017) Conversely, research indicates that as household heads age, there is an overall increase in migration trends, particularly in employment migration (Raabe Katharina et al., 2015) This discrepancy in findings suggests that variations in data collection across studies may lead to inconsistent conclusions regarding the impact of age on migration.
The impact of household head gender on migration has generated diverse perspectives, with research indicating that male-headed households in Vietnam exhibit a lower likelihood of migration compared to female-headed ones (Pham Tan Nhat and Huynh Hien Hai, 2014) Conversely, a study in Nigeria's Abia state found that 74.10% of migrant families were led by males (Ehirim et al., 2012) This disparity suggests that the influence of household head gender on migration is significantly shaped by variations in geographical context, temporal factors, and the specific demographics of the study population.
The marital status of a household head significantly influences the decision to migrate or stay in their place of origin, as it reflects the family responsibilities and economic pressures they face Research in rural Bangladesh indicates that married couples tend to relocate more often, highlighting the impact of family dynamics on migration patterns.
According to Huynh Hien Hai (2013), married household heads are less likely to migrate compared to individuals with different marital statuses Additionally, the education level of the head of the household plays a significant role in influencing migration patterns.
Higher education enhances an individual's ability to work and apply technical skills, driving the desire to relocate to cities that offer better living and working conditions, as well as higher financial rewards Research indicates that individuals with higher education levels are more likely to migrate For instance, Parrado and Cerrutti (2003) found that in their study of migrant workers in Paraguay and Argentina, increased education correlates with a higher probability of migration Similarly, Huynh Hien Hai (2013) identified a positive relationship between educational attainment and migration in his analysis of employment migration factors in Vietnam.
2.1.2.2 Group of factors related to household characteristics a Housing area
Housing is a crucial asset for households and significantly impacts their quality of life Pham Tan Nhat and Huynh Hien Hai (2014) explored the previously established inverse relationship between housing area and migration decisions Despite this, the connection between these two factors remains under-researched, prompting the authors to contribute to the understanding of how housing area influences migration choices Additionally, the ownership of agricultural land plays a vital role in this context.
Land ownership significantly influences a household's decision to relocate, as it reflects their economic status Households with land ownership are often in higher socioeconomic groups, which increases the likelihood of providing financial support for a family member seeking better opportunities in a new location, particularly in rural areas.
The rise of agricultural mechanization has led to a noticeable decline in agricultural employment, prompting surplus labor from households to migrate in search of alternative income sources (Mehrotra et al., 2013) Consequently, the author incorporated agricultural land ownership as a crucial variable in the research model to better understand its impact on this trend.
Households with financial loans are likely to experience migration, particularly those that send family members abroad in hopes of receiving remittances to help pay off their debts (Raabe Katharina et al., 2015).
Conclusion of previous studies
Migration has been a significant research topic for scientists and analysts, particularly regarding its impact on poverty reduction A study by Adams Jr and Page (2005) examined the effects of remittances and international migration on poverty in 71 developing countries, utilizing OLS regression for data analysis Their findings reveal that remittances and labor migration notably enhance household living standards, indicating that a 10% increase in per capita income correlates with a 3.5% decrease in poverty rates Furthermore, a 10% rise in international migration could lead to a 21% reduction in the number of individuals living below the poverty line These insights suggest that international migration policies could significantly benefit impoverished populations worldwide.
Fahad and Rehmat (2013) examined the effects of macroeconomic policies on poverty in Pakistan, analyzing data from 1994 to 2005 through a multiple regression model with the OLS method Their study utilized the Gini coefficient to measure income inequality and found that factors such as development expenditure, per capita income, unemployment rate, migration, and remittances significantly influence poverty reduction The researchers recommended that the government prioritize investments in social and development projects to create job opportunities, enhance per capita income, and improve living standards.
A study utilizing data from the Ghana Living Standards Survey, along with the PSM method and Stata processing tool, revealed significant insights into the effects of remittances from migration on poverty and investment Key findings indicate that households receiving remittances spend less on food compared to those without, allowing them to allocate more resources toward investments in education, housing, and health Additionally, these remittances notably decrease the likelihood of households falling into poverty Overall, the research underscores the potential of remittances to alleviate poverty and encourage investment in developing nations.
A study conducted in Germany utilized a panel data set of 2,200 households from Dak Lak, Thua Thien Hue, and Ha Tinh provinces in Vietnam, collected in 2015, to analyze the relationship between migration, vulnerability to poverty, and rural household well-being Employing models such as least squares regression (OLS), probit model, and propensity score matching (PSM) alongside the difference-in-differences technique, the findings reveal that migration, particularly for employment, is a crucial strategy for enhancing livelihoods affected by economic and agricultural shocks Conversely, educational migration is more common among households with better financial and human resources, while job availability in villages reduces migration likelihood Although many migrants experience improved conditions, economic shocks in rural areas can diminish household income and living standards The study's results indicate that migration positively influences income growth, especially in provinces with limited job opportunities, aiding migrant households in escaping poverty and contributing to overall poverty reduction in rural regions.
In Vietnam, Ho Nhut Khuong (2017) conducted a study using the OLS method and data from the 2012 and 2014 Vietnam Household Living Standards Surveys (VHLSS) to explore the effects of rural migration on household living standards The findings revealed no direct correlation between migration and living standards; however, remittances were found to have a significant positive impact Specifically, remittances increased household income, leading to greater spending capacity, and households with migrants experienced a lower proportion of living expenses relative to their total income compared to non-migrant households This suggests that migration and remittances play a crucial role in enhancing rural household living standards.
Nguyen Van Phuc (2012) utilized difference-in-differences (DID) methods, propensity score matching (PSM) techniques, and OLS regression with the Household Survey Survey (VHLSS) data set to analyze the effects of migration and remittances on the welfare of Vietnamese households The study revealed that remittances significantly enhance household income and per capita expenditure, although they do not influence spending on health and education These findings align with the results of Lensink Robert et al (2009).
METHODOLOGY
Methodology
This article outlines a three-step approach to data processing and analysis focused on labor migration Initially, it examines the characteristics of household heads and their households using the probit model The second phase employs the Propensity Score Matching (PSM) method to differentiate between the attributes of migrants and non-migrants Finally, the study assesses the effects of job migration, remittances, and other factors on rural household living standards through Ordinary Least Squares (OLS) regression Key variables used to measure these living standards include average monthly spending per capita, average monthly income per capita, average monthly medical expenses, and average monthly education costs for households.
A review of related studies identifies two primary groups of factors that influence migration decisions: demographic characteristics and planable explanatory variables The latter includes factors such as the household's housing area, credit status, average monthly income, and agricultural land ownership Key demographic variables for analysis will encompass age, gender, marital status, education level of the household head, household size, and the status of poor households This analysis serves as a crucial foundation for the subsequent stage of research, where Propensity Score Matching (PSM) will be utilized to compare the characteristics of migrants and non-migrants The general probit model will be employed to express these findings.
Migr = Pl credit + p2 house + p3 income + p4land + £pkx j+ £j (3.1)
Table 1 : Summary of variables used in the Probit model
Migr Migration situation of the household
Migr = 1 / Migr = 0: Households with/without migrants
Independent variables credit Credit status credit = 1 / credit = 0: Household has/has not a loan in 12 months house Housing area of the household
Housing area (land) (m 2) income Household income Average monthly income of the household (million VND/month) land Own agricultural land
Land = 1 / land = 0: Households with/without ownership of agricultural land
X 1 (age) Age of household head
X 2 (gender) Gender of household head
X 2 = 1 / X 2 = 0: Head of household is male/female
X 3 (educ) Education level of the head of household
The highest degree of the head of household
X 4 (mani age) Marital status of head of household
X 4 = 1 / X 4 = 0: Head of household is married or in another marital status
X 5 (hhsize) Household size Number of people in household
X 6 (poor) Households are poor X6 = 1 / X6=0: Households are/are not poor
The analytical techniques used in this project include three methods: probit regression, propensity score matching method (PSM) and least squares regression (OLS).
The probit regression model is utilized to analyze the factors influencing the job migration decisions of rural households in Vietnam, focusing on variables such as credit status, housing area, average monthly income, agricultural land ownership, age, gender, education level, and marital status of the household head This analysis serves as a foundation for matching and comparing trend scores in the subsequent stages of data analysis and processing The study examines two groups of households: those with migrants and those without, establishing a clear framework for the research.
Migr = {1 if Migr > 0 0 if Migr = 0
Migr = p] credit + p2 house + pl income + p4 land + £pk X i + 8j (3.1)
The author employed the PSM method to assess the average treatment effect (ATT) in order to explore the differences in key characteristics between households with migrants and those without The statistically significant differences between these two groups highlight the impact of migrant labor on the living standards of rural households.
T: a group of households with migrants (Treatment) and C: control group, households without migrants (Control)
The output variables for comparison include household housing area, credit status, average monthly income, agricultural land ownership, age, gender, marital status, education level of the household head, household size, and the status of being a poor household.
NT: Number of observations of the group of households with migrants
The weight w(ij) is calculated using the Optimal Full Matching method: It is the selection of numbers through optimal full matching.
In this study, we employ OLS regression on data post-PSM to analyze the impact of independent variables on household living standards, focusing on four key aspects: average monthly expenditure per capita, monthly income per capita, average monthly health expenditure, and average monthly education expenditure Additionally, the model incorporates two independent variables—migration and remittances from households with migrants—to assess their influence on living standards The OLS regression model is structured to provide insights into these relationships.
LS = Pj Migr + p2 Remitt + p3 credit + p4 house + p5 income + p6 land + y p, X j + £j
Table 2: Summary of variables used in the OLS regression model
LS Living standards of rural households
Representing four criteria including: expenditure and average income per capita/month of the household combined with average health and education expenditure/month of the household
Migr Migration situation of the household
Migr = 1 / Migr = 0: Households with/without migrants
Remittances from households with migrants (million VND) credit Credit status credit = 1 / credit = 0: Household has/no loan in 12 months house Household area of the household
Housing area (land) (m 2) income Household income Average monthly income of the household (million VND/month) land Own agricultural land Land = 1 / land = 0: Households with/without ownership of agricultural land
X 1 (age) Age of household head
X 2 (gender) Gender of household head
X 2 = 1 / X 2 = 0: Head of household is male/female
X 3 (educ) Education level of the head of household:
Highest degree of the head of household
X 4 (marriage) Marital status of head of household
X 4 = 1 / X 4 = 0: Head of household is married or in another marital status
X 5 (hhsize) Household size Number of people in household
X 6 (poor) Households are poor x6= 1 / X 6 = 0: Households are/are not poor
Data
The data for this study was sourced from the Household Living Standards Survey (VHLSS), which has been conducted biennially by the General Statistics Office of Vietnam since 2000, with technical support from the World Bank These nationally representative surveys gather comprehensive household information, including demographics, education, employment, labor force participation, income, expenditures, health, housing, durable goods, fixed assets, and involvement in poverty reduction programs Additionally, community surveys collect essential data on demographics, socioeconomic characteristics, and community infrastructure, covering 9,300 households since 2002.
This study analyzes the effects of rural migration on living standards in rural households by utilizing data from the 2018 Household Living Standards Survey The author focuses on income and expenses collected from a sample of rural homes, excluding non-rural households and those with migrant workers to establish a clear comparison group.
RESEARCH RESULTS
Statistical result from VHLSS data
Household has a credit loan (Yes=l) 2366 4776
Gender of household head (Male = 1) 5995 1147
From the statistical table, the results show that the number of households with credit loans is 2366 households out of a total observation of 7142 households Next, there are
In a study of 6329 families who own agricultural land and 813 families without it, it was found that 5995 households are headed by men, while 1147 are led by women The education levels of household heads reveal that most in rural areas have completed primary to lower secondary school, with primary education leading at 3313 households, followed by secondary education at 2573 households, high school education at 937 households, and only 319 households having graduated from high school Marital status data shows that 6169 households are headed by married individuals, while 973 have heads with other marital statuses Additionally, there are 2007 poor households compared to 5135 non-poor households.
Smallest Greatest The average Variable name value value value
The average housing area for rural households is 86.30 m², ranging from 10 m² to 360 m² Households earn an average monthly income of 11.47 million VND, with incomes varying from 0.82 million VND to a maximum of 103.54 million VND The average age of household heads is 48.85 years, with the oldest being 96 and the youngest 21 Additionally, the average household size consists of 5.46 members, with the largest household having 12 members and the smallest having just 2.
Factors affecting rural migrant workers
Tabic 5 : Results of the Probit model and marginal effects
Household has a credit loan (Yes=l)
Average monthly income of household
Gender of household head (Male - 1)
(Note: The values in the results table are: Beta coefficient, statistical significance level, and the value in parentheses is the standard error
" *** ” “ ** ” " * ”, “ ” are the ieveỊs of statistical significance, respectively 0 1%, Ỉ %, 5 %, 10%.
(I) Head of household completed high school level as reference value; (2) Head of household has a different marital status than married status as reference value)
The statistical analysis of the probit and marginal impact models reveals that various characteristics of both the household head and the household significantly influence the decisions of migrant workers Each factor impacting the migration choices of individuals in rural areas is driven by specific underlying causes.
Households with loans exhibit a higher likelihood of migration compared to those without Specifically, the presence of an unpaid loan increases the probability of migration by 7.8 percentage points This trend may be attributed to the economic burden that debt imposes on families, driving them to seek better opportunities to enhance their livelihoods and overall quality of life.
Research indicates that an increase in housing size correlates with a decreased likelihood of household migration Specifically, for every additional square meter of housing, the probability of migration decreases by 0.02 percentage points This trend highlights that housing is a significant asset for families, reflecting their economic conditions Larger homes typically signify better economic stability, reducing the necessity for households to relocate for employment purposes.
Research indicates that higher household income correlates with a reduced likelihood of migration Specifically, for every one million VND increase in income, the probability of migration decreases by 0.2 percentage points Income has long been considered a key indicator of a household's financial status, with higher earnings reflecting improved financial stability and living standards Consequently, households with better living conditions are less inclined to migrate for work, lacking the strong motivation to relocate for economic reasons.
Households that own agricultural land experience a 6.7 percentage point higher likelihood of migration compared to those without land ownership Typically, well-off families in rural areas own land and often lease it to others While many are rooted in agriculture, not everyone can pursue it as a primary occupation due to varying individual talents Wealthier families in rural regions tend to seek modern opportunities beyond agriculture, prompting them to migrate in search of new career paths and personal growth.
As the age of the household head increases, so does the likelihood of migration, with each additional year correlating to a 0.5 percentage point rise in migration probability This trend may stem from the fact that older household heads often face declining financial conditions, prompting family members to seek better work opportunities through migration Consequently, the pursuit of improved income sources becomes a key motivation for these households, enhancing their overall quality of life and personal development prospects.
The gender of the household head significantly influences migration patterns, with male-headed households exhibiting a 17.1 percentage point lower likelihood of migration compared to female-headed households This trend is likely rooted in traditional Vietnamese customs, where males are expected to inherit and maintain the family’s work Consequently, male heads of household are less inclined to seek employment opportunities elsewhere, opting instead to remain in their hometowns to further develop their family's traditional professions.
Households led by individuals with educational backgrounds from elementary to high school experience an increased migration probability of 16% for elementary, 14.9% for middle school, and a notable percentage for high school graduates.
Households led by individuals with a high school education or less experience a significant income disparity, with a 23 percentage point difference compared to those whose heads have studied beyond high school This gap can be attributed to the prevalence of manual labor jobs, often in farming, that these individuals endure in rural areas, leading to lower earnings Consequently, the lack of financial stability drives many of these households to seek migration for better work opportunities In contrast, households with heads who possess advanced degrees are more likely to secure stable, well-paying office jobs locally, reducing their motivation to migrate in search of better livelihoods.
Marriage typically leads to an increase in household size, resulting in higher spending levels that can strain the household's income, especially if one partner is unemployed Couples often aspire to create a prosperous life for themselves and their future children, which may drive them to migrate In fact, households headed by married individuals are 10.8 percentage points more likely to migrate compared to those with heads of different marital statuses.
Larger household sizes significantly reduce the likelihood of migration, with each additional family member decreasing the probability of relocation by 2.6 percentage points This trend is particularly evident in rural areas, where families often have more children due to limited knowledge of family planning The presence of multiple children creates challenges for parents seeking employment opportunities elsewhere, as there may be insufficient childcare options available.
Poor households face significant barriers to migration, primarily due to the financial burden of moving costs As a result, they are 3.2 percentage points less likely to migrate compared to non-poor households This trend aligns with Lee's (1966) migration theory, which suggests that migration opportunities are often inaccessible to those in poverty.
Comparing characteristics between rural migrant and non-migrant households
Table 6 : PSM results comparing characteristics between households with migrants and non-migrants
Summary of Balance for Matched Data Means Treated Means Control Std Mean Diff distance 0.200 0.200 0.001 credit 0.452 0.454 -0.005 house 83.622 81.745 0.042 income 10.388 10.880 -0.098 land 0.947 0.923 0.108 age 55.198 54.575 0.053 gender 0.741 0.773 -0.072
High school 0.164 0.221 -0.155 marriage 0.841 0.855 -0.040 hhsize 5.148 5,020 0.071 poor 0.263 0.240 0.053
The analysis reveals a balanced comparison between migrant and non-migrant households across various factors such as credit access, housing area, income, agricultural land ownership, age, gender, education level, marital status, household size, and poverty status Migrant households have a slightly lower credit loan rate at 45.2%, compared to 45.4% for non-migrants The average housing area for migrant households is 83.622 m², marginally larger than the 81.745 m² of non-migrant households In terms of income, migrant households earn an average of 10.388 million VND per month, slightly less than the 10.88 million VND of non-migrant households Agricultural land ownership is higher among migrants at 94.7%, versus 92.3% for non-migrants The average age of household heads is similar, with migrants at 55.198 years and non-migrants at 54.575 years Gender distribution shows 74.1% of migrant heads are male, compared to 77.3% for non-migrants Education levels are comparable, with 49.4% of migrant heads having primary education, and 33.1% holding secondary education, while non-migrants have 48.5% and 28.5%, respectively Married heads account for 84.1% of migrant households, slightly lower than the 85.5% in non-migrant households The average household size is 5.148 for migrants and 5.020 for non-migrants Lastly, poverty rates are 26.3% for migrant households and 24% for non-migrants, indicating a 5.3% difference.
Impacts of migration, remittances and other characteristics on the living
Table 7 : OLS results of the impact of migration, remittances and other characteristics on living standards of rural households
Average spending per capita/ month
Average income per capita/ month
(Note: The values in the results table are: Beta coefficient, statistical significance level, and the value in parentheses is the standard error.
“ *** ” , “ ** ”, “ * ” , - ” are f/je levels of statistical significance, respectively 0. ỉ%, 1 %, 5%, 10%.
(I) Head of household completed high school level as reference value; (2) Head of household has a different marital status than married status as reference value )
Households with migrants experience a higher average monthly expenditure per capita, approximately 0.004 million VND more than non-migrant households, and an increased average income per capita of 0.074 million VND, attributed to remittances that enhance family living standards This influx of remittances not only boosts income but also elevates household spending In terms of healthcare, migrant households incur an average monthly medical expenditure that is 0.029 million VND higher than their non-migrant counterparts, likely due to improved access to quality health services resulting from increased income Additionally, these households allocate 0.063 million VND more per month on education, facilitating better educational opportunities for their children Overall, the migration status of households positively influences the living standards of rural households in Vietnam across expenditure, income, health, and education.
Remittances from migration significantly enhance household income and health expenditures Specifically, for every increase of 1 million VND in monthly remittances, per capita income rises by 0.038 million VND, indicating that migration allows households to access additional financial resources Furthermore, health spending increases by 0.006 million VND per month with the same rise in remittances, suggesting that increased funds enable families to afford better healthcare services However, the study found no significant impact of remittances on average per capita or educational expenditures This lack of correlation may be attributed to the small or irregular nature of remittances, which can distort spending measurements, or the fact that those remaining at home may have more resources, thus diminishing the effect of remittances on overall living standards.
The OLS regression analysis reveals that households with loans generally experience a lower living standard, with the exception of education spending, which shows no significant difference Specifically, these households have an average monthly per capita expenditure that is 0.005 million VND less than those without loans, while their average monthly per capita income is 0.051 million VND lower This decline in financial well-being is likely attributed to the interest and principal payments required on their debts, leading to reduced household spending and income.
The housing area positively influences the living standards of rural households; however, it does not significantly affect the average monthly income per capita This is because a larger housing area often indicates more household members, which can lead to increased spending, but it does not necessarily correlate with the household's income-generating capacity, as not all members may be able to work.
In Vietnam, the average monthly household income significantly influences living standards, particularly in rural areas An increase of 1 million VND in a household's monthly income leads to a corresponding rise in health and education expenditures, with health spending increasing by 1 million VND and educational spending by 0.008 million VND This correlation suggests that higher income levels enable households to better afford essential health and education services.
Households that own agricultural land tend to have a lower average monthly expenditure per capita, approximately 0.009 million VND less than those without land This reduction in spending is likely due to their ability to cultivate crops and raise animals for personal use, significantly decreasing their food expenses Consequently, landowning households also experience lower average health expenditures, as they have access to fresh food sources they produce themselves, potentially leading to improved health outcomes.
The analysis reveals that as the age of the household head increases by one year, the average monthly expenditure per capita rises by 0.0002 million VND, likely due to higher costs associated with products tailored for the elderly, which demand greater quality and nutritional considerations Conversely, the average monthly income per capita decreases by 0.001 million VND with each additional year of age for the household head, attributed to a decline in labor capacity and income-generating potential as they age.
The analysis reveals that when the household head is male, the average monthly income per capita is 0.076 million VND higher compared to households led by females, likely due to traditional roles in rural areas where men are often the primary earners and women manage household duties Additionally, women typically prioritize the health of family members, leading to higher medical expenses Consequently, with a male household head, the average monthly medical expenditure is 0.072 million VND lower than in female-led households, assuming all other factors remain constant.
Households with children in elementary to high school exhibit lower average spending and income compared to those with heads of households who have education levels beyond high school Specifically, when controlling for other factors, households led by individuals with only a primary school education report an average monthly expenditure per capita that is 0.009 million VND lower than those with heads holding degrees above high school This trend is similarly observed in households where the head has secondary or high school qualifications The underlying reason for this disparity is that heads of households with education levels below high school are predominantly manual workers, resulting in lower income and, consequently, reduced average monthly expenditure per capita.
Marital status significantly impacts household expenditures, with married heads of households spending an average of 0.009 million VND more per capita per month compared to those with different marital statuses, likely due to an increase in family members Additionally, married heads of households allocate an average of 0.041 million VND more monthly on education expenses than their unmarried counterparts, which is likely attributed to costs associated with children's schooling.
Research indicates that an increase in household size negatively impacts per capita monthly expenditures and income Specifically, for each additional family member, the average monthly expenditure per person decreases by 0.004 million VND, highlighting how larger households often face lower living standards and increased poverty risk Additionally, the average income per capita also declines, with an increase of one family member resulting in a decrease of 0.339 million VND per month This trend is largely attributed to the fact that not all household members, such as children and the elderly, contribute to income generation.
Poor households in Vietnam typically spend 162,000 VND less per month on health care compared to their non-poor counterparts, indicating a significant disparity in health expenditure This trend also extends to per capita spending and educational expenses, as limited cash flow forces these families to reduce or eliminate spending on essential services like education and health care for their children Consequently, poverty adversely affects the overall living standards of rural households in Vietnam.