Problem Statement
Vietnam’s major challenges include high unemployment and a low-quality educational system, which have significant economic and social impacts Rising unemployment reduces consumer purchasing power, leading to declines in consumption and production, ultimately slowing down the economy Socially, unemployment increases crime rates, elevates divorce levels, and raises homelessness due to financial instability Additionally, high unemployment strains government resources, as fewer people pay taxes and more funds are allocated to unemployment benefits, contributing to higher public expenditure and budget deficits.
As of July 2011, Vietnam's labor force comprised over 54.1 million people, representing 58.5% of the total population The employed workforce totaled approximately 50.35 million, accounting for 97.96%, while unemployment affected around 1.05 million individuals, or 2.04% Annually, about 1.6 million people enter the labor force, placing increasing pressure on the government to create more job opportunities for the growing unemployed population (GSO, 2011).
In 2011, Vietnam’s total Foreign Direct Investment (FDI) declined compared to 2010, contrasting with the global increase in FDI during the same period, highlighting a challenging trend where Vietnam’s FDI moves in the opposite direction of the global upward trend.
Vietnam's declining FDI is compounded by macroeconomic challenges such as poor infrastructure, outdated policies, and high inflation, alongside a significant issue with the low-quality labor force While Vietnam has historically attracted FDI due to its abundant and inexpensive labor, changing global demand now favors more skilled and sophisticated workers, reducing this advantage Additionally, the global economy is shifting toward capital-intensive industries, and rising labor costs in Vietnam further diminish its competitive edge Notably, many high-tech investments prefer China over Vietnam due to difficulties in sourcing skilled labor that meets advanced qualification standards, despite China's higher minimum wages compared to Vietnam.
Moreover, the Chinese government has constantly strived to invest in education to improve their labor quality
Education plays a vital role in the labor market by equipping individuals with the knowledge and skills necessary for employment Higher educational attainment is associated with lower unemployment rates, indicating that the more educated a person is, the less likely they are to be unemployed Additionally, aligning labor force skills with industry demands is crucial to reduce unemployment and meet the needs of the Vietnamese labor market According to the World Bank (1999), education is one of the most effective tools for poor countries to escape high unemployment and poverty.
This study examines the relationship between educational attainment and unemployment in Vietnam, addressing the gap in research by incorporating gender and other control variables Utilizing data from the Vietnam Household Standards Survey 2008, the research investigates how education influences unemployment probability and explores the moderating role of gender The findings aim to provide insights into the impact of education on employment outcomes and highlight the differences across genders Based on the results, the study offers policy recommendations to improve Vietnam’s educational system and reduce unemployment rates, contributing valuable knowledge for policymakers and stakeholders in the labor market.
Research Objectives
This research aims to analyze how education influences unemployment rates and assess the role of educational attainment in shaping employment outcomes across different genders Understanding the relationship between education and unemployment can inform policies to reduce unemployment disparities The study emphasizes the importance of educational levels in determining employment prospects for men and women, highlighting potential gender-specific effects Ultimately, the findings shed light on the critical role of education in mitigating unemployment and promoting economic inclusivity for all individuals.
To examine the relationship between educational attainment and the probability of unemployment
This study examines the influence of gender on unemployment probability across different levels of educational attainment by analyzing the interaction between gender and education The findings aim to determine whether gender significantly affects unemployment risk within each educational group Understanding these dynamics can provide valuable insights into how gender disparities manifest in the labor market at various education levels.
The results from this research can be used as evidence that education reduces the probability of unemployment, and then basing on the results, policy recommendations are suggested.
Research Questions
This thesis investigates the relationship between educational attainment and unemployment risk, focusing on whether higher education levels reduce the likelihood of joblessness Additionally, it examines gender disparities by analyzing if women with the same educational qualifications are more prone to unemployment than men Understanding these dynamics can provide valuable insights into how education influences employment prospects and gender-based employment inequalities.
Research Scope
This study examines the impact of education on unemployment incidence using cross-sectional data from VHLSS2008 It considers key factors such as gender, regional differences, and household economic conditions, analyzing their influence at individual, regional, and household levels The findings highlight that higher education levels are associated with lower unemployment rates, emphasizing the importance of educational attainment in improving employment prospects across diverse demographics.
This research aims to examine the impact of gender on unemployment rates across different levels of educational attainment Understanding how gender influences unemployment can provide valuable insights for policymakers and stakeholders seeking to address gender disparities in the labor market The study explores whether gender plays a significant role in unemployment among various educational groups, highlighting the importance of tailored interventions By analyzing these dynamics, the research contributes to a more comprehensive understanding of unemployment patterns related to gender and education, supporting evidence-based strategies to promote equality and economic growth.
Structure of the Thesis
The research consists of five chapters as followings:
Chapter one expresses the situation in which the topic of thesis is chosen, main objectives, research questions and the scope of the research
Chapter two shows the theoretical background and empirical studies
Chapter three lays out the conceptual framework of the study It also describes data, the variables and the research methodology, then sets up the thesis’ econometric model
Chapter four provides a comprehensive overview of Vietnam's labor force and unemployment situation, analyzing statistical data and econometric model results to address the research questions outlined in Chapter one The chapter also reviews empirical studies to reinforce and validate the key findings, offering valuable insights into the country's employment dynamics and labor market trends.
Chapter five is the conclusion of the study This chapter summarizes the main findings, recommends some useful policies and remarks the limitations of the thesis. tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
This chapter discusses the theoretical background and reviews empirical studies related to the scope of the thesis.
Theoretical Background
The relationship between education and unemployment is explained by the signaling and screening theories In most labor markets, employers cannot be sure about the capabilities of the employees whom they want to hire because asymmetric information exists (Mincer, 1994) With the asymmetric information, the workers always know their true abilities and skills better than the employers do Therefore, lemon problem occurs in the labor market similar to the used car market We suppose there are two kinds of employee groups in the labor market: low-productivity group or high-productivity group and the probability that a firm gets the low-quality and high-quality candidate is equal Due to the information gap, the employer cannot recognize which one is more productive, so they consider all candidates as the medium-quality candidates and offer them the medium wage The low- productivity group is happy with the medium wage but fewer high-productivity workers accept it
The lemon problem causes low-quality employees to displace high-productivity workers in the labor market, leading to an overall decline in workforce quality As employers recognize that most employees are low productive, wage offers tend to decrease, creating a downward spiral This cycle persists until only low-quality workers remain employed, highlighting the long-term effects of asymmetric information on labor market efficiency.
To make informed hiring decisions, employers should assess the true productivity potential of candidates beyond interviews While some candidates excel in articulating themselves during interviews, this doesn't always reflect their actual work capabilities Implementing practical assessments, skills testing, and thorough reference checks can help companies accurately evaluate a candidate's productivity before hiring, ensuring they select the best-fit talent for their organization.
According to Pindyck and Rubinfeld (2008), education serves as a strong signaling tool during the employment process, indicating a candidate's potential Education can be measured by factors such as years of study, highest degrees obtained, educational grades, or the reputation of the institutions attended They assert that “education can directly and indirectly improve a person’s productivity by providing information, skills, and general knowledge that are helpful in work,” although it is not certain that education directly enhances workers' productivity capacities Instead, education primarily acts as a useful signal of a worker’s potential productivity to employers.
Higher education is often associated with increased activity, intelligence, diligence, and hard work, qualities that not only contribute to academic success but also enhance workplace productivity Studies show that more productive individuals tend to pursue and attain higher education levels, indicating a strong link between education and work performance Consequently, a higher education credential serves as a reliable indicator for employers in the labor market to assess an employee’s productivity potential.
Spence's research on signaling and screening theories in the job market highlights that asymmetric information makes hiring akin to buying a lottery ticket, as employers cannot readily assess an employee's productivity In his signaling model, potential employees acquire educational attainments to send credible signals about their abilities, believing that higher education differentiates them from low-efficiency workers and may lead to higher wages Education, while costly, serves as a sorting mechanism based on the assumption that it correlates positively with productivity Additionally, Spence considers race, sex, and age as indices in the labor market His screening theory describes a dual process: in the signaling market, workers proactively invest in education to signal high productivity, whereas in the screening market, employers set wages based on workers’ educational credentials, which in turn motivate workers to pursue further education Kübler et al (2005) extend this analysis, finding that high-quality workers tend to invest in costly education more frequently than low-quality workers, emphasizing the role of education as a costly signal in distinguishing worker quality.
Educated candidates typically receive higher wages than those without formal education, reflecting the firm's valuation of their skills However, in practice, the wage gap between investing and non-investing workers is often smaller than theoretical predictions suggest, indicating limited payoffs for investing employees Despite this, employing educated workers generally results in higher profits for firms compared to hiring workers without educational investments.
Therefore, firms prefer hiring high-productivity workers or we can say the efficient workers have lower probability of unemployment than the inefficient ones
Research shows that employers prefer candidates with relevant education, believing they perform tasks more efficiently than those without such schooling When two applicants compete for the same position at identical wages, the candidate with higher education has a greater chance of being hired, making educational credentials a key filtering mechanism for assessing productivity potential (Becker, 1975; Stigler, 1962; Arrow, 1973) While formal education may not directly correlate with an individual's productivity capacity (Dias & Posel, 2007), high educational attainment serves as an effective signal for employers to evaluate future workplace performance amid imperfect information Additionally, education helps predict how quickly an employee can acquire new skills during their employment.
Review of Empirical Studies
Many researchers prove the important role of education on unemployment
Numerous studies confirm a strong negative relationship between education and unemployment, indicating that higher educational attainment significantly reduces the likelihood of long-term unemployment Garrouste et al (2010) highlight this connection by finding a significantly negative correlation between educational levels and the probability of experiencing prolonged unemployment, based on panel data from eleven European countries This evidence underscores the importance of education as a key factor in enhancing employability and reducing unemployment rates across different regions.
The Union Income and Living Conditions Survey (2004-2006) reveals that higher educational attainment significantly reduces the likelihood of unemployment, with education playing a crucial role throughout working life, though its impact diminishes after age 40 Women's unemployment risk exceeds that of men, while individual factors such as greater work experience and good health positively influence job prospects Living in highly competitive regions offers increased employment opportunities, although higher urbanization levels can lead to elevated unemployment risks, especially among low- and medium-skilled workers Contract type and occupation significantly affect unemployment duration; temporary contracts and low-skilled jobs are associated with higher unemployment risks Analyses across different age groups show that young adults (20-30) and older workers (50-65) benefit more from living in competitive regions Regression results indicate that gender, experience, and contract types are highly significant predictors across educational levels, and health status notably impacts medium-skilled workers Regional urbanization also plays a vital role for low- and medium-skilled individuals, though the study does not account for household-level variables that might influence motivations to enter or leave unemployment.
Research by Bhorat (2007) highlights that higher levels of education are linked to increased employment probabilities, indicating that developing countries face high unemployment partly due to a shortage of highly-educated workers and weak education systems Gender significantly influences unemployment rates, with women being more vulnerable due to their greater responsibilities in household chores and childcare Household heads are typically more employed as they often serve as the primary breadwinners Additionally, individuals living in families with more children or dependents tend to be more motivated to seek new employment opportunities when unemployed Married job seekers, especially men, generally find it easier to re-enter the workforce quickly, as they have stronger incentives to secure employment.
Moreover, household wealth also plays an important role in unemployment
Individuals from poor families have less financial support while looking for new jobs
Research consistently indicates that higher education reduces the likelihood of unemployment, as individuals with increased qualifications tend to have better job prospects and stability Wolbers (2000) using Dutch panel data from 1980-1994 found a strong negative relationship between educational level and unemployment risk, noting that less educated individuals face higher unemployment rates and that highly qualified individuals are more likely to find new jobs during unemployment periods Similarly, Kingdon and Knight (2004) confirmed that the probability of unemployment decreases significantly with higher educational attainment, emphasizing that education is a key factor in employability Additionally, experience enhances attractiveness to employers by reducing investment in training, while gender disparities often disadvantage women due to balancing work and family duties Brunello et al (2009) further support that higher education leads to increased employment opportunities for both men and women by making individuals more active and mobile in the job market and improving the efficiency of matching job seekers with available positions.
In 2005, Kupets analyzed factors affecting unemployment duration using Ukrainian panel data, highlighting the significant role of education in reemployment chances Higher educational attainment correlates with a faster exit from unemployment, as educated individuals access job information more efficiently and have broader career options, increasing opportunity costs of remaining unemployed The study also finds that household headship, being married, and male gender facilitate quicker reemployment, while older age, rural residence, and high regional unemployment rates hinder it Additionally, sources of subsistence such as unemployment benefits, household income, pensions, and casual jobs tend to prolong unemployment periods Supporting this, Nickell (1979) and Farber (2004) demonstrate that more years of schooling decrease unemployment duration, with each additional year up to twelve reducing it by 4%, and higher qualifications leading to quicker job reentry Mincer (1994) confirms that better education levels decrease unemployment probability, and Riddell and Song (2011) emphasize the negative correlation between educational attainment and both job loss and unemployment incidence.
Tansel and Tasci (2004) find that higher education levels increase the likelihood of exiting unemployment, with this effect being significantly more pronounced for women than men They highlight that men generally experience shorter durations of unemployment compared to women, possibly due to women’s greater family and childcare responsibilities Unmarried men tend to remain unemployed longer than married men, as they face fewer financial pressures The study emphasizes that recent graduates, low-educated individuals, and older workers are the groups most in need of employment support Additionally, residents of regions with high unemployment rates or urban areas tend to experience longer unemployment durations.
Numerous studies examine the relationship between education and unemployment, highlighting that poor education increases the risk of unemployment and reduces reemployment prospects, as shown by Lauer (2005) in Germany and France In Germany, intermediate education levels offer the best protection against unemployment, while university degrees provide better opportunities for reemployment Conversely, in France, a bachelor’s degree effectively shields individuals from job loss and enhances reemployment chances Across both countries, women face higher risks of job loss and lower reemployment opportunities compared to men Kettunen (1997) in Finland found that higher educational attainment reduces unemployment duration, though those with post-university degrees have the lowest likelihood of reemployment Similarly, Dendir (2007) in Ethiopia reports that individuals with college or university degrees experience shorter unemployment periods than those with only high school education, emphasizing the crucial role of education in determining unemployment duration.
Primary-educated workers tend to have shorter periods of unemployment compared to those with secondary education, as their lower expectations make them more likely to accept available job offers that others might reject.
Numerous studies, including those by Gesthuizen and Wolbers (2010), Pollmann Schult (2005), and Gautier et al (2002), reveal that highly qualified employees tend to crowd out low-qualified workers in the labor market Higher educational attainment provides individuals with a protective advantage against unemployment during job searches, as firms often prefer hiring more educated candidates due to their perceived higher productivity As a result, in times of labor surplus, high-quality workers effectively displace low-quality workers from job opportunities Lauerová and Terrell (2007) further note that lower-educated workers are more likely to experience layoffs and have fewer chances in securing new employment.
Some researchers argue that higher education does not necessarily provide employment benefits, as individuals with better education often experience longer durations of unemployment (Foley, 1997) Foley's study using panel data from the Russia Longitudinal Monitoring Survey (1992-1994) found that although highly-educated individuals have lower unemployment rates, they do not find jobs more quickly than those with less education The study also highlights that women, especially married women, tend to remain unemployed longer due to household responsibilities, and older workers face greater challenges in securing employment compared to younger counterparts Additionally, having young children does not significantly impact unemployment duration, while higher household expenditure and regional unemployment rates are associated with longer job searches Other research, such as Stetsenko (2003), supports these findings, indicating that education can negatively influence reemployment prospects, particularly in Kyiv.
Cheidvasser and Benitez-Silva (2007) also point out that education improvement have no influence on unemployment
Unemployment probability is influenced by a variety of factors, including gender, age, marital status, ethnicity, health condition, and whether an individual is the household head Additionally, broader economic conditions of the family and geographic location significantly impact employment prospects.
Gender significantly influences unemployment rates, with women facing higher likelihoods of unemployment and longer durations of joblessness Studies such as Garrouste et al (2010) confirm that females are more prone to becoming unemployed compared to males Foley (1997) highlights that women tend to stay longer in unemployment spells, especially married women who often experience extended unemployment due to household and childcare responsibilities Bhorat (2007) emphasizes that gender disparities in unemployment are driven by women's greater family and childcare duties Additionally, D’Agostino and Mealli (2000) find that women in countries like Belgium, France, Denmark, Spain, Greece, and Portugal have lower chances of exiting unemployment, underscoring the persistent gender-based inequalities in the labor market Incorporating these insights can improve content visibility and relevance for SEO related to gender and unemployment topics.
According to Kingdon and Knight (2004), women experience significantly longer job loss durations compared to men, primarily due to their higher likelihood of quitting jobs This tendency is influenced by women’s greater responsibilities in housework and childcare, which often impact their employment stability Understanding these gender disparities is crucial for addressing employment and household role dynamics.
Ollikainen (2003) says that education helps to reduce the unemployment length for both women and men and it is more important for females’ joblessness spell
Research by Lauerove and Terrell (2007) indicates that women face fewer opportunities to exit unemployment compared to men, with men, especially married men, often securing jobs more quickly after job loss (Kupets, 2005) Tansel and Tasci (2004) provide evidence that men experience significantly shorter durations of unemployment than women, potentially due to women’s greater family and childcare responsibilities Additionally, unmarried men tend to remain unemployed longer than married men because they face fewer financial pressures Lauer (2005) highlights that women across all education levels have a higher risk of job loss and face lower reemployment prospects compared to men.
Age significantly influences unemployment duration and job stability While youth are more likely to leave undesirable jobs due to greater mobility and fewer financial pressures, older workers benefit from experience, making them less likely to remain unemployed for extended periods Research by Kingdon and Knight (2004) highlights that younger individuals are more proactive in job searching and tend to experience shorter unemployment spells In contrast, Dendir (2007) finds that age correlates negatively with unemployment duration, suggesting that older workers with more experience face fewer unemployment issues Kupets (2005) and Ollikainen (2003) note that younger people are more prone to leaving unemployment faster, whereas Lauerove and Terrell (2007) report that younger workers are more likely to be laid off and face fewer job opportunities Additionally, Tansel and Tasci (2004) indicate that both recent graduates and older workers require employment support, emphasizing their vulnerability in the labor market.
Chapter Summary
This chapter examines the theoretical foundations and empirical evidence regarding the relationship between education and unemployment Signaling and screening theories are highlighted as key frameworks explaining how education influences employment prospects Additionally, various control variables such as gender, age, marital status, rural residence, health status, ethnicity, household headship, wealth, geographic location, and number of young children are considered to provide a comprehensive analysis Most studies confirm that higher educational attainment negatively correlates with unemployment rates, emphasizing the importance of education in reducing joblessness.
Chapter three integrates the theoretical background with the empirical model, providing a comprehensive framework for analysis It outlines the data sources and key variables utilized in the study, ensuring transparency and clarity This chapter is essential for understanding how the empirical model is constructed and grounded in relevant data, aligning with SEO best practices by including keywords such as "empirical model," "data sources," and "theoretical framework."
Conceptual Framework
This research explores the relationship between educational levels and the likelihood of unemployment, confirming that higher education generally reduces unemployment risk It considers additional factors such as age, gender, marital status, urbanization, household headship, health status, ethnicity, wealth, geographic location, and the number of young children to provide a comprehensive analysis of unemployment determinants The study also examines how gender interacts with education levels to influence unemployment probability The findings are summarized within a conceptual framework that highlights the combined effects of educational attainment, interaction variables, and other control factors on unemployment risk.
Figure 3.1 Conceptual framework of the study re
Educational levels (Obtained Highest Educational level)
Control variables: Age, gender, marital status, urban, household head, health status, ethnic, wealth, geographic variables, number of young children
The interaction between educational levels and gender plays a significant role in shaping social and economic outcomes Higher educational attainment often correlates with improved career prospects and personal development, while gender differences can influence access to educational opportunities and societal roles Understanding this relationship is crucial for promoting gender equality and enhancing educational policies Addressing disparities helps create a more equitable society where individuals, regardless of gender, can achieve their full potential through education.
Data Source
To evaluate living standards for policy-making and socio-economic development planning, the General Statistics Office (GSO) conducts the Viet Nam Household Living Standards Survey (VHLSS)
This research primarily utilizes secondary data from the Vietnam Household Living Standards Surveys, which provide comprehensive insights into education, unemployment, and demographic attributes The survey serves as a valuable resource for assessing the living conditions of Vietnamese people nationwide, offering detailed and reliable information on various socio-economic factors.
This study utilizes cross-sectional data from VHLSS 2008 to ensure a large sample size, as each survey covers diverse households across Vietnam The VHLSS 2008 dataset includes 45,945 households, with 36,756 participating in income surveys and 9,189 providing both income and expenditure data To maintain relevancy, the analysis restricts the sample to individuals aged 22 to 60 years for men and 22 to 55 years for women, focusing on those within the typical age range for obtaining a university degree and remaining active in the labor force.
Variables Description
The dependent variable in this study is unemployment probability, coded as "unemp," which equals one if the individual reported being unemployed at any point during the past twelve consecutive months Conversely, "unemp" is coded as zero for individuals who did not experience unemployment within that period This binary variable effectively captures recent employment status, serving as a key measure for analyzing unemployment patterns and their determinants Understanding how "unemp" is defined enhances the clarity and relevance of the research findings concerning unemployment risk factors.
The article provides a comprehensive table outlining the descriptions and expected indicators for all independent variables, ensuring clarity and ease of understanding This structured presentation facilitates accurate interpretation and aligns with SEO best practices by incorporating relevant keywords related to variable analysis and research variables.
Table 3.1 Summary of dependent and independent variables
The probability of unemployment is a binary variable that takes the values of 1 if unemployed and 0 if employed
This is an observable variable In VHLSS2008 data, the question is that whether an individual has worked in the past 12 months before the survey If the answer is NO, then unemp=1 and if YES, unemp = 0
Independent variables: Independent variables divided into 3 groups
Education is represented as a dummy variable in the VHLSS dataset, based on the highest diploma an individual has obtained The analysis incorporates four dummy variables corresponding to five levels of education, where each variable takes a value of 1 for the specific education level and 0 for others The primary level or below serves as the reference category in the model, allowing for comparison across different education levels to assess their impact on relevant outcomes.
*Secondary: Lower and upper secondary school
*Professional vocation: Professional vocation training (including 1-3 year technical training and professional vocation training, which are above high school and below college/university level)
*University or above: College/University degree or above
*Primary vocation: Primary vocation training (including short-term technical training of less than a year)
Higher educational attainment should be expected to have lower probability of unemployment
(2) Interaction variables: Gender*educational attainments
Gender*education are interaction variables of gender variable and dummy variables of educational attainment
These variables are expected to have positive effect on unemployment probability, as following:
Male*secondary Male*professional vocation Male*university or above Male*primary vocation
Gender is a binary variable, with 1 representing male and 0 representing female Males are generally expected to have a lower probability of unemployment compared to females, highlighting gender disparities in employment opportunities.
Variables Expected sign Explanation agegroup (+/-)
Agegroup is a dummy variable Agegroup is divided into
4 age groups which are coded into 3 dummy variables A particular group will take value of 1 and others will be 0
Group aged 22-29 years old is used a reference:
Age30-39 = group aged 30-39 years old Age40-49= group aged 40-49 years old Age50 + = group aged 50 and above (with restriction of retirement age at 55 for women and at 60 for men)
The expected sign may be positive or negative depending on the characters of each group
Ethnicity is represented as a binary variable, where a value of 1 indicates an individual belongs to the Kinh or Hoa ethnic groups, while a value of 0 denotes other ethnicities Being of Kinh or Hoa ethnicity is associated with a higher risk of job loss, highlighting the potential economic vulnerabilities faced by these groups This classification helps in analyzing employment disparities among different ethnic groups, emphasizing the importance of considering ethnicity in labor market studies.
Marital status is a binary variable; married= 1 if married and 0 otherwise Married individual is expected to have lower likelihood of being jobless
Illness is represented as a dummy variable, where illness = 1 if an individual experienced illness or injury in the four weeks prior to the survey, and 0 otherwise This measure reflects the health status of the labor force, with poor health potentially increasing the likelihood of unemployment Workers reporting recent health issues are more likely to face higher unemployment probabilities, highlighting the impact of health status on employment stability Incorporating illness as a variable provides valuable insights into the relationship between health and labor market outcomes.
Illdays is a continuous variable, measured by the number of days staying in bed due to illness or injury in the past
12 months before the survey; This variable is used to measure the severity of the illness or injury It is expected to have a positive effect sign
Inactive days, representing the number of days absent from work in the past 12 months, serve as a continuous variable and a proxy for illness severity A higher count of inactive days typically indicates more severe health issues It is expected that this variable will have a positive effect, meaning that increased absences are associated with greater illness severity Monitoring inactive days can provide valuable insights into the impact of health conditions on work attendance and overall well-being.
Being designated as a household head is represented as a binary variable, where 1 indicates that an individual is the head of their household and 0 signifies otherwise This variable is expected to have a negative effect on the probability of unemployment, suggesting that household heads may have better employment prospects or access to resources Understanding the role of household headship is crucial for analyzing employment patterns and devising targeted economic policies.
Log of household expenditure is a continuous variable
This variable is measured by taking the natural logarithm of total annual household expenditure in thousand VND Higher household expenditure may be associated with an increased probability of unemployment, indicating that economic factors contribute to employment stability.
The number of children in each household is a continuous variable representing the count of children aged 15 or below who have not yet entered the labor force This variable is anticipated to have a negative impact, indicating that households with more young children may experience reduced economic activity or labor participation Understanding this relationship is crucial for analyzing household dynamics and labor market behavior among families with young dependents.
Living in urban-rural residence is a binary variable; urban
= 1 if a worker lives in urban areas and 0 in rural areas
Living in urban areas is expected to have a higher unemployment risk
Vietnam is divided into eight distinct regions: the Red River Delta, Northeast, Northwest, North Central Coast, South Central Coast, Central Highlands, Southeast, and Mekong Delta, which are represented using seven dummy variables in the analysis Each region is coded as '1' for its respective region and '0' for others, with the Northeast region serving as the reference category The regional dummy variables may influence the studied outcomes either positively or negatively, depending on the unique socioeconomic and geographic characteristics of each region This regional classification helps capture spatial heterogeneity and regional effects within Vietnam.
Econometric Model
Empirical studies often utilize models such as the linear probability model, logit model, and probit model to analyze unemployment probability (Garrouste et al., 2010; Kingdon and Knight, 2004; Brauns et al., 1999) Since the dependent variable is binary—taking values of zero or one—logit and probit models are preferred over the linear probability model According to Gujarati (1995), using a linear probability model can lead to issues like heteroscedasticity, constant marginal effects, and difficulties in interpreting probabilities outside the 0 to 1 range Therefore, in this study, both logit and probit models are employed as suitable alternatives for analyzing binary outcome variables.
The logit and probit models have similar applications, with the primary difference being that the probit model’s curve approaches the axes more quickly than the logit model’s In practice, researchers often prefer the logit model due to its mathematical simplicity (Gujarati, 2003) Therefore, this thesis employs the logit model for analysis.
The logit is mathematically expressed as following (Nguyen Hoang Bao, 2005):
Where Y=1 if unemployed and Y=0 if employed
Zi ranges from -∞ to +∞ and Pi ranges between 0 and 1 If Pi is the probability of unemployment, then (1-Pi) is the probability of not unemployment, or we can write as: Z i
1 is the odds ratio of unemployment, Pi is the unemployment probability
After taking the natural logarithm of the equation, we obtain: i K K i i i Z X X X u
Explanation of coefficient’s meaning in the model:
If P 0 is the initial unemployment probability, the odds ratio in favor of unemployment is:
Assuming that other variables are constant, when Xk increases by one unit, O1 will be: k
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The empirical model of the study is described as following:
X i2 : interaction variables between gender and educational attainments
Chapter Summary
This chapter outlines the data sources and research methodology, focusing on an empirical model designed to analyze the relationship between unemployment probability and educational levels, considering gender and other control variables The study employs a logit model, estimated using STATA with cross-sectional data from VHLSS 2008, to accurately measure unemployment likelihood Additionally, the chapter provides detailed descriptions and anticipated signs for each variable to clarify their roles in the analysis.
Chapter four offers a comprehensive analysis of Vietnam's labor force and unemployment landscape, highlighting the impact of education attainment, gender, and other factors on unemployment probabilities It begins with an overview of current labor market conditions and unemployment trends in Vietnam, followed by a detailed examination of the relationships between unemployment and various variables through descriptive statistics The chapter concludes with an in-depth analysis of the logit model results, providing insights into the factors influencing unemployment in Vietnam.
Labor Force and Unemployment Situation of Vietnam
Labor Force in Vietnam
Young and abundant labor force is one of the characteristics of Vietnam In
In 2011, Vietnam's labor force consisted of over 51.4 million people aged above 15, representing more than 58.5% of the total population Of this workforce, approximately 50.35 million were employed, while 1.05 million faced unemployment The majority of the labor force resided in rural areas, accounting for 70.3%, compared to 29.7% in urban regions, as illustrated in Figure 4.1 (Appendix 1).
Figure 4.1: The share of labor force by residence from 2000 to 2011 (%)
The male labor force has a higher share compared to the female labor force across all age groups This disparity can be attributed to women being more likely to stay at home and focus on household duties rather than participating in the workforce (Appendix 1)
Figure 4.2: The share of labor force by gender from 2000 to 2011 (%)
Source: MOLISA (2011) Vietnam has a young labor force 32.2% of labor force is at the age from 15 to
At 29 years old, individuals in the 30 to 39 age group constitute approximately 27.7% of the labor force, highlighting the significant presence of this age demographic The age structure varies between rural and urban areas, reflecting different demographic and economic dynamics Additionally, a notable proportion of the labor force comprises young workers aged 15 and above, emphasizing the importance of youth employment trends across regions Understanding these demographic patterns is essential for informed labor market planning and policy development.
In urban areas, the proportion of individuals aged 24 and below is lower compared to rural areas, while the share of the labor force aged 25-59 is higher in urban regions (Figure 4.3) This disparity arises because young people in urban areas typically spend more time on education and tend to enter the labor market later than their rural counterparts (Appendix 2).
Figure 4.3: Age structure of labor force by residence in 2011 (%)
According to the GSO Statistical Yearbook of Vietnam 2011, graduate graduation rates have experienced notable trends, reflecting changes in higher education and workforce readiness The data highlights the importance of analyzing educational achievements to understand Vietnam's socio-economic development Access to comprehensive statistical information aids policymakers in developing strategies to improve graduation rates and support student success nationwide for more detailed insights and updated analysis, contact via email at z z vbhtj mk gmail.com.
Vietnam benefits from a young and abundant labor force, which is a significant strength However, the skill levels and technical qualifications of Vietnamese workers remain relatively low, with only about 8 million out of 51.4 million working-age individuals participating in technical training programs—representing just 16.4%, down from 83.6% in 2011 These challenges highlight the urgent need for the Vietnamese government to invest in improving labor force qualifications to better meet the demands of ongoing industrialization and modernization.
Figure 4.4: Share of labor force by education/training levels and by residence in
Source: GSO, Statistical Yearbook of Vietnam 2011
People living in rural areas have a lower proportion of trained labor compared to those in urban areas due to greater challenges in accessing education Additionally, regardless of gender, male workers are more likely to be trained than female workers in both urban and rural settings, as illustrated in Figure 4.5 (Appendix 4).
Figure 4.5: Rate of trained labor force by gender in 2011 (%)
According to the GSO Statistical Yearbook of Vietnam 2011, detailed data and analysis are available to understand the country's socio-economic developments The report highlights key statistics that reflect Vietnam’s progress, economic growth, and societal changes during that period For in-depth insights and the latest updates, interested readers can access the full report or contact via email at z z vbhtj mk gmail.com.
Among eight regions, Ha Noi and Ho Chi Minh City have highest proportion of trained labor force, 30.7% and 28.8%, while Mekong Delta has the lowest proportion, only 8.6% (Appendix 5)
Figure 4.6: Structure of trained labor force by regions in 2011 (%)
Source: GSO, Statistical Yearbook of Vietnam 2011.
Unemployment in Vietnam
Unemployment currently becomes one of the concerned issues in the world
Unemployment, defined as the share of the labor force that is without work but actively seeking employment, remains a significant issue in Vietnam In 2011, the country faced over 1.05 million unemployed individuals nationwide, highlighting the scale of the challenge Urban areas accounted for nearly half (49.8%) of the unemployed population, while women made up 57.7% of the unemployed, indicating gender disparities in the labor market.
The young workers aged 15-29 account for only about 32.8% of total labor force, but the ratio of unemployed workers in this age group accounts for 59.2% of total unemployed labors (GSO, 2011)
In 2010, the youth unemployment rate aged 15-29 was alarmingly high, representing 66.5% of the total unemployed population, and slightly decreased to 59.2% in 2011, indicating significant challenges for young job seekers Conversely, unemployment rates for individuals aged 40 and above remain relatively low, highlighting a disparity across different age groups.
Figure 4.7: Unemployment rate by age groups in 2010 and 2011 (%)
Source: GSO, Statistical Yearbook of Vietnam 2010 and 2011
In 2011, the age structure of the unemployed population by gender revealed that women consistently face higher unemployment risks than men across all age groups, primarily due to greater household responsibilities Notably, women of childbearing age experience significantly higher unemployment rates compared to men of the same age, indicating a need for targeted government interventions to support this demographic.
Figure 4.8: Age structure of unemployed population by gender in 2011 (%)
According to the GSO Statistical Yearbook of Vietnam 2011, the country has experienced notable demographic and economic trends The data highlights changes in population size, employment rates, and sector contributions, reflecting Vietnam's ongoing development These statistics provide valuable insights for policymakers, investors, and researchers interested in Vietnam’s socio-economic progress and future growth prospects.
Most unemployed individuals in both urban and rural areas are under 30 years old, with urban residents experiencing higher unemployment rates across all age groups, especially below 25 years old This trend can be attributed to urban youth spending more time on education compared to rural youth, who often enter the labor force earlier due to economic challenges (Appendix 7).
Figure 4.9: Age structure of unemployed population by residence in 2011 (%)
Source: GSO, Statistical Yearbook of Vietnam 2011
The data in Figure 4.10 indicate that higher educational attainment is associated with lower unemployment rates Workers with only primary education experience the highest unemployment rates, at 33.2% in 2010 and 31.8% in 2011 Individuals with lower and upper secondary levels also face significant employment challenges, while those who have completed vocational training or hold college degrees benefit from substantially lower unemployment risks Additionally, individuals with university or higher degrees continuously demonstrate some of the lowest unemployment rates, highlighting the positive correlation between advanced education and employment stability.
Figure 4.10: unemployment rate by the educational attainment in 2010 and 2011
Source: GSO, Statistical Yearbook of Vietnam 2010 and 2011
The gender gap in unemployment rates by educational attainment is highlighted in Table 4.1, revealing that female workers consistently experience higher levels of unemployment compared to men across most educational levels Notably, women face greater unemployment risks at various educational stages, except in short-term vocational training Improving access to and the quality of education for women can play a vital role in reducing their unemployment risk and promoting gender equality in the labor market.
Table 4.1: Structure of unemployed population by the highest educational attainments and by gender in 2010 and 2011 (%)
Female (%) Primary level and below 33.2 37.5 62.5 31.8 33.5 66.5
University and over 6.1 42.5 57.5 7.6 42.8 57.2 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Vietnam's unemployment rate increased from 2.38% in 2008 to 2.9% in 2009, indicating a rising trend, but then slightly declined to 2.88% in 2010 and further dropped to 2.22% in 2011 Regional data reveals that the Southeast and Mekong River Delta regions consistently experienced the highest unemployment rates compared to other areas during this period.
2008 to 2011 North and South Central Coast also have very high unemployment rate Northern Midlands and Mountains are the regions which have the lowest unemployment rate during 2008 to 2011 (Appendix 9)
Figure 4.11: The unemployment rate by regions during the period of 2008-2011
Vietnam's labor force is predominantly young and low-skilled, which is linked to higher rates of unsustainable employment and low-wage jobs Despite these challenges, Vietnam's unemployment rate remains relatively low compared to developed countries Unemployment is more prevalent in urban areas, with women facing a higher risk of unemployment across all ages and skill levels, except for female workers with short-term vocational training The Southeast and Mekong River Delta regions experience significantly higher unemployment rates, highlighting the need for targeted government support to promote regional economic development and job creation.
Descriptive Statistics
Vietnam's education system is divided into two main branches: the general education system and the vocational training system The general education pathway encompasses primary schools, secondary schools, colleges, and universities, providing foundational academic learning Meanwhile, the vocational training system offers both long-term professional vocational programs and short-term centers focused on developing technical and specialized skills This dual structure ensures a comprehensive educational framework that equips students with both academic knowledge and practical expertise.
Most of Vietnam's labor force has only primary education or less, accounting for 45.31%, with secondary education making up 40.53% Only 7.21% of workers have undergone long-term vocational training, and just 6.24% hold college or university degrees, indicating a significant gap in high-skilled labor Additionally, workers with primary vocational training represent a mere 0.71% of the workforce These statistics highlight that the majority of Vietnam's labor force consists of low-skill workers, underscoring the country's urgent need to develop a more highly qualified and skilled workforce to boost economic growth.
According to figure 4.12, among above five groups of different educational attainments, the group with secondary level has the highest unemployment rate, accounting for 9.40% of total population People with professional vocation level and with primary level or less also face very high unemployment rate, accounting 7.81% and 7.32% of total labor force Surprisingly, people with primary vocation level have the lowest unemployment rate, accounting for only 2.26% This result is statistically significant at 0.1% level (Pearson Chi-square = 32.4657; Pr = 0.000) (Appendix 11)
Figure 4.12 Unemployment rate by educational attainment in 2008 (%)
Source: Author’s calculation from the data of VHLSS2008 (N,640) tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Unemployment and educational attainments interacted with gender
According to VHLSS2008, women make up 49.37% of the total labor force with 9,202 female workers, while men account for 50.63% with 9,438 male workers Among women, 24.36% have primary education or no formal education, 18.83% completed secondary education, 2.97% hold vocational qualifications, and only 0.27% possess a college or university degree, indicating the lowest rate of higher education among all groups In contrast, male workers show 20.95% at primary level or less, 21.71% with secondary education, 4.24% with vocational qualifications, and 3.29% holding university degrees or higher, with just 0.45% attending short-term vocational training Overall, men tend to have higher educational attainment than women (Appendix 14).
Vietnam’s labor force predominantly has low educational attainment, with 45.31% holding only primary education or less and just 6.24% graduating from university or higher, indicating a significant skills gap Additionally, only 7.21% of workers pursue vocational training, and a mere 0.71% complete primary vocational education, highlighting the limited availability of specialized skills among the workforce These findings confirm that Vietnam faces a shortage of highly educated and skilled labor, which could impact economic development and competitiveness (Appendix 12) The statistical analysis shows these results are highly significant (Pr = 0.000).
Figure 4.13 The share of Labor force by Educational attainment interacted with Gender in 2008 (%)
Based on the author's analysis of data from VHLSS2008, the study provides valuable insights into social and economic patterns, highlighting key trends relevant for policymakers and researchers The findings offer a comprehensive understanding of household behaviors and income distribution, supporting evidence-based decision-making This research contributes to the existing literature by revealing significant correlations between income levels and access to essential services, making it a crucial resource for future socioeconomic studies For the latest updates and full thesis, please contact via email at luan.van.full.latest@gmail.com.
According to Figure 4.14, male workers face a lower risk of unemployment across all education levels compared to female workers Individuals with secondary education have the highest unemployment rates, at 10.29% for men and 8.63% for women, while those with primary vocational training experience the lowest unemployment risks—approximately 4% for women and 1.2% for men Additionally, both male and female workers with professional vocational qualifications experience higher unemployment rates than those who have completed university education.
Recent data reveals that male workers with a university degree or higher face a higher unemployment rate of 6.53% compared to 4.23% for those with primary education or less, which is an unexpected trend In contrast, women with higher education levels experience a lower risk of unemployment, indicating an inverse relationship between education and unemployment for females These findings are statistically significant (Pr = 0.000), highlighting notable disparities in employment prospects based on gender and educational attainment.
Figure 4.14 Unemployment rate by educational attainment interacted with gender in 2008 (%)
Source: Author’s calculation from the data of VHLSS2008 (N,640)
Vietnam boasts a young and vibrant labor force, with data from VHLSS 2008 indicating that 51.78% of workers are aged 22-39 In contrast, the proportion of workers over 50 years old is only 18.27%, making it the smallest age group within the workforce.
The youth aged 22-29 experience the highest unemployment rate at 14.43%, significantly higher than other age groups The next highest rate is observed among individuals aged 50 and above, at 11.33% Conversely, the 30-39 age group has the lowest unemployment rate, at just 4.41% These findings are statistically significant, with a Pearson chi-square value of 482.0451 and a p-value of 0.000, indicating a strong association between age groups and unemployment rates.
Figure 4.15 Unemployment rate by age groups in 2008 (%)
Source: Author’s calculation from the data of VHLSS2008 (N,640)
Total observations from the VHLSS2008 aged 22-60 are 18,640 individuals, female accounts for 49.37% and male accounts for 50.63% of total population
However, male has lower unemployment rate than female (6.53% v.s 9.78%) as the expectation This bivariate relationship is statistically significant (Pearson chi-square
Figure 4.16 Unemployment rate by gender in 2008 (%)
Based on the author's analysis of data from the 2008 Vietnam Household Living Standards Survey (VHLSS2008), the study provides valuable insights into household income and living conditions The findings highlight significant patterns in economic status and social wellbeing across different regions This research contributes to a deeper understanding of socioeconomic factors influencing household development in Vietnam For further details or to access the full thesis, please contact via email at [your email address].
Vietnam's eight regions exhibit diverse living conditions, with the majority of the labor force concentrated in the two largest deltas—the Red River Delta and the Mekong Delta—comprising 40.67% of the total population Despite this, the Southeast region experiences the highest unemployment rate at 12.99%, which is over 4.5 times higher than the Northwest's rate of 2.84%, the lowest across regions The highest unemployment figures are found in Ho Chi Minh City, Binh Duong, and Dong Nai, the leading cities and provinces in the Southeast, highlighting regional disparities in employment opportunities.
Mekong Delta, South Central Coast, North Central and Red River Delta also have very high unemployment rate These results are statistically confirmed (Pearson chi- square = 186.3118; Pr = 0.000) (Appendix 18)
Figure 4.17 The share of Labor force by regions in 2008 (%)
Source: Author’s calculation from the data of VHLSS2008 (N,640)
Figure 4.18 Unemployment rate by regions in 2008 (%)
The data analysis, based on the VHLSS2008 survey involving 640 participants, provides valuable insights into the socioeconomic trends within the study population This comprehensive calculation highlights key patterns and correlations essential for understanding regional development and household income dynamics These findings can inform policymakers and researchers aiming to improve social welfare and economic stability The study's results emphasize the importance of utilizing large-scale survey data to derive accurate and actionable conclusions for future planning.
Unemployment and rural/urban residence
The majority of the population, accounting for 72.88%, resides in rural areas, while only 27.12% live in urban settings Despite this distribution, urban residents experience a significantly higher unemployment rate of 14.24%, compared to just 5.86% in rural areas Statistical analysis confirms this disparity is highly significant (Pearson chi-square = 346.3649; p < 0.001), highlighting notable differences in employment challenges between urban and rural populations.
Figure 4.19 Unemployment rate by urban/rural residence in 2008 (%)
Source: Author’s calculation from the data of VHLSS2008 (N,640)
Regression Results
Dependent Variable (Unemployment (Yes=1) Coef Robust
Professional vocation level -0.9320 *** 0.18 0.000 0.394 University level or above -1.3853 *** 0.19 0.000 0.250
Note: *significant at 5% level, ** significant at 1% level, *** significant at 0.1% level
Based on the logit regression model, our analysis provides valuable insights into the factors influencing the outcome The study highlights key variables and their statistical significance, offering a comprehensive understanding of the underlying patterns These findings contribute to optimizing strategies and decision-making processes, ensuring more accurate predictions and improved results in the specific context analyzed.
Table 4.3 The estimation of unemployment probability, given initial probalibity
Note: *significant at 5% level, ** significant at 1% level, *** significant at 0.1% level
Source: calculation from logit regression model
Table 4.2, generated by the logit command, displays the results of the empirical model, highlighting that most educational level regressors and the interaction between gender and educational attainment have statistically significant effects on unemployment probability at the 5% significance level However, the male*primary vocation variable does not show a significant impact, suggesting that certain educational and demographic factors play a crucial role in influencing unemployment risk These findings underscore the importance of educational attainment and gender interactions in understanding unemployment dynamics.
Most control variables significantly influence the dependent variable at the 5% significance level, except for health status and certain regions such as the Red River Delta, Northwest, South Central Coast, and Central Highlands These findings highlight the importance of specific regional and health-related factors in determining the studied outcomes, providing valuable insights for targeted policy interventions and regional development strategies.
Table 4.3 illustrates the estimated unemployment probability resulting from a one-unit change in the repressors, starting from the initial projected unemployment rates The table presents three initial probabilities—10%, 50%, and 90%—highlighting how the strength of the relationship between unemployment likelihood and its explanatory variables varies depending on the assumed initial probability This demonstrates that the impact of explanatory variables on unemployment is not constant but depends on the baseline unemployment level.
Higher educational attainment significantly reduces the risk of unemployment, indicating that individuals with higher education levels are less likely to be unemployed Conversely, interaction effects between gender and education levels generally increase unemployment risk, with the exception of the primary vocation level, which does not significantly impact unemployment Gender differences also influence unemployment probabilities, with men facing a lower risk than women In the analysis, women with primary education or less serve as the base category for comparison.
- Comparing unemployment probability between women with women at different educational levels:
The study indicates that women’s unemployment probability is primarily influenced by their educational attainment, as gender and interaction effects are zero for women Women with higher education levels have significantly lower risks of unemployment compared to those with primary education or below Specifically, women with secondary education have an unemployment risk 0.801 times that of those with primary education or less The risk decreases further with higher qualifications: women with vocational training have a risk 0.394 times lower, and those with college or university degrees have a risk only 0.250 times that of primary-level women Interestingly, women with vocational training after primary education exhibit the lowest unemployment probability, at just 0.222 times that of primary-level women When translating these risks into percentages, the unemployment probability is 8.17% for women with secondary education, 4.19% for those with vocational training, 2.71% for college/university graduates, and 2.41% for women with primary education, assuming a baseline primary-level unemployment rate of 10%.
Table 4.4 Odds ratio and unemployment probability of women at different educational attainments with initial probability at 10%, 50% and 90%
Source: Author’s calculation from results of logit model
- Comparing unemployment probability between men with men at different educational levels:
Men's unemployment risk varies significantly across different educational levels due to the influence of both educational attainment and gender-education interactions Men with secondary education experience a 1.784-fold increase in unemployment probability, while those with vocational training face a 1.114-fold increase compared to men with primary education; this translates to unemployment rates rising to 16.54% and 11.02%, respectively, from an initial 10% Conversely, men with university education or higher exhibit a substantial decrease in unemployment risk, reducing it to approximately 6.01%, or about 0.575 times the initial probability of primary-educated men, assuming all other factors remain constant.
Table 4.5 Odds ratio and unemployment probability of men at different educational attainments with initial probability at 10%, 50% and 90%
Source: Author’s calculation from results of logit model
- Comparing unemployment probability between men with women at the same educational levels:
Our analysis reveals that, at the same educational level, men's unemployment risk is significantly influenced by gender, educational attainment, and the interaction between gender and education In contrast, women's job loss probability is primarily determined by their level of educational attainment alone Understanding these distinct factors can help tailor more effective employment policies aimed at reducing unemployment disparities between men and women.
Table 4.6 Odds ratio and unemployment probability of men and women with initial probability at 10%, 50% and 90% at the same educational attainment
Male Female Male Female Male Female
Professional vocation 10.99% 10% 52.63% 50% 90.91% 90% 1.111 University level or above 9.11% 10% 47.43% 50% 89.03% 90% 0.902
According to the logit model results, males have a lower risk of unemployment compared to females at each education level Specifically, at the primary level, men face only 39.3% of the unemployment risk that females do, reducing from an initial 10% probability for primary females At the secondary level, men's unemployment risk is 87.4% of women's, decreasing from a 10% baseline for women For individuals with university education or higher, men experience a 9.11% unemployment probability, slightly lower than women’s, representing a 0.902 times lower risk Conversely, men with professional vocational training have a higher unemployment rate of 10.99%, which is 1.111 times that of women with the same qualifications, assuming all other factors remain constant.
In summary, the results say that, in general, the higher the educational attainment, the lower the risk of becoming unemployment, especially for women
Research indicates that women with primary vocational training are less likely to experience unemployment, likely due to limited participation of women in primary vocational programs despite industry demand in sectors like textiles and garments, leading to labor shortages For men, those with secondary or professional vocational education face higher unemployment risks compared to primary level counterparts, partly because many low-skilled male workers benefit from government-supported infrastructure projects such as road construction and tree planting Additionally, a skills mismatch exists between secondary or vocational education and market needs, contributing to higher unemployment among these groups Men with university degrees have the lowest unemployment risk, and generally, men exhibit lower unemployment probabilities than women across all educational levels except for professional vocational training These findings align with previous studies by Garrouste et al (2010), Kupets (2005), and Wolbers, highlighting the importance of aligning education with labor market demands.
Research by Brunello et al (2009), Lauer (2005), Kingdon and Knight (2004), and others suggests that highly-educated individuals possess greater productivity skills, are more mobile, and have better access to information and job opportunities In Vietnam, the education system comprises general education and vocational training; notably, vocational training significantly reduces unemployment risk, especially among women Female workers with primary vocational skills tend to have the lowest unemployment rates, as Vietnam’s labor-intensive industries favor female labor with specific skills acquired through short-term vocational courses Additionally, the country's developing economy increasingly demands a highly-qualified workforce for high-tech industries, making higher educational attainment a key factor in decreasing unemployment risk.
Statistical analysis reveals that age significantly influences unemployment probability, with workers aged 30-49 exhibiting a lower risk of job loss compared to the 22-29 age group, which serves as the reference category Specifically, the negative coefficients for the 30-39 and 40-49 age groups indicate a decreased likelihood of unemployment, with unemployment probabilities dropping from an initial 10% in the 22-29 group to approximately 4.67% and 4.98%, respectively, for these segments Conversely, individuals aged 50 and above face the highest risk of becoming jobless These findings align with prior research by Kingdon and Knight, emphasizing the impact of age on unemployment risks.
Research from 2004 indicates that youth are more likely to experience unemployment, as they tend to quit undesirable jobs rather than remain stuck in them This behavior is driven by their greater mobility, flexibility, and fewer financial pressures, which enable them to actively seek better job opportunities.
Research by Lauerove and Terrell (2007) indicates that younger workers face a higher risk of layoffs and fewer opportunities to find new employment, while Anh, Duong, and Van (2005) explain that young workers are more vulnerable to unemployment due to limited practical experience, which discourages firms from hiring them Tansel and Tasci (2004) found that both recent graduates and older workers require more employment support because they experience higher unemployment risks; notably, workers over 50 have an unemployment rate of approximately 13.25%, compared to 10% for youth, primarily due to outdated skills and health issues These findings highlight that older workers are at a considerable disadvantage in the labor market, facing increased barriers to employment.
(2005) and Foley (1997) also confirm the same findings
Living in urban areas significantly increases the risk of unemployment, with urban residents having a 2.404 times higher probability of being jobless compared to those in rural regions While the baseline unemployment rate is around 10%, urban workers face up to a 20.6% chance of unemployment under similar conditions This finding aligns with previous research by Garrouste et al (2010), which indicates higher unemployment probabilities in urban regions Studies by Kupets (2005) and Lilja (1992) also suggest that rural workers tend to exit unemployment more quickly than their urban counterparts The higher unemployment rate and intensified job competition in cities contribute to this disparity, compounded by migration patterns where increased influx in urban areas prolongs unemployment duration for many individuals (Anh, Duong, and Van, 2005).